From f5a28a0c00cae603d7a7b897f4b7ce8a6a1dc012 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Wed, 13 Jun 2018 10:59:25 +0200
Subject: [PATCH 001/719] basic adapter, packing is missing

---
 .../instrument_drivers/tektronix/AWG5014.py   | 39 +++++++++++++++++++
 1 file changed, 39 insertions(+)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index b5988b68769..32eadb91a6f 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -1022,6 +1022,45 @@ def mrkdeltrans(x):
 
         return AWG_channel_cfg
 
+    def generate_awg_file_from_forged_sequence(self, seq):
+        # TODO: create channel_cfg, sequence_cfg, preservechannelsettings
+        packed_waveforms = {}
+        # unfortunately the definitions of the sequence elements in terms of channel and step in `generate_awg_file` and the forged sequence definition are transposed. Start by filling out after schema and transpose at the end
+        wfname_l = []
+        nrep = []
+        trig_wait = []
+        goto_state = []
+        jump_to = []
+        for step, elem in seq.items():
+            assert elem['type'] == 'element'
+            content = elem['content']
+            assert len(content.keys()) == 1
+            # get first element
+            # this seems very complicated but is probably necessary
+            # one could use pop, but this would change the content
+            content = content[list(content.keys())[0]]
+
+            # waveform data
+            datadict = content['data']
+            step_waveform_names = []
+            for channel_name, data in datadict.items():
+                waveform_name = f'Step{step}_Channel{channel_name}'
+                packed_waveforms[waveform_name] = data
+                step_waveform_names.append(waveform_name)
+            wfname_l.append(step_waveform_names)
+
+            # sequencing
+            seq_opts = elem['sequencing']
+            nrep.append(seq_opts.get('nrep', 1))
+            trig_wait.append(seq_opts.get('trig_wait', 0))
+            goto_state.append(seq_opts.get('goto_state',0))
+            jump_to.append(seq_opts.get('jump_to', 0))
+        # transpose list of lists
+        wfname_l = [list(x) for x in zip(*wfname_l)]
+        channel_cfg = {}
+        self.generate_awg_file(packed_waveforms, wfname_l, nrep, trig_wait,
+                               goto_state, jump_to, channel_cfg)
+
     def generate_awg_file(self,
                           packed_waveforms, wfname_l, nrep, trig_wait,
                           goto_state, jump_to, channel_cfg,

From bc65f6486000d12cbbcd801e14f00e1f2cc630f4 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Wed, 20 Jun 2018 10:08:16 +0200
Subject: [PATCH 002/719] first working version

---
 .../instrument_drivers/tektronix/AWG5014.py   | 127 ++++++++++++++----
 1 file changed, 98 insertions(+), 29 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index 32eadb91a6f..395057bbf67 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -1,6 +1,7 @@
 import struct
 import logging
 import warnings
+import re
 
 import numpy as np
 import array as arr
@@ -1022,45 +1023,113 @@ def mrkdeltrans(x):
 
         return AWG_channel_cfg
 
-    def generate_awg_file_from_forged_sequence(self, seq):
-        # TODO: create channel_cfg, sequence_cfg, preservechannelsettings
-        packed_waveforms = {}
-        # unfortunately the definitions of the sequence elements in terms of channel and step in `generate_awg_file` and the forged sequence definition are transposed. Start by filling out after schema and transpose at the end
-        wfname_l = []
-        nrep = []
-        trig_wait = []
-        goto_state = []
-        jump_to = []
+    
+    # def make_awg_file_from_forged_sequence(self, seq, channel_mapping: Dict[Union[str, int], Union[int, str]],
+    def make_awg_file_from_forged_sequence(self, seq, channel_mapping,
+                                           filename='customawgfile.awg', preservechannelsettings=True):
+        """
+        Makes an awg file form a forged sequence as produced by broadbean.sequence.Sequence.forge. The forged sequence is a dictionary (see broadbean.sequence.fs_schmema) that does not need to be created by broadbean.
+
+        Args:
+            seq: the sequence dictionary
+
+            channel_mapping: a dictionary that maps the abstract channel id as assigned (e.g. in broadbean) to a physical channel of the device. For this particular device the channels are integers starting at TODO: 1?. Unmapped channels will be ignored, so that one can create forged sequences accross several instruments.
+            filename: filename of the uploaded awg file. See :meth:`~make_send_and_load_awg_file`
+            preservechannelsettings: see :meth:`~make_send_and_load_awg_file`
+
+        """
+        n_channels = 4
+        self.available_signal_channels = list(range(1,n_channels+1))
+        self.available_marker_channels = [f'{m}M{c}'
+                                          for c in self.available_signal_channels
+                                          for m in [1,2]]
+        self.available_channels = self.available_signal_channels + self.available_marker_channels
+
+        waveforms = []
+        m1s = []
+        m2s = []
+        # unfortunately the definitions of the sequence elements in terms of channel and step in :meth:`make_and_send_awg_file` and the forged sequence definition are transposed. Start by filling out after schema and transpose at the end
+        # make...: [[wfm1ch1, wfm2ch1, ...], [wfm1ch2, wfm2ch2], ...]
+        # dict efectively: {elementid: {channelid: wfm}}
+        physical_channels = []
+
+        nreps = []
+        trig_waits = []
+        goto_states = []
+        jump_tos = []
+
+        first_step = True
         for step, elem in seq.items():
+            # TODO: add support for subsequences
             assert elem['type'] == 'element'
             content = elem['content']
             assert len(content.keys()) == 1
-            # get first element
-            # this seems very complicated but is probably necessary
-            # one could use pop, but this would change the content
-            content = content[list(content.keys())[0]]
-
+            # there is only one element int the dict with key `1` in case of type == element
+            # and no sequencing information, i.e. there is only one entry with key 'data'
             # waveform data
-            datadict = content['data']
-            step_waveform_names = []
-            for channel_name, data in datadict.items():
-                waveform_name = f'Step{step}_Channel{channel_name}'
-                packed_waveforms[waveform_name] = data
-                step_waveform_names.append(waveform_name)
-            wfname_l.append(step_waveform_names)
-
+            datadict = content[1]['data']
+
+            # obtain list of channels defined in the first step
+            if first_step:
+                mapped_channels = [channel_mapping.get(k, None) for k in datadict.keys()]
+                # filter out all channels that are not relevant for this instrument
+                physical_channels = [v for v in mapped_channels if v in self.available_signal_channels]
+                first_step = False
+
+            # create empty list with size of number of used channels
+            step_waveforms = [None] * len(physical_channels)
+            step_marker = [[None] * n_channels] * 2
+            for channel_id, data in datadict.items():
+                physical_channel = channel_mapping.get(channel_id, None)
+
+                if physical_channel in self.available_marker_channels:
+                    res  = re.match('^(?P<channel>\d+)M(?P<marker>\d+)$',
+                                    physical_channel)
+                    assert res is not None
+                    step_marker[int(res.group('marker'))-1][int(res.group('channel'))-1] = data
+                elif physical_channel in self.available_signal_channels:
+                    # treat the first step differently: here the channel order is defined
+                    try:
+                        index = physical_channels.index(physical_channel)
+                    except IndexError:
+                        #TODO: write error message
+                        raise
+                    step_waveforms[index] = data
+                else:
+                    # only proceed if the defined channel is available on the instrument
+                    # TODO: this needs some extra information in case we are using two instruments with the same channel names
+                    # e.g. always add a prefix with the instrument name.
+                    continue
+
+            if any(x is None for x in step_waveforms):
+                # TODO: error message
+                raise RuntimeError
+            #TODO: make more general such that one can create a sequence without signals but only markers
+            n_samples = len(step_waveforms[0])
+            for j in range(n_channels):
+                for i in range(2):
+                    if step_marker[i][j] is None:
+                        step_marker[i][j] = np.zeros(n_samples)
+            waveforms.append(step_waveforms)
+            m1s.append(step_marker[0])
+            m2s.append(step_marker[1])
             # sequencing
             seq_opts = elem['sequencing']
-            nrep.append(seq_opts.get('nrep', 1))
-            trig_wait.append(seq_opts.get('trig_wait', 0))
-            goto_state.append(seq_opts.get('goto_state',0))
-            jump_to.append(seq_opts.get('jump_to', 0))
+            nreps.append(seq_opts.get('nrep', 1))
+            trig_waits.append(seq_opts.get('trig_wait', 0))
+            goto_states.append(seq_opts.get('goto_state',0))
+            jump_tos.append(seq_opts.get('jump_to', 0))
         # transpose list of lists
-        wfname_l = [list(x) for x in zip(*wfname_l)]
+        waveforms = [list(x) for x in zip(*waveforms)]
         channel_cfg = {}
-        self.generate_awg_file(packed_waveforms, wfname_l, nrep, trig_wait,
-                               goto_state, jump_to, channel_cfg)
 
+
+        self.make_send_and_load_awg_file(waveforms, m1s, m2s,
+                                    nreps, trig_waits,
+                                    goto_states, jump_tos,
+                                    channels=physical_channels,
+                                    filename=filename,
+                                    preservechannelsettings=preservechannelsettings)
     def generate_awg_file(self,
                           packed_waveforms, wfname_l, nrep, trig_wait,
                           goto_state, jump_to, channel_cfg,

From 687b213ddf2b66090c85ff5ae138ee2536035495 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Tue, 26 Jun 2018 15:46:53 +0200
Subject: [PATCH 003/719] cosmetics

---
 .../instrument_drivers/tektronix/AWG5014.py   | 27 +++++++++++++------
 1 file changed, 19 insertions(+), 8 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index 395057bbf67..4e0c1d2ca05 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -1024,17 +1024,27 @@ def mrkdeltrans(x):
         return AWG_channel_cfg
 
     
-    # def make_awg_file_from_forged_sequence(self, seq, channel_mapping: Dict[Union[str, int], Union[int, str]],
-    def make_awg_file_from_forged_sequence(self, seq, channel_mapping,
-                                           filename='customawgfile.awg', preservechannelsettings=True):
+    def parse_marker_channel_name(name:str)->Tupel[int, int]:
         """
-        Makes an awg file form a forged sequence as produced by broadbean.sequence.Sequence.forge. The forged sequence is a dictionary (see broadbean.sequence.fs_schmema) that does not need to be created by broadbean.
+        returns from the channel index and marker index from a marker descriptor string e.g. '1M1'->(1,1)
+        """
+        res  = re.match('^(?P<channel>\d+)M(?P<marker>\d+)$',
+                        name)
+        assert res is not None
+        MarkerDescriptor = namedtuple('MarkerDescriptor', 'marker', 'channel')
+        return MarkerDescriptor(int(res.group('marker')), int(res.group('channel')))
+
+    def make_awg_file_from_forged_sequence(self, seq, filename='customawgfile.awg', preservechannelsettings=True):
+        """
+        Makes an awg file form a forged sequence as produced by
+        broadbean.sequence.Sequence.forge. The forged sequence is a dictionary
+        (see :attr:`fs_schmea <broadbean.sequence.fs_schmema>`) that does not
+        need to be created by broadbean.
 
         Args:
             seq: the sequence dictionary
-
-            channel_mapping: a dictionary that maps the abstract channel id as assigned (e.g. in broadbean) to a physical channel of the device. For this particular device the channels are integers starting at TODO: 1?. Unmapped channels will be ignored, so that one can create forged sequences accross several instruments.
-            filename: filename of the uploaded awg file. See :meth:`~make_send_and_load_awg_file`
+            filename: filename of the uploaded awg file.
+                See :meth:`~make_send_and_load_awg_file`
             preservechannelsettings: see :meth:`~make_send_and_load_awg_file`
 
         """
@@ -1043,7 +1053,8 @@ def make_awg_file_from_forged_sequence(self, seq, channel_mapping,
         self.available_marker_channels = [f'{m}M{c}'
                                           for c in self.available_signal_channels
                                           for m in [1,2]]
-        self.available_channels = self.available_signal_channels + self.available_marker_channels
+        self.available_channels = (self.available_signal_channels +
+                                   self.available_marker_channels)
 
         waveforms = []
         m1s = []

From a6a7ec651e6cb5f2d818017a59e7aaabd61efd71 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Tue, 26 Jun 2018 15:50:11 +0200
Subject: [PATCH 004/719] more cosmetics

---
 .../instrument_drivers/tektronix/AWG5014.py   | 41 +++++++++++++------
 1 file changed, 28 insertions(+), 13 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index 4e0c1d2ca05..00e80f68350 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -1026,15 +1026,19 @@ def mrkdeltrans(x):
     
     def parse_marker_channel_name(name:str)->Tupel[int, int]:
         """
-        returns from the channel index and marker index from a marker descriptor string e.g. '1M1'->(1,1)
+        returns from the channel index and marker index from a marker
+        descriptor string e.g. '1M1'->(1,1)
         """
         res  = re.match('^(?P<channel>\d+)M(?P<marker>\d+)$',
                         name)
         assert res is not None
         MarkerDescriptor = namedtuple('MarkerDescriptor', 'marker', 'channel')
-        return MarkerDescriptor(int(res.group('marker')), int(res.group('channel')))
+        return MarkerDescriptor(int(res.group('marker')),
+                                int(res.group('channel')))
 
-    def make_awg_file_from_forged_sequence(self, seq, filename='customawgfile.awg', preservechannelsettings=True):
+    def make_awg_file_from_forged_sequence(self, seq,
+                                           filename='customawgfile.awg',
+                                           preservechannelsettings=True):
         """
         Makes an awg file form a forged sequence as produced by
         broadbean.sequence.Sequence.forge. The forged sequence is a dictionary
@@ -1059,7 +1063,10 @@ def make_awg_file_from_forged_sequence(self, seq, filename='customawgfile.awg',
         waveforms = []
         m1s = []
         m2s = []
-        # unfortunately the definitions of the sequence elements in terms of channel and step in :meth:`make_and_send_awg_file` and the forged sequence definition are transposed. Start by filling out after schema and transpose at the end
+        # unfortunately the definitions of the sequence elements in terms of
+        # channel and step in :meth:`make_and_send_awg_file` and the forged
+        # sequence definition are transposed. Start by filling out after schema
+        # and transpose at the end.
         # make...: [[wfm1ch1, wfm2ch1, ...], [wfm1ch2, wfm2ch2], ...]
         # dict efectively: {elementid: {channelid: wfm}}
         physical_channels = []
@@ -1075,16 +1082,20 @@ def make_awg_file_from_forged_sequence(self, seq, filename='customawgfile.awg',
             assert elem['type'] == 'element'
             content = elem['content']
             assert len(content.keys()) == 1
-            # there is only one element int the dict with key `1` in case of type == element
-            # and no sequencing information, i.e. there is only one entry with key 'data'
+            # there is only one element int the dict with key `1` in case of
+            # type == element and no sequencing information, i.e. there is only
+            # one entry with key 'data'
             # waveform data
             datadict = content[1]['data']
 
             # obtain list of channels defined in the first step
             if first_step:
-                mapped_channels = [channel_mapping.get(k, None) for k in datadict.keys()]
-                # filter out all channels that are not relevant for this instrument
-                physical_channels = [v for v in mapped_channels if v in self.available_signal_channels]
+                mapped_channels = [channel_mapping.get(k, None)
+                                   for k in datadict.keys()]
+                # filter out all channels that are not relevant for this
+                # instrument
+                physical_channels = [v for v in mapped_channels
+                                     if v in self.available_signal_channels]
                 first_step = False
 
             # create empty list with size of number of used channels
@@ -1099,7 +1110,8 @@ def make_awg_file_from_forged_sequence(self, seq, filename='customawgfile.awg',
                     assert res is not None
                     step_marker[int(res.group('marker'))-1][int(res.group('channel'))-1] = data
                 elif physical_channel in self.available_signal_channels:
-                    # treat the first step differently: here the channel order is defined
+                    # treat the first step differently: here the channel order
+                    # is defined
                     try:
                         index = physical_channels.index(physical_channel)
                     except IndexError:
@@ -1107,15 +1119,18 @@ def make_awg_file_from_forged_sequence(self, seq, filename='customawgfile.awg',
                         raise
                     step_waveforms[index] = data
                 else:
-                    # only proceed if the defined channel is available on the instrument
-                    # TODO: this needs some extra information in case we are using two instruments with the same channel names
+                    # only proceed if the defined channel is available on the
+                    # instrument
+                    # TODO: this needs some extra information in case we are
+                    # using two instruments with the same channel names
                     # e.g. always add a prefix with the instrument name.
                     continue
 
             if any(x is None for x in step_waveforms):
                 # TODO: error message
                 raise RuntimeError
-            #TODO: make more general such that one can create a sequence without signals but only markers
+            #TODO: make more general such that one can create a sequence
+            # without signals but only markers
             n_samples = len(step_waveforms[0])
             for j in range(n_channels):
                 for i in range(2):

From 76c0f3388fe1deb908006ce05f63c19494ca93d2 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Tue, 26 Jun 2018 16:39:47 +0200
Subject: [PATCH 005/719] converted to broadbean routing

---
 .../instrument_drivers/tektronix/AWG5014.py   | 56 ++++++++++---------
 1 file changed, 29 insertions(+), 27 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index 00e80f68350..4700576f719 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -12,6 +12,7 @@
 from qcodes import VisaInstrument, validators as vals
 from pyvisa.errors import VisaIOError
 
+from broadbean.tools import forged_sequence_dict_to_list
 
 log = logging.getLogger(__name__)
 
@@ -1076,44 +1077,45 @@ def make_awg_file_from_forged_sequence(self, seq,
         goto_states = []
         jump_tos = []
 
-        first_step = True
-        for step, elem in seq.items():
+        # convert to list
+        seq = forged_sequence_dict_to_list(seq)
+
+        # obtain list of channels defined in the first step
+        if len(seq) < 1:
+            # TODO: better error
+            raise RuntimeError('Sequences need to have at least one element')
+
+        # mapped_channels = [channel_mapping.get(k, None)
+        #                    for k in datadict.keys()]
+        # # filter out all channels that are not relevant for this
+        # # instrument
+        
+        # treat the first step differently: here the channel order
+        # is defined
+        used_channels = list(seq[0]['content'][0]['data'].keys())
+        # physical_channels = [v for v in mapped_channels
+        #                      if v in self.available_signal_channels]
+        for elem in seq:
             # TODO: add support for subsequences
             assert elem['type'] == 'element'
             content = elem['content']
-            assert len(content.keys()) == 1
+            assert len(content) == 1
             # there is only one element int the dict with key `1` in case of
             # type == element and no sequencing information, i.e. there is only
             # one entry with key 'data'
             # waveform data
-            datadict = content[1]['data']
-
-            # obtain list of channels defined in the first step
-            if first_step:
-                mapped_channels = [channel_mapping.get(k, None)
-                                   for k in datadict.keys()]
-                # filter out all channels that are not relevant for this
-                # instrument
-                physical_channels = [v for v in mapped_channels
-                                     if v in self.available_signal_channels]
-                first_step = False
+            datadict = content[0]['data']
 
             # create empty list with size of number of used channels
-            step_waveforms = [None] * len(physical_channels)
+            step_waveforms = [None] * len(used_channels)
             step_marker = [[None] * n_channels] * 2
-            for channel_id, data in datadict.items():
-                physical_channel = channel_mapping.get(channel_id, None)
-
-                if physical_channel in self.available_marker_channels:
-                    res  = re.match('^(?P<channel>\d+)M(?P<marker>\d+)$',
-                                    physical_channel)
-                    assert res is not None
-                    step_marker[int(res.group('marker'))-1][int(res.group('channel'))-1] = data
-                elif physical_channel in self.available_signal_channels:
-                    # treat the first step differently: here the channel order
-                    # is defined
+            for channel, data in datadict.items():
+                if channel in self.available_marker_channels:
+                    t = self.parse_marker_channel_name(channel)
+                    step_marker[t.marker-1][t.channel-1] = data
+                elif channel in self.available_signal_channels:
                     try:
-                        index = physical_channels.index(physical_channel)
+                        index = used_channels.index(channel)
                     except IndexError:
                         #TODO: write error message
                         raise

From 4b605697f9022c24ea790a9f16df06c2c10892ab Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Wed, 27 Jun 2018 10:47:04 +0200
Subject: [PATCH 006/719] debugging driver

---
 .../instrument_drivers/tektronix/AWG5014.py   | 25 +++++++++----------
 1 file changed, 12 insertions(+), 13 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index 4700576f719..28756abc66b 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -2,6 +2,9 @@
 import logging
 import warnings
 import re
+from collections import namedtuple
+
+from typing import Tuple
 
 import numpy as np
 import array as arr
@@ -1024,8 +1027,8 @@ def mrkdeltrans(x):
 
         return AWG_channel_cfg
 
-    
-    def parse_marker_channel_name(name:str)->Tupel[int, int]:
+    @staticmethod 
+    def parse_marker_channel_name(name:str)->Tuple[int, int]:
         """
         returns from the channel index and marker index from a marker
         descriptor string e.g. '1M1'->(1,1)
@@ -1033,7 +1036,8 @@ def parse_marker_channel_name(name:str)->Tupel[int, int]:
         res  = re.match('^(?P<channel>\d+)M(?P<marker>\d+)$',
                         name)
         assert res is not None
-        MarkerDescriptor = namedtuple('MarkerDescriptor', 'marker', 'channel')
+        MarkerDescriptor = namedtuple('MarkerDescriptor',
+                                      ('marker', 'channel'))
         return MarkerDescriptor(int(res.group('marker')),
                                 int(res.group('channel')))
 
@@ -1070,7 +1074,6 @@ def make_awg_file_from_forged_sequence(self, seq,
         # and transpose at the end.
         # make...: [[wfm1ch1, wfm2ch1, ...], [wfm1ch2, wfm2ch2], ...]
         # dict efectively: {elementid: {channelid: wfm}}
-        physical_channels = []
 
         nreps = []
         trig_waits = []
@@ -1084,17 +1087,13 @@ def make_awg_file_from_forged_sequence(self, seq,
         if len(seq) < 1:
             # TODO: better error
             raise RuntimeError('Sequences need to have at least one element')
-
-        # mapped_channels = [channel_mapping.get(k, None)
-        #                    for k in datadict.keys()]
-        # # filter out all channels that are not relevant for this
-        # # instrument
         
         # treat the first step differently: here the channel order
         # is defined
-        used_channels = list(seq[0]['content'][0]['data'].keys())
-        # physical_channels = [v for v in mapped_channels
-        #                      if v in self.available_signal_channels]
+        # TODO: this could made such that it fails if not all channels are
+        # accounted for. Now it will fail in the next step.
+        used_channels = [k for k in seq[0]['content'][0]['data'].keys()
+                         if k in self.available_signal_channels]
         for elem in seq:
             # TODO: add support for subsequences
             assert elem['type'] == 'element'
@@ -1155,7 +1154,7 @@ def make_awg_file_from_forged_sequence(self, seq,
         self.make_send_and_load_awg_file(waveforms, m1s, m2s,
                                     nreps, trig_waits,
                                     goto_states, jump_tos,
-                                    channels=physical_channels,
+                                    channels=used_channels,
                                     filename=filename,
                                     preservechannelsettings=preservechannelsettings)
     def generate_awg_file(self,

From 5e2844b3687802efab8e8b32754e07108e02f6cc Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Thu, 28 Jun 2018 16:42:52 +0200
Subject: [PATCH 007/719] fixing marker transpose

---
 qcodes/instrument_drivers/tektronix/AWG5014.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index 28756abc66b..652ea165c46 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -1148,6 +1148,8 @@ def make_awg_file_from_forged_sequence(self, seq,
             jump_tos.append(seq_opts.get('jump_to', 0))
         # transpose list of lists
         waveforms = [list(x) for x in zip(*waveforms)]
+        m1s = [list(x) for x in zip(*m1s)]
+        m2s = [list(x) for x in zip(*m2s)]
         channel_cfg = {}
 
 

From 96d18a1e0d39beedd908b0d066422ed72190920b Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Fri, 29 Jun 2018 14:00:36 +0200
Subject: [PATCH 008/719] gave method the right name

---
 qcodes/instrument_drivers/tektronix/AWG5014.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index f7a5a971302..d3ca4275d28 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -1042,7 +1042,7 @@ def parse_marker_channel_name(name:str)->Tuple[int, int]:
         return MarkerDescriptor(int(res.group('marker')),
                                 int(res.group('channel')))
 
-    def make_awg_file_from_forged_sequence(self, seq,
+    def make_send_and_load_awg_file_from_forged_sequence(self, seq,
                                            filename='customawgfile.awg',
                                            preservechannelsettings=True):
         """

From 46cade6129de0fd1f9af378181a31929832d8420 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Tue, 10 Jul 2018 10:51:41 +0200
Subject: [PATCH 009/719] adaption to lists

---
 qcodes/instrument_drivers/tektronix/AWG5014.py | 17 ++++-------------
 1 file changed, 4 insertions(+), 13 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index d3ca4275d28..95c0c2493c7 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -18,7 +18,7 @@
 from qcodes.utils.deprecate import deprecate
 from pyvisa.errors import VisaIOError
 
-from broadbean.tools import forged_sequence_dict_to_list
+from broadbean.tools import forged_sequence_dict_to_list, is_subsequence
 
 log = logging.getLogger(__name__)
 
@@ -1081,9 +1081,6 @@ def make_send_and_load_awg_file_from_forged_sequence(self, seq,
         goto_states = []
         jump_tos = []
 
-        # convert to list
-        seq = forged_sequence_dict_to_list(seq)
-
         # obtain list of channels defined in the first step
         if len(seq) < 1:
             # TODO: better error
@@ -1093,18 +1090,12 @@ def make_send_and_load_awg_file_from_forged_sequence(self, seq,
         # is defined
         # TODO: this could made such that it fails if not all channels are
         # accounted for. Now it will fail in the next step.
-        used_channels = [k for k in seq[0]['content'][0]['data'].keys()
+        used_channels = [k for k in seq[0]['data'].keys()
                          if k in self.available_signal_channels]
         for elem in seq:
             # TODO: add support for subsequences
-            assert elem['type'] == 'element'
-            content = elem['content']
-            assert len(content) == 1
-            # there is only one element int the dict with key `1` in case of
-            # type == element and no sequencing information, i.e. there is only
-            # one entry with key 'data'
-            # waveform data
-            datadict = content[0]['data']
+            assert not is_subsequence(elem)
+            datadict = elem['data']
 
             # create empty list with size of number of used channels
             step_waveforms = [None] * len(used_channels)

From 0217c445c85a13ea512f31b854696d90d9f4a68c Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Fri, 13 Jul 2018 17:53:26 +0200
Subject: [PATCH 010/719] fix double marker from * operator trap

---
 qcodes/instrument_drivers/tektronix/AWG5014.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index 95c0c2493c7..3951a6d232c 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -1099,7 +1099,7 @@ def make_send_and_load_awg_file_from_forged_sequence(self, seq,
 
             # create empty list with size of number of used channels
             step_waveforms = [None] * len(used_channels)
-            step_marker = [[None] * n_channels] * 2
+            step_marker = [[None] * n_channels,  [None] * n_channels]
             for channel, data in datadict.items():
                 if channel in self.available_marker_channels:
                     t = self.parse_marker_channel_name(channel)

From 9f5e9442299213bb67288dd282bbfbbb5a8c3a72 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Thu, 19 Jul 2018 14:05:11 +0200
Subject: [PATCH 011/719] add markers on empty channels

---
 .../instrument_drivers/tektronix/AWG5014.py   | 146 ++++++++++--------
 1 file changed, 84 insertions(+), 62 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index 3951a6d232c..87a19bc8bac 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -3,6 +3,7 @@
 import warnings
 import re
 from collections import namedtuple
+from collections import defaultdict
 
 from typing import Tuple
 
@@ -18,7 +19,9 @@
 from qcodes.utils.deprecate import deprecate
 from pyvisa.errors import VisaIOError
 
-from broadbean.tools import forged_sequence_dict_to_list, is_subsequence
+from broadbean.tools import (forged_sequence_dict_to_list,
+                             is_subsequence, get_element_channel_ids)
+
 
 log = logging.getLogger(__name__)
 
@@ -909,7 +912,7 @@ def generate_channel_cfg(self):
         been changed from their default value and put them in a
         dictionary that can easily be written into an awg file, so as
         to prevent said awg file from falling back to default values.
-        (See :meth:`~make_awg_file` and :meth:`qcodes.instrument_drivers.tektronix.AWG5014.Tektronix_AWG5014.AWG_FILE_FORMAT_CHANNEL`)
+        (See :meth:`~make_awg_file` and :meth:`~AWG_FILE_FORMAT_CHANNEL`)
         NOTE: This only works for settings changed via the corresponding
         QCoDeS parameter.
 
@@ -1028,23 +1031,24 @@ def mrkdeltrans(x):
 
         return AWG_channel_cfg
 
-    @staticmethod 
-    def parse_marker_channel_name(name:str)->Tuple[int, int]:
+    @staticmethod
+    def parse_marker_channel_name(name: str)->Tuple[int, int]:
         """
         returns from the channel index and marker index from a marker
         descriptor string e.g. '1M1'->(1,1)
         """
-        res  = re.match('^(?P<channel>\d+)M(?P<marker>\d+)$',
-                        name)
+        res = re.match(r'^(?P<channel>\d+)M(?P<marker>\d+)$',
+                       name)
         assert res is not None
         MarkerDescriptor = namedtuple('MarkerDescriptor',
                                       ('marker', 'channel'))
         return MarkerDescriptor(int(res.group('marker')),
                                 int(res.group('channel')))
 
-    def make_send_and_load_awg_file_from_forged_sequence(self, seq,
-                                           filename='customawgfile.awg',
-                                           preservechannelsettings=True):
+    def make_send_and_load_awg_file_from_forged_sequence(
+            self, seq,
+            filename='customawgfile.awg',
+            preservechannelsettings=True):
         """
         Makes an awg file form a forged sequence as produced by
         broadbean.sequence.Sequence.forge. The forged sequence is a dictionary
@@ -1059,11 +1063,12 @@ def make_send_and_load_awg_file_from_forged_sequence(self, seq,
 
         """
         n_channels = 4
-        self.available_signal_channels = list(range(1,n_channels+1))
-        self.available_marker_channels = [f'{m}M{c}'
-                                          for c in self.available_signal_channels
-                                          for m in [1,2]]
-        self.available_channels = (self.available_signal_channels +
+        self.available_waveform_channels = list(range(1, n_channels+1))
+        self.available_marker_channels = [
+            f'{c}M{m}'
+            for c in self.available_waveform_channels
+            for m in [1, 2]]
+        self.available_channels = (self.available_waveform_channels +
                                    self.available_marker_channels)
 
         waveforms = []
@@ -1085,72 +1090,87 @@ def make_send_and_load_awg_file_from_forged_sequence(self, seq,
         if len(seq) < 1:
             # TODO: better error
             raise RuntimeError('Sequences need to have at least one element')
-        
-        # treat the first step differently: here the channel order
-        # is defined
-        # TODO: this could made such that it fails if not all channels are
-        # accounted for. Now it will fail in the next step.
-        used_channels = [k for k in seq[0]['data'].keys()
-                         if k in self.available_signal_channels]
-        for elem in seq:
+
+        # assume that the channels are the same on every element
+        provided_channels = get_element_channel_ids(seq[0])
+        used_waveform_channels = list(set(provided_channels).intersection(
+            set(self.available_waveform_channels)))
+        used_marker_channels = list(set(provided_channels).intersection(
+                set(self.available_marker_channels)))
+        associated_marker_channels = [
+            self.parse_marker_channel_name(name).channel
+            for name in used_marker_channels]
+        used_channels = list(
+            set(used_waveform_channels).union(set(associated_marker_channels)))
+
+        for i_elem, elem in enumerate(seq):
             # TODO: add support for subsequences
             assert not is_subsequence(elem)
             datadict = elem['data']
 
-            # create empty list with size of number of used channels
-            step_waveforms = [None] * len(used_channels)
-            step_marker = [[None] * n_channels,  [None] * n_channels]
+            # Split up the dictionary into two, one for the markers the other
+            # for the waveforms
+            step_waveforms = {}
+            step_markers = defaultdict(None), defaultdict(None)
             for channel, data in datadict.items():
                 if channel in self.available_marker_channels:
                     t = self.parse_marker_channel_name(channel)
-                    step_marker[t.marker-1][t.channel-1] = data
-                elif channel in self.available_signal_channels:
-                    try:
-                        index = used_channels.index(channel)
-                    except IndexError:
-                        #TODO: write error message
-                        raise
-                    step_waveforms[index] = data
+                    step_markers[t.marker-1][t.channel] = data
+                elif channel in self.available_waveform_channels:
+                    step_waveforms[channel] = data
                 else:
-                    # only proceed if the defined channel is available on the
-                    # instrument
-                    # TODO: this needs some extra information in case we are
-                    # using two instruments with the same channel names
-                    # e.g. always add a prefix with the instrument name.
-                    continue
-
-            if any(x is None for x in step_waveforms):
-                # TODO: error message
-                raise RuntimeError
-            #TODO: make more general such that one can create a sequence
-            # without signals but only markers
-            n_samples = len(step_waveforms[0])
-            for j in range(n_channels):
+                    raise RuntimeError(
+                        f'The channel with name {channel} as defined in '
+                        f'the element with no. {i_elem} is not an available '
+                        f'marker channel or waveform channel.\n'
+                        f'Available channels are: '
+                        f'{self.available_marker_channels} and '
+                        f'{self.available_waveform_channels}')
+
+            # create empty trace as template for filling traces with markers
+            # only and traces without markers
+            n_samples = None
+            waveform_keys = list(step_waveforms.keys())
+            marker_keys = tuple(list(step_markers[i].keys()) for i in range(2))
+            if len(waveform_keys) != 0:
+                n_samples = len(step_waveforms[waveform_keys[0]])
+            else:
                 for i in range(2):
-                    if step_marker[i][j] is None:
-                        step_marker[i][j] = np.zeros(n_samples)
-            waveforms.append(step_waveforms)
-            m1s.append(step_marker[0])
-            m2s.append(step_marker[1])
+                    if len(marker_keys[i]) != 0:
+                        n_samples = len(step_markers[marker_keys[i][0]])
+                        break
+            if n_samples is None:
+                raise RuntimeError('It is not allowed to upload an element '
+                                   'without markers nor waveforms')
+            blank_trace = np.zeros(n_samples)
+            # I think this does might add some traces dynamically if they are
+            # not the same in all elements. Add check in the beginning
+            step_waveforms_list = [step_waveforms.get(key, blank_trace)
+                                   for key in used_channels]
+            step_markers_list = tuple([step_markers[i].get(key, blank_trace)
+                                       for key in used_channels]
+                                      for i in range(2))
+
+            waveforms.append(step_waveforms_list)
+            m1s.append(step_markers_list[0])
+            m2s.append(step_markers_list[1])
             # sequencing
             seq_opts = elem['sequencing']
             nreps.append(seq_opts.get('nrep', 1))
             trig_waits.append(seq_opts.get('trig_wait', 0))
-            goto_states.append(seq_opts.get('goto_state',0))
+            goto_states.append(seq_opts.get('goto_state', 0))
             jump_tos.append(seq_opts.get('jump_to', 0))
         # transpose list of lists
         waveforms = [list(x) for x in zip(*waveforms)]
         m1s = [list(x) for x in zip(*m1s)]
         m2s = [list(x) for x in zip(*m2s)]
-        channel_cfg = {}
-
 
         self.make_send_and_load_awg_file(waveforms, m1s, m2s,
-                                    nreps, trig_waits,
-                                    goto_states, jump_tos,
-                                    channels=used_channels,
-                                    filename=filename,
-                                    preservechannelsettings=preservechannelsettings)
+                                         nreps, trig_waits,
+                                         goto_states, jump_tos,
+                                         channels=used_channels,
+                                         filename=filename,
+                                         preservechannelsettings=preservechannelsettings)
 
     def _generate_awg_file(self,
                            packed_waveforms, wfname_l, nrep, trig_wait,
@@ -1294,7 +1314,8 @@ def _generate_awg_file(self,
         return awg_file
 
     @deprecate(alternative='make_awg_file, _generate_awg_file')
-    @wraps(_generate_awg_file, assigned=tuple(v for v in WRAPPER_ASSIGNMENTS if v != '__name__'))
+    @wraps(_generate_awg_file, assigned=tuple(v for v in WRAPPER_ASSIGNMENTS
+                                              if v != '__name__'))
     def generate_awg_file(self, *args, **kwargs):
         return self._generate_awg_file(*args, **kwargs)
 
@@ -1611,7 +1632,8 @@ def _pack_waveform(self, wf, m1, m2):
         return packed_wf
 
     @deprecate(reason='this function is for private use only.')
-    @wraps(_pack_waveform, assigned=tuple(v for v in WRAPPER_ASSIGNMENTS if v != '__name__'))
+    @wraps(_pack_waveform, assigned=tuple(v for v in WRAPPER_ASSIGNMENTS
+                                          if v != '__name__'))
     def pack_waveform(self, *args, **kwargs):
         return self._pack_waveform(*args, **kwargs)
 

From 1e1f993eb488360cfee478f5766bf7f8af1be67c Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Thu, 19 Jul 2018 14:21:31 +0200
Subject: [PATCH 012/719] Remove unused import

---
 qcodes/instrument_drivers/tektronix/AWG5014.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index 87a19bc8bac..d0bfd21c9d5 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -19,8 +19,7 @@
 from qcodes.utils.deprecate import deprecate
 from pyvisa.errors import VisaIOError
 
-from broadbean.tools import (forged_sequence_dict_to_list,
-                             is_subsequence, get_element_channel_ids)
+from broadbean.tools import is_subsequence, get_element_channel_ids
 
 
 log = logging.getLogger(__name__)

From b97618bcb1479cffc33e9b3b195158eb19f44cc3 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen <jenshnielsen@gmail.com>
Date: Thu, 4 Oct 2018 15:02:33 +0200
Subject: [PATCH 013/719] [WIP] Azure pipelines add coverage

This is WIP until we figure out how to set the right permissions
---
 azure-pipelines.yml | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index eedac3a5252..21b0598d221 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -40,3 +40,8 @@ jobs:
         inputs:
           testResultsFiles: 'qcodes\test-*.xml'
           testRunTitle: 'Publish test results'
+      - task: PublishCodeCoverageResults@1
+        inputs:
+          codeCoverageTool: Cobertura
+          summaryFileLocation: '$(System.DefaultWorkingDirectory)\qcodes\coverage.xml'
+          reportDirectory: '$(System.DefaultWorkingDirectory)\qcodes\htmlcov'

From 9de0f377934f77b8438e0c077d984aba2b5287b2 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 12 Oct 2018 17:20:29 +0200
Subject: [PATCH 014/719] Try to publish docs as a build artifact

---
 azure-pipelines.yml | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index a3b3a4119d1..09565394bbf 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -37,11 +37,16 @@ jobs:
           cd qcodes
           pytest --junitxml=test-results.xml --cov=qcodes --cov-report=xml --cov-report=html --cov-config=.coveragerc
         displayName: "Pytest"
+      - task: PublishTestResults@1
+        inputs:
+          testResultsFiles: 'qcodes\test-*.xml'
+          testRunTitle: 'Publish test results'
       - script: |
           cd docs
           make.bat htmlapi
         displayName: "docsbuild"
-      - task: PublishTestResults@1
+      - task: PublishBuildArtifacts@1
         inputs:
-          testResultsFiles: 'qcodes\test-*.xml'
-          testRunTitle: 'Publish test results'
+          pathtoPublish: 'docs/_build/html'
+          artifactName: 'docs'
+          publishLocation: 'Container'

From c6a89b93cf10487be9015768fccc2fce6ebe6a3a Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 15 Oct 2018 15:19:37 +0200
Subject: [PATCH 015/719] Add types to ctypes dll calls

These matches the calling conventions from the headers
---
 .../signal_hound/USB_SA124B.py                | 50 ++++++++++++++++---
 1 file changed, 42 insertions(+), 8 deletions(-)

diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index 01f24221d51..286a4f003e7 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -178,6 +178,40 @@ def __init__(self, name, dll_path=None, **kwargs):
         self._trace_updated = False
         log.info('Initializing instrument SignalHound USB 124B')
         self.dll = ct.CDLL(dll_path or self.dll_path)
+
+        self.dll.saConfigCenterSpan.argtypes = [ct.c_int,
+                                                ct.c_double,
+                                                ct.c_double]
+        self.dll.saConfigAcquisition.argtypes = [ct.c_int,
+                                                 ct.c_int,
+                                                 ct.c_int]
+        self.dll.saConfigLevel.argtypes = [ct.c_int,
+                                           ct.c_double]
+        self.dll.saSetTimebase.argtypes = [ct.c_int,
+                                           ct.c_int]
+        self.dll.saConfigSweepCoupling.argypes = [ct.c_int,
+                                                  ct.c_double,
+                                                  ct.c_double,
+                                                  ct.c_bool]
+        self.dll.saInitiate.argtypes = [ct.c_int,
+                                        ct.c_int,
+                                        ct.c_int]
+        self.dll.saOpenDevice.argtypes = [ct.POINTER(ct.c_int)]
+        self.dll.saCloseDevice.argtypes = [ct.c_int]
+        self.dll.saPreset.argtypes = [ct.c_int]
+        self.dll.saGetDeviceType.argtypes = [ct.c_int,
+                                             ct.POINTER(ct.c_int)]
+        self.dll.saQuerySweepInfo.argtypes = [ct.c_int,
+                                              ct.POINTER(ct.c_int),
+                                              ct.POINTER(ct.c_double),
+                                              ct.POINTER(ct.c_double)]
+        self.dll.saGetSweep_32f.argtypes = [ct.c_int, ct.POINTER(ct.c_float),
+                                            ct.POINTER(ct.c_float)]
+        self.dll.saGetSerialNumber.argtypes = [ct.c_int,
+                                               ct.POINTER(ct.c_int)]
+        self.dll.saGetFirmwareString.argtypes = [ct.c_int,
+                                                 ct.c_char_p]
+
         self.hf = Constants
         self.add_parameter('frequency',
                            label='Frequency',
@@ -361,14 +395,14 @@ def sync_parameters(self) -> None:
 
         # 2. Acquisition configuration
         detectorVals = {
-            'min-max': ct.c_uint(self.hf.sa_MIN_MAX),
-            'average': ct.c_uint(self.hf.sa_AVERAGE)
+            'min-max': ct.c_int(self.hf.sa_MIN_MAX),
+            'average': ct.c_int(self.hf.sa_AVERAGE)
         }
         scaleVals = {
-            'log-scale': ct.c_uint(self.hf.sa_LOG_SCALE),
-            'lin-scale': ct.c_uint(self.hf.sa_LIN_SCALE),
-            'log-full-scale': ct.c_uint(self.hf.sa_LOG_FULL_SCALE),
-            'lin-full-scale': ct.c_uint(self.hf.sa_LIN_FULL_SCALE)
+            'log-scale': ct.c_int(self.hf.sa_LOG_SCALE),
+            'lin-scale': ct.c_int(self.hf.sa_LIN_SCALE),
+            'log-full-scale': ct.c_int(self.hf.sa_LOG_FULL_SCALE),
+            'lin-full-scale': ct.c_int(self.hf.sa_LIN_FULL_SCALE)
         }
         detector = detectorVals[self.acquisition_mode()]
         scale = scaleVals[self.scale()]
@@ -502,7 +536,7 @@ def _get_device_type(self) -> str:
         """
         log.info('Querying device for model information')
 
-        devType = ct.c_uint(0)
+        devType = ct.c_int(0)
         devTypePnt = ct.pointer(devType)
 
         err = self.dll.saGetDeviceType(self.deviceHandle, devTypePnt)
@@ -605,7 +639,7 @@ def _get_power_at_freq(self) -> float:
             self.rbw(1e3)
         if not self._parameters_synced:
             # call configure to update both
-            # the paramters on the device and the
+            # the parameters on the device and the
             # setpoints and units
             self.configure()
         data = self._get_averaged_sweep_data()

From d2a6358201b5a4d4fbc119b693c7b5e908b6a03b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 15 Oct 2018 15:20:31 +0200
Subject: [PATCH 016/719] Simplify get data logic

Recursively call the code rather than duplication and store directly into numpy arrays
---
 .../signal_hound/USB_SA124B.py                | 35 +++++++++----------
 1 file changed, 17 insertions(+), 18 deletions(-)

diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index 286a4f003e7..ffb6be2b2c0 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -578,11 +578,15 @@ def QuerySweep(self) -> Tuple[int, float, float]:
 
         return sweep_len.value, start_freq.value, stepsize.value
 
-    def _get_sweep_data(self) -> np.ndarray:
+    def _get_sweep_data(self, retry: bool=False) -> np.ndarray:
         """
         This function performs a sweep over the configured ranges.
         The result of the sweep is returned along with the sweep points
 
+        Args:
+            retry: It this the second attempt? This method will attempt
+             to get the data exactly twice.
+
         returns:
             datamin numpy array
         """
@@ -590,25 +594,20 @@ def _get_sweep_data(self) -> np.ndarray:
             self.sync_parameters()
         sweep_len, _, _ = self.QuerySweep()
 
-        minarr = (ct.c_float * sweep_len)()
-        maxarr = (ct.c_float * sweep_len)()
-        sleep_time = self.sleep_time.get()
-        sleep(sleep_time)  # Added extra sleep for updating issue
+        datamin = np.zeros((sweep_len), dtype=np.float32)
+        datamax = np.zeros((sweep_len), dtype=np.float32)
+
+        minarr = datamin.ctypes.data_as(ct.POINTER(ct.c_float))
+        maxarr = datamax.ctypes.data_as(ct.POINTER(ct.c_float))
+
+        sleep(self.sleep_time.get())  # Added extra sleep for updating issue
         err = self.dll.saGetSweep_32f(self.deviceHandle, minarr, maxarr)
-        sleep(sleep_time)  # Added extra sleep
-        if not err == saStatus.saNoError:
-            # if an error occurs tries preparing the device and then asks again
+        if not err == saStatus.saNoError and not retry:
             log.warning('Error raised in _get_sweep_data, '
-                        'trying to get data')
-            sleep(sleep_time)
-            self.sync_parameters()
-            sleep(sleep_time)
-            minarr = (ct.c_float * sweep_len)()
-            maxarr = (ct.c_float * sweep_len)()
-            err = self.dll.saGetSweep_32f(self.deviceHandle, minarr, maxarr)
-        self.check_for_error(err, 'saGetSweep_32f')
-
-        datamin = np.array([minarr[elem] for elem in range(sweep_len)])
+                        'retrying to get data')
+            datamin = self._get_sweep_data(retry=True)
+        else:
+            self.check_for_error(err, 'saGetSweep_32f')
 
         return datamin
 

From 74013a4f239be2e0dd26265461278c507012caea Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 15 Oct 2018 15:40:14 +0200
Subject: [PATCH 017/719] Simplify avg logic

---
 .../signal_hound/USB_SA124B.py                | 35 +++++++++----------
 1 file changed, 16 insertions(+), 19 deletions(-)

diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index ffb6be2b2c0..76f54f59d1a 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -578,7 +578,7 @@ def QuerySweep(self) -> Tuple[int, float, float]:
 
         return sweep_len.value, start_freq.value, stepsize.value
 
-    def _get_sweep_data(self, retry: bool=False) -> np.ndarray:
+    def _get_sweep_data(self) -> np.ndarray:
         """
         This function performs a sweep over the configured ranges.
         The result of the sweep is returned along with the sweep points
@@ -594,34 +594,31 @@ def _get_sweep_data(self, retry: bool=False) -> np.ndarray:
             self.sync_parameters()
         sweep_len, _, _ = self.QuerySweep()
 
-        datamin = np.zeros((sweep_len), dtype=np.float32)
-        datamax = np.zeros((sweep_len), dtype=np.float32)
 
-        minarr = datamin.ctypes.data_as(ct.POINTER(ct.c_float))
-        maxarr = datamax.ctypes.data_as(ct.POINTER(ct.c_float))
+        data = np.zeros(sweep_len)
+        Navg = self.avg()
+        for i in range(Navg):
 
-        sleep(self.sleep_time.get())  # Added extra sleep for updating issue
-        err = self.dll.saGetSweep_32f(self.deviceHandle, minarr, maxarr)
-        if not err == saStatus.saNoError and not retry:
-            log.warning('Error raised in _get_sweep_data, '
-                        'retrying to get data')
-            datamin = self._get_sweep_data(retry=True)
-        else:
+            datamin = np.zeros((sweep_len), dtype=np.float32)
+            datamax = np.zeros((sweep_len), dtype=np.float32)
+
+            minarr = datamin.ctypes.data_as(ct.POINTER(ct.c_float))
+            maxarr = datamax.ctypes.data_as(ct.POINTER(ct.c_float))
+
+            sleep(self.sleep_time.get())  # Added extra sleep for updating issue
+            err = self.dll.saGetSweep_32f(self.deviceHandle, minarr, maxarr)
             self.check_for_error(err, 'saGetSweep_32f')
+            data += datamin
 
-        return datamin
+        return data / Navg
 
     def _get_averaged_sweep_data(self) -> np.ndarray:
         """
         Averages over SH.sweep Navg times
 
         """
-        sweep_len, _, _ = self.QuerySweep()
-        data = np.zeros(sweep_len)
-        Navg = self.avg()
-        for i in range(Navg):
-            data += self._get_sweep_data()
-        return data / Navg
+        data = self._get_sweep_data()
+        return data
 
     def _get_power_at_freq(self) -> float:
         """

From 884abe0ef1ff3a6dffab81d13cc8345023b2c1dc Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 15 Oct 2018 16:11:47 +0200
Subject: [PATCH 018/719] remove avg_data method that serves no use now

---
 qcodes/instrument_drivers/signal_hound/USB_SA124B.py | 12 ++----------
 1 file changed, 2 insertions(+), 10 deletions(-)

diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index 76f54f59d1a..6d8e657fa06 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -145,7 +145,7 @@ def get_raw(self) -> np.ndarray:
                                "for 'SignalHound_USB_SA124B'")
         if not self.instrument._trace_updated:
             raise RuntimeError('trace not updated, run configure to update')
-        data = self._instrument._get_averaged_sweep_data()
+        data = self._instrument._get_sweep_data()
         sleep(2*self.instrument.sleep_time.get())
         return data
 
@@ -612,14 +612,6 @@ def _get_sweep_data(self) -> np.ndarray:
 
         return data / Navg
 
-    def _get_averaged_sweep_data(self) -> np.ndarray:
-        """
-        Averages over SH.sweep Navg times
-
-        """
-        data = self._get_sweep_data()
-        return data
-
     def _get_power_at_freq(self) -> float:
         """
         Returns the maximum power in a window of 250 kHz
@@ -638,7 +630,7 @@ def _get_power_at_freq(self) -> float:
             # the parameters on the device and the
             # setpoints and units
             self.configure()
-        data = self._get_averaged_sweep_data()
+        data = self._get_sweep_data()
         max_power = np.max(data)
         if needs_reset:
             self.span(original_span)

From 5572ef22409eda2e935ac3de5f9fe07c87721039 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 16 Oct 2018 09:21:44 +0200
Subject: [PATCH 019/719] remove out of date arg

---
 qcodes/instrument_drivers/signal_hound/USB_SA124B.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index 6d8e657fa06..1ecf14f56ff 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -583,10 +583,6 @@ def _get_sweep_data(self) -> np.ndarray:
         This function performs a sweep over the configured ranges.
         The result of the sweep is returned along with the sweep points
 
-        Args:
-            retry: It this the second attempt? This method will attempt
-             to get the data exactly twice.
-
         returns:
             datamin numpy array
         """

From 495b8732293b63ed991ac70e93f18c147502f005 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 16 Oct 2018 09:25:27 +0200
Subject: [PATCH 020/719] refactor ctypes argtypes into their own method

---
 .../signal_hound/USB_SA124B.py                | 74 ++++++++++---------
 1 file changed, 41 insertions(+), 33 deletions(-)

diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index 1ecf14f56ff..a8cda68a834 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -179,38 +179,7 @@ def __init__(self, name, dll_path=None, **kwargs):
         log.info('Initializing instrument SignalHound USB 124B')
         self.dll = ct.CDLL(dll_path or self.dll_path)
 
-        self.dll.saConfigCenterSpan.argtypes = [ct.c_int,
-                                                ct.c_double,
-                                                ct.c_double]
-        self.dll.saConfigAcquisition.argtypes = [ct.c_int,
-                                                 ct.c_int,
-                                                 ct.c_int]
-        self.dll.saConfigLevel.argtypes = [ct.c_int,
-                                           ct.c_double]
-        self.dll.saSetTimebase.argtypes = [ct.c_int,
-                                           ct.c_int]
-        self.dll.saConfigSweepCoupling.argypes = [ct.c_int,
-                                                  ct.c_double,
-                                                  ct.c_double,
-                                                  ct.c_bool]
-        self.dll.saInitiate.argtypes = [ct.c_int,
-                                        ct.c_int,
-                                        ct.c_int]
-        self.dll.saOpenDevice.argtypes = [ct.POINTER(ct.c_int)]
-        self.dll.saCloseDevice.argtypes = [ct.c_int]
-        self.dll.saPreset.argtypes = [ct.c_int]
-        self.dll.saGetDeviceType.argtypes = [ct.c_int,
-                                             ct.POINTER(ct.c_int)]
-        self.dll.saQuerySweepInfo.argtypes = [ct.c_int,
-                                              ct.POINTER(ct.c_int),
-                                              ct.POINTER(ct.c_double),
-                                              ct.POINTER(ct.c_double)]
-        self.dll.saGetSweep_32f.argtypes = [ct.c_int, ct.POINTER(ct.c_float),
-                                            ct.POINTER(ct.c_float)]
-        self.dll.saGetSerialNumber.argtypes = [ct.c_int,
-                                               ct.POINTER(ct.c_int)]
-        self.dll.saGetFirmwareString.argtypes = [ct.c_int,
-                                                 ct.c_char_p]
+        self._set_ctypes_argtypes()
 
         self.hf = Constants
         self.add_parameter('frequency',
@@ -356,6 +325,45 @@ def __init__(self, name, dll_path=None, **kwargs):
 
         self.connect_message()
 
+    def _set_ctypes_argtypes(self) -> None:
+        """
+        Set the expected argtypes for function calls in the sa_api dll
+        These should match the function signatures defined in the sa-api
+        header files included with the signal hound sdk
+        """
+        self.dll.saConfigCenterSpan.argtypes = [ct.c_int,
+                                                ct.c_double,
+                                                ct.c_double]
+        self.dll.saConfigAcquisition.argtypes = [ct.c_int,
+                                                 ct.c_int,
+                                                 ct.c_int]
+        self.dll.saConfigLevel.argtypes = [ct.c_int,
+                                           ct.c_double]
+        self.dll.saSetTimebase.argtypes = [ct.c_int,
+                                           ct.c_int]
+        self.dll.saConfigSweepCoupling.argypes = [ct.c_int,
+                                                  ct.c_double,
+                                                  ct.c_double,
+                                                  ct.c_bool]
+        self.dll.saInitiate.argtypes = [ct.c_int,
+                                        ct.c_int,
+                                        ct.c_int]
+        self.dll.saOpenDevice.argtypes = [ct.POINTER(ct.c_int)]
+        self.dll.saCloseDevice.argtypes = [ct.c_int]
+        self.dll.saPreset.argtypes = [ct.c_int]
+        self.dll.saGetDeviceType.argtypes = [ct.c_int,
+                                             ct.POINTER(ct.c_int)]
+        self.dll.saQuerySweepInfo.argtypes = [ct.c_int,
+                                              ct.POINTER(ct.c_int),
+                                              ct.POINTER(ct.c_double),
+                                              ct.POINTER(ct.c_double)]
+        self.dll.saGetSweep_32f.argtypes = [ct.c_int, ct.POINTER(ct.c_float),
+                                            ct.POINTER(ct.c_float)]
+        self.dll.saGetSerialNumber.argtypes = [ct.c_int,
+                                               ct.POINTER(ct.c_int)]
+        self.dll.saGetFirmwareString.argtypes = [ct.c_int,
+                                                 ct.c_char_p]
+
     def _update_trace(self) -> None:
         """
         Private method to sync changes of the
@@ -536,7 +544,7 @@ def _get_device_type(self) -> str:
         """
         log.info('Querying device for model information')
 
-        devType = ct.c_int(0)
+        devType = ct.c_int32(0)
         devTypePnt = ct.pointer(devType)
 
         err = self.dll.saGetDeviceType(self.deviceHandle, devTypePnt)

From da1b072485b5a338f468184c45ac2b203bab269b Mon Sep 17 00:00:00 2001
From: "Esteban A. Martinez" <esteban.martinez@nbi.ku.dk>
Date: Fri, 19 Oct 2018 15:24:51 +0200
Subject: [PATCH 021/719] Added driver for Basel current preamplifier.

---
 qcodes/instrument_drivers/basel/__init__.py |  0
 qcodes/instrument_drivers/basel/sp983c.py   | 35 +++++++++++++++++++++
 2 files changed, 35 insertions(+)
 create mode 100644 qcodes/instrument_drivers/basel/__init__.py
 create mode 100644 qcodes/instrument_drivers/basel/sp983c.py

diff --git a/qcodes/instrument_drivers/basel/__init__.py b/qcodes/instrument_drivers/basel/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/qcodes/instrument_drivers/basel/sp983c.py b/qcodes/instrument_drivers/basel/sp983c.py
new file mode 100644
index 00000000000..a603e2453d0
--- /dev/null
+++ b/qcodes/instrument_drivers/basel/sp983c.py
@@ -0,0 +1,35 @@
+from qcodes.instrument.base import Instrument
+from qcodes.instrument.parameter import MultiParameter
+from qcodes.utils.validators import Enum, Bool
+
+
+class SP983C(Instrument):
+    """
+    A virtual driver for the Basel SP 983 current preamplifier.
+    """
+    
+    def __init__(self, name, **kwargs):
+        super().__init__(name, **kwargs)
+
+        self.add_parameter('gain',
+                           initial_value=1e8,
+                           label='Gain',
+                           unit='V/A',
+                           get_cmd=None, set_cmd=None,
+                           vals=Enum(1e09, 1e08, 1e07, 1e06, 1e05))
+
+        self.add_parameter('fcut',
+                           initial_value=1e3,
+                           label='Cutoff frequency',
+                           unit='Hz',
+                           get_cmd=None, set_cmd=None,
+                           vals=Enum(30., 100., 300., 1e3, 3e3, 10e3, 30e3,
+                                     100e3, 1e6))
+
+    def get_idn(self):
+        vendor = 'Physics Basel'
+        model = 'SP 983c'
+        serial = None
+        firmware = None
+        return {'vendor': vendor, 'model': model,
+                'serial': serial, 'firmware': firmware}

From 25d6150177cbcf38887e2d073ee8d56832b49647 Mon Sep 17 00:00:00 2001
From: GeneralSarsby <matt.sarsby@gmail.com>
Date: Tue, 23 Oct 2018 16:43:01 +0200
Subject: [PATCH 022/719] added the snap XY command to read X and Y together

The snap command records the values of X and Y at a single instant.
This is a way to query values at the same time.
This is important when the time constant is very short, < 100 ms.
 -> tuple (x,y)
---
 qcodes/instrument_drivers/stanford_research/SR830.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/stanford_research/SR830.py b/qcodes/instrument_drivers/stanford_research/SR830.py
index f2b4acb5f10..165e725ba79 100644
--- a/qcodes/instrument_drivers/stanford_research/SR830.py
+++ b/qcodes/instrument_drivers/stanford_research/SR830.py
@@ -386,7 +386,17 @@ def parse_offset_get(s):
                            get_cmd='OUTP? 4',
                            get_parser=float,
                            unit='deg')
-
+        
+        self.add_parameter('SNAP_XY',
+                           label='Coherent snapshot of X and Y',
+                           docstring=("The snap command records the values of X and Y at a single instant."
+                                     " This is a way to query values at the same time."
+                                     " This is important when the time constant is very short, < 100 ms. "
+                                     " -> tuple (x,y) " )
+                           get_cmd='SNAP? 1,2',
+                           get_parser=lambda s: tuple((float(el) for el in s.split(','))),
+                           unit=('V','V'))
+        
         # Data buffer settings
         self.add_parameter('buffer_SR',
                            label='Buffer sample rate',

From c69bb9368831e2ba9062857e02f189c137266062 Mon Sep 17 00:00:00 2001
From: GeneralSarsby <matt.sarsby@gmail.com>
Date: Tue, 23 Oct 2018 18:34:10 +0200
Subject: [PATCH 023/719] Update SR830.py

typographical error.
---
 qcodes/instrument_drivers/stanford_research/SR830.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/stanford_research/SR830.py b/qcodes/instrument_drivers/stanford_research/SR830.py
index 165e725ba79..9f5e7ea69ae 100644
--- a/qcodes/instrument_drivers/stanford_research/SR830.py
+++ b/qcodes/instrument_drivers/stanford_research/SR830.py
@@ -392,7 +392,7 @@ def parse_offset_get(s):
                            docstring=("The snap command records the values of X and Y at a single instant."
                                      " This is a way to query values at the same time."
                                      " This is important when the time constant is very short, < 100 ms. "
-                                     " -> tuple (x,y) " )
+                                     " -> tuple (x,y) " ),
                            get_cmd='SNAP? 1,2',
                            get_parser=lambda s: tuple((float(el) for el in s.split(','))),
                            unit=('V','V'))

From f48618ddf08d2d758d7903e7777247871d75117c Mon Sep 17 00:00:00 2001
From: GeneralSarsby <matt.sarsby@gmail.com>
Date: Tue, 23 Oct 2018 18:40:10 +0200
Subject: [PATCH 024/719] changed the parameter to a function ...

the snap_xy is now a function using "add_function". in turn get_cmd -> call_cmd, and get_parser -> return_parser.

SNAP_XY is also now lowercase
---
 qcodes/instrument_drivers/stanford_research/SR830.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/stanford_research/SR830.py b/qcodes/instrument_drivers/stanford_research/SR830.py
index 9f5e7ea69ae..72389375102 100644
--- a/qcodes/instrument_drivers/stanford_research/SR830.py
+++ b/qcodes/instrument_drivers/stanford_research/SR830.py
@@ -387,14 +387,14 @@ def parse_offset_get(s):
                            get_parser=float,
                            unit='deg')
         
-        self.add_parameter('SNAP_XY',
+        self.add_function('snap_xy',
                            label='Coherent snapshot of X and Y',
                            docstring=("The snap command records the values of X and Y at a single instant."
                                      " This is a way to query values at the same time."
                                      " This is important when the time constant is very short, < 100 ms. "
                                      " -> tuple (x,y) " ),
-                           get_cmd='SNAP? 1,2',
-                           get_parser=lambda s: tuple((float(el) for el in s.split(','))),
+                           call_cmd='SNAP? 1,2',
+                           return_parser=lambda s: tuple((float(el) for el in s.split(','))),
                            unit=('V','V'))
         
         # Data buffer settings

From 6ac42204ff04a29ca1efcacf0aad2998d2c6d3d5 Mon Sep 17 00:00:00 2001
From: GeneralSarsby <matt.sarsby@gmail.com>
Date: Wed, 24 Oct 2018 11:13:35 +0200
Subject: [PATCH 025/719] snap is a flexable class bound method
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

snap is now a class bound method.

it can be used as
lockin.snap('x','y') -> tuple(x,y)
it checks for a correct number of parameters and that the parameters are valid.
It is not fussy about  'x' or  'X', and the confusing phase option can be 'p', 'P', 'Phase', 'phase', or 'θ'. to help with compatibility between similar drivers.
---
 .../stanford_research/SR830.py                | 64 ++++++++++++++++---
 1 file changed, 54 insertions(+), 10 deletions(-)

diff --git a/qcodes/instrument_drivers/stanford_research/SR830.py b/qcodes/instrument_drivers/stanford_research/SR830.py
index 72389375102..68d9d45f4ac 100644
--- a/qcodes/instrument_drivers/stanford_research/SR830.py
+++ b/qcodes/instrument_drivers/stanford_research/SR830.py
@@ -387,16 +387,6 @@ def parse_offset_get(s):
                            get_parser=float,
                            unit='deg')
         
-        self.add_function('snap_xy',
-                           label='Coherent snapshot of X and Y',
-                           docstring=("The snap command records the values of X and Y at a single instant."
-                                     " This is a way to query values at the same time."
-                                     " This is important when the time constant is very short, < 100 ms. "
-                                     " -> tuple (x,y) " ),
-                           call_cmd='SNAP? 1,2',
-                           return_parser=lambda s: tuple((float(el) for el in s.split(','))),
-                           unit=('V','V'))
-        
         # Data buffer settings
         self.add_parameter('buffer_SR',
                            label='Buffer sample rate',
@@ -482,6 +472,60 @@ def parse_offset_get(s):
         self._buffer2_ready = False
 
         self.connect_message()
+    
+    
+    SNAP_PARAMETERS = {
+            'x': '1',   
+            'y': '2',  
+            'r': '3', 
+            'p': '4',   
+        'phase': '4',  
+           'θ' : '4',
+        'aux 1': '5',  
+        'aux 2': '6',   
+        'aux 3': '7',  
+        'aux 4': '8',  
+         'freq': '9',   
+         'ch 1': '10',
+         'ch 2': '11'  
+    }
+    
+    def snap(self, *parameter_names: str) -> Tuple[float, ...]:
+            """
+            Gets the values of either 2, 3, 4, 5 or 6 parameters at a single instant.
+            For example, SNAP? is a way to query values of
+            X and Y (or R and θ) which are taken at the same time. This is important
+            when the time constant is very short. Using the OUTP? or OUTR? com-
+            mands will result in time delays, which may be greater than the time con-
+            stant, between reading X and Y (or R and θ)
+            Thus reading  X,Y  OR  R,θ yields  a  coherent  snapshot  of  the  output  signal.
+            
+            Args:
+                *parameter_names
+                    2 or 3 names of parameters for which the values are
+                    requested; valid names can be found in `PARAMETER_NAMES`
+                    attribute of the driver class
+            Returns:
+                a tuple of floating point values
+                
+            Examples:
+                lockin.snap('x','y') -> tuple(x,y)
+                lockin.snap('aux 1','aux 2','freq','phase') -> tuple(aux1,aux2,freq,phase)
+            """
+            if not 2 <= len(parameter_names) <= 6:
+                raise KeyError(
+                    'It is only possible to request values of 2 to 6 parameters '
+                    'at a time.')
+
+            for name in parameter_names:
+                if name.lower() not in self.SNAP_PARAMETERS:
+                    raise KeyError(f'{name} is not a valid parameter name. Refer '
+                                   f'to `SNAP_PARAMETERS` for a list of valid '
+                                   f'parameter names')
+
+            p_ids = [self.PARAMETER_NAMES[name.lower()] for name in parameter_names]
+            output = self.ask(f'SNAP? {",".join(p_ids)}')
+            return tuple(float(val) for val in output.split(','))
 
     def _set_buffer_SR(self, SR):
         self.write('SRAT {}'.format(SR))

From aa07256ba29ade66fc170f9b16f5231775e7eabf Mon Sep 17 00:00:00 2001
From: GeneralSarsby <matt.sarsby@gmail.com>
Date: Thu, 25 Oct 2018 14:06:33 +0200
Subject: [PATCH 026/719] Update SR830.py

Rewritten the doc string cleaning up the text.
Removed all notions of visa commands.
Removed space from 'aux 4' -> 'aux4' and similar in the SNAP_PARAMETERS.
All lines are less than 80 characters long. To follow PEP8.
Fixed typos.
---
 .../stanford_research/SR830.py                | 98 +++++++++++--------
 1 file changed, 57 insertions(+), 41 deletions(-)

diff --git a/qcodes/instrument_drivers/stanford_research/SR830.py b/qcodes/instrument_drivers/stanford_research/SR830.py
index 68d9d45f4ac..07859286b26 100644
--- a/qcodes/instrument_drivers/stanford_research/SR830.py
+++ b/qcodes/instrument_drivers/stanford_research/SR830.py
@@ -481,51 +481,67 @@ def parse_offset_get(s):
             'p': '4',   
         'phase': '4',  
            'θ' : '4',
-        'aux 1': '5',  
-        'aux 2': '6',   
-        'aux 3': '7',  
-        'aux 4': '8',  
+         'aux1': '5',  
+         'aux2': '6',   
+         'aux3': '7',  
+         'aux4': '8',  
          'freq': '9',   
-         'ch 1': '10',
-         'ch 2': '11'  
+          'ch1': '10',
+          'ch2': '11'  
     }
     
-    def snap(self, *parameter_names: str) -> Tuple[float, ...]:
-            """
-            Gets the values of either 2, 3, 4, 5 or 6 parameters at a single instant.
-            For example, SNAP? is a way to query values of
-            X and Y (or R and θ) which are taken at the same time. This is important
-            when the time constant is very short. Using the OUTP? or OUTR? com-
-            mands will result in time delays, which may be greater than the time con-
-            stant, between reading X and Y (or R and θ)
-            Thus reading  X,Y  OR  R,θ yields  a  coherent  snapshot  of  the  output  signal.
-            
-            Args:
-                *parameter_names
-                    2 or 3 names of parameters for which the values are
-                    requested; valid names can be found in `PARAMETER_NAMES`
-                    attribute of the driver class
-            Returns:
-                a tuple of floating point values
-                
-            Examples:
-                lockin.snap('x','y') -> tuple(x,y)
-                lockin.snap('aux 1','aux 2','freq','phase') -> tuple(aux1,aux2,freq,phase)
+    def snap(self, *parameters: str) -> Tuple[float, ...]:
+        """
+Get between 2 and 6 parameters at a single instant. This provides a coherent
+snapshot of measured signals. Pick up to 6 from: X, Y, R, θ, the aux
+inputs 1-4, frequency, or what is currently displayed on channels 1 and 2.
+Reading X and Y (or R and θ) gives a coherent snapshot of the signal.
+Snap is important when the time constant is very short, a time constant less
+than 100 ms.
+
+Args:
+    *parameters
+        From 2 to 6 strings of names of parameters for which the values are
+        requested. including: 'x', 'y', 'r', 'p', 'phase' or 'θ',
+        'aux1', 'aux2', 'aux3', 'aux4', 'freq', 'ch1', and 'ch2'.
+    
+Returns:
+    A tuple of floating point values in the same order as requested.
+ 
+Units:
+    Volts for x, y, r, and aux 1-4
+    Degrees for θ
+    Hertz for freq
+    Unknown for ch1 and ch2. It will depend on what was set.
+
+Examples:
+    lockin.snap('x','y') -> tuple(x,y)
+    
+    lockin.snap('aux1','aux2','freq','phase') 
+        -> tuple(aux1,aux2,freq,phase)
+
+Limitations:
+     - If X,Y,R and θ are all read, then the values of X,Y are recorded
+       approximately 10 µs apart from R,θ. Thus, the values of X and Y may not
+       yield the exact values of R and θ from a single snap.
+     - The values of the Aux Inputs may have an uncertainty of up to 32 µs.
+     - The frequency is computed only every other period or 40 ms, whichever is
+       longer.  
             """
-            if not 2 <= len(parameter_names) <= 6:
-                raise KeyError(
-                    'It is only possible to request values of 2 to 6 parameters '
-                    'at a time.')
-
-            for name in parameter_names:
-                if name.lower() not in self.SNAP_PARAMETERS:
-                    raise KeyError(f'{name} is not a valid parameter name. Refer '
-                                   f'to `SNAP_PARAMETERS` for a list of valid '
-                                   f'parameter names')
-
-            p_ids = [self.PARAMETER_NAMES[name.lower()] for name in parameter_names]
-            output = self.ask(f'SNAP? {",".join(p_ids)}')
-            return tuple(float(val) for val in output.split(','))
+        if not 2 <= len(parameters) <= 6:
+            raise KeyError(
+                'It is only possible to request values of 2 to 6 parameters'
+                ' at a time.')
+
+        for name in parameters:
+            if name.lower() not in self.SNAP_PARAMETERS:
+                raise KeyError(f'{name} is an unknown parameter. Refer'
+                               f' to `SNAP_PARAMETERS` for a list of valid'
+                               f' parameter names')
+
+        p_ids = [self.SNAP_PARAMETERS[name.lower()] for name in parameters]
+        output = self.ask(f'SNAP? {",".join(p_ids)}')
+        return tuple(float(val) for val in output.split(','))
 
     def _set_buffer_SR(self, SR):
         self.write('SRAT {}'.format(SR))

From fe42007ee301b0e0c094a311ee643a8c297f6179 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 1 Nov 2018 18:40:09 +0100
Subject: [PATCH 027/719] Make ParamSpec hashable (implement __hash__)

---
 qcodes/dataset/param_spec.py | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/qcodes/dataset/param_spec.py b/qcodes/dataset/param_spec.py
index 3dce28f3ca2..d10473dc067 100644
--- a/qcodes/dataset/param_spec.py
+++ b/qcodes/dataset/param_spec.py
@@ -105,6 +105,26 @@ def __eq__(self, other):
                 return False
         return True
 
+    def __hash__(self) -> int:
+        """Allow ParamSpecs in data structures that use hashing (i.e. sets)"""
+        attrs_with_strings = ['name', 'type', 'label', 'unit']
+        attrs_with_lists = ['_inferred_from', '_depends_on']
+
+        # First, get the hash of the tuple with all the relevant attributes
+        all_attr_tuple_hash = hash(
+            tuple(getattr(self, attr) for attr in attrs_with_strings)
+            + tuple(tuple(getattr(self, attr)) for attr in attrs_with_lists)
+        )
+        hash_value = all_attr_tuple_hash
+
+        # Then, XOR it with the individual hashes of all relevant attributes
+        for attr in attrs_with_strings:
+            hash_value = hash_value ^ hash(getattr(self, attr))
+        for attr in attrs_with_lists:
+            hash_value = hash_value ^ hash(tuple(getattr(self, attr)))
+
+        return hash_value
+
     def serialize(self) -> Dict[str, Any]:
         """
         Write the ParamSpec as a dictionary

From 513149fd1e516e60d0ffc1370fb24916423ac314 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 1 Nov 2018 18:41:02 +0100
Subject: [PATCH 028/719] Test ParamSpec hashing with hypothesis

---
 qcodes/tests/dataset/test_paramspec.py | 94 +++++++++++++++++++++++++-
 1 file changed, 91 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_paramspec.py b/qcodes/tests/dataset/test_paramspec.py
index 1a573128ce4..016792ad484 100644
--- a/qcodes/tests/dataset/test_paramspec.py
+++ b/qcodes/tests/dataset/test_paramspec.py
@@ -1,6 +1,7 @@
-import pytest
+from keyword import iskeyword
 from numbers import Number
 
+import pytest
 from numpy import ndarray
 from hypothesis import given
 import hypothesis.strategies as hst
@@ -8,6 +9,30 @@
 from qcodes.dataset.param_spec import ParamSpec
 
 
+@hst.defines_strategy
+def valid_identifier(**kwargs):
+    """Return a strategy which generates a valid Python Identifier"""
+    if 'min_size' not in kwargs:
+        kwargs['min_size'] = 4
+    return hst.text(
+        alphabet="abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_",
+        **kwargs).filter(
+        lambda x: x[0].isalpha() and x.isidentifier() and not (iskeyword(x))
+    )
+
+
+# This strategy generates a dict of kwargs needed to instantiate a valid
+# ParamSpec object
+valid_paramspec_kwargs = hst.fixed_dictionaries(
+    {'name': valid_identifier(min_size=1),
+     'paramtype': hst.sampled_from(['numeric', 'array', 'text']),
+     'label': hst.one_of(hst.none(), hst.text()),
+     'unit': hst.one_of(hst.none(), hst.text()),
+     'depends_on': hst.lists(hst.text(min_size=1), min_size=0),
+     'inferred_from': hst.lists(hst.text(min_size=1), min_size=0)
+     })
+
+
 @pytest.fixture
 def version_0_serializations():
     sers = []
@@ -162,7 +187,6 @@ def test_copy(name1, name2, name3):
 
 
 def test_serialize():
-
     p1 = ParamSpec('p1', 'numeric', 'paramspec one', 'no unit',
                    depends_on=['some', 'thing'], inferred_from=['bab', 'bob'])
 
@@ -177,7 +201,71 @@ def test_serialize():
 
 
 def test_deserialize(version_0_serializations, version_0_deserializations):
-
     for sdict, ps in zip(version_0_serializations, version_0_deserializations):
         deps = ParamSpec.deserialize(sdict)
         assert ps == deps
+
+
+@given(paramspecs=hst.lists(valid_paramspec_kwargs, min_size=2, max_size=2))
+def test_hash(paramspecs):
+    p1 = ParamSpec(**paramspecs[0])
+    p2 = ParamSpec(**paramspecs[1])
+
+    # call __hash__
+    p1_h = hash(p1)
+    p2_h = hash(p2)
+
+    # make a set
+    p_set = {p1, p2}
+
+    # test that the hash equality follows object equality
+    if p1 == p2:
+        assert p1_h == p2_h
+        assert 1 == len(p_set)
+    else:
+        assert p1_h != p2_h
+        assert 2 == len(p_set)
+
+
+@given(paramspecs=hst.lists(valid_paramspec_kwargs, min_size=6, max_size=6),
+       add_to_1_inf=hst.booleans(),
+       add_to_1_dep=hst.booleans(),
+       add_to_2_inf=hst.booleans(),
+       add_to_2_dep=hst.booleans(),
+       )
+def test_hash_with_deferred_and_inferred_as_paramspecs(
+        paramspecs, add_to_1_inf, add_to_1_dep, add_to_2_inf, add_to_2_dep):
+    """
+    Test that hashing works if 'inferred_from' and/or 'depend_on' contain
+    actual ParamSpec instances and not just strings.
+    """
+    hst.assume(add_to_1_inf or add_to_1_dep or add_to_2_inf or add_to_2_dep)
+
+    # Add ParamSpecs to 'inferred_from' and/or 'depend_on' lists next to
+    # strings (that are generated by the main strategy)
+    if add_to_1_inf:
+        paramspecs[0]['inferred_from'].append(ParamSpec(**paramspecs[2]))
+    if add_to_1_dep:
+        paramspecs[0]['depends_on'].append(ParamSpec(**paramspecs[3]))
+    if add_to_2_inf:
+        paramspecs[1]['inferred_from'].append(ParamSpec(**paramspecs[4]))
+    if add_to_2_dep:
+        paramspecs[1]['depends_on'].append(ParamSpec(**paramspecs[5]))
+
+    p1 = ParamSpec(**paramspecs[0])
+    p2 = ParamSpec(**paramspecs[1])
+
+    # call __hash__
+    p1_h = hash(p1)
+    p2_h = hash(p2)
+
+    # make a set
+    p_set = {p1, p2}
+
+    # test that the hash equality follows object equality
+    if p1 == p2:
+        assert p1_h == p2_h
+        assert 1 == len(p_set)
+    else:
+        assert p1_h != p2_h
+        assert 2 == len(p_set)

From 8aa94f45bb757633f88e9a4f7769483c29762787 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 1 Nov 2018 18:41:24 +0100
Subject: [PATCH 029/719] Test hashes of equal/copied ParamSpecs explicitly

---
 qcodes/tests/dataset/test_paramspec.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/qcodes/tests/dataset/test_paramspec.py b/qcodes/tests/dataset/test_paramspec.py
index 016792ad484..fc6bbeb71a9 100644
--- a/qcodes/tests/dataset/test_paramspec.py
+++ b/qcodes/tests/dataset/test_paramspec.py
@@ -158,15 +158,16 @@ def test_add_inferred_from(name1, name2, name3):
 
 @given(
     name1=hst.text(min_size=4, alphabet=alphabet),
-    name2=hst.text(min_size=4, alphabet=alphabet),
-    name3=hst.text(min_size=4, alphabet=alphabet),
+    name2=hst.text(min_size=4, alphabet=alphabet)
 )
-def test_copy(name1, name2, name3):
-
+def test_copy(name1, name2):
     ps_indep = ParamSpec(name1, "numeric")
-    ps = ParamSpec(name3, "numeric", depends_on=[ps_indep])
+    ps = ParamSpec(name2, "numeric", depends_on=[ps_indep, 'other_param'])
     ps_copy = ps.copy()
 
+    assert ps_copy == ps
+    assert hash(ps_copy) == hash(ps)
+
     att_names = ["name", "type", "label", "unit",
                  "_inferred_from", "_depends_on"]
 
@@ -185,6 +186,9 @@ def test_copy(name1, name2, name3):
             setattr(ps_copy, att, attributes[att] + ['bob'])
         assert getattr(ps, att) == attributes[att]
 
+    assert ps_copy != ps
+    assert hash(ps_copy) != hash(ps)
+
 
 def test_serialize():
     p1 = ParamSpec('p1', 'numeric', 'paramspec one', 'no unit',

From 89520f4bcf5988f1b28201eec8d3552769a60506 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Tue, 13 Nov 2018 10:56:32 +0100
Subject: [PATCH 030/719] make import of legume conditional

---
 qcodes/instrument_drivers/tektronix/AWG5014.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index a66297d5d3e..fce55e8a328 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -19,7 +19,12 @@
 from qcodes.utils.deprecate import deprecate
 from pyvisa.errors import VisaIOError
 
-from broadbean.tools import is_subsequence, get_element_channel_ids
+# conditionally import legume for support of legume type sequences
+try:
+    from legume.tools import is_subsequence, get_element_channel_ids
+    USE_LEGUME = True
+except ImportError:
+    USE_LEGUME = False
 
 
 log = logging.getLogger(__name__)
@@ -1061,6 +1066,10 @@ def make_send_and_load_awg_file_from_forged_sequence(
             preservechannelsettings: see :meth:`~make_send_and_load_awg_file`
 
         """
+        if not USE_LEGUME:
+            raise RuntimeError(
+                'The method "make_send_and_load_awg_file_from_forged_sequence" is '
+                ' only available with the `legume` module installed')
         n_channels = 4
         self.available_waveform_channels = list(range(1, n_channels+1))
         self.available_marker_channels = [

From 0576efecffa3ba40022e7daae7fe384ae1d69099 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Tue, 13 Nov 2018 12:15:50 +0100
Subject: [PATCH 031/719] rename legume->lomentum

---
 qcodes/instrument_drivers/tektronix/AWG5014.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index fce55e8a328..3b36f40cf2b 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -19,12 +19,12 @@
 from qcodes.utils.deprecate import deprecate
 from pyvisa.errors import VisaIOError
 
-# conditionally import legume for support of legume type sequences
+# conditionally import lomentum for support of lomentum type sequences
 try:
-    from legume.tools import is_subsequence, get_element_channel_ids
-    USE_LEGUME = True
+    from lomentum.tools import is_subsequence, get_element_channel_ids
+    USE_LOMENTUM = True
 except ImportError:
-    USE_LEGUME = False
+    USE_LOMENTUM = False
 
 
 log = logging.getLogger(__name__)
@@ -1066,10 +1066,10 @@ def make_send_and_load_awg_file_from_forged_sequence(
             preservechannelsettings: see :meth:`~make_send_and_load_awg_file`
 
         """
-        if not USE_LEGUME:
+        if not USE_LOMENTUM:
             raise RuntimeError(
                 'The method "make_send_and_load_awg_file_from_forged_sequence" is '
-                ' only available with the `legume` module installed')
+                ' only available with the `lomentum` module installed')
         n_channels = 4
         self.available_waveform_channels = list(range(1, n_channels+1))
         self.available_marker_channels = [

From 063c15acf38c8b1b40f262b66865b2c6ab149235 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 24 Oct 2018 15:54:52 +0200
Subject: [PATCH 032/719] Add very rudimentary copy-paste module

---
 qcodes/dataset/database_copy_paste.py | 81 +++++++++++++++++++++++++++
 1 file changed, 81 insertions(+)
 create mode 100644 qcodes/dataset/database_copy_paste.py

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
new file mode 100644
index 00000000000..41e82ee0451
--- /dev/null
+++ b/qcodes/dataset/database_copy_paste.py
@@ -0,0 +1,81 @@
+from qcodes.dataset.data_set import DataSet
+from qcodes.dataset.sqlite_base import (atomic,
+                                        connect,
+                                        get_last_experiment,
+                                        insert_column,
+                                        SomeConnection)
+
+
+def copy_dataset_into_db(dataset: DataSet, path_to_db: str) -> None:
+    """
+    Insert the given dataset into the specified database file as the latest
+    run. The database file must exist and its latest experiment must have name
+    and sample_name matching that of the dataset's parent experiment
+
+    Args:
+        dataset: A dataset representing the run to be copied
+        path_to_db: The path to the target DB into which the run should be
+          inserted
+    """
+
+    if not dataset.completed:
+        raise ValueError('Dataset not completed. An incomplete dataset '
+                         'can not be copied.')
+
+    source_conn = dataset.conn
+    target_conn = connect(path_to_db)
+
+    exp_id = get_last_experiment(target_conn)
+
+    with atomic(target_conn) as target_conn:
+        _copy_runs_table_entries(source_conn,
+                                 target_conn,
+                                 dataset.run_id,
+                                 exp_id)
+
+
+def _copy_runs_table_entries(source_conn: SomeConnection,
+                             target_conn: SomeConnection,
+                             source_run_id: int,
+                             target_exp_id: int) -> None:
+    """
+    Copy an entire runs table row from one DB and paste it all
+    (expect the primary key) into another DB. The two DBs may be the same.
+
+    This function should be executed with an atomically guarded target_conn
+    as a part of a larger atomic transaction
+    """
+    runs_row_query = """
+                    SELECT *
+                    FROM runs
+                    WHERE run_id = ?
+                    """
+    cursor = source_conn.cursor()
+    cursor.execute(runs_row_query, (source_run_id,))
+    source_runs_row = cursor.fetchall()[0]
+    source_colnames = set(source_runs_row.keys())
+
+    # There might not be any runs in the target DB, hence we ask PRAGMA
+    cursor = target_conn.cursor()
+    cursor.execute("PRAGMA table_info(runs)", ())
+    tab_info = cursor.fetchall()
+    target_colnames = set([r['name'] for r in tab_info])
+
+    for colname in source_colnames.difference(target_colnames):
+        insert_column(target_conn, 'runs', colname)
+
+    # the first entry is "run_id"
+    sql_colnames = str(tuple(source_runs_row.keys()[1:])).replace("'", "")
+    sql_placeholders = '(' + ','.join('?'*len(sql_colnames)) + ')'
+
+    sql_insert_values = f"""
+                         INSERT INTO runs
+                         {sql_colnames}
+                         VALUES
+                         {sql_placeholders}
+                         """
+    # the first two entries in source_runs_row are run_id and exp_id
+    values = tuple([exp_id] + [val for val in source_runs_row[2:]])
+
+    cursor = target_conn.cursor()
+    cursor.execute(sql_insert_values, values)
\ No newline at end of file

From bb51e60f7431b42af141ec560a4fd69cf9b72996 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 25 Oct 2018 11:03:29 +0200
Subject: [PATCH 033/719] Add sql_placeholder_string function

---
 qcodes/dataset/database_copy_paste.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 41e82ee0451..87c366854d0 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -5,6 +5,11 @@
                                         insert_column,
                                         SomeConnection)
 
+def sql_placeholder_string(n: int) -> str:
+    """
+    Return an SQL placeholder string of length n.
+    """
+    return '(' + ','.join('?'*n) + ')'
 
 def copy_dataset_into_db(dataset: DataSet, path_to_db: str) -> None:
     """

From 89a45807106076381a54bec3a4366017fabc286d Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 25 Oct 2018 12:52:25 +0200
Subject: [PATCH 034/719] Make Experiment take a connection and use that in
 funcs

---
 qcodes/dataset/experiment_container.py | 46 +++++++++++++++++---------
 1 file changed, 31 insertions(+), 15 deletions(-)

diff --git a/qcodes/dataset/experiment_container.py b/qcodes/dataset/experiment_container.py
index 36ccdc69dcd..08b3653cb3c 100644
--- a/qcodes/dataset/experiment_container.py
+++ b/qcodes/dataset/experiment_container.py
@@ -12,7 +12,8 @@
                                         connect, transaction,
                                         get_last_experiment, get_experiments,
                                         get_experiment_name_from_experiment_id,
-                                        get_sample_name_from_experiment_id)
+                                        get_sample_name_from_experiment_id,
+                                        SomeConnection)
 from qcodes.dataset.sqlite_base import new_experiment as ne
 from qcodes.dataset.database import get_DB_location, get_DB_debug
 
@@ -25,7 +26,8 @@ def __init__(self, path_to_db: Optional[str]=None,
                  exp_id: Optional[int]=None,
                  name: Optional[str]=None,
                  sample_name: Optional[str]=None,
-                 format_string: str="{}-{}-{}") -> None:
+                 format_string: str="{}-{}-{}",
+                 conn: Optional[SomeConnection]=None) -> None:
         """
         Create or load an experiment. If exp_id is None, a new experiment is
         created. If exp_id is not None, an experiment is loaded.
@@ -39,9 +41,11 @@ def __init__(self, path_to_db: Optional[str]=None,
               is not None
             format_string: The format string used to name result-tables.
               Ignored if exp_id is not None.
+            conn: connection to the database. If not supplied, a new connection
+              to the DB file specified in the config is made
         """
         self._path_to_db = path_to_db or get_DB_location()
-        self.conn = connect(self.path_to_db, get_DB_debug())
+        self.conn = conn or connect(self.path_to_db, get_DB_debug())
 
         max_id = len(get_experiments(self.conn))
 
@@ -186,7 +190,8 @@ def experiments()->List[Experiment]:
 
 def new_experiment(name: str,
                    sample_name: str,
-                   format_string: Optional[str] = "{}-{}-{}") -> Experiment:
+                   format_string: Optional[str]="{}-{}-{}",
+                   conn: Optional[SomeConnection]=None) -> Experiment:
     """
     Create a new experiment (in the database file from config)
 
@@ -195,11 +200,15 @@ def new_experiment(name: str,
         sample_name: the name of the current sample
         format_string: basic format string for table-name
             must contain 3 placeholders.
+        conn: connection to the database. If not supplied, a new connection
+          to the DB file specified in the config is made
     Returns:
         the new experiment
     """
+    conn = conn or connect(get_DB_location())
     return Experiment(name=name, sample_name=sample_name,
-                      format_string=format_string)
+                      format_string=format_string,
+                      conn=conn)
 
 
 def load_experiment(exp_id: int) -> Experiment:
@@ -231,7 +240,8 @@ def load_last_experiment() -> Experiment:
 
 
 def load_experiment_by_name(name: str,
-                            sample: Optional[str] = None) -> Experiment:
+                            sample: Optional[str] = None,
+                            conn: Optional[SomeConnection]=None) -> Experiment:
     """
     Try to load experiment with the specified name.
 
@@ -241,6 +251,8 @@ def load_experiment_by_name(name: str,
     Args:
         name: the name of the experiment
         sample: the name of the sample
+        conn: connection to the database. If not supplied, a new connection
+          to the DB file specified in the config is made
 
     Returns:
         the requested experiment
@@ -248,7 +260,8 @@ def load_experiment_by_name(name: str,
     Raises:
         ValueError if the name is not unique and sample name is None.
     """
-    conn = connect(get_DB_location())
+    conn = conn or connect(get_DB_location())
+
     if sample:
         sql = """
         SELECT
@@ -288,26 +301,29 @@ def load_experiment_by_name(name: str,
 
 
 def load_or_create_experiment(experiment_name: str,
-                              sample_name: Optional[str] = None
-                              ) -> Experiment:
+                              sample_name: Optional[str] = None,
+                              conn: Optional[SomeConnection]=None)->Experiment:
     """
     Find and return an experiment with the given name and sample name,
     or create one if not found.
 
     Args:
-        experiment_name
-            Name of the experiment to find or create
-        sample_name
-            Name of the sample
+        experiment_name: Name of the experiment to find or create
+        sample_name: Name of the sample
+        conn: Connection to the database. If not supplied, a new connection
+          to the DB file specified in the config is made
 
     Returns:
         The found or created experiment
     """
+    conn = conn or connect(get_DB_location())
     try:
-        experiment = load_experiment_by_name(experiment_name, sample_name)
+        experiment = load_experiment_by_name(experiment_name, sample_name,
+                                             conn=conn)
     except ValueError as exception:
         if "Experiment not found" in str(exception):
-            experiment = new_experiment(experiment_name, sample_name)
+            experiment = new_experiment(experiment_name, sample_name,
+                                        conn=conn)
         else:
             raise exception
     return experiment

From 3d98f5856f5ec9504f275a0c910d269eb6cac911 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 24 Oct 2018 16:39:56 +0200
Subject: [PATCH 035/719] Add first partial copy function

---
 qcodes/dataset/database_copy_paste.py | 71 +++++++++++++++++++++++++--
 1 file changed, 68 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 87c366854d0..e11f902bc62 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -1,17 +1,82 @@
+import numpy as np
+
 from qcodes.dataset.data_set import DataSet
+from qcodes.dataset.experiment_container import load_or_create_experiment
 from qcodes.dataset.sqlite_base import (atomic,
                                         connect,
                                         get_last_experiment,
                                         insert_column,
                                         SomeConnection)
 
+
 def sql_placeholder_string(n: int) -> str:
     """
     Return an SQL placeholder string of length n.
     """
     return '(' + ','.join('?'*n) + ')'
 
-def copy_dataset_into_db(dataset: DataSet, path_to_db: str) -> None:
+
+def copy_runs_into_db(source_db_path: str,
+                      target_db_path: str, *run_ids) -> None:
+    """
+    Copy a selection of runs into another DB file. All runs must come from the
+    same experiment. They will be added to an experiment with the same name
+    and sample_name in the target db. If such an experiment does not exist,
+    it will be created.
+
+    Args:
+        source_db_path: Path to the source DB file
+        target_db_path: Path to the target DB file
+        run_ids: The run_ids of the runs to copy into the target DB file
+    """
+
+    # Validate that all runs are from the same experiment
+
+    sql_placeholders = sql_placeholder_string(len(run_ids))
+    exp_id_query = f"""
+                    SELECT exp_id
+                    FROM runs
+                    WHERE run_id IN {sql_placeholders}
+                    """
+    source_conn = connect(source_db_path)
+    cursor = source_conn.cursor()
+    cursor.execute(exp_id_query, run_ids)
+    rows = cursor.fetchall()
+    source_exp_ids = np.unique([exp_id for row in rows for exp_id in row])
+    if len(source_exp_ids) != 1:
+        raise ValueError('Did not receive runs from a single experiment. '
+                         f'Got runs from experiments {source_exp_ids}')
+
+    # Fetch the name and sample name of the runs' experiment
+
+    names_query = """
+                  SELECT name, sample_name
+                  FROM experiments
+                  WHERE exp_id = ?
+                  """
+    cursor = source_conn.cursor()
+    cursor.execute(names_query, (source_exp_ids[0],))
+    row = cursor.fetchall()[0]
+    (source_exp_name, source_sample_name) = (row['name'], row['sample_name'])
+
+    # Massage the target DB file to accomodate the runs
+    # (create new experiment if needed)
+
+    target_conn = connect(target_db_path)
+
+    # this function raises if the target DB file has several experiments
+    # matching both the name and sample_name
+
+    load_or_create_experiment(source_exp_name, source_sample_name,
+                              conn=target_conn)
+
+    # Finally insert the runs
+    for run_id in run_ids:
+        copy_single_dataset_into_db(DataSet(run_id=run_id, conn=source_conn),
+                                    target_db_path)
+
+
+def copy_single_dataset_into_db(dataset: DataSet, path_to_db: str) -> None:
     """
     Insert the given dataset into the specified database file as the latest
     run. The database file must exist and its latest experiment must have name
@@ -71,7 +136,7 @@ def _copy_runs_table_entries(source_conn: SomeConnection,
 
     # the first entry is "run_id"
     sql_colnames = str(tuple(source_runs_row.keys()[1:])).replace("'", "")
-    sql_placeholders = '(' + ','.join('?'*len(sql_colnames)) + ')'
+    sql_placeholders = sql_placeholder_string(len(source_runs_row.keys())-1)
 
     sql_insert_values = f"""
                          INSERT INTO runs
@@ -80,7 +145,7 @@ def _copy_runs_table_entries(source_conn: SomeConnection,
                          {sql_placeholders}
                          """
     # the first two entries in source_runs_row are run_id and exp_id
-    values = tuple([exp_id] + [val for val in source_runs_row[2:]])
+    values = tuple([target_exp_id] + [val for val in source_runs_row[2:]])
 
     cursor = target_conn.cursor()
     cursor.execute(sql_insert_values, values)
\ No newline at end of file

From de60f6bd6de661e569962e5bb6cba406f4203433 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 25 Oct 2018 15:19:57 +0200
Subject: [PATCH 036/719] Protect copy_single_dataset against inserting
 duplicates

---
 qcodes/dataset/database_copy_paste.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index e11f902bc62..992c7dc317d 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -95,6 +95,17 @@ def copy_single_dataset_into_db(dataset: DataSet, path_to_db: str) -> None:
     source_conn = dataset.conn
     target_conn = connect(path_to_db)
 
+    already_in_query = """
+                       SELECT run_id
+                       FROM runs
+                       WHERE guid = ?
+                       """
+    cursor = target_conn.cursor()
+    cursor.execute(already_in_query, (dataset.guid,))
+    res = cursor.fetchall()
+    if len(res) > 0:
+        return
+
     exp_id = get_last_experiment(target_conn)
 
     with atomic(target_conn) as target_conn:

From 6f7b0b748ee810b91b7fe2a2936c94dc8d15bf4f Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 26 Oct 2018 10:27:24 +0200
Subject: [PATCH 037/719] Add experiment table update to run copy

---
 qcodes/dataset/database_copy_paste.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 992c7dc317d..97c967242a9 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -113,6 +113,7 @@ def copy_single_dataset_into_db(dataset: DataSet, path_to_db: str) -> None:
                                  target_conn,
                                  dataset.run_id,
                                  exp_id)
+        _update_run_counter(target_conn, exp_id)
 
 
 def _copy_runs_table_entries(source_conn: SomeConnection,
@@ -159,4 +160,14 @@ def _copy_runs_table_entries(source_conn: SomeConnection,
     values = tuple([target_exp_id] + [val for val in source_runs_row[2:]])
 
     cursor = target_conn.cursor()
-    cursor.execute(sql_insert_values, values)
\ No newline at end of file
+    cursor.execute(sql_insert_values, values)
+
+
+def _update_run_counter(target_conn: SomeConnection, target_exp_id) -> None:
+    update_sql = """
+                 UPDATE experiments
+                 SET run_counter = run_counter + 1
+                 WHERE exp_id = ?
+                 """
+    cursor = target_conn.cursor()
+    cursor.execute(update_sql, (target_exp_id,))
\ No newline at end of file

From 01b2579960af52322686d994cae1d88d826453d5 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 26 Oct 2018 16:37:06 +0200
Subject: [PATCH 038/719] Add basic database copy_paste test

---
 qcodes/tests/dataset/temporary_databases.py   | 12 ++++++++++
 .../tests/dataset/test_database_copy_paste.py | 22 +++++++++++++++++++
 2 files changed, 34 insertions(+)
 create mode 100644 qcodes/tests/dataset/test_database_copy_paste.py

diff --git a/qcodes/tests/dataset/temporary_databases.py b/qcodes/tests/dataset/temporary_databases.py
index 2d8d1ca3cae..e60c6dc997c 100644
--- a/qcodes/tests/dataset/temporary_databases.py
+++ b/qcodes/tests/dataset/temporary_databases.py
@@ -25,6 +25,18 @@ def empty_temp_db():
         yield
 
 
+@pytest.fixture(scope='function')
+def two_empty_temp_dbs():
+    """
+    Yield the paths of two empty files. Meant for use with the
+    test_database_copy_paste
+    """
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        source_path = os.path.join(tmpdirname, 'source.db')
+        target_path = os.path.join(tmpdirname, 'target.db')
+        yield (source_path, target_path)
+
+
 @pytest.fixture(scope='function')
 def experiment(empty_temp_db):
     e = new_experiment("test-experiment", sample_name="test-sample")
diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
new file mode 100644
index 00000000000..c1877b949c3
--- /dev/null
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -0,0 +1,22 @@
+import pytest
+
+from qcodes.dataset.sqlite_base import connect
+from qcodes.dataset.experiment_container import Experiment
+from qcodes.dataset.data_set import DataSet
+from qcodes.dataset.database_copy_paste import copy_runs_into_db
+from qcodes.tests.dataset.temporary_databases import two_empty_temp_dbs
+
+
+def test_basic_copy_paste(two_empty_temp_dbs):
+    source_path, target_path = two_empty_temp_dbs
+
+    source_conn = connect(source_path)
+    target_conn = connect(target_path)
+
+    exp = Experiment(conn=source_conn)
+    dataset = DataSet(conn=source_conn)
+
+    with pytest.raises(ValueError, match='Dataset not completed'):
+        copy_runs_into_db(source_path, target_path, dataset.run_id)
+
+

From 78e767bb26798552b62257ec9c7c90f15b7dbec3 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Sat, 27 Oct 2018 15:31:29 +0200
Subject: [PATCH 039/719] Add complete single run copy function

---
 qcodes/dataset/database_copy_paste.py         | 152 +++++++++++++++++-
 .../tests/dataset/test_database_copy_paste.py |  19 ++-
 2 files changed, 168 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 97c967242a9..083f0583059 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -4,6 +4,7 @@
 from qcodes.dataset.experiment_container import load_or_create_experiment
 from qcodes.dataset.sqlite_base import (atomic,
                                         connect,
+                                        format_table_name,
                                         get_last_experiment,
                                         insert_column,
                                         SomeConnection)
@@ -114,6 +115,12 @@ def copy_single_dataset_into_db(dataset: DataSet, path_to_db: str) -> None:
                                  dataset.run_id,
                                  exp_id)
         _update_run_counter(target_conn, exp_id)
+        _copy_layouts_and_dependencies(source_conn,
+                                       target_conn,
+                                       dataset.run_id)
+        _copy_results_table(source_conn,
+                            target_conn,
+                            dataset.run_id)
 
 
 def _copy_runs_table_entries(source_conn: SomeConnection,
@@ -122,7 +129,8 @@ def _copy_runs_table_entries(source_conn: SomeConnection,
                              target_exp_id: int) -> None:
     """
     Copy an entire runs table row from one DB and paste it all
-    (expect the primary key) into another DB. The two DBs may be the same.
+    (expect the primary key) into another DB. The two DBs may not be the same.
+    Note that this function does not create a new results table
 
     This function should be executed with an atomically guarded target_conn
     as a part of a larger atomic transaction
@@ -164,10 +172,150 @@ def _copy_runs_table_entries(source_conn: SomeConnection,
 
 
 def _update_run_counter(target_conn: SomeConnection, target_exp_id) -> None:
+    """
+    Update the run_counter in the target DB experiments table
+    """
     update_sql = """
                  UPDATE experiments
                  SET run_counter = run_counter + 1
                  WHERE exp_id = ?
                  """
     cursor = target_conn.cursor()
-    cursor.execute(update_sql, (target_exp_id,))
\ No newline at end of file
+    cursor.execute(update_sql, (target_exp_id,))
+
+
+def _copy_layouts_and_dependencies(target_conn: SomeConnection,
+                                   source_conn: SomeConnection,
+                                   source_run_id: int) -> None:
+    """
+    Copy over the layouts and dependencies tables. Note that the layout_ids
+    are not preserved in the target DB, but of course their relationships are
+    (e.g. layout_id 10 that depends on layout_id 9 might be inserted as
+    layout_id 2 that depends on layout_id 1)
+    """
+    layout_query = """
+                   SELECT layout_id, run_id, parameter, label, unit, inferred_from
+                   FROM layouts
+                   WHERE run_id = ?
+                   """
+    cursor = source_conn.cursor()
+    cursor.execute(layout_query, (source_run_id,))
+    rows = cursor.fetchall()
+
+    layout_insert = """
+                    INSERT INTO layouts
+                    (run_id, parameter, label, unit, inferred_from)
+                    VALUES (?,?,?,?,?)
+                    """
+
+    colnames = ('run_id', 'parameter', 'label', 'unit', 'inferred_from')
+    cursor = target_conn.cursor()
+    source_layout_ids = []
+    target_layout_ids = []
+    for row in rows:
+        values = tuple(row[colname] for colname in colnames)
+        cursor.execute(layout_insert, values)
+        source_layout_ids.append(row['layout_id'])
+        target_layout_ids.append(cursor.lastrowid)
+
+    # for the dependencies, we need a map from source layout_id to
+    # target layout_id
+    layout_id_map = dict(zip(source_layout_ids, target_layout_ids))
+
+    placeholders = sql_placeholder_string(len(source_layout_ids))
+
+    deps_query = f"""
+                 SELECT dependent, independent, axis_num
+                 FROM dependencies
+                 WHERE dependent IN {placeholders}
+                 OR independent IN {placeholders}
+                 """
+
+    cursor = source_conn.cursor()
+    cursor.execute(deps_query, tuple(source_layout_ids*2))
+    rows = cursor.fetchall()
+
+    deps_insert = """
+                  INSERT INTO dependencies
+                  (dependent, independent, axis_num)
+                  VALUES (?,?,?)
+                  """
+    cursor = target_conn.cursor()
+
+    for row in rows:
+        values = (layout_id_map[row['dependent']],
+                  layout_id_map[row['independent']],
+                  row['axis_num'])
+        cursor.execute(deps_insert, values)
+
+
+def _copy_results_table(source_conn: SomeConnection,
+                        target_conn: SomeConnection,
+                        source_run_id) -> None:
+    """
+    Copy the contents of the results table. Creates a new results_table with
+    a name appropriate for the target DB and updates the
+    """
+    table_name_query = """
+                       SELECT result_table_name, name
+                       FROM runs
+                       WHERE run_id = ?
+                       """
+    cursor = source_conn.cursor()
+    cursor.execute(table_name_query, (source_run_id,))
+    row = cursor.fetchall()[0]
+    table_name = row['result_table_name']
+    run_name = row['name']
+
+    format_string_query = """
+                          SELECT format_string, run_counter, experiments.exp_id
+                          FROM experiments
+                          INNER JOIN runs
+                          ON runs.exp_id = experiments.exp_id
+                          WHERE runs.run_id = ?
+                          """
+    cursor.execute(format_string_query, (source_run_id,))
+    row = cursor.fetchall()[0]
+    format_string = row['format_string']
+    run_counter = row['run_counter']
+    exp_id = int(row['exp_id'])
+
+    get_data_query = f"""
+                     SELECT *
+                     FROM "{table_name}"
+                     """
+
+    cursor.execute(get_data_query)
+    data_rows = cursor.fetchall()
+    data_columns = data_rows[0].keys()
+    data_columns.remove('id')
+
+    target_table_name = format_table_name(format_string,
+                                          run_name,
+                                          exp_id,
+                                          run_counter)
+
+    column_names = ','.join(data_columns)
+    make_table = f"""
+                  CREATE TABLE "{target_table_name}" (
+                      id INTEGER PRIMARY KEY,
+                      {column_names}
+                  )
+                  """
+
+    cursor = target_conn.cursor()
+    cursor.execute(make_table)
+
+    # according to one of our reports, multiple single-row inserts
+    # are okay if there's only one commit
+
+    value_placeholders = sql_placeholder_string(len(data_columns))
+    insert_data = f"""
+                   INSERT INTO "{target_table_name}"
+                   ({column_names})
+                   values {value_placeholders}
+                   """
+
+    for row in data_rows:
+        # the first row entry is the ID, which is automatically inserted
+        cursor.execute(insert_data, tuple(v for v in row[1:]))
diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index c1877b949c3..3d68c9479db 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -1,15 +1,22 @@
 import pytest
+import numpy as np
 
 from qcodes.dataset.sqlite_base import connect
 from qcodes.dataset.experiment_container import Experiment
 from qcodes.dataset.data_set import DataSet
 from qcodes.dataset.database_copy_paste import copy_runs_into_db
 from qcodes.tests.dataset.temporary_databases import two_empty_temp_dbs
+from qcodes.tests.dataset.test_descriptions import some_paramspecs
 
 
-def test_basic_copy_paste(two_empty_temp_dbs):
+def test_basic_copy_paste(two_empty_temp_dbs, some_paramspecs):
     source_path, target_path = two_empty_temp_dbs
 
+    type_casters = {'numeric': float,
+                    'array': (lambda x: np.array(x) if hasattr(x, '__iter__')
+                              else np.array([x])),
+                    'text': str}
+
     source_conn = connect(source_path)
     target_conn = connect(target_path)
 
@@ -19,4 +26,14 @@ def test_basic_copy_paste(two_empty_temp_dbs):
     with pytest.raises(ValueError, match='Dataset not completed'):
         copy_runs_into_db(source_path, target_path, dataset.run_id)
 
+    for ps in some_paramspecs[1].values():
+        dataset.add_parameter(ps)
+
+    value = 0  # an arbitrary data value
+    result = {ps.name: type_casters[ps.type](value)
+              for ps in some_paramspecs[1].values()}
+
+    dataset.add_result(result)
+    dataset.mark_complete()
 
+    copy_runs_into_db(source_path, target_path, dataset.run_id)

From 2c04cac34a612266505f3ab1ce46a3a846f2f6f8 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 29 Oct 2018 13:01:07 +0100
Subject: [PATCH 040/719] Refactor table name formatting into function

---
 qcodes/dataset/sqlite_base.py | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 00dc00be102..3b2930ea399 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1515,6 +1515,22 @@ def data_sets(conn: SomeConnection) -> List[sqlite3.Row]:
     return c.fetchall()
 
 
+def format_table_name(fmt_str: str, name: str, exp_id: int,
+                      run_counter: int) -> str:
+    """
+    Format the format_string into a table name
+
+    Args:
+        fmt_str: a valid format string
+        name: the run name
+        exp_id: the experiment ID
+        run_counter: the intra-experiment runnumber of this run
+    """
+    table_name = fmt_str.format(name, exp_id, run_counter)
+    _validate_table_name(table_name)  # raises if table_name not valid
+    return table_name
+
+
 def _insert_run(conn: SomeConnection, exp_id: int, name: str,
                 guid: str,
                 parameters: Optional[List[ParamSpec]] = None,
@@ -1527,7 +1543,8 @@ def _insert_run(conn: SomeConnection, exp_id: int, name: str,
                                                    where_column="exp_id",
                                                    where_value=exp_id)
     run_counter += 1
-    formatted_name = format_string.format(name, exp_id, run_counter)
+    formatted_name = format_table_name(format_string, name, exp_id,
+                                       run_counter)
     table = "runs"
 
     parameters = parameters or []

From 52f5a8892325d5f7b57cff9b5040e6b1f7ea6b20 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 29 Oct 2018 14:09:45 +0100
Subject: [PATCH 041/719] Extend test to test for dataset equility

---
 .../tests/dataset/test_database_copy_paste.py | 41 +++++++++++++++----
 1 file changed, 34 insertions(+), 7 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index 3d68c9479db..c68e52f03b9 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -20,20 +20,47 @@ def test_basic_copy_paste(two_empty_temp_dbs, some_paramspecs):
     source_conn = connect(source_path)
     target_conn = connect(target_path)
 
-    exp = Experiment(conn=source_conn)
-    dataset = DataSet(conn=source_conn)
+    source_exp = Experiment(conn=source_conn)
+    source_dataset = DataSet(conn=source_conn)
 
     with pytest.raises(ValueError, match='Dataset not completed'):
-        copy_runs_into_db(source_path, target_path, dataset.run_id)
+        copy_runs_into_db(source_path, target_path, source_dataset.run_id)
 
     for ps in some_paramspecs[1].values():
-        dataset.add_parameter(ps)
+        source_dataset.add_parameter(ps)
 
     value = 0  # an arbitrary data value
     result = {ps.name: type_casters[ps.type](value)
               for ps in some_paramspecs[1].values()}
 
-    dataset.add_result(result)
-    dataset.mark_complete()
+    source_dataset.add_result(result)
+    source_dataset.mark_complete()
 
-    copy_runs_into_db(source_path, target_path, dataset.run_id)
+    copy_runs_into_db(source_path, target_path, source_dataset.run_id)
+
+    target_exp = Experiment(conn=target_conn, exp_id=1)
+
+    length1 = len(target_exp)
+
+    # trying to insert the same run again should be a NOOP
+    copy_runs_into_db(source_path, target_path, source_dataset.run_id)
+
+    assert len(target_exp) == length1
+
+    target_dataset = DataSet(conn=source_conn, run_id=1)
+
+    # Now make the interesting comparisons: are the target objects the same as
+    # the source objects?
+
+    exp_attrs = ['name', 'sample_name', 'started_at', 'finished_at',
+                 'format_string']
+
+    ds_attrs = ['name', 'table_name', 'guid', 'number_of_results',
+                'counter', 'parameters', 'paramspecs', 'exp_name',
+                'sample_name', 'completed', 'snapshot', 'run_timestamp_raw']
+
+    for ds_attr in ds_attrs:
+        assert getattr(source_dataset, ds_attr) == getattr(target_dataset, ds_attr)
+
+    # for exp_attr in exp_attrs:
+    #     assert getattr(source_exp, exp_attr) == getattr(target_exp, exp_attr)

From eb8ee094ebe98719cc695df79b0973736779c338 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 29 Oct 2018 14:15:57 +0100
Subject: [PATCH 042/719] Move atomicity further out

---
 qcodes/dataset/database_copy_paste.py | 50 +++++++++++++++------------
 1 file changed, 28 insertions(+), 22 deletions(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 083f0583059..7b04b00dba6 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -68,25 +68,33 @@ def copy_runs_into_db(source_db_path: str,
     # this function raises if the target DB file has several experiments
     # matching both the name and sample_name
 
-    load_or_create_experiment(source_exp_name, source_sample_name,
-                              conn=target_conn)
+    with atomic(target_conn) as target_conn:
+
+        load_or_create_experiment(source_exp_name, source_sample_name,
+                                  conn=target_conn)
 
-    # Finally insert the runs
-    for run_id in run_ids:
-        copy_single_dataset_into_db(DataSet(run_id=run_id, conn=source_conn),
-                                    target_db_path)
+        # Finally insert the runs
+        for run_id in run_ids:
+            _copy_single_dataset_into_db(DataSet(run_id=run_id,
+                                                 conn=source_conn),
+                                         target_conn)
 
 
-def copy_single_dataset_into_db(dataset: DataSet, path_to_db: str) -> None:
+def _copy_single_dataset_into_db(dataset: DataSet,
+                                 target_conn: SomeConnection) -> None:
     """
+    NB: This function should only be called from within
+    :meth:copy_runs_into_db
+
     Insert the given dataset into the specified database file as the latest
     run. The database file must exist and its latest experiment must have name
     and sample_name matching that of the dataset's parent experiment
 
+    Trying to insert a run already in the DB is a NOOP.
+
     Args:
         dataset: A dataset representing the run to be copied
-        path_to_db: The path to the target DB into which the run should be
-          inserted
+        path_to_db: connection to the DB. Must be atomically guarded
     """
 
     if not dataset.completed:
@@ -94,7 +102,6 @@ def copy_single_dataset_into_db(dataset: DataSet, path_to_db: str) -> None:
                          'can not be copied.')
 
     source_conn = dataset.conn
-    target_conn = connect(path_to_db)
 
     already_in_query = """
                        SELECT run_id
@@ -109,18 +116,17 @@ def copy_single_dataset_into_db(dataset: DataSet, path_to_db: str) -> None:
 
     exp_id = get_last_experiment(target_conn)
 
-    with atomic(target_conn) as target_conn:
-        _copy_runs_table_entries(source_conn,
-                                 target_conn,
-                                 dataset.run_id,
-                                 exp_id)
-        _update_run_counter(target_conn, exp_id)
-        _copy_layouts_and_dependencies(source_conn,
-                                       target_conn,
-                                       dataset.run_id)
-        _copy_results_table(source_conn,
-                            target_conn,
-                            dataset.run_id)
+    _copy_runs_table_entries(source_conn,
+                             target_conn,
+                             dataset.run_id,
+                             exp_id)
+    _update_run_counter(target_conn, exp_id)
+    _copy_layouts_and_dependencies(source_conn,
+                                   target_conn,
+                                   dataset.run_id)
+    _copy_results_table(source_conn,
+                        target_conn,
+                        dataset.run_id)
 
 
 def _copy_runs_table_entries(source_conn: SomeConnection,

From edc32b7b9318f367a8d03116ccebad0f0be3e20e Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 29 Oct 2018 15:34:02 +0100
Subject: [PATCH 043/719] Handle existing vs non-existing experiments

---
 qcodes/dataset/database_copy_paste.py | 77 ++++++++++++++++++++++-----
 1 file changed, 64 insertions(+), 13 deletions(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 7b04b00dba6..22862fd148a 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -1,3 +1,5 @@
+from typing import Union
+
 import numpy as np
 
 from qcodes.dataset.data_set import DataSet
@@ -48,17 +50,21 @@ def copy_runs_into_db(source_db_path: str,
         raise ValueError('Did not receive runs from a single experiment. '
                          f'Got runs from experiments {source_exp_ids}')
 
-    # Fetch the name and sample name of the runs' experiment
+    # Fetch the attributes of the runs' experiment
+    # hopefully, this is enough to uniquely identify the experiment
+
+    exp_attr_names = ['name', 'sample_name', 'start_time', 'end_time',
+                      'format_string']
 
-    names_query = """
-                  SELECT name, sample_name
+    attrs_query = f"""
+                  SELECT {','.join(exp_attr_names)}
                   FROM experiments
                   WHERE exp_id = ?
                   """
     cursor = source_conn.cursor()
-    cursor.execute(names_query, (source_exp_ids[0],))
+    cursor.execute(attrs_query, (source_exp_ids[0],))
     row = cursor.fetchall()[0]
-    (source_exp_name, source_sample_name) = (row['name'], row['sample_name'])
+    exp_attrs = {attr: row[attr] for attr in exp_attr_names}
 
     # Massage the target DB file to accomodate the runs
     # (create new experiment if needed)
@@ -70,18 +76,65 @@ def copy_runs_into_db(source_db_path: str,
 
     with atomic(target_conn) as target_conn:
 
-        load_or_create_experiment(source_exp_name, source_sample_name,
-                                  conn=target_conn)
+        target_exp_id = _create_exp_if_needed(target_conn,
+                                              exp_attrs['name'],
+                                              exp_attrs['sample_name'],
+                                              exp_attrs['format_string'],
+                                              exp_attrs['start_time'],
+                                              exp_attrs['end_time'])
 
         # Finally insert the runs
         for run_id in run_ids:
             _copy_single_dataset_into_db(DataSet(run_id=run_id,
                                                  conn=source_conn),
-                                         target_conn)
+                                         target_conn,
+                                         target_exp_id)
+
+
+def _create_exp_if_needed(target_conn: SomeConnection,
+                          exp_name: str, sample_name: str,
+                          fmt_str: str,
+                          start_time: float,
+                          end_time: Union[float, None]) -> int:
+    """
+    Look up in the database whether an experiment already exists and create
+    it if it doesn't. Note that experiments do not have GUIDs, so this method
+    is not guaranteed to work. Matching names and times is the best we can do.
+    """
+
+    time_eq = "=" if end_time is not None else "IS"
+
+    exp_exists_query = f"""
+                       SELECT exp_id
+                       FROM experiments
+                       WHERE name = ?
+                       AND sample_name = ?
+                       AND format_string = "{fmt_str}"
+                       AND start_time = ?
+                       AND end_time {time_eq} ?
+                       """
+    values = (exp_name, sample_name, start_time, end_time)
+    cursor = target_conn.execute(exp_exists_query, values)
+
+    rows = cursor.fetchall()
+
+    if len(rows) > 0:
+        return rows[0]['exp_id']
+    else:
+        create_exp = f"""
+                     INSERT INTO experiments
+                     (name, sample_name, format_string, start_time, end_time)
+                     VALUES
+                     (?,?,?,?,?)
+                     """
+        cursor.execute(create_exp, (exp_name, sample_name, fmt_str,
+                                    start_time, end_time))
+        return cursor.lastrowid
 
 
 def _copy_single_dataset_into_db(dataset: DataSet,
-                                 target_conn: SomeConnection) -> None:
+                                 target_conn: SomeConnection,
+                                 target_exp_id: int) -> None:
     """
     NB: This function should only be called from within
     :meth:copy_runs_into_db
@@ -114,13 +167,11 @@ def _copy_single_dataset_into_db(dataset: DataSet,
     if len(res) > 0:
         return
 
-    exp_id = get_last_experiment(target_conn)
-
     _copy_runs_table_entries(source_conn,
                              target_conn,
                              dataset.run_id,
-                             exp_id)
-    _update_run_counter(target_conn, exp_id)
+                             target_exp_id)
+    _update_run_counter(target_conn, target_exp_id)
     _copy_layouts_and_dependencies(source_conn,
                                    target_conn,
                                    dataset.run_id)

From 1dc6c445e47c8be9d6f3ecfb73c2716a7d6690ed Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 29 Oct 2018 15:44:14 +0100
Subject: [PATCH 044/719] Extend test to assert experiment equality

---
 qcodes/tests/dataset/test_database_copy_paste.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index c68e52f03b9..7b4912476c7 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -7,6 +7,7 @@
 from qcodes.dataset.database_copy_paste import copy_runs_into_db
 from qcodes.tests.dataset.temporary_databases import two_empty_temp_dbs
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
+from qcodes.tests.dataset.test_database_creation_and_upgrading import error_caused_by
 
 
 def test_basic_copy_paste(two_empty_temp_dbs, some_paramspecs):
@@ -23,9 +24,11 @@ def test_basic_copy_paste(two_empty_temp_dbs, some_paramspecs):
     source_exp = Experiment(conn=source_conn)
     source_dataset = DataSet(conn=source_conn)
 
-    with pytest.raises(ValueError, match='Dataset not completed'):
+    with pytest.raises(RuntimeError) as excinfo:
         copy_runs_into_db(source_path, target_path, source_dataset.run_id)
 
+    assert error_caused_by(excinfo, 'Dataset not completed')
+
     for ps in some_paramspecs[1].values():
         source_dataset.add_parameter(ps)
 
@@ -52,8 +55,8 @@ def test_basic_copy_paste(two_empty_temp_dbs, some_paramspecs):
     # Now make the interesting comparisons: are the target objects the same as
     # the source objects?
 
-    exp_attrs = ['name', 'sample_name', 'started_at', 'finished_at',
-                 'format_string']
+    exp_attrs = ['name', 'sample_name', 'format_string', 'started_at',
+                 'finished_at']
 
     ds_attrs = ['name', 'table_name', 'guid', 'number_of_results',
                 'counter', 'parameters', 'paramspecs', 'exp_name',
@@ -62,5 +65,5 @@ def test_basic_copy_paste(two_empty_temp_dbs, some_paramspecs):
     for ds_attr in ds_attrs:
         assert getattr(source_dataset, ds_attr) == getattr(target_dataset, ds_attr)
 
-    # for exp_attr in exp_attrs:
-    #     assert getattr(source_exp, exp_attr) == getattr(target_exp, exp_attr)
+    for exp_attr in exp_attrs:
+        assert getattr(source_exp, exp_attr) == getattr(target_exp, exp_attr)

From 084d1f00a481aa3b50ea5f4df1cf6a3b7228a741 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 29 Oct 2018 16:39:11 +0100
Subject: [PATCH 045/719] Change source-target fixture to yield conns

---
 qcodes/tests/dataset/temporary_databases.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/temporary_databases.py b/qcodes/tests/dataset/temporary_databases.py
index e60c6dc997c..19c80a50943 100644
--- a/qcodes/tests/dataset/temporary_databases.py
+++ b/qcodes/tests/dataset/temporary_databases.py
@@ -26,7 +26,7 @@ def empty_temp_db():
 
 
 @pytest.fixture(scope='function')
-def two_empty_temp_dbs():
+def two_empty_temp_db_connections():
     """
     Yield the paths of two empty files. Meant for use with the
     test_database_copy_paste
@@ -34,7 +34,13 @@ def two_empty_temp_dbs():
     with tempfile.TemporaryDirectory() as tmpdirname:
         source_path = os.path.join(tmpdirname, 'source.db')
         target_path = os.path.join(tmpdirname, 'target.db')
-        yield (source_path, target_path)
+        source_conn = connect(source_path)
+        target_conn = connect(target_path)
+        try:
+            yield (source_conn, target_conn)
+        finally:
+            source_conn.close()
+            target_conn.close()
 
 
 @pytest.fixture(scope='function')

From 8ffc7e3df58c9a146ae371275759714673c7a93d Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 29 Oct 2018 17:00:55 +0100
Subject: [PATCH 046/719] Close connections

---
 qcodes/dataset/database_copy_paste.py | 35 +++++++++++++++------------
 1 file changed, 20 insertions(+), 15 deletions(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 22862fd148a..a6fa2caf36f 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -47,6 +47,7 @@ def copy_runs_into_db(source_db_path: str,
     rows = cursor.fetchall()
     source_exp_ids = np.unique([exp_id for row in rows for exp_id in row])
     if len(source_exp_ids) != 1:
+        source_conn.close()
         raise ValueError('Did not receive runs from a single experiment. '
                          f'Got runs from experiments {source_exp_ids}')
 
@@ -74,21 +75,25 @@ def copy_runs_into_db(source_db_path: str,
     # this function raises if the target DB file has several experiments
     # matching both the name and sample_name
 
-    with atomic(target_conn) as target_conn:
-
-        target_exp_id = _create_exp_if_needed(target_conn,
-                                              exp_attrs['name'],
-                                              exp_attrs['sample_name'],
-                                              exp_attrs['format_string'],
-                                              exp_attrs['start_time'],
-                                              exp_attrs['end_time'])
-
-        # Finally insert the runs
-        for run_id in run_ids:
-            _copy_single_dataset_into_db(DataSet(run_id=run_id,
-                                                 conn=source_conn),
-                                         target_conn,
-                                         target_exp_id)
+    try:
+        with atomic(target_conn) as target_conn:
+
+            target_exp_id = _create_exp_if_needed(target_conn,
+                                                  exp_attrs['name'],
+                                                  exp_attrs['sample_name'],
+                                                  exp_attrs['format_string'],
+                                                  exp_attrs['start_time'],
+                                                  exp_attrs['end_time'])
+
+            # Finally insert the runs
+            for run_id in run_ids:
+                _copy_single_dataset_into_db(DataSet(run_id=run_id,
+                                                    conn=source_conn),
+                                             target_conn,
+                                             target_exp_id)
+    finally:
+        source_conn.close()
+        target_conn.close()
 
 
 def _create_exp_if_needed(target_conn: SomeConnection,

From 1de9771aa0e8677fdc69d3646c88cca560532247 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 29 Oct 2018 17:01:15 +0100
Subject: [PATCH 047/719] Adapt test to fixture change

---
 .../tests/dataset/test_database_copy_paste.py | 28 ++++++++++++-------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index 7b4912476c7..cab157d8fac 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -4,23 +4,24 @@
 from qcodes.dataset.sqlite_base import connect
 from qcodes.dataset.experiment_container import Experiment
 from qcodes.dataset.data_set import DataSet
+from qcodes.dataset.database import path_of_connection
 from qcodes.dataset.database_copy_paste import copy_runs_into_db
-from qcodes.tests.dataset.temporary_databases import two_empty_temp_dbs
+from qcodes.tests.dataset.temporary_databases import two_empty_temp_db_connections
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 from qcodes.tests.dataset.test_database_creation_and_upgrading import error_caused_by
 
 
-def test_basic_copy_paste(two_empty_temp_dbs, some_paramspecs):
-    source_path, target_path = two_empty_temp_dbs
+def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
+    source_conn, target_conn = two_empty_temp_db_connections
+
+    source_path = path_of_connection(source_conn)
+    target_path = path_of_connection(target_conn)
 
     type_casters = {'numeric': float,
                     'array': (lambda x: np.array(x) if hasattr(x, '__iter__')
                               else np.array([x])),
                     'text': str}
 
-    source_conn = connect(source_path)
-    target_conn = connect(target_path)
-
     source_exp = Experiment(conn=source_conn)
     source_dataset = DataSet(conn=source_conn)
 
@@ -32,11 +33,11 @@ def test_basic_copy_paste(two_empty_temp_dbs, some_paramspecs):
     for ps in some_paramspecs[1].values():
         source_dataset.add_parameter(ps)
 
-    value = 0  # an arbitrary data value
-    result = {ps.name: type_casters[ps.type](value)
-              for ps in some_paramspecs[1].values()}
+    for value in range(10):
+        result = {ps.name: type_casters[ps.type](value)
+                  for ps in some_paramspecs[1].values()}
+        source_dataset.add_result(result)
 
-    source_dataset.add_result(result)
     source_dataset.mark_complete()
 
     copy_runs_into_db(source_path, target_path, source_dataset.run_id)
@@ -67,3 +68,10 @@ def test_basic_copy_paste(two_empty_temp_dbs, some_paramspecs):
 
     for exp_attr in exp_attrs:
         assert getattr(source_exp, exp_attr) == getattr(target_exp, exp_attr)
+
+    source_data = source_dataset.get_data(*source_dataset.parameters.split(','))
+    target_data = target_dataset.get_data(*target_dataset.parameters.split(','))
+
+    assert source_data == target_data
+
+

From 1a2995d71417e3bb2dda4bd271c66e26e02a020f Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 29 Oct 2018 18:11:47 +0100
Subject: [PATCH 048/719] Remove wrong statement in docstring

---
 qcodes/dataset/database_copy_paste.py | 39 ++++++++++++++++-----------
 1 file changed, 23 insertions(+), 16 deletions(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index a6fa2caf36f..c1e1e5239f5 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -88,7 +88,7 @@ def copy_runs_into_db(source_db_path: str,
             # Finally insert the runs
             for run_id in run_ids:
                 _copy_single_dataset_into_db(DataSet(run_id=run_id,
-                                                    conn=source_conn),
+                                                     conn=source_conn),
                                              target_conn,
                                              target_exp_id)
     finally:
@@ -128,12 +128,13 @@ def _create_exp_if_needed(target_conn: SomeConnection,
     else:
         create_exp = f"""
                      INSERT INTO experiments
-                     (name, sample_name, format_string, start_time, end_time)
+                     (name, sample_name, format_string,
+                      run_counter, start_time, end_time)
                      VALUES
-                     (?,?,?,?,?)
+                     (?,?,?,?,?,?)
                      """
         cursor.execute(create_exp, (exp_name, sample_name, fmt_str,
-                                    start_time, end_time))
+                                    0, start_time, end_time))
         return cursor.lastrowid
 
 
@@ -145,14 +146,15 @@ def _copy_single_dataset_into_db(dataset: DataSet,
     :meth:copy_runs_into_db
 
     Insert the given dataset into the specified database file as the latest
-    run. The database file must exist and its latest experiment must have name
-    and sample_name matching that of the dataset's parent experiment
+    run.
 
     Trying to insert a run already in the DB is a NOOP.
 
     Args:
         dataset: A dataset representing the run to be copied
         path_to_db: connection to the DB. Must be atomically guarded
+        target_exp_id: The exp_id of the (target DB) experiment in which to
+          insert the run
     """
 
     if not dataset.completed:
@@ -172,23 +174,24 @@ def _copy_single_dataset_into_db(dataset: DataSet,
     if len(res) > 0:
         return
 
-    _copy_runs_table_entries(source_conn,
-                             target_conn,
-                             dataset.run_id,
-                             target_exp_id)
+    target_run_id = _copy_runs_table_entries(source_conn,
+                                             target_conn,
+                                             dataset.run_id,
+                                             target_exp_id)
     _update_run_counter(target_conn, target_exp_id)
     _copy_layouts_and_dependencies(source_conn,
                                    target_conn,
                                    dataset.run_id)
     _copy_results_table(source_conn,
                         target_conn,
-                        dataset.run_id)
+                        dataset.run_id,
+                        target_run_id)
 
 
 def _copy_runs_table_entries(source_conn: SomeConnection,
                              target_conn: SomeConnection,
                              source_run_id: int,
-                             target_exp_id: int) -> None:
+                             target_exp_id: int) -> int:
     """
     Copy an entire runs table row from one DB and paste it all
     (expect the primary key) into another DB. The two DBs may not be the same.
@@ -232,6 +235,8 @@ def _copy_runs_table_entries(source_conn: SomeConnection,
     cursor = target_conn.cursor()
     cursor.execute(sql_insert_values, values)
 
+    return cursor.lastrowid
+
 
 def _update_run_counter(target_conn: SomeConnection, target_exp_id) -> None:
     """
@@ -313,10 +318,11 @@ def _copy_layouts_and_dependencies(target_conn: SomeConnection,
 
 def _copy_results_table(source_conn: SomeConnection,
                         target_conn: SomeConnection,
-                        source_run_id) -> None:
+                        source_run_id: int,
+                        target_run_id: int) -> None:
     """
     Copy the contents of the results table. Creates a new results_table with
-    a name appropriate for the target DB and updates the
+    a name appropriate for the target DB and updates the rows of that table
     """
     table_name_query = """
                        SELECT result_table_name, name
@@ -336,7 +342,8 @@ def _copy_results_table(source_conn: SomeConnection,
                           ON runs.exp_id = experiments.exp_id
                           WHERE runs.run_id = ?
                           """
-    cursor.execute(format_string_query, (source_run_id,))
+    cursor = target_conn.cursor()
+    cursor.execute(format_string_query, (target_run_id,))
     row = cursor.fetchall()[0]
     format_string = row['format_string']
     run_counter = row['run_counter']
@@ -346,7 +353,7 @@ def _copy_results_table(source_conn: SomeConnection,
                      SELECT *
                      FROM "{table_name}"
                      """
-
+    cursor = source_conn.cursor()
     cursor.execute(get_data_query)
     data_rows = cursor.fetchall()
     data_columns = data_rows[0].keys()

From 32194537b23de301809c55c16c520a8acc2b4d7d Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Tue, 30 Oct 2018 11:17:10 +0100
Subject: [PATCH 049/719] Add test for experiment routing

---
 .../tests/dataset/test_database_copy_paste.py | 76 ++++++++++++++++++-
 1 file changed, 74 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index cab157d8fac..17ed2dbe4dc 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -1,7 +1,7 @@
 import pytest
 import numpy as np
 
-from qcodes.dataset.sqlite_base import connect
+from qcodes.dataset.sqlite_base import connect, get_experiments
 from qcodes.dataset.experiment_container import Experiment
 from qcodes.dataset.data_set import DataSet
 from qcodes.dataset.database import path_of_connection
@@ -59,7 +59,7 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
     exp_attrs = ['name', 'sample_name', 'format_string', 'started_at',
                  'finished_at']
 
-    ds_attrs = ['name', 'table_name', 'guid', 'number_of_results',
+    ds_attrs = ['name', 'guid', 'number_of_results',
                 'counter', 'parameters', 'paramspecs', 'exp_name',
                 'sample_name', 'completed', 'snapshot', 'run_timestamp_raw']
 
@@ -75,3 +75,75 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
     assert source_data == target_data
 
 
+def test_correct_experiment_routing(two_empty_temp_db_connections,
+                                    some_paramspecs):
+    """
+    Test that existing experiments are correctly identified AND that multiple
+    insertions of the same runs don't matter (run insertion is idempotent)
+    """
+    source_conn, target_conn = two_empty_temp_db_connections
+
+    source_exp_1 = Experiment(conn=source_conn)
+
+    # make 5 runs in first experiment
+
+    exp_1_run_ids = []
+    for _ in range(5):
+
+        source_dataset = DataSet(conn=source_conn, exp_id=source_exp_1.exp_id)
+        exp_1_run_ids.append(source_dataset.run_id)
+
+        for ps in some_paramspecs[2].values():
+            source_dataset.add_parameter(ps)
+
+        for val in range(10):
+            source_dataset.add_result({ps.name: val
+                                       for ps in some_paramspecs[2].values()})
+        source_dataset.mark_complete()
+
+    # make a new experiment with 1 run
+
+    source_exp_2 = Experiment(conn=source_conn)
+    ds = DataSet(conn=source_conn, exp_id=source_exp_2.exp_id)
+
+    for ps in some_paramspecs[2].values():
+        ds.add_parameter(ps)
+
+    for val in range(10):
+        ds.add_result({ps.name: val for ps in some_paramspecs[2].values()})
+
+    ds.mark_complete()
+
+    source_path = path_of_connection(source_conn)
+    target_path = path_of_connection(target_conn)
+
+    # now copy 2 runs
+    copy_runs_into_db(source_path, target_path, *exp_1_run_ids[:2])
+
+    test_exp1 = Experiment(conn=target_conn, exp_id=1)
+
+    assert len(test_exp1) == 2
+
+    # copy two other runs, one of them already in
+    copy_runs_into_db(source_path, target_path, *exp_1_run_ids[1:3])
+
+    assert len(test_exp1) == 3
+
+    # insert run from different experiment
+
+    copy_runs_into_db(source_path, target_path, ds.run_id)
+
+    assert len(test_exp1) == 3
+
+    test_exp2 = Experiment(conn=target_conn, exp_id=2)
+
+    assert len(test_exp2) == 1
+
+    # finally insert every single run from experiment 1
+
+    copy_runs_into_db(source_path, target_path, *exp_1_run_ids)
+
+    target_exps = get_experiments(target_conn)
+
+    assert len(target_exps) == 2
+    assert len(test_exp1) == 5

From fed3b61c2316ebd06e9de5e0dfbd36eee4408b27 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Tue, 30 Oct 2018 11:26:45 +0100
Subject: [PATCH 050/719] Change function name

---
 qcodes/tests/dataset/test_database_copy_paste.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index 17ed2dbe4dc..4729b8dbecc 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -4,7 +4,7 @@
 from qcodes.dataset.sqlite_base import connect, get_experiments
 from qcodes.dataset.experiment_container import Experiment
 from qcodes.dataset.data_set import DataSet
-from qcodes.dataset.database import path_of_connection
+from qcodes.dataset.database import path_to_dbfile
 from qcodes.dataset.database_copy_paste import copy_runs_into_db
 from qcodes.tests.dataset.temporary_databases import two_empty_temp_db_connections
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
@@ -14,8 +14,8 @@
 def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
     source_conn, target_conn = two_empty_temp_db_connections
 
-    source_path = path_of_connection(source_conn)
-    target_path = path_of_connection(target_conn)
+    source_path = path_to_dbfile(source_conn)
+    target_path = path_to_dbfile(target_conn)
 
     type_casters = {'numeric': float,
                     'array': (lambda x: np.array(x) if hasattr(x, '__iter__')
@@ -114,8 +114,8 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
 
     ds.mark_complete()
 
-    source_path = path_of_connection(source_conn)
-    target_path = path_of_connection(target_conn)
+    source_path = path_to_dbfile(source_conn)
+    target_path = path_to_dbfile(target_conn)
 
     # now copy 2 runs
     copy_runs_into_db(source_path, target_path, *exp_1_run_ids[:2])

From ff63bed43408bd9cf8edf9bcf709b40148a2126f Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Tue, 30 Oct 2018 13:33:11 +0100
Subject: [PATCH 051/719] Pass on connections correctly

---
 qcodes/dataset/data_set.py             | 12 ++++++++----
 qcodes/dataset/experiment_container.py |  2 +-
 2 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 7f93402a935..f357ad49e22 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -33,7 +33,7 @@
                                         get_run_timestamp_from_run_id,
                                         get_completed_timestamp_from_run_id,
                                         update_run_description,
-                                        run_exists)
+                                        run_exists, SomeConnection)
 
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
@@ -848,14 +848,16 @@ def __repr__(self) -> str:
 
 
 # public api
-def load_by_id(run_id: int) -> DataSet:
+def load_by_id(run_id: int, conn: Optional[SomeConnection]=None) -> DataSet:
     """
     Load dataset by run id
 
-    Lookup is performed in the database file that is specified in the config.
+    If no connection is provided, lookup is performed in the database file that
+    is specified in the config.
 
     Args:
         run_id: run id of the dataset
+        conn: connection to the database to load from
 
     Returns:
         dataset with the given run id
@@ -863,7 +865,9 @@ def load_by_id(run_id: int) -> DataSet:
     if run_id is None:
         raise ValueError('run_id has to be a positive integer, not None.')
 
-    d = DataSet(path_to_db=get_DB_location(), run_id=run_id)
+    conn = conn or connect(get_DB_location())
+
+    d = DataSet(conn=conn, run_id=run_id)
     return d
 
 
diff --git a/qcodes/dataset/experiment_container.py b/qcodes/dataset/experiment_container.py
index 08b3653cb3c..a377e830cbb 100644
--- a/qcodes/dataset/experiment_container.py
+++ b/qcodes/dataset/experiment_container.py
@@ -136,7 +136,7 @@ def data_sets(self) -> List[DataSet]:
         runs = get_runs(self.conn, self.exp_id)
         data_sets = []
         for run in runs:
-            data_sets.append(load_by_id(run['run_id']))
+            data_sets.append(load_by_id(run['run_id'], conn=self.conn))
         return data_sets
 
     def last_data_set(self) -> DataSet:

From ab05e699e7a05d14a45d88aa9ffe97c2af652d7f Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 31 Oct 2018 14:35:14 +0100
Subject: [PATCH 052/719] Add method for testing dataset similarity

---
 qcodes/dataset/data_set.py | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index f357ad49e22..de8e1f5a849 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -182,6 +182,15 @@ def _clean_up(self) -> None:
 
 
 class DataSet(Sized):
+
+    # the "persistent traits" are the attributes/properties of the DataSet
+    # that are NOT tied to the representation of the DataSet in any particular
+    # database
+    persistent_traits = ('name', 'guid', 'number_of_results', 'counter',
+                         'parameters', 'paramspecs', 'exp_name', 'sample_name',
+                         'completed', 'snapshot', 'run_timestamp_raw',
+                         'description', 'completed_timestamp_raw')
+
     def __init__(self, path_to_db: str=None,
                  run_id: Optional[int]=None,
                  conn=None,
@@ -350,6 +359,34 @@ def description(self) -> RunDescriber:
     def metadata(self) -> Dict:
         return self._metadata
 
+    def the_same_dataset_as(self, other: 'DataSet') -> bool:
+        """
+        Check if two datasets correspond to the same run by comparing
+        all their persistent traits. Note that this method
+        does not compare the data itself.
+
+        This function raises if the GUIDs match but anything else doesn't
+
+        Args:
+            other: the dataset to compare self to
+        """
+
+        if not isinstance(other, DataSet):
+            return False
+
+        guids_match = self.guid == other.guid
+
+        for attr in DataSet.persistent_traits:
+            if getattr(self, attr) != getattr(other, attr):
+                if guids_match:
+                    raise RuntimeError('Bad inconsistency detected! '
+                                       'The two datasets have the same GUID,'
+                                       f' but their "{attr}" differ.')
+                else:
+                    return False
+
+        return True
+
     def run_timestamp(self, fmt: str="%Y-%m-%d %H:%M:%S") -> str:
         """
         Returns run timestamp in a human-readable format

From 36a70c97066a5918483ff33c4a6f1204cfe21de1 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 31 Oct 2018 13:42:45 +0100
Subject: [PATCH 053/719] Add a load_by_GUID method to data_set module

---
 qcodes/dataset/data_set.py | 44 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 44 insertions(+)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index de8e1f5a849..fedd198e985 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -884,6 +884,8 @@ def __repr__(self) -> str:
         return "\n".join(out)
 
 
+log = logging.getLogger(__name__)
+
 # public api
 def load_by_id(run_id: int, conn: Optional[SomeConnection]=None) -> DataSet:
     """
@@ -908,6 +910,48 @@ def load_by_id(run_id: int, conn: Optional[SomeConnection]=None) -> DataSet:
     return d
 
 
+def load_by_guid(guid: str, conn: Optional[SomeConnection]=None) -> DataSet:
+    """
+    Load a dataset by its GUID
+
+    If no connection is provided, lookup is performed in the database file that
+    is specified in the config.
+
+    Args:
+        guid: guid of the dataset
+        conn: connection to the database to load from
+
+    Returns:
+        dataset with the given guid
+
+    Raises:
+        NameError if no run with the specified GUID exists in the database
+        RuntimError if several runs with the same GUID are found
+    """
+    conn = conn or connect(get_DB_location())
+
+    query = """
+            SELECT run_id
+            FROM runs
+            WHERE guid = ?
+            """
+    cursor = conn.cursor()
+    cursor.execute(query, (guid,))
+    rows = cursor.fetchall()
+    if len(rows) == 0:
+        raise NameError(f'No run with guid {guid} found in database.')
+    elif len(rows) > 1:
+        errormssg = ('Critical consistency error: multiple runs with'
+                     f' the same GUID found! {len(rows)} runs have GUID '
+                     f'{guid}')
+        log.critical(errormssg)
+        raise RuntimeError(errormssg)
+    else:
+        run_id = int(rows[0]['run_id'])
+
+    return DataSet(run_id=run_id, conn=conn)
+
+
 def load_by_counter(counter: int, exp_id: int) -> DataSet:
     """
     Load a dataset given its counter in a given experiment

From 669cb37e8a36a3d4700bd0f1d68f3551b09381a9 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 31 Oct 2018 14:30:48 +0100
Subject: [PATCH 054/719] Extend tests to check dataset similarity

---
 .../tests/dataset/test_database_copy_paste.py | 31 +++++++++++++------
 1 file changed, 21 insertions(+), 10 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index 4729b8dbecc..d532782d2f9 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -3,7 +3,7 @@
 
 from qcodes.dataset.sqlite_base import connect, get_experiments
 from qcodes.dataset.experiment_container import Experiment
-from qcodes.dataset.data_set import DataSet
+from qcodes.dataset.data_set import DataSet, load_by_guid
 from qcodes.dataset.database import path_to_dbfile
 from qcodes.dataset.database_copy_paste import copy_runs_into_db
 from qcodes.tests.dataset.temporary_databases import two_empty_temp_db_connections
@@ -59,21 +59,16 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
     exp_attrs = ['name', 'sample_name', 'format_string', 'started_at',
                  'finished_at']
 
-    ds_attrs = ['name', 'guid', 'number_of_results',
-                'counter', 'parameters', 'paramspecs', 'exp_name',
-                'sample_name', 'completed', 'snapshot', 'run_timestamp_raw']
-
-    for ds_attr in ds_attrs:
-        assert getattr(source_dataset, ds_attr) == getattr(target_dataset, ds_attr)
-
-    for exp_attr in exp_attrs:
-        assert getattr(source_exp, exp_attr) == getattr(target_exp, exp_attr)
+    assert source_dataset.the_same_dataset_as(target_dataset)
 
     source_data = source_dataset.get_data(*source_dataset.parameters.split(','))
     target_data = target_dataset.get_data(*target_dataset.parameters.split(','))
 
     assert source_data == target_data
 
+    for exp_attr in exp_attrs:
+        assert getattr(source_exp, exp_attr) == getattr(target_exp, exp_attr)
+
 
 def test_correct_experiment_routing(two_empty_temp_db_connections,
                                     some_paramspecs):
@@ -105,6 +100,7 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
 
     source_exp_2 = Experiment(conn=source_conn)
     ds = DataSet(conn=source_conn, exp_id=source_exp_2.exp_id)
+    exp_2_run_ids = [ds.run_id]
 
     for ps in some_paramspecs[2].values():
         ds.add_parameter(ps)
@@ -147,3 +143,18 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
 
     assert len(target_exps) == 2
     assert len(test_exp1) == 5
+
+    # check that all the datasets match up
+    for run_id in exp_1_run_ids + exp_2_run_ids:
+        source_ds = DataSet(conn=source_conn, run_id=run_id)
+        target_ds = load_by_guid(guid=source_ds.guid, conn=target_conn)
+
+        print(source_ds.paramspecs)
+        print(target_ds.paramspecs)
+
+        assert source_ds.the_same_dataset_as(target_ds)
+
+        source_data = source_ds.get_data(*source_ds.parameters.split(','))
+        target_data = target_ds.get_data(*target_ds.parameters.split(','))
+
+        assert source_data == target_data

From 7db1467b5be8c91ea33264c3797e14a9604baa30 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 31 Oct 2018 14:31:35 +0100
Subject: [PATCH 055/719] Handle data column types correctly

---
 qcodes/dataset/database_copy_paste.py | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index c1e1e5239f5..596cc3a2173 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -174,6 +174,9 @@ def _copy_single_dataset_into_db(dataset: DataSet,
     if len(res) > 0:
         return
 
+    parspecs = dataset.paramspecs.values()
+    data_column_names_and_types = ",".join([p.sql_repr() for p in parspecs])
+
     target_run_id = _copy_runs_table_entries(source_conn,
                                              target_conn,
                                              dataset.run_id,
@@ -185,7 +188,8 @@ def _copy_single_dataset_into_db(dataset: DataSet,
     _copy_results_table(source_conn,
                         target_conn,
                         dataset.run_id,
-                        target_run_id)
+                        target_run_id,
+                        data_column_names_and_types)
 
 
 def _copy_runs_table_entries(source_conn: SomeConnection,
@@ -251,8 +255,8 @@ def _update_run_counter(target_conn: SomeConnection, target_exp_id) -> None:
     cursor.execute(update_sql, (target_exp_id,))
 
 
-def _copy_layouts_and_dependencies(target_conn: SomeConnection,
-                                   source_conn: SomeConnection,
+def _copy_layouts_and_dependencies(source_conn: SomeConnection,
+                                   target_conn: SomeConnection,
                                    source_run_id: int) -> None:
     """
     Copy over the layouts and dependencies tables. Note that the layout_ids
@@ -261,7 +265,7 @@ def _copy_layouts_and_dependencies(target_conn: SomeConnection,
     layout_id 2 that depends on layout_id 1)
     """
     layout_query = """
-                   SELECT layout_id, run_id, parameter, label, unit, inferred_from
+                   SELECT layout_id, run_id, "parameter", label, unit, inferred_from
                    FROM layouts
                    WHERE run_id = ?
                    """
@@ -319,7 +323,8 @@ def _copy_layouts_and_dependencies(target_conn: SomeConnection,
 def _copy_results_table(source_conn: SomeConnection,
                         target_conn: SomeConnection,
                         source_run_id: int,
-                        target_run_id: int) -> None:
+                        target_run_id: int,
+                        column_names_and_types: str) -> None:
     """
     Copy the contents of the results table. Creates a new results_table with
     a name appropriate for the target DB and updates the rows of that table
@@ -364,11 +369,10 @@ def _copy_results_table(source_conn: SomeConnection,
                                           exp_id,
                                           run_counter)
 
-    column_names = ','.join(data_columns)
     make_table = f"""
                   CREATE TABLE "{target_table_name}" (
                       id INTEGER PRIMARY KEY,
-                      {column_names}
+                      {column_names_and_types}
                   )
                   """
 
@@ -378,6 +382,7 @@ def _copy_results_table(source_conn: SomeConnection,
     # according to one of our reports, multiple single-row inserts
     # are okay if there's only one commit
 
+    column_names = ','.join(data_columns)
     value_placeholders = sql_placeholder_string(len(data_columns))
     insert_data = f"""
                    INSERT INTO "{target_table_name}"

From 8de6a8a87eb49d3c86e0a6c1ea1f68449db5982b Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 5 Nov 2018 09:59:49 +0100
Subject: [PATCH 056/719] Improve two docstrings

---
 qcodes/dataset/database_copy_paste.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 596cc3a2173..e2ffb2d5205 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -14,7 +14,8 @@
 
 def sql_placeholder_string(n: int) -> str:
     """
-    Return an SQL placeholder string of length n.
+    Return an SQL value placeholder string of length n.
+    Example: sql_placeholder_string(5) returns '(?,?,?,?,?)'
     """
     return '(' + ','.join('?'*n) + ')'
 
@@ -29,7 +30,8 @@ def copy_runs_into_db(source_db_path: str,
 
     Args:
         source_db_path: Path to the source DB file
-        target_db_path: Path to the target DB file
+        target_db_path: Path to the target DB file. The target DB file will be
+          created if it does not exist.
         run_ids: The run_ids of the runs to copy into the target DB file
     """
 

From 9add02b6f3ee6ac7edec3db383affc3536684c32 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 10:51:55 +0100
Subject: [PATCH 057/719] Move log initiation line

---
 qcodes/dataset/data_set.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index fedd198e985..76f57bf2bff 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -42,6 +42,8 @@
 from qcodes.utils.deprecate import deprecate
 import qcodes.config
 
+log = logging.getLogger(__name__)
+
 # TODO: as of now every time a result is inserted with add_result the db is
 # saved same for add_results. IS THIS THE BEHAVIOUR WE WANT?
 
@@ -884,8 +886,6 @@ def __repr__(self) -> str:
         return "\n".join(out)
 
 
-log = logging.getLogger(__name__)
-
 # public api
 def load_by_id(run_id: int, conn: Optional[SomeConnection]=None) -> DataSet:
     """

From 95c2b49f446b8e708a8e887dda2f84c57379c07e Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 10:52:37 +0100
Subject: [PATCH 058/719] Fix typo

---
 qcodes/dataset/data_set.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 76f57bf2bff..feb1952c675 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -926,7 +926,7 @@ def load_by_guid(guid: str, conn: Optional[SomeConnection]=None) -> DataSet:
 
     Raises:
         NameError if no run with the specified GUID exists in the database
-        RuntimError if several runs with the same GUID are found
+        RuntimeError if several runs with the same GUID are found
     """
     conn = conn or connect(get_DB_location())
 

From f9a4285727191ad5c7cf36afe3e53694700dfa52 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 11:16:37 +0100
Subject: [PATCH 059/719] Refactor run_id from guid into sqlite_base

---
 qcodes/dataset/data_set.py            | 28 +++++++---------------
 qcodes/dataset/database_copy_paste.py | 13 ++++------
 qcodes/dataset/sqlite_base.py         | 34 ++++++++++++++++++++++++++-
 3 files changed, 45 insertions(+), 30 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index feb1952c675..eff84f2c623 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -30,6 +30,7 @@
                                         get_experiment_name_from_experiment_id,
                                         get_sample_name_from_experiment_id,
                                         get_guid_from_run_id,
+                                        get_runid_from_guid,
                                         get_run_timestamp_from_run_id,
                                         get_completed_timestamp_from_run_id,
                                         update_run_description,
@@ -925,29 +926,16 @@ def load_by_guid(guid: str, conn: Optional[SomeConnection]=None) -> DataSet:
         dataset with the given guid
 
     Raises:
-        NameError if no run with the specified GUID exists in the database
-        RuntimeError if several runs with the same GUID are found
+        NameError if no run with the given GUID exists in the database
+        RuntimeError if several runs with the given GUID are found
     """
     conn = conn or connect(get_DB_location())
 
-    query = """
-            SELECT run_id
-            FROM runs
-            WHERE guid = ?
-            """
-    cursor = conn.cursor()
-    cursor.execute(query, (guid,))
-    rows = cursor.fetchall()
-    if len(rows) == 0:
-        raise NameError(f'No run with guid {guid} found in database.')
-    elif len(rows) > 1:
-        errormssg = ('Critical consistency error: multiple runs with'
-                     f' the same GUID found! {len(rows)} runs have GUID '
-                     f'{guid}')
-        log.critical(errormssg)
-        raise RuntimeError(errormssg)
-    else:
-        run_id = int(rows[0]['run_id'])
+    # this function raises a RuntimeError if more than one run matches the GUID
+    run_id = get_runid_from_guid(conn, guid)
+
+    if run_id is None:
+        raise NameError(f'No run with GUID: {guid} found in database.')
 
     return DataSet(run_id=run_id, conn=conn)
 
diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index e2ffb2d5205..66517deafa8 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -8,6 +8,7 @@
                                         connect,
                                         format_table_name,
                                         get_last_experiment,
+                                        get_runid_from_guid,
                                         insert_column,
                                         SomeConnection)
 
@@ -165,15 +166,9 @@ def _copy_single_dataset_into_db(dataset: DataSet,
 
     source_conn = dataset.conn
 
-    already_in_query = """
-                       SELECT run_id
-                       FROM runs
-                       WHERE guid = ?
-                       """
-    cursor = target_conn.cursor()
-    cursor.execute(already_in_query, (dataset.guid,))
-    res = cursor.fetchall()
-    if len(res) > 0:
+    run_id = get_runid_from_guid(target_conn, dataset.guid)
+
+    if run_id is not None:
         return
 
     parspecs = dataset.paramspecs.values()
diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 3b2930ea399..c3b8f665d64 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1008,7 +1008,6 @@ def modify_values(conn: SomeConnection,
     c = atomic_transaction(conn, query, *values)
     return c.rowcount
 
-
 def modify_many_values(conn: SomeConnection,
                        formatted_name: str,
                        start_index: int,
@@ -1205,6 +1204,39 @@ def get_setpoints(conn: SomeConnection,
     return output
 
 
+def get_runid_from_guid(conn: SomeConnection, guid: str) -> Union[int, None]:
+    """
+    Get the run_id of a run based on the guid
+
+    Args:
+      conn: connection to the database
+      guid: the guid to look up
+
+    Raises:
+      RunTimeError if more than one run with the given GUID exists
+    """
+    query = """
+            SELECT run_id
+            FROM runs
+            WHERE guid = ?
+            """
+    cursor = conn.cursor()
+    cursor.execute(query, (guid,))
+    rows = cursor.fetchall()
+    if len(rows) == 0:
+        run_id = None
+    elif len(rows) > 1:
+        errormssg = ('Critical consistency error: multiple runs with'
+                     f' the same GUID found! {len(rows)} runs have GUID '
+                     f'{guid}')
+        log.critical(errormssg)
+        raise RuntimeError(errormssg)
+    else:
+        run_id = int(rows[0]['run_id'])
+
+    return run_id
+
+
 def get_layout(conn: SomeConnection,
                layout_id) -> Dict[str, str]:
     """

From d51455c098ecc8f4283422f23b0632b445fec73e Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 11:25:44 +0100
Subject: [PATCH 060/719] Be correct about conn and path_to_db in docstring

---
 qcodes/dataset/experiment_container.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/experiment_container.py b/qcodes/dataset/experiment_container.py
index a377e830cbb..b5baa5d1156 100644
--- a/qcodes/dataset/experiment_container.py
+++ b/qcodes/dataset/experiment_container.py
@@ -33,7 +33,9 @@ def __init__(self, path_to_db: Optional[str]=None,
         created. If exp_id is not None, an experiment is loaded.
 
         Args:
-            path_to_db: The path of the database file to create in/load from
+            path_to_db: The path of the database file to create in/load from.
+              If a conn is passed together with path_to_db, an exception is
+              raised
             exp_id: The id of the experiment to load
             name: The name of the experiment to create. Ignored if exp_id is
               not None
@@ -41,9 +43,16 @@ def __init__(self, path_to_db: Optional[str]=None,
               is not None
             format_string: The format string used to name result-tables.
               Ignored if exp_id is not None.
-            conn: connection to the database. If not supplied, a new connection
+            conn: connection to the database. If not supplied, the constructor
+              first tries to use path_to_db to figure out where to connect to.
+              If path_to_db is not supplied either, a new connection
               to the DB file specified in the config is made
         """
+
+        if path_to_db is not None and conn is not None:
+            raise ValueError('Received BOTH conn and path_to_db. Please '
+                             'provide only one or the other.')
+
         self._path_to_db = path_to_db or get_DB_location()
         self.conn = conn or connect(self.path_to_db, get_DB_debug())
 

From 41feda85e1fe5eb2dde699ce02464cd65250e0af Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 11:32:06 +0100
Subject: [PATCH 061/719] Move list definition closer to where it is used

---
 qcodes/tests/dataset/test_database_copy_paste.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index d532782d2f9..f7e26c8dfcc 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -56,9 +56,6 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
     # Now make the interesting comparisons: are the target objects the same as
     # the source objects?
 
-    exp_attrs = ['name', 'sample_name', 'format_string', 'started_at',
-                 'finished_at']
-
     assert source_dataset.the_same_dataset_as(target_dataset)
 
     source_data = source_dataset.get_data(*source_dataset.parameters.split(','))
@@ -66,6 +63,9 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
 
     assert source_data == target_data
 
+    exp_attrs = ['name', 'sample_name', 'format_string', 'started_at',
+                 'finished_at']
+
     for exp_attr in exp_attrs:
         assert getattr(source_exp, exp_attr) == getattr(target_exp, exp_attr)
 

From 69130ef9e3999e50fce4f2282489e4f8dbdf90ad Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 13:22:33 +0100
Subject: [PATCH 062/719] Check harder for idempotency of run insertion

---
 .../tests/dataset/test_database_copy_paste.py | 31 +++++++++++++++++--
 1 file changed, 29 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index f7e26c8dfcc..475bd02f832 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -1,3 +1,6 @@
+from os.path import getmtime
+from contextlib import contextmanager
+
 import pytest
 import numpy as np
 
@@ -11,6 +14,21 @@
 from qcodes.tests.dataset.test_database_creation_and_upgrading import error_caused_by
 
 
+@contextmanager
+def raise_if_file_changed(path_to_file: str):
+    """
+    Context manager that raises if a file is modified.
+    On Windows, the OS modification time resolution is 100 ns
+    """
+    pre_operation_time = getmtime(path_to_file)
+    # we don't want to catch and re-raise anything, since there is no clean-up
+    # that we need to perform. Hence no try-except here
+    yield
+    post_operation_time = getmtime(path_to_file)
+    if pre_operation_time != post_operation_time:
+        raise RuntimeError(f'File {path_to_file} was modified.')
+
+
 def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
     source_conn, target_conn = two_empty_temp_db_connections
 
@@ -47,7 +65,8 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
     length1 = len(target_exp)
 
     # trying to insert the same run again should be a NOOP
-    copy_runs_into_db(source_path, target_path, source_dataset.run_id)
+    with raise_if_file_changed(target_path):
+        copy_runs_into_db(source_path, target_path, source_dataset.run_id)
 
     assert len(target_exp) == length1
 
@@ -69,6 +88,10 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
     for exp_attr in exp_attrs:
         assert getattr(source_exp, exp_attr) == getattr(target_exp, exp_attr)
 
+    # trying to insert the same run again should be a NOOP
+    with raise_if_file_changed(target_path):
+        copy_runs_into_db(source_path, target_path, source_dataset.run_id)
+
 
 def test_correct_experiment_routing(two_empty_temp_db_connections,
                                     some_paramspecs):
@@ -126,7 +149,6 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
     assert len(test_exp1) == 3
 
     # insert run from different experiment
-
     copy_runs_into_db(source_path, target_path, ds.run_id)
 
     assert len(test_exp1) == 3
@@ -139,6 +161,11 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
 
     copy_runs_into_db(source_path, target_path, *exp_1_run_ids)
 
+    # check for idempotency once more by inserting all the runs but in another
+    # order
+    with raise_if_file_changed(target_path):
+        copy_runs_into_db(source_path, target_path, *exp_1_run_ids[::-1])
+
     target_exps = get_experiments(target_conn)
 
     assert len(target_exps) == 2

From 35872619a9003dae438ef30c966981caeecdb5be Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 13:26:54 +0100
Subject: [PATCH 063/719] Rename some variables and add an assert

---
 qcodes/tests/dataset/test_database_copy_paste.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index 475bd02f832..7fef790571f 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -139,23 +139,23 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
     # now copy 2 runs
     copy_runs_into_db(source_path, target_path, *exp_1_run_ids[:2])
 
-    test_exp1 = Experiment(conn=target_conn, exp_id=1)
+    target_exp1 = Experiment(conn=target_conn, exp_id=1)
 
-    assert len(test_exp1) == 2
+    assert len(target_exp1) == 2
 
     # copy two other runs, one of them already in
     copy_runs_into_db(source_path, target_path, *exp_1_run_ids[1:3])
 
-    assert len(test_exp1) == 3
+    assert len(target_exp1) == 3
 
     # insert run from different experiment
     copy_runs_into_db(source_path, target_path, ds.run_id)
 
-    assert len(test_exp1) == 3
+    assert len(target_exp1) == 3
 
-    test_exp2 = Experiment(conn=target_conn, exp_id=2)
+    target_exp2 = Experiment(conn=target_conn, exp_id=2)
 
-    assert len(test_exp2) == 1
+    assert len(target_exp2) == 1
 
     # finally insert every single run from experiment 1
 
@@ -169,7 +169,8 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
     target_exps = get_experiments(target_conn)
 
     assert len(target_exps) == 2
-    assert len(test_exp1) == 5
+    assert len(target_exp1) == 5
+    assert len(target_exp2) == 1
 
     # check that all the datasets match up
     for run_id in exp_1_run_ids + exp_2_run_ids:

From 4e42110bd51e7259cd22b0b285dd871ce5cdcd5f Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 13:27:47 +0100
Subject: [PATCH 064/719] Remove print calls

---
 qcodes/tests/dataset/test_database_copy_paste.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index 7fef790571f..be539b46738 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -177,9 +177,6 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
         source_ds = DataSet(conn=source_conn, run_id=run_id)
         target_ds = load_by_guid(guid=source_ds.guid, conn=target_conn)
 
-        print(source_ds.paramspecs)
-        print(target_ds.paramspecs)
-
         assert source_ds.the_same_dataset_as(target_ds)
 
         source_data = source_ds.get_data(*source_ds.parameters.split(','))

From 0484d96a884f4f703a9bdce8357207db7f025bd0 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 13:42:28 +0100
Subject: [PATCH 065/719] Add test for load_by_guid

---
 qcodes/tests/dataset/test_dataset_loading.py | 21 +++++++++++++++++++-
 1 file changed, 20 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_dataset_loading.py b/qcodes/tests/dataset/test_dataset_loading.py
index 43a6b955a47..30ecaf5c866 100644
--- a/qcodes/tests/dataset/test_dataset_loading.py
+++ b/qcodes/tests/dataset/test_dataset_loading.py
@@ -3,13 +3,19 @@
 
 import pytest
 
-from qcodes.dataset.data_set import (new_data_set, load_by_id, load_by_counter,
+from qcodes.dataset.data_set import (DataSet,
+                                     new_data_set,
+                                     load_by_guid,
+                                     load_by_id,
+                                     load_by_counter,
                                      ParamSpec)
 from qcodes.dataset.data_export import get_data_by_id
 from qcodes.dataset.experiment_container import new_experiment
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import (empty_temp_db,
                                                       experiment, dataset)
+# pylint: disable=unused-import
+from qcodes.tests.dataset.test_descriptions import some_paramspecs
 
 
 @pytest.mark.usefixtures("experiment")
@@ -178,3 +184,16 @@ def test_get_data_by_id_order(dataset):
     data_dict = {el['name']: el['data'] for el in data[1]}
     assert data_dict['indep1'] == 1
     assert data_dict['indep2'] == 2
+
+
+@pytest.mark.usefixtures('experiment')
+def test_load_by_guid(some_paramspecs):
+        paramspecs = some_paramspecs[2]
+        ds = DataSet()
+        ds.add_parameter(paramspecs['ps1'])
+        ds.add_parameter(paramspecs['ps2'])
+        ds.add_result({'ps1': 1, 'ps2': 2})
+
+        loaded_ds = load_by_guid(ds.guid)
+
+        assert loaded_ds.the_same_dataset_as(ds)

From 338966957ec8a231d684087570bd80c339d21ec2 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 14:51:55 +0100
Subject: [PATCH 066/719] Add mini-test for the_same_dataset_as

---
 qcodes/tests/dataset/test_dataset_basic.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index e0dbcfd830b..050f98ade27 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -556,3 +556,17 @@ def test_metadata():
     with pytest.raises(ValueError, match=match):
         for tag, value in sorry_metadata.items():
             ds1.add_metadata(tag, value)
+
+
+def test_the_same_dataset_as(some_paramspecs):
+        paramspecs = some_paramspecs[2]
+        ds = DataSet()
+        ds.add_parameter(paramspecs['ps1'])
+        ds.add_parameter(paramspecs['ps2'])
+        ds.add_result({'ps1': 1, 'ps2': 2})
+
+        same_ds_from_load = DataSet(run_id=ds.run_id)
+        assert ds.the_same_dataset_as(same_ds_from_load)
+
+        new_ds = DataSet()
+        assert not ds.the_same_dataset_as(new_ds)

From 7005584b16ced6c4ede8daf57005fc1c7789ac88 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 15:16:06 +0100
Subject: [PATCH 067/719] Test that runs from different experiments raise

---
 .../tests/dataset/test_database_copy_paste.py | 55 +++++++++++++++++++
 1 file changed, 55 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index be539b46738..cc7b2c10876 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -183,3 +183,58 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
         target_data = target_ds.get_data(*target_ds.parameters.split(','))
 
         assert source_data == target_data
+
+
+def test_runs_from_different_experiments_raises(two_empty_temp_db_connections,
+                                                some_paramspecs):
+    """
+    Test that inserting runs from multiple experiments raises
+    """
+    source_conn, target_conn = two_empty_temp_db_connections
+
+    source_path = path_to_dbfile(source_conn)
+    target_path = path_to_dbfile(target_conn)
+
+    source_exp_1 = Experiment(conn=source_conn)
+    source_exp_2 = Experiment(conn=source_conn)
+
+    # make 5 runs in first experiment
+
+    exp_1_run_ids = []
+    for _ in range(5):
+
+        source_dataset = DataSet(conn=source_conn, exp_id=source_exp_1.exp_id)
+        exp_1_run_ids.append(source_dataset.run_id)
+
+        for ps in some_paramspecs[2].values():
+            source_dataset.add_parameter(ps)
+
+        for val in range(10):
+            source_dataset.add_result({ps.name: val
+                                       for ps in some_paramspecs[2].values()})
+        source_dataset.mark_complete()
+
+    # make 5 runs in second experiment
+
+    exp_2_run_ids = []
+    for _ in range(5):
+
+        source_dataset = DataSet(conn=source_conn, exp_id=source_exp_2.exp_id)
+        exp_2_run_ids.append(source_dataset.run_id)
+
+        for ps in some_paramspecs[2].values():
+            source_dataset.add_parameter(ps)
+
+        for val in range(10):
+            source_dataset.add_result({ps.name: val
+                                       for ps in some_paramspecs[2].values()})
+        source_dataset.mark_complete()
+
+    run_ids = exp_1_run_ids + exp_2_run_ids
+    source_exp_ids = np.unique([1, 2])
+    matchstring = ('Did not receive runs from a single experiment\\. '
+                   f'Got runs from experiments {source_exp_ids}')
+    # make the matchstring safe to use as a regexp
+    matchstring = matchstring.replace('[', '\\[').replace(']', '\\]')
+    with pytest.raises(ValueError, match=matchstring):
+        copy_runs_into_db(source_path, target_path, *run_ids)

From 14619bdcef40b4bca0239bc4fb347964cc608da9 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 16:25:20 +0100
Subject: [PATCH 068/719] Add newline

---
 qcodes/dataset/database_copy_paste.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 66517deafa8..094c9eb5314 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -100,7 +100,8 @@ def copy_runs_into_db(source_db_path: str,
 
 
 def _create_exp_if_needed(target_conn: SomeConnection,
-                          exp_name: str, sample_name: str,
+                          exp_name: str,
+                          sample_name: str,
                           fmt_str: str,
                           start_time: float,
                           end_time: Union[float, None]) -> int:

From 894357fbb706a3686ecca7bd4f1582d1d75da7b3 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 16:37:28 +0100
Subject: [PATCH 069/719] Warn if multiple possible target experiments exist

---
 qcodes/dataset/database_copy_paste.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 094c9eb5314..7d51efbdbbe 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -1,4 +1,5 @@
 from typing import Union
+from warnings import warn
 
 import numpy as np
 
@@ -127,6 +128,13 @@ def _create_exp_if_needed(target_conn: SomeConnection,
 
     rows = cursor.fetchall()
 
+    if len(rows) > 1:
+
+        exp_id = rows[0]['exp_id']
+        warn(f'{len(rows)} experiments found in target DB that match name, '
+             'sample_name, fmt_str, start_time, and end_time. '
+             f'Inserting into the experiment with exp_id={exp_id}.')
+        return exp_id
     if len(rows) > 0:
         return rows[0]['exp_id']
     else:

From af6f188daca6c6925deea3629f72d38d0b78eee4 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 16:45:18 +0100
Subject: [PATCH 070/719] Fix typo

---
 qcodes/dataset/database_copy_paste.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 7d51efbdbbe..9989536c800 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -164,7 +164,7 @@ def _copy_single_dataset_into_db(dataset: DataSet,
 
     Args:
         dataset: A dataset representing the run to be copied
-        path_to_db: connection to the DB. Must be atomically guarded
+        target_conn: connection to the DB. Must be atomically guarded
         target_exp_id: The exp_id of the (target DB) experiment in which to
           insert the run
     """

From a2b9fc3570d9437a846851e23ac17dd1ffee343f Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 8 Nov 2018 16:45:38 +0100
Subject: [PATCH 071/719] Assert full reason for raise

---
 qcodes/tests/dataset/test_database_copy_paste.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index cc7b2c10876..274fa5d9fc8 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -46,7 +46,8 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
     with pytest.raises(RuntimeError) as excinfo:
         copy_runs_into_db(source_path, target_path, source_dataset.run_id)
 
-    assert error_caused_by(excinfo, 'Dataset not completed')
+    assert error_caused_by(excinfo, ('Dataset not completed. An incomplete '
+                                     'dataset can not be copied.'))
 
     for ps in some_paramspecs[1].values():
         source_dataset.add_parameter(ps)

From db222caed4ef11f34ea9ae2241f0008c618544aa Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 9 Nov 2018 12:48:34 +0100
Subject: [PATCH 072/719] Change "bad" -> "critical"

---
 qcodes/dataset/data_set.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index eff84f2c623..0a4e0c530ed 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -382,7 +382,7 @@ def the_same_dataset_as(self, other: 'DataSet') -> bool:
         for attr in DataSet.persistent_traits:
             if getattr(self, attr) != getattr(other, attr):
                 if guids_match:
-                    raise RuntimeError('Bad inconsistency detected! '
+                    raise RuntimeError('Critical inconsistency detected! '
                                        'The two datasets have the same GUID,'
                                        f' but their "{attr}" differ.')
                 else:

From b89e557a851bdae2450c773f903c5ed53cb8f9bc Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 9 Nov 2018 12:55:21 +0100
Subject: [PATCH 073/719] Add test for copying empty run

---
 .../tests/dataset/test_database_copy_paste.py | 23 +++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index 274fa5d9fc8..2f30f980745 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -239,3 +239,26 @@ def test_runs_from_different_experiments_raises(two_empty_temp_db_connections,
     matchstring = matchstring.replace('[', '\\[').replace(']', '\\]')
     with pytest.raises(ValueError, match=matchstring):
         copy_runs_into_db(source_path, target_path, *run_ids)
+
+
+def test_extracting_dataless_run(two_empty_temp_db_connections,
+                                 some_paramspecs):
+    """
+    Although contrived, it could happen that a run with no data is extracted
+    """
+    source_conn, target_conn = two_empty_temp_db_connections
+
+    source_path = path_to_dbfile(source_conn)
+    target_path = path_to_dbfile(target_conn)
+
+    Experiment(conn=source_conn)
+
+    source_ds = DataSet(conn=source_conn)
+
+    source_ds.mark_complete()
+
+    copy_runs_into_db(source_path, target_path, source_ds.run_id)
+
+    loaded_ds = DataSet(conn=target_conn, run_id=1)
+
+    assert loaded_ds.the_same_dataset_as(source_ds)

From 1527e4cccf1d3b80b0c6153464024d27a99a198e Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 9 Nov 2018 13:16:09 +0100
Subject: [PATCH 074/719] Make empty dataset extractable

---
 qcodes/dataset/database_copy_paste.py | 26 ++++++++++++++++++--------
 1 file changed, 18 insertions(+), 8 deletions(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 9989536c800..336eb08104a 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -367,20 +367,30 @@ def _copy_results_table(source_conn: SomeConnection,
     cursor = source_conn.cursor()
     cursor.execute(get_data_query)
     data_rows = cursor.fetchall()
-    data_columns = data_rows[0].keys()
-    data_columns.remove('id')
+    if len(data_rows) > 0:
+        data_columns = data_rows[0].keys()
+        data_columns.remove('id')
+    else:
+        data_columns = []
 
     target_table_name = format_table_name(format_string,
                                           run_name,
                                           exp_id,
                                           run_counter)
 
-    make_table = f"""
-                  CREATE TABLE "{target_table_name}" (
-                      id INTEGER PRIMARY KEY,
-                      {column_names_and_types}
-                  )
-                  """
+    if column_names_and_types != '':
+        make_table = f"""
+                    CREATE TABLE "{target_table_name}" (
+                        id INTEGER PRIMARY KEY,
+                        {column_names_and_types}
+                    )
+                    """
+    else:
+        make_table = f"""
+            CREATE TABLE "{target_table_name}" (
+                id INTEGER PRIMARY KEY
+            )
+            """
 
     cursor = target_conn.cursor()
     cursor.execute(make_table)

From 8b933238c9a8431e9338a966f9189db3dd8a10be Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 9 Nov 2018 13:01:50 +0100
Subject: [PATCH 075/719] Fix typo

---
 qcodes/dataset/sqlite_base.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index c3b8f665d64..8828d011364 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1024,7 +1024,7 @@ def modify_many_values(conn: SomeConnection,
     len_requested = start_index + len(list_of_values[0])
     available = _len - start_index
     if len_requested > _len:
-        reason = f""""Modify operation Out of bounds.
+        reason = f"""Modify operation Out of bounds.
         Trying to modify {len(list_of_values)} results,
         but therere are only {available} results.
         """
@@ -1213,7 +1213,7 @@ def get_runid_from_guid(conn: SomeConnection, guid: str) -> Union[int, None]:
       guid: the guid to look up
 
     Raises:
-      RunTimeError if more than one run with the given GUID exists
+      RuntimeError if more than one run with the given GUID exists
     """
     query = """
             SELECT run_id

From 9c8a4767eec3ded804e5d2f419ebef53426f0205 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 9 Nov 2018 13:38:38 +0100
Subject: [PATCH 076/719] Add test for correct result table names

---
 .../tests/dataset/test_database_copy_paste.py | 40 +++++++++++++++++++
 1 file changed, 40 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index 2f30f980745..88f95917334 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -262,3 +262,43 @@ def test_extracting_dataless_run(two_empty_temp_db_connections,
     loaded_ds = DataSet(conn=target_conn, run_id=1)
 
     assert loaded_ds.the_same_dataset_as(source_ds)
+
+
+def test_result_table_naming(two_empty_temp_db_connections,
+                             some_paramspecs):
+    """
+    Does this raise?
+    """
+    source_conn, target_conn = two_empty_temp_db_connections
+
+    source_path = path_to_dbfile(source_conn)
+    target_path = path_to_dbfile(target_conn)
+
+    source_exp1 = Experiment(conn=source_conn)
+    source_ds_1_1 = DataSet(conn=source_conn, exp_id=source_exp1.exp_id)
+    for ps in some_paramspecs[2].values():
+        source_ds_1_1.add_parameter(ps)
+    source_ds_1_1.add_result({ps.name: 0.0
+                              for ps in some_paramspecs[2].values()})
+    source_ds_1_1.mark_complete()
+
+    source_exp2 = Experiment(conn=source_conn)
+    source_ds_2_1 = DataSet(conn=source_conn, exp_id=source_exp2.exp_id)
+    for ps in some_paramspecs[2].values():
+        source_ds_2_1.add_parameter(ps)
+    source_ds_2_1.add_result({ps.name: 0.0
+                              for ps in some_paramspecs[2].values()})
+    source_ds_2_1.mark_complete()
+    source_ds_2_2 = DataSet(conn=source_conn, exp_id=source_exp2.exp_id)
+    for ps in some_paramspecs[2].values():
+        source_ds_2_2.add_parameter(ps)
+    source_ds_2_2.add_result({ps.name: 0.0
+                              for ps in some_paramspecs[2].values()})
+    source_ds_2_2.mark_complete()
+
+    copy_runs_into_db(source_path, target_path, source_ds_2_2.run_id)
+
+    # The target ds ought to have a runs table "results-1-1"
+    target_ds = DataSet(conn=target_conn, run_id=1)
+
+    assert target_ds.table_name == "results-1-1"

From 56f1abb98209d5b9fefe76bb7f577084fcdf5548 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 9 Nov 2018 13:49:31 +0100
Subject: [PATCH 077/719] Sprinkle custom dataset names in test

---
 qcodes/tests/dataset/test_database_copy_paste.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_copy_paste.py
index 88f95917334..8556b152e4a 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_copy_paste.py
@@ -41,7 +41,7 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
                     'text': str}
 
     source_exp = Experiment(conn=source_conn)
-    source_dataset = DataSet(conn=source_conn)
+    source_dataset = DataSet(conn=source_conn, name="basic_copy_paste_name")
 
     with pytest.raises(RuntimeError) as excinfo:
         copy_runs_into_db(source_path, target_path, source_dataset.run_id)
@@ -123,7 +123,7 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
     # make a new experiment with 1 run
 
     source_exp_2 = Experiment(conn=source_conn)
-    ds = DataSet(conn=source_conn, exp_id=source_exp_2.exp_id)
+    ds = DataSet(conn=source_conn, exp_id=source_exp_2.exp_id, name="lala")
     exp_2_run_ids = [ds.run_id]
 
     for ps in some_paramspecs[2].values():
@@ -289,7 +289,9 @@ def test_result_table_naming(two_empty_temp_db_connections,
     source_ds_2_1.add_result({ps.name: 0.0
                               for ps in some_paramspecs[2].values()})
     source_ds_2_1.mark_complete()
-    source_ds_2_2 = DataSet(conn=source_conn, exp_id=source_exp2.exp_id)
+    source_ds_2_2 = DataSet(conn=source_conn,
+                            exp_id=source_exp2.exp_id,
+                            name="customname")
     for ps in some_paramspecs[2].values():
         source_ds_2_2.add_parameter(ps)
     source_ds_2_2.add_result({ps.name: 0.0
@@ -298,7 +300,7 @@ def test_result_table_naming(two_empty_temp_db_connections,
 
     copy_runs_into_db(source_path, target_path, source_ds_2_2.run_id)
 
-    # The target ds ought to have a runs table "results-1-1"
+    # The target ds ought to have a runs table "customname-1-1"
     target_ds = DataSet(conn=target_conn, run_id=1)
 
-    assert target_ds.table_name == "results-1-1"
+    assert target_ds.table_name == "customname-1-1"

From 6fa904ad0ee696954da85433827d4fb5567e5f89 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 9 Nov 2018 13:52:42 +0100
Subject: [PATCH 078/719] Ensure correct results table name

---
 qcodes/dataset/database_copy_paste.py | 29 +++++++++++++++++++++------
 1 file changed, 23 insertions(+), 6 deletions(-)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 336eb08104a..4407d4c9776 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -191,11 +191,12 @@ def _copy_single_dataset_into_db(dataset: DataSet,
     _copy_layouts_and_dependencies(source_conn,
                                    target_conn,
                                    dataset.run_id)
-    _copy_results_table(source_conn,
-                        target_conn,
-                        dataset.run_id,
-                        target_run_id,
-                        data_column_names_and_types)
+    target_table_name = _copy_results_table(source_conn,
+                                            target_conn,
+                                            dataset.run_id,
+                                            target_run_id,
+                                            data_column_names_and_types)
+    _update_result_table_name(target_conn, target_table_name, target_run_id)
 
 
 def _copy_runs_table_entries(source_conn: SomeConnection,
@@ -330,10 +331,12 @@ def _copy_results_table(source_conn: SomeConnection,
                         target_conn: SomeConnection,
                         source_run_id: int,
                         target_run_id: int,
-                        column_names_and_types: str) -> None:
+                        column_names_and_types: str) -> str:
     """
     Copy the contents of the results table. Creates a new results_table with
     a name appropriate for the target DB and updates the rows of that table
+
+    Returns the name of the new results table
     """
     table_name_query = """
                        SELECT result_table_name, name
@@ -409,3 +412,17 @@ def _copy_results_table(source_conn: SomeConnection,
     for row in data_rows:
         # the first row entry is the ID, which is automatically inserted
         cursor.execute(insert_data, tuple(v for v in row[1:]))
+
+    return target_table_name
+
+
+def _update_result_table_name(target_conn: SomeConnection,
+                              target_table_name: str,
+                              target_run_id: int) -> None:
+    sql = """
+          UPDATE runs
+          SET result_table_name = ?
+          WHERE run_id = ?
+          """
+    cursor = target_conn.cursor()
+    cursor.execute(sql, (target_table_name, target_run_id))

From 5a1aefb85d80f92bc2391dc9219a557b2a167ecd Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 9 Nov 2018 14:01:18 +0100
Subject: [PATCH 079/719] Use run_id = -1 instead of None for missing runs

---
 qcodes/dataset/data_set.py            | 2 +-
 qcodes/dataset/database_copy_paste.py | 2 +-
 qcodes/dataset/sqlite_base.py         | 6 +++++-
 3 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 0a4e0c530ed..0e7a22284a9 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -934,7 +934,7 @@ def load_by_guid(guid: str, conn: Optional[SomeConnection]=None) -> DataSet:
     # this function raises a RuntimeError if more than one run matches the GUID
     run_id = get_runid_from_guid(conn, guid)
 
-    if run_id is None:
+    if run_id == -1:
         raise NameError(f'No run with GUID: {guid} found in database.')
 
     return DataSet(run_id=run_id, conn=conn)
diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_copy_paste.py
index 4407d4c9776..f7d220a5521 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_copy_paste.py
@@ -177,7 +177,7 @@ def _copy_single_dataset_into_db(dataset: DataSet,
 
     run_id = get_runid_from_guid(target_conn, dataset.guid)
 
-    if run_id is not None:
+    if run_id != -1:
         return
 
     parspecs = dataset.paramspecs.values()
diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 8828d011364..cc062d25933 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1212,6 +1212,9 @@ def get_runid_from_guid(conn: SomeConnection, guid: str) -> Union[int, None]:
       conn: connection to the database
       guid: the guid to look up
 
+    Returns:
+        The run_id if found, else -1.
+
     Raises:
       RuntimeError if more than one run with the given GUID exists
     """
@@ -1224,7 +1227,7 @@ def get_runid_from_guid(conn: SomeConnection, guid: str) -> Union[int, None]:
     cursor.execute(query, (guid,))
     rows = cursor.fetchall()
     if len(rows) == 0:
-        run_id = None
+        run_id = -1
     elif len(rows) > 1:
         errormssg = ('Critical consistency error: multiple runs with'
                      f' the same GUID found! {len(rows)} runs have GUID '
@@ -1322,6 +1325,7 @@ def get_dependencies(conn: SomeConnection,
     res = many_many(c, 'independent', 'axis_num')
     return res
 
+
 # Higher level Wrappers
 
 

From fbfa07de7cbe17c42a0ff901f460983f1a242234 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 12 Nov 2018 10:06:12 +0100
Subject: [PATCH 080/719] Rename from copy-paste to extract

---
 ...copy_paste.py => database_extract_runs.py} | 28 +++++++++----------
 ...paste.py => test_database_extract_runs.py} | 28 +++++++++----------
 2 files changed, 28 insertions(+), 28 deletions(-)
 rename qcodes/dataset/{database_copy_paste.py => database_extract_runs.py} (95%)
 rename qcodes/tests/dataset/{test_database_copy_paste.py => test_database_extract_runs.py} (90%)

diff --git a/qcodes/dataset/database_copy_paste.py b/qcodes/dataset/database_extract_runs.py
similarity index 95%
rename from qcodes/dataset/database_copy_paste.py
rename to qcodes/dataset/database_extract_runs.py
index f7d220a5521..65287e1c82d 100644
--- a/qcodes/dataset/database_copy_paste.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -22,13 +22,13 @@ def sql_placeholder_string(n: int) -> str:
     return '(' + ','.join('?'*n) + ')'
 
 
-def copy_runs_into_db(source_db_path: str,
-                      target_db_path: str, *run_ids) -> None:
+def extract_runs_into_db(source_db_path: str,
+                         target_db_path: str, *run_ids) -> None:
     """
-    Copy a selection of runs into another DB file. All runs must come from the
-    same experiment. They will be added to an experiment with the same name
-    and sample_name in the target db. If such an experiment does not exist,
-    it will be created.
+    Extract a selection of runs into another DB file. All runs must come from
+    the same experiment. They will be added to an experiment with the same name
+    and sample_name in the target db. If such an experiment does not exist, it
+    will be created.
 
     Args:
         source_db_path: Path to the source DB file
@@ -91,10 +91,10 @@ def copy_runs_into_db(source_db_path: str,
 
             # Finally insert the runs
             for run_id in run_ids:
-                _copy_single_dataset_into_db(DataSet(run_id=run_id,
-                                                     conn=source_conn),
-                                             target_conn,
-                                             target_exp_id)
+                _extract_single_dataset_into_db(DataSet(run_id=run_id,
+                                                        conn=source_conn),
+                                                target_conn,
+                                                target_exp_id)
     finally:
         source_conn.close()
         target_conn.close()
@@ -150,12 +150,12 @@ def _create_exp_if_needed(target_conn: SomeConnection,
         return cursor.lastrowid
 
 
-def _copy_single_dataset_into_db(dataset: DataSet,
-                                 target_conn: SomeConnection,
-                                 target_exp_id: int) -> None:
+def _extract_single_dataset_into_db(dataset: DataSet,
+                                    target_conn: SomeConnection,
+                                    target_exp_id: int) -> None:
     """
     NB: This function should only be called from within
-    :meth:copy_runs_into_db
+    :meth:extract_runs_into_db
 
     Insert the given dataset into the specified database file as the latest
     run.
diff --git a/qcodes/tests/dataset/test_database_copy_paste.py b/qcodes/tests/dataset/test_database_extract_runs.py
similarity index 90%
rename from qcodes/tests/dataset/test_database_copy_paste.py
rename to qcodes/tests/dataset/test_database_extract_runs.py
index 8556b152e4a..19c4115a5f8 100644
--- a/qcodes/tests/dataset/test_database_copy_paste.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -8,7 +8,7 @@
 from qcodes.dataset.experiment_container import Experiment
 from qcodes.dataset.data_set import DataSet, load_by_guid
 from qcodes.dataset.database import path_to_dbfile
-from qcodes.dataset.database_copy_paste import copy_runs_into_db
+from qcodes.dataset.database_extract_runs import extract_runs_into_db
 from qcodes.tests.dataset.temporary_databases import two_empty_temp_db_connections
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 from qcodes.tests.dataset.test_database_creation_and_upgrading import error_caused_by
@@ -29,7 +29,7 @@ def raise_if_file_changed(path_to_file: str):
         raise RuntimeError(f'File {path_to_file} was modified.')
 
 
-def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
+def test_basic_extraction(two_empty_temp_db_connections, some_paramspecs):
     source_conn, target_conn = two_empty_temp_db_connections
 
     source_path = path_to_dbfile(source_conn)
@@ -44,7 +44,7 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
     source_dataset = DataSet(conn=source_conn, name="basic_copy_paste_name")
 
     with pytest.raises(RuntimeError) as excinfo:
-        copy_runs_into_db(source_path, target_path, source_dataset.run_id)
+        extract_runs_into_db(source_path, target_path, source_dataset.run_id)
 
     assert error_caused_by(excinfo, ('Dataset not completed. An incomplete '
                                      'dataset can not be copied.'))
@@ -59,7 +59,7 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
 
     source_dataset.mark_complete()
 
-    copy_runs_into_db(source_path, target_path, source_dataset.run_id)
+    extract_runs_into_db(source_path, target_path, source_dataset.run_id)
 
     target_exp = Experiment(conn=target_conn, exp_id=1)
 
@@ -67,7 +67,7 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
 
     # trying to insert the same run again should be a NOOP
     with raise_if_file_changed(target_path):
-        copy_runs_into_db(source_path, target_path, source_dataset.run_id)
+        extract_runs_into_db(source_path, target_path, source_dataset.run_id)
 
     assert len(target_exp) == length1
 
@@ -91,7 +91,7 @@ def test_basic_copy_paste(two_empty_temp_db_connections, some_paramspecs):
 
     # trying to insert the same run again should be a NOOP
     with raise_if_file_changed(target_path):
-        copy_runs_into_db(source_path, target_path, source_dataset.run_id)
+        extract_runs_into_db(source_path, target_path, source_dataset.run_id)
 
 
 def test_correct_experiment_routing(two_empty_temp_db_connections,
@@ -138,19 +138,19 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
     target_path = path_to_dbfile(target_conn)
 
     # now copy 2 runs
-    copy_runs_into_db(source_path, target_path, *exp_1_run_ids[:2])
+    extract_runs_into_db(source_path, target_path, *exp_1_run_ids[:2])
 
     target_exp1 = Experiment(conn=target_conn, exp_id=1)
 
     assert len(target_exp1) == 2
 
     # copy two other runs, one of them already in
-    copy_runs_into_db(source_path, target_path, *exp_1_run_ids[1:3])
+    extract_runs_into_db(source_path, target_path, *exp_1_run_ids[1:3])
 
     assert len(target_exp1) == 3
 
     # insert run from different experiment
-    copy_runs_into_db(source_path, target_path, ds.run_id)
+    extract_runs_into_db(source_path, target_path, ds.run_id)
 
     assert len(target_exp1) == 3
 
@@ -160,12 +160,12 @@ def test_correct_experiment_routing(two_empty_temp_db_connections,
 
     # finally insert every single run from experiment 1
 
-    copy_runs_into_db(source_path, target_path, *exp_1_run_ids)
+    extract_runs_into_db(source_path, target_path, *exp_1_run_ids)
 
     # check for idempotency once more by inserting all the runs but in another
     # order
     with raise_if_file_changed(target_path):
-        copy_runs_into_db(source_path, target_path, *exp_1_run_ids[::-1])
+        extract_runs_into_db(source_path, target_path, *exp_1_run_ids[::-1])
 
     target_exps = get_experiments(target_conn)
 
@@ -238,7 +238,7 @@ def test_runs_from_different_experiments_raises(two_empty_temp_db_connections,
     # make the matchstring safe to use as a regexp
     matchstring = matchstring.replace('[', '\\[').replace(']', '\\]')
     with pytest.raises(ValueError, match=matchstring):
-        copy_runs_into_db(source_path, target_path, *run_ids)
+        extract_runs_into_db(source_path, target_path, *run_ids)
 
 
 def test_extracting_dataless_run(two_empty_temp_db_connections,
@@ -257,7 +257,7 @@ def test_extracting_dataless_run(two_empty_temp_db_connections,
 
     source_ds.mark_complete()
 
-    copy_runs_into_db(source_path, target_path, source_ds.run_id)
+    extract_runs_into_db(source_path, target_path, source_ds.run_id)
 
     loaded_ds = DataSet(conn=target_conn, run_id=1)
 
@@ -298,7 +298,7 @@ def test_result_table_naming(two_empty_temp_db_connections,
                               for ps in some_paramspecs[2].values()})
     source_ds_2_2.mark_complete()
 
-    copy_runs_into_db(source_path, target_path, source_ds_2_2.run_id)
+    extract_runs_into_db(source_path, target_path, source_ds_2_2.run_id)
 
     # The target ds ought to have a runs table "customname-1-1"
     target_ds = DataSet(conn=target_conn, run_id=1)

From 9f08566aef1c2a6179db37ed5c0b043676bca1b6 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 12 Nov 2018 10:17:10 +0100
Subject: [PATCH 081/719] Move sql_placeholder_string to sqlite_base

---
 qcodes/dataset/database_extract_runs.py | 11 ++---------
 qcodes/dataset/sqlite_base.py           |  8 ++++++++
 2 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 65287e1c82d..b603b45a9ad 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -11,15 +11,8 @@
                                         get_last_experiment,
                                         get_runid_from_guid,
                                         insert_column,
-                                        SomeConnection)
-
-
-def sql_placeholder_string(n: int) -> str:
-    """
-    Return an SQL value placeholder string of length n.
-    Example: sql_placeholder_string(5) returns '(?,?,?,?,?)'
-    """
-    return '(' + ','.join('?'*n) + ')'
+                                        SomeConnection,
+                                        sql_placeholder_string)
 
 
 def extract_runs_into_db(source_db_path: str,
diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index cc062d25933..229bfafc214 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -108,6 +108,14 @@
                       "run_description"]
 
 
+def sql_placeholder_string(n: int) -> str:
+    """
+    Return an SQL value placeholder string for n values.
+    Example: sql_placeholder_string(5) returns '(?,?,?,?,?)'
+    """
+    return '(' + ','.join('?'*n) + ')'
+
+
 class ConnectionPlus(wrapt.ObjectProxy):
     """
     A class to extend the sqlite3.Connection object with a single extra

From 15ce049f56d612c35833ca2de5f2cea11de380ba Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 12 Nov 2018 10:34:38 +0100
Subject: [PATCH 082/719] Refactor get_exp_ids_from_run_ids into sqlite_base

---
 qcodes/dataset/database_extract_runs.py | 13 ++-----------
 qcodes/dataset/sqlite_base.py           | 25 +++++++++++++++++++++++++
 2 files changed, 27 insertions(+), 11 deletions(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index b603b45a9ad..89ba6140ab4 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -8,6 +8,7 @@
 from qcodes.dataset.sqlite_base import (atomic,
                                         connect,
                                         format_table_name,
+                                        get_exp_ids_from_run_ids,
                                         get_last_experiment,
                                         get_runid_from_guid,
                                         insert_column,
@@ -31,18 +32,8 @@ def extract_runs_into_db(source_db_path: str,
     """
 
     # Validate that all runs are from the same experiment
-
-    sql_placeholders = sql_placeholder_string(len(run_ids))
-    exp_id_query = f"""
-                    SELECT exp_id
-                    FROM runs
-                    WHERE run_id IN {sql_placeholders}
-                    """
     source_conn = connect(source_db_path)
-    cursor = source_conn.cursor()
-    cursor.execute(exp_id_query, run_ids)
-    rows = cursor.fetchall()
-    source_exp_ids = np.unique([exp_id for row in rows for exp_id in row])
+    source_exp_ids = np.unique(get_exp_ids_from_run_ids(source_conn, run_ids))
     if len(source_exp_ids) != 1:
         source_conn.close()
         raise ValueError('Did not receive runs from a single experiment. '
diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 229bfafc214..2f118926d54 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1471,6 +1471,31 @@ def get_experiments(conn: SomeConnection) -> List[sqlite3.Row]:
     return c.fetchall()
 
 
+def get_exp_ids_from_run_ids(conn: SomeConnection,
+                             run_ids: Sequence[int]) -> List[int]:
+    """
+    Get the corresponding exp_id for a sequence of run_ids
+
+    Args:
+        conn: connection to the database
+        run_ids: a sequence of the run_ids to get the exp_id of
+
+    Returns:
+        A list of exp_ids matching the run_ids
+    """
+    sql_placeholders = sql_placeholder_string(len(run_ids))
+    exp_id_query = f"""
+                    SELECT exp_id
+                    FROM runs
+                    WHERE run_id IN {sql_placeholders}
+                    """
+    cursor = conn.cursor()
+    cursor.execute(exp_id_query, run_ids)
+    rows = cursor.fetchall()
+
+    return [exp_id for row in rows for exp_id in row]
+
+
 def get_last_experiment(conn: SomeConnection) -> Optional[int]:
     """
     Return last started experiment id

From b38931a0d6b32593e5c2b8898e2b9b3a6b3f3bb4 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 12 Nov 2018 12:40:13 +0100
Subject: [PATCH 083/719] Add input validation of run_ids

---
 qcodes/dataset/database_extract_runs.py       | 14 ++++++-
 qcodes/dataset/sqlite_base.py                 | 30 ++++++++++++++
 .../dataset/test_database_extract_runs.py     | 39 +++++++++++++++++++
 qcodes/tests/dataset/test_sqlite_base.py      | 23 +++++++++++
 4 files changed, 105 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 89ba6140ab4..6e4514a330c 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -12,6 +12,7 @@
                                         get_last_experiment,
                                         get_runid_from_guid,
                                         insert_column,
+                                        is_run_id_in_database,
                                         SomeConnection,
                                         sql_placeholder_string)
 
@@ -30,9 +31,20 @@ def extract_runs_into_db(source_db_path: str,
           created if it does not exist.
         run_ids: The run_ids of the runs to copy into the target DB file
     """
+    source_conn = connect(source_db_path)
+
+    # Validate that all runs are in the source database
+    do_runs_exist = is_run_id_in_database(source_conn, run_ids)
+    if False in do_runs_exist.values():
+        source_conn.close()
+        non_existing_ids = [rid for rid in run_ids if not do_runs_exist[rid]]
+        err_mssg = ("Error: not all run_ids exist in the source database. "
+                    "The following run(s) is/are not present: "
+                    f"{non_existing_ids}")
+        raise ValueError(err_mssg)
 
     # Validate that all runs are from the same experiment
-    source_conn = connect(source_db_path)
+
     source_exp_ids = np.unique(get_exp_ids_from_run_ids(source_conn, run_ids))
     if len(source_exp_ids) != 1:
         source_conn.close()
diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 2f118926d54..34ab8a3ee4c 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -792,6 +792,36 @@ def init_db(conn: SomeConnection)->None:
         transaction(conn, _dependencies_table_schema)
 
 
+def is_run_id_in_database(conn: SomeConnection,
+                          *run_ids: int) -> Dict[int, bool]:
+    """
+    Look up run_ids and return a dictionary with the answers to the question
+    "is this run_id in the database?"
+
+    Args:
+        conn: the connection to the database
+        run_ids: the run_ids to look up
+
+    Returns:
+        a dict with the run_ids as keys and bools as values. True means that
+        the run_id DOES exist in the database
+    """
+    run_ids = np.unique(run_ids)
+    placeholders = sql_placeholder_string(len(run_ids))
+
+    query = f"""
+             SELECT run_id
+             FROM runs
+             WHERE run_id in {placeholders}
+            """
+
+    cursor = conn.cursor()
+    cursor.execute(query, run_ids)
+    rows = cursor.fetchall()
+    existing_ids = [row[0] for row in rows]
+    return {run_id: (run_id in existing_ids) for run_id in run_ids}
+
+
 def is_column_in_table(conn: SomeConnection, table: str, column: str) -> bool:
     """
     A look-before-you-leap function to look up if a table has a certain column.
diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index 19c4115a5f8..b370ec63a8c 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -1,5 +1,6 @@
 from os.path import getmtime
 from contextlib import contextmanager
+import re
 
 import pytest
 import numpy as np
@@ -29,6 +30,44 @@ def raise_if_file_changed(path_to_file: str):
         raise RuntimeError(f'File {path_to_file} was modified.')
 
 
+def test_missing_runs_raises(two_empty_temp_db_connections, some_paramspecs):
+    """
+    Test that an error is raised if runs not present in the source DB are
+    attempted extracted
+    """
+    source_conn, target_conn = two_empty_temp_db_connections
+
+    source_exp_1 = Experiment(conn=source_conn)
+
+    # make 5 runs in first experiment
+
+    exp_1_run_ids = []
+    for _ in range(5):
+
+        source_dataset = DataSet(conn=source_conn, exp_id=source_exp_1.exp_id)
+        exp_1_run_ids.append(source_dataset.run_id)
+
+        for ps in some_paramspecs[2].values():
+            source_dataset.add_parameter(ps)
+
+        for val in range(10):
+            source_dataset.add_result({ps.name: val
+                                       for ps in some_paramspecs[2].values()})
+        source_dataset.mark_complete()
+
+    source_path = path_to_dbfile(source_conn)
+    target_path = path_to_dbfile(target_conn)
+
+    run_ids = [1, 8, 5, 3, 2, 4, 4, 4, 7, 8]
+    wrong_ids = [8, 7, 8]
+
+    expected_err = ("Error: not all run_ids exist in the source database. "
+                    "The following run(s) is/are not present: "
+                    f"{wrong_ids}")
+
+    with pytest.raises(ValueError, match=re.escape(expected_err)):
+        extract_runs_into_db(source_path, target_path, *run_ids)
+
 def test_basic_extraction(two_empty_temp_db_connections, some_paramspecs):
     source_conn, target_conn = two_empty_temp_db_connections
 
diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index fc309304aac..60499421f2f 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -9,12 +9,14 @@
 import hypothesis.strategies as hst
 from hypothesis import given
 import unicodedata
+import numpy as np
 
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
 import qcodes.dataset.sqlite_base as mut  # mut: module under test
 from qcodes.dataset.database import get_DB_location, path_to_dbfile
 from qcodes.dataset.guids import generate_guid
+from qcodes.dataset.data_set import DataSet
 from qcodes.dataset.param_spec import ParamSpec
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import \
@@ -185,3 +187,24 @@ def test_runs_table_columns(empty_temp_db):
         colnames.remove(row['name'])
 
     assert colnames == []
+
+
+def test_is_run_id_in_db(empty_temp_db):
+    conn = mut.connect(get_DB_location())
+    mut.new_experiment(conn, 'test_exp', 'no_sample')
+
+    for _ in range(5):
+        ds = DataSet(conn=conn, run_id=None)
+        print(ds.run_id)
+
+    # there should now be run_ids 1, 2, 3, 4, 5 in the database
+    good_ids = [1, 2, 3, 4, 5]
+    try_ids = [1, 3, 9999, 23, 0, 1, 1, 3, 34]
+
+    sorted_try_ids = np.unique(try_ids)
+
+    expected_dict = {tid: (tid in good_ids) for tid in sorted_try_ids}
+
+    acquired_dict = mut.is_run_id_in_database(conn, *try_ids)
+
+    assert expected_dict == acquired_dict

From 81caec62a42809ee0ae14c65cb5bca5ee590b500 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 12 Nov 2018 13:03:06 +0100
Subject: [PATCH 084/719] Refactor exp trait lookup into sqlite_base

---
 qcodes/dataset/database_extract_runs.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 6e4514a330c..185cde29f30 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -13,12 +13,13 @@
                                         get_runid_from_guid,
                                         insert_column,
                                         is_run_id_in_database,
+                                        select_many_where,
                                         SomeConnection,
                                         sql_placeholder_string)
 
 
 def extract_runs_into_db(source_db_path: str,
-                         target_db_path: str, *run_ids) -> None:
+                         target_db_path: str, *run_ids: int) -> None:
     """
     Extract a selection of runs into another DB file. All runs must come from
     the same experiment. They will be added to an experiment with the same name
@@ -57,15 +58,13 @@ def extract_runs_into_db(source_db_path: str,
     exp_attr_names = ['name', 'sample_name', 'start_time', 'end_time',
                       'format_string']
 
-    attrs_query = f"""
-                  SELECT {','.join(exp_attr_names)}
-                  FROM experiments
-                  WHERE exp_id = ?
-                  """
-    cursor = source_conn.cursor()
-    cursor.execute(attrs_query, (source_exp_ids[0],))
-    row = cursor.fetchall()[0]
-    exp_attrs = {attr: row[attr] for attr in exp_attr_names}
+    exp_attr_vals = select_many_where(source_conn,
+                                      'experiments',
+                                      *exp_attr_names,
+                                      where_column='exp_id',
+                                      where_value=source_exp_ids[0])
+
+    exp_attrs = dict(zip(exp_attr_names, exp_attr_vals))
 
     # Massage the target DB file to accomodate the runs
     # (create new experiment if needed)

From 25bc49ae751ec032c77c6dabe3afb3f3a28c42db Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 12 Nov 2018 14:09:24 +0100
Subject: [PATCH 085/719] Refactor finding matching exps into sqlite_base

---
 qcodes/dataset/database_extract_runs.py | 35 +++++++++-------------
 qcodes/dataset/sqlite_base.py           | 40 +++++++++++++++++++++++++
 2 files changed, 54 insertions(+), 21 deletions(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 185cde29f30..4f98d71dddc 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -10,6 +10,7 @@
                                         format_table_name,
                                         get_exp_ids_from_run_ids,
                                         get_last_experiment,
+                                        get_matching_exp_ids,
                                         get_runid_from_guid,
                                         insert_column,
                                         is_run_id_in_database,
@@ -107,31 +108,22 @@ def _create_exp_if_needed(target_conn: SomeConnection,
     is not guaranteed to work. Matching names and times is the best we can do.
     """
 
-    time_eq = "=" if end_time is not None else "IS"
+    matching_exp_ids = get_matching_exp_ids(target_conn,
+                                            name=exp_name,
+                                            sample_name=sample_name,
+                                            format_string=fmt_str,
+                                            start_time=start_time,
+                                            end_time=end_time)
 
-    exp_exists_query = f"""
-                       SELECT exp_id
-                       FROM experiments
-                       WHERE name = ?
-                       AND sample_name = ?
-                       AND format_string = "{fmt_str}"
-                       AND start_time = ?
-                       AND end_time {time_eq} ?
-                       """
-    values = (exp_name, sample_name, start_time, end_time)
-    cursor = target_conn.execute(exp_exists_query, values)
-
-    rows = cursor.fetchall()
-
-    if len(rows) > 1:
+    if len(matching_exp_ids) > 1:
 
-        exp_id = rows[0]['exp_id']
-        warn(f'{len(rows)} experiments found in target DB that match name, '
-             'sample_name, fmt_str, start_time, and end_time. '
+        exp_id = matching_exp_ids[0]
+        warn(f'{len(matching_exp_ids)} experiments found in target DB that '
+             'match name, sample_name, fmt_str, start_time, and end_time. '
              f'Inserting into the experiment with exp_id={exp_id}.')
         return exp_id
-    if len(rows) > 0:
-        return rows[0]['exp_id']
+    if len(matching_exp_ids) == 1:
+        return matching_exp_ids[0]
     else:
         create_exp = f"""
                      INSERT INTO experiments
@@ -140,6 +132,7 @@ def _create_exp_if_needed(target_conn: SomeConnection,
                      VALUES
                      (?,?,?,?,?,?)
                      """
+        cursor = target_conn.cursor()
         cursor.execute(create_exp, (exp_name, sample_name, fmt_str,
                                     0, start_time, end_time))
         return cursor.lastrowid
diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 34ab8a3ee4c..be7cccf6e98 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1501,6 +1501,46 @@ def get_experiments(conn: SomeConnection) -> List[sqlite3.Row]:
     return c.fetchall()
 
 
+def get_matching_exp_ids(conn: SomeConnection, **match_conditions) -> List:
+    """
+    Get exp_ids for experiments matching the match_conditions
+
+    Raises:
+        ValueError if a match_condition that is not "name", "sample_name",
+        "format_string", "run_counter", "start_time", or "end_time"
+    """
+    valid_conditions = ["name", "sample_name", "start_time", "end_time",
+                        "run_counter", "format_string"]
+
+    for mcond in match_conditions:
+        if mcond not in valid_conditions:
+            raise ValueError(f"{mcond} is not a valid match condition.")
+
+    end_time = match_conditions.get('end_time', None)
+    time_eq = "=" if end_time is not None else "IS"
+
+    query = "SELECT exp_id FROM experiments "
+    for n, mcond in enumerate(match_conditions):
+        if n == 0:
+            query += f"WHERE {mcond} = ? "
+        else:
+            query += f"AND {mcond} = ? "
+
+    # now some syntax clean-up
+    if "format_string" in match_conditions:
+        format_string = match_conditions["format_string"]
+        query = query.replace("format_string = ?",
+                              f'format_string = "{format_string}"')
+        match_conditions.pop("format_string")
+    query = query.replace("end_time = ?", f"end_time {time_eq} ?")
+
+    cursor = conn.cursor()
+    cursor.execute(query, tuple(match_conditions.values()))
+    rows = cursor.fetchall()
+
+    return [row[0] for row in rows]
+
+
 def get_exp_ids_from_run_ids(conn: SomeConnection,
                              run_ids: Sequence[int]) -> List[int]:
     """

From 8d82c163d8e6aa22f61e7feac8f088ed0914b313 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 12 Nov 2018 14:16:32 +0100
Subject: [PATCH 086/719] Extend a test to load by run_id

---
 qcodes/tests/dataset/test_database_extract_runs.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index b370ec63a8c..f111a39baa6 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -68,6 +68,7 @@ def test_missing_runs_raises(two_empty_temp_db_connections, some_paramspecs):
     with pytest.raises(ValueError, match=re.escape(expected_err)):
         extract_runs_into_db(source_path, target_path, *run_ids)
 
+
 def test_basic_extraction(two_empty_temp_db_connections, some_paramspecs):
     source_conn, target_conn = two_empty_temp_db_connections
 
@@ -303,10 +304,11 @@ def test_extracting_dataless_run(two_empty_temp_db_connections,
     assert loaded_ds.the_same_dataset_as(source_ds)
 
 
-def test_result_table_naming(two_empty_temp_db_connections,
-                             some_paramspecs):
+def test_result_table_naming_and_run_id(two_empty_temp_db_connections,
+                                        some_paramspecs):
     """
-    Does this raise?
+    Check that a correct result table name is given and that a correct run_id
+    is assigned
     """
     source_conn, target_conn = two_empty_temp_db_connections
 
@@ -340,6 +342,9 @@ def test_result_table_naming(two_empty_temp_db_connections,
     extract_runs_into_db(source_path, target_path, source_ds_2_2.run_id)
 
     # The target ds ought to have a runs table "customname-1-1"
+    # and ough to be the same dataset as its "ancestor"
     target_ds = DataSet(conn=target_conn, run_id=1)
 
     assert target_ds.table_name == "customname-1-1"
+    assert target_ds.the_same_dataset_as(source_ds_2_2)
+

From 5ade9166e85b555b9cb19d0d34aa2422b94dbf6a Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 12 Nov 2018 14:25:17 +0100
Subject: [PATCH 087/719] Apply fix for load by run_id test failure

---
 qcodes/dataset/database_extract_runs.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 4f98d71dddc..4528bf33974 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -116,7 +116,6 @@ def _create_exp_if_needed(target_conn: SomeConnection,
                                             end_time=end_time)
 
     if len(matching_exp_ids) > 1:
-
         exp_id = matching_exp_ids[0]
         warn(f'{len(matching_exp_ids)} experiments found in target DB that '
              'match name, sample_name, fmt_str, start_time, and end_time. '
@@ -178,7 +177,8 @@ def _extract_single_dataset_into_db(dataset: DataSet,
     _update_run_counter(target_conn, target_exp_id)
     _copy_layouts_and_dependencies(source_conn,
                                    target_conn,
-                                   dataset.run_id)
+                                   dataset.run_id,
+                                   target_run_id)
     target_table_name = _copy_results_table(source_conn,
                                             target_conn,
                                             dataset.run_id,
@@ -252,7 +252,8 @@ def _update_run_counter(target_conn: SomeConnection, target_exp_id) -> None:
 
 def _copy_layouts_and_dependencies(source_conn: SomeConnection,
                                    target_conn: SomeConnection,
-                                   source_run_id: int) -> None:
+                                   source_run_id: int,
+                                   target_run_id: int) -> None:
     """
     Copy over the layouts and dependencies tables. Note that the layout_ids
     are not preserved in the target DB, but of course their relationships are
@@ -279,7 +280,8 @@ def _copy_layouts_and_dependencies(source_conn: SomeConnection,
     source_layout_ids = []
     target_layout_ids = []
     for row in rows:
-        values = tuple(row[colname] for colname in colnames)
+        values = ((target_run_id,) +
+                  tuple(row[colname] for colname in colnames[1:]))
         cursor.execute(layout_insert, values)
         source_layout_ids.append(row['layout_id'])
         target_layout_ids.append(cursor.lastrowid)

From a73c133c9a8f8a89e359e7519985f71e9f4788e8 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 12 Nov 2018 14:36:00 +0100
Subject: [PATCH 088/719] Refactor new exp creation into sqlite_base

---
 qcodes/dataset/database_extract_runs.py | 19 +++++++--------
 qcodes/dataset/sqlite_base.py           | 32 +++++++++++++++++--------
 2 files changed, 30 insertions(+), 21 deletions(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 4528bf33974..1147857bc92 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -14,6 +14,7 @@
                                         get_runid_from_guid,
                                         insert_column,
                                         is_run_id_in_database,
+                                        new_experiment,
                                         select_many_where,
                                         SomeConnection,
                                         sql_placeholder_string)
@@ -124,17 +125,13 @@ def _create_exp_if_needed(target_conn: SomeConnection,
     if len(matching_exp_ids) == 1:
         return matching_exp_ids[0]
     else:
-        create_exp = f"""
-                     INSERT INTO experiments
-                     (name, sample_name, format_string,
-                      run_counter, start_time, end_time)
-                     VALUES
-                     (?,?,?,?,?,?)
-                     """
-        cursor = target_conn.cursor()
-        cursor.execute(create_exp, (exp_name, sample_name, fmt_str,
-                                    0, start_time, end_time))
-        return cursor.lastrowid
+        lastrowid = new_experiment(target_conn,
+                                   name=exp_name,
+                                   sample_name=sample_name,
+                                   format_string=fmt_str,
+                                   start_time=start_time,
+                                   end_time=end_time)
+        return lastrowid
 
 
 def _extract_single_dataset_into_db(dataset: DataSet,
diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index be7cccf6e98..0fd27591d5d 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1370,27 +1370,39 @@ def get_dependencies(conn: SomeConnection,
 def new_experiment(conn: SomeConnection,
                    name: str,
                    sample_name: str,
-                   format_string: Optional[str] = "{}-{}-{}"
+                   format_string: Optional[str]="{}-{}-{}",
+                   start_time: Optional[float]=None,
+                   end_time: Optional[float]=None,
                    ) -> int:
-    """ Add new experiment to container
+    """
+    Add new experiment to container.
 
     Args:
         conn: database connection
         name: the name of the experiment
         sample_name: the name of the current sample
         format_string: basic format string for table-name
-            must contain 3 placeholders.
+          must contain 3 placeholders.
+        start_time: time when the experiment was started. Do not supply this
+          unless you have a very good reason to do so.
+        end_time: time when the experiment was completed. Do not supply this
+          unless you have a VERY good reason to do so
+
     Returns:
         id: row-id of the created experiment
     """
     query = """
-    INSERT INTO experiments
-        (name, sample_name, start_time, format_string, run_counter)
-    VALUES
-        (?,?,?,?,?)
-    """
-    curr = atomic_transaction(conn, query, name, sample_name,
-                              time.time(), format_string, 0)
+            INSERT INTO experiments
+            (name, sample_name, format_string,
+            run_counter, start_time, end_time)
+            VALUES
+            (?,?,?,?,?,?)
+            """
+
+    start_time = start_time or time.time()
+    values = (name, sample_name, format_string, 0, start_time, end_time)
+
+    curr = atomic_transaction(conn, query, *values)
     return curr.lastrowid
 
 

From 2c10e5fd315c0733de415bca197f4ff2d3d3dbc8 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 12 Nov 2018 15:20:01 +0100
Subject: [PATCH 089/719] Remove a type annotation

---
 qcodes/dataset/sqlite_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 0fd27591d5d..3ff0142e1c5 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -793,7 +793,7 @@ def init_db(conn: SomeConnection)->None:
 
 
 def is_run_id_in_database(conn: SomeConnection,
-                          *run_ids: int) -> Dict[int, bool]:
+                          *run_ids) -> Dict[int, bool]:
     """
     Look up run_ids and return a dictionary with the answers to the question
     "is this run_id in the database?"

From 986f51709277d5130f91e3b288f40eb907f33fed Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 12 Nov 2018 15:36:01 +0100
Subject: [PATCH 090/719] Add test for different load methods

---
 .../dataset/test_database_extract_runs.py     | 48 ++++++++++++++++++-
 1 file changed, 47 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index f111a39baa6..a61f1995da1 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -7,7 +7,8 @@
 
 from qcodes.dataset.sqlite_base import connect, get_experiments
 from qcodes.dataset.experiment_container import Experiment
-from qcodes.dataset.data_set import DataSet, load_by_guid
+from qcodes.dataset.data_set import (DataSet, load_by_guid, load_by_counter,
+                                     load_by_id)
 from qcodes.dataset.database import path_to_dbfile
 from qcodes.dataset.database_extract_runs import extract_runs_into_db
 from qcodes.tests.dataset.temporary_databases import two_empty_temp_db_connections
@@ -348,3 +349,48 @@ def test_result_table_naming_and_run_id(two_empty_temp_db_connections,
     assert target_ds.table_name == "customname-1-1"
     assert target_ds.the_same_dataset_as(source_ds_2_2)
 
+
+def test_load_by_X_functions(two_empty_temp_db_connections,
+                             some_paramspecs):
+    """
+    Test some different loading functions
+    """
+    source_conn, target_conn = two_empty_temp_db_connections
+
+    source_path = path_to_dbfile(source_conn)
+    target_path = path_to_dbfile(target_conn)
+
+    source_exp1 = Experiment(conn=source_conn)
+    source_ds_1_1 = DataSet(conn=source_conn, exp_id=source_exp1.exp_id)
+    for ps in some_paramspecs[2].values():
+        source_ds_1_1.add_parameter(ps)
+    source_ds_1_1.add_result({ps.name: 0.0
+                              for ps in some_paramspecs[2].values()})
+    source_ds_1_1.mark_complete()
+
+    source_exp2 = Experiment(conn=source_conn)
+    source_ds_2_1 = DataSet(conn=source_conn, exp_id=source_exp2.exp_id)
+    for ps in some_paramspecs[2].values():
+        source_ds_2_1.add_parameter(ps)
+    source_ds_2_1.add_result({ps.name: 0.0
+                              for ps in some_paramspecs[2].values()})
+    source_ds_2_1.mark_complete()
+    source_ds_2_2 = DataSet(conn=source_conn,
+                            exp_id=source_exp2.exp_id,
+                            name="customname")
+    for ps in some_paramspecs[2].values():
+        source_ds_2_2.add_parameter(ps)
+    source_ds_2_2.add_result({ps.name: 0.0
+                              for ps in some_paramspecs[2].values()})
+    source_ds_2_2.mark_complete()
+
+    extract_runs_into_db(source_path, target_path, source_ds_2_2.run_id)
+
+    test_ds = load_by_guid(source_ds_2_2.guid, target_conn)
+    assert source_ds_2_2.the_same_dataset_as(test_ds)
+
+    test_ds = load_by_id(1, target_conn)
+    assert source_ds_2_2.the_same_dataset_as(test_ds)
+
+    test_ds = load_by_counter(1, 1, target_conn)
+    assert source_ds_2_2.the_same_dataset_as(test_ds)

From 3e4ab8322d1cb060a57beb6fd99507946f6fcfac Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Tue, 13 Nov 2018 10:50:18 +0100
Subject: [PATCH 091/719] Remove "counter" from persistent dataset traits

---
 qcodes/dataset/data_set.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 0e7a22284a9..18fad27da4b 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -189,7 +189,7 @@ class DataSet(Sized):
     # the "persistent traits" are the attributes/properties of the DataSet
     # that are NOT tied to the representation of the DataSet in any particular
     # database
-    persistent_traits = ('name', 'guid', 'number_of_results', 'counter',
+    persistent_traits = ('name', 'guid', 'number_of_results',
                          'parameters', 'paramspecs', 'exp_name', 'sample_name',
                          'completed', 'snapshot', 'run_timestamp_raw',
                          'description', 'completed_timestamp_raw')
@@ -940,7 +940,8 @@ def load_by_guid(guid: str, conn: Optional[SomeConnection]=None) -> DataSet:
     return DataSet(run_id=run_id, conn=conn)
 
 
-def load_by_counter(counter: int, exp_id: int) -> DataSet:
+def load_by_counter(counter: int, exp_id: int,
+                    conn: Optional[SomeConnection]=None) -> DataSet:
     """
     Load a dataset given its counter in a given experiment
 
@@ -949,11 +950,13 @@ def load_by_counter(counter: int, exp_id: int) -> DataSet:
     Args:
         counter: counter of the dataset within the given experiment
         exp_id: id of the experiment where to look for the dataset
+        conn: connection to the database to load from. If not provided, a
+          connection to the DB file specified in the config is made
 
     Returns:
         dataset of the given counter in the given experiment
     """
-    conn = connect(get_DB_location())
+    conn = conn or connect(get_DB_location())
     sql = """
     SELECT run_id
     FROM

From 92b268f84eeedf62275aaa86da88f8e46fcc9f27 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Tue, 13 Nov 2018 10:52:19 +0100
Subject: [PATCH 092/719] Refactor runs extractor to use create_run from
 sqlite_base

---
 qcodes/dataset/database_extract_runs.py | 285 +++++-------------------
 1 file changed, 54 insertions(+), 231 deletions(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 1147857bc92..865585915b8 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -7,6 +7,7 @@
 from qcodes.dataset.experiment_container import load_or_create_experiment
 from qcodes.dataset.sqlite_base import (atomic,
                                         connect,
+                                        create_run,
                                         format_table_name,
                                         get_exp_ids_from_run_ids,
                                         get_last_experiment,
@@ -14,6 +15,7 @@
                                         get_runid_from_guid,
                                         insert_column,
                                         is_run_id_in_database,
+                                        mark_run_complete,
                                         new_experiment,
                                         select_many_where,
                                         SomeConnection,
@@ -165,251 +167,72 @@ def _extract_single_dataset_into_db(dataset: DataSet,
         return
 
     parspecs = dataset.paramspecs.values()
-    data_column_names_and_types = ",".join([p.sql_repr() for p in parspecs])
-
-    target_run_id = _copy_runs_table_entries(source_conn,
-                                             target_conn,
-                                             dataset.run_id,
-                                             target_exp_id)
-    _update_run_counter(target_conn, target_exp_id)
-    _copy_layouts_and_dependencies(source_conn,
-                                   target_conn,
-                                   dataset.run_id,
-                                   target_run_id)
-    target_table_name = _copy_results_table(source_conn,
-                                            target_conn,
-                                            dataset.run_id,
-                                            target_run_id,
-                                            data_column_names_and_types)
-    _update_result_table_name(target_conn, target_table_name, target_run_id)
-
-
-def _copy_runs_table_entries(source_conn: SomeConnection,
-                             target_conn: SomeConnection,
-                             source_run_id: int,
-                             target_exp_id: int) -> int:
-    """
-    Copy an entire runs table row from one DB and paste it all
-    (expect the primary key) into another DB. The two DBs may not be the same.
-    Note that this function does not create a new results table
-
-    This function should be executed with an atomically guarded target_conn
-    as a part of a larger atomic transaction
-    """
-    runs_row_query = """
-                    SELECT *
-                    FROM runs
-                    WHERE run_id = ?
-                    """
-    cursor = source_conn.cursor()
-    cursor.execute(runs_row_query, (source_run_id,))
-    source_runs_row = cursor.fetchall()[0]
-    source_colnames = set(source_runs_row.keys())
-
-    # There might not be any runs in the target DB, hence we ask PRAGMA
-    cursor = target_conn.cursor()
-    cursor.execute("PRAGMA table_info(runs)", ())
-    tab_info = cursor.fetchall()
-    target_colnames = set([r['name'] for r in tab_info])
-
-    for colname in source_colnames.difference(target_colnames):
-        insert_column(target_conn, 'runs', colname)
-
-    # the first entry is "run_id"
-    sql_colnames = str(tuple(source_runs_row.keys()[1:])).replace("'", "")
-    sql_placeholders = sql_placeholder_string(len(source_runs_row.keys())-1)
-
-    sql_insert_values = f"""
-                         INSERT INTO runs
-                         {sql_colnames}
-                         VALUES
-                         {sql_placeholders}
-                         """
-    # the first two entries in source_runs_row are run_id and exp_id
-    values = tuple([target_exp_id] + [val for val in source_runs_row[2:]])
 
-    cursor = target_conn.cursor()
-    cursor.execute(sql_insert_values, values)
+    #metadata = dataset.get_metadata()
 
-    return cursor.lastrowid
+    _, target_run_id, target_table_name = create_run(target_conn,
+                                                     target_exp_id,
+                                                     name=dataset.name,
+                                                     guid=dataset.guid,
+                                                     parameters=list(parspecs))
+    _populate_results_table(source_conn,
+                            target_conn,
+                            dataset.table_name,
+                            target_table_name)
+    mark_run_complete(target_conn, target_run_id)
+    _rewrite_timestamps(target_conn,
+                        target_run_id,
+                        dataset.run_timestamp_raw,
+                        dataset.completed_timestamp_raw)
 
 
-def _update_run_counter(target_conn: SomeConnection, target_exp_id) -> None:
-    """
-    Update the run_counter in the target DB experiments table
-    """
-    update_sql = """
-                 UPDATE experiments
-                 SET run_counter = run_counter + 1
-                 WHERE exp_id = ?
-                 """
-    cursor = target_conn.cursor()
-    cursor.execute(update_sql, (target_exp_id,))
 
 
-def _copy_layouts_and_dependencies(source_conn: SomeConnection,
-                                   target_conn: SomeConnection,
-                                   source_run_id: int,
-                                   target_run_id: int) -> None:
+def _populate_results_table(source_conn: SomeConnection,
+                            target_conn: SomeConnection,
+                            source_table_name: str,
+                            target_table_name: str) -> None:
     """
-    Copy over the layouts and dependencies tables. Note that the layout_ids
-    are not preserved in the target DB, but of course their relationships are
-    (e.g. layout_id 10 that depends on layout_id 9 might be inserted as
-    layout_id 2 that depends on layout_id 1)
+    Copy over all the entries of the results table
     """
-    layout_query = """
-                   SELECT layout_id, run_id, "parameter", label, unit, inferred_from
-                   FROM layouts
-                   WHERE run_id = ?
-                   """
-    cursor = source_conn.cursor()
-    cursor.execute(layout_query, (source_run_id,))
-    rows = cursor.fetchall()
-
-    layout_insert = """
-                    INSERT INTO layouts
-                    (run_id, parameter, label, unit, inferred_from)
-                    VALUES (?,?,?,?,?)
-                    """
-
-    colnames = ('run_id', 'parameter', 'label', 'unit', 'inferred_from')
-    cursor = target_conn.cursor()
-    source_layout_ids = []
-    target_layout_ids = []
-    for row in rows:
-        values = ((target_run_id,) +
-                  tuple(row[colname] for colname in colnames[1:]))
-        cursor.execute(layout_insert, values)
-        source_layout_ids.append(row['layout_id'])
-        target_layout_ids.append(cursor.lastrowid)
-
-    # for the dependencies, we need a map from source layout_id to
-    # target layout_id
-    layout_id_map = dict(zip(source_layout_ids, target_layout_ids))
-
-    placeholders = sql_placeholder_string(len(source_layout_ids))
-
-    deps_query = f"""
-                 SELECT dependent, independent, axis_num
-                 FROM dependencies
-                 WHERE dependent IN {placeholders}
-                 OR independent IN {placeholders}
-                 """
-
-    cursor = source_conn.cursor()
-    cursor.execute(deps_query, tuple(source_layout_ids*2))
-    rows = cursor.fetchall()
-
-    deps_insert = """
-                  INSERT INTO dependencies
-                  (dependent, independent, axis_num)
-                  VALUES (?,?,?)
-                  """
-    cursor = target_conn.cursor()
+    get_data_query = f"""
+                     SELECT *
+                     FROM "{source_table_name}"
+                     """
 
-    for row in rows:
-        values = (layout_id_map[row['dependent']],
-                  layout_id_map[row['independent']],
-                  row['axis_num'])
-        cursor.execute(deps_insert, values)
+    source_cursor = source_conn.cursor()
+    target_cursor = target_conn.cursor()
 
+    for row in source_cursor.execute(get_data_query):
+        column_names = ','.join(row.keys()[1:])  # the first key is "id"
+        values = tuple(val for val in row[1:])
+        value_placeholders = sql_placeholder_string(len(values))
+        insert_data_query = f"""
+                             INSERT INTO "{target_table_name}"
+                             ({column_names})
+                             values {value_placeholders}
+                             """
+        target_cursor.execute(insert_data_query, values)
 
-def _copy_results_table(source_conn: SomeConnection,
-                        target_conn: SomeConnection,
-                        source_run_id: int,
-                        target_run_id: int,
-                        column_names_and_types: str) -> str:
-    """
-    Copy the contents of the results table. Creates a new results_table with
-    a name appropriate for the target DB and updates the rows of that table
 
-    Returns the name of the new results table
+def _rewrite_timestamps(target_conn: SomeConnection, target_run_id: int,
+                        correct_run_timestamp: float,
+                        correct_completed_timestamp: float) -> None:
+    """
+    Update the timestamp to match the original one
     """
-    table_name_query = """
-                       SELECT result_table_name, name
-                       FROM runs
-                       WHERE run_id = ?
-                       """
-    cursor = source_conn.cursor()
-    cursor.execute(table_name_query, (source_run_id,))
-    row = cursor.fetchall()[0]
-    table_name = row['result_table_name']
-    run_name = row['name']
-
-    format_string_query = """
-                          SELECT format_string, run_counter, experiments.exp_id
-                          FROM experiments
-                          INNER JOIN runs
-                          ON runs.exp_id = experiments.exp_id
-                          WHERE runs.run_id = ?
-                          """
+    query = """
+            UPDATE runs
+            SET run_timestamp = ?
+            WHERE run_id = ?
+            """
     cursor = target_conn.cursor()
-    cursor.execute(format_string_query, (target_run_id,))
-    row = cursor.fetchall()[0]
-    format_string = row['format_string']
-    run_counter = row['run_counter']
-    exp_id = int(row['exp_id'])
+    cursor.execute(query, (correct_run_timestamp, target_run_id))
 
-    get_data_query = f"""
-                     SELECT *
-                     FROM "{table_name}"
-                     """
-    cursor = source_conn.cursor()
-    cursor.execute(get_data_query)
-    data_rows = cursor.fetchall()
-    if len(data_rows) > 0:
-        data_columns = data_rows[0].keys()
-        data_columns.remove('id')
-    else:
-        data_columns = []
-
-    target_table_name = format_table_name(format_string,
-                                          run_name,
-                                          exp_id,
-                                          run_counter)
-
-    if column_names_and_types != '':
-        make_table = f"""
-                    CREATE TABLE "{target_table_name}" (
-                        id INTEGER PRIMARY KEY,
-                        {column_names_and_types}
-                    )
-                    """
-    else:
-        make_table = f"""
-            CREATE TABLE "{target_table_name}" (
-                id INTEGER PRIMARY KEY
-            )
+    query = """
+            UPDATE runs
+            SET completed_timestamp = ?
+            WHERE run_id = ?
             """
-
-    cursor = target_conn.cursor()
-    cursor.execute(make_table)
-
-    # according to one of our reports, multiple single-row inserts
-    # are okay if there's only one commit
-
-    column_names = ','.join(data_columns)
-    value_placeholders = sql_placeholder_string(len(data_columns))
-    insert_data = f"""
-                   INSERT INTO "{target_table_name}"
-                   ({column_names})
-                   values {value_placeholders}
-                   """
-
-    for row in data_rows:
-        # the first row entry is the ID, which is automatically inserted
-        cursor.execute(insert_data, tuple(v for v in row[1:]))
-
-    return target_table_name
-
-
-def _update_result_table_name(target_conn: SomeConnection,
-                              target_table_name: str,
-                              target_run_id: int) -> None:
-    sql = """
-          UPDATE runs
-          SET result_table_name = ?
-          WHERE run_id = ?
-          """
     cursor = target_conn.cursor()
-    cursor.execute(sql, (target_table_name, target_run_id))
+    cursor.execute(query, (correct_completed_timestamp, target_run_id))

From 3cdca93cd62264fb7333d7aefc1d971cae88faa5 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 14 Nov 2018 14:53:00 +0100
Subject: [PATCH 093/719] Fix incorrect conn handling in load_by_counter

---
 qcodes/dataset/data_set.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 18fad27da4b..ccdff6a17df 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -192,7 +192,7 @@ class DataSet(Sized):
     persistent_traits = ('name', 'guid', 'number_of_results',
                          'parameters', 'paramspecs', 'exp_name', 'sample_name',
                          'completed', 'snapshot', 'run_timestamp_raw',
-                         'description', 'completed_timestamp_raw')
+                         'description', 'completed_timestamp_raw', 'metadata')
 
     def __init__(self, path_to_db: str=None,
                  run_id: Optional[int]=None,
@@ -967,9 +967,8 @@ def load_by_counter(counter: int, exp_id: int,
     """
     c = transaction(conn, sql, counter, exp_id)
     run_id = one(c, 'run_id')
-    conn.close()
 
-    d = DataSet(get_DB_location(), run_id=run_id)
+    d = DataSet(conn=conn, run_id=run_id)
     return d
 
 

From e1817dbdc35487921dc4e461ed89648f0826b965 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 15 Nov 2018 11:26:47 +0100
Subject: [PATCH 094/719] Don't use mark.usefixtures decorator

---
 qcodes/tests/dataset/test_dataset_basic.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 050f98ade27..9f52c5636c3 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -495,8 +495,7 @@ def fb(xv, yv):
     assert dataset.get_setpoints("b")['y'] == expected_setpoints[1]
 
 
-@pytest.mark.usefixtures('experiment')
-def test_get_description(some_paramspecs):
+def test_get_description(experiment, some_paramspecs):
 
     paramspecs = some_paramspecs[2]
 
@@ -528,8 +527,7 @@ def test_get_description(some_paramspecs):
     assert loaded_ds.description == desc
 
 
-@pytest.mark.usefixtures('experiment')
-def test_metadata():
+def test_metadata(experiment):
 
     metadata1 = {'number': 1, "string": "Once upon a time..."}
     metadata2 = {'more': 'meta'}
@@ -558,7 +556,7 @@ def test_metadata():
             ds1.add_metadata(tag, value)
 
 
-def test_the_same_dataset_as(some_paramspecs):
+def test_the_same_dataset_as(some_paramspecs, experiment):
         paramspecs = some_paramspecs[2]
         ds = DataSet()
         ds.add_parameter(paramspecs['ps1'])

From 501b3abe6c8fca24412f99f0c9b2ee5f1f17a71a Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Sep 2018 15:23:21 +0200
Subject: [PATCH 095/719] Add simple prototype of new ArrayParameter

---
 qcodes/instrument/parameter.py | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 5c6faac94e5..2b928145439 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -947,6 +947,36 @@ def sweep(self, start, stop, step=None, num=None):
                                 step=step, num=num)
 
 
+class ArrayParameter2(Parameter):
+    """
+
+
+
+    """
+    def __init__(self, *args, setpoints=None, **kwargs):
+        if setpoints is None:
+            self.setpoints = []
+        else:
+            self.setpoints = setpoints
+        super().__init__(*args, **kwargs)
+
+
+class GeneratedSetPoints(Parameter):
+    """
+    A parameter that generates a setpoint array from start, stop and num points
+    parameters.
+    """
+    def __init__(self, startparam, stopparam, numpointsparam, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._startparam = startparam
+        self._stopparam = stopparam
+        self._numpointsparam = numpointsparam
+
+    def get_raw(self):
+        return numpy.linspace(self._startparam(), self._stopparam(),
+                              self._numpointsparam())
+
+
 class ArrayParameter(_BaseParameter):
     """
     A gettable parameter that returns an array of values.

From 9e622c5c6897722c201c298597635dde83f655eb Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Sep 2018 15:23:47 +0200
Subject: [PATCH 096/719] Handle new array parameters in dataset

---
 qcodes/dataset/measurements.py | 59 +++++++++++++++++++++++++++++++++-
 1 file changed, 58 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index 4e8e47baefb..78d7d68fc60 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -12,7 +12,7 @@
 import qcodes as qc
 from qcodes import Station
 from qcodes.instrument.parameter import ArrayParameter, _BaseParameter, \
-    Parameter, MultiParameter
+    Parameter, MultiParameter, ArrayParameter2
 from qcodes.dataset.experiment_container import Experiment
 from qcodes.dataset.param_spec import ParamSpec
 from qcodes.dataset.data_set import DataSet
@@ -156,6 +156,10 @@ def add_result(self, *res_tuple: res_type) -> None:
                     self._unbundle_arrayparameter(parameter,
                                                   res,
                                                   found_parameters)
+                if isinstance(parameter, ArrayParameter2):
+                    self._unbundle_arrayparameter2(parameter,
+                                                   res,
+                                                   found_parameters)
 
         for partial_result in res:
             parameter = partial_result[0]
@@ -288,6 +292,24 @@ def _unbundle_arrayparameter(self,
                                             parameter.setpoints,
                                             res, found_parameters)
 
+    def _unbundle_arrayparameter2(self, parameter: ArrayParameter2,
+                                  res: List[res_type],
+                                  found_parameters: List[str]) -> None:
+        setpoint_names = []
+        setpoint_data = []
+        for setpointparam in parameter.setpoints:
+            these_setpoints = setpointparam.get()
+            setpoint_names.append(setpointparam.full_name)
+            setpoint_data.append(these_setpoints)
+            found_parameters.append(setpointparam.full_name)
+            if setpointparam.full_name not in self.parameters.keys():
+                raise RuntimeError(f'{setpointparam.full_name} '
+                                   f'which is a setpoint parameter for'
+                                   f'{parameter.full_name} is not registered!')
+        output_grids = np.meshgrid(*setpoint_data, indexing='ij')
+        for name, grid in zip(setpoint_names, output_grids):
+            res.append((name, grid))
+
     def _unbundle_setpoints_from_param(self, parameter: _BaseParameter,
                                        sp_names: Sequence[str],
                                        fallback_sp_name: str,
@@ -631,6 +653,11 @@ def register_parameter(
                                           setpoints,
                                           basis,
                                           paramtype)
+        if isinstance(parameter, ArrayParameter2):
+            self._register_arrayparameter2(parameter,
+                                           setpoints,
+                                           basis,
+                                           paramtype)
         elif isinstance(parameter, MultiParameter):
             self._register_multiparameter(parameter,
                                           setpoints,
@@ -726,6 +753,36 @@ def _register_arrayparameter(self,
                                  basis,
                                  paramtype)
 
+    def _register_arrayparameter2(self,
+                                 parameter: ArrayParameter2,
+                                 setpoints: setpoints_type,
+                                 basis: setpoints_type,
+                                 paramtype: str) -> None:
+        """
+        Register an ArrayParameter2 and the setpoints belonging to the
+        ArrayParameter2
+        """
+        name = str(parameter)
+        my_setpoints = list(setpoints) if setpoints else []
+        for i, setpoints in enumerate(parameter.setpoints):
+            spname = setpoints.full_name
+            splabel = setpoints.label
+            spunit = setpoints.unit
+
+            sp = ParamSpec(name=spname, paramtype=paramtype,
+                           label=splabel, unit=spunit)
+
+            self.parameters[spname] = sp
+
+            my_setpoints += [spname]
+
+        self._register_parameter(name,
+                                 parameter.label,
+                                 parameter.unit,
+                                 my_setpoints,
+                                 basis,
+                                 paramtype)
+
     def _register_multiparameter(self,
                                  multiparameter: MultiParameter,
                                  setpoints: setpoints_type,

From 876c6e45e7ca23a2344cc383efcd1e349d55da0d Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Sep 2018 15:31:52 +0200
Subject: [PATCH 097/719] Notebook with simple example of new ArrayParameter

---
 ...Simple Example of new ArrayParameter.ipynb | 505 ++++++++++++++++++
 1 file changed, 505 insertions(+)
 create mode 100644 docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb

diff --git a/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb b/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb
new file mode 100644
index 00000000000..fa609674392
--- /dev/null
+++ b/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb	
@@ -0,0 +1,505 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Example of new ArrayParameter\n",
+    "\n",
+    "This notebook has an example of a new simpler ArrayParameter\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument.parameter import Parameter, _BaseParameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Optional, Union, Iterable, Callable\n",
+    "Number = Union[float, int]\n",
+    "from qcodes.utils.validators import Validator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.utils.validators import Numbers\n",
+    "from qcodes.instrument.base import Instrument\n",
+    "from qcodes.dataset.measurements import Measurement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument.parameter import ArrayParameter2, GeneratedSetPoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we define an dummy instrument that returns something like a frequency spectrum from `f_start` to `f_stop`\n",
+    "in `n_points` steps. The functionality of the ArrayParameter is implemented only by having a reference to it's setpoints which is consumed by the dataset context manager. To do this we only have to define the parameter for the setpoints and the spectrum and let the parameter know that the frequency axis is the setpoints "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DummyArray(ArrayParameter2):\n",
+    "    \n",
+    "    def get_raw(self):\n",
+    "        npoints = self.root_instrument.n_points()\n",
+    "        return np.random.rand(npoints)\n",
+    "    \n",
+    "\n",
+    "class DummySpectrumAnalyzer(Instrument):\n",
+    "    \n",
+    "    def __init__(self, name, **kwargs):\n",
+    "        \n",
+    "        super().__init__(name, **kwargs)\n",
+    "            \n",
+    "\n",
+    "        self.add_parameter('f_start',\n",
+    "                           initial_value=0,\n",
+    "                           unit='Hz',\n",
+    "                           label='f start',\n",
+    "                           vals=Numbers(0,1e3),\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "\n",
+    "        self.add_parameter('f_stop',\n",
+    "                           unit='Hz',\n",
+    "                           label='f stop',\n",
+    "                           vals=Numbers(1,1e3),\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "\n",
+    "        self.add_parameter('n_points',\n",
+    "                           unit='',\n",
+    "                           vals=Numbers(1,1e3),\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "        \n",
+    "        self.add_parameter('freq_axis',\n",
+    "                           unit='Hz',\n",
+    "                           label='Freq Axis',\n",
+    "                           parameter_class=GeneratedSetPoints,\n",
+    "                           startparam=self.f_start,\n",
+    "                           stopparam=self.f_stop,\n",
+    "                           numpointsparam=self.n_points)\n",
+    "                           \n",
+    "        self.add_parameter('spectrum',\n",
+    "                   unit='dBm',\n",
+    "                   setpoints=(self.freq_axis,),\n",
+    "                   label='Spectrum',\n",
+    "                   parameter_class=DummyArray)\n",
+    "                "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = DummySpectrumAnalyzer('foobar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we setup the limits of the spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.f_start(0)\n",
+    "a.f_stop(500)\n",
+    "a.n_points(501)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can grab the axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
+       "        11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
+       "        22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,\n",
+       "        33.,  34.,  35.,  36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,\n",
+       "        44.,  45.,  46.,  47.,  48.,  49.,  50.,  51.,  52.,  53.,  54.,\n",
+       "        55.,  56.,  57.,  58.,  59.,  60.,  61.,  62.,  63.,  64.,  65.,\n",
+       "        66.,  67.,  68.,  69.,  70.,  71.,  72.,  73.,  74.,  75.,  76.,\n",
+       "        77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,\n",
+       "        88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,  96.,  97.,  98.,\n",
+       "        99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109.,\n",
+       "       110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.,\n",
+       "       121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,\n",
+       "       132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142.,\n",
+       "       143., 144., 145., 146., 147., 148., 149., 150., 151., 152., 153.,\n",
+       "       154., 155., 156., 157., 158., 159., 160., 161., 162., 163., 164.,\n",
+       "       165., 166., 167., 168., 169., 170., 171., 172., 173., 174., 175.,\n",
+       "       176., 177., 178., 179., 180., 181., 182., 183., 184., 185., 186.,\n",
+       "       187., 188., 189., 190., 191., 192., 193., 194., 195., 196., 197.,\n",
+       "       198., 199., 200., 201., 202., 203., 204., 205., 206., 207., 208.,\n",
+       "       209., 210., 211., 212., 213., 214., 215., 216., 217., 218., 219.,\n",
+       "       220., 221., 222., 223., 224., 225., 226., 227., 228., 229., 230.,\n",
+       "       231., 232., 233., 234., 235., 236., 237., 238., 239., 240., 241.,\n",
+       "       242., 243., 244., 245., 246., 247., 248., 249., 250., 251., 252.,\n",
+       "       253., 254., 255., 256., 257., 258., 259., 260., 261., 262., 263.,\n",
+       "       264., 265., 266., 267., 268., 269., 270., 271., 272., 273., 274.,\n",
+       "       275., 276., 277., 278., 279., 280., 281., 282., 283., 284., 285.,\n",
+       "       286., 287., 288., 289., 290., 291., 292., 293., 294., 295., 296.,\n",
+       "       297., 298., 299., 300., 301., 302., 303., 304., 305., 306., 307.,\n",
+       "       308., 309., 310., 311., 312., 313., 314., 315., 316., 317., 318.,\n",
+       "       319., 320., 321., 322., 323., 324., 325., 326., 327., 328., 329.,\n",
+       "       330., 331., 332., 333., 334., 335., 336., 337., 338., 339., 340.,\n",
+       "       341., 342., 343., 344., 345., 346., 347., 348., 349., 350., 351.,\n",
+       "       352., 353., 354., 355., 356., 357., 358., 359., 360., 361., 362.,\n",
+       "       363., 364., 365., 366., 367., 368., 369., 370., 371., 372., 373.,\n",
+       "       374., 375., 376., 377., 378., 379., 380., 381., 382., 383., 384.,\n",
+       "       385., 386., 387., 388., 389., 390., 391., 392., 393., 394., 395.,\n",
+       "       396., 397., 398., 399., 400., 401., 402., 403., 404., 405., 406.,\n",
+       "       407., 408., 409., 410., 411., 412., 413., 414., 415., 416., 417.,\n",
+       "       418., 419., 420., 421., 422., 423., 424., 425., 426., 427., 428.,\n",
+       "       429., 430., 431., 432., 433., 434., 435., 436., 437., 438., 439.,\n",
+       "       440., 441., 442., 443., 444., 445., 446., 447., 448., 449., 450.,\n",
+       "       451., 452., 453., 454., 455., 456., 457., 458., 459., 460., 461.,\n",
+       "       462., 463., 464., 465., 466., 467., 468., 469., 470., 471., 472.,\n",
+       "       473., 474., 475., 476., 477., 478., 479., 480., 481., 482., 483.,\n",
+       "       484., 485., 486., 487., 488., 489., 490., 491., 492., 493., 494.,\n",
+       "       495., 496., 497., 498., 499., 500.])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.freq_axis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or the spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.31281518, 0.96887871, 0.42191194, 0.51714415, 0.25538286,\n",
+       "       0.92696486, 0.76954697, 0.51712345, 0.48111713, 0.61014766,\n",
+       "       0.42801603, 0.43561301, 0.64152132, 0.24845664, 0.68491269,\n",
+       "       0.57899917, 0.16974739, 0.18945099, 0.77348716, 0.32345713,\n",
+       "       0.32283512, 0.03025562, 0.91810724, 0.57521798, 0.58470989,\n",
+       "       0.38120617, 0.46536914, 0.48650925, 0.30560833, 0.78322953,\n",
+       "       0.93716312, 0.60605105, 0.75239911, 0.96351199, 0.32925186,\n",
+       "       0.71956489, 0.49513814, 0.40072415, 0.71484636, 0.47744867,\n",
+       "       0.99183756, 0.42884688, 0.87046585, 0.97636722, 0.80768148,\n",
+       "       0.47275855, 0.04567205, 0.80345861, 0.16429275, 0.98433221,\n",
+       "       0.31342919, 0.25533898, 0.71032451, 0.03503889, 0.69431169,\n",
+       "       0.85934569, 0.15493456, 0.350528  , 0.13867757, 0.88801413,\n",
+       "       0.12544615, 0.71695272, 0.80233482, 0.95412588, 0.08481348,\n",
+       "       0.88858004, 0.20740531, 0.62895786, 0.7094034 , 0.71663199,\n",
+       "       0.04516419, 0.25645949, 0.3619041 , 0.22624793, 0.03096165,\n",
+       "       0.50486112, 0.38058129, 0.38549274, 0.93265042, 0.69570252,\n",
+       "       0.9715744 , 0.26551269, 0.0278466 , 0.47564881, 0.14327013,\n",
+       "       0.65544137, 0.37554881, 0.81038091, 0.4373319 , 0.63655817,\n",
+       "       0.84999389, 0.48549494, 0.37636679, 0.24317791, 0.46666367,\n",
+       "       0.93993374, 0.2030196 , 0.20372072, 0.55596547, 0.64384574,\n",
+       "       0.55032748, 0.18346428, 0.13315848, 0.4444626 , 0.57653102,\n",
+       "       0.28088507, 0.47296397, 0.19810506, 0.02489858, 0.08292104,\n",
+       "       0.15122852, 0.02681189, 0.1825864 , 0.83923332, 0.82979598,\n",
+       "       0.12532781, 0.28667028, 0.06138414, 0.54201822, 0.28361495,\n",
+       "       0.04006816, 0.55588607, 0.08477299, 0.2226377 , 0.74544186,\n",
+       "       0.248556  , 0.90649953, 0.09848282, 0.63803225, 0.54836512,\n",
+       "       0.09148153, 0.14109837, 0.62075636, 0.52826137, 0.8763575 ,\n",
+       "       0.48961966, 0.24877176, 0.89193214, 0.7763051 , 0.6191885 ,\n",
+       "       0.93309929, 0.48183231, 0.38190092, 0.76675634, 0.31256475,\n",
+       "       0.64081171, 0.23996295, 0.17831513, 0.70911486, 0.3689516 ,\n",
+       "       0.77318391, 0.26551174, 0.9332016 , 0.80833114, 0.94929732,\n",
+       "       0.05899685, 0.14579488, 0.50757545, 0.84313475, 0.67681984,\n",
+       "       0.09312281, 0.17850811, 0.30471426, 0.70653125, 0.94016408,\n",
+       "       0.77204967, 0.78658342, 0.96252718, 0.05135915, 0.6104287 ,\n",
+       "       0.57842423, 0.54790475, 0.18708696, 0.40089462, 0.61792494,\n",
+       "       0.78666442, 0.45664661, 0.57400101, 0.21632045, 0.65836317,\n",
+       "       0.41728132, 0.09314095, 0.39881779, 0.69329242, 0.89670779,\n",
+       "       0.39077226, 0.36796047, 0.51413186, 0.24017188, 0.96503661,\n",
+       "       0.39737534, 0.14966974, 0.05272144, 0.07344577, 0.65384573,\n",
+       "       0.3244456 , 0.93881849, 0.77149932, 0.34256363, 0.57171536,\n",
+       "       0.99633436, 0.7128691 , 0.53621015, 0.20434432, 0.95889797,\n",
+       "       0.14452839, 0.52756577, 0.59106331, 0.3081482 , 0.4549849 ,\n",
+       "       0.20716477, 0.46569662, 0.74935475, 0.43593272, 0.27032474,\n",
+       "       0.76637407, 0.88225447, 0.65898922, 0.80142354, 0.14939703,\n",
+       "       0.54069258, 0.78950072, 0.83605732, 0.62979079, 0.21252879,\n",
+       "       0.60680212, 0.97419554, 0.77182435, 0.75763177, 0.97771958,\n",
+       "       0.91581111, 0.7219678 , 0.96016159, 0.65960683, 0.23570303,\n",
+       "       0.33695877, 0.23561085, 0.82518603, 0.94735652, 0.52801753,\n",
+       "       0.62309437, 0.98345045, 0.35983667, 0.07843121, 0.09146102,\n",
+       "       0.66973072, 0.93494067, 0.05280839, 0.93756376, 0.7293141 ,\n",
+       "       0.50768684, 0.14962689, 0.2658266 , 0.27975887, 0.96575044,\n",
+       "       0.86352484, 0.00677849, 0.60706878, 0.60814407, 0.12029253,\n",
+       "       0.34831538, 0.34223691, 0.58388414, 0.681435  , 0.21728724,\n",
+       "       0.94635087, 0.33127465, 0.64361487, 0.92241564, 0.59245856,\n",
+       "       0.1148949 , 0.7021584 , 0.11996621, 0.64843461, 0.94555061,\n",
+       "       0.70387657, 0.77887487, 0.72795828, 0.68279723, 0.39879606,\n",
+       "       0.1533654 , 0.38515401, 0.71375073, 0.17799896, 0.0252395 ,\n",
+       "       0.06283647, 0.84102243, 0.45017289, 0.29994363, 0.58409358,\n",
+       "       0.80281348, 0.79188333, 0.1162714 , 0.32685503, 0.75942253,\n",
+       "       0.38373489, 0.79152529, 0.97062294, 0.67297296, 0.79336539,\n",
+       "       0.43884364, 0.24561457, 0.65555045, 0.87768973, 0.64103826,\n",
+       "       0.2666453 , 0.1391131 , 0.86647697, 0.61714395, 0.41203585,\n",
+       "       0.11707366, 0.95913778, 0.05950737, 0.74316187, 0.24973516,\n",
+       "       0.55441886, 0.19049959, 0.72943378, 0.14485682, 0.23767495,\n",
+       "       0.23655284, 0.75358185, 0.11639362, 0.22699845, 0.58859088,\n",
+       "       0.19922609, 0.59756317, 0.31772437, 0.31891757, 0.61702834,\n",
+       "       0.2307571 , 0.71656227, 0.77801287, 0.61430522, 0.43949158,\n",
+       "       0.904222  , 0.13305298, 0.97109297, 0.03946522, 0.27349123,\n",
+       "       0.60728714, 0.11184083, 0.20264691, 0.41851126, 0.34208404,\n",
+       "       0.3516932 , 0.72207464, 0.62215237, 0.83973124, 0.61972589,\n",
+       "       0.00746499, 0.67415024, 0.64700587, 0.31843831, 0.85093758,\n",
+       "       0.36074848, 0.26198126, 0.55974394, 0.7755382 , 0.96214801,\n",
+       "       0.60038967, 0.5871515 , 0.97103239, 0.25660708, 0.8394608 ,\n",
+       "       0.61362974, 0.7721306 , 0.41902284, 0.56507279, 0.97512555,\n",
+       "       0.1757342 , 0.12194126, 0.72601828, 0.46489845, 0.20108013,\n",
+       "       0.12265486, 0.32739885, 0.23558657, 0.02516245, 0.90309388,\n",
+       "       0.71132001, 0.35639495, 0.99692481, 0.13415441, 0.65833817,\n",
+       "       0.11460708, 0.51822322, 0.53973366, 0.81536775, 0.13440548,\n",
+       "       0.48233264, 0.25445079, 0.00434424, 0.84816563, 0.02711536,\n",
+       "       0.21425029, 0.07758158, 0.02883053, 0.56527235, 0.19727065,\n",
+       "       0.49378938, 0.66257068, 0.99112211, 0.49515019, 0.45807055,\n",
+       "       0.24407957, 0.80594336, 0.52918217, 0.7366867 , 0.12172603,\n",
+       "       0.50720039, 0.89600683, 0.03299381, 0.87402446, 0.03942597,\n",
+       "       0.4398926 , 0.56602433, 0.1827016 , 0.34461014, 0.48292586,\n",
+       "       0.37908904, 0.05441919, 0.20705179, 0.34375954, 0.6649455 ,\n",
+       "       0.0731173 , 0.10959237, 0.98139206, 0.03566843, 0.00688507,\n",
+       "       0.91668891, 0.91156237, 0.65782263, 0.15321753, 0.12761921,\n",
+       "       0.70015197, 0.5353978 , 0.48899851, 0.92041438, 0.58646024,\n",
+       "       0.24802929, 0.58023147, 0.51882816, 0.49366176, 0.03196893,\n",
+       "       0.56670287, 0.06277128, 0.54048722, 0.85678255, 0.31840659,\n",
+       "       0.60387901, 0.25282476, 0.62576788, 0.34045001, 0.78477856,\n",
+       "       0.19135518, 0.89802393, 0.56130128, 0.54358126, 0.70889138,\n",
+       "       0.23780924, 0.43899571, 0.04051872, 0.73162057, 0.7630328 ,\n",
+       "       0.42628733, 0.5824199 , 0.51831109, 0.5821532 , 0.01817897,\n",
+       "       0.20330086, 0.02747431, 0.15170385, 0.63457024, 0.30606336,\n",
+       "       0.15690499, 0.75987837, 0.87250445, 0.34596773, 0.35767279,\n",
+       "       0.42499385, 0.14849033, 0.79368317, 0.08853673, 0.87720111,\n",
+       "       0.3156627 , 0.73408487, 0.16822168, 0.13283555, 0.35081781,\n",
+       "       0.23496507, 0.11859108, 0.19449802, 0.8220064 , 0.30034061,\n",
+       "       0.61608037, 0.64891939, 0.15747444, 0.40561231, 0.23300349,\n",
+       "       0.11877202])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.spectrum.get()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also directly consume the parameter in a measurement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 614\n"
+     ]
+    }
+   ],
+   "source": [
+    "meas = Measurement()\n",
+    "meas.register_parameter(a.spectrum)  # register the first independent parameter\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((a.spectrum, a.spectrum.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.dataset.plotting import plot_by_id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And plot it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x2046acfe860>], [None])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 3b90878b6aea45161e3f9201fafede0ab802e9f6 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 12 Sep 2018 18:13:44 +0200
Subject: [PATCH 098/719] Add raw arrayparameter time/freq example notebook

---
 .../examples/DataSet/Simple Example of new ArrayParameter.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb b/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb
index fa609674392..ba8cdbc751f 100644
--- a/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb	
+++ b/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb	
@@ -455,7 +455,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.5"
   },
   "toc": {
    "base_numbering": 1,

From 30e21e2a32f30e186a0cc134365dc862ba882b1f Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 12 Sep 2018 18:17:00 +0200
Subject: [PATCH 099/719] Add example notebook of time/freq measurement

---
 ...arameter Example with Dual Setpoints.ipynb | 411 ++++++++++++++++++
 1 file changed, 411 insertions(+)
 create mode 100644 docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb

diff --git a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb
new file mode 100644
index 00000000000..7c53af62806
--- /dev/null
+++ b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb	
@@ -0,0 +1,411 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# An ArrayParameter Example with Dual Setpoints\n",
+    "\n",
+    "In this example we consider a dummy instrument that can return a time trace or the DFT (magnitude square) of that trace. The setpoints are accounted for in an easy way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from qcodes.instrument.base import Instrument, Parameter\n",
+    "from qcodes.instrument.parameter import ArrayParameter2\n",
+    "from qcodes.dataset.measurements import Measurement\n",
+    "from qcodes.dataset.plotting import plot_by_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def timetrace(npts: int, dt: float) -> np.ndarray:\n",
+    "    \"\"\"\n",
+    "    A very realistic-looking signal\n",
+    "    \"\"\"\n",
+    "    #freq = 10/(dt*npts)\n",
+    "    #decay = 1/(dt*npts)\n",
+    "    freq = 10\n",
+    "    decay = 1\n",
+    "    time = np.linspace(0, npts*dt, npts, endpoint=False)\n",
+    "    signal = np.exp(-decay*time)*np.sin(2*np.pi*freq*time)\n",
+    "    noise = 0.1*np.random.randn(npts)\n",
+    "    return signal + noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "class TimeTrace(ArrayParameter2):\n",
+    "    \n",
+    "    def get_raw(self):\n",
+    "        npts = self.root_instrument.npts()\n",
+    "        dt = self.root_instrument.dt()\n",
+    "        \n",
+    "        return timetrace(npts, dt)\n",
+    "    \n",
+    "\n",
+    "class Periodogram(ArrayParameter2):\n",
+    "    \n",
+    "    def get_raw(self):\n",
+    "        npts = self.root_instrument.npts()\n",
+    "        dt = self.root_instrument.dt()\n",
+    "        \n",
+    "        tt = self.root_instrument.trace()\n",
+    "        \n",
+    "        return np.abs(np.fft.fft(tt))**2\n",
+    "        \n",
+    "        \n",
+    "class TimeAxis(Parameter):\n",
+    "\n",
+    "    def get_raw(self):\n",
+    "        npts = self.root_instrument.npts()\n",
+    "        dt = self.root_instrument.dt()\n",
+    "        return np.linspace(0, dt*npts, npts, endpoint=False)\n",
+    "\n",
+    "    \n",
+    "class FrequencyAxis(Parameter):\n",
+    "    \n",
+    "    def get_raw(self):\n",
+    "        npts = self.root_instrument.npts()\n",
+    "        dt = self.root_instrument.dt()\n",
+    "\n",
+    "        return np.linspace(0, 1/dt, npts)\n",
+    "    \n",
+    "        \n",
+    "class OzzyLowScope(Instrument):\n",
+    "    \n",
+    "    def __init__(self, name, **kwargs):\n",
+    "        \n",
+    "        super().__init__(name, **kwargs)\n",
+    "        \n",
+    "        self.add_parameter(name='npts',\n",
+    "                           initial_value=500,\n",
+    "                           label='Number of points',\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "        \n",
+    "        self.add_parameter(name='dt',\n",
+    "                           initial_value=1e-3,\n",
+    "                           label='Time resolution',\n",
+    "                           unit='s',\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "        \n",
+    "        self.add_parameter(name='time_axis',\n",
+    "                           label='Time',\n",
+    "                           unit='s',\n",
+    "                           parameter_class=TimeAxis)\n",
+    "        \n",
+    "        self.add_parameter(name='freq_axis',\n",
+    "                           label='Frequency',\n",
+    "                           unit='Hz',\n",
+    "                           parameter_class=FrequencyAxis)\n",
+    "        \n",
+    "        self.add_parameter(name='trace',\n",
+    "                           label='Signal',\n",
+    "                           unit='V',\n",
+    "                           setpoints=(self.time_axis,),\n",
+    "                           parameter_class=TimeTrace)\n",
+    "        \n",
+    "        self.add_parameter(name='periodogram',\n",
+    "                           label='Periodogram',\n",
+    "                           unit='V^2/Hz',\n",
+    "                           setpoints=(self.freq_axis,),\n",
+    "                           parameter_class=Periodogram)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "osc = OzzyLowScope('osc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Measurement 1: Time Trace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 68\n"
+     ]
+    }
+   ],
+   "source": [
+    "timemeas = Measurement()\n",
+    "timemeas.register_parameter(osc.trace)\n",
+    "\n",
+    "osc.dt(0.001)\n",
+    "\n",
+    "with timemeas.run() as datasaver:\n",
+    "    datasaver.add_result((osc.trace, osc.trace.get()))\n",
+    "    \n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22fce779b38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 69\n"
+     ]
+    }
+   ],
+   "source": [
+    "osc.dt(0.01)  # make the trace 10 times longer\n",
+    "\n",
+    "with timemeas.run() as datasaver:\n",
+    "    datasaver.add_result((osc.trace, osc.trace.get()))\n",
+    "    \n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22fce753320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Measurement 2: Periodogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 71\n"
+     ]
+    }
+   ],
+   "source": [
+    "freqmeas = Measurement()\n",
+    "freqmeas.register_parameter(osc.periodogram)\n",
+    "\n",
+    "with freqmeas.run() as datasaver:\n",
+    "    datasaver.add_result((osc.periodogram, osc.periodogram.get()))\n",
+    "    \n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22fd07c62b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 72\n"
+     ]
+    }
+   ],
+   "source": [
+    "with freqmeas.run() as datasaver:\n",
+    "    datasaver.add_result((osc.periodogram, np.log10(osc.periodogram.get())))\n",
+    "    \n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AexDRHQB+xszdtShovaJXrO51NP/WlGH+BgaNDHvzdk8U6WpLYeHaHYv5nwjgi8y8Uv2CiFIATgZwHIC/ValsDQHhCBqtOHxNTn8Dg8bEoLN/r0BXJt2UDt9Q48/M/xXxXQ7A/1WlRA0GL52z0Pwd2ceEehoYNCTEftwCXW1p9A7lYFmMhCMFNQMKOnyJKE9EVxERScdeKvZGRDSdiB4jogVE9AYRXVjsNeoRIgqgM+Nn/v0m2sfAoCHRP5x31+sANrFjbr6FXnGifd5wfvcQEY1zjpUy/OUAfI2ZZwF4D4D/IKJZJVynriAcQVNGZwAA6WQCk7syWL65bySLZWBgUCKWberD9HFt7ufJTt9es21wpIpUFcQx/jlmvhjA7wE8RUSHACg6XzEzr2Xml5y/ewAsBDC12OvUG1Zu6UeCgKljvcYya+cuLFizQ/vBDQwaEoPZPJZs7MWsKV3usV3HjQJg9/VmQhzjTwDAzH8BcCaAGwHsXs5NiWgGgIMAPKccP4+I5hHRvI0bN5Zzi5ph5ZZ+7DymDemkV5WzpnRh8cZeE/FjYNBgeGt9Dyy2CZzALuPaAQArtzTXbD6O8T9X/MHMrwM4EsAFpd6QiDpgRwh9RQ0TZebrmXkOM8+ZMGFCqbeoKVZs7seuO7X7ju01qQN5i7Fic3MxBQODZsfiDb0AgL0mdbrHRrenMbot3XTMPyrO/zTp712Vr3tLuRkRpWEb/luY+e+lXKPesHb7AI7ayz9Q7bqTPU1csbkP+0zu1J1mYGBQh1ixuR9EwDRJxgWAqWPasLbJNP+oOP9TlL/vkT4zgKKMtxMtdAOAhcx8TTHn1jP6h/JupI/Aru40sbmYgoFBs2Plln7sPLrNXawp0JFJoW+4uTJ7RsX5f078TUQvy59LxBEAPgXgNSKa7xz7BjPfV+Z1RwzMjL7hHEa1+hvKmPY0OjMpY/wNDBoMKzb3uRq/jFEtSWzuGx6BElUPBXP7OCh7N3JmfhqlhYjWJSyL0Z/Nw2KgvcVfjUSEXca1Y/XW5ssHYmDQzFi9dQBH7x30N7a32mQum7eQShCkZU8NC5PVs0Sc9Munsd93HwSAAPMH7CXhvU246bOBQTOjdyjnpmqRMaolid6hHPb65v244h8LR6BklUeUw/ceeIx/dyK6W/6emT9czYLVOxau9QKVVOZvH0tiXXdzOYgMDJoZlsXoH86jvSVI5tpbUljfPQQA+PNzK/CdUxp+fWqk7PNT6e+fVbsgjYxRusbSmjIbuhgYNBDEDl7trUGzKM/uJ3W11qxM1USU8T8bwP0AHnFW5BqEQNdY2tPJposOMDBoZvQNOcY/hPkLTOzM1KxM1USU5n8DgNkA7iOifxLRJUQ0u0blaijomX/SJHczMGggiJm6TsaV+/jEziZn/sz8HOz0C5cT0U4APgTga0S0P+zNXR5g5ttrU8z6Q2sqgSFnt64wzb9/OA9mborIAAODZoeYqWuZvzS7b001R5xMrFBPZt4M4FbnH5zkbsdXsVx1jwmdrW4opy7ap70lhbzFGM5bgQUjBgYG9Yf+4XDZZ5RE8LL5siPf6wKRQxgRzSSiDzr5eGRMYOYfVrFcdQ/L8hpAGPMH7BXAa7cPYE0T7gFqYNAMWLyhB92DWfS7zF/TnyWCN5xvjo2aQo0/EV0A4C4A/wngdSL6iPT1j6pdsHrHsDT665i/YAr92TwOv/JRvPeqR2tWNgMDg/g49ponccZvn41k/sM5z+Bnm8T4R8k+XwRwCDP3OimY7yCiGcz8CzTRSt1SMZzznLkZjawjmEL/kIn4MTCodyxa1yMx/2B/3muiJ37IA0EjI0r2STBzLwAw83IA7wdwAhFdA2P8kc0zvnjkblh25YnafT1d2cdE/BgY1C1k+Vb01VGa0O3dJ3Rg6Y9OxOG779Q0zD/K+K8nogPFB2cgOBnAeAD7V7tgtcJzSzfj2SWbiz4vm7fQkkqERvII3fBLtxS93bGBgUGNIOv3P3voLQB65g8AiQShJZXwSb5xcc8ra7BkY0mZ8KuGKOP/aQDr5APMnGPmTwM4qqqlqiHOvP5fOOt//lXUOZbFyFns271LhWhA7xhHr4FB3UJm8VucrJ06h69AOplAtgTZ5z9vfRknXftU8QWsIkKtFzOvZuZ1Id/NrV6R6h9Zy375UcZfN3VUsb0/W7Ey7WjY1j+M/374LeSt5gi7i4OtfcP4+SNv+aQKg/iwLEb3oL/PqWGbmXQCSY2MK9CSoqJlH7Gd62C2vuSigqsViOjyGpSjoSAcPi0Rxr8rE8wMKGPu4k2Y/f2H8ORbjbFXcb3he/cswC/++TYef3PDSBelZvjWXa/j54+8jacXbxrpojQkrnpgEQ64/CH0SkEYqiEv1G/TyUTRoZ5iwKm3tZ5RoZ4JIroBQHOsZa4gBFtIJ8PfZmcmmvm/tGIrAOD5ZVsqV7AdCENOtNVAdsdxqHcP2EbE8P7S8PeX3gEAN6oHCEbuFOq3pcg+PU5q91ERctJIIIr5/wPAFma+rFaFqSR6h3J4c138fHTFhG8JtpCOWOadSUev6hURQjkzhS8JqYRd9zuS7JMTpCNCljAIR96RaxMSBVdZfJTeDwjmX1ybE4N2mCNZxvxV22om60UZ/0NQ5D699YRb/rUCH71uLpjjVWTPYHz9PY7sUwhCV7Rils/Aj5RTf82y1D4Oco7xitKkDcIhiILc51TZp1DVtqYSRWv+3YL5F/ADPrN4E0799Vz8Ye6yoq5fKqKs1zEArieid9ekJBVG71AO/cP52PpcdxG7bomX31JkgieZpSYd9lEvzPWu+e/gpZVbiz6PmXH/a2tr/hwpR3LLVTDmelWF91wezllYt71yG/qIgS5VBukoBdv7s3jq7dJ8U/e/thb/Wlp8KHU1IJqoJTWZbM7fbgslYUwni3f4CmJZiPkv29wHAFi8oTYhoVHRPgsA/BuAn9SkJBXEBbe+jF8+uhgA3MybhSCmZgL9Ebn4Pc0/Xic867DpznleWYTsUy/G/8Lb5uO0654p+ry/v/QO/v2Wl3Dzs8srXqYoCAOYrVD9zV28CUf++DHcNf+dilwPAC7526t4z5X/dP0T5WKkmP8Xb56HT93wfFGzY4F/v+UlfOL64kKpqwVRfznJ+gtyKPpooapNJ6OZv2VxYBOn7oF4mr+Q9W57YRV+9tCb0QWpACKtFzOvAXBS1UtRYdz9yhr376GY4VU9EvN/ZskmzPrOg3hGE1XxyIL1brRAXOM/tr0FgN/4pwoY/2zewkNvrIstW40UNvTYW9vVestKoXvnpTrNa0L54kJsy/nKqu3lF87BQ2/YkdJxCYgKNRRYGIdat4m3NvT47l+vGMzm8dii8OgvYfN9zN9pP6KPFmb+CWTzjA09g9pgjWsefgv7fucBX0SRaJPtmhxgMmT/Xy1IYVS0z+lElGn0Xbziyz72C9rSN4xrnJV+IqTuf55cik/d8BxeXrkV5948D9+/5w0A0dE+Mjzj771Ql/mHdORf/vNtnPenF/HEDhoKet3ji/HoovWh3ycdh6/cYa52QvlK2T5TdLZKKiquU78Eo/mvpZsx+/sP4TEplDXnatbh5/3xmeU+8lNJ1LfpBy6/+w187qYX8MYa/QAu+lpeo/kLPb4Q8xdS74m/eApn/O5ZDGbzeN/Vj+Lpt21bcevzKwEAf3lhlStJihlTIR+hLGEmahAXGlWaTwJYSUR/IqITiaghk9IPxQwF7HWY/2dvfB7znDBMwdh+eN9CPPX2Jtd4v7LablxxNX/RsOSIIqH5WxYjl7fw68cWo09iCysc/Xlr/3Cse4w4KmwZfvzAm/j8TfO8yzPjfGkwFAOvbPzvnm8bvU29Q0XfTxgEXZ6mUiHkmVISgT3jpBx5eeU295jqsLzt+ZX41aNv+8777t1v4IJbXy6pvGEQtrLegxOWbbI18+2ShLticx/+9znbIIv6y8uyj/NuxEycCqQtEwZ8U6/dL5ds7MXqrQP47t2vA/BsxhX/WIDfP207boVtKVR/cluuZDsMQ5Tm/1EAewJ4BHZa59VE9FsiOrrqpaogCjF/0UEfWrAOMy69F6+u9ljDDU8vw19eWOl+lrVCoPBI/sOP7ofDd9/JNVRhss/9r6/DTx58E1c/sMj9XrQDmQFs7BnCHS+ujrxnraEjKDfOXYZXV28LfqHBZX9/TSuvqRjOW3jgjXX47I3PA/Dem8yWxLS6FOMvwuuSFWRc4lqlGH+RDVbePlC0H1HW+19fh/+bH4/l3/r8SvzuiSWxfvvA6+vw4BvBxf31tLI4m7dw09xlWrYs29gP/2ouvnHna76y5zWyz8G7jsX0cW24+Ph9Iu+rzvbFYNE3lMdlf3/NJ/f8+rHFmHHpvW6Kl0Jh3fIMsRZunUKafzcz/5GZTwCwH+ztG68lolXVL1pl8OiiDZFOPPEyH1mo1wp/8qDneOkf8s8iCmn+Z797V9x63nvcGcK1/3w7EDWRt9g1oLLREixhKGdhqZMQ6vw/v4iv//UVrN1eP/mC5I62vT+LU375NL53zwJ8+FdzCxq9bN7Crc+vxBMxIkkGh/3XEg5fuUMJh9rGnhKYv3P5SjpTBXsbzhcvQ/U50pW8XsRlrk6lD2bzsSWu+15biztfLuzMfmfbAM7/84v4f396EZ+/6QXfd/W0JuXGuctw+T0L8Od/rQAALFjTDUcJ9DFsMQuQj4l6vO35le7Cr7HtLXjq4g9gzoxxkfdV1/aIwJC+oZwr+QgIP+Jb63t991XBzPjtE0vw1npPYR9p2ccFEY0FcBqAMwGMA3BHNQtVSfz4gTdx4W3zQ79PJ6KrQGb3fUoEUCqm5i8Gib++uBqfuuF5PLxgPS7+26sA7I4sQsD6pMFFOPUuvuNVfOBnT+CRBevxoiNH1Ws+8YcXrsdr73gzp8/e+HxkYjshc6nhdjoMOhEzog+nJT19U+8QvvoX7x2LKXkxcGWfkE63eEMP5vzgYbyzbQCbeoewoXsQcxdvwhFXPRpqgAXzL8XhK4zKonU9uPRvryKXt1zZUdTBYDYfO5JoOGf5ghp0eGH5FnzOmVkBNnGyynBCVjIMV8VWxxn+g3sX4oanl+HEa5/C3MW2VKYrpnzsxGufwvJNfbj076/hoQW2Xynumh2V8PU4bVi1DTJEPYTV39b+LK66fxHufW2te6wWEV2hsUfO1o0fBXAWgIMA3A3gCgCPc72HoGhgWazV0Qo9SKvEvNRO3lZgFa+A2rC+drtnqPIWoy2dClxfreFzb/b070ozsEplHs0rstgzSzbjkjtexZ/P1S8VEcYoDjNW6168y6xl4ccPLPKx2rJkn5BOd+Pc5djUO4wjNDuyLd7Qi/2njQ4cL0fzFwOjYJMfP2SaKzsKIzKYtWIz/+G85ZMkdDj9t89qzxMott3J527qHcL4jsplihF1kLMYV/xjge87nXlS9XZ1IVVc/526ct/T88PPybq+Bv2PNvQEI+VqkQco6omXw47zvw7ALsz8/5j5sUY0/EC441TV8VX4mb+/oxVaCi6gThXlNmAxu0aiP5vzHQ9DJbXXDd2DWoMWF3Ij1RG9KMPXWwTzD8vhk8uzG0ctUJLsw9HGP4r1hrUhMaksxfiL9MICPUM5VxMWbWMwl8dANh8r9DPrGP9iu++wEkpb1LnSc8/5wSNFnVsIUWXRPaLan5Zv9i/oixu5164a/xg79Yl6CCvz+u5ge62F7BNlvaYzc8XEZSL6A+zNYDYw836Vum5crOsexE4a5lHA9iOd8l7CgDK1a4uRqwMINiy5weTy7DZM2acQ1c8qxfy39A1jTQVXoKrMHwgPZQW8eogTjqsa/7zEptQOuG2g+Fh/MaCGdbqoOg/r1K7DtwT5Y7vyDL2DOXeQEe1lYDgPi+3rt2q2EpUxnLOQtxiDWSt2uxXnCRRr/FW5a/tAFskEoSNGuvNCiCqLjjipv1+qbKwSladLhrpKty+G8Re/CWtD6zV9sJKBB2GIeuK/ENHJRBTIcUpEuxPR94no80Xc6yYAxxdbwEphfcg6mfyzAAAgAElEQVQipELMX/bAB5l/abKPDIvZNTxLN/VhxqX3YmPPUCRDq5Tmf/AVD+OM3/mn+kf++FHcPm9V0QyRoe+QUUazdzC+8R9U6l508KxGzihlZlQozj9Kvw7LL5QoQ/ZR63JL37DH/J3LuXnihwtfX5SxZ6i4gTGbt9y2UKivCAzl8vj9U0vx7h/903d89vcewvt/8lhR9w9D5MyYgaff3oQZl97rOyZjgzI7jKv5qwNnIT8KIK/P0JdZt0BypGWf82Dv2LWIiF4govuI6FEiWgrgdwBeZOY/xL0RMz8JYMTyF6/b7n/Zzy/bgt88viSSYQP+jqvqq3FX+Eb9Lm9xoAyvrNqmnbq6ZSqBSX76D8/jotuDjm/VMK3aMoCL73gVb6zpDr3W6+9sx4xL78UKJxeJgM4GRhli4SxTy6AbeFTmL66rM/5xjZQMMUMJq/eymH8Jxl+936beIfeYF+1jXzdOWmtRht4icljJ5wH2cz7w+lrMuPTeSLnjmofewg/uXaj9TnXGf+i/n8BPHyw+lUG07MO46ZnlgWMy1HcSty+rUm8c2UdgKGvh8rvfwOqtfslJZ/xr4fCNivNfx8wXM/MeAE6H7ey9CMB+zHwcM99V6cIQ0XlENI+I5m3cWNmVrWoFn/G7Z31x9WGQp65qg4qLqIa1rnso4PDsz+YjmY2Iwlix2Z4piJjs659cghmX3qs1nk++tdENawMKpwh4JSRO/51tAzjnhucAILCUXmfoRSfd0jeMfy70r9gVhkjNlaLr2AHj7/zk9nmrA4mwSlkaL8qesxjn/P45nPP753zfR63SzSqDzf7ffRA/eXCR5/AtYbBWn+HeV71IEGZG3mL3unGMv2jHsrGyLMZd89+JnNWoxv/nj9iLyr5/zxuhzuZni0jk9tb6XvzqscWB4ys392PGpffiOeda379ngcvkewazmLc8PAmhxcBA1m+UCzH0uMZWne3f8PSykF8G8eb6Htz0zHJc9vfXfMd1sk/dhHoy83JmfpaZ5zNzZVMf+u9zPTPPYeY5EyZMqOi1N5SYe6aYkT0MUZEEC9d247t3v+E7NjCci5yR/ObxJbjxmeUuO7/TMepiTUIcY1AoFXIY8//Ir+ZimxNmJ2eXJOjZsRjEPv6bZ/CFP87zhSb2OhKEysJ0fgLV0ET5EkrxiYjr5fKMpxdvCuyWFXVNOX9U31AOPUM5/PqxJW4HLiXUU73f0k3eLCtveZIPEKwbHcQAKzP/x9/agAtvm4+fPfxW6HlDOcvNd5OX9q2+fd7qUPIUNWuUEUVAnnjLJhYiVYWIzslbjC/ePA9vrg/POsPM6Ffq5IRfVGb/3GL8JWFQyc56TbTPiC/yaiaUmnislDwxKgrpiau2+P3q/cP5giGob67rdo2LMBSdzhZ0qrNQh8EC8eFrtg1g7uJNgcgZOYxSdmTbmr/G4Sv5MwC/g11m/ve/ttY1aDrmPhhg/sXLMFHQLf3P+sIcww24PKCJFAOjWpJlRftEyWUWs9/4Rwz267sH8cySTW4ZeiQyk3GcxCIvjQ7DPs2ffWtbVNlv8YZevP7O9sj6l4lQ1IxIpFjvVLZVHMzm8a+l0eqxxcF+WwkSB8T380UhpawtUiVpoDbpHeprX7EqotS86qVM2VVM6Cwuvrl/uHD4Xv9wPrAhTEdrClv6htE9kMPETkb3QBZjR7Vozy+U7XRgOI+zf/8c9prYgYcvOlr7G7UR66oqz+x7FtloC0P0wvKteGH5Vnz2vTNw+YffFTAem3uHAoNQlHEsxfgLdi4z7lVb+rH7hA77eMRMSTbEwvhPHdtWUc1fhsXsM/jvbBvAPpM7tVE0p/zyaWzoGXLTicjMX8zcREZTHbKK7CMvilRJxrHXPBF6HfkaAn1DwUFLrAcQxlrdVjHOrDbPHLnoSkUxCkumQFRVHMiGPZu3sLlvZEI9a8b8iehWAM8C2IeIVhPRF2p1byA82icKs6ePCRy79ISZRV+n2Kli/3CuYBKogeG8L9wRgNv5tw9k8b173sBBVzwcOnNRmbQKEWe+eGP4xhKpJPmXzesW11iMzVLMuvwL1fm40klmpxrvQ37wCK591NOFLY2TXEYpso8uFnvJRo/ZRjN/77slTn3tPKZNSu9QvuYvw2b+3jUvuPVlfOiaJ7SEQUS1iDqRGbDM6MOgxvnLzL+YDZDkawioYZLPLNmEOT94BI8sWO9mwlQHtELtFtDn1I9CMYvPZMN91mG7BL6frVnsp0I26xt6hrRBBiMd6gkAcMI9XyaiLUTUTUQ9RBRP1JPAzGcx8xRmTjPzNGa+obQil4at/dnQpfBhzp62dLB6jt67PF/EKM1AEIwdzgfWHxyy61jf54FsPiCTdGQ843+Xk/BL7SyiwxdKCyDknXQy4cSH5/UOXYkR677PWYy127yBVx4sVHYmvivE3Aey+cjflML8hyXm3+kYHDlVRdQ15Tp+3TknSVQm8w8/R9X8AWDN9sGCkWuA4vCVfh/WHlSHr+znEcx/IMZMVQf1/Yu6e2H5FpcYqFsf6oz/sftO9H3OxTT+ot9NKHHl8Qn7TQ4cC9u7OyXZGPkdhJHSkQ71FPg5gM8A2ImZu5i5k5m7qlyuqkCwJbUB6QwyoE/fUO4ClYldmcAx9T13D2bBEkc+au8JOGYf/6AzkM27U2BhmLoc4989kHWPqV1SsNTBArKPyJ3SkkzgotvnY+a3H/AxeMCWQgRrZGa9w9din7+FpduqERji9ELGW8hiunfRmkqUZvyl/CtCl56/yot4inKQi7pkZjcFs8yYS9P8g4ECop2qmr9AnBBXHfMHwrewlMues9hnxLoHsli5uR/7fucB3D4vfq5HcV+V+cs+LNE2VF42oFnTcPZ7dsVOkryZtyz0x5ghCNJXrCyrni8jbJYvE7xtUrYBdf2KQL3IPqsAvN6oaR1kiJC2bcoOSWGjte5FqhpkXFx12v6YNaULY9oDa+YwqBiH7gF/tE9LktzNSwRWbRlwIxqEsZMdvm7uE0VyEB0ubkKwdJJCZxHZvOWydYv1Dt+sYvxl5h+2OCsqkkeUI8+sjaKa2NVaJvO3XMP92upgLn0dRF2u6x50B8hs3nIHw1Jkn5xlYVKX3yiJPFOWxVrtO87yBllqkx9p6cY+za+Dso9s8BJEWLTOFgEeXhC+8Y4KQUCE5t/qvMeEFFXUE5IzRxfMkEqQL5w6m+fIdTICR+09AeM7WnHRcXvHLrsMnX0Oy/cl2xL5GcK2IR3ROH8JFwO4j4guI6KLxL9qF6ySEI1LsDe1AbVq5B0AbsI1GXEXg6j4xGG74L4LjwxEL+w9qSNgWLYPDPtYWTqZ8DEuwJZlfvO4nZ9dMD7BLrYPZL3QReXaosMVYv7yvQVUpi4buLzFWna8sWcIiySHos/4B5i/F24ZhaGcndZAx44mdWYCz7x9IIubn12OXzzytm/VpwxZ8xdRPtsGsr7FZGEQdbm1T+rUeXbfq475v7NtADMuvRcPvL4ON85d5pPMhE9jYqd/liiixizWv79imb/8Lh4P2TFOlX3kCC+i4vezBrxgA0FERP8U/gSL2Q0GUKXE//jflwLXSybIl4YlbKA+bDd/uuZdxrVj3reO1fr24kCny4cxfzn9hkw+hUM9o9igWsg+cWjsDwH0AsgA0IeO1DnaWpIYylluB1Y7Y1helLaWYINOEOGIPXfCSfvvXFJZ1MRQOva6pW8YY9q9qk4nE1omIHR5YexEg+ke9IyWakiFzhqf+YdHdwxLBi5nWdpoHwC45Tkvz7nP4asyf3cWUYj5W3aWVk0HGTeqBd2Dfhb7zTtfwz+kRVJ9Q7mAliwYbi5vD2ItqQSGcxb6hnPozKQjnaJiRiSep8XZ5Fuco4vzn+uEV57/5xcBAFNGZ3D8flMAeDOfiZ0q80+435fK/OUBXH6i/5XekYxhJdxVjvCy68r+Pm5WTEC0vXRgzwKZ+Ys1IJYSLaZn/glfOw0L61R9a2pfjIvPvncGiPThmGHMv1Wqn5xlPxMRuQN2WzrpG9BHNKWzhHHM/KGql6SKaE8nsQ3ZwPJ4gdaQhqt7kYkEcMu57ym5LOosQzfwbO3PoqvNmyGkk4nA3gGHzhiLF5xVjqKxC/s0MJx3n1Fdfdo3lMONc5fhHs0+r/I1BeROrXa8bN5yB5e8xRjWZOdsb0n6FtxEMX/xVaFoHZv5s7aDpFOJwPkrFT17Y89Q0Pg7Bno4bydAm9jZirXbB9EzaBv/aNnHz2THjkpjOOdJYjrmr/pP5LQH4l6TuvTMn8vS/L13qA6yM3ZqD2S7HM5Z7iCRt9g3YGQty322MOZ/2IxxeH65Py7/63e8is+9d4bH/NN+2cdidmcHulnOh2fv7NunOJkg31qasKyuan8Om/EXwuUffhcAuPtrRN0j7F4ickrMnNrSSWyF927qRfN/hIga2vgLZpF1nXoq8y/C+Jf5UtQFX7oFYFv7h33GpiVFAUM3fWy7+7cwooIhMXuGNG/5mVPvUA7fu2cBXloZTN+g832kk+Qy7G7F+OckzT+XZ622PbbdP1l094O1GL0h0T5hMfxC+hrM2rMM3btIJch3/ta+4cBqz42afP+i7MKojnMciIIpRxlWcY6QKsa2t/iYv04yUlOMdw9m0e2EN4p3rzoiXeZv6Y1/HF9HmMMX0AcjqLKPHPfP7C0WDDP+OgP75Fsb8bmbXnDLIgiQvLWpgC0B+dvdPpM7fZ9VzT/U+CvMP24ytzDoyHkc2QdAoG1klPPqZYXvfwB4gIgGygn1HEkEjb/++8B5mhdZbvytOj3WTZeZ/cahRaP5Tx/nGX9h3IR9khld1mGyAioLlqEbBNPJhFtGnewjbzAyrJGS1EHLTV+dzYPZ70AXxQxj/kIKG3Ry2Ms+8EuOn4lnL/sAkglCzmJ0D2bxyqptOOiKhwO5f3TGQcxgRF16xj/r+14HwUzFIDy2vcWdQdjPo2H+DtMX7/XHD7yJAy5/CIPZvDvgZNJJPPq1o3H6IdMAqJq/xvgXkMs6Mym/w1cpli7qLWD8lc4j6iuMQIUdB7wII7dPkbiP9xvmYNjmXhM7fJ+TCfL1I93mKEBQ9mkpc8GWbuYZakuUQVD1b6lEc6Tz+QMAmLmz0G/qHeKli4pWO2NYA9Wt5iv3nagMIOzemyUZwNb8/b+bMd4z/q6zUoq8EZDDMQHguYil8bqBKJW09dTBrKWVfeQdlXQOX3XQEkURRmjcqBYpsiMY5y+z07HtaWzqHbKZP7Ovg0zqasWU0faq2t6hHA64/CFf+J8MnfEXRu0pR4sX567a2o+Pa3a4kvHIwvXY7bJ78Y0T9rXLOSqNZ5d6KTt0cpgY3NWB7tRfz8WidXbemiQBu0/ocCVA0XbsRUwah2/eX2+kNNZxo1pCHb6AfnMif4oLDkSniPeovmeBqH0GRAI40R/VzWoAuy2oPpMZ40f5PqeS5HNEq+maBQKyTxF+Ch10BjrM+Icyf0nzL3TtSiP2Hr5EdBgRHSX+VbtglYSYiqnb4AmENVDdBg9qhyoWcZg/AJ9UkU4Fmf/Myd5SC5F/xXJlH+/5chb7nve5ZeEZF3X18Mqqba5xDhj/nKT5M2u1bZUdibIJ7VmWhXRx/vLfIkxWRPvIszCx+CiVJDeaQtXVBWRmuNmRgFRGO26ULbm8smo74oDZk31Gt/kHHTFIbpXKE1Y2YfgBICk9E+C1FYsZg7l8QLaQjaZu8jS2vSXU4StfX8ZQPlz2Abw28fuQ7JZRBnbFZv+K7rzUP8WrtTjoM9ldNf6K7LNBszMWALQpg1sxTmoddAa60AxI3FMsjhT9Rx006iLUk4jOBfAkgAcBfM/5//LqFquyEBUrGFjA+Ic4ftJVeAFq44jTANVonwmdrdhjgn/qO5SzPD1dNv6SU3a38aMiNzdXp6YqxA5ZXz3WjovOWezONvL5oPHvyqQ0xt/+X2y2PWMnbwaji/M/9bq57t9iUZeI9pH7nnhXcTrN6+94semH/OARPLtkc2DWslOHbcDj7NQk0DeUw6iWZKAec5aFax5+Ewdd8TC29A3DshhvriusnIoBX+TTEW0n70ghremErw6O/snj7t86/X+38aMwlLPw4gp79qdq/rq608k+6SThrMOmAyi8a1ohp+pu40e5dZ/NB9+/xRyITEspg14ykfANhAPZvPZZVNmnbOavOT2sP4vIoIz0DgGPdKi7/dXLCt8LARwKYAUzHwN7M/fKJtuvMsSU6sr7F/py4giENYJqjL5q4yi0/R5gL/IShqC9JYkn/uv9gesMKwuuBPKWp8sfVCCeWS7L6LbgYjTB8j4wcyLGd7Qqujb7WOJt570HL377uECUkijjX15YhQOnj8F79xgf+E5+P8JQA94MbtBJ7yC/H5f5Sz0ybOX2s0s3Y2A4j7lO2uYFa7s1zN82/sVkg+wdtENIVUaezbG7CGp99yDeWNMda52FeD5Rh4LdWpZtENvSycDqcAGd8T/73btgdFsatz1vr8ZVf5JKEN78wfH47imz3GMB428x3rvHeMyeZrclXejlTMkhG9W+kwnC/lNHB3wjssOe2R8qO3VMGwDgle94MSgq8wf0izGDmn95xl/n/wsdUJxHEkQ0b9n7cfza2ctAnUXUi+wzyMyDAEBErcy8CMA+1S1WZSGM/6urt+P6J5fGln1Uw1UJqI0jDvuQmX9XJu1qs/+67IO44IN7AYATWmj/3ufwlWSf/aaOjowiEJ1hbHsaL3/7OJx8wBTf96KjC401m5MXeVm+PY5bUwmtr8JiYMGabqza2o99JnX6nOo62UeG6DiDTqhnwif72H/Lx8Z16DX/4ZyF+au2uYwyk04EHLqj29JIUHDrzij0DufQkUkFDFHWsryyZ/OR0pvMAIVxEdcT7NFiO5w3k06GypBqNlXAHtCmj2vDW+t7sHJzf0DzTyYIramkj1kPDOddw5VzZB879FgfBHDJ8TPxwFc8VTiqfe8xYRQ6Mikp8iU4+FuSnPjzMw/E/V85EgAwWlopby/yUlNhBI2/Kq3EIV5R0MX5F1rsJhv/z974gptGRX2NdSH7AFhNRGMA/B+Ah4noLgDBIPE6hjwNzzMXwfwrn/S0FNmnJRWM8weAyaMzmDrGDs8bzlkuY5L7dE4KORzVmvR1ADU3jly2hBJBAQA9TkdPO07gbN7ytEuLfSl6BQNXfRX9wzmceO1T6BnMYUx7Gh2tXnm8aCy98ReD+FDWChh/IY3I9aSLzhzvDAgL13rsO5NKBsJUW5IJdLSm0F8k8+9o1Rj/vOUGDwxmLSxc2+OWQ4W8Alw8i6hD8WjuRuzpZOhgns8HHaUdrSmMaWvBK6u346ifPBZIgSDuE5aELG9ZruwjBqkedbW80maijOHotjRSCfJp/YDi8JWM/54TO9CVCc5IU0qcP6DPu19K34uCjp2HhY+KFRIZKVw3KvlcXazwZeaPOn9eTkSPARgN4IGqlqrCkNllOkGu3pZywgLDdMmwCIZyECfaR5TL/SwxaFbcdKIBy4uKfJq/xS6rTSb8juPRbWlf51aZkdqQt7nG32H+suZv+XOoi3FTZTCyz2FMe4uPoUVt5iKu1ZJKOMzfr7kKQynfT7fuYPLoDJht4y+Yv25gTacS6Myki5N9hhzjn/JfL5tjtLbbhR3M5bFwbTdm7TwaT729MWCAZbbuyT72uYLls+PwzaQTzrFgfeWlhVICo1pTvtxSQeZv30eWM+R3mrfgGH9vRa3K/FUGHsVgUwl7RutG4TnvK5tnEMSzeu8xzFjb7cJ/n/aQpH9Rn4uFTvbRBYkAHiGTmb/c7tQ2P+KyDxElieh18ZmZn2Dmu5k53GtYh2iXcvSkkl7WR/EiWpL66V8tNH8dU1BXn8qaf+B6TtmHwoy/FItvG23vfl2Krq92BrX9iRz/Ivwzm/Nr/rJzNKmwSMEUN0sLrMa0p9001OIZgHDjT7AdZkNZ+75y50sng6xVlzExlUhg70mdWLyx12X+uvulk4TOTKrg3q8ytg9kMbotHTDoWctyB/3+oTwWb+zFPpM6tMZDdjyrdQfY9SocvpkIzT9nWQFHaXtL0mf8xb1cxi+kM6kO+4byki/GQjbPvlxTatRSizKQRhp/pz16YY/2/3K5LcsbxMJYdSqRCGwsJM8o3bJV2Pjr7HMo8xfGPyUiD9nXF9Uo6RGXfZg5D+AVIgruWtBAkGWfdJLczl7o5ase+EogztRTdVQmE/rcPvL1/Jq/933Oslwjl0yQj22oHUQtS1ga47TjYJNXsQ5m81rDlXQNmH1tef3C2Pa0T3oacpj/va95eXhkEBG6B3O46ZnleP2d7T69W3R+mTHJ+W+EDJBKEDoyKQwMe/sh6PLktCQT6Mqkfel3C2Fb/zC6MumA8zibt9w22DOYxXDOQkerZ4RliUIui3gW2bAlCPjrvNV4btkWtKaToQzx2SWbA7IPEflCa8WziXejDtiAHcEkJwnM5i20pIIOVgH1eJQRE76s4byFnBQ8IJfbkph/2Aw9mQyugNetWVBJXrmav+7ZwmYnYsYunsFif3psNetAvazwnQLgDSL6JxHdLf5Vu2CVhLzkWpZUBPNXpRSBamj+ceL81SlrOor5O+d/667XXW1UjpYYylo48Vp78+pUgnyG5JsnzYIMdWBS00EL2MyfnMyV9m/U1A+e4fL/v6lPZv4tvllO33Aeizf04I4XV2vvKxu6zX3DSrRP0HDJ0pkYZJIJW7LKSSkSdNprOplAZyZVlMN3a38Wo9vTgZDXbI7dtiYcfKkkua1OHgDzPrnPz8gBuw7EIqYN3YOhRuLC2+ZrZy1ynQkJTq27hGr8Xae+bfxTCb0PCihs/H/88QO850uQo/kz9vzm/b7Ei6JPru8ZxHfussWHcOZPAaKmi/SKu8YmLoqJ8xfMXww4ubx/Yxw14KAuVvjCju1vaMir59KphGscxSgctiq+Fpq/jkEFmX+Q2QiIBvyKtPGIvDm7zCTt7If2db545G44UAn9VDX/sFzjYrqezVvu1FfVfsUgIwZQUc4tPs0/HTB8us2sBQ7bbRz+MNdbTCRXicteQ4xSRyaFDT1D9uwnkUAub0kb2+iNf0eRezfkLUZXJhWoi5xluUZh24Bg294WmKJsKlQNnhUn95KNvTh9zvTQjJy6HEbyrGSLMxCrszTV4aum7bZln5jMXzFiZ8yZjjtfegfPLt0cuI4wgPKaFfnZojR/lajpNH91gKhlnL/oSRmJ+csSmep/qQvjz8xPVL0UVYZs1CyL8YKzyGW/nUdj6ca+0MiLWmj+OmlJnbLKmqY6UMnXI7K/l5msrJ8mkySthA020rjMP52w8/30DeXcOlINnugYnm7tyD6SRjyqJRW4pzCOKq48bX8cr2yb54v2ceP89e+sU2L+IptilOwDlLZxT1dbOrCQbjjnDZLbnJz/qUTCfZedIbvDiWeR7YDcJpMJwvc//C6kEoSbn10ROF+XxkJevyEkOHWWJtdr71DOl201m7eQTgWZtoB6XNeH5BmNPIOQJUQd7wg1/hSP+asDUzWYf5gcttfEDjy8YD2mjrXXKTz59kY/8x8Bh2/B1k1EPQiGE2wHMA/A15h5aTUKVknIzP/bd73h/v3Nk/bFJw6dHnB8ClSD+asNTictjVK0+FQE85eNZ0sygaGcPwePHPEhptni7+C1lPwjIZq/x/w9wUztrKK8gol7mr9tkH56+mxfcjqBddv1SbkmOxknf3b6bHztr68A8HcQj73qO59g8SIsUGbAujw5w3krsPFOHIxuSwe0djniymX+0nsLm2EkFePP8A8Ed37pCKSSidA9aDdpmP9n37sbtvVncd3jS7DJGYiF78SdaUhtQ10wmM1bdqLBECOnOs918pDXBv2+LEE2eodyWmd4mOyT0PQPneav/qYa0T5hA8pXj9sbR+w5HjmL8bsnluLHD7zp+z4Q7VN5xTmAOLe4BsB/AZgKYBqArwP4HwC3AfhD9YpWOehifgH75b93z/Gh+mU1mL9qdHUMSo32SSoMSYbcgEXz2SrtFCQbIlmr1T2b2hlCZR9nEFEzhvrKrAwyLa7skcWMndrxcSdTpYo12/TGX5T7g9Jm3fIzuLJPyCvzNH+7Djb0DOGdbXbytYFsUBufsVN7acw/k8ZHD5rqOyY7xh98w17pK6cOCdsXWveOxLGpY9qw7xQ7v1OYId7QPRRgwC2pBL78gT0BeA7fpDJLCyM99radYiaq/43KYHW/EoOM6ssS524fyAauA0Tn1VLLo6tTta+F1VtcFBPnn04mcMSe40OzAtddqKeD45n5d8zcw8zdzHw9gBOZ+S8Axla5fGWhM2MvtdcxTEA2UPH0y0pAfam6Dq7+Jh0R7SNHMAhH4xZJWpH17FSS3PhpLfNXc9JI7FiWC4g8zT/U+CuRKuJ+fUP5QIItGWu3D2iPu9eR3olcTe6gFvLORHSN6vQGPJnskuNn4qbPHYrlV52EMe0tJTH/rrY0DtttHJZfdRJ+dvpsfObwXZHNc0BCk59DjvyR4TJ/yYSKtiEbsjAJZu32Ae3m5CLcUCQPdNuEJtRThmhLYuangxrppGsdshSoM/7M+rQRUVANuS6vfqUDOIrR/L0y6Ov2zEOnx/pdJRGnNiwiOoOIEs6/M6TvQlyl9YGZkzvx5g+ODzX+YatQBarxAtRr6gYeNZlYMlE42icMMvNPJsg1mLqOIGQfwbBk9qUyyJQTMRNm/NW6FeUcGM6FJpDLpBNYsy3E+CeDRs/H/Au8S8Hik8mgPiw0/5lTOvH+fbyZRZgWH4UuabbwsUOmucZXzeUjl0GV+QR0z+IZf68Ow9rpmu2DGO9IQjITFqu3xaAnTi/E/MUzpCNmoqpUqAumSEqavzxYh/mY4kAts444V1rG1fozCtwjrN4+ffgMLLvyRPdzvYR6ng3gUwA2OP8+BeAcImoD8OUqljpbc4UAACAASURBVK0iiJoquk7JkBdSDc0/aPxlVgf817/tg2+fPAvXnX2wezwtxTGrfamw8ZejfTwOqXs2UQ9ilenVH/PC8lQpyo6YYe30HPDqVvwvjFV/Nh/IXf7AV47EQ189Ch2t6dAskWqGS0DR/CPkLMAf5y/KIqboA8KoKQNiKQ7BsGiugaw/BbNsvMOkAv2sMHh+mHyxrX8YHZkU/nr+4XjkoqN937Wlk+h3Vu+qmn+Y5OCuiE4kAnUFAIfsOhYn7O93yqv5hezz9cw/bF1JHIjr7D5hFK7+2P748Oydcey+kzC2PZguo1LQ1VMhuSbqe9lWlZs6Pg7iRPssBXBKyNdPV7Y4tYXHTuN3vnKx+/hROO+o3XHg9DGYPX0M3lrv5W9feuVJ7t/ybEWEJ+pQyGk1mPVr/oISqR3h3PftFkiGtdv4UfjvM2fjq395RWP87c2nQ4JzpLrzM3bm4MYVYm+CtpZE6IpadxGX49zLW4UTu8nwfAJexFNbSxLDA5a7EjhOpEohqKGmsvEX97OfR5q1FJFV1ls0Jz17SDl7BnNoSydx6Ixxge/a0klXWlEjs8LsTrfYuCWE+f/Pp+cEHK1nHDod/1y0wd0kx76PN0uTyYO60KkYiBlEgghnHmqvSf39Z+Zg+aY+vP+nj9u/SRDuv/BIvLZ6uy9tRanQGv8CfCEuoSx3x8A4KEhtiGgaEd1JRBucf38jIr23rs5QYFc7b7obyvyroPknCN84cV+cuP8UTB3TpmVQKtLJROg0MIydtqbscEwf80+GM/9vnTzLNVQy6xANPBCBlLRzsmwJ2ZRENVwyUw3d6i6VdLdNVJHSaNy+OH+nHsP2/3UHHwRz/4sNxlUGHaejqhJWmEP/+WVbfNKZXB/j2vWhxnFln3Djn421rWCC/PURdl2xCU7YIi8dEWlvSeHnZx7oO+Y6mJMJd1U3YMuMulTicSDeqS5TqfebBPad0oUzDp2Ozx2xW0n3kaGr9kLMPy6hqBeH740A7gaws/PvHudYw4Ncp2SI5l+F9A6Be8RoDNHJsfTfjWm3MyYWo/lHOZ9VRpdOJtA7lAuNkfeYC7u/FwgzSG0tyfCUEj7j74UknuSknRZOSp0M9YtPHOgadmZvZaX6tKr8otaHLCEIqIY7atDzrTSXnmffKV04c47f4Wdfyz53T2fP2vfusVNARlP/lpHNc4Tx9467hEBp7+oaErFuIJUkLWkJm4WGBlQkyJd8L5u33E10ioUriyqv37dGosL9WTszq5Txr5NQzwnMfCMz55x/NwGYUOVyVQRxB88wzbQaO3kF7hGjQUb9Jkwb7Mo4xl+SfeQOq2eVwevoZAZxXAwsIgZfXYAU9gyhDt/IjT+Cxi6RIPz8zAMx/zvHud/pNjE/fI+d3OdlqSxq3anZOFWj9bMzZuNXnzzI/fzbcw7B59+3m3JOuPGXB1C/Zk84XtHK5WvtN3U0XvjmsThjznQviirE8a0i3Lnu1XUY85eN/6TOjCshphJ62SesH6lGV7yiVDIR2CwmbN/lQnA3uolYKRtnll0MdH2vkM1pNOa/mYjOcTJ8JonoHADhu1FEgIiOJ6I3iWgxEV1ayjWKQSHZR6CW0T6l3COZSGBMewsSBFx6/MzA9598dzDvXspZiOPLkMjssry4z/bBfSfizDnT8Z2T36Vc3zt/0mjb+MtXVDtGi2TYVc1fIBOyHgNQNHJJ308n7boR0EWMJMlz8jJLg4dSBaqxDzL/Fpx8wM7u5+P3mxxg1mqYpFxPbT7Zx/88OsYoX2tCZyuIyDUKfudx+LsMq2v5uLiP2g/ke4h3DMDN6irwt38/HP/prB3QQb2u6JbppH9mGjVTufpj+weOfebwXbHfVNtfJN5VlOxTi/5cyFHbaMb/8wDOALAOwFoAHwfwuWJvRERJAL8GcAKAWQDOIqJZ0WdVDrOnjQ79Lsz4V0PzVxFnLUHKCc1beuVJ+JhmcdQlmgEhmbDPkx2+di6eIHMUEE7dc96zq3usNZXE1R8/AJOlzi/KJDBJE0uuQp5Fhco+CkuVBzW/xh2+oEuEnqqdPiVp/imX+fvPLST76N5VYOFQRCivLIukEv6/dZ1dOztLBN/fsCOV7T91NKaPa/P9PnSg9Wn+9v+qFCiv+5jc5b1jddvEQ3Ydh699yL+535xdvSVA6rO5zD9BPoI27OwSplbFtLFtrhNXxvc+sh/+8Z/2zl7iPag+Y99akFrETxaAbpD/lNTfBOpC9mHmFcz8YWaewMwTmflUZl5Zwr0OA7CYmZc6+wHcBuAjJVynJNz15feFjrqhx+tE8y8UoqYLFRSsWDD/WVO6sOfEjkjmn0knsfRHJ+Krx+5VuExS6xQDQxRZkRmRbgGOuL/A9z/yLvzoox7bk8sr5AidwXT3avDp1Z5Gzczu36So/qrsE3CAat6DajDVc/rlDW400Unib11njxvqKZzk79q5C098/RgcN2uS+12Uf0UgbOGf3K6EtCd+V6jd/vX8w7H0Rydqr+teJ5nAl47Zw/08mMsjlaDAew0LhZWRlN6v77gcvFAPxl8pw0G7jMEVp+4X+N2I5vYhol8iYhEXM19Q5L2mAlglfV4N4N3KPc8DcB4A7LJL5bcQCN0kJKSia8EU4mj+hWYgOqOUIPLp8pedOBNEnsNXXPOgXcZg1RZvYVXcDiKXe1JXJuKXojze32GOQZmlfuRAO0WC2FDFF9kTsRJVOHxb00k3HbPK/IWRLyT7BFm8RuNVPqvXkFeqypKEGvMft7PrZB8RHtuZSSGRIHzuiBnuhvFhUprsX/GCAMKluonSO5YHnsN2C4aR2tf02pp4T+cfbRt6doMACF2ZNC45fiaufmARhrKWPRASIIcRxJodC+avdPFaGNFioPbVMAfxSCd2m+f8fwRsmeYvzufTAbxYjcI4qSOuB4A5c+aM+Orh2mj+8WSfYr8Xq4J7hjwDqDvnzi8dEbeoeOnbx0myihSqGMNJlyiS+Qvj9v2PvAuX/f01X/Izb+OW4DWEw3DqmDY3DFWO7Qd75wccvgXi/HWDsNpI1TJNGe3JMPLq15RPxvIz6dFtaWwfyOr9ABrZ7rhZk3DV/Yvw8UOmO88hRVaFDLTyoKBm9RRXlnNiyY5YUdanLj4mdnTO8qu8NSyi0kQbEvfN5i0kEwlnJuLVlToj00FcYyRSIxcD9Z2Gka1axPmHGn9m/iMAENFnARzDzFnn828BPFTCvd4BIMezTXOO1S1qwfzj3KOQ7ENkZ6qUw+aSREgkyHWAisbkruYsQdKSjbxsKOXUAXecfzheXrkNKmS7GRbVIxt/8cwfPWgaPnqQ388hFkXpBucz5kxHW0sSeYtx0e129k8i7/ktZinaxzvvxx8/wOc4lssQ9lkHdUA5cf/JOOuwXXDr8yv9G7X4HNh+5n/DZ+ZgW38WYzWDqrozGgDsMaHDZ1zDwktlyO9A3W95zoxx+PwRu+ELR+6GI656FIA/86ioh7C0KYUgakE8cUI2/oTAdCqO7038JsD8a6CdF4NCaykEqB40f9ix/Z3S5w7nWLF4AcBeRLQbEbUA+ATs9QN1i5ossa6A7KO7TiJhs1B3n1Zh8Nxrlvds8vmyXDNnxjh88ajd3c+CiMXT/AsvXAK8fWJ17yeRIHzkwKm+MEV5fQP7mL93nrqxja4MpQQAEBEO2812fuYkb6Qa6infanxHK46VdHsZOs1fhS+sNmSgbWsJLvKSU2R855RZmDrGm7XIA3y5CQ+FLu9JkML426u21bcaR/NXU5MI1B3zV9pUqL+xHtI7ALgKwMtE9Bhs23EUgMuLvREz54joywAeBJAE8AdmfqPAaTXDSQdMwcI13Vi6qa+m9y3FsOtgd0j//q8JIjfLYlIxeOU2LjkhV5wcOHIbD4s9lweR6PS9Iton6jeSo08yKAx2Zw6yYdAZSVWSKzU3jMj3IzN/f3oGf9bWKLkx4TL/8N8Uy/zFlaLqXE5vXa4carmEwH+9YSkaTUY5sk8tpNsozJzc6duEKK7xH2nNHwDAzDcS0f2wnbMM4BJmXlfKzZj5PgD3lXJutfHrTx6M3z6xBFfdv6im940l+8T4jW7vVCJvBaUr+zhdvVyHihy6Gc/4F2ajYZEpgXu7xjv8N7LhThB8zF+UXT5dNyCpg4uIErri1P0wbUxb4PdhENfOhmj+6aQ/wiVqkNGld1DhX01deJEXKFgfKuS00+UulvJkH2+mAdiyj/yu3PvFCoe2f6MGdYw08f/mSfviyL28NbEB4x9SwFqUO27O2sMAHOn8zbBTPNQldFkE42IkSEIpOrIOKhO0mb8nu6i7QpULXehlFPzGrTDzj4KQfaIik+QB064/b9AT95fLpGbjBIJ+EfFZF5cdhUwh5q9o/pHMv1jZJ6ROi53FdFSQ+auQUzPYs7Ti5TZP9vEfH2nZp9D+HcWGn1cSBWuViK4CcCGABc6/C4joR9UuWKmQB/5ih4GRaCiVWkimY/5RUkIZY2TgfnE0WV+OlZCGHbXCV4YX7RNh/BXj5tP8xXfST9SNbOz7xHPOFYK4tqz5+xd5+d9VFLMW7Tu27BNm/It8llEhq5NLgar5q7H4gcV35cg+I2z81dvHLU9dyD4ATgRwIDNbAEBEfwTwMoBvVLNgpaIc5l8LB6+KSuUY1zF/0rBJ1wiWKfzI5Y6zF2ocZjsmZkZHcX7UfdV7eJ/YHazkX+iuFWeFbxwU0vzVrK1RkVjJImUfNYJJoNgFT/LMqNztD13ZRxO2qitWvDh/fbTPSMs+hZh/yNKjutnMBQDkUIjwPAl1ALkui62/EZF9KnRTtYMkyP88Xgy3fnpcLORyx5F95KcMYz/TxsbT0dd326mFZ+w0KqJ8/jIJQyMzf7lj6gZ+9d3oXtVeTsbNKGRc5u9Vujowy8Y4qk1MGWMvtooywPLAostEChTPiOPsHxAbwuEryhLinPfuF38tjMr8R4LQyVCNv1oetbxhv6sG4hj/K2FH+9zksP4XAfywusUqHdxgsk+lXrJO9tGxbY/5lwd/zprCck0c2WfnmE7U1VvtFckzxkcYf1X2cf6Xn7uY3OvPfeOD2nc1e/oY/O5Th0ReR8f8A/eSfSIRxk4MkN0Re9zKbSGsfeneQVSbSCmhqeVAzDq1sg/pkgLGkH1CNP+RRqFxMsz41wJxcvvcCuA9AP4O4G8ADnc2b69LlFOZIxwVVhYCsk/CHzsuOrvY1WnK6MIpGaLgsedo5v/1f9sHpx08Facd7C3UCtX8Yzp81znMf7co46/cQ+TEP3bfSW7yr0LjrmyEJ0Ykr9t5dPSgJepH3WvgBCkE0OcQj2iI08baC6vEAKhDHJlEfgfXfuJAnHXYdBwwNd6kvlw/1UHT7XUPuziLxOSykIb5F/M8I2lMdShE7qIIQbURldtnJjMvIiKxmexq5/+diWhnZn6p+sWrLUZ6ilgOAsyf9Mz/y8fsiRP3n+Iaw1LhxtonyDVuwW5rL1i65owDsXJzf6AspeLYfSfikYUbMD4itYB6jxnjR+G1yz+EjtYUFqztjnUf2cbF2Qu6UFnUjv7Lsw5yQ3Hla0Tp8SJb5v5OKuOo+8UpEwDsutMoXHnaARG/9qNc2efcI3fDMTMnYM+JnYGyJBMU0GvjGH93J7c6M/71zPyjHL4XwU6y9jPNdwzgA1UpUZkopy5lY/mTj8fvDPWAuLJPIkFlG35AWjHspJYAwhcU2b/zly0M1519MAaG9buDCfzqkwejbygXayGYjM6MrX8Lpn72u3cBiLBY2ke50DV0KGRsx7a34Ki9J+D8o3bHn59bgftes5fJiD0XgPiS416TOvHcNz6ICR3RabQvOm5vHL13+J5L5QzA5co+ROQafrUsumLFuZ9wko8gkXbxpffvgeseXwKg8HutS+bPzOcRUQLAt5h5bg3LVBYqIfscsutYnO5sq3fNGbMBwM0TU69QpRciP5usdJ6itCL7XHL8TBy778TQ3/uMf0SHOHH/KQXvnUknC0pEUQZj7KgWf6KxEMStsjhb9938+cMAAO/dc3zob+IiThbVCz4YnZY7rsP35s8fhq39/n2ay03voCLlW5AXnD+2FsH8Rwo/+uj+rgx58fEzYxv/kRysIkM9mdkiop8COLxG5Skb5dSl7kWddvA0bOvXb1JeT1BDFVXZp9K5zJNKioV/f/8eUT/3z0JqsE9CJQa7uDJgJeq21sEGcQebozSzh0ovQFLlNbXeW2P4gkY6jYNuNz2gsF/JqkfmL+EhIvoYgL9zOUH0NUKcIv71/MN9m1MIeAuB6itcLA5U5i/SOwhUmvm7oaMlLFqpxcKbWhqDShjuWtuusmSfirclfSoOgViJ3eo0WkPXNu674EgsXNuNr/31Fe2e07VCHON/EYBRAPJENADbHcPMHO5xGkHIA+lXj91b+5t9JneiKxOMfw7rxCPNKuJA7SCJBPm2Oaz0M4RthRgGXeRRNVFpaSIKlRjMat3GyrlfpcmQ6vBVrx4rfYhzjSg/x0hAp0bN2rnL3eRHJf6XnjAT/3h1TQ1KFi+xW2eh39QVnMr89smz8L699PpqGJMIkw1HyvbP/85xgfDAMKjpCQLRPhXusHK0TxzoVhtXEzVl/hUYZ2q9xWA9ERo11FNFnBXkADD30g/4Np2pB4QRSrF2QZV9zj96D3fHs2qjoPEn+22cDWA3Zr6CiKYDmMLMz1e9dCXAXUAS8ZswViheVHB3ppHpKGFL83VoSfp1UXWxTKU7e1qzSjYKcR2+lUItZYBK1G3NNf86kjL96bd1i7ziGf+pRWRarRXCmoawQSMZ7ROnVq+D7fD9pPO5F8Cvq1aiMiHqMqo/hnXWsOlsHfWTUKjMX17kpetQ5cINHS1F86+Fw7fRZJ86dfjWAr7ABF20T4wV5IUQx29QDYTvDz7y6xLiaP7vZuaDiehlAGDmrc5OXHUJL2Ng8Y1b9Idap4X959eOjr26NQxRi7wqlTlURtQ+ujokmpj5V2JgrTXBqCfjryZ2C2b1LK/9PviVo0JzHFUbhfyI9W78s0SUhKOGENEEAFb0KSMHL2Ng8eeGvqgq98w9JlRg0ZXSmeWUztXo6KLRxpd9mlfzr8S9GsnhW2n4FnlpylWu8d9n8si5LcOqWRyvd9nnWgB3AphIRD8E8DSAus3nr9szNi5c5q8cbwTZJ5DVM+HlRa9GRxcdMm7HirvCt1KoqeZvZJ+ykFRkH9VjF9fhW48II0dikBvJ4Pk40T63ENGLAD4I+62cyswLq16yEuHKPiWcG675109HCYOa2E2WfarR0cd3tOJPXzgMszUbn+vg13UrXpwAavnOKqGqNYLs8/Qlx2BLX+UXPKrpHdSilcv8RxJh71X0h7qM8yeiDIDzAewJ4DUAv2PmXK0KViqiZJ+fn3kgXntne+i5roHSvJBJXa348jF7VqCE1YEu97w4VC2WJ+9NWghyERphMC0GlajfWtdJKWWeNrbdzSparbLYmxD5vx8pZ20cnDlnOjb1DoV+H8b8p49tw4dmTcKXRtCmRDH/PwLIAngKwAkA9gXwlVoUqhywG+0TrPRTD5qKUw+aGnpuVH947hvHllu0qiIQ3VJl5l8sRnov1WqiEZ+tXkM97ZTO/rKFbUJfD7i6QALIsLaRSiZw/afnVKNIsRFl/Gcx8/4AQEQ3AKjLuH4VVhmyTyN2YgFV9iF4umI9LH1v4KotiEZsN/VACAR8K3w1xVLXsDQS6qiaA4gaUt2tghpB7hEoJ9rHVX0qVpraIbhtodfw6sE46XL9NwvqyZDGRT2VWY32qXSo50iiniXOKOY/m4jErhcEoM35TKjj3D7lxfnX74sqhLSa0hlSnH8NFlUVQh3ZmoqjEZ+tXo2/bievxo72GekShCMqn39DzrXcUM8SznXTOzQg9U9rWhkZzb8mqGd2F4Z6aBMCwaye/rI1MvOv53bfuLUagkrE+TciVIevLPvUg3OvDopgIKEe2oSAbwtLTbmM8a8OGrdWQxAnsVsYxIDBDaj6q9IOAVVd4VssGpEdA0BHa5xF8DaOf9fkwj+qE9RDmxCQmb9uUKqHgIVSQXVsYeO37AaBG+pZQqU3cBsLbGNnM//6Mf6NinnfOjaWDPjq5R9CW5n5mWqJemoT/jUgwVlioxIHwDB/ENFMInqWiIaI6OvVvJcX6lmC7FNHHaJYjFESV5G0WKaRmdNII5NORm5ML9CVSdd0A5lyUU/Gn8jLQ6Vb5NXIqKNqDqBWzH8LgAsAnFrtG5WX2M25RuOpPthv6mj89pxD8K+lm3HTM8sBGOZ/2sFTtdt11is+MHMi3rP7uJrcq97aRDJByFuMRMIjbteedRAmdraOcMnKQz0z/5oYf2beAGADEZ1U/XvZ/5cyVWzk6SUAHL/fZMxftc39XO30DvWOa844cKSLUBT+8NlDa3avejNKQuuXyzVtbBsO3mXsSBWpIqizavahruapRHQeEc0jonkbN24s6RrlJHZr5FBPAdfhTZ6MtaMaf4Nw1JsUmNLIPvU2QJWCen6GujL+zHw9M89h5jkTJpS2EXMlZJ+GhrvOob5y+xjUF+qtTSRk4y+O1VcRS8IOafyJ6D+IaL7zb+dq3UdFVGK3QhDNroGJvwtfnH8VdvIyaGzUm8TpMX+vbPVsOOOinp+gapo/M/8aI7DXbzmJ3ZqgrfkGLm8bxyZ4MIOmhpiJyOkdmqE/1vMz1MThS0STAcwD0AXAIqKvwM4a2h19ZvHwHL6VvnJjQPZ5NBODMmhuJCXmL9AM7bbeZlgyahXtsw7AtJrcC6Undqvj9xQb8uCXNMzfoEHgW43eRA7fekbTicHlJHZrBrgOb5Cn+ddBVk8DgyikNA5fY/uri+Y1/mW0HG7kWE8HvlBP04sM6hwJV/Ovb6mkmdB8xr+cxG5NMF+Qxy2T3sGgUaBj/gbVRfMZ/zISuzUD5IykQjNt5JxFBjsGEprghCaYgNc1mi6rZzmJ3ZphtinLXknD/A0aBCIleSIhb6dqrH810XT82G0uZdi7ZmAcdqin/Xe9reY0MFAhFiLask/jp1lpBDQd8y9rhW8T2UgiIAGT3sGgMSBmqXJuH2P8q4vmY/5lrPB1r9HA0005UmlHz+pp0DhIuczfO9bI/bAR0HzM3/m/mVh8MbCkmY8r+9RRZdQqX71BPMyePmakiwDAC9CwNyEysk8t0HzGvwKJ3RoZB0wbDQDYa2IHlm7qA1A/i7yWXXniSBfBQEI9vQ/B/JMJwvv23AkL13Zjp46WES5Vc6PpjH85id0EGplxfPyQaThk17HY/f+3d+excpVlHMe/P+6FLrSy1lJKpRAKFQxtoWAJBFmasqgUExAMsgQQYkCBsEs0kmiMkbhjIykiGgIFRKwkyiaIAtKNApWyFMtSKLayb7GUPv5x3nvv8XI3eme77/l9ksnM2Wbed96ZZ86855znHTOKZ195F2ids3188U5raaX2KOf2ufjwyZw4YyLjthjR5FLlLcM+//RgIz7XE7YewaZt4vxZu9W0TI0kiZ3HjAJKff4t9CU360l5DN/2tk34xDYjm1yiwTn74F2YvN3oZhejT9nt+Xdd4fvRA97Izdp5+rut81d4sLoGc8nuN94y01ZK75CDCw7bjQsOa+2dyPyiQmeff3OL0Qo60zu0SJ+/WW/K6R2sMbIL/htqkNgtF23+QtkQ4fGmGy+74F8ewLzqPJKXDRXtPQzmYvWVX/B3t0+njh9AJ3azVtdxUoL/sTdOdsF/w2BO98mM9/xtqHAXZeNlF/yrfoVvWdfZPn4zrLV1ZvX0R7Vhsgv+DOIK39y0bdJx7/fCWltP+fytvrIL/rW4wjcX8p6/DRGdB3z9WW2Y7IJ/12AmzS1HK+js9vGbYS1uE5/t03D5Bf9077+PXV8kX+Rlrc4XeTVedsF/w1DOylZjPuBrQ0VHChLH/sbJLvi726dL51WTfjOsxXWenODPasNkF/wZRGK33HgkLxsqymP4WmNkF/w7r/DNrmYfXedFXu7ztxbnPv/Gyy5EdiZ2855/V3oHf6GsxXWmdM4uIrWuhrzVkk6Q9KikxyQ9IGlKvV7Lid26tHWmd/A3ylqb0zs0XqMGc1kJfCYiXpN0BHAV8Ol6vNChk8fy4KWHsO2oYfV4+iFlyoQtOfPAndl7x62aXRSzPh2+x3Z8sCEYNSy78aValqLBp0ZK2gpYFhHj+1pv+vTpsWjRogaVyswsD5IWR8T0/tZrRn/AacCfelog6QxJiyQtWrt2bYOLZWZWHQ0N/pIOpgj+F/e0PCKuiojpETF9zJgxjSyamVml1C34SzpL0tJ0217SnsBcYHZEvFKv1zUzs/7VLfhHxJURMTUiplIcWL4FODEinqrXa5qZ2cA06tD6t4BtgF+kNMPrB3JAwszM6qMhwT8iTgdOb8RrmZlZ/3z1j5lZBTn4m5lVUMMv8hooSWuB5zZy822B/9SwOEOB61wNrnM1DKbOO0ZEv+fKt2zwHwxJi6p2QNl1rgbXuRoaUWd3+5iZVZCDv5lZBeUa/K9qdgGawHWuBte5Gupe5yz7/M3MrG+57vmbmVkfHPzNzCoou+Av6XBJT0paIemSZpenHiRNkHSPpMcl/VPSOWn+1pLulPR0us9qCC9JbZIelnRbmt5J0kOpredJ2qzZZawlSVtKulnSE5KWS9qvAm18XvpML5N0vaThubWzpF9JWiNpWWlej+2qwk9T3R+VtFetypFV8JfUBlwJHAHsDnxJ0u7NLVVdrAfOj4jdgRnAWamelwB3R8Qk4O40nZNzgOWl6e8DP4qIXYDXKMaKyMlPgD9HxGRgCkXds21jSeOBrwPTI+JTQBtwPPm186+Bw7vN661djwAmpdsZwJxaFSKr4A/sC6yIiH9FxDrgBmB2k8tUcxGxOiKWpMdvUQSF8RR1vTatdi1wdHNKWHuSdgA+SzEmBCrSwx4C3JxWNlWRKQAABXdJREFUya2+WwAHAlcDRMS6iHidjNs4aQdGSGoHRgKryaydI+I+4NVus3tr19nAb6LwD2BLSeNqUY7cgv944IXS9Ko0L1uSJgLTgIeAsRGxOi16GRjbpGLVw4+Bi4ANaXob4PWIWJ+mc2vrnYC1wDWpq2uupM3JuI0j4kXgCuB5iqD/BrCYvNu5Q2/tWreYllvwrxRJo4DfAedGxJvlZVGcw5vFebySPgesiYjFzS5LA7UDewFzImIa8A7dunhyamOA1M89m+KHb3tgcz7cPZK9RrVrbsH/RWBCaXqHNC87kjalCPzXRcQtafa/O/4Spvs1zSpfje0PHCXpWYquvEMo+sO3TN0DkF9brwJWRcRDafpmih+DXNsYYCawMiLWRsT7FKP/7U/e7dyht3atW0zLLfgvBCalswM2ozhYNL/JZaq51N99NbA8In5YWjQfODk9Phn4Q6PLVg8RcWlE7BAREyna9C8RcQJwD3BMWi2b+gJExMvAC5J2S7MOBR4n0zZOngdmSBqZPuMddc62nUt6a9f5wEnprJ8ZwBul7qHBiYisbsCRwFPAM8BlzS5Pnep4AMXfwkeBpel2JEU/+N3A08BdwNbNLmsd6n4QcFt6vDOwAFgB3AQMa3b5alzXqcCi1M63Alvl3sbA5cATwDLgt8Cw3NoZuJ7imMb7FP/wTuutXQFRnMH4DPAYxZlQNSmH0zuYmVVQbt0+ZmY2AA7+ZmYV5OBvZlZBDv5mZhXk4G9mVkEO/tbyJH0gaWnpNrHZZaolSdMkXZ0enyLp592W3yup18G8Jd0gaVK9y2l5ae9/FbOmey8ipva2UFJ7dOV+GYq+AXxnENvPoch79JXaFMeqwHv+NiSlPeSbJP0RuCPNu1DSwpT3/PLSupelMR7uSjniL0jzO/eoJW2b0kd0jBvwg9JznZnmH5S26cixf126EhVJ+0h6QNIjkhZIGi3pPklTS+X4u6Qp3eoxGtgzIh4ZQJ2PKv37eVLSyrTob8DMUgoEs375w2JDwQhJS9PjlRHxhfR4P4rA+aqkWRQ5z/eluCpyvqQDKRKiHU+R+bQdWEKRKbIvp1FcRr+PpGHA/ZLuSMumAXsALwH3A/tLWgDMA46LiIWSPga8R5GC4xTgXEm7AsN7CPLTKa5mLTtO0gGl6V0AImI+KV2JpBuBv6b5GyStoMj5X6XkdzYIDv42FPTW7XNnRHTkRZ+Vbg+n6VEUPwajgd9HxLsAkgaS62kWsKekjnwyW6TnWgcsiIhV6bmWAhMpUg+vjoiFAJEyrEq6CfimpAuBUykG8ehuHEXq5rJ5EXF2x4Ske8sLJV1E8Z5cWZq9hiITpoO/DYiDvw1l75QeC/heRPyyvIKkc/vYfj1dXZ/Duz3X1yLi9m7PdRDw39KsD+jjOxQR70q6kyJN8ReBvXtY7b1ur90nSTOBYykGeikbnp7LbEDc52+5uB04NY1xgKTxkj4O3AccLWlE6l//fGmbZ+kKyMd0e66vprTZSNo1DaTSmyeBcZL2SeuPLvW/zwV+CiyMiNd62HY5qVunP5J2pEjydWxEdA/0u/Lh7iOzXnnP37IQEXdI+iTwYDoG+zbw5YhYImkeRebT5ygOjna4ArhR0okUGRU7zKXozlmSDuiupY+hAyNinaTjgJ9JGkGxBz4TeDsiFkt6E7iml22fkLSFpNFRDMnZl1Mosj/emur4UkQcKWksRTfQy/1sb9bJWT2tUiR9myIoX9Gg19seuBeYHBEbelnnPOCtiJi7ka9xHvBmRFy90QW1ynG3j1mdSDqJYmzly3oL/Mkc/v9Ywkf1Ol2Df5sNiPf8zcwqyHv+ZmYV5OBvZlZBDv5mZhXk4G9mVkEO/mZmFfQ/HUa4zErgvEYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22fd0958ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Measurement 3: 2D Sweeping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 77\n"
+     ]
+    }
+   ],
+   "source": [
+    "meas = Measurement()\n",
+    "meas.register_parameter(osc.dt)\n",
+    "meas.register_parameter(osc.time_axis, setpoints=[osc.dt])\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "\n",
+    "    for dt in [0.01, 0.02, 0.03]:\n",
+    "        osc.dt(dt)\n",
+    "        datasaver.add_result((osc.time_axis, osc.time_axis.get()),\n",
+    "                             (osc.dt, osc.dt.get()))\n",
+    "        \n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22fd1c1a390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 38cdf9f0e44582d67fd408cbc8dd6953161094ea Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 13 Sep 2018 09:05:47 +0200
Subject: [PATCH 100/719] Update notebook example to look crisp

---
 ...arameter Example with Dual Setpoints.ipynb | 83 ++++++++++++-------
 1 file changed, 53 insertions(+), 30 deletions(-)

diff --git a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb
index 7c53af62806..a58fd20b436 100644
--- a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb	
+++ b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb	
@@ -21,7 +21,7 @@
     "from qcodes.instrument.base import Instrument, Parameter\n",
     "from qcodes.instrument.parameter import ArrayParameter2\n",
     "from qcodes.dataset.measurements import Measurement\n",
-    "from qcodes.dataset.plotting import plot_by_id"
+    "from qcodes.dataset.plotting import plot_by_id, load_by_id"
    ]
   },
   {
@@ -156,7 +156,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 68\n"
+      "Starting experimental run with id: 101\n"
      ]
     }
    ],
@@ -179,9 +179,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22fce779b38>"
+       "<matplotlib.figure.Figure at 0x257927cb940>"
       ]
      },
      "metadata": {},
@@ -201,7 +201,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 69\n"
+      "Starting experimental run with id: 102\n"
      ]
     }
    ],
@@ -221,9 +221,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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SI8kmlTyBHQAuuW0JvnfjM1v5CRwcHBxGF448MhAwo1RQ5CEnemkxrO6uGPtrNKN5ZBVb1Pj2Dc/gB7cu2ZrhOzg4OIw6HHlkIGSOkwQleYQMlArqK1vdPRhvl6SSG6pbz7E8nGDu4OCwDcKRRwbCEPCIQGQSQ8iMnSe3AADW9AjLI68Kb06Zk8BpHg4ODts4HHlkIGSGR4pA5EQfhoxZkxR5SMsjlzzCdJgvAASBJA/Z80qMwRVVdHBwmMBw5JGBkBm+R/CJDDE8ZKC9XEBnuYC1wvKQSYLVHGujWc1Do2fQFUx0cHCYuHDkkYGQASKC56XdVh4B5aJn9irPCdXNi6oKczQSic2ubImDg8MEhiOPDIShcFtZgjkRgYgMt5Lep+CR1Ugq223VjOVhk8ejKzbhhidWbeEncnBwcBhZjFkzqG0JysJQbispPXBkeajt6RDelqLfVIZ52ES0lU0ep/zwbgDACxeevAWfyMHBwWFkMSEsDyI6kYieIaIlRHROxvtnENFaIloU/fvQaI4nZMD3VLSVJIkgjEjFM7UQ/Xe54DWlbcjtldrWua1ueWo1PvPbR5ra18HBwWGkMO7kQUQ+gEsAnARgfwCnE9H+GbtexcyHRP8uG80xhcwgQkQSlubhKS3EIJXo73LBQzUnw1wK6UETpdr7Ks0J5h+8fCH+8NCK1PbuwRrueHZtU+dwcHBwGC7GnTwALACwhJmfZ+YqgCsBnDqeAwqFhWG6pxC7s+zMc49UAqHhnpJCupEkmFwrt+ZVDqnUc7bbzaY+deUivO9n92NNz2Dm/g4ODg5bg4lAHnMALBevV0TbbLyNiB4lot8T0S5ZJyKiM4loIREtXLt2y1fdidvKJI84/8Mz8z+0O6tU8FKZ5OUoIz0v/0PuX80ppCjRX8vOC7H3f3ZND4CRCfl9fm0v/vRw2rqpBSFWbhrY6vM7ODhse5gI5NEMrgUwn5kPAnATgMuzdmLmS5n5cGY+vKura4svFrutiCBbd4TC8jCirSJ3lu95qUKKurR7XnmSihGRNXQUVn+lOfIoeOqnHU6nwjyd5aSL78SnrkrrKl+79kkcdeGt2Njn+rA7OOxomAjksRKAtCTmRttiMPN6ZtZZeZcBOGw0B6TdUx4hZWFkaSHMimh8SwsJI8uDKDv/o+Sblop0beWRR18125KwM9X9qDbXpibJ457n1uHgr96YqZPoEiq2a+y2Z9YAcAmNDg47IiYCeTwAYC8i2o2ISgDeBeAauQMR7SxengLgqdEcUCDcU6GlbfhE8Cx3VhDlhfieZ0ywQbR/0fcswVz93VryjUlfEkxe2ZKBavZ2u0aWLuy4ub858nhg6UYAwD+Wrs/dxx4TUVOndnBw2A4x7nkezFwnorMA3ADAB/AzZn6CiL4GYCEzXwPgE0R0CoA6gA0AzhjNMYXRpG8L5rHbyrI8gjByW5FVhTfablsY2rXVVvKN7ZVmLI+cKKwUefiR5dHfnEtJEwFz/j6DtRBtpfR2hqvD5eCwo2HcyQMAmPk6ANdZ274s/v4cgM+N0VjAujwJEYTBEIXqasHcPMb3Mkgl2l70KTNJsKXoY7CWbXnkhfD251oe5naPhue2ij9Lg/dSlgfUNRoRjoODw/aJieC2mlDQc7/WPGzLg0hZGCnBXFskYv96qMjDDuGtC83DzEIfWjDP1zzM/bV7a1OTbqtmMJiT0Bg69nBw2OHgyMOCngh9TxGIJAkdqpt2W2W7s8JQaB4y2iq6Rqng5RZPzI22yrE87P21e6tZt5U+b951gXwdph468nBw2NHgyMOCJg/KIgMhmEsLQ7mt0oUUA215+FYV3iDJSM/rKmgnCeoOhv1Nah490X4DOXkhNnorykLpGcy3VPIsD5lJ7+DgsGPAkYcFNtxWZmHEMOSYVOyquh4RCimBPUkezHRbWeRhRFtZE3VLRB6bB0zyiIKqUBEkwczojcij1uTErsNtG4XdVmrZ0VZ2CK+Dg8P2jwkhmE8k6IlQheoiuzyJnWEuNI96kLY8fJAxiYfCbZXXbdAWzCmaqZdv7De267Bhuf9ALYhJsJYjvNvQpNHdyPLIcWnVwuau4eDgsP3AkYeFRPOgVOl1nf9BqVLtan87/0ML5h5lV9UtFzwjg13v01L0ULX0BV3Tatn6PmO7iqpiw1Kp1bN1lEbozbE8ZL2tQdvyiP53loeDw44HRx4W9Dyomz5lVdVNRVtFSYIFP0Mj8ZQ7y9A8IsYoFfxMy6OjXExN+tpyWbbetDy066hikFO2NdMI2uKwyaOSk4eirq0u7jQPB4cdD07zsKBX2jqqqpkkwXoYouh72YJ5ZsFE9X/J9xByck2dhd5R9lNuq1oYgghY01Mxssx1GRIZCZUX/tsIWljvt0KBZXSXbXnIzzla+NV9y1zxRQeHCQhHHhYSzYNShRF16XXbPVWtM4q+l9JCwlDpJraQnlgennFNvb21VDDcUEGoEhe7OsoAEIvhepxqDEN3MGwEbT3Y1oUkE/u9+BqjpHn0V+v44p8fx58fXjn0zg4ODmMKRx4W4iTBqJNg0ITlUQtCFAteboa573mGBSA1D/laT+BtJdPy0ASgK/RKt1SW2yowmlA1N7HrcdvWhQzPTUVbZVxvJKG/M/u6Dg4O4w9HHhZstxWz6YbK6udRC0KU/HSTKCWYeyh4ZHQPDC3y0Mdo68SueaXJpbUYkYeYrPUwTLdVGH+Gpi2P6JjBWmh8ZrNYY06eh+W2unrRSiyJ+olsDXSQQN51HRwcxg+OPCwEMXmQoWHoCZUy+nnUgjBxW6UyzJWQbvf5AITbirXbSr3fUrTIIwjj7YDVNCr6O49sZOQVAKzrreDrf3kyRSpGX/WcTPc8zaNuua0+eeUiHPe9OzL3HQ70mBx5ODhMPDjysKDnUJ/IKIAYb/fSeR7VINE87FLtOtrKFtIBJZgDidtHn7PVIg/tvsmyPDSx9IomUfr91pKfIolvXPc0LrtrKW56crWxXbqejLDfoAF5jHKSYD3HldYI37nhGfz2geVD7+jg4LBVcKG6FrRFoToJJhZHKNxZniWkV+uJ5SFX8DrD3Pc8Y8LXZKAtCX1MKCyGrNBbrXnoCT0IOSY12c1P9guxCyNqjcTuMFgPGe0lH33VIHKBFePPppHrtsogs5HAcN1WDy7bgB/etgQA8I5XZXYqdnBwGCE4y8OCXZ7E1iMo6hhoC+alAmWH6mZYHjostr1cMM6t9ykXzczzum15RPtJq2CDII+YbIp+KgejI7pmn1VgsR6G8XikSC5dZHkFEOVYR9LFFAvmOQUZJTb0VfG2H987Ytd2cHBoDEceFmLNI+rboedLPT9mlSdRgrkSxu3kQd8j+D4ZE+xgLUBL0Yu7/elJWbqzzPwPU/PQK3JJHuv7KvHfhuZhWQJtkfViN5WqhxwTy6CYrKs5UVxAEm0lSUXuf8b/3Y+tgf7O7DpfWbDzUxwcHEYXY0YeRHQiET1DREuI6JyM9z9NRE8S0aNEdAsR7SreC4hoUfTvGvvYkURoCOaJK6lRqfZajttKh+oWrO2DtQCtRT9O8LM1Dzv/I9Ew1Ha9IpdWxUbhnqoLt1g9ZGOsGovX9CafLcojSSwP2aBKfE5LGNcZ5nmVgW9/Jt0PfTjQn2OwCcvDtRRxcBhbjAl5EJEP4BIAJwHYH8DpRLS/tdvDAA5n5oMA/B7At8R7A8x8SPTvlNEcKwvykBZGmLMdiATzghdZKukM84LnGav2gWqAlqIft4rVK+zY8rDyP7T10FYqGPvr7TM6StjYXxXJhknIL2BO+jpj/NpHXsK1j75kXKe9rPY3oq2CZOLOE8ZrOWK7jXoQ4rI7n2/KDSXH1Yzl4XqKODiMLcbK8lgAYAkzP8/MVQBXAjhV7sDMtzGzLtx0H4C5YzQ2A4FwT8lMclnzytY2pNsqq4e5Hao7EFkeOjs8voYdwqstD9G2FkhW5LVo+8zOFjAnjZ/SArtp9Wis7h40rtORZXnUExdY3gQtc1gk2di44oHl+Ppfn8Jldy7N3UdiOIJ5swUgtwaDtaBhvxMHhx0JY0UecwDI+MkV0bY8fBDA38TrFiJaSET3EdFb8w4iojOj/RauXbtlLpN0VJWZ5xHXvErleST9P/S+QdQ8ys7/GKyFyvKwNQ/ttvLN/A89iWrBXFsctWjC3GmSKluiRfO6Fc1Vq6ctD0CVQQESyyRLMK/EVo+fiqTK0jzyGkYBwECkS0hxH1CRYvY2ed5mLJVmrZmtwQkX3YEDz71x1K/j4LAtYMIJ5kT0HgCHA/i22LwrMx8O4F8BfJ+I9sg6lpkvZebDmfnwrq6uLbp+TB5RPoeeF2XNK88qWyKTBOW+Qcjwfa15pAVzvb+e7FWzKcSkol1dSZ6HVc4kOmdXpyKPTVH4rZ2RLkXzgVqAnSe3ROcPjetkah71xIrJc1s1Ww6l4EXjt/Y59LybcMJFf0/tH2seTbitxsLyeHFD/9A7OThEYObMRdH2grEij5UAZOD93GibASI6DsAXAJzCzHH4EDOvjP5/HsDtAA4drYHKUF0iWTok2u6REYXFzKiJJEFAZownlkfIiVtqoBagtZQI5pqw6mJ//RpIax76dTVyKc2ICiZujkRzW/MwGkVVg9g9VbfcYlluq6q0PCzy0K9qYnujSbwYaTxy/8dXbgYArOtNP2TaIsqyKj726wdx7SMvNXVdB4fxwE/vWopXnncTXly/fS46xoo8HgCwFxHtRkQlAO8CYERNEdGhAH4CRRxrxPapRFSO/p4B4CgAT47WQGUnQdkMynBbCXeWnlxLhRzLI4q2ApJJeqBqRltJt5UXFVIEElJJFUYMzO0xecSWh+3mMvWWjpZCapwA0B6Rk9QYYsujmLY8wvj45vI8ClZGPQA8syq/BlZepd8wZFz32Cp8/IqHk+uOYHLieCEMGTc+scqoLeaw7eK2Z9Q0tmxD3xB7bpsYE/Jg5jqAswDcAOApAL9l5ieI6GtEpKOnvg2gA8DvrJDc/QAsJKJHANwG4EJmHjXykG6rLMHcjrbSE3MxKowImNaKR5RMmrrcRj2KtvKswogho5BBNjWx+lfbQ+P/GZbbSp+vpZR2W/VXA3S2FI3z12PNQ+1vWx4eAeVCOmfEtlyAxhZATIYyATKjYKRGkJPnsXkgLVo3uu6a7sFhlTgZK1xx/4s4/dL74te//scynPnLB/H7B1eM46i2Dj+7aynmn/NXZwkiaZewvUYCjll5Ema+DsB11rYvi7+PyznuHgAHju7oEkiS8DyKo69sIZ0jYVyvzIu+F7u89HyoLA8IMggB+BiMQnUj7hAWQFJTC0hW6LpJVOK2irZHbqvp7SUAwvKwMtLt4oadkeVRt/JFtNvKDNVVek7BpxR52Hko6tj8SVqPI6tUC6C6GU5pK6XeG6wHYOY4r0QnROrv1f6MNhZccAtevcd0/ObDR+buMxxoi3JrcM0jL+Fzf3wMAOLP9vJmFf22Kvp/W4QuD9M9WIst4mbx49ufw+wpLTj1kEaxNNm4e8k6HLn79K3+XUYS+v7MyrPaHtDQ8iCiuUT0WSK6mogeIKI7iOhHRHQyEU04sX0kYEZVicipuOYVGe6pWobbSuZt+F7anaVDdVOWR+S2KljaiSaoNsuS0NcpFzx0thTielUNBfNqgI5SIdJzQnP/UkaeRz2MP5u9grLdXnr/POjzmqVXskusyP2YTZJZH+kj2v3W6Lr697vnufWGRrI1GIlV9SeEy01/r3qlui1PNbGutQVuxG9e/zQ+eeWiYR935+K1ePdl/8D//P25YR87HKzpHsTCFzY0vb/tlt7ekEsARPR/AH4GoArgmwBOB/AxADcDOBHAXUR0zFgMcixhRlUl7iltVUhBO2CONY+i7yUWgwzVNSyPJHpICuayPInvCcvDSgZsy9E8Cr6HKW3F2PIIGuR59FfraC35Rta7/sxF30PJ96yKvkkOi10ny3Z7AY0nVv1eVmMsANjYb5FHjkWjSaZTkkfOZCWvJTWSrcFIu8A0qepFczgMzaMehFg5LmFzAAAgAElEQVTbUxl6x1HCYC3AB37+ABavVtqVXhCNpdtqTbf6/M+t6d3qcz31cjfmn/NXLM+IrPvqtU/i7f9zLx56cWNT54rdVhnN0v7y6Eu49I6tI7vrHnsZ88/5K37zjxe36jxbikbWw3eZ+QRm/gEz38PMS5j5cWb+IzN/HMBrAYzMUm4CwXBbZRRG9LzkpgjDZHLSE6zeDshoq8TCYGYM1AK0CEslFp6jKrx5mkerlTEu9ZbJrUWRJBhpHhmWR0VYEoE1+fteut+6tjwKvpdheaTdUI0Ec00AeeVPNvSlK/1mnXdNNFl2lovGOBtdcyTRTLmUoaDDqwExdm15DGOh+pVrnsCrzr953Gp7PfTiRtz69Bp88c+PA0gSXLPCq6955KURs/4kYvfvCAQaXBWV87dbFgDJIuv6x1el3rv3ufWpBmi6gkTWYuOs3zyMC657OnMM1z++CvPP+StWbGwcpXXXknUAgGdXb33jtS1BI/I4iYhys7yZucrMS0ZhTOMKztA2ALs8idoWcOK2KvpeLJjXwzAmBOmGqgVhPAm2lDKSBANlqdhuLlvzsC2Pou9hcmsxpXno6Cl58waxKJ+0xtX7FzR5BObkXvTT3RDluLME8w8dvZv63kQwgK6/JRMV5Tk32m6rjF4ia3sq+Mo1TwBINBp5XRuj0Ugqr1zKJ698GF+5+vGmziGtJk1w2vK4+JbFmZNXFv74kIp4H6iOPEkOVANcfPPihlaEXSVBu60GMibMT1zxcK71tzW6QDIGc/tgLdji70W3LpDwo8+W1Rb59P+9L9UATS8aB2oB6kGIvz+bn7i8oa8aP886YOKplxuTQhz6Pwq/fTNoRB6zAdxLRHcS0ceIaMuy7rYxBCLaSpZet8uTAOq9aiyYJ+6mMEzO41saiZ7MSkZSYRhf26d01Ja+qVqKZpJdPbY8PJQLSd9zPZlPaVMr896ogi4zox4yCnHXQ3N/36OU28rQPCzzO0vz0BNhm5VL8q0bnonNa1niw7A8LLdVLcOieVGEPUr3Tp7baijyqAchDv7qjcOKcMqyPAZrAa5e9BIuv3dZU+cw+qTUtNsqmbH+8/ePNHUePUkPhyRtks7DZXc+j4tufha/us/8TGu6B7GuV1l/9sSt3VbDde1lkU2zIIvANF7/ndux35evH9a54tYLGe8NVof3XUdcg4FqgEtuew7/9rP7cceza2MPgUa1HuKV592Er177hDGGH966GC9vHsg9v362+sbJ6swlD2b+FIB5AL4IFe30KBFdT0T/RkSdYzXAsYYdbZWE6kohPXE3xav/gmcI3XpC9X0SBRCT7QVPkgri8/k+xSuc2G0lCKfoU5xkpydMfS59Hk0Kk1sVefRFXQaTB5xQ9HM0D9ttpaOtMgTzrL4i+tik94h6LcXMZ1f3xqZ/PQzhe4RywUtNagYpRROsdm1Nby8ZhJH3QGetEiU29FWxeaDWtMUAZLtkTv7BnU0fD6jvrNOKbpOBQsMVWbNyYZ56uTu13+3PrMGh592EuxavG3qM0Rg2WaHRCy64BYd//eZonDqoIbqHIrfV/Us34PrHX256/E83yPcZCnqxNVANcOMTiUvppS2IWouThDOitjTB2ZZYntWkzzFQC+LqBKs2D+K5tWbexwvr1evbnlaWiZ5rHlmxGa/7zu25Y9XX7Z+AlgdY4e/M/FGorPCLAJwNoDmbehtEqraVRR62YC41Dyl0x+QRVdVV2wWpeJSOzmJdhdfUQmpBqDobRu4mu59HqaBcZrYlMSkmj7qxf9xKN0/zsMhAax52kmBmqG6g9i9aBGhDT2z1QLnRprWXjGir1d2DOP+6p5LzRqt9TTA7TWqxuhyaD5Ae21CrxLXRCno4U3UWIS1dpyaA3bvaje21IMTl97yQij6q1sPYdaXHTsLyGG5rX3ul/83rn8ZJF9+Z8ocvfEGJvc2IvtrSbUTA+vvVwy1Fv/v3bnoWH/nVQ5nH2PpMX6WOt/34ntR+9y/dgP++ZbGxbaAapERi7Ua+9ek1OPOXD2LR8k0NPpWJc695AvPP+Wv8mpFvecRWnvVb9uas/DURDdQCUV0hNLp4MjMWr1ZC/86TW1ALQsP9NlgLc5NGY8ujUseSNb347cKxbb/cVLgtER0I4GtQZdUrAD43moMaTxgl2aVgHmuaJATzxPIoeNLdJNxWkiQCNpIQY0tFWACeOE89tjCU7kBEUb6FOWkXfS9qOJVs9yjRBLTbSlo9Bc9LWR6FDLeVirZK1+diYV1JN06lFqIsrLCsSJOCl+SMaE1lalvJiLb6tRVBolf76yPymDW5JUVyEvr8Q5GHLosyHK11sB7irsXrEIaM1d2DWN09mKqBpvGzu5biK9c8gSutvuqKPNKtfjX098bMqAchlm/ob5h5bpPHT+54HgDw4LLmIoP0ODaLvjDaemzkgtLEosemF0qNsN4qRdOdU6n4HT+5F9+96Vnz2CjH58K/PSW2mlP9cCof//yeFwDIdtOI/z/3midwefQ+kGgL9u/VM5hNHnq/gWqQeB8CNu7bWsBYHAntC5dtxDl/eCz1O9cyniEgmWP6qwGueuBF/OfvH42f9bFAo1DdvYjoS0T0BIBfA+gDcAIzH8nMF4/ZCMcYZkl2eTOl3VaGe8oztY1YMCeTJKRF4lnaRsic0kiAJFwWUERh9/MoRJN7GFsSjEKUX9Ja9GPLQ5OF1jxs7URbHnLCfXFDP2Z0lFOah4w46a8kk8tANUBbyYfv6yKOYephLvqekatS8NOWR7lg3pqx5dFfRbngYUprMaXNSCS9QBqb9Ot6tOVhPqDPru7JdUf84cEVeM9P/4Hf3P8ijrjgFhxxwS3xezVrHEui8FGZ0AioCaGjxXRbSetE/8ZX3L8ce37hb3jNt27Df9+aH5+ybH1/XCdMfhcPDYM8Pv3bRTj4azfGk5eO1svTI5g5Id9oW9H63bIIT+slGvbk++CyDQaJSegJvFscY2sdvqV2N1PuRbt+9K6DtQA/v+eFODhDbwPSC5LujIoHcr+BahCTai0IUxbzyo2JrrF0XW/q8+TpeTqJuK9aj9tKj2XkVaNlwvUAygDeycwHMfMFUWHC7RpGSK5wH8mCiVLQtqvw6u0GqYimT3EeiZdoIdL942ds1yXfARj5FtJl5pPUMML4HO3lQiyomZaHsFRyNI+VmwawYuMAFuw2LSKt5Kb+6K8Tl4QU7HQCZFFYHvbkUxTWUy1QRDe1vWR0Q9SfV0M/iBv6qpjeXsoMKZa46clVxnF50BOZfF6fX9uLEy66A9+96ZnMY56MXG5ZmeBVa5W4KuqZMlVkznOUH5Rk9KvvR5JHyMBza3vx+T89Fm/T2dsacv+zr1qEN//3XQhCNiw47U5rBn95VGkUOm9E891ATnTZbp+7Lg7RfXTFZsw/569Y021+J1nff8rysCbft/34Xrz7p0nZFjn5Z62sUwmJls9JT76rNg/mJi/KoBIgW9fqjy0P837OtTyia0m3VV14K/R15HfUPVhPWa95C6DY8qgkUWWNasWNNBqRx17M/EVmzlUSiSyK3w5gu60ARSh2b3NAubLyQngleciQ3CztpC4sDy/DItEl3wG9ajeF6kQwT8hAn7uj7MeCubY0NEEFgmz09rLQPB6O/OKvmj8t1ZNk/vS2+G8p2A3UdJfEROexze5SQVgeETFOaysalof+vBr6AdvYV8VUTR7SbWVNCp+66hHjOA3bmtATZTUI4zHp1fQltz2XqQ1sikhO591otBb91MSiE9iyWvXGlkctceFJvOG7fzde2wSZRV5PvtRtfI+NSuRLyBpbmnDi8jDDiISyhe++jMnerk2WNfk+vjIR++X3kkUe9mRrf4/9lQAb+qo48hu34Ds3mAsCnZeix6C/Y+mKvee5ddjQV00J5j+6fQnuXrIu102m74X+ahA/D3VxnwFq4SB/1+6BWirkOO831Pfyqu5B/OlhFbL9dEaQxGihEXncSkQfJ6J5ciMRlYjo9UR0OYB/G93hjT0Mt5VwT9m9zQE12WsZwE4GlJpH0sfCskisEMOkCq/Zt6Na53gyVV0Jk4k3FtIFGWgRGogsD8ttVfTVWG3Lw9Y89HFT20uGTgEozWFmZxmnHTrHmCAGo3LzMrfFnvSk2yoIGQWf0NlSNB5Ce0LQE9j6viqmtZfUOTLCXW3YQrr9IOqJljmZ1OTk+5OMkhc61LKl6BsWUmdLAd2DdbxdiL9retQEL/UiPblNauC2agZZVsU/lq6Pxz+1rZj67vME4XufXx//raN/9Hi2JqO+r5I+th6GuOqBF/GLe18AkK95aMjvRd5r2mq0dTV7vH3VerwQshcD2j3aW6mjFoR4KQqNled470/vxxX3vxhv0/fQt65/Bu++7B94NhK8dYBBMu5E0C7GzwMbv8lgLTTuye7BWsrNlndvZwWjbE3U2nDRiDxOBBAAuIKIXiKiJ4noeQCLoUqVfJ+Zfz4GYxxT2NFWgLIw7N7mQGRh6LhwguHOMqKthBvKFMwTUlHnS2snQKR5RDe5dFtVA0bR8+J6W6blofZvLxdSgrnvmS1zbc3DLiNStMgJUK6MfXeehCltJdPyiDQPGZ6cTR7RZwvVZ2gpegg5mSjsxKfY8uhX5GFbHv05E5z94Mljeit1Y+KSrjGN6VFxP/lA64dWRZUlj5COnlq4bGNs5ekwV7ka1t+HLZg3Qx5yHz3JSyxZ0xsHFew0qWVIt92tT6/G9yxR+oWo/0R9CywPG1k5CCs3DeK//vAYvny10hO6c9w+GvozP7hsA55ZlZQgOfzrN+Pvz65NTaL2Z+6vBnHgwJ4zO4z3ygVlPb71krvxocsX4r7nVe0q6bYKQkb3QE0UJDXP/83rVaZ4a9HH4tU9KWH9+XW9cSRdPQwN12alHhgLHNuNlfV5NLLK2Dy9qmfMSvrnVtVl5kEAPwLwIyIqApgBYICZm4+D2wZhlmRPthn5HyLaioWFIavk6oWm1EKU5qG2q+q5yf76fHnkoVe4htgsttuah97eUS7Eq1/bzZWI1kmkjJyUjez5yFLRFWArtQAtnWW0l330V+vx9oFagCltRSPaSpLOSa+YhWdX9yQJjYHSZ/RDXKkrF51NBtrvu6GviqltJZQiAtJ94vsz3BlhyKkHT1sry9b34dhv355zDbWindZeikXJrFVera7dieo4HRoNKJIqiErLctLXf6c0j/rQD/1gLYgJK0scfWF9H/bbeRIAFfqpV8V5+MDPF6a2abecLoOj3TXPru7JrPnUCFluq3uWmDkm2uJ804GzcN1j6dIf+l5524/vTb13wxOrsN8sM+3M1gj6KvVYC7h/6QY8u7oHe++kjpHWgswAtwlTutoq9ezw2XrIOP6iO3DaK+fge+84JCaP1d0VrI60oMFamLY8rHvUduvZ7z+yfBP2mNmRGcm4eaCGVd2D2Hlya+q9kUZTobrMXGPml7d34gDMToLSwkiip+xoq2R/bUlIjUQWRjSFdKTcU7LzoD4/YGoe0gKoRROUOp9nRFv5htsqiM+v9iXD8tCaR8FXbis9wcZlS/x0WLHuhthWKiBkM7JE9ipRKy313uUfWIAfv+cww+VUC5SVVLZyCrIsj2o9RM9gPbY8gGRi6ctIlKrUw1y3lRaH7f0BlYjYUS6gtejHE2jWg6p7nWhoS0J9Div6KyOsuHML3FZ6Il+6rg9X3r8cr5gzyXh/2fp+rO+rggiY2Tm05ZF5jcha0GT2+EpVLPCEi+7ABy9Pk00j2EI0kOTW6FDgnsE6ij5hwfxpmefQi4QsbOqvpjSOQeszP/FSN1ZEEU3Pre3DCRclZUR0RJkNW8SXE3q1HuKhF82pcN60tlg3eXRFFPUWhHFZep17MlALGmoeQLoCgGmZBDj1krvxkV8+mKrlpb/PsSqUuV2WVd8ayE6CcYnsEMI9ZQraoSAJ7cFQmeRahJal2tkS2NOah2eQjTpHpZ6Qh9QqdP4HoLWQ5Dyx5lFKh+oWo6z3TM1DWh6htDxMcX+wFqCl4McNpPQ14nLzGW4rHW5sCOaRlaR9z3qys1d+lVoQaw3T2kvx/n0VZfVkFQYcrAXx+T5z/N7qM9XzXTEJeVQwtb1oEnWYnoQrtdAgFVmv6sO/WGiIu6arwrY8wmifoSf6BeffgjXdg3hmVQ/qIeOcE/eL39t9Rjte3jyIpev6ML29hNaSKeD//sEVcYZ+owx2TVD1jM/cLPT9EgdriOvp8Gi96u8eqGFSSxGlQvZEXosWDVnY2FfLjE6SZPPFPz+OZzKstKsXrYxDqW3YOowmj7aSj5c3DxpJjW89ZDZevcf0+PWsSS0AFMnst7OycJ6PssoHqhZ5ZLipbDeeJBe9qHpw2UYEIWPfWZ1JcEx0/41VReNthjyI6EQieoaIlhDROaN1nSxhPOAkVFe6lZhFLRzLnSXdU3mCuRSV9XXspEL9vp54fZKhvYl7SlYA1iG/gFpZ6UnJ1Dw8YXmYmke8Eq5rskkyxuPEwKisvC7WqHWPgVgwTz6zbNWrzycTHQue6baS59t7pw6UozFtEOShSfOwr9+My+95IbNEw5qeSmwB7DJNRYfpoo/ygdU1wCqGKF82y9ZnWh6BQSqTBHksXLYRvxMZv/UMt1W7Jo9aOlT3qD2TycjG0d+8DU++pFa3s6e0xNv3m62skNufXoNXzJlsLASWruvDZ3/3CK64/8XUtWzcuXgd7ly8Njc5bSjsO6sTN3/6WADp6gZA2krsGayjs6UQ3x82akGYKtevsbG/miL2Sj3M7DZpo1HvkO4BcwLX55ssXJManS3FeLEEqAATQC0GZk9uRcGjJGy3aloaT6/qia2iPMh7VRN7GEV0zpzUgi+drBYQWpQfjWKgWdgmyIOIfKjs9pMA7A/gdCLafzSuJWvbSMvAFNLVPoblkVuGRDaFCY3Ci/oasWAecqZFUgsYxYLO8zCTBGPLQ2SAq9W82l4ueCIU1NQ8ktpUluZRT86jo7l8y8U2UAtQLnpxjxEtjOr+7ElGbZiyPIo+GbpKwfeSUhj1hIT223kSbvzUsQl59OooopIx0fxp0UuZmscbv38HVmzsR8Gj2D2RZdlMiSaEpPhiP+ZMaTFLweRYHjXD8jAnFvkQZ1UeLhc89dmsZE31XvYqHFCT0g+ihMF2UVl475lqldtTqeOV86aqsOvIP29bZo1Wp/3VAO/96f0G4dl4zV4zjNd/+Og/4Zi9Ve3UjnIBUyNC1vdFFhHpMWzsr2KK9ZvanzePPFZuHEi5FCu1AIeed1Pu2IHG5V86yoUU+WgdSHa6jPdvKRiBE3FUVj1ES9HDTpMSgu+vBYaF+c3rnx4yK1zeRzKZUbm5k/tOu7BHow1BFhplmPcQUXfGvx4iGrtgYoUFAJYw8/PMXAVwJYBTR+NCBknIjPHoXiMRwhtamocn3FbSgpGrdm1O68isomjvGjIbFoksPJi4rSgW42shJ4K5Rwg5KRuiCUhNvIFRTkRfox5bJInmUfbValUnsklyAhQZhJErqqXgJ+RRCWKButUqN1/LtDwSsb4oBHMd5aJISO3fUvRRqQex5TG9oxQTEaA+c1601aruwXiSVt+l+g6kJjE5mhCUplLDsvX92G/WJDOCLWPyG6gFxiTUWTbjT+Q1vn3DM7jlKVUSTlpiktzlpGJn2OdB5prIulp779SJkq8i2GRBTo1aEA7ZA+SOxfklxF9l6ROH7ToN7dFYfI9iizR2mWYQkf68OoIu7zPXAo4nbwCYKXqh9FTqcdFBDTnZHrn7NJxy8GwcNHcy5k5NRGRd5sTGPx86B0fuPi1FHjoCb1p7luVhkkevyBkpFTzsPDkhj8FqWuMYCpV6gKXr+hCEHBNTGD/nXrww0s9cXmjvSKNRVd1OZp6U8a+TmSflHTdKmANAFgdaEW0zQERnEtFCIlq4dm3+jd8IgZj0pUgsBXNZhlpmpPsGqSQTtdQL5HYAKHqe4VYqeGSQFqBuwqw8j1rdJBV9TF1oHuWiH08gSXkSM7RXT6jabcV6/4BjU1iGG+sEqtaSH698+yr12KRuFUmC9VBYHtHkULLIo+Alk3ssmNeCeAIqFz0M1sJYSLQtj4FqkFubqh4wykU//p76q3Xc8MQqIwlMWh46Kmf/2ZOMysNZ5GFHEknNQ53PJLSPRoUCZd/7rOi2fWd1Nk0ebULw1foToHzzcVBBPUxpHNUgTLX9tdEoUku7ZiTicHKfolwiwpMvd2PFxv5My0ML4Rv7apjSVsy1PN56yd1x6RUAOGC2Of3Y5fSfErkOpx06Fz84/VBcc9bRmCbGvOD8pKTMofOmxH9Pailgj66OVFUE/TrL8lAtpRO3VW+kw+nF185TWo33akHY9O8LKOJ63Xdux4d/sTCxPKDzwpJnX/8/4dxWRDSTiObpf6M5qC0FM1/KzIcz8+FdXVvWfkSG5Mqkv/wkwWyykT0yDHeWOA+gagHJhDn7PICZ52Hnc9jkoSd9aXkA6oZKypN4RrmR2DKIJjNATTi1IIxrFUlLQlsHLVGNKUDlM8TkIS0PkRRlWB51U7eJo62sqC31GXwM1oI4f8GeaBqVpO6p1FAuJJ/rx7c/h3//5YO4elHS0U77sX90+5K42u+eMzsMl2KW26rXSoCb1JrvtgISAboiLA+zSjLjqD2n4/qzj2nottIo+V5M0oDp6mopmuRhBwhU62rSlnjPkfPwL4fl9n8zME1Monf+5+sAJFUBPCIQEVoKHq57bBWO/uZtuRqLJrFpbSWU/fzp6C4R3nvQ3Cm5+wHAHSLkVt4neSt+6W6c1FrEe47cNffc0zLIw/fItDwqdVQD9by1lwvYybCUVL7I1Izz5EF3zrz16TVxFJiO6Cx4SRHShDzG2W2lQUSnENFiAEsB/B3ACwD+NsrjsrESwC7i9dxo24hDdhKU0VOaVHzPFMaztptRWKblEWeka8vDp3gi1e6mdJ5HYgHIfI5akNSwisu4M6tig16ieQDKD5tXkr0WhLHFo91Bijw4Po8vBPBBQRI6iW59byWOBGmxo63ifJE0YdYDbXmYmoQW3vVnqNaV5TG5tRj3WtfQfvWvv/UVuPaso/H7j/xT/F7PYN0gj6w8hUmtymJ44qVuXPg3lfA1ubUY60sbRZc3id6KOfl2WG4r2y2k7xWpARULhN8uXIH/+N0jhnuyXBx6Xae/nz9+7NW49TPHGqvZlqJnhDOnyCMIU772A+dMNjSURmiLrJyZneU4GKFkLTSkSy2PPLoH1aJjanu+5gEkgvW/HjEPHz5m93j7R47do+E45TnzxtAhLLZJLUXDvWVDazkSJFzTgCIPXSy0reQb5NQzWEe1HhpW4lCQobu6VprWPDyP4pyVUw6ZDWBiWR7nATgSwLPMvBuANwC4e1RHlcYDAPYiot2IqATgXQCuGY0LBcKSSCyPUERVmW4rI8M8K59D9iQPQiP/A4hW4aKfh+ela15p36Y6zpz0sywPXfIDgDEpJ02fzBLruiw6kAiwPYN1I5orsTzC2MJoKfqY0lqE7xHW9VbiCUrmecjyJGXfj68vw4ELRqiu0mdWdw9ieuRm0K6dDf212PWQZXnM6CjhwLnmBKjIIykjIrv1abSIFbuOBGovF1DwCas2D+LQ827CxTebfSVKBS9VesPOGdiUUxlWakDF6Hv6x9INxu/QjFtD602vnDcVu3d1pC2P6FyPrtiMlZvMOli1elrzyMt5yELZ9/DrDx2Ba846Ot4WRwTGC5fkfLbbTFvvqzcnCZlZkUway9b3Y++dOnDBPx8Yk3S54OGQXRpbIXKRcdIrdh5yn0mtBaOvio0sdx0BKc1DL2jaSwWDKLoHaqjUw9yw5CxI96IsPKnd3POmt+HJr70xtpjGXfMQqDHzegAeEXnMfBuAQ0Z5XAaYuQ7gLAA3AHgKwG+Z+YnGR20ZYreVJ91HZnkSfeMziwzzvKRCL6mqG4j8Dz2J6UxpIMowzyjJrn2bgJkkqLQQ02QNAjaSBGXynXSlSZeMPI82pzf2V1PJiXoskiQ8T5VTX99bjVc80gcchBmhup5teYhQ3VqItb0V9FeDuPhiyY+irfoq8covyx2hNZJWMQn2DNZQLiaaSta8YJcRL0dlR3yPsDoqbPi3qAT9a/dR7tDdprenNA+7mGNeuKgmu5ZCYqGt760YIdmNVuEadmFGaa20FBK31Yd/sRBf+rNZ37QahCl3X7mQbviVh1LBw1F7zsAsIQbr71h/DY0sD70I0G1Wp7YVMbOzBXkYqAXxChtQrrJ7znl96juwIX/bTx+/Nx7+0vGpfaTrTyf1ZYEoO1T3yN2nGecYqAVxqG9b2TcWMyEDmweqKPmEkw/KJjMbMtJM349A4uYG1L3fEi8UJ4jbCsAmIuoAcAeAXxPRxQDGvGkuM1/HzHsz8x7MfP4oXgeAmUkuy4rYBRNNS0WQhHBbFUWYqz6PXpnLbGud5yE1ErldnS95wKtBGE+6sgWuLIwoNY+6CMmVSYJSU5kaRZOo+HlOucVqgUkegHrgpOVRLnjxcdLySNxWsqGVGao7WA/wYlRbadcZKnqoXPQj8qhhWrt6uEsZ/nG9wpMr6FrAUbSV2rauNy0S2+fSwnfBo9SD+P6jdsMLF56Mqe1F9ETk8a23H4RHvnxCasLfmGN5vLRpAETATpPLMeH0VQP0DNbi76gZzaO9ZLqYDLdVyWt4jqxoq3LBbzoxMIvcksg8HSWX7PPESjNAU0/CL29OStZr92Ee9hHkscu0NkzvKBsLhRcuPDl1jNQ5PI8yLYdik+TRXirg9fvOxJffnGQJ3PKZY7HnzM5UC4HVUUmg9lIhthA11vdWUfQ9fPdfDsaC3bKz6jUKHpkdNntMy0PeukWfQDSx3FanAhgA8CmoHh/PAXjLaA5qPBHmRVsJd5MZbaWOkx0AbcE8y52lw3rVRBqF6oamZWO6rbTmIavthoKqpVEAACAASURBVGK1lxCOLIwo3Vay3axMEpQWho4m2dRfQ62erITLuqtcPcBANbEwAOUuWicsj3LBM88TlfHQqzOjPIkVqluphXFhvl2nJZaH1jx0qGTW5KVXrnZ103LBx9yprThit3QIJpB2ZWm3SCEKdZXQ2lOpkGTut5cKmCzqeWlszslNeHFDP2ZNakG54Bsr1rU9lfh7sK2YdxyeFrJTlofltmrk+qrUw5TbrVxMrOChkPX929tkU6b//MOjxnv6/tD1uXad3t7QXQTAiFrSaLVcbd9620HG66xw5NfvO9N4LX+3rs588mgr+ZjSVsIHjt4t3qa/Y/176eflhajicVvJT5H8ut4KSgUVYnto5Hbbw2pfrFEqeMYiRFoe9TCMn3NAaS86J2osMCR5MHMfMwfMXGfmy5n5B5Eba7tEwfPQ2VIwLAyZt0G50VZmqK7M55CRR1JIB9RNZ4fq6iq5oSQPSlselbqMwkI8VlkYUYbASs1DdhKsivNIt1VdWB46JHSgGsQx8tr1oC0PvUovF320l3y0Fn2s7akY5wciMpCFEWVtq3qIF9b1wfcIc6e2xZ9B53lMzdA8NGZOUg++7bsvF1TlYTuxTcPuIqjdDDYZAMnvVi54sdtHf9d2ldM8y2P5hv5EZBYr1pCTEhP2PPqttx+cOo+9ojUsD+G2ysL9SzekWtSWC35uYuCsSS24/uzXxG5Dm9yA5DfR91kjGtLneXpVD9pLPnaalD9pa9iLAgBoLVkk+6pdjNd7zTSLJgLApe89DPOmtaW2AyqHKO+6WcEE9r22yzRFcF+99sn4GPu47sF6yh381kPmZFpO5YJnWB5PiX4dPYN12D9DueBPnPIkRHQaES0mos3jmCQ4ZvjA0bvhsXPfaISbyk6CBFjRVhlRVVYZEs9T5mQgOgkmSYJmzoMmLJkBHgpLQhY0rArLQL8fhjmah4j1twsjSqF2cmsRRGrikxaJXuX2V4O48JpepU1pK2Jzfy0W6lqKarLu6ixjbW9FkZy4y2WYsCYo/X6lHmDJml7sOq0tnozKBQ/dAypKRbs7bFfTjI5SvPIuFzwjYa6c8/Afv/9OeO0+XXivFZqpLQ8/gzz05C4nZu1Xt8kjr33r8g0D2CUiRrvnt762vPL+O2enVaXIoyi/YxpSN/nrY2ZxyJaih/e9ej4A4DcfPsJ4jwjYd9akeHGRpY3oe0W7bBtVBp/cqs7z9Mvd2K1raKsDyHblNRL5rzzzSOw/O/3dFXzP+O7k75ZYfsl4ZkcWT1aElCZsTbq7TjctiLaSn3mcbO4GKAscAN5s6SC7d5kl5CUq9TB1/+iF1ligGbfVtwCcwsyTxzFJcFxg5E6IHInEwjCTCmXmuXR/qePIzDzX7g8pmHPiw/SJYnG9LnybvtAqKvUwnjBkHoapeQi3ldA8fJ/iG7ZqRW1Nbi1G1UrDWK9pi8mjjrU9yuzWtZymtJbQU6nHoZ/6ml2dZaztqRiaCqAeGO3C0wSlw4Qr9RBL1vYaD02p4MXWjg57tCdGKdwSEa4888j4tR6P7T6YPbkFP3//gjjcWENrHlmr69lRqeuyZUkBQGtx6DDXj/7qQazqHsScqCZVwfKV62vr++bgXabg1x9SE/lj556AP37s1fE+9vUkoRJRpi7UCOWCj1fOm4oXLjwZu80wJ0E9v557ygHo6ixnhrPq3ySMLY989pgSly8JMF9MuP/vdXvg4LmTc8aX/jyNyGNmAxeUJKusvhj6vOedegBev49yc7WV0r+vvrf0MyxrjeljsiwW3SYhJo+IfL7/zkPw5NfeGO93+oLslDptudku13LRm1DRVquZ+alRH8kEhFmrKtILIlEKUDedLOFull6PzuGblkTa8kg0D+meUpaBOocO4U22C3dTFP6aZKWHRmivDIGVZUh0eRJmjqJ8kptwapvqJ14XNbVaowdnILI8ujrK8QOoJwKdzKSv2dVRTtxW0vKIzvmXR1/CYC3E7loYL3joq9Txwro+o2mPznoHkuKD9sQ4a5I5memwYDke+yEu50w87TmWh9JyitHfybH6d99/9iT88F8PNY6xXWU6aqsrqndkf47Y8ogufdi8qbGrrrOliFfOmxpXcLVLZRSsczUTsSUh3UJtOUR4zN5deOALx2VPpJbl0Uh7nyKilmTC3H+8cV9cLcJ/jfNnfB5b8zDeaxCJJb+qMAS+ePJ++MTr94y3XfDPB2JGRwnvfNW8mEhk4Uv9OyW9yXXirHnNtnJa8wCSsu36nPo4ZRUVcOtnjsXNnz4Gr5o/NXXsQXMnY370zNiLj3LBnziaB4CFRHQVEZ0eubBOI6LTRn1kEwCmjqD+LohkQDairZJVgLJUzJBclUmcLk9S8JNChLqxEaBISk/2YZhYErJ6bjXP8jA0j0SIlqXX28tJHw7pngJUlq22PDSBxppHLcDa3kqsLwBJ5Mxamzwit1XVsjz0WD955SL4HuGEA2YBAKa0F3H/0g2oh2yQh5w0JuVYHl2dpq9avq+Pb7PcB1maBiAEc+v9IOSYMMsZbisAePNBs41j3n1E9sqxK/Kt2w9/h1XiJGv1rjP8Zw3R8CfPbfSeI+fhNx86IrVdRhq1WFpCE16leFEQNzdr4LeaIpLtmk1MzCL7RpZHI2LxLMvjQ6/ZHZ8+YZ9421sOno2FXzze6BY5Rwj2f/n40fjuvxwc3w9xcVHfw7lvSaKx2oqm22rpN94EIBHt37VgHj57wt7492N3N8a3e1cH9pzZGbs3Ja456+j4/ktZHmPotmrmV5sEoB/ACWIbA/jjqIxoAkHqCIGIVEoyyc1y5vpRsbUQQOdnhDluKxGqq4VxMvttmBaJEuSrslS7FdGVqXmIGlZ6Eu4eqKEmeqQDqsBfX6UeaSHa8jA1Dyk4To4mAt0tTT/Qsya3YFN/DZv6a8b5n1uTtE+dNaklFt4PmjMl9sPbloeGDue0ycNe3RnkEY3HzgCXTXP+/djd8ZO/P6/206G61sQuE92y3FYaR+05HXcvUTEle+2UFmyBRC+yXWOJ5aEXKOljdV+TWZPy8yIAVc7+3UfMw0A1wB8fTgoyzOxsydQC5EQ8XJeXOkYd34g0NCYLa6N9iFwNjSzLI0uX0mhELCZ5NL6ujtCT0V7zZ7THq39ANE7zCGcctRvOjQTzgu+hTVyLiPDIV06Irbyi7+Gs1++VP07x+X7xgQUxwdvFEDUmWrTV+zP+fWAsBjfeMFfzyc2hn6tQJAkSiVBdNvt56OOkBWO6rQRJiHazddFBLc4wjzQPHa2kycEOE07neQSGbqPrMHUP1pTmIR7Mjqj7oFEuo+DBI+W22jxQM5KltAtCk4e+pvZlP72q23Ah/FskygJmPaiDd0l83TJ0sSRcUHH5aeuhabOIQQYwxG4ri2CWb0xKlXzupP3iybijpC0PdZwmNzlZ2xqOxK8/lOgtu+ZE9XR1tGQem2ge6nVWu1PdF13qPFko+B7O/+cDsbfVprW9XGiYzQ2gKQHbRirayhr6ESKnQZb5sH+7PAynmOBQ+0vSGYrsdCLj7IxQYY3j998JAHDSgcqKloK8TXCTW4tN5fFofPWUA/Cp4/bGMXt3xaQfWx4p8vDHTPMY8lcjoh9kbN4MYCEzXz3yQ5o4SFbzoWFh6AdLFyfT+yVRVTLDPDmXURgx2q5IIglb1QK1zv9IIqTU/try0KuL2PIQtaTqQXaeh9Q89CTVPVg3MpsBNbn0VuqqcF+0nUiV2e6PSkrLyJ7JMXlUVCRXdIyOeFrdXTGiUPafPQmnL9gFV9y/3PAjH7v3TFxwnaotJesByWt1tpgrc42OjIgWvUeieZj7vOlAM7JFX6ejxXRbdZQL+OUHF2B6e+LWMdxWfnqi3aOrHc+t7UvpEBozIjebTYIdZfW5j9xd6RpvjFx6ErrW0VDkoWG7bzrLjUtwbCnikOVo7rJdbueecgBOuvhOAKbOkfXbafz7MbvjJ3coizBPo7Jx7VlH4+7n1jX8jN/5l4Px+u/ebjR0y4NePOzZIPJp/9mTjFDb2z/72rgOFQCcfdxeQyYE5kEutjTsVgkaLUXPyAUZTTRD+S0A9gXwu+j12wA8AeCDRPQ6Zj57tAY33siLtvIEeYScrBIBHSWVkEQhDrH1osKIlmBe8FCNLIyQE1eJzv+w3Vw+EQJOKtXqSUyGFdcz8jz6q0E8Tp9Mt5XKw0g+RGdLAT2DNXS2FI2JsaXoY6BWj0JvkwdZJ3yt6Rk0JlUZRWNPYNrikCvgfWZ14ltvPyguy65h1B5qyV4xZwm4ekqIo63ECnfx+SelHjx9nThUN/rs7eUCDphtRgA1sjwA4C8ff01mJV57vDa5aOLab+dJmXH/APDqPWbg+idWNYwmkrAbKelrXHXmkSgWPJz2o3uyDsMvP7gAtSDEB36+sGHYrUZseUQ7K7JNyrrLe0D2uMj67fbZqROTW4v43Jv2S8ijScvjwLmTcWBOxJbGbjPa8f13HoJPXrloSLfV59+0H47ff6dMV18eZk5qwUxhqZ593N5NH9sM7Jp2Gnvv1Im7l6xXbaKHUatsS9AMeewJ4PVRfSkQ0Y8B3AjgeACPjeLYxh2ZhQ5FJnkYRiK3WOH4nprc6xmWRz0IU4K51jxkv3C5PRDuMnWcijxKSoH4xvl0RJe0hqa2FbGhr4IpraW4ydXkVtPykBNgR2R5tJb82BIClCmuLQ9Dh2gpgEiJhp0tyQ3bWvIxs7OMNT2VNHlEJGBHxLzjcDPJCzAtDzu3QSMrll67fOI8DzFJNUpyswXzrJWxdDtkRTW1lny0wjzukS+fgBfW9xkrUhu2LpOFi955CM7pHsz+DL6XChe1S27oaxyx+3Sj17eN1+zVhRUb01WI81C23FY/OP1QXP/4y/jS1aoMnfztpLsy6zPf8Kljcs8/UpAehEZoLxfw2n1mNtxnrGGXYdc4bNep+Mkdz+OxlZtTDbtGfAxN7DMHQDuUqwrR37OZOSCisbGPxglGf45Auq3U+zqfI0UeQdrCyMvz0KG6UnBT21X+R0xC+jzRalhnN8cZ5jmaB6DE2XU9VXSUi7ElpCfvnsGakSQIqJVpyCqDtSgskraSj75KENXUEtFTvoc9ujqwZE1v6gHvbCko8ihlWx7NOE/s/IUsZIVDJpaHOj4rQ9m4jiaPFlPzyFoZS8IYqjjfV085AI8s34TJbUUc3DYF6Vxx1ZCoUgubIo/Wkm+ItRIyR0Dj9AXzsNfMDpzzx8ewdF2fYYF5HmFmZzm3tPlw3FtxgEl0j3d1lvHef5ofk0dryY+rEcj7LW9BYCNPJ/jJew9r6nuz0UhXmujQFqtPafIAgEeWb5oQ5PEtAIuI6HaoZ/0YABcQUTuAm0dxbOMOczWvQ28Buw2tZH/tVkqH5FKqVDuQZJjXRaif3r8WtXyV59H/65o9sdsqIhXVQjZ5kAG18lzbW8Eu01rj/Tpjt1XdSBIEEvdOfzUwMlhbSz66B5VYa6+2D5o7GUvW9Ka264faNqG1JdLM5KTP2WjXrHBPPSfoSWeoa2mSssuTZE1MkiQbhYQC2T5rMUoAwCkHz8b7j9qt4XmaQZbG4nuEI3afHrs67Y6H93/huNzz6X1PfEVae0ldRyxgstBWKuDqs47Cs6LTH9B8qG6WtgRk60LNQIfB2i7JbQFxiwHL8pjeUcZd//U6I6x4tDDkr8bMPyWi66D6iAPA55lZt2H7j1Eb2QSAXSVX151KalshsjzEMT4ZobqecDfVQ7PaLpBYGKmGSVH+R912W0UP6IBteUSTvI60kGGmXZ1lPPziJtREh8GWooeiT+gerKWSBGUvbkkGbSUf63qU/9y2MA6eOwV/fGil0bgGSFbx9gQ7nNWenvylYG2j0QQk33vLwbPxmj2za1zpz9qZ0jzS5DCU5tEs9NeQJ6yPJHSQxXBW6ZNainjgC8dlNkGyodcZeeThe4Q5U1pTE1uz5DHSIv/Bu0zBdZ94DfadlR1OPZGh77ms52huRm7IaCD3VyOifZn5aSJ6ZbRpefT/LCKaxcwPjf7wxhe2YO5bE3jstrIsD5lJrid9mZ8BiH4e0SSkNQy90i/5HgZqQQYJmW6rWPOIzqcThKTbShYubBEr/kktxaR6rhWqqyHP01r0sWkghzyi6qDdg2YVU+1OsgvYaWQ1Z7KhrzUjo2hdcp30BD9rUgtWdQ8aWd7/ffqhqf3s62jCKzZwWw0n1LIZ2O6H0YC+N5p1E2k0qjQrIQuDDgfN5nmMBoYjgk8kJC0Pxs/l1ojyPw3gTADfzXiPAbx+VEY0gVCQSYKiXlQitEX1qMSD73kUtYI1SSIpT4L4NZBYGpoMkmgrQvdgWjDX7+tOZbK3OZD0L5CutK7OMvqrAdb1Vg0LYM7UVqzY2J/SPNpzLI9y0Y+b3Njuqf12zl696f1sy+Pkg3bGXUvW4T9P3CfrMBPRR5mW0YtBIytX4OqzjkLBo6ajTvRY7fIkQ7mttgZxwc3R5w78+N2H4X/vfD43Ym1rkee2uv2zr8WyjPa/Gs1aHg4J4h5BOVWQxwK5vxoznxn9/7qtvQip2fZiAG+CylY/w7ZciKgNKhx4DwABgGuZ+ZzovTMAfBtJ3/IfMvNlWzuuoZBnecRuq4gMpDltWxi+YXmEopCi3l+H0qpJ2XZbBRkkBKTdVppUBiPykBbDtCiU9qVNA8YkPn96Ox5evjGleXTkkEdLwY8rxdrkoVfindZEkLjJrJo/pQIufle+FSChM6rtAoYSHRnWwU5DZGDbsAsoFkSoro3h1o0aCmPAHTh6rxk4Oqcs/UhgTlQs8ZNWWKqdja3xjdMOxIV/e3pIzcghDdlsbdzGkPcGEb0KwHJmXhW9fh9UjscyAOcy84ZhXOckAHtF/44A8OPofxvfYebboj7ltxDRScz8t+i9q5j5rGFcc6thJwkWrNhqnWEu3dWedluxKYz7nsokV6G9CeHozG5NBnGHwYIZqitJCJBuKy++LoA4R8LPsCQ29FWN2PP509twzSNKvjJIQkQlyUinsrE9/cA/+MXjUm4o/fmzXD/N4vBdVdTIGQ2E56EinppByffQXvLFd52dXAiMfNjo9oC2UiE3NyULpy+Yl1s11qEx7FLu44FGT/RPABwHAER0DIALAXwcqn/5pQDePozrnArgF6zUnfuIaAoR7czMcUMBZu4HcFv0d5WIHgKQbp82hrDLk/gZbivZRxhA3MRJ9jAH1I89UAuMjHQgmYT6rMZCxYhs6hZ56GvZFoAeqxZFi+IauiDg+t4qdp2eiGky61tGskh/vt1gKGu7RpZl4FmktyXYZVpb5qT0mw8dgefX9WFya3FELIED506OS78DyZizwoBHyvLQWdhj4bZy2H6QNJibgJYHAF9YF+8EcCkz/wHAH4ho0TCvMweJ4A4AK6JtL2ftTERToFrdXiw2vy0isWcBfIqZl2ccdyaUToN587Z+RRNbGFGorl5FS7eVyjC33FZRnw85YWrNw04q1JNwb6UWHR9ZHlEIb1aBRSBxc9mFEbM0Dz35VYPQcBHMEEKodFsZXf/yLJImJ09Z72uk8eo9Z+DVOZFTW4L3HLkr3iMaQ+nvMMttNdKCuUM+JrcWM9sH78jQXpDxFMwbzQA+Eemn5g0AbhXvjZrCFV3zCgA/YObno83XApjPzAcBuAnA5VnHMvOlzHw4Mx/e1dW11WPRE58Wuu1ciyTaKjnGi7QN2RUQEJpHaFoeWgvorViCeSE/wxxIh+Qmmod5HsCMrpHuHWk95FkbpZx9miYPv3Hs/0RGoUGo7ogL5mOiemyb+Mfn34CnzztxvIcxoaBD6+uNmqaMMho9AVcA+DsRXQ1gAMCdAEBEeyLJNs8FEf0/IloUWSkvA5B1J+YiEb9tXApgMTN/X29g5vXMrP0JlwE4bKjrjwQ8T+V06GZQejLxGkRb6dpWYWht90j187D2j8ljUAvmsjwJpwRz2z0la2cBCanIJEEpgBtltzN6XgD52sZWWR7bInnEmsfoRVvFcNyRi5aiP+p1mrY1tFqVn8cDuVdm5vMBfAbAzwEczUk2igelfTQEM1/CzIcw8yEA/gzgfaRwJIDNUu/QIKKvA5gM4Gxruyx/egqAMetsqN1Npuah3gujTPJUeZIQqcxznWEeWhZJ2m2VhOpmCeb6WlUrqkq7r7TwnqV5ACZ55BOGTIBLE519bCO87TAlWx2z99ZbgmONPWa2Y+fJLUbvEo2R0jy0VegEeIfh4NRDZuOs1+2Jz5wwsgUXh4OG7idmvi9j27NbcJ3roMJ0l0CF6r5fv0FEi5j5ECKaC+ALAJ4G8ND/b+/uY+SqzjOAP8+d2V0bL8YGltrYLk75stySbMjyoRhKoVACoZAqUktKEoHSoqimQBGq6qpRG6mqUqmiaSSERJN+oOZThaJSaANRadOk0LAGAw4k1K1SYdepNyGlNvhjZ+btH3POzLl37t27d3fm3t2d5yetdmfm7swZW57X73nPeY8rSvsluXeSvBFAA8AbAG5dwBgWxBfAm4nT/IB2zcMs3iKgFnUPfYrtPA92mKdNW73VmbaK1zySO9K7mYdfVeWnuVwtxK+2Sql5APH9FvGpquBY1Yz6R5h5zPfDbnLLukIrcJaSbRvW4pldP5v6WL9qHvdeez5OOWkU70+0hxeZy0gtwr3XzmOP1ACVsjvHZS07Mx6bdN/3IyN5N7NdAHYNbIBz8K3U25lHvBlZd7VV9/rIF8yDpb3t5yEaruaRVjA/fKx3n8dsM96QEegGi+M5mUdY8wgDxuqszCMjGGTWPFKW6g4T/+cSHlq1ECevGsE911T3v0eRhdLWzhydQ5xa4Q7z9mNpXXXrLlNptHrvbzbjzwOEmUfDXeczj0SG0ZN5xFdV+W6/R2cb7nb3Qz+K2DmxMLtgnh48xrIyj5wOtStdLSL+4raLlmVTPZF+UPDIUXMZQ3KfB5nRkp3t61ut+ObBeo09q7aAsObRm3kA3Uwi3GwIdIOKb1NAEiO1qLN5MKx5+McBS9Qtgp8zCpJhhhFes5AzrleapXbGg0iZ9AmQo1sAb/W0XvcnCYb3R1H7kKjkqiqfwSSX9voP88M+8whqHkBKhtHZSd4CGa+3jNWibrBJBA//e7Fpq3nUMJLtSdLuF5Hho0+AHFn7MyIyteZRjyI0rXdVla+dNFvJpbpuh3ln2iretuRYogBeD2oeyV3bI/UoaLAY/6v1l4bdbWOtR+ZT81hAwVxEViZ9AuTodsON1ypIdM7tiGKZR7e3VXKHuT8XJL5UN2ufh9v0lwgenU2CjWZPdjFaizqHNSVPzfu1K88BEG9JEhtH1rRVRuZRxvkTIrJ0qeaRw3/oN1qGVSPxYJBe80C3YJ7YYe5PBkxOZ43U2Kl51BM1j2Oz/gTD9v3+bIXX/udIT6vwkTpx6HA7eCQ7le688hx8/IqzewKOl5l5BEEi7xhXERke+jTIkbbaCuhOW7VaSASDKHWHeXgMbfIDfKxe6xbMo3jNwzdA9EFl07rV2L5xbWdsoZFaBL+RO63LbFbgaI8hf9rKrwQr44hLEVnaFDxy+PM5Gk2LLX/1q62aZrGOqLUIqUGiFuwXCfdgAPH/0YeHQQHBtFXwIheetS52jRfLEgpuYsva9BYGlU3rV+OXL/lxPPSxi1OvFZHhoWmrHLWg0N2TebTa53mE8/+1iO2CeWI6yy+dPdFoxYIQkNzd7Tb9uQ/tt1NWT3WOnk3WPIIP+qLnWyQDUec5E+/tD37hgkLPKyIrkzKPHD6TaCSX6kbMOM8j6rYzqYXTVt1pqOQqqXAVk5+28kt4fev18LX9lFayKdrIPFZPZQlPQwxFc0x1icjwUvDIEWYe8aW66OzziPW2YvfwqFjmEUxDJTMGP8UUBfs2fPB45PkD7rHu7/iVWMmGmj5LWDUSZQYDEZF+UPDI4duNJJfekuFqq+71UbAZMC1bSMs8xlf5M7ODaScXPPxJgmEWk7VBz+8N0ZnQIjJoqnnk8O1Jms1EAZxEq9V7nkfdLeFNbgbs7BifbfVkHqeeNNq+JqXnVfh6yedKHs7XzTwWHzyeuPNy/Puhw4t+HhFZmRQ8cvheVclVUn7aqtmK1wu6x83Gp5X876ZlHuvXtIPHqozut/55PR88/Dnp3mi9twXJQm0/cy22n7l20c8jIiuTgkeOeo041uitedDt8zCLN0AMz+EYCaLHaGfTX7NntdWpa0YAdDcAAr0b8sLX9tNWycP5fFDJ2i2e5u6rz8WBHx2d9/UiIoCCRy5/GFSjZbHVTVGUfpLgSC3CbKOF2WYLa1d1/3jDzCPZ2WO9m7bKOi62/Xphwbz9BM3EvJW/f3WBneB3X62zJESkOBXMc9SD3lbpXXXjbUhG6+1DnE40WrGls2GdIrnE9tSUaavkUtt6SuZhieDRKZgX3OMhIlJUpcHDnWn+GZL7SL5E8sKM6/6J5HdJ7nFfpR2k0O1t1Uos1XXtSQw9mceJZgsnmq3Oh3n7/nhdJOSDRjhVlVxqGwacTs0jq2DepyNSRUSyVD1tdR2Ac93XJQAecN/T3GJm02UNzAu76oZBwnfVba+q6l7v92AcPdGM7c4Os5Bkwdxfl9Uz6rE7Lostz/WBqJWctnLXrFLmISIDVnXwuAnAQ+6M82dJriO50cwOVjyujtiO8Z4d5uk1D6B9MmCYbYwk2nyErtp2Bn7357fjly7akjqGs8+In5PdKZgnUg9/Dvr4aNV/rSKy0lVd89gE4PXg9n53X5o/d1NWn2DG9mmSt5OcJjk9MzPTlwHWI2K21erZSR65msdssxXLCsKeVGnZAoCexohRRNy24x04KeNDPzkNNZqxz+OcM8YBAB++9Kz5vj0RkQWpOnjM1y1mdgGAy93XR9IuMrMHzWzKzKYmJib68sK1iJhttD+la4wv1W22gBOJ4OEzjGbLUgvm/jmLSPaX8rWU5GqrW9+7Qa0oygAAC35JREFUFS984hpcsPmUQs8vIlJU6cGD5E5f+AZwEEA4V7MZwIHk75jZAff9MIAvACitJ3iN7UOcAMSW2Nai9mqnE41WrLaR9XNY8E6utipqtFMwjwePWsTOhkMRkUEqPXiY2f1mNmlmkwAeBfBRt+rqUgBvJusdJOskT3c/jwC4AcDessZbqxEnGu40v5RpqxONROZRT69zjNazV1sVldWeRESkLFVXVp8AcD2AfQDeBnCbf4DkHhdgxgB81QWOGoCvAfjTsgZYj4jjzfhRsG58ne65sZpHrVufGMnMPBYXPHwgSmYeIiJlqTR4uFVWOzMem3Tf3wLwnjLHFapFwbQVw8yj3eQQQKK2kd79NtzzMd/M45ldV3VeI+QDVHKfh4hIWarOPJa8GtmZHooSO8yPNdqn/I1lBInYUt3gd+ebeWw8JX3fx4gyDxGp2HJZbVWZWrgzPDy3g+ycLx6ftupdtgskV1st7o9dNQ8RqZqCR47kxkCPBI65KaX5BIz6HPs8iso6DEpEpCz6FMpRi3XSjQeStMwja2/HYvZ5JIXBSkSkCvoUyhEWyeMF827wmFfBfI7eVkWNKHiISMX0KZQjdnpgctrK7f/IrHlkdNJdbObhf/+iresX9TwiIgul1VY5kmd4eBG7mwfTelsB2RnCYoMHAHztniuw4ZRVi34eEZGFUPDIkVUwD38em0edI9SP4OGbIIqIVEHTVjliB0BlBI/5FMxDi615iIhUTcEjR9a0VdZZHVnLduPPqT92EVne9CmWIz5V1b0/7FWVVfPIWlKrzENEljsFjxzhB33YGLGeuSQ3yEjq8SCx2p1V3o+ah4hIlRQ8coRTTLWM/lSjGRsAkzWPNWP1nt8VEVmOFDxyhJ//YcG8HjwQNkYkic3rV/fcDwDjY8o8RGRl0FLdHLHMIyyYz5FhPHbHZXhi70FsWhfvitvJPBbZ20pEpGoKHjmy9nnU51hVtX7NKG655Kye5/LBI6ydiIgsR6VMW7ljZj9Dch/Jl0hemHLNyf5sc/f1A5Kfdo/dSnImeOxXyhg3kNjnMY+C+VzGXfCYbaqXuogsb2VlHtcBONd9XQLgAfe9w8wOA5j0t0nuBvBIcMmXzeyOwQ81LtYYMZyqctNZ5PwL4D7zeOt4o48jFBEpX1kF85sAPGRtzwJYR3Jj1sUkzwNwBoB/KWl8mWKHQYX7PNz9I1EEznMaymceRxQ8RGSZKyt4bALwenB7v7svy81oZxrh/M4H3ZTXX5PckvZLJG8nOU1yemZmZvGjRvY+D18kL1K+uPW9WzE+VsdV287oy9hERKqyVJfq3gzgi8HtxwBsNbN3AngKwF+m/ZKZPWhmU2Y2NTEx0ZeBZDVD9EGlSPH7/A0nY+8nr8WZ69LPJhcRWS4GFjxI7vQFbgAHAYTZwmYABzJ+710A6ma2299nZj80s+Pu5mcBvGdAw+6RbMPu+dVW2rIhIsNoYMHDzO43s0kzmwTwKICPulVXlwJ408wOZvzqhxDPOpCoj9wI4NWBDDpFPeNAJ9+GRMtuRWQYlbXa6gkA1wPYB+BtALf5B0jucQHG+0V3behOkjcCaAB4A8CtAx1tILs9ibtfsUNEhlApwcMVvndmPDaZuP0TKdfsArBrMKObW15jRGUeIjKMlmrBfMkIW4+E9Y3utFXZIxIRqZ6CR46wuWHatJUyDxEZRgoeOcLWI1HKSYLz3SAoIrKSKHjkyMs8FDtEZBgpeOQYc6f/Acmuuqp5iMjwUvDIMZY5baWah4gMLwWPHJnneSygPYmIyEqh4JEjLIjXUtqTiIgMI30CFhCFLdl95qE/QREZQvroKyC9YK5pKxEZPgoeBaQVzGsKHiIyhBQ8CoifZ97+rtghIsNIwaOAMMvwZxxqh7mIDCMFjwKiIPNoueChTYIiMowUPBbotPFRAMC1P7mh4pGIiJSvrMOgVpzTx8ew+3euxvqTRqseiohI6ZZF5kFyG8lnSB4neW/V4/FOGx+LTWWJiAyL5ZJ5vAHgTgAfqHogIiKyTDIPMztkZs8BmK16LCIiskyCx3yRvJ3kNMnpmZmZqocjIrJiLZdpq3kxswcBPAgAU1NT1q/nfXTnDuw98Ga/nk5EZNlbssGD5E4Av+puXm9m/13VWCa3rMPklnVVvbyIyJKzZIOHmd0P4P6qxyEiIr2WbPAIkdwAYBrAWgAtkncD2G5m/1ftyEREhtOyCB5m9n0Am6seh4iItK2o1VYiIlIOBQ8RESlMwUNERApT8BARkcIUPEREpDCa9W0j9pJCcgbAfy3w108H8IM+Dmc50HseDnrPw2Ex7/ksM5vIu2jFBo/FIDltZlNVj6NMes/DQe95OJTxnjVtJSIihSl4iIhIYQoe6R6segAV0HseDnrPw2Hg71k1DxERKUyZh4iIFKbgISIihSl4JJB8H8nvktxH8reqHs+gkfwzkodI7q16LGUhuYXk0yRfIfltkndVPaZBI7mK5LdIvuje8yerHlNZSNZIvkDy76oeSxlIfo/kyyT3kJwe2Ouo5tFFsgbgNQDXANgP4DkAHzKzVyod2ACR/GkARwA8ZGY/VfV4ykByI4CNZvY8yZMB7AbwgRX+90wAa8zsCMkRAN8AcJeZPVvx0AaO5D0ApgCsNbMbqh7PoJH8HoApMxvoxkhlHnEXA9hnZv9pZicAfAnATRWPaaDM7OsA3qh6HGUys4Nm9rz7+TCAVwFsqnZUg2VtR9zNEfe14v/nSHIzgPcD+GzVY1lpFDziNgF4Pbi9Hyv8Q2XYkdwK4N0A/q3akQyem77ZA+AQgKfMbMW/ZwCfBvCbAFpVD6REBuBJkrtJ3j6oF1HwkKFFchzAwwDuHoYjjc2saWaTaJ/KeTHJFT1NSfIGAIfMbHfVYynZZWZ2IYDrAOx0U9N9p+ARdwDAluD2ZnefrDBu3v9hAJ83s0eqHk+ZzOx/ATwN4H1Vj2XAdgC40dUAvgTgKpJ/Ve2QBs/MDrjvhwD8DdrT8X2n4BH3HIBzSb6D5CiAmwH8bcVjkj5zxePPAXjVzO6rejxlIDlBcp37eTXai0K+U+2oBsvMdpnZZjPbiva/5X80sw9XPKyBIrnGLQIByTUAfg7AQFZSKngEzKwB4A4AX0W7iPoVM/t2taMaLJJfBPAMgPNJ7if5sarHVIIdAD6C9v9E97iv66se1IBtBPA0yZfQ/k/SU2Y2FEtXh8yPAfgGyRcBfAvA42b2D4N4IS3VFRGRwpR5iIhIYQoeIiJSmIKHiIgUpuAhIiKFKXiIiEhhCh4iOUieFizp/T7JA8Htfx3Qa76b5OfmeHyC5ECWYIrMR73qAYgsdWb2QwCTAEDy9wAcMbM/GvDL/jaA359jTDMkD5LcYWbfHPBYRHoo8xBZBJJH3PefIfnPJL9C8jWSnyJ5iztD42WSZ7vrJkg+TPI597Uj5TlPBvBOM3vR3b4iyHRe8DuIATwK4JaS3qpIjIKHSP+8C8BdAC5Aewf7eWZ2MdrtwH/dXfMnAP7YzC4C8EGktwqfQrylxL0AdrqmhpcDOOrun3a3RUqnaSuR/nnOzA4CAMn/APCku/9lAFe6n68GsL3dXgsAsJbkeHDWBtBuJTIT3P4mgPtIfh7AI2a2391/CMCZ/X8bIvkUPET653jwcyu43UL331oE4FIzOzbH8xwFsMrfMLNPkXwcwPUAniV5tZl9x11zNOM5RAZK01Yi5XoS3SkskJxMueZVAOcE15xtZi+b2R+iPVW1zT10HgbUMVUkj4KHSLnuBDBF8iWSrwD4ePICl1WcEhTG7ya513XEPQrg7939VwJ4vIxBiySpq67IEkTyNwAcNrPMs7dJfh3ATWb2o/JGJtKmzENkaXoA8RpKDMkJAPcpcEhVlHmIiEhhyjxERKQwBQ8RESlMwUNERApT8BARkcIUPEREpLD/Bx5Uo+E1yPKbAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22fce753320>"
+       "<matplotlib.figure.Figure at 0x257948e5fd0>"
       ]
      },
      "metadata": {},
@@ -243,14 +243,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 71\n"
+      "Starting experimental run with id: 103\n"
      ]
     }
    ],
@@ -258,6 +258,8 @@
     "freqmeas = Measurement()\n",
     "freqmeas.register_parameter(osc.periodogram)\n",
     "\n",
+    "osc.dt(0.01)\n",
+    "\n",
     "with freqmeas.run() as datasaver:\n",
     "    datasaver.add_result((osc.periodogram, osc.periodogram.get()))\n",
     "    \n",
@@ -271,9 +273,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22fd07c62b0>"
+       "<matplotlib.figure.Figure at 0x257927cf0b8>"
       ]
      },
      "metadata": {},
@@ -281,39 +283,56 @@
     }
    ],
    "source": [
-    "_ = plot_by_id(dataid)"
+    "axs, cbax = plot_by_id(dataid)\n",
+    "aa = axs[0]\n",
+    "aa.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just for the fun of it, let's make a measurement with the averaged periodogram."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 72\n"
+      "Starting experimental run with id: 104\n"
      ]
     }
    ],
    "source": [
+    "no_of_avgs = 100\n",
+    "\n",
     "with freqmeas.run() as datasaver:\n",
-    "    datasaver.add_result((osc.periodogram, np.log10(osc.periodogram.get())))\n",
     "    \n",
+    "    temp_per = osc.periodogram()\n",
+    "    \n",
+    "    for _ in range(no_of_avgs-1):\n",
+    "        temp_per += osc.periodogram()\n",
+    "        \n",
+    "    datasaver.add_result((osc.periodogram, temp_per/no_of_avgs))\n",
+    "\n",
     "dataid = datasaver.run_id"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22fd0958ef0>"
+       "<matplotlib.figure.Figure at 0x2579489db70>"
       ]
      },
      "metadata": {},
@@ -321,7 +340,9 @@
     }
    ],
    "source": [
-    "_ = plot_by_id(dataid)"
+    "axs, cbax = plot_by_id(dataid)\n",
+    "aa = axs[0]\n",
+    "aa.set_yscale('log')"
    ]
   },
   {
@@ -333,42 +354,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 77\n"
+      "Starting experimental run with id: 105\n"
      ]
     }
    ],
    "source": [
     "meas = Measurement()\n",
-    "meas.register_parameter(osc.dt)\n",
-    "meas.register_parameter(osc.time_axis, setpoints=[osc.dt])\n",
+    "meas.register_parameter(osc.npts)\n",
+    "meas.register_parameter(osc.trace, setpoints=[osc.npts])\n",
     "\n",
     "with meas.run() as datasaver:\n",
     "\n",
-    "    for dt in [0.01, 0.02, 0.03]:\n",
-    "        osc.dt(dt)\n",
-    "        datasaver.add_result((osc.time_axis, osc.time_axis.get()),\n",
-    "                             (osc.dt, osc.dt.get()))\n",
+    "    osc.dt(0.001)\n",
+    "    \n",
+    "    for npts in [200, 400, 600, 800, 1000, 1200]:\n",
+    "        osc.npts(npts)\n",
+    "        datasaver.add_result((osc.trace, osc.trace()),\n",
+    "                             (osc.npts, osc.npts()))\n",
     "        \n",
     "dataid = datasaver.run_id"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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PfyQnqRG4EHgNsB64RdJVZjZ5ofT3zOz90z5gTtwYOE4Ath8Wn5uoqTe0gnAU5CY6GnjAzB4CkPRd4CSKyZoxbdwYOE4AGoZDK8jP4OL4RjNFkLO4TbekNSXvV5vZ6vT1UmBdyXfrgWPKtHGypJcD9wMfNLN1ZbYpHDcGjhOAgSXxRRPRHKHmgsgxZ7DFzFZN41D/CVxmZkOS/hz4JvCqabSXGTcGjhOCzviK21CfAwOwwtxEG4DlJe+X8dREcXIos9IMhhcB/1zEgbPgxsBxQqD4eta5C/pDSwhCURPIwC3AIZIOJDECpwCnlm4gaT8zeyx9eyJwTxEHzoIbA8cJwMJFPaEl5KanL758SkWQc85g6nbMRiW9H7iGJLT0YjO7S9LZwBozuwo4S9KJwCiwDTh92gfOiBsDxwnA/LaB0BJyMzDUHFpCMIrK2GpmVwNXT/rskyWvPwJ8pJCD5cSNgeMEoK0xvjmDzrYIQ6AKwhPVTZMMq+1WkMyWz0+3+XBqOR1nVtMQ4ZxBY0N9RhNZcRPINU3VjEHG1XYfB75vZl+W9CyS4dPKamlynFrhoW0LQ0vITd/2OaElBKMeCvtUc2SQZbWdAXPT1/OAjVXU4zg1Q+/W9tAScjNn/mBoCYEQY8VEE9U01TQGWVbbfQq4VtIHgA7guHINSToTOBNgxYoVhQt14mflVz8XWkIuGvvjm64b3tkZWkIQvJ7BzPA24BIzO0/SHwD/JukIM9vNOZku514NsGrVqvicrU7VWXBrXGUJ+5aGVpCfxqHQCgJhybzBbKeaxqDiajvgDOB4ADP7laQ2oBvYXEVdzixkeG7lbWqJti2hFeRnpD4HBoBHE02XiqvtgEeBVwOXSDocaAOeqKImZ5bSsSmuRzeNxaUXYKRr9vvNy2H4BPK0yLja7q+Br0n6IMk5P92sHgZkTtGoPqMeZ5RFv4tvbUQxuRy80tm0ybDa7m7gJdXU4NQHg/PjulljG8kADM6Pa16mSMbH47q+9obQE8iOUwgWWT81NC++zmW8OT7NRWDmbiLHiYbmvrietGOMzBmtzzx1gIeWOk409C6P62a1COdiW3aGVhCOepjJdGPgzApi61xjrCfc84yx0BKC4W4ix4mEoRVxZdS0zrj0AtiO1tASglBUPYNax42BMytYuSyu5SmDo/HVBuhauC20hNw8WkQjPoHsOPEQW3rlsQhDFXuG6ngG2ecMHCcOYqsPsKQzvrKXO4fr1xj4yMBxIqG7rS+0hFz0jsTnf2+oh8fjKaj1aCJJy0hS/rwM2B8YAO4Efgz8ZHLyz3K4MXBmBXdv2Te0hFws7owvnKgekrWVo9ZzE0n6BknJgB8B55Ik+mwDDiVJBPoxSR82sxv31I4bA2dWsKC9P7SEXPQOxzcy2DVQp24iA6vtOZ7zzOzOMp/fCVwhqQWoWAjGjYEzK3j4/v1CS8hHe3xJ39RY476SalLbP/31knaY2fpyX5rZMPBApUYqGoO06MzbSXxR+7G7L+pbZlbH6xKdWsFa4oomUm98z2ENg5Gt7CsM1bSbiGSO4FeS1gKXAZebWe5Y6z1ekZJ+QlKX+ErgM+zuizoWuFLS59N01I4TjLYNccXtN8cXTMRofGWbi6OGRwZm9kFJHwJeTjKJ/AlJt5MYhivMLNPVVunx5B1mNrkmUy9wa/p3nqTufNIdp3jmPVDDd2sZGofj0guw/bA6HRlEsOgsrQNzA3BDWkfmOOAc4MtAJjO+R2MwYQgkdQADZjYu6VDgMJJwpZEyxsJxZpzm/rg616G5td25lCPGUp2FUePGYAJJzyEZHbwV2AJ8JOu+WR2XNwIvk7QP8HNgTXqw0/JJdZzqMLhPXE+tXetGQkvITf++8c1zFEYNP2tIOoTEAJwCjAHfBV5rZg/laSfr/67MrF/SGcC/mNk/S7otl2LHqSKxuV36lsTXscZWM6JQavun/5RkfuCtU4SYZiKzMUijik4Dzkg/i6y2lDObGYusClfbjriinwBG58R1jgvDqHU30SGVVhhLUqX68lmNwV+S+J7+Iy1qfxBwXcZ9HafqqNbzBUxirLWmO5eyjLbFp7koavzy+m9JPwCuNLMnE7Wmi81eCvwJSX99yZ4ayWQM0mXMN5a8fwg4K79mx6kO/fvG1VE1xFfOgNGO0AoCUtvG4HjgXcBlkg4EdpAsAWgErgW+YGa/rdRIpXUGXwO+aGZ3lPmug2QSecjMvp1fv+MUx0hXaAX5UHwLkBntrO0esZqohtNRmNkg8CXgS5KagW6S6M8dedqpNDK4kGQBw3NIVh0/QWJxDgHmAhcDbgic4IwdMBBaQi7G+uObQNZwXBFbhWHU+sjgScxsBHhsb/attM7gNuAtkjqBVTyVjuIeM7tvbw7oONWgs3MwtIRcNMyNpHcpYfncXA+aNcEjhbSiWp9ALoSscwa9wPXVleI4e097S1xx+31DLaEl5GZj79zQEsIRn+3OTXxjVccpQ1PjWGgJuTj5oPiW6XznvlWhJYTDjYHjxME+rXHNGTzcH19Kr8Xz4suu9/uiGqphYyCph/IKRZK2KNOQLpcxkNRuZpmriEg6HriAJMTpIjM7p8w2bwE+RfJjbjezU/NochyAdbvmh5aQi7Z94gsnetGiYjzwM8lNRTRiNR9NVEgsXSZjIOnFwEVAJ7BC0pHAn5vZ+/awTyNJNNJrgPXALZKuMrO7S7Y5hGQx20vMbLukxXv/U5x6Zp85cVU6W7tzn9AScjNqdRpNBDU9MphM2o8+WZaudCHansg6MjgfeB1wVdr47ZJeXmGfo4EHJpIlSfoucBJwd8k27wYuNLPtabubM+pxnN3Y1h9Xsv2d2zpDS8jNE+vjM2C1RhZvyTTaPhE4j6TYzWbgAOAe4NlZ9s/sJjKzddJuQ6VKM3ZLgXUl79cDx0za5lAASTeRnJxPmdlPJzck6UzgTIAVKyqW8nSmySHnnB9aQm7G2uLqqBrj8xIx1h1XxFaRqICRQRZvyTT5NPAi4Gdm9jxJxwJvy7pzVmOwLnUVWbrC7S9JLM50aSJZwPZKYBlwo6TnTF45Z2argdUAq1atimjAFicxro7tWF+7Pt1yjESY2qF1e3zhsIVRzDqDLN6S6TBiZlslNUhqMLPrJJ2bdeesxuA9JEObpcAGknwXf1Fhnw3A8pL3y9LPSlkP/DpdNfewpPtJjMMtGXU5VaArk4exthiMa2BA54b4nmmaBuLTXAgGZE8y2y1pTcn71enDLGTzlkyHHekC4RuBb0vaDGR+tMu66GwL+QvZ3AIckiZO2kBSeGFypNAPSYYx30jLZx4K5CrI4BSPRZicvKU3tIJ8tPRE2LFGKLkocriJtphZqAUZJwGDwAdJ+ut5wNlZd84aTXQg8AFgZek+ZnbiVPuY2Whai/MakvmAi9P012cDa8zsqvS710q6m2QO4m/NbGtW8U51aN8c1wIugJH2uCJdmgbiq2cQY0GewijGEGbxluw1ZtZX8vabeffP+r/7Q+DrwH+SY8BkZlcDV0/67JMlrw34UPrn1AgNI/E9ArbuisuAxXiOux6NMO92URTz35XFW7LXSPpj4FxgMcmCs6osOhs0sy/unUQnNhqG43tqbRqIyxiMt8Q1kgEYi1BzEciKiSaaylsy/Zaf5J+BPzKzvQruyWoMLpD09yQTx0MTH5rZrXtzUKe22frs+KJG5j4alwEbi+8UM7hPfRoDoLCspeW8JQWyaW8NAWQ3Bs8B3gG8iqfcRJa+d2YZXevi6lgB+pbE1VHFGFra3Fd5m9mK4rgl1kj6Holbv/Sh/YosO2c1Bv8HOMjM6thpWD9sOzyujhVgPLIn7fEI52J7D6nfRWeRRFLNBfqB15Z8ZkChxuB2YD7JEmdnljN48FDljWqNXc2hFeRC8+J7rlIdFHgpS0FzBtXGzP50OvtnNQb7AvdKuoXdhx9ThpY68dLYEtdkLMDBRzweWkIuWhriO8f3Pr5vaAnhiMAYSCoX5LOTJJT/ykr7ZzUGf59LlRM1y7u3h5aQmy39cTnhD93nidAScrNPV1yZYQslAmNAkqn0MODy9P3JwF3AGZKONbO/2tPOWVcg3zAtiU5UzGmOzzf8vIXrQ0vIRc9oW+WNaowYayAXldcmBjcRcDDwKjMbBZD0ZZII0NcAd1TaeY/GQNIvzOylZSrp5FrM4DjVprMxrnmOzUOF1COZUZ7REd9opjDiMAZLgQ4S1xDp6/3NbExSxRuk0sigA4qrpOPEwZa+uFwuAAPz4gonmtccV5lOgHt2LQktIQyRTCCTLDq7TdL1JA/sLwf+SVIH8LNKO1cyBnGcAqdQDl0Q3xPgb7bEVeeiIcJba2uEDwmFEcF/l5l9XdLVJKmyAT5qZhvT139baf9KxmCxpCnzBpnZ57PJdGJiQ++80BJy88jDcVVM3We/XaEl5KZ3V3zzHIVRw8ZA0mFmdq+k56cfTaTJXiJpSdZMEZWMQSNJ3eM6DTCuT9ZuWBRaQm7UGleo5q77IyvAANjC+AILikDUvJvoQySVIM8r813mTBGVjMFjZpY5H7YzO5h3a1z+d4D+yNzZzZHVXwDo74xvZXohWG2nozCzM9N/j51OO5WMgY8I6pDxCIvbzHugth/dJjPWEt+tNTQ/wgujKGr48pL0QmCdmT2evn8nyRqDR0jqym/L0k4lY/Dqaal0omTe2rhcLhBfcZt5D8QXTdQa4ZxBYWUTa9gYAF8FjgOQ9HLgHJJiZEeR1I5/U5ZG9mgMsloUZ3YxNDeujhVgzta4DNiuA+PrWGvZVVJtanzOoLGkr34rSd3lHwA/kHRb1kYizJ3oVJv2TZlraNcMDWO1fbdOpnE4PoM7/4740pQURm1fXo2SmtKVx68mmUyeIHMf78bAeRqDC+O7LGIre9k0UNu9Szl2HRZfBFTlJAwZMGrdGFwG3CBpCzAA/A+ApIN5ajVyReK7652qU+ND4rKMtsb1pD2wKC69AG3b6tdPVMsuMjP7jKSfA/sB16a15QEaSOYOMuHGwHkaPcvii3Rp7o1L83hc5RcAGO6Kz4AVRa0/IJnZzWU+uz9PG24MnKcRY0WrOY/G1bs294RWkJ+dh9R4j1hN6uCnuzFwnoaa4rvyY6vONtgf4a3XFte8TGHU/pxBIUR4RTrVRk017CCdgq65ccXtN3fH17Eu7ohv2fQjBbQh6mP1rRsD52kcvDS+Ute9w62hJeRifltcxgtgcLSOuwsfGTj1yNyWuFwuAC9auDa0hFwMjMeX/+mRvgWhJQSjlqOJisKNgfM0Ysy13zsW18hgZduW0BJys2EgvtTmhRHfLZGbqhoDSccDF5Ckwr7IzM6ZYruTgX8HXmhma6qpyanMgR3xdVQDY3E9ad/Tt19oCbnpbukLLSEM8VQ6mxZVMwaSGoELSYoxrwdukXSVmd09absu4C+BX1dLi5OPdQPxrTR9tCcuzYfvsym0hNzc/PjK0BLC4cZgWhwNPGBmDwFI+i5wEnD3pO0+DZxLhrJszsxw37a4qoYBjIzGlV75Fz0HhZaQmzroD6fERwbTYylPlV+DZHRwTOkGaZm25Wb2Y0lTGgNJZ5ImX1qxIq5at8/89PmhJeRmqHt+aAn5mRNZqGaMi3nrYBJ1KnwCuYpIagA+D5xeaVszW02Sl5tVq1ZFZaNjXGlqDXE9ZQO0bo9Lc4yVznYcHtWtVxy+6GzabACWl7xfln42QRdwBHC9JIAlwFWSTpxNk8gRRhAy9+H4rvw5W+N6dOvdLy7jBbDw9tAK8vNwUQ3Fd0vkpprG4BbgEEkHkhiBU4BTJ740s51A98R7SdcDfzObDAHAgrsjc18QX20AgIEFcXWuHY/Hd10MdMd1jotC+JzBtDCzUUnvB64hCS292MzuknQ2sMbMrqrWsWuJti3xLeAabY9v+UnnxrhGBkPz4jvH8x6M71ouDDcG08PMrgaunvTZJ6fY9pXV1BIKa4pvprBpIL6n1ubNcU3ONC7uCi0hNxqJy+AWiWz2W4P4Hk8iwxrjS3E10hnfZdEwMCe0hFzEHvyiAAAUoUlEQVTEeF0Mz4trlXdhmEcTOQUw0B1Xnn2AhpH4noJ2HtYZWkIu2rbGV2e6uTc+zYUR3y2RGzcGVWbHwfG5iRriq21D647QCvIx0h7hQ0KMWUuvK6YZn0B2po1FGIChCOcJ+5aGVpCPhuHQCvIzHOFaxMJwY+BMl6GDB0NLyM3Q9vgWR4zPicupG6MPuqErwiFjEXiiOqcIuubFV8TE5sZnwFQPd2tgDpi/PbSE3BSx6EwUb7yVrLS9ADgB6AdON7Nby2x3PbAfMNGRvNbMqlJ9yo1BlXn2osdDS8jN4Fh8/uyVHVtDS8jFo/3xFYo5cu760BJy86OiGio+tPT1wCHp3zHAl5mUu62E02ZiMa4bgypzUHt8tQEWt+wKLSE3awe7K29UQ8RYQKi9McLJpIKowsDzJOBSMzPgZknzJe1nZo8VfqSMuDGoMkd1FFGSe2a5cedhoSXkZlFLXIvOelraQkvIzSMDcRncwsiXqK5bUulT/Oo00eZkymV1XgqUMwbfkDQG/AD4x9SAFI4bgyrzyHB8N9DB7fEVXtkwFFdxm0MjPMetMcYcF0SOOYMtZraqwEOfZmYb0iJgPwDeAVxaYPtP4sagyjzQH1+hmCeG4kuVsKg1rpFBa0N8C7ju6IksfrdICngWl/QXwLvTt7ew56zOyWHNNqT/9kj6DknRMDcGMfKbTQeEllAX9HfGNen9q8dWhpaQm/aWOh0ZGGh8+tbAzC4kKQWMpD8E3p9WgDwG2Dl5vkBSEzDfzLZIagbeAPxs2kKmwI1Bldm2Lr6VOg398a2a3r6oI7SEXIwPxnfr9bTXqTGgKhPIV5OElT5AElr6p08eS7rNzI4CWoFrUkPQSGIIvla4kpT4rsjIaN0c3xLkoQXxrYhqfTiuCdnxuAYyAIy1xXctF0bBxiCdBP6LKb47Kv23D3hBsUeeGjcGVaYtvshSFt4RWkF+BuOaP460BnJ8mVaLwIvbOIXQ8Vh8T9n9+8bXU7Vvius8D82L7xzXLWbVWHRWc7gxqDLtm+JbqNO6Mz53QFNfXNE5c7bEZwyat8WXWqUofGTgTBtriG9o3TAc11M2QNMTcYWWji2Ia8IbQE/El5uoKGJMLJgXNwZVpnEwridWgNHIwjQBhpfOCy0hFy1r48qlBDC+OL58SmwsoA0DCggtrXXcGFSZ4fnxlQosIqZ6phnuiutS3vXK/UNLyE3rjggfj28rqJ34boncxHUHRUjv0vhO8Uh7fK6t2Ji3Nr4R40hHfPMcReFzBs606Tkgvo51ZF58T4BNu+LqqIbnx3frWVynuFg8msiZLqPt8V1E4/Pie2odjmvKAIbj61kbd8UXZVYI5hPIThEsiy8c7/iD7w0tITc/f+iZoSXkYrwtvhHjfgfuDC0hNw8V0Eay6Cy+h7q8uDGoMm8+/LehJeTmwd5FoSXk5tgDfx9aQi7u3RlfNttFc/pCSwiHjwyc6fLsOfGVClzWsi20hNzc179faAm5eN2Se0JLyM3hbU/LsFzzXFFQOz4ycKbNg0P7hpaQm0Pb4qvbfNCcJ0JLyMUvtz8jtITcjEU5g1xAbGm+SmfRUlVjIOl44AKS9KsXmdk5k77/EPBnwCjwBPAuM4uvTuQe6B9rCS0hN7f2rQwtITfHdD4QWkIuXtcd31xSq+o1hbXnJpoWkhpJCjm8hqS+5y2SrjKzu0s2+y2wysz6Jb0X+GfgrdXSFIJ1A7Gl04TH+uaGlpCbTYNxVWcbHItvlfc+Lf2hJewFvy6klRgXYualmiODo4EHzOwhgLSiz0nAk8bAzK4r2f5m4O1V1BOErqb4EtXtaI7vCfDhnrhSJfQOxbcyvaNlOLSEMHho6bRZCqwreb+epLzbVJwB/KRSo3c+tolnfvr8aUqbOYYOeFZoCblZuGhXaAm52batM7SEXNiu+EYG25tn/9PxlLibaGaQ9HZgFfCKKb4/EzgToGnePlGthGxZH9+cwc7HukNLyE1sD63N8dlbmgZDK8jPo0U1NPttQVWNwQZgecn7ZelnuyHpOOBjwCvMrKxPxcxWA6sB2vddbnM2Fy+2WjT3xXcVjTfHtyAqOiJ0O4zNCa0gHB5aOj1uAQ6RdCCJETgFOLV0A0nPA74KHG9mmbr4hhFo3xzPndQ4HN9FNNYSnzHQWGgF+WgaiucanqAhQs2F4cZg7zGzUUnvB64hCS292MzuknQ2sMbMrgI+C3QCl0sCeNTMTtxTuw2jRvumeHwCjf3xTcaOt9RpDpoZZLS9Jjy0uZjzcH0Wt5EZGnNjMC3M7Grg6kmffbLk9XF529TIGM1P9BagbmbQ9rgqcAFoSVyROQANu+KK22/eHl+eHxuLbPhVJD4yqEHGx1FPPPHOtiC+mP2Gx+NLR0FsHVVne2gFudFgfGHSheHGoPaw1maGD4onydd4c0ShTynDh84PLSE3HRviGhnESO/y+AwYlxfQhhHlhH9eojMG441iaJ94YrQH58dnDJoH4nsK6t8/rlCX1u3xzSWN1nEFPI8mqkFG2+GJo+KZ4ByP7gxD6/b4bvrxeJ4PAOhfEVpBfjoeiu+6KAaD8dk/NIiuq2ruHGG/F8eTSnftfUtCS8jNSHxeImiM68mtoSu+kcHwqtnfIZbF8DmDWmR0rIHNPfGkHmhcGN+k24K58RUxeWJzXHUvu7rim+Po7ojvuigsl20d2MHojEFnyzAvXfZwaBmZWdcf32P2YXM3hZaQm/al8aw9AWhvjEtvrPx3Qe34nEENMq+xn+P3+V1oGZm5Z87S0BJyc39ffAV5WttGQ0vIRUOEaTAPaomrgFChuDGoPXrH27i59+DQMjJz8vw1oSXkZlV7EWXEZ5YNI3EtlHtZhOf45oEIZ72LwACvZ1B7tGmEQ+fEU5Zx13hbaAm5WTeyMLSE3AxGFk70P/0HhZaQm2e3xhO4USweTVSTjCP6xuMpDPLQcDwL5CZ4eGhRaAm52TIcT1ABwJjFF6a5qSO+1fSwtphm3E1Ue2wb7uCyR14YWkZmtvfFtRgKYNXSdZU3qjG6W+LJVwWweSiuMp0Av9gSj3u2UNxNVJuYwch4PKt6GxTfRXTT/c8ILSE3Le1xxe0P98Xl1gJoimySvjgMzN1ENcdYfxO9t8RTiSu2PPsA7BufaK2Nx3UIEJfahOZdMaouCHcT1R5NA7Dwrnis9I6D4xnFTLDgd/Gk+5igdVc81wTAWITV5GJMrVII7iaqTRoHRpl7dzwplufeE+FN3xZf3ebBJXHNzbT3xOdyGZ4fn2urMDyaqPawxgbG5sZz4yvCJwoNx9dRtT8SV4X5sa74Qo47fxXf2ohisMLdRJIOA74BPB/4mJl9rtAD7AXRGQMNDdN0//rQMrIzP75wPNu8JbSE3GhJXCG8TVvjq3RGW53OGRjVGBlsA84C3lh0w3tLdMaAxiZYEFG+n974kntp3/jWGURnwJbHl82W9fHlrCqMgkcGZrYZ2CzpDwtteBpEZwyspZHhpfFkqGzqicelNUHDxsg6VsCesSy0hFyMdcQ3L9NkcY2+ANheUDseTVR7WAOMdMUju2//+IbWne3xTRQOLIqrc23ujS98t+eo+NKUcGcBbZhh2Wtsd0sqTUi22sxWF6Ci6sTTq6aMN4u+xfHIjixlDgB9L4xwNBPXmjNQfOG7Tf2hFQQkeyDIFjNbVe4LSX8BvDt9e4KZbSxCWlHE06umjLVAz4GhVWRndE58w8vxjvieWls3xXUpx/iQMLJvbBYX+FpB7RTgJjKzC4ELpy+mOsR1BwFNHSMsPvqx0DIyYxEmJHtsSzxzMhO07tcTWkIuWpviC98dGY9vNPNIEY1Y8VlLJS0B1gBzgXFJfwU8y8yCxUhHZwzGTfQPx+MffsmSeKqyTdA7f3NoCbn5/c64IqDevOzW0BJyc0dvXJP0ALcX1VDx0USPAzV1QqMzBgfM2cZXj/hWaBmZuXto/9AScrNpJL6RwbK2HaEl5OJHjz8ntITcvHPpr0JLyM3XC2rHfAVy7dEiY2lE9WPHW+JxaU0wYvG5A94097bQEnJx4rzfhpaQm42jEa3vKRIzGHNjUHMMmVg7Gs9S/p4IK52d1Blf2oGNo3Fdyiub4puMXdJYz4vO3BjMCJKOBy4AGoGLzOycqbYdtUYeH43HjfHCtnhKdE5wec9hoSXkZiiy8JxlLVtDS8jNiNVEd5GT6d9/BliEOcbyEvx/V1IjSbjVa4D1wC2SrjKzu8ttv320gx9sKRvGW5N8Yzi+kUFn81BoCbnpbokr7Uf/eDxBEBM0aPY/HZfFvLjNTHE08ICZPQQg6bvASUBZYzAy3sDj/fGUDNzcE4/WCXp3xWfAuuYNhJaQi13bO0JLyM2crsHQEvaCnxTSio8MZoalQGnR3fXAMaUbSDoTODN92/vfrzr/vipp6QZiSswTm16IT3NsesE1l3LAdBvoYfs1Pxv/ftbyirGd9yepBWNQkTS3R9Xze0haM9VS8lokNr0Qn+bY9IJrLhozOz60hpmgFmoybgCWl7xfln7mOI7jzBC1YAxuAQ6RdKCkFuAU4KrAmhzHceqK4G4iMxuV9H7gGpLQ0ovN7K5AcqJINVtCbHohPs2x6QXX7OwFsjoo2uA4juPsmVpwEzmO4ziBcWPgOI7j1J8xkHS8pPskPSDpw2W+/5CkuyX9TtLPJU07Tnm6VNJcst3JkkxS0BC9LHolvSU9z3dJ+s5Mayyjp9J1sULSdZJ+m14bJ4TQuSckXSxps6Qiij0WTiV9kk5Lz+0dkn4p6ciZ1ljXmFnd/JFMUD8IHAS0kKQ7f9akbY4F2tPX7wW+V+ua0+26gBuBm4FVtawXOAT4LbBP+n5xrZ9jkgnO96avnwWsDal5it/xcuD5wJ2hteyNPuDFJdfE64Ffh9ZcT3/1NjJ4MvWFmQ0DE6kvnsTMrjOziWqvNxO+AEVFzSmfBs4FQucMyKL33cCFZrYdwMxCV9PJotlIqlIBzANqqn4tgJndCGwLrWMqKukzs19OXBPUxr1XV9SbMSiX+mLpHrY/g6KSm+w9FTVLej6w3Mx+PJPCpiDLOT4UOFTSTZJuTrPWhiSL5k8Bb5e0Hrga+MDMSKtbauHeqyuCrzOoVSS9HVgFvCK0lj0hqQH4PHB6YCl5aCJxFb2S5OnvRknPMbNaLlf2NuASMztP0h8A/ybpCLM6SGc5w0g6lsQYvDS0lnqi3kYGmVJfSDoO+BhwopmFzudcSXMXcARwvaS1wIuAqwJOImc5x+uBq8xsxMweBu4nMQ6hyKL5DOD7AGb2K6CNJLmaUyCSngtcBJxkZvEVfYiYejMGFVNfSHoe8FUSQxDalw0VNJvZTjPrNrOVZraSxNd6opmtCSM3U3qRH5KMCpDUTeI2ClleLYvmR4FXA0g6nMQYPDGjKmc5klYAVwDvMLP7Q+upN+rKGJjZKDCR+uIe4PtmdpeksyWdmG72WaATuFzSbZKC5knKqLlmyKj3GmCrpLuB64C/DfkUmFHzXwPvlnQ7cBlwupnV1PJ9SZcBvwKeKWm9pDNCayqlnD5J75H0nnSTTwILgS+l916oB5q6xNNROI7jOPU1MnAcx3HK48bAcRzHcWPgOI7juDFwHMdxcGPgOI7j4MbAcRzHwY2B4wRH0uGSviLp3yW9N7Qepz5xYzDLSesbnFfy/m8kfaqAdlfOVN58SWdJukfStwto65cVvp8v6X170e4cSTdIatxT3v5ydRPM7B4zew/wFpJ8WJP3aZF0oyTPJeZUDTcGs58h4I/TtA81gxKyXn/vA04ws9Ome1wze3GFTeanx8vLu4ArzGwMuAR4WiZWSY3AhSS5+p8FvE3Ss9LvTgR+Afy8jObh9PO37oUux8mEG4PZzyhJYZYPln44+cl+YsSQfn6vpIsk3Snp25KOS9NN/17S0SXNNEn6Zlqd6t8ltadtvV3Sb9KUAl9NO8GJY94j6UvAreyeHG6iytyd6d9fpZ99haTozFWSyv2Ge6fQ8LS20s97J2n5mpJqa9dKmgOcAzwj1f5ZSR2Sfizp9rStqTrk04ArYY95+6esm2BmV6WGaiqD98M9fOc408aNQX1wIXCapHkZtz8YuAB4LnAYcCpJOuG/AT5ast0zgdVm9lxgF/C+NInbW4GXmNlRwBi7d2LPBC41s+eZ2SMTH0p6AfCnwDEkmVffLel5qftkI3CsmZ1fRms5DWXbKrPvISRFdp4N7ABOBj4MPGhmR5nZ35I84W80syPN7Ajgp5MbSZPbHWRma8udzBLK1k2Q9EpJX5T0VZJaCeW4E3hhhfYdZ69xH2QdYGa7JF0KnAUMZNjlYTO7A0DSXcDPzcwk3QGsLNlunZndlL7+Vtr+IPAC4BZJAHOA0uyvj5jZzWWO+VLgP8ysLz3uFcDLSMpj7olyGkYytvWwmd2Wvv7f9Lf9YtI2dwDnSToX+JGZ/U8ZDd0kxmSvMLPrgesrbDMmaVhSl5n17O2xHGcq3BjUD18gcc18I30/yu4jw7aS16U1HMZL3o+z+zUzOcuhAQK+aWYfmUJHXw7NWSinISulv3OMxHDt3pjZ/UoqyZ0A/D9J15rZ2ZM2G2D38zcVmepp7IFWwpc1dWYp7iaqE8xsG0lxlom0xpuAxZIWSmoF3rAXza5QUvULElfSxATomyQtBpC0QNIBGdr6H+CNktoldQD/J/1sbzTsbVsAPSQFg0j17w/0m9m3gM+RFHTfjbRub6OkSgYhS92EskhaCGwxs5FsP8Nx8uHGoL44j7Q6V9qpnA38GvhP4N69aO9e4E8k/Q7YB/iymd0NfBy4Nv38v4D9KjVkZreSROH8JtV0kZlVchFNpWFv2yKtq3BTOln8WeA5wG8k3UZS/e4fp9j1WtIyjZqirsBUdROy6AKOBWqhxrUzS/F6Bk60SFpJ4sc/IrAUUlfSB83sHVVq/wrgw14BzKkWPjJwnAJIRyPXTYTRFknqUvqhGwKnmvjIwHEcx/GRgeM4juPGwHEcx8GNgeM4joMbA8dxHAc3Bo7jOA5uDBzHcRzcGDiO4zi4MXAcx3FwY+A4juMA/x+jylXZKBt5VgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x22fd1c1a390>"
+       "<matplotlib.figure.Figure at 0x25795c74c50>"
       ]
      },
      "metadata": {},

From fb6b62e0248acb938351851a7f26cd77c60ce80b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 25 Sep 2018 09:33:23 +0200
Subject: [PATCH 101/719] If to elif

---
 qcodes/dataset/measurements.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index 78d7d68fc60..a1c2242b013 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -156,7 +156,7 @@ def add_result(self, *res_tuple: res_type) -> None:
                     self._unbundle_arrayparameter(parameter,
                                                   res,
                                                   found_parameters)
-                if isinstance(parameter, ArrayParameter2):
+                elif isinstance(parameter, ArrayParameter2):
                     self._unbundle_arrayparameter2(parameter,
                                                    res,
                                                    found_parameters)
@@ -653,7 +653,7 @@ def register_parameter(
                                           setpoints,
                                           basis,
                                           paramtype)
-        if isinstance(parameter, ArrayParameter2):
+        elif isinstance(parameter, ArrayParameter2):
             self._register_arrayparameter2(parameter,
                                            setpoints,
                                            basis,

From 0aa26b26414407a4d444dd29eb8eebb89754bd68 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 11 Oct 2018 10:17:53 +0200
Subject: [PATCH 102/719] First attempt of porting SignalHound driver

---
 .../signal_hound/USB_SA124B.py                | 31 +++++++++++++++++--
 1 file changed, 29 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index 01f24221d51..ce41b97323a 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -5,13 +5,15 @@
 from enum import IntEnum
 from typing import Dict, Union, Optional, Any, Tuple
 
-from qcodes import Instrument, ArrayParameter, Parameter, validators as vals
+from qcodes.instrument.base import Instrument
+import qcodes.utils.validators as vals
+from qcodes.instrument.parameter import Parameter, ArrayParameter, \
+                                         ArrayParameter2
 
 log = logging.getLogger(__name__)
 
 number = Union[int, float]
 
-
 class TraceParameter(Parameter):
     """
     A parameter that used a flag on the instrument to keeps track of if it's
@@ -317,6 +319,25 @@ def __init__(self, name, dll_path=None, **kwargs):
                            vals=vals.Enum('log-scale', 'lin-scale',
                                           'log-full-scale', 'lin-full-scale'),
                            parameter_class=ScaleParameter)
+
+        self.add_parameter('frequency_axis',
+                           label='Frequency',
+                           unit='Hz',
+                           get_cmd=self._get_freq_axis,
+                           set_cmd=False,
+                           vals=vals.Arrays(),
+                           snapshot_value=False
+                           )
+        self.add_parameter('freq_sweep',
+                           label='Power',
+                           unit='depends on mode',
+                           get_cmd=self._get_averaged_sweep_data,
+                           set_cmd=False,
+                           parameter_class=ArrayParameter2,
+                           vals=vals.Arrays(),
+                           setpoints=(self.frequency_axis,),
+                           snapshot_value=False)
+
         self.openDevice()
         self.configure()
 
@@ -657,6 +678,12 @@ def get_idn(self) -> Dict[str, Optional[str]]:
         output['firmware'] = fw_version.value.decode('ascii')
         return output
 
+    def _get_freq_axis(self) -> np.ndarray:
+        sweep_len, start_freq, stepsize = self.QuerySweep()
+        end_freq = start_freq + stepsize*(sweep_len-1)
+        freq_points = np.linspace(start_freq, end_freq, sweep_len)
+        return freq_points
+
 
 class Constants:
     """

From 35d9bd868b4ec036222b69de1fc9215fe1d129da Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 12 Sep 2018 15:23:33 +0200
Subject: [PATCH 103/719] example of new signal hound

---
 ...example with SignalHound  USB-SA124B.ipynb | 371 ++++++++++++++++++
 1 file changed, 371 insertions(+)
 create mode 100644 docs/examples/driver_examples/QCoDeS example with SignalHound  USB-SA124B.ipynb

diff --git a/docs/examples/driver_examples/QCoDeS example with SignalHound  USB-SA124B.ipynb b/docs/examples/driver_examples/QCoDeS example with SignalHound  USB-SA124B.ipynb
new file mode 100644
index 00000000000..92fcc566e14
--- /dev/null
+++ b/docs/examples/driver_examples/QCoDeS example with SignalHound  USB-SA124B.ipynb	
@@ -0,0 +1,371 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import qcodes as qc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument_drivers.signal_hound.USB_SA124B import SignalHound_USB_SA124B\n",
+    "from qcodes.dataset.measurements import Measurement\n",
+    "from qcodes.dataset.plotting import plot_by_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialized SignalHound in 6.08s\n"
+     ]
+    }
+   ],
+   "source": [
+    "mysa = SignalHound_USB_SA124B('mysa', dll_path='C:\\\\Program Files\\\\Signal Hound\\\\Spike\\\\sa_api.dll')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'vendor': 'Signal Hound',\n",
+       " 'model': 'sa124B',\n",
+       " 'serial': 17172185,\n",
+       " 'firmware': 'Version 3.13'}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mysa.get_idn()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-93.19955444, -91.95262146, -91.95391083, ..., -93.46353912,\n",
+       "       -92.25684357, -91.50266266])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mysa.freq_sweep()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mysa.frequency(2e9)\n",
+    "mysa.span(0.5e6)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 638\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bb3eb73908>], [None])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.n_avg(1)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
+    "mysa.prepare_for_measurement()\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 639\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bb3eb74c50>], [None])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.n_avg(10)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
+    "mysa.prepare_for_measurement()\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 640\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bb3fc6e518>], [None])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.n_avg(100)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
+    "mysa.prepare_for_measurement()\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 641\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bb410e2390>], [None])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.frequency(3e9)\n",
+    "mysa.span(1e6)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
+    "mysa.prepare_for_measurement()\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: saBandwidthClamped\n",
+      "Starting experimental run with id: 642\n",
+      "Warning: saBandwidthClamped\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bb3ff66518>], [None])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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rQhtGstz1+VRu+GByoHMnzF8NwOs/z0Mi1NKW7TuZu2JjyvKMnesohPfGZsdUs3OXMnDkbDYmYYd//7eFnDXol6jHI53tH45bSNNbhrB5W5bmnSTRczjvlbFFagZ3xhSDiFwhIpNEZBKOj+ETdZgNzAVaA4uAiao6R1V3AJ8B+2ZKJsNIDGXQ6L8Y8N2fCV/5xs/z+XiCf+TKwlWbeHjojEDRRFe9O5HDHh+VcP255ovJS3hs2CweHx7fiR7Jiz/M5ecERjZPj3C+n9CII+OUABNjxhSDqg5U1c6q2hmYCfQCEJF6QCtgDvAbUENEQkOAw4HpmZLJMIIgrvdZFR76aiaPD/8jreVf9tZ4Xvh+Dq3v/JqFqzbFPPfHP+OvwZBpX8iMpesSDond4s4a37Q1eC8+aA1/LS8crshQkMLaTds5sP93TF28NuUyV27Ymr2RTwyyZUq6HzhARKYAI4CbVXWFqu7EMSONcI8J8GKWZDIMXzLtY9ix02kCt+7YxaDRc3wb9niN5Ky/13PeK2MTrnv0nytoessQFqzcxMQFq1m+PnYve8zsFRz19A+8lWBIbCZ1Ve8nR+d/Xrt5e/ZGChGEvrcxf61g8ZrNDBw5O+UyuzzwLac+/1PK5aRKVhSDqi5R1T6q2kFV26vqW55j36hqR/fY+aq6LRsyGUZhQ5DAobLJmGgAVm10/l4TFqzmpP/9xNHP/BDz/JB/Y8bf6+OW/cc/62l6yxAmLFidvy+Tob+qSqd7h7Nle5ad1RnuOUxbsi5se+6KjVz5zgS27cjefdrMZ8OIQrp7vcvXb+X053+O2cMN9ULHzl3FgpWbMtawqnt3y9dvDbTWwYYtO9i1K/YTGTXLmYI0dMrSrIT6fjxhceYrKQTc+snvfPn7UsbNDzYbPh2YYjCMCGI1xjP/Xsd/v0nO5/Dmz/MYO28VKzcGGxQf8tjIqMciG95Zf69nUpKL2ZwwcEz0etz3wZOXcPPHv8csxyvTC6P/Ago+y3mxIqwSVCa2eE/mMMVgGFHwc7ieNPAnnh7xJ1t3hDsId+1S1m7aHrs8n30iu/d//8dyznoxephmLPo+NZoTYzTwkWGwQfjzn/Xc+dnU/O0PA+YHEhHmrww51cPr7ZnBCKtdquzYGW5uufrdiVz6ZuFfJ/rXOSsZNu3v/O2/124pcC/ZxBSDkRUmLVzDmk1Fw30Ua8QQmnwV2dD2/u/3dLpvOMvWb0m63sTWf1DPp8TtNkFMPZe8OT6xMt33XGUUOe+VsbS4fWjYvsGTlzBsWmZThiRiNtuxcxeXvDGOd34Nn09yxqBfuNTzvHs8PIJ7vsjdvAdTDEZWOHHgGP714q+5FiMhYv3fL35jHIe7vd9VG7cxZ7ljIlm2Lr7/IBVe/2kef/wT7gj2lvvF5CXMWLqOXPDhuIUA/OLJDJvNvFOhUcq7GZrEd+agn7ni7Qm7dyic8txPfD7J8XWE7vWziYu5/8vpviPOL35fwvDp/3Dbp1MAp8MULZpp5MzcZXiwNZ+NrDE9Rw1WosQyu4T+69//sftPG2vIv33nLsrkOf2vaD37RBRGaPZsr9Z1fY9f9e5EAOb1P4ZJC9dQrUIZVJVnIibpxavzsWEzA8+4Hj9/FX+v3cpfrnL02v4T0Qvp8lff+skUuu9Vk+Z1KvseP+rpH+jWtAb3ndA+oXJ/meMovKrldzeb4+evDjtn5YatXPv+JAA+Gr+Ib68/lDpVyuUf90YWvTt2Abd+MiVuvbnI2WUjBiNj/PDncv714i9xo1ki2b5zFwtWxp74VVj48vclMY8/OGRGzONCbFNQkIZ1exTFdOLAMRz2+CjOfunX/BFNUAaO/CvQeV9PXcopz/3MFe9M8D2+etM2Hhs2M6G6vXzgjkIiiae0YqXimLF0HW/8PJ9/1iVn9ov1a97kmZy2dvN2Bk+O/vuIpxQWr9nM/JWpp0NJBlMMRsa44u0J/PTXStZvCZ4vZ9Dov2h5+1AOeWwkK3MwcWnZui3cNdhxuAbpqV35zkQWr9kc9fjvi3b3nqNNZNuZoOIMXRfit3mro54HsHRtwQYwXZ3Qy97yVwghvprydwElM+T3pTEd5V7+85F/JNTImctirkMxeNISWt85NOpxgP0eGhG2vW3HLq5/f5Lv97nE7zvOgpns0MdG5SQNvJmSjJyzedtOdqpSuVxpHvpqd+9y3ZYd1KpcLsaVwVi2bgtVypehQlnfxL1h3P7ZVBauchoBb+PZ9JYh/Hv/Jr4N6qNf755sFkuZ+B16/7eFvPFzyVpoJzS62LVLKVVqd6uXSPv30FexR2K7lMAT3zZv28mGrTuYvHANn0xczNrN23n5/G5h5/hmG474Qsf5KOjIe0omOiwX2IjByDn79x9B+7uH8ec//rNrD3rkO458arTvsSB0f2gEZwYMA41l9no9SgPuTfh23IDwBsRbmp/S2BpnNuvGKHlzvpuZ2/WsBk9ewtApS1Mqo9ltXzFtye78QomMYnbEGWV96GOCWrp2s+9v7PQXfqbbg9/mb/v5wlZ55p5Ea9qX+aQX+XXuyrCV3jZtKxqrvgUeMYhIJWCLm9/IKAHMX7mRSuVKUzsNvfZYrHHj/y99yz88ctHq6KaaoEROhtq6Yydl80ohIrz1y3yO6bAnNSqVzWiOn2TCSrPFyg1befTrWdx7QjuOejp6mozZyzYwe9kGrnad3Kny65xV5JUSWu9RNS3lhVjv42PY/+HvCuzbsn0nU9zkdxe94cx38DO9Jcuwaf+w871J+dtv/1r4V6iDGIpBREoBZwJn46yVsBUoJyLLga+AQaqaeD5ig127lF/nrmL/5rVyLUpMDn1sFGXyhD8fPDqlcpJtEDO1yP36LdvpcM9wrjq8BUe135M7PpvKiBn/8Gq/7mmXyXt6NnPdvDpmbkLn9x86kw/HL6JLkxoxHbu9n/w+VdHCePCrGezcpfzwn8PSWm5QWt/5dVrL805SC/HtjN3zKJL5RYeiobJJLFPSSKA5cCuwh6o2UtW6OAvr/AL0F5FzsiBjseO1n+Zx1ou/pG2t3tUbt8WddZss291MoMvWbUl48pYUwsWTPx6/iHFuiOGz381m5UZn+L/KfX7ehj8diklxFNGKDVt5dcy8lMsLyr1fxM5eH3lvuRrLhBzvl745Pi0LEmWDdTGCKR6IE4U2e1nyKcOz6Z+IZUrqraoFWhtVXQV8DHwsImUyJlkxJvQHWLo2dRMJwD73fwM4seuZorsbwZHJOrLBDR+Gr6q2OqRQMzQ62bR1Bx3uGZ6RslNhdcQs9FCTEzSPU7rJ9ByXVEJmCwvLsxilF3XE4FUKInKQiPRzP9cRkb0izzESp6QvNv/wVzNoesuQ/O1cjy+279zFyFm7J675rVWQ6Ff2Zwo9xEzijf6C3bN2H/m68DSg3kmEqRJ0XoYf0Ub2scKUM8HV707kkyirAqabuFFJInI3cDOOSQmgDPBW9CuMeOTawrJ1x07WbUlNp6/euC1m6oV1W7azdnPsOl4YPSdsO9L0FM0UtWHrDqZ7ctav27Kda96byNrN21m2fgsH9v+OOe4qX9OXBOuJKgUVwUyfNQiKqzIvjGGU/05iIaJMcPEb/kn44q1lkQmuD7iOeKoECVc9CTge2AjOojtAlUwKVVIYOSs3IYfnvPQrHVM0b5wwcAxHPf0D/YfO5DKfZGuHe7JoehvTJWs2M3FB7AlZIaKtLnbha79x9DM/5IeWvvLjXD6ftISXf5zLkN+XsnjNZga5SifenzfUHG5IYtH64sT7UWYYGyWTIIphmzqeKoX8sFUjBUKN0ahZuUmSFW+mbCxCkTUL3LWKn//+L772icRYscHfVn3QI99x0v9+YrSPmSDS+Xj6Cz/7ljF2nn+UxjMj/swPPXzvt4IN3fPfRzcnzFm+MSxWPRU631f4fAqGkQhBFMMHIvICUF1ELga+xdZljsrOXcrsZfGXQYzHjp27uOvzqWlzUE9dvDZ/ha1UePGHOfFPisBrow3NS/JbrzhoaohIc453+5MYq3r1H1rQfu4tKl1rB6/JUISYYWSLuIpBVR8HPsKJRGoF3KWqz2ZasKLKs9/9Se8nRzMrxhq5Xtt5tJDIX+as4o2f5xfIFaOq9B86M+Gwt2Of/ZHzX/0toWv8COKbiDTL/CfOyl+xiKUs4vlqvI5twzCCEyglhqp+o6o3qeqNqvpNpoUqykxY4MywHT9/daA4+Pu/nMEH4xYWSN0cmhQWWcQ/67by/Pd/cd7LBdc2iNYQvvyj/2SnoD4ObyI4iJ5m+u7Pp3LDB5N5NI2RLR3vGRb1WDzndhC8uqWY+pWNJDj0sZFJzd0pLkRVDCKyXkTWRXtlU8iiyG2fTmGvW7/i2RGxJ4e/MmYu//nod176cS7bduzijs+mRHW6etmZQHjM/V/6T3bq9+pvXPrmuLjph48fsDsT5votO/Lt+JG8/vN8Pp6wKCz1cKpE5gp6xaPkej3hzMJNpUHPdYSYUTiZv3IT3R8aQfcHR8Q/uRgSax5DFVWtCjwF3AI0ABrihK4+kEglIlJNRL4QkckiMs0zJ+IwEZnkeW0RkROTv53MsmLD1oRnwz4RcOH4VRu38e2Mf3jrlwXcMzj6kn7pzrczbNo/3OIx9XwUZ13fd35dkNMZqvd5lNzKjdtYsmYzz49KPkY9DBsyGAYQzJTUV1X/p6rrVXWdqj4HnJJgPVcA01W1E9ATeEJEyqrqSFXtrKqdgcOBTUChDOmYvmQdXR/4lvd9ol28pNIBDa0/MGTK0vzlCSMVQag3ns64c68Z/8YP48dJZyuWOggXvT6ObSksmu7V88msi2AYxZEgimGniJwtInkiUkpEzgYStRUoUEUcr2tlYBUQGTh+KjBUVbO6dNfaTdsDrTD2pxtpNOavlXHOTJ47P989UvhqSsEQUNhtPonGm7/Mz1jyuaDEWj0rFfxmwoZyHSXLVZ4soekKVzWMok4QxfAv4HTgH2AZcJq7LxEGAG2AJcAU4BpVjezmnQm8G60AEblERMaJyLjly9MT/79iw1Y63Tecp+L4ARIhns167abtGW247/xsasLZGNNtZx861V+ppcoQn2U0/1mXvvwx4+ZnP4ulYRRGgoSrzlPVE1S1tvs6UVXnJVhPX2ASUB/oDAwQkfwE7CKyJ9ABiBqCoqqDVLWrqnatU6dOgtU7bN+5iyG/L81vmENx68My1JBFsmHrDjrdN9x3wZdBo/3nB0TTIbEa87Ne/IUvoqw1W5RDOD8Yl9k8MZku3zCKCkFyJTUUkU9FZJmI/CMiH4tIwwDXXRFyKuP4GD5Rh9nAXKC15/TTgU8znZTvmRF/csU7Exg69W+WrduSVN6bVHr7GxJY+zgo66PMK3gyoNMbcp+8zjCMwkUQU9KrwGCc3n4D4At3X0xUdaDHsTwT6AUgIvVwJsp5u8hnEcOMlC6WrHHCMi9/e0J+GmlHpvDznhw+ix4PjeDrqX/z0+wV7jkFm8/pS9bR9JYhYYnaYjWysVIyJMPy9VvTktJ5+05N2bwVa9UvwzCKFkGW9qyjql5F8JqIXJtgPfe7103BaTtvVtUVACLSFGgEpHdpqASIbBOf+W42AJd5lpqsWalsgeu+nuqseTt8+t+MnLWMahViL0/x2k/zEpbNO9PYO9tZcCbhRENVmbxwTdjqUdH4cfYKHhs2i/8c2TruudGIlWnVMIyiRRDFsMJdqS3Uoz8LSCg0x83I2ifKsXk4I5Gcs2j1Jj6f5G+b94tYCekTQXhs2CwADmuVnP8jGlMX725wI5dVjDWRbN7KTZwwcEzU45G8O3ZBSorBMIziQxDFcAFOVNF/cdrCn9x9xYaQleii18f55uD3Es/gUhiXswzCakv8ZhiGS1zFoKoLcNZjKHZEmpASTeUQShY3fenuFBHZUgtFVQEZhlH4iasY3GU8rwKaes9X1WKpLBIhtLj7sGm77fh+7bWqWkNuGEaRIYgp6TPgZZxopORzDxRCRv2RxPoEcWxJfqOOn/9aSbWKsR3TsViyZrOlazAMI2sEUQxbVPWZjEuSAx79elbay/zJJ2XGqz/Ny1/5LBkO6P9dKiIZhmEkRBDF8LSI3I2T3C4//4CqTsiYVBni4wmpz2zdtG0HDw6UU7CBAAAgAElEQVSZnpA/wruCmWEYRmEniGLoAJyLk/001O1Vd7tYkMjcrpGzljMyR2s1Z5pJC9fEP8kwjGJPEMVwEtBMVYtt6snFazYHWrKyuLMszoI9hmGUDIKkxJgMVM+0ILlkw9YdHPVU0UrpsHjN5rSXefV7E+OfZBhGsSfIiKEeMFNEfiPcx1CswlUXr9lM45oVcy1GTtmyvVgFnRmGkSRBFMPdGZfCMAzDKDQEmfmcs+R2hmEYRvYJ4mMwDMMwShCmGAzDMIwwTDEYhmEYYQRJoncgcA/QxD1fAFXVZpkVLftYnjvDMIxgUUkvA9cB44HE8lIXMVJc3dIwDKNYEEQxrFXVoRmXpBCwYNWmXItgGIaRc4IohpEi8hjwCUU8iZ5hGIYRnyCKYT/3vatnX7FKomcYhmHsJsgEt8OyIYhhGIZROIgbrioi1UTkSREZ576eEJFq2RDOMAzDyD5B5jG8AqwHTndf64BXMymUYRiGkTuCKIbmqnq3qs5xX/cCCc1hcEcdX4jIZBGZJiL9PMcedffNEJFnRGw2gWEYRi4Johg2i8hBoQ13wluiiwFcAUxX1U5AT+AJESkrIgcABwIdgfZAN+DQBMs2DMMw0kiQqKTLgDc8foXVwL8TrEeBKu5ooDKwCtjh7i8PlMWZUV0GsAWSDcMwckiQqKTJQCcRqepur0uingHAYGAJUAU4Q1V3AT+LyEhgKY5iGKCqM/wKEJFLgEsAGjdunIQIhmEYRhACJ9FT1XVJKgWAvsAkoD7QGRggIlVFpAXQBmgINAAOF5FDotQ/SFW7qmrXOnXqJCmGYRiGEY8gpqSkEJErgIvdzdXAXaqqwGwRmQu0xvEn/KKqG9xrhgI9gNGZksswDMOITcbSbqvqQFXtrKqdgZlALwARqQe0AuYAC4BDRaS0iJTBURS+piTDMAwjOwSZ4HaaiFRxP98hIp+IyL4J1nM/cICITAFGADer6grgI+AvYAowGZisql8kWLZhGIaRRoKYku5U1Q/dkNW+wOPAc+zOoRQXVV0C9PHZvxO4NGg5hmEYRuYJYkoKrcFwDPCcqn6OE15qGIZhFEOCKIbFIvICTjqMr0SkXMDrDMMwjCJIkAb+dGAYcKSqrgFqAjdlVCrDMAwjZ0T1MYhITc/mKM++rcC4zIplGIZh5IpYzufxOCkrBGiMMxdBgOo4YaZ7ZVw6wzAMI+tENSWp6l6q2gzHjHScqtZW1VrAsTjLfBqGYRjFkCA+hm6q+lVoQ1WHYhlQDcMwii1B5jGsEJE7gLdwTEvnACszKpVhGIaRM4KMGM4C6gCfAp8Bdd19hmEYRjEkSNrtVcA1WZDFMAzDKATEVQwisjdwI9DUe76qHp45sQzDMIxcEcTH8CHwPPASu9NjGIZhGMWUIIphh6o+l3FJDMMwjEJBEOfzFyJyuYjsKSI1Q6+MS2YYhmHkhCAjhn+77978SAo0S784hmEYRq4JEpVkqS8MwzBKEEFWcKvortw2yN1uKSLHZl40wzAMIxcE8TG8CmwDDnC3FwEPZEwiwzAMI6cEUQzNVfVRYDuAqm7GybJqGIZhFEOCKIZtIlIBx+GMiDTHWZPBMAzDKIYEiUq6G/gaaCQibwMHAudnUijDMAwjdwSJSvpGRCYAPXBMSNeo6oqMS2YYhmHkhCCmJHDWX+gFHAYcnGglIlJNRL4QkckiMk1E+nmOPSIiU93XGYmWbRiGYaSXIEn0/ge0AN51d10qIr1V9YoE6rkCmK6qx4lIHWCWa5Y6AtgX6AyUA74XkaGqui6huzAMwzDSRhAfw6FAe1UNOZ9fB6YkWI8CVUREgMrAKmAH0Bb4XlV3ADtEZDJwJPBBguUbhmEYaSKIKWkW0Niz3Qj4PcF6BgBtgCU4SuUaVd0FTAaOcifR1cYxVTXyK0BELhGRcSIybvny5QlWbxiGYQQliGKoBcwQkVEiMgqYDtQRkcEiMjhgPX2BSUB9HLPRABGpqqrDga+An3BMVT/jjCQKoKqDVLWrqnatU6dOwGoNwzCMRAliSrormYJF5ArgYndzNXCXa46aLSJzgdbAWFV9EHjQveYd4M9k6jMMwzDSQ5Bw1e9FpAnQUlW/dSe7lVbV9XGuGwgMBBCR53Cimn4QkXpAK2COiOQB1VV1pYh0BDoCw1O7JcMwDCMVgkQlXQxcAtQEmgMNcVZ065VAPfcDr4nIFJy5EDer6goRKY+jLADWAee4jmjDMAwjRwQxJV0BdAd+BVDVP0WkbiKVqOoSoI/P/i04kUmGYRhGISGI83mrqm4LbYhIady8SYZhGEbxI4hi+F5EbgMqiMgRwIfAF5kVyzAMw8gVQRTDLcBynPkHl+KEl96RSaEMwzCM3BEkKmkX8KL7MgzDMIo5URWDG0EU1Zegqh0zIpFhGIaRU2KNGELrOoeS5b3pvp8NbMqYRIZhGEZOiaoYVHU+gIgcqKoHeg7dIiJjgPsyLZxhGIaRfYI4nyuJyEGhDRE5AKiUOZEMwzCMXBJkgtuFwCsiUg3H57AWuCCjUhmGYRg5I0hU0nigk4hUBURV12ZeLMMwDCNXRDUlicg5IpJ/XFXXeZWCiDT3mpgMwzCM4kGsEUMtYKKIjAfG40xyK4+zzOehwAqcyW+GYRhGMSJWVNLTIjIAOBw4ECcl9mZgBnCuqi7IjoiGYRhGNonpY1DVncA37sswDMMoAQQJVzUMwzBKEKYYDMMwjDBiKgYRKSUip2dLGMMwDCP3xFQMbmbVK7Mki2EYhlEICGJK+kZEbhSRRiJSM/TKuGSGYRhGTgiSEiOU/uIKzz4FmqVfHMMwDCPXBEmJsVc2BDEMwzAKB3FNSSJSUUTuEJFB7nZLETk23nWGYRhG0SSIj+FVYBtwgLu9CHggYxIZhmFE8PDJHbiu9965FqPEEEQxNFfVR4HtAKq6GZBEKhGRGiLyqYj8LiJjRaS959iRIjJLRGaLiOVeMowoDLk6/Tkrj+6wB7Url017uclyy1GtffeXEihXxqZdZYsgT3qbiFTAXf9ZRJoDWxOs5zZgkrtO9HnA025ZecBA4CigLXCWiLRNsGzDMJKgRsUy/O/sLoy74wgkoa5e5qheoUyuRTAIphjuAb4GGonI28AI4D8J1tPWvQ5VnQk0FZF6QHdgtqrOUdVtwHvACQmWbRglAklsoM7w6w7h/Ut6RD3+4nld8z+rJi5PrUqpjTQePKl9/JPi0HqPKkld17FhNQDO7NYoZRmS4dV+3XJSb1DiKgZVHQ6cDJwPvAt0VdVRCdYz2S0DEekONAEaAg2AhZ7zFrn7CiAil4jIOBEZt3z58gSrN4ySR53K5divWa2oxyuWDRKtHp3zD2ia0vVn79ekwL4KZfN8z42muJrXqZxU3fs0qg4kr1hCDPzXvkld16B6haSuK1c6O+a0IFFJb+I06n+p6pequiKJevoDNURkEnAVMBHYgb+vwvcnoKqDVLWrqnatU6dOEiIYRtEm3eaeNnsWbBTP6t440LU39W3FVb1aplcgQKLcZLQBzUMndeDglrUTquM1T289iYFSGL3a1GXuw0enWEpwUpU3KEGjkvYEnhWRv0TkYxG5Jt5FInKFiExylUFlVe2nqp1xfAx1gLk4IwTvWK4hsCThuzCMEsAeVcsHOq9aADt9lfKlfRvhCw9qmqhYaUVj2LT8VEa1imW44MDEplrtXa9K/r0nY0LzUr6M/wgnY2RJMwQxJX0HPAjcCbwEdAX+L8B1A1W1s6sMNolIyCB5ETBaVdcBvwEtRWQv9/iZwODkbsUwii8HtahNjYA2/SAji/tOaOe7P2hDWbdKOQBuP7pNsAuSoEKMRrdZnUrM63+Ms5HgSKqm5zl6b7dSFDOWH6UEald2nkG0UU4skh38aZY0QxBT0ghgDHAGMAvopqr+MWXRaQNME5GZOBFI1wCo6g6cJH3DcFaG+0BVpyVYtmFknINb1qZ3m3pxz6teMTNRNX4903uPb0e/A5sW2P/IKR1pXqcSVco7PoQHTizo5D1pn4ZJyzLo3C6c2sW5/uJDdmfGuezQ5vmfj+tUP+ny/fAqrO571eSjyw6IfrKHr64+ON88dv0RezOv/zGUL5Pnqzz3jvA3VCiTxwvnduE9Hwf+md0bM+6O3sFvoIgRxJT0O84Et/Y4y3u2d8NXA6OqP6tqS1Vtraonq+pqz7GvVHVvVW2uqg8mJL1hZIlHTunIf8/oFNdZWbV8corh6iTs9XmlhA4NqoXte/jkDvRttwcjbuhJ6Tzn731OjybUq1ouKbn86NNuD99ecrQ5CIlyXKf63H50m7DG29tT7tyoelivP1bvu239qvkO28rlCjrbQ6arFnUrFxgtzbj/SPq224MePg78VN09hSU8OBpBTEnXqeohwEnAShyfw5pMC2YYhY0q5ctw8r6+QXMAzOt/DPWrx/YD7B8lSqh8kpO3TtqnAc+ctQ8AZfNKRXUexwt1fem8rhzcsnZ+89uibmUePaUj5/TYXd5dx7Zlv70ym1hZRHj2rH24+JBmfHjZ/r7nRPoh8koVvLeHTurA8OsOiV6P53lMvqsPX151UNYcu6mQqk8kKEFMSVeKyPvAJOBE4BUcc5BhFGqGXRu9YYgkWoMdSfv61WIef+L0zlGP3R/Frp8sitOQ9mlbz91OvtXo3bYeb164X/62AKd3a8QDJ3bI33fBQXvx/qX+jXUmaFe/Wr6iU43ey45Uege1qM2/9mvM3vXCR3fe672fq1Usk1En8hOndeKJ0zoBjtO/Z6s6NKlVKf94LAUWSWGKSqoAPAm0VtVeqnqv65A2jKwRtOH2UrVC8Dj9hjWCWUebuXHz0UwzDapX2O0U9fCv/RpzWtfdAXjxHJ3RIov+r+duO36o+5hOs0S6eqTpEinMnJSkbH6RTq1cpeFtoDPZHa/jOus7N6rOa/26UyZvd9MbqcC8jLujNzf2yX6OqCCmpMeALcBl7uihU+bFMoxwQjNVvdzUt1XC5Vx1eAvf/fGahHiNb+82dcO2y+SFX/DQSR3CeqUDzt6XFnWjT8566kz/kcfNR7YOM+8EJVHlUdht4LGIPrLYzWldG/LFlQdxRFv/gAI/p36QOtJN7crlwiYiHtV+j6zUG8SUdDXwNlDXfb0lIldlWjDD8OPkfXbb+C86eHf8etmAM0Jv6OOvTFSDpXgI9eTP7bF71u7Y23ox8OzEZsCWyysVM3ldkHYnE/3bUEe2ko+jNhGC+kyODxi95JjNUhAoAhGhg09nI0TQOSMhqpQrzb3H+5sKRdLzXR3bcU+ejGGqTCdBvr2LgP1U9S5VvQvoAVycWbEMwx9vSGG50nn5s1i7NK5R4NxI23Nk2OGbF3bnzmOj52z8n09jX6FsHvP6H8OVh++OIqpbtTzlSoebhhLJaxRLBj8SzZkE0LhmxUDnNa9TmVuPas3z53TJ39d9r5qB8xqFGvqODasHOv/EfWIrhvw79Zh5Ii0+8RRG0Eb5pr67o6rqJ5iyYsq9ffl3gilCbuyzN+3qVw18fu3K5QJ3gFIlSC0C7PRs7yR9JkTDSImereoyr/8x/Gu/2OaVM7s1KhB2eHDLOlR1Y/39HLeHt95tHiqbl7k/ZJs9qxRo7MNs33EIoiheOLdLWNK8qGWJcOmhzann6TF/cOn+vnmNvITSUqQ60igoTzrLil3YQS1rM/fho3n7ov04tuOe6as4Clce3pIhVx8c97zQ/JnQ3JFsEDQlxq8ico+I3AP8ArycUakMIwrR/IPHdarPF1dGX68gqt05yoF9G1cPS1hWvWL61yw4xzVHtaxb0Pm4V+1K/HZ7b/5zZHQ/SiK+0uoVy3JE23o0qx1c4QRlwp1H8NK/w5VOJm3wiZadyHMSEQ5sUTuuEklk1FYvQbNUJI1rVWRe/2No3yB2RFw6CeJ8fhLoB6wCVgP9VPWpTAtmFF8SdRpfcOBegcaotauEN941Knkje+LbGyLbAm/jkGg7N+i8Lhyyd/Rkjwoc27E+8/ofkx+xEkmdKuU4qr3Tc+3TbreTNJVGd9h1hzDz/iOTL8CHmpXKFjClBaVahdgKN5QWwxvFE9nQh/w+8SLLsu1Qv/jgvXjvkh4c2KJ2zBxQhZGoikFEyovItSIyAOgG/E9Vn1bVidkTzzCIGb0TjWHXHhKoscpUW9GzVV3euKB7fj6d/PqiVLh/c/9w3L1qV2LGfUdyetf46wYEaXvK5JXKeuK3qw5vEVUZdWlSg5f/3TVqdNB1R+zN1b1ackqXhlF76e0bVOO1ft24K4qv5gD32Wazxw1QSqSA+TKZvEq5INaI4XWchHlTcCa0PZ4ViYxiT7zeU6y1fWNN4vLG/hfs/ftfc2T7PejZqg7X99mbV87vRrM6ldx64nP70W3iTlobdVNPxgfIqdO5UfWo6Zsj1yi4tvfeHNepPqe4NufC2NZ4v2IRiamMerWpl+9UjbyVimVLc/0Re4eNGPzo2aou5aLUcVSHPZl8dx/29QlQyCgJfC+hvFaFhVhPu62qnqOqLwCnAsGn5xnFEm+StEwS2XsUCWbTrVi2NC2jjC4a1fCPyqlUrjSv9etOwxoV6diwOo+dGnyazsWHNOPc/ZvGPKdyudLU8owaYunEoL3JmpXK8uxZ+/jm/inOhExzR3VI3DEcJBV5IiSqjEPBC4VNAUQjlpTbQx9UdUdRGQIVRcqVLsXWHbvSWmbZvFJs25neMtP1o471W/oqQJRGp4bVmLxobUJ1XuLJAloYKO7/pljNxVsX7sfclRsTLrPVHlV8Z5WHyIYdv82eVZmxdF2gc2t6Ahb2b16LW49qzZndEp+cmAtijRg6icg697Ue6Bj6LCLBnowRiNuPSX9O+59vPTyl6x87tWPUY41qJrcsYRDaRsR116xUll6esFFV+PzKg6I2ENGaBr9Ea36EIpG8f+p29atSKuD1uaSouDcPalk7bIJguslEJ/akfRrQsm7lwGtEP3pqRy48aPcEzFAYcLU4admfPL0T1x+R/RQYkURVDKqap6pV3VcVVS3t+Rx8VoYRl6CTj2JRNyKypVbl1NIsnxbD2Xlsx8Ry7fdtF24aSqRnN+HOI6hbtXzgoXuo7GSbhnb1q3L/ie153E16Nv2+vgyOEQabLOlsxAu/yorP4a0c5d8qxTWYM8V/z+jMN9cfGvi3e3rXRvlpzxOhV5t6+SnY98pAaHFQsjONzohJ0EaiSa3oCuSgBNe9vfIw/5xBiRJraB8iUpFkI3Iv2U6jiHBujyb5q6VVLFs68Ggjk3LFotCPFNwv/I5j2vDIKR18TzmlS0Om3ts3ZkK5wkTav8bItSDuOzKh7MDpxhRDEeK2GMsoJpom4YAo4ZFBiFfTo6d0jKnEMtmQFfpGMoMU1pHDRa5/56KDm3FGDBt7SXOm+xHqOFQom5e19Bd+mGIoYrxz8X6+i7H49UT/r2dzPoiSPz+TwQTly+ZRx2PK6tOuXth0/l0RQ4aLPcnwvHgnfiW7qm6zOpWill/cKAxK8fKezdm3cXWO8UQOJbuqXaIUhvtPllAkXiZTryRC4ZCihDHk6uRt1gc0r+3bG/drOG8+sjXdk1hx6+ooqamTpVzpvHybPRQ0Jd1+TPjEpFD0UzJrMETy3Q09C5Rf3ChMI4VGNSvyyeUHZiSFSFAy+TxCpq4OAZMEBqX/KR355dZeWZ98GA1TDDE4vWtmkla1i7MKWCQHB/AfxBoADPjXPgX2xZooFm00EdTxlmzYYGiE0KhmRd65eD8eOaVgZFQ6QhJv6tsq7kI5huHHAS1qM/LGnpwSY4nXZChbuhR7VEstp1I6McUQA+9EmhM6JxaJE499G+/ucezfrFZ+BsUQ1d2wtpP3bVCgF+HXNsbyMSQaRRQPEScFsNc81ClGbvtI/Jr2H/5zGN9ed2j+9gHNa4fN+A1q+ernpj6OtsIawBWHtWDafenNFxSUrk2c2bex5DMKN3vVrlRkUlskiymGgKTb9nemx09QvkxegeyUoca/a5NgpqDQ7/SyQ5vz7sU9Chw/rFVEQrcUO97j7uidbx769vpDeeui/eJc4a27YOWNalaMG+MdhHP3b8q8/sdQJUt27US5pvfejLjhUFr4ZFRNlaKWqM0ovGRFMYhIDRH5VER+F5GxItLec+wVEVkmIlMzLUfQxUbSSWRkQfsG4VNA4vUcY3VMvMsPhs5rVLOCb0K2Ugn0cELNy21Ht+bkAEPmFnUrhzXEPfeuy0k+1+UnlCvmva1Y5JUSmtdJPClgLIp77zUoPfaqxf7NanFHBiaMljSyNWK4DZikqh2B84CnPcdeA7Iyrt8zQRue9+/m7Ys1qF6hQO6VU7s0LNgrB+7zLPf34nldeb1f97DjB7f0T80cSjccJoMrRFPX+dxmD6+SkbBz4uF3WqSSuuSQ5mFLCYZMa8d3iq0sqlUsw798IqdCnNalIWfHWVgnkn/t14RmdSpxapdgM0+NkkeFsnm8e0kPWhaRuRCFmWwphrbACABVnQk0FZF67vZonLUeCh3RemLN61bm62udnD6hsLzqFcqELTsJjn/Aa4c/om29QDOSHzqpQ77D2UkgF85RHfbkk8sP4DSPczyZTuMFB+4VFs7qFwYLMOmuI5h8Vx+a16nMvP7HBJqdGqsXW650KR48yX+iUzQaVK/Adzf0LFQOOsMormRLMUwGTgYQke5AEyChkB8RuURExonIuOXLlyclRLxJYBUChoqpKntWq8C8/sfQ0XW6+rWD1/Xem9J5pahcrjR9ouSb98O7TGU0mfdtXAMRYdSNPRl7Wy/6HdCUWpXKRq3n+j5706Ju5Xx5VeGu49oGCmetXrFsWuz/ZvHIDKGJ2RcXskSBRtElW1MN+wNPi8gknPUdJgI7EilAVQcBgwC6du2alJctXk93j2rlmbvCP+tjp4bV+Gj8oqjXxuohT723bzABPdR1zTpV46QLburmU6lbtTzj7zwi6nnt6lfj2+sP5dI3x/H7orWUzisob74SStKJ+f1NPdO+5q8RHxEJlJrEMIKSsX+xiFwBXOxuHq2q/dz9Asx1X1kl2ZzsZUuX4pweTdi+U7nvy+lpkaVP23q8skcVLu/pv8bBNb2cHn7fdvX4ZEJ0hZQo/U/uSKdGC9jPZ6QQ0m3JxrYEXcDeYmdiE21NiaLGR5ftX2AFO6NokDHFoKoDgYEAIlJdRMqq6jbgImC0qmY9dbdf7pEKZfJ47px9Of/V3wocE+CnWw6nQpk8xLNMX2Rq6GSoXrEsX0ckyapavjTrtuzIl/WkfdI/wa5GpbJc3jO9M5uDEhqjWFRldD68bP+0Ry3liq5NE591XxxoUD1zaemzRbbG/W2AN0RkJzAduDB0QETeBXoCtUVkEXC3qr6cCSHK5JVi9oNH0eL2ofn7SknsUM76ni+5bf2qfHbFgbT3KIZ0tnHf3nAoy9ZtTWOJhQvzMcSnWwltTIsLr/XrRts9i/6qBFlRDKr6M9AyyrGzsiFDiNJ5pRh85YEAHD9gTMINe+dG/jlSBGidYi75ulXKU7dKwaibmm4K6Mi1fzNFtnr0z5/ThfVbtsc/0TCKCD1b1Y1/UhGgRHoKOzaszsatu33fId9DhwbVojqfg3Bi5wa0qleVS94cx6LVm1OWM8Sdx7alXYNqHJLgmguJku4O/RFt6/HnP+sL7A/laTqy/R5prtEwjHRgKTGATo2q88K5XXj01I5h0R3VA4RoenvXIkLb+lXzU+imK91wpXKlObdHkyI3w/XF87oy6qbD8rcTXTPCMIzcUCJHDOD4GwD6ttsj7N1Lx0RS63ravNuPbsP/9Yy/vmth4d2LezBuXqGcY2gYRg4osYqhbOlSjL29FzVSzBvvl766dF4pX19BYWX/5rXYv3kt/lq+gSe++YPjOqU3G6thGEWLEqsYgLQ23sXBTBJKeZFpLFzVMAo35mMwskaqE+gMw8gOphiMrFH0x1SGUTIwxWBkHVtQxjAKN6YYUiQ0U/XAFqkvXF/cKWrhtoZRUinRzud00K1pTWbef2SBdZkNwzCKKjZiSAOmFAzDKE6YYjCyjrkYDKNwY4rBMAzDCMMUg2EYhhGGKQYjazSq6axt4bdgkmEYhQeLSjKyxvPndOHnv1ZSr2rRySNlGCURUww+PHJKB1oUk3V3CxPVK5blqA575loMwzDiYIrBhzO6Nc61CIZhGDnDjL2GYRhGGKYYDMMwjDBMMRiGYRhhmGIwDMMwwjDFYBiGYYSRFcUgIjVE5FMR+V1ExopIe3d/IxEZKSIzRGSaiFyTDXkMwzCM6GRrxHAbMElVOwLnAU+7+3cAN6hqG6AHcIWItM2STIZhGIYP2VIMbYERAKo6E2gqIvVUdamqTnD3rwdmAA2yJJNhGIbhQ7YmuE0GTgZ+FJHuQBOgIfBP6AQRaQrsA/zqV4CIXAJc4m5uEJFZGZQ3E9QGVuRaiCxj91wysHsuGjQJeqJkY/1dEamKYz7aB5gCtAYuUtXJ7vHKwPfAg6r6ScYFygEiMk5Vu+Zajmxi91wysHsufmRsxCAiVwAXu5tHq2o/d78Ac90XIlIG+Bh4u7gqBcMwjKJExnwMqjpQVTuramdgk4iUdQ9dBIxW1XWukngZmKGqT2ZKFsMwDCM42XI+twGmichM4CggFJZ6IHAucLiITHJfR2dJpmwzKNcC5AC755KB3XMxIys+BsMwDKPoYDOfDcMwjDBMMRiGYRhhmGJIkSBpPaKlBHGPVReRj0RkplvG/tm9g8RJwz1f5143VUTeFZEiv9aniJR373Oye2/35lqmVAlyTyJSTkTeF5HZIvKrOx8JETlCRMaLyBT3/fBsy58oqdyv53hjEdkgIjdmS+6MoKr2SuEF7Ans636uAvwBtI045zHgbvdza2CE59jrOHM6AMoC1XN9T5m8Z5yZ7XOBCu72B8D5ub6nNDwTASq7n8vgTNTskWu5Mn1PwOXA8+7nM4H33c/7AH9ocUoAAAaZSURBVPXdz+2Bxbm+n0zer+f4x8CHwI25vp9UXjZiSBENltbDNyWIO/HvEJyQXVR1m6quyZrwSZLKPbvHSgMVRKQ0UBFYkhXBM4g6bHA3y7ivIh3ZEfCeTsDp3AB8BPQSEVHViaoa+l6nAeVFpFzGhU6BVO4XQEROBObg3G+RxhRDGomR1iOUEoSIlCDNgOXAqyIyUUReEpFKWRM4DSR6z6q6GHgcWAAsBdaq6vBsyZtJRCRPRCYBy4BvVNU3vUtRIsA9NQAWAqjqDmAtUCvinFOAiaq6NdPypkqy9+v+b28GirwJEUwxpA03rcfHwLWqui7icH+ghvuDuwqYiJNZtjSwL/Ccqu4DbARuyZ7UqZHMPYtIDZxe115AfaCSiJyTRbEzhqruVGdCZ0Ogu9evUlQJcE/id1n+QZF2wCPApZmTMn2kcL/3Av/1jDiKNNlKolesiZfWw200/VKCVAQWeXolH1FEFEMK99wXmKuqy91jnwAHAG9lSfSMo6prRGQUcCQwNcfipIUY97QIaAQsck2D1YBVACLSEPgUOE9V/8quxKmRxP3uB5wqIo8C1YFdIrJFVQdkV/L0YCOGFAmS1sONPCqQEkRV/wYWikgr91gvYHrGhU6RVO4Zx4TUQ0QquuX0wvFRFGlEpI6IVHc/VwB6AzNzK1VqBLynwcC/3c+nAt+pqrrXDQFuVdUx2ZI5FVK5X1U9WFWbqmpT4CngoaKqFMBGDOkglNZjims2AWdhosYAqvo8TkqQN0RkJ07Df6Hn+quAt91GdA5uL7uQk/Q9q+qvIvIRMAHHnDaR4pFeYE/gdRHJw+lwfaCqX+ZYplTxvScRuQ8Yp6qDcToIb4rIbJye85nutVcCLYA7ReROd18fVV2W3VtIiFTut1hhKTEMwzCMMMyUZBiGYYRhisEwDMMIwxSDYRiGEYYpBsMwDCMMUwyGYRhGGKYYjCKDiOyU3Sv9TYrMbFnUEZF9ROQlz/aRbrbPme79vi8ijd1jr4nIqRHXR511KyJlRWS0OynLMGJiPxKjKLHZTVfgi4iUdvPXFFVuAx4AcFMxPAscr6oz3H3HA01xJgkmhKpuE5ERwBnA2+kS2Cie2IjBKNKIyPki8qGIfAEMd/fdJCK/ibMWxL2ec28XkVki8q0460Dc6O4fJSJd3c+1RWSe+zlPRB7zlHWpu7+ne01oHY233VnciEg3EflJnJz+Y0Wkioj8ICKdPXKMEZGOEfdRBeioqpPdXTfjzJ7NnxWuqoNVdXSAZ3KfZ1S1WERedQ99Bpyd0AM2SiQ2YjCKEhU8M63nqupJ7uf9cRrVVSLSB2gJdMdJeDZYRA7BSVB4Jk4m2NI4M6/Hx6nvQpzsr93ESRk9RkRCmWD3AdrhpAwfAxwoImOB94EzVPU3cdKqbwZeAs4HrhWRvYFyqvp7RF1dCc/J0w4nC20sHhOROyJ3qupdwF0iUg34AQilZpgKdItTpmGYYjCKFNFMSd+o6ir3cx/3NdHdroyjKKoAn6rqJgARGRygvj5AR48tv5pb1jZgrKoucsuahGPiWQssVdXfID+RICLyIU5qiJuAC4DXfOraEycFewFEpBbO2hYVgUGqGlIYN6nqR57zNng+C47J6L+qOt6VZ6eIbBORKu46GobhiykGoziw0fNZgIdV9QXvCSJyLdEXztnBbrOqd5lRAa5S1WERZfUEvGsL7MT5L4lfHaq6SUS+wUk3fjrO6CCSzRF1T8NJyT5ZVVcCnV3TV+Uo9xDJPTiZe1+N2F8O2BKwDKOEYj4Go7gxDLhAnLUiEJEGIlIXGA2cJCIVXHv+cZ5r5gFd3M+nRpT1f+KkGEdE9pbYCynNBOqLSDf3/CqeKKCXgGeA3zyjGy8zcJLOhXgUuF1E2nj2VYxRdz4icixwBHB1xP5awHJV3R6kHKPkYiMGo1ihqsPdxvRn1x+8AThHVSeIyPvAJGA+ju09xOPAByJyLvCdZ/9LOCaiCa5pZjlwYoy6t4nIGcCz4qRt3oyTunmDqo4XkXVAZA8+dO1MEakWMvOo6hQRuQYnQ20VYCVONNLdAR7DDTiLII11n8Fg1+9wGPBVgOuNEo5lVzVKJCJyD06DHc/Bm6766gOjgNaquivKOdcB61X1Jb/jaZDhE5z1EWZlonyj+GCmJMPIMCJyHs6a2LdHUwouzxHuu0inDGWBz0wpGEGwEYNhGIYRho0YDMMwjDBMMRiGYRhhmGIwDMMwwjDFYBiGYYRhisEwDMMI4/8BDFQkq0Ihd0YAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.frequency(3e9)\n",
+    "mysa.span(1e8)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
+    "mysa.prepare_for_measurement()\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mysa.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 82a4e8532b443802968b7a4f2d7efb2dafec4df3 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 11 Oct 2018 10:55:54 +0200
Subject: [PATCH 104/719] Add a typecheck to make mypy happy

---
 qcodes/dataset/measurements.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index a1c2242b013..ae8d6e4e3d7 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -765,6 +765,10 @@ def _register_arrayparameter2(self,
         name = str(parameter)
         my_setpoints = list(setpoints) if setpoints else []
         for i, setpoints in enumerate(parameter.setpoints):
+            if not isinstance(setpoints, Parameter):
+                raise RuntimeError("The setpoints of a "
+                                   "ParameterWithSetpoints "
+                                   "must be a Parameter")
             spname = setpoints.full_name
             splabel = setpoints.label
             spunit = setpoints.unit

From 308af2d8256d75cc9efd644c63f1ab1576b572df Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 11 Oct 2018 13:37:48 +0200
Subject: [PATCH 105/719] rename driver example notebook

---
 ...example with SignalHound  USB-SA124B.ipynb | 371 ------------------
 ...ound  USB-SA124B new Array Parameter.ipynb | 370 +++++++++++++++++
 2 files changed, 370 insertions(+), 371 deletions(-)
 delete mode 100644 docs/examples/driver_examples/QCoDeS example with SignalHound  USB-SA124B.ipynb
 create mode 100644 docs/examples/driver_examples/Qcodes example with Signal Hound  USB-SA124B new Array Parameter.ipynb

diff --git a/docs/examples/driver_examples/QCoDeS example with SignalHound  USB-SA124B.ipynb b/docs/examples/driver_examples/QCoDeS example with SignalHound  USB-SA124B.ipynb
deleted file mode 100644
index 92fcc566e14..00000000000
--- a/docs/examples/driver_examples/QCoDeS example with SignalHound  USB-SA124B.ipynb	
+++ /dev/null
@@ -1,371 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import qcodes as qc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.instrument_drivers.signal_hound.USB_SA124B import SignalHound_USB_SA124B\n",
-    "from qcodes.dataset.measurements import Measurement\n",
-    "from qcodes.dataset.plotting import plot_by_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initialized SignalHound in 6.08s\n"
-     ]
-    }
-   ],
-   "source": [
-    "mysa = SignalHound_USB_SA124B('mysa', dll_path='C:\\\\Program Files\\\\Signal Hound\\\\Spike\\\\sa_api.dll')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'vendor': 'Signal Hound',\n",
-       " 'model': 'sa124B',\n",
-       " 'serial': 17172185,\n",
-       " 'firmware': 'Version 3.13'}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mysa.get_idn()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-93.19955444, -91.95262146, -91.95391083, ..., -93.46353912,\n",
-       "       -92.25684357, -91.50266266])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mysa.freq_sweep()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mysa.frequency(2e9)\n",
-    "mysa.span(0.5e6)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 638\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bb3eb73908>], [None])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.n_avg(1)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
-    "mysa.prepare_for_measurement()\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 639\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bb3eb74c50>], [None])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.n_avg(10)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
-    "mysa.prepare_for_measurement()\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 640\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bb3fc6e518>], [None])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.n_avg(100)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
-    "mysa.prepare_for_measurement()\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 641\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bb410e2390>], [None])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.frequency(3e9)\n",
-    "mysa.span(1e6)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
-    "mysa.prepare_for_measurement()\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Warning: saBandwidthClamped\n",
-      "Starting experimental run with id: 642\n",
-      "Warning: saBandwidthClamped\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bb3ff66518>], [None])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.frequency(3e9)\n",
-    "mysa.span(1e8)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
-    "mysa.prepare_for_measurement()\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mysa.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/examples/driver_examples/Qcodes example with Signal Hound  USB-SA124B new Array Parameter.ipynb b/docs/examples/driver_examples/Qcodes example with Signal Hound  USB-SA124B new Array Parameter.ipynb
new file mode 100644
index 00000000000..2e7b30d6726
--- /dev/null
+++ b/docs/examples/driver_examples/Qcodes example with Signal Hound  USB-SA124B new Array Parameter.ipynb	
@@ -0,0 +1,370 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import qcodes as qc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument_drivers.signal_hound.USB_SA124B import SignalHound_USB_SA124B\n",
+    "from qcodes.dataset.measurements import Measurement\n",
+    "from qcodes.dataset.plotting import plot_by_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected to: Signal Hound sa124B (serial:17172185, firmware:Version 3.13) in 6.15s\n"
+     ]
+    }
+   ],
+   "source": [
+    "mysa = SignalHound_USB_SA124B('mysa', dll_path='C:\\\\Program Files\\\\Signal Hound\\\\Spike\\\\sa_api.dll')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'vendor': 'Signal Hound',\n",
+       " 'model': 'sa124B',\n",
+       " 'serial': '17172185',\n",
+       " 'firmware': 'Version 3.13'}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mysa.get_idn()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-95.04801941, -90.23478699, -90.57860565, ..., -92.38912201,\n",
+       "       -99.49388123, -96.53659058])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mysa.freq_sweep()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mysa.frequency(2e9)\n",
+    "mysa.span(0.5e6)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 928\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d5bf5f1a90>], [None])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.avg(1)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 929\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d5c09ad240>], [None])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.avg(10)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 930\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d5c0a91128>], [None])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.avg(100)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 932\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d5c0aa29b0>], [None])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.frequency(3e9)\n",
+    "mysa.span(1e6)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 934\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "During call of saInitiate the followingWarning: saBandwidthClamped was raised\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d5c0bd2828>], [None])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.frequency(3e9)\n",
+    "mysa.span(1e8)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mysa.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From c78aff5ebb93be14ccbc3badd639cad93cfc2c25 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 11 Oct 2018 13:38:50 +0200
Subject: [PATCH 106/719] move once again

---
 ...xample with Signal Hound USB-SA124B new Array Parameter.ipynb} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename docs/examples/driver_examples/{Qcodes example with Signal Hound  USB-SA124B new Array Parameter.ipynb => Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb} (100%)

diff --git a/docs/examples/driver_examples/Qcodes example with Signal Hound  USB-SA124B new Array Parameter.ipynb b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb
similarity index 100%
rename from docs/examples/driver_examples/Qcodes example with Signal Hound  USB-SA124B new Array Parameter.ipynb
rename to docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb

From ac9f7625fc4d9b368ab86d84b51744a3c46e1151 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 11 Oct 2018 13:47:45 +0200
Subject: [PATCH 107/719] Make sure that data is synced before acq

---
 qcodes/instrument_drivers/signal_hound/USB_SA124B.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index ce41b97323a..a83a6adc711 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -604,7 +604,10 @@ def _get_averaged_sweep_data(self) -> np.ndarray:
         Averages over SH.sweep Navg times
 
         """
+        if not self._parameters_synced:
+            self.sync_parameters()
         sweep_len, _, _ = self.QuerySweep()
+        print(sweep_len)
         data = np.zeros(sweep_len)
         Navg = self.avg()
         for i in range(Navg):

From 3a39607ac87c6e074a041139a2a6386c705f8d66 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 11 Oct 2018 14:37:49 +0200
Subject: [PATCH 108/719] Reneame new array parameter

---
 ...arameter Example with Dual Setpoints.ipynb |  72 ++++--
 ...Simple Example of new ArrayParameter.ipynb | 241 +++++++++---------
 ...Hound USB-SA124B new Array Parameter.ipynb |  74 +++---
 qcodes/instrument/parameter.py                |  13 +-
 .../signal_hound/USB_SA124B.py                |   4 +-
 5 files changed, 222 insertions(+), 182 deletions(-)

diff --git a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb
index a58fd20b436..1845a1fdc40 100644
--- a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb	
+++ b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb	
@@ -19,7 +19,7 @@
     "import numpy as np\n",
     "\n",
     "from qcodes.instrument.base import Instrument, Parameter\n",
-    "from qcodes.instrument.parameter import ArrayParameter2\n",
+    "from qcodes.instrument.parameter import ParameterWithSetpoints\n",
     "from qcodes.dataset.measurements import Measurement\n",
     "from qcodes.dataset.plotting import plot_by_id, load_by_id"
    ]
@@ -52,7 +52,7 @@
    "source": [
     "\n",
     "\n",
-    "class TimeTrace(ArrayParameter2):\n",
+    "class TimeTrace(ParameterWithSetpoints):\n",
     "    \n",
     "    def get_raw(self):\n",
     "        npts = self.root_instrument.npts()\n",
@@ -61,7 +61,7 @@
     "        return timetrace(npts, dt)\n",
     "    \n",
     "\n",
-    "class Periodogram(ArrayParameter2):\n",
+    "class Periodogram(ParameterWithSetpoints):\n",
     "    \n",
     "    def get_raw(self):\n",
     "        npts = self.root_instrument.npts()\n",
@@ -156,7 +156,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 101\n"
+      "Starting experimental run with id: 947\n"
      ]
     }
    ],
@@ -179,12 +179,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x257927cb940>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -201,7 +203,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 102\n"
+      "Starting experimental run with id: 948\n"
      ]
     }
    ],
@@ -221,12 +223,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x257948e5fd0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -250,7 +254,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 103\n"
+      "Starting experimental run with id: 949\n"
      ]
     }
    ],
@@ -273,12 +277,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x257927cf0b8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -304,7 +310,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 104\n"
+      "Starting experimental run with id: 950\n"
      ]
     }
    ],
@@ -330,12 +336,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2579489db70>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -361,7 +369,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 105\n"
+      "Starting experimental run with id: 951\n"
      ]
     }
    ],
@@ -389,12 +397,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x25795c74c50>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -402,6 +412,20 @@
     "_ = plot_by_id(dataid)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -426,7 +450,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb b/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb
index ba8cdbc751f..32d14da0704 100644
--- a/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb	
+++ b/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb	
@@ -4,9 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Simple Example of new ArrayParameter\n",
-    "\n",
-    "This notebook has an example of a new simpler ArrayParameter\n"
+    "# Simple Example of ParameterWithSetpoints\n",
+    "This notebook has an example of a new simpler ArrayParameter replacement\n"
    ]
   },
   {
@@ -55,7 +54,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qcodes.instrument.parameter import ArrayParameter2, GeneratedSetPoints"
+    "from qcodes.instrument.parameter import ParameterWithSetpoints, GeneratedSetPoints"
    ]
   },
   {
@@ -68,11 +67,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "class DummyArray(ArrayParameter2):\n",
+    "class DummyArray(ParameterWithSetpoints):\n",
     "    \n",
     "    def get_raw(self):\n",
     "        npoints = self.root_instrument.n_points()\n",
@@ -125,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
@@ -215,7 +214,7 @@
        "       495., 496., 497., 498., 499., 500.])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -233,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 10,
    "metadata": {
     "scrolled": true
    },
@@ -241,110 +240,110 @@
     {
      "data": {
       "text/plain": [
-       "array([0.31281518, 0.96887871, 0.42191194, 0.51714415, 0.25538286,\n",
-       "       0.92696486, 0.76954697, 0.51712345, 0.48111713, 0.61014766,\n",
-       "       0.42801603, 0.43561301, 0.64152132, 0.24845664, 0.68491269,\n",
-       "       0.57899917, 0.16974739, 0.18945099, 0.77348716, 0.32345713,\n",
-       "       0.32283512, 0.03025562, 0.91810724, 0.57521798, 0.58470989,\n",
-       "       0.38120617, 0.46536914, 0.48650925, 0.30560833, 0.78322953,\n",
-       "       0.93716312, 0.60605105, 0.75239911, 0.96351199, 0.32925186,\n",
-       "       0.71956489, 0.49513814, 0.40072415, 0.71484636, 0.47744867,\n",
-       "       0.99183756, 0.42884688, 0.87046585, 0.97636722, 0.80768148,\n",
-       "       0.47275855, 0.04567205, 0.80345861, 0.16429275, 0.98433221,\n",
-       "       0.31342919, 0.25533898, 0.71032451, 0.03503889, 0.69431169,\n",
-       "       0.85934569, 0.15493456, 0.350528  , 0.13867757, 0.88801413,\n",
-       "       0.12544615, 0.71695272, 0.80233482, 0.95412588, 0.08481348,\n",
-       "       0.88858004, 0.20740531, 0.62895786, 0.7094034 , 0.71663199,\n",
-       "       0.04516419, 0.25645949, 0.3619041 , 0.22624793, 0.03096165,\n",
-       "       0.50486112, 0.38058129, 0.38549274, 0.93265042, 0.69570252,\n",
-       "       0.9715744 , 0.26551269, 0.0278466 , 0.47564881, 0.14327013,\n",
-       "       0.65544137, 0.37554881, 0.81038091, 0.4373319 , 0.63655817,\n",
-       "       0.84999389, 0.48549494, 0.37636679, 0.24317791, 0.46666367,\n",
-       "       0.93993374, 0.2030196 , 0.20372072, 0.55596547, 0.64384574,\n",
-       "       0.55032748, 0.18346428, 0.13315848, 0.4444626 , 0.57653102,\n",
-       "       0.28088507, 0.47296397, 0.19810506, 0.02489858, 0.08292104,\n",
-       "       0.15122852, 0.02681189, 0.1825864 , 0.83923332, 0.82979598,\n",
-       "       0.12532781, 0.28667028, 0.06138414, 0.54201822, 0.28361495,\n",
-       "       0.04006816, 0.55588607, 0.08477299, 0.2226377 , 0.74544186,\n",
-       "       0.248556  , 0.90649953, 0.09848282, 0.63803225, 0.54836512,\n",
-       "       0.09148153, 0.14109837, 0.62075636, 0.52826137, 0.8763575 ,\n",
-       "       0.48961966, 0.24877176, 0.89193214, 0.7763051 , 0.6191885 ,\n",
-       "       0.93309929, 0.48183231, 0.38190092, 0.76675634, 0.31256475,\n",
-       "       0.64081171, 0.23996295, 0.17831513, 0.70911486, 0.3689516 ,\n",
-       "       0.77318391, 0.26551174, 0.9332016 , 0.80833114, 0.94929732,\n",
-       "       0.05899685, 0.14579488, 0.50757545, 0.84313475, 0.67681984,\n",
-       "       0.09312281, 0.17850811, 0.30471426, 0.70653125, 0.94016408,\n",
-       "       0.77204967, 0.78658342, 0.96252718, 0.05135915, 0.6104287 ,\n",
-       "       0.57842423, 0.54790475, 0.18708696, 0.40089462, 0.61792494,\n",
-       "       0.78666442, 0.45664661, 0.57400101, 0.21632045, 0.65836317,\n",
-       "       0.41728132, 0.09314095, 0.39881779, 0.69329242, 0.89670779,\n",
-       "       0.39077226, 0.36796047, 0.51413186, 0.24017188, 0.96503661,\n",
-       "       0.39737534, 0.14966974, 0.05272144, 0.07344577, 0.65384573,\n",
-       "       0.3244456 , 0.93881849, 0.77149932, 0.34256363, 0.57171536,\n",
-       "       0.99633436, 0.7128691 , 0.53621015, 0.20434432, 0.95889797,\n",
-       "       0.14452839, 0.52756577, 0.59106331, 0.3081482 , 0.4549849 ,\n",
-       "       0.20716477, 0.46569662, 0.74935475, 0.43593272, 0.27032474,\n",
-       "       0.76637407, 0.88225447, 0.65898922, 0.80142354, 0.14939703,\n",
-       "       0.54069258, 0.78950072, 0.83605732, 0.62979079, 0.21252879,\n",
-       "       0.60680212, 0.97419554, 0.77182435, 0.75763177, 0.97771958,\n",
-       "       0.91581111, 0.7219678 , 0.96016159, 0.65960683, 0.23570303,\n",
-       "       0.33695877, 0.23561085, 0.82518603, 0.94735652, 0.52801753,\n",
-       "       0.62309437, 0.98345045, 0.35983667, 0.07843121, 0.09146102,\n",
-       "       0.66973072, 0.93494067, 0.05280839, 0.93756376, 0.7293141 ,\n",
-       "       0.50768684, 0.14962689, 0.2658266 , 0.27975887, 0.96575044,\n",
-       "       0.86352484, 0.00677849, 0.60706878, 0.60814407, 0.12029253,\n",
-       "       0.34831538, 0.34223691, 0.58388414, 0.681435  , 0.21728724,\n",
-       "       0.94635087, 0.33127465, 0.64361487, 0.92241564, 0.59245856,\n",
-       "       0.1148949 , 0.7021584 , 0.11996621, 0.64843461, 0.94555061,\n",
-       "       0.70387657, 0.77887487, 0.72795828, 0.68279723, 0.39879606,\n",
-       "       0.1533654 , 0.38515401, 0.71375073, 0.17799896, 0.0252395 ,\n",
-       "       0.06283647, 0.84102243, 0.45017289, 0.29994363, 0.58409358,\n",
-       "       0.80281348, 0.79188333, 0.1162714 , 0.32685503, 0.75942253,\n",
-       "       0.38373489, 0.79152529, 0.97062294, 0.67297296, 0.79336539,\n",
-       "       0.43884364, 0.24561457, 0.65555045, 0.87768973, 0.64103826,\n",
-       "       0.2666453 , 0.1391131 , 0.86647697, 0.61714395, 0.41203585,\n",
-       "       0.11707366, 0.95913778, 0.05950737, 0.74316187, 0.24973516,\n",
-       "       0.55441886, 0.19049959, 0.72943378, 0.14485682, 0.23767495,\n",
-       "       0.23655284, 0.75358185, 0.11639362, 0.22699845, 0.58859088,\n",
-       "       0.19922609, 0.59756317, 0.31772437, 0.31891757, 0.61702834,\n",
-       "       0.2307571 , 0.71656227, 0.77801287, 0.61430522, 0.43949158,\n",
-       "       0.904222  , 0.13305298, 0.97109297, 0.03946522, 0.27349123,\n",
-       "       0.60728714, 0.11184083, 0.20264691, 0.41851126, 0.34208404,\n",
-       "       0.3516932 , 0.72207464, 0.62215237, 0.83973124, 0.61972589,\n",
-       "       0.00746499, 0.67415024, 0.64700587, 0.31843831, 0.85093758,\n",
-       "       0.36074848, 0.26198126, 0.55974394, 0.7755382 , 0.96214801,\n",
-       "       0.60038967, 0.5871515 , 0.97103239, 0.25660708, 0.8394608 ,\n",
-       "       0.61362974, 0.7721306 , 0.41902284, 0.56507279, 0.97512555,\n",
-       "       0.1757342 , 0.12194126, 0.72601828, 0.46489845, 0.20108013,\n",
-       "       0.12265486, 0.32739885, 0.23558657, 0.02516245, 0.90309388,\n",
-       "       0.71132001, 0.35639495, 0.99692481, 0.13415441, 0.65833817,\n",
-       "       0.11460708, 0.51822322, 0.53973366, 0.81536775, 0.13440548,\n",
-       "       0.48233264, 0.25445079, 0.00434424, 0.84816563, 0.02711536,\n",
-       "       0.21425029, 0.07758158, 0.02883053, 0.56527235, 0.19727065,\n",
-       "       0.49378938, 0.66257068, 0.99112211, 0.49515019, 0.45807055,\n",
-       "       0.24407957, 0.80594336, 0.52918217, 0.7366867 , 0.12172603,\n",
-       "       0.50720039, 0.89600683, 0.03299381, 0.87402446, 0.03942597,\n",
-       "       0.4398926 , 0.56602433, 0.1827016 , 0.34461014, 0.48292586,\n",
-       "       0.37908904, 0.05441919, 0.20705179, 0.34375954, 0.6649455 ,\n",
-       "       0.0731173 , 0.10959237, 0.98139206, 0.03566843, 0.00688507,\n",
-       "       0.91668891, 0.91156237, 0.65782263, 0.15321753, 0.12761921,\n",
-       "       0.70015197, 0.5353978 , 0.48899851, 0.92041438, 0.58646024,\n",
-       "       0.24802929, 0.58023147, 0.51882816, 0.49366176, 0.03196893,\n",
-       "       0.56670287, 0.06277128, 0.54048722, 0.85678255, 0.31840659,\n",
-       "       0.60387901, 0.25282476, 0.62576788, 0.34045001, 0.78477856,\n",
-       "       0.19135518, 0.89802393, 0.56130128, 0.54358126, 0.70889138,\n",
-       "       0.23780924, 0.43899571, 0.04051872, 0.73162057, 0.7630328 ,\n",
-       "       0.42628733, 0.5824199 , 0.51831109, 0.5821532 , 0.01817897,\n",
-       "       0.20330086, 0.02747431, 0.15170385, 0.63457024, 0.30606336,\n",
-       "       0.15690499, 0.75987837, 0.87250445, 0.34596773, 0.35767279,\n",
-       "       0.42499385, 0.14849033, 0.79368317, 0.08853673, 0.87720111,\n",
-       "       0.3156627 , 0.73408487, 0.16822168, 0.13283555, 0.35081781,\n",
-       "       0.23496507, 0.11859108, 0.19449802, 0.8220064 , 0.30034061,\n",
-       "       0.61608037, 0.64891939, 0.15747444, 0.40561231, 0.23300349,\n",
-       "       0.11877202])"
+       "array([0.37372587, 0.54861787, 0.53155288, 0.82667582, 0.94220873,\n",
+       "       0.0698023 , 0.89762473, 0.40402402, 0.94676787, 0.72479779,\n",
+       "       0.90016702, 0.50196169, 0.67613499, 0.96529924, 0.15008748,\n",
+       "       0.44617032, 0.77400385, 0.36104437, 0.6286802 , 0.69278277,\n",
+       "       0.12981568, 0.70792455, 0.63326382, 0.79600711, 0.77696625,\n",
+       "       0.60408452, 0.34930137, 0.33298155, 0.99237339, 0.72181006,\n",
+       "       0.24177301, 0.38409252, 0.13977054, 0.46336893, 0.29702444,\n",
+       "       0.32585009, 0.85559257, 0.506369  , 0.79234633, 0.91855242,\n",
+       "       0.91216349, 0.82632103, 0.15051881, 0.38302095, 0.39616307,\n",
+       "       0.32864887, 0.67891701, 0.01545452, 0.32810263, 0.84456954,\n",
+       "       0.30411356, 0.83792683, 0.18423625, 0.76232546, 0.62204577,\n",
+       "       0.28122632, 0.90895218, 0.47174043, 0.0835899 , 0.880365  ,\n",
+       "       0.09111603, 0.50584601, 0.56714919, 0.57147067, 0.85644402,\n",
+       "       0.22211664, 0.05509187, 0.95254653, 0.93371861, 0.97754025,\n",
+       "       0.0207392 , 0.28635372, 0.63367136, 0.0663695 , 0.70019442,\n",
+       "       0.40926177, 0.42111847, 0.85448678, 0.98820638, 0.94779916,\n",
+       "       0.25279355, 0.38715305, 0.30811057, 0.8744069 , 0.50608295,\n",
+       "       0.58995161, 0.58200316, 0.58201891, 0.93918669, 0.6899701 ,\n",
+       "       0.95871603, 0.14629169, 0.80691971, 0.99692501, 0.17309138,\n",
+       "       0.40671056, 0.87719149, 0.68771378, 0.08381251, 0.14822247,\n",
+       "       0.69180229, 0.03358827, 0.30626825, 0.33936864, 0.88937098,\n",
+       "       0.99683227, 0.7537689 , 0.35666654, 0.23870022, 0.80237453,\n",
+       "       0.83317602, 0.56290746, 0.4612086 , 0.59187958, 0.35496415,\n",
+       "       0.76680147, 0.72366515, 0.53806547, 0.73491729, 0.87097106,\n",
+       "       0.22250074, 0.4319    , 0.25040755, 0.4204853 , 0.57152887,\n",
+       "       0.67989035, 0.51488572, 0.12774031, 0.86615569, 0.78384103,\n",
+       "       0.04802505, 0.65392064, 0.96986131, 0.47990375, 0.3774105 ,\n",
+       "       0.06543123, 0.41360314, 0.1389294 , 0.23166719, 0.67111829,\n",
+       "       0.59806864, 0.73826979, 0.4013829 , 0.63824427, 0.44717827,\n",
+       "       0.15403842, 0.39021108, 0.10353845, 0.96664997, 0.41367465,\n",
+       "       0.22059292, 0.17918976, 0.96500464, 0.51467301, 0.89966624,\n",
+       "       0.93987921, 0.94166864, 0.64683108, 0.55641748, 0.9484579 ,\n",
+       "       0.66408577, 0.37486359, 0.65857553, 0.00120706, 0.76021654,\n",
+       "       0.95142316, 0.74106431, 0.3687487 , 0.81576676, 0.89905403,\n",
+       "       0.60252817, 0.52068992, 0.99020434, 0.01758782, 0.26871668,\n",
+       "       0.42934743, 0.74147531, 0.02036298, 0.90464033, 0.67078621,\n",
+       "       0.55625392, 0.61639495, 0.68145904, 0.18213379, 0.03731415,\n",
+       "       0.28217922, 0.82413099, 0.41451497, 0.06819495, 0.67933438,\n",
+       "       0.48485664, 0.02562523, 0.99595955, 0.8278784 , 0.56357999,\n",
+       "       0.45688242, 0.15482338, 0.88100967, 0.75521795, 0.66280644,\n",
+       "       0.43293394, 0.70636499, 0.83458264, 0.16970696, 0.47818101,\n",
+       "       0.08842654, 0.77442703, 0.56663918, 0.24441029, 0.55706783,\n",
+       "       0.08530588, 0.48254471, 0.90653557, 0.86716833, 0.92738514,\n",
+       "       0.29735555, 0.5957369 , 0.34662178, 0.14905592, 0.84934635,\n",
+       "       0.37038126, 0.91797818, 0.10743678, 0.33358987, 0.61751291,\n",
+       "       0.56708866, 0.96416561, 0.9008846 , 0.5365646 , 0.33294292,\n",
+       "       0.7055552 , 0.22700866, 0.21832081, 0.37975166, 0.69098247,\n",
+       "       0.10769966, 0.14557844, 0.14896331, 0.37194517, 0.45441072,\n",
+       "       0.83939733, 0.99817352, 0.36444098, 0.043637  , 0.19484866,\n",
+       "       0.39253182, 0.75448118, 0.35597998, 0.80725856, 0.95531812,\n",
+       "       0.79183714, 0.36342638, 0.45123011, 0.92610209, 0.25520695,\n",
+       "       0.85375401, 0.01310321, 0.48663791, 0.48385191, 0.8494894 ,\n",
+       "       0.76816717, 0.72400051, 0.0430897 , 0.15606243, 0.53290784,\n",
+       "       0.09257425, 0.75460404, 0.74206088, 0.61720626, 0.51084909,\n",
+       "       0.58411453, 0.47311666, 0.53830169, 0.9681255 , 0.79663431,\n",
+       "       0.57598398, 0.77865371, 0.65301602, 0.18408085, 0.80375723,\n",
+       "       0.23633886, 0.58244941, 0.29898102, 0.02252307, 0.98082483,\n",
+       "       0.23015448, 0.70841724, 0.05523285, 0.49451749, 0.35346533,\n",
+       "       0.85056982, 0.33981886, 0.06833761, 0.79821593, 0.11604247,\n",
+       "       0.8953603 , 0.46323585, 0.23137089, 0.44835628, 0.80901788,\n",
+       "       0.42361957, 0.51037851, 0.23792164, 0.7941132 , 0.76321701,\n",
+       "       0.11911182, 0.84181059, 0.60137409, 0.34603324, 0.22177182,\n",
+       "       0.10613795, 0.89584437, 0.64215604, 0.2739601 , 0.76154826,\n",
+       "       0.04708115, 0.81540537, 0.12827219, 0.01783416, 0.62473422,\n",
+       "       0.6454418 , 0.35580161, 0.25226381, 0.15315039, 0.06409371,\n",
+       "       0.82481876, 0.26274587, 0.77689317, 0.28502571, 0.11676692,\n",
+       "       0.21020535, 0.58340203, 0.50298104, 0.66517868, 0.91702034,\n",
+       "       0.41895255, 0.48976598, 0.71751388, 0.55612794, 0.79018665,\n",
+       "       0.08031651, 0.93057851, 0.56953056, 0.04527181, 0.88304957,\n",
+       "       0.70831083, 0.90265482, 0.94031171, 0.2569966 , 0.66892006,\n",
+       "       0.66635678, 0.27309862, 0.03279321, 0.7976703 , 0.50451827,\n",
+       "       0.75593387, 0.35091271, 0.76169186, 0.57906099, 0.98890751,\n",
+       "       0.96141125, 0.25731111, 0.67022349, 0.30738175, 0.22752395,\n",
+       "       0.88743371, 0.6975126 , 0.90094769, 0.55723621, 0.17498322,\n",
+       "       0.23819869, 0.3564374 , 0.80282856, 0.30243351, 0.80448796,\n",
+       "       0.48466329, 0.98000332, 0.02199852, 0.18488749, 0.54550893,\n",
+       "       0.86812643, 0.83792429, 0.89461096, 0.88250996, 0.42808942,\n",
+       "       0.59296736, 0.79860916, 0.30380101, 0.43271408, 0.15841563,\n",
+       "       0.56869869, 0.04184787, 0.10550598, 0.60942529, 0.4265301 ,\n",
+       "       0.57083876, 0.82435567, 0.59211889, 0.96031419, 0.67952074,\n",
+       "       0.37334926, 0.3645627 , 0.00962004, 0.46270116, 0.24393275,\n",
+       "       0.61877639, 0.72515794, 0.74112891, 0.78423151, 0.38865816,\n",
+       "       0.52355693, 0.98749161, 0.0240849 , 0.55514309, 0.01958487,\n",
+       "       0.849384  , 0.59643945, 0.72927759, 0.50640776, 0.47260815,\n",
+       "       0.02341713, 0.18155492, 0.92489797, 0.25403256, 0.10852141,\n",
+       "       0.64066968, 0.77210979, 0.11233653, 0.67487216, 0.66589406,\n",
+       "       0.69246635, 0.81106247, 0.04426596, 0.72241094, 0.61934481,\n",
+       "       0.67953836, 0.33271667, 0.70431201, 0.49538386, 0.70481405,\n",
+       "       0.51488965, 0.35365375, 0.96189495, 0.08626799, 0.65469915,\n",
+       "       0.75678647, 0.13191874, 0.68800181, 0.3976978 , 0.75082862,\n",
+       "       0.9238525 , 0.19926841, 0.52864871, 0.84631558, 0.08041432,\n",
+       "       0.61617623, 0.47972956, 0.41253579, 0.89873016, 0.49668532,\n",
+       "       0.28110437, 0.71319872, 0.38127862, 0.2969393 , 0.6869966 ,\n",
+       "       0.93906833, 0.05512929, 0.46130136, 0.31505291, 0.57178749,\n",
+       "       0.77410248, 0.43127678, 0.46184098, 0.30312938, 0.93041753,\n",
+       "       0.51114825, 0.79740875, 0.76236662, 0.46370778, 0.98322976,\n",
+       "       0.03513679, 0.3587125 , 0.13594183, 0.59490432, 0.77843549,\n",
+       "       0.69222248, 0.89102323, 0.69021418, 0.81951953, 0.13221865,\n",
+       "       0.86460461, 0.83135051, 0.15770558, 0.18465204, 0.17177554,\n",
+       "       0.61530353, 0.64739044, 0.40496839, 0.49301272, 0.86611209,\n",
+       "       0.86025078])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -362,14 +361,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 614\n"
+      "Starting experimental run with id: 952\n"
      ]
     }
    ],
@@ -385,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -401,22 +400,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x2046acfe860>], [None])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d6f0446c18>], [None])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -455,7 +454,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb
index 2e7b30d6726..7c0db5b1a9b 100644
--- a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb	
@@ -29,7 +29,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: Signal Hound sa124B (serial:17172185, firmware:Version 3.13) in 6.15s\n"
+      "Connected to: Signal Hound sa124B (serial:17172185, firmware:Version 3.13) in 5.92s\n"
      ]
     }
    ],
@@ -65,11 +65,18 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1054\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "array([-95.04801941, -90.23478699, -90.57860565, ..., -92.38912201,\n",
-       "       -99.49388123, -96.53659058])"
+       "array([-91.55989838, -98.06058502, -95.64240265, ..., -91.06426239,\n",
+       "       -91.20920563, -91.69102478])"
       ]
      },
      "execution_count": 5,
@@ -93,29 +100,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 928\n"
+      "Starting experimental run with id: 937\n",
+      "2107\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d5bf5f1a90>], [None])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x15228190358>], [None])"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -139,29 +147,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 929\n"
+      "Starting experimental run with id: 938\n",
+      "2107\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d5c09ad240>], [None])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x152281a7c50>], [None])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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NgJjguqqmFuM+W4zqGtd4qUWNhGAkSJCg6DBv5Ra8+vWyfDcDtWyQDCLCE58uxm1vzcXzXy7Nc6uiQ0IwEiRIkCAgagVmYtP2KgDAT6aYqi4iIRgJIsfm7VUov2483puzKt9NSZAgVFgchvi7JEX5ak7kSAhGgsixYM1WAMBjkxbluSUJEoQLgV6gttb4U4fpRUIwEiRIkCAo2FR7EwCTXiBFdZdiJAQjQWxgcTuWIEEdQK3IYQgK8LqKhGC4YN1Pu1BTmyxyCRIkkCNDJDIbIr8iqbGTF2H28s1hNy0SJARDgY3bKjH0jg8x+n/zfN332cJ1+HHD9ohaVdyoyzuvBD9PiFxzTUCl9x3j52HUQ5+G2q6okBAMBTbtMEzkPpi72td9Z439Ekfe90kUTUqQIEGBwRJAECjzuw5vjBKCESIsK4mdVXXX0zMXJDqMBHUNtYLIOqhIqpiQEIwQ8enCdfluQoIECWJEhqvIOPElVlIJtNCkQZLx1g11mVVP8POEyDX/HIZ3QjAiQLum9XMuo6aW8fd3v8OarTtDaFGCYscXi9bj+S+X5LsZCRyQGVH6kbw6xbRTKzag/03vYuO2yhxbFg0SgqGAtVnwI3W3+j4MSf3nP6zDwxN/wI2vzw6htMJAosMIjjMe+wJ/rkNjoa6gVsJhsI8VwElwxkxYiB1VNZjx48Ywmhc6EoKhQLrzA6xxYSyM2yuNDF51YYn9ObDqurj/w/kov268TVmaoHhhd9YzBnouHIYFQmFOmoRghAqj88NYC3ZVGxq0+qXF30UJY5HBPz9eCCBjs5+guCF2Y4bDAK59ZRaOvm+S5/3OtaLQh0Xxr0YFhLRIKoRe32XmCK6nQTCWrt+O8uvG+/YZiRuJ0jvDMRb6wpBAD1YkiAx/Adz0xmy8PG0Zvl+91fP+WsdASP8r0KmSEIyQsGbLzsxiEEJ51kAs0VhkD757AgDg5Wk/hlBzdEh0GBn4kXMnKFzUCKFBci6rltNzpEDpRUIwwsCnC9Zh37s+wruzjXwPm8z8Dx/msOMPMgDLSgpzmBUrY8HMWLbRf5iXrxZvwOQFa5VlGt85NS1BgYAFghFknIscxgMfzk//LlRuPCEYIWDOCiNw2PQldsuGt79ZEbhMS+nlZ10pSRVmdxbr4vjq18sx/G8TMLVig6/7Tnt0Cs554ivXa4r1nSSwo0Zw1guiqBZ1GNOWFKZllIjCXGGKDJZnp1MemZPyO4CVVlmBxyQo1F2TCrN+3AQAmLtiS+hlO8dKguJErSBCchve0yo24PIXZ2RZx4li2qZF4PibEIwQYA0U5yKgsoT5dME6rNrs7pAXZGkt9NSQxabDKCsxpkdVTfixwRKCUTeQ7kdy5y8ufGoq3pq1Alt32vN9i/RDnL+FOpMTghECrJ2zc11R2dqf/cSXGPngZK0y/ShHS0vcu/OjeatRft14LFr7k3aZAPDXd+Zhv7s+9HWPiEJjLEbc+wle+3qZ53VlpUbDK30QDF2imLGWYvx35nLsNK3iosTXSzfi8yTeWagQraTcoBoVzvEi0J+CREIwQoC1MXASCLfkS+s9XP/T48WPSMpD6f32NysBADOWbtIvFMCjkxZh9ZZdvu4RUUibaWbGwjU/4aqXZ3leW8/iMKqDe+5m1W99mzRoyg/rccVLM3HXO0belZ92VaM6Ao4GAE56+HOcOfbLSMr+uSKTllVT5Oq4xDk3MilfC5NiJATDAzo7fEuH4RRBhSF28Kf0dh9kqnb+nOBLJxRAJKXb59a42mKKKCwR5cBb3sMfNIhZgsKAtUnUjlDrGB5ZfhgFPjUTguEBnQ5MKXQYXrtNy7pKWmbKql9/BJV5iKRKU5borMBHZYTwQ8SD7PG8yrdOu3XBW7OCW9cliBdWf3sSjHScOfUaIfMaLzQkBEMBX5Te0jc47vFamEc+mEnLOH3JBrw8NeN4F8SsttSLw0gIRihhW9ygO25kG4EkvlRh4ub/zsap//pcek503NNZ5LNDgYgJmAqfw9C24yKixgB2MnP02rkCgJ9+s9Zp50Kss5t9a9YKHDuwA05+ZAoA4LR9ugIIFvzQS+ltnY7bQqeQ5kAQD2t/0Uf1rrWGyuOTF6WPVScEwxNL12/HK9PjjWjwzBR1WHlRJOWmd7B6VhkKxIECZTDUHAYRpYjoTCIaT0RrAHwHYCURzSGiu4moT3zNjB+6oqAxExbixjeMsNNOgiErwlnu5S/OwKOTFmVfaF2v1QoDZSnCH/49E2Mny8srNeVccXMYhbRr8tOWYJ67uu1gbNtVbXP2TExtvXHRM1PxoBnAMSps3FapLRZMK71TuhyG+6YyvTkpUIrhtiWdAKAXgOsBdGDmrszcDsBBAL4AMJqIzo6hjXmB7tS9+73v04tQFsGQlCJbE9ZuVVsg+fVdeH3Gctwxfp70XFrpHTPBKKSF0F/oaf/l6yu9s+HkMBav25alcC82X5awsSMG8+PfvzQDl784Az9u8A4LU2PjMNRQhYSx6TDAGbPaAqUYbgRjBDPfzszfMHN61DLzBmZ+lZlPBvDv6JuYHwSZl07rI1kZugtKxg8jgx83bMfqLWqHv1ZN6rmWOd+MnlmIHMb3q7ZijcuzhYUgxMvP5GVNg6paZtuOdNOOKmzdWZX+v3rLThx2z0Tc8fZcx33aTcGu6hq8MWN5nSIytdFYHNuwfNMOAMb784I9H4Y3ZizdhJ92ZZz3svww0t+F2WdKgsHM6dFLRMOJ6ALzd1si6uG8JgiIqCURvU5E3xDRV0Q0UDjXgoheIaLviGgeER2QS13+EYJJrFnEGzOWY/mmHXh39kq8ODVb/iobazI/jIP+PgH73fVR1rUNylK2+lT41HTaitusVmfwH33/JBw4+uPI2xI1t6PNYbCdEH21eAMOv+eT9P9N242p9fkP6wOVDwD3vPc9rvz3THwyXx4IsRgRB/FLZ9vUqMrqj0nz12LyArVTpFXUJc9Nx8VPT8scF+p4b87qzGauMOmFt9KbiG4BMBRAXwDjAJQBeA7AsBDqvwHATGb+JRH1AzAGwBHmuQcAvMvMpxBRPQCNQqhPG2GNy5paxpX/nomOzRtgpSIciMwkz4/8vMDHmHbDclH6rt26C7uqa9ClpfswieIdWV7aDcpKbAv6lS/NwP1nDJbe8+3yzTioTxvbMVHcojbV1n+CpaZIZUdl3bFTiYM5lnH3KogSQ538FwDwxeLMJsDZn5ZOq1DtH3TMan8J4BcAtgEAM68A0DSk+gcA+Mgs9zsA5UTUnoiaATgYwBPmuUpm9uee7AObtlfijRnLbceC9JfMa9MaEGtc9BRWkDsRGbNa75akzTEVi8mzUypQft14ZTujRtSDf+O2Suxz54cY/rcJntdaIiM/BNmrD/rf/C72vv0DAPZnfWPmCtTUMl76ammW9/ZLXy117QdSmGr76btKM2ujThKuYkEcopogHIYfiLeobi8kvZ8InZFUyQYfyEDavDYszAJwklnuvgC6A+gCoCeAtQDGEdEMIhqrqpeIfkNE04ho2tq1wVjvy1+cgSv/PRNL1m9LHwvWX9k6DDGapQqysMbpgIYaMltrZ65q8t3vfe9dSIiYWrEBS9dnFIZRTvJ3Z6/EYHOx1kEUbWEWcrA7Bs4LXy3Fda99i6cdppmlJSnXllgchvMaPwuJFQOrfmmJ9j2Fhvmrt2LCd2vS/+PhMIxvX5s1D/glDLKjtbWMzTty0gLkDB2C8TIRPQqgBRFdDOBDAI+HVP9oAC2JaCaAywHMAFANQ1S2N4BHmHkwDO7mOlkBzPwYMw9l5qFt27YN1AhLkbyzKrM6W4MlF6saFo75NdFM73J8LHCqtjZrWOav8hxx6r+mpLMAAtFyNJc897XnNRu2VeK2t+agsrrW14IThlntT2boD6dCvzRFSnl80waloYTMt2JglYaUWEvHaihsHHXfJFzw1NT0/3h0GHLuToZc9YGq/pQRkvs/WoA9b3sfGzzi0EUJT4LBzPcAeAXAqzD0GDcz80NBKySi3xHRTJNINGHmC5h5LwDnAmgLYDGAZQCWMbMVKe0VGAQkEsjMTd3GQXVNLV77ell2bHvnhSxEs/S5+qStJXwRLPnF9RwOfXFbzbDjO27c/vZcjPusAh/MXa3F8VlwvqYbXv/WJtpz4vrXvs2KOlvfFAftqraziiUpUr6P0hS5EIwgVl654+PvVuOgv09IZ5XMF+LkMHSsCXXbU60UFSgKkBx+2/QNySfB0PL0ZuYPAOjz/e5ljYGh3LYsoeoxcyWAiwBMYuYtALYQ0Y9E1JeZv4ehCJ+rLjU3qCanCo9PXoy/vfud52Iu6jD8Ttoga7rslvmrt2LRum2SM/Eh32adlhljSYoCvVdrx/nCl0tdr3vxq6Xo0rKh7Vg9BcEo9WjLxu3GouBcZ0Sz3aqaWuyqrkWT+u7TOIy3P2e5kUTq2+WbcMzADiGUGAxxyva1CIbHNdt2VaNx/VJU1civ88NhWKJnrxBAUcLN03srEW1RfUKqvz+AOUT0HYBjAVwhnLscwPNE9A2AvQDcFVKdWcgE+ssccxuXlqOdNalVMHQYxu9cgok9PHEhBtz8rud1ssEny38Q9/qdcUaSw29+Dr+wlL/1S1OREy9nxGAr5Lyl9G5cz9AnHNq3netKfsKYzwBkE1txIbn4mWkYeMt76kIChJdRwYpDlm9dbNjxth6ftCjtd2HBkgboWO15ETCZGbzO/bLDFgHLZ6I0Nz+MpszcDMD9MPQHnWEopP8E4I4wKmfmKczch5n7MfNJzLxRODfT1E0MYuYTxXNhQxb22013oFr8ZU441gDXdf664+252LCt0lb/39/9Pq1UBWATe9jFaNltDjMtatDFVnwWZsbb36ywtXu+pjliUIjWQlGvd8687k4dVofmDQB4Rxa24FyzxAVm4vfuRh5RLCv5NvcMk2Ct/2kX7nxnHs55wp4jREWUPpq3Gk98uth2zEuHITrpyeBHGZ72Ki9EgiHgaGZ+mJm3MvMWZn4EwMlRNyxOkEQkpTMwZUpuJzKeoHptGfvpYtzw2rd4fPJi5X373JnJficuvGL7b3j9W2UdQedc0Mlq3ccArn55Fi57YQaeFCZeSSpas0/LWqheacq3Zy7gz/Dgg7mrHfcacEYfrmXWKtctHHacEC2Hamo5bxGPo6h12UY7h5EhAvbafv30NNz+9tw097d43TY8+ok6DpwO/FhJWe3Kp4hXZ6bWENFZRFRiBiQ8C0Dd8QQCYBmRyNJ2ih1aVVOLN12CkmXbzHNGJOWjPe/OWSX1zbAg5gW2794z11jy9jDj6odBaF4z/V3EECdhWfGoYHEYKaKsBbe6phZPf14hTZIUxrtL50uwZppAPFXzfuP2jOmkWzhsXYRhSpzKUAwMuvU9HP6PiTmXGQR+CNXiddtw+D8mYv1Pch8oS+RU6dAveYmZrLn58ITcgyCqqpL1s/Xs+RQL6hCMMwGcBmA1gDUATjWP1RlY+qjnvsgoNa1OEeWYYyYsxO9fnKFtKfL10k2YWrEBgI+MXD4hDh79SKnB6gqqcJQp/sXXISrxokhPmlkQOGsiPv/lUtzy5hwbx2MhjImZKcPOYcxYulFrGV+7dRdmLM2IuQI1ycdNzGzzR7Jg9VAtM7ZV1mDJ+vhNbAF/xO+xST9g0dpteG/Oaun59T/JdZBpjjikhXmFQ0ciwkuHMa1iAy56ehpqajMGNPl06tMxq61g5hOYuY35OZGZK2JoW2zYJYmAaQ1McQGzdsVeym4Rlz5v+gnEIHbUHUgff7/G+yIJAoukPMoqEajHaY9OCVaJCyyRVC1nK+CtmE1usuYxE36w/fezy0+LpBz9P+6zCu1yxPzjtcxYvmmH1r1p508f/fbCV0txyN0Ts3QxKYXneVwIYs7r5QN1839nK+4L9yHdYqSpdRjG9yXPfY0P563G+p92CUQ71Ob5gifBIKIuZoDANUS0moheJaIucTQuLshCJss4DFW4hsw98fekV5WyueIm7nJD0J2NqMPIlJX5LeoTvl4qb9vS9dtt0Vz9wBI31Qq7tHTb4F+n4WvCuphV6xazWDCLnrtiC4aN/hjjPqvwvM/Sm/jpN4tQLHaYYgchPmHi/bnBCYYKqk06ax/TAAAgAElEQVRCnI+o1mHY9RVEJNW1xg0dkdQ4AG8C6ATDUuot81idQXnr7KgjVpdUC/bTTu9rJ3vs1o1RiKRufXMObn1zTvq/07rj6c8r8NY3K0Ov1z+y34w46BvW8w5dcfDdE3DKI97ch9xSzKozexFJJ8DxFewxOIfhTMnpF9ZCPmXReun5zTuqcNbYL2yhWYLFO3JfyFRYs2Unlm0MX1wVREyU3gz4rMt6X3Esy2o/DNjakKKMw97SPIkDAT2C0ZaZxzFztfl5CoZHdp3Bvj1aAQAO3i3zWNaEET00PdlylxEWhQrjqc8r8O9pmXDpzsF3y5tz8NXiDaHVlzOHEYAze3/OqrR3tU40ULcdsGGEwFnHAH8E3Z/3vfEtM6vORRmtemcTv1+Dzxaux+h35wVK86sy/9YVSe1710daQSD9Isgi7iWSUvW57vsKh6CodBh2fYU49v/4yizpPXFAh2CsI6KzTSupEjPLnnx7U+Sw7f7Mb9FDUzAUMa933O9SdlRKb3v90e6JgivLrftZOMZZ52Vw2r17QcphpEUz2X0UxBPfF4fhMKu23RngfWbEEvLzVtywbbsyYtZgecyd9ZrH8yQOCVJthruT966Ks9XhZqYvCWcjpupHq25LamD32zGeZ+n67ZEYibhBh2BcCMNKahWAlQBOMY/VGcgmgWywyJIa6SJscuFUSgL+5MtBnOWCLhXWgiUSX/H9ui1Cfgmt7B1kRFKMpz+vsB2zqpY5Q6kdNK3vbKurrGutshz3iueCQEa0Plu4DjMlOqAwstSlOQzJuWkVG/DslIrcK3FB+nk1Xtqk+WuxaO1PnpuBE/bqBADo1qoR1mzZiWtfmYVHJv6gRRRPfmRKKOliVU6CtczYWVWDLaYJvWhOnCLDAOfguyfgr//7Luc2+IFnLClmXgojH0adhdf4WPfTLhx13yT0btfEuD6tw8gfTn7k86xjfnZ/R903CRWjR/qqM1eRlMrJ0FnqTW/Mxu0nGskX/fr0ubWxqqYWzzjCjNc4OAB7We51nDX2S0+R321vzTXLz85vkstmXWzbwjVb0btdU5w11u6xbD3TvJVbcHi/dsErg53oWpi3cgtSRDjlX4Zu6ZwDynOqww2qVzXk9g/w+qXD0K11JnHWuU9+BQA4aXBnAGoOwyKCDctKcP64qZi70oh41NH0xveaT1XVuVNipXSb7WkJamzGN8aaBBibhDihk3GvB4y4TuXi9cxcZ4iIXMSU+XPek19hw7bK9OIQRBYfZogOFaK2nsjVrFYMoyAueM5d1rNfLMkQjBDem1WCLDZQmsNw1PP+nFW494P50vKs9+xMn+oXgURFlixfeJcj7nUn/v/4YL52OAnV65ZxSMc+MFmrzDCgmlvrt1Xila+X4aojd8OQ2z/Amft1y9xjfqueXPRrWLsl49ynO87DsBhTzdlaZltUWtGxNEWZwJVxrCsidPZvbwCoAPAQgH8InzqDDLcr3/3NWWGPtZhWwPnQYcTRr1GLlwPHkrImpiLuVZjNdiOatl1aOueBccy5nr4x056BUQTD/7sIooCWwSJ6XgsFkV2B7RRhTvlhfVYodhsUSox8cdVuYrUHP1oAZsb6bZV46OOM97VTf5Rdpl2xbMHy2/F61jA2aMrNp+O/uDnJXyQpPYKxk5kfZOYJzPyJ9Ym8ZTFClizJbSi4sZEqxNHJue54Ppm/FuXXjccmhWNiYA7DvE8Ms76ruja9gLtNPOfOv/y68ZhWoRYDLd+4A+/Psdvsu0UfzZjV2usRlcZOcC3Q4/p3lOdlkCXlCfI+d5lJvjZ65ERwlr2rOvM8P6z9Cb96/Avc9Ea245pqnOZzkQJEMbD8pW2X5C3PKL3lZd5qigvXb6u07ea3V2b8M9Zs3Zl1n4VICYbjxGOTMjGrxM1C3P2iQzAeIKJbiOgAItrb+kTeshghs4pwGwsqc70gEW69oBvlFsjdguWRicbubO4KefR6ndKlBgSSO1+fsRy/eWaaeY+6PJkk5dWv1bv/kQ9+it88O116rkayTVUFI5TFlnLe4wey/g/SW9bCX+Ezx4kYL2mLmeZTZvgQFgfhFaXVL7w2Q858IwDw35lG3DevObRpu90hVBwmV/1bbcIahkhKtWYw6xGDmCVSWgmU9gBwDoDDAWSC8hj/6wTSOgxHGG7P+3wMGD8Lv4h35+h7uOY6fr3aqLNQyi5R3faRmavZnWBktyllKv3++k62hUily0IvOmE6c0U4a3HLORBW1rsgBN5KI9y4fim2uizKRPbFRLaghg3xeQbe8h6m/nkE2jatH1LZ7ufdghL6XVRFkbMb4QvDxFhVhBsxWr5pB+7/UK5fixo6BOOXAHqaWfHqJtLKL+FQyFXEo8MIp9VBRG5u93rd57YAy2T1JSnCY5MW4VVJdGFpGea3bFFR6TDcdARBdpaZBERyPZkuLA5Dxzvedl+VhGBIntFrmLq12SnyW71lZ4gEQ643tBBmuHXdPN1h1Kmcax4r0IfzjM1W3ByGjkhqFoAWUTckn0hzGD4nc1ZoEJ875bCxThF90wtVNbW48qUZ+MEj850OQZIt/l47ctl51UJuHCPPtKS2ssxv2UKQMasljJ28CCs371DWmykvOIeR6xpjiZb8jqb5ayR+Ny794nxGneEbZAHdUVmTFV5chqYN3Pt7W6Ubt+XvbYlGMG5PFArBUFpJ5Vx0JNAhGO0BfEdE7xHRm9Yn6obFibRXpY3D8N9j+e7j12eoZftumL18M96YuQJrzNSzupYb0mtkIimveyTH0tnFFBxGEPIrm+DWoWUbt+OO8fPwf88Z0YVLXBaZfe90T7sphcSPIQiHsbNaz4IHsC+UYl1RmWI6OYxRD33q6anf/+Z3cfxDn3qWPbyPezSiDS5GAFFt1UJReitP6JUdVNQdFDrbtFsib0WeYREH2wDQ4TCc17jcEzfrqINVm3emU4bqYPKCdThliHugYukk8niX0mQxzCiF/L2VpMibCDFnLYzPCk57BOCBDxekPaMtL/TF67Zh4ZqtWffmmjBIDE+SbmOALYYVCiKfWddUkPX97W/Pxa+H93C9TydGWFokpTjvxqUE5u4Zrgt3rhzG+p92KR9It+iCE0mJprR11qxWymGEi0IkGEfdZ3RjNt2TP/01/1FbjGzaXokTx3yGpRuyI2l6LYyyOWlZqsiV3uS5AbNbvBl/Fqyxi9zu+3B+2rvXmvybd1RhxL2T4Ey5vWitP6skJ2Se0kHW/Eo/HIbHeVkZuYxTDlmvbvPbsb4VL83Nqo3IILTXv/aN70ivriKpHBaJd2evwpA7PsQXi+XOn/kMYe4GfUFwHYZfHQaR/8keN+uogy07FfkAAozVt79ZiZk/bsIjE3/IOue1W5Kdd8uF3rRBqesCAfgn+E79Rtg6p1Wbd+Ld2StdQ6LoIO1UFvF6oi7fZccdcqNqfHD8rlZSAL5dvhkvfvUj5q7Uj6H2moeIV2amrQsrasS3yzZLzxcovdDSYdR5WJ2TK4sZxA+jVeN6OdUZBsIYnNYCLjNDDGIlZS0WsoW7aYNSDQ7D30MtcIhFwiYYr89Yjkue+9pW7jpFrmk3WKbBUUUmVm1sdDY8Ye+Ka2wchnvZlkhR1W2WVdkOF+W4E69Md7fCyyWoo4zjtJWt+S7jJiwJwYDgRaopX1Z7Z6rrkHmiAv4S98QFK3ibH1iikg/mZudPdnuXNbWsEEmpraR0lLas+K3C1ApHStKIOqZtk4yZ6an/8p+OtsoHh+H1mlQ5rYMibIIhE9+pavDyw2hQahAMNw9+GdweKUpPb+37Yza10UnROoyIPiCi+US0iIgWE9Eir/uKCpLBqNORVo5vC24DaO1W+W4yDnPbOCATEf20qxpzVmx2FUmN+2yxlBtQhewAgK+XbMTXS7PDu4vw6j+v1x4VIc91kfFDMCZ+v1Z63Hq05Zt25NQWJ3J5tEnz12LP297HNoFDrZHoMFSwEp3Juk3cYITpr/HdKv8pApxQO+7ptTOM0PV+oMNhPAHgXgDDAewDYKj5XWfg1GE8+8USjNdIbeoMlR1kKJYWIosRAFUSDeDVL8/EyAc/dY17tHrLTul721lVg51VNWlTXxGvz1iOyQvcwzqfMOYzjPvM3aTTDVER8lyXq0qFpvXd2eGm4nXWMmbCQul1InIhhne/9z0276jCQsEwwc8GbofJwUvzoQhl+e3W6ER/BtZtk28kmb11KED8ynEdgrGZmf/HzGuYeb31ibxlMcK5w73pjdm21KdRoqSk+AjG9spq3Dl+bnqSAnIO4705hnhq5WZ1ALeG9Uqlg/7A0R/j4mem4dOA8f7nrdySzkURZE5FRTBUCXN0oVL2X2L6j/iFKmOb8+ktbsTtXap279+tkscmE/HtckP5q7Iik4mNRazeotYHEVGgzIpxQGV9pztMClGHMYGI7v65BR+MCz3aNIm/0iz4e/Cxkxfj8cmL8aSwg3ezWtq8Q81hNChLKSeHFxeRC7yUuIUvkgpejkgLZ/yYnaHPCV0ip2rSlh36imabGM1mGu1+3/YqF09vjftViGpNGOvh0KjL2ej4sIQJHYKxHwwx1F3I5MK4J8pGxQ2ra3KdzEFu79yiASZec2hO9cYNa1cqEgmZSMrCO9+qAygaPhWFZ0MYGYeR46NmrKSCw/m67blJskt+cepS5b0iVPPHT/8+8NECaVu8SnBz3DPM4C0rqmBhQuJGAU4JAHqOe4dJPnUmUi3gbYERLShvTn0l6YB4Pm+UNLg6oPYtl92fLoLIoaOykspV6Rq2H8ap/5qCq17OdsgUu3iVIFJ021SFHf/IVpXHA7txuCnyjgwQFq49pm8o5RTiJgrQs5JqTkT3EtE08/MPImoeR+PiQkY+Gn8nOTOjxQm3eEk6CON1ERVmatmoRFK5WialRVIeS6Azx4MIZ7eLMci83pUbwQvTAgmwb+BqGZjpIj6rqnav280R1A0/bvTnGX6wR8wrXRRq8EEdT+8nAcwGcJr5/xwA4wCcFFWj4obIYaiUgFEiXxzGiYM7AfDPWVnNDWtMF+Jmyi0fRj5RZYpe3JS8gPviqgNxEyO+CVnWQgs6G67NO6pABDRrUOZ5rbiRGPfZYlS4hPVwy4PCkPv66GCrIhpC1CjEOQHo6TB6MfMtzLzI/NwGoGfUDcsLOBMNNGqcagbxI+SPYDSqZ+wX/O4M0+0NYVQTqEA5jAIlGLkEMNKAV+lvzlqhPKczjPa87X0MuvV9vbYI5bkRC8Bdh8GcKatAuzULb3+jfs/5hA7B2EFEw60/RDQMQLgeP3mGGAlzZ5U/T9CgaN8sEyU2qnDTuvC7YMtEaEHFakTR6o7e/maFVAzkJdIpVILhtpPWhayvXp+xTK0H0HwXs5fL4yLJ4JaX3YIf3ZNbRkFmQSRVcIa1cjgDZRYKdERSlwB4RtBbbARwXnRNih+ilVTYclgVSk3/C0OHkV8E9RYV31SDsuBRZqLUHV32woxA9zmj1dYlyBbiP/x7FpY4dvHfLtuMWcv0RVtXu0QzduKUf01BxeiR7hf5GBZbdqp1NgnCg46V1Cxm3hPAIACDmHkwM38TfdPig+iHEdbaVebhkFdmrkiURyspa9fll8N44CMjn7B4W9CNr+FUFezeXOAdOiTfZDw6qN736i27Mu+FgOP/+SlufGN2zvWd/tgXge7zMyw2uyj5GcGV3n5RqLqHsKAd3pyZvd01ixRpKylwaPL0khS5ypvFkCD5YpMzeUD8PbNbOHK/iMOsNoEd6r5i6c+4R+eGbZVGkiwf48LNgY2Z03laon6WuIMBxo06zHjrw8ZhhFSmpVBWodTiMCh/ijhppkETVTW1eOHLpSi/brztuCrkdFBRXhxmtUFQqHbwYUD1bMb4z2yeLETVP7J2tGtaH3vf/gEG/+X90Bbfxeu2pUWTdZlzjAMJwYAYfDD3WD8WWjRyNxsUJVZxDGGZlaj1qDIdxq+fnoa73pmXdVxUjI6Z8EOaoDz7xZKsa3UgBoaLE151vvp1sPzoxQDVEFfFcXroY+/Ag0Ego0PH79nJbEt4nOeKkKPyFhri1N/oOO6dSkRNzd83EtFrdTWWVJgob93YvU7z2zCrjZ5kyOqwnluWKW3S/LVKzsOJhWuCx7OhAg0NssElwm6xQ7UpYga+XGRYL8XRI7LxZQ84GD7qIn9x8sOfx1aXDodxEzNvNU1rjwbwNIBHwqiciFoS0etE9A0RfUVEA4VzfyCiOUQ0m4heJKIGbmXlhoyndxhr172n7YkDerbWupYof0rvnVU1OOeJL/G9Iq6/7F3IPIhH3Dspp3aExdX5QuHRqNiget2MjCd6HGLCzTuyx5KdywmnDU9PCcb9BkE+9j5xmuDqEAzLMWEkgEeY+b8AwsoregOAmcw8CMC5AB4AACLqDOD3AIYy80AAJQDOCKnOLFidvGLzTtz/4fycyzuiX3tPT2FxYKmubNu0vuKMf8jqmFqxAZMXrMO9H8ifWcZ5nBcgG59ruyL2w0iQDXWQQPnvqHD+uKmSNsjFYgkKAzoEYzkRPQojNMg7RFRf8z4dDADwEQAw83cAyomovXmuFEBDIioF0AhAZK6P4sDUSVriCfIXWkIlkgozOoWsCq8JKdvhLVonj98fFITCjZtTV6EiGGIAyTi65FuJo1/U9YY9fp3o26Gp8tyfjumXU9l7dM5/CD+dhf80AO8BOIaZNwFoBeCPIdU/C2ZMKiLaF0B3AF2YeTmMEOpLAayEkcRJGk+AiH5jBUZcu1aektILYZvCEXlHOxVrjEMiJTPd9RI7xLLDy5MOo66bP7pBRaBt4TUi6BMdSzqbFV4RdlGDshL8+bj+tmO924WT8+aqo3YLpZxcoCQYRNSKiFoBaABgIoD15v9dAKaFVP9oAC2JaCaAywHMAFBNRC0BnACgB4BOABoT0dmyApj5MWYeysxD27YNFikyioHpFQk2E59ffY1skW/WQNt1xgZZSAmvCRyHHJsgl2UniA6qfhX9hqLoeR1xrzgf6gpRD2tDWJbKv1GrWwumwyAM0wGsBTAfwALz9/SgFRLR74hopkkkmjDzBcy8FwwdRlsAiwGMALCYmdcycxWA1wAcGLROL4Q9LP0EFPTjtNe3fVN8c+vRwRolgRdBiENUVFVTG5nZpht0aeFvD657cTZ3KeKliRZwUewVdCLoivWGNf66t24UTkGacBI6ay3IlQCWpAiDuuRXLKUkGMzcg5l7whBHHc/MbZi5NYBRMBbwQGDmMcy8l0kkthORpUC/CMAk06N8KYD9iagRGQL+IwBkOwWEhLAnhx8zWTelr1cxvzusl36jJIgrbpYb3KKMRgndJ2/XLELjvDxBFRrdTjDCHxs6gT3FWv8z7cdQ6nXGyCpWlJUQOjVvmNc26PA4+zDzO9YfZv4fgENCqr8/gDlE9B2AYwFcYdbxJYBXAHwN4FuznY+FVGcWQtdhwB8bqjs5nQQk1wRIBUAv8pZ3ohC9y+OCikiLBOP9uatDr9fveHt44g++rt+7Wwt/FYSE9s3s1ozZKXDDqackRci3VEqn+nWmw145EXUnoj8DWB9G5cw8hZn7MHM/Zj6JmTcK524xjw9k5nOY2T1jTE4NCbc4nXAf9UqNV1+/NKXmMDzryZVg5H/R/DnmTC5v3Qj3nb5n3up/dNIi6fE2TTIL3+c/hDLF09hRWYPpSzZ6XvfCl0s9r1GheUPvpExh4/7T98JLvznAdsw5tMLysypJUd5Dm+hoUH8F4BYAr5v/J5nH6gzC12F4d+oZ+3TDmi27cOlhvQLnXsg1Z0NeHOYckPl6FBKisuDKZ16GdT/J9179OjTD/2aviqTO12Ysi6RcEY3qBzMIyQXHDOyQtfFyDpmw+poKIJuHTnjzDcx8hRnWfLD52zv7SREhikXBq2vrlaZwzdF90aheKRqUleAfp2bvOL12E7lyCNsq40kW5YZC4HLiRj69+90QZV+s2xp9qJV6OSQxOWGvToHuM/LZ2DtTqfQO4fXmO7GXJ0kmot0AXAOgXLyemQ+PrlnxIuyNNvlVYkDO5ezwUBIWgtI6V0TJ5TRvWFaQJrt+hkbnFg2lGQOjQJQE474QIih4wSsHjRuCLsRGPht3DiMsEIXrzBsEOiT5PzD8I26E4bBnfeoMCjXImVcAPC/nQMBYcG4aNSCE1kSDEDKOKpHLAmIhqsmvW+5+PVtF0wAAHZvbLcCKndsry4HDCLpxT1E2sTllSBf0bNMYfUJy2BORbx2GzhuuZuZHmPkrZp5ufSJvWYwIWyRlKL39dWyQYaBzz5Uj+uDXw3sEKD0eRKnDcCs6HwrSNHx0dpRSa2fJxc6w+iUYLRuVYfRJewDIJSc9Ze362zdrgI+vORSdW4ZrAqsypomT69B5w28R0aVE1NHy/jY9vusMolB6xxLuQ6OSfO9IvBClSGq9C4eWz5DqOj1yYC+9aMe5wMmh1tZyFtdRTPDLUXZq0TD9DnLhMLzmWFhzkEBS0Vmcpuk6ZgXnmd+iGIoB1B0X2Ch0GDFAhywVNrnIn5WUbrVRiWm8xkgcOaidZdeGFN4/X5BxGCUpUur6iICWjQy/4U4BCSURQW0YHy4MBbu8DXFBx0qqh+RTd4gFoolZE0UfBhkYcTIY5x9YjjZNvCPfd26RYdXzJTfXrdfL8CAIjKRR7tdYa1yU3efccESpT4oDMoLhtvkmEEb0b4cxZ+6Ny4/oE7hea146uTN29CEzo2nAWHBGPXLlfK4OvH6gk3Gvkem495j5vw8RjYq+afEh9NAg8L9QB+lzPZGU/3KDol+HpujV1lvRJ3qr5ssXRLfabbuqo22ICjG8Fqcoo5a5qAP+Wc6wIsSAijIQEUYO6oiykhTeumx44Lr/dfYQvHapPNydOAc/uuoQDA7okU6Qe3rHKZLS0WGMA1CJTPC/ZQDuiKxFeUA+Y0nlVI/w+7FzhiiuyW7Lr/btGkl73Ky2Du/XLv1b3BHla1ery2H8Ys/OodetMzriihQsIqyMk/mCX0MG5zSVERxdHDOwAzoq4jyJ77RdswYY0q1loDqM9maPnjg3hTpvqBcz/x1AFQAw8w4UvmjcF6KJJZV5RccO7BBq+el6hF7o2koekVM2mP560qDAYdLdUEKkfJMiKy0SltVbd4beDh3oLox7RBAdVGeC/8aMkism5OnfsVnIDbH/reFi5i+MnfY9EgdYFZzdEPZGvZs5J52ErFG9ksBlFoMfRiURNYTJJBNRLxg5MeoMwucw4qH6IicThs9BrkiloBSliO9DJB7jv1kZbaMUKCSfg/9cckDWscP7tUPF6JFoJiw2uSwWo0/aA1c45PROeXixm9XmupiGPWf/PLI/Hj1nCPbubhdBNQhIMAj59/TWIRi3AHgXQFcieh5GStVrI21VzAjdrJbItjMMq4+dxYjllijCWKrEY1GIzQgKMw7YD8eppEvXKZHXFwoGdlJzMaL5by6vbbcOTdHBoZR1LrCFEFssF5Df7I1ZLzTccdmgrARH794hrYzfZUYJblAakGAUg6c3M38AI43q+QBeBDCUmSdG26x4EcXa0a9DM9z2i90jK9+JUsVIUo0v1eKTy6JEZETf9YKOh3rYaOBoV5Rro/UOWzWWW4yJ42HkHh3RULLjtAi6eG0u3j0EmQhGovQuYprhd/fttgELE80aGFziFjNMTXAldf6j1epqeQ6BkcToMAAHRdecfCGaWdKiUbTexOIColqElYRBUWauLK+TYFhmtnZuKKcqAuHIAe0jr+Pg3YwUwdY7HNCxGSpGj8y6ThxtpR6ixJqQOAydgIcGES1eipGzSCqcZmTh0L7GuDh2j44A/G2Yxl2wT/q3KOq+cFgPNM1DdF4ds9qHAVwCI5HRbAC/JaIxUTcsTsS5q3rqgn3w398NU55v17S+8pwTdr0AcM7+3bOvUUwD1U4lV3FR/TL7btmqx0bcPOoImwE594Du+Psp0eef6G2aFHu1X0dsYhVRy9nHgsI5Fpz9MK1iA7bszJMZcQhIuRhdyOAchlHt3nu2bYKK0SOxf0/De9/P+CbHb6vPOrXIj0e+zl7vEABHM/M4Zh4H4DgAh0baqpgRFb2QDcBD+7bDnl2z7bCtSy3PU3l5jv/C75RiB+mXw8h1F3v2fnaiZU0OldJbhqMGhGtV1q1Vo5xMJnVh2Rh5LTzMQDszS1vPNh5+KzaZVG4iKWenO9VeFeu35y1lbhjw7fvk8T8q+NmUiWsTEQkbCZZfFDF0ZtH3ALoJ/7sC+Caa5uQHXju+secOzbH8YPcN660fTygswpCLSIoAHNCrtU0MYw1xu0jKvY6wHZHilvvq1HZgrzZ44eL9cNnhveVlmIUcv2cmT0PuHIbzf/4t68JEiggDcjA9jmuY+NLhOThM617m/AgPdQhGawDziGgiEU0EMBdAWyJ6k4jejLR1McHrxecSmMwvxLrcuA3ntWrRk/Ju6VHdxXpE/2ydgC6H45XoJmyleNzLovXMqndvjbcDe7XxfN8tGtXDJYf0wpUj+uRskJBvhWnUSBFhYOfm0mRkMjjfR1wE1B+HYddhWbfWsrDRjbFbdbQmN0feijzjsL7tMPH7tcrzQedZrgPQMwqmUD6DVSRDUbaqTr22yWLiyJ6XJOcaeyjrwtZhxGWUZc1fLy5Nx6RX7Pvrju0HAJg0Xz1GPcsLeTlsUJbCzqrCEl9Zr0zX2CSIlVT/js3AzPhu1VZ/jROgsICXQpXutWBFUsz8CYAKAGXm768AfM3Mn5j/ix7nHVjumjNCNtVGDTIsHkb0b5d1Ln1fxFYbQWJJ9WzTWFl2SYrQT/AfybVu+w2ZnzJiIyrsw17fvQhvWCKwvUzd1GlD3UOvNKoXzLolVw4hTAajEMVZ6Q13hE3r16Ep3r3y4JzK8PPu7EYP2bk34oaOldTFAF4B8Kh5qAuAN6JsVDGgS0t5KA4Rw3q3Qc+2jSQFyxQAACAASURBVHHFCP1ImOKi4Bz4lxzSS32jysPa8f8fp+0pLRsAfrjrODTRNNWTchMug1k81bh+tt+BqJQOW3TiNcna+7BMc0OnFg1RMXqkLW6WDGPPC6YTy+WtyKIP5PKaCzGISNroQPNNBQr46f8W3/VeKawXTsdNi3vNl5OlDnP0OwDDAGwBAGZeAMB9RhQhXKUEkg7OiOHVvd+8YRk+vvpQXzGAVKX169DUpgAFJDJYyUh0Hmtgmr2qJpXuMLSKFUOSiCWOPXco7js9I0s+c9+M3YRMJCUu6qFvorxEeyERKN1ixPDuUZRvYc+uLWwcYyFyBWHC7xrqfB96HHv07/DKEbulfzsfyabDiLwl2dAhGLuYOZ26jIhKUczePQEgFd9Y/gWa4+ekwZ3x3K/3C1yvlBgIv9sqdsnOu1I+262CtcD/n8D1iGWOGNAevxzcJd3uds0yduN7dnEP7xw3hxFV6BY/6N7am2P1u+AHkdEXM3JVAuuMu7jfodOq2mpjvrzydQjGJ0R0A4CGRHQkgP8AeCvaZsUPV3EKUZZlj99F7d7T98LwPm3UdUhG+fbKGuG8rF3G96hBHV1iRsn/q1qvOwjli5f6nYjtOHi3tvjwqkPQRZHz2NnmqX8eodcoVd0eK0i+A7oBwCuXyHMpiPDbTJFQlqSyw0rk/6lzR9dWmTHkm14454b/WwJBNldn3XyU4mpRJJXRYeQrxbAOwbgOwFoYnt6/BfAOgBujbFShgZBRjDYsK8GpQ7qk/zMbeZBzsf8Wsb0y42n7/tzVmTZIRqq10LkteM5TYVjwABlLD7tjkfp6cvzu3c7usCZW6+QIlNyT5uz1ui4qemE906lDunheq5OJzbdjGhGqTTlNSaquC6QgTcX6wR/UCuogHFgYY0VWRHOFZZc9llhG17crT4lkdKykapn5cWY+lZlPMX//rERSQGagTLn+cNx96p4CpQemXH8E3rxMHe7DDwZ0khMe2UA9ZmAHnLlfN9w0aoCyvOxwEFZ5cvntBcPKpeV8ft3hWSXrwM03QzWKtJWWWld5i6TC5jCcxV1zdN9wypU88UgzPpEMKcooR1WRAPzgw6sOcW1LmAiS+8NaTMWx7cfDX+eZwnhuP/1Q6xBJNTR1kDsrw08drAPltoaIvoWLroKZB0XSogKEaJ1gDRjLuczakYdl8lhfEfpYNlDrlaRw1y/38CjY/ldlQmodPbyfPEhfJ4eiVkoI3JohWn8hw51Z0OVUgsBrkkctkdLJBBd0dztiQDuM/1aeU4RA6eCFBoehkE9qoodpkh0HOrdogHkrt/i6J2PQkYEf7js+DkO/EJvjHiidfGlHVU36XJy7dzfyOwrA8TByYbwL4Czz8w4MM9ufDWxTzfxhKb1rc9OzpXHswI4494DuuHFkf3kbXHbqbtBVevvdZcvojhvRtImkzD8qqxbdpqiIq9/ywqYXTs6pQVkJnrlwX9d7dN6/f5FURkxTmsqdwxBvj9qsVrdvRXQ0832Iz+kWNSDLSkqjjlAIho8ynLkzrPeSL6dJJcFg5iXMvATAMGa+lpm/NT/XATg6vibGD+nOwzrm8ObNcBi51VmvNIW/nDAQrZv4iFarYRboXMQtDiNXgiHWPbR7S/OYrH51GSp9iS63JssjEaS8sERSThm6U9HvBj2Fq88+EghGKgQdRpy2ASMG+LfcH9g5OxGVL0c3rWvj1QSJPj1EmXD41bW1BWsl1ZiIhlt/iOhAAPHxpnmAcwEhZBOInVU1tu+o7bOli7FG7yk5DMcZvztG8XEtha27pVn2b3F9dVN6q9CwTI9gxKXDkClddRGGSaczNH6KKN2mEokOw+9Ty8SKUaFHmybYu5u7+TUgj7emG0o/+314P9OrXy8DALx52bDAeks/b07kkAhAqWltUlVTuFZSvwYwhogqiGgxgIcBXBhts+KH2Imy4GBpRa35/58TFgIAplZsjLZhzgaIhwLcZlk3OY/7d3rKoNyUbbdwmbyyzHFKDkPjyXq2bYxRe6oVvrbyvERSIa19VbkQjID3ia/wQkd4m1pm9Gxr9E2DslQOtYSLE/fq5HlNitwX+34dmuK2X+wuDTQo25zI4EeH0d4MR2+Ffx/UpQUGefgT6darf2PGUTaXzUku0LGSms7MewIYBGAvZt6Lmb+Ovmn5Q1aHEvAL08va2tXGnRP6YIkPh060TeextMOh4zq/hm/O4Hjjzt8HQ0zRlP062b3Gt+od6nAYD54xWJvDCMsP44e7jkuPAxlqaoPLlfUUru4XOTc6XyzagEfPHoqnLtgHLRrVKxjHPR1uKkXkwR0Qzjuw3FOE6ycyrOrK3do3CZmjspd12WHyEPfZdxFKSywOo7awPL2J6GyijNCDmbcw82bhfC9RVFXsEF9+tkiKcMvxu2PGTUem5eb3nz4YANAy4jSsAHDy3l1s4QIy7fKGc75kdBhOkZQ/HDOwQ/q7fmkJDvOIn2Rrk/ktxsNhh4OSXjl613mJ28TooYvuOk55XUmKsKNKbc5YnYOYQEsk5XFepuBt3qgMh/ZtJ70/FwJy+RF6i5wMOvWWpMj1ga1HlUoDbNe5EJ0sHaD62poIN4i6ZtdEQJn54LmMtVzgxmG0BjCDiJ4kot8R0WlEdC4R/YWIPgHwdwCrXe4vKojjwWl6SmQca9k4I3KxQjnEwRl2btlQag6rGt9uSXdIyWH4a1O/Dk1RMXokdu+UrWj0gtUGpR+G1m5bvz6vjb+4qHjl4rh51ACcf2A59inP5qaiFhN4PbNHevBQ9WwDOzXH5GsPyzr+uEayscGSjJNOlKTcI7NafVYieWi3zZ8IN52OyC2niHDWfkYstDA2iEEjzhIyfiWlJZSXAE1uVlIPANgbwIsA2gI4wvy/HMA5zHyyGYiwTkDchTo7VNa/lrVCHFEjVeNLGUDQ6R4qIE14clw7/C4+Nj8L8zsXHYYfRzSvHmqgKdoCgK6tGuHWX+xuI+BWZsRuGvGgcoFfDuPaY+w71zDIhRg4Ufb+G5aV4Jjd3VPsnr1/d3x41SGueSu8RFJuHIZt+PvIPSEWJYo7iSgtiuzWKvc+Dkq4iQh7d2uJyw7rjbtjyFEvg+vrZOYaZv6AmW9l5t8y85XM/CgzL42rgfmATsa3MlOWGCWrakE1cXSUdro6DL/QHfOyy6x7xXenSl2t2tH5mXNO/UybJhlOsbx1Izx4xmD9wiQ4Z//u+OL6IwJxW37gmVDLcb6pIypwGAyG6LwnG5dEwENnur9PIkLvdk3Q2CUvSImH34j1rDLO277586PDyFwrbiJSZDz3tcf0xSNnD9EuL2wQjLXpmqP7okPzBnkJMe+D/tZt2M06vWWbZabgOw5rhTCVlWGV5bcYe1x/kztTvDpxDRj/+4MU9eu3wKrmjd8Nw6d/Ogwn752J7XTliN3QoXkDPH7uULxwkb9owmJrOjRv4H1ZjpA9sejglrV2Zo1j7/K020KqDYr7It1MiJm1X49W6d9tm9bHrFsyAfhKyD1HYJrDkBEMUbzsoTi3H8j8FH18DG6WcOmhvbMiHgRB2LYHcUZqSgiGBNkEI/uaslJr0YuDw5AfV80FWZiNty4bjguH9UB9SbydYG3SVExb+grJOdVAF8tWTVA/cmCrnr26tshKfGVVdeSA9jiwtzqasBPiYuZ8FVGNCFmfiUELnX2SLVpVvzQvx0InmBVjwF1Xjak3ZiIP//XkPXCzGQetUb0SWwgVFUGykAm8KWmb2Bw3LsX5XzhQllL3b64IWl4hWLm5EgwiShHRablWQkT9iGgKEe0iomsc544hou+JaCERXScc70FEXxLRAiL6NxFlG/mHCLEztHQYJocRh9JbtbjLHAyz7jW/9+jSHDcfP0Cp9PbfJs3rXM6pQ4N4F04eC5OIqGl6XOHRZdXsKSiQneM2q10uzSzX1L9Yeof6pSlpe7x0SyJHVL+0BH0VKYFl4did9QDysSJuRIL2TamYGCzk/lUFGBUxon97HDvQrgsqhHjDXjqMWgCXhVDPBgC/B3CPeJCISgCMAXAsgAEAfkVEVujVvwG4j5n7ANgIw4EwFuhwGFZ+jHyKpFTDRzyumyfDL8IYvCoOQ69tpL2Tj7qH4prGsnqaNyxLi3m8OQzHf9E6THNA3PnLPXDr8QOwb49WCr8ffwus6kpD6e1yn8s5u5WUfhni31Ih/03YebQ7NjdS+bph7HlDs/QlTgW+NX22VdbgmSkV4TXQBToiqQ+I6Boi6kpErayPn0qYeQ0zTwVQ5Ti1L4CFzLzIzOr3EoATyBhxhyMT5PBpACf6qdMvxMGviugqotTLhjEGlJbIu0+HJfez4B+3R7bVi1+CI6MNOjoMFYiQzvXgBTexYRi7x7hEBV4iSJn/kP1+N3m+XhuaNyzD+cN6gBQLuu/3qbg8lXInYq4EzsVEHgCePH+otGqx7aJIqhASbAFIi5MtiKP65v/OQVUMOTJ0CMaFMPJ6TwIw3fxMC6n+zgB+FP4vM4+1BrCJmasdx7NARL8homlENG3t2rWBGyKOiezxkT1g4iQYzonfT8HGW+3uJChglVxIgOb3FCxktO8PUI/OBE0RKU2aK0aPxIj+GUdCp+JTZuKbC4KGiPALT491x2z2o+T24xGdrk9hJRUGUuT+tGI99UtTNm9pLydQdR6WDMTNWJzkosxlXXFm/XQiDt23TmiQHpJPz5Dql70ddjkua99jzDyUmYe2betPcadqiJbS2zk7Q8T/rjgIX1x/hPL865cOw4ybjlSeFzPU6U7g/XuqmUZr6tqIaoBpdLjCG/zUIV2kVlRuIMhNmi0HKxFlHhNNBZHoqPD8RfspMwKGDa/Xki2S8h7H6WtdduJ+2uN3VKjGUQnp6TAA4Ps7jrV5SzuHhVP8k07l6vJ+Du3bVno8Ssy8+Uh87TKvveZFLAY4XhcQUSMiupGIHjP/9yGiURr3/Y6IZpofVQCeZQC6Cv+7AFgBYB2AFkRU6jgeGcS+yPL0llxvTTDVbj8X9O/YzGam2ayh3V69Yb0Sm9e5DJmdikqHkTn+wsX74cnz9/HVRv+TiPHI2Xvj21uzcxcf41Du6Yqk0qG7zev/eHRf3ClJKOXkBu06HnUdY89Tv5MoF5E+jvS1unU6F5QsjsNlOZeVrUqmpapPVY4OnLeliHBAr9bK6y86qIev8g/erS16tGlsIx5ZIinhyEF92mLMmXsbx2OiGC0a1UPTBtl+RzoiciAeDsM7kTAwDoYYyspSvwzAfwC87XYTM4+BodB2w1QAfYioBwwP8jMAnMnMTEQTAJwCQ69xHoD/arQ1MGyhm52svGLAvHnZsFA8P71wxj7Zu2Y3MFttZi2FedeWjdDIxYnK636d65gNqxhZYhxnG3VFUjVC+tFaVj9raUot+y0EyxMnPhBSoYpwtnXWzXbi6xRZ+FGCB5HTh6HDUI7PFHDhsHIcM7ADho3+2HbuhuP6peNjySBbOMUEVqp11dkWKwxH2Epvv5hw9aFYsmGb53VxOBHrrBK9mPl0IvoVADDzDvI5KoioAwy9RzMAtUR0JYABzLyFiC4D8B6AEgBPMvMc87Y/AXiJiO4AMAPAE37qzAW6kycu2bXuDsNu9WIeU17rvx26MXpUbXK7JoheQUwO5NSgi3OnS8vcna0KAo4X09zhBW+FMte938LJe3cJRDalHEaAcmSwRFKdJX44XkTea9m0xJ+eHJtmfVGjW+tG0rAzTivDOERSOgSjkogawuwHIuoFYJefSph5FQyxkuzcOzDSvjqPL4JhRRULxCHhVAAW3h5UjiZmKIhG9Utd7dSN4/Lf8ovd788VWaIBTZGUNUHcFLZXHNHH5qsgKydsRDVe/HaTl9WUhdP36YqJ36/x3Z5QOAxl2f7EZyL0PZ/ddTzW/wjVlTnB+ZQcQ9ZWnVdxK4yc3l2J6HkAHwG4NspG5QUuC2iBWNV54reH9MSfj+uPX+3TVcisJ4fdU1nzAW3xnvy9FLcprBOKRXbPniaHt5upR5ItiHtI0nba6vKsSY58pMdUvpe0Etd+2Evpbf1nF3GeG5z5poHg79NN//Lq/x3gqyxPDkPVBgUBKRSzWi8UhNKbmd8HcBKA82FErh3KzBOjbVZ+obszixp/OWF3DOqiH9CufmkJLj64J0pLUhmRlEpGHOCR7jnNf4TM8w7oDgBo30wda4nIPZaX6p6T9u6Mydcehv3NmETibfnJFhBt3RpvxfZPJ2KBhXol+hF70+VrhNz3q6hPly0cH9Ldl9uXJzFnBYHNEAjzf9o6sDgIRkHoMIjoWRg+GJOZ+bvIW5QnuDnu5Wu8nHtAOc49oDzQvdZkVhE7Uvx2w97dsnNAeOGcA8pxjsczeClnZSAYMu6urRq5LtDeC5Z3XWHelwuUdSo3Bfqcm5HCNXcE3Vx5idNs13q+fL2FM0sUan47x3m+ld66KAgOA4aVVEcADxHRD0T0KhFdEXG7YocY8rpYBogbMjoM+Xk3q7Csa8NqlKr8ACJA8ZqjdzfMPw/uk7Gd15Vj/7hhh+c1HVy4ozjhxXl5vces/0LP+skJIuLCYT1wzVGZbJB+CanqeteQHh5lWl1/oNIsVz42SktSeON3w/DkBYY5tbUAF+py4BziBWFWy8wfmxn29gFwGIBLAOwO4IGI2xYrLhjWA3eMnwegeFhQN3gRvUIiigSye+fqCF+ES4Z0b6WMzePVldsr1SlXLUz+02G+JuOAjkZwufMPLNe/SQPKR2H5eZ3glBZk+dh1cPPxRui3e96fb9QhEYMFWcfCmIPNJD4NgFokBRgRjZ3XJTqMDHREUh8BaAxgCoDJAPZhZv8mFQUOUQxVKCKpXEA+OIx8KHBFBCFeYemVdOr26ynetml9z+BygeBTH5Clw3DRmQ/0MA7Qhd9+CdKLXn1mDWdvztn9gjSHEeECMLhbeKb5cQRC1TGr/QbAEAADAWwGsImIpjCzNy9fpPCTR6BQ4VTcqc4D3juTqDmuIOV7mla6nbOFIfFddd7gNQ699QA+tOAB4axSewnz0RbdEBmqy2pdOAzZdVFx44vuOi7U8golltQfmPlgAL8EsB6GTmNT1A3LJ1TmdcUETx2Gjxka9eOnKHuw//7w3njt0gPlN8CH46DXIptj5/qdpJ1yyMwnNnWoIEJS7ajzYR6eRTA83o9um168eH9vx0Rn2Yq+r6wxxJD1Sr2C+ek5+AVFKkVa6aB1USgiqcsAHASDy1gC4EkYoqm6iyIkEE54K70zv/MtkpItbFcd1Vd+sXVNTvWpxY/6Zfi/Z8Gdx+bWbvP7lCFdcM+p2SbOXuHMVXWH2f9RceMH9GqNA3u1xqK12zQc99zPV1UbF3hGfzW/i0eHEX0dOiKphgDuBTBdCDdep5HFyGuOl/euPBjrfvLlBB8ZLMcnpVltQRGMCERSLs9kz8jmu+rA8NKFXDS8B8Z+ulh53npmD0FTGtk6DH0leFD478oAfe9x3kuHscvMG1HmyWH4a1e+URA6DGa+m4j2BHCJOeAmM/OsyFtWQNDdNfXt0BR9EX702iDw4jDEXRPn1c1Nb8mY9MfDsGF7JU4c85lxT0i7vkLST904agBuHDVAed4P1yhenz6vKDfM/g9KgHVus/Kxt23qLtZjD2V1VbVBMLw4DCth0a7qGGJuhIA4wuzrhDf/PYDnAbQzP88R0eVRNyyfuNRMxmLFZioSjtQGz9AgGhzG/x3aK+vaKHwSUuSdbrVb60Y2k0evPmlo+hV4iZyKqW8zHIa90brRV+N5Vp9WUj4uv/ignnjy/KFpv5ug6GHqQryiKDSsZ4yh7ZWFL1hp06QemjeUmxGHCR2R1EUA9mPmbQBARH+DYWL7UJQNyycO2a0tKkaPxLDRH+OnXdUFtAfVR3px0eAwVMqy7pLQ7W//fjhWbArXQC7IQuZ1y52/HIg+7ZtgeO82rtcVi3wa0DGVtv/PR4ibKF9nSYo8c3QAgp+F4vxhfdvhw6sORu927tKAHmaGyRH9cyNQcWDzDmf262igY2BOAETvphrUCbVwNl6/9ED88Wh3ZWuxwFosdOSwujtUAGjTpH7oYd2DLNpeIqnWTerj6qP6elqh5LrAxSnO844P5k9HMWpPI69Z99b+rI/c4Kzz7cuH+7o+DBy1e3uM6N8efzq2n/IaL2IBAJ1aNMQ3tx6FXw/vEWbzIkFVTTzjUDeB0pdE9Lr5/0TEmJsiTgzu1hKDJfGSimgTmoa1uKj0YDYdRgEo9/y2Id+e6vnQfSw1w5iodpPOFjmJpZO4nb1fN5w6pEvgsCDSNjgmi65DYJi+Po3qlWLsee7pZXWh8hb/uUJH6X0vEU0EMBzGmLyAmWdE3bDCQvFRDIsgqCwnxLWkaQP3YRC9H0YQS5lwWlUIxFIXk+avBQD8b/Yq6flskZT9v3MoEFGoxAJwHytHDsgW7VCaEy6ijvgZQ7lSEFEDGHGjegP4FsDDPxezWgtRO+5ECWsiqvQT1iL9hxG7KUOPx7WLDqTDCKlpxbhMObtUTFUrwrlrj3JRJrJSA7tco3ksQeHCTYfxNIChMIjFsQDuiaVFBYRic9wRYVkMqtYI65H6d8y/GbDxfsVwHd7vuwi7JDJU1cjNRJ2vSJdctGtaH03rB8vxHnSu1IWAnz8HuI2KAcy8BwAQ0RMAvoqnSYWDTLTK/LYjCFIeHEZaFOBWSEzPbe1OLejshHPhfrzqKklR2ga/GFBtchjOUBdBF+8p1x+Rc5t0kdCJ4oIbwUhr1pi5+ue4A7AW22LkMLxEUtYTuS3OjUw79GYR23cHilYbYZfMue3ovNYfFF4EQ1ciFSRcCplU31UkVYDvLIE/uBGMPYloi/mbADQ0/xMAZuZmkbcuz0hHqyxCFkPXSkq2iFg7/qN374AbR/bHmft1i6iV6Rod9Xu/71yIuNetOorgv58yCGMmLMQBPVVJeuJHqWdY/gh1GOk6fTruJVqMooKSYDBzuOYTRQhr911ShFujDEFQiaSMb7fwMykiXHRQz7CbJqkHaNGonveFAnLpEbtIKlgZHZs3xB0n7pFDK8JD11YN8eOGHZJ8GP44jH3KW2JqxUbpOa8ou5YeKmi/WPddNLwHvl+9NWApP190btEwlnr8abZ+ZsiIpPLckAAYNagjpi/ZiM4t5QMpTVAKwE6IiHDliD4YO3lRWh7vfU/EjSoivPZ/w7B43bas48535PVqX7x4f+X7/9xDr+EVWcDrPgtusbQSqBHXfCgezV4eUMwiqfMPLMe8vxyDjs0VOw8XDsMrnEbYSJEhBjp7/+4A9LiHn6NOTYW2Tetj3x6tso5ncxjuFKO0JBXYL8Oqy01UmIifokNCMAoAxa70toKnyeAmsnr0nCH46OpDAueK8It8LiSFwGGFifeuPDj929l9UT5pJsNjgrjwy8Gd07/jmkMJwXCBtZYWow7DCxkrqexzjeqVolfbJvG1JebXe9BubeOtMEb07dAUXUwxpPO9RulMnd5UmV/nHdAdN47sb7vGbQNSB6dY5Lj7lEG4//S9AMQnNk90GC5Ie3rXQbJqDbBC2GHHvVgcsltbnH9gOZ76vKKoQoPoIr3RSdkHbpR9bYltrZ3ubScMzLpG1s8JoQiO0pIUGqdTMCQcRt6RSQJf90Z12k+jAHLD5OP9Wj4LdZBepEOFOM1so3xYr0i6xjV1bx7lGxlz5njqSwiGC2qL2KzWC5TmMPKPfCT6qXs9mkFa9+YgGO0iSH5lQSecvkxskijCc4NDEhg5EoLhgnQiljo4pq2JqvIEjxPJzjNcWH3q5DB6t2uCD686JJI6daLOulkbJmMgGDLmzIlIKu8oZispL6Tnbv7phVaYkqhQAPRSGy9ctB8AoEUj91Atli+FTMncu10THLN7h3T63bCQ0uBY6+I8yjesjV+i9P7/9u4+SI6ijOP49wd5uyNHEkgCSY5wIIGEd/JmUgqGt4goRFQEUd4CpShCUKQUQcCXUhB8KRBFKryIlVIMgsYqIAQUQ0EgAUwgMQEjIJxGQVAMr4Hw+Ef33g3L7t3c3e7M7t7zqZq62Z637tm96enume4aMKplMBtefK0uX9zrTueb3vlfMXN5p6KGGv3TahuZbmS8sm0Y0VXHT6lYnAq6G38FSlftFs6/v1fTSx1VUl7CyN3C02byvaP3YcCWjXeaOt/0zl/WxWqoz7rztKeno5flDO90ZuwcXhxs6uLFvy1K/Bt1N/6261rWjd5ewuhC64hmWqc05x2NqqilEkZ3/V5VUw0kP7W047QX7vIr8bDGbfP259U3Nne73sUf3ZvPHbgLI7Yq3ydYVzcEXsDoncI59TYMV1VKefHJQvFPPYuffuGOeMqO7xzDvValPS+brXwbRk9NGrM1k0uMc19syMAt2XW7rgfjKhWdRn6wJAsq+lttnmH0U3k2NBfLozF01m6jefSi2cyooe7Ju5XyNE3aPly4s+raJa1S33NHG4ZXSvVKbzt97C2vkuqnaqkNo7MBOlstQ6o7MFSlpb2o/mzudNb9cyMDa6zt7cyDJ7wjzEsYfdP5lJRXSbkq2qt1GAA7j8yuz6hyim+E/YmZ0tKeluHNg2qy5DRy6OB3hBXa0Pwb7x0vYbhMHD2llcnjR7DL6PwzjOIMohaqyWpRQ19U/SahVxqyDUPSREnLJL0u6UtFyw6T9Jik9ZK+kghfEMNXS7pWUn3VH9Q4STWRWUB9DlCVh0YseRVuDRovZRlp0De9XwDOBC5LBkraErgS+ACwO/AJSYUhtxYAE4G9gCbg1Izi6jJWXDffiBfGSqilBxUqxdsw+qbwv9NQnQ+a2bNmtgJ4o2jRdGC9mT1hZpuAXwJz4ja3WgQsB1qziKvLnl8s0qmpBxUqxtsw+iLrzgfzbsMYBzyT+NwOvDu5QqyKOh6Yl2G8XIY6es5trCth5dXpVfXnp0wv+wZ4ZwmjThOXs8JZy+opqbwzjFKpewGwkgAACoFJREFULL5s/BhYamb3lNyB9Gng0wDjx4+vbOxcJop/7H7taCz7Tyg/wqG3YVRG3VdJSTpd0so4jS2zWjuwQ+JzK/CPxD4uBEYBXyx3HDO72symmtnUUaMad+jNRtaI4424dLwNo286Mtx6L2GY2ZWEBu2urAAmSNoJ+DtwLHAcgKRTgfcDB5tZDYwL56ql0Ene5w58F4//ayNz9hnXzRauUXQMg+xljF7J+j2WrB6r3V5SO6GkcL6kdklbm9mbwOeBxcBa4FdmtiZudhWwHbAsllIuyCKuLj9jhjVx42dmMqyL8R4OmTQ6wxi5auuof/b8oncyLqFl0oZhZv+kzFNOZnYrcGuJ8LzbV1wNmn/itLyj4CrIuzfvm0KG612DOOdKa6CnyToHUMo5InWqo0qq3hu9nXOVVXgjvmVIAxW+O0oYnmP0xVsZtfI20C/PucbWMmQgFx6xOwdNbJx2nM6nfHKNRt2aMDp0ZT+1LZtxXTzDcK6OnPyenfKOQkX5Y7V9s/2wISw79yBGlegJuBo8w3DO5WbnUVsBMGdff5S6t8YMa8rsWJ5hOOdyM3Z4E09+53DvGqROeIbhcrPwtJk8+e+X846Gy5lnFvXDMwyXm2lt2zCtbZu8o+GcS8kfq3XOOZeKZxjOOedS8QzDOedcKp5hOOecS8UzDOecc6l4huGccy4VzzCcc86l4hmGc865VFQYIrERSHoO+Fve8eihkcC/845ExjzN/YOnuX7saGajulupoTKMeiTpQTObmnc8suRp7h88zY3Hq6Scc86l4hmGc865VDzDyN/VeUcgB57m/sHT3GC8DcM551wqXsJwzjmXimcYzjnnUvEMo48kXSvpWUmryywfIekWSY9IWi5pz8SyeZJWS1oj6axE+D6Slkl6VNLvJG0dwwdK+lkMXyvp3OqnsGSaskzzIEnXxfBVkmZVPYEVJGkHSX+I39caSfPyjlNaaeKu4HJJ6+P3PTmx7ERJf4nTiYnwKfH7XB+3VQw/Oh7nLUmZP5qaQ3ovlbQu7ucWScOzSWkfmJlPfZiAA4DJwOoyyy8FLozzE4G74vyewGqgmTDy4Z3AhLhsBfC+OD8X+GacPw74ZZxvBp4C2ho8zacD18X50cBDwBZ5f+89OFdjgMlxvgV4HNg973hVKu7A4cBtgIAZwAMxfBvgifh3RJwfEZctB2bGbW4DPhDDJwG7AXcDU/tBemcDA+L8JcAleX/n3U1ewugjM1sKvNDFKrsDd8V11wFtkrYj/HPcb2avmNmbwB+Bo+I2uwFL4/wS4KOFwwFbSRoANAGbgP9VMDmpZJzm5L6eBf4L1M2LUWa2wcwejvMbgbXAuHxjlU7KuM8BbrDgfmC4pDHA+4ElZvaCmf2H8J0eFpdtbWbLLFwpbwA+HI+x1sweyyZ175RDeu+I/wcA9wOt1U5jX3mGUX2rgI8ASJoO7Ej4YawGDpC0raRmwp3LDnGb1cCRcf7oRPhNwMvABuBp4DIz6+rCnZdKpnkVMEfSAEk7AVMSy+qKpDZgP+CBfGPSc13EfRzwTOJzewzrKry9RHhNySG9cwmlj5rmGUb1XQyMkLQSOAP4E/Cmma0lFEOXALcTLoyFu425wOmSHiIUjTfF8OnAZmAssBNwtqSds0pID1QyzdcS/skeBH4I3JfYpm5IGgr8GjjLzDIvFfZFN3FXiU2sF+E1I+v0SjqP8Jte0PPYZmtA3hFodPEHdzKEBjPgyThhZtcA18Rl3ybeicRqnNkxfFfgg3F3xwG3m9kbwLOS7iVUzzyRVXrSqGSaY5H9C4V9S7oP+EtGSakISQMJF6AFZnZz3vHpiRRxb+ftJb5W4B8xfFZR+N0xvLXE+jUh6/TGxvEPAQfHKqua5iWMKpM0XNKg+PFUYGnhrkXS6Ph3PKEK5xdF4VsA5wNXxe2fBg6KT2psRWh0W5dVWtKqZJolNce0IulQQknlzxkmp09ihnkNsNbMvp93fHoiZdwXASfE3+QM4EUz2wAsBmYrPDE3gnAzsDgu2yhpRtz/CcBvq5+a7mWdXkmHAV8GjjSzV6qbugrJu9W93ifCBW8D8AbhbuIU4DTgtLh8JuGOeB1wM/HJibjsHuDPhKqZgxPh8whPaDxOqN4pvJE/FFgIrInbndMP0twGPEZogLyT0A1z7t97D87VewlVEI8AK+N0eN7x6kvci75rAVcCfwUeJfF0E6GacX2cTk6ETyW0Wf0V+FHiuz4q/p5eB/5FuOA2cnrXE9o9Cse6Ku/vvLvJuwZxzjmXildJOeecS8UzDOecc6l4huGccy4VzzCcc86l4hmGc865VDzDcHVP0mZJKxNTW95xqiRJ+0man/h8mEIvwOtiem+M77Ug6XpJHyva/qUu9j1I0tLYP5lzXfIfiWsEr5rZvuUWShpgnZ281aOvAt8CUOgq/grCy15rY9iRhPdVnu7pjs1sk6S7gGOog64pXL68hOEakqSTJC2U9Dvgjhh2jqQVcfyBryfWPU/SY5LulPQLSV+K4XcrjssgaaSkp+L8lgpjGRT29ZkYPituc1O8+18Q3+5F0jRJ9ymM6bFcUoukeyTtm4jHvZL2LkpHC7C3ma2KQV8Gvl3ILADMbJGFHoS7OyffSJTC/i7purjoN8Ane3SCXb/kJQzXCJoUOjoEeNLMCl2mzyRcbF+QNBuYQOjAUcAiSQcQev89ltAz6QDgYcKYG105hdAlxDRJg4F7Jd0Rl+0H7EHoL+he4D2SlgM3AseY2QqFwaFeBeYDJwFnxf6zBpvZI0XHKrwlXLAHcFk38btU0vnFgWZ2AXCBpGGEN+5/FBetBqZ1s0/nPMNwDaFcldQS6+z+fXac/hQ/DyVkIC3ALRb78pG0KMXxZgN7J9oKhsV9bQKWm1l73NdKQlXRi8AGM1sBHZ0zImkh8DVJ5xC6lbi+xLHGAM+VioSkbQljhTQDV5tZISM5x8xuSqz3UmJehKqnH5jZQzE+myVtktRiYRwI50ryDMM1spcT8wK+Y2Y/Ta6gMExsuf5x3qSz2nZI0b7OMLPFRfuaRegHqWAz4X9MpY5hZq9IWkIYlOfjlB4Y6tWiY68hjHa4ysyeB/aNVWhDy6Sh2EVAu5ldVxQ+GHgt5T5cP+VtGK6/WAzMVRjrAEnjYg+5S4GjJDXF9oIjEts8RRiwCeBjRfv6rEJX2EjatdCjbhnrgLGSpsX1WxJPJc0HLgdWWOnBsNYCuyQ+fxc4T9KkRFhzF8fuIOlDwKHAmUXh2wLPWeg237myvITh+gUzuyNeZJfFduiXgE+Z2cOSbiT0Fvo3Qt1+wWXAryQdD/w+ET6fUNX0cKzieY447GaZY2+SdAxwhaQmQqnhEOAlM3tI0v+A4jv+wrbrJA0rVBeZ2aOS5gE3xAzuecLTURemOA1nEwbfWh7PwaLYrnEgcGuK7V0/573VOpcg6SLChby7huVKHW8sYaCdiWb2Vpl1vgBsNLP5pZZXIA43A+dajuNpu/rgVVLO5UTSCYQxo88rl1lEP+HtbSOVjMMg4DeeWbg0vIThnHMuFS9hOOecS8UzDOecc6l4huGccy4VzzCcc86l4hmGc865VP4PIG3t/CWXyxoAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -185,29 +194,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 930\n"
+      "Starting experimental run with id: 939\n",
+      "2107\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d5c0a91128>], [None])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x152281f2278>], [None])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -231,29 +241,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 932\n"
+      "Starting experimental run with id: 940\n",
+      "4215\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d5c0aa29b0>], [None])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1522960fcc0>], [None])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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lIvK1iJj5vA0GgyFhktYwrgHGq+qOwJnAPWnrrwQmx16rmDD6hcFgaEgkLTD6ACMBVHUKUCEimwCISDdgCPBYctUzGAwGg0PSAmMCcCyAiAwAegLd7HV3A1cDNclULT6ML8NgMDQEkhYYw4COIjIeuBz4GqgSkcOBhao6LtsBRORCERkrImMXLVoUcXUNBoOh6RK7wBCRS0VkvC0k2qjqOaraD8uHUQ7MAPYCjhSRmcALwP4i8g+v46nqI6raX1X7l5eXx3QWBoPB0PSIXWCo6nBV7WcLiTUi0txedT4wRlVXqOofVbWbqlYAJwMfqOrpcdc1aowlymAwNCTKEi5/O+AZEakGvgPOS7g+BoPBYPAhUYGhqp8BW2XZZjQwOo76xI2awFqDwdCASNrpbcCMxzAYDA0DIzAMBoPBEAgjMAwGg8EQCCMwEsRESRkMhoaEERgGg8FgCIQRGEWA0TQMBkNDwAgMg8FgMATCCAyDwWAwBMIIjAQxliiDwdCQCCwwRGQjESmNsjIGg8FgKF58BYaIlIjIqSIyQkQWAlOAeSIySURuF5GMKT0MATAqhsFgaEBk0jBGAVsCfwQ2VdXuqtoF2Bv4HzBMRBpdBtkkMDmlDAZDQyBT8sEDVHVD+kJVXQK8ArwiIs0iq1lTQJKugMFgMATHV8NwCwsRGSQi59jfy0Vki/RtDHlgFAuDwdCAyOr0FpHrgT9gmaYAmgGes98ZDAaDofESJErqGOBIYDWAqs4F2kZZqaaC8V0YDIaGRBCBUamqim1AEZGNoq2SwWAwGIqRIALjJRF5GOggIhcA7wOPRlutJkZCisakucupGDqCD79flEwFDAZDgyKrwFDVO4CXsSKjtgGuU9X7oq6YIXrGzlwKwMjJCxKuicFgaAgEmtNbVd8D3gu7cBHpCDyBNd5jHXCuqk60180EVgLVQJWq9g+7/KRJOkut2GG9NUlXxGAwNAh8BYaIrCSDsURV24VQ/jXAeFU9RkS2BYYDg13r91PVxSGUY/DADAMxGAy54CswVLUtgIjcCMwHnsVqY04jvCipPsBtdnlTRKRCRDZRVWMjiQNbxTAKhsFgCEIQp/fBqvqAqq5U1RWq+iBwXEjlTwCOBRCRAUBPoJu9ToF3RWSciFzodwARuVBExorI2EWLGpbzNul22tEwapKuiMFgaBAEERjVInKaiJTaCQlPw/IrhMEwoKOIjAcuB74Gqux1e6nqLsChwKUiso/XAVT1EVXtr6r9y8vLQ6pWvCTVXkvKJmUkhsFgyE4QgXEqcCKwAFgInGAvywsRuVRExttCoo2qnqOq/YAzgXJgBqQGCKKqC4HXgAH5lhmEFes2cNT9HzN90aooiykqBGOSMhgMwQkSVjtTVY9S1Y3tz9GqOjPfAlV1uKr2s4XEGhFpbq86HxijqivsuTccH8pGwEHAxHzLDMLoqYuYMHs5d773fZTFFBWOhmEEhsFgCEKQXFLdROQ1EVkoIgtE5BUR6ZZtv4BsB0wSkSlYpqcr7eWbAB+LyATgC2CEqr4dUpmelKYcwPG1nnGW5YVjkTIpSgwGQxCCjMN4EvgnlikK4HR72YGFFq6qnwH1JmJS1R+BnQo9fi6UOGMSauIsNVmMhmEwGHIhiA+jXFWfVNUq+/MUlq+hUSF26xnnILak2+mUDyPhehgMhoZBEIGxWEROt6OkSu1Z9n6JumJxk9IwEmg9k+7hmwF8BoMhCEEExrlYUVLzgXnA8fayRkVJAhqGaagNBkNDIqsPQ1V/xpoPo1FTYovOpmSSMhgaKwtWrGPpmkq23TSMDEYGh6wCw56O9XKgwr29qjYqISJNsL9voqMMjZW9hn1AVY0yc9iQpKvSqAgSJfVv4HHgDaAJxRBFT9K+C4OhsVJl8t1EQhCBsU5V7428JgaDwWAoaoIIjHtE5HrgXWC9s1BVv4qsVgmSRK/fmIYMhmiorKqheVmQ2B5DEIIIjB2AM4D9qTVJqf278dD0XBgGg8GQE0EExjFAL1WtjLoyxUBT7OtLExKWlVU1PPXpDM7ZawualZqeZ2PHaO/hEuSNmQB0iLoiSdOE2sx6NCXn+5OfzODWN6fw9Kczk66KwdDgCKJhbAJMEZEvqevDaFRhtYamwcp11nQrayvDmtLFUMw0pc5QHAQRGNdHXosiIukMsknQlExSzsDMpnTOBkNYBBnp/WEcFUkaSbAFaYIyKjGc8Pwk77fB0FAxXj9Dk8LRIEtLjMAwGHLFCIwmTFPUbByTlJEXTYOm+IxHiREYNqb9aBqkTFJN6I4vW1PJ2xPnJ10NQyMgyBSte4nIeyLyvYj8KCIzROTHOCqXBKZH0rjRlA8j2XrEyUXPjuPif4xj4cp1SVcldsw4jHAJEiX1OPAbYBwQaiyiiHQEngC2BNYB56rqRHtdB+AxoC/WeLpz7SldIyHJBiTpR7op9bZrTVJN55x/XLwaMJ0hQ+EEERjLVfWtiMq/BhivqseIyLbAcGCwve4e4G1VPV5EmgOtI6qDoQlR0wSd3lXVVkafpnTODkZIhksQgTFKRG4HXiX85IN9gNvs400RkQoR2QRYC+wDnG2vqwRiSU1iVNjGTVN0etf6bQyGwggiMHa3//d3LQsr+eAE4FjgYxEZAPQEumGZvhYBT4rITljmsCtVdXX6AUTkQuBCgB49euRdkaZklmnKpKZJaEImKUlwvvqkaYKnHClZnd6qup/HJ6xMtcOAjiIyHmtWv6+BKixBtgvwoKruDKwGhvrU7xFV7a+q/cvLywuukFFhGzfaBDUM51SN9mwolCBRUu1F5E4RGWt//i4i7fMtUEQuFZHxtpBoo6rnqGo/4EygHJgBzAZmq+rn9m4vYwmQyGhCHc4mTY2doL8pOb2dUe1NsTPUFFP9REmQcRhPACuBE+3PCuDJfAtU1eGq2s8WEmtshzbA+cAYVV2hqvOBWSKyjb1uMPBdvmUWO0k91OZValqYttNQKEF8GFuq6nGu33+xtYMw2A54RkSqsQTCea51lwPP2QLlR+CckMr0xLxMhsZKScqH0fQe8qZ3xtESRGCsFZFBqvoxWAP5sKKYCsYeV7GVz7rx1HW0R4pj322C71SToglZolxYJ90UBYYhXIIIjIuxtADHb7EUOCu6KiWLcQwaGhuOkGyK8qIpnnOUBElvPgHYSUTa2b9XRF6rBDAPliEq1m2oZk1lNZ02ap594whIRUmZZ9xQIIGTD9rO6EYpLKBp2zqbppkmPk5+5H/sctN7SVejaWrPTfCUo8Rkq7VJMvzO9PwaN+NnLUu0fCeEuCkO3DOEixEYNuZdMjRWpElHSTW9c46SIAP3ThCRtvb3P4nIqyIS6SC6JGmC75ShiWCebUOhBNEw/qyqK0VkEHAw8DTwYLTVSgCt88/QyGlKjWet07sJnbRNEzzlSAkiMJw5MIZg5XZ6HUgm3CNCjOraNGiKDv5UapCE62Fo+AQRGHNE5GGstCBvikiLgPs1KJpiT6QpnnNTpmn6MAxhEqThPxF4BzhEVZcBnYDfR1qrJDFPmKGRkXJ61yRbD0PDx3fgnoh0cv0c7Vq2HhgbbbXiR1M+DCMxDI2L1Ehv82wbCiTTSO9xWP1tAXpgpQQRoAPwM7BF5LWLkab8KjVFu35TosSkNzeEhK9JSlW3UNVeWOaoI1R1Y1XtDByONV1ro8I8WIbGitMfaIo+DEO4BPFh7Kaqbzo/VPUtYN/oqpQM5lUyNHaaorxogqccKUEExmJ7wF6FiPQUkWuBX6KuWFIk9VKd99SXvPXtvGQKNzRqREx6c0M4BBEYp2BNnfoa8G+gi72sURHHu7S+qpprX/uWX1atr1f2yCkL+fVzX0VfCQOQjAM4KbNn7ZzeTQ8jI8MlSHrzJcCVMdQlYdT1Nxre+nY+z33+M2srq7nzpH6p5abnFyfJefhVEwowSM2HYZ4zQ2FkFRgisjXwO6DCvb2q7h9dteInjnfJ6dVWpxVWlVCAfFMOs5QEBEeNKiUJlFvr9I696MRpys94FASZce9fwEPAY9SmCQkFEekIPAFsCawDzlXViSKyDfCia9NewHWqeneY5btxXqYoX2e/RqqyyjzUcZOISSr2Ei2kCYfVxsm3s5fzy+r1/GqbLklXJTKCCIwqVY0q2eA1wHhVPUZEtgWGA4NVdSrQD0BESoE5WD6UyIizAUl/cZPSMAz5o6p8N28F22/ePvvGNkmZHpt0WG2Mp3zE/R8DMHPYkPgKjZkgTu83ROQSEdlMRDo5n5DK7wOMBFDVKUCFiGySts1gYLqq/hRSmZ7E8S7Vjrity4bqpAVGNHrV6KkL+WHBykiOnTSvfjWHIfd+zDuT5gfeJ6n2Osk5vX/+ZQ0LV6yLv2BDJATRMM6y/7vzRymWmahQJgDHAh+LyACgJ9ANWODa5mTgeb8DiMiFwIUAPXr0yLsicbxLtaaBuqVtqG6cPb+zn/wSqN/j+mHBSlo1L6Vbx9ZJVCsUvrcF4YzFqwPvk5jAwPu5i4N9bh8FJNfrbpxvVnJk1TDsEd/pnzCEBcAwoKOIjAcuB74GqpyVItIcOBLLj+JXv0dUtb+q9i8vL8+7InG+TOkl1TQxb+SBd41h0F9HMXneClavr8q+QxGS6Y4tX7uBOcvW1lvumITGz1pGxdARfPXz0ohqB4P++gH73TEacM+4F1lxjYYPpixg6erKpKtRtASZca+1PXDvEfv3ViJyeL4FisilIjLeFhJtVPUcVe0HnIk13mOGa/NDga9UdYHXscIkFpNUqrC6y90v8vqqUOMKippD7/mI859umHksnQ6GlzFv8N8/ZK9hHwCwtrL2fjq3efTUhQB8OHVRZPWbvXRtPe0nyYihpEJ6cyl2+ZoNnPvUWC54prBnMhczZUMjiA/jSaAS2NP+PRu4Od8CVXW4qvazhcQaW4sAOB8Yo6orXJufQgZzVJjE8TL5xeC7nZFPfDwz8noUE5/PSCZpQKHtl7O/1z1d7BqYud11b6e+O/e5JsO+UVA70jue8rwY9vaU5AoPyAY7+OTHHMyMXlz72kSe/nRmCDXKTlV1TazCOIjA2FJV/wZsAFDVtYTnJd0OmCQiU7C0idQAQRFpDRxITIkOnUClKF9ix5Y8Zf6KOsvdAmNtZcM00eRL3I1YWPfXqXYu4zlSt9n+UhWT7yruKVorho5g2Ft1BcRTn8yMpex0cukIlvj4GL1Ytb6KQ+4ew8Q5y+utW7xqPdf/Z1K95eurqrn0n18xs0CB5Kb3tW/xmxfHh3a8bAQRGJUi0gr7HRGRLbHmxCgYVf1MVbdS1W1V9VhVXepat0ZVO6tq/TsSAfE4va3/0xfVfWBe+WpOrPUwFC44MmkY/vtYO81aavk37h81rbBKBCTOKCnnHB/6cHrd5dEXzfK1GwryB+YywHHcT0uZMn8lf81Bc/r8xyWM+GYef/r3xPwq6MO/x88N9XiZCCIwrgfeBrqLyHNYYbBXR1qrBHAe9Cheqp9+Wc3iVet9+6JvTKi94U0xVP6pT2bw8IfT+fiHxQA8/OF07n7/+0jLLNgklUcT6DRES2J2qtY6vaN/uHwb24iLXrxqPTv95V3u/eCHusXmUW5UmlhpiXUjqkNSq5PwCwXJJfWeiHwFDMQSwleq6uLIaxYzUV76fW8fTWmJcO/JO0dYSu4Ui3C64Y3vUt9nDhvCbbY546oDtk6qSlmp1TD8VYz6SSZtR3nM2UFqw2qjL8stlH5ctMpzeRQ4fqO3vp2f93NTE2GnEWpNXmFdiyTe3yDjMMCa/2IQVrvajIhHXSdB1C9zdY3WyyHlWY9GbpT66IfoIoOSIP1xWbluQ+r7J9PrOvSdnmXc2aTi1TBqy9j/7x+mvkddsl9jnEu5UfvTSkK+D0Hak7AJElb7AHAx8C0wEbhIRIZHXbG4CfvaV9coP/+yps6yti1r5fOsJWvSd4mkHkkw8NaRDLx1ZOr3sjWVzFm2loqhIzjj8S8CH+ftifNZtqY4Y+L9OhjHP/hZ6ntV2gj+C58dZ+9Tu9On06NX1uNIPjhj8WqWrK70fX6jNp+E0RiH3Vm7673vueqFr1O/wzZJJZHqJYgPY1/gYFV9UlWfBA4DfhVprRIg7Et/z/vfs8/to+pGRLgKOfRqi368AAAgAElEQVSejzz3SyL0MWytav6Kdcx3pYPYa9gHqXEJuXDxP8ZxSZHOEVIbJVWXqa5UKOur6goMZ8DeB1MWppad+ujnTJobcVxH6gZH93Dtd8dodrnpPd9GrEYtX146T30yI6fR8n74JVjMRVCp3031Ki/A8e4Z+UMdh3TY4c1JpKALIjCmAu6cG92Bb6KpTnKEKax/WbWe5z7/GYC5rhG/7pdpVQMd4ZwPqyvzH4z4s48mViiF3u4gPox0DcOPZWs2ZN+oANI1jNXrq1i4Mpr8Tpl6z/vePrrO78qqGm544zuOe/DTgssNI8FiVD32dGtCWB20ojRJAZ2BySIyWkRGA98B5SLyHxH5T6S1i5EwH5Zf3T6aX+xImN+8VBsj/fjHM/x2SfH6+DlmopsICUuZcswXzss/Y/FqKoaOqLNNZcBxFlH7NNLDaofc+xEDbhnpv0MB5NN7Xr62cIHpJ7hzeZXyqbtzfGf0vhd7/20Ub0+sHf0d1uudhEkqiNP7ushrUQSEeelXurSHBStqI2U+nZ59VPO85esYOXkhB/RJT9obDv/+2hrzcfTOXQOd84p1G2hRVkKLstJI6pONqIIQCj1sSsOwf5//9Jf1trnpv9/VW5YE6b3vmb9Eo7UBfJblGVdVRIQj7/+Y9q2a1alXGBRypFw6asvShJyTaNOPtyfOC328RBI56IKE1X4oIj2BrVT1fXsQX5mqNq681UXUq1+zoZqTHv6Mw3fanDMG9gz12FfZo0KP3rlroO13vOFddu3ZkVd+vWf2jSMgqpnxCjZJOV9EWLq6st5gzGJCQg7ndPhk2mJOe+xzbjxq+9Syi/8xLuM+NQqlAt/MrvXbhFGtMMZRpXcCMnHF85Yz++Npi1OZizMxae6KrNvkShL+ziBRUhcALwMP24u6Af+OslJJUDziAspKhM9nLOHPIY8IdbNuQ3XgpnjcT8GyqqoqTwQwuzUGnMZFVdn5pveSrUwWojJ5PWXnS7ru9fppMPyIyoziNJ5BIp0WrlznaQbzqlt1jfLImOl1kkimc9BdY7KW6S5v/KxlWbcPQljRVrkQxCR1KTAA+BxAVX8QkUY3B2FNKkY+/jmX04kjMqiyuiZ0IfnFjCXcWKAZJh81e/X6KsZ8v4hDd9gs+E4hNVz/tIMbiplcxmGMnLyAb+csDzT4LahT301UAsPPnOS1eMAtI2ndvJTvbjykznKvR++NCXO59c0pLF5Vyawla/hh4SqeOXdAzvVbuDKUbEp1KMqR3sB6Va101FoRKaO4OuSh0OhOKAulETgH1lUVHue3Mi16LEg1b39nKk99OpN/XbwHu1WENRlkNqwnJgyHbVx9lCDty3l2uvlAAiMP4R5VG5fSMAIef42HxuDVADuzYT4y5sfUsj3zCBGPgmKNkvpQRK4BWonIgViTGb0RbbXiR3NQaQ31WVNZxVlPBB+U58fv/zUh531m28n8gkx84zdNbq44z4szGCsuZi1Zw3EPflonXDsb+cT/qypbXfsmT33ib2LMZ2rh6ExSPhpGDne6Nu187T0tK03e4uBHUfowgKHAIqyR3hcBbwJ/irJSSVCsYiKMQU1eKKRavTBeiee/mBXCUeDd73KfK6t21LX3mfz2pfGpkGbNsSfqx4ffWylOmpcGeYUyk4sZ9KWxsxj301L+PX5O9o1Tx7fIpbGuUWvqYHeer3TysaFH1ciFkQfKS8OYvzx8U1JYJBElFWSK1hpVfVRVT1DV4+3vxdq+5k3tDGr+L+897//AhQXOxpUrzjSbYZPtFr4+fg5fzlwS+HhRhZAGaUqdxsKvs//qV3Pq1a/QR3jecmvgW+sWhYcb59ILdoRipW3+W7xqPeN+ynyf8hnoHeT65GOSis6HkdtyL7xOJ5f05VEyd9naehp0TaqjFF89fAWGiHwrIt/4feKrYjwEebDuev/7vHrAhfLZ9F880yoUgpK5/bjyhfGc8NBnGbaIh0wjqR1SGR3EcsS++tVs396X1+FUlTvemcp0V3bVdEZNWUjF0BFMX7SqTmMaxviUe97/gZoaZeHKdazJMoFWKmeSfX5H3f8Jxz2Y+T45naBcGmu/LT+ZtjjVcOWjYdw38gfmLa9vTnt9/BzenjjPd7/qGmX4qGm+GRLCEETFYI6+ZcR3VAwdQcXQEXVmbtxz2AcMuPX9Ots6178kRomRScM4HDgCay6Mt4HT7M+bWGG2jYpieFj8OOXR/9VLqxAGjUVPdM5j/YYanv7sJ3770gReGpvZROY+9YUr13P/qGmc6ZMYUVU55ylrYNZXPy2t0xNdGkJyxM9nLGH7699hwC0jOfy+jzNu6wQrOA7POTn4MnK53V7Pxi+r1nPaY5+z7+2jGPbWlJz8KA6PfjTDc78rXxjPxf+wogNHTVnIwXeNqROF9c6k+dz+zlRue3NyatmTn8xIJfdzhznXOY8c6hbHrJvZePSjWp/RT2kDLDekZQ5wnsM43Wi+AkNVf1LVn4C9VPVqVf3W/gwFDo6vivHQEBrPTL3PgbeO5J73f/Bdn05DON+gOM7Xy57/muV2A+5OfuiF+/yd71U+2dzScz3VnfMhHM1v7YbqQMcrKcndgV1iv+W5aRj1t931ZquHu2JdFQ99OJ3Fq/ITlnOXZb43V7/yDVMXrKwz0dT6Kuv6rHZpGH9547vU6OmUD8PnmKrKsQ98wpvf+msxKROP/Xv+8mjybXlx+H0f0f/m3Mbz1JqkikPDcNhIRAY5P0RkT2CjMAoXkY4i8ppt5vpCRPq61v1GRCaJyEQReV5EWoZRph+5DPzJRCazRqH0ue4d3+PPX7GOu3KZpU797dSZBikVE4tXrWfawpUpX0t1jdLMdkJni+B5aewsps4PlqwgvaFNYsCUg9NgfjJtMXveFiwfVK1JKng5UXYoLn/+6+wb+aBYA0nT83b5nZt7BPhXPy/zHOPU78Z3ufz5r+udsyPEo2TawpVMnb+SiXNW1BPAv//XhIyO7Wy+uygIIjDOA4aLyEwRmQE8AJwbUvnXAONVdUfgTOAeABHpClwB9FfVvkApcHJIZXqSSiaXZ8zQvOVrWb52Q+BR0fly5Qv5vWyzlqypkyDNTzCuraxmu+veznisFes28MdXv81qbw8Dr7tRXaOs21DNvn8bxQF3jqmjqqf8GVnu45T5Kzn47jH2Pt4RNsvXbGDawpV1rpR6bBcnD4y25sr+ZvZy5rp6wBPnBEiRnk+q7wRxV8E9a+Ad70ytv22GKKmaGmVDhlzgy9Zs4I0Jc+u9E3HE9hxw55jUc5jOj4tXsyBDVuEkfBhBckmNA3YSkXaAqGqYyfv7ALfZ5UwRkQoRcbLulWGN/dgAtAYinem80Gdjj9s+oH2rZlw7ZLtwKuRDs9ISlq/dwJrKKjZr36re+v9+M5fDd9y83vKD7hpTp8ek6q2+X/1K9niGB0dP5/kvfqaic2su2nfLnOqfMwIf/7CY/hUdadnMcjBf/I9xvJcl+CCXd8itMcxbvpY9bvuAZ88bwHWvT2LG4tX8Ke2eRp0ltKZGeWjMdE7bvWcqQV82vpm9nL5d23uuqx3pHbwOSfr00m9dVXVNnYF2Xhqer4YBHP3AJ3VyV/kRp+JoRbZlf0h/WVXJHrd5DxTMJfdVWPgKDBE5HfinqtYAqOqKtPVbApupamYvXWYmAMcCH4vIAKAn0E1Vx4nIHcDPwFrgXVV9t4ByYiGUUb9ZKBFh/zus9Okzhw1h6vyVdNyotlG57J9fs2vPjvWESbp6vWZDNes21O91vffd/HrL/MgnrDJXfly0mtMf/xyw5vsGMgqLbG25l+bhdD4dcwfAC1/MSo2BuXnE5DrbRz3C9sPvF/G3t6cybcEq7jypH0tWV9Jpo+YZ96msym4+yWsyIWDBinU5hViHTe9r30p9V7wFdq0Po+66kx7+zNfXUllVwxsTavuh6T6BKO9ytsg2hykZzKbFFiXVGfhaRJ4QkUtF5EQROVNEbhSRD4G/AYXGmA4DOorIeOBy4GugSkQ6AkcBWwCbY/lRTvc6gIhcKCJjRWTsokX5zxedshUGuPZf/WzZUL1mSlsUQc4YNwKpuTYADr57DIOGjaqzzW9fzD5aeq9hH3jGmPfs5O2eckIhq2uUB22ziKqiqvz93fomgihYvnZDyvnpR61pMfN6N+5BX9MW2j6iDM+BRjzT2TpbuK+urOL97xawy03vZZ3KtbK6hpfHzfYUCn4jvdO3Xe5y7rvXnPbY51z2z/z9DvniJd9U1VNg+2l9mRzz9478gf9zZRaoF2FVBGa5ZhlGmhfVOAxVvQfYBXgeKAcG27/nAGeo6nGqGjwsx8YWPuNtIdFGVc9R1X5YPoxyYAZwADBDVRep6gbgVcAzv7aqPqKq/VW1f3l5ea7VqT1OvS/+vGVHWoz5vv5LfLuHfTUqnHmzK9McvF8U0Bts2dx7XIEjCN2RK5PnrWTdhhru+2Ba3uXlQk2Nss2fMvtXUuTwFtUKDOVuO9LMt9emsNON8Si770xawPn2QNFsJpX7Rk7jd/+awAiPKCC/kd6qlrnnv9/MRVV5f3Jt/8+9bUqIxkC2oJF1G2o8HcH5jOBPn3WwGAREOunPYVV1DRVDR3D3+9/XOr1j9Hpn9GGoajXwnv0JBVUdDgwHEJEOItJcVSuB84ExqrpCRH4GBopIayyT1GAg0iHWuTwsTqx0kvHa4B86Wl2jvPXtvFT21he+yJxR1X0e2dINLHONO6isrmF1DI5vhyC+g7xMUvZObs3N79bGMcuZVwnZHjUnaeOKtfXvRwbZxyMf/cjf3p5KzSlp6xJqPA+95yM6ZPDbvD95ATt4+Gpusc2GhVQ7weA3X9Kr5CT4fGDU9FTnpih8GDGxHfCMiFRjTf16HoCqfi4iLwNfAVVYpqpHoqyIl6niomfHss/W5Zy2e88oi46EiXOXs+eWG9O+dTOGvvpt4P2yNYgnPfK/1PcubVtw+9vxaVRB3ufaKKlgLF1dyQF31o9S8Wtk42hUFmYZQ5IJkfpJGEtSJqn0gV+aGmvw8IfT2XbTdql1SaVtr6yqSV37g+8eQ4uy+kYQL6f3d/MsF+uilevrhdwGxT0Oo7KqJnLzchDSzWROHd1WhaKKkooSVf0M2Mpn3fXA9fHVpe7vFes28M6kBbwzaQGn7d4zY9bOYmT4qOkMHzWdabccmtN+2WYGc5ukfliwis5tMjtjwyRI7/7ekf5W0mte+5YvZ9Q11/mNCPd7CeNIKe2V8C9om/DFjCX1fEq1JimY4Jq857rXJ7Le7rFOmruizr0vhhxKfkEkjnAolHRt0/18Xff6RF74MpyEmmHi5T+Lc+BeRoEhIiXA8ar6Ukz1SYxUHDfKrCVr2PtvtY7kl76c5fkSx5zZOi/cESZh88XMJRy8fTRzj3uRS1v91c/1x8N49Zr9fAO+TvOEbDVBxwe99rV/FltVOGr4J6nfYWUYbjS4bm0u2YDTKZHwNNErXxhf57dXp6loBu7ZIbWXxVSXRHFugypckTY4zm9sQpyqoEOSfpNlHnmT8pgSIW9mL12TfSObj37IHFXkMMvnmH69tnxSSu/as2PO+3iRr4kklxn3ioG4Zr18MU27PPWxz0M57oe/3y+U43jhFcpeLGG1Du+JyO9EpLuIdHI+kdcsRqprNBXpowRP/bA+hBnmHLLF2UfNtIWrstp++91YP/Yhzh53ttj1is6tcz6mn4YxbaF3/Hs+Pcf+FYULjPcmL2C3W97PvmEaVvSTNcJ/SYAJpoqBbHnAoqbQ9jfKqKWi1jBszsWa13sMMM7+xDspRMS4M46O+2lpYNNH0FG46fToVNuwvf/bfRlxxSC++vOBeR0rLEZ845+UDfzNQSOnLPRekQDOSPAwmOAjSHLtpZ+6ew8qOheeeu2LGfmFSjupRMCarzsOunaoO2g0LA0rToJ2Brcsr39vW3o46sPCS8MoquSDqrqFx6dXHJWLi/Q24NsgeXmAjq3z0wrcEVm9u7Rh+829Uzp4EZW6nm0qSrftO2x+c8DWXHtY4SlV0jXD8576kk+nBTNNBSVXharv5u1jGRHvh3tc0Mxfgpv0CiFdYLx00R6cvWdFZOUtXxN+hoWg9/mli/ao8/vuk/rRuU0LXr90L+48cafQ63Wixxw1RTFwz0FEWovIn0TkEfv3ViJyePRVi4+fl+T3Is1bvpY73pka61SJhQzKy0Tcc1O76dymOecN2qLg46QLjJFTFoZml06VkaEladOifgzJRi1KfZ+PI3aqn/OrMRL1o3X8Q5+GfMTgFU73H+y2hWWt36l7B3qGoFmm4zX/SbH5MJ4EKqkdaT0buDmyGiXAcQ/m98DdPGIy94+alnMI4qwl1k0/fMfN8io3bAShLEaBceE+9RXUMOy+6SPeo+CBUf6j2r38Oa2aldKhtbfpcqduwTXLBkXarRQRBvbqXGfZbw/cOrTifohxJHo67dLM0kn0u4rNh7Glqv4N2ACgqmuJd3Bh0fPwmB+zbuNl6zyxf/c6v/9+QvgqbFDi1DDSfQ1h6WdV1dFreivW+Y9s9yq9tEQ40kOTOKhPfOHIceP1JB3Sd9M6v68Y7Dn8qkFx/RF96r03SUROFpuGUSkirbDfBztLbfJDIBsYl+7Xu96y9F79kAQ1jjg1jHolhRRpFYeGkQmvayji7ZQcftoucVQpEZJOmRMXXibIJE69qHwYwA1Yc3p3F5HngJHA1VFWqjEyqPfGPH/BQB46vbahSO+dhBnlkyulJeFFduzSo0PG9ekPuCMuZg4bwqm798i73KTDRjfv0Iq/Hb8jD52+a9ZtnZkBGyOX/Ko3x+/ajXtO7sdR/RqenyZoA+x0BF6+eI8sW0ZLUaUGUdV3RWQcMBBLgF6pquGGnjQFBPbYsq4dN85wuDh57Kzd2OUm/3yV6Q+4W8H4y5Hbc8HevdjvjtER1S46Tt29Byf2717H+Z4pqi3d/t1YaNW8lDts8+pR/bqmlvfo1DrvAJNixOnv9a/wHpbm57sKvx5FZJISkWexJjmarqr/NcIiP9wNR/dOVtihl5O0z2bt6i2LgzBzJLVrmbkfkv54u69Ds9ISNmsf6fTtkbDNJm05c48KwNIcf7VN9lT7x+/SLeJaJcMGnzEMcZo948CzoXYt2rK8DS9eOJDvbjw40npMXRBsbvowCBoltRlwn4hMF5FXROTKiOvV6HC/K5u1qz+1qsN/LtsrlDEJuRJmaHA2B3p6Sd071R2hnWSIb748e94A7xVpp3LmHj1TIcQlJcJJaYEPhuIl3dQapGO/e6/OtG5exikD8je1FhNBBu59ANwC/Bl4DOgP/DriejU63OanNnYP3GuwXFlpScZZtqLi/gzhormSi6ntlV/vweDt6kYMJRFpUihd2tXVivwUthuP6sufD++T+h2i6yhW+natqwmPuGJQ9p0ayG31y9m1edqARC8/lJ8JMupH+sUv40lHH8QkNRL4BDgJmArspqrbRl2xxob7ebn9+B35/cHbsEsP75QJm9rzce/UPbPzOCxGTl4Qa+7/5q7UCbv2rG//bYgaRjrp83L43ctN2jU88xvAgIo0f5zrCW8YKQ5zx92ROXaXrp5mRz/BsPsW0abf+8Mrwee8KYQg/ZtvsAbu9QV2BPraYbaGHHA/bJ3btODS/Xr79sQP3n4Tnj1vAI+ekT3aJgzmLo8+2Vt52xbs1bszvz94G/bftkvk5RULzj1+/oLd+fyawfXWX7zvlnFXqWBalJUw9NBtOc7lg2mASmFO9O7Spk4E350n9qN181pf3fu/3YffHrg1G7dp4bn/Uf26NsicWukEiZL6DYCItAHOwfJpbAp4XxmDJ7mYHkSEvbcqrzfncDFzzl4VPPnJTN/1V+zfmzNsp/APMTrpkiI9oKF187I6DYxDQwyvfeTM/jQvK+GOE3bkla9mA3UFhp857qJ9esXWEw6TZ88bQNcOrehV3oYfbz2MDTX1nfq9u7TlisFtMx6nY0xRU1ESxCR1mYi8CIwHjgaeAHKbxq0J8sy5dZ2g+djlSxtQt02QOqam9GRzbm3K8a9v1aVNHFUrahqi9c0JkHDf0626ZG4sAU7arWE6fvfeqpxe5dazWlIitCjLb7xUIWOMioUg3ZtWwJ3Atqo6WFX/YjvCDRno2rGu1S4fu3xnH/W2GBGBkb/dNxUtdMOR2/P8BQPrrHdwsvU2IHmYN9lOUUQaXJoMrxTvpSXCXr0tv4Y2Wi9GYey/7SY86DPCv6FoH0GipG4H1gEX29pGaAmPRKSjiLwmIt+IyBci0te17koRmSgik0TkqrDKjIv0d6qxN44lYoXH7r1VrSMwfaCig3NtGmI0lEOLEOc8CDMRX5i8fulensvDisC+/9Sd+cd5u4dzsAZOx4QnUAtKEJPUFcBzQBf78w8RuTyk8q8BxqvqjsCZwD12mX2BC4ABwE7A4SISWTcsiqyh6TbshmReyodsjX9JHZNUuD3QyTceEurxgvC7g7aJvUyHuAZ3+t1Tvxkpz7J9VNtu6l+/j67ej9cusRJfH77j5gzaauPCKtnA8BO2DWT23EAmqfOB3VX1OlW9DitFyAUhld8HKzcVqjoFqBCRTYDtgP+p6hpVrQI+BI4Jqcx6bFkevi29WpV/nl/be4q6N905rYfSO2b/QLaxF+61reycWekD9tx8cc1gnjs/WO+zVfPSRMauZMJxZm/kkaCuUPbeemNmDhsS+nHT8bulftPyHrT9pswcNoTytv6m1O6dWrOzTzh5XLSN4J4Exc9c11DmWw8iMASodv2uJrwhOBOw0o4gIgOAnkA3YCKwj4h0FpHWwGGA55BYEblQRMaKyNhFixblVYkoblVNDezZe2P62fH3Uc7zC9YkRG5OjXlkabbTczc+vcrb8MBpu/D3DDOSdWnXMlCKkEv3s8JSz92r8AmY8sWrnsOO3YHfHbR11kSM+fD4RzNCP6YXfp2cBCcQLJg3r9ib/3mEN8eFn1xoKAIjiKh9EvhcRF6zfx8NPB5S+cOAe0RkPPAt8DVQpaqTReSvwHvAKizB4jkRgao+AjwC0L9//7yuut/NGrLDZoz4NvNc1344YbTPnDeAn2OYGjP95Y7bAnbawJ45bX/YDtlTuQfRylJZdmM+X3dP8Z8u575Dl3YtuWz//KyoLcpKMs4p7Uz52mezdnw3b0VeZQTBLxQ8ycatY+tmLC1gStbeXdrUieaLG+fKtW1Rxsr1tU2aR6RuURLE6X0n1viLJcBS4BxVvTvfAkXkUhEZbwuJNqp6jqr2w/JhlAMz7HIfV9VdVHUfu+wf8i0zG37P/9BD8xvQvvdWG7PNJlaYYbuWzejbtXAfSa75pQptP5/3aAQzkT6Pczr5tDG5mPHc2+4cQa8+HXcvu6Kzv2ktH4KetpPEMhfuOblf8Hr4PEVJCoxChAUkH8bsmPP2TRsl7mfmKzZ8BYaItBSRq0TkfmA34AFVvUdVvy6kQFUdrqr9bCGxRkQcW8r5wBhVXWGX38X+3wPLbPV8IeVmwu8FyNf+fOeJ/UJPXX6Bx7Sm6bzy6z1T31sUOLfG7lt04oyBPXnhwtwERzon72ZZEvMxY3hdwnqT1tj3zr1pPllRN2puXa/HzuwfaPtWrusbxr3eepNan1Mmp7GbTKnT/TiqX1f+GLAj5HcZG4r5xItiSTuT/szsEELgTZgJRP3IpGE8jZVo8FusgXp3RFD+dsAkEZlil+HOgvuKiHwHvAFcqqpLIygfgKnzvUcet89zvoJMTr8o2bVnx5Tz96A+mxQU+llSItx0dF/6F5jOwHkv8onN92qHnzlvAL8/eJvU2AXnxdtzy9pom3waBccBv2dv71DgdMpKhak3HxJahNa7v9k3lRb+/lN3DuWYfgQ1ybgbtbP2qDU5NhTziRdJz0HjyFoBbjt2h9Tyw3bYjIsCdArTObF/bXqWMKco8CPTk9NHVU9X1YeB44F9wi5cVT9T1a1UdVtVPdYtFFR1b1Xto6o7qerIsMt24zeJvLvhSXL61HwoKynhygMKj0QuvEeW//5eDVufzdpZ0906moV9eHd4ZropK4jgfOa8ATx0+i6e6TuAeumpBWvEb6vm4c2SeI7tuA/a4Yi67XNufctmJVx3xPap5WHPDPnKr/ekdYjX0Y+/Hbdj5GVkY+kaa1bIecvXcsqAHnXme+/mETX4lyO3r7fMTbeOtfv4hTuHSaY3KWUstENbmzSHbF93Evt0G39Uw/4H9urENYdZJoRcNBcpyc9kUe84BbZKKQ0jj2e5S9v60UdO41+bDbZ+/YIOmvzLkduz79blfHHtYLq0bckhff07BWfukZtTPx9+c+DWzBw2JO/UE2HjvvelJcLkGw/hz4f34dC+m2bYK3d27dmxTmLGr/98YJ31XXye+y3LN8qpnBN3S37ukf9+YwXRfDmzvsHE6zE9Ky3FDsDGrohI97MeR16yTCXsJCIr7M9KYEfnu4hEF5qRMK18ek/7bdulTn6kdMeq3yxjhfLChXtw4T5W6OgZdiTSZfv1zrpfcVhq4fxBW9CjU2sO3j6/RmbEFYPqONSdRqx2tHj9fdJ7/WU+4T4bqmt4+twBnoIpnXTfSRzqvx9Ogx2GhpFpTEJJmrBv1byU8wZtEUmIeNuWtebfdFOwV/h1aYmEPn7KL9NslLg7VEHv59GuaW/dpt44/DO+AkNVS1W1nf1pq6plru/JzCMaA37TKbYoK+EGl3qYfnPz9XfkQkkAf4DT4y5koOD46w7MvhFw09F9s27Tq7wNY67eL2+/zvabt+f3B9cfVe04Xr0ar/Rghd0qvP0wy9dmj7i59rDteP6CgfUGGYbdy86Fe0+xfBxec4kEYYBrboZv/+I/fajzDGUSjS9dtAePBgwUCEpJifDxH/bjrSv3ZuawIXRsXT9tRhRt4ydD98s5OjBX0qOhnNNQNLBF4I+uiEnH0R1X6vSGl1s5YnnKaHcAABLQSURBVPxMMPXGObhu7ogrBqWyWUaJU7cazW4Cc1f3on16Mfp3vwpcToe0F9TP8X1GjmMv8qXSQ3tzzLVetyv9pfRz8p4YYHrUFs1KPHNixd0bvcr2R3Xt0Cplejh3r4qc7quDkwXAmYvjnz4j6ktreyi+DNiiEwf22cR/gzzp1rE129kpULyUOff75wjQQmlRVuqb/ywsnKy+229unZtbY/br4227ad1MwF6aRFxzzBiBEZD0e+S+udtv3j6WDJ1OmTWq3HrMDnx09X71e/n2NiUidepYsXFu9l43SYdRrq+uLzDOHVTBwF6dODlAyuwjXI5FN1GPvg8TJ0+Tu6MgIr73dVBv/xxNLcpK+eGWQ/nDIZbmtqfHtoftsGnRhKB6vluuqjUvLSlIwz/S5/mIgg4bWfV0gmhSGob6m5Hfvmofrtjf2wx9wT69OH1gD87ZqyLcivpgBEZA0jWPJDKtlqTZ77t3as2uMeTlydQoD9lhs6yRHIVykEcPtkvblrxw4R508sjy6W5efrz1MA7f0UdgBLiFxTLkoONGzfnuxoO55Ff+M/S5NalsucSalZbUeabdIZ4OhYRE58OjZ/avM4ufg9c9SL93/708wJziPtx7ys6x5OYC2HJj675s0bmuoFesMTLH7tLVY69ajTr9vNu2bMbNR+/gG90XNsll4WrgpLc1cTQstU7IeFuxE3frzom7dadi6Ih664b75PcPk1zmvd6qS5s6mYEzaRF+NuOuHVoxZ9laILkRuPeesjN9NmvLAXeOSS3L1ii4z+aqA7aiXatm3DsyWIKEk/p3Z92Gatq2bMbv/jUBqM2wHFfuqAP7bOJp3nI03J26tWfC7OWAJfC22qQN7363gPK2zeneqTXtWpaxYp13QGc+PrSnzx0QerjvCf27sWWXNrU+B9c73ap5KXee2I9Xv5pTb78O9nwZN0TcOcuG0TCyMNjHNpiuYHwze1nkdSnxeIE3amE90BV2j8XRONwaUJjve1JWCvdIaD8m/eVg/nvFIP58eJ966246ui//TpvfwU9JfP+3+2Ys56Or98tal0I5cqfN6R1gFjs3bq2iQ+vmnLBr/d66HyUlwjl7bUHLZrVNgmOSKpq0FSIMszWhARWd+M0BW/PChQNTzn+/ar588R6MuCK4BjLuTwfwydD92XfrcnaryC+wwA8RqeOgdjot7qp7BVScvWcFtx6zA6ftHo/f0A+jYWThwdN3ZfX6+r2WdBPVWI+46rA5qM+m3DxiMse7GoKenTfi8bP6s3svy1n36Fn9mbFoNc3LSur1n5uXlXg6kIPyzlX70KF1M3a/NdJxlJ68ecXeWXu6TnRUedv6vUIvB72f7HPSpW+oVo5zXetvbzgIxcoPVow8c+4Adr35/dTvbPm9MiFISjtLWlzUjrmpnWhIRCgrLWFgr1ontV+oc/8cG/04Z7pMNSOuqj94+q71tPmy0pKimOLVaBhZaF5WEmg2LKdh2aIA53I2enRuzcxhQ1LRIw6Dt9skNU6gTYsy37w0twQIg83ENpu2ZZN2LSM9Rz/KSkvCzzKaQVsae+2B/O+Pg+uMD2jbsllRCovHzuzPrcfsUK+hcxr8XNK7uNvcYpkRMZVOQ2o16UEeKVxyGel8Z4bU+nHiDqt1k09iyTgwGoaLXNTWdC7apxcLVqzj1xmcknFzyu49+PrnZakcNWHl0Xnzir1ZX1WdfcMiJ1Pce/vWzWhP8QkHLw7IENb68R/2o9NGzelz3TuBjuXMRb9913YpH0byFqnaBJPbbNqWz68Z7Dn6O5d6HrtLN3770oSQ6pc/fq/kiCv2ZnmBmXmjwAgMF9tvXtszH9grNzW2rLSEG48qrAcfNu1aNuOhM3ZN/T6k76a8Pn4OH/2wuKDjtmoebg6lpGjRrPEr2O5cQ0HYpUdH/nv5IPps1o4NRZJlsHbMjdW6+gVBdO3YihmLV9OyWQnrNlh1L5LI4KykC7t2RarNGoHhwfjrDszYIPbKMYdNsdCmRRnPnre7Z7RTU2HoodsyfeEqztijZ1G+kOlMuO4gJGa55szfUqrF1dpmq83hO27GfR9M47xBWzB81HSg/iDUYsPL6R2Ez/64P1XV8at+RmB4kOkhG/P7/VKDbwwND2d0c0OhfevknrViGbhXbvtmdtsis9b/mwO25oJ9etG6WSkdWzfn5hGT46heQeSbnHOz9sn4OIzAyJEertnVLt+/N/tuXZ5ha4Oh4eKYgM72yJgaJxUbb8QH/7cvPTtn1uxLSiSlNR6zc9cGITCctB/dOhankzsdIzAK4P8Oqp8Uz2AoRj66er/UXAy5MP3Ww4rCD5BrrrbE/fQBOWvPCvr16Ei/7tFPKxwGRmAAd520U9GEEBoMhXD8rt3YwWMO+e6dWtfLuBuEYjFL5Yp7ZrtiRkQajLAAIzAAOGbn4CNiDYZi5o4TimN8QbFg+oHhEkv8hYhsKyKfich6Efld2rpDRGSqiEwTkaGu5VuIyOci8oOIvCgixR3uYDAYDI2cuAL2lgBXAHe4F4pIKTAcOBToA5wiIk4ioL8Cd6nqVsBS4LyY6troufnovlR0rm+eeO2SPflk6P4J1MhgCJeOrZvRa+ONuOWY+ll4DfkTi8BQ1YWq+iWuecJtBgDTVPVHVa0EXgCOEis8Y3/gZXu7p4Gj46hrU+D0gT0Z/fv6CfR27tGxoPxDBkOxUFZawge/+1XeUwMbvEl6qGtXYJbr92x7WWdgmapWpS2vh4hcKCJjRWTsokWLIq2soWHRrNQYsA2GMEna6e31RmuG5fUXqj4CPALQv3//hhJNZ4iYL64ZHH6yQoOhiRPZGyUil4rIePvjNwfibMA9sXI3YC6wGOggImVpyw0hcszOltJ28Pbhz8mcNF3atSz6tBAGQ0MjMg1DVYdjObQz8SWwlYhsAcwBTgZOVVUVkVHA8Vh+jbOA16Oqa1PlrpP6cddJ/aiqrmFDAnlpDAZDwyIWk5SIbAqMBdoBNSJyFdBHVVeIyGXAO0Ap8ISqTrJ3+wPwgojcDHwNPB5HXZsiZaUllDX85LMGgyFiYhEYqjofy6zkte5N4E2P5T9iRVEZDAaDoQgwXkGDwWAwBMIIDIPBYDAEwggMg8FgMATCCAyDwWAwBMIIDIPBYDAEwggMg8FgMATCCAyDwWAwBMIIDIPBYDAEIunkgwaDwZAIj53Zn2o1KXFywQgMg8HQJDmgT+NLuhk1xiRlMBgMhkAYgWEwGAyGQBiBYTAYDIZAGIFhMBgMhkAYgWEwGAyGQBiBYTAYDIZAGIFhMBgMhkAYgWEwGAyGQIg2opGOIrII+CnpeuTIxsDipCsRM+acmwbmnBsOPVW1PNtGjUpgNEREZKyq9k+6HnFizrlpYM658WFMUgaDwWAIhBEYBoPBYAiEERjJ80jSFUgAc85NA3POjQzjwzAYDAZDIIyGYTAYDIZAGIFhMBgMhkAYgVEgItJdREaJyGQRmSQiV3ps01FEXhORb0TkCxHp61p3pYhMtPe9yrV8JxH5TES+FZE3RKSda92O9rpJ9vqW0Z9pquxYz1dEmonI0/byySLyx3jONBxEpKV9DSbY5/yXpOsUhCD1FpEWIvKiiEwTkc9FpMK17o/28qkicrBr+SH2smkiMtS1/Dl7+UQReUJEmkV9junEfc6u9feJyKqozitUVNV8CvgAmwG72N/bAt8DfdK2uR243v6+LTDS/t4XmAi0xpr98H1gK3vdl8C+9vdzgZvs72XAN8BO9u/OQGkjPt9TgRfs762BmUBF0vc9h+slQBv7ezPgc2Bg0vUKo97AJcBD9veTgRft732ACUALYAtgOlBqf6YDvYDm9jZ97H0Os8sU4Hng1439nO39+gPPAquSvudBPkbDKBBVnaeqX9nfVwKTga5pm/UBRtrbTAEqRGQTYDvgf6q6RlWrgA+BY+x9tgHG2N/fA46zvx8EfKOqE+zj/aKq1ZGcnAcJnK8CG4lIGdAKqARWRHFuUaAWTu+xmf0p+kiTgPU+Cnja/v4yMFhExF7+gqquV9UZwDRggP2Zpqo/qmol8IK9Lar6pl2mAl8A3SI8PU/iPmcRKcXqXF0d4WmFihEYIWKrpztj9UzcTACOtbcZAPTEeiEmAvuISGcRaY3Vy+pu7zMRONL+foJr+daAisg7IvKViCT2sMV0vi8Dq4F5wM/AHaq6JILTiQwRKRWR8cBC4D1VTb9eRUmAencFZgHYHYDlWBpvarnNbHuZ33J3mc2AM4C3wzuT4MR8zpcB/1HVeWGfR1QYgRESItIGeAW4SlXTe8DDgI72g3g58DVQpaqTgb9i9ajfxmpoq+x9zgUuFZFxWKafSnt5GTAIOM3+f4yIDI7sxHyI8XwHANXA5liq/v+JSK/ITiwCVLVaVfthCc0Bbp9OMROg3uK1Wx7L3TwAjFHVj3KtbxjEdc4isjlWx+i+QuobN2VJV6AxYPeKXgGeU9VX09fbDeo59rYCzLA/qOrjwOP2uluxeiCOKecge/nWwBD7cLOBD1V1sb3uTWAXbBNQHMR8vqcCb6vqBmChiHyCZff9MarziwpVXSYio4FDsDSqBkGGes/G0gRn2ybD9sAS13KHbsBc+7vfckTkeqAcuCjkU8iZGM55Z6A3MM16RWgtItNUtXf4ZxMiSTtRGvoHqwfxDHB3hm06AM3t7xcAz7jWdbH/9wCmAB3TlpfYxz/X/t0R+Iq6juMhjfh8/wA8aZe7EfAdsGPS9z2H61UOdLC/t4L/b+/uQqyqwjCO/x8SbczBCyPIICwoCkGUnAsJwqikxKRAsgsLqSAjyqKGqCkziQIVggqiGFACCT8wmSBQK0TRYAZNLVP7NLC6EAPLtAbt7WKto3s258xsaXKYM88PDuxZ+2Otfc7Mfmevtc+72AHMGep2DUa7gcfpOwC8Li9Ppu8A8A+kwd9Refkazg8AT877PALsAlpGyjmXjjssBr2HvAHD/UXqFgrSk0t782s2sAhYlLeZAXybL5AbaxfJvG5HvgjuA24rlC8mPYH0DamLR4V1C4ADpP98ljfz+QLjgPX5fL8G2of6M7/A92sKqUtuf/68lgx1m/5Lu4FlwNy8fGn+bL4jDVRfW9i/g/R00GHgrkL57PwZfw90FMrP5LLa79RFf58u9jmX6h4WAcOpQczMrBIPepuZWSUOGGZmVokDhpmZVeKAYWZmlThgmJlZJQ4YNuxJOitpb+E1aajbNJgkTZPUWfj5zpxV9VA+37WSrs7rVkuaV9q/YSZUSaMlbc9fQjPrl39JrBmcjpTOoS5JoyLl/RmuXgBeBcipKt4ifS/gYC6bC0wi5dq6IBHRK+lTYD6wZrAabM3JdxjWlCQtlLRe0kfAllzWLqlHaZ6OVwrbduT5Cj6R9IGkZ3P5NknT8/Llko7k5UskrSgc69FcPjPvsyH/978mp0ZBUpukXUpzLXRLapW0Q9LUQjt2SppSOo9W0jfb9+Wi54DXasECICK6ImI7A5C0rHAX9rOkVXnVJlJuMrN++Q7DmkFLTnQI8GNE1FKmzyBdbH+TNAu4jpTMUECXpFtImXDvJ+X2GUVKu7J7gPoeBk5ERJukMcBOSVvyummkNBG/ADuBmyV1A2uB+RHRozQ51GmgE1gIPJXzZ42JiP2luqbTN5fRZGDlAO1bIenFcmFELAGWSBpP+sb923nVV0DbAMc0c8CwptCoS2prnE+FPiu/vsg/jyMFkFbgw4g4BSCpq0J9s4AphbGC8flYvUB3RBzNx9pL6io6AfwaET1wLjkjktYDL0lqJ2XrXV2nriuBY/UaIWkCKenkWOC9iKgFkvaI2FDY7mRhWaSupzciYnduz1lJvZJaI81xYlaXA4Y1sz8LywJej4h3ixsoTRPbKD/OGc532xanwRXwRERsLh1rJvB3oegs6W9M9eqIiFOStpIm1LmPdDdRdrpU9wFSduJ9EXEcmJq70MY1OIeypcDRiFhVKh8D/FXxGDZCeQzDRorNwEN5Hg8kXSXpCtIsf/dKasnjBXcX9jkC3JSX55WO9VhO846k6yVd1k/dh4CJktry9q2Fp5I6gTeBnqg/MdRBUhrsmuVAh6QbC2Vj+6n7HElzgDuAJ0vlE4BjkVLImzXkOwwbESJiS77Ifp7HoU8CCyJij6S1pAypP5H69mtWAuskPQB8VijvJHU17cldPMeAe/qpu1fSfOAtSS2ku4bbSRlKd0v6nZTCvd6+hySNr3UXRcSXkhYD7+cAd5z0dNTLFd6GZ0gTUXXn96Arj2vcCnxcYX8b4Zyt1qxA0lLShXyggeXBqm8isA24ISL+abDN08AfEdFZb/0gtGEj8HxEHP4/jm/Nw11SZkNE0oOk+dA7GgWL7B36jo0MZhtGA5scLKwK32GYmVklvsMwM7NKHDDMzKwSBwwzM6vEAcPMzCpxwDAzs0r+BXY39LAwd4wFAAAAAElFTkSuQmCC\n",
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GYRQAxknKYDAUA0ZhGAwGgyEQRmEYDAaDIRBGYSSIGsdag8FQRFQkmbiItAUeBvoA24ELVPVje98iYBNQBVSq6vCk5IwKM+htMBiKiUQVBnAtMENVTxGR/sBY4CjX/m+q6upkRIsPozgMBkMxkLRJaiAwEUBV5wE9RaRTsiIZDAaDwY+kFcZM4FQAERkJ9AC62vsUeFVEponIxekuICIXi8hUEZm6atWqyAUOE9OxMBgMxURghSEiLUSkPOT0bwfaisgM4HJgOlBp7ztEVfcDjgMuFZHD/S6gqg+q6nBVHd6hQ4eQxYsHozgMBkMxkFZhiEiZiJwlIi+JyEpgHrBcROaIyF0i0i+fBEXkUhGZYSuJlqp6vqoOA84FOgALAVT1K/t7JfAcMDKf9AwGg8EQDpl6GG9geS9dA+yhqt1UtSNwGPAecLuIfC/XBFV1rKoOs5XEVhFpbO+6EJisqhvt3kwrsHo2wDHAx7mmVeiYwW6DwVBMZPKSGqWqu7wbVXUt8AzwjIg0qmf6A4BHRaQK+AT4gb29E/CcvbBQBfC4qo6vZ1oFi1l5z2AwFANpFYZbWYjIoUA/Vf27iHTAMiUt9FMouaCqU4A6pi1VXQAMrc+1DQaDwRAuWQe9ReRG4Cos0xRAI+CfUQplMBgMhsIjiJfUKcC3gS1QMxjdKkqhSg1jkTIYDMVAEIWxUy0ju0LNILQhFIymMBgMxUMQhfFvEfkL0EZELgImAH+NViyDwWAwFBpZY0mp6u9F5GhgI7A3cIOqvha5ZAaDwWAoKAIFH7QVRINWEqrK09OWcuLQzjRtFPaE9nRpxpKMwWAwhEJahSEim8hgZFfV1pFIlBCTP1/NL5+exZyvNnLTt/dJWhyDwWAoODLNw3BmWt8MrAD+AQhwNg3QS2rHrioAlq7bFnvapqdhMBiKgSCD3t9S1ftVdZOqblTVB4AxUQsWN40rrEexo7IqYUkMBoOhMAmiMKpE5GwRKbcDEp6NtQpeg6KizHoUVdXxNfdNx8JgMBQTQRTGWcDpwNfASuA79rYGha0v4lUYxhZlMBiKiCButYuAk6IXJVmS6GE4qOlrGAyGIiBILKmuIvKciKwUka9F5BkR6ZrtvGKjvEwAqExAYRgMBkMxEMQk9XfgBaAz0AX4r73NUE+MajLEwcbtu5j06cqkxTA0AIIojA6q+ndVrbQ/47BWxjOEhBnKMETJZY9P57y/f8iqTTuSFsVQ5ARRGKtF5Hu2l1S5vcremqgFMxgM4TD/602AcRk31J8gCuMCLC+pFcBy4DR7W4PEWuQvHkzPwhAnEmfmLgCqzXhk6ATxklqMtR5GAye5zGWytSEOSqkCveQf0xg/ZwWLbh+dtCgNiiBeUr1E5G4ReVZEXnA+cQhnMITNglWbOXnsO2zcXq/VhYsKp2eRhMt4UoyfsyJpERokQaLV/gd4CMs7qjpacQyGaLlnwufMWLKeN+at5KRhXZIWJxYcS1TpqAtDVARRGNtV9b7IJSkQkrDymhnfhjgw+cxQX4IojHtF5EbgVaDGL09VP4pMqhLBFOD4cRoEpfToTQ/DEBZBFMZg4BzgSGpNUmr/rxci0hZ4GOgDbAcuUNWP7X1tgL8Bg+z0LlDVKfVN01BLZVU1c5dvYnDX3ZIWJTZqK8/Sqz5LSUkaoiGIwjgF6K2qOyNI/1pghqqeIiL9gbHAUfa+e4HxqnqaiDQGmkeQfg1JFqakkv7DhM/50xvzeemKQ9mnc2kojVLsYdRSkjdtCJEg8zBmAm0iSn8gMBFAVecBPUWkk4i0Bg7HGmxHVXeq6vqIZEiMpIvv7GUbAFi5sXRmADseQ6WkMITSu2dDNATpYXQC5onIh6SOYYQxN2MmcCrwtoiMBHoAXbHW21gF/F1EhgLTgCtVdYv3AiJyMXAxQPfu3UMQqXQoRfNMTQ8jUSnixYxhGMIiiMK4McL0b8caVJ8BzAamA5VAI2A/4HJVfV9E7gWuBq73XkBVHwQeBBg+fHi9y0QSs2GTavmV1rxfmxK8aeeWq00Xw1BPgsz0fjPMBEXkUuAi++/xqnq+vV2AhfanObBUVd+3j3saS2E0LAqk/JZiPVKKHmoleMuGkAkyhhEqqjpWVYep6jBgqz2gDXAhMNleN3wFsERE9rb3HQV8EqlcUV68QCm12ELgsucnLEeclOK4jSEagpikomQA8KiIVGEphB+49l0OPGYrlAXA+VEKkkRhKpTyW0oViZTiIIZNKY1VGaIhUYVhz6vol2bfDGB4vBIlRTIFuYTrzpIcyyilhoGDqpZkTzoqsioMETkEuAnLg6kCq6ipqvaOVrR4KUWbdimWoxJ8zaWoG2tQLc18HhVBehgPAT/Fcm1tsCuwOPVInHmrUJRUocgRB45ZpqTqEPtmS9FLqvTuOFqCKIwNqvq/yCUpYZIrxyVVbZYspTy73WoMmXweFkEUxhsichfwLA04+GApFiaHkrp1+2aNXbs0KKm8HQNBFMYB9rd7ADqU4IOFRBIeJEln5poZwEkLkgClqC5K8DWXpBkuSoJM3PtmHIIkTgkGHyzFSrMUKeUq0+iLcAmyROtu9hKtU+3P/4lIaYQ2beCs22oFIF61aXvCkhgMhmIgyEzvh4FNwOn2ZyPw9yiFSoIkGiJJt34alVuv/6sNpaMwSrnBWUrecA4leMuREkRh9FHVG1V1gf35DdCg5mBAbcZKYiw0qUy9V6dWAHRs1SQZARLEjHmXBmZ2e7gEURjbRORQ5489kW9bdCIZ4qJRuVVr7qysznKkoSFQilWn6WGESxAvqUuw4j054xbrgO9HJ1IylGJLpExKLxBfKZplShnztsMliJfUTGCovQoeqroxcqkSINklWk22jptSNEmVoq40brXhEjj4YENVFEmSuKIo4XkYJUUJv1+Tt8Ml9vUwCpVSzFe1a0M07Lt/94vVvPX5KqA033NJY154qCS9HkbB4Ni2JcbpbKb1Ew9n/dVauHHR7aNrtsX5nguH0stwDb0xFDdBJu59R0Ra2b+vE5FnRWS/6EUrHZJWHEmnbzBEhcnb4RLEJHW9qm6yXWu/BTwCPBCtWPFTivnKDPyWFqV47yV4y5ESRGE4a2CMBh5Q1eeBxhmOL05KeInWUqJm3ZMSVJalSFxu1E98sJjPvt4US1pJEkRhLBORv2CFBXlZRJoEPM8QkKRafrXrJBjVVQrE+Za376ri3gmfs6My2TXXqmO66aufnc0x90yOJ7EECVLxnw68AhyrquuB3YFfRipVApTi4FgptrJLUTkmcccPv7OQeyZ8xrh3FiWQei2lWK6jJK3CEJHdRWR3oCkwCVhj/98BTI1HvPioqUdiXaM1xrQyUIJ1qFlAKWKccDPTF6/ntpfnJqeoSzBvR0mmHsY0LMUwDVgFfAZ8bv+eFr1opUNSraDaeRjxo6oc+4fJ/HfmVwmkXprEWWc7kZDHz1nBg5MXsGrzjixnREOYt/zlmi1858/vsnH7rhCvWlykVRiq2ktVe2OZo05U1faq2g44AWu51nojIm1F5DkRmSUiH4jIIHv73iIyw/XZKCI/CSPNdJRmKzu5tHdVKfNWbOJn/54BwCdfbeQfUxZFnm6Ur3nlxu1UxWU0LzB2VFax2qUUnMCWSRNmuf7DhM/5cNE6XpvzdXgXLTKCjGGMUNWXnT+q+j/giJDSvxaYoapDgHOBe+00PlXVYao6DNgf2Ao8F1KaviTSyvakWl2tPD9jWeyVTtzKcu7yjVww7kPAUhwAx9/3Ftc/Pyc2GcKuztZs3sHI2yZyx/h5IV85PKI0C1362EcMv2VCzf8mFeWexCNLOiNh9t5rnERCu2LxEURhrLYn7PUUkR4i8mtgTUjpDwQmAqjqPKCniHTyHHMU8IWqfhlSmr7UzvSOjvVbd7J9V12vEaccPzt9GVc+MYOH314YoRS1JNUGPO7et3h7/uqEUo+G1Zut1Qtf+6T0Wp/TvlzHhLkrU7Y1bVQYjpSh6sia2GvZL/qvDxYzY8n6EBMvDIK81TOBDlgt/P8AHe1tYTATOBVAREYCPYCunmPOAP6V7gIicrGzfOyqVatCEisaht38Gt/585S0+zfZttFl6+NdbiRpTxInzhPAqk076Hn1S7y/IKw2STz85EnLtFZZXc3mHZVc95/ZbN1ZmbBUqUTxljfvqGTMA+/W2d64IrVqSSqHhasvUptYS9ZuZeaS9b4K5JpnZ3Py2HdCTL0wyKowVHWtql6pqvvanytVdW1I6d8OtBWRGcDlwHSgppSJSGPg28BTGeR7UFWHq+rwDh065C1IXBl69rINtWl6Eq2wBwp3VlWzaPUWel79ElMXhfWok2fmkvW+leg5D31Q89u5379H5Y4Z0Yueu9wK5lxdDX+e9AX/fG8xj7wbaac4MFGaonalWXyrUGJ1VUdo3j3szjc4aew7JTX+mTX4oIjsBfwC6Ok+XlWPzCdBEbkUuMj+e7yqnm9vF2Ch/XE4DvhIVSPv58f90mcv3UClJzM7RWzR6i28ZZtsnp2+jOE9d49XuAhYt2UnJ419h2/t47U4phLXa4hqwL+sDKrszFRoazFEIY73Oaoqva552f/gBoL3MRbae46SICapp7Ba/tdhTdhzPnmhqmNdA9pb7V4EwIXAZM+6G2eSwRxVrFRVKyf+6e06253C9+4Xa1yzsOOTK0q22zN+g9p1C32axPyVm3jHZxymTIQla7cCtS37p6ct5cjfT/K9zvINxb3asbcnkU5ZJDYNI8R0Jc2odyk5xgVRGJWq+oCqfqCq05xPSOkPAOaIyDys3sSVzg4RaQ4cTUguvNmJ7q1v31WVMi7huJJ6cRe+ByZ9AcDitVsikytOauZ8ZHnMzv6oFEZY4zWj7p7M2X9733ffi7OWAzBvhRVb6BdPzWTB6i388qmZrNuys+a4d+ev5qDfvc5L9vHFSNDn6W4o7Ky0xnniIJ18G7fv4qscxwpr9UXqNfe67n/5iFaUBFEY/xWRH4vIns7sb3vGd71R1Smq2k9V+6vqqaq6zrVvq6q2U9UNma4RFlFWVFf8azqH3P56zf/nZ6ROVvNL21EwKzZsD18gD2Hbm6uqNcUbbNWmHWyxxy6yVS9O9/7l2StClSkJtuyoZOWm2vf31LSl/N9rn9b8f/CtBQBMX7yuzrlREKZzw/ZdVahq4Nb1A5Pm1/w+66/vMejGV0KTJRPpGijH/eEtDnaVySA45fPTFZsZclM88hcaQRTG97FMUO9izfB2ZoAbAjJhbrAhGL9qO+wQFne/9hk9r34p1Gt6ueKJ6fS/fnzN/xG3TuCo/3sTyN7DmLU0HlfEOEwkb3y6ipG3TkzZVlWt7KqqprpamfRpYXv1pWPDtl30v348f3x9fmD7/cylte2+qV/GoyAhfQMlH09Ep2H1yJRFbNxeWB5wcRHES6qXz6d3HMLFSZT1R9BKPw67/X0TP488DcfE0vPql3jig8WevZmf9F/finYOShSKYv3WWjNTtutXVin9fv0/fvXMrJptsdnAQ0rHMas9PW1ppF5IuXLPa58xb8XGlG1heog55TPTxNpKuzHQUAmy4l5ze+Leg/b/fiJyQvSixUuULc5seuCpaUuYOPdrX9NQlC6RUVzZW1j++X6qa2nSg/hO+mGKccBtE7MfZFPlGgh3iNrLJuyrl0ntWvC51I3XPjc75X9llb9Lbj5s31XFvRM/Z8z9qXNCwqy7gzTo+v76f1z5pP8YZUMgiEnq78BO4GD7/1LglsgkakC8NGs5+978ah33WS+PTvmSHzwylR0hFqA5X22oiRgaF89NX0rva1O9ZLx14RrXoG82IlWWIV57h+s5L7Y9pNJR5lPrxBUKJlsqqzfv4MwH32PVpsyBAp1bqK7OTdk9/n5qb3NniPndkWlHnTwfZg8jmAmgIQfUDKIw+qjqncAuAFXdRnJRJSIj7NnOKzdt5xdPzWTd1uCRLatCKkBfrtnC6Pve5taXPsn7GuPeWUjPq1/KyZvlvzPrevvUp15esnZb6N40Sc9q9ys4VUl3u2wee28xUxasyRoEsqzM8XjTevWOwny3jhheecJ4tKrKtC/XsSahiLuFRBCFsVNEmmGrahHpg7UmRoMi7DI78taJbPOJG5UJvxaW3zjMAAAgAElEQVRMPoPeTlwj90Bjrtxvu/VuzmFwz6/V/snyjT5HBuPwu97glAYYXsFLmKaZ+uDEf1qexTPPyZEbt1fWq9z8LcTxqlqF4dme5Ty3F1s6npq2lDEPvMsrJRyl1iGIwrgRGA90E5HHsIIF/ipSqUqUbKar4NiBFOvRD3TMJBU5hKmOop38+crNEVw1ubGUhavrzqsJ773X8sCkL3h+xrKUbc49r9y4nZ/9e0adQJiOuewp1/iKH06+2ryjkr9M/iJvGTeFuK5Eup5jtve8fWd2Zb1gVcOYCxUGQbykXsMKEHge1qzr4ao6KVqx4idTvorL6+G3L9Y1IeXT5a+Z1xHweD/F4qSaS/IFYlnJiJ+MC1Zt5qGYIgT7KcCpi9bx9cZw59vcMX4eVz7hP/h668tzefajZYz/OL+5Lu5n+M/3vF5wmXFHYg5z7CbdpbKZIIM0qgo96kCcBI1BfARWmPFvAodFJ05y1IY3T80dd7/6Kb2vfZn5EbV0s5FPJeycUp85HM7zSNrmHxXu+xrzwLv89sVPYnES2LCtbqt68dqtjLLnqURJWO+yPuMWN7saRf33aB2GOEB6J4ZsorrvpapaffOA0Re1BHGrvR+4BJgNfAz8UETGRi1YIbBp+y7ue92aoTrnq1gmnNehPq2wnZXV3PrSJ2ypz+BiwOQH3/gKb35W2BPR3JWKuyLxTsJSVf721oLQW/2Z2BRhqIyg9XtQhRJWT7J3hxbhXIj0PYxsys1dvs588D3fMB9h9DDemb+aq56exTNZzH2FTtZotVi9i0FqlzYReQRLeTR4KqtqM5OfO2Qc5KMwnDIye9kGZi/bQKPyMn51bH/PMZmv6+xdsm4rHVs3zZpmlBVeWKjW3teDkxdw6n7W0ivOM3YqzAWrt3DLS3MZ//EKnv7RwX6XKkrCqugL4To9r36Jcw/qwc0nDbIvll8a7uL1QZqlBOoTOufSxz/isxWbakyRT05dwpj9vUv+FA9BTFKfAt1d/7sBs9IcW7T4ZSy3kigTYfzHy/nuX9IvgBQF+WQurzLY5eOFE7Swjnkg3vv1I8iciZ2V1b6rGaZcx/XbCQyYmo717cyG37IzNy+3uPl42QYeeXdR7OkWgmkLrPlL9b1WkLz1ZZa5NZl4adbyOuNW906IPtpCVARRGO2AuSIySUQmAZ8AHUTkBRF5IVLpYkR9PIvcBePFWV9xyT8/4v2FayOdUOalddPsncAVG7az2uUj7pXOT9xsd1BIA9i9rnmZu1/9NOMxo+97i/7Xj88YgTTbe3MqHSc45FyPS/AHC9fy93eiGxxfmaMJ7IQ/vs2NLwRfBz2sVxrWWPXNL37C2X97L6Oin79yc6Dylu6IXHoY6Qh7It49Ez5j4A3jOT3D6puFShCT1A2RS1GguDPT/1weJar+ds1/T11ClzbNOKRv+9BkCFJxH/g7KzTFottHBz4nTqUXBve9Pp+fHbN32v1OK+7g21+veQ5elMzPJtsjOd3uXZ5/SK/MB+bJyNsmct7BPdm3extOGtYl8HmqmtbBYe7yjYHmebwyZwW3vTwvZdv6rTtp1ricz7/ezH+mL+PXowcgIqHlnS/XbOXLNVt5auoS/vrWQk7etws/O3qvmv3vzF/N2X97n57tmnNw3/Y8/v5ifjKqHz8ZtVeda6XrYTiNvkmfruSJD5bw53P2T9kf1yx7L1t3VqU1gTl8tHgdKzdu59hBe8YkVXayKgxVfVNEegD9VHWCPYmvQlXr9umLGL/8li4TVqtS5mPX/NXTlqUuXYWVD7nMAl69eQcrNmyvYzLwu0L2HkbqEWPfmM9dr3zK3JuPpVnj8sAyFRK5eMwkxbh3FzHuXXJSGDurqmlS4f9Ojrv3rUDX+OE/6i5xM+zm1zig1+7MWrqBbbuq+OnRe9GiSUXowRK37qxi8dqt3Dfx8xSF8cUqqxGwaM1WFq2x3Hf/MOFzzhjRvc410r06Z/t5f/8QsBREeVlt2S1kL8BT7bhYYdYn9SWIl9RFwNPAX+xNXYH/RClUEuSmMCIWxsWd4+fx0ydnBFoX4/h73+KEP74dyPagmpvZybGVb7QnW23ZUckF4z7k0SmLIg+Xng3vPJl0pp1sCiHTXndE2qR58sPFKeurBPaCSpeffTK0c+z7C9emmGv/N3s5o+4O1wU4XaMoXa/prL++V2dbuns7yY4U4OgI73heAbQRioogYxiXAocAGwFU9XOgY5RCFQrpMlPWiifEXFit8Nz0ZZYiyMJKO2ict/xXlNUteOlaVj8Y9yF/efOLlL27qqprTHDOrb36yQpen7eSG54PbkOvD+1bNk6778+e2caz8gyJomksN9t3VTHs5td89yWhLK96ZnbKeg75ZjcnD/itDeE3+1wQfvTYR/klloGN2/w97HyyLeAfuiRbI66izKrqvPeVb14pVYIojB2qWtO8EpEKol0+IhH8biitXTTL3W+NwLtmdQ6Bz7zKoNxPYaS5h4nzVvK7/6Xaso+/963aJVbtazdNYwKJjtR72LB1F/dO+Jwqn4WI0r239xeuJVPWrVatE04Donmf+eLXewo8f8L+3ugzedCLuyVeGxI+mmI/7Ut/W346V3a/cYdsstn6os54jjfketxUVlUz/JbX+FeddWNqWb15R8Gs/R5k0PtNEbkWaCYiRwM/Bv4brVjx4/QKZi3dUDOImG5ALFsPI65Wdzq84vn1MNz47d3kmszmdgusXU423nkp3uSuf/5jXpj5FUO77Vbn2HStzW07qzIq+6emLakz8Fto+K30lquJ9A2XglVVXy+lrzfWbaBEZb7ZWeV/4eZpxsoqq+t2BbM9A6fBE2SQ2+1EsMgn9leYTP58Fas37+SaZ2dz5si6YzMAw2+ZAMC/LjqQM21z3K2nDOLsA3pEKpsfQXoYVwOrsCbr/RB4GbguSqGSZPOOSp6aas3G9MmX1na75FRWVfu2/J3BurDpefVL/GDch1mP8xaJivK6rzl/M4ZF0vF11trravj1ntIr9Mw3nU5ZJO1Rtm1nFfNXWj4mfs898GC9z2H3Tvyco++ZXGd7WHHNgrArTUiWJhX+1ZNfnZ/tHdWs4RHg3Mdc63Z84/eTMl63vuSyTO+TH9bKdcuLc6MQJytBgg9Wq+pfVfU7qnqa/btBm6Q+/XoTW3ZUsmpzmsFTO3/f+MIcht8ygbtemcc/36udRBSlq97EeStzPsevbp+yYHXN71ykdQZI49YXgqXM7371U2tNbDsL+pkt7hw/z7cCcc/0zgXvOdtiMFGt2rSjJjTJFU9MZ9Tdk9m+q8r3uacbewnCsx/VNcEBvO6Tz6LK1fUJgd/7mpcY+8b8rA0g57n5KT1vcX32o/jCd7gnH2bDndeT8u5Ka5ISkdlkyCOqOiQSiZLCdaeVVdUcc8/ktAvFO5nulTnW3Iyxb6QOuia90Iq3svR7iReMm8pl3+yb87WT8lsHa83mh95eSNe2zWu27ays5oOFqTbwRWu28tnXm9l7j1Yp28OSfMAN40O6UnpG3GqZIRbdPpr3vlgDWO6zfqbA+as2s3+Ptnmlk22FQHBFLo5h2Y7X533Nkf07WekFeGHVCne98iknDumc8TjnuW3ctosWTVKrvYWrt/Cf6bWKM6ks/tfJC9iys9J3ngnULlyVJJnGMJx1uy+1v/9hf58N5D9X3oWItAUeBvoA24ELVPVje99PgQux8uts4HxVjSQa3F2vzEsJnFdZrWmVBbhbKf4v8KsALrD1YfGarXRv1zztfm9+z1bwcukvOl4mcY9hKNTY2t1L2S5IY2P2s3N773PJ2q3cOzF7mIak+9NOnC6t9s9xYx54N5Cvfn1bpXG0al/5+GsuGDeVV396eE5zkLKZy5zs6md+u2DchymKM6n5OLe+bJmZ0ikM97tPKk+mNUmp6peq+iVwiKr+SlVn25+rgW+FlP61wAy7t3IucC+AiHQBrsBae2MQUA6cEVKadZj82Wo+XlbbLf7868xjEE4LJCmFf/hdb2Tcv3hNqj7PVtCd/UHCe1er8uSHi/lyTbyLyqzatKPGtuzuQTm9PC+VPgOpiqace82zs3m6iKKHVquyIs0ckwWrNmeNpeUlaFA9J1/E0fIeb7/P52cs47LHpwc+L5No23b6m/Jq9nuem1/eiZN04W2SCoCaIkOAY1qIyKHOHxE5GAgrLvFArBX8UNV5QE8R6WTvq8DyzKoAmgORrazu9cZYl2WSVs3aGQm+v8++3sQbacYzvPGFqtVyXdy6M3NE2SDmpsoq5apnZnPLS+ENun0nxwCL7olmXnOUg58d+n8fr0j1EArsjloYQ3bVqlz1jH/czx8/9hH9rx+fcfBXNdWFdVyOgQvjGLpsbA9051pp+425OPztrQUZz/Xel9PDiNpDKh3fSRNjqsxVWyeVI4MojB8AY0VkkYgsBO4HLggp/ZlYq/khIiOBHkBXVV0G/B5YDCwHNqjqq34XEJGLRWSqiExdtSq/9Ri8njbZFMHI2yYy56sNiWr8Y+6ZzPkBPKYAVm3azpgHpvDTJ/1XYKt1lc1+rWxKJx92zzApz491W3dl7ZL7hWR/adbynNJxSNok5VCt6ZW6E313c5Yw8+7owzOWrM85/ahxZtTvylFh+Hl1OeyorM5o//c+02pVdlRWRe4hlY5M5vAabJGXrtvqG406KoJ4SU1T1aHAEGCYqg5T1bCme94OtBWRGcDlwHSg0h7bOAnoBXTG6uV8L418D6rqcFUd3qFDh7yEqKMwAnTVp3yxpiC6iEFwfPfnLk8N/1Un5lSAMpr0RCewXEEXrM5sNtyrUyv+OPHzjCslvjN/Tda0vv2ntwsixhTAyWPfyfqOMoXOru9trNkSvTOHoyjCbJhUVmvGEu1VGJ99vZm9r4vesaG+LFu/jUPveCPWEPdpFYaIfE9Eavar6kZV3eDa38dtqgqKiFwqIjNsJdFSVc9X1WFYYxgdgIXAKGChqq5S1V3As0BkK9n4+fI3JJzZrelu0yku05esy3qtz7KM7+TDHgEWaPLiN7HMzYZtu/i/1z6rmeiUL7OWbkjMa8ZLkJbn8g3bIzMdHfuHYIEMw8BvKdt8yR7KJ7SkYkPRmt7YM2lco6Mgk5dUO2C6iEwDpmFN3msK9MVahW811qS+nFDVscBYABFpIyKN7dAjFwKTVXWjiCwGDhSR5sA2rPXEp+aaVlDKJTeTlHWMpNgU68M+nVsz56v8fdGz4bTavD0ib0F5Z/5qkuDcg3qyevOOOu7J9cGpJHbkOBDsRyFNO8omS1W1FoyCqw9+sazypapaM1oDCqUH6SZblOHG5WU19+QXPDIqMnlJ3QvsB/wLq+V/lP1/GXCOqo6xAxHWhwHAHBGZBxwHXGmn/T5WhNyPsFxqy4AH65lWWmYtSw1A5rcamx9hmaSiNm1Ntl2G07mgOpojqYqmvEz45bf6Zz8wD8K4pUKqT7K9o2rVDGtDFA9h2uWFzI3AXNx348K7eBekRp5o1ri8NiBojG82YywpVa0CXrM/oaOqU4B+afbdCNwYRbpeVm3KzzYbnsII5TJpSdda8259YFJ4LfykcVZJ2+QTeylXCqkFms6t1uGIvTsUlLz5ks21PRdmLFnP6s3pPR+374pv0Lg+PDl1Sc1v1VoFsqvKigfWpKIs8vlRIRlVSg8hRDur6yVfdFg0q7n5URuFND4e+v5w+nZsGXk6b30ennktSRNPrnmsU6umaXtE0xdnH6MKgyDLCmfDb+Jlvkz9Mp77jhunYbBw9Rb6Xz+eHQHmUdUXozDqgRMAr76UiTXwe2T/jsToIcdbn+fnhlwfOrRqwtOXHFTz/84xhR9hJskW+w3Pf5zT8Up619v7Y+pB/ufSQ+p9jSRD0BQDfu85DqfNjApDRMpE5PToxShtykR479qjePi8ESmV0+AudUN3h4kz0K6afXJTWJSJ0KZ57byL00d0iyXd+pDkoPf6rbn1MDTDGEZcdN89fdiaoIQ56N1Q8c4IDzpzvz5kVBiqWg1cFrkUCZPPGMLNGSYK1Sf9pFpWYc7czkSRTF1J4bVPco8QHBbuGGdBUJI1oUE4ccaChKkpZdZu2ck1njlRccwOCGKSek1EfiEi3URkd+cTuWQxkvQEPHfLoFmaRWOiJE4vi6SfdT7cMb6wF1Vys37rTpati2d1tnevPtJ3u7fiyieSbn1WOYzTzTRJvL3POAKCBlEYF2BFrJ2MNR9jGhHOiUiCpLu/7vf8k1G1TmNxVeRxWjCKUWGEwche8bSxrnpmNsffF88EO79X2al1k5SK66Prj+aZH0U259aXaTEN7gehX8eW/OgbfWJJK46SFSQ0SC+fT+8YZCsZ3JVo88a1HiZxVeRxDYZCbevzzjFDeOUnh8eWbtLs271N0iLEgnfcbfcW1niVs/ZKh1ZN+OTmsIJd+zPHM68qTP5z6SEc1b8jFxwSzJuxR7sWNG8Uj9Ug8UFvSwhpLiLXiciD9v9+InJCtvMM/hzYe3f+/cODUralmzEeRGHkG1AvKZzW5+kjutVZ4KhYeezCA7If1MCtJIfv5cRx86+1Th/erWavu1EUBTf9N7zxRS/DurXhofNG8J3hdSMs+w32i8Q3MbBQTFJ/B3ZSG8tpKXBLZBI1MPbr3iZlcZuh3dow3GPTTefdEMTb5dLHw4oDGQ8N0SLVyp53cGT/jglLEi/ufDuyp5Wn3e/XvWyAs72hvH+/+HP3fHcoh/VrX2d7GJNHsxHXcw2iMPqo6p3ALgBV3Ub8Szo3KMrKJGXA8IQhe/oed9KwLnGJFBsNYBJyHQRh0e2jefi8EUmLkhjeYcA7TxvCi5fXxiatURgNpOrwG4vbY7dm/OnM/TzHxeP5GNfYYBCFsVNEmmF3qkWkD5DsotUhM6RrtPMd/HC/3zNGdvc95pIjenPHmMExSWQw5IY7D3t7w6cP70bvDi1dx0qdc4qFts0b1dnm7WG8+ctv0KVNM3bzHCtILAojrscaRGHcBIwHuonIY1gr5P0qSqHiJkrtnM6u6KSZyXdaRGga04BZIXDOgT3qbDtu0B4JSJIbQbKPAm18Kp5ipsKVeb+5d0dOH96V3540KOM5RagvfMuwN8L1HrulD9EfxxhGwZik7JXuTgXOw4pcO1xVJ0UrVrxEOeHF2zpxClltpMnMNDwTTvob+u3JdSuboAUhnzU14mS3Zo0Ken5A4/LcowS1a9mk5ndFuXDnaUPTVpy1yxoXn8rwk9jrqJKunIrEMy8krucaxEvqH1gK4wtVfVFVk1k0IUKiWkBp9xaNGXu2ZdN0Wsqn7md5V5TqfISoFGBFeeE+zwF7tubiw3szamCn7AcnRL5zfgbu2do6P8vp2fbnuq57nPgV1QqXxrjpxIFpLQEicPK+0Y9FxjUzPqiX1J7AH0XkCxF5RkSujFiuWIlKO390/dE0qbAy0gPf259Ft4+mj23XLdzqrf48ZQcXHO0azI86Qu3SmGY3+9G9Xa07ZbsWddcnP+fAHjQqL6txLXXo3b5Fze/rRg/g7wEHzcOIBuslX0Xu1JvF2BPeu1NQt+66pdW57yYVZZyXZU7Ggb3bMeFnR+QoXWESxCT1OnArcD3wN2A48KOI5YoVrz0yDpweRraUc2357dYseTv5iJ67s+j20Yw9a786+4qwXslK66a1z/x/PzmM60YPSNnvWHs6tmqSsv31X3yj5vcRewVfjz6KHrECH//mWzyfY6TZP525H2cd0J2BnVtnvr794gupY/29g+qOmfnhJ7NTZ2TLzw3FK8whiElqIvAO8F3gU2CEqkazPFpCJLGmd1mGDPffyw7ld6fm5x1VUaDrk+crVcdWhT024aVjq6YM65Y6q7uRrTF6d2hZx+vNWf+kb8eWgRsHUeXXlk0qGNottxnpPdu34LZTBgeWKZ3CSEKReBV4OvxEc+43WyTjsgItj/kSxCQ1C2vi3iBgCDDIdrM11IcM+Whw190403a1LYSu/qAumVuPAPNvPS7j/muO70+b5o3o1ja30NdXH9efnu2yn/PNvYO30KPGeWV9O7bkosN6ccKQzjX7vjsi1YX616MHsuj20TmZRaMY/4o6hHs2ZZiuJf6Xc/aPQhy6tGlGlzbBqjG/x11WozAyn+sMrdXnlTVtVDjLFgUxSf1UVQ8HTgHWYI1prI9asDgJ00/6vWuOCnSc0/AohvZHkG51RRYvmyP7d2LGDcfkHI23aaPyQIOG+3bPPSJq1LRp1ohfjx5I44rUZzPlmiOZ+PP8bdpRtMajbpd0a9uc7w7vxoPnDM/pvEP71s6cDlrBB6Fr22aBn6OfgnZMUn7RGFq5xpjKPe5UrZpWMO26UTlIWlhmrSAmqctE5ElgBnAy8DCQuTlZZIRp98/kj+0maCvx+MF70rVtsh06R7mN2S8ZT5bNAUIrrNlcv7mkg7vsxuAuu9G4vIw3XGML+ZCt1bnnbs1qnB9yOc8higok6p5sWZlwx2lDGGB7Vb31q28GOq+8TJj0i29w44kD+fclB/Hbk/aJUkxffBVGWXqTcnuXu7HjHdmrXQvOGNGNpy85OMUdOQjZxofiJEhfpxlwN9BfVY9S1d/YA+ENhh8ekX/w3eZ5rl+RaQzDTdNG5dx12lAgmFdHFOXeMZckNaD+5NQlWY9x93DyWRddBF647BA+veVYerVvwd/OHZ63gnTm3kQVXLGQBo7zpZsnUF+6e6ooE3q2b8H5h/SiS5tmnHNQz0jk8fYCs1GewSTlTECdeeMxNa7UZWXC7WOG5JUnTt2vcEIEBTFJ3QVsBy6xextDoxcrXvbt3jZvpeG0mHIll0LfyDaEtmxawdkH+IcRiRKncDTJw5Z6YO/dOWlY5+wHZqB/gELmHux3XJlzRURqlOOogZ246ri987pOv06tePLiA7nhxIF5ne/HGSEuZfvtoZnfx8+P3otTs5gBg/YQgpKuPPgNpkehMA/s3S7tvkxeUn5ccGgvFt0+OmMD67kfx7tGSFgEMUldATwGdLQ//xSRy8NIXETaishzIjJLRD4QkUGufVeKyMciMkdEfhJGepm4+tj8HL/ydcmtDcaWHcfLZldVsstW5uPw8cTFB3HvGfsGPv7lKw7jcU+48PYBuvDuiiWshacymQ2zLQp0QO92OSuuoGYh57h8bfo/OLRuD8w9sHr5Uf24+7vDav77pePtIdQXVWutDC9RzZHyXjZziJ6625xB72P3yS90TSGOuQUhyAygC4EDVHULgIjcAUwB/hhC+tcCM1T1FBHpD4wFjrIVx0XASCwPrfEi8pKqfh5Cmr7kmzHzdXHMxdPFSaOyKs7FVGtxPGiyyfzUJQexrR5La4K/vTbIo3L3ME4f3o2xb+S2KJRv+IcMCeez7Gh9aOIymdQ3F3hdZ399/ACOyOBl1rVtM5atj3Zi5BMfLuG5Hx/MKfe/G+p1O7ZqwspN/uNb7rGgTA2/dGNGH/z6qMjNtLmayqImiDQCuGuBKsJz7hmIFcwQVZ0H9BSRTsAA4D1V3aqqlcCbWF5aseOerXzFkX3r7M9XYeRzVlK2a6d62rBtV8bjRvTc3bWQTry4vVF6tGvB3afnaDn1ebhxu9Bner/uBk3Yk+AuOrw3e2UYH4tqQPxnR++V8r9V02CVby633cs1m96LW/Fm8t5LN+7QsVXTvM2fQbnl5EGs2bwz0jRyIWhokPdF5CYRuQl4D3gopPRnYsWpQkRGAj2ArsDHwOEi0k5EmgPHA+EZcQPgeGO4gwceNaBuLKB8J+Y4rdcj+2ePL+QusFF7s3hnKQPsb3efl6zdGm3iaQjm1pt6TKYKMHC6MWvoTN7dB9RjTfCnLzko+0EZCLKQVz5ccVQ/7hwzBIBD+raLREGnk9ybp3570qCaZWS9/MFlnouTRbeP5vTh3Xjtk68TSd+PIIPedwPnA2uBdcD5qvqHkNK/HWgrIjOAy4HpQKWqzgXuAF7DCq0+E/D1rRSRi0VkqohMXbVqVShCuWdZZysrgwNMavOjrEx461ff5E9nBbfvW/VX9sJbJvmv8XFI37orhl11XH9e/enh9Gpf1xX0zJHd+e7wiHV5jiYpgEFddqN3h/StyyBJxN3DqEwzRnXGiG4cs88eNa3lbbtqO/x/Ozf7vIb6ms/cOa5r22Z878DwHC/a2rG3mlaURxOQM0NxaeTyrGvbojG/+FZdJ4cygRZNKvjjmfty8eH5e1PWh0Lyiks7hiEiTYFLgL7AbOB+2zxUL0TkUqzxCYDjVfV8e7sAC+0PqvoQdk9GRG7DWhq2Dqr6IPAgwPDhw0NpCp05sjv/eO9L6/opsqce98PDe/P6vPyVVNgDhw4LfjcaVaXXNS/nfK6fia1ReRl7dWqF39y8fEOY5EKQ8uInd8dWTViwakv+6cZcUivTdDEO69eB8jLh+MF7MPaNL2qW/BSBXhmU4g8P783Pj9k77/u4bvQAbnlpbsos8LevOjLDGbnjliyomGH1d/oFCIj53rXWRNwTh3bmxCzeZWHQs11zFq1JpicfhEw9jEewAg3Oxpqo9/swElTVsao6TFWHAVtFxAnveSEwWVU3AohIR/u7O5bZ6l9hpJ+JXu1b5GSDb1RextzlG+tsv270gDqePmEReHJXBIP4hRwXp1Ee6zlkw+9ufzpqr3rN28mE04DwXv/4wZYnzsZtddtr7vzg9X46Yq8OaQdND+6T3o20ThqBj8wdR75mjYP3MLxlwDFr+R6bQXoR4b1rjuLZDC6ucccy26153WjHhVTqMnlJDVTVwQAi8hDwQQTpDwAeFZEq4BPgB659z4hIO6y1xC9V1XURpJ9Cthm+Xrtnusr1wsOS6bpC/WMCZQpemERUXwim/JwxjJF52vp9XSd9Nl45ql9e1w/CsG5tmPCzI2jdrIK/vLnAJVv6+3fGF/p2bMnuntDqB/uYFx3OPagn736xJqM8TrpOxyeKFQMP7dueXxyzF987sEVlufMAABVoSURBVAebd+RnwDhyQMe8099jt6Yp0RlGDejIhLkr875eLvzprH257PHpKdv8yu/+PXZn5tINsciUjUzNshqXmDBMUX6o6hRV7aeq/VX1VLdSUNXDVHWgqg5V1YlRpJ8JvyLqLbf3TozMyzct2fRBfVvamVp5SUT1hdp34a0Q3V5rjrdKyya1baBcdKfffSehH/t2bJmimCdlacQ4c3Nyj1Kc/eE4V3QqsUweR/lSViZcdmQ/2jRvnPcYhvesUS4Fkmv7Kc4lkd1BKR1U605UzaU3GDWZapehIrLR/mwChji/RaSuHaYE6NKmGe1b1lZaowZ0ij2ceDYf/JauwGdv/eqbgey0bjKZnZJaJdBxa7z/7P3410UHura35t2rj+SOMYPp1Nqa9NUqzeJCRw/sxCVH9EmbRlK9Jz/SKeaT902tYASpeSd+k978cCp9pyId2TN7j6zGjTdQCvmT7yvw5suD+qTvWRUDL11xWEr051EDOzE0TyeWsEmrMFS1XFVb259Wqlrh+l040bAixt1CadqonKnXHV3z/1v7dIrFrr/3Hq04tG97bjsl+wDzuPNG1vzutntzOrbOLdBZprtJqodxyRF9ePqSgziwdzsO6tOuZlnQMoHObZrx3RHdObRve645rj83n1S7LrhbtTZrVF5nfXU3QU1SXvbtntv6EUFwm6DcInRpY41xtG3eiCP26sA93x1K/z1acfNJ+9SZTZ+uIeP0Fpxn065lXZu5N+24povm2yDxnhckm6YzfyW95niTijLKy6RO9OdHLhiZcawmLgprGmEB4SyfOXDP2u6hNy+t27ozlpZp44oy/nnhAQzp2iZjF/vQvu1TlgsFGLBHqm7fq1PmHoe78B3ZP9U2nJTCKC8ThrtawjVhVVziiAg/PKJP6sxbTf2ZqULyWxM826udeeMxPHHxgZkPyoN0z9mZm1gmwiMXjGT/HrsjIpx7UM865rqWWZZxDTL5z9nVo51VFs4YEW0cM0cWR9kF7R2LpxZz5O7QqklaVTd7mf+YQNL9TGcdHC9tmjfm9BDjieVL+IsDNxAO7tuel684jAF7tuL65+cAdQtXZbWm+MQ/flE0nlFu0q07MeumY2jqM+v0quP6c9zgPTjtz1NQhcP7deCzrzenvb77Hh/2rDE9akAn7nrl0/wED5FahRG8eKuqb+XYrFE523ZVcczAujGBsl09qrAQbn3hF74i3UQ694BpNtlz6TW0a9GYRbePDnx8vjj3qljKuEkaD682zRuxfusu13me64gw4WeH065FE/7vtU+Z9mXu/jJ3jhnCCUP3zH5gyIzZP1iE5F9+a++UsnhBlnXFw8L0MDIwsHPrVPOAJ2tWe/zmWwcMbVAf9k7TQ2jdtJGvC2Wj8jL271G3dZ6OTLv36tSSg3q348GIVkHLlWyVYuc2/i6R7mB63XdvzqybjuFcn/Wd3T2SET3b5h2ZOFfS9YScnke6GeE7q3I3HWWaRR+3G7WTnKqyW7NGaQegn7/0EPZzmQK9z0sE+nZsRdsWjbn+hNwiBjtu9YO67EbzxoXXnnYiV3/vwNT8GtVsfC9GYeSAt/xUVcO480f4HxwR3z+4Z15zPBzRs7bKM5koRPjXxQdyTJ4ROsPCqeSy2bxvdY35KLX3fvTATtx3Zq3Nv3XTRr7Pxb3p58fszf+uPKweUtcfydLDeGDS/Jrf6Z6Nc6bTQvebR+Twnf27cfrwrvx01F5pjwmToGvE9GjXImVWtvdeh7saSLnGejpt/67MuOHoglq0yI3jBZnUUItRGDngrVSqVDkoZpc3EcnoX5/1fM//8w7umfI/6MDj/Wfvl1hPw28Mww8npAMAmqrwjxu0B9/YuwN3fSf9QGJq7zI+3KYYd1C82ha4/3m7XD2MbM9mygJrDsaC1elnwjdrXM6dpw2tCd8RNTUKI0Bj+WCXJ5T7Xhf+7vhAlf0dY9I7kLTxmTxXKDhWhMoq5f6z96sJ/R/1muwOhdfnKmC8PYzqaqVJRTllkjlwXCEgIlZJ9NyDd4BVgFtPGcTQrpm9f44fHL9918GROIhyc3v6OEdXq9KovIxx549Me17d68SnMkSEuTcfy9wVG1PcZcuy9DAy4Q0dvkfreGcwByLPR+x1fvAy6RffYOP2XVz06FR2b9GEFy8/NDEHjvry8HkjeOTdRbRp1ojjB+/J6s07uOH5ObHVP0Zh5IA3Mzqxf4Z1a8NHi9cXRSbMFvlVRDj7gLr2/Li5/oSB6V1Wc1h9yn2/kkMLNl2ScdGscTn7eRbZcfJXurkmqaQK3NbTanYUhreHmST5Fp9sDYeetsfj+9eOyi+BCOnathlL121jZK/dGdYtu4v2ft3bpuQLdyMoDozCqAfOS7rztKH8e+qSQGtuJ0XtGIb13aSijB2V1XU9TOIUKgN+q8I55NLDcFCt37yCpCYtumnaqJybT9qHIwLEO0snrlOvVNk/CmmBnrDmYRQTz/74YD5dsYnD+uW3jkxNIyhMoTJgFEY9qKqujeNz7fF115EoRJyi5bRWO3uW3yyGspfL8rY1SsJljcunMVYovcdzD+oZ6Li6rqap/528W0iVbVihQYqJjq2a1ivAYe24j/GSKniqEhy4mHnjMTkd7x0o/t6BPbj+hIF13EmTnukahJzGMOxvRevVGisQfREY76NxBq5PHmaFF+ljh0Xfe4/cQsdESb5ZrwiybGS4G0RxYHoYeXDTiQO56b+fxGY39CPfSWOOLXuP1k25wMfsUwyFz6n4g8ia0sOoR+EqpJZ4ELxjVa2bNmLuzcfWeGAdO2hPXrz8UPYpIPfR/BVGcb2bMHGCbUa9triDURgBePyiA3h1Tu0yiY55IskeBsCD5+zPxf+YFujYFk0qWL91F2cf0IOWTSo4Lc2M0mIoet7xmGBHu8n9vXlDrhQ6vrPaPetWD+pSGAHtHIpNKRcCowfvyfqtO/lO1Kte2hiFEYCD+7RP8fsut4P6JNnDAHKaQPf0JQfz+ryvada4nDPSxKspFmrHMHIY9HYdn89ri2MWf6lTH3Ux4WdHsHH7ruwHNjDKyoRzAo5rhYFRGHkwyF7Hu5jCKPft2JK+AYK5Ffh0EqC24g/SIHW67O1aNM468a0hEee6DmFRnx5GkLxtqD9GYeTBkK5tmHHD0QU9IzRfiqIyzaFeOaRvO247ZTAnDevM1p1VdGz1me/YTUMj7pA1YWAsUoWP8ZLKk4akLM46oHtNULN0EUILiVwmK4kIZx3QnRZNKujQqgkf/HpUzYJMDZXG5WU1IcmLiVIevC4WTA/DwG2nDOamE/dh9eYdRWHKcGbY5zKGUUrEteCRofQo/OakIRYaV5TVmcRXqGyxV0uLy5Ww2CgKs6KhKDE9DEPR4VSIZaa50yBp0ThYL/fFyw9l+uLcF0cy5I9RGIaiwzG5GJOUP8XcwfjTWfsyOOD8kEFddiu4uSQNnVjaaCLSX0SmiMgOEfmFZ9+xIvKpiMwXkatd23uJyPsi8rmIPCkiDWeU2VAvgqxHHSajEwzlngvOwlovXn5owpLkzwlDOhflgH2pEFcPYy1wBXCye6OIlANjgaOBpcCHIvKCqn4C3AHco6pPiMifgR8AD8Qkr6GAcfz149AXcaxlHRYH921fVPIaio9YehiqulJVPwS8UzFHAvNVdYGq7gSeAE4Sy7/uSOBp+7hH8CgbQ+ny53P25weH9qJPBzNZy2CIk6SHDbsAS1z/l9rb2gHrVbXSs91goFf7Flx/wkDKii2ErMFQ5CStMNJFhgscMU5ELhaRqSIyddWqVaEKZzAYDIZaIlMYInKpiMywP53THLYUcIdZ7Ap8BawG2ohIhWd7HVT1QVUdrqrDO3TIb9Uqg8FgMGQnMoWhqmNVdZj98a3sgQ+BfrZHVGPgDOAFtZaPegM4zT7u+8DzUclqMBgMhuzE4iUlInsAU4HWQLWI/AQYqKobReQy4BWgHHhYVefYp10FPCEitwDTgYfikNVgMBgM/sSiMFR1BZZZyW/fy8DLPtsXYHlRGQwGg6EASHrQ22AwGAxFglEYBoPBYAiEURgGg8FgCIQJPljk3DlmCL07mNg7BoMheozCKHJOH9Et+0EGg8EQAsYkZTAYDIZAGIVhMBgMhkAYhWEwGAyGQBiFYTAYDIZAGIVhMBgMhkAYhWEwGAyGQBiFYTAYDIZAGIVhMBgMhkCItfREw0BEVgFfJi1HjrTHWjCqlDD3XBqYey4eeqhq1hXoGpTCKEZEZKqqDk9ajjgx91wamHtueBiTlMFgMBgCYRSGwWAwGAJhFEbyPJi0AAlg7rk0MPfcwDBjGAaDwWAIhOlhGAwGgyEQRmEYDAaDIRBGYdQTEekmIm+IyFwRmSMiV/oc01ZEnhORWSLygYgMcu27UkQ+ts/9iWv7UBGZIiKzReS/ItLatW+IvW+Ovb9p9Hdak3as9ysijUTkEXv7XBG5Jp47DQcRaWo/g5n2Pf8maZmCEERuEWkiIk+KyHwReV9Eerr2XWNv/1REvuXafqy9bb6IXO3a/pi9/WMReVhEGkV9j17ivmfX/j+KyOao7itUVNV86vEB9gT2s3+3Aj4DBnqOuQu40f7dH5ho/x4EfAw0x1r9cALQz973IXCE/fsC4Lf27wpgFjDU/t8OKG/A93sW8IT9uzmwCOiZ9HvP4XkJ0NL+3Qh4HzgwabnCkBv4MfBn+/cZwJP274HATKAJ0Av4Aii3P18AvYHG9jED7XOOt9MU4F/Ajxr6PdvnDQf+AWxO+p0H+ZgeRj1R1eWq+pH9exMwF+jiOWwgMNE+Zh7QU0Q6AQOA91R1q6pWAm8Cp9jn7A1Mtn+/Boyxfx8DzFLVmfb11qhqVSQ350MC96tACxGpAJoBO4GNUdxbFKiF03psZH8K3tMkoNwnAY/Yv58GjhIRsbc/oao7VHUhMB8YaX/mq+oCVd0JPGEfi6q+bKepwAdA1whvz5e471lEyrEaV7+K8LZCxSiMELG7p/titUzczAROtY8ZCfTAKhAfA4eLSDsRaY7VynIW6f4Y+Lb9+zuu7XsBKiKviMhHIpJYZovpfp8GtgDLgcXA71V1bQS3ExkiUi4iM4CVwGuq6n1eBUkAubsASwDsBsAGrB5vzXabpfa2dNvdaTYCzgHGh3cnwYn5ni8DXlDV5WHfR1QYhRESItISeAb4iap6W8C3A23tjHg5MB2oVNW5wB1YLerxWBVtpX3OBcClIjINy/Sz095eARwKnG1/nyIiR0V2Y2mI8X5HAlVAZ6yu/s9FpHdkNxYBqlqlqsOwlOZI95hOIRNAbvE7LY/tbu4HJqvqW7nKGwZx3bOIdMZqGP2xPvLGTUXSAjQE7FbRM8Bjqvqsd79doZ5vHyvAQvuDqj4EPGTvuw2rBeKYco6xt+8FjLYvtxR4U1VX2/teBvbDNgHFQcz3exYwXlV3AStF5B0su++CqO4vKlR1vYhMAv6/vbMNsaqKwvDzlmijDpOYQhZiYVEIg5LzQ4IwKjEzKZDsh4VYoRFlUYPUlJpEgQpFBlEMKIKEH5hMEKgVomgwg6aWOpaVwWQ/RMEyrUFb/djr6pnLnZkzdPMyd9YDh9l37XP2xzl3zrp7r3PePZ00ouoX9NDuDtJIsMOnDOuAMxl7gZuBk57uzo6kpcAoYEGZu9BnrkKfJwHjgePpX4Shko6b2fjy96aMVDqI0t830i+IdcB7PexzPTDY088A6zJ5o/3vWKAdGFFkv8bLn++fRwD76Ro4fqiK+7sYWOP1DgOOAPWVvu59OF+jgOs9XQPsBmZWul3laDfwHF0DwBs9PYGuAeCfSMHfQZ6+hSsB4Al+zNPAXqBmoPS5qNx+EfSueAP6+0aaFjLSk0sHfJsBLAQW+j5TgB/8BrmlcJP0vN1+EzwI3JexLyI9gfQ9aYpHmby5wGHSL58V1dxfYDiwyft7BGis9DXv4/mqJ03JHfLrtaTSbfov7QaWA7M8fZ1fm+OkQPWtmeObSE8HHQMezNhn+DX+EWjK2C+6rfCduurn6Wr3uajufuEwQhokCIIgyEUEvYMgCIJchMMIgiAIchEOIwiCIMhFOIwgCIIgF+EwgiAIglyEwwj6PZIuSTqQ2cZVuk3lRNIkSc2Zz9NdVbXd+7tB0ljPWytpdtHx3SqhShosaZe/hBYEPRJfkqAauGBJzqEkkgZZ0v3pr7wGvAXgUhWrSe8FHHXbLGAcSWurT5hZp6QvgTnA+nI1OKhOYoQRVCWS5knaJOkzYLvbGiW1Ka3T8WZm3yZfr+ALSZ9IesXtOyVN9vQNkk54+lpJKzNlLXD7VD9ms//6X+/SKEhqkLRXaa2FVkm1knZLmphpxx5J9UX9qCW92X7QTYuBtwvOAsDMWsxsF70gaXlmFParpDWetZWkTRYEPRIjjKAaqHGhQ4CfzawgmT6FdLM9I2kacBtJzFBAi6R7SEq4j5O0fQaRZFf29VLfU8BZM2uQNATYI2m7500iyUScBPYAd0tqBTYAc8ysTWlxqAtAMzAPeNH1s4aY2aGiuibTVctoArCql/atlPR6sdHMlgBLJNWR3rj/wLO+Axp6KTMIwmEEVUF3U1I77IoU+jTfvvHPw0kOpBb41MzOA0hqyVHfNKA+Eyuo87I6gVYz6/CyDpCmis4Cv5lZG1wWZ0TSJuANSY0ktd61Jeq6EThVqhGSRpJEJ4cCH5tZwZE0mtnmzH7nMmmRpp7eNbN93p5Lkjol1Vpa4yQIShIOI6hm/sykBbxjZh9ld1BaJrY7fZyLXJm2zS6DK+B5M9tWVNZU4O+M6RLpf0yl6jCz85J2kBbUeYw0mijmQlHdh0nqxAfN7DQw0afQhnfTh2KWAR1mtqbIPgT4K2cZwQAlYhjBQGEbMN/X8UDSTZJGk1b5e1RSjccLHs4ccwK4y9Ozi8p61mXekXS7pGE91N0OjJHU4PvXZp5KagbeB9qs9MJQR0ky2AVWAE2S7szYhvZQ92UkzQQeAF4oso8ETlmSkA+CbokRRjAgMLPtfpP92uPQ54C5ZrZf0gaSQuovpLn9AquAjZKeAL7K2JtJU037fYrnFPBID3V3SpoDrJZUQxo13E9SKN0n6XeShHupY9sl1RWmi8zsW0mLgHXu4E6Tno5amuM0vExaiKrVz0GLxzXuBT7PcXwwwAm12iDIIGkZ6UbeW2C5XPWNAXYCd5jZP93s8xLwh5k1l8ovQxu2AK+a2bH/o/ygeogpqSCoEJKeJK2H3tSds3A+pGtspJxtGAxsDWcR5CFGGEEQBEEuYoQRBEEQ5CIcRhAEQZCLcBhBEARBLsJhBEEQBLkIhxEEQRDk4l+2lFeTqssLDQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -277,36 +288,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 934\n"
+      "During call of saInitiate the followingWarning: saBandwidthClamped was raised\n"
      ]
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "During call of saInitiate the followingWarning: saBandwidthClamped was raised\n"
+      "Starting experimental run with id: 941\n",
+      "52500\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d5c0bd2828>], [None])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1522a614ac8>], [None])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 2b928145439..5d45c3202f5 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -947,11 +947,16 @@ def sweep(self, start, stop, step=None, num=None):
                                 step=step, num=num)
 
 
-class ArrayParameter2(Parameter):
+class ParameterWithSetpoints(Parameter):
     """
-
-
-
+    A parameter that has associated setpoints. The setpoints is nothing
+    more than a list of other paramameters that descripe the values, names
+    and units of the setpoint axis for this parameter.
+
+    In most cases this will probably be a parameter that returns an array.
+    It is expected that the setpoint arrays are 1D arrays such that the
+    combined shape of the parameter. E.G if parameter is of shape (m,n)
+    self.setpoints is a list of parameters of shape (m,) and (n,)
     """
     def __init__(self, *args, setpoints=None, **kwargs):
         if setpoints is None:
diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index a83a6adc711..ac7525c547f 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -8,7 +8,7 @@
 from qcodes.instrument.base import Instrument
 import qcodes.utils.validators as vals
 from qcodes.instrument.parameter import Parameter, ArrayParameter, \
-                                         ArrayParameter2
+    ParameterWithSetpoints
 
 log = logging.getLogger(__name__)
 
@@ -333,7 +333,7 @@ def __init__(self, name, dll_path=None, **kwargs):
                            unit='depends on mode',
                            get_cmd=self._get_averaged_sweep_data,
                            set_cmd=False,
-                           parameter_class=ArrayParameter2,
+                           parameter_class=ParameterWithSetpoints,
                            vals=vals.Arrays(),
                            setpoints=(self.frequency_axis,),
                            snapshot_value=False)

From aaa0f01b6a23fd5c0cd173e4d464854b4085f6e1 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 11 Oct 2018 14:38:12 +0200
Subject: [PATCH 109/719] use new name in measurements

---
 qcodes/dataset/measurements.py | 41 +++++++++++++++++-----------------
 1 file changed, 21 insertions(+), 20 deletions(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index ae8d6e4e3d7..e0f8db2d557 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -12,7 +12,7 @@
 import qcodes as qc
 from qcodes import Station
 from qcodes.instrument.parameter import ArrayParameter, _BaseParameter, \
-    Parameter, MultiParameter, ArrayParameter2
+    Parameter, MultiParameter, ParameterWithSetpoints
 from qcodes.dataset.experiment_container import Experiment
 from qcodes.dataset.param_spec import ParamSpec
 from qcodes.dataset.data_set import DataSet
@@ -156,10 +156,10 @@ def add_result(self, *res_tuple: res_type) -> None:
                     self._unbundle_arrayparameter(parameter,
                                                   res,
                                                   found_parameters)
-                elif isinstance(parameter, ArrayParameter2):
-                    self._unbundle_arrayparameter2(parameter,
-                                                   res,
-                                                   found_parameters)
+                elif isinstance(parameter, ParameterWithSetpoints):
+                    self._unbundle_parameter_with_setpoints(parameter,
+                                                            res,
+                                                            found_parameters)
 
         for partial_result in res:
             parameter = partial_result[0]
@@ -292,9 +292,10 @@ def _unbundle_arrayparameter(self,
                                             parameter.setpoints,
                                             res, found_parameters)
 
-    def _unbundle_arrayparameter2(self, parameter: ArrayParameter2,
-                                  res: List[res_type],
-                                  found_parameters: List[str]) -> None:
+    def _unbundle_parameter_with_setpoints(self,
+                                           parameter: ParameterWithSetpoints,
+                                           res: List[res_type],
+                                           found_parameters: List[str]) -> None:
         setpoint_names = []
         setpoint_data = []
         for setpointparam in parameter.setpoints:
@@ -653,11 +654,11 @@ def register_parameter(
                                           setpoints,
                                           basis,
                                           paramtype)
-        elif isinstance(parameter, ArrayParameter2):
-            self._register_arrayparameter2(parameter,
-                                           setpoints,
-                                           basis,
-                                           paramtype)
+        elif isinstance(parameter, ParameterWithSetpoints):
+            self._register_parameter_with_setpoints(parameter,
+                                                    setpoints,
+                                                    basis,
+                                                    paramtype)
         elif isinstance(parameter, MultiParameter):
             self._register_multiparameter(parameter,
                                           setpoints,
@@ -753,14 +754,14 @@ def _register_arrayparameter(self,
                                  basis,
                                  paramtype)
 
-    def _register_arrayparameter2(self,
-                                 parameter: ArrayParameter2,
-                                 setpoints: setpoints_type,
-                                 basis: setpoints_type,
-                                 paramtype: str) -> None:
+    def _register_parameter_with_setpoints(self,
+                                           parameter: ParameterWithSetpoints,
+                                           setpoints: setpoints_type,
+                                           basis: setpoints_type,
+                                           paramtype: str) -> None:
         """
-        Register an ArrayParameter2 and the setpoints belonging to the
-        ArrayParameter2
+        Register an ParameterWithSetpoints and the setpoints belonging to the
+        Parameter
         """
         name = str(parameter)
         my_setpoints = list(setpoints) if setpoints else []

From 3ba891e24384e26b44b01cb575d81f8b7fc01bc3 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 12 Oct 2018 17:13:49 +0200
Subject: [PATCH 110/719] make setpoints a property

---
 qcodes/instrument/parameter.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 5d45c3202f5..0f5eff6f3d6 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -958,13 +958,25 @@ class ParameterWithSetpoints(Parameter):
     combined shape of the parameter. E.G if parameter is of shape (m,n)
     self.setpoints is a list of parameters of shape (m,) and (n,)
     """
-    def __init__(self, *args, setpoints=None, **kwargs):
+    def __init__(self, *args,
+                 setpoints: Sequence[Parameter]=None, **kwargs) -> None:
         if setpoints is None:
-            self.setpoints = []
+            self._setpoints = ()
         else:
             self.setpoints = setpoints
         super().__init__(*args, **kwargs)
 
+    @property
+    def setpoints(self):
+        return self._setpoints
+
+    @setpoints.setter
+    def setpoints(self, setpoints):
+        for setpointarray in setpoints:
+            if not isinstance(setpointarray, Parameter):
+                raise RuntimeError
+        self._setpoints = setpoints
+
 
 class GeneratedSetPoints(Parameter):
     """

From 482530d56a900335e58821ba1d8a03a3c85f5011 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 22 Oct 2018 13:52:35 +0200
Subject: [PATCH 111/719] Import ParameterWithSetpoints to main qcodes
 namespace

---
 qcodes/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qcodes/__init__.py b/qcodes/__init__.py
index ceed84f6a63..f09c3ff247e 100644
--- a/qcodes/__init__.py
+++ b/qcodes/__init__.py
@@ -62,6 +62,7 @@
     Parameter,
     ArrayParameter,
     MultiParameter,
+    ParameterWithSetpoints,
     StandardParameter,
     ManualParameter,
     ScaledParameter,

From 81c407a8b7236547e4c7c9ab584badc3c8fc97ab Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 22 Oct 2018 13:53:02 +0200
Subject: [PATCH 112/719] Add ParameterWithSetpoints to docs

---
 docs/api/public.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/api/public.rst b/docs/api/public.rst
index 302e6ae104e..ca634cef9cb 100644
--- a/docs/api/public.rst
+++ b/docs/api/public.rst
@@ -107,7 +107,7 @@ Instrument
 
    Function
    Parameter
-   StandardParameter
+   ParameterWithSetpoints
    ArrayParameter
    MultiParameter
    ManualParameter

From 1fd85af2f98766d2c839826988e82e7228e261a3 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 22 Oct 2018 13:53:33 +0200
Subject: [PATCH 113/719] Add link to parameter from ParameterWithSetpoints

---
 qcodes/instrument/parameter.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 0f5eff6f3d6..6df6baef8ca 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -957,6 +957,9 @@ class ParameterWithSetpoints(Parameter):
     It is expected that the setpoint arrays are 1D arrays such that the
     combined shape of the parameter. E.G if parameter is of shape (m,n)
     self.setpoints is a list of parameters of shape (m,) and (n,)
+
+    In all other ways this is identical to  :class:`Parameter` See the
+    documentation of :class:`Parameter` for more details.
     """
     def __init__(self, *args,
                  setpoints: Sequence[Parameter]=None, **kwargs) -> None:

From ce2d7e28711555c7cb302829c05f14bf37868c4c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 22 Oct 2018 13:53:57 +0200
Subject: [PATCH 114/719] add title to notebook

---
 ... with Signal Hound USB-SA124B new Array Parameter.ipynb | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb
index 7c0db5b1a9b..38287c9bb69 100644
--- a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb	
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Qcodes example with Signal Hound USB-SA124B new Array Parameter"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

From 31d9c4b887194d31b84f00328bc731bafca5e5b0 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 22 Oct 2018 14:39:32 +0200
Subject: [PATCH 115/719] Make sure that database exists

---
 ...Simple Example of new ArrayParameter.ipynb | 504 ----------------
 ...xample of new ParameterWithSetpoints.ipynb | 536 ++++++++++++++++++
 2 files changed, 536 insertions(+), 504 deletions(-)
 delete mode 100644 docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb
 create mode 100644 docs/examples/DataSet/Simple Example of new ParameterWithSetpoints.ipynb

diff --git a/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb b/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb
deleted file mode 100644
index 32d14da0704..00000000000
--- a/docs/examples/DataSet/Simple Example of new ArrayParameter.ipynb	
+++ /dev/null
@@ -1,504 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Simple Example of ParameterWithSetpoints\n",
-    "This notebook has an example of a new simpler ArrayParameter replacement\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.instrument.parameter import Parameter, _BaseParameter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Optional, Union, Iterable, Callable\n",
-    "Number = Union[float, int]\n",
-    "from qcodes.utils.validators import Validator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.utils.validators import Numbers\n",
-    "from qcodes.instrument.base import Instrument\n",
-    "from qcodes.dataset.measurements import Measurement"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.instrument.parameter import ParameterWithSetpoints, GeneratedSetPoints"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we define an dummy instrument that returns something like a frequency spectrum from `f_start` to `f_stop`\n",
-    "in `n_points` steps. The functionality of the ArrayParameter is implemented only by having a reference to it's setpoints which is consumed by the dataset context manager. To do this we only have to define the parameter for the setpoints and the spectrum and let the parameter know that the frequency axis is the setpoints "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class DummyArray(ParameterWithSetpoints):\n",
-    "    \n",
-    "    def get_raw(self):\n",
-    "        npoints = self.root_instrument.n_points()\n",
-    "        return np.random.rand(npoints)\n",
-    "    \n",
-    "\n",
-    "class DummySpectrumAnalyzer(Instrument):\n",
-    "    \n",
-    "    def __init__(self, name, **kwargs):\n",
-    "        \n",
-    "        super().__init__(name, **kwargs)\n",
-    "            \n",
-    "\n",
-    "        self.add_parameter('f_start',\n",
-    "                           initial_value=0,\n",
-    "                           unit='Hz',\n",
-    "                           label='f start',\n",
-    "                           vals=Numbers(0,1e3),\n",
-    "                           get_cmd=None,\n",
-    "                           set_cmd=None)\n",
-    "\n",
-    "        self.add_parameter('f_stop',\n",
-    "                           unit='Hz',\n",
-    "                           label='f stop',\n",
-    "                           vals=Numbers(1,1e3),\n",
-    "                           get_cmd=None,\n",
-    "                           set_cmd=None)\n",
-    "\n",
-    "        self.add_parameter('n_points',\n",
-    "                           unit='',\n",
-    "                           vals=Numbers(1,1e3),\n",
-    "                           get_cmd=None,\n",
-    "                           set_cmd=None)\n",
-    "        \n",
-    "        self.add_parameter('freq_axis',\n",
-    "                           unit='Hz',\n",
-    "                           label='Freq Axis',\n",
-    "                           parameter_class=GeneratedSetPoints,\n",
-    "                           startparam=self.f_start,\n",
-    "                           stopparam=self.f_stop,\n",
-    "                           numpointsparam=self.n_points)\n",
-    "                           \n",
-    "        self.add_parameter('spectrum',\n",
-    "                   unit='dBm',\n",
-    "                   setpoints=(self.freq_axis,),\n",
-    "                   label='Spectrum',\n",
-    "                   parameter_class=DummyArray)\n",
-    "                "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = DummySpectrumAnalyzer('foobar')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we setup the limits of the spectrum"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a.f_start(0)\n",
-    "a.f_stop(500)\n",
-    "a.n_points(501)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can grab the axis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
-       "        11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
-       "        22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,\n",
-       "        33.,  34.,  35.,  36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,\n",
-       "        44.,  45.,  46.,  47.,  48.,  49.,  50.,  51.,  52.,  53.,  54.,\n",
-       "        55.,  56.,  57.,  58.,  59.,  60.,  61.,  62.,  63.,  64.,  65.,\n",
-       "        66.,  67.,  68.,  69.,  70.,  71.,  72.,  73.,  74.,  75.,  76.,\n",
-       "        77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,\n",
-       "        88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,  96.,  97.,  98.,\n",
-       "        99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109.,\n",
-       "       110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.,\n",
-       "       121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,\n",
-       "       132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142.,\n",
-       "       143., 144., 145., 146., 147., 148., 149., 150., 151., 152., 153.,\n",
-       "       154., 155., 156., 157., 158., 159., 160., 161., 162., 163., 164.,\n",
-       "       165., 166., 167., 168., 169., 170., 171., 172., 173., 174., 175.,\n",
-       "       176., 177., 178., 179., 180., 181., 182., 183., 184., 185., 186.,\n",
-       "       187., 188., 189., 190., 191., 192., 193., 194., 195., 196., 197.,\n",
-       "       198., 199., 200., 201., 202., 203., 204., 205., 206., 207., 208.,\n",
-       "       209., 210., 211., 212., 213., 214., 215., 216., 217., 218., 219.,\n",
-       "       220., 221., 222., 223., 224., 225., 226., 227., 228., 229., 230.,\n",
-       "       231., 232., 233., 234., 235., 236., 237., 238., 239., 240., 241.,\n",
-       "       242., 243., 244., 245., 246., 247., 248., 249., 250., 251., 252.,\n",
-       "       253., 254., 255., 256., 257., 258., 259., 260., 261., 262., 263.,\n",
-       "       264., 265., 266., 267., 268., 269., 270., 271., 272., 273., 274.,\n",
-       "       275., 276., 277., 278., 279., 280., 281., 282., 283., 284., 285.,\n",
-       "       286., 287., 288., 289., 290., 291., 292., 293., 294., 295., 296.,\n",
-       "       297., 298., 299., 300., 301., 302., 303., 304., 305., 306., 307.,\n",
-       "       308., 309., 310., 311., 312., 313., 314., 315., 316., 317., 318.,\n",
-       "       319., 320., 321., 322., 323., 324., 325., 326., 327., 328., 329.,\n",
-       "       330., 331., 332., 333., 334., 335., 336., 337., 338., 339., 340.,\n",
-       "       341., 342., 343., 344., 345., 346., 347., 348., 349., 350., 351.,\n",
-       "       352., 353., 354., 355., 356., 357., 358., 359., 360., 361., 362.,\n",
-       "       363., 364., 365., 366., 367., 368., 369., 370., 371., 372., 373.,\n",
-       "       374., 375., 376., 377., 378., 379., 380., 381., 382., 383., 384.,\n",
-       "       385., 386., 387., 388., 389., 390., 391., 392., 393., 394., 395.,\n",
-       "       396., 397., 398., 399., 400., 401., 402., 403., 404., 405., 406.,\n",
-       "       407., 408., 409., 410., 411., 412., 413., 414., 415., 416., 417.,\n",
-       "       418., 419., 420., 421., 422., 423., 424., 425., 426., 427., 428.,\n",
-       "       429., 430., 431., 432., 433., 434., 435., 436., 437., 438., 439.,\n",
-       "       440., 441., 442., 443., 444., 445., 446., 447., 448., 449., 450.,\n",
-       "       451., 452., 453., 454., 455., 456., 457., 458., 459., 460., 461.,\n",
-       "       462., 463., 464., 465., 466., 467., 468., 469., 470., 471., 472.,\n",
-       "       473., 474., 475., 476., 477., 478., 479., 480., 481., 482., 483.,\n",
-       "       484., 485., 486., 487., 488., 489., 490., 491., 492., 493., 494.,\n",
-       "       495., 496., 497., 498., 499., 500.])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.freq_axis()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or the spectrum"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.37372587, 0.54861787, 0.53155288, 0.82667582, 0.94220873,\n",
-       "       0.0698023 , 0.89762473, 0.40402402, 0.94676787, 0.72479779,\n",
-       "       0.90016702, 0.50196169, 0.67613499, 0.96529924, 0.15008748,\n",
-       "       0.44617032, 0.77400385, 0.36104437, 0.6286802 , 0.69278277,\n",
-       "       0.12981568, 0.70792455, 0.63326382, 0.79600711, 0.77696625,\n",
-       "       0.60408452, 0.34930137, 0.33298155, 0.99237339, 0.72181006,\n",
-       "       0.24177301, 0.38409252, 0.13977054, 0.46336893, 0.29702444,\n",
-       "       0.32585009, 0.85559257, 0.506369  , 0.79234633, 0.91855242,\n",
-       "       0.91216349, 0.82632103, 0.15051881, 0.38302095, 0.39616307,\n",
-       "       0.32864887, 0.67891701, 0.01545452, 0.32810263, 0.84456954,\n",
-       "       0.30411356, 0.83792683, 0.18423625, 0.76232546, 0.62204577,\n",
-       "       0.28122632, 0.90895218, 0.47174043, 0.0835899 , 0.880365  ,\n",
-       "       0.09111603, 0.50584601, 0.56714919, 0.57147067, 0.85644402,\n",
-       "       0.22211664, 0.05509187, 0.95254653, 0.93371861, 0.97754025,\n",
-       "       0.0207392 , 0.28635372, 0.63367136, 0.0663695 , 0.70019442,\n",
-       "       0.40926177, 0.42111847, 0.85448678, 0.98820638, 0.94779916,\n",
-       "       0.25279355, 0.38715305, 0.30811057, 0.8744069 , 0.50608295,\n",
-       "       0.58995161, 0.58200316, 0.58201891, 0.93918669, 0.6899701 ,\n",
-       "       0.95871603, 0.14629169, 0.80691971, 0.99692501, 0.17309138,\n",
-       "       0.40671056, 0.87719149, 0.68771378, 0.08381251, 0.14822247,\n",
-       "       0.69180229, 0.03358827, 0.30626825, 0.33936864, 0.88937098,\n",
-       "       0.99683227, 0.7537689 , 0.35666654, 0.23870022, 0.80237453,\n",
-       "       0.83317602, 0.56290746, 0.4612086 , 0.59187958, 0.35496415,\n",
-       "       0.76680147, 0.72366515, 0.53806547, 0.73491729, 0.87097106,\n",
-       "       0.22250074, 0.4319    , 0.25040755, 0.4204853 , 0.57152887,\n",
-       "       0.67989035, 0.51488572, 0.12774031, 0.86615569, 0.78384103,\n",
-       "       0.04802505, 0.65392064, 0.96986131, 0.47990375, 0.3774105 ,\n",
-       "       0.06543123, 0.41360314, 0.1389294 , 0.23166719, 0.67111829,\n",
-       "       0.59806864, 0.73826979, 0.4013829 , 0.63824427, 0.44717827,\n",
-       "       0.15403842, 0.39021108, 0.10353845, 0.96664997, 0.41367465,\n",
-       "       0.22059292, 0.17918976, 0.96500464, 0.51467301, 0.89966624,\n",
-       "       0.93987921, 0.94166864, 0.64683108, 0.55641748, 0.9484579 ,\n",
-       "       0.66408577, 0.37486359, 0.65857553, 0.00120706, 0.76021654,\n",
-       "       0.95142316, 0.74106431, 0.3687487 , 0.81576676, 0.89905403,\n",
-       "       0.60252817, 0.52068992, 0.99020434, 0.01758782, 0.26871668,\n",
-       "       0.42934743, 0.74147531, 0.02036298, 0.90464033, 0.67078621,\n",
-       "       0.55625392, 0.61639495, 0.68145904, 0.18213379, 0.03731415,\n",
-       "       0.28217922, 0.82413099, 0.41451497, 0.06819495, 0.67933438,\n",
-       "       0.48485664, 0.02562523, 0.99595955, 0.8278784 , 0.56357999,\n",
-       "       0.45688242, 0.15482338, 0.88100967, 0.75521795, 0.66280644,\n",
-       "       0.43293394, 0.70636499, 0.83458264, 0.16970696, 0.47818101,\n",
-       "       0.08842654, 0.77442703, 0.56663918, 0.24441029, 0.55706783,\n",
-       "       0.08530588, 0.48254471, 0.90653557, 0.86716833, 0.92738514,\n",
-       "       0.29735555, 0.5957369 , 0.34662178, 0.14905592, 0.84934635,\n",
-       "       0.37038126, 0.91797818, 0.10743678, 0.33358987, 0.61751291,\n",
-       "       0.56708866, 0.96416561, 0.9008846 , 0.5365646 , 0.33294292,\n",
-       "       0.7055552 , 0.22700866, 0.21832081, 0.37975166, 0.69098247,\n",
-       "       0.10769966, 0.14557844, 0.14896331, 0.37194517, 0.45441072,\n",
-       "       0.83939733, 0.99817352, 0.36444098, 0.043637  , 0.19484866,\n",
-       "       0.39253182, 0.75448118, 0.35597998, 0.80725856, 0.95531812,\n",
-       "       0.79183714, 0.36342638, 0.45123011, 0.92610209, 0.25520695,\n",
-       "       0.85375401, 0.01310321, 0.48663791, 0.48385191, 0.8494894 ,\n",
-       "       0.76816717, 0.72400051, 0.0430897 , 0.15606243, 0.53290784,\n",
-       "       0.09257425, 0.75460404, 0.74206088, 0.61720626, 0.51084909,\n",
-       "       0.58411453, 0.47311666, 0.53830169, 0.9681255 , 0.79663431,\n",
-       "       0.57598398, 0.77865371, 0.65301602, 0.18408085, 0.80375723,\n",
-       "       0.23633886, 0.58244941, 0.29898102, 0.02252307, 0.98082483,\n",
-       "       0.23015448, 0.70841724, 0.05523285, 0.49451749, 0.35346533,\n",
-       "       0.85056982, 0.33981886, 0.06833761, 0.79821593, 0.11604247,\n",
-       "       0.8953603 , 0.46323585, 0.23137089, 0.44835628, 0.80901788,\n",
-       "       0.42361957, 0.51037851, 0.23792164, 0.7941132 , 0.76321701,\n",
-       "       0.11911182, 0.84181059, 0.60137409, 0.34603324, 0.22177182,\n",
-       "       0.10613795, 0.89584437, 0.64215604, 0.2739601 , 0.76154826,\n",
-       "       0.04708115, 0.81540537, 0.12827219, 0.01783416, 0.62473422,\n",
-       "       0.6454418 , 0.35580161, 0.25226381, 0.15315039, 0.06409371,\n",
-       "       0.82481876, 0.26274587, 0.77689317, 0.28502571, 0.11676692,\n",
-       "       0.21020535, 0.58340203, 0.50298104, 0.66517868, 0.91702034,\n",
-       "       0.41895255, 0.48976598, 0.71751388, 0.55612794, 0.79018665,\n",
-       "       0.08031651, 0.93057851, 0.56953056, 0.04527181, 0.88304957,\n",
-       "       0.70831083, 0.90265482, 0.94031171, 0.2569966 , 0.66892006,\n",
-       "       0.66635678, 0.27309862, 0.03279321, 0.7976703 , 0.50451827,\n",
-       "       0.75593387, 0.35091271, 0.76169186, 0.57906099, 0.98890751,\n",
-       "       0.96141125, 0.25731111, 0.67022349, 0.30738175, 0.22752395,\n",
-       "       0.88743371, 0.6975126 , 0.90094769, 0.55723621, 0.17498322,\n",
-       "       0.23819869, 0.3564374 , 0.80282856, 0.30243351, 0.80448796,\n",
-       "       0.48466329, 0.98000332, 0.02199852, 0.18488749, 0.54550893,\n",
-       "       0.86812643, 0.83792429, 0.89461096, 0.88250996, 0.42808942,\n",
-       "       0.59296736, 0.79860916, 0.30380101, 0.43271408, 0.15841563,\n",
-       "       0.56869869, 0.04184787, 0.10550598, 0.60942529, 0.4265301 ,\n",
-       "       0.57083876, 0.82435567, 0.59211889, 0.96031419, 0.67952074,\n",
-       "       0.37334926, 0.3645627 , 0.00962004, 0.46270116, 0.24393275,\n",
-       "       0.61877639, 0.72515794, 0.74112891, 0.78423151, 0.38865816,\n",
-       "       0.52355693, 0.98749161, 0.0240849 , 0.55514309, 0.01958487,\n",
-       "       0.849384  , 0.59643945, 0.72927759, 0.50640776, 0.47260815,\n",
-       "       0.02341713, 0.18155492, 0.92489797, 0.25403256, 0.10852141,\n",
-       "       0.64066968, 0.77210979, 0.11233653, 0.67487216, 0.66589406,\n",
-       "       0.69246635, 0.81106247, 0.04426596, 0.72241094, 0.61934481,\n",
-       "       0.67953836, 0.33271667, 0.70431201, 0.49538386, 0.70481405,\n",
-       "       0.51488965, 0.35365375, 0.96189495, 0.08626799, 0.65469915,\n",
-       "       0.75678647, 0.13191874, 0.68800181, 0.3976978 , 0.75082862,\n",
-       "       0.9238525 , 0.19926841, 0.52864871, 0.84631558, 0.08041432,\n",
-       "       0.61617623, 0.47972956, 0.41253579, 0.89873016, 0.49668532,\n",
-       "       0.28110437, 0.71319872, 0.38127862, 0.2969393 , 0.6869966 ,\n",
-       "       0.93906833, 0.05512929, 0.46130136, 0.31505291, 0.57178749,\n",
-       "       0.77410248, 0.43127678, 0.46184098, 0.30312938, 0.93041753,\n",
-       "       0.51114825, 0.79740875, 0.76236662, 0.46370778, 0.98322976,\n",
-       "       0.03513679, 0.3587125 , 0.13594183, 0.59490432, 0.77843549,\n",
-       "       0.69222248, 0.89102323, 0.69021418, 0.81951953, 0.13221865,\n",
-       "       0.86460461, 0.83135051, 0.15770558, 0.18465204, 0.17177554,\n",
-       "       0.61530353, 0.64739044, 0.40496839, 0.49301272, 0.86611209,\n",
-       "       0.86025078])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.spectrum.get()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also directly consume the parameter in a measurement"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 952\n"
-     ]
-    }
-   ],
-   "source": [
-    "meas = Measurement()\n",
-    "meas.register_parameter(a.spectrum)  # register the first independent parameter\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((a.spectrum, a.spectrum.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.dataset.plotting import plot_by_id"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And plot it"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d6f0446c18>], [None])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
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-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
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-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
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-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
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-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
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-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
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-   "types_to_exclude": [
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-    "function",
-    "builtin_function_or_method",
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diff --git a/docs/examples/DataSet/Simple Example of new ParameterWithSetpoints.ipynb b/docs/examples/DataSet/Simple Example of new ParameterWithSetpoints.ipynb
new file mode 100644
index 00000000000..ed29993a991
--- /dev/null
+++ b/docs/examples/DataSet/Simple Example of new ParameterWithSetpoints.ipynb	
@@ -0,0 +1,536 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Example of ParameterWithSetpoints\n",
+    "This notebook has an example of a new simpler ArrayParameter replacement\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument.parameter import Parameter, _BaseParameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Optional, Union, Iterable, Callable\n",
+    "Number = Union[float, int]\n",
+    "from qcodes.utils.validators import Validator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.utils.validators import Numbers\n",
+    "from qcodes.instrument.base import Instrument\n",
+    "from qcodes.dataset.measurements import Measurement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.dataset.database import initialise_database\n",
+    "from qcodes.dataset.experiment_container import new_experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument.parameter import ParameterWithSetpoints, GeneratedSetPoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we define an dummy instrument that returns something like a frequency spectrum from `f_start` to `f_stop`\n",
+    "in `n_points` steps. The functionality of the ArrayParameter is implemented only by having a reference to it's setpoints which is consumed by the dataset context manager. To do this we only have to define the parameter for the setpoints and the spectrum and let the parameter know that the frequency axis is the setpoints "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DummyArray(ParameterWithSetpoints):\n",
+    "    \n",
+    "    def get_raw(self):\n",
+    "        npoints = self.root_instrument.n_points()\n",
+    "        return np.random.rand(npoints)\n",
+    "    \n",
+    "\n",
+    "class DummySpectrumAnalyzer(Instrument):\n",
+    "    \n",
+    "    def __init__(self, name, **kwargs):\n",
+    "        \n",
+    "        super().__init__(name, **kwargs)\n",
+    "            \n",
+    "\n",
+    "        self.add_parameter('f_start',\n",
+    "                           initial_value=0,\n",
+    "                           unit='Hz',\n",
+    "                           label='f start',\n",
+    "                           vals=Numbers(0,1e3),\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "\n",
+    "        self.add_parameter('f_stop',\n",
+    "                           unit='Hz',\n",
+    "                           label='f stop',\n",
+    "                           vals=Numbers(1,1e3),\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "\n",
+    "        self.add_parameter('n_points',\n",
+    "                           unit='',\n",
+    "                           vals=Numbers(1,1e3),\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "        \n",
+    "        self.add_parameter('freq_axis',\n",
+    "                           unit='Hz',\n",
+    "                           label='Freq Axis',\n",
+    "                           parameter_class=GeneratedSetPoints,\n",
+    "                           startparam=self.f_start,\n",
+    "                           stopparam=self.f_stop,\n",
+    "                           numpointsparam=self.n_points)\n",
+    "                           \n",
+    "        self.add_parameter('spectrum',\n",
+    "                   unit='dBm',\n",
+    "                   setpoints=(self.freq_axis,),\n",
+    "                   label='Spectrum',\n",
+    "                   parameter_class=DummyArray)\n",
+    "                "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tutorial_exp#no sample#142@c:\\Users\\jenielse\\mymainfolder\\experiments.db\n",
+       "------------------------------------------------------------------------"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "initialise_database()\n",
+    "new_experiment(name='tutorial_exp', sample_name=\"no sample\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = DummySpectrumAnalyzer('foobar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we setup the limits of the spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.f_start(0)\n",
+    "a.f_stop(500)\n",
+    "a.n_points(501)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can grab the axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
+       "        11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
+       "        22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,\n",
+       "        33.,  34.,  35.,  36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,\n",
+       "        44.,  45.,  46.,  47.,  48.,  49.,  50.,  51.,  52.,  53.,  54.,\n",
+       "        55.,  56.,  57.,  58.,  59.,  60.,  61.,  62.,  63.,  64.,  65.,\n",
+       "        66.,  67.,  68.,  69.,  70.,  71.,  72.,  73.,  74.,  75.,  76.,\n",
+       "        77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,\n",
+       "        88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,  96.,  97.,  98.,\n",
+       "        99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109.,\n",
+       "       110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.,\n",
+       "       121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,\n",
+       "       132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142.,\n",
+       "       143., 144., 145., 146., 147., 148., 149., 150., 151., 152., 153.,\n",
+       "       154., 155., 156., 157., 158., 159., 160., 161., 162., 163., 164.,\n",
+       "       165., 166., 167., 168., 169., 170., 171., 172., 173., 174., 175.,\n",
+       "       176., 177., 178., 179., 180., 181., 182., 183., 184., 185., 186.,\n",
+       "       187., 188., 189., 190., 191., 192., 193., 194., 195., 196., 197.,\n",
+       "       198., 199., 200., 201., 202., 203., 204., 205., 206., 207., 208.,\n",
+       "       209., 210., 211., 212., 213., 214., 215., 216., 217., 218., 219.,\n",
+       "       220., 221., 222., 223., 224., 225., 226., 227., 228., 229., 230.,\n",
+       "       231., 232., 233., 234., 235., 236., 237., 238., 239., 240., 241.,\n",
+       "       242., 243., 244., 245., 246., 247., 248., 249., 250., 251., 252.,\n",
+       "       253., 254., 255., 256., 257., 258., 259., 260., 261., 262., 263.,\n",
+       "       264., 265., 266., 267., 268., 269., 270., 271., 272., 273., 274.,\n",
+       "       275., 276., 277., 278., 279., 280., 281., 282., 283., 284., 285.,\n",
+       "       286., 287., 288., 289., 290., 291., 292., 293., 294., 295., 296.,\n",
+       "       297., 298., 299., 300., 301., 302., 303., 304., 305., 306., 307.,\n",
+       "       308., 309., 310., 311., 312., 313., 314., 315., 316., 317., 318.,\n",
+       "       319., 320., 321., 322., 323., 324., 325., 326., 327., 328., 329.,\n",
+       "       330., 331., 332., 333., 334., 335., 336., 337., 338., 339., 340.,\n",
+       "       341., 342., 343., 344., 345., 346., 347., 348., 349., 350., 351.,\n",
+       "       352., 353., 354., 355., 356., 357., 358., 359., 360., 361., 362.,\n",
+       "       363., 364., 365., 366., 367., 368., 369., 370., 371., 372., 373.,\n",
+       "       374., 375., 376., 377., 378., 379., 380., 381., 382., 383., 384.,\n",
+       "       385., 386., 387., 388., 389., 390., 391., 392., 393., 394., 395.,\n",
+       "       396., 397., 398., 399., 400., 401., 402., 403., 404., 405., 406.,\n",
+       "       407., 408., 409., 410., 411., 412., 413., 414., 415., 416., 417.,\n",
+       "       418., 419., 420., 421., 422., 423., 424., 425., 426., 427., 428.,\n",
+       "       429., 430., 431., 432., 433., 434., 435., 436., 437., 438., 439.,\n",
+       "       440., 441., 442., 443., 444., 445., 446., 447., 448., 449., 450.,\n",
+       "       451., 452., 453., 454., 455., 456., 457., 458., 459., 460., 461.,\n",
+       "       462., 463., 464., 465., 466., 467., 468., 469., 470., 471., 472.,\n",
+       "       473., 474., 475., 476., 477., 478., 479., 480., 481., 482., 483.,\n",
+       "       484., 485., 486., 487., 488., 489., 490., 491., 492., 493., 494.,\n",
+       "       495., 496., 497., 498., 499., 500.])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.freq_axis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or the spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.72571352, 0.90632183, 0.33183049, 0.02012441, 0.47057386,\n",
+       "       0.90672402, 0.07999109, 0.61693661, 0.30530647, 0.10985965,\n",
+       "       0.60918186, 0.34274291, 0.43832254, 0.76568971, 0.21721021,\n",
+       "       0.30198089, 0.47201838, 0.97928559, 0.7715346 , 0.23269826,\n",
+       "       0.36167025, 0.80635443, 0.04952206, 0.58616747, 0.40224088,\n",
+       "       0.45277086, 0.02814571, 0.07327414, 0.63791345, 0.18959364,\n",
+       "       0.65795672, 0.67999541, 0.05855894, 0.02249179, 0.40130862,\n",
+       "       0.54845244, 0.2443827 , 0.73592797, 0.9013913 , 0.59558038,\n",
+       "       0.24921754, 0.00510943, 0.85173873, 0.99469415, 0.3457128 ,\n",
+       "       0.61190606, 0.86541492, 0.03245227, 0.75744   , 0.01621978,\n",
+       "       0.86318191, 0.99017436, 0.60923479, 0.67913571, 0.4967267 ,\n",
+       "       0.03686535, 0.36743032, 0.53714849, 0.72401575, 0.94879797,\n",
+       "       0.84441913, 0.89020992, 0.21399512, 0.88068879, 0.36922398,\n",
+       "       0.95177315, 0.39291993, 0.06345901, 0.12453757, 0.59359891,\n",
+       "       0.47722406, 0.11144605, 0.26755104, 0.76426021, 0.46995979,\n",
+       "       0.6373366 , 0.38905457, 0.28229169, 0.75277916, 0.86484352,\n",
+       "       0.95307285, 0.61320484, 0.18912997, 0.5053552 , 0.95201004,\n",
+       "       0.29930822, 0.96947378, 0.37089328, 0.29869885, 0.67549094,\n",
+       "       0.59300973, 0.73171001, 0.79902709, 0.59934004, 0.70940563,\n",
+       "       0.56030827, 0.75177476, 0.72089084, 0.9550844 , 0.57847848,\n",
+       "       0.60957107, 0.1517757 , 0.38754644, 0.09730288, 0.59988402,\n",
+       "       0.26167866, 0.00287584, 0.76134196, 0.72431838, 0.72975611,\n",
+       "       0.22905784, 0.18651577, 0.48891715, 0.57899863, 0.4710521 ,\n",
+       "       0.10136816, 0.69808045, 0.02561995, 0.08323249, 0.77192287,\n",
+       "       0.38029957, 0.9954691 , 0.64841041, 0.90485035, 0.45524682,\n",
+       "       0.89615322, 0.1358363 , 0.48303273, 0.2959501 , 0.13139568,\n",
+       "       0.8123182 , 0.7683797 , 0.67372457, 0.96291618, 0.84549104,\n",
+       "       0.95942812, 0.27444138, 0.23508412, 0.26097708, 0.40263297,\n",
+       "       0.28233152, 0.54483201, 0.91830492, 0.1457073 , 0.40617229,\n",
+       "       0.35904864, 0.02131232, 0.57335326, 0.31579837, 0.23954332,\n",
+       "       0.83839741, 0.28382389, 0.52414372, 0.85262061, 0.06880117,\n",
+       "       0.40455194, 0.90178558, 0.22408739, 0.7744848 , 0.02481246,\n",
+       "       0.80764393, 0.26593597, 0.02677597, 0.40965144, 0.8421576 ,\n",
+       "       0.53731458, 0.89145735, 0.38181248, 0.38428735, 0.80712207,\n",
+       "       0.09220307, 0.74547953, 0.52355824, 0.14421889, 0.09984613,\n",
+       "       0.86976546, 0.86983597, 0.75526311, 0.35561927, 0.95147586,\n",
+       "       0.46415392, 0.91923755, 0.48889277, 0.22755345, 0.26840527,\n",
+       "       0.62062403, 0.80705705, 0.71891185, 0.69817039, 0.88091927,\n",
+       "       0.72549422, 0.69001979, 0.10264036, 0.88963743, 0.05742265,\n",
+       "       0.25475254, 0.63174133, 0.85631804, 0.46140504, 0.89153781,\n",
+       "       0.09246152, 0.4553433 , 0.82194924, 0.10917984, 0.73418867,\n",
+       "       0.19001657, 0.45568429, 0.54007574, 0.58392099, 0.30775546,\n",
+       "       0.4394131 , 0.91735479, 0.50491593, 0.19709832, 0.04327409,\n",
+       "       0.12994578, 0.60389174, 0.60880184, 0.86196128, 0.461006  ,\n",
+       "       0.93866109, 0.07467717, 0.96665901, 0.07662807, 0.00650676,\n",
+       "       0.05117541, 0.5180313 , 0.8942333 , 0.31172786, 0.03383395,\n",
+       "       0.16083572, 0.98726038, 0.23029171, 0.03815381, 0.27830724,\n",
+       "       0.78089892, 0.26262639, 0.34292734, 0.74449311, 0.79762933,\n",
+       "       0.43790091, 0.51195497, 0.28181043, 0.5153651 , 0.30021213,\n",
+       "       0.33442989, 0.09322682, 0.03990131, 0.99979069, 0.34127417,\n",
+       "       0.86015426, 0.30417118, 0.9575738 , 0.30270029, 0.50181287,\n",
+       "       0.11860836, 0.72601674, 0.7365407 , 0.00710955, 0.68799828,\n",
+       "       0.17416455, 0.53911492, 0.91384936, 0.18843829, 0.63088603,\n",
+       "       0.29193789, 0.03401586, 0.2651537 , 0.21846207, 0.80779887,\n",
+       "       0.44912494, 0.00771518, 0.33588898, 0.99925868, 0.32591454,\n",
+       "       0.64306746, 0.59037939, 0.62749703, 0.82225998, 0.77850614,\n",
+       "       0.23233915, 0.3631501 , 0.2458376 , 0.87561822, 0.07585749,\n",
+       "       0.67926546, 0.40504241, 0.91947842, 0.78828305, 0.7828465 ,\n",
+       "       0.00427593, 0.00283294, 0.43546984, 0.90332049, 0.15497613,\n",
+       "       0.45345279, 0.64433748, 0.12109508, 0.75966004, 0.23897154,\n",
+       "       0.21001474, 0.11993474, 0.31404692, 0.68167124, 0.09595568,\n",
+       "       0.15755628, 0.51734827, 0.07594637, 0.55267866, 0.44948865,\n",
+       "       0.25025314, 0.38526441, 0.65159328, 0.62017123, 0.25803987,\n",
+       "       0.4264115 , 0.14115278, 0.26872908, 0.4629822 , 0.08718998,\n",
+       "       0.57810156, 0.99622881, 0.95761661, 0.89062367, 0.79028311,\n",
+       "       0.88585828, 0.89394358, 0.04451967, 0.52583018, 0.27224092,\n",
+       "       0.14832532, 0.7204601 , 0.88138712, 0.16166671, 0.84870318,\n",
+       "       0.04202383, 0.12059793, 0.16675446, 0.85509329, 0.24051516,\n",
+       "       0.31051694, 0.65789458, 0.84415465, 0.95522144, 0.55012256,\n",
+       "       0.06439362, 0.0471405 , 0.4298978 , 0.97980461, 0.30204309,\n",
+       "       0.1332781 , 0.57028329, 0.71668634, 0.65833129, 0.61765729,\n",
+       "       0.59033497, 0.67498614, 0.86193156, 0.0719664 , 0.75907468,\n",
+       "       0.47468334, 0.75966527, 0.09815679, 0.37776018, 0.8208667 ,\n",
+       "       0.51770517, 0.69423553, 0.56905415, 0.73752259, 0.5912302 ,\n",
+       "       0.543815  , 0.27079868, 0.13084457, 0.63898715, 0.98408504,\n",
+       "       0.23782107, 0.29082979, 0.78321311, 0.04698455, 0.43149004,\n",
+       "       0.29898713, 0.31190781, 0.96342633, 0.85667897, 0.30544318,\n",
+       "       0.09077967, 0.82440842, 0.10259322, 0.41499891, 0.22650351,\n",
+       "       0.41287407, 0.00468798, 0.10084611, 0.83298202, 0.84158234,\n",
+       "       0.23697744, 0.7171173 , 0.22766355, 0.25294449, 0.69188399,\n",
+       "       0.28100622, 0.12540229, 0.3447357 , 0.16122461, 0.13464566,\n",
+       "       0.27402472, 0.45150249, 0.59151639, 0.86097983, 0.80896133,\n",
+       "       0.46221015, 0.64527635, 0.42510658, 0.67139835, 0.80904429,\n",
+       "       0.30068385, 0.44338639, 0.53308842, 0.90475476, 0.92110973,\n",
+       "       0.816617  , 0.42493616, 0.22609153, 0.71772722, 0.2188498 ,\n",
+       "       0.91914612, 0.19662348, 0.98499926, 0.31563325, 0.97379735,\n",
+       "       0.6651961 , 0.32430088, 0.63782434, 0.83354675, 0.55140114,\n",
+       "       0.22303504, 0.04978941, 0.51909567, 0.15775337, 0.22066878,\n",
+       "       0.8610816 , 0.6267583 , 0.28182854, 0.06605268, 0.18431065,\n",
+       "       0.77677561, 0.52523021, 0.44120459, 0.41657094, 0.44568815,\n",
+       "       0.77413594, 0.87491547, 0.06874434, 0.55776904, 0.72884754,\n",
+       "       0.82478796, 0.98673168, 0.7940176 , 0.5361103 , 0.51926828,\n",
+       "       0.82189625, 0.20471795, 0.24458432, 0.06947301, 0.00567292,\n",
+       "       0.81583788, 0.66837088, 0.99109122, 0.89918589, 0.94710167,\n",
+       "       0.74186421, 0.00774864, 0.53096422, 0.7902904 , 0.79997898,\n",
+       "       0.50096739, 0.28782579, 0.93802907, 0.318469  , 0.08039758,\n",
+       "       0.17068301, 0.88139886, 0.65884923, 0.14605981, 0.41048688,\n",
+       "       0.35022301, 0.8821677 , 0.15931134, 0.40747103, 0.81513538,\n",
+       "       0.11041708, 0.62856525, 0.9785561 , 0.32482764, 0.0215816 ,\n",
+       "       0.5345427 , 0.50194307, 0.90729726, 0.43601256, 0.62007211,\n",
+       "       0.96943269])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.spectrum.get()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also directly consume the parameter in a measurement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 1075\n"
+     ]
+    }
+   ],
+   "source": [
+    "meas = Measurement()\n",
+    "meas.register_parameter(a.spectrum)  # register the first independent parameter\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((a.spectrum, a.spectrum.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id  # convenient to have for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.dataset.plotting import plot_by_id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And plot it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x175f49b5160>], [None])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From baaa572a7b1f40ccf21222ccdfe6e381c4091019 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 25 Oct 2018 15:02:31 +0200
Subject: [PATCH 116/719] make sure notebook has a dataset to save to:

---
 ...arameter Example with Dual Setpoints.ipynb | 99 ++++++++++---------
 1 file changed, 55 insertions(+), 44 deletions(-)

diff --git a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb
index 1845a1fdc40..00a20b83626 100644
--- a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb	
+++ b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb	
@@ -29,6 +29,16 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "from qcodes.dataset.database import initialise_database\n",
+    "from qcodes.dataset.experiment_container import new_experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def timetrace(npts: int, dt: float) -> np.ndarray:\n",
     "    \"\"\"\n",
@@ -46,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,13 +143,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "osc = OzzyLowScope('osc')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tutorial_exp#no sample#1@./exp_container_tutorial.db\n",
+       "----------------------------------------------------"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "initialise_database()\n",
+    "new_experiment(name='tutorial_exp', sample_name=\"no sample\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -149,14 +181,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 947\n"
+      "Starting experimental run with id: 1\n"
      ]
     }
    ],
@@ -174,12 +206,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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eD3zI7ZP47iMfYER1EIdMqrd2cBbi82jrKF6GRSIZajhKSDiNzW0RvLZ6D15bvSe9bb3RJyFoEuubuzFheDhtonAzSd3c5PVQugRJNsq9B3bAp2kSMrpJMlRx/2xWQoz5U8PC/j6TRXc8BcYYdrRHccIti/CbF1ZbOMLSoagqvGmfRO5VtN9L6I6n8KtnP+kTHlwOcIEfLcNzk0gKQQqJPBjt0COqQ332UVSGWFJFs76iXrKhPOz0KUUzNwV9Xmxui2DWz1/EJzs6++zn9Xhwx8J1uGfxRjy8bIsNIzWfj3d0IGpYDERkuXTJEEUKiTwYJ4oRNcGs+3XHU2m7vddDJR+XFaSjm3werNvTjUhCwUNLN2Xdl2saShk4d2NJBaffthjf/sf7AHqjmm5/fT329iTsHJpEYgtSSOTBKCQaqzUhMbwykLG9O55CR1Srb+ShMhISXk1IcB55ZysWr23J2C+RUsHTSMrh3Lk5kfuhxITBW19da8uYJOaTUlQccfNreGblDruH4nikkMiD0Q49skYzN6UUFRPrwzhuupbt3R0ThQTw6qe7XV8QrjcE1pux/Sv3Lst4HU8pUPVzpTIQEka/ithXI+T3GneXuJSeuILt7VFc9cRKu4fieKSQyINRSCyYPQqANhkuuuo4fOOYyQC0ZLv2iGaKeH9LO772wHI899FOawdrMimVpR3X+UgI4bHuFxGZv/ntr6/LOL+aChkMWC5wf2NMhjb3i7zr82A0N00ZUYWHvnYwaiv8AICqoHb5Lr3/3T6fbY+4u7x2SlFRGfRlmJuyEU+paU2iHNwx4m/+u5fWZLwXkc2XygaZQV84UkjkwWh68Hs9OGpqb0FB7qPIRn+Tq9NRuCbhy29iiaeUdKhwOZibsuWE8Kzzcs2uN6KqDC098azRfOVCcggUpzQLd89kJaQnnsKvDTkPvCIoZ2RNCMPC/pyfdzNadJOnf00iqYKB+ySsGFlpiSa0yWN0be8EOXN0DUbVhIZMGOxDSzfj4Btf7VOnrJzIVbRS0hcpJHJw95sbMhLnAl5P1pXyc987CpMbK/tsd3u5jpTSW+AvHwmlN7pJVd2vwnOfxN0XzcePTp4GAGAAKoPedG+JcmVHexSt3XGs3NoOAHhnoyP7f5mCqEm4Pcik1EghkYOAYQVt1CI4Y+oqcIru0BZxe5+JlKrqIbCZ5qYpI6oyXsdTavohS5WRkAj5PagIaNbYlKKiKugre3PT4Te/hgN/9V+MGx4GAGxoLl9NIiX4JNz+rJYaKSRy0FCV6W/w5zG7cCFx5v5j0ts6o+52XMeSWmc6o7lp/3F1Ga/jyV6fRDk4A2NpIeFFOKAJyJTCUBn0ud6EWCg8N8RYzLKcEM1NsnhjfqSQyIFRb8hXtG//8XXYdPPp+L8v7o9l15yAyY2VrjY3JRUVuzpjGDusok9uwAkzRwAArj51BkZUB5FQVEFIuPthiyYUXP/0xwCACkFIJBUV4YAPXbEU/vDKZ/jcHW9j1fYOO4dqKorKMn67j/XyKz1l7IMROw26/b4tNTK6KQfGVXGggMqufq8HI2tCqKnwozPmXk3i1v+uhaIyNNVX9ongOmHmCPzrm4djv3G1eHjZZm0VpktUtz9sf1+6GT2JXk2CC8ikqqIq6MWa3V1Ys7sLgGavnzO21raxmsl5f12C5XoPdwBYqtcfS5WBZpgL8fl2+31baqSQyIHxxjH6KPJRHfKnM7DdxOK1LQj5Pfjz6+sAAE0NYUxqyPRBBH3edH8NxoAnP9iOsXUVANwfMSLW3RLNTcmUZm4ScfMiwAgXEAGfB4mUmq5DliwDH1MuxOdbCon8SHNTDviN89Uj9gEA+AaQKVYd8qHbhZPIV+5dhs/fuST9uqm+EsMMdapEtu3VOvZt1zv3JVPunlS4UAA0gZH2SagqhoUD6X2qgj50RsvPFKMYhEJKUbF6V2e6mkA5IS5oEi6/b0uN1CSy0NaTwBK9NScvxaAMIEwuX8tPt3D23DGo1533T1xxGPZGkqgO5b9d3L4iM5ZhqfBr55tIqZg5ugaAVgBwTG2orDQJTl8hwbDgj29i2sgqvPyDY2waVWlIGcxNsaSC9zfvxeFTGmwclTORQiILF9y1NG175kX9BuKIDvq8rhMSxgn+Cr0uFQDMbxpe0DHc3gfaGL3Uq0kw7Deu1/9QU+F3ffRaLkJ+T7qeUateGv2z3eUXCms0N13/zCd45J0t+O8Pj8aUEdU2jsx5SHNTFriAAIBRupDoGEAtpqBu23UTxo573LwyENyutncbkuXEENhxwzS/y7nzxro+MCEf+wg+qJbu8m1Nm2Fu0s1qANBRhmbEwSKFRD/w6J6BOGU1c5O7snONJSfqcpQbEXn9R8fi1+fum37tVnOTojL84j+r8PjyrSAC3r76eABASBcSCUUFEeHTXy7Abz+3H2pC/rL0SQDApCzVA8oR8V69+40Nae2pHErLmI00N/XDiDxF/HLBo0QYY64pemc0tRTSO2GfhkqEhLIdbhUSm1t78MCSzQA04ThGj9YKG65BhS40aip8WL2rPDWJSQ3ZhUQipQ4ows+pMMbAWGaexOtrmtN/r93dhQPG17nmubUC9//qJaa+auBCIujzQGXuKlNRbF0isVLoos+aXWmG4dFZQGaJd5/Xg2OnN+KvFx6Ysb+mSbjvPAuhqT67kNjdGbN4JKXh5hdXY9I1z+e0DPz4Xx/h/rc3WTsohyOFRD8U07Oar7jc5JcoNrtWvD6RhIKr/um+Tl/b90Zzvnf/pQf3qc1VW+FHVzzlqt+3UOrCfvzxvLk4Uc+s5zSXiX/ir4s2ANC6SeaCFziUaEghUQDXnTkL911yUMH786J4bopw4g11nrjiMHz6ywUD+uzKn5+c/ltclbuFgY55yogqMAZ8JgQ4uJUHl2zKeB30eXHOAWOxj8Hs1Nrt/lyJT/RyI0DfIAWRcjCrmYm8GgVwyRH74LgZI/rfUYffZG5yXnNNYlhlIG17L5RawcldXzlw85zdbN8bzegf0R+zx2g5E+Kk41aM3fd4aXifoQxNaxloEre83Huu+Yo1SiGRibwaJSDoQnMTD4GtDAwulsFYPdcNtEUSaKwOYsLwMC46bGK/+zfVV6Iy4MUnO90nJBhjmHzN8/jti6vREUn2yf/h965fNyOO0YVna08CbT0J/OCxFa4tmb5L8KvkM69m68b42LtbMONnL7g2OGMwyOimEtCrSbjnhuIrq3BwYFqEEd7/201EEwoq/F48/f+OLGh/j4cwuq4Ce7q0SeeLdy7B7LE1+MWZs0s5TFPY0haBojL8ZeF6/GfFjj7v8wmSaxLhoA/VQR9auuO47dW1+PcH27HfuFpcqpercRN7exLwewlJheXVJLJ1Y/zxvz4CoGXl56sIXY4MrbPth2k/fQG/fXF1/zv2A3/Q3KRJ8OgmY9jnQFFd2OUrllQGbGKrq/CnI6He2dSG+97aVIKRmc8yodtcNl8MnyB9epMtDwH1VQG0difSGfUDqWPmFBhjaO1JYJSuGfXElYxaXSL5glXKofviQJFCQiCRUvGXhesHfRw3+iQ6Y0mEA94+tuhC+dc3Dwfgzkqw0aSmSQyEurAfeyNJ17W+XN9P32ruk/B7tP89RKivCqKlOw7+03pcKCQiCQXxlJquoNAVTyGcw7Sab3FXDo21BooUEjpm2hqDLjQ3tXbHB+VPOHDiMIyuDWUkKbmFaFIpKHlQpC4cQEck4brMa2MRQyO95iauSRBG1YSwoz2aXkV7XZho1qbXoRpVqyVKfra7C7nWQ8bnVix8aCyCOBSQQkIn1s/DMxC4JtEdS+GWl9egywUJZq09CdRXDbxek4jf63FVAiEnllQHLiQq/GiPJtHS466on1z3+TWnzcBvP7cfhuul4blG6fFofUW27Y2mNWM3ahJcSPAoNkVlOTUGozYslkp3exHLYpBCQidmYp9brkk88+FO/Om1dfiNCX6OUtPcFR90+KrfS640N8USxZmbIgkFuzrclYmc6z6vCfnxxYPGp1/z6CYvESbWVyKlMmxpi6S3uY02faLnVZ0B4EenTE///c8rDkv/bex5LWoW5dytLxdSSOiYqUlwIaFr7Fizy/lJV609CTSYoEm41dxUERjYo1CnV8ld3+yuMtq57nOjuZVrEkSUTqx7f4uWiTyQ3ipOgVdxHlnTuxA6fHJv74iDmobj2tNmAtB8iWIZkgwh4UJNebBIIaEjPjx+7+BWStyu+5QeYri1zZlZyNc9/TGarn4OqsrQZoK5KeDzuCqO/IMte/H7l9YgpbKiNAmg1xGcLWzSicRyCHGjHd4vRDdNG5nZX8FNUXscXlNM9LtVGqKbLjt6EqaPrMazH+7EITe9ilXbOwBkBqBIc9MQRlTDxaJ1xWDM2Nzl0OJovJBZezQJRWUmmJs8ror+OO+vS9P9vAfqkxg3LAwAeHNdCwAMOITWLmJC35BbvrA/fnDiNADoE/vPw0C9HkJthR+/PLs3B8RNCwEOL8goColwsG90U1Coavz48q24580NGXPDUDQ3WZZMR0QLANwKwAvgHsbYzYb3LwHwOwDb9U1/ZozdY9X4YsJqobE6iCtPnoapRXaoyraqdHLZ8I93aCsmXiK7WNzkk1BVljHWgU7y+4+rxaSGSmxo7jF7aCUlllJw7PRG/OqcORg3LIxYUoHPS7jg4AkZ+/k8veYmoNe8BmhCoiOaRMDrcY1w7IylEPJ7UCUIhmzaY0AQlg/q5eMf+OrB6W0yuqlEEJEXwO0ATgUwC8AFRDQry66PMcbm6v8sExBAprmptsKPc+eNw75Cy8qBkC3+usvBpQxe/XQPAGB+07BBHUfTJNwhJDa1Zk7uAzU3EREOmNB7vdxigoklFYR83rQmFPJ78e3jpvTRfrm5iTupxUz6RErF/te/jJP+sMiiUQ+Odze14a43NqAm5M84T64tzRlbk94mahIcsUqwNDeVjoMBrGOMbWCMJQA8CuBsi767IESVsiqLGjoQAj4P/v61QzK2tTmwiiafCF5dvRuTGisHXXcp4CIhsVmP1OEMVEgAWvMhjltyYrRw3/4fe+645hNphpDQTS7b8pRYdxJfuHMJAG0RYxSG6286DU9/u7ccSyBL8sT29t57ZSiam6wSEmMBbBVeb9O3GfkcEX1IRE8Q0fgs7wMAiOhyIlpORMubm5tz7TYgRE1isEIC6Nv+s9WB8fR8YtzaFsWBEwanRQA8uolhY4vzTTBbdSHBf+uB+iQALWyUo6gMKRcIyEJLkPBMcm4hrTNoEm6CL4Y6dROZiNdDGXkfohDhm0VhKM1NpSObMd54tZ8B0MQY2w/AfwE8kOtgjLG7GGPzGWPzGxsbTRlghpAIDV5IiBMIALQ4UJMQzWL7j68b9PH8Pg/W7O7Ccb9fiKc+2N7/B2xkS2sEIb8Hs0ZrpoZimkvVGIoZOt0fo6gMsaSStcqpER7lmk2TcIu2yKmt0PwpXfFUv5GL/NrMGFWNt/Re56KQSEohUTK2ARA1g3EAMkpQMsZaGWN8uX03gMyekSVGDA00Q5MwChonNm0RC5zNNUNICA/gCod399rSFsGE4WE0NWi2+UgRnflqDL+xMQnLaZx66xu6A7d/IcFXzB5dlRAFYjHXyk5qBbNgf8EjXJOoDvnS0X7b9vaam5Qh6JOwKrrpXQBTiWgfaNFL5wP4krgDEY1mjO3UX54F4FOLxgYAiAuaRLUJmkSloeS2E5u28LLgAZ8H00cVF8klks2e61RauuMYUR3CtafPQn1VEMfPGDngY7hJk2iPJPDZbi2noxCfBK/myxUsUdMS+4DHU4VpJnbCw3vHD+8/eq9XSGhO7toKP3Z39j67bgrxNgtLnmrGWArAdwC8BG3yf5wx9jER/ZKIztJ3+x4RfUxEKwF8D8AlVoyNI5qbirFPGxEfnMqAF+1R59VvCvs1YThnTI0pNfLFYzgx2pcxhn9/sA3xlILueArVIR9qK/z48YIZRXUjM5oUnaxJvL2+Nf13Ib81t6p4hB+S970WhYT4t1PpjCYxfWR1ulJxPrifji94jAtG6ZMoIYyx5xlj0xhjkxljN+rbfs4Ye1r/+yeMsdmMsf0ZY8cxxiwteBQ1MePaSF2MfRiQAAAgAElEQVQ44MyHST/Ng/epN+Vw4uTjxMoNa3Z34QePrcTrq/egO5YatFlRjG4CnF0aXiwzwYvd5YOHhX7uwHHpbfdcfBAmNVRmLHj2RpxnRjXSGUvhiCkNBSXJnjhT0yg3tGhalzHqbSiW5ZCd6XTEEFieSGQWtRX+jEqSTiGlqJjUWInvnzjVlOP5fb3C1Yl9Fnhjpb16287BBihUBzM1iU92dmLqyMGb7UrBXmGR0tzVv+lz3LAwNt18ep/tfq8HHcK9vLfHgYsfgZSiojueyhDoT3/nCNTnCPc+dNJwfOPoSThmuhYQY2xM5IYINrNxjxG5xHQLvX79JtXhufncfXHvxfMxrNLvSHOTojJMGB42xbwGZPokIgnnrar5Sr8jmkR3IoXqQWoStXqY88R6zfn9v4+uGNwAS4i4SDnngDFFHyfg82Tcy04vg8/7cYumwf3G1WFsjuoCRISfnDYzXfzP+GwMRU1CCgmd9mjvQ1Q1yD7PnPMPnoATZo5EXUXAkZpEUmGmtqIUzU1dMedFwPD4/l0dMTA2+FDn2go/3rr6ePzk1JlmDK+k7I0k0VSvaQfFOOk5Xg9lLAB6HB7pxJtCFRuMYswp+WRHpyO15FIizU067ZEkZoyqxllzx+DYaSNMPXZt2I8OB2oSKVU11bQmColuB5Yh4UJih97budrgeC6GsXUVruj53B5JZNRfKhZjlnW3AxcDIjyJtdgKx9zcFPJ7EEuquP/tTThgQh3OnpstF7g8kZqETkc0ifHDw/jWsVNM77xVV+FHuwP7IacUlm5TaQai/daJtap46YztupAwIx8G0BrZfPu4yUUl5FnF3kgCw8KDF4othlBuJ/7OItxJP7zICsfc3FQpJJ5+utP5/WHMZMhrEuubu3HnwvVo6Y5j37HFFfTrj7qwHymVoSehmDYxmUFSVU0JfeUc1DQ8/bcTbdVGTcKMzHpO0OdNl+bwOTBfZG9PEtOKrGqcjx4HC4mmq59LtyutryxOk+DRTbUVfrTqAseJ4d2lxHl3s8X8+vlP8c/3tqGlO9Gn3pJZ1OllAZzml1AUZurqd+bo3knIiWYIrknwSJ/BOq5FeHl4pxb6M8vcxBPxPjdvHGpCPkf+zptaetB09XMAgJ16e9lizU1cSDRWD674pZtxzrLWJoYLKwwzHqJsVOqTEQ/BdApJlZmaE0JEWPzj43DzC6vxxmfmFF40k4Qhj8EMnwRHFBKD7N1kOoxxLXbwARkLf3QcIokUJjVW4YibWx1pbnpzbea9V+H3Zi3fXwjchNoghcTQRbx5aitKo0nwEh1Oc+amFHMd14AWX681s3Heitq4yjfV3KSvOJ2YUMfPO2hCqPOo2t6EtOqQz5HmJuPvPLxIUxMAhHQhYazTNZTIe+ZENA5anaWjAIwBEAWwCsBzAF5gjDlvJhggYtTRYG6mfHA/xP1vb8Ks0TWO6eZltuOaE/J7kFBUKKq55qzBYixxbaZ/KK1JOFA48pIzZuXDcKqCPsctfF5fvQe7OjLbBTcMonc7j1yr8Esh0Qciug9az4dnAfwGwB4AIQDTACwAcC0RXc0Ye8OKgZaKjmgSM0fX4AcnTsXR08wpO26Em5ueWbkDI6uD+OkZ2ZryWY/ZjmsOt+PGkkr63J2AsQCfuUKCaxJOFBLamAop7DcQKoM+7I0k0BFNlkwLHwixpIJL73+3z/YJ9ZVFHzOe5dqJdd6GAvmeklsYY6uybF8F4EkiCgCYkOV9V9EeSWB4pR8nzx5Vsu8Qw+eclC9RqpV+yKFCQpzAwwGvqefe65Nw3gTCJ7Viuu/loyrkw6LPmrH/9S9j6U9OyDBF2UGuKrxzxtRk3V4I0SzXbqgJiXxLiwW6uSkrjLEEY2xdCcZkKR3RZDr6qFSIZcONtWDsgjGGpMLgL4GQ4A/Uzo4YfvjYij7qvx2s2Nqe0dPDjHLwIrw3spM0CVVl6IgmEUuVxtwkRoet3WN/7kAyx7UfTO92Rc9tCgvn6sSSM6Ukn5AYC2AJEb1BRN8kogarBmUlHdFkn74AZiOupiuKjLIwG17yuBQx/dzZd8afFuPJD7bj5hcsbQ3Sh5Si4pzb38K/3t+W3mZ2vkra3OQgn8Qdi9Zj/+tfxtY2LS/EbHNTrRAyvqfT/n4p2TSJH508DQdOHJ5l78L45jGTccHB43HBwb0906SQ0GGM/QCaOelnAPYD8CERvUBEFxGRM0tdDhDGmCX21KBQMNBslb9YUmkhUQJzk6FAYin8HgMh2+q+ysTwVyDT3KSozBFmpxdWaT28Nuplr0MmNwcSn5vNbZE8e1pDMtW3osFhkwe3tq0LB/Drc/fLiILc1REbUn0l8j69TGMRY+yb0NqP/hHADwDstmJwpSaeUpFUWMmFhNgyUXFAaY51e7px5eMrAQB+k0Nggb5F0YImr2AHijGqCTA3kQ7INDfduWg9Tr9tsanHLwYunHnyYMhkU6f43Gxp7TH12MWQUPoKZrO1JwD4aHsHrnx8BfZ02W9GtYKCriAR7QvglwBuB5AAcE0pB2UVvFKpmfHy/eGEFeZ/VmzHcx9pq8xSOK6N2lLAa6/2JJohuC+iZOamlIL1zd3Y7IBJk5du3637hMzWJERfXmsBjYxKTSKLJmG2H4bz1IodOPjGV0tybKeRLwR2KrQciQsAKAAeBXAyY2yDRWMrOTzG26zS4IXgBJu16LQ1uwsf0PfBLKY1qJlkXHN9HjHdcS3kSXREkkgqDImUauu58+/e0VEin4SgSTjBYZ/NJ1EqITGUyHfXvAQtL+I8xti+jLEby0lAAL31haqC1sV4O+FhEifNkjiuDQ9m0GYhIZohuuIpEJmvPfJzvH3hunRLz4jNvRa4uYnXLzJ7wnSakEhmExIm3nsv/O9RuP6s2aYdzy3ku4JTGGPXMsY+yrUDkbvrIfZqEqU3N638+cmorfA7wtwkPtCl+AGNK1bbNQnDBPbd46fi9H1Hm/odvOTF1rYoPtnZCQDosTkKhjtXd7aXRkiIBTHjDsgdyBYCa+Y5zxxdg4sPb8KoGnvzQawm39P7GhF9l4gyEuaIKEBExxPRAwAuLu3wSgsXEmabHrJRG/ajoSrgiBWXmAxUCluy0Seh2hwJIjquP3/gOPzwpGmY31R8WGQ2RG2JZzhHbC5ZwesqcTOM2ZF1Yuh4tuAAq4lbZG4qhTPcyeSbHRcA+CqAR4hoHwDt0MxPXgAvA/gDY8y5TX0LoDuuRX1Y1eMh6PM6wichCqo9neZHaBijm5I2CglFZXh42RYAwMNfPwRHTClNuo/f68GRUxqweF1LepvdmoSxrpLZZr/qoA91Ya2hlhMWP9k0iVIEZgRNDgBwOvnyJGKMsb8wxo4AMBHACQDmMcYmMsYuc7uAAASfhEXRTUG/xxHmJlGTONVkswvQN4omlaNcghU88d5WPPGelkRXarPXt46bnPHabk2iO57KCEwwu+Oix0NY8fOTccHBExwhJHKV5TAbv8/VVvYBU9BTwxhLMsZ2MsbaSz0gK+my0CcBaCs5p2gSTfVhbLr5dBw6qd7043s8hFd+cHT6dTaHolW0R3prZQVKnNRn7FlgtybRE0/h/INKX14t6LN/8fPWuhas2t6Zft1QFcSkxuIL++VDTHWycwFkFc6oEWET3TFtpWVV9E3Q53VEd7pYUil5aODUkb1J+UnFPnOTR4itKHVSn7Eul929FnriWoHF//7wGGxsKV3ehqYh2ztZfvmeZRmvf7xgOr4wf3yOvc0jklRQ48B2tWYytIVEPIWqoA9WBWlpKy77Vx7xlGqJYOTnm1LtO2fxpy21JmF0DPfYGAIbTylIKCqqgl5MGVGFKSOqSvZdQZ8XiZQKxphlz1J/WNWzJRJXUGNyiRenUd4isB964krRbQ2LIej32iok7ly0Hg8v24xYUjGlS1l/fHTdKRheGUDKRk1CnLRK7ZMwlkWP2NiutjOqCSgr+jzwBYdVPoFCMDu7PBd258JYQb6M6y6k81Mz34JW1qn4Iu0OIZZSLA1n03wS9k0cN7+wGoC2oj5kkrkhoNkI+Dyo8HuRVBj+99EPEE+quPPCA0v+vSJMMCCXWkj0MTfZOIHwviWlrnAMZPb3dkrkTyk1CdEnMRQqwuYUEoyxsqj0mo9YovS2eZGQ34POWAovrtqJk2eNMj3apFASimrZefu9hKSi4umVOyz5PiNiJFewxDWkgj4PiLRJxEP2TiC2CImkqgXJW0w257FV9/dQEBIFL62IaAQRTeD/Sjkoq9A0CeuERDig9QS+4u/v4x/vbLHsewEgariZrXLW+7we2wQE0NtZDCi9JkFECPu9CPg8qAz6bI3q6oxpQsIac1NvcUM7iGUx4ZbS/ySaV4aCuanfK0lEZxHRWgAbASwCsAnACyUelyXEkqql/R1GC+0dn15h7cS51xBVZZVw9NmkLXGiid4JxIryIBUBH+oq/PB7Pbb5Yhhj6fvLEiGhm2z//No6fLSto+TfZyRbO9ESVMBPI5oweSVpK3l99R48KTTQKjWFeG1vAHAogP8yxg4gouOgVYZ1PbGkgmFh6yITxg8Lp/+OJK29udoM5TfsLrpnFaImUYrsWyPhgBdBnwcd0aRtmsRzH+3Evz/YDsBax/Wj725FwOfBvuNqS/6dIkYtGbDmtwaA1m7rO/Jdev+7AIBz5+XsLm0qhcwUScZYKwAPEXkYY68DmFvicVlC1KIoH8744b1CwupaN2JSGWBdaQG7I16sblofDnhRF9Y0ieauuC2TiPhbWxGeKd5LdvSVMJq5murDaKovTSKdkZZu+/OeSk0hQqKdiKoAvAHgYSK6FUBZGOLiSdWyUDkAGDesIv231UKirY+5yRpNwu7Cb9lWmaXk5FkjcdKskfB7Ca+u3oMDf/VfS78fyDxnK0xsolbaZsOkKZoUAWDhVcdZZk5t7bG/t3epKeQOOhtAFFrb0hcBrAdwZikHZRVa5rF1ZpfKoA+TGrQVjtWTp7GQn1WahJ3OWyDT3GQFPzx5Oi4/enJJ+nQUSrPF2osoiIxmTSuIWeww//0X9scJM0ZgyogqNHdJTQKMsR7GmMIYSzHGHmCM3aabn1xPLKlY6rgGgNd+dCy+fIi1BdEUleGBJZsysm6NMf2lwnZNQhcSx88YYen32umwb+7ShMQDXz3Yku8TTYp2mJtEzelbx07Os6c5zBlbi3svOQija0NSkwAAIjqXiNYSUQcRdRJRFxF19ve5YiGiBUS0hojWEdHVpfqedXu60GNxngSHlzGwitbuOLa2RXHhoRPT20JDREjEkgqOm96Iv11ykKXf67dRk9jTFcO8CXU4ZlqjJd93wPhhOGfuGJx7wFjsjSQs7x/C/U43nD0bV50y3bLvra8MoMUGn5PVFHIn/xbAWYyxWsZYDWOsulTZ1kTkBXA7gFMBzAJwARHNKsV38QgBOxqIBHyerA1SSgVPrBpe2du43ioNym7HddSmhYCvBL3DC6W5K47G6qBl31cR8OKP5x+A2WNroagMk655Hk/p0VVWwLXFwyY3WFo7amRtCLs74mVfCbaQGXI3Y+zTko9E42AA6xhjGxhjCQCPQvOJmM6wsDZh2jGBBHyedEE0K+BCQgyHtMrcZGcFWEATUnaE+/pLGajfD1YLCc7wyt77i/fwsAJeft+qon6cyY1VSCgqtu2NWvq9VlNInsRyInoMwFMA0roVY+zJEoxnLICtwuttAA4x7kRElwO4HAAmTCgu+ZtPmFaGwHLEgmhWOJCzCQmrfTEcqyuFJlOqLU5kUZNIKqpl5qdESsXeSBIjqq2vj1Hh751OrMpTAHo1iZDFi4HJjZqPb31zN5oarAm5FbHqWSrkqtYAiAA4GVpU05kAzijReLKdcZ+lKGPsLsbYfMbY/MbG4uyudbomEbDBLMBLBlhlr+dx86KQsEODAjQnupUkFGaLf0AUTFbW9+E2cjs0CbFfh5WOe+6TsPqenqILiXV7ugEAdyxcj7fXt+T7iKlY9Sz1q0kwxi61YiA62wCInULGAShJ/Qqead1tQzln/jBZJSS4JlEnZJdbrZpzUiqDlYVCU6qa0cLTKsTFRzShWJL5DPRGNjVWWS8kxJwjKzWJ7ngKHrLOhMqpDftRF/Zjc1sEv3tpNW5/fT0AYNPNp1vy/QnFGi25XyFBRLdl2dwBYDlj7D8mj+ddAFOJaB8A2wGcD+BLJn8HgF5Nwo5OcVyTsCoMlguJ6pD1PglObYUfHdEkVmxtL0nL1FwkU9aZekR8HlGTsC73NC0kbNAkxCAQKx33XTFrm4eJjKwO4ekVO9Bd4i6EPN9IvJdn/fwlfPrLBSVf8BXy9ISgleFYq//bD8BwAF8joj+aORjGWArAdwC8BOBTAI8zxj428zs45x00HmPrKvD5A62pfyLCk4+s1CSqQ76M1Z1VPokHvnowvnrEPvj+iVMBAOfftdQyhz0AJFW7zE2919oqc5OiMnz9weUAgBE1dgiJ3nuqK5ayrCRKZyyZsQCykhE1wZILCAA45KZXccAvX+mz3YqgjEIc11MAHK9P4CCiOwC8DOAkAB+ZPSDG2PMAnjf7uEbG1lXgrauPL/XXZCVgcSevjmgyw9QEWGe/PWZaI46Z1oiHlm5Ob4unrOtnoTmNrV9hijZ5q7K+xTpRtpibhN/0zbUtOP+upXjq20eU/Hu7YilUh+zpxDyyxpoAgVyZ7Fb0pClEDI0FILruKwGMYYwpEKKdJIXDI5qs0iQ0dTxTSFjtk/B77FlZM2ZvYhtgXf2oLn1Fe+v5c22J6DKualdsbbfke7tiSdv6TI+0WGOzOlERKEyT+C2AFUS0EFr00dEAbiKiSgDWVy8rAwLpdo/WTB7RZAqVBqFgdQisOGlZtbLmdlw7EtsyG9NYc749upCotLBvu4hdEXNdsRRGWbSiN2KVJsFJqtYn7hUS3XQvET0PLdGNAFzDGOMRR1eVcnDlitWO65640kcdtzL6BECGySdqkSOXm/NK2aUsF6LbJWpR75BuvQFOlU2mFzuqFwCakJg6wp5zFsv/W4Edjaxy/qpENEP/fx6A0dCS3LYAGKVvkxSJ1Y7raEKxbXXJyYz2sWZlzR8oO4rtqYKUsOp35g7UqqBNQsLK2GaBLhsd13PGWNtgyY6qyvnuph9Cy2q+Jct7DIA9Xt8yIN043ipNIpFKh7yG/B7EkjbcaIa8gVITSaQw7wYtGsRvcxc+q0qT2C0krHCicl5fvQfPfLgDt3xhf1sd11aHGovBLlZF/Oa8soyxy/X/j7NmKEOHoA2aBHdUv3HVcX0aEFmBaG6KWOCTaBHq/NvhuBbFglUF4NI+CZuEhFUwxtIFOn9w4jSkVGabJgEAlxzehPvf3pR+XcpyGaK5ySoNOZ+56SAiGiW8voiI/kNEtxHRcEtGV6ZYbW6KJJT0xDGiJoQZo0pSxDcvornJCk1CEcw9doTAilIiZVFECq8eYNeq2irEgnqvfrobADCq1vqQX851Z83OeF1KTV00N1nlV8y3xPorgAQAENHRAG4G8CC0bOu7Sj+08oVHgVz5z5V47N0tJf0uVWWI2tBcyYjV5iYxkcseTaJXMFgnJJLwesiWqrdW0hXrDQR4bLlWbXbehGF2DQcAcPq+o9N/f7Blb8m+R2zq5LOo0nC+b/Eyxtr0v88DcBdj7F+MsZ9BS7CTFImYo/Djf5mej5gBDzetDNorJMSJ2gpzk+gct+phEhGjm6wzNymoDHhtKU9hJT1CdNynOzvRUBXABIujjIz88fy5uPa0mQCAL92zzNRjixUKzv3L2+m/rdIk8umlXiLy6ZnWJ0AvzV3A5yT9ELZwVc8fqArbo5usDYEVNYmAz4Y8CUFIWOW41hy49tnms7GlNYIJ9eZO4NxB//0Tp2LV9k58/sBxtgtGv9eToT2aSa77x3afBIBHACwiov8AiAJ4EwCIaAo0k5OkSHxej2WRCdy0Y6VgyoaoSUQTpV9Zi5qE/eYm7Xw/3NaOnz71kWm1qyKJFB59Z0v6eJ2xpG2RTbk4+nevm35M7qA/dc5o3HPxfCyYM6qfT1jDCTNHluS4ucJebfdJMMZuBHAlgPsBHMl672wPgO+WfmgSM+iJO8PcJBKxILlMzOq2w9ykZpibtBfn37UUf1+6BZ1Rc87/ty+uwdVPfoSFnzUDALa2RTBuWIUpxy6Wo6Y29Nn2zb+/hweE6J/B0hvF5Zx7GtCaEH3pkAloqAr0v/MAyBXg4gRNAoyxpYyxfzPGeoRtnzHG3i/90Mobqwqh8mxfu81NYny3FY5r0aRlh7lJhJsLuHZjVlkSXvStI5KEqjJsbOnBPjZ0SBN56GuH9Jm8Xli1C7942rxizjyKy2laE6CFt8dNjm7KqUlYFLVX3mEQkvQDZazdZDVifLc1QsJeTUIMu1UM9XZ6TPLJiKHUuzpjiKdUW9poGin1+ifi4HyQkN+LmMk12XIl3Vp1XzvvKktMZXdnDID1hciMTB9VDa+HoKjMmugmm0Ngrz9rDkZUh/DUiu1Iqgyrtve68SImdUNMF4pUVGxq1ZR9uzUJAFl9LmYW4OtOpBDweWyv7puNoM+DpMKgqMw0n8ET723LeL1g9ijEUgquPGm6KcfvDykkypwd7VEQ2S8kaiv8WH/TaTjjT29akyeR4bi23tzUWB3EdWfNxourdiGlqDjjT4vT75mmSQi90nnugLFviB1kSwupN9FO3xNPOdLUBPTmQMVTCsImmXhvfXVt+u9wwIs7LzzQlOMWivNEscRUdrRHMaI6mF512k3Y77OodpO9mgTH56U+lTvNamfKf9MtrT3pkN+gTUX28jG2rsLU8vA9ccXy9ruFEtJ/k1hSxetr9uDBJZsGdTyjVua1IdTXmeJYYho72mMYU2dvxItIKOBN99w2E8YYtrRFMLG+Ep/u7MSWtkj6PTsL/Pm9HiQNS+ses8xNuvB7YMlmPPnBdgD2levOxVWnTMemlh4sXtdi2jG7HaxJBAVN4tL7tPpSFx3WVPTxjN0rrSyimP5Oy79RYik72qMYU+scIRH2e0uSTPfA25twzO8W4qNtHTj11jfx8ie70+/5bXiwOD4PIaWoGWYgszQJUUPi5ianaRIVfi8qgz5Ty8N3OTAfhMOFtFi/6ZaX1xSdG2MMf7XjVpZCwgEoJazt09qTMD1uezBUBLzY2NKDDc3dph733c1avZw31zX3ec+OVp4cr4eQUhnqKvyY3Kg5lc3SJJQsE0/QYZrEvInDUBHwmiYYAWBPZxwja+31seWCC2mx6+SfXluHrW3RXB/JizGyyWtDpJ6z7qghhFhrplRtTFWVaf1/K+x3ZnIqAl4kFYbjb1lk6nE9uq324+2dfd6zM0vC7/UgpaiIJhXsP74OQG8y2GDJVhPKrsY/2Xjp+0dj7vg6hP3ab25GwxzGGHZ2xDDa5kCMXGTTJIDicxqMmoQd6x0pJGzi8W8chiOm1AMoXWnhnkQKKoNtTeKzUaryIPwZ/GRnr5C44ODx+NU5czCs0j5NyufVNIloQkFNyA+/l9Bjkukl26RrS1n0HPCIprBuGjLD5NQRTSKaVDDK6ZqEwVGvFmkt6CMkbHBcSyFhE6NqQzhzvzEAMovRmUmnbqeuqXCO/VasgGtmK0auSezs0NT60/cdjR8vmIGvHDrRtO8oBr/Hg82tEXTGUqgIeBEO+EwzvWQr/GZ3oTsR7jPhkUhmnPfODi3vZ7SD/GwiaU3CMLkXc68rKsNtQvgrYM/vK4WEjQTTqmmJhIQeReQkTUIUEm095nXI41EfsaSKfRoqcfuX56EubL8vxueldKRVhd+LyoA3XcV0sKRU69vQDoRAHyEx+Pt8FxcSde7SJIqpBPzyx7vSUWvnzhsLwLqifiJSSNgItx+XytyUFhIO8kmI6nJLd9y044rPzjAHJJRxRKd5hd+Lmgp/+ncZLMb8C6fBG03xhldm5Mds3asJ3LEOCusWMVOTEM2SXNBKITHECOk/vNm1Xjhpc5ODNAkxqWrhmmZsFfIZBoPY/W24jT4II+IjHQp40VgdRHO3ORpUUmGorwzgwa8ebMrxzIYX+uM1lsSOcsWyqSWCCr8XI6rta1eaj9yaxMCFhCpEr1Xq2dsyBHaIYeYKKxu9moRzfBLiuf7upTU46rfm9BsQTXbDHGBm4ojjqvB70VAVREuXORpUSlVRFfI5SigCwA1nz8aomlDaft6oT+jNJmiOm1t7MLE+7Cjfi0guE3Ix5iYxtyKcFhJSkxhScBVy8boWU+PIOZ0x5/kkShVpJAofJ02aYpy7JiQCaOmOm9J4KKUw+L2eDD+PE7jwsCYsveaE9OvReiTSro7icgVENrX2oKne/iKGuajVTbvLNrZlbC+mha1470hz0xCF//B3LFyPq/75oenH5+p9Vcg5msTXjtwHt54/N6MzX7HhgSKiU9TOkFcj4orS6yHUVwURT6mmhMEmFRU+Dzm2jhGnOuRHVdCXjkwaDNv2RjF+uDP9EYBmbqoK+vDshzszthvLaxSCGODAFwJSkxhiiI2AVmxtN/34kYSCgNdZJZX9Xg/Onjs2ozHNrs7BTx7iZDzaQTH0Ypx7LKmgoUozvZhhckqpmiYR9jtnEZCL+qoA7ntrE97TM+OLIZZUEE+pjohay0dtlkCRYsxN3YIPh3fhsyHhWgoJOxETy8zqeywSSyqOK9PAEW3KZjivRU3ilNnO6HkMZAovn5fSJVLMsM8nFRU+LyEUcOZvLLK5VfuN73trY9HH4IUhs03CTqcYc1OGJqHPFTKZbogh2pLNFBGX3PcO7n5jA2JJJV3f3mmIN7sZ8fORhIITZ47A+z87yVHnzEMhv3TIBJw6ZzTGDdPKsWxpHbxgTCoq/B5POh/Byfzk1BkAgEmDaIrkFiGRLZLJaG7qiCTxh1c+yxv1JAoJ3oVOVoEdYgSFEtZmKhIL1zTjxuc/1YWEM39i0d6XsAAAABpQSURBVAFnRjJhLKlgZE3IUU5roPfcvnXsZHg9hAnDw/B6CBtaBl/gMKUw+Lzk2EgfkW8cMxkBrweJQeR2uFlIGM1Nd7+5Abe+uhaPL9+a8ziiuYnnnEhNYoghPtysBJ2Bn1qxI62mOg3xXjcjT6QnkXKkA5dHqPDS1gGfBxOHh7FyawdeEcqZF0NSZRnJenP1AoJOJeDz9KlFVCgd0SS+cOcSAG4QEn2fZaO5iZsdP9za0WdfjqhJcL+idFwPYcyqFm70bTjJ9CJy78UH4YAJ2qQWH2TGeUpREUuq6aQtJ8HLwItjm9RYicXrWnDZg8uxvb34sNCUoqZ7Zaz8+cl49PJDBzfYEuP3UtH1ul79tFegOqFFaz4uObwp/Tc3s1395EdoFoIVeNFDsSCliKqyjMZZXDRIx/UQxixzk3EV46TS0SIH7zMcf7v4IACDNzfxcFInNqK59+L5OGnWyIwIs8bq3uirjc09BR+LMYZT/vAGnnx/G4DePAkAqA37Hbsg4AR8nqKFRKuQpe50TeLKk6fhnLla8U5x5f9/r3yW/puXVNmdI7Jv6cZWbNvbu4CYN3EYDptUj+vOml2KIefFEiFBGrcR0Toi+pCI5uXYbyERrSGiFfq/EVaMzwmoJkkJ40MYcqAJhhPMUedmoPBERCdqEifMHIm7L5qfsU3MgN/YWriQUFSGNbu78MPHVwIAkqqatlW7Ab+3eHPTuj29PpxqByWHZoOI0vei+FyL586LM+ayIKzdrZ3vGD2cO+T34pHLD8WMUTWlGHJerHqqTgUwVf93CIA79P+z8WXG2HKLxuUYzKoMyoVEOOBFJKGkG7M7Ea7lrNregV8+8wl+evrMoqI3eBMfJwqJbIgr4U0thQsJY4SMqEm4Ac1xXZyQ2ChcJzuyjgcKr+EkCgFxAcc1/lyLQ65d/+c7R5a0c2UhWHWHnQ3gQaaxFEAdEY226LtdQSKlmtKhjj+EdfpEZIejq1A8HkLA68GzH+7E397aiJ1FJtV1x7m5yblak4hYJmUgQiKZypwseMa1WyjGcc19bB3RJI6b3oj3f3ZSKYZmOrzKgfj7ZGgSCtckcgkJ7f3hlQHbGyxZJSTGAhBjvbbp27Jxn25q+hnliO0josuJaDkRLW9u7tvT2K2YUSWT2zp5VmrK5lVIf4hRXV2x4kpopzWJgDs0CbF0+0DMTXGlb9E4O/t3DxS/14NFnzXjw22FVRf4dGcn9vnJ83hrXQsiyRSGhQOOC3HOxTeOnoRLj2jKaHolahL8ucylJcRSCvxecoTWZNUdlu1Ms12dLzPG9gVwlP7vwmwHY4zdxRibzxib39jYaOIwrefVK49JR0OYIST4jcgfJsXhjWlER/tAz39HexS/evaTtHBxo7lpa1sEisoKysg1rsJTquqodqX9EfB5EE+pOOvPbxW0/wdbNGHy1AfbEYkrjitkmI/KoA+/OHN2xphFUxv/LXPVLYsmFMcEnZRMSBDRt7kDGsAOAOOFt8fp2zJgjG3X/+8C8A8AziyUbyKTG6tw5JQGAMWvpEW4kOBhgk7XJEQGev5XPbES9yzeiEWftQBwZnRTNmqEgotJRWtROeXaF9KtV3MhCtRp176A9kgynYnrBgYq0LjJpiOaRE8i5ZpFQC7eXNuCt9dp9yp3XOeq6RRPKY4JOinZHcYYu50xNpcxNhfAUwAu0qOcDgXQwRjLKJNIRD4iatD/9gM4A8CqUo3PSVSHzGvKkkhxc5MmJOx2eg2EgZ4/P7f2iBYe6ZZJxNgp8Fa9j/Hmfkp1iJoEX5VOGVFl8uhKh+hkZ4xha1sETVc/h//mSCrkztv2SBKxpOrYxNCB8KfX1gHoNQsnFDVr3bZYUnVMtQSrnqrnAZwGYB2ACIBL+RtEtEIXJEEAL+kCwgvgvwDutmh8tsJD+szUJPgD5SZNonOAQoJPOrxcg3s0iewhnD39RLhlyzE4amqDKWOyArEMzX9W7EgHajy/aidOnDWyz/68LAWvElzpksCEfHDtSNQg4ikVL328C+v2dOP4GSNwwIRhWkkdh5ibLHmqmCYqv53jvbn6/z0ADrRiPE6DaxIDnSSzYRQSbtIkugd4/typ1x5JwkNwzMqrP3iexJFTGrBYNz8AmQlj2RCb0AS8Hnxh/jiMHx4uzSBLgGga+/5jK3DVKdMBAI1V2VuRcqG5S+9DUeGSwIRcHDu9MZ08l1IztcL/fXQFAE3T+ON5cx1VnNMdT1WZU5PWJMwQEppQmKB37zpiintWmgPVpPik0x5JoDLoc0WhO0CLoV/5i5NxwzlzMra39OQvHy5qEl+YPw43/s++JRlfqVAMZpXfvbQGgNZrIhs8d4ib1iodYqMvltG1IezsiOH6Zz7Gg0s2p7cby9I8s3IHog4qzulu0VwmcDXaTHPTPg1hvHX18RhV45wGPPmorfAPWEhyR2hLTwLDHd6Ixkhthb9Py9q2fjQJ0SfhpD7ehcK12v3H1WLlttyF7ThdBvNb2OWaxKiaCjR3xXHfW5sythvzo1p7tPug2iEdJZ0xiiGOz+tBOOA1NQTW7/VgbJ1z2zwa0YSEJiTb9Iekv5h4niOQSKmutFcbs6X55JCLDCHhknwBEX5vTh9VnSEkbnp+NRIpFd85fmrG/kYfjROr/BbCs989EuGAF8tzdOUzhja39SQQDnjRWJ3dDGc1ztBnJKgO+UzVJNxSrmHWaK0WTW2FH+26A3reDa9g3g2v5P3ce5vb8MzK3ihqtzitRYy/UUs/3epEc9Mwh1dCzQYf/+TGvhFZv3/5sz7bjD4qNy4EAGDO2FpMaqxK3+tGvvOPDzJet3bHEU+p0ichyWRYOICW7gT2ve4lPPLOlqKPw5u6uCXJ6slvHY4PrzsZo2pD2NleeFmOh5dmXiO3hL+KGDvKdUbzLxLEInduPF9ubmrSu9ONqQ3l7apnNDdVuKCXdz6mj6rOut1YLrwnoaA9knBM3TVnjEKCMXUV+Gh7B7piKVz7748G/PnFa1vQdPVzuOqfWoVQt2gSIb8XNSE/xg2rwLa9kYJ7fRsjwdw4aRoFebbotlc+2Y0H3t6Ej3d04Bah1LQbcwZ4UEVthR93XzQf//rW4Rm1GG5/fR2arn4unX1eLpoEx6+blfOxjy5A90aSjskwd8dMMgQYUxdKNyUpJmr13sUbAPSGSbpFSHDG1lWgJ6Gkcx76w2iac6O5SazLE/B6smoSlz24HL94+mN8vCNztemWGkYiXJOo8Htx0qyRGF1bkbEouGPhegC9eRGdsWSGX83pfSQK4Z1rT8TN52aPSrv4sIm45rSZ6dfS3CTJYMwgnczGaBe3CYlxw7TzFxut5MPo5HfjKlMM2W2oCqAzlsypSYmlsq8/azbmjK0t+fjMhvskxB4Y4oKIlyvh90BHJJm+L4DyEBJVQV9OAR8O+jBCcFZLc5Mkg8FGItUZhEQ+W68TGV2rnT9PnAJyFz8DtFWmiBvNTSIN1UEkFZYuEW1kQ3OvP+JzB46zalimwrP/xQWMmOzJy5VsbYsgqajoiqcykgXdkgfTH7k0hJDPi8lCmZXRDolOdNdMUsY06clvnN+8uHpA0U7GVZbf564HittqY0LMeCRPW1OjJlHl8hj6Bj3r2Cj8OKIm4bYFAOfiw7Sy2bn6I3BBv3VvNG12HD/MPRnlhZJTSPg9GWbTfR2iLbrzbitD9huXeUPcsXA9bskSFpgLY9l5N1UHBbQy0kBmzHiuWkaMsT6d/FyvSehZx6Jf4jv/eD/9tygk3BK5ZuTCw5qw6ebT+61dtbm1J120cfxwbTU9qaEy62fcSK5MaqPwmDrSGcUb3TWTlDFEhH9cdgiOnd7bH2MgneqMxd/cNpFwISGaW3K1dI0klD41qfZG8ieiOZ1smsSzH/YWShYLwpWL2cUIF5Cf7OhEe0T7u6EqiPsuPQiPfeMwO4dmKqIwuOvC3nJ1PGLtzq/Mw/eOn5JugWo37l5+lRmHT26AojIsXKN12yukK1VnLAmfh9L5ERy3TSTchCKa2HJpEtky0w+YUFeagVlEWkhEzel17ka4iWldczd26r6purAf+41z929rRKzuevLsURg/vAJb26II6hrGgjmjsWCOc7o7S03CYVQLqnghJqP9rnsZx/xuIZKKiqqgD7PHZM/qdDpckxAFQDZN4umVO3Dor19Nv66t8GPNrxbg+Bl9S027iYbq/D6JoUBPQsH+42rBGPDdR7QsZDfWqOoPo7mpvjKob3eG5mBEahIOQyzqlUuTiCYUeD2Unlibu+JIKSp8XsJj3zgMLV35yzs4kV4h0TtJRuJ9zW1/eCXTT6OqzDFq+WDgZTYiidwmxtoKf8F5JG7lfw4Yi2GVgbQ27cZ8kP4IGoQB90cVmkhqNVJIOAxRSOQyGM38+YvYb1wtnv7OkeltCYXB79WiI9yYWNZrburVHnoSfTUJY2SPsfy0W+Er5p54Css2tGY1qS380bHp+lblynEzRmBifWVaSLg9ICEbRk2Cmxq5H8ZplN8v4HLEyI9YHsf1h9s6MlYeS9a3uDY0EtB8KAGvB3sELSiaZVXtMzjk3dRUKR88hDmaUHDeXUuz7jOsMuDK6q+Fctikekysr0yf45UnTbN5RKXB+Jxedcp0xFMqzth/jE0jyo8UEg5DbPGYz/QAAFEhj2BTawRN9e6OKQ/4PBmd2mJZ8iSMmeRqmWgS4YAXAa8HPYbf/ORZI/Fyjh7Q5cZcPfigJuTHJ788xZX1qQrBGFRSXxXEH86ba9No+kcKCYch3kDZVtIiRvu020pxGDG6YGKpvtnHAUOpAjf18M5HwOdBOOhFm6E7XTmUoiiE0bUhXHjoxPRrtzcY6o8/nLc/Zo12RrJcf5T3L+FysmkSonllQ3NPxns+lwsJ44SfXZPIlCRlokhoQsLvxepdXRnba8pcSHg9BEVlWPKTE+weiqX8zwHuKa0ihYSDWfRZM9bu7sLUkb116MWJc41hQnFqdEShpBSjkOirSRi1pfPmjy/pmKwi4PUgHPThQ0NbT65JFJIz40Ze+v5RWLm1/1amEvtw99KzTHn32hMxXRcMX7l3WcZ7oh9iS1sk4z23O3GTaqZQEAXii6t2YdX2jozckY+vPwU35Si77DaIKF2/SvRL8cqobg5KyMeUEdWuLVg4VCjPO8/lNFYH00lVxolfnDi3t2eW1Xa7fd6oCPGyJKrKcMXf38MZf1qMhFB+pDLoK6sVNk+m+qKgHVXp0W5GX4xEYhXyznMovCyBWCoZyDTBbN9rFBLZy0y7jSkjqjCmNpQ+V1FjiuepDOtGZgp9jyN6XohY2K2xOgivh/DT02f2+axEYgVSSDiUI6bUA0CfipmiJrFtrzZ53vg/cwD0tem7lT98cS4qg770uYpd2XIV/XMrT1xxGJZdozlt9/Zo2qPYaKcq6MX6m07DF8rE9yJxH1JIOJS7L5qPSQ2VWLaxFR8JzkyxMizvicwbFrnd3MQJB70I+b2IJRWoKsP9b29Mv7d2dzcmNVZi8Y+Ps3GE5lEZ9GFkjdZfoa1Hq2Q7TuihUA4lRyTuRgoJhxIO+DCpsQqxpIoz/7w4vT2ayDQp1YR6y3C43XHNqQz4EPJ7EE0qeH/LXry7aS8+N09zbiYUFXPG1GZMpOUCD0oQuxRKX4TEbuQd6GDEOk4cY+5AXTiQdnimlPLwSfRqEipeW70HXg/h8qMnpd8PlunE+ecvHYCjpjZk1Csq13OVuAeZJ+FgKoN9TQ28nlNDVQAt3QnUhf3p0Mly0STCfi+CPi+ak3Gs2tGJmaOrMbmxEh4CVIZ03f1y44z9xuCM/TLr90hNQmI38g50CTxRjkf8jKjW7Ni1FX5U6EIiWSZCwuf1IOT3IJ5S0RFNYnhlED6vJ227Dw0hO730SUjsRgoJByP6HzpjKdz0/Kf4y8J1ALQwUUAzSVWUiblp7vjeDmTccd0VTaYTykbXakKiXDWJbEhNQmI30tzkYKLJ3nDP/a9/OeO9AybU4emVO7C3J5n2SbhdkXjkskPTIa4hvyedK3LoZC0ceNywMN7f0o6eLM2Iyo2xdRXY3h4t20xriXuQQsLBVAdzF3c7YMIwAMCuzljauXnU1AZLxlUqKgLetOlMFAQ8V+SKYybj6ZU7MHlEVdbPlxOPX3EY3tnYKjUJie1IIeFgrj1jJtqjCbz0cWY/gS8fMgEzRmm1na44ZhKICIuuOhaNep/kcuDyoyfh3x9sBwDUVGi36awxNfjgZycNifLZY+sqXFUpVFK+SCHhYGpCfnzjmMkZQuK9n56I4ZUBEBE23Xx6evvE+ko7hlgyZo6uwdzxdVixtT0j67ycO7NJJE5E6rIOZ+aomoxmPPVVwT6drcoVHtpb7j0VJBIn4xghQUQziGgJEcWJ6Ed2j8cpVAS8eO57R9k9DFvgQiLgHRpCUSJxIk4yN7UB+B6Ac+weiNMYFh6aJpY6/byTZVK4UCJxI44REoyxPQD2ENHp/e48xKgLD01zyzWnzURlwIuTZo20eygSyZDFMeamgUBElxPRciJa3tzcbPdwSg7PgxhqDK8M4Pqz5wzZ85dInIBjNImBwBi7C8BdADB//vwhYYu44Zw5mD2mpv8dJRKJxERsFRJE9G0Al+kvT2OM7bBzPE7mwkMn2j0EiUQyBLFVSDDGbgdwu51jkEgkEkluHGNuIqJRAJYDqAGgEtH3AcxijHXm/6REIpFISoVjhARjbBcAWYdAIpFIHIQro5skEolEYg1SSEgkEokkJ1JISCQSiSQnUkhIJBKJJCdSSEgkEokkJ8SYuxOWiagZwOYiP94AoMXE4bgBec5DA3nOQ4PBnPNExlhjfzu5XkgMBiJazhibb/c4rESe89BAnvPQwIpzluYmiUQikeRECgmJRCKR5GSoC4m77B6ADchzHhrIcx4alPych7RPQiKRSCT5GeqahEQikUjyIIWERCKRSHIyJIUEES0gojVEtI6IrrZ7PGZCRH8joj1EtErYNpyIXiGitfr/w/TtRES36dfhQyKaZ9/Ii4OIxhPR60T0KRF9TET/q28v53MOEdE7RLRSP+fr9e37ENEy/ZwfI6KAvj2ov16nv99k5/gHAxF5iegDInpWf13W50xEm4joIyJaQUTL9W2W3ttDTkgQkRdao6NTAcwCcAERzbJ3VP+/vbsNsaKK4zj+/ZU9WZviUy0ZiE9koe6CmbYFahZiUVS+KMR8YUiglRZEEoUvfFEhaa8kMCooisQisdI1NV/0osxS13xKQUiUNkhN00Tdfy/mf3Va710f7t257dz/B4Y758zx3PMfZ++5c2bunIp6H5jULu9lYK2ZDQHWehqSfTDEl5nAkozaWEmngRfNbBgwBpjl/595jvkkMMHMRgINwCRJY4A3gEUe8yFghpefARwys8HAIi/XVT0P7EilayHm8WbWkPo9RLbHtpnV1AKMBVan0vOAedVuV4VjHABsS6V3AfW+Xg/s8vV3gCeLleuqC/AFcH+txAx0B34C7iL55W03zz97nAOrgbG+3s3Lqdptv4xY+5N8KE4AVgKqgZj3AX3a5WV6bNfcmQRwC/BbKr3f8/LsJjM7COCv/Tw/V/vChxQage/Jecw+7LIZaAXWAHuBw2Z22ouk4zobs28/AvTOtsUVsRh4CWjzdG/yH7MBzZI2SZrpeZke2/+bmekypCJ5tXofcG72haQbgOXAHDP7SyoWWlK0SF6Xi9nMzgANknoCnwPDihXz1y4fs6SHgFYz2yRpXCG7SNHcxOyazOyApH7AGkk7OyjbKTHX4pnEfuDWVLo/cKBKbcnK75LqAfy11fNzsS8kXUXSQXxkZp95dq5jLjCzw8C3JNdjekoqfPFLx3U2Zt/eA/gz25aWrQl4WNI+4BOSIafF5DtmzOyAv7aSfBkYTcbHdi12EhuBIX5XxNXAE8CKKreps60Apvv6dJJx+0L+U35XxBjgSOE0tqtQcsrwLrDDzN5KbcpzzH39DAJJ1wETSS7mrgemeLH2MRf2xRRgnfmgdVdhZvPMrL+ZDSD5m11nZlPJccySrpdUV1gHHgC2kfWxXe0LM1W6GDQZ2E0yjvtKtdtT4dg+Bg4Cp0i+WcwgGYtdC/zqr728rEju9NoLtACjqt3+y4j3HpJT6q3AZl8m5zzmEcDPHvM24DXPHwj8AOwBlgHXeP61nt7j2wdWO4Yy4x8HrMx7zB7bFl9+KXxWZX1sx2M5QgghlFSLw00hhBAuUnQSIYQQSopOIoQQQknRSYQQQigpOokQQggl1eIvrkNAUuE2QoCbgTPAH54+bmZ3d8J7NgKzzOzpMuuZDfxtZu9VpmUhlBa3wIaaJ2k+cMzMFnby+ywDFpjZljLr6Q58Z2aNlWlZCKXFcFMI7Ug65q/jJG2Q9Kmk3ZJelzTV53JokTTIy/WVtFzSRl+aitRZB4wodBCS5kv6QFKzzxnwmKQ3vd5V/qgR/D23+/wACwHM7DiwT9LorPZJqF3RSYTQsZEkcxgMB6YBQ81sNLAUeNbLvE0yp8GdwOO+rb1RJL+OThsEPAg8AnwIrDez4cAJ4EFJvYBHgTvMbASwIPVvfwTuLT+8EDoW1yRC6NhG8+ffSNoLNHt+CzDe1ycCt6eePHujpDozO5qqp55z1zwKvjazU5JagCuBVam6B5DMmfAPsFTSl54uaAVuKzO2EC4oOokQOnYytd6WSrdx7u/nCpIJbk50UM8JkucJnVe3mbVJOmXnLhC2kUykc9qHlO4jeajdbJKnn+J1dfR+IVREDDeFUL5mkg9wACQ1FCmzAxh8KZX6HBk9zOwrYA7JVKUFQzl/+CqEiotOIoTyPQeM8ovL24Fn2hcws51Aj8Kjny9SHbBS0lZgAzA3ta0J+KaMNodwUeIW2BAyImkucNTMil3YvpR6GoEXzGxaZVoWQmlxJhFCdpbw32scl6sP8GoF6gnhguJMIoQQQklxJhFCCKGk6CRCCCGUFJ1ECCGEkqKTCCGEUFJ0EiGEEEr6F0tQmx5J+rPWAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -196,14 +228,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 948\n"
+      "Starting experimental run with id: 2\n"
      ]
     }
    ],
@@ -218,12 +250,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -247,14 +279,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 949\n"
+      "Starting experimental run with id: 3\n"
      ]
     }
    ],
@@ -272,12 +304,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -303,14 +335,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 950\n"
+      "Starting experimental run with id: 4\n"
      ]
     }
    ],
@@ -331,12 +363,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -362,14 +394,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 951\n"
+      "Starting experimental run with id: 5\n"
      ]
     }
    ],
@@ -392,12 +424,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -411,27 +443,6 @@
    "source": [
     "_ = plot_by_id(dataid)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -450,7 +461,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,

From 8ea1d9f6140be7d21b4b3d72b93f62511d146c85 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 25 Oct 2018 15:02:57 +0200
Subject: [PATCH 117/719] This cannot be executed

---
 ...le with Signal Hound USB-SA124B new Array Parameter.ipynb | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb
index 38287c9bb69..6d024cfbd5c 100644
--- a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb	
@@ -381,7 +381,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.7"
+  },
+  "nbsphinx": {
+   "execute": "never"
   }
  },
  "nbformat": 4,

From 161c7df14abc62d1e532c556c01fde46444bb0f6 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 6 Nov 2018 14:48:16 +0100
Subject: [PATCH 118/719] Add parameter with setpoints to dummy instrument

---
 qcodes/tests/instrument_mocks.py | 56 ++++++++++++++++++++++++++++++--
 1 file changed, 53 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/instrument_mocks.py b/qcodes/tests/instrument_mocks.py
index c47ea7c5fd8..8bb2b4391a2 100644
--- a/qcodes/tests/instrument_mocks.py
+++ b/qcodes/tests/instrument_mocks.py
@@ -5,8 +5,9 @@
 import numpy as np
 
 from qcodes.instrument.base import Instrument
-from qcodes.utils.validators import Numbers
-from qcodes.instrument.parameter import MultiParameter, Parameter, ArrayParameter
+from qcodes.utils.validators import Numbers, Arrays
+from qcodes.instrument.parameter import MultiParameter, Parameter, \
+    ArrayParameter, ParameterWithSetpoints, GeneratedSetPoints
 from qcodes.instrument.channel import InstrumentChannel, ChannelList
 
 log = logging.getLogger(__name__)
@@ -95,7 +96,7 @@ def _get_skew_parabola(self):
 
 class DummyInstrument(Instrument):
 
-    def __init__(self, name='dummy', gates=['dac1', 'dac2', 'dac3'], **kwargs):
+    def __init__(self, name='dummy', gates=('dac1', 'dac2', 'dac3'), **kwargs):
 
         """
         Create a dummy instrument that can be used for testing
@@ -149,6 +150,43 @@ def __init__(self, parent, name, channel):
         self.add_parameter(name='dummy_array_parameter',
                            parameter_class=ArraySetPointParam)
 
+        self.add_parameter('dummy_start',
+                           initial_value=0,
+                           unit='some unit',
+                           label='f start',
+                           vals=Numbers(0, 1e3),
+                           get_cmd=None,
+                           set_cmd=None)
+
+        self.add_parameter('dummy_stop',
+                           unit='some unit',
+                           label='f stop',
+                           vals=Numbers(1, 1e3),
+                           get_cmd=None,
+                           set_cmd=None)
+
+        self.add_parameter('dummy_n_points',
+                           unit='',
+                           vals=Numbers(1, 1e3),
+                           get_cmd=None,
+                           set_cmd=None)
+
+        self.add_parameter('dummy_sp_axis',
+                           unit='some unit',
+                           label='Dummy sp axis',
+                           parameter_class=GeneratedSetPoints,
+                           startparam=self.dummy_start,
+                           stopparam=self.dummy_stop,
+                           numpointsparam=self.dummy_n_points,
+                           vals=Arrays(shape=(self.dummy_n_points,)))
+
+        self.add_parameter(name='dummy_parameter_with_setpoints',
+                           label='Dummy Parameter with Setpoints',
+                           unit='some other unit',
+                           setpoints=(self.dummy_sp_axis,),
+                           vals=Arrays(shape=(self.dummy_n_points,)),
+                           parameter_class=DummyParameterWithSetpoints1D)
+
         self.add_function(name='log_my_name',
                           call_cmd=partial(log.debug, f'{name}'))
 
@@ -284,6 +322,7 @@ def get_raw(self):
         self._save_val(items)
         return items
 
+
 class ArraySetPointParam(ArrayParameter):
     """
     Arrayparameter which only purpose it to test that units, setpoints
@@ -315,6 +354,17 @@ def get_raw(self):
         return item
 
 
+class DummyParameterWithSetpoints1D(ParameterWithSetpoints):
+    """
+    Dummy parameter that returns data with a shape based on the
+    `n_points` parameter in the instrument.
+    """
+
+    def get_raw(self):
+        npoints = self.instrument.dummy_n_points()
+        return np.random.rand(npoints)
+
+
 def setpoint_generator(*sp_bases):
     """
     Helper function to generate setpoints in the format that ArrayParameter

From 7e3616278ace84fa375ae8267c87c6e7176da2db Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 6 Nov 2018 14:50:26 +0100
Subject: [PATCH 119/719] Add tests of saving data from parameter with
 setpoints

---
 .../test_measurement_context_manager.py       | 45 ++++++++++++++++++-
 1 file changed, 44 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 461e25df5fd..71c0c4f967b 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -640,7 +640,7 @@ def test_datasaver_scalars(experiment, DAC, DMM, set_values, get_values,
 @settings(max_examples=10, deadline=None)
 @given(N=hst.integers(min_value=2, max_value=500))
 @pytest.mark.usefixtures("empty_temp_db")
-def test_datasaver_arrays_lists_tuples(N):
+def test_datasaver_arrays_lists_tuples(N: int) -> None:
     new_experiment('firstexp', sample_name='no sample')
 
     meas = Measurement()
@@ -976,6 +976,49 @@ def test_datasaver_array_parameters_channel(channel_array_instrument,
         assert datadict['data'].shape == (N * M,)
 
 
+@settings(max_examples=5, deadline=None)
+@given(n=hst.integers(min_value=5, max_value=500))
+@pytest.mark.usefixtures("experiment")
+def test_datasaver_parameter_with_setpoints(channel_array_instrument,
+                                            DAC, n):
+
+    chan = channel_array_instrument.A
+    param = chan.dummy_parameter_with_setpoints
+    chan.dummy_n_points(n)
+    chan.dummy_start(0)
+    chan.dummy_stop(100)
+    meas = Measurement()
+    meas.register_parameter(param)
+
+    assert len(meas.parameters) == 2
+    dependency_name = 'dummy_channel_inst_ChanA_dummy_sp_axis'
+
+    assert meas.parameters[str(param)].depends_on == dependency_name
+    assert meas.parameters[str(param)].type == 'numeric'
+    assert meas.parameters[dependency_name].type == 'numeric'
+
+    # Now for a real measurement
+    with meas.run() as datasaver:
+        datasaver.add_result((param, param()))
+    assert datasaver.points_written == n
+
+    expected_params = ('dummy_channel_inst_ChanA_dummy_sp_axis',
+                       'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints')
+    ds = load_by_id(datasaver.run_id)
+    for param in expected_params:
+        data = ds.get_data(param)
+        assert len(data) == n
+        assert len(data[0]) == 1
+    datadicts = get_data_by_id(datasaver.run_id)
+    # one dependent parameter
+    assert len(datadicts) == 1
+    datadicts = datadicts[0]
+    assert len(datadicts) == len(meas.parameters)
+    for datadict in datadicts:
+        assert datadict['data'].shape == (n,)
+
+
+
 @settings(max_examples=5, deadline=None)
 @given(N=hst.integers(min_value=5, max_value=500))
 @pytest.mark.usefixtures("experiment")

From 80a771223d8e34dddc4e0ec4be6d4cebb606286f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 6 Nov 2018 14:51:38 +0100
Subject: [PATCH 120/719] Reflect that results may be Lists and tuples too

---
 qcodes/dataset/measurements.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index e0f8db2d557..cb318176eea 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -22,8 +22,10 @@
 log = logging.getLogger(__name__)
 
 array_like_types = (tuple, list, np.ndarray)
+scalar_res_types = Union[str, int, float, np.dtype]
 res_type = Tuple[Union[_BaseParameter, str],
-                 Union[str, int, float, np.dtype, np.ndarray]]
+                 Union[scalar_res_types, np.ndarray,
+                 Sequence[scalar_res_types]]]
 setpoints_type = Sequence[Union[str, _BaseParameter]]
 numeric_types = Union[float, int]
 

From b9527c8b2b76e0e36ae5beb54c60c42493284b39 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 6 Nov 2018 14:52:27 +0100
Subject: [PATCH 121/719] Remove redundant type

---
 qcodes/tests/test_parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/tests/test_parameter.py b/qcodes/tests/test_parameter.py
index f7deac8c177..18167594297 100644
--- a/qcodes/tests/test_parameter.py
+++ b/qcodes/tests/test_parameter.py
@@ -48,7 +48,7 @@ def validate(self, value, context=''):
     None,  # no instrument at all
     namedtuple('noname', '')(),  # no .name
     namedtuple('blank', 'name')('')  # blank .name
-) # type: Tuple
+)
 named_instrument = namedtuple('yesname', 'name')('astro')
 
 

From a0a40a4e47256d1ae1e61dc219140b082f59e363 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 6 Nov 2018 14:54:26 +0100
Subject: [PATCH 122/719] Add validator and some cleanup of parameter

---
 qcodes/instrument/parameter.py | 62 ++++++++++++++++++++++++++--------
 1 file changed, 48 insertions(+), 14 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 6df6baef8ca..2113fabe24b 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -72,7 +72,7 @@
                                   warn_units)
 from qcodes.utils.metadata import Metadatable
 from qcodes.utils.command import Command
-from qcodes.utils.validators import Validator, Ints, Strings, Enum
+from qcodes.utils.validators import Validator, Ints, Strings, Enum, Arrays
 from qcodes.instrument.sweep_values import SweepFixedValues
 from qcodes.data.data_array import DataArray
 
@@ -112,16 +112,16 @@ class _BaseParameter(Metadatable):
     Note that ``CombinedParameter`` is not yet a subclass of ``_BaseParameter``
 
     Args:
-        name (str): the local name of the parameter. Must be a valid
+        name: the local name of the parameter. Must be a valid
             identifier, ie no spaces or special characters or starting with a
             number. If this parameter is part of an Instrument or Station,
             this should match how it will be referenced from that parent,
             ie ``instrument.name`` or ``instrument.parameters[name]``
 
-        instrument (Optional[Instrument]): the instrument this parameter
+        instrument: the instrument this parameter
             belongs to, if any
 
-        snapshot_get (Optional[bool]): False prevents any update to the
+        snapshot_get: False prevents any update to the
             parameter during a snapshot, even if the snapshot was called with
             ``update=True``, for example if it takes too long to update.
             Default True.
@@ -189,11 +189,11 @@ def __init__(self, name: str,
                  instrument: Optional['Instrument'],
                  snapshot_get: bool=True,
                  metadata: Optional[dict]=None,
-                 step: Optional[Union[int, float]]=None,
+                 step: Optional[Number]=None,
                  scale: Optional[Union[Number, Iterable[Number]]]=None,
                  offset: Optional[Union[Number, Iterable[Number]]]=None,
-                 inter_delay: Union[int, float]=0,
-                 post_delay: Union[int, float]=0,
+                 inter_delay: Number=0,
+                 post_delay: Number=0,
                  val_mapping: Optional[dict]=None,
                  get_parser: Optional[Callable]=None,
                  set_parser: Optional[Callable]=None,
@@ -950,7 +950,7 @@ def sweep(self, start, stop, step=None, num=None):
 class ParameterWithSetpoints(Parameter):
     """
     A parameter that has associated setpoints. The setpoints is nothing
-    more than a list of other paramameters that descripe the values, names
+    more than a list of other parameters that describe the values, names
     and units of the setpoint axis for this parameter.
 
     In most cases this will probably be a parameter that returns an array.
@@ -962,9 +962,10 @@ class ParameterWithSetpoints(Parameter):
     documentation of :class:`Parameter` for more details.
     """
     def __init__(self, *args,
-                 setpoints: Sequence[Parameter]=None, **kwargs) -> None:
+                 setpoints: Optional[Sequence[_BaseParameter]]=None,
+                 **kwargs) -> None:
         if setpoints is None:
-            self._setpoints = ()
+            self._setpoints = []
         else:
             self.setpoints = setpoints
         super().__init__(*args, **kwargs)
@@ -974,12 +975,45 @@ def setpoints(self):
         return self._setpoints
 
     @setpoints.setter
-    def setpoints(self, setpoints):
+    def setpoints(self, setpoints: Sequence[_BaseParameter]):
         for setpointarray in setpoints:
-            if not isinstance(setpointarray, Parameter):
+            if not isinstance(setpointarray, _BaseParameter):
                 raise RuntimeError
         self._setpoints = setpoints
 
+    def validate_consistent_shape(self) -> bool:
+        """
+        Verifies that the shape of the Array Validator of the parameter
+        is consistent with the Validator of the Setpoints. This requires that
+        both the setpoints and the actual parameters have validators
+        of type Arrays with a defined shape.
+        """
+        output_shape = self.vals.shape
+
+        if not isinstance(self.vals, Arrays):
+            raise RuntimeError("Can only validate shapes for Array "
+                               "Parameters")
+
+        setpoints_shape_list = []
+        for sp in self.setpoints:
+            if not isinstance(self.vals, Arrays):
+                raise RuntimeError("Can only validate shapes for Array "
+                                   "Parameters")
+            setpoints_shape_list.extend(sp.vals.shape)
+        setpoints_shape = tuple(setpoints_shape_list)
+
+        if output_shape is None:
+            raise RuntimeError("Trying to verify shape but output "
+                               "does not have any shape")
+        if None in output_shape or None in setpoints_shape:
+            raise RuntimeError(f"One or more dimensions have unknown shape"
+                               f"when comparing output: {output_shape} to "
+                               f"setpoints: {setpoints_shape}")
+
+        if output_shape != setpoints_shape:
+            return False
+        return True
+
 
 class GeneratedSetPoints(Parameter):
     """
@@ -1352,8 +1386,8 @@ def __init__(self,
     @property
     def short_names(self):
         """
-        short_names is indentical to names i.e. the names of the paramter parts
-        but does not add the intrument name.
+        short_names is identical to names i.e. the names of the parameter
+        parts but does not add the instrument name.
 
         It exists for consistency with instruments and other parameters.
         """

From 3e82c559656bb723793a8e2cd9728512dd907522 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 6 Nov 2018 15:39:16 +0100
Subject: [PATCH 123/719] Correct some types

---
 qcodes/instrument/parameter.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 2113fabe24b..4eba0c98dfa 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -965,7 +965,7 @@ def __init__(self, *args,
                  setpoints: Optional[Sequence[_BaseParameter]]=None,
                  **kwargs) -> None:
         if setpoints is None:
-            self._setpoints = []
+            self._setpoints: Sequence[_BaseParameter] = []
         else:
             self.setpoints = setpoints
         super().__init__(*args, **kwargs)
@@ -988,13 +988,11 @@ def validate_consistent_shape(self) -> bool:
         both the setpoints and the actual parameters have validators
         of type Arrays with a defined shape.
         """
-        output_shape = self.vals.shape
-
         if not isinstance(self.vals, Arrays):
             raise RuntimeError("Can only validate shapes for Array "
                                "Parameters")
-
-        setpoints_shape_list = []
+        output_shape = self.vals.shape
+        setpoints_shape_list: List[int] = []
         for sp in self.setpoints:
             if not isinstance(self.vals, Arrays):
                 raise RuntimeError("Can only validate shapes for Array "

From 134d067a1a6ec959dfd1ba64c8aaffb7ea5a0eca Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 6 Nov 2018 15:39:34 +0100
Subject: [PATCH 124/719] Dont type this just yet

---
 qcodes/tests/dataset/test_measurement_context_manager.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 71c0c4f967b..2631ee962a5 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -640,7 +640,7 @@ def test_datasaver_scalars(experiment, DAC, DMM, set_values, get_values,
 @settings(max_examples=10, deadline=None)
 @given(N=hst.integers(min_value=2, max_value=500))
 @pytest.mark.usefixtures("empty_temp_db")
-def test_datasaver_arrays_lists_tuples(N: int) -> None:
+def test_datasaver_arrays_lists_tuples(N):
     new_experiment('firstexp', sample_name='no sample')
 
     meas = Measurement()

From 09af75e051e1b9d1b68e057286fa68240ef20352 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 15 Nov 2018 15:44:27 +0100
Subject: [PATCH 125/719] Typo

---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 4eba0c98dfa..fa5ee84b2ed 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -994,7 +994,7 @@ def validate_consistent_shape(self) -> bool:
         output_shape = self.vals.shape
         setpoints_shape_list: List[int] = []
         for sp in self.setpoints:
-            if not isinstance(self.vals, Arrays):
+            if not isinstance(sp.vals, Arrays):
                 raise RuntimeError("Can only validate shapes for Array "
                                    "Parameters")
             setpoints_shape_list.extend(sp.vals.shape)

From 325f32d84abe51c2125744bc6b4518f4b0caad91 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 19 Nov 2018 15:27:44 +0100
Subject: [PATCH 126/719] Extract making ConnectionPlus into separate function

---
 qcodes/dataset/sqlite_base.py                 | 27 +++++++++++++++----
 .../tests/dataset/test_sqlite_connection.py   | 25 +++++++++++++++++
 2 files changed, 47 insertions(+), 5 deletions(-)
 create mode 100644 qcodes/tests/dataset/test_sqlite_connection.py

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 00dc00be102..313593a49d9 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -744,11 +744,7 @@ def atomic(conn: SomeConnection):
         - conn: connection to guard
     """
 
-    if not(hasattr(conn, 'atomic_in_progress')):
-        conn = ConnectionPlus(conn)
-        conn.atomic_in_progress = False
-    else:
-        conn = cast(ConnectionPlus, conn)
+    conn = make_plus_connection_from(conn)
 
     is_outmost = not(conn.atomic_in_progress)
     conn.atomic_in_progress = True
@@ -776,6 +772,27 @@ def atomic(conn: SomeConnection):
             conn.isolation_level = old_level
 
 
+def make_plus_connection_from(conn: SomeConnection) -> ConnectionPlus:
+    """
+    Makes a ConnectionPlus connection object out of a given argument.
+
+    If the given connection is already a ConnectionPlus, then it is returned
+    without any changes.
+
+    Args:
+        conn: an sqlite connection object as defined by SomeConnection type
+
+    Returns:
+        the same connection as ConnectionPlus object
+    """
+    if not (hasattr(conn, 'atomic_in_progress')):
+        conn = ConnectionPlus(conn)
+        conn.atomic_in_progress = False
+    else:
+        conn = cast(ConnectionPlus, conn)
+    return conn
+
+
 def init_db(conn: SomeConnection)->None:
     with atomic(conn) as conn:
         transaction(conn, _experiment_table_schema)
diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
new file mode 100644
index 00000000000..73cd2dec358
--- /dev/null
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -0,0 +1,25 @@
+import sqlite3
+
+import pytest
+
+from qcodes.dataset.sqlite_base import ConnectionPlus, make_plus_connection_from
+
+
+@pytest.mark.parametrize(
+    argnames='conn',
+    argvalues=(sqlite3.connect(':memory:'),
+               ConnectionPlus(sqlite3.connect(':memory:'))),
+    ids=('sqlite3.Connection', 'ConnectionPlus')
+)
+def test_make_plus_connection_from(conn):
+    plus_conn = make_plus_connection_from(conn)
+
+    assert isinstance(plus_conn, ConnectionPlus)
+
+    if isinstance(conn, ConnectionPlus):
+        # make_plus_connection_from does not change this, hence it should be
+        # equal to the value from `conn` (which is True, see ConnectionPlus)
+        assert conn.atomic_in_progress is plus_conn.atomic_in_progress
+    else:
+        # make_plus_connection_from explicitly sets this to False
+        assert False is plus_conn.atomic_in_progress

From 07cc37c1767c9365df30cd3a397eb503b3450dbf Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 19 Nov 2018 15:52:23 +0100
Subject: [PATCH 127/719] Add basic test for ConnectionPlus

---
 qcodes/tests/dataset/test_sqlite_connection.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index 73cd2dec358..ffab0eedc92 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -5,6 +5,16 @@
 from qcodes.dataset.sqlite_base import ConnectionPlus, make_plus_connection_from
 
 
+def test_connection_plus():
+    sqlite_conn = sqlite3.connect(':memory:')
+    plus_conn = ConnectionPlus(sqlite_conn)
+
+    assert isinstance(plus_conn, ConnectionPlus)
+    assert isinstance(plus_conn, sqlite3.Connection)
+    # reason for the value of "True" here is unknown
+    assert True is plus_conn.atomic_in_progress
+
+
 @pytest.mark.parametrize(
     argnames='conn',
     argvalues=(sqlite3.connect(':memory:'),

From 98bf334ba548767514436c12c33271633956c593 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 19 Nov 2018 17:03:38 +0100
Subject: [PATCH 128/719] Capture behavior of atomic context manager and
 ConnectionPlus

This behavior is quite questionable, hence comments in test code
---
 .../tests/dataset/test_sqlite_connection.py   | 142 +++++++++++++++++-
 1 file changed, 141 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index ffab0eedc92..e8e1e04fa21 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -2,7 +2,39 @@
 
 import pytest
 
-from qcodes.dataset.sqlite_base import ConnectionPlus, make_plus_connection_from
+from qcodes.dataset.sqlite_base import ConnectionPlus, \
+    make_plus_connection_from, atomic
+
+
+def sqlite_conn_in_transaction(conn: sqlite3.Connection):
+    assert isinstance(conn, sqlite3.Connection)
+    assert True is conn.in_transaction
+    assert None is conn.isolation_level
+    return True
+
+
+def plus_conn_in_transaction(conn: ConnectionPlus):
+    assert isinstance(conn, ConnectionPlus)
+    assert True is conn.atomic_in_progress
+    assert None is conn.isolation_level
+    assert True is conn.in_transaction
+    return True
+
+
+def sqlite_conn_is_idle(conn: sqlite3.Connection, isolation=None):
+    assert isinstance(conn, sqlite3.Connection)
+    assert False is conn.in_transaction
+    assert isolation == conn.isolation_level
+    return True
+
+
+def plus_conn_is_idle(conn: ConnectionPlus, isolation=None):
+    assert isinstance(conn, ConnectionPlus)
+    # assert False is conn.atomic_in_progress <-- should it be?
+    assert True is conn.atomic_in_progress
+    assert isolation == conn.isolation_level
+    assert False is conn.in_transaction
+    return True
 
 
 def test_connection_plus():
@@ -13,6 +45,7 @@ def test_connection_plus():
     assert isinstance(plus_conn, sqlite3.Connection)
     # reason for the value of "True" here is unknown
     assert True is plus_conn.atomic_in_progress
+    # assert False is plus_conn.atomic_in_progress <-- should it be?
 
 
 @pytest.mark.parametrize(
@@ -33,3 +66,110 @@ def test_make_plus_connection_from(conn):
     else:
         # make_plus_connection_from explicitly sets this to False
         assert False is plus_conn.atomic_in_progress
+
+
+def test_atomic_on_outmost_sqlite_connection():
+    sqlite_conn = sqlite3.connect(':memory:')
+    isolation_level = sqlite_conn.isolation_level
+    assert False is sqlite_conn.in_transaction
+
+    with atomic(sqlite_conn) as atomic_conn:
+        assert sqlite_conn_in_transaction(sqlite_conn)
+        assert plus_conn_in_transaction(atomic_conn)
+
+    assert sqlite_conn_is_idle(sqlite_conn, isolation_level)
+    assert plus_conn_is_idle(atomic_conn, isolation_level)
+
+
+def test_atomic_on_outmost_plus_connection():
+    sqlite_conn = sqlite3.connect(':memory:')
+    plus_conn = ConnectionPlus(sqlite_conn)
+
+    atomic_in_progress = plus_conn.atomic_in_progress
+
+    isolation_level = plus_conn.isolation_level
+    assert False is plus_conn.in_transaction
+
+    with atomic(plus_conn) as atomic_conn:
+        assert isinstance(atomic_conn, ConnectionPlus)
+
+        assert True is atomic_conn.atomic_in_progress
+
+        # assert None is atomic_conn.isolation_level <-- should it be?
+        assert isolation_level == atomic_conn.isolation_level
+        # assert None is plus_conn.isolation_level <-- should it be?
+        assert isolation_level == plus_conn.isolation_level
+
+        # assert True is plus_conn.in_transaction <-- should it be?
+        assert False is plus_conn.in_transaction
+        # assert True is atomic_conn.in_transaction <-- should it be?
+        assert False is atomic_conn.in_transaction
+
+    assert isolation_level == plus_conn.isolation_level
+    assert False is plus_conn.in_transaction
+
+    assert False is atomic_conn.in_transaction
+
+    assert atomic_in_progress is atomic_conn.atomic_in_progress
+
+
+def test_two_atomics_on_outmost_sqlite_connection():
+    sqlite_conn = sqlite3.connect(':memory:')
+
+    isolation_level = sqlite_conn.isolation_level
+    assert False is sqlite_conn.in_transaction
+
+    with atomic(sqlite_conn) as atomic_conn_1:
+        assert sqlite_conn_in_transaction(sqlite_conn)
+        assert plus_conn_in_transaction(atomic_conn_1)
+
+        with atomic(atomic_conn_1) as atomic_conn_2:
+            assert sqlite_conn_in_transaction(sqlite_conn)
+            assert plus_conn_in_transaction(atomic_conn_2)
+            assert plus_conn_in_transaction(atomic_conn_1)
+
+        assert sqlite_conn_in_transaction(sqlite_conn)
+        assert plus_conn_in_transaction(atomic_conn_1)
+        assert plus_conn_in_transaction(atomic_conn_2)
+
+    assert sqlite_conn_is_idle(sqlite_conn, isolation_level)
+    assert plus_conn_is_idle(atomic_conn_1, isolation_level)
+    assert plus_conn_is_idle(atomic_conn_2, isolation_level)
+
+
+def test_two_atomics_on_outmost_plus_connection():
+    sqlite_conn = sqlite3.connect(':memory:')
+    plus_conn = ConnectionPlus(sqlite_conn)
+    atomic_in_progress = plus_conn.atomic_in_progress
+
+    isolation_level = plus_conn.isolation_level
+    assert False is plus_conn.in_transaction
+
+    with atomic(plus_conn) as atomic_conn_1:
+        # assert plus_conn_in_transaction(plus_conn)
+        assert plus_conn_is_idle(plus_conn, isolation_level)
+        # assert plus_conn_in_transaction(atomic_conn_1)
+        assert plus_conn_is_idle(atomic_conn_1, isolation_level)
+
+        with atomic(atomic_conn_1) as atomic_conn_2:
+            # assert plus_conn_in_transaction(plus_conn) <-- should it be?
+            assert plus_conn_is_idle(plus_conn, isolation_level)
+            # assert plus_conn_in_transaction(atomic_conn_1) <-- should it be?
+            assert plus_conn_is_idle(atomic_conn_1, isolation_level)
+            # assert plus_conn_in_transaction(atomic_conn_2) <-- should it be?
+            assert plus_conn_is_idle(atomic_conn_2, isolation_level)
+
+        # assert plus_conn_in_transaction(plus_conn) <-- should it be?
+        assert plus_conn_is_idle(plus_conn, isolation_level)
+        # assert plus_conn_in_transaction(atomic_conn_1) <-- should it be?
+        assert plus_conn_is_idle(atomic_conn_1, isolation_level)
+        # assert plus_conn_in_transaction(atomic_conn_2) <-- should it be?
+        assert plus_conn_is_idle(atomic_conn_2, isolation_level)
+
+    assert plus_conn_is_idle(plus_conn, isolation_level)
+    assert plus_conn_is_idle(atomic_conn_1, isolation_level)
+    assert plus_conn_is_idle(atomic_conn_2, isolation_level)
+
+    assert atomic_in_progress == plus_conn.atomic_in_progress
+    assert atomic_in_progress == atomic_conn_1.atomic_in_progress
+    assert atomic_in_progress == atomic_conn_2.atomic_in_progress

From de15768550a55aeeb1cfa180bcdc9c9d6d5c6e5d Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 19 Nov 2018 20:33:50 +0100
Subject: [PATCH 129/719] Fix DataSet.unsubscribe bug by adding trigger_id to
 _Subscriber

DataSet.unsubscribe used to unsubscribe by _Subscriber._id but this
raises an exception because unsubscribing involved removing a
corresponding trigger from the database, and the id of the trigger is
not equal to the id of the _Subscriber itself: the trigger id has a
prefix 'sub'.
---
 qcodes/dataset/data_set.py               |  5 +++--
 qcodes/tests/dataset/test_subscribing.py | 12 ++++++++++++
 2 files changed, 15 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 7f93402a935..2d023a71cce 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -109,6 +109,7 @@ def __init__(self,
             self.callback = functools.partial(callback, **callback_kwargs)
 
         self.callback_id = f"callback{self._id}"
+        self.trigger_id = f"sub{self._id}"
 
         conn = dataSet.conn
 
@@ -117,7 +118,7 @@ def __init__(self,
         parameters = dataSet.get_parameters()
         sql_param_list = ",".join([f"NEW.{p.name}" for p in parameters])
         sql_create_trigger_for_callback = f"""
-        CREATE TRIGGER sub{self._id}
+        CREATE TRIGGER {self.trigger_id}
             AFTER INSERT ON '{self.table_name}'
         BEGIN
             SELECT {self.callback_id}({sql_param_list});
@@ -805,8 +806,8 @@ def unsubscribe(self, uuid: str) -> None:
         Remove subscriber with the provided uuid
         """
         with atomic(self.conn) as self.conn:
-            self._remove_trigger(uuid)
             sub = self.subscribers[uuid]
+            self._remove_trigger(sub.trigger_id)
             sub.schedule_stop()
             sub.join()
             del self.subscribers[uuid]
diff --git a/qcodes/tests/dataset/test_subscribing.py b/qcodes/tests/dataset/test_subscribing.py
index d5aefe1d91b..42c8d478e93 100644
--- a/qcodes/tests/dataset/test_subscribing.py
+++ b/qcodes/tests/dataset/test_subscribing.py
@@ -9,6 +9,7 @@
 import qcodes
 from qcodes.dataset.param_spec import ParamSpec
 # pylint: disable=unused-import
+from qcodes.dataset.sqlite_base import atomic_transaction
 from qcodes.tests.dataset.temporary_databases import (
     empty_temp_db, experiment, dataset)
 # pylint: enable=unused-import
@@ -85,6 +86,17 @@ def test_basic_subscription(dataset, basic_subscriber):
         expected_state[x+1] = [(x, y)]
         assert dataset.subscribers[sub_id].state == expected_state
 
+    dataset.unsubscribe(sub_id)
+
+    assert len(dataset.subscribers) == 0
+    assert list(dataset.subscribers.keys()) == []
+
+    # Ensure the trigger for the subscriber have been removed from the database
+    get_triggers_sql = "SELECT * FROM sqlite_master WHERE TYPE = 'trigger';"
+    triggers = atomic_transaction(
+        dataset.conn, get_triggers_sql).fetchall()
+    assert len(triggers) == 0
+
 
 def test_subscription_from_config(dataset, basic_subscriber):
     """

From b435c75b9ccfaca4fca8d185f15738ca98d1988a Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 19 Nov 2018 21:09:08 +0100
Subject: [PATCH 130/719] Deprecate DataSet.modify_results (w/ 's') + test;
 does not always work

Also add an xfail test that covers working functionality and non-working
---
 qcodes/dataset/data_set.py                 |  3 +++
 qcodes/tests/dataset/test_dataset_basic.py | 28 ++++++++++++++++++++++
 2 files changed, 31 insertions(+)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 2d023a71cce..490cb3c6bd1 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -606,6 +606,9 @@ def modify_result(self, index: int, results: Dict[str, VALUES]) -> None:
                           list(results.values())
                           )
 
+    @deprecate(reason='it is an experimental functionality, and it is not '
+                      'known whether it will remain or it will be removed.',
+               alternative='modify_result')
     def modify_results(self, start_index: int,
                        updates: List[Dict[str, VALUES]]):
         """
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index e0dbcfd830b..1676d28bb37 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -291,6 +291,34 @@ def test_adding_too_many_results():
     dataset.add_results(results)
 
 
+def test_modify_results(dataset):
+    xparam = ParamSpec("x", "numeric")
+    dataset.add_parameter(xparam)
+    dataset.add_result({'x': 0})
+    dataset.add_result({'x': 1})
+
+    pytest.deprecated_call(dataset.modify_results, 0, [{'x': [10]}])
+    assert [[10], [1]] == dataset.get_data(xparam)
+
+    pytest.deprecated_call(dataset.modify_results, 1, [{'x': [14]}])
+    assert [[10], [14]] == dataset.get_data(xparam)
+
+    with pytest.raises(RuntimeError,
+                       match='Rolling back due to unhandled exception'):
+        # not sure calling `modify_results` like this is correct, anyway it
+        # is difficult to find out what the call signature for multiple
+        # results is supposed to look like...
+        pytest.deprecated_call(
+            dataset.modify_results, 0, [{'x': [5]}, {'x': [6]}])
+        assert [[5], [6]] == dataset.get_data(xparam)
+
+    pytest.xfail('modify_results does not seem to work for cases where '
+                 'multiple values of multiple parameters need to be changed. '
+                 'Anyway, the signature needs to be revisited, '
+                 'and consequently the correct behavior needs to be '
+                 'implemented and covered with tests.')
+
+
 @pytest.mark.usefixtures("experiment")
 def test_modify_result():
     dataset = new_data_set("test_modify_result")

From 815a457a7b3bcc1f353a4a33b459810dbbf7d5cd Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 19 Nov 2018 21:11:24 +0100
Subject: [PATCH 131/719] Add failing tests for some DataSet methods which do
 not commit to db

This is due to both: bug in `atomic` context manager, and the fact that
in those methods DataSet's connection object gets reassigned to a
modified one that `atomic` returns.

These bugs will be fixed in further commits.
---
 qcodes/tests/dataset/test_dataset_basic.py | 138 ++++++++++++++++-----
 qcodes/tests/dataset/test_subscribing.py   |  46 +++++++
 2 files changed, 151 insertions(+), 33 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 1676d28bb37..069378f3d63 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -1,4 +1,5 @@
 import itertools
+import re
 
 import pytest
 import numpy as np
@@ -10,6 +11,8 @@
 from qcodes import load_by_id, load_by_counter
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
+from qcodes.tests.dataset.test_database_creation_and_upgrading import \
+    error_caused_by
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 from qcodes.dataset.sqlite_base import _unicode_categories
 from qcodes.dataset.database import get_DB_location
@@ -22,6 +25,19 @@
 n_experiments = 0
 
 
+def make_shadow_dataset(dataset: DataSet):
+    """
+    Creates a new DataSet object that points to the same run_id in the same
+    database file as the given dataset object.
+
+    Note that in order to achieve it `path_to_db` because this will create a
+    new sqlite3 connection object behind the scenes. This is very useful for
+    situations where one needs to assert the underlying modifications to the
+    database file.
+    """
+    return DataSet(path_to_db=dataset.path_to_db, run_id=dataset.run_id)
+
+
 @pytest.mark.usefixtures("experiment")
 def test_has_attributes_after_init():
     """
@@ -161,7 +177,10 @@ def test_add_paramspec(dataset):
 
     # Now retrieve the paramspecs
 
-    paramspecs = dataset.paramspecs
+    shadow_ds = make_shadow_dataset(dataset)
+
+    paramspecs = shadow_ds.paramspecs
+
     expected_keys = ['a_param', 'b_param', 'c_param']
     keys = sorted(list(paramspecs.keys()))
     assert keys == expected_keys
@@ -171,6 +190,8 @@ def test_add_paramspec(dataset):
 
     assert paramspecs['c_param'].inferred_from == 'a_param, b_param'
 
+    assert paramspecs == dataset.paramspecs
+
 
 def test_add_paramspec_one_by_one(dataset):
     exps = experiments()
@@ -185,7 +206,11 @@ def test_add_paramspec_one_by_one(dataset):
                   ParamSpec("c", "array")]
     for parameter in parameters:
         dataset.add_parameter(parameter)
-    paramspecs = dataset.paramspecs
+
+    shadow_ds = make_shadow_dataset(dataset)
+
+    paramspecs = shadow_ds.paramspecs
+
     expected_keys = ['a', 'b', 'c']
     keys = sorted(list(paramspecs.keys()))
     assert keys == expected_keys
@@ -193,12 +218,15 @@ def test_add_paramspec_one_by_one(dataset):
         ps = paramspecs[expected_param_name]
         assert ps.name == expected_param_name
 
+    assert paramspecs == dataset.paramspecs
+
     # Test that is not possible to add the same parameter again to the dataset
     with pytest.raises(ValueError, match=f'Duplicate parameter name: '
                                          f'{parameters[0].name}'):
         dataset.add_parameter(parameters[0])
 
     assert len(dataset.paramspecs.keys()) == 3
+    assert len(shadow_ds.paramspecs.keys()) == 3
 
 
 @pytest.mark.usefixtures("experiment")
@@ -222,8 +250,13 @@ def test_add_data_1d():
         y = 3 * x + 10
         expected_y.append([y])
         mydataset.add_result({"x": x, "y": y})
+
+    shadow_ds = make_shadow_dataset(mydataset)
+
     assert mydataset.get_data('x') == expected_x
     assert mydataset.get_data('y') == expected_y
+    assert shadow_ds.get_data('x') == expected_x
+    assert shadow_ds.get_data('y') == expected_y
 
     with pytest.raises(ValueError):
         mydataset.add_result({'y': 500})
@@ -258,9 +291,16 @@ def test_add_data_array():
         y = np.random.random_sample(10)
         expected_y.append([y])
         mydataset.add_result({"x": x, "y": y})
+
+    shadow_ds = make_shadow_dataset(mydataset)
+
     assert mydataset.get_data('x') == expected_x
+    assert shadow_ds.get_data('x') == expected_x
+
     y_data = mydataset.get_data('y')
     np.testing.assert_allclose(y_data, expected_y)
+    y_data = shadow_ds.get_data('y')
+    np.testing.assert_allclose(y_data, expected_y)
 
 
 @pytest.mark.usefixtures("experiment")
@@ -338,54 +378,86 @@ def test_modify_result():
 
     dataset.add_result({'x': 0, 'y': 1, 'z': zdata})
 
-    assert dataset.get_data('x')[0][0] == xdata
-    assert dataset.get_data('y')[0][0] == ydata
-    assert (dataset.get_data('z')[0][0] == zdata).all()
+    shadow_ds = make_shadow_dataset(dataset)
 
-    with pytest.raises(ValueError):
-        dataset.modify_result(0, {' x': 1})
+    try:
+        assert dataset.get_data('x')[0][0] == xdata
+        assert dataset.get_data('y')[0][0] == ydata
+        assert (dataset.get_data('z')[0][0] == zdata).all()
 
-    xdata = 1
-    ydata = 12
-    zdata = np.linspace(0, 1, 99)
+        assert shadow_ds.get_data('x')[0][0] == xdata
+        assert shadow_ds.get_data('y')[0][0] == ydata
+        assert (shadow_ds.get_data('z')[0][0] == zdata).all()
 
-    dataset.modify_result(0, {'x': xdata})
-    assert dataset.get_data('x')[0][0] == xdata
+        with pytest.raises(ValueError):
+            dataset.modify_result(0, {' x': 1})
 
-    dataset.modify_result(0, {'y': ydata})
-    assert dataset.get_data('y')[0][0] == ydata
+        xdata = 1
+        ydata = 12
+        zdata = np.linspace(0, 1, 99)
 
-    dataset.modify_result(0, {'z': zdata})
-    assert (dataset.get_data('z')[0][0] == zdata).all()
+        dataset.modify_result(0, {'x': xdata})
+        assert dataset.get_data('x')[0][0] == xdata
+        assert shadow_ds.get_data('x')[0][0] == xdata
 
-    dataset.mark_complete()
+        dataset.modify_result(0, {'y': ydata})
+        assert dataset.get_data('y')[0][0] == ydata
+        assert shadow_ds.get_data('y')[0][0] == ydata
 
-    with pytest.raises(CompletedError):
-        dataset.modify_result(0, {'x': 2})
+        dataset.modify_result(0, {'z': zdata})
+        assert (dataset.get_data('z')[0][0] == zdata).all()
+        assert (shadow_ds.get_data('z')[0][0] == zdata).all()
 
+        dataset.mark_complete()
 
-@settings(max_examples=25, deadline=None)
-@given(N=hst.integers(min_value=1, max_value=10000),
-       M=hst.integers(min_value=1, max_value=10000))
-@pytest.mark.usefixtures("experiment")
-def test_add_parameter_values(N, M):
+        with pytest.raises(CompletedError):
+            dataset.modify_result(0, {'x': 2})
+
+    finally:
+        shadow_ds.conn.close()
 
-    mydataset = new_data_set("test_add_parameter_values")
+
+# @settings(max_examples=25, deadline=None)
+# @given(N=hst.integers(min_value=1, max_value=10000),
+#        M=hst.integers(min_value=1, max_value=10000))
+# def test_add_parameter_values(dataset, N, M):
+def test_add_parameter_values(dataset, N=2, M=3):
     xparam = ParamSpec('x', 'numeric')
-    mydataset.add_parameter(xparam)
+    dataset.add_parameter(xparam)
 
     x_results = [{'x': x} for x in range(N)]
-    mydataset.add_results(x_results)
+    dataset.add_results(x_results)
+
+    yparam = ParamSpec("y", "numeric")
 
     if N != M:
-        with pytest.raises(ValueError):
-            mydataset.add_parameter_values(ParamSpec("y", "numeric"),
-                                           [y for y in range(M)])
+        match_str = f'Need to have {N} values but got {M}.'
+        match_str = re.escape(match_str)
+        with pytest.raises(ValueError, match=match_str):
+            dataset.add_parameter_values(yparam, [y for y in range(M)])
 
-    mydataset.add_parameter_values(ParamSpec("y", "numeric"),
-                                   [y for y in range(N)])
+    yvals = [y for y in range(N)]
+    y_expected = [[None]] * N + [[y] for y in yvals]  # <-- should it have None?
+    dataset.add_parameter_values(yparam, yvals)
 
-    mydataset.mark_complete()
+    shadow_ds = make_shadow_dataset(dataset)
+
+    try:
+        assert y_expected == dataset.get_data(yparam)
+        assert y_expected == shadow_ds.get_data(yparam)
+
+        dataset.mark_complete()
+
+        # and now let's test that dataset's connection does not commit anymore
+        # when `atomic` is used
+        dataset.add_results([{yparam.name: -2}])
+        y_expected_2 = y_expected + [[-2]]
+
+        assert y_expected_2 == dataset.get_data(yparam)
+        assert y_expected_2 == shadow_ds.get_data(yparam)
+
+    finally:
+        shadow_ds.conn.close()
 
 
 @pytest.mark.usefixtures("dataset")
diff --git a/qcodes/tests/dataset/test_subscribing.py b/qcodes/tests/dataset/test_subscribing.py
index 42c8d478e93..8d862f7b332 100644
--- a/qcodes/tests/dataset/test_subscribing.py
+++ b/qcodes/tests/dataset/test_subscribing.py
@@ -13,6 +13,7 @@
 from qcodes.tests.dataset.temporary_databases import (
     empty_temp_db, experiment, dataset)
 # pylint: enable=unused-import
+from qcodes.tests.dataset.test_dataset_basic import make_shadow_dataset
 
 from qcodes.tests.test_config import default_config
 from qcodes.tests.common import retry_until_does_not_throw
@@ -196,3 +197,48 @@ def test_subscription_from_config_wrong_name(dataset):
         with pytest.raises(RuntimeError):
             sub_id_c = dataset.subscribe_from_config('test_subscriber')
 
+
+def test_unsubscribe_all_makes_atomic_connections_never_commit(
+        dataset, basic_subscriber):
+    xparam = ParamSpec(name='x', paramtype='numeric')
+    dataset.add_parameter(xparam)
+
+    sub_id = dataset.subscribe(basic_subscriber, min_wait=0, min_count=1,
+                               state={})
+
+    assert len(dataset.subscribers) == 1
+    assert list(dataset.subscribers.keys()) == [sub_id]
+
+    dataset.unsubscribe_all()
+
+    dataset.add_result({'x': 1})
+
+    shadow_ds = make_shadow_dataset(dataset)
+    try:
+        assert dataset.get_data('x') == [[1]]
+        assert shadow_ds.get_data('x') == [[1]]
+    finally:
+        shadow_ds.conn.close()
+
+
+def test_unsubscribe_makes_atomic_connections_never_commit(
+        dataset, basic_subscriber):
+    xparam = ParamSpec(name='x', paramtype='numeric')
+    dataset.add_parameter(xparam)
+
+    sub_id = dataset.subscribe(basic_subscriber, min_wait=0, min_count=1,
+                               state={})
+
+    assert len(dataset.subscribers) == 1
+    assert list(dataset.subscribers.keys()) == [sub_id]
+
+    dataset.unsubscribe(sub_id)
+
+    dataset.add_result({'x': 1})
+
+    shadow_ds = make_shadow_dataset(dataset)
+    try:
+        assert dataset.get_data('x') == [[1]]
+        assert shadow_ds.get_data('x') == [[1]]
+    finally:
+        shadow_ds.conn.close()

From dcdd8cf8d01b095dba71f1c992d57610a34c477f Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Mon, 19 Nov 2018 12:52:54 -0800
Subject: [PATCH 132/719] initial commit

---
 .../Qcodes example with Stahl.ipynb           | 110 ++++++++++++++++++
 1 file changed, 110 insertions(+)
 create mode 100644 docs/examples/driver_examples/Qcodes example with Stahl.ipynb

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
new file mode 100644
index 00000000000..8d079ed6513
--- /dev/null
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes import VisaInstrument "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#stahl = VisaInstrument(\"stahl\", \"COM3\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import visa\n",
+    "rm = visa.ResourceManager(visa_library=r\"C:\\Windows\\SysWOW64\\visa32.dll\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#rm.open_resource(\"COM3\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'Serial'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-10-de5902db3797>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mSerial\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'Serial'"
+     ]
+    }
+   ],
+   "source": [
+    "import Serial "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "()"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rm.list_resources()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 0de01219c057cae37bd8d19804f22993096a1438 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 20 Nov 2018 12:13:17 +0100
Subject: [PATCH 133/719] Deprecate DataSet.modify_result, to be removed soon

"it's a fundamentally flawed idea"
---
 qcodes/dataset/data_set.py                 |  2 ++
 qcodes/tests/dataset/test_dataset_basic.py | 11 ++++++-----
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 490cb3c6bd1..c63f165e3fb 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -576,6 +576,8 @@ def add_results(self, results: List[Dict[str, VALUE]]) -> int:
                            values)
         return len_before_add
 
+    @deprecate(reason='it is an experimental functionality, and is most '
+                      'probably will be removed soon.')
     def modify_result(self, index: int, results: Dict[str, VALUES]) -> None:
         """
         Modify a logically single result of existing parameters
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 069378f3d63..8a40e0b3171 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -390,28 +390,29 @@ def test_modify_result():
         assert (shadow_ds.get_data('z')[0][0] == zdata).all()
 
         with pytest.raises(ValueError):
-            dataset.modify_result(0, {' x': 1})
+            pytest.deprecated_call(
+                dataset.modify_result, 0, {' x': 1})
 
         xdata = 1
         ydata = 12
         zdata = np.linspace(0, 1, 99)
 
-        dataset.modify_result(0, {'x': xdata})
+        pytest.deprecated_call(dataset.modify_result, 0, {'x': xdata})
         assert dataset.get_data('x')[0][0] == xdata
         assert shadow_ds.get_data('x')[0][0] == xdata
 
-        dataset.modify_result(0, {'y': ydata})
+        pytest.deprecated_call(dataset.modify_result, 0, {'y': ydata})
         assert dataset.get_data('y')[0][0] == ydata
         assert shadow_ds.get_data('y')[0][0] == ydata
 
-        dataset.modify_result(0, {'z': zdata})
+        pytest.deprecated_call(dataset.modify_result, 0, {'z': zdata})
         assert (dataset.get_data('z')[0][0] == zdata).all()
         assert (shadow_ds.get_data('z')[0][0] == zdata).all()
 
         dataset.mark_complete()
 
         with pytest.raises(CompletedError):
-            dataset.modify_result(0, {'x': 2})
+            pytest.deprecated_call(dataset.modify_result, 0, {'x': 2})
 
     finally:
         shadow_ds.conn.close()

From 7cf0d9969e1f28cb5b011410353934b205b2caf6 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 31 Oct 2018 22:05:03 +0100
Subject: [PATCH 134/719] min and max is allowed to be None

---
 qcodes/utils/validators.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/utils/validators.py b/qcodes/utils/validators.py
index 15bd4d658b1..e7ff3d827fc 100644
--- a/qcodes/utils/validators.py
+++ b/qcodes/utils/validators.py
@@ -31,7 +31,8 @@ def validate_all(*args, context: str='') -> None:
         validator.validate(value, 'argument ' + str(i) + context)
 
 
-def range_str(min_val: Union[float, int], max_val: Union[float, int],
+def range_str(min_val: Optional[Union[float, int]],
+              max_val: Optional[Union[float, int]],
               name: str) -> str:
     """
     utility to represent ranges in Validator repr's

From 61bbfc599ad715a12809176760751fdd72734cde Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 31 Oct 2018 22:19:19 +0100
Subject: [PATCH 135/719] Config fix mypy strict issues

---
 qcodes/config/config.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/qcodes/config/config.py b/qcodes/config/config.py
index 1b1de22b53b..fcdda742f2a 100644
--- a/qcodes/config/config.py
+++ b/qcodes/config/config.py
@@ -9,7 +9,7 @@
 from pathlib import Path
 
 import jsonschema
-from typing import Dict
+from typing import Dict, Tuple
 
 logger = logging.getLogger(__name__)
 
@@ -102,7 +102,7 @@ def __init__(self, path: str=None) -> None:
         self.defaults, self.defaults_schema = self.load_default()
         self.update_config()
 
-    def load_default(self):
+    def load_default(self) -> Tuple[dict, dict]:
         defaults = self.load_config(self.default_file_name)
         defaults_schema = self.load_config(self.schema_default_file_name)
         self.validate(defaults, defaults_schema)
@@ -156,7 +156,9 @@ def update_config(self, path: str=None) -> dict:
             schema_file = os.path.join(self.config_file_path,
                                        self.schema_file_name)
             self._update_config_from_file(config_file, schema_file, config)
-
+        if config is None:
+            raise RuntimeError("Could not load config from any of the "
+                               "expected locations.")
         self.current_config = config
         self.current_config_path = self._loaded_config_files[-1]
 

From 44b7d0135192f0b5bf7d532128c40490a579bde4 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 31 Oct 2018 22:19:45 +0100
Subject: [PATCH 136/719] Ensure that lhandler is not None

---
 qcodes/logger/logger.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/qcodes/logger/logger.py b/qcodes/logger/logger.py
index 809a09a294d..f6d5315c083 100644
--- a/qcodes/logger/logger.py
+++ b/qcodes/logger/logger.py
@@ -240,6 +240,8 @@ def console_level(level: LevelType):
         level: level to set the console handler to
     """
     global console_handler
+    if console_handler is None:
+        raise RuntimeError("Console handler is None")
     with handler_level(level, handler=console_handler):
         yield
 

From 58aeb63619e1d1e84d357b08fc3c0f337845b418 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 31 Oct 2018 22:20:07 +0100
Subject: [PATCH 137/719] This cannot be optional

---
 qcodes/utils/log_analysis.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/utils/log_analysis.py b/qcodes/utils/log_analysis.py
index dfa9a7ef920..e8c809c710b 100644
--- a/qcodes/utils/log_analysis.py
+++ b/qcodes/utils/log_analysis.py
@@ -10,7 +10,7 @@
 
 @deprecate(reason="The logging infrastructure has moved to `qcodes.utils.logger`",
            alternative="`qcodes.utils.logger.logfile_to_dataframe`")
-def logfile_to_dataframe(logfile: Optional[str]=None,
+def logfile_to_dataframe(logfile: str,
                          columns: Optional[List[str]]=None,
                          separator: Optional[str]=None) -> pd.DataFrame:
 

From 707cfc7d57f95eea7c66e8686ed4cc1dbb398e27 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 1 Nov 2018 08:33:30 +0100
Subject: [PATCH 138/719] Improve optional typing

---
 qcodes/dataset/measurements.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index 4e8e47baefb..f200dca02ba 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -533,7 +533,7 @@ def __init__(self, exp: Optional[Experiment] = None,
         self.name = ''
 
     @property
-    def write_period(self) -> float:
+    def write_period(self) -> Optional[float]:
         return self._write_period
 
     @write_period.setter
@@ -650,10 +650,10 @@ def register_parameter(
         return self
 
     def _register_parameter(self : T, name: str,
-                            label: str,
-                            unit: str,
-                            setpoints: setpoints_type,
-                            basis: setpoints_type,
+                            label: Optional[str],
+                            unit: Optional[str],
+                            setpoints: Optional[setpoints_type],
+                            basis: Optional[setpoints_type],
                             paramtype: str) -> T:
         """
         Generate ParamSpecs and register them for an individual parameter
@@ -688,8 +688,8 @@ def _register_parameter(self : T, name: str,
 
     def _register_arrayparameter(self,
                                  parameter: ArrayParameter,
-                                 setpoints: setpoints_type,
-                                 basis: setpoints_type,
+                                 setpoints: Optional[setpoints_type],
+                                 basis: Optional[setpoints_type],
                                  paramtype: str, ) -> None:
         """
         Register an Array paramter and the setpoints belonging to the
@@ -728,8 +728,8 @@ def _register_arrayparameter(self,
 
     def _register_multiparameter(self,
                                  multiparameter: MultiParameter,
-                                 setpoints: setpoints_type,
-                                 basis: setpoints_type,
+                                 setpoints: Optional[setpoints_type],
+                                 basis: Optional[setpoints_type],
                                  paramtype: str) -> None:
         """
         Find the individual multiparameter components and their setpoints

From 2f065067f29652a420020c1fe1c92f21c30daad7 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 1 Nov 2018 08:38:35 +0100
Subject: [PATCH 139/719] Clarify types

---
 qcodes/utils/plotting.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/utils/plotting.py b/qcodes/utils/plotting.py
index 20a9b53047a..292fbf91b0c 100644
--- a/qcodes/utils/plotting.py
+++ b/qcodes/utils/plotting.py
@@ -146,7 +146,7 @@ def apply_color_scale_limits(colorbar: matplotlib.pyplot.colorbar,
             raise RuntimeError('You may not specify `data_lim` and `data_array` '
                                'at the same time. Please refer to the docstring of '
                                '`apply_color_scale_limits for details:\n\n`'
-                               + apply_color_scale_limits.__doc__)
+                               + str(apply_color_scale_limits.__doc__))
         else:
             data_lim = cast(Tuple[float, float], tuple(sorted(data_lim)))
     # if `None` is provided in the new limits don't change this limit
@@ -173,8 +173,8 @@ def apply_color_scale_limits(colorbar: matplotlib.pyplot.colorbar,
 
 def apply_auto_color_scale(colorbar: matplotlib.pyplot.colorbar,
                            data_array: Optional[np.ndarray]=None,
-                           cutoff_percentile: Optional[Union[Tuple[
-                               Number, Number], Number]]=DEFAULT_PERCENTILE,
+                           cutoff_percentile: Union[Tuple[
+                               Number, Number], Number]=DEFAULT_PERCENTILE,
                            color_over: Optional[Any]=DEFAULT_COLOR_OVER,
                            color_under: Optional[Any]=DEFAULT_COLOR_UNDER
                            ) -> None:

From cbefa2cbb4098644638a47057edeb46b43562b51 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 1 Nov 2018 08:45:41 +0100
Subject: [PATCH 140/719] Strict optional for ATS

---
 qcodes/instrument_drivers/AlazarTech/ATS.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index 9078d7d9d20..efc04727315 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -250,7 +250,6 @@ def get_board_info(cls, dll: ctypes.CDLL, system_id: int,
     def __init__(self, name: str, system_id: int=1, board_id: int=1,
                  dll_path: str=None, **kwargs) -> None:
         super().__init__(name, **kwargs)
-        self._ATS_dll = None
 
         if os.name == 'nt':
             self._ATS_dll = ctypes.cdll.LoadLibrary(dll_path or self.dll_path)
@@ -886,7 +885,10 @@ def _call_dll(self, func_name: str, *args) -> None:
         update_params: List[Parameter] = []
         for arg in args:
             if isinstance(arg, Parameter):
-                args_out.append(arg.raw_value)
+                if arg.raw_value is not None:
+                    args_out.append(arg.raw_value)
+                else:
+                    raise RuntimeError(f"{arg} has value None")
                 update_params.append(arg)
             else:
                 args_out.append(arg)

From 3aebba0580189b74a3915293b4bae7b957a1bdf2 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 1 Nov 2018 14:07:56 +0100
Subject: [PATCH 141/719] Match baseclass signature

---
 qcodes/instrument_drivers/tektronix/Keithley_2600_channels.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/Keithley_2600_channels.py b/qcodes/instrument_drivers/tektronix/Keithley_2600_channels.py
index a1784036ad0..0ea28b4fd89 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_2600_channels.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_2600_channels.py
@@ -1,7 +1,7 @@
 import logging
 import struct
 import numpy as np
-from typing import List, Dict
+from typing import List, Dict, Optional
 
 import qcodes as qc
 from qcodes import VisaInstrument, DataSet
@@ -430,7 +430,7 @@ def __init__(self, name: str, address: str, **kwargs) -> None:
     def _display_settext(self, text: str) -> None:
         self.visa_handle.write('display.settext("{}")'.format(text))
 
-    def get_idn(self) -> Dict[str, str]:
+    def get_idn(self) -> Dict[str, Optional[str]]:
         IDN = self.ask_raw('*IDN?')
         vendor, model, serial, firmware = map(str.strip, IDN.split(','))
         model = model[6:]

From e7284144ced22d28dcf22b13b139839b26de9168 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 1 Nov 2018 14:08:18 +0100
Subject: [PATCH 142/719] g could be none

---
 qcodes/tests/drivers/test_ami430.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/qcodes/tests/drivers/test_ami430.py b/qcodes/tests/drivers/test_ami430.py
index 9d7cff55038..65485cc14c2 100644
--- a/qcodes/tests/drivers/test_ami430.py
+++ b/qcodes/tests/drivers/test_ami430.py
@@ -253,6 +253,8 @@ def get_ramp_down_order(messages: List[str]) -> List[str]:
             continue
 
         g = re.search(r"\[(.*).*\] Writing: CONF:FIELD:TARG", msg)
+        if g is None:
+            raise RuntimeError("No match is found")
         name = g.groups()[0]
         order.append(name)
 

From b715bdf55cb03ec523e4e3487e398df2ace4cf90 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 1 Nov 2018 15:09:36 +0100
Subject: [PATCH 143/719] Signal hound strict optional

---
 .../instrument_drivers/signal_hound/USB_SA124B.py  | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index 01f24221d51..23b1be9823e 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -140,12 +140,15 @@ def set_sweep(self, sweep_len: int, start_freq: number,
         self.instrument._trace_updated = True
 
     def get_raw(self) -> np.ndarray:
+        if self.instrument is None:
+            raise RuntimeError("No instrument is attached to"
+                               "'FrequencySweep'")
         if not isinstance(self.instrument, SignalHound_USB_SA124B):
             raise RuntimeError("'FrequencySweep' is only implemented"
                                "for 'SignalHound_USB_SA124B'")
         if not self.instrument._trace_updated:
             raise RuntimeError('trace not updated, run configure to update')
-        data = self._instrument._get_averaged_sweep_data()
+        data = self.instrument._get_averaged_sweep_data()
         sleep(2*self.instrument.sleep_time.get())
         return data
 
@@ -409,6 +412,7 @@ def sync_parameters(self) -> None:
         # the third argument to saInitiate is a flag that is
         # currently not used
         err = self.dll.saInitiate(self.deviceHandle, mode, 0)
+        extrainfo: Optional[str] = None
         if err == saStatus.saInvalidParameterErr:
             extrainfo = """
                  In real-time mode, this value may be returned if the span
@@ -425,8 +429,6 @@ def sync_parameters(self) -> None:
              """
         elif err == saStatus.saBandwidthErr:
             extrainfo = 'RBW is larger than your span. (Sweep Mode)!'
-        else:
-            extrainfo = None
         self.check_for_error(err, 'saInitiate', extrainfo)
 
         self._parameters_synced = True
@@ -478,11 +480,11 @@ def abort(self) -> None:
         log.info('Stopping acquisition')
 
         err = self.dll.saAbort(self.deviceHandle)
+        extrainfo: Optional[str] = None
         if err == saStatus.saDeviceNotConfiguredErr:
             extrainfo = 'Device was already idle! Did you call abort ' \
                         'without ever calling initiate()'
-        else:
-            extrainfo = None
+
         self.check_for_error(err, 'saAbort', extrainfo)
 
     def preset(self) -> None:
@@ -640,7 +642,7 @@ def check_for_error(err: int, source: str, extrainfo: str=None) -> None:
             log.info(msg)
 
     def get_idn(self) -> Dict[str, Optional[str]]:
-        output = {}
+        output: Dict[str, Optional[str]] = {}
         output['vendor'] = 'Signal Hound'
         output['model'] = self._get_device_type()
         serialnumber = ct.c_int32()

From 46329ecbd01485863c2d281889738606b69faa64 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 1 Nov 2018 15:19:16 +0100
Subject: [PATCH 144/719] Mercury strict typing

---
 qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py b/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py
index 0ad60dac98e..811d3fa6057 100644
--- a/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py
+++ b/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py
@@ -213,7 +213,9 @@ class MercuryiPS(VisaInstrument):
     """
 
     def __init__(self, name: str, address: str, visalib=None,
-                 field_limits: Optional[Callable]=None,
+                 field_limits: Optional[Callable[[float,
+                                                  float,
+                                                  float], bool]]=None,
                  **kwargs) -> None:
         """
         Args:
@@ -306,8 +308,8 @@ def _set_target(self, coordinate: str, target: float) -> None:
         valid_vec = FieldVector()
         valid_vec.copy(self._target_vector)
         valid_vec.set_component(**{coordinate: target})
-
-        if not self._field_limits(*valid_vec.get_components('x', 'y', 'z')):
+        components = valid_vec.get_components('x', 'y', 'z')
+        if not self._field_limits(*components):
             raise ValueError(f'Cannot set {coordinate} target to {target}, '
                              'that would violate the field_limits. ')
 

From 518f01c4eef18a3dc4d872c3bd9d0d0ada7347ec Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 1 Nov 2018 15:29:30 +0100
Subject: [PATCH 145/719] Correct strict typing in dataset

---
 qcodes/dataset/data_set.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 7f93402a935..802ceeeae8a 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -372,7 +372,7 @@ def completed_timestamp_raw(self) -> Union[float, None]:
         return get_completed_timestamp_from_run_id(self.conn, self.run_id)
 
     def completed_timestamp(self,
-                            fmt: str="%Y-%m-%d %H:%M:%S") -> Union[str, None]:
+                            fmt: str="%Y-%m-%d %H:%M:%S") -> Optional[str]:
         """
         Returns timestamp when measurement run was completed
         in a human-readable format
@@ -384,7 +384,7 @@ def completed_timestamp(self,
         completed_timestamp_raw = self.completed_timestamp_raw
 
         if completed_timestamp_raw:
-            completed_timestamp = time.strftime(
+            completed_timestamp: Optional[str] = time.strftime(
                 fmt, time.localtime(completed_timestamp_raw))
         else:
             completed_timestamp = None

From 6c511fc6a37157046d864ff605f5ea7efbfab7ba Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 1 Nov 2018 15:42:35 +0100
Subject: [PATCH 146/719] use python 3.6 syntax and add a needed cast for
 strict optional

---
 qcodes/instrument/base.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/qcodes/instrument/base.py b/qcodes/instrument/base.py
index cce4e5f1d13..fc6912bef9c 100644
--- a/qcodes/instrument/base.py
+++ b/qcodes/instrument/base.py
@@ -120,7 +120,9 @@ def add_function(self, name: str, **kwargs) -> None:
         func = Function(name=name, instrument=self, **kwargs)
         self.functions[name] = func
 
-    def add_submodule(self, name: str, submodule:  Union['InstrumentBase', 'ChannelList']) -> None:
+    def add_submodule(self, name: str,
+                      submodule:  Union['InstrumentBase',
+                                        'ChannelList']) -> None:
         """
         Bind one submodule to this instrument.
 
@@ -401,9 +403,9 @@ class Instrument(InstrumentBase):
 
     shared_kwargs = ()
 
-    _all_instruments = {} # type: Dict[str, weakref.ref[Instrument]]
+    _all_instruments: Dict[str, weakref.ref] = {}
     _type = None
-    _instances = [] # type: List[weakref.ref]
+    _instances: List[weakref.ref] = []
 
     def __init__(self, name: str,
                  metadata: Optional[Dict]=None, **kwargs) -> None:
@@ -440,7 +442,7 @@ def get_idn(self) -> Dict[str, Optional[str]]:
             idstr = self.ask('*IDN?')
             # form is supposed to be comma-separated, but we've seen
             # other separators occasionally
-            idparts = [] # type: List[Optional[str]]
+            idparts: List[Optional[str]] = []
             for separator in ',;:':
                 # split into no more than 4 parts, so we don't lose info
                 idparts = [p.strip() for p in idstr.split(separator, 3)]
@@ -628,7 +630,7 @@ def find_instrument(cls, name: str,
                     'Instrument {} is {} but {} was requested'.format(
                         name, type(ins), instrument_class))
 
-        return ins
+        return cast('Instrument', ins)
 
     @staticmethod
     def exist(name: str, instrument_class: Optional[type]=None) -> bool:

From d69eccf38d2657493fa80ba689ad7c76e6a2dd30 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 1 Nov 2018 15:43:15 +0100
Subject: [PATCH 147/719] Experiment container strict optional

---
 qcodes/dataset/experiment_container.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/experiment_container.py b/qcodes/dataset/experiment_container.py
index 36ccdc69dcd..4c1ce52f751 100644
--- a/qcodes/dataset/experiment_container.py
+++ b/qcodes/dataset/experiment_container.py
@@ -186,7 +186,7 @@ def experiments()->List[Experiment]:
 
 def new_experiment(name: str,
                    sample_name: str,
-                   format_string: Optional[str] = "{}-{}-{}") -> Experiment:
+                   format_string: str = "{}-{}-{}") -> Experiment:
     """
     Create a new experiment (in the database file from config)
 
@@ -306,7 +306,7 @@ def load_or_create_experiment(experiment_name: str,
     try:
         experiment = load_experiment_by_name(experiment_name, sample_name)
     except ValueError as exception:
-        if "Experiment not found" in str(exception):
+        if "Experiment not found" in str(exception) and sample_name is not None:
             experiment = new_experiment(experiment_name, sample_name)
         else:
             raise exception

From 41f82f986f9203253251fb14385d2883c0b8447e Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 2 Nov 2018 16:05:10 +0100
Subject: [PATCH 148/719] Add abstract method helper

---
 qcodes/utils/helpers.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/qcodes/utils/helpers.py b/qcodes/utils/helpers.py
index 53757b65288..9980d4d94e4 100644
--- a/qcodes/utils/helpers.py
+++ b/qcodes/utils/helpers.py
@@ -654,3 +654,16 @@ def inner(**inner_kwargs):
     inner.__doc__ = docstring
 
     return inner
+
+
+def abstractmethod(funcobj):
+    """A decorator indicating abstract methods.
+
+    This is heavily inspired by the decorator of the same name in
+    the ABC standard library. But we make our own version because
+    we actually want to allow the class with the abstract method to be
+    instantiated and we will use this property to detect if the
+    method is abstract and should be overwritten.
+    """
+    funcobj.__qcodesisabstractmethod__ = True
+    return funcobj

From ab06ee9fc034c47fc14fc933ae3baa812c949be1 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 2 Nov 2018 16:06:45 +0100
Subject: [PATCH 149/719] Make get_raw/set_raw abstract methods so that typing
 is consistent with subclasses

---
 qcodes/instrument/parameter.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 1de617b086f..94b603eae5f 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -65,7 +65,7 @@
     Dict, Any, Sized, Iterable, cast, Type
 from functools import partial, wraps
 import numpy
-
+from qcodes.utils.helpers import abstractmethod
 
 from qcodes.utils.helpers import (permissive_range, is_sequence_of,
                                   DelegateAttributes, full_class, named_repr,
@@ -182,8 +182,6 @@ class _BaseParameter(Metadatable):
         metadata (Optional[dict]): extra information to include with the
             JSON snapshot of the parameter
     """
-    get_raw = None  # type: Optional[Callable]
-    set_raw = None  # type: Optional[Callable]
 
     def __init__(self, name: str,
                  instrument: Optional['Instrument'],
@@ -242,14 +240,14 @@ def __init__(self, name: str,
         self._latest = {'value': None, 'ts': None, 'raw_value': None}
         self.get_latest = GetLatest(self, max_val_age=max_val_age)
 
-        if hasattr(self, 'get_raw') and self.get_raw is not None:
+        if hasattr(self, 'get_raw') and not getattr(self.get_raw, '__qcodesisabstractmethod__', False):
             self.get = self._wrap_get(self.get_raw)
         elif hasattr(self, 'get'):
             warnings.warn('Wrapping get method, original get method will not '
                           'be directly accessible. It is recommended to '
                           'define get_raw in your subclass instead.' )
             self.get = self._wrap_get(self.get)
-        if hasattr(self, 'set_raw') and self.set_raw is not None:
+        if hasattr(self, 'set_raw') and not getattr(self.set_raw, '__qcodesisabstractmethod__', False):
             self.set = self._wrap_set(self.set_raw)
         elif hasattr(self, 'set'):
             warnings.warn('Wrapping set method, original set method will not '
@@ -266,6 +264,14 @@ def __init__(self, name: str,
         # check if additional waiting time is needed before next set
         self._t_last_set = time.perf_counter()
 
+    @abstractmethod
+    def get_raw(self):
+        raise NotImplementedError
+
+    @abstractmethod
+    def set_raw(self, value):
+        raise NotImplementedError
+
     def __str__(self):
         """Include the instrument name with the Parameter name if possible."""
         inst_name = getattr(self._instrument, 'name', '')

From 36b309cbf630adf457ee5d8717e396a3473a1db3 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 2 Nov 2018 16:07:22 +0100
Subject: [PATCH 150/719] add a few none checks to make mypy happy in strict
 optional

---
 qcodes/instrument_drivers/Keysight/N52xx.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/N52xx.py b/qcodes/instrument_drivers/Keysight/N52xx.py
index af52202372d..d53db1e3744 100644
--- a/qcodes/instrument_drivers/Keysight/N52xx.py
+++ b/qcodes/instrument_drivers/Keysight/N52xx.py
@@ -34,6 +34,9 @@ def shape(self, val: Sequence[int]) -> None:
 
     @property # type: ignore
     def setpoints(self) -> Sequence: # type: ignore
+        if self._instrument is None:
+            raise RuntimeError("Cannot return setpoints if not attached "
+                               "to instrument")
         start = self._instrument.root_instrument.start()
         stop = self._instrument.root_instrument.stop()
         return (np.linspace(start, stop, self.shape[0]),)
@@ -65,6 +68,8 @@ def __init__(self,
         self.memory = memory
 
     def get_raw(self) -> Sequence[float]:
+        if self._instrument is None:
+            raise RuntimeError("Cannot get data without instrument")
         root_instr = self._instrument.root_instrument
         # Check if we should run a new sweep
         if root_instr.auto_sweep():

From 3547602727494738d0d8b83cd364521eb1037728 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 2 Nov 2018 16:18:27 +0100
Subject: [PATCH 151/719] Make mypy strict happy in logger

---
 qcodes/logger/instrument_logger.py | 21 ++++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/qcodes/logger/instrument_logger.py b/qcodes/logger/instrument_logger.py
index 059aaffd8db..b3fe82beca8 100644
--- a/qcodes/logger/instrument_logger.py
+++ b/qcodes/logger/instrument_logger.py
@@ -1,6 +1,6 @@
 from contextlib import contextmanager
 import logging
-from typing import Optional, Sequence, Union, TYPE_CHECKING
+from typing import Optional, Sequence, Union, TYPE_CHECKING, cast
 import collections.abc
 from .logger import get_console_handler, LevelType, handler_level
 if TYPE_CHECKING:
@@ -103,20 +103,27 @@ def filter_instrument(instrument: Union['InstrumentBase',
         level: level to set the handlers to
         handler: single or sequence of handlers which to change
     """
+    handler_int: Sequence[logging.Handler]
     if handler is None:
-        handler = (get_console_handler(),)
-    if not isinstance(handler, collections.abc.Sequence):
-        handler = (handler,)
+        myhandler = get_console_handler()
+        if myhandler is None:
+            raise RuntimeError("Trying to filter instrument but no handler "
+                               "defined. Did you call `start_logger`")
+        handler_int = (myhandler,)
+    elif not isinstance(handler, collections.abc.Sequence):
+        handler_int = (handler,)
+    else:
+        handler_int = handler
 
     instrument_filter = InstrumentFilter(instrument)
-    for h in handler:
+    for h in handler_int:
         h.addFilter(instrument_filter)
     try:
         if level is not None:
-            with handler_level(level, handler):
+            with handler_level(level, handler_int):
                 yield
         else:
             yield
     finally:
-        for h in handler:
+        for h in handler_int:
             h.removeFilter(instrument_filter)

From f595ffde11311736de44f0be9f579869ed6aeace Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 2 Nov 2018 16:29:28 +0100
Subject: [PATCH 152/719] Add some checks for none to group parameter

---
 qcodes/instrument/group_parameter.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument/group_parameter.py b/qcodes/instrument/group_parameter.py
index 8d7b5e4baac..31d67bfca54 100644
--- a/qcodes/instrument/group_parameter.py
+++ b/qcodes/instrument/group_parameter.py
@@ -54,10 +54,12 @@ def __init__(self,
         self.get = self._wrap_get(self.get_raw)
 
     def _get_raw_value(self) -> Any:
+        if self.group is None:
+            raise RuntimeError("Trying to get Group value but no "
+                               "group defined")
         self.group.update()
         return self.raw_value
 
-
 class Group:
     """
     The group combines `GroupParameter`s that are to be gotten or set via the
@@ -190,7 +192,12 @@ def set(self, set_parameter: GroupParameter, value: Any):
         calling_dict = {name: p.raw_value
                         for name, p in self.parameters.items()}
         calling_dict[set_parameter.name] = value
+        if self.set_cmd is None:
+            raise RuntimeError("Calling set but no `set_cmd` defined")
         command_str = self.set_cmd.format(**calling_dict)
+        if self.instrument is None:
+            raise RuntimeError("Trying to set GroupParameter not attached "
+                               "to any instrument.")
         self.instrument.write(command_str)
 
     def update(self):

From 39c8c1cd7a2271a3258c8fab0629a954f32abe0c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 2 Nov 2018 16:36:10 +0100
Subject: [PATCH 153/719] Make set_raw check if the group is None to make mypy
 happy

---
 qcodes/instrument/group_parameter.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument/group_parameter.py b/qcodes/instrument/group_parameter.py
index 31d67bfca54..907a3cdbfbb 100644
--- a/qcodes/instrument/group_parameter.py
+++ b/qcodes/instrument/group_parameter.py
@@ -45,7 +45,6 @@ def __init__(self,
         self.group: Union[Group, None] = None
         super().__init__(name, instrument=instrument, **kwargs)
 
-        self.set_raw = lambda value: self.group.set(self, value)
         self.set = self._wrap_set(self.set_raw)
 
         self.get_raw = lambda result=None: result if result is not None \
@@ -60,6 +59,13 @@ def _get_raw_value(self) -> Any:
         self.group.update()
         return self.raw_value
 
+    def set_raw(self, value: Any) -> None:
+        if self.group is None:
+            raise RuntimeError("Trying to get Group value but no "
+                               "group defined")
+        self.group.set(self, value)
+
+
 class Group:
     """
     The group combines `GroupParameter`s that are to be gotten or set via the

From 47ef0f7b85a0c07af0647cefd8acef2523532e00 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 2 Nov 2018 16:36:35 +0100
Subject: [PATCH 154/719] Check that setpoints are not None before using them

---
 qcodes/dataset/measurements.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index f200dca02ba..82887a5c91a 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -283,6 +283,9 @@ def _unbundle_arrayparameter(self,
         """
         sp_names = parameter.setpoint_full_names
         fallback_sp_name = f"{parameter.full_name}_setpoint"
+        if parameter.setpoints is None:
+            raise RuntimeError(f"{parameter.full_name} is an {type(parameter)} "
+                               f"without setpoints. Cannot handle this.")
         self._unbundle_setpoints_from_param(parameter, sp_names,
                                             fallback_sp_name,
                                             parameter.setpoints,
@@ -358,6 +361,9 @@ def _unbundle_multiparameter(self,
             found_parameters: The list of all parameters that we know of by now
               This is modified in place with new parameters found here.
         """
+        if parameter.setpoints is None:
+            raise RuntimeError(f"{parameter.full_name} is an {type(parameter)} "
+                               f"without setpoints. Cannot handle this.")
         for i in range(len(parameter.shapes)):
             shape = parameter.shapes[i]
             res.append((parameter.names[i], data[i]))

From f809449c0c80de5f647110db18aac9546d5f0828 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 5 Nov 2018 13:59:57 +0100
Subject: [PATCH 155/719] Should now be passing with strict optional

---
 mypy.ini | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mypy.ini b/mypy.ini
index 2b38716299c..ef6027adb92 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -1,5 +1,5 @@
 [mypy]
-strict_optional = False
+strict_optional = True
 disallow_untyped_decorators = True
 ignore_missing_imports = True
 

From 3e8f06470b504bd8a4fc275a8fd430b69cb5aa95 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 20 Nov 2018 12:41:48 +0100
Subject: [PATCH 156/719] Update qcodes/tests/drivers/test_ami430.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/tests/drivers/test_ami430.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/tests/drivers/test_ami430.py b/qcodes/tests/drivers/test_ami430.py
index 65485cc14c2..dde781af72e 100644
--- a/qcodes/tests/drivers/test_ami430.py
+++ b/qcodes/tests/drivers/test_ami430.py
@@ -254,7 +254,7 @@ def get_ramp_down_order(messages: List[str]) -> List[str]:
 
         g = re.search(r"\[(.*).*\] Writing: CONF:FIELD:TARG", msg)
         if g is None:
-            raise RuntimeError("No match is found")
+            raise RuntimeError(f"No match found in {msg!r} when getting ramp down order")
         name = g.groups()[0]
         order.append(name)
 

From b5ec215cce9c7d8475e8967267bc0112545dc2f6 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 20 Nov 2018 12:41:57 +0100
Subject: [PATCH 157/719] Update qcodes/utils/helpers.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/utils/helpers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/utils/helpers.py b/qcodes/utils/helpers.py
index 9980d4d94e4..40aa3502df6 100644
--- a/qcodes/utils/helpers.py
+++ b/qcodes/utils/helpers.py
@@ -665,5 +665,5 @@ def abstractmethod(funcobj):
     instantiated and we will use this property to detect if the
     method is abstract and should be overwritten.
     """
-    funcobj.__qcodesisabstractmethod__ = True
+    funcobj.__qcodes_is_abstract_method__ = True
     return funcobj

From 9f7d02543f0e088fb3c54d6786f9db4458d83eb9 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 20 Nov 2018 12:49:05 +0100
Subject: [PATCH 158/719] Update qcodes/logger/instrument_logger.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/logger/instrument_logger.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/logger/instrument_logger.py b/qcodes/logger/instrument_logger.py
index b3fe82beca8..fffddc4300f 100644
--- a/qcodes/logger/instrument_logger.py
+++ b/qcodes/logger/instrument_logger.py
@@ -108,7 +108,7 @@ def filter_instrument(instrument: Union['InstrumentBase',
         myhandler = get_console_handler()
         if myhandler is None:
             raise RuntimeError("Trying to filter instrument but no handler "
-                               "defined. Did you call `start_logger`")
+                               "defined. Did you forget to call `start_logger` before?")
         handler_int = (myhandler,)
     elif not isinstance(handler, collections.abc.Sequence):
         handler_int = (handler,)

From fa89d2ef39c44d05d3af36ba873208b416a0ef48 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 20 Nov 2018 12:45:16 +0100
Subject: [PATCH 159/719] change name here too

---
 qcodes/instrument/parameter.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 94b603eae5f..5b3a7b76959 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -240,19 +240,19 @@ def __init__(self, name: str,
         self._latest = {'value': None, 'ts': None, 'raw_value': None}
         self.get_latest = GetLatest(self, max_val_age=max_val_age)
 
-        if hasattr(self, 'get_raw') and not getattr(self.get_raw, '__qcodesisabstractmethod__', False):
+        if hasattr(self, 'get_raw') and not getattr(self.get_raw, '__qcodes_is_abstract_method__', False):
             self.get = self._wrap_get(self.get_raw)
         elif hasattr(self, 'get'):
             warnings.warn('Wrapping get method, original get method will not '
                           'be directly accessible. It is recommended to '
-                          'define get_raw in your subclass instead.' )
+                          'define get_raw in your subclass instead.')
             self.get = self._wrap_get(self.get)
-        if hasattr(self, 'set_raw') and not getattr(self.set_raw, '__qcodesisabstractmethod__', False):
+        if hasattr(self, 'set_raw') and not getattr(self.set_raw, '__qcodes_is_abstract_method__', False):
             self.set = self._wrap_set(self.set_raw)
         elif hasattr(self, 'set'):
             warnings.warn('Wrapping set method, original set method will not '
                           'be directly accessible. It is recommended to '
-                          'define set_raw in your subclass instead.' )
+                          'define set_raw in your subclass instead.')
             self.set = self._wrap_set(self.set)
 
         # subclasses should extend this list with extra attributes they

From 39edc4ae1455bde0e5a098cb8f8c85e98e1694f3 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 20 Nov 2018 12:47:57 +0100
Subject: [PATCH 160/719] allow sample_name to be None

---
 qcodes/dataset/experiment_container.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/experiment_container.py b/qcodes/dataset/experiment_container.py
index 4c1ce52f751..1d2100f6257 100644
--- a/qcodes/dataset/experiment_container.py
+++ b/qcodes/dataset/experiment_container.py
@@ -185,7 +185,7 @@ def experiments()->List[Experiment]:
 
 
 def new_experiment(name: str,
-                   sample_name: str,
+                   sample_name: Optional[str],
                    format_string: str = "{}-{}-{}") -> Experiment:
     """
     Create a new experiment (in the database file from config)
@@ -306,7 +306,7 @@ def load_or_create_experiment(experiment_name: str,
     try:
         experiment = load_experiment_by_name(experiment_name, sample_name)
     except ValueError as exception:
-        if "Experiment not found" in str(exception) and sample_name is not None:
+        if "Experiment not found" in str(exception):
             experiment = new_experiment(experiment_name, sample_name)
         else:
             raise exception

From a80319f53a7ad22ca0e87b9b916184b5426d3ebd Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 20 Nov 2018 12:57:19 +0100
Subject: [PATCH 161/719] Handle the rare case that one of the snapshots is
 empty

---
 qcodes/utils/metadata.py | 20 ++++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/qcodes/utils/metadata.py b/qcodes/utils/metadata.py
index 170be108ef1..495c9859393 100644
--- a/qcodes/utils/metadata.py
+++ b/qcodes/utils/metadata.py
@@ -87,9 +87,10 @@ def extract_param_values(snapshot: Snapshot) -> Dict[ParameterKey, Any]:
 
     return parameters
 
+
 def diff_param_values(left_snapshot: Snapshot,
                       right_snapshot: Snapshot
-                     ) -> ParameterDiff:
+                      ) -> ParameterDiff:
     """
     Given two snapshots, returns the differences between parameter values
     in each.
@@ -114,7 +115,8 @@ def diff_param_values(left_snapshot: Snapshot,
         }
     )
 
-def diff_param_values_by_id(left_id : RunId, right_id : RunId):
+
+def diff_param_values_by_id(left_id: RunId, right_id: RunId) -> ParameterDiff:
     """
     Given the IDs of two datasets, returns the differences between
     parameter values in each of their snapshots.
@@ -122,7 +124,13 @@ def diff_param_values_by_id(left_id : RunId, right_id : RunId):
     # Local import to reduce load time and
     # avoid circular references.
     from qcodes.dataset.data_set import load_by_id
-    return diff_param_values(
-        load_by_id(left_id).snapshot,
-        load_by_id(right_id).snapshot
-    )
+
+    left_snapshot = load_by_id(left_id).snapshot
+    right_snapshot = load_by_id(right_id).snapshot
+
+    if left_snapshot is None or right_snapshot is None:
+        raise RuntimeError(f"Tried to compare {left_snapshot} "
+                           f"and {right_snapshot}"
+                           f"but on of them is empty")
+
+    return diff_param_values(left_snapshot, right_snapshot)

From 67702912344e93f417b7817079950ba5b9898c54 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 20 Nov 2018 13:01:39 +0100
Subject: [PATCH 162/719] This class cannot handle a missing parent

---
 qcodes/tests/test_autoloadable_channels.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/qcodes/tests/test_autoloadable_channels.py b/qcodes/tests/test_autoloadable_channels.py
index e7b9d913b6f..aa8290d0375 100644
--- a/qcodes/tests/test_autoloadable_channels.py
+++ b/qcodes/tests/test_autoloadable_channels.py
@@ -129,6 +129,8 @@ def _get_new_instance_kwargs(
         Find the smallest channel number not yet occupied. An optional keyword
         `greeting` is extracted from the kwargs. The default is "Hello"
         """
+        if parent is None:
+            raise RuntimeError("SimpleTestChannel needs a parent instrument")
         channels_str = parent.channel_catalog()
         existing_channels = [int(i) for i in channels_str.split(",")]
 

From 9026d92bb13822b70af4cd510f3216a53df1be69 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 20 Nov 2018 13:05:26 +0100
Subject: [PATCH 163/719] better variable name and fix some docstrings

---
 qcodes/logger/instrument_logger.py | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/qcodes/logger/instrument_logger.py b/qcodes/logger/instrument_logger.py
index fffddc4300f..0bef73ce75e 100644
--- a/qcodes/logger/instrument_logger.py
+++ b/qcodes/logger/instrument_logger.py
@@ -100,30 +100,33 @@ def filter_instrument(instrument: Union['InstrumentBase',
         >>>     v = dmm2.v()  # not logged
 
     Args:
+        instrument: The instrument or sequence of instruments to enable
+            messages from.
         level: level to set the handlers to
         handler: single or sequence of handlers which to change
     """
-    handler_int: Sequence[logging.Handler]
+    handlers: Sequence[logging.Handler]
     if handler is None:
         myhandler = get_console_handler()
         if myhandler is None:
             raise RuntimeError("Trying to filter instrument but no handler "
-                               "defined. Did you forget to call `start_logger` before?")
-        handler_int = (myhandler,)
+                               "defined. Did you forget to call "
+                               "`start_logger` before?")
+        handlers = (myhandler,)
     elif not isinstance(handler, collections.abc.Sequence):
-        handler_int = (handler,)
+        handlers = (handler,)
     else:
-        handler_int = handler
+        handlers = handler
 
     instrument_filter = InstrumentFilter(instrument)
-    for h in handler_int:
+    for h in handlers:
         h.addFilter(instrument_filter)
     try:
         if level is not None:
-            with handler_level(level, handler_int):
+            with handler_level(level, handlers):
                 yield
         else:
             yield
     finally:
-        for h in handler_int:
+        for h in handlers:
             h.removeFilter(instrument_filter)

From e6560811e2ba817c576de3b66ff5120632bb1956 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 20 Nov 2018 13:08:15 +0100
Subject: [PATCH 164/719] better message

---
 qcodes/logger/logger.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/qcodes/logger/logger.py b/qcodes/logger/logger.py
index f6d5315c083..fee0ea9c64a 100644
--- a/qcodes/logger/logger.py
+++ b/qcodes/logger/logger.py
@@ -234,14 +234,15 @@ def console_level(level: LevelType):
     handler.
     Example:
         >>> with logger.console_level(level=logging.DEBUG):
-        >>>     root_logger.debug('this is now visible)
+        >>>     root_logger.debug('this is now visible')
 
     Args:
         level: level to set the console handler to
     """
     global console_handler
     if console_handler is None:
-        raise RuntimeError("Console handler is None")
+        raise RuntimeError("Console handler is None. Cannot set the level"
+                           " on it")
     with handler_level(level, handler=console_handler):
         yield
 

From b4d075ab11cee6cfb96e8bee3dc2c6f91db61a52 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 20 Nov 2018 13:23:24 +0100
Subject: [PATCH 165/719] typo in error message

---
 qcodes/instrument/group_parameter.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument/group_parameter.py b/qcodes/instrument/group_parameter.py
index 907a3cdbfbb..88d84f03cb0 100644
--- a/qcodes/instrument/group_parameter.py
+++ b/qcodes/instrument/group_parameter.py
@@ -36,7 +36,7 @@ def __init__(self,
                  name: str,
                  instrument: Optional['Instrument'] = None,
                  **kwargs
-                ) -> None:
+                 ) -> None:
 
         if "set_cmd" in kwargs or "get_cmd" in kwargs:
             raise ValueError("A GroupParameter does not use 'set_cmd' or "
@@ -61,7 +61,7 @@ def _get_raw_value(self) -> Any:
 
     def set_raw(self, value: Any) -> None:
         if self.group is None:
-            raise RuntimeError("Trying to get Group value but no "
+            raise RuntimeError("Trying to set Group value but no "
                                "group defined")
         self.group.set(self, value)
 

From cb13636506ecf8f138b045fdf3d39af1f203edd9 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 20 Nov 2018 13:41:01 +0100
Subject: [PATCH 166/719] Add tests for GroupParameter without group

---
 qcodes/tests/test_group_parameter.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/qcodes/tests/test_group_parameter.py b/qcodes/tests/test_group_parameter.py
index e001c5e8d2d..f8182bea134 100644
--- a/qcodes/tests/test_group_parameter.py
+++ b/qcodes/tests/test_group_parameter.py
@@ -77,3 +77,14 @@ def test_raise_on_get_set_cmd():
 
         assert str(e.value) == "A GroupParameter does not use 'set_cmd' or " \
                                "'get_cmd' kwarg"
+
+def test_raises_on_get_set_without_group():
+    param = GroupParameter(name='b')
+
+    with pytest.raises(RuntimeError) as e:
+        param.get()
+    assert str(e.value) == "('Trying to get Group value but no group defined', 'getting b')"
+
+    with pytest.raises(RuntimeError) as e:
+        param.set(1)
+    assert str(e.value) == "('Trying to set Group value but no group defined', 'setting b to 1')"

From 5ea3fd27f84e0dadffc25a7dbfa0c33a48af9a31 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 20 Nov 2018 16:24:07 +0100
Subject: [PATCH 167/719] Deprecate DataSet.add_parameter_values (doesnt work
 as expected) + test

---
 qcodes/dataset/data_set.py                 |  3 ++
 qcodes/tests/dataset/test_dataset_basic.py | 63 ++++++++++++++++------
 2 files changed, 51 insertions(+), 15 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index c63f165e3fb..9d1d24e107b 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -653,6 +653,9 @@ def modify_results(self, start_index: int,
                                flattened_keys,
                                flattened_values)
 
+    @deprecate(reason='it is an experimental functionality, and it is not '
+                      'known whether it will remain or it will be removed.',
+               alternative='add_parameter, add_result, add_results')
     def add_parameter_values(self, spec: ParamSpec, values: VALUES):
         """
         Add a parameter to the DataSet and associates result values with the
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 8a40e0b3171..8e27ddd7e30 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -418,28 +418,61 @@ def test_modify_result():
         shadow_ds.conn.close()
 
 
-# @settings(max_examples=25, deadline=None)
-# @given(N=hst.integers(min_value=1, max_value=10000),
-#        M=hst.integers(min_value=1, max_value=10000))
-# def test_add_parameter_values(dataset, N, M):
-def test_add_parameter_values(dataset, N=2, M=3):
+@pytest.mark.xfail(reason='This function does not seem to work the way its '
+                          'docstring suggests. See the test body for more '
+                          'information.')
+def test_add_parameter_values(dataset):
+    n = 2
+    m = n + 1
+
     xparam = ParamSpec('x', 'numeric')
     dataset.add_parameter(xparam)
 
-    x_results = [{'x': x} for x in range(N)]
+    x_results = [{'x': x} for x in range(n)]
     dataset.add_results(x_results)
 
     yparam = ParamSpec("y", "numeric")
 
-    if N != M:
-        match_str = f'Need to have {N} values but got {M}.'
-        match_str = re.escape(match_str)
-        with pytest.raises(ValueError, match=match_str):
-            dataset.add_parameter_values(yparam, [y for y in range(M)])
-
-    yvals = [y for y in range(N)]
-    y_expected = [[None]] * N + [[y] for y in yvals]  # <-- should it have None?
-    dataset.add_parameter_values(yparam, yvals)
+    match_str = f'Need to have {n} values but got {m}.'
+    match_str = re.escape(match_str)
+    with pytest.raises(ValueError, match=match_str):
+        pytest.deprecated_call(
+            dataset.add_parameter_values, yparam, [y for y in range(m)])
+
+    yvals = [y for y in range(n)]
+
+    # Unlike what the docstring of the method suggests,
+    # `add_parameter_values` does NOT add a new parameter and values for it
+    # "NEXT TO the columns of values of existing parameters".
+    #
+    # In other words, if the initial state of the table is:
+    #
+    # |   x  |
+    # --------
+    # |   1  |
+    # |   2  |
+    #
+    # then the state of the table after calling `add_parameter_values` is
+    # going to be:
+    #
+    # |   x  |   y  |
+    # ---------------
+    # |   1  | NULL |
+    # |   2  | NULL |
+    # | NULL |  25  |
+    # | NULL |  42  |
+    #
+    # while the docstring suggests the following state:
+    #
+    # |   x  |   y  |
+    # ---------------
+    # |   1  |  25  |
+    # |   2  |  42  |
+    #
+
+    y_expected = [[None]] * n + [[y] for y in yvals]
+    pytest.deprecated_call(
+        dataset.add_parameter_values, yparam, yvals)
 
     shadow_ds = make_shadow_dataset(dataset)
 

From ae5cd997472f93996f05c40e4f9f09ddbb60678e Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 20 Nov 2018 16:29:45 +0100
Subject: [PATCH 168/719] Fix bug for some DataSet methods making it
 not-committable afterwards

List of methods:
- unsubscribe
- unsubscribe_all
- modify_result
- modify_results
- add_parameter_values

Note that ConnctionPlus still behaves weirdly.
---
 qcodes/dataset/sqlite_base.py                 |  2 +
 .../tests/dataset/test_sqlite_connection.py   | 81 +++++++++++++++----
 2 files changed, 66 insertions(+), 17 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 313593a49d9..3426cc2514f 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -746,6 +746,7 @@ def atomic(conn: SomeConnection):
 
     conn = make_plus_connection_from(conn)
 
+    old_atomic_in_progress = conn.atomic_in_progress
     is_outmost = not(conn.atomic_in_progress)
     conn.atomic_in_progress = True
 
@@ -770,6 +771,7 @@ def atomic(conn: SomeConnection):
     finally:
         if is_outmost:
             conn.isolation_level = old_level
+        conn.atomic_in_progress = old_atomic_in_progress
 
 
 def make_plus_connection_from(conn: SomeConnection) -> ConnectionPlus:
diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index e8e1e04fa21..5b66ad5661e 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -30,13 +30,13 @@ def sqlite_conn_is_idle(conn: sqlite3.Connection, isolation=None):
 
 def plus_conn_is_idle(conn: ConnectionPlus, isolation=None):
     assert isinstance(conn, ConnectionPlus)
-    # assert False is conn.atomic_in_progress <-- should it be?
-    assert True is conn.atomic_in_progress
+    assert False is conn.atomic_in_progress
     assert isolation == conn.isolation_level
     assert False is conn.in_transaction
     return True
 
 
+@pytest.mark.xfail('this test showcases a weird behavior of `ConnectionPlus`')
 def test_connection_plus():
     sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
@@ -81,6 +81,8 @@ def test_atomic_on_outmost_sqlite_connection():
     assert plus_conn_is_idle(atomic_conn, isolation_level)
 
 
+@pytest.mark.xfail('this test showcases a weird behavior of `atomic`, '
+                   'needs fixes, apparently also for `ConnectionPlus`')
 def test_atomic_on_outmost_plus_connection():
     sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
@@ -137,6 +139,8 @@ def test_two_atomics_on_outmost_sqlite_connection():
     assert plus_conn_is_idle(atomic_conn_2, isolation_level)
 
 
+@pytest.mark.xfail('this test showcases a weird behavior of `atomic`, '
+                   'needs fixes, apparently also for `ConnectionPlus`')
 def test_two_atomics_on_outmost_plus_connection():
     sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
@@ -146,29 +150,72 @@ def test_two_atomics_on_outmost_plus_connection():
     assert False is plus_conn.in_transaction
 
     with atomic(plus_conn) as atomic_conn_1:
-        # assert plus_conn_in_transaction(plus_conn)
-        assert plus_conn_is_idle(plus_conn, isolation_level)
-        # assert plus_conn_in_transaction(atomic_conn_1)
-        assert plus_conn_is_idle(atomic_conn_1, isolation_level)
+        # assert plus_conn_in_transaction(plus_conn) <-- should it be?
+        assert isinstance(plus_conn, ConnectionPlus)
+        assert True is plus_conn.atomic_in_progress
+        assert isolation_level == plus_conn.isolation_level
+        assert False is plus_conn.in_transaction # not True??
+
+        # assert plus_conn_in_transaction(atomic_conn_1) <-- should it be?
+        assert isinstance(atomic_conn_1, ConnectionPlus)
+        assert True is atomic_conn_1.atomic_in_progress
+        assert isolation_level is atomic_conn_1.isolation_level
+        assert False is atomic_conn_1.in_transaction # not True??
 
         with atomic(atomic_conn_1) as atomic_conn_2:
-            # assert plus_conn_in_transaction(plus_conn) <-- should it be?
-            assert plus_conn_is_idle(plus_conn, isolation_level)
+            # assert plus_conn_in_transaction(plus_conn) # <-- should it be?
+            assert isinstance(plus_conn, ConnectionPlus)
+            assert True is plus_conn.atomic_in_progress
+            assert isolation_level is plus_conn.isolation_level
+            assert False is plus_conn.in_transaction  # not True??
+
             # assert plus_conn_in_transaction(atomic_conn_1) <-- should it be?
-            assert plus_conn_is_idle(atomic_conn_1, isolation_level)
-            # assert plus_conn_in_transaction(atomic_conn_2) <-- should it be?
-            assert plus_conn_is_idle(atomic_conn_2, isolation_level)
+            assert isinstance(atomic_conn_1, ConnectionPlus)
+            assert True is atomic_conn_1.atomic_in_progress
+            assert isolation_level == atomic_conn_1.isolation_level  # not None??
+            assert False is atomic_conn_1.in_transaction  # not True???
+
+            # assert plus_conn_in_transaction(atomic_conn_2) # <-- should it be?
+            assert isinstance(atomic_conn_2, ConnectionPlus)
+            assert True is atomic_conn_2.atomic_in_progress
+            assert isolation_level == atomic_conn_2.isolation_level  # not None??
+            assert False is atomic_conn_2.in_transaction  # not True???
 
         # assert plus_conn_in_transaction(plus_conn) <-- should it be?
-        assert plus_conn_is_idle(plus_conn, isolation_level)
+        assert isinstance(plus_conn, ConnectionPlus)
+        assert True is plus_conn.atomic_in_progress
+        assert isolation_level == plus_conn.isolation_level  # not None??
+        assert False is plus_conn.in_transaction # not True?
+
         # assert plus_conn_in_transaction(atomic_conn_1) <-- should it be?
-        assert plus_conn_is_idle(atomic_conn_1, isolation_level)
+        assert isinstance(atomic_conn_1, ConnectionPlus)
+        assert True is atomic_conn_1.atomic_in_progress
+        assert isolation_level == atomic_conn_1.isolation_level  # not None??
+        assert False is atomic_conn_1.in_transaction  # not True?
+
         # assert plus_conn_in_transaction(atomic_conn_2) <-- should it be?
-        assert plus_conn_is_idle(atomic_conn_2, isolation_level)
+        assert isinstance(atomic_conn_2, ConnectionPlus)
+        assert True is atomic_conn_2.atomic_in_progress
+        assert isolation_level == atomic_conn_2.isolation_level # not None??
+        assert False is atomic_conn_2.in_transaction # not True?
 
-    assert plus_conn_is_idle(plus_conn, isolation_level)
-    assert plus_conn_is_idle(atomic_conn_1, isolation_level)
-    assert plus_conn_is_idle(atomic_conn_2, isolation_level)
+    # assert plus_conn_is_idle(plus_conn, isolation_level) <-- should it be?
+    assert isinstance(plus_conn, ConnectionPlus)
+    assert True is plus_conn.atomic_in_progress # not False??
+    assert isolation_level == plus_conn.isolation_level
+    assert False is plus_conn.in_transaction
+
+    # assert plus_conn_is_idle(atomic_conn_1, isolation_level) <-- should it be?
+    assert isinstance(atomic_conn_1, ConnectionPlus)
+    assert True is atomic_conn_1.atomic_in_progress # not False??
+    assert isolation_level == atomic_conn_1.isolation_level
+    assert False is atomic_conn_1.in_transaction
+
+    # assert plus_conn_is_idle(atomic_conn_2, isolation_level) <-- should it be?
+    assert isinstance(atomic_conn_2, ConnectionPlus)
+    assert True is atomic_conn_2.atomic_in_progress # not False??
+    assert isolation_level == atomic_conn_2.isolation_level
+    assert False is atomic_conn_2.in_transaction
 
     assert atomic_in_progress == plus_conn.atomic_in_progress
     assert atomic_in_progress == atomic_conn_1.atomic_in_progress

From 3cc06bd5a33677d2165a28a353fc9acec02391ed Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 20 Nov 2018 16:30:11 +0100
Subject: [PATCH 169/719] Use dataset fixture in test_modify_result directly

---
 qcodes/tests/dataset/test_dataset_basic.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 8e27ddd7e30..8300141d5a6 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -359,9 +359,7 @@ def test_modify_results(dataset):
                  'implemented and covered with tests.')
 
 
-@pytest.mark.usefixtures("experiment")
-def test_modify_result():
-    dataset = new_data_set("test_modify_result")
+def test_modify_result(dataset):
     xparam = ParamSpec("x", "numeric", label="x parameter",
                        unit='V')
     yparam = ParamSpec("y", 'numeric', label='y parameter',

From 71e6554aba74393b6458a3c5672e5ac1962ec489 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 20 Nov 2018 16:31:44 +0100
Subject: [PATCH 170/719] Add failing test for ConnectionPlus that showcases a
 bug

ConnectionPlus created via constructor does not commit in atomic
context manager.
---
 .../tests/dataset/test_sqlite_connection.py   | 108 +++++++++++++++++-
 1 file changed, 102 insertions(+), 6 deletions(-)

diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index 5b66ad5661e..fea27c63681 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -3,7 +3,7 @@
 import pytest
 
 from qcodes.dataset.sqlite_base import ConnectionPlus, \
-    make_plus_connection_from, atomic
+    make_plus_connection_from, atomic, connect
 
 
 def sqlite_conn_in_transaction(conn: sqlite3.Connection):
@@ -36,7 +36,8 @@ def plus_conn_is_idle(conn: ConnectionPlus, isolation=None):
     return True
 
 
-@pytest.mark.xfail('this test showcases a weird behavior of `ConnectionPlus`')
+@pytest.mark.xfail(reason='this test showcases a weird behavior of '
+                          '`ConnectionPlus`')
 def test_connection_plus():
     sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
@@ -81,8 +82,8 @@ def test_atomic_on_outmost_sqlite_connection():
     assert plus_conn_is_idle(atomic_conn, isolation_level)
 
 
-@pytest.mark.xfail('this test showcases a weird behavior of `atomic`, '
-                   'needs fixes, apparently also for `ConnectionPlus`')
+@pytest.mark.xfail(reason='this test showcases a weird behavior of `atomic`, '
+                          'needs fixes, apparently also for `ConnectionPlus`')
 def test_atomic_on_outmost_plus_connection():
     sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
@@ -139,8 +140,8 @@ def test_two_atomics_on_outmost_sqlite_connection():
     assert plus_conn_is_idle(atomic_conn_2, isolation_level)
 
 
-@pytest.mark.xfail('this test showcases a weird behavior of `atomic`, '
-                   'needs fixes, apparently also for `ConnectionPlus`')
+@pytest.mark.xfail(reason='this test showcases a weird behavior of `atomic`, '
+                          'needs fixes, apparently also for `ConnectionPlus`')
 def test_two_atomics_on_outmost_plus_connection():
     sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
@@ -220,3 +221,98 @@ def test_two_atomics_on_outmost_plus_connection():
     assert atomic_in_progress == plus_conn.atomic_in_progress
     assert atomic_in_progress == atomic_conn_1.atomic_in_progress
     assert atomic_in_progress == atomic_conn_2.atomic_in_progress
+
+
+@pytest.mark.parametrize(argnames='create_conn_plus',
+                         argvalues=(
+                                 make_plus_connection_from,
+                                 pytest.param(ConnectionPlus,
+                                              marks=pytest.mark.xfail(
+                                                  strict=True))
+                         ),
+                         ids=(
+                                 'make_plus_connection_from',
+                                 'ConnectionPlus'
+                         ))
+def test_use_of_atomic_that_does_not_commit(tmp_path, create_conn_plus):
+    """
+    This test tests the behavior of `ConnectionPlus` that is created from
+    `sqlite3.Connection` with respect to `atomic` context manager and commits.
+    """
+    dbfile = str(tmp_path / 'temp.db')
+
+    sqlite_conn = connect(dbfile)
+    plus_conn = create_conn_plus(sqlite_conn)
+
+    # this connection is going to be used to test whether changes have been
+    # committed to the database file
+    control_conn = connect(dbfile)
+
+    get_all_runs = 'SELECT * FROM runs'
+    insert_run_with_name = 'INSERT INTO runs (name) VALUES (?)'
+
+    # assert that at the beginning of the test there are no runs in the
+    # table; we'll be adding new rows to the runs table below
+
+    assert 0 == len(plus_conn.execute(get_all_runs).fetchall())
+    assert 0 == len(control_conn.execute(get_all_runs).fetchall())
+
+    # add 1 new row, and assert the state of the runs table at every step
+    # note that control_conn will only detect the change after the `atomic`
+    # context manager is exited
+
+    with atomic(plus_conn) as atomic_conn:
+
+        assert 0 == len(plus_conn.execute(get_all_runs).fetchall())
+        assert 0 == len(atomic_conn.execute(get_all_runs).fetchall())
+        assert 0 == len(control_conn.execute(get_all_runs).fetchall())
+
+        atomic_conn.cursor().execute(insert_run_with_name, ['aaa'])
+
+        assert 1 == len(plus_conn.execute(get_all_runs).fetchall())
+        assert 1 == len(atomic_conn.execute(get_all_runs).fetchall())
+        assert 0 == len(control_conn.execute(get_all_runs).fetchall())
+
+    assert 1 == len(plus_conn.execute(get_all_runs).fetchall())
+    assert 1 == len(atomic_conn.execute(get_all_runs).fetchall())
+    assert 1 == len(control_conn.execute(get_all_runs).fetchall())
+
+    # let's add two new rows but each inside its own `atomic` context manager
+    # we expect to see the actual change in the database only after we exit
+    # the outermost context.
+
+    with atomic(plus_conn) as atomic_conn_1:
+
+        assert 1 == len(plus_conn.execute(get_all_runs).fetchall())
+        assert 1 == len(atomic_conn_1.execute(get_all_runs).fetchall())
+        assert 1 == len(control_conn.execute(get_all_runs).fetchall())
+
+        atomic_conn_1.cursor().execute(insert_run_with_name, ['bbb'])
+
+        assert 2 == len(plus_conn.execute(get_all_runs).fetchall())
+        assert 2 == len(atomic_conn_1.execute(get_all_runs).fetchall())
+        assert 1 == len(control_conn.execute(get_all_runs).fetchall())
+
+        with atomic(atomic_conn_1) as atomic_conn_2:
+
+            assert 2 == len(plus_conn.execute(get_all_runs).fetchall())
+            assert 2 == len(atomic_conn_1.execute(get_all_runs).fetchall())
+            assert 2 == len(atomic_conn_2.execute(get_all_runs).fetchall())
+            assert 1 == len(control_conn.execute(get_all_runs).fetchall())
+
+            atomic_conn_2.cursor().execute(insert_run_with_name, ['ccc'])
+
+            assert 3 == len(plus_conn.execute(get_all_runs).fetchall())
+            assert 3 == len(atomic_conn_1.execute(get_all_runs).fetchall())
+            assert 3 == len(atomic_conn_2.execute(get_all_runs).fetchall())
+            assert 1 == len(control_conn.execute(get_all_runs).fetchall())
+
+        assert 3 == len(plus_conn.execute(get_all_runs).fetchall())
+        assert 3 == len(atomic_conn_1.execute(get_all_runs).fetchall())
+        assert 3 == len(atomic_conn_2.execute(get_all_runs).fetchall())
+        assert 1 == len(control_conn.execute(get_all_runs).fetchall())
+
+    assert 3 == len(plus_conn.execute(get_all_runs).fetchall())
+    assert 3 == len(atomic_conn_1.execute(get_all_runs).fetchall())
+    assert 3 == len(atomic_conn_2.execute(get_all_runs).fetchall())
+    assert 3 == len(control_conn.execute(get_all_runs).fetchall())

From bf7c17f0020c36b792713a614d37f507949b1ab2 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 20 Nov 2018 17:43:49 +0100
Subject: [PATCH 171/719] Fix atomic called on constructor-created
 ConnectionPlus doesnt commit

(as opposed to on ConnectionPlus created by `make_plus_connection_from`)

Setting initial atomic_in_progress to True fixes it.
---
 qcodes/dataset/sqlite_base.py                 |   3 +-
 .../tests/dataset/test_sqlite_connection.py   | 157 +++++++-----------
 2 files changed, 58 insertions(+), 102 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 3426cc2514f..bdc1475ccac 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -118,7 +118,7 @@ class ConnectionPlus(wrapt.ObjectProxy):
     currently in the middle of an atomic block of transactions, thus allowing
     us to nest atomic context managers
     """
-    atomic_in_progress: bool = True
+    atomic_in_progress: bool = False
 
 
 SomeConnection = Union[sqlite3.Connection, ConnectionPlus]
@@ -789,7 +789,6 @@ def make_plus_connection_from(conn: SomeConnection) -> ConnectionPlus:
     """
     if not (hasattr(conn, 'atomic_in_progress')):
         conn = ConnectionPlus(conn)
-        conn.atomic_in_progress = False
     else:
         conn = cast(ConnectionPlus, conn)
     return conn
diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index fea27c63681..3a6bec31a56 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -36,17 +36,13 @@ def plus_conn_is_idle(conn: ConnectionPlus, isolation=None):
     return True
 
 
-@pytest.mark.xfail(reason='this test showcases a weird behavior of '
-                          '`ConnectionPlus`')
 def test_connection_plus():
     sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
 
     assert isinstance(plus_conn, ConnectionPlus)
     assert isinstance(plus_conn, sqlite3.Connection)
-    # reason for the value of "True" here is unknown
-    assert True is plus_conn.atomic_in_progress
-    # assert False is plus_conn.atomic_in_progress <-- should it be?
+    assert False is plus_conn.atomic_in_progress
 
 
 @pytest.mark.parametrize(
@@ -61,11 +57,8 @@ def test_make_plus_connection_from(conn):
     assert isinstance(plus_conn, ConnectionPlus)
 
     if isinstance(conn, ConnectionPlus):
-        # make_plus_connection_from does not change this, hence it should be
-        # equal to the value from `conn` (which is True, see ConnectionPlus)
         assert conn.atomic_in_progress is plus_conn.atomic_in_progress
     else:
-        # make_plus_connection_from explicitly sets this to False
         assert False is plus_conn.atomic_in_progress
 
 
@@ -82,38 +75,61 @@ def test_atomic_on_outmost_sqlite_connection():
     assert plus_conn_is_idle(atomic_conn, isolation_level)
 
 
-@pytest.mark.xfail(reason='this test showcases a weird behavior of `atomic`, '
-                          'needs fixes, apparently also for `ConnectionPlus`')
 def test_atomic_on_outmost_plus_connection():
     sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
+    assert False is plus_conn.atomic_in_progress
 
     atomic_in_progress = plus_conn.atomic_in_progress
-
     isolation_level = plus_conn.isolation_level
+
     assert False is plus_conn.in_transaction
 
     with atomic(plus_conn) as atomic_conn:
-        assert isinstance(atomic_conn, ConnectionPlus)
+        assert plus_conn_in_transaction(atomic_conn)
+        assert plus_conn_in_transaction(plus_conn)
 
-        assert True is atomic_conn.atomic_in_progress
+    assert isolation_level == plus_conn.isolation_level
+    assert False is plus_conn.in_transaction
+    assert atomic_in_progress is plus_conn.atomic_in_progress
 
-        # assert None is atomic_conn.isolation_level <-- should it be?
-        assert isolation_level == atomic_conn.isolation_level
-        # assert None is plus_conn.isolation_level <-- should it be?
+    assert isolation_level == plus_conn.isolation_level
+    assert False is atomic_conn.in_transaction
+    assert atomic_in_progress is atomic_conn.atomic_in_progress
+
+
+@pytest.mark.parametrize('in_transaction', (True, False))
+def test_atomic_on_outmost_plus_connection_that_is_in_progress(in_transaction):
+    sqlite_conn = sqlite3.connect(':memory:')
+    plus_conn = ConnectionPlus(sqlite_conn)
+
+    # explicitly set to True for testing purposes
+    plus_conn.atomic_in_progress = True
+
+    # implement parametrizing over connection's `in_transaction` attribute
+    if in_transaction:
+        plus_conn.cursor().execute('BEGIN')
+    assert in_transaction is plus_conn.in_transaction
+
+    isolation_level = plus_conn.isolation_level
+    in_transaction = plus_conn.in_transaction
+
+    with atomic(plus_conn) as atomic_conn:
+        assert True is plus_conn.atomic_in_progress
         assert isolation_level == plus_conn.isolation_level
+        assert in_transaction is plus_conn.in_transaction
 
-        # assert True is plus_conn.in_transaction <-- should it be?
-        assert False is plus_conn.in_transaction
-        # assert True is atomic_conn.in_transaction <-- should it be?
-        assert False is atomic_conn.in_transaction
+        assert True is atomic_conn.atomic_in_progress
+        assert isolation_level == atomic_conn.isolation_level
+        assert in_transaction is atomic_conn.in_transaction
 
+    assert True is plus_conn.atomic_in_progress
     assert isolation_level == plus_conn.isolation_level
-    assert False is plus_conn.in_transaction
+    assert in_transaction is plus_conn.in_transaction
 
-    assert False is atomic_conn.in_transaction
-
-    assert atomic_in_progress is atomic_conn.atomic_in_progress
+    assert True is atomic_conn.atomic_in_progress
+    assert isolation_level == atomic_conn.isolation_level
+    assert in_transaction is atomic_conn.in_transaction
 
 
 def test_two_atomics_on_outmost_sqlite_connection():
@@ -140,83 +156,31 @@ def test_two_atomics_on_outmost_sqlite_connection():
     assert plus_conn_is_idle(atomic_conn_2, isolation_level)
 
 
-@pytest.mark.xfail(reason='this test showcases a weird behavior of `atomic`, '
-                          'needs fixes, apparently also for `ConnectionPlus`')
 def test_two_atomics_on_outmost_plus_connection():
     sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
-    atomic_in_progress = plus_conn.atomic_in_progress
 
+    atomic_in_progress = plus_conn.atomic_in_progress
     isolation_level = plus_conn.isolation_level
+
     assert False is plus_conn.in_transaction
 
     with atomic(plus_conn) as atomic_conn_1:
-        # assert plus_conn_in_transaction(plus_conn) <-- should it be?
-        assert isinstance(plus_conn, ConnectionPlus)
-        assert True is plus_conn.atomic_in_progress
-        assert isolation_level == plus_conn.isolation_level
-        assert False is plus_conn.in_transaction # not True??
-
-        # assert plus_conn_in_transaction(atomic_conn_1) <-- should it be?
-        assert isinstance(atomic_conn_1, ConnectionPlus)
-        assert True is atomic_conn_1.atomic_in_progress
-        assert isolation_level is atomic_conn_1.isolation_level
-        assert False is atomic_conn_1.in_transaction # not True??
+        assert plus_conn_in_transaction(plus_conn)
+        assert plus_conn_in_transaction(atomic_conn_1)
 
         with atomic(atomic_conn_1) as atomic_conn_2:
-            # assert plus_conn_in_transaction(plus_conn) # <-- should it be?
-            assert isinstance(plus_conn, ConnectionPlus)
-            assert True is plus_conn.atomic_in_progress
-            assert isolation_level is plus_conn.isolation_level
-            assert False is plus_conn.in_transaction  # not True??
-
-            # assert plus_conn_in_transaction(atomic_conn_1) <-- should it be?
-            assert isinstance(atomic_conn_1, ConnectionPlus)
-            assert True is atomic_conn_1.atomic_in_progress
-            assert isolation_level == atomic_conn_1.isolation_level  # not None??
-            assert False is atomic_conn_1.in_transaction  # not True???
-
-            # assert plus_conn_in_transaction(atomic_conn_2) # <-- should it be?
-            assert isinstance(atomic_conn_2, ConnectionPlus)
-            assert True is atomic_conn_2.atomic_in_progress
-            assert isolation_level == atomic_conn_2.isolation_level  # not None??
-            assert False is atomic_conn_2.in_transaction  # not True???
-
-        # assert plus_conn_in_transaction(plus_conn) <-- should it be?
-        assert isinstance(plus_conn, ConnectionPlus)
-        assert True is plus_conn.atomic_in_progress
-        assert isolation_level == plus_conn.isolation_level  # not None??
-        assert False is plus_conn.in_transaction # not True?
-
-        # assert plus_conn_in_transaction(atomic_conn_1) <-- should it be?
-        assert isinstance(atomic_conn_1, ConnectionPlus)
-        assert True is atomic_conn_1.atomic_in_progress
-        assert isolation_level == atomic_conn_1.isolation_level  # not None??
-        assert False is atomic_conn_1.in_transaction  # not True?
-
-        # assert plus_conn_in_transaction(atomic_conn_2) <-- should it be?
-        assert isinstance(atomic_conn_2, ConnectionPlus)
-        assert True is atomic_conn_2.atomic_in_progress
-        assert isolation_level == atomic_conn_2.isolation_level # not None??
-        assert False is atomic_conn_2.in_transaction # not True?
-
-    # assert plus_conn_is_idle(plus_conn, isolation_level) <-- should it be?
-    assert isinstance(plus_conn, ConnectionPlus)
-    assert True is plus_conn.atomic_in_progress # not False??
-    assert isolation_level == plus_conn.isolation_level
-    assert False is plus_conn.in_transaction
+            assert plus_conn_in_transaction(plus_conn)
+            assert plus_conn_in_transaction(atomic_conn_1)
+            assert plus_conn_in_transaction(atomic_conn_2)
 
-    # assert plus_conn_is_idle(atomic_conn_1, isolation_level) <-- should it be?
-    assert isinstance(atomic_conn_1, ConnectionPlus)
-    assert True is atomic_conn_1.atomic_in_progress # not False??
-    assert isolation_level == atomic_conn_1.isolation_level
-    assert False is atomic_conn_1.in_transaction
+        assert plus_conn_in_transaction(plus_conn)
+        assert plus_conn_in_transaction(atomic_conn_1)
+        assert plus_conn_in_transaction(atomic_conn_2)
 
-    # assert plus_conn_is_idle(atomic_conn_2, isolation_level) <-- should it be?
-    assert isinstance(atomic_conn_2, ConnectionPlus)
-    assert True is atomic_conn_2.atomic_in_progress # not False??
-    assert isolation_level == atomic_conn_2.isolation_level
-    assert False is atomic_conn_2.in_transaction
+    assert plus_conn_is_idle(plus_conn, isolation_level)
+    assert plus_conn_is_idle(atomic_conn_1, isolation_level)
+    assert plus_conn_is_idle(atomic_conn_2, isolation_level)
 
     assert atomic_in_progress == plus_conn.atomic_in_progress
     assert atomic_in_progress == atomic_conn_1.atomic_in_progress
@@ -224,17 +188,10 @@ def test_two_atomics_on_outmost_plus_connection():
 
 
 @pytest.mark.parametrize(argnames='create_conn_plus',
-                         argvalues=(
-                                 make_plus_connection_from,
-                                 pytest.param(ConnectionPlus,
-                                              marks=pytest.mark.xfail(
-                                                  strict=True))
-                         ),
-                         ids=(
-                                 'make_plus_connection_from',
-                                 'ConnectionPlus'
-                         ))
-def test_use_of_atomic_that_does_not_commit(tmp_path, create_conn_plus):
+                         argvalues=(make_plus_connection_from, ConnectionPlus),
+                         ids=('make_plus_connection_from', 'ConnectionPlus'))
+def test_that_use_of_atomic_commits_only_at_outermost_context(
+        tmp_path, create_conn_plus):
     """
     This test tests the behavior of `ConnectionPlus` that is created from
     `sqlite3.Connection` with respect to `atomic` context manager and commits.

From b9e326636001ba8e3e5d585843cda54aebef6b9e Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Tue, 20 Nov 2018 17:07:45 -0800
Subject: [PATCH 172/719] initial commit

---
 .../Qcodes example with Stahl.ipynb           | 309 ++++++++++++++++--
 .../Spectrum/stahl/__init__.py                |   0
 .../Spectrum/stahl/stahl.py                   | 173 ++++++++++
 3 files changed, 460 insertions(+), 22 deletions(-)
 create mode 100644 qcodes/instrument_drivers/Spectrum/stahl/__init__.py
 create mode 100644 qcodes/instrument_drivers/Spectrum/stahl/stahl.py

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index 8d079ed6513..ae1284076f2 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -6,76 +6,341 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qcodes import VisaInstrument "
+    "from typing import Dict, Optional, List, Tuple, Callable\n",
+    "import re \n",
+    "import numpy as np \n",
+    "import logging\n",
+    "\n",
+    "from qcodes import VisaInstrument, InstrumentChannel, ChannelList\n",
+    "from qcodes.instrument.parameter import Parameter\n",
+    "from qcodes.utils.validators import Numbers"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#stahl = VisaInstrument(\"stahl\", \"COM3\")"
+    "logger = logging.getLogger()\n",
+    "logger.setLevel(logging.DEBUG)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import visa\n",
-    "rm = visa.ResourceManager(visa_library=r\"C:\\Windows\\SysWOW64\\visa32.dll\")"
+    "class StahlChannel(InstrumentChannel): \n",
+    "    def __init__(self, parent, name, channel_number): \n",
+    "        super().__init__(parent, name)\n",
+    "    \n",
+    "        self._channel_string = f\"{channel_number:02d}\"\n",
+    "        self._channel_number = channel_number\n",
+    "        self._acknowledge_reply = chr(6)\n",
+    "    \n",
+    "        self.add_parameter(\n",
+    "            \"voltage\", \n",
+    "            get_cmd=f\"{self.parent.identifier} U{self._channel_string}\", \n",
+    "            get_parser=self._stahl_get_parser(\"V\"), \n",
+    "            set_cmd=self._set_voltage, \n",
+    "            unit=\"V\",\n",
+    "            vals=Numbers(\n",
+    "                -self.parent.voltage_range, \n",
+    "                self.parent.voltage_range\n",
+    "            )\n",
+    "        )\n",
+    "        \n",
+    "        self.add_parameter(\n",
+    "            \"current\", \n",
+    "            get_cmd=f\"{self.parent.identifier} I{self._channel_string}\", \n",
+    "            get_parser=self._stahl_get_parser(\"mA\"), \n",
+    "            unit=\"mA\",\n",
+    "        )\n",
+    "        \n",
+    "        self.add_parameter(\n",
+    "            \"is_locked\", \n",
+    "            get_cmd=self._get_lock_status, \n",
+    "            get_parser={\"0\": False, \"1\": True}.get, \n",
+    "        )\n",
+    "        \n",
+    "    def _stahl_get_parser(self, unit):\n",
+    "        \n",
+    "        regex = f\"([\\+\\-][\\d]?\\d,\\d{{3}} ){unit}$\"\n",
+    "        \n",
+    "        def parser(response):\n",
+    "            result = re.search(regex, response).groups()[0]\n",
+    "            return float(result.replace(\",\", \".\"))\n",
+    "        \n",
+    "        return parser\n",
+    "        \n",
+    "    def _set_voltage(self, voltage): \n",
+    "        \n",
+    "        voltage_normalized = np.interp(\n",
+    "            voltage, \n",
+    "            self.parent.voltage_range * np.array([-1, 1]), \n",
+    "            [0, 1]\n",
+    "        )\n",
+    "        \n",
+    "        send_string = f\"{self.parent.identifier} CH{self._channel_string} {voltage_normalized:.5f}\"\n",
+    "        response = self.ask(send_string)  # This is not a bug. We are asking in a set \n",
+    "        \n",
+    "        if response != self._acknowledge_reply: \n",
+    "            logger.warning(f\"Command {expected_reply} did not produce an acknowledge reply\")\n",
+    "            \n",
+    "    def _get_lock_status(self): \n",
+    "        \n",
+    "        send_string = f\"{self.parent.identifier} LOCK\"\n",
+    "        \n",
+    "        response = self.parent.visa_handle.query_binary_values(\n",
+    "            send_string, \n",
+    "            datatype='B',\n",
+    "            header_fmt=\"empty\"\n",
+    "        )\n",
+    "        \n",
+    "        chnr = self._channel_number - 1\n",
+    "        channel_group = chnr // 4\n",
+    "        lock_code_group = response[channel_group]\n",
+    "        return format(lock_code_group, \"b\")[chnr % 4 + 1]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#rm.open_resource(\"COM3\")"
+    "class Stahl(VisaInstrument): \n",
+    "    def __init__(self, name, address): \n",
+    "        super().__init__(name, address, terminator=\"\\r\")\n",
+    "        self.visa_handle.baud_rate = 115200\n",
+    "        \n",
+    "        instrument_info = self._parse_idn_string(\n",
+    "            self.ask(\"IDN\")\n",
+    "        )\n",
+    "        \n",
+    "        for key, value in instrument_info.items(): \n",
+    "            setattr(self, key, value)\n",
+    "                    \n",
+    "        channels = ChannelList(\n",
+    "            self, \"channel\", StahlChannel, snapshotable=False\n",
+    "        )\n",
+    "        \n",
+    "        for channel_number in range(1, self.n_channels + 1):\n",
+    "            name = f\"channel{channel_number}\"\n",
+    "            channel = StahlChannel(\n",
+    "                self, \n",
+    "                name, \n",
+    "                channel_number\n",
+    "            )\n",
+    "            self.add_submodule(name, channel)\n",
+    "            channels.append(channel)\n",
+    "        \n",
+    "        self.add_submodule(\"channel\", channels)\n",
+    "        \n",
+    "        self.add_parameter(\n",
+    "            \"temperature\", \n",
+    "            get_cmd=self._get_temperature, \n",
+    "            unit=\"C\"\n",
+    "        )\n",
+    "        \n",
+    "        self.connect_message()\n",
+    "    \n",
+    "    def _get_temperature(self):\n",
+    "        \n",
+    "        send_string = f\"{self.identifier} TEMP\"\n",
+    "        response_characters = self.visa_handle.query_binary_values(\n",
+    "            send_string, \n",
+    "            datatype='B', \n",
+    "            header_fmt=\"empty\"\n",
+    "        )\n",
+    "        \n",
+    "        response_string = \"\".join(map(chr, response_characters))\n",
+    "        # The '°' is the reason why we cannot simply call 'ask'\n",
+    "        # Stupid instrument :-(\n",
+    "        groups = re.search(\"TEMP (.*)°C\", response_string).groups()\n",
+    "        return float(groups[0])\n",
+    "        \n",
+    "    @staticmethod\n",
+    "    def _parse_idn_string(ind_string): \n",
+    "            \n",
+    "        groups = re.search(\n",
+    "            \"(HV|BS)(\\d{3}) (\\d{3}) (\\d{2}) [buqsm]\", \n",
+    "            ind_string\n",
+    "        ).groups()\n",
+    "    \n",
+    "        idparsers = {\n",
+    "            \"model\": str, \n",
+    "            \"serial_number\": str, \n",
+    "            \"voltage_range\": float, \n",
+    "            \"n_channels\": int, \n",
+    "            \"output_type\": {\n",
+    "                \"b\": \"bipolar\", \n",
+    "                \"u\": \"unipolar\", \n",
+    "                \"q\": \"quadrupole\", \n",
+    "                \"s\": \"steerer\", \n",
+    "                \"m\": \"bipolar milivolt\"\n",
+    "            }.get\n",
+    "        }\n",
+    "        \n",
+    "        return {\n",
+    "            name: idparsers[name](value) \n",
+    "            for name, value in zip(idparsers.keys(), groups)\n",
+    "        }\n",
+    "    \n",
+    "    def get_idn(self) -> Dict[str, Optional[str]]:\n",
+    "        \n",
+    "        return {\n",
+    "            \"vendor\": \"Stahl\", \n",
+    "            \"model\": self.model, \n",
+    "            \"serial\": self.serial_number, \n",
+    "            \"firmware\": None\n",
+    "        }\n",
+    "    \n",
+    "    @property \n",
+    "    def identifier(self): \n",
+    "        return f\"{self.model}{self.serial_number}\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'Serial'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-10-de5902db3797>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mSerial\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'Serial'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected to: Stahl HV (serial:171, firmware:None) in 0.07s\n"
      ]
     }
    ],
    "source": [
-    "import Serial "
+    "stahl = Stahl(\"stahl\", \"ASRL3\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.101\n"
+     ]
+    }
+   ],
+   "source": [
+    "v = stahl.channel[0].voltage()\n",
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.001\n"
+     ]
+    }
+   ],
+   "source": [
+    "v = stahl.channel[4].voltage()\n",
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.001\n"
+     ]
+    }
+   ],
+   "source": [
+    "v = stahl.channel[9].voltage()\n",
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stahl.channel[0].voltage(0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.002"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stahl.channel[0].current()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25.3"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stahl.temperature()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "()"
+       "False"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "rm.list_resources()"
+    "stahl.channel[1].is_locked()"
    ]
   },
   {
diff --git a/qcodes/instrument_drivers/Spectrum/stahl/__init__.py b/qcodes/instrument_drivers/Spectrum/stahl/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/qcodes/instrument_drivers/Spectrum/stahl/stahl.py b/qcodes/instrument_drivers/Spectrum/stahl/stahl.py
new file mode 100644
index 00000000000..90bd4fd3c7d
--- /dev/null
+++ b/qcodes/instrument_drivers/Spectrum/stahl/stahl.py
@@ -0,0 +1,173 @@
+from typing import Dict, Optional, Any, Callable
+import re
+import numpy as np
+import logging
+
+from qcodes import VisaInstrument, InstrumentChannel, ChannelList
+from qcodes.utils.validators import Numbers
+
+
+logger = logging.getLogger()
+
+
+class StahlChannel(InstrumentChannel):
+    def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
+        super().__init__(parent, name)
+
+        self._channel_string = f"{channel_number:02d}"
+        self._channel_number = channel_number
+        self._acknowledge_reply = chr(6)
+
+        self.add_parameter(
+            "voltage",
+            get_cmd=f"{self.parent.identifier} U{self._channel_string}",
+            get_parser=self._stahl_get_parser("V"),
+            set_cmd=self._set_voltage,
+            unit="V",
+            vals=Numbers(
+                -self.parent.voltage_range,
+                self.parent.voltage_range
+            )
+        )
+
+        self.add_parameter(
+            "current",
+            get_cmd=f"{self.parent.identifier} I{self._channel_string}",
+            get_parser=self._stahl_get_parser("mA"),
+            unit="mA",
+        )
+
+        self.add_parameter(
+            "is_locked",
+            get_cmd=self._get_lock_status,
+            get_parser={"0": False, "1": True}.get,
+        )
+
+    @staticmethod
+    def _stahl_get_parser(unit: str) -> Callable:
+        regex = f"([\+\-][\d]?\d,\d{{3}} ){unit}$"
+
+        def parser(response):
+            result = re.search(regex, response).groups()[0]
+            return float(result.replace(",", "."))
+
+        return parser
+
+    def _set_voltage(self, voltage: float) -> None:
+        voltage_normalized = np.interp(
+            voltage,
+            self.parent.voltage_range * np.array([-1, 1]),
+            [0, 1]
+        )
+
+        send_string = f"{self.parent.identifier} CH{self._channel_string} {voltage_normalized:.5f}"
+        response = self.ask(send_string)  # This is not a bug. We are asking in a set
+
+        if response != self._acknowledge_reply:
+            logger.warning(f"Command {send_string} did not produce an acknowledge reply")
+
+    def _get_lock_status(self) -> chr:
+        send_string = f"{self.parent.identifier} LOCK"
+
+        response = self.parent.visa_handle.query_binary_values(
+            send_string,
+            datatype='B',
+            header_fmt="empty"
+        )
+
+        chnr = self._channel_number - 1
+        channel_group = chnr // 4
+        lock_code_group = response[channel_group]
+        return format(lock_code_group, "b")[chnr % 4 + 1]
+
+
+class Stahl(VisaInstrument):
+    def __init__(self, name: str, address: str):
+        super().__init__(name, address, terminator="\r")
+        self.visa_handle.baud_rate = 115200
+
+        instrument_info = self._parse_idn_string(
+            self.ask("IDN")
+        )
+
+        for key, value in instrument_info.items():
+            setattr(self, key, value)
+
+        channels = ChannelList(
+            self, "channel", StahlChannel, snapshotable=False
+        )
+
+        for channel_number in range(1, self.n_channels + 1):
+            name = f"channel{channel_number}"
+            channel = StahlChannel(
+                self,
+                name,
+                channel_number
+            )
+            self.add_submodule(name, channel)
+            channels.append(channel)
+
+        self.add_submodule("channel", channels)
+
+        self.add_parameter(
+            "temperature",
+            get_cmd=self._get_temperature,
+            unit="C"
+        )
+
+        self.connect_message()
+
+    def _get_temperature(self) -> float:
+
+        send_string = f"{self.identifier} TEMP"
+        response_characters = self.visa_handle.query_binary_values(
+            send_string,
+            datatype='B',
+            header_fmt="empty"
+        )
+
+        response_string = "".join(map(chr, response_characters))
+        # The '°' is the reason why we cannot simply call 'ask'
+        # Stupid instrument :-(
+        groups = re.search("TEMP (.*)°C", response_string).groups()
+        return float(groups[0])
+
+    @staticmethod
+    def _parse_idn_string(ind_string) -> Dict[str, Any]:
+
+        groups = re.search(
+            "(HV|BS)(\d{3}) (\d{3}) (\d{2}) [buqsm]",
+            ind_string
+        ).groups()
+
+        idparsers = {
+            "model": str,
+            "serial_number": str,
+            "voltage_range": float,
+            "n_channels": int,
+            "output_type": {
+                "b": "bipolar",
+                "u": "unipolar",
+                "q": "quadrupole",
+                "s": "steerer",
+                "m": "bipolar milivolt"
+            }.get
+        }
+
+        return {
+            name: idparsers[name](value)
+            for name, value in zip(idparsers.keys(), groups)
+        }
+
+    def get_idn(self) -> Dict[str, Optional[str]]:
+
+        return {
+            "vendor": "Stahl",
+            "model": self.model,
+            "serial": self.serial_number,
+            "firmware": None
+        }
+
+    @property
+    def identifier(self):
+        return f"{self.model}{self.serial_number}"

From ef7dabdb3a0cd76418dc2e1307781c3d74e2016a Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Tue, 20 Nov 2018 17:09:52 -0800
Subject: [PATCH 173/719] initial commit

---
 .../Qcodes example with Stahl.ipynb           | 226 ++----------------
 .../Spectrum/stahl/__init__.py                |   0
 qcodes/instrument_drivers/stahl/__init__.py   |   1 +
 .../{Spectrum => }/stahl/stahl.py             |   0
 4 files changed, 17 insertions(+), 210 deletions(-)
 delete mode 100644 qcodes/instrument_drivers/Spectrum/stahl/__init__.py
 create mode 100644 qcodes/instrument_drivers/stahl/__init__.py
 rename qcodes/instrument_drivers/{Spectrum => }/stahl/stahl.py (100%)

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index ae1284076f2..4e2a31d3904 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -6,213 +6,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Dict, Optional, List, Tuple, Callable\n",
-    "import re \n",
-    "import numpy as np \n",
-    "import logging\n",
-    "\n",
-    "from qcodes import VisaInstrument, InstrumentChannel, ChannelList\n",
-    "from qcodes.instrument.parameter import Parameter\n",
-    "from qcodes.utils.validators import Numbers"
+    "from qcodes.instrument_drivers.stahl import Stahl"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "logger = logging.getLogger()\n",
-    "logger.setLevel(logging.DEBUG)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class StahlChannel(InstrumentChannel): \n",
-    "    def __init__(self, parent, name, channel_number): \n",
-    "        super().__init__(parent, name)\n",
-    "    \n",
-    "        self._channel_string = f\"{channel_number:02d}\"\n",
-    "        self._channel_number = channel_number\n",
-    "        self._acknowledge_reply = chr(6)\n",
-    "    \n",
-    "        self.add_parameter(\n",
-    "            \"voltage\", \n",
-    "            get_cmd=f\"{self.parent.identifier} U{self._channel_string}\", \n",
-    "            get_parser=self._stahl_get_parser(\"V\"), \n",
-    "            set_cmd=self._set_voltage, \n",
-    "            unit=\"V\",\n",
-    "            vals=Numbers(\n",
-    "                -self.parent.voltage_range, \n",
-    "                self.parent.voltage_range\n",
-    "            )\n",
-    "        )\n",
-    "        \n",
-    "        self.add_parameter(\n",
-    "            \"current\", \n",
-    "            get_cmd=f\"{self.parent.identifier} I{self._channel_string}\", \n",
-    "            get_parser=self._stahl_get_parser(\"mA\"), \n",
-    "            unit=\"mA\",\n",
-    "        )\n",
-    "        \n",
-    "        self.add_parameter(\n",
-    "            \"is_locked\", \n",
-    "            get_cmd=self._get_lock_status, \n",
-    "            get_parser={\"0\": False, \"1\": True}.get, \n",
-    "        )\n",
-    "        \n",
-    "    def _stahl_get_parser(self, unit):\n",
-    "        \n",
-    "        regex = f\"([\\+\\-][\\d]?\\d,\\d{{3}} ){unit}$\"\n",
-    "        \n",
-    "        def parser(response):\n",
-    "            result = re.search(regex, response).groups()[0]\n",
-    "            return float(result.replace(\",\", \".\"))\n",
-    "        \n",
-    "        return parser\n",
-    "        \n",
-    "    def _set_voltage(self, voltage): \n",
-    "        \n",
-    "        voltage_normalized = np.interp(\n",
-    "            voltage, \n",
-    "            self.parent.voltage_range * np.array([-1, 1]), \n",
-    "            [0, 1]\n",
-    "        )\n",
-    "        \n",
-    "        send_string = f\"{self.parent.identifier} CH{self._channel_string} {voltage_normalized:.5f}\"\n",
-    "        response = self.ask(send_string)  # This is not a bug. We are asking in a set \n",
-    "        \n",
-    "        if response != self._acknowledge_reply: \n",
-    "            logger.warning(f\"Command {expected_reply} did not produce an acknowledge reply\")\n",
-    "            \n",
-    "    def _get_lock_status(self): \n",
-    "        \n",
-    "        send_string = f\"{self.parent.identifier} LOCK\"\n",
-    "        \n",
-    "        response = self.parent.visa_handle.query_binary_values(\n",
-    "            send_string, \n",
-    "            datatype='B',\n",
-    "            header_fmt=\"empty\"\n",
-    "        )\n",
-    "        \n",
-    "        chnr = self._channel_number - 1\n",
-    "        channel_group = chnr // 4\n",
-    "        lock_code_group = response[channel_group]\n",
-    "        return format(lock_code_group, \"b\")[chnr % 4 + 1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Stahl(VisaInstrument): \n",
-    "    def __init__(self, name, address): \n",
-    "        super().__init__(name, address, terminator=\"\\r\")\n",
-    "        self.visa_handle.baud_rate = 115200\n",
-    "        \n",
-    "        instrument_info = self._parse_idn_string(\n",
-    "            self.ask(\"IDN\")\n",
-    "        )\n",
-    "        \n",
-    "        for key, value in instrument_info.items(): \n",
-    "            setattr(self, key, value)\n",
-    "                    \n",
-    "        channels = ChannelList(\n",
-    "            self, \"channel\", StahlChannel, snapshotable=False\n",
-    "        )\n",
-    "        \n",
-    "        for channel_number in range(1, self.n_channels + 1):\n",
-    "            name = f\"channel{channel_number}\"\n",
-    "            channel = StahlChannel(\n",
-    "                self, \n",
-    "                name, \n",
-    "                channel_number\n",
-    "            )\n",
-    "            self.add_submodule(name, channel)\n",
-    "            channels.append(channel)\n",
-    "        \n",
-    "        self.add_submodule(\"channel\", channels)\n",
-    "        \n",
-    "        self.add_parameter(\n",
-    "            \"temperature\", \n",
-    "            get_cmd=self._get_temperature, \n",
-    "            unit=\"C\"\n",
-    "        )\n",
-    "        \n",
-    "        self.connect_message()\n",
-    "    \n",
-    "    def _get_temperature(self):\n",
-    "        \n",
-    "        send_string = f\"{self.identifier} TEMP\"\n",
-    "        response_characters = self.visa_handle.query_binary_values(\n",
-    "            send_string, \n",
-    "            datatype='B', \n",
-    "            header_fmt=\"empty\"\n",
-    "        )\n",
-    "        \n",
-    "        response_string = \"\".join(map(chr, response_characters))\n",
-    "        # The '°' is the reason why we cannot simply call 'ask'\n",
-    "        # Stupid instrument :-(\n",
-    "        groups = re.search(\"TEMP (.*)°C\", response_string).groups()\n",
-    "        return float(groups[0])\n",
-    "        \n",
-    "    @staticmethod\n",
-    "    def _parse_idn_string(ind_string): \n",
-    "            \n",
-    "        groups = re.search(\n",
-    "            \"(HV|BS)(\\d{3}) (\\d{3}) (\\d{2}) [buqsm]\", \n",
-    "            ind_string\n",
-    "        ).groups()\n",
-    "    \n",
-    "        idparsers = {\n",
-    "            \"model\": str, \n",
-    "            \"serial_number\": str, \n",
-    "            \"voltage_range\": float, \n",
-    "            \"n_channels\": int, \n",
-    "            \"output_type\": {\n",
-    "                \"b\": \"bipolar\", \n",
-    "                \"u\": \"unipolar\", \n",
-    "                \"q\": \"quadrupole\", \n",
-    "                \"s\": \"steerer\", \n",
-    "                \"m\": \"bipolar milivolt\"\n",
-    "            }.get\n",
-    "        }\n",
-    "        \n",
-    "        return {\n",
-    "            name: idparsers[name](value) \n",
-    "            for name, value in zip(idparsers.keys(), groups)\n",
-    "        }\n",
-    "    \n",
-    "    def get_idn(self) -> Dict[str, Optional[str]]:\n",
-    "        \n",
-    "        return {\n",
-    "            \"vendor\": \"Stahl\", \n",
-    "            \"model\": self.model, \n",
-    "            \"serial\": self.serial_number, \n",
-    "            \"firmware\": None\n",
-    "        }\n",
-    "    \n",
-    "    @property \n",
-    "    def identifier(self): \n",
-    "        return f\"{self.model}{self.serial_number}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: Stahl HV (serial:171, firmware:None) in 0.07s\n"
+      "Connected to: Stahl HV (serial:171, firmware:None) in 0.20s\n"
      ]
     }
    ],
@@ -222,14 +28,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.101\n"
+      "0.1\n"
      ]
     }
    ],
@@ -240,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -258,14 +64,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.001\n"
+      "-0.0\n"
      ]
     }
    ],
@@ -276,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -285,16 +91,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.002"
+       "0.001"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -305,16 +111,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "25.3"
+       "25.9"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -325,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -334,7 +140,7 @@
        "False"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/qcodes/instrument_drivers/Spectrum/stahl/__init__.py b/qcodes/instrument_drivers/Spectrum/stahl/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/qcodes/instrument_drivers/stahl/__init__.py b/qcodes/instrument_drivers/stahl/__init__.py
new file mode 100644
index 00000000000..3b522091e04
--- /dev/null
+++ b/qcodes/instrument_drivers/stahl/__init__.py
@@ -0,0 +1 @@
+from .stahl import Stahl
diff --git a/qcodes/instrument_drivers/Spectrum/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
similarity index 100%
rename from qcodes/instrument_drivers/Spectrum/stahl/stahl.py
rename to qcodes/instrument_drivers/stahl/stahl.py

From 79eb07f2a5a8f3661ff83afed689cf246d41afd4 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Wed, 21 Nov 2018 16:53:33 +0100
Subject: [PATCH 174/719] bugfix: allow empty traces

---
 qcodes/instrument_drivers/tektronix/AWG5014.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG5014.py b/qcodes/instrument_drivers/tektronix/AWG5014.py
index 3b36f40cf2b..7a194a252f1 100644
--- a/qcodes/instrument_drivers/tektronix/AWG5014.py
+++ b/qcodes/instrument_drivers/tektronix/AWG5014.py
@@ -1145,12 +1145,13 @@ def make_send_and_load_awg_file_from_forged_sequence(
             else:
                 for i in range(2):
                     if len(marker_keys[i]) != 0:
-                        n_samples = len(step_markers[marker_keys[i][0]])
+                        n_samples = len(step_markers[i][marker_keys[i][0]])
                         break
             if n_samples is None:
                 raise RuntimeError('It is not allowed to upload an element '
                                    'without markers nor waveforms')
             blank_trace = np.zeros(n_samples)
+
             # I think this does might add some traces dynamically if they are
             # not the same in all elements. Add check in the beginning
             step_waveforms_list = [step_waveforms.get(key, blank_trace)

From 40289b81b01fde62c181c8ae740b84d3d63e59f5 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Wed, 21 Nov 2018 12:08:48 -0800
Subject: [PATCH 175/719] doc strings

---
 .../Qcodes example with Stahl.ipynb           | 76 +++++++++++----
 qcodes/instrument_drivers/stahl/stahl.py      | 93 ++++++++++++++-----
 2 files changed, 127 insertions(+), 42 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index 4e2a31d3904..b7a5cd6ed8e 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -18,7 +18,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: Stahl HV (serial:171, firmware:None) in 0.20s\n"
+      "Connected to: Stahl HV (serial:171, firmware:None) in 0.07s\n"
      ]
     }
    ],
@@ -35,7 +35,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1\n"
+      "0.001\n"
      ]
     }
    ],
@@ -48,12 +48,21 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = stahl.channel[4].voltage(-2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.001\n"
+      "-1.997\n"
      ]
     }
    ],
@@ -64,7 +73,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = stahl.channel[4].voltage(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -82,16 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stahl.channel[0].voltage(0.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +109,7 @@
        "0.001"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -111,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -120,7 +129,7 @@
        "25.9"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -131,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -140,7 +149,7 @@
        "False"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -149,6 +158,35 @@
     "stahl.channel[1].is_locked()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = '+/-10,103 V'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'+/-10.103'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.strip(\"V\").strip().replace(\",\", \".\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index 90bd4fd3c7d..0e527ec72b0 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -1,3 +1,7 @@
+"""
+This is a driver for the Stahl power supplies
+"""
+
 from typing import Dict, Optional, Any, Callable
 import re
 import numpy as np
@@ -11,6 +15,14 @@
 
 
 class StahlChannel(InstrumentChannel):
+    """
+    A Stahl source channel
+
+    Args:
+        parent
+        name
+        channel_number
+    """
     def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
         super().__init__(parent, name)
 
@@ -39,21 +51,37 @@ def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
 
         self.add_parameter(
             "is_locked",
-            get_cmd=self._get_lock_status,
-            get_parser={"0": False, "1": True}.get,
+            get_cmd=self._get_lock_status
         )
 
     @staticmethod
     def _stahl_get_parser(unit: str) -> Callable:
-        regex = f"([\+\-][\d]?\d,\d{{3}} ){unit}$"
+        """
+        Upon querying the voltage and current, the response from the instrument
+        is `+/-yy,yyy V` or `+/-yy,yyy mA'. We strip the unit and replace the
+        comma with a dot.
+
+        Args:
+            unit: The unit to strip
 
-        def parser(response):
+        Return:
+            parser: Parse a response to a float
+        """
+        regex = f"([\+\-]\d+,\d{{3}} ){unit}$"
+
+        def parser(response: str) -> float:
             result = re.search(regex, response).groups()[0]
             return float(result.replace(",", "."))
 
         return parser
 
     def _set_voltage(self, voltage: float) -> None:
+        """
+        Args:
+            voltage
+        """
+        # Normalize the voltage in the range 0 to 1, where 0 is maximum negative
+        # voltage and 1 is maximum positive voltage
         voltage_normalized = np.interp(
             voltage,
             self.parent.voltage_range * np.array([-1, 1]),
@@ -61,12 +89,18 @@ def _set_voltage(self, voltage: float) -> None:
         )
 
         send_string = f"{self.parent.identifier} CH{self._channel_string} {voltage_normalized:.5f}"
-        response = self.ask(send_string)  # This is not a bug. We are asking in a set
+        response = self.ask(send_string)
 
         if response != self._acknowledge_reply:
             logger.warning(f"Command {send_string} did not produce an acknowledge reply")
 
-    def _get_lock_status(self) -> chr:
+    def _get_lock_status(self) -> bool:
+        """
+        A lock occurs when an output is overloaded
+
+        Return:
+            lock_status: True when locked
+        """
         send_string = f"{self.parent.identifier} LOCK"
 
         response = self.parent.visa_handle.query_binary_values(
@@ -78,10 +112,17 @@ def _get_lock_status(self) -> chr:
         chnr = self._channel_number - 1
         channel_group = chnr // 4
         lock_code_group = response[channel_group]
-        return format(lock_code_group, "b")[chnr % 4 + 1]
+        return format(lock_code_group, "b")[chnr % 4 + 1] == "1"
 
 
 class Stahl(VisaInstrument):
+    """
+    Stahl driver.
+
+    Args:
+        name
+        address: A serial port address
+    """
     def __init__(self, name: str, address: str):
         super().__init__(name, address, terminator="\r")
         self.visa_handle.baud_rate = 115200
@@ -118,29 +159,32 @@ def __init__(self, name: str, address: str):
         self.connect_message()
 
     def _get_temperature(self) -> float:
-
+        """
+        When querying the temperature a non-ascii character to denote
+        the degree symbol '°' is present in the return string. For this
+        reason, we cannot use the `ask` method as it will attempt to
+        recode the response with utf-8, which will fail. We need to
+        manually set the decoding to latin-1
+        """
         send_string = f"{self.identifier} TEMP"
-        response_characters = self.visa_handle.query_binary_values(
-            send_string,
-            datatype='B',
-            header_fmt="empty"
-        )
-
-        response_string = "".join(map(chr, response_characters))
-        # The '°' is the reason why we cannot simply call 'ask'
-        # Stupid instrument :-(
-        groups = re.search("TEMP (.*)°C", response_string).groups()
+        self.write(send_string)
+        response = self.visa_handle.read(encoding="latin-1")
+        groups = re.search("TEMP (.*)°C", response).groups()
         return float(groups[0])
 
     @staticmethod
     def _parse_idn_string(ind_string) -> Dict[str, Any]:
-
+        """
+        Return:
+             dict with keys: "model", "serial_number", "voltage_range",
+             "n_channels", "output_type"
+        """
         groups = re.search(
             "(HV|BS)(\d{3}) (\d{3}) (\d{2}) [buqsm]",
             ind_string
         ).groups()
 
-        idparsers = {
+        id_parsers = {
             "model": str,
             "serial_number": str,
             "voltage_range": float,
@@ -155,12 +199,15 @@ def _parse_idn_string(ind_string) -> Dict[str, Any]:
         }
 
         return {
-            name: idparsers[name](value)
-            for name, value in zip(idparsers.keys(), groups)
+            name: id_parsers[name](value)
+            for name, value in zip(id_parsers.keys(), groups)
         }
 
     def get_idn(self) -> Dict[str, Optional[str]]:
-
+        """
+        The Stahl sends a uncommon IDN string which does not include a
+        firmware version.
+        """
         return {
             "vendor": "Stahl",
             "model": self.model,

From 1cfeb4145e8d6a9bce1e3d9db20985b7b0e1b56e Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Wed, 21 Nov 2018 12:20:05 -0800
Subject: [PATCH 176/719] mypy needs type hint for id_parsers

---
 qcodes/instrument_drivers/stahl/stahl.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index 0e527ec72b0..4f5e826f8fb 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -184,7 +184,7 @@ def _parse_idn_string(ind_string) -> Dict[str, Any]:
             ind_string
         ).groups()
 
-        id_parsers = {
+        id_parsers: Dict[str, Callable] = {
             "model": str,
             "serial_number": str,
             "voltage_range": float,

From 2bdd2a48a8f4d187be044edfc0e462707073fa48 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Wed, 21 Nov 2018 12:25:26 -0800
Subject: [PATCH 177/719] example notebook

---
 .../Qcodes example with Stahl.ipynb           | 85 ++++++-------------
 1 file changed, 25 insertions(+), 60 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index b7a5cd6ed8e..f4a21ccf526 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -1,5 +1,14 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction \n",
+    "\n",
+    "Example notebook how to set voltages with the Stahl bias sources"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -35,12 +44,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.001\n"
+      "2.001\n"
      ]
     }
    ],
    "source": [
-    "v = stahl.channel[0].voltage()\n",
+    "v = stahl.channel[4].voltage(2)\n",
+    "v = stahl.channel[4].voltage()\n",
     "print(v)"
    ]
   },
@@ -48,15 +58,6 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "v = stahl.channel[4].voltage(-2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -67,49 +68,42 @@
     }
    ],
    "source": [
+    "v = stahl.channel[4].voltage(-2)\n",
     "v = stahl.channel[4].voltage()\n",
     "print(v)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v = stahl.channel[4].voltage(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.0\n"
+      "0.001\n"
      ]
     }
    ],
    "source": [
-    "v = stahl.channel[9].voltage()\n",
+    "v = stahl.channel[4].voltage(0)\n",
+    "v = stahl.channel[4].voltage()\n",
     "print(v)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.001"
+       "0.002"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -120,16 +114,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "25.9"
+       "26.3"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -140,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -149,7 +143,7 @@
        "False"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -158,35 +152,6 @@
     "stahl.channel[1].is_locked()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = '+/-10,103 V'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'+/-10.103'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.strip(\"V\").strip().replace(\",\", \".\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From 0f8a725ee2f4f85117bfc4f26cf81a292932881a Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Wed, 21 Nov 2018 15:12:03 -0800
Subject: [PATCH 178/719] unpack tuple with comma's

---
 qcodes/instrument_drivers/stahl/stahl.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index 4f5e826f8fb..45a672024ef 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -67,10 +67,10 @@ def _stahl_get_parser(unit: str) -> Callable:
         Return:
             parser: Parse a response to a float
         """
-        regex = f"([\+\-]\d+,\d{{3}} ){unit}$"
+        regex = f"([\+\-]\d+,\d+ ){unit}$"
 
         def parser(response: str) -> float:
-            result = re.search(regex, response).groups()[0]
+            result, = re.search(regex, response).groups()
             return float(result.replace(",", "."))
 
         return parser
@@ -163,14 +163,14 @@ def _get_temperature(self) -> float:
         When querying the temperature a non-ascii character to denote
         the degree symbol '°' is present in the return string. For this
         reason, we cannot use the `ask` method as it will attempt to
-        recode the response with utf-8, which will fail. We need to
+        decode the response with utf-8, which will fail. We need to
         manually set the decoding to latin-1
         """
         send_string = f"{self.identifier} TEMP"
         self.write(send_string)
         response = self.visa_handle.read(encoding="latin-1")
-        groups = re.search("TEMP (.*)°C", response).groups()
-        return float(groups[0])
+        temperature_string, = re.search("TEMP (.*)°C", response).groups()
+        return float(temperature_string)
 
     @staticmethod
     def _parse_idn_string(ind_string) -> Dict[str, Any]:

From 2914303314b01423b407f93c4d3b254b019c974f Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 22 Nov 2018 13:03:40 +0100
Subject: [PATCH 179/719] Add metadata to runs to be extracted

---
 qcodes/tests/dataset/test_database_extract_runs.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index a61f1995da1..ada869522f2 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -98,6 +98,9 @@ def test_basic_extraction(two_empty_temp_db_connections, some_paramspecs):
                   for ps in some_paramspecs[1].values()}
         source_dataset.add_result(result)
 
+    source_dataset.add_metadata('goodness', 'fair')
+    source_dataset.add_metadata('test', True)
+
     source_dataset.mark_complete()
 
     extract_runs_into_db(source_path, target_path, source_dataset.run_id)

From 4a0feb74edbbc9a22f8691ce5ee40b795d027191 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 22 Nov 2018 18:58:13 +0100
Subject: [PATCH 180/719] Dont allow creating ConnectionPlus from
 ConnectionPlus objects

---
 qcodes/dataset/sqlite_base.py                  | 9 +++++++++
 qcodes/tests/dataset/test_sqlite_connection.py | 6 ++++++
 2 files changed, 15 insertions(+)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 516e598b0bf..e978149b051 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -120,6 +120,15 @@ class ConnectionPlus(wrapt.ObjectProxy):
     """
     atomic_in_progress: bool = False
 
+    def __init__(self, sqlite3_connection: sqlite3.Connection):
+        super(ConnectionPlus, self).__init__(sqlite3_connection)
+
+        if isinstance(sqlite3_connection, ConnectionPlus):
+            raise ValueError('Attempted to create `ConnectionPlus` from a '
+                             '`ConnectionPlus` object which is not allowed.')
+
+
+
 
 SomeConnection = Union[sqlite3.Connection, ConnectionPlus]
 
diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index 3a6bec31a56..0f634c2f79e 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -1,3 +1,4 @@
+import re
 import sqlite3
 
 import pytest
@@ -44,6 +45,11 @@ def test_connection_plus():
     assert isinstance(plus_conn, sqlite3.Connection)
     assert False is plus_conn.atomic_in_progress
 
+    match_str = re.escape('Attempted to create `ConnectionPlus` from a '
+                          '`ConnectionPlus` object which is not allowed.')
+    with pytest.raises(ValueError, match=match_str):
+        ConnectionPlus(plus_conn)
+
 
 @pytest.mark.parametrize(
     argnames='conn',

From 098989ab8c93fd226f5372d75926c67630b4feda Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 22 Nov 2018 18:58:30 +0100
Subject: [PATCH 181/719] Improve docstring of ConnectionPlus

---
 qcodes/dataset/sqlite_base.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index e978149b051..1162d26c22c 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -110,13 +110,17 @@
 
 class ConnectionPlus(wrapt.ObjectProxy):
     """
-    A class to extend the sqlite3.Connection object with a single extra
-    attribute. Since sqlite3.Connection has no __dict__, we can not directly
-    add the attribute to a normal instance.
+    A class to extend the sqlite3.Connection object. Since sqlite3.Connection
+    has no __dict__, we can not directly add attributes to its instance
+    directly.
 
-    The extra attribute is a bool describing whether the connection is
-    currently in the middle of an atomic block of transactions, thus allowing
-    us to nest atomic context managers
+    It is not allowed to instantiate a new `ConnectionPlus` object from a
+    `ConnectionPlus` object.
+
+    Attributes:
+        atomic_in_progress: a bool describing whether the connection is
+            currently in the middle of an atomic block of transactions, thus
+            allowing to nest `atomic` context managers
     """
     atomic_in_progress: bool = False
 

From ceb0e97bf300d7763f800c3dcd2f8e3e9d23413b Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 22 Nov 2018 19:36:43 +0100
Subject: [PATCH 182/719] Now connect returns ConnectionPlus instead of
 sqlite3.Connection

---
 qcodes/dataset/sqlite_base.py                  | 17 +++++++++--------
 qcodes/tests/dataset/test_sqlite_connection.py | 15 ++++++++++++++-
 2 files changed, 23 insertions(+), 9 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 1162d26c22c..2caf310abf6 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -132,8 +132,6 @@ def __init__(self, sqlite3_connection: sqlite3.Connection):
                              '`ConnectionPlus` object which is not allowed.')
 
 
-
-
 SomeConnection = Union[sqlite3.Connection, ConnectionPlus]
 
 
@@ -307,21 +305,21 @@ def many_many(curr: sqlite3.Cursor, *columns: str) -> List[List[Any]]:
 
 
 def connect(name: str, debug: bool = False,
-            version: int=-1) -> sqlite3.Connection:
+            version: int=-1) -> ConnectionPlus:
     """
     Connect or create  database. If debug the queries will be echoed back.
     This function takes care of registering the numpy/sqlite type
     converters that we need.
 
-
     Args:
         name: name or path to the sqlite file
         debug: whether or not to turn on tracing
-        version: which version to create. We count from 0. -1 means 'latest'
-          Should always be left at -1 except when testing.
+        version: which version to create. We count from 0. -1 means 'latest'.
+            Should always be left at -1 except when testing.
 
     Returns:
-        conn: connection object to the database
+        conn: connection object to the database (note, it is
+            `ConnectionPlus`, not `sqlite3.Connection`
 
     """
     # register numpy->binary(TEXT) adapter
@@ -331,7 +329,10 @@ def connect(name: str, debug: bool = False,
     # register binary(TEXT) -> numpy converter
     # for some reasons mypy complains about this
     sqlite3.register_converter("array", _convert_array)
-    conn = sqlite3.connect(name, detect_types=sqlite3.PARSE_DECLTYPES)
+
+    sqlite3_conn = sqlite3.connect(name, detect_types=sqlite3.PARSE_DECLTYPES)
+    conn = ConnectionPlus(sqlite3_conn)
+
     # sqlite3 options
     conn.row_factory = sqlite3.Row
 
diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index 0f634c2f79e..6ecafe72501 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -203,8 +203,11 @@ def test_that_use_of_atomic_commits_only_at_outermost_context(
     `sqlite3.Connection` with respect to `atomic` context manager and commits.
     """
     dbfile = str(tmp_path / 'temp.db')
+    # just initialize the database file, connection objects needed for
+    # testing in this test function are created separately, see below
+    connect(dbfile)
 
-    sqlite_conn = connect(dbfile)
+    sqlite_conn = sqlite3.connect(dbfile)
     plus_conn = create_conn_plus(sqlite_conn)
 
     # this connection is going to be used to test whether changes have been
@@ -279,3 +282,13 @@ def test_that_use_of_atomic_commits_only_at_outermost_context(
     assert 3 == len(atomic_conn_1.execute(get_all_runs).fetchall())
     assert 3 == len(atomic_conn_2.execute(get_all_runs).fetchall())
     assert 3 == len(control_conn.execute(get_all_runs).fetchall())
+
+
+def test_connect():
+    conn = connect(':memory:')
+
+    assert isinstance(conn, sqlite3.Connection)
+    assert isinstance(conn, ConnectionPlus)
+    assert False is conn.atomic_in_progress
+
+    assert sqlite3.Row is conn.row_factory

From ef1dfe89fe549d211f0917f9af04ce2e8955264f Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 22 Nov 2018 19:40:36 +0100
Subject: [PATCH 183/719] Test that connect changes user version (because it
 performs upgrades)

---
 .../dataset/test_database_creation_and_upgrading.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index 71b77b06180..83b49fbc256 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -69,6 +69,19 @@ def location_and_station_set_to(location: int, work_station: int):
         cfg.save_to_home()
 
 
+LATEST_VERSION = 3
+VERSIONS = tuple(range(LATEST_VERSION + 1))
+LATEST_VERSION_ARG = -1
+
+
+@pytest.mark.parametrize('ver',
+                         VERSIONS + (LATEST_VERSION_ARG,))
+def test_connect_upgrades_user_version(ver):
+    expected_version = ver if ver != LATEST_VERSION_ARG else LATEST_VERSION
+    conn = connect(':memory:', version=ver)
+    assert expected_version == get_user_version(conn)
+
+
 @pytest.mark.usefixtures("empty_temp_db")
 def test_tables_exist():
     for version in [-1, 0, 1]:

From a79d160f7ad2ffdb0a3f503abde220b1aa5fc6b5 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 22 Nov 2018 19:43:00 +0100
Subject: [PATCH 184/719] test_tables_exist is now parametrized for all
 database versions

---
 .../test_database_creation_and_upgrading.py   | 30 +++++++++----------
 1 file changed, 14 insertions(+), 16 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index 83b49fbc256..6f7b7804935 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -74,28 +74,26 @@ def location_and_station_set_to(location: int, work_station: int):
 LATEST_VERSION_ARG = -1
 
 
-@pytest.mark.parametrize('ver',
-                         VERSIONS + (LATEST_VERSION_ARG,))
+@pytest.mark.parametrize('ver', VERSIONS + (LATEST_VERSION_ARG,))
 def test_connect_upgrades_user_version(ver):
     expected_version = ver if ver != LATEST_VERSION_ARG else LATEST_VERSION
     conn = connect(':memory:', version=ver)
     assert expected_version == get_user_version(conn)
 
 
-@pytest.mark.usefixtures("empty_temp_db")
-def test_tables_exist():
-    for version in [-1, 0, 1]:
-        conn = connect(qc.config["core"]["db_location"],
-                       qc.config["core"]["db_debug"],
-                       version=version)
-        cursor = conn.execute("select sql from sqlite_master"
-                              " where type = 'table'")
-        expected_tables = ['experiments', 'runs', 'layouts', 'dependencies']
-        rows = [row for row in cursor]
-        assert len(rows) == len(expected_tables)
-        for row, expected_table in zip(rows, expected_tables):
-            assert expected_table in row['sql']
-        conn.close()
+@pytest.mark.parametrize('version', VERSIONS + (LATEST_VERSION_ARG,))
+def test_tables_exist(empty_temp_db, version):
+    conn = connect(qc.config["core"]["db_location"],
+                   qc.config["core"]["db_debug"],
+                   version=version)
+    cursor = conn.execute("select sql from sqlite_master"
+                          " where type = 'table'")
+    expected_tables = ['experiments', 'runs', 'layouts', 'dependencies']
+    rows = [row for row in cursor]
+    assert len(rows) == len(expected_tables)
+    for row, expected_table in zip(rows, expected_tables):
+        assert expected_table in row['sql']
+    conn.close()
 
 
 def test_initialise_database_at_for_nonexisting_db():

From 3a57fc79229ecd96f7087637b39d0860f8ace508 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 22 Nov 2018 19:49:10 +0100
Subject: [PATCH 185/719] Extract upgrade_actions and newest_version as
 constants

Additionally, this allows to correctly parametrize tests.
---
 qcodes/dataset/sqlite_base.py                    | 16 +++++++++-------
 .../test_database_creation_and_upgrading.py      |  5 +++--
 2 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 2caf310abf6..2eae6275051 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -364,19 +364,16 @@ def perform_db_upgrade(conn: SomeConnection, version: int=-1) -> None:
     upgrade and be a NOOP if the current version is higher than their target.
 
     Args:
+        conn: object for connection to the database
         version: Which version to upgrade to. We count from 0. -1 means
           'newest version'
     """
-
-    upgrade_actions = [perform_db_upgrade_0_to_1, perform_db_upgrade_1_to_2,
-                       perform_db_upgrade_2_to_3]
-    newest_version = len(upgrade_actions)
-    version = newest_version if version == -1 else version
+    version = NEWEST_VERSION if version == -1 else version
 
     current_version = get_user_version(conn)
-    if current_version < newest_version:
+    if current_version < NEWEST_VERSION:
         log.info("Commencing database upgrade")
-        for action in upgrade_actions[:version]:
+        for action in UPGRADE_ACTIONS[:version]:
             action(conn)
 
 
@@ -698,6 +695,11 @@ def perform_db_upgrade_2_to_3(conn: SomeConnection) -> None:
             log.debug(f"Upgrade in transition, run number {run_id}: OK")
 
 
+UPGRADE_ACTIONS = [perform_db_upgrade_0_to_1, perform_db_upgrade_1_to_2,
+                   perform_db_upgrade_2_to_3]
+NEWEST_VERSION = len(UPGRADE_ACTIONS)
+
+
 def transaction(conn: SomeConnection,
                 sql: str, *args: Any) -> sqlite3.Cursor:
     """Perform a transaction.
diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index 6f7b7804935..1b651108b66 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -25,7 +25,8 @@
                                         atomic_transaction,
                                         perform_db_upgrade_0_to_1,
                                         perform_db_upgrade_1_to_2,
-                                        perform_db_upgrade_2_to_3)
+                                        perform_db_upgrade_2_to_3,
+                                        NEWEST_VERSION)
 
 from qcodes.dataset.guids import parse_guid
 import qcodes.tests.dataset
@@ -69,7 +70,7 @@ def location_and_station_set_to(location: int, work_station: int):
         cfg.save_to_home()
 
 
-LATEST_VERSION = 3
+LATEST_VERSION = NEWEST_VERSION
 VERSIONS = tuple(range(LATEST_VERSION + 1))
 LATEST_VERSION_ARG = -1
 

From b83404de1f3ac279f80c0fb2e5815fa542dd362e Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 23 Nov 2018 01:06:40 +0100
Subject: [PATCH 186/719] Make atomic accept only ConnectionPlus

---
 qcodes/dataset/sqlite_base.py                 | 12 ++--
 .../tests/dataset/test_sqlite_connection.py   | 71 +++++++++----------
 2 files changed, 39 insertions(+), 44 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 2eae6275051..2dfe564e415 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -741,13 +741,14 @@ def atomic_transaction(conn: SomeConnection,
         sqlite cursor
 
     """
-    with atomic(conn) as conn:
-        c = transaction(conn, sql, *args)
+    plus_conn = make_plus_connection_from(conn)
+    with atomic(plus_conn) as atomic_conn:
+        c = transaction(atomic_conn, sql, *args)
     return c
 
 
 @contextmanager
-def atomic(conn: SomeConnection):
+def atomic(conn: ConnectionPlus):
     """
     Guard a series of transactions as atomic.
     If one fails the transaction is rolled back and no more transactions
@@ -759,8 +760,9 @@ def atomic(conn: SomeConnection):
     Args:
         - conn: connection to guard
     """
-
-    conn = make_plus_connection_from(conn)
+    if not isinstance(conn, ConnectionPlus):
+        raise ValueError('atomic context manager only accepts ConnectionPlus '
+                         'database connection objects.')
 
     old_atomic_in_progress = conn.atomic_in_progress
     is_outmost = not(conn.atomic_in_progress)
diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index 6ecafe72501..f2f0035a57d 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -4,7 +4,7 @@
 import pytest
 
 from qcodes.dataset.sqlite_base import ConnectionPlus, \
-    make_plus_connection_from, atomic, connect
+    make_plus_connection_from, atomic, connect, atomic_transaction
 
 
 def sqlite_conn_in_transaction(conn: sqlite3.Connection):
@@ -68,21 +68,15 @@ def test_make_plus_connection_from(conn):
         assert False is plus_conn.atomic_in_progress
 
 
-def test_atomic_on_outmost_sqlite_connection():
+def test_atomic():
     sqlite_conn = sqlite3.connect(':memory:')
-    isolation_level = sqlite_conn.isolation_level
-    assert False is sqlite_conn.in_transaction
-
-    with atomic(sqlite_conn) as atomic_conn:
-        assert sqlite_conn_in_transaction(sqlite_conn)
-        assert plus_conn_in_transaction(atomic_conn)
-
-    assert sqlite_conn_is_idle(sqlite_conn, isolation_level)
-    assert plus_conn_is_idle(atomic_conn, isolation_level)
 
+    match_str = re.escape('atomic context manager only accepts ConnectionPlus '
+                          'database connection objects.')
+    with pytest.raises(ValueError, match=match_str):
+        with atomic(sqlite_conn):  # type: ignore
+            pass
 
-def test_atomic_on_outmost_plus_connection():
-    sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
     assert False is plus_conn.atomic_in_progress
 
@@ -105,7 +99,7 @@ def test_atomic_on_outmost_plus_connection():
 
 
 @pytest.mark.parametrize('in_transaction', (True, False))
-def test_atomic_on_outmost_plus_connection_that_is_in_progress(in_transaction):
+def test_atomic_on_plus_connection_that_is_in_progress(in_transaction):
     sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
 
@@ -138,31 +132,7 @@ def test_atomic_on_outmost_plus_connection_that_is_in_progress(in_transaction):
     assert in_transaction is atomic_conn.in_transaction
 
 
-def test_two_atomics_on_outmost_sqlite_connection():
-    sqlite_conn = sqlite3.connect(':memory:')
-
-    isolation_level = sqlite_conn.isolation_level
-    assert False is sqlite_conn.in_transaction
-
-    with atomic(sqlite_conn) as atomic_conn_1:
-        assert sqlite_conn_in_transaction(sqlite_conn)
-        assert plus_conn_in_transaction(atomic_conn_1)
-
-        with atomic(atomic_conn_1) as atomic_conn_2:
-            assert sqlite_conn_in_transaction(sqlite_conn)
-            assert plus_conn_in_transaction(atomic_conn_2)
-            assert plus_conn_in_transaction(atomic_conn_1)
-
-        assert sqlite_conn_in_transaction(sqlite_conn)
-        assert plus_conn_in_transaction(atomic_conn_1)
-        assert plus_conn_in_transaction(atomic_conn_2)
-
-    assert sqlite_conn_is_idle(sqlite_conn, isolation_level)
-    assert plus_conn_is_idle(atomic_conn_1, isolation_level)
-    assert plus_conn_is_idle(atomic_conn_2, isolation_level)
-
-
-def test_two_atomics_on_outmost_plus_connection():
+def test_two_nested_atomics():
     sqlite_conn = sqlite3.connect(':memory:')
     plus_conn = ConnectionPlus(sqlite_conn)
 
@@ -284,6 +254,29 @@ def test_that_use_of_atomic_commits_only_at_outermost_context(
     assert 3 == len(control_conn.execute(get_all_runs).fetchall())
 
 
+@pytest.mark.parametrize(argnames='create_connection',
+                         argvalues=(
+                                 sqlite3.connect,
+                                 lambda x: ConnectionPlus(sqlite3.connect(x))),
+                         ids=(
+                                 'sqlite3.Connection',
+                                 'ConnectionPlus'))
+def test_atomic_transaction(create_connection, tmp_path):
+    """Test that atomic_transaction works for both types of connection"""
+    dbfile = str(tmp_path / 'temp.db')
+
+    conn = create_connection(dbfile)
+
+    ctrl_conn = sqlite3.connect(dbfile)
+
+    sql_create_table = 'CREATE TABLE smth (name TEXT)'
+    sql_table_exists = 'SELECT sql FROM sqlite_master WHERE TYPE = "table"'
+
+    atomic_transaction(conn, sql_create_table)
+
+    assert sql_create_table in ctrl_conn.execute(sql_table_exists).fetchall()[0]
+
+
 def test_connect():
     conn = connect(':memory:')
 

From b92b0547ab54e5a5e3672ed40666470bedaadb7e Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 23 Nov 2018 01:39:05 +0100
Subject: [PATCH 187/719] make_plus_connection_from is stricter when processing
 given connection

---
 qcodes/dataset/sqlite_base.py                 | 10 ++++----
 .../tests/dataset/test_sqlite_connection.py   | 23 ++++++++++---------
 2 files changed, 17 insertions(+), 16 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 2dfe564e415..b7176d137d4 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -803,13 +803,13 @@ def make_plus_connection_from(conn: SomeConnection) -> ConnectionPlus:
         conn: an sqlite connection object as defined by SomeConnection type
 
     Returns:
-        the same connection as ConnectionPlus object
+        the "same" connection but as ConnectionPlus object
     """
-    if not (hasattr(conn, 'atomic_in_progress')):
-        conn = ConnectionPlus(conn)
+    if not isinstance(conn, ConnectionPlus):
+        conn_plus = ConnectionPlus(conn)
     else:
-        conn = cast(ConnectionPlus, conn)
-    return conn
+        conn_plus = cast(ConnectionPlus, conn)
+    return conn_plus
 
 
 def init_db(conn: SomeConnection)->None:
diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index f2f0035a57d..95336fde203 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -51,21 +51,22 @@ def test_connection_plus():
         ConnectionPlus(plus_conn)
 
 
-@pytest.mark.parametrize(
-    argnames='conn',
-    argvalues=(sqlite3.connect(':memory:'),
-               ConnectionPlus(sqlite3.connect(':memory:'))),
-    ids=('sqlite3.Connection', 'ConnectionPlus')
-)
-def test_make_plus_connection_from(conn):
+def test_make_plus_connection_from_sqlite3_connection():
+    conn = sqlite3.connect(':memory:')
     plus_conn = make_plus_connection_from(conn)
 
     assert isinstance(plus_conn, ConnectionPlus)
+    assert False is plus_conn.atomic_in_progress
+    assert plus_conn is not conn
+
 
-    if isinstance(conn, ConnectionPlus):
-        assert conn.atomic_in_progress is plus_conn.atomic_in_progress
-    else:
-        assert False is plus_conn.atomic_in_progress
+def test_make_plus_connection_from_connecton_plus():
+    conn = ConnectionPlus(sqlite3.connect(':memory:'))
+    plus_conn = make_plus_connection_from(conn)
+
+    assert isinstance(plus_conn, ConnectionPlus)
+    assert conn.atomic_in_progress is plus_conn.atomic_in_progress
+    assert plus_conn is conn
 
 
 def test_atomic():

From 485dc2aed332be4afcaa6f8747b399ff80265c3f Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 23 Nov 2018 01:49:01 +0100
Subject: [PATCH 188/719] Rename all plus_conn to conn_plus to resemble
 ConnectionPlus

---
 qcodes/dataset/sqlite_base.py                 |   6 +-
 .../tests/dataset/test_sqlite_connection.py   | 152 +++++++++---------
 2 files changed, 79 insertions(+), 79 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index b7176d137d4..5535ac17b74 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -741,8 +741,8 @@ def atomic_transaction(conn: SomeConnection,
         sqlite cursor
 
     """
-    plus_conn = make_plus_connection_from(conn)
-    with atomic(plus_conn) as atomic_conn:
+    conn_plus = make_connection_plus_from(conn)
+    with atomic(conn_plus) as atomic_conn:
         c = transaction(atomic_conn, sql, *args)
     return c
 
@@ -792,7 +792,7 @@ def atomic(conn: ConnectionPlus):
         conn.atomic_in_progress = old_atomic_in_progress
 
 
-def make_plus_connection_from(conn: SomeConnection) -> ConnectionPlus:
+def make_connection_plus_from(conn: SomeConnection) -> ConnectionPlus:
     """
     Makes a ConnectionPlus connection object out of a given argument.
 
diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index 95336fde203..23c1fee665c 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -4,7 +4,7 @@
 import pytest
 
 from qcodes.dataset.sqlite_base import ConnectionPlus, \
-    make_plus_connection_from, atomic, connect, atomic_transaction
+    make_connection_plus_from, atomic, connect, atomic_transaction
 
 
 def sqlite_conn_in_transaction(conn: sqlite3.Connection):
@@ -14,7 +14,7 @@ def sqlite_conn_in_transaction(conn: sqlite3.Connection):
     return True
 
 
-def plus_conn_in_transaction(conn: ConnectionPlus):
+def conn_plus_in_transaction(conn: ConnectionPlus):
     assert isinstance(conn, ConnectionPlus)
     assert True is conn.atomic_in_progress
     assert None is conn.isolation_level
@@ -29,7 +29,7 @@ def sqlite_conn_is_idle(conn: sqlite3.Connection, isolation=None):
     return True
 
 
-def plus_conn_is_idle(conn: ConnectionPlus, isolation=None):
+def conn_plus_is_idle(conn: ConnectionPlus, isolation=None):
     assert isinstance(conn, ConnectionPlus)
     assert False is conn.atomic_in_progress
     assert isolation == conn.isolation_level
@@ -39,34 +39,34 @@ def plus_conn_is_idle(conn: ConnectionPlus, isolation=None):
 
 def test_connection_plus():
     sqlite_conn = sqlite3.connect(':memory:')
-    plus_conn = ConnectionPlus(sqlite_conn)
+    conn_plus = ConnectionPlus(sqlite_conn)
 
-    assert isinstance(plus_conn, ConnectionPlus)
-    assert isinstance(plus_conn, sqlite3.Connection)
-    assert False is plus_conn.atomic_in_progress
+    assert isinstance(conn_plus, ConnectionPlus)
+    assert isinstance(conn_plus, sqlite3.Connection)
+    assert False is conn_plus.atomic_in_progress
 
     match_str = re.escape('Attempted to create `ConnectionPlus` from a '
                           '`ConnectionPlus` object which is not allowed.')
     with pytest.raises(ValueError, match=match_str):
-        ConnectionPlus(plus_conn)
+        ConnectionPlus(conn_plus)
 
 
-def test_make_plus_connection_from_sqlite3_connection():
+def test_make_connection_plus_from_sqlite3_connection():
     conn = sqlite3.connect(':memory:')
-    plus_conn = make_plus_connection_from(conn)
+    conn_plus = make_connection_plus_from(conn)
 
-    assert isinstance(plus_conn, ConnectionPlus)
-    assert False is plus_conn.atomic_in_progress
-    assert plus_conn is not conn
+    assert isinstance(conn_plus, ConnectionPlus)
+    assert False is conn_plus.atomic_in_progress
+    assert conn_plus is not conn
 
 
-def test_make_plus_connection_from_connecton_plus():
+def test_make_connection_plus_from_connecton_plus():
     conn = ConnectionPlus(sqlite3.connect(':memory:'))
-    plus_conn = make_plus_connection_from(conn)
+    conn_plus = make_connection_plus_from(conn)
 
-    assert isinstance(plus_conn, ConnectionPlus)
-    assert conn.atomic_in_progress is plus_conn.atomic_in_progress
-    assert plus_conn is conn
+    assert isinstance(conn_plus, ConnectionPlus)
+    assert conn.atomic_in_progress is conn_plus.atomic_in_progress
+    assert conn_plus is conn
 
 
 def test_atomic():
@@ -78,55 +78,55 @@ def test_atomic():
         with atomic(sqlite_conn):  # type: ignore
             pass
 
-    plus_conn = ConnectionPlus(sqlite_conn)
-    assert False is plus_conn.atomic_in_progress
+    conn_plus = ConnectionPlus(sqlite_conn)
+    assert False is conn_plus.atomic_in_progress
 
-    atomic_in_progress = plus_conn.atomic_in_progress
-    isolation_level = plus_conn.isolation_level
+    atomic_in_progress = conn_plus.atomic_in_progress
+    isolation_level = conn_plus.isolation_level
 
-    assert False is plus_conn.in_transaction
+    assert False is conn_plus.in_transaction
 
-    with atomic(plus_conn) as atomic_conn:
-        assert plus_conn_in_transaction(atomic_conn)
-        assert plus_conn_in_transaction(plus_conn)
+    with atomic(conn_plus) as atomic_conn:
+        assert conn_plus_in_transaction(atomic_conn)
+        assert conn_plus_in_transaction(conn_plus)
 
-    assert isolation_level == plus_conn.isolation_level
-    assert False is plus_conn.in_transaction
-    assert atomic_in_progress is plus_conn.atomic_in_progress
+    assert isolation_level == conn_plus.isolation_level
+    assert False is conn_plus.in_transaction
+    assert atomic_in_progress is conn_plus.atomic_in_progress
 
-    assert isolation_level == plus_conn.isolation_level
+    assert isolation_level == conn_plus.isolation_level
     assert False is atomic_conn.in_transaction
     assert atomic_in_progress is atomic_conn.atomic_in_progress
 
 
 @pytest.mark.parametrize('in_transaction', (True, False))
-def test_atomic_on_plus_connection_that_is_in_progress(in_transaction):
+def test_atomic_on_connection_plus_that_is_in_progress(in_transaction):
     sqlite_conn = sqlite3.connect(':memory:')
-    plus_conn = ConnectionPlus(sqlite_conn)
+    conn_plus = ConnectionPlus(sqlite_conn)
 
     # explicitly set to True for testing purposes
-    plus_conn.atomic_in_progress = True
+    conn_plus.atomic_in_progress = True
 
     # implement parametrizing over connection's `in_transaction` attribute
     if in_transaction:
-        plus_conn.cursor().execute('BEGIN')
-    assert in_transaction is plus_conn.in_transaction
+        conn_plus.cursor().execute('BEGIN')
+    assert in_transaction is conn_plus.in_transaction
 
-    isolation_level = plus_conn.isolation_level
-    in_transaction = plus_conn.in_transaction
+    isolation_level = conn_plus.isolation_level
+    in_transaction = conn_plus.in_transaction
 
-    with atomic(plus_conn) as atomic_conn:
-        assert True is plus_conn.atomic_in_progress
-        assert isolation_level == plus_conn.isolation_level
-        assert in_transaction is plus_conn.in_transaction
+    with atomic(conn_plus) as atomic_conn:
+        assert True is conn_plus.atomic_in_progress
+        assert isolation_level == conn_plus.isolation_level
+        assert in_transaction is conn_plus.in_transaction
 
         assert True is atomic_conn.atomic_in_progress
         assert isolation_level == atomic_conn.isolation_level
         assert in_transaction is atomic_conn.in_transaction
 
-    assert True is plus_conn.atomic_in_progress
-    assert isolation_level == plus_conn.isolation_level
-    assert in_transaction is plus_conn.in_transaction
+    assert True is conn_plus.atomic_in_progress
+    assert isolation_level == conn_plus.isolation_level
+    assert in_transaction is conn_plus.in_transaction
 
     assert True is atomic_conn.atomic_in_progress
     assert isolation_level == atomic_conn.isolation_level
@@ -135,38 +135,38 @@ def test_atomic_on_plus_connection_that_is_in_progress(in_transaction):
 
 def test_two_nested_atomics():
     sqlite_conn = sqlite3.connect(':memory:')
-    plus_conn = ConnectionPlus(sqlite_conn)
+    conn_plus = ConnectionPlus(sqlite_conn)
 
-    atomic_in_progress = plus_conn.atomic_in_progress
-    isolation_level = plus_conn.isolation_level
+    atomic_in_progress = conn_plus.atomic_in_progress
+    isolation_level = conn_plus.isolation_level
 
-    assert False is plus_conn.in_transaction
+    assert False is conn_plus.in_transaction
 
-    with atomic(plus_conn) as atomic_conn_1:
-        assert plus_conn_in_transaction(plus_conn)
-        assert plus_conn_in_transaction(atomic_conn_1)
+    with atomic(conn_plus) as atomic_conn_1:
+        assert conn_plus_in_transaction(conn_plus)
+        assert conn_plus_in_transaction(atomic_conn_1)
 
         with atomic(atomic_conn_1) as atomic_conn_2:
-            assert plus_conn_in_transaction(plus_conn)
-            assert plus_conn_in_transaction(atomic_conn_1)
-            assert plus_conn_in_transaction(atomic_conn_2)
+            assert conn_plus_in_transaction(conn_plus)
+            assert conn_plus_in_transaction(atomic_conn_1)
+            assert conn_plus_in_transaction(atomic_conn_2)
 
-        assert plus_conn_in_transaction(plus_conn)
-        assert plus_conn_in_transaction(atomic_conn_1)
-        assert plus_conn_in_transaction(atomic_conn_2)
+        assert conn_plus_in_transaction(conn_plus)
+        assert conn_plus_in_transaction(atomic_conn_1)
+        assert conn_plus_in_transaction(atomic_conn_2)
 
-    assert plus_conn_is_idle(plus_conn, isolation_level)
-    assert plus_conn_is_idle(atomic_conn_1, isolation_level)
-    assert plus_conn_is_idle(atomic_conn_2, isolation_level)
+    assert conn_plus_is_idle(conn_plus, isolation_level)
+    assert conn_plus_is_idle(atomic_conn_1, isolation_level)
+    assert conn_plus_is_idle(atomic_conn_2, isolation_level)
 
-    assert atomic_in_progress == plus_conn.atomic_in_progress
+    assert atomic_in_progress == conn_plus.atomic_in_progress
     assert atomic_in_progress == atomic_conn_1.atomic_in_progress
     assert atomic_in_progress == atomic_conn_2.atomic_in_progress
 
 
 @pytest.mark.parametrize(argnames='create_conn_plus',
-                         argvalues=(make_plus_connection_from, ConnectionPlus),
-                         ids=('make_plus_connection_from', 'ConnectionPlus'))
+                         argvalues=(make_connection_plus_from, ConnectionPlus),
+                         ids=('make_connection_plus_from', 'ConnectionPlus'))
 def test_that_use_of_atomic_commits_only_at_outermost_context(
         tmp_path, create_conn_plus):
     """
@@ -179,7 +179,7 @@ def test_that_use_of_atomic_commits_only_at_outermost_context(
     connect(dbfile)
 
     sqlite_conn = sqlite3.connect(dbfile)
-    plus_conn = create_conn_plus(sqlite_conn)
+    conn_plus = create_conn_plus(sqlite_conn)
 
     # this connection is going to be used to test whether changes have been
     # committed to the database file
@@ -191,26 +191,26 @@ def test_that_use_of_atomic_commits_only_at_outermost_context(
     # assert that at the beginning of the test there are no runs in the
     # table; we'll be adding new rows to the runs table below
 
-    assert 0 == len(plus_conn.execute(get_all_runs).fetchall())
+    assert 0 == len(conn_plus.execute(get_all_runs).fetchall())
     assert 0 == len(control_conn.execute(get_all_runs).fetchall())
 
     # add 1 new row, and assert the state of the runs table at every step
     # note that control_conn will only detect the change after the `atomic`
     # context manager is exited
 
-    with atomic(plus_conn) as atomic_conn:
+    with atomic(conn_plus) as atomic_conn:
 
-        assert 0 == len(plus_conn.execute(get_all_runs).fetchall())
+        assert 0 == len(conn_plus.execute(get_all_runs).fetchall())
         assert 0 == len(atomic_conn.execute(get_all_runs).fetchall())
         assert 0 == len(control_conn.execute(get_all_runs).fetchall())
 
         atomic_conn.cursor().execute(insert_run_with_name, ['aaa'])
 
-        assert 1 == len(plus_conn.execute(get_all_runs).fetchall())
+        assert 1 == len(conn_plus.execute(get_all_runs).fetchall())
         assert 1 == len(atomic_conn.execute(get_all_runs).fetchall())
         assert 0 == len(control_conn.execute(get_all_runs).fetchall())
 
-    assert 1 == len(plus_conn.execute(get_all_runs).fetchall())
+    assert 1 == len(conn_plus.execute(get_all_runs).fetchall())
     assert 1 == len(atomic_conn.execute(get_all_runs).fetchall())
     assert 1 == len(control_conn.execute(get_all_runs).fetchall())
 
@@ -218,38 +218,38 @@ def test_that_use_of_atomic_commits_only_at_outermost_context(
     # we expect to see the actual change in the database only after we exit
     # the outermost context.
 
-    with atomic(plus_conn) as atomic_conn_1:
+    with atomic(conn_plus) as atomic_conn_1:
 
-        assert 1 == len(plus_conn.execute(get_all_runs).fetchall())
+        assert 1 == len(conn_plus.execute(get_all_runs).fetchall())
         assert 1 == len(atomic_conn_1.execute(get_all_runs).fetchall())
         assert 1 == len(control_conn.execute(get_all_runs).fetchall())
 
         atomic_conn_1.cursor().execute(insert_run_with_name, ['bbb'])
 
-        assert 2 == len(plus_conn.execute(get_all_runs).fetchall())
+        assert 2 == len(conn_plus.execute(get_all_runs).fetchall())
         assert 2 == len(atomic_conn_1.execute(get_all_runs).fetchall())
         assert 1 == len(control_conn.execute(get_all_runs).fetchall())
 
         with atomic(atomic_conn_1) as atomic_conn_2:
 
-            assert 2 == len(plus_conn.execute(get_all_runs).fetchall())
+            assert 2 == len(conn_plus.execute(get_all_runs).fetchall())
             assert 2 == len(atomic_conn_1.execute(get_all_runs).fetchall())
             assert 2 == len(atomic_conn_2.execute(get_all_runs).fetchall())
             assert 1 == len(control_conn.execute(get_all_runs).fetchall())
 
             atomic_conn_2.cursor().execute(insert_run_with_name, ['ccc'])
 
-            assert 3 == len(plus_conn.execute(get_all_runs).fetchall())
+            assert 3 == len(conn_plus.execute(get_all_runs).fetchall())
             assert 3 == len(atomic_conn_1.execute(get_all_runs).fetchall())
             assert 3 == len(atomic_conn_2.execute(get_all_runs).fetchall())
             assert 1 == len(control_conn.execute(get_all_runs).fetchall())
 
-        assert 3 == len(plus_conn.execute(get_all_runs).fetchall())
+        assert 3 == len(conn_plus.execute(get_all_runs).fetchall())
         assert 3 == len(atomic_conn_1.execute(get_all_runs).fetchall())
         assert 3 == len(atomic_conn_2.execute(get_all_runs).fetchall())
         assert 1 == len(control_conn.execute(get_all_runs).fetchall())
 
-    assert 3 == len(plus_conn.execute(get_all_runs).fetchall())
+    assert 3 == len(conn_plus.execute(get_all_runs).fetchall())
     assert 3 == len(atomic_conn_1.execute(get_all_runs).fetchall())
     assert 3 == len(atomic_conn_2.execute(get_all_runs).fetchall())
     assert 3 == len(control_conn.execute(get_all_runs).fetchall())

From 0f63565744648911f9a9ed5d404475485134b3bc Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 23 Nov 2018 01:55:47 +0100
Subject: [PATCH 189/719] Add explicit test for exception within atomic context
 manager

---
 .../tests/dataset/test_sqlite_connection.py   | 21 +++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index 23c1fee665c..52815ad85d7 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -5,6 +5,8 @@
 
 from qcodes.dataset.sqlite_base import ConnectionPlus, \
     make_connection_plus_from, atomic, connect, atomic_transaction
+from qcodes.tests.dataset.test_database_creation_and_upgrading import \
+    error_caused_by
 
 
 def sqlite_conn_in_transaction(conn: sqlite3.Connection):
@@ -99,6 +101,25 @@ def test_atomic():
     assert atomic_in_progress is atomic_conn.atomic_in_progress
 
 
+def test_atomic_with_exception():
+    sqlite_conn = sqlite3.connect(':memory:')
+    conn_plus = ConnectionPlus(sqlite_conn)
+
+    sqlite_conn.execute('PRAGMA user_version(25)')
+    sqlite_conn.commit()
+
+    assert 25 == sqlite_conn.execute('PRAGMA user_version').fetchall()[0][0]
+
+    with pytest.raises(RuntimeError,
+                       match="Rolling back due to unhandled exception") as e:
+        with atomic(conn_plus) as atomic_conn:
+            atomic_conn.execute('PRAGMA user_version(42)')
+            raise Exception('intended exception')
+    assert error_caused_by(e, 'intended exception')
+
+    assert 25 == sqlite_conn.execute('PRAGMA user_version').fetchall()[0][0]
+
+
 @pytest.mark.parametrize('in_transaction', (True, False))
 def test_atomic_on_connection_plus_that_is_in_progress(in_transaction):
     sqlite_conn = sqlite3.connect(':memory:')

From a5ae48d68eca522748a77ed48d8e32870cdf7985 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 23 Nov 2018 02:04:55 +0100
Subject: [PATCH 190/719] Minor rearrangement of code of atomic

---
 qcodes/dataset/sqlite_base.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 5535ac17b74..c18c4a5d5fa 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -751,28 +751,31 @@ def atomic_transaction(conn: SomeConnection,
 def atomic(conn: ConnectionPlus):
     """
     Guard a series of transactions as atomic.
-    If one fails the transaction is rolled back and no more transactions
-    are performed.
+
+    If one transaction fails, all the previous transactions are rolled back
+    and no more transactions are performed.
+
     NB: 'BEGIN' is by default only inserted before INSERT/UPDATE/DELETE/REPLACE
     but we want to guard any transaction that modifies the database (e.g. also
     ALTER)
 
     Args:
-        - conn: connection to guard
+        conn: connection to guard
     """
     if not isinstance(conn, ConnectionPlus):
         raise ValueError('atomic context manager only accepts ConnectionPlus '
                          'database connection objects.')
 
-    old_atomic_in_progress = conn.atomic_in_progress
     is_outmost = not(conn.atomic_in_progress)
-    conn.atomic_in_progress = True
 
     if conn.in_transaction and is_outmost:
         raise RuntimeError('SQLite connection has uncommited transactions. '
                            'Please commit those before starting an atomic '
                            'transaction.')
 
+    old_atomic_in_progress = conn.atomic_in_progress
+    conn.atomic_in_progress = True
+
     try:
         if is_outmost:
             old_level = conn.isolation_level

From 1fd54cdf1fdfcc9a04427a3ccc731f0d08bf300f Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 23 Nov 2018 02:05:24 +0100
Subject: [PATCH 191/719] Test atomic raises on outmost connection that is in
 transaction

---
 qcodes/dataset/sqlite_base.py                  |  2 +-
 qcodes/tests/dataset/test_sqlite_connection.py | 14 ++++++++++++++
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index c18c4a5d5fa..0f7a0d4fed8 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -769,7 +769,7 @@ def atomic(conn: ConnectionPlus):
     is_outmost = not(conn.atomic_in_progress)
 
     if conn.in_transaction and is_outmost:
-        raise RuntimeError('SQLite connection has uncommited transactions. '
+        raise RuntimeError('SQLite connection has uncommitted transactions. '
                            'Please commit those before starting an atomic '
                            'transaction.')
 
diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index 52815ad85d7..31701481895 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -120,6 +120,20 @@ def test_atomic_with_exception():
     assert 25 == sqlite_conn.execute('PRAGMA user_version').fetchall()[0][0]
 
 
+def test_atomic_on_outmost_connection_that_is_in_transaction():
+    conn = ConnectionPlus(sqlite3.connect(':memory:'))
+
+    conn.execute('BEGIN')
+    assert True is conn.in_transaction
+
+    match_str = re.escape('SQLite connection has uncommitted transactions. '
+                          'Please commit those before starting an atomic '
+                          'transaction.')
+    with pytest.raises(RuntimeError, match=match_str):
+        with atomic(conn):
+            pass
+
+
 @pytest.mark.parametrize('in_transaction', (True, False))
 def test_atomic_on_connection_plus_that_is_in_progress(in_transaction):
     sqlite_conn = sqlite3.connect(':memory:')

From 29bce594598d9f4176b5b240cf0d8805661eee4a Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 26 Nov 2018 10:02:43 +0100
Subject: [PATCH 192/719] Use ConnectionPlus where it is needed instead of
 sqlite3.Connection

also restrict the type of database upgrader functions.
---
 qcodes/dataset/sqlite_base.py                 | 47 ++++++++++---------
 .../tests/dataset/test_sqlite_connection.py   |  2 +-
 2 files changed, 25 insertions(+), 24 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 0f7a0d4fed8..7c20af7f9e2 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -135,14 +135,15 @@ def __init__(self, sqlite3_connection: sqlite3.Connection):
 SomeConnection = Union[sqlite3.Connection, ConnectionPlus]
 
 
-def upgrader(func: Callable[[SomeConnection], None]):
+def upgrader(func: Callable[[ConnectionPlus], None]):
     """
     Decorator for database version upgrade functions. An upgrade function
-    must have the name `perform_db_upgrade_N_to_M` where N = M-1.
-    The upgrade function must either perform the upgrade and return (no return
-    values allowed) or fail to perform the upgrade, in which case it must raise
-    a RuntimeError. A failed upgrade must be completely rolled back before the
-    RuntimeError is raises.
+    must have the name `perform_db_upgrade_N_to_M` where N = M-1. For
+    simplicity, an upgrade function must take a single argument of type
+    `ConnectionPlus`. The upgrade function must either perform the upgrade
+    and return (no return values allowed) or fail to perform the upgrade,
+    in which case it must raise a RuntimeError. A failed upgrade must be
+    completely rolled back before the RuntimeError is raises.
 
     The decorator takes care of logging about the upgrade and managing the
     database versioning.
@@ -165,7 +166,7 @@ def upgrader(func: Callable[[SomeConnection], None]):
                          ' to version N+1')
 
     @wraps(func)
-    def do_upgrade(conn: SomeConnection) -> None:
+    def do_upgrade(conn: ConnectionPlus) -> None:
 
         log.info(f'Starting database upgrade version {from_version} '
                  f'to {to_version}')
@@ -378,7 +379,7 @@ def perform_db_upgrade(conn: SomeConnection, version: int=-1) -> None:
 
 
 @upgrader
-def perform_db_upgrade_0_to_1(conn: SomeConnection) -> None:
+def perform_db_upgrade_0_to_1(conn: ConnectionPlus) -> None:
     """
     Perform the upgrade from version 0 to version 1
 
@@ -419,7 +420,7 @@ def perform_db_upgrade_0_to_1(conn: SomeConnection) -> None:
 
 
 @upgrader
-def perform_db_upgrade_1_to_2(conn: SomeConnection) -> None:
+def perform_db_upgrade_1_to_2(conn: ConnectionPlus) -> None:
     """
     Perform the upgrade from version 1 to version 2
 
@@ -623,7 +624,7 @@ def _2to3_get_paramspecs(conn: SomeConnection,
 
 
 @upgrader
-def perform_db_upgrade_2_to_3(conn: SomeConnection) -> None:
+def perform_db_upgrade_2_to_3(conn: ConnectionPlus) -> None:
     """
     Perform the upgrade from version 2 to version 3
 
@@ -811,11 +812,11 @@ def make_connection_plus_from(conn: SomeConnection) -> ConnectionPlus:
     if not isinstance(conn, ConnectionPlus):
         conn_plus = ConnectionPlus(conn)
     else:
-        conn_plus = cast(ConnectionPlus, conn)
+        conn_plus = conn
     return conn_plus
 
 
-def init_db(conn: SomeConnection)->None:
+def init_db(conn: ConnectionPlus)->None:
     with atomic(conn) as conn:
         transaction(conn, _experiment_table_schema)
         transaction(conn, _runs_table_schema)
@@ -842,7 +843,7 @@ def is_column_in_table(conn: SomeConnection, table: str, column: str) -> bool:
     return False
 
 
-def insert_column(conn: SomeConnection, table: str, name: str,
+def insert_column(conn: ConnectionPlus, table: str, name: str,
                   paramtype: Optional[str] = None) -> None:
     """Insert new column to a table
 
@@ -947,7 +948,7 @@ def insert_values(conn: SomeConnection,
     return c.lastrowid
 
 
-def insert_many_values(conn: SomeConnection,
+def insert_many_values(conn: ConnectionPlus,
                        formatted_name: str,
                        columns: List[str],
                        values: List[VALUES],
@@ -1466,7 +1467,7 @@ def get_last_experiment(conn: SomeConnection) -> Optional[int]:
     return c.fetchall()[0][0]
 
 
-def get_runs(conn: SomeConnection,
+def get_runs(conn: ConnectionPlus,
              exp_id: Optional[int] = None)->List[sqlite3.Row]:
     """ Get a list of runs.
 
@@ -1543,7 +1544,7 @@ def data_sets(conn: SomeConnection) -> List[sqlite3.Row]:
     return c.fetchall()
 
 
-def _insert_run(conn: SomeConnection, exp_id: int, name: str,
+def _insert_run(conn: ConnectionPlus, exp_id: int, name: str,
                 guid: str,
                 parameters: Optional[List[ParamSpec]] = None,
                 ):
@@ -1725,7 +1726,7 @@ def get_paramspec(conn: SomeConnection,
     return parspec
 
 
-def update_run_description(conn: SomeConnection, run_id: int,
+def update_run_description(conn: ConnectionPlus, run_id: int,
                            description: str) -> None:
     """
     Update the run_description field for the given run_id. The description
@@ -1746,7 +1747,7 @@ def update_run_description(conn: SomeConnection, run_id: int,
         conn.cursor().execute(sql, (description, run_id))
 
 
-def add_parameter(conn: SomeConnection,
+def add_parameter(conn: ConnectionPlus,
                   formatted_name: str,
                   *parameter: ParamSpec):
     """
@@ -1787,7 +1788,7 @@ def add_parameter(conn: SomeConnection,
                                                *parameter)
 
 
-def _add_parameters_to_layout_and_deps(conn: SomeConnection,
+def _add_parameters_to_layout_and_deps(conn: ConnectionPlus,
                                        formatted_name: str,
                                        *parameter: ParamSpec) -> sqlite3.Cursor:
     # get the run_id
@@ -1848,7 +1849,7 @@ def _validate_table_name(table_name: str) -> bool:
     return valid
 
 
-def _create_run_table(conn: SomeConnection,
+def _create_run_table(conn: ConnectionPlus,
                       formatted_name: str,
                       parameters: Optional[List[ParamSpec]] = None,
                       values: Optional[VALUES] = None
@@ -1893,7 +1894,7 @@ def _create_run_table(conn: SomeConnection,
             transaction(conn, query)
 
 
-def create_run(conn: SomeConnection, exp_id: int, name: str,
+def create_run(conn: ConnectionPlus, exp_id: int, name: str,
                guid: str,
                parameters: Optional[List[ParamSpec]]=None,
                values:  List[Any] = None,
@@ -1972,7 +1973,7 @@ def get_metadata_from_run_id(conn: SomeConnection, run_id: int) -> Dict:
     return metadata
 
 
-def insert_meta_data(conn: SomeConnection, row_id: int, table_name: str,
+def insert_meta_data(conn: ConnectionPlus, row_id: int, table_name: str,
                      metadata: Dict[str, Any]) -> None:
     """
     Insert new metadata column and add values. Note that None is not a valid
@@ -2007,7 +2008,7 @@ def update_meta_data(conn: SomeConnection, row_id: int, table_name: str,
     update_where(conn, table_name, 'rowid', row_id, **metadata)
 
 
-def add_meta_data(conn: SomeConnection,
+def add_meta_data(conn: ConnectionPlus,
                   row_id: int,
                   metadata: Dict[str, Any],
                   table_name: str = "runs") -> None:
diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index 31701481895..6128fe59b9a 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -77,7 +77,7 @@ def test_atomic():
     match_str = re.escape('atomic context manager only accepts ConnectionPlus '
                           'database connection objects.')
     with pytest.raises(ValueError, match=match_str):
-        with atomic(sqlite_conn):  # type: ignore
+        with atomic(sqlite_conn):
             pass
 
     conn_plus = ConnectionPlus(sqlite_conn)

From c8454e5ee4540749f46ec968677ef220d355bb14 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 26 Nov 2018 10:11:57 +0100
Subject: [PATCH 193/719] Unify deprecation messages for deprecated DataSet
 methods

---
 qcodes/dataset/data_set.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 9d1d24e107b..0e8a712dab6 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -576,8 +576,8 @@ def add_results(self, results: List[Dict[str, VALUE]]) -> int:
                            values)
         return len_before_add
 
-    @deprecate(reason='it is an experimental functionality, and is most '
-                      'probably will be removed soon.')
+    @deprecate(reason='it is an experimental functionality, and is likely '
+                      'to be removed soon.')
     def modify_result(self, index: int, results: Dict[str, VALUES]) -> None:
         """
         Modify a logically single result of existing parameters
@@ -608,8 +608,8 @@ def modify_result(self, index: int, results: Dict[str, VALUES]) -> None:
                           list(results.values())
                           )
 
-    @deprecate(reason='it is an experimental functionality, and it is not '
-                      'known whether it will remain or it will be removed.',
+    @deprecate(reason='it is an experimental functionality, and is likely '
+                      'to be removed soon.',
                alternative='modify_result')
     def modify_results(self, start_index: int,
                        updates: List[Dict[str, VALUES]]):
@@ -653,8 +653,8 @@ def modify_results(self, start_index: int,
                                flattened_keys,
                                flattened_values)
 
-    @deprecate(reason='it is an experimental functionality, and it is not '
-                      'known whether it will remain or it will be removed.',
+    @deprecate(reason='it is an experimental functionality, and is likely '
+                      'to be removed soon.',
                alternative='add_parameter, add_result, add_results')
     def add_parameter_values(self, spec: ParamSpec, values: VALUES):
         """

From 326375fcd40b91c8d568e4155e8c2b3557dabfb8 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 26 Nov 2018 10:24:25 +0100
Subject: [PATCH 194/719] Remove not needed test about unsubscribe_all and
 unsubscribe methods of DataSet which used to make atomic connections never
 commit

---
 qcodes/tests/dataset/test_subscribing.py | 46 ------------------------
 1 file changed, 46 deletions(-)

diff --git a/qcodes/tests/dataset/test_subscribing.py b/qcodes/tests/dataset/test_subscribing.py
index 8d862f7b332..397283664ef 100644
--- a/qcodes/tests/dataset/test_subscribing.py
+++ b/qcodes/tests/dataset/test_subscribing.py
@@ -196,49 +196,3 @@ def test_subscription_from_config_wrong_name(dataset):
         assert 'test_subscriber' not in qcodes.config.subscription.subscribers
         with pytest.raises(RuntimeError):
             sub_id_c = dataset.subscribe_from_config('test_subscriber')
-
-
-def test_unsubscribe_all_makes_atomic_connections_never_commit(
-        dataset, basic_subscriber):
-    xparam = ParamSpec(name='x', paramtype='numeric')
-    dataset.add_parameter(xparam)
-
-    sub_id = dataset.subscribe(basic_subscriber, min_wait=0, min_count=1,
-                               state={})
-
-    assert len(dataset.subscribers) == 1
-    assert list(dataset.subscribers.keys()) == [sub_id]
-
-    dataset.unsubscribe_all()
-
-    dataset.add_result({'x': 1})
-
-    shadow_ds = make_shadow_dataset(dataset)
-    try:
-        assert dataset.get_data('x') == [[1]]
-        assert shadow_ds.get_data('x') == [[1]]
-    finally:
-        shadow_ds.conn.close()
-
-
-def test_unsubscribe_makes_atomic_connections_never_commit(
-        dataset, basic_subscriber):
-    xparam = ParamSpec(name='x', paramtype='numeric')
-    dataset.add_parameter(xparam)
-
-    sub_id = dataset.subscribe(basic_subscriber, min_wait=0, min_count=1,
-                               state={})
-
-    assert len(dataset.subscribers) == 1
-    assert list(dataset.subscribers.keys()) == [sub_id]
-
-    dataset.unsubscribe(sub_id)
-
-    dataset.add_result({'x': 1})
-
-    shadow_ds = make_shadow_dataset(dataset)
-    try:
-        assert dataset.get_data('x') == [[1]]
-        assert shadow_ds.get_data('x') == [[1]]
-    finally:
-        shadow_ds.conn.close()

From 3ad23c18255f801707b3d37a047157d41e330898 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 26 Nov 2018 10:30:35 +0100
Subject: [PATCH 195/719] Add transaction function that removes a trigger from
 database

---
 qcodes/dataset/sqlite_base.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 7c20af7f9e2..1d973bba428 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -2135,3 +2135,16 @@ def _both_zero(run_id: int, conn, guid_comps) -> None:
 
         log.info(f'Updating run number {run_id}...')
         actions[(loc == 0, ws == 0)](run_id, conn, guid_comps)
+
+
+def remove_trigger(conn: SomeConnection, trigger_id: str) -> None:
+    """
+    Removes a trigger with a given id if it exists.
+
+    Note that this transaction is not atomic!
+
+    Args:
+        conn: database connection object
+        name: id of the trigger
+    """
+    transaction(conn, f"DROP TRIGGER IF EXISTS {trigger_id};")

From d62b72ef46bdfe09affc2a93251e7db3782f8974 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 26 Nov 2018 10:31:25 +0100
Subject: [PATCH 196/719] Do not redefine self.conn in DataSet when doing
 atomic transactions

+ use remove_trigger from sqlite_base
---
 qcodes/dataset/data_set.py | 25 +++++++++++--------------
 1 file changed, 11 insertions(+), 14 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 0e8a712dab6..caf4ac09938 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -33,7 +33,7 @@
                                         get_run_timestamp_from_run_id,
                                         get_completed_timestamp_from_run_id,
                                         update_run_description,
-                                        run_exists)
+                                        run_exists, remove_trigger)
 
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
@@ -602,8 +602,8 @@ def modify_result(self, index: int, results: Dict[str, VALUES]) -> None:
             if param not in self.paramspecs.keys():
                 raise ValueError(f'No such parameter: {param}.')
 
-        with atomic(self.conn) as self.conn:
-            modify_values(self.conn, self.table_name, index,
+        with atomic(self.conn) as conn:
+            modify_values(conn, self.table_name, index,
                           list(results.keys()),
                           list(results.values())
                           )
@@ -646,8 +646,8 @@ def modify_results(self, start_index: int,
         values = [list(val.values()) for val in updates]
         flattened_values = [item for sublist in values for item in sublist]
 
-        with atomic(self.conn) as self.conn:
-            modify_many_values(self.conn,
+        with atomic(self.conn) as conn:
+            modify_many_values(conn,
                                self.table_name,
                                start_index,
                                flattened_keys,
@@ -676,8 +676,8 @@ def add_parameter_values(self, spec: ParamSpec, values: VALUES):
                     len(values)
                 ))
 
-        with atomic(self.conn) as self.conn:
-            add_parameter(self.conn, self.table_name, spec)
+        with atomic(self.conn) as conn:
+            add_parameter(conn, self.table_name, spec)
             # now add values!
             results = [{spec.name: value} for value in values]
             self.add_results(results)
@@ -813,25 +813,22 @@ def unsubscribe(self, uuid: str) -> None:
         """
         Remove subscriber with the provided uuid
         """
-        with atomic(self.conn) as self.conn:
+        with atomic(self.conn) as conn:
             sub = self.subscribers[uuid]
-            self._remove_trigger(sub.trigger_id)
+            remove_trigger(conn, sub.trigger_id)
             sub.schedule_stop()
             sub.join()
             del self.subscribers[uuid]
 
-    def _remove_trigger(self, name):
-        transaction(self.conn, f"DROP TRIGGER IF EXISTS {name};")
-
     def unsubscribe_all(self):
         """
         Remove all subscribers
         """
         sql = "select * from sqlite_master where type = 'trigger';"
         triggers = atomic_transaction(self.conn, sql).fetchall()
-        with atomic(self.conn) as self.conn:
+        with atomic(self.conn) as conn:
             for trigger in triggers:
-                self._remove_trigger(trigger['name'])
+                remove_trigger(conn, trigger['name'])
             for sub in self.subscribers.values():
                 sub.schedule_stop()
                 sub.join()

From 05b2617a213b9679b57cee309970486629c6c5cd Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 26 Nov 2018 11:37:31 +0100
Subject: [PATCH 197/719] Ensure ConnectionPlus is used within DataSet

---
 qcodes/dataset/data_set.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index caf4ac09938..a8811d02d27 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -33,7 +33,9 @@
                                         get_run_timestamp_from_run_id,
                                         get_completed_timestamp_from_run_id,
                                         update_run_description,
-                                        run_exists, remove_trigger)
+                                        run_exists, remove_trigger,
+                                        make_connection_plus_from,
+                                        SomeConnection)
 
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
@@ -185,7 +187,7 @@ def _clean_up(self) -> None:
 class DataSet(Sized):
     def __init__(self, path_to_db: str=None,
                  run_id: Optional[int]=None,
-                 conn=None,
+                 conn: Optional[SomeConnection]=None,
                  exp_id=None,
                  name: str=None,
                  specs: SPECS=None,
@@ -221,7 +223,8 @@ def __init__(self, path_to_db: str=None,
                              "This is not allowed.")
         self._path_to_db = path_to_db or get_DB_location()
 
-        self.conn = conn or connect(self.path_to_db)
+        self.conn = make_connection_plus_from(conn) if conn is not None else \
+            connect(self.path_to_db)
 
         self._run_id = run_id
         self._debug = False

From b523b59ed7650fabda43471a6fe2c8f087c5f25f Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Mon, 26 Nov 2018 16:23:34 -0800
Subject: [PATCH 198/719] 1) Example notebook should not execute 2) Processed
 PR comments.

---
 .../Qcodes example with Stahl.ipynb           |  41 +++--
 qcodes/instrument_drivers/stahl/stahl.py      | 147 ++++++++++++------
 2 files changed, 135 insertions(+), 53 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index f4a21ccf526..c7cc7841481 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -27,7 +27,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: Stahl HV (serial:171, firmware:None) in 0.07s\n"
+      "Connected to: Stahl HV (serial:171, firmware:None) in 0.08s\n"
      ]
     }
    ],
@@ -49,7 +49,7 @@
     }
    ],
    "source": [
-    "v = stahl.channel[4].voltage(2)\n",
+    "stahl.channel[4].voltage(2)\n",
     "v = stahl.channel[4].voltage()\n",
     "print(v)"
    ]
@@ -63,12 +63,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-1.997\n"
+      "-2.0\n"
      ]
     }
    ],
    "source": [
-    "v = stahl.channel[4].voltage(-2)\n",
+    "stahl.channel[4].voltage(-2)\n",
     "v = stahl.channel[4].voltage()\n",
     "print(v)"
    ]
@@ -87,7 +87,7 @@
     }
    ],
    "source": [
-    "v = stahl.channel[4].voltage(0)\n",
+    "stahl.channel[4].voltage(0)\n",
     "v = stahl.channel[4].voltage()\n",
     "print(v)"
    ]
@@ -100,7 +100,7 @@
     {
      "data": {
       "text/plain": [
-       "0.002"
+       "2e-06"
       ]
      },
      "execution_count": 6,
@@ -120,7 +120,7 @@
     {
      "data": {
       "text/plain": [
-       "26.3"
+       "'A'"
       ]
      },
      "execution_count": 7,
@@ -129,7 +129,7 @@
     }
    ],
    "source": [
-    "stahl.temperature()"
+    "stahl.channel[0].current.unit"
    ]
   },
   {
@@ -140,7 +140,7 @@
     {
      "data": {
       "text/plain": [
-       "False"
+       "25.9"
       ]
      },
      "execution_count": 8,
@@ -148,6 +148,26 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "stahl.temperature()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "stahl.channel[1].is_locked()"
    ]
@@ -177,6 +197,9 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.7"
+  },
+  "nbsphinx": {
+   "execute": "never"
   }
  },
  "nbformat": 4,
diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index 45a672024ef..4d9e0779402 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -6,6 +6,7 @@
 import re
 import numpy as np
 import logging
+from collections import OrderedDict
 
 from qcodes import VisaInstrument, InstrumentChannel, ChannelList
 from qcodes.utils.validators import Numbers
@@ -14,6 +15,41 @@
 logger = logging.getLogger()
 
 
+class UnexpectedInstrumentResponse(Exception):
+    def __init__(self):
+        super().__init__(
+            "Unexpected instrument response. Perhaps the model of the "
+            "instrument does not match the drivers expectation or a "
+            "firmware upgrade has taken place. Please get in touch "
+            "with a QCoDeS core developer"
+        )
+
+
+def chain(*functions: Callable) -> Callable:
+    """
+    The output of the first callable is piped to the input of the second, etc.
+
+    Example:
+        >>> def f():
+        >>>>   return "1.2"
+        >>> chain(f, float)()  # return 1.2 as float
+    """
+    def make_tuple(args):
+        if not isinstance(args, tuple):
+            return args,
+        return args
+
+    def inner(*args):
+        result = args
+        for fun in functions:
+            new_args = make_tuple(result)
+            result = fun(*new_args)
+
+        return result
+
+    return inner
+
+
 class StahlChannel(InstrumentChannel):
     """
     A Stahl source channel
@@ -23,17 +59,22 @@ class StahlChannel(InstrumentChannel):
         name
         channel_number
     """
+
+    acknowledge_reply = chr(6)
+
     def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
         super().__init__(parent, name)
 
         self._channel_string = f"{channel_number:02d}"
         self._channel_number = channel_number
-        self._acknowledge_reply = chr(6)
 
         self.add_parameter(
             "voltage",
             get_cmd=f"{self.parent.identifier} U{self._channel_string}",
-            get_parser=self._stahl_get_parser("V"),
+            get_parser=chain(
+                self.parent.regex_parser("([+\-]\d+,\d+) V$"),
+                self._string_to_float()
+            ),
             set_cmd=self._set_voltage,
             unit="V",
             vals=Numbers(
@@ -45,8 +86,13 @@ def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
         self.add_parameter(
             "current",
             get_cmd=f"{self.parent.identifier} I{self._channel_string}",
-            get_parser=self._stahl_get_parser("mA"),
-            unit="mA",
+            get_parser=chain(
+                self.parent.regex_parser("([+\-]\d+,\d+) mA$"),
+                self._string_to_float(
+                    scale_factor=1/1000  # We want results in Ampere
+                )
+            ),
+            unit="A",
         )
 
         self.add_parameter(
@@ -55,25 +101,20 @@ def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
         )
 
     @staticmethod
-    def _stahl_get_parser(unit: str) -> Callable:
+    def _string_to_float(
+            decimal_separator: str=",",
+            scale_factor: float=1
+    ) -> Callable:
         """
-        Upon querying the voltage and current, the response from the instrument
-        is `+/-yy,yyy V` or `+/-yy,yyy mA'. We strip the unit and replace the
-        comma with a dot.
-
-        Args:
-            unit: The unit to strip
-
-        Return:
-            parser: Parse a response to a float
+        Querying the voltage and current gives back strings containing a
+        comma denoting a decimal symbol (e.g. 1,4 = 1.4). Correct this
+        madness (and send an angry email to Stahl)
         """
-        regex = f"([\+\-]\d+,\d+ ){unit}$"
+        def converter(string):
+            sane_str = string.replace(decimal_separator, ".")
+            return float(sane_str) * scale_factor
 
-        def parser(response: str) -> float:
-            result, = re.search(regex, response).groups()
-            return float(result.replace(",", "."))
-
-        return parser
+        return converter
 
     def _set_voltage(self, voltage: float) -> None:
         """
@@ -91,8 +132,8 @@ def _set_voltage(self, voltage: float) -> None:
         send_string = f"{self.parent.identifier} CH{self._channel_string} {voltage_normalized:.5f}"
         response = self.ask(send_string)
 
-        if response != self._acknowledge_reply:
-            logger.warning(f"Command {send_string} did not produce an acknowledge reply")
+        if response != self.acknowledge_reply:
+            self.log.warning(f"Command {send_string} did not produce an acknowledge reply")
 
     def _get_lock_status(self) -> bool:
         """
@@ -109,10 +150,10 @@ def _get_lock_status(self) -> bool:
             header_fmt="empty"
         )
 
-        chnr = self._channel_number - 1
-        channel_group = chnr // 4
+        channel_index = self._channel_number - 1
+        channel_group = channel_index // 4
         lock_code_group = response[channel_group]
-        return format(lock_code_group, "b")[chnr % 4 + 1] == "1"
+        return format(lock_code_group, "b")[channel_index % 4 + 1] == "1"
 
 
 class Stahl(VisaInstrument):
@@ -152,39 +193,57 @@ def __init__(self, name: str, address: str):
 
         self.add_parameter(
             "temperature",
-            get_cmd=self._get_temperature,
+            get_cmd=f"{self.identifier} TEMP",
+            get_parser=chain(
+                self.regex_parser("TEMP (.*)°C"),
+                float
+            ),
             unit="C"
         )
 
         self.connect_message()
 
-    def _get_temperature(self) -> float:
+    def ask_raw(self, cmd: str) -> str:
         """
-        When querying the temperature a non-ascii character to denote
-        the degree symbol '°' is present in the return string. For this
-        reason, we cannot use the `ask` method as it will attempt to
-        decode the response with utf-8, which will fail. We need to
-        manually set the decoding to latin-1
+        Sometimes the instrument returns non-ascii characters in response strings
+        Manually adjust the encoding to latin-1
         """
-        send_string = f"{self.identifier} TEMP"
-        self.write(send_string)
+        self.visa_log.debug(f"Querying: {cmd}")
+        self.visa_handle.write(cmd)
         response = self.visa_handle.read(encoding="latin-1")
-        temperature_string, = re.search("TEMP (.*)°C", response).groups()
-        return float(temperature_string)
+        self.visa_log.debug(f"Response: {response}")
+        return response
 
     @staticmethod
-    def _parse_idn_string(ind_string) -> Dict[str, Any]:
+    def regex_parser(match_string: str) -> Callable:
+
+        regex = re.compile(match_string)
+
+        def parser(input_string):
+            result = regex.search(input_string)
+            if result is None:
+                raise UnexpectedInstrumentResponse()
+
+            result_groups = result.groups()
+            if len(result_groups) == 1:
+                return result_groups[0]
+            else:
+                return result_groups
+
+        return parser
+
+    def _parse_idn_string(self, ind_string) -> Dict[str, Any]:
         """
         Return:
              dict with keys: "model", "serial_number", "voltage_range",
              "n_channels", "output_type"
         """
-        groups = re.search(
-            "(HV|BS)(\d{3}) (\d{3}) (\d{2}) [buqsm]",
-            ind_string
-        ).groups()
+        idn_parser = self.regex_parser(
+            "(HV|BS)(\d{3}) (\d{3}) (\d{2}) [buqsm]"
+        )
+        parsed_idn = idn_parser(ind_string)
 
-        id_parsers: Dict[str, Callable] = {
+        id_parsers: Dict[str, Callable] = OrderedDict({
             "model": str,
             "serial_number": str,
             "voltage_range": float,
@@ -196,11 +255,11 @@ def _parse_idn_string(ind_string) -> Dict[str, Any]:
                 "s": "steerer",
                 "m": "bipolar milivolt"
             }.get
-        }
+        })
 
         return {
             name: id_parsers[name](value)
-            for name, value in zip(id_parsers.keys(), groups)
+            for name, value in zip(id_parsers.keys(), parsed_idn)
         }
 
     def get_idn(self) -> Dict[str, Optional[str]]:

From 7a544631f73ec367f29387c278f0441484b120c4 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Mon, 26 Nov 2018 16:35:56 -0800
Subject: [PATCH 199/719] More type hints

---
 .../driver_examples/Qcodes example with Stahl.ipynb  |  8 ++++----
 qcodes/instrument_drivers/stahl/stahl.py             | 12 ++++++------
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index c7cc7841481..22d3182a4f5 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -27,7 +27,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: Stahl HV (serial:171, firmware:None) in 0.08s\n"
+      "Connected to: Stahl HV (serial:171, firmware:None) in 0.06s\n"
      ]
     }
    ],
@@ -44,7 +44,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.001\n"
+      "2.0\n"
      ]
     }
    ],
@@ -63,7 +63,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-2.0\n"
+      "-1.997\n"
      ]
     }
    ],
@@ -140,7 +140,7 @@
     {
      "data": {
       "text/plain": [
-       "25.9"
+       "26.9"
       ]
      },
      "execution_count": 8,
diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index 4d9e0779402..093767d887b 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -2,7 +2,7 @@
 This is a driver for the Stahl power supplies
 """
 
-from typing import Dict, Optional, Any, Callable
+from typing import Dict, Optional, Any, Callable, Union, Sequence
 import re
 import numpy as np
 import logging
@@ -107,7 +107,7 @@ def _string_to_float(
     ) -> Callable:
         """
         Querying the voltage and current gives back strings containing a
-        comma denoting a decimal symbol (e.g. 1,4 = 1.4). Correct this
+        comma denoting a decimal separator (e.g. 1,4 = 1.4). Correct this
         madness (and send an angry email to Stahl)
         """
         def converter(string):
@@ -219,7 +219,7 @@ def regex_parser(match_string: str) -> Callable:
 
         regex = re.compile(match_string)
 
-        def parser(input_string):
+        def parser(input_string: str) -> Union[str, Sequence[str]]:
             result = regex.search(input_string)
             if result is None:
                 raise UnexpectedInstrumentResponse()
@@ -243,7 +243,7 @@ def _parse_idn_string(self, ind_string) -> Dict[str, Any]:
         )
         parsed_idn = idn_parser(ind_string)
 
-        id_parsers: Dict[str, Callable] = OrderedDict({
+        converters: Dict[str, Callable] = OrderedDict({
             "model": str,
             "serial_number": str,
             "voltage_range": float,
@@ -258,8 +258,8 @@ def _parse_idn_string(self, ind_string) -> Dict[str, Any]:
         })
 
         return {
-            name: id_parsers[name](value)
-            for name, value in zip(id_parsers.keys(), parsed_idn)
+            name: converter(value)
+            for (name, converter), value in zip(converters.items(), parsed_idn)
         }
 
     def get_idn(self) -> Dict[str, Optional[str]]:

From a002bd097f896b4d27c4a13af0e9dd659bc459e0 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Mon, 26 Nov 2018 17:16:15 -0800
Subject: [PATCH 200/719] Add tests

---
 .../Qcodes example with Stahl.ipynb           | 60 +++++++++++++++++++
 qcodes/instrument_drivers/stahl/stahl.py      | 18 ++++--
 qcodes/tests/drivers/test_stahl.py            | 36 +++++++++++
 3 files changed, 109 insertions(+), 5 deletions(-)
 create mode 100644 qcodes/tests/drivers/test_stahl.py

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index 22d3182a4f5..8da3df5affb 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -172,6 +172,66 @@
     "stahl.channel[1].is_locked()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parser = Stahl.regex_parser(\"^QCoDeS is (.*)$\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = parser(\"QCoDeS is Cool\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Cool'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "UnexpectedInstrumentResponse",
+     "evalue": "Unexpected instrument response. Perhaps the model of the instrument does not match the drivers expectation or a firmware upgrade has taken place. Please get in touch with a QCoDeS core developer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mUnexpectedInstrumentResponse\u001b[0m              Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-13-63fea0925a8b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mparser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"QCoDe is Cool\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mc:\\users\\sochatoo\\development\\qcodes\\qcodes\\instrument_drivers\\stahl\\stahl.py\u001b[0m in \u001b[0;36mparser\u001b[1;34m(input_string)\u001b[0m\n\u001b[0;32m    223\u001b[0m         \"\"\"\n\u001b[0;32m    224\u001b[0m         \u001b[0mregex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmatch_string\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 225\u001b[1;33m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    226\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mparser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_string\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mregex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_string\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mUnexpectedInstrumentResponse\u001b[0m: Unexpected instrument response. Perhaps the model of the instrument does not match the drivers expectation or a firmware upgrade has taken place. Please get in touch with a QCoDeS core developer"
+     ]
+    }
+   ],
+   "source": [
+    "parser(\"QCoDe is Cool\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index 093767d887b..596fb68554c 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -72,7 +72,7 @@ def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
             "voltage",
             get_cmd=f"{self.parent.identifier} U{self._channel_string}",
             get_parser=chain(
-                self.parent.regex_parser("([+\-]\d+,\d+) V$"),
+                self.parent.regex_parser(r"([+\-]\d+,\d+) V$"),
                 self._string_to_float()
             ),
             set_cmd=self._set_voltage,
@@ -87,7 +87,7 @@ def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
             "current",
             get_cmd=f"{self.parent.identifier} I{self._channel_string}",
             get_parser=chain(
-                self.parent.regex_parser("([+\-]\d+,\d+) mA$"),
+                self.parent.regex_parser(r"([+\-]\d+,\d+) mA$"),
                 self._string_to_float(
                     scale_factor=1/1000  # We want results in Ampere
                 )
@@ -216,7 +216,15 @@ def ask_raw(self, cmd: str) -> str:
 
     @staticmethod
     def regex_parser(match_string: str) -> Callable:
+        """
+        Example:
+            >>> parser = Stahl.regex_parser("^QCoDeS is (.*)$")
+            >>> result = parser("QCoDeS is Cool")  # Will return 'Cool'
 
+        Raises:
+            UnexpectedInstrumentResponse if a match could not be found with
+            regular expressions
+        """
         regex = re.compile(match_string)
 
         def parser(input_string: str) -> Union[str, Sequence[str]]:
@@ -232,16 +240,16 @@ def parser(input_string: str) -> Union[str, Sequence[str]]:
 
         return parser
 
-    def _parse_idn_string(self, ind_string) -> Dict[str, Any]:
+    def _parse_idn_string(self, idn_string) -> Dict[str, Any]:
         """
         Return:
              dict with keys: "model", "serial_number", "voltage_range",
              "n_channels", "output_type"
         """
         idn_parser = self.regex_parser(
-            "(HV|BS)(\d{3}) (\d{3}) (\d{2}) [buqsm]"
+            r"(HV|BS)(\d{3}) (\d{3}) (\d{2}) [buqsm]"
         )
-        parsed_idn = idn_parser(ind_string)
+        parsed_idn = idn_parser(idn_string)
 
         converters: Dict[str, Callable] = OrderedDict({
             "model": str,
diff --git a/qcodes/tests/drivers/test_stahl.py b/qcodes/tests/drivers/test_stahl.py
new file mode 100644
index 00000000000..e0db5429b5b
--- /dev/null
+++ b/qcodes/tests/drivers/test_stahl.py
@@ -0,0 +1,36 @@
+import pytest
+
+from qcodes.instrument_drivers.stahl import Stahl
+from qcodes.instrument_drivers.stahl.stahl import UnexpectedInstrumentResponse, chain
+
+
+def test_parse_simple():
+    parser = Stahl.regex_parser("^QCoDeS is (.*)$")
+    result = parser("QCoDeS is Cool")
+    assert result == "Cool"
+
+    with pytest.raises(UnexpectedInstrumentResponse):
+        parser("QCoDe is Cool")
+
+
+def test_parse_tuple():
+    parser = Stahl.regex_parser(r"^QCoDeS version is (\d),(\d)$")
+    a, b = parser("QCoDeS version is 3,4")
+
+    assert a == "3"
+    assert b == "4"
+
+
+def test_chain():
+
+    def f(a, b):
+        return int(a) + int(b)
+
+    parser = chain(
+        Stahl.regex_parser(r"^QCoDeS version is (\d),(\d)$"),
+        f
+    )
+
+    answer = parser("QCoDeS version is 3,4")
+    assert answer == 7
+

From dded1fb228ff80068282e64fb22db7b2df1ece50 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Mon, 26 Nov 2018 17:23:15 -0800
Subject: [PATCH 201/719] add docstring to test functions

---
 qcodes/tests/drivers/test_stahl.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/drivers/test_stahl.py b/qcodes/tests/drivers/test_stahl.py
index e0db5429b5b..e17246805db 100644
--- a/qcodes/tests/drivers/test_stahl.py
+++ b/qcodes/tests/drivers/test_stahl.py
@@ -5,6 +5,11 @@
 
 
 def test_parse_simple():
+    """
+    Test that we can parse simple messages from the instrument
+    and that the proper exception is raised when the received
+    response does not match the expectation
+    """
     parser = Stahl.regex_parser("^QCoDeS is (.*)$")
     result = parser("QCoDeS is Cool")
     assert result == "Cool"
@@ -14,6 +19,9 @@ def test_parse_simple():
 
 
 def test_parse_tuple():
+    """
+    Test that we can extract multiple values from a string
+    """
     parser = Stahl.regex_parser(r"^QCoDeS version is (\d),(\d)$")
     a, b = parser("QCoDeS version is 3,4")
 
@@ -22,7 +30,9 @@ def test_parse_tuple():
 
 
 def test_chain():
-
+    """
+    Test chaining of callables
+    """
     def f(a, b):
         return int(a) + int(b)
 

From fc4e8a561ec1adedc6f9917115811108b9825bf5 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Mon, 26 Nov 2018 17:24:44 -0800
Subject: [PATCH 202/719] clean up example notebook

---
 .../Qcodes example with Stahl.ipynb           | 60 -------------------
 1 file changed, 60 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index 8da3df5affb..22d3182a4f5 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -172,66 +172,6 @@
     "stahl.channel[1].is_locked()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "parser = Stahl.regex_parser(\"^QCoDeS is (.*)$\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result = parser(\"QCoDeS is Cool\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Cool'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "UnexpectedInstrumentResponse",
-     "evalue": "Unexpected instrument response. Perhaps the model of the instrument does not match the drivers expectation or a firmware upgrade has taken place. Please get in touch with a QCoDeS core developer",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mUnexpectedInstrumentResponse\u001b[0m              Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-13-63fea0925a8b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mparser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"QCoDe is Cool\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mc:\\users\\sochatoo\\development\\qcodes\\qcodes\\instrument_drivers\\stahl\\stahl.py\u001b[0m in \u001b[0;36mparser\u001b[1;34m(input_string)\u001b[0m\n\u001b[0;32m    223\u001b[0m         \"\"\"\n\u001b[0;32m    224\u001b[0m         \u001b[0mregex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmatch_string\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 225\u001b[1;33m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    226\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mparser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_string\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mregex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_string\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mUnexpectedInstrumentResponse\u001b[0m: Unexpected instrument response. Perhaps the model of the instrument does not match the drivers expectation or a firmware upgrade has taken place. Please get in touch with a QCoDeS core developer"
-     ]
-    }
-   ],
-   "source": [
-    "parser(\"QCoDe is Cool\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From 886cda10ed0bdf61e6eaf00f4d01371eef5c2c2e Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Mon, 26 Nov 2018 17:48:30 -0800
Subject: [PATCH 203/719] bugfix: the output type was not extracted from the
 IDN string

---
 .../Qcodes example with Stahl.ipynb           | 113 +++++++++++++++++-
 qcodes/instrument_drivers/stahl/stahl.py      |   2 +-
 2 files changed, 112 insertions(+), 3 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index 22d3182a4f5..3dac68d516c 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -44,7 +44,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.0\n"
+      "2.001\n"
      ]
     }
    ],
@@ -140,7 +140,7 @@
     {
      "data": {
       "text/plain": [
-       "26.9"
+       "25.9"
       ]
      },
      "execution_count": 8,
@@ -172,6 +172,115 @@
     "stahl.channel[1].is_locked()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Stahl' object and its delegates have no attribute 'output_type'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-16-a4e96dbb1ae2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstahl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mc:\\users\\sochatoo\\development\\qcodes\\qcodes\\utils\\helpers.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    418\u001b[0m         raise AttributeError(\n\u001b[0;32m    419\u001b[0m             \"'{}' object and its delegates have no attribute '{}'\".format(\n\u001b[1;32m--> 420\u001b[1;33m                 self.__class__.__name__, key))\n\u001b[0m\u001b[0;32m    421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    422\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'Stahl' object and its delegates have no attribute 'output_type'"
+     ]
+    }
+   ],
+   "source": [
+    "stahl.output_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idn_str = stahl.ask(\"IDN\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'model': 'HV', 'serial_number': '171', 'voltage_range': 5.0, 'n_channels': 10}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stahl._parse_idn_string(idn_str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'HV171 005 10 b'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idn_str"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regex = \"(HV|BS)(\\d{3}) (\\d{3}) (\\d{2}) ([buqsm])\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('HV', '171', '005', '10', 'b')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "re.search(regex, idn_str).groups()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index 596fb68554c..f0d4dec1bdd 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -247,7 +247,7 @@ def _parse_idn_string(self, idn_string) -> Dict[str, Any]:
              "n_channels", "output_type"
         """
         idn_parser = self.regex_parser(
-            r"(HV|BS)(\d{3}) (\d{3}) (\d{2}) [buqsm]"
+            r"(HV|BS)(\d{3}) (\d{3}) (\d{2}) ([buqsm])"
         )
         parsed_idn = idn_parser(idn_string)
 

From 63f7a4ff90e9d28d54c5b81c0ac7a2b32996d928 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Mon, 26 Nov 2018 17:48:51 -0800
Subject: [PATCH 204/719] bugfix: the output type was not extracted from the
 IDN string

---
 .../Qcodes example with Stahl.ipynb           | 97 +------------------
 1 file changed, 4 insertions(+), 93 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index 3dac68d516c..d279f493e00 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -174,111 +174,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'Stahl' object and its delegates have no attribute 'output_type'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-16-a4e96dbb1ae2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstahl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mc:\\users\\sochatoo\\development\\qcodes\\qcodes\\utils\\helpers.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    418\u001b[0m         raise AttributeError(\n\u001b[0;32m    419\u001b[0m             \"'{}' object and its delegates have no attribute '{}'\".format(\n\u001b[1;32m--> 420\u001b[1;33m                 self.__class__.__name__, key))\n\u001b[0m\u001b[0;32m    421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    422\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'Stahl' object and its delegates have no attribute 'output_type'"
-     ]
-    }
-   ],
-   "source": [
-    "stahl.output_type"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "idn_str = stahl.ask(\"IDN\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'model': 'HV', 'serial_number': '171', 'voltage_range': 5.0, 'n_channels': 10}"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stahl._parse_idn_string(idn_str)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'HV171 005 10 b'"
+       "'bipolar'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "idn_str"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "regex = \"(HV|BS)(\\d{3}) (\\d{3}) (\\d{2}) ([buqsm])\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('HV', '171', '005', '10', 'b')"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "re.search(regex, idn_str).groups()"
+    "stahl.output_type"
    ]
   },
   {

From 494bdc6967b12bfe41cc04cd42a227f94dc013fd Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Tue, 27 Nov 2018 10:13:34 +0100
Subject: [PATCH 205/719] Add safety measures against old source DB versions

---
 qcodes/dataset/database_extract_runs.py       | 14 +++++++++-
 .../dataset/test_database_extract_runs.py     | 28 ++++++++++++++++++-
 2 files changed, 40 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 865585915b8..d11d2bffade 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -9,6 +9,7 @@
                                         connect,
                                         create_run,
                                         format_table_name,
+                                        get_db_version_and_newest_available_version,
                                         get_exp_ids_from_run_ids,
                                         get_last_experiment,
                                         get_matching_exp_ids,
@@ -23,7 +24,8 @@
 
 
 def extract_runs_into_db(source_db_path: str,
-                         target_db_path: str, *run_ids: int) -> None:
+                         target_db_path: str, *run_ids: int,
+                         upgrade_source_db: bool=False) -> None:
     """
     Extract a selection of runs into another DB file. All runs must come from
     the same experiment. They will be added to an experiment with the same name
@@ -35,7 +37,17 @@ def extract_runs_into_db(source_db_path: str,
         target_db_path: Path to the target DB file. The target DB file will be
           created if it does not exist.
         run_ids: The run_ids of the runs to copy into the target DB file
+        upgrade_source_db: If the source DB is found to be in a version that is
+          not the newest, should it be upgraded?
     """
+    # Check for versions
+    (s_v, new_v) = get_db_version_and_newest_available_version(source_db_path)
+    if s_v < new_v and not upgrade_source_db:
+        warn(f'Source DB version is {s_v}, but this function needs it to be'
+             f' in version {new_v}. Run this function again with '
+             'upgrade_source_db=True to auto-upgrade the source DB file.')
+        return
+
     source_conn = connect(source_db_path)
 
     # Validate that all runs are in the source database
diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index ada869522f2..988083b41ae 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -1,11 +1,13 @@
 from os.path import getmtime
 from contextlib import contextmanager
 import re
+import os
 
 import pytest
 import numpy as np
 
-from qcodes.dataset.sqlite_base import connect, get_experiments
+import qcodes.tests.dataset
+from qcodes.dataset.sqlite_base import get_experiments
 from qcodes.dataset.experiment_container import Experiment
 from qcodes.dataset.data_set import (DataSet, load_by_guid, load_by_counter,
                                      load_by_id)
@@ -397,3 +399,27 @@ def test_load_by_X_functions(two_empty_temp_db_connections,
 
     test_ds = load_by_counter(1, 1, target_conn)
     assert source_ds_2_2.the_same_dataset_as(test_ds)
+
+
+def test_old_versions_not_touched(two_empty_temp_db_connections):
+
+    _, target_conn = two_empty_temp_db_connections
+
+    target_path = path_to_dbfile(target_conn)
+
+    fixturepath = os.sep.join(qcodes.tests.dataset.__file__.split(os.sep)[:-1])
+    fixturepath = os.path.join(fixturepath,
+                               'fixtures', 'db_files', 'version2',
+                               'some_runs.db')
+    if not os.path.exists(fixturepath):
+        pytest.skip("No db-file fixtures found. You can generate test db-files"
+                    " using the scripts in the legacy_DB_generation folder")
+    with raise_if_file_changed(fixturepath):
+        with pytest.warns(UserWarning) as warning:
+            extract_runs_into_db(fixturepath, target_path, 1)
+            expected_mssg = ('Source DB version is 2, but this '
+                             'function needs it to be in version 3. '
+                             'Run this function again with '
+                             'upgrade_source_db=True to auto-upgrade '
+                             'the source DB file.')
+            assert warning[0].message.args[0] == expected_mssg

From b9d5f52d6547c7c1ed7737d0c2971cf9cebdeb39 Mon Sep 17 00:00:00 2001
From: qSaevar <39056113+qSaevar@users.noreply.github.com>
Date: Tue, 16 Oct 2018 14:22:17 +0200
Subject: [PATCH 206/719]  [DEM-564] Create a new HDAWG8 driver from ZI example
 (#1)

* [DEM-564] Create a new HDAWG8 driver from ZI example
---
 qcodes/instrument_drivers/ZI/ZIHDAWG8 | 198 ++++++++++++++++++++++++++
 qcodes/tests/drivers/test_zihdawg8.py |  86 +++++++++++
 2 files changed, 284 insertions(+)
 create mode 100644 qcodes/instrument_drivers/ZI/ZIHDAWG8
 create mode 100644 qcodes/tests/drivers/test_zihdawg8.py

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8 b/qcodes/instrument_drivers/ZI/ZIHDAWG8
new file mode 100644
index 00000000000..cfe3a2e2988
--- /dev/null
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8
@@ -0,0 +1,198 @@
+import json
+import time
+from functools import partial
+
+import zhinst
+import zhinst.utils
+
+from qcodes import Instrument
+from qcodes.utils import validators as validators
+
+
+class ZIHDAWG8(Instrument):
+    """
+    QCoDeS driver for ZI HDAWG8.
+
+    Requires ZI LabOne software to be installed on the computer running QCoDeS (tested using LabOne (18.05.54618)
+    and firmware (53866).
+    Furthermore, the Data Server and Web Server must be running and a connection
+    between the two must be made.
+    """
+
+    def __init__(self, name: str, device_id: str, **kwargs) -> None:
+        """
+        Create an instance of the instrument.
+
+        Args:
+            name (str): The internal QCoDeS name of the instrument
+            device_ID (str): The device name as listed in the web server.
+        """
+        super().__init__(name, **kwargs)
+        self.api_level = 6
+        (self.daq, self.device, self.props) = zhinst.utils.create_api_session(device_id, self.api_level,
+                                                                              required_devtype='HDAWG')
+        self.awg_module = self.daq.awgModule()
+        self.awg_module.set('awgModule/device', self.device)
+        self.awg_module.execute()
+        node_tree = self.download_device_node_tree()
+        self.create_parameters_from_node_tree(node_tree)
+
+    def enable_channel(self, channel_number):
+        """
+        Enable a signal output, turns on a blue LED on the device.
+        Args:
+            channel_number (int): Output channel that should be enabled.
+
+        Returns: None
+        """
+        self.set('sigouts_{}_on'.format(channel_number), 1)
+
+    def disable_channel(self, channel_number):
+        """
+        Disable a signal output, turns off a blue LED on the device.
+        Args:
+            channel_number (int): Output channel that should be disabled.
+
+        Returns: None
+        """
+        self.set('sigouts_{}_on'.format(channel_number), 0)
+
+    def start_awg(self, awg_number):
+        """
+        Activate an AWG
+        Args:
+            awg_number (int): The AWG that should be enabled.
+
+        Returns: None
+        """
+        self.set('awgs_{}_enable'.format(awg_number), 1)
+
+    def stop_awg(self, awg_number):
+        """
+        Deactivate an AWG
+        Args:
+            awg_number (int): The AWG that should be disabled.
+
+        Returns: None
+        """
+        self.set('awgs_{}_enable'.format(awg_number), 0)
+
+    def upload_sequence_program(self, awg, sequence_program):
+        """
+        Uploads a sequence program to the device equivalent to using the the sequencer tab in the device's gui.
+        Args:
+            awg (int): The AWG that the sequence program will be uploaded to.
+            sequence_program (str): A sequence program that should be played on the device.
+        Returns (int): 0: Compilation was successful with no warnings.
+                       1: Compilation was successful but with warnings.
+        """
+        self.awg_module.set('awgModule/index', awg)
+        self.awg_module.set('awgModule/compiler/sourcestring', sequence_program)
+        while self.awg_module.getInt('awgModule/compiler/status') == -1:
+            time.sleep(0.1)
+
+        if self.awg_module.getInt('awgModule/compiler/status') == 1:
+            raise Exception(self.awg_module.getString('awgModule/compiler/statusstring'))
+        while self.awg_module.getDouble('awgModule/progress') < 1.0:
+            time.sleep(0.1)
+
+        return self.awg_module.getInt('awgModule/compiler/status')
+
+    def upload_waveform(self, awg, waveform, index):
+        """
+        Upload a waveform to the device memory at a given index.
+        Node: There needs to be a place holder on the device as this only replaces a data in the device memory and there
+              for does not allocate new memory space.
+        Args:
+            awg (int): The AWG where waveform should be uploaded to.
+            waveform: An array of floating point values from -1.0 to 1.0, or integers in the range (-32768...+32768)
+            index: Index of the waveform that will be replaced. If there are more than 1 waveforms used then the index
+                   corresponds to the position of the waveform in the Waveforms sub-tab of the AWG tab in the GUI.
+
+        Returns: None
+
+        """
+        self.set('awgs_{}_waveform_index'.format(awg), index)
+        self.daq.sync()
+        self.set('awgs_{}_waveform_data'.format(awg), waveform)
+
+    def set_channel_grouping(self, group):
+        """
+        Set the channel grouping mode of the device.
+
+        Args:
+            group (int): 0: Use the outputs in groups of 2. One sequencer program controls 2 outputs.
+                         1: Use the outputs in groups of 4. One sequencer program controls 4 outputs.
+                         2: Use the outputs in groups of 8. One sequencer program controls 8 outputs.
+        Returns: None
+
+        """
+        self.set('system_awg_channelgrouping', group)
+
+    def create_parameters_from_node_tree(self, parameters):
+        """
+        Create QuCoDeS parameters from the device node tree.
+        Args:
+            parameters (dict): A device node tree.
+
+        Returns: None
+
+        """
+        for parameter in parameters.values():
+            getter = partial(self._getter, parameter['Node'], parameter['Type']) if 'Read' in parameter[
+                'Properties'] else None
+            setter = partial(self._setter, parameter['Node'], parameter['Type']) if 'Write' in parameter[
+                'Properties'] else None
+            options = validators.Enum(*[int(val) for val in parameter['Options'].keys()]) \
+                if parameter['Type'] == 'Integer (enumerated)' else None
+            parameter_name = self._generate_parameter_name(parameter['Node'])
+            self.add_parameter(name=parameter_name,
+                               set_cmd=setter,
+                               get_cmd=getter,
+                               vals=options,
+                               docstring=parameter['Description'],
+                               unit=parameter['Unit']
+                               )
+
+    @staticmethod
+    def _generate_parameter_name(node):
+        values = node.split('/')
+        return '_'.join(values[2:]).lower()
+
+    def download_device_node_tree(self, flags=0):
+        """
+        Args:
+            flags: ziPython.ziListEnum.settingsonly -> 0x08
+                        Returns only nodes which are marked as setting
+                   ziPython.ziListEnum.streamingonly -> 0x10
+                        Returns only streaming nodes
+                   ziPython.ziListEnum.subscribedonly -> 0x20
+                        Returns only subscribed nodes
+                   ziPython.ziListEnum.basechannel -> 0x40
+                        Return only one instance of a node in case of multiple channels
+                   Or any combination of flags can be used.
+
+        Returns: A dictionary of the device node tree.
+        """
+        node_tree = self.daq.listNodesJSON('/{}/'.format(self.device), flags)
+        return json.loads(node_tree)
+
+    def _setter(self, name, param_type, value):
+        if param_type == "Integer (64 bit)" or param_type == 'Integer (enumerated)':
+            self.daq.setInt(name, value)
+        elif param_type == "Double":
+            self.daq.setDouble(name, value)
+        elif param_type == "String":
+            self.daq.setString(name, value)
+        elif param_type == "ZIVectorData":
+            self.daq.vectorWrite(name, value)
+
+    def _getter(self, name, param_type):
+        if param_type == "Integer (64 bit)" or param_type == 'Integer (enumerated)':
+            self.daq.getInt(name)
+        elif param_type == "Double":
+            self.daq.getDouble(name)
+        elif param_type == "String":
+            self.daq.getString(name)
+        elif param_type == "ZIVectorData":
+            self.daq.vectorRead(name)
diff --git a/qcodes/tests/drivers/test_zihdawg8.py b/qcodes/tests/drivers/test_zihdawg8.py
new file mode 100644
index 00000000000..19f9b3e5c5d
--- /dev/null
+++ b/qcodes/tests/drivers/test_zihdawg8.py
@@ -0,0 +1,86 @@
+import unittest
+from unittest.mock import patch, MagicMock
+
+import zhinst
+
+import qcodes
+from qcodes.instrument_drivers.ZI.ZIHDAWG8 import ZIHDAWG8
+from qcodes.utils import validators
+
+
+class TestZIHDAWG8(unittest.TestCase):
+    def setUp(self):
+        self.node_tree = {"/DEV8049/SYSTEM/AWG/CHANNELGROUPING": {
+            "Node": "/DEV8049/SYSTEM/AWG/CHANNELGROUPING",
+            "Description": "Sets the channel grouping mode of the device.",
+            "Properties": "Read, Write, Setting",
+            "Type": "Integer (enumerated)",
+            "Unit": "None",
+            "Options": {
+                "0": "Use the outputs in groups of 2. One sequencer program controls 2 outputs ",
+                "1": "Use the outputs in groups of 4. One sequencer program controls 4 outputs ",
+                "2": "Use the outputs in groups of 8. One sequencer program controls 8 outputs "
+            }
+        }, "/DEV8049/SIGOUTS/0/ON": {
+            "Node": "/DEV8049/SIGOUTS/0/ON",
+            "Description": "Enabling/Disabling the Signal Output. Corresponds to the blue LED indicator",
+            "Properties": "Read, Write, Setting",
+            "Type": "Integer (64 bit)",
+            "Unit": "None"
+        }, "/DEV8049/SYSTEM/OWNER": {
+            "Node": "/DEV8049/SYSTEM/OWNER",
+            "Description": "Returns the current owner of the device (IP).",
+            "Properties": "Read",
+            "Type": "String",
+            "Unit": "None"
+        }, "/DEV8049/SINES/0/AMPLITUDES/0": {
+            "Node": "/DEV8049/SINES/0/AMPLITUDES/0",
+            "Description": "Sets the peak amplitude that the sine signal contributes to the signal output. Note that\
+             the last index is either 0 or 1 and will map to the pair of outputs given by the first index.\
+              (e.g. sines/3/amplitudes/0 corresponds to wave output 2)",
+            "Properties": "Read, Write, Setting",
+            "Type": "Double",
+            "Unit": "None"
+        }, "/DEV8049/AWGS/1/WAVEFORM/MEMORYUSAGE": {
+            "Node": "/DEV8049/AWGS/1/WAVEFORM/MEMORYUSAGE",
+            "Description": "Amount of the used waveform data relative to the device cache memory. The cache memory \
+            provides space for 32 kSa of waveform data. Memory Usage over 100% means that waveforms must be loaded from\
+             the main memory (128 MSa per channel) during playback, which can lead to delays.",
+            "Properties": "Read",
+            "Type": "Double",
+            "Unit": "%"
+        }}
+
+    def test_create_parameters_from_node_tree(self):
+        with patch.object(zhinst.utils, 'create_api_session', return_value=3 * (MagicMock(),)), \
+             patch.object(qcodes.instrument_drivers.ZI.ZIHDAWG8.ZIHDAWG8, 'download_device_node_tree',
+                          return_value=self.node_tree):
+            hdawg8 = ZIHDAWG8('Name', 'dev-test')
+
+            self.assertIn('system_awg_channelgrouping', hdawg8.parameters)
+            with self.assertRaises(ValueError):
+                hdawg8.system_awg_channelgrouping.set(4)
+            self.assertEqual('None', hdawg8.system_awg_channelgrouping.unit)
+            self.assertEqual('system_awg_channelgrouping', hdawg8.system_awg_channelgrouping.name)
+            self.assertIsNotNone(hdawg8.system_awg_channelgrouping.vals)
+            self.assertIsInstance(hdawg8.system_awg_channelgrouping.vals, validators.Enum)
+
+            self.assertIn('sigouts_0_on', hdawg8.parameters)
+            self.assertEqual('None', hdawg8.sigouts_0_on.unit)
+            self.assertEqual('sigouts_0_on', hdawg8.sigouts_0_on.name)
+            self.assertIsNone(hdawg8.sigouts_0_on.vals)
+
+            self.assertIn('system_owner', hdawg8.parameters)
+            self.assertEqual('None', hdawg8.system_owner.unit)
+            self.assertEqual('system_owner', hdawg8.system_owner.name)
+            self.assertIsNone(hdawg8.system_owner.vals)
+
+            self.assertIn('sines_0_amplitudes_0', hdawg8.parameters)
+            self.assertEqual('None', hdawg8.sines_0_amplitudes_0.unit)
+            self.assertEqual('sines_0_amplitudes_0', hdawg8.sines_0_amplitudes_0.name)
+            self.assertIsNone(hdawg8.sines_0_amplitudes_0.vals)
+
+            self.assertIn('awgs_1_waveform_memoryusage', hdawg8.parameters)
+            self.assertEqual('%', hdawg8.awgs_1_waveform_memoryusage.unit)
+            self.assertEqual('awgs_1_waveform_memoryusage', hdawg8.awgs_1_waveform_memoryusage.name)
+            self.assertIsNone(hdawg8.awgs_1_waveform_memoryusage.vals)

From e231ac5189f3c4c0c6694159ef583248ad505960 Mon Sep 17 00:00:00 2001
From: qSaevar <39056113+qSaevar@users.noreply.github.com>
Date: Fri, 19 Oct 2018 13:22:47 +0200
Subject: [PATCH 207/719] [DEM-525] Improve raw waveform upload speed (#2)

* [DEM-525] Improve raw waveform upload speed
---
 .../ZI/{ZIHDAWG8 => ZIHDAWG8.py}              | 78 ++++++++++++++++---
 qcodes/tests/drivers/test_zihdawg8.py         | 47 ++++++++++-
 2 files changed, 113 insertions(+), 12 deletions(-)
 rename qcodes/instrument_drivers/ZI/{ZIHDAWG8 => ZIHDAWG8.py} (70%)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8 b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
similarity index 70%
rename from qcodes/instrument_drivers/ZI/ZIHDAWG8
rename to qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index cfe3a2e2988..361b3103f16 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -1,4 +1,7 @@
+import csv
 import json
+import os
+import textwrap
 import time
 from functools import partial
 
@@ -77,16 +80,69 @@ def stop_awg(self, awg_number):
         """
         self.set('awgs_{}_enable'.format(awg_number), 0)
 
-    def upload_sequence_program(self, awg, sequence_program):
+    def waveform_to_csv(self, wave_name, *waveforms):
+        """
+        Write waveforms to a CSV file in the modules data directory so that it can be referenced and used in a
+        sequence program. If more than one waveform is provided they will be played simultaneously but on separate
+        outputs.
+
+        Args:
+            wave_name: Name of the CSV file, is used by a sequence program.
+            waveforms (list): One or more waveforms that are to be written to a CSV file. Note if there are more than
+            one waveforms then they have to be of equal length, if not the longer ones will be truncated.
+
+        Returns: None
+
+        """
+        data_dir = self.awg_module.getString('awgModule/directory')
+        wave_dir = os.path.join(data_dir, "awg", "waves")
+        if not os.path.isdir(wave_dir):
+            raise Exception("AWG module wave directory {} does not exist or is not a directory".format(wave_dir))
+        csv_file = os.path.join(wave_dir, wave_name + '.csv')
+        with open(csv_file, "w", newline='') as f:
+            writer = csv.writer(f, delimiter=';')
+            writer.writerows(zip(*waveforms))
+
+    @staticmethod
+    def generate_csv_sequence_program(wave_names, channels=None):
+        """
+        Generates and returns a sequencing program that plays the given waves on the given channels. There has to be a
+        CSV file with a corresponding name to a wave in wave_names.
+        Args:
+            wave_names (list): List of wave names that are to be played.
+            channels (list, optional): Channels to play the waveforms on.
+
+        Returns (str): A sequencing program that can be uploaded to the device.
+
+        """
+        awg_program = textwrap.dedent("""
+            HEADER
+            while(true){
+                playWave(WAVES);
+            }
+            """)
+        sequence_header = '// generated by {}\n'.format(__name__)
+        awg_program = awg_program.replace('HEADER', sequence_header)
+        if channels is None:
+            argument_string = ('"{}"' * len(wave_names)).replace('""', '", "')
+            waves = argument_string.format(*wave_names)
+        else:
+            argument_string = ('{}, "{}"' * len(wave_names)).replace('}"{', '}", {')
+            waves = argument_string.format(*[value for pair in zip(channels, wave_names) for value in pair])
+        awg_program = awg_program.replace('WAVES', waves)
+
+        return awg_program
+
+    def upload_sequence_program(self, awg_number, sequence_program):
         """
         Uploads a sequence program to the device equivalent to using the the sequencer tab in the device's gui.
         Args:
-            awg (int): The AWG that the sequence program will be uploaded to.
+            awg_number (int): The AWG that the sequence program will be uploaded to.
             sequence_program (str): A sequence program that should be played on the device.
         Returns (int): 0: Compilation was successful with no warnings.
                        1: Compilation was successful but with warnings.
         """
-        self.awg_module.set('awgModule/index', awg)
+        self.awg_module.set('awgModule/index', awg_number)
         self.awg_module.set('awgModule/compiler/sourcestring', sequence_program)
         while self.awg_module.getInt('awgModule/compiler/status') == -1:
             time.sleep(0.1)
@@ -98,13 +154,13 @@ def upload_sequence_program(self, awg, sequence_program):
 
         return self.awg_module.getInt('awgModule/compiler/status')
 
-    def upload_waveform(self, awg, waveform, index):
+    def upload_waveform(self, awg_number, waveform, index):
         """
         Upload a waveform to the device memory at a given index.
         Node: There needs to be a place holder on the device as this only replaces a data in the device memory and there
               for does not allocate new memory space.
         Args:
-            awg (int): The AWG where waveform should be uploaded to.
+            awg_number (int): The AWG where waveform should be uploaded to.
             waveform: An array of floating point values from -1.0 to 1.0, or integers in the range (-32768...+32768)
             index: Index of the waveform that will be replaced. If there are more than 1 waveforms used then the index
                    corresponds to the position of the waveform in the Waveforms sub-tab of the AWG tab in the GUI.
@@ -112,9 +168,9 @@ def upload_waveform(self, awg, waveform, index):
         Returns: None
 
         """
-        self.set('awgs_{}_waveform_index'.format(awg), index)
+        self.set('awgs_{}_waveform_index'.format(awg_number), index)
         self.daq.sync()
-        self.set('awgs_{}_waveform_data'.format(awg), waveform)
+        self.set('awgs_{}_waveform_data'.format(awg_number), waveform)
 
     def set_channel_grouping(self, group):
         """
@@ -189,10 +245,10 @@ def _setter(self, name, param_type, value):
 
     def _getter(self, name, param_type):
         if param_type == "Integer (64 bit)" or param_type == 'Integer (enumerated)':
-            self.daq.getInt(name)
+            return self.daq.getInt(name)
         elif param_type == "Double":
-            self.daq.getDouble(name)
+            return self.daq.getDouble(name)
         elif param_type == "String":
-            self.daq.getString(name)
+            return self.daq.getString(name)
         elif param_type == "ZIVectorData":
-            self.daq.vectorRead(name)
+            return self.daq.vectorRead(name)
diff --git a/qcodes/tests/drivers/test_zihdawg8.py b/qcodes/tests/drivers/test_zihdawg8.py
index 19f9b3e5c5d..ecc275f9ea3 100644
--- a/qcodes/tests/drivers/test_zihdawg8.py
+++ b/qcodes/tests/drivers/test_zihdawg8.py
@@ -1,10 +1,11 @@
+import textwrap
 import unittest
 from unittest.mock import patch, MagicMock
 
 import zhinst
+from qcodes.instrument_drivers.ZI.ZIHDAWG8 import ZIHDAWG8
 
 import qcodes
-from qcodes.instrument_drivers.ZI.ZIHDAWG8 import ZIHDAWG8
 from qcodes.utils import validators
 
 
@@ -84,3 +85,47 @@ def test_create_parameters_from_node_tree(self):
             self.assertEqual('%', hdawg8.awgs_1_waveform_memoryusage.unit)
             self.assertEqual('awgs_1_waveform_memoryusage', hdawg8.awgs_1_waveform_memoryusage.name)
             self.assertIsNone(hdawg8.awgs_1_waveform_memoryusage.vals)
+
+    def test_generate_csv_sequence_program(self):
+        expected = textwrap.dedent("""
+                        // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
+    
+                        while(true){
+                            playWaves(1, "wave_1", 2, "wave_2", 3, "wave_3");
+                        }
+                        """)
+        sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1', 'wave_2', 'wave_3'], [1, 2, 3])
+        self.assertEqual(expected, sequence_program)
+
+    def test_generate_csv_sequence_program_no_channels(self):
+        expected = textwrap.dedent("""
+                        // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
+    
+                        while(true){
+                            playWaves("wave_1", "wave_2", "wave_3");
+                        }
+                        """)
+        sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1', 'wave_2', 'wave_3'])
+        self.assertEqual(expected, sequence_program)
+
+    def test_generate_csv_sequence_program_1_wave_no_channels(self):
+        expected = textwrap.dedent("""
+                        // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
+    
+                        while(true){
+                            playWaves("wave_1");
+                        }
+                        """)
+        sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1'])
+        self.assertEqual(expected, sequence_program)
+
+    def test_generate_csv_sequence_program_1_wave(self):
+        expected = textwrap.dedent("""
+                        // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
+    
+                        while(true){
+                            playWaves(7, "wave_1");
+                        }
+                        """)
+        sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1'], [7])
+        self.assertEqual(expected, sequence_program)

From 147653baca21bace07e85a1c2810795153cb3636 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 16 Oct 2018 13:24:35 +0200
Subject: [PATCH 208/719] [DEM-564] Create a new HDAWG8 driver from ZI example

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8 | 198 ++++++++++++++++++++++++++
 qcodes/tests/drivers/test_zihdawg8.py |   7 +-
 2 files changed, 202 insertions(+), 3 deletions(-)
 create mode 100644 qcodes/instrument_drivers/ZI/ZIHDAWG8

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8 b/qcodes/instrument_drivers/ZI/ZIHDAWG8
new file mode 100644
index 00000000000..f590b0b7e1a
--- /dev/null
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8
@@ -0,0 +1,198 @@
+import json
+import time
+from functools import partial
+
+import zhinst
+import zhinst.utils
+
+from qcodes import Instrument
+from qcodes.utils import validators as validators
+
+
+class ZIHDAWG8(Instrument):
+    """
+    QCoDeS driver for ZI HDAWG8.
+
+    Requires ZI LabOne software to be installed on the computer running QCoDeS (tested using LabOne (18.05.54618)
+    and firmware (53866).
+    Furthermore, the Data Server and Web Server must be running and a connection
+    between the two must be made.
+    """
+
+    def __init__(self, name: str, device_id: str, **kwargs) -> None:
+        """
+        Create an instance of the instrument.
+
+        Args:
+            name (str): The internal QCoDeS name of the instrument
+            device_ID (str): The device name as listed in the web server.
+        """
+        super().__init__(name, **kwargs)
+        self.api_level = 6
+        (self.daq, self.device, self.props) = zhinst.utils.create_api_session(device_id, self.api_level,
+                                                                              required_devtype='HDAWG')
+        self.awg_module = self.daq.awgModule()
+        self.awg_module.set('awgModule/device', self.device)
+        self.awg_module.execute()
+        node_tree = self.download_device_node_tree()
+        self.create_parameters_from_node_tree(node_tree)
+
+    def enable_channel(self, channel_number):
+        """
+        Enable a signal output, turns on a blue LED on the device.
+        Args:
+            channel_number (int): Output channel that should be enabled.
+
+        Returns: None
+        """
+        self.set('sigouts_{}_on'.format(channel_number), 1)
+
+    def disable_channel(self, channel_number):
+        """
+        Disable a signal output, turns off a blue LED on the device.
+        Args:
+            channel_number (int): Output channel that should be disabled.
+
+        Returns: None
+        """
+        self.set('sigouts_{}_on'.format(channel_number), 0)
+
+    def start_awg(self, awg_number):
+        """
+        Activate an AWG
+        Args:
+            awg_number (int): The AWG that should be enabled.
+
+        Returns: None
+        """
+        self.set('awgs_{}_enable'.format(awg_number), 1)
+
+    def stop_awg(self, awg):
+        """
+        Deactivate an AWG
+        Args:
+            awg (int): The AWG that should be disabled.
+
+        Returns: None
+        """
+        self.set('awgs_{}_enable'.format(awg), 0)
+
+    def upload_sequence_program(self, awg, sequence_program):
+        """
+        Uploads a sequence program to the device equivalent to using the the sequencer tab in the device's gui.
+        Args:
+            awg (int): The AWG that the sequence program will be uploaded to.
+            sequence_program (str): A sequence program that should be played on the device.
+        Returns (int): 0: Compilation was successful with no warnings.
+                       1: Compilation was successful but with warnings.
+        """
+        self.awg_module.set('awgModule/index', awg)
+        self.awg_module.set('awgModule/compiler/sourcestring', sequence_program)
+        while self.awg_module.getInt('awgModule/compiler/status') == -1:
+            time.sleep(0.1)
+
+        if self.awg_module.getInt('awgModule/compiler/status') == 1:
+            raise Exception(self.awg_module.getString('awgModule/compiler/statusstring'))
+        while self.awg_module.getDouble('awgModule/progress') < 1.0:
+            time.sleep(0.1)
+
+        return self.awg_module.getInt('awgModule/compiler/status')
+
+    def upload_waveform(self, awg, waveform, index):
+        """
+        Upload a waveform to the device memory at a given index.
+        Node: There needs to be a place holder on the device as this only replaces a data in the device memory and there
+              for does not allocate new memory space.
+        Args:
+            awg (int): The AWG where waveform should be uploaded to.
+            waveform: An array of floating point values from -1.0 to 1.0, or integers in the range (-32768...+32768)
+            index: Index of the waveform that will be replaced. If there are more than 1 waveforms used then the index
+                   corresponds to the position of the waveform in the Waveforms sub-tab of the AWG tab in the GUI.
+
+        Returns: None
+
+        """
+        self.set('awgs_{}_waveform_index'.format(awg), index)
+        self.daq.sync()
+        self.set('awgs_{}_waveform_data'.format(awg), waveform)
+
+    def set_channel_grouping(self, group):
+        """
+        Set the channel grouping mode of the device.
+
+        Args:
+            group (int): 0: Use the outputs in groups of 2. One sequencer program controls 2 outputs.
+                         1: Use the outputs in groups of 4. One sequencer program controls 4 outputs.
+                         2: Use the outputs in groups of 8. One sequencer program controls 8 outputs.
+        Returns: None
+
+        """
+        self.set('system_awg_channelgrouping', group)
+
+    def create_parameters_from_node_tree(self, parameters):
+        """
+        Create QuCoDeS parameters from the device node tree.
+        Args:
+            parameters (dict): A device node tree.
+
+        Returns: None
+
+        """
+        for parameter in parameters.values():
+            getter = partial(self._getter, parameter['Node'], parameter['Type']) if 'Read' in parameter[
+                'Properties'] else None
+            setter = partial(self._setter, parameter['Node'], parameter['Type']) if 'Write' in parameter[
+                'Properties'] else None
+            options = validators.Enum(*[int(val) for val in parameter['Options'].keys()]) \
+                if parameter['Type'] == 'Integer (enumerated)' else None
+            parameter_name = self._generate_parameter_name(parameter['Node'])
+            self.add_parameter(name=parameter_name,
+                               set_cmd=setter,
+                               get_cmd=getter,
+                               vals=options,
+                               docstring=parameter['Description'],
+                               unit=parameter['Unit']
+                               )
+
+    @staticmethod
+    def _generate_parameter_name(node):
+        values = node.split('/')
+        return '_'.join(values[2:]).lower()
+
+    def download_device_node_tree(self, flags=0):
+        """
+        Args:
+            flags: ziPython.ziListEnum.settingsonly -> 0x08
+                        Returns only nodes which are marked as setting
+                   ziPython.ziListEnum.streamingonly -> 0x10
+                        Returns only streaming nodes
+                   ziPython.ziListEnum.subscribedonly -> 0x20
+                        Returns only subscribed nodes
+                   ziPython.ziListEnum.basechannel -> 0x40
+                        Return only one instance of a node in case of multiple channels
+                   Or any combination of flags can be used.
+
+        Returns: A dictionary of the device node tree.
+        """
+        node_tree = self.daq.listNodesJSON('/{}/'.format(self.device), flags)
+        return json.loads(node_tree)
+
+    def _setter(self, name, param_type, value):
+        if param_type == "Integer (64 bit)" or param_type == 'Integer (enumerated)':
+            self.daq.setInt(name, value)
+        elif param_type == "Double":
+            self.daq.setDouble(name, value)
+        elif param_type == "String":
+            self.daq.setString(name, value)
+        elif param_type == "ZIVectorData":
+            self.daq.vectorWrite(name, value)
+
+    def _getter(self, name, param_type):
+        if param_type == "Integer (64 bit)" or param_type == 'Integer (enumerated)':
+            self.daq.getInt(name)
+        elif param_type == "Double":
+            self.daq.getDouble(name)
+        elif param_type == "String":
+            self.daq.getString(name)
+        elif param_type == "ZIVectorData":
+            self.daq.vectorRead(name)
diff --git a/qcodes/tests/drivers/test_zihdawg8.py b/qcodes/tests/drivers/test_zihdawg8.py
index ecc275f9ea3..2c59d043945 100644
--- a/qcodes/tests/drivers/test_zihdawg8.py
+++ b/qcodes/tests/drivers/test_zihdawg8.py
@@ -1,11 +1,12 @@
+import sys
 import textwrap
 import unittest
 from unittest.mock import patch, MagicMock
 
+sys.modules['zhinst'] = MagicMock(name='zhinst')
 import zhinst
-from qcodes.instrument_drivers.ZI.ZIHDAWG8 import ZIHDAWG8
 
-import qcodes
+from qcodes.instrument_drivers.ZI.ZIHDAWG8 import ZIHDAWG8
 from qcodes.utils import validators
 
 
@@ -54,7 +55,7 @@ def setUp(self):
 
     def test_create_parameters_from_node_tree(self):
         with patch.object(zhinst.utils, 'create_api_session', return_value=3 * (MagicMock(),)), \
-             patch.object(qcodes.instrument_drivers.ZI.ZIHDAWG8.ZIHDAWG8, 'download_device_node_tree',
+             patch.object(ZIHDAWG8, 'download_device_node_tree',
                           return_value=self.node_tree):
             hdawg8 = ZIHDAWG8('Name', 'dev-test')
 

From 957fe7a7a0905306e2099c7980e5ddd717bb8950 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 16 Oct 2018 14:08:22 +0200
Subject: [PATCH 209/719] fixed small detail

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8 | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8 b/qcodes/instrument_drivers/ZI/ZIHDAWG8
index f590b0b7e1a..cfe3a2e2988 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8
@@ -67,15 +67,15 @@ class ZIHDAWG8(Instrument):
         """
         self.set('awgs_{}_enable'.format(awg_number), 1)
 
-    def stop_awg(self, awg):
+    def stop_awg(self, awg_number):
         """
         Deactivate an AWG
         Args:
-            awg (int): The AWG that should be disabled.
+            awg_number (int): The AWG that should be disabled.
 
         Returns: None
         """
-        self.set('awgs_{}_enable'.format(awg), 0)
+        self.set('awgs_{}_enable'.format(awg_number), 0)
 
     def upload_sequence_program(self, awg, sequence_program):
         """

From 9af1dce4360826780a176dc183012f18053d1e0d Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 16 Oct 2018 16:04:54 +0200
Subject: [PATCH 210/719] Added a needed .py to ZIHDAWG8

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8    | 198 -----------------------
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py |   3 -
 2 files changed, 201 deletions(-)
 delete mode 100644 qcodes/instrument_drivers/ZI/ZIHDAWG8

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8 b/qcodes/instrument_drivers/ZI/ZIHDAWG8
deleted file mode 100644
index cfe3a2e2988..00000000000
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8
+++ /dev/null
@@ -1,198 +0,0 @@
-import json
-import time
-from functools import partial
-
-import zhinst
-import zhinst.utils
-
-from qcodes import Instrument
-from qcodes.utils import validators as validators
-
-
-class ZIHDAWG8(Instrument):
-    """
-    QCoDeS driver for ZI HDAWG8.
-
-    Requires ZI LabOne software to be installed on the computer running QCoDeS (tested using LabOne (18.05.54618)
-    and firmware (53866).
-    Furthermore, the Data Server and Web Server must be running and a connection
-    between the two must be made.
-    """
-
-    def __init__(self, name: str, device_id: str, **kwargs) -> None:
-        """
-        Create an instance of the instrument.
-
-        Args:
-            name (str): The internal QCoDeS name of the instrument
-            device_ID (str): The device name as listed in the web server.
-        """
-        super().__init__(name, **kwargs)
-        self.api_level = 6
-        (self.daq, self.device, self.props) = zhinst.utils.create_api_session(device_id, self.api_level,
-                                                                              required_devtype='HDAWG')
-        self.awg_module = self.daq.awgModule()
-        self.awg_module.set('awgModule/device', self.device)
-        self.awg_module.execute()
-        node_tree = self.download_device_node_tree()
-        self.create_parameters_from_node_tree(node_tree)
-
-    def enable_channel(self, channel_number):
-        """
-        Enable a signal output, turns on a blue LED on the device.
-        Args:
-            channel_number (int): Output channel that should be enabled.
-
-        Returns: None
-        """
-        self.set('sigouts_{}_on'.format(channel_number), 1)
-
-    def disable_channel(self, channel_number):
-        """
-        Disable a signal output, turns off a blue LED on the device.
-        Args:
-            channel_number (int): Output channel that should be disabled.
-
-        Returns: None
-        """
-        self.set('sigouts_{}_on'.format(channel_number), 0)
-
-    def start_awg(self, awg_number):
-        """
-        Activate an AWG
-        Args:
-            awg_number (int): The AWG that should be enabled.
-
-        Returns: None
-        """
-        self.set('awgs_{}_enable'.format(awg_number), 1)
-
-    def stop_awg(self, awg_number):
-        """
-        Deactivate an AWG
-        Args:
-            awg_number (int): The AWG that should be disabled.
-
-        Returns: None
-        """
-        self.set('awgs_{}_enable'.format(awg_number), 0)
-
-    def upload_sequence_program(self, awg, sequence_program):
-        """
-        Uploads a sequence program to the device equivalent to using the the sequencer tab in the device's gui.
-        Args:
-            awg (int): The AWG that the sequence program will be uploaded to.
-            sequence_program (str): A sequence program that should be played on the device.
-        Returns (int): 0: Compilation was successful with no warnings.
-                       1: Compilation was successful but with warnings.
-        """
-        self.awg_module.set('awgModule/index', awg)
-        self.awg_module.set('awgModule/compiler/sourcestring', sequence_program)
-        while self.awg_module.getInt('awgModule/compiler/status') == -1:
-            time.sleep(0.1)
-
-        if self.awg_module.getInt('awgModule/compiler/status') == 1:
-            raise Exception(self.awg_module.getString('awgModule/compiler/statusstring'))
-        while self.awg_module.getDouble('awgModule/progress') < 1.0:
-            time.sleep(0.1)
-
-        return self.awg_module.getInt('awgModule/compiler/status')
-
-    def upload_waveform(self, awg, waveform, index):
-        """
-        Upload a waveform to the device memory at a given index.
-        Node: There needs to be a place holder on the device as this only replaces a data in the device memory and there
-              for does not allocate new memory space.
-        Args:
-            awg (int): The AWG where waveform should be uploaded to.
-            waveform: An array of floating point values from -1.0 to 1.0, or integers in the range (-32768...+32768)
-            index: Index of the waveform that will be replaced. If there are more than 1 waveforms used then the index
-                   corresponds to the position of the waveform in the Waveforms sub-tab of the AWG tab in the GUI.
-
-        Returns: None
-
-        """
-        self.set('awgs_{}_waveform_index'.format(awg), index)
-        self.daq.sync()
-        self.set('awgs_{}_waveform_data'.format(awg), waveform)
-
-    def set_channel_grouping(self, group):
-        """
-        Set the channel grouping mode of the device.
-
-        Args:
-            group (int): 0: Use the outputs in groups of 2. One sequencer program controls 2 outputs.
-                         1: Use the outputs in groups of 4. One sequencer program controls 4 outputs.
-                         2: Use the outputs in groups of 8. One sequencer program controls 8 outputs.
-        Returns: None
-
-        """
-        self.set('system_awg_channelgrouping', group)
-
-    def create_parameters_from_node_tree(self, parameters):
-        """
-        Create QuCoDeS parameters from the device node tree.
-        Args:
-            parameters (dict): A device node tree.
-
-        Returns: None
-
-        """
-        for parameter in parameters.values():
-            getter = partial(self._getter, parameter['Node'], parameter['Type']) if 'Read' in parameter[
-                'Properties'] else None
-            setter = partial(self._setter, parameter['Node'], parameter['Type']) if 'Write' in parameter[
-                'Properties'] else None
-            options = validators.Enum(*[int(val) for val in parameter['Options'].keys()]) \
-                if parameter['Type'] == 'Integer (enumerated)' else None
-            parameter_name = self._generate_parameter_name(parameter['Node'])
-            self.add_parameter(name=parameter_name,
-                               set_cmd=setter,
-                               get_cmd=getter,
-                               vals=options,
-                               docstring=parameter['Description'],
-                               unit=parameter['Unit']
-                               )
-
-    @staticmethod
-    def _generate_parameter_name(node):
-        values = node.split('/')
-        return '_'.join(values[2:]).lower()
-
-    def download_device_node_tree(self, flags=0):
-        """
-        Args:
-            flags: ziPython.ziListEnum.settingsonly -> 0x08
-                        Returns only nodes which are marked as setting
-                   ziPython.ziListEnum.streamingonly -> 0x10
-                        Returns only streaming nodes
-                   ziPython.ziListEnum.subscribedonly -> 0x20
-                        Returns only subscribed nodes
-                   ziPython.ziListEnum.basechannel -> 0x40
-                        Return only one instance of a node in case of multiple channels
-                   Or any combination of flags can be used.
-
-        Returns: A dictionary of the device node tree.
-        """
-        node_tree = self.daq.listNodesJSON('/{}/'.format(self.device), flags)
-        return json.loads(node_tree)
-
-    def _setter(self, name, param_type, value):
-        if param_type == "Integer (64 bit)" or param_type == 'Integer (enumerated)':
-            self.daq.setInt(name, value)
-        elif param_type == "Double":
-            self.daq.setDouble(name, value)
-        elif param_type == "String":
-            self.daq.setString(name, value)
-        elif param_type == "ZIVectorData":
-            self.daq.vectorWrite(name, value)
-
-    def _getter(self, name, param_type):
-        if param_type == "Integer (64 bit)" or param_type == 'Integer (enumerated)':
-            self.daq.getInt(name)
-        elif param_type == "Double":
-            self.daq.getDouble(name)
-        elif param_type == "String":
-            self.daq.getString(name)
-        elif param_type == "ZIVectorData":
-            self.daq.vectorRead(name)
diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index 361b3103f16..418ff87a106 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -1,7 +1,4 @@
-import csv
 import json
-import os
-import textwrap
 import time
 from functools import partial
 

From 838b3cd274caa3197e6cbc092ea9409848b8d79d Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Mon, 22 Oct 2018 15:48:37 +0200
Subject: [PATCH 211/719] feat: Insturment driver for ZI HDAWG8

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 19 ++++---------------
 1 file changed, 4 insertions(+), 15 deletions(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index 418ff87a106..ce61436f259 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -1,4 +1,7 @@
+import csv
 import json
+import os
+import textwrap
 import time
 from functools import partial
 
@@ -12,7 +15,6 @@
 class ZIHDAWG8(Instrument):
     """
     QCoDeS driver for ZI HDAWG8.
-
     Requires ZI LabOne software to be installed on the computer running QCoDeS (tested using LabOne (18.05.54618)
     and firmware (53866).
     Furthermore, the Data Server and Web Server must be running and a connection
@@ -22,7 +24,6 @@ class ZIHDAWG8(Instrument):
     def __init__(self, name: str, device_id: str, **kwargs) -> None:
         """
         Create an instance of the instrument.
-
         Args:
             name (str): The internal QCoDeS name of the instrument
             device_ID (str): The device name as listed in the web server.
@@ -42,7 +43,6 @@ def enable_channel(self, channel_number):
         Enable a signal output, turns on a blue LED on the device.
         Args:
             channel_number (int): Output channel that should be enabled.
-
         Returns: None
         """
         self.set('sigouts_{}_on'.format(channel_number), 1)
@@ -52,7 +52,6 @@ def disable_channel(self, channel_number):
         Disable a signal output, turns off a blue LED on the device.
         Args:
             channel_number (int): Output channel that should be disabled.
-
         Returns: None
         """
         self.set('sigouts_{}_on'.format(channel_number), 0)
@@ -62,7 +61,6 @@ def start_awg(self, awg_number):
         Activate an AWG
         Args:
             awg_number (int): The AWG that should be enabled.
-
         Returns: None
         """
         self.set('awgs_{}_enable'.format(awg_number), 1)
@@ -72,7 +70,6 @@ def stop_awg(self, awg_number):
         Deactivate an AWG
         Args:
             awg_number (int): The AWG that should be disabled.
-
         Returns: None
         """
         self.set('awgs_{}_enable'.format(awg_number), 0)
@@ -89,7 +86,6 @@ def waveform_to_csv(self, wave_name, *waveforms):
             one waveforms then they have to be of equal length, if not the longer ones will be truncated.
 
         Returns: None
-
         """
         data_dir = self.awg_module.getString('awgModule/directory')
         wave_dir = os.path.join(data_dir, "awg", "waves")
@@ -110,7 +106,6 @@ def generate_csv_sequence_program(wave_names, channels=None):
             channels (list, optional): Channels to play the waveforms on.
 
         Returns (str): A sequencing program that can be uploaded to the device.
-
         """
         awg_program = textwrap.dedent("""
             HEADER
@@ -161,9 +156,7 @@ def upload_waveform(self, awg_number, waveform, index):
             waveform: An array of floating point values from -1.0 to 1.0, or integers in the range (-32768...+32768)
             index: Index of the waveform that will be replaced. If there are more than 1 waveforms used then the index
                    corresponds to the position of the waveform in the Waveforms sub-tab of the AWG tab in the GUI.
-
         Returns: None
-
         """
         self.set('awgs_{}_waveform_index'.format(awg_number), index)
         self.daq.sync()
@@ -172,13 +165,11 @@ def upload_waveform(self, awg_number, waveform, index):
     def set_channel_grouping(self, group):
         """
         Set the channel grouping mode of the device.
-
         Args:
             group (int): 0: Use the outputs in groups of 2. One sequencer program controls 2 outputs.
                          1: Use the outputs in groups of 4. One sequencer program controls 4 outputs.
                          2: Use the outputs in groups of 8. One sequencer program controls 8 outputs.
         Returns: None
-
         """
         self.set('system_awg_channelgrouping', group)
 
@@ -187,9 +178,7 @@ def create_parameters_from_node_tree(self, parameters):
         Create QuCoDeS parameters from the device node tree.
         Args:
             parameters (dict): A device node tree.
-
         Returns: None
-
         """
         for parameter in parameters.values():
             getter = partial(self._getter, parameter['Node'], parameter['Type']) if 'Read' in parameter[
@@ -224,7 +213,6 @@ def download_device_node_tree(self, flags=0):
                    ziPython.ziListEnum.basechannel -> 0x40
                         Return only one instance of a node in case of multiple channels
                    Or any combination of flags can be used.
-
         Returns: A dictionary of the device node tree.
         """
         node_tree = self.daq.listNodesJSON('/{}/'.format(self.device), flags)
@@ -249,3 +237,4 @@ def _getter(self, name, param_type):
             return self.daq.getString(name)
         elif param_type == "ZIVectorData":
             return self.daq.vectorRead(name)
+

From 906152b1f2d9db0a6f1adb31b3d3238a99bf1849 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 23 Oct 2018 10:04:29 +0200
Subject: [PATCH 212/719] Added zhinst to setup.py

---
 environment.yml                          | 1 +
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 2 +-
 setup.py                                 | 2 +-
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/environment.yml b/environment.yml
index 9bd0a05354a..02266992e2a 100644
--- a/environment.yml
+++ b/environment.yml
@@ -30,3 +30,4 @@ dependencies:
  - pip:
     - websockets>=3.2
     - broadbean>=0.9.1
+    - zhinst
diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index ce61436f259..ce28a7e10b6 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -213,6 +213,7 @@ def download_device_node_tree(self, flags=0):
                    ziPython.ziListEnum.basechannel -> 0x40
                         Return only one instance of a node in case of multiple channels
                    Or any combination of flags can be used.
+
         Returns: A dictionary of the device node tree.
         """
         node_tree = self.daq.listNodesJSON('/{}/'.format(self.device), flags)
@@ -237,4 +238,3 @@ def _getter(self, name, param_type):
             return self.daq.getString(name)
         elif param_type == "ZIVectorData":
             return self.daq.vectorRead(name)
-
diff --git a/setup.py b/setup.py
index a90ef6a89be..0340193a1ac 100644
--- a/setup.py
+++ b/setup.py
@@ -124,4 +124,4 @@ def readme():
         print(valueerror_template.format(
             module_name, module.__version__, min_version, extra))
     except:
-        print(othererror_template.format(module_name))
+        print(othererror_template.format(module_name))
\ No newline at end of file

From 9ccc40b524fe70960b8d7c1c800d781f20f20430 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 23 Oct 2018 10:21:14 +0200
Subject: [PATCH 213/719] Fixed broken tests

---
 qcodes/tests/drivers/test_zihdawg8.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/qcodes/tests/drivers/test_zihdawg8.py b/qcodes/tests/drivers/test_zihdawg8.py
index 2c59d043945..9e7b7cea46d 100644
--- a/qcodes/tests/drivers/test_zihdawg8.py
+++ b/qcodes/tests/drivers/test_zihdawg8.py
@@ -92,7 +92,7 @@ def test_generate_csv_sequence_program(self):
                         // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
     
                         while(true){
-                            playWaves(1, "wave_1", 2, "wave_2", 3, "wave_3");
+                            playWave(1, "wave_1", 2, "wave_2", 3, "wave_3");
                         }
                         """)
         sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1', 'wave_2', 'wave_3'], [1, 2, 3])
@@ -103,7 +103,7 @@ def test_generate_csv_sequence_program_no_channels(self):
                         // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
     
                         while(true){
-                            playWaves("wave_1", "wave_2", "wave_3");
+                            playWave("wave_1", "wave_2", "wave_3");
                         }
                         """)
         sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1', 'wave_2', 'wave_3'])
@@ -114,7 +114,7 @@ def test_generate_csv_sequence_program_1_wave_no_channels(self):
                         // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
     
                         while(true){
-                            playWaves("wave_1");
+                            playWave("wave_1");
                         }
                         """)
         sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1'])
@@ -125,7 +125,7 @@ def test_generate_csv_sequence_program_1_wave(self):
                         // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
     
                         while(true){
-                            playWaves(7, "wave_1");
+                            playWave(7, "wave_1");
                         }
                         """)
         sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1'], [7])

From 14b05bdcf106f27b7ae2b704adcd38d4e12b67af Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 23 Oct 2018 10:44:52 +0200
Subject: [PATCH 214/719] Fixed a docstring

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index ce28a7e10b6..f417dd22eac 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -213,7 +213,6 @@ def download_device_node_tree(self, flags=0):
                    ziPython.ziListEnum.basechannel -> 0x40
                         Return only one instance of a node in case of multiple channels
                    Or any combination of flags can be used.
-
         Returns: A dictionary of the device node tree.
         """
         node_tree = self.daq.listNodesJSON('/{}/'.format(self.device), flags)

From 6cf167f4cd64df204e6c50f29a21099ea88feef4 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 23 Oct 2018 13:48:09 +0200
Subject: [PATCH 215/719] Improving docstrings format

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 41 ++++++++++++++++--------
 1 file changed, 28 insertions(+), 13 deletions(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index f417dd22eac..86a9dfb2879 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -24,6 +24,7 @@ class ZIHDAWG8(Instrument):
     def __init__(self, name: str, device_id: str, **kwargs) -> None:
         """
         Create an instance of the instrument.
+
         Args:
             name (str): The internal QCoDeS name of the instrument
             device_ID (str): The device name as listed in the web server.
@@ -41,8 +42,10 @@ def __init__(self, name: str, device_id: str, **kwargs) -> None:
     def enable_channel(self, channel_number):
         """
         Enable a signal output, turns on a blue LED on the device.
+
         Args:
             channel_number (int): Output channel that should be enabled.
+
         Returns: None
         """
         self.set('sigouts_{}_on'.format(channel_number), 1)
@@ -50,8 +53,10 @@ def enable_channel(self, channel_number):
     def disable_channel(self, channel_number):
         """
         Disable a signal output, turns off a blue LED on the device.
+
         Args:
             channel_number (int): Output channel that should be disabled.
+
         Returns: None
         """
         self.set('sigouts_{}_on'.format(channel_number), 0)
@@ -59,8 +64,10 @@ def disable_channel(self, channel_number):
     def start_awg(self, awg_number):
         """
         Activate an AWG
+
         Args:
             awg_number (int): The AWG that should be enabled.
+
         Returns: None
         """
         self.set('awgs_{}_enable'.format(awg_number), 1)
@@ -68,8 +75,10 @@ def start_awg(self, awg_number):
     def stop_awg(self, awg_number):
         """
         Deactivate an AWG
+
         Args:
             awg_number (int): The AWG that should be disabled.
+
         Returns: None
         """
         self.set('awgs_{}_enable'.format(awg_number), 0)
@@ -101,6 +110,7 @@ def generate_csv_sequence_program(wave_names, channels=None):
         """
         Generates and returns a sequencing program that plays the given waves on the given channels. There has to be a
         CSV file with a corresponding name to a wave in wave_names.
+
         Args:
             wave_names (list): List of wave names that are to be played.
             channels (list, optional): Channels to play the waveforms on.
@@ -128,9 +138,11 @@ def generate_csv_sequence_program(wave_names, channels=None):
     def upload_sequence_program(self, awg_number, sequence_program):
         """
         Uploads a sequence program to the device equivalent to using the the sequencer tab in the device's gui.
+
         Args:
             awg_number (int): The AWG that the sequence program will be uploaded to.
             sequence_program (str): A sequence program that should be played on the device.
+
         Returns (int): 0: Compilation was successful with no warnings.
                        1: Compilation was successful but with warnings.
         """
@@ -149,13 +161,17 @@ def upload_sequence_program(self, awg_number, sequence_program):
     def upload_waveform(self, awg_number, waveform, index):
         """
         Upload a waveform to the device memory at a given index.
-        Node: There needs to be a place holder on the device as this only replaces a data in the device memory and there
-              for does not allocate new memory space.
+
+        Note:
+            There needs to be a place holder on the device as this only replaces a data in the device memory but
+            does not allocate new memory space.
+
         Args:
             awg_number (int): The AWG where waveform should be uploaded to.
             waveform: An array of floating point values from -1.0 to 1.0, or integers in the range (-32768...+32768)
             index: Index of the waveform that will be replaced. If there are more than 1 waveforms used then the index
                    corresponds to the position of the waveform in the Waveforms sub-tab of the AWG tab in the GUI.
+
         Returns: None
         """
         self.set('awgs_{}_waveform_index'.format(awg_number), index)
@@ -165,10 +181,10 @@ def upload_waveform(self, awg_number, waveform, index):
     def set_channel_grouping(self, group):
         """
         Set the channel grouping mode of the device.
+
         Args:
-            group (int): 0: Use the outputs in groups of 2. One sequencer program controls 2 outputs.
-                         1: Use the outputs in groups of 4. One sequencer program controls 4 outputs.
-                         2: Use the outputs in groups of 8. One sequencer program controls 8 outputs.
+            group (int): 0: groups of 2. 1: groups of 4. 2: groups of 8 i.e., one sequencer program controls 8 outputs.
+
         Returns: None
         """
         self.set('system_awg_channelgrouping', group)
@@ -176,8 +192,10 @@ def set_channel_grouping(self, group):
     def create_parameters_from_node_tree(self, parameters):
         """
         Create QuCoDeS parameters from the device node tree.
+
         Args:
             parameters (dict): A device node tree.
+
         Returns: None
         """
         for parameter in parameters.values():
@@ -204,15 +222,12 @@ def _generate_parameter_name(node):
     def download_device_node_tree(self, flags=0):
         """
         Args:
-            flags: ziPython.ziListEnum.settingsonly -> 0x08
-                        Returns only nodes which are marked as setting
-                   ziPython.ziListEnum.streamingonly -> 0x10
-                        Returns only streaming nodes
-                   ziPython.ziListEnum.subscribedonly -> 0x20
-                        Returns only subscribed nodes
-                   ziPython.ziListEnum.basechannel -> 0x40
-                        Return only one instance of a node in case of multiple channels
+            flags: ziPython.ziListEnum.settingsonly -> 0x08: Returns only nodes which are marked as setting
+                   ziPython.ziListEnum.streamingonly -> 0x10: Returns only streaming nodes
+                   ziPython.ziListEnum.subscribedonly -> 0x20: Returns only subscribed nodes
+                   ziPython.ziListEnum.basechannel -> 0x40: Return only one instance of a node in case of multiple channels
                    Or any combination of flags can be used.
+
         Returns: A dictionary of the device node tree.
         """
         node_tree = self.daq.listNodesJSON('/{}/'.format(self.device), flags)

From f2fd52ddc19f4ff8cff4b5ca0fc1f6bee16f89e2 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 23 Oct 2018 16:21:34 +0200
Subject: [PATCH 216/719] Removed zhinst as a dependency

---
 environment.yml                          |  1 -
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 12 +++++++++---
 qcodes/tests/drivers/test_zihdawg8.py    |  2 ++
 3 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/environment.yml b/environment.yml
index 02266992e2a..9bd0a05354a 100644
--- a/environment.yml
+++ b/environment.yml
@@ -30,4 +30,3 @@ dependencies:
  - pip:
     - websockets>=3.2
     - broadbean>=0.9.1
-    - zhinst
diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index 86a9dfb2879..5228dcbd271 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -5,8 +5,13 @@
 import time
 from functools import partial
 
-import zhinst
-import zhinst.utils
+try:
+    import zhinst
+except ImportError:
+    raise ImportError('''Could not find Zurich Instruments Lab One software.
+                         Please refer to the Zi UHF-LI User Manual for
+                         download and installation instructions.
+                      ''')
 
 from qcodes import Instrument
 from qcodes.utils import validators as validators
@@ -225,7 +230,8 @@ def download_device_node_tree(self, flags=0):
             flags: ziPython.ziListEnum.settingsonly -> 0x08: Returns only nodes which are marked as setting
                    ziPython.ziListEnum.streamingonly -> 0x10: Returns only streaming nodes
                    ziPython.ziListEnum.subscribedonly -> 0x20: Returns only subscribed nodes
-                   ziPython.ziListEnum.basechannel -> 0x40: Return only one instance of a node in case of multiple channels
+                   ziPython.ziListEnum.basechannel -> 0x40: Return only one instance of a node in case of multiple
+                   channels
                    Or any combination of flags can be used.
 
         Returns: A dictionary of the device node tree.
diff --git a/qcodes/tests/drivers/test_zihdawg8.py b/qcodes/tests/drivers/test_zihdawg8.py
index 9e7b7cea46d..ce9b16f91d2 100644
--- a/qcodes/tests/drivers/test_zihdawg8.py
+++ b/qcodes/tests/drivers/test_zihdawg8.py
@@ -10,6 +10,8 @@
 from qcodes.utils import validators
 
 
+
+
 class TestZIHDAWG8(unittest.TestCase):
     def setUp(self):
         self.node_tree = {"/DEV8049/SYSTEM/AWG/CHANNELGROUPING": {

From 60fb5bd86cfd01116785832f1dc646897e0e24fc Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 23 Oct 2018 16:35:53 +0200
Subject: [PATCH 217/719] small cleanup

---
 qcodes/tests/drivers/test_zihdawg8.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/qcodes/tests/drivers/test_zihdawg8.py b/qcodes/tests/drivers/test_zihdawg8.py
index ce9b16f91d2..9e7b7cea46d 100644
--- a/qcodes/tests/drivers/test_zihdawg8.py
+++ b/qcodes/tests/drivers/test_zihdawg8.py
@@ -10,8 +10,6 @@
 from qcodes.utils import validators
 
 
-
-
 class TestZIHDAWG8(unittest.TestCase):
     def setUp(self):
         self.node_tree = {"/DEV8049/SYSTEM/AWG/CHANNELGROUPING": {

From c9623ee325c3b7ddac3aacfac5893cff784c2a58 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 30 Oct 2018 21:35:22 +0100
Subject: [PATCH 218/719] setters for parameters should be False, not None,
 when not settable

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index 5228dcbd271..6d2c262ab9d 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -5,13 +5,7 @@
 import time
 from functools import partial
 
-try:
-    import zhinst
-except ImportError:
-    raise ImportError('''Could not find Zurich Instruments Lab One software.
-                         Please refer to the Zi UHF-LI User Manual for
-                         download and installation instructions.
-                      ''')
+import zhinst
 
 from qcodes import Instrument
 from qcodes.utils import validators as validators
@@ -207,7 +201,7 @@ def create_parameters_from_node_tree(self, parameters):
             getter = partial(self._getter, parameter['Node'], parameter['Type']) if 'Read' in parameter[
                 'Properties'] else None
             setter = partial(self._setter, parameter['Node'], parameter['Type']) if 'Write' in parameter[
-                'Properties'] else None
+                'Properties'] else False
             options = validators.Enum(*[int(val) for val in parameter['Options'].keys()]) \
                 if parameter['Type'] == 'Integer (enumerated)' else None
             parameter_name = self._generate_parameter_name(parameter['Node'])
@@ -258,3 +252,4 @@ def _getter(self, name, param_type):
             return self.daq.getString(name)
         elif param_type == "ZIVectorData":
             return self.daq.vectorRead(name)
+

From 7cd8dac7ecb5de69c59f85e6d0f1d30a9ea3cfc3 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Fri, 2 Nov 2018 09:46:57 +0100
Subject: [PATCH 219/719] rebase on master

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index 6d2c262ab9d..5f8b4b027a0 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -251,5 +251,5 @@ def _getter(self, name, param_type):
         elif param_type == "String":
             return self.daq.getString(name)
         elif param_type == "ZIVectorData":
-            return self.daq.vectorRead(name)
+            return self.daq.getAsEvent(name)
 

From 4a004bada89e625bcc019c6705bc61a560ed175d Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 6 Nov 2018 13:30:31 +0100
Subject: [PATCH 220/719] added import of zhinst.utils

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index 5f8b4b027a0..b825a4a3fd9 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -6,6 +6,7 @@
 from functools import partial
 
 import zhinst
+import zhinst.utils
 
 from qcodes import Instrument
 from qcodes.utils import validators as validators

From 804fb9f8f51be5cc39b2ab0cd00e08c92f1e98f1 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 6 Nov 2018 15:54:46 +0100
Subject: [PATCH 221/719] Addressing code style review comments

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 170 +++++++++++++----------
 1 file changed, 93 insertions(+), 77 deletions(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index b825a4a3fd9..53c6aa70bfa 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -4,6 +4,7 @@
 import textwrap
 import time
 from functools import partial
+from typing import Optional, Any
 
 import zhinst
 import zhinst.utils
@@ -15,8 +16,8 @@
 class ZIHDAWG8(Instrument):
     """
     QCoDeS driver for ZI HDAWG8.
-    Requires ZI LabOne software to be installed on the computer running QCoDeS (tested using LabOne (18.05.54618)
-    and firmware (53866).
+    Requires ZI LabOne software to be installed on the computer running QCoDeS
+    (tested using LabOne (18.05.54618) and firmware (53866).
     Furthermore, the Data Server and Web Server must be running and a connection
     between the two must be made.
     """
@@ -26,96 +27,95 @@ def __init__(self, name: str, device_id: str, **kwargs) -> None:
         Create an instance of the instrument.
 
         Args:
-            name (str): The internal QCoDeS name of the instrument
-            device_ID (str): The device name as listed in the web server.
+            name: The internal QCoDeS name of the instrument
+            device_ID: The device name as listed in the web server.
         """
         super().__init__(name, **kwargs)
         self.api_level = 6
-        (self.daq, self.device, self.props) = zhinst.utils.create_api_session(device_id, self.api_level,
-                                                                              required_devtype='HDAWG')
+        (self.daq, self.device, self.props) = zhinst.utils.create_api_session(
+            device_id, self.api_level,
+            required_devtype='HDAWG')
         self.awg_module = self.daq.awgModule()
         self.awg_module.set('awgModule/device', self.device)
         self.awg_module.execute()
         node_tree = self.download_device_node_tree()
         self.create_parameters_from_node_tree(node_tree)
 
-    def enable_channel(self, channel_number):
+    def enable_channel(self, channel_number: int) -> None:
         """
         Enable a signal output, turns on a blue LED on the device.
 
         Args:
-            channel_number (int): Output channel that should be enabled.
-
-        Returns: None
+            channel_number: Output channel that should be enabled.
         """
         self.set('sigouts_{}_on'.format(channel_number), 1)
 
-    def disable_channel(self, channel_number):
+    def disable_channel(self, channel_number: int) -> None:
         """
         Disable a signal output, turns off a blue LED on the device.
 
         Args:
-            channel_number (int): Output channel that should be disabled.
-
-        Returns: None
+            channel_number: Output channel that should be disabled.
         """
         self.set('sigouts_{}_on'.format(channel_number), 0)
 
-    def start_awg(self, awg_number):
+    def start_awg(self, awg_number: int):
         """
         Activate an AWG
 
         Args:
-            awg_number (int): The AWG that should be enabled.
-
-        Returns: None
+            awg_number: The AWG that should be enabled.
         """
         self.set('awgs_{}_enable'.format(awg_number), 1)
 
-    def stop_awg(self, awg_number):
+    def stop_awg(self, awg_number: int) -> None:
         """
         Deactivate an AWG
 
         Args:
-            awg_number (int): The AWG that should be disabled.
-
-        Returns: None
+            awg_number: The AWG that should be disabled.
         """
         self.set('awgs_{}_enable'.format(awg_number), 0)
 
-    def waveform_to_csv(self, wave_name, *waveforms):
+    def waveform_to_csv(self, wave_name: str, *waveforms: list) -> None:
         """
-        Write waveforms to a CSV file in the modules data directory so that it can be referenced and used in a
-        sequence program. If more than one waveform is provided they will be played simultaneously but on separate
+        Write waveforms to a CSV file in the modules data directory so that it
+        can be referenced and used in a sequence program. If more than one
+        waveform is provided they will be played simultaneously but on separate
         outputs.
 
         Args:
             wave_name: Name of the CSV file, is used by a sequence program.
-            waveforms (list): One or more waveforms that are to be written to a CSV file. Note if there are more than
-            one waveforms then they have to be of equal length, if not the longer ones will be truncated.
-
-        Returns: None
+            waveforms: One or more waveforms that are to be written to a
+            CSV file. Note if there are more than one waveforms then they have
+            to be of equal length, if not the longer ones will be truncated.
         """
         data_dir = self.awg_module.getString('awgModule/directory')
         wave_dir = os.path.join(data_dir, "awg", "waves")
         if not os.path.isdir(wave_dir):
-            raise Exception("AWG module wave directory {} does not exist or is not a directory".format(wave_dir))
+            raise Exception(
+                "AWG module wave directory {} does not exist or is not a "
+                "directory".format(
+                    wave_dir))
         csv_file = os.path.join(wave_dir, wave_name + '.csv')
         with open(csv_file, "w", newline='') as f:
             writer = csv.writer(f, delimiter=';')
             writer.writerows(zip(*waveforms))
 
     @staticmethod
-    def generate_csv_sequence_program(wave_names, channels=None):
+    def generate_csv_sequence_program(wave_names: list,
+                                      channels: Optional[list] = None) -> str:
         """
-        Generates and returns a sequencing program that plays the given waves on the given channels. There has to be a
-        CSV file with a corresponding name to a wave in wave_names.
+        Generates and returns a sequencing program that plays the given waves on
+         the given channels. There has to be a CSV file with a corresponding
+         name to a wave in wave_names.
 
         Args:
-            wave_names (list): List of wave names that are to be played.
-            channels (list, optional): Channels to play the waveforms on.
+            wave_names: List of wave names that are to be played.
+            channels: Channels to play the waveforms on.
 
-        Returns (str): A sequencing program that can be uploaded to the device.
+        Returns:
+            A sequencing program that can be uploaded to the device.
         """
         awg_program = textwrap.dedent("""
             HEADER
@@ -129,22 +129,29 @@ def generate_csv_sequence_program(wave_names, channels=None):
             argument_string = ('"{}"' * len(wave_names)).replace('""', '", "')
             waves = argument_string.format(*wave_names)
         else:
-            argument_string = ('{}, "{}"' * len(wave_names)).replace('}"{', '}", {')
-            waves = argument_string.format(*[value for pair in zip(channels, wave_names) for value in pair])
+            argument_string = ('{}, "{}"' * len(wave_names)).replace('}"{',
+                                                                     '}", {')
+            waves = argument_string.format(
+                *[value for pair in zip(channels, wave_names) for value in
+                  pair])
         awg_program = awg_program.replace('WAVES', waves)
 
         return awg_program
 
-    def upload_sequence_program(self, awg_number, sequence_program):
+    def upload_sequence_program(self, awg_number: int,
+                                sequence_program: str) -> int:
         """
-        Uploads a sequence program to the device equivalent to using the the sequencer tab in the device's gui.
+        Uploads a sequence program to the device equivalent to using the the
+        sequencer tab in the device's gui.
 
         Args:
-            awg_number (int): The AWG that the sequence program will be uploaded to.
-            sequence_program (str): A sequence program that should be played on the device.
+            awg_number: The AWG that the sequence program will be uploaded to.
+            sequence_program: A sequence program that should be played on the
+                device.
 
-        Returns (int): 0: Compilation was successful with no warnings.
-                       1: Compilation was successful but with warnings.
+        Returns:
+            0: Compilation was successful with no warnings.
+            1: Compilation was successful but with warnings.
         """
         self.awg_module.set('awgModule/index', awg_number)
         self.awg_module.set('awgModule/compiler/sourcestring', sequence_program)
@@ -152,58 +159,61 @@ def upload_sequence_program(self, awg_number, sequence_program):
             time.sleep(0.1)
 
         if self.awg_module.getInt('awgModule/compiler/status') == 1:
-            raise Exception(self.awg_module.getString('awgModule/compiler/statusstring'))
+            raise Exception(
+                self.awg_module.getString('awgModule/compiler/statusstring'))
         while self.awg_module.getDouble('awgModule/progress') < 1.0:
             time.sleep(0.1)
 
         return self.awg_module.getInt('awgModule/compiler/status')
 
-    def upload_waveform(self, awg_number, waveform, index):
+    def upload_waveform(self, awg_number: int, waveform: list,
+                        index: int) -> None:
         """
         Upload a waveform to the device memory at a given index.
 
         Note:
-            There needs to be a place holder on the device as this only replaces a data in the device memory but
-            does not allocate new memory space.
+            There needs to be a place holder on the device as this only replaces
+            a data in the device memory but does not allocate new memory space.
 
         Args:
-            awg_number (int): The AWG where waveform should be uploaded to.
-            waveform: An array of floating point values from -1.0 to 1.0, or integers in the range (-32768...+32768)
-            index: Index of the waveform that will be replaced. If there are more than 1 waveforms used then the index
-                   corresponds to the position of the waveform in the Waveforms sub-tab of the AWG tab in the GUI.
-
-        Returns: None
+            awg_number: The AWG where waveform should be uploaded to.
+            waveform: An array of floating point values from -1.0 to 1.0, or
+                integers in the range (-32768...+32768)
+            index: Index of the waveform that will be replaced. If there are
+                more than 1 waveforms used then the index corresponds to the
+                position of the waveform in the Waveforms sub-tab of the AWG tab
+                in the GUI.
         """
         self.set('awgs_{}_waveform_index'.format(awg_number), index)
         self.daq.sync()
         self.set('awgs_{}_waveform_data'.format(awg_number), waveform)
 
-    def set_channel_grouping(self, group):
+    def set_channel_grouping(self, group: int) -> None:
         """
         Set the channel grouping mode of the device.
 
         Args:
-            group (int): 0: groups of 2. 1: groups of 4. 2: groups of 8 i.e., one sequencer program controls 8 outputs.
-
-        Returns: None
+            group: 0: groups of 2. 1: groups of 4. 2: groups of 8 i.e., one
+                sequencer program controls 8 outputs.
         """
         self.set('system_awg_channelgrouping', group)
 
-    def create_parameters_from_node_tree(self, parameters):
+    def create_parameters_from_node_tree(self, parameters: dict) -> None:
         """
         Create QuCoDeS parameters from the device node tree.
 
         Args:
-            parameters (dict): A device node tree.
-
-        Returns: None
+            parameters: A device node tree.
         """
         for parameter in parameters.values():
-            getter = partial(self._getter, parameter['Node'], parameter['Type']) if 'Read' in parameter[
+            getter = partial(self._getter, parameter['Node'],
+                             parameter['Type']) if 'Read' in parameter[
                 'Properties'] else None
-            setter = partial(self._setter, parameter['Node'], parameter['Type']) if 'Write' in parameter[
+            setter = partial(self._setter, parameter['Node'],
+                             parameter['Type']) if 'Write' in parameter[
                 'Properties'] else False
-            options = validators.Enum(*[int(val) for val in parameter['Options'].keys()]) \
+            options = validators.Enum(
+                *[int(val) for val in parameter['Options'].keys()]) \
                 if parameter['Type'] == 'Integer (enumerated)' else None
             parameter_name = self._generate_parameter_name(parameter['Node'])
             self.add_parameter(name=parameter_name,
@@ -219,23 +229,29 @@ def _generate_parameter_name(node):
         values = node.split('/')
         return '_'.join(values[2:]).lower()
 
-    def download_device_node_tree(self, flags=0):
+    def download_device_node_tree(self, flags: int = 0) -> dict:
         """
         Args:
-            flags: ziPython.ziListEnum.settingsonly -> 0x08: Returns only nodes which are marked as setting
-                   ziPython.ziListEnum.streamingonly -> 0x10: Returns only streaming nodes
-                   ziPython.ziListEnum.subscribedonly -> 0x20: Returns only subscribed nodes
-                   ziPython.ziListEnum.basechannel -> 0x40: Return only one instance of a node in case of multiple
-                   channels
+            flags: ziPython.ziListEnum.settingsonly -> 0x08:
+                        Returns only nodes which are marked as setting
+                   ziPython.ziListEnum.streamingonly -> 0x10:
+                        Returns only streaming nodes
+                   ziPython.ziListEnum.subscribedonly -> 0x20:
+                        Returns only subscribed nodes
+                   ziPython.ziListEnum.basechannel -> 0x40:
+                        Return only one instance of a node in case of multiple
+                        channels
                    Or any combination of flags can be used.
 
-        Returns: A dictionary of the device node tree.
+        Returns:
+            A dictionary of the device node tree.
         """
         node_tree = self.daq.listNodesJSON('/{}/'.format(self.device), flags)
         return json.loads(node_tree)
 
-    def _setter(self, name, param_type, value):
-        if param_type == "Integer (64 bit)" or param_type == 'Integer (enumerated)':
+    def _setter(self, name: str, param_type: str, value: Any) -> None:
+        if param_type == "Integer (64 bit)" or \
+                param_type == 'Integer (enumerated)':
             self.daq.setInt(name, value)
         elif param_type == "Double":
             self.daq.setDouble(name, value)
@@ -244,8 +260,9 @@ def _setter(self, name, param_type, value):
         elif param_type == "ZIVectorData":
             self.daq.vectorWrite(name, value)
 
-    def _getter(self, name, param_type):
-        if param_type == "Integer (64 bit)" or param_type == 'Integer (enumerated)':
+    def _getter(self, name: str, param_type: str) -> Any:
+        if param_type == "Integer (64 bit)" or \
+                param_type == 'Integer (enumerated)':
             return self.daq.getInt(name)
         elif param_type == "Double":
             return self.daq.getDouble(name)
@@ -253,4 +270,3 @@ def _getter(self, name, param_type):
             return self.daq.getString(name)
         elif param_type == "ZIVectorData":
             return self.daq.getAsEvent(name)
-

From 0cca32649b8da468b25eed59fd4afcede0b6c559 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Wed, 7 Nov 2018 09:02:10 +0100
Subject: [PATCH 222/719] Addressing review comments

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 11 +++++------
 qcodes/tests/drivers/test_zihdawg8.py    |  3 ++-
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index 53c6aa70bfa..bdba7ce30ee 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -6,7 +6,6 @@
 from functools import partial
 from typing import Optional, Any
 
-import zhinst
 import zhinst.utils
 
 from qcodes import Instrument
@@ -17,7 +16,7 @@ class ZIHDAWG8(Instrument):
     """
     QCoDeS driver for ZI HDAWG8.
     Requires ZI LabOne software to be installed on the computer running QCoDeS
-    (tested using LabOne (18.05.54618) and firmware (53866).
+    (tested using LabOne 18.05.54618 and firmware 53866).
     Furthermore, the Data Server and Web Server must be running and a connection
     between the two must be made.
     """
@@ -232,13 +231,13 @@ def _generate_parameter_name(node):
     def download_device_node_tree(self, flags: int = 0) -> dict:
         """
         Args:
-            flags: ziPython.ziListEnum.settingsonly -> 0x08:
+            flags: ziPython.ziListEnum.settingsonly -> 0x08
                         Returns only nodes which are marked as setting
-                   ziPython.ziListEnum.streamingonly -> 0x10:
+                   ziPython.ziListEnum.streamingonly -> 0x10
                         Returns only streaming nodes
-                   ziPython.ziListEnum.subscribedonly -> 0x20:
+                   ziPython.ziListEnum.subscribedonly -> 0x20
                         Returns only subscribed nodes
-                   ziPython.ziListEnum.basechannel -> 0x40:
+                   ziPython.ziListEnum.basechannel -> 0x40
                         Return only one instance of a node in case of multiple
                         channels
                    Or any combination of flags can be used.
diff --git a/qcodes/tests/drivers/test_zihdawg8.py b/qcodes/tests/drivers/test_zihdawg8.py
index 9e7b7cea46d..73fd962754f 100644
--- a/qcodes/tests/drivers/test_zihdawg8.py
+++ b/qcodes/tests/drivers/test_zihdawg8.py
@@ -3,8 +3,9 @@
 import unittest
 from unittest.mock import patch, MagicMock
 
+sys.modules['zhinst.utils'] = MagicMock(name='zhinst.utils')
 sys.modules['zhinst'] = MagicMock(name='zhinst')
-import zhinst
+import zhinst.utils
 
 from qcodes.instrument_drivers.ZI.ZIHDAWG8 import ZIHDAWG8
 from qcodes.utils import validators

From 344fd17d96af73d5ff3b5d1841496caa7d1ae971 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Wed, 7 Nov 2018 12:59:56 +0100
Subject: [PATCH 223/719] Improved one docstring

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index bdba7ce30ee..551935cd897 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -231,15 +231,12 @@ def _generate_parameter_name(node):
     def download_device_node_tree(self, flags: int = 0) -> dict:
         """
         Args:
-            flags: ziPython.ziListEnum.settingsonly -> 0x08
-                        Returns only nodes which are marked as setting
-                   ziPython.ziListEnum.streamingonly -> 0x10
-                        Returns only streaming nodes
-                   ziPython.ziListEnum.subscribedonly -> 0x20
-                        Returns only subscribed nodes
-                   ziPython.ziListEnum.basechannel -> 0x40
-                        Return only one instance of a node in case of multiple
-                        channels
+            flags: 0x08, Returns only nodes which are marked as setting
+                   0x10, Returns only streaming nodes
+                   0x20, Returns only subscribed nodes
+                   0x40, Return only one instance of a node in case of multiple
+                   channels
+
                    Or any combination of flags can be used.
 
         Returns:

From 4306e866156efec8b396843f1026fff089bc8276 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 13 Nov 2018 10:17:48 +0100
Subject: [PATCH 224/719] [DEM-612] Changed compile condition in
 upload_sequence_program

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index 551935cd897..c0af5f45556 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -150,11 +150,12 @@ def upload_sequence_program(self, awg_number: int,
 
         Returns:
             0: Compilation was successful with no warnings.
-            1: Compilation was successful but with warnings.
+            2: Compilation was successful but with warnings.
         """
         self.awg_module.set('awgModule/index', awg_number)
         self.awg_module.set('awgModule/compiler/sourcestring', sequence_program)
-        while self.awg_module.getInt('awgModule/compiler/status') == -1:
+        while len(self.awg_module.get('awgModule/compiler/sourcestring')
+                      ['compiler']['sourcestring'][0]) > 0:
             time.sleep(0.1)
 
         if self.awg_module.getInt('awgModule/compiler/status') == 1:

From 2de26d0b53f18c7904e9630848a7a74b6e2b8054 Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 13 Nov 2018 16:28:30 +0100
Subject: [PATCH 225/719] Mentioned ziPython.ziListEnum.* in docstring

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index c0af5f45556..150dec323a6 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -232,13 +232,17 @@ def _generate_parameter_name(node):
     def download_device_node_tree(self, flags: int = 0) -> dict:
         """
         Args:
-            flags: 0x08, Returns only nodes which are marked as setting
-                   0x10, Returns only streaming nodes
-                   0x20, Returns only subscribed nodes
-                   0x40, Return only one instance of a node in case of multiple
-                   channels
-
-                   Or any combination of flags can be used.
+            flags:
+                ziPython.ziListEnum.settingsonly (0x08): Returns only nodes
+                which are marked as setting
+                ziPython.ziListEnum.streamingonly (0x10): Returns only
+                streaming nodes
+                ziPython.ziListEnum.subscribedonly (0x20): Returns only
+                subscribed nodes
+                ziPython.ziListEnum.basechannel (0x40): Return only one instance
+                of a node in case of multiple channels
+
+                Or any combination of flags can be used.
 
         Returns:
             A dictionary of the device node tree.

From c04dfbef9160c76847b2700bef72dc6dbe8e4b03 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Tue, 27 Nov 2018 11:05:21 +0100
Subject: [PATCH 226/719] Fix wrong use of connection

---
 qcodes/dataset/experiment_container.py | 10 +++++++---
 qcodes/dataset/sqlite_base.py          | 22 ++++++++++++++++++++++
 2 files changed, 29 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/experiment_container.py b/qcodes/dataset/experiment_container.py
index b5baa5d1156..e3d56406407 100644
--- a/qcodes/dataset/experiment_container.py
+++ b/qcodes/dataset/experiment_container.py
@@ -12,6 +12,7 @@
                                         connect, transaction,
                                         get_last_experiment, get_experiments,
                                         get_experiment_name_from_experiment_id,
+                                        get_runid_from_expid_and_counter,
                                         get_sample_name_from_experiment_id,
                                         SomeConnection)
 from qcodes.dataset.sqlite_base import new_experiment as ne
@@ -126,7 +127,8 @@ def new_data_set(self, name, specs: SPECS = None, values=None,
             values: the values to associate with the parameters
             metadata: the metadata to associate with the dataset
         """
-        return new_data_set(name, self.exp_id, specs, values, metadata)
+        return new_data_set(name, self.exp_id, specs, values, metadata,
+                            conn=self.conn)
 
     def data_set(self, counter: int) -> DataSet:
         """
@@ -138,7 +140,9 @@ def data_set(self, counter: int) -> DataSet:
         Returns:
             the dataset
         """
-        return load_by_counter(counter, self.exp_id)
+        run_id = get_runid_from_expid_and_counter(self.conn, self.exp_id,
+                                                  counter)
+        return DataSet(run_id=run_id, conn=self.conn)
 
     def data_sets(self) -> List[DataSet]:
         """Get all the datasets of this experiment"""
@@ -305,7 +309,7 @@ def load_experiment_by_name(name: str,
         raise ValueError(f"Many experiments matching your request"
                          f" found:\n{_repr_str}")
     else:
-        e = Experiment(exp_id=rows[0]['exp_id'])
+        e = Experiment(exp_id=rows[0]['exp_id'], conn=conn)
     return e
 
 
diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 2ed21382990..39cb49ff17a 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1258,6 +1258,28 @@ def get_setpoints(conn: SomeConnection,
     return output
 
 
+def get_runid_from_expid_and_counter(conn: SomeConnection, exp_id: int,
+                                     counter: int) -> int:
+    """
+    Get the run_id of a run in the specified experiment with the specified
+    counter
+
+    Args:
+        conn: connection to the database
+        exp_id: the exp_id of the experiment containing the run
+        counter: the intra-experiment run counter of that run
+    """
+    sql = """
+          SELECT run_id
+          FROM runs
+          WHERE result_counter= ? AND
+          exp_id = ?
+          """
+    c = transaction(conn, sql, counter, exp_id)
+    run_id = one(c, 'run_id')
+    return run_id
+
+
 def get_runid_from_guid(conn: SomeConnection, guid: str) -> Union[int, None]:
     """
     Get the run_id of a run based on the guid

From 15298c90e56ff8bf2ec3e0d28fc97d9f2fbf0dea Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 27 Nov 2018 11:05:27 +0100
Subject: [PATCH 227/719] Updated generate_csv_sequence_program due to a bug

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py |  70 ++++++++-----
 qcodes/tests/drivers/test_zihdawg8.py    | 125 +++++++++++++++++++----
 2 files changed, 149 insertions(+), 46 deletions(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index 150dec323a6..98fc94dfde1 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -4,8 +4,7 @@
 import textwrap
 import time
 from functools import partial
-from typing import Optional, Any
-
+from typing import Optional, Any, List, Tuple, Union
 import zhinst.utils
 
 from qcodes import Instrument
@@ -102,41 +101,60 @@ def waveform_to_csv(self, wave_name: str, *waveforms: list) -> None:
             writer.writerows(zip(*waveforms))
 
     @staticmethod
-    def generate_csv_sequence_program(wave_names: list,
-                                      channels: Optional[list] = None) -> str:
+    def generate_csv_sequence_program(wave_info: List[
+        Tuple[int, Union[str, None], Union[str, None]]]) -> str:
         """
-        Generates and returns a sequencing program that plays the given waves on
-         the given channels. There has to be a CSV file with a corresponding
-         name to a wave in wave_names.
+        A method that generates a sequence program that plays waveforms from
+        csv files.
 
         Args:
-            wave_names: List of wave names that are to be played.
-            channels: Channels to play the waveforms on.
+            wave_info: A list of tuples containing information about the waves
+            that are to be played. Every tuple should have a channel number and
+            wave, marker or both wave and marker.
 
         Returns:
-            A sequencing program that can be uploaded to the device.
+            A sequence program that can be compiled and uploaded.
+
         """
         awg_program = textwrap.dedent("""
-            HEADER
-            while(true){
-                playWave(WAVES);
-            }
-            """)
+                HEADER
+                DECLARATIONS
+                while(true){
+                    playWave(WAVES);
+                }
+                """)
         sequence_header = '// generated by {}\n'.format(__name__)
         awg_program = awg_program.replace('HEADER', sequence_header)
-        if channels is None:
-            argument_string = ('"{}"' * len(wave_names)).replace('""', '", "')
-            waves = argument_string.format(*wave_names)
-        else:
-            argument_string = ('{}, "{}"' * len(wave_names)).replace('}"{',
-                                                                     '}", {')
-            waves = argument_string.format(
-                *[value for pair in zip(channels, wave_names) for value in
-                  pair])
-        awg_program = awg_program.replace('WAVES', waves)
-
+        declarations = ZIHDAWG8._generate_declarations(wave_info)
+        awg_program = awg_program.replace('DECLARATIONS', declarations)
+        play_wave_arguments = ZIHDAWG8._get_waveform_arguments(wave_info)
+        awg_program = awg_program.replace('WAVES', play_wave_arguments)
         return awg_program
 
+    @staticmethod
+    def _generate_declarations(wave_info):
+        declarations = ""
+        for _, wave, marker in wave_info:
+            if wave is not None and marker is not None:
+                declarations += ('wave {0} = "{0}";\n'.format(wave))
+                declarations += ('wave {0} = "{0}";\n'.format(marker))
+                declarations += ('{0} = {0} + {1};\n'.format(wave, marker))
+            elif wave is not None:
+                declarations += ('wave {0} = "{0}";\n'.format(wave))
+            elif marker is not None:
+                declarations += ('wave {0} = "{0}";\n'.format(marker))
+        return declarations
+
+    @staticmethod
+    def _get_waveform_arguments(wave_info):
+        argument_string = ('{}, {}' * len(wave_info)).replace('}{', '}, {')
+        play_wave_arguments = []
+        for channel, wave, marker in wave_info:
+            play_wave_arguments.append(channel)
+            wave = wave if wave is not None else marker
+            play_wave_arguments.append(wave)
+        return argument_string.format(*play_wave_arguments)
+
     def upload_sequence_program(self, awg_number: int,
                                 sequence_program: str) -> int:
         """
diff --git a/qcodes/tests/drivers/test_zihdawg8.py b/qcodes/tests/drivers/test_zihdawg8.py
index 73fd962754f..60c59489768 100644
--- a/qcodes/tests/drivers/test_zihdawg8.py
+++ b/qcodes/tests/drivers/test_zihdawg8.py
@@ -55,7 +55,8 @@ def setUp(self):
         }}
 
     def test_create_parameters_from_node_tree(self):
-        with patch.object(zhinst.utils, 'create_api_session', return_value=3 * (MagicMock(),)), \
+        with patch.object(zhinst.utils, 'create_api_session',
+                          return_value=3 * (MagicMock(),)), \
              patch.object(ZIHDAWG8, 'download_device_node_tree',
                           return_value=self.node_tree):
             hdawg8 = ZIHDAWG8('Name', 'dev-test')
@@ -64,9 +65,11 @@ def test_create_parameters_from_node_tree(self):
             with self.assertRaises(ValueError):
                 hdawg8.system_awg_channelgrouping.set(4)
             self.assertEqual('None', hdawg8.system_awg_channelgrouping.unit)
-            self.assertEqual('system_awg_channelgrouping', hdawg8.system_awg_channelgrouping.name)
+            self.assertEqual('system_awg_channelgrouping',
+                             hdawg8.system_awg_channelgrouping.name)
             self.assertIsNotNone(hdawg8.system_awg_channelgrouping.vals)
-            self.assertIsInstance(hdawg8.system_awg_channelgrouping.vals, validators.Enum)
+            self.assertIsInstance(hdawg8.system_awg_channelgrouping.vals,
+                                  validators.Enum)
 
             self.assertIn('sigouts_0_on', hdawg8.parameters)
             self.assertEqual('None', hdawg8.sigouts_0_on.unit)
@@ -80,54 +83,136 @@ def test_create_parameters_from_node_tree(self):
 
             self.assertIn('sines_0_amplitudes_0', hdawg8.parameters)
             self.assertEqual('None', hdawg8.sines_0_amplitudes_0.unit)
-            self.assertEqual('sines_0_amplitudes_0', hdawg8.sines_0_amplitudes_0.name)
+            self.assertEqual('sines_0_amplitudes_0',
+                             hdawg8.sines_0_amplitudes_0.name)
             self.assertIsNone(hdawg8.sines_0_amplitudes_0.vals)
 
             self.assertIn('awgs_1_waveform_memoryusage', hdawg8.parameters)
             self.assertEqual('%', hdawg8.awgs_1_waveform_memoryusage.unit)
-            self.assertEqual('awgs_1_waveform_memoryusage', hdawg8.awgs_1_waveform_memoryusage.name)
+            self.assertEqual('awgs_1_waveform_memoryusage',
+                             hdawg8.awgs_1_waveform_memoryusage.name)
             self.assertIsNone(hdawg8.awgs_1_waveform_memoryusage.vals)
 
     def test_generate_csv_sequence_program(self):
         expected = textwrap.dedent("""
                         // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
-    
+                        
+                        wave wave_1 = "wave_1";
+                        wave wave_2 = "wave_2";
+                        wave wave_3 = "wave_3";
+                        
                         while(true){
-                            playWave(1, "wave_1", 2, "wave_2", 3, "wave_3");
+                            playWave(1, wave_1, 2, wave_2, 3, wave_3);
                         }
                         """)
-        sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1', 'wave_2', 'wave_3'], [1, 2, 3])
+        sequence_program = ZIHDAWG8.generate_csv_sequence_program(
+            [(1, 'wave_1', None), (2, 'wave_2', None), (3, 'wave_3', None)])
         self.assertEqual(expected, sequence_program)
 
-    def test_generate_csv_sequence_program_no_channels(self):
+    def test_generate_csv_sequence_program_1_wave(self):
         expected = textwrap.dedent("""
                         // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
-    
+                        
+                        wave wave_1 = "wave_1";
+                        
                         while(true){
-                            playWave("wave_1", "wave_2", "wave_3");
+                            playWave(7, wave_1);
                         }
                         """)
-        sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1', 'wave_2', 'wave_3'])
+        sequence_program = ZIHDAWG8.generate_csv_sequence_program(
+            [(7, 'wave_1', None)])
         self.assertEqual(expected, sequence_program)
 
-    def test_generate_csv_sequence_program_1_wave_no_channels(self):
+    def test_generate_csv_sequence_program_with_marker(self):
         expected = textwrap.dedent("""
                         // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
-    
+                        
+                        wave wave_1 = "wave_1";
+                        wave marker_1 = "marker_1";
+                        wave_1 = wave_1 + marker_1;
+                        
                         while(true){
-                            playWave("wave_1");
+                            playWave(6, wave_1);
                         }
                         """)
-        sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1'])
+        sequence_program = ZIHDAWG8.generate_csv_sequence_program(
+            [(6, 'wave_1', "marker_1")])
         self.assertEqual(expected, sequence_program)
 
-    def test_generate_csv_sequence_program_1_wave(self):
+    def test_generate_csv_sequence_program_with_marker_2_waves(self):
+        expected = textwrap.dedent("""
+                        // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
+                        
+                        wave wave_1 = "wave_1";
+                        wave marker_1 = "marker_1";
+                        wave_1 = wave_1 + marker_1;
+                        wave wave_2 = "wave_2";
+                        
+                        while(true){
+                            playWave(6, wave_1, 5, wave_2);
+                        }
+                        """)
+        sequence_program = ZIHDAWG8.generate_csv_sequence_program(
+            [(6, 'wave_1', "marker_1"), (5, "wave_2", None)])
+        self.assertEqual(expected, sequence_program)
+
+    def test_generate_csv_sequence_program_with_marker_no_waves(self):
+        expected = textwrap.dedent("""
+                        // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
+                        
+                        wave marker_1 = "marker_1";
+                        
+                        while(true){
+                            playWave(6, marker_1);
+                        }
+                        """)
+        sequence_program = ZIHDAWG8.generate_csv_sequence_program(
+            [(6, None, "marker_1")])
+        self.assertEqual(expected, sequence_program)
+
+    def test_generate_csv_sequence_program_with_2_markers_1_wave(self):
         expected = textwrap.dedent("""
                         // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
-    
+                        
+                        wave wave_1 = "wave_1";
+                        wave marker_1 = "marker_1";
+                        wave_1 = wave_1 + marker_1;
+                        wave marker_2 = "marker_2";
+                        
                         while(true){
-                            playWave(7, "wave_1");
+                            playWave(1, wave_1, 2, marker_2);
                         }
                         """)
-        sequence_program = ZIHDAWG8.generate_csv_sequence_program(['wave_1'], [7])
+        sequence_program = ZIHDAWG8.generate_csv_sequence_program(
+            [(1, "wave_1", "marker_1"), (2, None, "marker_2")])
         self.assertEqual(expected, sequence_program)
+
+    def test_generate_large_csv_sequence_program(self):
+        expected = textwrap.dedent("""
+                        // generated by qcodes.instrument_drivers.ZI.ZIHDAWG8
+                        
+                        wave wave_1 = "wave_1";
+                        wave marker_1 = "marker_1";
+                        wave_1 = wave_1 + marker_1;
+                        wave marker_2 = "marker_2";
+                        wave marker_3 = "marker_3";
+                        wave wave_4 = "wave_4";
+                        wave wave_5 = "wave_5";
+                        wave marker_5 = "marker_5";
+                        wave_5 = wave_5 + marker_5;
+                        wave wave_6 = "wave_6";
+                        wave wave_7 = "wave_7";
+                        wave marker_7 = "marker_7";
+                        wave_7 = wave_7 + marker_7;
+                        wave marker_8 = "marker_8";
+                        
+                        while(true){
+                            playWave(1, wave_1, 2, marker_2, 3, marker_3, 4, wave_4, 5, wave_5, 6, wave_6, 7, wave_7, 8, marker_8);
+                        }
+                        """)
+        sequence_program = ZIHDAWG8.generate_csv_sequence_program(
+            [(1, "wave_1", "marker_1"), (2, None, "marker_2"),
+             (3, None, "marker_3"), (4, "wave_4", None),
+             (5, "wave_5", "marker_5"), (6, "wave_6", None),
+             (7, "wave_7", "marker_7"), (8, None, "marker_8")])
+        self.assertEqual(expected, sequence_program)
\ No newline at end of file

From a01531126ef3ec78c2751844dbd7afeb87908130 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Tue, 27 Nov 2018 11:06:47 +0100
Subject: [PATCH 228/719] Add explanatory notebook

---
 ...ing runs from one DB file to another.ipynb | 239 ++++++++++++++++++
 1 file changed, 239 insertions(+)
 create mode 100644 docs/examples/DataSet/Extracting runs from one DB file to another.ipynb

diff --git a/docs/examples/DataSet/Extracting runs from one DB file to another.ipynb b/docs/examples/DataSet/Extracting runs from one DB file to another.ipynb
new file mode 100644
index 00000000000..56a164572cd
--- /dev/null
+++ b/docs/examples/DataSet/Extracting runs from one DB file to another.ipynb	
@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extracting runs from one DB file to another\n",
+    "\n",
+    "This notebook shows how to use the `extract_runs_into_db` function to extract runs from DB file (the source DB) into another DB file (the target DB). If the target DB does not exist, it will be created. The runs are **NOT** removed from the original DB file; they are copied over.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Let us set up a DB file with some runs in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os \n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "from qcodes.dataset.sqlite_base import connect\n",
+    "from qcodes.dataset.database_extract_runs import extract_runs_into_db\n",
+    "from qcodes.dataset.experiment_container import new_experiment, load_experiment_by_name\n",
+    "from qcodes.tests.instrument_mocks import DummyInstrument\n",
+    "from qcodes.dataset.measurements import Measurement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_path = os.path.join(os.getcwd(), 'extract_runs_notebook_source.db')\n",
+    "target_path = os.path.join(os.getcwd(), 'extract_runs_notebook_target.db')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_conn = connect(source_path)\n",
+    "target_conn = connect(target_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exp = new_experiment(name='extract_runs_experiment',\n",
+    "                     sample_name='no_sample',\n",
+    "                     conn=source_conn)\n",
+    "\n",
+    "my_inst = DummyInstrument('my_inst', gates=['voltage', 'current'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 1\n",
+      "Starting experimental run with id: 2\n",
+      "Starting experimental run with id: 3\n",
+      "Starting experimental run with id: 4\n",
+      "Starting experimental run with id: 5\n",
+      "Starting experimental run with id: 6\n",
+      "Starting experimental run with id: 7\n",
+      "Starting experimental run with id: 8\n",
+      "Starting experimental run with id: 9\n",
+      "Starting experimental run with id: 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "meas = Measurement(exp=exp)\n",
+    "meas.register_parameter(my_inst.voltage)\n",
+    "meas.register_parameter(my_inst.current, setpoints=(my_inst.voltage,))\n",
+    "\n",
+    "#Add 10 runs with gradually more and more data\n",
+    "\n",
+    "for run_id in range(1, 11):\n",
+    "    with meas.run() as datasaver:\n",
+    "        for step, noise in enumerate(np.random.randn(run_id)):\n",
+    "            datasaver.add_result((my_inst.voltage, step),\n",
+    "                                 (my_inst.current, noise))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extraction\n",
+    "\n",
+    "Now let us extract runs 3 and 7 into our desired target DB file. All runs must come from the same experiment. To extract runs from different experiments, call the function several times.\n",
+    "\n",
+    "The function will look in the target DB to see if an experiment with matching attributes already exists. If not, such an experiment is created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "extract_runs_into_db(source_path, target_path, 3, 7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_exp = load_experiment_by_name(name='extract_runs_experiment', conn=target_conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method Experiment.data_sets of extract_runs_experiment#no_sample#1@C:\\Users\\wihpniel/src/Qcodes/docs/examples/DataSet/experiments.db\n",
+       "-----------------------------------------------------------------------------------------------------\n",
+       "1-results-1-my_inst_voltage,my_inst_current-3\n",
+       "2-results-2-my_inst_voltage,my_inst_current-7>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target_exp.data_sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The last number printed in each line is the number of data points. As expected, we get 3 and 7.\n",
+    "\n",
+    "Note that the runs will have different `run_id`s in the new database. Their GUIDs are, however, the same (as they must be)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'aaaaaaaa-0c00-0007-0000-016754a23c83'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exp.data_set(3).guid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'aaaaaaaa-0c00-0007-0000-016754a23c83'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target_exp.data_set(1).guid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 726c1b527e64a6bfbd82eddce86ac5fea086fec1 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 27 Nov 2018 11:41:07 +0100
Subject: [PATCH 229/719] Make _fix_wrong_run_descriptions accept only
 ConnectionPlus

---
 qcodes/dataset/sqlite_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 74c0e791318..706e20add1d 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -2150,7 +2150,7 @@ def remove_trigger(conn: SomeConnection, trigger_id: str) -> None:
     transaction(conn, f"DROP TRIGGER IF EXISTS {trigger_id};")
 
 
-def _fix_wrong_run_descriptions(conn: SomeConnection,
+def _fix_wrong_run_descriptions(conn: ConnectionPlus,
                                 run_ids: Sequence[int]) -> None:
     """
     NB: This is a FIX function. Do not use it unless your database has been

From 5660e8282b08e2f5c17c16393e940de1412fd679 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 27 Nov 2018 12:01:49 +0100
Subject: [PATCH 230/719] Get rid of SomeConnection in favor of ConnectionPlus
 everywhere

---
 qcodes/dataset/data_set.py    |   4 +-
 qcodes/dataset/database.py    |   4 +-
 qcodes/dataset/sqlite_base.py | 108 +++++++++++++++++-----------------
 3 files changed, 57 insertions(+), 59 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index a8811d02d27..9fe296e6541 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -35,7 +35,7 @@
                                         update_run_description,
                                         run_exists, remove_trigger,
                                         make_connection_plus_from,
-                                        SomeConnection)
+                                        ConnectionPlus)
 
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
@@ -187,7 +187,7 @@ def _clean_up(self) -> None:
 class DataSet(Sized):
     def __init__(self, path_to_db: str=None,
                  run_id: Optional[int]=None,
-                 conn: Optional[SomeConnection]=None,
+                 conn: Optional[ConnectionPlus]=None,
                  exp_id=None,
                  name: str=None,
                  specs: SPECS=None,
diff --git a/qcodes/dataset/database.py b/qcodes/dataset/database.py
index 6ea49fdaacc..55d7971ab63 100644
--- a/qcodes/dataset/database.py
+++ b/qcodes/dataset/database.py
@@ -2,7 +2,7 @@
 
 from os.path import expanduser
 
-from qcodes.dataset.sqlite_base import SomeConnection
+from qcodes.dataset.sqlite_base import ConnectionPlus
 from qcodes.dataset.sqlite_base import connect as _connect
 from qcodes.dataset.sqlite_base import init_db as _init_db
 import qcodes.config
@@ -46,7 +46,7 @@ def initialise_or_create_database_at(db_file_with_abs_path: str) -> None:
     initialise_database()
 
 
-def path_to_dbfile(conn: SomeConnection) -> str:
+def path_to_dbfile(conn: ConnectionPlus) -> str:
     """
     Return the path of the database file that the conn object is connected to
     """
diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 706e20add1d..5005f07b65e 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -132,9 +132,6 @@ def __init__(self, sqlite3_connection: sqlite3.Connection):
                              '`ConnectionPlus` object which is not allowed.')
 
 
-SomeConnection = Union[sqlite3.Connection, ConnectionPlus]
-
-
 def upgrader(func: Callable[[ConnectionPlus], None]):
     """
     Decorator for database version upgrade functions. An upgrade function
@@ -357,7 +354,7 @@ def connect(name: str, debug: bool = False,
     return conn
 
 
-def perform_db_upgrade(conn: SomeConnection, version: int=-1) -> None:
+def perform_db_upgrade(conn: ConnectionPlus, version: int=-1) -> None:
     """
     This is intended to perform all upgrades as needed to bring the
     db from version 0 to the most current version (or the version specified).
@@ -449,7 +446,7 @@ def perform_db_upgrade_1_to_2(conn: ConnectionPlus) -> None:
         raise RuntimeError(f"found {n_run_tables} runs tables expected 1")
 
 
-def _2to3_get_result_tables(conn: SomeConnection) -> Dict[int, str]:
+def _2to3_get_result_tables(conn: ConnectionPlus) -> Dict[int, str]:
     rst_query = "SELECT run_id, result_table_name FROM runs"
     cur = conn.cursor()
     cur.execute(rst_query)
@@ -462,7 +459,7 @@ def _2to3_get_result_tables(conn: SomeConnection) -> Dict[int, str]:
     return results
 
 
-def _2to3_get_layout_ids(conn: SomeConnection) -> DefaultDict[int, List[int]]:
+def _2to3_get_layout_ids(conn: ConnectionPlus) -> DefaultDict[int, List[int]]:
     query = """
             select runs.run_id, layouts.layout_id
             FROM layouts
@@ -483,7 +480,7 @@ def _2to3_get_layout_ids(conn: SomeConnection) -> DefaultDict[int, List[int]]:
     return results
 
 
-def _2to3_get_indeps(conn: SomeConnection) -> DefaultDict[int, List[int]]:
+def _2to3_get_indeps(conn: ConnectionPlus) -> DefaultDict[int, List[int]]:
     query = """
             SELECT layouts.run_id, layouts.layout_id
             FROM layouts
@@ -504,7 +501,7 @@ def _2to3_get_indeps(conn: SomeConnection) -> DefaultDict[int, List[int]]:
     return results
 
 
-def _2to3_get_deps(conn: SomeConnection) -> DefaultDict[int, List[int]]:
+def _2to3_get_deps(conn: ConnectionPlus) -> DefaultDict[int, List[int]]:
     query = """
             SELECT layouts.run_id, layouts.layout_id
             FROM layouts
@@ -525,7 +522,7 @@ def _2to3_get_deps(conn: SomeConnection) -> DefaultDict[int, List[int]]:
     return results
 
 
-def _2to3_get_dependencies(conn: SomeConnection) -> DefaultDict[int, List[int]]:
+def _2to3_get_dependencies(conn: ConnectionPlus) -> DefaultDict[int, List[int]]:
     query = """
             SELECT dependent, independent
             FROM dependencies
@@ -548,7 +545,7 @@ def _2to3_get_dependencies(conn: SomeConnection) -> DefaultDict[int, List[int]]:
     return results
 
 
-def _2to3_get_layouts(conn: SomeConnection) -> Dict[int,
+def _2to3_get_layouts(conn: ConnectionPlus) -> Dict[int,
                                                     Tuple[str, str, str, str]]:
     query = """
             SELECT layout_id, parameter, label, unit, inferred_from
@@ -566,7 +563,7 @@ def _2to3_get_layouts(conn: SomeConnection) -> Dict[int,
     return results
 
 
-def _2to3_get_paramspecs(conn: SomeConnection,
+def _2to3_get_paramspecs(conn: ConnectionPlus,
                          layout_ids: List[int],
                          layouts: Dict[int, Tuple[str, str, str, str]],
                          dependencies: Dict[int, List[int]],
@@ -701,7 +698,7 @@ def perform_db_upgrade_2_to_3(conn: ConnectionPlus) -> None:
 NEWEST_VERSION = len(UPGRADE_ACTIONS)
 
 
-def transaction(conn: SomeConnection,
+def transaction(conn: ConnectionPlus,
                 sql: str, *args: Any) -> sqlite3.Cursor:
     """Perform a transaction.
     The transaction needs to be committed or rolled back.
@@ -724,7 +721,7 @@ def transaction(conn: SomeConnection,
     return c
 
 
-def atomic_transaction(conn: SomeConnection,
+def atomic_transaction(conn: ConnectionPlus,
                        sql: str, *args: Any) -> sqlite3.Cursor:
     """Perform an **atomic** transaction.
     The transaction is committed if there are no exceptions else the
@@ -796,7 +793,8 @@ def atomic(conn: ConnectionPlus):
         conn.atomic_in_progress = old_atomic_in_progress
 
 
-def make_connection_plus_from(conn: SomeConnection) -> ConnectionPlus:
+def make_connection_plus_from(conn: Union[sqlite3.Connection, ConnectionPlus]
+                              ) -> ConnectionPlus:
     """
     Makes a ConnectionPlus connection object out of a given argument.
 
@@ -804,7 +802,7 @@ def make_connection_plus_from(conn: SomeConnection) -> ConnectionPlus:
     without any changes.
 
     Args:
-        conn: an sqlite connection object as defined by SomeConnection type
+        conn: an sqlite database connection object
 
     Returns:
         the "same" connection but as ConnectionPlus object
@@ -824,7 +822,7 @@ def init_db(conn: ConnectionPlus)->None:
         transaction(conn, _dependencies_table_schema)
 
 
-def is_column_in_table(conn: SomeConnection, table: str, column: str) -> bool:
+def is_column_in_table(conn: ConnectionPlus, table: str, column: str) -> bool:
     """
     A look-before-you-leap function to look up if a table has a certain column.
 
@@ -871,7 +869,7 @@ def insert_column(conn: ConnectionPlus, table: str, name: str,
                         f'ALTER TABLE "{table}" ADD COLUMN "{name}"')
 
 
-def select_one_where(conn: SomeConnection, table: str, column: str,
+def select_one_where(conn: ConnectionPlus, table: str, column: str,
                      where_column: str, where_value: Any) -> Any:
     query = f"""
     SELECT {column}
@@ -885,7 +883,7 @@ def select_one_where(conn: SomeConnection, table: str, column: str,
     return res
 
 
-def select_many_where(conn: SomeConnection, table: str, *columns: str,
+def select_many_where(conn: ConnectionPlus, table: str, *columns: str,
                       where_column: str, where_value: Any) -> Any:
     _columns = ",".join(columns)
     query = f"""
@@ -912,7 +910,7 @@ def _massage_dict(metadata: Dict[str, Any]) -> Tuple[str, List[Any]]:
     return ','.join(template), values
 
 
-def update_where(conn: SomeConnection, table: str,
+def update_where(conn: ConnectionPlus, table: str,
                  where_column: str, where_value: Any, **updates) -> None:
     _updates, values = _massage_dict(updates)
     query = f"""
@@ -926,7 +924,7 @@ def update_where(conn: SomeConnection, table: str,
     atomic_transaction(conn, query, *values, where_value)
 
 
-def insert_values(conn: SomeConnection,
+def insert_values(conn: ConnectionPlus,
                   formatted_name: str,
                   columns: List[str],
                   values: VALUES,
@@ -1022,7 +1020,7 @@ def insert_many_values(conn: ConnectionPlus,
     return return_value
 
 
-def modify_values(conn: SomeConnection,
+def modify_values(conn: ConnectionPlus,
                   formatted_name: str,
                   index: int,
                   columns: List[str],
@@ -1049,7 +1047,7 @@ def modify_values(conn: SomeConnection,
     return c.rowcount
 
 
-def modify_many_values(conn: SomeConnection,
+def modify_many_values(conn: ConnectionPlus,
                        formatted_name: str,
                        start_index: int,
                        columns: List[str],
@@ -1075,7 +1073,7 @@ def modify_many_values(conn: SomeConnection,
         start_index += 1
 
 
-def length(conn: SomeConnection,
+def length(conn: ConnectionPlus,
            formatted_name: str
            ) -> int:
     """
@@ -1097,7 +1095,7 @@ def length(conn: SomeConnection,
         return _len
 
 
-def get_data(conn: SomeConnection,
+def get_data(conn: ConnectionPlus,
              table_name: str,
              columns: List[str],
              start: Optional[int] = None,
@@ -1141,7 +1139,7 @@ def get_data(conn: SomeConnection,
     return res
 
 
-def get_values(conn: SomeConnection,
+def get_values(conn: ConnectionPlus,
                table_name: str,
                param_name: str) -> List[List[Any]]:
     """
@@ -1165,7 +1163,7 @@ def get_values(conn: SomeConnection,
     return res
 
 
-def get_setpoints(conn: SomeConnection,
+def get_setpoints(conn: ConnectionPlus,
                   table_name: str,
                   param_name: str) -> Dict[str, List[List[Any]]]:
     """
@@ -1234,7 +1232,7 @@ def get_setpoints(conn: SomeConnection,
     return output
 
 
-def get_layout(conn: SomeConnection,
+def get_layout(conn: ConnectionPlus,
                layout_id) -> Dict[str, str]:
     """
     Get the layout of a single parameter for plotting it
@@ -1255,7 +1253,7 @@ def get_layout(conn: SomeConnection,
     return res
 
 
-def get_layout_id(conn: SomeConnection,
+def get_layout_id(conn: ConnectionPlus,
                   parameter: Union[ParamSpec, str],
                   run_id: int) -> int:
     """
@@ -1287,7 +1285,7 @@ def get_layout_id(conn: SomeConnection,
     return res
 
 
-def get_dependents(conn: SomeConnection,
+def get_dependents(conn: ConnectionPlus,
                    run_id: int) -> List[int]:
     """
     Get dependent layout_ids for a certain run_id, i.e. the layout_ids of all
@@ -1302,7 +1300,7 @@ def get_dependents(conn: SomeConnection,
     return res
 
 
-def get_dependencies(conn: SomeConnection,
+def get_dependencies(conn: ConnectionPlus,
                      layout_id: int) -> List[List[int]]:
     """
     Get the dependencies of a certain dependent variable (indexed by its
@@ -1322,7 +1320,7 @@ def get_dependencies(conn: SomeConnection,
 # Higher level Wrappers
 
 
-def new_experiment(conn: SomeConnection,
+def new_experiment(conn: ConnectionPlus,
                    name: str,
                    sample_name: str,
                    format_string: Optional[str] = "{}-{}-{}"
@@ -1351,7 +1349,7 @@ def new_experiment(conn: SomeConnection,
 
 # TODO(WilliamHPNielsen): we should remove the redundant
 # is_completed
-def mark_run_complete(conn: SomeConnection, run_id: int):
+def mark_run_complete(conn: ConnectionPlus, run_id: int):
     """ Mark run complete
 
     Args:
@@ -1370,7 +1368,7 @@ def mark_run_complete(conn: SomeConnection, run_id: int):
     atomic_transaction(conn, query, time.time(), True, run_id)
 
 
-def completed(conn: SomeConnection, run_id)->bool:
+def completed(conn: ConnectionPlus, run_id)->bool:
     """ Check if the run scomplete
 
     Args:
@@ -1382,7 +1380,7 @@ def completed(conn: SomeConnection, run_id)->bool:
 
 
 def get_completed_timestamp_from_run_id(
-        conn: SomeConnection, run_id: int) -> float:
+        conn: ConnectionPlus, run_id: int) -> float:
     """
     Retrieve the timestamp when the given measurement run was completed
 
@@ -1400,7 +1398,7 @@ def get_completed_timestamp_from_run_id(
                             "run_id", run_id)
 
 
-def get_guid_from_run_id(conn: SomeConnection, run_id: int) -> str:
+def get_guid_from_run_id(conn: ConnectionPlus, run_id: int) -> str:
     """
     Get the guid of the given run
 
@@ -1411,7 +1409,7 @@ def get_guid_from_run_id(conn: SomeConnection, run_id: int) -> str:
     return select_one_where(conn, "runs", "guid", "run_id", run_id)
 
 
-def finish_experiment(conn: SomeConnection, exp_id: int):
+def finish_experiment(conn: ConnectionPlus, exp_id: int):
     """ Finish experiment
 
     Args:
@@ -1424,7 +1422,7 @@ def finish_experiment(conn: SomeConnection, exp_id: int):
     atomic_transaction(conn, query, time.time(), exp_id)
 
 
-def get_run_counter(conn: SomeConnection, exp_id: int) -> int:
+def get_run_counter(conn: ConnectionPlus, exp_id: int) -> int:
     """ Get the experiment run counter
 
     Args:
@@ -1440,7 +1438,7 @@ def get_run_counter(conn: SomeConnection, exp_id: int) -> int:
                             where_value=exp_id)
 
 
-def get_experiments(conn: SomeConnection) -> List[sqlite3.Row]:
+def get_experiments(conn: ConnectionPlus) -> List[sqlite3.Row]:
     """ Get a list of experiments
      Args:
          conn: database connection
@@ -1456,7 +1454,7 @@ def get_experiments(conn: SomeConnection) -> List[sqlite3.Row]:
     return c.fetchall()
 
 
-def get_last_experiment(conn: SomeConnection) -> Optional[int]:
+def get_last_experiment(conn: ConnectionPlus) -> Optional[int]:
     """
     Return last started experiment id
 
@@ -1493,7 +1491,7 @@ def get_runs(conn: ConnectionPlus,
     return c.fetchall()
 
 
-def get_last_run(conn: SomeConnection, exp_id: int) -> Optional[int]:
+def get_last_run(conn: ConnectionPlus, exp_id: int) -> Optional[int]:
     """
     Get run_id of the last run in experiment with exp_id
 
@@ -1514,7 +1512,7 @@ def get_last_run(conn: SomeConnection, exp_id: int) -> Optional[int]:
     return one(c, 'run_id')
 
 
-def run_exists(conn: SomeConnection, run_id: int) -> bool:
+def run_exists(conn: ConnectionPlus, run_id: int) -> bool:
     # the following query always returns a single sqlite3.Row with an integer
     # value of `1` or `0` for existing and non-existing run_id in the database
     query = """
@@ -1529,7 +1527,7 @@ def run_exists(conn: SomeConnection, run_id: int) -> bool:
     return bool(res[0])
 
 
-def data_sets(conn: SomeConnection) -> List[sqlite3.Row]:
+def data_sets(conn: ConnectionPlus) -> List[sqlite3.Row]:
     """ Get a list of datasets
     Args:
         conn: database connection
@@ -1620,7 +1618,7 @@ def _insert_run(conn: ConnectionPlus, exp_id: int, name: str,
     return run_counter, formatted_name, run_id
 
 
-def _update_experiment_run_counter(conn: SomeConnection, exp_id: int,
+def _update_experiment_run_counter(conn: ConnectionPlus, exp_id: int,
                                    run_counter: int) -> None:
     query = """
     UPDATE experiments
@@ -1630,7 +1628,7 @@ def _update_experiment_run_counter(conn: SomeConnection, exp_id: int,
     atomic_transaction(conn, query, run_counter, exp_id)
 
 
-def get_parameters(conn: SomeConnection,
+def get_parameters(conn: ConnectionPlus,
                    run_id: int) -> List[ParamSpec]:
     """
     Get the list of param specs for run
@@ -1659,7 +1657,7 @@ def get_parameters(conn: SomeConnection,
     return parspecs
 
 
-def get_paramspec(conn: SomeConnection,
+def get_paramspec(conn: ConnectionPlus,
                   run_id: int,
                   param_name: str) -> ParamSpec:
     """
@@ -1933,14 +1931,14 @@ def create_run(conn: ConnectionPlus, exp_id: int, name: str,
     return run_counter, run_id, formatted_name
 
 
-def get_metadata(conn: SomeConnection, tag: str, table_name: str):
+def get_metadata(conn: ConnectionPlus, tag: str, table_name: str):
     """ Get metadata under the tag from table
     """
     return select_one_where(conn, "runs", tag,
                             "result_table_name", table_name)
 
 
-def get_metadata_from_run_id(conn: SomeConnection, run_id: int) -> Dict:
+def get_metadata_from_run_id(conn: ConnectionPlus, run_id: int) -> Dict:
     """
     Get all metadata associated with the specified run
     """
@@ -1994,7 +1992,7 @@ def insert_meta_data(conn: ConnectionPlus, row_id: int, table_name: str,
     update_meta_data(conn, row_id, table_name, metadata)
 
 
-def update_meta_data(conn: SomeConnection, row_id: int, table_name: str,
+def update_meta_data(conn: ConnectionPlus, row_id: int, table_name: str,
                      metadata: Dict[str, Any]) -> None:
     """
     Updates metadata (they must exist already)
@@ -2033,36 +2031,36 @@ def add_meta_data(conn: ConnectionPlus,
             raise e
 
 
-def get_user_version(conn: SomeConnection) -> int:
+def get_user_version(conn: ConnectionPlus) -> int:
 
     curr = atomic_transaction(conn, 'PRAGMA user_version')
     res = one(curr, 0)
     return res
 
 
-def set_user_version(conn: SomeConnection, version: int) -> None:
+def set_user_version(conn: ConnectionPlus, version: int) -> None:
 
     atomic_transaction(conn, 'PRAGMA user_version({})'.format(version))
 
 
 def get_experiment_name_from_experiment_id(
-        conn: SomeConnection, exp_id: int) -> str:
+        conn: ConnectionPlus, exp_id: int) -> str:
     return select_one_where(
         conn, "experiments", "name", "exp_id", exp_id)
 
 
 def get_sample_name_from_experiment_id(
-        conn: SomeConnection, exp_id: int) -> str:
+        conn: ConnectionPlus, exp_id: int) -> str:
     return select_one_where(
         conn, "experiments", "sample_name", "exp_id", exp_id)
 
 
-def get_run_timestamp_from_run_id(conn: SomeConnection,
+def get_run_timestamp_from_run_id(conn: ConnectionPlus,
                                   run_id: int) -> float:
     return select_one_where(conn, "runs", "run_timestamp", "run_id", run_id)
 
 
-def update_GUIDs(conn: SomeConnection) -> None:
+def update_GUIDs(conn: ConnectionPlus) -> None:
     """
     Update all GUIDs in this database where either the location code or the
     work_station code is zero to use the location and work_station code from
@@ -2137,7 +2135,7 @@ def _both_zero(run_id: int, conn, guid_comps) -> None:
         actions[(loc == 0, ws == 0)](run_id, conn, guid_comps)
 
 
-def remove_trigger(conn: SomeConnection, trigger_id: str) -> None:
+def remove_trigger(conn: ConnectionPlus, trigger_id: str) -> None:
     """
     Removes a trigger with a given id if it exists.
 

From 9eefd792987adee477136147c0d19f1f4a5f71eb Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 27 Nov 2018 12:06:05 +0100
Subject: [PATCH 231/719] Skip database-file-dependent tests on file existence,
 not directory

---
 .../test_database_creation_and_upgrading.py   | 30 +++++++++----------
 qcodes/tests/dataset/test_database_fixes.py   |  6 ++--
 2 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index 1b651108b66..1a275d28a0c 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -136,12 +136,12 @@ def test_perform_actual_upgrade_0_to_1():
 
     v0fixpath = os.path.join(fixturepath, 'db_files', 'version0')
 
-    if not os.path.exists(v0fixpath):
+    dbname_old = os.path.join(v0fixpath, 'empty.db')
+
+    if not os.path.exists(dbname_old):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
                     " using the scripts in the legacy_DB_generation folder")
 
-    dbname_old = os.path.join(v0fixpath, 'empty.db')
-
     with temporarily_copied_DB(dbname_old, debug=False, version=0) as conn:
 
         assert get_user_version(conn) == 0
@@ -164,12 +164,12 @@ def test_perform_actual_upgrade_1_to_2():
 
     v1fixpath = os.path.join(fixturepath, 'db_files', 'version1')
 
-    if not os.path.exists(v1fixpath):
+    dbname_old = os.path.join(v1fixpath, 'empty.db')
+
+    if not os.path.exists(dbname_old):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
                     " using the scripts in the legacy_DB_generation folder")
 
-    dbname_old = os.path.join(v1fixpath, 'empty.db')
-
     with temporarily_copied_DB(dbname_old, debug=False, version=1) as conn:
 
         assert get_user_version(conn) == 1
@@ -194,12 +194,12 @@ def test_perform_actual_upgrade_2_to_3_empty():
 
     v2fixpath = os.path.join(fixturepath, 'db_files', 'version2')
 
-    if not os.path.exists(v2fixpath):
+    dbname_old = os.path.join(v2fixpath, 'empty.db')
+
+    if not os.path.exists(dbname_old):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
                     " using the scripts in the legacy_DB_generation folder")
 
-    dbname_old = os.path.join(v2fixpath, 'empty.db')
-
     with temporarily_copied_DB(dbname_old, debug=False, version=2) as conn:
 
         assert get_user_version(conn) == 2
@@ -223,12 +223,12 @@ def test_perform_actual_upgrade_2_to_3_empty_runs():
 
     v2fixpath = os.path.join(fixturepath, 'db_files', 'version2')
 
-    if not os.path.exists(v2fixpath):
+    dbname_old = os.path.join(v2fixpath, 'empty_runs.db')
+
+    if not os.path.exists(dbname_old):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
                     " using the scripts in the legacy_DB_generation folder")
 
-    dbname_old = os.path.join(v2fixpath, 'empty_runs.db')
-
     with temporarily_copied_DB(dbname_old, debug=False, version=2) as conn:
 
         perform_db_upgrade_2_to_3(conn)
@@ -238,12 +238,12 @@ def test_perform_actual_upgrade_2_to_3_some_runs():
 
     v2fixpath = os.path.join(fixturepath, 'db_files', 'version2')
 
-    if not os.path.exists(v2fixpath):
+    dbname_old = os.path.join(v2fixpath, 'some_runs.db')
+
+    if not os.path.exists(dbname_old):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
                     " using the scripts in the legacy_DB_generation folder")
 
-    dbname_old = os.path.join(v2fixpath, 'some_runs.db')
-
     with temporarily_copied_DB(dbname_old, debug=False, version=2) as conn:
 
         assert get_user_version(conn) == 2
diff --git a/qcodes/tests/dataset/test_database_fixes.py b/qcodes/tests/dataset/test_database_fixes.py
index bea9ad33148..d82356c8566 100644
--- a/qcodes/tests/dataset/test_database_fixes.py
+++ b/qcodes/tests/dataset/test_database_fixes.py
@@ -17,13 +17,13 @@
 def test_fix_wrong_run_descriptions():
     v3fixpath = os.path.join(fixturepath, 'db_files', 'version3')
 
-    if not os.path.exists(v3fixpath):
+    dbname_old = os.path.join(v3fixpath, 'some_runs_without_run_description.db')
+
+    if not os.path.exists(dbname_old):
         pytest.skip(
             "No db-file fixtures found. You can generate test db-files"
             " using the scripts in the legacy_DB_generation folder")
 
-    dbname_old = os.path.join(v3fixpath, 'some_runs_without_run_description.db')
-
     with temporarily_copied_DB(dbname_old, debug=False, version=3) as conn:
 
         assert get_user_version(conn) == 3

From 4ea67f9465beb161d283b2fffacfbc8f7ce98e30 Mon Sep 17 00:00:00 2001
From: GeneralSarsby <matt.sarsby@gmail.com>
Date: Tue, 27 Nov 2018 13:21:17 +0100
Subject: [PATCH 232/719] Update SR830.py

included Tuple from the typing module.

fixed the docstring indent.
rearranged the section headings in the docstring.
---
 .../stanford_research/SR830.py                | 77 ++++++++++---------
 1 file changed, 41 insertions(+), 36 deletions(-)

diff --git a/qcodes/instrument_drivers/stanford_research/SR830.py b/qcodes/instrument_drivers/stanford_research/SR830.py
index 07859286b26..90baa867833 100644
--- a/qcodes/instrument_drivers/stanford_research/SR830.py
+++ b/qcodes/instrument_drivers/stanford_research/SR830.py
@@ -5,6 +5,8 @@
 from qcodes.instrument.parameter import ArrayParameter
 from qcodes.utils.validators import Numbers, Ints, Enum, Strings
 
+from typing import Tuple
+
 
 class ChannelBuffer(ArrayParameter):
     """
@@ -492,42 +494,44 @@ def parse_offset_get(s):
     
     def snap(self, *parameters: str) -> Tuple[float, ...]:
         """
-Get between 2 and 6 parameters at a single instant. This provides a coherent
-snapshot of measured signals. Pick up to 6 from: X, Y, R, θ, the aux
-inputs 1-4, frequency, or what is currently displayed on channels 1 and 2.
-Reading X and Y (or R and θ) gives a coherent snapshot of the signal.
-Snap is important when the time constant is very short, a time constant less
-than 100 ms.
-
-Args:
-    *parameters
-        From 2 to 6 strings of names of parameters for which the values are
-        requested. including: 'x', 'y', 'r', 'p', 'phase' or 'θ',
-        'aux1', 'aux2', 'aux3', 'aux4', 'freq', 'ch1', and 'ch2'.
-    
-Returns:
-    A tuple of floating point values in the same order as requested.
- 
-Units:
-    Volts for x, y, r, and aux 1-4
-    Degrees for θ
-    Hertz for freq
-    Unknown for ch1 and ch2. It will depend on what was set.
-
-Examples:
-    lockin.snap('x','y') -> tuple(x,y)
-    
-    lockin.snap('aux1','aux2','freq','phase') 
-        -> tuple(aux1,aux2,freq,phase)
-
-Limitations:
-     - If X,Y,R and θ are all read, then the values of X,Y are recorded
-       approximately 10 µs apart from R,θ. Thus, the values of X and Y may not
-       yield the exact values of R and θ from a single snap.
-     - The values of the Aux Inputs may have an uncertainty of up to 32 µs.
-     - The frequency is computed only every other period or 40 ms, whichever is
-       longer.  
-            """
+        Get between 2 and 6 parameters at a single instant. This provides a 
+        coherent snapshot of measured signals. Pick up to 6 from: X, Y, R, θ, 
+        the aux inputs 1-4, frequency, or what is currently displayed on 
+        channels 1 and 2.
+
+        Reading X and Y (or R and θ) gives a coherent snapshot of the signal.
+        Snap is important when the time constant is very short, a time constant
+        less than 100 ms.
+
+        Args:
+            *parameters
+                From 2 to 6 strings of names of parameters for which the values
+                are requested. including: 'x', 'y', 'r', 'p', 'phase' or 'θ',
+                'aux1', 'aux2', 'aux3', 'aux4', 'freq', 'ch1', and 'ch2'.
+            
+        Returns:
+            A tuple of floating point values in the same order as requested.
+
+        Examples:
+            lockin.snap('x','y') -> tuple(x,y)
+            
+            lockin.snap('aux1','aux2','freq','phase') 
+                -> tuple(aux1,aux2,freq,phase)
+
+        Note:
+            Volts for x, y, r, and aux 1-4
+            Degrees for θ
+            Hertz for freq
+            Unknown for ch1 and ch2. It will depend on what was set.
+
+             - If X,Y,R and θ are all read, then the values of X,Y are recorded
+               approximately 10 µs apart from R,θ. Thus, the values of X and Y 
+               may not yield the exact values of R and θ from a single snap.
+             - The values of the Aux Inputs may have an uncertainty of 
+               up to 32 µs.
+             - The frequency is computed only every other period or 40 ms, 
+               whichever is longer.  
+        """
         if not 2 <= len(parameters) <= 6:
             raise KeyError(
                 'It is only possible to request values of 2 to 6 parameters'
@@ -541,6 +545,7 @@ def snap(self, *parameters: str) -> Tuple[float, ...]:
 
         p_ids = [self.SNAP_PARAMETERS[name.lower()] for name in parameters]
         output = self.ask(f'SNAP? {",".join(p_ids)}')
+
         return tuple(float(val) for val in output.split(','))
 
     def _set_buffer_SR(self, SR):

From 5b254854b7b811fa271b1dbc7b1975cb201c3894 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Tue, 27 Nov 2018 13:50:09 +0100
Subject: [PATCH 233/719] Add failing test for sample_name == None

---
 .../dataset/test_database_extract_runs.py     | 52 +++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index 988083b41ae..9abe331d19d 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -423,3 +423,55 @@ def test_old_versions_not_touched(two_empty_temp_db_connections):
                              'upgrade_source_db=True to auto-upgrade '
                              'the source DB file.')
             assert warning[0].message.args[0] == expected_mssg
+
+
+def test_experiments_with_NULL_sample_name(two_empty_temp_db_connections,
+                                           some_paramspecs):
+    """
+    In older API versions (corresponding to DB version 3),
+    users could get away with setting the sample name to None
+
+    This test checks that such an experiment gets correctly recognised and
+    is thus not ever re-inserted into the target DB
+    """
+    source_conn, target_conn = two_empty_temp_db_connections
+    source_exp_1 = Experiment(conn=source_conn, name='null_sample_name')
+
+    source_path = path_to_dbfile(source_conn)
+    target_path = path_to_dbfile(target_conn)
+
+    # make 5 runs in experiment
+
+    exp_1_run_ids = []
+    for _ in range(5):
+
+        source_dataset = DataSet(conn=source_conn, exp_id=source_exp_1.exp_id)
+        exp_1_run_ids.append(source_dataset.run_id)
+
+        for ps in some_paramspecs[2].values():
+            source_dataset.add_parameter(ps)
+
+        for val in range(10):
+            source_dataset.add_result({ps.name: val
+                                       for ps in some_paramspecs[2].values()})
+        source_dataset.mark_complete()
+
+    sql = """
+          UPDATE experiments
+          SET sample_name = NULL
+          WHERE exp_id = 1
+          """
+    source_conn.execute(sql)
+    source_conn.commit()
+
+    assert source_exp_1.sample_name is None
+
+    extract_runs_into_db(source_path, target_path, 1, 2, 3, 4, 5)
+
+    assert len(get_experiments(target_conn)) == 1
+
+    extract_runs_into_db(source_path, target_path, 1, 2, 3, 4, 5)
+
+    assert len(get_experiments(target_conn)) == 1
+
+    assert len(Experiment(exp_id=1, conn=target_conn)) == 5

From 08826741fee751ec7d79ed380416325a9ffcbc17 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Tue, 27 Nov 2018 13:56:18 +0100
Subject: [PATCH 234/719] Make experiments match on sample_name is None

---
 qcodes/dataset/sqlite_base.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 39cb49ff17a..fadaa6a3de3 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1569,6 +1569,9 @@ def get_matching_exp_ids(conn: SomeConnection, **match_conditions) -> List:
     end_time = match_conditions.get('end_time', None)
     time_eq = "=" if end_time is not None else "IS"
 
+    sample_name = match_conditions.get('sample_name', None)
+    sample_name_eq = "=" if sample_name is not None else "IS"
+
     query = "SELECT exp_id FROM experiments "
     for n, mcond in enumerate(match_conditions):
         if n == 0:
@@ -1583,6 +1586,7 @@ def get_matching_exp_ids(conn: SomeConnection, **match_conditions) -> List:
                               f'format_string = "{format_string}"')
         match_conditions.pop("format_string")
     query = query.replace("end_time = ?", f"end_time {time_eq} ?")
+    query = query.replace("sample_name = ?", f"sample_name {sample_name_eq} ?")
 
     cursor = conn.cursor()
     cursor.execute(query, tuple(match_conditions.values()))

From 679c292af1e1c07d775f396d7dfe8e42b44a7f0f Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Tue, 27 Nov 2018 15:35:24 +0100
Subject: [PATCH 235/719] Override the snapshot_base method so all values are
 always up to date

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 8 +++++++-
 qcodes/tests/drivers/test_zihdawg8.py    | 2 +-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index 98fc94dfde1..d65fbd26c18 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -4,7 +4,7 @@
 import textwrap
 import time
 from functools import partial
-from typing import Optional, Any, List, Tuple, Union
+from typing import Optional, Any, List, Tuple, Union, Sequence, Dict
 import zhinst.utils
 
 from qcodes import Instrument
@@ -39,6 +39,12 @@ def __init__(self, name: str, device_id: str, **kwargs) -> None:
         node_tree = self.download_device_node_tree()
         self.create_parameters_from_node_tree(node_tree)
 
+    def snapshot_base(self, update: bool = False,
+                      params_to_skip_update: Sequence[str] = None) -> Dict:
+        """ Override the base method to ensure all values are up-to-date"""
+        return super(ZIHDAWG8, self).snapshot_base(update=True,
+                                                   params_to_skip_update=None)
+
     def enable_channel(self, channel_number: int) -> None:
         """
         Enable a signal output, turns on a blue LED on the device.
diff --git a/qcodes/tests/drivers/test_zihdawg8.py b/qcodes/tests/drivers/test_zihdawg8.py
index 60c59489768..bf4cc300e89 100644
--- a/qcodes/tests/drivers/test_zihdawg8.py
+++ b/qcodes/tests/drivers/test_zihdawg8.py
@@ -215,4 +215,4 @@ def test_generate_large_csv_sequence_program(self):
              (3, None, "marker_3"), (4, "wave_4", None),
              (5, "wave_5", "marker_5"), (6, "wave_6", None),
              (7, "wave_7", "marker_7"), (8, None, "marker_8")])
-        self.assertEqual(expected, sequence_program)
\ No newline at end of file
+        self.assertEqual(expected, sequence_program)

From 6300a6133ac99a14808d1d1a0b4bcf7496b1de6b Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 27 Nov 2018 18:58:12 +0100
Subject: [PATCH 236/719] Use atomic for add_meta_data in DataSet instead of
 explicit commit

---
 qcodes/dataset/data_set.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 9fe296e6541..6396fd80399 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -478,9 +478,9 @@ def add_metadata(self, tag: str, metadata: Any):
         """
 
         self._metadata[tag] = metadata
-        add_meta_data(self.conn, self.run_id, {tag: metadata})
-        # `add_meta_data` does not commit, hence we commit here:
-        self.conn.commit()
+        # `add_meta_data` is not atomic by itself, hence using `atomic`
+        with atomic(self.conn) as conn:
+            add_meta_data(conn, self.run_id, {tag: metadata})
 
     @property
     def started(self) -> bool:

From cd28f708e35fdf040a7aa70d679bf30fdc438744 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Tue, 27 Nov 2018 09:58:38 -0800
Subject: [PATCH 237/719] 1) more tests for better coverage 2) use re.sub to
 replace comma with period.

---
 qcodes/instrument_drivers/stahl/stahl.py | 57 +++++++++---------------
 qcodes/tests/drivers/test_stahl.py       | 37 ++++++++++++++-
 2 files changed, 55 insertions(+), 39 deletions(-)

diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index f0d4dec1bdd..2e224b095f2 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -2,11 +2,12 @@
 This is a driver for the Stahl power supplies
 """
 
-from typing import Dict, Optional, Any, Callable, Union, Sequence
+from typing import Dict, Optional, Any, Callable, Sequence
 import re
 import numpy as np
 import logging
 from collections import OrderedDict
+from functools import partial
 
 from qcodes import VisaInstrument, InstrumentChannel, ChannelList
 from qcodes.utils.validators import Numbers
@@ -31,7 +32,7 @@ def chain(*functions: Callable) -> Callable:
 
     Example:
         >>> def f():
-        >>>>   return "1.2"
+        >>>   return "1.2"
         >>> chain(f, float)()  # return 1.2 as float
     """
     def make_tuple(args):
@@ -73,7 +74,8 @@ def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
             get_cmd=f"{self.parent.identifier} U{self._channel_string}",
             get_parser=chain(
                 self.parent.regex_parser(r"([+\-]\d+,\d+) V$"),
-                self._string_to_float()
+                partial(re.sub, ",", "."),
+                float
             ),
             set_cmd=self._set_voltage,
             unit="V",
@@ -88,9 +90,8 @@ def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
             get_cmd=f"{self.parent.identifier} I{self._channel_string}",
             get_parser=chain(
                 self.parent.regex_parser(r"([+\-]\d+,\d+) mA$"),
-                self._string_to_float(
-                    scale_factor=1/1000  # We want results in Ampere
-                )
+                partial(re.sub, ",", "."),
+                lambda ma: float(ma) / 1000  # Convert mA to A
             ),
             unit="A",
         )
@@ -100,22 +101,6 @@ def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
             get_cmd=self._get_lock_status
         )
 
-    @staticmethod
-    def _string_to_float(
-            decimal_separator: str=",",
-            scale_factor: float=1
-    ) -> Callable:
-        """
-        Querying the voltage and current gives back strings containing a
-        comma denoting a decimal separator (e.g. 1,4 = 1.4). Correct this
-        madness (and send an angry email to Stahl)
-        """
-        def converter(string):
-            sane_str = string.replace(decimal_separator, ".")
-            return float(sane_str) * scale_factor
-
-        return converter
-
     def _set_voltage(self, voltage: float) -> None:
         """
         Args:
@@ -129,11 +114,13 @@ def _set_voltage(self, voltage: float) -> None:
             [0, 1]
         )
 
-        send_string = f"{self.parent.identifier} CH{self._channel_string} {voltage_normalized:.5f}"
+        send_string = f"{self.parent.identifier} CH{self._channel_string} " \
+                      f"{voltage_normalized:.5f}"
         response = self.ask(send_string)
 
         if response != self.acknowledge_reply:
-            self.log.warning(f"Command {send_string} did not produce an acknowledge reply")
+            self.log.warning(
+                f"Command {send_string} did not produce an acknowledge reply")
 
     def _get_lock_status(self) -> bool:
         """
@@ -168,7 +155,7 @@ def __init__(self, name: str, address: str):
         super().__init__(name, address, terminator="\r")
         self.visa_handle.baud_rate = 115200
 
-        instrument_info = self._parse_idn_string(
+        instrument_info = self.parse_idn_string(
             self.ask("IDN")
         )
 
@@ -205,8 +192,8 @@ def __init__(self, name: str, address: str):
 
     def ask_raw(self, cmd: str) -> str:
         """
-        Sometimes the instrument returns non-ascii characters in response strings
-        Manually adjust the encoding to latin-1
+        Sometimes the instrument returns non-ascii characters in response
+        strings manually adjust the encoding to latin-1
         """
         self.visa_log.debug(f"Querying: {cmd}")
         self.visa_handle.write(cmd)
@@ -219,7 +206,7 @@ def regex_parser(match_string: str) -> Callable:
         """
         Example:
             >>> parser = Stahl.regex_parser("^QCoDeS is (.*)$")
-            >>> result = parser("QCoDeS is Cool")  # Will return 'Cool'
+            >>> result = parser("QCoDeS is Cool")  # Will return ('Cool',)
 
         Raises:
             UnexpectedInstrumentResponse if a match could not be found with
@@ -227,26 +214,22 @@ def regex_parser(match_string: str) -> Callable:
         """
         regex = re.compile(match_string)
 
-        def parser(input_string: str) -> Union[str, Sequence[str]]:
+        def parser(input_string: str) -> Sequence[str]:
             result = regex.search(input_string)
             if result is None:
                 raise UnexpectedInstrumentResponse()
 
-            result_groups = result.groups()
-            if len(result_groups) == 1:
-                return result_groups[0]
-            else:
-                return result_groups
-
+            return result.groups()
         return parser
 
-    def _parse_idn_string(self, idn_string) -> Dict[str, Any]:
+    @staticmethod
+    def parse_idn_string(idn_string) -> Dict[str, Any]:
         """
         Return:
              dict with keys: "model", "serial_number", "voltage_range",
              "n_channels", "output_type"
         """
-        idn_parser = self.regex_parser(
+        idn_parser = Stahl.regex_parser(
             r"(HV|BS)(\d{3}) (\d{3}) (\d{2}) ([buqsm])"
         )
         parsed_idn = idn_parser(idn_string)
diff --git a/qcodes/tests/drivers/test_stahl.py b/qcodes/tests/drivers/test_stahl.py
index e17246805db..f236b6d2b45 100644
--- a/qcodes/tests/drivers/test_stahl.py
+++ b/qcodes/tests/drivers/test_stahl.py
@@ -1,7 +1,11 @@
 import pytest
+import re
+from functools import partial
 
 from qcodes.instrument_drivers.stahl import Stahl
-from qcodes.instrument_drivers.stahl.stahl import UnexpectedInstrumentResponse, chain
+from qcodes.instrument_drivers.stahl.stahl import (
+    UnexpectedInstrumentResponse, chain
+)
 
 
 def test_parse_simple():
@@ -11,7 +15,7 @@ def test_parse_simple():
     response does not match the expectation
     """
     parser = Stahl.regex_parser("^QCoDeS is (.*)$")
-    result = parser("QCoDeS is Cool")
+    result, = parser("QCoDeS is Cool")
     assert result == "Cool"
 
     with pytest.raises(UnexpectedInstrumentResponse):
@@ -29,6 +33,24 @@ def test_parse_tuple():
     assert b == "4"
 
 
+def test_query_voltage_current():
+    """
+    Simulate Stahl weirdness when querying voltage or current. We do not simply
+    get a string representing a floating point value which we can convert by
+    casting to float. Instead, we need to use regular expressions. Additionally,
+    when querying voltage/current a comma instead of a period is used as the
+    decimal separator! Send an angry email to Stahl about this!
+    """
+    parser = chain(
+        Stahl.regex_parser("channel 1 voltage: (.*)$"),
+        partial(re.sub, ",", "."),
+        float
+    )
+
+    answer = parser("channel 1 voltage: 2,3")
+    assert answer == 2.3
+
+
 def test_chain():
     """
     Test chaining of callables
@@ -44,3 +66,14 @@ def f(a, b):
     answer = parser("QCoDeS version is 3,4")
     assert answer == 7
 
+
+def test_parse_idn_string():
+
+    idn_reply = "BS123 005 16 b"
+    assert Stahl.parse_idn_string(idn_reply) == {
+        "model": "BS",
+        "serial_number": "123",
+        "voltage_range": 5.0,
+        "n_channels": 16,
+        "output_type": "bipolar"
+    }

From 3522387445ec93b0c0fa6aec4471d7f143bf697c Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 27 Nov 2018 19:00:27 +0100
Subject: [PATCH 238/719] Ignore versioned .db files that are generated for
 some tests

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index fa1da1dcef9..616fe3dde34 100644
--- a/.gitignore
+++ b/.gitignore
@@ -99,3 +99,6 @@ experiments.db
 benchmarking/env/
 benchmarking/html/
 benchmarking/results/
+
+# Ignore .db files that are generated for some tests
+qcodes/tests/dataset/fixtures/db_files/*

From ef0dc30ee1d61abd93454b7d5780fb92e44f8f3e Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Tue, 27 Nov 2018 15:49:12 -0800
Subject: [PATCH 239/719] added stahl simulator and added more tests to improve
 coverage

---
 .../Qcodes example with Stahl.ipynb           | 267 +++++++++++++++++-
 qcodes/instrument/sims/stahl.yaml             |  43 +++
 qcodes/instrument_drivers/stahl/stahl.py      |  21 +-
 qcodes/tests/drivers/test_stahl.py            |  94 +++++-
 4 files changed, 397 insertions(+), 28 deletions(-)
 create mode 100644 qcodes/instrument/sims/stahl.yaml

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index d279f493e00..c632c10ce0e 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -27,7 +27,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: Stahl HV (serial:171, firmware:None) in 0.06s\n"
+      "Connected to: Stahl HV (serial:171, firmware:None) in 0.19s\n"
      ]
     }
    ],
@@ -44,7 +44,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.001\n"
+      "2.002\n"
      ]
     }
    ],
@@ -100,7 +100,7 @@
     {
      "data": {
       "text/plain": [
-       "2e-06"
+       "1e-06"
       ]
      },
      "execution_count": 6,
@@ -192,6 +192,267 @@
     "stahl.output_type"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\x06'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chr(6)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging \n",
+    "import io"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iostream = io.StringIO()\n",
+    "logger = logging.getLogger()\n",
+    "logger.setLevel(logging.DEBUG)\n",
+    "lh = logging.StreamHandler(iostream)\n",
+    "logger.addHandler(lh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger.debug(\"some warning\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'some warning\\n'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iostream.getvalue()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " '_name',\n",
+       " 'acquire',\n",
+       " 'addFilter',\n",
+       " 'close',\n",
+       " 'createLock',\n",
+       " 'emit',\n",
+       " 'filter',\n",
+       " 'filters',\n",
+       " 'flush',\n",
+       " 'format',\n",
+       " 'formatter',\n",
+       " 'get_name',\n",
+       " 'handle',\n",
+       " 'handleError',\n",
+       " 'level',\n",
+       " 'lock',\n",
+       " 'name',\n",
+       " 'release',\n",
+       " 'removeFilter',\n",
+       " 'setFormatter',\n",
+       " 'setLevel',\n",
+       " 'set_name',\n",
+       " 'stream',\n",
+       " 'terminator']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(logger.handlers[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'some warning\\n'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logger.handlers[0].stream.getvalue()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "176"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ord(\"°\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b10110000'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(176)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'°'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(\"°\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0xb0'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(176)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'°'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(\"\\xb0\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/qcodes/instrument/sims/stahl.yaml b/qcodes/instrument/sims/stahl.yaml
new file mode 100644
index 00000000000..2bd71135052
--- /dev/null
+++ b/qcodes/instrument/sims/stahl.yaml
@@ -0,0 +1,43 @@
+spec: "1.0"
+devices:
+  device 1:
+    eom:
+      ASRL INSTR:
+        q: "\r"
+        r: "\r"
+    error: ERROR
+    dialogues:
+      - q: "IDN"
+        r: "BS123 005 16 b"
+
+    properties:
+      voltage_ch1:
+        default: 1.2
+        getter:
+          q: "BS123 U01"
+          r: "-1,2 V"
+        setter:
+          q: "BS123 CH01 {}"
+          r: "\x06"
+      voltage_ch2:
+        default: 1.2
+        getter:
+          q: "BS123 U02"
+          r: "-1,2 V"
+        setter:
+          q: "BS123 CH02 {}"
+          r: ""
+      current_ch1:
+        default: 0.001
+        getter:
+          q: "BS123 I01"
+          r: "+0,001 mA"
+      temperature:
+        getter:
+          q: "BS123 TEMP"
+          r: "TEMP 27\xb0C"
+
+
+resources:
+  "ASRL3":
+    device: device 1
diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index 2e224b095f2..407b9000044 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -16,16 +16,6 @@
 logger = logging.getLogger()
 
 
-class UnexpectedInstrumentResponse(Exception):
-    def __init__(self):
-        super().__init__(
-            "Unexpected instrument response. Perhaps the model of the "
-            "instrument does not match the drivers expectation or a "
-            "firmware upgrade has taken place. Please get in touch "
-            "with a QCoDeS core developer"
-        )
-
-
 def chain(*functions: Callable) -> Callable:
     """
     The output of the first callable is piped to the input of the second, etc.
@@ -151,8 +141,8 @@ class Stahl(VisaInstrument):
         name
         address: A serial port address
     """
-    def __init__(self, name: str, address: str):
-        super().__init__(name, address, terminator="\r")
+    def __init__(self, name: str, address: str, **kwargs):
+        super().__init__(name, address, terminator="\r", **kwargs)
         self.visa_handle.baud_rate = 115200
 
         instrument_info = self.parse_idn_string(
@@ -217,7 +207,12 @@ def regex_parser(match_string: str) -> Callable:
         def parser(input_string: str) -> Sequence[str]:
             result = regex.search(input_string)
             if result is None:
-                raise UnexpectedInstrumentResponse()
+                raise RuntimeError(
+                    "Unexpected instrument response. Perhaps the model of the "
+                    "instrument does not match the drivers expectation or a "
+                    "firmware upgrade has taken place. Please get in touch "
+                    "with a QCoDeS core developer"
+                )
 
             return result.groups()
         return parser
diff --git a/qcodes/tests/drivers/test_stahl.py b/qcodes/tests/drivers/test_stahl.py
index f236b6d2b45..c08186c3fe7 100644
--- a/qcodes/tests/drivers/test_stahl.py
+++ b/qcodes/tests/drivers/test_stahl.py
@@ -1,11 +1,31 @@
 import pytest
 import re
 from functools import partial
+import logging
+import io
 
 from qcodes.instrument_drivers.stahl import Stahl
-from qcodes.instrument_drivers.stahl.stahl import (
-    UnexpectedInstrumentResponse, chain
-)
+from qcodes.instrument_drivers.stahl.stahl import chain
+import qcodes.instrument.sims as sims
+
+
+@pytest.fixture(scope="function")
+def stahl_instrument():
+    visa_lib = sims.__file__.replace(
+        '__init__.py',
+        'stahl.yaml@sim'
+    )
+
+    inst = Stahl('Stahl', 'ASRL3', visalib=visa_lib)
+    inst.log.setLevel(logging.DEBUG)
+    iostream = io.StringIO()
+    lh = logging.StreamHandler(iostream)
+    inst.log.logger.addHandler(lh)
+
+    try:
+        yield inst
+    finally:
+        inst.close()
 
 
 def test_parse_simple():
@@ -18,7 +38,10 @@ def test_parse_simple():
     result, = parser("QCoDeS is Cool")
     assert result == "Cool"
 
-    with pytest.raises(UnexpectedInstrumentResponse):
+    with pytest.raises(
+            RuntimeError,
+            match=r"Unexpected instrument response"
+    ):
         parser("QCoDe is Cool")
 
 
@@ -67,13 +90,60 @@ def f(a, b):
     assert answer == 7
 
 
-def test_parse_idn_string():
-
-    idn_reply = "BS123 005 16 b"
-    assert Stahl.parse_idn_string(idn_reply) == {
+def test_parse_idn_string(stahl_instrument):
+    """
+    1) Assert that the correct IDN message is received and parsed
+    2) Instrument attributes are set correctly
+    """
+    assert stahl_instrument.IDN() == {
+        "vendor": "Stahl",
         "model": "BS",
-        "serial_number": "123",
-        "voltage_range": 5.0,
-        "n_channels": 16,
-        "output_type": "bipolar"
+        "serial": "123",
+        "firmware": None
     }
+
+    assert stahl_instrument.n_channels == 16
+    assert stahl_instrument.voltage_range == 5.0
+    assert stahl_instrument.output_type == "bipolar"
+
+
+def test_get_set_voltage(stahl_instrument):
+    """
+    Test that we can correctly get/set voltages
+    """
+    stahl_instrument.channel[0].voltage(1.2)
+    assert stahl_instrument.channel[0].voltage() == -1.2
+    logger = stahl_instrument.log.logger
+    log_messages = logger.handlers[0].stream.getvalue()
+    assert "did not produce an acknowledge reply" not in log_messages
+
+
+def test_get_set_voltage_assert_warning(stahl_instrument):
+    """
+    On channel 2 we have deliberately introduced an error in the
+    visa simulation; setting a voltage does not produce an acknowledge
+    string. Test that a warning is correctly issued.
+    """
+    stahl_instrument.channel[1].voltage(1.0)
+    logger = stahl_instrument.log.logger
+    log_messages = logger.handlers[0].stream.getvalue()
+    assert "did not produce an acknowledge reply" in log_messages
+
+
+def test_get_current(stahl_instrument):
+    """
+    Test that we can read currents and that the unit is in Ampere
+    """
+    assert stahl_instrument.channel[0].current() == 1E-6
+    assert stahl_instrument.channel[0].current.unit == "A"
+
+
+def test_get_temperature(stahl_instrument):
+    """
+    Due to limitations in pyvisa-sim, we cannot test this.
+    Line 191 of pyvisa-sim\component.py  should read
+    "return response.encode('latin-1')" for this to work.
+    """
+    pass
+
+

From 75408f251bace9c9dc3034d6caa16f03ee6d4d4f Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Tue, 27 Nov 2018 15:52:13 -0800
Subject: [PATCH 240/719] clean up notebook

---
 .../Qcodes example with Stahl.ipynb           | 261 ------------------
 1 file changed, 261 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb
index c632c10ce0e..9ac6e14a42a 100644
--- a/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Stahl.ipynb	
@@ -192,267 +192,6 @@
     "stahl.output_type"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\x06'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chr(6)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging \n",
-    "import io"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iostream = io.StringIO()\n",
-    "logger = logging.getLogger()\n",
-    "logger.setLevel(logging.DEBUG)\n",
-    "lh = logging.StreamHandler(iostream)\n",
-    "logger.addHandler(lh)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "logger.debug(\"some warning\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'some warning\\n'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iostream.getvalue()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__class__',\n",
-       " '__delattr__',\n",
-       " '__dict__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__module__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " '__weakref__',\n",
-       " '_name',\n",
-       " 'acquire',\n",
-       " 'addFilter',\n",
-       " 'close',\n",
-       " 'createLock',\n",
-       " 'emit',\n",
-       " 'filter',\n",
-       " 'filters',\n",
-       " 'flush',\n",
-       " 'format',\n",
-       " 'formatter',\n",
-       " 'get_name',\n",
-       " 'handle',\n",
-       " 'handleError',\n",
-       " 'level',\n",
-       " 'lock',\n",
-       " 'name',\n",
-       " 'release',\n",
-       " 'removeFilter',\n",
-       " 'setFormatter',\n",
-       " 'setLevel',\n",
-       " 'set_name',\n",
-       " 'stream',\n",
-       " 'terminator']"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(logger.handlers[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'some warning\\n'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "logger.handlers[0].stream.getvalue()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "176"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ord(\"°\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0b10110000'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bin(176)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'°'"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "str(\"°\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0xb0'"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hex(176)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'°'"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "str(\"\\xb0\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From a3c9c2e2f8d4388dba892e7d8217afd7e5acd9e8 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Tue, 27 Nov 2018 15:55:34 -0800
Subject: [PATCH 241/719] 1) Docstring in regex_parser: raises runtimerror 2)
 extra newlines removal in test

---
 qcodes/instrument_drivers/stahl/stahl.py | 2 +-
 qcodes/tests/drivers/test_stahl.py       | 2 --
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index 407b9000044..fece0b26fa0 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -199,7 +199,7 @@ def regex_parser(match_string: str) -> Callable:
             >>> result = parser("QCoDeS is Cool")  # Will return ('Cool',)
 
         Raises:
-            UnexpectedInstrumentResponse if a match could not be found with
+            RuntimeError if a match could not be found with
             regular expressions
         """
         regex = re.compile(match_string)
diff --git a/qcodes/tests/drivers/test_stahl.py b/qcodes/tests/drivers/test_stahl.py
index c08186c3fe7..02baa1a90b3 100644
--- a/qcodes/tests/drivers/test_stahl.py
+++ b/qcodes/tests/drivers/test_stahl.py
@@ -145,5 +145,3 @@ def test_get_temperature(stahl_instrument):
     "return response.encode('latin-1')" for this to work.
     """
     pass
-
-

From dd0fdb8e145c03a31f9b41258381351628418120 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Tue, 27 Nov 2018 17:21:07 -0800
Subject: [PATCH 242/719] initial commit

---
 .../tektronix/Keithley_s46.py                 | 105 ++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 qcodes/instrument_drivers/tektronix/Keithley_s46.py

diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
new file mode 100644
index 00000000000..e578ca9309a
--- /dev/null
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -0,0 +1,105 @@
+import numpy as np
+import itertools
+from collections import defaultdict
+
+from qcodes import VisaInstrument, InstrumentChannel, ChannelList
+
+
+class RelayLock:
+    def __init__(self):
+        self.acquired = False
+        self.acquired_by = None
+
+    def acquire(self, requester_id):
+        if self.acquired and self.acquired_by != requester_id:
+            raise RuntimeError(
+                "Relay {self.name} already in use by another channel on the "
+                "same relay"
+            )
+
+        self.acquired = True
+        self.acquired_by = requester_id
+
+    def release(self, requester_id):
+        if self.acquired_by != requester_id:
+            raise RuntimeError(
+                "Relay can only be freed by the channel that acquired the lock")
+
+        self.acquired = False
+        self.acquired_by = None
+
+
+class ChannelNotPresentError(Exception):
+    pass
+
+
+class RFSwitchChannel(InstrumentChannel):
+    def __init__(self, parent, name, channel_number, relay_locks):
+        super().__init__(parent, name)
+        self._channel_number = channel_number
+
+        relay_id = self._get_relay()
+        self._relay_lock = relay_locks[relay_id]
+
+        self.add_parameter(
+            "is_closed",
+            get_cmd=lambda: self._channel_number in
+                            self.parent.query_closed_channels()
+        )
+
+    def _get_relay(self):
+        relay_layout = self.parent.get_relay_layout()
+        found = False
+
+        count = 0
+        for count, total_count in enumerate(np.cumsum(relay_layout)):
+            if total_count >= self._channel_number:
+                found = True
+                break
+
+        if not found:
+            raise ChannelNotPresentError()
+
+        return ["A", "B", "C", "D", "1", "2", "3", "4", "5", "6", "7", "8"][
+            count]
+
+    def close_channel(self):
+        self._relay_lock.acquire(self._channel_number)
+        self.write(f":CLOSE (@{self._channel_number})")
+
+    def open_channel(self):
+        self._relay_lock.release(self._channel_number)
+        self.write(f":OPEN (@{self._channel_number})")
+
+
+class S46(VisaInstrument):
+    def __init__(self, name, address, **kwargs):
+        super().__init__(name, address, terminator="\n", **kwargs)
+
+        channels = ChannelList(
+            self, "channel", RFSwitchChannel, snapshotable=False
+        )
+
+        relay_locks = defaultdict(RelayLock)
+
+        for chn in itertools.count(1):
+            chn_name = f"channel{chn}"
+
+            try:
+                channel = RFSwitchChannel(self, chn_name, chn, relay_locks)
+            except ChannelNotPresentError:
+                break
+
+            self.add_submodule(chn_name, channel)
+            channels.append(channel)
+
+        self.add_submodule("channels", channels)
+        self.connect_message()
+
+    def query_closed_channels(self):
+        response = self.ask(":CLOS?")
+        channels_list_string = response.lstrip("(@").rstrip(")")
+        return [int(i) for i in channels_list_string.split(",")]
+
+    def get_relay_layout(self):
+        return [int(i) for i in self.ask(":CONF:CPOL?").split(",")]
\ No newline at end of file

From 72a554e26bd1ef2ae25136beab3f1845fb66ada9 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Tue, 27 Nov 2018 18:32:49 -0800
Subject: [PATCH 243/719] initial commit

---
 .../tektronix/Keithley_s46.py                 | 55 +++++++++++--------
 1 file changed, 31 insertions(+), 24 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index e578ca9309a..9116c3ee7e3 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -5,6 +5,10 @@
 from qcodes import VisaInstrument, InstrumentChannel, ChannelList
 
 
+class ChannelNotPresentError(Exception):
+    pass
+
+
 class RelayLock:
     def __init__(self):
         self.acquired = False
@@ -13,8 +17,7 @@ def __init__(self):
     def acquire(self, requester_id):
         if self.acquired and self.acquired_by != requester_id:
             raise RuntimeError(
-                "Relay {self.name} already in use by another channel on the "
-                "same relay"
+                f"Relay already in use by channel {self.acquired_by}"
             )
 
         self.acquired = True
@@ -23,32 +26,33 @@ def acquire(self, requester_id):
     def release(self, requester_id):
         if self.acquired_by != requester_id:
             raise RuntimeError(
-                "Relay can only be freed by the channel that acquired the lock")
+                f"Relay can only be freed by channel {self.acquired_by} "
+                f"that acquired the lock"
+            )
 
         self.acquired = False
         self.acquired_by = None
 
 
-class ChannelNotPresentError(Exception):
-    pass
-
-
 class RFSwitchChannel(InstrumentChannel):
     def __init__(self, parent, name, channel_number, relay_locks):
         super().__init__(parent, name)
         self._channel_number = channel_number
 
-        relay_id = self._get_relay()
-        self._relay_lock = relay_locks[relay_id]
+        self._relay_id = self._get_relay()
+        self._relay_lock = relay_locks[self._relay_id]
 
         self.add_parameter(
-            "is_closed",
-            get_cmd=lambda: self._channel_number in
-                            self.parent.query_closed_channels()
+            "close",
+            get_cmd=lambda: str(self._channel_number) in self.ask(":CONF:CPOL?"),
+            set_cmd=lambda state: {
+                True: self._close_channel,
+                False: self._open_channel
+            }[state]()
         )
 
     def _get_relay(self):
-        relay_layout = self.parent.get_relay_layout()
+        relay_layout = self.parent.relay_layout()
         found = False
 
         count = 0
@@ -60,22 +64,29 @@ def _get_relay(self):
         if not found:
             raise ChannelNotPresentError()
 
-        return ["A", "B", "C", "D", "1", "2", "3", "4", "5", "6", "7", "8"][
-            count]
+        return (["A", "B", "C", "D"] + [chr(i) for i in range(1, 9)])[count]
 
-    def close_channel(self):
+    def _close_channel(self):
         self._relay_lock.acquire(self._channel_number)
         self.write(f":CLOSE (@{self._channel_number})")
 
-    def open_channel(self):
+    def _open_channel(self):
         self._relay_lock.release(self._channel_number)
         self.write(f":OPEN (@{self._channel_number})")
 
+    @property
+    def relay_id(self):
+        return self._relay_id
+
 
 class S46(VisaInstrument):
     def __init__(self, name, address, **kwargs):
         super().__init__(name, address, terminator="\n", **kwargs)
 
+        self._relay_layout = [
+            int(i) for i in self.ask(":CONF:CPOL?").split(",")
+        ]
+
         channels = ChannelList(
             self, "channel", RFSwitchChannel, snapshotable=False
         )
@@ -96,10 +107,6 @@ def __init__(self, name, address, **kwargs):
         self.add_submodule("channels", channels)
         self.connect_message()
 
-    def query_closed_channels(self):
-        response = self.ask(":CLOS?")
-        channels_list_string = response.lstrip("(@").rstrip(")")
-        return [int(i) for i in channels_list_string.split(",")]
-
-    def get_relay_layout(self):
-        return [int(i) for i in self.ask(":CONF:CPOL?").split(",")]
\ No newline at end of file
+    @property
+    def relay_layout(self):
+        return self._relay_layout

From 447afaa8b5deb0a421135b3ccebe77fcd6e97747 Mon Sep 17 00:00:00 2001
From: sohailc <schatoor@gmail.com>
Date: Tue, 27 Nov 2018 23:26:50 -0800
Subject: [PATCH 244/719] get rid of regex_parser

---
 qcodes/instrument_drivers/stahl/stahl.py | 61 +++++++------------
 qcodes/tests/drivers/test_stahl.py       | 75 +++++-------------------
 2 files changed, 36 insertions(+), 100 deletions(-)

diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index fece0b26fa0..7783d2d9ab6 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -2,7 +2,7 @@
 This is a driver for the Stahl power supplies
 """
 
-from typing import Dict, Optional, Any, Callable, Sequence
+from typing import Dict, Optional, Any, Callable, Sequence, Iterable
 import re
 import numpy as np
 import logging
@@ -12,7 +12,6 @@
 from qcodes import VisaInstrument, InstrumentChannel, ChannelList
 from qcodes.utils.validators import Numbers
 
-
 logger = logging.getLogger()
 
 
@@ -25,15 +24,16 @@ def chain(*functions: Callable) -> Callable:
         >>>   return "1.2"
         >>> chain(f, float)()  # return 1.2 as float
     """
-    def make_tuple(args):
-        if not isinstance(args, tuple):
+
+    def make_iter(args):
+        if not isinstance(args, Iterable) or isinstance(args, str):
             return args,
         return args
 
     def inner(*args):
         result = args
         for fun in functions:
-            new_args = make_tuple(result)
+            new_args = make_iter(result)
             result = fun(*new_args)
 
         return result
@@ -63,7 +63,7 @@ def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
             "voltage",
             get_cmd=f"{self.parent.identifier} U{self._channel_string}",
             get_parser=chain(
-                self.parent.regex_parser(r"([+\-]\d+,\d+) V$"),
+                re.compile(r"^([+\-]\d+,\d+) V$").findall,
                 partial(re.sub, ",", "."),
                 float
             ),
@@ -79,7 +79,7 @@ def __init__(self, parent: VisaInstrument, name: str, channel_number: int):
             "current",
             get_cmd=f"{self.parent.identifier} I{self._channel_string}",
             get_parser=chain(
-                self.parent.regex_parser(r"([+\-]\d+,\d+) mA$"),
+                re.compile(r"^([+\-]\d+,\d+) mA$").findall,
                 partial(re.sub, ",", "."),
                 lambda ma: float(ma) / 1000  # Convert mA to A
             ),
@@ -105,7 +105,7 @@ def _set_voltage(self, voltage: float) -> None:
         )
 
         send_string = f"{self.parent.identifier} CH{self._channel_string} " \
-                      f"{voltage_normalized:.5f}"
+            f"{voltage_normalized:.5f}"
         response = self.ask(send_string)
 
         if response != self.acknowledge_reply:
@@ -141,6 +141,7 @@ class Stahl(VisaInstrument):
         name
         address: A serial port address
     """
+
     def __init__(self, name: str, address: str, **kwargs):
         super().__init__(name, address, terminator="\r", **kwargs)
         self.visa_handle.baud_rate = 115200
@@ -172,7 +173,7 @@ def __init__(self, name: str, address: str, **kwargs):
             "temperature",
             get_cmd=f"{self.identifier} TEMP",
             get_parser=chain(
-                self.regex_parser("TEMP (.*)°C"),
+                re.compile("^TEMP (.*)°C$").findall,
                 float
             ),
             unit="C"
@@ -191,32 +192,6 @@ def ask_raw(self, cmd: str) -> str:
         self.visa_log.debug(f"Response: {response}")
         return response
 
-    @staticmethod
-    def regex_parser(match_string: str) -> Callable:
-        """
-        Example:
-            >>> parser = Stahl.regex_parser("^QCoDeS is (.*)$")
-            >>> result = parser("QCoDeS is Cool")  # Will return ('Cool',)
-
-        Raises:
-            RuntimeError if a match could not be found with
-            regular expressions
-        """
-        regex = re.compile(match_string)
-
-        def parser(input_string: str) -> Sequence[str]:
-            result = regex.search(input_string)
-            if result is None:
-                raise RuntimeError(
-                    "Unexpected instrument response. Perhaps the model of the "
-                    "instrument does not match the drivers expectation or a "
-                    "firmware upgrade has taken place. Please get in touch "
-                    "with a QCoDeS core developer"
-                )
-
-            return result.groups()
-        return parser
-
     @staticmethod
     def parse_idn_string(idn_string) -> Dict[str, Any]:
         """
@@ -224,10 +199,18 @@ def parse_idn_string(idn_string) -> Dict[str, Any]:
              dict with keys: "model", "serial_number", "voltage_range",
              "n_channels", "output_type"
         """
-        idn_parser = Stahl.regex_parser(
-            r"(HV|BS)(\d{3}) (\d{3}) (\d{2}) ([buqsm])"
+        result = re.search(
+            r"(HV|BS)(\d{3}) (\d{3}) (\d{2}) ([buqsm])",
+            idn_string
         )
-        parsed_idn = idn_parser(idn_string)
+
+        if result is None:
+            raise RuntimeError(
+                "Unexpected instrument response. Perhaps the model of the "
+                "instrument does not match the drivers expectation or a "
+                "firmware upgrade has taken place. Please get in touch "
+                "with a QCoDeS core developer"
+            )
 
         converters: Dict[str, Callable] = OrderedDict({
             "model": str,
@@ -245,7 +228,7 @@ def parse_idn_string(idn_string) -> Dict[str, Any]:
 
         return {
             name: converter(value)
-            for (name, converter), value in zip(converters.items(), parsed_idn)
+            for (name, converter), value in zip(converters.items(), result.groups())
         }
 
     def get_idn(self) -> Dict[str, Optional[str]]:
diff --git a/qcodes/tests/drivers/test_stahl.py b/qcodes/tests/drivers/test_stahl.py
index 02baa1a90b3..1a3ffc11136 100644
--- a/qcodes/tests/drivers/test_stahl.py
+++ b/qcodes/tests/drivers/test_stahl.py
@@ -1,11 +1,8 @@
 import pytest
-import re
-from functools import partial
 import logging
 import io
 
 from qcodes.instrument_drivers.stahl import Stahl
-from qcodes.instrument_drivers.stahl.stahl import chain
 import qcodes.instrument.sims as sims
 
 
@@ -28,72 +25,28 @@ def stahl_instrument():
         inst.close()
 
 
-def test_parse_simple():
+def test_parse_idn_string():
     """
-    Test that we can parse simple messages from the instrument
-    and that the proper exception is raised when the received
-    response does not match the expectation
+    Test that we can parse IDN strings correctly
     """
-    parser = Stahl.regex_parser("^QCoDeS is (.*)$")
-    result, = parser("QCoDeS is Cool")
-    assert result == "Cool"
+    assert Stahl.parse_idn_string("HV123 005 16 b") == {
+        "model": "HV",
+        "serial_number": "123",
+        "voltage_range": 5.0,
+        "n_channels": 16,
+        "output_type": "bipolar"
+    }
 
     with pytest.raises(
             RuntimeError,
-            match=r"Unexpected instrument response"
+            match="Unexpected instrument response"
     ):
-        parser("QCoDe is Cool")
-
-
-def test_parse_tuple():
-    """
-    Test that we can extract multiple values from a string
-    """
-    parser = Stahl.regex_parser(r"^QCoDeS version is (\d),(\d)$")
-    a, b = parser("QCoDeS version is 3,4")
-
-    assert a == "3"
-    assert b == "4"
-
-
-def test_query_voltage_current():
-    """
-    Simulate Stahl weirdness when querying voltage or current. We do not simply
-    get a string representing a floating point value which we can convert by
-    casting to float. Instead, we need to use regular expressions. Additionally,
-    when querying voltage/current a comma instead of a period is used as the
-    decimal separator! Send an angry email to Stahl about this!
-    """
-    parser = chain(
-        Stahl.regex_parser("channel 1 voltage: (.*)$"),
-        partial(re.sub, ",", "."),
-        float
-    )
-
-    answer = parser("channel 1 voltage: 2,3")
-    assert answer == 2.3
-
-
-def test_chain():
-    """
-    Test chaining of callables
-    """
-    def f(a, b):
-        return int(a) + int(b)
-
-    parser = chain(
-        Stahl.regex_parser(r"^QCoDeS version is (\d),(\d)$"),
-        f
-    )
-
-    answer = parser("QCoDeS version is 3,4")
-    assert answer == 7
+        Stahl.parse_idn_string("HS123 005 16 bla b")
 
 
-def test_parse_idn_string(stahl_instrument):
+def test_get_idn(stahl_instrument):
     """
-    1) Assert that the correct IDN message is received and parsed
-    2) Instrument attributes are set correctly
+    Instrument attributes are set correctly after getting the IDN
     """
     assert stahl_instrument.IDN() == {
         "vendor": "Stahl",
@@ -141,7 +94,7 @@ def test_get_current(stahl_instrument):
 def test_get_temperature(stahl_instrument):
     """
     Due to limitations in pyvisa-sim, we cannot test this.
-    Line 191 of pyvisa-sim\component.py  should read
+    Line 191 of pyvisa-sim/component.py  should read
     "return response.encode('latin-1')" for this to work.
     """
     pass

From b4eb7184f64d8b10b4c301022f555bccedda3d22 Mon Sep 17 00:00:00 2001
From: sohailc <schatoor@gmail.com>
Date: Wed, 28 Nov 2018 00:20:47 -0800
Subject: [PATCH 245/719] remove unused import

---
 qcodes/instrument_drivers/stahl/stahl.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/stahl/stahl.py b/qcodes/instrument_drivers/stahl/stahl.py
index 7783d2d9ab6..ba26191dcf1 100644
--- a/qcodes/instrument_drivers/stahl/stahl.py
+++ b/qcodes/instrument_drivers/stahl/stahl.py
@@ -2,7 +2,7 @@
 This is a driver for the Stahl power supplies
 """
 
-from typing import Dict, Optional, Any, Callable, Sequence, Iterable
+from typing import Dict, Optional, Any, Callable, Iterable
 import re
 import numpy as np
 import logging

From 180fa2a01204af54f0bc5b327990077fb3791a45 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 28 Nov 2018 12:02:15 +0100
Subject: [PATCH 246/719] Fix test for metadata (due to use of atomic context
 manager)

---
 qcodes/tests/dataset/test_dataset_basic.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 9bd3440f82a..ccf6685ff21 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -684,13 +684,16 @@ def test_metadata():
     badtag = 'lex luthor'
     sorry_metadata = {'superman': 1, badtag: None, 'spiderman': 'two'}
 
-    match = (f'Tag {badtag} has value None. '
-             ' That is not a valid metadata value!')
+    bad_tag_msg = (f'Tag {badtag} has value None. '
+                    ' That is not a valid metadata value!')
 
-    with pytest.raises(ValueError, match=match):
+    with pytest.raises(RuntimeError,
+                       match='Rolling back due to unhandled exception') as e:
         for tag, value in sorry_metadata.items():
             ds1.add_metadata(tag, value)
 
+    assert error_caused_by(e, bad_tag_msg)
+
 
 class TestGetData:
     x = ParamSpec("x", paramtype='numeric')

From 0ddac10a8efbf83cf82e30976208af456642b981 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 28 Nov 2018 12:03:16 +0100
Subject: [PATCH 247/719] Remove redundant use of make_connection_plus_from

---
 qcodes/dataset/sqlite_base.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index bdd58f41f4d..f817468deda 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -768,8 +768,7 @@ def atomic_transaction(conn: ConnectionPlus,
         sqlite cursor
 
     """
-    conn_plus = make_connection_plus_from(conn)
-    with atomic(conn_plus) as atomic_conn:
+    with atomic(conn) as atomic_conn:
         c = transaction(atomic_conn, sql, *args)
     return c
 

From efb6b89e2cd4810972c01f1dc40ae738fed3d0fe Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 28 Nov 2018 12:31:45 +0100
Subject: [PATCH 248/719] Fix test for atomic_transaction since only
 ConnectionPlus is allowed

---
 .../tests/dataset/test_sqlite_connection.py   | 26 ++++++++++++-------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index 6128fe59b9a..adbfc230584 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -290,18 +290,11 @@ def test_that_use_of_atomic_commits_only_at_outermost_context(
     assert 3 == len(control_conn.execute(get_all_runs).fetchall())
 
 
-@pytest.mark.parametrize(argnames='create_connection',
-                         argvalues=(
-                                 sqlite3.connect,
-                                 lambda x: ConnectionPlus(sqlite3.connect(x))),
-                         ids=(
-                                 'sqlite3.Connection',
-                                 'ConnectionPlus'))
-def test_atomic_transaction(create_connection, tmp_path):
-    """Test that atomic_transaction works for both types of connection"""
+def test_atomic_transaction(tmp_path):
+    """Test that atomic_transaction works for ConnectionPlus"""
     dbfile = str(tmp_path / 'temp.db')
 
-    conn = create_connection(dbfile)
+    conn = ConnectionPlus(sqlite3.connect(dbfile))
 
     ctrl_conn = sqlite3.connect(dbfile)
 
@@ -313,6 +306,19 @@ def test_atomic_transaction(create_connection, tmp_path):
     assert sql_create_table in ctrl_conn.execute(sql_table_exists).fetchall()[0]
 
 
+def test_atomic_transaction_on_sqlite3_connection_raises(tmp_path):
+    """Test that atomic_transaction does not work for sqlite3.Connection"""
+    dbfile = str(tmp_path / 'temp.db')
+
+    conn = sqlite3.connect(dbfile)
+
+    match_str = re.escape('atomic context manager only accepts ConnectionPlus '
+                          'database connection objects.')
+
+    with pytest.raises(ValueError, match=match_str):
+        atomic_transaction(conn, 'whatever sql query')
+
+
 def test_connect():
     conn = connect(':memory:')
 

From 5b11e75b69d242d861004fe198c609b324fc4fec Mon Sep 17 00:00:00 2001
From: qSaevar <saevar.steinn@gmail.com>
Date: Wed, 28 Nov 2018 13:40:14 +0100
Subject: [PATCH 249/719] optimized imports

---
 qcodes/instrument_drivers/ZI/ZIHDAWG8.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
index d65fbd26c18..495f41b8e98 100644
--- a/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
+++ b/qcodes/instrument_drivers/ZI/ZIHDAWG8.py
@@ -4,7 +4,8 @@
 import textwrap
 import time
 from functools import partial
-from typing import Optional, Any, List, Tuple, Union, Sequence, Dict
+from typing import Any, List, Tuple, Union, Sequence, Dict
+
 import zhinst.utils
 
 from qcodes import Instrument

From 7dcc77c26e19d5fffd52ce0f79266de4bc85ed9e Mon Sep 17 00:00:00 2001
From: Christopher Granade <cgranade@cgranade.com>
Date: Wed, 28 Nov 2018 13:57:35 +0100
Subject: [PATCH 250/719] Homogeneous coordinates for field vectors in Mercury
 iPS

Adds functionality to FieldVector that allows for it to be used as a
snapshotable parameter, and that adds conversion to and from
homogeneous coordinate representations to make it easier to transform
FieldVector instances. This functionality is used by the new
field-valued parameters of the MercuryiPS instrument.

This commit has been made via `git checkout <files> -p` in order to
take only changes of interest from a branch that contained more
changes. In order to preserve the reference to the original author of
this work, the "cherry picked from..." lines are added below.

(cherry picked from commit f1bf38b54f266e52f86fdc03e59cb6cdbc6b400a)
(cherry picked from commit 3c8a0a11cb082cf6ea21251ee17581c1a8d9e976)
(cherry picked from commit 251a2f4d23b5b30e556b8954ce1fe4c938c75db7)
(cherry picked from commit b31fa4643998fc9f51f85d56c183d4f257d955d3)
(cherry picked from commit 5e23cd6d1d66c3a57b29850a5e784b426bc92822)
---
 qcodes/instrument/sims/MercuryiPS.yaml        |  27 ++++
 .../oxford/MercuryiPS_VISA.py                 |  50 +++++++-
 qcodes/math/field_vector.py                   | 121 ++++++++++++++++--
 qcodes/tests/drivers/test_MercuryiPS.py       |  23 +++-
 qcodes/tests/test_field_vector.py             |  36 ++++++
 qcodes/utils/helpers.py                       |  14 +-
 6 files changed, 255 insertions(+), 16 deletions(-)

diff --git a/qcodes/instrument/sims/MercuryiPS.yaml b/qcodes/instrument/sims/MercuryiPS.yaml
index baf09369ce0..e489ac952d7 100644
--- a/qcodes/instrument/sims/MercuryiPS.yaml
+++ b/qcodes/instrument/sims/MercuryiPS.yaml
@@ -84,6 +84,33 @@ devices:
           q: "SET:DEV:GRPZ:PSU:ACTN:{}"
           r: ""
 
+      x ramp rate:
+        default: 0
+        getter:
+          q: "READ:DEV:GRPX:PSU:SIG:RFST"
+          r: "{}"
+        setter:
+          q: "SET:DEV:GRPX:PSU:SIG:RFST:{}"
+          r: ""
+
+      y ramp rate:
+        default: 0
+        getter:
+          q: "READ:DEV:GRPY:PSU:SIG:RFST"
+          r: "{}"
+        setter:
+          q: "SET:DEV:GRPY:PSU:SIG:RFST:{}"
+          r: ""
+
+      z ramp rate:
+        default: 0
+        getter:
+          q: "READ:DEV:GRPZ:PSU:SIG:RFST"
+          r: "{}"
+        setter:
+          q: "SET:DEV:GRPZ:PSU:SIG:RFST:{}"
+          r: ""
+
 
 resources:
   GPIB::1::INSTR:
diff --git a/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py b/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py
index 0ad60dac98e..c2a3b421068 100644
--- a/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py
+++ b/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py
@@ -277,11 +277,48 @@ def __init__(self, name: str, address: str, visalib=None,
                                unit='T',
                                get_cmd=partial(self._get_measured, [coord]))
 
+        # FieldVector-valued parameters #
+        self.add_parameter(name="field_target",
+                           label="target field",
+                           unit="T",
+                           get_cmd=self._get_target_field,
+                           set_cmd=self._set_target_field)
+        self.add_parameter(name="field_measured",
+                           label="measured field",
+                           unit="T",
+                           get_cmd=self._get_field)
+
+        self.add_parameter(name="field_ramp_rate",
+                           label="ramp rate",
+                           unit="T/s",
+                           get_cmd=self._get_ramp_rate,
+                           set_cmd=self._set_ramp_rate)
+
         self.connect_message()
 
     def _get_component(self, coordinate: str) -> float:
         return self._target_vector.get_components(coordinate)[0]
 
+    def _get_target_field(self) -> FieldVector:
+        return FieldVector(
+            **{
+                coord: self._get_component(coord)
+                for coord in 'xyz'
+            }
+        )
+
+    def _get_ramp_rate(self) -> FieldVector:
+        return FieldVector(
+            x=self.GRPX.field_ramp_rate(),
+            y=self.GRPY.field_ramp_rate(),
+            z=self.GRPZ.field_ramp_rate(),
+        )
+
+    def _set_ramp_rate(self, rate : FieldVector) -> None:
+        self.GRPX.field_ramp_rate(rate.x)
+        self.GRPY.field_ramp_rate(rate.y)
+        self.GRPZ.field_ramp_rate(rate.z)
+
     def _get_measured(self, coordinates: List[str]) -> Union[float,
                                                              List[float]]:
         """
@@ -297,6 +334,13 @@ def _get_measured(self, coordinates: List[str]) -> Union[float,
         else:
             return meas_field.get_components(*coordinates)
 
+    def _get_field(self) -> FieldVector:
+        return FieldVector(
+            x=self.x_measured(),
+            y=self.y_measured(),
+            z=self.z_measured()
+        )
+
     def _set_target(self, coordinate: str, target: float) -> None:
         """
         The function to set a target value for a coordinate, i.e. the set_cmd
@@ -319,6 +363,10 @@ def _set_target(self, coordinate: str, target: float) -> None:
         for targ, slave in zip(cartesian_targ, self.submodules.values()):
             slave.field_target(targ)
 
+    def _set_target_field(self, field : FieldVector) -> None:
+        for coord in 'xyz':
+            self._set_target(coord, field[coord])
+
     def _idn_getter(self) -> Dict[str, str]:
         """
         Parse the raw non-SCPI compliant IDN string into an IDN dict
@@ -391,7 +439,7 @@ def set_new_field_limits(self, limit_func: Callable) -> None:
 
         self._field_limits = limit_func
 
-    def ramp(self, mode: str) -> None:
+    def ramp(self, mode : str = "safe") -> None:
         """
         Ramp the fields to their present target value
 
diff --git a/qcodes/math/field_vector.py b/qcodes/math/field_vector.py
index e71139746e4..c7c4f21c40f 100644
--- a/qcodes/math/field_vector.py
+++ b/qcodes/math/field_vector.py
@@ -8,9 +8,14 @@
 
 import numpy as np
 
+from typing import Union, Type, TypeVar
+NormOrder = Union[str, float]
+T = TypeVar('T', bound='FieldVector')
+
 
 class FieldVector(object):
     attributes = ["x", "y", "z", "r", "theta", "phi", "rho"]
+    repr_format = "cartesian"
 
     def __init__(self, x=None, y=None, z=None, r=None, theta=None, phi=None,
                  rho=None):
@@ -74,6 +79,9 @@ def _set_attribute_values(self, attr_names, values):
         for attr_name, value in zip(attr_names, values):
             self._set_attribute_value(attr_name, value)
 
+    def __getnewargs__(self):
+        return (self.x, self.y, self.z)
+
     @staticmethod
     def _cartesian_to_other(x, y, z):
         """ Convert a cartesian set of coordinates to values of interest."""
@@ -145,7 +153,7 @@ def _compute_unknowns(self):
                 self._set_attribute_values(FieldVector.attributes, new_values)
                 break
 
-    def copy(self, other):
+    def copy(self : T, other : T):
         """Copy the properties of other vector to yourself"""
         for att in FieldVector.attributes:
             value = getattr(other, "_" + att)
@@ -250,30 +258,127 @@ def is_equal(self, other):
 
         return True
 
+    def __getitem__(self, component):
+        return self.get_components(component)[0]
+
+    def __setitem__(self, component, value):
+        self.set_component(**{component: value})
+
+    def __mul__(self, other):
+        if not isinstance(other, (float, int)):
+            return NotImplemented
+
+        return FieldVector(**{
+            component: self[component] * other
+            for component in 'xyz'
+        })
+
+    def __rmul__(self, other):
+        if not isinstance(other, (int, float)):
+            return NotImplemented
+
+        return self * other
+
+    def __neg__(self):
+        return -1 * self
+
+    def __add__(self, other):
+        if not isinstance(other, FieldVector):
+            return NotImplemented
+
+        return FieldVector(**{
+            component: self[component] + other[component]
+            for component in 'xyz'
+        })
+
+    def __sub__(self, other):
+        if not isinstance(other, FieldVector):
+            return NotImplemented
+
+        return FieldVector(**{
+            component: self[component] - other[component]
+            for component in 'xyz'
+        })
+
+    # NB: we disable the pylint warning here so that we can match
+    #     NumPy's naming convention for the norm method.
+    def norm(self, ord : NormOrder = 2) -> float: # pylint: disable=redefined-builtin
+        """
+        Returns the norm of this field vector. See np.norm
+        for the definition of the ord keyword argument.
+        """
+        return np.linalg.norm([self.x, self.y, self.z], ord=ord)
+
+    def distance(self, other, ord : NormOrder = 2) -> float: # pylint: disable=redefined-builtin
+        return (self - other).norm(ord=ord)
+
     @property
-    def x(self):
+    def x(self) -> float:
         return self._x
 
     @property
-    def y(self):
+    def y(self) -> float:
         return self._y
 
     @property
-    def z(self):
+    def z(self) -> float:
         return self._z
 
     @property
-    def rho(self):
+    def rho(self) -> float:
         return self._rho
 
     @property
-    def theta(self):
+    def theta(self) -> float:
         return np.degrees(self._theta)
 
     @property
-    def r(self):
+    def r(self) -> float:
         return self._r
 
     @property
-    def phi(self):
+    def phi(self) -> float:
         return np.degrees(self._phi)
+
+    ## Representation Methods ##
+
+    def repr_cartesian(self) -> str:
+        return f"FieldVector(x={self.x}, y={self.y}, z={self.z})"
+
+    def repr_spherical(self) -> str:
+        return f"FieldVector(r={self.r}, phi={self.phi}, theta={self.theta})"
+
+    def repr_cylindrical(self) -> str:
+        return f"FieldVector(rho={self.rho}, phi={self.phi}, z={self.z})"
+
+    def __repr__(self) -> str:
+        if self.repr_format == "cartesian":
+            return self.repr_cartesian()
+        elif self.repr_format == "spherical":
+            return self.repr_spherical()
+        elif self.repr_format == "cylindrical":
+            return self.repr_cylindrical()
+        else:
+            return super().__repr__()
+
+    ## Homogenous Coordinates ##
+
+    def as_homogeneous(self) -> np.ndarray:
+        return np.array([self.x, self.y, self.z, 1])
+
+    @classmethod
+    def from_homogeneous(cls : Type[T], hvec : np.ndarray) -> T:
+        # Homogenous coordinates define an equivalence relation
+        #     [x / s, y / s, z / s, 1] == [x, y, z, s].
+        # More generally,
+        #     [x, y, z, s] == [x', y', z', s']
+        #     iff x / s == x' / s',
+        #         y / s == y' / s', and
+        #         z / s == z' / s'.
+        # This definition has the consequence that for any
+        # w, w * [x, y, z, s] == [x, y, z, s].
+        # Thus, we start by rescaling such that s == 1.
+        hvec /= hvec[-1]
+        return cls(
+            x=hvec[0], y=hvec[1], z=hvec[2]
+        )
diff --git a/qcodes/tests/drivers/test_MercuryiPS.py b/qcodes/tests/drivers/test_MercuryiPS.py
index 3878e0032c7..09649f3f9f8 100644
--- a/qcodes/tests/drivers/test_MercuryiPS.py
+++ b/qcodes/tests/drivers/test_MercuryiPS.py
@@ -6,7 +6,7 @@
 
 from qcodes.instrument_drivers.oxford.MercuryiPS_VISA import MercuryiPS
 import qcodes.instrument.sims as sims
-
+from qcodes.math.field_vector import FieldVector
 
 visalib = sims.__file__.replace('__init__.py', 'MercuryiPS.yaml@sim')
 
@@ -68,6 +68,21 @@ def test_simple_setting(driver):
     assert driver.GRPX.field_target() == 0.1
 
 
+def test_vector_setting(driver):
+    assert driver.field_target().distance(FieldVector(0, 0, 0)) <= 1e-8
+    driver.field_target(FieldVector(r=0.1, theta=0, phi=0))
+    assert driver.field_target().distance(
+        FieldVector(r=0.1, theta=0, phi=0)
+    ) <= 1e-8
+
+
+def test_vector_ramp_rate(driver):
+    driver.field_ramp_rate(FieldVector(0.1, 0.1, 0.1))
+    assert driver.field_ramp_rate().distance(
+        FieldVector(0.1, 0.1, 0.1)
+    ) <= 1e-8
+
+
 def test_wrong_field_limit_raises():
 
     # check that a non-callable input fails
@@ -133,10 +148,8 @@ def test_ramp_safely(driver, x, y, z, caplog):
     driver.GRPZ.ramp_status('HOLD')
 
     # the current field values are always zero for the sim. instr.
-
-    driver.x_target(x)
-    driver.y_target(y)
-    driver.z_target(z)
+    # Use the FieldVector interface here to increase coverage.
+    driver.field_target(FieldVector(x=x, y=y, z=z))
 
     exp_order = np.array(['x', 'y', 'z'])[np.argsort(np.abs(np.array([x, y, z])))]
 
diff --git a/qcodes/tests/test_field_vector.py b/qcodes/tests/test_field_vector.py
index b5f56b424b4..f41650c99bd 100644
--- a/qcodes/tests/test_field_vector.py
+++ b/qcodes/tests/test_field_vector.py
@@ -3,11 +3,13 @@
 implemented.
 """
 import numpy as np
+import json
 from hypothesis import given, settings
 from hypothesis.strategies import floats
 from hypothesis.strategies import tuples
 
 from qcodes.math.field_vector import FieldVector
+from qcodes.utils.helpers import NumpyJSONEncoder
 
 random_coordinates = {
     "cartesian": tuples(
@@ -82,3 +84,37 @@ def test_cylindrical_properties(cylindrical0):
     cartisian1 = FieldVector(**cylindrical1).get_components("x", "y", "z")
 
     assert np.allclose(cartisian0, cartisian1 * np.array([-1, -1, 1]))
+
+@given(
+    random_coordinates["cylindrical"],
+    random_coordinates["spherical"]
+)
+@settings(max_examples=10)
+def test_triangle_inequality(cylindrical0, spherical0):
+    cylindrical0 = FieldVector(**dict(zip(["rho", "phi", "z"], cylindrical0)))
+    spherical0 = FieldVector(**dict(zip(["r", "phi", "theta"], spherical0)))
+
+    assert (cylindrical0 + spherical0).norm() <= (cylindrical0.norm() + spherical0.norm())
+    assert cylindrical0.distance(spherical0) <= (cylindrical0.norm() + spherical0.norm())
+
+@given(random_coordinates["cartesian"])
+@settings(max_examples=10)
+def test_homogeneous_roundtrip(cartesian0):
+    vec = FieldVector(**dict(zip("xyz", cartesian0)))
+    h_vec = 13 * vec.as_homogeneous()
+
+    assert np.allclose(
+        vec.get_components(*"xyz"),
+        FieldVector.from_homogeneous(h_vec).get_components(*"xyz")
+    )
+
+@given(random_coordinates["spherical"])
+@settings(max_examples=10)
+def test_json_dump(spherical0):
+    vec = FieldVector(**dict(zip(["r", "phi", "theta"], spherical0)))
+    dump = json.dumps(vec, cls=NumpyJSONEncoder)
+
+    assert json.loads(dump) == {
+        '__class__': FieldVector.__name__,
+        '__args__': [vec.x, vec.y, vec.z]
+    }
diff --git a/qcodes/utils/helpers.py b/qcodes/utils/helpers.py
index 53757b65288..4c55395fe8a 100644
--- a/qcodes/utils/helpers.py
+++ b/qcodes/utils/helpers.py
@@ -46,6 +46,8 @@ def default(self, obj):
         "__dtype__" containing value "complex"
         * object with a `_JSONEncoder` method get converted the return value of
         that method
+        * objects which support the pickle protocol get converted using the data
+        provided by that protocol
         * other objects which cannot be serialized get converted to their
         string representation (suing the `str` function)
         """
@@ -70,8 +72,16 @@ def default(self, obj):
             try:
                 s = super(NumpyJSONEncoder, self).default(obj)
             except TypeError:
-                # we cannot convert the object to JSON, just take a string
-                s = str(obj)
+                # See if the object supports the pickle protocol.
+                # If so, we should be able to use that to serialize.
+                if hasattr(obj, '__getnewargs__'):
+                    return {
+                        '__class__': type(obj).__name__,
+                        '__args__': obj.__getnewargs__()
+                    }
+                else:
+                    # we cannot convert the object to JSON, just take a string
+                    s = str(obj)
             return s
 
 

From cd5401f856d166df46e0d95ab831f5faa68c996b Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 28 Nov 2018 14:08:41 +0100
Subject: [PATCH 251/719] Pass fixture directly into metadata test instead of
 mark decorator

---
 qcodes/tests/dataset/test_dataset_basic.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index ccf6685ff21..52b28941bcd 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -662,8 +662,7 @@ def test_get_description(some_paramspecs):
     assert loaded_ds.description == desc
 
 
-@pytest.mark.usefixtures('experiment')
-def test_metadata():
+def test_metadata(experiment):
 
     metadata1 = {'number': 1, "string": "Once upon a time..."}
     metadata2 = {'more': 'meta'}

From 10f735d30a75d655abfdffdbbb9f72edc078b34f Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Wed, 28 Nov 2018 14:14:30 +0100
Subject: [PATCH 252/719] add `get_columns`

---
 qcodes/dataset/sqlite_base.py | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 2dde25d5f9e..374dc4cdc4d 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1128,6 +1128,38 @@ def get_data(conn: SomeConnection,
     return res
 
 
+def get_columns(conn: SomeConnection,
+                table_name: str,
+                columns: List[str],
+                start: int = None,
+                end: int = None) -> Dict[str, np.ndarray]:
+    """
+    Get data from the columns of a table.
+    Allows to specfiy a range.
+
+    Args:
+        conn: database connection
+        table_name: name of the table
+        columns: list of columns
+        start: start of range (1 indedex)
+        end: start of range (1 indedex)
+
+    Returns:
+        the data requested
+    """
+    # Benchmarking shows that transposing the data with python types is faster
+    # than transposing the data using np.array.transpose
+    # This method is going to form the entry point for a compiled version
+
+    query = _get_data_query(table_name, columns, start, end)
+    c = atomic_transaction(conn, query)
+    res = many_many(c, *columns)
+    res_t = list(map(list, zip(*res)))
+    return {name: np.array(column)
+            for name, column
+            in zip(res_t, columns)}
+
+
 def get_values(conn: SomeConnection,
                table_name: str,
                param_name: str) -> List[List[Any]]:

From cc6b838c42e9bf79987c69fac5d103d5cc77cd39 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Wed, 28 Nov 2018 15:26:24 +0100
Subject: [PATCH 253/719] insert missing hunk for _build_data_query

---
 qcodes/dataset/sqlite_base.py | 51 ++++++++++++++++++++---------------
 1 file changed, 29 insertions(+), 22 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 374dc4cdc4d..a2d19a4ba72 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1084,6 +1084,30 @@ def length(conn: SomeConnection,
         return _len
 
 
+def _build_data_query( table_name: str,
+             columns: List[str],
+             start: Optional[int] = None,
+             end: Optional[int] = None,
+             ) -> str:
+
+    _columns = ",".join(columns)
+    query = f"""
+            SELECT {_columns}
+            FROM "{table_name}"
+            """
+
+    start_specified = start is not None
+    end_specified = end is not None
+
+    where = ' WHERE' if start_specified or end_specified else ''
+    start_condition = f' rowid >= {start}' if start_specified else ''
+    end_condition = f' rowid <= {end}' if end_specified else ''
+    and_ = ' AND' if start_specified and end_specified else ''
+
+    query += where + start_condition + and_ + end_condition
+    return query
+
+
 def get_data(conn: SomeConnection,
              table_name: str,
              columns: List[str],
@@ -1105,23 +1129,8 @@ def get_data(conn: SomeConnection,
     Returns:
         the data requested in the format of list of rows of values
     """
-    _columns = ",".join(columns)
-
-    query = f"""
-            SELECT {_columns}
-            FROM "{table_name}"
-            """
-
-    start_specified = start is not None
-    end_specified = end is not None
-
-    where = ' WHERE' if start_specified or end_specified else ''
-    start_condition = f' rowid >= {start}' if start_specified else ''
-    end_condition = f' rowid <= {end}' if end_specified else ''
-    and_ = ' AND' if start_specified and end_specified else ''
-
-    query += where + start_condition + and_ + end_condition
 
+    query = _build_data_query(table_name, columns, start, end)
     c = atomic_transaction(conn, query)
     res = many_many(c, *columns)
 
@@ -1151,13 +1160,11 @@ def get_columns(conn: SomeConnection,
     # than transposing the data using np.array.transpose
     # This method is going to form the entry point for a compiled version
 
-    query = _get_data_query(table_name, columns, start, end)
-    c = atomic_transaction(conn, query)
-    res = many_many(c, *columns)
+    res = get_data(conn, table_name, columns, start, end)
     res_t = list(map(list, zip(*res)))
-    return {name: np.array(column)
-            for name, column
-            in zip(res_t, columns)}
+    return {name: np.array(column_data)
+            for name, column_data
+            in zip(columns, res_t)}
 
 
 def get_values(conn: SomeConnection,

From cc8141f3b15fbf5efba0d6d0049a638005534b32 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Wed, 28 Nov 2018 15:26:46 +0100
Subject: [PATCH 254/719] add  tests

---
 qcodes/tests/dataset/dataset_fixtures.py | 31 ++++++++++++++++++++++++
 qcodes/tests/dataset/test_sqlite_base.py | 26 ++++++++++++++++++++
 2 files changed, 57 insertions(+)
 create mode 100644 qcodes/tests/dataset/dataset_fixtures.py

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
new file mode 100644
index 00000000000..ca79a46a178
--- /dev/null
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -0,0 +1,31 @@
+import pytest
+import numpy as np
+from qcodes.dataset.param_spec import ParamSpec
+
+# pylint: disable=unused-import
+from qcodes.tests.dataset.temporary_databases import dataset 
+# pylint: enable=unused-import
+
+
+@pytest.fixture
+def scalar_dataset(dataset):
+    n_params = 3
+    n_rows = 10**3
+    params_indep = [ParamSpec(f'param_{i}',
+                              'numeric',
+                              label=f'param_{i}',
+                              unit='V')
+                    for i in range(n_params)]
+    params = params_indep + [ParamSpec(f'param_{n_params}',
+                                       'numeric',
+                                       label=f'param_{n_params}',
+                                       unit='Ohm',
+                                       depends_on=params_indep)]
+    for p in params:
+        dataset.add_parameter(p)
+    dataset.add_results([{p.name: np.random.rand(1)[0] for p in params}
+                         for _ in range(n_rows)])
+    dataset.mark_complete()
+    yield dataset
+
+
diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index fc309304aac..799a869f777 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -9,6 +9,7 @@
 import hypothesis.strategies as hst
 from hypothesis import given
 import unicodedata
+import numpy as np
 
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
@@ -19,8 +20,10 @@
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import \
     empty_temp_db, experiment, dataset
+from qcodes.tests.dataset.dataset_fixtures import scalar_dataset
 from qcodes.tests.dataset.test_database_creation_and_upgrading import \
     error_caused_by
+# pylint: enable=unused-import
 
 _unicode_categories = ('Lu', 'Ll', 'Lt', 'Lm', 'Lo', 'Nd', 'Pc', 'Pd', 'Zs')
 
@@ -185,3 +188,26 @@ def test_runs_table_columns(empty_temp_db):
         colnames.remove(row['name'])
 
     assert colnames == []
+
+
+def test_get_columns(scalar_dataset):
+    ds = scalar_dataset
+    params = ds.parameters.split(',')
+    # delete some random parameter to test it with an incomplete list
+    del params[-2]
+
+    ref = mut.get_data(ds.conn, ds.table_name, params)
+    dut = mut.get_columns(ds.conn, ds.table_name, params)
+    for i_row, row in enumerate(ref):
+        for i_param, param_name in enumerate(params):
+            v_ref = row[i_param]
+            v_test = dut[param_name][i_row]
+            if type(v_ref) == float:
+                assert type(v_test) == np.float64
+            elif type(v_ref) == int:
+                assert type(v_test) == np.int
+            else:
+                raise RuntimeError('Unsupported data type')
+            assert np.isclose(v_test, v_ref)
+            
+

From e4df3d5c87f85e97de02482cf7279bc869d8e190 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 28 Nov 2018 15:40:19 +0100
Subject: [PATCH 255/719] Make another subscriber test more robust

---
 qcodes/tests/dataset/test_subscribing.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_subscribing.py b/qcodes/tests/dataset/test_subscribing.py
index 397283664ef..0596010049a 100644
--- a/qcodes/tests/dataset/test_subscribing.py
+++ b/qcodes/tests/dataset/test_subscribing.py
@@ -85,7 +85,13 @@ def test_basic_subscription(dataset, basic_subscriber):
         y = -x**2
         dataset.add_result({'x': x, 'y': y})
         expected_state[x+1] = [(x, y)]
-        assert dataset.subscribers[sub_id].state == expected_state
+
+        @retry_until_does_not_throw(
+            exception_class_to_expect=AssertionError, delay=0, tries=10)
+        def assert_expected_state():
+            assert dataset.subscribers[sub_id].state == expected_state
+
+        assert_expected_state()
 
     dataset.unsubscribe(sub_id)
 
@@ -161,7 +167,7 @@ def assert_expected_state():
                 assert dataset.subscribers[sub_id].state == expected_state
                 assert dataset.subscribers[sub_id_c].state == expected_state
 
-        assert_expected_state()
+            assert_expected_state()
 
 
 def test_subscription_from_config_wrong_name(dataset):

From b561b98ef103eca89cc3919b39c7e64449bb1c74 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Wed, 28 Nov 2018 15:54:37 +0100
Subject: [PATCH 256/719] rename get_colums->get_parameter_data

---
 qcodes/dataset/sqlite_base.py            | 10 +++++-----
 qcodes/tests/dataset/test_sqlite_base.py |  4 ++--
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index a2d19a4ba72..981f160dc56 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1137,11 +1137,11 @@ def get_data(conn: SomeConnection,
     return res
 
 
-def get_columns(conn: SomeConnection,
-                table_name: str,
-                columns: List[str],
-                start: int = None,
-                end: int = None) -> Dict[str, np.ndarray]:
+def get_parameter_data(conn: SomeConnection,
+                       table_name: str,
+                       columns: List[str],
+                       start: int = None,
+                       end: int = None) -> Dict[str, np.ndarray]:
     """
     Get data from the columns of a table.
     Allows to specfiy a range.
diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index 799a869f777..7353a08c4f9 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -190,14 +190,14 @@ def test_runs_table_columns(empty_temp_db):
     assert colnames == []
 
 
-def test_get_columns(scalar_dataset):
+def test_get_parameter_data(scalar_dataset):
     ds = scalar_dataset
     params = ds.parameters.split(',')
     # delete some random parameter to test it with an incomplete list
     del params[-2]
 
     ref = mut.get_data(ds.conn, ds.table_name, params)
-    dut = mut.get_columns(ds.conn, ds.table_name, params)
+    dut = mut.get_parameter_data(ds.conn, ds.table_name, params)
     for i_row, row in enumerate(ref):
         for i_param, param_name in enumerate(params):
             v_ref = row[i_param]

From 5ea0f029cccbf6ab446bc958763d400e1814f643 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Wed, 28 Nov 2018 15:54:52 +0100
Subject: [PATCH 257/719] implement in DataSet

---
 qcodes/dataset/data_set.py | 71 ++++++++++++++++++++++++++++++--------
 1 file changed, 57 insertions(+), 14 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 7f93402a935..ceaa855bc23 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -673,6 +673,29 @@ def add_parameter_values(self, spec: ParamSpec, values: VALUES):
             results = [{spec.name: value} for value in values]
             self.add_results(results)
 
+    @staticmethod
+    def _validate_parameters(*params: Union[str, ParamSpec, _BaseParameter]
+        ) -> List[str]:
+        """
+        validate the provided parameters have a name and return them as a list. 
+        names. The Parameters may be a mix of strings, ParamSpecs or ordinary
+        QCoDeS parameters.
+        """
+
+        valid_param_names = []
+        for maybeParam in params:
+            if isinstance(maybeParam, str):
+                valid_param_names.append(maybeParam)
+                continue
+            else:
+                try:
+                    maybeParam = maybeParam.name
+                except Exception as e:
+                    raise ValueError(
+                        "This parameter does not have  a name") from e
+                valid_param_names.append(maybeParam)
+        return valid_param_names
+
     def get_data(self,
                  *params: Union[str, ParamSpec, _BaseParameter],
                  start: Optional[int] = None,
@@ -708,21 +731,41 @@ def get_data(self,
             be of the datatypes stored in the database (numeric, array or
             string)
         """
-        valid_param_names = []
-        for maybeParam in params:
-            if isinstance(maybeParam, str):
-                valid_param_names.append(maybeParam)
-                continue
-            else:
-                try:
-                    maybeParam = maybeParam.name
-                except Exception as e:
-                    raise ValueError(
-                        "This parameter does not have  a name") from e
-            valid_param_names.append(maybeParam)
-        data = get_data(self.conn, self.table_name, valid_param_names,
+        valid_param_names = self._validate_parameters(*params)
+        return get_data(self.conn, self.table_name, valid_param_names,
+                        start, end)
+
+    def get_parameter_data(
+            self,
+            *params: Union[str, ParamSpec, _BaseParameter],
+            start: Optional[int] = None,
+            end: Optional[int] = None) -> Dict[str, numpy.ndarray]:
+        """
+        Returns the values stored in the DataSet for the specified parameters.
+        The values are returned as a dictionary of numpy arrays where the key,
+        is given by the respective parameter name and the value arrays represent
+        the data points. This method returns the transpose of `get_data`.
+
+        If provided, the start and end arguments select a range of results
+        by result count (index). If the range is empty - that is, if the end is
+        less than or equal to the start, or if start is after the current end
+        of the DataSet – then a list of empty arrays is returned.
+
+        Args:
+            *params: string parameter names, QCoDeS Parameter objects, and
+                ParamSpec objects
+            start: start value of selection range (by result count); ignored
+                if None
+            end: end value of selection range (by results count); ignored if
+                None
+
+        Returns:
+            Dictionary of numpy arrays containing the data points of type
+            numeric, array or string.
+        """
+        valid_param_names = self._validate_parameters(*params)
+        return get_parameter_data(self.conn, self.table_name, valid_param_names,
                         start, end)
-        return data
 
     def get_values(self, param_name: str) -> List[List[Any]]:
         """

From 041eeb9affe129b46ccc0c4cd8809e3564cd4d9e Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Wed, 28 Nov 2018 16:36:40 +0100
Subject: [PATCH 258/719] add imports

---
 qcodes/dataset/data_set.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index ceaa855bc23..5cd561aa921 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -7,6 +7,7 @@
 import logging
 import uuid
 from queue import Queue, Empty
+import numpy
 
 from qcodes.dataset.param_spec import ParamSpec
 from qcodes.instrument.parameter import _BaseParameter
@@ -22,6 +23,7 @@
                                         modify_many_values, insert_values,
                                         insert_many_values,
                                         VALUE, VALUES, get_data,
+                                        get_parameter_data,
                                         get_values,
                                         get_setpoints,
                                         get_metadata,

From e15cfcbd48afec69f0363f419eda5c28c0774305 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Wed, 28 Nov 2018 16:37:03 +0100
Subject: [PATCH 259/719] add dataset test

---
 qcodes/tests/dataset/test_dataset_basic.py | 26 ++++++++++++++++++++++
 1 file changed, 26 insertions(+)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 1a23d894091..2ffad6bd1ba 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -1,4 +1,5 @@
 import itertools
+from copy import copy
 
 import pytest
 import numpy as np
@@ -17,6 +18,7 @@
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import (empty_temp_db,
                                                       experiment, dataset)
+from qcodes.tests.dataset.dataset_fixtures import scalar_dataset
 # pylint: disable=unused-import
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 
@@ -614,3 +616,27 @@ def ds_with_vals(self, dataset):
     def test_get_data_with_start_and_end_args(self, ds_with_vals,
                                               start, end, expected):
         assert expected == ds_with_vals.get_data(self.x, start=start, end=end)
+
+def test_get_parameter_data(scalar_dataset):
+    ds = scalar_dataset
+    params = ds.parameters.split(',')
+    # delete some random parameter to test it with an incomplete list
+    del params[-2]
+    # replace first list entry by paramspec
+    param_names = copy(params)
+    params[0] = ds.paramspecs[params[0]]
+
+    ref = ds.get_data(*params)
+    dut = ds.get_parameter_data(*params)
+
+    for i_row, row in enumerate(ref):
+        for i_param, param_name in enumerate(param_names):
+            v_ref = row[i_param]
+            v_test = dut[param_name][i_row]
+            if type(v_ref) == float:
+                assert type(v_test) == np.float64
+            elif type(v_ref) == int:
+                assert type(v_test) == np.int
+            else:
+                raise RuntimeError('Unsupported data type')
+            assert np.isclose(v_test, v_ref)

From ae21c0e9fcb70e4796f3583560a0949cb3b9574a Mon Sep 17 00:00:00 2001
From: "Robert R. Henry" <robhenry@microsoft.com>
Date: Wed, 28 Nov 2018 14:54:59 -0800
Subject: [PATCH 260/719] Fix some typos, grammar glitches and minor python
 formatting.

---
 docs/community/objects.rst                     |  2 +-
 docs/dataset/introduction.rst                  |  2 +-
 .../Comprehensive Plotting How-To.ipynb        | 18 +++++++++---------
 .../DataSet/Offline Plotting Tutorial.ipynb    |  4 ++--
 docs/examples/plotting/live_plotting.ipynb     |  2 +-
 docs/user/tutorial.rst                         | 14 +++++++-------
 .../QuantumDesign/DynaCoolPPMS/DynaCool.py     |  2 +-
 .../tektronix/Keithley_2600.py                 |  2 +-
 .../tektronix/Keithley_2600_channels.py        |  2 +-
 9 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/docs/community/objects.rst b/docs/community/objects.rst
index 559b09177e0..3b37576f612 100644
--- a/docs/community/objects.rst
+++ b/docs/community/objects.rst
@@ -8,7 +8,7 @@ Rough linkages:
 
 In **bold** the containing class creates this object. In *italics* the
 container just holds this object (or class) as a default for derivatives
-to use. Normal text the container includes and uses this object.
+to use. Normal text shows the container includes and uses of this object.
 
 -  Station
 -  Instrument: IPInstrument, VisaInstrument, MockInstrument
diff --git a/docs/dataset/introduction.rst b/docs/dataset/introduction.rst
index f7409084875..5f7e1e3410c 100644
--- a/docs/dataset/introduction.rst
+++ b/docs/dataset/introduction.rst
@@ -11,7 +11,7 @@ This page explains the basics of the QCoDeS dataset. Read here to figure out wha
 Basics
 ======
 
-The DataSet is directly linked to an SQLite database on the machine hard drive. Here we provide an overview of the layout of that database.
+The DataSet is directly linked to an SQLite database on the host machine's hard drive. Here we provide an overview of the layout of that database.
 The specifics of the python objects implementing the API on top of that can be found in the section :ref:`datasetdesign`.
 
 The database holds a number of ``Experiments`` that each holds a number of ``DataSets``. One ``DataSet`` contains exactly one ``run``, and we may use the words ``DataSet`` and ``run`` interchangeably.
diff --git a/docs/examples/Comprehensive Plotting How-To.ipynb b/docs/examples/Comprehensive Plotting How-To.ipynb
index 5e75fbbc31b..88ae99df9b4 100644
--- a/docs/examples/Comprehensive Plotting How-To.ipynb	
+++ b/docs/examples/Comprehensive Plotting How-To.ipynb	
@@ -23,7 +23,7 @@
     "Plotting data in QCoDeS can be done using either MatPlot or QTPlot, with matplotlib and pyqtgraph as backends, respectively. \n",
     "MatPlot and QTPlot tailor these plotting backends to QCoDeS, providing many features.\n",
     "For example, when plotting a DataArray in a DataSet, the corresponding ticks, labels, etc. are automatically added to the plot.\n",
-    "Both MatPlot and QTPlot support live plotting while a measurement is running\n",
+    "Both MatPlot and QTPlot support live plotting while a measurement is running.\n",
     "\n",
     "One of the main differences between the two backends is that matplotlib is more strongly integrated with Jupyter Notebook, while pyqtgraph uses the PyQT GUI.\n",
     "For matplotlib, this has the advantage that plots can be displayed within a notebook (though it also has a gui).\n",
@@ -39,7 +39,7 @@
    "outputs": [],
    "source": [
     "loc_provider = qc.data.location.FormatLocation(fmt='data/{date}/#{counter}_{name}_{time}')\n",
-    "qc.data.data_set.DataSet.location_provider=loc_provider"
+    "qc.data.data_set.DataSet.location_provider = loc_provider"
    ]
   },
   {
@@ -921,7 +921,7 @@
     "# Attach updating of plot to loop\n",
     "loop.with_bg_task(plot.update)\n",
     "\n",
-    "loop.run();"
+    "loop.run()"
    ]
   },
   {
@@ -1786,7 +1786,7 @@
     "# Attach updating of plot to loop\n",
     "loop.with_bg_task(plot.update)\n",
     "\n",
-    "loop.run();"
+    "loop.run()"
    ]
   },
   {
@@ -2627,7 +2627,7 @@
     "# Attach updating of plot to loop\n",
     "loop.with_bg_task(plot.update)\n",
     "\n",
-    "loop.run();"
+    "loop.run()"
    ]
   },
   {
@@ -2635,7 +2635,7 @@
    "metadata": {},
    "source": [
     "Note that we passed the kwarg `subplots=3` to specify that we need 3 subplots.\n",
-    "the `subplots` kwarg can be either an int or a tuple.\n",
+    "The `subplots` kwarg can be either an int or a tuple.\n",
     "If it is an int, it will segment the value such that there are at most three columns.\n",
     "If a tuple is provided, its first element indicates the number of rows, and the second the number of columns.\n",
     "\n",
@@ -3494,7 +3494,7 @@
     "# Attach updating of plot to loop\n",
     "loop.with_bg_task(plot.update)\n",
     "\n",
-    "loop.run();"
+    "loop.run()"
    ]
   },
   {
@@ -4341,7 +4341,7 @@
     "# Attach updating of plot to loop\n",
     "loop.with_bg_task(plot.update)\n",
     "\n",
-    "loop.run();"
+    "loop.run()"
    ]
   },
   {
@@ -4410,7 +4410,7 @@
     "Every time a new plot is created, it will update the active axis and figure.\n",
     "The active Figure and Axis can be changed via `plt.scf(fig)` and `plt.sca(ax)`, respectively.\n",
     "\n",
-    "As an example, the following code will change the title of the last-created plot (the right subplot of the previous figure)"
+    "As an example, the following code will change the title of the last-created plot (the right subplot of the previous figure):"
    ]
   },
   {
diff --git a/docs/examples/DataSet/Offline Plotting Tutorial.ipynb b/docs/examples/DataSet/Offline Plotting Tutorial.ipynb
index ad2f7b82433..be4b13fe34b 100644
--- a/docs/examples/DataSet/Offline Plotting Tutorial.ipynb	
+++ b/docs/examples/DataSet/Offline Plotting Tutorial.ipynb	
@@ -115,9 +115,9 @@
     "\n",
     "xvals = np.linspace(-3.4, 4.2, 250)\n",
     "\n",
-    "# shuffle randomly the values in order to test that plot\n",
+    "# Randomly shuffle the values in order to test the plot\n",
     "# that is to be created for this data is a correct line\n",
-    "# that does not depend on the order of the data\n",
+    "# that does not depend on the order of the data.\n",
     "np.random.shuffle(xvals)\n",
     "\n",
     "with meas.run() as datasaver:\n",
diff --git a/docs/examples/plotting/live_plotting.ipynb b/docs/examples/plotting/live_plotting.ipynb
index edf8c465b2a..4375831ce9b 100644
--- a/docs/examples/plotting/live_plotting.ipynb
+++ b/docs/examples/plotting/live_plotting.ipynb
@@ -44,7 +44,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With this simple chang you can run all your notebooks without any changes and live data will be streamed to plottr. For that lets consider the simple example adapted from the dataset docs:"
+    "With this simple change you can run all your notebooks without any changes and live data will be streamed to plottr. For that let us consider the simple example adapted from the dataset docs:"
    ]
   },
   {
diff --git a/docs/user/tutorial.rst b/docs/user/tutorial.rst
index 65fc0cedbac..4199ad6fab1 100644
--- a/docs/user/tutorial.rst
+++ b/docs/user/tutorial.rst
@@ -119,7 +119,7 @@ and we save the sum of them.
     z_vals = np.linspace(1, 2, 2)
 
     loop = qc.Loop(magnet.sweep(x_vals, y_vals, z_vals), delay=0.001).each(p4)
-    data =loop.run()
+    data = loop.run()
 
 
 
@@ -134,8 +134,8 @@ Meta Instruments
 The concept of a meta instrument is that of having
 two separate Instrument, real or virtual, whose actions can
 the be controlled from the meta instrument.
-In the following example we will create two dummy instruments and a meta instruments.
-All the instruments will live on a InstrumentServer.
+In the following example we will create two dummy instruments and a meta instrument.
+All the instruments will live on an InstrumentServer.
 
 
 .. note:: this is rather non-trival due to the limitation of the
@@ -254,9 +254,9 @@ Async Meta
 Say you want to set two instruments at the same time.
 You can use the following:
 
-.. note:: the curernt architecture is so that you MUST one  server per base instrument
+.. note:: the current architecture is so that you MUST one server per base instrument
 
-The base instrument class stays the same, meta gets a new method f.ex:
+The base instrument class stays the same, Meta gets a new method for example:
 
 .. code:: python
 
@@ -303,12 +303,12 @@ The base instrument class stays the same, meta gets a new method f.ex:
 This way:
     >>> meta.setBothAsync(0)
 
-will set both instrument at the same time, say it takes 10 seconds per set,
+will set both instruments at the same time, say it takes 10 seconds per set,
 then setting two things will take 10 seconds, not 20 seconds.
 
 For a complete working example see :download:`this example script <./meta.py>`.
 
-Avanced
+Advanced
 -------
 
 .. todo::  missing
diff --git a/qcodes/instrument_drivers/QuantumDesign/DynaCoolPPMS/DynaCool.py b/qcodes/instrument_drivers/QuantumDesign/DynaCoolPPMS/DynaCool.py
index 2168453b258..7359307ef00 100644
--- a/qcodes/instrument_drivers/QuantumDesign/DynaCoolPPMS/DynaCool.py
+++ b/qcodes/instrument_drivers/QuantumDesign/DynaCoolPPMS/DynaCool.py
@@ -17,7 +17,7 @@ class DynaCool(VisaInstrument):
 
     Args:
         name: The name used internaly by QCoDeS for this driver
-        address: The VISA ressource name.
+        address: The VISA resource name.
           E.g. 'TCPIP0::127.0.0.1::5000::SOCKET' with the appropriate IP
           address instead of 127.0.0.1. Note that the port number is
           hard-coded into the server.
diff --git a/qcodes/instrument_drivers/tektronix/Keithley_2600.py b/qcodes/instrument_drivers/tektronix/Keithley_2600.py
index 78d79aba78c..f92f55602ff 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_2600.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_2600.py
@@ -22,7 +22,7 @@ def __init__(self, name: str, address: str, channel: str,
         """
         Args:
             name: Name to use internally in QCoDeS
-            address: VISA ressource address
+            address: VISA resource address
             channel: Either 'a' or 'b'
             model: The model type, e.g. '2614B'
         """
diff --git a/qcodes/instrument_drivers/tektronix/Keithley_2600_channels.py b/qcodes/instrument_drivers/tektronix/Keithley_2600_channels.py
index a1784036ad0..5592ffd0bec 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_2600_channels.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_2600_channels.py
@@ -349,7 +349,7 @@ def __init__(self, name: str, address: str, **kwargs) -> None:
         """
         Args:
             name: Name to use internally in QCoDeS
-            address: VISA ressource address
+            address: VISA resource address
         """
         super().__init__(name, address, terminator='\n', **kwargs)
 

From 7cf8e2020cecd19d0cdd640c4c43cbfcac038786 Mon Sep 17 00:00:00 2001
From: "Robert R. Henry" <robhenry@microsoft.com>
Date: Wed, 28 Nov 2018 15:44:19 -0800
Subject: [PATCH 261/719] Fix 'title underline too short' issue which breaks
 CI.

---
 docs/user/tutorial.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/user/tutorial.rst b/docs/user/tutorial.rst
index 4199ad6fab1..ae0bcd42af0 100644
--- a/docs/user/tutorial.rst
+++ b/docs/user/tutorial.rst
@@ -309,6 +309,6 @@ then setting two things will take 10 seconds, not 20 seconds.
 For a complete working example see :download:`this example script <./meta.py>`.
 
 Advanced
--------
+--------
 
 .. todo::  missing

From 2b440c1c78653bbdd1bdd1a48e10a01bef16bb19 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Wed, 28 Nov 2018 16:42:22 -0800
Subject: [PATCH 262/719] added instrument sim yaml file

---
 qcodes/instrument/sims/Keithley_s46.yaml      | 54 ++++++++++++
 .../tektronix/Keithley_s46.py                 | 82 +++++++++++++------
 qcodes/tests/drivers/test_keithley_s46.py     | 59 +++++++++++++
 3 files changed, 171 insertions(+), 24 deletions(-)
 create mode 100644 qcodes/instrument/sims/Keithley_s46.yaml
 create mode 100644 qcodes/tests/drivers/test_keithley_s46.py

diff --git a/qcodes/instrument/sims/Keithley_s46.yaml b/qcodes/instrument/sims/Keithley_s46.yaml
new file mode 100644
index 00000000000..0b9bcca7605
--- /dev/null
+++ b/qcodes/instrument/sims/Keithley_s46.yaml
@@ -0,0 +1,54 @@
+# SIMULATED INSTRUMENT FOR Keysight 33xxx series function generator
+spec: "1.0"
+devices:
+  device bad_state:
+    eom:
+      GPIB INSTR:
+        q: "\n"
+        r: "\n"
+    error: ERROR
+    dialogues:
+      - q: "*IDN?"
+        r: "KEITHLEY INSTRUMENTS INC.,MODEL SYSTEM 46, 1327388, A03  "
+      - q: ":CONF:CPOL?"
+        r: "6,6,6,6,0,0,0,0,1,0,0,1"
+
+    properties:
+
+      close:
+        getter:
+          q: ":CLOS?"
+          r: "(@1,2,8)"
+
+  device default:
+    eom:
+      GPIB INSTR:
+        q: "\n"
+        r: "\n"
+    error: ERROR
+    dialogues:
+    - q: "*IDN?"
+      r: "KEITHLEY INSTRUMENTS INC.,MODEL SYSTEM 46, 1327388, A03  "
+    - q: ":CONF:CPOL?"
+      r: "6,6,6,6,0,0,0,0,1,0,0,1"
+
+    properties:
+
+      close:
+        getter:
+          q: ":CLOS?"
+          r: "(@1,8,13)"
+        setter:
+          q: ":CLOS {}"
+          r: ""
+
+      open:
+        setter:
+          q: ":OPEN {}"
+          r: ""
+
+resources:
+  GPIB::1::INSTR:
+    device: device bad_state
+  GPIB::2::INSTR:
+    device: device default
\ No newline at end of file
diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index 9116c3ee7e3..4c2acdb4b04 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -1,37 +1,50 @@
 import numpy as np
 import itertools
 from collections import defaultdict
+import re
 
 from qcodes import VisaInstrument, InstrumentChannel, ChannelList
+from qcodes.utils.validators import Enum
 
 
 class ChannelNotPresentError(Exception):
     pass
 
 
+class LockAcquisitionError(Exception):
+    pass
+
+
 class RelayLock:
     def __init__(self):
-        self.acquired = False
-        self.acquired_by = None
+        self._acquired = False
+        self._acquired_by = None
 
     def acquire(self, requester_id):
-        if self.acquired and self.acquired_by != requester_id:
-            raise RuntimeError(
+        if self._acquired and self._acquired_by != requester_id:
+            raise LockAcquisitionError(
                 f"Relay already in use by channel {self.acquired_by}"
             )
 
-        self.acquired = True
-        self.acquired_by = requester_id
+        self._acquired = True
+        self._acquired_by = requester_id
 
     def release(self, requester_id):
-        if self.acquired_by != requester_id:
+        if self._acquired_by != requester_id:
+            # It should be impossible to get here. There is a driver bug
+            # if we do.
             raise RuntimeError(
                 f"Relay can only be freed by channel {self.acquired_by} "
-                f"that acquired the lock"
+                f"that acquired the lock. Please get in touch with a QCoDeS "
+                f"developer to get to the root cause of this error"
             )
 
-        self.acquired = False
-        self.acquired_by = None
+        self._acquired = False
+        self._acquired_by = None
+
+    @property
+    def acquired_by(self):
+        return self._acquired_by
 
 
 class RFSwitchChannel(InstrumentChannel):
@@ -42,17 +55,29 @@ def __init__(self, parent, name, channel_number, relay_locks):
         self._relay_id = self._get_relay()
         self._relay_lock = relay_locks[self._relay_id]
 
+        if self._get_state() == "close":
+            try:
+                self._relay_lock.acquire(self._channel_number)
+            except LockAcquisitionError as e:
+                raise RuntimeError(
+                    "The driver is initialized from an undesirable instrument "
+                    "state where more then one channel on a single relay is "
+                    "closed. It is advised to power cycle the instrument or to "
+                    "manually send the 'OPEN:ALL' SCPI command to get the "
+                    "instrument back into a normal state. Refusing to "
+                    "initialize driver!"
+                ) from e
+
         self.add_parameter(
-            "close",
-            get_cmd=lambda: str(self._channel_number) in self.ask(":CONF:CPOL?"),
-            set_cmd=lambda state: {
-                True: self._close_channel,
-                False: self._open_channel
-            }[state]()
+            "state",
+            get_cmd=self._get_state,
+            set_cmd=f":{{}} (@{self._channel_number})",
+            set_parser=self._set_state_parser,
+            vals=Enum("open", "close")
         )
 
     def _get_relay(self):
-        relay_layout = self.parent.relay_layout()
+        relay_layout = self.parent.relay_layout
         found = False
 
         count = 0
@@ -64,15 +89,24 @@ def _get_relay(self):
         if not found:
             raise ChannelNotPresentError()
 
-        return (["A", "B", "C", "D"] + [chr(i) for i in range(1, 9)])[count]
+        return (["A", "B", "C", "D"] + [str(i) for i in range(1, 9)])[count]
+
+    def _get_state(self):
+        closed_channels = re.findall(r"(\d+)[,)]",  self.ask(":CLOS?"))
+        return "close" \
+            if str(self._channel_number) in closed_channels \
+            else "open"
+
+    def _set_state_parser(self, new_state: str):
+
+        if new_state == "close":
+            self._relay_lock.acquire(self._channel_number)
+        elif new_state == "open" \
+                and self._relay_lock.acquired_by == self._channel_number:
 
-    def _close_channel(self):
-        self._relay_lock.acquire(self._channel_number)
-        self.write(f":CLOSE (@{self._channel_number})")
+            self._relay_lock.release(self._channel_number)
 
-    def _open_channel(self):
-        self._relay_lock.release(self._channel_number)
-        self.write(f":OPEN (@{self._channel_number})")
+        return new_state.upper()
 
     @property
     def relay_id(self):
diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
new file mode 100644
index 00000000000..13f29a400d0
--- /dev/null
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -0,0 +1,59 @@
+import pytest
+
+from qcodes.instrument_drivers.tektronix.Keithley_s46 import (
+    S46, LockAcquisitionError
+)
+
+import qcodes.instrument.sims as sims
+visalib = sims.__file__.replace('__init__.py', 'Keithley_s46.yaml@sim')
+
+
+@pytest.fixture(scope='module')
+def s46():
+    driver = S46('s46_sim',address='GPIB::2::INSTR', visalib=visalib)
+    yield driver
+    driver.close()
+
+
+def test_runtime_error_on_bad_init():
+
+    with pytest.raises(
+        RuntimeError,
+        match="The driver is initialized from an undesirable instrument state"
+    ):
+        S46('s46_bad_state', address='GPIB::1::INSTR', visalib=visalib)
+
+
+def test_init(s46):
+
+    n_channels = len(s46.channels)
+    assert n_channels == 26
+
+    closed_channels = [0, 7, 12]
+
+    for channel_nr in range(n_channels):
+        assert s46.channels[channel_nr].state() == "close" \
+            if channel_nr in closed_channels else "open"
+
+
+def test_open_close(s46):
+
+    with pytest.raises(
+        LockAcquisitionError,
+        match="Relay already in use by channel"
+    ):
+        s46.channels[1].state("close")
+
+    s46.channels[0].state("open")
+    s46.channels[1].state("close")
+
+    s46.channels[18].state("close")
+
+    with pytest.raises(
+        LockAcquisitionError,
+        match="Relay already in use by channel"
+    ):
+        s46.channels[19].state("close")
+
+    s46.channels[18].state("open")
+    s46.channels[19].state("close")

From 6aed56fb82a257a88d2af7cf1b6ad435e2dd2d0d Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Thu, 29 Nov 2018 09:22:24 +0100
Subject: [PATCH 263/719] handle empty parameter list

---
 qcodes/dataset/data_set.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 5cd561aa921..face1f8302a 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -734,6 +734,10 @@ def get_data(self,
             string)
         """
         valid_param_names = self._validate_parameters(*params)
+        if len(valid_param_names) == 0:
+            raise RuntimeError('Error calling `get_data`: you need to supply '
+                               'at least one parameter for which to get the '
+                               'data.')
         return get_data(self.conn, self.table_name, valid_param_names,
                         start, end)
 

From 03a550f54455af2894e43a1d8e586c4de6f55cb0 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Thu, 29 Nov 2018 09:31:10 +0100
Subject: [PATCH 264/719] update comment

---
 qcodes/dataset/data_set.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index face1f8302a..e2b714c485e 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -677,10 +677,11 @@ def add_parameter_values(self, spec: ParamSpec, values: VALUES):
 
     @staticmethod
     def _validate_parameters(*params: Union[str, ParamSpec, _BaseParameter]
-        ) -> List[str]:
+                             ) -> List[str]:
         """
-        validate the provided parameters have a name and return them as a list. 
-        names. The Parameters may be a mix of strings, ParamSpecs or ordinary
+        Validate that the provided parameters have a name and return those
+        names as a list.
+        The Parameters may be a mix of strings, ParamSpecs or ordinary
         QCoDeS parameters.
         """
 

From af09c3728683b22619e76fc41e3ab7219a2c7302 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 10:15:04 +0100
Subject: [PATCH 265/719] Improve __repr__ string formatting

---
 qcodes/config/config.py     |  6 +++---
 qcodes/tests/test_config.py | 11 +++++++++++
 2 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/qcodes/config/config.py b/qcodes/config/config.py
index 1b1de22b53b..ab2e57bf7af 100644
--- a/qcodes/config/config.py
+++ b/qcodes/config/config.py
@@ -392,9 +392,9 @@ def __getattr__(self, name):
 
     def __repr__(self):
         old = super().__repr__()
-        output = f"""Current values: \n {current_config} \n
-                     Current paths: \n {self._loaded_config_files} \n
-                     {old}"""
+        output = (f"Current values: \n {self.current_config} \n"
+                  f"Current paths: \n {self._loaded_config_files} \n"
+                  f"{old}")
         return output
 
 
diff --git a/qcodes/tests/test_config.py b/qcodes/tests/test_config.py
index 0d39ed5b785..41afd621384 100644
--- a/qcodes/tests/test_config.py
+++ b/qcodes/tests/test_config.py
@@ -325,3 +325,14 @@ def test_update_from_path(path_to_config_file_on_disk):
         expected_path = os.path.join(path_to_config_file_on_disk,
                                      'qcodesrc.json')
         assert cfg.current_config_path == expected_path
+
+
+def test_repr():
+    cfg = Config()
+    rep = cfg.__repr__()
+
+    expected_rep = (f"Current values: \n {cfg.current_config} \n"
+                    f"Current paths: \n {cfg._loaded_config_files} \n"
+                    f"{super(Config, cfg).__repr__()}")
+
+    assert rep == expected_rep

From 71c3f8855be41ca9ed13bb6b76f7f5eee74d1073 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 29 Nov 2018 10:16:29 +0100
Subject: [PATCH 266/719] Fix codacy issues

---
 qcodes/tests/dataset/dataset_fixtures.py   |  2 +-
 qcodes/tests/dataset/test_dataset_basic.py |  8 ++++----
 qcodes/tests/dataset/test_sqlite_base.py   | 10 ++++------
 3 files changed, 9 insertions(+), 11 deletions(-)

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
index ca79a46a178..2e0d8d18b3e 100644
--- a/qcodes/tests/dataset/dataset_fixtures.py
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -3,7 +3,7 @@
 from qcodes.dataset.param_spec import ParamSpec
 
 # pylint: disable=unused-import
-from qcodes.tests.dataset.temporary_databases import dataset 
+from qcodes.tests.dataset.temporary_databases import dataset
 # pylint: enable=unused-import
 
 
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 2ffad6bd1ba..73cdb4bac46 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -633,10 +633,10 @@ def test_get_parameter_data(scalar_dataset):
         for i_param, param_name in enumerate(param_names):
             v_ref = row[i_param]
             v_test = dut[param_name][i_row]
-            if type(v_ref) == float:
-                assert type(v_test) == np.float64
-            elif type(v_ref) == int:
-                assert type(v_test) == np.int
+            if isinstance(v_ref, float):
+                assert isinstance(v_test, np.float64)
+            elif isinstance(v_ref, int):
+                assert isinstance(v_test, np.int)
             else:
                 raise RuntimeError('Unsupported data type')
             assert np.isclose(v_test, v_ref)
diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index 7353a08c4f9..7e71e698b39 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -202,12 +202,10 @@ def test_get_parameter_data(scalar_dataset):
         for i_param, param_name in enumerate(params):
             v_ref = row[i_param]
             v_test = dut[param_name][i_row]
-            if type(v_ref) == float:
-                assert type(v_test) == np.float64
-            elif type(v_ref) == int:
-                assert type(v_test) == np.int
+            if isinstance(v_ref, float):
+                assert isinstance(v_test, np.float64)
+            elif isinstance(v_ref, int):
+                assert isinstance(v_test, np.int)
             else:
                 raise RuntimeError('Unsupported data type')
             assert np.isclose(v_test, v_ref)
-            
-

From d573602aef2097c41bda1d60333b6831b308e702 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 10:23:56 +0100
Subject: [PATCH 267/719] Update describe docstring

---
 qcodes/config/config.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/qcodes/config/config.py b/qcodes/config/config.py
index ab2e57bf7af..70b2a4d7602 100644
--- a/qcodes/config/config.py
+++ b/qcodes/config/config.py
@@ -355,12 +355,13 @@ def save_to_cwd(self):
         self.save_config(self.cwd_file_name)
         self.save_schema(self.schema_cwd_file_name)
 
-    def describe(self, name):
+    def describe(self, name: str) -> str:
         """
         Describe a configuration entry
 
         Args:
-            name (str): name of entry to describe
+            name: name of entry to describe in 'dotdict' notation,
+              e.g. name="user.scriptfolder"
         """
         val = self.current_config
         sch = self.current_schema["properties"]

From a697e24999ae28f3c613a7bb5662eb95f6854311 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 10:32:12 +0100
Subject: [PATCH 268/719] Add a test for add and describe

---
 qcodes/tests/test_config.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/qcodes/tests/test_config.py b/qcodes/tests/test_config.py
index 41afd621384..01fcc7f2673 100644
--- a/qcodes/tests/test_config.py
+++ b/qcodes/tests/test_config.py
@@ -336,3 +336,27 @@ def test_repr():
                     f"{super(Config, cfg).__repr__()}")
 
     assert rep == expected_rep
+
+
+def test_add_and_describe():
+    """
+    Test that a key an be added and described
+    """
+    with default_config():
+
+        key = 'newkey'
+        value ='testvalue'
+        value_type ='string'
+        description ='A test'
+        default = 'testdefault'
+
+        cfg = Config()
+        cfg.add(key=key, value=value, value_type=value_type,
+                description=description, default=default)
+
+
+        desc = cfg.describe(f'user.{key}')
+        expected_desc = (f"{description}.\nCurrent value: {value}. "
+                         f"Type: {value_type}. Default: {default}.")
+
+        assert desc == expected_desc

From 6adeddd19de5a88ecabac7945f184a5864c712ca Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 29 Nov 2018 11:09:55 +0100
Subject: [PATCH 269/719] add some more tests

---
 qcodes/tests/dataset/dataset_fixtures.py   | 25 ++++++++++++++++++++++
 qcodes/tests/dataset/test_dataset_basic.py | 16 ++++++++++++--
 2 files changed, 39 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
index 2e0d8d18b3e..d0525105cc3 100644
--- a/qcodes/tests/dataset/dataset_fixtures.py
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -2,6 +2,9 @@
 import numpy as np
 from qcodes.dataset.param_spec import ParamSpec
 
+from qcodes.dataset.measurements import Measurement
+from qcodes.tests.instrument_mocks import ArraySetPointParam, Multi2DSetPointParam
+
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import dataset
 # pylint: enable=unused-import
@@ -29,3 +32,25 @@ def scalar_dataset(dataset):
     yield dataset
 
 
+@pytest.fixture
+def array_dataset(dataset):
+    meas = Measurement()
+    param = ArraySetPointParam()
+    meas.register_parameter(param)
+
+    with meas.run() as datasaver:
+        datasaver.add_result((param, param.get(),))
+    datasaver.dataset.conn.close()
+    yield dataset
+
+
+@pytest.fixture
+def multi_dataset(dataset):
+    meas = Measurement()
+    param = Multi2DSetPointParam()
+    meas.register_parameter(param)
+
+    with meas.run() as datasaver:
+        datasaver.add_result((param, param.get(),))
+    datasaver.dataset.conn.close()
+    yield dataset
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 73cdb4bac46..c38d18a278a 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -18,7 +18,7 @@
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import (empty_temp_db,
                                                       experiment, dataset)
-from qcodes.tests.dataset.dataset_fixtures import scalar_dataset
+from qcodes.tests.dataset.dataset_fixtures import scalar_dataset, array_dataset, multi_dataset
 # pylint: disable=unused-import
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 
@@ -617,8 +617,20 @@ def test_get_data_with_start_and_end_args(self, ds_with_vals,
                                               start, end, expected):
         assert expected == ds_with_vals.get_data(self.x, start=start, end=end)
 
+
 def test_get_parameter_data(scalar_dataset):
-    ds = scalar_dataset
+    parameter_test_helper(scalar_dataset)
+
+
+def test_get_array_parameter_data(array_dataset):
+    parameter_test_helper(array_dataset)
+
+
+def test_get_array_parameter_data(multi_dataset):
+    parameter_test_helper(multi_dataset)
+
+
+def parameter_test_helper(ds):
     params = ds.parameters.split(',')
     # delete some random parameter to test it with an incomplete list
     del params[-2]

From 63723f55db5c61a84d1d671f405bdc879e61939f Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Thu, 29 Nov 2018 11:18:13 +0100
Subject: [PATCH 270/719] move error and make warning

---
 qcodes/dataset/data_set.py    | 4 ----
 qcodes/dataset/sqlite_base.py | 8 +++++++-
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index e2b714c485e..d3d21416eac 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -735,10 +735,6 @@ def get_data(self,
             string)
         """
         valid_param_names = self._validate_parameters(*params)
-        if len(valid_param_names) == 0:
-            raise RuntimeError('Error calling `get_data`: you need to supply '
-                               'at least one parameter for which to get the '
-                               'data.')
         return get_data(self.conn, self.table_name, valid_param_names,
                         start, end)
 
diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 981f160dc56..c5853925122 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -4,6 +4,7 @@
 import sqlite3
 import time
 import io
+import warnings
 from typing import (Any, List, Optional, Tuple, Union, Dict, cast, Callable,
                     Sequence, DefaultDict)
 import itertools
@@ -1129,7 +1130,12 @@ def get_data(conn: SomeConnection,
     Returns:
         the data requested in the format of list of rows of values
     """
-
+    if len(columns) == 0:
+        warnings.warn(
+            'get_data: requested data without specifying parameters/columns.'
+            'Returning empyt list.'
+        )
+        return [[]]
     query = _build_data_query(table_name, columns, start, end)
     c = atomic_transaction(conn, query)
     res = many_many(c, *columns)

From de89df5aa18f62bb5998443fdb44e6d20e8eed14 Mon Sep 17 00:00:00 2001
From: Dominik Vogel <a-dovoge@microsoft.com>
Date: Thu, 29 Nov 2018 11:18:43 +0100
Subject: [PATCH 271/719] add test for empty columns

---
 qcodes/tests/dataset/test_sqlite_base.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index 7e71e698b39..e1521914dd4 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -190,6 +190,12 @@ def test_runs_table_columns(empty_temp_db):
     assert colnames == []
 
 
+def test_get_data_no_columns(scalar_dataset):
+    ds = scalar_dataset
+    ref = mut.get_data(ds.conn, ds.table_name, [])
+    assert ref == [[]]
+
+
 def test_get_parameter_data(scalar_dataset):
     ds = scalar_dataset
     params = ds.parameters.split(',')

From f75678e811ba4f6f8867892beb97d0a4a3abd80f Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 29 Nov 2018 12:37:25 +0100
Subject: [PATCH 272/719] Use pytest request.addfinalizer to avoid unclosed
 sqlite connections

---
 qcodes/tests/dataset/test_dataset_basic.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 52b28941bcd..9889223cb94 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -662,13 +662,15 @@ def test_get_description(some_paramspecs):
     assert loaded_ds.description == desc
 
 
-def test_metadata(experiment):
+def test_metadata(experiment, request):
 
     metadata1 = {'number': 1, "string": "Once upon a time..."}
     metadata2 = {'more': 'meta'}
 
     ds1 = DataSet(metadata=metadata1)
+    request.addfinalizer(ds1.conn.close)
     ds2 = DataSet(metadata=metadata2)
+    request.addfinalizer(ds2.conn.close)
 
     assert ds1.run_id == 1
     assert ds1.metadata == metadata1
@@ -676,8 +678,10 @@ def test_metadata(experiment):
     assert ds2.metadata == metadata2
 
     loaded_ds1 = DataSet(run_id=1)
+    request.addfinalizer(loaded_ds1.conn.close)
     assert loaded_ds1.metadata == metadata1
     loaded_ds2 = DataSet(run_id=2)
+    request.addfinalizer(loaded_ds2.conn.close)
     assert loaded_ds2.metadata == metadata2
 
     badtag = 'lex luthor'

From 72706de1987c062dfc0d79b5f2ae250bdbd6061a Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 11:12:35 +0100
Subject: [PATCH 273/719] Add failing integration test

---
 .../dataset/test_database_extract_runs.py     | 38 +++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index 9abe331d19d..3d9f8d47c52 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -16,6 +16,9 @@
 from qcodes.tests.dataset.temporary_databases import two_empty_temp_db_connections
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 from qcodes.tests.dataset.test_database_creation_and_upgrading import error_caused_by
+from qcodes.dataset.measurements import Measurement
+from qcodes import Station
+from qcodes.tests.instrument_mocks import DummyInstrument
 
 
 @contextmanager
@@ -475,3 +478,38 @@ def test_experiments_with_NULL_sample_name(two_empty_temp_db_connections,
     assert len(get_experiments(target_conn)) == 1
 
     assert len(Experiment(exp_id=1, conn=target_conn)) == 5
+
+
+def test_integration_station_and_measurement(two_empty_temp_db_connections):
+    """
+    An integration test where the runs in the source DB file are produced
+    with the Measurement object and there is a Station as well
+    """
+    source_conn, target_conn = two_empty_temp_db_connections
+    source_path = path_to_dbfile(source_conn)
+    target_path = path_to_dbfile(target_conn)
+
+    source_exp = Experiment(conn=source_conn)
+
+    # Set up measurement scenario
+    inst = DummyInstrument('inst', gates=['back', 'plunger', 'cutter'])
+    station = Station(inst)
+
+    meas = Measurement(exp=source_exp, station=station)
+    meas.register_parameter(inst.back)
+    meas.register_parameter(inst.plunger)
+    meas.register_parameter(inst.cutter, setpoints=(inst.back, inst.plunger))
+
+    with meas.run() as datasaver:
+        for back_v in [1, 2, 3]:
+            for plung_v in [-3, -2.5, 0]:
+                datasaver.add_result((inst.back, back_v),
+                                     (inst.plunger, plung_v),
+                                     (inst.cutter, back_v+plung_v))
+
+    extract_runs_into_db(source_path, target_path, 1)
+
+    target_ds = DataSet(conn=target_conn, run_id=1)
+
+    assert datasaver.dataset.the_same_dataset_as(target_ds)
+

From 043f03cc8e24620c6200fa73e5667c8fc841600e Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 11:12:56 +0100
Subject: [PATCH 274/719] Remove dead line of code

---
 qcodes/dataset/database_extract_runs.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index d11d2bffade..87718208d79 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -180,8 +180,6 @@ def _extract_single_dataset_into_db(dataset: DataSet,
 
     parspecs = dataset.paramspecs.values()
 
-    #metadata = dataset.get_metadata()
-
     _, target_run_id, target_table_name = create_run(target_conn,
                                                      target_exp_id,
                                                      name=dataset.name,
@@ -198,8 +196,6 @@ def _extract_single_dataset_into_db(dataset: DataSet,
                         dataset.completed_timestamp_raw)
 
 
-
-
 def _populate_results_table(source_conn: SomeConnection,
                             target_conn: SomeConnection,
                             source_table_name: str,

From fd9d04654190f49c4e186513ecd9aaaf5a117792 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 12:27:32 +0100
Subject: [PATCH 275/719] Fix critical typo in test

---
 qcodes/tests/dataset/test_database_extract_runs.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index 3d9f8d47c52..4dc6a62487f 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -120,7 +120,7 @@ def test_basic_extraction(two_empty_temp_db_connections, some_paramspecs):
 
     assert len(target_exp) == length1
 
-    target_dataset = DataSet(conn=source_conn, run_id=1)
+    target_dataset = DataSet(conn=target_conn, run_id=1)
 
     # Now make the interesting comparisons: are the target objects the same as
     # the source objects?

From 4a2b8c89a9600c8e160cc24654159e83d679c4c1 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 12:29:13 +0100
Subject: [PATCH 276/719] Also extract metadata

---
 qcodes/dataset/database_extract_runs.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 87718208d79..a10b15de554 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -179,12 +179,14 @@ def _extract_single_dataset_into_db(dataset: DataSet,
         return
 
     parspecs = dataset.paramspecs.values()
+    metadata = dataset.metadata
 
     _, target_run_id, target_table_name = create_run(target_conn,
                                                      target_exp_id,
                                                      name=dataset.name,
                                                      guid=dataset.guid,
-                                                     parameters=list(parspecs))
+                                                     parameters=list(parspecs),
+                                                     metadata=metadata)
     _populate_results_table(source_conn,
                             target_conn,
                             dataset.table_name,

From 3ac56777d3bb9c80febe2f6f2239b5007a7468ec Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:06:33 +0100
Subject: [PATCH 277/719] Refactor how snapshot get into dataset

---
 qcodes/dataset/data_set.py     | 14 ++++++++++++++
 qcodes/dataset/measurements.py |  6 ++----
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index ccdff6a17df..047e1466a82 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -518,6 +518,20 @@ def add_metadata(self, tag: str, metadata: Any):
         # `add_meta_data` does not commit, hence we commit here:
         self.conn.commit()
 
+    def add_snapshot(self, snapshot: str, overwrite: bool=False) -> None:
+        """
+        Adds a snapshot to this run
+
+        Args:
+            snapshot: the raw JSON dump of the snapshot
+            overwrite: force overwrite an existing snapshot
+        """
+        if self.snapshot is None or overwrite:
+            add_meta_data(self.conn, self.run_id, {'snapshot': snapshot})
+        elif self.snapshot is not None and not overwrite:
+            log.warning('This dataset already has a snapshot. Use overwrite'
+                        '=True to overwrite that')
+
     @property
     def started(self) -> bool:
         return self._started
diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index 4e8e47baefb..130c3f2c7f1 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -465,10 +465,8 @@ def __enter__(self) -> DataSaver:
             station = self.station
 
         if station:
-            self.ds.add_metadata('snapshot',
-                                 json.dumps({'station': station.snapshot()},
-                                            cls=NumpyJSONEncoder)
-                                 )
+            self.ds.add_snapshot(json.dumps({'station': station.snapshot()},
+                                            cls=NumpyJSONEncoder))
 
         if self.parameters is not None:
             for paramspec in self.parameters.values():

From 9200cce80a3268ba8f06a04106f9010a28ccd5eb Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:07:06 +0100
Subject: [PATCH 278/719] Also extract snapshot

---
 qcodes/dataset/database_extract_runs.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index a10b15de554..8444d9c7f56 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -5,7 +5,8 @@
 
 from qcodes.dataset.data_set import DataSet
 from qcodes.dataset.experiment_container import load_or_create_experiment
-from qcodes.dataset.sqlite_base import (atomic,
+from qcodes.dataset.sqlite_base import (add_meta_data,
+                                        atomic,
                                         connect,
                                         create_run,
                                         format_table_name,
@@ -180,6 +181,7 @@ def _extract_single_dataset_into_db(dataset: DataSet,
 
     parspecs = dataset.paramspecs.values()
     metadata = dataset.metadata
+    snapshot_raw = dataset.snapshot_raw
 
     _, target_run_id, target_table_name = create_run(target_conn,
                                                      target_exp_id,
@@ -197,6 +199,9 @@ def _extract_single_dataset_into_db(dataset: DataSet,
                         dataset.run_timestamp_raw,
                         dataset.completed_timestamp_raw)
 
+    if snapshot_raw is not None:
+        add_meta_data(target_conn, target_run_id, {'snapshot': snapshot_raw})
+
 
 def _populate_results_table(source_conn: SomeConnection,
                             target_conn: SomeConnection,

From 965be0bc20647cf5640f610677879eb31ff40d3d Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:10:50 +0100
Subject: [PATCH 279/719] Add a station to the example notebook

---
 ...ing runs from one DB file to another.ipynb | 36 ++++++++++---------
 1 file changed, 20 insertions(+), 16 deletions(-)

diff --git a/docs/examples/DataSet/Extracting runs from one DB file to another.ipynb b/docs/examples/DataSet/Extracting runs from one DB file to another.ipynb
index 56a164572cd..09772e7fdbc 100644
--- a/docs/examples/DataSet/Extracting runs from one DB file to another.ipynb	
+++ b/docs/examples/DataSet/Extracting runs from one DB file to another.ipynb	
@@ -30,9 +30,10 @@
     "\n",
     "from qcodes.dataset.sqlite_base import connect\n",
     "from qcodes.dataset.database_extract_runs import extract_runs_into_db\n",
-    "from qcodes.dataset.experiment_container import new_experiment, load_experiment_by_name\n",
+    "from qcodes.dataset.experiment_container import Experiment, load_experiment_by_name\n",
     "from qcodes.tests.instrument_mocks import DummyInstrument\n",
-    "from qcodes.dataset.measurements import Measurement"
+    "from qcodes.dataset.measurements import Measurement\n",
+    "from qcodes import Station"
    ]
   },
   {
@@ -49,7 +50,16 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Upgrading database: : 0it [00:00, ?it/s]\n",
+      "Upgrading database: : 0it [00:00, ?it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "source_conn = connect(source_path)\n",
     "target_conn = connect(target_path)"
@@ -61,11 +71,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "exp = new_experiment(name='extract_runs_experiment',\n",
-    "                     sample_name='no_sample',\n",
-    "                     conn=source_conn)\n",
+    "exp = Experiment(name='extract_runs_experiment',\n",
+    "                 sample_name='no_sample',\n",
+    "                 conn=source_conn)\n",
     "\n",
-    "my_inst = DummyInstrument('my_inst', gates=['voltage', 'current'])"
+    "my_inst = DummyInstrument('my_inst', gates=['voltage', 'current'])\n",
+    "station = Station(my_inst)"
    ]
   },
   {
@@ -175,7 +186,7 @@
     {
      "data": {
       "text/plain": [
-       "'aaaaaaaa-0c00-0007-0000-016754a23c83'"
+       "'aaaaaaaa-0c00-0007-0000-01675f5f9479'"
       ]
      },
      "execution_count": 9,
@@ -195,7 +206,7 @@
     {
      "data": {
       "text/plain": [
-       "'aaaaaaaa-0c00-0007-0000-016754a23c83'"
+       "'aaaaaaaa-0c00-0007-0000-01675f5f9479'"
       ]
      },
      "execution_count": 10,
@@ -206,13 +217,6 @@
    "source": [
     "target_exp.data_set(1).guid"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 7311b057d50ae25e32662650d86e4045a6b5f87c Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:15:22 +0100
Subject: [PATCH 280/719] Add version check of target DB file

---
 qcodes/dataset/database_extract_runs.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 8444d9c7f56..92a9b040131 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -1,5 +1,6 @@
 from typing import Union
 from warnings import warn
+import os
 
 import numpy as np
 
@@ -26,7 +27,8 @@
 
 def extract_runs_into_db(source_db_path: str,
                          target_db_path: str, *run_ids: int,
-                         upgrade_source_db: bool=False) -> None:
+                         upgrade_source_db: bool=False,
+                         upgrade_target_db: bool=False) -> None:
     """
     Extract a selection of runs into another DB file. All runs must come from
     the same experiment. They will be added to an experiment with the same name
@@ -49,6 +51,15 @@ def extract_runs_into_db(source_db_path: str,
              'upgrade_source_db=True to auto-upgrade the source DB file.')
         return
 
+    if os.path.exists(target_db_path):
+        (t_v, new_v) = get_db_version_and_newest_available_version(target_db_path)
+        if t_v < new_v and not upgrade_target_db:
+            warn(f'Target DB version is {t_v}, but this function needs it to '
+                 f'be in version {new_v}. Run this function again with '
+                 'upgrade_target_db=True to auto-upgrade the target DB file.')
+            return
+
+
     source_conn = connect(source_db_path)
 
     # Validate that all runs are in the source database

From b0b731da57153d37f895ca95019b1094c207ee58 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:35:52 +0100
Subject: [PATCH 281/719] Make an error message more informative

---
 qcodes/dataset/database_extract_runs.py            | 3 ++-
 qcodes/tests/dataset/test_database_extract_runs.py | 5 ++++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 92a9b040131..080b2c714fc 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -181,7 +181,8 @@ def _extract_single_dataset_into_db(dataset: DataSet,
 
     if not dataset.completed:
         raise ValueError('Dataset not completed. An incomplete dataset '
-                         'can not be copied.')
+                         'can not be copied. The incomplete dataset has '
+                         f'GUID: {dataset.guid} and run_id: {dataset.run_id}')
 
     source_conn = dataset.conn
 
diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index 4dc6a62487f..d3c68394228 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -93,7 +93,10 @@ def test_basic_extraction(two_empty_temp_db_connections, some_paramspecs):
         extract_runs_into_db(source_path, target_path, source_dataset.run_id)
 
     assert error_caused_by(excinfo, ('Dataset not completed. An incomplete '
-                                     'dataset can not be copied.'))
+                                     'dataset can not be copied. The '
+                                     'incomplete dataset has GUID: '
+                                     f'{source_dataset.guid} and run_id: '
+                                     f'{source_dataset.run_id}'))
 
     for ps in some_paramspecs[1].values():
         source_dataset.add_parameter(ps)

From c5d9bbb4c60cf342ea6a16c51b84e614f003ed27 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:37:23 +0100
Subject: [PATCH 282/719] Change the indentation in a docstring

---
 qcodes/dataset/sqlite_base.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index fadaa6a3de3..146278c3540 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1285,14 +1285,14 @@ def get_runid_from_guid(conn: SomeConnection, guid: str) -> Union[int, None]:
     Get the run_id of a run based on the guid
 
     Args:
-      conn: connection to the database
-      guid: the guid to look up
+        conn: connection to the database
+        guid: the guid to look up
 
     Returns:
         The run_id if found, else -1.
 
     Raises:
-      RuntimeError if more than one run with the given GUID exists
+        RuntimeError if more than one run with the given    GUID exists
     """
     query = """
             SELECT run_id

From c813da9432167cc66843316434c1242cd06a8f17 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:40:06 +0100
Subject: [PATCH 283/719] Unindent a block

---
 qcodes/tests/dataset/test_dataset_basic.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 24f2460ecb2..88f13a23e7b 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -557,17 +557,17 @@ def test_metadata(experiment):
 
 
 def test_the_same_dataset_as(some_paramspecs, experiment):
-        paramspecs = some_paramspecs[2]
-        ds = DataSet()
-        ds.add_parameter(paramspecs['ps1'])
-        ds.add_parameter(paramspecs['ps2'])
-        ds.add_result({'ps1': 1, 'ps2': 2})
+    paramspecs = some_paramspecs[2]
+    ds = DataSet()
+    ds.add_parameter(paramspecs['ps1'])
+    ds.add_parameter(paramspecs['ps2'])
+    ds.add_result({'ps1': 1, 'ps2': 2})
 
-        same_ds_from_load = DataSet(run_id=ds.run_id)
-        assert ds.the_same_dataset_as(same_ds_from_load)
+    same_ds_from_load = DataSet(run_id=ds.run_id)
+    assert ds.the_same_dataset_as(same_ds_from_load)
 
-        new_ds = DataSet()
-        assert not ds.the_same_dataset_as(new_ds)
+    new_ds = DataSet()
+    assert not ds.the_same_dataset_as(new_ds)
 
 
 class TestGetData:

From 08061c4e4c12a41ee69bd6eefafa51806c95212a Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:42:08 +0100
Subject: [PATCH 284/719] Fix indentation error

---
 qcodes/tests/dataset/test_dataset_loading.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_loading.py b/qcodes/tests/dataset/test_dataset_loading.py
index 30ecaf5c866..823557f78c0 100644
--- a/qcodes/tests/dataset/test_dataset_loading.py
+++ b/qcodes/tests/dataset/test_dataset_loading.py
@@ -188,12 +188,12 @@ def test_get_data_by_id_order(dataset):
 
 @pytest.mark.usefixtures('experiment')
 def test_load_by_guid(some_paramspecs):
-        paramspecs = some_paramspecs[2]
-        ds = DataSet()
-        ds.add_parameter(paramspecs['ps1'])
-        ds.add_parameter(paramspecs['ps2'])
-        ds.add_result({'ps1': 1, 'ps2': 2})
+    paramspecs = some_paramspecs[2]
+    ds = DataSet()
+    ds.add_parameter(paramspecs['ps1'])
+    ds.add_parameter(paramspecs['ps2'])
+    ds.add_result({'ps1': 1, 'ps2': 2})
 
-        loaded_ds = load_by_guid(ds.guid)
+    loaded_ds = load_by_guid(ds.guid)
 
-        assert loaded_ds.the_same_dataset_as(ds)
+    assert loaded_ds.the_same_dataset_as(ds)

From e20d4d099bc66d446ec30d7d08e2496b10ae86da Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:42:53 +0100
Subject: [PATCH 285/719] Remove print call

---
 qcodes/tests/dataset/test_sqlite_base.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index 60499421f2f..779f12244d9 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -195,7 +195,6 @@ def test_is_run_id_in_db(empty_temp_db):
 
     for _ in range(5):
         ds = DataSet(conn=conn, run_id=None)
-        print(ds.run_id)
 
     # there should now be run_ids 1, 2, 3, 4, 5 in the database
     good_ids = [1, 2, 3, 4, 5]

From 18cbcea6aba3b0ea35cc104d552ece2356275881 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:45:14 +0100
Subject: [PATCH 286/719] Reword the docstring of a test

---
 qcodes/tests/dataset/test_database_extract_runs.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index d3c68394228..701b8b01f76 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -38,8 +38,8 @@ def raise_if_file_changed(path_to_file: str):
 
 def test_missing_runs_raises(two_empty_temp_db_connections, some_paramspecs):
     """
-    Test that an error is raised if runs not present in the source DB are
-    attempted extracted
+    Test that an error is raised if we attempt to extract a run not present in
+    the source DB
     """
     source_conn, target_conn = two_empty_temp_db_connections
 

From b3d2060339773a117857378c7a0cf39d6ce54b32 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:46:50 +0100
Subject: [PATCH 287/719] Assert some more

---
 qcodes/tests/dataset/test_database_extract_runs.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index 701b8b01f76..785a0acaeeb 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -116,6 +116,7 @@ def test_basic_extraction(two_empty_temp_db_connections, some_paramspecs):
     target_exp = Experiment(conn=target_conn, exp_id=1)
 
     length1 = len(target_exp)
+    assert length1 == 1
 
     # trying to insert the same run again should be a NOOP
     with raise_if_file_changed(target_path):

From ea58bfd8527074231d04889974c6bada669530fd Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 13:48:09 +0100
Subject: [PATCH 288/719] Fix typo in a comment

---
 qcodes/tests/dataset/test_database_extract_runs.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index 785a0acaeeb..3bc084c9099 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -355,7 +355,7 @@ def test_result_table_naming_and_run_id(two_empty_temp_db_connections,
     extract_runs_into_db(source_path, target_path, source_ds_2_2.run_id)
 
     # The target ds ought to have a runs table "customname-1-1"
-    # and ough to be the same dataset as its "ancestor"
+    # and ought to be the same dataset as its "ancestor"
     target_ds = DataSet(conn=target_conn, run_id=1)
 
     assert target_ds.table_name == "customname-1-1"

From 47d93ac4c27ad9a6c90b6d80fb9feb9f38f1df16 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 29 Nov 2018 13:54:21 +0100
Subject: [PATCH 289/719] Fix stupidly broken tests

---
 qcodes/tests/dataset/dataset_fixtures.py   | 20 +++++++++++++-------
 qcodes/tests/dataset/test_dataset_basic.py |  4 ++--
 2 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
index d0525105cc3..548bcac7ce4 100644
--- a/qcodes/tests/dataset/dataset_fixtures.py
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -6,7 +6,7 @@
 from qcodes.tests.instrument_mocks import ArraySetPointParam, Multi2DSetPointParam
 
 # pylint: disable=unused-import
-from qcodes.tests.dataset.temporary_databases import dataset
+from qcodes.tests.dataset.temporary_databases import dataset, experiment
 # pylint: enable=unused-import
 
 
@@ -33,24 +33,30 @@ def scalar_dataset(dataset):
 
 
 @pytest.fixture
-def array_dataset(dataset):
+def array_dataset(experiment):
     meas = Measurement()
     param = ArraySetPointParam()
     meas.register_parameter(param)
 
     with meas.run() as datasaver:
         datasaver.add_result((param, param.get(),))
-    datasaver.dataset.conn.close()
-    yield dataset
+    try:
+        yield datasaver.dataset
+    finally:
+        datasaver.dataset.conn.close()
+
 
 
 @pytest.fixture
-def multi_dataset(dataset):
+def multi_dataset(experiment):
     meas = Measurement()
     param = Multi2DSetPointParam()
+
     meas.register_parameter(param)
 
     with meas.run() as datasaver:
         datasaver.add_result((param, param.get(),))
-    datasaver.dataset.conn.close()
-    yield dataset
+    try:
+        yield datasaver.dataset
+    finally:
+        datasaver.dataset.conn.close()
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index c38d18a278a..52cb9b249db 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -626,7 +626,7 @@ def test_get_array_parameter_data(array_dataset):
     parameter_test_helper(array_dataset)
 
 
-def test_get_array_parameter_data(multi_dataset):
+def test_get_multi_parameter_data(multi_dataset):
     parameter_test_helper(multi_dataset)
 
 
@@ -648,7 +648,7 @@ def parameter_test_helper(ds):
             if isinstance(v_ref, float):
                 assert isinstance(v_test, np.float64)
             elif isinstance(v_ref, int):
-                assert isinstance(v_test, np.int)
+                assert isinstance(v_test, np.int32)
             else:
                 raise RuntimeError('Unsupported data type')
             assert np.isclose(v_test, v_ref)

From 150fdb0fa51e087fb3af0ee3ec8bb82fbd72250f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 29 Nov 2018 14:04:18 +0100
Subject: [PATCH 290/719] This takes a connectionplus

---
 qcodes/dataset/sqlite_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 126ac0dcde0..dc94c362a9e 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1182,7 +1182,7 @@ def get_data(conn: ConnectionPlus,
     return res
 
 
-def get_parameter_data(conn: SomeConnection,
+def get_parameter_data(conn: ConnectionPlus,
                        table_name: str,
                        columns: List[str],
                        start: int = None,

From 848373b89e2c263f9d7ef5283e3c2ee5c2c7b4cc Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 14:14:26 +0100
Subject: [PATCH 291/719] Replace SomeConnection with ConnectionPlus

---
 qcodes/dataset/data_set.py              |  6 +++---
 qcodes/dataset/database_extract_runs.py | 12 ++++++------
 qcodes/dataset/experiment_container.py  | 10 +++++-----
 3 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 940ab93e1d4..5c8bf052d2f 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -911,7 +911,7 @@ def __repr__(self) -> str:
 
 
 # public api
-def load_by_id(run_id: int, conn: Optional[SomeConnection]=None) -> DataSet:
+def load_by_id(run_id: int, conn: Optional[ConnectionPlus]=None) -> DataSet:
     """
     Load dataset by run id
 
@@ -934,7 +934,7 @@ def load_by_id(run_id: int, conn: Optional[SomeConnection]=None) -> DataSet:
     return d
 
 
-def load_by_guid(guid: str, conn: Optional[SomeConnection]=None) -> DataSet:
+def load_by_guid(guid: str, conn: Optional[ConnectionPlus]=None) -> DataSet:
     """
     Load a dataset by its GUID
 
@@ -964,7 +964,7 @@ def load_by_guid(guid: str, conn: Optional[SomeConnection]=None) -> DataSet:
 
 
 def load_by_counter(counter: int, exp_id: int,
-                    conn: Optional[SomeConnection]=None) -> DataSet:
+                    conn: Optional[ConnectionPlus]=None) -> DataSet:
     """
     Load a dataset given its counter in a given experiment
 
diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 080b2c714fc..1fe782c6c1e 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -21,7 +21,7 @@
                                         mark_run_complete,
                                         new_experiment,
                                         select_many_where,
-                                        SomeConnection,
+                                        ConnectionPlus,
                                         sql_placeholder_string)
 
 
@@ -123,7 +123,7 @@ def extract_runs_into_db(source_db_path: str,
         target_conn.close()
 
 
-def _create_exp_if_needed(target_conn: SomeConnection,
+def _create_exp_if_needed(target_conn: ConnectionPlus,
                           exp_name: str,
                           sample_name: str,
                           fmt_str: str,
@@ -161,7 +161,7 @@ def _create_exp_if_needed(target_conn: SomeConnection,
 
 
 def _extract_single_dataset_into_db(dataset: DataSet,
-                                    target_conn: SomeConnection,
+                                    target_conn: ConnectionPlus,
                                     target_exp_id: int) -> None:
     """
     NB: This function should only be called from within
@@ -215,8 +215,8 @@ def _extract_single_dataset_into_db(dataset: DataSet,
         add_meta_data(target_conn, target_run_id, {'snapshot': snapshot_raw})
 
 
-def _populate_results_table(source_conn: SomeConnection,
-                            target_conn: SomeConnection,
+def _populate_results_table(source_conn: ConnectionPlus,
+                            target_conn: ConnectionPlus,
                             source_table_name: str,
                             target_table_name: str) -> None:
     """
@@ -242,7 +242,7 @@ def _populate_results_table(source_conn: SomeConnection,
         target_cursor.execute(insert_data_query, values)
 
 
-def _rewrite_timestamps(target_conn: SomeConnection, target_run_id: int,
+def _rewrite_timestamps(target_conn: ConnectionPlus, target_run_id: int,
                         correct_run_timestamp: float,
                         correct_completed_timestamp: float) -> None:
     """
diff --git a/qcodes/dataset/experiment_container.py b/qcodes/dataset/experiment_container.py
index e3d56406407..5fc798cd264 100644
--- a/qcodes/dataset/experiment_container.py
+++ b/qcodes/dataset/experiment_container.py
@@ -14,7 +14,7 @@
                                         get_experiment_name_from_experiment_id,
                                         get_runid_from_expid_and_counter,
                                         get_sample_name_from_experiment_id,
-                                        SomeConnection)
+                                        ConnectionPlus)
 from qcodes.dataset.sqlite_base import new_experiment as ne
 from qcodes.dataset.database import get_DB_location, get_DB_debug
 
@@ -28,7 +28,7 @@ def __init__(self, path_to_db: Optional[str]=None,
                  name: Optional[str]=None,
                  sample_name: Optional[str]=None,
                  format_string: str="{}-{}-{}",
-                 conn: Optional[SomeConnection]=None) -> None:
+                 conn: Optional[ConnectionPlus]=None) -> None:
         """
         Create or load an experiment. If exp_id is None, a new experiment is
         created. If exp_id is not None, an experiment is loaded.
@@ -204,7 +204,7 @@ def experiments()->List[Experiment]:
 def new_experiment(name: str,
                    sample_name: str,
                    format_string: Optional[str]="{}-{}-{}",
-                   conn: Optional[SomeConnection]=None) -> Experiment:
+                   conn: Optional[ConnectionPlus]=None) -> Experiment:
     """
     Create a new experiment (in the database file from config)
 
@@ -254,7 +254,7 @@ def load_last_experiment() -> Experiment:
 
 def load_experiment_by_name(name: str,
                             sample: Optional[str] = None,
-                            conn: Optional[SomeConnection]=None) -> Experiment:
+                            conn: Optional[ConnectionPlus]=None) -> Experiment:
     """
     Try to load experiment with the specified name.
 
@@ -315,7 +315,7 @@ def load_experiment_by_name(name: str,
 
 def load_or_create_experiment(experiment_name: str,
                               sample_name: Optional[str] = None,
-                              conn: Optional[SomeConnection]=None)->Experiment:
+                              conn: Optional[ConnectionPlus]=None)->Experiment:
     """
     Find and return an experiment with the given name and sample name,
     or create one if not found.

From a6d1ca3e4094e62fcf7427410bd006af982ea80f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 29 Nov 2018 14:44:45 +0100
Subject: [PATCH 292/719] Fix tests on travis

---
 qcodes/tests/dataset/dataset_fixtures.py   | 19 +++++++++++++++++++
 qcodes/tests/dataset/test_dataset_basic.py | 13 +++++++++++--
 2 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
index 548bcac7ce4..89a9cfbd9d3 100644
--- a/qcodes/tests/dataset/dataset_fixtures.py
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -4,6 +4,7 @@
 
 from qcodes.dataset.measurements import Measurement
 from qcodes.tests.instrument_mocks import ArraySetPointParam, Multi2DSetPointParam
+from qcodes.instrument.parameter import Parameter
 
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import dataset, experiment
@@ -60,3 +61,21 @@ def multi_dataset(experiment):
         yield datasaver.dataset
     finally:
         datasaver.dataset.conn.close()
+
+@pytest.fixture
+def array_in_scalar_dataset(experiment):
+    meas = Measurement()
+    scalar_param = Parameter('scalarparam', set_cmd=None)
+    param = ArraySetPointParam()
+    meas.register_parameter(scalar_param)
+    meas.register_parameter(param, setpoints=(scalar_param,))
+
+    with meas.run() as datasaver:
+        for i in range(1, 10):
+            scalar_param.set(i)
+            datasaver.add_result((scalar_param, scalar_param.get()),
+                                 (param, param.get()))
+    try:
+        yield datasaver.dataset
+    finally:
+        datasaver.dataset.conn.close()
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index c230615ba2a..5bccd53a117 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -23,7 +23,7 @@
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import (empty_temp_db,
                                                       experiment, dataset)
-from qcodes.tests.dataset.dataset_fixtures import scalar_dataset, array_dataset, multi_dataset
+from qcodes.tests.dataset.dataset_fixtures import scalar_dataset, array_dataset, multi_dataset, array_in_scalar_dataset
 # pylint: disable=unused-import
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 
@@ -770,6 +770,8 @@ def test_get_array_parameter_data(array_dataset):
 def test_get_multi_parameter_data(multi_dataset):
     parameter_test_helper(multi_dataset)
 
+def test_get_array_in_scalar_param_data(array_in_scalar_dataset):
+    parameter_test_helper(array_in_scalar_dataset)
 
 def parameter_test_helper(ds):
     params = ds.parameters.split(',')
@@ -782,6 +784,10 @@ def parameter_test_helper(ds):
     ref = ds.get_data(*params)
     dut = ds.get_parameter_data(*params)
 
+    column_lenghts = []
+    for i_col, col in enumerate(dut):
+        column_lenghts.append(len(col))
+
     for i_row, row in enumerate(ref):
         for i_param, param_name in enumerate(param_names):
             v_ref = row[i_param]
@@ -789,7 +795,10 @@ def parameter_test_helper(ds):
             if isinstance(v_ref, float):
                 assert isinstance(v_test, np.float64)
             elif isinstance(v_ref, int):
-                assert isinstance(v_test, np.int32)
+                # default datatype for int is c_long which is 32 bit on
+                # windows and 64bit on linux
+                assert isinstance(v_test, np.int32) or \
+                       isinstance(v_test, np.int64)
             else:
                 raise RuntimeError('Unsupported data type')
             assert np.isclose(v_test, v_ref)

From 306a46eb3970ac9bdf60c5737d049a5303b392b3 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 29 Nov 2018 14:53:52 +0100
Subject: [PATCH 293/719] Test that all columns have the same lenght

---
 qcodes/tests/dataset/test_dataset_basic.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 5bccd53a117..88cc59ad313 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -770,9 +770,11 @@ def test_get_array_parameter_data(array_dataset):
 def test_get_multi_parameter_data(multi_dataset):
     parameter_test_helper(multi_dataset)
 
+
 def test_get_array_in_scalar_param_data(array_in_scalar_dataset):
     parameter_test_helper(array_in_scalar_dataset)
 
+
 def parameter_test_helper(ds):
     params = ds.parameters.split(',')
     # delete some random parameter to test it with an incomplete list
@@ -784,9 +786,11 @@ def parameter_test_helper(ds):
     ref = ds.get_data(*params)
     dut = ds.get_parameter_data(*params)
 
-    column_lenghts = []
-    for i_col, col in enumerate(dut):
-        column_lenghts.append(len(col))
+    column_lengths = []
+    for col in dut.values():
+        column_lengths.append(col.size)
+    column_lenghts = np.array(column_lengths)
+    assert np.all(column_lenghts == column_lenghts[0])
 
     for i_row, row in enumerate(ref):
         for i_param, param_name in enumerate(param_names):

From 8eadd1995f5af07d070fba26fe8aa62631a5eabc Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 15:03:36 +0100
Subject: [PATCH 294/719] Add test for old target db file

---
 .../dataset/test_database_extract_runs.py     | 30 +++++++++++++++++--
 1 file changed, 28 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index 3bc084c9099..9a3e8688e22 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -408,11 +408,13 @@ def test_load_by_X_functions(two_empty_temp_db_connections,
     assert source_ds_2_2.the_same_dataset_as(test_ds)
 
 
-def test_old_versions_not_touched(two_empty_temp_db_connections):
+def test_old_versions_not_touched(two_empty_temp_db_connections,
+                                  some_paramspecs):
 
-    _, target_conn = two_empty_temp_db_connections
+    source_conn, target_conn = two_empty_temp_db_connections
 
     target_path = path_to_dbfile(target_conn)
+    source_path = path_to_dbfile(source_conn)
 
     fixturepath = os.sep.join(qcodes.tests.dataset.__file__.split(os.sep)[:-1])
     fixturepath = os.path.join(fixturepath,
@@ -421,6 +423,9 @@ def test_old_versions_not_touched(two_empty_temp_db_connections):
     if not os.path.exists(fixturepath):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
                     " using the scripts in the legacy_DB_generation folder")
+
+    # First test that we can not use an old version as source
+
     with raise_if_file_changed(fixturepath):
         with pytest.warns(UserWarning) as warning:
             extract_runs_into_db(fixturepath, target_path, 1)
@@ -431,6 +436,27 @@ def test_old_versions_not_touched(two_empty_temp_db_connections):
                              'the source DB file.')
             assert warning[0].message.args[0] == expected_mssg
 
+    # Then test that we can not use an old version as target
+
+    # first create a run in the new version source
+    source_exp = Experiment(conn=source_conn)
+    source_ds = DataSet(conn=source_conn, exp_id=source_exp.exp_id)
+
+    for ps in some_paramspecs[2].values():
+        source_ds.add_parameter(ps)
+    source_ds.add_result({ps.name: 0.0
+                              for ps in some_paramspecs[2].values()})
+    source_ds.mark_complete()
+
+    with raise_if_file_changed(fixturepath):
+        with pytest.warns(UserWarning) as warning:
+            extract_runs_into_db(source_path, fixturepath, 1)
+            expected_mssg = ('Target DB version is 2, but this '
+                             'function needs it to be in version 3. '
+                             'Run this function again with '
+                             'upgrade_target_db=True to auto-upgrade '
+                             'the target DB file.')
+            assert warning[0].message.args[0] == expected_mssg
 
 def test_experiments_with_NULL_sample_name(two_empty_temp_db_connections,
                                            some_paramspecs):

From 5d7557e8d37677964d82dc1ee232612530450eb3 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 15:14:51 +0100
Subject: [PATCH 295/719] Add test for atomicity

---
 .../dataset/test_database_extract_runs.py     | 38 +++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index 9a3e8688e22..935183728b8 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -2,6 +2,7 @@
 from contextlib import contextmanager
 import re
 import os
+from pathlib import Path
 
 import pytest
 import numpy as np
@@ -543,3 +544,40 @@ def test_integration_station_and_measurement(two_empty_temp_db_connections):
 
     assert datasaver.dataset.the_same_dataset_as(target_ds)
 
+
+def test_atomicity(two_empty_temp_db_connections, some_paramspecs):
+    """
+    Test the atomicity of the transaction by extracting and inserting two
+    runs where the second one is not completed. The not completed error must
+    roll back any changes to the target
+    """
+    source_conn, target_conn = two_empty_temp_db_connections
+
+    source_path = path_to_dbfile(source_conn)
+    target_path = path_to_dbfile(target_conn)
+
+    # The target file must exist for us to be able to see whether it has
+    # changed
+    Path(target_path).touch()
+
+    source_exp = Experiment(conn=source_conn)
+    source_ds_1 = DataSet(conn=source_conn, exp_id=source_exp.exp_id)
+    for ps in some_paramspecs[2].values():
+        source_ds_1.add_parameter(ps)
+    source_ds_1.add_result({ps.name: 2.1
+                            for ps in some_paramspecs[2].values()})
+    source_ds_1.mark_complete()
+
+    source_ds_2 = DataSet(conn=source_conn, exp_id=source_exp.exp_id)
+    for ps in some_paramspecs[2].values():
+        source_ds_2.add_parameter(ps)
+    source_ds_2.add_result({ps.name: 2.1
+                            for ps in some_paramspecs[2].values()})
+    # This dataset is NOT marked as completed
+
+    # now check that the target file is untouched
+    with raise_if_file_changed(target_path):
+        # although the not completed error is a ValueError, we get the
+        # RuntimeError from SQLite
+        with pytest.raises(RuntimeError):
+            extract_runs_into_db(source_path, target_path, 1, 2)

From 6a7abfe6d2ea4b152351a96be073f95576aff315 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 29 Nov 2018 15:21:01 +0100
Subject: [PATCH 296/719] Add tests for datasets with strings

---
 qcodes/tests/dataset/dataset_fixtures.py   | 19 +++++++++++++++++++
 qcodes/tests/dataset/test_dataset_basic.py | 13 +++++++++++--
 2 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
index 89a9cfbd9d3..0493fb65840 100644
--- a/qcodes/tests/dataset/dataset_fixtures.py
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -79,3 +79,22 @@ def array_in_scalar_dataset(experiment):
         yield datasaver.dataset
     finally:
         datasaver.dataset.conn.close()
+
+
+@pytest.fixture
+def array_in_str_dataset(experiment):
+    meas = Measurement()
+    scalar_param = Parameter('scalarparam', set_cmd=None)
+    param = ArraySetPointParam()
+    meas.register_parameter(scalar_param, paramtype='text')
+    meas.register_parameter(param, setpoints=(scalar_param,))
+
+    with meas.run() as datasaver:
+        for i in ['A', 'B', 'C']:
+            scalar_param.set(i)
+            datasaver.add_result((scalar_param, scalar_param.get()),
+                                 (param, param.get()))
+    try:
+        yield datasaver.dataset
+    finally:
+        datasaver.dataset.conn.close()
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 88cc59ad313..ea81bb0dd12 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -23,7 +23,8 @@
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import (empty_temp_db,
                                                       experiment, dataset)
-from qcodes.tests.dataset.dataset_fixtures import scalar_dataset, array_dataset, multi_dataset, array_in_scalar_dataset
+from qcodes.tests.dataset.dataset_fixtures import scalar_dataset, \
+    array_dataset, multi_dataset, array_in_scalar_dataset, array_in_str_dataset
 # pylint: disable=unused-import
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 
@@ -775,6 +776,10 @@ def test_get_array_in_scalar_param_data(array_in_scalar_dataset):
     parameter_test_helper(array_in_scalar_dataset)
 
 
+def test_get_array_in_str_param_data(array_in_str_dataset):
+    parameter_test_helper(array_in_str_dataset)
+
+
 def parameter_test_helper(ds):
     params = ds.parameters.split(',')
     # delete some random parameter to test it with an incomplete list
@@ -798,11 +803,15 @@ def parameter_test_helper(ds):
             v_test = dut[param_name][i_row]
             if isinstance(v_ref, float):
                 assert isinstance(v_test, np.float64)
+                assert np.isclose(v_test, v_ref)
             elif isinstance(v_ref, int):
                 # default datatype for int is c_long which is 32 bit on
                 # windows and 64bit on linux
                 assert isinstance(v_test, np.int32) or \
                        isinstance(v_test, np.int64)
+                assert np.isclose(v_test, v_ref)
+            elif isinstance(v_ref, str):
+                assert isinstance(v_test, np.str_)
+                assert v_ref == v_test
             else:
                 raise RuntimeError('Unsupported data type')
-            assert np.isclose(v_test, v_ref)

From c37c1fb8158715636eea66711c9fd5a8dde75279 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Thu, 29 Nov 2018 15:23:04 +0100
Subject: [PATCH 297/719] Add test for column mismatch between target and
 source

---
 .../dataset/test_database_extract_runs.py     | 45 +++++++++++++++++++
 1 file changed, 45 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index 935183728b8..c385920af69 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -581,3 +581,48 @@ def test_atomicity(two_empty_temp_db_connections, some_paramspecs):
         # RuntimeError from SQLite
         with pytest.raises(RuntimeError):
             extract_runs_into_db(source_path, target_path, 1, 2)
+
+
+def test_column_mismatch(two_empty_temp_db_connections, some_paramspecs):
+    """
+    Test insertion of runs with no metadata and no snapshot into a DB already
+    containing a run that has both
+    """
+
+    source_conn, target_conn = two_empty_temp_db_connections
+    source_path = path_to_dbfile(source_conn)
+    target_path = path_to_dbfile(target_conn)
+
+    target_exp = Experiment(conn=target_conn)
+
+    # Set up measurement scenario
+    inst = DummyInstrument('inst', gates=['back', 'plunger', 'cutter'])
+    station = Station(inst)
+
+    meas = Measurement(exp=target_exp, station=station)
+    meas.register_parameter(inst.back)
+    meas.register_parameter(inst.plunger)
+    meas.register_parameter(inst.cutter, setpoints=(inst.back, inst.plunger))
+
+    with meas.run() as datasaver:
+        for back_v in [1, 2, 3]:
+            for plung_v in [-3, -2.5, 0]:
+                datasaver.add_result((inst.back, back_v),
+                                     (inst.plunger, plung_v),
+                                     (inst.cutter, back_v+plung_v))
+    datasaver.dataset.add_metadata('meta_tag', 'meta_value')
+
+    Experiment(conn=source_conn)
+    source_ds = DataSet(conn=source_conn)
+    for ps in some_paramspecs[2].values():
+        source_ds.add_parameter(ps)
+    source_ds.add_result({ps.name: 2.1
+                          for ps in some_paramspecs[2].values()})
+    source_ds.mark_complete()
+
+    extract_runs_into_db(source_path, target_path, 1)
+
+    # compare
+    target_copied_ds = DataSet(conn=target_conn, run_id=2)
+
+    assert target_copied_ds.the_same_dataset_as(source_ds)

From 114253de72e1d2da0779208ef441166281203666 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 29 Nov 2018 15:55:41 +0100
Subject: [PATCH 298/719] Use instrument fixture in tests to ensure instrument
 gets closed

---
 .../dataset/test_database_extract_runs.py     | 20 +++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index c385920af69..ec44885bb29 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -37,6 +37,18 @@ def raise_if_file_changed(path_to_file: str):
         raise RuntimeError(f'File {path_to_file} was modified.')
 
 
+@pytest.fixture(scope='function')
+def inst():
+    """
+    Dummy instrument for testing, ensuring that it's instance gets closed
+    and removed from the global register of instruments, which, if not done,
+    make break other tests
+    """
+    inst = DummyInstrument('inst', gates=['back', 'plunger', 'cutter'])
+    yield inst
+    inst.close()
+
+
 def test_missing_runs_raises(two_empty_temp_db_connections, some_paramspecs):
     """
     Test that an error is raised if we attempt to extract a run not present in
@@ -459,6 +471,7 @@ def test_old_versions_not_touched(two_empty_temp_db_connections,
                              'the target DB file.')
             assert warning[0].message.args[0] == expected_mssg
 
+
 def test_experiments_with_NULL_sample_name(two_empty_temp_db_connections,
                                            some_paramspecs):
     """
@@ -511,7 +524,8 @@ def test_experiments_with_NULL_sample_name(two_empty_temp_db_connections,
     assert len(Experiment(exp_id=1, conn=target_conn)) == 5
 
 
-def test_integration_station_and_measurement(two_empty_temp_db_connections):
+def test_integration_station_and_measurement(two_empty_temp_db_connections,
+                                             inst):
     """
     An integration test where the runs in the source DB file are produced
     with the Measurement object and there is a Station as well
@@ -523,7 +537,6 @@ def test_integration_station_and_measurement(two_empty_temp_db_connections):
     source_exp = Experiment(conn=source_conn)
 
     # Set up measurement scenario
-    inst = DummyInstrument('inst', gates=['back', 'plunger', 'cutter'])
     station = Station(inst)
 
     meas = Measurement(exp=source_exp, station=station)
@@ -583,7 +596,7 @@ def test_atomicity(two_empty_temp_db_connections, some_paramspecs):
             extract_runs_into_db(source_path, target_path, 1, 2)
 
 
-def test_column_mismatch(two_empty_temp_db_connections, some_paramspecs):
+def test_column_mismatch(two_empty_temp_db_connections, some_paramspecs, inst):
     """
     Test insertion of runs with no metadata and no snapshot into a DB already
     containing a run that has both
@@ -596,7 +609,6 @@ def test_column_mismatch(two_empty_temp_db_connections, some_paramspecs):
     target_exp = Experiment(conn=target_conn)
 
     # Set up measurement scenario
-    inst = DummyInstrument('inst', gates=['back', 'plunger', 'cutter'])
     station = Station(inst)
 
     meas = Measurement(exp=target_exp, station=station)

From 104dc2c83592a1bbf14baf88d62a273030ee987c Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 29 Nov 2018 18:18:29 +0100
Subject: [PATCH 299/719] Use log instead of print in AWG70000A driver

Substitute the annoying `print` statements in the driver with `log.debug`. Logging can be easily enabled when needed, qcodes has convenient infrastructure as well.
---
 qcodes/instrument_drivers/tektronix/AWG70000A.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG70000A.py b/qcodes/instrument_drivers/tektronix/AWG70000A.py
index 122d0388b1c..cf4762fd112 100644
--- a/qcodes/instrument_drivers/tektronix/AWG70000A.py
+++ b/qcodes/instrument_drivers/tektronix/AWG70000A.py
@@ -941,8 +941,7 @@ def make_SEQX_from_forged_sequence(
         # STEP 2:
         # Make all subsequence .sml files
 
-        print('Waveforms done')
-        print(wfmx_filenames)
+        log.debug(f'Waveforms done: {wfmx_filenames}')
 
         subseqsml_files: List[str] = []
         subseqsml_filenames: List[str] = []
@@ -974,7 +973,7 @@ def make_SEQX_from_forged_sequence(
 
                 subseqname = f'subsequence_{pos1}'
 
-                print(ss_wfm_names)
+                log.debug(f'Subsequence waveform names: {ss_wfm_names}')
 
                 subseqsml = AWG70000A._makeSMLFile(trig_waits=seqing['twait'],
                                                    nreps=seqing['nrep'],
@@ -1013,8 +1012,7 @@ def make_SEQX_from_forged_sequence(
                                     if f'wfm_{pos1}' in wn])
         seqing = {k: [d[k] for d in seqings] for k in seqings[0].keys()}
 
-        print('True debug')
-        print(asset_names)
+        log.debug(f'Assets for SML file: {asset_names}')
 
         mainseqname = seqname
         mainseqsml = AWG70000A._makeSMLFile(trig_waits=seqing['twait'],

From 5eeed70d9ed60b0b7087519c201ce2b339fdc7dd Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Thu, 29 Nov 2018 09:32:11 -0800
Subject: [PATCH 300/719] removed superfluous newline

---
 .../QCodes example with Keithley S46.ipynb    | 350 ++++++++++++++++++
 qcodes/tests/drivers/test_keithley_s46.py     |   1 -
 2 files changed, 350 insertions(+), 1 deletion(-)
 create mode 100644 docs/examples/driver_examples/QCodes example with Keithley S46.ipynb

diff --git a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb
new file mode 100644
index 00000000000..20ce0d8c4a0
--- /dev/null
+++ b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
@@ -0,0 +1,350 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument_drivers.tektronix.Keithley_s46 import S46"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.36s\n"
+     ]
+    }
+   ],
+   "source": [
+    "s46 = S46(\"s2\", \"GPIB0::7::INSTR\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'KEITHLEY INSTRUMENTS INC.,MODEL SYSTEM 46, 1327388, A03  '"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s46.ask(\"*IDN?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'6,6,6,6,0,0,0,0,1,0,0,1'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s46.ask(\":CONF:CPOL?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'(@1,7)'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s46.ask(\":CLOS?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(s46.channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "channel1 is on relay A\n",
+      "channel2 is on relay A\n",
+      "channel3 is on relay A\n",
+      "channel4 is on relay A\n",
+      "channel5 is on relay A\n",
+      "channel6 is on relay A\n",
+      "channel7 is on relay B\n",
+      "channel8 is on relay B\n",
+      "channel9 is on relay B\n",
+      "channel10 is on relay B\n",
+      "channel11 is on relay B\n",
+      "channel12 is on relay B\n",
+      "channel13 is on relay C\n",
+      "channel14 is on relay C\n",
+      "channel15 is on relay C\n",
+      "channel16 is on relay C\n",
+      "channel17 is on relay C\n",
+      "channel18 is on relay C\n",
+      "channel19 is on relay D\n",
+      "channel20 is on relay D\n",
+      "channel21 is on relay D\n",
+      "channel22 is on relay D\n",
+      "channel23 is on relay D\n",
+      "channel24 is on relay D\n",
+      "channel25 is on relay 5\n",
+      "channel26 is on relay 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "for channel in s46.channels: \n",
+    "    print(f\"{channel.short_name} is on relay {channel.relay_id}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'open'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s46.channels[0].state()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s46.channels[0].state(\"close\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'close'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s46.channels[0].state()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Relay already in use by channel 1', 'setting s2_channel2_state to close')\n"
+     ]
+    }
+   ],
+   "source": [
+    "try: \n",
+    "    s46.channels[1].state(\"close\")\n",
+    "    raise Exception(\"We should not be here \")\n",
+    "except RuntimeError as e: \n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s46.channels[1].state(\"open\")  # This should have no effect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Relay already in use by channel 1', 'setting s2_channel6_state to close')\n"
+     ]
+    }
+   ],
+   "source": [
+    "try: \n",
+    "    s46.channels[5].state(\"close\")\n",
+    "    raise Exception(\"We should not be here \")\n",
+    "except RuntimeError as e: \n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s46.channels[6].state(\"close\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Relay already in use by channel 7', 'setting s2_channel8_state to close')\n"
+     ]
+    }
+   ],
+   "source": [
+    "try: \n",
+    "    s46.channels[7].state(\"close\")\n",
+    "    raise Exception(\"We should not be here \")\n",
+    "except RuntimeError as e: \n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s46.channels[0].state(\"open\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s46.channels[1].state(\"close\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Relay already in use by channel 2', 'setting s2_channel1_state to close')\n"
+     ]
+    }
+   ],
+   "source": [
+    "try: \n",
+    "    s46.channels[0].state(\"close\")\n",
+    "    raise Exception(\"We should not be here \")\n",
+    "except RuntimeError as e: \n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
index 13f29a400d0..5d90fa37b2d 100644
--- a/qcodes/tests/drivers/test_keithley_s46.py
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -46,7 +46,6 @@ def test_open_close(s46):
 
     s46.channels[0].state("open")
     s46.channels[1].state("close")
-
     s46.channels[18].state("close")
 
     with pytest.raises(

From 67ecfbe7ded55e9c6244d7ea47a45661ca0773f5 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Thu, 29 Nov 2018 17:52:53 -0800
Subject: [PATCH 301/719] major rewrite of driver: organized in terms of relays

---
 .../QCodes example with Keithley S46.ipynb    | 230 ++++++------------
 .../tektronix/Keithley_s46.py                 | 189 +++++++-------
 qcodes/tests/drivers/test_keithley_s46.py     |   7 +-
 3 files changed, 177 insertions(+), 249 deletions(-)

diff --git a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb
index 20ce0d8c4a0..9d42179bd51 100644
--- a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
+++ b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
@@ -6,7 +6,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qcodes.instrument_drivers.tektronix.Keithley_s46 import S46"
+    "from qcodes.instrument_drivers.tektronix.Keithley_s46 import S46, LockAcquisitionError"
    ]
   },
   {
@@ -18,7 +18,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.36s\n"
+      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.34s\n"
      ]
     }
    ],
@@ -28,294 +28,222 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'KEITHLEY INSTRUMENTS INC.,MODEL SYSTEM 46, 1327388, A03  '"
+       "['A1']"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "s46.ask(\"*IDN?\")"
+    "s46.get_closed_channels(by_name=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'6,6,6,6,0,0,0,0,1,0,0,1'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "s46.ask(\":CONF:CPOL?\")"
+    "s46.A1.state(\"open\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'(@1,7)'"
+       "'open'"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "s46.ask(\":CLOS?\")"
+    "s46.A1.state()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s46.A1.state(\"close\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "26"
+       "'close'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(s46.channels)"
+    "s46.A1.state()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "channel1 is on relay A\n",
-      "channel2 is on relay A\n",
-      "channel3 is on relay A\n",
-      "channel4 is on relay A\n",
-      "channel5 is on relay A\n",
-      "channel6 is on relay A\n",
-      "channel7 is on relay B\n",
-      "channel8 is on relay B\n",
-      "channel9 is on relay B\n",
-      "channel10 is on relay B\n",
-      "channel11 is on relay B\n",
-      "channel12 is on relay B\n",
-      "channel13 is on relay C\n",
-      "channel14 is on relay C\n",
-      "channel15 is on relay C\n",
-      "channel16 is on relay C\n",
-      "channel17 is on relay C\n",
-      "channel18 is on relay C\n",
-      "channel19 is on relay D\n",
-      "channel20 is on relay D\n",
-      "channel21 is on relay D\n",
-      "channel22 is on relay D\n",
-      "channel23 is on relay D\n",
-      "channel24 is on relay D\n",
-      "channel25 is on relay 5\n",
-      "channel26 is on relay 8\n"
+      "('Relay A is already in use by channel 1', 'setting s2_A2_state to close')\n"
      ]
     }
    ],
    "source": [
-    "for channel in s46.channels: \n",
-    "    print(f\"{channel.short_name} is on relay {channel.relay_id}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'open'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s46.channels[0].state()"
+    "try: \n",
+    "    s46.A2.state(\"close\")\n",
+    "    raise(\"We should not be here\")\n",
+    "except LockAcquisitionError as e: \n",
+    "    print(e)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.channels[0].state(\"close\")"
+    "s46.A1.state(\"open\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'close'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "s46.channels[0].state()"
+    "s46.A2.state(\"close\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('Relay already in use by channel 1', 'setting s2_channel2_state to close')\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "try: \n",
-    "    s46.channels[1].state(\"close\")\n",
-    "    raise Exception(\"We should not be here \")\n",
-    "except RuntimeError as e: \n",
-    "    print(e)"
+    "s46.A2.state(\"open\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.channels[1].state(\"open\")  # This should have no effect"
+    "assert s46.A1 is s46.channels[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('Relay already in use by channel 1', 'setting s2_channel6_state to close')\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "try: \n",
-    "    s46.channels[5].state(\"close\")\n",
-    "    raise Exception(\"We should not be here \")\n",
-    "except RuntimeError as e: \n",
-    "    print(e)"
+    "assert s46.A2 is s46.channels[1]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.channels[6].state(\"close\")"
+    "assert s46.B1 is s46.channels[6]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('Relay already in use by channel 7', 'setting s2_channel8_state to close')\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'B1'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "try: \n",
-    "    s46.channels[7].state(\"close\")\n",
-    "    raise Exception(\"We should not be here \")\n",
-    "except RuntimeError as e: \n",
-    "    print(e)"
+    "s46.channels[6].short_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.channels[0].state(\"open\")"
+    "s46.A1.state(\"close\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.channels[1].state(\"close\")"
+    "s46.B1.state(\"close\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('Relay already in use by channel 2', 'setting s2_channel1_state to close')\n"
-     ]
+     "data": {
+      "text/plain": [
+       "['A1', 'B1']"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "try: \n",
-    "    s46.channels[0].state(\"close\")\n",
-    "    raise Exception(\"We should not be here \")\n",
-    "except RuntimeError as e: \n",
-    "    print(e)"
+    "s46.get_closed_channels(by_name=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s46.R8.state(\"close\")"
    ]
   },
   {
diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index 4c2acdb4b04..29bf91a4191 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -1,146 +1,149 @@
 import numpy as np
-import itertools
-from collections import defaultdict
 import re
 
+from typing import cast
 from qcodes import VisaInstrument, InstrumentChannel, ChannelList
 from qcodes.utils.validators import Enum
 
 
-class ChannelNotPresentError(Exception):
-    pass
-
-
 class LockAcquisitionError(Exception):
     pass
 
 
-class RelayLock:
-    def __init__(self):
-        self._acquired = False
-        self._acquired_by = None
-
-    def acquire(self, requester_id):
-        if self._acquired and self._acquired_by != requester_id:
-            raise LockAcquisitionError(
-                f"Relay already in use by channel {self.acquired_by}"
-            )
-
-        self._acquired = True
-        self._acquired_by = requester_id
-
-    def release(self, requester_id):
-        if self._acquired_by != requester_id:
-            # It should be impossible to get here. There is a driver bug
-            # if we do.
-            raise RuntimeError(
-                f"Relay can only be freed by channel {self.acquired_by} "
-                f"that acquired the lock. Please get in touch with a QCoDeS "
-                f"developer to get to the root cause of this error"
-            )
-
-        self._acquired = False
-        self._acquired_by = None
-
-    @property
-    def acquired_by(self):
-        return self._acquired_by
-
-
-class RFSwitchChannel(InstrumentChannel):
-    def __init__(self, parent, name, channel_number, relay_locks):
+class S46Channel(InstrumentChannel):
+    def __init__(self, parent, name, channel_number, relay):
         super().__init__(parent, name)
-        self._channel_number = channel_number
-
-        self._relay_id = self._get_relay()
-        self._relay_lock = relay_locks[self._relay_id]
 
+        self._channel_number = channel_number
+        self._relay = relay
         if self._get_state() == "close":
             try:
-                self._relay_lock.acquire(self._channel_number)
+                self._relay.acquire_lock(self._channel_number)
             except LockAcquisitionError as e:
                 raise RuntimeError(
                     "The driver is initialized from an undesirable instrument "
                     "state where more then one channel on a single relay is "
-                    "closed. It is advised to power cycle the instrument or to "
-                    "manually send the 'OPEN:ALL' SCPI command to get the "
-                    "instrument back into a normal state. Refusing to "
-                    "initialize driver!"
+                    "closed. It is advised to power cycle the instrument. "
+                    "Refusing to initialize driver!"
                 ) from e
 
         self.add_parameter(
             "state",
             get_cmd=self._get_state,
-            set_cmd=f":{{}} (@{self._channel_number})",
-            set_parser=self._set_state_parser,
+            set_cmd=self._set_state,
             vals=Enum("open", "close")
         )
 
-    def _get_relay(self):
-        relay_layout = self.parent.relay_layout
-        found = False
+    def _get_state(self):
+        is_closed = self._channel_number in \
+                    self.root_instrument.get_closed_channels()
 
-        count = 0
-        for count, total_count in enumerate(np.cumsum(relay_layout)):
-            if total_count >= self._channel_number:
-                found = True
-                break
+        return {True: "close", False: "open"}[is_closed]
 
-        if not found:
-            raise ChannelNotPresentError()
+    def _set_state(self, new_state: str):
 
-        return (["A", "B", "C", "D"] + [str(i) for i in range(1, 9)])[count]
+        current_state = self._get_state()
+        if new_state == current_state:
+            return
 
-    def _get_state(self):
-        closed_channels = re.findall(r"(\d+)[,)]",  self.ask(":CLOS?"))
-        return "close" \
-            if str(self._channel_number) in closed_channels \
-            else "open"
+        if new_state == "close":
+            self._relay.acquire_lock(self._channel_number)
+        elif new_state == "open":
+            self._relay.release_lock(self._channel_number)
 
-    def _set_state_parser(self, new_state: str):
+        self.write(f":{new_state} (@{self._channel_number})")
 
-        if new_state == "close":
-            self._relay_lock.acquire(self._channel_number)
-        elif new_state == "open" \
-                and self._relay_lock.acquired_by == self._channel_number:
 
-            self._relay_lock.release(self._channel_number)
+class S46Relay(InstrumentChannel):
+    def __init__(self, parent, name, channel_offset, channel_count):
+        super().__init__(parent, name)
 
-        return new_state.upper()
+        self._channel_offset = channel_offset
+        self._locked_by = None
 
-    @property
-    def relay_id(self):
-        return self._relay_id
+        channels = ChannelList(
+            cast(VisaInstrument, self),
+            "channel",
+            S46Channel,
+            snapshotable=False
+        )
+
+        for count, channel_number in enumerate(range(
+                channel_offset, channel_offset + channel_count)):
+
+            if channel_count == 1:
+                channel_name = self.short_name
+            else:
+                channel_name = f"{self.short_name}{count + 1}"
+
+            channel = S46Channel(
+                cast(VisaInstrument, self.parent),
+                channel_name,
+                channel_number + 1,
+                self
+            )
+            channels.append(channel)
+
+        self.add_submodule("channels", channels)
+
+    def acquire_lock(self, channel_number):
+        if self._locked_by is None:
+            self._locked_by = channel_number
+        else:
+            raise LockAcquisitionError(
+                f"Relay {self.short_name} is already in use by channel "
+                f"{self._locked_by}"
+            )
+
+    def release_lock(self, channel_number):
+        if self._locked_by == channel_number:
+            self._locked_by = None
+        else:
+            raise RuntimeError(
+                "This run time error should never occur. Please get in touch "
+                "with a QCoDeS developer"
+            )
 
 
 class S46(VisaInstrument):
     def __init__(self, name, address, **kwargs):
         super().__init__(name, address, terminator="\n", **kwargs)
 
-        self._relay_layout = [
+        relay_layout = [
             int(i) for i in self.ask(":CONF:CPOL?").split(",")
         ]
+        relay_names = (["A", "B", "C", "D"] + [f"R{i}" for i in range(1, 9)])
+        # Relay offsets are independent of pole configuration. See page
+        # 2-5 of the manual
+        channel_offsets = np.cumsum([0] + 4 * [6] + 7 * [1])
 
         channels = ChannelList(
-            self, "channel", RFSwitchChannel, snapshotable=False
+            self,
+            "channel",
+            S46Channel,
+            snapshotable=False
         )
 
-        relay_locks = defaultdict(RelayLock)
-
-        for chn in itertools.count(1):
-            chn_name = f"channel{chn}"
+        for name, channel_offset, channel_count in zip(
+                relay_names, channel_offsets, relay_layout):
 
-            try:
-                channel = RFSwitchChannel(self, chn_name, chn, relay_locks)
-            except ChannelNotPresentError:
-                break
-
-            self.add_submodule(chn_name, channel)
-            channels.append(channel)
+            relay = S46Relay(self, name, channel_offset, channel_count)
+            for channel in relay.channels:
+                channels.append(channel)
+                self.add_submodule(channel.short_name, channel)
 
         self.add_submodule("channels", channels)
         self.connect_message()
 
-    @property
-    def relay_layout(self):
-        return self._relay_layout
+    def get_closed_channels(self, by_name=False):
+
+        closed_channels_str = re.findall(r"\d+", self.ask(":CLOS?"))
+        closed_channels_numbers = [int(i) for i in closed_channels_str]
+
+        if not by_name:
+            return closed_channels_numbers
+        else:
+            return [
+                self.channels[i-1].short_name
+                for i in closed_channels_numbers
+            ]
diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
index 5d90fa37b2d..2f2a7e43a0e 100644
--- a/qcodes/tests/drivers/test_keithley_s46.py
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -32,8 +32,8 @@ def test_init(s46):
     closed_channels = [0, 7, 12]
 
     for channel_nr in range(n_channels):
-        assert s46.channels[channel_nr].state() == "close" \
-            if channel_nr in closed_channels else "open"
+        state = "close" if channel_nr in closed_channels else "open"
+        assert s46.channels[channel_nr].state() == state
 
 
 def test_open_close(s46):
@@ -53,6 +53,3 @@ def test_open_close(s46):
         match="Relay already in use by channel"
     ):
         s46.channels[19].state("close")
-
-    s46.channels[18].state("open")
-    s46.channels[19].state("close")

From af646aec542a289a4010bb8b28fe704552a20da2 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Thu, 29 Nov 2018 18:47:33 -0800
Subject: [PATCH 302/719] do not raise a run time warning when attempting to
 release a lock that does not belong to you

---
 .../QCodes example with Keithley S46.ipynb    | 72 ++++++++++++-------
 .../tektronix/Keithley_s46.py                 | 50 +++++++------
 2 files changed, 70 insertions(+), 52 deletions(-)

diff --git a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb
index 9d42179bd51..05698e3beb9 100644
--- a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
+++ b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
@@ -18,7 +18,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.34s\n"
+      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.36s\n"
      ]
     }
    ],
@@ -34,7 +34,9 @@
     {
      "data": {
       "text/plain": [
-       "['A1']"
+       "[<S46Channel: s2_A1 of S46: s2>,\n",
+       " <S46Channel: s2_B1 of S46: s2>,\n",
+       " <S46Channel: s2_R8 of S46: s2>]"
       ]
      },
      "execution_count": 3,
@@ -43,13 +45,33 @@
     }
    ],
    "source": [
-    "s46.get_closed_channels(by_name=True)"
+    "s46.get_closed_channels()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(s46.channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [],
    "source": [
     "s46.A1.state(\"open\")"
@@ -57,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -66,7 +88,7 @@
        "'open'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -77,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -95,7 +117,7 @@
        "'close'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -106,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,20 +243,9 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['A1', 'B1']"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "s46.get_closed_channels(by_name=True)"
+    "s46.R8.state(\"close\")"
    ]
   },
   {
@@ -243,7 +254,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.R8.state(\"close\")"
+    "s46.open_all_channels()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s46.A1.state(\"open\")"
    ]
   },
   {
diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index 29bf91a4191..e61ac3fd8ba 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -36,16 +36,12 @@ def __init__(self, parent, name, channel_number, relay):
 
     def _get_state(self):
         is_closed = self._channel_number in \
-                    self.root_instrument.get_closed_channels()
+                    self.root_instrument.get_closed_channel_numbers()
 
         return {True: "close", False: "open"}[is_closed]
 
     def _set_state(self, new_state: str):
 
-        current_state = self._get_state()
-        if new_state == current_state:
-            return
-
         if new_state == "close":
             self._relay.acquire_lock(self._channel_number)
         elif new_state == "open":
@@ -53,6 +49,10 @@ def _set_state(self, new_state: str):
 
         self.write(f":{new_state} (@{self._channel_number})")
 
+    @property
+    def channel_number(self):
+        return self._channel_number
+
 
 class S46Relay(InstrumentChannel):
     def __init__(self, parent, name, channel_offset, channel_count):
@@ -79,7 +79,7 @@ def __init__(self, parent, name, channel_offset, channel_count):
             channel = S46Channel(
                 cast(VisaInstrument, self.parent),
                 channel_name,
-                channel_number + 1,
+                channel_number,
                 self
             )
             channels.append(channel)
@@ -87,22 +87,19 @@ def __init__(self, parent, name, channel_offset, channel_count):
         self.add_submodule("channels", channels)
 
     def acquire_lock(self, channel_number):
-        if self._locked_by is None:
-            self._locked_by = channel_number
-        else:
+
+        if self._locked_by is not None and self._locked_by != channel_number:
             raise LockAcquisitionError(
                 f"Relay {self.short_name} is already in use by channel "
                 f"{self._locked_by}"
             )
+        else:
+            self._locked_by = channel_number
 
     def release_lock(self, channel_number):
+
         if self._locked_by == channel_number:
             self._locked_by = None
-        else:
-            raise RuntimeError(
-                "This run time error should never occur. Please get in touch "
-                "with a QCoDeS developer"
-            )
 
 
 class S46(VisaInstrument):
@@ -113,9 +110,9 @@ def __init__(self, name, address, **kwargs):
             int(i) for i in self.ask(":CONF:CPOL?").split(",")
         ]
         relay_names = (["A", "B", "C", "D"] + [f"R{i}" for i in range(1, 9)])
-        # Relay offsets are independent of pole configuration. See page
+        # Channel offsets are independent of pole configuration. See page
         # 2-5 of the manual
-        channel_offsets = np.cumsum([0] + 4 * [6] + 7 * [1])
+        channel_offsets = np.cumsum([0] + 4 * [6] + 7 * [1]) + 1
 
         channels = ChannelList(
             self,
@@ -135,15 +132,16 @@ def __init__(self, name, address, **kwargs):
         self.add_submodule("channels", channels)
         self.connect_message()
 
-    def get_closed_channels(self, by_name=False):
-
+    def get_closed_channel_numbers(self):
         closed_channels_str = re.findall(r"\d+", self.ask(":CLOS?"))
-        closed_channels_numbers = [int(i) for i in closed_channels_str]
+        return [int(i) for i in closed_channels_str]
 
-        if not by_name:
-            return closed_channels_numbers
-        else:
-            return [
-                self.channels[i-1].short_name
-                for i in closed_channels_numbers
-            ]
+    def get_closed_channels(self):
+        return [
+            channel for channel in self.channels if
+            channel.channel_number in self.get_closed_channel_numbers()
+        ]
+
+    def open_all_channels(self):
+        for channel in self.get_closed_channels():
+            cast(S46Channel, channel).state("open")

From ff28261ef72a247dc94483edddc78da3ee897464 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 30 Nov 2018 11:40:45 +0100
Subject: [PATCH 303/719] Use instance log instead of module level

log.<> --> self.log.<>
---
 .../instrument_drivers/tektronix/AWG70000A.py | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG70000A.py b/qcodes/instrument_drivers/tektronix/AWG70000A.py
index cf4762fd112..aa91b0a514d 100644
--- a/qcodes/instrument_drivers/tektronix/AWG70000A.py
+++ b/qcodes/instrument_drivers/tektronix/AWG70000A.py
@@ -658,12 +658,12 @@ def _sendBinaryFile(self, binfile: bytes, filename: str,
         self.current_directory(path)
 
         if overwrite:
-            log.debug(f'Pre-deleting file {filename} at {path}')
+            self.log.debug(f'Pre-deleting file {filename} at {path}')
             self.visa_handle.write(f'MMEMory:DELete "{filename}"')
             # if the file does not exist,
             # an error code -256 is put in the error queue
             resp = self.visa_handle.query(f'SYSTem:ERRor:CODE?')
-            log.debug(f'Pre-deletion finished with return code {resp}')
+            self.log.debug(f'Pre-deletion finished with return code {resp}')
 
         self.visa_handle.write_raw(msg)
 
@@ -839,12 +839,12 @@ def _makeWFMXFileBinaryData(data: np.ndarray, amplitude: float) -> bytes:
             binary_marker = struct.pack(fmt, *markers)
 
         if wfm.max() > channel_max or wfm.min() < channel_min:
-            log.warning('Waveform exceeds specified channel range.'
-                        ' The resulting waveform will be clipped. '
-                        'Waveform min.: {} (V), waveform max.: {} (V),'
-                        'Channel min.: {} (V), channel max.: {} (V)'
-                        ''.format(wfm.min(), wfm.max(), channel_min,
-                                  channel_max))
+            self.log.warning('Waveform exceeds specified channel range.'
+                             ' The resulting waveform will be clipped. '
+                             'Waveform min.: {} (V), waveform max.: {} (V),'
+                             'Channel min.: {} (V), channel max.: {} (V)'
+                             ''.format(wfm.min(), wfm.max(), channel_min,
+                                       channel_max))
 
         # the data must be such that channel_max becomes 1 and
         # channel_min becomes -1
@@ -941,7 +941,7 @@ def make_SEQX_from_forged_sequence(
         # STEP 2:
         # Make all subsequence .sml files
 
-        log.debug(f'Waveforms done: {wfmx_filenames}')
+        self.log.debug(f'Waveforms done: {wfmx_filenames}')
 
         subseqsml_files: List[str] = []
         subseqsml_filenames: List[str] = []
@@ -973,7 +973,7 @@ def make_SEQX_from_forged_sequence(
 
                 subseqname = f'subsequence_{pos1}'
 
-                log.debug(f'Subsequence waveform names: {ss_wfm_names}')
+                self.log.debug(f'Subsequence waveform names: {ss_wfm_names}')
 
                 subseqsml = AWG70000A._makeSMLFile(trig_waits=seqing['twait'],
                                                    nreps=seqing['nrep'],
@@ -1012,7 +1012,7 @@ def make_SEQX_from_forged_sequence(
                                     if f'wfm_{pos1}' in wn])
         seqing = {k: [d[k] for d in seqings] for k in seqings[0].keys()}
 
-        log.debug(f'Assets for SML file: {asset_names}')
+        self.log.debug(f'Assets for SML file: {asset_names}')
 
         mainseqname = seqname
         mainseqsml = AWG70000A._makeSMLFile(trig_waits=seqing['twait'],

From 8cb21a8bbe69e41895234b4d11a1d88ee74869ea Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 30 Nov 2018 12:09:25 +0100
Subject: [PATCH 304/719] In static methods use log of the module level

---
 .../instrument_drivers/tektronix/AWG70000A.py  | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/AWG70000A.py b/qcodes/instrument_drivers/tektronix/AWG70000A.py
index aa91b0a514d..e23953c4f6a 100644
--- a/qcodes/instrument_drivers/tektronix/AWG70000A.py
+++ b/qcodes/instrument_drivers/tektronix/AWG70000A.py
@@ -839,12 +839,12 @@ def _makeWFMXFileBinaryData(data: np.ndarray, amplitude: float) -> bytes:
             binary_marker = struct.pack(fmt, *markers)
 
         if wfm.max() > channel_max or wfm.min() < channel_min:
-            self.log.warning('Waveform exceeds specified channel range.'
-                             ' The resulting waveform will be clipped. '
-                             'Waveform min.: {} (V), waveform max.: {} (V),'
-                             'Channel min.: {} (V), channel max.: {} (V)'
-                             ''.format(wfm.min(), wfm.max(), channel_min,
-                                       channel_max))
+            log.warning('Waveform exceeds specified channel range.'
+                        ' The resulting waveform will be clipped. '
+                        'Waveform min.: {} (V), waveform max.: {} (V),'
+                        'Channel min.: {} (V), channel max.: {} (V)'
+                        ''.format(wfm.min(), wfm.max(), channel_min,
+                                  channel_max))
 
         # the data must be such that channel_max becomes 1 and
         # channel_min becomes -1
@@ -941,7 +941,7 @@ def make_SEQX_from_forged_sequence(
         # STEP 2:
         # Make all subsequence .sml files
 
-        self.log.debug(f'Waveforms done: {wfmx_filenames}')
+        log.debug(f'Waveforms done: {wfmx_filenames}')
 
         subseqsml_files: List[str] = []
         subseqsml_filenames: List[str] = []
@@ -973,7 +973,7 @@ def make_SEQX_from_forged_sequence(
 
                 subseqname = f'subsequence_{pos1}'
 
-                self.log.debug(f'Subsequence waveform names: {ss_wfm_names}')
+                log.debug(f'Subsequence waveform names: {ss_wfm_names}')
 
                 subseqsml = AWG70000A._makeSMLFile(trig_waits=seqing['twait'],
                                                    nreps=seqing['nrep'],
@@ -1012,7 +1012,7 @@ def make_SEQX_from_forged_sequence(
                                     if f'wfm_{pos1}' in wn])
         seqing = {k: [d[k] for d in seqings] for k in seqings[0].keys()}
 
-        self.log.debug(f'Assets for SML file: {asset_names}')
+        log.debug(f'Assets for SML file: {asset_names}')
 
         mainseqname = seqname
         mainseqsml = AWG70000A._makeSMLFile(trig_waits=seqing['twait'],

From 7c07882184e39d67202234f913e7b849023d1d50 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 30 Nov 2018 15:52:56 +0100
Subject: [PATCH 305/719] Add get data function that expands arrays

---
 qcodes/dataset/sqlite_base.py | 80 ++++++++++++++++++++++++++++++++++-
 1 file changed, 79 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index dc94c362a9e..f15ba935a5a 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1312,7 +1312,7 @@ def get_layout(conn: ConnectionPlus,
 
     Args:
         conn: The database connection
-        run_id: The run_id as in the runs table
+        layout_id: The run_id as in the layouts table
 
     Returns:
         A dict with name, label, and unit
@@ -1390,9 +1390,87 @@ def get_dependencies(conn: ConnectionPlus,
     res = many_many(c, 'independent', 'axis_num')
     return res
 
+
+def get_independent_parameters(conn: ConnectionPlus,
+                               run_id: int):
+    """"
+    This should return all parameters that are neither
+    dependent on other parameters or dependencies of other parameters
+    """
+    raise NotADirectoryError("TODO")
 # Higher level Wrappers
 
 
+def get_dependency_parameters(conn: ConnectionPlus, param: str,
+                              run_id: int) -> List[ParamSpec]:
+    """
+    Given a parameter name return its ParamSpec of the parameter along with
+    the ParamSpecs of all it's dependencies.
+
+    Args:
+        conn:
+        param:
+        run_id:
+
+    """
+    layout_id = get_layout_id(conn, param, run_id)
+    deps = get_dependencies(conn, layout_id)
+    parameters = [get_paramspec(conn, run_id, param)]
+
+    for dep in deps:
+        depinfo = get_layout(conn, dep[0])
+        parameters.append(get_paramspec(conn, run_id, depinfo['name']))
+    return parameters
+
+
+def get_data_of_param_and_deps_as_columns(conn: ConnectionPlus,
+                                          params: List[str], run_id: int):
+    """
+    Load
+    Args:
+        conn:
+        params:
+        run_id:
+
+    Returns:
+
+    """
+    output = {}
+    if len(params) == 0:
+        raise NotImplementedError("HERE we should look up all "
+                                  "dependent standalone parameters")
+
+    for param in params:
+        params = get_dependency_parameters(conn, param, run_id)
+        columns = [param.name for param in params]
+        types = [param.type for param in params]
+        # !!! fix this, there should probably be a method to get the table
+        # from the run_id
+        sql = f"""
+        SELECT result_table_name FROM runs WHERE run_id = {run_id}
+        """
+        c = conn.execute(sql)
+        result_table_name = one(c, 'result_table_name')
+
+        res = get_data(conn, result_table_name, columns)
+
+        if 'array' in types and 'numeric' in types: # todo handle str
+            # todo: Should we check that all the arrays are the same size?
+            first_array_element = types.index('array')
+            non_array_elements = [i for i, x in enumerate(types)
+                                  if x == "numeric"]
+            for dat in res:
+                for element in non_array_elements:
+                    dat[element] = np.zeros_like(dat[first_array_element]) \
+                                   + dat[element]
+
+        res_t = list(map(list, zip(*res)))
+        output[param] = {name: np.array(column_data)
+                         for name, column_data
+                         in zip(columns, res_t)}
+    return output
+
+
 def new_experiment(conn: ConnectionPlus,
                    name: str,
                    sample_name: str,

From 2b5f06fdc093fe9d7adfbe1bd2bf994e59edb595 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 30 Nov 2018 16:16:08 +0100
Subject: [PATCH 306/719] make syntax match other funcs better

---
 qcodes/dataset/sqlite_base.py | 43 ++++++++++++++++++++---------------
 1 file changed, 25 insertions(+), 18 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index f15ba935a5a..792e21ca5d4 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1424,35 +1424,42 @@ def get_dependency_parameters(conn: ConnectionPlus, param: str,
 
 
 def get_data_of_param_and_deps_as_columns(conn: ConnectionPlus,
-                                          params: List[str], run_id: int):
+                                          table_name: str,
+                                          columns: List[str],
+                                          start: Optional[int]=None,
+                                          end: Optional[int]=None) -> \
+        Dict[str, Dict[str, np.ndarray]]:
     """
-    Load
-    Args:
-        conn:
-        params:
-        run_id:
-
-    Returns:
+    Get data for one or more parameters and its dependencies. The data
+    is returned as numpy arrays nested 2 layers of dicts. The keys or the
+    outermost are the requested parameters and the second level by the loaded
+    parameters.
 
+    Args:
+        conn: database connection
+        table_name: name of the table
+        columns: list of columns
+        start: start of range; if None, then starts from the top of the table
+        end: end of range; if None, then ends at the bottom of the table
     """
     output = {}
-    if len(params) == 0:
+    if len(columns) == 0:
         raise NotImplementedError("HERE we should look up all "
                                   "dependent standalone parameters")
 
-    for param in params:
+    # get run_id
+    sql = """
+    SELECT run_id FROM runs WHERE result_table_name = ?
+    """
+    c = atomic_transaction(conn, sql, table_name)
+    run_id = one(c, 'run_id')
+
+    for param in columns:
         params = get_dependency_parameters(conn, param, run_id)
         columns = [param.name for param in params]
         types = [param.type for param in params]
-        # !!! fix this, there should probably be a method to get the table
-        # from the run_id
-        sql = f"""
-        SELECT result_table_name FROM runs WHERE run_id = {run_id}
-        """
-        c = conn.execute(sql)
-        result_table_name = one(c, 'result_table_name')
 
-        res = get_data(conn, result_table_name, columns)
+        res = get_data(conn, table_name, columns, start=start, end=end)
 
         if 'array' in types and 'numeric' in types: # todo handle str
             # todo: Should we check that all the arrays are the same size?

From 4a11e76f934519f3704d2782fcbe382811f142e8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 30 Nov 2018 16:16:30 +0100
Subject: [PATCH 307/719] Add dataset wrapper

---
 qcodes/dataset/data_set.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 56a466e15de..b80e4ebcf55 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -37,7 +37,8 @@
                                         update_run_description,
                                         run_exists, remove_trigger,
                                         make_connection_plus_from,
-                                        ConnectionPlus)
+                                        ConnectionPlus,
+                                        get_data_of_param_and_deps_as_columns)
 
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
@@ -782,6 +783,18 @@ def get_parameter_data(
         return get_parameter_data(self.conn, self.table_name, valid_param_names,
                         start, end)
 
+    def get_data_of_param_and_deps_as_columns(
+            self,
+            *params: Union[str, ParamSpec, _BaseParameter],
+            start: Optional[int] = None,
+            end: Optional[int] = None) -> Dict[str, Dict[str, numpy.ndarray]]:
+        valid_param_names = self._validate_parameters(*params)
+        return get_data_of_param_and_deps_as_columns(self.conn,
+                                                     self.table_name,
+                                                     valid_param_names,
+                                                     start, end)
+
+
     def get_values(self, param_name: str) -> List[List[Any]]:
         """
         Get the values (i.e. not NULLs) of the specified parameter

From 1d5c9b95d81ba050d26129901088bb2bf76d7419 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Fri, 30 Nov 2018 15:05:51 -0800
Subject: [PATCH 308/719] major rewrite, again

---
 .../QCodes example with Keithley S46.ipynb    | 206 +++++++++++++++++-
 .../tektronix/Keithley_s46.py                 |  94 ++++----
 qcodes/tests/drivers/test_keithley_s46.py     |  22 +-
 3 files changed, 266 insertions(+), 56 deletions(-)

diff --git a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb
index 05698e3beb9..6405988ca0a 100644
--- a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
+++ b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,14 +11,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.36s\n"
+      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.20s\n"
      ]
     }
    ],
@@ -266,6 +266,206 @@
     "s46.A1.state(\"open\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'A1': 1, 'A2': 2, 'A3': 3, 'A4': 4, 'A5': 5, 'A6': 6, 'B1': 7, 'B2': 8, 'B3': 9, 'B4': 10, 'B5': 11, 'B6': 12, 'C1': 13, 'C2': 14, 'C3': 15, 'C4': 16, 'C5': 17, 'C6': 18, 'D1': 19, 'D2': 20, 'D3': 21, 'D4': 22, 'D5': 23, 'D6': 24, 'R1': 25, 'R2': 26, 'R3': 27, 'R4': 28, 'R5': 29, 'R6': 30, 'R7': 31, 'R8': 32}\n"
+     ]
+    }
+   ],
+   "source": [
+    "channel_aliases = [\n",
+    "    f\"{relay_name}{index}\" for\n",
+    "    relay_name, index in product(\n",
+    "        [\"A\", \"B\", \"C\", \"D\"],\n",
+    "        range(1, 7)\n",
+    "    )\n",
+    "]\n",
+    "channel_aliases += [f\"R{i}\" for i in range(1, 9)]\n",
+    "\n",
+    "print(dict(zip(channel_aliases, range(1, 33))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'A1': 1,\n",
+       " 'A2': 2,\n",
+       " 'A3': 3,\n",
+       " 'A4': 4,\n",
+       " 'A5': 5,\n",
+       " 'A6': 6,\n",
+       " 'B1': 7,\n",
+       " 'B2': 8,\n",
+       " 'B3': 9,\n",
+       " 'B4': 10,\n",
+       " 'B5': 11,\n",
+       " 'B6': 12,\n",
+       " 'C1': 13,\n",
+       " 'C2': 14,\n",
+       " 'C3': 15,\n",
+       " 'C4': 16,\n",
+       " 'C5': 17,\n",
+       " 'C6': 18,\n",
+       " 'D1': 19,\n",
+       " 'D2': 20,\n",
+       " 'D3': 21,\n",
+       " 'D4': 22,\n",
+       " 'D5': 23,\n",
+       " 'D6': 24,\n",
+       " 'R1': 25,\n",
+       " 'R2': 26,\n",
+       " 'R3': 27,\n",
+       " 'R4': 28,\n",
+       " 'R5': 29,\n",
+       " 'R6': 30,\n",
+       " 'R7': 31,\n",
+       " 'R8': 32}"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(\n",
+    "    zip(\n",
+    "        [\"{}{}\".format(*a) for a in product([\"A\", \"B\", \"C\", \"D\"], range(1, 7))] + [f\"R{i}\" for i in range(1, 9)], \n",
+    "        range(1, 33)\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6, 6, 6, 6, 0, 0, 0, 0, 1, 0, 0, 1]\n",
+      "['A', 'B', 'C', 'D', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8']\n"
+     ]
+    }
+   ],
+   "source": [
+    "relay_layout = [int(i) for i in s46.ask(\":CONF:CPOL?\").split(\",\")]\n",
+    "relay_names = ([\"A\", \"B\", \"C\", \"D\"] + [f\"R{i}\" for i in range(1, 9)])\n",
+    "print(relay_layout)\n",
+    "print(relay_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class RelayLock: \n",
+    "    def __init__(self, relay_name): \n",
+    "        self._relay_name = relay_name\n",
+    "    \n",
+    "    def acquire(self, channel_number): \n",
+    "        pass \n",
+    "    \n",
+    "    def release(self, channel_number):\n",
+    "        pass "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class s46Channel: \n",
+    "    def __init__(self, name, channel_number, lock): \n",
+    "        self._name = name \n",
+    "        self._lock = lock\n",
+    "        self._channel_number = channel_number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for relay_name, channel_count in zip(relay_names, relay_layout): \n",
+    "    \n",
+    "    relay_lock = RelayLock(relay_name)\n",
+    "    \n",
+    "    for channel_index in range(1, channel_count + 1):\n",
+    "        \n",
+    "        if channel_count > 1:\n",
+    "            alias = f\"{relay_name}{channel_index}\"\n",
+    "        else:\n",
+    "            alias = relay_name\n",
+    "            \n",
+    "        channel_number = aliases[alias]\n",
+    "        channel = s46Channel(alias, channel_number, relay_lock)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class classproperty:\n",
+    "    def __init__(self, f):\n",
+    "        self.f = f\n",
+    "    def __get__(self, obj, owner):\n",
+    "        return self.f(owner)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class A: \n",
+    "    @classmethod\n",
+    "    @property\n",
+    "    def aclassprop(cls): \n",
+    "        return 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method ? of <class '__main__.A'>>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.aclassprop"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index e61ac3fd8ba..51488e3a4d9 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -1,4 +1,4 @@
-import numpy as np
+import itertools
 import re
 
 from typing import cast
@@ -11,14 +11,14 @@ class LockAcquisitionError(Exception):
 
 
 class S46Channel(InstrumentChannel):
-    def __init__(self, parent, name, channel_number, relay):
+    def __init__(self, parent, name, channel_number, relay_lock):
         super().__init__(parent, name)
 
         self._channel_number = channel_number
-        self._relay = relay
+        self._relay_lock = relay_lock
         if self._get_state() == "close":
             try:
-                self._relay.acquire_lock(self._channel_number)
+                self._relay_lock.acquire(self._channel_number)
             except LockAcquisitionError as e:
                 raise RuntimeError(
                     "The driver is initialized from an undesirable instrument "
@@ -43,9 +43,9 @@ def _get_state(self):
     def _set_state(self, new_state: str):
 
         if new_state == "close":
-            self._relay.acquire_lock(self._channel_number)
+            self._relay_lock.acquire(self._channel_number)
         elif new_state == "open":
-            self._relay.release_lock(self._channel_number)
+            self._relay_lock.release(self._channel_number)
 
         self.write(f":{new_state} (@{self._channel_number})")
 
@@ -54,66 +54,45 @@ def channel_number(self):
         return self._channel_number
 
 
-class S46Relay(InstrumentChannel):
-    def __init__(self, parent, name, channel_offset, channel_count):
-        super().__init__(parent, name)
-
-        self._channel_offset = channel_offset
+class RelayLock:
+    def __init__(self, relay_name):
+        self.relay_name = relay_name
         self._locked_by = None
 
-        channels = ChannelList(
-            cast(VisaInstrument, self),
-            "channel",
-            S46Channel,
-            snapshotable=False
-        )
-
-        for count, channel_number in enumerate(range(
-                channel_offset, channel_offset + channel_count)):
-
-            if channel_count == 1:
-                channel_name = self.short_name
-            else:
-                channel_name = f"{self.short_name}{count + 1}"
-
-            channel = S46Channel(
-                cast(VisaInstrument, self.parent),
-                channel_name,
-                channel_number,
-                self
-            )
-            channels.append(channel)
-
-        self.add_submodule("channels", channels)
-
-    def acquire_lock(self, channel_number):
+    def acquire(self, channel_number):
 
         if self._locked_by is not None and self._locked_by != channel_number:
             raise LockAcquisitionError(
-                f"Relay {self.short_name} is already in use by channel "
+                f"Relay {self.relay_name} is already in use by channel "
                 f"{self._locked_by}"
             )
         else:
             self._locked_by = channel_number
 
-    def release_lock(self, channel_number):
+    def release(self, channel_number):
 
         if self._locked_by == channel_number:
             self._locked_by = None
 
 
 class S46(VisaInstrument):
+
+    channel_aliases = dict(
+            zip([
+                    "{}{}".format(*a)
+                    for a in itertools.product(["A", "B", "C", "D"], range(1, 7))
+                ] +
+                [
+                    f"R{i}" for i in range(1, 9)
+                ],
+                range(1, 33))
+    )
+
+    relay_names = ["A", "B", "C", "D"] + [f"R{j}" for j in range(1, 9)]
+
     def __init__(self, name, address, **kwargs):
         super().__init__(name, address, terminator="\n", **kwargs)
 
-        relay_layout = [
-            int(i) for i in self.ask(":CONF:CPOL?").split(",")
-        ]
-        relay_names = (["A", "B", "C", "D"] + [f"R{i}" for i in range(1, 9)])
-        # Channel offsets are independent of pole configuration. See page
-        # 2-5 of the manual
-        channel_offsets = np.cumsum([0] + 4 * [6] + 7 * [1]) + 1
-
         channels = ChannelList(
             self,
             "channel",
@@ -121,11 +100,19 @@ def __init__(self, name, address, **kwargs):
             snapshotable=False
         )
 
-        for name, channel_offset, channel_count in zip(
-                relay_names, channel_offsets, relay_layout):
+        for relay_name, channel_count in zip(S46.relay_names, self.relay_layout):
 
-            relay = S46Relay(self, name, channel_offset, channel_count)
-            for channel in relay.channels:
+            relay_lock = RelayLock(relay_name)
+
+            for channel_index in range(1, channel_count + 1):
+
+                if channel_count > 1:
+                    alias = f"{relay_name}{channel_index}"
+                else:
+                    alias = relay_name
+
+                channel_number = S46.channel_aliases[alias]
+                channel = S46Channel(self, alias, channel_number, relay_lock)
                 channels.append(channel)
                 self.add_submodule(channel.short_name, channel)
 
@@ -145,3 +132,8 @@ def get_closed_channels(self):
     def open_all_channels(self):
         for channel in self.get_closed_channels():
             cast(S46Channel, channel).state("open")
+
+    @property
+    def relay_layout(self):
+        return [int(i) for i in self.ask(":CONF:CPOL?").split(",")]
+
diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
index 2f2a7e43a0e..4e7ebe8abcf 100644
--- a/qcodes/tests/drivers/test_keithley_s46.py
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -1,4 +1,5 @@
 import pytest
+from itertools import product
 
 from qcodes.instrument_drivers.tektronix.Keithley_s46 import (
     S46, LockAcquisitionError
@@ -40,7 +41,7 @@ def test_open_close(s46):
 
     with pytest.raises(
         LockAcquisitionError,
-        match="Relay already in use by channel"
+        match="is already in use by channel"
     ):
         s46.channels[1].state("close")
 
@@ -50,6 +51,23 @@ def test_open_close(s46):
 
     with pytest.raises(
         LockAcquisitionError,
-        match="Relay already in use by channel"
+        match="is already in use by channel"
     ):
         s46.channels[19].state("close")
+
+
+def test_aliases(s46):
+
+    hex_aliases = [
+        f"{a}{b}" for a, b in product(
+            ["A", "B", "C", "D"],
+            list(range(1, 7))
+        )
+    ]
+
+    aliases = hex_aliases + [f"R{i}" for i in range(1, 9)]
+
+    for channel in s46.channels:
+        idx = channel.channel_number - 1
+        alias = aliases[idx]
+        assert getattr(s46, alias) is channel

From 3fb4007f24a10bfea4d4c7c12ec29ad3e432e48d Mon Sep 17 00:00:00 2001
From: Neil Dick <a-nedick@microsoft.com>
Date: Mon, 3 Dec 2018 14:05:38 +1100
Subject: [PATCH 309/719] feat: Add model number 33511B

The 33511B is a single channel model with a maximum frequency
of 20 MHz.
---
 qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py b/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
index eb00573be4f..f85897ff794 100644
--- a/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
+++ b/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
@@ -290,11 +290,13 @@ def __init__(self, name, address, silent=False, **kwargs):
         # TODO: Fill out this dict with all models
         no_of_channels = {'33210A': 1,
                           '33250A': 1,
+                          '33511B': 1,
                           '33522B': 2,
                           '33622A': 2
                           }
 
         self._max_freqs = {'33210A': 10e6,
+                           '33511B': 20e6,
                            '33250A': 80e6,
                            '33522B': 30e6,
                            '33622A': 120e6}

From d99f93d89f34062ff3a0f312ced2240c03a7d3ab Mon Sep 17 00:00:00 2001
From: Neil Dick <a-nedick@microsoft.com>
Date: Mon, 3 Dec 2018 14:09:30 +1100
Subject: [PATCH 310/719] feat: Add parameter to set pulse width

Add a channel parameter to set the width of the pulse function
---
 .../instrument_drivers/Keysight/KeysightAgilent_33XXX.py   | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py b/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
index f85897ff794..3ef0e300626 100644
--- a/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
+++ b/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
@@ -128,6 +128,13 @@ def val_parser(parser: type, inputstring: str) -> Union[float,int]:
                            vals=vals.Numbers(0, 100)
                            )
 
+        self.add_parameter('pulse_width',
+                           label="Channel {} pulse width".format(channum),
+                           set_cmd='SOURce{}:FUNCtion:PULSE:WIDTh {{}}'.format(channum),
+                           get_cmd='SOURce{}:FUNCtion:PULSE:WIDTh?'.format(channum),
+                           get_parser=float,
+                           unit='S')
+
         # TRIGGER MENU
         self.add_parameter('trigger_source',
                            label='Channel {} trigger source'.format(channum),

From 200561c5fc1facfb8dfcd2ea44b72ad7a1cb20b7 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 15:40:30 +1100
Subject: [PATCH 311/719] Close websockets connection on disconnect

---
 qcodes/monitor/monitor.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index c12f255533a..56fbc8a43b8 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -11,6 +11,7 @@
 """
 
 import asyncio
+from asyncio import CancelledError
 import logging
 import os
 import time
@@ -21,12 +22,8 @@
 import datetime
 from copy import deepcopy
 from contextlib import suppress
-
 from threading import Thread
 from typing import Dict, Any
-from asyncio import CancelledError
-import functools
-
 import websockets
 
 SERVER_PORT = 3000
@@ -85,6 +82,7 @@ async def serverFunc(websocket, path):
                 # mute browser disconnects
                 except websockets.exceptions.ConnectionClosed as e:
                     log.debug(e)
+                    break
                 await asyncio.sleep(interval)
             except CancelledError:
                 log.debug("Got CancelledError")
@@ -171,6 +169,11 @@ def join(self, timeout=None) -> None:
         joining avoiding a potential deadlock.
         """
         log.debug("Shutting down server")
+        if not self.isAlive():
+            # we run this check before trying to run to prevent a cryptic
+            # error message
+            log.debug("monitor is dead")
+            return
         try:
             asyncio.run_coroutine_threadsafe(self.__stop_server(), self.loop)
         except RuntimeError as e:
@@ -183,6 +186,8 @@ def join(self, timeout=None) -> None:
         log.debug("Loop reported closed")
         super().join(timeout=timeout)
         log.debug("Monitor Thread has joined")
+        if Monitor.running == self:
+            Monitor.running = None
 
     @staticmethod
     def show():

From dec4d74487345b8c3abf659f75610bb5bdb86fa0 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 16:41:41 +1100
Subject: [PATCH 312/719] Improve documentation of _handler

---
 qcodes/monitor/monitor.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 56fbc8a43b8..48fe682abb0 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -67,8 +67,10 @@ def _get_metadata(*parameters) -> Dict[str, Any]:
 
 
 def _handler(parameters, interval: int):
-
-    async def serverFunc(websocket, path):
+    """
+    Return the websockets server handler
+    """
+    async def server_func(websocket, path):
         while True:
             try:
                 try:
@@ -88,9 +90,9 @@ async def serverFunc(websocket, path):
                 log.debug("Got CancelledError")
                 break
 
-        log.debug("Stopping Websocket handler")
+        log.debug("Closing websockets connection")
 
-    return serverFunc
+    return server_func
 
 
 class Monitor(Thread):

From 426bb4a564687fbfc64878854a39f986243f5289 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 16:42:22 +1100
Subject: [PATCH 313/719] Don't use private parameters to find instrument root

---
 qcodes/monitor/monitor.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 48fe682abb0..e090cda463c 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -52,9 +52,7 @@ def _get_metadata(*parameters) -> Dict[str, Any]:
         meta["name"] = parameter.label or parameter.name
         meta["unit"] = parameter.unit
         # find the base instrument in case this is a channel parameter
-        baseinst = parameter._instrument
-        while hasattr(baseinst, '_parent'):
-            baseinst = baseinst._parent
+        baseinst = parameter.root_instrument
         accumulator = metas.get(str(baseinst), [])
         accumulator.append(meta)
         metas[str(baseinst)] = accumulator

From fee75c49a70d12cee6f3559a1aac5968ee41c0b1 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 16:43:27 +1100
Subject: [PATCH 314/719] Use Event to flag server shutdown instead of global
 flag

---
 qcodes/monitor/monitor.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index e090cda463c..4d2833392cc 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -22,7 +22,7 @@
 import datetime
 from copy import deepcopy
 from contextlib import suppress
-from threading import Thread
+from threading import Thread, Event
 from typing import Dict, Any
 import websockets
 
@@ -110,7 +110,7 @@ def __init__(self, *parameters, interval=1):
         super().__init__()
         self.loop = None
         self._parameters = parameters
-        self._monitor(*parameters, interval=interval)
+        self.loop_is_closed = Event()
         Monitor.running = self
 
     def run(self):
@@ -137,8 +137,8 @@ def run(self):
             while not self.loop.is_closed():
                 log.debug("waiting for loop to stop and close")
                 time.sleep(0.01)
-            self.loop_is_closed = True
             log.debug("loop closed")
+            self.loop_is_closed.set()
 
     def update_all(self):
         for p in self._parameters:
@@ -180,9 +180,7 @@ def join(self, timeout=None) -> None:
             # the above may throw a runtime error if the loop is already
             # stopped in which case there is nothing more to do
             log.exception("Could not close loop")
-        while not self.loop_is_closed:
-            log.debug("waiting for loop to stop and close")
-            time.sleep(0.01)
+        self.loop_is_closed.wait()
         log.debug("Loop reported closed")
         super().join(timeout=timeout)
         log.debug("Monitor Thread has joined")

From ed7b18dad334490dd2be2cee1a69caf8475aecb1 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 16:43:50 +1100
Subject: [PATCH 315/719] Refactor monitor initializer, remove unessecary
 function

---
 qcodes/monitor/monitor.py | 29 ++++++++++-------------------
 1 file changed, 10 insertions(+), 19 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 4d2833392cc..878b8990502 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -111,6 +111,16 @@ def __init__(self, *parameters, interval=1):
         self.loop = None
         self._parameters = parameters
         self.loop_is_closed = Event()
+        # TODO (giulioungaretti) read from config
+        self.handler = _handler(parameters, interval=interval)
+
+        log.debug("Start monitoring thread")
+        if Monitor.running:
+            # stop the old server
+            log.debug("Stopping and restarting server")
+            Monitor.running.stop()
+        self.start()
+
         Monitor.running = self
 
     def run(self):
@@ -119,7 +129,6 @@ def run(self):
         """
         log.debug("Running Websocket server")
         self.loop = asyncio.new_event_loop()
-        self.loop_is_closed = False
         asyncio.set_event_loop(self.loop)
         try:
             server_start = websockets.serve(self.handler, '127.0.0.1', 5678)
@@ -203,24 +212,6 @@ def show():
         """
         webbrowser.open("http://localhost:{}".format(SERVER_PORT))
 
-    def _monitor(self, *parameters, interval=1):
-        self.handler = _handler(parameters, interval=interval)
-        # TODO (giulioungaretti) read from config
-
-        log.debug("Start monitoring thread")
-
-        if Monitor.running:
-            # stop the old server
-            log.debug("Stopping and restarting server")
-            Monitor.running.stop()
-
-        self.start()
-
-        # let the thread start
-        time.sleep(0.01)
-        log.debug("Start monitoring server")
-
-
 class Server():
 
     def __init__(self, port=3000):

From 6fe807efcbe74e34f21ac5d139b6004a3aeaddbe Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 17:27:16 +1100
Subject: [PATCH 316/719] Use correct is_alive for threads

---
 qcodes/monitor/monitor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 878b8990502..aa3386c1c28 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -178,7 +178,7 @@ def join(self, timeout=None) -> None:
         joining avoiding a potential deadlock.
         """
         log.debug("Shutting down server")
-        if not self.isAlive():
+        if not self.is_alive():
             # we run this check before trying to run to prevent a cryptic
             # error message
             log.debug("monitor is dead")

From ecd8b3b3a7c7495dd2997df3d7d9124615ade77f Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 20:24:02 +1100
Subject: [PATCH 317/719] Refactor, remove references to private functions.

- _handler now uses get_latest to access parameter values, causing
update after max_age.
- added get_timestamp to get_latest which returns age of data
- various readability improvements
---
 qcodes/instrument/parameter.py |  7 ++++
 qcodes/monitor/monitor.py      | 74 ++++++++++++++++++----------------
 2 files changed, 46 insertions(+), 35 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 1de617b086f..f70466d83b4 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1364,6 +1364,13 @@ def get(self):
             else:
                 return state['value']
 
+    def get_timestamp(self):
+        """
+        Return the age of the last get_latest call.
+        """
+        state = self.parameter._latest
+        return state["ts"]
+
     def __call__(self):
         return self.get()
 
diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index aa3386c1c28..8d9d213f2dd 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -10,21 +10,25 @@
 stream opuput over websocket
 """
 
-import asyncio
-from asyncio import CancelledError
 import logging
 import os
 import time
 import json
-import http.server
-import socketserver
-import webbrowser
 import datetime
-from copy import deepcopy
 from contextlib import suppress
-from threading import Thread, Event
 from typing import Dict, Any
+from collections import defaultdict
+
+import asyncio
+from asyncio import CancelledError
+from threading import Thread, Event
+
+import http.server
+import socketserver
 import websockets
+import webbrowser
+
+from qcodes.base.parameter import _BaseParameter
 
 SERVER_PORT = 3000
 
@@ -37,29 +41,33 @@ def _get_metadata(*parameters) -> Dict[str, Any]:
     instrument it belongs to.
     """
     ts = time.time()
-    # group meta data by instrument if any
-    metas = {} # type: Dict
+    # group metadata by instrument
+    metas = defaultdict(list) # type: dict
     for parameter in parameters:
-        _meta = getattr(parameter, "_latest", None)
-        if _meta:
-            meta = deepcopy(_meta)
-        else:
-            raise ValueError("Input is not a parameter; Refusing to proceed")
-        # convert to string
-        meta['value'] = str(meta['value'])
-        if isinstance(meta["ts"], datetime.datetime):
-            meta["ts"] = time.mktime(meta["ts"].timetuple())
+        # Check we have a list of parameters
+        if not isinstance(parameter, _BaseParameter):
+            raise ValueError("{} is not a parameter".format(parameter))
+
+        # Get the latest value from the parameter, respecting the max_val_age parameter
+        meta = {}
+        meta["value"] = str(parameter.get_latest())
+        meta["ts"] = str(time.mktime(parameter.get_latest.get_timestamp.timestamp()))
         meta["name"] = parameter.label or parameter.name
         meta["unit"] = parameter.unit
-        # find the base instrument in case this is a channel parameter
+
+        # find the base instrument that this parameter belongs to
         baseinst = parameter.root_instrument
-        accumulator = metas.get(str(baseinst), [])
-        accumulator.append(meta)
-        metas[str(baseinst)] = accumulator
+        if baseinst is None:
+            metas["No Instrument"].append(meta)
+        else:
+            metas[str(baseinst)].append(meta)
+
+    # Create list of parameters, grouped by instrument
     parameters_out = []
     for instrument in metas:
         temp = {"instrument": instrument, "parameters": metas[instrument]}
         parameters_out.append(temp)
+
     state = {"ts": ts, "parameters": parameters_out}
     return state
 
@@ -69,30 +77,29 @@ def _handler(parameters, interval: int):
     Return the websockets server handler
     """
     async def server_func(websocket, path):
+        """
+        Create a websockets handler that sends parameter values to a listener
+        every "interval" seconds
+        """
         while True:
             try:
+                # Update the parameter values
                 try:
                     meta = _get_metadata(*parameters)
                 except ValueError as e:
                     log.exception(e)
                     break
                 log.debug(f"sending.. to {websocket}")
-                try:
-                    await websocket.send(json.dumps(meta))
-                # mute browser disconnects
-                except websockets.exceptions.ConnectionClosed as e:
-                    log.debug(e)
-                    break
+                await websocket.send(json.dumps(meta))
+                # Wait for interval seconds and then send again
                 await asyncio.sleep(interval)
-            except CancelledError:
-                log.debug("Got CancelledError")
+            except (CancelledError, websockets.exceptions.ConnectionClosed):
+                log.debug("Got CancelledError or ConnectionClosed")
                 break
-
         log.debug("Closing websockets connection")
 
     return server_func
 
-
 class Monitor(Thread):
     running = None
     server = None
@@ -105,13 +112,10 @@ def __init__(self, *parameters, interval=1):
             *parameters: Parameters to monitor
             interval: How often one wants to refresh the values
         """
-        # let the thread start
-        time.sleep(0.01)
         super().__init__()
         self.loop = None
         self._parameters = parameters
         self.loop_is_closed = Event()
-        # TODO (giulioungaretti) read from config
         self.handler = _handler(parameters, interval=interval)
 
         log.debug("Start monitoring thread")

From 4d5cfb8c8cdb4da506d76d17feb6a98e38b660a9 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 20:51:44 +1100
Subject: [PATCH 318/719] Reorder/fix monitor imports

---
 qcodes/monitor/monitor.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 8d9d213f2dd..0280c74d2fc 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -14,7 +14,6 @@
 import os
 import time
 import json
-import datetime
 from contextlib import suppress
 from typing import Dict, Any
 from collections import defaultdict
@@ -25,10 +24,10 @@
 
 import http.server
 import socketserver
-import websockets
 import webbrowser
+import websockets
 
-from qcodes.base.parameter import _BaseParameter
+from qcodes.instrument.parameter import _BaseParameter
 
 SERVER_PORT = 3000
 

From ac7788d0ce62c6b9dad07e9813825a9efa466f38 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 20:52:22 +1100
Subject: [PATCH 319/719] Make server port configurable in file

---
 qcodes/monitor/monitor.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 0280c74d2fc..d26abebb4e3 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -29,6 +29,7 @@
 
 from qcodes.instrument.parameter import _BaseParameter
 
+WEBSOCKET_PORT = 5678
 SERVER_PORT = 3000
 
 log = logging.getLogger(__name__)
@@ -134,7 +135,7 @@ def run(self):
         self.loop = asyncio.new_event_loop()
         asyncio.set_event_loop(self.loop)
         try:
-            server_start = websockets.serve(self.handler, '127.0.0.1', 5678)
+            server_start = websockets.serve(self.handler, '127.0.0.1', WEBSOCKET_PORT)
             self.server = self.loop.run_until_complete(server_start)
             self.loop.run_forever()
         except OSError as e:
@@ -217,7 +218,7 @@ def show():
 
 class Server():
 
-    def __init__(self, port=3000):
+    def __init__(self, port=SERVER_PORT):
         self.port = port
         self.handler = http.server.SimpleHTTPRequestHandler
         self.httpd = socketserver.TCPServer(("", self.port), self.handler)
@@ -226,7 +227,6 @@ def __init__(self, port=3000):
     def run(self):
         os.chdir(self.static_dir)
         log.debug("serving directory %s", self.static_dir)
-        log.info("Open browser at http://localhost::{}".format(self.port))
         self.httpd.serve_forever()
 
     def stop(self):
@@ -234,7 +234,7 @@ def stop(self):
 
 
 if __name__ == "__main__":
-    server = Server(SERVER_PORT)
+    server = Server()
     print("Open browser at http://localhost:{}".format(server.port))
     try:
         webbrowser.open("http://localhost:{}".format(server.port))

From a7dce6adadfa60a9fdb7f47914ab2bdac27066e7 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 20:52:47 +1100
Subject: [PATCH 320/719] Improve documentation

---
 qcodes/monitor/monitor.py | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index d26abebb4e3..ef1a204073c 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -89,7 +89,7 @@ async def server_func(websocket, path):
                 except ValueError as e:
                     log.exception(e)
                     break
-                log.debug(f"sending.. to {websocket}")
+                log.debug("sending.. to %r", websocket)
                 await websocket.send(json.dumps(meta))
                 # Wait for interval seconds and then send again
                 await asyncio.sleep(interval)
@@ -101,8 +101,10 @@ async def server_func(websocket, path):
     return server_func
 
 class Monitor(Thread):
+    """
+    QCodes Monitor - WebSockets server to monitor qcodes parameters
+    """
     running = None
-    server = None
 
     def __init__(self, *parameters, interval=1):
         """
@@ -114,6 +116,7 @@ def __init__(self, *parameters, interval=1):
         """
         super().__init__()
         self.loop = None
+        self.server = None
         self._parameters = parameters
         self.loop_is_closed = Event()
         self.handler = _handler(parameters, interval=interval)
@@ -138,22 +141,22 @@ def run(self):
             server_start = websockets.serve(self.handler, '127.0.0.1', WEBSOCKET_PORT)
             self.server = self.loop.run_until_complete(server_start)
             self.loop.run_forever()
-        except OSError as e:
+        except OSError:
             # The code above may throw an OSError
             # if the socket cannot be bound
-            log.exception(e)
+            log.exception("Server could not be started")
         finally:
             log.debug("loop stopped")
-            log.debug("Pending tasks at close: {}".format(
-                asyncio.Task.all_tasks(self.loop)))
+            log.debug("Pending tasks at close: %r",
+                      asyncio.Task.all_tasks(self.loop))
             self.loop.close()
-            while not self.loop.is_closed():
-                log.debug("waiting for loop to stop and close")
-                time.sleep(0.01)
             log.debug("loop closed")
             self.loop_is_closed.set()
 
     def update_all(self):
+        """
+        Update all parameters in the monitor
+        """
         for p in self._parameters:
             # call get if it can be called without arguments
             with suppress(TypeError):
@@ -173,7 +176,8 @@ async def __stop_server(self):
         log.debug("waiting for server to close")
         await self.loop.create_task(self.server.wait_closed())
         log.debug("stopping loop")
-        log.debug("Pending tasks at stop: {}".format(asyncio.Task.all_tasks(self.loop)))
+        log.debug("Pending tasks at stop: %r",
+                  asyncio.Task.all_tasks(self.loop))
         self.loop.stop()
 
     def join(self, timeout=None) -> None:
@@ -189,7 +193,7 @@ def join(self, timeout=None) -> None:
             return
         try:
             asyncio.run_coroutine_threadsafe(self.__stop_server(), self.loop)
-        except RuntimeError as e:
+        except RuntimeError:
             # the above may throw a runtime error if the loop is already
             # stopped in which case there is nothing more to do
             log.exception("Could not close loop")
@@ -197,8 +201,6 @@ def join(self, timeout=None) -> None:
         log.debug("Loop reported closed")
         super().join(timeout=timeout)
         log.debug("Monitor Thread has joined")
-        if Monitor.running == self:
-            Monitor.running = None
 
     @staticmethod
     def show():

From 56c387a7781748b37b8da412ac2f0d47e4312821 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 21:08:54 +1100
Subject: [PATCH 321/719] Monitor can only really look at parameters.

This is since labels and units are only really defined for those, and
they are necessary for display
---
 qcodes/monitor/monitor.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index ef1a204073c..c76c9fa7c59 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -27,7 +27,7 @@
 import webbrowser
 import websockets
 
-from qcodes.instrument.parameter import _BaseParameter
+from qcodes.instrument.parameter import Parameter
 
 WEBSOCKET_PORT = 5678
 SERVER_PORT = 3000
@@ -45,13 +45,13 @@ def _get_metadata(*parameters) -> Dict[str, Any]:
     metas = defaultdict(list) # type: dict
     for parameter in parameters:
         # Check we have a list of parameters
-        if not isinstance(parameter, _BaseParameter):
+        if not isinstance(parameter, Parameter):
             raise ValueError("{} is not a parameter".format(parameter))
 
         # Get the latest value from the parameter, respecting the max_val_age parameter
         meta = {}
         meta["value"] = str(parameter.get_latest())
-        meta["ts"] = str(time.mktime(parameter.get_latest.get_timestamp.timestamp()))
+        meta["ts"] = str(time.mktime(parameter.get_latest.get_timestamp().timestamp()))
         meta["name"] = parameter.label or parameter.name
         meta["unit"] = parameter.unit
 

From 0163ec09d2892ce4825e708df30f28f91815ef00 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 21:54:09 +1100
Subject: [PATCH 322/719] Handle unset parameters in monitor

---
 qcodes/monitor/monitor.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index c76c9fa7c59..d7a485f5a9c 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -51,14 +51,17 @@ def _get_metadata(*parameters) -> Dict[str, Any]:
         # Get the latest value from the parameter, respecting the max_val_age parameter
         meta = {}
         meta["value"] = str(parameter.get_latest())
-        meta["ts"] = str(time.mktime(parameter.get_latest.get_timestamp().timestamp()))
+        if parameter.get_latest.get_timestamp() is not None:
+            meta["ts"] = str(parameter.get_latest.get_timestamp().timestamp())
+        else:
+            meta["ts"] = None
         meta["name"] = parameter.label or parameter.name
         meta["unit"] = parameter.unit
 
         # find the base instrument that this parameter belongs to
         baseinst = parameter.root_instrument
         if baseinst is None:
-            metas["No Instrument"].append(meta)
+            metas["Unbound Parameter"].append(meta)
         else:
             metas[str(baseinst)].append(meta)
 

From a21a5528bb2e4d81fddf8b3fd400d1dcb134c94b Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 21:54:16 +1100
Subject: [PATCH 323/719] Add monitor tests

---
 qcodes/tests/test_monitor.py | 125 +++++++++++++++++++++++++++++++++++
 1 file changed, 125 insertions(+)
 create mode 100644 qcodes/tests/test_monitor.py

diff --git a/qcodes/tests/test_monitor.py b/qcodes/tests/test_monitor.py
new file mode 100644
index 00000000000..e133da6bceb
--- /dev/null
+++ b/qcodes/tests/test_monitor.py
@@ -0,0 +1,125 @@
+"""
+Test suite for monitor
+"""
+from unittest import TestCase
+
+import time
+import asyncio
+import websockets
+import json
+import random
+
+import qcodes as qc
+from qcodes.instrument.base import Parameter
+from qcodes.tests.instrument_mocks import DummyInstrument
+
+class TestMonitor(TestCase):
+    """
+    Test cases for the qcodes monitor
+    """
+    def test_setup_teardown(self):
+        """
+        Check that monitor starts up and closes correctly
+        """
+        m = qc.Monitor()
+        self.assertTrue(m.is_alive())
+        # Wait for loop to start
+        while m.loop is None:
+            time.sleep(0.01)
+        self.assertTrue(m.loop.is_running())
+        self.assertEqual(qc.Monitor.running, m)
+        m.stop()
+        self.assertFalse(m.loop.is_running())
+        self.assertTrue(m.loop.is_closed())
+        self.assertFalse(m.is_alive())
+        self.assertIsNone(qc.Monitor.running)
+
+    def test_connection(self):
+        """
+        Test that we can connect to a monitor instance
+        """
+        m = qc.Monitor()
+
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+
+        @asyncio.coroutine
+        def async_test_connection():
+            websocket = yield from websockets.connect(f"ws://localhost:{qc.monitor.monitor.WEBSOCKET_PORT}")
+            yield from websocket.close()
+        loop.run_until_complete(async_test_connection())
+
+        m.stop()
+
+class TestMonitorWithInstr(TestCase):
+    """
+    Test monitor values from instruments
+    """
+    def setUp(self):
+        """
+        Create a dummy instrument for use in monitor tests, and hook it into
+        a monitor
+        """
+        self.instr = DummyInstrument("MonitorDummy")
+        self.param = Parameter("DummyParam",
+                               unit="V",
+                               get_cmd=None,
+                               set_cmd=None)
+        self.param(1)
+        self.monitor_parameters = tuple(self.instr.parameters.values())[1:]
+        self.monitor = qc.Monitor(*self.monitor_parameters, self.param)
+
+    def tearDown(self):
+        """
+        Close the dummy instrument
+        """
+        self.monitor.stop()
+        self.instr.close()
+
+    def test_parameter(self):
+        """
+        Test instrument updates
+        """
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+
+        @asyncio.coroutine
+        def async_test_monitor():
+            websocket = yield from websockets.connect(f"ws://localhost:{qc.monitor.monitor.WEBSOCKET_PORT}")
+
+            # Recieve data from monitor
+            data = yield from websocket.recv()
+            data = json.loads(data)
+            # Check fields
+            self.assertIn("ts", data)
+            self.assertIn("parameters", data)
+            # Check one instrument and "No Instrument" is being sent
+            self.assertEqual(len(data["parameters"]), 2)
+            self.assertEqual(data["parameters"][1]["instrument"], "Unbound Parameter")
+            metadata = data["parameters"][0]
+            self.assertEqual(metadata["instrument"], str(self.instr))
+
+            # Check parameter values
+            old_timestamps = {}
+            for local, monitor in zip(self.monitor_parameters, metadata["parameters"]):
+                self.assertEqual(str(local.get_latest()), monitor["value"])
+                self.assertEqual(local.label, monitor["name"])
+                old_timestamps[local.label] = float(monitor["ts"])
+                local(random.random())
+
+            # Check parameter updates
+            data = yield from websocket.recv()
+            data = yield from websocket.recv()
+            data = json.loads(data)
+            metadata = data["parameters"][0]
+            for local, monitor in zip(self.monitor_parameters, metadata["parameters"]):
+                self.assertEqual(str(local.get_latest()), monitor["value"])
+                self.assertEqual(local.label, monitor["name"])
+                self.assertGreater(float(monitor["ts"]), old_timestamps[local.label])
+
+            # Check unbound parameter
+            metadata = data["parameters"][1]["parameters"]
+            self.assertEqual(len(metadata), 1)
+            self.assertEqual(self.param.label, metadata[0]["name"])
+
+        loop.run_until_complete(async_test_monitor())

From eee6fe01c99f312dda33b771c99ca1a87bdc417c Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 22:13:32 +1100
Subject: [PATCH 324/719] Correct datatype for timestamp

---
 qcodes/monitor/monitor.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index d7a485f5a9c..8ce7309f070 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -52,7 +52,7 @@ def _get_metadata(*parameters) -> Dict[str, Any]:
         meta = {}
         meta["value"] = str(parameter.get_latest())
         if parameter.get_latest.get_timestamp() is not None:
-            meta["ts"] = str(parameter.get_latest.get_timestamp().timestamp())
+            meta["ts"] = parameter.get_latest.get_timestamp().timestamp()
         else:
             meta["ts"] = None
         meta["name"] = parameter.label or parameter.name
@@ -131,6 +131,9 @@ def __init__(self, *parameters, interval=1):
             Monitor.running.stop()
         self.start()
 
+        # Wait until the loop is running
+        while self.loop is None:
+            time.sleep(0.01)
         Monitor.running = self
 
     def run(self):

From 97ca268bef9958aef3dd64121ec6d700145e9086 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 22:32:20 +1100
Subject: [PATCH 325/719] Use randomized port for monitor tests

---
 qcodes/tests/test_monitor.py | 36 +++++++++++++++++++-----------------
 1 file changed, 19 insertions(+), 17 deletions(-)

diff --git a/qcodes/tests/test_monitor.py b/qcodes/tests/test_monitor.py
index e133da6bceb..c7aa987db69 100644
--- a/qcodes/tests/test_monitor.py
+++ b/qcodes/tests/test_monitor.py
@@ -5,14 +5,16 @@
 
 import time
 import asyncio
-import websockets
 import json
 import random
+import websockets
 
-import qcodes as qc
+from qcodes.monitor import monitor
 from qcodes.instrument.base import Parameter
 from qcodes.tests.instrument_mocks import DummyInstrument
 
+monitor.WEBSOCKET_PORT = random.randint(50000, 60000)
+
 class TestMonitor(TestCase):
     """
     Test cases for the qcodes monitor
@@ -21,31 +23,31 @@ def test_setup_teardown(self):
         """
         Check that monitor starts up and closes correctly
         """
-        m = qc.Monitor()
+        m = monitor.Monitor()
         self.assertTrue(m.is_alive())
         # Wait for loop to start
         while m.loop is None:
             time.sleep(0.01)
         self.assertTrue(m.loop.is_running())
-        self.assertEqual(qc.Monitor.running, m)
+        self.assertEqual(monitor.Monitor.running, m)
         m.stop()
         self.assertFalse(m.loop.is_running())
         self.assertTrue(m.loop.is_closed())
         self.assertFalse(m.is_alive())
-        self.assertIsNone(qc.Monitor.running)
+        self.assertIsNone(monitor.Monitor.running)
 
     def test_connection(self):
         """
         Test that we can connect to a monitor instance
         """
-        m = qc.Monitor()
+        m = monitor.Monitor()
 
         loop = asyncio.new_event_loop()
         asyncio.set_event_loop(loop)
 
         @asyncio.coroutine
         def async_test_connection():
-            websocket = yield from websockets.connect(f"ws://localhost:{qc.monitor.monitor.WEBSOCKET_PORT}")
+            websocket = yield from websockets.connect(f"ws://localhost:{monitor.WEBSOCKET_PORT}")
             yield from websocket.close()
         loop.run_until_complete(async_test_connection())
 
@@ -67,7 +69,7 @@ def setUp(self):
                                set_cmd=None)
         self.param(1)
         self.monitor_parameters = tuple(self.instr.parameters.values())[1:]
-        self.monitor = qc.Monitor(*self.monitor_parameters, self.param)
+        self.monitor = monitor.Monitor(*self.monitor_parameters, self.param, interval=0.1)
 
     def tearDown(self):
         """
@@ -85,7 +87,7 @@ def test_parameter(self):
 
         @asyncio.coroutine
         def async_test_monitor():
-            websocket = yield from websockets.connect(f"ws://localhost:{qc.monitor.monitor.WEBSOCKET_PORT}")
+            websocket = yield from websockets.connect(f"ws://localhost:{monitor.WEBSOCKET_PORT}")
 
             # Recieve data from monitor
             data = yield from websocket.recv()
@@ -101,10 +103,10 @@ def async_test_monitor():
 
             # Check parameter values
             old_timestamps = {}
-            for local, monitor in zip(self.monitor_parameters, metadata["parameters"]):
-                self.assertEqual(str(local.get_latest()), monitor["value"])
-                self.assertEqual(local.label, monitor["name"])
-                old_timestamps[local.label] = float(monitor["ts"])
+            for local, mon in zip(self.monitor_parameters, metadata["parameters"]):
+                self.assertEqual(str(local.get_latest()), mon["value"])
+                self.assertEqual(local.label, mon["name"])
+                old_timestamps[local.label] = float(mon["ts"])
                 local(random.random())
 
             # Check parameter updates
@@ -112,10 +114,10 @@ def async_test_monitor():
             data = yield from websocket.recv()
             data = json.loads(data)
             metadata = data["parameters"][0]
-            for local, monitor in zip(self.monitor_parameters, metadata["parameters"]):
-                self.assertEqual(str(local.get_latest()), monitor["value"])
-                self.assertEqual(local.label, monitor["name"])
-                self.assertGreater(float(monitor["ts"]), old_timestamps[local.label])
+            for local, mon in zip(self.monitor_parameters, metadata["parameters"]):
+                self.assertEqual(str(local.get_latest()), mon["value"])
+                self.assertEqual(local.label, mon["name"])
+                self.assertGreater(float(mon["ts"]), old_timestamps[local.label])
 
             # Check unbound parameter
             metadata = data["parameters"][1]["parameters"]

From 85858ff8ba06d5ba7628fccd41fd8be6f307b017 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 3 Dec 2018 23:53:45 +1100
Subject: [PATCH 326/719] Wait for loop to start before returning monitor

---
 qcodes/monitor/monitor.py    | 2 +-
 qcodes/tests/test_monitor.py | 3 ---
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 8ce7309f070..1f0fae77ecd 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -132,7 +132,7 @@ def __init__(self, *parameters, interval=1):
         self.start()
 
         # Wait until the loop is running
-        while self.loop is None:
+        while self.loop is None or not self.loop.is_running():
             time.sleep(0.01)
         Monitor.running = self
 
diff --git a/qcodes/tests/test_monitor.py b/qcodes/tests/test_monitor.py
index c7aa987db69..c17943e6cbe 100644
--- a/qcodes/tests/test_monitor.py
+++ b/qcodes/tests/test_monitor.py
@@ -25,9 +25,6 @@ def test_setup_teardown(self):
         """
         m = monitor.Monitor()
         self.assertTrue(m.is_alive())
-        # Wait for loop to start
-        while m.loop is None:
-            time.sleep(0.01)
         self.assertTrue(m.loop.is_running())
         self.assertEqual(monitor.Monitor.running, m)
         m.stop()

From 7483cc658c2dfb3c7e1eeb792c4978aac3fd32e0 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Tue, 4 Dec 2018 00:00:16 +1100
Subject: [PATCH 327/719] Use event to wait for server start

---
 qcodes/monitor/monitor.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 1f0fae77ecd..b090709784a 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -122,6 +122,7 @@ def __init__(self, *parameters, interval=1):
         self.server = None
         self._parameters = parameters
         self.loop_is_closed = Event()
+        self.server_is_started = Event()
         self.handler = _handler(parameters, interval=interval)
 
         log.debug("Start monitoring thread")
@@ -132,8 +133,7 @@ def __init__(self, *parameters, interval=1):
         self.start()
 
         # Wait until the loop is running
-        while self.loop is None or not self.loop.is_running():
-            time.sleep(0.01)
+        self.server_is_started.wait()
         Monitor.running = self
 
     def run(self):
@@ -146,6 +146,7 @@ def run(self):
         try:
             server_start = websockets.serve(self.handler, '127.0.0.1', WEBSOCKET_PORT)
             self.server = self.loop.run_until_complete(server_start)
+            self.server_is_started.set()
             self.loop.run_forever()
         except OSError:
             # The code above may throw an OSError

From 561578248b2ad99beabec29e5762426beea55f6a Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Mon, 3 Dec 2018 11:13:06 -0800
Subject: [PATCH 328/719] test work now

---
 .../QCodes example with Keithley S46.ipynb    | 228 +++++++-----------
 qcodes/instrument/sims/Keithley_s46.yaml      |  33 ++-
 .../tektronix/Keithley_s46.py                 |  51 ++--
 qcodes/tests/drivers/test_keithley_s46.py     |  73 ++++--
 4 files changed, 193 insertions(+), 192 deletions(-)

diff --git a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb
index 6405988ca0a..6685d57991b 100644
--- a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
+++ b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,14 +11,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.20s\n"
+      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.62s\n"
      ]
     }
    ],
@@ -34,9 +34,7 @@
     {
      "data": {
       "text/plain": [
-       "[<S46Channel: s2_A1 of S46: s2>,\n",
-       " <S46Channel: s2_B1 of S46: s2>,\n",
-       " <S46Channel: s2_R8 of S46: s2>]"
+       "[]"
       ]
      },
      "execution_count": 3,
@@ -223,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -250,220 +248,160 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s46.open_all_channels()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s46.A1.state(\"open\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'A1': 1, 'A2': 2, 'A3': 3, 'A4': 4, 'A5': 5, 'A6': 6, 'B1': 7, 'B2': 8, 'B3': 9, 'B4': 10, 'B5': 11, 'B6': 12, 'C1': 13, 'C2': 14, 'C3': 15, 'C4': 16, 'C5': 17, 'C6': 18, 'D1': 19, 'D2': 20, 'D3': 21, 'D4': 22, 'D5': 23, 'D6': 24, 'R1': 25, 'R2': 26, 'R3': 27, 'R4': 28, 'R5': 29, 'R6': 30, 'R7': 31, 'R8': 32}\n"
-     ]
+     "data": {
+      "text/plain": [
+       "[<S46Channel: s2_A1 of S46: s2>,\n",
+       " <S46Channel: s2_B1 of S46: s2>,\n",
+       " <S46Channel: s2_R8 of S46: s2>]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "channel_aliases = [\n",
-    "    f\"{relay_name}{index}\" for\n",
-    "    relay_name, index in product(\n",
-    "        [\"A\", \"B\", \"C\", \"D\"],\n",
-    "        range(1, 7)\n",
-    "    )\n",
-    "]\n",
-    "channel_aliases += [f\"R{i}\" for i in range(1, 9)]\n",
-    "\n",
-    "print(dict(zip(channel_aliases, range(1, 33))))"
+    "s46.get_closed_channels()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'A1': 1,\n",
-       " 'A2': 2,\n",
-       " 'A3': 3,\n",
-       " 'A4': 4,\n",
-       " 'A5': 5,\n",
-       " 'A6': 6,\n",
-       " 'B1': 7,\n",
-       " 'B2': 8,\n",
-       " 'B3': 9,\n",
-       " 'B4': 10,\n",
-       " 'B5': 11,\n",
-       " 'B6': 12,\n",
-       " 'C1': 13,\n",
-       " 'C2': 14,\n",
-       " 'C3': 15,\n",
-       " 'C4': 16,\n",
-       " 'C5': 17,\n",
-       " 'C6': 18,\n",
-       " 'D1': 19,\n",
-       " 'D2': 20,\n",
-       " 'D3': 21,\n",
-       " 'D4': 22,\n",
-       " 'D5': 23,\n",
-       " 'D6': 24,\n",
-       " 'R1': 25,\n",
-       " 'R2': 26,\n",
-       " 'R3': 27,\n",
-       " 'R4': 28,\n",
-       " 'R5': 29,\n",
-       " 'R6': 30,\n",
-       " 'R7': 31,\n",
-       " 'R8': 32}"
+       "[1, 7, 32]"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dict(\n",
-    "    zip(\n",
-    "        [\"{}{}\".format(*a) for a in product([\"A\", \"B\", \"C\", \"D\"], range(1, 7))] + [f\"R{i}\" for i in range(1, 9)], \n",
-    "        range(1, 33)\n",
-    "    )\n",
-    ")"
+    "s46.get_closed_channel_numbers()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[6, 6, 6, 6, 0, 0, 0, 0, 1, 0, 0, 1]\n",
-      "['A', 'B', 'C', 'D', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8']\n"
+      "1\n",
+      "7\n",
+      "32\n"
      ]
     }
    ],
    "source": [
-    "relay_layout = [int(i) for i in s46.ask(\":CONF:CPOL?\").split(\",\")]\n",
-    "relay_names = ([\"A\", \"B\", \"C\", \"D\"] + [f\"R{i}\" for i in range(1, 9)])\n",
-    "print(relay_layout)\n",
-    "print(relay_names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class RelayLock: \n",
-    "    def __init__(self, relay_name): \n",
-    "        self._relay_name = relay_name\n",
-    "    \n",
-    "    def acquire(self, channel_number): \n",
-    "        pass \n",
-    "    \n",
-    "    def release(self, channel_number):\n",
-    "        pass "
+    "print(S46.alias_to_channel_number(\"A1\"))\n",
+    "print(S46.alias_to_channel_number(\"B1\"))\n",
+    "print(S46.alias_to_channel_number(\"R8\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
-    "class s46Channel: \n",
-    "    def __init__(self, name, channel_number, lock): \n",
-    "        self._name = name \n",
-    "        self._lock = lock\n",
-    "        self._channel_number = channel_number"
+    "def channel_number_to_alias(num): \n",
+    "    n = num - 24\n",
+    "    if n > 0: \n",
+    "        return f\"R{n}\"\n",
+    "    index = num % 6\n",
+    "    relay = {0: \"A\", 1: \"B\", 2: \"C\", 3: \"D\"}[(num - 1)//6]\n",
+    "    return f\"{relay}{index}\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A1\n",
+      "B1\n",
+      "R8\n"
+     ]
+    }
+   ],
    "source": [
-    "for relay_name, channel_count in zip(relay_names, relay_layout): \n",
-    "    \n",
-    "    relay_lock = RelayLock(relay_name)\n",
-    "    \n",
-    "    for channel_index in range(1, channel_count + 1):\n",
-    "        \n",
-    "        if channel_count > 1:\n",
-    "            alias = f\"{relay_name}{channel_index}\"\n",
-    "        else:\n",
-    "            alias = relay_name\n",
-    "            \n",
-    "        channel_number = aliases[alias]\n",
-    "        channel = s46Channel(alias, channel_number, relay_lock)"
+    "print(channel_number_to_alias(1))\n",
+    "print(channel_number_to_alias(7))\n",
+    "print(channel_number_to_alias(32))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A2\n",
+      "B2\n",
+      "R7\n"
+     ]
+    }
+   ],
    "source": [
-    "class classproperty:\n",
-    "    def __init__(self, f):\n",
-    "        self.f = f\n",
-    "    def __get__(self, obj, owner):\n",
-    "        return self.f(owner)"
+    "print(channel_number_to_alias(2))\n",
+    "print(channel_number_to_alias(8))\n",
+    "print(channel_number_to_alias(31))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "class A: \n",
-    "    @classmethod\n",
-    "    @property\n",
-    "    def aclassprop(cls): \n",
-    "        return 1"
+    "s46.open_all_channels()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<bound method ? of <class '__main__.A'>>"
+       "[]"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "A.aclassprop"
+    "s46.get_closed_channels()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s46.A1.state(\"open\")"
    ]
   },
   {
diff --git a/qcodes/instrument/sims/Keithley_s46.yaml b/qcodes/instrument/sims/Keithley_s46.yaml
index 0b9bcca7605..66f9789933d 100644
--- a/qcodes/instrument/sims/Keithley_s46.yaml
+++ b/qcodes/instrument/sims/Keithley_s46.yaml
@@ -20,7 +20,7 @@ devices:
           q: ":CLOS?"
           r: "(@1,2,8)"
 
-  device default:
+  device six:
     eom:
       GPIB INSTR:
         q: "\n"
@@ -47,8 +47,37 @@ devices:
           q: ":OPEN {}"
           r: ""
 
+  device four:
+    eom:
+      GPIB INSTR:
+        q: "\n"
+        r: "\n"
+    error: ERROR
+    dialogues:
+    - q: "*IDN?"
+      r: "KEITHLEY INSTRUMENTS INC.,MODEL SYSTEM 46, 1327388, A03  "
+    - q: ":CONF:CPOL?"
+      r: "4,4,4,4,0,0,0,0,1,0,0,1"
+
+    properties:
+
+      close:
+        getter:
+          q: ":CLOS?"
+          r: "(@1,8)"
+        setter:
+          q: ":CLOS {}"
+          r: ""
+
+      open:
+        setter:
+          q: ":OPEN {}"
+          r: ""
+
 resources:
   GPIB::1::INSTR:
     device: device bad_state
   GPIB::2::INSTR:
-    device: device default
\ No newline at end of file
+    device: device six
+  GPIB::3::INSTR:
+    device: device four
\ No newline at end of file
diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index 51488e3a4d9..ba8de24da95 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -1,5 +1,5 @@
-import itertools
 import re
+from itertools import product
 
 from typing import cast
 from qcodes import VisaInstrument, InstrumentChannel, ChannelList
@@ -10,13 +10,27 @@ class LockAcquisitionError(Exception):
     pass
 
 
+def cached_method(method):
+    def inner(self, *args, get_cached=False, **kwargs):
+        if not hasattr(self, "__cached_values__"):
+            self.__cached_values__ = {}
+
+        if method not in self.__cached_values__ or not get_cached:
+            self.__cached_values__[method] = method(self, *args, **kwargs)
+
+        return self.__cached_values__[method]
+
+    return inner
+
+
 class S46Channel(InstrumentChannel):
     def __init__(self, parent, name, channel_number, relay_lock):
         super().__init__(parent, name)
 
         self._channel_number = channel_number
         self._relay_lock = relay_lock
-        if self._get_state() == "close":
+
+        if self._get_state(get_cached=True) == "close":
             try:
                 self._relay_lock.acquire(self._channel_number)
             except LockAcquisitionError as e:
@@ -34,10 +48,9 @@ def __init__(self, parent, name, channel_number, relay_lock):
             vals=Enum("open", "close")
         )
 
-    def _get_state(self):
+    def _get_state(self, get_cached=False):
         is_closed = self._channel_number in \
-                    self.root_instrument.get_closed_channel_numbers()
-
+                    self.parent.get_closed_channel_numbers(get_cached=get_cached)
         return {True: "close", False: "open"}[is_closed]
 
     def _set_state(self, new_state: str):
@@ -77,19 +90,18 @@ def release(self, channel_number):
 
 class S46(VisaInstrument):
 
-    channel_aliases = dict(
-            zip([
-                    "{}{}".format(*a)
-                    for a in itertools.product(["A", "B", "C", "D"], range(1, 7))
-                ] +
-                [
-                    f"R{i}" for i in range(1, 9)
-                ],
-                range(1, 33))
-    )
-
     relay_names = ["A", "B", "C", "D"] + [f"R{j}" for j in range(1, 9)]
 
+    _aliases_list = [
+        f"{a}{b}" for a, b in product(
+            ["A", "B", "C", "D"],
+            list(range(1, 7))
+        )
+    ]
+
+    _aliases_list += [f"R{i}" for i in range(1, 9)]
+    aliases = dict(zip(_aliases_list, range(1, 33)))
+
     def __init__(self, name, address, **kwargs):
         super().__init__(name, address, terminator="\n", **kwargs)
 
@@ -101,7 +113,6 @@ def __init__(self, name, address, **kwargs):
         )
 
         for relay_name, channel_count in zip(S46.relay_names, self.relay_layout):
-
             relay_lock = RelayLock(relay_name)
 
             for channel_index in range(1, channel_count + 1):
@@ -111,14 +122,15 @@ def __init__(self, name, address, **kwargs):
                 else:
                     alias = relay_name
 
-                channel_number = S46.channel_aliases[alias]
+                channel_number = S46.aliases[alias]
                 channel = S46Channel(self, alias, channel_number, relay_lock)
                 channels.append(channel)
-                self.add_submodule(channel.short_name, channel)
+                self.add_submodule(alias, channel)
 
         self.add_submodule("channels", channels)
         self.connect_message()
 
+    @cached_method
     def get_closed_channel_numbers(self):
         closed_channels_str = re.findall(r"\d+", self.ask(":CLOS?"))
         return [int(i) for i in closed_channels_str]
@@ -136,4 +148,3 @@ def open_all_channels(self):
     @property
     def relay_layout(self):
         return [int(i) for i in self.ask(":CONF:CPOL?").split(",")]
-
diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
index 4e7ebe8abcf..0989da2dfdf 100644
--- a/qcodes/tests/drivers/test_keithley_s46.py
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -10,8 +10,15 @@
 
 
 @pytest.fixture(scope='module')
-def s46():
-    driver = S46('s46_sim',address='GPIB::2::INSTR', visalib=visalib)
+def s46_six():
+    driver = S46('s46_six',address='GPIB::2::INSTR', visalib=visalib)
+    yield driver
+    driver.close()
+
+
+@pytest.fixture(scope='module')
+def s46_four():
+    driver = S46('s46_four',address='GPIB::3::INSTR', visalib=visalib)
     yield driver
     driver.close()
 
@@ -25,49 +32,65 @@ def test_runtime_error_on_bad_init():
         S46('s46_bad_state', address='GPIB::1::INSTR', visalib=visalib)
 
 
-def test_init(s46):
+def test_init_six(s46_six):
 
-    n_channels = len(s46.channels)
+    n_channels = len(s46_six.channels)
     assert n_channels == 26
 
-    closed_channels = [0, 7, 12]
+    closed_channels = [1, 8, 13]
+
+    for channel in s46_six.channels:
+        channel_nr = S46.aliases[channel.short_name]
+        state = "close" if channel_nr in closed_channels else "open"
+        assert channel.state() == state
+
+
+def test_init_four(s46_four):
 
-    for channel_nr in range(n_channels):
+    n_channels = len(s46_four.channels)
+    assert n_channels == 18
+
+    closed_channels = [1, 8]
+
+    for channel in s46_four.channels:
+        channel_nr = S46.aliases[channel.short_name]
         state = "close" if channel_nr in closed_channels else "open"
-        assert s46.channels[channel_nr].state() == state
+        assert channel.state() == state
 
 
-def test_open_close(s46):
+def test_open_close(s46_six):
 
     with pytest.raises(
         LockAcquisitionError,
         match="is already in use by channel"
     ):
-        s46.channels[1].state("close")
+        s46_six.channels[1].state("close")
 
-    s46.channels[0].state("open")
-    s46.channels[1].state("close")
-    s46.channels[18].state("close")
+    s46_six.channels[0].state("open")
+    s46_six.channels[1].state("close")
+    s46_six.channels[18].state("close")
 
     with pytest.raises(
         LockAcquisitionError,
         match="is already in use by channel"
     ):
-        s46.channels[19].state("close")
+        s46_six.channels[19].state("close")
+
 
+def alias_to_channel_nr(alias):
+    offset = dict(zip(["A", "B", "C", "D", "R"], range(0, 32, 6)))[alias[0]]
+    return offset + int(alias[1:])
 
-def test_aliases(s46):
 
-    hex_aliases = [
-        f"{a}{b}" for a, b in product(
-            ["A", "B", "C", "D"],
-            list(range(1, 7))
-        )
-    ]
+def test_aliases_six(s46_six):
+    for channel in s46_six.channels:
+        alias = channel.short_name
+        assert getattr(s46_six, alias) is channel
+        assert channel.channel_number == alias_to_channel_nr(alias)
 
-    aliases = hex_aliases + [f"R{i}" for i in range(1, 9)]
 
-    for channel in s46.channels:
-        idx = channel.channel_number - 1
-        alias = aliases[idx]
-        assert getattr(s46, alias) is channel
+def test_aliases_four(s46_four):
+    for channel in s46_four.channels:
+        alias = channel.short_name
+        assert getattr(s46_four, alias) is channel
+        assert channel.channel_number == alias_to_channel_nr(alias)

From ce2fb0f12d32011b329e694d65e411d78b8e3f81 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Mon, 3 Dec 2018 12:06:05 -0800
Subject: [PATCH 329/719] added test to assert that the 'close' query is only
 asked once during init

---
 .../QCodes example with Keithley S46.ipynb    | 56 +++++++++++++++++++
 qcodes/tests/drivers/test_keithley_s46.py     | 17 +++++-
 2 files changed, 70 insertions(+), 3 deletions(-)

diff --git a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb
index 6685d57991b..7418c8a49de 100644
--- a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
+++ b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
@@ -404,6 +404,62 @@
     "s46.A1.state(\"open\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class A: \n",
+    "    @property \n",
+    "    def a(self): \n",
+    "        return 1\n",
+    "\n",
+    "class B(A): \n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = B()\n",
+    "b.a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[1, 2, 3, 1].count(1)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
index 0989da2dfdf..6821efd7b6c 100644
--- a/qcodes/tests/drivers/test_keithley_s46.py
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -1,5 +1,4 @@
 import pytest
-from itertools import product
 
 from qcodes.instrument_drivers.tektronix.Keithley_s46 import (
     S46, LockAcquisitionError
@@ -9,16 +8,26 @@
 visalib = sims.__file__.replace('__init__.py', 'Keithley_s46.yaml@sim')
 
 
+class S46LoggedAsk(S46):
+    def __init__(self, *args, **kwargs):
+        self._ask_log = []
+        super().__init__(*args, **kwargs)
+
+    def ask(self, cmd: str):
+        self._ask_log.append(cmd)
+        return super().ask(cmd)
+
+
 @pytest.fixture(scope='module')
 def s46_six():
-    driver = S46('s46_six',address='GPIB::2::INSTR', visalib=visalib)
+    driver = S46LoggedAsk('s46_six', address='GPIB::2::INSTR', visalib=visalib)
     yield driver
     driver.close()
 
 
 @pytest.fixture(scope='module')
 def s46_four():
-    driver = S46('s46_four',address='GPIB::3::INSTR', visalib=visalib)
+    driver = S46LoggedAsk('s46_four', address='GPIB::3::INSTR', visalib=visalib)
     yield driver
     driver.close()
 
@@ -33,6 +42,7 @@ def test_runtime_error_on_bad_init():
 
 
 def test_init_six(s46_six):
+    assert s46_six._ask_log.count(":CLOS?") == 1
 
     n_channels = len(s46_six.channels)
     assert n_channels == 26
@@ -46,6 +56,7 @@ def test_init_six(s46_six):
 
 
 def test_init_four(s46_four):
+    assert s46_four._ask_log.count(":CLOS?") == 1
 
     n_channels = len(s46_four.channels)
     assert n_channels == 18

From 2834048afb331f071ffe5f386ecbc0954e3698ad Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Tue, 4 Dec 2018 09:50:16 +1100
Subject: [PATCH 330/719] Set timeout on server start

---
 qcodes/monitor/monitor.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index b090709784a..41d37dbba77 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -133,7 +133,9 @@ def __init__(self, *parameters, interval=1):
         self.start()
 
         # Wait until the loop is running
-        self.server_is_started.wait()
+        self.server_is_started.wait(timeout=5)
+        if not self.server_is_started.is_set():
+            raise RuntimeError("Failed to start server")
         Monitor.running = self
 
     def run(self):

From 31bb84897cb56262738cc0c6fc00de930d10df1f Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Tue, 4 Dec 2018 11:21:12 +1100
Subject: [PATCH 331/719] Remove unused import

---
 qcodes/tests/test_monitor.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/qcodes/tests/test_monitor.py b/qcodes/tests/test_monitor.py
index c17943e6cbe..b1832d0969e 100644
--- a/qcodes/tests/test_monitor.py
+++ b/qcodes/tests/test_monitor.py
@@ -3,7 +3,6 @@
 """
 from unittest import TestCase
 
-import time
 import asyncio
 import json
 import random

From f1e3852c10f6341071323cd9593e2a4bfc7d0580 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Mon, 3 Dec 2018 16:29:44 -0800
Subject: [PATCH 332/719] docstrings + type hints

---
 .../QCodes example with Keithley S46.ipynb    |  56 ------
 .../tektronix/Keithley_s46.py                 | 171 +++++++++++++-----
 qcodes/tests/drivers/test_keithley_s46.py     |  92 +++++++---
 3 files changed, 189 insertions(+), 130 deletions(-)

diff --git a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb
index 7418c8a49de..6685d57991b 100644
--- a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
+++ b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
@@ -404,62 +404,6 @@
     "s46.A1.state(\"open\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class A: \n",
-    "    @property \n",
-    "    def a(self): \n",
-    "        return 1\n",
-    "\n",
-    "class B(A): \n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = B()\n",
-    "b.a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[1, 2, 3, 1].count(1)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index ba8de24da95..4c73c057680 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -1,8 +1,11 @@
+"""
+Driver for the Tekronix S46 RF switch
+"""
 import re
 from itertools import product
 
-from typing import cast
-from qcodes import VisaInstrument, InstrumentChannel, ChannelList
+from typing import cast, Callable, Any, Union, Dict, List
+from qcodes import Instrument, VisaInstrument, InstrumentChannel, ChannelList
 from qcodes.utils.validators import Enum
 
 
@@ -10,8 +13,14 @@ class LockAcquisitionError(Exception):
     pass
 
 
-def cached_method(method):
-    def inner(self, *args, get_cached=False, **kwargs):
+def cached_method(method: Callable) -> Callable:
+    """
+    A decorator which adds a keyword 'get_cached' to a method. When
+    'get_cached=True', the decorated method returns a cached return value. If
+    the method has not been called before, or 'get_cached=False' the original
+    method is called.
+    """
+    def inner(self, *args: Any, get_cached: bool=False, **kwargs: Any) -> Any:
         if not hasattr(self, "__cached_values__"):
             self.__cached_values__ = {}
 
@@ -19,21 +28,73 @@ def inner(self, *args, get_cached=False, **kwargs):
             self.__cached_values__[method] = method(self, *args, **kwargs)
 
         return self.__cached_values__[method]
-
     return inner
 
 
+class RelayLock:
+    """
+    The S46 either has six pole or a four pole relays. For example, channels
+    'A1' to 'A6' are all on relay 'A'. However, channels 'R1' to 'R8' are all on
+    individual relays.
+
+    Only one channel per relay may be closed at any given time to prevent
+    degradation of RF performance and even switch damage. See page 2-11
+    of the manual. To enforce this, a lock mechanism has been implemented.
+    """
+    def __init__(self, relay_name: str):
+        self.relay_name = relay_name
+        self._locked_by = None
+
+    def acquire(self, channel_number: int):
+        """
+        Request a lock acquisition
+        """
+        if self._locked_by is not None and self._locked_by != channel_number:
+            raise LockAcquisitionError(
+                f"Relay {self.relay_name} is already in use by channel "
+                f"{self._locked_by}"
+            )
+        else:
+            self._locked_by = channel_number
+
+    def release(self, channel_number: int):
+        """
+        Release a lock.
+        """
+        if self._locked_by == channel_number:
+            self._locked_by = None
+
+
 class S46Channel(InstrumentChannel):
-    def __init__(self, parent, name, channel_number, relay_lock):
+    """
+    A channel class for the S46
+
+    Args:
+        parent
+        name
+        channel_number: unlike other instruments, channel numbers on the
+            S46 will not be contiguous. That is, we may have channels 1, 2 and 4
+            but channel 3 may be missing.
+        relay_lock: When closing the channel, request a lock acquisition.
+            Release the lock when opening
+    """
+    def __init__(
+            self,
+            parent: Union[Instrument, 'InstrumentChannel'],
+            name: str,
+            channel_number: int,
+            relay_lock: RelayLock
+    ):
         super().__init__(parent, name)
 
         self._channel_number = channel_number
         self._relay_lock = relay_lock
-
-        if self._get_state(get_cached=True) == "close":
+        # Acquire the lock if upon init we find the channel closed.
+        if self._get_state(init=True) == "close":
             try:
                 self._relay_lock.acquire(self._channel_number)
             except LockAcquisitionError as e:
+                # another channel has already acquired the lock
                 raise RuntimeError(
                     "The driver is initialized from an undesirable instrument "
                     "state where more then one channel on a single relay is "
@@ -48,13 +109,23 @@ def __init__(self, parent, name, channel_number, relay_lock):
             vals=Enum("open", "close")
         )
 
-    def _get_state(self, get_cached=False):
-        is_closed = self._channel_number in \
-                    self.parent.get_closed_channel_numbers(get_cached=get_cached)
+    def _get_state(self, init: bool=False) -> str:
+        """
+        Args:
+            init: When calling this method from self.__init__, make this
+                value 'True'. This will prevent the instrument being queried
+                ':CLOS?' for each channel.
+        """
+        closed_channels = self.parent.get_closed_channel_numbers(
+            get_cached=init)
+
+        is_closed = self._channel_number in closed_channels
         return {True: "close", False: "open"}[is_closed]
 
-    def _set_state(self, new_state: str):
-
+    def _set_state(self, new_state: str) -> None:
+        """
+        Open/Close the channel
+        """
         if new_state == "close":
             self._relay_lock.acquire(self._channel_number)
         elif new_state == "open":
@@ -63,36 +134,15 @@ def _set_state(self, new_state: str):
         self.write(f":{new_state} (@{self._channel_number})")
 
     @property
-    def channel_number(self):
+    def channel_number(self) -> int:
         return self._channel_number
 
 
-class RelayLock:
-    def __init__(self, relay_name):
-        self.relay_name = relay_name
-        self._locked_by = None
-
-    def acquire(self, channel_number):
-
-        if self._locked_by is not None and self._locked_by != channel_number:
-            raise LockAcquisitionError(
-                f"Relay {self.relay_name} is already in use by channel "
-                f"{self._locked_by}"
-            )
-        else:
-            self._locked_by = channel_number
-
-    def release(self, channel_number):
-
-        if self._locked_by == channel_number:
-            self._locked_by = None
-
-
 class S46(VisaInstrument):
 
-    relay_names = ["A", "B", "C", "D"] + [f"R{j}" for j in range(1, 9)]
+    relay_names: list = ["A", "B", "C", "D"] + [f"R{j}" for j in range(1, 9)]
 
-    _aliases_list = [
+    _aliases_list: list = [
         f"{a}{b}" for a, b in product(
             ["A", "B", "C", "D"],
             list(range(1, 7))
@@ -100,9 +150,17 @@ class S46(VisaInstrument):
     ]
 
     _aliases_list += [f"R{i}" for i in range(1, 9)]
-    aliases = dict(zip(_aliases_list, range(1, 33)))
+    # Make a dictionary where keys are channel aliases (e.g. 'A1', 'B3', etc)
+    # and values are corresponding channel numbers.
+    aliases: Dict[str, int] = dict(zip(_aliases_list, range(1, 33)))
+
+    def __init__(
+            self,
+            name: str,
+            address: str,
+            **kwargs: Any
+    ):
 
-    def __init__(self, name, address, **kwargs):
         super().__init__(name, address, terminator="\n", **kwargs)
 
         channels = ChannelList(
@@ -112,15 +170,18 @@ def __init__(self, name, address, **kwargs):
             snapshotable=False
         )
 
-        for relay_name, channel_count in zip(S46.relay_names, self.relay_layout):
+        for relay_name, channel_count in zip(
+                S46.relay_names, self.relay_layout):
+
             relay_lock = RelayLock(relay_name)
 
             for channel_index in range(1, channel_count + 1):
-
+                # E.g. For channel 'B2', channel_index is 2
                 if channel_count > 1:
                     alias = f"{relay_name}{channel_index}"
                 else:
-                    alias = relay_name
+                    alias = relay_name  # For channels R1 to R8, we have one
+                    # channel per relay. Channel alias = relay name
 
                 channel_number = S46.aliases[alias]
                 channel = S46Channel(self, alias, channel_number, relay_lock)
@@ -131,20 +192,36 @@ def __init__(self, name, address, **kwargs):
         self.connect_message()
 
     @cached_method
-    def get_closed_channel_numbers(self):
+    def get_closed_channel_numbers(self) -> List[int]:
+        """
+        Query the instrument for closed channels. Add an option to return
+        a cached response so we can prevent this method from being called
+        repeatedly for each channel during initialization of the driver.
+        """
         closed_channels_str = re.findall(r"\d+", self.ask(":CLOS?"))
         return [int(i) for i in closed_channels_str]
 
-    def get_closed_channels(self):
+    def get_closed_channels(self) -> List[S46Channel]:
+        """
+        Return a list of closed channels as a list of channel objects
+        """
         return [
             channel for channel in self.channels if
             channel.channel_number in self.get_closed_channel_numbers()
         ]
 
-    def open_all_channels(self):
+    def open_all_channels(self) -> None:
+        """
+        Please do not write ':OPEN ALL' to the instrument as this will
+        circumvent the lock.
+        """
         for channel in self.get_closed_channels():
-            cast(S46Channel, channel).state("open")
+            channel.state("open")
 
     @property
-    def relay_layout(self):
+    def relay_layout(self) -> List[int]:
+        """
+        The relay layout tells us how many channels we have per relay. Note
+        that we can have zero channels per relay.
+        """
         return [int(i) for i in self.ask(":CONF:CPOL?").split(",")]
diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
index 6821efd7b6c..67a5862b884 100644
--- a/qcodes/tests/drivers/test_keithley_s46.py
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -8,7 +8,26 @@
 visalib = sims.__file__.replace('__init__.py', 'Keithley_s46.yaml@sim')
 
 
+def test_aliases_dict():
+    """
+    Test the class attribute 'aliases' which maps channel aliases
+    (e.g. A1, B2, etc) to channel numbers.
+    """
+    def calc_channel_nr(alias: str) -> int:
+        """
+        We perform the calculation in a different way to verify correctness
+        """
+        offset_dict = dict(zip(["A", "B", "C", "D", "R"], range(0, 32, 6)))
+        return offset_dict[alias[0]] + int(alias[1:])
+
+    assert all([nr == calc_channel_nr(al) for al, nr in S46.aliases.items()])
+
+
 class S46LoggedAsk(S46):
+    """
+    A version of the driver which logs every ask command. We need this to
+    assert that ':CLOS?' is called once during initialization
+    """
     def __init__(self, *args, **kwargs):
         self._ask_log = []
         super().__init__(*args, **kwargs)
@@ -20,6 +39,9 @@ def ask(self, cmd: str):
 
 @pytest.fixture(scope='module')
 def s46_six():
+    """
+    A six channel-per-relay instrument
+    """
     driver = S46LoggedAsk('s46_six', address='GPIB::2::INSTR', visalib=visalib)
     yield driver
     driver.close()
@@ -27,13 +49,20 @@ def s46_six():
 
 @pytest.fixture(scope='module')
 def s46_four():
+    """
+    A four channel-per-relay instrument
+    """
     driver = S46LoggedAsk('s46_four', address='GPIB::3::INSTR', visalib=visalib)
     yield driver
     driver.close()
 
 
 def test_runtime_error_on_bad_init():
-
+    """
+    If we initialize the driver from an instrument state with more then one
+    channel per relay closed, raise a runtime error. An instrument can come to
+    this state if previously, other software was used to control the instrument
+    """
     with pytest.raises(
         RuntimeError,
         match="The driver is initialized from an undesirable instrument state"
@@ -42,9 +71,13 @@ def test_runtime_error_on_bad_init():
 
 
 def test_init_six(s46_six):
+    """
+    Test that the six channel instrument initializes correctly.
+    """
     assert s46_six._ask_log.count(":CLOS?") == 1
 
     n_channels = len(s46_six.channels)
+    # Channels A1 to D6 + R5 + R8 (4 * 6 + 2)
     assert n_channels == 26
 
     closed_channels = [1, 8, 13]
@@ -53,12 +86,17 @@ def test_init_six(s46_six):
         channel_nr = S46.aliases[channel.short_name]
         state = "close" if channel_nr in closed_channels else "open"
         assert channel.state() == state
+        assert channel_nr == S46.aliases[channel.short_name]
 
 
 def test_init_four(s46_four):
+    """
+    Test that the six channel instrument initializes correctly.
+    """
     assert s46_four._ask_log.count(":CLOS?") == 1
 
     n_channels = len(s46_four.channels)
+    # Channels A1 to D4 + R5 + R8 (4 * 4 + 2)
     assert n_channels == 18
 
     closed_channels = [1, 8]
@@ -67,41 +105,41 @@ def test_init_four(s46_four):
         channel_nr = S46.aliases[channel.short_name]
         state = "close" if channel_nr in closed_channels else "open"
         assert channel.state() == state
+        assert channel_nr == S46.aliases[channel.short_name]
 
+    # A four channel instrument will have channels missing
+    for relay in ["A", "B", "C", "D"]:
+        for index in [5, 6]:
+            alias = f"{relay}{index}"
+            assert not hasattr(s46_four, alias)
 
-def test_open_close(s46_six):
 
+def test_locking_mechanism(s46_six):
+    """
+    1) Test that the lock acquisition error is raised if we try to close
+    more then once channel per replay
+    2) Test that the lock is released when opening a channel that was closed
+    """
     with pytest.raises(
         LockAcquisitionError,
         match="is already in use by channel"
     ):
-        s46_six.channels[1].state("close")
-
-    s46_six.channels[0].state("open")
-    s46_six.channels[1].state("close")
-    s46_six.channels[18].state("close")
-
+        # A1 should be closed already
+        s46_six.A2.state("close")
+    # release the lock
+    s46_six.A1.state("open")
+    # now we should be able to close A2
+    s46_six.A2.state("close")
+
+    # Let C1 acquire the lock
+    s46_six.C1.state("close")
+    # closing C2 should raise an error
     with pytest.raises(
         LockAcquisitionError,
         match="is already in use by channel"
     ):
-        s46_six.channels[19].state("close")
-
+        s46_six.C2.state("close")
 
-def alias_to_channel_nr(alias):
-    offset = dict(zip(["A", "B", "C", "D", "R"], range(0, 32, 6)))[alias[0]]
-    return offset + int(alias[1:])
-
-
-def test_aliases_six(s46_six):
-    for channel in s46_six.channels:
-        alias = channel.short_name
-        assert getattr(s46_six, alias) is channel
-        assert channel.channel_number == alias_to_channel_nr(alias)
-
-
-def test_aliases_four(s46_four):
-    for channel in s46_four.channels:
-        alias = channel.short_name
-        assert getattr(s46_four, alias) is channel
-        assert channel.channel_number == alias_to_channel_nr(alias)
+    # Upon opening C1 we should be able to close C2
+    s46_six.C1.state("open")
+    s46_six.C2.state("close")

From 1e16106a9495384e8615081eaaaf5431c2e1ab9f Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Tue, 4 Dec 2018 12:13:09 +1100
Subject: [PATCH 333/719] Move parameter type check to initializer

---
 qcodes/monitor/monitor.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 41d37dbba77..117aa9f5842 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -44,10 +44,6 @@ def _get_metadata(*parameters) -> Dict[str, Any]:
     # group metadata by instrument
     metas = defaultdict(list) # type: dict
     for parameter in parameters:
-        # Check we have a list of parameters
-        if not isinstance(parameter, Parameter):
-            raise ValueError("{} is not a parameter".format(parameter))
-
         # Get the latest value from the parameter, respecting the max_val_age parameter
         meta = {}
         meta["value"] = str(parameter.get_latest())
@@ -118,6 +114,12 @@ def __init__(self, *parameters, interval=1):
             interval: How often one wants to refresh the values
         """
         super().__init__()
+
+        # Check that all values are valid parameters
+        for parameter in parameters:
+            if not isinstance(parameter, Parameter):
+                raise TypeError("We can only monitor QCodes Parameters")
+
         self.loop = None
         self.server = None
         self._parameters = parameters

From c9abbe41c4e34039e197576b5995921227d02c62 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Mon, 3 Dec 2018 17:20:00 -0800
Subject: [PATCH 334/719] edit notebook meta data to stop exec on CI

---
 .../QCodes example with Keithley S46.ipynb    | 276 +++++-------------
 .../tektronix/Keithley_s46.py                 |   2 +-
 2 files changed, 75 insertions(+), 203 deletions(-)

diff --git a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb
index 6685d57991b..2e9132e83a4 100644
--- a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
+++ b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
@@ -1,5 +1,22 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tektronix Keithley S46\n",
+    "\n",
+    "The S46 is an RF swicth with four relays \"A\" to \"D\". These relays either have four or six poles, depending on the instrument model. Each pole constitutes a channel and can either be \"open\" or \"closed\". Channel \"A1\" is attached to the first pole on relay \"A\", channel \"B2\" is attached to the second pole of relay \"B\", etc...  \n",
+    "\n",
+    "Channels \"A1\" to \"D6\" are all \"normally open\". Only one channel per relay may be closed.  \n",
+    "\n",
+    "Additionally, there are optionally eight relays \"R1\" to \"R8\" which are two pole relays. One pole is \"normally closed\" and the other \"normally open\". For these relays, we have one channel per relay, so \"R1\" is both a channel and a relay. Upon closing the channel, the normally open pole will close. \n",
+    "\n",
+    "In this notebook, we have verified with a multi-meter that channels indeed open and close as expected. \n",
+    "\n",
+    "Note: We have performed tests with a six pole instrument. Although it is expected that this driver should work with a four pole instrument, this has not been verified due to a lack of instrument availability "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -18,7 +35,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.62s\n"
+      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.54s\n"
      ]
     }
    ],
@@ -34,7 +51,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "26"
       ]
      },
      "execution_count": 3,
@@ -43,32 +60,32 @@
     }
    ],
    "source": [
-    "s46.get_closed_channels()"
+    "len(s46.channels)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "26"
+       "[]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(s46.channels)"
+    "s46.get_closed_channels()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -86,7 +103,7 @@
        "'open'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -97,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -115,7 +132,7 @@
        "'close'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -124,251 +141,119 @@
     "s46.A1.state()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('Relay A is already in use by channel 1', 'setting s2_A2_state to close')\n"
-     ]
-    }
-   ],
-   "source": [
-    "try: \n",
-    "    s46.A2.state(\"close\")\n",
-    "    raise(\"We should not be here\")\n",
-    "except LockAcquisitionError as e: \n",
-    "    print(e)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s46.A1.state(\"open\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "s46.A2.state(\"close\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s46.A2.state(\"open\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert s46.A1 is s46.channels[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert s46.A2 is s46.channels[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert s46.B1 is s46.channels[6]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'B1'"
+       "[<S46Channel: s2_A1 of S46: s2>]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "s46.channels[6].short_name"
+    "s46.get_closed_channels()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Relay A is already in use by channel 1', 'setting s2_A2_state to close')\n"
+     ]
+    }
+   ],
    "source": [
-    "s46.A1.state(\"close\")"
+    "try: \n",
+    "    s46.A2.state(\"close\")\n",
+    "    raise(\"We should not be here\")\n",
+    "except LockAcquisitionError as e: \n",
+    "    print(e)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.B1.state(\"close\")"
+    "s46.A1.state(\"open\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.R8.state(\"close\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<S46Channel: s2_A1 of S46: s2>,\n",
-       " <S46Channel: s2_B1 of S46: s2>,\n",
-       " <S46Channel: s2_R8 of S46: s2>]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s46.get_closed_channels()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 7, 32]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s46.get_closed_channel_numbers()"
+    "s46.A2.state(\"close\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1\n",
-      "7\n",
-      "32\n"
+      "('Relay A is already in use by channel 2', 'setting s2_A1_state to close')\n"
      ]
     }
    ],
    "source": [
-    "print(S46.alias_to_channel_number(\"A1\"))\n",
-    "print(S46.alias_to_channel_number(\"B1\"))\n",
-    "print(S46.alias_to_channel_number(\"R8\"))"
+    "try: \n",
+    "    s46.A1.state(\"close\")\n",
+    "    raise(\"We should not be here\")\n",
+    "except LockAcquisitionError as e: \n",
+    "    print(e)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def channel_number_to_alias(num): \n",
-    "    n = num - 24\n",
-    "    if n > 0: \n",
-    "        return f\"R{n}\"\n",
-    "    index = num % 6\n",
-    "    relay = {0: \"A\", 1: \"B\", 2: \"C\", 3: \"D\"}[(num - 1)//6]\n",
-    "    return f\"{relay}{index}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "A1\n",
-      "B1\n",
-      "R8\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(channel_number_to_alias(1))\n",
-    "print(channel_number_to_alias(7))\n",
-    "print(channel_number_to_alias(32))"
+    "s46.B1.state(\"close\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "A2\n",
-      "B2\n",
-      "R7\n"
+      "('Relay B is already in use by channel 7', 'setting s2_B2_state to close')\n"
      ]
     }
    ],
    "source": [
-    "print(channel_number_to_alias(2))\n",
-    "print(channel_number_to_alias(8))\n",
-    "print(channel_number_to_alias(31))"
+    "try: \n",
+    "    s46.B2.state(\"close\")\n",
+    "    raise(\"We should not be here\")\n",
+    "except LockAcquisitionError as e: \n",
+    "    print(e)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -386,7 +271,7 @@
        "[]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -394,22 +279,6 @@
    "source": [
     "s46.get_closed_channels()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s46.A1.state(\"open\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -429,6 +298,9 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.7"
+  },
+  "nbsphinx": {
+   "execute": "never"
   }
  },
  "nbformat": 4,
diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index 4c73c057680..30a1362b394 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -43,7 +43,7 @@ class RelayLock:
     """
     def __init__(self, relay_name: str):
         self.relay_name = relay_name
-        self._locked_by = None
+        self._locked_by: int = None
 
     def acquire(self, channel_number: int):
         """

From c4bbc08d12accff0342545821ce244ea5e0d7f4f Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Mon, 3 Dec 2018 17:20:39 -0800
Subject: [PATCH 335/719] remove unused cast import

---
 qcodes/instrument_drivers/tektronix/Keithley_s46.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index 30a1362b394..cbf02769321 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -4,7 +4,7 @@
 import re
 from itertools import product
 
-from typing import cast, Callable, Any, Union, Dict, List
+from typing import Callable, Any, Union, Dict, List
 from qcodes import Instrument, VisaInstrument, InstrumentChannel, ChannelList
 from qcodes.utils.validators import Enum
 

From ac173596cb1bd7e306deb7793592608387db98cb Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Mon, 3 Dec 2018 17:28:43 -0800
Subject: [PATCH 336/719] add newline in simulation yaml

---
 qcodes/instrument/sims/Keithley_s46.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/sims/Keithley_s46.yaml b/qcodes/instrument/sims/Keithley_s46.yaml
index 66f9789933d..4e3de706d37 100644
--- a/qcodes/instrument/sims/Keithley_s46.yaml
+++ b/qcodes/instrument/sims/Keithley_s46.yaml
@@ -80,4 +80,4 @@ resources:
   GPIB::2::INSTR:
     device: device six
   GPIB::3::INSTR:
-    device: device four
\ No newline at end of file
+    device: device four

From 84f382643a67df1b118acf2b032f24b70d88be40 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Mon, 3 Dec 2018 17:29:44 -0800
Subject: [PATCH 337/719] wrong comment in simulation yaml file

---
 qcodes/instrument/sims/Keithley_s46.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/sims/Keithley_s46.yaml b/qcodes/instrument/sims/Keithley_s46.yaml
index 4e3de706d37..2f2b6a840d1 100644
--- a/qcodes/instrument/sims/Keithley_s46.yaml
+++ b/qcodes/instrument/sims/Keithley_s46.yaml
@@ -1,4 +1,4 @@
-# SIMULATED INSTRUMENT FOR Keysight 33xxx series function generator
+# SIMULATED INSTRUMENT FOR Keysight S46 RF switch
 spec: "1.0"
 devices:
   device bad_state:

From 0b453b55d08906cf4eb08936e18f1180296cf989 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Mon, 3 Dec 2018 17:38:13 -0800
Subject: [PATCH 338/719] remove nonsense assert from test

---
 qcodes/tests/drivers/test_keithley_s46.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
index 67a5862b884..72e0db48645 100644
--- a/qcodes/tests/drivers/test_keithley_s46.py
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -86,7 +86,6 @@ def test_init_six(s46_six):
         channel_nr = S46.aliases[channel.short_name]
         state = "close" if channel_nr in closed_channels else "open"
         assert channel.state() == state
-        assert channel_nr == S46.aliases[channel.short_name]
 
 
 def test_init_four(s46_four):
@@ -105,7 +104,6 @@ def test_init_four(s46_four):
         channel_nr = S46.aliases[channel.short_name]
         state = "close" if channel_nr in closed_channels else "open"
         assert channel.state() == state
-        assert channel_nr == S46.aliases[channel.short_name]
 
     # A four channel instrument will have channels missing
     for relay in ["A", "B", "C", "D"]:

From f499cb7c239d0ec18fc8dfc45e8388ff540b4dc9 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Mon, 3 Dec 2018 18:00:17 -0800
Subject: [PATCH 339/719] explicitly test channel number invariance

---
 qcodes/tests/drivers/test_keithley_s46.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
index 72e0db48645..5a38c5dc91e 100644
--- a/qcodes/tests/drivers/test_keithley_s46.py
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -112,6 +112,19 @@ def test_init_four(s46_four):
             assert not hasattr(s46_four, alias)
 
 
+def test_channel_number_invariance(s46_four, s46_six):
+    """
+    Regardless of the channel layout (that is, number of channels per relay),
+    channel aliases should represent the same channel. See also page 2-5 of the
+    manual (e.g. B1 is *always* channel 7)
+    """
+    for alias in S46.aliases.keys():
+        if hasattr(s46_four, alias) and hasattr(s46_six, alias):
+            channel_four = getattr(s46_four, alias)
+            channel_six = getattr(s46_six, alias)
+            assert channel_four.channel_number == channel_six.channel_number
+
+
 def test_locking_mechanism(s46_six):
     """
     1) Test that the lock acquisition error is raised if we try to close

From 8d91bb181a0867bf25a7721ea1d2db8eefc10935 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Tue, 4 Dec 2018 14:10:08 +1100
Subject: [PATCH 340/719] Set reasonable timeouts on waiting for clients to
 disconnect

---
 qcodes/monitor/monitor.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 117aa9f5842..060bc8d5905 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -148,7 +148,8 @@ def run(self):
         self.loop = asyncio.new_event_loop()
         asyncio.set_event_loop(self.loop)
         try:
-            server_start = websockets.serve(self.handler, '127.0.0.1', WEBSOCKET_PORT)
+            server_start = websockets.serve(self.handler, '127.0.0.1',
+                                            WEBSOCKET_PORT, close_timeout=1)
             self.server = self.loop.run_until_complete(server_start)
             self.server_is_started.set()
             self.loop.run_forever()
@@ -182,13 +183,13 @@ def stop(self) -> None:
         Monitor.running = None
 
     async def __stop_server(self):
-        log.debug("asking server to close")
+        log.debug("asking server %r to close", self.server)
         self.server.close()
         log.debug("waiting for server to close")
         await self.loop.create_task(self.server.wait_closed())
         log.debug("stopping loop")
         log.debug("Pending tasks at stop: %r",
-                  asyncio.Task.all_tasks(self.loop))
+                asyncio.Task.all_tasks(self.loop))
         self.loop.stop()
 
     def join(self, timeout=None) -> None:
@@ -208,7 +209,9 @@ def join(self, timeout=None) -> None:
             # the above may throw a runtime error if the loop is already
             # stopped in which case there is nothing more to do
             log.exception("Could not close loop")
-        self.loop_is_closed.wait()
+        self.loop_is_closed.wait(timeout=5)
+        if not self.loop_is_closed.is_set():
+            raise RuntimeError("Failed to join loop")
         log.debug("Loop reported closed")
         super().join(timeout=timeout)
         log.debug("Monitor Thread has joined")

From 5f0aa3d4f352a54881101345284e4d8be230495a Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 4 Dec 2018 14:16:28 +0100
Subject: [PATCH 341/719] support for expaning scalar parameters

---
 qcodes/dataset/sqlite_base.py | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 792e21ca5d4..5a47cb871a5 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1461,18 +1461,27 @@ def get_data_of_param_and_deps_as_columns(conn: ConnectionPlus,
 
         res = get_data(conn, table_name, columns, start=start, end=end)
 
-        if 'array' in types and 'numeric' in types: # todo handle str
+        if 'array' in types and ('numeric' in types or 'text' in types):
             # todo: Should we check that all the arrays are the same size?
             first_array_element = types.index('array')
-            non_array_elements = [i for i, x in enumerate(types)
-                                  if x == "numeric"]
-            for dat in res:
-                for element in non_array_elements:
-                    dat[element] = np.zeros_like(dat[first_array_element]) \
-                                   + dat[element]
+            numeric_elms = [i for i, x in enumerate(types)
+                            if x == "numeric"]
+            text_elms = [i for i, x in enumerate(types)
+                         if x == "text"]
+            for row in res:
+                for element in numeric_elms:
+                    row[element] = np.full_like(row[first_array_element],
+                                                row[element],
+                                                dtype=np.float)
+                    # todo should we handle int/float types here
+                for element in text_elms:
+                    strlen = len(row[element])
+                    row[element] = np.full_like(row[first_array_element],
+                                                row[element],
+                                                dtype=f'U{strlen}')
 
         res_t = list(map(list, zip(*res)))
-        output[param] = {name: np.array(column_data)
+        output[param] = {name: np.squeeze(np.array(column_data))
                          for name, column_data
                          in zip(columns, res_t)}
     return output

From 81b04ab75ff0ddcced828a4ba2dfdc28be254e4a Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 4 Dec 2018 14:17:00 +0100
Subject: [PATCH 342/719] Improve tests

---
 qcodes/tests/dataset/dataset_fixtures.py   |  34 ++++---
 qcodes/tests/dataset/test_dataset_basic.py | 101 +++++++++++++++++++--
 2 files changed, 112 insertions(+), 23 deletions(-)

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
index 0493fb65840..dd6729244c6 100644
--- a/qcodes/tests/dataset/dataset_fixtures.py
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -33,11 +33,12 @@ def scalar_dataset(dataset):
     yield dataset
 
 
-@pytest.fixture
-def array_dataset(experiment):
+@pytest.fixture(scope="function",
+                params=["array", "numeric"])
+def array_dataset(experiment, request):
     meas = Measurement()
     param = ArraySetPointParam()
-    meas.register_parameter(param)
+    meas.register_parameter(param, paramtype=request.param)
 
     with meas.run() as datasaver:
         datasaver.add_result((param, param.get(),))
@@ -47,13 +48,13 @@ def array_dataset(experiment):
         datasaver.dataset.conn.close()
 
 
-
-@pytest.fixture
-def multi_dataset(experiment):
+@pytest.fixture(scope="function",
+                params=["array", "numeric"])
+def multi_dataset(experiment, request):
     meas = Measurement()
     param = Multi2DSetPointParam()
 
-    meas.register_parameter(param)
+    meas.register_parameter(param, paramtype=request.param)
 
     with meas.run() as datasaver:
         datasaver.add_result((param, param.get(),))
@@ -62,13 +63,16 @@ def multi_dataset(experiment):
     finally:
         datasaver.dataset.conn.close()
 
-@pytest.fixture
-def array_in_scalar_dataset(experiment):
+
+@pytest.fixture(scope="function",
+                params=["array", "numeric"])
+def array_in_scalar_dataset(experiment, request):
     meas = Measurement()
     scalar_param = Parameter('scalarparam', set_cmd=None)
     param = ArraySetPointParam()
     meas.register_parameter(scalar_param)
-    meas.register_parameter(param, setpoints=(scalar_param,))
+    meas.register_parameter(param, setpoints=(scalar_param,),
+                            paramtype=request.param)
 
     with meas.run() as datasaver:
         for i in range(1, 10):
@@ -81,13 +85,15 @@ def array_in_scalar_dataset(experiment):
         datasaver.dataset.conn.close()
 
 
-@pytest.fixture
-def array_in_str_dataset(experiment):
+@pytest.fixture(scope="function",
+                params=["array", "numeric"])
+def array_in_str_dataset(experiment, request):
     meas = Measurement()
-    scalar_param = Parameter('scalarparam', set_cmd=None)
+    scalar_param = Parameter('textparam', set_cmd=None)
     param = ArraySetPointParam()
     meas.register_parameter(scalar_param, paramtype='text')
-    meas.register_parameter(param, setpoints=(scalar_param,))
+    meas.register_parameter(param, setpoints=(scalar_param,),
+                            paramtype=request.param)
 
     with meas.run() as datasaver:
         for i in ['A', 'B', 'C']:
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index ea81bb0dd12..061c476e1b9 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -1,7 +1,7 @@
 import itertools
 from copy import copy
 import re
-
+from typing import Sequence, Tuple
 
 import pytest
 import numpy as np
@@ -765,19 +765,86 @@ def test_get_parameter_data(scalar_dataset):
 
 
 def test_get_array_parameter_data(array_dataset):
-    parameter_test_helper(array_dataset)
+    paramspecs = array_dataset.paramspecs
+    types = [param.type for param in paramspecs.values()]
+
+    if 'array' not in types:
+        parameter_test_helper(array_dataset)
+
+    inputnames = ['testparameter']
+    data = array_dataset.get_data_of_param_and_deps_as_columns(*inputnames)
+    assert len(data.keys()) == len(inputnames)
+    expected_names = {}
+    expected_names['testparameter'] = ['testparameter', 'this_setpoint']
+    expected_shapes = {}
+    expected_shapes['testparameter'] = [(5, ), (5, )]
+
+    verify_data_dict(data, inputnames, expected_names, expected_shapes)
 
 
 def test_get_multi_parameter_data(multi_dataset):
-    parameter_test_helper(multi_dataset)
+    paramspecs = multi_dataset.paramspecs
+    types = [param.type for param in paramspecs.values()]
+
+    if 'array' not in types:
+        parameter_test_helper(multi_dataset)
+
+    inputnames = ['this', 'that']
+    data = multi_dataset.get_data_of_param_and_deps_as_columns(*inputnames)
+    assert len(data.keys()) == len(inputnames)
+    expected_names = {}
+    expected_names['this'] = ['this', 'this_setpoint', 'that_setpoint']
+    expected_names['that'] = ['that', 'this_setpoint', 'that_setpoint']
+    expected_shapes = {}
+    if 'array' in types:
+        expected_shapes['this'] = [(5, 3), (5, 3)]
+        expected_shapes['that'] = [(5, 3), (5, 3)]
+    else:
+        expected_shapes['this'] = [(15,), (15,)]
+        expected_shapes['that'] = [(15,), (15,)]
+    verify_data_dict(data, inputnames, expected_names, expected_shapes)
 
 
 def test_get_array_in_scalar_param_data(array_in_scalar_dataset):
-    parameter_test_helper(array_in_scalar_dataset)
+    paramspecs = array_in_scalar_dataset.paramspecs
+    types = [param.type for param in paramspecs.values()]
+
+    if 'array' not in types:
+        parameter_test_helper(array_in_scalar_dataset)
+
+    inputnames = ['testparameter']
+    data = array_in_scalar_dataset.get_data_of_param_and_deps_as_columns(*inputnames)
+    assert len(data.keys()) == len(inputnames)
+    expected_names = {}
+    expected_names['testparameter'] = ['testparameter', 'scalarparam',
+                                       'this_setpoint']
+    expected_shapes = {}
+    if 'array' in types:
+        expected_shapes['testparameter'] = [(9, 5), (9, 5)]
+    else:
+        expected_shapes['testparameter'] = [(45,), (45,)]
+    verify_data_dict(data, inputnames, expected_names, expected_shapes)
 
 
 def test_get_array_in_str_param_data(array_in_str_dataset):
-    parameter_test_helper(array_in_str_dataset)
+    paramspecs = array_in_str_dataset.paramspecs
+    types = [param.type for param in paramspecs.values()]
+
+    if 'array' not in types:
+        parameter_test_helper(array_in_str_dataset)
+
+    inputnames = ['testparameter']
+    data = array_in_str_dataset.get_data_of_param_and_deps_as_columns(*inputnames)
+    assert len(data.keys()) == len(inputnames)
+    expected_names = {}
+    expected_names['testparameter'] = ['testparameter', 'textparam',
+                                       'this_setpoint']
+    expected_shapes = {}
+    if 'array' in types:
+        expected_shapes['testparameter'] = [(3, 5), (3, 5)]
+    else:
+        expected_shapes['testparameter'] = [(15,), (15,)]
+    verify_data_dict(data, inputnames, expected_names, expected_shapes)
 
 
 def parameter_test_helper(ds):
@@ -788,14 +855,16 @@ def parameter_test_helper(ds):
     param_names = copy(params)
     params[0] = ds.paramspecs[params[0]]
 
+
+
     ref = ds.get_data(*params)
     dut = ds.get_parameter_data(*params)
 
     column_lengths = []
     for col in dut.values():
         column_lengths.append(col.size)
-    column_lenghts = np.array(column_lengths)
-    assert np.all(column_lenghts == column_lenghts[0])
+    column_lengths = np.array(column_lengths)
+    assert np.all(column_lengths == column_lengths[0])
 
     for i_row, row in enumerate(ref):
         for i_param, param_name in enumerate(param_names):
@@ -813,5 +882,19 @@ def parameter_test_helper(ds):
             elif isinstance(v_ref, str):
                 assert isinstance(v_test, np.str_)
                 assert v_ref == v_test
-            else:
-                raise RuntimeError('Unsupported data type')
+
+
+def verify_data_dict(data, parameter_names, expected_names,
+                     expected_shapes):
+    for param in parameter_names:
+        innerdata = data[param]
+        verify_data_dict_for_single_param(innerdata,
+                                          expected_names[param],
+                                          expected_shapes[param])
+
+
+def verify_data_dict_for_single_param(datadict,
+                                      names: Sequence[str],
+                                      shapes: Sequence[Tuple[int, ...]]):
+    for name, shape in zip(names, shapes):
+        assert datadict[name].shape == shape

From 5c0af86c7ff0c6a182e527c5c94c9a37607a5b86 Mon Sep 17 00:00:00 2001
From: nyxus <gercoversloot@gmail.com>
Date: Tue, 4 Dec 2018 15:32:09 +0100
Subject: [PATCH 343/719]  RTO1024:  Add measurment and run continues commands;
 Added error count and next; corrected the stop_opc and opc paramaters; Added
 trigger mode paramater and added docstring to High defintion mode

---
 .../rohde_schwarz/RTO1000.py                  | 180 +++++++++++++++++-
 1 file changed, 177 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py b/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py
index cfa4a23ad04..1112920028e 100644
--- a/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py
+++ b/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py
@@ -129,6 +129,146 @@ def get_raw(self):
 
         return output
 
+class ScopeMeasurement(InstrumentChannel):
+    """
+    Class to hold a mesurement of the scope
+    """
+
+    def __init__(self, parent: Instrument, name: str, meas_nr: int):
+        """
+        Args:
+            parent: The instrument to which the channel is attached
+            name: The name of the mesurement
+            meas_nr: The number of the mesurment in question. Must match the
+                actual number as used by the instrument (1..8)
+        """
+
+        if meas_nr not in range(1, 9):
+            raise ValueError('Invalid measurement number; Min: 1, max 8')
+
+        self.meas_nr = meas_nr
+        super().__init__(parent, name)
+
+        self.sources = vals.Enum('C1W1',     'C1W2',     'C1W3',
+                        'C2W1',     'C2W2',     'C2W3',
+                        'C3W1',     'C3W2',     'C3W3',
+                        'C4W1',     'C4W2',     'C4W3',
+                        'M1',       'M2',       'M3',       'M4',
+                        'R1',       'R2',       'R3',       'R4',
+                        'SBUS1',    'SBUS2',    'SBUS3',    'SBUS4',
+                        'D0',       'D1',       'D2',       'D3',
+                        'D4',       'D5',       'D6',       'D7',
+                        'D8',       'D9',       'D10',       'D11',
+                        'D12',      'D13',      'D14',      'D15',
+                        'TRK1',     'TRK2',     'TRK3',     'TRK4',
+                        'TRK5',     'TRK6',     'TRK7',     'TRK8',
+                        'SG1TL1',   'SG1TL2',
+                        'SG2TL1',   'SG2TL2',
+                        'SG3TL1',   'SG3TL2',
+                        'SG4TL1',   'SG4TL2',
+                        'Z1V1',     'Z1V2',     'Z1V3',     'Z1V4',
+                        'Z1I1',     'Z1I2',     'Z1I3',     'Z1I4',
+                        'Z2V1',     'Z2V2',     'Z2V3',     'Z2V4',
+                        'Z2I1',     'Z2I2',     'Z2I3',     'Z2I4')
+
+        self.categories = vals.Enum('AMPTime',   'JITTer', 'EYEJitter', 'SPECtrum',
+                                    'HISTogram', 'PROTocol')
+
+        self.meas_type = vals.Enum(
+                        # Amplitude/time measurements
+                        'HIGH',        'LOW',          'AMPLitude',    'MAXimum',
+                        'MINimum',     'PDELta',       'MEAN',         'RMS',
+                        'STDDev',      'POVershoot',   'NOVershoot',   'AREA',
+                        'RTIMe',       'FTIMe',        'PPULse',       'NPULse',
+                        'PERiod',      'FREQuency',    'PDCYcle',      'NDCYcle',
+                        'CYCarea',     'CYCMean',      'CYCRms',       'CYCStddev',
+                        'PULCnt',      'DELay',        'PHASe',        'BWIDth',
+                        'PSWitching',  'NSWitching',   'PULSetrain',   'EDGecount',
+                        'SHT',         'SHR',          'DTOTrigger',   'PROBemeter',
+                        'SLERising',   'SLEFalling'
+                        # Jitter measurements
+                        'CCJitter',     'NCJitter',    'CCWidth',      'CCDutycycle',
+                        'TIE',          'UINTerval',   'DRATe',        'SKWDelay',
+                        'SKWPhase',
+                        # Eye diagram measurements
+                        'ERPercent',    'ERDB',         'EHEight',      'EWIDth',
+                        'ETOP',         'EBASe',        'QFACtor',      'RMSNoise',
+                        'SNRatio',      'DCDistortion', 'ERTime',       'EFTime',
+                        'EBRate',       'EAMPlitude',   'PPJitter',     'STDJitter',
+                        'RMSJitter',
+                        # Spectrum measurements
+                        'CPOWer',       'OBWidth',      'SBWidth',      'THD',
+                        'THDPCT',       'THDA',         'THDU',         'THDR',
+                        'HAR',          'PLISt',
+                        # Histogram measurements
+                        'WCOunt',       'WSAMples',     'HSAMples',     'HPEak',
+                        'PEAK',         'UPEakvalue',   'LPEakvalue',   'HMAXimum',
+                        'HMINimum',     'MEDian',       'MAXMin',       'HMEan',
+                        'HSTDdev',      'M1STddev',     'M2STddev',     'M3STddev',
+                        'MKPositive',   'MKNegative'
+                        )
+
+        self.add_parameter('enable',
+                           label='Measurement {} enable'.format(meas_nr),
+                           set_cmd='MEASurement{}:ENABle {{}}'.format(meas_nr),
+                           vals=vals.Enum('ON', 'OFF'),
+                           docstring='Switches the mesurment on or off')
+
+        self.add_parameter('source',
+                           label='Measurement {} source'.format(meas_nr),
+                           set_cmd='MEASurement{}:SOURce {{}}'.format(meas_nr),
+                           vals=self.sources,
+                           docstring='Set the source of a measurement if the ' \
+                                     'measurement only needs one source')
+
+        self.add_parameter('source_first',
+                           label='Measurement {} first source'.format(meas_nr),
+                           set_cmd='MEASurement{}:FSRC {{}}'.format(meas_nr),
+                           vals=self.sources,
+                           docstring='Set the first source of a measurement if the ' \
+                                     'measurement only needs mutliple sources')
+
+        self.add_parameter('source_second',
+                           label='Measurement {} second source'.format(meas_nr),
+                           set_cmd='MEASurement{}:SSRC {{}}'.format(meas_nr),
+                           vals=self.sources,
+                           docstring='Set the second source of a measurement if the ' \
+                                     'measurement only needs mutliple sources')
+
+        self.add_parameter('category',
+                           label='Measurement {} category'.format(meas_nr),
+                           set_cmd='MEASurement{}:CATegory {{}}'.format(meas_nr),
+                           vals=self.categories,
+                           docstring='Set the category of a measurement')
+
+        self.add_parameter('main',
+                           label='Measurement {} main'.format(meas_nr),
+                           set_cmd='MEASurement{}:MAIN {{}}'.format(meas_nr),
+                           vals=self.meas_type,
+                           docstring='Set the main of a measurement')
+
+        self.add_parameter('statistics_enable',
+                           label='Measurement {} enable statistics'.format(meas_nr),
+                           set_cmd='MEASurement{}:STATistics:ENABle {{}}'.format(meas_nr),
+                           vals=vals.Enum('ON', 'OFF'),
+                           docstring='Switches the mesurment on or off')
+
+        self.add_parameter('clear',
+                           label='Measurement {} clear statistics'.format(meas_nr),
+                           set_cmd='MEASurement{}:CLEar'.format(meas_nr),
+                           docstring='Clears/reset measurement')
+
+        self.add_parameter('event_count',
+                           label='Measurement {} number of events'.format(meas_nr),
+                           get_cmd='MEASurement{}:RESult:EVTCount?'.format(meas_nr),
+                           get_parser=int,
+                           docstring='Number of measurement results in the long-term measurement')
+
+        self.add_parameter('result_avg',
+                           label='Measurement {} averages'.format(meas_nr),
+                           get_cmd='MEASurement{}:RESult:AVG?'.format(meas_nr),
+                           get_parser=float,
+                           docstring='Average of the long-term measurement results')
 
 class ScopeChannel(InstrumentChannel):
     """
@@ -335,6 +475,7 @@ def __init__(self, name: str, address: str,
 
         # Now assign model-specific values
         self.num_chans = int(self.model[-1])
+        self.num_meas = 8
 
         self._horisontal_divs = int(self.ask('TIMebase:DIVisions?'))
 
@@ -367,6 +508,18 @@ def __init__(self, name: str, address: str,
                                         'CH4': 'CHAN4',
                                         'EXT': 'EXT'})
 
+        self.add_parameter('trigger_mode',
+                           label='Trigger mode',
+                           set_cmd='TRIGger:MODE {}',
+                           get_cmd='TRIGger1:SOURce?',
+                           val_mapping={'AUTO': 'AUTO',
+                                        'NORMAL': 'NORMal',
+                                        'FREERUN': 'FREerun'},
+                           docstring='Sets the trigger mode which determines the ' \
+                           ' behaviour of the instrument if no trigger occurs.\n'    \
+                           'Options: AUTO, NORMAL, FREERUN.',
+                           unit='none')
+
         self.add_parameter('trigger_type',
                            label='Trigger type',
                            set_cmd='TRIGger1:TYPE {}',
@@ -488,7 +641,12 @@ def __init__(self, name: str, address: str,
                                label='High definition (16 bit) state',
                                set_cmd=self._set_hd_mode,
                                get_cmd='HDEFinition:STAte?',
-                               val_mapping={'ON': 1, 'OFF': 0})
+                               val_mapping={'ON': 1, 'OFF': 0},
+                               docstring='Sets the filter bandwidth for the high definition mode.\n' \
+                               'ON: high definition mode, up to 16 bit digital resolution\n' \
+                               'Options: ON, OFF\n\n' \
+                               'Warning/Bug: By opening the HD acquisition menu on the scope, ' \
+                               'this value will be set to "ON"')
 
             self.add_parameter('high_definition_bandwidth',
                                label='High definition mode bandwidth',
@@ -498,15 +656,31 @@ def __init__(self, name: str, address: str,
                                get_parser=float,
                                vals=vals.Numbers(1e4, 1e9))
 
+        self.add_parameter('error_count',
+                           label='Number of errors in the error stack',
+                           get_cmd='SYSTem:ERRor:COUNt?',
+                           unit='#',
+                           get_parser=int)
+
+        self.add_parameter('error_next',
+                           label='Next error from the error stack',
+                           get_cmd='SYSTem:ERRor:NEXT?',
+                           get_parser=str)
+
         # Add the channels to the instrument
         for ch in range(1, self.num_chans+1):
             chan = ScopeChannel(self, 'channel{}'.format(ch), ch)
             self.add_submodule('ch{}'.format(ch), chan)
 
+        for meas in range(1, self.num_meas+1):
+            measCh = ScopeMeasurement(self, 'measurement{}'.format(meas), meas)
+            self.add_submodule('meas{}'.format(meas), measCh)
+
         self.add_function('stop', call_cmd='STOP')
         self.add_function('reset', call_cmd='*RST')
-        self.add_function('opc', call_cmd='*OPC?')
-        self.add_function('stop_opc', call_cmd='*STOP;OPC?')
+        self.add_parameter('opc', get_cmd='*OPC?')
+        self.add_parameter('stop_opc', get_cmd='STOP;*OPC?')
+        self.add_function('run_continues', call_cmd='RUNContinous')
         # starts the shutdown of the system
         self.add_function('system_shutdown', call_cmd='SYSTem:EXIT')
 

From 52ffd702092465b76d7cae8c0e4977c6e645e1ff Mon Sep 17 00:00:00 2001
From: spxtr <spxtr@google.com>
Date: Tue, 4 Dec 2018 12:38:29 -0800
Subject: [PATCH 344/719] Fix change_sensitivity in SR830 driver.

---
 qcodes/instrument_drivers/stanford_research/SR830.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/stanford_research/SR830.py b/qcodes/instrument_drivers/stanford_research/SR830.py
index 89fd6ac7e1a..f7e47bb8ff0 100644
--- a/qcodes/instrument_drivers/stanford_research/SR830.py
+++ b/qcodes/instrument_drivers/stanford_research/SR830.py
@@ -572,7 +572,7 @@ def decrement_sensitivity(self):
 
     def _change_sensitivity(self, dn):
         _ = self.sensitivity.get()
-        n = self.sensitivity.raw_value
+        n = int(self.sensitivity.raw_value)
         if self.input_config() in ['a', 'a-b']:
             n_to = self._N_TO_VOLT
         else:

From 70902becdca13f31703ff11ae4ee814c646d7bd1 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Tue, 4 Dec 2018 13:34:46 -0800
Subject: [PATCH 345/719] minor improvement in dict comprehension

---
 .../instrument_drivers/tektronix/Keithley_s46.py | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index cbf02769321..06ead1f73c6 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -142,17 +142,15 @@ class S46(VisaInstrument):
 
     relay_names: list = ["A", "B", "C", "D"] + [f"R{j}" for j in range(1, 9)]
 
-    _aliases_list: list = [
-        f"{a}{b}" for a, b in product(
-            ["A", "B", "C", "D"],
-            list(range(1, 7))
-        )
-    ]
-
-    _aliases_list += [f"R{i}" for i in range(1, 9)]
     # Make a dictionary where keys are channel aliases (e.g. 'A1', 'B3', etc)
     # and values are corresponding channel numbers.
-    aliases: Dict[str, int] = dict(zip(_aliases_list, range(1, 33)))
+    aliases: Dict[str, int] = {
+        f"{a}{b}": count + 1 for count, (a, b) in enumerate(product(
+            ["A", "B", "C", "D"],
+            range(1, 7)
+        ))
+    }
+    aliases.update({f"R{i}": i + 24 for i in range(1, 9)})
 
     def __init__(
             self,

From 678ac1274b4b2dea70101f602c5c0b4ee39c5bd3 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Wed, 5 Dec 2018 10:39:29 +1100
Subject: [PATCH 346/719] Fix error in get_timestamp docstring

---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index f70466d83b4..3ed741b9cb8 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1364,7 +1364,7 @@ def get(self):
             else:
                 return state['value']
 
-    def get_timestamp(self):
+    def get_timestamp(self) -> datetime:
         """
         Return the age of the last get_latest call.
         """

From f4283cd0e704490f9944485fe12a609a142544eb Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Wed, 5 Dec 2018 10:39:53 +1100
Subject: [PATCH 347/719] Add extra info to errors/logs

---
 qcodes/monitor/monitor.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 060bc8d5905..faf4d3208de 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -93,7 +93,7 @@ async def server_func(websocket, path):
                 # Wait for interval seconds and then send again
                 await asyncio.sleep(interval)
             except (CancelledError, websockets.exceptions.ConnectionClosed):
-                log.debug("Got CancelledError or ConnectionClosed")
+                log.debug("Got CancelledError or ConnectionClosed", exc_info=True)
                 break
         log.debug("Closing websockets connection")
 
@@ -118,7 +118,7 @@ def __init__(self, *parameters, interval=1):
         # Check that all values are valid parameters
         for parameter in parameters:
             if not isinstance(parameter, Parameter):
-                raise TypeError("We can only monitor QCodes Parameters")
+                raise TypeError(f"We can only monitor QCodes Parameters, not {type(parameter)}")
 
         self.loop = None
         self.server = None

From aa530985046f93d685408aa8425c9c7724dac087 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Wed, 5 Dec 2018 10:40:25 +1100
Subject: [PATCH 348/719] Add test for get_latest/timestamp

---
 qcodes/tests/test_parameter.py | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/qcodes/tests/test_parameter.py b/qcodes/tests/test_parameter.py
index f7deac8c177..4ed3c068708 100644
--- a/qcodes/tests/test_parameter.py
+++ b/qcodes/tests/test_parameter.py
@@ -6,6 +6,7 @@
 from unittest import TestCase
 from typing import Tuple
 import pytest
+from datetime import datetime
 
 import numpy as np
 from hypothesis import given, event, settings
@@ -181,6 +182,22 @@ def test_snapshot_value(self):
         snap = p_no_snapshot.snapshot()
         self.assertNotIn('value', snap)
 
+    def test_get_latest(self):
+        # Create a gettable parameter
+        local_parameter = Parameter('test_param', set_cmd=None, get_cmd=None)
+        before_set = datetime.now()
+        local_parameter.set(1)
+        after_set = datetime.now()
+
+        # Check we return last set value, with the correct timestamp
+        self.assertEqual(local_parameter.get_latest(), 1)
+        self.assertTrue(before_set < local_parameter.get_latest.get_timestamp() < after_set)
+
+        # Check that updating the value updates the timestamp
+        local_parameter.set(2)
+        self.assertEqual(local_parameter.get_latest(), 2)
+        self.assertGreater(local_parameter.get_latest.get_timestamp(), after_set)
+
     def test_has_set_get(self):
         # Create parameter that has no set_cmd, and get_cmd returns last value
         gettable_parameter = Parameter('one', set_cmd=False, get_cmd=None)

From f38d68bb0e1b5d2399c311f130b7c88eee363b07 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Wed, 5 Dec 2018 10:54:01 +1100
Subject: [PATCH 349/719] Update get_latest docstring

---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 3ed741b9cb8..e48410a09b3 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1366,7 +1366,7 @@ def get(self):
 
     def get_timestamp(self) -> datetime:
         """
-        Return the age of the last get_latest call.
+        Return the age of the latest parameter value.
         """
         state = self.parameter._latest
         return state["ts"]

From b23810a9f1cb5857c48fa7757295482bd6a8ea1e Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Wed, 5 Dec 2018 11:16:58 +1100
Subject: [PATCH 350/719] Get rid of Server class, serve in main

---
 qcodes/monitor/monitor.py | 64 +++++++++++++++++++--------------------
 1 file changed, 32 insertions(+), 32 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index faf4d3208de..bfb96eed8d9 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -6,8 +6,19 @@
 #
 # Distributed under terms of the MIT license.
 """
-Monitor a set of parameter in a background thread
-stream opuput over websocket
+Monitor a set of parameters in a background thread
+stream output over websocket
+
+To start monitor, run this file, or if qcodes is installed as a module:
+```
+% python -m qcodes.monitor.monitor
+```
+
+Add parameters to monitor in your measurement by creating a new monitor with a list
+of parameters to monitor:
+```
+monitor = qcodes.Monitor(param1, param2, param3, ...)
+```
 """
 
 import logging
@@ -40,7 +51,7 @@ def _get_metadata(*parameters) -> Dict[str, Any]:
     Return a dict that contains the parameter metadata grouped by the
     instrument it belongs to.
     """
-    ts = time.time()
+    metadata_timestamp = time.time()
     # group metadata by instrument
     metas = defaultdict(list) # type: dict
     for parameter in parameters:
@@ -67,7 +78,7 @@ def _get_metadata(*parameters) -> Dict[str, Any]:
         temp = {"instrument": instrument, "parameters": metas[instrument]}
         parameters_out.append(temp)
 
-    state = {"ts": ts, "parameters": parameters_out}
+    state = {"ts": metadata_timestamp, "parameters": parameters_out}
     return state
 
 
@@ -75,7 +86,7 @@ def _handler(parameters, interval: int):
     """
     Return the websockets server handler
     """
-    async def server_func(websocket, path):
+    async def server_func(websocket, _):
         """
         Create a websockets handler that sends parameter values to a listener
         every "interval" seconds
@@ -85,8 +96,8 @@ async def server_func(websocket, path):
                 # Update the parameter values
                 try:
                     meta = _get_metadata(*parameters)
-                except ValueError as e:
-                    log.exception(e)
+                except ValueError:
+                    log.exception("Error getting parameters")
                     break
                 log.debug("sending.. to %r", websocket)
                 await websocket.send(json.dumps(meta))
@@ -169,10 +180,10 @@ def update_all(self):
         """
         Update all parameters in the monitor
         """
-        for p in self._parameters:
+        for parameter in self._parameters:
             # call get if it can be called without arguments
             with suppress(TypeError):
-                p.get()
+                parameter.get()
 
     def stop(self) -> None:
         """
@@ -189,7 +200,7 @@ async def __stop_server(self):
         await self.loop.create_task(self.server.wait_closed())
         log.debug("stopping loop")
         log.debug("Pending tasks at stop: %r",
-                asyncio.Task.all_tasks(self.loop))
+                  asyncio.Task.all_tasks(self.loop))
         self.loop.stop()
 
     def join(self, timeout=None) -> None:
@@ -232,28 +243,17 @@ def show():
         """
         webbrowser.open("http://localhost:{}".format(SERVER_PORT))
 
-class Server():
-
-    def __init__(self, port=SERVER_PORT):
-        self.port = port
-        self.handler = http.server.SimpleHTTPRequestHandler
-        self.httpd = socketserver.TCPServer(("", self.port), self.handler)
-        self.static_dir = os.path.join(os.path.dirname(__file__), 'dist')
-
-    def run(self):
-        os.chdir(self.static_dir)
-        log.debug("serving directory %s", self.static_dir)
-        self.httpd.serve_forever()
-
-    def stop(self):
-        self.httpd.shutdown()
-
-
 if __name__ == "__main__":
-    server = Server()
-    print("Open browser at http://localhost:{}".format(server.port))
+    # If this file is run, create a simple webserver that serves a simple website
+    # that can be used to view monitored parameters.
+    STATIC_DIR = os.path.join(os.path.dirname(__file__), 'dist')
+    os.chdir(STATIC_DIR)
     try:
-        webbrowser.open("http://localhost:{}".format(server.port))
-        server.run()
+        log.info("Starting HTTP Server at http://localhost:%i", SERVER_PORT)
+        with socketserver.TCPServer(("", SERVER_PORT),
+                                    http.server.SimpleHTTPRequestHandler) as httpd:
+            log.debug("serving directory %s", STATIC_DIR)
+            webbrowser.open("http://localhost:{}".format(SERVER_PORT))
+            httpd.serve_forever()
     except KeyboardInterrupt:
-        exit()
+        log.info("Shutting Down HTTP Server")

From 0d299c9b374ab35b2363deb5ba5b8ec5f7e17b27 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Wed, 5 Dec 2018 11:58:59 +1100
Subject: [PATCH 351/719] Resolution of windows time causes test to fail
 without equal check

---
 qcodes/tests/test_parameter.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/test_parameter.py b/qcodes/tests/test_parameter.py
index 4ed3c068708..f55791bd6ee 100644
--- a/qcodes/tests/test_parameter.py
+++ b/qcodes/tests/test_parameter.py
@@ -191,12 +191,12 @@ def test_get_latest(self):
 
         # Check we return last set value, with the correct timestamp
         self.assertEqual(local_parameter.get_latest(), 1)
-        self.assertTrue(before_set < local_parameter.get_latest.get_timestamp() < after_set)
+        self.assertTrue(before_set <= local_parameter.get_latest.get_timestamp() <= after_set)
 
         # Check that updating the value updates the timestamp
         local_parameter.set(2)
         self.assertEqual(local_parameter.get_latest(), 2)
-        self.assertGreater(local_parameter.get_latest.get_timestamp(), after_set)
+        self.assertGreaterEqual(local_parameter.get_latest.get_timestamp(), after_set)
 
     def test_has_set_get(self):
         # Create parameter that has no set_cmd, and get_cmd returns last value

From 999c3eb3422df63d36e4fa410f5aa6b33f2c894d Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Wed, 5 Dec 2018 12:05:04 +1100
Subject: [PATCH 352/719] Add additional monitor tests for correct join
 behaviour

---
 qcodes/tests/test_monitor.py | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/qcodes/tests/test_monitor.py b/qcodes/tests/test_monitor.py
index b1832d0969e..555db39f506 100644
--- a/qcodes/tests/test_monitor.py
+++ b/qcodes/tests/test_monitor.py
@@ -32,6 +32,29 @@ def test_setup_teardown(self):
         self.assertFalse(m.is_alive())
         self.assertIsNone(monitor.Monitor.running)
 
+    def test_monitor_replace(self):
+        """
+        Check that monitors get correctly replaced
+        """
+        m = monitor.Monitor()
+        self.assertEqual(monitor.Monitor.running, m)
+        m2 = monitor.Monitor()
+        self.assertEqual(monitor.Monitor.running, m2)
+        self.assertTrue(m.loop.is_closed())
+        self.assertFalse(m.is_alive())
+        self.assertTrue(m2.is_alive())
+        m2.stop()
+
+    def test_double_join(self):
+        """
+        Check that a double join doesn't cause a hang
+        """
+        m = monitor.Monitor()
+        self.assertEqual(monitor.Monitor.running, m)
+        m.stop()
+        m.stop()
+
+
     def test_connection(self):
         """
         Test that we can connect to a monitor instance

From 0cf77f897a6d1edcc416c27469f1c28ad59f573f Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Tue, 4 Dec 2018 18:07:21 -0800
Subject: [PATCH 353/719] add acquire and release lock return type hints

---
 qcodes/instrument_drivers/tektronix/Keithley_s46.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index 06ead1f73c6..77d4f59d337 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -45,7 +45,7 @@ def __init__(self, relay_name: str):
         self.relay_name = relay_name
         self._locked_by: int = None
 
-    def acquire(self, channel_number: int):
+    def acquire(self, channel_number: int) -> None:
         """
         Request a lock acquisition
         """
@@ -57,7 +57,7 @@ def acquire(self, channel_number: int):
         else:
             self._locked_by = channel_number
 
-    def release(self, channel_number: int):
+    def release(self, channel_number: int) -> None:
         """
         Release a lock.
         """

From 9c6a4410391c69fb387efb7d491156716aa6a7e9 Mon Sep 17 00:00:00 2001
From: nyxus <gercoversloot@gmail.com>
Date: Wed, 5 Dec 2018 10:10:49 +0100
Subject: [PATCH 354/719] RTO1000: rework after comments, removed .format() and
 change trigger_mode val_mapping to vals

---
 .../rohde_schwarz/RTO1000.py                  | 50 +++++++++----------
 1 file changed, 24 insertions(+), 26 deletions(-)

diff --git a/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py b/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py
index 1112920028e..375784820ee 100644
--- a/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py
+++ b/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py
@@ -209,64 +209,64 @@ def __init__(self, parent: Instrument, name: str, meas_nr: int):
                         )
 
         self.add_parameter('enable',
-                           label='Measurement {} enable'.format(meas_nr),
-                           set_cmd='MEASurement{}:ENABle {{}}'.format(meas_nr),
+                           label=f'Measurement {meas_nr} enable',
+                           set_cmd=f'MEASurement{meas_nr}:ENABle {{}}',
                            vals=vals.Enum('ON', 'OFF'),
                            docstring='Switches the mesurment on or off')
 
         self.add_parameter('source',
-                           label='Measurement {} source'.format(meas_nr),
-                           set_cmd='MEASurement{}:SOURce {{}}'.format(meas_nr),
+                           label=f'Measurement {meas_nr} source',
+                           set_cmd=f'MEASurement{meas_nr}:SOURce {{}}',
                            vals=self.sources,
                            docstring='Set the source of a measurement if the ' \
                                      'measurement only needs one source')
 
         self.add_parameter('source_first',
-                           label='Measurement {} first source'.format(meas_nr),
-                           set_cmd='MEASurement{}:FSRC {{}}'.format(meas_nr),
+                           label=f'Measurement {meas_nr} first source',
+                           set_cmd=f'MEASurement{meas_nr}:FSRC {{}}',
                            vals=self.sources,
                            docstring='Set the first source of a measurement if the ' \
                                      'measurement only needs mutliple sources')
 
         self.add_parameter('source_second',
-                           label='Measurement {} second source'.format(meas_nr),
-                           set_cmd='MEASurement{}:SSRC {{}}'.format(meas_nr),
+                           label=f'Measurement {meas_nr} second source',
+                           set_cmd=f'MEASurement{meas_nr}:SSRC {{}}',
                            vals=self.sources,
                            docstring='Set the second source of a measurement if the ' \
                                      'measurement only needs mutliple sources')
 
         self.add_parameter('category',
-                           label='Measurement {} category'.format(meas_nr),
-                           set_cmd='MEASurement{}:CATegory {{}}'.format(meas_nr),
+                           label=f'Measurement {meas_nr} category',
+                           set_cmd=f'MEASurement{meas_nr}:CATegory {{}}',
                            vals=self.categories,
                            docstring='Set the category of a measurement')
 
         self.add_parameter('main',
-                           label='Measurement {} main'.format(meas_nr),
-                           set_cmd='MEASurement{}:MAIN {{}}'.format(meas_nr),
+                           label=f'Measurement {meas_nr} main',
+                           set_cmd=f'MEASurement{meas_nr}:MAIN {{}}',
                            vals=self.meas_type,
                            docstring='Set the main of a measurement')
 
         self.add_parameter('statistics_enable',
-                           label='Measurement {} enable statistics'.format(meas_nr),
-                           set_cmd='MEASurement{}:STATistics:ENABle {{}}'.format(meas_nr),
+                           label=f'Measurement {meas_nr} enable statistics',
+                           set_cmd=f'MEASurement{meas_nr}:STATistics:ENABle {{}}',
                            vals=vals.Enum('ON', 'OFF'),
                            docstring='Switches the mesurment on or off')
 
         self.add_parameter('clear',
-                           label='Measurement {} clear statistics'.format(meas_nr),
-                           set_cmd='MEASurement{}:CLEar'.format(meas_nr),
+                           label=f'Measurement {meas_nr} clear statistics',
+                           set_cmd=f'MEASurement{meas_nr}:CLEar',
                            docstring='Clears/reset measurement')
 
         self.add_parameter('event_count',
-                           label='Measurement {} number of events'.format(meas_nr),
-                           get_cmd='MEASurement{}:RESult:EVTCount?'.format(meas_nr),
+                           label=f'Measurement {meas_nr} number of events',
+                           get_cmd=f'MEASurement{meas_nr}:RESult:EVTCount?',
                            get_parser=int,
                            docstring='Number of measurement results in the long-term measurement')
 
         self.add_parameter('result_avg',
-                           label='Measurement {} averages'.format(meas_nr),
-                           get_cmd='MEASurement{}:RESult:AVG?'.format(meas_nr),
+                           label=f'Measurement {meas_nr} averages',
+                           get_cmd=f'MEASurement{meas_nr}:RESult:AVG?',
                            get_parser=float,
                            docstring='Average of the long-term measurement results')
 
@@ -512,9 +512,7 @@ def __init__(self, name: str, address: str,
                            label='Trigger mode',
                            set_cmd='TRIGger:MODE {}',
                            get_cmd='TRIGger1:SOURce?',
-                           val_mapping={'AUTO': 'AUTO',
-                                        'NORMAL': 'NORMal',
-                                        'FREERUN': 'FREerun'},
+                           vals=vals.Enum('AUTO', 'NORMAL', 'FREERUN'),
                            docstring='Sets the trigger mode which determines the ' \
                            ' behaviour of the instrument if no trigger occurs.\n'    \
                            'Options: AUTO, NORMAL, FREERUN.',
@@ -672,9 +670,9 @@ def __init__(self, name: str, address: str,
             chan = ScopeChannel(self, 'channel{}'.format(ch), ch)
             self.add_submodule('ch{}'.format(ch), chan)
 
-        for meas in range(1, self.num_meas+1):
-            measCh = ScopeMeasurement(self, 'measurement{}'.format(meas), meas)
-            self.add_submodule('meas{}'.format(meas), measCh)
+        for measId in range(1, self.num_meas+1):
+            measCh = ScopeMeasurement(self, f'measurement{measId}', measId)
+            self.add_submodule(f'meas{measId}', measCh)
 
         self.add_function('stop', call_cmd='STOP')
         self.add_function('reset', call_cmd='*RST')

From ab7bff4a3bb8fc8e3d1a76558211f02371edb53f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 10:36:05 +0100
Subject: [PATCH 355/719] filter warning

---
 qcodes/tests/dataset/test_sqlite_base.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index e1521914dd4..bc45b99f6d1 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -190,6 +190,7 @@ def test_runs_table_columns(empty_temp_db):
     assert colnames == []
 
 
+@pytest.mark.filterwarnings("ignore:get_data")
 def test_get_data_no_columns(scalar_dataset):
     ds = scalar_dataset
     ref = mut.get_data(ds.conn, ds.table_name, [])

From 88a1b89c5b7856bf80075f112d807c28d03bed2b Mon Sep 17 00:00:00 2001
From: Shawn Storm <stormsrs@yahoo.com>
Date: Tue, 4 Dec 2018 22:23:14 +0100
Subject: [PATCH 356/719] Changed 3.5+ to 3.6+

---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index e5e6c7d08d6..f5230ad3568 100644
--- a/README.rst
+++ b/README.rst
@@ -13,7 +13,7 @@ To get  a feeling of qcodes browse the Jupyter notebooks in `docs/examples
 <https://github.com/QCoDeS/Qcodes/tree/master/docs/examples>`__ .
 
 
-QCoDeS is compatible with Python 3.5+. It is primarily intended for use
+QCoDeS is compatible with Python 3.6+. It is primarily intended for use
 from Jupyter notebooks, but can be used from traditional terminal-based
 shells and in stand-alone scripts as well. The features in
 `qcodes.utils.magic` are exclusively for Jupyter notebooks.

From c8fac47e3a82afb027ebf0d2f65b39a35ced1c86 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 13:53:38 +0100
Subject: [PATCH 357/719] Remove old get_columns

---
 qcodes/dataset/sqlite_base.py            | 133 +++++++++--------------
 qcodes/tests/dataset/helper_functions.py |   0
 qcodes/tests/dataset/test_sqlite_base.py |  33 +++---
 3 files changed, 69 insertions(+), 97 deletions(-)
 create mode 100644 qcodes/tests/dataset/helper_functions.py

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 8d1f9324534..36e26774394 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1223,31 +1223,68 @@ def get_data(conn: ConnectionPlus,
 def get_parameter_data(conn: ConnectionPlus,
                        table_name: str,
                        columns: List[str],
-                       start: int = None,
-                       end: int = None) -> Dict[str, np.ndarray]:
+                       start: Optional[int]=None,
+                       end: Optional[int]=None) -> \
+        Dict[str, Dict[str, np.ndarray]]:
     """
-    Get data from the columns of a table.
-    Allows to specfiy a range.
+    Get data for one or more parameters and its dependencies. The data
+    is returned as numpy arrays nested 2 layers of dicts. The keys or the
+    outermost are the requested parameters and the second level by the loaded
+    parameters.
 
     Args:
         conn: database connection
         table_name: name of the table
         columns: list of columns
-        start: start of range (1 indedex)
-        end: start of range (1 indedex)
+        start: start of range; if None, then starts from the top of the table
+        end: end of range; if None, then ends at the bottom of the table
+    """
+    output = {}
+    if len(columns) == 0:
+        raise NotImplementedError("HERE we should look up all "
+                                  "dependent standalone parameters")
 
-    Returns:
-        the data requested
+    # get run_id
+    sql = """
+    SELECT run_id FROM runs WHERE result_table_name = ?
     """
-    # Benchmarking shows that transposing the data with python types is faster
-    # than transposing the data using np.array.transpose
-    # This method is going to form the entry point for a compiled version
+    c = atomic_transaction(conn, sql, table_name)
+    run_id = one(c, 'run_id')
 
-    res = get_data(conn, table_name, columns, start, end)
-    res_t = list(map(list, zip(*res)))
-    return {name: np.array(column_data)
-            for name, column_data
-            in zip(columns, res_t)}
+    for param in columns:
+        params = get_dependency_parameters(conn, param, run_id)
+        columns = [param.name for param in params]
+        types = [param.type for param in params]
+
+        res = get_data(conn, table_name, columns, start=start, end=end)
+
+        if 'array' in types and ('numeric' in types or 'text' in types):
+            # todo: Should we check that all the arrays are the same size?
+            first_array_element = types.index('array')
+            numeric_elms = [i for i, x in enumerate(types)
+                            if x == "numeric"]
+            text_elms = [i for i, x in enumerate(types)
+                         if x == "text"]
+            for row in res:
+                for element in numeric_elms:
+                    row[element] = np.full_like(row[first_array_element],
+                                                row[element],
+                                                dtype=np.float)
+                    # todo should we handle int/float types here
+                for element in text_elms:
+                    strlen = len(row[element])
+                    row[element] = np.full_like(row[first_array_element],
+                                                row[element],
+                                                dtype=f'U{strlen}')
+
+        # Benchmarking shows that transposing the data with python types is faster
+        # than transposing the data using np.array.transpose
+        # This method is going to form the entry point for a compiled version
+        res_t = list(map(list, zip(*res)))
+        output[param] = {name: np.squeeze(np.array(column_data))
+                         for name, column_data
+                         in zip(columns, res_t)}
+    return output
 
 
 def get_values(conn: ConnectionPlus,
@@ -1520,70 +1557,6 @@ def get_dependency_parameters(conn: ConnectionPlus, param: str,
     return parameters
 
 
-def get_data_of_param_and_deps_as_columns(conn: ConnectionPlus,
-                                          table_name: str,
-                                          columns: List[str],
-                                          start: Optional[int]=None,
-                                          end: Optional[int]=None) -> \
-        Dict[str, Dict[str, np.ndarray]]:
-    """
-    Get data for one or more parameters and its dependencies. The data
-    is returned as numpy arrays nested 2 layers of dicts. The keys or the
-    outermost are the requested parameters and the second level by the loaded
-    parameters.
-
-    Args:
-        conn: database connection
-        table_name: name of the table
-        columns: list of columns
-        start: start of range; if None, then starts from the top of the table
-        end: end of range; if None, then ends at the bottom of the table
-    """
-    output = {}
-    if len(columns) == 0:
-        raise NotImplementedError("HERE we should look up all "
-                                  "dependent standalone parameters")
-
-    # get run_id
-    sql = """
-    SELECT run_id FROM runs WHERE result_table_name = ?
-    """
-    c = atomic_transaction(conn, sql, table_name)
-    run_id = one(c, 'run_id')
-
-    for param in columns:
-        params = get_dependency_parameters(conn, param, run_id)
-        columns = [param.name for param in params]
-        types = [param.type for param in params]
-
-        res = get_data(conn, table_name, columns, start=start, end=end)
-
-        if 'array' in types and ('numeric' in types or 'text' in types):
-            # todo: Should we check that all the arrays are the same size?
-            first_array_element = types.index('array')
-            numeric_elms = [i for i, x in enumerate(types)
-                            if x == "numeric"]
-            text_elms = [i for i, x in enumerate(types)
-                         if x == "text"]
-            for row in res:
-                for element in numeric_elms:
-                    row[element] = np.full_like(row[first_array_element],
-                                                row[element],
-                                                dtype=np.float)
-                    # todo should we handle int/float types here
-                for element in text_elms:
-                    strlen = len(row[element])
-                    row[element] = np.full_like(row[first_array_element],
-                                                row[element],
-                                                dtype=f'U{strlen}')
-
-        res_t = list(map(list, zip(*res)))
-        output[param] = {name: np.squeeze(np.array(column_data))
-                         for name, column_data
-                         in zip(columns, res_t)}
-    return output
-
-
 def new_experiment(conn: ConnectionPlus,
                    name: str,
                    sample_name: str,
diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index 94f9164d924..0d00e7f7798 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -26,6 +26,8 @@
     error_caused_by
 # pylint: enable=unused-import
 
+from .helper_functions import verify_data_dict
+
 _unicode_categories = ('Lu', 'Ll', 'Lt', 'Lm', 'Lo', 'Nd', 'Pc', 'Pd', 'Zs')
 
 
@@ -197,25 +199,22 @@ def test_get_data_no_columns(scalar_dataset):
     ref = mut.get_data(ds.conn, ds.table_name, [])
     assert ref == [[]]
 
+
 def test_get_parameter_data(scalar_dataset):
     ds = scalar_dataset
-    params = ds.parameters.split(',')
-    # delete some random parameter to test it with an incomplete list
-    del params[-2]
-
-    ref = mut.get_data(ds.conn, ds.table_name, params)
-    dut = mut.get_parameter_data(ds.conn, ds.table_name, params)
-    for i_row, row in enumerate(ref):
-        for i_param, param_name in enumerate(params):
-            v_ref = row[i_param]
-            v_test = dut[param_name][i_row]
-            if isinstance(v_ref, float):
-                assert isinstance(v_test, np.float64)
-            elif isinstance(v_ref, int):
-                assert isinstance(v_test, np.int)
-            else:
-                raise RuntimeError('Unsupported data type')
-            assert np.isclose(v_test, v_ref)
+    input_names = ['param_3']
+
+    data = mut.get_parameter_data(ds.conn, ds.table_name, input_names)
+
+    assert len(data.keys()) == len(input_names)
+
+    expected_names = {}
+    expected_names['param_3'] = ['param_0', 'param_1', 'param_2',
+                                 'param_3']
+    expected_shapes = {}
+    expected_shapes['param_3'] = [(10**3, )]*4
+
+    verify_data_dict(data, input_names, expected_names, expected_shapes)
 
 def test_is_run_id_in_db(empty_temp_db):
     conn = mut.connect(get_DB_location())

From 8bb57c8cf81a6da3cf986717a06fdd307f5f6050 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 13:54:22 +0100
Subject: [PATCH 358/719] remove method from dataset

---
 qcodes/dataset/data_set.py                 | 19 ++------
 qcodes/tests/dataset/test_dataset_basic.py | 55 ++++++++--------------
 2 files changed, 22 insertions(+), 52 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 1d05c8513d8..20e081a812d 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -38,8 +38,7 @@
                                         update_run_description,
                                         run_exists, remove_trigger,
                                         make_connection_plus_from,
-                                        ConnectionPlus,
-                                        get_data_of_param_and_deps_as_columns)
+                                        ConnectionPlus)
 
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
@@ -809,7 +808,7 @@ def get_parameter_data(
             self,
             *params: Union[str, ParamSpec, _BaseParameter],
             start: Optional[int] = None,
-            end: Optional[int] = None) -> Dict[str, numpy.ndarray]:
+            end: Optional[int] = None) -> Dict[str, Dict[str, numpy.ndarray]]:
         """
         Returns the values stored in the DataSet for the specified parameters.
         The values are returned as a dictionary of numpy arrays where the key,
@@ -835,19 +834,7 @@ def get_parameter_data(
         """
         valid_param_names = self._validate_parameters(*params)
         return get_parameter_data(self.conn, self.table_name, valid_param_names,
-                        start, end)
-
-    def get_data_of_param_and_deps_as_columns(
-            self,
-            *params: Union[str, ParamSpec, _BaseParameter],
-            start: Optional[int] = None,
-            end: Optional[int] = None) -> Dict[str, Dict[str, numpy.ndarray]]:
-        valid_param_names = self._validate_parameters(*params)
-        return get_data_of_param_and_deps_as_columns(self.conn,
-                                                     self.table_name,
-                                                     valid_param_names,
-                                                     start, end)
-
+                                  start, end)
 
     def get_values(self, param_name: str) -> List[List[Any]]:
         """
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 7ed2950cce3..74460a7665f 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -1,7 +1,6 @@
 import itertools
 from copy import copy
 import re
-from typing import Sequence, Tuple
 
 import pytest
 import numpy as np
@@ -28,6 +27,8 @@
 # pylint: disable=unused-import
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 
+from .helper_functions import verify_data_dict
+
 n_experiments = 0
 
 
@@ -774,18 +775,25 @@ def test_get_data_with_start_and_end_args(self, ds_with_vals,
 
 
 def test_get_parameter_data(scalar_dataset):
-    parameter_test_helper(scalar_dataset)
+    ds = scalar_dataset
+    input_names = ['param_3']
 
+    data = ds.get_parameter_data(*input_names)
 
-def test_get_array_parameter_data(array_dataset):
-    paramspecs = array_dataset.paramspecs
-    types = [param.type for param in paramspecs.values()]
+    assert len(data.keys()) == len(input_names)
+
+    expected_names = {}
+    expected_names['param_3'] = ['param_0', 'param_1', 'param_2',
+                                 'param_3']
+    expected_shapes = {}
+    expected_shapes['param_3'] = [(10**3, )]*4
 
-    if 'array' not in types:
-        parameter_test_helper(array_dataset)
+    verify_data_dict(data, input_names, expected_names, expected_shapes)
+
+def test_get_array_parameter_data(array_dataset):
 
     inputnames = ['testparameter']
-    data = array_dataset.get_data_of_param_and_deps_as_columns(*inputnames)
+    data = array_dataset.get_parameter_data(*inputnames)
     assert len(data.keys()) == len(inputnames)
     expected_names = {}
     expected_names['testparameter'] = ['testparameter', 'this_setpoint']
@@ -799,11 +807,8 @@ def test_get_multi_parameter_data(multi_dataset):
     paramspecs = multi_dataset.paramspecs
     types = [param.type for param in paramspecs.values()]
 
-    if 'array' not in types:
-        parameter_test_helper(multi_dataset)
-
     inputnames = ['this', 'that']
-    data = multi_dataset.get_data_of_param_and_deps_as_columns(*inputnames)
+    data = multi_dataset.get_parameter_data(*inputnames)
     assert len(data.keys()) == len(inputnames)
     expected_names = {}
     expected_names['this'] = ['this', 'this_setpoint', 'that_setpoint']
@@ -822,11 +827,8 @@ def test_get_array_in_scalar_param_data(array_in_scalar_dataset):
     paramspecs = array_in_scalar_dataset.paramspecs
     types = [param.type for param in paramspecs.values()]
 
-    if 'array' not in types:
-        parameter_test_helper(array_in_scalar_dataset)
-
     inputnames = ['testparameter']
-    data = array_in_scalar_dataset.get_data_of_param_and_deps_as_columns(*inputnames)
+    data = array_in_scalar_dataset.get_parameter_data(*inputnames)
     assert len(data.keys()) == len(inputnames)
     expected_names = {}
     expected_names['testparameter'] = ['testparameter', 'scalarparam',
@@ -843,11 +845,8 @@ def test_get_array_in_str_param_data(array_in_str_dataset):
     paramspecs = array_in_str_dataset.paramspecs
     types = [param.type for param in paramspecs.values()]
 
-    if 'array' not in types:
-        parameter_test_helper(array_in_str_dataset)
-
     inputnames = ['testparameter']
-    data = array_in_str_dataset.get_data_of_param_and_deps_as_columns(*inputnames)
+    data = array_in_str_dataset.get_parameter_data(*inputnames)
     assert len(data.keys()) == len(inputnames)
     expected_names = {}
     expected_names['testparameter'] = ['testparameter', 'textparam',
@@ -895,19 +894,3 @@ def parameter_test_helper(ds):
             elif isinstance(v_ref, str):
                 assert isinstance(v_test, np.str_)
                 assert v_ref == v_test
-
-
-def verify_data_dict(data, parameter_names, expected_names,
-                     expected_shapes):
-    for param in parameter_names:
-        innerdata = data[param]
-        verify_data_dict_for_single_param(innerdata,
-                                          expected_names[param],
-                                          expected_shapes[param])
-
-
-def verify_data_dict_for_single_param(datadict,
-                                      names: Sequence[str],
-                                      shapes: Sequence[Tuple[int, ...]]):
-    for name, shape in zip(names, shapes):
-        assert datadict[name].shape == shape

From 60b2eca8184013588e3f34f38d9aa8df1fe54cac Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 13:56:58 +0100
Subject: [PATCH 359/719] Update docstring

---
 qcodes/dataset/data_set.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 20e081a812d..f91ed7e9ebb 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -813,7 +813,10 @@ def get_parameter_data(
         Returns the values stored in the DataSet for the specified parameters.
         The values are returned as a dictionary of numpy arrays where the key,
         is given by the respective parameter name and the value arrays represent
-        the data points. This method returns the transpose of `get_data`.
+        the data points. If some of the parameters are stored as
+        arrays the remaining parameters are expanded to the same shape as these.
+        Apart from this expansion this method returns the transpose of
+        `get_data`.
 
         If provided, the start and end arguments select a range of results
         by result count (index). If the range is empty - that is, if the end is
@@ -829,8 +832,8 @@ def get_parameter_data(
                 None
 
         Returns:
-            Dictionary of numpy arrays containing the data points of type
-            numeric, array or string.
+            Dictionary from requested parameters to Dict of numpy arrays
+            containing the data points of type numeric, array or string.
         """
         valid_param_names = self._validate_parameters(*params)
         return get_parameter_data(self.conn, self.table_name, valid_param_names,

From 653fbb3b8051029d7390853d6494a67e9ccfbcf8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 13:59:13 +0100
Subject: [PATCH 360/719] Add missing file

---
 qcodes/tests/dataset/helper_functions.py | 33 ++++++++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index e69de29bb2d..87b68c1c4a2 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -0,0 +1,33 @@
+from typing import Sequence, Tuple, Dict
+
+import numpy as np
+
+
+def verify_data_dict(data: Dict[str, Dict[str, np.ndarray]],
+                     parameter_names: Sequence[str],
+                     expected_names: Dict[str, Sequence[str]],
+                     expected_shapes: Dict[str, Sequence[Tuple[int, ...]]]) -> None:
+    """
+    Simple helper function to verify a dict of data
+
+    Args:
+        data: The data to verify
+        parameter_names: names of the parameters requested
+        expected_names: names of the paramerters expected to be loaded
+        expected_shapes: shapes of the paramters loaded
+
+    Returns:
+
+    """
+    for param in parameter_names:
+        innerdata = data[param]
+        verify_data_dict_for_single_param(innerdata,
+                                          expected_names[param],
+                                          expected_shapes[param])
+
+
+def verify_data_dict_for_single_param(datadict: Dict[str, np.ndarray],
+                                      names: Sequence[str],
+                                      shapes: Sequence[Tuple[int, ...]]):
+    for name, shape in zip(names, shapes):
+        assert datadict[name].shape == shape

From d5cb99266460d501ce0a8dea0d0d6ede927d941c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 14:00:19 +0100
Subject: [PATCH 361/719] typo

---
 qcodes/dataset/sqlite_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 36e26774394..a6d690f840b 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1210,7 +1210,7 @@ def get_data(conn: ConnectionPlus,
     if len(columns) == 0:
         warnings.warn(
             'get_data: requested data without specifying parameters/columns.'
-            'Returning empyt list.'
+            'Returning empty list.'
         )
         return [[]]
     query = _build_data_query(table_name, columns, start, end)

From c86579addf956d2ef55769c207095193ac5a3fb6 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 16:18:34 +0100
Subject: [PATCH 362/719] add function to get all non dependent parameters

---
 qcodes/dataset/sqlite_base.py | 33 +++++++++++++++++++++++++++------
 1 file changed, 27 insertions(+), 6 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index a6d690f840b..1f1f3279e00 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1524,13 +1524,34 @@ def get_dependencies(conn: ConnectionPlus,
     return res
 
 
-def get_independent_parameters(conn: ConnectionPlus,
-                               run_id: int):
-    """"
-    This should return all parameters that are neither
-    dependent on other parameters or dependencies of other parameters
+def get_non_dependencies(conn: ConnectionPlus,
+                         run_id: int) -> List[str]:
     """
-    raise NotADirectoryError("TODO")
+    Return all parameters for a given run that are not dependencies of
+    other parameters.
+
+    Args:
+        conn: connection to the database
+        run_id: The run_id of the run in question
+
+    Returns:
+
+    """
+    parameters = get_parameters(conn, run_id)
+    maybe_independent = []
+    dependent = []
+    dependencies = []
+
+    for param in parameters:
+        if len(param.depends_on) == 0:
+            maybe_independent.append(param.name)
+        else:
+            dependent.append(param.name)
+            dependencies.extend(param.depends_on)
+
+    independent = set(maybe_independent) - set(dependencies)
+    return sorted(list(independent))
+
 
 # Higher level Wrappers
 

From 999bd2824eacc82bb6ef0b47953839f89f20d781 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 16:18:57 +0100
Subject: [PATCH 363/719] Cleanup logic and use new function

---
 qcodes/dataset/sqlite_base.py | 39 ++++++++++++++++++-----------------
 1 file changed, 20 insertions(+), 19 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 1f1f3279e00..6fdb790da49 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1239,25 +1239,26 @@ def get_parameter_data(conn: ConnectionPlus,
         start: start of range; if None, then starts from the top of the table
         end: end of range; if None, then ends at the bottom of the table
     """
-    output = {}
-    if len(columns) == 0:
-        raise NotImplementedError("HERE we should look up all "
-                                  "dependent standalone parameters")
-
-    # get run_id
     sql = """
     SELECT run_id FROM runs WHERE result_table_name = ?
     """
     c = atomic_transaction(conn, sql, table_name)
     run_id = one(c, 'run_id')
 
-    for param in columns:
-        params = get_dependency_parameters(conn, param, run_id)
-        columns = [param.name for param in params]
-        types = [param.type for param in params]
-
-        res = get_data(conn, table_name, columns, start=start, end=end)
-
+    output = {}
+    if len(columns) == 0:
+        columns = get_non_dependencies(conn, run_id)
+
+    # loop over all the requested parameters
+    for output_param in columns:
+        # find all the dependencies of this param
+        paramspecs = get_parameter_dependencies(conn, output_param, run_id)
+        param_names = [param.name for param in paramspecs]
+        types = [param.type for param in paramspecs]
+
+        res = get_data(conn, table_name, param_names, start=start, end=end)
+        # if we have array type parameters expand all other parameters
+        # to arrays
         if 'array' in types and ('numeric' in types or 'text' in types):
             # todo: Should we check that all the arrays are the same size?
             first_array_element = types.index('array')
@@ -1277,13 +1278,13 @@ def get_parameter_data(conn: ConnectionPlus,
                                                 row[element],
                                                 dtype=f'U{strlen}')
 
-        # Benchmarking shows that transposing the data with python types is faster
-        # than transposing the data using np.array.transpose
+        # Benchmarking shows that transposing the data with python types is
+        # faster than transposing the data using np.array.transpose
         # This method is going to form the entry point for a compiled version
         res_t = list(map(list, zip(*res)))
-        output[param] = {name: np.squeeze(np.array(column_data))
+        output[output_param] = {name: np.squeeze(np.array(column_data))
                          for name, column_data
-                         in zip(columns, res_t)}
+                         in zip(param_names, res_t)}
     return output
 
 
@@ -1556,10 +1557,10 @@ def get_non_dependencies(conn: ConnectionPlus,
 # Higher level Wrappers
 
 
-def get_dependency_parameters(conn: ConnectionPlus, param: str,
+def get_parameter_dependencies(conn: ConnectionPlus, param: str,
                               run_id: int) -> List[ParamSpec]:
     """
-    Given a parameter name return its ParamSpec of the parameter along with
+    Given a parameter name return the ParamSpec of the parameter along with
     the ParamSpecs of all it's dependencies.
 
     Args:

From 950f752dd2bd88c0167edaa335e20d1f7aea63d7 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 16:22:04 +0100
Subject: [PATCH 364/719] better tests

---
 qcodes/tests/dataset/dataset_fixtures.py | 22 ++++++++++++++++++++++
 qcodes/tests/dataset/test_sqlite_base.py | 11 ++++++++++-
 2 files changed, 32 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
index dd6729244c6..8f49ef5d820 100644
--- a/qcodes/tests/dataset/dataset_fixtures.py
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -104,3 +104,25 @@ def array_in_str_dataset(experiment, request):
         yield datasaver.dataset
     finally:
         datasaver.dataset.conn.close()
+
+@pytest.fixture
+def standalone_parameters_dataset(dataset):
+    n_params = 3
+    n_rows = 10**3
+    params_indep = [ParamSpec(f'param_{i}',
+                              'numeric',
+                              label=f'param_{i}',
+                              unit='V')
+                    for i in range(n_params)]
+
+    params = params_indep + [ParamSpec(f'param_{n_params}',
+                                       'numeric',
+                                       label=f'param_{n_params}',
+                                       unit='Ohm',
+                                       depends_on=params_indep[0:1])]
+    for p in params:
+        dataset.add_parameter(p)
+    dataset.add_results([{p.name: np.random.rand(1)[0] for p in params}
+                         for _ in range(n_rows)])
+    dataset.mark_complete()
+    yield dataset
diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index 0d00e7f7798..708bef3cb3e 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -21,7 +21,8 @@
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import \
     empty_temp_db, experiment, dataset
-from qcodes.tests.dataset.dataset_fixtures import scalar_dataset
+from qcodes.tests.dataset.dataset_fixtures import scalar_dataset, \
+    standalone_parameters_dataset
 from qcodes.tests.dataset.test_database_creation_and_upgrading import \
     error_caused_by
 # pylint: enable=unused-import
@@ -216,6 +217,14 @@ def test_get_parameter_data(scalar_dataset):
 
     verify_data_dict(data, input_names, expected_names, expected_shapes)
 
+
+def test_get_parameter_data_independent_parameters(standalone_parameters_dataset):
+    ds = standalone_parameters_dataset
+    params = mut.get_non_dependencies(ds.conn,
+                                      ds.run_id)
+    assert params == ['param_0', 'param_1', 'param_2']
+
+
 def test_is_run_id_in_db(empty_temp_db):
     conn = mut.connect(get_DB_location())
     mut.new_experiment(conn, 'test_exp', 'no_sample')

From 25c9b9d0f936ae92682dad337e30fd5078eeea5e Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 17:07:22 +0100
Subject: [PATCH 365/719] look up parameters if missing

---
 qcodes/dataset/data_set.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index f91ed7e9ebb..5f108e0b39f 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -38,7 +38,8 @@
                                         update_run_description,
                                         run_exists, remove_trigger,
                                         make_connection_plus_from,
-                                        ConnectionPlus)
+                                        ConnectionPlus,
+                                        get_non_dependencies)
 
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
@@ -835,7 +836,11 @@ def get_parameter_data(
             Dictionary from requested parameters to Dict of numpy arrays
             containing the data points of type numeric, array or string.
         """
-        valid_param_names = self._validate_parameters(*params)
+        if len(params) == 0:
+            valid_param_names = get_non_dependencies(self.conn,
+                                                     self.run_id)
+        else:
+            valid_param_names = self._validate_parameters(*params)
         return get_parameter_data(self.conn, self.table_name, valid_param_names,
                                   start, end)
 

From 21c57d6e1bef06295a44db05a2525b8991dccbbd Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 17:08:41 +0100
Subject: [PATCH 366/719] add tests for dataset interface

---
 qcodes/tests/dataset/test_dataset_basic.py | 30 ++++++++++++++++++++--
 qcodes/tests/dataset/test_sqlite_base.py   | 20 ++++++++++++++-
 2 files changed, 47 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 74460a7665f..9d97c57ee07 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -15,7 +15,7 @@
 from qcodes.tests.dataset.test_database_creation_and_upgrading import \
     error_caused_by
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
-from qcodes.dataset.sqlite_base import _unicode_categories
+from qcodes.dataset.sqlite_base import _unicode_categories, get_non_dependencies
 from qcodes.dataset.database import get_DB_location
 from qcodes.dataset.data_set import CompletedError, DataSet
 from qcodes.dataset.guids import parse_guid
@@ -23,7 +23,8 @@
 from qcodes.tests.dataset.temporary_databases import (empty_temp_db,
                                                       experiment, dataset)
 from qcodes.tests.dataset.dataset_fixtures import scalar_dataset, \
-    array_dataset, multi_dataset, array_in_scalar_dataset, array_in_str_dataset
+    array_dataset, multi_dataset, array_in_scalar_dataset, array_in_str_dataset, \
+    standalone_parameters_dataset
 # pylint: disable=unused-import
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 
@@ -859,6 +860,31 @@ def test_get_array_in_str_param_data(array_in_str_dataset):
     verify_data_dict(data, inputnames, expected_names, expected_shapes)
 
 
+def test_get_parameter_data_independent_parameters(standalone_parameters_dataset):
+    ds = standalone_parameters_dataset
+    params = get_non_dependencies(ds.conn,
+                                      ds.run_id)
+    expected_toplevel_params = ['param_1', 'param_2', 'param_3']
+    assert params == expected_toplevel_params
+
+    data = standalone_parameters_dataset.get_parameter_data()
+
+    assert len(data.keys()) == len(expected_toplevel_params)
+
+    expected_names = {}
+    expected_names['param_1'] = []
+    expected_names['param_2'] = []
+    expected_names['param_3'] = ['param_0']
+
+    expected_shapes = {}
+    expected_shapes['param_1'] = [(10 ** 3,)]
+    expected_shapes['param_2'] = [(10 ** 3,)]
+    expected_shapes['param_3'] = [(10**3, )]*2
+
+    verify_data_dict(data, expected_toplevel_params, expected_names,
+                     expected_shapes)
+
+
 def parameter_test_helper(ds):
     params = ds.parameters.split(',')
     # delete some random parameter to test it with an incomplete list
diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index 708bef3cb3e..fc408c7ef51 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -222,7 +222,25 @@ def test_get_parameter_data_independent_parameters(standalone_parameters_dataset
     ds = standalone_parameters_dataset
     params = mut.get_non_dependencies(ds.conn,
                                       ds.run_id)
-    assert params == ['param_0', 'param_1', 'param_2']
+    expected_toplevel_params = ['param_1', 'param_2', 'param_3']
+    assert params == expected_toplevel_params
+
+    data = mut.get_parameter_data(ds.conn, ds.table_name)
+
+    assert len(data.keys()) == len(expected_toplevel_params)
+
+    expected_names = {}
+    expected_names['param_1'] = []
+    expected_names['param_2'] = []
+    expected_names['param_3'] = ['param_0']
+
+    expected_shapes = {}
+    expected_shapes['param_1'] = [(10 ** 3,)]
+    expected_shapes['param_2'] = [(10 ** 3,)]
+    expected_shapes['param_3'] = [(10**3, )]*2
+
+    verify_data_dict(data, expected_toplevel_params, expected_names,
+                     expected_shapes)
 
 
 def test_is_run_id_in_db(empty_temp_db):

From 9a39480dd47f8715938540e903b82c35aef1d35f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 17:09:16 +0100
Subject: [PATCH 367/719] include dependent in output and correct a bug

---
 qcodes/dataset/sqlite_base.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 6fdb790da49..e3976ac3c4a 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1222,7 +1222,7 @@ def get_data(conn: ConnectionPlus,
 
 def get_parameter_data(conn: ConnectionPlus,
                        table_name: str,
-                       columns: List[str],
+                       columns: Sequence[str]=(),
                        start: Optional[int]=None,
                        end: Optional[int]=None) -> \
         Dict[str, Dict[str, np.ndarray]]:
@@ -1548,10 +1548,12 @@ def get_non_dependencies(conn: ConnectionPlus,
             maybe_independent.append(param.name)
         else:
             dependent.append(param.name)
-            dependencies.extend(param.depends_on)
+            dependencies.extend(param.depends_on.split(','))
 
     independent = set(maybe_independent) - set(dependencies)
-    return sorted(list(independent))
+    dependent = set(dependent)
+    result = independent.union(dependent)
+    return sorted(list(result))
 
 
 # Higher level Wrappers

From 1b382400591ba22e6aa2d9b118f42516692d51d9 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 17:15:31 +0100
Subject: [PATCH 368/719] clarify docstring

---
 qcodes/dataset/data_set.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 5f108e0b39f..0d3e6704e5d 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -826,7 +826,9 @@ def get_parameter_data(
 
         Args:
             *params: string parameter names, QCoDeS Parameter objects, and
-                ParamSpec objects
+                ParamSpec objects. If no parameters are supplied data for
+                all parameters that are not a dependency of another
+                parameter will be returned.
             start: start value of selection range (by result count); ignored
                 if None
             end: end value of selection range (by results count); ignored if

From 7347f7dfa5620c6fe1b5ce3f13f73dbcf222d07f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 17:24:16 +0100
Subject: [PATCH 369/719] more docstring work

---
 qcodes/dataset/data_set.py | 21 +++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 0d3e6704e5d..b788ba51b12 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -811,13 +811,17 @@ def get_parameter_data(
             start: Optional[int] = None,
             end: Optional[int] = None) -> Dict[str, Dict[str, numpy.ndarray]]:
         """
-        Returns the values stored in the DataSet for the specified parameters.
-        The values are returned as a dictionary of numpy arrays where the key,
-        is given by the respective parameter name and the value arrays represent
-        the data points. If some of the parameters are stored as
+        Returns the values stored in the DataSet for the specified parameters
+        and their dependencies. If no paramerers are supplied the values will
+        be returned for all parameters that are not them self depdendencies.
+
+        The values are returned as a dictionary with names of the requested
+        parameters as keys and values consisting of dictionaries with the
+        names of the parameters and its dependencies as keys and numpy arrays
+        of the data as values. If some of the parameters are stored as
         arrays the remaining parameters are expanded to the same shape as these.
-        Apart from this expansion this method returns the transpose of
-        `get_data`.
+        Apart from this expansion the data returned by this method
+        is the transpose of the date returned by `get_data`.
 
         If provided, the start and end arguments select a range of results
         by result count (index). If the range is empty - that is, if the end is
@@ -835,8 +839,9 @@ def get_parameter_data(
                 None
 
         Returns:
-            Dictionary from requested parameters to Dict of numpy arrays
-            containing the data points of type numeric, array or string.
+            Dictionary from requested parameters to Dict of parameter names
+            to numpy arrays containing the data points of type numeric,
+            array or string.
         """
         if len(params) == 0:
             valid_param_names = get_non_dependencies(self.conn,

From b2fc15447b1eb3b2fc44d087e3b1fc3d0be00426 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 17:44:36 +0100
Subject: [PATCH 370/719] Fix typeing issues

---
 qcodes/dataset/sqlite_base.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index e3976ac3c4a..a3564cefb60 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1541,7 +1541,7 @@ def get_non_dependencies(conn: ConnectionPlus,
     parameters = get_parameters(conn, run_id)
     maybe_independent = []
     dependent = []
-    dependencies = []
+    dependencies: List[str] = []
 
     for param in parameters:
         if len(param.depends_on) == 0:
@@ -1550,9 +1550,9 @@ def get_non_dependencies(conn: ConnectionPlus,
             dependent.append(param.name)
             dependencies.extend(param.depends_on.split(','))
 
-    independent = set(maybe_independent) - set(dependencies)
-    dependent = set(dependent)
-    result = independent.union(dependent)
+    independent_set = set(maybe_independent) - set(dependencies)
+    dependent_set = set(dependent)
+    result = independent_set.union(dependent_set)
     return sorted(list(result))
 
 

From 03a67e65cf29a7666c551f208523f516eb7ac035 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 5 Dec 2018 20:48:35 +0100
Subject: [PATCH 371/719] Fix parsing of depends string

---
 qcodes/dataset/sqlite_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index a3564cefb60..13425a74a05 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1548,7 +1548,7 @@ def get_non_dependencies(conn: ConnectionPlus,
             maybe_independent.append(param.name)
         else:
             dependent.append(param.name)
-            dependencies.extend(param.depends_on.split(','))
+            dependencies.extend(param.depends_on.split(', '))
 
     independent_set = set(maybe_independent) - set(dependencies)
     dependent_set = set(dependent)

From 22e3e444f9c151b920caac366f146636e122cc5f Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Wed, 5 Dec 2018 19:02:12 -0800
Subject: [PATCH 372/719] 1) Instead of using InstrumentChannel, make
 individual parameters (e.g. A1, B2, etc) 2) Use the caching functions of the
 parameters instead of rolling your own 3) Use the visa logger in the test to
 verify that ":CLOS ?" is called only once.

---
 .../QCodes example with Keithley S46.ipynb    | 130 ++++++++++-----
 .../tektronix/Keithley_s46.py                 | 157 +++++++-----------
 qcodes/tests/drivers/test_keithley_s46.py     | 104 ++++++------
 3 files changed, 202 insertions(+), 189 deletions(-)

diff --git a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb
index 2e9132e83a4..f111489e1e9 100644
--- a/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
+++ b/docs/examples/driver_examples/QCodes example with Keithley S46.ipynb	
@@ -30,37 +30,57 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "s46 = S46(\"s2\", \"GPIB0::7::INSTR\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: KEITHLEY INSTRUMENTS INC. SYSTEM 46 (serial:1327388, firmware:A03) in 0.54s\n"
+      "['A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'R5', 'R8']\n",
+      "26\n"
      ]
     }
    ],
    "source": [
-    "s46 = S46(\"s2\", \"GPIB0::7::INSTR\")"
+    "print(s46.available_channels)\n",
+    "print(len(s46.available_channels))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "26"
+       "['A2', 'B1']"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(s46.channels)"
+    "s46.closed_channels()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s46.open_all_channels()"
    ]
   },
   {
@@ -80,22 +100,13 @@
     }
    ],
    "source": [
-    "s46.get_closed_channels()"
+    "s46.closed_channels()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "s46.A1.state(\"open\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -103,27 +114,27 @@
        "'open'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "s46.A1.state()"
+    "s46.A1()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.A1.state(\"close\")"
+    "s46.A1(\"close\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -132,51 +143,51 @@
        "'close'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "s46.A1.state()"
+    "s46.A1()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<S46Channel: s2_A1 of S46: s2>]"
+       "['A1']"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "s46.get_closed_channels()"
+    "s46.closed_channels()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "('Relay A is already in use by channel 1', 'setting s2_A2_state to close')\n"
+      "Relay A is already in use by channel 1\n"
      ]
     }
    ],
    "source": [
     "try: \n",
-    "    s46.A2.state(\"close\")\n",
+    "    s46.A2(\"close\")\n",
     "    raise(\"We should not be here\")\n",
     "except LockAcquisitionError as e: \n",
     "    print(e)"
@@ -184,38 +195,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.A1.state(\"open\")"
+    "s46.A1(\"open\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.A2.state(\"close\")"
+    "s46.A2(\"close\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "('Relay A is already in use by channel 2', 'setting s2_A1_state to close')\n"
+      "Relay A is already in use by channel 2\n"
      ]
     }
    ],
    "source": [
     "try: \n",
-    "    s46.A1.state(\"close\")\n",
+    "    s46.A1(\"close\")\n",
     "    raise(\"We should not be here\")\n",
     "except LockAcquisitionError as e: \n",
     "    print(e)"
@@ -223,29 +234,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "s46.B1.state(\"close\")"
+    "s46.B1(\"close\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "('Relay B is already in use by channel 7', 'setting s2_B2_state to close')\n"
+      "Relay B is already in use by channel 7\n"
      ]
     }
    ],
    "source": [
     "try: \n",
-    "    s46.B2.state(\"close\")\n",
+    "    s46.B2(\"close\")\n",
     "    raise(\"We should not be here\")\n",
     "except LockAcquisitionError as e: \n",
     "    print(e)"
@@ -253,7 +264,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A2', 'B1']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s46.closed_channels()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -271,14 +302,21 @@
        "[]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "s46.get_closed_channels()"
+    "s46.closed_channels()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index 77d4f59d337..3a91064ab08 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -3,34 +3,16 @@
 """
 import re
 from itertools import product
+from functools import partial
 
-from typing import Callable, Any, Union, Dict, List
-from qcodes import Instrument, VisaInstrument, InstrumentChannel, ChannelList
-from qcodes.utils.validators import Enum
+from typing import Any, Dict, List, Optional
+from qcodes import Instrument, VisaInstrument, Parameter
 
 
 class LockAcquisitionError(Exception):
     pass
 
 
-def cached_method(method: Callable) -> Callable:
-    """
-    A decorator which adds a keyword 'get_cached' to a method. When
-    'get_cached=True', the decorated method returns a cached return value. If
-    the method has not been called before, or 'get_cached=False' the original
-    method is called.
-    """
-    def inner(self, *args: Any, get_cached: bool=False, **kwargs: Any) -> Any:
-        if not hasattr(self, "__cached_values__"):
-            self.__cached_values__ = {}
-
-        if method not in self.__cached_values__ or not get_cached:
-            self.__cached_values__[method] = method(self, *args, **kwargs)
-
-        return self.__cached_values__[method]
-    return inner
-
-
 class RelayLock:
     """
     The S46 either has six pole or a four pole relays. For example, channels
@@ -65,36 +47,35 @@ def release(self, channel_number: int) -> None:
             self._locked_by = None
 
 
-class S46Channel(InstrumentChannel):
+class S46Parameter(Parameter):
     """
-    A channel class for the S46
+    A parameter class for S46 channels. We do not use the QCoDeS
+    InstrumentChannel class because our channel has one state parameter,
+    which can either be "open" or "close".
 
     Args:
-        parent
         name
-        channel_number: unlike other instruments, channel numbers on the
-            S46 will not be contiguous. That is, we may have channels 1, 2 and 4
-            but channel 3 may be missing.
-        relay_lock: When closing the channel, request a lock acquisition.
-            Release the lock when opening
+        instrument
+        channel_number
+        lock: Acquire the lock when closing and release when opening
     """
     def __init__(
             self,
-            parent: Union[Instrument, 'InstrumentChannel'],
             name: str,
+            instrument: Optional[Instrument],
             channel_number: int,
-            relay_lock: RelayLock
+            lock: RelayLock
     ):
-        super().__init__(parent, name)
+        super().__init__(name, instrument)
 
+        self._lock = lock
         self._channel_number = channel_number
-        self._relay_lock = relay_lock
-        # Acquire the lock if upon init we find the channel closed.
-        if self._get_state(init=True) == "close":
+        self.get = partial(self._get, get_cached=False)
+
+        if self._get(get_cached=True) == "close":
             try:
-                self._relay_lock.acquire(self._channel_number)
+                self._lock.acquire(self._channel_number)
             except LockAcquisitionError as e:
-                # another channel has already acquired the lock
                 raise RuntimeError(
                     "The driver is initialized from an undesirable instrument "
                     "state where more then one channel on a single relay is "
@@ -102,36 +83,25 @@ def __init__(
                     "Refusing to initialize driver!"
                 ) from e
 
-        self.add_parameter(
-            "state",
-            get_cmd=self._get_state,
-            set_cmd=self._set_state,
-            vals=Enum("open", "close")
-        )
+        self.set = self._set
 
-    def _get_state(self, init: bool=False) -> str:
-        """
-        Args:
-            init: When calling this method from self.__init__, make this
-                value 'True'. This will prevent the instrument being queried
-                ':CLOS?' for each channel.
-        """
-        closed_channels = self.parent.get_closed_channel_numbers(
-            get_cached=init)
+    def _get(self, get_cached):
 
-        is_closed = self._channel_number in closed_channels
-        return {True: "close", False: "open"}[is_closed]
+        closed_channels = self._instrument.closed_channels.get_latest()
+
+        if not get_cached or closed_channels is None:
+            closed_channels = self._instrument.closed_channels.get()
+
+        return "close" if self.name in closed_channels else "open"
+
+    def _set(self, new_state) -> None:
 
-    def _set_state(self, new_state: str) -> None:
-        """
-        Open/Close the channel
-        """
         if new_state == "close":
-            self._relay_lock.acquire(self._channel_number)
+            self._lock.acquire(self._channel_number)
         elif new_state == "open":
-            self._relay_lock.release(self._channel_number)
+            self._lock.release(self._channel_number)
 
-        self.write(f":{new_state} (@{self._channel_number})")
+        self._instrument.write(f":{new_state} (@{self._channel_number})")
 
     @property
     def channel_number(self) -> int:
@@ -144,13 +114,15 @@ class S46(VisaInstrument):
 
     # Make a dictionary where keys are channel aliases (e.g. 'A1', 'B3', etc)
     # and values are corresponding channel numbers.
-    aliases: Dict[str, int] = {
+    channel_numbers: Dict[str, int] = {
         f"{a}{b}": count + 1 for count, (a, b) in enumerate(product(
             ["A", "B", "C", "D"],
             range(1, 7)
         ))
     }
-    aliases.update({f"R{i}": i + 24 for i in range(1, 9)})
+    channel_numbers.update({f"R{i}": i + 24 for i in range(1, 9)})
+    # Make a reverse dict for efficient alias lookup given a channel number
+    aliases = dict((v, k) for k, v in channel_numbers.items())
 
     def __init__(
             self,
@@ -161,13 +133,14 @@ def __init__(
 
         super().__init__(name, address, terminator="\n", **kwargs)
 
-        channels = ChannelList(
-            self,
-            "channel",
-            S46Channel,
-            snapshotable=False
+        self.add_parameter(
+            "closed_channels",
+            get_cmd=":CLOS?",
+            get_parser=self._get_closed_channels_parser
         )
 
+        self._available_channels: List[str] = []
+
         for relay_name, channel_count in zip(
                 S46.relay_names, self.relay_layout):
 
@@ -181,40 +154,28 @@ def __init__(
                     alias = relay_name  # For channels R1 to R8, we have one
                     # channel per relay. Channel alias = relay name
 
-                channel_number = S46.aliases[alias]
-                channel = S46Channel(self, alias, channel_number, relay_lock)
-                channels.append(channel)
-                self.add_submodule(alias, channel)
+                self.add_parameter(
+                    alias,
+                    channel_number=S46.channel_numbers[alias],
+                    lock=relay_lock,
+                    parameter_class=S46Parameter
+                )
 
-        self.add_submodule("channels", channels)
-        self.connect_message()
+                self._available_channels.append(alias)
 
-    @cached_method
-    def get_closed_channel_numbers(self) -> List[int]:
+    @staticmethod
+    def _get_closed_channels_parser(reply: str) -> List[str]:
         """
-        Query the instrument for closed channels. Add an option to return
-        a cached response so we can prevent this method from being called
-        repeatedly for each channel during initialization of the driver.
+        The SCPI command ":CLOS ?" returns a reply in the form
+        "(@1,9)", if channels 1 and 9 are closed. Return a list of
+        strings, representing the aliases of the closed channels
         """
-        closed_channels_str = re.findall(r"\d+", self.ask(":CLOS?"))
-        return [int(i) for i in closed_channels_str]
-
-    def get_closed_channels(self) -> List[S46Channel]:
-        """
-        Return a list of closed channels as a list of channel objects
-        """
-        return [
-            channel for channel in self.channels if
-            channel.channel_number in self.get_closed_channel_numbers()
-        ]
+        closed_channels_str = re.findall(r"\d+", reply)
+        return [S46.aliases[int(i)] for i in closed_channels_str]
 
     def open_all_channels(self) -> None:
-        """
-        Please do not write ':OPEN ALL' to the instrument as this will
-        circumvent the lock.
-        """
-        for channel in self.get_closed_channels():
-            channel.state("open")
+        for channel_name in self.closed_channels():
+            self.parameters[channel_name].set("open")
 
     @property
     def relay_layout(self) -> List[int]:
@@ -223,3 +184,7 @@ def relay_layout(self) -> List[int]:
         that we can have zero channels per relay.
         """
         return [int(i) for i in self.ask(":CONF:CPOL?").split(",")]
+
+    @property
+    def available_channels(self) -> List[str]:
+        return self._available_channels
diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
index 5a38c5dc91e..c5417f1fe1b 100644
--- a/qcodes/tests/drivers/test_keithley_s46.py
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -1,8 +1,12 @@
+from io import StringIO
+import logging
+
 import pytest
 
 from qcodes.instrument_drivers.tektronix.Keithley_s46 import (
     S46, LockAcquisitionError
 )
+from qcodes.instrument.visa import VISA_LOGGER
 
 import qcodes.instrument.sims as sims
 visalib = sims.__file__.replace('__init__.py', 'Keithley_s46.yaml@sim')
@@ -20,21 +24,9 @@ def calc_channel_nr(alias: str) -> int:
         offset_dict = dict(zip(["A", "B", "C", "D", "R"], range(0, 32, 6)))
         return offset_dict[alias[0]] + int(alias[1:])
 
-    assert all([nr == calc_channel_nr(al) for al, nr in S46.aliases.items()])
-
-
-class S46LoggedAsk(S46):
-    """
-    A version of the driver which logs every ask command. We need this to
-    assert that ':CLOS?' is called once during initialization
-    """
-    def __init__(self, *args, **kwargs):
-        self._ask_log = []
-        super().__init__(*args, **kwargs)
-
-    def ask(self, cmd: str):
-        self._ask_log.append(cmd)
-        return super().ask(cmd)
+    assert all([
+        nr == calc_channel_nr(al) for al, nr in S46.channel_numbers.items()
+    ])
 
 
 @pytest.fixture(scope='module')
@@ -42,7 +34,15 @@ def s46_six():
     """
     A six channel-per-relay instrument
     """
-    driver = S46LoggedAsk('s46_six', address='GPIB::2::INSTR', visalib=visalib)
+    io_stream = StringIO()
+    handler = logging.StreamHandler(io_stream)
+    logger = logging.getLogger(VISA_LOGGER)
+    logger.setLevel(logging.DEBUG)
+    logger.addHandler(handler)
+
+    driver = S46('s46_six', address='GPIB::2::INSTR', visalib=visalib)
+    driver.io_stream = io_stream
+
     yield driver
     driver.close()
 
@@ -52,7 +52,15 @@ def s46_four():
     """
     A four channel-per-relay instrument
     """
-    driver = S46LoggedAsk('s46_four', address='GPIB::3::INSTR', visalib=visalib)
+    io_stream = StringIO()
+    handler = logging.StreamHandler(io_stream)
+    logger = logging.getLogger(VISA_LOGGER)
+    logger.setLevel(logging.DEBUG)
+    logger.addHandler(handler)
+
+    driver = S46('s46_four', address='GPIB::3::INSTR', visalib=visalib)
+    driver.io_stream = io_stream
+
     yield driver
     driver.close()
 
@@ -74,36 +82,28 @@ def test_init_six(s46_six):
     """
     Test that the six channel instrument initializes correctly.
     """
-    assert s46_six._ask_log.count(":CLOS?") == 1
+    log_messages = s46_six.io_stream.getvalue()
+    assert log_messages.count(":CLOS?") == 1
+    assert len(s46_six.available_channels) == 26
 
-    n_channels = len(s46_six.channels)
-    # Channels A1 to D6 + R5 + R8 (4 * 6 + 2)
-    assert n_channels == 26
-
-    closed_channels = [1, 8, 13]
-
-    for channel in s46_six.channels:
-        channel_nr = S46.aliases[channel.short_name]
-        state = "close" if channel_nr in closed_channels else "open"
-        assert channel.state() == state
+    closed_channel_numbers = [1, 8, 13]
+    assert s46_six.closed_channels() == [
+        S46.aliases[i] for i in closed_channel_numbers
+    ]
 
 
 def test_init_four(s46_four):
     """
     Test that the six channel instrument initializes correctly.
     """
-    assert s46_four._ask_log.count(":CLOS?") == 1
-
-    n_channels = len(s46_four.channels)
-    # Channels A1 to D4 + R5 + R8 (4 * 4 + 2)
-    assert n_channels == 18
+    log_messages = s46_four.io_stream.getvalue()
+    assert log_messages.count(":CLOS?") == 1
+    assert len(s46_four.available_channels) == 18
 
-    closed_channels = [1, 8]
-
-    for channel in s46_four.channels:
-        channel_nr = S46.aliases[channel.short_name]
-        state = "close" if channel_nr in closed_channels else "open"
-        assert channel.state() == state
+    closed_channel_numbers = [1, 8]
+    assert s46_four.closed_channels() == [
+        S46.aliases[i] for i in closed_channel_numbers
+    ]
 
     # A four channel instrument will have channels missing
     for relay in ["A", "B", "C", "D"]:
@@ -118,7 +118,7 @@ def test_channel_number_invariance(s46_four, s46_six):
     channel aliases should represent the same channel. See also page 2-5 of the
     manual (e.g. B1 is *always* channel 7)
     """
-    for alias in S46.aliases.keys():
+    for alias in S46.channel_numbers.keys():
         if hasattr(s46_four, alias) and hasattr(s46_six, alias):
             channel_four = getattr(s46_four, alias)
             channel_six = getattr(s46_six, alias)
@@ -136,21 +136,31 @@ def test_locking_mechanism(s46_six):
         match="is already in use by channel"
     ):
         # A1 should be closed already
-        s46_six.A2.state("close")
+        s46_six.A2("close")
     # release the lock
-    s46_six.A1.state("open")
+    s46_six.A1("open")
     # now we should be able to close A2
-    s46_six.A2.state("close")
+    s46_six.A2("close")
 
     # Let C1 acquire the lock
-    s46_six.C1.state("close")
+    s46_six.C1("close")
     # closing C2 should raise an error
     with pytest.raises(
         LockAcquisitionError,
         match="is already in use by channel"
     ):
-        s46_six.C2.state("close")
+        s46_six.C2("close")
 
     # Upon opening C1 we should be able to close C2
-    s46_six.C1.state("open")
-    s46_six.C2.state("close")
+    s46_six.C1("open")
+    s46_six.C2("close")
+
+
+def test_open_all(s46_six):
+    """
+    Verify that calling 'open_all_channels' does not cause an exception.
+    Unfortunately, pyvisa-sim is not flexible enough for us to test if channels
+    have truly opened.
+    """
+    s46_six.open_all_channels()
+

From 9168549ce799e1f753ef927249bfabb9cdaa4f70 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 6 Dec 2018 12:49:42 +0100
Subject: [PATCH 373/719] Better tests

---
 qcodes/tests/dataset/test_dataset_basic.py | 112 +++++++++------------
 1 file changed, 46 insertions(+), 66 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 9d97c57ee07..240ee680440 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -779,38 +779,39 @@ def test_get_parameter_data(scalar_dataset):
     ds = scalar_dataset
     input_names = ['param_3']
 
-    data = ds.get_parameter_data(*input_names)
-
-    assert len(data.keys()) == len(input_names)
-
     expected_names = {}
     expected_names['param_3'] = ['param_0', 'param_1', 'param_2',
                                  'param_3']
     expected_shapes = {}
     expected_shapes['param_3'] = [(10**3, )]*4
 
-    verify_data_dict(data, input_names, expected_names, expected_shapes)
+    parameter_test_helper(scalar_dataset,
+                          input_names,
+                          expected_names,
+                          expected_shapes)
+
 
 def test_get_array_parameter_data(array_dataset):
 
-    inputnames = ['testparameter']
-    data = array_dataset.get_parameter_data(*inputnames)
-    assert len(data.keys()) == len(inputnames)
+    input_names = ['testparameter']
+
     expected_names = {}
     expected_names['testparameter'] = ['testparameter', 'this_setpoint']
     expected_shapes = {}
     expected_shapes['testparameter'] = [(5, ), (5, )]
 
-    verify_data_dict(data, inputnames, expected_names, expected_shapes)
+    parameter_test_helper(array_dataset,
+                          input_names,
+                          expected_names,
+                          expected_shapes)
 
 
 def test_get_multi_parameter_data(multi_dataset):
     paramspecs = multi_dataset.paramspecs
     types = [param.type for param in paramspecs.values()]
 
-    inputnames = ['this', 'that']
-    data = multi_dataset.get_parameter_data(*inputnames)
-    assert len(data.keys()) == len(inputnames)
+    input_names = ['this', 'that']
+
     expected_names = {}
     expected_names['this'] = ['this', 'this_setpoint', 'that_setpoint']
     expected_names['that'] = ['that', 'this_setpoint', 'that_setpoint']
@@ -821,16 +822,18 @@ def test_get_multi_parameter_data(multi_dataset):
     else:
         expected_shapes['this'] = [(15,), (15,)]
         expected_shapes['that'] = [(15,), (15,)]
-    verify_data_dict(data, inputnames, expected_names, expected_shapes)
+    parameter_test_helper(multi_dataset,
+                          input_names,
+                          expected_names,
+                          expected_shapes)
 
 
 def test_get_array_in_scalar_param_data(array_in_scalar_dataset):
     paramspecs = array_in_scalar_dataset.paramspecs
     types = [param.type for param in paramspecs.values()]
 
-    inputnames = ['testparameter']
-    data = array_in_scalar_dataset.get_parameter_data(*inputnames)
-    assert len(data.keys()) == len(inputnames)
+    input_names = ['testparameter']
+
     expected_names = {}
     expected_names['testparameter'] = ['testparameter', 'scalarparam',
                                        'this_setpoint']
@@ -839,16 +842,18 @@ def test_get_array_in_scalar_param_data(array_in_scalar_dataset):
         expected_shapes['testparameter'] = [(9, 5), (9, 5)]
     else:
         expected_shapes['testparameter'] = [(45,), (45,)]
-    verify_data_dict(data, inputnames, expected_names, expected_shapes)
+    parameter_test_helper(array_in_scalar_dataset,
+                          input_names,
+                          expected_names,
+                          expected_shapes)
 
 
 def test_get_array_in_str_param_data(array_in_str_dataset):
     paramspecs = array_in_str_dataset.paramspecs
     types = [param.type for param in paramspecs.values()]
 
-    inputnames = ['testparameter']
-    data = array_in_str_dataset.get_parameter_data(*inputnames)
-    assert len(data.keys()) == len(inputnames)
+    input_names = ['testparameter']
+
     expected_names = {}
     expected_names['testparameter'] = ['testparameter', 'textparam',
                                        'this_setpoint']
@@ -857,20 +862,19 @@ def test_get_array_in_str_param_data(array_in_str_dataset):
         expected_shapes['testparameter'] = [(3, 5), (3, 5)]
     else:
         expected_shapes['testparameter'] = [(15,), (15,)]
-    verify_data_dict(data, inputnames, expected_names, expected_shapes)
+    parameter_test_helper(array_in_str_dataset,
+                          input_names,
+                          expected_names,
+                          expected_shapes)
 
 
 def test_get_parameter_data_independent_parameters(standalone_parameters_dataset):
     ds = standalone_parameters_dataset
-    params = get_non_dependencies(ds.conn,
-                                      ds.run_id)
+    params = get_non_dependencies(ds.conn, ds.run_id)
+
     expected_toplevel_params = ['param_1', 'param_2', 'param_3']
     assert params == expected_toplevel_params
 
-    data = standalone_parameters_dataset.get_parameter_data()
-
-    assert len(data.keys()) == len(expected_toplevel_params)
-
     expected_names = {}
     expected_names['param_1'] = []
     expected_names['param_2'] = []
@@ -881,42 +885,18 @@ def test_get_parameter_data_independent_parameters(standalone_parameters_dataset
     expected_shapes['param_2'] = [(10 ** 3,)]
     expected_shapes['param_3'] = [(10**3, )]*2
 
-    verify_data_dict(data, expected_toplevel_params, expected_names,
-                     expected_shapes)
-
-
-def parameter_test_helper(ds):
-    params = ds.parameters.split(',')
-    # delete some random parameter to test it with an incomplete list
-    del params[-2]
-    # replace first list entry by paramspec
-    param_names = copy(params)
-    params[0] = ds.paramspecs[params[0]]
-
-
-
-    ref = ds.get_data(*params)
-    dut = ds.get_parameter_data(*params)
-
-    column_lengths = []
-    for col in dut.values():
-        column_lengths.append(col.size)
-    column_lengths = np.array(column_lengths)
-    assert np.all(column_lengths == column_lengths[0])
-
-    for i_row, row in enumerate(ref):
-        for i_param, param_name in enumerate(param_names):
-            v_ref = row[i_param]
-            v_test = dut[param_name][i_row]
-            if isinstance(v_ref, float):
-                assert isinstance(v_test, np.float64)
-                assert np.isclose(v_test, v_ref)
-            elif isinstance(v_ref, int):
-                # default datatype for int is c_long which is 32 bit on
-                # windows and 64bit on linux
-                assert isinstance(v_test, np.int32) or \
-                       isinstance(v_test, np.int64)
-                assert np.isclose(v_test, v_ref)
-            elif isinstance(v_ref, str):
-                assert isinstance(v_test, np.str_)
-                assert v_ref == v_test
+    parameter_test_helper(ds,
+                          expected_toplevel_params,
+                          expected_names,
+                          expected_shapes)
+
+
+def parameter_test_helper(ds, toplevel_names, expected_names, expected_shapes):
+    data = ds.get_parameter_data(*toplevel_names)
+    all_data = ds.get_parameter_data()
+
+    assert data.keys() == all_data.keys()
+    assert len(data.keys()) == len(toplevel_names)
+
+    verify_data_dict(data, toplevel_names, expected_names, expected_shapes)
+    verify_data_dict(all_data, toplevel_names, expected_names, expected_shapes)

From 2740aa05add0a212d90018ec349768f675aa07e5 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 6 Dec 2018 12:50:00 +0100
Subject: [PATCH 374/719] pep8

---
 qcodes/tests/dataset/dataset_fixtures.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
index 8f49ef5d820..bd1a749f8f2 100644
--- a/qcodes/tests/dataset/dataset_fixtures.py
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -105,6 +105,7 @@ def array_in_str_dataset(experiment, request):
     finally:
         datasaver.dataset.conn.close()
 
+
 @pytest.fixture
 def standalone_parameters_dataset(dataset):
     n_params = 3

From d78d662036bd882ffd23ff5d5ad221f2fa3fad7a Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Fri, 7 Dec 2018 08:24:25 -0800
Subject: [PATCH 375/719] make a seperate test to verify that "close?" is
 called once during init and use caplog for this

---
 qcodes/tests/drivers/test_keithley_s46.py | 29 +++++++----------------
 1 file changed, 9 insertions(+), 20 deletions(-)

diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
index c5417f1fe1b..29beb918a31 100644
--- a/qcodes/tests/drivers/test_keithley_s46.py
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -1,4 +1,3 @@
-from io import StringIO
 import logging
 
 import pytest
@@ -6,7 +5,6 @@
 from qcodes.instrument_drivers.tektronix.Keithley_s46 import (
     S46, LockAcquisitionError
 )
-from qcodes.instrument.visa import VISA_LOGGER
 
 import qcodes.instrument.sims as sims
 visalib = sims.__file__.replace('__init__.py', 'Keithley_s46.yaml@sim')
@@ -34,14 +32,7 @@ def s46_six():
     """
     A six channel-per-relay instrument
     """
-    io_stream = StringIO()
-    handler = logging.StreamHandler(io_stream)
-    logger = logging.getLogger(VISA_LOGGER)
-    logger.setLevel(logging.DEBUG)
-    logger.addHandler(handler)
-
     driver = S46('s46_six', address='GPIB::2::INSTR', visalib=visalib)
-    driver.io_stream = io_stream
 
     yield driver
     driver.close()
@@ -52,14 +43,7 @@ def s46_four():
     """
     A four channel-per-relay instrument
     """
-    io_stream = StringIO()
-    handler = logging.StreamHandler(io_stream)
-    logger = logging.getLogger(VISA_LOGGER)
-    logger.setLevel(logging.DEBUG)
-    logger.addHandler(handler)
-
     driver = S46('s46_four', address='GPIB::3::INSTR', visalib=visalib)
-    driver.io_stream = io_stream
 
     yield driver
     driver.close()
@@ -78,12 +62,19 @@ def test_runtime_error_on_bad_init():
         S46('s46_bad_state', address='GPIB::1::INSTR', visalib=visalib)
 
 
+def test_query_close_once_at_init(caplog):
+    """
+    Test that, during initialisation, we query the closed channels once only
+    """
+    with caplog.at_level(logging.DEBUG):
+        S46('s46_test_query_once', address='GPIB::2::INSTR', visalib=visalib)
+        assert caplog.text.count(":CLOS?") == 1
+
+
 def test_init_six(s46_six):
     """
     Test that the six channel instrument initializes correctly.
     """
-    log_messages = s46_six.io_stream.getvalue()
-    assert log_messages.count(":CLOS?") == 1
     assert len(s46_six.available_channels) == 26
 
     closed_channel_numbers = [1, 8, 13]
@@ -96,8 +87,6 @@ def test_init_four(s46_four):
     """
     Test that the six channel instrument initializes correctly.
     """
-    log_messages = s46_four.io_stream.getvalue()
-    assert log_messages.count(":CLOS?") == 1
     assert len(s46_four.available_channels) == 18
 
     closed_channel_numbers = [1, 8]

From ac94abb310a226875c55fff70e6bd48715055c08 Mon Sep 17 00:00:00 2001
From: sochatoo <schatoor@gmail.com>
Date: Fri, 7 Dec 2018 10:07:59 -0800
Subject: [PATCH 376/719] test that open_all_channels issues the right visa
 commands and locks are freed

---
 qcodes/tests/drivers/test_keithley_s46.py | 27 ++++++++++++-----------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/qcodes/tests/drivers/test_keithley_s46.py b/qcodes/tests/drivers/test_keithley_s46.py
index 29beb918a31..e9704e2f27e 100644
--- a/qcodes/tests/drivers/test_keithley_s46.py
+++ b/qcodes/tests/drivers/test_keithley_s46.py
@@ -1,5 +1,4 @@
 import logging
-
 import pytest
 
 from qcodes.instrument_drivers.tektronix.Keithley_s46 import (
@@ -64,14 +63,14 @@ def test_runtime_error_on_bad_init():
 
 def test_query_close_once_at_init(caplog):
     """
-    Test that, during initialisation, we query the closed channels once only
+    Test that, during initialisation, we query the closed channels only once
     """
     with caplog.at_level(logging.DEBUG):
         S46('s46_test_query_once', address='GPIB::2::INSTR', visalib=visalib)
         assert caplog.text.count(":CLOS?") == 1
 
 
-def test_init_six(s46_six):
+def test_init_six(s46_six, caplog):
     """
     Test that the six channel instrument initializes correctly.
     """
@@ -82,6 +81,16 @@ def test_init_six(s46_six):
         S46.aliases[i] for i in closed_channel_numbers
     ]
 
+    with caplog.at_level(logging.DEBUG):
+        s46_six.open_all_channels()
+        assert ":open (@1)" in caplog.text
+        assert ":open (@8)" in caplog.text
+        assert ":open (@13)" in caplog.text
+
+        assert s46_six.A1._lock._locked_by is None
+        assert s46_six.B1._lock._locked_by is None
+        assert s46_six.C1._lock._locked_by is None
+
 
 def test_init_four(s46_four):
     """
@@ -120,6 +129,8 @@ def test_locking_mechanism(s46_six):
     more then once channel per replay
     2) Test that the lock is released when opening a channel that was closed
     """
+    s46_six.A1("close")
+
     with pytest.raises(
         LockAcquisitionError,
         match="is already in use by channel"
@@ -143,13 +154,3 @@ def test_locking_mechanism(s46_six):
     # Upon opening C1 we should be able to close C2
     s46_six.C1("open")
     s46_six.C2("close")
-
-
-def test_open_all(s46_six):
-    """
-    Verify that calling 'open_all_channels' does not cause an exception.
-    Unfortunately, pyvisa-sim is not flexible enough for us to test if channels
-    have truly opened.
-    """
-    s46_six.open_all_channels()
-

From 74dd9dfd49292e8b18ec53a898fa60bdff05a47d Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 13:16:00 +0100
Subject: [PATCH 377/719] improve docstrings

---
 qcodes/dataset/sqlite_base.py | 35 ++++++++++++++++++++++++-----------
 1 file changed, 24 insertions(+), 11 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 13425a74a05..6e1f2e6beeb 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1228,9 +1228,17 @@ def get_parameter_data(conn: ConnectionPlus,
         Dict[str, Dict[str, np.ndarray]]:
     """
     Get data for one or more parameters and its dependencies. The data
-    is returned as numpy arrays nested 2 layers of dicts. The keys or the
-    outermost are the requested parameters and the second level by the loaded
-    parameters.
+    is returned as numpy arrays within 2 layers of nested dicts. The keys of
+    the outermost dict are the requested parameters and the keys of the second
+    level are the loaded parameters (requested parameter followed by its
+    dependencies). Start and End  sllows one to specify a range of rows
+    (1-based indexing, both ends are included).
+
+    Note that this assumes that all array type parameters have the same length.
+    This should always be the case for a parameter and its dependencies.
+
+    Note that all numeric data will at the moment be returned as floating point
+    values.
 
     Args:
         conn: database connection
@@ -1260,7 +1268,6 @@ def get_parameter_data(conn: ConnectionPlus,
         # if we have array type parameters expand all other parameters
         # to arrays
         if 'array' in types and ('numeric' in types or 'text' in types):
-            # todo: Should we check that all the arrays are the same size?
             first_array_element = types.index('array')
             numeric_elms = [i for i, x in enumerate(types)
                             if x == "numeric"]
@@ -1272,6 +1279,10 @@ def get_parameter_data(conn: ConnectionPlus,
                                                 row[element],
                                                 dtype=np.float)
                     # todo should we handle int/float types here
+                    # we would in practice have to perform another
+                    # loop to check that all elements of a given can be cast to
+                    # int without loosing precision before choosing an interger
+                    # representation of the array
                 for element in text_elms:
                     strlen = len(row[element])
                     row[element] = np.full_like(row[first_array_element],
@@ -1280,7 +1291,6 @@ def get_parameter_data(conn: ConnectionPlus,
 
         # Benchmarking shows that transposing the data with python types is
         # faster than transposing the data using np.array.transpose
-        # This method is going to form the entry point for a compiled version
         res_t = list(map(list, zip(*res)))
         output[output_param] = {name: np.squeeze(np.array(column_data))
                          for name, column_data
@@ -1536,7 +1546,7 @@ def get_non_dependencies(conn: ConnectionPlus,
         run_id: The run_id of the run in question
 
     Returns:
-
+        A list of the parameter names.
     """
     parameters = get_parameters(conn, run_id)
     maybe_independent = []
@@ -1562,14 +1572,17 @@ def get_non_dependencies(conn: ConnectionPlus,
 def get_parameter_dependencies(conn: ConnectionPlus, param: str,
                               run_id: int) -> List[ParamSpec]:
     """
-    Given a parameter name return the ParamSpec of the parameter along with
-    the ParamSpecs of all it's dependencies.
+    Given a parameter name return a list of ParamSpecs where the first
+    element is the ParamSpec of the given parameter and the rest of the
+    elements are ParamSpecs of its dependencies.
 
     Args:
-        conn:
-        param:
-        run_id:
+        conn: connection to the database
+        param: the name of the parameter to look up
+        run_id: run_id: The run_id of the run in question
 
+    Returns:
+        List of ParameterSpecs of the parameter followed by its dependencies.
     """
     layout_id = get_layout_id(conn, param, run_id)
     deps = get_dependencies(conn, layout_id)

From 96fcae00cdc362e6ea9910181559141a058d69fa Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 13:45:29 +0100
Subject: [PATCH 378/719] ensure thet the expeced results do no miss an element

---
 qcodes/tests/dataset/helper_functions.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index 87b68c1c4a2..16a80ce107b 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -13,12 +13,15 @@ def verify_data_dict(data: Dict[str, Dict[str, np.ndarray]],
     Args:
         data: The data to verify
         parameter_names: names of the parameters requested
-        expected_names: names of the paramerters expected to be loaded
-        expected_shapes: shapes of the paramters loaded
+        expected_names: names of the parameters expected to be loaded
+        expected_shapes: shapes of the parameters loaded
 
     Returns:
 
     """
+    # check that all the expected parameters in the dict are
+    # included in the list of parameters
+    assert all(param in parameter_names for param in list(data.keys())) is True
     for param in parameter_names:
         innerdata = data[param]
         verify_data_dict_for_single_param(innerdata,

From 75b85c7b3b933f07d8e64e4e8c1e12e9bde7cbd4 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 14:03:46 +0100
Subject: [PATCH 379/719] Test removing one element

---
 qcodes/tests/dataset/test_dataset_basic.py | 23 ++++++++++++++++++++--
 1 file changed, 21 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 240ee680440..f15132fb645 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -1,6 +1,7 @@
 import itertools
 from copy import copy
 import re
+import random
 
 import pytest
 import numpy as np
@@ -891,12 +892,30 @@ def test_get_parameter_data_independent_parameters(standalone_parameters_dataset
                           expected_shapes)
 
 
-def parameter_test_helper(ds, toplevel_names, expected_names, expected_shapes):
+def parameter_test_helper(ds, toplevel_names,
+                          expected_names,
+                          expected_shapes):
     data = ds.get_parameter_data(*toplevel_names)
     all_data = ds.get_parameter_data()
 
-    assert data.keys() == all_data.keys()
+    all_parameters = list(all_data.keys())
+    assert set(data.keys()).issubset(set(all_parameters))
     assert len(data.keys()) == len(toplevel_names)
 
     verify_data_dict(data, toplevel_names, expected_names, expected_shapes)
     verify_data_dict(all_data, toplevel_names, expected_names, expected_shapes)
+
+    # now lets remove a random element from the list
+    # and test that we don't get data back for that element.
+    # This only makes sense if there originally where more than
+    # one parameter in the list
+    if len(all_parameters) > 1:
+        elem_to_remove = random.randint(0, len(toplevel_names) - 1)
+        subset_names = copy(all_parameters)
+        name_removed = subset_names.pop(elem_to_remove)
+        expected_names.pop(name_removed)
+        expected_shapes.pop(name_removed)
+
+        subset_data = ds.get_parameter_data(*subset_names)
+        verify_data_dict(subset_data, subset_names, expected_names,
+                         expected_shapes)

From d7f7fb7f790c81fbf86152030316365ba5f70600 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 14:09:03 +0100
Subject: [PATCH 380/719] Continue removing until there is only one element

---
 qcodes/tests/dataset/test_dataset_basic.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index f15132fb645..8b30f1aeafa 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -905,13 +905,11 @@ def parameter_test_helper(ds, toplevel_names,
     verify_data_dict(data, toplevel_names, expected_names, expected_shapes)
     verify_data_dict(all_data, toplevel_names, expected_names, expected_shapes)
 
-    # now lets remove a random element from the list
-    # and test that we don't get data back for that element.
-    # This only makes sense if there originally where more than
-    # one parameter in the list
-    if len(all_parameters) > 1:
-        elem_to_remove = random.randint(0, len(toplevel_names) - 1)
-        subset_names = copy(all_parameters)
+    # Now lets remove a random element from the list
+    # We do this one by one until there is only one element in the list
+    subset_names = copy(all_parameters)
+    while len(subset_names) > 1:
+        elem_to_remove = random.randint(0, len(subset_names) - 1)
         name_removed = subset_names.pop(elem_to_remove)
         expected_names.pop(name_removed)
         expected_shapes.pop(name_removed)

From 442c4bfc5743035e4249c898697aea46975bb88a Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 14:23:49 +0100
Subject: [PATCH 381/719] Test content of data arrays

---
 qcodes/tests/dataset/helper_functions.py | 28 ++++++++++++++----------
 1 file changed, 17 insertions(+), 11 deletions(-)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index 16a80ce107b..8fd37dbdc7c 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -1,22 +1,25 @@
 from typing import Sequence, Tuple, Dict
 
 import numpy as np
-
+from numpy.testing import assert_allclose
 
 def verify_data_dict(data: Dict[str, Dict[str, np.ndarray]],
                      parameter_names: Sequence[str],
                      expected_names: Dict[str, Sequence[str]],
-                     expected_shapes: Dict[str, Sequence[Tuple[int, ...]]]) -> None:
+                     expected_shapes: Dict[str, Sequence[Tuple[int, ...]]],
+                     expected_values: Dict[str, Sequence[np.ndarray]]) -> None:
     """
     Simple helper function to verify a dict of data
 
     Args:
-        data: The data to verify
-        parameter_names: names of the parameters requested
-        expected_names: names of the parameters expected to be loaded
-        expected_shapes: shapes of the parameters loaded
-
-    Returns:
+        data: The dict data to verify the shape and content of.
+        parameter_names: names of the parameters loaded as top level
+            keys in the dict.
+        expected_names: names of the parameters expected as keys in the second
+            level.
+        expected_shapes: expected shapes of the parameters loaded in the values
+            of the dict.
+        expected_values: expected content of the data arrays.
 
     """
     # check that all the expected parameters in the dict are
@@ -26,11 +29,14 @@ def verify_data_dict(data: Dict[str, Dict[str, np.ndarray]],
         innerdata = data[param]
         verify_data_dict_for_single_param(innerdata,
                                           expected_names[param],
-                                          expected_shapes[param])
+                                          expected_shapes[param],
+                                          expected_values[param])
 
 
 def verify_data_dict_for_single_param(datadict: Dict[str, np.ndarray],
                                       names: Sequence[str],
-                                      shapes: Sequence[Tuple[int, ...]]):
-    for name, shape in zip(names, shapes):
+                                      shapes: Sequence[Tuple[int, ...]],
+                                      values):
+    for name, shape, value in zip(names, shapes, values):
         assert datadict[name].shape == shape
+        np.testing.assert_allclose(datadict[name], value)

From 5719317a3869c870f313181aaf45bcd08eca3e11 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 14:24:21 +0100
Subject: [PATCH 382/719] Convert testing of scalar data

---
 qcodes/tests/dataset/dataset_fixtures.py   |  5 +++--
 qcodes/tests/dataset/test_dataset_basic.py | 19 +++++++++++++------
 2 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
index bd1a749f8f2..d7c64eaa00c 100644
--- a/qcodes/tests/dataset/dataset_fixtures.py
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -27,8 +27,9 @@ def scalar_dataset(dataset):
                                        depends_on=params_indep)]
     for p in params:
         dataset.add_parameter(p)
-    dataset.add_results([{p.name: np.random.rand(1)[0] for p in params}
-                         for _ in range(n_rows)])
+    dataset.add_results([{p.name: np.int(n_rows*10*pn+i)
+                          for pn, p in enumerate(params)}
+                         for i in range(n_rows)])
     dataset.mark_complete()
     yield dataset
 
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 8b30f1aeafa..474520d4d93 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -777,7 +777,6 @@ def test_get_data_with_start_and_end_args(self, ds_with_vals,
 
 
 def test_get_parameter_data(scalar_dataset):
-    ds = scalar_dataset
     input_names = ['param_3']
 
     expected_names = {}
@@ -785,11 +784,15 @@ def test_get_parameter_data(scalar_dataset):
                                  'param_3']
     expected_shapes = {}
     expected_shapes['param_3'] = [(10**3, )]*4
+    expected_values = {}
+    expected_values['param_3'] = [np.arange(10000*a, 10000*a+1000)
+                                  for a in range(4)]
 
     parameter_test_helper(scalar_dataset,
                           input_names,
                           expected_names,
-                          expected_shapes)
+                          expected_shapes,
+                          expected_values)
 
 
 def test_get_array_parameter_data(array_dataset):
@@ -894,7 +897,8 @@ def test_get_parameter_data_independent_parameters(standalone_parameters_dataset
 
 def parameter_test_helper(ds, toplevel_names,
                           expected_names,
-                          expected_shapes):
+                          expected_shapes,
+                          expected_values):
     data = ds.get_parameter_data(*toplevel_names)
     all_data = ds.get_parameter_data()
 
@@ -902,8 +906,10 @@ def parameter_test_helper(ds, toplevel_names,
     assert set(data.keys()).issubset(set(all_parameters))
     assert len(data.keys()) == len(toplevel_names)
 
-    verify_data_dict(data, toplevel_names, expected_names, expected_shapes)
-    verify_data_dict(all_data, toplevel_names, expected_names, expected_shapes)
+    verify_data_dict(data, toplevel_names, expected_names, expected_shapes,
+                     expected_values)
+    verify_data_dict(all_data, toplevel_names, expected_names, expected_shapes,
+                     expected_values)
 
     # Now lets remove a random element from the list
     # We do this one by one until there is only one element in the list
@@ -913,7 +919,8 @@ def parameter_test_helper(ds, toplevel_names,
         name_removed = subset_names.pop(elem_to_remove)
         expected_names.pop(name_removed)
         expected_shapes.pop(name_removed)
+        expected_values.pop(name_removed)
 
         subset_data = ds.get_parameter_data(*subset_names)
         verify_data_dict(subset_data, subset_names, expected_names,
-                         expected_shapes)
+                         expected_shapes, expected_values)

From e91387a1fa1401088bb55bf42f12f0ffbe0a388a Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 14:45:04 +0100
Subject: [PATCH 383/719] add shapes to 2 more tests

---
 qcodes/tests/dataset/test_dataset_basic.py | 39 +++++++++++++++++++---
 1 file changed, 34 insertions(+), 5 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 474520d4d93..aa46df7c995 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -802,12 +802,17 @@ def test_get_array_parameter_data(array_dataset):
     expected_names = {}
     expected_names['testparameter'] = ['testparameter', 'this_setpoint']
     expected_shapes = {}
-    expected_shapes['testparameter'] = [(5, ), (5, )]
+    expected_len = 5
+    expected_shapes['testparameter'] = [(expected_len, ), (expected_len, )]
+    expected_values = {}
+    expected_values['testparameter'] = [np.ones(expected_len) + 1,
+                                        np.linspace(5, 9, expected_len)]
 
     parameter_test_helper(array_dataset,
                           input_names,
                           expected_names,
-                          expected_shapes)
+                          expected_shapes,
+                          expected_values)
 
 
 def test_get_multi_parameter_data(multi_dataset):
@@ -820,16 +825,39 @@ def test_get_multi_parameter_data(multi_dataset):
     expected_names['this'] = ['this', 'this_setpoint', 'that_setpoint']
     expected_names['that'] = ['that', 'this_setpoint', 'that_setpoint']
     expected_shapes = {}
+    expected_values = {}
+    shape_1 = 5
+    shape_2 = 3
+
+    this_data = np.zeros((shape_1, shape_2))
+    that_data = np.ones((shape_1, shape_2))
+    sp_1_data = np.tile(np.linspace(5, 9, shape_1).reshape(shape_1, 1),
+                                           (1, shape_2))
+    sp_2_data = np.tile(np.linspace(9, 11, shape_2), (shape_1, 1))
     if 'array' in types:
-        expected_shapes['this'] = [(5, 3), (5, 3)]
-        expected_shapes['that'] = [(5, 3), (5, 3)]
+        expected_shapes['this'] = [(shape_1, shape_2), (shape_1, shape_2)]
+        expected_shapes['that'] = [(shape_1, shape_2), (shape_1, shape_2)]
+        expected_values['this'] = [this_data,
+                                   sp_1_data,
+                                   sp_2_data]
+        expected_values['that'] = [that_data,
+                                   sp_1_data,
+                                   sp_2_data]
+
     else:
         expected_shapes['this'] = [(15,), (15,)]
         expected_shapes['that'] = [(15,), (15,)]
+        expected_values['this'] = [this_data.ravel(),
+                                   sp_1_data.ravel(),
+                                   sp_2_data.ravel()]
+        expected_values['that'] = [that_data.ravel(),
+                                   sp_1_data.ravel(),
+                                   sp_2_data.ravel()]
     parameter_test_helper(multi_dataset,
                           input_names,
                           expected_names,
-                          expected_shapes)
+                          expected_shapes,
+                          expected_values)
 
 
 def test_get_array_in_scalar_param_data(array_in_scalar_dataset):
@@ -899,6 +927,7 @@ def parameter_test_helper(ds, toplevel_names,
                           expected_names,
                           expected_shapes,
                           expected_values):
+
     data = ds.get_parameter_data(*toplevel_names)
     all_data = ds.get_parameter_data()
 

From 25c0bcdbd0318fb97c8686b10136933b90f763fb Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 15:41:29 +0100
Subject: [PATCH 384/719] update one more test

---
 qcodes/tests/dataset/test_dataset_basic.py | 23 +++++++++++++++++++++-
 1 file changed, 22 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index aa46df7c995..fe74f216250 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -870,14 +870,35 @@ def test_get_array_in_scalar_param_data(array_in_scalar_dataset):
     expected_names['testparameter'] = ['testparameter', 'scalarparam',
                                        'this_setpoint']
     expected_shapes = {}
+
+    shape_1 = 9
+    shape_2 = 5
+
+    test_parameter_values = np.tile((np.ones(shape_2) + 1).reshape(1, shape_2),
+                                    (shape_1, 1))
+    scalar_param_values = np.tile(np.arange(1, 10).reshape(shape_1, 1),
+                                  (1, shape_2))
+    setpoint_param_values = np.tile((np.linspace(5, 9, shape_2)).reshape(1, shape_2),
+                                    (shape_1, 1))
+    expected_shapes['testparameter'] = {}
+    expected_values = {}
     if 'array' in types:
         expected_shapes['testparameter'] = [(9, 5), (9, 5)]
+        expected_values['testparameter'] = [
+            test_parameter_values,
+            scalar_param_values,
+            setpoint_param_values]
     else:
         expected_shapes['testparameter'] = [(45,), (45,)]
+        expected_values['testparameter'] = [
+            test_parameter_values.ravel(),
+            scalar_param_values.ravel(),
+            setpoint_param_values.ravel()]
     parameter_test_helper(array_in_scalar_dataset,
                           input_names,
                           expected_names,
-                          expected_shapes)
+                          expected_shapes,
+                          expected_values)
 
 
 def test_get_array_in_str_param_data(array_in_str_dataset):

From f15f6abaae15cfc159f91ddb38e80b5506d97f95 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 16:08:28 +0100
Subject: [PATCH 385/719] Add expected results to last 2 tests

---
 qcodes/tests/dataset/test_dataset_basic.py | 35 ++++++++++++++++++++--
 1 file changed, 32 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index fe74f216250..8084b680737 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -911,14 +911,36 @@ def test_get_array_in_str_param_data(array_in_str_dataset):
     expected_names['testparameter'] = ['testparameter', 'textparam',
                                        'this_setpoint']
     expected_shapes = {}
+
+    shape_1 = 3
+    shape_2 = 5
+
+    test_parameter_values = np.tile((np.ones(shape_2) + 1).reshape(1, shape_2),
+                                    (shape_1, 1))
+    scalar_param_values = np.tile(np.array(['A', 'B', 'C']).reshape(shape_1, 1),
+                                  (1, shape_2))
+    setpoint_param_values = np.tile((np.linspace(5, 9, shape_2)).reshape(1, shape_2),
+                                    (shape_1, 1))
+    expected_shapes['testparameter'] = {}
+    expected_values = {}
+
     if 'array' in types:
         expected_shapes['testparameter'] = [(3, 5), (3, 5)]
+        expected_values['testparameter'] = [
+            test_parameter_values,
+            scalar_param_values,
+            setpoint_param_values]
     else:
         expected_shapes['testparameter'] = [(15,), (15,)]
+        expected_values['testparameter'] = [
+            test_parameter_values.ravel(),
+            scalar_param_values.ravel(),
+            setpoint_param_values.ravel()]
     parameter_test_helper(array_in_str_dataset,
                           input_names,
                           expected_names,
-                          expected_shapes)
+                          expected_shapes,
+                          expected_values)
 
 
 def test_get_parameter_data_independent_parameters(standalone_parameters_dataset):
@@ -931,17 +953,24 @@ def test_get_parameter_data_independent_parameters(standalone_parameters_dataset
     expected_names = {}
     expected_names['param_1'] = []
     expected_names['param_2'] = []
-    expected_names['param_3'] = ['param_0']
+    expected_names['param_3'] = ['param_3', 'param_0']
 
     expected_shapes = {}
     expected_shapes['param_1'] = [(10 ** 3,)]
     expected_shapes['param_2'] = [(10 ** 3,)]
     expected_shapes['param_3'] = [(10**3, )]*2
 
+    expected_values = {}
+    expected_values['param_1'] = [np.arange(10000, 10000 + 1000)]
+    expected_values['param_2'] = [np.arange(20000, 20000 + 1000)]
+    expected_values['param_3'] = [np.arange(30000, 30000 + 1000),
+                                  np.arange(0, 1000)]
+
     parameter_test_helper(ds,
                           expected_toplevel_params,
                           expected_names,
-                          expected_shapes)
+                          expected_shapes,
+                          expected_values)
 
 
 def parameter_test_helper(ds, toplevel_names,

From d1d06f50f17d72362d6cc15e694e13783746de71 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 16:22:09 +0100
Subject: [PATCH 386/719] Add missing data

---
 qcodes/tests/dataset/test_dataset_basic.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 8084b680737..254914ceb51 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -951,8 +951,8 @@ def test_get_parameter_data_independent_parameters(standalone_parameters_dataset
     assert params == expected_toplevel_params
 
     expected_names = {}
-    expected_names['param_1'] = []
-    expected_names['param_2'] = []
+    expected_names['param_1'] = ['param_1']
+    expected_names['param_2'] = ['param_2']
     expected_names['param_3'] = ['param_3', 'param_0']
 
     expected_shapes = {}

From ba504db70c62ac75a1f2f98f935f90b20b90286d Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 16:26:10 +0100
Subject: [PATCH 387/719] No need to use close. This should match exactly.

That also works for strings.
---
 qcodes/tests/dataset/helper_functions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index 8fd37dbdc7c..80e0b0c11bf 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -39,4 +39,4 @@ def verify_data_dict_for_single_param(datadict: Dict[str, np.ndarray],
                                       values):
     for name, shape, value in zip(names, shapes, values):
         assert datadict[name].shape == shape
-        np.testing.assert_allclose(datadict[name], value)
+        np.testing.assert_array_equal(datadict[name], value)

From 15a837748d1d840e3307d936f99b9302c40aaaf1 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 16:26:22 +0100
Subject: [PATCH 388/719] Add an extra assert

---
 qcodes/tests/dataset/helper_functions.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index 80e0b0c11bf..1548963df0e 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -37,6 +37,8 @@ def verify_data_dict_for_single_param(datadict: Dict[str, np.ndarray],
                                       names: Sequence[str],
                                       shapes: Sequence[Tuple[int, ...]],
                                       values):
+    # check that there are no unexpected elements in the dict
+    assert all(param in names for param in list(datadict.keys())) is True
     for name, shape, value in zip(names, shapes, values):
         assert datadict[name].shape == shape
         np.testing.assert_array_equal(datadict[name], value)

From 7ecb914ad4e9584abe7746ab42b5ac13513fe237 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 16:50:07 +0100
Subject: [PATCH 389/719] Update fixtures to match expected result

---
 qcodes/tests/dataset/dataset_fixtures.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
index d7c64eaa00c..a58365bf52a 100644
--- a/qcodes/tests/dataset/dataset_fixtures.py
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -124,7 +124,8 @@ def standalone_parameters_dataset(dataset):
                                        depends_on=params_indep[0:1])]
     for p in params:
         dataset.add_parameter(p)
-    dataset.add_results([{p.name: np.random.rand(1)[0] for p in params}
-                         for _ in range(n_rows)])
+    dataset.add_results([{p.name: np.int(n_rows*10*pn+i)
+                          for pn, p in enumerate(params)}
+                         for i in range(n_rows)])
     dataset.mark_complete()
     yield dataset

From 4338a1fc792603a1f6c60090b5f87955c4fde945 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 10 Dec 2018 18:40:46 +0100
Subject: [PATCH 390/719] update sqlite tests

---
 qcodes/tests/dataset/test_sqlite_base.py | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index fc408c7ef51..93ec6dc0d96 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -215,7 +215,11 @@ def test_get_parameter_data(scalar_dataset):
     expected_shapes = {}
     expected_shapes['param_3'] = [(10**3, )]*4
 
-    verify_data_dict(data, input_names, expected_names, expected_shapes)
+    expected_values = {}
+    expected_values['param_3'] = [np.arange(10000*a, 10000*a+1000)
+                                  for a in range(4)]
+    verify_data_dict(data, input_names, expected_names, expected_shapes,
+                     expected_values)
 
 
 def test_get_parameter_data_independent_parameters(standalone_parameters_dataset):
@@ -230,17 +234,23 @@ def test_get_parameter_data_independent_parameters(standalone_parameters_dataset
     assert len(data.keys()) == len(expected_toplevel_params)
 
     expected_names = {}
-    expected_names['param_1'] = []
-    expected_names['param_2'] = []
-    expected_names['param_3'] = ['param_0']
+    expected_names['param_1'] = ['param_1']
+    expected_names['param_2'] = ['param_2']
+    expected_names['param_3'] = ['param_3', 'param_0']
 
     expected_shapes = {}
     expected_shapes['param_1'] = [(10 ** 3,)]
     expected_shapes['param_2'] = [(10 ** 3,)]
     expected_shapes['param_3'] = [(10**3, )]*2
 
+    expected_values = {}
+    expected_values['param_1'] = [np.arange(10000, 10000 + 1000)]
+    expected_values['param_2'] = [np.arange(20000, 20000 + 1000)]
+    expected_values['param_3'] = [np.arange(30000, 30000 + 1000),
+                                  np.arange(0, 1000)]
+
     verify_data_dict(data, expected_toplevel_params, expected_names,
-                     expected_shapes)
+                     expected_shapes, expected_values)
 
 
 def test_is_run_id_in_db(empty_temp_db):

From a9a9739d9fe05d3fc0fb5bf2e4f6fef3d4bbb2c8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 11 Dec 2018 10:51:55 +0100
Subject: [PATCH 391/719] Cleanup import

---
 qcodes/tests/dataset/helper_functions.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index 1548963df0e..e74a4b84142 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -1,7 +1,7 @@
 from typing import Sequence, Tuple, Dict
 
 import numpy as np
-from numpy.testing import assert_allclose
+from numpy.testing import assert_array_equal
 
 def verify_data_dict(data: Dict[str, Dict[str, np.ndarray]],
                      parameter_names: Sequence[str],
@@ -41,4 +41,4 @@ def verify_data_dict_for_single_param(datadict: Dict[str, np.ndarray],
     assert all(param in names for param in list(datadict.keys())) is True
     for name, shape, value in zip(names, shapes, values):
         assert datadict[name].shape == shape
-        np.testing.assert_array_equal(datadict[name], value)
+        assert_array_equal(datadict[name], value)

From 35781210108c94b9b338fbb9f55b58a7cdc6e331 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 11 Dec 2018 11:20:40 +0100
Subject: [PATCH 392/719] typo

---
 qcodes/dataset/sqlite_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 6e1f2e6beeb..6a9643524c5 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1281,7 +1281,7 @@ def get_parameter_data(conn: ConnectionPlus,
                     # todo should we handle int/float types here
                     # we would in practice have to perform another
                     # loop to check that all elements of a given can be cast to
-                    # int without loosing precision before choosing an interger
+                    # int without loosing precision before choosing an integer
                     # representation of the array
                 for element in text_elms:
                     strlen = len(row[element])

From e2c9298aa6f4d07af58bc65f9c17cb5df39e0a04 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 11 Dec 2018 16:16:56 +0100
Subject: [PATCH 393/719] build feature branch

---
 .travis.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.travis.yml b/.travis.yml
index 55c548856f8..685b50a574e 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -17,6 +17,7 @@ python:
 branches:
   only:
     - master
+    - DataStorageInterface
 
 # command to install dependencies and qcodes
 # We want to fail early if there is an installation problem, so

From 17f311642af5ea18f6f5840eaa55ae90d6030429 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 12 Dec 2018 14:02:17 +0100
Subject: [PATCH 394/719] Ensure that we still get an array in the case where
 we request exactly one result

---
 qcodes/dataset/sqlite_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 6a9643524c5..71215dbcce1 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1292,7 +1292,7 @@ def get_parameter_data(conn: ConnectionPlus,
         # Benchmarking shows that transposing the data with python types is
         # faster than transposing the data using np.array.transpose
         res_t = list(map(list, zip(*res)))
-        output[output_param] = {name: np.squeeze(np.array(column_data))
+        output[output_param] = {name: np.atleast_1d(np.squeeze(np.array(column_data)))
                          for name, column_data
                          in zip(param_names, res_t)}
     return output

From c9433591c438d0c1722f70474c439c6c87e6e939 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 12 Dec 2018 14:17:07 +0100
Subject: [PATCH 395/719] Start testing start and end

---
 qcodes/tests/dataset/test_dataset_basic.py | 50 +++++++++++++++++++---
 1 file changed, 44 insertions(+), 6 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 254914ceb51..d00eadb2ecf 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -776,7 +776,9 @@ def test_get_data_with_start_and_end_args(self, ds_with_vals,
         assert expected == ds_with_vals.get_data(self.x, start=start, end=end)
 
 
-def test_get_parameter_data(scalar_dataset):
+@given(start=hst.one_of(hst.integers(1, 10**3), hst.none()),
+       end=hst.one_of(hst.integers(1, 10**3), hst.none()))
+def test_get_parameter_data(scalar_dataset, start, end):
     input_names = ['param_3']
 
     expected_names = {}
@@ -788,11 +790,17 @@ def test_get_parameter_data(scalar_dataset):
     expected_values['param_3'] = [np.arange(10000*a, 10000*a+1000)
                                   for a in range(4)]
 
+    start, end = limit_data_to_start_end(start, end, input_names,
+                                         expected_names, expected_shapes,
+                                         expected_values)
+
     parameter_test_helper(scalar_dataset,
                           input_names,
                           expected_names,
                           expected_shapes,
-                          expected_values)
+                          expected_values,
+                          start,
+                          end)
 
 
 def test_get_array_parameter_data(array_dataset):
@@ -976,10 +984,12 @@ def test_get_parameter_data_independent_parameters(standalone_parameters_dataset
 def parameter_test_helper(ds, toplevel_names,
                           expected_names,
                           expected_shapes,
-                          expected_values):
+                          expected_values,
+                          start=None,
+                          end=None):
 
-    data = ds.get_parameter_data(*toplevel_names)
-    all_data = ds.get_parameter_data()
+    data = ds.get_parameter_data(*toplevel_names, start=start, end=end)
+    all_data = ds.get_parameter_data(start=start, end=end)
 
     all_parameters = list(all_data.keys())
     assert set(data.keys()).issubset(set(all_parameters))
@@ -1000,6 +1010,34 @@ def parameter_test_helper(ds, toplevel_names,
         expected_shapes.pop(name_removed)
         expected_values.pop(name_removed)
 
-        subset_data = ds.get_parameter_data(*subset_names)
+        subset_data = ds.get_parameter_data(*subset_names,
+                                            start=start, end=end)
         verify_data_dict(subset_data, subset_names, expected_names,
                          expected_shapes, expected_values)
+
+
+def limit_data_to_start_end(start, end, input_names, expected_names,
+                            expected_shapes, expected_values):
+    if not (start is None and end is None):
+        if start is None:
+            start = 1
+        elif end is None:
+            # all the shapes are the same so pick the first one
+            end = expected_shapes[input_names[0]][0][0]
+        if end < start:
+            for name in input_names:
+                expected_names[name] = []
+                expected_shapes[name] = ()
+                expected_values[name] = {}
+        else:
+            for name in input_names:
+                new_shapes = []
+                for shape in expected_shapes[name]:
+                    shape_list = list(shape)
+                    shape_list[0] = end - start + 1
+                    new_shapes.append(tuple(shape_list))
+                expected_shapes[name] = new_shapes
+                for i in range(len(expected_values[name])):
+                    expected_values[name][i] = \
+                        expected_values[name][i][start - 1:end]
+    return start, end

From 9ad90bf79b0fe6c9e7e43ea68e24acf5cb7aad04 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 12 Dec 2018 15:44:52 +0100
Subject: [PATCH 396/719] Don't squeeze singleton dimension. This leads to
 inconsistent results

---
 qcodes/dataset/sqlite_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 71215dbcce1..9c18f02f236 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -1292,7 +1292,7 @@ def get_parameter_data(conn: ConnectionPlus,
         # Benchmarking shows that transposing the data with python types is
         # faster than transposing the data using np.array.transpose
         res_t = list(map(list, zip(*res)))
-        output[output_param] = {name: np.atleast_1d(np.squeeze(np.array(column_data)))
+        output[output_param] = {name: np.array(column_data)
                          for name, column_data
                          in zip(param_names, res_t)}
     return output

From ea8444cebb89af59bcabddd07bf768fe778450cb Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 12 Dec 2018 15:47:06 +0100
Subject: [PATCH 397/719] Update tests to reflect changes in dims

---
 qcodes/tests/dataset/test_dataset_basic.py | 27 +++++++++++++---------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index d00eadb2ecf..8e227fe869f 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -804,18 +804,23 @@ def test_get_parameter_data(scalar_dataset, start, end):
 
 
 def test_get_array_parameter_data(array_dataset):
-
+    paramspecs = array_dataset.paramspecs
+    types = [param.type for param in paramspecs.values()]
     input_names = ['testparameter']
 
     expected_names = {}
     expected_names['testparameter'] = ['testparameter', 'this_setpoint']
     expected_shapes = {}
     expected_len = 5
-    expected_shapes['testparameter'] = [(expected_len, ), (expected_len, )]
+    expected_shapes['testparameter'] = [(expected_len,), (expected_len,)]
     expected_values = {}
     expected_values['testparameter'] = [np.ones(expected_len) + 1,
                                         np.linspace(5, 9, expected_len)]
-
+    if 'array' in types:
+        expected_shapes['testparameter'] = [(1, expected_len),
+                                            (1, expected_len)]
+        for i in range(len(expected_values['testparameter'])):
+            expected_values['testparameter'][i] = expected_values['testparameter'][i].reshape(1, expected_len)
     parameter_test_helper(array_dataset,
                           input_names,
                           expected_names,
@@ -843,14 +848,14 @@ def test_get_multi_parameter_data(multi_dataset):
                                            (1, shape_2))
     sp_2_data = np.tile(np.linspace(9, 11, shape_2), (shape_1, 1))
     if 'array' in types:
-        expected_shapes['this'] = [(shape_1, shape_2), (shape_1, shape_2)]
-        expected_shapes['that'] = [(shape_1, shape_2), (shape_1, shape_2)]
-        expected_values['this'] = [this_data,
-                                   sp_1_data,
-                                   sp_2_data]
-        expected_values['that'] = [that_data,
-                                   sp_1_data,
-                                   sp_2_data]
+        expected_shapes['this'] = [(1, shape_1, shape_2), (1, shape_1, shape_2)]
+        expected_shapes['that'] = [(1, shape_1, shape_2), (1, shape_1, shape_2)]
+        expected_values['this'] = [this_data.reshape(1, shape_1, shape_2),
+                                   sp_1_data.reshape(1, shape_1, shape_2),
+                                   sp_2_data.reshape(1, shape_1, shape_2)]
+        expected_values['that'] = [that_data.reshape(1, shape_1, shape_2),
+                                   sp_1_data.reshape(1, shape_1, shape_2),
+                                   sp_2_data.reshape(1, shape_1, shape_2)]
 
     else:
         expected_shapes['this'] = [(15,), (15,)]

From 150f31a495b521ae9f7b579a4ad726ff423880a1 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 12 Dec 2018 16:15:05 +0100
Subject: [PATCH 398/719] Split test to allow hypothesis to generate the
 correct start and end values

---
 qcodes/tests/dataset/dataset_fixtures.py   | 27 ++++++--
 qcodes/tests/dataset/test_dataset_basic.py | 75 ++++++++++++++++------
 2 files changed, 80 insertions(+), 22 deletions(-)

diff --git a/qcodes/tests/dataset/dataset_fixtures.py b/qcodes/tests/dataset/dataset_fixtures.py
index a58365bf52a..7cdcfbffcf2 100644
--- a/qcodes/tests/dataset/dataset_fixtures.py
+++ b/qcodes/tests/dataset/dataset_fixtures.py
@@ -65,15 +65,34 @@ def multi_dataset(experiment, request):
         datasaver.dataset.conn.close()
 
 
-@pytest.fixture(scope="function",
-                params=["array", "numeric"])
-def array_in_scalar_dataset(experiment, request):
+@pytest.fixture(scope="function")
+def array_in_scalar_dataset(experiment):
     meas = Measurement()
     scalar_param = Parameter('scalarparam', set_cmd=None)
     param = ArraySetPointParam()
     meas.register_parameter(scalar_param)
     meas.register_parameter(param, setpoints=(scalar_param,),
-                            paramtype=request.param)
+                            paramtype='array')
+
+    with meas.run() as datasaver:
+        for i in range(1, 10):
+            scalar_param.set(i)
+            datasaver.add_result((scalar_param, scalar_param.get()),
+                                 (param, param.get()))
+    try:
+        yield datasaver.dataset
+    finally:
+        datasaver.dataset.conn.close()
+
+
+@pytest.fixture(scope="function")
+def array_in_scalar_dataset_unrolled(experiment):
+    meas = Measurement()
+    scalar_param = Parameter('scalarparam', set_cmd=None)
+    param = ArraySetPointParam()
+    meas.register_parameter(scalar_param)
+    meas.register_parameter(param, setpoints=(scalar_param,),
+                            paramtype='numeric')
 
     with meas.run() as datasaver:
         for i in range(1, 10):
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 8e227fe869f..100753be42d 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -25,7 +25,7 @@
                                                       experiment, dataset)
 from qcodes.tests.dataset.dataset_fixtures import scalar_dataset, \
     array_dataset, multi_dataset, array_in_scalar_dataset, array_in_str_dataset, \
-    standalone_parameters_dataset
+    standalone_parameters_dataset, array_in_scalar_dataset_unrolled
 # pylint: disable=unused-import
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 
@@ -873,10 +873,10 @@ def test_get_multi_parameter_data(multi_dataset):
                           expected_values)
 
 
-def test_get_array_in_scalar_param_data(array_in_scalar_dataset):
-    paramspecs = array_in_scalar_dataset.paramspecs
-    types = [param.type for param in paramspecs.values()]
-
+@given(start=hst.one_of(hst.integers(1, 9), hst.none()),
+       end=hst.one_of(hst.integers(1, 9), hst.none()))
+def test_get_array_in_scalar_param_data(array_in_scalar_dataset,
+                                        start, end):
     input_names = ['testparameter']
 
     expected_names = {}
@@ -894,24 +894,63 @@ def test_get_array_in_scalar_param_data(array_in_scalar_dataset):
     setpoint_param_values = np.tile((np.linspace(5, 9, shape_2)).reshape(1, shape_2),
                                     (shape_1, 1))
     expected_shapes['testparameter'] = {}
+    expected_shapes['testparameter'] = [(9, 5), (9, 5)]
     expected_values = {}
-    if 'array' in types:
-        expected_shapes['testparameter'] = [(9, 5), (9, 5)]
-        expected_values['testparameter'] = [
-            test_parameter_values,
-            scalar_param_values,
-            setpoint_param_values]
-    else:
-        expected_shapes['testparameter'] = [(45,), (45,)]
-        expected_values['testparameter'] = [
-            test_parameter_values.ravel(),
-            scalar_param_values.ravel(),
-            setpoint_param_values.ravel()]
+    expected_values['testparameter'] = [
+        test_parameter_values,
+        scalar_param_values,
+        setpoint_param_values]
+
+    start, end = limit_data_to_start_end(start, end, input_names,
+                                         expected_names, expected_shapes,
+                                         expected_values)
     parameter_test_helper(array_in_scalar_dataset,
                           input_names,
                           expected_names,
                           expected_shapes,
-                          expected_values)
+                          expected_values,
+                          start,
+                          end)
+
+
+@given(start=hst.one_of(hst.integers(1, 45), hst.none()),
+       end=hst.one_of(hst.integers(1, 45), hst.none()))
+def test_get_array_in_scalar_param_unrolled(array_in_scalar_dataset_unrolled,
+                                            start, end):
+    input_names = ['testparameter']
+
+    expected_names = {}
+    expected_names['testparameter'] = ['testparameter', 'scalarparam',
+                                       'this_setpoint']
+    expected_shapes = {}
+
+    shape_1 = 9
+    shape_2 = 5
+
+    test_parameter_values = np.tile((np.ones(shape_2) + 1).reshape(1, shape_2),
+                                    (shape_1, 1))
+    scalar_param_values = np.tile(np.arange(1, 10).reshape(shape_1, 1),
+                                  (1, shape_2))
+    setpoint_param_values = np.tile((np.linspace(5, 9, shape_2)).reshape(1, shape_2),
+                                    (shape_1, 1))
+    expected_shapes['testparameter'] = {}
+    expected_shapes['testparameter'] = [(45,), (45,)]
+    expected_values = {}
+    expected_values['testparameter'] = [
+        test_parameter_values.ravel(),
+        scalar_param_values.ravel(),
+        setpoint_param_values.ravel()]
+
+    start, end = limit_data_to_start_end(start, end, input_names,
+                                         expected_names, expected_shapes,
+                                         expected_values)
+    parameter_test_helper(array_in_scalar_dataset_unrolled,
+                          input_names,
+                          expected_names,
+                          expected_shapes,
+                          expected_values,
+                          start,
+                          end)
 
 
 def test_get_array_in_str_param_data(array_in_str_dataset):

From 37901e106bf9fb8937e9f5619b2f234a69d665f5 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 13 Dec 2018 10:00:24 +0100
Subject: [PATCH 399/719] Remove some magic numbers

---
 qcodes/tests/dataset/test_dataset_basic.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 100753be42d..58e49386ec1 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -894,7 +894,7 @@ def test_get_array_in_scalar_param_data(array_in_scalar_dataset,
     setpoint_param_values = np.tile((np.linspace(5, 9, shape_2)).reshape(1, shape_2),
                                     (shape_1, 1))
     expected_shapes['testparameter'] = {}
-    expected_shapes['testparameter'] = [(9, 5), (9, 5)]
+    expected_shapes['testparameter'] = [(shape_1, shape_2), (shape_1, shape_2)]
     expected_values = {}
     expected_values['testparameter'] = [
         test_parameter_values,
@@ -934,7 +934,7 @@ def test_get_array_in_scalar_param_unrolled(array_in_scalar_dataset_unrolled,
     setpoint_param_values = np.tile((np.linspace(5, 9, shape_2)).reshape(1, shape_2),
                                     (shape_1, 1))
     expected_shapes['testparameter'] = {}
-    expected_shapes['testparameter'] = [(45,), (45,)]
+    expected_shapes['testparameter'] = [(shape_1*shape_2,), (shape_1*shape_2,)]
     expected_values = {}
     expected_values['testparameter'] = [
         test_parameter_values.ravel(),

From 3753a1591500d8a802ee4b2e69d9c936d2d44157 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 13 Dec 2018 20:31:00 +0100
Subject: [PATCH 400/719] Require websockets >=7.0

https://github.com/QCoDeS/Qcodes/pull/1407 broke support for older versions
---
 environment.yml | 2 +-
 setup.py        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/environment.yml b/environment.yml
index 9bd0a05354a..2aca03ca0bd 100644
--- a/environment.yml
+++ b/environment.yml
@@ -28,5 +28,5 @@ dependencies:
  - testpath>=0.4.2 # 0.4.1 is bad due to https://github.com/conda-forge/testpath-feedstock/issues/7
  - tqdm
  - pip:
-    - websockets>=3.2
+    - websockets>=7.0
     - broadbean>=0.9.1
diff --git a/setup.py b/setup.py
index a90ef6a89be..15cf520aff8 100644
--- a/setup.py
+++ b/setup.py
@@ -64,7 +64,7 @@ def readme():
           'numpy>=1.10',
           'pyvisa>=1.9.1',
           'h5py>=2.6',
-          'websockets>=3.2',
+          'websockets>=7.0',
           'jsonschema',
           'pyzmq',
           'wrapt',

From 3118de7da403322a797b1e23decda841d1a664a0 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Wed, 19 Dec 2018 17:05:14 -0800
Subject: [PATCH 401/719] added function to parse a curve file

---
 .../instrument_drivers/Lakeshore/Model_325.py | 56 ++++++++++++++++++-
 1 file changed, 55 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Lakeshore/Model_325.py b/qcodes/instrument_drivers/Lakeshore/Model_325.py
index 4b174c11951..58258a557aa 100644
--- a/qcodes/instrument_drivers/Lakeshore/Model_325.py
+++ b/qcodes/instrument_drivers/Lakeshore/Model_325.py
@@ -1,11 +1,65 @@
 import numpy as np
-from typing import cast
+from typing import cast, Union, List, Tuple, Iterable
+from itertools import takewhile, repeat
 
 from qcodes import VisaInstrument, InstrumentChannel, ChannelList
 from qcodes.utils.validators import Enum, Numbers
 from qcodes.instrument.group_parameter import GroupParameter, Group
 
 
+def read_curve_file(file_path: str) -> dict:
+    """
+    Read a curve file with extension *.330
+    The format of the file is as follows:
+
+    Item1: value
+    Item2: value
+    Item3: value
+
+    Header1   Header2   Header3
+    data11     data12    data13
+    data21     data22    data23
+    """
+
+    def split_data_line(
+            line: str,
+            parsers: Union[type, List]=str
+    ) -> List[str]:
+
+        if not isinstance(parsers, list):
+            parsers = repeat(parsers)
+
+        line_split = [i for i in line.split("  ") if i != ""]
+
+        return [
+            parser(i) for parser, i in zip(parsers, line_split)
+        ]
+
+    def strip(strings: Iterable[str]) -> Tuple:
+        return tuple(s.strip() for s in strings)
+
+    with open(file_path, "r") as curve_file:
+        file_content = curve_file.read()
+
+    lines = iter(file_content.split("\n"))
+    # Meta data lines contain a colon
+    metadata_lines = takewhile(lambda s: ":" in s, lines)
+    curve_data = dict([strip(line.split(":")) for line in metadata_lines])
+    # After meta data we have a data header
+    header_items = strip(split_data_line(next(lines)))
+    # After that we have the curve data
+    data = [
+        split_data_line(line, parsers=float)
+        for line in lines if line != ""
+    ]
+
+    curve_data["data"] = dict(
+        zip(header_items, zip(*data))
+    )
+
+    return curve_data
+
+
 class Model_325_Curve(InstrumentChannel):
 
     valid_sensor_units = ["mV", "V", "Ohm", "log Ohm"]

From f6ba46e002b054d436efed69111c90f740f41a73 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Wed, 19 Dec 2018 18:53:42 -0800
Subject: [PATCH 402/719] 1) add driver method to upload a curve from file 2)
 bug fix in "upload_curve": should be index - 1 when selecting the right curve

---
 .../instrument_drivers/Lakeshore/Model_325.py | 57 ++++++++++++++++---
 1 file changed, 49 insertions(+), 8 deletions(-)

diff --git a/qcodes/instrument_drivers/Lakeshore/Model_325.py b/qcodes/instrument_drivers/Lakeshore/Model_325.py
index 58258a557aa..a1bad1b5bbc 100644
--- a/qcodes/instrument_drivers/Lakeshore/Model_325.py
+++ b/qcodes/instrument_drivers/Lakeshore/Model_325.py
@@ -1,6 +1,7 @@
 import numpy as np
 from typing import cast, Union, List, Tuple, Iterable
 from itertools import takewhile, repeat
+import os
 
 from qcodes import VisaInstrument, InstrumentChannel, ChannelList
 from qcodes.utils.validators import Enum, Numbers
@@ -39,25 +40,47 @@ def strip(strings: Iterable[str]) -> Tuple:
         return tuple(s.strip() for s in strings)
 
     with open(file_path, "r") as curve_file:
-        file_content = curve_file.read()
+        lines = iter(curve_file.readlines())
 
-    lines = iter(file_content.split("\n"))
     # Meta data lines contain a colon
     metadata_lines = takewhile(lambda s: ":" in s, lines)
-    curve_data = dict([strip(line.split(":")) for line in metadata_lines])
+    # Data from the file is collected in the following dict
+    file_data = dict([strip(line.split(":")) for line in metadata_lines])
     # After meta data we have a data header
     header_items = strip(split_data_line(next(lines)))
     # After that we have the curve data
     data = [
         split_data_line(line, parsers=float)
-        for line in lines if line != ""
+        for line in lines if line.strip() != ""
     ]
 
-    curve_data["data"] = dict(
+    file_data["data"] = dict(
         zip(header_items, zip(*data))
     )
 
-    return curve_data
+    return file_data
+
+
+def sanitize_data(file_data: dict) -> None:
+    """
+    Data as found in the curve files are slightly different then
+    the dictionary as expected by the 'upload_curve' method of the
+    driver
+
+    Note: This function modifies the input
+    """
+    data_dict = file_data["data"]
+    # We do not need the index column
+    del data_dict["No."]
+    # Rename the 'Units' column to the appropriate name
+    # Look up under the 'Data Format' entry to find what units we have
+    data_format = file_data['Data Format']
+    # This is a string in the form '4      (Log Ohms/Kelvin)'
+    data_format_int = int(data_format[0])
+    correct_name = Model_325_Curve.valid_sensor_units[data_format_int - 1]
+    # Rename the column
+    data_dict[correct_name] = data_dict["Units"]
+    del data_dict["Units"]
 
 
 class Model_325_Curve(InstrumentChannel):
@@ -186,7 +209,7 @@ def set_data(self, data_dict: dict, sensor_unit: str=None) ->None:
                 enumerate(zip(temperature_values, sensor_values)):
 
             cmd_str = f"CRVPT {self._index}, {value_index + 1}, " \
-                      f"{sensor_value:3.3}, {temperature_value:3.3}"
+                      f"{sensor_value:3.3f}, {temperature_value:3.3f}"
 
             self.write(cmd_str)
 
@@ -523,8 +546,26 @@ def upload_curve(
 
         sensor_unit = Model_325_Curve.validate_datadict(data_dict)
 
-        curve = self.curve[index]
+        curve = self.curve[index - 1]
         curve.curve_name(name)
         curve.serial_number(serial_number)
         curve.format(f"{sensor_unit}/K")
         curve.set_data(data_dict, sensor_unit=sensor_unit)
+
+    def upload_curve_from_file(self, index: int, file_path: str) -> None:
+        """
+        Upload a curve from a curve file. Note that we only support
+        curve files with extension *.330
+        """
+        _, filename = os.path.split(file_path)
+        if not filename.endswith(".330"):
+            raise ValueError("Only curve files with extension *.330 is supported")
+
+        file_data = read_curve_file(file_path)
+        sanitize_data(file_data)
+
+        name = file_data["Sensor Model"]
+        serial_number = file_data["Serial Number"]
+        data_dict = file_data["data"]
+
+        self.upload_curve(index, name, serial_number, data_dict)

From 1cb3553712b1f468811fe89ec67c2c6bb41508b0 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Wed, 19 Dec 2018 19:11:23 -0800
Subject: [PATCH 403/719] get rid of superfluous newline

---
 qcodes/instrument_drivers/Lakeshore/Model_325.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Lakeshore/Model_325.py b/qcodes/instrument_drivers/Lakeshore/Model_325.py
index a1bad1b5bbc..adbe6130b3e 100644
--- a/qcodes/instrument_drivers/Lakeshore/Model_325.py
+++ b/qcodes/instrument_drivers/Lakeshore/Model_325.py
@@ -31,7 +31,6 @@ def split_data_line(
             parsers = repeat(parsers)
 
         line_split = [i for i in line.split("  ") if i != ""]
-
         return [
             parser(i) for parser, i in zip(parsers, line_split)
         ]

From 67374fa51587f162ada51b919b862d12fc9e54ed Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 20 Dec 2018 12:30:06 +0100
Subject: [PATCH 404/719] S46 pass with strict optional

---
 qcodes/instrument_drivers/tektronix/Keithley_s46.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/tektronix/Keithley_s46.py b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
index 3a91064ab08..9ec6c246739 100644
--- a/qcodes/instrument_drivers/tektronix/Keithley_s46.py
+++ b/qcodes/instrument_drivers/tektronix/Keithley_s46.py
@@ -25,7 +25,7 @@ class RelayLock:
     """
     def __init__(self, relay_name: str):
         self.relay_name = relay_name
-        self._locked_by: int = None
+        self._locked_by: Optional[int] = None
 
     def acquire(self, channel_number: int) -> None:
         """
@@ -101,6 +101,9 @@ def _set(self, new_state) -> None:
         elif new_state == "open":
             self._lock.release(self._channel_number)
 
+        if self._instrument is None:
+            raise RuntimeError("Cannot set the value on a parameter "
+                               "that is not attached to an instrument.")
         self._instrument.write(f":{new_state} (@{self._channel_number})")
 
     @property

From 83f83ee31ac81b6986450669d5d9e3a7475ca6c3 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 20 Dec 2018 13:50:30 +0100
Subject: [PATCH 405/719] Fix pep8 and other style stuff

---
 .../oxford/MercuryiPS_VISA.py                 | 17 +++---
 qcodes/math/field_vector.py                   | 56 +++++++++----------
 qcodes/tests/drivers/test_MercuryiPS.py       | 10 ++--
 qcodes/tests/test_field_vector.py             | 48 +++++++++-------
 qcodes/utils/helpers.py                       |  4 +-
 5 files changed, 73 insertions(+), 62 deletions(-)

diff --git a/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py b/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py
index c2a3b421068..f2f4036513c 100644
--- a/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py
+++ b/qcodes/instrument_drivers/oxford/MercuryiPS_VISA.py
@@ -202,8 +202,8 @@ def _param_setter(self, set_cmd: str, value: Union[float, str]) -> None:
         # holds the value reported back by the instrument
         self._parent.ask(dressed_cmd)
 
-        # TODO: we could use the opportunity to check that we did set/ achieve
-        # the intended value
+        # TODO: we could use the opportunity to check that we did set/achieve
+        #  the intended value
 
 
 class MercuryiPS(VisaInstrument):
@@ -264,8 +264,9 @@ def __init__(self, name: str, address: str, visalib=None,
                                           y=self.GRPY.field(),
                                           z=self.GRPZ.field())
 
-        for coord, unit in zip(['x', 'y', 'z', 'r', 'theta', 'phi', 'rho'],
-                               ['T', 'T', 'T', 'T', 'degrees', 'degrees', 'T']):
+        for coord, unit in zip(
+                ['x', 'y', 'z', 'r', 'theta',   'phi',     'rho'],
+                ['T', 'T', 'T', 'T', 'degrees', 'degrees', 'T']):
             self.add_parameter(name=f'{coord}_target',
                                label=f'{coord.upper()} target field',
                                unit=unit,
@@ -278,11 +279,13 @@ def __init__(self, name: str, address: str, visalib=None,
                                get_cmd=partial(self._get_measured, [coord]))
 
         # FieldVector-valued parameters #
+
         self.add_parameter(name="field_target",
                            label="target field",
                            unit="T",
                            get_cmd=self._get_target_field,
                            set_cmd=self._set_target_field)
+
         self.add_parameter(name="field_measured",
                            label="measured field",
                            unit="T",
@@ -314,7 +317,7 @@ def _get_ramp_rate(self) -> FieldVector:
             z=self.GRPZ.field_ramp_rate(),
         )
 
-    def _set_ramp_rate(self, rate : FieldVector) -> None:
+    def _set_ramp_rate(self, rate: FieldVector) -> None:
         self.GRPX.field_ramp_rate(rate.x)
         self.GRPY.field_ramp_rate(rate.y)
         self.GRPZ.field_ramp_rate(rate.z)
@@ -363,7 +366,7 @@ def _set_target(self, coordinate: str, target: float) -> None:
         for targ, slave in zip(cartesian_targ, self.submodules.values()):
             slave.field_target(targ)
 
-    def _set_target_field(self, field : FieldVector) -> None:
+    def _set_target_field(self, field: FieldVector) -> None:
         for coord in 'xyz':
             self._set_target(coord, field[coord])
 
@@ -439,7 +442,7 @@ def set_new_field_limits(self, limit_func: Callable) -> None:
 
         self._field_limits = limit_func
 
-    def ramp(self, mode : str = "safe") -> None:
+    def ramp(self, mode: str="safe") -> None:
         """
         Ramp the fields to their present target value
 
diff --git a/qcodes/math/field_vector.py b/qcodes/math/field_vector.py
index c7c4f21c40f..ab4045c23fb 100644
--- a/qcodes/math/field_vector.py
+++ b/qcodes/math/field_vector.py
@@ -59,7 +59,6 @@ def __init__(self, x=None, y=None, z=None, r=None, theta=None, phi=None,
         self._compute_unknowns()
 
     def _set_attribute_value(self, attr_name, value):
-
         if value is None:
             return
 
@@ -75,17 +74,15 @@ def _set_attribute_value(self, attr_name, value):
                 )
 
     def _set_attribute_values(self, attr_names, values):
-
         for attr_name, value in zip(attr_names, values):
             self._set_attribute_value(attr_name, value)
 
     def __getnewargs__(self):
-        return (self.x, self.y, self.z)
+        return self.x, self.y, self.z
 
     @staticmethod
     def _cartesian_to_other(x, y, z):
         """ Convert a cartesian set of coordinates to values of interest."""
-
         if any([i is None for i in [x, y, z]]):
             return None
 
@@ -102,7 +99,6 @@ def _cartesian_to_other(x, y, z):
     @staticmethod
     def _spherical_to_other(r, theta, phi):
         """Convert from spherical to other representations"""
-
         if any([i is None for i in [r, theta, phi]]):
             return None
 
@@ -116,7 +112,6 @@ def _spherical_to_other(r, theta, phi):
     @staticmethod
     def _cylindrical_to_other(phi, rho, z):
         """Convert from cylindrical to other representations"""
-
         if any([i is None for i in [phi, rho, z]]):
             return None
 
@@ -139,7 +134,6 @@ def _compute_unknowns(self):
         This function will raise an error if there are contradictory inputs
         (e.g. x=3, y=4, z=0 and rho=6).
         """
-
         for f in [
             lambda: FieldVector._cartesian_to_other(self._x, self._y, self._z),
             lambda: FieldVector._spherical_to_other(self._r, self._theta,
@@ -153,7 +147,7 @@ def _compute_unknowns(self):
                 self._set_attribute_values(FieldVector.attributes, new_values)
                 break
 
-    def copy(self : T, other : T):
+    def copy(self: T, other: T):
         """Copy the properties of other vector to yourself"""
         for att in FieldVector.attributes:
             value = getattr(other, "_" + att)
@@ -167,12 +161,14 @@ def set_vector(self, **new_values):
             >>> f = FieldVector(x=0, y=2, z=6)
             >>> f.set_vector(x=9, y=3, z=1)
             >>> f.set_vector(r=1, theta=30.0, phi=10.0)
-            >>> f.set_vector(x=9, y=0)  # this should raise a value error:
+            # The following should raise a value error:
             # "Can only set vector with a complete value set"
-            >>> f.set_vector(x=9, y=0, r=3)  # although mathematically it is
-            # possible to compute the complete vector from the values given,
-            # this is too hard to implement with generality (and not worth it)
-            # so this to will raise the above mentioned ValueError
+            >>> f.set_vector(x=9, y=0)
+            # Although mathematically it is possible to compute the complete
+            # vector from the values given, this is too hard to implement with
+            # generality (and not worth it), so the following will raise the
+            # above-mentioned ValueError too
+            >>> f.set_vector(x=9, y=0, r=3)
         """
         names = sorted(list(new_values.keys()))
         groups = [["x", "y", "z"], ["phi", "r", "theta"], ["phi", "rho", "z"]]
@@ -191,18 +187,18 @@ def set_component(self, **new_values):
 
         Examples:
             >>> f = FieldVector(x=2, y=3, z=4)
-            >>> f.set_component(r=10) # Since r is part of the set
-            # (r, theta, phi) representing spherical coordinates, setting r
-            # means that theta and phi are kept constant and only r is changed.
-            # After changing r, (x, y, z) values are recomputed, as is the rho
-            # coordinate. Internally we arrange this by setting x, y, z and
-            # rho to None and calling self._compute_unknowns()
+            # Since r is part of the set (r, theta, phi) representing
+            # spherical coordinates, setting r means that theta and phi are
+            # kept constant and only r is changed. After changing r,
+            # (x, y, z) values are recomputed, as is the rho coordinate.
+            # Internally we arrange this by setting x, y, z and rho to None
+            # and calling self._compute_unknowns()
+            >>> f.set_component(r=10)
 
         Parameters:
             new_values (dict): keys representing parameter names and values the
             values to be set
         """
-
         if len(new_values) > 1:
             raise NotImplementedError("Cannot set multiple components at once")
 
@@ -302,14 +298,18 @@ def __sub__(self, other):
 
     # NB: we disable the pylint warning here so that we can match
     #     NumPy's naming convention for the norm method.
-    def norm(self, ord : NormOrder = 2) -> float: # pylint: disable=redefined-builtin
+    def norm(self,
+             ord: NormOrder=2  # pylint: disable=redefined-builtin
+             ) -> float:
         """
         Returns the norm of this field vector. See np.norm
         for the definition of the ord keyword argument.
         """
         return np.linalg.norm([self.x, self.y, self.z], ord=ord)
 
-    def distance(self, other, ord : NormOrder = 2) -> float: # pylint: disable=redefined-builtin
+    def distance(self, other,
+                 ord: NormOrder=2  # pylint: disable=redefined-builtin
+                 ) -> float:
         return (self - other).norm(ord=ord)
 
     @property
@@ -340,7 +340,7 @@ def r(self) -> float:
     def phi(self) -> float:
         return np.degrees(self._phi)
 
-    ## Representation Methods ##
+    # Representation Methods #
 
     def repr_cartesian(self) -> str:
         return f"FieldVector(x={self.x}, y={self.y}, z={self.z})"
@@ -361,22 +361,22 @@ def __repr__(self) -> str:
         else:
             return super().__repr__()
 
-    ## Homogenous Coordinates ##
+    # Homogeneous Coordinates #
 
     def as_homogeneous(self) -> np.ndarray:
         return np.array([self.x, self.y, self.z, 1])
 
     @classmethod
-    def from_homogeneous(cls : Type[T], hvec : np.ndarray) -> T:
-        # Homogenous coordinates define an equivalence relation
+    def from_homogeneous(cls: Type[T], hvec: np.ndarray) -> T:
+        # Homogeneous coordinates define an equivalence relation
         #     [x / s, y / s, z / s, 1] == [x, y, z, s].
         # More generally,
         #     [x, y, z, s] == [x', y', z', s']
         #     iff x / s == x' / s',
         #         y / s == y' / s', and
         #         z / s == z' / s'.
-        # This definition has the consequence that for any
-        # w, w * [x, y, z, s] == [x, y, z, s].
+        # This definition has the consequence that for any w,
+        # w * [x, y, z, s] == [x, y, z, s].
         # Thus, we start by rescaling such that s == 1.
         hvec /= hvec[-1]
         return cls(
diff --git a/qcodes/tests/drivers/test_MercuryiPS.py b/qcodes/tests/drivers/test_MercuryiPS.py
index 09649f3f9f8..61a6f8ba6fc 100644
--- a/qcodes/tests/drivers/test_MercuryiPS.py
+++ b/qcodes/tests/drivers/test_MercuryiPS.py
@@ -13,7 +13,6 @@
 
 @pytest.fixture(scope='function')
 def driver():
-
     mips = MercuryiPS('mips', address='GPIB::1::INSTR',
                       visalib=visalib)
     yield mips
@@ -84,11 +83,9 @@ def test_vector_ramp_rate(driver):
 
 
 def test_wrong_field_limit_raises():
-
     # check that a non-callable input fails
     with pytest.raises(ValueError):
-        MercuryiPS('mips', address='GPIB::1::INSTR',
-                   visalib=visalib,
+        MercuryiPS('mips', address='GPIB::1::INSTR', visalib=visalib,
                    field_limits=0)
 
 
@@ -102,7 +99,7 @@ def test_field_limits(x, y, z, driver_spher_lim, driver_cyl_lim):
     """
 
     # TODO: there really isn't a reason to do this tuple-by-tuple
-    # and not point-by-point. Extend the test to do that.
+    #  and not point-by-point. Extend the test to do that.
 
     for mip in [driver_spher_lim, driver_cyl_lim]:
         # reset (PyVISA-sim unfortunately has memory)
@@ -151,7 +148,8 @@ def test_ramp_safely(driver, x, y, z, caplog):
     # Use the FieldVector interface here to increase coverage.
     driver.field_target(FieldVector(x=x, y=y, z=z))
 
-    exp_order = np.array(['x', 'y', 'z'])[np.argsort(np.abs(np.array([x, y, z])))]
+    exp_order = \
+        np.array(['x', 'y', 'z'])[np.argsort(np.abs(np.array([x, y, z])))]
 
     with caplog.at_level(logging.DEBUG, logger='qcodes.instrument.visa'):
         caplog.clear()
diff --git a/qcodes/tests/test_field_vector.py b/qcodes/tests/test_field_vector.py
index f41650c99bd..b80847e2a51 100644
--- a/qcodes/tests/test_field_vector.py
+++ b/qcodes/tests/test_field_vector.py
@@ -1,6 +1,6 @@
 """
-Test properties of the coordinate transforms in the field vector module to test if everything has been correctly
-implemented.
+Test properties of the coordinate transforms in the field vector module to
+test if everything has been correctly implemented.
 """
 import numpy as np
 import json
@@ -15,17 +15,17 @@
     "cartesian": tuples(
         floats(min_value=0, max_value=1),  # x
         floats(min_value=0, max_value=1),  # y
-        floats(min_value=0, max_value=1)  # z
+        floats(min_value=0, max_value=1)   # z
     ),
     "spherical": tuples(
-        floats(min_value=0, max_value=1),  # r
+        floats(min_value=0, max_value=1),    # r
         floats(min_value=0, max_value=180),  # theta
-        floats(min_value=0, max_value=180)  # phi
+        floats(min_value=0, max_value=180)   # phi
     ),
     "cylindrical": tuples(
-        floats(min_value=0, max_value=1),  # rho
+        floats(min_value=0, max_value=1),    # rho
         floats(min_value=0, max_value=180),  # phi
-        floats(min_value=0, max_value=1)  # z
+        floats(min_value=0, max_value=1)     # z
     )
 }
 
@@ -34,33 +34,38 @@
 @settings(max_examples=10)
 def test_spherical_properties(spherical0):
     """
-    If the cartesian to spherical transform has been correctly implemented then we expect certain symmetrical properties
+    If the cartesian to spherical transform has been correctly implemented
+    then we expect certain symmetrical properties
     """
     # Generate a random coordinate in spherical representation
     spherical0 = dict(zip(["r", "theta", "phi"], spherical0))
     cartisian0 = FieldVector(**spherical0).get_components("x", "y", "z")
 
-    # Mirror the theta angle in the xy-plane. This should flip the sign of the z-coordinate
+    # Mirror the theta angle in the xy-plane.
+    # This should flip the sign of the z-coordinate
     spherical1 = dict(spherical0)
     spherical1["theta"] = 180 - spherical0["theta"]
     cartisian1 = FieldVector(**spherical1).get_components("x", "y", "z")
     assert np.allclose(cartisian0, cartisian1 * np.array([1, 1, -1]))
 
-    # Add 180 to the phi coordinate. This should flip the sign of the xy coordinate
+    # Add 180 to the phi coordinate.
+    # This should flip the sign of the xy coordinate
     spherical2 = dict(spherical0)
     spherical2["phi"] = 180 + spherical0["phi"]
     cartisian2 = FieldVector(**spherical2).get_components("x", "y", "z")
     assert np.allclose(cartisian0, cartisian2 * np.array([-1, -1, 1]))
 
-    # Mirroring the theta angle in the xy-plane and adding 180 to the phi coordinate should flip all cartesian
-    # coordinates
+    # Mirroring the theta angle in the xy-plane
+    # and adding 180 to the phi coordinate
+    # should flip all cartesian coordinates
     spherical3 = dict(spherical0)
-    spherical3["theta"] = 180 - spherical0["theta"]  # This should only flip the z-coordinate
-    spherical3["phi"] = 180 + spherical0["phi"]  # This should flip the xy-coordinate
+    spherical3["theta"] = 180 - spherical0["theta"]  # should only flip z
+    spherical3["phi"] = 180 + spherical0["phi"]  # should flip xy
     cartisian3 = FieldVector(**spherical3).get_components("x", "y", "z")
     assert np.allclose(cartisian0, cartisian3 * np.array([-1, -1, -1]))
 
-    # Finally flipping the sign of the r coordinate should flip all cartesian coordinates
+    # Finally, flipping the sign of the r coordinate
+    # should flip all cartesian coordinates
     spherical4 = dict(spherical0)
     spherical4["r"] = -spherical0["r"]  # This should flip all coordinates
     cartisian4 = FieldVector(**spherical4).get_components("x", "y", "z")
@@ -71,8 +76,8 @@ def test_spherical_properties(spherical0):
 @settings(max_examples=10)
 def test_cylindrical_properties(cylindrical0):
     """
-    If the cartesian to cylindrical transform has been correctly implemented then we expect certain symmetrical
-    properties
+    If the cartesian to cylindrical transform has been correctly implemented
+    then we expect certain symmetrical properties
     """
     # Generate a random coordinate in cylindrical representation
     cylindrical0 = dict(zip(["rho", "phi", "z"], cylindrical0))
@@ -85,6 +90,7 @@ def test_cylindrical_properties(cylindrical0):
 
     assert np.allclose(cartisian0, cartisian1 * np.array([-1, -1, 1]))
 
+
 @given(
     random_coordinates["cylindrical"],
     random_coordinates["spherical"]
@@ -94,8 +100,11 @@ def test_triangle_inequality(cylindrical0, spherical0):
     cylindrical0 = FieldVector(**dict(zip(["rho", "phi", "z"], cylindrical0)))
     spherical0 = FieldVector(**dict(zip(["r", "phi", "theta"], spherical0)))
 
-    assert (cylindrical0 + spherical0).norm() <= (cylindrical0.norm() + spherical0.norm())
-    assert cylindrical0.distance(spherical0) <= (cylindrical0.norm() + spherical0.norm())
+    assert (cylindrical0 + spherical0).norm() \
+           <= (cylindrical0.norm() + spherical0.norm())
+    assert cylindrical0.distance(spherical0) \
+           <= (cylindrical0.norm() + spherical0.norm())
+
 
 @given(random_coordinates["cartesian"])
 @settings(max_examples=10)
@@ -108,6 +117,7 @@ def test_homogeneous_roundtrip(cartesian0):
         FieldVector.from_homogeneous(h_vec).get_components(*"xyz")
     )
 
+
 @given(random_coordinates["spherical"])
 @settings(max_examples=10)
 def test_json_dump(spherical0):
diff --git a/qcodes/utils/helpers.py b/qcodes/utils/helpers.py
index 4c55395fe8a..cc6949ad888 100644
--- a/qcodes/utils/helpers.py
+++ b/qcodes/utils/helpers.py
@@ -46,8 +46,8 @@ def default(self, obj):
         "__dtype__" containing value "complex"
         * object with a `_JSONEncoder` method get converted the return value of
         that method
-        * objects which support the pickle protocol get converted using the data
-        provided by that protocol
+        * objects which support the pickle protocol get converted using the
+        data provided by that protocol
         * other objects which cannot be serialized get converted to their
         string representation (suing the `str` function)
         """

From 3b8f9b120afb58b1764a517045f7faa4d598e346 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 20 Dec 2018 14:47:24 +0100
Subject: [PATCH 406/719] Add typing to monitor

---
 qcodes/monitor/monitor.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index bfb96eed8d9..25236da5033 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -26,7 +26,7 @@
 import time
 import json
 from contextlib import suppress
-from typing import Dict, Any
+from typing import Dict, Any, Optional, Union
 from collections import defaultdict
 
 import asyncio
@@ -56,7 +56,7 @@ def _get_metadata(*parameters) -> Dict[str, Any]:
     metas = defaultdict(list) # type: dict
     for parameter in parameters:
         # Get the latest value from the parameter, respecting the max_val_age parameter
-        meta = {}
+        meta: Dict[str, Optional[str]] = {}
         meta["value"] = str(parameter.get_latest())
         if parameter.get_latest.get_timestamp() is not None:
             meta["ts"] = parameter.get_latest.get_timestamp().timestamp()

From 03e982a38de7ac6e7503fcd8d2c32a7cd28a76da Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 20 Dec 2018 16:35:21 +0100
Subject: [PATCH 407/719] Allow this to be None to match signature of caller

---
 qcodes/dataset/database_extract_runs.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/database_extract_runs.py b/qcodes/dataset/database_extract_runs.py
index 1fe782c6c1e..50be33cea37 100644
--- a/qcodes/dataset/database_extract_runs.py
+++ b/qcodes/dataset/database_extract_runs.py
@@ -1,4 +1,4 @@
-from typing import Union
+from typing import Union, Optional
 from warnings import warn
 import os
 
@@ -244,7 +244,7 @@ def _populate_results_table(source_conn: ConnectionPlus,
 
 def _rewrite_timestamps(target_conn: ConnectionPlus, target_run_id: int,
                         correct_run_timestamp: float,
-                        correct_completed_timestamp: float) -> None:
+                        correct_completed_timestamp: Optional[float]) -> None:
     """
     Update the timestamp to match the original one
     """

From 345846edcf8aba7fa6b06dfc6368a7f95d8d9d50 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 20 Dec 2018 17:13:26 +0100
Subject: [PATCH 408/719] Handle potential missing config and schema

---
 qcodes/config/config.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/qcodes/config/config.py b/qcodes/config/config.py
index 8f323ad8687..09581e99098 100644
--- a/qcodes/config/config.py
+++ b/qcodes/config/config.py
@@ -366,8 +366,14 @@ def describe(self, name: str) -> str:
               e.g. name="user.scriptfolder"
         """
         val = self.current_config
+        if val is None:
+            raise RuntimeError(f"Config is empty, cannot describe entry.")
+        if self.current_schema is None:
+            raise RuntimeError("No schema found, cannot describe entry.")
         sch = self.current_schema["properties"]
         for key in name.split('.'):
+            if val is None:
+                raise RuntimeError(f"{name} is not found in config")
             val = val[key]
             if sch.get(key):
                 sch = sch[key]

From f28b48a39175b406cb117b04a1da13b3deb8db38 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 20 Dec 2018 17:23:29 +0100
Subject: [PATCH 409/719] Cast raw values since these are not actually None

---
 .../QuantumDesign/DynaCoolPPMS/DynaCool.py     | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/qcodes/instrument_drivers/QuantumDesign/DynaCoolPPMS/DynaCool.py b/qcodes/instrument_drivers/QuantumDesign/DynaCoolPPMS/DynaCool.py
index 7359307ef00..517f76408e1 100644
--- a/qcodes/instrument_drivers/QuantumDesign/DynaCoolPPMS/DynaCool.py
+++ b/qcodes/instrument_drivers/QuantumDesign/DynaCoolPPMS/DynaCool.py
@@ -217,11 +217,12 @@ def _field_setter(self, param: str, value: float) -> None:
         The combined set function for the three field parameters,
         field_setpoint, field_rate, and field_approach
         """
-        vals: List[Union[int, float]]
-        vals = list(self.parameters[p].raw_value for p in self.field_params)
-        vals[self.field_params.index(param)] = value
+        temp_values = list(self.parameters[p].raw_value
+                           for p in self.field_params)
+        values = cast(List[Union[int, float]], temp_values)
+        values[self.field_params.index(param)] = value
 
-        self.write(f'FELD {vals[0]}, {vals[1]}, {vals[2]}, 0')
+        self.write(f'FELD {values[0]}, {values[1]}, {values[2]}, 0')
 
     def _temp_getter(self, param_name: str) -> Union[int, float]:
         """
@@ -240,11 +241,12 @@ def _temp_setter(self, param: str, value: float) -> None:
         The setter function for the temperature parameters. All three are set
         with the same call to the instrument API
         """
-        vals: List[Union[int, float]]
-        vals = list(self.parameters[par].raw_value for par in self.temp_params)
-        vals[self.temp_params.index(param)] = value
+        temp_values = list(self.parameters[par].raw_value
+                           for par in self.temp_params)
+        values = cast(List[Union[int, float]], temp_values)
+        values[self.temp_params.index(param)] = value
 
-        self.write(f'TEMP {vals[0]}, {vals[1]}, {vals[2]}')
+        self.write(f'TEMP {values[0]}, {values[1]}, {values[2]}')
 
     def write(self, cmd: str) -> None:
         """

From 7ef1dec39ec6cdf4707a1d5fad731547a6e70da3 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Thu, 20 Dec 2018 18:15:46 -0800
Subject: [PATCH 410/719] 1) Processed PR comments 2) Added file parser test

---
 .../instrument_drivers/Lakeshore/Model_325.py | 52 +++++++----------
 .../drivers/test_lakeshore_file_parser.py     | 58 +++++++++++++++++++
 2 files changed, 79 insertions(+), 31 deletions(-)
 create mode 100644 qcodes/tests/drivers/test_lakeshore_file_parser.py

diff --git a/qcodes/instrument_drivers/Lakeshore/Model_325.py b/qcodes/instrument_drivers/Lakeshore/Model_325.py
index adbe6130b3e..c47aea2b199 100644
--- a/qcodes/instrument_drivers/Lakeshore/Model_325.py
+++ b/qcodes/instrument_drivers/Lakeshore/Model_325.py
@@ -1,6 +1,6 @@
 import numpy as np
-from typing import cast, Union, List, Tuple, Iterable
-from itertools import takewhile, repeat
+from typing import cast, List, Tuple, Iterable, TextIO
+from itertools import takewhile
 import os
 
 from qcodes import VisaInstrument, InstrumentChannel, ChannelList
@@ -8,7 +8,7 @@
 from qcodes.instrument.group_parameter import GroupParameter, Group
 
 
-def read_curve_file(file_path: str) -> dict:
+def read_curve_file(curve_file: TextIO) -> dict:
     """
     Read a curve file with extension *.330
     The format of the file is as follows:
@@ -22,34 +22,24 @@ def read_curve_file(file_path: str) -> dict:
     data21     data22    data23
     """
 
-    def split_data_line(
-            line: str,
-            parsers: Union[type, List]=str
-    ) -> List[str]:
-
-        if not isinstance(parsers, list):
-            parsers = repeat(parsers)
-
-        line_split = [i for i in line.split("  ") if i != ""]
-        return [
-            parser(i) for parser, i in zip(parsers, line_split)
-        ]
+    def split_data_line(line: str, parser: type = str) -> List[str]:
+        return [parser(i) for i in line.split("  ") if i != ""]
 
     def strip(strings: Iterable[str]) -> Tuple:
         return tuple(s.strip() for s in strings)
 
-    with open(file_path, "r") as curve_file:
-        lines = iter(curve_file.readlines())
-
+    lines = iter(curve_file.readlines())
     # Meta data lines contain a colon
     metadata_lines = takewhile(lambda s: ":" in s, lines)
     # Data from the file is collected in the following dict
-    file_data = dict([strip(line.split(":")) for line in metadata_lines])
+    file_data = dict(metadata={}, data={})
+    # Capture meta data
+    file_data["metadata"] = dict([strip(line.split(":")) for line in metadata_lines])
     # After meta data we have a data header
     header_items = strip(split_data_line(next(lines)))
     # After that we have the curve data
     data = [
-        split_data_line(line, parsers=float)
+        split_data_line(line, parser=float)
         for line in lines if line.strip() != ""
     ]
 
@@ -60,27 +50,27 @@ def strip(strings: Iterable[str]) -> Tuple:
     return file_data
 
 
-def sanitize_data(file_data: dict) -> None:
+def get_sanitize_data(file_data: dict) -> dict:
     """
     Data as found in the curve files are slightly different then
     the dictionary as expected by the 'upload_curve' method of the
     driver
-
-    Note: This function modifies the input
     """
-    data_dict = file_data["data"]
+    data_dict = dict(file_data["data"])
     # We do not need the index column
     del data_dict["No."]
     # Rename the 'Units' column to the appropriate name
     # Look up under the 'Data Format' entry to find what units we have
-    data_format = file_data['Data Format']
+    data_format = file_data['metadata']['Data Format']
     # This is a string in the form '4      (Log Ohms/Kelvin)'
-    data_format_int = int(data_format[0])
+    data_format_int = int(data_format.split()[0])
     correct_name = Model_325_Curve.valid_sensor_units[data_format_int - 1]
     # Rename the column
     data_dict[correct_name] = data_dict["Units"]
     del data_dict["Units"]
 
+    return data_dict
+
 
 class Model_325_Curve(InstrumentChannel):
 
@@ -560,11 +550,11 @@ def upload_curve_from_file(self, index: int, file_path: str) -> None:
         if not filename.endswith(".330"):
             raise ValueError("Only curve files with extension *.330 is supported")
 
-        file_data = read_curve_file(file_path)
-        sanitize_data(file_data)
+        with open(file_path, "r") as curve_file:
+            file_data = read_curve_file(curve_file)
 
-        name = file_data["Sensor Model"]
-        serial_number = file_data["Serial Number"]
-        data_dict = file_data["data"]
+        data_dict = get_sanitize_data(file_data)
+        name = file_data["metadata"]["Sensor Model"]
+        serial_number = file_data["metadata"]["Serial Number"]
 
         self.upload_curve(index, name, serial_number, data_dict)
diff --git a/qcodes/tests/drivers/test_lakeshore_file_parser.py b/qcodes/tests/drivers/test_lakeshore_file_parser.py
new file mode 100644
index 00000000000..74897ee6991
--- /dev/null
+++ b/qcodes/tests/drivers/test_lakeshore_file_parser.py
@@ -0,0 +1,58 @@
+import io
+import pytest
+
+from qcodes.instrument_drivers.Lakeshore.Model_325 import read_curve_file, get_sanitize_data
+
+curve_file_content = """Sensor Model:   CX-1050-SD-HT-1.4L
+Serial Number:  X116121
+Interpolation Method:   Lagrangian
+SetPoint Limit: 325.0      (Kelvin)
+Data Format:    4      (Log Ohms/Kelvin)
+Number of Breakpoints:   52
+
+No.   Units      Temperature (K)
+
+  1  1.70333       325.0
+  2  1.70444       324.0
+  3  1.72168       309.0
+  4  1.73995       294.0
+  5  1.75936       279.0
+  6  1.78000       264.0
+"""
+
+
+@pytest.fixture(scope="function")
+def curve_file():
+    yield io.StringIO(curve_file_content)
+
+
+def test_file_parser(curve_file):
+    file_data = read_curve_file(curve_file)
+
+    assert list(file_data.keys()) == ["metadata", "data"]
+
+    assert file_data["metadata"] == {
+        "Sensor Model": "CX-1050-SD-HT-1.4L",
+        "Serial Number":  "X116121",
+        "Interpolation Method": "Lagrangian",
+        "SetPoint Limit": "325.0      (Kelvin)",
+        "Data Format": "4      (Log Ohms/Kelvin)",
+        "Number of Breakpoints": "52"
+    }
+
+    assert file_data["data"] == {
+        "No.": (1.0, 2.0, 3.0, 4.0, 5.0, 6.0),
+        "Units": (1.70333, 1.70444, 1.72168, 1.73995, 1.75936, 1.78),
+        "Temperature (K)": (325.0, 324.0, 309.0, 294.0, 279.0, 264.0)
+    }
+
+
+def test_sanitise_data(curve_file):
+
+    file_data = read_curve_file(curve_file)
+    data_dict = get_sanitize_data(file_data)
+
+    assert data_dict == {
+        "log Ohm": (1.70333, 1.70444, 1.72168, 1.73995, 1.75936, 1.78),
+        "Temperature (K)": (325.0, 324.0, 309.0, 294.0, 279.0, 264.0)
+    }

From 29dc926df0622aa8c74b7b357757b73a95243cd2 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Thu, 20 Dec 2018 18:23:42 -0800
Subject: [PATCH 411/719] Better docstrings

---
 qcodes/instrument_drivers/Lakeshore/Model_325.py | 16 ++++++----------
 1 file changed, 6 insertions(+), 10 deletions(-)

diff --git a/qcodes/instrument_drivers/Lakeshore/Model_325.py b/qcodes/instrument_drivers/Lakeshore/Model_325.py
index c47aea2b199..e230651b129 100644
--- a/qcodes/instrument_drivers/Lakeshore/Model_325.py
+++ b/qcodes/instrument_drivers/Lakeshore/Model_325.py
@@ -11,15 +11,12 @@
 def read_curve_file(curve_file: TextIO) -> dict:
     """
     Read a curve file with extension *.330
-    The format of the file is as follows:
+    The file format of this file is shown in qcodes\tests\drivers\test_lakeshore_file_parser.py
 
-    Item1: value
-    Item2: value
-    Item3: value
-
-    Header1   Header2   Header3
-    data11     data12    data13
-    data21     data22    data23
+    The output is a dictionary with keys: "metadata" and "data".
+    The metadata dictionary contains the first n lines of the curve file which
+    are in the format "item: value". The data dictionary contains the actual
+    curve data.
     """
 
     def split_data_line(line: str, parser: type = str) -> List[str]:
@@ -546,8 +543,7 @@ def upload_curve_from_file(self, index: int, file_path: str) -> None:
         Upload a curve from a curve file. Note that we only support
         curve files with extension *.330
         """
-        _, filename = os.path.split(file_path)
-        if not filename.endswith(".330"):
+        if not file_path.endswith(".330"):
             raise ValueError("Only curve files with extension *.330 is supported")
 
         with open(file_path, "r") as curve_file:

From 6ec1c687e26bc5b6a9f2d7dbcde3a888d56cf7d0 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Thu, 20 Dec 2018 18:41:24 -0800
Subject: [PATCH 412/719] Mypy passing

---
 qcodes/instrument_drivers/Lakeshore/Model_325.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/qcodes/instrument_drivers/Lakeshore/Model_325.py b/qcodes/instrument_drivers/Lakeshore/Model_325.py
index e230651b129..957f4ff3f63 100644
--- a/qcodes/instrument_drivers/Lakeshore/Model_325.py
+++ b/qcodes/instrument_drivers/Lakeshore/Model_325.py
@@ -1,7 +1,6 @@
 import numpy as np
 from typing import cast, List, Tuple, Iterable, TextIO
 from itertools import takewhile
-import os
 
 from qcodes import VisaInstrument, InstrumentChannel, ChannelList
 from qcodes.utils.validators import Enum, Numbers
@@ -11,7 +10,8 @@
 def read_curve_file(curve_file: TextIO) -> dict:
     """
     Read a curve file with extension *.330
-    The file format of this file is shown in qcodes\tests\drivers\test_lakeshore_file_parser.py
+    The file format of this file is shown in test_lakeshore_file_parser.py
+    in the test module
 
     The output is a dictionary with keys: "metadata" and "data".
     The metadata dictionary contains the first n lines of the curve file which
@@ -29,9 +29,10 @@ def strip(strings: Iterable[str]) -> Tuple:
     # Meta data lines contain a colon
     metadata_lines = takewhile(lambda s: ":" in s, lines)
     # Data from the file is collected in the following dict
-    file_data = dict(metadata={}, data={})
+    file_data: dict = dict(metadata={}, data={})
     # Capture meta data
-    file_data["metadata"] = dict([strip(line.split(":")) for line in metadata_lines])
+    parsed_lines = [strip(line.split(":")) for line in metadata_lines]
+    file_data["metadata"] = {key: value for key, value in parsed_lines}
     # After meta data we have a data header
     header_items = strip(split_data_line(next(lines)))
     # After that we have the curve data

From c124bd09099277242044c8c9d64bd1a0150854fc Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Thu, 20 Dec 2018 18:43:09 -0800
Subject: [PATCH 413/719] Mypy passing

---
 qcodes/instrument_drivers/Lakeshore/Model_325.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Lakeshore/Model_325.py b/qcodes/instrument_drivers/Lakeshore/Model_325.py
index 957f4ff3f63..c31ecd08337 100644
--- a/qcodes/instrument_drivers/Lakeshore/Model_325.py
+++ b/qcodes/instrument_drivers/Lakeshore/Model_325.py
@@ -29,7 +29,7 @@ def strip(strings: Iterable[str]) -> Tuple:
     # Meta data lines contain a colon
     metadata_lines = takewhile(lambda s: ":" in s, lines)
     # Data from the file is collected in the following dict
-    file_data: dict = dict(metadata={}, data={})
+    file_data = dict()
     # Capture meta data
     parsed_lines = [strip(line.split(":")) for line in metadata_lines]
     file_data["metadata"] = {key: value for key, value in parsed_lines}

From ac7c19263ccf326347f2d421d00ff2aa892fc13e Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Thu, 20 Dec 2018 18:45:47 -0800
Subject: [PATCH 414/719] fix grammar mistake in exception message

---
 qcodes/instrument_drivers/Lakeshore/Model_325.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Lakeshore/Model_325.py b/qcodes/instrument_drivers/Lakeshore/Model_325.py
index c31ecd08337..4d74634bdb5 100644
--- a/qcodes/instrument_drivers/Lakeshore/Model_325.py
+++ b/qcodes/instrument_drivers/Lakeshore/Model_325.py
@@ -545,7 +545,7 @@ def upload_curve_from_file(self, index: int, file_path: str) -> None:
         curve files with extension *.330
         """
         if not file_path.endswith(".330"):
-            raise ValueError("Only curve files with extension *.330 is supported")
+            raise ValueError("Only curve files with extension *.330 are supported")
 
         with open(file_path, "r") as curve_file:
             file_data = read_curve_file(curve_file)

From b597a6490f19db409077e9ed6a142bdd51ad301e Mon Sep 17 00:00:00 2001
From: Thibaud Ruelle <thibaud.ruelle@gmail.com>
Date: Fri, 21 Dec 2018 07:25:23 +0100
Subject: [PATCH 415/719] feat: add specs for model '33512B'

---
 qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py b/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
index 3ef0e300626..56ef0ad18bb 100644
--- a/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
+++ b/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
@@ -298,12 +298,14 @@ def __init__(self, name, address, silent=False, **kwargs):
         no_of_channels = {'33210A': 1,
                           '33250A': 1,
                           '33511B': 1,
+                          '33512B': 2,
                           '33522B': 2,
                           '33622A': 2
                           }
 
         self._max_freqs = {'33210A': 10e6,
                            '33511B': 20e6,
+                           '33512B': 20e6,
                            '33250A': 80e6,
                            '33522B': 30e6,
                            '33622A': 120e6}

From 251b9d234c8f909118384eadf8b88972523fd7c9 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 6 Dec 2018 15:22:12 +0100
Subject: [PATCH 416/719] Add function to export data as pandas array

---
 qcodes/dataset/data_set.py | 54 +++++++++++++++++++++++++++++++++++++-
 1 file changed, 53 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index b788ba51b12..072b7439e6a 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -8,6 +8,7 @@
 import uuid
 from queue import Queue, Empty
 import numpy
+import pandas as pd
 
 from qcodes.dataset.param_spec import ParamSpec
 from qcodes.instrument.parameter import _BaseParameter
@@ -813,7 +814,7 @@ def get_parameter_data(
         """
         Returns the values stored in the DataSet for the specified parameters
         and their dependencies. If no paramerers are supplied the values will
-        be returned for all parameters that are not them self depdendencies.
+        be returned for all parameters that are not them self dependencies.
 
         The values are returned as a dictionary with names of the requested
         parameters as keys and values consisting of dictionaries with the
@@ -851,6 +852,57 @@ def get_parameter_data(
         return get_parameter_data(self.conn, self.table_name, valid_param_names,
                                   start, end)
 
+    def get_data_as_pandas_dataframe(self,
+                                     *params: Union[str,
+                                                    ParamSpec,
+                                                    _BaseParameter],
+                                     start: Optional[int] = None,
+                                     end: Optional[int] = None) -> \
+            List[pd.DataFrame]:
+        """
+        Returns the values stored in the DataSet for the specified parameters
+        and their dependencies. If no paramerers are supplied the values will
+        be returned for all parameters that are not them self dependencies.
+
+        The values are returned as a list of Pandas DataFrames. One for each
+        names of the parameters and its dependencies as keys and numpy arrays
+        of the data as values. If some of the parameters are stored as
+        arrays the remaining parameters are expanded to the same shape as these.
+        Apart from this expansion the data returned by this method
+        is the transpose of the date returned by `get_data`.
+
+        If provided, the start and end arguments select a range of results
+        by result count (index). If the range is empty - that is, if the end is
+        less than or equal to the start, or if start is after the current end
+        of the DataSet – then a list of empty arrays is returned.
+
+        Args:
+            *params: string parameter names, QCoDeS Parameter objects, and
+                ParamSpec objects. If no parameters are supplied data for
+                all parameters that are not a dependency of another
+                parameter will be returned.
+            start: start value of selection range (by result count); ignored
+                if None
+            end: end value of selection range (by results count); ignored if
+                None
+
+        Returns:
+
+        """
+        dfs = []
+        datadict = self.get_parameter_data(*params,
+                                           start=start,
+                                           end=end)
+        for subdict in datadict.values():
+            keys = list(subdict.keys())
+            multiindex = pd.MultiIndex.from_arrays(
+                tuple(subdict[key].ravel() for key in keys[1:]),
+                names=keys[1:])
+            df = pd.DataFrame(subdict[keys[0]].ravel(), index=multiindex,
+                              columns=[keys[0]])
+            dfs.append(df)
+        return dfs
+
     def get_values(self, param_name: str) -> List[List[Any]]:
         """
         Get the values (i.e. not NULLs) of the specified parameter

From 0f75e4ddc79f892f1618bbfe28e6dc5714046599 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 16 Dec 2018 20:21:42 +0100
Subject: [PATCH 417/719] Return dataframes in a dict too

---
 qcodes/dataset/data_set.py | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 072b7439e6a..ba75c5eac7d 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -861,15 +861,14 @@ def get_data_as_pandas_dataframe(self,
             List[pd.DataFrame]:
         """
         Returns the values stored in the DataSet for the specified parameters
-        and their dependencies. If no paramerers are supplied the values will
-        be returned for all parameters that are not them self dependencies.
+        and their dependencies as a dict of Pandas DataFrames. Each element in
+        the dict is indexed by the names of the requested parameters.
 
-        The values are returned as a list of Pandas DataFrames. One for each
-        names of the parameters and its dependencies as keys and numpy arrays
-        of the data as values. If some of the parameters are stored as
-        arrays the remaining parameters are expanded to the same shape as these.
-        Apart from this expansion the data returned by this method
-        is the transpose of the date returned by `get_data`.
+        Each DataFrame contains a column for the data and is indexed by a
+        MultiIndex formed from all the setpoints of the parameter.
+
+        If no parameters are supplied dataframes will be be returned for all
+        parameters that are not them self dependencies of other parameters.
 
         If provided, the start and end arguments select a range of results
         by result count (index). If the range is empty - that is, if the end is
@@ -889,18 +888,18 @@ def get_data_as_pandas_dataframe(self,
         Returns:
 
         """
-        dfs = []
+        dfs = {}
         datadict = self.get_parameter_data(*params,
                                            start=start,
                                            end=end)
-        for subdict in datadict.values():
+        for name, subdict in datadict.items():
             keys = list(subdict.keys())
             multiindex = pd.MultiIndex.from_arrays(
                 tuple(subdict[key].ravel() for key in keys[1:]),
                 names=keys[1:])
             df = pd.DataFrame(subdict[keys[0]].ravel(), index=multiindex,
                               columns=[keys[0]])
-            dfs.append(df)
+            dfs[name] = df
         return dfs
 
     def get_values(self, param_name: str) -> List[List[Any]]:

From 8994590bc73de504f3277db28a505f95a629702b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 16 Dec 2018 20:49:53 +0100
Subject: [PATCH 418/719] start testing dataframes

---
 qcodes/tests/dataset/helper_functions.py   |  4 ++++
 qcodes/tests/dataset/test_dataset_basic.py | 22 ++++++++++++++++------
 2 files changed, 20 insertions(+), 6 deletions(-)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index e74a4b84142..b1e6870c595 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -1,9 +1,12 @@
 from typing import Sequence, Tuple, Dict
 
 import numpy as np
+import pandas
 from numpy.testing import assert_array_equal
 
+
 def verify_data_dict(data: Dict[str, Dict[str, np.ndarray]],
+                     dataframe: Dict[str, pandas.DataFrame],
                      parameter_names: Sequence[str],
                      expected_names: Dict[str, Sequence[str]],
                      expected_shapes: Dict[str, Sequence[Tuple[int, ...]]],
@@ -13,6 +16,7 @@ def verify_data_dict(data: Dict[str, Dict[str, np.ndarray]],
 
     Args:
         data: The dict data to verify the shape and content of.
+        dataframe: The data represented as a dict of Pandas DataFrames.
         parameter_names: names of the parameters loaded as top level
             keys in the dict.
         expected_names: names of the parameters expected as keys in the second
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 58e49386ec1..bd65323edc6 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -1033,16 +1033,23 @@ def parameter_test_helper(ds, toplevel_names,
                           end=None):
 
     data = ds.get_parameter_data(*toplevel_names, start=start, end=end)
+    dataframe = ds.get_data_as_pandas_dataframe(*toplevel_names,
+                                                start=start,
+                                                end=end)
+
     all_data = ds.get_parameter_data(start=start, end=end)
+    all_dataframe = ds.get_data_as_pandas_dataframe(start=start, end=end)
 
     all_parameters = list(all_data.keys())
     assert set(data.keys()).issubset(set(all_parameters))
+    assert list(data.keys()) == list(dataframe.keys())
     assert len(data.keys()) == len(toplevel_names)
+    assert len(dataframe.keys()) == len(toplevel_names)
 
-    verify_data_dict(data, toplevel_names, expected_names, expected_shapes,
-                     expected_values)
-    verify_data_dict(all_data, toplevel_names, expected_names, expected_shapes,
-                     expected_values)
+    verify_data_dict(data, dataframe, toplevel_names, expected_names,
+                     expected_shapes, expected_values)
+    verify_data_dict(all_data, all_dataframe, toplevel_names, expected_names,
+                     expected_shapes, expected_values)
 
     # Now lets remove a random element from the list
     # We do this one by one until there is only one element in the list
@@ -1056,8 +1063,11 @@ def parameter_test_helper(ds, toplevel_names,
 
         subset_data = ds.get_parameter_data(*subset_names,
                                             start=start, end=end)
-        verify_data_dict(subset_data, subset_names, expected_names,
-                         expected_shapes, expected_values)
+        subset_dataframe = ds.get_data_as_pandas_dataframe(*subset_names,
+                                                           start=start,
+                                                           end=end)
+        verify_data_dict(subset_data, subset_dataframe, subset_names,
+                         expected_names, expected_shapes, expected_values)
 
 
 def limit_data_to_start_end(start, end, input_names, expected_names,

From 5896b5e83b4946df74ac0a3772da5aef592ce463 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 16 Dec 2018 20:50:41 +0100
Subject: [PATCH 419/719] handle the case where a dataframe is empty or the
 parameter has no values

---
 qcodes/dataset/data_set.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index ba75c5eac7d..6efe577febf 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -894,10 +894,16 @@ def get_data_as_pandas_dataframe(self,
                                            end=end)
         for name, subdict in datadict.items():
             keys = list(subdict.keys())
-            multiindex = pd.MultiIndex.from_arrays(
-                tuple(subdict[key].ravel() for key in keys[1:]),
-                names=keys[1:])
-            df = pd.DataFrame(subdict[keys[0]].ravel(), index=multiindex,
+            if len(keys) == 0:
+                dfs[name] = {}
+                continue
+            if len(keys) == 1:
+                index = None
+            else:
+                index = pd.MultiIndex.from_arrays(
+                    tuple(subdict[key].ravel() for key in keys[1:]),
+                    names=keys[1:])
+            df = pd.DataFrame(subdict[keys[0]].ravel(), index=index,
                               columns=[keys[0]])
             dfs[name] = df
         return dfs

From fcbd23acdf55deb524a25150aff297bbf1ade7ad Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 16 Dec 2018 20:53:47 +0100
Subject: [PATCH 420/719] return an empty dict

---
 qcodes/dataset/data_set.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 6efe577febf..806cff05ec1 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -895,7 +895,7 @@ def get_data_as_pandas_dataframe(self,
         for name, subdict in datadict.items():
             keys = list(subdict.keys())
             if len(keys) == 0:
-                dfs[name] = {}
+                dfs[name] = pd.DataFrame()
                 continue
             if len(keys) == 1:
                 index = None

From df568a45fc627abdcdb30e56983f65e5f307feba Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 16 Dec 2018 20:55:46 +0100
Subject: [PATCH 421/719] correct type

---
 qcodes/dataset/data_set.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 806cff05ec1..297cbc244be 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -858,7 +858,7 @@ def get_data_as_pandas_dataframe(self,
                                                     _BaseParameter],
                                      start: Optional[int] = None,
                                      end: Optional[int] = None) -> \
-            List[pd.DataFrame]:
+            Dict[str, pd.DataFrame]:
         """
         Returns the values stored in the DataSet for the specified parameters
         and their dependencies as a dict of Pandas DataFrames. Each element in

From 3e3a9e88091777af5d8af30db370b92bc671df76 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 16 Dec 2018 21:08:02 +0100
Subject: [PATCH 422/719] pass dataframe down one more level

---
 qcodes/tests/dataset/helper_functions.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index b1e6870c595..5057ab078cc 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -29,15 +29,20 @@ def verify_data_dict(data: Dict[str, Dict[str, np.ndarray]],
     # check that all the expected parameters in the dict are
     # included in the list of parameters
     assert all(param in parameter_names for param in list(data.keys())) is True
+    assert all(param in parameter_names for
+               param in list(dataframe.keys())) is True
     for param in parameter_names:
         innerdata = data[param]
+        innerdataframe = dataframe[param]
         verify_data_dict_for_single_param(innerdata,
+                                          innerdataframe,
                                           expected_names[param],
                                           expected_shapes[param],
                                           expected_values[param])
 
 
 def verify_data_dict_for_single_param(datadict: Dict[str, np.ndarray],
+                                      dataframe: pandas.DataFrame,
                                       names: Sequence[str],
                                       shapes: Sequence[Tuple[int, ...]],
                                       values):

From d4021963e75900afe2a0e319af5c74b9cf07dfd0 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 17 Dec 2018 09:49:21 +0100
Subject: [PATCH 423/719] Enable assert introspection on helper module

---
 qcodes/tests/dataset/test_dataset_basic.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index bd65323edc6..5199a78cece 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -29,7 +29,8 @@
 # pylint: disable=unused-import
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 
-from .helper_functions import verify_data_dict
+pytest.register_assert_rewrite('qcodes.tests.dataset.helper_functions')
+from qcodes.tests.dataset.helper_functions import verify_data_dict
 
 n_experiments = 0
 

From e35ba79ee8be8ffe85f5a7e33254f9a7b87f1a8f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 17 Dec 2018 09:50:10 +0100
Subject: [PATCH 424/719] Start testing dataframes too

---
 qcodes/tests/dataset/helper_functions.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index 5057ab078cc..8bc90d10a5d 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -47,7 +47,20 @@ def verify_data_dict_for_single_param(datadict: Dict[str, np.ndarray],
                                       shapes: Sequence[Tuple[int, ...]],
                                       values):
     # check that there are no unexpected elements in the dict
-    assert all(param in names for param in list(datadict.keys())) is True
+    key_names = list(datadict.keys())
+    assert set(key_names) == set(names)
+    # check that the dataframe has the same elements as index and columns
+    pandas_index_names = list(dataframe.index.names)
+    pandas_column_names = list(dataframe)
+    pandas_names = []
+    for i in pandas_index_names:
+        if i is not None:
+            pandas_names.append(i)
+    for i in pandas_column_names:
+        if i is not None:
+            pandas_names.append(i)
+    assert set(pandas_names) == set(names)
+
     for name, shape, value in zip(names, shapes, values):
         assert datadict[name].shape == shape
         assert_array_equal(datadict[name], value)

From 6128c1fd484b2ab4adea0405f258ea15aef5f16f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 17 Dec 2018 10:36:50 +0100
Subject: [PATCH 425/719] test content of dataframe

---
 qcodes/tests/dataset/helper_functions.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index 8bc90d10a5d..b90c4130cd5 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -1,4 +1,6 @@
 from typing import Sequence, Tuple, Dict
+from operator import mul
+from functools import reduce
 
 import numpy as np
 import pandas
@@ -61,6 +63,10 @@ def verify_data_dict_for_single_param(datadict: Dict[str, np.ndarray],
             pandas_names.append(i)
     assert set(pandas_names) == set(names)
 
+    simpledf = dataframe.reset_index()
+
     for name, shape, value in zip(names, shapes, values):
         assert datadict[name].shape == shape
+        assert len(simpledf[name]) == reduce(mul, shape)
+        assert_array_equal(dataframe.reset_index()[name].values, value.ravel())
         assert_array_equal(datadict[name], value)

From ada3b866c2eda8ea4b3c9e7f5e6f41920a71867c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 17 Dec 2018 11:29:40 +0100
Subject: [PATCH 426/719] Test multiindexes

---
 qcodes/tests/dataset/helper_functions.py | 38 +++++++++++++++++++-----
 1 file changed, 30 insertions(+), 8 deletions(-)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index b90c4130cd5..08c4252d29e 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -14,18 +14,24 @@ def verify_data_dict(data: Dict[str, Dict[str, np.ndarray]],
                      expected_shapes: Dict[str, Sequence[Tuple[int, ...]]],
                      expected_values: Dict[str, Sequence[np.ndarray]]) -> None:
     """
-    Simple helper function to verify a dict of data
+    Simple helper function to verify a dict of data. The expected names values
+    and shapes should be given as a dict with keys given by the dependent
+    parameters. Each value in the dicts should be the sequence of expected
+    names/shapes/values for that requested parameter and its dependencies.
+    The first element in the sequence must be the dependent parameter loaded.
 
     Args:
         data: The dict data to verify the shape and content of.
         dataframe: The data represented as a dict of Pandas DataFrames.
-        parameter_names: names of the parameters loaded as top level
-            keys in the dict.
-        expected_names: names of the parameters expected as keys in the second
-            level.
-        expected_shapes: expected shapes of the parameters loaded in the values
-            of the dict.
-        expected_values: expected content of the data arrays.
+        parameter_names: names of the parameters requested. These are expected
+            as top level keys in the dict.
+        expected_names: names of the parameters expected to be loaded for a
+            given parameter as a sequence indexed by the parameter.
+        expected_shapes: expected shapes of the parameters loaded. The shapes
+            should be stored as a tuple per parameter in a sequence containing
+            all the loaded parameters for a given requested parameter.
+        expected_values: expected content of the data arrays stored in a
+            sequence
 
     """
     # check that all the expected parameters in the dict are
@@ -63,6 +69,22 @@ def verify_data_dict_for_single_param(datadict: Dict[str, np.ndarray],
             pandas_names.append(i)
     assert set(pandas_names) == set(names)
 
+    # lets check that the index is made up
+    # from all but the first column as expected
+    if len(values) > 1:
+        expected_index_values = values[1:]
+        index_values = dataframe.index.values
+
+        nindexes = len(expected_index_values)
+        nrows = shapes[0]
+
+        for row in range(len(nrows)):
+            row_index_values = index_values[row]
+            expected_values = \
+                tuple(expected_index_values[indexnum].ravel()[row]
+                      for indexnum in range(nindexes))
+            assert row_index_values == expected_values
+
     simpledf = dataframe.reset_index()
 
     for name, shape, value in zip(names, shapes, values):

From d46e784a631051f1809ee1ffe6c35dbe6ae3f5a9 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 17 Dec 2018 11:29:59 +0100
Subject: [PATCH 427/719] Make this test conform to the same layout as the rest

---
 qcodes/tests/dataset/test_dataset_basic.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 5199a78cece..924aaa387e4 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -783,13 +783,13 @@ def test_get_parameter_data(scalar_dataset, start, end):
     input_names = ['param_3']
 
     expected_names = {}
-    expected_names['param_3'] = ['param_0', 'param_1', 'param_2',
-                                 'param_3']
+    expected_names['param_3'] = ['param_3', 'param_0', 'param_1', 'param_2']
     expected_shapes = {}
     expected_shapes['param_3'] = [(10**3, )]*4
     expected_values = {}
-    expected_values['param_3'] = [np.arange(10000*a, 10000*a+1000)
-                                  for a in range(4)]
+    expected_values['param_3'] = [np.arange(30000, 31000)] + \
+                                 [np.arange(10000*a, 10000*a+1000)
+                                  for a in range(3)]
 
     start, end = limit_data_to_start_end(start, end, input_names,
                                          expected_names, expected_shapes,

From 059e24daebac2777d10637070f23942818e6d8c5 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 17 Dec 2018 12:41:14 +0100
Subject: [PATCH 428/719] Split pandas funcs out of test helper so that it can
 be used independently

---
 qcodes/tests/dataset/helper_functions.py | 33 +++++++++++++++++-------
 1 file changed, 24 insertions(+), 9 deletions(-)

diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index 08c4252d29e..25e58cbaeb1 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -1,5 +1,6 @@
 from typing import Sequence, Tuple, Dict
 from operator import mul
+from typing import Optional
 from functools import reduce
 
 import numpy as np
@@ -8,13 +9,15 @@
 
 
 def verify_data_dict(data: Dict[str, Dict[str, np.ndarray]],
-                     dataframe: Dict[str, pandas.DataFrame],
+                     dataframe: Optional[Dict[str, pandas.DataFrame]],
                      parameter_names: Sequence[str],
                      expected_names: Dict[str, Sequence[str]],
                      expected_shapes: Dict[str, Sequence[Tuple[int, ...]]],
                      expected_values: Dict[str, Sequence[np.ndarray]]) -> None:
     """
-    Simple helper function to verify a dict of data. The expected names values
+    Simple helper function to verify a dict of data. It can also optionally
+
+    The expected names values
     and shapes should be given as a dict with keys given by the dependent
     parameters. Each value in the dicts should be the sequence of expected
     names/shapes/values for that requested parameter and its dependencies.
@@ -37,26 +40,40 @@ def verify_data_dict(data: Dict[str, Dict[str, np.ndarray]],
     # check that all the expected parameters in the dict are
     # included in the list of parameters
     assert all(param in parameter_names for param in list(data.keys())) is True
-    assert all(param in parameter_names for
-               param in list(dataframe.keys())) is True
+    if dataframe is not None:
+        assert all(param in parameter_names for
+                   param in list(dataframe.keys())) is True
     for param in parameter_names:
         innerdata = data[param]
-        innerdataframe = dataframe[param]
         verify_data_dict_for_single_param(innerdata,
-                                          innerdataframe,
                                           expected_names[param],
                                           expected_shapes[param],
                                           expected_values[param])
+        if dataframe is not None:
+            innerdataframe = dataframe[param]
+            verify_dataframe_for_single_param(innerdataframe,
+                                              expected_names[param],
+                                              expected_shapes[param],
+                                              expected_values[param])
 
 
 def verify_data_dict_for_single_param(datadict: Dict[str, np.ndarray],
-                                      dataframe: pandas.DataFrame,
                                       names: Sequence[str],
                                       shapes: Sequence[Tuple[int, ...]],
                                       values):
     # check that there are no unexpected elements in the dict
     key_names = list(datadict.keys())
     assert set(key_names) == set(names)
+
+    for name, shape, value in zip(names, shapes, values):
+        assert datadict[name].shape == shape
+        assert_array_equal(datadict[name], value)
+
+
+def verify_dataframe_for_single_param(dataframe: pandas.DataFrame,
+                                      names: Sequence[str],
+                                      shapes: Sequence[Tuple[int, ...]],
+                                      values):
     # check that the dataframe has the same elements as index and columns
     pandas_index_names = list(dataframe.index.names)
     pandas_column_names = list(dataframe)
@@ -88,7 +105,5 @@ def verify_data_dict_for_single_param(datadict: Dict[str, np.ndarray],
     simpledf = dataframe.reset_index()
 
     for name, shape, value in zip(names, shapes, values):
-        assert datadict[name].shape == shape
         assert len(simpledf[name]) == reduce(mul, shape)
         assert_array_equal(dataframe.reset_index()[name].values, value.ravel())
-        assert_array_equal(datadict[name], value)

From 135d7eadb2da4baa08a6dfdfb6d3764599d36404 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 17 Dec 2018 12:41:43 +0100
Subject: [PATCH 429/719] Adapt sqlite base tests to support new helpers

---
 qcodes/tests/dataset/test_sqlite_base.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index 93ec6dc0d96..7b4b3489bfb 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -218,8 +218,8 @@ def test_get_parameter_data(scalar_dataset):
     expected_values = {}
     expected_values['param_3'] = [np.arange(10000*a, 10000*a+1000)
                                   for a in range(4)]
-    verify_data_dict(data, input_names, expected_names, expected_shapes,
-                     expected_values)
+    verify_data_dict(data, None, input_names, expected_names,
+                     expected_shapes, expected_values)
 
 
 def test_get_parameter_data_independent_parameters(standalone_parameters_dataset):
@@ -249,7 +249,7 @@ def test_get_parameter_data_independent_parameters(standalone_parameters_dataset
     expected_values['param_3'] = [np.arange(30000, 30000 + 1000),
                                   np.arange(0, 1000)]
 
-    verify_data_dict(data, expected_toplevel_params, expected_names,
+    verify_data_dict(data, None, expected_toplevel_params, expected_names,
                      expected_shapes, expected_values)
 
 

From a1bdb5c5a030ded1bfe64b35b34a7ab8a7250228 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 18 Dec 2018 12:43:46 +0100
Subject: [PATCH 430/719] Don't return multiindexes for 1d index data

---
 qcodes/dataset/data_set.py               | 2 ++
 qcodes/tests/dataset/helper_functions.py | 6 ++++++
 2 files changed, 8 insertions(+)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 297cbc244be..28d7d2d495e 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -899,6 +899,8 @@ def get_data_as_pandas_dataframe(self,
                 continue
             if len(keys) == 1:
                 index = None
+            elif len(keys) == 2:
+                index = pd.Index(subdict[keys[1]].ravel(), name=keys[1])
             else:
                 index = pd.MultiIndex.from_arrays(
                     tuple(subdict[key].ravel() for key in keys[1:]),
diff --git a/qcodes/tests/dataset/helper_functions.py b/qcodes/tests/dataset/helper_functions.py
index 25e58cbaeb1..8238c7910ed 100644
--- a/qcodes/tests/dataset/helper_functions.py
+++ b/qcodes/tests/dataset/helper_functions.py
@@ -97,6 +97,12 @@ def verify_dataframe_for_single_param(dataframe: pandas.DataFrame,
 
         for row in range(len(nrows)):
             row_index_values = index_values[row]
+            # one dimensional arrays will have single values for there indexed
+            # not tuples as they don't use multiindex. Put these in tuples
+            # for easy comparison
+            if not isinstance(dataframe.index, pandas.MultiIndex):
+                row_index_values = (row_index_values,)
+
             expected_values = \
                 tuple(expected_index_values[indexnum].ravel()[row]
                       for indexnum in range(nindexes))

From fd435adc298bbc30a486dbadd8108292907ac26e Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 21 Dec 2018 13:39:05 +0100
Subject: [PATCH 431/719] Add DataSet to public api

---
 docs/api/public.rst        | 2 ++
 docs/conf.py               | 1 +
 qcodes/dataset/data_set.py | 2 +-
 3 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/docs/api/public.rst b/docs/api/public.rst
index 302e6ae104e..9a8bff909dd 100644
--- a/docs/api/public.rst
+++ b/docs/api/public.rst
@@ -95,7 +95,9 @@ DataSet
     qcodes.dataset.database.initialise_database
     qcodes.dataset.database.initialise_or_create_database_at
 
+    qcodes.dataset.data_set.DataSet
     qcodes.dataset.data_set.load_by_id
+    qcodes.dataset.data_set.load_by_guid
 
     qcodes.dataset.plotting.plot_by_id
 
diff --git a/docs/conf.py b/docs/conf.py
index 7a320545dff..048556b47fd 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -358,6 +358,7 @@
 
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
+    'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None),
     'matplotlib': ('https://matplotlib.org/', None),
     'python': ('https://docs.python.org/3.6', None),
     'numpy': ('https://docs.scipy.org/doc/numpy', None),
diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index 28d7d2d495e..b361bba2fd1 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -873,7 +873,7 @@ def get_data_as_pandas_dataframe(self,
         If provided, the start and end arguments select a range of results
         by result count (index). If the range is empty - that is, if the end is
         less than or equal to the start, or if start is after the current end
-        of the DataSet – then a list of empty arrays is returned.
+        of the DataSet – then a dict of empty dataframes is returned.
 
         Args:
             *params: string parameter names, QCoDeS Parameter objects, and

From da0eb57d982b817c2b714b77ce6f4e2eef6bb608 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 21 Dec 2018 15:18:52 +0100
Subject: [PATCH 432/719] Improve docstring

---
 qcodes/dataset/data_set.py | 21 ++++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/qcodes/dataset/data_set.py b/qcodes/dataset/data_set.py
index b361bba2fd1..696f8df7029 100644
--- a/qcodes/dataset/data_set.py
+++ b/qcodes/dataset/data_set.py
@@ -861,19 +861,23 @@ def get_data_as_pandas_dataframe(self,
             Dict[str, pd.DataFrame]:
         """
         Returns the values stored in the DataSet for the specified parameters
-        and their dependencies as a dict of Pandas DataFrames. Each element in
-        the dict is indexed by the names of the requested parameters.
+        and their dependencies as a dict of :py:class:`pandas.DataFrame`\s
+        Each element in the dict is indexed by the names of the requested
+        parameters.
 
         Each DataFrame contains a column for the data and is indexed by a
-        MultiIndex formed from all the setpoints of the parameter.
+        :py:class:`pandas.MultiIndex` formed from all the setpoints
+        of the parameter.
 
-        If no parameters are supplied dataframes will be be returned for all
-        parameters that are not them self dependencies of other parameters.
+        If no parameters are supplied data will be be
+        returned for all parameters in the dataset that are not them self
+        dependencies of other parameters.
 
         If provided, the start and end arguments select a range of results
         by result count (index). If the range is empty - that is, if the end is
         less than or equal to the start, or if start is after the current end
-        of the DataSet – then a dict of empty dataframes is returned.
+        of the DataSet – then a dict of empty :py:class:`pandas.DataFrame`\s is
+        returned.
 
         Args:
             *params: string parameter names, QCoDeS Parameter objects, and
@@ -886,7 +890,10 @@ def get_data_as_pandas_dataframe(self,
                 None
 
         Returns:
-
+            Dictionary from requested parameter names to
+            :py:class:`pandas.DataFrame`\s with the requested parameter as
+            a column and a indexed by a :py:class:`pandas.MultiIndex` formed
+            by the dependencies.
         """
         dfs = {}
         datadict = self.get_parameter_data(*params,

From cd354fee5ca616d84d23d55fd5bcbd19c8a0c5a1 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 21 Dec 2018 15:52:58 +0100
Subject: [PATCH 433/719] Fix ambiguous type

---
 qcodes/data/format.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/qcodes/data/format.py b/qcodes/data/format.py
index 0f8f012e5b4..03e6ab21b7b 100644
--- a/qcodes/data/format.py
+++ b/qcodes/data/format.py
@@ -2,6 +2,11 @@
 from traceback import format_exc
 from operator import attrgetter
 import logging
+from typing import TYPE_CHECKING, Set
+
+if TYPE_CHECKING:
+    from .data_set import DataSet
+
 
 log = logging.getLogger(__name__)
 
@@ -69,7 +74,7 @@ def write(self, data_set, io_manager, location, write_metadata=True,
         """
         raise NotImplementedError
 
-    def read(self, data_set):
+    def read(self, data_set: 'DataSet') -> None:
         """
         Read the entire ``DataSet``.
 
@@ -80,7 +85,7 @@ def read(self, data_set):
         initialization functionality defined here.
 
         Args:
-            data_set (DataSet): the data to read into. Should already have
+            data_set: the data to read into. Should already have
                 attributes ``io`` (an io manager), ``location`` (string),
                 and ``arrays`` (dict of ``{array_id: array}``, can be empty
                 or can already have some or all of the arrays present, they
@@ -100,7 +105,7 @@ def read(self, data_set):
 
         self.read_metadata(data_set)
 
-        ids_read = set()
+        ids_read: Set[str] = set()
         for fn in data_files:
             with io_manager.open(fn, 'r') as f:
                 try:

From 3949a8d7b5c3c3c41c30b2dce8871df8453d81e7 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 21 Dec 2018 16:13:00 +0100
Subject: [PATCH 434/719] remove all non explicit refs to dataset

---
 qcodes/data/format.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/qcodes/data/format.py b/qcodes/data/format.py
index 03e6ab21b7b..43f3b458735 100644
--- a/qcodes/data/format.py
+++ b/qcodes/data/format.py
@@ -53,7 +53,7 @@ class Formatter:
     """
     ArrayGroup = namedtuple('ArrayGroup', 'shape set_arrays data name')
 
-    def write(self, data_set, io_manager, location, write_metadata=True,
+    def write(self, data_set: DataSet, io_manager, location, write_metadata=True,
               force_write=False, only_complete=True):
         """
         Write the DataSet to storage.
@@ -64,7 +64,7 @@ def write(self, data_set, io_manager, location, write_metadata=True,
         and when to just append or otherwise update the file(s).
 
         Args:
-            data_set (DataSet): the data we are writing.
+            data_set: the data we are writing.
             io_manager (io_manager): base physical location to write to.
             location (str): the file location within the io_manager.
             write_metadata (bool): if True, then the metadata is written to disk
@@ -114,14 +114,15 @@ def read(self, data_set: 'DataSet') -> None:
                     log.warning('error reading file ' + fn)
                     log.warning(format_exc())
 
-    def write_metadata(self, data_set, io_manager, location, read_first=True):
+    def write_metadata(self, data_set: 'DataSet',
+                       io_manager, location, read_first=True):
         """
         Write the metadata for this DataSet to storage.
 
         Subclasses must override this method.
 
         Args:
-            data_set (DataSet): the data we are writing.
+            data_set: the data we are writing.
             io_manager (io_manager): base physical location to write to.
             location (str): the file location within the io_manager.
             read_first (bool, optional): whether to first look for previously
@@ -130,18 +131,18 @@ def write_metadata(self, data_set, io_manager, location, read_first=True):
         """
         raise NotImplementedError
 
-    def read_metadata(self, data_set):
+    def read_metadata(self, data_set: 'DataSet'):
         """
         Read the metadata from this DataSet from storage.
 
         Subclasses must override this method.
 
         Args:
-            data_set (DataSet): the data to read metadata into
+            data_set: the data to read metadata into
         """
         raise NotImplementedError
 
-    def read_one_file(self, data_set, f, ids_read):
+    def read_one_file(self, data_set: 'DataSet', f, ids_read):
         """
         Read data from a single file into a ``DataSet``.
 
@@ -150,7 +151,7 @@ def read_one_file(self, data_set, f, ids_read):
         time, or ``read`` which finds matching files on its own.
 
         Args:
-            data_set (DataSet): the data we are reading into.
+            data_set: the data we are reading into.
 
             f (file-like): a file-like object to read from, as provided by
                 ``io_manager.open``.

From 47a14075f486d17052f734a34f8c7b9e5b4f6175 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 21 Dec 2018 16:17:00 +0100
Subject: [PATCH 435/719] This needs to be a forward ref

---
 qcodes/data/format.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/data/format.py b/qcodes/data/format.py
index 43f3b458735..79ac290cd30 100644
--- a/qcodes/data/format.py
+++ b/qcodes/data/format.py
@@ -53,7 +53,7 @@ class Formatter:
     """
     ArrayGroup = namedtuple('ArrayGroup', 'shape set_arrays data name')
 
-    def write(self, data_set: DataSet, io_manager, location, write_metadata=True,
+    def write(self, data_set: 'DataSet', io_manager, location, write_metadata=True,
               force_write=False, only_complete=True):
         """
         Write the DataSet to storage.

From 2d259dcfe93756377281937db03ed9afc89cdb21 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 21 Dec 2018 16:35:35 +0100
Subject: [PATCH 436/719] Same for the gnuplot formatter

---
 qcodes/data/gnuplot_format.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/qcodes/data/gnuplot_format.py b/qcodes/data/gnuplot_format.py
index 82512a8f3d1..4e0cd9c9b37 100644
--- a/qcodes/data/gnuplot_format.py
+++ b/qcodes/data/gnuplot_format.py
@@ -7,6 +7,10 @@
 from qcodes.utils.helpers import deep_update, NumpyJSONEncoder
 from .data_array import DataArray
 from .format import Formatter
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from .data_set import DataSet
 
 
 log = logging.getLogger(__name__)
@@ -243,15 +247,16 @@ def _get_labels(self, labelstr):
             parts = re.split('"\\s+"', labelstr[1:-1])
             return [l.replace('\\"', '"').replace('\\\\', '\\') for l in parts]
 
-    def write(self, data_set, io_manager, location, force_write=False,
-              write_metadata=True, only_complete=True, filename=None):
+    def write(self, data_set: 'DataSet', io_manager, location,
+              force_write=False, write_metadata=True, only_complete=True,
+              filename=None):
         """
         Write updates in this DataSet to storage.
 
         Will choose append if possible, overwrite if not.
 
         Args:
-            data_set (DataSet): the data we're storing
+            data_set: the data we're storing
             io_manager (io_manager): the base location to write to
             location (str): the file location within io_manager
             only_complete (bool): passed to match_save_range, answers the
@@ -336,12 +341,13 @@ def write(self, data_set, io_manager, location, force_write=False,
             self.write_metadata(
                 data_set, io_manager=io_manager, location=location)
 
-    def write_metadata(self, data_set, io_manager, location, read_first=True):
+    def write_metadata(self, data_set: 'DataSet', io_manager, location,
+                       read_first=True):
         """
         Write all metadata in this DataSet to storage.
 
         Args:
-            data_set (DataSet): the data we're storing
+            data_set: the data we're storing
 
             io_manager (io_manager): the base location to write to
 

From e551376c815a7c1dc592f78e3d6c87f1b3deb7a7 Mon Sep 17 00:00:00 2001
From: nyxus <gercoversloot@gmail.com>
Date: Fri, 21 Dec 2018 16:47:11 +0100
Subject: [PATCH 437/719] RTO1000 driver: Added is_triggered, is_running and is
 acquiring functions, trigger value_mapping to  validator, Fixed typos; Added
 measurement to the RTO1000 example program

---
 ...Schwarz RTO 1000 series Oscilloscope.ipynb | 1296 ++++-------------
 .../rohde_schwarz/RTO1000.py                  |   26 +-
 2 files changed, 339 insertions(+), 983 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Rohde Schwarz RTO 1000 series Oscilloscope.ipynb b/docs/examples/driver_examples/Qcodes example with Rohde Schwarz RTO 1000 series Oscilloscope.ipynb
index 3837ada70e7..6b900285068 100644
--- a/docs/examples/driver_examples/Qcodes example with Rohde Schwarz RTO 1000 series Oscilloscope.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Rohde Schwarz RTO 1000 series Oscilloscope.ipynb	
@@ -10,7 +10,12 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:00.839361Z",
+     "start_time": "2018-12-21T15:20:59.084361Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%matplotlib notebook\n",
@@ -23,24 +28,36 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:00.909861Z",
+     "start_time": "2018-12-21T15:21:00.841361Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: Rohde&Schwarz RTO (serial:1316.1000k44/400363, firmware:3.70.1.0) in 0.23s\n"
+      "Connected to: Rohde&Schwarz RTO (serial:1316.1000k24/400376, firmware:3.70.1.0) in 0.06s\n"
      ]
     }
    ],
    "source": [
-    "rto = RTO1000('rto', 'TCPIP0::172.20.3.86::inst0::INSTR')"
+    "# Select your scope based on its ip address\n",
+    "# rto = RTO1000('rto', 'TCPIP0::172.20.3.86::inst0::INSTR')\n",
+    "rto = RTO1000('rto', 'TCPIP0::192.168.0.3::inst0::INSTR', model='RTO1024')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:00.913861Z",
+     "start_time": "2018-12-21T15:21:00.910861Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Before we do anything, let's make sure that the instrument is in a clean state\n",
@@ -53,7 +70,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:00.949361Z",
+     "start_time": "2018-12-21T15:21:00.945861Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# The display should be set to 'remote' for better performance regarding data acquisition\n",
@@ -65,102 +87,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "rto:\n",
-      "\tparameter                value\n",
-      "--------------------------------------------------------------------------------\n",
-      "IDN                       :\t{'firmware': '3.70.1.0', 'serial': '1316.1000k44/...\n",
-      "acquisition_sample_rate   :\t1e+10 (Sa/s)\n",
-      "completed_acquisitions    :\t270421 \n",
-      "dataformat                :\tASC,0 \n",
-      "display                   :\tview \n",
-      "high_definition_bandwidth :\t1e+06 (Hz)\n",
-      "high_definition_state     :\tOFF \n",
-      "num_acquisitions          :\t1 \n",
-      "run_mode                  :\tRUN CONT \n",
-      "sampling_rate             :\t10000000000 (Sa/s)\n",
-      "timebase_position         :\t0 (s)\n",
-      "timebase_range            :\t1e-07 (s)\n",
-      "timebase_scale            :\t1e-08 (s/div)\n",
-      "timeout                   :\t5 (s)\n",
-      "trigger_display           :\tOFF \n",
-      "trigger_edge_slope        :\tPOS \n",
-      "trigger_level             :\t0 \n",
-      "trigger_source            :\tCH1 \n",
-      "trigger_type              :\tEDGE \n",
-      "rto_channel3:\n",
-      "\tparameter  value\n",
-      "--------------------------------------------------------------------------------\n",
-      "arithmetics :\tOFF \n",
-      "bandwidth   :\tFULL \n",
-      "coupling    :\tDCL \n",
-      "ground      :\t0 \n",
-      "impedance   :\t50 (Ohm)\n",
-      "invert      :\t0 \n",
-      "offset      :\t0 (V)\n",
-      "overload    :\t0 \n",
-      "position    :\t0 (div)\n",
-      "range       :\t0.5 (V)\n",
-      "scale       :\t0.05 (V/div)\n",
-      "state       :\t0 \n",
-      "trace       :\tNot available (V)\n",
-      "rto_channel4:\n",
-      "\tparameter  value\n",
-      "--------------------------------------------------------------------------------\n",
-      "arithmetics :\tOFF \n",
-      "bandwidth   :\tFULL \n",
-      "coupling    :\tDCL \n",
-      "ground      :\t0 \n",
-      "impedance   :\t50 (Ohm)\n",
-      "invert      :\t0 \n",
-      "offset      :\t0 (V)\n",
-      "overload    :\t0 \n",
-      "position    :\t0 (div)\n",
-      "range       :\t0.5 (V)\n",
-      "scale       :\t0.05 (V/div)\n",
-      "state       :\t0 \n",
-      "trace       :\tNot available (V)\n",
-      "rto_channel2:\n",
-      "\tparameter  value\n",
-      "--------------------------------------------------------------------------------\n",
-      "arithmetics :\tOFF \n",
-      "bandwidth   :\tFULL \n",
-      "coupling    :\tDCL \n",
-      "ground      :\t0 \n",
-      "impedance   :\t50 (Ohm)\n",
-      "invert      :\t0 \n",
-      "offset      :\t0 (V)\n",
-      "overload    :\t0 \n",
-      "position    :\t0 (div)\n",
-      "range       :\t0.5 (V)\n",
-      "scale       :\t0.05 (V/div)\n",
-      "state       :\t0 \n",
-      "trace       :\tNot available (V)\n",
-      "rto_channel1:\n",
-      "\tparameter  value\n",
-      "--------------------------------------------------------------------------------\n",
-      "arithmetics :\tOFF \n",
-      "bandwidth   :\tFULL \n",
-      "coupling    :\tDCL \n",
-      "ground      :\t0 \n",
-      "impedance   :\t50 (Ohm)\n",
-      "invert      :\t0 \n",
-      "offset      :\t0 (V)\n",
-      "overload    :\t0 \n",
-      "position    :\t0 (div)\n",
-      "range       :\t0.5 (V)\n",
-      "scale       :\t0.05 (V/div)\n",
-      "state       :\t1 \n",
-      "trace       :\tNot available (V)\n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T11:03:26.701022Z",
+     "start_time": "2018-12-21T11:00:30.886366Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# To get an overview of all instrument settings, print_readable_sanpshot is great\n",
     "rto.print_readable_snapshot(update=True)"
@@ -187,8 +121,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:07.927861Z",
+     "start_time": "2018-12-21T15:21:07.923861Z"
+    }
+   },
    "outputs": [],
    "source": [
     "rto.ch1.coupling('DC')  # 'DC' means DC 50 Ohm, 'DCLimit' means DC 1 MOhm, 'AC' means AC 1 MOhm.\n",
@@ -206,8 +145,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:08.990361Z",
+     "start_time": "2018-12-21T15:21:08.979861Z"
+    }
+   },
    "outputs": [],
    "source": [
     "rto.trigger_source('CH1')\n",
@@ -227,8 +171,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:10.215361Z",
+     "start_time": "2018-12-21T15:21:10.211861Z"
+    }
+   },
    "outputs": [],
    "source": [
     "rto.run_cont() # run continuously"
@@ -245,9 +194,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": true
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:11.334861Z",
+     "start_time": "2018-12-21T15:21:11.331361Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -266,8 +218,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:14.559361Z",
+     "start_time": "2018-12-21T15:21:14.555861Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# This is EXACTLY matching the waveform...\n",
@@ -277,9 +234,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": true
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:16.710361Z",
+     "start_time": "2018-12-21T15:21:16.707361Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -303,8 +263,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:18.917361Z",
+     "start_time": "2018-12-21T15:21:18.906361Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Let's do 1000 averages of our sine wave\n",
@@ -316,9 +281,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": true
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:21.334361Z",
+     "start_time": "2018-12-21T15:21:21.330361Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -337,9 +305,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": true
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:24.054861Z",
+     "start_time": "2018-12-21T15:21:24.050861Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -351,8 +322,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:26.051361Z",
+     "start_time": "2018-12-21T15:21:26.048361Z"
+    }
+   },
    "outputs": [],
    "source": [
     "##########################\n",
@@ -367,8 +343,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:27.625861Z",
+     "start_time": "2018-12-21T15:21:27.490361Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Next, the trace must be prepared. This ensures that all settings are correct\n",
@@ -377,18 +358,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:29.821861Z",
+     "start_time": "2018-12-21T15:21:28.528861Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "DataSet:\n",
-      "   location = 'data/2017-12-14/#007_{name}_10-09-48'\n",
+      "   location = 'data/2018-12-21/#003_{name}_16-21-28'\n",
       "   <Type>   | <array_id> | <array.name> | <array.shape>\n",
       "   Measured | trace      | trace        | (100000,)\n",
-      "acquired at 2017-12-14 10:09:49\n"
+      "acquired at 2018-12-21 16:21:29\n"
      ]
     },
     {
@@ -473,7 +459,7 @@
        "        };\n",
        "\n",
        "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
+       "        fig.ws.close();\n",
        "    }\n",
        "\n",
        "    this.ws.onmessage = this._make_on_message_function(this);\n",
@@ -948,9 +934,9 @@
        "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
        "    this.message.textContent = tooltip;\n",
        "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
        "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
        "\n",
        "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
        "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
@@ -968,7 +954,7 @@
        "    // Register the callback with on_msg.\n",
        "    comm.on_msg(function(msg) {\n",
        "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
        "        ws.onmessage(msg['content']['data'])\n",
        "    });\n",
        "    return ws;\n",
@@ -1120,9 +1106,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -1171,7 +1160,7 @@
     {
      "data": {
       "text/html": [
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\" 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\" width=\"600\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1189,18 +1178,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:39.668861Z",
+     "start_time": "2018-12-21T15:21:38.208861Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "DataSet:\n",
-      "   location = 'data/2017-12-14/#008_{name}_10-09-51'\n",
+      "   location = 'data/2018-12-21/#004_{name}_16-21-38'\n",
       "   <Type>   | <array_id> | <array.name> | <array.shape>\n",
       "   Measured | trace      | trace        | (100000,)\n",
-      "acquired at 2017-12-14 10:09:52\n"
+      "acquired at 2018-12-21 16:21:39\n"
      ]
     },
     {
@@ -1285,7 +1279,7 @@
        "        };\n",
        "\n",
        "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
+       "        fig.ws.close();\n",
        "    }\n",
        "\n",
        "    this.ws.onmessage = this._make_on_message_function(this);\n",
@@ -1760,9 +1754,9 @@
        "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
        "    this.message.textContent = tooltip;\n",
        "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
        "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
        "\n",
        "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
        "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
@@ -1780,7 +1774,7 @@
        "    // Register the callback with on_msg.\n",
        "    comm.on_msg(function(msg) {\n",
        "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
        "        ws.onmessage(msg['content']['data'])\n",
        "    });\n",
        "    return ws;\n",
@@ -1932,9 +1926,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -1983,7 +1980,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" width=\"600\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2015,9 +2012,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 18,
    "metadata": {
-    "collapsed": true
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:52.234861Z",
+     "start_time": "2018-12-21T15:21:52.231361Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -2027,9 +2027,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 19,
    "metadata": {
-    "collapsed": true
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:55.264861Z",
+     "start_time": "2018-12-21T15:21:55.126861Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -2038,9 +2041,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "metadata": {
-    "collapsed": true
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:21:56.419361Z",
+     "start_time": "2018-12-21T15:21:56.414861Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -2051,18 +2057,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:22:07.554861Z",
+     "start_time": "2018-12-21T15:21:57.837861Z"
+    },
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "DataSet:\n",
-      "   location = 'data/2017-12-14/#011_{name}_10-43-29'\n",
+      "   location = 'data/2018-12-21/#005_{name}_16-21-58'\n",
       "   <Type>   | <array_id> | <array.name> | <array.shape>\n",
       "   Measured | trace      | trace        | (1000000,)\n",
-      "acquired at 2017-12-14 10:43:50\n"
+      "acquired at 2018-12-21 16:22:07\n"
      ]
     },
     {
@@ -2147,7 +2159,7 @@
        "        };\n",
        "\n",
        "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
+       "        fig.ws.close();\n",
        "    }\n",
        "\n",
        "    this.ws.onmessage = this._make_on_message_function(this);\n",
@@ -2622,9 +2634,9 @@
        "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
        "    this.message.textContent = tooltip;\n",
        "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
        "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
        "\n",
        "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
        "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
@@ -2642,7 +2654,7 @@
        "    // Register the callback with on_msg.\n",
        "    comm.on_msg(function(msg) {\n",
        "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
        "        ws.onmessage(msg['content']['data'])\n",
        "    });\n",
        "    return ws;\n",
@@ -2794,9 +2806,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -2845,7 +2860,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" width=\"599.5333285686705\">"
+       "<div id='36f9463f-4f45-4c3c-97ea-a73b8d1d13d9'></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2862,828 +2877,142 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:22:49.949861Z",
+     "start_time": "2018-12-21T15:22:07.556861Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# We can see the averaging effect by increasing the number of averages\n",
+    "\n",
+    "rto.num_acquisitions(500)\n",
+    "rto.ch1.arithmetics('AVERAGE')  # other options: 'ENVELOPE' and 'OFF'\n",
+    "rto.run_single()  # and perform a single run. NOTE: YOU MUST CALL THIS AS THE LAST THING BEFORE GETTING THE TRACE!!!\n",
+    "data_avgd = qc.Measure(rto.ch1.trace).run()\n",
+    "plot = qc.MatPlot(data_avgd.arrays['trace'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T13:17:01.236984Z",
+     "start_time": "2018-12-21T13:17:01.233484Z"
+    },
+    "collapsed": true
+   },
+   "source": [
+    "# Measurements\n",
+    "Start 1 of the 8 mesurements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:23:06.811361Z",
+     "start_time": "2018-12-21T15:23:06.804861Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "def do_measurement(rto, nr_measurements, nr_samples):\n",
+    "    \"\"\"\n",
+    "    Will clean prevous measurement en start a new one\n",
+    "    Args:\n",
+    "        rto: scope driver object\n",
+    "        nr_measurements: number of used/enabled measurements (up to 8)\n",
+    "        nr_samples: minimal number of samples before the measurement will stop\n",
+    "    \"\"\"\n",
+    "    # Clear the prevous mesurement\n",
+    "    for meas_ch in range(1, nr_measurements + 1):\n",
+    "        rto.submodules['meas{}'.format(meas_ch)].clear()\n",
+    "    rto.opc()\n",
+    "    rto.run_continues()\n",
+    "    \n",
+    "    # Check if measurement is running\n",
+    "    if(rto.is_acquiring() == False):\n",
+    "        raise RuntimeError('Cannot start measuremet; scope is not trigged')\n",
+    "        \n",
+    "    # Wait for the scope to be ready\n",
+    "    while rto.submodules['meas{}'.format(nr_measurements)].event_count() < 10:\n",
+    "        time.sleep(0.1)\n",
+    "    rto.opc() \n",
+    "\n",
+    "    ## Actual measurement \n",
+    "    for meas_ch in range(1, nr_measurements + 1):\n",
+    "        rto.submodules['meas{}'.format(meas_ch)].clear()\n",
+    "    while rto.submodules['meas{}'.format(nr_measurements)].event_count() < nr_samples:\n",
+    "        time.sleep(0.1)\n",
+    "    rto.stop_opc()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:25:39.973861Z",
+     "start_time": "2018-12-21T15:25:39.884861Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DataSet:\n",
-      "   location = 'data/2017-12-14/#012_{name}_10-45-40'\n",
-      "   <Type>   | <array_id> | <array.name> | <array.shape>\n",
-      "   Measured | trace      | trace        | (1000000,)\n",
-      "acquired at 2017-12-14 10:46:46\n"
+      "Wait for results\n",
+      "Ch: 1; frequency: 100kHz\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "rto.timebase_range(1.5 * (1/100e3)) # Need a bit more than 1 period \n",
+    "rto.ch1.range(0.11) # Need a bit more the 100mV\n",
+    "\n",
+    "# Configure measurement 1 to determine frequency \n",
+    "rto.meas1.enable(\"ON\")\n",
+    "rto.meas1.source(\"C1W1\") # See rto.meas1.sources for more options\n",
+    "rto.meas1.category(\"AMPTime\") # See rto.meas1.categories for more options\n",
+    "rto.meas1.main(\"FREQuency\")  # See rto.meas1.meas_type for more options\n",
+    "rto.meas1.clear()\n",
+    "rto.meas1.statistics_enable(\"ON\")\n",
+    "rto.run_continues()\n",
+    "\n",
+    "#start measurement\n",
+    "print(\"Wait for results\")\n",
+    "do_measurement(rto, 1, 500)\n",
+    "actualFq = rto.meas1.result_avg() * 1e-3\n",
+    "print(f\"Ch: 1; frequency: {actualFq:.0f}kHz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-21T15:25:06.628361Z",
+     "start_time": "2018-12-21T15:25:06.624361Z"
+    }
+   },
+   "outputs": [
     {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "<img 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\" width=\"599.5333285686705\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Enum: {'D13', 'Z2I4', 'C4W2', 'Z1I3', 'TRK4', 'SG2TL1', 'Z1I2', 'D12', 'SBUS3', 'Z1I1', 'Z2I3', 'R1', 'M1', 'C2W1', 'D10', 'SBUS4', 'Z2V1', 'R4', 'D0', 'D2', 'D7', 'M4', 'R2', 'R3', 'C2W3', 'D8', 'M2', 'D4', 'D6', 'TRK2', 'TRK8', 'Z1V3', 'Z2V4', 'C3W1', 'D3', 'Z2V3', 'TRK5', 'TRK1', 'SG1TL2', 'C4W3', 'SG3TL1', 'Z1I4', 'SG3TL2', 'C1W3', 'SG2TL2', 'C2W2', 'Z2I1', 'SG4TL2', 'C1W1', 'SG4TL1', 'Z2V2', 'C3W2', 'D1', 'TRK3', 'TRK6', 'C3W3', 'Z2I2', 'C1W2', 'D11', 'Z1V2', 'SBUS2', 'Z1V1', 'D9', 'TRK7', 'D5', 'SG1TL1', 'Z1V4', 'C4W1', 'D15', 'D14', 'M3', 'SBUS1'}>\n"
+     ]
     }
    ],
    "source": [
-    "# We can see the averaging effect by increasing the number of averages\n",
-    "\n",
-    "rto.num_acquisitions(500)\n",
-    "rto.ch1.arithmetics('AVERAGE')  # other options: 'ENVELOPE' and 'OFF'\n",
-    "rto.run_single()  # and perform a single run. NOTE: YOU MUST CALL THIS AS THE LAST THING BEFORE GETTING THE TRACE!!!\n",
-    "data_avgd = qc.Measure(rto.ch1.trace).run()\n",
-    "plot = qc.MatPlot(data_avgd.arrays['trace'])"
+    "print(rto.meas1.sources)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -3702,10 +3031,23 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.7"
   },
   "nbsphinx": {
    "execute": "never"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,
diff --git a/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py b/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py
index 375784820ee..e4475e578f6 100644
--- a/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py
+++ b/qcodes/instrument_drivers/rohde_schwarz/RTO1000.py
@@ -131,15 +131,15 @@ def get_raw(self):
 
 class ScopeMeasurement(InstrumentChannel):
     """
-    Class to hold a mesurement of the scope
+    Class to hold a measurement of the scope
     """
 
     def __init__(self, parent: Instrument, name: str, meas_nr: int):
         """
         Args:
             parent: The instrument to which the channel is attached
-            name: The name of the mesurement
-            meas_nr: The number of the mesurment in question. Must match the
+            name: The name of the measurement
+            meas_nr: The number of the measurement in question. Must match the
                 actual number as used by the instrument (1..8)
         """
 
@@ -212,7 +212,7 @@ def __init__(self, parent: Instrument, name: str, meas_nr: int):
                            label=f'Measurement {meas_nr} enable',
                            set_cmd=f'MEASurement{meas_nr}:ENABle {{}}',
                            vals=vals.Enum('ON', 'OFF'),
-                           docstring='Switches the mesurment on or off')
+                           docstring='Switches the measurement on or off')
 
         self.add_parameter('source',
                            label=f'Measurement {meas_nr} source',
@@ -233,7 +233,7 @@ def __init__(self, parent: Instrument, name: str, meas_nr: int):
                            set_cmd=f'MEASurement{meas_nr}:SSRC {{}}',
                            vals=self.sources,
                            docstring='Set the second source of a measurement if the ' \
-                                     'measurement only needs mutliple sources')
+                                     'measurement only needs multiple sources')
 
         self.add_parameter('category',
                            label=f'Measurement {meas_nr} category',
@@ -251,7 +251,7 @@ def __init__(self, parent: Instrument, name: str, meas_nr: int):
                            label=f'Measurement {meas_nr} enable statistics',
                            set_cmd=f'MEASurement{meas_nr}:STATistics:ENABle {{}}',
                            vals=vals.Enum('ON', 'OFF'),
-                           docstring='Switches the mesurment on or off')
+                           docstring='Switches the measurement on or off')
 
         self.add_parameter('clear',
                            label=f'Measurement {meas_nr} clear statistics',
@@ -446,6 +446,8 @@ def __init__(self, name: str, address: str,
         super().__init__(name=name, address=address, timeout=timeout,
                          terminator=terminator, **kwargs)
 
+
+
         # With firmware versions earlier than 3.65, it seems that the
         # model number can NOT be queried from the instrument
         # (at least fails with RTO1024, fw 2.52.1.1), so in that case
@@ -678,6 +680,7 @@ def __init__(self, name: str, address: str,
         self.add_function('reset', call_cmd='*RST')
         self.add_parameter('opc', get_cmd='*OPC?')
         self.add_parameter('stop_opc', get_cmd='STOP;*OPC?')
+        self.add_parameter('status_operation', get_cmd='STATus:OPERation:CONDition?', get_parser=int)
         self.add_function('run_continues', call_cmd='RUNContinous')
         # starts the shutdown of the system
         self.add_function('system_shutdown', call_cmd='SYSTem:EXIT')
@@ -698,6 +701,17 @@ def run_single(self) -> None:
         self.write('SINGLE')
         self.run_mode.set('RUN Nx SINGLE')
 
+    def is_triggered(self) -> bool:
+        waitTriggerMask = 0b01000;
+        return bool(self.status_operation() & waitTriggerMask) == False
+
+    def is_running(self) -> bool:
+        measuringMask = 0b10000;
+        return bool(self.status_operation() & measuringMask)
+
+    def is_acquiring(self) -> bool:
+        return self.is_triggered() & self.is_running()
+
     #########################
     # Specialised set/get functions
 

From 8066c2edfe29936787b68b6b77469499e6ec9956 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 21 Dec 2018 16:54:11 +0100
Subject: [PATCH 438/719] ignore incompatible signature

---
 qcodes/data/gnuplot_format.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/qcodes/data/gnuplot_format.py b/qcodes/data/gnuplot_format.py
index 4e0cd9c9b37..b22b846cd5d 100644
--- a/qcodes/data/gnuplot_format.py
+++ b/qcodes/data/gnuplot_format.py
@@ -247,8 +247,12 @@ def _get_labels(self, labelstr):
             parts = re.split('"\\s+"', labelstr[1:-1])
             return [l.replace('\\"', '"').replace('\\\\', '\\') for l in parts]
 
-    def write(self, data_set: 'DataSet', io_manager, location,
-              force_write=False, write_metadata=True, only_complete=True,
+    # this signature is unfortunatly incompatible with the super class
+    # so we have to ignore type errors
+    def write(self,
+              data_set: 'DataSet',  # type: ignore
+              io_manager, location, force_write=False,
+              write_metadata=True, only_complete=True,
               filename=None):
         """
         Write updates in this DataSet to storage.

From 6b2436e8e74a5fdf505670cce1bb6d5fabfc5385 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 21 Dec 2018 17:01:02 +0100
Subject: [PATCH 439/719] wrong line

---
 qcodes/data/gnuplot_format.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/data/gnuplot_format.py b/qcodes/data/gnuplot_format.py
index b22b846cd5d..ef0869781da 100644
--- a/qcodes/data/gnuplot_format.py
+++ b/qcodes/data/gnuplot_format.py
@@ -249,8 +249,8 @@ def _get_labels(self, labelstr):
 
     # this signature is unfortunatly incompatible with the super class
     # so we have to ignore type errors
-    def write(self,
-              data_set: 'DataSet',  # type: ignore
+    def write(self,  # type: ignore
+              data_set: 'DataSet',
               io_manager, location, force_write=False,
               write_metadata=True, only_complete=True,
               filename=None):

From 0cc1cda6e1ed56eab92d98544bbea949bb0ec2d3 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Fri, 21 Dec 2018 09:59:02 -0800
Subject: [PATCH 440/719] make sphynx pass

---
 qcodes/instrument_drivers/Lakeshore/Model_325.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/Lakeshore/Model_325.py b/qcodes/instrument_drivers/Lakeshore/Model_325.py
index 4d74634bdb5..0ac625a5d92 100644
--- a/qcodes/instrument_drivers/Lakeshore/Model_325.py
+++ b/qcodes/instrument_drivers/Lakeshore/Model_325.py
@@ -9,7 +9,7 @@
 
 def read_curve_file(curve_file: TextIO) -> dict:
     """
-    Read a curve file with extension *.330
+    Read a curve file with extension .330
     The file format of this file is shown in test_lakeshore_file_parser.py
     in the test module
 
@@ -542,10 +542,10 @@ def upload_curve(
     def upload_curve_from_file(self, index: int, file_path: str) -> None:
         """
         Upload a curve from a curve file. Note that we only support
-        curve files with extension *.330
+        curve files with extension .330
         """
         if not file_path.endswith(".330"):
-            raise ValueError("Only curve files with extension *.330 are supported")
+            raise ValueError("Only curve files with extension .330 are supported")
 
         with open(file_path, "r") as curve_file:
             file_data = read_curve_file(curve_file)

From 13fed7163c2362fa8ff4cd1351bbd632cc0604b8 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Fri, 21 Dec 2018 10:01:04 -0800
Subject: [PATCH 441/719] nicer looking test

---
 qcodes/tests/drivers/test_lakeshore_file_parser.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/drivers/test_lakeshore_file_parser.py b/qcodes/tests/drivers/test_lakeshore_file_parser.py
index 74897ee6991..e9923efb0ca 100644
--- a/qcodes/tests/drivers/test_lakeshore_file_parser.py
+++ b/qcodes/tests/drivers/test_lakeshore_file_parser.py
@@ -3,7 +3,8 @@
 
 from qcodes.instrument_drivers.Lakeshore.Model_325 import read_curve_file, get_sanitize_data
 
-curve_file_content = """Sensor Model:   CX-1050-SD-HT-1.4L
+curve_file_content = """ \
+Sensor Model:   CX-1050-SD-HT-1.4L
 Serial Number:  X116121
 Interpolation Method:   Lagrangian
 SetPoint Limit: 325.0      (Kelvin)

From 21a092995b0400ebd153b289bba009e2e400316a Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 28 Dec 2018 11:15:11 +0100
Subject: [PATCH 442/719] Add a failing test for line plot + heatmap

---
 qcodes/tests/dataset/test_plotting.py | 33 +++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/qcodes/tests/dataset/test_plotting.py b/qcodes/tests/dataset/test_plotting.py
index 2247b0f5e06..7f18321d050 100644
--- a/qcodes/tests/dataset/test_plotting.py
+++ b/qcodes/tests/dataset/test_plotting.py
@@ -6,6 +6,11 @@
 from qcodes.dataset.plotting import _make_rescaled_ticks_and_units, \
     _ENGINEERING_PREFIXES, _UNITS_FOR_RESCALING
 
+from qcodes.dataset.plotting import plot_by_id
+from qcodes.dataset.measurements import Measurement
+from qcodes.tests.instrument_mocks import DummyInstrument
+from qcodes.tests.dataset.temporary_databases import empty_temp_db, experiment
+
 
 @given(param_name=text(min_size=1, max_size=10),
        param_label=text(min_size=0, max_size=15),
@@ -83,3 +88,31 @@ def test_rescaled_ticks_and_units(scale, unit,
     # also test the fact that "{:g}" is used in ticks formatter function
     assert '2.12346' == ticks_formatter(2.123456789 / (10 ** (-scale)))
 
+
+def test_plot_by_id_line_and_heatmap(experiment):
+    """
+    Test that line plots and heatmaps can be plotted together
+    """
+    inst = DummyInstrument('dummy', gates=['s1', 'm1', 's2', 'm2'])
+
+    inst.m1.get = lambda: np.random.randn()
+    inst.m2.get = lambda: np.random.randint(0, 5)
+
+    meas = Measurement()
+    meas.register_parameter(inst.s1)
+    meas.register_parameter(inst.s2)
+    meas.register_parameter(inst.m2, setpoints=(inst.s1, inst.s2) )
+    meas.register_parameter(inst.m1, setpoints=(inst.s1,))
+
+    with meas.run() as datasaver:
+        for outer in range(10):
+            datasaver.add_result((inst.s1, outer),
+                                (inst.m1, inst.m1()))
+            for inner in range(10):
+                datasaver.add_result((inst.s1, outer),
+                                    (inst.s2, inner),
+                                    (inst.m2, inst.m2()))
+
+    dataid = datasaver.run_id
+    plot_by_id(dataid)
+    plot_by_id(dataid, cmap='bone')

From 5b9ef7d18ae7c1ec499fe404439817c5da1ba20f Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 28 Dec 2018 11:52:29 +0100
Subject: [PATCH 443/719] Add plot-kwarg handling contextmanager

---
 qcodes/dataset/plotting.py | 59 ++++++++++++++++++++++++++++++++------
 1 file changed, 51 insertions(+), 8 deletions(-)

diff --git a/qcodes/dataset/plotting.py b/qcodes/dataset/plotting.py
index db44f5f2d4a..07ea9be1e6d 100644
--- a/qcodes/dataset/plotting.py
+++ b/qcodes/dataset/plotting.py
@@ -8,6 +8,7 @@
 import matplotlib
 import matplotlib.pyplot as plt
 from matplotlib.ticker import FuncFormatter
+from contextlib import contextmanager
 
 import qcodes as qc
 from qcodes.dataset.data_set import load_by_id
@@ -37,6 +38,44 @@
 SUBPLOTS_KWARGS = SUBPLOTS_OWN_KWARGS.union(FIGURE_KWARGS)
 
 
+@contextmanager
+def appropriate_kwargs(plottype: str,
+                       ax: matplotlib.axes.Axes,
+                       colorbar: Optional[matplotlib.colorbar.Colorbar] = None,
+                       **kwargs):
+    """
+    NB: Only to be used inside :meth"`plot_by_id`.
+
+    Context manager to temporarily mutate the plotting kwargs to be appropriate
+    for a specific plottype. This is helpful since :meth:`plot_by_id` may have
+    to generate different kinds of plots (e.g. heatmaps and line plots) and
+    the user may want to specify kwargs only relevant to some of them
+    (e.g. 'cmap', that line plots cannot consume). Those kwargs should then not
+    be passed to all plots, which is what this contextmanager handles.
+
+    Args:
+        plottype: The plot type for which the kwargs should be adjusted
+        ax: The ax that is to be plotted on
+        colorbar: The colorbar that is to be used (if any)
+    """
+
+    def linehandler(**kwargs):
+        kwargs.pop('cmap', None)
+        return kwargs
+
+    def heatmaphandler(**kwargs):
+        if colorbar is None and 'cmap' not in kwargs:
+            kwargs['cmap'] = qc.config.plotting.default_color_map
+        return kwargs
+
+    switch = {'line': linehandler,
+              'point': linehandler,
+              'bar': linehandler,
+              'heatmap': heatmaphandler}
+
+    yield switch[plottype](**kwargs.copy())
+
+
 def plot_by_id(run_id: int,
                axes: Optional[Union[matplotlib.axes.Axes,
                               Sequence[matplotlib.axes.Axes]]]=None,
@@ -139,6 +178,7 @@ def plot_by_id(run_id: int,
 
         if len(data) == 2:  # 1D PLOTTING
             log.debug('Plotting by id, doing a 1D plot')
+            log.debug(f"kwargs are {kwargs}")
 
             xpoints = data[0]['data']
             ypoints = data[1]['data']
@@ -152,11 +192,14 @@ def plot_by_id(run_id: int,
                 xpoints = xpoints[order]
                 ypoints = ypoints[order]
 
-                ax.plot(xpoints, ypoints, **kwargs)
+                with appropriate_kwargs(plottype, ax, colorbar, **kwargs) as k:
+                    ax.plot(xpoints, ypoints, **k)
             elif plottype == 'point':
-                ax.scatter(xpoints, ypoints, **kwargs)
+                with appropriate_kwargs(plottype, ax, colorbar, **kwargs) as k:
+                    ax.scatter(xpoints, ypoints, **k)
             elif plottype == 'bar':
-                ax.bar(xpoints, ypoints, **kwargs)
+                with appropriate_kwargs(plottype, ax, colorbar, **kwargs) as k:
+                    ax.bar(xpoints, ypoints, **k)
             else:
                 raise ValueError('Unknown plottype. Something is way wrong.')
 
@@ -171,6 +214,7 @@ def plot_by_id(run_id: int,
 
         elif len(data) == 3:  # 2D PLOTTING
             log.debug('Plotting by id, doing a 2D plot')
+            log.debug(f"kwargs are {kwargs}")
 
             # From the setpoints, figure out which 2D plotter to use
             # TODO: The "decision tree" for what gets plotted how and how
@@ -190,11 +234,10 @@ def plot_by_id(run_id: int,
                            'unknown': plot_2d_scatterplot}
             plot_func = how_to_plot[plottype]
 
-            if colorbar is None and 'cmap' not in kwargs:
-                kwargs['cmap'] = qc.config.plotting.default_color_map
-
-            ax, colorbar = plot_func(xpoints, ypoints, zpoints, ax, colorbar,
-                                     **kwargs)
+            with appropriate_kwargs('heatmap', ax, colorbar, **kwargs) as k:
+                ax, colorbar = plot_func(xpoints, ypoints, zpoints,
+                                         ax, colorbar,
+                                         **k)
 
             _set_data_axes_labels(ax, data, colorbar)
 

From 7a7eabc654d6ca9ab518136e214c66b3223d1e8b Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 28 Dec 2018 11:59:45 +0100
Subject: [PATCH 444/719] Add test for context manager

---
 qcodes/tests/dataset/test_plotting.py | 35 ++++++++++++++++++++++++++-
 1 file changed, 34 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_plotting.py b/qcodes/tests/dataset/test_plotting.py
index 7f18321d050..8f961d5331c 100644
--- a/qcodes/tests/dataset/test_plotting.py
+++ b/qcodes/tests/dataset/test_plotting.py
@@ -2,11 +2,13 @@
 from hypothesis import given, example, assume
 from hypothesis.strategies import text, sampled_from, floats, lists, data, \
     one_of, just
+import matplotlib.pyplot as plt
 
+import qcodes as qc
 from qcodes.dataset.plotting import _make_rescaled_ticks_and_units, \
     _ENGINEERING_PREFIXES, _UNITS_FOR_RESCALING
 
-from qcodes.dataset.plotting import plot_by_id
+from qcodes.dataset.plotting import plot_by_id, appropriate_kwargs
 from qcodes.dataset.measurements import Measurement
 from qcodes.tests.instrument_mocks import DummyInstrument
 from qcodes.tests.dataset.temporary_databases import empty_temp_db, experiment
@@ -116,3 +118,34 @@ def test_plot_by_id_line_and_heatmap(experiment):
     dataid = datasaver.run_id
     plot_by_id(dataid)
     plot_by_id(dataid, cmap='bone')
+
+
+def test_appropriate_kwargs():
+    _, ax = plt.subplots()
+
+    kwargs = {'cmap': 'bone'}
+    check = kwargs.copy()
+
+    with appropriate_kwargs('line', ax, None, **kwargs) as ap_kwargs:
+        assert ap_kwargs == {}
+
+    assert kwargs == check
+
+    with appropriate_kwargs('point', ax, None, **kwargs) as ap_kwargs:
+        assert ap_kwargs == {}
+
+    assert kwargs == check
+
+    with appropriate_kwargs('bar', ax, None, **kwargs) as ap_kwargs:
+        assert ap_kwargs == {}
+
+    assert kwargs == check
+
+    with appropriate_kwargs('heatmap', ax, None, **kwargs) as ap_kwargs:
+        assert ap_kwargs == kwargs
+
+    assert kwargs == check
+
+    with appropriate_kwargs('heatmap', ax, None, **{}) as ap_kwargs:
+        assert len(ap_kwargs) == 1
+        assert ap_kwargs['cmap'] == qc.config.plotting.default_color_map

From cf2b8e6b40916018066205163b55016b44728033 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 2 Jan 2019 11:00:48 +0100
Subject: [PATCH 445/719] Remove non needed enumaration

---
 qcodes/dataset/measurements.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index 4b6d895a698..5cccebe9054 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -765,7 +765,7 @@ def _register_parameter_with_setpoints(self,
         """
         name = str(parameter)
         my_setpoints = list(setpoints) if setpoints else []
-        for i, setpoints in enumerate(parameter.setpoints):
+        for setpoints in parameter.setpoints:
             if not isinstance(setpoints, Parameter):
                 raise RuntimeError("The setpoints of a "
                                    "ParameterWithSetpoints "

From a222adf19e096ee8b69fd65514fcfd9cddcdd08b Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 2 Jan 2019 11:12:49 +0100
Subject: [PATCH 446/719] Make _appropriate_kwargs pure and private

---
 qcodes/dataset/plotting.py            | 24 +++++++++++++-----------
 qcodes/tests/dataset/test_plotting.py | 13 ++++++-------
 2 files changed, 19 insertions(+), 18 deletions(-)

diff --git a/qcodes/dataset/plotting.py b/qcodes/dataset/plotting.py
index 07ea9be1e6d..f9879bb75e0 100644
--- a/qcodes/dataset/plotting.py
+++ b/qcodes/dataset/plotting.py
@@ -39,10 +39,9 @@
 
 
 @contextmanager
-def appropriate_kwargs(plottype: str,
-                       ax: matplotlib.axes.Axes,
-                       colorbar: Optional[matplotlib.colorbar.Colorbar] = None,
-                       **kwargs):
+def _appropriate_kwargs(plottype: str,
+                        colorbar_present: bool,
+                        **kwargs):
     """
     NB: Only to be used inside :meth"`plot_by_id`.
 
@@ -55,8 +54,7 @@ def appropriate_kwargs(plottype: str,
 
     Args:
         plottype: The plot type for which the kwargs should be adjusted
-        ax: The ax that is to be plotted on
-        colorbar: The colorbar that is to be used (if any)
+        colorbar_present: Is there a non-None colorbar in this plot iteration?
     """
 
     def linehandler(**kwargs):
@@ -64,7 +62,7 @@ def linehandler(**kwargs):
         return kwargs
 
     def heatmaphandler(**kwargs):
-        if colorbar is None and 'cmap' not in kwargs:
+        if not(colorbar_present) and 'cmap' not in kwargs:
             kwargs['cmap'] = qc.config.plotting.default_color_map
         return kwargs
 
@@ -192,13 +190,16 @@ def plot_by_id(run_id: int,
                 xpoints = xpoints[order]
                 ypoints = ypoints[order]
 
-                with appropriate_kwargs(plottype, ax, colorbar, **kwargs) as k:
+                with _appropriate_kwargs(plottype,
+                                         colorbar is not None, **kwargs) as k:
                     ax.plot(xpoints, ypoints, **k)
             elif plottype == 'point':
-                with appropriate_kwargs(plottype, ax, colorbar, **kwargs) as k:
+                with _appropriate_kwargs(plottype,
+                                         colorbar is not None, **kwargs) as k:
                     ax.scatter(xpoints, ypoints, **k)
             elif plottype == 'bar':
-                with appropriate_kwargs(plottype, ax, colorbar, **kwargs) as k:
+                with _appropriate_kwargs(plottype,
+                                         colorbar is not None, **kwargs) as k:
                     ax.bar(xpoints, ypoints, **k)
             else:
                 raise ValueError('Unknown plottype. Something is way wrong.')
@@ -234,7 +235,8 @@ def plot_by_id(run_id: int,
                            'unknown': plot_2d_scatterplot}
             plot_func = how_to_plot[plottype]
 
-            with appropriate_kwargs('heatmap', ax, colorbar, **kwargs) as k:
+            with _appropriate_kwargs('heatmap',
+                                     colorbar is not None, **kwargs) as k:
                 ax, colorbar = plot_func(xpoints, ypoints, zpoints,
                                          ax, colorbar,
                                          **k)
diff --git a/qcodes/tests/dataset/test_plotting.py b/qcodes/tests/dataset/test_plotting.py
index 8f961d5331c..c61b751af58 100644
--- a/qcodes/tests/dataset/test_plotting.py
+++ b/qcodes/tests/dataset/test_plotting.py
@@ -8,7 +8,7 @@
 from qcodes.dataset.plotting import _make_rescaled_ticks_and_units, \
     _ENGINEERING_PREFIXES, _UNITS_FOR_RESCALING
 
-from qcodes.dataset.plotting import plot_by_id, appropriate_kwargs
+from qcodes.dataset.plotting import plot_by_id, _appropriate_kwargs
 from qcodes.dataset.measurements import Measurement
 from qcodes.tests.instrument_mocks import DummyInstrument
 from qcodes.tests.dataset.temporary_databases import empty_temp_db, experiment
@@ -121,31 +121,30 @@ def test_plot_by_id_line_and_heatmap(experiment):
 
 
 def test_appropriate_kwargs():
-    _, ax = plt.subplots()
 
     kwargs = {'cmap': 'bone'}
     check = kwargs.copy()
 
-    with appropriate_kwargs('line', ax, None, **kwargs) as ap_kwargs:
+    with _appropriate_kwargs('line', False, **kwargs) as ap_kwargs:
         assert ap_kwargs == {}
 
     assert kwargs == check
 
-    with appropriate_kwargs('point', ax, None, **kwargs) as ap_kwargs:
+    with _appropriate_kwargs('point', False, **kwargs) as ap_kwargs:
         assert ap_kwargs == {}
 
     assert kwargs == check
 
-    with appropriate_kwargs('bar', ax, None, **kwargs) as ap_kwargs:
+    with _appropriate_kwargs('bar', False, **kwargs) as ap_kwargs:
         assert ap_kwargs == {}
 
     assert kwargs == check
 
-    with appropriate_kwargs('heatmap', ax, None, **kwargs) as ap_kwargs:
+    with _appropriate_kwargs('heatmap', False, **kwargs) as ap_kwargs:
         assert ap_kwargs == kwargs
 
     assert kwargs == check
 
-    with appropriate_kwargs('heatmap', ax, None, **{}) as ap_kwargs:
+    with _appropriate_kwargs('heatmap', False, **{}) as ap_kwargs:
         assert len(ap_kwargs) == 1
         assert ap_kwargs['cmap'] == qc.config.plotting.default_color_map

From ac0a95003d408931f98964301731b5dc423cc529 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 2 Jan 2019 11:14:29 +0100
Subject: [PATCH 447/719] Rename switch dict to plot_handler_mapping

---
 qcodes/dataset/plotting.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/qcodes/dataset/plotting.py b/qcodes/dataset/plotting.py
index f9879bb75e0..59d689cf213 100644
--- a/qcodes/dataset/plotting.py
+++ b/qcodes/dataset/plotting.py
@@ -66,12 +66,12 @@ def heatmaphandler(**kwargs):
             kwargs['cmap'] = qc.config.plotting.default_color_map
         return kwargs
 
-    switch = {'line': linehandler,
-              'point': linehandler,
-              'bar': linehandler,
-              'heatmap': heatmaphandler}
+    plot_handler_mapping = {'line': linehandler,
+                            'point': linehandler,
+                            'bar': linehandler,
+                            'heatmap': heatmaphandler}
 
-    yield switch[plottype](**kwargs.copy())
+    yield plot_handler_mapping[plottype](**kwargs.copy())
 
 
 def plot_by_id(run_id: int,

From e379aa9f6e001cbba6204fd40c2564f57f87cf35 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 2 Jan 2019 11:20:57 +0100
Subject: [PATCH 448/719] Change log messages

For debug, it's nice to see the kwargs. We do not, however
want the log message to contain redundant information
about which function that produced the message
---
 qcodes/dataset/plotting.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/qcodes/dataset/plotting.py b/qcodes/dataset/plotting.py
index 59d689cf213..5f97a2082fa 100644
--- a/qcodes/dataset/plotting.py
+++ b/qcodes/dataset/plotting.py
@@ -175,8 +175,7 @@ def plot_by_id(run_id: int,
     for data, ax, colorbar in zip(alldata, axes, colorbars):
 
         if len(data) == 2:  # 1D PLOTTING
-            log.debug('Plotting by id, doing a 1D plot')
-            log.debug(f"kwargs are {kwargs}")
+            log.debug(f'Doing a 1D plot with kwargs: {kwargs}')
 
             xpoints = data[0]['data']
             ypoints = data[1]['data']
@@ -214,8 +213,7 @@ def plot_by_id(run_id: int,
             ax.set_title(title)
 
         elif len(data) == 3:  # 2D PLOTTING
-            log.debug('Plotting by id, doing a 2D plot')
-            log.debug(f"kwargs are {kwargs}")
+            log.debug(f'Doing a 2D plot with kwargs: {kwargs}')
 
             # From the setpoints, figure out which 2D plotter to use
             # TODO: The "decision tree" for what gets plotted how and how

From 8b9c9da7f8a668550e5909bb7956dbdb58692e25 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 2 Jan 2019 12:28:32 +0100
Subject: [PATCH 449/719] Remove unused import

---
 qcodes/tests/dataset/test_plotting.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_plotting.py b/qcodes/tests/dataset/test_plotting.py
index c61b751af58..2f88122d340 100644
--- a/qcodes/tests/dataset/test_plotting.py
+++ b/qcodes/tests/dataset/test_plotting.py
@@ -2,7 +2,6 @@
 from hypothesis import given, example, assume
 from hypothesis.strategies import text, sampled_from, floats, lists, data, \
     one_of, just
-import matplotlib.pyplot as plt
 
 import qcodes as qc
 from qcodes.dataset.plotting import _make_rescaled_ticks_and_units, \

From 97c441a38ff417a5cae3b0cad070a3b5e4346a2b Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 2 Jan 2019 12:28:50 +0100
Subject: [PATCH 450/719] Remove superflous lambda

---
 qcodes/tests/dataset/test_plotting.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_plotting.py b/qcodes/tests/dataset/test_plotting.py
index 2f88122d340..854c6d141b5 100644
--- a/qcodes/tests/dataset/test_plotting.py
+++ b/qcodes/tests/dataset/test_plotting.py
@@ -96,7 +96,7 @@ def test_plot_by_id_line_and_heatmap(experiment):
     """
     inst = DummyInstrument('dummy', gates=['s1', 'm1', 's2', 'm2'])
 
-    inst.m1.get = lambda: np.random.randn()
+    inst.m1.get = np.random.randn
     inst.m2.get = lambda: np.random.randint(0, 5)
 
     meas = Measurement()

From 7dced0385b7118469d55b86444f8cebb6cf23c69 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 2 Jan 2019 12:43:48 +0100
Subject: [PATCH 451/719] rewrite test to use get_parameter_data and test the
 content of the arrays

---
 .../test_measurement_context_manager.py       | 26 ++++++++++++-------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 2631ee962a5..1264543ba9c 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -976,12 +976,12 @@ def test_datasaver_array_parameters_channel(channel_array_instrument,
         assert datadict['data'].shape == (N * M,)
 
 
-@settings(max_examples=5, deadline=None)
+@settings(max_examples=5, deadline=None, use_coverage=False)
 @given(n=hst.integers(min_value=5, max_value=500))
 @pytest.mark.usefixtures("experiment")
 def test_datasaver_parameter_with_setpoints(channel_array_instrument,
                                             DAC, n):
-
+    random_seed = 1
     chan = channel_array_instrument.A
     param = chan.dummy_parameter_with_setpoints
     chan.dummy_n_points(n)
@@ -999,6 +999,9 @@ def test_datasaver_parameter_with_setpoints(channel_array_instrument,
 
     # Now for a real measurement
     with meas.run() as datasaver:
+        # we seed the random number generator
+        # so we can test that we get the expected numbers
+        np.random.seed(random_seed)
         datasaver.add_result((param, param()))
     assert datasaver.points_written == n
 
@@ -1009,14 +1012,17 @@ def test_datasaver_parameter_with_setpoints(channel_array_instrument,
         data = ds.get_data(param)
         assert len(data) == n
         assert len(data[0]) == 1
-    datadicts = get_data_by_id(datasaver.run_id)
-    # one dependent parameter
-    assert len(datadicts) == 1
-    datadicts = datadicts[0]
-    assert len(datadicts) == len(meas.parameters)
-    for datadict in datadicts:
-        assert datadict['data'].shape == (n,)
-
+    datadict = ds.get_parameter_data()
+    assert len(datadict) == 1
+    subdata = datadict['dummy_channel_inst_ChanA_dummy_parameter_with_setpoints']
+    assert_allclose(subdata['dummy_channel_inst_ChanA_dummy_sp_axis'],
+                    np.linspace(chan.dummy_start(),
+                                chan.dummy_stop(),
+                                chan.dummy_n_points()))
+    np.random.seed(random_seed)
+    assert_allclose(subdata['dummy_channel_inst_ChanA_'
+                            'dummy_parameter_with_setpoints'],
+                    np.random.rand(n))
 
 
 @settings(max_examples=5, deadline=None)

From 1e574b2f1aebdfeedfcfbbf6f01cc4bf5ba604cc Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 2 Jan 2019 13:00:09 +0100
Subject: [PATCH 452/719] This change should never have been committed

---
 qcodes/tests/dataset/test_measurement_context_manager.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 1264543ba9c..2aa91994cd3 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -976,7 +976,7 @@ def test_datasaver_array_parameters_channel(channel_array_instrument,
         assert datadict['data'].shape == (N * M,)
 
 
-@settings(max_examples=5, deadline=None, use_coverage=False)
+@settings(max_examples=5, deadline=None)
 @given(n=hst.integers(min_value=5, max_value=500))
 @pytest.mark.usefixtures("experiment")
 def test_datasaver_parameter_with_setpoints(channel_array_instrument,

From f60f8c737fa943247bc9cd7edcb2bf3b1edeef61 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 2 Jan 2019 14:33:20 +0100
Subject: [PATCH 453/719] Add tests that ensures that errors are raised
 correctly when logging is not started

In the process
* Refactor sim instrument to a fixture and ensure that it is closed
* ensure that the global handlers are reset in the fixtures to avoid
  leaking handles from one test to another
---
 qcodes/tests/test_logger.py | 77 +++++++++++++++++++++++++++----------
 1 file changed, 57 insertions(+), 20 deletions(-)

diff --git a/qcodes/tests/test_logger.py b/qcodes/tests/test_logger.py
index ea43bb244e0..64d79cee2fc 100644
--- a/qcodes/tests/test_logger.py
+++ b/qcodes/tests/test_logger.py
@@ -17,6 +17,9 @@ def remove_root_handlers():
     for handler in handlers:
         handler.close()
         root_logger.removeHandler(handler)
+    logger.logger.file_handler = None
+    logger.logger.console_handler = None
+
 
 @pytest.fixture
 def model372():
@@ -39,6 +42,34 @@ def model372():
     finally:
         inst.close()
 
+
+@pytest.fixture()
+def AMI430_3D():
+    from qcodes.instrument.ip_to_visa import AMI430_VISA
+    from qcodes.instrument_drivers.american_magnetics.AMI430 import AMI430_3D
+    import qcodes.instrument.sims as sims
+    import numpy as np
+    visalib = sims.__file__.replace('__init__.py', 'AMI430.yaml@sim')
+    mag_x = AMI430_VISA('x', address='GPIB::1::INSTR', visalib=visalib,
+                        terminator='\n', port=1)
+    mag_y = AMI430_VISA('y', address='GPIB::2::INSTR', visalib=visalib,
+                        terminator='\n', port=1)
+    mag_z = AMI430_VISA('z', address='GPIB::3::INSTR', visalib=visalib,
+                        terminator='\n', port=1)
+    field_limit = [
+        lambda x, y, z: x == 0 and y == 0 and z < 3,
+        lambda x, y, z: np.linalg.norm([x, y, z]) < 2
+    ]
+    driver = AMI430_3D("AMI430-3D", mag_x, mag_y, mag_z, field_limit)
+    try:
+        yield driver, mag_x, mag_y, mag_z
+    finally:
+        driver.close()
+        mag_x.close()
+        mag_y.close()
+        mag_z.close()
+
+
 def test_get_log_file_name():
     fp = logger.logger.get_log_file_name().split(os.sep)
     assert fp[-1] == logger.logger.PYTHON_LOG_NAME
@@ -66,7 +97,16 @@ def test_start_logger_twice():
     logger.start_logger()
     handlers = logging.getLogger().handlers
     # there is always one logger registered from pytest
-    assert len(logging.getLogger().handlers) == 2+1
+    assert len(handlers) == 2+1
+
+
+@pytest.mark.usefixtures("remove_root_handlers")
+def test_set_level_without_starting_raises():
+    with pytest.raises(RuntimeError):
+        with logger.console_level('DEBUG'):
+            pass
+    # there is always one logger registered from pytest
+    assert len(logging.getLogger().handlers) == 1
 
 
 @pytest.mark.usefixtures("remove_root_handlers")
@@ -83,27 +123,11 @@ def test_handler_level():
     assert logs.value.strip() == TEST_LOG_MESSAGE
 
 @pytest.mark.usefixtures("remove_root_handlers")
-def test_filter_instrument():
-    from qcodes.instrument.ip_to_visa import AMI430_VISA
-    from qcodes.instrument_drivers.american_magnetics.AMI430 import AMI430_3D
-    import qcodes.instrument.sims as sims
-    import numpy as np
+def test_filter_instrument(AMI430_3D):
 
-    logger.start_logger()
-
-    visalib = sims.__file__.replace('__init__.py', 'AMI430.yaml@sim')
-    mag_x = AMI430_VISA('x', address='GPIB::1::INSTR', visalib=visalib,
-                        terminator='\n', port=1)
-    mag_y = AMI430_VISA('y', address='GPIB::2::INSTR', visalib=visalib,
-                        terminator='\n', port=1)
-    mag_z = AMI430_VISA('z', address='GPIB::3::INSTR', visalib=visalib,
-                        terminator='\n', port=1)
-    field_limit = [
-        lambda x, y, z: x == 0 and y == 0 and z < 3,
-        lambda x, y, z: np.linalg.norm([x, y, z]) < 2
-    ]
+    driver, mag_x, mag_y, mag_z = AMI430_3D
 
-    driver = AMI430_3D("AMI430-3D", mag_x, mag_y, mag_z, field_limit)
+    logger.start_logger()
 
     # filter one instrument
     driver.cartesian((0, 0, 0))
@@ -136,6 +160,19 @@ def test_filter_instrument():
     assert any_x
     assert any_y
 
+
+@pytest.mark.usefixtures("remove_root_handlers")
+def test_filter_without_started_logger_raises(AMI430_3D):
+
+    driver, mag_x, mag_y, mag_z = AMI430_3D
+
+    # filter one instrument
+    driver.cartesian((0, 0, 0))
+    with pytest.raises(RuntimeError):
+        with logger.filter_instrument(mag_x):
+            pass
+
+
 @pytest.mark.usefixtures("remove_root_handlers")
 def test_capture_dataframe():
     root_logger = logging.getLogger()

From 3f6bf5cdc84c684db6df7ccded44d73cbd6c095c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 2 Jan 2019 14:44:22 +0100
Subject: [PATCH 454/719] Less verbose error message

---
 qcodes/utils/metadata.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/qcodes/utils/metadata.py b/qcodes/utils/metadata.py
index 495c9859393..9eff2ed2a0b 100644
--- a/qcodes/utils/metadata.py
+++ b/qcodes/utils/metadata.py
@@ -129,8 +129,12 @@ def diff_param_values_by_id(left_id: RunId, right_id: RunId) -> ParameterDiff:
     right_snapshot = load_by_id(right_id).snapshot
 
     if left_snapshot is None or right_snapshot is None:
-        raise RuntimeError(f"Tried to compare {left_snapshot} "
-                           f"and {right_snapshot}"
-                           f"but on of them is empty")
+        if left_snapshot is None:
+            empty = left_id
+        else:
+            empty = right_id
+        raise RuntimeError(f"Tried to compare two snapshots"
+                           f"but the snapshot of {empty} "
+                           f"is empty.")
 
     return diff_param_values(left_snapshot, right_snapshot)

From f96da3df98faf8b423786ce3c2d43f4a72df7c63 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 3 Jan 2019 10:19:59 +0100
Subject: [PATCH 455/719] Better error message

---
 qcodes/config/config.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/config/config.py b/qcodes/config/config.py
index 09581e99098..93cb56022ce 100644
--- a/qcodes/config/config.py
+++ b/qcodes/config/config.py
@@ -373,7 +373,8 @@ def describe(self, name: str) -> str:
         sch = self.current_schema["properties"]
         for key in name.split('.'):
             if val is None:
-                raise RuntimeError(f"{name} is not found in config")
+                raise RuntimeError(f"Cannot describe {name} Some part of it "
+                                   f"is null")
             val = val[key]
             if sch.get(key):
                 sch = sch[key]

From dc555ad353f52355f4301f06e7d2f7914d7d7cd7 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 3 Jan 2019 13:25:15 +0100
Subject: [PATCH 456/719] Validate shapes on set and get

---
 qcodes/instrument/parameter.py | 23 +++++++++++++++++------
 1 file changed, 17 insertions(+), 6 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index f6d48e38bc4..6a7f5d969dc 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -346,11 +346,11 @@ def _save_val(self, value, validate=False):
         if validate:
             self.validate(value)
         if (self.get_parser is None and
-            self.set_parser is None and
-            self.val_mapping is None and
-            self.scale is None and
-            self.offset is None):
-                self.raw_value = value
+                self.set_parser is None and
+                self.val_mapping is None and
+                self.scale is None and
+                self.offset is None):
+            self.raw_value = value
         self._latest = {'value': value, 'ts': datetime.now(),
                         'raw_value': self.raw_value}
 
@@ -964,6 +964,7 @@ def validate_consistent_shape(self) -> bool:
         both the setpoints and the actual parameters have validators
         of type Arrays with a defined shape.
         """
+
         if not isinstance(self.vals, Arrays):
             raise RuntimeError("Can only validate shapes for Array "
                                "Parameters")
@@ -972,7 +973,7 @@ def validate_consistent_shape(self) -> bool:
         for sp in self.setpoints:
             if not isinstance(sp.vals, Arrays):
                 raise RuntimeError("Can only validate shapes for Array "
-                                   "Parameters")
+                                   "Parameters.")
             setpoints_shape_list.extend(sp.vals.shape)
         setpoints_shape = tuple(setpoints_shape_list)
 
@@ -988,6 +989,16 @@ def validate_consistent_shape(self) -> bool:
             return False
         return True
 
+    def _save_val(self, value, validate=False):
+        """
+        Overwrites the standard `_save_val` to also check the
+        the parameter has consistent shape with it's setpoints
+
+        Arguments are passed to the super method
+        """
+        self.validate_consistent_shape()
+        super()._save_val(value, validate)
+
 
 class GeneratedSetPoints(Parameter):
     """

From fc148d1c38b10a54c6e7739a07965157f831029a Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 3 Jan 2019 13:29:05 +0100
Subject: [PATCH 457/719] Changed error to TypeError

---
 qcodes/instrument/parameter.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 6a7f5d969dc..d5d17bad0a0 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -954,7 +954,8 @@ def setpoints(self):
     def setpoints(self, setpoints: Sequence[_BaseParameter]):
         for setpointarray in setpoints:
             if not isinstance(setpointarray, _BaseParameter):
-                raise RuntimeError
+                raise TypeError(f"Setpoints is of type {type(setpointarray)}"
+                                f" expcected a QCoDeS parameter")
         self._setpoints = setpoints
 
     def validate_consistent_shape(self) -> bool:

From 66904d284aa83f070d938843122dff88ff5b7672 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 3 Jan 2019 13:32:08 +0100
Subject: [PATCH 458/719] Raise an error if the shape of the parameter is not
 consistent with the setpoints

---
 qcodes/instrument/parameter.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index d5d17bad0a0..e69e1bcb692 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -997,7 +997,11 @@ def _save_val(self, value, validate=False):
 
         Arguments are passed to the super method
         """
-        self.validate_consistent_shape()
+        consistent = self.validate_consistent_shape()
+        if consistent is False:
+            raise RuntimeError(f"Inconsistent shape between parameter and "
+                               f"setpoints for parameter: "
+                               f"{self.full_name}")
         super()._save_val(value, validate)
 
 

From 43ee0129d62c30e1461b6ff3be90d790a69ab68f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 3 Jan 2019 16:35:17 +0100
Subject: [PATCH 459/719] Add validators to Parameters in example

---
 ...arameter Example with Dual Setpoints.ipynb | 40 ++++++++++++-------
 1 file changed, 26 insertions(+), 14 deletions(-)

diff --git a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb
index 00a20b83626..c91dfad491a 100644
--- a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb	
+++ b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb	
@@ -21,7 +21,8 @@
     "from qcodes.instrument.base import Instrument, Parameter\n",
     "from qcodes.instrument.parameter import ParameterWithSetpoints\n",
     "from qcodes.dataset.measurements import Measurement\n",
-    "from qcodes.dataset.plotting import plot_by_id, load_by_id"
+    "from qcodes.dataset.plotting import plot_by_id, load_by_id\n",
+    "import qcodes.utils.validators as vals"
    ]
   },
   {
@@ -121,22 +122,26 @@
     "        self.add_parameter(name='time_axis',\n",
     "                           label='Time',\n",
     "                           unit='s',\n",
+    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
     "                           parameter_class=TimeAxis)\n",
     "        \n",
     "        self.add_parameter(name='freq_axis',\n",
     "                           label='Frequency',\n",
     "                           unit='Hz',\n",
+    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
     "                           parameter_class=FrequencyAxis)\n",
     "        \n",
     "        self.add_parameter(name='trace',\n",
     "                           label='Signal',\n",
     "                           unit='V',\n",
+    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
     "                           setpoints=(self.time_axis,),\n",
     "                           parameter_class=TimeTrace)\n",
     "        \n",
     "        self.add_parameter(name='periodogram',\n",
     "                           label='Periodogram',\n",
     "                           unit='V^2/Hz',\n",
+    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
     "                           setpoints=(self.freq_axis,),\n",
     "                           parameter_class=Periodogram)"
    ]
@@ -158,8 +163,8 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#1@./exp_container_tutorial.db\n",
-       "----------------------------------------------------"
+       "tutorial_exp#no sample#48@C:\\Users\\jenielse/experiments.db\n",
+       "----------------------------------------------------------"
       ]
      },
      "execution_count": 6,
@@ -188,7 +193,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 1\n"
+      "Starting experimental run with id: 184\n"
      ]
     }
    ],
@@ -211,7 +216,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -235,7 +240,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 2\n"
+      "Starting experimental run with id: 185\n"
      ]
     }
    ],
@@ -255,7 +260,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -286,7 +291,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 3\n"
+      "Starting experimental run with id: 186\n"
      ]
     }
    ],
@@ -309,7 +314,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -342,7 +347,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 4\n"
+      "Starting experimental run with id: 187\n"
      ]
     }
    ],
@@ -368,7 +373,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -401,7 +406,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 5\n"
+      "Starting experimental run with id: 188\n"
      ]
     }
    ],
@@ -429,7 +434,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -443,6 +448,13 @@
    "source": [
     "_ = plot_by_id(dataid)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -461,7 +473,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,

From f86c2d12356b2e176f00c9ae375495067247f64d Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 4 Jan 2019 09:45:33 +0100
Subject: [PATCH 460/719] Move validation to a more logical place

---
 qcodes/instrument/parameter.py | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index e69e1bcb692..1a3321181dc 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -990,19 +990,21 @@ def validate_consistent_shape(self) -> bool:
             return False
         return True
 
-    def _save_val(self, value, validate=False):
+    def validate(self, value):
         """
-        Overwrites the standard `_save_val` to also check the
-        the parameter has consistent shape with it's setpoints
+        Overwrites the standard `validate` to also check the
+        the parameter has consistent shape with it's setpoints.
+        This only makes sense if the parameter has an Arrays validator
 
         Arguments are passed to the super method
         """
-        consistent = self.validate_consistent_shape()
-        if consistent is False:
-            raise RuntimeError(f"Inconsistent shape between parameter and "
-                               f"setpoints for parameter: "
-                               f"{self.full_name}")
-        super()._save_val(value, validate)
+        if isinstance(self.vals, Arrays):
+            consistent = self.validate_consistent_shape()
+            if consistent is False:
+                raise RuntimeError(f"Inconsistent shape between parameter and "
+                                   f"setpoints for parameter: "
+                                   f"{self.full_name}")
+        super().validate(value)
 
 
 class GeneratedSetPoints(Parameter):

From 294647e5bdd7eadf4aef3f6058eb57356f0b24f8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 4 Jan 2019 10:11:17 +0100
Subject: [PATCH 461/719] Improve parameter typing somewhat

---
 qcodes/instrument/parameter.py | 40 +++++++++++++++++++---------------
 1 file changed, 22 insertions(+), 18 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 1a3321181dc..d85f1a92560 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -80,7 +80,8 @@
     from .base import Instrument, InstrumentBase
 
 Number = Union[float, int]
-
+# for now the type the parameter may contain is not restricted at all
+ParamDataType = Any
 
 class _SetParamContext:
     """
@@ -239,7 +240,8 @@ def __init__(self, name: str,
         # record of latest value and when it was set or measured
         # what exactly this means is different for different subclasses
         # but they all use the same attributes so snapshot is consistent.
-        self._latest = {'value': None, 'ts': None, 'raw_value': None}
+        self._latest: Dict[str, Optional[Union[ParamDataType, datetime]]] = \
+            {'value': None, 'ts': None, 'raw_value': None}
         self.get_latest = GetLatest(self, max_val_age=max_val_age)
 
         if hasattr(self, 'get_raw') and self.get_raw is not None:
@@ -266,7 +268,7 @@ def __init__(self, name: str,
         # check if additional waiting time is needed before next set
         self._t_last_set = time.perf_counter()
 
-    def __str__(self):
+    def __str__(self) -> str:
         """Include the instrument name with the Parameter name if possible."""
         inst_name = getattr(self._instrument, 'name', '')
         if inst_name:
@@ -274,7 +276,7 @@ def __str__(self):
         else:
             return self.name
 
-    def __repr__(self):
+    def __repr__(self) -> str:
         return named_repr(self)
 
     def __call__(self, *args, **kwargs):
@@ -291,8 +293,9 @@ def __call__(self, *args, **kwargs):
                 raise NotImplementedError('no set cmd found in' +
                                           ' Parameter {}'.format(self.name))
 
-    def snapshot_base(self, update: bool=False,
-                      params_to_skip_update: Sequence[str]=None) -> Dict[str, Any]:
+    def snapshot_base(self, update: bool = False,
+                      params_to_skip_update: Sequence[str] = None) -> \
+            Dict[str, Any]:
         """
         State of the parameter as a JSON-compatible dict.
 
@@ -339,7 +342,7 @@ def snapshot_base(self, update: bool=False,
 
         return state
 
-    def _save_val(self, value, validate=False):
+    def _save_val(self, value: ParamDataType, validate: bool = False) -> None:
         """
         Update latest
         """
@@ -485,9 +488,9 @@ def set_wrapper(value, **kwargs):
         return set_wrapper
 
     def get_ramp_values(self, value: Union[float, int, Sized],
-                        step: Union[float, int]=None) -> List[Union[float,
-                                                                    int,
-                                                                    Sized]]:
+                        step: Number = None) -> List[Union[float,
+                                                           int,
+                                                           Sized]]:
         """
         Return values to sweep from current value to target value.
         This method can be overridden to have a custom sweep behaviour.
@@ -503,7 +506,8 @@ def get_ramp_values(self, value: Union[float, int, Sized],
             return [value]
         else:
             if isinstance(value, collections.abc.Sized) and len(value) > 1:
-                raise RuntimeError("Don't know how to step a parameter with more than one value")
+                raise RuntimeError("Don't know how to step a parameter"
+                                   " with more than one value")
             if self.get_latest() is None:
                 self.get()
             start_value = self.get_latest()
@@ -522,7 +526,7 @@ def get_ramp_values(self, value: Union[float, int, Sized],
             # drop the initial value, we're already there
             return permissive_range(start_value, value, step)[1:] + [value]
 
-    def validate(self, value):
+    def validate(self, value: Any) -> None:
         """
         Validate value
 
@@ -539,11 +543,11 @@ def validate(self, value):
             self.vals.validate(value, 'Parameter: ' + context)
 
     @property
-    def step(self):
+    def step(self) -> Optional[Number]:
         return self._step
 
     @step.setter
-    def step(self, step: Union[int, float]):
+    def step(self, step: Number):
         """
         Configure whether this Parameter uses steps during set operations.
         If step is a positive number, this is the maximum value change
@@ -562,7 +566,7 @@ def step(self, step: Union[int, float]):
             TypeError: if step is not a number
         """
         if step is None:
-            self._step = step # type: Optional[Union[float, int]]
+            self._step: Optional[Number] = step
         elif not getattr(self.vals, 'is_numeric', True):
             raise TypeError('you can only step numeric parameters')
         elif not isinstance(step, (int, float)):
@@ -577,12 +581,12 @@ def step(self, step: Union[int, float]):
             self._step = step
 
     @property
-    def post_delay(self):
+    def post_delay(self) -> Number:
         """Delay time after *start* of set operation, for each set"""
         return self._post_delay
 
     @post_delay.setter
-    def post_delay(self, post_delay):
+    def post_delay(self, post_delay: Number) -> None:
         """
         Configure this parameter with a delay after the *start* of every set
         operation.
@@ -990,7 +994,7 @@ def validate_consistent_shape(self) -> bool:
             return False
         return True
 
-    def validate(self, value):
+    def validate(self, value: Any) -> None:
         """
         Overwrites the standard `validate` to also check the
         the parameter has consistent shape with it's setpoints.

From e42c0400c9c14584de9c39d497d0335c875fc380 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 4 Jan 2019 10:12:38 +0100
Subject: [PATCH 462/719] Make typing a bit clearer by using alias for
 paramdatatype

---
 qcodes/instrument/parameter.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index d85f1a92560..e9f01755611 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -526,7 +526,7 @@ def get_ramp_values(self, value: Union[float, int, Sized],
             # drop the initial value, we're already there
             return permissive_range(start_value, value, step)[1:] + [value]
 
-    def validate(self, value: Any) -> None:
+    def validate(self, value: ParamDataType) -> None:
         """
         Validate value
 
@@ -994,7 +994,7 @@ def validate_consistent_shape(self) -> bool:
             return False
         return True
 
-    def validate(self, value: Any) -> None:
+    def validate(self, value: ParamDataType) -> None:
         """
         Overwrites the standard `validate` to also check the
         the parameter has consistent shape with it's setpoints.

From 4851dd09c05113ae7a28d68b5c9506a752bb339b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 4 Jan 2019 10:30:07 +0100
Subject: [PATCH 463/719] Add types to get and set wrappers

---
 qcodes/instrument/parameter.py | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index e9f01755611..b7a28255bbf 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -357,9 +357,10 @@ def _save_val(self, value: ParamDataType, validate: bool = False) -> None:
         self._latest = {'value': value, 'ts': datetime.now(),
                         'raw_value': self.raw_value}
 
-    def _wrap_get(self, get_function):
+    def _wrap_get(self, get_function: Callable[..., ParamDataType]) ->\
+            Callable[..., ParamDataType]:
         @wraps(get_function)
-        def get_wrapper(*args, **kwargs):
+        def get_wrapper(*args: Any, **kwargs: Any) -> ParamDataType:
             try:
                 # There might be cases where a .get also has args/kwargs
                 value = get_function(*args, **kwargs)
@@ -373,11 +374,11 @@ def get_wrapper(*args, **kwargs):
                     # offset values
                     if isinstance(self.offset, collections.abc.Iterable):
                         # offset contains multiple elements, one for each value
-                        value = tuple(value - offset for value, offset
+                        value = tuple(val - offset for val, offset
                                       in zip(value, self.offset))
                     elif isinstance(value, collections.abc.Iterable):
                         # Use single offset for all values
-                        value = tuple(value - self.offset for value in value)
+                        value = tuple(val - self.offset for val in value)
                     else:
                         value -= self.offset
 
@@ -386,11 +387,11 @@ def get_wrapper(*args, **kwargs):
                     # Scale values
                     if isinstance(self.scale, collections.abc.Iterable):
                         # Scale contains multiple elements, one for each value
-                        value = tuple(value / scale for value, scale
+                        value = tuple(val / scale for val, scale
                                       in zip(value, self.scale))
                     elif isinstance(value, collections.abc.Iterable):
                         # Use single scale for all values
-                        value = tuple(value / self.scale for value in value)
+                        value = tuple(val / self.scale for val in value)
                     else:
                         value /= self.scale
 
@@ -410,9 +411,10 @@ def get_wrapper(*args, **kwargs):
 
         return get_wrapper
 
-    def _wrap_set(self, set_function):
+    def _wrap_set(self, set_function: Callable[..., None]) -> \
+            Callable[..., None]:
         @wraps(set_function)
-        def set_wrapper(value, **kwargs):
+        def set_wrapper(value: ParamDataType, **kwargs: Any) -> None:
             try:
                 self.validate(value)
 

From adad5363c495dfcf9677f8ab02ecfe7401f67cd7 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 4 Jan 2019 10:31:45 +0100
Subject: [PATCH 464/719] Add a few more types

---
 qcodes/instrument/parameter.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index b7a28255bbf..51687536eb8 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -618,12 +618,12 @@ def post_delay(self, post_delay: Number) -> None:
         self._post_delay = post_delay
 
     @property
-    def inter_delay(self):
+    def inter_delay(self) -> Number:
         """Delay time between consecutive set operations"""
         return self._inter_delay
 
     @inter_delay.setter
-    def inter_delay(self, inter_delay):
+    def inter_delay(self, inter_delay: Number) -> None:
         """
         Configure this parameter with a delay between set operations.
 
@@ -649,7 +649,7 @@ def inter_delay(self, inter_delay):
         self._inter_delay = inter_delay
 
     @property
-    def full_name(self):
+    def full_name(self) -> str:
         return "_".join(self.name_parts)
 
     def set_validator(self, vals):

From 5da560838e7d584e92152a252b55ae0620e7fe57 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 4 Jan 2019 11:12:34 +0100
Subject: [PATCH 465/719] Make plottypes have more elaborate names

---
 qcodes/dataset/data_export.py | 45 ++++++++++++++++++-----------------
 qcodes/dataset/plotting.py    | 28 ++++++++++++----------
 2 files changed, 39 insertions(+), 34 deletions(-)

diff --git a/qcodes/dataset/data_export.py b/qcodes/dataset/data_export.py
index 718feecaf6e..e1ae7af59b3 100644
--- a/qcodes/dataset/data_export.py
+++ b/qcodes/dataset/data_export.py
@@ -238,9 +238,9 @@ def get_1D_plottype(xpoints: np.ndarray, ypoints: np.ndarray) -> str:
     Determine plot type for a 1D plot by inspecting the data
 
     Possible plot types are:
-    * 'bar' - bar plot
-    * 'point' - scatter plot
-    * 'line' - line plot
+    * '1D_bar' - bar plot
+    * '1D_point' - scatter plot
+    * '1D_line' - line plot
 
     Args:
         xpoints: The x-axis values
@@ -252,11 +252,11 @@ def get_1D_plottype(xpoints: np.ndarray, ypoints: np.ndarray) -> str:
 
     if isinstance(xpoints[0], str) and not isinstance(ypoints[0], str):
         if len(xpoints) == len(np.unique(xpoints)):
-            return 'bar'
+            return '1D_bar'
         else:
-            return 'point'
+            return '1D_point'
     if isinstance(xpoints[0], str) or isinstance(ypoints[0], str):
-        return 'point'
+        return '1D_point'
     else:
         return datatype_from_setpoints_1d(xpoints)
 
@@ -267,8 +267,8 @@ def datatype_from_setpoints_1d(setpoints: np.ndarray) -> str:
     provided setpoints.
 
     The type is:
-        * 'point' (scatter plot) when all setpoints are identical
-        * 'line' otherwise
+        * '1D_point' (scatter plot) when all setpoints are identical
+        * '1D_line' otherwise
 
     Args:
         setpoints: The x-axis values
@@ -277,9 +277,9 @@ def datatype_from_setpoints_1d(setpoints: np.ndarray) -> str:
         A string representing the plot type as described above
     """
     if np.allclose(setpoints, setpoints[0]):
-        return 'point'
+        return '1D_point'
     else:
-        return 'line'
+        return '1D_line'
 
 
 def get_2D_plottype(xpoints: np.ndarray,
@@ -289,10 +289,10 @@ def get_2D_plottype(xpoints: np.ndarray,
     Determine plot type for a 2D plot by inspecting the data
 
     Plot types are:
-    * 'grid' - colormap plot for data that is on a grid
-    * 'equidistant' - colormap plot for data that is on equidistant grid
-    * 'scatter' - scatter plot
-    * 'unknown' - returned in case the data did not match any criteria of the
+    * '2D_grid' - colormap plot for data that is on a grid
+    * '2D_equidistant' - colormap plot for data that is on equidistant grid
+    * '2D_scatter' - scatter plot
+    * '2D_unknown' - returned in case the data did not match any criteria of the
     other plot types
 
     Args:
@@ -316,10 +316,11 @@ def datatype_from_setpoints_2d(xpoints: np.ndarray,
     to display the data.
 
     Plot types are:
-    * 'grid' - colormap plot for data that is on a grid
-    * 'equidistant' - colormap plot for data that is on equidistant grid
-    * 'scatter' - scatter plot
-    * 'unknown' - returned in case the data did not match any criteria of the
+    * '2D_point' - all setpoint are the same in each direction; one point
+    * '2D_grid' - colormap plot for data that is on a grid
+    * '2D_equidistant' - colormap plot for data that is on equidistant grid
+    * '2D_scatter' - scatter plot
+    * '2D_unknown' - returned in case the data did not match any criteria of the
     other plot types
 
     Args:
@@ -341,7 +342,7 @@ def datatype_from_setpoints_2d(xpoints: np.ndarray,
     y_all_the_same = np.allclose(ypoints, ypoints[0])
 
     if x_all_the_same or y_all_the_same:
-        return 'point'
+        return '2D_point'
 
     # Now check if this is a simple rectangular sweep,
     # possibly interrupted in the middle of one row
@@ -357,16 +358,16 @@ def datatype_from_setpoints_2d(xpoints: np.ndarray,
 
     # this is the check that we are on a "simple" grid
     if y_check and x_check:
-        return 'grid'
+        return '2D_grid'
 
     x_check = _all_steps_multiples_of_min_step(xrows)
     y_check = _all_steps_multiples_of_min_step(yrows)
 
     # this is the check that we are on an equidistant grid
     if y_check and x_check:
-        return 'equidistant'
+        return '2D_equidistant'
 
-    return 'unknown'
+    return '2D_unknown'
 
 
 def reshape_2D_data(x: np.ndarray, y: np.ndarray, z: np.ndarray
diff --git a/qcodes/dataset/plotting.py b/qcodes/dataset/plotting.py
index 5f97a2082fa..209742168d2 100644
--- a/qcodes/dataset/plotting.py
+++ b/qcodes/dataset/plotting.py
@@ -66,10 +66,14 @@ def heatmaphandler(**kwargs):
             kwargs['cmap'] = qc.config.plotting.default_color_map
         return kwargs
 
-    plot_handler_mapping = {'line': linehandler,
-                            'point': linehandler,
-                            'bar': linehandler,
-                            'heatmap': heatmaphandler}
+    plot_handler_mapping = {'1D_line': linehandler,
+                            '1D_point': linehandler,
+                            '1D_bar': linehandler,
+                            '2D_point': heatmaphandler,
+                            '2D_grid': heatmaphandler,
+                            '2D_scatter': heatmaphandler,
+                            '2D_equidistant': heatmaphandler,
+                            '2D_unknown': heatmaphandler}
 
     yield plot_handler_mapping[plottype](**kwargs.copy())
 
@@ -183,7 +187,7 @@ def plot_by_id(run_id: int,
             plottype = get_1D_plottype(xpoints, ypoints)
             log.debug(f'Determined plottype: {plottype}')
 
-            if plottype == 'line':
+            if plottype == '1D_line':
                 # sort for plotting
                 order = xpoints.argsort()
                 xpoints = xpoints[order]
@@ -192,11 +196,11 @@ def plot_by_id(run_id: int,
                 with _appropriate_kwargs(plottype,
                                          colorbar is not None, **kwargs) as k:
                     ax.plot(xpoints, ypoints, **k)
-            elif plottype == 'point':
+            elif plottype == '1D_point':
                 with _appropriate_kwargs(plottype,
                                          colorbar is not None, **kwargs) as k:
                     ax.scatter(xpoints, ypoints, **k)
-            elif plottype == 'bar':
+            elif plottype == '1D_bar':
                 with _appropriate_kwargs(plottype,
                                          colorbar is not None, **kwargs) as k:
                     ax.bar(xpoints, ypoints, **k)
@@ -227,13 +231,13 @@ def plot_by_id(run_id: int,
 
             log.debug(f'Determined plottype: {plottype}')
 
-            how_to_plot = {'grid': plot_on_a_plain_grid,
-                           'equidistant': plot_on_a_plain_grid,
-                           'point': plot_2d_scatterplot,
-                           'unknown': plot_2d_scatterplot}
+            how_to_plot = {'2D_grid': plot_on_a_plain_grid,
+                           '2D_equidistant': plot_on_a_plain_grid,
+                           '2D_point': plot_2d_scatterplot,
+                           '2D_unknown': plot_2d_scatterplot}
             plot_func = how_to_plot[plottype]
 
-            with _appropriate_kwargs('heatmap',
+            with _appropriate_kwargs(plottype,
                                      colorbar is not None, **kwargs) as k:
                 ax, colorbar = plot_func(xpoints, ypoints, zpoints,
                                          ax, colorbar,

From e2bc21b176ea377d84fc853e5e093f4213b73e99 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 4 Jan 2019 11:16:09 +0100
Subject: [PATCH 466/719] Fix codeacy errors

---
 qcodes/logger/instrument_logger.py | 2 +-
 qcodes/monitor/monitor.py          | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/logger/instrument_logger.py b/qcodes/logger/instrument_logger.py
index 0bef73ce75e..2dc7b4ceb92 100644
--- a/qcodes/logger/instrument_logger.py
+++ b/qcodes/logger/instrument_logger.py
@@ -1,6 +1,6 @@
 from contextlib import contextmanager
 import logging
-from typing import Optional, Sequence, Union, TYPE_CHECKING, cast
+from typing import Optional, Sequence, Union, TYPE_CHECKING
 import collections.abc
 from .logger import get_console_handler, LevelType, handler_level
 if TYPE_CHECKING:
diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 25236da5033..0b07ce7727d 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -26,7 +26,7 @@
 import time
 import json
 from contextlib import suppress
-from typing import Dict, Any, Optional, Union
+from typing import Dict, Any, Optional
 from collections import defaultdict
 
 import asyncio

From 3369232b6b2f06cc18159f10277f5925ce8d6a54 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Fri, 4 Jan 2019 13:06:35 +0100
Subject: [PATCH 467/719] Update test

---
 qcodes/tests/dataset/test_plotting.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/qcodes/tests/dataset/test_plotting.py b/qcodes/tests/dataset/test_plotting.py
index 854c6d141b5..c07b09ffdf2 100644
--- a/qcodes/tests/dataset/test_plotting.py
+++ b/qcodes/tests/dataset/test_plotting.py
@@ -124,26 +124,26 @@ def test_appropriate_kwargs():
     kwargs = {'cmap': 'bone'}
     check = kwargs.copy()
 
-    with _appropriate_kwargs('line', False, **kwargs) as ap_kwargs:
+    with _appropriate_kwargs('1D_line', False, **kwargs) as ap_kwargs:
         assert ap_kwargs == {}
 
     assert kwargs == check
 
-    with _appropriate_kwargs('point', False, **kwargs) as ap_kwargs:
+    with _appropriate_kwargs('1D_point', False, **kwargs) as ap_kwargs:
         assert ap_kwargs == {}
 
     assert kwargs == check
 
-    with _appropriate_kwargs('bar', False, **kwargs) as ap_kwargs:
+    with _appropriate_kwargs('1D_bar', False, **kwargs) as ap_kwargs:
         assert ap_kwargs == {}
 
     assert kwargs == check
 
-    with _appropriate_kwargs('heatmap', False, **kwargs) as ap_kwargs:
+    with _appropriate_kwargs('2D_grid', False, **kwargs) as ap_kwargs:
         assert ap_kwargs == kwargs
 
     assert kwargs == check
 
-    with _appropriate_kwargs('heatmap', False, **{}) as ap_kwargs:
+    with _appropriate_kwargs('2D_point', False, **{}) as ap_kwargs:
         assert len(ap_kwargs) == 1
         assert ap_kwargs['cmap'] == qc.config.plotting.default_color_map

From 02b68a120b4fb3d8aeef223ba3c7a866f6844ca3 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 14:01:57 +0100
Subject: [PATCH 468/719] Add missing space to parameter docstring

---
 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 0cbd5fcbfc1..3e1e48ffb5d 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -174,7 +174,7 @@ def __init__(self, name, address, silent=False,
                            set_cmd=False,
                            label='Line Frequency',
                            unit='Hz',
-                           docstring=('The frequency of the power line where'
+                           docstring=('The frequency of the power line where '
                                       'the instrument is plugged')
                            )
 

From 95b0cccac4ebc8226a920aa503e4e721e8c231e8 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 14:02:37 +0100
Subject: [PATCH 469/719] Add seconds as unit for trigger_delay parameter

---
 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 3e1e48ffb5d..a8e84755b71 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -237,6 +237,7 @@ def __init__(self, name, address, silent=False,
 
         self.add_parameter('trigger_delay',
                            label='Trigger Delay',
+                           unit='s',
                            set_cmd='TRIGger:DELay {}',
                            get_cmd='TRIGger:DELay?',
                            vals=vals.MultiType(vals.Numbers(0, 3600),

From 99c82b1964650673a5b2b2bdfa6810d4ff500576 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 14:07:25 +0100
Subject: [PATCH 470/719] Add docstrings for NPLC, display_text, autozero,
 trigger_delay params

---
 .../Keysight/private/Keysight_344xxA.py       | 56 +++++++++++++++++--
 1 file changed, 51 insertions(+), 5 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index a8e84755b71..6353c25eb42 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -1,5 +1,5 @@
 # QCoDeS driver for the Keysight 344xxA Digital Multimeter
-
+import textwrap
 from functools import partial
 import numpy as np
 import logging
@@ -184,7 +184,21 @@ def __init__(self, name, address, silent=False,
                            set_cmd=self._set_NPLC,
                            vals=vals.Enum(*self.NPLC_list),
                            label='Integration time',
-                           unit='NPLC')
+                           unit='NPLC',
+                           docstring=textwrap.dedent("""\
+            Sets the integration time in number of power line cycles (PLC) 
+            for DC voltage and ratio measurements. Integration time is the 
+            period that the instrument's analog-to-digital (A/D) converter 
+            samples the input signal for a measurement. A longer integration 
+            time gives better measurement resolution but slower measurement 
+            speed.
+            
+            Only integration times of 1, 10, or 100 PLC provide normal mode 
+            (line frequency noise) rejection.
+            
+            Setting the integration time also sets the measurement 
+            resolution.""")
+                           )
 
         self.add_parameter('volt',
                            get_cmd=self._get_voltage,
@@ -215,14 +229,41 @@ def __init__(self, name, address, silent=False,
                            label='Display text',
                            set_cmd='DISPLAY:TEXT "{}"',
                            get_cmd='DISPLAY:TEXT?',
-                           vals=vals.Strings())
+                           vals=vals.Strings(),
+                           docstring=textwrap.dedent("""\
+            Displays the given text on the screen. Specifying empty string 
+            moves the display back to its normal state. The same can be 
+            achieved by calling `display_clear`.""")
+                           )
 
         self.add_parameter('autozero',
                            label='Autozero',
                            set_cmd='SENSe:VOLTage:DC:ZERO:AUTO {}',
                            get_cmd='SENSe:VOLTage:DC:ZERO:AUTO?',
                            val_mapping={'ON': 1, 'OFF': 0, 'ONCE': 'ONCE'},
-                           vals=vals.Enum('ON', 'OFF', 'ONCE'))
+                           vals=vals.Enum('ON', 'OFF', 'ONCE'),
+                           docstring=textwrap.dedent("""\
+            Disables or enables the autozero mode for DC voltage and ratio 
+            measurements.
+            
+            ON:   the DMM internally measures the offset following each 
+                  measurement. It then subtracts that measurement from the 
+                  preceding reading. This prevents offset voltages present on
+                  the DMM’s input circuitry from affecting measurement 
+                  accuracy.
+            OFF:  the instrument uses the last measured zero measurement and 
+                  subtracts it from each measurement. It takes a new zero 
+                  measurement each time you change the function, range or 
+                  integration time.
+            ONCE: the instrument takes one zero measurement and sets 
+                  autozero OFF. The zero measurement taken is used for all 
+                  subsequent measurements until the next change to the 
+                  function, range or integration time. If the specified 
+                  integration time is less than 1 PLC, the zero measurement 
+                  is taken at 1 PLC to optimize noise rejection. Subsequent 
+                  measurements are taken at the specified fast (< 1 PLC) 
+                  integration time.""")
+                           )
 
         # TRIGGERING
 
@@ -242,7 +283,12 @@ def __init__(self, name, address, silent=False,
                            get_cmd='TRIGger:DELay?',
                            vals=vals.MultiType(vals.Numbers(0, 3600),
                                                vals.Enum('MIN', 'MAX', 'DEF')),
-                           get_parser=float)
+                           get_parser=float,
+                           docstring="Step size for DC measurements is "
+                                     "approximately 1 µs.\nFor AC "
+                                     "measurements, step size depends on AC "
+                                     "bandwidth."
+                           )
 
         self.add_parameter('trigger_slope',
                            label='Trigger Slope',

From 844334bb154525fc1ba9e2c4193e3e588b436760 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 14:37:38 +0100
Subject: [PATCH 471/719] Add get parser to display_text parameter to remove
 technical quotes

---
 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 6353c25eb42..d85b8015005 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -229,6 +229,7 @@ def __init__(self, name, address, silent=False,
                            label='Display text',
                            set_cmd='DISPLAY:TEXT "{}"',
                            get_cmd='DISPLAY:TEXT?',
+                           get_parser=lambda s: s.strip('"'),
                            vals=vals.Strings(),
                            docstring=textwrap.dedent("""\
             Displays the given text on the screen. Specifying empty string 

From 40f837938f997b51446e8c69cf935458147b83b0 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 14:40:04 +0100
Subject: [PATCH 472/719] Recast add_function in Keysight_344xxA drivers

(cherry picked from commit 24dc67fb7a758ec6b1f1050fead235e659d88df0)

(that commit has incorrect cast for 'reset' function which is taken
care of here)
---
 .../Keysight/private/Keysight_344xxA.py       | 26 +++++++++++++++----
 1 file changed, 21 insertions(+), 5 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index d85b8015005..4250a2081bd 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -388,14 +388,30 @@ def __init__(self, name, address, silent=False,
                                           'automatically enables the aperture'
                                           ' mode.'))
 
-        self.add_function('init_measurement', call_cmd='INIT')
-        self.add_function('reset', call_cmd='*RST')
-        self.add_function('display_clear', call_cmd=('DISPLay:TEXT:CLEar'))
-        self.add_function('abort_measurement', call_cmd='ABORt')
-
         if not silent:
             self.connect_message()
 
+    def init_measurement(self) -> None:
+        """
+        Change the state of the triggering system from "idle" to
+        "wait-for-trigger", and clear the previous set of measurements from
+        reading memory
+        """
+        self.write('INIT')
+
+    def reset(self) -> None:
+        self.write('*RST')
+
+    def display_clear(self) -> None:
+        self.write('DISPLay:TEXT:CLEar')
+
+    def abort_measurement(self) -> None:
+        """
+        Abort a measurement in progress, returning the instrument to the
+        trigger idle state.
+        """
+        self.write('ABORt')
+
     def _licenses(self):
         licenses_raw = self.ask('SYST:LIC:CAT?')
         licenses_list = [x.strip('"') for x in licenses_raw.split(',')]

From d3f36e1cb72d4183348399e1e8d32c8b9360df03 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 14:41:57 +0100
Subject: [PATCH 473/719] Update display_text value when calling display_clear

---
 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 4250a2081bd..922b6ecbdc6 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -404,6 +404,7 @@ def reset(self) -> None:
 
     def display_clear(self) -> None:
         self.write('DISPLay:TEXT:CLEar')
+        self.display_text.get()  # also update the parameter value
 
     def abort_measurement(self) -> None:
         """

From 435c1427581d5b850fba93b7e884bc463ca87786 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 15:04:26 +0100
Subject: [PATCH 474/719] Add display_enabled parameter to control display
 on/off state

---
 .../Keysight/private/Keysight_344xxA.py         | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 922b6ecbdc6..927df2848b4 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -225,6 +225,23 @@ def __init__(self, name, address, silent=False,
                            val_mapping={'ON': 1, 'OFF': 0},
                            vals=vals.Enum('ON', 'OFF'))
 
+        self.add_parameter('display_enabled',
+                           label='Display enabled',
+                           set_cmd='DISPlay:STATe {}',
+                           get_cmd='DISPlay:STATe?',
+                           val_mapping={True: 1, False: 0},
+                           docstring=textwrap.dedent("""\
+            Disables or enables the front panel display. When disabled, 
+            the display dims, and all annunciators are disabled. However, 
+            the screen remains on.
+            
+            Disabling the display improves command execution speed from the 
+            remote interface and provides basic security.
+            
+            Displaying text with `display_text` parameter will work even 
+            when the display is disabled.""")
+                           )
+
         self.add_parameter('display_text',
                            label='Display text',
                            set_cmd='DISPLAY:TEXT "{}"',

From d56c1c796a65ab2b3eab1ef3043788c008db67b5 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 4 Jan 2019 14:45:36 +0100
Subject: [PATCH 475/719] Always publish results even if tests fail

---
 azure-pipelines.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index d157f96026d..890eea3a4f0 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -46,6 +46,7 @@ jobs:
           make.bat htmlapi
         displayName: "docsbuild"
       - task: PublishTestResults@1
+        condition: always() # this step will always run, even if the pipeline is cancelled
         inputs:
           testResultsFiles: 'qcodes\test-*.xml'
           testRunTitle: 'Publish test results'

From 67a7c5e1f63ed639ddac403d07997a55ade74e7e Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen <jenshnielsen@gmail.com>
Date: Fri, 4 Jan 2019 15:24:32 +0100
Subject: [PATCH 476/719] Update azure-pipelines.yml

---
 azure-pipelines.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 8072152abee..6289828bd95 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -52,5 +52,5 @@ jobs:
       - task: PublishBuildArtifacts@1
         inputs:
           pathtoPublish: 'docs/_build/html'
-          artifactName: 'docs'
+          artifactName: 'qcodes_docs'
           publishLocation: 'Container'

From b893d22a6f477143bba357d50146174a2bd2fc53 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 15:39:45 +0100
Subject: [PATCH 477/719] Add _fetch method, FETCh? waits for completion and
 retrieves data

---
 .../Keysight/private/Keysight_344xxA.py         | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 927df2848b4..a644f0e6bf2 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -446,6 +446,23 @@ def _get_voltage(self):
 
         return float(response)
 
+    def _fetch(self) -> np.array:
+        """
+        Waits for measurements to complete and copies all available
+        measurements to the instrument's output buffer. The readings remain
+        in reading memory.
+
+        This query does not erase measurements from the reading memory. You
+        can call this method multiple times to retrieve the same data.
+
+        Returns:
+            a 1D numpy array of all measured values that are currently in the
+            reading memory
+        """
+        raw_vals: str = self.ask('FETCH?')
+        vals = np.array(list(map(float, raw_vals.split(','))))
+        return vals
+
     def _set_databuffer_setpoints(self, cmd, value):
         """
         set_cmd for all databuffer-setpoint related parameters

From e8f44fab261a0499e4bf0cf6f989b7f355e85a72 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 15:48:19 +0100
Subject: [PATCH 478/719] CLarify display_clear docstring

---
 .../instrument_drivers/Keysight/private/Keysight_344xxA.py   | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index a644f0e6bf2..cca80db1315 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -420,6 +420,11 @@ def reset(self) -> None:
         self.write('*RST')
 
     def display_clear(self) -> None:
+        """
+        Clear text from display. Depending on the display being
+        enabled/disabled, this either returns to display's normal state or
+        leaves it black, respectively.
+        """
         self.write('DISPLay:TEXT:CLEar')
         self.display_text.get()  # also update the parameter value
 

From 64dd967bce1aab8c26988e0b013d939c6cabe65b Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 15:49:42 +0100
Subject: [PATCH 479/719] Clarify init_measurement docstring with extra info

---
 .../Keysight/private/Keysight_344xxA.py               | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index cca80db1315..4bed593f64b 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -412,7 +412,16 @@ def init_measurement(self) -> None:
         """
         Change the state of the triggering system from "idle" to
         "wait-for-trigger", and clear the previous set of measurements from
-        reading memory
+        reading memory.
+
+        This method is an "overlapped" command. This means that after
+        executing it, you can send other commands that do not affect the
+        measurements.
+
+        Storing measurements in reading memory with this method is faster than
+        sending measurements to the instrument's output buffer using
+        `read` method ("READ?" command) (provided you do not `_fetch`,
+        "FETCh?" command, until done).
         """
         self.write('INIT')
 

From 009420cef663402f4a77650f7d8fa5eab88dd5bf Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 15:59:20 +0100
Subject: [PATCH 480/719] Extract parsing retrieved data into a function
 _raw_vals_to_array

---
 .../Keysight/private/Keysight_344xxA.py        | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 4bed593f64b..0eab24886d0 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -474,8 +474,7 @@ def _fetch(self) -> np.array:
             reading memory
         """
         raw_vals: str = self.ask('FETCH?')
-        vals = np.array(list(map(float, raw_vals.split(','))))
-        return vals
+        return _raw_vals_to_array(raw_vals)
 
     def _set_databuffer_setpoints(self, cmd, value):
         """
@@ -568,3 +567,18 @@ def flush_error_queue(self, verbose: bool=True) -> None:
                 print(err_code, err_message)
 
         log.debug('...flushing complete')
+
+
+def _raw_vals_to_array(raw_vals: str) -> np.array:
+    """
+    Helper function that converts comma-delimited string of floating-point
+    values to a numpy 1D array of them. Most data retrieval command of these
+    instruments return data in this format.
+
+    Args:
+        raw_vals: comma-delimited string of floating-point values
+
+    Returns:
+        numpy 1D array of data
+    """
+    return np.array(list(map(float, raw_vals.split(','))))

From ba208f50822f7385d322290b65ca9db9676eb5af Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 15:59:41 +0100
Subject: [PATCH 481/719] Add _read method for READ? command

---
 .../Keysight/private/Keysight_344xxA.py            | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 0eab24886d0..c3e3caf6658 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -476,6 +476,20 @@ def _fetch(self) -> np.array:
         raw_vals: str = self.ask('FETCH?')
         return _raw_vals_to_array(raw_vals)
 
+    def _read(self) -> np.array:
+        """
+        Starts a new set of measurements, waits for all measurements to
+        complete, and transfers all available measurements.
+
+        This method is similar to calling :meth:`init_measurement` followed
+        immediately by :meth:`_fetch`.
+
+        Returns:
+            a 1D numpy array of all measured values
+        """
+        raw_vals: str = self.ask('READ?')
+        return _raw_vals_to_array(raw_vals)
+
     def _set_databuffer_setpoints(self, cmd, value):
         """
         set_cmd for all databuffer-setpoint related parameters

From b76942bf6f6e6953590eae327741108d4274704d Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 18:34:26 +0100
Subject: [PATCH 482/719] Add docstring to sample_timer parameter

---
 .../Keysight/private/Keysight_344xxA.py       | 20 ++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index c3e3caf6658..82cf645376f 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -360,7 +360,25 @@ def __init__(self, name, address, silent=False,
                            unit='s',
                            vals=vals.MultiType(vals.Numbers(0, 3600),
                                                vals.Enum('MIN', 'MAX', 'DEF')),
-                           get_parser=float)
+                           get_parser=float,
+                           docstring=textwrap.dedent("""\
+            The value is rounded by the instrument to the nearest step. For DC 
+            measurements, the step size is 1 µs. For AC measurements, 
+            it is AC bandwidth dependent.
+            
+            Special values are: MIN - recommended minimum, MAX - maximum, 
+            DEF - default. In order to obtain the actual value of the 
+            parameter that gets set when setting it to one of these special 
+            values, just call the get method of the parameter, or use 
+            corresponding parameters in this driver, 
+            like `sample_timer_minimum`.
+
+            Specifying a value that is between the absolute minimum (assumes 
+            no range changes) and the recommended minimum value, 
+            may generate a timing violation error when making measurements.
+            
+            Applying a value less than the absolute minimum will generate an 
+            error."""))
 
         self.add_parameter('sample_timer_minimum',
                            label='Minimal sample time',

From eb6c06060857c4919c605c3c9af1ba2d9856e96a Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 18:34:52 +0100
Subject: [PATCH 483/719] Add docstring to sample_timer_minimum parameter

---
 .../Keysight/private/Keysight_344xxA.py       | 20 ++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 82cf645376f..79e63c737bf 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -384,7 +384,25 @@ def __init__(self, name, address, silent=False,
                            label='Minimal sample time',
                            get_cmd='SAMPle:TIMer? MIN',
                            get_parser=float,
-                           unit='s')
+                           unit='s',
+                           docstring=textwrap.dedent("""\
+            This value is measurement dependent. It depends on such things 
+            as the integration time, autozero on or off, autorange on or 
+            off, and the measurement range. Basically, the minimum is 
+            automatically determined by the instrument so that the sample 
+            interval is always greater than the sampling time.
+            
+            Since the minimum value changes depending on configuration, a 
+            command order dependency exists. You must completely configure 
+            the measurement before setting the sample timer to minimum, 
+            or you may generate an error. A complete configuration includes 
+            such things as math statistics or scaling.
+            
+            When using autorange, the minimum value is the recommended value, 
+            not the absolute minimum value. With autorange enabled, minimum 
+            value is calculated assuming a single range change will occur 
+            for every measurement (not multiple ranges, just one range up or 
+            down per measurement)."""))
 
         # The array parameter
         self.add_parameter('data_buffer',

From 0c8caeccaa3217ea6168437ffc99ce288bdfec21 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 18:35:22 +0100
Subject: [PATCH 484/719] Correct label of sample_timer_minimum parameter to
 mention "recommended"

---
 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 79e63c737bf..fa1e79dd306 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -381,7 +381,7 @@ def __init__(self, name, address, silent=False,
             error."""))
 
         self.add_parameter('sample_timer_minimum',
-                           label='Minimal sample time',
+                           label='Minimal recommended sample time',
                            get_cmd='SAMPle:TIMer? MIN',
                            get_parser=float,
                            unit='s',

From 89219b6f3431db7849143bfbb450aa9ed65d83f4 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 18:35:41 +0100
Subject: [PATCH 485/719] Rename _fetch method to fetch

---
 .../instrument_drivers/Keysight/private/Keysight_344xxA.py  | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index fa1e79dd306..9467916bc31 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -456,7 +456,7 @@ def init_measurement(self) -> None:
 
         Storing measurements in reading memory with this method is faster than
         sending measurements to the instrument's output buffer using
-        `read` method ("READ?" command) (provided you do not `_fetch`,
+        `read` method ("READ?" command) (provided you do not `fetch`,
         "FETCh?" command, until done).
         """
         self.write('INIT')
@@ -496,7 +496,7 @@ def _get_voltage(self):
 
         return float(response)
 
-    def _fetch(self) -> np.array:
+    def fetch(self) -> np.array:
         """
         Waits for measurements to complete and copies all available
         measurements to the instrument's output buffer. The readings remain
@@ -518,7 +518,7 @@ def _read(self) -> np.array:
         complete, and transfers all available measurements.
 
         This method is similar to calling :meth:`init_measurement` followed
-        immediately by :meth:`_fetch`.
+        immediately by :meth:`fetch`.
 
         Returns:
             a 1D numpy array of all measured values

From fb301d6d88c75cab087eb1b5afc6fb884eae0d34 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 18:35:57 +0100
Subject: [PATCH 486/719] Rename _read method to read

---
 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 9467916bc31..72454bf2642 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -512,7 +512,7 @@ def fetch(self) -> np.array:
         raw_vals: str = self.ask('FETCH?')
         return _raw_vals_to_array(raw_vals)
 
-    def _read(self) -> np.array:
+    def read(self) -> np.array:
         """
         Starts a new set of measurements, waits for all measurements to
         complete, and transfers all available measurements.

From b9683ed4cec3e4f4cb5eb0c98222f2bedca594bd Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 18:47:23 +0100
Subject: [PATCH 487/719] Deprecate data_buffer ArrayParameter via its prepare
 method

---
 .../Keysight/private/Keysight_344xxA.py                  | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 72454bf2642..204b1f4a47a 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -7,6 +7,7 @@
 
 from qcodes.instrument.parameter import ArrayParameter
 import qcodes.utils.validators as vals
+from qcodes.utils.deprecate import deprecate
 from qcodes import VisaInstrument
 from pyvisa import VisaIOError
 
@@ -26,6 +27,14 @@ def __init__(self, name, shape=(1,), *args, **kwargs):
         self.unit = ''
         self.properly_prepared = False
 
+    @deprecate(reason='the use of this ArrayParameter is limiting as it '
+                      'represents only a single way of performing '
+                      'measurements and performs preparations that are not '
+                      'generally applicable',
+               alternative='driver methods like `fetch` and `read` (together '
+                           'with other methods and parameters) and custom '
+                           'functions and parameters implemented on top of '
+                           'them')
     def prepare(self):
         """
         Prepare the measurement, create the setpoints.

From a623777ce06b4aca26a30b62f543a13a0fb63f77 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 19:40:48 +0100
Subject: [PATCH 488/719] Make DIG boolean instance attribute of driver

---
 .../Keysight/private/Keysight_344xxA.py              | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 204b1f4a47a..3fab1aac2d3 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -138,17 +138,17 @@ def __init__(self, name, address, silent=False,
         idn = self.IDN.get()
         self.model = idn['model']
 
-        DIG = 'DIG' in self._licenses()
-
         ####################################
         # Instrument specifications
 
+        self.has_DIG = 'DIG' in self._licenses()
+
         PLCs = {'34460A': [0.02, 0.2, 1, 10, 100],
                 '34461A': [0.02, 0.2, 1, 10, 100],
                 '34465A': [0.02, 0.06, 0.2, 1, 10, 100],
                 '34470A': [0.02, 0.06, 0.2, 1, 10, 100]
                 }
-        if DIG:
+        if self.has_DIG:
             PLCs['34465A'] = [0.001, 0.002, 0.006] + PLCs['34465A']
             PLCs['34470A'] = [0.001, 0.002, 0.006] + PLCs['34470A']
 
@@ -166,7 +166,7 @@ def __init__(self, name, address, silent=False,
                        '34470A': [1e-6, 0.5e-6, 0.3e-6, 0.1e-6, 0.03e-6,
                                   0.01e-6]
                        }
-        if DIG:
+        if self.has_DIG:
             res_factors['34465A'] = [30e-6, 15e-6, 6e-6] + res_factors['34465A']
             res_factors['34470A'] = [30e-6, 10e-6, 3e-6] + res_factors['34470A']
 
@@ -340,7 +340,7 @@ def __init__(self, name, address, silent=False,
                                                vals.Enum('MIN', 'MAX', 'DEF')),
                            get_parser=int)
 
-        if DIG:
+        if self.has_DIG:
             self.add_parameter('sample_count_pretrigger',
                                label='Sample Pretrigger Count',
                                set_cmd='SAMPle:COUNt:PRETrigger {}',
@@ -429,7 +429,7 @@ def __init__(self, name, address, silent=False,
             elif utility_freq == 60:
                 apt_times = {'34465A': [0.3e-3, 1.67],
                             '34470A': [0.3e-3, 1.67]}
-            if DIG:
+            if self.has_DIG:
                 apt_times['34465A'][0] = 20e-6
                 apt_times['34470A'][0] = 20e-6
 

From 0601323fdaf3764c0ef4937cfa1baf6cb561b574 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 19:41:27 +0100
Subject: [PATCH 489/719] data_buffer now works even if instrument does not
 have DIG

---
 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 3fab1aac2d3..cd3245d39c5 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -50,9 +50,10 @@ def prepare(self):
         inst.aperture_mode('OFF')  # aperture mode seems slower ON than OFF
         inst.trigger_count(1)
         inst.trigger_delay(0)
-        inst.sample_count_pretrigger(0)
         inst.sample_source('TIM')
         inst.autorange('OFF')
+        if inst.has_DIG:
+            inst.sample_count_pretrigger(0)
 
         if inst.trigger_source() is None:
             raise ValueError('Trigger source unspecified! Please set '

From 6104cd7fc51444439e35201e17302184a7079478 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 19:41:46 +0100
Subject: [PATCH 490/719] CLarify docstrings for error and flush_error_queue
 methods

---
 .../Keysight/private/Keysight_344xxA.py            | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index cd3245d39c5..e318d3507ff 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -593,7 +593,17 @@ def _set_resolution(self, value):
 
     def error(self) -> Tuple[int, str]:
         """
-        Return the first error message in the queue.
+        Return the first error message in the queue. It also clears it from
+        the error queue.
+
+        Up to 20 errors can be stored in the instrument's error queue.
+        Error retrieval is first-in-first-out (FIFO).
+
+        If more than 20 errors have occurred, the most recent error stored
+        in the queue is replaced with -350,"Queue overflow". No additional
+        errors are stored until you remove errors from the queue. If no
+        errors have occurred when you read the error queue, the instrument
+        responds with +0,"No error".
 
         Returns:
             The error code and the error message.
@@ -606,7 +616,7 @@ def error(self) -> Tuple[int, str]:
 
     def flush_error_queue(self, verbose: bool=True) -> None:
         """
-        Clear the instrument error queue.
+        Clear the instrument error queue, and prints it.
 
         Args:
             verbose: If true, the error messages are printed.

From eec3d684fe9a8da03104baa55066566593d3e639 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 19:52:56 +0100
Subject: [PATCH 491/719] Add autorange once method for DC voltage measurements

it autoranges and then sets autoranging off.
---
 .../Keysight/private/Keysight_344xxA.py                  | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index e318d3507ff..8e8b961dc7a 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -591,6 +591,15 @@ def _set_resolution(self, value):
 
         self.NPLC.get()
 
+    def autorange_once(self) -> None:
+        """
+        Performs immediate autorange and then turns autoranging off.
+
+        The value of the `range` parameter is also updated.
+        """
+        self.write('SENSe:VOLTage:DC:RANGe:AUTO ONCE')
+        self.range.get()
+
     def error(self) -> Tuple[int, str]:
         """
         Return the first error message in the queue. It also clears it from

From bbfa7b4d633dc00e37de03be270f0695ad60a807 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:12:13 +0100
Subject: [PATCH 492/719] Set maximum value for sample_count parameter
 depending on the model

---
 .../Keysight/private/Keysight_344xxA.py                | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 8e8b961dc7a..6350e556949 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -332,13 +332,19 @@ def __init__(self, name, address, silent=False,
 
         # SAMPLING
 
+        if self.model in ['34465A', '34470A']:
+            _max_sample_count = 1e9
+        else:
+            _max_sample_count = 1e6
+
         self.add_parameter('sample_count',
                            label='Sample Count',
                            set_cmd=partial(self._set_databuffer_setpoints,
                                            'SAMPle:COUNt {}'),
                            get_cmd='SAMPle:COUNt?',
-                           vals=vals.MultiType(vals.Numbers(1, 1e6),
-                                               vals.Enum('MIN', 'MAX', 'DEF')),
+                           vals=vals.MultiType(
+                               vals.Numbers(1, _max_sample_count),
+                               vals.Enum('MIN', 'MAX', 'DEF')),
                            get_parser=int)
 
         if self.has_DIG:

From ee64dcf739cf367abce3a069e4e277b0e92d4dd1 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:12:49 +0100
Subject: [PATCH 493/719] Add dcostring for sample_count parameter

---
 .../Keysight/private/Keysight_344xxA.py                  | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 6350e556949..3fe60c91dba 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -345,7 +345,14 @@ def __init__(self, name, address, silent=False,
                            vals=vals.MultiType(
                                vals.Numbers(1, _max_sample_count),
                                vals.Enum('MIN', 'MAX', 'DEF')),
-                           get_parser=int)
+                           get_parser=int,
+                           docstring=textwrap.dedent("""\
+            Specifies the number of measurements (samples) the instrument 
+            takes per trigger.
+            
+            MAX selects 1 billion readings. However, when pretrigger is 
+            selected, the maximum is 50,000 readings (without the MEM 
+            option) or 2,000,000 readings (with the MEM option)"""))
 
         if self.has_DIG:
             self.add_parameter('sample_count_pretrigger',

From 8c9ce647aa97532b0f8dd7cf0377b51dc3bfe273 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:13:19 +0100
Subject: [PATCH 494/719] Add docstring for aperture_mode parameter

---
 .../instrument_drivers/Keysight/private/Keysight_344xxA.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 3fe60c91dba..6197f685f7c 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -452,7 +452,12 @@ def __init__(self, name, address, silent=False,
                                set_cmd='SENSe:VOLTage:DC:APERture:ENABled {}',
                                get_cmd='SENSe:VOLTage:DC:APERture:ENABled?',
                                val_mapping={'ON': 1, 'OFF': 0},
-                               vals=vals.Enum('ON', 'OFF'))
+                               vals=vals.Enum('ON', 'OFF'),
+                               docstring=textwrap.dedent("""\
+                Enables the setting of integration time in seconds (called 
+                aperture time) for DC voltage measurements. If aperture time 
+                mode is disabled (default), the integration time is set in PLC 
+                (power-line cycles)."""))
 
             self.add_parameter('aperture_time',
                                label='Aperture time',

From 8562b10787ed5ec809b5500717c93ca6e9fc1731 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:13:29 +0100
Subject: [PATCH 495/719] Add docstring for aperture_time parameter

---
 .../Keysight/private/Keysight_344xxA.py             | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 6197f685f7c..fc7303283d0 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -465,9 +465,16 @@ def __init__(self, name, address, silent=False,
                                get_cmd='SENSe:VOLTage:DC:APERture?',
                                get_parser=float,
                                vals=vals.Numbers(*apt_times[self.model]),
-                               docstring=('Setting the aperture time '
-                                          'automatically enables the aperture'
-                                          ' mode.'))
+                               docstring=textwrap.dedent("""\
+                Specifies the integration time in seconds (called aperture 
+                time) with 2 µs resolution for DC voltage measurements.
+                
+                Use this command for precise control of the DMM's 
+                integration time. Use `NPLC` for better power-line noise 
+                rejection characteristics (NPLC > 1).
+
+                Setting the aperture time automatically enables the aperture 
+                mode."""))
 
         if not silent:
             self.connect_message()

From 3cafeeb4f60ecee614daa37128ec32a91b4ae203 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:17:13 +0100
Subject: [PATCH 496/719] sample_source parameter is only available for 34465A,
 34470A models

---
 .../Keysight/private/Keysight_344xxA.py           | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index fc7303283d0..8919aa7f2a9 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -368,13 +368,14 @@ def __init__(self, name, address, silent=False,
                                           ' specified num. of pretrigger samples.')
                                )
 
-        self.add_parameter('sample_source',
-                           label='Sample Timing Source',
-                           set_cmd='SAMPle:SOURce {}',
-                           get_cmd='SAMPle:SOURce?',
-                           vals=vals.Enum('IMM', 'TIM'),
-                           docstring=('Determines sampling time, immediate'
-                                      ' or using sample_timer'))
+        if self.model in ['34465A', '34470A']:
+            self.add_parameter('sample_source',
+                               label='Sample Timing Source',
+                               set_cmd='SAMPle:SOURce {}',
+                               get_cmd='SAMPle:SOURce?',
+                               vals=vals.Enum('IMM', 'TIM'),
+                               docstring=('Determines sampling time, '
+                                          'immediate or using sample_timer'))
 
         self.add_parameter('sample_timer',
                            label='Sample Timer',

From fef45fbd3f8c22c4b14f5c2d244c60575ea79e93 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:20:30 +0100
Subject: [PATCH 497/719] Fix line length for sample_count_pretrigger parameter
 definition

---
 .../Keysight/private/Keysight_344xxA.py            | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 8919aa7f2a9..0f8930c1189 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -359,14 +359,14 @@ def __init__(self, name, address, silent=False,
                                label='Sample Pretrigger Count',
                                set_cmd='SAMPle:COUNt:PRETrigger {}',
                                get_cmd='SAMPle:COUNt:PRETrigger?',
-                               vals=vals.MultiType(vals.Numbers(0, 2e6-1),
-                                                   vals.Enum('MIN', 'MAX', 'DEF')),
+                               vals=vals.MultiType(
+                                   vals.Numbers(0, 2e6-1),
+                                   vals.Enum('MIN', 'MAX', 'DEF')),
                                get_parser=int,
-                               docstring=('Allows collection of the data '
-                                          'being digitized the trigger. Reserves '
-                                          'memory for pretrigger samples up to the'
-                                          ' specified num. of pretrigger samples.')
-                               )
+                               docstring=textwrap.dedent("""\
+                Allows collection of the data being digitized the trigger. 
+                Reserves memory for pretrigger samples up to the specified 
+                num. of pretrigger samples."""))
 
         if self.model in ['34465A', '34470A']:
             self.add_parameter('sample_source',

From 23480bb3fe7f0b26c5ceee4fdee6e46cc314ef1b Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:20:57 +0100
Subject: [PATCH 498/719] Unify formatting within __init__

---
 .../instrument_drivers/Keysight/private/Keysight_344xxA.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 0f8930c1189..f7e6b3668dc 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -293,6 +293,7 @@ def __init__(self, name, address, silent=False,
                   integration time.""")
                            )
 
+        ####################################
         # TRIGGERING
 
         self.add_parameter('trigger_count',
@@ -330,6 +331,7 @@ def __init__(self, name, address, silent=False,
                            get_cmd='TRIGger:SOURce?',
                            vals=vals.Enum('IMM', 'EXT', 'BUS', 'INT'))
 
+        ####################################
         # SAMPLING
 
         if self.model in ['34465A', '34470A']:
@@ -428,7 +430,9 @@ def __init__(self, name, address, silent=False,
             for every measurement (not multiple ranges, just one range up or 
             down per measurement)."""))
 
+        ####################################
         # The array parameter
+
         self.add_parameter('data_buffer',
                            parameter_class=ArrayMeasurement)
 
@@ -477,6 +481,9 @@ def __init__(self, name, address, silent=False,
                 Setting the aperture time automatically enables the aperture 
                 mode."""))
 
+        ####################################
+        # Connect message
+
         if not silent:
             self.connect_message()
 

From 7aa1a89ca40f10e08429bf15a8fbaefb5be67d76 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:21:12 +0100
Subject: [PATCH 499/719] Clarify section name

---
 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index f7e6b3668dc..b2203e64e5f 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -437,7 +437,7 @@ def __init__(self, name, address, silent=False,
                            parameter_class=ArrayMeasurement)
 
         ####################################
-        # Model-specific parameters
+        # Aperture parameters
 
         if self.model in ['34465A', '34470A']:
             # Define the extreme aperture time values for the 34465A and 34470A

From e26e0038e9d56162dcf349c860ce3c5a9bd2bd3d Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 4 Jan 2019 20:19:44 +0100
Subject: [PATCH 500/719] Latest python 3.6 point release

---
 environment.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environment.yml b/environment.yml
index 2aca03ca0bd..eeff5dc2bac 100644
--- a/environment.yml
+++ b/environment.yml
@@ -3,7 +3,7 @@ channels:
  - defaults
  - conda-forge
 dependencies:
- - python=3.6.7
+ - python=3.6.8
  - numpy=1.15
  - matplotlib=3.0
  - pyqtgraph=0.10.0

From 7bfd9246e3b9da1a894edca68ced63aa1859e188 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 4 Jan 2019 20:22:14 +0100
Subject: [PATCH 501/719] h5py 2.9

---
 environment.yml  | 2 +-
 requirements.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/environment.yml b/environment.yml
index eeff5dc2bac..645e6fd0f25 100644
--- a/environment.yml
+++ b/environment.yml
@@ -8,7 +8,7 @@ dependencies:
  - matplotlib=3.0
  - pyqtgraph=0.10.0
  - pyvisa=1.9.1
- - h5py=2.8.0
+ - h5py=2.9.0
  - pyqt=5.9
  - QtPy
  - jsonschema
diff --git a/requirements.txt b/requirements.txt
index 5f03f0ce089..1ca4a326446 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,7 +2,7 @@ numpy==1.15.*
 matplotlib==3.0.*
 pyqtgraph==0.10.0
 PyVISA==1.9.1
-h5py==2.8.0
+h5py==2.9.0
 PyQt5==5.9.*
 QtPy
 jsonschema

From de3b334c7214cd1353d2a4ec19f572c261e94ce0 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:41:07 +0100
Subject: [PATCH 502/719] Clarify trigger_source parameter and add
 trigger_level parameter

These parameters are model and DIG-option dependent
---
 .../Keysight/private/Keysight_344xxA.py       | 53 ++++++++++++++++++-
 1 file changed, 52 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index b2203e64e5f..76df934ff25 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -325,11 +325,58 @@ def __init__(self, name, address, silent=False,
                            get_cmd='TRIGger:SLOPe?',
                            vals=vals.Enum('POS', 'NEG'))
 
+        if self.model in ['34465A', '34470A'] and self.has_DIG:
+            self.add_parameter('trigger_level',
+                               label='Trigger Level',
+                               unit='V',
+                               set_cmd='TRIGger:LEVel {}',
+                               get_cmd='TRIGger:LEVel?',
+                               vals=vals.MultiType(
+                                   vals.Numbers(-1000, 1000),
+                                   vals.Enum('MIN', 'MAX', 'DEF')),
+                               docstring=textwrap.dedent("""\
+                Sets the level on which a trigger occurs when level 
+                triggering is enabled (`trigger_source set to "INT").
+                
+                Note that for 100 mV to 100 V ranges and autorange is off, 
+                the trigger level can only be set within ±120% of the 
+                range."""))
+
+        _trigger_source_docstring = textwrap.dedent("""\
+            Selects the trigger source for measurements.
+            
+            IMMediate: The trigger signal is always present. When you place 
+                the instrument in the "wait-for-trigger" state, the trigger is 
+                issued immediately.
+                
+            BUS: The instrument is triggered by `trigger` method of this 
+                driver once the DMM is in the "wait-for-trigger" state.
+            
+            EXTernal: The instrument accepts hardware triggers applied to 
+                the rear-panel Ext Trig input and takes the specified number 
+                of measurements (`sample_count`), each time a TTL pulse 
+                specified by `trigger_slope` is received. If the 
+                instrument receives an external trigger before it is ready, 
+                it buffers one trigger.""")
+        _trigger_source_vals = vals.Enum('IMM', 'EXT', 'BUS')
+
+        if self.model in ['34465A', '34470A'] and self.has_DIG:
+            _trigger_source_vals = vals.Enum('IMM', 'EXT', 'BUS', 'INT')
+            # extra empty lines are needed for readability of the docstring
+            _trigger_source_docstring += textwrap.dedent("""\
+            
+            
+            INTernal: Provides level triggering capability. To trigger on a 
+                level on the input signal, select INTernal for the source, 
+                and set the level and slope with the `trigger_level` and 
+                `trigger_slope` parameters.""")
+
         self.add_parameter('trigger_source',
                            label='Trigger Source',
                            set_cmd='TRIGger:SOURce {}',
                            get_cmd='TRIGger:SOURce?',
-                           vals=vals.Enum('IMM', 'EXT', 'BUS', 'INT'))
+                           vals=_trigger_source_vals,
+                           docstring=_trigger_source_docstring)
 
         ####################################
         # SAMPLING
@@ -633,6 +680,10 @@ def autorange_once(self) -> None:
         self.write('SENSe:VOLTage:DC:RANGe:AUTO ONCE')
         self.range.get()
 
+    def trigger(self) -> None:
+        """Triggers the instrument if `trigger_source` is "BUS"."""
+        self.write('*TRG')
+
     def error(self) -> Tuple[int, str]:
         """
         Return the first error message in the queue. It also clears it from

From c520b7f33de437de8e5a752f834002ee7c0a132f Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:49:53 +0100
Subject: [PATCH 503/719] Clarify docstring for trigger_delat parameter

---
 .../Keysight/private/Keysight_344xxA.py          | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 76df934ff25..d623667d9f0 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -313,11 +313,17 @@ def __init__(self, name, address, silent=False,
                            vals=vals.MultiType(vals.Numbers(0, 3600),
                                                vals.Enum('MIN', 'MAX', 'DEF')),
                            get_parser=float,
-                           docstring="Step size for DC measurements is "
-                                     "approximately 1 µs.\nFor AC "
-                                     "measurements, step size depends on AC "
-                                     "bandwidth."
-                           )
+                           docstring=textwrap.dedent("""\
+            Sets the delay between the trigger signal and the first 
+            measurement. This may be useful in applications where you want 
+            to allow the input to settle before taking a measurement or for 
+            pacing a burst of measurements.
+            
+            Step size for DC measurements is approximately 1 µs. For AC 
+            measurements, step size depends on AC bandwidth.
+            
+            Selecting a specific trigger delay disables the automatic 
+            trigger delay."""))
 
         self.add_parameter('trigger_slope',
                            label='Trigger Slope',

From 9d3db4be9daad2dfbc102be79692160cb8b6ea31 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:50:12 +0100
Subject: [PATCH 504/719] Add auto_trigger_delay_enabled parameter

---
 .../Keysight/private/Keysight_344xxA.py            | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index d623667d9f0..4d55e060004 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -325,6 +325,20 @@ def __init__(self, name, address, silent=False,
             Selecting a specific trigger delay disables the automatic 
             trigger delay."""))
 
+        self.add_parameter('auto_trigger_delay_enabled',
+                           label='Auto Trigger Delay Enabled',
+                           set_cmd='TRIGger:DELay:AUTO {}',
+                           get_cmd='TRIGger:DELay:AUTO?',
+                           get_parser=int,
+                           val_mapping={True: 1, False: 0},
+                           docstring=textwrap.dedent("""\
+            Disables or enables automatic trigger delay. If enabled, 
+            the instrument determines the delay based on function, range, 
+            and integration time or bandwidth.
+            
+            Selecting a specific trigger delay using `trigger_delay` disables 
+            the automatic trigger delay."""))
+
         self.add_parameter('trigger_slope',
                            label='Trigger Slope',
                            set_cmd='TRIGger:SLOPe {}',

From 3b00674861fa4c0d26a73ac4941230755da57ac6 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:52:05 +0100
Subject: [PATCH 505/719] Maximum trigger_count differs for models 34465A,
 34470A

---
 .../Keysight/private/Keysight_344xxA.py               | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 4d55e060004..7e8a4f83ea5 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -296,14 +296,19 @@ def __init__(self, name, address, silent=False,
         ####################################
         # TRIGGERING
 
+        if self.model in ['34465A', '34470A']:
+            _max_trigger_count = 1e9
+        else:
+            _max_trigger_count = 1e6
+
         self.add_parameter('trigger_count',
                            label='Trigger Count',
                            set_cmd='TRIGger:COUNt {}',
                            get_cmd='TRIGger:COUNt?',
                            get_parser=float,
-                           vals=vals.MultiType(vals.Numbers(1, 1e6),
-                                               vals.Enum('MIN', 'MAX', 'DEF',
-                                                         'INF')))
+                           vals=vals.MultiType(
+                               vals.Numbers(1, _max_trigger_count),
+                               vals.Enum('MIN', 'MAX', 'DEF', 'INF')))
 
         self.add_parameter('trigger_delay',
                            label='Trigger Delay',

From f54f868b4caf11f8caacd6cd8806de5d1f7e5140 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:54:33 +0100
Subject: [PATCH 506/719] Add docstring for trigger_count

---
 .../Keysight/private/Keysight_344xxA.py             | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 7e8a4f83ea5..e333c8b67c2 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -308,7 +308,18 @@ def __init__(self, name, address, silent=False,
                            get_parser=float,
                            vals=vals.MultiType(
                                vals.Numbers(1, _max_trigger_count),
-                               vals.Enum('MIN', 'MAX', 'DEF', 'INF')))
+                               vals.Enum('MIN', 'MAX', 'DEF', 'INF')),
+                           docstring=textwrap.dedent("""\
+            Selects the number of triggers that are accepted by the 
+            instrument before returning to the "idle" trigger state.
+
+            You can use the specified trigger count in conjunction with 
+            `sample_count`. In this case, the number of measurements 
+            returned is the sample count multiplied by the trigger count.
+
+            A variable trigger count is not available from the front panel. 
+            However, when you return to remote control of the instrument, 
+            the trigger count returns to the previous value you selected."""))
 
         self.add_parameter('trigger_delay',
                            label='Trigger Delay',

From 0bb1ee6e37103f2c2de2e77016ecc8bb5bf4100d Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 20:56:54 +0100
Subject: [PATCH 507/719] Unify the use of parentheses

---
 .../Keysight/private/Keysight_344xxA.py              | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index e333c8b67c2..97eae6e0d9e 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -207,8 +207,7 @@ def __init__(self, name, address, silent=False,
             (line frequency noise) rejection.
             
             Setting the integration time also sets the measurement 
-            resolution.""")
-                           )
+            resolution."""))
 
         self.add_parameter('volt',
                            get_cmd=self._get_voltage,
@@ -249,8 +248,7 @@ def __init__(self, name, address, silent=False,
             remote interface and provides basic security.
             
             Displaying text with `display_text` parameter will work even 
-            when the display is disabled.""")
-                           )
+            when the display is disabled."""))
 
         self.add_parameter('display_text',
                            label='Display text',
@@ -261,8 +259,7 @@ def __init__(self, name, address, silent=False,
                            docstring=textwrap.dedent("""\
             Displays the given text on the screen. Specifying empty string 
             moves the display back to its normal state. The same can be 
-            achieved by calling `display_clear`.""")
-                           )
+            achieved by calling `display_clear`."""))
 
         self.add_parameter('autozero',
                            label='Autozero',
@@ -290,8 +287,7 @@ def __init__(self, name, address, silent=False,
                   integration time is less than 1 PLC, the zero measurement 
                   is taken at 1 PLC to optimize noise rejection. Subsequent 
                   measurements are taken at the specified fast (< 1 PLC) 
-                  integration time.""")
-                           )
+                  integration time."""))
 
         ####################################
         # TRIGGERING

From d46d57a402509e68f9c06b969ef7bb21f2c7bc6b Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 21:04:53 +0100
Subject: [PATCH 508/719] Add MIN/MAX/DEF values and docstring to resolution
 parameter

---
 .../Keysight/private/Keysight_344xxA.py       | 20 ++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 97eae6e0d9e..da9a64c7703 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -225,7 +225,25 @@ def __init__(self, name, address, silent=False,
                            get_parser=float,
                            set_cmd=self._set_resolution,
                            label='Resolution',
-                           unit='V')
+                           unit='V',
+                           vals=vals.MultiType(
+                               vals.Numbers(0),
+                               vals.Enum('MIN', 'MAX', 'DEF')),
+                           docstring=textwrap.dedent("""\
+            Selects the measurement resolution for DC voltage and ratio 
+            measurements. The resolution is specified in the same units as the 
+            selected measurement function, not in number of digits.
+            
+            You can also specify MIN (best resolution) or MAX (worst 
+            resolution).
+            
+            To achieve normal mode (line frequency noise) rejection, 
+            use a resolution that corresponds to an integration time that is 
+            an integral number of power line cycles.
+            
+            Refer to "Resolution Table" or "Range, Resolution and NPLC" 
+            sections of the instrument's manual for the available ranges for 
+            the resolution values."""))
 
         self.add_parameter('autorange',
                            label='Autorange',

From e9306b6855a2955175e11f972280e8444c080832 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Fri, 4 Jan 2019 21:12:23 +0100
Subject: [PATCH 509/719] Add example notebook for Keysight 344xxA DMM, WIP
 measurement sequence

---
 .../Qcodes example with Keysight 344xxA.ipynb | 781 ++++++++++++++++++
 1 file changed, 781 insertions(+)
 create mode 100644 docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb

diff --git a/docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb b/docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb
new file mode 100644
index 00000000000..16c2e80533c
--- /dev/null
+++ b/docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb	
@@ -0,0 +1,781 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Qcodes example with Keysight 344xxA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`344xxA` models of Keysight digital multimeters have similar QCoDeS drivers. In this tutorial, `Keysight_34465A` is chosen for showcasing the usage of the instrument.\n",
+    "\n",
+    "Note however that not every feature/parameter is available on all `344xxA` models. This, when possible, is reflected in the instantiated driver object. Also note that models like `34465A` have options (like `DIG` and `MEM`) that can either be enabled or disabled on a particular instrument; this also has impact on availability of some features/parameters and/or their settings. In general, refer to the instrument's manual for detailed information.\n",
+    "\n",
+    "The driver does not cover all the features of the instrument because it is being mostly used for voltage DC measurements. Contribution is welcome."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "import qcodes\n",
+    "from qcodes.instrument_drivers.Keysight.Keysight_34465A import Keysight_34465A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected to: Keysight Technologies 34465A (serial:MY57504787, firmware:A.02.16-02.40-02.16-00.51-03-01) in 0.48s\n"
+     ]
+    }
+   ],
+   "source": [
+    "dmm = Keysight_34465A('dmm', 'USB0::10893::257::MY57504787::0::INSTR')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parameters overview:\n",
+    "\n",
+    "Here we present __some__ of the parameters of the driver.\n",
+    "\n",
+    "* Single-point measurement\n",
+    "  * `dmm.volt` - measure one voltage point now (note: may not always work)\n",
+    "* Range settings\n",
+    "  * `dmm.range`\n",
+    "  * `dmm.autorange`\n",
+    "* Triggering settings\n",
+    "  * `dmm.trigger_source`\n",
+    "  * `dmm.trigger_delay`\n",
+    "  * `dmm.auto_trigger_delay_enabled`\n",
+    "  * `dmm.trigger_count`\n",
+    "  * `dmm.trigger_slope`\n",
+    "  * `dmm.trigger_level`\n",
+    "* Sample settings\n",
+    "  * `dmm.sample_count`\n",
+    "  * `dmm.sample_source`\n",
+    "  * `dmm.sample_timer`\n",
+    "* Display control\n",
+    "  * `dmm.display_text`\n",
+    "  * `dmm.display_clear`\n",
+    "  * `dmm.display_enabled`\n",
+    "* Related measurement accuracy\n",
+    "  * `dmm.NPLC`\n",
+    "  * `dmm.resolution`\n",
+    "  * `dmm.line_frequency`\n",
+    "  * `dmm.aperture_mode`\n",
+    "  * `dmm.aperture_time`\n",
+    "  * `dmm.autozero`\n",
+    "  \n",
+    "Use `dmm.parameters` (or other ways) to explore all the parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Single value reading\n",
+    "\n",
+    "If one simpy wants to measure a single voltage value right now, the convenient `volt` parameter can be used.\n",
+    "\n",
+    "Note that it may not work for some configurations of the instrument, for example, when `sample_count` is not `1`. Later in this notebook, we will present more prowerful and robust ways of performing measurements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.00927792071"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dmm.volt()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multivalue triggered measurements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__NOTE__: Refer to the instrument manual for more information on how to perform measurements with the instrument; here, only the most basic and frequently used ones are demonstated.\n",
+    "\n",
+    "Measurements with the instrument are performed in the following way: the instrument's settings are set for a particular kind of measurement, then the measurement is started/initialized, then after all the data has been acquired, it is retrieved from the instrument. Below is an example of such a measurement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use `range`, `autorange` parameters or `autorange_once` method\n",
+    "# to set the measurement range\n",
+    "dmm.autorange_once()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Todo\n",
+    "# - document measurement process with `read` and `fetch`\n",
+    "#   (consult with experimentalists)\n",
+    "# - add info on how to create array of setpoints \n",
+    "#   (inst.sample_timer has resolution of some us, \n",
+    "#    hence it's important to keep that in mind for setpoint values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.01263095, 0.01261663, 0.01246853]), ((0.0, 6.75e-05, 0.000135),))"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# REFERENCE MEASUREMENT SEQUENCE>>>>\n",
+    "\n",
+    "\n",
+    "dmm.sample_count(3)\n",
+    "\n",
+    "# PREPARE\n",
+    "\n",
+    "n = dmm.sample_count()\n",
+    "\n",
+    "# ensure correct instrument settings\n",
+    "\n",
+    "dmm.aperture_mode('OFF')  # aperture mode seems slower ON than OFF\n",
+    "dmm.autorange('OFF')\n",
+    "\n",
+    "dmm.trigger_count(1)\n",
+    "dmm.trigger_delay(0)\n",
+    "\n",
+    "dmm.sample_count_pretrigger(0)\n",
+    "\n",
+    "# Final step\n",
+    "\n",
+    "time_per_point = dmm.sample_timer_minimum()\n",
+    "\n",
+    "dmm.sample_timer(time_per_point)\n",
+    "dmm.sample_source('TIM')\n",
+    "\n",
+    "setpoints = (tuple(np.linspace(0, n*time_per_point, n)),)\n",
+    "shape = (n,)\n",
+    "\n",
+    "# MEASURE\n",
+    "\n",
+    "n = dmm.sample_count()\n",
+    "\n",
+    "# Ensure that the measurement doesn't time out\n",
+    "# TODO (WilliamHPNielsen): What if we wait really long for a trigger?\n",
+    "old_timeout = dmm.visa_handle.timeout\n",
+    "dmm.visa_handle.timeout = old_timeout + n * time_per_point * 1.2 * 1000\n",
+    "\n",
+    "# Turn off the display to increase measurement speed\n",
+    "dmm.display_text(f'Acquiring {n} samples')\n",
+    "\n",
+    "dmm.init_measurement()\n",
+    "\n",
+    "numvals = dmm.fetch()\n",
+    "\n",
+    "dmm.visa_handle.timeout = old_timeout\n",
+    "\n",
+    "dmm.display_clear()\n",
+    "\n",
+    "numvals, setpoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Special values of some parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some parameters can be set to special values like `MIN`/`MAX`/`DEF` which usually mean minimum/maximum/default, respectively. \n",
+    "\n",
+    "In order to obtain the actual value of the parameter that gets set when setting it to one of these special values, just call the get method of the parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3600.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find out what the maximum value of `sample_timer` can be\n",
+    "dmm.sample_timer('MAX')\n",
+    "dmm.sample_timer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find out what the default value of `sample_timer` is\n",
+    "dmm.sample_timer('DEF')\n",
+    "dmm.sample_timer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.5e-05"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find out what the recommended minumum value of `sample_timer` is\n",
+    "dmm.sample_timer('MIN')\n",
+    "dmm.sample_timer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.5e-05"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Alternatively, if available, use a conveniently implemented\n",
+    "# get-only parameter to find out the actual value,\n",
+    "# for example, for MIN value of `sample_timer` there is such\n",
+    "# a convenient get-only parameter:\n",
+    "dmm.sample_timer_minimum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Display state impacts command execution speed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although it is indeed just nice to be able to display useful text on the screen of the instrument, it turn out that disabling the display (or making it display does text instead of all the indicators and values) improves command execution speed from the remote interface and provides basic security (quoted from the instrument's manual).\n",
+    "\n",
+    "Hence, the driver provides `display_text` parameter that displays a given text on the insrturment, and a `display_clear` method that clears the text from display.\n",
+    "\n",
+    "The driver also provides `display_enabled` parameter. When it is set to `False`, the state of the display is such that it does not show anything. Note, however, that displaying text is still possible when the `display_enabled` is `False` (when `display_enabled` is `False`, `display_clear` clears the text from the screen but does not enable it)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Displays the text\n",
+    "dmm.display_text('Hello, World!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Returns display to its normal state\n",
+    "dmm.display_clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note that a call to `display_clear` also updates \n",
+    "# the value of the `display_text` parameter:\n",
+    "assert dmm.display_text() == ''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Display can also be cleared by setting \n",
+    "# `display_text` to an empty string\n",
+    "dmm.display_text('some text')  # Displays some text\n",
+    "time.sleep(0.5)\n",
+    "dmm.display_text('')  # Returns display to its normal state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Disables the display, which makes it turn black\n",
+    "dmm.display_enabled(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Shows some text on a disabled display\n",
+    "dmm.display_text(\"i'm disabled but still showing text\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Enabling display in this state \n",
+    "# won't change what's being displayed\n",
+    "dmm.display_enabled(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ... but since the display is now enabled,\n",
+    "# clearing the display will not only remove the text\n",
+    "# but also show all the normal display indicators.\n",
+    "dmm.display_clear()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Error handling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the following methods to read the error queue of the instrument. The instrument has an error queue of length up to 20 messages. The queue message retrieval is first-in-first-out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 'No error')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Retrieve the first (i.e. oldest) error message \n",
+    "# in the queue (and thereby remove from the queue)\n",
+    "dmm.error()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The entire queue can be flushed out\n",
+    "# using `flush_error_queue` method.\n",
+    "# Printing the messages in enabled by default\n",
+    "# and can be disables with the `verbose` kwarg.\n",
+    "\n",
+    "# generate a few errors\n",
+    "for _ in range(3):\n",
+    "    dmm.write('produce an error!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-113 Undefined header\n",
+      "-113 Undefined header\n",
+      "-113 Undefined header\n",
+      "0 No error\n"
+     ]
+    }
+   ],
+   "source": [
+    "dmm.flush_error_queue(verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using data_buffer for triggered multisample measurement (deprecated)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__NOTE__: This approach is deprecated.\n",
+    "\n",
+    "One may use `data_buffer` `ArrayParameter` in order to perform a triggered measurement of a predefined number of samples with a single trigger that has not delay. The following code shows how to do it.\n",
+    "\n",
+    "__NOTE__: The reason this approach is deprecated is that the internal implementation of `data_buffer` is quite specific and sets up the instrument into a mode that is not generally useful. This is why it is recommended that the users use driver methods like `read` and `fetch` in order to implement the measurements. It is also up to user to decide to wrap his code into his own measurement function/routine or even a QCoDeS parameter for his own convenience."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the trigger mode. For the sake of this example,\n",
+    "# we are going to use \"immediate\" which will start measuring\n",
+    "# right after `init_measurement` is called (it will be called\n",
+    "# for you by `data_buffer`'s get method')\n",
+    "dmm.trigger_source('IMM')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set sample count to number of samples that\n",
+    "# should be acquired\n",
+    "dmm.sample_count(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\a-miasta\\AppData\\Local\\Continuum\\anaconda3\\envs\\qcodes\\lib\\site-packages\\ipykernel_launcher.py:4: DeprecationWarning: The function \"prepare\" is deprecated, because the use of this ArrayParameter is limiting as it represents only a single way of performing measurements and performs preparations that are not generally applicable Use \"driver methods like `fetch` and `read` (together with other methods and parameters) and custom functions and parameters implemented on top of them\" as an alternative.\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# One needs to prepare the `data_buffer` parameter\n",
+    "# and the instrument for this measurement, otherwise\n",
+    "# getting `data_buffer` will raise an exception\n",
+    "dmm.data_buffer.prepare()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Finally perform the measurement,\n",
+    "# and obtain the measured data\n",
+    "# (note the text on the instrument's display\n",
+    "# during the measurement)\n",
+    "data = dmm.data_buffer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.01319693, 0.01314679, 0.01309664, 0.01305843, 0.01299873])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Now, here's the measured data\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((0.0,\n",
+       "  5.6250000000000005e-05,\n",
+       "  0.00011250000000000001,\n",
+       "  0.00016875,\n",
+       "  0.00022500000000000002),)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Here are the values of setpoints,\n",
+    "# in this case its time in seconds when\n",
+    "# each of the points have been measured relative\n",
+    "# to the first point\n",
+    "dmm.data_buffer.setpoints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5,)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The shape of the setpoints reflects\n",
+    "# the number of samples\n",
+    "dmm.data_buffer.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.5e-05"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For internal use, the amount of time used for\n",
+    "# measuring every point is saved into the following \n",
+    "# attribute:\n",
+    "dmm.data_buffer.time_per_point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.5e-05"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ... which is equal to the value of the\n",
+    "# `sample_timer` parameter:\n",
+    "dmm.sample_timer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  },
+  "nbsphinx": {
+   "execute": "never"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 398e0df79c821cec07006ec01ac71bba082507c1 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 6 Jan 2019 16:27:40 +0100
Subject: [PATCH 510/719] Add tests of validation

---
 qcodes/tests/test_parameter_with_setpoints.py | 43 +++++++++++++++++++
 1 file changed, 43 insertions(+)
 create mode 100644 qcodes/tests/test_parameter_with_setpoints.py

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
new file mode 100644
index 00000000000..bff1a0dac87
--- /dev/null
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -0,0 +1,43 @@
+from numpy.random import rand
+import pytest
+
+
+from qcodes.instrument.parameter import ParameterWithSetpoints, Parameter
+import qcodes.utils.validators as vals
+
+
+
+
+
+def test_verification_invalid_shape():
+    """
+    Test that various parameters with setpoints and invalid shape combinations
+    raises when validated.
+    """
+
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(10)
+
+    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
+                          vals=vals.Arrays(shape=(n_points_1,)))
+    setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: rand(n_points_2()),
+                          vals=vals.Arrays(shape=(n_points_2,)))
+
+    param_with_setpoints = ParameterWithSetpoints('param',
+                                                  get_cmd=lambda:
+                                                  rand(n_points_2()),
+                                                  setpoints=(setpoints_1,),
+                                                  vals=vals.Arrays(shape=(n_points_2,)))
+
+    # the two shapes are the same so validation works
+    param_with_setpoints.validate_consistent_shape()
+    param_with_setpoints.validate(param_with_setpoints.get())
+
+    n_points_2(20)
+    with pytest.raises(ValueError):
+        param_with_setpoints.validate_consistent_shape()
+    with pytest.raises(ValueError):
+        param_with_setpoints.validate(param_with_setpoints.get())

From 211d8abc6cfc472c05960ec1795e25709e77d744 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 6 Jan 2019 16:31:45 +0100
Subject: [PATCH 511/719] Always raise valueerrors

---
 qcodes/instrument/parameter.py | 30 ++++++++++++++++--------------
 1 file changed, 16 insertions(+), 14 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 51687536eb8..00516341f9c 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -964,7 +964,7 @@ def setpoints(self, setpoints: Sequence[_BaseParameter]):
                                 f" expcected a QCoDeS parameter")
         self._setpoints = setpoints
 
-    def validate_consistent_shape(self) -> bool:
+    def validate_consistent_shape(self):
         """
         Verifies that the shape of the Array Validator of the parameter
         is consistent with the Validator of the Setpoints. This requires that
@@ -973,27 +973,29 @@ def validate_consistent_shape(self) -> bool:
         """
 
         if not isinstance(self.vals, Arrays):
-            raise RuntimeError("Can only validate shapes for Array "
-                               "Parameters")
+            raise ValueError("Can only validate shapes for Array "
+                             "Parameters")
         output_shape = self.vals.shape
         setpoints_shape_list: List[int] = []
         for sp in self.setpoints:
             if not isinstance(sp.vals, Arrays):
-                raise RuntimeError("Can only validate shapes for Array "
-                                   "Parameters.")
+                raise ValueError("Can only validate shapes for Array "
+                                 "Parameters.")
             setpoints_shape_list.extend(sp.vals.shape)
         setpoints_shape = tuple(setpoints_shape_list)
 
         if output_shape is None:
-            raise RuntimeError("Trying to verify shape but output "
-                               "does not have any shape")
+            raise ValueError("Trying to verify shape but output "
+                             "does not have any shape")
         if None in output_shape or None in setpoints_shape:
-            raise RuntimeError(f"One or more dimensions have unknown shape"
-                               f"when comparing output: {output_shape} to "
-                               f"setpoints: {setpoints_shape}")
+            raise ValueError(f"One or more dimensions have unknown shape"
+                             f"when comparing output: {output_shape} to "
+                             f"setpoints: {setpoints_shape}")
 
         if output_shape != setpoints_shape:
-            return False
+            raise ValueError(f"Shape of output is not consistent with"
+                             f"setpoints. Output is shape {output_shape} and "
+                             f"setpoints are shape {setpoints_shape}")
         return True
 
     def validate(self, value: ParamDataType) -> None:
@@ -1007,9 +1009,9 @@ def validate(self, value: ParamDataType) -> None:
         if isinstance(self.vals, Arrays):
             consistent = self.validate_consistent_shape()
             if consistent is False:
-                raise RuntimeError(f"Inconsistent shape between parameter and "
-                                   f"setpoints for parameter: "
-                                   f"{self.full_name}")
+                raise ValueError(f"Inconsistent shape between parameter and "
+                                 f"setpoints for parameter: "
+                                 f"{self.full_name}")
         super().validate(value)
 
 

From e0d5a743b05f642b28132b3207c84681dc739530 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 6 Jan 2019 16:32:06 +0100
Subject: [PATCH 512/719] Better test logic

---
 qcodes/tests/test_parameter_with_setpoints.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index bff1a0dac87..960628c0a96 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -19,7 +19,7 @@ def test_verification_invalid_shape():
     n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
 
     n_points_1.set(10)
-    n_points_2.set(10)
+    n_points_2.set(20)
 
     setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
                           vals=vals.Arrays(shape=(n_points_1,)))
@@ -28,15 +28,16 @@ def test_verification_invalid_shape():
 
     param_with_setpoints = ParameterWithSetpoints('param',
                                                   get_cmd=lambda:
-                                                  rand(n_points_2()),
+                                                  rand(n_points_1()),
                                                   setpoints=(setpoints_1,),
-                                                  vals=vals.Arrays(shape=(n_points_2,)))
+                                                  vals=vals.Arrays(shape=(n_points_1,)))
 
     # the two shapes are the same so validation works
     param_with_setpoints.validate_consistent_shape()
     param_with_setpoints.validate(param_with_setpoints.get())
 
-    n_points_2(20)
+    param_with_setpoints.setpoints = (setpoints_2,)
+    param_with_setpoints.get_raw = lambda: rand(n_points_2())
     with pytest.raises(ValueError):
         param_with_setpoints.validate_consistent_shape()
     with pytest.raises(ValueError):

From 7c270198b08c360e3cc3f1262f0e38fa37530d47 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 6 Jan 2019 16:51:03 +0100
Subject: [PATCH 513/719] Tests of more permutations

---
 qcodes/tests/test_parameter_with_setpoints.py | 49 ++++++++++++++++---
 1 file changed, 41 insertions(+), 8 deletions(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index 960628c0a96..df2ceae5783 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -9,7 +9,7 @@
 
 
 
-def test_verification_invalid_shape():
+def test_verification_shapes():
     """
     Test that various parameters with setpoints and invalid shape combinations
     raises when validated.
@@ -26,19 +26,52 @@ def test_verification_invalid_shape():
     setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: rand(n_points_2()),
                           vals=vals.Arrays(shape=(n_points_2,)))
 
-    param_with_setpoints = ParameterWithSetpoints('param',
+    param_with_setpoints_1 = ParameterWithSetpoints('param_1',
                                                   get_cmd=lambda:
                                                   rand(n_points_1()),
                                                   setpoints=(setpoints_1,),
                                                   vals=vals.Arrays(shape=(n_points_1,)))
 
     # the two shapes are the same so validation works
-    param_with_setpoints.validate_consistent_shape()
-    param_with_setpoints.validate(param_with_setpoints.get())
+    param_with_setpoints_1.validate_consistent_shape()
+    param_with_setpoints_1.validate(param_with_setpoints_1.get())
 
-    param_with_setpoints.setpoints = (setpoints_2,)
-    param_with_setpoints.get_raw = lambda: rand(n_points_2())
+    param_with_setpoints_2 = ParameterWithSetpoints('param_2',
+                                                    get_cmd=lambda:
+                                                    rand(n_points_1(),
+                                                         n_points_2()),
+                                                    setpoints=(setpoints_1,
+                                                               setpoints_2),
+                                                    vals=vals.Arrays(
+                                                    shape=(n_points_1,
+                                                           n_points_2)))
+    # 2d
+    param_with_setpoints_2.validate_consistent_shape()
+    param_with_setpoints_2.validate(param_with_setpoints_2.get())
+
+    param_with_setpoints_3 = ParameterWithSetpoints('param_3',
+                                                    get_cmd=lambda:
+                                                    rand(n_points_2()),
+                                                    setpoints=(setpoints_1,),
+                                                    vals=vals.Arrays(
+                                                        shape=(n_points_2,)))
+
+    # inconsistent shapes
+    with pytest.raises(ValueError):
+        param_with_setpoints_3.validate_consistent_shape()
     with pytest.raises(ValueError):
-        param_with_setpoints.validate_consistent_shape()
+        param_with_setpoints_3.validate(param_with_setpoints_3.get())
+
+    # output is not consistent with validator
+    param_with_setpoints_4 = ParameterWithSetpoints('param_4',
+                                                    get_cmd=lambda:
+                                                    rand(n_points_2()),
+                                                    setpoints=(setpoints_1,),
+                                                    vals=vals.Arrays(
+                                                        shape=(n_points_1,)))
+
+    # this does not raise because the validator shapes are consistent
+    param_with_setpoints_4.validate_consistent_shape()
+    # but the output is not consistent with the validator
     with pytest.raises(ValueError):
-        param_with_setpoints.validate(param_with_setpoints.get())
+        param_with_setpoints_4.validate(param_with_setpoints_4())
\ No newline at end of file

From ccc14cc004c851b73d7168c57ba5ba10928cf094 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 6 Jan 2019 17:06:43 +0100
Subject: [PATCH 514/719] Formatting fix

---
 qcodes/utils/validators.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/utils/validators.py b/qcodes/utils/validators.py
index 15bd4d658b1..db7f04ad376 100644
--- a/qcodes/utils/validators.py
+++ b/qcodes/utils/validators.py
@@ -549,7 +549,7 @@ def validate(self, value: np.ndarray, context: str='') -> None:
             if np.shape(value) != shape:
                 raise ValueError(
                     f'{repr(value)} does not have expected shape {shape},'
-                    f'it has shape {np.shape(value)}; {context}')
+                    f' it has shape {np.shape(value)}; {context}')
 
         # Only check if max is not inf as it can be expensive for large arrays
         if self._max_value != (float("inf")):

From 8241ced3d58c58f8a86271b4048c2037282f00f8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 6 Jan 2019 17:07:22 +0100
Subject: [PATCH 515/719] Better formatting

---
 qcodes/instrument/parameter.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 00516341f9c..f2a2dc894fd 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -988,12 +988,12 @@ def validate_consistent_shape(self):
             raise ValueError("Trying to verify shape but output "
                              "does not have any shape")
         if None in output_shape or None in setpoints_shape:
-            raise ValueError(f"One or more dimensions have unknown shape"
+            raise ValueError(f"One or more dimensions have unknown shape "
                              f"when comparing output: {output_shape} to "
                              f"setpoints: {setpoints_shape}")
 
         if output_shape != setpoints_shape:
-            raise ValueError(f"Shape of output is not consistent with"
+            raise ValueError(f"Shape of output is not consistent with "
                              f"setpoints. Output is shape {output_shape} and "
                              f"setpoints are shape {setpoints_shape}")
         return True

From cd1378b5120bd21dfe2a5d4ef2a1d7a3fc08a3bb Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sun, 6 Jan 2019 17:08:16 +0100
Subject: [PATCH 516/719] Test that the right exceptions are thrown

---
 qcodes/tests/test_parameter_with_setpoints.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index df2ceae5783..6849663f62a 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -57,9 +57,15 @@ def test_verification_shapes():
                                                         shape=(n_points_2,)))
 
     # inconsistent shapes
-    with pytest.raises(ValueError):
+    with pytest.raises(ValueError, match=r'Shape of output is not consistent '
+                                         r'with setpoints\. Output is shape '
+                                         r'\(20,\) and setpoints are shape '
+                                         r'\(10,\)'):
         param_with_setpoints_3.validate_consistent_shape()
-    with pytest.raises(ValueError):
+    with pytest.raises(ValueError, match=r'Shape of output is not consistent '
+                                         r'with setpoints\. Output is shape '
+                                         r'\(20,\) and setpoints are shape '
+                                         r'\(10,\)'):
         param_with_setpoints_3.validate(param_with_setpoints_3.get())
 
     # output is not consistent with validator
@@ -73,5 +79,8 @@ def test_verification_shapes():
     # this does not raise because the validator shapes are consistent
     param_with_setpoints_4.validate_consistent_shape()
     # but the output is not consistent with the validator
-    with pytest.raises(ValueError):
-        param_with_setpoints_4.validate(param_with_setpoints_4())
\ No newline at end of file
+    with pytest.raises(ValueError, match=r'does not have expected shape'
+                                         r' \(10,\), '
+                                         r'it has shape \(20,\); '
+                                         r'Parameter: param_4'):
+        param_with_setpoints_4.validate(param_with_setpoints_4())

From 0f26334fe50346dbc38034864eb18c28996ff00b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 09:44:10 +0100
Subject: [PATCH 517/719] Better error messages

---
 qcodes/instrument/parameter.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index f2a2dc894fd..f6653314d13 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -973,14 +973,17 @@ def validate_consistent_shape(self):
         """
 
         if not isinstance(self.vals, Arrays):
-            raise ValueError("Can only validate shapes for Array "
-                             "Parameters")
+            raise ValueError(f"Can only validate shapes for parameters "
+                             f"with Arrays validator. {self.name} does "
+                             f"not have an Arrays validator.")
         output_shape = self.vals.shape
         setpoints_shape_list: List[int] = []
         for sp in self.setpoints:
             if not isinstance(sp.vals, Arrays):
-                raise ValueError("Can only validate shapes for Array "
-                                 "Parameters.")
+                raise ValueError(f"Can only validate shapes for parameters "
+                                 f"with Arrays validator. {sp.name} is "
+                                 f"a setpoint vector but does not have an "
+                                 f"Arrays validator")
             setpoints_shape_list.extend(sp.vals.shape)
         setpoints_shape = tuple(setpoints_shape_list)
 

From e22117d0526cfd533b4e995659cce50ba9e3eb92 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 09:44:29 +0100
Subject: [PATCH 518/719] Test a few more cases that should fail

---
 qcodes/tests/test_parameter_with_setpoints.py | 82 ++++++++++++++-----
 1 file changed, 62 insertions(+), 20 deletions(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index 6849663f62a..675d6a098b2 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -11,8 +11,8 @@
 
 def test_verification_shapes():
     """
-    Test that various parameters with setpoints and invalid shape combinations
-    raises when validated.
+    Test that various parameters with setpoints and shape combinations
+    validate correctly.
     """
 
     n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
@@ -27,10 +27,11 @@ def test_verification_shapes():
                           vals=vals.Arrays(shape=(n_points_2,)))
 
     param_with_setpoints_1 = ParameterWithSetpoints('param_1',
-                                                  get_cmd=lambda:
-                                                  rand(n_points_1()),
-                                                  setpoints=(setpoints_1,),
-                                                  vals=vals.Arrays(shape=(n_points_1,)))
+                                                    get_cmd=lambda:
+                                                    rand(n_points_1()),
+                                                    setpoints=(setpoints_1,),
+                                                    vals=vals.Arrays(
+                                                        shape=(n_points_1,)))
 
     # the two shapes are the same so validation works
     param_with_setpoints_1.validate_consistent_shape()
@@ -43,13 +44,26 @@ def test_verification_shapes():
                                                     setpoints=(setpoints_1,
                                                                setpoints_2),
                                                     vals=vals.Arrays(
-                                                    shape=(n_points_1,
-                                                           n_points_2)))
+                                                        shape=(n_points_1,
+                                                               n_points_2)))
     # 2d
     param_with_setpoints_2.validate_consistent_shape()
     param_with_setpoints_2.validate(param_with_setpoints_2.get())
 
-    param_with_setpoints_3 = ParameterWithSetpoints('param_3',
+
+def test_verification_inconsistent_shape():
+
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(20)
+
+    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
+                            vals=vals.Arrays(shape=(n_points_1,)))
+    setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: rand(n_points_2()))
+
+    param_with_diff_lenght = ParameterWithSetpoints('param_1',
                                                     get_cmd=lambda:
                                                     rand(n_points_2()),
                                                     setpoints=(setpoints_1,),
@@ -61,26 +75,54 @@ def test_verification_shapes():
                                          r'with setpoints\. Output is shape '
                                          r'\(20,\) and setpoints are shape '
                                          r'\(10,\)'):
-        param_with_setpoints_3.validate_consistent_shape()
+        param_with_diff_lenght.validate_consistent_shape()
     with pytest.raises(ValueError, match=r'Shape of output is not consistent '
                                          r'with setpoints\. Output is shape '
                                          r'\(20,\) and setpoints are shape '
                                          r'\(10,\)'):
-        param_with_setpoints_3.validate(param_with_setpoints_3.get())
+        param_with_diff_lenght.validate(param_with_diff_lenght.get())
 
     # output is not consistent with validator
-    param_with_setpoints_4 = ParameterWithSetpoints('param_4',
-                                                    get_cmd=lambda:
-                                                    rand(n_points_2()),
-                                                    setpoints=(setpoints_1,),
-                                                    vals=vals.Arrays(
-                                                        shape=(n_points_1,)))
+    param_with_wrong_validator = ParameterWithSetpoints('param_2',
+                                                        get_cmd=lambda:
+                                                        rand(n_points_2()),
+                                                        setpoints=(
+                                                            setpoints_1,),
+                                                        vals=vals.Arrays(
+                                                            shape=(
+                                                                n_points_1,)))
 
     # this does not raise because the validator shapes are consistent
-    param_with_setpoints_4.validate_consistent_shape()
+    param_with_wrong_validator.validate_consistent_shape()
     # but the output is not consistent with the validator
     with pytest.raises(ValueError, match=r'does not have expected shape'
                                          r' \(10,\), '
                                          r'it has shape \(20,\); '
-                                         r'Parameter: param_4'):
-        param_with_setpoints_4.validate(param_with_setpoints_4())
+                                         r'Parameter: param_2'):
+        param_with_wrong_validator.validate(param_with_wrong_validator())
+
+    # output does not have a validator
+    param_without_validator = ParameterWithSetpoints('param_3',
+                                                     get_cmd=lambda:
+                                                     rand(n_points_1()),
+                                                     setpoints=(setpoints_1,))
+
+    with pytest.raises(ValueError, match=r"Can only validate shapes for "
+                                         r"parameters with Arrays validator. "
+                                         r"param_3 does not"):
+        param_without_validator.validate_consistent_shape()
+
+    # setpoints do not have a validator
+    param_sp_without_validator = ParameterWithSetpoints('param_4',
+                                                        get_cmd=lambda:
+                                                        rand(n_points_2()),
+                                                        setpoints=(
+                                                            setpoints_2,),
+                                                        vals=vals.Arrays(
+                                                            shape=(
+                                                                n_points_1,)))
+
+    with pytest.raises(ValueError, match=r"Can only validate shapes for "
+                                         r"parameters with Arrays validator. "
+                                         r"setpoints_2 is a setpoint"):
+        param_sp_without_validator.validate_consistent_shape()

From 6f9fde0ae10dc360080601860e7c3a820e240dca Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 09:58:27 +0100
Subject: [PATCH 519/719] Slightly more informative message

---
 qcodes/instrument/parameter.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index f6653314d13..4e1a526008d 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -988,8 +988,8 @@ def validate_consistent_shape(self):
         setpoints_shape = tuple(setpoints_shape_list)
 
         if output_shape is None:
-            raise ValueError("Trying to verify shape but output "
-                             "does not have any shape")
+            raise ValueError(f"Trying to validate shape but parameter "
+                             f"{self.name} does not define a shape")
         if None in output_shape or None in setpoints_shape:
             raise ValueError(f"One or more dimensions have unknown shape "
                              f"when comparing output: {output_shape} to "

From 1c0b42b039431e46baef67a30037a5b8dcfa50a5 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 10:04:31 +0100
Subject: [PATCH 520/719] split tests

---
 qcodes/tests/test_parameter_with_setpoints.py | 80 +++++++++++++++----
 1 file changed, 65 insertions(+), 15 deletions(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index 675d6a098b2..70f2a101091 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -61,7 +61,6 @@ def test_verification_inconsistent_shape():
 
     setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
                             vals=vals.Arrays(shape=(n_points_1,)))
-    setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: rand(n_points_2()))
 
     param_with_diff_lenght = ParameterWithSetpoints('param_1',
                                                     get_cmd=lambda:
@@ -71,17 +70,24 @@ def test_verification_inconsistent_shape():
                                                         shape=(n_points_2,)))
 
     # inconsistent shapes
-    with pytest.raises(ValueError, match=r'Shape of output is not consistent '
-                                         r'with setpoints\. Output is shape '
-                                         r'\(20,\) and setpoints are shape '
-                                         r'\(10,\)'):
+    expected_err_msg = (r'Shape of output is not consistent '
+                        r'with setpoints. Output is shape '
+                        r'\(20,\) and setpoints are shape '
+                        r'\(10,\)')
+    with pytest.raises(ValueError, match=expected_err_msg):
         param_with_diff_lenght.validate_consistent_shape()
-    with pytest.raises(ValueError, match=r'Shape of output is not consistent '
-                                         r'with setpoints\. Output is shape '
-                                         r'\(20,\) and setpoints are shape '
-                                         r'\(10,\)'):
+    with pytest.raises(ValueError, match=expected_err_msg):
         param_with_diff_lenght.validate(param_with_diff_lenght.get())
 
+
+def test_verification_wrong_validator():
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(20)
+    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
+                            vals=vals.Arrays(shape=(n_points_1,)))
     # output is not consistent with validator
     param_with_wrong_validator = ParameterWithSetpoints('param_2',
                                                         get_cmd=lambda:
@@ -101,18 +107,37 @@ def test_verification_inconsistent_shape():
                                          r'Parameter: param_2'):
         param_with_wrong_validator.validate(param_with_wrong_validator())
 
+def test_verification_no_validator():
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(20)
+    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
+                            vals=vals.Arrays(shape=(n_points_1,)))
     # output does not have a validator
     param_without_validator = ParameterWithSetpoints('param_3',
                                                      get_cmd=lambda:
                                                      rand(n_points_1()),
                                                      setpoints=(setpoints_1,))
 
-    with pytest.raises(ValueError, match=r"Can only validate shapes for "
-                                         r"parameters with Arrays validator. "
-                                         r"param_3 does not"):
+    expected_err_msg = (r"Can only validate shapes for "
+                        r"parameters with Arrays validator. "
+                        r"param_3 does not")
+    with pytest.raises(ValueError, match=expected_err_msg):
         param_without_validator.validate_consistent_shape()
+    # this does not raise as the shape is only validated for arrays
+    param_without_validator.validate(param_without_validator.get())
 
+
+def test_verification_sp_no_validator():
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(20)
     # setpoints do not have a validator
+    setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: rand(n_points_2()))
     param_sp_without_validator = ParameterWithSetpoints('param_4',
                                                         get_cmd=lambda:
                                                         rand(n_points_2()),
@@ -122,7 +147,32 @@ def test_verification_inconsistent_shape():
                                                             shape=(
                                                                 n_points_1,)))
 
-    with pytest.raises(ValueError, match=r"Can only validate shapes for "
-                                         r"parameters with Arrays validator. "
-                                         r"setpoints_2 is a setpoint"):
+    expected_err_msg = (r"Can only validate shapes for "
+                        r"parameters with Arrays validator. "
+                        r"setpoints_2 is a setpoint")
+    with pytest.raises(ValueError, match=expected_err_msg):
         param_sp_without_validator.validate_consistent_shape()
+    with pytest.raises(ValueError, match=expected_err_msg):
+        param_sp_without_validator.validate(param_sp_without_validator.get())
+
+
+def test_verification_without_shape():
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(20)
+    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
+                            vals=vals.Arrays(shape=(n_points_1,)))
+    param_without_shape = ParameterWithSetpoints('param_5',
+                                                 get_cmd=lambda:
+                                                 rand(n_points_1()),
+                                                 setpoints=(setpoints_1,),
+                                                 vals=vals.Arrays())
+    expected_err_msg = (r"Trying to validate shape but "
+                        r"parameter param_5 does not "
+                        r"define a shape")
+    with pytest.raises(ValueError, match=expected_err_msg):
+        param_without_shape.validate_consistent_shape()
+    with pytest.raises(ValueError, match=expected_err_msg):
+        param_without_shape.validate(param_without_shape.get())

From fa45ed3863d540363a332badb79ea64b7296c13e Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 10:30:14 +0100
Subject: [PATCH 521/719] Handle None setpoint shapes correctly

---
 qcodes/instrument/parameter.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 4e1a526008d..e0016142996 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -984,7 +984,10 @@ def validate_consistent_shape(self):
                                  f"with Arrays validator. {sp.name} is "
                                  f"a setpoint vector but does not have an "
                                  f"Arrays validator")
-            setpoints_shape_list.extend(sp.vals.shape)
+            if sp.vals.shape is not None:
+                setpoints_shape_list.extend(sp.vals.shape)
+            else:
+                setpoints_shape_list.append(sp.vals.shape)
         setpoints_shape = tuple(setpoints_shape_list)
 
         if output_shape is None:

From c81769d8273b691d952361fe6f563116c58e0e73 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 10:30:46 +0100
Subject: [PATCH 522/719] More tests and docstrings

---
 qcodes/tests/test_parameter_with_setpoints.py | 132 +++++++++++++++---
 1 file changed, 112 insertions(+), 20 deletions(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index 70f2a101091..34f35b7b2c8 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -1,15 +1,11 @@
 from numpy.random import rand
 import pytest
 
-
 from qcodes.instrument.parameter import ParameterWithSetpoints, Parameter
 import qcodes.utils.validators as vals
 
 
-
-
-
-def test_verification_shapes():
+def test_validation_shapes():
     """
     Test that various parameters with setpoints and shape combinations
     validate correctly.
@@ -22,9 +18,9 @@ def test_verification_shapes():
     n_points_2.set(20)
 
     setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
-                          vals=vals.Arrays(shape=(n_points_1,)))
+                            vals=vals.Arrays(shape=(n_points_1,)))
     setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: rand(n_points_2()),
-                          vals=vals.Arrays(shape=(n_points_2,)))
+                            vals=vals.Arrays(shape=(n_points_2,)))
 
     param_with_setpoints_1 = ParameterWithSetpoints('param_1',
                                                     get_cmd=lambda:
@@ -51,8 +47,11 @@ def test_verification_shapes():
     param_with_setpoints_2.validate(param_with_setpoints_2.get())
 
 
-def test_verification_inconsistent_shape():
-
+def test_validation_inconsistent_shape():
+    """
+    Parameters with shapes inconsistent with their setpoints should not
+    validate
+    """
     n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
     n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
 
@@ -80,7 +79,11 @@ def test_verification_inconsistent_shape():
         param_with_diff_lenght.validate(param_with_diff_lenght.get())
 
 
-def test_verification_wrong_validator():
+def test_validation_wrong_validator():
+    """
+    If the validator does not match the actual content the validation should
+    fail
+    """
     n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
     n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
 
@@ -107,12 +110,14 @@ def test_verification_wrong_validator():
                                          r'Parameter: param_2'):
         param_with_wrong_validator.validate(param_with_wrong_validator())
 
-def test_verification_no_validator():
+
+def test_validation_no_validator():
+    """
+    If a parameter does not use array validators it cannot be validated.
+    """
     n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
-    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
 
     n_points_1.set(10)
-    n_points_2.set(20)
     setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
                             vals=vals.Arrays(shape=(n_points_1,)))
     # output does not have a validator
@@ -130,11 +135,13 @@ def test_verification_no_validator():
     param_without_validator.validate(param_without_validator.get())
 
 
-def test_verification_sp_no_validator():
-    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+def test_validation_sp_no_validator():
+    """
+    If the setpoints do not have an Arrays validator validation
+    will fail.
+    """
     n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
 
-    n_points_1.set(10)
     n_points_2.set(20)
     # setpoints do not have a validator
     setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: rand(n_points_2()))
@@ -145,7 +152,7 @@ def test_verification_sp_no_validator():
                                                             setpoints_2,),
                                                         vals=vals.Arrays(
                                                             shape=(
-                                                                n_points_1,)))
+                                                                n_points_2,)))
 
     expected_err_msg = (r"Can only validate shapes for "
                         r"parameters with Arrays validator. "
@@ -156,12 +163,13 @@ def test_verification_sp_no_validator():
         param_sp_without_validator.validate(param_sp_without_validator.get())
 
 
-def test_verification_without_shape():
+def test_validation_without_shape():
+    """
+    If the Arrays validator does not have a shape the validation will fail
+    """
     n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
-    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
 
     n_points_1.set(10)
-    n_points_2.set(20)
     setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
                             vals=vals.Arrays(shape=(n_points_1,)))
     param_without_shape = ParameterWithSetpoints('param_5',
@@ -176,3 +184,87 @@ def test_verification_without_shape():
         param_without_shape.validate_consistent_shape()
     with pytest.raises(ValueError, match=expected_err_msg):
         param_without_shape.validate(param_without_shape.get())
+
+
+def test_validation_without_sp_shape():
+    """
+    If the setpoints validator has no shape the validation will fail
+    """
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(20)
+
+    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
+                            vals=vals.Arrays())
+    param_sp_without_shape = ParameterWithSetpoints('param_6',
+                                                    get_cmd=lambda:
+                                                    rand(n_points_1()),
+                                                    setpoints=(setpoints_1,),
+                                                    vals=vals.Arrays(
+                                                        shape=(
+                                                            n_points_1,)))
+    expected_err_msg = (r"One or more dimensions have unknown shape "
+                        r"when comparing output: \(10,\) to setpoints: "
+                        r"\(None,\)")
+    with pytest.raises(ValueError, match=expected_err_msg):
+        param_sp_without_shape.validate_consistent_shape()
+    with pytest.raises(ValueError, match=expected_err_msg):
+        param_sp_without_shape.validate(param_sp_without_shape.get())
+
+
+def test_validation_one_dim_missing():
+    """
+    If one or more dims of the output does not have a shape the validation
+    will fail.
+    """
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(20)
+    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
+                            vals=vals.Arrays(shape=(n_points_1, n_points_2)))
+    param_sp_without_shape = ParameterWithSetpoints('param_6',
+                                                    get_cmd=lambda:
+                                                    rand(n_points_1()),
+                                                    setpoints=(setpoints_1,),
+                                                    vals=vals.Arrays(
+                                                        shape=(
+                                                            n_points_1, None)))
+    expected_err_msg = (r"One or more dimensions have unknown shape "
+                        r"when comparing output: \(10, None\) to setpoints: "
+                        r"\(10, 20\)")
+    with pytest.raises(ValueError, match=expected_err_msg):
+        param_sp_without_shape.validate_consistent_shape()
+    with pytest.raises(ValueError, match=expected_err_msg):
+        param_sp_without_shape.validate(param_sp_without_shape.get())
+
+
+def test_validation_one_sp_dim_missing():
+    """
+    If one or more setpoint validators has no shape the validation will fail.
+    """
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(20)
+    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
+                            vals=vals.Arrays(shape=(n_points_1, None)))
+    param_sp_without_shape = ParameterWithSetpoints('param_6',
+                                                    get_cmd=lambda:
+                                                    rand(n_points_1()),
+                                                    setpoints=(setpoints_1,),
+                                                    vals=vals.Arrays(
+                                                        shape=(
+                                                            n_points_1,
+                                                            n_points_2)))
+    expected_err_msg = (r"One or more dimensions have unknown shape "
+                        r"when comparing output: \(10, 20\) to setpoints: "
+                        r"\(10, None\)")
+    with pytest.raises(ValueError, match=expected_err_msg):
+        param_sp_without_shape.validate_consistent_shape()
+    with pytest.raises(ValueError, match=expected_err_msg):
+        param_sp_without_shape.validate(param_sp_without_shape.get())

From f1328e483e975716c487026ba632cbda0c99b397 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Mon, 7 Jan 2019 11:24:04 +0100
Subject: [PATCH 523/719] Don't call fixtures in notebook

---
 docs/examples/plotting/auto_color_scale.ipynb | 69 ++++++++++++-------
 1 file changed, 45 insertions(+), 24 deletions(-)

diff --git a/docs/examples/plotting/auto_color_scale.ipynb b/docs/examples/plotting/auto_color_scale.ipynb
index 3397f10ef6c..6d74b29fb53 100644
--- a/docs/examples/plotting/auto_color_scale.ipynb
+++ b/docs/examples/plotting/auto_color_scale.ipynb
@@ -83,9 +83,7 @@
     "new_experiment(name='tutorial_auto_color_scale', sample_name=\"outliers\")\n",
     "\n",
     "# import methods for creating datasets with outliers:\n",
-    "from qcodes.tests.test_plot_utils import (dataset_with_data_outside_iqr_high_outlier,\n",
-    "                                          dataset_with_data_outside_iqr_low_outlier,\n",
-    "                                          dataset_with_outliers)"
+    "from qcodes.tests.test_plot_utils import dataset_with_outliers_generator"
    ]
   },
   {
@@ -103,7 +101,9 @@
    "outputs": [],
    "source": [
     "# create a new dataset with outliers\n",
-    "ds = dataset_with_outliers(new_data_set('data_with_outliers'))"
+    "ds = dataset_with_outliers_generator(new_data_set('data_with_outliers'),\n",
+    "                                     low_outlier=-3, high_outlier=3,\n",
+    "                                     background_noise=False)"
    ]
   },
   {
@@ -122,12 +122,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -149,12 +151,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -196,7 +200,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x2c084f7eba8>"
+       "<matplotlib.lines.Line2D at 0x1d387f78358>"
       ]
      },
      "execution_count": 5,
@@ -205,12 +209,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -266,7 +272,9 @@
    "outputs": [],
    "source": [
     "# create a new dataset with outliers\n",
-    "ds = dataset_with_data_outside_iqr_high_outlier(new_data_set('data_outside_iqr'))"
+    "ds = dataset_with_outliers_generator(new_data_set('data_outside_iqr'),\n",
+    "                                     data_offset=2, low_outlier=-2,\n",
+    "                                     high_outlier=3)"
    ]
   },
   {
@@ -276,12 +284,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -306,12 +316,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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wtRu2B+3f28gae3YzstlnpXg7O6+U/m/YF2FeCg+Fmn52BxtH2c6mKV5dZfdsMLLlB/kRErqttv2lz1vWh27eab4v6ZgHbB/Wnt2NbPFn7DUFGPKT142s7lgvwgX8+6kr3lHVPdwftwARmVy2y/7vbq6rZcHMdwrawmm1M7DdrxoKBAKBtmK3A49797Onf4e3//kYnz7pGw20zrO909LhNoJsEJHzRWSqiExtqLNfuYFAINDeiMjkxsYGxu6wF0efchl/feQX200ekZyYEcRri++GSDXUwc0JBAKfQJpmAwC9+gykfPKntptZQU4MBMloHtQXNb/uCccmtSBF+oteThiolbva8OYNVpUOwBDHmT5vs9X7Dn/QT9Ew/4KWMetg/Sj/Nnj69PW7Wb1r3QBfTTn7VKujLnugysik3q+ft77ayGZ9zXrdl908z60//cfDjGzijettwWPc6h1O+R129rn0ZHv/ADZMtMr73tPsfR35pG+76rLRyhNd7cdm9UA/FH/3lfb7qKcTrm6zs1gCoHqk7Wz93vcio8OGkY6do9razlK9HjfubBcHrNjTnquk0L6vuNRGoR76jDXstyUiMnnX/Y5m7A4f2yKOPuUyfn75cci1p+Xnuq2gw1VDIvIwUdySCRJl5NraIHWBQCDQpux24HHvHn9W80gyTbOCC37w+3RBBnOCDh8IVPVkVR2sql1UdZiq/q6j2xQIBAJNJNsGWrK92ApyTjUUCAQC25Jk20BLthdbQYfPCAKBQKCzkm420MT2MCvIuRlBl3V1DHxydjNZ1f5jTLkelf6p1To+Mv0/sg5hFfv7RrmaftYyXTrVOtMsOsM3KnZxbKUbxjleZsC4h60BbvGR1oo9/K++naronx8Z2bJzdjayIff5sdAaJ4wwMteA18M6AwH0/MBew4++lTNpXElMm2Fkmz+7j194s/2mKlpuL9bqHX1v26IVtr963vEF1X5fyWu0ZTcOsvtcs78fIn/iFYuMrGo/+1wBlMx3jMhOHxjzO7tPAF1vFyyMXDHKyApWWic3ALrY81rwpRSJEH/ki1vB5cedcWXaAr36DKRs1/1ZMPOdY0nKiZBL5NxAEAgEAtuQwd889AxKUoTbaKJ2r/X87dG0iWw6NWEgCAQCgTQkUBL4M7EmtE3Tj297wkAQCAQCaWjUBI7mrRmJThCzbWvIuYGgoVdXVh/VXP9eNdIa6wtTqBeLKuzIvvgwq8vukiKSRZdNtn7CiXzYZ6a/tLjLJqs3HvoP/2ANvaxDWJGN10VdiW/z715tHcI857uqw/yESiv2tvv1Ir1Ov8IP7jjoX/bhGHbXdFtwm6WM2XqGvuI7Wa3e0d6rgk3W9tRnVoqouD3stU44T2dtH19FsXoP65A24atT7HGK9nTrz/y2tWmNfdz2H4B1Zba/d+1rn6Glh/iLaEY9ZZ+N+p72vHpu9K/1ghP7GtnoX892SoK18rSeBhppyPDFn2nG0NnJuYEgEAgEtiWNqjRm+OJPBNVQIBAIbL9ENoL0L/rcHgbCQBAIBAJpSaA0ZlQN5fZQkHMDgSjkNTS/6H2m25uwfnT2vnLdV1rZ5hK/bI9XZxlZ1aETjCwvRQiq5XvagF0jnUQdAFW72sQcxYus3rlyd9/noeq7extZ75lWl5lK79xvmi1bNdxe1yGXT3Xrr7rArrmfcetEt2xnZPPRNp/Jqp38R6b3LHvD10y092XQr95y6xeNGWVkMy6ytpcRf/NtT73m2Wdgzem2/cOetT4vAHNPt4HgNo70/UO8jHrL93GetxTqlMLV1vbQ2N0Gvavcz9oCAEb9ebWRVXxhvFuWX/ni1lCvCeozvOczqY46Ozk3EAQCgcC2JBFv6cjtYSCEmAgEAoG0NMaqoXRba1RDEvFLEZkjIu+LyG4Zyj8lIh9s9YmkIcwIAoFAIA2NSmY/gtbt8jPA+HjbG7gz/tcgIp8DbPLwNibMCAKBQCANiSy2VqqGjgce0Ig3gVIRMeEpRKQn8E3aImJSBnJuRlDfAyr3aO6oUjLPOq40+HYuKne3Y9+4H71vZNroW3tn/MIGbZvwdZv2bN73/dlekZM1KlXQtqph9rxqHMedTWP9rFcTf2k/JOpLrePT0qP8brDDT9YYWe83rLFy6SV+ILZ8J77ZxCsXWuHjbvUOp3CtNcyXLPS/nZYc7gjz7bXaeJMfxXLIv+035bB/2tdLtxenufWrPz3JyPo/a7PkLTvJN6puHmHPtfTX/iKG2ZeMNLL8WttXh77sG7bzltt+teIEG4ywz0f+d/bG8bZsrwXtlxumQYV6TR9hujH6/YAWibXujtPstmQosDjp7yWxrKJFueuAmwHfs68NybmBIBAIBLYljQiNGVINxEPWq6p6aha79HbWbNQXkUnAOFX9hoiMyqadW0MYCAKBQCAN2Q0E6X8XkQuBL8d/TgGGJ/08DGg5/doX2F1EFhC9pweIyMuqenDWDW8FwUYQCAQCaUioZNw0g+pIVW9X1UmqOokoZ8EZ8eqhfYD1qlrRovydqjpEVUcBBwCz2msQgBycEYhCXn3zi15QbXWpNZN9neEOVy6wwtJe9jhFvt5enaFz+ZetPWDQ276Noesaq4ude5afVGP0DVYfPOsuGyCuaLrV+wNUHGwd0ob8ZbGRFc0bbmQA6ydZh6Ze7yw3ssINvqls1d72GnRb6yc76YwkCuzDvX6U/+00/kEbOHD2V+3j1f/fvvNe8QfWq3HFIbZf9Ni5zK1ftMAme6nec7SRrdvZtyft8K35Rjb7MusoCdDNccDsucz2AS+QHEDFibYPjHjBqsHnXOBf68J5tr8PeC+FB2cbsJl8Nmf4Zm5oXZbK54CjgTlE+v+zm34QkWnxYLFNybmBIBAIBLYlGn/1py3TioFAVRW4MMVvZhBQ1QXATlkfYAsIA0EgEAikoS1sBJ2dMBAEAoFAGho1j0ZPJ5xEJhtBZyfnBoLC9QlGPds8qXuXWXZx/sr97FpngOk3Wnn5zTaLzaxz/YBXZfdYXWZdP1uux7+cBCzA/MusH0J9SQq/xPGjjKhgidWP9p7p60crDrCdd9nx1h4w8jE/ENmq/QYYmZeEp3qw/xD0nG27V/GCdneSbDMWfNZe68YS3/a04LgiIxvwD8cPYI1fv6F/sZGt2c9JbDPdf2QL5i4xsvy+9l6N+4Nvz5n+I5uYZuJ11m4AsPx4a3vY6PSB4Q9aPwaAgr1t/fyN1umkzyvWxpWKvLr2SwxTTx71pM9Z3JDj625ybiAIBAKBbUlWM4KgGgoEAoHtlwSS0QaQ24kqw0AQCAQCaUmQR2MG1Y8G1VAgEAhsv9RrAfWa3kaQSXXU2cm5gUAaEnSpbO48s+J4a+jK35giO9I6Z4qXb2/yhFut8Q0gsdx60xT1soa+RV+1RmGAzaNrjWzoE13cssxeYESNXXc1spJ/z3WrLz3EXhcVe/5a4Hfy6gG27MoLbCapoS/4xupNg+x+5x9n63dWulfa8x/8hBNJDyhYbbNmLfy8zfrV9wUn6B6g1TVGNuIJm81tznn+I9vntR2MbOATM41s+rW2TwDk1doX2ZwLrVEXfIeyRs+nsSCFYXuj7S8Vn7KG4XW7pgimeMt6I5txhX0GAXjeF7eGRpWmoHIpyeRn0NnJuYEgEAgEtiWNWaiGgh9BIBAIbMckNI9EWDUUCAQCn1zCjKATUl9SwLIjmzs6DXjHOnkVL/L17j0+cJJtOLrMhiF93PqN46yTVU1/e6zqof6CsvLLre1h0Rm+3rZ4qtWbtgy4BzD9h2Pd+r3ft523yyZrO6ma6DvudLV+dtSts3r/krcXufWLe1knq9phNsAfn3KrdzjDn7B9ZfMw/1qtPMD2iwInnYjW+3rv2d+3oWTGXf2ekeUfONmt36XGsYl1t4r7vu/59qAaa86gZIFvZ/NsRw3Wd43Zl/g2hjFP2AB5BTVdjazbMv8ZnnFZiZENesEv61tkWkcDecFYHAgEAp9ksnEoy/UZQbsOYyLyaRGZKSJzROQ7zu8jROQlEXlPRN4XkaPbsz2BQCDQWpocytJtwUaQAhHJB24HjiDKyTlFRJ5S1Y+Sil0NPKqqd4rIDkRxuke1V5sCgUCgtWQ1IwjLR1OyFzBHVecBiMgjwPFA8kCgQJPCrxc2XZuhy7p6hjzdPMhcTZlNoFK4wdfFLjjLBp0b9etZRib1Vg8JsHIXR5e51upSy39V6dZfeLa1B/Se5a/Dn/nNEUZW4rgMbOjmd8K1+9g17+U/swlUFh7n20NG/vK/Rla6h02M4l1TgJrx9viFC1P4THRCGuYtMLK537a2AIBxv7f+IR41e/n2oIZetg/IBKtjb+zh254q97R9oHJPG2Cwh+8ew4i7bJDE2VfaJEgAZT+3wehqdh5mZN0q/ACDSw+zdpaqchuMb+JPV7n1Z1zs2GNq2jHonOZT0IY2AhER4Fai5DTVwFmq+q5TrhD4FXAwURSLq1T1T1kfqBW050AwFEhOh7UE2LtFmWuAv4nIxUARcLi3IxE5HzgfoFt+CseRQCAQaAeyCTHRShvBZ4Dx8bY3cCf23QhwFVCpqmUikgf4X2xtQHvaCLwr0/LT+WTgPlUdRjQ6PhifcPNKqner6h6qukdhvrM8IRAIBNqJJj+CdFsrbQTHAw9oxJtAqYgMdsqdA/wYQFUTqupPkQAR2VdEbo9trStFZJGIPCciF4qIs1SvOe05ECwBkuemw7Cqn3OBRwFU9Q2gG+BE9w8EAoGOoSlDWbqtlTMCT1syNLmAiJTG/71ORN4VkcdExFnkCyLyPHAe8ALwaWAwsAORDbYb8BcROS5dg9pzIJgCjBeR0bGu6yTgqRZlFgGHAYjIRKJGO5FMAoFAoGOo18iPIN0W2wgOEJGpSdv5KXaZjbakgOjj+TVV3Q14A/hZiv2drqrnqupTqrpMVRtUdaOqvquqN6vqwcDr6c6x3WwEqtogIhcRjVL5wD2q+qGI/BCYqqpPAd8CfiMi3yC6EGfFiZ1T0tijC1WTBjWTrZloDTnV1qYJwKC/WaOSDrCqt3lf8IOjDfm3NWotOsqOp1XD3cEbda740kPdoox93Bpb8zbb9vf9wB/Pl37KOhRNv8SWHfNHmwkLoOYAayxcvo819o552B+764Zag3tDDydD1yFu9Q5n9m37GFnv9/xrXbGPvbH5zmUd8tJat37X5Y6j2kK7dmLir/wMZ9MvsX247D7r0ZY/v8Kt37DGtqv78hRfuY5TXOVk2y9K+vgaiYTzDJS+b4Vr9vGfoQm/WWNktUPaz3aYVYiJaNXQq6p6qve7iFwIfDn+cwqZtSWriQzJf47/foxIg+IcO7XKKNsy7epQpqrPES0JTZZ9P+n/HwH7t2cbAoFAYGtoC4cyVb2daDk9InIMcFG8knJvYL2qVrQoryLyNNGKoX8SaU4+Ig0i8jngJ8AAolmHxLvyl0AmETyLA4FAIA2ahQ2glcbi54gWx8wh+uo/u+kHEZmmqpPiP79NtIDmFiKV+dktd9SCm4BjVdVPmJ6GMBAEAoFAGuoT+eQl2s6PIFZ/X5jit0lJ/19I6yJxrdiSQQBycCCoL1WWnNhcT5q/0t6EiT+yiUIAFp9gV2nV9rELlRoH+w5CFfs5GTh6WJ1pXoPfMYY/a5Nq1Ay1wdkACpfb4Fyzz7ZtHfYP33lu2EtWR7z4CLv8dkkKHX2vudZc01BkZbPPtA59AONu/NDIFn1nR/9gnZCCavuVV5diJffI+6yT1aLTrUPY2p1LjQyg3/vW9rPiZHut+k+1fQL8xDLygfU+nHWtTWwEUPYLq+PfOMZ3dKyZPMrIujtmot6v+MEIq06x9eudVeGDX1jh1t800T4Di05IYVp8wRe3hhyKPjpVRP4IPAn8z8Coqk9kqphzA0EgEAhsSxIqGUNIaOcIMVFCpGo6MkmmQBgIAoFAYGtIkEciB2YEqprJhpCS3A6iHQgEAu1MU87idFv6Re/bBhEZJiJ/FpFKEVkhIn8SERsEyiHnZgRdF1Qz/sx3msnqPruXKVc3wlfmVg+zd2zA21bW5R82uBxAwomZVvwvK1t4qm9j2DjT+ieUvO+vw5975iAj6+4sA+/+3gK3PqV2HXd+jVXG9pnvB+ykQeJqAAAgAElEQVQqfXmerb/ZJsFZcbif0J2RQ+yxPnKemAP86h1N/3dtW72kLAC1Oww1sjzHj6C2r19/w1gncOH1s41s/gXj3foDpth7uOqUSUZWavPZAzDnImvPGP+ADVAIkLfZ+jIsO6DQyGodW0C0Aysa+LbtQ9O/7gf4o8Beq4nf96PptUVimsZEPg1taCxuR+4FHgK+EP99Wiw7IlPFTtH6QCAQ6Ky0Q4iJ9qK/qt4bexY3qOp9gL+SowVhIAgEAoE0JPRjg3GqrRNohgBWichpIpIfb6cReShnJAwEgUAgkIasoo92DtXQOcAXgeVABfD5WJaRnLMRBAKBwLakXvOQDC/6xs6xfHSjqqaNMpqKnBsIGgYUUXnyfs1kg1+2AbNkhT8j6ravzRDVdYN1nGno6t/YdeOs0ajPb6fY4x+1r1u/womstHxfP7jWoDdsu1ZOtsdPjLBGZYD1ZdYwXV1mLZibxvrn2n2VzXC1yvFHKqjwDeubRtvu1evBN2zB87/u1u9ouldaR72Ko/xr1XOZPdeqcfb+eY5fAN2XW/n0G23mt4nfW+DW37SbXRyyZqLd58i/WidDAHXyfBQs9hcxVJwwysjGPGYDwS07zF+wMfRR63y35CRrrJ5481IjAyDfntfy42ybALjDF7eGrILOdQ4bwVsiMg24B/hrpgCeyXSK+UwgEAh0VhKktw9k43C2jSgD7gbOAOaIyA0ikiIOc3PCQBAIBAJpaAo6l27rDMbiOOPZi6p6MlGimjOBt0XkXyLiqyhick41FAgEAtuShkQ+ksGPIJPqaFsgIn2JfAdOB1YAFxMlA5tElM/A6t9icnMgaDH8zrjAhtse/zU/CF/1iFFGVviy1Zsn+vt6726O6aHqZJvApOfCFI5Dk+2x8gr84F4lb1ca2cYhVm+88Gg/3HieE4tuwEvWI25tud/WeSfadkkv6/iTv8y/Vqt2sg9PSXHaD5NOReFqq0/v/y8/aFzJhzbvx7qxdgn38OdSBEM8pq+R9XnLOmnNuGKEW7/8Jhvgrf4IW7ZgiZ+fZM0p1iFuoJMYCWDAr61NbObNuzslfUdFL7FNoY3FSO04fwn8yl3tdRl293/9Y7UBORRr6A3gQeAEVU32sJsqInelq5ibA0EgEAhsIxJZOIylGPK2NRNSGYhV9SfpKoaBIBAIBNKQ1Yygc6waGi8ilwGjSHq3q2qKZLgfEwaCQCAQSENDIg9JZIg+2grVkIgIcCtRlrJqolzt7zrlTgauJFKGLwNOy5B7+DHgLuC3gK9vTkHODQT5tUrp7OY6xsH3WnvAjLtsIDoAz6Yz9/+6G1miW4rJXp5zfbs6sjx/HUH5RTbqlzjB4QDqR1kdaekcq19NFPqdcMnnbXCw6nJ7Acru8HXBs063CXPKv+1Evevm2wiqy2z7JdEZ1ldkx4yv2YTo3Rf713rNTjZZSncnr0pi+hy3fs05ds29NNhj9XnffyHVldmES4VrbdnVB1vfEIB8x72gocwPXFk7wCZnKlxnj1Xfy3+GNk+0bej/uE1iJF2tLQCguK8T+PDUnd2y3OmLW0M72Ag+A4yPt72JWrl3cgERKSAaLHZQ1VUichNwEXBNmv02qOoWnXHHm7oDgUCgE6OQcfloK20ExwMPxMs93wRKRaTlSN6UfL4onkGUEM0K0vG0iHxNRAaLSJ+mLZsG5dyMIBAIBLYlrZgRHCAi5yaJ71bVu53iQ4HFSX8viWX/m26rar2IfBX4L7AJmE2KPMdJnBn/e3ly04AxGeqFgSAQCATS0ZDIg+xsBK+q6qlZ7NIbVZrpTEWkC/BVYDIwD7gN+C7wo1Q7VVXjJyAivn6tBUE1FAgEAmnIJrxEphmDiFwoItPiWEDLgGRDyTCs2mcSgKrOjZeEPgrsRxZIxKEi8luazzxSknMzgvpiYelBzZ2iEkdZQ1H5rb7jzoLPW6Ne7UBr7O031R8jV+1htYFdF2c16AIw+xrb1rI7l7tlV06ygcDyHSexBmu7A2CcYwResbctrFPMggUAJtSVG9msS0cZWdldjgEZKFxvnecaipwUb52Ukhn28ai39nMAxv3eekTVDbKFZ9/nRO0D8pfaF0m/D+zNXjfOv351vW1bh79oLcDzLvBfWCPvt85/1UPsIgqATYPsszHmHud9U+g/F/NOs0EWe+y4o5ENfMVfILNykmOY7uc8GG2Eqmy1w5iq3g7cDiAixwAXicgjREbi9ara8iFaCuwgIv1VdSVRljHfSzZGRPYGTgFOBPoQqZIuT1eniZwbCAKBQGBbko1DWSv9CJ4jWjo6h2j56P+SzovINFWdpKrLRORa4BURqSfKunmWtzMRuZ4oD8Ei4GHgh8BUVb0/2waFgSAQCATSkI3qpzV+BLGqxzX8quqkpP/fReQXkInzgZlEy1CfUdVaEWnVOu0wEAQCgUAaGrNwKOvgWEODgCOBk4FbROQloLuIFKiqdSZyyLmBQAuVxqG1zWTdZ1i996zzbRAvgMaeVpcozj3uPaPWCoGqUVbvO/pxm5QjFbJqnZEl1jkRt4AhT1od+9yv2KBzdQP9ey2Ntq2FG+yHQu2xe7j1u620NoaxV061BUf6jkezv2z12T1mZ29P6WgGv2Lvy5LDfec//cA6Cub32sXIyr/p2+6W/58NG999oe0rC77kH7/LK/ZaL/2s83ineC0Uvmoduioum+yW3dzH9qF111i9f9F0/173mW7tbHXF9iGs3M/a8wAaetuT6Dmn/WxP2dgIOnIgUNVG4HngeRHpBnwW6AEsFZF/qOopmfaRcwNBIBAIbEs0q1hDnQNVrQUeBx4XkRIiw3FGwvLRQCAQSIMCqpm3jkJEThOxeg1V3aCq94vIWBE5IN0+wowgEAgE0tCoeX6QsiQyrSpqZ/oC74nIO8A7wEqgGzAOOAhYBXwn3Q5ybyBoFLSquT6w11yrcxz8qh9Ibdmn7NroUb+3SbJn/NC3MQx6ztFvDrRJ4isn+/rRfv+1gczyGn0de+Eaa6cYc/NHRibdfUeCxkqbfFyctd2LvuXrgrtstN1j85F7GtmoW6x+GWDidzcZ2byvjXPLdkY8e8Cgt3zb0apz9zayWifKi+zrp5Bt9LrLZmdtfIr3Td931hqZ5vU2sjV2uT4ACy+3faBrCtNXl422EfmO7afBd0Ng5WRbv3iBLVc1yv/MzttkfR6G3+EnprFPS+tJqCCd20Zwq4j8CjgU2B/YBagh8js4XVVt1qIW5N5AEAgEAtuQbFQ/Hakaio6vjcCL8dZq2tVGICKfFpGZIjJHRNypiYh8UUQ+EpEPReSh9mxPIBAItJamVUPptpRTtRyh3WYEIpJP5FJ9BFF0vSki8pSqfpRUZjxRIKX9VXWtiAxor/YEAoHAltCYfdC5nKU9ZwR7AXNUdZ6qbgYeIYrDncyXgdtVdS2Aqtps7YFAINCBZLNiqKNVQyKSJyJf3NL67Wkj8GJut7SolQGIyGtAPnCNqv615Y5E5HwiN2oKevWme0VzY9HKSS1rQNEA31I1YKo1wC09fqiRDXnSz/TWc0GVkeUtt1a1jV/0M0H1XGYveZ+p1qgL0NDHOoQ17GGNrfOP92/jiBdGGNlmx3EnlVFwwDsbjUzzbX2tqXHrV37RWiZHP+YEA8yYUbVjEKcLLDnUN8zXjbDOf10qrAG1ZK5/rAEvLDQyrbLXf/ALg9z6C4+zlunBb9oFE72n+x5lBZUbjGzZMTbrGcCwp5wgg+ts/USVfVYAZt+4u5Gt2d0+lxNvsfsE2DS21MiWfCVFhrKf+uLWEL3oM3zxd7yNICEiFxFFKW017TkQZIy5HR9/PHAwUSjWf4vITqrazKUyTu5wN0D3IcM7i+9GIBD4BJCVZ3HnsBG8GCev/yNRMhsAVDVj6IP2HAiWkDnm9hLgTVWtB+aLyEyigWFKO7YrEAgEsqazLx9N4pz43+SAdm2ToUxE9gVOAw4EBhOtT/0AeBb4var6gXKil/l4ERlNFFv7JKJY2ck8SRQo6T4R6UekKpqXqU2BQCCwzVA6XPWTDV6GsmxJOxCIyPNEX/F/Aa4HKok81sqAQ4C/iMjPVfUpp1ENsc7qBSL9/z2q+qGINMXKfir+7UgR+QhoBC5XVT+jTEzhqs2Muq+5PjUxwOoME139U8vfZHW5K/ayjjdLd/VtBF5mkgmX2rGrYMMot/rqneyXw/KD/PzSE39hg47lbbbtGvZPP1tKj7m2/uIrrfPbiIesgw7AhjE2MU6vJ/9jZMu+5getG/p3eytnXGDvVWcl3/FJHPqkvaYA68tLjKzrWqv37v7GLLe+jhxiZDO+be1MZfdbJz2AjYOto2LX2StswRTOh9XjbYC3IU9ZR0uAWRfatkYBMJsz+HU/pXvpTPsMDHxivpHN+Lm1cQEMf9S+lYfeMc0t67s6tpKsgs61xYG2DhHpAXwTGKGq58erMieo6jOZ6maaEZyuqi3TBG0E3o23m+MveRdVfY4oCUOy7PtJ/9e44d/M1NBAIBDoCLJaFdSKgUBEyoF7gd2Aq1T1Z1veumbcSxRioiml5RLgMSDjQJB2+WjTICAiRU1BjUSkTESOi5Mr4wwUgUAgsN2QjUNZK43Fa4BLgLYaAJoYq6o3AfVRu7WGLD3dsvUjeAXoJiJDgX8QpVa7r/XtDAQCgdxCE5Jxa82MQFUrVXUK8Qu7DdksIt2JWyMiYwE/6FoLsh0IRFWrgc8Bt6nqicAOW9LSQCAQyCk0yw0OEJGpSdv527ilPwD+CgwXkT8QfbRfkU3FbJePSrx66FTg3FbWbVsaGki0iKo592JrVBr7/ffc6jN+sauRDXzdDuf1Pf2MR2U/m2OFPaxRdfwNM9z6usk6X6WKHlqz73h7qHk2ymTxf6vd+rWjrRG67Ge2rCR8o97aCbZ+lyNt1q1NKVw7Zp9h65fMcWaq9pZ0Cga94Ts0eSzfz8qG/cN+Z1Wc6Yf/7FFp70F+jb1WSw+zRmGAEU9Yw7BusA5pUmcXSwAsPNHeqx7zraMlgBbY+1063bZ142B/EUK1Y2uu/pqNyqob/AUbCWe3eSP8tjLdF7eGVmQoe1VVT936I24ZqvqiiLwL7EOkEvp6tqr7bGcEXyeKCfTneOXPGOClLWptIBAI5BqZZwNpEZELRWRavHnLrtqKg4DDiFZ1Hphtpay+6lX1FSI7QdPf84iMHYFAILBd8z87QIYyaX9XvZ0oCGe7ISJ3ECWjeTgWfUVEDlfVC9NUAzL7EdxNZBMwWR9EpAj4ElCnqn9ofbMDgUAgF2jbMNMiMgiYCpQACRG5FNhBVbPXRfocBOwUL8tHRO4H/Iw9Lcg0I7gD+J6I7EzkTdyUAm080UncA2zTQaB2eA9mfqd5NqX+TkCKWTc5keiAga/ZG7piHzu3G/2UH5xrxtVjjUzq7T7HfusNt/6GU/c1spW+PxZ5dXa/DV+yWbO6ze/q1m/oYc+reL516Fq3n591q+wr1s6iO9qgd8Xzfb31Jkdt22OFb4/ojDQU26Bx80/wM8+Ne9Quzlh2oA182Ge6r/euL/KCAdr7P/Rnb7n1GT3SiGr2sTamZZ/ybV8jnrb9fZMf347a/lZW7ZTN97sVY2+3DpiVR9soCNLo2xhWOgn1EgV+RsG2sBG0tWexqi4nCrnT1swERgBNHrfDgfezqZh2IFDVacAXRaQnsAcfh5iYrqozt7i5gUAgkCtkMxB0bPL6p+MW9AKmi8jb8d97A69ns49sbQQbgZe3rJmBQCCQu2RjI6Bjg85ttWNayFkcCAQC6ejkMwJV/Vfy3yJSQivf7Tk3EBSuU0Y91VzP7K1rLpnjn1qfVxcb2YojbYbMhu6+frL8xwuMbPo11o9h9fnOwnKgoMa2tU8Kc061k7hz6MtWl7wsxSIxz8Yw6E826NmGcVaXDNCw10Qjq+tjdczFS329d21/ew0rDskdG0GXVTbAW+Fa3+djycHWHtDvv/a6LD3U/3Is/3nLCO2w4gqreM/b0a63B2CdTQLTY4rVxY9+0Q+aN+/6ljmjYMBU/16V3Wf3MedU64fgnT+AVltfmp7LrJNtQZ3/DK/ZwV7DzSXt+EWu0tFf/FkRO7BdR6S+TxBZuNsmDHWLAxWpqh/+MBAIBLZDRKMtLZ0g+ihwObDjlsR/y8qhTET2i0NFT4//3jVesxoIBALbNwnJvHWOGcNcwA8zkIFsZwS/AI4CngJQ1f+IyKe25ICBQCCQU3RyG0ES3wVeF5G3SAo2p6oZnX+zVg2p6mKRZqNeiswtgUAgsB2RIxnKgF8D/yRyImuVMS7bgWCxiOwHqIgUEoWXaAtXjVaTKBBq+jVvdlcnONWQvyxy68+8wXrDdJ9hHbIau/rXsXpXmzVq4o9twK/GgdbxC2DOyTZAXaLEd14bf+67Rrb6vH2MTFMo+MbdZ9s18yrrEFb2g4/c+jX7WsPkmonWADz8ed8AubmnvQb5NU6XszbpTkHjh7ONbHixbyzOq7XGzopPWee9svv8zK7Tr7X5nfo5CwPmf85m0wMY/IbNPNf1ZbsKoeazvvdi6Uz7pqvp73esXv+2udDz66yxuGqov+Bi9UU28N7mUnv88df7r5geK2wfrtjXd/RrE3JnRtCgqluU5CvboHMXECVEHkqU9WYSzRMkBwKBwPZJ06qhdFsbhqDYCl4SkfNFZLCI9GnasqmYrUPZKqIQ1IFAIPCJQhLRlqlMJ+CU+N/vJsnabvmoiIwGLgZGJddR1eOybmIgEAgE2g1VHb2ldbO1ETwJ/A54mlYaIdoaLYDavs2nYWt2tKcx9mVfF9tYO9jIRv7K6sirDit36y88ySoDRz5iPb9WTvaDe439xptGtvZs68wDMPt3uxlZ/1fs8fv/x1dQbtjZ6p09e4L0LHLr93h3gZENW2OdnOac6ttDyn6z0sjyJ9k2dVbWn27vS169f62LF1jZpqG27MwLrY0IQNZbfXq3tfZR6/+Cb/ta9nn7DhjyX2tPSGVPKllkE9as2tkPZri53MZL21xq25oo8A/WMMJGoyv/3mojm3epnwRx5HU28F7Xcv8ZaguEzH4EnUExJCJneHJVfSBT3WwHglpV/WWrWhUIBALbA9l4FncOY/GeSf/vRpSg5l2gzQaCW0XkB8DfaL4+1S5rCQQCge2JBJn1IJ1gIFDVi5P/FpFewIPZ1M12INgZOB04lI8vicZ/BwKBwHZLW4eYEJFy4F5gN+AqVXWjh8YJ6PcA6oG3ga+oql2nnJpqotwxGcl2IDgRGKOqfubrbYgKJFq0esgr9tqsPW4nt/64B20CERlo9dYFm3x/ufKbbUJwL+DWuN9bnSeADLfpSvv/xQaCA1hbbtfxV+5vfQ6K5vr2iJH32HXwS4+wCwh0vZ8YqeJsm6i+5zL7aTT+Ot8PYd1nrINAxQGd4NMpSzaMsuqAomW+iqByD2tnGfdHe10XHl3i1m+0MesoXG/76oZ9bIBDgKIKe1+0yvbVysn+2v78zVbec4l/r/JemWZk5XOczDQNvn/M4tOtHwA1NhDdmD9YPxiA2kNsZhrv/NuMtncoW0Pki3VChnJ/AE6L//8QcB5wZ6rCSXkJIHIN2AF4NJsGZTsQ/AcoBSqzLB8IBALbB23sUKaqlUCliByTodxzTf+Pk81kymqWPLNoABaq6pJs2pTtQDAQmCEiU2huIwjLRwOBwHaNJATJkJhG2jHonIh0IVLNfz1duZZ5CVpDtgPBD7b0AIFAIJDTZD8jOEBEzk2S3q2qd7dBC+4AXlHVf6crJCKfA34CDCBe9Qqoqvr6yCSy9Sze4pEmEAgEcpmsjMURr6qqG4FBRC4Evhz/ebSq2kxEfr0fAP2Br2RR/CbgWFVtdRy4tAOBiLyqqgeISBXNx8SsR5q2RvOhroWfzMpJ1lg6/Nb33PoybqSRzTvFOoSNud+/T2v3tkaxoS9bY/XSI/q69TdOtkbF8sts1jSIzrUlYx+yRrH6Yt8ol9hocwgVzbM7lSLfyWnwAx8a2fSfTjCyEQ2+812XTbatfd53utyubvUOp2a4va91fX1j65gn7DqKZQfZx6N0jm/UTHSxqoX8Ortgoeczfjq7RVfYYHIFNdZYP/Y2f2FCw7ihtk2F/rnOv8EGPhx/yxxbsLtjAQfyrT8ZDSus+XH9UWPd+lUj7LUa8J7/DLQJbWAjUNXbgdtbc1gROY8o/P9hqpqNNXzFlgwCkHlGUASgqsVbsvNAIBDIebKJNdS65aODgKlACZAQkUuBHVR1g4g8B5wXzxjuAhYCb8QpAJ5Q1R+m2fVUEfkjUSSIZFvuE5nalGkgyJ21foFAINAetP2qoeWkWAGkqkcn/b+1OeVLiHwHjmzRsq0eCAaISMr41qr686yaFwgEAjlKNjaCzhBrSFXP3tK6mQaCfKAnW3ieIvJp4NZ4P79V1RtTlPs88Biwp6pOTbfPwg0Jhr/Y3Plk/ok2WUjtoTu79Ssn2VOuG2j1i7ouRdC6rjZo3bIDrY2iYbijCAWK37FtnXuJ7/xXvNDKVu1qA4GlCiS2fswkIxv6UpWRzbjKj1I74jmro97hBuvks+RzVr8MMMQJ/LdxsB+gLlsePPAkIzv9349s1T5T0esDe19rU8TMW3qwva+DX7N2g/mnGREAxe/bxCqVh1kdfflHvj2nrq99Uy3f2/b10S9aJzOA2v62X+Vt9t9+nj0gMdQmfFo12b/XG/a2z8bAO+25Fm7w9TGjHrPOmjO/muLGPOOLA83JNBBUZNBJpURE8omMI0cQJbOZIiJPqepHLcoVE3nZ2ZCCgUAg0NHkToayLSZThrKtmfHsBcxR1XlxaIpHgOOdctcRLXvyP6EDgUCgAxH9ODlNqi3XB4JMM4LDtmLfQ4HkdZFLgGZBw0VkMjBcVZ8RkctS7UhEzgfOB+jadetUC4FAINAqcmhGEIet2JEoDDUA2Wh10g4EqmqzVLeiTd4u//ejSB7wC+CsTDuKvfPuBug6epjOPbu5PnHi1VaZrhusLhyg2whrO6gZYpuqo2xwOIBGZ723x8C/+kk98uqt3rM2RZJwL/Rtt7W2xzV089s08ImZdpcjBhpZ+c1L3frTr7Fl2WT9KKTR1+XO/ZJdR99QunXrvdvLHuDR4OTr6TM9RWKah94wsvk37mdko//gx20sePF1e6zP7GlkFWf4yVoS3e11Hf2AtQfMuN23ne1wg13Hrz38Pjz9J8ONrGCFtXGMv8P3j+m1wPrt5Dk+B8Xv+b48Kw+1C24m3O6HQZvvSltHrhiLReQuoAdwCPBb4PNEUUszkm3y+i1hCZDcY4YByXe2GNgJeFlEFgD7AE+JiPWMCQQCgY5Cs9w6nv1U9QxgrapeC+xL83dwSlq7TrU1TAHGx/mOlwIn8XFyZVR1PfA/U7+IvAxclmnVUCAQCGxLskle30kGgqbllNUiMgRYDWSVx7jdZgSq2gBcBLwATAceVdUPReSHIhKilgYCgdwgd2YEz4hIKfBTohSVC4gW6WSkPWcETfG0n2sh+36Ksge3Z1sCgUBgS8gVG4GqXhf/908i8gzQLda8ZKRdB4L2IH+T0OfN5oap2ReNMuVKZ/t3rt80G4ht7T72MqzZxY+n122dnSPmzbXdoNefUgS9G2+D3q3a2Q9Q12uhNQDm19jj1/T3b+P6Q6yj2srJdhJYvLDUrZ+/yl7Dsp9aZyId5DvzzLjAXsPCSqetvv2zwxl57zwjqx9nHQoB1pxnDcPiJLmbf4qVAfD5vYxo8D/tvSqo9qv3nWKv68xzbYiwQS/5SoDpV1mHsNEP+8/QhPPft8KdHKfIfD9oXU1/66i37hSbDW/tjr4+RottMMD+L7bj6vPcWjW0HzCK+N0uIqhqmyWvDwQCgU8k2dgIWhN0rr0QkQeBscA0oOkzRIEwEAQCgcBWkTszgj2Iopi2ujXtuXw0EAgEcp4mG0G6rZMMBB8A1tEnC3JuRtClqoEBr65qJqseYHXUdSW++aZif+slNPZ3NUZWuGKlW3/6pVaf37XSjqf9HFsAgM6zTjYNRX3cst3/bpOQrD/BZnFZfoDfC/Nqbbsm3GR1/ItPG+fWb+htbRQNlfa6bN59lFt/zJ9s/a7TnAQmB7vVO5wlJ9lgfHX+raJ4gZWN+5W1Mcy+1F/N13Oh7a9Vzgrw6mG+jqLbcnuvu66yOvoNo9zq9J5i9fard/TLDlluz2H5Ptbjf+Bb/jO47HBrPJl46QdG1r/Mf4ZmfNUG3pv19RFuWVLGK2gFnedFn4l+wEdxovtW5ZbPuYEgEAgEtiVZrRrqHAPFNVtaMQwEgUAgkI42nhGISDlwL7AbcJWq/ixD+duAs1W1Z7pyW5NbPtgIAoFAIA3tYCNYQxR6P+0AABCH3PHXd9uy+4jIFBHZKCKbRaRRRDZkUzfnZgS1A/KZcXHz6zLgdXsXavv4+snBt0wxMim0+tHKk21SF4DyO9da4UIbHGvGjX5C94k/ssHwumz027roW5NtWSeW3tg/WhsHwNxz7DifWLvOyIa96JwTULXA6n0LRlrFdVWpv16899urjGzmlWVu2c5IrV1aT/1gP2hc37/UGdn0H9uEPaVT/Hvdo9Lq/pefYPdZ/KafEH7w/daeVLO/7YM93l3g1q927DzdX53hlpUetg1Vo63xJK/R98WRLva8GG6DPC4+0o807NUf+Jb/JrZWmi2g7VNVVgKVcaTQlMQ5XX5KFJrnxCx2/SuiUD6PEa0gOgPws161IMwIAoFAIA3Z5COIbQQHiMjUpO38rTz0RcBTqlqRbQVVnQPkq2qjqt5Llksxcm5GEAgEAtuU7FU/r6rqqW1xyDho3Bdo3Zq6ahEpBKaJyE1ABeAEU7eEGUEgEAikIRsbQcZVRSIXisi0ePOTnTRnMjAOmBOH6e8hIs7a62acTvROvwjYRBSC+v+yOFaYEQQCgUBa2sBGoKq3E+Vwz+6Qqs+S5BwmIov3vbcAAB0jSURBVBtV1Xf44X/2hOtV9TSitL/XZnssyMGBIL9WKJnevNmJAnsXes+0gakANh9mjcDLDrDG4oYi/86u3cka8Ivn9TaysovecuszzH4MbC71nYSk0RoWRzxnFwEsPNY3qg15xu530zHWAL1ykm/sHfG8DdBXW2azlq3czTeAFm601tYJN862BZ9wq3c44261H2Ab9vcdwmadbZ2cesyw13Xdzn6Gttp+9lHsMtcaZbuvSfHG8aIKOLdl+k2+k1bJe/YZWPjFCW7Z3m/ZbGT5jv23ZL5vWAeb+WzVvnaf1eXOToFBf7Nlu61NdaytJ5sv/lbtT2QQMBUoARIicilRaIgNIvIccJ6q+unZUqCqjSLSX0QK4xzxrSLnBoJAIBDYpiRw08Y2o3WrhpYTZWz0fjs6hTytD0HMAuA1EXmKSDXUVPfnmSqGgSAQCATSkEOexcviLY8oFXDWhIEgEAgE0pEj0UfjPMWISHH0p27Mtm7ODQRd1tQx9KFZzWSNa20Snrk37OHWH/eI1bGP+dkSI5t9tZ8tpeyu5Ua24lCbrGTR1Xu79Xsusj2maJGvY68eYstu7mP1xo1W5QrApsF2Udjgf1nnsfoi33GxYL5dvpxXZfvW2PVj3frSYOfTcy/OHYey2d+05zXmT/6zNeEbM41s4bdtHyy7x3f+W72L/YDr9451/ku895Fbf8Ydtr8VbLA2ioIV/htr03Arn3jFAresDra2n/retl8WzrDPFcDKXa3Ns2aoPX7hYr9jL9/fBq3r/UGKh+Dvvrg1iCqSIbKzdIKRQER2Ah4E+sR/rwLOUNUPM9XNuYEgEAgEtiU5lLz+buCbqvoSgIgcDPwGsOnzWhAGgkAgEEhHjqiGgKKmQQBAVV8WkawcysJAEAgEAmnIIWPxPBH5HpF6COA0YH42FXNvICgsIDG8+Vr2BZdYvXNjX9+PYOa5dhVW2SVW79vfzz1PotQOsN1XOzrL2f7xV+zRzcjqrBsCACNesPtYfLizhtrGdgOgaoztnUOetTrqxkLfRrDyGKsjT3Sx9owBr61260uVzbQ+8vuOuvLfl7j1O5oCJxjg0oP9xRiDC61NadSf7XWp72P9DQA8M0vVSHtfBg2zSe7BtweM/6m1WzSus3YHgJl37WZkC8/17TndnNu9yVkMObBklFu/xrF9jX3E9suFx/gfs17CpSrfPaJtyJ3ENOcQOZI9QeRF8gpwdjYVc28gCAQCgW1IVjaCTL9vA1R1LVF461YTBoJAIBDIQEbV0LZphn9skVtU9VIReRo7d1Gi/Ae/VtU3U+0jDASBQCCQDlU/hEezMtumKSlosgmkSnTTD7gH8NfEEwaCQCAQSEtWxuJt0xQXVX0n/jdlqkoRSRt/KOcGgvoe+azcvXmQta5Ogq0xD1knM4C6YTZrUvVx1vGn9OkP3PozbrOBuAoqrKGuy0Y/kFvNOHs/yu7y79H8461he+AUq4ys7+F3Qy/D1vQrBhhZz9l+/UZr12bU45VGVuMYNQE0z17rwqF93bKdkZE3vWtkq06zRlWANTtaI/CaXayTk3bx3yhl579uZAtusMu/V+zhP7JdHQNuYoQNEJg3wL/++Ztsfx3yqjX2Ayz8jD3XUc/asrPOsYHsALovtP3Nc9gada3NJgggO9qkWzJvsVt2rittHdIIkilgfyewEYjIeODHRF/+/3t6VXWMqj6drm7IRxAIBALp0Cy3jude4E6gATgEeICP1UZpCQNBIBAIpCGrxDQd3ciI7qr6D0BUdaGqXgMcmk3FnFMNBQKBwDYlK2Nxp5gS1IpIHjBbRC4ClgJWF+yQcwNBfr1SvKx5co9FJ9lkH/VTfWeUgk22bF69o3ef7CcDyl9p9Z6NPWz9sde979bPG2AV9wtPGeqWHfsH6ym2bpc+RrZmJ7c643+50Mi01Nod5pzh6417OrrcxcfaflU92H8IGouto11+lQ1O1lmZe621B9T38RPLeE5Ovf9r9e6FG/1rteJSaw+oH2oTs5TfVOXWX35QPyPbUGZtNKV/dxIDAf3etX0gf8oMt2z9absY2Yq9rN1g4q1r3PrTL7btyl9pg0HOuHl3t/6E31ij4OrPpXgI7vPFrSGrxDStGAdEpJxIjbMbcJWquqt9RESAHxHlLm4E7lTVX6bZ9aVADyJfguuIZgNnZtOmnBsIAoFAYFuSjUNZK0NMrCF6WZ+QodxZRHmHy1U1ISJpv+5Vtcm6vpEsPYqbCANBIBAIpCMb1VArpgSqWglUisgxGYp+FThFVRNJ9QxxRrJ0xzsuU5vCQBAIBAJpaEXQuQNE5Nwk8d2qevdWHHos8CURORFYCVyiqp5ub19gMfAw8BZbYLtu14FARD4N3ArkA79V1Rtb/P5N4Dyi5U4rgXNU1Sq2k+usr6bwuanNZKXD9zHl5p/o37nS6fYard3Jzvu6rvYXVI2/3iYGqdvD2hOkm58oo/Jwaw8YeoNdQw4w58dWb1w6255XQw//XFceaSNxbRjrBI2b6s97S6Y7Ot4FS+1xTrI6Y4Cu6+w1XHqUr2PvjIy739poKo6wuniAOseVoniJPdeij9yPOqZfbtf8T7jFBmKb/o1eRgZQfqH1eVh0ubVxFM8f4tbv84y1Byy4wveZGPd76zPQxVnHv+LY0W79Ec9a25H2tLajCXf5NobGXrbsmp3dom1D9mGoX1XVU9vwyF2BWlXdQ0Q+R+QdfKBTbhBwBHAycArwLPBwNglpmmi35aMikg/cDnyGyMHhZBFp6eL8HrCHqu4CPA7c1F7tCQQCgS1BGjXjlmmgEJELRWRavPmjsWUJ8Kf4/38G3C8uVW1U1b+q6pnAPsAc4GURuTjL47SrH8FewBxVnaeqm4FHgOOTC6jqS6ra9HnxJuAEsw0EAoEOpA0cylT1dlWdFG/Lsjzyk3zsB3AQMCtVQRHpGs8afg9cCPySKBx1VrSnamgokd6qiSWAn8g34lzgee8HETkfOB+gG34890AgEGgPhLZNTCMig4CpQAmQEJFLgR1UdYOIPAecFw8WNwJ/EJFvEK0EOi/F/u4HdiJ6f16rqn58nDS050DgGSzcyyUipwF7EI16tlJkcLkboET6dArPjUAg8Amh7VcNLSeF9kNVj076/zog08oigNOBTUAZcEnkfgBE72BVVeu40YL2HAiWEK2BbWIYYKZEInI4cBVwkKpaD5oW1I3uwZwfTmom86qV3e4Hcstfaw1dAx61EbvmXu5HbJ1/qZWro2DbfJqTcgqYcJt1nJl5n+84M/7Xm2y7vmBnRIVrfQ1ftbU/0v9da6hbW+YHyFs5yToZjXnclu1Z4RuA86vtsfq96RjRy93qHY4XoLDXPP9cu62sNbKlB1nnve5LU8xoG+x304r9rAV64hXT3erzr7SG3QHOvc77yM9cuPxMq34e+m97TgCaZ9u68AxrGB75gH+sRafaskVO0YrDfMN86VybuW/8A37mtXmutHV09sQ0qt4bqHW0p41gCjBeREaLSCFwEtBsvauITAZ+DRyXao1sIBAIdCSimnnr6EZuJe02I1DVhjjexQtEy0fvUdUPReSHwFRVfQr4KdATeCyezizKxvkhEAgEthkJMn/x57jCul39CFT1OeC5FrLvJ/3/8PY8fiAQCGwtTV/9mcrkMjnnWdx1fjXjzpzWTLbiEutQtuCzvkMXUmxEY++0doNRV/vpPfN7W4ceHW2dxCoO9O0zs86wl3z82W+7ZeuOsQlzBr5tO1zF/n4nHH2jDXw36y6rkO//d19DuG6cE3TuKKu3Hv43PwnQguPstRrzsKMBPMut3uG4uvAU89XyO63toNd8+xm5epLvEFawyR5r4JvWnrTknIlu/TF3WW149aThRibdnWxDwKB/Wue5DTvaAIcAvT6wNrX+/7H9evqP/eXyw5+wtgtZZ4PpFVb5CY8aelg7VX2pf15tQkKjLR1hIAgEAoHtl7aOPtoZCQNBIBAIpCOr5aO5TRgIAoFAIA1ZhaHuBDmLt4YwEAQCgUA62tihrDOScwNBoncR1Yft1UzWdb29CUP+4UculAprFFv+xQlG1u/OCre+Dh9sZI09bNayqt19Z5zhf7SXXPbc0S276HP2M2PMg9bQNvov/irmOT/c1cgmXOI4JDlGUYCVP7DXZfNwaxSd38M3gHZzbsHss7PKnNcpyK+z17rXh75RMm+JNYKrkyGsobt/rTXf9uFZXy80sh4p4knOvMw6aRXPt8fqkaL9LLbhbyqutA5xAOvG2Sx7JYts+wudDHcA3Z+dYmQV5+9pZKn08pUH2D444mn7DLYVklAko7G43Q6/Tci5gSAQCAS2KbmTs3iLCQNBIBAIpEMJDmWBQCDwSSY4lHVC8usa6TmvuaNNY0/rPFbft8itn1dsdaQDX1trZDMfSJGd6Q6rN158uM2YVP49m8kLoHHxEiPLHzXCLdttvnV+m/d/9vj9p/gOYWW/snrfud+0QfMKbCIsAMpvtfVnXmRtJKlWTBQvtj+ULHAemP39+h3NkkNsXxn1pB/cbOZ3xhvZ2Meto2LPRf6xNg63/bX8Wtsv559mrz9A2T3WyUu7Wr1546Debn3pbe0Z5T/w7WyJihVGtv4Ea48STWG7+ol1lBzyqu3XRQuskxnA+vHWJtV1TYpO3BYkNPOyoDAQBAKBwHZMgsxZgHN7HGjX6KOBQCCQ82QVfbR1iWnKReQNEakTkcvSlDtMRN6N01u+KiI2OXobEQaCQCAQSEfTqqFMW/asAS4Bfpah3J3Aqao6CXgIuHrLTiAzuacaqm8gb3lz3WVeV2sjqJngr1fvstome/n/9s49yqr6uuOfLwMDCkh4CFoFRxBEQxQTHzVRRNEsXzErq6baGhMraGttu9TG1WbZqEl0NYmraR5iG5plXCspVuuD0MTEGJcRY8Tgs4ICPhhxsApY5KHgMDO7f5wz6YX9m7lnmMvce2f2Z62z1r2/c37n7N895959f3v/9t5bD/d204l3p22Cr/2Rv9bUH3lbbstnfCI6gG1N3sY7ekVaHw/y9Tc44oZX/DlPSv9R2Hysv9aU2xO+i9Z0EZ+XrvdJy6b90H9+LXPS683XnePXew9f6dfG1yqTv7vKtb1+uY+tAJh2s4/PUOK5bDv0gGT/w27x/V+6eZprG/t0sjuvfN4XEdr/mUSCwlld/GANS8SsLPS+L4D2aT6OYOgW318d6ee6Y5x/3oav9cWlBm3tojDOOO+72XpIWlYeSzf3iI4CocU9UAR57ZX1kspVHzOycpYAo0gU9qoU9acIgiAI+pLiPoKTJM0taV2Ql9ndU+YBD0jaDmwBfJrlChGKIAiCoBuKLB/NZwS/MbOLKnjpq4GzzexJSdcC36KLAva9JXwEQRAE3VEB/4CkK3On73OS0oUadj1+f+BoM3syb7oL+HjvBtI1MSMIgiDojvYCtSo7ut9vZvOB+T246iZglKRpZrYaOANIJAqrDHWnCFrHDaX50sm7tDX9cI07bs356f4jVnun2kGP+sCVhtd90AyAzZni2lbN8wEuU69+0rUBvH3lCa5t/D3p+7vzKJ9IjAY/iWs5LW3APPyGla7tpa97B+Tgd9OPwRFfXu3amv/c9z/wce/oA1gzwQc0HbA0EfjzqWT3qvPqVd4xPMLHAwLpZITa4BcRrJu9b7L/9gv8tQ5f4CuUvfWJdNWuqd9p9tf6rH9+xviidQDsGOOrfr3t474AGJqIqWtMOIvHLk//OA7/mXcWvznLB7R1DElX+bMO33/Uy9uSx1aGIrmGip9N0gHAU2SO4A5JVwFHmtkWSQ8A88zsTUmXAfdK6iBTDJfumfzlqTtFEARB0KdUOA21mb0FHNzFvrNLXt8P3F/4xL0gFEEQBEF3dBhlf+gjxUQQBEE/xjqyrdtjQhH0KWqHIbuZA1unevts06K03XzthYkglW+96Jo6GtOBT5Pv9Tbu5nO83bfhsIR9HxiyzT8wGjsmeaza/bFrL/HJzfZ/Kv0Qrv1Ln2BupI+RwrpYO9Z6pE+G15aI2xn65uZk/+nf9YnENp6cDqiqRYYmcq4Neyf9g7DpKO8nGr3CP4NjXvSfCUDrG/4m/G/inH/wX28k+7OfD+rbcoy3pY9Ynn6uh3p3RpfPxYRfeBk+OMwHcA5qTX9WNtif+MDv+WI1qeR0APsmghK3H+h9HBWjvYAiKFe4psapO0UQBEHQp0SpyiAIggGOUdFVQ7VIKIIgCILuiFKVtcegNth3/a72uk2HJ4rN3JswhgNceIhrWv09b4scvDltc5w6/3XX1rjFn7NjbbowzaZ5PmHXoJ3pBHnvj/c25gN+530cr16QLtw94XFvi337NJ/JbvrVLyf7r/yGX9s+1pty2Xqkj80AWP8x/xk2LU4XG6lF3pvkv9wjukj7NeYZXxhm5bV+HfyQdennqukflrq2wZN84sK2Lp6r17/m09A0LfQ+gsZ3u/j8n/Z+snXX+JgXSPsDms/xdvvGTWk/3bgXvJ9k6Cc+4toadqT7H/Jj/x200b6IU8VobwdL+3Z+T5mAslqn7hRBEARBn9LzNNN1RyiCIAiC7og4giAIgoGNWQcWcQRBEAQDmI6II6g51G40bt31poxa9Lw77vWrjkn2H5mo8DTh2791bS/PTzvKtpzgq3a9P90nXWs74Yhk/4Me9U6n9sa0U6xxi3csdgzxDuBJD6Qfwo0f8ecd/4h3LK/7sw8n+0+/1TtA35zjHcM2KO0AnfID79jccFrZDLw1w1j/WPHWuelqbu98eJxrm3KHd+yvm5W+1ltXeWfvQb/yEW1rb0zXJulIfJOHPeEXTGj/tGP/vbM+5tosvQaBjTN85bUP+fyGTPhV2rP+8uX+GZj4sP9ejF2Rfq6bL/GLM6yrwjFdJNnrERWuUFaL1J0iCIIg6FOs//sIql6YRtKZklZJekXS31dbniAIglKso6P8VueKoKozAkkNZMUazgBagGWSFpuZX9QcBEFQDdoLmIYijqBXHA+8YmavAUj6D+DTQJeKoL1RbJm0q9gNJ3kb96SfJjKGAetO9wne3rjeV4Cbfl1ahLVX+ERuDRt8ME3LqWmj5fhnvC105EPpaw050Qd0rT/GX6tteLI7gxLm7NHLfYK4Haeki51om7dxN271/3xGv/h+sn/bAf68+2wsE5hTQ4y95wXXtmHW9OSxqQR1rfv5r9ehd6xN9reRPnHhhhO93+HQuzcm+288PmH7H+Kvv2XmhGT/zZO9n6fNx2kCMGyDb/sgkTexdWI6meJhN3nD/crveJ/a+F+nrz/xIV+EpvlTPulexTCjbIWyOs8xUW3T0EFAaSrDlrwtCIKgJrAOK7/1rELZRZL+O99+K+novSd9Mao9I0j9bXYfqaTLgcsBhowYvbdlCoIg+H+sQM3icstLd2UNcIqZbZJ0FrAASC9T7COqrQhagNL1mAcDbs2ZmS0g+7DYd/zE+p6DBUFQT+xc2f40g8oYT95jK4BP5JXAzErXqy+li7KVfYmq6e2WNBhYDcwB1gHLgD81sxXd9NkAdGadGgekjab1SX8bD/S/MfW38UD/G1PpeA4xM5/psSCSJlD8h/p4YG7J+wX5n9juzv9FYLqZzdtDEStCVRUBgKSzgW8DDcDtZnZzD/o+ZWbpMkZ1SH8bD/S/MfW38UD/G1O9jEfSqcBtwElm5qM3+5Bqm4YwsweAB6otRxAEwd5C0pXAZfnbs8lmLT8Azqq2EoDqrxoKgiDo95jZfDObaWYzyf6A3wdcbGarqywaUAMzgl7Srf2tDulv44H+N6b+Nh7of2Oq9fFcD4wFbpME0FZtU1bVfQRBEARBdQnTUBAEwQAnFEEQBMEApy4VQb1nLC0nv6RrJL2Yh6A/LMknYK8hit4PSedLMkn1sLSv7Jgk/XF+n1ZIWtjXMvaEAs/cJEmPSHo2f+7OroacvUHS7ZLWS1pebVnqDjOrq40s3uBVYDLQCDwPHFltuSopP3AqsG/++grgrmrL3dv7AYwElpBFUh5bbbkrcI+mAs8Co/P346stdy/HswC4In99JNBcbbn3YJyzgI8Cy6stS71t9Tgj+H3GUjNrBTozltYLZeU3s0fMrDOlZ02EoHdD0fvxNeCbgE9pWnsUGdNlwHwz2wRgZuv7WMaeUGQ8BuyXvx5FItVLrWNmS4B02uGgW+pREdR7xtKeyj8X+Plelah3lB2PpGOAiWb2074UrBcUuUfTgGmSHpe0VNKZfSZdzykynhuBz0lqIQvw/Ou+ES2oBeoxjqBQxtIaprD8kj4HHAucslcl6h3djkfSIOCfgUv6SqAKUOQeDSYzD80mm7E9JmmGmb27l2XbE4qM50+AO8zsnySdCPwoH099V1wJClGPM4JCGUtrmELySzoduA44z8w+6CPZ9oRy4xkJzAB+LakZ+ENgcY07jIvcoxbgJ2a208zWAKvIFEMtUmQ8c4G7AczsCWAYWRqEYABQj4pgGTBV0qGSGoELgcVVlqknlJU/N6V8n0wJ1LLtGcqMx8w2m9k4M2sysyYyn8d5ZvZUdcQtRJFnbBGZUx9J48hMRa/1qZTFKTKetWRZgJF0BJkiSNQiC/ojdacIzKwN+CvgQeAl4G7rJm11rdGV/JK+Kum8/LBbgBHAf0p6TlLNKrqC46krCo7pQeAdSS8CjwDXWg0kD0tRcDx/C1wm6XngTuASy5fi1AuS7gSeAA6X1CJpbrk+QUakmAiCIBjg1N2MIAiCIKgsoQiCIAgGOKEIgiAIBjihCIIgCAY4oQiCIAgGOKEIgh4hqT1f0tq5NUmaLaki6SMk7SPpUUkNBY4dLOlnkjZKmrHbvlskrcwzad4v6UO77X9aUqOkbV2c+w5J5/diHI2Slkiqx+j9YIARiiDoKdstr72ab80VPv+lwH1m1l7g2H8hi+j9NHCXpNLkfA8BM8zsKGA18KXOHZKagHV5AraKI6khP/fDwAV74xpBUElCEQQVRdKNkr5Y8n55Pms4Lv93PkzS8DyH/4zEKS4CflLS/1pJy/K+XylpvwHYbGbXmNnjwDzgTkmjAMzsl3kgFfgMrmcBvyg5182Sns+Tx00oOe50SY9JWi3p3PzYprztmXz7eN4+O8/nvxB4Ie+/KB9PENQ0MW0Neso+kp7LX68xs88U6WRmy/II6ZuAfYAfm9kuBUTy9AeTO2cZkj5Jlr/neLLEaYslzTKzJWb2ld3O/wRwcheXvxS4q+T9mcDV+evhwFIzu07SN8nSS9+U72siS/g3BXhE0mHAeuAMM9shaSpZFG5n3qTjyWYha/L3y4Hjyn44QVBlQhEEPWW7mc3cw75fJct7swP4m8T+cUBp9s5P5tuz+fsRZIphSdELSroOaAP+PX/fCBxsZp15gVqBTv/G08AZJd3vzrNvvizpNWA6sAa4VdJMoJ0sx1AnvytRAphZu6RWSSPNbGtRmYOgrwlFEFSaNnY1OQ4reT2G7Md8SN7+3m59t+92vIB/NLPv74kgkr4AnAvMKcmbczLwm5LDdpbsa2fX78Tu+VeMbCbxNnA02ThLC+3sPh6AodRHMZ5gABM+gqDSNJOVC0TSR4FDS/YtAL5M9u/8G7t3zKt9NUjqVAYPApdKGpGf7yBJ44sIkReK+TuyTKfvl+w6k+KFfj4raZCkKWRlHleRVe/6n3ymcDFZGciuZBgLbDCznQWvFwRVIRRBUGnuBcbkfoQryFbsIOnzQJuZLQS+Dhwn6bRE/18CJ0Hm8AUWAk9IegG4h6y+QRFuzY99KF/m+q95+2zg0YLnWJUf+3PgL8xsB3Ab8AVJS8nMQqlZQCenklX7CoKaJrKPBjVFXovhGjO7eC+c+2Dg38zsrEqfu4vr3Qd8ycxW9cX1gmBPiRlBUFOY2bNkK3TKBpTtwblb+lAJNAKLQgkE9UDMCIIgCAY4MSMIgiAY4IQiCIIgGOCEIgiCIBjghCIIgiAY4IQiCIIgGOD8H/T97yp/whAxAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -334,7 +346,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x2c0850e5710>"
+       "<matplotlib.lines.Line2D at 0x1d388235a58>"
       ]
      },
      "execution_count": 9,
@@ -343,12 +355,14 @@
     },
     {
      "data": {
-      "image/png": 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L8mSSvx93QcOS5GNJZoAPAQ8k+ca4a+qjO2l+4JEcO4G7W3okR5I7gW8Bq5LMJNkw7pqG5CLgWuCS7t/Yk0k+Mu6ihuQs4OEkTzE3ENlWVV8b1c68M1aSGueIXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4/wP7Mpf1RqTppwAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -383,12 +397,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -418,17 +434,22 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "ds = dataset_with_outliers(new_data_set('data_with_outliers'))\n",
+    "ds = dataset_with_outliers_generator(new_data_set('data_with_outliers'),\n",
+    "                                     low_outlier=-3,\n",
+    "                                     high_outlier=3,\n",
+    "                                     background_noise=False)\n",
     "ax, cb = plot_by_id(ds.run_id, auto_color_scale=True, cutoff_percentile=(0,5));"
    ]
   },

From d53042c7ac58fc1da119797c2971e8e3949c41fd Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 11:39:51 +0100
Subject: [PATCH 524/719] Rename trigger to force_trigger

---
 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index da9a64c7703..2417fb13252 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -730,7 +730,7 @@ def autorange_once(self) -> None:
         self.write('SENSe:VOLTage:DC:RANGe:AUTO ONCE')
         self.range.get()
 
-    def trigger(self) -> None:
+    def force_trigger(self) -> None:
         """Triggers the instrument if `trigger_source` is "BUS"."""
         self.write('*TRG')
 

From e662d513b5e3777d4fe764cdddf14b971428a3a8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 12:38:00 +0100
Subject: [PATCH 525/719] Test parameter with setpoints that is not an Array

---
 .../test_measurement_context_manager.py       | 38 ++++++++++++++++++-
 qcodes/tests/instrument_mocks.py              |  9 +++++
 2 files changed, 46 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 2aa91994cd3..1b9277cc20d 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -359,6 +359,7 @@ def test_setting_write_period(wp):
         with meas.run() as datasaver:
             assert datasaver.write_period == float(wp)
 
+
 @pytest.mark.usefixtures("experiment")
 def test_method_chaining(DAC):
     meas = (
@@ -372,6 +373,7 @@ def test_method_chaining(DAC):
             .add_subscriber((lambda values, idx, state: None), state=[])
     )
 
+
 @pytest.mark.usefixtures("experiment")
 @settings(deadline=None)
 @given(words=hst.lists(elements=hst.text(), min_size=4, max_size=10))
@@ -1014,7 +1016,8 @@ def test_datasaver_parameter_with_setpoints(channel_array_instrument,
         assert len(data[0]) == 1
     datadict = ds.get_parameter_data()
     assert len(datadict) == 1
-    subdata = datadict['dummy_channel_inst_ChanA_dummy_parameter_with_setpoints']
+    subdata = datadict[
+        'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints']
     assert_allclose(subdata['dummy_channel_inst_ChanA_dummy_sp_axis'],
                     np.linspace(chan.dummy_start(),
                                 chan.dummy_stop(),
@@ -1025,6 +1028,39 @@ def test_datasaver_parameter_with_setpoints(channel_array_instrument,
                     np.random.rand(n))
 
 
+@pytest.mark.usefixtures("experiment")
+def test_datasaver_scalar_parameter_with_setpoints(channel_array_instrument,
+                                                   DAC):
+    chan = channel_array_instrument.A
+    param = chan.dummy_scalar_parameter_with_setpoints
+
+    meas = Measurement()
+    meas.register_parameter(param)
+
+    assert len(meas.parameters) == 2
+    dependency_name = 'dummy_channel_inst_ChanA_dummy_start'
+
+    assert meas.parameters[str(param)].depends_on == dependency_name
+    assert meas.parameters[str(param)].type == 'numeric'
+    assert meas.parameters[dependency_name].type == 'numeric'
+
+    # Now for a real measurement
+    with meas.run() as datasaver:
+        datasaver.add_result((param, param()))
+    assert datasaver.points_written == 1
+
+    ds = load_by_id(datasaver.run_id)
+    data = ds.get_parameter_data()
+    assert len(data) == 1
+    subdata = data[
+        'dummy_channel_inst_ChanA_dummy_scalar_parameter_with_setpoints']
+    assert len(subdata) == 2
+    assert subdata[
+               'dummy_channel_inst_ChanA_dummy_scalar_parameter_with_setpoints'] == np.array(
+        [51])
+    assert subdata['dummy_channel_inst_ChanA_dummy_start'] == np.array([0])
+
+
 @settings(max_examples=5, deadline=None)
 @given(N=hst.integers(min_value=5, max_value=500))
 @pytest.mark.usefixtures("experiment")
diff --git a/qcodes/tests/instrument_mocks.py b/qcodes/tests/instrument_mocks.py
index 8bb2b4391a2..42f21341993 100644
--- a/qcodes/tests/instrument_mocks.py
+++ b/qcodes/tests/instrument_mocks.py
@@ -9,6 +9,7 @@
 from qcodes.instrument.parameter import MultiParameter, Parameter, \
     ArrayParameter, ParameterWithSetpoints, GeneratedSetPoints
 from qcodes.instrument.channel import InstrumentChannel, ChannelList
+import random
 
 log = logging.getLogger(__name__)
 
@@ -187,6 +188,14 @@ def __init__(self, parent, name, channel):
                            vals=Arrays(shape=(self.dummy_n_points,)),
                            parameter_class=DummyParameterWithSetpoints1D)
 
+        self.add_parameter(name='dummy_scalar_parameter_with_setpoints',
+                           label='Dummy Scalar Parameter with Setpoints',
+                           unit='some other unit',
+                           setpoints=(self.dummy_start,),
+                           vals=Numbers(),
+                           get_cmd=lambda: 51,
+                           parameter_class=ParameterWithSetpoints)
+
         self.add_function(name='log_my_name',
                           call_cmd=partial(log.debug, f'{name}'))
 

From c7a4f50a00f3f9fc6b4db58dadbe3ea843a655eb Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 13:39:25 +0100
Subject: [PATCH 526/719] Remove unnecessary return from docstring of __init__

---
 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 2417fb13252..6fc76c6213a 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -130,8 +130,6 @@ def __init__(self, name, address, silent=False,
             address (str): Visa-resolvable instrument address.
             silent (bool): If True, the connect_message of the instrument
                 is supressed. Default: False
-        Returns:
-            _Keysight_344xxA
         """
 
         super().__init__(name, address, terminator='\n', **kwargs)

From 1b38fbf2297b54bed5aee859b8836a6f0d4bc933 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 13:41:08 +0100
Subject: [PATCH 527/719] Add files for Keysight 344xxA DMM drivers version
 with submodules

---
 .../Keysight/Keysight_34460A_submodules.py    |  10 +
 .../Keysight/Keysight_34461A_submodules.py    |  10 +
 .../Keysight/Keysight_34465A_submodules.py    |  10 +
 .../Keysight/Keysight_34470A_submodules.py    |  10 +
 .../private/Keysight_344xxA_submodules.py     | 795 ++++++++++++++++++
 5 files changed, 835 insertions(+)
 create mode 100644 qcodes/instrument_drivers/Keysight/Keysight_34460A_submodules.py
 create mode 100644 qcodes/instrument_drivers/Keysight/Keysight_34461A_submodules.py
 create mode 100644 qcodes/instrument_drivers/Keysight/Keysight_34465A_submodules.py
 create mode 100644 qcodes/instrument_drivers/Keysight/Keysight_34470A_submodules.py
 create mode 100644 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py

diff --git a/qcodes/instrument_drivers/Keysight/Keysight_34460A_submodules.py b/qcodes/instrument_drivers/Keysight/Keysight_34460A_submodules.py
new file mode 100644
index 00000000000..adc2930bb00
--- /dev/null
+++ b/qcodes/instrument_drivers/Keysight/Keysight_34460A_submodules.py
@@ -0,0 +1,10 @@
+from .private.Keysight_344xxA_submodules import _Keysight_344xxA
+
+
+class Keysight_34460A(_Keysight_344xxA):
+    """
+    This is the qcodes driver for the Keysight 34460A Multimeter
+    """
+    def __init__(self, name, address, silent=False,
+                 **kwargs):
+        super().__init__(name, address, silent, **kwargs)
diff --git a/qcodes/instrument_drivers/Keysight/Keysight_34461A_submodules.py b/qcodes/instrument_drivers/Keysight/Keysight_34461A_submodules.py
new file mode 100644
index 00000000000..8a29f38b09f
--- /dev/null
+++ b/qcodes/instrument_drivers/Keysight/Keysight_34461A_submodules.py
@@ -0,0 +1,10 @@
+from .private.Keysight_344xxA_submodules import _Keysight_344xxA
+
+
+class Keysight_34461A(_Keysight_344xxA):
+    """
+    This is the qcodes driver for the Keysight 34461A Multimeter
+    """
+    def __init__(self, name, address, silent=False,
+                 **kwargs):
+        super().__init__(name, address, silent, **kwargs)
diff --git a/qcodes/instrument_drivers/Keysight/Keysight_34465A_submodules.py b/qcodes/instrument_drivers/Keysight/Keysight_34465A_submodules.py
new file mode 100644
index 00000000000..d42f44f0cbe
--- /dev/null
+++ b/qcodes/instrument_drivers/Keysight/Keysight_34465A_submodules.py
@@ -0,0 +1,10 @@
+from .private.Keysight_344xxA_submodules import _Keysight_344xxA
+
+
+class Keysight_34465A(_Keysight_344xxA):
+    """
+    This is the qcodes driver for the Keysight 34465A Multimeter
+    """
+    def __init__(self, name, address, silent=False,
+                 **kwargs):
+        super().__init__(name, address, silent, **kwargs)
diff --git a/qcodes/instrument_drivers/Keysight/Keysight_34470A_submodules.py b/qcodes/instrument_drivers/Keysight/Keysight_34470A_submodules.py
new file mode 100644
index 00000000000..73a50e2b331
--- /dev/null
+++ b/qcodes/instrument_drivers/Keysight/Keysight_34470A_submodules.py
@@ -0,0 +1,10 @@
+from .private.Keysight_344xxA_submodules import _Keysight_344xxA
+
+
+class Keysight_34470A(_Keysight_344xxA):
+    """
+    This is the qcodes driver for the Keysight 34470A Multimeter
+    """
+    def __init__(self, name, address, silent=False,
+                 **kwargs):
+        super().__init__(name, address, silent, **kwargs)
diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
new file mode 100644
index 00000000000..6fc76c6213a
--- /dev/null
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
@@ -0,0 +1,795 @@
+# QCoDeS driver for the Keysight 344xxA Digital Multimeter
+import textwrap
+from functools import partial
+import numpy as np
+import logging
+from typing import Tuple
+
+from qcodes.instrument.parameter import ArrayParameter
+import qcodes.utils.validators as vals
+from qcodes.utils.deprecate import deprecate
+from qcodes import VisaInstrument
+from pyvisa import VisaIOError
+
+log = logging.getLogger(__name__)
+
+
+class ArrayMeasurement(ArrayParameter):
+    """
+    Class to return several values. Really represents a measurement routine.
+    """
+
+    def __init__(self, name, shape=(1,), *args, **kwargs):
+
+        super().__init__(name, shape=shape, *args, **kwargs)
+
+        self.label = ''
+        self.unit = ''
+        self.properly_prepared = False
+
+    @deprecate(reason='the use of this ArrayParameter is limiting as it '
+                      'represents only a single way of performing '
+                      'measurements and performs preparations that are not '
+                      'generally applicable',
+               alternative='driver methods like `fetch` and `read` (together '
+                           'with other methods and parameters) and custom '
+                           'functions and parameters implemented on top of '
+                           'them')
+    def prepare(self):
+        """
+        Prepare the measurement, create the setpoints.
+
+        There is some randomness in the measurement times.
+        """
+
+        inst = self._instrument
+
+        N = inst.sample_count()
+
+        # ensure correct instrument settings
+        inst.aperture_mode('OFF')  # aperture mode seems slower ON than OFF
+        inst.trigger_count(1)
+        inst.trigger_delay(0)
+        inst.sample_source('TIM')
+        inst.autorange('OFF')
+        if inst.has_DIG:
+            inst.sample_count_pretrigger(0)
+
+        if inst.trigger_source() is None:
+            raise ValueError('Trigger source unspecified! Please set '
+                             "trigger_source to 'INT' or 'EXT'.")
+
+        # Final step
+        self.time_per_point = inst.sample_timer_minimum()
+        inst.sample_timer(self.time_per_point)
+
+        self.setpoints = (tuple(np.linspace(0, N*self.time_per_point, N)),)
+        self.shape = (N,)
+
+        self.properly_prepared = True
+
+    def get_raw(self):
+
+        if not self.properly_prepared:
+            raise ValueError('ArrayMeasurement not properly_prepared. '
+                             'Please run prepare().')
+
+        N = self._instrument.sample_count()
+        log.debug("Acquiring {} samples.".format(N))
+
+        # Ensure that the measurement doesn't time out
+        # TODO (WilliamHPNielsen): What if we wait really long for a trigger?
+        old_timeout = self._instrument.visa_handle.timeout
+        self._instrument.visa_handle.timeout = N*1000*1.2*self.time_per_point
+        self._instrument.visa_handle.timeout += old_timeout
+
+        # Turn off the display to increase measurement speed
+        self._instrument.display_text('Acquiring {} samples'.format(N))
+
+        self._instrument.init_measurement()
+        try:
+            rawvals = self._instrument.ask('FETCH?')
+        except VisaIOError:
+            rawvals = None
+            log.error('Could not pull data from DMM. Perhaps no trigger?')
+
+        self._instrument.visa_handle.timeout = old_timeout
+
+        # parse the acquired values
+        try:
+            numvals = np.array(list(map(float, rawvals.split(','))))
+        except AttributeError:
+            numvals = None
+
+        self._instrument.display_clear()
+
+        return numvals
+
+
+class _Keysight_344xxA(VisaInstrument):
+    """
+    Instrument class for Keysight 34460A, 34461A, 34465A and 34470A multimeters.
+
+    Tested with: 34461A, 34465A.
+
+    The driver currently only supports using the instrument as a voltmeter.
+
+    Attributes:
+        model (str): The model number of the instrument
+        NPLC_list (list): A list of the available Power Line Cycle settings
+        ranges (list): A list of the available voltage ranges
+    """
+
+    def __init__(self, name, address, silent=False,
+                 **kwargs):
+        """
+        Create an instance of the instrument.
+
+        Args:
+            name (str): Name used by QCoDeS. Appears in the DataSet
+            address (str): Visa-resolvable instrument address.
+            silent (bool): If True, the connect_message of the instrument
+                is supressed. Default: False
+        """
+
+        super().__init__(name, address, terminator='\n', **kwargs)
+
+        idn = self.IDN.get()
+        self.model = idn['model']
+
+        ####################################
+        # Instrument specifications
+
+        self.has_DIG = 'DIG' in self._licenses()
+
+        PLCs = {'34460A': [0.02, 0.2, 1, 10, 100],
+                '34461A': [0.02, 0.2, 1, 10, 100],
+                '34465A': [0.02, 0.06, 0.2, 1, 10, 100],
+                '34470A': [0.02, 0.06, 0.2, 1, 10, 100]
+                }
+        if self.has_DIG:
+            PLCs['34465A'] = [0.001, 0.002, 0.006] + PLCs['34465A']
+            PLCs['34470A'] = [0.001, 0.002, 0.006] + PLCs['34470A']
+
+        ranges = {'34460A': [10**n for n in range(-3, 9)],  # 1 m to 100 M
+                  '34461A': [10**n for n in range(-3, 9)],  # 1 m to 100 M
+                  '34465A': [10**n for n in range(-3, 10)],  # 1 m to 1 G
+                  '34470A': [10**n for n in range(-3, 10)],  # 1 m to 1 G
+                  }
+
+        # The resolution factor order matches the order of PLCs
+        res_factors = {'34460A': [300e-6, 100e-6, 30e-6, 10e-6, 3e-6],
+                       '34461A': [100e-6, 10e-6, 3e-6, 1e-6, 0.3e-6],
+                       '34465A': [3e-6, 1.5e-6, 0.7e-6, 0.3e-6, 0.1e-6,
+                                  0.03e-6],
+                       '34470A': [1e-6, 0.5e-6, 0.3e-6, 0.1e-6, 0.03e-6,
+                                  0.01e-6]
+                       }
+        if self.has_DIG:
+            res_factors['34465A'] = [30e-6, 15e-6, 6e-6] + res_factors['34465A']
+            res_factors['34470A'] = [30e-6, 10e-6, 3e-6] + res_factors['34470A']
+
+        self._resolution_factors = res_factors[self.model]
+        self.ranges = ranges[self.model]
+        self.NPLC_list = PLCs[self.model]
+
+        ####################################
+        # PARAMETERS
+
+        self.add_parameter('line_frequency',
+                           get_cmd='SYSTem:LFRequency?',
+                           get_parser=int,
+                           set_cmd=False,
+                           label='Line Frequency',
+                           unit='Hz',
+                           docstring=('The frequency of the power line where '
+                                      'the instrument is plugged')
+                           )
+
+        self.add_parameter('NPLC',
+                           get_cmd='SENSe:VOLTage:DC:NPLC?',
+                           get_parser=float,
+                           set_cmd=self._set_NPLC,
+                           vals=vals.Enum(*self.NPLC_list),
+                           label='Integration time',
+                           unit='NPLC',
+                           docstring=textwrap.dedent("""\
+            Sets the integration time in number of power line cycles (PLC) 
+            for DC voltage and ratio measurements. Integration time is the 
+            period that the instrument's analog-to-digital (A/D) converter 
+            samples the input signal for a measurement. A longer integration 
+            time gives better measurement resolution but slower measurement 
+            speed.
+            
+            Only integration times of 1, 10, or 100 PLC provide normal mode 
+            (line frequency noise) rejection.
+            
+            Setting the integration time also sets the measurement 
+            resolution."""))
+
+        self.add_parameter('volt',
+                           get_cmd=self._get_voltage,
+                           label='Voltage',
+                           unit='V')
+
+        self.add_parameter('range',
+                           get_cmd='SENSe:VOLTage:DC:RANGe?',
+                           get_parser=float,
+                           set_cmd='SENSe:VOLTage:DC:RANGe {:f}',
+                           vals=vals.Enum(*self.ranges))
+
+        self.add_parameter('resolution',
+                           get_cmd='SENSe:VOLTage:DC:RESolution?',
+                           get_parser=float,
+                           set_cmd=self._set_resolution,
+                           label='Resolution',
+                           unit='V',
+                           vals=vals.MultiType(
+                               vals.Numbers(0),
+                               vals.Enum('MIN', 'MAX', 'DEF')),
+                           docstring=textwrap.dedent("""\
+            Selects the measurement resolution for DC voltage and ratio 
+            measurements. The resolution is specified in the same units as the 
+            selected measurement function, not in number of digits.
+            
+            You can also specify MIN (best resolution) or MAX (worst 
+            resolution).
+            
+            To achieve normal mode (line frequency noise) rejection, 
+            use a resolution that corresponds to an integration time that is 
+            an integral number of power line cycles.
+            
+            Refer to "Resolution Table" or "Range, Resolution and NPLC" 
+            sections of the instrument's manual for the available ranges for 
+            the resolution values."""))
+
+        self.add_parameter('autorange',
+                           label='Autorange',
+                           set_cmd='SENSe:VOLTage:DC:RANGe:AUTO {}',
+                           get_cmd='SENSe:VOLTage:DC:RANGe:AUTO?',
+                           val_mapping={'ON': 1, 'OFF': 0},
+                           vals=vals.Enum('ON', 'OFF'))
+
+        self.add_parameter('display_enabled',
+                           label='Display enabled',
+                           set_cmd='DISPlay:STATe {}',
+                           get_cmd='DISPlay:STATe?',
+                           val_mapping={True: 1, False: 0},
+                           docstring=textwrap.dedent("""\
+            Disables or enables the front panel display. When disabled, 
+            the display dims, and all annunciators are disabled. However, 
+            the screen remains on.
+            
+            Disabling the display improves command execution speed from the 
+            remote interface and provides basic security.
+            
+            Displaying text with `display_text` parameter will work even 
+            when the display is disabled."""))
+
+        self.add_parameter('display_text',
+                           label='Display text',
+                           set_cmd='DISPLAY:TEXT "{}"',
+                           get_cmd='DISPLAY:TEXT?',
+                           get_parser=lambda s: s.strip('"'),
+                           vals=vals.Strings(),
+                           docstring=textwrap.dedent("""\
+            Displays the given text on the screen. Specifying empty string 
+            moves the display back to its normal state. The same can be 
+            achieved by calling `display_clear`."""))
+
+        self.add_parameter('autozero',
+                           label='Autozero',
+                           set_cmd='SENSe:VOLTage:DC:ZERO:AUTO {}',
+                           get_cmd='SENSe:VOLTage:DC:ZERO:AUTO?',
+                           val_mapping={'ON': 1, 'OFF': 0, 'ONCE': 'ONCE'},
+                           vals=vals.Enum('ON', 'OFF', 'ONCE'),
+                           docstring=textwrap.dedent("""\
+            Disables or enables the autozero mode for DC voltage and ratio 
+            measurements.
+            
+            ON:   the DMM internally measures the offset following each 
+                  measurement. It then subtracts that measurement from the 
+                  preceding reading. This prevents offset voltages present on
+                  the DMM’s input circuitry from affecting measurement 
+                  accuracy.
+            OFF:  the instrument uses the last measured zero measurement and 
+                  subtracts it from each measurement. It takes a new zero 
+                  measurement each time you change the function, range or 
+                  integration time.
+            ONCE: the instrument takes one zero measurement and sets 
+                  autozero OFF. The zero measurement taken is used for all 
+                  subsequent measurements until the next change to the 
+                  function, range or integration time. If the specified 
+                  integration time is less than 1 PLC, the zero measurement 
+                  is taken at 1 PLC to optimize noise rejection. Subsequent 
+                  measurements are taken at the specified fast (< 1 PLC) 
+                  integration time."""))
+
+        ####################################
+        # TRIGGERING
+
+        if self.model in ['34465A', '34470A']:
+            _max_trigger_count = 1e9
+        else:
+            _max_trigger_count = 1e6
+
+        self.add_parameter('trigger_count',
+                           label='Trigger Count',
+                           set_cmd='TRIGger:COUNt {}',
+                           get_cmd='TRIGger:COUNt?',
+                           get_parser=float,
+                           vals=vals.MultiType(
+                               vals.Numbers(1, _max_trigger_count),
+                               vals.Enum('MIN', 'MAX', 'DEF', 'INF')),
+                           docstring=textwrap.dedent("""\
+            Selects the number of triggers that are accepted by the 
+            instrument before returning to the "idle" trigger state.
+
+            You can use the specified trigger count in conjunction with 
+            `sample_count`. In this case, the number of measurements 
+            returned is the sample count multiplied by the trigger count.
+
+            A variable trigger count is not available from the front panel. 
+            However, when you return to remote control of the instrument, 
+            the trigger count returns to the previous value you selected."""))
+
+        self.add_parameter('trigger_delay',
+                           label='Trigger Delay',
+                           unit='s',
+                           set_cmd='TRIGger:DELay {}',
+                           get_cmd='TRIGger:DELay?',
+                           vals=vals.MultiType(vals.Numbers(0, 3600),
+                                               vals.Enum('MIN', 'MAX', 'DEF')),
+                           get_parser=float,
+                           docstring=textwrap.dedent("""\
+            Sets the delay between the trigger signal and the first 
+            measurement. This may be useful in applications where you want 
+            to allow the input to settle before taking a measurement or for 
+            pacing a burst of measurements.
+            
+            Step size for DC measurements is approximately 1 µs. For AC 
+            measurements, step size depends on AC bandwidth.
+            
+            Selecting a specific trigger delay disables the automatic 
+            trigger delay."""))
+
+        self.add_parameter('auto_trigger_delay_enabled',
+                           label='Auto Trigger Delay Enabled',
+                           set_cmd='TRIGger:DELay:AUTO {}',
+                           get_cmd='TRIGger:DELay:AUTO?',
+                           get_parser=int,
+                           val_mapping={True: 1, False: 0},
+                           docstring=textwrap.dedent("""\
+            Disables or enables automatic trigger delay. If enabled, 
+            the instrument determines the delay based on function, range, 
+            and integration time or bandwidth.
+            
+            Selecting a specific trigger delay using `trigger_delay` disables 
+            the automatic trigger delay."""))
+
+        self.add_parameter('trigger_slope',
+                           label='Trigger Slope',
+                           set_cmd='TRIGger:SLOPe {}',
+                           get_cmd='TRIGger:SLOPe?',
+                           vals=vals.Enum('POS', 'NEG'))
+
+        if self.model in ['34465A', '34470A'] and self.has_DIG:
+            self.add_parameter('trigger_level',
+                               label='Trigger Level',
+                               unit='V',
+                               set_cmd='TRIGger:LEVel {}',
+                               get_cmd='TRIGger:LEVel?',
+                               vals=vals.MultiType(
+                                   vals.Numbers(-1000, 1000),
+                                   vals.Enum('MIN', 'MAX', 'DEF')),
+                               docstring=textwrap.dedent("""\
+                Sets the level on which a trigger occurs when level 
+                triggering is enabled (`trigger_source set to "INT").
+                
+                Note that for 100 mV to 100 V ranges and autorange is off, 
+                the trigger level can only be set within ±120% of the 
+                range."""))
+
+        _trigger_source_docstring = textwrap.dedent("""\
+            Selects the trigger source for measurements.
+            
+            IMMediate: The trigger signal is always present. When you place 
+                the instrument in the "wait-for-trigger" state, the trigger is 
+                issued immediately.
+                
+            BUS: The instrument is triggered by `trigger` method of this 
+                driver once the DMM is in the "wait-for-trigger" state.
+            
+            EXTernal: The instrument accepts hardware triggers applied to 
+                the rear-panel Ext Trig input and takes the specified number 
+                of measurements (`sample_count`), each time a TTL pulse 
+                specified by `trigger_slope` is received. If the 
+                instrument receives an external trigger before it is ready, 
+                it buffers one trigger.""")
+        _trigger_source_vals = vals.Enum('IMM', 'EXT', 'BUS')
+
+        if self.model in ['34465A', '34470A'] and self.has_DIG:
+            _trigger_source_vals = vals.Enum('IMM', 'EXT', 'BUS', 'INT')
+            # extra empty lines are needed for readability of the docstring
+            _trigger_source_docstring += textwrap.dedent("""\
+            
+            
+            INTernal: Provides level triggering capability. To trigger on a 
+                level on the input signal, select INTernal for the source, 
+                and set the level and slope with the `trigger_level` and 
+                `trigger_slope` parameters.""")
+
+        self.add_parameter('trigger_source',
+                           label='Trigger Source',
+                           set_cmd='TRIGger:SOURce {}',
+                           get_cmd='TRIGger:SOURce?',
+                           vals=_trigger_source_vals,
+                           docstring=_trigger_source_docstring)
+
+        ####################################
+        # SAMPLING
+
+        if self.model in ['34465A', '34470A']:
+            _max_sample_count = 1e9
+        else:
+            _max_sample_count = 1e6
+
+        self.add_parameter('sample_count',
+                           label='Sample Count',
+                           set_cmd=partial(self._set_databuffer_setpoints,
+                                           'SAMPle:COUNt {}'),
+                           get_cmd='SAMPle:COUNt?',
+                           vals=vals.MultiType(
+                               vals.Numbers(1, _max_sample_count),
+                               vals.Enum('MIN', 'MAX', 'DEF')),
+                           get_parser=int,
+                           docstring=textwrap.dedent("""\
+            Specifies the number of measurements (samples) the instrument 
+            takes per trigger.
+            
+            MAX selects 1 billion readings. However, when pretrigger is 
+            selected, the maximum is 50,000 readings (without the MEM 
+            option) or 2,000,000 readings (with the MEM option)"""))
+
+        if self.has_DIG:
+            self.add_parameter('sample_count_pretrigger',
+                               label='Sample Pretrigger Count',
+                               set_cmd='SAMPle:COUNt:PRETrigger {}',
+                               get_cmd='SAMPle:COUNt:PRETrigger?',
+                               vals=vals.MultiType(
+                                   vals.Numbers(0, 2e6-1),
+                                   vals.Enum('MIN', 'MAX', 'DEF')),
+                               get_parser=int,
+                               docstring=textwrap.dedent("""\
+                Allows collection of the data being digitized the trigger. 
+                Reserves memory for pretrigger samples up to the specified 
+                num. of pretrigger samples."""))
+
+        if self.model in ['34465A', '34470A']:
+            self.add_parameter('sample_source',
+                               label='Sample Timing Source',
+                               set_cmd='SAMPle:SOURce {}',
+                               get_cmd='SAMPle:SOURce?',
+                               vals=vals.Enum('IMM', 'TIM'),
+                               docstring=('Determines sampling time, '
+                                          'immediate or using sample_timer'))
+
+        self.add_parameter('sample_timer',
+                           label='Sample Timer',
+                           set_cmd='SAMPle:TIMer {}',
+                           get_cmd='SAMPle:TIMer?',
+                           unit='s',
+                           vals=vals.MultiType(vals.Numbers(0, 3600),
+                                               vals.Enum('MIN', 'MAX', 'DEF')),
+                           get_parser=float,
+                           docstring=textwrap.dedent("""\
+            The value is rounded by the instrument to the nearest step. For DC 
+            measurements, the step size is 1 µs. For AC measurements, 
+            it is AC bandwidth dependent.
+            
+            Special values are: MIN - recommended minimum, MAX - maximum, 
+            DEF - default. In order to obtain the actual value of the 
+            parameter that gets set when setting it to one of these special 
+            values, just call the get method of the parameter, or use 
+            corresponding parameters in this driver, 
+            like `sample_timer_minimum`.
+
+            Specifying a value that is between the absolute minimum (assumes 
+            no range changes) and the recommended minimum value, 
+            may generate a timing violation error when making measurements.
+            
+            Applying a value less than the absolute minimum will generate an 
+            error."""))
+
+        self.add_parameter('sample_timer_minimum',
+                           label='Minimal recommended sample time',
+                           get_cmd='SAMPle:TIMer? MIN',
+                           get_parser=float,
+                           unit='s',
+                           docstring=textwrap.dedent("""\
+            This value is measurement dependent. It depends on such things 
+            as the integration time, autozero on or off, autorange on or 
+            off, and the measurement range. Basically, the minimum is 
+            automatically determined by the instrument so that the sample 
+            interval is always greater than the sampling time.
+            
+            Since the minimum value changes depending on configuration, a 
+            command order dependency exists. You must completely configure 
+            the measurement before setting the sample timer to minimum, 
+            or you may generate an error. A complete configuration includes 
+            such things as math statistics or scaling.
+            
+            When using autorange, the minimum value is the recommended value, 
+            not the absolute minimum value. With autorange enabled, minimum 
+            value is calculated assuming a single range change will occur 
+            for every measurement (not multiple ranges, just one range up or 
+            down per measurement)."""))
+
+        ####################################
+        # The array parameter
+
+        self.add_parameter('data_buffer',
+                           parameter_class=ArrayMeasurement)
+
+        ####################################
+        # Aperture parameters
+
+        if self.model in ['34465A', '34470A']:
+            # Define the extreme aperture time values for the 34465A and 34470A
+            utility_freq = self.line_frequency()
+            if utility_freq == 50:
+                apt_times = {'34465A': [0.3e-3, 2],
+                            '34470A': [0.3e-3, 2]}
+            elif utility_freq == 60:
+                apt_times = {'34465A': [0.3e-3, 1.67],
+                            '34470A': [0.3e-3, 1.67]}
+            if self.has_DIG:
+                apt_times['34465A'][0] = 20e-6
+                apt_times['34470A'][0] = 20e-6
+
+            self.add_parameter('aperture_mode',
+                               label='Aperture mode',
+                               set_cmd='SENSe:VOLTage:DC:APERture:ENABled {}',
+                               get_cmd='SENSe:VOLTage:DC:APERture:ENABled?',
+                               val_mapping={'ON': 1, 'OFF': 0},
+                               vals=vals.Enum('ON', 'OFF'),
+                               docstring=textwrap.dedent("""\
+                Enables the setting of integration time in seconds (called 
+                aperture time) for DC voltage measurements. If aperture time 
+                mode is disabled (default), the integration time is set in PLC 
+                (power-line cycles)."""))
+
+            self.add_parameter('aperture_time',
+                               label='Aperture time',
+                               set_cmd=self._set_apt_time,
+                               get_cmd='SENSe:VOLTage:DC:APERture?',
+                               get_parser=float,
+                               vals=vals.Numbers(*apt_times[self.model]),
+                               docstring=textwrap.dedent("""\
+                Specifies the integration time in seconds (called aperture 
+                time) with 2 µs resolution for DC voltage measurements.
+                
+                Use this command for precise control of the DMM's 
+                integration time. Use `NPLC` for better power-line noise 
+                rejection characteristics (NPLC > 1).
+
+                Setting the aperture time automatically enables the aperture 
+                mode."""))
+
+        ####################################
+        # Connect message
+
+        if not silent:
+            self.connect_message()
+
+    def init_measurement(self) -> None:
+        """
+        Change the state of the triggering system from "idle" to
+        "wait-for-trigger", and clear the previous set of measurements from
+        reading memory.
+
+        This method is an "overlapped" command. This means that after
+        executing it, you can send other commands that do not affect the
+        measurements.
+
+        Storing measurements in reading memory with this method is faster than
+        sending measurements to the instrument's output buffer using
+        `read` method ("READ?" command) (provided you do not `fetch`,
+        "FETCh?" command, until done).
+        """
+        self.write('INIT')
+
+    def reset(self) -> None:
+        self.write('*RST')
+
+    def display_clear(self) -> None:
+        """
+        Clear text from display. Depending on the display being
+        enabled/disabled, this either returns to display's normal state or
+        leaves it black, respectively.
+        """
+        self.write('DISPLay:TEXT:CLEar')
+        self.display_text.get()  # also update the parameter value
+
+    def abort_measurement(self) -> None:
+        """
+        Abort a measurement in progress, returning the instrument to the
+        trigger idle state.
+        """
+        self.write('ABORt')
+
+    def _licenses(self):
+        licenses_raw = self.ask('SYST:LIC:CAT?')
+        licenses_list = [x.strip('"') for x in licenses_raw.split(',')]
+        return licenses_list
+
+    def _get_voltage(self):
+        # TODO: massive improvements!
+        # The 'READ?' command will return anything the instrument is set up
+        # to return, i.e. not necessarily a voltage (might be current or
+        # or resistance) and not necessarily a single value. This function
+        # should be aware of the configuration.
+
+        response = self.ask('READ?')
+
+        return float(response)
+
+    def fetch(self) -> np.array:
+        """
+        Waits for measurements to complete and copies all available
+        measurements to the instrument's output buffer. The readings remain
+        in reading memory.
+
+        This query does not erase measurements from the reading memory. You
+        can call this method multiple times to retrieve the same data.
+
+        Returns:
+            a 1D numpy array of all measured values that are currently in the
+            reading memory
+        """
+        raw_vals: str = self.ask('FETCH?')
+        return _raw_vals_to_array(raw_vals)
+
+    def read(self) -> np.array:
+        """
+        Starts a new set of measurements, waits for all measurements to
+        complete, and transfers all available measurements.
+
+        This method is similar to calling :meth:`init_measurement` followed
+        immediately by :meth:`fetch`.
+
+        Returns:
+            a 1D numpy array of all measured values
+        """
+        raw_vals: str = self.ask('READ?')
+        return _raw_vals_to_array(raw_vals)
+
+    def _set_databuffer_setpoints(self, cmd, value):
+        """
+        set_cmd for all databuffer-setpoint related parameters
+        """
+
+        self.data_buffer.properly_prepared = False
+        self.write(cmd.format(value))
+
+    def _set_apt_time(self, value):
+        self.write('SENSe:VOLTage:DC:APERture {:f}'.format(value))
+
+        # setting aperture time switches aperture mode ON
+        self.aperture_mode.get()
+
+    def _set_NPLC(self, value):
+        self.write('SENSe:VOLTage:DC:NPLC {:f}'.format(value))
+
+        # This will change data_buffer setpoints (timebase)
+        self.data_buffer.properly_prepared = False
+
+        # resolution settings change with NPLC
+        self.resolution.get()
+
+        # setting NPLC switches off aperture mode
+        if self.model in ['34465A', '34470A']:
+            self.aperture_mode.get()
+
+    def _set_range(self, value):
+        self.write('SENSe:VOLTage:DC:RANGe {:f}'.format(value))
+
+        # resolution settings change with range
+
+        self.resolution.get()
+
+    def _set_resolution(self, value):
+        rang = self.range.get()
+
+        # convert both value*range and the resolution factors
+        # to strings with few digits, so we avoid floating point
+        # rounding errors.
+        res_fac_strs = ['{:.1e}'.format(v * rang)
+                        for v in self._resolution_factors]
+        if '{:.1e}'.format(value) not in res_fac_strs:
+            raise ValueError(
+                'Resolution setting {:.1e} ({} at range {}) '
+                'does not exist. '
+                'Possible values are {}'.format(value, value, rang,
+                                                res_fac_strs))
+
+        self.write('VOLT:DC:RES {:.1e}'.format(value))
+
+        # NPLC settings change with resolution
+
+        self.NPLC.get()
+
+    def autorange_once(self) -> None:
+        """
+        Performs immediate autorange and then turns autoranging off.
+
+        The value of the `range` parameter is also updated.
+        """
+        self.write('SENSe:VOLTage:DC:RANGe:AUTO ONCE')
+        self.range.get()
+
+    def force_trigger(self) -> None:
+        """Triggers the instrument if `trigger_source` is "BUS"."""
+        self.write('*TRG')
+
+    def error(self) -> Tuple[int, str]:
+        """
+        Return the first error message in the queue. It also clears it from
+        the error queue.
+
+        Up to 20 errors can be stored in the instrument's error queue.
+        Error retrieval is first-in-first-out (FIFO).
+
+        If more than 20 errors have occurred, the most recent error stored
+        in the queue is replaced with -350,"Queue overflow". No additional
+        errors are stored until you remove errors from the queue. If no
+        errors have occurred when you read the error queue, the instrument
+        responds with +0,"No error".
+
+        Returns:
+            The error code and the error message.
+        """
+        rawmssg = self.ask('SYSTem:ERRor?')
+        code = int(rawmssg.split(',')[0])
+        mssg = rawmssg.split(',')[1].strip().replace('"', '')
+
+        return code, mssg
+
+    def flush_error_queue(self, verbose: bool=True) -> None:
+        """
+        Clear the instrument error queue, and prints it.
+
+        Args:
+            verbose: If true, the error messages are printed.
+                Default: True.
+        """
+
+        log.debug('Flushing error queue...')
+
+        err_code, err_message = self.error()
+        log.debug('    {}, {}'.format(err_code, err_message))
+        if verbose:
+            print(err_code, err_message)
+
+        while err_code != 0:
+            err_code, err_message = self.error()
+            log.debug('    {}, {}'.format(err_code, err_message))
+            if verbose:
+                print(err_code, err_message)
+
+        log.debug('...flushing complete')
+
+
+def _raw_vals_to_array(raw_vals: str) -> np.array:
+    """
+    Helper function that converts comma-delimited string of floating-point
+    values to a numpy 1D array of them. Most data retrieval command of these
+    instruments return data in this format.
+
+    Args:
+        raw_vals: comma-delimited string of floating-point values
+
+    Returns:
+        numpy 1D array of data
+    """
+    return np.array(list(map(float, raw_vals.split(','))))

From 6247785a16eaf66efd21f87349f17be260f24017 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 13:37:16 +0100
Subject: [PATCH 528/719] Don't use deprecated message in pytest raises

---
 qcodes/tests/dataset/test_dataset_basic.py        | 5 ++---
 qcodes/tests/dataset/test_experiment_container.py | 5 ++---
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index 58e49386ec1..dcef4f21dd2 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -82,10 +82,9 @@ def test_dataset_read_only_properties(dataset):
                        'run_timestamp_raw', 'completed_timestamp_raw',
                        'snapshot', 'snapshot_raw']
 
+    # It is not expected to be possible to set readonly properties
     for prop in read_only_props:
-        with pytest.raises(AttributeError, match="can't set attribute",
-                           message=f"It is not expected to be possible to set "
-                                   f"property {prop!r}"):
+        with pytest.raises(AttributeError, match="can't set attribute"):
             setattr(dataset, prop, True)
 
 
diff --git a/qcodes/tests/dataset/test_experiment_container.py b/qcodes/tests/dataset/test_experiment_container.py
index b0a44be7fff..d2586f7dd87 100644
--- a/qcodes/tests/dataset/test_experiment_container.py
+++ b/qcodes/tests/dataset/test_experiment_container.py
@@ -131,10 +131,9 @@ def test_experiment_read_only_properties(experiment):
                        'path_to_db', 'started_at', 'finished_at',
                        'format_string']
 
+    # It is not expected to be possible to set read only properties
     for prop in read_only_props:
-        with pytest.raises(AttributeError, match="can't set attribute",
-                           message=f"It is not expected to be possible to set "
-                                   f"property {prop!r}"):
+        with pytest.raises(AttributeError, match="can't set attribute"):
             setattr(experiment, prop, True)
 
 

From 899639f15352a525714224071886b4d63afe2f95 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 13:46:50 +0100
Subject: [PATCH 529/719] Move dataset generator so that it does not depend on
 pytest

---
 qcodes/tests/dataset_generators.py | 30 +++++++++++++++++++++++++++
 qcodes/tests/test_plot_utils.py    | 33 ++----------------------------
 2 files changed, 32 insertions(+), 31 deletions(-)
 create mode 100644 qcodes/tests/dataset_generators.py

diff --git a/qcodes/tests/dataset_generators.py b/qcodes/tests/dataset_generators.py
new file mode 100644
index 00000000000..cc81d4278da
--- /dev/null
+++ b/qcodes/tests/dataset_generators.py
@@ -0,0 +1,30 @@
+import numpy as np
+from qcodes.dataset.param_spec import ParamSpec
+
+def dataset_with_outliers_generator(ds, data_offset=5, low_outlier=-3,
+                 high_outlier=1, background_noise=True):
+    x = ParamSpec('x', 'numeric', label='Flux', unit='e^2/hbar')
+    t = ParamSpec('t', 'numeric', label='Time', unit='s')
+    z = ParamSpec('z', 'numeric', label='Majorana number', unit='Anyon',
+                depends_on=[x, t])
+    ds.add_parameter(x)
+    ds.add_parameter(t)
+    ds.add_parameter(z)
+
+    npoints = 50
+    xvals = np.linspace(0, 1, npoints)
+    tvals = np.linspace(0, 1, npoints)
+    for counter, xv in enumerate(xvals):
+        if background_noise and (
+            counter < round(npoints/2.3) or counter > round(npoints/1.8)):
+            data = np.random.rand(npoints)-data_offset
+        else:
+            data = xv * np.linspace(0,1,npoints)
+        if counter == round(npoints/1.9):
+            data[round(npoints/1.9)] = high_outlier
+        if counter == round(npoints/2.1):
+            data[round(npoints/2.5)] = low_outlier
+        ds.add_results([{'x': xv, 't': tv, 'z': z}
+                            for z, tv in zip(data, tvals)])
+    ds.mark_complete()
+    return ds
diff --git a/qcodes/tests/test_plot_utils.py b/qcodes/tests/test_plot_utils.py
index 08b8cba948a..03e564bc793 100644
--- a/qcodes/tests/test_plot_utils.py
+++ b/qcodes/tests/test_plot_utils.py
@@ -3,10 +3,9 @@
 """
 
 from pytest import fixture
-import numpy as np
+
 from matplotlib import pyplot as plt
 
-from qcodes.dataset.param_spec import ParamSpec
 # we only need `dataset` here, but pytest does not discover the dependencies
 # by itself so we also need to import all the fixtures this one is dependent
 # on
@@ -16,37 +15,9 @@
 
 from qcodes.tests.test_config import default_config
 from qcodes.dataset.plotting import plot_by_id
+from .dataset_generators import dataset_with_outliers_generator
 import qcodes.config
 
-def dataset_with_outliers_generator(ds, data_offset=5, low_outlier=-3,
-                 high_outlier=1, background_noise=True):
-    x = ParamSpec('x', 'numeric', label='Flux', unit='e^2/hbar')
-    t = ParamSpec('t', 'numeric', label='Time', unit='s')
-    z = ParamSpec('z', 'numeric', label='Majorana number', unit='Anyon',
-                depends_on=[x, t])
-    ds.add_parameter(x)
-    ds.add_parameter(t)
-    ds.add_parameter(z)
-
-    npoints = 50
-    xvals = np.linspace(0, 1, npoints)
-    tvals = np.linspace(0, 1, npoints)
-    for counter, xv in enumerate(xvals):
-        if background_noise and (
-            counter < round(npoints/2.3) or counter > round(npoints/1.8)):
-            data = np.random.rand(npoints)-data_offset
-        else:
-            data = xv * np.linspace(0,1,npoints)
-        if counter == round(npoints/1.9):
-            data[round(npoints/1.9)] = high_outlier
-        if counter == round(npoints/2.1):
-            data[round(npoints/2.5)] = low_outlier
-        ds.add_results([{'x': xv, 't': tv, 'z': z}
-                            for z, tv in zip(data, tvals)])
-    ds.mark_complete()
-    return ds
-
-
 @fixture(scope='function')
 def dataset_with_data_outside_iqr_high_outlier(dataset):
     return dataset_with_outliers_generator(dataset, data_offset=2, low_outlier=-2,

From 53fdad6ea332fe49535da1bfa1815940e7be19a8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 13:47:17 +0100
Subject: [PATCH 530/719] update import

---
 docs/examples/plotting/auto_color_scale.ipynb | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/docs/examples/plotting/auto_color_scale.ipynb b/docs/examples/plotting/auto_color_scale.ipynb
index 6d74b29fb53..f5df85a1bd7 100644
--- a/docs/examples/plotting/auto_color_scale.ipynb
+++ b/docs/examples/plotting/auto_color_scale.ipynb
@@ -83,7 +83,7 @@
     "new_experiment(name='tutorial_auto_color_scale', sample_name=\"outliers\")\n",
     "\n",
     "# import methods for creating datasets with outliers:\n",
-    "from qcodes.tests.test_plot_utils import dataset_with_outliers_generator"
+    "from qcodes.tests.dataset_generators import dataset_with_outliers_generator"
    ]
   },
   {
@@ -122,7 +122,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -151,7 +151,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -200,7 +200,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x1d387f78358>"
+       "<matplotlib.lines.Line2D at 0x2113042e9b0>"
       ]
      },
      "execution_count": 5,
@@ -209,7 +209,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -284,7 +284,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -316,7 +316,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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wtRu2B+3f28gae3YzstlnpXg7O6+U/m/YF2FeCg+Fmn52BxtH2c6mKV5dZfdsMLLlB/kRErqttv2lz1vWh27eab4v6ZgHbB/Wnt2NbPFn7DUFGPKT142s7lgvwgX8+6kr3lHVPdwftwARmVy2y/7vbq6rZcHMdwrawmm1M7DdrxoKBAKBtmK3A49797Onf4e3//kYnz7pGw20zrO909LhNoJsEJHzRWSqiExtqLNfuYFAINDeiMjkxsYGxu6wF0efchl/feQX200ekZyYEcRri++GSDXUwc0JBAKfQJpmAwC9+gykfPKntptZQU4MBMloHtQXNb/uCccmtSBF+oteThiolbva8OYNVpUOwBDHmT5vs9X7Dn/QT9Ew/4KWMetg/Sj/Nnj69PW7Wb1r3QBfTTn7VKujLnugysik3q+ft77ayGZ9zXrdl908z60//cfDjGzijettwWPc6h1O+R129rn0ZHv/ADZMtMr73tPsfR35pG+76rLRyhNd7cdm9UA/FH/3lfb7qKcTrm6zs1gCoHqk7Wz93vcio8OGkY6do9razlK9HjfubBcHrNjTnquk0L6vuNRGoR76jDXstyUiMnnX/Y5m7A4f2yKOPuUyfn75cci1p+Xnuq2gw1VDIvIwUdySCRJl5NraIHWBQCDQpux24HHvHn9W80gyTbOCC37w+3RBBnOCDh8IVPVkVR2sql1UdZiq/q6j2xQIBAJNJNsGWrK92ApyTjUUCAQC25Jk20BLthdbQYfPCAKBQKCzkm420MT2MCvIuRlBl3V1DHxydjNZ1f5jTLkelf6p1To+Mv0/sg5hFfv7RrmaftYyXTrVOtMsOsM3KnZxbKUbxjleZsC4h60BbvGR1oo9/K++naronx8Z2bJzdjayIff5sdAaJ4wwMteA18M6AwH0/MBew4++lTNpXElMm2Fkmz+7j194s/2mKlpuL9bqHX1v26IVtr963vEF1X5fyWu0ZTcOsvtcs78fIn/iFYuMrGo/+1wBlMx3jMhOHxjzO7tPAF1vFyyMXDHKyApWWic3ALrY81rwpRSJEH/ki1vB5cedcWXaAr36DKRs1/1ZMPOdY0nKiZBL5NxAEAgEAtuQwd889AxKUoTbaKJ2r/X87dG0iWw6NWEgCAQCgTQkUBL4M7EmtE3Tj297wkAQCAQCaWjUBI7mrRmJThCzbWvIuYGgoVdXVh/VXP9eNdIa6wtTqBeLKuzIvvgwq8vukiKSRZdNtn7CiXzYZ6a/tLjLJqs3HvoP/2ANvaxDWJGN10VdiW/z715tHcI857uqw/yESiv2tvv1Ir1Ov8IP7jjoX/bhGHbXdFtwm6WM2XqGvuI7Wa3e0d6rgk3W9tRnVoqouD3stU44T2dtH19FsXoP65A24atT7HGK9nTrz/y2tWmNfdz2H4B1Zba/d+1rn6Glh/iLaEY9ZZ+N+p72vHpu9K/1ghP7GtnoX892SoK18rSeBhppyPDFn2nG0NnJuYEgEAgEtiWNqjRm+OJPBNVQIBAIbL9ENoL0L/rcHgbCQBAIBAJpSaA0ZlQN5fZQkHMDgSjkNTS/6H2m25uwfnT2vnLdV1rZ5hK/bI9XZxlZ1aETjCwvRQiq5XvagF0jnUQdAFW72sQcxYus3rlyd9/noeq7extZ75lWl5lK79xvmi1bNdxe1yGXT3Xrr7rArrmfcetEt2xnZPPRNp/Jqp38R6b3LHvD10y092XQr95y6xeNGWVkMy6ytpcRf/NtT73m2Wdgzem2/cOetT4vAHNPt4HgNo70/UO8jHrL93GetxTqlMLV1vbQ2N0Gvavcz9oCAEb9ebWRVXxhvFuWX/ni1lCvCeozvOczqY46Ozk3EAQCgcC2JBFv6cjtYSCEmAgEAoG0NMaqoXRba1RDEvFLEZkjIu+LyG4Zyj8lIh9s9YmkIcwIAoFAIA2NSmY/gtbt8jPA+HjbG7gz/tcgIp8DbPLwNibMCAKBQCANiSy2VqqGjgce0Ig3gVIRMeEpRKQn8E3aImJSBnJuRlDfAyr3aO6oUjLPOq40+HYuKne3Y9+4H71vZNroW3tn/MIGbZvwdZv2bN73/dlekZM1KlXQtqph9rxqHMedTWP9rFcTf2k/JOpLrePT0qP8brDDT9YYWe83rLFy6SV+ILZ8J77ZxCsXWuHjbvUOp3CtNcyXLPS/nZYc7gjz7bXaeJMfxXLIv+035bB/2tdLtxenufWrPz3JyPo/a7PkLTvJN6puHmHPtfTX/iKG2ZeMNLL8WttXh77sG7bzltt+teIEG4ywz0f+d/bG8bZsrwXtlxumQYV6TR9hujH6/YAWibXujtPstmQosDjp7yWxrKJFueuAmwHfs68NybmBIBAIBLYljQiNGVINxEPWq6p6aha79HbWbNQXkUnAOFX9hoiMyqadW0MYCAKBQCAN2Q0E6X8XkQuBL8d/TgGGJ/08DGg5/doX2F1EFhC9pweIyMuqenDWDW8FwUYQCAQCaUioZNw0g+pIVW9X1UmqOokoZ8EZ8eqhfYD1qlrRovydqjpEVUcBBwCz2msQgBycEYhCXn3zi15QbXWpNZN9neEOVy6wwtJe9jhFvt5enaFz+ZetPWDQ276Noesaq4ude5afVGP0DVYfPOsuGyCuaLrV+wNUHGwd0ob8ZbGRFc0bbmQA6ydZh6Ze7yw3ssINvqls1d72GnRb6yc76YwkCuzDvX6U/+00/kEbOHD2V+3j1f/fvvNe8QfWq3HFIbZf9Ni5zK1ftMAme6nec7SRrdvZtyft8K35Rjb7MusoCdDNccDsucz2AS+QHEDFibYPjHjBqsHnXOBf68J5tr8PeC+FB2cbsJl8Nmf4Zm5oXZbK54CjgTlE+v+zm34QkWnxYLFNybmBIBAIBLYlGn/1py3TioFAVRW4MMVvZhBQ1QXATlkfYAsIA0EgEAikoS1sBJ2dMBAEAoFAGho1j0ZPJ5xEJhtBZyfnBoLC9QlGPds8qXuXWXZx/sr97FpngOk3Wnn5zTaLzaxz/YBXZfdYXWZdP1uux7+cBCzA/MusH0J9SQq/xPGjjKhgidWP9p7p60crDrCdd9nx1h4w8jE/ENmq/QYYmZeEp3qw/xD0nG27V/GCdneSbDMWfNZe68YS3/a04LgiIxvwD8cPYI1fv6F/sZGt2c9JbDPdf2QL5i4xsvy+9l6N+4Nvz5n+I5uYZuJ11m4AsPx4a3vY6PSB4Q9aPwaAgr1t/fyN1umkzyvWxpWKvLr2SwxTTx71pM9Z3JDj625ybiAIBAKBbUlWM4KgGgoEAoHtlwSS0QaQ24kqw0AQCAQCaUmQR2MG1Y8G1VAgEAhsv9RrAfWa3kaQSXXU2cm5gUAaEnSpbO48s+J4a+jK35giO9I6Z4qXb2/yhFut8Q0gsdx60xT1soa+RV+1RmGAzaNrjWzoE13cssxeYESNXXc1spJ/z3WrLz3EXhcVe/5a4Hfy6gG27MoLbCapoS/4xupNg+x+5x9n63dWulfa8x/8hBNJDyhYbbNmLfy8zfrV9wUn6B6g1TVGNuIJm81tznn+I9vntR2MbOATM41s+rW2TwDk1doX2ZwLrVEXfIeyRs+nsSCFYXuj7S8Vn7KG4XW7pgimeMt6I5txhX0GAXjeF7eGRpWmoHIpyeRn0NnJuYEgEAgEtiWNWaiGgh9BIBAIbMckNI9EWDUUCAQCn1zCjKATUl9SwLIjmzs6DXjHOnkVL/L17j0+cJJtOLrMhiF93PqN46yTVU1/e6zqof6CsvLLre1h0Rm+3rZ4qtWbtgy4BzD9h2Pd+r3ft523yyZrO6ma6DvudLV+dtSts3r/krcXufWLe1knq9phNsAfn3KrdzjDn7B9ZfMw/1qtPMD2iwInnYjW+3rv2d+3oWTGXf2ekeUfONmt36XGsYl1t4r7vu/59qAaa86gZIFvZ/NsRw3Wd43Zl/g2hjFP2AB5BTVdjazbMv8ZnnFZiZENesEv61tkWkcDecFYHAgEAp9ksnEoy/UZQbsOYyLyaRGZKSJzROQ7zu8jROQlEXlPRN4XkaPbsz2BQCDQWpocytJtwUaQAhHJB24HjiDKyTlFRJ5S1Y+Sil0NPKqqd4rIDkRxuke1V5sCgUCgtWQ1IwjLR1OyFzBHVecBiMgjwPFA8kCgQJPCrxc2XZuhy7p6hjzdPMhcTZlNoFK4wdfFLjjLBp0b9etZRib1Vg8JsHIXR5e51upSy39V6dZfeLa1B/Se5a/Dn/nNEUZW4rgMbOjmd8K1+9g17+U/swlUFh7n20NG/vK/Rla6h02M4l1TgJrx9viFC1P4THRCGuYtMLK537a2AIBxv7f+IR41e/n2oIZetg/IBKtjb+zh254q97R9oHJPG2Cwh+8ew4i7bJDE2VfaJEgAZT+3wehqdh5mZN0q/ACDSw+zdpaqchuMb+JPV7n1Z1zs2GNq2jHonOZT0IY2AhER4Fai5DTVwFmq+q5TrhD4FXAwURSLq1T1T1kfqBW050AwFEhOh7UE2LtFmWuAv4nIxUARcLi3IxE5HzgfoFt+CseRQCAQaAeyCTHRShvBZ4Dx8bY3cCf23QhwFVCpqmUikgf4X2xtQHvaCLwr0/LT+WTgPlUdRjQ6PhifcPNKqner6h6qukdhvrM8IRAIBNqJJj+CdFsrbQTHAw9oxJtAqYgMdsqdA/wYQFUTqupPkQAR2VdEbo9trStFZJGIPCciF4qIs1SvOe05ECwBkuemw7Cqn3OBRwFU9Q2gG+BE9w8EAoGOoSlDWbqtlTMCT1syNLmAiJTG/71ORN4VkcdExFnkCyLyPHAe8ALwaWAwsAORDbYb8BcROS5dg9pzIJgCjBeR0bGu6yTgqRZlFgGHAYjIRKJGO5FMAoFAoGOo18iPIN0W2wgOEJGpSdv5KXaZjbakgOjj+TVV3Q14A/hZiv2drqrnqupTqrpMVRtUdaOqvquqN6vqwcDr6c6x3WwEqtogIhcRjVL5wD2q+qGI/BCYqqpPAd8CfiMi3yC6EGfFiZ1T0tijC1WTBjWTrZloDTnV1qYJwKC/WaOSDrCqt3lf8IOjDfm3NWotOsqOp1XD3cEbda740kPdoox93Bpb8zbb9vf9wB/Pl37KOhRNv8SWHfNHmwkLoOYAayxcvo819o552B+764Zag3tDDydD1yFu9Q5n9m37GFnv9/xrXbGPvbH5zmUd8tJat37X5Y6j2kK7dmLir/wMZ9MvsX247D7r0ZY/v8Kt37DGtqv78hRfuY5TXOVk2y9K+vgaiYTzDJS+b4Vr9vGfoQm/WWNktUPaz3aYVYiJaNXQq6p6qve7iFwIfDn+cwqZtSWriQzJf47/foxIg+IcO7XKKNsy7epQpqrPES0JTZZ9P+n/HwH7t2cbAoFAYGtoC4cyVb2daDk9InIMcFG8knJvYL2qVrQoryLyNNGKoX8SaU4+Ig0i8jngJ8AAolmHxLvyl0AmETyLA4FAIA2ahQ2glcbi54gWx8wh+uo/u+kHEZmmqpPiP79NtIDmFiKV+dktd9SCm4BjVdVPmJ6GMBAEAoFAGuoT+eQl2s6PIFZ/X5jit0lJ/19I6yJxrdiSQQBycCCoL1WWnNhcT5q/0t6EiT+yiUIAFp9gV2nV9rELlRoH+w5CFfs5GTh6WJ1pXoPfMYY/a5Nq1Ay1wdkACpfb4Fyzz7ZtHfYP33lu2EtWR7z4CLv8dkkKHX2vudZc01BkZbPPtA59AONu/NDIFn1nR/9gnZCCavuVV5diJffI+6yT1aLTrUPY2p1LjQyg3/vW9rPiZHut+k+1fQL8xDLygfU+nHWtTWwEUPYLq+PfOMZ3dKyZPMrIujtmot6v+MEIq06x9eudVeGDX1jh1t800T4Di05IYVp8wRe3hhyKPjpVRP4IPAn8z8Coqk9kqphzA0EgEAhsSxIqGUNIaOcIMVFCpGo6MkmmQBgIAoFAYGtIkEciB2YEqprJhpCS3A6iHQgEAu1MU87idFv6Re/bBhEZJiJ/FpFKEVkhIn8SERsEyiHnZgRdF1Qz/sx3msnqPruXKVc3wlfmVg+zd2zA21bW5R82uBxAwomZVvwvK1t4qm9j2DjT+ieUvO+vw5975iAj6+4sA+/+3gK3PqV2HXd+jVXG9pnvB+ykQeJqAAAgAElEQVQqfXmerb/ZJsFZcbif0J2RQ+yxPnKemAP86h1N/3dtW72kLAC1Oww1sjzHj6C2r19/w1gncOH1s41s/gXj3foDpth7uOqUSUZWavPZAzDnImvPGP+ADVAIkLfZ+jIsO6DQyGodW0C0Aysa+LbtQ9O/7gf4o8Beq4nf96PptUVimsZEPg1taCxuR+4FHgK+EP99Wiw7IlPFTtH6QCAQ6Ky0Q4iJ9qK/qt4bexY3qOp9gL+SowVhIAgEAoE0JPRjg3GqrRNohgBWichpIpIfb6cReShnJAwEgUAgkIasoo92DtXQOcAXgeVABfD5WJaRnLMRBAKBwLakXvOQDC/6xs6xfHSjqqaNMpqKnBsIGgYUUXnyfs1kg1+2AbNkhT8j6ravzRDVdYN1nGno6t/YdeOs0ajPb6fY4x+1r1u/womstHxfP7jWoDdsu1ZOtsdPjLBGZYD1ZdYwXV1mLZibxvrn2n2VzXC1yvFHKqjwDeubRtvu1evBN2zB87/u1u9ouldaR72Ko/xr1XOZPdeqcfb+eY5fAN2XW/n0G23mt4nfW+DW37SbXRyyZqLd58i/WidDAHXyfBQs9hcxVJwwysjGPGYDwS07zF+wMfRR63y35CRrrJ5481IjAyDfntfy42ybALjDF7eGrILOdQ4bwVsiMg24B/hrpgCeyXSK+UwgEAh0VhKktw9k43C2jSgD7gbOAOaIyA0ikiIOc3PCQBAIBAJpaAo6l27rDMbiOOPZi6p6MlGimjOBt0XkXyLiqyhick41FAgEAtuShkQ+ksGPIJPqaFsgIn2JfAdOB1YAFxMlA5tElM/A6t9icnMgaDH8zrjAhtse/zU/CF/1iFFGVviy1Zsn+vt6726O6aHqZJvApOfCFI5Dk+2x8gr84F4lb1ca2cYhVm+88Gg/3HieE4tuwEvWI25tud/WeSfadkkv6/iTv8y/Vqt2sg9PSXHaD5NOReFqq0/v/y8/aFzJhzbvx7qxdgn38OdSBEM8pq+R9XnLOmnNuGKEW7/8Jhvgrf4IW7ZgiZ+fZM0p1iFuoJMYCWDAr61NbObNuzslfUdFL7FNoY3FSO04fwn8yl3tdRl293/9Y7UBORRr6A3gQeAEVU32sJsqInelq5ibA0EgEAhsIxJZOIylGPK2NRNSGYhV9SfpKoaBIBAIBNKQ1Yygc6waGi8ilwGjSHq3q2qKZLgfEwaCQCAQSENDIg9JZIg+2grVkIgIcCtRlrJqolzt7zrlTgauJFKGLwNOy5B7+DHgLuC3gK9vTkHODQT5tUrp7OY6xsH3WnvAjLtsIDoAz6Yz9/+6G1miW4rJXp5zfbs6sjx/HUH5RTbqlzjB4QDqR1kdaekcq19NFPqdcMnnbXCw6nJ7Acru8HXBs063CXPKv+1Evevm2wiqy2z7JdEZ1ldkx4yv2YTo3Rf713rNTjZZSncnr0pi+hy3fs05ds29NNhj9XnffyHVldmES4VrbdnVB1vfEIB8x72gocwPXFk7wCZnKlxnj1Xfy3+GNk+0bej/uE1iJF2tLQCguK8T+PDUnd2y3OmLW0M72Ag+A4yPt72JWrl3cgERKSAaLHZQ1VUichNwEXBNmv02qOoWnXHHm7oDgUCgE6OQcfloK20ExwMPxMs93wRKRaTlSN6UfL4onkGUEM0K0vG0iHxNRAaLSJ+mLZsG5dyMIBAIBLYlrZgRHCAi5yaJ71bVu53iQ4HFSX8viWX/m26rar2IfBX4L7AJmE2KPMdJnBn/e3ly04AxGeqFgSAQCATS0ZDIg+xsBK+q6qlZ7NIbVZrpTEWkC/BVYDIwD7gN+C7wo1Q7VVXjJyAivn6tBUE1FAgEAmnIJrxEphmDiFwoItPiWEDLgGRDyTCs2mcSgKrOjZeEPgrsRxZIxKEi8luazzxSknMzgvpiYelBzZ2iEkdZQ1H5rb7jzoLPW6Ne7UBr7O031R8jV+1htYFdF2c16AIw+xrb1rI7l7tlV06ygcDyHSexBmu7A2CcYwResbctrFPMggUAJtSVG9msS0cZWdldjgEZKFxvnecaipwUb52Ukhn28ai39nMAxv3eekTVDbKFZ9/nRO0D8pfaF0m/D+zNXjfOv351vW1bh79oLcDzLvBfWCPvt85/1UPsIgqATYPsszHmHud9U+g/F/NOs0EWe+y4o5ENfMVfILNykmOY7uc8GG2Eqmy1w5iq3g7cDiAixwAXicgjREbi9ara8iFaCuwgIv1VdSVRljHfSzZGRPYGTgFOBPoQqZIuT1eniZwbCAKBQGBbko1DWSv9CJ4jWjo6h2j56P+SzovINFWdpKrLRORa4BURqSfKunmWtzMRuZ4oD8Ei4GHgh8BUVb0/2waFgSAQCATSkI3qpzV+BLGqxzX8quqkpP/fReQXkInzgZlEy1CfUdVaEWnVOu0wEAQCgUAaGrNwKOvgWEODgCOBk4FbROQloLuIFKiqdSZyyLmBQAuVxqG1zWTdZ1i996zzbRAvgMaeVpcozj3uPaPWCoGqUVbvO/pxm5QjFbJqnZEl1jkRt4AhT1od+9yv2KBzdQP9ey2Ntq2FG+yHQu2xe7j1u620NoaxV061BUf6jkezv2z12T1mZ29P6WgGv2Lvy5LDfec//cA6Cub32sXIyr/p2+6W/58NG999oe0rC77kH7/LK/ZaL/2s83ineC0Uvmoduioum+yW3dzH9qF111i9f9F0/173mW7tbHXF9iGs3M/a8wAaetuT6Dmn/WxP2dgIOnIgUNVG4HngeRHpBnwW6AEsFZF/qOopmfaRcwNBIBAIbEs0q1hDnQNVrQUeBx4XkRIiw3FGwvLRQCAQSIMCqpm3jkJEThOxeg1V3aCq94vIWBE5IN0+wowgEAgE0tCoeX6QsiQyrSpqZ/oC74nIO8A7wEqgGzAOOAhYBXwn3Q5ybyBoFLSquT6w11yrcxz8qh9Ibdmn7NroUb+3SbJn/NC3MQx6ztFvDrRJ4isn+/rRfv+1gczyGn0de+Eaa6cYc/NHRibdfUeCxkqbfFyctd2LvuXrgrtstN1j85F7GtmoW6x+GWDidzcZ2byvjXPLdkY8e8Cgt3zb0apz9zayWifKi+zrp5Bt9LrLZmdtfIr3Td931hqZ5vU2sjV2uT4ACy+3faBrCtNXl422EfmO7afBd0Ng5WRbv3iBLVc1yv/MzttkfR6G3+EnprFPS+tJqCCd20Zwq4j8CjgU2B/YBagh8js4XVVt1qIW5N5AEAgEAtuQbFQ/Hakaio6vjcCL8dZq2tVGICKfFpGZIjJHRNypiYh8UUQ+EpEPReSh9mxPIBAItJamVUPptpRTtRyh3WYEIpJP5FJ9BFF0vSki8pSqfpRUZjxRIKX9VXWtiAxor/YEAoHAltCYfdC5nKU9ZwR7AXNUdZ6qbgYeIYrDncyXgdtVdS2Aqtps7YFAINCBZLNiqKNVQyKSJyJf3NL67Wkj8GJut7SolQGIyGtAPnCNqv615Y5E5HwiN2oKevWme0VzY9HKSS1rQNEA31I1YKo1wC09fqiRDXnSz/TWc0GVkeUtt1a1jV/0M0H1XGYveZ+p1qgL0NDHOoQ17GGNrfOP92/jiBdGGNlmx3EnlVFwwDsbjUzzbX2tqXHrV37RWiZHP+YEA8yYUbVjEKcLLDnUN8zXjbDOf10qrAG1ZK5/rAEvLDQyrbLXf/ALg9z6C4+zlunBb9oFE72n+x5lBZUbjGzZMTbrGcCwp5wgg+ts/USVfVYAZt+4u5Gt2d0+lxNvsfsE2DS21MiWfCVFhrKf+uLWEL3oM3zxd7yNICEiFxFFKW017TkQZIy5HR9/PHAwUSjWf4vITqrazKUyTu5wN0D3IcM7i+9GIBD4BJCVZ3HnsBG8GCev/yNRMhsAVDVj6IP2HAiWkDnm9hLgTVWtB+aLyEyigWFKO7YrEAgEsqazLx9N4pz43+SAdm2ToUxE9gVOAw4EBhOtT/0AeBb4var6gXKil/l4ERlNFFv7JKJY2ck8SRQo6T4R6UekKpqXqU2BQCCwzVA6XPWTDV6GsmxJOxCIyPNEX/F/Aa4HKok81sqAQ4C/iMjPVfUpp1ENsc7qBSL9/z2q+qGINMXKfir+7UgR+QhoBC5XVT+jTEzhqs2Muq+5PjUxwOoME139U8vfZHW5K/ayjjdLd/VtBF5mkgmX2rGrYMMot/rqneyXw/KD/PzSE39hg47lbbbtGvZPP1tKj7m2/uIrrfPbiIesgw7AhjE2MU6vJ/9jZMu+5getG/p3eytnXGDvVWcl3/FJHPqkvaYA68tLjKzrWqv37v7GLLe+jhxiZDO+be1MZfdbJz2AjYOto2LX2StswRTOh9XjbYC3IU9ZR0uAWRfatkYBMJsz+HU/pXvpTPsMDHxivpHN+Lm1cQEMf9S+lYfeMc0t67s6tpKsgs61xYG2DhHpAXwTGKGq58erMieo6jOZ6maaEZyuqi3TBG0E3o23m+MveRdVfY4oCUOy7PtJ/9e44d/M1NBAIBDoCLJaFdSKgUBEyoF7gd2Aq1T1Z1veumbcSxRioiml5RLgMSDjQJB2+WjTICAiRU1BjUSkTESOi5Mr4wwUgUAgsN2QjUNZK43Fa4BLgLYaAJoYq6o3AfVRu7WGLD3dsvUjeAXoJiJDgX8QpVa7r/XtDAQCgdxCE5Jxa82MQFUrVXUK8Qu7DdksIt2JWyMiYwE/6FoLsh0IRFWrgc8Bt6nqicAOW9LSQCAQyCk0yw0OEJGpSdv527ilPwD+CgwXkT8QfbRfkU3FbJePSrx66FTg3FbWbVsaGki0iKo592JrVBr7/ffc6jN+sauRDXzdDuf1Pf2MR2U/m2OFPaxRdfwNM9z6usk6X6WKHlqz73h7qHk2ymTxf6vd+rWjrRG67Ge2rCR8o97aCbZ+lyNt1q1NKVw7Zp9h65fMcWaq9pZ0Cga94Ts0eSzfz8qG/cN+Z1Wc6Yf/7FFp70F+jb1WSw+zRmGAEU9Yw7BusA5pUmcXSwAsPNHeqx7zraMlgBbY+1063bZ142B/EUK1Y2uu/pqNyqob/AUbCWe3eSP8tjLdF7eGVmQoe1VVT936I24ZqvqiiLwL7EOkEvp6tqr7bGcEXyeKCfTneOXPGOClLWptIBAI5BqZZwNpEZELRWRavHnLrtqKg4DDiFZ1Hphtpay+6lX1FSI7QdPf84iMHYFAILBd8z87QIYyaX9XvZ0oCGe7ISJ3ECWjeTgWfUVEDlfVC9NUAzL7EdxNZBMwWR9EpAj4ElCnqn9ofbMDgUAgF2jbMNMiMgiYCpQACRG5FNhBVbPXRfocBOwUL8tHRO4H/Iw9Lcg0I7gD+J6I7EzkTdyUAm080UncA2zTQaB2eA9mfqd5NqX+TkCKWTc5keiAga/ZG7piHzu3G/2UH5xrxtVjjUzq7T7HfusNt/6GU/c1spW+PxZ5dXa/DV+yWbO6ze/q1m/oYc+reL516Fq3n591q+wr1s6iO9qgd8Xzfb31Jkdt22OFb4/ojDQU26Bx80/wM8+Ne9Quzlh2oA182Ge6r/euL/KCAdr7P/Rnb7n1GT3SiGr2sTamZZ/ybV8jnrb9fZMf347a/lZW7ZTN97sVY2+3DpiVR9soCNLo2xhWOgn1EgV+RsG2sBG0tWexqi4nCrnT1swERgBNHrfDgfezqZh2IFDVacAXRaQnsAcfh5iYrqozt7i5gUAgkCtkMxB0bPL6p+MW9AKmi8jb8d97A69ns49sbQQbgZe3rJmBQCCQu2RjI6Bjg85ttWNayFkcCAQC6ejkMwJV/Vfy3yJSQivf7Tk3EBSuU0Y91VzP7K1rLpnjn1qfVxcb2YojbYbMhu6+frL8xwuMbPo11o9h9fnOwnKgoMa2tU8Kc061k7hz6MtWl7wsxSIxz8Yw6E826NmGcVaXDNCw10Qjq+tjdczFS329d21/ew0rDskdG0GXVTbAW+Fa3+djycHWHtDvv/a6LD3U/3Is/3nLCO2w4gqreM/b0a63B2CdTQLTY4rVxY9+0Q+aN+/6ljmjYMBU/16V3Wf3MedU64fgnT+AVltfmp7LrJNtQZ3/DK/ZwV7DzSXt+EWu0tFf/FkRO7BdR6S+TxBZuNsmDHWLAxWpqh/+MBAIBLZDRKMtLZ0g+ihwObDjlsR/y8qhTET2i0NFT4//3jVesxoIBALbNwnJvHWOGcNcwA8zkIFsZwS/AI4CngJQ1f+IyKe25ICBQCCQU3RyG0ES3wVeF5G3SAo2p6oZnX+zVg2p6mKRZqNeiswtgUAgsB2RIxnKgF8D/yRyImuVMS7bgWCxiOwHqIgUEoWXaAtXjVaTKBBq+jVvdlcnONWQvyxy68+8wXrDdJ9hHbIau/rXsXpXmzVq4o9twK/GgdbxC2DOyTZAXaLEd14bf+67Rrb6vH2MTFMo+MbdZ9s18yrrEFb2g4/c+jX7WsPkmonWADz8ed8AubmnvQb5NU6XszbpTkHjh7ONbHixbyzOq7XGzopPWee9svv8zK7Tr7X5nfo5CwPmf85m0wMY/IbNPNf1ZbsKoeazvvdi6Uz7pqvp73esXv+2udDz66yxuGqov+Bi9UU28N7mUnv88df7r5geK2wfrtjXd/RrE3JnRtCgqluU5CvboHMXECVEHkqU9WYSzRMkBwKBwPZJ06qhdFsbhqDYCl4SkfNFZLCI9GnasqmYrUPZKqIQ1IFAIPCJQhLRlqlMJ+CU+N/vJsnabvmoiIwGLgZGJddR1eOybmIgEAgE2g1VHb2ldbO1ETwJ/A54mlYaIdoaLYDavs2nYWt2tKcx9mVfF9tYO9jIRv7K6sirDit36y88ySoDRz5iPb9WTvaDe439xptGtvZs68wDMPt3uxlZ/1fs8fv/x1dQbtjZ6p09e4L0LHLr93h3gZENW2OdnOac6ttDyn6z0sjyJ9k2dVbWn27vS169f62LF1jZpqG27MwLrY0IQNZbfXq3tfZR6/+Cb/ta9nn7DhjyX2tPSGVPKllkE9as2tkPZri53MZL21xq25oo8A/WMMJGoyv/3mojm3epnwRx5HU28F7Xcv8ZaguEzH4EnUExJCJneHJVfSBT3WwHglpV/WWrWhUIBALbA9l4FncOY/GeSf/vRpSg5l2gzQaCW0XkB8DfaL4+1S5rCQQCge2JBJn1IJ1gIFDVi5P/FpFewIPZ1M12INgZOB04lI8vicZ/BwKBwHZLW4eYEJFy4F5gN+AqVXWjh8YJ6PcA6oG3ga+oql2nnJpqotwxGcl2IDgRGKOqfubrbYgKJFq0esgr9tqsPW4nt/64B20CERlo9dYFm3x/ufKbbUJwL+DWuN9bnSeADLfpSvv/xQaCA1hbbtfxV+5vfQ6K5vr2iJH32HXwS4+wCwh0vZ8YqeJsm6i+5zL7aTT+Ot8PYd1nrINAxQGd4NMpSzaMsuqAomW+iqByD2tnGfdHe10XHl3i1m+0MesoXG/76oZ9bIBDgKIKe1+0yvbVysn+2v78zVbec4l/r/JemWZk5XOczDQNvn/M4tOtHwA1NhDdmD9YPxiA2kNsZhrv/NuMtncoW0Pki3VChnJ/AE6L//8QcB5wZ6rCSXkJIHIN2AF4NJsGZTsQ/AcoBSqzLB8IBALbB23sUKaqlUCliByTodxzTf+Pk81kymqWPLNoABaq6pJs2pTtQDAQmCEiU2huIwjLRwOBwHaNJATJkJhG2jHonIh0IVLNfz1duZZ5CVpDtgPBD7b0AIFAIJDTZD8jOEBEzk2S3q2qd7dBC+4AXlHVf6crJCKfA34CDCBe9Qqoqvr6yCSy9Sze4pEmEAgEcpmsjMURr6qqG4FBRC4Evhz/ebSq2kxEfr0fAP2Br2RR/CbgWFVtdRy4tAOBiLyqqgeISBXNx8SsR5q2RvOhroWfzMpJ1lg6/Nb33PoybqSRzTvFOoSNud+/T2v3tkaxoS9bY/XSI/q69TdOtkbF8sts1jSIzrUlYx+yRrH6Yt8ol9hocwgVzbM7lSLfyWnwAx8a2fSfTjCyEQ2+812XTbatfd53utyubvUOp2a4va91fX1j65gn7DqKZQfZx6N0jm/UTHSxqoX8Ortgoeczfjq7RVfYYHIFNdZYP/Y2f2FCw7ihtk2F/rnOv8EGPhx/yxxbsLtjAQfyrT8ZDSus+XH9UWPd+lUj7LUa8J7/DLQJbWAjUNXbgdtbc1gROY8o/P9hqpqNNXzFlgwCkHlGUASgqsVbsvNAIBDIebKJNdS65aODgKlACZAQkUuBHVR1g4g8B5wXzxjuAhYCb8QpAJ5Q1R+m2fVUEfkjUSSIZFvuE5nalGkgyJ21foFAINAetP2qoeWkWAGkqkcn/b+1OeVLiHwHjmzRsq0eCAaISMr41qr686yaFwgEAjlKNjaCzhBrSFXP3tK6mQaCfKAnW3ieIvJp4NZ4P79V1RtTlPs88Biwp6pOTbfPwg0Jhr/Y3Plk/ok2WUjtoTu79Ssn2VOuG2j1i7ouRdC6rjZo3bIDrY2iYbijCAWK37FtnXuJ7/xXvNDKVu1qA4GlCiS2fswkIxv6UpWRzbjKj1I74jmro97hBuvks+RzVr8MMMQJ/LdxsB+gLlsePPAkIzv9349s1T5T0esDe19rU8TMW3qwva+DX7N2g/mnGREAxe/bxCqVh1kdfflHvj2nrq99Uy3f2/b10S9aJzOA2v62X+Vt9t9+nj0gMdQmfFo12b/XG/a2z8bAO+25Fm7w9TGjHrPOmjO/muLGPOOLA83JNBBUZNBJpURE8omMI0cQJbOZIiJPqepHLcoVE3nZ2ZCCgUAg0NHkToayLSZThrKtmfHsBcxR1XlxaIpHgOOdctcRLXvyP6EDgUCgAxH9ODlNqi3XB4JMM4LDtmLfQ4HkdZFLgGZBw0VkMjBcVZ8RkctS7UhEzgfOB+jadetUC4FAINAqcmhGEIet2JEoDDUA2Wh10g4EqmqzVLeiTd4u//ejSB7wC+CsTDuKvfPuBug6epjOPbu5PnHi1VaZrhusLhyg2whrO6gZYpuqo2xwOIBGZ723x8C/+kk98uqt3rM2RZJwL/Rtt7W2xzV089s08ImZdpcjBhpZ+c1L3frTr7Fl2WT9KKTR1+XO/ZJdR99QunXrvdvLHuDR4OTr6TM9RWKah94wsvk37mdko//gx20sePF1e6zP7GlkFWf4yVoS3e11Hf2AtQfMuN23ne1wg13Hrz38Pjz9J8ONrGCFtXGMv8P3j+m1wPrt5Dk+B8Xv+b48Kw+1C24m3O6HQZvvSltHrhiLReQuoAdwCPBb4PNEUUszkm3y+i1hCZDcY4YByXe2GNgJeFlEFgD7AE+JiPWMCQQCgY5Cs9w6nv1U9QxgrapeC+xL83dwSlq7TrU1TAHGx/mOlwIn8XFyZVR1PfA/U7+IvAxclmnVUCAQCGxLskle30kGgqbllNUiMgRYDWSVx7jdZgSq2gBcBLwATAceVdUPReSHIhKilgYCgdwgd2YEz4hIKfBTohSVC4gW6WSkPWcETfG0n2sh+36Ksge3Z1sCgUBgS8gVG4GqXhf/908i8gzQLda8ZKRdB4L2IH+T0OfN5oap2ReNMuVKZ/t3rt80G4ht7T72MqzZxY+n122dnSPmzbXdoNefUgS9G2+D3q3a2Q9Q12uhNQDm19jj1/T3b+P6Q6yj2srJdhJYvLDUrZ+/yl7Dsp9aZyId5DvzzLjAXsPCSqetvv2zwxl57zwjqx9nHQoB1pxnDcPiJLmbf4qVAfD5vYxo8D/tvSqo9qv3nWKv68xzbYiwQS/5SoDpV1mHsNEP+8/QhPPft8KdHKfIfD9oXU1/66i37hSbDW/tjr4+RottMMD+L7bj6vPcWjW0HzCK+N0uIqhqmyWvDwQCgU8k2dgIWhN0rr0QkQeBscA0oOkzRIEwEAQCgcBWkTszgj2Iopi2ujXtuXw0EAgEcp4mG0G6rZMMBB8A1tEnC3JuRtClqoEBr65qJqseYHXUdSW++aZif+slNPZ3NUZWuGKlW3/6pVaf37XSjqf9HFsAgM6zTjYNRX3cst3/bpOQrD/BZnFZfoDfC/Nqbbsm3GR1/ItPG+fWb+htbRQNlfa6bN59lFt/zJ9s/a7TnAQmB7vVO5wlJ9lgfHX+raJ4gZWN+5W1Mcy+1F/N13Oh7a9Vzgrw6mG+jqLbcnuvu66yOvoNo9zq9J5i9fard/TLDlluz2H5Ptbjf+Bb/jO47HBrPJl46QdG1r/Mf4ZmfNUG3pv19RFuWVLGK2gFnedFn4l+wEdxovtW5ZbPuYEgEAgEtiVZrRrqHAPFNVtaMQwEgUAgkI42nhGISDlwL7AbcJWq/ixD+duAs1W1Z7pyW5NbPtgIAoFAIA3tYCNYQxR6P+0AABCH3PHXd9uy+4jIFBHZKCKbRaRRRDZkUzfnZgS1A/KZcXHz6zLgdXsXavv4+snBt0wxMim0+tHKk21SF4DyO9da4UIbHGvGjX5C94k/ssHwumz027roW5NtWSeW3tg/WhsHwNxz7DifWLvOyIa96JwTULXA6n0LRlrFdVWpv16899urjGzmlWVu2c5IrV1aT/1gP2hc37/UGdn0H9uEPaVT/Hvdo9Lq/pefYPdZ/KafEH7w/daeVLO/7YM93l3g1q927DzdX53hlpUetg1Vo63xJK/R98WRLva8GG6DPC4+0o807NUf+Jb/JrZWmi2g7VNVVgKVcaTQlMQ5XX5KFJrnxCx2/SuiUD6PEa0gOgPws161IMwIAoFAIA3Z5COIbQQHiMjUpO38rTz0RcBTqlqRbQVVnQPkq2qjqt5Llksxcm5GEAgEAtuU7FU/r6rqqW1xyDho3Bdo3Zq6ahEpBKaJyE1ABeAEU7eEGUEgEAikIRsbQcZVRSIXisi0ePOTnTRnMjAOmBOH6e8hIs7a62acTvROvwjYRBSC+v+yOFaYEQQCgUBa2sBGoKq3E+Vwz+6Qqs+S5BwmIov3vbcAAB0jSURBVBtV1Xf44X/2hOtV9TSitL/XZnssyMGBIL9WKJnevNmJAnsXes+0gakANh9mjcDLDrDG4oYi/86u3cka8Ivn9TaysovecuszzH4MbC71nYSk0RoWRzxnFwEsPNY3qg15xu530zHWAL1ykm/sHfG8DdBXW2azlq3czTeAFm601tYJN862BZ9wq3c44261H2Ab9vcdwmadbZ2cesyw13Xdzn6Gttp+9lHsMtcaZbuvSfHG8aIKOLdl+k2+k1bJe/YZWPjFCW7Z3m/ZbGT5jv23ZL5vWAeb+WzVvnaf1eXOToFBf7Nlu61NdaytJ5sv/lbtT2QQMBUoARIicilRaIgNIvIccJ6q+unZUqCqjSLSX0QK4xzxrSLnBoJAIBDYpiRw08Y2o3WrhpYTZWz0fjs6hTytD0HMAuA1EXmKSDXUVPfnmSqGgSAQCATSkEOexcviLY8oFXDWhIEgEAgE0pEj0UfjPMWISHH0p27Mtm7ODQRd1tQx9KFZzWSNa20Snrk37OHWH/eI1bGP+dkSI5t9tZ8tpeyu5Ua24lCbrGTR1Xu79Xsusj2maJGvY68eYstu7mP1xo1W5QrApsF2Udjgf1nnsfoi33GxYL5dvpxXZfvW2PVj3frSYOfTcy/OHYey2d+05zXmT/6zNeEbM41s4bdtHyy7x3f+W72L/YDr9451/ku895Fbf8Ydtr8VbLA2ioIV/htr03Arn3jFAresDra2n/retl8WzrDPFcDKXa3Ns2aoPX7hYr9jL9/fBq3r/UGKh+Dvvrg1iCqSIbKzdIKRQER2Ah4E+sR/rwLOUNUPM9XNuYEgEAgEtiU5lLz+buCbqvoSgIgcDPwGsOnzWhAGgkAgEEhHjqiGgKKmQQBAVV8WkawcysJAEAgEAmnIIWPxPBH5HpF6COA0YH42FXNvICgsIDG8+Vr2BZdYvXNjX9+PYOa5dhVW2SVW79vfzz1PotQOsN1XOzrL2f7xV+zRzcjqrBsCACNesPtYfLizhtrGdgOgaoztnUOetTrqxkLfRrDyGKsjT3Sx9owBr61260uVzbQ+8vuOuvLfl7j1O5oCJxjg0oP9xRiDC61NadSf7XWp72P9DQA8M0vVSHtfBg2zSe7BtweM/6m1WzSus3YHgJl37WZkC8/17TndnNu9yVkMObBklFu/xrF9jX3E9suFx/gfs17CpSrfPaJtyJ3ENOcQOZI9QeRF8gpwdjYVc28gCAQCgW1IVjaCTL9vA1R1LVF461YTBoJAIBDIQEbV0LZphn9skVtU9VIReRo7d1Gi/Ae/VtU3U+0jDASBQCCQDlU/hEezMtumKSlosgmkSnTTD7gH8NfEEwaCQCAQSEtWxuJt0xQXVX0n/jdlqkoRSRt/KOcGgvoe+azcvXmQta5Ogq0xD1knM4C6YTZrUvVx1vGn9OkP3PozbrOBuAoqrKGuy0Y/kFvNOHs/yu7y79H8461he+AUq4ys7+F3Qy/D1vQrBhhZz9l+/UZr12bU45VGVuMYNQE0z17rwqF93bKdkZE3vWtkq06zRlWANTtaI/CaXayTk3bx3yhl579uZAtusMu/V+zhP7JdHQNuYoQNEJg3wL/++Ztsfx3yqjX2Ayz8jD3XUc/asrPOsYHsALovtP3Nc9gada3NJgggO9qkWzJvsVt2rittHdIIkilgfyewEYjIeODHRF/+/3t6VXWMqj6drm7IRxAIBALp0Cy3jude4E6gATgEeICP1UZpCQNBIBAIpCGrxDQd3ciI7qr6D0BUdaGqXgMcmk3FnFMNBQKBwDYlK2Nxp5gS1IpIHjBbRC4ClgJWF+yQcwNBfr1SvKx5co9FJ9lkH/VTfWeUgk22bF69o3ef7CcDyl9p9Z6NPWz9sde979bPG2AV9wtPGeqWHfsH6ym2bpc+RrZmJ7c643+50Mi01Nod5pzh6417OrrcxcfaflU92H8IGouto11+lQ1O1lmZe621B9T38RPLeE5Ovf9r9e6FG/1rteJSaw+oH2oTs5TfVOXWX35QPyPbUGZtNKV/dxIDAf3etX0gf8oMt2z9absY2Yq9rN1g4q1r3PrTL7btyl9pg0HOuHl3t/6E31ij4OrPpXgI7vPFrSGrxDStGAdEpJxIjbMbcJWquqt9RESAHxHlLm4E7lTVX6bZ9aVADyJfguuIZgNnZtOmnBsIAoFAYFuSjUNZK0NMrCF6WZ+QodxZRHmHy1U1ISJpv+5Vtcm6vpEsPYqbCANBIBAIpCMb1VArpgSqWglUisgxGYp+FThFVRNJ9QxxRrJ0xzsuU5vCQBAIBAJpaEXQuQNE5Nwk8d2qevdWHHos8CURORFYCVyiqp5ub19gMfAw8BZbYLtu14FARD4N3ArkA79V1Rtb/P5N4Dyi5U4rgXNU1Sq2k+usr6bwuanNZKXD9zHl5p/o37nS6fYard3Jzvu6rvYXVI2/3iYGqdvD2hOkm58oo/Jwaw8YeoNdQw4w58dWb1w6255XQw//XFceaSNxbRjrBI2b6s97S6Y7Ot4FS+1xTrI6Y4Cu6+w1XHqUr2PvjIy739poKo6wuniAOseVoniJPdeij9yPOqZfbtf8T7jFBmKb/o1eRgZQfqH1eVh0ubVxFM8f4tbv84y1Byy4wveZGPd76zPQxVnHv+LY0W79Ec9a25H2tLajCXf5NobGXrbsmp3dom1D9mGoX1XVU9vwyF2BWlXdQ0Q+R+QdfKBTbhBwBHAycArwLPBwNglpmmi35aMikg/cDnyGyMHhZBFp6eL8HrCHqu4CPA7c1F7tCQQCgS1BGjXjlmmgEJELRWRavPmjsWUJ8Kf4/38G3C8uVW1U1b+q6pnAPsAc4GURuTjL47SrH8FewBxVnaeqm4FHgOOTC6jqS6ra9HnxJuAEsw0EAoEOpA0cylT1dlWdFG/Lsjzyk3zsB3AQMCtVQRHpGs8afg9cCPySKBx1VrSnamgokd6qiSWAn8g34lzgee8HETkfOB+gG34890AgEGgPhLZNTCMig4CpQAmQEJFLgR1UdYOIPAecFw8WNwJ/EJFvEK0EOi/F/u4HdiJ6f16rqn58nDS050DgGSzcyyUipwF7EI16tlJkcLkboET6dArPjUAg8Amh7VcNLSeF9kNVj076/zog08oigNOBTUAZcEnkfgBE72BVVeu40YL2HAiWEK2BbWIYYKZEInI4cBVwkKpaD5oW1I3uwZwfTmom86qV3e4Hcstfaw1dAx61EbvmXu5HbJ1/qZWro2DbfJqTcgqYcJt1nJl5n+84M/7Xm2y7vmBnRIVrfQ1ftbU/0v9da6hbW+YHyFs5yToZjXnclu1Z4RuA86vtsfq96RjRy93qHY4XoLDXPP9cu62sNbKlB1nnve5LU8xoG+x304r9rAV64hXT3erzr7SG3QHOvc77yM9cuPxMq34e+m97TgCaZ9u68AxrGB75gH+sRafaskVO0YrDfMN86VybuW/8A37mtXmutHV09sQ0qt4bqHW0p41gCjBeREaLSCFwEtBsvauITAZ+DRyXao1sIBAIdCSimnnr6EZuJe02I1DVhjjexQtEy0fvUdUPReSHwFRVfQr4KdATeCyezizKxvkhEAgEthkJMn/x57jCul39CFT1OeC5FrLvJ/3/8PY8fiAQCGwtTV/9mcrkMjnnWdx1fjXjzpzWTLbiEutQtuCzvkMXUmxEY++0doNRV/vpPfN7W4ceHW2dxCoO9O0zs86wl3z82W+7ZeuOsQlzBr5tO1zF/n4nHH2jDXw36y6rkO//d19DuG6cE3TuKKu3Hv43PwnQguPstRrzsKMBPMut3uG4uvAU89XyO63toNd8+xm5epLvEFawyR5r4JvWnrTknIlu/TF3WW149aThRibdnWxDwKB/Wue5DTvaAIcAvT6wNrX+/7H9evqP/eXyw5+wtgtZZ4PpFVb5CY8aelg7VX2pf15tQkKjLR1hIAgEAoHtl7aOPtoZCQNBIBAIpCOr5aO5TRgIAoFAIA1ZhaHuBDmLt4YwEAQCgUA62tihrDOScwNBoncR1Yft1UzWdb29CUP+4UculAprFFv+xQlG1u/OCre+Dh9sZI09bNayqt19Z5zhf7SXXPbc0S276HP2M2PMg9bQNvov/irmOT/c1cgmXOI4JDlGUYCVP7DXZfNwaxSd38M3gHZzbsHss7PKnNcpyK+z17rXh75RMm+JNYKrkyGsobt/rTXf9uFZXy80sh4p4knOvMw6aRXPt8fqkaL9LLbhbyqutA5xAOvG2Sx7JYts+wudDHcA3Z+dYmQV5+9pZKn08pUH2D444mn7DLYVklAko7G43Q6/Tci5gSAQCAS2KbmTs3iLCQNBIBAIpEMJDmWBQCDwSSY4lHVC8usa6TmvuaNNY0/rPFbft8itn1dsdaQDX1trZDMfSJGd6Q6rN158uM2YVP49m8kLoHHxEiPLHzXCLdttvnV+m/d/9vj9p/gOYWW/snrfud+0QfMKbCIsAMpvtfVnXmRtJKlWTBQvtj+ULHAemP39+h3NkkNsXxn1pB/cbOZ3xhvZ2Meto2LPRf6xNg63/bX8Wtsv559mrz9A2T3WyUu7Wr1546Debn3pbe0Z5T/w7WyJihVGtv4Ea48STWG7+ol1lBzyqu3XRQuskxnA+vHWJtV1TYpO3BYkNPOyoDAQBAKBwHZMgsxZgHN7HGjX6KOBQCCQ82QVfbR1iWnKReQNEakTkcvSlDtMRN6N01u+KiI2OXobEQaCQCAQSEfTqqFMW/asAS4Bfpah3J3Aqao6CXgIuHrLTiAzuacaqm8gb3lz3WVeV2sjqJngr1fvstome/n/9s49yqr6uuOfLwMDCkh4CFoFRxBEQxQTHzVRRNEsXzErq6baGhMraGttu9TG1WbZqEl0NYmraR5iG5plXCspVuuD0MTEGJcRY8Tgs4ICPhhxsApY5KHgMDO7f5wz6YX9m7lnmMvce2f2Z62z1r2/c37n7N895959f3v/9t5bD/d204l3p22Cr/2Rv9bUH3lbbstnfCI6gG1N3sY7ekVaHw/y9Tc44oZX/DlPSv9R2Hysv9aU2xO+i9Z0EZ+XrvdJy6b90H9+LXPS683XnePXew9f6dfG1yqTv7vKtb1+uY+tAJh2s4/PUOK5bDv0gGT/w27x/V+6eZprG/t0sjuvfN4XEdr/mUSCwlld/GANS8SsLPS+L4D2aT6OYOgW318d6ee6Y5x/3oav9cWlBm3tojDOOO+72XpIWlYeSzf3iI4CocU9UAR57ZX1kspVHzOycpYAo0gU9qoU9acIgiAI+pLiPoKTJM0taV2Ql9ndU+YBD0jaDmwBfJrlChGKIAiCoBuKLB/NZwS/MbOLKnjpq4GzzexJSdcC36KLAva9JXwEQRAE3VEB/4CkK3On73OS0oUadj1+f+BoM3syb7oL+HjvBtI1MSMIgiDojvYCtSo7ut9vZvOB+T246iZglKRpZrYaOANIJAqrDHWnCFrHDaX50sm7tDX9cI07bs356f4jVnun2kGP+sCVhtd90AyAzZni2lbN8wEuU69+0rUBvH3lCa5t/D3p+7vzKJ9IjAY/iWs5LW3APPyGla7tpa97B+Tgd9OPwRFfXu3amv/c9z/wce/oA1gzwQc0HbA0EfjzqWT3qvPqVd4xPMLHAwLpZITa4BcRrJu9b7L/9gv8tQ5f4CuUvfWJdNWuqd9p9tf6rH9+xviidQDsGOOrfr3t474AGJqIqWtMOIvHLk//OA7/mXcWvznLB7R1DElX+bMO33/Uy9uSx1aGIrmGip9N0gHAU2SO4A5JVwFHmtkWSQ8A88zsTUmXAfdK6iBTDJfumfzlqTtFEARB0KdUOA21mb0FHNzFvrNLXt8P3F/4xL0gFEEQBEF3dBhlf+gjxUQQBEE/xjqyrdtjQhH0KWqHIbuZA1unevts06K03XzthYkglW+96Jo6GtOBT5Pv9Tbu5nO83bfhsIR9HxiyzT8wGjsmeaza/bFrL/HJzfZ/Kv0Qrv1Ln2BupI+RwrpYO9Z6pE+G15aI2xn65uZk/+nf9YnENp6cDqiqRYYmcq4Neyf9g7DpKO8nGr3CP4NjXvSfCUDrG/4m/G/inH/wX28k+7OfD+rbcoy3pY9Ynn6uh3p3RpfPxYRfeBk+OMwHcA5qTX9WNtif+MDv+WI1qeR0APsmghK3H+h9HBWjvYAiKFe4psapO0UQBEHQp0SpyiAIggGOUdFVQ7VIKIIgCILuiFKVtcegNth3/a72uk2HJ4rN3JswhgNceIhrWv09b4scvDltc5w6/3XX1rjFn7NjbbowzaZ5PmHXoJ3pBHnvj/c25gN+530cr16QLtw94XFvi337NJ/JbvrVLyf7r/yGX9s+1pty2Xqkj80AWP8x/xk2LU4XG6lF3pvkv9wjukj7NeYZXxhm5bV+HfyQdennqukflrq2wZN84sK2Lp6r17/m09A0LfQ+gsZ3u/j8n/Z+snXX+JgXSPsDms/xdvvGTWk/3bgXvJ9k6Cc+4toadqT7H/Jj/x200b6IU8VobwdL+3Z+T5mAslqn7hRBEARBn9LzNNN1RyiCIAiC7og4giAIgoGNWQcWcQRBEAQDmI6II6g51G40bt31poxa9Lw77vWrjkn2H5mo8DTh2791bS/PTzvKtpzgq3a9P90nXWs74Yhk/4Me9U6n9sa0U6xxi3csdgzxDuBJD6Qfwo0f8ecd/4h3LK/7sw8n+0+/1TtA35zjHcM2KO0AnfID79jccFrZDLw1w1j/WPHWuelqbu98eJxrm3KHd+yvm5W+1ltXeWfvQb/yEW1rb0zXJulIfJOHPeEXTGj/tGP/vbM+5tosvQaBjTN85bUP+fyGTPhV2rP+8uX+GZj4sP9ejF2Rfq6bL/GLM6yrwjFdJNnrERWuUFaL1J0iCIIg6FOs//sIql6YRtKZklZJekXS31dbniAIglKso6P8VueKoKozAkkNZMUazgBagGWSFpuZX9QcBEFQDdoLmIYijqBXHA+8YmavAUj6D+DTQJeKoL1RbJm0q9gNJ3kb96SfJjKGAetO9wne3rjeV4Cbfl1ahLVX+ERuDRt8ME3LqWmj5fhnvC105EPpaw050Qd0rT/GX6tteLI7gxLm7NHLfYK4Haeki51om7dxN271/3xGv/h+sn/bAf68+2wsE5hTQ4y95wXXtmHW9OSxqQR1rfv5r9ehd6xN9reRPnHhhhO93+HQuzcm+288PmH7H+Kvv2XmhGT/zZO9n6fNx2kCMGyDb/sgkTexdWI6meJhN3nD/crveJ/a+F+nrz/xIV+EpvlTPulexTCjbIWyOs8xUW3T0EFAaSrDlrwtCIKgJrAOK7/1rELZRZL+O99+K+novSd9Mao9I0j9bXYfqaTLgcsBhowYvbdlCoIg+H+sQM3icstLd2UNcIqZbZJ0FrAASC9T7COqrQhagNL1mAcDbs2ZmS0g+7DYd/zE+p6DBUFQT+xc2f40g8oYT95jK4BP5JXAzErXqy+li7KVfYmq6e2WNBhYDcwB1gHLgD81sxXd9NkAdGadGgekjab1SX8bD/S/MfW38UD/G1PpeA4xM5/psSCSJlD8h/p4YG7J+wX5n9juzv9FYLqZzdtDEStCVRUBgKSzgW8DDcDtZnZzD/o+ZWbpMkZ1SH8bD/S/MfW38UD/G1O9jEfSqcBtwElm5qM3+5Bqm4YwsweAB6otRxAEwd5C0pXAZfnbs8lmLT8Azqq2EoDqrxoKgiDo95jZfDObaWYzyf6A3wdcbGarqywaUAMzgl7Srf2tDulv44H+N6b+Nh7of2Oq9fFcD4wFbpME0FZtU1bVfQRBEARBdQnTUBAEwQAnFEEQBMEApy4VQb1nLC0nv6RrJL2Yh6A/LMknYK8hit4PSedLMkn1sLSv7Jgk/XF+n1ZIWtjXMvaEAs/cJEmPSHo2f+7OroacvUHS7ZLWS1pebVnqDjOrq40s3uBVYDLQCDwPHFltuSopP3AqsG/++grgrmrL3dv7AYwElpBFUh5bbbkrcI+mAs8Co/P346stdy/HswC4In99JNBcbbn3YJyzgI8Cy6stS71t9Tgj+H3GUjNrBTozltYLZeU3s0fMrDOlZ02EoHdD0fvxNeCbgE9pWnsUGdNlwHwz2wRgZuv7WMaeUGQ8BuyXvx5FItVLrWNmS4B02uGgW+pREdR7xtKeyj8X+Plelah3lB2PpGOAiWb2074UrBcUuUfTgGmSHpe0VNKZfSZdzykynhuBz0lqIQvw/Ou+ES2oBeoxjqBQxtIaprD8kj4HHAucslcl6h3djkfSIOCfgUv6SqAKUOQeDSYzD80mm7E9JmmGmb27l2XbE4qM50+AO8zsnySdCPwoH099V1wJClGPM4JCGUtrmELySzoduA44z8w+6CPZ9oRy4xkJzAB+LakZ+ENgcY07jIvcoxbgJ2a208zWAKvIFEMtUmQ8c4G7AczsCWAYWRqEYABQj4pgGTBV0qGSGoELgcVVlqknlJU/N6V8n0wJ1LLtGcqMx8w2m9k4M2sysyYyn8d5ZvZUdcQtRJFnbBGZUx9J48hMRa/1qZTFKTKetWRZgJF0BJkiSNQiC/ojdacIzKwN+CvgQeAl4G7rJm11rdGV/JK+Kum8/LBbgBHAf0p6TlLNKrqC46krCo7pQeAdSS8CjwDXWg0kD0tRcDx/C1wm6XngTuASy5fi1AuS7gSeAA6X1CJpbrk+QUakmAiCIBjg1N2MIAiCIKgsoQiCIAgGOKEIgiAIBjihCIIgCAY4oQiCIAgGOKEIgh4hqT1f0tq5NUmaLaki6SMk7SPpUUkNBY4dLOlnkjZKmrHbvlskrcwzad4v6UO77X9aUqOkbV2c+w5J5/diHI2Slkiqx+j9YIARiiDoKdstr72ab80VPv+lwH1m1l7g2H8hi+j9NHCXpNLkfA8BM8zsKGA18KXOHZKagHV5AraKI6khP/fDwAV74xpBUElCEQQVRdKNkr5Y8n55Pms4Lv93PkzS8DyH/4zEKS4CflLS/1pJy/K+XylpvwHYbGbXmNnjwDzgTkmjAMzsl3kgFfgMrmcBvyg5182Sns+Tx00oOe50SY9JWi3p3PzYprztmXz7eN4+O8/nvxB4Ie+/KB9PENQ0MW0Neso+kp7LX68xs88U6WRmy/II6ZuAfYAfm9kuBUTy9AeTO2cZkj5Jlr/neLLEaYslzTKzJWb2ld3O/wRwcheXvxS4q+T9mcDV+evhwFIzu07SN8nSS9+U72siS/g3BXhE0mHAeuAMM9shaSpZFG5n3qTjyWYha/L3y4Hjyn44QVBlQhEEPWW7mc3cw75fJct7swP4m8T+cUBp9s5P5tuz+fsRZIphSdELSroOaAP+PX/fCBxsZp15gVqBTv/G08AZJd3vzrNvvizpNWA6sAa4VdJMoJ0sx1AnvytRAphZu6RWSSPNbGtRmYOgrwlFEFSaNnY1OQ4reT2G7Md8SN7+3m59t+92vIB/NLPv74kgkr4AnAvMKcmbczLwm5LDdpbsa2fX78Tu+VeMbCbxNnA02ThLC+3sPh6AodRHMZ5gABM+gqDSNJOVC0TSR4FDS/YtAL5M9u/8G7t3zKt9NUjqVAYPApdKGpGf7yBJ44sIkReK+TuyTKfvl+w6k+KFfj4raZCkKWRlHleRVe/6n3ymcDFZGciuZBgLbDCznQWvFwRVIRRBUGnuBcbkfoQryFbsIOnzQJuZLQS+Dhwn6bRE/18CJ0Hm8AUWAk9IegG4h6y+QRFuzY99KF/m+q95+2zg0YLnWJUf+3PgL8xsB3Ab8AVJS8nMQqlZQCenklX7CoKaJrKPBjVFXovhGjO7eC+c+2Dg38zsrEqfu4vr3Qd8ycxW9cX1gmBPiRlBUFOY2bNkK3TKBpTtwblb+lAJNAKLQgkE9UDMCIIgCAY4MSMIgiAY4IQiCIIgGOCEIgiCIBjghCIIgiAY4IQiCIIgGOD8H/T97yp/whAxAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -346,7 +346,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x1d388235a58>"
+       "<matplotlib.lines.Line2D at 0x211306cd358>"
       ]
      },
      "execution_count": 9,
@@ -355,7 +355,7 @@
     },
     {
      "data": {
-      "image/png": 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P5dgN3N3KYzmS3An8O7AmyXSSjeOuaYguBK4DLu7+jj2Z5KPjLmoIlgMPJXmK2UnIjqr6xqgG885YSWqcM3pJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4/4f+wmXsycPLhIAAAAASUVORK5CYII=\n",
+      "image/png": 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uaChJ3pZkDngDcEeSr0y6plF1X5gfeTTHAeCWVh7NkeQm4GvAliRzSXZOuqaBvBF4J3Bh9//WA0neOumiBnAmcFeSb7IwANlXVV8Y18m8M1aSGueIXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4/wUR1J3ER6809AAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -397,7 +397,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -434,7 +434,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -514,7 +514,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.8"
   },
   "toc": {
    "base_numbering": 1,

From 05c71b165193723632f1a021e42364704cbcb5f8 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 13:47:21 +0100
Subject: [PATCH 531/719] Remove data_buffer ArrayParameter form Keysight
 344xxA submodules driver

---
 .../private/Keysight_344xxA_submodules.py     | 116 +-----------------
 1 file changed, 1 insertion(+), 115 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
index 6fc76c6213a..35d67aa24eb 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
@@ -1,111 +1,15 @@
 # QCoDeS driver for the Keysight 344xxA Digital Multimeter
 import textwrap
-from functools import partial
 import numpy as np
 import logging
 from typing import Tuple
 
-from qcodes.instrument.parameter import ArrayParameter
 import qcodes.utils.validators as vals
-from qcodes.utils.deprecate import deprecate
 from qcodes import VisaInstrument
-from pyvisa import VisaIOError
 
 log = logging.getLogger(__name__)
 
 
-class ArrayMeasurement(ArrayParameter):
-    """
-    Class to return several values. Really represents a measurement routine.
-    """
-
-    def __init__(self, name, shape=(1,), *args, **kwargs):
-
-        super().__init__(name, shape=shape, *args, **kwargs)
-
-        self.label = ''
-        self.unit = ''
-        self.properly_prepared = False
-
-    @deprecate(reason='the use of this ArrayParameter is limiting as it '
-                      'represents only a single way of performing '
-                      'measurements and performs preparations that are not '
-                      'generally applicable',
-               alternative='driver methods like `fetch` and `read` (together '
-                           'with other methods and parameters) and custom '
-                           'functions and parameters implemented on top of '
-                           'them')
-    def prepare(self):
-        """
-        Prepare the measurement, create the setpoints.
-
-        There is some randomness in the measurement times.
-        """
-
-        inst = self._instrument
-
-        N = inst.sample_count()
-
-        # ensure correct instrument settings
-        inst.aperture_mode('OFF')  # aperture mode seems slower ON than OFF
-        inst.trigger_count(1)
-        inst.trigger_delay(0)
-        inst.sample_source('TIM')
-        inst.autorange('OFF')
-        if inst.has_DIG:
-            inst.sample_count_pretrigger(0)
-
-        if inst.trigger_source() is None:
-            raise ValueError('Trigger source unspecified! Please set '
-                             "trigger_source to 'INT' or 'EXT'.")
-
-        # Final step
-        self.time_per_point = inst.sample_timer_minimum()
-        inst.sample_timer(self.time_per_point)
-
-        self.setpoints = (tuple(np.linspace(0, N*self.time_per_point, N)),)
-        self.shape = (N,)
-
-        self.properly_prepared = True
-
-    def get_raw(self):
-
-        if not self.properly_prepared:
-            raise ValueError('ArrayMeasurement not properly_prepared. '
-                             'Please run prepare().')
-
-        N = self._instrument.sample_count()
-        log.debug("Acquiring {} samples.".format(N))
-
-        # Ensure that the measurement doesn't time out
-        # TODO (WilliamHPNielsen): What if we wait really long for a trigger?
-        old_timeout = self._instrument.visa_handle.timeout
-        self._instrument.visa_handle.timeout = N*1000*1.2*self.time_per_point
-        self._instrument.visa_handle.timeout += old_timeout
-
-        # Turn off the display to increase measurement speed
-        self._instrument.display_text('Acquiring {} samples'.format(N))
-
-        self._instrument.init_measurement()
-        try:
-            rawvals = self._instrument.ask('FETCH?')
-        except VisaIOError:
-            rawvals = None
-            log.error('Could not pull data from DMM. Perhaps no trigger?')
-
-        self._instrument.visa_handle.timeout = old_timeout
-
-        # parse the acquired values
-        try:
-            numvals = np.array(list(map(float, rawvals.split(','))))
-        except AttributeError:
-            numvals = None
-
-        self._instrument.display_clear()
-
-        return numvals
-
-
 class _Keysight_344xxA(VisaInstrument):
     """
     Instrument class for Keysight 34460A, 34461A, 34465A and 34470A multimeters.
@@ -436,8 +340,7 @@ def __init__(self, name, address, silent=False,
 
         self.add_parameter('sample_count',
                            label='Sample Count',
-                           set_cmd=partial(self._set_databuffer_setpoints,
-                                           'SAMPle:COUNt {}'),
+                           set_cmd='SAMPle:COUNt {}',
                            get_cmd='SAMPle:COUNt?',
                            vals=vals.MultiType(
                                vals.Numbers(1, _max_sample_count),
@@ -525,12 +428,6 @@ def __init__(self, name, address, silent=False,
             for every measurement (not multiple ranges, just one range up or 
             down per measurement)."""))
 
-        ####################################
-        # The array parameter
-
-        self.add_parameter('data_buffer',
-                           parameter_class=ArrayMeasurement)
-
         ####################################
         # Aperture parameters
 
@@ -664,14 +561,6 @@ def read(self) -> np.array:
         raw_vals: str = self.ask('READ?')
         return _raw_vals_to_array(raw_vals)
 
-    def _set_databuffer_setpoints(self, cmd, value):
-        """
-        set_cmd for all databuffer-setpoint related parameters
-        """
-
-        self.data_buffer.properly_prepared = False
-        self.write(cmd.format(value))
-
     def _set_apt_time(self, value):
         self.write('SENSe:VOLTage:DC:APERture {:f}'.format(value))
 
@@ -681,9 +570,6 @@ def _set_apt_time(self, value):
     def _set_NPLC(self, value):
         self.write('SENSe:VOLTage:DC:NPLC {:f}'.format(value))
 
-        # This will change data_buffer setpoints (timebase)
-        self.data_buffer.properly_prepared = False
-
         # resolution settings change with NPLC
         self.resolution.get()
 

From 72fa3da1905e16d4786733b3d636a6d4d21654b3 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 13:48:31 +0100
Subject: [PATCH 532/719] Improve class docstring for Keysight 344xxA
 submodules driver

... for Keysight 344xxA submodules driver
---
 .../Keysight/private/Keysight_344xxA_submodules.py     | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
index 35d67aa24eb..2fa9425b237 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
@@ -1,4 +1,3 @@
-# QCoDeS driver for the Keysight 344xxA Digital Multimeter
 import textwrap
 import numpy as np
 import logging
@@ -12,11 +11,14 @@
 
 class _Keysight_344xxA(VisaInstrument):
     """
-    Instrument class for Keysight 34460A, 34461A, 34465A and 34470A multimeters.
+    Instrument class for Keysight 34460A, 34461A, 34465A and 34470A
+    multimeters.
 
-    Tested with: 34461A, 34465A.
+    The driver currently only supports using the instrument as a voltmeter
+    for DC measurements.
 
-    The driver currently only supports using the instrument as a voltmeter.
+    This driver makes use of submodules for implementing different
+    subsystems of the instrument.
 
     Attributes:
         model (str): The model number of the instrument

From e955d933cfe41cd85324f03965408aae547d3b4e Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 14:33:39 +0100
Subject: [PATCH 533/719] Extract display submodule from Keysight 344xxA
 submodules driver

---
 .../private/Keysight_344xxA_submodules.py     | 86 +++++++++++--------
 1 file changed, 49 insertions(+), 37 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
index 2fa9425b237..5ecbc91f934 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
@@ -4,11 +4,54 @@
 from typing import Tuple
 
 import qcodes.utils.validators as vals
-from qcodes import VisaInstrument
+from qcodes import VisaInstrument, InstrumentChannel
 
 log = logging.getLogger(__name__)
 
 
+class Display(InstrumentChannel):
+    """Implements interaction with the display of Keysight 344xxA."""
+
+    def __init__(self, parent: '_Keysight_344xxA', name: str, **kwargs):
+        super(Display, self).__init__(parent, name, **kwargs)
+
+        self.add_parameter('enabled',
+                           label='Display enabled',
+                           set_cmd='DISPlay:STATe {}',
+                           get_cmd='DISPlay:STATe?',
+                           val_mapping={True: 1, False: 0},
+                           docstring=textwrap.dedent("""\
+            Disables or enables the front panel display. When disabled, 
+            the display dims, and all annunciators are disabled. However, 
+            the screen remains on.
+
+            Disabling the display improves command execution speed from the 
+            remote interface and provides basic security.
+
+            Displaying text with `display.text` parameter will work even 
+            when the display is disabled."""))
+
+        self.add_parameter('text',
+                           label='Display text',
+                           set_cmd='DISPLAY:TEXT "{}"',
+                           get_cmd='DISPLAY:TEXT?',
+                           get_parser=lambda s: s.strip('"'),
+                           vals=vals.Strings(),
+                           docstring=textwrap.dedent("""\
+            Displays the given text on the screen. Specifying empty string 
+            moves the display back to its normal state. The same can be 
+            achieved by calling `display.clear`."""))
+
+    def clear(self) -> None:
+        """
+        Clear text from display. Depending on the display being
+        enabled/disabled, this either returns to display's normal state or
+        leaves it black, respectively.
+        """
+        self.write('DISPLay:TEXT:CLEar')
+        self.text.get()  # also update the parameter value
+
+
 class _Keysight_344xxA(VisaInstrument):
     """
     Instrument class for Keysight 34460A, 34461A, 34465A and 34470A
@@ -156,33 +199,6 @@ def __init__(self, name, address, silent=False,
                            val_mapping={'ON': 1, 'OFF': 0},
                            vals=vals.Enum('ON', 'OFF'))
 
-        self.add_parameter('display_enabled',
-                           label='Display enabled',
-                           set_cmd='DISPlay:STATe {}',
-                           get_cmd='DISPlay:STATe?',
-                           val_mapping={True: 1, False: 0},
-                           docstring=textwrap.dedent("""\
-            Disables or enables the front panel display. When disabled, 
-            the display dims, and all annunciators are disabled. However, 
-            the screen remains on.
-            
-            Disabling the display improves command execution speed from the 
-            remote interface and provides basic security.
-            
-            Displaying text with `display_text` parameter will work even 
-            when the display is disabled."""))
-
-        self.add_parameter('display_text',
-                           label='Display text',
-                           set_cmd='DISPLAY:TEXT "{}"',
-                           get_cmd='DISPLAY:TEXT?',
-                           get_parser=lambda s: s.strip('"'),
-                           vals=vals.Strings(),
-                           docstring=textwrap.dedent("""\
-            Displays the given text on the screen. Specifying empty string 
-            moves the display back to its normal state. The same can be 
-            achieved by calling `display_clear`."""))
-
         self.add_parameter('autozero',
                            label='Autozero',
                            set_cmd='SENSe:VOLTage:DC:ZERO:AUTO {}',
@@ -475,6 +491,11 @@ def __init__(self, name, address, silent=False,
                 Setting the aperture time automatically enables the aperture 
                 mode."""))
 
+        ####################################
+        # Submodules
+
+        self.add_submodule('display', Display(self, 'display'))
+
         ####################################
         # Connect message
 
@@ -501,15 +522,6 @@ def init_measurement(self) -> None:
     def reset(self) -> None:
         self.write('*RST')
 
-    def display_clear(self) -> None:
-        """
-        Clear text from display. Depending on the display being
-        enabled/disabled, this either returns to display's normal state or
-        leaves it black, respectively.
-        """
-        self.write('DISPLay:TEXT:CLEar')
-        self.display_text.get()  # also update the parameter value
-
     def abort_measurement(self) -> None:
         """
         Abort a measurement in progress, returning the instrument to the

From 535795f548f62a073ef1b330ab3b6a44f935e152 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 14:52:38 +0100
Subject: [PATCH 534/719] Add float get_parser to trigger_level parameter

---
 qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 6fc76c6213a..dc09d410688 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -379,6 +379,7 @@ def __init__(self, name, address, silent=False,
                                unit='V',
                                set_cmd='TRIGger:LEVel {}',
                                get_cmd='TRIGger:LEVel?',
+                               get_parser=float,
                                vals=vals.MultiType(
                                    vals.Numbers(-1000, 1000),
                                    vals.Enum('MIN', 'MAX', 'DEF')),

From 7d9cd38a9d451defd6451bd50ab75d416815e861 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 14:57:27 +0100
Subject: [PATCH 535/719] Extract triggering submodule in Keysight 344xxA
 submodules driver

also, add float get_parser to trigger.level parameter
---
 .../private/Keysight_344xxA_submodules.py     | 256 +++++++++---------
 1 file changed, 131 insertions(+), 125 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
index 5ecbc91f934..eb057f60cc4 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
@@ -9,6 +9,136 @@
 log = logging.getLogger(__name__)
 
 
+class Trigger(InstrumentChannel):
+    """Implements triggering parameters and methods of Keysight 344xxA."""
+
+    def __init__(self, parent: '_Keysight_344xxA', name: str, **kwargs):
+        super(Trigger, self).__init__(parent, name, **kwargs)
+
+        if self.parent.model in ['34465A', '34470A']:
+            _max_trigger_count = 1e9
+        else:
+            _max_trigger_count = 1e6
+
+        self.add_parameter('count',
+                           label='Trigger Count',
+                           set_cmd='TRIGger:COUNt {}',
+                           get_cmd='TRIGger:COUNt?',
+                           get_parser=float,
+                           vals=vals.MultiType(
+                               vals.Numbers(1, _max_trigger_count),
+                               vals.Enum('MIN', 'MAX', 'DEF', 'INF')),
+                           docstring=textwrap.dedent("""\
+            Selects the number of triggers that are accepted by the 
+            instrument before returning to the "idle" trigger state.
+
+            You can use the specified trigger count in conjunction with 
+            `sample_count`. In this case, the number of measurements 
+            returned is the sample count multiplied by the trigger count.
+
+            A variable trigger count is not available from the front panel. 
+            However, when you return to remote control of the instrument, 
+            the trigger count returns to the previous value you selected."""))
+
+        self.add_parameter('delay',
+                           label='Trigger Delay',
+                           unit='s',
+                           set_cmd='TRIGger:DELay {}',
+                           get_cmd='TRIGger:DELay?',
+                           vals=vals.MultiType(vals.Numbers(0, 3600),
+                                               vals.Enum('MIN', 'MAX', 'DEF')),
+                           get_parser=float,
+                           docstring=textwrap.dedent("""\
+            Sets the delay between the trigger signal and the first 
+            measurement. This may be useful in applications where you want 
+            to allow the input to settle before taking a measurement or for 
+            pacing a burst of measurements.
+
+            Step size for DC measurements is approximately 1 µs. For AC 
+            measurements, step size depends on AC bandwidth.
+
+            Selecting a specific trigger delay disables the automatic 
+            trigger delay."""))
+
+        self.add_parameter('auto_delay_enabled',
+                           label='Auto Trigger Delay Enabled',
+                           set_cmd='TRIGger:DELay:AUTO {}',
+                           get_cmd='TRIGger:DELay:AUTO?',
+                           get_parser=int,
+                           val_mapping={True: 1, False: 0},
+                           docstring=textwrap.dedent("""\
+            Disables or enables automatic trigger delay. If enabled, 
+            the instrument determines the delay based on function, range, 
+            and integration time or bandwidth.
+
+            Selecting a specific trigger delay using `trigger.delay` disables 
+            the automatic trigger delay."""))
+
+        self.add_parameter('slope',
+                           label='Trigger Slope',
+                           set_cmd='TRIGger:SLOPe {}',
+                           get_cmd='TRIGger:SLOPe?',
+                           vals=vals.Enum('POS', 'NEG'))
+
+        if self.parent.model in ['34465A', '34470A'] and self.parent.has_DIG:
+            self.add_parameter('level',
+                               label='Trigger Level',
+                               unit='V',
+                               set_cmd='TRIGger:LEVel {}',
+                               get_cmd='TRIGger:LEVel?',
+                               get_parser=float,
+                               vals=vals.MultiType(
+                                   vals.Numbers(-1000, 1000),
+                                   vals.Enum('MIN', 'MAX', 'DEF')),
+                               docstring=textwrap.dedent("""\
+                Sets the level on which a trigger occurs when level 
+                triggering is enabled (`trigger.source` set to "INT").
+
+                Note that for 100 mV to 100 V ranges and autorange is off, 
+                the trigger level can only be set within ±120% of the 
+                range."""))
+
+        _trigger_source_docstring = textwrap.dedent("""\
+            Selects the trigger source for measurements.
+
+            IMMediate: The trigger signal is always present. When you place 
+                the instrument in the "wait-for-trigger" state, the trigger is 
+                issued immediately.
+
+            BUS: The instrument is triggered by `trigger.force` method of this 
+                driver once the DMM is in the "wait-for-trigger" state.
+
+            EXTernal: The instrument accepts hardware triggers applied to 
+                the rear-panel Ext Trig input and takes the specified number 
+                of measurements (`sample_count`), each time a TTL pulse 
+                specified by `trigger.slope` is received. If the 
+                instrument receives an external trigger before it is ready, 
+                it buffers one trigger.""")
+        _trigger_source_vals = vals.Enum('IMM', 'EXT', 'BUS')
+
+        if self.parent.model in ['34465A', '34470A'] and self.parent.has_DIG:
+            _trigger_source_vals = vals.Enum('IMM', 'EXT', 'BUS', 'INT')
+            # extra empty lines are needed for readability of the docstring
+            _trigger_source_docstring += textwrap.dedent("""\
+
+
+            INTernal: Provides level triggering capability. To trigger on a 
+                level on the input signal, select INTernal for the source, 
+                and set the level and slope with the `trigger.level` and 
+                `trigger.slope` parameters.""")
+
+        self.add_parameter('source',
+                           label='Trigger Source',
+                           set_cmd='TRIGger:SOURce {}',
+                           get_cmd='TRIGger:SOURce?',
+                           vals=_trigger_source_vals,
+                           docstring=_trigger_source_docstring)
+
+    def force(self) -> None:
+        """Triggers the instrument if `trigger.source` is "BUS"."""
+        self.write('*TRG')
+
+
 class Display(InstrumentChannel):
     """Implements interaction with the display of Keysight 344xxA."""
 
@@ -227,127 +357,6 @@ def __init__(self, name, address, silent=False,
                   measurements are taken at the specified fast (< 1 PLC) 
                   integration time."""))
 
-        ####################################
-        # TRIGGERING
-
-        if self.model in ['34465A', '34470A']:
-            _max_trigger_count = 1e9
-        else:
-            _max_trigger_count = 1e6
-
-        self.add_parameter('trigger_count',
-                           label='Trigger Count',
-                           set_cmd='TRIGger:COUNt {}',
-                           get_cmd='TRIGger:COUNt?',
-                           get_parser=float,
-                           vals=vals.MultiType(
-                               vals.Numbers(1, _max_trigger_count),
-                               vals.Enum('MIN', 'MAX', 'DEF', 'INF')),
-                           docstring=textwrap.dedent("""\
-            Selects the number of triggers that are accepted by the 
-            instrument before returning to the "idle" trigger state.
-
-            You can use the specified trigger count in conjunction with 
-            `sample_count`. In this case, the number of measurements 
-            returned is the sample count multiplied by the trigger count.
-
-            A variable trigger count is not available from the front panel. 
-            However, when you return to remote control of the instrument, 
-            the trigger count returns to the previous value you selected."""))
-
-        self.add_parameter('trigger_delay',
-                           label='Trigger Delay',
-                           unit='s',
-                           set_cmd='TRIGger:DELay {}',
-                           get_cmd='TRIGger:DELay?',
-                           vals=vals.MultiType(vals.Numbers(0, 3600),
-                                               vals.Enum('MIN', 'MAX', 'DEF')),
-                           get_parser=float,
-                           docstring=textwrap.dedent("""\
-            Sets the delay between the trigger signal and the first 
-            measurement. This may be useful in applications where you want 
-            to allow the input to settle before taking a measurement or for 
-            pacing a burst of measurements.
-            
-            Step size for DC measurements is approximately 1 µs. For AC 
-            measurements, step size depends on AC bandwidth.
-            
-            Selecting a specific trigger delay disables the automatic 
-            trigger delay."""))
-
-        self.add_parameter('auto_trigger_delay_enabled',
-                           label='Auto Trigger Delay Enabled',
-                           set_cmd='TRIGger:DELay:AUTO {}',
-                           get_cmd='TRIGger:DELay:AUTO?',
-                           get_parser=int,
-                           val_mapping={True: 1, False: 0},
-                           docstring=textwrap.dedent("""\
-            Disables or enables automatic trigger delay. If enabled, 
-            the instrument determines the delay based on function, range, 
-            and integration time or bandwidth.
-            
-            Selecting a specific trigger delay using `trigger_delay` disables 
-            the automatic trigger delay."""))
-
-        self.add_parameter('trigger_slope',
-                           label='Trigger Slope',
-                           set_cmd='TRIGger:SLOPe {}',
-                           get_cmd='TRIGger:SLOPe?',
-                           vals=vals.Enum('POS', 'NEG'))
-
-        if self.model in ['34465A', '34470A'] and self.has_DIG:
-            self.add_parameter('trigger_level',
-                               label='Trigger Level',
-                               unit='V',
-                               set_cmd='TRIGger:LEVel {}',
-                               get_cmd='TRIGger:LEVel?',
-                               vals=vals.MultiType(
-                                   vals.Numbers(-1000, 1000),
-                                   vals.Enum('MIN', 'MAX', 'DEF')),
-                               docstring=textwrap.dedent("""\
-                Sets the level on which a trigger occurs when level 
-                triggering is enabled (`trigger_source set to "INT").
-                
-                Note that for 100 mV to 100 V ranges and autorange is off, 
-                the trigger level can only be set within ±120% of the 
-                range."""))
-
-        _trigger_source_docstring = textwrap.dedent("""\
-            Selects the trigger source for measurements.
-            
-            IMMediate: The trigger signal is always present. When you place 
-                the instrument in the "wait-for-trigger" state, the trigger is 
-                issued immediately.
-                
-            BUS: The instrument is triggered by `trigger` method of this 
-                driver once the DMM is in the "wait-for-trigger" state.
-            
-            EXTernal: The instrument accepts hardware triggers applied to 
-                the rear-panel Ext Trig input and takes the specified number 
-                of measurements (`sample_count`), each time a TTL pulse 
-                specified by `trigger_slope` is received. If the 
-                instrument receives an external trigger before it is ready, 
-                it buffers one trigger.""")
-        _trigger_source_vals = vals.Enum('IMM', 'EXT', 'BUS')
-
-        if self.model in ['34465A', '34470A'] and self.has_DIG:
-            _trigger_source_vals = vals.Enum('IMM', 'EXT', 'BUS', 'INT')
-            # extra empty lines are needed for readability of the docstring
-            _trigger_source_docstring += textwrap.dedent("""\
-            
-            
-            INTernal: Provides level triggering capability. To trigger on a 
-                level on the input signal, select INTernal for the source, 
-                and set the level and slope with the `trigger_level` and 
-                `trigger_slope` parameters.""")
-
-        self.add_parameter('trigger_source',
-                           label='Trigger Source',
-                           set_cmd='TRIGger:SOURce {}',
-                           get_cmd='TRIGger:SOURce?',
-                           vals=_trigger_source_vals,
-                           docstring=_trigger_source_docstring)
-
         ####################################
         # SAMPLING
 
@@ -495,6 +504,7 @@ def __init__(self, name, address, silent=False,
         # Submodules
 
         self.add_submodule('display', Display(self, 'display'))
+        self.add_submodule('trigger', Trigger(self, 'trigger'))
 
         ####################################
         # Connect message
@@ -628,10 +638,6 @@ def autorange_once(self) -> None:
         self.write('SENSe:VOLTage:DC:RANGe:AUTO ONCE')
         self.range.get()
 
-    def force_trigger(self) -> None:
-        """Triggers the instrument if `trigger_source` is "BUS"."""
-        self.write('*TRG')
-
     def error(self) -> Tuple[int, str]:
         """
         Return the first error message in the queue. It also clears it from

From 9164397f4648265106af4c0e1b0d08bfd4c52442 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 15:10:15 +0100
Subject: [PATCH 536/719] validate_consistent_shape now raises on its own

---
 qcodes/instrument/parameter.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index e0016142996..2675e004f47 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1013,11 +1013,7 @@ def validate(self, value: ParamDataType) -> None:
         Arguments are passed to the super method
         """
         if isinstance(self.vals, Arrays):
-            consistent = self.validate_consistent_shape()
-            if consistent is False:
-                raise ValueError(f"Inconsistent shape between parameter and "
-                                 f"setpoints for parameter: "
-                                 f"{self.full_name}")
+            self.validate_consistent_shape()
         super().validate(value)
 
 

From 4f478f047117af22645a1e1668fc0d8d5c95f627 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 15:12:52 +0100
Subject: [PATCH 537/719] Test setting setpoints after construction

---
 qcodes/tests/test_parameter_with_setpoints.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index 34f35b7b2c8..617e68785f1 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -37,11 +37,11 @@ def test_validation_shapes():
                                                     get_cmd=lambda:
                                                     rand(n_points_1(),
                                                          n_points_2()),
-                                                    setpoints=(setpoints_1,
-                                                               setpoints_2),
                                                     vals=vals.Arrays(
                                                         shape=(n_points_1,
                                                                n_points_2)))
+
+    param_with_setpoints_2.setpoints = (setpoints_1, setpoints_2)
     # 2d
     param_with_setpoints_2.validate_consistent_shape()
     param_with_setpoints_2.validate(param_with_setpoints_2.get())

From da407373e886716861f5621cd09bcccd61fdc15e Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 15:22:04 +0100
Subject: [PATCH 538/719] Extract sampling submodule of Keysight 344xxA
 submodules driver

---
 .../private/Keysight_344xxA_submodules.py     | 201 +++++++++---------
 1 file changed, 103 insertions(+), 98 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
index eb057f60cc4..84eca1995a2 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
@@ -139,6 +139,108 @@ def force(self) -> None:
         self.write('*TRG')
 
 
+class Sample(InstrumentChannel):
+    """Implements sampling parameters of Keysight 344xxA."""
+
+    def __init__(self, parent: '_Keysight_344xxA', name: str, **kwargs):
+        super(Sample, self).__init__(parent, name, **kwargs)
+
+        if self.parent.model in ['34465A', '34470A']:
+            _max_sample_count = 1e9
+        else:
+            _max_sample_count = 1e6
+
+        self.add_parameter('count',
+                           label='Sample Count',
+                           set_cmd='SAMPle:COUNt {}',
+                           get_cmd='SAMPle:COUNt?',
+                           vals=vals.MultiType(
+                               vals.Numbers(1, _max_sample_count),
+                               vals.Enum('MIN', 'MAX', 'DEF')),
+                           get_parser=int,
+                           docstring=textwrap.dedent("""\
+            Specifies the number of measurements (samples) the instrument 
+            takes per trigger.
+
+            MAX selects 1 billion readings. However, when pretrigger is 
+            selected, the maximum is 50,000 readings (without the MEM 
+            option) or 2,000,000 readings (with the MEM option)"""))
+
+        if self.parent.has_DIG:
+            self.add_parameter('pretrigger_count',
+                               label='Sample Pretrigger Count',
+                               set_cmd='SAMPle:COUNt:PRETrigger {}',
+                               get_cmd='SAMPle:COUNt:PRETrigger?',
+                               vals=vals.MultiType(
+                                   vals.Numbers(0, 2e6 - 1),
+                                   vals.Enum('MIN', 'MAX', 'DEF')),
+                               get_parser=int,
+                               docstring=textwrap.dedent("""\
+                Allows collection of the data being digitized the trigger. 
+                Reserves memory for pretrigger samples up to the specified 
+                num. of pretrigger samples."""))
+
+        if self.parent.model in ['34465A', '34470A']:
+            self.add_parameter('source',
+                               label='Sample Timing Source',
+                               set_cmd='SAMPle:SOURce {}',
+                               get_cmd='SAMPle:SOURce?',
+                               vals=vals.Enum('IMM', 'TIM'),
+                               docstring=('Determines sampling time, '
+                                          'immediate or using `sample.timer`'))
+
+        self.add_parameter('timer',
+                           label='Sample Timer',
+                           set_cmd='SAMPle:TIMer {}',
+                           get_cmd='SAMPle:TIMer?',
+                           unit='s',
+                           vals=vals.MultiType(vals.Numbers(0, 3600),
+                                               vals.Enum('MIN', 'MAX', 'DEF')),
+                           get_parser=float,
+                           docstring=textwrap.dedent("""\
+            The value is rounded by the instrument to the nearest step. For DC 
+            measurements, the step size is 1 µs. For AC measurements, 
+            it is AC bandwidth dependent.
+
+            Special values are: MIN - recommended minimum, MAX - maximum, 
+            DEF - default. In order to obtain the actual value of the 
+            parameter that gets set when setting it to one of these special 
+            values, just call the get method of the parameter, or use 
+            corresponding parameters in this driver, 
+            like `sample.timer_minimum`.
+
+            Specifying a value that is between the absolute minimum (assumes 
+            no range changes) and the recommended minimum value, 
+            may generate a timing violation error when making measurements.
+
+            Applying a value less than the absolute minimum will generate an 
+            error."""))
+
+        self.add_parameter('timer_minimum',
+                           label='Minimal recommended sample time',
+                           get_cmd='SAMPle:TIMer? MIN',
+                           get_parser=float,
+                           unit='s',
+                           docstring=textwrap.dedent("""\
+            This value is measurement dependent. It depends on such things 
+            as the integration time, autozero on or off, autorange on or 
+            off, and the measurement range. Basically, the minimum is 
+            automatically determined by the instrument so that the sample 
+            interval is always greater than the sampling time.
+
+            Since the minimum value changes depending on configuration, a 
+            command order dependency exists. You must completely configure 
+            the measurement before setting the sample timer to minimum, 
+            or you may generate an error. A complete configuration includes 
+            such things as math statistics or scaling.
+
+            When using autorange, the minimum value is the recommended value, 
+            not the absolute minimum value. With autorange enabled, minimum 
+            value is calculated assuming a single range change will occur 
+            for every measurement (not multiple ranges, just one range up or 
+            down per measurement)."""))
+
+
 class Display(InstrumentChannel):
     """Implements interaction with the display of Keysight 344xxA."""
 
@@ -357,104 +459,6 @@ def __init__(self, name, address, silent=False,
                   measurements are taken at the specified fast (< 1 PLC) 
                   integration time."""))
 
-        ####################################
-        # SAMPLING
-
-        if self.model in ['34465A', '34470A']:
-            _max_sample_count = 1e9
-        else:
-            _max_sample_count = 1e6
-
-        self.add_parameter('sample_count',
-                           label='Sample Count',
-                           set_cmd='SAMPle:COUNt {}',
-                           get_cmd='SAMPle:COUNt?',
-                           vals=vals.MultiType(
-                               vals.Numbers(1, _max_sample_count),
-                               vals.Enum('MIN', 'MAX', 'DEF')),
-                           get_parser=int,
-                           docstring=textwrap.dedent("""\
-            Specifies the number of measurements (samples) the instrument 
-            takes per trigger.
-            
-            MAX selects 1 billion readings. However, when pretrigger is 
-            selected, the maximum is 50,000 readings (without the MEM 
-            option) or 2,000,000 readings (with the MEM option)"""))
-
-        if self.has_DIG:
-            self.add_parameter('sample_count_pretrigger',
-                               label='Sample Pretrigger Count',
-                               set_cmd='SAMPle:COUNt:PRETrigger {}',
-                               get_cmd='SAMPle:COUNt:PRETrigger?',
-                               vals=vals.MultiType(
-                                   vals.Numbers(0, 2e6-1),
-                                   vals.Enum('MIN', 'MAX', 'DEF')),
-                               get_parser=int,
-                               docstring=textwrap.dedent("""\
-                Allows collection of the data being digitized the trigger. 
-                Reserves memory for pretrigger samples up to the specified 
-                num. of pretrigger samples."""))
-
-        if self.model in ['34465A', '34470A']:
-            self.add_parameter('sample_source',
-                               label='Sample Timing Source',
-                               set_cmd='SAMPle:SOURce {}',
-                               get_cmd='SAMPle:SOURce?',
-                               vals=vals.Enum('IMM', 'TIM'),
-                               docstring=('Determines sampling time, '
-                                          'immediate or using sample_timer'))
-
-        self.add_parameter('sample_timer',
-                           label='Sample Timer',
-                           set_cmd='SAMPle:TIMer {}',
-                           get_cmd='SAMPle:TIMer?',
-                           unit='s',
-                           vals=vals.MultiType(vals.Numbers(0, 3600),
-                                               vals.Enum('MIN', 'MAX', 'DEF')),
-                           get_parser=float,
-                           docstring=textwrap.dedent("""\
-            The value is rounded by the instrument to the nearest step. For DC 
-            measurements, the step size is 1 µs. For AC measurements, 
-            it is AC bandwidth dependent.
-            
-            Special values are: MIN - recommended minimum, MAX - maximum, 
-            DEF - default. In order to obtain the actual value of the 
-            parameter that gets set when setting it to one of these special 
-            values, just call the get method of the parameter, or use 
-            corresponding parameters in this driver, 
-            like `sample_timer_minimum`.
-
-            Specifying a value that is between the absolute minimum (assumes 
-            no range changes) and the recommended minimum value, 
-            may generate a timing violation error when making measurements.
-            
-            Applying a value less than the absolute minimum will generate an 
-            error."""))
-
-        self.add_parameter('sample_timer_minimum',
-                           label='Minimal recommended sample time',
-                           get_cmd='SAMPle:TIMer? MIN',
-                           get_parser=float,
-                           unit='s',
-                           docstring=textwrap.dedent("""\
-            This value is measurement dependent. It depends on such things 
-            as the integration time, autozero on or off, autorange on or 
-            off, and the measurement range. Basically, the minimum is 
-            automatically determined by the instrument so that the sample 
-            interval is always greater than the sampling time.
-            
-            Since the minimum value changes depending on configuration, a 
-            command order dependency exists. You must completely configure 
-            the measurement before setting the sample timer to minimum, 
-            or you may generate an error. A complete configuration includes 
-            such things as math statistics or scaling.
-            
-            When using autorange, the minimum value is the recommended value, 
-            not the absolute minimum value. With autorange enabled, minimum 
-            value is calculated assuming a single range change will occur 
-            for every measurement (not multiple ranges, just one range up or 
-            down per measurement)."""))
-
         ####################################
         # Aperture parameters
 
@@ -505,6 +509,7 @@ def __init__(self, name, address, silent=False,
 
         self.add_submodule('display', Display(self, 'display'))
         self.add_submodule('trigger', Trigger(self, 'trigger'))
+        self.add_submodule('sample', Sample(self, 'sample'))
 
         ####################################
         # Connect message

From 75cd7c2ce26b28cb715e08e44a46d4ea43327707 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 15:23:38 +0100
Subject: [PATCH 539/719] Move volt parameter for readability

---
 .../Keysight/private/Keysight_344xxA_submodules.py  | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
index 84eca1995a2..7dda628413b 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
@@ -388,11 +388,6 @@ def __init__(self, name, address, silent=False,
             Setting the integration time also sets the measurement 
             resolution."""))
 
-        self.add_parameter('volt',
-                           get_cmd=self._get_voltage,
-                           label='Voltage',
-                           unit='V')
-
         self.add_parameter('range',
                            get_cmd='SENSe:VOLTage:DC:RANGe?',
                            get_parser=float,
@@ -511,6 +506,14 @@ def __init__(self, name, address, silent=False,
         self.add_submodule('trigger', Trigger(self, 'trigger'))
         self.add_submodule('sample', Sample(self, 'sample'))
 
+        ####################################
+        # Measuring parameter
+
+        self.add_parameter('volt',
+                           get_cmd=self._get_voltage,
+                           label='Voltage',
+                           unit='V')
+
         ####################################
         # Connect message
 

From 6c8593883e19b71005926803ad2fee5f55c2359f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 15:30:52 +0100
Subject: [PATCH 540/719] Test that setpoints that are not parameters raises

---
 qcodes/tests/test_parameter_with_setpoints.py | 38 +++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index 617e68785f1..c6f8cfe1afd 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -47,6 +47,44 @@ def test_validation_shapes():
     param_with_setpoints_2.validate(param_with_setpoints_2.get())
 
 
+def test_setpoints_non_parameter_raises():
+
+    """
+    Test that putting some random function as a setpoint parameter will
+    raise as expected.
+    """
+
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(20)
+
+
+    err_msg = (r"Setpoints is of type <class 'function'> "
+               r"expcected a QCoDeS parameter")
+    with pytest.raises(TypeError, match=err_msg):
+        param_with_setpoints_1 = ParameterWithSetpoints('param_1',
+                                                        get_cmd=lambda:
+                                                        rand(n_points_1()),
+                                                        setpoints=(
+                                                        lambda x: x,),
+                                                        vals=vals.Arrays(
+                                                            shape=(
+                                                                n_points_1,)))
+
+    param_with_setpoints_1 = ParameterWithSetpoints('param_1',
+                                                    get_cmd=lambda:
+                                                    rand(n_points_1()),
+                                                    vals=vals.Arrays(
+                                                        shape=(
+                                                            n_points_1,)))
+
+    with pytest.raises(TypeError, match=err_msg):
+        param_with_setpoints_1.setpoints = (lambda x: x,)
+
+
+
 def test_validation_inconsistent_shape():
     """
     Parameters with shapes inconsistent with their setpoints should not

From 9ebb446f59c375878ec88964af471a04d7d74fe6 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 15:38:48 +0100
Subject: [PATCH 541/719] Typo

---
 qcodes/dataset/measurements.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index 5cccebe9054..15b16a0b75d 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -307,7 +307,7 @@ def _unbundle_parameter_with_setpoints(self,
             found_parameters.append(setpointparam.full_name)
             if setpointparam.full_name not in self.parameters.keys():
                 raise RuntimeError(f'{setpointparam.full_name} '
-                                   f'which is a setpoint parameter for'
+                                   f'which is a setpoint parameter for '
                                    f'{parameter.full_name} is not registered!')
         output_grids = np.meshgrid(*setpoint_data, indexing='ij')
         for name, grid in zip(setpoint_names, output_grids):

From fd32b87cfb596a1a1270d808b01d44f2a20a7c04 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 7 Jan 2019 15:39:16 +0100
Subject: [PATCH 542/719] Test that missing setpoints fails as expected

---
 .../test_measurement_context_manager.py       | 26 ++++++++++++++++++-
 1 file changed, 25 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 1b9277cc20d..5985f71fd99 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -19,7 +19,7 @@
     DummyChannelInstrument, setpoint_generator
 from qcodes.dataset.param_spec import ParamSpec
 from qcodes.dataset.sqlite_base import atomic_transaction
-from qcodes.instrument.parameter import ArrayParameter
+from qcodes.instrument.parameter import ArrayParameter, Parameter
 from qcodes.dataset.legacy_import import import_dat_file
 from qcodes.dataset.data_set import load_by_id
 # pylint: disable=unused-import
@@ -1061,6 +1061,30 @@ def test_datasaver_scalar_parameter_with_setpoints(channel_array_instrument,
     assert subdata['dummy_channel_inst_ChanA_dummy_start'] == np.array([0])
 
 
+@pytest.mark.usefixtures("experiment")
+def test_datasaver_parameter_with_setpoints_missing_reg_raises(
+        channel_array_instrument,
+        DAC):
+    chan = channel_array_instrument.A
+    param = chan.dummy_scalar_parameter_with_setpoints
+
+    meas = Measurement()
+    meas.register_parameter(param)
+
+    myparam = Parameter('foo', set_cmd=None)
+    myparam.set(1)
+    setpoints = (param.setpoints[0], myparam)
+    param.setpoints = setpoints
+
+    # Now for a real measurement
+    with meas.run() as datasaver:
+        with pytest.raises(RuntimeError, match=r'foo which is a setpoint '
+                                               r'parameter for dummy_channel_'
+                                               r'inst_ChanA_dummy_scalar_'
+                                               r'parameter_with_setpoints '
+                                               r'is not registered!'):
+            datasaver.add_result((param, param()))
+
 @settings(max_examples=5, deadline=None)
 @given(N=hst.integers(min_value=5, max_value=500))
 @pytest.mark.usefixtures("experiment")

From ca13f5710094d567ecfb951928e12ee5944fab52 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 16:44:24 +0100
Subject: [PATCH 543/719] Extract Keysight error handling into mixin class

---
 .../Keysight/KeysightAgilent_33XXX.py         | 49 +---------------
 .../Keysight/Keysight_33500B.py               | 46 ---------------
 .../Keysight/Keysight_33500B_channels.py      | 50 ++--------------
 .../Keysight/private/Keysight_344xxA.py       | 52 +----------------
 .../private/Keysight_344xxA_submodules.py     | 52 +----------------
 .../Keysight/private/error_handling.py        | 57 +++++++++++++++++++
 6 files changed, 70 insertions(+), 236 deletions(-)
 create mode 100644 qcodes/instrument_drivers/Keysight/private/error_handling.py

diff --git a/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py b/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
index 56ef0ad18bb..21dbd8d3828 100644
--- a/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
+++ b/qcodes/instrument_drivers/Keysight/KeysightAgilent_33XXX.py
@@ -5,6 +5,8 @@
 from qcodes import VisaInstrument, validators as vals
 from qcodes.instrument.channel import InstrumentChannel
 from qcodes.instrument.base import Instrument
+from qcodes.instrument_drivers.Keysight.private.error_handling import \
+    KeysightErrorQueueMixin
 
 log = logging.getLogger(__name__)
 
@@ -273,7 +275,7 @@ def __init__(self, parent, name):
                                vals=vals.Enum(1, 2))
 
 
-class WaveformGenerator_33XXX(VisaInstrument):
+class WaveformGenerator_33XXX(KeysightErrorQueueMixin, VisaInstrument):
     """
     QCoDeS driver for the Keysight/Agilent 33XXX series of
     waveform generators
@@ -312,21 +314,6 @@ def __init__(self, name, address, silent=False, **kwargs):
 
         self.num_channels = no_of_channels[self.model]
 
-        def errorparser(rawmssg):
-            """
-            Parses the error message.
-
-            Args:
-                rawmssg (str): The raw return value of 'SYSTem:ERRor?'
-
-            Returns:
-                tuple (int, str): The error code and the error message.
-            """
-            code = int(rawmssg.split(',')[0])
-            mssg = rawmssg.split(',')[1].strip().replace('"', '')
-
-            return code, mssg
-
         for i in range(1, self.num_channels+1):
             channel = OutputChannel(self, 'ch{}'.format(i), i)
             self.add_submodule('ch{}'.format(i), channel)
@@ -334,39 +321,9 @@ def errorparser(rawmssg):
         sync = SyncChannel(self, 'sync')
         self.add_submodule('sync', sync)
 
-        self.add_parameter('error',
-                           label='Error message',
-                           get_cmd='SYSTem:ERRor?',
-                           get_parser=errorparser
-                           )
-
         self.add_function('force_trigger', call_cmd='*TRG')
 
         self.add_function('sync_channel_phases', call_cmd='PHAS:SYNC')
 
         if not silent:
             self.connect_message()
-
-    def flush_error_queue(self, verbose=True):
-        """
-        Clear the instrument error queue.
-
-        Args:
-            verbose (Optional[bool]): If true, the error messages are printed.
-                Default: True.
-        """
-
-        log.debug('Flushing error queue...')
-
-        err_code, err_message = self.error()
-        log.debug('    {}, {}'.format(err_code, err_message))
-        if verbose:
-            print(err_code, err_message)
-
-        while err_code != 0:
-            err_code, err_message = self.error()
-            log.debug('    {}, {}'.format(err_code, err_message))
-            if verbose:
-                print(err_code, err_message)
-
-        log.debug('...flushing complete')
diff --git a/qcodes/instrument_drivers/Keysight/Keysight_33500B.py b/qcodes/instrument_drivers/Keysight/Keysight_33500B.py
index 71593ea5342..ac5be8e2405 100644
--- a/qcodes/instrument_drivers/Keysight/Keysight_33500B.py
+++ b/qcodes/instrument_drivers/Keysight/Keysight_33500B.py
@@ -36,21 +36,6 @@ def setcmd(channel, setting):
         def getcmd(channel, setting):
             return 'SOURce{}:'.format(channel) + setting + '?'
 
-        def errorparser(rawmssg):
-            """
-            Parses the error message.
-
-            Args:
-                rawmssg (str): The raw return value of 'SYSTem:ERRor?'
-
-            Returns:
-                tuple (int, str): The error code and the error message.
-            """
-            code = int(rawmssg.split(',')[0])
-            mssg = rawmssg.split(',')[1].strip().replace('"', '')
-
-            return code, mssg
-
         def val_parser(parser, inputstring):
             """
             Parses return values from instrument. Meant to be used when a query
@@ -268,38 +253,7 @@ def val_parser(parser, inputstring):
                            vals=vals.Enum('ON', 'OFF')
                            )
 
-
-        self.add_parameter('error',
-                           label='Error message',
-                           get_cmd='SYSTem:ERRor?',
-                           get_parser=errorparser
-                           )
-
         self.add_function('force_trigger', call_cmd='*TRG')
 
         if not silent:
             self.connect_message()
-
-    def flush_error_queue(self, verbose=True):
-        """
-        Clear the instrument error queue.
-
-        Args:
-            verbose (Optional[bool]): If true, the error messages are printed.
-                Default: True.
-        """
-
-        log.debug('Flushing error queue...')
-
-        err_code, err_message = self.error()
-        log.debug('    {}, {}'.format(err_code, err_message))
-        if verbose:
-            print(err_code, err_message)
-
-        while err_code != 0:
-            err_code, err_message = self.error()
-            log.debug('    {}, {}'.format(err_code, err_message))
-            if verbose:
-                print(err_code, err_message)
-
-        log.debug('...flushing complete')
diff --git a/qcodes/instrument_drivers/Keysight/Keysight_33500B_channels.py b/qcodes/instrument_drivers/Keysight/Keysight_33500B_channels.py
index be63ac3bf80..875d9c77ce4 100644
--- a/qcodes/instrument_drivers/Keysight/Keysight_33500B_channels.py
+++ b/qcodes/instrument_drivers/Keysight/Keysight_33500B_channels.py
@@ -5,6 +5,9 @@
 from qcodes.instrument.channel import InstrumentChannel, ChannelList
 import logging
 
+from qcodes.instrument_drivers.Keysight.private.error_handling import \
+    KeysightErrorQueueMixin
+
 log = logging.getLogger(__name__)
 
 
@@ -230,7 +233,7 @@ def val_parser(parser, inputstring):
                            )
 
 
-class Keysight_33500B_Channels(VisaInstrument):
+class Keysight_33500B_Channels(KeysightErrorQueueMixin, VisaInstrument):
     """
     QCoDeS driver for the Keysight 33500B Waveform Generator using QCoDeS
     channels
@@ -253,21 +256,6 @@ def __init__(self, name, address, silent=False, **kwargs):
 
         super().__init__(name, address, **kwargs)
 
-        def errorparser(rawmssg):
-            """
-            Parses the error message.
-
-            Args:
-                rawmssg (str): The raw return value of 'SYSTem:ERRor?'
-
-            Returns:
-                tuple (int, str): The error code and the error message.
-            """
-            code = int(rawmssg.split(',')[0])
-            mssg = rawmssg.split(',')[1].strip().replace('"', '')
-
-            return code, mssg
-
         channels = ChannelList(self, "Channels", KeysightChannel,
                                snapshotable=False)
         for i in range(1, 3):
@@ -292,39 +280,9 @@ def errorparser(rawmssg):
                            vals=vals.Enum('ON', 'OFF')
                            )
 
-        self.add_parameter('error',
-                           label='Error message',
-                           get_cmd='SYSTem:ERRor?',
-                           get_parser=errorparser
-                           )
-
         self.add_function('force_trigger', call_cmd='*TRG')
 
         self.add_function('sync_channel_phases', call_cmd='PHAS:SYNC')
 
         if not silent:
             self.connect_message()
-
-    def flush_error_queue(self, verbose=True):
-        """
-        Clear the instrument error queue.
-
-        Args:
-            verbose (Optional[bool]): If true, the error messages are printed.
-                Default: True.
-        """
-
-        log.debug('Flushing error queue...')
-
-        err_code, err_message = self.error()
-        log.debug('    {}, {}'.format(err_code, err_message))
-        if verbose:
-            print(err_code, err_message)
-
-        while err_code != 0:
-            err_code, err_message = self.error()
-            log.debug('    {}, {}'.format(err_code, err_message))
-            if verbose:
-                print(err_code, err_message)
-
-        log.debug('...flushing complete')
diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index dc09d410688..4fd122d3ad7 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -3,10 +3,11 @@
 from functools import partial
 import numpy as np
 import logging
-from typing import Tuple
 
 from qcodes.instrument.parameter import ArrayParameter
 import qcodes.utils.validators as vals
+from qcodes.instrument_drivers.Keysight.private.error_handling import \
+    KeysightErrorQueueMixin
 from qcodes.utils.deprecate import deprecate
 from qcodes import VisaInstrument
 from pyvisa import VisaIOError
@@ -106,7 +107,7 @@ def get_raw(self):
         return numvals
 
 
-class _Keysight_344xxA(VisaInstrument):
+class _Keysight_344xxA(KeysightErrorQueueMixin, VisaInstrument):
     """
     Instrument class for Keysight 34460A, 34461A, 34465A and 34470A multimeters.
 
@@ -733,53 +734,6 @@ def force_trigger(self) -> None:
         """Triggers the instrument if `trigger_source` is "BUS"."""
         self.write('*TRG')
 
-    def error(self) -> Tuple[int, str]:
-        """
-        Return the first error message in the queue. It also clears it from
-        the error queue.
-
-        Up to 20 errors can be stored in the instrument's error queue.
-        Error retrieval is first-in-first-out (FIFO).
-
-        If more than 20 errors have occurred, the most recent error stored
-        in the queue is replaced with -350,"Queue overflow". No additional
-        errors are stored until you remove errors from the queue. If no
-        errors have occurred when you read the error queue, the instrument
-        responds with +0,"No error".
-
-        Returns:
-            The error code and the error message.
-        """
-        rawmssg = self.ask('SYSTem:ERRor?')
-        code = int(rawmssg.split(',')[0])
-        mssg = rawmssg.split(',')[1].strip().replace('"', '')
-
-        return code, mssg
-
-    def flush_error_queue(self, verbose: bool=True) -> None:
-        """
-        Clear the instrument error queue, and prints it.
-
-        Args:
-            verbose: If true, the error messages are printed.
-                Default: True.
-        """
-
-        log.debug('Flushing error queue...')
-
-        err_code, err_message = self.error()
-        log.debug('    {}, {}'.format(err_code, err_message))
-        if verbose:
-            print(err_code, err_message)
-
-        while err_code != 0:
-            err_code, err_message = self.error()
-            log.debug('    {}, {}'.format(err_code, err_message))
-            if verbose:
-                print(err_code, err_message)
-
-        log.debug('...flushing complete')
-
 
 def _raw_vals_to_array(raw_vals: str) -> np.array:
     """
diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
index 7dda628413b..386d0eff56f 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
@@ -1,10 +1,11 @@
 import textwrap
 import numpy as np
 import logging
-from typing import Tuple
 
 import qcodes.utils.validators as vals
 from qcodes import VisaInstrument, InstrumentChannel
+from qcodes.instrument_drivers.Keysight.private.error_handling import \
+    KeysightErrorQueueMixin
 
 log = logging.getLogger(__name__)
 
@@ -284,7 +285,7 @@ def clear(self) -> None:
         self.text.get()  # also update the parameter value
 
 
-class _Keysight_344xxA(VisaInstrument):
+class _Keysight_344xxA(KeysightErrorQueueMixin, VisaInstrument):
     """
     Instrument class for Keysight 34460A, 34461A, 34465A and 34470A
     multimeters.
@@ -646,53 +647,6 @@ def autorange_once(self) -> None:
         self.write('SENSe:VOLTage:DC:RANGe:AUTO ONCE')
         self.range.get()
 
-    def error(self) -> Tuple[int, str]:
-        """
-        Return the first error message in the queue. It also clears it from
-        the error queue.
-
-        Up to 20 errors can be stored in the instrument's error queue.
-        Error retrieval is first-in-first-out (FIFO).
-
-        If more than 20 errors have occurred, the most recent error stored
-        in the queue is replaced with -350,"Queue overflow". No additional
-        errors are stored until you remove errors from the queue. If no
-        errors have occurred when you read the error queue, the instrument
-        responds with +0,"No error".
-
-        Returns:
-            The error code and the error message.
-        """
-        rawmssg = self.ask('SYSTem:ERRor?')
-        code = int(rawmssg.split(',')[0])
-        mssg = rawmssg.split(',')[1].strip().replace('"', '')
-
-        return code, mssg
-
-    def flush_error_queue(self, verbose: bool=True) -> None:
-        """
-        Clear the instrument error queue, and prints it.
-
-        Args:
-            verbose: If true, the error messages are printed.
-                Default: True.
-        """
-
-        log.debug('Flushing error queue...')
-
-        err_code, err_message = self.error()
-        log.debug('    {}, {}'.format(err_code, err_message))
-        if verbose:
-            print(err_code, err_message)
-
-        while err_code != 0:
-            err_code, err_message = self.error()
-            log.debug('    {}, {}'.format(err_code, err_message))
-            if verbose:
-                print(err_code, err_message)
-
-        log.debug('...flushing complete')
-
 
 def _raw_vals_to_array(raw_vals: str) -> np.array:
     """
diff --git a/qcodes/instrument_drivers/Keysight/private/error_handling.py b/qcodes/instrument_drivers/Keysight/private/error_handling.py
new file mode 100644
index 00000000000..a433a61e3dc
--- /dev/null
+++ b/qcodes/instrument_drivers/Keysight/private/error_handling.py
@@ -0,0 +1,57 @@
+from typing import Tuple
+
+
+class KeysightErrorQueueMixin:
+    """
+    Mixin class for visa instruments that happen to implement an error queue.
+
+    It is meant to work ONLY with drivers for Keysight
+    instruments (which inherit from VisaInstrument class).
+    """
+
+    def error(self) -> Tuple[int, str]:
+        """
+        Return the first error message in the queue. It also clears it from
+        the error queue.
+
+        Up to 20 errors can be stored in the instrument's error queue.
+        Error retrieval is first-in-first-out (FIFO).
+
+        If more than 20 errors have occurred, the most recent error stored
+        in the queue is replaced with -350,"Queue overflow". No additional
+        errors are stored until you remove errors from the queue. If no
+        errors have occurred when you read the error queue, the instrument
+        responds with +0,"No error".
+
+        Returns:
+            The error code and the error message.
+        """
+        rawmssg = self.ask('SYSTem:ERRor?')
+        code = int(rawmssg.split(',')[0])
+        mssg = rawmssg.split(',')[1].strip().replace('"', '')
+
+        return code, mssg
+
+    def flush_error_queue(self, verbose: bool=True) -> None:
+        """
+        Clear the instrument error queue, and prints it.
+
+        Args:
+            verbose: If true, the error messages are printed.
+                Default: True.
+        """
+
+        self.log.debug('Flushing error queue...')
+
+        err_code, err_message = self.error()
+        self.log.debug('    {}, {}'.format(err_code, err_message))
+        if verbose:
+            print(err_code, err_message)
+
+        while err_code != 0:
+            err_code, err_message = self.error()
+            self.log.debug('    {}, {}'.format(err_code, err_message))
+            if verbose:
+                print(err_code, err_message)
+
+        self.log.debug('...flushing complete')

From d18f214276b7a0eba90f49227c5de73e7165ad8a Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 16:50:19 +0100
Subject: [PATCH 544/719] Deprecate Keysight 344xxA drivers in favor of
 submoduled ones

... the ones in Keysight_344xxA_submodules.py files
---
 .../Keysight/private/Keysight_344xxA.py                  | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
index 4fd122d3ad7..5f2a464f1d4 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA.py
@@ -1,5 +1,6 @@
 # QCoDeS driver for the Keysight 344xxA Digital Multimeter
 import textwrap
+import warnings
 from functools import partial
 import numpy as np
 import logging
@@ -133,6 +134,14 @@ def __init__(self, name, address, silent=False,
                 is supressed. Default: False
         """
 
+        warnings.warn("This driver is old and will be removed from QCoDeS "
+                      "soon. Please use the Keysight_344xxA classes from the "
+                      "files "
+                      "instrument_drivers/Keysight"
+                          "/Keysight_344xxA_submodules "
+                      "instead.",
+                      UserWarning)
+
         super().__init__(name, address, terminator='\n', **kwargs)
 
         idn = self.IDN.get()

From d570f5080abf29bfaca7edbc201ec15036e6a744 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Mon, 7 Jan 2019 08:29:55 -0800
Subject: [PATCH 545/719] stip function returns a tuple of strings.

---
 qcodes/instrument_drivers/Lakeshore/Model_325.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/Lakeshore/Model_325.py b/qcodes/instrument_drivers/Lakeshore/Model_325.py
index 0ac625a5d92..80f4ecf5c10 100644
--- a/qcodes/instrument_drivers/Lakeshore/Model_325.py
+++ b/qcodes/instrument_drivers/Lakeshore/Model_325.py
@@ -22,7 +22,7 @@ def read_curve_file(curve_file: TextIO) -> dict:
     def split_data_line(line: str, parser: type = str) -> List[str]:
         return [parser(i) for i in line.split("  ") if i != ""]
 
-    def strip(strings: Iterable[str]) -> Tuple:
+    def strip(strings: Iterable[str]) -> Tuple[str]:
         return tuple(s.strip() for s in strings)
 
     lines = iter(curve_file.readlines())

From 19a96cbc34d40fa41d108ed78ef349270e893084 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Mon, 7 Jan 2019 17:57:59 +0100
Subject: [PATCH 546/719] Update example notebook Keysight 344xxA to use driver
 with submodules

... also add example of a measurement routine.
---
 .../Qcodes example with Keysight 344xxA.ipynb | 592 +++++++++++++-----
 1 file changed, 433 insertions(+), 159 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb b/docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb
index 16c2e80533c..f4bfdab2c1c 100644
--- a/docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb	
@@ -15,7 +15,17 @@
     "\n",
     "Note however that not every feature/parameter is available on all `344xxA` models. This, when possible, is reflected in the instantiated driver object. Also note that models like `34465A` have options (like `DIG` and `MEM`) that can either be enabled or disabled on a particular instrument; this also has impact on availability of some features/parameters and/or their settings. In general, refer to the instrument's manual for detailed information.\n",
     "\n",
-    "The driver does not cover all the features of the instrument because it is being mostly used for voltage DC measurements. Contribution is welcome."
+    "The driver does not cover all the features of the instrument because it is being mostly used for voltage DC measurements. Contribution is welcome.\n",
+    "\n",
+    "__NOTE__: use drivers for these models which originate from `*_submodules` python modules, from example:\n",
+    "```python\n",
+    "from qcodes.instrument_drivers.Keysight.Keysight_34465A_submodules import Keysight_34465A\n",
+    "```\n",
+    "instead of\n",
+    "```python\n",
+    "from qcodes.instrument_drivers.Keysight.Keysight_34465A import Keysight_34465A\n",
+    "```\n",
+    "The latter ones are deprecated due to their deficiencies."
    ]
   },
   {
@@ -26,8 +36,10 @@
    "source": [
     "import time\n",
     "\n",
+    "import numpy\n",
+    "\n",
     "import qcodes\n",
-    "from qcodes.instrument_drivers.Keysight.Keysight_34465A import Keysight_34465A"
+    "from qcodes.instrument_drivers.Keysight.Keysight_34465A_submodules import Keysight_34465A"
    ]
   },
   {
@@ -39,7 +51,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: Keysight Technologies 34465A (serial:MY57504787, firmware:A.02.16-02.40-02.16-00.51-03-01) in 0.48s\n"
+      "Connected to: Keysight Technologies 34465A (serial:MY57504787, firmware:A.02.16-02.40-02.16-00.51-03-01) in 0.46s\n"
      ]
     }
    ],
@@ -47,43 +59,114 @@
     "dmm = Keysight_34465A('dmm', 'USB0::10893::257::MY57504787::0::INSTR')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dmm.reset()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Parameters overview:\n",
+    "## Parameters and methods overview\n",
+    "\n",
+    "Here is an overview (not exhaustive) of the parameters and methods that are available in the driver.\n",
     "\n",
-    "Here we present __some__ of the parameters of the driver.\n",
+    "The driver is not only comprised of root-level parameters and methods but also contains submodules which logically group some functionality.\n",
     "\n",
-    "* Single-point measurement\n",
+    "* Measurements\n",
+    "  * `dmm.init_measurement()`\n",
+    "  * `dmm.fetch()`\n",
+    "  * `dmm.read()`\n",
     "  * `dmm.volt` - measure one voltage point now (note: may not always work)\n",
     "* Range settings\n",
     "  * `dmm.range`\n",
     "  * `dmm.autorange`\n",
-    "* Triggering settings\n",
-    "  * `dmm.trigger_source`\n",
-    "  * `dmm.trigger_delay`\n",
-    "  * `dmm.auto_trigger_delay_enabled`\n",
-    "  * `dmm.trigger_count`\n",
-    "  * `dmm.trigger_slope`\n",
-    "  * `dmm.trigger_level`\n",
+    "  * `dmm.autorange_once()`\n",
+    "* Triggering\n",
+    "  * `dmm.trigger.source`\n",
+    "  * `dmm.trigger.delay`\n",
+    "  * `dmm.trigger.auto_delay_enabled`\n",
+    "  * `dmm.trigger.count`\n",
+    "  * `dmm.trigger.slope`\n",
+    "  * `dmm.trigger.level`\n",
+    "  * `dmm.trigger.force()`\n",
     "* Sample settings\n",
-    "  * `dmm.sample_count`\n",
-    "  * `dmm.sample_source`\n",
-    "  * `dmm.sample_timer`\n",
+    "  * `dmm.sample.count`\n",
+    "  * `dmm.sample.source`\n",
+    "  * `dmm.sample.timer`\n",
+    "  * `dmm.sample.pretrigger_count`\n",
     "* Display control\n",
-    "  * `dmm.display_text`\n",
-    "  * `dmm.display_clear`\n",
-    "  * `dmm.display_enabled`\n",
+    "  * `dmm.display.text`\n",
+    "  * `dmm.display.clear()`\n",
+    "  * `dmm.display.enabled`\n",
     "* Related measurement accuracy\n",
     "  * `dmm.NPLC`\n",
     "  * `dmm.resolution`\n",
     "  * `dmm.line_frequency`\n",
-    "  * `dmm.aperture_mode`\n",
-    "  * `dmm.aperture_time`\n",
+    "  * `dmm.aperture.mode`\n",
+    "  * `dmm.aperture.time`\n",
     "  * `dmm.autozero`\n",
     "  \n",
-    "Use `dmm.parameters` (or other ways) to explore all the parameters."
+    "Use `dmm.parameters` (or other ways) to explore all the parameters.\n",
+    "\n",
+    "As an extra, let's print the readable snapshot of the instrument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dmm:\n",
+      "\tparameter     value\n",
+      "--------------------------------------------------------------------------------\n",
+      "IDN            :\t{'vendor': 'Keysight Technologies', 'model': '34465A', 'seri...\n",
+      "NPLC           :\t10 (NPLC)\n",
+      "aperture_mode  :\tOFF \n",
+      "aperture_time  :\t0.1 \n",
+      "autorange      :\tON \n",
+      "autozero       :\tON \n",
+      "line_frequency :\t50 (Hz)\n",
+      "range          :\t1000 \n",
+      "resolution     :\t0.0001 (V)\n",
+      "timeout        :\t5 (s)\n",
+      "volt           :\t-5.9611e-08 (V)\n",
+      "dmm_display:\n",
+      "\tparameter value\n",
+      "--------------------------------------------------------------------------------\n",
+      "enabled :\tTrue \n",
+      "text    :\t \n",
+      "dmm_trigger:\n",
+      "\tparameter         value\n",
+      "--------------------------------------------------------------------------------\n",
+      "auto_delay_enabled :\tTrue \n",
+      "count              :\t1 \n",
+      "delay              :\t0.00016 (s)\n",
+      "level              :\t0 (V)\n",
+      "slope              :\tNEG \n",
+      "source             :\tIMM \n",
+      "dmm_sample:\n",
+      "\tparameter       value\n",
+      "--------------------------------------------------------------------------------\n",
+      "count            :\t1 \n",
+      "pretrigger_count :\t0 \n",
+      "source           :\tIMM \n",
+      "timer            :\t1 (s)\n",
+      "timer_minimum    :\t0.43913 (s)\n"
+     ]
+    }
+   ],
+   "source": [
+    "dmm.print_readable_snapshot(update=True)"
    ]
   },
   {
@@ -94,21 +177,21 @@
     "\n",
     "If one simpy wants to measure a single voltage value right now, the convenient `volt` parameter can be used.\n",
     "\n",
-    "Note that it may not work for some configurations of the instrument, for example, when `sample_count` is not `1`. Later in this notebook, we will present more prowerful and robust ways of performing measurements."
+    "Note that it may not work for some configurations of the instrument, for example, when `sample.count` is not `1`. Later in this notebook, we will present more prowerful and robust ways of performing measurements."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.00927792071"
+       "-9.30337575e-06"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -133,98 +216,231 @@
     "Measurements with the instrument are performed in the following way: the instrument's settings are set for a particular kind of measurement, then the measurement is started/initialized, then after all the data has been acquired, it is retrieved from the instrument. Below is an example of such a measurement."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `range`, `autorange` parameters or `autorange_once` method to set the measurement range. Disabling autorange is recommended by the instrument's manual for speeding up the measurement."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Use `range`, `autorange` parameters or `autorange_once` method\n",
-    "# to set the measurement range\n",
     "dmm.autorange_once()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to set up the accuracy of the measurements and related settings, set up `NPLC` or `aperture_*` parameters (if available)."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Todo\n",
-    "# - document measurement process with `read` and `fetch`\n",
-    "#   (consult with experimentalists)\n",
-    "# - add info on how to create array of setpoints \n",
-    "#   (inst.sample_timer has resolution of some us, \n",
-    "#    hence it's important to keep that in mind for setpoint values)"
+    "dmm.aperture_mode('ON')\n",
+    "dmm.aperture_time(2e-5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up triggering mechanism. Note that trigger settings and methods are inside `trigger` submodule of the instrument driver. Here, we will use immediate triggering (the measurement is triggered uppon measurement initialization, that is when `init_measurement` is called) with 1 trigger without any delays. Consulm the instrument's manual for more information on various triggering options."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dmm.trigger.source('IMM')\n",
+    "dmm.trigger.count(1)\n",
+    "dmm.trigger.delay(0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up sampling settings. Note that sampling parameters and method are inside `sample` submodule of the instrument driver. Here, we set to measure 15 samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dmm.sample.count(15)\n",
+    "dmm.sample.pretrigger_count(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are also going to set the sample source to timer (not avaliable in all models) so that the instrument ensures that the samples are taken with fixed periods between them. The `timer` parameter allows to set the value of that fixed period. For simplicity, we are going to let the instrument deduct the minimum value of it according to the current instrument configuration (!) and set it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dmm.sample.source('TIM')\n",
+    "dmm.sample.timer('MIN')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It turns out that commands are executed faster when the display of the instrument is disabled or is displaying text. One of the further section expands on it. Here, we will just set the display to some text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dmm.display.text('Example with 15 samples')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to initiate the measurement, call `init_measurement` method of the driver. In the case of this example, the instrument will get into \"wait-for-trigger\" mode but because the trigger source is \"immediate\", the instrument will immediately start measuring the 15 samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dmm.init_measurement()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The insrtument is going to measure 15 samples and save them to its memory. Once the measurement is completed, we can call `fetch` method of the driver to retreive the acquired data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While the measurement is going, there are two things we can do. One is to `sleep` until the end of the measurement, and then call `fetch`. The other one is to call `fetch` immediately after the measurement has been initiated - this way the instrument will return the acquired data right when the measurement is finished. This sounds pretty useful, however there are two considerations to keep in mind: the instrument manual hints that calling fetching immediately after initiation may be slower than waiting for the measurement to complete; if the measurement takes longer than the VISA command timeout, the code may raise a VISA timeout exception while the measurement is properly running. To overcome the latter consideration, one could in principle wrap `fetch` call into the following `try-finally` block that takes care of the VISA timeout value:\n",
+    "```python\n",
+    "old_timeout = dmm.visa_handle.timeout  # it is in milliseconds\n",
+    "new_timeout = old_timeout + n_samples * time_per_sample * 1000\n",
+    "# where, n_samples == dmm.sample.count(),\n",
+    "# and time_per_sample == dmm.sample.timer()\n",
+    "dmm.visa_handle.timeout = new_timeout\n",
+    "try:\n",
+    "    data = dmm.fetch()\n",
+    "finally:\n",
+    "    dmm.visa_handle.timeout = old_timeout\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assuming that we've just slept or waited enough for the measurement to complete, let's `fetch` the data from the instrument. Note that due to the nature of the `fetch` command of the instrument, one can fetch the same measured data more than once (until, for example, a new measurement has been initiated; refer to the instrument's manual for more information on this)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([0.01263095, 0.01261663, 0.01246853]), ((0.0, 6.75e-05, 0.000135),))"
+       "array([-0.00469509, -0.00495776, -0.00509389, -0.00502464, -0.00457326,\n",
+       "       -0.00368487, -0.00033184,  0.00395255,  0.00707154,  0.00978691,\n",
+       "        0.01218942,  0.01157326,  0.01100726,  0.01077802,  0.0099947 ])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# REFERENCE MEASUREMENT SEQUENCE>>>>\n",
-    "\n",
-    "\n",
-    "dmm.sample_count(3)\n",
-    "\n",
-    "# PREPARE\n",
-    "\n",
-    "n = dmm.sample_count()\n",
-    "\n",
-    "# ensure correct instrument settings\n",
-    "\n",
-    "dmm.aperture_mode('OFF')  # aperture mode seems slower ON than OFF\n",
-    "dmm.autorange('OFF')\n",
-    "\n",
-    "dmm.trigger_count(1)\n",
-    "dmm.trigger_delay(0)\n",
-    "\n",
-    "dmm.sample_count_pretrigger(0)\n",
-    "\n",
-    "# Final step\n",
-    "\n",
-    "time_per_point = dmm.sample_timer_minimum()\n",
-    "\n",
-    "dmm.sample_timer(time_per_point)\n",
-    "dmm.sample_source('TIM')\n",
-    "\n",
-    "setpoints = (tuple(np.linspace(0, n*time_per_point, n)),)\n",
-    "shape = (n,)\n",
-    "\n",
-    "# MEASURE\n",
-    "\n",
-    "n = dmm.sample_count()\n",
-    "\n",
-    "# Ensure that the measurement doesn't time out\n",
-    "# TODO (WilliamHPNielsen): What if we wait really long for a trigger?\n",
-    "old_timeout = dmm.visa_handle.timeout\n",
-    "dmm.visa_handle.timeout = old_timeout + n * time_per_point * 1.2 * 1000\n",
-    "\n",
-    "# Turn off the display to increase measurement speed\n",
-    "dmm.display_text(f'Acquiring {n} samples')\n",
-    "\n",
-    "dmm.init_measurement()\n",
-    "\n",
-    "numvals = dmm.fetch()\n",
-    "\n",
-    "dmm.visa_handle.timeout = old_timeout\n",
-    "\n",
-    "dmm.display_clear()\n",
-    "\n",
-    "numvals, setpoints"
+    "data = dmm.fetch()\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that there is also a `read` method. It's difference from `fetch` is that it also initiates the new measurement. Using `read` might be convenient for some cases while `init_measurement` + `fetch` definitely allow for more control."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the measurement is finished, let's bring back the display to life."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dmm.display.clear()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If needed, it is straightforward to calculate a vector of times when the acquired data points were measured; for example, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.     , 0.00063, 0.00126, 0.00189, 0.00252, 0.00315, 0.00378,\n",
+       "       0.00441, 0.00504, 0.00567, 0.0063 , 0.00693, 0.00756, 0.00819,\n",
+       "       0.00882])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n = dmm.sample.count()\n",
+    "t = dmm.sample.timer()\n",
+    "setpoints = numpy.linspace(0, n*t, n)\n",
+    "setpoints  # in seconds"
    ]
   },
   {
@@ -245,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -254,20 +470,20 @@
        "3600.0"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Find out what the maximum value of `sample_timer` can be\n",
-    "dmm.sample_timer('MAX')\n",
-    "dmm.sample_timer()"
+    "dmm.sample.timer('MAX')\n",
+    "dmm.sample.timer()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -276,51 +492,51 @@
        "1.0"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Find out what the default value of `sample_timer` is\n",
-    "dmm.sample_timer('DEF')\n",
-    "dmm.sample_timer()"
+    "dmm.sample.timer('DEF')\n",
+    "dmm.sample.timer()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4.5e-05"
+       "0.000588"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Find out what the recommended minumum value of `sample_timer` is\n",
-    "dmm.sample_timer('MIN')\n",
-    "dmm.sample_timer()"
+    "dmm.sample.timer('MIN')\n",
+    "dmm.sample.timer()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4.5e-05"
+       "0.000588"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -330,7 +546,7 @@
     "# get-only parameter to find out the actual value,\n",
     "# for example, for MIN value of `sample_timer` there is such\n",
     "# a convenient get-only parameter:\n",
-    "dmm.sample_timer_minimum()"
+    "dmm.sample.timer_minimum()"
    ]
   },
   {
@@ -346,96 +562,96 @@
    "source": [
     "Although it is indeed just nice to be able to display useful text on the screen of the instrument, it turn out that disabling the display (or making it display does text instead of all the indicators and values) improves command execution speed from the remote interface and provides basic security (quoted from the instrument's manual).\n",
     "\n",
-    "Hence, the driver provides `display_text` parameter that displays a given text on the insrturment, and a `display_clear` method that clears the text from display.\n",
+    "Hence, the driver provides `display` submodule with `text` parameter that displays a given text on the insrturment, and a `clear` method that clears the text from display.\n",
     "\n",
-    "The driver also provides `display_enabled` parameter. When it is set to `False`, the state of the display is such that it does not show anything. Note, however, that displaying text is still possible when the `display_enabled` is `False` (when `display_enabled` is `False`, `display_clear` clears the text from the screen but does not enable it)."
+    "The driver's `display` submodule also provides `enabled` parameter. When it is set to `False`, the state of the display is such that it does not show anything. Note, however, that displaying text is still possible when the `display.enabled` is `False` (when `display.enabled` is `False`, `display.clear` clears the text from the screen but does not enable it)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Displays the text\n",
-    "dmm.display_text('Hello, World!')"
+    "dmm.display.text('Hello, World!')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Returns display to its normal state\n",
-    "dmm.display_clear()"
+    "dmm.display.clear()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Note that a call to `display_clear` also updates \n",
     "# the value of the `display_text` parameter:\n",
-    "assert dmm.display_text() == ''"
+    "assert dmm.display.text() == ''"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Display can also be cleared by setting \n",
     "# `display_text` to an empty string\n",
-    "dmm.display_text('some text')  # Displays some text\n",
+    "dmm.display.text('some text')  # Displays some text\n",
     "time.sleep(0.5)\n",
-    "dmm.display_text('')  # Returns display to its normal state"
+    "dmm.display.text('')  # Returns display to its normal state"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Disables the display, which makes it turn black\n",
-    "dmm.display_enabled(False)"
+    "dmm.display.enabled(False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Shows some text on a disabled display\n",
-    "dmm.display_text(\"i'm disabled but still showing text\")"
+    "dmm.display.text(\"i'm disabled but still showing text\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Enabling display in this state \n",
     "# won't change what's being displayed\n",
-    "dmm.display_enabled(True)"
+    "dmm.display.enabled(True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
     "# ... but since the display is now enabled,\n",
     "# clearing the display will not only remove the text\n",
     "# but also show all the normal display indicators.\n",
-    "dmm.display_clear()"
+    "dmm.display.clear()"
    ]
   },
   {
@@ -454,7 +670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -463,7 +679,7 @@
        "(0, 'No error')"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -492,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -521,7 +737,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "__NOTE__: This approach is deprecated.\n",
+    "__NOTE__: This approach is deprecated and only available in the deprecated Keysight 344xxA drivers (those which do not originate from `*_submodules.py` files).\n",
     "\n",
     "One may use `data_buffer` `ArrayParameter` in order to perform a triggered measurement of a predefined number of samples with a single trigger that has not delay. The following code shows how to do it.\n",
     "\n",
@@ -530,7 +746,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument_drivers.Keysight.Keysight_34465A import Keysight_34465A as Keysight_34465A_old"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\a-miasta\\pycharmprojects\\qcodes\\qcodes\\instrument_drivers\\Keysight\\private\\Keysight_344xxA.py:143: UserWarning: This driver is old and will be removed from QCoDeS soon. Please use the Keysight_344xxA classes from the files instrument_drivers/Keysight/Keysight_344xxA_submodules instead.\n",
+      "  UserWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected to: Keysight Technologies 34465A (serial:MY57504787, firmware:A.02.16-02.40-02.16-00.51-03-01) in 0.51s\n"
+     ]
+    }
+   ],
+   "source": [
+    "dmm_old = Keysight_34465A_old('dmm_old', 'USB0::10893::257::MY57504787::0::INSTR')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dmm_old.reset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the sample timer for now to default,\n",
+    "# because due to aperture mode usage above\n",
+    "# there might be configuration compatibility \n",
+    "# errors on the instrument. They are not harmful,\n",
+    "# we do this just for the sake of a clean demo.\n",
+    "dmm_old.sample_timer('DEF')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -538,23 +811,23 @@
     "# we are going to use \"immediate\" which will start measuring\n",
     "# right after `init_measurement` is called (it will be called\n",
     "# for you by `data_buffer`'s get method')\n",
-    "dmm.trigger_source('IMM')"
+    "dmm_old.trigger_source('IMM')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Set sample count to number of samples that\n",
     "# should be acquired\n",
-    "dmm.sample_count(5)"
+    "dmm_old.sample_count(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -570,12 +843,12 @@
     "# One needs to prepare the `data_buffer` parameter\n",
     "# and the instrument for this measurement, otherwise\n",
     "# getting `data_buffer` will raise an exception\n",
-    "dmm.data_buffer.prepare()"
+    "dmm_old.data_buffer.prepare()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -583,21 +856,22 @@
     "# and obtain the measured data\n",
     "# (note the text on the instrument's display\n",
     "# during the measurement)\n",
-    "data = dmm.data_buffer()"
+    "data = dmm_old.data_buffer()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.01319693, 0.01314679, 0.01309664, 0.01305843, 0.01299873])"
+       "array([-1.22270673e-05,  2.16857420e-05,  1.70717544e-05,  8.30517780e-06,\n",
+       "        1.91480488e-05])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -609,20 +883,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "((0.0,\n",
-       "  5.6250000000000005e-05,\n",
-       "  0.00011250000000000001,\n",
-       "  0.00016875,\n",
-       "  0.00022500000000000002),)"
+       "  0.5056200000000001,\n",
+       "  1.0112400000000001,\n",
+       "  1.5168600000000003,\n",
+       "  2.0224800000000003),)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -632,12 +906,12 @@
     "# in this case its time in seconds when\n",
     "# each of the points have been measured relative\n",
     "# to the first point\n",
-    "dmm.data_buffer.setpoints"
+    "dmm_old.data_buffer.setpoints"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -646,7 +920,7 @@
        "(5,)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -654,21 +928,21 @@
    "source": [
     "# The shape of the setpoints reflects\n",
     "# the number of samples\n",
-    "dmm.data_buffer.shape"
+    "dmm_old.data_buffer.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4.5e-05"
+       "0.404496"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -677,12 +951,12 @@
     "# For internal use, the amount of time used for\n",
     "# measuring every point is saved into the following \n",
     "# attribute:\n",
-    "dmm.data_buffer.time_per_point"
+    "dmm_old.data_buffer.time_per_point"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 43,
    "metadata": {
     "scrolled": true
    },
@@ -690,10 +964,10 @@
     {
      "data": {
       "text/plain": [
-       "4.5e-05"
+       "0.404496"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -701,7 +975,7 @@
    "source": [
     "# ... which is equal to the value of the\n",
     "# `sample_timer` parameter:\n",
-    "dmm.sample_timer()"
+    "dmm_old.sample_timer()"
    ]
   },
   {

From 81d4ec271b079460686dc352c0a52536ad81fdd8 Mon Sep 17 00:00:00 2001
From: sochatoo <sohail.chatoor@microsoft.com>
Date: Mon, 7 Jan 2019 09:16:50 -0800
Subject: [PATCH 547/719] fix mypy

---
 qcodes/instrument_drivers/Lakeshore/Model_325.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/Lakeshore/Model_325.py b/qcodes/instrument_drivers/Lakeshore/Model_325.py
index 80f4ecf5c10..7f806a87e5c 100644
--- a/qcodes/instrument_drivers/Lakeshore/Model_325.py
+++ b/qcodes/instrument_drivers/Lakeshore/Model_325.py
@@ -19,10 +19,10 @@ def read_curve_file(curve_file: TextIO) -> dict:
     curve data.
     """
 
-    def split_data_line(line: str, parser: type = str) -> List[str]:
+    def split_data_line(line: str, parser: type=str) -> List[str]:
         return [parser(i) for i in line.split("  ") if i != ""]
 
-    def strip(strings: Iterable[str]) -> Tuple[str]:
+    def strip(strings: Iterable[str]) -> Tuple:
         return tuple(s.strip() for s in strings)
 
     lines = iter(curve_file.readlines())

From 38217fea398a4c4d2d324d7315685f960933f9c2 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 8 Jan 2019 10:40:58 +0100
Subject: [PATCH 548/719] Add AbstractInstrument ABC to allow clean instrument
 mixins definition

---
 qcodes/instrument/base.py                          | 14 ++++++++++++--
 .../Keysight/private/error_handling.py             |  4 +++-
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument/base.py b/qcodes/instrument/base.py
index cce4e5f1d13..4c5fcbca0d3 100644
--- a/qcodes/instrument/base.py
+++ b/qcodes/instrument/base.py
@@ -3,6 +3,7 @@
 import warnings
 import weakref
 import logging
+from abc import ABC
 from typing import Sequence, Optional, Dict, Union, Callable, Any, List, \
     TYPE_CHECKING, cast, Type
 
@@ -10,10 +11,11 @@
 import numpy as np
 if TYPE_CHECKING:
     from qcodes.instrument.channel import ChannelList
+    from qcodes.logger.instrument_logger import get_instrument_logger, \
+        InstrumentLoggerAdapter
 from qcodes.utils.helpers import DelegateAttributes, strip_attrs, full_class
 from qcodes.utils.metadata import Metadatable
 from qcodes.utils.validators import Anything
-from qcodes.logger.instrument_logger import get_instrument_logger
 from .parameter import Parameter, _BaseParameter
 from .function import Function
 
@@ -372,7 +374,15 @@ def validate_status(self, verbose: bool=False) -> None:
                 p.validate(value)
 
 
-class Instrument(InstrumentBase):
+class AbstractInstrument(ABC):
+    """ABC that is useful for defining mixin classes for Instrument class"""
+    log: InstrumentLoggerAdapter = None  # for logging from instrument
+
+    def ask(self, cmd: str) -> str:
+        pass
+
+
+class Instrument(InstrumentBase, AbstractInstrument):
 
     """
     Base class for all QCodes instruments.
diff --git a/qcodes/instrument_drivers/Keysight/private/error_handling.py b/qcodes/instrument_drivers/Keysight/private/error_handling.py
index a433a61e3dc..249eaa5d310 100644
--- a/qcodes/instrument_drivers/Keysight/private/error_handling.py
+++ b/qcodes/instrument_drivers/Keysight/private/error_handling.py
@@ -1,7 +1,9 @@
 from typing import Tuple
 
+from qcodes.instrument.base import AbstractInstrument
 
-class KeysightErrorQueueMixin:
+
+class KeysightErrorQueueMixin(AbstractInstrument):
     """
     Mixin class for visa instruments that happen to implement an error queue.
 

From 8a267e40ab44b2edbd6cc6b72b4590947e0104df Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 8 Jan 2019 10:51:11 +0100
Subject: [PATCH 549/719] Make mypy strict_optional==True happy by removing
 None value from ABC

---
 qcodes/instrument/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/base.py b/qcodes/instrument/base.py
index 4c5fcbca0d3..d739b75e1b4 100644
--- a/qcodes/instrument/base.py
+++ b/qcodes/instrument/base.py
@@ -376,7 +376,7 @@ def validate_status(self, verbose: bool=False) -> None:
 
 class AbstractInstrument(ABC):
     """ABC that is useful for defining mixin classes for Instrument class"""
-    log: InstrumentLoggerAdapter = None  # for logging from instrument
+    log: InstrumentLoggerAdapter  # instrument logging
 
     def ask(self, cmd: str) -> str:
         pass

From 1cdd8fbe76e0ed7e4019741116aad15726ad2565 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 8 Jan 2019 10:54:13 +0100
Subject: [PATCH 550/719] These are optional

---
 qcodes/dataset/measurements.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index 15b16a0b75d..7fe2e19f5ce 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -756,8 +756,8 @@ def _register_arrayparameter(self,
 
     def _register_parameter_with_setpoints(self,
                                            parameter: ParameterWithSetpoints,
-                                           setpoints: setpoints_type,
-                                           basis: setpoints_type,
+                                           setpoints: Optional[setpoints_type],
+                                           basis: Optional[setpoints_type],
                                            paramtype: str) -> None:
         """
         Register an ParameterWithSetpoints and the setpoints belonging to the

From 33244f0123beef3234dda23e44f3a01c3e43e923 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 8 Jan 2019 10:55:44 +0100
Subject: [PATCH 551/719] Check that inverse_val_mapping is defined before
 using it

In practice these checks are equivalent as the constructor ensures an inverse_val_mapping is defined when a val_mapping is given but mypy cannot see this
---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 2675e004f47..ec9c08bd8c8 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -395,7 +395,7 @@ def get_wrapper(*args: Any, **kwargs: Any) -> ParamDataType:
                     else:
                         value /= self.scale
 
-                if self.val_mapping is not None:
+                if self.inverse_val_mapping is not None:
                     if value in self.inverse_val_mapping:
                         value = self.inverse_val_mapping[value]
                     else:

From 8f7452cc922d50209709ad22764b96150bac2eba Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 8 Jan 2019 10:56:36 +0100
Subject: [PATCH 552/719] Fix bug with undefined symbol in type hint

---
 qcodes/instrument/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/base.py b/qcodes/instrument/base.py
index d739b75e1b4..7a25d0251b7 100644
--- a/qcodes/instrument/base.py
+++ b/qcodes/instrument/base.py
@@ -376,7 +376,7 @@ def validate_status(self, verbose: bool=False) -> None:
 
 class AbstractInstrument(ABC):
     """ABC that is useful for defining mixin classes for Instrument class"""
-    log: InstrumentLoggerAdapter  # instrument logging
+    log: 'InstrumentLoggerAdapter'  # instrument logging
 
     def ask(self, cmd: str) -> str:
         pass

From 0f864caf1b3cb35b97b0e548abb03cb642967c29 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 8 Jan 2019 10:58:06 +0100
Subject: [PATCH 553/719] Fix import bug of function that ended up in "if
 TYPE_CHECKING"

..introduced couple commits ago
---
 qcodes/instrument/base.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument/base.py b/qcodes/instrument/base.py
index 7a25d0251b7..e52e2597ba2 100644
--- a/qcodes/instrument/base.py
+++ b/qcodes/instrument/base.py
@@ -11,11 +11,11 @@
 import numpy as np
 if TYPE_CHECKING:
     from qcodes.instrument.channel import ChannelList
-    from qcodes.logger.instrument_logger import get_instrument_logger, \
-        InstrumentLoggerAdapter
+    from qcodes.logger.instrument_logger import InstrumentLoggerAdapter
 from qcodes.utils.helpers import DelegateAttributes, strip_attrs, full_class
 from qcodes.utils.metadata import Metadatable
 from qcodes.utils.validators import Anything
+from qcodes.logger.instrument_logger import get_instrument_logger
 from .parameter import Parameter, _BaseParameter
 from .function import Function
 

From c3331292185c3b4f548737ca13e921317c2d407d Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 8 Jan 2019 12:00:11 +0100
Subject: [PATCH 554/719] Test new Keysight 34456A driver (with submodules),
 not old one

---
 qcodes/tests/drivers/test_keysight_34465a.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/drivers/test_keysight_34465a.py b/qcodes/tests/drivers/test_keysight_34465a.py
index 0c70a10e031..ac22af26f1c 100644
--- a/qcodes/tests/drivers/test_keysight_34465a.py
+++ b/qcodes/tests/drivers/test_keysight_34465a.py
@@ -1,7 +1,8 @@
 import pytest
 
 import qcodes.instrument.sims as sims
-from qcodes.instrument_drivers.Keysight.Keysight_34465A import Keysight_34465A
+from qcodes.instrument_drivers.Keysight.Keysight_34465A_submodules import \
+    Keysight_34465A
 visalib = sims.__file__.replace('__init__.py', 'Keysight_34465A.yaml@sim')
 
 

From 0b818e244816aa0341e19643e2a21ac436bc3c47 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 8 Jan 2019 12:01:01 +0100
Subject: [PATCH 555/719] Add test for Keysight 34465A display text parameter

---
 qcodes/instrument/sims/Keysight_34465A.yaml  | 10 ++++++++++
 qcodes/tests/drivers/test_keysight_34465a.py |  9 +++++++++
 2 files changed, 19 insertions(+)

diff --git a/qcodes/instrument/sims/Keysight_34465A.yaml b/qcodes/instrument/sims/Keysight_34465A.yaml
index 82f449e0dd5..ae4ac4f482e 100644
--- a/qcodes/instrument/sims/Keysight_34465A.yaml
+++ b/qcodes/instrument/sims/Keysight_34465A.yaml
@@ -10,6 +10,8 @@ devices:
     dialogues:
       - q: "*IDN?"
         r: "Keysight, 34465A, 1000, 0.1"
+      - q: "DISPLay:TEXT:CLEar"
+        r: null_response
     properties:
       voltage:
         default: 10
@@ -71,6 +73,14 @@ devices:
         getter:
           q: "SYSTem:LFRequency?"
           r: "+{}"
+      display_text:
+        default: ""
+        getter:
+          q: "DISPLAY:TEXT?"
+          r: "\"{}\""
+          type: string
+        setter:
+          q: "DISPLAY:TEXT \"{}\""
 
 
 resources:
diff --git a/qcodes/tests/drivers/test_keysight_34465a.py b/qcodes/tests/drivers/test_keysight_34465a.py
index ac22af26f1c..6a1525615b3 100644
--- a/qcodes/tests/drivers/test_keysight_34465a.py
+++ b/qcodes/tests/drivers/test_keysight_34465a.py
@@ -43,3 +43,12 @@ def test_set_get_autorange(driver):
     driver.autorange.set('OFF')
     ar = driver.autorange.get()
     assert ar == 'OFF'
+
+
+def test_display_text(driver):
+    assert "" == driver.display.text()
+
+    driver.display.text("qwe")
+    assert "qwe" == driver.display.text()
+
+    driver.display.clear()

From 6d4c0d796bfc2cfb82b8e6224f876f39ce6b1fbb Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 8 Jan 2019 12:02:09 +0100
Subject: [PATCH 556/719] Add test for DIG option for Keysight 34456A

---
 qcodes/tests/drivers/test_keysight_34465a.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/qcodes/tests/drivers/test_keysight_34465a.py b/qcodes/tests/drivers/test_keysight_34465a.py
index 6a1525615b3..b8334bff1ff 100644
--- a/qcodes/tests/drivers/test_keysight_34465a.py
+++ b/qcodes/tests/drivers/test_keysight_34465a.py
@@ -22,6 +22,10 @@ def test_init(driver):
     assert idn['vendor'] == 'Keysight'
 
 
+def test_has_dig_option(driver):
+    assert True is driver.has_DIG
+
+
 def test_NPLC(driver):
     assert driver.NPLC.get() == 10.0
     driver.NPLC.set(0.2)

From a36b0797d59dc857f81cde9e32675953a6accadb Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 8 Jan 2019 12:19:33 +0100
Subject: [PATCH 557/719] Extract is_34465A_34470A attribute in Keysight 344xxA
 driver

this avoids reference to '34465A', '34470A' in the submodules.
---
 .../private/Keysight_344xxA_submodules.py        | 16 +++++++++-------
 qcodes/tests/drivers/test_keysight_34465a.py     |  4 ++++
 2 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
index 386d0eff56f..ba08b147d7e 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
@@ -16,7 +16,7 @@ class Trigger(InstrumentChannel):
     def __init__(self, parent: '_Keysight_344xxA', name: str, **kwargs):
         super(Trigger, self).__init__(parent, name, **kwargs)
 
-        if self.parent.model in ['34465A', '34470A']:
+        if self.parent.is_34465A_34470A:
             _max_trigger_count = 1e9
         else:
             _max_trigger_count = 1e6
@@ -81,7 +81,7 @@ def __init__(self, parent: '_Keysight_344xxA', name: str, **kwargs):
                            get_cmd='TRIGger:SLOPe?',
                            vals=vals.Enum('POS', 'NEG'))
 
-        if self.parent.model in ['34465A', '34470A'] and self.parent.has_DIG:
+        if self.parent.is_34465A_34470A and self.parent.has_DIG:
             self.add_parameter('level',
                                label='Trigger Level',
                                unit='V',
@@ -117,7 +117,7 @@ def __init__(self, parent: '_Keysight_344xxA', name: str, **kwargs):
                 it buffers one trigger.""")
         _trigger_source_vals = vals.Enum('IMM', 'EXT', 'BUS')
 
-        if self.parent.model in ['34465A', '34470A'] and self.parent.has_DIG:
+        if self.parent.is_34465A_34470A and self.parent.has_DIG:
             _trigger_source_vals = vals.Enum('IMM', 'EXT', 'BUS', 'INT')
             # extra empty lines are needed for readability of the docstring
             _trigger_source_docstring += textwrap.dedent("""\
@@ -146,7 +146,7 @@ class Sample(InstrumentChannel):
     def __init__(self, parent: '_Keysight_344xxA', name: str, **kwargs):
         super(Sample, self).__init__(parent, name, **kwargs)
 
-        if self.parent.model in ['34465A', '34470A']:
+        if self.parent.is_34465A_34470A:
             _max_sample_count = 1e9
         else:
             _max_sample_count = 1e6
@@ -181,7 +181,7 @@ def __init__(self, parent: '_Keysight_344xxA', name: str, **kwargs):
                 Reserves memory for pretrigger samples up to the specified 
                 num. of pretrigger samples."""))
 
-        if self.parent.model in ['34465A', '34470A']:
+        if self.parent.is_34465A_34470A:
             self.add_parameter('source',
                                label='Sample Timing Source',
                                set_cmd='SAMPle:SOURce {}',
@@ -319,6 +319,8 @@ def __init__(self, name, address, silent=False,
         idn = self.IDN.get()
         self.model = idn['model']
 
+        self.is_34465A_34470A = self.model in ['34465A', '34470A']
+
         ####################################
         # Instrument specifications
 
@@ -458,7 +460,7 @@ def __init__(self, name, address, silent=False,
         ####################################
         # Aperture parameters
 
-        if self.model in ['34465A', '34470A']:
+        if self.is_34465A_34470A:
             # Define the extreme aperture time values for the 34465A and 34470A
             utility_freq = self.line_frequency()
             if utility_freq == 50:
@@ -607,7 +609,7 @@ def _set_NPLC(self, value):
         self.resolution.get()
 
         # setting NPLC switches off aperture mode
-        if self.model in ['34465A', '34470A']:
+        if self.is_34465A_34470A:
             self.aperture_mode.get()
 
     def _set_range(self, value):
diff --git a/qcodes/tests/drivers/test_keysight_34465a.py b/qcodes/tests/drivers/test_keysight_34465a.py
index b8334bff1ff..7579189fc16 100644
--- a/qcodes/tests/drivers/test_keysight_34465a.py
+++ b/qcodes/tests/drivers/test_keysight_34465a.py
@@ -26,6 +26,10 @@ def test_has_dig_option(driver):
     assert True is driver.has_DIG
 
 
+def test_model_flag(driver):
+    assert True is driver.is_34465A_34470A
+
+
 def test_NPLC(driver):
     assert driver.NPLC.get() == 10.0
     driver.NPLC.set(0.2)

From daf9f970e4a2c3ddaa045a4d2e0756a07b62b537 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 8 Jan 2019 13:23:40 +0100
Subject: [PATCH 558/719] Note that DIG is included in 3.0 firmware for
 Keysight 34465A/34470A

See https://www.keysight.com/upload/cmc_upload/All/Firmware_3.0_Release_Notes.pdf
---
 .../driver_examples/Qcodes example with Keysight 344xxA.ipynb   | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb b/docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb
index f4bfdab2c1c..8cbb67bb6e5 100644
--- a/docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Keysight 344xxA.ipynb	
@@ -15,6 +15,8 @@
     "\n",
     "Note however that not every feature/parameter is available on all `344xxA` models. This, when possible, is reflected in the instantiated driver object. Also note that models like `34465A` have options (like `DIG` and `MEM`) that can either be enabled or disabled on a particular instrument; this also has impact on availability of some features/parameters and/or their settings. In general, refer to the instrument's manual for detailed information.\n",
     "\n",
+    "__NOTE__: Beginning with firmware revision 3.0, the digitizing and advanced triggering option, referred to as \"DIG\", for models 34465A-DIG/34470A-DIG, is now standard.\n",
+    "\n",
     "The driver does not cover all the features of the instrument because it is being mostly used for voltage DC measurements. Contribution is welcome.\n",
     "\n",
     "__NOTE__: use drivers for these models which originate from `*_submodules` python modules, from example:\n",

From 0c760934154f4b1fed52e4b3eed5ac5cd437cf5d Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 8 Jan 2019 14:31:09 +0100
Subject: [PATCH 559/719] Remove unused module-level log of Keysight 344xxA
 submodules driver

---
 .../Keysight/private/Keysight_344xxA_submodules.py             | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
index ba08b147d7e..9fd70def23e 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
@@ -1,14 +1,11 @@
 import textwrap
 import numpy as np
-import logging
 
 import qcodes.utils.validators as vals
 from qcodes import VisaInstrument, InstrumentChannel
 from qcodes.instrument_drivers.Keysight.private.error_handling import \
     KeysightErrorQueueMixin
 
-log = logging.getLogger(__name__)
-
 
 class Trigger(InstrumentChannel):
     """Implements triggering parameters and methods of Keysight 344xxA."""

From a2f37946f99ed9c7ad80df76a547441984b9de79 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Tue, 8 Jan 2019 16:22:13 +0100
Subject: [PATCH 560/719] Remove type hints in docstrings of Keysight 344xxA
 driver

---
 .../Keysight/private/Keysight_344xxA_submodules.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
index 9fd70def23e..6478f66c543 100644
--- a/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
+++ b/qcodes/instrument_drivers/Keysight/private/Keysight_344xxA_submodules.py
@@ -294,20 +294,20 @@ class _Keysight_344xxA(KeysightErrorQueueMixin, VisaInstrument):
     subsystems of the instrument.
 
     Attributes:
-        model (str): The model number of the instrument
-        NPLC_list (list): A list of the available Power Line Cycle settings
-        ranges (list): A list of the available voltage ranges
+        model: The model number of the instrument
+        NPLC_list: A list of the available Power Line Cycle settings
+        ranges: A list of the available voltage ranges
     """
 
-    def __init__(self, name, address, silent=False,
+    def __init__(self, name: str, address: str, silent: bool=False,
                  **kwargs):
         """
         Create an instance of the instrument.
 
         Args:
-            name (str): Name used by QCoDeS. Appears in the DataSet
-            address (str): Visa-resolvable instrument address.
-            silent (bool): If True, the connect_message of the instrument
+            name: Name used by QCoDeS. Appears in the DataSet
+            address: Visa-resolvable instrument address.
+            silent: If True, the connect_message of the instrument
                 is supressed. Default: False
         """
 

From b62be47600cf057b804a05450c69620c2e7cc36b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <j.h.nielsen@nbi.ku.dk>
Date: Tue, 8 Jan 2019 14:39:50 +0100
Subject: [PATCH 561/719] Update example to use new driver

---
 .../Qcodes example with Keysight 33500B.ipynb | 108 +++++++-----------
 1 file changed, 44 insertions(+), 64 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Keysight 33500B.ipynb b/docs/examples/driver_examples/Qcodes example with Keysight 33500B.ipynb
index 5b09632dc93..4604d01ad69 100644
--- a/docs/examples/driver_examples/Qcodes example with Keysight 33500B.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Keysight 33500B.ipynb	
@@ -11,18 +11,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pyqtgraph plotting not supported, try \"from qcodes.plots.pyqtgraph import QtPlot\" to see the full error\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import qcodes as qc\n",
-    "from qcodes.instrument_drivers.Keysight.Keysight_33500B import Keysight_33500B"
+    "from qcodes.instrument_drivers.Keysight.KeysightAgilent_33XXX import WaveformGenerator_33XXX "
    ]
   },
   {
@@ -34,12 +26,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: Agilent Technologies 33522B (serial:MY57800256, firmware:3.05-1.19-2.00-52-00) in 0.11s\n"
+      "Connected to: Agilent Technologies 33522B (serial:MY57800256, firmware:5.00-1.19-2.00-58-00) in 1.85s\n"
      ]
     }
    ],
    "source": [
-    "ks = Keysight_33500B('ks', 'TCPIP0::K-33522B-00256::inst0::INSTR')"
+    "ks = WaveformGenerator_33XXX('ks', 'TCPIP0::K-33522B-00256::inst0::INSTR')"
    ]
   },
   {
@@ -51,57 +43,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# SET up a sawtooth\n",
-    "ks.ch1_function_type('RAMP')\n",
-    "ks.ch1_ramp_symmetry(100)\n",
-    "ks.ch1_amplitude_unit('VPP')\n",
-    "ks.ch1_amplitude(1)\n",
-    "ks.ch1_offset(0)\n",
-    "ks.ch1_frequency(2e3)\n",
-    "ks.sync_source(1)"
+    "ks.ch1.function_type('RAMP')\n",
+    "ks.ch1.ramp_symmetry(100)\n",
+    "ks.ch1.amplitude_unit('VPP')\n",
+    "ks.ch1.amplitude(1)\n",
+    "ks.ch1.offset(0)\n",
+    "ks.ch1.frequency(2e3)\n",
+    "ks.sync.source(1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Start it\n",
-    "ks.sync_output('ON')\n",
-    "ks.ch1_output('ON')\n"
+    "ks.sync.output('ON')\n",
+    "ks.ch1.output('ON')\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "ks.ch1_frequency(1e3)"
+    "ks.ch1.frequency(1e3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# stop it\n",
-    "ks.sync_output('OFF')\n",
-    "ks.ch1_output('OFF')"
+    "ks.sync.output('OFF')\n",
+    "ks.ch1.output('OFF')"
    ]
   },
   {
@@ -112,50 +96,46 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
-   "source": []
+   "source": [
+    "In burst mode, the instrument starts running a task (e.g. a waveform generation) upon receiving a trigger\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# In burst mode, the instrument starts running a task (e.g. a waveform generation) upon receiving a trigger\n",
-    "\n",
     "# TRIGGERING\n",
     "\n",
     "# Can be 'EXT' (external), 'IMM' (immediate, internal),\n",
     "# 'BUS' (software trigger), 'TIM' (timed)\n",
-    "ks.ch1_trigger_source('EXT')  \n",
+    "ks.ch1.trigger_source('EXT')  \n",
     "\n",
-    "ks.ch1_trigger_count(1)\n",
-    "ks.ch1_trigger_delay(0)  # seconds\n",
+    "ks.ch1.trigger_count(1)\n",
+    "ks.ch1.trigger_delay(0)  # seconds\n",
     "\n",
     "# for external triggering, a slope should be specified\n",
-    "ks.ch1_trigger_slope('POS')\n",
+    "ks.ch1.trigger_slope('POS')\n",
     "\n",
     "# For timed triggering, the time between each trigger should be set\n",
-    "ks.ch1_trigger_timer(50e-3)\n",
+    "ks.ch1.trigger_timer(50e-3)\n",
     "\n",
     "# BURSTING\n",
     "\n",
-    "ks.ch1_burst_state('ON')\n",
-    "ks.ch1_burst_mode('N Cycle')  # Can be 'N Cycle' or 'Gated'\n",
+    "ks.ch1.burst_state('ON')\n",
+    "ks.ch1.burst_mode('N Cycle')  # Can be 'N Cycle' or 'Gated'\n",
     "\n",
     "# when in 'N Cycle' mode, the following options are available\n",
-    "ks.ch1_burst_ncycles(1)  # Can be 1, 2, 3,... , 'MIN', 'MAX', or 'INF'\n",
-    "ks.ch1_burst_phase(180)  # the starting phase (degrees)\n",
+    "ks.ch1.burst_ncycles(1)  # Can be 1, 2, 3,... , 'MIN', 'MAX', or 'INF'\n",
+    "ks.ch1.burst_phase(180)  # the starting phase (degrees)\n",
     "\n",
     "# If in 'Gated' mode, the following should be specified\n",
-    "ks.ch1_burst_polarity('NORM')  # Can be 'NORM' or 'INV'"
+    "ks.ch1.burst_polarity('NORM')  # Can be 'NORM' or 'INV'"
    ]
   },
   {
@@ -167,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -176,7 +156,7 @@
        "(0, 'No error')"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -191,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -241,7 +221,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.8"
   },
   "nbsphinx": {
    "execute": "never"

From c39ed1b950be96044fb24a3f2263737011f50cf2 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <j.h.nielsen@nbi.ku.dk>
Date: Tue, 8 Jan 2019 14:41:27 +0100
Subject: [PATCH 562/719] remove really old keysigth waveform driver

---
 .../Keysight/Keysight_33500B.py               | 259 ------------------
 1 file changed, 259 deletions(-)
 delete mode 100644 qcodes/instrument_drivers/Keysight/Keysight_33500B.py

diff --git a/qcodes/instrument_drivers/Keysight/Keysight_33500B.py b/qcodes/instrument_drivers/Keysight/Keysight_33500B.py
deleted file mode 100644
index ac5be8e2405..00000000000
--- a/qcodes/instrument_drivers/Keysight/Keysight_33500B.py
+++ /dev/null
@@ -1,259 +0,0 @@
-import warnings
-
-from qcodes import VisaInstrument, validators as vals
-from functools import partial
-import logging
-
-log = logging.getLogger(__name__)
-
-
-class Keysight_33500B(VisaInstrument):
-    """
-    QCoDeS driver for the Keysight 33500B Waveform Generator
-    """
-
-    def __init__(self, name, address, silent=False, **kwargs):
-        """
-        Args:
-            name (string): The name of the instrument used internally
-                by QCoDeS. Must be unique.
-            address (string): The VISA resource name.
-            silent (Optional[bool]): If True, no connect message is printed.
-        """
-
-        warnings.warn("This driver is old and will be removed "
-                      "from QCoDeS soon. Please use the "
-                      "WaveformGenerator_33XXX from the file "
-                      "instrument_drivers/Keysight/KeysightAgilent_33XXX"
-                      " instead.", UserWarning)
-
-        super().__init__(name, address, **kwargs)
-
-        # convenient little helper functions
-        def setcmd(channel, setting):
-            return 'SOURce{}:'.format(channel) + setting + ' {}'
-
-        def getcmd(channel, setting):
-            return 'SOURce{}:'.format(channel) + setting + '?'
-
-        def val_parser(parser, inputstring):
-            """
-            Parses return values from instrument. Meant to be used when a query
-            can return a meaningful finite number or a numeric representation
-            of infinity
-
-            Args:
-                inputstring (str): The raw return value
-                parser (type): Either int or float, what to return in finite
-                    cases
-            """
-
-            inputstring = inputstring.strip()
-
-            if float(inputstring) == 9.9e37:
-                output = float('inf')
-            else:
-                output = float(inputstring)
-                if parser == int:
-                    output = parser(output)
-
-            return output
-
-        for chan in [1, 2]:
-
-            self.add_parameter('ch{}_function_type'.format(chan),
-                               label='Channel {} function type'.format(chan),
-                               set_cmd=setcmd(chan, 'FUNCtion'),
-                               get_cmd=getcmd(chan, 'FUNCtion'),
-                               vals=vals.Enum('SIN', 'SQU', 'TRI', 'RAMP',
-                                              'PULS', 'PRBS', 'NOIS', 'ARB',
-                                              'DC')
-                               )
-
-            self.add_parameter('ch{}_frequency_mode'.format(chan),
-                               label='Channel {} frequency mode'.format(chan),
-                               set_cmd=setcmd(chan, 'FREQuency:MODE'),
-                               get_cmd=getcmd(chan, 'FREQuency:MODE'),
-                               vals=vals.Enum('CW', 'LIST', 'SWEEP', 'FIXED')
-                               )
-
-            self.add_parameter('ch{}_frequency'.format(chan),
-                               label='Channel {} frequency'.format(chan),
-                               set_cmd=setcmd(chan, 'FREQuency'),
-                               get_cmd=getcmd(chan, 'FREQUency'),
-                               get_parser=float,
-                               unit='Hz',
-                               # TODO: max. freq. actually really tricky
-                               vals=vals.Numbers(1e-6, 30e6)
-                               )
-
-            self.add_parameter('ch{}_phase'.format(chan),
-                               label='Channel {} phase'.format(chan),
-                               set_cmd=setcmd(chan, 'PHASe'),
-                               get_cmd=getcmd(chan, 'PHASe'),
-                               get_parser=float,
-                               unit='deg',
-                               vals=vals.Numbers(0, 360)
-                               )
-            self.add_parameter('ch{}_amplitude_unit'.format(chan),
-                               label='Channel {} amplitude unit'.format(chan),
-                               set_cmd=setcmd(chan, 'VOLTage:UNIT'),
-                               get_cmd=getcmd(chan, 'VOLTage:UNIT'),
-                               vals=vals.Enum('VPP', 'VRMS', 'DBM')
-                               )
-
-            self.add_parameter('ch{}_amplitude'.format(chan),
-                               label='Channel {} amplitude'.format(chan),
-                               set_cmd=setcmd(chan, 'VOLTage'),
-                               get_cmd=getcmd(chan, 'VOLTage'),
-                               unit='',  # see amplitude_unit
-                               get_parser=float)
-
-            self.add_parameter('ch{}_offset'.format(chan),
-                               label='Channel {} voltage offset'.format(chan),
-                               set_cmd=setcmd(chan, 'VOLTage:OFFSet'),
-                               get_cmd=getcmd(chan, 'VOLTage:OFFSet'),
-                               unit='V',
-                               get_parser=float
-                               )
-            self.add_parameter('ch{}_output'.format(chan),
-                               label='Channel {} output state'.format(chan),
-                               set_cmd='OUTPut{}'.format(chan) + ' {}',
-                               get_cmd='OUTPut{}?'.format(chan),
-                               val_mapping={'ON': 1, 'OFF': 0}
-                               )
-
-            self.add_parameter('ch{}_output_polarity'.format(chan),
-                               label='Channel {} output polarity'.format(chan),
-                               set_cmd='OUTPut{}:POL'.format(chan) + ' {}',
-                               get_cmd='OUTPut{}:POL?'.format(chan),
-                               get_parser=str.strip,
-                               vals=vals.Enum('NORM', 'INV')
-                               )
-
-            self.add_parameter('ch{}_ramp_symmetry'.format(chan),
-                               label='Channel {} ramp symmetry'.format(chan),
-                               set_cmd=setcmd(chan, 'FUNCtion:RAMP:SYMMetry'),
-                               get_cmd=getcmd(chan, 'FUNCtion:RAMP:SYMMetry'),
-                               get_parser=float,
-                               vals=vals.Numbers(0, 100)
-                               )
-
-            # TRIGGER MENU
-            self.add_parameter('ch{}_trigger_source'.format(chan),
-                               label='Channel {} trigger source'.format(chan),
-                               set_cmd='TRIGger{}:SOURce {}'.format(chan, '{}'),
-                               get_cmd='TRIGger{}:SOURce?'.format(chan),
-                               vals=vals.Enum('IMM', 'EXT', 'TIM', 'BUS')
-                               )
-
-            self.add_parameter('ch{}_trigger_count'.format(chan),
-                               label='Channel {} trigger count'.format(chan),
-                               set_cmd='TRIGger{}:COUNt {}'.format(chan, '{}'),
-                               get_cmd='TRIGger{}:COUNt?'.format(chan),
-                               vals=vals.Ints(1, 1000000),
-                               get_parser=partial(val_parser, int)
-                               )
-
-            self.add_parameter('ch{}_trigger_delay'.format(chan),
-                               label='Channel {} trigger delay'.format(chan),
-                               set_cmd='TRIGger{}:DELay {}'.format(chan, '{}'),
-                               get_cmd='TRIGger{}:DELay?'.format(chan),
-                               vals=vals.Numbers(0, 1000),
-                               get_parser=float,
-                               unit='s'
-                               )
-
-            # TODO: trigger level doesn't work remotely. Why?
-
-            self.add_parameter('ch{}_trigger_slope'.format(chan),
-                               label='Channel {} trigger slope'.format(chan),
-                               set_cmd='TRIGger{}:SLOPe {}'.format(chan, '{}'),
-                               get_cmd='TRIGger{}:SLOPe?'.format(chan),
-                               vals=vals.Enum('POS', 'NEG')
-                               )
-
-            self.add_parameter('ch{}_trigger_timer'.format(chan),
-                               label='Channel {} trigger timer'.format(chan),
-                               set_cmd='TRIGger{}:TIMer {}'.format(chan, '{}'),
-                               get_cmd='TRIGger{}:TIMer?'.format(chan),
-                               vals=vals.Numbers(1e-6, 8000),
-                               get_parser=float
-                               )
-
-            # BURST MENU
-            self.add_parameter('ch{}_burst_state'.format(chan),
-                               label='Channel {} burst state'.format(chan),
-                               set_cmd=setcmd(chan, 'BURSt:STATe'),
-                               get_cmd=getcmd(chan, 'BURSt:STATe'),
-                               val_mapping={'ON': 1, 'OFF': 0},
-                               vals=vals.Enum('ON', 'OFF')
-                               )
-
-            self.add_parameter('ch{}_burst_mode'.format(chan),
-                               label='Channel {} burst mode'.format(chan),
-                               set_cmd=setcmd(chan, 'BURSt:MODE'),
-                               get_cmd=getcmd(chan, 'BURSt:MODE'),
-                               val_mapping={'N Cycle': 'TRIG', 'Gated': 'GAT'},
-                               vals=vals.Enum('N Cycle', 'Gated')
-                               )
-
-            self.add_parameter('ch{}_burst_ncycles'.format(chan),
-                               label='Channel {} burst no. of cycles'.format(chan),
-                               set_cmd=setcmd(chan, 'BURSt:NCYCles'),
-                               get_cmd=getcmd(chan, 'BURSt:NCYCLes'),
-                               get_parser=partial(val_parser, int),
-                               vals=vals.MultiType(vals.Ints(1),
-                                                   vals.Enum('MIN', 'MAX',
-                                                             'INF'))
-                               )
-
-            self.add_parameter('ch{}_burst_phase'.format(chan),
-                               label='Channel {} burst start phase'.format(chan),
-                               set_cmd=setcmd(chan, 'BURSt:PHASe'),
-                               get_cmd=getcmd(chan, 'BURSt:PHASe'),
-                               vals=vals.Numbers(-360, 360),
-                               unit='degrees',
-                               get_parser=float
-                               )
-
-            self.add_parameter('ch{}_burst_polarity'.format(chan),
-                               label='Channel {} burst gated polarity'.format(chan),
-                               set_cmd=setcmd(chan, 'BURSt:GATE:POLarity'),
-                               get_cmd=getcmd(chan, 'BURSt:GATE:POLarity'),
-                               vals=vals.Enum('NORM', 'INV')
-                               )
-
-            self.add_parameter('ch{}_burst_int_period'.format(chan),
-                               label=('Channel {}'.format(chan) +
-                                      ' burst internal period'),
-                               set_cmd=setcmd(chan, 'BURSt:INTernal:PERiod'),
-                               get_cmd=getcmd(chan, 'BURSt:INTernal:PERiod'),
-                               unit='s',
-                               vals=vals.Numbers(1e-6, 8e3),
-                               get_parser=float,
-                               docstring=('The burst period is the time '
-                                          'between the starts of consecutive '
-                                          'bursts when trigger is immediate.')
-                               )
-
-        self.add_parameter('sync_source',
-                           label='Source of sync function',
-                           set_cmd='OUTPut:SYNC:SOURce {}',
-                           get_cmd='OUTPut:SYNC:SOURce?',
-                           val_mapping={1: 'CH1', 2: 'CH2'},
-                           vals=vals.Enum(1, 2)
-                           )
-
-        self.add_parameter('sync_output',
-                           label='Sync output state',
-                           set_cmd='OUTPut:SYNC {}',
-                           get_cmd='OUTPut:SYNC?',
-                           val_mapping={'ON': 1, 'OFF': 0},
-                           vals=vals.Enum('ON', 'OFF')
-                           )
-
-        self.add_function('force_trigger', call_cmd='*TRG')
-
-        if not silent:
-            self.connect_message()

From d692a1669384a5f7ad5618c4b3e6761f7e7cbebe Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <j.h.nielsen@nbi.ku.dk>
Date: Tue, 8 Jan 2019 14:41:45 +0100
Subject: [PATCH 563/719] remove old keysight waveform driver

---
 .../Keysight/Keysight_33500B_channels.py      | 288 ------------------
 1 file changed, 288 deletions(-)
 delete mode 100644 qcodes/instrument_drivers/Keysight/Keysight_33500B_channels.py

diff --git a/qcodes/instrument_drivers/Keysight/Keysight_33500B_channels.py b/qcodes/instrument_drivers/Keysight/Keysight_33500B_channels.py
deleted file mode 100644
index 875d9c77ce4..00000000000
--- a/qcodes/instrument_drivers/Keysight/Keysight_33500B_channels.py
+++ /dev/null
@@ -1,288 +0,0 @@
-import warnings
-
-from qcodes import VisaInstrument, validators as vals
-from functools import partial
-from qcodes.instrument.channel import InstrumentChannel, ChannelList
-import logging
-
-from qcodes.instrument_drivers.Keysight.private.error_handling import \
-    KeysightErrorQueueMixin
-
-log = logging.getLogger(__name__)
-
-
-class KeysightChannel(InstrumentChannel):
-    """
-
-
-    """
-    def __init__(self, parent, name, channum):
-        """
-        Args:
-            parent (Instrument): The instrument to which the channel is
-                attached.
-            name (str): The name of the channel
-            channum (int): The number of the channel in question (1-2)
-        """
-        super().__init__(parent, name)
-
-        def val_parser(parser, inputstring):
-            """
-            Parses return values from instrument. Meant to be used when a query
-            can return a meaningful finite number or a numeric representation
-            of infinity
-
-            Args:
-                inputstring (str): The raw return value
-                parser (type): Either int or float, what to return in finite
-                    cases
-            """
-
-            inputstring = inputstring.strip()
-
-            if float(inputstring) == 9.9e37:
-                output = float('inf')
-            else:
-                output = float(inputstring)
-                if parser == int:
-                    output = parser(output)
-
-            return output
-
-        self.add_parameter('function_type',
-                           label='Channel {} function type'.format(channum),
-                           set_cmd='SOURce{}:FUNCtion {{}}'.format(channum),
-                           get_cmd='SOURce{}:FUNCtion?'.format(channum),
-                           get_parser=str.rstrip,
-                           vals=vals.Enum('SIN', 'SQU', 'TRI', 'RAMP',
-                                          'PULS', 'PRBS', 'NOIS', 'ARB',
-                                          'DC')
-                           )
-
-        self.add_parameter('frequency_mode',
-                           label='Channel {} frequency mode'.format(channum),
-                           set_cmd='SOURce{}:FREQuency:MODE {{}}'.format(channum),
-                           get_cmd='SOURce{}:FREQuency:MODE?'.format(channum),
-                           get_parser=str.rstrip,
-                           vals=vals.Enum('CW', 'LIST', 'SWEEP', 'FIXED')
-                           )
-
-        self.add_parameter('frequency',
-                           label='Channel {} frequency'.format(channum),
-                           set_cmd='SOURce{}:FREQuency {{}}'.format(channum),
-                           get_cmd='SOURce{}:FREQuency?'.format(channum),
-                           get_parser=float,
-                           unit='Hz',
-                           # TODO: max. freq. actually really tricky
-                           vals=vals.Numbers(1e-6, 30e6)
-                           )
-
-        self.add_parameter('phase',
-                           label='Channel {} phase'.format(channum),
-                           set_cmd='SOURce{}:PHASe {{}}'.format(channum),
-                           get_cmd='SOURce{}:PHASe?'.format(channum),
-                           get_parser=float,
-                           unit='deg',
-                           vals=vals.Numbers(0, 360)
-                           )
-        self.add_parameter('amplitude_unit',
-                           label='Channel {} amplitude unit'.format(channum),
-                           set_cmd='SOURce{}:VOLTage:UNIT {{}}'.format(channum),
-                           get_cmd='SOURce{}:VOLTage:UNIT?'.format(channum),
-                           vals=vals.Enum('VPP', 'VRMS', 'DBM'),
-                           get_parser=str.rstrip
-                           )
-
-        self.add_parameter('amplitude',
-                           label='Channel {} amplitude'.format(channum),
-                           set_cmd='SOURce{}:VOLTage {{}}'.format(channum),
-                           get_cmd='SOURce{}:VOLTage?'.format(channum),
-                           unit='',  # see amplitude_unit
-                           get_parser=float)
-
-        self.add_parameter('offset',
-                           label='Channel {} voltage offset'.format(channum),
-                           set_cmd='SOURce{}:VOLTage:OFFSet {{}}'.format(channum),
-                           get_cmd='SOURce{}:VOLTage:OFFSet?'.format(channum),
-                           unit='V',
-                           get_parser=float
-                           )
-        self.add_parameter('output',
-                           label='Channel {} output state'.format(channum),
-                           set_cmd='OUTPut{} {{}}'.format(channum),
-                           get_cmd='OUTPut{}?'.format(channum),
-                           val_mapping={'ON': 1, 'OFF': 0}
-                           )
-
-        self.add_parameter('ramp_symmetry',
-                           label='Channel {} ramp symmetry'.format(channum),
-                           set_cmd='SOURce{}:FUNCtion:RAMP:SYMMetry {{}}'.format(channum),
-                           get_cmd='SOURce{}:FUNCtion:RAMP:SYMMetry?'.format(channum),
-                           get_parser=float,
-                           unit='%',
-                           vals=vals.Numbers(0, 100)
-                           )
-
-        # TRIGGER MENU
-        self.add_parameter('trigger_source',
-                           label='Channel {} trigger source'.format(channum),
-                           set_cmd='TRIGger{}:SOURce {{}}'.format(channum),
-                           get_cmd='TRIGger{}:SOURce?'.format(channum),
-                           vals=vals.Enum('IMM', 'EXT', 'TIM', 'BUS'),
-                           get_parser=str.rstrip,
-                           )
-
-        self.add_parameter('trigger_count',
-                           label='Channel {} trigger count'.format(channum),
-                           set_cmd='TRIGger{}:COUNt {{}}'.format(channum),
-                           get_cmd='TRIGger{}:COUNt?'.format(channum),
-                           vals=vals.Ints(1, 1000000),
-                           get_parser=partial(val_parser, int)
-                           )
-
-        self.add_parameter('trigger_delay',
-                           label='Channel {} trigger delay'.format(channum),
-                           set_cmd='TRIGger{}:DELay {{}}'.format(channum),
-                           get_cmd='TRIGger{}:DELay?'.format(channum),
-                           vals=vals.Numbers(0, 1000),
-                           get_parser=float,
-                           unit='s'
-                           )
-
-        # TODO: trigger level doesn't work remotely. Why?
-
-        self.add_parameter('trigger_slope',
-                           label='Channel {} trigger slope'.format(channum),
-                           set_cmd='TRIGger{}:SLOPe {{}}'.format(channum),
-                           get_cmd='TRIGger{}:SLOPe?'.format(channum),
-                           vals=vals.Enum('POS', 'NEG'),
-                           get_parser=str.rstrip
-                           )
-
-        self.add_parameter('trigger_timer',
-                           label='Channel {} trigger timer'.format(channum),
-                           set_cmd='TRIGger{}:TIMer {{}}'.format(channum),
-                           get_cmd='TRIGger{}:TIMer?'.format(channum),
-                           vals=vals.Numbers(1e-6, 8000),
-                           get_parser=float
-                           )
-
-        # output menu
-        self.add_parameter('output_polarity',
-                           label='Channel {} output polarity'.format(channum),
-                           set_cmd='OUTPut{}:POLarity {{}}'.format(channum),
-                           get_cmd='OUTPut{}:POLarity?'.format(channum),
-                           get_parser=str.rstrip,
-                           vals=vals.Enum('NORM', 'INV')
-                           )
-        # BURST MENU
-        self.add_parameter('burst_state',
-                           label='Channel {} burst state'.format(channum),
-                           set_cmd='SOURce{}:BURSt:STATe {{}}'.format(channum),
-                           get_cmd='SOURce{}:BURSt:STATe?'.format(channum),
-                           val_mapping={'ON': 1, 'OFF': 0},
-                           vals=vals.Enum('ON', 'OFF')
-                           )
-
-        self.add_parameter('burst_mode',
-                           label='Channel {} burst mode'.format(channum),
-                           set_cmd='SOURce{}:BURSt:MODE {{}}'.format(channum),
-                           get_cmd='SOURce{}:BURSt:MODE?'.format(channum),
-                           get_parser=str.rstrip,
-                           val_mapping={'N Cycle': 'TRIG', 'Gated': 'GAT'},
-                           vals=vals.Enum('N Cycle', 'Gated')
-                           )
-
-        self.add_parameter('burst_ncycles',
-                           label='Channel {} burst no. of cycles'.format(channum),
-                           set_cmd='SOURce{}:BURSt:NCYCles {{}}'.format(channum),
-                           get_cmd='SOURce{}:BURSt:NCYCLes?'.format(channum),
-                           get_parser=partial(val_parser, int),
-                           vals=vals.MultiType(vals.Ints(1),
-                                               vals.Enum('MIN', 'MAX',
-                                                         'INF'))
-                           )
-
-        self.add_parameter('burst_phase',
-                           label='Channel {} burst start phase'.format(channum),
-                           set_cmd='SOURce{}:BURSt:PHASe {{}}'.format(channum),
-                           get_cmd='SOURce{}:BURSt:PHASe?'.format(channum),
-                           vals=vals.Numbers(-360, 360),
-                           unit='degrees',
-                           get_parser=float
-                           )
-
-        self.add_parameter('burst_polarity',
-                           label='Channel {} burst gated polarity'.format(channum),
-                           set_cmd='SOURce{}:BURSt:GATE:POLarity {{}}'.format(channum),
-                           get_cmd='SOURce{}:BURSt:GATE:POLarity?'.format(channum),
-                           vals=vals.Enum('NORM', 'INV')
-                           )
-
-        self.add_parameter('burst_int_period',
-                           label=('Channel {}'.format(channum) +
-                                  ' burst internal period'),
-                           set_cmd='SOURce{}:BURSt:INTernal:PERiod {{}}'.format(channum),
-                           get_cmd='SOURce{}:BURSt:INTernal:PERiod'.format(channum),
-                           unit='s',
-                           vals=vals.Numbers(1e-6, 8e3),
-                           get_parser=float,
-                           docstring=('The burst period is the time '
-                                      'between the starts of consecutive '
-                                      'bursts when trigger is immediate.')
-                           )
-
-
-class Keysight_33500B_Channels(KeysightErrorQueueMixin, VisaInstrument):
-    """
-    QCoDeS driver for the Keysight 33500B Waveform Generator using QCoDeS
-    channels
-    """
-
-    def __init__(self, name, address, silent=False, **kwargs):
-        """
-        Args:
-            name (string): The name of the instrument used internally
-                by QCoDeS. Must be unique.
-            address (string): The VISA resource name.
-            silent (Optional[bool]): If True, no connect message is printed.
-        """
-
-        warnings.warn("This driver is old and will be removed "
-                      "from QCoDeS soon. Please use the "
-                      "WaveformGenerator_33XXX from the file "
-                      "instrument_drivers/Keysight/KeysightAgilent_33XXX"
-                      " instead.", UserWarning)
-
-        super().__init__(name, address, **kwargs)
-
-        channels = ChannelList(self, "Channels", KeysightChannel,
-                               snapshotable=False)
-        for i in range(1, 3):
-            channel = KeysightChannel(self, 'ch{}'.format(i), i)
-            channels.append(channel)
-        channels.lock()
-        self.add_submodule('channels', channels)
-
-        self.add_parameter('sync_source',
-                           label='Source of sync function',
-                           set_cmd='OUTPut:SYNC:SOURce {}',
-                           get_cmd='OUTPut:SYNC:SOURce?',
-                           val_mapping={1: 'CH1', 2: 'CH2'},
-                           vals=vals.Enum(1, 2)
-                           )
-
-        self.add_parameter('sync_output',
-                           label='Sync output state',
-                           set_cmd='OUTPut:SYNC {}',
-                           get_cmd='OUTPut:SYNC?',
-                           val_mapping={'ON': 1, 'OFF': 0},
-                           vals=vals.Enum('ON', 'OFF')
-                           )
-
-        self.add_function('force_trigger', call_cmd='*TRG')
-
-        self.add_function('sync_channel_phases', call_cmd='PHAS:SYNC')
-
-        if not silent:
-            self.connect_message()

From 835840e1ab715b4cd81212fb3bcc86e1162667a6 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <j.h.nielsen@nbi.ku.dk>
Date: Tue, 8 Jan 2019 14:44:25 +0100
Subject: [PATCH 564/719] remove note in tutorial too

---
 docs/examples/Tutorial.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/examples/Tutorial.ipynb b/docs/examples/Tutorial.ipynb
index c0688a4c048..bf359af28fb 100644
--- a/docs/examples/Tutorial.ipynb
+++ b/docs/examples/Tutorial.ipynb
@@ -49,7 +49,7 @@
     "# In this tutorial, we import the dummy instrument\n",
     "from qcodes.tests.instrument_mocks import DummyInstrument\n",
     "# real instruments are imported in a similar way, e.g.\n",
-    "# from qcodes.instrument_drivers.Keysight.Keysight_33500B import Keysight_33500B"
+    "# from qcodes.instrument_drivers.Keysight.KeysightAgilent_33XXX import WaveformGenerator_33XXX"
    ]
   },
   {
@@ -595,7 +595,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,

From a003426f6548f6cdd34632b3b3f6e09571452ece Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 10:20:40 +0100
Subject: [PATCH 565/719] Check that shape is a Sequence. It is way to easy to
 forget this and use a single value causing strange errors later on

---
 qcodes/tests/test_validators.py | 6 ++++++
 qcodes/utils/validators.py      | 3 +++
 2 files changed, 9 insertions(+)

diff --git a/qcodes/tests/test_validators.py b/qcodes/tests/test_validators.py
index 0908527f6d1..96bf265a03b 100644
--- a/qcodes/tests/test_validators.py
+++ b/qcodes/tests/test_validators.py
@@ -623,6 +623,12 @@ def test_valid_values(self):
         for vval in val.valid_values:
             val.validate(vval)
 
+    def test_shape_non_sequence_raises(self):
+        with self.assertRaises(ValueError):
+            m = Arrays(shape=5)
+        with self.assertRaises(ValueError):
+            m = Arrays(shape=lambda: 10)
+
 
 class TestLists(TestCase):
 
diff --git a/qcodes/utils/validators.py b/qcodes/utils/validators.py
index 99b3c3e4359..3153aca75b8 100644
--- a/qcodes/utils/validators.py
+++ b/qcodes/utils/validators.py
@@ -511,6 +511,9 @@ def __init__(self, min_value: numbertypes=-float("inf"),
             self._max_value = max_value
         else:
             raise TypeError('max_value must be a number bigger than min_value')
+        if not isinstance(shape, collections.abc.Sequence):
+            raise ValueError(f"Shape must be a sequence (List, Tuple ...)"
+                             f"got a {type(shape)}")
         self._shape = shape
 
     @property

From f7a36759f8961ddbe4f81cd0fdad6ad1e53a165e Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 10:30:51 +0100
Subject: [PATCH 566/719] update example notebook

---
 ...arameter Example with Dual Setpoints.ipynb | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb
index c91dfad491a..0c3499c7c04 100644
--- a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb	
+++ b/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb	
@@ -163,7 +163,7 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#48@C:\\Users\\jenielse/experiments.db\n",
+       "tutorial_exp#no sample#49@C:\\Users\\jenielse/experiments.db\n",
        "----------------------------------------------------------"
       ]
      },
@@ -193,7 +193,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 184\n"
+      "Starting experimental run with id: 189\n"
      ]
     }
    ],
@@ -216,7 +216,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -240,7 +240,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 185\n"
+      "Starting experimental run with id: 190\n"
      ]
     }
    ],
@@ -260,7 +260,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -291,7 +291,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 186\n"
+      "Starting experimental run with id: 191\n"
      ]
     }
    ],
@@ -314,7 +314,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -347,7 +347,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 187\n"
+      "Starting experimental run with id: 192\n"
      ]
     }
    ],
@@ -373,7 +373,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -406,7 +406,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 188\n"
+      "Starting experimental run with id: 193\n"
      ]
     }
    ],
@@ -434,7 +434,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]

From 032d1252452c4b9acd2c2c93cd4adc5054db4d1b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 10:33:35 +0100
Subject: [PATCH 567/719] update simple example notebook

---
 ...xample of new ParameterWithSetpoints.ipynb | 288 ++++++++++--------
 1 file changed, 157 insertions(+), 131 deletions(-)

diff --git a/docs/examples/DataSet/Simple Example of new ParameterWithSetpoints.ipynb b/docs/examples/DataSet/Simple Example of new ParameterWithSetpoints.ipynb
index ed29993a991..de94897ec14 100644
--- a/docs/examples/DataSet/Simple Example of new ParameterWithSetpoints.ipynb	
+++ b/docs/examples/DataSet/Simple Example of new ParameterWithSetpoints.ipynb	
@@ -23,34 +23,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qcodes.instrument.parameter import Parameter, _BaseParameter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Optional, Union, Iterable, Callable\n",
-    "Number = Union[float, int]\n",
     "from qcodes.utils.validators import Validator"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qcodes.utils.validators import Numbers\n",
+    "from qcodes.utils.validators import Numbers, Arrays\n",
     "from qcodes.instrument.base import Instrument\n",
     "from qcodes.dataset.measurements import Measurement"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,6 +101,7 @@
     "\n",
     "        self.add_parameter('n_points',\n",
     "                           unit='',\n",
+    "                           initial_value=10,\n",
     "                           vals=Numbers(1,1e3),\n",
     "                           get_cmd=None,\n",
     "                           set_cmd=None)\n",
@@ -122,29 +112,31 @@
     "                           parameter_class=GeneratedSetPoints,\n",
     "                           startparam=self.f_start,\n",
     "                           stopparam=self.f_stop,\n",
-    "                           numpointsparam=self.n_points)\n",
+    "                           numpointsparam=self.n_points,\n",
+    "                           vals=Arrays(shape=(self.n_points,)))\n",
     "                           \n",
     "        self.add_parameter('spectrum',\n",
     "                   unit='dBm',\n",
     "                   setpoints=(self.freq_axis,),\n",
     "                   label='Spectrum',\n",
-    "                   parameter_class=DummyArray)\n",
+    "                   parameter_class=DummyArray,\n",
+    "                   vals=Arrays(shape=(self.n_points,)))\n",
     "                "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#142@c:\\Users\\jenielse\\mymainfolder\\experiments.db\n",
-       "------------------------------------------------------------------------"
+       "tutorial_exp#no sample#63@C:\\Users\\jenielse/experiments.db\n",
+       "----------------------------------------------------------"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -156,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -172,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "scrolled": true
    },
@@ -246,7 +238,7 @@
        "       495., 496., 497., 498., 499., 500.])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -262,6 +254,15 @@
     "Or the spectrum"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.spectrum.validate(a.spectrum.get())"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 12,
@@ -272,107 +273,132 @@
     {
      "data": {
       "text/plain": [
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-       "       0.60918186, 0.34274291, 0.43832254, 0.76568971, 0.21721021,\n",
-       "       0.30198089, 0.47201838, 0.97928559, 0.7715346 , 0.23269826,\n",
-       "       0.36167025, 0.80635443, 0.04952206, 0.58616747, 0.40224088,\n",
-       "       0.45277086, 0.02814571, 0.07327414, 0.63791345, 0.18959364,\n",
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-       "       0.24921754, 0.00510943, 0.85173873, 0.99469415, 0.3457128 ,\n",
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-       "       0.86318191, 0.99017436, 0.60923479, 0.67913571, 0.4967267 ,\n",
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-       "       0.5345427 , 0.50194307, 0.90729726, 0.43601256, 0.62007211,\n",
-       "       0.96943269])"
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+       "       2.54100442e-01, 1.21613964e-01, 2.06075373e-01, 9.57780926e-01,\n",
+       "       1.67754656e-01, 8.44389250e-01, 4.90915971e-01, 9.85315419e-01,\n",
+       "       3.58931990e-01, 1.75352462e-01, 5.48442741e-01, 9.69724630e-01,\n",
+       "       7.84696761e-01, 5.95453211e-01, 4.46227847e-01, 5.56367034e-01,\n",
+       "       1.32768080e-01, 2.40457994e-01, 5.16025790e-01, 3.35198116e-01,\n",
+       "       1.96859303e-02, 4.73916440e-01, 5.13924078e-01, 7.95614141e-03,\n",
+       "       7.03834387e-01, 2.30139796e-01, 9.22670026e-01, 8.06222913e-01,\n",
+       "       2.74788244e-01, 8.01547933e-01, 5.26186281e-01, 3.67969257e-01,\n",
+       "       6.67493545e-02, 8.19918941e-01, 8.22979906e-01, 8.47044716e-01,\n",
+       "       8.86409061e-01, 7.54259578e-01, 7.75318427e-01, 9.39424840e-01,\n",
+       "       8.91715831e-01, 5.89820922e-01, 2.22696854e-01, 6.73415611e-02,\n",
+       "       1.82561702e-01, 2.83042062e-01, 4.54999958e-01, 5.62660691e-01,\n",
+       "       5.05646810e-01, 6.39395318e-01, 3.04989084e-01, 7.34171101e-01,\n",
+       "       2.62020818e-01, 8.67640847e-01, 1.33128948e-01, 5.65729831e-01,\n",
+       "       6.77766201e-01, 8.79206033e-01, 3.01816459e-01, 1.48754984e-01,\n",
+       "       3.53535420e-01, 6.74319003e-01, 6.86249130e-01, 5.38895789e-01,\n",
+       "       7.90107305e-01, 4.97196863e-01, 3.92791954e-01, 9.00572811e-01,\n",
+       "       8.64999057e-01, 2.25876316e-01, 2.38182449e-01, 6.67462145e-01,\n",
+       "       4.57156070e-01, 2.28598486e-01, 8.56032752e-01, 3.19034835e-01,\n",
+       "       1.54671766e-02, 5.65654395e-01, 7.06621873e-01, 7.50065321e-01,\n",
+       "       1.02797119e-01, 2.74504727e-02, 2.81663345e-01, 2.20485667e-01,\n",
+       "       1.03373229e-01, 2.95338198e-01, 5.20948016e-01, 6.82498498e-01,\n",
+       "       2.52836581e-01, 1.79905515e-01, 5.93305874e-01, 8.96879916e-01,\n",
+       "       3.26623208e-01, 9.07221366e-01, 6.56687833e-01, 9.73822788e-01,\n",
+       "       4.09865141e-01, 2.45423235e-01, 2.69359733e-01, 1.49531897e-01,\n",
+       "       9.34080269e-01, 6.67603782e-01, 1.86377318e-01, 7.40802937e-01,\n",
+       "       6.30561849e-01, 7.18895820e-01, 4.08513231e-01, 3.17966177e-01,\n",
+       "       6.17779009e-01, 3.90437029e-01, 7.94862578e-01, 9.85788627e-02,\n",
+       "       3.28066179e-01, 2.26438034e-01, 6.17329521e-01, 3.85326397e-01,\n",
+       "       6.01781844e-01, 6.89534572e-01, 8.56048988e-01, 8.11034554e-01,\n",
+       "       7.04480431e-03, 6.18811079e-01, 1.04159197e-01, 6.77885586e-01,\n",
+       "       3.11216630e-01, 8.82639409e-01, 4.98601025e-02, 8.62054586e-02,\n",
+       "       3.90111802e-01, 4.86802713e-01, 3.86178727e-01, 8.16806377e-01,\n",
+       "       5.59569936e-01, 9.31851945e-01, 3.38537080e-01, 6.65107848e-01,\n",
+       "       2.87820375e-01, 1.66643374e-01, 1.52961276e-01, 8.12295908e-01,\n",
+       "       1.23274592e-01, 8.76236833e-01, 7.33592633e-01, 1.30967942e-01,\n",
+       "       9.30508799e-01, 4.33689890e-02, 5.00731512e-02, 3.48616422e-01,\n",
+       "       2.14162784e-01, 7.06106345e-01, 8.15929613e-01, 9.80794906e-01,\n",
+       "       2.15223564e-01, 7.14210665e-01, 9.54456416e-01, 4.12783683e-01,\n",
+       "       9.25716133e-01, 6.43724159e-01, 2.47726754e-01, 5.34835031e-01,\n",
+       "       8.28760836e-01, 7.48169391e-01, 2.77494772e-01, 7.17041031e-01,\n",
+       "       6.25623702e-01, 8.55655412e-01, 6.78380144e-02, 5.66745731e-01,\n",
+       "       6.77679139e-01, 2.86716794e-01, 3.25670901e-01, 9.96884751e-01,\n",
+       "       1.68874871e-01, 9.49903707e-01, 9.24780280e-01, 4.47229079e-01,\n",
+       "       8.81187926e-01, 9.12028693e-01, 5.33108739e-01, 7.19084041e-01,\n",
+       "       3.22300678e-02, 7.55049706e-01, 9.64847183e-01, 8.02197067e-01,\n",
+       "       8.84905621e-01])"
       ]
      },
      "execution_count": 12,
@@ -400,7 +426,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 1075\n"
+      "Starting experimental run with id: 194\n"
      ]
     }
    ],
@@ -438,7 +464,7 @@
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x175f49b5160>], [None])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1a94560a080>], [None])"
       ]
      },
      "execution_count": 15,
@@ -447,7 +473,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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Od5l/CqBXGMrFALSn1vuhtn/V59+XeXsdBWBDphK7AsCxRLRjZow/NjvXRwLa2bKASNftMjEC9JyxZsswLr9vBU78pbSz8dhBr0L1/ycLTr74ftyxMN0WUBWL127F6k3DY9a+xoIntgAALrlneX7urwvW4G0/uAU/nGAmXzAPMs7BO/d0xUS4DZ8H4FYABxPREiL6MIDTARxDRPMBHJP9BoDLoPbJfgTAT5Dt8czMawGcCuCO7O9L2bk+EtAOT6DsYxnP9WE963PbOKue+tYbGa/83+vwoq9c7Z3/w11LcOGcJV3r59l77AAAWLd1JD+nVUwPr9zUtX7agWYoZDEU/5zGojVbJ1yqGk+Me7ZhZv6HwKWjhbIM4F8D7fwMwM+6SFoUzIyrH1yFtVuG8aJZO2H/XbYfr667jnakjPGUUDQa2Yc4UZLC03W9OTTaxO9mL8Z7XrIfagl6nP88/x4AwNtfuHdH/TIzmAsJeqLm4WaLsXjtVsx6xnbeNU2T+VT0N+Hyk0dWbcbrvnUDPnnMQfjE0Qd2lcbPXHAvLpu7HHO/eFxX2+0UvaLy6nlc99Aq/NMvZ+MzF87FG868aVz6PO3yB3HEqVcBAIYbTSzfsK0r7XIba3CCllDG7yvve3pNDL555UP4wp/ux+X3rRjXfk+/fB72/9xl+Yq/NUG7yX7tz/Pwmm9cj6Xr/e+NHXvJ0vXb8JObFljnNPT3evtj3VeenD97MTYNN8oLjjP6DCURpu54uNEalz5/dMMCrN2ixP4fXv8oXnratZi9sPPB2cl3Op7f+EQxFH2PP7pxAb555UMTQsNEYk025sZK1bh60zDuXeK72J59s3LGmOhdyW98WIUXbNjqx5poqVnbSz708zuwPitHiTLt1/48D+/68a3dILXn0GcoiZjoQb58vdIhT5RrYrftjSs2DOGJzXEDb6MHJJTvXvvIRJMw7mBBrdNNHPftG/GW7/3FO9/MOtb9S7aHqt/h0GgTp1x8P9aUjDXVNuP6h1blC8ZJA/4TaDRthrJmS9FuqpfXD69/FLcteGqafPsMJRETPbdNnzLQNTrasqHkdbvzII467Roc+WXfwGuiUwnlH39yG2addClmnXRpR+388taFHdV/soFzI/PYtK+lbr/f7D+6Z0P5w11L8YtbFuIH15d7h11+3wp84Od34LHMy2xSve6VGW0pZqOfzWizINI1ypd9Kt36lnoJfYaSiPG0HUhoOR9bCFtHGviXX8/Byo3hwLC2bCgTYpTvTLV4y6Nr2qrnPp8v/On+juh4skHf/US5weqxbtpQ2iVlWWYH2W5yuf/RcieYsibMjqOZ9KKfTaNZjNGqz+vJ5paegj5DScRELyZaiZ4vl967HJfNXYGv/Xmedf7Tv78nP27nXvTHEup/+YZt+Pwf51ofWKd4qhnlR5stXHH/ip5fmeaeTG1O4p3eH+eqr/bamXXSpXjr95VKTS+stp/sSxvldPjntBo2l1CMMeqqvMqe3/ou5AObddKlQYlvItBnKInolSmg7BurZ6PaLfd7I06grTiUkusnXTgX5962CH9pUyqQ0As2lDKcdvmDVs4qZsbpl8/D3CUbvLI3Prwa//yrOXhk1ebxJLEyOmUInb42vZDopJl7srxaKzYqG8fm4XJpIIV/jjZtCWXUWEClMmBdbl2XGMEiJz/fObcsHPfsEhp9hpKKCV5VFuJ/nA490GOr+04mjJC6bCxcPM176KbkE8PPbn4sV5Ok4Ec3LLByVjVbjLNueBRv/f7NXlmt4hgaHZ97KcNwoykmNOxU5dWpZKnNEt0YUxuy4MgtCS627u1K3WubiaQClgIbJcycOggAuXdYp6g7/Z588f14+w8nxousz1ASMdGL5VzlVTIX6UC0ZuRjbOtWSJZ8YrjuoVV4/89ub5uBNQyD53hEy6/eNIwvXfIAzr1tkXV+ymD6Z6IplsaLtgm1M1Fu2Daa5Kkk4U93L8Vrvu4nBb1s7nK87Qd/8VQmnRrlO2UEms5ufHO6jc1D5QzFZaDS4qnhSCgmUp/XzGmTANiZADpBL2V86TOUREy03jvVKK/1uEvWbcNnLrjXEsk1OvLyqlDnn86ZjRseXt226spc6Xb69FPeX+jDfO6eM7xz9y3dgDuEmKDYZKpXt+1MuId98Uq8sMQrLoRP//5eLFyzFSPOWNg83ESLfWZduA1PkITSav85udDfy+aRbkkomqH411Iluhm5hNIdhlLvoayU45565cmKidbmp6aj0OLvPYvX457F63H8oXvgVQc5afs7MMpX4UadPjNTyup0bmm0GIP1+Ifnqg40pLNv+q6v0gLidDY6YCidIPjqshPNJkun286eG5KOmy2Oeh9q5M+niy7yKRKKV1c4N+rEoZhIfV7TJikHgW55efVSUsq+hJKIiVZ55d9YgI7/+v09ePXXr/NyLw2UTKKp0GO27Dl0U5JrGvq9lHaveXBlUFfeaJbXD32YVRhAlKHkKq/k5roCfV9Do00rJbymw3XPbnWq8grc4LeueggvO/3a0vpdlVA0Q0mxoXh1BZVXHociMZSKcSgl9Cxdvw3v+elfsXEobmup99As3kOk9DYmXuUVd6W8YM4SPL5mqzeoBwRn+vZyeZVcFz6wwv2zcncAbCZQ1saC1Zvx4XNm478vvFe8PpoQ0xJ2OCitWtoGUKxuO1UJ/dfv78Hrv31jcnm9xjj22zfiOV8odnnQ78dVSRa/5Lde9i2E7u+m+U+U0goUEo7UTNUnp7+bFKO8y0FjEsrVD67E16+wXfNDRvmQKq1sTJ959cO4+ZEncPnc5dFyqc4A44E+QxkDPPbEFsw66VI8uHxj19osbChxuKsVSULpKJdXG8yo3aDQKjYUvQJdtEbe4jhFQgk9lxYzmDlplRvjFc0OjPImLpizBPNWpKdx1xOOu5dJLqFUVHmV2cRiDiEpaHVRQtHYlKDyquI2DADfv86Ovq86rXcrWLqv8noSosrgvuoBlaX1ojuX4II5S3DF/Z1nbY1JKKYawzWkDgizQjvDuJNI+bYlFJOhlDRSFownOSe4CPXQYsZvbl+E5518BRZmaTnCdCQY5cfZazj0TDStrkRReHnJFcuYc6f35+b06gS6DXf3RwkpRvnYvVe1OfV4fGtb6BvlE1Hl5dczNVOjxfivLEJ94enHd0iA+ictDh83VuXuhCaqvNrasXH8N9hqx8sr9E0nMZTAc2m1gKsfUNsnP7p6s7hPRtFGuH09GV330Cocvu/MpHQg3UDZPOfaUPQthOqNNFuYinDkeecSSkZHF728XKb5pu/e5Emzvleb339sHIX2jmn3NlLrTbQ63kSfoSSiyivT3kQpapZU5BKKcM0U590PR3Ip7ERCaUcN0R0JJVzusrnLPR25a8hMUnkFzreY8+fYiVOCnrjPvvkxrNw4hO/94xGlNLl479l/rVwnNNGVSShSLiugnDl3ukNhYZTvqBmrDfe13LfUV0dXCWyU4Ep0ZZqoMkZQMPZ4Q73DTvoMJRlVJtKBXELpjm6DmcWEeRJtKXS2F4eidV4lbQvH7dtQDC+vSBv/8us782MiwubhBg495UqrTMq7iNlQ9GRRZlCPPVtzMnqsRHUWQqph20RoOiq8vFg8H5rIyhhK6BmljrvCKN8FCaVCW5J8snzDNsycOglTM1ff2DhKVXlV/h5KGVO15sYSfRtKIqq8NG0Ij61mqvYd8823GYpTV6jQiWG9nTvqhoSS2jERsElws0x5FzEvLx2jUjYxxa53kpm2Hdy/bAMO+vzlWBdI8aFJ9SQUfRCyRzXafwZ2/yEVY3cklCvuXxHNXOBCklBeetq1eP/Pbs/PxZipy4BDjyHVyyv9u+kdjtJnKImooqfUhvBuZctlGINQGjzGKV99EWiwKg0d3EqKd5QE817M2/rWVQ/jZaddE6wnrayT1I8hCaVlqrzKJtPwNZNBjiU/0WP10dVbMBLZXVTfi+c2rFVeASLdiPtQu+V0yucLejr7fv75V3MM22P7i6jbjYwIsYVJSEIJvetUisqGSl9CeRLCfWl3LVoXLDuQ+e6mGIJT0GIuNh0SmjTnA/fDkQZbO+NPrxpdxnrMt26w9PrS4H/JV68Jbl0c07dbcSgG1d+5Zj6WbZAjrgnyB5wWhyJDqbz0cVkb4QJ2ZtrOOUrIc0m/oqGS/Gf6XTZdo3yu8pJRrvKKXi76CZzPJRShnequudn/FAklQcKIJSmtKnV2ixFYamaj0Ykw1vcZSiLcV/P1K8J7jWsJpR2j/LaRppfWmtnwfDEoWbZ+G+av3BS1oYgqrzbGWWHDsc/PX7U5qNc3+7lrkZ/VFojHNLSsjyONTiISV6OjkZV6WR8tNvaD6cCGYo6HbqRfes4XrhClP93LcClDUf/dVXeZSjTEUB5ZtQnrtozYkmUbWa9jNpSqQ7cVactDh0b5qtyu7DmHrrvPzfxpHndL5V4FfYaSCHdAxlQJOUNpQ+X15u/djMNPvco6x2Bxg62XnX4tjjnjRos291uXJZTqdBVxMJWrRhEzclpeXontEWRVY8q7CD2XZoLK64hTr8Jlc5fHjfLGvabOPY+u3hxdaUr2Il2+LEOzfiQhNWmo1xBDed23bsQbzrzJHo8R2kNXuunlpbtPYSgp2YbjNpRqaPdb8m1e8ndSppocC/QZSpuITVBanSFNlmu3jEQnCGnzJWWUL46l6xrd9PJ6dPVmvOp/r8OKDUPJ3jJVv5HYc7TiUBKJvn/ZRixd5+9nkhaH4p8jUn3ruSZkF1u7ZQRf+r8HSozyxbUUldfitVtx9DdvwP9GpOGYmqVs35WQDaUszc9IxCi/YuOQ9Yzi+/LE6epGJHmV/en9XF5+mW5s+sbO/zK4Y8W3eRXH5vgrk1DHAn2Gkgh3cMU+lJB//6OrN+OIU6/CObcsbKPv8EduqbwSjPKpA/l3sxdj0dqtOO/2RcHVrEap4TDQa0wtWCWXl8a20Sb+/se3VepHQ1IfDdTI8vKK0VGvyeq2nIaW6eVVSg6GMyn4/DsWB8tIzWgKyiQUXS5kQ6kqoWiYUknKd+LVz+110W6SYKuDOm8wxlDKWt80NIp7l6yvZthJoCGk8hpOUPN2G32Gkgh3MMY+Kl3WLfPYahV7cGPFWAKl8tLHPszx5aoYZBuKf27+yk2YddKluCqLCAeAg3adDgB4cPnGvN1Oo6BXbBjC/cuK7XFjKi93ousEZXEo181bhWPP8BMu1ojQ5ELlFbt/Jc0Uv7c56clNnXbKXiN6YRrbM1xOyqn+pxrlvVxe3oGNUk83U0JhxuNrVG67h1am5R+LxVxVhb16j5dNCWyM2tACl3Q759+xGO8469ZcFVV6d4EC3nYDRkFLQukzlN6F+25TRPnQh9qOrjWmhojGoZSoyDTuyraCNfOOTRpQw+PBFRuNHSM7+8iPOu0aHP+dYi+RqISSGCmfgjID5Y3zV4vnB2qUqbzKXcFrRBadL/+anardrJviEJRkRxba0RNMqsorFCkfNgrHabJUXk3GJfeqbLmu3bGsne7YUOTJVoLHUIT7jy1MylR0W4abGGm0KocTuK/Y9Vg8/js347qHVikajKZHGi386e6lmPN42CO12+gzlERUU3mp/6MVk+5JZVV7ZqR8SdlgKvLq0B/g4rXb8skgybjNjAeWpWVajj1HOw5FYKQVPswyCSXE2Opa5ZV9KTG1Sb1G1qTiSha223AZxUC7by9dQlH/QyqUMhtHCKYU12hx0IGlbALuzo6N6e25UqM0vGLDKNR8nlg1o0Y/724Z5QHgojuXWn0AiqH8+2/vxtt/eEt7HbWBPkNJhDsYU9xdXZ91XSVFf256aDDMVaMPk7TQajNUPgaz3OyFapWTMomfffNjeON3bkrqY+GaLfjU7+4RJ50yL68qE05ZdHfofWq7SD1BQiEqCWy03IZTFhWlRaJlyhhK0MurpO2yIWBOui3moLdR6f21MeGmutRKWLzWThYpja+4hBJH8by1yiteI3RVUrdLXqCdqqfbQZ+hJKKKyqs8LXh5f6OOQbpYNUoqr+I4lDnWPpc20MwPav4qpf8uHaQMzF26IV7GwKd/fy8uvHMJbl2wxrtW5uVV5YMpC2wMBazVazU0W4aofScgAAAgAElEQVTKK9KlUnnFVHjVUq+kCGCxOKMQQ3FVWkEvr1CfXZNQ4khZMCxasxUPG7YZt0qVPHffvOphmz5xzIXrl34ajldd6vB1h4o09xSb2XG03FijpxgKEf0nEd1PRPcR0XlENIWInklEfyWi+UR0PhFNyspOzn4/kl2fNZa0uS8/bpSXy+gmUiYT6yPkeBxIy/mAJVrKzkmwIvAzcoJeXsYtVRnH+hlJUlujZUtpHn0VbI5lXl4hCUXZUIqszVGVF1H03i2jfIoNJYXxR4qEvLxcF3SXmcYWL2b9ECyjfJMx3AjR0bnK61Vfvw7HnnEjNmT5yvzAXrO90uac/v1zMUeRc297PJr0U5OWe7FVIyeHZA/UZMWyZowHeoahENFeAD4B4Ehmfh6AOoB3AfgagDOY+UAA6wB8OKvyYQDrmPkAAGdk5cYM7sfVjoSiz6cwFJMZKRsK58eh/gDfA0QatrFhxoEBWeyHLte2VQvyqll6ZiM5Q/GfiTnPlTHSMpS5uoaua5WXZnixPnXMSghVs08nqbzEc+psyCifBw62ZAmFnf8+XSUSiuPlNRygox2jfKjvw750JYZGm1GnlKoTrDReY8PogeUb8Zbv3Ry8XqjCq0koKXTliw9rEfg0ZigZBgBMJaIBANMALAfwWgAXZNfPAXBCdvzW7Dey60fTGG6u7L6auA1F/fdSWuifKTaUhr06Tw1s9B0BpPL+STGeQfAeK1vpf/aiuVi63g8sBOSobn2f0puzV4OS+iH9gylzJogZ5ZstzhmenlAkemtEUWZd1YbSbpBqmcrLVWkFvbwC3ZfNU3YcSiscsZ2wCKmCxWu3Cs+s+M0VvWil51/myh7bati1WZXaUAIPQLah6P82Mx9v9AxDYealAL4BYBEUI9kAYA6A9cys39ISAHtlx3sBWJzVbWTld3bbJaITiWg2Ec1evVp2DU2kz/rdlg1F05TQn2WUZ0axAIlLKJ76Qmg7dZiJXi4lg3TFxqFg3i4pjfpoREIpcxuu5OVVIqGUqbxqTuqVQWH3qbLAxqpeXmkSSlgCLWMo4WzDbkvu4iJOmPleUr28pOefEoioXdsBtceMW6UTCUUq3oldwrWhuK9uzeZh3CfYH6vYUOwg5+L6HYHkrN1GzzAUItoRSup4JoA9AWwH4A1C0di87D1pZv4xMx/JzEfusssubdPnvkNXfTE02sRZNzyKRrNlDBxXN63OpwhSo01XQtEDJk5bSvp68ZxAg7xCa/+DWr/VD9ArPN8klZcxoYn0pfddJqGEVp41x8srnyyFV/jo6s14/bfD3m0mDd3aDyUmgYZUXq6060XK51kZ5H6quA03YwwlMm5D/dy1aD2e2Dyc/95vp2n58cI1WzwGW3XzubL+q4z/kKxUeHnZOP47N+NN3w2rzDSksSzxKPM9vPOsW0sXVd1AzzAUAK8D8Bgzr2bmUQAXAXgZgJmZCgwA9gawLDteAmAfAMiuzwAwZmzYHVvuwPrh9Y/i9Mvn4fzZi/Oy7odU2FDK+zPrmjYUaWY1B76vZqsmo5QZ1ztJYBpLqCkb5eOTQZWPu0yaiUkoTcOGElMjbHUi46c7e8Zbubyi1Cgkqbwi50LG8CK1iSyhFJmt4/2E4ObyCkVsm23GJkmgWIQtXb8NJ3z/L/n53WdMARGw3aQ6Fq7Z6ttQAMP+VeEmELChdKBGCtmsNFZstLdkCPcUZr6xNEz3LJE1B91ELzGURQCOIqJpmS3kaAAPALgOwDuyMu8H8Kfs+OLsN7Lr1/IYbgDgftyjTbYma50HautwYRgMSQtVjfLKy0umQ7VrfsAJKq/EpyS6KCd4eYUQT1siSCglubyqrDjLJoKYUd5WeSV3iYN3nx7sIy24tbyPuBOAfK1Qj6jffioPv/9KKi/LmSMmoRjjVvJcCvSzxEn++YJ9ZmKn7Sdh20jT9/Jiw0Ovol+VqPKqsKJy37Brh0ydrtZsHsGVRgYL+VvQF4tz7vxzw8PVt4+uip5hKMz8Vyjj+p0A5kLR9mMAnwHwSSJ6BMpGcnZW5WwAO2fnPwngpPGm2XxfehVkppp3pYWItiSHnmeGHaN8amBjklE+0n9Z3aqeSibiaUt0n/ZkFKOlxZz8UZZJ+yHa9GSko6g1Q21HwrBVXuX1U+4sptIM3VORHl5eMUupV8wSZUPAfNbNFmM48PBj8VOpYFbvok7KecI1vLeYDZfvam2LUnEnEopjW9VNXTtvJWaddGmw3pcvfRAn/mpOLnHGvN/Ma+4i6d/+5oB2SU/GQHmR8QMznwzgZOf0AgAvFsoOAXjneNCV9eeda7RaqNfqAIoVZ4uLsl6ixgQbykCNMNpkP7AxO5bdhg2aPKO8JNEYdVuM5558BQ5yVtPBvtrkJ4y4HaNGvgTQtOJQhI+7xckSQ9mqOpTrq+7M/FWkIrdkVaN823EEJQzF1beHyoXsJlUkFOawqtNKExKJ/o6BoYJOa9nGam6dVsuwf1W2ofjn2rUh3v7Y2jzfVjNn2ApnXvNIpbZiHnHmPZrP9ODdplsODGOFnpFQeh1lgyvP18O+e6BGofIK96MnMHMvA/NDKVP9NFqMqYN1fOK12WpElFAMVQMzto02cc9iX78q21DaX6HF1AWFW24FCaWVrjYomwhCK+QB52U180VBeZ8uabYNRW5g+YZtWLVpSKwvQVSBwl4Jh+qEnEekSPmQgV5s33pv4cBGswPX/qTaQdAF3aSFUDhPuM+jyZyrKxtNxplXz8caw6gfvQ9xEcl4yTN3wt8duXdpfbP23/3oVjy+RqV2cSWU0FByu4+YUcV3ZjLysQuosNEWQyGi7Yio3m1inmwwJ7xcJSIMag39ocde7kDmjmpGOTNkQ2l+3WQoTWVAfvkBzwiWt+vGrvkX5zy+Dr+L7M8RQ1nqd8Dxoy9hAi1O14qXMpQSCSXfljZXeVVL8OnSEBoDLz3tWrz4K9foFhL6CJ8LSYRF5uisXCBeKsSsy566+Z4ZMQmlgJvqHwBuXbAGLz/9Wlxy7zLvmkkrUaHycm+51Sre4a0L1uCMqx/GSRfNjdJftC1Lxc/eYwf85zEHJde/+ZHAFtmB+aBskRRbVJrMvGe3ACaiGhH9IxFdSkSrAMwDsDxLk/J1IjpwbMmceIiprAOpNEJzV7EiUYWvvH8F/u6sW60BVEgodhxKPH19cdxsqRWZVqvFJhzpvkJqDhM/uWmBeL4MKeoCcxJslkgoTeZkvXi5yitslAeKDzVV46EyDzt9jEkuL/lczKvNDawLxUtZ56xxYZ4XxqPz3oJeXkbVWCLLuwNxTYpWBoHyxJweE+dC5aXpKEuaqRHSSgzUKC25p/5fMh9IfcTKy7FofvmqueO6gVQJ5ToAzwLwWQC7M/M+zLwrgFcCuA3A6UT0njGisSdQ/sKycq2woViv3HRM3EfPnYPbF661BtBgXTU0ZKgJytQNtttwCzUiL2W2iYdWbMJfs2SMsXk2NCfttN0k71zKcE0xSoe2jxU/ohYne+6Uxk4EjfLqZTVyhlIuZQI6r5d9LyYJXQtsFAqt2TyM/T93WWm7udeR6zYsqFdDNhSR0TsqrxQbStnukiEwA6AsqFSQUJot9hYFqQi5qtfrlKZCSuzObcq1sRTN5RzF74rt8QlMjMor1Sj/uiw2xAIzrwVwIYALiWiwq5T1GMo+nJpllJfbKMpnnkPZFn9mcT34zVWUssv4A0bDlVDqtUIhI9HyH+ffDQBYePrxMqF5u/KNSAwlBbaR3UY+yQVsKOfdvhhnXj0fZ3/gRfnOiFUkFEkAuXPROuw1cyp222FKUD2gbSia9tRJqVazn70rAVXdEydYRjj3wPL4XjSul5fHTIWVsFnCLC57QtlNhZ6ZeVqyoWjEHlXGT1SKnIC6WX9TejylrtblXF5K4klSeSZyFHcshBxf4jYUuwxgG+V7iqFIzKSdMk9u+K9RClQz3YZdNPMBbdcxy2sbynm3F3YKawvgyOoEUB+NLaHEEVu5hy6NNlv4830rMG9FMXGlfDqxZI+5+iVgQznv9kUAgEdWbUadCA3mSh5n0qT20V/NwZsO3RNfePNzgkZ5N4dX7D2YcDMPu1LAWLoNl02YbpBsaMsDWzJm8VjiFa7KK3QfZjsxNVSU+TJANWReXnJf+WJP278SJ9f5Kzd755qsVV7l9cvGSJkmw0WKY05IQhkvlVclt2EiOhLA/wDYL6tLAJiZDx0D2noK0nxjG1kLm0VoEdt0BrTpGaYxkKm8zDTY5RKKyVBaqJFJT3xUR1VegRtZv3UUHz13TrRdqZ9YYr0yCWXapDq2jjRx7byVlgE/WUIRCg6NNrElC0gNGeW1hJKv5iPvwUQ92zp43oqN2GPGVO89dCtSXppCyxmK3X4oK/ZDKzZhpNHCpIFa2ONLcue2jPIRJxXjtGSUt8sG2gCDUEON1NiRxqzrWJE6uX7vOtudt5WpLWupNpQyhhI472YycMvHtBShOJRxElAqx6H8GsCnoQIPxz4xTA+hbH/pgjmEPyBX5FZis13ejXtQfafbOkab5Sovt+2UdgHg+XvNwF4zp+LR1f7KLQWxOJTiYynOmQxo5tRBbB1pYttIK2OWrOwSqTYUoW/mQi0QNMrXtbrEVnmVSihZhP3rv30TDt5tOs79yEus60mJsRNuTaKjXmIZdV1MQ8khf3TjAmwZaeDLJzzfs6cc+eWrsceMKfj9R1/qte86U6QwxpjKKwbmzMsrywotdVWoLbMFXVs9FQypzCjPzIkqTYj0hBZyMZVXwXyKq5YatxclFACrmfniMaGkxyHNN5LbsJr8A6KsO6CFHEPSazcjwmWVl92HUnkV6SZiUkrsmjsR1AiYMXUQG7b52s2U4Rrz8pKisy2PL+N6rUsSSsswGAeN8p7Ky6dTgkpln630V27y1Epd4idimbLJzE006nt5Fb/veExt/eyquZ7YPIwnNg8H4oNMCSXNyylmlKdYG1DPMhTYCBRpcwoNQXuTa66yrhEowrRHm4xJA/GtDIDiOXvZhGM3C/mblZLH2iqvEmK6hKoM5WQi+imAawDk0UHMfFFXqepBiJHy1v4WulyKyktLKAplKzizzVKVV7OFWg2WhBJrPtazp6YhwoxpMkNJQZShZP9dB4PiuLhuxvwk8pNANtvClTSWHBIoJknXlhJCLZNQNFyVWopRNylSXJJQjBmqRj6trlHelc5M3pczcqONsrxeZvqfGNM3316ZK2/oTTMrt+EaKSlSZCjZ42jk31+0qyD0cyuTUIYbTaUmbFPdHHZisCVLqS2zzZEngcrrgwAOATCIQuXFUJmBn9KQVg1SoFossNGdtHI1WcnOhKZaLC7uqj7qplGey5iG/TsWT6MllFBcQQxEZRKK/m8yR2HiYiPzb4VI+RAj1h9diKG4HkK5t1eZDcVxG3Yn7W4YdQF5ojW3ahmo1by0Jq52JPZepO1qLbdhoY6f2DQ0QRbH5TaUwHkUKq/hhpyKxzXKX/XAymhfIZgq69j7G260MB3lEqa+7i4u7l++EbvuMEVwG3YPCkgLTtttuDdVXocx8/PHhJIehxyHYjKUQuUV+j7dlYe50i76kVfSLAwYiTYVKU+WCi7uycXOb7Ndtla49Rphcgf5gOJ7kvgrYXOi05MUw3TR7kxCYQZGsnifsuSQZiJFTlC1uYGNvpdXemBcDJKfgzl5DNQJ7lydmhzSLBvy7BIlFCuxafgdmW1ujUkoVK42U6lX5AWGdnTpdAdD/f2WSSh6Ii+1XxYcxcIHf34HLv3EK7zyR5x6Fa78z1eJT1SKQ5kIo3zV2eE2InrOmFDS4wgFOWlYHlslEoobHPf//nRfvj2uNK+x+aGIqxNjELVaWaS8rhuWmHJ6A2ix7STQ6SonTUIpzklR88wobE8BI6zct3COw0F3GnVH/55qtymPQylvIzU5oguzacnJw/UY9L28imNJQrHVXz5NVqZsjkR+G8dDsTgUhO0RSkKhbOEjSyiFHawzhqLHY70WD2ws+kmVUXys2ijnG/vzfSuibsMhNWuvRcprvALA3UT0EBHdS0RziejesSCs1yDpNRvWCqBw0w1KKM6HrF/xJfcuxzevfNi6ZsKKQxHbLY514FVRtzMbSs3Rx7fLVGITi0mHOUG6SS/1uTw2pEyfZ0C3dfDnL8f3rp2fnwvud57B9RBqNOMMuqhn69A9G0rKY0y4N4kU89yg4PLlTj5eLi/LMcJX8dmR8j4B20blLA8xOssi5YOqTWYrfX2KUb5d6LbrtVp0gs6l6cTupJYyR0YPDWMR9eUTnmfQBq9PSzU9TiJKVYbyegAHAjgWwJsBvCn7/5RHKPOohhlIGLShNPWk6F+7cb7a7z40QcTiUNyJi8jNfhyTDMLXWszWClep0mSkMBpXtWJm8i1UekZ5Y7I3V8rmDnwxpdffH7mPV3+40cI3rnw4V1uVSSh5ptpW4Q2WMi+5xvDwvu1hVN0QSsMOlJUkFPt/bNvopmkpza+zWFbDTWwaRnF12YahSLlwO0pCUe8pxFC6L6HEV/ySVCdBkyo1JUmWgPomCsZWlJG8D3te5cXMj+s/AFsALMqOn/KQxqKdekX9j0ko+V7SucqreM0LVm/J+vErm6J8TNwF1CBScSi5n1fbEkqL4UgoiTmMAnADG7VuW/WlV81slPePmW0ja+jeBuuE/XfZzmtfQ/volzGUQkLJ6IioEM1no+MiNLx9akpmm2YrLROAPB6KY4mh+FsAu+nrzbL+/udlNhRTfZWi5hwabeKBZRvwvL12CJaNtUFQ359SDftluiah5AylFjXKjzaLsRpD7Ho98KE1WgXLsGgQFmQTESmfmm34KCK6noguIqLDieg+APcBWElErx9bEnsDosrLlFByA3t41a+NgrEAK1kCkY1uRR2jj5aTeqVUQgleQottaYeos5WOu0o31TGihCIa0jmnp9kKr+HdD8idTI449SoA5QxFJ4cM5r0yYPao4yI0QgkYQxhtthLjUPxSJo0Dgsrr3NsexzUPrsxr+vckM3KNMi8vU0JJUXPev2wjRpuMI/bdUSwXUv+oNlQQoc7lJT3WfFGQqoMKwJRQzMXgp4872CrnZnMOIY9DEb6qWlBCYUOyKcoU7t0BCaXHVF7fA/BVAOcBuBbAR5h5dwCvAnDaGNHWU5CN8lKkfHiyKAy7upLUj9y/Pl2mM2+02DbKR9pUdePMxlRz1Y20+FXa0XA32JpkMhTDipKXb7G3wlYqDsPLK9CvqzJwJ7bNWcqVVBtK4TacZkNxGYprlC9rQXuTlUEeDyZD8d/XJfcux4fPmW3EodiN2MxD/b/qwZXi9T/fV+x1rrFttJmPv9jWvrr/1ZuUAXrvHacGy4bjULSEorMN++XazTbsopkzFHvafPVBu1i/RxOTzMVUXmZgrAn1PDkvoyEGNvYwQxlg5iuZ+fcAVjDzbQDAzPPGjrTegjQWR639UIpJLjRuCxtKWEIxJ4Mdpw1m52SVkFRHGeVNJ4ESphG8UrgN63sr878vg7tCtLYkDUgoLmNgdiLlA325ZIaYQFlMTc2ZjEYarSQbykCdLM8y1/BdxpSUrry8ny9cfH/uIahhVpNUXgUN6n8olxegJrC1W0bw//54n3j988Z5jW0jzdy9PLbJU9GMP0GaIMiMU7dRGinfJRtKzlAcOkPScKnKK3ItlD5n1JBQzFebb8BndNqzKi/Aytvl7snZ2Vt6kqDUbbikLOBHHUurfbNNMwYizyFVQpu0H0pcQglf0x5VmsoaoaOljvtBWyqvAD2ehMIFs1SBjel9S4w13YaSMZSmHI0N2O+mRmQ7S7ir1hK61eqy/ObuWbwe373WSWJoGeXDn3hhQ3EkFLOtlv+MyqS6odFmLn0mJQTNJ0jbozBEk3++UHmJbsNdUnkVEorDUJxHbMZMxRAjJ8QAmq1CFWqWKVIX+XSMJ1IDGw8joo1Q8+ZUItqETPsAYMpYEddLKPPyYqNcLWBpcAPKpDFjfhB6wDCKwVdmhM1VXijKxyUUdzIpfrdYMb3ChtK+lxeDBRuK5OVll3E/XuUoUJSN6anNK02Wn91IsxV9Pm4cytBo08psEKvXtBh9eNKWYOrKy+BO+JZRXlB55TTkEkrYYUBSWY024oQNjbYwaaAOoBGXUJxgVvNdmxH+RDEJhQsJJZA5ofsqL/uZuhJLKFuwi9iW4GpB4p+3bShmWwqhLYB7KlKemZ/2+8fL6evtbXqBzJAdaKOIpVC/y4zyOUMxVDtlubzyDYCSbSixa5nKK8uKXKPOdLGuDWVQsKF4Eooj+2sjLJDdd4B+9wMKBXgyxyP43dXtcCMsoVj1yPHy8jyp4m2kGuWltqUtpSWEIuXtmBP/nZetfLeNNrH9ZDW1uN5tNp3Z/1zlVVyr1wgwQlNiCweCUhGVBTbGMzWUw8w2bMI1oIe2QvCQzwP+Owq1YErIkg0lJKH0VHJIIvpk7Dozf6s75PQuJHHZklAMCSL0CbU8CUUYSEY3ejJgyAPGbVdDieAGM2rXhtLKBm1GppnSpR24z9BM45IqobAxwcW8vACbYcfiR2JqL1flNTTaDKu8TOmyFl4tumUljCYGUCra7N9mvcGoykvXd/op6bZMTbhttImds109UybxXOVlvGvpvUswbSjBOBQnfU670AtIl4G4Ekojl1Da7yuWsVxfseNQ9P+inpXLq31SKiHVhjI9+zsSwMcA7JX9fRTA0yIVS1m2YXPCL0sOme8cJ5Qx6+YMxVhdS3S4E7UbLxL7pmNqgNwor9stSTlRhhQbiotB0SivJ4iKNpRAL7FMt67KazjRKO+qvPxo9DgarZZ3b8/aZTu8/Yi9vbLuOzQFlnYklDLaymwoelMuIG6Ud8d0yIYSEUSVxArKcnmNbaS8foeehOIylNyGUqbyUihTfZtQRnlfopO2t7C9vHpL5fVFACCiKwEcwcybst+nAPj9mFHXQ5AGo2RDUeopSfJgQULx+7FVXrqunFqhoM3+bRrS1ceY/iGZEohrQ4lFyqcgHocir+rqjg2AYdhQIkzCRSwHVyzth5tteGi0mdSn1ulrVFZ5Nfxe3vHCfbBms5/jafXmYUu9YRnlYzaU7H/Mywvw30mZhAIUHnxJKq/sv7nSN1WdLRaIMNogKrZcloq5Uma7CO346H7HqRJK3MYij1d7Uz9fQjHrTITKq2rqlX0BjBi/RwDM6ho1PQxpLJo2FDM/krwBjm9DkWQUyyhvqLyigY2uyovM7Mfte3kxM2pGmomObSjOpDo44Eso7v25XkrMxoozwiRcMkO7+QFpEkrLlFASjfK29101tdJoy3cWCE0K185bhf++oEipZ9aScnnl5bL2XQ849zm5rC1l+wLt5ZWyS6dknJbSioTaUCqv8Dvuttuwy6Q9G0qeESPeXuxyiFTzHl0byi2PPIGbshROgMv4e0hCMfArALcT0R+gnsffAjin61T1IESVV8CGEkov7Xp/SJOzZVDNjfKmAdOHZHcwJRQ9GR7//D1w6dzldn+RYe27DYcDG1OGq/tBm4GNCNyfp0s35L+YDcU93+LwxBTbfrYIbCwmiZQVursfirtSL5dQfJWXCnaTYb7XdKN8cdxoce515z5Vd3IrU3kBwORBzVBiEkq2SGr5tJpqpeh7ZkPlFbShIKOlOwzFcxt2JRSdeqWkPU1qmS3VhHLWkFRewD/+9K9WWfN2ey2wEQDAzF+B2mRrHYD1AD7IzE+TSHn/nOm1pD+O82cvxpzH14n1vUh5AcE4lIBKCPBtKKaaymRGM7JASROxeU256BZMpJPUK8wCQxkoWtMfiS+h2D1qmlSbYYeDKYM12204MinFNncy7TUaW0cbVpkz3/UCv162P4dG1eSQDUGdF5sUBqwVfXF+MOo2XBQ03407Pl0JeDSBoU4eUI6hMY8ndv6bK+66y1ACzTAAZCqvUJ41c/+cdnH65fPwgZ/foWgrCWxMZVy6VMiWKkbKN8MSSgw95eVlgpnvBHDnGNDS0yiLQzHH0KNZokcTDMZwtplTKFLeTSxZMyQUyYsj71tSeQkbbEkJ5+JiN1tMJCahpCAll5dnQxG8fUwVRug7mjbJHtqxlCkxG0q+OZNBu8uA9t1pGg7ZfTrmrdhU0O0Zah2GUrJ+HWn6qrVYHFA9MLnEAhuDDM8h7ZFVm63fKQFzWvpMiZTP3WADXl6xFDvgLPVKpmIUVV5dsKGcdcOjIm1AzIYS768s7ZEEM329FYdScmudWT/TUZmhPF0hjcWGYEMJgVkZT8223IHoDng9F1iTjyih2L8J9gZbxQrQr1uWONJ2G+5MExvz8npo5SYs3zCEQ3afbpXxU4cYySEjj3zapLqXvSAUkBi3oeiI7zBDkZisO+k0Wi0rQK9sbms0fZYTe/Z1K3NzcT6WesWSULKHeffi9V4uqg/+4g7rd4rKKzfKRw1ObP3z4lAyxFLsMPQGW8oJQhrP3TLKa0xydi0Ne3nFEbseYjaNVjx9fQg9qfJ6OiPVhhJCiznfhS23ocCddOxG8ky3xjeZJKE4pglpBajpiBlgXbdhk1G5SPlUYwzlwjlL8KVLHii1obRahsNBxCg/bZIdixtzG06xoVgMxWFAphNEfs6he7TJVkxI2XjZOtJINsoDtoRSlhwyL2ccN1ot3Ld0A074/l9Kaavi5RV3G9b/fQnaZIRzHl+PDdvsfGUayoai7CSh1CvdMsprTKrHGUreT2J3oi0V8hgxFxpVVF5PS4ZCRDOJ6AIimkdEDxLRS4loJyK6iojmZ/93zMoSEX2HiB7Jdo88YixpE20Xgg0lhA3bRnPvmDx9vfOSXVWCngvMVZ7Ui/uhmDYUcEGbO/DV6dgHz5aaq1brbGC6DHOSMdlpTyOXOUqR8nquiTGJ7SbbwncsaWdM5WXtDhkoLyXNdOdxM+1+dibYJwCs2zLiG+UjHMVkvGY9KX29hvpfSJsAACAASURBVJthYcm6rVGaNKowlJRcXrktwbKhFHQ/uHwjvn31fLkNlCeHLLIdlJIdhPnuBj0JxS6b74dSxlG0pkK4FI1lEzQcMcatyo4PRyllKET0USL6CRG9i4guIaKPjSE9ZwL4MzMfAuAwAA8COAnANcx8IIBrst8A8Aao3SMPBHAigB+OIV2VbCgSVhg70oVsKK6eXTImltGh60luw77HVLlR3mJOCEfKpwzXmITSaCk6XXLE5JC5yquQUHZ0HA5ECSVkQ0mQUFpRlZfPaD1pEOq9nH/iUao9gRRTT79266hvlA9S6RrlWTz//pfuZ9Wx42QYQ6NpyQSTVF7abThqQ2GLXlvllUSKGg8ocl9JgbrdyOVlPkdXQnEn62ay23Bc3SyhYXl52Qsymya73jgJKEkSymuhJux/Y+Y3QU30XQcR7QC1v8rZAMDMI8y8HsBbUbgmnwPghOz4rQB+yQq3AZhJRHuMBW1AuWRQthpZuVExFCLThuKqRewPtTAmGnToVV2EyRCKAWR6ebmDLKab1n1YkfLCxFkFnlHeWOnpCb/UKA/bWUEX/8Kbn4PtDCaynWiUl+mK2lAyUcOk3ZVQSGC0rlG+1VISykv23xmH7j3DY26jzRZOv7zYDWL9Vl9Cia0y63V5ctFG+UP3noFPHH2gVcfdEbNsX3eNFAlFuw2PpsShCAsed8+RcBvFBluh/tzg1HZQjzIUu+xoF9yGYxKK5OXlwl2I9YyEAmANq9H/tey3H6rbHewPYDWAnxPRXUT0UyLaDsBuzLwcALL/u2bl9wKw2Ki/JDtngYhOJKLZRDR79erV7uVkyJKBaZSP11+VbSK06/TJwZWy+zFIe2HrI7OoZ8w3Jn6GrKMG4tHjug/PbbiDgemuECdJEkqp23CRHNKUOtwJfaojoUhta8RsKNI7cCUUFfzp1IswQkL5eFm3ddQrI0lCGgMB+4y2oehVvAlT0lASSiJDqSShpETK+yvumDOB24ayoej35PdXvMP207mbz7fMKJ+8H0pE5cWQGZJtQ0mjt6xsN5HCUM4EAGb+v+z3RWNEywCAIwD8kJkPh9qz/qRI+dB7sE8w/5iZj2TmI3fZZRehSiKEtyvl8gpBr+qmDNaDXl7ux+cmtRuoFXtsuBKKOWDMJI7K5Zit9vJb4jjdottw9C7j8GwoxodZpKWx63grVS7oMWNL3Gfp2lCicSgxt+Eko7w/07sqLysLtRCg6L6GdVtGPKk3tiINefzkEzORR5MpaTSarWSVV0ociqY1KX29rhNJDhlsgwEYj19Ssel2k7MACzDpcWN7PBuKVnmVyChxlVdIQmlFJRsN1xmjZ1ReeldGInpG9vuGMaJlCYAlzKzDPS+AYjArtSor+7/KKL+PUX9vAMvGiDZx4rUkg5KxakbZhnJ5hVVenP+W3E5bzNaKxApsRFhENtVhElwbCgk0V4GXesUY9PkWuyUSirlyc1VkZk3PhhIw2ALVkkMCgg0F/gfrSoOm7adG/oTh0rZOUnkFqXTjUIrzplHefZbDjZaxVW93JZRCzZSey8skL1VCAZR0GktR34309VYUf5mXl1Z5pUoowq0GbSiGI0pcQuldlZfGz8aMCgDMvALAYiI6ODt1NIAHAFwM4P3ZufcD+FN2fDGA92XeXkcB2KBVY2MBOQ4lXULRZQeMCGp3vT/ibFyk51tTZaWPvT1QjAFk2j2iEgpKNqjKJR+t8uowfX3EKJ9nEXDVfs5Kq2VwQcV8NHO2y/3NwbtaTKUVCYKMGeWl3f7cVbftuGDX02BDVUfwJwz39/qto4LbcHUJJWc0zB6THWk083fQbDGGGmkMpcyjSNGKvN0QisWRr5JNl1CUFK3Lf/aiuV4ZvVqPqd9MHL7vTO9cjJ5gYGNJP8V1yYYSdhsOOdmYcJneeLkNVwlsHA+SPg7g10Q0CcACqDQvNQC/I6IPA1gE4J1Z2csAvBHAIwC2ZmXHDNLEe8uja7B8wzbsMWNqeRxKLqHUivT13kCUVV6mdFOszs227RUJGUtmhrwCBMKDVoPZToUf0+GXDVjJ4G6qvPS9u6tIycuL8zpG1LBR5uJ/ezkO3XsmnrPnDtg41MCGbaP4yU0L2srlJUkorqRVI3+yd38re1RGK5E3ntzfm4cbfmBj5BmHJhezjst0R5otDNYII8gklMhzqAotXcei6t1N1agdhgItOYfL1ypKKD949xF46WnXWufcbRSk9jXiwZwF4t+efJGNBVUlo/w4Kb2qSCjty4upHTDfndk7DmXmE5h5HTOvYeajmfnA7P/arCwz878y87OY+fnMPHssaZPGyBObh3HcGTeq6yVPRw/mwToFJzZ35ecGZClVibpmTkBNZmslb22EZah6JL1+DN5+KB3YUExGoGG5DWf37q5oY67OI41iV0Mik3FS3v6//s0BmDpYj6r34jaUgkZNijsxSSlRXLdXFSlPeTsuLe74kRhw1MvLlFCMxmLva6TRyj3tmq10G0oK8km8KUvHgCChmGqlKkZ58uN+TOh3kRrYKI3yWAxQOLAx3t/QaBMnXXgvNgpBmzFSzTEfQll6mLFCr0koTzpsHFKJAssm54IpEEZzzyQbpUb57Mtwc37pbX81bBtKMTglvX7chsJds6Ew/GfkenlBKOPuOKhSyagyQ6OGgRLm/dt9l7mNxmwHZvf1GqEl7KQouVO7zgSjzcJxguAvKtwVqXo3fj8hSMkh//ivL8fN88OejS0umHqjme42nAI91rTn4kCNvAndfRuh1Csx6A22YuULB4E0himN8RiDCwc2xnHzI09ErgYkFPgLJwn+JmAlxHQJVSSUz44ZFU8CdLx9qOCp5cehOBOIE4diJ4ssyrbYt6FYsRpBLy/ZUL1h6yiOOPUqzHl8ndUWGfYUFykGSLeMtFeHJ6G4G2wZ7Qwbm12Zj9L90GIupUAVCcVedZvn3Xfp3pqazIrnWCqhCOdibts1i6EIC5ZAvUmmDaWLDKXwrGpZ/ZjwAhvb9PIqc2eX1JYxSC3FMg74gY22Kq8dqCb8BmLfswnPhtJrKi9mvm8sCel1xBjKojVbcd7ti+L1JS8vp4yblM/1n9djxI0fabbYs6HoX2bKEXfgS2ooAJj9+Fqs3TKSrart/VBCY7js21ER++Ur7tkL11q/fS+v4t6HG6aEYrYri/shY3KKUd48lqKSPZWXq1c3JRSBobgPUAo6TV2RmvvtlHn3DBqBm92VUNR/zXzddCWKTvu/nb4+NbARllFeQrGnTeIMLzQlZeoOQUtCZemYYojaV7L/1by82ialEpJVXkR0CFR0+l5Q97QMwMXM/OAY0dZTiL3gd5x1S+kuduZub+E4FNl+4KqslNhr2FBagg2lMKEUKxqnv01DDXFVat6rtfqOTFBlH492ADAnU23kN6uec+vjVj0pdkZjaLRptRVyp5SCE03EbAcSQ3EDUCUvLy/mo1m46BIIjPiGW5LKS52X78H28ir6KcOAIaEMd9OGkj8r1aakMir2wFG/Q8khY1CPg6ITftX09dJzS5WYzH460WmEFrDmgirVplZWtptIWgYQ0WcA/BaKd98O4I7s+DwiigUfPmUQG4trt4yEL2Zo5BJKzYhDcVex9getL+deXnXNYOI2FCuw0aDdHWQf+MXt+Opl/nrAHMyuKik0LEs/HlZR7a6t57pPvSZaLeblNdQw93e32zWRM4KADj0lsNE8bsfLq9Fs5edqNcFt2OlXymJgtvm3h9tJIazkkIYa0J1H3ChvrXYcbbaS3YZTQA4Tl9SbroOJSWv6BM5R70OgehyKaEOJWf0djCbGocRg2kqs88b4T3UjB8ZPQklVeX0YwIuY+XRmPjf7Ox3Ai7NrTwPIo2OgRtEXq5FLKEZwolvLDRjLExM6fvplNhTLbZiL9CTuqnn1xuE8x5gJ87uz3IYRHphl3ypnf+azirWn4ao+zM2Whk2jvMX47DZ0n2s2y4x/60hDPK/69yUU2YYSrgeoyUyfkYzynoQC35W4RoTXP293AMCrDnqGU18+9lLSDNqxKJOMDcS6ld4dMCZxrfISJmRX5VXFy6vIGAEr9YqElO/ThFS6ioSSbxdtnJs+udrWU/HNt+z5QIKn9q3Ue/tIZSgtAHsK5/fIrj3lEfrWJg3Ukri/NsrXDW8Xt56r43e9k0yGwM4EYkXKO/uWhLy8Gi0W01GYg9n0YJImTqmOfD1zQzZGXEqgpBQprzHUaOLie1RyBLNUaAL5yC9lz/KYysvsP7SvhuLfslSkMdos3IaVes6GJ7GIbsPAC/fbCQtPPx7P2WOGde3GhwtvLjMTg/so3voC+zM21apd5Ce5vU9LhZJRmwHcs3g9vn/dIwDs9+ZKUl7dXLopt6FUYQaArB6qErlfeA0XD/QFQrBkDFEbSkBlbqLXbSj/AeAaIpqPIiHjvgAOgApGfMojpNMcrNfAxlaAB+66PeY7W6YCRToGMw7F22DLU3nJEoq7JWqzxagPmhNfMcEqEVnbFuz+mi0W1UDmnZrqM2V8lkdmqZcXlJrOUnkhRUIRVF5ZX/ct3Yj7lm7MaCskP/c+n9jcfj5TyfNIjEPxpCL792iz2A/FpFXDT8XiT/Bmm7HnZrpSk3PyC296Dv7m4F3zXRj1QqTpjKlO4QYTyiovxlu//5f8t1nkoN2me+VNNJlRg/KYJPjP00RlhtJhG9JzrEpDKBO4KblGY2M8t+Hx4ShJEgoz/xnAQQC+COAKAFcCOAXAwcx8+ZhR10MIDdhJA7X8Q58yWMP33y3v86UllJoxmXgSiusyqxmI4SGmaXFtKKaOt1aT90NxB1mT2dcrs32vVhyK5M5k0BCDZgQmDSmOPK6qxIxDMWGWcp/rB14+CwCw83aTvHqSO6uJAYGhyNmdXRWD/XvLcMNSHXpxJw798n2ScOTDzMTgPouBeg27z5hi/C7GWKeu8SYKF2vtNiyovJzf5jM8dO8ZiEHTqiWUWEr92MQrQbShJHqdAfJcUUXCCbXhXos16Wojek3lBWZuMfNtzHwhM1+QHTeJ6INjSWCvIPSxTarXrJiPKQN1sZz+yAdq4Uj5UGCjPm0GOrpb9bo2FFNCKSKR7f6aLYGhwL5XU9ohhAdm2WSkjIwOnQir0DRcG0rIWGnZUJwvbdfpU/DC/XYUV3x6345w/5KE4hrlfbnNva+N20YLo7yg8nLzb0lpcVIlFNOLUJIoTWnB9PLqIj/xpDlJ5bVhqx0hbk6CO07zmb8J/Qq0DSWWsLKKyy8gP7MqTeTMzrFFpuCQ3adbbXjgdo3yPSShlOCLXWij9xGRUIwsJ5gSmKAKG0rYy8vLNpxdH2kq7xs9ETAECSUwUeuJ3GzPhJQwz1V5mQM4NIjLJiMtwlv1hZW9C9/LS1YFxIzy+px0r1MG5QWAhsVQQjYU8vt072vjUKNgzCRFypu06gWKr1ozfgVpbhnvW3q8ptQ3YC1Sugfdb8ORrk08scVWRVoOGwTc/rmjg+0Xk7bK5hCTUFJ3fyw6909VcVgopCdjkZAooeh3fO5fF2HTkJ+SBUizofR06hUiujd0CcBu3SOnd5EiobSYMTkwQUnZhl34Rnn1f122ktPb3HLLztckRcpb+6E4kfb2ffl0WEZ5y4geHphu2nm/USHNPspFcTmXl6TyoujKjUh+7qEFgIbZVs1ZdZttuwzEnT82Dzew6/TJGa0+A265z7xp79Tpthm3oWgbnQxTWjCl3hSVV43SjPeFR1wL9ZocJ+JmlrYXBYRdd5iCEEyVFxCXULTathMJrIo6UErllap206/mnsXrxetmhvBKEso4Kb1SjfK7ATgOwDrnPAG4pasU9ShiXl65bQMRCUUnyatT8IP33FGzdtdlcS47ZjYAM7gJkNLXO/uhZOcrbVqUwZwoFQOQ20hTedmTYsxrTMP1/29xQFgsmWxrJGeBDako8/4TJBRTLSiQ452UjfLFcd4Pu/3Y70Jj1s7TsHDN1vy3pfISHoYpoQzmbsNpE67OZ1ZaLl9kKfuJZIKIxYWUqYjyV8nqPmObflHWXumiR5cXutb9vfOFe5fX1QzFOJ+qdisrZzqlxJ5RKFvEWCOVoVwCYHtmvtu9QETXd5WiHkVIITBYL6bYFnPQyJurvCjsNuwGlunBtW5rxlAyvbLpuQVohuJnxdVlTRVIKci1oRjeU7UwAyiNQ8lWwL6tJ06TlH9MehVmKek+a+QnJwSqqbyKlOzlEoo0kZs50aIqLy01eMm85PZdF9uibfnpmgk364aXV5qEYsyYsXIOIxal46iEEm/fMsqDSiWUGgGpYZtS1y1mvPyAnfH1dx4WrWvuWWQtEhIXcymSTEocykQlh0xiKMwcDF5k5n/sHjm9i3gcSqFeCtkEmi2Vy6leC6de+eH1j1q/67mEolReO2USSosZW4aLz0PFoZgTtROHYqjbUmAHNtrXQi2kxKGA3VV2goQipuzwYT73agwlrvLSk1GLdUS8HCnvdik9anL+m5D07X7uM1utWZR3GYqmPeCxJNlQEgMbkydGsvuQvgsphY10LKGZT9rKHftFs3YKlqW8vVQJRWB+zEkLMjNXn/lOU43yZeW0pA8AFBm6PW2UpwRqUso8qREYi7ctWBuMc/inVz4Tnzj6QACaoZC1Oi1bnevBtXbrCOo1wvQpAzkpb/zOTXk53baGufJn5jzPWNlqXINdCcXU2QZILnUbRtwbLYRYpLwJ0p0gMJmTvCiQnsmbDt3D+m1KFjUiT1UjpqSJSCg1wSjvZicAfJUXWcc+U9AwVaqihBKyoaQwlOSJ0TiuyUlFpQBRqb4EW0IBXnXQLvjQy58p01KrtkKXijYji0UT5oLRfH2pi7lylVfBpqIqL8+GMj5I9X+4jog+TkT7mieJaBIRvZaIzkGxTe9TEu346P/P8c/BYZk/fbPF+YSU4qUBFANm/dYRzJw6aBn/TTSZLU8WNznkcKZKK1uNa9g2FPs4xART4lD0Kt9qu6KEEtB4OXSGJ3MXkwUbyklvOMSum9FAIFHS0e/V7i9Mo2sgfmjFJixZV9hA8vcsMC63LZM+DdOLsIhHKmAylEEz9UqKyqsN1c1ATR410nPM62c/XjRrR7F9NiZtXU8vuFwQqFIciTRUVB668rp1InHBk/rcUvh1WhyK224PSSgAXg+lgjyPiJYR0QNEtADAfAD/AOAMZv7FGNHYE3An8U8dc1BSPf0eFUPR6hPZKO+iSF/BmDltsBhsznhtORKKFSkPVJZQzBW4126A6DS3YbY+LLJjuUW4RnltlHzzYXvi/BOPstoy6XQR+vgkJhvy2NIqJH9l7avu5FiGQkIxWzju2zfivWffnv8udhhMuweJ6Soa5PdVN2xheqJtMXv9SWhH5VWvyTYU6Tm69X/zT0fhFQfYecvMugy2nqsIqhapLr27VJUXUfF5mowl1XW5jE6lOS63ibqSfU8Z5Zl5CMAPAPyAiAYBPAPANmaWfduegnDny91nTMGLZ+2E2539OwDgwo+9DDMzF9886ypzpoM31B3CW/7IK56Jm+Y/gYdWbrIm3xmWhGLXabKr8jJWplzsSJgkobBtI7Akisj0nyLBealXIgxKQ9wPBSru5pA9drDaKmhOl1AkJuuWzFVe2bHrjSfdR8yG4jo+uDBT7IQIM/tzJyE7sFHGYL2GkUbLCp5NeYfpDMWuI62QYxKKtg8M1muYNsl/R1ZGbKFPq11Ui1SXhkqzVUXl5T/HdFVhCUMxvLxiRV0GNk78pHpgIzOPMvPypxMzAfwVuJS/SeOF++2IZ+2yvSqXnStsKIZRXqg7dVI9b9cchNtNHjCkDt9DyPTAMicSRrHPhaTekeBKKOYADonuZeoS7Z1lR8pXs6Fo/TSzntyLcoT4yi00GaQw2brxYJX7sernjc/fPb/ue3n57eTNAFH7cGj/DnfR4NGXobChhDn2YNbHoLElQpJRvsLEmI/jgA3Fi+dx6ud9CpXNSPm8TmBs1ii+RXAK2FErh1CvUSXaXKSUS7HBuvfbU7m8nu4IGYFTBqmZoVbveGjuqOdikmAwBdRKzbSLuJgyUDNWaq4NRa1EpQR9EsyJxfW8Cd1xQlyjpzaoBVauJrxcWnqFRs5HEli9530FupHiUDz1lcEIajXKJbj/Pu4QLDz9eHUfXhthKclVebnIpQZmBG7ROvZjdYrxFXq6OrhRM+xUL6/UedlcLIRVXo6OzZIyjWOJoZiBjcZCSqSFuiGhpKm8TA2E+TTT41DKy2h7bFxCKV/gjAX6DCUB0ncmGWIlmAxF18njUITyZm4pK513vXBPlkTqyYPFdWXrUMfKhtLE5MQ0+wxXQrGNgKE2SgMbM2O6FXmPcgnFNe7mmzE5dgtzSpcZSkhCkVRezupOG+XJ9vIKSQyqDbNvmy6iNJWXuyGZZX8yOgipvEL0AIVhfsBgXkkMxenr8H1n4h9evK9XzlS71mtpgY2SDQWQJ+O8Lhf1QpM2gawdTcuQakPZf5ftvHJmmXbiUFLKNbO9dWIlJyo5ZLVdX56mkD7+GlGSeKrfa0NLKFYcil/fVEuZg2uSIYFIc5FZzxr4mdvwZKN+DMxs5bzyo7PlVsoj5bPAxg5sKIXKiz2GTgbjkyaEkJPPVEE/79KkaSYo5qADG227jduGQ3fTNh7HHpeu2mxlTgzCAiQ0+QLx/VA0tKordxtO9PJyJ7zdd5iC0972fJx3+yLvHnIJRVAJAiU2FONYki6KTawKKS60aCCqliBSKqrVyiYu//dXevcQikOpmssrBu3gEyvrzU29qPIioiOJ6A9EdCcR3UtEcyN5vp4ykL4zNaGV19XvsdXSRnndpvzxTjainmsOQzGzGkv1XHKIMglltKUYThsSijVp1sJ2I1N7IXnAaWOim+AwNRYH0HvPFJOrqw668GMvw0de8Uxxd8DQxzdZ2MjJf46U/zcllJA6StHt3wMZZaMSSla50fK3TC6Ow32zsWAJPd+6IXVpRp0Uh5I6MRo0pnt5FZByqJloGNvs5s81QJq+x1RIJbVTjYnJA3VMm2SvyWs1OZdXsoSSwlC4XEJJcWMfC1SVUH4N4NMA5uJpslMjEP74k9wIUagU9ISk2pQHhJlGwxwE5s6QUhqYyQN1b6VGyLy8Gk2lEkvgKMz2h55qRDef0Ymv3h/fvOph57rfXq3sq4BtH6jXKHOb1PXtyfawfWbisH3knfGqqLxcmnKVVdZPM0XlZfzU95zfOsmLApdWM8bCqu+TaKFQC4YnWd3/YF3t59PkRAklcp8mlF2JAKi9eqQJzZdQZKlLmmR1Zm42aAhKKKgah+K340rXIVipV0ralJBCZivBhuIJKOOk9KrKUFYz88VjQsmTDCZziEG/WFdCaTkGVw1TdWWK+nZWY6FezjCKlRQR5V5e7dpQLHVDRMy23TiFD1KnMXcm4TKa3H3GC0knHOgnIRyHkmBDMVRV9RoCDMXtz2aEqgx51ySYG3mFmFZsgtLSoql2cqGDXadPGcjteimxu6neQpaEEvhWUiPlRQlFx6Ew5+8r6DbcBQmlleg2XKsZ6kzBtbm0frKEEld5pXgdjgWqMpSTieinAK4BkOcbYeaLukpVj0GSUAhpYqSZ8tzUe6oUIn55S+VljAKTIUjqsinGvixFZHcRKT9lsJ5sQ7EllELNVItMUGZQnDR48x0rKxrlTQY0kGVqZrCnLCtrJyyhKIImDdTyPTU8G4ohooTyaZnUvPsltpHalVAIaSovrdoQ+wscw2jb3RfHhHYl337yAOq1+H4iJtIjvm23YalW3IZiMmS/riihBGgj+J5wMUhDpSWovCQolWhBW6zNUP0ytDIvxxjGi4G4qMpQPgjgEACDKFReDOApzlD8c7VECUWXaLaKwEYgnEJkUoChWEkohXrmPiyFzr+IlJ9s1I+BYafRN+0ROvWIWK9kNSZlPFZie9pqHVBR3YxCQolNqi5C17Xb8OS6wVACdcmj35dQpk2q4yt/+3z83z3LinvIVZDGe4ka5bNFh6MXtTzZjPJuWy1rASDfuM6esP3kAdSJvM3dQkh1vyWj71BgY8jLy+1C9PISbChho3y1OJSgyiuhjToRRgQbSqpkl9JHq8VtL6DGGlUZymHM/PwxoaSHIcahJIrReaS8EYeif0vtmhKKuaqaVDe9vOR6+nrxn/I4lKmVJJRictF2CyDuNmzq38UPUqu8LBtKuWbX3YJXM2KfGZUwphIbyqSBWi5z+6lXCkYQsmmY0ocum18zDOC6vdj+iLkrr5D0szgufrgt5fN0RATU6d63nzIAIuDSucuD9JhIDtBzJJQUGwqM51PWp7W3TYkqsUbV93R3kRopXwt4eaX2nqTySoiJmSABpXIcym1E9JwxoaSHEfLrT1kE6DLabdjeF96HKWn4Eoo6DrkNk/tBkhrUQ6MV4lA4bEOJ1S/zENKXfe+TKhIKGapCu165DSXEUAqVV95WgAZXQrNdqu1Jzbw24DAUovj+MWbqFZNsMzDVllDsxiyVV7gbAIXKa/1WebtZn7akYhYvq9dkCcHd8Mwbv3mfft3RZrEgKySUAC2Ji78YOFHlVa+FvUJTkBJ7rGPaYpgoCaUqQ3kFgHuI6KGxchsmojoR3UVEl2S/n0lEfyWi+UR0PhFNys5Pzn4/kl2f1U06LATdhhNWLM7kUNhQ5IFnRsq7LrNRo/xADa7KgDLahxutDry8arYbaqAJkyapSNNQw+TlIhKPhmvcVs34H1SpCiAw0jUjMSVDr22DEbgSllfHkVTMcoWkkx6HYrazx8wpXhnAH55mqpyyVbVmKKlInah0miFAjSEx2j0QKO92ITKjJlv3GaetmpeXhCoqL83ozHec7OWVIqFwisrL/j1e7KXqU349gAMAHAvgzQDelP3vJv4dwIPG769BZTM+EGoLYr3Z14cBrGPmAwCckZUbE4hG+US9rC7SaLYslRc76Q4nSAAAIABJREFUEopuy4yUN1crVmCj5DY8WPM+rMKG0qwQh8LWjoSWhIKwTt7y8hKKSCqvFAY34EgouQ3FKVf2wYaua+ZpSyiyFOUxQIs5knXKLFd3JRTAmHT8d1lIsTbdO0wZFGkM2VBMKSGE7acMJJQq4O+zIddVNhSFgRpFgxOLOr50J/Wp6xaaPfv5SrSkqupCSE69ksX03LN4PX5gbJiX7B2XUG5otNn2eB9rVGUo7w/8dQVEtDeA4wH8NPtNAF4L4IKsyDkATsiO35r9Rnb9aBqjpxjy8kpSecGcHGBLGUa72vgd8vKaNFDDbjPUCvXuRX5ezskDho0kn7jUaml4tIUpg+kqL9+GUhjUwxJK3IYiudrWauXP0LehsOXZk/cZbyaorth7x2kAgI+95lnBxkyjetDLK5diyPoPFEzRZfSAvI+MKdUG58EkCaXcLXv65MHS3TZNSEGjIZgLHNFtWDahJBnlLZVXiYRSI5mhVQFzGlPQcShnXjPfOp86M6WUu+L+ldg83Oi4nbFAVYayxfhrAngDgFldpOfbAP4bhQfZzgDWM7N+eksA7JUd7wVgMQBk1zdk5bsO6XNLjUMpbCi2hOLaULR+3Fwpm5Pp5IEaDt9nJg7cdXtcdOdSr58pVi4vY+LKVF6mUT8Ghm1Dsby83BW6gbIgaymwMWU/FLPPgXoti0Nhr167OuUZUwex8PTj8beH7x1sy1R52YZ4W3qzypp9O5NZzVCLSMGEuat5M+wfaqm8gjaU8ucyZbBWafO4VNWRqfIaCNhQ3OSQIcYgR8qbEopCSGNAkWupSHYbziQU95mm9t4NPmB6k443Knl5MfM3zd9E9A0AXQl0JKI3AVjFzHOI6DX6tERGwjWz3RMBnAgA++7rJ7FLQUhCSYmc1Z5ao03XhmLHoeyy/WRsGmoEkwHq5JD77DQNS9dt8/qxc3kVNCo34BYG6oluw4INpWg3zABco7xmZvn1oNtwnB7tCcYokkPKEkq8odAHJk007pmQIV46JuFaLuEYZfTjcu0Iqnx2zTDKv/2Iva0ysbu1sw2XMWwqXQyYSJVQzHcbNMq7+8oEVFeihNLybSgxlVfHXl7MSWozVcSPMUtXeVWnzYVK09R5O+2g0+SQ0wDs3w1CALwcwFuI6I0ApgDYAUpimUlEA5kUsjcA7eC/BMA+AJYQ0QCAGQC83a6Y+ccAfgwARx55ZIVPx2zEP1WrhQ29JrSRvXAbpowu2xZyzodejCvuX4Gdt5+cnzM/JC251Igwms1CL91/Z9y6YA0A223YNf42Wir9RdoYY5gxbq6XV2igSisyFq7bOzaWr8jyGIbMKJpHyjvlyj7E0HXpflzGWwQm2vEUZjF3dW1eq+X1i/ZjEorpCVgjYOHpx0dpDO1PbwU2Ot287Fk7///2vjzejqLO9/vrc869N8vNfrPvJCEJkAUCYScBEsImIiigIigOIiAoIAMuuD1GnvPe4Ig6DwbE5Tkyo4igDz/KsIz4XEB2kEEQyLAJyL4m955b80dXdVdXV3VXdfe559yb+n4+ybmnu9Y+1fWr347f/PkFfq9FHAr/20RQTI6N6sats3wKORRBOOPfRwdC+Xwog7YiLxOHYtl9FZxFw/Lw2Ao4ERQiug/xPlED0Afgi1UMhDF2PoDzeT/rAJzDGHsfEf0QwNEArkKor7mWV7mOf/8tv38Tc3kzHKA3G87P5QEkTT0DaUNuDrLE6XTOpNH48D5J2qxGGw6vxSe7hEisEXvKyyc2BhYSlCBfng6EG4/8MtjmVcg75cY6FLmt/GcYnrJDxGbDzPmFyXJ6S10DcNnxu+CPz7yaGDNRcnPLEnlpzYalMjY6lNDKSz9u+Tmqqz56DTIe0Xc/tFsk2nThUFIe5xlcQWzlRVouwxQu307kZc8FVMGh2JoNB6TP2NilCUKqQxVkoKtmpy9tBawJCld4nwZgM780AOBZSb/RKvwtgKuI6H8AuAvAFfz6FQC+R0SPIORMjm3VAEwyZhuRl+r5Hkinz7z3OKGUj/JXBPHmnNCx1KQNSxKt8MRJ9cDObHiQsYSyNAx3Io+n2EoVzzChQ6H81mRDgHotSASHlJEvOrMeKoiAjTtMx8YdpvPv8fM0K+Wjp5/4AGQORbQTP1Od/04ceiVDjJNh5WVjNlyvBRBSUhcdim2StkD6bcN8KBqCYjjFq0V1xKBfOo2Z6sn31RzrrrC18gozNrLUM100dSw+d/hybH7hTXz7N48b61fBWcguBkMNa4LCGGNEdDFjbJdWDoj3dQuAW/jfjwLYTVPmbQDvbvVYAP0GBthtUglnNFXklfMi6ziUIIjDZMj9y6FZZNFKP9+wGjVLDgVJZaks4rDReZggiGAiOCRsdSgE8DzygqJUpUPRIW02zK8rHJXOD0XHodRS95Ap8ooOHRmbmHxZPZq4mA3L5W1gf9JXlPKaeZh1KOrzz+ZQTPXkdsvqFAaZnelxjWKxrIyACB/cawG+ecsjfKzZ4XfKoJ06lCKe8ru2ZCQdDJMXuJ3IK3mila288qASDCDcnAY0m7P8ostyfJEsy/aE9vrbA3j0+Tei78nQK8WDYAsapcbCyo3lJbExcvh6Vysvl5NfSiksiax0psKQxqPwKcn60u8SK+WzRV42SPuhiPHZHSLcRF4OHIogoAFpgzMadSiG5y8jqUMxlxP3XeZogs0mTRT7SiWu80/xztocFIqiUUsbzwwVw+KqlF8P4CNEtBmh6TAhZF5WVD6yEYIkh5L0QxGL7rOH6aPZqFZe4lrEoShERH0hCXF2QVsO5Q+bX0p8Tzk2Wq5M9f3VKeWtXtAACdFJrENRyuVyOvl9GccgEQJTcEhZz6KORyZIop7YDLVKeenQYRR5JTiUJBJJyCyOAK3wQyHp8FEP9Cb26eCQIVKhV7QiL5lDEfVMY3GbowlW+VCC8HdLK+WJ3xcEJfS7SI21Ai1KQ6NDaRU3pMKVoBzcklF0OLTchOUiVUOpkLRZMDBs2mE6Ttp7gbauvCZEqtoaUayUVzgUUmoSURQEMAwh7r5Ya5IORd4kXKENvSKZiZoepbCMEyHQQ+u4tCinUpGXUjQeM0knTH0dNaYXkLQSE2XFfLVKeaFDyRJ5yfM1cijmOenK28DWWirBoRBpLbXSfij607te5CX5oZC5nLhexX5qJfIShx7lujrGcK7pUVXBSXTX26dDcRV5ncoY2yz/A3BqKwbWSShD3YOApOCAsg6FpTLypepKN6ePC73ka0GcbyERVyogpHQoiEVe9YKWH6rZcNF1qnVslMZpQo0TYZGXXBVzqG2Z4KSUV0Ykh04xbV66Zx/3HdcX7cdK+XT/gmg1rTkUkw7FVuRlv8Btc7PLTqsmDsWUYMskcpSRDA6ZfL7psVTDodhMPbTySvcnc9ny91QfxYcXQcehDBVcCcoGzbURz7WYXjjbJSrEXrIHa3MwmRxIh4TpaULklRYfAWmRAZEk8gqKcRfhCyC4C7voADoIXYGufpYYTahQRJgWncITyH8Ry1jPyE6LumjCYpxyP3J/Wg4lS+QViEOH3cajchgJKy/jrNLlbZAyIzeUk4lvLdCHPjHrUBSCrhWX2XMo4fMuDxtiGvDOUjqUaIxSOV39CrTpO80eb1yfrYaVyIuIPoqQE1moRBfuBfD/WzGwToJJJGD7IjZqhLf6k6x/pAvIeOV1i0Be1CZxdqx/j/UtpiRH+WM3h3XPgvpsIisvhavKa1c4E9YQn+wZ0n4o+RyK/ejT3E9MCMSzTZXJUILWFbYloGyRl+ivyf2H9GOMrxtDr5TgKE2wz9gYP3OjY6NBz5ASOWp8oVz8UAC3aAAm2Kwh4SuVPoTyQ0UQPxMdyv5cPzplD6ycMwFX3fZfieudpkP5FwA/B/BlAOdJ119jjKW800ceyv0asZd7/NJEctaMFSTW7+yJo6Jrug1ZLS+LAARBaVjG8lIRh4yPuYQikLMIRuMVnxltCt0JccfMSFSYKpk9MDeRlzIG6bnmcyhI3VfrEGLnNx33KyfYMqWula+aDCBIMhyuakOxFnnJ3LXJD8XSsVHVvzRqQch5S7o9QD5Iqe0NocjLoEMR068ZiGbcSeHhAQDWzJ/E2x8ilkSBFUFhjL2CMPjica0dTmfCyKFYEhpxylf9UJAh0gDCDeHa0/bCvMmjo2u6GFFyjbCf+JsQedUtrbxUJK28inE5gCH0SoIAmjYXABRn/RNmw+qDy+VQHCiKyQ8iCGTioBkn0oQFSMvNxTwA/aYqh14x7jvSDbPZcAs4FMv2VLNhvdhKr5ROcSjKhe5awEVesTWbrlzcZjVKedt8KGHolfQYgHgdWhlblICrjrEqOOlQiOg7RDRB+j6RiL5V/bA6C6bDjb3IS9ahhNfEKSaPjV45ZwImjO6KvsunNVNYFJ0fSr2gyKumsBRF16UufH308mRyaWGpGlF8smcaEVNO/y5TT5FpibOQnRyTdSjxKT822VRUDCZb5BXfMzrrySIv5Z6rY6MLerpq+YUgDh/h36ZowyZdWJ7ZcKMeOCXYIqASJYqNyEsQDKNS3nAgicpV9IN1vKc8xwrGWJSMgzH2EhGtrnhMHYfySnl5QxKnz2w/AxMSfhx5SnkQtgoOpWDoiXoQJB0bC67TyMpLJij59ISXI27FBkmHki6ThTI6lIiIU9piS60TX5c4SdWxkV9nWll7/IxsQ6arO3PCzLvCjeXKE3fFi29sTVwztR+QRFwNBEWHkItPXlO5m0aN8PgLb0Q6GJnz07fpZslmgn20YbPBgWqgoaKqX6sC3X6xfl3LE9FE8YWIJqF8xOKOh5GgWK7RLh40SdZBiPD1rr+7LIJSXzR1U5N1KCZZfB6S+UuKs+Q6kZeaydCEgGLzYQZDtOGc/p10KAaRF0n3zGbD6TnVFEIiiz2zRF5ZwSFlDBWHsn7pVPt1RMjlUAzV0n4oSt1nX92CB55+Fd//Xah41lnWJeqTPmCjK2ymIP92MlTTZjPnWXx8uv6GGq7E4H8D+C2PAMwAvAfAhZWPqsNgNlNN3jhVzvonoasWbzYJPxSNtVIeElZeKQ5FWbSQRV7FOJRk6JjiHIoulleDj8kmZ4ewUoscGw0cggl6c+X8cct15dNzyrFR+Uwo5RUrL/lQodvoZMdGmzFmB4fMr+8CW05PDtPjYm4ejjlZ1mQI8MRLb0Z1wn7M7VZhlGAl8iIDQUlxKPr6rdKhDBWcdhnG2HcBvAvAswCeB/Auxtj3WjGwToLNYly7YBLO3bRUe0+nlG8OZvsZmKCGW9GWkTZAWSlfBKnowAUXapxTPr4mxpTXJoFzdxBWXpqMjblWXun7thZLsnlznpWXPGaBiKsUYj/+fWCQoalxbIz1bPmiPAD4W2XdxWbDxQ8AJpjMmPt6uxPf5WflxqFoRF6GuvO5sYqOiKsYKj+UOFK0vse8WF5ViaqGhac8D2G/M4BJjLFLALxORKlIwCMNJmuuQkr5hB8KnClKkkNJ3tMpJ7dGZsPFFlg9CBK5u4ueoJrSJhe1HemWskGkiLxQhENJX3MJIxKNQ9GHxGWSPKuWQ+EQv8UATy2QGpd0yrUZ4d6Lp+D09Yui7/K6rFr0YXpmPz19b3z7g3HcWAoXS1inFqTqGR895SvlF0wZA0CypOLlTWPb2hy0Nhvef+lU4z2bPTo6DCi/a2wpmM2hVHUCUAUSQ2VG7CoH+SaAPRCbD78G4BuVjqgDUdqxUcq2GCvlZV8Be9gp5fl3co82rCKVA74oh6KczgFJ5JXbaKiUD4gy/FCyoVOo2hOUmPCZxCtqSzodilguQvzYbGaLvFyMNuRyiXpVcyiGg8n08T1Yt328GQcxPUGN0mbD5lDz6Wer/k7fOjEkXMLsOJqqYa79zUGrd3XymK6obR1crLxMuV7UdNACahK2slD3lRblHkzBdZdZyxg7DcDbQGjlBaAru8rwhzanvLIgsn6uWIeSVMjqoubmIeHYaHhJk0p5YeVVlEORw9cXX/Bx6JX4mo2nPCBxKEifSqH/qmlDQ1Ac5PqiDWuRl0xQlGcvNuX+wUGtL0bSysv9icv+K+LT1mcqD7YHE9lnSSfyyjqg5+VDEWtZrCkdZ66OpYrZ24iNI+5SzfUixhgkPwW660GiXFkMCx0KgH4iqkH4lhH1AdBIgUcWjH4ojo6NkOTDwg/F9XdPhl4xvEDRiTqONlxUhyLXK2OGGnvKp+vb6FBipXzyVCqPLQtakZflM5GttFQHRrV/MT55A41jcyVNuMM00OWV8mJsArLuxeX3euALBxnvHb5yJgAHIiylHQiCtKe8qRkbHYqsgxJ1gPTaOnL1LPzdkTth++m9WL99n9W4s2BD3E06FHWMKWfNRi1RrixMh81Ww5WgfA3ANQCmEdGFAH4N4O8qH1WHwcQuFtGhxKFXkBttWIcskVd0XeJUylp5hToU0W5xDkUXy0sgb7GTwtmJa4kyOf2XUspLm7Mth1LXEBT1Xn9zMDOn/KCLaE8agCwSdPm9sg4dlxwXuptZcxrSkOpBOjikaeOUf2sBVVcYh6YZjOoA6UPDhNENvHftXADA+3efh7sv0MW2TfadBRsRaRyHTd+2eA/VtddVGxkcipPZMGPs+0R0B4AD+KV3MsYerH5YnYWy7HJXQocSXhP5UFxPJPI7n3pJlRdLFhG5KOWJ4o27KisvXXtyu5njAfHQKyQ5s9mdeAVKKeWlgJCmAIZqGlqZgKsKe7FxDzSZ1hoomTLAkiOQ/pZFqS6/V8Pi0GHL6cr6QqH/SsDIoWj8UAxKerFpR8RTFUPLxglEiYgTRWDFoSg60qh//rlm/kScccBiPPnim3j0r3Fm1O5GkChXFupYO1KHQkQ9AA4BcCCA/QFs4tdGNEwpgO095UXoeen0OZifD0UHnShFINpElM1NVzYLibD5gSRmomJJugC9lZdAPneByFNejqSbbCOPy9FwKAWU8rEfirrphZ96kVeyPZGKYCBH5BWO22qIaaW8WAMOv5dtAinX8eh0KHLA02S9tOFHmsMTIsMkh+KyxovARg8pfut0RIHws6dRw1kblkSHTAE5I2sVaPGjMPfrWP67AHZAKPr6OoBlAEa8H4qOnoSnf+lGBnXpivwt4hfrvB/fhy0Dg86yzSw/FJ1jo0BDlzLP1IdUMbG5qY06QMi7dWKmfJEXoUZJjkutkc+hpAu4OOmJPkzBIVMiL+kkLxTZYrmIjWlgcFDLocht2z5umXAMSsq5qkUfLmJCOe2tuvzOP3iZtp4VhyKZXYd19L+JO7IbsCFYZoKQXVdwKNVBPWwODYVxncX2jLGTGGM3838nA1jSioF1EspayPRwhZvsaf78a1vwylv92hdepASepTnFOfmhKKdEW2QFJMxr5tAVM7TX45O7pl2LMdW4Yle17LFtQzduW/FNXYrFZgy9ooxAFnmpm3BEUJoGPxQL59Us6Ky8qoILFyBK1ohS1mFjuw3SdkpbQJl0UNGBzsA1Vg0rDs4wBrWqKhKrWocyXDiUu4hod/GFiNZiG0iwZRQ/WtKZyCQQuo0ojXevmYPHLzoU40c1UvdsNptI5i9dc1HKBwR86pClmDwmlDknzIYzVvwJe8zDN967s/aeLtqwQK4OhXMGMleYGkcBDsX2tN3NY7GF44jHlGg/2uTC70mlfPiZ0qEMGvxQpMYLibw01mFZoi/Vyz0LtkRY1rfVamk/lN4ePUHRhWkxWnk17fxQqoKdyMt8GJOh/uzRGmuRlddQwTWW11oAHyAikQ5sLoAHieg+AIwxtqLS0XUIzGbDdhAmgU2NX0EZHYpJNKDTobiYDdeIcPK+2+HkfZOxyfI0KFnERufYKLecBSJej8xOpvk6lPQ1W3l1TyMWWZmixQqjB0E45eet9iOI+0BzUBt6RXUmdcUg03OpJlx/xj74yytvW7Vty+nK3HiNKMV1jDFwKLLhg9yWDDmSQFjHwDVWvKe6xPJSoV5Vl3FXxX4oBY06S8OVoGxqySg6HGUjlQoOZevAoMbD2m0FZSrl+Weg2UtcCErqpagw2GAxPxQeHDLjWRXRodhujkJkubU5iFFdMbeSbEso2tNm2qKf2A9F4lAyHBt1/ZggF2NgEPk5bdZXX2+3NZdiu2nL3LhOKT+mW59XJayXvJYK28INNNLh68stzvGjsrdDqwRbRg4l+V3dU2QphsCH916Ay3/9WG6f2v4qF3bawYqOEdGuRDSdMbaZMbYZwH4IFfNnI0wDLK6PSJjD18fXs/QsgkPZqlHCu74DWTGi1Da3SsdfF5GXcZOg7IVqMxfdmPOqCZFXQlntJvEqpZTv4S/7loGm0Su7HnEo4XcdJxmLvASHojcblpsuYlWVdGwE77sas1HbdZSINqzxQ+ntTotzAaGn03Mk6rUBRZ9W5lQ+tbcb3z1pber65w5fHvdZQimfem9UkVcjfVD5zGHLURRtknhZ61AuBbAVAIhoXwAXIbT4egXAZa0ZWuegpAolOn30N3UcihsSHIpxVw3/2PzCm9p6echymMxaqDanIl3T+RyK8JQ391XIU97ymYiXfUv/YMLJUUYs8oozZJr6iXUoesfGLC7UhEQGx1Yq5R10KOIHq2v8UHoMVk06DkVHKHQGGmVO5SfsOR+zJqSNYOQWrQhKxmFMhlEpX2IOe243WRpHeyiKrcirxhh7kf99DIDLGGNXA7iaiO5uzdA6ByanoI/tvwjX3v10bv0eiUMpGxJBrm+2BC53GjXKgfM2foupaM2Gc14ioaiVy7lyKGX8UMTmt1U6EKhVY5EXNz6QCqjh62UrL23GxgIERUbSbLjajcVWTChSNwPCbNhu3et0KLpnEFDabLgqLiw5nowDnAa2hh5V61Bu//SBCUOHTudQakQkRnsAgJukeyM+Y6NJhbJoai8uetdOufVlDqWs85WNUl4d79xJo536UIcYW3llcyg2U9OKvHKpQUg8kxyKWxtlOJSeeppDyVPKJ/o2KeUNHErguIkBqshL4lAq3liczIYprmN/YrbLh1IjkpTy/GILnMFdxY9G7l65nrbyso28rUdfb3d0cAXax6HYEpQfAPgPIroWwFsAbgUAIlqEUOw1oiHe+QuP3DH64SfyMA42P1yklG/qcqG7jUXmSkxKefW9uviYlZltpkQMGZYqWfO1eRn0HEo2umoBuuu1RPtpDiWfy7EZiw7C6WyLdCDI41B0/YgTdD0nH0qSC3XfGJisQ3GunQ3bZyb3XQvIydzYRocSBKR9dsn+y8/eNdqEMWCr8j2llK849Eq7OBQr7oIxdiER3QhgBoBfslgGFAD4WKsG1ykQP/7K2RNw7+c34rbHXsROs8cDkJSeGWtb2JhvHWjmOsTlwWazMdm4m0BEiUqmOECyGEPbTmYvvG2dY2PO6r/wyB0xflQDP73nGakvN5mXrl93DqUZKdTVN7buxKHkibzcx2iKbVY5h+IUEy4sWwvSfijGOtAQFK3Ii1Jmw60wbJKbtAk2YWvllXpHK3ds7GwOBYyx3zHGrmGMvSFd+xNj7M4qBkJEc4joZiJ6kIgeIKIz+fVJRHQDET3MPyfy60REXyOiR4joXiLSe9RVgDjaLqG7XsM+i+NQ2DanciGD72+21g/F1JYwdTVBrZalWMycr43Iq8BC32XeJCya2psUeTlyemV0KN2S2bAIo6Oa+4rQNjqCEpsNJ8sODDItR2NjyafCrFOqdmNxibggitaDdPh6IIwIccIe8xLXwjWmtKMTeQUUmWiLu31ju3HG/ovwPh5huArIY7FZu6YiKsFPcyjCsbEatIlBcfaUbyUGAJzNGFsGYHcApxHRcgDnAbiRMbYYwI38OwAcDGAx/3cygH9q1cDkFLgqbN6vmEOpwMorg0MxKSdl2aoO6c3ZzLZncygWIoEiOhSOrBc672XX3TeFiVEhDgRb+gcjDkUlHFkbrdnTezCViEktb60EV79H1mhW1a3hFnpFiAf1HMpnD1uOzyqmsWE+FAuRFxEGRbThiBsjnLVx+yhFcBWQ17SNyXRRDiUOvVLND1ZVkEnnftvSqwaMsWcEt8MYew3AgwBmATgCwHd4se8AeCf/+wgA32UhfgdgAhHZ7RCuY+OfRZzygFg+urWZ9kNx/eF1wSFV8cag4n3dU8/+mVVCoL43tkp5kymoDH1wyNxqYblEHZWYuvX7teNW49hd51j1Kw4EWwYG03GkOOoZ8hDVWELWoeg4lIQiuIBSHoifR9XbSiqVb1bhiEMJjKIyXcRsG6V8aOUlOJTWbZ4JDsVG5MUrqCbI6gjVQ1+kQ6lM5FVNO879tqfbbBDRfACrAfwewDTG2DNASHQAiMTVswA8IVV7kl9T2zqZiP5ARH94/vnnC40nzjaYvmfDBgsOob8CDkXn3yDGMJMvYjVyaR6Hog4iy2w46wQ1Oke0BpgcG903zfSJPKdf5f6oRs36NBiZDQ80E+IqGVn5ZtRukqFXmJHTBBxEXkYdSpKYlUUtIHxk34U4cc/5FmPidTSxvKIyGgKVF21YjCNl5cVx3G5z8c5VM/Gx/RfljjEPctN2CbYMZZWv6jki9kOpCu2hKB1n8ktEYwFcDeDjjLFXM1563Y3Ua8MYuwzc+XLNmjWFXqsoZLqmR5tNSVh5bdGZDTv+7kGCoPBPPoaL37MKt/zpOWzXNzZRJ4+g5Fl5yTqkLIzqyl9ORbm8sBxJfyv3ch6kK0cjQzy/LQODRl1Jljgk9pRPW3kNcIIitycP1UVnkQRJ/1cHIsL5hyzDj+980qJs+BlGG7YbSaMepFItGJXyhnTQY7rr+Oqxq636y0OCW7QiKKQtq67PlFK+UbHIizcze+Io7L1oitUBoJJ+h6QXSxBRAyEx+T5j7Mf88rNClMU/n+PXnwQgyyxmA8j3MiwA2copNWaL+rIfSnpjc1tA8klPFXmNH93AEatSTFrui5ASeRVc06PzOCHYha+7+BuhAAAZl0lEQVS/9dz12rpVcigu761MUEzWXFkcitp3QzIxbg4OpoiGvEYKK+VJf30oIdaVix/KV45agVPWJYOSaiUDgRRtuIVzlN8NG/Gj+k7G15PfVWdpcSCp2sprbHcdFx21whiMs2p0DIdC4Vt0BYAHGWP/IN26DsAJCMO9nADgWun66UR0FcIoyK8I0VjVUNIuJKDGadJBbEiMldvYAL0XdVkTwfTiV05TSHt/62Aj8tISZeXaHIMjZtJT3m3O6thdqndLOiiTA2OWDkVATYM80ByMOJTE2KS/Xfw+dG20K0ggEOscdJ7yJqxdODl1TeThkR+57NjYUvGOI4ciiqR0Tal3Sq0nOErC3RdsKC2i7Hiz4SHAXgCOB7A/Ed3N/x2CkJBsIKKHAWzg3wHgegCPAngEwD8DOLVVA4s21ILiGnEC7a4HGj8UNyRMSkkQFMdGFKjVbUNwq8gzTwaKOTZG5Uj/t+67irSk0f6hySIYcZJUlfJZHIqYYRQcUhAU7tiY5lDiv4vE8pLbaPW+kkXYZQ6lFhCu/uiehfvRRRxuZlhfVoUEcS+hQ1FrqusnkH6vCaO7MJHnIiqKdnGmHcOhMMZ+DfPecoCmPANwWksHxSGspvQRa/PrExEuPHJH7DZ/Ukv8UMqYCHbVAuNmJGCrQxltoUPRvpSE1AlUB7lqSuTlqENxpeTnHbwUe243GU+/HOYNSSvlHRKYBeGJe6ApdCjJugkxS1Gz4ZKn9kuOW41BxnDmVfpQfQcun4Zd50/Exw9cbB6TsrnuMm9i4fEERJg3eRS+esyq6Ht/M+mH0gq4esrb5mZR17p4h6uayzZPUDoZgxknIVvRy/vWhg5cb/c3k/VddSjS3hOnpi2Oei09gqLBIa2svDQyZkL4sg5qfDIS/csjLUmYXXHKfqFc//nXngWQdmzMUp6LscmH0notQP9g6Iei1pW/Fo39VpZr3X/pVIzprhsJyrieBn54ih3HUdywIEYtIEzt7cbquSFRqhHhraZZt1kV5KG7JNhSbTTSSnmVQ0m/FzY4aIdp2syu7RJ1eoJigUhSqz9cO6Esh6IVebk4m5GyseksaAx+KHljtRF5ibZXzJ4gjSnM1b567ngctmKmsW6mUj6n37e2Jgl50UCCkWOjgx+KbmyNgNBsMq0OxVVuD+hEgFzMVnCeVcjgTaf1IqhRMtp0EBCeevmtsJ/SrZvhKn406VDyBhmJvHIK/ttH9sB7Lv1t9P3S49fkjmko4QmKBbKsvFxfltKe8lIDZFq8GagHhH6JE2jUgpT4xhzgLk/klU9QGrUAPzxlDyyZ2iu1G/Z5zal75daP6hg2UBNe3zJg3XYWGvz5qx7u2ToUgbjOG1ubuPzXj2HHWeNSgRNdLYvCOjn3HRdaFYd+0UQVHEoQUGKSCfFnCylKcSuvPJFXuBYuPX4X7LekD7999AXRYSZ2WzApdwzthCcoFoh1COl7sTjD7iiYPo26EqR0edNm+vMz99H2rxKUJlPEcAbDgVyRVyN/ORGAXecnX4qA7KyAMiMd59TtyokWYItGXe/YmOWHkkXs7n/qVSzsS4YKKaKUT/dZqFpl9YHwfSGqJgxIPUgeZ3QHq1ZA1o3ZzMNERE2+XV31AD2NmjEtwnBDJ1l5dSwGM5TSrgugjIMdkHyRsggdACybMQ7LZoxLXFM3Pr0OJfk9K/SMDCuRl8FSzs64Qfrb4Bluwrolfbj4mJXYQ5ilFnxvxUahirxsOBTTmSNl5SX9XdbKK69vY3sVCJKI7KIML+wbg7M2LMkso27m8jpqpb7AJpxQAnwoKTN1pZjgUGoRIdGXKwqvlO9gDBo8coHyP5wrQUqetPmidDgBqmUbGiuvombDNlyAabpWFjQZ7eSJvIgIR66ejZ/cVc73VZxYVaV8tvlsGr09dbz2diiGS1l5OVoWhXXUPkl73Rai24CAg3aYXqgNgt34bzp7XW6ZGiXjyCWabcHmeeWJu6K7EaTi4uXCcMjLs5wUhGWYMyieoNhg/KgGlkwbq1W8lrUwca0u6ynG9YTWHYfsZB8TUz0Nq6IEQENQLM2GbWAS2VlZ0MhijtIjKYaGQSlvA7nGvZ/biDOvuhvX3fN0KT+UUYaw5+VFXmEDj3750BJtFBfZqagFSaW8/CxbsRbWLw1DBt7++Is5JZMQ5wzb0Cux/4knKNsMDl85E4ev1FsfVe1UmIceKVnW+FEN3PXZDRinMRs0wYZDMS5qw/Wbz1mHR59/3XoMumZd5NNABS9eYSuvsGMXemIyNxce+OpvkggAmjHR2z51QJw8raQoVUU1dMDeQz4PgZICejAR+6x1u3C3o+5NHDTy/VCS0Sdsrbw6HZ6glIRN6JUsOJsNJ5SR5OxRq56GG7V0SHp1E4gjBejbXDBljHUOClNOeStrpgrk5mX3noZNDHMDVMMNISKUf5OzNyzBDjPHR9+zCO3UcT3R32kOpSznXIUOpUIORRF5yUYRrdyCcyN1KxCELm/eql4ycmw0VNu0w3TsPG+C/mYHwROUkii7mMu8uEXeVTUvRb0WODg2VrDJaNu123gWTJaIVsGhlI2RZJsbXYbpsXVpOJSPHbAYA81YcF/U5LYTzrmEakyGgXDDlQ8RsgqrlWIiZw5l0I1DEVL0PJHv/zl+F6dxtAveyqskWslu56GITkPlBOpBmkMxKxCdu0tBq0MBpZwpx/XUcdTOsxPX9l48BbMnjuJ1SqJgAy4hVlSotCziUFQ/FOkZFY023AkUxdYc3AYqh5IQeVVikaa/3l135FBMIi+DDkX8ULERRLU/XFV5cGzhOZSSaKe9fyGCorzgXfVAY4Jr4FBKvLhE4eLW6xPSc7n38wdp2xEnxnYRcjsHxhATRzfw0pv9xucmkiqlE0rFf5eNNtxO6ESZf390sVDq6rodkMyvyiyFMV01vGPVLGO+EFcOxZSMz2yKLz69Ut4DFYSOL7NJFzgsq34oAVFurgbb0CtZqBFhgDFj+BrbjVOcGNv13tmEqRf45Sf2w19eeRt/fX0LAHMecRUysbQVGanr8M/Pv2E9zlaBKC1iffcau7TLKsL00waRV6EWQ/T2NPDld+1kvK9mP82DkFamUxurHEoy+oYor+4Hlx2/C15+q99pDO2EJyglIdaNC2t56fG74JKbHsb9T71aapMuUjUVBpzCRT1/8mg8/sKbANK5PqL+ynBTPJywlgATWYt21Nzbo7tqeFON02UDzRRPW78dHvpLtrVa1gb/f09ai4ljYou7vt5u9PV24+aHntN2KUReWWvHPgVwNmzX55Uf3BU/vaeaPHUEO8dGG9QU8/ZmRRQl7/G6iryaivWWgNpN2mw4+SmwsagPUJtOXJ6glESRH+6gHabjhj8+GxKUEn0X4Y6EvH78qAZeeasfJ++7HV5+cysO3mkGrvj1Y/jSz/6YCivCDHJhF0SOW5p7bhxKkqD8/Mx9cP9Tr1qPI6ubTx60NLd+lg5l78VT9H0ayguCYiLggD7DZSuxfvupWL/91EraWjBlTCq6dlEcvnJGQlTWrEiHkic6ddUBCb+gKWO6cPjKmRFxNinlxdjHj2qACJjg4AKQhRnjQl3jsbsV4wiLwhOU0ii2mNWNsQjK6FC+cvQKLJnWmzD37TJkIxQoOtS9Fk3GPU+8AiAj9IothxKJvMLy8yaPwbzJdibLQHklZRklsypKtHGSVL3oTehE2fs5B21fWVsn75tMCywnqOqkuR+4bCq+dMQOOHqXORjVVZMIil4pLy7Pnjgat5yzDnMN2UplXH/GPhibo4caP7qBxy8q7pRaFJ6glETRxezKSlfVt+AEuupBynekyxD4MO7PvcNHLjwYARFWf+kGYxmC/Um8CkIcdTpEMD038bwHBxlOXbcdbn34r6ky9tGGO2hXHQI0K/JDKeFWpAUR4fg95ueW01mD2R6Mls8cl1+oTfAEpU0Qm0l/TlKpLJThUHQ1oxOzEsDoX/5md/zb7U9o5c2mCAICQokt+tWdxm1DrwBANxcpDLU5ZBkI/5mNy6clrgvi2GQM525ainM3pesOtchruKBZkad8u6L7qhzKSIEnKBXBdX8Tm8nWAdfoczGKSF+iLI+alSwIyoBC5HZfOBm7iyi9Ei45brV14EBBUNSgigAPvWL5ZvVU8NyqwLzJ+aIJgbmTR+O+z29MiSmElVdW6uPhLPJqJaoSebXrscXRJ0bWD+fPP22C4FC2FNgYo4CAhTgUYVmU3sXyRF4q5k8eY51n5CtHrcDS6b3aUDEuITqEldeWgWqUvUVw09n74brT9naq09vTSP1epsjFMmw5lHY62LYDlYm8KnpuS6f35heSMOg5FI8q0V0vvjFed/peuPE/nyukII7yeWg2sa5avtWRjDHd9nqg9UunRhFcVYTmpXbtCN1TEUKcQAmR2cK+seX65rCx8hppJ9iqkBR5lWioosf7k9P2wpb+9JqcN3k0NnNzfBmRH0o13XcMPEFpE4QuoMjGuHhaLxZPczsRCQgipONCIpGXZRKIPEsTW6gZC7PQXYKz6zRESvkMhVBWJkgZI21jyoOsiyujT6vqufU0atpAkld/dE88qnEyjZ2FR9Yv5wlKm1CFDqUIsjgUEVZEzZduQpEQGjpcdNQK67KCQynt39AB73EWh1ILCM1BZm2FJGKcje2u4/UtA6n7Q7Vvbdc3Bv/1YvpEXjXkM4+tiFYHGw7w38/aN1PPlYUpY7sxZWx36rpXyntoEa0Hx2NSu07aWRyKqw5ltEXK36ohUrJW5TDXTsRK+fTz7q4HeHNr09pseO3Cybj2tL2wbMY4LPnMz1P3h8oq7oZP7FdGmmgNmUOxFdHqYPN4F00tJg3IwkgVeXmlfJsQ6QKGeGOMzHc1Yq2Gow6lHez6yBR5pe91GyIRZ2HlnAnWRhKtQhBUF2E4C/Ia7W8WXwvt8t8Rwx9pOjJPUNqEdnMouncw9o3p3M060j1pFKDDDVlGEOLAUXbDWdg3BqO7ajjnoCWl2ulkqGbuLthtwaQKR2IPU5j74Q4v8moTylh5lUG9Qg6lHVi3fR8A4L1r57Z5JOVRy9BnCdFe2dP+6K46/vhFjcfkCEJRHcpH9luIczZWFx7GBSNVh+I5lDYh9qcYag7FrCdx1aG0AzPGj8LjFx2KlXM6Px1qHsRmovMJqopD2RZga5WoYlHf2FIJ08rg/bvPAwBMckzh3enwHEpJFNUjLJsxDvMmj8Z5m/Ij3FYJGyuvLDNWW9zxmQM7mjB1AoSZ6YwJo1L3xIHDP8N8uIi8rjl1Txz5zd+0cDR2+Oi67fDRddvlFxxm8BxKmzC6q47/+OR6rNWENGklIisvzUsoZPpl4osJTB7bjWnjekq3UzV2nT8RADBzfHoTH2pMGduNr793NS7V5AuPRKIjwJqtFfjqMauiv13OdKvnTkyllvaoDp5DqQjD5Ry546zxAJCKNAzY50u/4LDlmDmh84iFDU5dtwiHrpipnX87cNgKfXDNcT1hXgzPoejxztWzcMCyqfjnWx/DoTvNaPdwPDiGNUEhok0A/hFADcDljLGL2jykjsdRO8/CjrPGYen0dAhsW5PTD+29oOphDRmCgDqGmGThoqNW4PJbH9UG5fQI0dvTwFkbRq712nDEsCUoRFQD8A0AGwA8CeB2IrqOMfbH9o6ss0FEWmIC2Ocv92g9+nq7cf4hywrV/Zt9Foy4kB6tgOf9qsewJSgAdgPwCGPsUQAgoqsAHAFgSAmK2IR7KkiY1W6ITWjfJX1tHolHGXz60OXtHkJHY9yocNvTxd7yKIfhTFBmAXhC+v4kgLVqISI6GcDJADB3bvW+CzvMHIczDliM44Y4d3Or8PtPHYDxFeW19vDoRHzyoO0xbVyP1720AMPZykvH06e4WMbYZYyxNYyxNX191Z+8iQhnbViCGR1gNVQFpo3r8Sc3jxGN0V11nLLfdkMSImZbw3AmKE8CkNmC2QCebtNYPDw8PLZ5DGeCcjuAxUS0gIi6ABwL4Lo2j8nDw8Njm8Ww1aEwxgaI6HQAv0BoNvwtxtgDbR6Wh4eHxzaLYUtQAIAxdj2A69s9Dg8PDw+P4S3y8vDw8PDoIHiC4uHh4eFRCTxB8fDw8PCoBJ6geHh4eHhUAtIl9xmpIKLnAWwuWH0KgL9WOJzhAD/nbQN+ztsGysx5HmMs1zN8myIoZUBEf2CMrWn3OIYSfs7bBvyctw0MxZy9yMvDw8PDoxJ4guLh4eHhUQk8QbHHZe0eQBvg57xtwM9520DL5+x1KB4eHh4elcBzKB4eHh4elcATFA8PDw+PSuAJigWIaBMRPUREjxDRee0eT1Ugom8R0XNEdL90bRIR3UBED/PPifw6EdHX+DO4l4h2bt/Ii4GI5hDRzUT0IBE9QERn8usjec49RHQbEd3D5/wFfn0BEf2ez/lfeQoIEFE3//4Ivz+/neMvAyKqEdFdRPQz/n1Ez5mIHiei+4jobiL6A782pGvbE5QcEFENwDcAHAxgOYDjiGikJO3+NoBNyrXzANzIGFsM4Eb+HQjnv5j/OxnAPw3RGKvEAICzGWPLAOwO4DT+W47kOW8BsD9jbCWAVQA2EdHuAP4ngIv5nF8CcBIvfxKAlxhjiwBczMsNV5wJ4EHp+7Yw5/WMsVWSv8nQrm3GmP+X8Q/AHgB+IX0/H8D57R5XhfObD+B+6ftDAGbwv2cAeIj/fSmA43Tlhus/ANcC2LCtzBnAaAB3AliL0GO6zq9HaxxhfqE9+N91Xo7aPfYCc52NcAPdH8DPEKYMH+lzfhzAFOXakK5tz6HkYxaAJ6TvT/JrIxXTGGPPAAD/nMqvj6jnwMUaqwH8HiN8zlz0czeA5wDcAODPAF5mjA3wIvK8ojnz+68AmDy0I64EXwVwLoBB/n0yRv6cGYBfEtEdRHQyvzaka3tYJ9gaIpDm2rZoaz1ingMRjQVwNYCPM8ZeJdJNLSyquTbs5swYawJYRUQTAFwDYJmuGP8c9nMmosMAPMcYu4OI1onLmqIjZs4cezHGniaiqQBuIKL/zCjbkjl7DiUfTwKYI32fDeDpNo1lKPAsEc0AAP75HL8+Ip4DETUQEpPvM8Z+zC+P6DkLMMZeBnALQv3RBCISB0p5XtGc+f3xAF4c2pGWxl4A3kFEjwO4CqHY66sY2XMGY+xp/vkcwoPDbhjite0JSj5uB7CYW4h0ATgWwHVtHlMrcR2AE/jfJyDUM4jrH+DWIbsDeEWw0sMFFLIiVwB4kDH2D9KtkTznPs6ZgIhGATgQoaL6ZgBH82LqnMWzOBrATYwL2YcLGGPnM8ZmM8bmI3xfb2KMvQ8jeM5ENIaIesXfADYCuB9DvbbbrUgaDv8AHALgTwhlz59u93gqnNcPADwDoB/hieUkhLLjGwE8zD8n8bKE0NrtzwDuA7Cm3eMvMN+9EbL19wK4m/87ZITPeQWAu/ic7wdwAb++EMBtAB4B8EMA3fx6D//+CL+/sN1zKDn/dQB+NtLnzOd2D//3gNinhnpt+9ArHh4eHh6VwIu8PDw8PDwqgScoHh4eHh6VwBMUDw8PD49K4AmKh4eHh0cl8ATFw8PDw6MSeILisc2DiJo8Qqv4N7+Ffd1DRD+wLHu5SyBSIvo4EX2A//1tIjpauf96Rt0uIvqV5Pjn4eEMv3g8PIC3GGOrTDeJqM7iGFCFQUTLEB7i9iWiMYyxN7LKM8Y+7NB2HcCHABQKQ84Y20pENwI4BsD3i7Th4eE5FA8PDYjoRCL6IRH9FMAv+bVPEtHtPH/EF6Syn6YwX86/E9EPiOgcQ7PvBfA93t47eN06b3Md//5lIrqQ/30LEa3hwR2/TUT383wXn9C0vT+AO20IHxF9UeLGniKiK/mtnwB4n83z8fDQwXMoHh7AKB6NFwAeY4wdyf/eA8AKxtiLRLQRYe6I3RB6GV9HRPsCeANheI/VCN+nOwHcYejnGITh8rcHcDqAHzDGBojoRAA/IqIzEOanWavUWwVgFmNsRwAQoVQU7KXp9++J6DNqQcbYBQAuIKLxAG4F8HV+634AuxrG7uGRC09QPDzMIq8bGGMiSOBG/u8u/n0sQgLTC+AaxtibAEBE2jhvRLQrgOcZY5uJ6EkA3yKiiYyxlxhjDxDR9wD8FGFejq1K9UcBLCSiSwD8P3COScEMJJNJAcAnGWM/ksbwuvQ3IRRtXcwYuwMIoxIT0VYi6mWMvaabh4dHFrzIy8PDDFnHQQC+zMJseKsYY4sYY1fwezbxi44DsJRHwP0zgHEAjpLu7wTgZQDT1IqMsZcArEQYKfg0AJdr2n8LYUwqW3wewJOMsSuV690A3nZox8MjgicoHh52+AWAD/FcKiCiWTzvxK8AHElEo3i018PVikQUAHg3QvHZfBZGwT0CIZEBEb0LYRC/fQF8TRVpEdEUAAFj7GoAn4Ve8f4ggEU2E+H5QjYAOEO5PhkhF9Vv046Hhwov8vLwsABj7JfcSuu3PCHX6wDezxi7k4j+FWHk4s0IdRIq9gXwFGPsKenarwAsJ6I5AC4CcABj7Aki+jqAf0QcchwIM+ldyQkTEKahVvFzhAp/G5wNYCaA2/hcruN6lfUArrdsw8MjBR9t2MOjQhDR5wG8zhj7X23o+xoA5zLGHi5Y/8cAzmeMPVTtyDy2FXiRl4fHyMF5CJXzzuDJ437iiYlHGXgOxcPDw8OjEngOxcPDw8OjEniC4uHh4eFRCTxB8fDw8PCoBJ6geHh4eHhUAk9QPDw8PDwqwX8DK86LtFni7rYAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -486,7 +512,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.8"
   },
   "toc": {
    "base_numbering": 1,

From 5da112b72a363adcb6f38c6802e3185ebdb57f88 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 10:53:51 +0100
Subject: [PATCH 568/719] move examples that are only useful for the old
 dataset/plotting to legacy folder

---
 docs/examples/{ => legacy}/Combined Parameters.ipynb           | 0
 docs/examples/{ => legacy}/Comprehensive Plotting How-To.ipynb | 0
 docs/examples/{ => legacy}/Datasaving examples.ipynb           | 0
 docs/examples/{ => legacy}/Measure without a Loop.ipynb        | 0
 docs/examples/{ => legacy}/The Location Formatter.ipynb        | 0
 5 files changed, 0 insertions(+), 0 deletions(-)
 rename docs/examples/{ => legacy}/Combined Parameters.ipynb (100%)
 rename docs/examples/{ => legacy}/Comprehensive Plotting How-To.ipynb (100%)
 rename docs/examples/{ => legacy}/Datasaving examples.ipynb (100%)
 rename docs/examples/{ => legacy}/Measure without a Loop.ipynb (100%)
 rename docs/examples/{ => legacy}/The Location Formatter.ipynb (100%)

diff --git a/docs/examples/Combined Parameters.ipynb b/docs/examples/legacy/Combined Parameters.ipynb
similarity index 100%
rename from docs/examples/Combined Parameters.ipynb
rename to docs/examples/legacy/Combined Parameters.ipynb
diff --git a/docs/examples/Comprehensive Plotting How-To.ipynb b/docs/examples/legacy/Comprehensive Plotting How-To.ipynb
similarity index 100%
rename from docs/examples/Comprehensive Plotting How-To.ipynb
rename to docs/examples/legacy/Comprehensive Plotting How-To.ipynb
diff --git a/docs/examples/Datasaving examples.ipynb b/docs/examples/legacy/Datasaving examples.ipynb
similarity index 100%
rename from docs/examples/Datasaving examples.ipynb
rename to docs/examples/legacy/Datasaving examples.ipynb
diff --git a/docs/examples/Measure without a Loop.ipynb b/docs/examples/legacy/Measure without a Loop.ipynb
similarity index 100%
rename from docs/examples/Measure without a Loop.ipynb
rename to docs/examples/legacy/Measure without a Loop.ipynb
diff --git a/docs/examples/The Location Formatter.ipynb b/docs/examples/legacy/The Location Formatter.ipynb
similarity index 100%
rename from docs/examples/The Location Formatter.ipynb
rename to docs/examples/legacy/The Location Formatter.ipynb

From 5c1aebbec18364a91d46ec354b712e27ec6bc662 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 11:05:59 +0100
Subject: [PATCH 569/719] Add legacy folder to docs build

---
 docs/examples/index.rst                   | 7 +++++++
 docs/examples/{ => legacy}/Tutorial.ipynb | 0
 2 files changed, 7 insertions(+)
 rename docs/examples/{ => legacy}/Tutorial.ipynb (100%)

diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index c418ce60b37..527d1c7c970 100644
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -49,3 +49,10 @@ Logging
     logging/*
 
 
+Legacy examples
+---------------
+
+.. toctree::
+    :glob:
+
+    legacy/*
\ No newline at end of file
diff --git a/docs/examples/Tutorial.ipynb b/docs/examples/legacy/Tutorial.ipynb
similarity index 100%
rename from docs/examples/Tutorial.ipynb
rename to docs/examples/legacy/Tutorial.ipynb

From 6b810b704b325a88819865c67015d0d1e8ec5f5a Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 11:15:23 +0100
Subject: [PATCH 570/719] Add note about legacy examples

---
 docs/examples/index.rst         | 2 ++
 docs/examples/legacy/readme.txt | 3 +++
 2 files changed, 5 insertions(+)
 create mode 100644 docs/examples/legacy/readme.txt

diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index 527d1c7c970..4637f900456 100644
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -52,6 +52,8 @@ Logging
 Legacy examples
 ---------------
 
+.. include:: legacy/readme.txt
+
 .. toctree::
     :glob:
 
diff --git a/docs/examples/legacy/readme.txt b/docs/examples/legacy/readme.txt
new file mode 100644
index 00000000000..e488ee3c596
--- /dev/null
+++ b/docs/examples/legacy/readme.txt
@@ -0,0 +1,3 @@
+This folder contains examples of the legacy QCoDeS dataset
+and plotting utilities. They are not recommended for new users
+but may be useful as reference for existing users.
\ No newline at end of file

From c3a247979e77e5e09ef52311ee1e952ba360c3cc Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 11:18:22 +0100
Subject: [PATCH 571/719] Move snapshot example

This uses the old loop and there is a better one in the dataset folder
---
 docs/examples/{ => legacy}/The Snapshot.ipynb | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename docs/examples/{ => legacy}/The Snapshot.ipynb (100%)

diff --git a/docs/examples/The Snapshot.ipynb b/docs/examples/legacy/The Snapshot.ipynb
similarity index 100%
rename from docs/examples/The Snapshot.ipynb
rename to docs/examples/legacy/The Snapshot.ipynb

From dabef7b894f0dc01a6f215e8e8cffea402e1f2f6 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 12:19:08 +0100
Subject: [PATCH 572/719] Remove add_inferred_from add_depends_on from
 ParamSpec

---
 qcodes/dataset/param_spec.py           | 30 ++++++--------------------
 qcodes/tests/dataset/test_paramspec.py | 14 ++++++------
 2 files changed, 13 insertions(+), 31 deletions(-)

diff --git a/qcodes/dataset/param_spec.py b/qcodes/dataset/param_spec.py
index d10473dc067..694ff47e8b9 100644
--- a/qcodes/dataset/param_spec.py
+++ b/qcodes/dataset/param_spec.py
@@ -44,8 +44,13 @@ def __init__(self, name: str,
         inferred_from = [] if inferred_from is None else inferred_from
         depends_on = [] if depends_on is None else depends_on
 
-        self.add_inferred_from(inferred_from)
-        self.add_depends_on(depends_on)
+        self._inferred_from.extend(
+            p.name if isinstance(p, ParamSpec) else p
+            for p in inferred_from)
+
+        self._depends_on.extend(
+            p.name if isinstance(p, ParamSpec) else p
+            for p in depends_on)
 
         if metadata:
             self.metadata = metadata
@@ -58,27 +63,6 @@ def inferred_from(self):
     def depends_on(self):
         return ', '.join(self._depends_on)
 
-    def add_inferred_from(
-            self,
-            inferred_from: Sequence[Union['ParamSpec', str]]) -> None:
-        """
-        Args:
-            inferred_from: the parameters that this parameter is inferred_from
-        """
-        self._inferred_from.extend(
-            p.name if isinstance(p, ParamSpec) else p
-            for p in inferred_from)
-
-    def add_depends_on(self,
-                       depends_on: Sequence[Union['ParamSpec', str]]) -> None:
-        """
-        Args:
-            depends_on: the parameters that this parameter depends on
-        """
-        self._depends_on.extend(
-            p.name if isinstance(p, ParamSpec) else p
-            for p in depends_on)
-
     def copy(self) -> 'ParamSpec':
         """
         Make a copy of self
diff --git a/qcodes/tests/dataset/test_paramspec.py b/qcodes/tests/dataset/test_paramspec.py
index fc6bbeb71a9..ec121aa70a9 100644
--- a/qcodes/tests/dataset/test_paramspec.py
+++ b/qcodes/tests/dataset/test_paramspec.py
@@ -131,12 +131,11 @@ def test_repr(name):
     name2=hst.text(min_size=4, alphabet=alphabet),
     name3=hst.text(min_size=4, alphabet=alphabet)
 )
-def test_add_depends_on(name1, name2, name3):
-
-    ps1 = ParamSpec(name1, "numeric")
+def test_depends_on(name1, name2, name3):
     ps2 = ParamSpec(name2, "numeric")
     ps3 = ParamSpec(name3, "numeric")
-    ps1.add_depends_on([ps2, ps3])
+
+    ps1 = ParamSpec(name1, "numeric", depends_on=[ps2, ps3])
 
     assert ps1.depends_on == f"{ps2.name}, {ps3.name}"
 
@@ -146,12 +145,11 @@ def test_add_depends_on(name1, name2, name3):
     name2=hst.text(min_size=4, alphabet=alphabet),
     name3=hst.text(min_size=4, alphabet=alphabet)
 )
-def test_add_inferred_from(name1, name2, name3):
-
-    ps1 = ParamSpec(name1, "numeric")
+def test_inferred_from(name1, name2, name3):
     ps2 = ParamSpec(name2, "numeric")
     ps3 = ParamSpec(name3, "numeric")
-    ps1.add_inferred_from([ps2, ps3])
+
+    ps1 = ParamSpec(name1, "numeric", inferred_from=[ps2, ps3])
 
     assert ps1.inferred_from == f"{ps2.name}, {ps3.name}"
 

From 07ac6a75b28235efcaedba85e2f177581db76f9f Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 12:22:03 +0100
Subject: [PATCH 573/719] Test ParamSpec inferred_from depends_on for string
 inputs as well

---
 qcodes/tests/dataset/test_paramspec.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/qcodes/tests/dataset/test_paramspec.py b/qcodes/tests/dataset/test_paramspec.py
index ec121aa70a9..62f8e18630a 100644
--- a/qcodes/tests/dataset/test_paramspec.py
+++ b/qcodes/tests/dataset/test_paramspec.py
@@ -135,9 +135,9 @@ def test_depends_on(name1, name2, name3):
     ps2 = ParamSpec(name2, "numeric")
     ps3 = ParamSpec(name3, "numeric")
 
-    ps1 = ParamSpec(name1, "numeric", depends_on=[ps2, ps3])
+    ps1 = ParamSpec(name1, "numeric", depends_on=[ps2, ps3, 'foo'])
 
-    assert ps1.depends_on == f"{ps2.name}, {ps3.name}"
+    assert ps1.depends_on == f"{ps2.name}, {ps3.name}, foo"
 
 
 @given(
@@ -149,9 +149,9 @@ def test_inferred_from(name1, name2, name3):
     ps2 = ParamSpec(name2, "numeric")
     ps3 = ParamSpec(name3, "numeric")
 
-    ps1 = ParamSpec(name1, "numeric", inferred_from=[ps2, ps3])
+    ps1 = ParamSpec(name1, "numeric", inferred_from=[ps2, ps3, 'bar'])
 
-    assert ps1.inferred_from == f"{ps2.name}, {ps3.name}"
+    assert ps1.inferred_from == f"{ps2.name}, {ps3.name}, bar"
 
 
 @given(

From 84e51fa405664fccf1cd2f728922f4d4a5350eff Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 12:26:41 +0100
Subject: [PATCH 574/719] Add inferred_from_, depends_on_ ParamSpec props
 returning string list

---
 qcodes/dataset/param_spec.py           | 8 ++++++++
 qcodes/tests/dataset/test_paramspec.py | 2 ++
 2 files changed, 10 insertions(+)

diff --git a/qcodes/dataset/param_spec.py b/qcodes/dataset/param_spec.py
index 694ff47e8b9..252fe351151 100644
--- a/qcodes/dataset/param_spec.py
+++ b/qcodes/dataset/param_spec.py
@@ -55,6 +55,14 @@ def __init__(self, name: str,
         if metadata:
             self.metadata = metadata
 
+    @property
+    def inferred_from_(self) -> List[str]:
+        return deepcopy(self._inferred_from)
+
+    @property
+    def depends_on_(self) -> List[str]:
+        return deepcopy(self._depends_on)
+
     @property
     def inferred_from(self):
         return ', '.join(self._inferred_from)
diff --git a/qcodes/tests/dataset/test_paramspec.py b/qcodes/tests/dataset/test_paramspec.py
index 62f8e18630a..203d529fd58 100644
--- a/qcodes/tests/dataset/test_paramspec.py
+++ b/qcodes/tests/dataset/test_paramspec.py
@@ -138,6 +138,7 @@ def test_depends_on(name1, name2, name3):
     ps1 = ParamSpec(name1, "numeric", depends_on=[ps2, ps3, 'foo'])
 
     assert ps1.depends_on == f"{ps2.name}, {ps3.name}, foo"
+    assert ps1.depends_on_ == [ps2.name, ps3.name, "foo"]
 
 
 @given(
@@ -152,6 +153,7 @@ def test_inferred_from(name1, name2, name3):
     ps1 = ParamSpec(name1, "numeric", inferred_from=[ps2, ps3, 'bar'])
 
     assert ps1.inferred_from == f"{ps2.name}, {ps3.name}, bar"
+    assert ps1.inferred_from_ == [ps2.name, ps3.name, "bar"]
 
 
 @given(

From 12ddadbb73ef5d5c69a213805f4ee77c278be692 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 12:26:58 +0100
Subject: [PATCH 575/719] Add type hint to inferred_from, depends_on of
 ParamSpec

---
 qcodes/dataset/param_spec.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/param_spec.py b/qcodes/dataset/param_spec.py
index 252fe351151..eb58f6f422f 100644
--- a/qcodes/dataset/param_spec.py
+++ b/qcodes/dataset/param_spec.py
@@ -64,11 +64,11 @@ def depends_on_(self) -> List[str]:
         return deepcopy(self._depends_on)
 
     @property
-    def inferred_from(self):
+    def inferred_from(self) -> str:
         return ', '.join(self._inferred_from)
 
     @property
-    def depends_on(self):
+    def depends_on(self) -> str:
         return ', '.join(self._depends_on)
 
     def copy(self) -> 'ParamSpec':

From 769909dc7d08985f1c0d0e11e423c4fccebc2afd Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 12:31:39 +0100
Subject: [PATCH 576/719] move driver writing examples to their own folder

---
 .../An ArrayParameter Example with Dual Setpoints.ipynb           | 0
 .../Simple Example of new ParameterWithSetpoints.ipynb            | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename docs/examples/{ => writing drivers}/An ArrayParameter Example with Dual Setpoints.ipynb (100%)
 rename docs/examples/{DataSet => writing drivers}/Simple Example of new ParameterWithSetpoints.ipynb (100%)

diff --git a/docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb b/docs/examples/writing drivers/An ArrayParameter Example with Dual Setpoints.ipynb
similarity index 100%
rename from docs/examples/An ArrayParameter Example with Dual Setpoints.ipynb
rename to docs/examples/writing drivers/An ArrayParameter Example with Dual Setpoints.ipynb
diff --git a/docs/examples/DataSet/Simple Example of new ParameterWithSetpoints.ipynb b/docs/examples/writing drivers/Simple Example of new ParameterWithSetpoints.ipynb
similarity index 100%
rename from docs/examples/DataSet/Simple Example of new ParameterWithSetpoints.ipynb
rename to docs/examples/writing drivers/Simple Example of new ParameterWithSetpoints.ipynb

From e7305187a8bc844425806b8c9b5c2992afb61ee6 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 12:34:04 +0100
Subject: [PATCH 577/719] Formatting typos fixes

---
 qcodes/dataset/param_spec.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/qcodes/dataset/param_spec.py b/qcodes/dataset/param_spec.py
index eb58f6f422f..beb7183ec66 100644
--- a/qcodes/dataset/param_spec.py
+++ b/qcodes/dataset/param_spec.py
@@ -2,7 +2,6 @@
 from copy import deepcopy
 
 
-
 class ParamSpec:
 
     allowed_types = ['array', 'numeric', 'text']
@@ -19,7 +18,7 @@ def __init__(self, name: str,
             name: name of the parameter
             paramtype: type of the parameter, i.e. the SQL storage class
             label: label of the parameter
-            inferred_from: the parameters that this parameter is inferred_from
+            inferred_from: the parameters that this parameter is inferred from
             depends_on: the parameters that this parameter depends on
         """
         if not isinstance(paramtype, str):

From 14ba6aa4ab3e6ef288735f031ff2fd82d62972d0 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 12:46:22 +0100
Subject: [PATCH 578/719] add driver writing examples to docs

---
 docs/examples/index.rst                  | 10 ++++++++++
 docs/examples/writing drivers/readme.txt |  1 +
 2 files changed, 11 insertions(+)
 create mode 100644 docs/examples/writing drivers/readme.txt

diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index c418ce60b37..f718a19818d 100644
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -40,6 +40,16 @@ Drivers
 
     driver_examples/*
 
+Writing Drivers
+---------------
+
+.. include:: writing drivers/readme.txt
+
+.. toctree::
+    :glob:
+
+    writing drivers/*
+
 Logging
 --------
 
diff --git a/docs/examples/writing drivers/readme.txt b/docs/examples/writing drivers/readme.txt
new file mode 100644
index 00000000000..5e8c728a0ce
--- /dev/null
+++ b/docs/examples/writing drivers/readme.txt	
@@ -0,0 +1 @@
+This folder contains examples that are useful when you want to write your own driver for a new instrument.
\ No newline at end of file

From d96b467fa9d44e298c9a9f696f13dbf8296b01f6 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 13:08:03 +0100
Subject: [PATCH 579/719] Remove hypothsis defines_strategy decorator, no need
 for it

Also in ~3.86 it is moved to hypothesis._strategies - private module,
hence we should not use it.
---
 qcodes/tests/dataset/test_paramspec.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_paramspec.py b/qcodes/tests/dataset/test_paramspec.py
index 203d529fd58..c74256bccb6 100644
--- a/qcodes/tests/dataset/test_paramspec.py
+++ b/qcodes/tests/dataset/test_paramspec.py
@@ -9,7 +9,6 @@
 from qcodes.dataset.param_spec import ParamSpec
 
 
-@hst.defines_strategy
 def valid_identifier(**kwargs):
     """Return a strategy which generates a valid Python Identifier"""
     if 'min_size' not in kwargs:

From 1c20d81f72e89464d6b45a751266971b00057272 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 13:08:41 +0100
Subject: [PATCH 580/719] Use hypothesis assume from root module

Apparently it has been moved. Tested with version ~3.86
---
 qcodes/tests/dataset/test_paramspec.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_paramspec.py b/qcodes/tests/dataset/test_paramspec.py
index c74256bccb6..535c7c85608 100644
--- a/qcodes/tests/dataset/test_paramspec.py
+++ b/qcodes/tests/dataset/test_paramspec.py
@@ -3,7 +3,7 @@
 
 import pytest
 from numpy import ndarray
-from hypothesis import given
+from hypothesis import given, assume
 import hypothesis.strategies as hst
 
 from qcodes.dataset.param_spec import ParamSpec
@@ -242,7 +242,7 @@ def test_hash_with_deferred_and_inferred_as_paramspecs(
     Test that hashing works if 'inferred_from' and/or 'depend_on' contain
     actual ParamSpec instances and not just strings.
     """
-    hst.assume(add_to_1_inf or add_to_1_dep or add_to_2_inf or add_to_2_dep)
+    assume(add_to_1_inf or add_to_1_dep or add_to_2_inf or add_to_2_dep)
 
     # Add ParamSpecs to 'inferred_from' and/or 'depend_on' lists next to
     # strings (that are generated by the main strategy)

From c134fb0ea5b9e5b42d2f652e1ee52e9ca013d98c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 13:55:31 +0100
Subject: [PATCH 581/719] Dont return from validation method

---
 qcodes/instrument/parameter.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 7597dca24ec..7c28561d206 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -970,7 +970,7 @@ def setpoints(self, setpoints: Sequence[_BaseParameter]):
                                 f" expcected a QCoDeS parameter")
         self._setpoints = setpoints
 
-    def validate_consistent_shape(self):
+    def validate_consistent_shape(self) -> None:
         """
         Verifies that the shape of the Array Validator of the parameter
         is consistent with the Validator of the Setpoints. This requires that
@@ -1008,7 +1008,6 @@ def validate_consistent_shape(self):
             raise ValueError(f"Shape of output is not consistent with "
                              f"setpoints. Output is shape {output_shape} and "
                              f"setpoints are shape {setpoints_shape}")
-        return True
 
     def validate(self, value: ParamDataType) -> None:
         """

From 351582906e1e2ac24c0da3cf14374ce5802fe866 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 13:56:05 +0100
Subject: [PATCH 582/719] The shape may be None

---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 7c28561d206..c637d2d0b69 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -983,7 +983,7 @@ def validate_consistent_shape(self) -> None:
                              f"with Arrays validator. {self.name} does "
                              f"not have an Arrays validator.")
         output_shape = self.vals.shape
-        setpoints_shape_list: List[int] = []
+        setpoints_shape_list: List[Optional[int]] = []
         for sp in self.setpoints:
             if not isinstance(sp.vals, Arrays):
                 raise ValueError(f"Can only validate shapes for parameters "

From 0ff0e9d8d1b277efccab6d0970ae1a0c981d3e5b Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 14:46:20 +0100
Subject: [PATCH 583/719] Add size constraints to valid_paramspec_kwargs
 strategy, paramspec test

---
 qcodes/tests/dataset/test_paramspec.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/qcodes/tests/dataset/test_paramspec.py b/qcodes/tests/dataset/test_paramspec.py
index 535c7c85608..74a0f5d7a34 100644
--- a/qcodes/tests/dataset/test_paramspec.py
+++ b/qcodes/tests/dataset/test_paramspec.py
@@ -23,12 +23,14 @@ def valid_identifier(**kwargs):
 # This strategy generates a dict of kwargs needed to instantiate a valid
 # ParamSpec object
 valid_paramspec_kwargs = hst.fixed_dictionaries(
-    {'name': valid_identifier(min_size=1),
+    {'name': valid_identifier(min_size=1, max_size=6),
      'paramtype': hst.sampled_from(['numeric', 'array', 'text']),
-     'label': hst.one_of(hst.none(), hst.text()),
-     'unit': hst.one_of(hst.none(), hst.text()),
-     'depends_on': hst.lists(hst.text(min_size=1), min_size=0),
-     'inferred_from': hst.lists(hst.text(min_size=1), min_size=0)
+     'label': hst.one_of(hst.none(), hst.text(min_size=0, max_size=6)),
+     'unit': hst.one_of(hst.none(), hst.text(min_size=0, max_size=2)),
+     'depends_on': hst.lists(hst.text(min_size=1, max_size=3),
+                             min_size=0, max_size=3),
+     'inferred_from': hst.lists(hst.text(min_size=1, max_size=3),
+                                min_size=0, max_size=3)
      })
 
 
@@ -239,7 +241,7 @@ def test_hash(paramspecs):
 def test_hash_with_deferred_and_inferred_as_paramspecs(
         paramspecs, add_to_1_inf, add_to_1_dep, add_to_2_inf, add_to_2_dep):
     """
-    Test that hashing works if 'inferred_from' and/or 'depend_on' contain
+    Test that hashing works if 'inferred_from' and/or 'depends_on' contain
     actual ParamSpec instances and not just strings.
     """
     assume(add_to_1_inf or add_to_1_dep or add_to_2_inf or add_to_2_dep)

From 9cd5dee908cb5d7a6cd741fb6a92fccfe0c59927 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 15:00:13 +0100
Subject: [PATCH 584/719] Use valid Keysight 344xxA driver in test, assert
 exceptions properly

---
 qcodes/tests/test_wrong_address.py | 34 +++++++++++++++++++-----------
 1 file changed, 22 insertions(+), 12 deletions(-)

diff --git a/qcodes/tests/test_wrong_address.py b/qcodes/tests/test_wrong_address.py
index ff0403529fd..c3fc019a3d5 100644
--- a/qcodes/tests/test_wrong_address.py
+++ b/qcodes/tests/test_wrong_address.py
@@ -1,21 +1,31 @@
 import logging
+import re
 
+import pytest
 import qcodes.instrument.sims as sims
-from qcodes.instrument_drivers.Keysight.Keysight_34465A import Keysight_34465A
+from qcodes.instrument_drivers.Keysight.Keysight_34465A_submodules import \
+    Keysight_34465A
+from qcodes.tests.dataset.test_database_creation_and_upgrading import \
+    error_caused_by
+
 visalib = sims.__file__.replace('__init__.py', 'Keysight_34465A.yaml@sim')
 
 logging.basicConfig(level=logging.DEBUG)
 
 
 def test_wrong_address():
-    try:
-        # wrong address
-        keysight_sim = Keysight_34465A('keysight_34465A_sim',
-                                       address='GPIB::2::INSTR',
-                                       visalib=visalib)
-    except Exception as e:
-        pass
-    # right address
-    keysight_sim = Keysight_34465A('keysight_34465A_sim',
-                                   address='GPIB::1::INSTR',
-                                   visalib=visalib)
+    wrong_address = 'GPIB::2::INSTR'
+
+    match_str = re.escape(f'ERROR: resource {wrong_address} not found')
+    with pytest.raises(Exception, match=match_str) as exc_info:
+        _ = Keysight_34465A('keysight_34465A_sim',
+                            address=wrong_address,
+                            visalib=visalib)
+
+    assert error_caused_by(exc_info, wrong_address)
+
+    right_address = 'GPIB::1::INSTR'  # from the simulation yaml file
+
+    _ = Keysight_34465A('keysight_34465A_sim',
+                        address=right_address,
+                        visalib=visalib)

From effb96f96b0af4560e75cacfeb52f164e213f947 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 15:08:42 +0100
Subject: [PATCH 585/719] Move error_caused_by to common.py in tests

---
 qcodes/tests/common.py                        | 22 ++++++++++++++++++-
 .../test_database_creation_and_upgrading.py   | 20 +----------------
 .../dataset/test_database_extract_runs.py     |  2 +-
 qcodes/tests/dataset/test_dataset_basic.py    |  3 +--
 qcodes/tests/dataset/test_metadata.py         |  3 +--
 qcodes/tests/dataset/test_sqlite_base.py      |  3 +--
 .../tests/dataset/test_sqlite_connection.py   |  3 +--
 qcodes/tests/test_wrong_address.py            |  5 +++--
 8 files changed, 30 insertions(+), 31 deletions(-)

diff --git a/qcodes/tests/common.py b/qcodes/tests/common.py
index 0df027d4571..9930e1c413c 100644
--- a/qcodes/tests/common.py
+++ b/qcodes/tests/common.py
@@ -1,9 +1,13 @@
-from typing import Callable, Type
+from typing import Callable, Type, TYPE_CHECKING
 from functools import wraps
 from time import sleep
 import cProfile
 
 
+if TYPE_CHECKING:
+    from _pytest._code.code import ExceptionInfo
+
+
 def strip_qc(d, keys=('instrument', '__class__')):
     # depending on how you run the tests, __module__ can either
     # have qcodes on the front or not. Just strip it off.
@@ -89,3 +93,19 @@ def wrapper(*args, **kwargs):
         profiler.dump_stats(profile_filename)
         return result
     return wrapper
+
+
+def error_caused_by(excinfo: 'ExceptionInfo', cause: str) -> bool:
+    """
+    Helper function to figure out whether an exception was caused by another
+    exception with the message provided.
+
+    Args:
+        excinfo: the output of with pytest.raises() as excinfo
+        cause: the error message or a substring of it
+    """
+    chain = excinfo.getrepr().chain
+    cause_found = False
+    for link in chain:
+        cause_found = cause_found or cause in str(link[1])
+    return cause_found
diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index a5d8c33d8c9..4b40ef87dc2 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -4,7 +4,6 @@
 import logging
 import tempfile
 import json
-from typing import TYPE_CHECKING
 
 import pytest
 
@@ -15,6 +14,7 @@
 from qcodes.dataset.database import (initialise_database,
                                      initialise_or_create_database_at)
 # pylint: disable=unused-import
+from qcodes.tests.common import error_caused_by
 from qcodes.tests.dataset.temporary_databases import (empty_temp_db,
                                                       experiment,
                                                       temporarily_copied_DB)
@@ -32,29 +32,11 @@
 from qcodes.dataset.guids import parse_guid
 import qcodes.tests.dataset
 
-if TYPE_CHECKING:
-    from _pytest._code.code import ExceptionInfo
 
 fixturepath = os.sep.join(qcodes.tests.dataset.__file__.split(os.sep)[:-1])
 fixturepath = os.path.join(fixturepath, 'fixtures')
 
 
-def error_caused_by(excinfo: 'ExceptionInfo', cause: str) -> bool:
-    """
-    Helper function to figure out whether an exception was caused by another
-    exception with the message provided.
-
-    Args:
-        excinfo: the output of with pytest.raises() as excinfo
-        cause: the error message or a substring of it
-    """
-    chain = excinfo.getrepr().chain
-    cause_found = False
-    for link in chain:
-        cause_found = cause_found or cause in str(link[1])
-    return cause_found
-
-
 @contextmanager
 def location_and_station_set_to(location: int, work_station: int):
     cfg = qc.Config()
diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index ec44885bb29..e0131649674 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -16,7 +16,7 @@
 from qcodes.dataset.database_extract_runs import extract_runs_into_db
 from qcodes.tests.dataset.temporary_databases import two_empty_temp_db_connections
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
-from qcodes.tests.dataset.test_database_creation_and_upgrading import error_caused_by
+from qcodes.tests.common import error_caused_by
 from qcodes.dataset.measurements import Measurement
 from qcodes import Station
 from qcodes.tests.instrument_mocks import DummyInstrument
diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index dcef4f21dd2..df24d879350 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -13,8 +13,7 @@
 from qcodes import load_by_id, load_by_counter
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
-from qcodes.tests.dataset.test_database_creation_and_upgrading import \
-    error_caused_by
+from qcodes.tests.common import error_caused_by
 from qcodes.tests.dataset.test_descriptions import some_paramspecs
 from qcodes.dataset.sqlite_base import _unicode_categories, get_non_dependencies
 from qcodes.dataset.database import get_DB_location
diff --git a/qcodes/tests/dataset/test_metadata.py b/qcodes/tests/dataset/test_metadata.py
index 60ea5625099..dcc3e39768c 100644
--- a/qcodes/tests/dataset/test_metadata.py
+++ b/qcodes/tests/dataset/test_metadata.py
@@ -3,8 +3,7 @@
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import dataset, experiment, \
     empty_temp_db
-from qcodes.tests.dataset.test_database_creation_and_upgrading \
-    import error_caused_by
+from qcodes.tests.common import error_caused_by
 
 
 def test_get_metadata_from_dataset(dataset):
diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index 93ec6dc0d96..a5e722f8fde 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -23,8 +23,7 @@
     empty_temp_db, experiment, dataset
 from qcodes.tests.dataset.dataset_fixtures import scalar_dataset, \
     standalone_parameters_dataset
-from qcodes.tests.dataset.test_database_creation_and_upgrading import \
-    error_caused_by
+from qcodes.tests.common import error_caused_by
 # pylint: enable=unused-import
 
 from .helper_functions import verify_data_dict
diff --git a/qcodes/tests/dataset/test_sqlite_connection.py b/qcodes/tests/dataset/test_sqlite_connection.py
index adbfc230584..61732793407 100644
--- a/qcodes/tests/dataset/test_sqlite_connection.py
+++ b/qcodes/tests/dataset/test_sqlite_connection.py
@@ -5,8 +5,7 @@
 
 from qcodes.dataset.sqlite_base import ConnectionPlus, \
     make_connection_plus_from, atomic, connect, atomic_transaction
-from qcodes.tests.dataset.test_database_creation_and_upgrading import \
-    error_caused_by
+from qcodes.tests.common import error_caused_by
 
 
 def sqlite_conn_in_transaction(conn: sqlite3.Connection):
diff --git a/qcodes/tests/test_wrong_address.py b/qcodes/tests/test_wrong_address.py
index c3fc019a3d5..93e0de18789 100644
--- a/qcodes/tests/test_wrong_address.py
+++ b/qcodes/tests/test_wrong_address.py
@@ -2,11 +2,12 @@
 import re
 
 import pytest
+
 import qcodes.instrument.sims as sims
 from qcodes.instrument_drivers.Keysight.Keysight_34465A_submodules import \
     Keysight_34465A
-from qcodes.tests.dataset.test_database_creation_and_upgrading import \
-    error_caused_by
+from qcodes.tests.common import error_caused_by
+
 
 visalib = sims.__file__.replace('__init__.py', 'Keysight_34465A.yaml@sim')
 

From 05526089c9b89bfbf9b7c0694514cce018cf41dd Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 15:12:31 +0100
Subject: [PATCH 586/719] update notebook

---
 ...le Example of ParameterWithSetpoints.ipynb | 497 ++++++++++++++++
 ...xample of new ParameterWithSetpoints.ipynb | 562 ------------------
 2 files changed, 497 insertions(+), 562 deletions(-)
 create mode 100644 docs/examples/writing drivers/Simple Example of ParameterWithSetpoints.ipynb
 delete mode 100644 docs/examples/writing drivers/Simple Example of new ParameterWithSetpoints.ipynb

diff --git a/docs/examples/writing drivers/Simple Example of ParameterWithSetpoints.ipynb b/docs/examples/writing drivers/Simple Example of ParameterWithSetpoints.ipynb
new file mode 100644
index 00000000000..c64ea95bba8
--- /dev/null
+++ b/docs/examples/writing drivers/Simple Example of ParameterWithSetpoints.ipynb	
@@ -0,0 +1,497 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Example of ParameterWithSetpoints\n",
+    "This notebook has an example of writing a simple driver with a parameter that has setpoints. This is most likely to be useful for instruments that return arrays of results. In this notebook we will show an example of this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.dataset.plotting import plot_by_id\n",
+    "from qcodes.utils.validators import Numbers, Arrays\n",
+    "from qcodes.instrument.base import Instrument\n",
+    "from qcodes.dataset.measurements import Measurement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.dataset.database import initialise_database\n",
+    "from qcodes.dataset.experiment_container import new_experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument.parameter import ParameterWithSetpoints, GeneratedSetPoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we define an dummy instrument that returns something like a frequency spectrum from a starting frequency give by `f_start` to a stop frequency given by `f_stop`\n",
+    "in `n_points` steps. \n",
+    "\n",
+    "The functionality of the ParameterWithSetpoints is implemented only by having a reference to it's setpoints which is consumed by the dataset context manager. To do this we only have to define the parameter for the setpoints and the spectrum and let the spectrum parameter know that the frequency axis is the setpoints axis of the spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DummyArray(ParameterWithSetpoints):\n",
+    "    \n",
+    "    def get_raw(self):\n",
+    "        npoints = self.root_instrument.n_points()\n",
+    "        return np.random.rand(npoints)\n",
+    "    \n",
+    "\n",
+    "class DummySpectrumAnalyzer(Instrument):\n",
+    "    \n",
+    "    def __init__(self, name, **kwargs):\n",
+    "        \n",
+    "        super().__init__(name, **kwargs)\n",
+    "            \n",
+    "\n",
+    "        self.add_parameter('f_start',\n",
+    "                           initial_value=0,\n",
+    "                           unit='Hz',\n",
+    "                           label='f start',\n",
+    "                           vals=Numbers(0,1e3),\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "\n",
+    "        self.add_parameter('f_stop',\n",
+    "                           unit='Hz',\n",
+    "                           label='f stop',\n",
+    "                           vals=Numbers(1,1e3),\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "\n",
+    "        self.add_parameter('n_points',\n",
+    "                           unit='',\n",
+    "                           initial_value=10,\n",
+    "                           vals=Numbers(1,1e3),\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "        \n",
+    "        self.add_parameter('freq_axis',\n",
+    "                           unit='Hz',\n",
+    "                           label='Freq Axis',\n",
+    "                           parameter_class=GeneratedSetPoints,\n",
+    "                           startparam=self.f_start,\n",
+    "                           stopparam=self.f_stop,\n",
+    "                           numpointsparam=self.n_points,\n",
+    "                           vals=Arrays(shape=(self.n_points,)))\n",
+    "                           \n",
+    "        self.add_parameter('spectrum',\n",
+    "                   unit='dBm',\n",
+    "                   setpoints=(self.freq_axis,),\n",
+    "                   label='Spectrum',\n",
+    "                   parameter_class=DummyArray,\n",
+    "                   vals=Arrays(shape=(self.n_points,)))\n",
+    "                "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tutorial_exp#no sample#67@C:\\Users\\jenielse/experiments.db\n",
+       "----------------------------------------------------------"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "initialise_database()\n",
+    "new_experiment(name='tutorial_exp', sample_name=\"no sample\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = DummySpectrumAnalyzer('foobar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we setup the limits of the spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.f_start(0)\n",
+    "a.f_stop(500)\n",
+    "a.n_points(501)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can grab the frequency axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "501"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "freq_axis = a.freq_axis()\n",
+    "len(freq_axis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "freq_axis[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As expected we get a result wit 501 points as we asked for an axis with 501 points. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Above we have added a validator to the `freq_axis` parameter encoding the fact that this is an Array with `n_points`. Note that we do not have to supply the number of points as an integer but can supply a function that returns the valid number of points. This would normally be a QCoDeS parameter.\n",
+    "\n",
+    "This will be checked if we validate the output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.freq_axis.validate(a.freq_axis.get())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Naturally we can also grap the spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "501"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spectrum = a.spectrum.get()\n",
+    "len(spectrum)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we validate a ParameterWithSetpoints we automatically validate that the shape is consistent between the parameters and its setpoints. As well as validating the shape as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.spectrum.validate(a.spectrum.get())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The consistent shapes can be validated explicitly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.spectrum.validate_consistent_shape()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can inspect the setpoints of the spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<qcodes.instrument.parameter.GeneratedSetPoints: freq_axis at 1914952775384>,)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.spectrum.setpoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or even change them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.spectrum.setpoints = (a.freq_axis,)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Measurement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also directly consume the parameter in a measurement. This implicitly calls get on the setpoint parameter(s) when the data is added to the datasaver."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 198\n"
+     ]
+    }
+   ],
+   "source": [
+    "meas = Measurement()\n",
+    "meas.register_parameter(a.spectrum)\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((a.spectrum, a.spectrum.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And plot it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bddc6ef4e0>], [None])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/writing drivers/Simple Example of new ParameterWithSetpoints.ipynb b/docs/examples/writing drivers/Simple Example of new ParameterWithSetpoints.ipynb
deleted file mode 100644
index de94897ec14..00000000000
--- a/docs/examples/writing drivers/Simple Example of new ParameterWithSetpoints.ipynb	
+++ /dev/null
@@ -1,562 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Simple Example of ParameterWithSetpoints\n",
-    "This notebook has an example of a new simpler ArrayParameter replacement\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.utils.validators import Validator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.utils.validators import Numbers, Arrays\n",
-    "from qcodes.instrument.base import Instrument\n",
-    "from qcodes.dataset.measurements import Measurement"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.dataset.database import initialise_database\n",
-    "from qcodes.dataset.experiment_container import new_experiment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.instrument.parameter import ParameterWithSetpoints, GeneratedSetPoints"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we define an dummy instrument that returns something like a frequency spectrum from `f_start` to `f_stop`\n",
-    "in `n_points` steps. The functionality of the ArrayParameter is implemented only by having a reference to it's setpoints which is consumed by the dataset context manager. To do this we only have to define the parameter for the setpoints and the spectrum and let the parameter know that the frequency axis is the setpoints "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class DummyArray(ParameterWithSetpoints):\n",
-    "    \n",
-    "    def get_raw(self):\n",
-    "        npoints = self.root_instrument.n_points()\n",
-    "        return np.random.rand(npoints)\n",
-    "    \n",
-    "\n",
-    "class DummySpectrumAnalyzer(Instrument):\n",
-    "    \n",
-    "    def __init__(self, name, **kwargs):\n",
-    "        \n",
-    "        super().__init__(name, **kwargs)\n",
-    "            \n",
-    "\n",
-    "        self.add_parameter('f_start',\n",
-    "                           initial_value=0,\n",
-    "                           unit='Hz',\n",
-    "                           label='f start',\n",
-    "                           vals=Numbers(0,1e3),\n",
-    "                           get_cmd=None,\n",
-    "                           set_cmd=None)\n",
-    "\n",
-    "        self.add_parameter('f_stop',\n",
-    "                           unit='Hz',\n",
-    "                           label='f stop',\n",
-    "                           vals=Numbers(1,1e3),\n",
-    "                           get_cmd=None,\n",
-    "                           set_cmd=None)\n",
-    "\n",
-    "        self.add_parameter('n_points',\n",
-    "                           unit='',\n",
-    "                           initial_value=10,\n",
-    "                           vals=Numbers(1,1e3),\n",
-    "                           get_cmd=None,\n",
-    "                           set_cmd=None)\n",
-    "        \n",
-    "        self.add_parameter('freq_axis',\n",
-    "                           unit='Hz',\n",
-    "                           label='Freq Axis',\n",
-    "                           parameter_class=GeneratedSetPoints,\n",
-    "                           startparam=self.f_start,\n",
-    "                           stopparam=self.f_stop,\n",
-    "                           numpointsparam=self.n_points,\n",
-    "                           vals=Arrays(shape=(self.n_points,)))\n",
-    "                           \n",
-    "        self.add_parameter('spectrum',\n",
-    "                   unit='dBm',\n",
-    "                   setpoints=(self.freq_axis,),\n",
-    "                   label='Spectrum',\n",
-    "                   parameter_class=DummyArray,\n",
-    "                   vals=Arrays(shape=(self.n_points,)))\n",
-    "                "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tutorial_exp#no sample#63@C:\\Users\\jenielse/experiments.db\n",
-       "----------------------------------------------------------"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "initialise_database()\n",
-    "new_experiment(name='tutorial_exp', sample_name=\"no sample\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = DummySpectrumAnalyzer('foobar')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we setup the limits of the spectrum"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a.f_start(0)\n",
-    "a.f_stop(500)\n",
-    "a.n_points(501)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can grab the axis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
-       "        11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
-       "        22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,\n",
-       "        33.,  34.,  35.,  36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,\n",
-       "        44.,  45.,  46.,  47.,  48.,  49.,  50.,  51.,  52.,  53.,  54.,\n",
-       "        55.,  56.,  57.,  58.,  59.,  60.,  61.,  62.,  63.,  64.,  65.,\n",
-       "        66.,  67.,  68.,  69.,  70.,  71.,  72.,  73.,  74.,  75.,  76.,\n",
-       "        77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,\n",
-       "        88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,  96.,  97.,  98.,\n",
-       "        99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109.,\n",
-       "       110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.,\n",
-       "       121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,\n",
-       "       132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142.,\n",
-       "       143., 144., 145., 146., 147., 148., 149., 150., 151., 152., 153.,\n",
-       "       154., 155., 156., 157., 158., 159., 160., 161., 162., 163., 164.,\n",
-       "       165., 166., 167., 168., 169., 170., 171., 172., 173., 174., 175.,\n",
-       "       176., 177., 178., 179., 180., 181., 182., 183., 184., 185., 186.,\n",
-       "       187., 188., 189., 190., 191., 192., 193., 194., 195., 196., 197.,\n",
-       "       198., 199., 200., 201., 202., 203., 204., 205., 206., 207., 208.,\n",
-       "       209., 210., 211., 212., 213., 214., 215., 216., 217., 218., 219.,\n",
-       "       220., 221., 222., 223., 224., 225., 226., 227., 228., 229., 230.,\n",
-       "       231., 232., 233., 234., 235., 236., 237., 238., 239., 240., 241.,\n",
-       "       242., 243., 244., 245., 246., 247., 248., 249., 250., 251., 252.,\n",
-       "       253., 254., 255., 256., 257., 258., 259., 260., 261., 262., 263.,\n",
-       "       264., 265., 266., 267., 268., 269., 270., 271., 272., 273., 274.,\n",
-       "       275., 276., 277., 278., 279., 280., 281., 282., 283., 284., 285.,\n",
-       "       286., 287., 288., 289., 290., 291., 292., 293., 294., 295., 296.,\n",
-       "       297., 298., 299., 300., 301., 302., 303., 304., 305., 306., 307.,\n",
-       "       308., 309., 310., 311., 312., 313., 314., 315., 316., 317., 318.,\n",
-       "       319., 320., 321., 322., 323., 324., 325., 326., 327., 328., 329.,\n",
-       "       330., 331., 332., 333., 334., 335., 336., 337., 338., 339., 340.,\n",
-       "       341., 342., 343., 344., 345., 346., 347., 348., 349., 350., 351.,\n",
-       "       352., 353., 354., 355., 356., 357., 358., 359., 360., 361., 362.,\n",
-       "       363., 364., 365., 366., 367., 368., 369., 370., 371., 372., 373.,\n",
-       "       374., 375., 376., 377., 378., 379., 380., 381., 382., 383., 384.,\n",
-       "       385., 386., 387., 388., 389., 390., 391., 392., 393., 394., 395.,\n",
-       "       396., 397., 398., 399., 400., 401., 402., 403., 404., 405., 406.,\n",
-       "       407., 408., 409., 410., 411., 412., 413., 414., 415., 416., 417.,\n",
-       "       418., 419., 420., 421., 422., 423., 424., 425., 426., 427., 428.,\n",
-       "       429., 430., 431., 432., 433., 434., 435., 436., 437., 438., 439.,\n",
-       "       440., 441., 442., 443., 444., 445., 446., 447., 448., 449., 450.,\n",
-       "       451., 452., 453., 454., 455., 456., 457., 458., 459., 460., 461.,\n",
-       "       462., 463., 464., 465., 466., 467., 468., 469., 470., 471., 472.,\n",
-       "       473., 474., 475., 476., 477., 478., 479., 480., 481., 482., 483.,\n",
-       "       484., 485., 486., 487., 488., 489., 490., 491., 492., 493., 494.,\n",
-       "       495., 496., 497., 498., 499., 500.])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.freq_axis()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or the spectrum"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a.spectrum.validate(a.spectrum.get())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
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-       "       5.06606897e-01, 8.29893568e-02, 3.33735907e-01, 4.42400625e-01,\n",
-       "       9.16681461e-02, 5.81358311e-01, 4.51932652e-01, 1.87074773e-01,\n",
-       "       2.02380668e-01, 6.53316860e-01, 1.72141579e-01, 1.86279204e-02,\n",
-       "       9.64464161e-01, 9.49980096e-01, 2.18509360e-01, 8.73908097e-01,\n",
-       "       6.95477171e-01, 8.45280249e-02, 9.88475564e-01, 7.48583006e-01,\n",
-       "       3.75698256e-01, 6.63416424e-01, 4.45721082e-02, 2.01031788e-02,\n",
-       "       9.11969530e-01, 3.18978737e-01, 4.55256870e-02, 7.69627991e-02,\n",
-       "       6.15589717e-01, 8.30054206e-01, 9.85316349e-01, 1.99833448e-03,\n",
-       "       8.13712260e-01, 9.19068159e-01, 3.33893582e-01, 3.88520350e-02,\n",
-       "       1.83501588e-01, 5.81598202e-02, 8.79058536e-01, 2.77580615e-01,\n",
-       "       3.98459361e-01, 7.45159171e-01, 7.02160997e-01, 5.60560683e-02,\n",
-       "       5.76319201e-02, 9.78713128e-01, 4.29086064e-01, 7.81239698e-01,\n",
-       "       6.19173707e-01, 4.12860279e-01, 8.23556372e-01, 7.49529829e-01,\n",
-       "       7.55891724e-01, 7.42013651e-01, 9.37166401e-01, 5.43522184e-01,\n",
-       "       1.74112046e-01, 3.25670153e-01, 4.02071966e-01, 3.51439899e-01,\n",
-       "       4.79960705e-03, 3.14422078e-01, 4.57832590e-01, 7.29089167e-01,\n",
-       "       2.58896393e-01, 2.87889367e-01, 8.61067921e-01, 9.25230633e-01,\n",
-       "       6.44048295e-01, 3.64113298e-01, 2.64303767e-01, 4.01498595e-01,\n",
-       "       8.68587905e-01, 4.46729418e-01, 1.79973147e-01, 8.55623848e-01,\n",
-       "       7.05578618e-01, 6.49133124e-02, 3.40334113e-01, 3.34551352e-01,\n",
-       "       2.23539275e-01, 3.27454771e-01, 5.52446482e-01, 5.16947526e-01,\n",
-       "       2.08712682e-02, 9.85773753e-01, 3.88270161e-01, 5.96030381e-01,\n",
-       "       3.81211576e-01, 4.55289491e-01, 2.37034394e-01, 6.91144419e-01,\n",
-       "       6.42137779e-01, 3.89827594e-01, 4.29574203e-01, 6.50335229e-01,\n",
-       "       7.50633240e-01, 1.32563218e-01, 4.68072279e-01, 6.75685283e-02,\n",
-       "       5.08124884e-01, 3.34996761e-01, 7.40747513e-01, 9.16839049e-01,\n",
-       "       3.24649033e-01, 5.80205772e-01, 2.93499564e-01, 2.47584909e-01,\n",
-       "       2.23377815e-01, 1.77509937e-02, 1.84711321e-02, 1.35751273e-01,\n",
-       "       4.58604245e-01, 6.28305184e-01, 2.11246334e-01, 4.86252224e-01,\n",
-       "       8.85202409e-01, 7.17454767e-01, 6.17601745e-01, 6.07385077e-01,\n",
-       "       7.55295664e-01, 2.45392849e-01, 1.39976166e-01, 5.75345114e-01,\n",
-       "       1.56436241e-01, 8.83996738e-01, 1.46138948e-01, 9.42592999e-01,\n",
-       "       4.12792520e-01, 3.28642418e-01, 6.11616517e-01, 5.60769417e-03,\n",
-       "       2.38672892e-01, 6.15462478e-01, 7.00537136e-01, 8.22390906e-01,\n",
-       "       1.55402367e-01, 6.28009565e-01, 2.68966090e-01, 7.90871837e-01,\n",
-       "       2.28346655e-01, 2.75343171e-01, 8.13750054e-01, 7.53443969e-01,\n",
-       "       7.99290638e-01, 2.90218717e-01, 2.55830396e-01, 7.17757247e-01,\n",
-       "       8.43734674e-02, 5.20829398e-01, 8.86829782e-01, 8.55842105e-01,\n",
-       "       6.64248055e-01, 4.04387578e-02, 8.75280574e-01, 1.68050943e-01,\n",
-       "       7.98076907e-01, 8.33409292e-01, 7.40736072e-01, 2.39010957e-01,\n",
-       "       1.99455494e-01, 9.60740289e-01, 3.25391594e-01, 9.54422202e-01,\n",
-       "       8.46239593e-01, 9.70279000e-01, 1.32264057e-01, 4.03132418e-01,\n",
-       "       9.10979553e-01, 3.93828183e-01, 2.46929853e-01, 3.05602650e-01,\n",
-       "       2.71775923e-01, 6.14691862e-01, 2.52237141e-01, 8.73804861e-01,\n",
-       "       6.52974507e-01, 7.16051855e-01, 5.84852911e-01, 6.02846059e-01,\n",
-       "       1.91431642e-01, 5.65150815e-01, 1.82410861e-01, 3.23469888e-01,\n",
-       "       7.01655609e-01, 4.51610492e-01, 1.58709564e-02, 1.49868905e-01,\n",
-       "       4.97765761e-01, 6.83040352e-01, 8.20060050e-01, 7.18731554e-01,\n",
-       "       7.66145447e-01, 6.08697758e-01, 4.00422571e-01, 1.52821461e-01,\n",
-       "       8.48815424e-01, 8.91558712e-01, 9.38945586e-01, 6.91928620e-01,\n",
-       "       2.89137947e-01, 8.23763098e-01, 6.82799643e-01, 4.10494746e-01,\n",
-       "       2.69825570e-01, 4.05327956e-02, 8.41091147e-01, 6.81156126e-01,\n",
-       "       9.14766597e-01, 8.46775579e-01, 1.91966078e-01, 2.68157194e-01,\n",
-       "       3.78167197e-01, 4.82941997e-01, 5.50622873e-01, 7.09060906e-01,\n",
-       "       7.10077724e-01, 1.17737378e-01, 8.49259825e-02, 7.53163443e-01,\n",
-       "       3.68951299e-01, 5.42090007e-04, 2.99659038e-01, 4.96339383e-01,\n",
-       "       2.23692424e-01, 7.42260669e-01, 7.44031887e-02, 6.32988177e-01,\n",
-       "       5.17318883e-01, 9.48007537e-01, 9.83153736e-01, 8.67224519e-01,\n",
-       "       6.49466971e-01, 6.16388135e-01, 5.13909254e-01, 8.70723203e-01,\n",
-       "       5.98542774e-01, 1.19243613e-01, 1.76926389e-01, 9.88427472e-01,\n",
-       "       5.64004832e-01, 1.27173880e-01, 5.45575938e-01, 8.52239332e-01,\n",
-       "       9.62916301e-01, 8.36671286e-01, 7.02507891e-01, 4.37875020e-01,\n",
-       "       8.48639742e-01, 8.37612792e-01, 2.44061132e-01, 8.19182148e-01,\n",
-       "       7.80829582e-01, 7.11999637e-01, 5.69630037e-01, 2.37832838e-01,\n",
-       "       8.51984166e-01, 4.04782371e-01, 5.02426170e-01, 1.58531658e-01,\n",
-       "       1.44148505e-01, 8.65042477e-01, 1.40721404e-01, 6.19958375e-01,\n",
-       "       9.04469382e-01, 8.04154797e-01, 5.51566063e-01, 4.35646948e-01,\n",
-       "       3.89373126e-01, 5.68588723e-01, 5.82620818e-01, 1.72044353e-01,\n",
-       "       5.84698666e-01, 6.96358353e-01, 2.80064847e-01, 1.11318866e-01,\n",
-       "       8.35137290e-01, 8.74821612e-01, 7.64265540e-01, 7.07735272e-01,\n",
-       "       9.31654395e-01, 7.67736303e-01, 6.76776277e-01, 7.48104296e-01,\n",
-       "       9.30726201e-01, 7.14602610e-01, 9.36748783e-01, 6.04344153e-01,\n",
-       "       5.60057974e-01, 9.24687580e-01, 7.50175115e-02, 9.70927896e-01,\n",
-       "       9.91568314e-03, 7.47778250e-01, 8.92419334e-01, 3.74285844e-01,\n",
-       "       1.41937210e-01, 2.81440512e-02, 2.16553368e-01, 1.52492342e-01,\n",
-       "       4.61153793e-01, 1.68838884e-01, 7.76991673e-01, 6.62240902e-01,\n",
-       "       7.09309322e-01, 3.96205376e-01, 6.58291602e-01, 7.58788544e-01,\n",
-       "       5.97356848e-01, 5.24229596e-01, 5.44891543e-01, 8.32668515e-01,\n",
-       "       2.54100442e-01, 1.21613964e-01, 2.06075373e-01, 9.57780926e-01,\n",
-       "       1.67754656e-01, 8.44389250e-01, 4.90915971e-01, 9.85315419e-01,\n",
-       "       3.58931990e-01, 1.75352462e-01, 5.48442741e-01, 9.69724630e-01,\n",
-       "       7.84696761e-01, 5.95453211e-01, 4.46227847e-01, 5.56367034e-01,\n",
-       "       1.32768080e-01, 2.40457994e-01, 5.16025790e-01, 3.35198116e-01,\n",
-       "       1.96859303e-02, 4.73916440e-01, 5.13924078e-01, 7.95614141e-03,\n",
-       "       7.03834387e-01, 2.30139796e-01, 9.22670026e-01, 8.06222913e-01,\n",
-       "       2.74788244e-01, 8.01547933e-01, 5.26186281e-01, 3.67969257e-01,\n",
-       "       6.67493545e-02, 8.19918941e-01, 8.22979906e-01, 8.47044716e-01,\n",
-       "       8.86409061e-01, 7.54259578e-01, 7.75318427e-01, 9.39424840e-01,\n",
-       "       8.91715831e-01, 5.89820922e-01, 2.22696854e-01, 6.73415611e-02,\n",
-       "       1.82561702e-01, 2.83042062e-01, 4.54999958e-01, 5.62660691e-01,\n",
-       "       5.05646810e-01, 6.39395318e-01, 3.04989084e-01, 7.34171101e-01,\n",
-       "       2.62020818e-01, 8.67640847e-01, 1.33128948e-01, 5.65729831e-01,\n",
-       "       6.77766201e-01, 8.79206033e-01, 3.01816459e-01, 1.48754984e-01,\n",
-       "       3.53535420e-01, 6.74319003e-01, 6.86249130e-01, 5.38895789e-01,\n",
-       "       7.90107305e-01, 4.97196863e-01, 3.92791954e-01, 9.00572811e-01,\n",
-       "       8.64999057e-01, 2.25876316e-01, 2.38182449e-01, 6.67462145e-01,\n",
-       "       4.57156070e-01, 2.28598486e-01, 8.56032752e-01, 3.19034835e-01,\n",
-       "       1.54671766e-02, 5.65654395e-01, 7.06621873e-01, 7.50065321e-01,\n",
-       "       1.02797119e-01, 2.74504727e-02, 2.81663345e-01, 2.20485667e-01,\n",
-       "       1.03373229e-01, 2.95338198e-01, 5.20948016e-01, 6.82498498e-01,\n",
-       "       2.52836581e-01, 1.79905515e-01, 5.93305874e-01, 8.96879916e-01,\n",
-       "       3.26623208e-01, 9.07221366e-01, 6.56687833e-01, 9.73822788e-01,\n",
-       "       4.09865141e-01, 2.45423235e-01, 2.69359733e-01, 1.49531897e-01,\n",
-       "       9.34080269e-01, 6.67603782e-01, 1.86377318e-01, 7.40802937e-01,\n",
-       "       6.30561849e-01, 7.18895820e-01, 4.08513231e-01, 3.17966177e-01,\n",
-       "       6.17779009e-01, 3.90437029e-01, 7.94862578e-01, 9.85788627e-02,\n",
-       "       3.28066179e-01, 2.26438034e-01, 6.17329521e-01, 3.85326397e-01,\n",
-       "       6.01781844e-01, 6.89534572e-01, 8.56048988e-01, 8.11034554e-01,\n",
-       "       7.04480431e-03, 6.18811079e-01, 1.04159197e-01, 6.77885586e-01,\n",
-       "       3.11216630e-01, 8.82639409e-01, 4.98601025e-02, 8.62054586e-02,\n",
-       "       3.90111802e-01, 4.86802713e-01, 3.86178727e-01, 8.16806377e-01,\n",
-       "       5.59569936e-01, 9.31851945e-01, 3.38537080e-01, 6.65107848e-01,\n",
-       "       2.87820375e-01, 1.66643374e-01, 1.52961276e-01, 8.12295908e-01,\n",
-       "       1.23274592e-01, 8.76236833e-01, 7.33592633e-01, 1.30967942e-01,\n",
-       "       9.30508799e-01, 4.33689890e-02, 5.00731512e-02, 3.48616422e-01,\n",
-       "       2.14162784e-01, 7.06106345e-01, 8.15929613e-01, 9.80794906e-01,\n",
-       "       2.15223564e-01, 7.14210665e-01, 9.54456416e-01, 4.12783683e-01,\n",
-       "       9.25716133e-01, 6.43724159e-01, 2.47726754e-01, 5.34835031e-01,\n",
-       "       8.28760836e-01, 7.48169391e-01, 2.77494772e-01, 7.17041031e-01,\n",
-       "       6.25623702e-01, 8.55655412e-01, 6.78380144e-02, 5.66745731e-01,\n",
-       "       6.77679139e-01, 2.86716794e-01, 3.25670901e-01, 9.96884751e-01,\n",
-       "       1.68874871e-01, 9.49903707e-01, 9.24780280e-01, 4.47229079e-01,\n",
-       "       8.81187926e-01, 9.12028693e-01, 5.33108739e-01, 7.19084041e-01,\n",
-       "       3.22300678e-02, 7.55049706e-01, 9.64847183e-01, 8.02197067e-01,\n",
-       "       8.84905621e-01])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.spectrum.get()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also directly consume the parameter in a measurement"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 194\n"
-     ]
-    }
-   ],
-   "source": [
-    "meas = Measurement()\n",
-    "meas.register_parameter(a.spectrum)  # register the first independent parameter\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((a.spectrum, a.spectrum.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.dataset.plotting import plot_by_id"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And plot it"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1a94560a080>], [None])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
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-   "types_to_exclude": [
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-    "builtin_function_or_method",
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From 0f1d97cec40deb3d23c81d875f225988c2fb5d31 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 9 Jan 2019 15:12:48 +0100
Subject: [PATCH 587/719] handle that shape can be none

---
 qcodes/utils/validators.py | 85 +++++++++++++++++++++-----------------
 1 file changed, 46 insertions(+), 39 deletions(-)

diff --git a/qcodes/utils/validators.py b/qcodes/utils/validators.py
index 3153aca75b8..69c945482b7 100644
--- a/qcodes/utils/validators.py
+++ b/qcodes/utils/validators.py
@@ -16,7 +16,7 @@
 numbertypes = Union[float, int, np.floating, np.integer]
 
 
-def validate_all(*args, context: str='') -> None:
+def validate_all(*args, context: str = '') -> None:
     """
     Takes a list of (validator, value) couplets and tests whether they are
     all valid, raising ValueError otherwise
@@ -84,7 +84,7 @@ class Validator:
     _valid_values: Tuple = ()
     is_numeric = False  # is this a numeric type (so it can be swept)?
 
-    def validate(self, value, context: str=''):
+    def validate(self, value, context: str = ''):
         raise NotImplementedError
 
     @property
@@ -98,8 +98,9 @@ class Anything(Validator):
     def __init__(self) -> None:
         self._valid_values = (0,)
 
-    def validate(self, value: Any, context: str=''):
+    def validate(self, value: Any, context: str = ''):
         pass
+
     # NOTE(giulioungaretti): why is_numeric?
     # it allows for set_step in parameter
     # TODO(giulioungaretti): possible refactor
@@ -133,7 +134,7 @@ class Bool(Validator):
     def __init__(self) -> None:
         self._valid_values = (True, False)
 
-    def validate(self, value: bool, context: str='') -> None:
+    def validate(self, value: bool, context: str = '') -> None:
         if not isinstance(value, bool) and not isinstance(value, np.bool8):
             raise TypeError(
                 '{} is not Boolean; {}'.format(repr(value), context))
@@ -149,7 +150,8 @@ class Strings(Validator):
     to min_length <= len(value) <= max_length
     """
 
-    def __init__(self, min_length: int=0, max_length: int=BIGSTRING) -> None:
+    def __init__(self, min_length: int = 0,
+                 max_length: int = BIGSTRING) -> None:
         if isinstance(min_length, int) and min_length >= 0:
             self._min_length = min_length
         else:
@@ -159,9 +161,9 @@ def __init__(self, min_length: int=0, max_length: int=BIGSTRING) -> None:
         else:
             raise TypeError('max_length must be a positive integer '
                             'no smaller than min_length')
-        self._valid_values = ('.'*min_length,)
+        self._valid_values = ('.' * min_length,)
 
-    def validate(self, value: str, context: str=''):
+    def validate(self, value: str, context: str = ''):
         if not isinstance(value, str):
             raise TypeError(
                 '{} is not a string; {}'.format(repr(value), context))
@@ -193,8 +195,8 @@ class Numbers(Validator):
 
     validtypes = (float, int, np.integer, np.floating)
 
-    def __init__(self, min_value: numbertypes=-float("inf"),
-                 max_value: numbertypes=float("inf")) -> None:
+    def __init__(self, min_value: numbertypes = -float("inf"),
+                 max_value: numbertypes = float("inf")) -> None:
 
         if isinstance(min_value, self.validtypes):
             self._min_value = min_value
@@ -210,7 +212,7 @@ def __init__(self, min_value: numbertypes=-float("inf"),
 
         self._valid_values = (min_value, max_value)
 
-    def validate(self, value: numbertypes, context: str='') -> None:
+    def validate(self, value: numbertypes, context: str = '') -> None:
         if not isinstance(value, self.validtypes):
             raise TypeError(
                 '{} is not an int or float; {}'.format(repr(value), context))
@@ -239,8 +241,8 @@ class Ints(Validator):
     validtypes = (int, np.integer)
     inttypes = Union[int, np.integer]
 
-    def __init__(self, min_value: inttypes=-BIGINT,
-                 max_value: inttypes=BIGINT) -> None:
+    def __init__(self, min_value: inttypes = -BIGINT,
+                 max_value: inttypes = BIGINT) -> None:
         if isinstance(min_value, self.validtypes):
             self._min_value = int(min_value)
         else:
@@ -256,7 +258,7 @@ def __init__(self, min_value: inttypes=-BIGINT,
 
         self._valid_values = (min_value, max_value)
 
-    def validate(self, value: inttypes, context: str='') -> None:
+    def validate(self, value: inttypes, context: str = '') -> None:
         if not isinstance(value, self.validtypes):
             raise TypeError(
                 '{} is not an int; {}'.format(repr(value), context))
@@ -284,7 +286,7 @@ class PermissiveInts(Ints):
     set_parser that converts the float repr to an actual int
     """
 
-    def validate(self, value: numbertypes, context: str='') -> None:
+    def validate(self, value: numbertypes, context: str = '') -> None:
         if isinstance(value, (float, np.floating)):
             intrepr = int(round(value))
             remainder = abs(value - intrepr)
@@ -311,7 +313,7 @@ def __init__(self, *values: Hashable) -> None:
         self._values = set(values)
         self._valid_values = tuple(values)
 
-    def validate(self, value, context: str='') -> None:
+    def validate(self, value, context: str = '') -> None:
         try:
             if value not in self._values:
                 raise ValueError('{} is not in {}; {}'.format(
@@ -335,7 +337,7 @@ def __init__(self) -> None:
         self._validator = Enum('on', 'off')
         self._valid_values = self._validator._valid_values
 
-    def validate(self, value: str, context: str='') -> None:
+    def validate(self, value: str, context: str = '') -> None:
         self._validator.validate(value, context)
 
 
@@ -354,14 +356,14 @@ class Multiples(Ints):
         min_value, value must be >= min_value
     """
 
-    def __init__(self, divisor: int=1, **kwargs) -> None:
+    def __init__(self, divisor: int = 1, **kwargs) -> None:
         super().__init__(**kwargs)
         if not isinstance(divisor, int) or divisor <= 0:
             raise TypeError('divisor must be a positive integer')
         self._divisor = divisor
         self._valid_values = (divisor,)
 
-    def validate(self, value: int, context: str='') -> None:
+    def validate(self, value: int, context: str = '') -> None:
         super().validate(value=value, context=context)
         if not value % self._divisor == 0:
             raise ValueError('{} is not a multiple of {}; {}'.format(
@@ -389,9 +391,8 @@ class PermissiveMultiples(Validator):
             the nearest true multiple
     """
 
-
     def __init__(self, divisor: numbertypes,
-                 precision: float=1e-9) -> None:
+                 precision: float = 1e-9) -> None:
         if divisor == 0:
             raise ValueError('Can not meaningfully check for multiples of'
                              ' zero.')
@@ -405,7 +406,7 @@ def __init__(self, divisor: numbertypes,
         self._valid_values = (divisor,)
 
     def validate(self, value: numbertypes,
-                 context: str='') -> None:
+                 context: str = '') -> None:
         """
         Validate the given value. Note that this validator does not use
         context for anything.
@@ -422,8 +423,9 @@ def validate(self, value: numbertypes,
             # multiples in the relevant range and see if `value` is one
             # of them (within rounding errors)
             divs = int(divmod(value, self.divisor)[0])
-            true_vals = np.array([n*self.divisor for n in range(divs, divs+2)])
-            abs_errs = [abs(tv-value) for tv in true_vals]
+            true_vals = np.array(
+                [n * self.divisor for n in range(divs, divs + 2)])
+            abs_errs = [abs(tv - value) for tv in true_vals]
             if min(abs_errs) > self.precision:
                 raise ValueError('{} is not a multiple'.format(value) +
                                  ' of {}.'.format(self.divisor))
@@ -465,7 +467,7 @@ def __init__(self, *validators: Validator) -> None:
         self._valid_values = tuple(vval for v in self._validators
                                    for vval in v._valid_values)
 
-    def validate(self, value: Any, context: str='') -> None:
+    def validate(self, value: Any, context: str = '') -> None:
         args: TList[str] = []
         for v in self._validators:
             try:
@@ -496,9 +498,10 @@ class Arrays(Validator):
 
     validtypes = (int, float, np.integer, np.floating)
 
-    def __init__(self, min_value: numbertypes=-float("inf"),
-                 max_value: numbertypes=float("inf"),
-                 shape: TSequence[Union[int, TCallable[[], int]]]=None) -> None:
+    def __init__(self, min_value: numbertypes = -float("inf"),
+                 max_value: numbertypes = float("inf"),
+                 shape: TSequence[
+                     Union[int, TCallable[[], int]]] = None) -> None:
 
         if isinstance(min_value, self.validtypes):
             self._min_value = min_value
@@ -511,7 +514,8 @@ def __init__(self, min_value: numbertypes=-float("inf"),
             self._max_value = max_value
         else:
             raise TypeError('max_value must be a number bigger than min_value')
-        if not isinstance(shape, collections.abc.Sequence):
+        if not isinstance(shape,
+                          collections.abc.Sequence) and shape is not None:
             raise ValueError(f"Shape must be a sequence (List, Tuple ...)"
                              f"got a {type(shape)}")
         self._shape = shape
@@ -539,7 +543,7 @@ def shape(self) -> Optional[Tuple[int, ...]]:
         shape = tuple(shape_array)
         return shape
 
-    def validate(self, value: np.ndarray, context: str='') -> None:
+    def validate(self, value: np.ndarray, context: str = '') -> None:
 
         if not isinstance(value, np.ndarray):
             raise TypeError(
@@ -589,7 +593,7 @@ class Lists(Validator):
         elt_validator: used to validate the individual elements of the list
     """
 
-    def __init__(self, elt_validator: Validator=Anything()) -> None:
+    def __init__(self, elt_validator: Validator = Anything()) -> None:
         self._elt_validator = elt_validator
         self._valid_values = ([vval for vval in elt_validator._valid_values],)
 
@@ -598,7 +602,7 @@ def __repr__(self) -> str:
         msg += self._elt_validator.__repr__() + '>'
         return msg
 
-    def validate(self, value: TList[Anything], context: str='') -> None:
+    def validate(self, value: TList[Anything], context: str = '') -> None:
         if not isinstance(value, list):
             raise TypeError(
                 '{} is not a list; {}'.format(repr(value), context))
@@ -615,8 +619,8 @@ class Sequence(Validator):
         elt_validator: used to validate the individual elements of the Sequence
     """
 
-    def __init__(self, elt_validator: Validator=Anything(),
-                 length: int=None, require_sorted: bool=False) -> None:
+    def __init__(self, elt_validator: Validator = Anything(),
+                 length: int = None, require_sorted: bool = False) -> None:
         self._elt_validator = elt_validator
         self._length = length
         self._require_sorted = require_sorted
@@ -629,7 +633,8 @@ def __repr__(self) -> str:
         msg += self._elt_validator.__repr__() + '>'
         return msg
 
-    def validate(self, value: collections.abc.Sequence, context: str='') -> None:
+    def validate(self, value: collections.abc.Sequence,
+                 context: str = '') -> None:
         if not isinstance(value, collections.abc.Sequence):
             raise TypeError(
                 '{} is not a sequence; {}'.format(repr(value), context))
@@ -649,10 +654,11 @@ class Callable(Validator):
     """
     Validator for callables such as functions.
     """
+
     def __init__(self) -> None:
         self._valid_values = (lambda: 0,)
 
-    def validate(self, value: TCallable, context: str='') -> None:
+    def validate(self, value: TCallable, context: str = '') -> None:
         if not callable(value):
             raise TypeError(
                 '{} is not a callable; {}'.format(repr(value), context))
@@ -666,22 +672,23 @@ class Dict(Validator):
     Validator for dictionaries
     """
 
-    def __init__(self, allowed_keys: TSequence[Hashable]=None) -> None:
+    def __init__(self, allowed_keys: TSequence[Hashable] = None) -> None:
         """
         Validator for dictionary keys
         Args:
             allowed_keys (List): if set, all keys must be in allowed_keys
         """
         self.allowed_keys = allowed_keys
-        self._valid_values =({0: 1},)
+        self._valid_values = ({0: 1},)
 
-    def validate(self, value: dict, context: str='') -> None:
+    def validate(self, value: dict, context: str = '') -> None:
         if not isinstance(value, dict):
             raise TypeError(
                 '{} is not a dictionary; {}'.format(repr(value), context))
 
         if self.allowed_keys is not None:
-            forbidden_keys = [key for key in value if key not in self.allowed_keys]
+            forbidden_keys = [key for key in value if
+                              key not in self.allowed_keys]
             if forbidden_keys:
                 raise SyntaxError('Dictionary keys {} are not in allowed keys '
                                   '{}'.format(forbidden_keys,

From a0e794311246d087b6c3103247b4edc10ea4a487 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 18:00:38 +0100
Subject: [PATCH 588/719] Suppress MultiParameter "set" warning in
 MultiChannelInstrumentParameter

MultiChannelInstrumentParameter defines set_raw method that makes its
parent class MultiParameter add a set method (well, _BaseParameter adds
the set method), which in turn raises a warning from the MultiParameter
class that MultiParameter's don't support "setting" yet.
---
 qcodes/instrument/channel.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument/channel.py b/qcodes/instrument/channel.py
index 6b6e6bcc123..f394b67bc40 100644
--- a/qcodes/instrument/channel.py
+++ b/qcodes/instrument/channel.py
@@ -1,4 +1,5 @@
 """ Base class for the channel of an instrument """
+import warnings
 from typing import (
     List, Union, Optional, Dict, Sequence,
     cast, Any
@@ -102,7 +103,9 @@ def __init__(self,
                  channels: Sequence[InstrumentChannel],
                  param_name: str,
                  *args, **kwargs) -> None:
-        super().__init__(*args, **kwargs)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            super().__init__(*args, **kwargs)
         self._channels = channels
         self._param_name = param_name
 

From b0d7b41e62a52f9341553ffb1e25bd5bf8c9e9e9 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 18:01:35 +0100
Subject: [PATCH 589/719] Suppress MultiParameter "set" warning in
 MercuryiPSArray (old driver)

MercuryiPSArray defines set_raw method that makes its parent class
MultiParameter add a set method (well, _BaseParameter adds the set
method), which in turn raises a warning from the MultiParameter
class that MultiParameter's don't support "setting" yet.
---
 qcodes/instrument_drivers/oxford/mercuryiPS.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/oxford/mercuryiPS.py b/qcodes/instrument_drivers/oxford/mercuryiPS.py
index 2ffcfdda89f..54cc80a2683 100644
--- a/qcodes/instrument_drivers/oxford/mercuryiPS.py
+++ b/qcodes/instrument_drivers/oxford/mercuryiPS.py
@@ -1,3 +1,4 @@
+import warnings
 from functools import partial
 import re
 import time
@@ -17,7 +18,10 @@ class MercuryiPSArray(MultiParameter):
     """
     def __init__(self, name, instrument, names, units, get_cmd, set_cmd, **kwargs):
         shapes = tuple(() for i in names)
-        super().__init__(name, names, shapes, snapshot_value=True, **kwargs)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            super().__init__(name, names, shapes, snapshot_value=True,
+                             **kwargs)
         self._get = get_cmd
         self._set = set_cmd
         self._instrument = instrument

From 665899dc8887e55da12a797235cb19aea4c7b0f4 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 18:28:50 +0100
Subject: [PATCH 590/719] Avoid use of deprecated .value in hdf5 indexing

Deprecated singe h5py 2.1.
See http://docs.h5py.org/en/stable/whatsnew/2.1.html#dataset-value-property-is-now-deprecated
---
 qcodes/data/hdf5_format.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/data/hdf5_format.py b/qcodes/data/hdf5_format.py
index 4ec0a4bfac3..6c19a8ee10e 100644
--- a/qcodes/data/hdf5_format.py
+++ b/qcodes/data/hdf5_format.py
@@ -97,7 +97,7 @@ def read(self, data_set, location=None):
             set_arrays = [s.decode() for s in set_arrays]
             # else:
             #     set_arrays = ()
-            vals = dat_arr.value[:, 0]
+            vals = dat_arr[:, 0]
             if 'shape' in dat_arr.attrs.keys():
                 # extend with NaN if needed
                 esize = np.prod(dat_arr.attrs['shape'])
@@ -428,9 +428,9 @@ def read_dict_from_hdf5(self, data_dict, h5_group):
                                                               item)
                 else:  # item either a group or a dataset
                     if 'list_type' not in item.attrs:
-                        data_dict[key] = item.value
+                        data_dict[key] = item[...]
                     else:
-                        data_dict[key] = list(item.value)
+                        data_dict[key] = list(item[...])
             for key, item in h5_group.attrs.items():
                 if type(item) is str:
                     # Extracts "None" as an exception as h5py does not support

From e7b18a783a1d726cd4eaf4159d2cdf3f0d6ea773 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 18:40:58 +0100
Subject: [PATCH 591/719] Use pytest parametrization instead of hypothesis
 strategy, its faster

---
 qcodes/tests/dataset/test_datasaver.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_datasaver.py b/qcodes/tests/dataset/test_datasaver.py
index 945ea04754e..1bd10511594 100644
--- a/qcodes/tests/dataset/test_datasaver.py
+++ b/qcodes/tests/dataset/test_datasaver.py
@@ -84,9 +84,9 @@ def test_numpy_types():
 
 
 @pytest.mark.usefixtures("experiment")
-@given(numeric_type=hst.sampled_from([int, float, np.int8, np.int16, np.int32,
-                                      np.int64, np.float16, np.float32,
-                                      np.float64]))
+@pytest.mark.parametrize('numeric_type',
+                         [int, float, np.int8, np.int16, np.int32, np.int64,
+                          np.float16, np.float32, np.float64])
 def test_saving_numeric_values_as_text(numeric_type):
     """
     Test the saving numeric values into 'text' parameter raises an exception

From 14472db850718b8f56c8e6da635e04fc5c64f8e3 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Wed, 9 Jan 2019 19:15:42 +0100
Subject: [PATCH 592/719] Limit max_examples - avoid 'timeout' hypothesis
 deprecation warning

The original warning is:
qcodes/tests/dataset/test_dataset_basic.py::test_add_experiments
\site-packages\hypothesis\core.py:1050:
HypothesisDeprecationWarning: Your tests are hitting the settings
timeout (60.00s). This functionality will go away in a future release
and you should not rely on it. Instead, try setting max_examples to be
some value lower than 79 (the number of examples your test successfully
ran here). Or, if you would prefer your tests to run to completion,
regardless of how long they take, you can set the timeout value to
hypothesis.unlimited.

The recommendation about 'unlimited' was not followed because 'timeout'
is going to be deprecated anyway.

Also, this test does not need very many examples.

So, this change also made this test run ~10 faster (~2s from ~22s)
---
 qcodes/tests/dataset/test_dataset_basic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_dataset_basic.py b/qcodes/tests/dataset/test_dataset_basic.py
index df24d879350..c3975f97f27 100644
--- a/qcodes/tests/dataset/test_dataset_basic.py
+++ b/qcodes/tests/dataset/test_dataset_basic.py
@@ -127,7 +127,7 @@ def test_load_by_id_for_none():
         _ = load_by_id(None)
 
 
-@settings(deadline=None)
+@settings(deadline=None, max_examples=6)
 @given(experiment_name=hst.text(min_size=1),
        sample_name=hst.text(min_size=1),
        dataset_name=hst.text(hst.characters(whitelist_categories=_unicode_categories),

From 1434276836f95be5c1ecf48f86bcab1904c46cce Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 10 Jan 2019 12:43:16 +0100
Subject: [PATCH 593/719] Update deprecated imports

---
 qcodes/plots/qcmatplotlib.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/plots/qcmatplotlib.py b/qcodes/plots/qcmatplotlib.py
index 45f04061e17..110928b7ee6 100644
--- a/qcodes/plots/qcmatplotlib.py
+++ b/qcodes/plots/qcmatplotlib.py
@@ -2,8 +2,8 @@
 Live plotting in Jupyter notebooks
 using the nbagg backend and matplotlib
 """
-from collections import Mapping
-from collections import Sequence
+from collections.abc import Mapping
+from collections.abc import Sequence
 from functools import partial
 from copy import deepcopy
 

From 14de5f75e6727567ff1a54540fd962f202f13795 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 10 Jan 2019 13:04:57 +0100
Subject: [PATCH 594/719] Work around python 3.6/3.7 differences in asyncio

---
 qcodes/monitor/monitor.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 0b07ce7727d..3d5e55f29ba 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -20,7 +20,7 @@
 monitor = qcodes.Monitor(param1, param2, param3, ...)
 ```
 """
-
+import sys
 import logging
 import os
 import time
@@ -40,6 +40,13 @@
 
 from qcodes.instrument.parameter import Parameter
 
+# all tasks has moved in python 3.7. Once we drop support for 3.6
+# this can be replaced by the else case only.
+if sys.version_info.major == 3 and sys.version_info.minor == 6:
+    all_tasks = asyncio.Task.all_tasks
+else:
+    all_tasks = asyncio.all_tasks
+
 WEBSOCKET_PORT = 5678
 SERVER_PORT = 3000
 
@@ -171,7 +178,7 @@ def run(self):
         finally:
             log.debug("loop stopped")
             log.debug("Pending tasks at close: %r",
-                      asyncio.Task.all_tasks(self.loop))
+                      all_tasks(self.loop))
             self.loop.close()
             log.debug("loop closed")
             self.loop_is_closed.set()
@@ -200,7 +207,7 @@ async def __stop_server(self):
         await self.loop.create_task(self.server.wait_closed())
         log.debug("stopping loop")
         log.debug("Pending tasks at stop: %r",
-                  asyncio.Task.all_tasks(self.loop))
+                  all_tasks(self.loop))
         self.loop.stop()
 
     def join(self, timeout=None) -> None:

From 8464c554e8eca06108cf0dab55648f70db6bb117 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 10 Jan 2019 13:06:21 +0100
Subject: [PATCH 595/719] Remove warning about multiparameter and set.

This is only relevant for the Loop and setting a multiparameter
works fine otherwise
---
 qcodes/instrument/parameter.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 36289cf9991..4fccca40e4d 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1219,10 +1219,6 @@ def __init__(self,
         super().__init__(name, instrument, snapshot_get, metadata,
                          snapshot_value=snapshot_value)
 
-        if hasattr(self, 'set'):
-            # TODO (alexcjohnson): can we support, ala Combine?
-            warnings.warn('MultiParameters do not support set at this time.')
-
         self._meta_attrs.extend(['setpoint_names', 'setpoint_labels',
                                  'setpoint_units', 'names', 'labels', 'units'])
 

From c77fe7226c473fdee75fbbfca1adbea877470eb5 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 10 Jan 2019 13:06:48 +0100
Subject: [PATCH 596/719] git Revert "Suppress MultiParameter "set" warning in
 MultiChannelInstrumentParameter"

This reverts commit a0e794311246d087b6c3103247b4edc10ea4a487.
---
 qcodes/instrument/channel.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/qcodes/instrument/channel.py b/qcodes/instrument/channel.py
index f394b67bc40..6b6e6bcc123 100644
--- a/qcodes/instrument/channel.py
+++ b/qcodes/instrument/channel.py
@@ -1,5 +1,4 @@
 """ Base class for the channel of an instrument """
-import warnings
 from typing import (
     List, Union, Optional, Dict, Sequence,
     cast, Any
@@ -103,9 +102,7 @@ def __init__(self,
                  channels: Sequence[InstrumentChannel],
                  param_name: str,
                  *args, **kwargs) -> None:
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")
-            super().__init__(*args, **kwargs)
+        super().__init__(*args, **kwargs)
         self._channels = channels
         self._param_name = param_name
 

From 5a57173c6e9bc1d35897af811f8bb8fa7e6dec02 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 10 Jan 2019 13:07:13 +0100
Subject: [PATCH 597/719] Revert "Suppress MultiParameter "set" warning in
 MercuryiPSArray (old driver)"

This reverts commit b0d7b41e62a52f9341553ffb1e25bd5bf8c9e9e9.
---
 qcodes/instrument_drivers/oxford/mercuryiPS.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/qcodes/instrument_drivers/oxford/mercuryiPS.py b/qcodes/instrument_drivers/oxford/mercuryiPS.py
index 54cc80a2683..2ffcfdda89f 100644
--- a/qcodes/instrument_drivers/oxford/mercuryiPS.py
+++ b/qcodes/instrument_drivers/oxford/mercuryiPS.py
@@ -1,4 +1,3 @@
-import warnings
 from functools import partial
 import re
 import time
@@ -18,10 +17,7 @@ class MercuryiPSArray(MultiParameter):
     """
     def __init__(self, name, instrument, names, units, get_cmd, set_cmd, **kwargs):
         shapes = tuple(() for i in names)
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")
-            super().__init__(name, names, shapes, snapshot_value=True,
-                             **kwargs)
+        super().__init__(name, names, shapes, snapshot_value=True, **kwargs)
         self._get = get_cmd
         self._set = set_cmd
         self._instrument = instrument

From 31f4f718fcbd31d976313100f43f2b30fc61d1ac Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 10 Jan 2019 13:20:22 +0100
Subject: [PATCH 598/719] Adapt test now that we no longer warn

---
 qcodes/tests/test_parameter.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/qcodes/tests/test_parameter.py b/qcodes/tests/test_parameter.py
index f55791bd6ee..bf71c849e8f 100644
--- a/qcodes/tests/test_parameter.py
+++ b/qcodes/tests/test_parameter.py
@@ -676,11 +676,9 @@ def get_raw(self):
 
 
 class SettableMulti(SimpleMultiParam):
-    # this is not fully suported - just created to raise a warning in the test below.
-    # We test that the warning is raised
     def set_raw(self, v):
         print("Calling set")
-        self.v = v
+        self._return_val = v
 
 
 class TestMultiParameter(TestCase):
@@ -786,9 +784,13 @@ def test_has_set_get(self):
         self.assertFalse(hasattr(p, 'set'))
         # We allow creation of Multiparameters with set to support
         # instruments that already make use of them.
-        with self.assertWarns(UserWarning):
-            SettableMulti([0, [1, 2, 3], [[4, 5], [6, 7]]],
-                          name, names, shapes)
+
+        p = SettableMulti([0, [1, 2, 3], [[4, 5], [6, 7]]], name, names, shapes)
+        self.assertTrue(hasattr(p, 'get'))
+        self.assertTrue(hasattr(p, 'set'))
+        value_to_set = [2, [1, 5, 2], [[8, 2], [4, 9]]]
+        p.set(value_to_set)
+        assert p.get() == value_to_set
 
     def test_full_name_s(self):
         name = 'mixed_dimensions'

From 18789a957de99fc3ac04b40f275abe80749d811b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 10 Jan 2019 13:44:30 +0100
Subject: [PATCH 599/719] mypy on 3.6 complains about this line which is only
 valid python 3.7 code

---
 qcodes/monitor/monitor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 3d5e55f29ba..4efe982cf64 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -45,7 +45,7 @@
 if sys.version_info.major == 3 and sys.version_info.minor == 6:
     all_tasks = asyncio.Task.all_tasks
 else:
-    all_tasks = asyncio.all_tasks
+    all_tasks = asyncio.all_tasks  # type: ignore
 
 WEBSOCKET_PORT = 5678
 SERVER_PORT = 3000

From 81320889ecee906c5db76af79c71536f17a72bad Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 10 Jan 2019 14:02:18 +0100
Subject: [PATCH 600/719] Silly workaround to keep mypy happy on both python
 3.6 and 3.7

---
 qcodes/monitor/monitor.py | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 4efe982cf64..28bb0d3f7ef 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -40,12 +40,21 @@
 
 from qcodes.instrument.parameter import Parameter
 
-# all tasks has moved in python 3.7. Once we drop support for 3.6
-# this can be replaced by the else case only.
-if sys.version_info.major == 3 and sys.version_info.minor == 6:
-    all_tasks = asyncio.Task.all_tasks
-else:
-    all_tasks = asyncio.all_tasks  # type: ignore
+
+def _get_all_tasks():
+    # all tasks has moved in python 3.7. Once we drop support for 3.6
+    # this can be replaced by the else case only.
+    # we wrap this in a function to trick mypy into not inspecting it
+    # as there seems to be no good way of writing this code in a way
+    # which keeps mypy happy on both 3.6 and 3.7
+    if sys.version_info.major == 3 and sys.version_info.minor == 6:
+        all_tasks = asyncio.Task.all_tasks
+    else:
+        all_tasks = asyncio.all_tasks
+    return all_tasks
+
+
+all_tasks = _get_all_tasks()
 
 WEBSOCKET_PORT = 5678
 SERVER_PORT = 3000

From b24c97b953d59fc17880fa21a13fec8692542e52 Mon Sep 17 00:00:00 2001
From: Pieter Eendebak <pieter.eendebak@tno.nl>
Date: Fri, 11 Jan 2019 11:48:25 +0100
Subject: [PATCH 601/719] add flush option to hdf5 write_metadata

---
 qcodes/data/hdf5_format.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/qcodes/data/hdf5_format.py b/qcodes/data/hdf5_format.py
index 6c19a8ee10e..73d130b56de 100644
--- a/qcodes/data/hdf5_format.py
+++ b/qcodes/data/hdf5_format.py
@@ -272,7 +272,7 @@ def _create_dataarray_dset(self, array, group):
 
         return dset
 
-    def write_metadata(self, data_set, io_manager=None, location=None, read_first=True):
+    def write_metadata(self, data_set, io_manager=None, location=None, read_first=True, flush = True):
         """
         Writes metadata of dataset to file using write_dict_to_hdf5 method
 
@@ -290,6 +290,11 @@ def write_metadata(self, data_set, io_manager=None, location=None, read_first=Tr
         metadata_group = data_set._h5_base_group.create_group('metadata')
         self.write_dict_to_hdf5(data_set.metadata, metadata_group)
 
+        # flush ensures buffers are written to disk
+        # (useful for ensuring openable by other files)
+        if flush:
+            data_set._h5_base_group.file.flush()
+
     def _read_list_group(self, entry_point, list_type):
         d = {}
         self.read_dict_from_hdf5(data_dict=d,

From d66489391724de0abadcd7abb4c1fcdf41194469 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 11 Jan 2019 13:39:22 +0100
Subject: [PATCH 602/719] Move notebooks so they can be linked

---
 .../An-ArrayParameter-Example-with-Dual-Setpoints.ipynb}          | 0
 .../Simple-Example-of-ParameterWithSetpoints.ipynb}               | 0
 docs/examples/{writing drivers => writing_drivers}/readme.txt     | 0
 3 files changed, 0 insertions(+), 0 deletions(-)
 rename docs/examples/{writing drivers/An ArrayParameter Example with Dual Setpoints.ipynb => writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb} (100%)
 rename docs/examples/{writing drivers/Simple Example of ParameterWithSetpoints.ipynb => writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb} (100%)
 rename docs/examples/{writing drivers => writing_drivers}/readme.txt (100%)

diff --git a/docs/examples/writing drivers/An ArrayParameter Example with Dual Setpoints.ipynb b/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb
similarity index 100%
rename from docs/examples/writing drivers/An ArrayParameter Example with Dual Setpoints.ipynb
rename to docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb
diff --git a/docs/examples/writing drivers/Simple Example of ParameterWithSetpoints.ipynb b/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
similarity index 100%
rename from docs/examples/writing drivers/Simple Example of ParameterWithSetpoints.ipynb
rename to docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
diff --git a/docs/examples/writing drivers/readme.txt b/docs/examples/writing_drivers/readme.txt
similarity index 100%
rename from docs/examples/writing drivers/readme.txt
rename to docs/examples/writing_drivers/readme.txt

From b20fdca760f6b21cc8c7dc10d2835fc9bb50ad30 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 11 Jan 2019 18:47:28 +0100
Subject: [PATCH 603/719] move location

---
 docs/examples/index.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index 289e823866d..04792db8255 100644
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -43,12 +43,12 @@ Drivers
 Writing Drivers
 ---------------
 
-.. include:: writing drivers/readme.txt
+.. include:: writing_drivers/readme.txt
 
 .. toctree::
     :glob:
 
-    writing drivers/*
+    writing_drivers/*
 
 Logging
 --------

From b2758bb857890da81bd581cbe4ace4e6c887ba87 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 11 Jan 2019 18:47:56 +0100
Subject: [PATCH 604/719] Add ref to new array parameter notebook

---
 docs/examples/Creating Instrument Drivers.ipynb | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/docs/examples/Creating Instrument Drivers.ipynb b/docs/examples/Creating Instrument Drivers.ipynb
index 90f467c00f6..cc912af7403 100644
--- a/docs/examples/Creating Instrument Drivers.ipynb	
+++ b/docs/examples/Creating Instrument Drivers.ipynb	
@@ -465,7 +465,18 @@
     "  - Accepts get and/or set commands as either strings for the instrument's `ask` and `write` methods, or functions/methods. The set and get commands may also be set to `False` and `None`. `False` corresponds to \"no get/set method available\" (example: the reading of a voltmeter is not settable, so we set `set_cmd=False`). `None` corresponds to a manually updated parameter (example: an instrument with no remote interface).\n",
     "  - Has options for translating between instrument codes and more meaningful data values\n",
     "  - Supports software-controlled ramping\n",
-    "- Any other parameter class may be used in `add_parameter`, if it accepts `name` and `instrument` as constructor kwargs. Generally these should subclass `Parameter`, `ArrayParameter`, or `MultiParameter`."
+    "- Any other parameter class may be used in `add_parameter`, if it accepts `name` and `instrument` as constructor kwargs. Generally these should subclasses of `Parameter`, `ParameterWithSetpoints`, `ArrayParameter`, or `MultiParameter`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`ParameterWithSetpoints` is specifically designed to handle the situations where the instrument returns an array of data with assosiated setpoints. An example of how to use it can be found in the notebook [Simple Example of ParameterWithSetpoints](writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb)\n",
+    "\n",
+    "`ArrayParameter` is an older alternative that does the same thing. However, it is significantly less flexible and much harder to use correct but used in a significant number of drivers. It is not recommended for any new driver.\n",
+    "\n",
+    "`MultiParameter` is designed to for the situation where multiple different types of data is captured from the same instrument command."
    ]
   },
   {
@@ -546,7 +557,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,

From 1936253feb48cee21576ee6165ee015404b102f7 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 11 Jan 2019 19:06:52 +0100
Subject: [PATCH 605/719] Build docs with sphinx 1.8.3

---
 docs_requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs_requirements.txt b/docs_requirements.txt
index 24a066f94a1..a811e5d86bb 100644
--- a/docs_requirements.txt
+++ b/docs_requirements.txt
@@ -1,4 +1,4 @@
-sphinx==1.7.9
+sphinx==1.8.3
 sphinx_rtd_theme
 sphinx-jsonschema
 nbsphinx

From 15855483efb3b2fcd9583bb30cc490cfdcf59a39 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 14 Jan 2019 15:23:26 +0100
Subject: [PATCH 606/719] Try different way of starting xvfb

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 685b50a574e..4a467ab86f5 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -31,7 +31,7 @@ install:
 
 before_script: # configure a headless display to test plot generation
     - "export DISPLAY=:99.0"
-    - "sh -e /etc/init.d/xvfb start"
+    - Xvfb :99 -screen 0 1024x768x24 > /dev/null 2>&1 &
     - sleep 3 # give xvfb some time to start
 
 script:

From caf6beb75d293d6495235f95fd6199c19a61429f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 14 Jan 2019 17:10:00 +0100
Subject: [PATCH 607/719] Add atomic context manager

To ensure that runs are not partially created
---
 qcodes/dataset/sqlite_base.py | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 9c18f02f236..223c96bdde2 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -2290,15 +2290,17 @@ def create_run(conn: ConnectionPlus, exp_id: int, name: str,
         - formatted_name: the name of the newly created table
     """
 
-    run_counter, formatted_name, run_id = _insert_run(conn,
-                                                      exp_id,
-                                                      name,
-                                                      guid,
-                                                      parameters)
-    if metadata:
-        add_meta_data(conn, run_id, metadata)
-    _update_experiment_run_counter(conn, exp_id, run_counter)
-    _create_run_table(conn, formatted_name, parameters, values)
+    with atomic(conn):
+        run_counter, formatted_name, run_id = _insert_run(conn,
+                                                          exp_id,
+                                                          name,
+                                                          guid,
+                                                          parameters)
+        print(f"create run {run_id}")
+        if metadata:
+            add_meta_data(conn, run_id, metadata)
+        _update_experiment_run_counter(conn, exp_id, run_counter)
+        _create_run_table(conn, formatted_name, parameters, values)
 
     return run_counter, run_id, formatted_name
 

From 47cf0ec86ff7161c204d5d9fd5f23a6b1307486c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 14 Jan 2019 17:10:55 +0100
Subject: [PATCH 608/719] remove print

---
 qcodes/dataset/sqlite_base.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 223c96bdde2..fee22744eac 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -2296,7 +2296,6 @@ def create_run(conn: ConnectionPlus, exp_id: int, name: str,
                                                           name,
                                                           guid,
                                                           parameters)
-        print(f"create run {run_id}")
         if metadata:
             add_meta_data(conn, run_id, metadata)
         _update_experiment_run_counter(conn, exp_id, run_counter)

From 6af183c8a051ce688e56e42e13122a3ab2a29418 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 15 Jan 2019 14:06:24 +0100
Subject: [PATCH 609/719] Add test of atomic create_run

---
 qcodes/tests/dataset/test_sqlite_base.py | 45 +++++++++++++++++++-----
 1 file changed, 37 insertions(+), 8 deletions(-)

diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index a5e722f8fde..7da436ba258 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -10,6 +10,7 @@
 from hypothesis import given
 import unicodedata
 import numpy as np
+from unittest.mock import patch
 
 from qcodes.dataset.descriptions import RunDescriber
 from qcodes.dataset.dependencies import InterDependencies
@@ -32,7 +33,6 @@
 
 
 def test_path_to_dbfile():
-
     with tempfile.TemporaryDirectory() as tempdir:
         tempdb = os.path.join(tempdir, 'database.db')
         conn = mut.connect(tempdb)
@@ -100,7 +100,6 @@ def test__validate_table_raises(table_name):
 
 
 def test_get_dependents(experiment):
-
     x = ParamSpec('x', 'numeric')
     t = ParamSpec('t', 'numeric')
     y = ParamSpec('y', 'numeric', depends_on=['x', 't'])
@@ -142,7 +141,8 @@ def test_get_dependents(experiment):
 
 def test_column_in_table(dataset):
     assert mut.is_column_in_table(dataset.conn, "runs", "run_id")
-    assert not mut.is_column_in_table(dataset.conn, "runs", "non-existing-column")
+    assert not mut.is_column_in_table(dataset.conn, "runs",
+                                      "non-existing-column")
 
 
 def test_run_exist(dataset):
@@ -168,7 +168,6 @@ def test_get_last_experiment_no_experiments(empty_temp_db):
 
 
 def test_update_runs_description(dataset):
-
     invalid_descs = ['{}', 'description']
 
     for idesc in invalid_descs:
@@ -212,16 +211,17 @@ def test_get_parameter_data(scalar_dataset):
     expected_names['param_3'] = ['param_0', 'param_1', 'param_2',
                                  'param_3']
     expected_shapes = {}
-    expected_shapes['param_3'] = [(10**3, )]*4
+    expected_shapes['param_3'] = [(10 ** 3,)] * 4
 
     expected_values = {}
-    expected_values['param_3'] = [np.arange(10000*a, 10000*a+1000)
+    expected_values['param_3'] = [np.arange(10000 * a, 10000 * a + 1000)
                                   for a in range(4)]
     verify_data_dict(data, input_names, expected_names, expected_shapes,
                      expected_values)
 
 
-def test_get_parameter_data_independent_parameters(standalone_parameters_dataset):
+def test_get_parameter_data_independent_parameters(
+        standalone_parameters_dataset):
     ds = standalone_parameters_dataset
     params = mut.get_non_dependencies(ds.conn,
                                       ds.run_id)
@@ -240,7 +240,7 @@ def test_get_parameter_data_independent_parameters(standalone_parameters_dataset
     expected_shapes = {}
     expected_shapes['param_1'] = [(10 ** 3,)]
     expected_shapes['param_2'] = [(10 ** 3,)]
-    expected_shapes['param_3'] = [(10**3, )]*2
+    expected_shapes['param_3'] = [(10 ** 3,)] * 2
 
     expected_values = {}
     expected_values['param_1'] = [np.arange(10000, 10000 + 1000)]
@@ -270,3 +270,32 @@ def test_is_run_id_in_db(empty_temp_db):
     acquired_dict = mut.is_run_id_in_database(conn, *try_ids)
 
     assert expected_dict == acquired_dict
+
+
+def test_atomic_creation(experiment):
+    def just_throw(*args):
+        raise RuntimeError("This breaks adding metadata")
+
+    # first we patch add_meta_data to throw an exception
+    # if create_data is not atomic this would create a partial
+    # run in the db. Causing the next create_run to fail
+    with patch('qcodes.dataset.sqlite_base.add_meta_data', new=just_throw):
+        x = ParamSpec('x', 'numeric')
+        t = ParamSpec('t', 'numeric')
+        y = ParamSpec('y', 'numeric', depends_on=['x', 't'])
+        with pytest.raises(RuntimeError):
+            mut.create_run(experiment.conn,
+                           experiment.exp_id,
+                           name='testrun',
+                           guid=generate_guid(),
+                           parameters=[x, t,
+                                       y],
+                           metadata={'a': 1})
+
+    mut.create_run(experiment.conn,
+                   experiment.exp_id,
+                   name='testrun',
+                   guid=generate_guid(),
+                   parameters=[x, t,
+                               y],
+                   metadata={'a': 1})

From b9e495c4569ad192ea416214f16d920ddaaa398d Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 15 Jan 2019 14:18:41 +0100
Subject: [PATCH 610/719] Improve test

---
 qcodes/tests/dataset/test_sqlite_base.py | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index 7da436ba258..26a66447667 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -273,6 +273,10 @@ def test_is_run_id_in_db(empty_temp_db):
 
 
 def test_atomic_creation(experiment):
+    """"
+    Test that dataset creation is atomic. Test for
+    https://github.com/QCoDeS/Qcodes/issues/1444
+    """
     def just_throw(*args):
         raise RuntimeError("This breaks adding metadata")
 
@@ -283,7 +287,8 @@ def just_throw(*args):
         x = ParamSpec('x', 'numeric')
         t = ParamSpec('t', 'numeric')
         y = ParamSpec('y', 'numeric', depends_on=['x', 't'])
-        with pytest.raises(RuntimeError):
+        with pytest.raises(RuntimeError,
+                           match="Rolling back due to unhandled exception")as e:
             mut.create_run(experiment.conn,
                            experiment.exp_id,
                            name='testrun',
@@ -291,7 +296,12 @@ def just_throw(*args):
                            parameters=[x, t,
                                        y],
                            metadata={'a': 1})
-
+    assert error_caused_by(e, "This breaks adding metadata")
+    # since we are starting from an empty database and the above transaction
+    # should be rolled back there should be no runs in the run table
+    runs = mut.transaction(experiment.conn,
+                           'SELECT run_id FROM runs').fetchall()
+    assert len(runs) == 0
     mut.create_run(experiment.conn,
                    experiment.exp_id,
                    name='testrun',
@@ -299,3 +309,7 @@ def just_throw(*args):
                    parameters=[x, t,
                                y],
                    metadata={'a': 1})
+    runs = mut.transaction(experiment.conn,
+                           'SELECT run_id FROM runs').fetchall()
+    assert len(runs) == 1
+

From 33c8400e3595417bec6502913008f8c8384f08bc Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 15 Jan 2019 14:55:57 +0100
Subject: [PATCH 611/719] Test the number of runs from a different connection

This ensures that the runs are not only in memory but matches the
db on disk
---
 qcodes/tests/dataset/test_sqlite_base.py | 25 ++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/qcodes/tests/dataset/test_sqlite_base.py b/qcodes/tests/dataset/test_sqlite_base.py
index 26a66447667..256072160b8 100644
--- a/qcodes/tests/dataset/test_sqlite_base.py
+++ b/qcodes/tests/dataset/test_sqlite_base.py
@@ -4,6 +4,7 @@
 from sqlite3 import OperationalError
 import tempfile
 import os
+from contextlib import contextmanager
 
 import pytest
 import hypothesis.strategies as hst
@@ -32,6 +33,17 @@
 _unicode_categories = ('Lu', 'Ll', 'Lt', 'Lm', 'Lo', 'Nd', 'Pc', 'Pd', 'Zs')
 
 
+@contextmanager
+def shadow_conn(path_to_db: str):
+    """
+    Simple context manager to create a connection for testing and
+    close it on exit
+    """
+    conn = mut.connect(path_to_db)
+    yield conn
+    conn.close()
+
+
 def test_path_to_dbfile():
     with tempfile.TemporaryDirectory() as tempdir:
         tempdb = os.path.join(tempdir, 'database.db')
@@ -277,6 +289,7 @@ def test_atomic_creation(experiment):
     Test that dataset creation is atomic. Test for
     https://github.com/QCoDeS/Qcodes/issues/1444
     """
+
     def just_throw(*args):
         raise RuntimeError("This breaks adding metadata")
 
@@ -302,6 +315,13 @@ def just_throw(*args):
     runs = mut.transaction(experiment.conn,
                            'SELECT run_id FROM runs').fetchall()
     assert len(runs) == 0
+    with shadow_conn(experiment.path_to_db) as new_conn:
+        runs = mut.transaction(new_conn,
+                               'SELECT run_id FROM runs').fetchall()
+        assert len(runs) == 0
+
+    # if the above was not correctly rolled back we
+    # expect the next creation of a run to fail
     mut.create_run(experiment.conn,
                    experiment.exp_id,
                    name='testrun',
@@ -309,7 +329,12 @@ def just_throw(*args):
                    parameters=[x, t,
                                y],
                    metadata={'a': 1})
+
     runs = mut.transaction(experiment.conn,
                            'SELECT run_id FROM runs').fetchall()
     assert len(runs) == 1
 
+    with shadow_conn(experiment.path_to_db) as new_conn:
+        runs = mut.transaction(new_conn,
+                               'SELECT run_id FROM runs').fetchall()
+        assert len(runs) == 1

From 2c488486c085ea8f34603f4b7cef0af4f376b980 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 17 Jan 2019 13:30:21 +0100
Subject: [PATCH 612/719] Add docstring clarifying models

---
 qcodes/instrument_drivers/basel/sp983c.py | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

diff --git a/qcodes/instrument_drivers/basel/sp983c.py b/qcodes/instrument_drivers/basel/sp983c.py
index a603e2453d0..5d06dd78399 100644
--- a/qcodes/instrument_drivers/basel/sp983c.py
+++ b/qcodes/instrument_drivers/basel/sp983c.py
@@ -1,11 +1,21 @@
 from qcodes.instrument.base import Instrument
-from qcodes.instrument.parameter import MultiParameter
-from qcodes.utils.validators import Enum, Bool
+from qcodes.utils.validators import Enum
 
 
 class SP983C(Instrument):
     """
-    A virtual driver for the Basel SP 983 current preamplifier.
+    A virtual driver for the Basel SP 983 and SP 983C current to voltage
+    converter.
+
+    This driver supports both the SP 983 and SP 983C models. These differ only
+    in their handling of input offset voltage. It is the responsibility of the
+    user to capture the input offset, (from the voltage supply) and compensate
+    that as needed depending on the model.
+
+    Note that, as this is a purely virtual driver, there is no support
+    for the the remote control interface (SP 983a). It is the responsibility of
+    the user to ensure that values set here are in accordance with the values
+    set on the instrument.
     """
     
     def __init__(self, name, **kwargs):
@@ -28,7 +38,7 @@ def __init__(self, name, **kwargs):
 
     def get_idn(self):
         vendor = 'Physics Basel'
-        model = 'SP 983c'
+        model = 'SP 983(c)'
         serial = None
         firmware = None
         return {'vendor': vendor, 'model': model,

From f916e9de8013ee4e938004977cba7c5e22c1f26c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 18 Jan 2019 10:48:05 +0100
Subject: [PATCH 613/719] Don't automatically unpack ParameterWithSetpoints

---
 qcodes/dataset/measurements.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index cbc6abf2892..6b3a3a06a36 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -158,10 +158,6 @@ def add_result(self, *res_tuple: res_type) -> None:
                     self._unbundle_arrayparameter(parameter,
                                                   res,
                                                   found_parameters)
-                elif isinstance(parameter, ParameterWithSetpoints):
-                    self._unbundle_parameter_with_setpoints(parameter,
-                                                            res,
-                                                            found_parameters)
 
         for partial_result in res:
             parameter = partial_result[0]

From 5be8057b426f0ac03e134826c810a12b15bb2128 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 18 Jan 2019 10:49:53 +0100
Subject: [PATCH 614/719] Update notebooks to use expansion

---
 ...arameter-Example-with-Dual-Setpoints.ipynb | 35 +++++------
 ...le-Example-of-ParameterWithSetpoints.ipynb | 60 +++++++++----------
 2 files changed, 45 insertions(+), 50 deletions(-)

diff --git a/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb b/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb
index 0c3499c7c04..22d522edae5 100644
--- a/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb
+++ b/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb
@@ -19,7 +19,7 @@
     "import numpy as np\n",
     "\n",
     "from qcodes.instrument.base import Instrument, Parameter\n",
-    "from qcodes.instrument.parameter import ParameterWithSetpoints\n",
+    "from qcodes.instrument.parameter import ParameterWithSetpoints, expand_setpoints_helper\n",
     "from qcodes.dataset.measurements import Measurement\n",
     "from qcodes.dataset.plotting import plot_by_id, load_by_id\n",
     "import qcodes.utils.validators as vals"
@@ -163,7 +163,7 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#49@C:\\Users\\jenielse/experiments.db\n",
+       "tutorial_exp#no sample#93@C:\\Users\\jenielse/experiments.db\n",
        "----------------------------------------------------------"
       ]
      },
@@ -193,7 +193,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 189\n"
+      "Starting experimental run with id: 268\n"
      ]
     }
    ],
@@ -204,7 +204,7 @@
     "osc.dt(0.001)\n",
     "\n",
     "with timemeas.run() as datasaver:\n",
-    "    datasaver.add_result((osc.trace, osc.trace.get()))\n",
+    "    datasaver.add_result(*expand_setpoints_helper(osc.trace))\n",
     "    \n",
     "dataid = datasaver.run_id"
    ]
@@ -216,7 +216,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -240,7 +240,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 190\n"
+      "Starting experimental run with id: 269\n"
      ]
     }
    ],
@@ -248,7 +248,7 @@
     "osc.dt(0.01)  # make the trace 10 times longer\n",
     "\n",
     "with timemeas.run() as datasaver:\n",
-    "    datasaver.add_result((osc.trace, osc.trace.get()))\n",
+    "    datasaver.add_result(*expand_setpoints_helper(osc.trace))\n",
     "    \n",
     "dataid = datasaver.run_id"
    ]
@@ -260,7 +260,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -291,7 +291,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 191\n"
+      "Starting experimental run with id: 270\n"
      ]
     }
    ],
@@ -302,7 +302,7 @@
     "osc.dt(0.01)\n",
     "\n",
     "with freqmeas.run() as datasaver:\n",
-    "    datasaver.add_result((osc.periodogram, osc.periodogram.get()))\n",
+    "    datasaver.add_result(*expand_setpoints_helper(osc.periodogram))\n",
     "    \n",
     "dataid = datasaver.run_id"
    ]
@@ -314,7 +314,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -347,7 +347,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 192\n"
+      "Starting experimental run with id: 271\n"
      ]
     }
    ],
@@ -361,7 +361,8 @@
     "    for _ in range(no_of_avgs-1):\n",
     "        temp_per += osc.periodogram()\n",
     "        \n",
-    "    datasaver.add_result((osc.periodogram, temp_per/no_of_avgs))\n",
+    "    datasaver.add_result((osc.periodogram, temp_per/no_of_avgs),\n",
+    "                         (osc.freq_axis, osc.freq_axis.get()))\n",
     "\n",
     "dataid = datasaver.run_id"
    ]
@@ -373,7 +374,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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qqkMFSzehLCiKZhjZgsU4OpcPgLEiMlpECoCzgadSOYCqPq2qF5WVlaVFwHQRKVFgFodhZDzZUAmiq9JxHwbeBcaLSIWIXKCqTcClwPPAYmCGqn7UFfJ1Np6LqjHApq1hZAuR+TgC/D7vUoxDRHoA9aoa3pXvq+o5CdY/RxsB8KDSlAUDhgwjW2jKgnFZSVkcIpIjIl8VkWdFZBPwCbBeRD4SkVtFZGx6xUyOTIxxqGpLOm6ATVvDyAaamxVvdoTGcDOqwVQeybqqXgX2BH4CDFLV4ao6ADgSeA/4jYh8PU0yJk0mxji8XkmOOJ+D+qAZRjbguZ2L83OBljFaQSNZV9XxqhqKXamq24D/AP8RkfwOlSxL8KyMkoI8djQ00dSs5OfGy0w2DKO747mdiwtyqQuFaQorrg4JFElZHJ7SEJGXReQU/zYRudu/j5EaId+D5iybu8owMpWI4nC1RVBLq6eaVTUa+JGIXO9b120KD2ZijMMLpJVEFEcwTVvDyAY8RdGjMNgdwVQVRyVwHDBQRJ4WkW4VTMjEGIfnA/V6KEF90AwjG/Bcz8UFThQgqO9zqopDVLVJVb+HE9t4CxjQ8WJlD97YDc9VZYUODSNz8d7fEq8jGNBqEKmO4/ib90FV7xeRD4FLOlak7MKzOEosxmEYGU8o1vUc0BT7lCwOVb0rZnmOqn6rY0XadTI5xmGuKsPIfLyOYFHAO4JJWRwi8mcgoc2lqt/vMIl2A1V9Gnh68uTJF3a1LMnSklWVF7VsGEbmEbE4zFUFwGzf518A1yfa0UiNyDgOszgMI+OJxDgKgp2Om5TiUNUHvM8icoV/2dg9PEVh4zgMI/OxrKrEBNP26iJiBwAGtUSBYWQDoXB2JLt0p/k4spLW6XvBfNAMIxuIdVUFNb0+2eB4DY6lIUCxiFR7m3Cmd+2VJvlSQkSmA9PHjBnT1aIkTaQoWiR9L5gPmmFkA977XGQlRwA4CShX1VJVzVPVXu5faXdRGpChI8cjPRTXJ2oWh2FkLLEWR7a7qs4DZovIIyJyvogMSqdQ2UTsgCGbk8MwMpfWteeC+T4nm1V1MYCITABOBu5361S9CvwPeHtXZwPMdrwHq8W0NVeVYWQqjRHF4XkQgvk+pzpy/BNV/YOqTgOOxalVdRbwfjqEywZaB9OC2UMxjGyglasqoB6EpGtVudbGUOB9Vd2hqnXAcyLSrKqXpU3CgNMUGxw3xWEYGUur9zmgMctk5xz/PvAkcBmwSERO9W2+OR2CZQuhcGxZ9WCatoaRDWTL+5ysxXEhcJCq7hCRUcBjIjJKVf+Ik5LbLcjIdNwsCaYZRjbQFBPjyPZ03FxV3QGgqiuBY4CTReT3dCPFEYR03KAOGDKMbCAyMVvAO4LJKo4NIrK/t+AqkS8A/YB90iFYthAZABjwAUOGkQ14rqmC3BzyciTrFcd5wAb/CncmwPOAozpcqiyiKbZWlVkchpGxeK6qvFwhPzcnsO9zsuM4KtrY9nbHiZN9hMLNiEB+rpAjwTVtDSMb8EoG5eUIebkSWA9CSuM4RKQ0XYJkK41NzRTm5SAi5OXmBDbv2zCygaZwM3k5gohQkJsT2I5g0opDRIYCz6RRlqykoamZglznNhTk5gR2pKlhZAONTc0U5Dnvc36A3+dkq+PuDTyCk5ZrdCANTWEK3cB4Xq5YrSrDyGBC4Wby3Y5gfl5wg+PJjuN4FThVVd9LpzDZSIPrqgK3hxLQB80wsoHGcIzFEdBpEpJ1VX0AfCmdgnQEIjJdRO6uqqrqalGSpsFv2uZIYEeaGkY20NikMa7nYHYEk1UcXwR6ichv0ynM7pKJAwAbQs0U5jmuqvw8szgMI5PxWxx5ucF1VSWlOFQ1rKoXATvSLE/W0RhucVXl5Uhg874NIxtobApHLI783BxLxwVQ1V+mS5BspSEUjvKJBvVBM4xsIBRW8vOcKkxBjlkmWx33LyJyeLqFyUZig+M2H4dhZC6Nsen1AfUgJGtxLAVuE5GVInKLv26VsXs4AwDdGEeuBccNI5OJHschge0IJhvj+KOqTgGOBrYB94nIYhG5TkTGpVXCgNPQFG6JcQTYtDWMbKDRP44jNyewU0GnGuNYpaq3qOoBwFeB04HFaZEsS/C7qoJcosAwsoHGLBmXlWqtqnx3rMSDwH+BT8mA8R3dmcamZgrzW9L3mgI6YMgwsoGokeMBTsdNtuTICcA5wOeBWTjlRy5S1Z1plC0r8Neqys/NoTGgA4YMIxtoNXI8oO9zsiVHfgo8BFylqtvSKE/W4a9VlW8Wh2FkNP6sqvy84JYcSXY+jqnpFiQbUVWrVWUYASIUbiY/C2KWKcU4ujuZVquqqVlRxTdyPLgzhhlGNhDtepbAuqoCpTgyrVZVg/tQeT7RgrzgzhhmGNlAyF9CyAYAGumgIRQG8A0AtJHjhpHJNDbFjuNoRjV4yqNdxSEiF4vI30XkbBF5RkS+2xmCZQOedVHgc1UFtYdiGEGnKdxMs/o8CLlOzaogvtPJBMePBb4CvKmqR4jI39IsU9ZQ2+hYHMVeVlWAZwwzjKDjKQjP4ihy3+v6ppZCpkEhmbPZqo6tdYu73JBGebKKOk9xFLiKIye4WRiGEXQaY2KW3nvtvedBIhnF8UdwAs/u8n/SJ0524VkcJQUtMY5mhXBAc78NI8g0hJ332VMc3ntdG0DF0a6rSlU/ARCRCcCpwFAR+RKwDnhKVa1W1S5S29gEtDxgeRGfaDO5ObldJpdhGKnjuaq82EZxvtO8eu95kEh2Po4f4ZQZEZySIx+4nx8WkR+nT7xgE3FVuQ+Yl8bXENDcb8MIMl6WZKzFEURXVbIlRy4A9lbVkH+liPwe+Aj4TUcLlg3EuqpKCpzbUdcYpqw4v8vkMgwjdepC0R3BILuqkg31NwND4qwf7G4zdoHaULTi6FHo/N8ZQNPWMIJOXUxHsDjAiiNZi+MK4GURWQqscdeNAMYAl6VDsGygzlUQxTEWR21D8B40wwg6EYsj1oMQCl5HMNkih/9zZ/o7BBiKE9+oAD5QVWvldpEWV5VzG3pEeijBe9AMI+jUxYzLCrKrKlmLA1VtBt6LXS8i31TV+zpUqiyhrtEZGJSb42RhlBR6WRjBe9AMI+jEWhzZPo6jPX7RAcfISmobw5FeCbT0UCzGYRiZRyuLIz/LLQ4RWZhoEzCw48TJLmobw5GHC3ymrcU4DCPjiM2SzMvNoSA3J3sVB45yOAnYHrNegHc6VKIsoi7UFDFnAXq4sQ6zOAwj8/BcVUW+zmBxQW4kCSZIJKs4ngF6qur82A0i8lqHSpRFOK6qlltQUhhc09Ywgk59KEyOtAzkBcf6COL7nGxW1QVtbPtqx4nTGhE5Dfg8MAC4Q1VfSOfvdSa1jeEoi6MgN4e8HLGsKsPIQGobwxTn5yIikXXFBbmR8VpBItmSI9IR+/j2vVdENonIopj100RkiYgs80qZqOpMVb0QOB+nvHtgqIsJjosIJQW57LQYh2FkHHWhMMUF0X3xkoLcrM6qelVELhOREf6VIlIgIseKyAPAN1L43fuBaTHHygXuAE4GJgLniMhE3y4/c7cHhtrGpijFAdCjMM8sDsPIQOoawxQXRDepJfnBfJ+TVRzTgDBOUcN1IvKxiCwHlgLnAH9Q1fuT/VFVfQPYFrP6EGCZqi5X1UacooqnisMtwH9VdW6yv5EJ1DWGI3VtPIoLctkZwB6KYQSdOtdV5ac4y2Mc9cCdwJ0ikg/0A+pUtbIDZRlKSzkTcEamH4pT0uR4oExExqhq3BkIReQi4CKAESNGxNul21EbCre2OAryqG0IXg/FMIJObQJX1drKLFUcftwKuevTIEu8GImq6p+AP7X3ZVW9G7gbYPLkyRkxE1LsAEBwHjSzOAwj86hvDFOcH+3EKc7yGEdnUAEM9y0Pw5ksKpCEm5XGpuaorCqwGIdhZCp1oej0evDScYP3PncnxfEBMFZERotIAXA28FQqBxCR6SJyd1VVVVoE7EhiZ//zCGret2EEndrGplYxjpKCvEC+z12iOETkYeBdYLyIVIjIBaraBFwKPA8sBmao6kepHFdVn1bVi8rKyjpe6A4mUtcmpofixDiC96AZRtCpD7X2IBTn59LQ1Ey4OSO850mTUoxDRCYD1wIj3e8KThxi31SOo6rnJFj/HPBcKsfKVCJ1bWJ7KIW5VnLEMDKQ+BaHWyE3FKZnYcoh5W5LqmfyIHA18CE2899uEVsQzcNzValq1AhUwzC6N84AwPiKo7axKasVx2ZVTSnu0JmIyHRg+pgxY7palHbxZgVr/aDlEW5WGpqao4qlGYbRfWluVsdV1WochzsLYMDiHKnGOK4XkXtE5BwROcP7S4tku0AmxThiZ//zaJkFMFgPmmEEmfqm6EmcPII6C2CqFsc3gQlAPi2uKgUe70ihsoGErqrILIBN9OlR0OlyGYaROnUJ3udiUxwA7Keq+6RFkixjR73jqupR2DqrCoL3oBlGkPHe11j3cqn7fu8IWDWIVF1V78UUHuxWZNI4jsq6EAC9S/Kj1ntzcuwM2INmGEGmPhTf4ih33+/K2sZOlymdpKo4jgDmu6XPF4rIh21MK9vpZFKMo6q2EREoLYpWHGZxGEbm4c3+FxscLy9x3M2VtaFOlymdpOqqmtb+LkYyVNaF6FWUT25OdMqt12Mxi8MwMofaxvjB8fJip2O4PWAWR0qKQ1VXiUhvYCxQ5Nu0qkOlygIqa0Ot3FQQ3CwMwwgyiSyOvNwcSgvzstviEJFvA5fjFCCcDxyGUzrk2I4XLXUyaRzH9tpGykpaZ031KDRXlWFkGnUJLA6A8h75WR/juBw4GFilqlOBA4DNHS7VLpJRMY66UMSM9eMfaWoYRmbguZZ7FLTui/cuKWB7wCyOVBVHvTupEyJSqKqfAOM7XqzgU1kbimRc+PEGBNq844aROVS76fW94nQGy4qDZ3GkGhyvEJFyYCbwoohsJ8BzZqSTytpGesdxVeXmCEX5OWZxGEYGUVUXcrIk49Sj6l1SwOpttV0gVfpIWnGIU3Hv++50sTeIyKtAGfC/dAkXVMLNSnV9E2VxeifgmLtWIdcwMofquhA9C/PIyWldmLR3ST7bd2apxaGqKiIzgYPc5dfTJlXAqXYH/8VzVYE7wb25qgwjY6iuDyXsCJaVFFBd30S4WVul32cquzJy/OC0SNIBZMrIcS+nO5Hi6BHQWcMMI6hUu+Oy4uGl3VfVBSdAnqrimAq8KyKf2cjxXccrN1JeHL+IoU3mZBiZRXVdE72K4ztwvFhmkAYBphocPzktUmQZVbVtu6rM4jCMzKK6PsSIPiVxt5VF6lUFx+JIeeR4ugTJJirrPFdVAoujIJctOxo6UyTDMHaDqrrEMY7ekXpVWWpxiMgP46yuAuao6vyOESn4eD2PeAMAwRk9bq4qw8gcqutCrQqWengxjiANAkw1xjEZuBgY6v5dBBwD/F1ErulY0YKL9wDFGywEUFqUR029KQ7DyARC4WZ2NoYTup69WGbWWhxAX+BAVd0BICLXA48BRwFzgN92rHjBpLK2kV5FeQlT88qL86mqC9HcrHHzwg3D6D5UtZNeX1qUR44EK8aRqsUxAvCrzRAwUlXrgC53ymdKOu7WHY30Ky1MuL2spABVzOowjAzAUwiJYhw5OUJ5SUGgsqpSVRwP4YzluF5EbgDeBh4WkR7Axx0tXKpkSjrulh0N9OuZWHF4sY8g5X0bRlCpaifZBZx3ujJA73OqWVW/EpHncGYCFOBiVZ3tbv5aRwsXVLbsaGD8oNKE272eS2VdIyOIn+JnGEb3wOvgJbI4wHFjBSnGkarFAdAENLv/g6NCO5GtOxvp26MNiyOAed+GEVTay5IEJyU3SO9zSopDRC4HHgT6AQOAf4nIZekQLKiEws1U1obo27MNs9ZTHAEybQ0jqFS2M6AXnEGAQVIcqWZVXQAcqqo7AUTkFpwZAP/c0YIFFa9KZlsxjl4W4zCMjKHSK6meYBwHeJM5Za+rSgB/LYywu85Ikq2u4ujTI7HF4flKqwL0oBlGUKmuC1FamDi9HpxBgLWNYRqaglFKKFWL4z7gfRF5AkdhnArc2+FSBZhkAmmFebmUFOTKDD+/AAAgAElEQVQGyrQ1jKBSWdvYZkYVOCn24NSpG9Cr9bzkmUaqWVW/F5HXcLKqAL6pqvM6XKoAk4zi8LZbjMMwuj+VdfGngfbjLzsyoFdRZ4iVVpJSHAlqVAFMFZGpqvr7DpRplxGR6cD0MWPGdLUoCUlFcViMwzC6P20VOPQIWqHDZGMcpe7fZOC7tNSquhiYmB7RUicTBgB6s/8lqlPlUV6SHym/bhhG96Wqtn3F4W0PSqHDpCwOVf0FgIi8gFOrqsZdvgF4NG3SBZC2JrX3U1acz4otOztJKsMwdpWkXFU9stPi8IitVdUIjOowabKAKneKyfaKF5YXB2vAkGEEEVWlqi6UcDZPj94BG5uValbVP4FZblYVwGnAAx0rUrCprgslnGLST3mJxTgMo7uzo6GJcLO2a3EU5+dSkJsTmLEcqWZV3SQi/wWOBBTLqkqZZAJp4Iw0bWhqpj4Upig/89P3DCOItFcZ10NEAhW33JVaVWGcWlXen5EC23Y2tmvWQksWxradweihGEYQ8SyI9sZxgDPoNyhTQlutqk6kuVlZtmkHYwb0bHff/m5Jkk01wXjQDCOIbKp23s8Bbcyv47Fn/54s3bQj3SJ1CqlaHF6tqutV9TrgMODCjhcrmNz95nJ2NobbLKnuMdAdJLSxuj7dYhmGsYtsrHHez4FJDOqbMKiUVVtreWbhunSLlXasVlUnUdvYxG/++wngPEDtMbCXWRyG0d3ZVN2ACPRro9q1h9dhvPSheahqukVLK7tTqwqcrKp/dKxIwWT2yu0A9CzMY+8h7Q9Q7NuzkByBTWZxGEa3ZVNNPX17FJKX234ffMqefSOfF6+vYeKQXukULa2kZHG4pUW+BWwDtuNkVd2eDsGCxKK1VZx37ywAZnxnCgV57V/23ByhX89Cc1UZRjdmY3VDxDvQHqVF+fzftw4B4JQ/vcmGqsx9t1O1OFDVOcCcNMiy23TXWlWfbqyJfB7VL/mpYAf2KmJjtbmqDKO7srG6Pqn4hsfofj0in1ds2cmgsswseJiUxSEib7n/a0Sk2vdXIyLV6RUxebprraoNPquhpCB5XT2wV6HFOAyjG7OppiGpjCqPIeXFkc9rttemQ6ROISnFoapHiIgAe6tqL99fqapmrqOuk1izrY6y4nw++dW0lL43oFeRxTgMo5vSFG5my46GlMqk5+YIC284EYCKbQFXHADqpAE80e6ORisqttcyqm9JyiPAB5QWsnVnI41NNs7SMLobW3Y0okrSMQ6PXkX5DC0vpmJ7XZokSz+ppuO+JyIHp0WSALOuso6hvYvb3zEGz3e6OSCjTQ0jSHiJKwNKU49TDC0vZm1l9iiOqTjK4zMRWSgiH4rIwnQIFiS27GiMjARPhchYDnNXGUa3w4s/pmpxAPQrzezyI6lmVZ2cFikCTGNTM1V1IfruguIY1tvJwHpy/joOGNG7o0UzDGMXaW5WnlrgjAD3B7yTpW+PQrbu3NrRYnUaqVocq3Eq435DVVfhVMgd2OFSBQivSGHfJEaWxjJ2QE++MWUk97+zkpnz1na0aIZh7CL3vr2Cpxes44cnjKPfLnQK+/Z05tsJhTMzfpmq4rgTmAKc4y7XAHd0qEQBwzNH+/ZI/eESEa6bvjf9ehbwzmdbOlo0wzB2kXc+28q4gT257NhdGzPmKZvtGVr9OlXFcaiqXgLUA6jqdiD1rnQWsdV9MJKpZROP3BxhWO8S1lVanMMwugvrKusY0acHziiF1PHag0xNfElVcYREJBfHRYWI9Mfm5GiTLW4AbVdiHB5Dy4tZl8EZGIYRNNZW1jG0fNdHfXvtwdYd2WFx/AlnLMcAEbkJeAu4ucOlChCrtu4kR3Yt88JjaG8ndS/TK2oaRhCorg9RU9+0Syn2HoPdUiMrt+7sKLE6lVSnjn1QROYAx+GUUz9NVRenRbIAsKGqnveWb2PS0LKUSo3EMqSsiIamZrbtbNwty8UwjN3Hs/53JZvKY2h5MYPLinhv+VZO2ntQSvWuugNJtWYiUgRcDIwBPgTuUtWmdAqW6bz48UYu/L/ZAFx01B67dSzvAV1bWWeKwzC6mI5QHCLClD378vjctTz34QaeuewIJg3tXjX22iJZV9UDwGQcpXEycFvaJAoIby9ryYLaezfr7nsPqMU5DKPrWesmqgzdDcUBRM3Ls2ht1W4dq7NJ1n8yUVX3ARCRfwCz0idS5lOxvZaa+haDbM/+7c8x3hZDIxaHZVYZRlezdnsd+bmyS9Ug/OzR31difetO1lfVMbhs95RRZ5GsxRHyPpiLqm0+2VDNEbe8yn/mVkTW+Wvw7wrlJfmUFOSaxWEY3YB1lU4Dn5Oze7Nm79mvpUN51+vLmfLrV9hUkxmdw2QVx37+OTiAfbvjfBzdgYffX91qXY/CXQ+Mg+MPHWIpuYbRLVhXWceQ3UjF9YiXlfXvWWt2+7idQVItmqqmVg+8AxGRPYBrgTJVPbOr5EiWpZt2RD5fM208h47u28beyTO6Xw8WratCVXd50JFhGLtHKNzMkg01TN9/yG4fKzdHeOjbh/LWsi3c+dpnQHT70Z1JdRxHhyAi94rIJhFZFLN+mogsEZFlIvJjAFVdrqoXdIWc7RE7rmLBmko+29xy4885eAQHjeyY4oRHj+vPmm11Ucc3DKNz+WDlNmoamjh6XP8OOd7nxvTjtAOGRpYXr69m8foWJ46qdsvxW12iOID7gajp8NwR6XfgZG1NBM4RkYmdL1pyfLqxhtE/eY5XPtkIODf41DveZmN1AydMHMiz3z+C3j06rhrL4WP6ATBn1fYOO6ZhGKkxb3UlAJ/bs2M8CQDjBpYy85LDOWR0H5Zu2sHJf3wzsu2hWasZ/ZPnul0J9i5RHKr6BrAtZvUhwDLXwmgEHgFO7XThkuSDlY74L3zkKI7K2kj+AAN7FUal2nUE3kjTLTsaufGZj/n5zEXtfMMwjI7ikgfncsery9hc00DPwjxKi/I79Pj7Dy+nT0lLRzPc7FgZM2Y7STaru9k0s11lccRjKOCPDFUAQ0Wkr4j8DThARH6S6MsicpGIzBaR2Zs3b063rDS7N9bLrPBPA9mrgx8qgKL8XHoW5rFlRwP3vLWCf763qsN/wzCM+Dz74XpufX4JW3c27nLB0vboVdwScvayq3LdcKbX3nQXdi/dp2OJF/FVVd2KM2q9TVT1buBugMmTJ6f9Kje5N7K52fFBrq1s6RF0dG/Eo1/PAj6syKyBQoYRJFZvq92l+TeSwd9urN1ex8DSIryGLBTuXoqjO1kcFcBw3/IwYF0XydIunin5yAdr+P4j86MsjtKi9Ojjfj0Lme2LcTy9oNteHsMIBKrKw7NaUuwXrKlMm+Lwp+1XbK/jK3e/G4mp7GzoXsPnupPF8QEwVkRGA2uBs4GvpnIAEZkOTB8zZtcmV0mFZl+mw9ML1kU14ulSHAV50Xr+sofncdgefelfavWrDCMdfLByOz95/MOodeUl6fEo+LOnrnp0QcSrAbCjmymOrkrHfRh4FxgvIhUicoE7Iv1S4HlgMTBDVT9K5biq+rSqXlRW1rGB6Ydnreb3L37q/QY/eXwhNz/3SYf+RjJ8sqEGgOMmDIisW7qpptPlMIxsYeWWlrLnXgpuukqh+91RTTExjSv+PZ9fP9dSiPyGpz7iuQ/Xp0WOZOiqrKpzVHWwquar6jBV/Ye7/jlVHaeqe6rqTV0hG8CfX17K7JUtSV9PzV/HfW+vQFXZXNPAwzGjO68+aTxHju3HhEGlQPr8kTefPokpe/Rlii8V8NMNpjgMI12s2taiJE7ZZxAHjCjn8uPGpeW3mtz5x6eO78+xEwbwvWP2jNp+1xvLCYWbqW1s4oF3V/K/RRsi215dsol731qRFrni0Z1cVbtNR7iqGpua+d2Ln8KLsPI3nwececNr6puo2F4Xt5bM6H49uGTqGFZvreWa/yzgxL0H7vLvt8W0SYOZNmkwj/h8rovWWcUXw0gXH/ner749Cnnie4en7be+feQefLi2itvO2o++PQv5v3dXttpnXWUd23Y2okrU2I5v3vcBAN86YnTa5PPTnYLju83uuqqWbKjh040tPfjGJqcH4N2gxeurmbuqstX3vKDWiL4lPHLRlLSk4/opLmipAPPch+upqgu1sbdhGLvC2so63lzaMj1CSUF6Ky8NKivi39+ZEplzp0ecyd/mrt7O4vVOG+W1S147Bc7shJ1BoBTH7nLlo/P5wp/fiiyP+9l/uenZj9nuDu67/52V3PRc6wkPexZ2bikv/wNV2xjm0dktrrNws7IkSfdVVW2Ix+ZUtL+jYWQgzc3KQ++vpj4UTmr/xeurowLUsT3+kt0sVpoq8Yqj/uDfCyKJOJtrGvjhjPnsc8Pzke373vACFdvTP1gwUK6q3eW6L+zNl+96N2rd399s8Ru+89nWuN/b3eq3qVLiKqrBZUUM713CvW+tYENVPbNXbadfzwJeWryJN6+ZyvA+JW0e56czP+TZhevZa3Bph490N4yu5tkP1/PTJz5kfVUdV544vs1931++la/c/R5fOnAYSzfVcPS4/jz8/mpOnDiQ/7qxhB5ptjhi6ZmgXXl3udMOba8N8fjcta22D+mEOT3M4vBxyOg+7e4ztLyYf190WNS6eCZlOvHmL8/NES4/fizrquq5560VzF9TyUuLNwHRJVASsbm6Iel9DSPT2FjtxCOTceVu29kIwH/mVrCwooo/v7KMmoYmLjt2bGSf4k5WHD18ngwRuP+bB1NW3L4bfHfnCUmGQCkOEZkuIndXVe366Opbz9w3avmcQ5wxiYe6SuX2s/fn0D36MvfnJ0T2Sde4jUR4vta8HOHwMf3Icx+UovyW2+n3db708UbOufu9VlU2C939O6uA2rzV29nnhufZ2s0Kthmdw8otO9n7uv9FpbimkwbX91+UH93g14fCnPqXt5izqiVzsjHcEicodvcfP7CUib5pnzu7g+hvV+ZfdyLHjB/AjadNAuCAEeUAfDsmGH7PeZM7RbZAKY6OGMdx1uTh/O6s/SLLvzp1EvecN5kHvnUIr151DAePchRIH1/l2852VXkdilz3g/f7t57ZIre/l3Xxv+bw7vKtrSwL74Xyembp5m+vf0ZNfVOkQKSRXTw+t4KdjWGemNfavZIOElkai9dXs6Ciihue+rjVviLw8y84Rblje/clnR3LdN/rovyciCzT9xvCSz88iocvPIx7zpvM949vsYge+NYhHD8xPRmdsQRKcXQU/nESebk5HD9xIEX5uQmngM3P7dzLOLS8hJKCXH40bQIA3/jcKMApvf70pUcAzotw83OLeenjjRS6I87XV9Xz3vKtkVGoXtmUZZt2sCpNg5r8iFuOrBtOL5BWtu9sZNSPn+X1T53im/+ZU8H4n/03KhsmG/Bue2fMQ7ZsU03kma5yO0xbdzQwZ9V2quud578wL4fH51bwl1eWRvZ5/yfHcfAoZw6dMw505sm46fRJ9C8tpKCT33NPcTTHPCZjBpRSlJ/L8RMHRmVwelZIZ2DB8TgMKS/m6HH9o6yKeHxuz74JA+bppLggl49/2TKdyRXHjeX8z42iT4+CiJLYUtPA3W8s5+43lkdM3rmrt/OzmYs4bf8h3H72AZFe1ozZFcyYXREZt5KIY3/3GkPLi/nnBYe22tYUbiYvhRdr7LXPcer+Q7nNZ90FlQUVTgr3399YztHj+vPr/y6moamZbTsbGVS2+1OQdnfO/cf7rNiyk9N9Exa1haoSbta4z9NJf3iDvj0LeOjCw+J806E+FOb4378RWa6sc+IXf39zBfe+tYJrP78X4Mzm98MZCwC48MjRFOfnMqBXEQN6FTHnZ8dH0mK/duhIvnboyOROtgPxXGPtXbcjx/ZjQGlR2ocB+AmU4ujIWlUPfOuQdve5/5uHUN+UXKpfOsnJkYiSKynIJS9HogYu1bg9rFc/cQLnn2yo4a+vfcbHMYMH60PhVv5gP8s372T55taWycbqeg69+WVuO2s/zjxoGACVtY3k5+ZEufG8nmazOqPrH5tTkRWKoz7k+dqdhrAwz7nG2aI4vLEQnqUpMYWwq+pCiLRMR3Dna59x6/NLWHLjtMi18liysQY2tv17sTG7OasquefN5VRsr6Ux3MxLi50DLPBVml69rTbKNdU3TYUMUyE3R5h/3QkJs6s84nXk0k2gXFXpqlWViIK8nE7V8skgIpQV57OwovVAxZd9iuOW/31CXUx++4/+szBuzvvtL33KmX99J7LsBdk9V9d7bnqgv3bO/r98kam3vcb8NZWtBiXF+p6rakNc8ci83Q6ah5uV+lCYnQ1N1KQwEOrtZVvY0dDE659uTroKaSjcTIPbadhUXc/ymCl9V23dydpKp2JyXch1jbhK2VMgXibPii07WVdZF/X9JRtq2O5urw+FI9e6PapqQ7y1dAtVtSHeX568NVxVF6I+FKauMbzbU5Wu2VbLD2fMb3f8xJYdDSxeX81+v3iB43/3emT9v9y5Zha6Dbt37rWNLffm+N+/HndOmqq6EFc9uqDV79z47GKeWeg8n/5BfR4fVlQllbHU2ZSXFKRkyXcWgbI4DIdexfms2IXMlSfnr2OPfj253BdwA7j9paVRy2f+7V1O2Wcwf3p5Kd+YMtLpBQIDezm9Z6/h2VTTwGl3vM2kob145rIjIxbH+qroRvLBWauYOX8dM+ev4z/fncJBI/u433eC9gNKW/fKP1pXxeh+PSKpyQBXP7qAx+etZXifYtZsq+MvXz2AL+w7BHBG1+blSCRV0ZOxYnsdX7vn/ahjT99vCH86e39EhAfeWUmfHgVM3885Tn0oTFVdiB/OmM+SDTX061kYKT658IYTueHJj/jJKXtx9K2vAbD85lPYttNRYkV5nuJw/m/d2UB9KMzU215jUK8iZnxnCn9+ZSk3nj6Jk253XC2HjO7DrBXbOOPAoVx54ngGlhZGGpKH3l9NUX4OZxw4jMamZi544INWjeKiX5wUGX8g7g0IhZvJESE3R1BVHp+7lisfXcB+w8pYUFHF947Zk2vc+Bk4jfGm6nrGDixtdR/WVdZRnJ8bmSb51SWbIuUvTpk0OCpY6xXuUzfacextr0XiDZtqWjoNI/uWsL6qnpnz1vLk/LU8/9FGLjxydKThBycu9/OZizj3sGgX0s9mLuK95aknX6yrqufg3ukf/xAUTHEEEK/nVJiXE0lJjOWbh48iR4Sp4wdwxb/nsWWH07t9dcmmVoojljmrtkfmPv/TK8si66vrQjz4/qpWLrBFa51lz0Wx1te73lRdH3GlAVz96EJeueoYAA7/zSuEwtoq9rKwopIv/uVtLjxyNAeO6M0JEweSl5vD4262zpptzvEvfWgeuSIcNa4/J93+BhMGlfLjk/di285Gnpi3lmcWrOPG0ydFHXvq+P48vWAdx4zrz1Hj+nP9U06BZk9xnH/frKiGybtu4Ewj/Pi8tVEl91/4eAN/fMmprJyf66VNOw35H178lAbXjbWhup5LH57LwoqqKJ/2rBXObz0+dy2Pz13LWQcN41bXvffTJ5xy30eP689jcyp4c+kWjhnfn9eWtMyAOW/1ds79xyx+cPw4Dh7Vm4FlRVz96AIampqZecnhPDxrNdc96Zyj57q587XPuGbaBOoaw7y9bAsz56/lmYXreePqqYzo2zKoVFX53G9eYXS/Hrzq3jNPaYBzn6PutdsR8Czd6vpo6+7aJz5kyp59I8HgB99vqckWrxp1YV7rnvjby1oU577DyvjBCeOYt7qSlVt28lSc+WtEnLT2UFgpK07PzH5BxBRHADl+rwHMX1PJ5/cdHHdkKcCkIWV8yY1HzLzkcI645VUA5q+p5OpHF/DbM/eN9FATIdLitz5kVB8Wrq3k2QSlnutD4UiD6u85HnLzy5zo65XWh8Ksr6pjcFlxpMrwlh0NUZPn/NlVVs6o/hUM6lXEL07dO+7v/vO9Vby/YhsV2+uo2F4XGSDpcfkj86OW7zp3Msfc+ipXxrg7wEkAaKs3e/2TzjzwL/t+4+pHF1Ljur+8BtNTICu31vK7F5dE9vVcM+fdOyvhbzw6p4JbvrRv1CCvg258CYC9BvfivvMPZvRPnotsO/cfzrH+4CovP3e/sZyZCVJjn124nu8/Mi/KRfbX1z/j12fsE1leuslxz3nW7ZqYebGXbKyJKF5wFCtAXWOYzTWt3ZIPvr+aB99fTb+eBRw3YUDEtZoIf/JKKNzMpQ/Njbj/AGZ8ZwpF+blMHT+AO15dBr5b2rskn+21IQb1KmJoeTGzV21PW3HSINL9nGfGbnPBEXvw7SNGc81JLe6GW8/cl8PH9I28bOMHtbgdYmc0e3RORZTrwD8QtdQXqPvb1w/i2AkDuOn0SQzoVRjp6cfjtSWbIw1nbBqqPzNtXVU9U379Cp9saLFavF5kuFmp2F7La0uiG5QN1fV8559zotZ9Y8pIPr/PYN75bCv3v7OSaXsPSigbOI3ujadNoiAvh5mXHs4RY/q12if2N/7z3c/x1UNHRJZ3NjrnV+OLk/g/72xwtnvBcoCN1a0bUP9cDFeeMI67zj0oavvPn1zUag7qU/YZxMMXHoqIcOUJ4xgfx63kUZCXw5Fj+3Hr80tYumkHZx40LJLU4HHJQ3NbxVX+u2g9W3Y0RNx8frfYrBXbOPK3r0btHztDpZcGvqO+KRKgjseWHY0M71PC1SeN54wDh/JLX6dgYK+WZ7W8pEVxfLK+huc/ij6mP9FjsJuEsN+wMo4Y048fnuCURt9YXc8tZ+7Ld4/ZkzOSzPoyAmZxdOYMgN2Z4oJcfuYOYvKYvt8Qzpo8nPlrKrnv7RWRuUPAecFyc4RBvYoiroWP11fz+qebmbViG1778cez9+eYcQPYvKOBgtwcRvQt4SS3QV66MTo4HMvF/5qTcNuOhibe+tFUfvXMx5GX/5/vtgQ+567aTv/SQv713iqe+9CpG3TshAG80kaP9BenTuK1JZsiFtCZBw3jnENH8I0EvfknLzk8MsPigNIi/nnBIUy6/vmIMlDVVj3gEX1K2szt79ujgK2+HrAX3PUHecGZS97v8vLTv7SQ4b2ja449OX8dV/lqL+07rIw7v9aiXC47bixnTh7GlF+/EveYT3zvc3xYURVp+M//3Cj2HtKrzYKX3vWefONLnHvYSL64/xDmrnbclSLwxLyW71501B58cb8hUQVD/Tw+b23ErZiI/qWFXDK15T0+amx/6pvCjOzTg8fnVXDtE4tYvL6aqx5dwLETBkQlNQzqVURs1Y1pkwbx/Ecb+NG0CezRvyebquv5+ZMf0aywZ/+ekTFRRnIESnGo6tPA05MnT76wq2XpLpy090CWb94Z6X3tP7ycP559QKv9Prv5FN75bAtf/bsTKPb7qgEuO3YMp+7v9MjK4kydubdbmmHP/j34LE7KblvkCAzrXRLlZvD7tx94dxUPvBudQXPJ1DFRimPGd6Ywul8PDr7ppYgC8BduHNWvhFJfBtzpBwyNGsEcOy2viFCUnxtRHGu21VGUn8Pw3iURF03fdsb5TBhcytvLtnLk2H7kiFDpZpPtbAjzpQOH8dG6Kj7ZUMO+w8oTKsGcHIlK2R07oCe1jeGoWejilbyJHYN09sHDeeQDp4rynv17Ul3X0tCOG1ga5ZZ858fHsmhtFRf5LKzvHzc2IuM/31sVldGkStTkZvWhcOR5SJU9+vdg+ead7DU42mIa5Rt8+7VDR7JobRUPz1rDY3MqWim8Gd+ZEhWLAae+213ntpTjGNCriF5FeZFn2kiNQCkOozX+l6U99h5SRp8eBVEN+IRBpXyyoabdtOOzJg/nsD36Em5WjrntNcYM6MmyTW1bIUeP68/rn26OZEZ9cf+hfLByO1+ePIwZs53GYOr4/rzqC/Z6HBgzSvaAEeXk5+Zwz3mTGee6afxzsQ93rYNLp45h2qRBlBXnt1v6ItfXbT3qVscNs8+wsojiyMkRLjxqD178eGNUENhjWHkJsJXhfUqorG3k9U8386/3VrG2so4ehbkM71PCJxtqmDCoNK7iOGBEOSdNHESv4pbXdJ9hZTw+dy2n39mSHp2b09rqiR3/cN30iZx2wFDmra5sVQXBU5rPX3EUW3Y0MKS8OKoaQmFeDvsNi5/i7g/Ge/ftqLH9oxTRCRMH8uLHbQ++yM0Rws3KYxd/jvpQmCHlbWc4ec/joaP78P6KlrjT0PJiBpcnNzZm4Q0nJbWf0RpTHEaEsuL8SPHGXzz9EVt2NNKsyicbauiZRCFHr4z7r8/Yh6njB3DYr19OuO8H1x4faUy9qqNfP3QEZx00jLwc4ZKpY/jfog0ct9eAVorj+L0GICIsuO5Env94A4vXV0caukS1eryG9KqTHBdPXWP7AzfjlZKJjR0MLS/mjWumcvWjCzj7kBFU1YW48P9mA04pmOKCXM45ZAR/edUJ6P9sphNALy7I5XvH7MnQ8mK+PHk4d772GeDETSprG3nj08384tTojC9wrIVWciZRDbWkII/D9ujLYXs45XQGuErV72obP6iU8Tjn169nAWccOJTj9xrIkWP7ISLsPaRX1MBSgJtP34eZ89dy+gFDGVhaxC9PndRqEOmtZ+5Lfai5zefhxR8cxYdrq9qt1hDLkWP7kZcrTNmjL5ce23Y2oNFxmOIw4nL9dCcgea2b8pnsZDgA5xziBIyP32tg3CBoeUk+/UsLI1V+v3+c88J77iGAkX178J2jnTmXb/nSPkwcXMb6qjoOGtk7km5cVpLPlycPb1OW/11xZJRbxqPYHWH/lYOHJ0w/zo3TII8d2Lrhzs0Rfv+V/QFYsKZl4OXEIb244YvOdYy1vjZXN3DAiN4cMKJ31PpBZUUcNLI3x+0VXwEOKG09ojmenOC4nH71zMdRMnnk5AgPfOsQRvWNP2eLiPD7L+8ftW7mJYezZUcDn27cQWFeDptqHFwV9w0AAAlqSURBVOvke8e0xCKKclqUxiVT9+SOVz+jrDif8pLEyu0bU0ayR/+e7BFHKSZie61jFZeXFPDgtxOXHzHSgykOo032Geq4KHqXpJ7jfve5B9GsypWPLnCVhHD8XgM4elx/wCni1l59LICvHOwoon0SuEvaYsKgxL72ZTef0uZ3rz5pPJc9PC9qnae0TkqQupmox+wveQ8tBf9iSRQ3mTS0F4vWVscdQf7Nw+PPMz2kvJi/fv2guNuAyH1IlvzcHAaXFTM4yYmCrj5pAlf7MvuW3nQyzy5cz+IN1SzfvJPhvUv46SkTEiq+tvBcWfEsMCP9yO6WF+iOTJ48WWfPnt3VYgQCVeWNpVs4ynVXZCNPLVjHo7PXUFKQyx/PPgBVZyxGvFIQtY1NTLzueXoV5UX50NdV1nHJQ3OZt7qSaXsP4uYz9olSMqN+/CxAQkXa2NRMsyoNoWZ+OGM+daEwFx+9J0el2PgHhVC4mfeXb+OIsa3Tpo1dR0TmqGq7gdFAKQ5fOu6FS5cubXd/w0gHf3v9M6aOHxA1Vgacxu75jzbw+X0Gt1LCc1dv56N11a1KaBhGZ5KVisPDLA7DMIzUSVZx2MhxwzAMIyVMcRiGYRgpYYrDMAzDSAlTHIZhGEZKmOIwDMMwUsIUh2EYhpESpjgMwzCMlAiU4hCR6SJyd1VVVVeLYhiGEVgCOQBQRDYDq9rdMT79gC3t7hUs7Jyzg2w752w7X9j9cx6pqu3WsQmk4tgdRGR2MiMng4Sdc3aQbeecbecLnXfOgXJVGYZhGOnHFIdhGIaREqY4WnN3VwvQBdg5ZwfZds7Zdr7QSedsMQ7DMAwjJcziMAzDMFLCFIdhGIaREqY4XERkmogsEZFlIvLjrpYnHYjIcBF5VUQWi8hHInK5u76PiLwoIkvd/727WtaORkRyRWSeiDzjLo8Wkffdc/63iKQ+qXo3RkTKReQxEfnEvd9Tgn6fReQH7nO9SEQeFpGioN1nEblXRDaJyCLfurj3VRz+5LZpC0XkwI6SwxQHTqMC3AGcDEwEzhGRiV0rVVpoAq5U1b2Aw4BL3PP8MfCyqo4FXnaXg8blwGLf8i3AH9xz3g5c0CVSpY8/Av9T1QnAfjjnHtj7LCJDge8Dk1V1EpALnE3w7vP9wLSYdYnu68nAWPfvIuCvHSWEKQ6HQ4BlqrpcVRuBR4BTu1imDkdV16vqXPdzDU5jMhTnXB9wd3sAOK1rJEwPIjIM+Dxwj7sswLHAY+4ugTpnEekFHAX8A0BVG1W1koDfZyAPKBaRPKAEWE/A7rOqvgFsi1md6L6eCvyfOrwHlIvI4I6QwxSHw1BgjW+5wl0XWERkFHAA8D4wUFXXg6NcgAFdJ1lauB24Bmh2l/sClara5C4H7X7vAWwG7nPdc/eISA8CfJ9VdS1wG7AaR2FUAXMI9n32SHRf09aumeJwkDjrApunLCI9gf8AV6hqdVfLk05E5AvAJlWd418dZ9cg3e884EDgr6p6ALCTALml4uH69U8FRgNDgB44rppYgnSf2yNtz7kpDocKYLhveRiwrotkSSsiko+jNB5U1cfd1Rs9E9b9v6mr5EsDhwNfFJGVOC7IY3EskHLXpQHBu98VQIWqvu8uP4ajSIJ8n48HVqjqZlUNAY8DnyPY99kj0X1NW7tmisPhA2Csm4FRgBNUe6qLZepwXN/+P4DFqvp736angG+4n78BPNnZsqULVf2Jqg5T1VE49/UVVf0a8Cpwprtb0M55A7BGRMa7q44DPibA9xnHRXWYiJS4z7l3zoG9zz4S3dengPPc7KrDgCrPpbW72MhxFxE5Bacnmgvcq6o3dbFIHY6IHAG8CXxIi7//pzhxjhnACJwX8CxVjQ3AZTwicgxwlap+QUT2wLFA+gDzgK+rakNXyteRiMj+OMkABcBy4Js4HcXA3mcR+QXwFZzswXnAt3F8+oG5zyLyMHAMTvn0jcD1wEzi3FdXgf4FJwurFvimqs7uEDlMcRiGYRipYK4qwzAMIyVMcRiGYRgpYYrDMAzDSAlTHIZhGEZKmOIwDMMwUsIUhxFYRCQsIvN9f6O6WqaOREQOEBGv/tb5IvKXmO2vicjkNr7/iIiMTbecRvDIa38Xw8hY6lR1/0QbRSTPV8coE/kpcONufP+vODW8LuwYcYxswSwOI6twe+aPisjTwAvuuqtF5AN3zoJf+Pa9Vpw5Wl5y53e4yl0f6cmLSD+3nIk358etvmN9x11/jPsdb36MB93BWYjIwSLyjogsEJFZIlIqIm+6A/g8Od4WkX1jzqMU2FdVFyRxzl/0WV1LRGSFu+lN4HhfSQ7DSAp7YIwgUywi893PK1T1dPfzFJxGd5uInIgzX8EhOEXhnhKRo3AKA56NU0E4D5iLU221LS7AKetwsIgUAm+LyAvutgOAvXFqBb0NHC4is4B/A19R1Q/ccuh1OCO+zweuEJFxQKGqLoz5rcnAoph1X3GrA3iMAVDVp3BL6IjIDOB1d32ziCzDma+jvXMzjAimOIwgk8hV9aKv1MaJ7t88d7knjiIpBZ5Q1VoAEUmmdtmJwL4i4tVGKnOP1QjMUtUK91jzgVE4pb/Xq+oHAF6lYhF5FPi5iFwNfAtn8p5YBuOUTvfzb1W91FsQkdf8G0XkGpxrcodv9SacarKmOIykMcVhZCM7fZ8F+LWq3uXfQUSuIHEJ6iZa3LxFMce6TFWfjznWMYC/PlIY592TeL+hqrUi8iJOmfAv41gXsdTF/HabiMhxwFk4Ezz5KXKPZRhJYzEOI9t5HviWO0cJIjJURAYAbwCni0ixG0+Y7vvOSuAg9/OZMcf6rlu6HhEZ506glIhPgCEicrC7f6kv3nAP8CfggwSFCBfjuqLaQ0RGAncCX1bVWCUxDvgomeMYhodZHEZWo6oviMhewLtuvHoHTgXVuSLyb2A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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -406,7 +407,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 193\n"
+      "Starting experimental run with id: 272\n"
      ]
     }
    ],
@@ -421,7 +422,7 @@
     "    \n",
     "    for npts in [200, 400, 600, 800, 1000, 1200]:\n",
     "        osc.npts(npts)\n",
-    "        datasaver.add_result((osc.trace, osc.trace()),\n",
+    "        datasaver.add_result(*expand_setpoints_helper(osc.trace),\n",
     "                             (osc.npts, osc.npts()))\n",
     "        \n",
     "dataid = datasaver.run_id"
@@ -434,7 +435,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
diff --git a/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb b/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
index c64ea95bba8..9565fb9b818 100644
--- a/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
+++ b/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,11 +41,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qcodes.instrument.parameter import ParameterWithSetpoints, GeneratedSetPoints"
+    "from qcodes.instrument.parameter import ParameterWithSetpoints, GeneratedSetPoints\n",
+    "from qcodes.instrument.parameter import expand_setpoints_helper"
    ]
   },
   {
@@ -126,7 +127,7 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#67@C:\\Users\\jenielse/experiments.db\n",
+       "tutorial_exp#no sample#94@C:\\Users\\jenielse/experiments.db\n",
        "----------------------------------------------------------"
       ]
      },
@@ -176,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
@@ -187,7 +188,7 @@
        "501"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -199,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -208,7 +209,7 @@
        "array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -235,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -251,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
@@ -262,7 +263,7 @@
        "501"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -281,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,16 +314,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(<qcodes.instrument.parameter.GeneratedSetPoints: freq_axis at 1914952775384>,)"
+       "(<qcodes.instrument.parameter.GeneratedSetPoints: freq_axis at 2910773430144>,)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -340,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -363,14 +364,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 198\n"
+      "Starting experimental run with id: 273\n"
      ]
     }
    ],
@@ -379,7 +380,7 @@
     "meas.register_parameter(a.spectrum)\n",
     "\n",
     "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((a.spectrum, a.spectrum.get(),))\n",
+    "    datasaver.add_result(*expand_setpoints_helper(a.spectrum))\n",
     "    \n",
     "    dataid = datasaver.run_id"
    ]
@@ -393,22 +394,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1bddc6ef4e0>], [None])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x2a5b7a25390>], [None])"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -422,13 +423,6 @@
    "source": [
     "plot_by_id(dataid)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From f864ef48a544457e217cd30b071e00504b5aa40c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 18 Jan 2019 11:28:54 +0100
Subject: [PATCH 615/719] Add helper function to expand parameterWithSetPoints

---
 qcodes/instrument/parameter.py | 40 +++++++++++++++++++++++++++++++++-
 1 file changed, 39 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index a308e612497..f6005f69018 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -62,7 +62,7 @@
 import warnings
 import enum
 from typing import Optional, Sequence, TYPE_CHECKING, Union, Callable, List, \
-    Dict, Any, Sized, Iterable, cast, Type
+    Dict, Any, Sized, Iterable, cast, Type, Tuple
 from functools import partial, wraps
 import numpy
 from qcodes.utils.helpers import abstractmethod
@@ -1910,3 +1910,41 @@ def set_raw(self, value: Union[int, float]) -> None:
 
         self._save_val(value)
         self._wrapped_parameter.set(instrument_value)
+
+
+def expand_setpoints_helper(parameter: ParameterWithSetpoints,
+                            update_setpoints_from_inst: bool = True) -> List[
+        Tuple[_BaseParameter, numpy.ndarray]]:
+    """
+
+
+
+    Args:
+        parameter: A ParameterWithSetpoints to be acquired
+        update_setpoints_from_inst:
+    Returns:
+
+    """
+    if not isinstance(parameter, ParameterWithSetpoints):
+        raise TypeError(
+            f"Expanding setpoints only works for ParameterWithSetpoints."
+            f"Supplied a {type(parameter)}")
+    res = []
+    found_parameters = []
+    setpoint_params = []
+    setpoint_names = []
+    setpoint_data = []
+    for setpointparam in parameter.setpoints:
+        if update_setpoints_from_inst:
+            these_setpoints = setpointparam.get()
+        else:
+            these_setpoints = setpointparam.get_latest()
+        setpoint_names.append(setpointparam.full_name)
+        setpoint_params.append(setpointparam)
+        setpoint_data.append(these_setpoints)
+        found_parameters.append(setpointparam.full_name)
+    output_grids = numpy.meshgrid(*setpoint_data, indexing='ij')
+    for param, grid in zip(setpoint_params, output_grids):
+        res.append((param, grid))
+    res.append((parameter, parameter.get()))
+    return res

From 50b2cf79d240b57e9f290838528e102b063b8877 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 18 Jan 2019 11:29:31 +0100
Subject: [PATCH 616/719] adapt tests to new api

---
 .../dataset/test_measurement_context_manager.py  | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 5985f71fd99..39c154e4ce0 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -22,6 +22,7 @@
 from qcodes.instrument.parameter import ArrayParameter, Parameter
 from qcodes.dataset.legacy_import import import_dat_file
 from qcodes.dataset.data_set import load_by_id
+from qcodes.instrument.parameter import expand_setpoints_helper
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import (empty_temp_db,
                                                       experiment)
@@ -1004,7 +1005,7 @@ def test_datasaver_parameter_with_setpoints(channel_array_instrument,
         # we seed the random number generator
         # so we can test that we get the expected numbers
         np.random.seed(random_seed)
-        datasaver.add_result((param, param()))
+        datasaver.add_result(*expand_setpoints_helper(param))
     assert datasaver.points_written == n
 
     expected_params = ('dummy_channel_inst_ChanA_dummy_sp_axis',
@@ -1046,7 +1047,7 @@ def test_datasaver_scalar_parameter_with_setpoints(channel_array_instrument,
 
     # Now for a real measurement
     with meas.run() as datasaver:
-        datasaver.add_result((param, param()))
+        datasaver.add_result(*expand_setpoints_helper(param))
     assert datasaver.points_written == 1
 
     ds = load_by_id(datasaver.run_id)
@@ -1078,12 +1079,11 @@ def test_datasaver_parameter_with_setpoints_missing_reg_raises(
 
     # Now for a real measurement
     with meas.run() as datasaver:
-        with pytest.raises(RuntimeError, match=r'foo which is a setpoint '
-                                               r'parameter for dummy_channel_'
-                                               r'inst_ChanA_dummy_scalar_'
-                                               r'parameter_with_setpoints '
-                                               r'is not registered!'):
-            datasaver.add_result((param, param()))
+        with pytest.raises(ValueError, match=r'Can not add a result for foo, '
+                                               r'no such parameter registered '
+                                               r'in this measurement.'):
+            datasaver.add_result(*expand_setpoints_helper(param))
+
 
 @settings(max_examples=5, deadline=None)
 @given(N=hst.integers(min_value=5, max_value=500))

From 6087685818dfe7e178b4d13e8d4fd78f05b63020 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 18 Jan 2019 15:14:34 +0100
Subject: [PATCH 617/719] Implement validate on get option

---
 qcodes/instrument/parameter.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index f6005f69018..5e5d983ba7f 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -265,6 +265,7 @@ def __init__(self, name: str,
         # Specify time of last set operation, used when comparing to delay to
         # check if additional waiting time is needed before next set
         self._t_last_set = time.perf_counter()
+        self._validate_on_get = False
 
     @abstractmethod
     def get_raw(self):
@@ -409,7 +410,7 @@ def get_wrapper(*args: Any, **kwargs: Any) -> ParamDataType:
                             value = self.inverse_val_mapping[int(value)]
                         except (ValueError, KeyError):
                             raise KeyError("'{}' not in val_mapping".format(value))
-                self._save_val(value)
+                self._save_val(value, validate=self._validate_on_get)
                 return value
             except Exception as e:
                 e.args = e.args + ('getting {}'.format(self),)
@@ -949,14 +950,21 @@ class ParameterWithSetpoints(Parameter):
     In all other ways this is identical to  :class:`Parameter` See the
     documentation of :class:`Parameter` for more details.
     """
-    def __init__(self, *args,
-                 setpoints: Optional[Sequence[_BaseParameter]]=None,
+
+    def __init__(self, name: str, *,
+                 vals: Validator = None,
+                 setpoints: Optional[Sequence[_BaseParameter]] = None,
+                 snapshot_get: bool = False,
+                 snapshot_value: bool = False,
                  **kwargs) -> None:
+        super().__init__(name=name, vals=vals, snapshot_get=snapshot_get,
+                         snapshot_value=snapshot_value, **kwargs)
         if setpoints is None:
             self._setpoints: Sequence[_BaseParameter] = []
         else:
             self.setpoints = setpoints
-        super().__init__(*args, **kwargs)
+
+        self._validate_on_get = True
 
     @property
     def setpoints(self):

From 302ed80af2c03293af369d3ed4850541b066a8d0 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 18 Jan 2019 15:39:20 +0100
Subject: [PATCH 618/719] Add logging to val

---
 qcodes/instrument/parameter.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 5e5d983ba7f..2b20adc92c6 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -83,6 +83,9 @@
 # for now the type the parameter may contain is not restricted at all
 ParamDataType = Any
 
+
+log = logging.getLogger(__name__)
+
 class _SetParamContext:
     """
     This class is returned by the set method of parameters
@@ -527,7 +530,7 @@ def get_ramp_values(self, value: Union[float, int, Sized],
                 # isn't, even though it's valid.
                 # probably MultiType with a mix of numeric and non-numeric types
                 # just set the endpoint and move on
-                logging.warning(
+                log.warning(
                     'cannot sweep {} from {} to {} - jumping.'.format(
                         self.name, start_value, value))
                 return []
@@ -1016,6 +1019,8 @@ def validate_consistent_shape(self) -> None:
             raise ValueError(f"Shape of output is not consistent with "
                              f"setpoints. Output is shape {output_shape} and "
                              f"setpoints are shape {setpoints_shape}")
+        log.info(f"For parameter {self.full_name} verified "
+                    f"that {output_shape} matches {setpoints_shape}")
 
     def validate(self, value: ParamDataType) -> None:
         """

From 34820c60e623c0ffbdbb25fb737630c643c52116 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 18 Jan 2019 15:41:05 +0100
Subject: [PATCH 619/719] correct indent

---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 2b20adc92c6..1779173ae7e 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1020,7 +1020,7 @@ def validate_consistent_shape(self) -> None:
                              f"setpoints. Output is shape {output_shape} and "
                              f"setpoints are shape {setpoints_shape}")
         log.info(f"For parameter {self.full_name} verified "
-                    f"that {output_shape} matches {setpoints_shape}")
+                 f"that {output_shape} matches {setpoints_shape}")
 
     def validate(self, value: ParamDataType) -> None:
         """

From d135ac531f98ca5c5a8c6b43c115d96dd5c414fb Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 18 Jan 2019 16:28:41 +0100
Subject: [PATCH 620/719] Rewrite to not shadow variable name

---
 qcodes/dataset/measurements.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index 6b3a3a06a36..31884124dd4 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -767,19 +767,19 @@ def _register_parameter_with_setpoints(self,
         """
         name = str(parameter)
         my_setpoints = list(setpoints) if setpoints else []
-        for setpoints in parameter.setpoints:
-            if not isinstance(setpoints, Parameter):
+        for sp in parameter.setpoints:
+            if not isinstance(sp, Parameter):
                 raise RuntimeError("The setpoints of a "
                                    "ParameterWithSetpoints "
                                    "must be a Parameter")
-            spname = setpoints.full_name
-            splabel = setpoints.label
-            spunit = setpoints.unit
+            spname = sp.full_name
+            splabel = sp.label
+            spunit = sp.unit
 
-            sp = ParamSpec(name=spname, paramtype=paramtype,
-                           label=splabel, unit=spunit)
+            spparamspec = ParamSpec(name=spname, paramtype=paramtype,
+                                    label=splabel, unit=spunit)
 
-            self.parameters[spname] = sp
+            self.parameters[spname] = spparamspec
 
             my_setpoints += [spname]
 

From 9e301e5525dbed166f8c37b1ebf6f049e1ca06b6 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 18 Jan 2019 16:33:58 +0100
Subject: [PATCH 621/719] Correct typeinfo in parameter clases

---
 qcodes/instrument/parameter.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 1779173ae7e..7fe89127d31 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -963,14 +963,14 @@ def __init__(self, name: str, *,
         super().__init__(name=name, vals=vals, snapshot_get=snapshot_get,
                          snapshot_value=snapshot_value, **kwargs)
         if setpoints is None:
-            self._setpoints: Sequence[_BaseParameter] = []
+            self.setpoints: Sequence[_BaseParameter] = []
         else:
             self.setpoints = setpoints
 
         self._validate_on_get = True
 
     @property
-    def setpoints(self):
+    def setpoints(self) -> Sequence[_BaseParameter]:
         return self._setpoints
 
     @setpoints.setter

From c94ab973bbbb8174ed682d8ecfd74edd92cc688a Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Mon, 21 Jan 2019 17:37:47 +1100
Subject: [PATCH 622/719] Use getattr to get ask/write from instruments

This allows us to use InstrumentBase as the base class for
virtual instruments
---
 qcodes/instrument/parameter.py | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 4fccca40e4d..2b62b14d20b 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -520,9 +520,8 @@ def get_ramp_values(self, value: Union[float, int, Sized],
                 # isn't, even though it's valid.
                 # probably MultiType with a mix of numeric and non-numeric types
                 # just set the endpoint and move on
-                logging.warning(
-                    'cannot sweep {} from {} to {} - jumping.'.format(
-                        self.name, start_value, value))
+                logging.warning('cannot sweep %s from %r to %r - jumping.',
+                                self.name, start_value, value)
                 return []
 
             # drop the initial value, we're already there
@@ -851,7 +850,7 @@ def __init__(self, name: str,
                                       'when max_val_age is set')
                 self.get_raw = lambda: self._latest['raw_value']
             else:
-                exec_str_ask = instrument.ask if instrument else None
+                exec_str_ask = getattr(instrument, "ask", None) if instrument else None
                 self.get_raw = Command(arg_count=0, cmd=get_cmd, exec_str=exec_str_ask)
             self.get = self._wrap_get(self.get_raw)
 
@@ -859,7 +858,7 @@ def __init__(self, name: str,
             if set_cmd is None:
                 self.set_raw = partial(self._save_val, validate=False)# type: Callable
             else:
-                exec_str_write = instrument.write if instrument else None
+                exec_str_write = getattr(instrument, "write", None) if instrument else None
                 self.set_raw = Command(arg_count=1, cmd=set_cmd, exec_str=exec_str_write)# type: Callable
             self.set = self._wrap_set(self.set_raw)
 
@@ -1593,10 +1592,10 @@ def __init__(self, name, instrument=None,
                  delay=0, max_delay=None, step=None, max_val_age=3600,
                  vals=None, val_mapping=None, **kwargs):
         super().__init__(name, instrument=instrument,
-                 get_cmd=get_cmd, get_parser=get_parser,
-                 set_cmd=set_cmd, set_parser=set_parser,
-                 post_delay=delay, step=step, max_val_age=max_val_age,
-                 vals=vals, val_mapping=val_mapping, **kwargs)
+                         get_cmd=get_cmd, get_parser=get_parser,
+                         set_cmd=set_cmd, set_parser=set_parser,
+                         post_delay=delay, step=step, max_val_age=max_val_age,
+                         vals=vals, val_mapping=val_mapping, **kwargs)
         warnings.warn('`StandardParameter` is deprecated, '
                         'use `Parameter` instead. {}'.format(self))
 

From 345a87572b9550e7a8cbb0970171bf0cf5ce0452 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 10:15:31 +0100
Subject: [PATCH 623/719] Enforce validator for ParameterWithSetpoints

---
 qcodes/instrument/parameter.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 7fe89127d31..74a536eeac2 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -960,6 +960,14 @@ def __init__(self, name: str, *,
                  snapshot_get: bool = False,
                  snapshot_value: bool = False,
                  **kwargs) -> None:
+
+        if not isinstance(vals, Arrays):
+            raise ValueError(f"A ParameterWithSetpoints must have an Arrays "
+                             f"validator got {type(vals)}")
+        if vals.shape is None:
+            raise RuntimeError("A ParameterWithSetpoints must have a shape "
+                               "defined for its validator.")
+
         super().__init__(name=name, vals=vals, snapshot_get=snapshot_get,
                          snapshot_value=snapshot_value, **kwargs)
         if setpoints is None:

From 4f7a2f9b674c88c524c3aed2196e7bf9f54092b7 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 10:22:15 +0100
Subject: [PATCH 624/719] Change tests to match new api

---
 qcodes/tests/test_parameter_with_setpoints.py | 40 ++++++++-----------
 1 file changed, 17 insertions(+), 23 deletions(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index c6f8cfe1afd..0a7961f1c33 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -159,18 +159,16 @@ def test_validation_no_validator():
     setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
                             vals=vals.Arrays(shape=(n_points_1,)))
     # output does not have a validator
-    param_without_validator = ParameterWithSetpoints('param_3',
-                                                     get_cmd=lambda:
-                                                     rand(n_points_1()),
-                                                     setpoints=(setpoints_1,))
 
-    expected_err_msg = (r"Can only validate shapes for "
-                        r"parameters with Arrays validator. "
-                        r"param_3 does not")
-    with pytest.raises(ValueError, match=expected_err_msg):
-        param_without_validator.validate_consistent_shape()
-    # this does not raise as the shape is only validated for arrays
-    param_without_validator.validate(param_without_validator.get())
+    with pytest.raises(ValueError, match=r"A ParameterWithSetpoints must have "
+                                         r"an Arrays validator got "
+                                         r"<class 'NoneType'>"):
+        param_without_validator = ParameterWithSetpoints('param_3',
+                                                         get_cmd=lambda:
+                                                         rand(n_points_1()),
+                                                         setpoints=(
+                                                         setpoints_1,))
+
 
 
 def test_validation_sp_no_validator():
@@ -210,18 +208,14 @@ def test_validation_without_shape():
     n_points_1.set(10)
     setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
                             vals=vals.Arrays(shape=(n_points_1,)))
-    param_without_shape = ParameterWithSetpoints('param_5',
-                                                 get_cmd=lambda:
-                                                 rand(n_points_1()),
-                                                 setpoints=(setpoints_1,),
-                                                 vals=vals.Arrays())
-    expected_err_msg = (r"Trying to validate shape but "
-                        r"parameter param_5 does not "
-                        r"define a shape")
-    with pytest.raises(ValueError, match=expected_err_msg):
-        param_without_shape.validate_consistent_shape()
-    with pytest.raises(ValueError, match=expected_err_msg):
-        param_without_shape.validate(param_without_shape.get())
+    with pytest.raises(RuntimeError, match=r"A ParameterWithSetpoints must "
+                                           r"have a shape defined "
+                                           r"for its validator."):
+        param_without_shape = ParameterWithSetpoints('param_5',
+                                                     get_cmd=lambda:
+                                                     rand(n_points_1()),
+                                                     setpoints=(setpoints_1,),
+                                                     vals=vals.Arrays())
 
 
 def test_validation_without_sp_shape():

From c5d2881be822241e33c84388a7b960be0be11c83 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 10:22:42 +0100
Subject: [PATCH 625/719] Remove now no longer allowed parameter type

---
 qcodes/tests/instrument_mocks.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/qcodes/tests/instrument_mocks.py b/qcodes/tests/instrument_mocks.py
index 42f21341993..8a02ffebaed 100644
--- a/qcodes/tests/instrument_mocks.py
+++ b/qcodes/tests/instrument_mocks.py
@@ -188,14 +188,6 @@ def __init__(self, parent, name, channel):
                            vals=Arrays(shape=(self.dummy_n_points,)),
                            parameter_class=DummyParameterWithSetpoints1D)
 
-        self.add_parameter(name='dummy_scalar_parameter_with_setpoints',
-                           label='Dummy Scalar Parameter with Setpoints',
-                           unit='some other unit',
-                           setpoints=(self.dummy_start,),
-                           vals=Numbers(),
-                           get_cmd=lambda: 51,
-                           parameter_class=ParameterWithSetpoints)
-
         self.add_function(name='log_my_name',
                           call_cmd=partial(log.debug, f'{name}'))
 

From f08bdb3bada206fa46265f35938861eafd616cde Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 10:25:24 +0100
Subject: [PATCH 626/719] Update tests to match new api

---
 .../test_measurement_context_manager.py       | 60 ++++++-------------
 1 file changed, 17 insertions(+), 43 deletions(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 39c154e4ce0..06981122d15 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -23,6 +23,7 @@
 from qcodes.dataset.legacy_import import import_dat_file
 from qcodes.dataset.data_set import load_by_id
 from qcodes.instrument.parameter import expand_setpoints_helper
+from qcodes.utils.validators import Arrays
 # pylint: disable=unused-import
 from qcodes.tests.dataset.temporary_databases import (empty_temp_db,
                                                       experiment)
@@ -1029,59 +1030,32 @@ def test_datasaver_parameter_with_setpoints(channel_array_instrument,
                     np.random.rand(n))
 
 
-@pytest.mark.usefixtures("experiment")
-def test_datasaver_scalar_parameter_with_setpoints(channel_array_instrument,
-                                                   DAC):
-    chan = channel_array_instrument.A
-    param = chan.dummy_scalar_parameter_with_setpoints
-
-    meas = Measurement()
-    meas.register_parameter(param)
-
-    assert len(meas.parameters) == 2
-    dependency_name = 'dummy_channel_inst_ChanA_dummy_start'
-
-    assert meas.parameters[str(param)].depends_on == dependency_name
-    assert meas.parameters[str(param)].type == 'numeric'
-    assert meas.parameters[dependency_name].type == 'numeric'
-
-    # Now for a real measurement
-    with meas.run() as datasaver:
-        datasaver.add_result(*expand_setpoints_helper(param))
-    assert datasaver.points_written == 1
-
-    ds = load_by_id(datasaver.run_id)
-    data = ds.get_parameter_data()
-    assert len(data) == 1
-    subdata = data[
-        'dummy_channel_inst_ChanA_dummy_scalar_parameter_with_setpoints']
-    assert len(subdata) == 2
-    assert subdata[
-               'dummy_channel_inst_ChanA_dummy_scalar_parameter_with_setpoints'] == np.array(
-        [51])
-    assert subdata['dummy_channel_inst_ChanA_dummy_start'] == np.array([0])
-
-
 @pytest.mark.usefixtures("experiment")
 def test_datasaver_parameter_with_setpoints_missing_reg_raises(
         channel_array_instrument,
         DAC):
+    """
+    Test that if for whatever reason the setpoints are changed between
+    registering and adding this raises correctly
+    """
     chan = channel_array_instrument.A
-    param = chan.dummy_scalar_parameter_with_setpoints
+    param = chan.dummy_parameter_with_setpoints
+    chan.dummy_n_points(11)
+    chan.dummy_start(0)
+    chan.dummy_stop(10)
 
+    old_setpoints = param.setpoints
+    param.setpoints = ()
     meas = Measurement()
     meas.register_parameter(param)
 
-    myparam = Parameter('foo', set_cmd=None)
-    myparam.set(1)
-    setpoints = (param.setpoints[0], myparam)
-    param.setpoints = setpoints
-
-    # Now for a real measurement
+    param.setpoints = old_setpoints
     with meas.run() as datasaver:
-        with pytest.raises(ValueError, match=r'Can not add a result for foo, '
-                                               r'no such parameter registered '
-                                               r'in this measurement.'):
+        with pytest.raises(ValueError, match=r'Can not add a result for dummy_'
+                                             r'channel_inst_ChanA_dummy_'
+                                             r'sp_axis,'
+                                             r' no such parameter registered '
+                                             r'in this measurement.'):
             datasaver.add_result(*expand_setpoints_helper(param))
 
 

From 509db0576fdb2cf53917a6c6527366d17f290be2 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 11:01:32 +0100
Subject: [PATCH 627/719] Add missing whitespace

---
 qcodes/utils/validators.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/utils/validators.py b/qcodes/utils/validators.py
index 69c945482b7..70fb7155c80 100644
--- a/qcodes/utils/validators.py
+++ b/qcodes/utils/validators.py
@@ -516,7 +516,7 @@ def __init__(self, min_value: numbertypes = -float("inf"),
             raise TypeError('max_value must be a number bigger than min_value')
         if not isinstance(shape,
                           collections.abc.Sequence) and shape is not None:
-            raise ValueError(f"Shape must be a sequence (List, Tuple ...)"
+            raise ValueError(f"Shape must be a sequence (List, Tuple ...) "
                              f"got a {type(shape)}")
         self._shape = shape
 

From 787437a65442121583eb9947c0c56d4d6958eaf0 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 12:05:17 +0100
Subject: [PATCH 628/719] Make deferred shape argument not executed in repr

---
 qcodes/tests/test_parameter_with_setpoints.py | 2 ++
 qcodes/utils/validators.py                    | 5 ++++-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index 0a7961f1c33..c2593b43fdc 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -28,6 +28,8 @@ def test_validation_shapes():
                                                     setpoints=(setpoints_1,),
                                                     vals=vals.Arrays(
                                                         shape=(n_points_1,)))
+    assert "<Arrays, shape: (<qcodes.instrument.parameter." \
+           "Parameter: n_points_1 at" in param_with_setpoints_1.__doc__
 
     # the two shapes are the same so validation works
     param_with_setpoints_1.validate_consistent_shape()
diff --git a/qcodes/utils/validators.py b/qcodes/utils/validators.py
index 70fb7155c80..bc36e39b74e 100644
--- a/qcodes/utils/validators.py
+++ b/qcodes/utils/validators.py
@@ -582,8 +582,11 @@ def validate(self, value: np.ndarray, context: str = '') -> None:
     def __repr__(self) -> str:
         minv = self._min_value if math.isfinite(self._min_value) else None
         maxv = self._max_value if math.isfinite(self._max_value) else None
+        # we don't want the repr to execute any deferred shape argument
+        # so we directly use _shape
         return '<Arrays{}, shape: {}>'.format(range_str(minv, maxv, 'v'),
-                                              self.shape)
+                                              self._shape)
+
 
 
 class Lists(Validator):

From 558efea6fce2cd909265a1bbe62a1485283569f9 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 13:14:07 +0100
Subject: [PATCH 629/719] Make shape not actually evaluate any callable but add
 a new property that does

---
 qcodes/utils/validators.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/qcodes/utils/validators.py b/qcodes/utils/validators.py
index bc36e39b74e..c20f42a2ea2 100644
--- a/qcodes/utils/validators.py
+++ b/qcodes/utils/validators.py
@@ -526,12 +526,16 @@ def valid_values(self) -> Tuple[np.ndarray]:
         if shape is None:
             return (np.array([self._min_value]),)
         else:
-            val_arr = np.empty(shape)
+            val_arr = np.empty(self.shape_evaluated)
             val_arr.fill(self._min_value)
             return (val_arr,)
 
     @property
     def shape(self) -> Optional[Tuple[int, ...]]:
+        return self._shape
+
+    @property
+    def shape_evaluated(self) -> Optional[Tuple[int, ...]]:
         if self._shape is None:
             return None
         shape_array = []
@@ -553,7 +557,7 @@ def validate(self, value: np.ndarray, context: str = '') -> None:
             raise TypeError(
                 '{} is not an int or float; {}'.format(repr(value), context))
         if self.shape is not None:
-            shape = self.shape
+            shape = self.shape_evaluated
             if np.shape(value) != shape:
                 raise ValueError(
                     f'{repr(value)} does not have expected shape {shape},'
@@ -585,7 +589,7 @@ def __repr__(self) -> str:
         # we don't want the repr to execute any deferred shape argument
         # so we directly use _shape
         return '<Arrays{}, shape: {}>'.format(range_str(minv, maxv, 'v'),
-                                              self._shape)
+                                              self.shape)
 
 
 

From 88cddfdf25d0ee31e3643759134c366e19bdbb2b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 13:14:39 +0100
Subject: [PATCH 630/719] Change tests to reflect that shapes are no longer
 evaluated when not needed

---
 qcodes/tests/test_parameter_with_setpoints.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index c2593b43fdc..def67b65823 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -111,8 +111,9 @@ def test_validation_inconsistent_shape():
     # inconsistent shapes
     expected_err_msg = (r'Shape of output is not consistent '
                         r'with setpoints. Output is shape '
-                        r'\(20,\) and setpoints are shape '
-                        r'\(10,\)')
+                        r'\(<qcodes.instrument.parameter.Parameter: n_points_2 at [0-9]+>,\) '
+                        r'and setpoints are shape '
+                        r'\(<qcodes.instrument.parameter.Parameter: n_points_1 at [0-9]+>,\)')
     with pytest.raises(ValueError, match=expected_err_msg):
         param_with_diff_lenght.validate_consistent_shape()
     with pytest.raises(ValueError, match=expected_err_msg):
@@ -240,7 +241,8 @@ def test_validation_without_sp_shape():
                                                         shape=(
                                                             n_points_1,)))
     expected_err_msg = (r"One or more dimensions have unknown shape "
-                        r"when comparing output: \(10,\) to setpoints: "
+                        r"when comparing output: \(<qcodes.instrument.parameter"
+                        r".Parameter: n_points_1 at [0-9]+>,\) to setpoints: "
                         r"\(None,\)")
     with pytest.raises(ValueError, match=expected_err_msg):
         param_sp_without_shape.validate_consistent_shape()
@@ -268,8 +270,8 @@ def test_validation_one_dim_missing():
                                                         shape=(
                                                             n_points_1, None)))
     expected_err_msg = (r"One or more dimensions have unknown shape "
-                        r"when comparing output: \(10, None\) to setpoints: "
-                        r"\(10, 20\)")
+                        r"when comparing output: \(<qcodes.instrument.parameter.Parameter: n_points_1 at [0-9]+>, None\) to setpoints: "
+                        r"\(<qcodes.instrument.parameter.Parameter: n_points_1 at [0-9]+>, <qcodes.instrument.parameter.Parameter: n_points_2 at [0-9]+>\)")
     with pytest.raises(ValueError, match=expected_err_msg):
         param_sp_without_shape.validate_consistent_shape()
     with pytest.raises(ValueError, match=expected_err_msg):
@@ -296,8 +298,8 @@ def test_validation_one_sp_dim_missing():
                                                             n_points_1,
                                                             n_points_2)))
     expected_err_msg = (r"One or more dimensions have unknown shape "
-                        r"when comparing output: \(10, 20\) to setpoints: "
-                        r"\(10, None\)")
+                        r"when comparing output: \(<qcodes.instrument.parameter.Parameter: n_points_1 at [0-9]+>, <qcodes.instrument.parameter.Parameter: n_points_2 at [0-9]+>\) to setpoints: "
+                        r"\(<qcodes.instrument.parameter.Parameter: n_points_1 at [0-9]+>, None\)")
     with pytest.raises(ValueError, match=expected_err_msg):
         param_sp_without_shape.validate_consistent_shape()
     with pytest.raises(ValueError, match=expected_err_msg):

From 96c56b0b0a1855b92d6dc2c86f1014e5837dfa2d Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 13:15:36 +0100
Subject: [PATCH 631/719] Update Signal Hound driver to reflect changes in api

---
 ...Hound USB-SA124B new Array Parameter.ipynb | 392 ------------------
 .../signal_hound/USB_SA124B.py                |  17 +-
 2 files changed, 12 insertions(+), 397 deletions(-)
 delete mode 100644 docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb

diff --git a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb
deleted file mode 100644
index 6d024cfbd5c..00000000000
--- a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B new Array Parameter.ipynb	
+++ /dev/null
@@ -1,392 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Qcodes example with Signal Hound USB-SA124B new Array Parameter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import qcodes as qc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.instrument_drivers.signal_hound.USB_SA124B import SignalHound_USB_SA124B\n",
-    "from qcodes.dataset.measurements import Measurement\n",
-    "from qcodes.dataset.plotting import plot_by_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connected to: Signal Hound sa124B (serial:17172185, firmware:Version 3.13) in 5.92s\n"
-     ]
-    }
-   ],
-   "source": [
-    "mysa = SignalHound_USB_SA124B('mysa', dll_path='C:\\\\Program Files\\\\Signal Hound\\\\Spike\\\\sa_api.dll')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'vendor': 'Signal Hound',\n",
-       " 'model': 'sa124B',\n",
-       " 'serial': '17172185',\n",
-       " 'firmware': 'Version 3.13'}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mysa.get_idn()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1054\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([-91.55989838, -98.06058502, -95.64240265, ..., -91.06426239,\n",
-       "       -91.20920563, -91.69102478])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mysa.freq_sweep()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mysa.frequency(2e9)\n",
-    "mysa.span(0.5e6)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 937\n",
-      "2107\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x15228190358>], [None])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.avg(1)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 938\n",
-      "2107\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x152281a7c50>], [None])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.avg(10)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 939\n",
-      "2107\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x152281f2278>], [None])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.avg(100)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 940\n",
-      "4215\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1522960fcc0>], [None])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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GYRQAxknKYDAUA0ZhGAwGgyEQRmEYDAaDIRBGYSSIGsdag8FQRFQkmbiItAUeBvoA24ELVPVje98iYBNQBVSq6vCk5IwKM+htMBiKiUQVBnAtMENVTxGR/sBY4CjX/m+q6upkRIsPozgMBkMxkLRJaiAwEUBV5wE9RaRTsiIZDAaDwY+kFcZM4FQAERkJ9AC62vsUeFVEponIxekuICIXi8hUEZm6atWqyAUOE9OxMBgMxURghSEiLUSkPOT0bwfaisgM4HJgOlBp7ztEVfcDjgMuFZHD/S6gqg+q6nBVHd6hQ4eQxYsHozgMBkMxkFZhiEiZiJwlIi+JyEpgHrBcROaIyF0i0i+fBEXkUhGZYSuJlqp6vqoOA84FOgALAVT1K/t7JfAcMDKf9AwGg8EQDpl6GG9geS9dA+yhqt1UtSNwGPAecLuIfC/XBFV1rKoOs5XEVhFpbO+6EJisqhvt3kwrsHo2wDHAx7mmVeiYwW6DwVBMZPKSGqWqu7wbVXUt8AzwjIg0qmf6A4BHRaQK+AT4gb29E/CcvbBQBfC4qo6vZ1oFi1l5z2AwFANpFYZbWYjIoUA/Vf27iHTAMiUt9FMouaCqU4A6pi1VXQAMrc+1DQaDwRAuWQe9ReRG4Cos0xRAI+CfUQplMBgMhsIjiJfUKcC3gS1QMxjdKkqhSg1jkTIYDMVAEIWxUy0ju0LNILQhFIymMBgMxUMQhfFvEfkL0EZELgImAH+NViyDwWAwFBpZY0mp6u9F5GhgI7A3cIOqvha5ZAaDwWAoKAIFH7QVRINWEqrK09OWcuLQzjRtFPaE9nRpxpKMwWAwhEJahSEim8hgZFfV1pFIlBCTP1/NL5+exZyvNnLTt/dJWhyDwWAoODLNw3BmWt8MrAD+AQhwNg3QS2rHrioAlq7bFnvapqdhMBiKgSCD3t9S1ftVdZOqblTVB4AxUQsWN40rrEexo7IqYUkMBoOhMAmiMKpE5GwRKbcDEp6NtQpeg6KizHoUVdXxNfdNx8JgMBQTQRTGWcDpwNfASuA79rYGha0v4lUYxhZlMBiKiCButYuAk6IXJVmS6GE4qOlrGAyGIiBILKmuIvKciKwUka9F5BkR6ZrtvGKjvEwAqExAYRgMBkMxEMQk9XfgBaAz0AX4r73NUE+MajLEwcbtu5j06cqkxTA0AIIojA6q+ndVrbQ/47BWxjOEhBnKMETJZY9P57y/f8iqTTuSFsVQ5ARRGKtF5Hu2l1S5vcremqgFMxgM4TD/602AcRk31J8gCuMCLC+pFcBy4DR7W4PEWuQvHkzPwhAnEmfmLgCqzXhk6ATxklqMtR5GAye5zGWytSEOSqkCveQf0xg/ZwWLbh+dtCgNiiBeUr1E5G4ReVZEXnA+cQhnMITNglWbOXnsO2zcXq/VhYsKp2eRhMt4UoyfsyJpERokQaLV/gd4CMs7qjpacQyGaLlnwufMWLKeN+at5KRhXZIWJxYcS1TpqAtDVARRGNtV9b7IJSkQkrDymhnfhjgw+cxQX4IojHtF5EbgVaDGL09VP4pMqhLBFOD4cRoEpfToTQ/DEBZBFMZg4BzgSGpNUmr/rxci0hZ4GOgDbAcuUNWP7X1tgL8Bg+z0LlDVKfVN01BLZVU1c5dvYnDX3ZIWJTZqK8/Sqz5LSUkaoiGIwjgF6K2qOyNI/1pghqqeIiL9gbHAUfa+e4HxqnqaiDQGmkeQfg1JFqakkv7DhM/50xvzeemKQ9mnc2kojVLsYdRSkjdtCJEg8zBmAm0iSn8gMBFAVecBPUWkk4i0Bg7HGmxHVXeq6vqIZEiMpIvv7GUbAFi5sXRmADseQ6WkMITSu2dDNATpYXQC5onIh6SOYYQxN2MmcCrwtoiMBHoAXbHW21gF/F1EhgLTgCtVdYv3AiJyMXAxQPfu3UMQqXQoRfNMTQ8jUSnixYxhGMIiiMK4McL0b8caVJ8BzAamA5VAI2A/4HJVfV9E7gWuBq73XkBVHwQeBBg+fHi9y0QSs2GTavmV1rxfmxK8aeeWq00Xw1BPgsz0fjPMBEXkUuAi++/xqnq+vV2AhfanObBUVd+3j3saS2E0LAqk/JZiPVKKHmoleMuGkAkyhhEqqjpWVYep6jBgqz2gDXAhMNleN3wFsERE9rb3HQV8EqlcUV68QCm12ELgsucnLEeclOK4jSEagpikomQA8KiIVGEphB+49l0OPGYrlAXA+VEKkkRhKpTyW0oViZTiIIZNKY1VGaIhUYVhz6vol2bfDGB4vBIlRTIFuYTrzpIcyyilhoGDqpZkTzoqsioMETkEuAnLg6kCq6ipqvaOVrR4KUWbdimWoxJ8zaWoG2tQLc18HhVBehgPAT/Fcm1tsCuwOPVInHmrUJRUocgRB45ZpqTqEPtmS9FLqvTuOFqCKIwNqvq/yCUpYZIrxyVVbZYspTy73WoMmXweFkEUxhsichfwLA04+GApFiaHkrp1+2aNXbs0KKm8HQNBFMYB9rd7ADqU4IOFRBIeJEln5poZwEkLkgClqC5K8DWXpBkuSoJM3PtmHIIkTgkGHyzFSrMUKeUq0+iLcAmyROtu9hKtU+3P/4lIaYQ2beCs22oFIF61aXvCkhgMhmIgyEzvh4FNwOn2ZyPw9yiFSoIkGiJJt34alVuv/6sNpaMwSrnBWUrecA4leMuREkRh9FHVG1V1gf35DdCg5mBAbcZKYiw0qUy9V6dWAHRs1SQZARLEjHmXBmZ2e7gEURjbRORQ5489kW9bdCIZ4qJRuVVr7qysznKkoSFQilWn6WGESxAvqUuw4j054xbrgO9HJ1IylGJLpExKLxBfKZplShnztsMliJfUTGCovQoeqroxcqkSINklWk22jptSNEmVoq40brXhEjj4YENVFEmSuKIo4XkYJUUJv1+Tt8Ml9vUwCpVSzFe1a0M07Lt/94vVvPX5KqA033NJY154qCS9HkbB4Ni2JcbpbKb1Ew9n/dVauHHR7aNrtsX5nguH0stwDb0xFDdBJu59R0Ra2b+vE5FnRWS/6EUrHZJWHEmnbzBEhcnb4RLEJHW9qm6yXWu/BTwCPBCtWPFTivnKDPyWFqV47yV4y5ESRGE4a2CMBh5Q1eeBxhmOL05KeInWUqJm3ZMSVJalSFxu1E98sJjPvt4US1pJEkRhLBORv2CFBXlZRJoEPM8QkKRafrXrJBjVVQrE+Za376ri3gmfs6My2TXXqmO66aufnc0x90yOJ7EECVLxnw68AhyrquuB3YFfRipVApTi4FgptrJLUTkmcccPv7OQeyZ8xrh3FiWQei2lWK6jJK3CEJHdRWR3oCkwCVhj/98BTI1HvPioqUdiXaM1xrQyUIJ1qFlAKWKccDPTF6/ntpfnJqeoSzBvR0mmHsY0LMUwDVgFfAZ8bv+eFr1opUNSraDaeRjxo6oc+4fJ/HfmVwmkXprEWWc7kZDHz1nBg5MXsGrzjixnREOYt/zlmi1858/vsnH7rhCvWlykVRiq2ktVe2OZo05U1faq2g44AWu51nojIm1F5DkRmSUiH4jIIHv73iIyw/XZKCI/CSPNdJRmKzu5tHdVKfNWbOJn/54BwCdfbeQfUxZFnm6Ur3nlxu1UxWU0LzB2VFax2qUUnMCWSRNmuf7DhM/5cNE6XpvzdXgXLTKCjGGMUNWXnT+q+j/giJDSvxaYoapDgHOBe+00PlXVYao6DNgf2Ao8F1KaviTSyvakWl2tPD9jWeyVTtzKcu7yjVww7kPAUhwAx9/3Ftc/Pyc2GcKuztZs3sHI2yZyx/h5IV85PKI0C1362EcMv2VCzf8mFeWexCNLOiNh9t5rnERCu2LxEURhrLYn7PUUkR4i8mtgTUjpDwQmAqjqPKCniHTyHHMU8IWqfhlSmr7UzvSOjvVbd7J9V12vEaccPzt9GVc+MYOH314YoRS1JNUGPO7et3h7/uqEUo+G1Zut1Qtf+6T0Wp/TvlzHhLkrU7Y1bVQYjpSh6sia2GvZL/qvDxYzY8n6EBMvDIK81TOBDlgt/P8AHe1tYTATOBVAREYCPYCunmPOAP6V7gIicrGzfOyqVatCEisaht38Gt/585S0+zfZttFl6+NdbiRpTxInzhPAqk076Hn1S7y/IKw2STz85EnLtFZZXc3mHZVc95/ZbN1ZmbBUqUTxljfvqGTMA+/W2d64IrVqSSqHhasvUptYS9ZuZeaS9b4K5JpnZ3Py2HdCTL0wyKowVHWtql6pqvvanytVdW1I6d8OtBWRGcDlwHSgppSJSGPg28BTGeR7UFWHq+rwDh065C1IXBl69rINtWl6Eq2wBwp3VlWzaPUWel79ElMXhfWok2fmkvW+leg5D31Q89u5379H5Y4Z0Yueu9wK5lxdDX+e9AX/fG8xj7wbaac4MFGaonalWXyrUGJ1VUdo3j3szjc4aew7JTX+mTX4oIjsBfwC6Ok+XlWPzCdBEbkUuMj+e7yqnm9vF2Ch/XE4DvhIVSPv58f90mcv3UClJzM7RWzR6i28ZZtsnp2+jOE9d49XuAhYt2UnJ419h2/t47U4phLXa4hqwL+sDKrszFRoazFEIY73Oaoqva552f/gBoL3MRbae46SICapp7Ba/tdhTdhzPnmhqmNdA9pb7V4EwIXAZM+6G2eSwRxVrFRVKyf+6e06253C9+4Xa1yzsOOTK0q22zN+g9p1C32axPyVm3jHZxymTIQla7cCtS37p6ct5cjfT/K9zvINxb3asbcnkU5ZJDYNI8R0Jc2odyk5xgVRGJWq+oCqfqCq05xPSOkPAOaIyDys3sSVzg4RaQ4cTUguvNmJ7q1v31WVMi7huJJ6cRe+ByZ9AcDitVsikytOauZ8ZHnMzv6oFEZY4zWj7p7M2X9733ffi7OWAzBvhRVb6BdPzWTB6i388qmZrNuys+a4d+ev5qDfvc5L9vHFSNDn6W4o7Ky0xnniIJ18G7fv4qscxwpr9UXqNfe67n/5iFaUBFEY/xWRH4vIns7sb3vGd71R1Smq2k9V+6vqqaq6zrVvq6q2U9UNma4RFlFWVFf8azqH3P56zf/nZ6ROVvNL21EwKzZsD18gD2Hbm6uqNcUbbNWmHWyxxy6yVS9O9/7l2StClSkJtuyoZOWm2vf31LSl/N9rn9b8f/CtBQBMX7yuzrlREKZzw/ZdVahq4Nb1A5Pm1/w+66/vMejGV0KTJRPpGijH/eEtDnaVySA45fPTFZsZclM88hcaQRTG97FMUO9izfB2ZoAbAjJhbrAhGL9qO+wQFne/9hk9r34p1Gt6ueKJ6fS/fnzN/xG3TuCo/3sTyN7DmLU0HlfEOEwkb3y6ipG3TkzZVlWt7KqqprpamfRpYXv1pWPDtl30v348f3x9fmD7/cylte2+qV/GoyAhfQMlH09Ep2H1yJRFbNxeWB5wcRHES6qXz6d3HMLFSZT1R9BKPw67/X0TP488DcfE0vPql3jig8WevZmf9F/finYOShSKYv3WWjNTtutXVin9fv0/fvXMrJptsdnAQ0rHMas9PW1ppF5IuXLPa58xb8XGlG1heog55TPTxNpKuzHQUAmy4l5ze+Leg/b/fiJyQvSixUuULc5seuCpaUuYOPdrX9NQlC6RUVzZW1j++X6qa2nSg/hO+mGKccBtE7MfZFPlGgh3iNrLJuyrl0ntWvC51I3XPjc75X9llb9Lbj5s31XFvRM/Z8z9qXNCwqy7gzTo+v76f1z5pP8YZUMgiEnq78BO4GD7/1LglsgkakC8NGs5+978ah33WS+PTvmSHzwylR0hFqA5X22oiRgaF89NX0rva1O9ZLx14RrXoG82IlWWIV57h+s5L7Y9pNJR5lPrxBUKJlsqqzfv4MwH32PVpsyBAp1bqK7OTdk9/n5qb3NniPndkWlHnTwfZg8jmAmgIQfUDKIw+qjqncAuAFXdRnJRJSIj7NnOKzdt5xdPzWTd1uCRLatCKkBfrtnC6Pve5taXPsn7GuPeWUjPq1/KyZvlvzPrevvUp15esnZb6N40Sc9q9ys4VUl3u2wee28xUxasyRoEsqzM8XjTevWOwny3jhheecJ4tKrKtC/XsSahiLuFRBCFsVNEmmGrahHpg7UmRoMi7DI78taJbPOJG5UJvxaW3zjMAAAgAElEQVRMPoPeTlwj90Bjrtxvu/VuzmFwz6/V/snyjT5HBuPwu97glAYYXsFLmKaZ+uDEf1qexTPPyZEbt1fWq9z8LcTxqlqF4dme5Ty3F1s6npq2lDEPvMsrJRyl1iGIwrgRGA90E5HHsIIF/ipSqUqUbKar4NiBFOvRD3TMJBU5hKmOop38+crNEVw1ubGUhavrzqsJ773X8sCkL3h+xrKUbc49r9y4nZ/9e0adQJiOuewp1/iKH06+2ryjkr9M/iJvGTeFuK5Eup5jtve8fWd2Zb1gVcOYCxUGQbykXsMKEHge1qzr4ao6KVqx4idTvorL6+G3L9Y1IeXT5a+Z1xHweD/F4qSaS/IFYlnJiJ+MC1Zt5qGYIgT7KcCpi9bx9cZw59vcMX4eVz7hP/h668tzefajZYz/OL+5Lu5n+M/3vF5wmXFHYg5z7CbdpbKZIIM0qgo96kCcBI1BfARWmPFvAodFJ05y1IY3T80dd7/6Kb2vfZn5EbV0s5FPJeycUp85HM7zSNrmHxXu+xrzwLv89sVPYnES2LCtbqt68dqtjLLnqURJWO+yPuMWN7saRf33aB2GOEB6J4ZsorrvpapaffOA0Re1BHGrvR+4BJgNfAz8UETGRi1YIbBp+y7ue92aoTrnq1gmnNehPq2wnZXV3PrSJ2ypz+BiwOQH3/gKb35W2BPR3JWKuyLxTsJSVf721oLQW/2Z2BRhqIyg9XtQhRJWT7J3hxbhXIj0PYxsys1dvs588D3fMB9h9DDemb+aq56exTNZzH2FTtZotVi9i0FqlzYReQRLeTR4KqtqM5OfO2Qc5KMwnDIye9kGZi/bQKPyMn51bH/PMZmv6+xdsm4rHVs3zZpmlBVeWKjW3teDkxdw6n7W0ivOM3YqzAWrt3DLS3MZ//EKnv7RwX6XKkrCqugL4To9r36Jcw/qwc0nDbIvll8a7uL1QZqlBOoTOufSxz/isxWbakyRT05dwpj9vUv+FA9BTFKfAt1d/7sBs9IcW7T4ZSy3kigTYfzHy/nuX9IvgBQF+WQurzLY5eOFE7Swjnkg3vv1I8iciZ2V1b6rGaZcx/XbCQyYmo717cyG37IzNy+3uPl42QYeeXdR7OkWgmkLrPlL9b1WkLz1ZZa5NZl4adbyOuNW906IPtpCVARRGO2AuSIySUQmAZ8AHUTkBRF5IVLpYkR9PIvcBePFWV9xyT8/4v2FayOdUOalddPsncAVG7az2uUj7pXOT9xsd1BIA9i9rnmZu1/9NOMxo+97i/7Xj88YgTTbe3MqHSc45FyPS/AHC9fy93eiGxxfmaMJ7IQ/vs2NLwRfBz2sVxrWWPXNL37C2X97L6Oin79yc6Dylu6IXHoY6Qh7It49Ez5j4A3jOT3D6puFShCT1A2RS1GguDPT/1weJar+ds1/T11ClzbNOKRv+9BkCFJxH/g7KzTFottHBz4nTqUXBve9Pp+fHbN32v1OK+7g21+veQ5elMzPJtsjOd3uXZ5/SK/MB+bJyNsmct7BPdm3extOGtYl8HmqmtbBYe7yjYHmebwyZwW3vTwvZdv6rTtp1ricz7/ezH+mL+PXowcgIqHlnS/XbOXLNVt5auoS/vrWQk7etws/O3qvmv3vzF/N2X97n57tmnNw3/Y8/v5ifjKqHz8ZtVeda6XrYTiNvkmfruSJD5bw53P2T9kf1yx7L1t3VqU1gTl8tHgdKzdu59hBe8YkVXayKgxVfVNEegD9VHWCPYmvQlXr9umLGL/8li4TVqtS5mPX/NXTlqUuXYWVD7nMAl69eQcrNmyvYzLwu0L2HkbqEWPfmM9dr3zK3JuPpVnj8sAyFRK5eMwkxbh3FzHuXXJSGDurqmlS4f9Ojrv3rUDX+OE/6i5xM+zm1zig1+7MWrqBbbuq+OnRe9GiSUXowRK37qxi8dqt3Dfx8xSF8cUqqxGwaM1WFq2x3Hf/MOFzzhjRvc410r06Z/t5f/8QsBREeVlt2S1kL8BT7bhYYdYn9SWIl9RFwNPAX+xNXYH/RClUEuSmMCIWxsWd4+fx0ydnBFoX4/h73+KEP74dyPagmpvZybGVb7QnW23ZUckF4z7k0SmLIg+Xng3vPJl0pp1sCiHTXndE2qR58sPFKeurBPaCSpeffTK0c+z7C9emmGv/N3s5o+4O1wU4XaMoXa/prL++V2dbuns7yY4U4OgI73heAbQRioogYxiXAocAGwFU9XOgY5RCFQrpMlPWiifEXFit8Nz0ZZYiyMJKO2ict/xXlNUteOlaVj8Y9yF/efOLlL27qqprTHDOrb36yQpen7eSG54PbkOvD+1bNk6778+e2caz8gyJomksN9t3VTHs5td89yWhLK96ZnbKeg75ZjcnD/itDeE3+1wQfvTYR/klloGN2/w97HyyLeAfuiRbI66izKrqvPeVb14pVYIojB2qWtO8EpEKol0+IhH8biitXTTL3W+NwLtmdQ6Bz7zKoNxPYaS5h4nzVvK7/6Xaso+/963aJVbtazdNYwKJjtR72LB1F/dO+Jwqn4WI0r239xeuJVPWrVatE04Donmf+eLXewo8f8L+3ugzedCLuyVeGxI+mmI/7Ut/W346V3a/cYdsstn6os54jjfketxUVlUz/JbX+FeddWNqWb15R8Gs/R5k0PtNEbkWaCYiRwM/Bv4brVjx4/QKZi3dUDOImG5ALFsPI65Wdzq84vn1MNz47d3kmszmdgusXU423nkp3uSuf/5jXpj5FUO77Vbn2HStzW07qzIq+6emLakz8Fto+K30lquJ9A2XglVVXy+lrzfWbaBEZb7ZWeV/4eZpxsoqq+t2BbM9A6fBE2SQ2+1EsMgn9leYTP58Fas37+SaZ2dz5si6YzMAw2+ZAMC/LjqQM21z3K2nDOLsA3pEKpsfQXoYVwOrsCbr/RB4GbguSqGSZPOOSp6aas3G9MmX1na75FRWVfu2/J3BurDpefVL/GDch1mP8xaJivK6rzl/M4ZF0vF11trravj1ntIr9Mw3nU5ZJO1Rtm1nFfNXWj4mfs898GC9z2H3Tvyco++ZXGd7WHHNgrArTUiWJhX+1ZNfnZ/tHdWs4RHg3Mdc63Z84/eTMl63vuSyTO+TH9bKdcuLc6MQJytBgg9Wq+pfVfU7qnqa/btBm6Q+/XoTW3ZUsmpzmsFTO3/f+MIcht8ygbtemcc/36udRBSlq97EeStzPsevbp+yYHXN71ykdQZI49YXgqXM7371U2tNbDsL+pkt7hw/z7cCcc/0zgXvOdtiMFGt2rSjJjTJFU9MZ9Tdk9m+q8r3uacbewnCsx/VNcEBvO6Tz6LK1fUJgd/7mpcY+8b8rA0g57n5KT1vcX32o/jCd7gnH2bDndeT8u5Ka5ISkdlkyCOqOiQSiZLCdaeVVdUcc8/ktAvFO5nulTnW3Iyxb6QOuia90Iq3svR7iReMm8pl3+yb87WT8lsHa83mh95eSNe2zWu27ays5oOFqTbwRWu28tnXm9l7j1Yp28OSfMAN40O6UnpG3GqZIRbdPpr3vlgDWO6zfqbA+as2s3+Ptnmlk22FQHBFLo5h2Y7X533Nkf07WekFeGHVCne98iknDumc8TjnuW3ctosWTVKrvYWrt/Cf6bWKM6ks/tfJC9iys9J3ngnULlyVJJnGMJx1uy+1v/9hf58N5D9X3oWItAUeBvoA24ELVPVje99PgQux8uts4HxVjSQa3F2vzEsJnFdZrWmVBbhbKf4v8KsALrD1YfGarXRv1zztfm9+z1bwcukvOl4mcY9hKNTY2t1L2S5IY2P2s3N773PJ2q3cOzF7mIak+9NOnC6t9s9xYx54N5Cvfn1bpXG0al/5+GsuGDeVV396eE5zkLKZy5zs6md+u2DchymKM6n5OLe+bJmZ0ikM97tPKk+mNUmp6peq+iVwiKr+SlVn25+rgW+FlP61wAy7t3IucC+AiHQBrsBae2MQUA6cEVKadZj82Wo+XlbbLf7868xjEE4LJCmFf/hdb2Tcv3hNqj7PVtCd/UHCe1er8uSHi/lyTbyLyqzatKPGtuzuQTm9PC+VPgOpiqace82zs3m6iKKHVquyIs0ckwWrNmeNpeUlaFA9J1/E0fIeb7/P52cs47LHpwc+L5No23b6m/Jq9nuem1/eiZN04W2SCoCaIkOAY1qIyKHOHxE5GAgrLvFArBX8UNV5QE8R6WTvq8DyzKoAmgORrazu9cZYl2WSVs3aGQm+v8++3sQbacYzvPGFqtVyXdy6M3NE2SDmpsoq5apnZnPLS+ENun0nxwCL7olmXnOUg58d+n8fr0j1EArsjloYQ3bVqlz1jH/czx8/9hH9rx+fcfBXNdWFdVyOgQvjGLpsbA9051pp+425OPztrQUZz/Xel9PDiNpDKh3fSRNjqsxVWyeVI4MojB8AY0VkkYgsBO4HLggp/ZlYq/khIiOBHkBXVV0G/B5YDCwHNqjqq34XEJGLRWSqiExdtSq/9Ri8njbZFMHI2yYy56sNiWr8Y+6ZzPkBPKYAVm3azpgHpvDTJ/1XYKt1lc1+rWxKJx92zzApz491W3dl7ZL7hWR/adbynNJxSNok5VCt6ZW6E313c5Yw8+7owzOWrM85/ahxZtTvylFh+Hl1OeyorM5o//c+02pVdlRWRe4hlY5M5vAabJGXrtvqG406KoJ4SU1T1aHAEGCYqg5T1bCme94OtBWRGcDlwHSg0h7bOAnoBXTG6uV8L418D6rqcFUd3qFDh7yEqKMwAnTVp3yxpiC6iEFwfPfnLk8N/1Un5lSAMpr0RCewXEEXrM5sNtyrUyv+OPHzjCslvjN/Tda0vv2ntwsixhTAyWPfyfqOMoXOru9trNkSvTOHoyjCbJhUVmvGEu1VGJ99vZm9r4vesaG+LFu/jUPveCPWEPdpFYaIfE9Eavar6kZV3eDa38dtqgqKiFwqIjNsJdFSVc9X1WFYYxgdgIXAKGChqq5S1V3As0BkK9n4+fI3JJzZrelu0yku05esy3qtz7KM7+TDHgEWaPLiN7HMzYZtu/i/1z6rmeiUL7OWbkjMa8ZLkJbn8g3bIzMdHfuHYIEMw8BvKdt8yR7KJ7SkYkPRmt7YM2lco6Mgk5dUO2C6iEwDpmFN3msK9MVahW811qS+nFDVscBYABFpIyKN7dAjFwKTVXWjiCwGDhSR5sA2rPXEp+aaVlDKJTeTlHWMpNgU68M+nVsz56v8fdGz4bTavD0ib0F5Z/5qkuDcg3qyevOOOu7J9cGpJHbkOBDsRyFNO8omS1W1FoyCqw9+sazypapaM1oDCqUH6SZblOHG5WU19+QXPDIqMnlJ3QvsB/wLq+V/lP1/GXCOqo6xAxHWhwHAHBGZBxwHXGmn/T5WhNyPsFxqy4AH65lWWmYtSw1A5rcamx9hmaSiNm1Ntl2G07mgOpojqYqmvEz45bf6Zz8wD8K4pUKqT7K9o2rVDGtDFA9h2uWFzI3AXNx348K7eBekRp5o1ri8NiBojG82YywpVa0CXrM/oaOqU4B+afbdCNwYRbpeVm3KzzYbnsII5TJpSdda8259YFJ4LfykcVZJ2+QTeylXCqkFms6t1uGIvTsUlLz5ks21PRdmLFnP6s3pPR+374pv0Lg+PDl1Sc1v1VoFsqvKigfWpKIs8vlRIRlVSg8hRDur6yVfdFg0q7n5URuFND4e+v5w+nZsGXk6b30ennktSRNPrnmsU6umaXtE0xdnH6MKgyDLCmfDb+Jlvkz9Mp77jhunYbBw9Rb6Xz+eHQHmUdUXozDqgRMAr76UiTXwe2T/jsToIcdbn+fnhlwfOrRqwtOXHFTz/84xhR9hJskW+w3Pf5zT8Up619v7Y+pB/ufSQ+p9jSRD0BQDfu85DqfNjApDRMpE5PToxShtykR479qjePi8ESmV0+AudUN3h4kz0K6afXJTWJSJ0KZ57byL00d0iyXd+pDkoPf6rbn1MDTDGEZcdN89fdiaoIQ56N1Q8c4IDzpzvz5kVBiqWg1cFrkUCZPPGMLNGSYK1Sf9pFpWYc7czkSRTF1J4bVPco8QHBbuGGdBUJI1oUE4ccaChKkpZdZu2ck1njlRccwOCGKSek1EfiEi3URkd+cTuWQxkvQEPHfLoFmaRWOiJE4vi6SfdT7cMb6wF1Vys37rTpati2d1tnevPtJ3u7fiyieSbn1WOYzTzTRJvL3POAKCBlEYF2BFrJ2MNR9jGhHOiUiCpLu/7vf8k1G1TmNxVeRxWjCKUWGEwche8bSxrnpmNsffF88EO79X2al1k5SK66Prj+aZH0U259aXaTEN7gehX8eW/OgbfWJJK46SFSQ0SC+fT+8YZCsZ3JVo88a1HiZxVeRxDYZCbevzzjFDeOUnh8eWbtLs271N0iLEgnfcbfcW1niVs/ZKh1ZN+OTmsIJd+zPHM68qTP5z6SEc1b8jFxwSzJuxR7sWNG8Uj9Ug8UFvSwhpLiLXiciD9v9+InJCtvMM/hzYe3f+/cODUralmzEeRGHkG1AvKZzW5+kjutVZ4KhYeezCA7If1MCtJIfv5cRx86+1Th/erWavu1EUBTf9N7zxRS/DurXhofNG8J3hdSMs+w32i8Q3MbBQTFJ/B3ZSG8tpKXBLZBI1MPbr3iZlcZuh3dow3GPTTefdEMTb5dLHw4oDGQ8N0SLVyp53cGT/jglLEi/ufDuyp5Wn3e/XvWyAs72hvH+/+HP3fHcoh/VrX2d7GJNHsxHXcw2iMPqo6p3ALgBV3Ub8Szo3KMrKJGXA8IQhe/oed9KwLnGJFBsNYBJyHQRh0e2jefi8EUmLkhjeYcA7TxvCi5fXxiatURgNpOrwG4vbY7dm/OnM/TzHxeP5GNfYYBCFsVNEmmF3qkWkD5DsotUhM6RrtPMd/HC/3zNGdvc95pIjenPHmMExSWQw5IY7D3t7w6cP70bvDi1dx0qdc4qFts0b1dnm7WG8+ctv0KVNM3bzHCtILAojrscaRGHcBIwHuonIY1gr5P0qSqHiJkrtnM6u6KSZyXdaRGga04BZIXDOgT3qbDtu0B4JSJIbQbKPAm18Kp5ipsKVeb+5d0dOH96V3540KOM5RagvfMuwN8L1HrulD9EfxxhGwZik7JXuTgXOw4pcO1xVJ0UrVrxEOeHF2zpxClltpMnMNDwTTvob+u3JdSuboAUhnzU14mS3Zo0Ken5A4/LcowS1a9mk5ndFuXDnaUPTVpy1yxoXn8rwk9jrqJKunIrEMy8krucaxEvqH1gK4wtVfVFVk1k0IUKiWkBp9xaNGXu2ZdN0Wsqn7md5V5TqfISoFGBFeeE+zwF7tubiw3szamCn7AcnRL5zfgbu2do6P8vp2fbnuq57nPgV1QqXxrjpxIFpLQEicPK+0Y9FxjUzPqiX1J7AH0XkCxF5RkSujFiuWIlKO390/dE0qbAy0gPf259Ft4+mj23XLdzqrf48ZQcXHO0azI86Qu3SmGY3+9G9Xa07ZbsWddcnP+fAHjQqL6txLXXo3b5Fze/rRg/g7wEHzcOIBuslX0Xu1JvF2BPeu1NQt+66pdW57yYVZZyXZU7Ggb3bMeFnR+QoXWESxCT1OnArcD3wN2A48KOI5YoVrz0yDpweRraUc2357dYseTv5iJ67s+j20Yw9a786+4qwXslK66a1z/x/PzmM60YPSNnvWHs6tmqSsv31X3yj5vcRewVfjz6KHrECH//mWzyfY6TZP525H2cd0J2BnVtnvr794gupY/29g+qOmfnhJ7NTZ2TLzw3FK8whiElqIvAO8F3gU2CEqkazPFpCJLGmd1mGDPffyw7ld6fm5x1VUaDrk+crVcdWhT024aVjq6YM65Y6q7uRrTF6d2hZx+vNWf+kb8eWgRsHUeXXlk0qGNottxnpPdu34LZTBgeWKZ3CSEKReBV4OvxEc+43WyTjsgItj/kSxCQ1C2vi3iBgCDDIdrM11IcM+Whw190403a1LYSu/qAumVuPAPNvPS7j/muO70+b5o3o1ja30NdXH9efnu2yn/PNvYO30KPGeWV9O7bkosN6ccKQzjX7vjsi1YX616MHsuj20TmZRaMY/4o6hHs2ZZiuJf6Xc/aPQhy6tGlGlzbBqjG/x11WozAyn+sMrdXnlTVtVDjLFgUxSf1UVQ8HTgHWYI1prI9asDgJ00/6vWuOCnSc0/AohvZHkG51RRYvmyP7d2LGDcfkHI23aaPyQIOG+3bPPSJq1LRp1ohfjx5I44rUZzPlmiOZ+PP8bdpRtMajbpd0a9uc7w7vxoPnDM/pvEP71s6cDlrBB6Fr22aBn6OfgnZMUn7RGFq5xpjKPe5UrZpWMO26UTlIWlhmrSAmqctE5ElgBnAy8DCQuTlZZIRp98/kj+0maCvx+MF70rVtsh06R7mN2S8ZT5bNAUIrrNlcv7mkg7vsxuAuu9G4vIw3XGML+ZCt1bnnbs1qnB9yOc8higok6p5sWZlwx2lDGGB7Vb31q28GOq+8TJj0i29w44kD+fclB/Hbk/aJUkxffBVGWXqTcnuXu7HjHdmrXQvOGNGNpy85OMUdOQjZxofiJEhfpxlwN9BfVY9S1d/YA+ENhh8ekX/w3eZ5rl+RaQzDTdNG5dx12lAgmFdHFOXeMZckNaD+5NQlWY9x93DyWRddBF647BA+veVYerVvwd/OHZ63gnTm3kQVXLGQBo7zpZsnUF+6e6ooE3q2b8H5h/SiS5tmnHNQz0jk8fYCs1GewSTlTECdeeMxNa7UZWXC7WOG5JUnTt2vcEIEBTFJ3QVsBy6xextDoxcrXvbt3jZvpeG0mHIll0LfyDaEtmxawdkH+IcRiRKncDTJw5Z6YO/dOWlY5+wHZqB/gELmHux3XJlzRURqlOOogZ246ri987pOv06tePLiA7nhxIF5ne/HGSEuZfvtoZnfx8+P3otTs5gBg/YQgpKuPPgNpkehMA/s3S7tvkxeUn5ccGgvFt0+OmMD67kfx7tGSFgEMUldATwGdLQ//xSRy8NIXETaishzIjJLRD4QkUGufVeKyMciMkdEfhJGepm4+tj8HL/ydcmtDcaWHcfLZldVsstW5uPw8cTFB3HvGfsGPv7lKw7jcU+48PYBuvDuiiWshacymQ2zLQp0QO92OSuuoGYh57h8bfo/OLRuD8w9sHr5Uf24+7vDav77pePtIdQXVWutDC9RzZHyXjZziJ6625xB72P3yS90TSGOuQUhyAygC4EDVHULgIjcAUwB/hhC+tcCM1T1FBHpD4wFjrIVx0XASCwPrfEi8pKqfh5Cmr7kmzHzdXHMxdPFSaOyKs7FVGtxPGiyyfzUJQexrR5La4K/vTbIo3L3ME4f3o2xb+S2KJRv+IcMCeez7Gh9aOIymdQ3F3hdZ399/ACOyOBl1rVtM5atj3Zi5BMfLuG5Hx/MKfe/G+p1O7ZqwspN/uNb7rGgTA2/dGNGH/z6qMjNtLmayqImiDQCuGuBKsJz7hmIFcwQVZ0H9BSRTsAA4D1V3aqqlcCbWF5aseOerXzFkX3r7M9XYeRzVlK2a6d62rBtV8bjRvTc3bWQTry4vVF6tGvB3afnaDn1ebhxu9Bner/uBk3Yk+AuOrw3e2UYH4tqQPxnR++V8r9V02CVby633cs1m96LW/Fm8t5LN+7QsVXTvM2fQbnl5EGs2bwz0jRyIWhokPdF5CYRuQl4D3gopPRnYsWpQkRGAj2ArsDHwOEi0k5EmgPHA+EZcQPgeGO4gwceNaBuLKB8J+Y4rdcj+2ePL+QusFF7s3hnKQPsb3efl6zdGm3iaQjm1pt6TKYKMHC6MWvoTN7dB9RjTfCnLzko+0EZCLKQVz5ccVQ/7hwzBIBD+raLREGnk9ybp3570qCaZWS9/MFlnouTRbeP5vTh3Xjtk68TSd+PIIPedwPnA2uBdcD5qvqHkNK/HWgrIjOAy4HpQKWqzgXuAF7DCq0+E/D1rRSRi0VkqohMXbVqVShCuWdZZysrgwNMavOjrEx461ff5E9nBbfvW/VX9sJbJvmv8XFI37orhl11XH9e/enh9Gpf1xX0zJHd+e7wiHV5jiYpgEFddqN3h/StyyBJxN3DqEwzRnXGiG4cs88eNa3lbbtqO/x/Ozf7vIb6ms/cOa5r22Z878DwHC/a2rG3mlaURxOQM0NxaeTyrGvbojG/+FZdJ4cygRZNKvjjmfty8eH5e1PWh0Lyiks7hiEiTYFLgL7AbOB+2zxUL0TkUqzxCYDjVfV8e7sAC+0PqvoQdk9GRG7DWhq2Dqr6IPAgwPDhw0NpCp05sjv/eO9L6/opsqce98PDe/P6vPyVVNgDhw4LfjcaVaXXNS/nfK6fia1ReRl7dWqF39y8fEOY5EKQ8uInd8dWTViwakv+6cZcUivTdDEO69eB8jLh+MF7MPaNL2qW/BSBXhmU4g8P783Pj9k77/u4bvQAbnlpbsos8LevOjLDGbnjliyomGH1d/oFCIj53rXWRNwTh3bmxCzeZWHQs11zFq1JpicfhEw9jEewAg3Oxpqo9/swElTVsao6TFWHAVtFxAnveSEwWVU3AohIR/u7O5bZ6l9hpJ+JXu1b5GSDb1RextzlG+tsv270gDqePmEReHJXBIP4hRwXp1Ee6zlkw+9ufzpqr3rN28mE04DwXv/4wZYnzsZtddtr7vzg9X46Yq8OaQdND+6T3o20ThqBj8wdR75mjYP3MLxlwDFr+R6bQXoR4b1rjuLZDC6ucccy26153WjHhVTqMnlJDVTVwQAi8hDwQQTpDwAeFZEq4BPgB659z4hIO6y1xC9V1XURpJ9Cthm+Xrtnusr1wsOS6bpC/WMCZQpemERUXwim/JwxjJF52vp9XSd9Nl45ql9e1w/CsG5tmPCzI2jdrIK/vLnAJVv6+3fGF/p2bMnuntDqB/uYFx3OPagn736xJqM8TrpOxyeKFQMP7dueXxyzF987sEVlufMAABVoSURBVAebd+RnwDhyQMe8099jt6Yp0RlGDejIhLkr875eLvzprH257PHpKdv8yu/+PXZn5tINsciUjUzNshqXmDBMUX6o6hRV7aeq/VX1VLdSUNXDVHWgqg5V1YlRpJ8JvyLqLbf3TozMyzct2fRBfVvamVp5SUT1hdp34a0Q3V5rjrdKyya1baBcdKfffSehH/t2bJmimCdlacQ4c3Nyj1Kc/eE4V3QqsUweR/lSViZcdmQ/2jRvnPcYhvesUS4Fkmv7Kc4lkd1BKR1U605UzaU3GDWZapehIrLR/mwChji/RaSuHaYE6NKmGe1b1lZaowZ0ij2ceDYf/JauwGdv/eqbgey0bjKZnZJaJdBxa7z/7P3410UHura35t2rj+SOMYPp1Nqa9NUqzeJCRw/sxCVH9EmbRlK9Jz/SKeaT902tYASpeSd+k978cCp9pyId2TN7j6zGjTdQCvmT7yvw5suD+qTvWRUDL11xWEr051EDOzE0TyeWsEmrMFS1XFVb259Wqlrh+l040bAixt1CadqonKnXHV3z/1v7dIrFrr/3Hq04tG97bjsl+wDzuPNG1vzutntzOrbOLdBZprtJqodxyRF9ePqSgziwdzsO6tOuZlnQMoHObZrx3RHdObRve645rj83n1S7LrhbtTZrVF5nfXU3QU1SXvbtntv6EUFwm6DcInRpY41xtG3eiCP26sA93x1K/z1acfNJ+9SZTZ+uIeP0Fpxn065lXZu5N+24povm2yDxnhckm6YzfyW95niTijLKy6RO9OdHLhiZcawmLgprGmEB4SyfOXDP2u6hNy+t27ozlpZp44oy/nnhAQzp2iZjF/vQvu1TlgsFGLBHqm7fq1PmHoe78B3ZP9U2nJTCKC8ThrtawjVhVVziiAg/PKJP6sxbTf2ZqULyWxM826udeeMxPHHxgZkPyoN0z9mZm1gmwiMXjGT/HrsjIpx7UM865rqWWZZxDTL5z9nVo51VFs4YEW0cM0cWR9kF7R2LpxZz5O7QqklaVTd7mf+YQNL9TGcdHC9tmjfm9BDjieVL+IsDNxAO7tuel684jAF7tuL65+cAdQtXZbWm+MQ/flE0nlFu0q07MeumY2jqM+v0quP6c9zgPTjtz1NQhcP7deCzrzenvb77Hh/2rDE9akAn7nrl0/wED5FahRG8eKuqb+XYrFE523ZVcczAujGBsl09qrAQbn3hF74i3UQ694BpNtlz6TW0a9GYRbePDnx8vjj3qljKuEkaD682zRuxfusu13me64gw4WeH065FE/7vtU+Z9mXu/jJ3jhnCCUP3zH5gyIzZP1iE5F9+a++UsnhBlnXFw8L0MDIwsHPrVPOAJ2tWe/zmWwcMbVAf9k7TQ2jdtJGvC2Wj8jL271G3dZ6OTLv36tSSg3q348GIVkHLlWyVYuc2/i6R7mB63XdvzqybjuFcn/Wd3T2SET3b5h2ZOFfS9YScnke6GeE7q3I3HWWaRR+3G7WTnKqyW7NGaQegn7/0EPZzmQK9z0sE+nZsRdsWjbn+hNwiBjtu9YO67EbzxoXXnnYiV3/vwNT8GtVsfC9GYeSAt/xUVcO480f4HxwR3z+4Z15zPBzRs7bKM5koRPjXxQdyTJ4ROsPCqeSy2bxvdY35KLX3fvTATtx3Zq3Nv3XTRr7Pxb3p58fszf+uPKweUtcfydLDeGDS/Jrf6Z6Nc6bTQvebR+Twnf27cfrwrvx01F5pjwmToGvE9GjXImVWtvdeh7saSLnGejpt/67MuOHoglq0yI3jBZnUUItRGDngrVSqVDkoZpc3EcnoX5/1fM//8w7umfI/6MDj/Wfvl1hPw28Mww8npAMAmqrwjxu0B9/YuwN3fSf9QGJq7zI+3KYYd1C82ha4/3m7XD2MbM9mygJrDsaC1elnwjdrXM6dpw2tCd8RNTUKI0Bj+WCXJ5T7Xhf+7vhAlf0dY9I7kLTxmTxXKDhWhMoq5f6z96sJ/R/1muwOhdfnKmC8PYzqaqVJRTllkjlwXCEgIlZJ9NyDd4BVgFtPGcTQrpm9f44fHL9918GROIhyc3v6OEdXq9KovIxx549Me17d68SnMkSEuTcfy9wVG1PcZcuy9DAy4Q0dvkfreGcwByLPR+x1fvAy6RffYOP2XVz06FR2b9GEFy8/NDEHjvry8HkjeOTdRbRp1ojjB+/J6s07uOH5ObHVP0Zh5IA3Mzqxf4Z1a8NHi9cXRSbMFvlVRDj7gLr2/Li5/oSB6V1Wc1h9yn2/kkMLNl2ScdGscTn7eRbZcfJXurkmqaQK3NbTanYUhreHmST5Fp9sDYeetsfj+9eOyi+BCOnathlL121jZK/dGdYtu4v2ft3bpuQLdyMoDozCqAfOS7rztKH8e+qSQGtuJ0XtGIb13aSijB2V1XU9TOIUKgN+q8I55NLDcFCt37yCpCYtumnaqJybT9qHIwLEO0snrlOvVNk/CmmBnrDmYRQTz/74YD5dsYnD+uW3jkxNIyhMoTJgFEY9qKqujeNz7fF115EoRJyi5bRWO3uW3yyGspfL8rY1SsJljcunMVYovcdzD+oZ6Li6rqap/528W0iVbVihQYqJjq2a1ivAYe24j/GSKniqEhy4mHnjMTkd7x0o/t6BPbj+hIF13EmTnukahJzGMOxvRevVGisQfREY76NxBq5PHmaFF+ljh0Xfe4/cQsdESb5ZrwiybGS4G0RxYHoYeXDTiQO56b+fxGY39CPfSWOOLXuP1k25wMfsUwyFz6n4g8ia0sOoR+EqpJZ4ELxjVa2bNmLuzcfWeGAdO2hPXrz8UPYpIPfR/BVGcb2bMHGCbUa9triDURgBePyiA3h1Tu0yiY55IskeBsCD5+zPxf+YFujYFk0qWL91F2cf0IOWTSo4Lc2M0mIoet7xmGBHu8n9vXlDrhQ6vrPaPetWD+pSGAHtHIpNKRcCowfvyfqtO/lO1Kte2hiFEYCD+7RP8fsut4P6JNnDAHKaQPf0JQfz+ryvada4nDPSxKspFmrHMHIY9HYdn89ri2MWf6lTH3Ux4WdHsHH7ruwHNjDKyoRzAo5rhYFRGHkwyF7Hu5jCKPft2JK+AYK5Ffh0EqC24g/SIHW67O1aNM468a0hEee6DmFRnx5GkLxtqD9GYeTBkK5tmHHD0QU9IzRfiqIyzaFeOaRvO247ZTAnDevM1p1VdGz1me/YTUMj7pA1YWAsUoWP8ZLKk4akLM46oHtNULN0EUILiVwmK4kIZx3QnRZNKujQqgkf/HpUzYJMDZXG5WU1IcmLiVIevC4WTA/DwG2nDOamE/dh9eYdRWHKcGbY5zKGUUrEteCRofQo/OakIRYaV5TVmcRXqGyxV0uLy5Ww2CgKs6KhKDE9DEPR4VSIZaa50yBp0ThYL/fFyw9l+uLcF0cy5I9RGIaiwzG5GJOUP8XcwfjTWfsyOOD8kEFddiu4uSQNnVjaaCLSX0SmiMgOEfmFZ9+xIvKpiMwXkatd23uJyPsi8rmIPCkiDWeU2VAvgqxHHSajEwzlngvOwlovXn5owpLkzwlDOhflgH2pEFcPYy1wBXCye6OIlANjgaOBpcCHIvKCqn4C3AHco6pPiMifgR8AD8Qkr6GAcfz149AXcaxlHRYH921fVPIaio9YehiqulJVPwS8UzFHAvNVdYGq7gSeAE4Sy7/uSOBp+7hH8CgbQ+ny53P25weH9qJPBzNZy2CIk6SHDbsAS1z/l9rb2gHrVbXSs91goFf7Flx/wkDKii2ErMFQ5CStMNJFhgscMU5ELhaRqSIyddWqVaEKZzAYDIZaIlMYInKpiMywP53THLYUcIdZ7Ap8BawG2ohIhWd7HVT1QVUdrqrDO3TIb9Uqg8FgMGQnMoWhqmNVdZj98a3sgQ+BfrZHVGPgDOAFtZaPegM4zT7u+8DzUclqMBgMhuzE4iUlInsAU4HWQLWI/AQYqKobReQy4BWgHHhYVefYp10FPCEitwDTgYfikNVgMBgM/sSiMFR1BZZZyW/fy8DLPtsXYHlRGQwGg6EASHrQ22AwGAxFglEYBoPBYAiEURgGg8FgCIQJPljk3DlmCL07mNg7BoMheozCKHJOH9Et+0EGg8EQAsYkZTAYDIZAGIVhMBgMhkAYhWEwGAyGQBiFYTAYDIZAGIVhMBgMhkAYhWEwGAyGQBiFYTAYDIZAGIVhMBgMhkCItfREw0BEVgFfJi1HjrTHWjCqlDD3XBqYey4eeqhq1hXoGpTCKEZEZKqqDk9ajjgx91wamHtueBiTlMFgMBgCYRSGwWAwGAJhFEbyPJi0AAlg7rk0MPfcwDBjGAaDwWAIhOlhGAwGgyEQRmEYDAaDIRBGYdQTEekmIm+IyFwRmSMiV/oc01ZEnhORWSLygYgMcu27UkQ+ts/9iWv7UBGZIiKzReS/ItLatW+IvW+Ovb9p9Hdak3as9ysijUTkEXv7XBG5Jp47DQcRaWo/g5n2Pf8maZmCEERuEWkiIk+KyHwReV9Eerr2XWNv/1REvuXafqy9bb6IXO3a/pi9/WMReVhEGkV9j17ivmfX/j+KyOao7itUVNV86vEB9gT2s3+3Aj4DBnqOuQu40f7dH5ho/x4EfAw0x1r9cALQz973IXCE/fsC4Lf27wpgFjDU/t8OKG/A93sW8IT9uzmwCOiZ9HvP4XkJ0NL+3Qh4HzgwabnCkBv4MfBn+/cZwJP274HATKAJ0Av4Aii3P18AvYHG9jED7XOOt9MU4F/Ajxr6PdvnDQf+AWxO+p0H+ZgeRj1R1eWq+pH9exMwF+jiOWwgMNE+Zh7QU0Q6AQOA91R1q6pWAm8Cp9jn7A1Mtn+/Boyxfx8DzFLVmfb11qhqVSQ350MC96tACxGpAJoBO4GNUdxbFKiF03psZH8K3tMkoNwnAY/Yv58GjhIRsbc/oao7VHUhMB8YaX/mq+oCVd0JPGEfi6q+bKepwAdA1whvz5e471lEyrEaV7+K8LZCxSiMELG7p/titUzczAROtY8ZCfTAKhAfA4eLSDsRaY7VynIW6f4Y+Lb9+zuu7XsBKiKviMhHIpJYZovpfp8GtgDLgcXA71V1bQS3ExkiUi4iM4CVwGuq6n1eBUkAubsASwDsBsAGrB5vzXabpfa2dNvdaTYCzgHGh3cnwYn5ni8DXlDV5WHfR1QYhRESItISeAb4iap6W8C3A23tjHg5MB2oVNW5wB1YLerxWBVtpX3OBcClIjINy/Sz095eARwKnG1/nyIiR0V2Y2mI8X5HAlVAZ6yu/s9FpHdkNxYBqlqlqsOwlOZI95hOIRNAbvE7LY/tbu4HJqvqW7nKGwZx3bOIdMZqGP2xPvLGTUXSAjQE7FbRM8Bjqvqsd79doZ5vHyvAQvuDqj4EPGTvuw2rBeKYco6xt+8FjLYvtxR4U1VX2/teBvbDNgHFQcz3exYwXlV3AStF5B0su++CqO4vKlR1vYhMAv6/vbMNsaqKwvDzlmijDpOYQhZiYVEIg5LzQ4IwKjEzKZDsh4VYoRFlUYPUlJpEgQpFBlEMKIKEH5hMEKgVomgwg6aWOpaVwWQ/RMEyrUFb/djr6pnLnZkzdPMyd9YDh9l37XP2xzl3zrp7r3PePZ00ouoX9NDuDtJIsMOnDOuAMxl7gZuBk57uzo6kpcAoYEGZu9BnrkKfJwHjgePpX4Shko6b2fjy96aMVDqI0t830i+IdcB7PexzPTDY088A6zJ5o/3vWKAdGFFkv8bLn++fRwD76Ro4fqiK+7sYWOP1DgOOAPWVvu59OF+jgOs9XQPsBmZWul3laDfwHF0DwBs9PYGuAeCfSMHfQZ6+hSsB4Al+zNPAXqBmoPS5qNx+EfSueAP6+0aaFjLSk0sHfJsBLAQW+j5TgB/8BrmlcJP0vN1+EzwI3JexLyI9gfQ9aYpHmby5wGHSL58V1dxfYDiwyft7BGis9DXv4/mqJ03JHfLrtaTSbfov7QaWA7M8fZ1fm+OkQPWtmeObSE8HHQMezNhn+DX+EWjK2C+6rfCduurn6Wr3uajufuEwQhokCIIgyEUEvYMgCIJchMMIgiAIchEOIwiCIMhFOIwgCIIgF+EwgiAIglyEwwj6PZIuSTqQ2cZVuk3lRNIkSc2Zz9NdVbXd+7tB0ljPWytpdtHx3SqhShosaZe/hBYEPRJfkqAauGBJzqEkkgZZ0v3pr7wGvAXgUhWrSe8FHHXbLGAcSWurT5hZp6QvgTnA+nI1OKhOYoQRVCWS5knaJOkzYLvbGiW1Ka3T8WZm3yZfr+ALSZ9IesXtOyVN9vQNkk54+lpJKzNlLXD7VD9ms//6X+/SKEhqkLRXaa2FVkm1knZLmphpxx5J9UX9qCW92X7QTYuBtwvOAsDMWsxsF70gaXlmFParpDWetZWkTRYEPRIjjKAaqHGhQ4CfzawgmT6FdLM9I2kacBtJzFBAi6R7SEq4j5O0fQaRZFf29VLfU8BZM2uQNATYI2m7500iyUScBPYAd0tqBTYAc8ysTWlxqAtAMzAPeNH1s4aY2aGiuibTVctoArCql/atlPR6sdHMlgBLJNWR3rj/wLO+Axp6KTMIwmEEVUF3U1I77IoU+jTfvvHPw0kOpBb41MzOA0hqyVHfNKA+Eyuo87I6gVYz6/CyDpCmis4Cv5lZG1wWZ0TSJuANSY0ktd61Jeq6EThVqhGSRpJEJ4cCH5tZwZE0mtnmzH7nMmmRpp7eNbN93p5Lkjol1Vpa4yQIShIOI6hm/sykBbxjZh9ld1BaJrY7fZyLXJm2zS6DK+B5M9tWVNZU4O+M6RLpf0yl6jCz85J2kBbUeYw0mijmQlHdh0nqxAfN7DQw0afQhnfTh2KWAR1mtqbIPgT4K2cZwQAlYhjBQGEbMN/X8UDSTZJGk1b5e1RSjccLHs4ccwK4y9Ozi8p61mXekXS7pGE91N0OjJHU4PvXZp5KagbeB9qs9MJQR0ky2AVWAE2S7szYhvZQ92UkzQQeAF4oso8ETlmSkA+CbokRRjAgMLPtfpP92uPQ54C5ZrZf0gaSQuovpLn9AquAjZKeAL7K2JtJU037fYrnFPBID3V3SpoDrJZUQxo13E9SKN0n6XeShHupY9sl1RWmi8zsW0mLgHXu4E6Tno5amuM0vExaiKrVz0GLxzXuBT7PcXwwwAm12iDIIGkZ6UbeW2C5XPWNAXYCd5jZP93s8xLwh5k1l8ovQxu2AK+a2bH/o/ygeogpqSCoEJKeJK2H3tSds3A+pGtspJxtGAxsDWcR5CFGGEEQBEEuYoQRBEEQ5CIcRhAEQZCLcBhBEARBLsJhBEEQBLkIhxEEQRDk4l+2lFeTqssLDQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.frequency(3e9)\n",
-    "mysa.span(1e6)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "During call of saInitiate the followingWarning: saBandwidthClamped was raised\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 941\n",
-      "52500\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1522a614ac8>], [None])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.frequency(3e9)\n",
-    "mysa.span(1e8)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)  # register the first independent parameter\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id  # convenient to have for plotting\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mysa.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.7"
-  },
-  "nbsphinx": {
-   "execute": "never"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index 63fbf8e9a1e..ac7cf6254cd 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -210,7 +210,7 @@ def __init__(self, name, dll_path=None, **kwargs):
                            )
         self.add_parameter('npts',
                            label='Number of Points',
-                           get_cmd=None,
+                           get_cmd=self._get_npts,
                            set_cmd=False,
                            docstring='Number of points in frequency sweep.')
         self.add_parameter('avg',
@@ -331,16 +331,16 @@ def __init__(self, name, dll_path=None, **kwargs):
                            unit='Hz',
                            get_cmd=self._get_freq_axis,
                            set_cmd=False,
-                           vals=vals.Arrays(),
+                           vals=vals.Arrays(shape=(self.npts,)),
                            snapshot_value=False
                            )
         self.add_parameter('freq_sweep',
                            label='Power',
                            unit='depends on mode',
-                           get_cmd=self._get_averaged_sweep_data,
+                           get_cmd=self._get_sweep_data,
                            set_cmd=False,
                            parameter_class=ParameterWithSetpoints,
-                           vals=vals.Arrays(),
+                           vals=vals.Arrays(shape=(self.npts,)),
                            setpoints=(self.frequency_axis,),
                            snapshot_value=False)
 
@@ -388,6 +388,12 @@ def _set_ctypes_argtypes(self) -> None:
         self.dll.saGetFirmwareString.argtypes = [ct.c_int,
                                                  ct.c_char_p]
 
+    def _get_npts(self) -> int:
+        if not self._parameters_synced:
+            self.sync_parameters()
+        sweep_info = self.QuerySweep()
+        return sweep_info[0]
+
     def _update_trace(self) -> None:
         """
         Private method to sync changes of the
@@ -606,7 +612,6 @@ def QuerySweep(self) -> Tuple[int, float, float]:
                                         ct.pointer(start_freq),
                                         ct.pointer(stepsize))
         self.check_for_error(err, 'saQuerySweepInfo')
-
         return sweep_len.value, start_freq.value, stepsize.value
 
     def _get_sweep_data(self) -> np.ndarray:
@@ -707,6 +712,8 @@ def get_idn(self) -> Dict[str, Optional[str]]:
         return output
 
     def _get_freq_axis(self) -> np.ndarray:
+        if not self._parameters_synced:
+            self.sync_parameters()
         sweep_len, start_freq, stepsize = self.QuerySweep()
         end_freq = start_freq + stepsize*(sweep_len-1)
         freq_points = np.linspace(start_freq, end_freq, sweep_len)

From 4260dad544baa5ead7a34bf7f8ff8677c2c0e319 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 13:23:32 +0100
Subject: [PATCH 632/719] refresh notebooks

---
 ...nd USB-SA124B ParameterWithSetpoints.ipynb | 363 ++++++++++++++++++
 ...arameter-Example-with-Dual-Setpoints.ipynb |  24 +-
 ...le-Example-of-ParameterWithSetpoints.ipynb |  65 ++--
 3 files changed, 416 insertions(+), 36 deletions(-)
 create mode 100644 docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints.ipynb

diff --git a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints.ipynb b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints.ipynb
new file mode 100644
index 00000000000..8c0fa438b1f
--- /dev/null
+++ b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints.ipynb	
@@ -0,0 +1,363 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import qcodes as qc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument_drivers.signal_hound.USB_SA124B import SignalHound_USB_SA124B\n",
+    "from qcodes.dataset.measurements import Measurement\n",
+    "from qcodes.dataset.plotting import plot_by_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected to: Signal Hound sa124B (serial:17172185, firmware:Version 3.13) in 5.93s\n"
+     ]
+    }
+   ],
+   "source": [
+    "mysa = SignalHound_USB_SA124B('mysa', dll_path='C:\\\\Program Files\\\\Signal Hound\\\\Spike\\\\sa_api.dll')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'vendor': 'Signal Hound',\n",
+       " 'model': 'sa124B',\n",
+       " 'serial': '17172185',\n",
+       " 'firmware': 'Version 3.13'}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mysa.get_idn()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mysa.frequency(2e9)\n",
+    "mysa.span(0.5e6)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 309\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1da84b9e4a8>], [None])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.avg(1)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
+    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 310\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1da84cb27b8>], [None])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.avg(10)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
+    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 311\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1da84cb96a0>], [None])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.avg(100)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
+    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 312\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1da8607e2e8>], [None])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.frequency(3e9)\n",
+    "mysa.span(1e6)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
+    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    dataid = datasaver.run_id\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 313\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "During call of saInitiate the followingWarning: saBandwidthClamped was raised\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1da86131198>], [None])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.frequency(3e9)\n",
+    "mysa.span(1e8)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
+    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    dataid = datasaver.run_id\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mysa.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  },
+  "nbsphinx": {
+   "execute": "never"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb b/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb
index 22d522edae5..c13a27e5f85 100644
--- a/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb
+++ b/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb
@@ -163,8 +163,8 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#93@C:\\Users\\jenielse/experiments.db\n",
-       "----------------------------------------------------------"
+       "tutorial_exp#no sample#102@C:\\Users\\jenielse/experiments.db\n",
+       "-----------------------------------------------------------"
       ]
      },
      "execution_count": 6,
@@ -193,7 +193,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 268\n"
+      "Starting experimental run with id: 314\n"
      ]
     }
    ],
@@ -216,7 +216,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -240,7 +240,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 269\n"
+      "Starting experimental run with id: 315\n"
      ]
     }
    ],
@@ -260,7 +260,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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Sbj7PI0k4V87Ew8PDY0fFeJHHbAByWr4VelkORDQPwB4AbhaLW4loIRHdQ0Svb3QSInq/3m5hV1fXZjc2SsnDrmE1nNvquzc+jYPOvd7P9eHh4fGiwHiRhysfAEAjv88ZAK5g5lgsm8vMRwJ4K4DziWhB0Y7M/DNmPpKZj5w1a9ZmN1YSg+SI4ZIEf79QcWO/Uw+re6CGahTDw8PDY0fCeJHHCgC7i+9zAKxqsO0ZcFxWzLxK/18C4BbYeshWR9SAJIZzWw3U4sLlh37pBrz7ovvHopkeHh4eE4bxIo/7AbyEiPYgogoUQeSipohoHwDTANwtlk0johb9eSaA4wE87u67NREnMp8jWz4ceQzVFXkUWRl3Ll6/lVvo4eHhMbEYl2grZo6I6MMArgMQAvglMz9GRF8CsJCZDZGcCeBytifL2A/AT4kogSK7r8sorbFAlLiWR6g+D1OexLi0huq+5pWHh8eOj3EhDwBg5msAXOMs+7zz/dyC/e4CcNCYNs6BpXlIy6OJUN1qNDYTRr3v1wtx29NdeOorp2y1Y3p4eHhsLsaNPLYnSGKIBQHUo5GTBKXbamvmgtzw+JqtdiwPDw+PLYUvT1KAOOe2yn9uTB5i+wY1sjw8PDy2d3jyKEDUwG1Va6B5yCKJVaF51Ddjzo8Hnt+IRcs2NlzvCzJ6eHhsC/DkUYC4gbURxQlay4FennXiA/XMVWW5rQTZ1KLmiOQNP7oLb/rxXQ3Xy3N5eHh4TBQ8eRRAdvrSeIgSRiVUt0wSjNRCpOUhM9K3Vub5gJOE6OHh4TER8ORRgLiBYJ4kjEpJh+2KbWT+h7Q8ZHTW1iKPPk8eHh4e2wA8eRTAiraSCYPMaCkZy0O4pCzyKLY8hkbpbmokyPdXvdvKw8Nj4uHJowCW5SHcVjED5ZBy28iaV5I85HJpnTSDRpZKf81bHh4eHhMPTx4FaBSeq9xWecHcclvV48J966MM2904UCtc7hZe9PDw8JgIePIoQKMM84QZ5bDAbRU1clsNH201HBF0C/KQ5OQ1Dw8Pj20BnjwK0CjDPE6KyaPeQPOQy1231d3PrscBX7gOdy5eV9iGjf2Z20rqJV7z8PDw2BbgyaMAccIoBUbbsAXzSoFgbmsexeVJqo7lceMTqtzIoys3FbahW2gegxZ5eMvDw8Nj4uHJowBRLEkiW54w0mirxppHc5ZHV28VALDT5JbCNkgSGqoVhwJ7eHh4TBQ8eRQgtoRxWzAvFyQJNg7VbRxttbZ3CADQqvNGXEiNZEgQRm07qJe1sb+Gb/z9SStU2cPDY8eCJ48C1JMktTDygrlxZ4nto2IikcTjCubG8qg3UWBxsCYSD7eDDvnLf3scP77l2dQ15+HhsePBk0cBpIVhVdLlYsvDaB6VMLDm8Bguz8OQhxydy32l+0tqHvUma2RNJAzxjTY82cPDY/vBNkEeRHQyET1FRIuJ6JyC9WcRURcRPaj/3juW7YmFMJ6wHbZblOdRi1Xn3lIKIA2JqEHyIAD0DEW5beKkWHivW9aM75A9PDwmHhNOHkQUAvghgFMA7A/gTCLav2DT3zHzofrv52PZpiSBKIAolrOKwiJyoq0i9bmlHFhkYwvmxZ2+PeVtcV6IlS/SpNvq3iXrccbP7p4QNxcpzx48zXl47LiYcPIAcBSAxcy8hJlrAC4HcPpENihp4J6KE0ZAhFJAhbWtWkphw/wP+TmxCGNk4d06TpNuq09c8RDuWbIBz28YaGp7AHhuXX/T2w4H0uyxNafh9fDw2LawLZDHbADLxfcVepmLNxHRw0R0BRHtXnQgIno/ES0kooVdXV2b3SCVDJgXxhNmBAEhdMjDdO4t5QCyv2xkSfSKXA1pkURN5Is0a0l0VNQMwxv6i8ucuLjx8TV45bdvwbWPvNDU9sOBtvgIHh4e2zq2BfIo6mvcIetfAcxn5oMB3Ajg4qIDMfPPmPlIZj5y1qxZm90gmQxoheoyEBKhFASFHXpLKbTcVlEDy6NHJADKbaIRLA+i5kXo9ooKAV69aaip7Z9c3QMAeKRB0uLmwBseHh47LrYF8lgBQFoScwCskhsw83pmruqvFwI4YiwblDBSt1XilCcJCAhczUN36K3loGHmuXRJ9Q5llkdU4P4CbEvFHKe9HDateXS0KMtjTU9z5BEG+cz5kfDoyk346OUP5PbJNA/PHh4eOyq2BfK4H8BLiGgPIqoAOAPAVXIDItpVfD0NwBNj2SC7hlW2PEmU26oUBoU5HCpUN9vebNNaDiwy6BnKLA8rkqpBdJaxSNpbSk27rYzu0KzloS/XIsuRcMfidfjLg6ty5eONKektDw+PHReliW4AM0dE9GEA1wEIAfySmR8joi8BWMjMVwH4CBGdBiACsAHAWWPZpqTBdLMJK8E8ILKTBOME5ZBQCskpT6IthkqpodsqbiCeF81I2F4JmyaPXk1Qa3qrI2ypEFBe4xkJpmCj26ZMMG/+WB4eHtsXJpw8AICZrwFwjbPs8+LzpwB8arzaEzOjXFDbKmZGGBDCwI6YUuQRICByNA/1ua0cOpZHsWBuFVgsmAu9rRymYcEG6/qquPD2JfjXfXbCMXvOSJcb11hPk9PfGvIYjeVhkhfd7PnU8mj6SB4eHtsbtgW31TYHuwyJLZgHRAiJrFLt9Vi5uYgIseO2IlJuK0kMvUPFgrkcwdcK3Fltlbzm8Ys7luKnty7Bhy75p1Vxt0+TR7NVeENdRVhaPyNhSJdNySUuavZolojqcYJzrnwYK7sHmz63h4fHxMKTRwHsJEGZYa4F84Asy6OmLY+QkCtPUg4CVEp2p29G7C0lN2orc09Z7i/doRe5rQxJbOiv4bn1WZ6GIahmJ48KAmN5NLW5dR05t5Vmj6jJyLB7l2zA5fcvxzlXPtz8yT08PCYUnjwKYNWwYnt5aPI8JElECSohFbitEpRCQiUky7VjPrtkkArjlZKd55G6v0q5DllOFGWILk4Y/doqkJFdwyE0bqtRsMegJriiWRJVO5oV99X/0bjMPDw8JhaePApQlGHOzGDptnI0j5J2W8n+MtKTSpXDwHZJRQlKAaFSCpyy7erzpNaSU54ki9pyR/mD9Xwy4UAtI4z+Wp48rnpoFa5ctKLw2kcTqmuq/eYFc+jlzR0rJY9tv+ajh4eHxjYhmG9LMCRRLtnRR6ZPDUhZHklO8yAEhFxtq3IYoFLKk0elFOSTDXXv2dESWkJ3XWe8V0pBTvMYque1kVhEZ/UNRWDmNAIKAD5y2QMAgDcdMSddZkgyHsXoP4u2svfRHrCm9RPj5vJ5IR4e2w+85eHAdLwtjuVhloeBEpfdiZ7KYZAjlShmlEKVF1Jz5vyolAKUQ7I6WHPMjkopl+dRCgJUwrzlYZcxSfR/dZwpbWVECecq+hbB7DM6t1Wctk8i1TyaPFbmtmr61B4eHhMMTx4OTAeWzeehO1VNCkGQ1zbiRGkhanl2rHqiOv2So5HUogSVMFDJhlZtK9UJd7aUnJLsioSU+yuvebjifizIA2hONE/3HY1grt1WjbLemxXMU1eZJw8Pj+0GnjwcGFIItRsqdsmDCgojJqw1D+Qsj3Jots/OUYuUpVIKqDA8t7USoh5zGrllLJtyGOSq6g7VE3S0qDpWUSPyaCCaF5WMH43lYabHdQnNuJ+anYbWXLcXzD08th948nCQuqd0AcS6oyOERAgCJ58jTlA2loeTMV4KAy2wi+zxWE1zWwrJcu2Yc5h5zc0qQ0LlEhVoHjE6W0vpdvI4U9uHtzw2WZnutnuuGQw1EMzN10ZT7LowhOjJw8Nj+4EnDwfGvRQGqtyI6VRNP0gEhOTOyZGF8Fpuq1hFW4WhY6kIwbxolsDWsl3R17i/ykFe8xiKYnS2KJKICzQPoDF5dA+IZMXUbTV6zSNPHqMjotTq8dzh4bHdwJOHA9b9IGn3lOvOSfM8JHlot1LebZW5p9zJo4xgbh9Hi/Xa8jDfU8sjVNPcyn2G6gk6c24rE7WlLJLBglwQANg0mM31Yc41mpkHBxtEW5l2NHsss53nDg+P7QeePByklgcp0dzVPIwwLkfoUaIE7bzbSi0PyXZPpYJ5YAvmsajCa/ZX/5X7y4QPy055qB6nJJG5raCPo0glFueQOSBFlkejhD8XScJpmLBLEuZeNSuYG1ecn3nQw2P7gScPB7FjYURO/oOxSBLHYigFStuw3VYJykGQ2z7N8wgpze0ApNvKWB6mc1buLxNVZU1XW0/QachDkA2gyp/I70AWIQXY5GGIq5mwXnc7lzxSi6nJPI/U8vDc4TFOiBPG4rV9E92M7RqePBywCMkti3wO0w+GlC9PEiUqYzwI8tFWpdCQUJHbyrU8jNvKDr2NRLSVOa5ZX4sz8nC1htT9lUjLQ4YA56OtmrU8pCvM3Sdr9/gK5o+u3IRP/OGh3PwiHh4uvn39UzjxO7di6br+kTf2KIQnDwexDMkV0VCZ2woFmociCcrleagQXnf7zG1FhR14anmklkSW56G2U/uYDO8Ox/JwSSiKi8mjKNLLJYK/PbwKf3vYmtgRgE0ejTSPZpMEzXZbanlc//gaXLFoBb7018e37EAeOzzuX7oBANDV5Hw3HnmMG3kQ0clE9BQRLSaicwrWf4yIHieih4noJiKaJ9bFRPSg/rvK3XdrQobkyugms5yKalslZj6PAsE8oMIkwbKxPKwO3NE8hIhdDoK0TLzp4A15dOY0D00ejnYCAIP1TPOwJ6JSn2VyIgB8+NIH8OFLH8jdJ+n+cvM5zHGbFcy3Vp5HSddFWd/vOwSP5iCq9niMEuNCHkQUAvghgFMA7A/gTCLa39nsAQBHMvPBAK4A8E2xbpCZD9V/p41lW03/FThRVanlYfI8ZKcf61DdgsmgSqHe3pnPvKXAIoly7qbEOk6l5FgeUZaRLvfPHUd04tLycCPGAKCvapOHwZk/uweX3Lss/T5UL3Z/yeM2Haobjc7yeGp1L353//O55YZUmyWtJOFRJUWOBeafczU+enmenD3GFl5e23KMl+VxFIDFzLyEmWsALgdwutyAmf/BzAP66z0A5mACYDq8gICSKAdi+pgikjCFC8mJtqrrKKnC8iRGMHcmfTL5JYAIedXur8xtpZa7bqu85pG3PBqRh/m8caBWGPV095L1+MyfHk2/S/KoOW6rzPJoNs8jy2dpBqd87zb875WP5NpZS915zR1nz09fg7f+/J6mth1L/OXBvFvQY3zgDY/Nx3iRx2wAy8X3FXpZI7wHwLXieysRLSSie4jo9Y12IqL36+0WdnV1bVZDZZJgWSQJZm6rIs1DJfEFRNboOYoZ5YAQOtVzjWDu5n+YZEPjfsnyPJT7q7HmUVyexI3aAhx3U4HVEydsTZPbCIPDWB5ZgcbRRVtVm+z0TbP7a7aVZCyPwVqx9VSEe5ZsaHrbrQ0fmjz22DRQx31L87+xv/dbjvEijyKCL/z1iOjtAI4E8C2xeC4zHwngrQDOJ6IFRfsy88+Y+UhmPnLWrFmb1dAktTzsKCmWeR5OJrnp9AOyM7QjPc9HGKBQMA+DwBn9q6itUmBHW2W1rbTm4YywJzUoTzKy5ZGIz9k2G/uz5EEJczzA7qAbua1Gm+fRqMBiI7hRVSZ8eChqnjxGi6F6jCdX92yVYzUbFu2x+XjfrxfiP356t2UpjxceWt6N7oHid2lHwHiRxwoAu4vvcwDkbHUiOhHAZwCcxsyp6snMq/T/JQBuAXDYWDXU9KGBFszTDllGYZGrF2QFEIvcWYYkDAEZt5VLKmbyqFCTRN3RPFLLIzIjdfVCtFcaaR75qXRlkqA9BW7WkW1o8MBPa6+kn5uLthqt5TG6F9x9MdNAglFYHqPFOVc+jJPPvx0bGhDsaNCoQ1u8tndCOrsdEY+t2gSg8cBkLAXz0394J8688N6xO8EEY7zI434ALyGiPYioAuAMAFbUFBEdBuCnUMSxViyfRkQt+vNMAMcDGLNYzNy8HY7bKigQzE0GuBuqa9xZxg2VsLJsooTT2lZxTmBXNazkOeWMhIDQPPQIu7UcaheYo3mU83kegyNoHkBmebimvSm0CGQdX0CjszyKErOMYD7akbhredScQIJm0WxuCwDcq10gg1vYuV+xaAWINCcoAAAgAElEQVQuuTcv+g/UIpz4ndvw8d8/tEXHbxZ3LV6Hn9327LicayIQkB2haGCezLGKlzDvzhMvKCv1f694GH9YuHy4XbY7jAt5MHME4MMArgPwBIDfM/NjRPQlIjLRU98C0AngD05I7n4AFhLRQwD+AeDrzDxm5CFLr8uqt6YfNZqEFVWVsK6qaxdMrIskQbVdknZwlZLSSGQUliywqPbPyCAUobp1x23VWg6sCaoyzaMgz6Me6zpZeb1lRoeyLMyo2rUoTKFFICOhzpZSQ/Jwl9/w+Bqc+J1bcfXDL1jLU8sjStA7lE/wc6O2DBlvGrC3rW+G5gEAXX3Nh/aan31LB6yf+MND+NZ1T+WWG93n5ifX5taNBd7683vx1WueHJdzTQSMZdFogDCaKtKjgZvj9LuFy/HJKx7erGP1VSPMP+dq/OaeZSNvPI4YtzwPZr6Gmfdm5gXMfJ5e9nlmvkp/PpGZd3ZDcpn5LmY+iJkP0f9/MZbtlDWsSgUdckCwtBDljgJCLZgXTUNryCBJstFxxWgh1qRSWvMIyTqnIo9sgipzDNNJtpVDK2fEWEulQBVrdMuTtJXDgjDhBLMmtQDIyMPVDtorYXYc3clNbis3JA/XknhkRTcA4Ok1vdZy6VJ4YdOQte639yzDgk9fY7mJzH3obmB5DNbjEQVRSfJre4aG2dLZj0cXhjxayGsYa1x059IxPwcAXLloBT71x83rOLcUgX733GfRPB5j9Ts2Ou7mJCWafX51x/j8Xs3CZ5g7kO6pkuiQU83DmbfDdJwmn8MV0ks6tFd9T5zqvHYUVjptbWCH6sasLJKKkzEu3VaSDMz/UqB1G0fzaK+UcvOnRzFjclsZAWUl3I1r6rOv2w8zOipOsmFmedSiYs3DHe2ZF9hEgRlI8lnZPWitu0qHsT6yclO6zFhgObeVOJ/bWTy/fgDL1melKCRhNRNdZmBuwWiqD48GjSLOkoRH5V5rBueOUyb+x//wEC67b2JcNq7b6n2/XoivXvNE6rZqtgqCRJwwvvH3J4cddMjnQw5kXtg0WLT5sNhWI8M8eTiQ080qy8Ou+OrWtjIPX1nPPGi2ZWbECVuWR5ywRU5hkJ8XRGkkNkkk2p2VD9XVnXFJaR6uPpMWd3SSBNsroTVLotmnHBI6KiX060TBqrAu9pzVYbm/qvUYreUALaUgJ4w3sjzMdxm1tXzDAK57bE1q1axyyGP36e0AbK3EvEsbGwjm6t7YI/dXfOsf+Jdv3ZJta4UvN08exlu+OZ1OM6jFxRbHx//wEPb+7LWF6zwaw7yTpnLCDY+vwc9uW5Ku35wk0YXPbcCPb3l2WDdUUfIv0FwO0sHnXofv3/RMfsU2lpTiycNBmgyoLY9sNK+WB6RLspvlsemog3SUEyec6gXl0HZDpdFc2vKw3VZ2kqCc3CkkKgjVVS9ESwPNoxRSbrbCwVqMtkqorSpRV0vrKu0tIfody0MJ8vb2g/U4dZc1yvPIk0fWXoMrFq0AoEitFBBWblTkwcx4fFVPqts8vqonXT6g2/XYSjtktmqVqi8e+aXXG9mE2izMobbE8ihqj1nWKGjgTw+sBDB2bpZL731+mx3hAsCiZRtypXOaATUQzMGbPwgwx+yvRogTxv9c8RCeWm27YhtFMjYTRdczFOH/bng6a+ow2z61uhdv+endVhTleGFY8iCiOUT0CSL6CxHdT0S3EdGPiOh1RLRDEo+VYR5kJdPT5ToKK3Vb6fVlIYwnLHSH0CaVTJBXBMWcjX6iNM/DdlslCSMQJdllaCuRGsnbmkdmJUndBsgsD3dedaO3dLSU0Fcz5GEsG5MN75CQIQ/htlq0bAPW9Cgfbc152Y0lY64PAHae3Jp+ntpeTnWMax9djddecDt+d79yd5iJq2pxkv4W9y5dnxKdOp9NbgZSEDeCfC3ePPIwv1+z2fNFKCIIc7yRIs6KAgoeeH7jFleH/fSfHsGiZRu36BhFaOS+aRYLPn0N3vWr+/CmH9+Nb/5dBRis7B7M1VNrBPOkbU3BPBTv57L1/fj9whX44G8XWdvId06+HyORR9E9So9V0NSvXvME7l26AfcsWd9s87caGhIAEf0KwC8B1AB8A8CZAD4E4EYAJwO4g4heMR6NHE+kSYKmoGFsoq2ku0m4rWKjLwRpZEfCmeUhySBmtmpkaS6wjmVFZ4kcE2V52HkeQ5GaC93MMVI0cZWrqwzUY7RVSlZob3rugNDZUsKAsTyEplIO85ZHayVEuRRYHfGHLvln+tmNrTcdY82KMFPLrv3oy9U16HXL1g/o9XZnPaBdakfvMR31mPGc1DGiOL138iVdvCZzeS3fMKjvYfMvtETqK3eubTQdY9G0wCb4YSRdw9V5mBlv+NFdOOm7tzZ9/kbYONB8KftbnlrbVAe+RugCm0O4ccL4x1OqWsTSdf3oHqjh+K/fjC/9rTm9xgzcqlGSC9Qwxx8tzHtu3NLm+BLyXZHvwUhh5EWWUFFAymodWGJcwM1WZ9iaGM56+D9mPomZL9ART4uZ+VFm/iMznw3glShI9Nve4c5hXncEczOToHk2MguD0geVOetcpOYRxa7mkc/nUCG5dmZ4nDDCkFAuOXke9TgVn2V5d0lo5dDWPAZrEdq1wO6WJymFhPZKmGoettvKtmCGtNuq4tTn2mNmR/q5GiVWp2rcDtJlZDrL3aa2qTnd0yl0bVHd3Od+bRWZyDC7xlaCya35qXfX9GYd2PMb+vW22fpRWR4OmQHAz29fgpd85tqmBe3+AvIY0NWO5TGKXGMueTzb1Z9rz+ai2QivO55Zh7N+dT9+8I/FI267vi/TpUbr6nMJubOllP5W1z66uuF+L2waxD90qLN5957t6sNJ370tO7b+32wiq0Q22RmLatTFuh9gRxeOlMBadI/SZZq0vnvD0zjmazdhTc9Q+v5PRLWC4cjjZCJqWJyQmWvMPPLTs50h1SRIdb7ZaN4sJyvENhIWhvHGxJw9VNKSiBNOSUdpHkiXm/+lQFoeIs+jgebRqivnFkVbGRebnWGu3FZuXS2TS9LZUko7aJlH4mobg5q4SkFQSB777zpZkWgiyUNtZxWD1OsroXKNpTkqJTciSy03I/SZnS3p9aTbRJzmosiXVI7K1unOTEaINSKPNT1DOV+y2Utew4W3L0GUMP7+WOMOTWI4y0N2Ar0FUWAueSx8TiUtTtLFMbcEzWbmm4ih5zcMjLBlRvbA6MnDtQomtZbSZcNZi6/69q1410X3oxrFqZXQqPjk5kwDkOVfJeln10Urn/u3/TzLMh+pdE49yrfHtUZuf0ZZYis2Dqaa4ERUJBiOPGYDuFvrHB/U2d07PNzaVubhyJYjnc+DmQu1DeW20pZHIKKtpNsqQGZ5pOJdUlhV14TqmsxzGW1lHp5SgeZR0lZMXTx8RjAPggIhvRygo6VULJgXCe9l5bZyZ0PcdUorXn/YbgDszrBaMOe5sULKoa3PVJ2OxhCpGR2bUikyIbAWJ5jUWs6d1w7Lrefa0Cja6uiv3oR3/uK+9Pva3qG0Q5cj1sPnTgOgsrWbQX9B2fuBAreVuf9SKO4ZtNu6Xue/mPpmIyFOGFc9tKrQXdPfhOjaV41wzSMqydM8j8PB/X0aIYqTXKSda011tpbSe9HITbOhv5Y+I8s3DKTvpAz1BrLAh2brr0mY64hE+LR7bY2OKzv5KxatwFphFXcP1PCei+/P7VN3rApTRbu/GqXTLhQNSMYaDX99Zv5/AOYC+ByAgwE8TETXEtE7iWjSeDVwvCHDXJXLh3PLTaefiJF1ORBuq0RYJI7l4dbIArKILVMjSxZGVGG/WVkUSWiu26pY87C1jcG6sDzEA96v8z/aK6V0To+UPEo62koK5vVEayFkvTgmSz6db10/+E+u7sGy1GVkR6KYSsUyossdBUfpiFOtn95RTq/HoBYlaSdaRFpA1vmOJJibdi8UIvJR590k2p1dg3lxL79/Of74zxW4+9n1mH/O1fjiXx/D/HOuzrmzitxWaacoiMLc/439mbXxsE60zK5NbTOSy+nJ1T1YtGwjfnXnUnzksgdw1UMrc9ts7K+NOIXv5/78aKpBmEGOi75qlLbdnva4cUf9pb89juO+frNVNcDtkDsrpfT3b0REMo9iSVf/iLWrRmN5LN8wgNuf6Uo7czMNNDC85iFh2r9poI5P/OEhHHXeTViuLbivX/uk9bwZ1B2iN+SxabCe9i0TMfXysEMHVriVmT8IVdjwfAD/D8Ca8WjcRCCxhPEg1yEHZLubTIca5txWmUUiS6y7wrvZHsg0D1meRCb8AWqEnrpw6nFav0qSinSlydE8M6fuJhkmzMwYqMXoaAnR2RKmrhoj7rVWAiuPBNCaRyVExXFnGeG9JfXFxuivRjj5/NvTKCy5fS1WoiOZcjDi2iTqzvJpHQWWh0Ue+RHvpJZSZnnIUN2CjtfNIXH973XLmsnI4GO/fygNP/7Vnc8ByJPFmoLkMkNwtuWhOxrRMfz0tiVYvDbzoZtOq3coGla0P/n82/GmH9+FB55X5CMJyeCCmxfjkC9eby27a/E6fOEvj2LFRtXBSevAaHNPvNCDn9++JF1+4Beuw7//5G4ATvXlYfzy1z+mupSBenH0HKBchiNVXpauvqXr+tMBXSNECeONP7oTl92XrzPm4sTv3Ip3/OK+9FmMYk6vyb31jYR48/xK99UJ31HBDrc9XTyNhLG6zZWYyd829NfS3797FMEOWwtNhdsS0UEAvgQ1G2ANwKfHslETCUkS5TAL1ZXLTckDyz0VBtZy84yHJC2VbHmRYG40j7LICzEj7iAljyB9qapRglYtostaVcbSCAI7z6MWJ2CWhRQzsS9OOLU8BmoxkoTTUW3qtsoJ5kYLcbUT2/IwneeBsycDAH58y7M4/0YVx16Pk3TbknClueQRCWsLAKZrt5UZ2TKrUWBnS95tZT7PnNSCHt0RVy23VZ48pNALAM+tt/378l70OeGztzqdgJzkipnx7evzNa1MZ2JH5uStEUCNqA3MtUUJNyV4G53CdRFZbRHH+cUdS3Hx3ctSQmwTJWrMIOfU79+Br1z9BIZEWZhHVm4CM1uusKKO//rHVuOxVZuysHTR5+bzh9iyIr9+bb4mV48g2ufWD6QDOhes1auN/TX88/lufOqPjxRvKOBqdlGSNLSmGi0393bIsZiBvIWRHcu+D8bbsL6/lr6j25TlQUQvIaLPEdHjAC4FMADgJGY+mpnPH7cWjjPSTl+7fEwehrWc8p27jLZKmIWbC6mlEiWu5oH0OIB6SGRhRHt7tawiQmbrelIps15qJMZSkWVIzEi2pRRY25vOs6MSpqOagXpsua2KBPM2TSpyRBnp+dxNImA1StKX7j+PnZ9ex09vXZJegyFLmbg4pN1rl77vaJyw707iGhzLo253usbykKNWM3/K5LZyaiWYNqsIHtsy6Oqt4rUX3A4gK4XynJNHIe+FK2yvcwotyg5vqJ5gXV8tLUIpl+e3tUV0E4ywfGPW8Q+nhxTB+NiHywuRo1jjkjMhzm1lmzyeeKEn/W3W9lStcN8DvnCd5bYqikZ7/28W4XUX3JE+57Isfz5ENbGu9ye35qsBm99icmsJq7oH04Q+F8ZSeHJ1PnzX4OzLHsB5V+dDgs3vopKBiy2hRpaH+Z0l0RvdshHRuURkzrm+r5oOMLYp8oCqgNsK4C26IOF5ej6NHRqyc0/zKpLE6fQzd5PttspCdV33F2CXJyEiK3nQ/C+HmTAuQ3sNYcmkPOMiAjRJCCEvTJdnobrSkpBaiBkdtldKaNUjy8FajKF6goCEmO0I5q3GbeVMKuVaHrKSsNSOgKx4pFmW1u2qx+hsKeG4BTMxpa1skQqAtA6Xmx9RqHlEMSqlAJNbS+nI1LRpSls5Z3lc/3gWNdVaDrFpsI47HTFcvtC9QxFed/CuOOXAXVAEOeLu1smOJtTYjZaR21YdQvnGmw5GRyVMfeQ/vuVZq2ZUT0ECoQvTmQ9HHrIIpemMl2u3lSSPn922BKd87/b0++qeIcuiGajFw04aJmGeC3n9OcsjHrm+l0mi3HeXyVjVPdhQ03DJQ1aMNvjrQ6tw4e1Lc+5AQ6hRwg1DZBtpHtXU8sjW772zkpDDBkQnj7Wxv5Y+Kxv6a+lxjFtxPDEceezFzJ9h5ob2HDWi9e0Y5mEjEsl9wgKQnX6S2El/qeZhZZIXHyekRtVzlfuLSI203M62XBLaRqLm/zDrZcmUlDyE2yoLvQ2tjtp0KO0tIcqp1ZOkgjyZIpGi1lY1SkR5kuzlMsUgpeZhXnhZ08q0rxZliVbl0LaGjIvE0kJqxhoK0F4pZZaHPsfkNNpKLb/72fX41Z3PoaUUYHJrOe1c6oI8XMFcZlr3DkU45IvX4+dORVNp/Q3WY+yz8yT84K2HowiPrcrKqJhRvSEPEzWWWh7DRFu1lgPsPr09JY9v/N122/Q0Mfo0ZLBkWMsjI4/M8lDnbK2EhfsASstZ7VRFblYwN9GMwxW3dDtrcw8ljGW5zy6TsKp7cMTwYFMzrbVsd4XScljmuCzNOboH6rjr2eIIu4bRVpHttioLj0Wj7tTct2e7+nHYl29I9SEZmPDc+oHNKt+yJRiOPG4morOJaK5cSEQVInoVEV0M4D/HtnnjDznSz0bHXLhcWhJmelrAdVtlD0eUJFntLLFcCuYlYTHUneMDWvNIhfHEEtJlqG5YYJEMiU5IzkliNImOSslKaByKsmguqf+4mefyPkSxbXlUoyTtECqCPErC8shcb5mLzbjFzPJsAix1rLZKiNZymHZOQ47lYc555oX3pOee3FZKX3yzvog83DpFRTDt6dPHm9Sq7p0kSIOPXPYA/vSA0gyMe2GWzlNJ81IKwnIzzcOQb4hdprRibYOy3kWWxzNrenHJvfY8EDKs99RDdsvtI2eSNGS7umcI/dXhRfk1PUN4wQkGGBQCuNuRy+/mHfjzA6vS59Xc40++Zh9M0aX/b3oii9VxXX+mvW3lEHOnt6O/FmNDX3MzPm7sr4OZ8WxXH4792k3pJE4AsHS9TbSyRMyv787u7Yb+Gq555AUs+PQ1hTk6lTBIBz+G6Ce3ltNnsZGr63mHvMz73y/II04Yv75rGeafczWe7cpPuDYWGC44/GQA7wZwGRHtAaAbyo0VArgewHeZ+cGxb+L4wg7VzdxNMknQkITM2wiCBm4rkbeRJBBuK+TmKo+05gFkCYqyFDwAK7opbmB5JFLzEEK61DDCgDBYt0fz7ZXQym4fqmeCfCkIwKzOKecRyWo9JQiDMJ1VUWoepUCTR5iNWgNBHkZXKAsX25AOBQYMMdpuq9ZSiPZKmHW6+n9bRYUPu6PWUkiY3FrGpsG6FtdVu6e0lXNC85qeKl46fxoeXrGpoVvC/Aa9KXmU03tYtM8/nuzCGw6b09jyiJTYfO+SDek+mTWi2tdSUnk4bnLetPYyNg7ULc2DmbG2t4pXi6xqg312npSGhLaX85aEnEmydyhS7r6hCCf8361YPUwZ8tWbhqw5XwA7p8UVzOV9N8/oL+9cis7WEl570C7pPT5kzlSUArI66qP3mF7Ylp7BCJPbSth1qqqZ1t8g8dHtpmtxgp6hCL+8Yyle2DRkEa7rKutrUML/8C/fkH4uSqCc1KpCje9cvA7v/42qhTW5rZzelyIr6aI7lzbM5B+oxQiIMHtqG1Z2D+KCm1Ul3rsWr8OCWZ2F+2xNDJfnMcTMP2Lm4wHMA3ACgMOZeR4zv2+8iYOITiaip4hoMRGdM1bnSWeKI1EALU5EzavMN5kk2faBcFslnJGNtFSiJCvXERZkmEda8wCyciPG3WlpHrrjqyeJpXlId5bRWdRx8m4rGW3Vn5JHSVhVmdsKyGL6jZsG0JNQOYmLJmLM0jwKLA9zPa7mEQvLI02AFDkm/dVIzcIYENrKWVixHJ23lMJcElkUM6a2V1CLEgzUYsfysEfH6/urOG7BTLz9mHloBENyXX2qAzOjYDOfvAujBWxyNA8ztW+1nuC6x9bgweVZHsdQPcaFty3BwytUgltLOUBHJUzrexmYbHtpeVx813M4+qs3oQj77JKlabUVuKGM6D1UTxAljNnTVFn84YjDnH/DQA3TOyr45Gv20debtckKrIgTfOx3WRciR90X3PQMTj7/9lSXkZWpDaa1Vwrzc7oHa5jUWsbUtrxVIlFkQa3rq6bPdp8kPYc8iqwKF0XazGQ9ULlD6GeTW0tZtFUBeRTNNmkwUIsxFMXYa6dOq12NCHNro6lQXWauM/MLzNw98tZbH0QUQoUJnwJgfwBnEtH+Y3Gu2Orcsyq57gyDgHFDGW0DlgBuzTxYuLxYMA8diyGbFVDmeeiOOi62MJTmAb1fkLc8yoFyEaWahxbMW0Ix1a2yPFrE6F9dM2fHqWTby3o/YUBpx68E82zkbJBqHrHUPAIrqqpNusziBFcuWoELb1+avmxtFeG2EuXpW0pBbl4Md5rdlDzayxiqZ4ODdX1VMKtqv64fXOLb1z+NGx5fg2fXqg5uz1kd6b0twsJlG7Fo2YbU8jAdfls5RKUUYCiKsb7fdket7a3ivGueSPNFWkoh2iulXCb4ZO36kprHXc82rrJqBFqgmDxe2DSI/mqE392vch9mT21reCyJoXqCjf11TGsvp79190AtLZ1SjxlL1/XjLw+uxFNrenHjE9lUu0UFAY32Ui5lc9wYTOtQgQ791QgfuewB/Pz2Jbhr8Tpcp/WAzoKM+4+/em+87uBdAdid+947q85302A9taplbo5rSfZW6yiHhB+89bCG96JoXpZdJrdifX81HVgB2vKIEvzlwZW5ScmShIclgoFahGo9wczOFqs8zWhqtW0Jtpey6kcBWMzMS5i5BuByAKePxYmktiGT/mRmeKptJCh0ZyVsV+e1aluJ7V3BvB4n6Uti/PyyFhagwlllXLg5Rj5UVx9HahVWtFV2XuNaUJpH5kqrRtnoP10eMwZruvPW5UlM281+yvLICrYVah6GpKIsz0NOXJUTzBPGdU7tqLaycFsJUb5SCgosjwTTNXms76+he6CGSilIl5kRp0lk3HlyCwr6Mwvv+/VCPNvVh0oYYI4enTeyPADgxifWonuwjlJAqdbRWgnRqtvrhls++YI9X4lyWynClCPngNS9kJ1PS4E7ymDnyZnQ3Faw3WX3LccBX7gunWlwzrSRyaNSCjBYVwQ4vaOSnn/jQD0lt3qc4LQf3IGPXv4gGgQjWTD3w9Q9k5jaXsFALcJfH1qFqx5ahYvueg6P6/v1oVcuSEPO7X3KOGj2FAC2y+wlmkw3DdbT5fK3KHJblcMAMzrygr1BUQc+f2Y7Vm8asqYkMK7Uj16ed+S4+pGL/mqsvQMBdpmSTW0wME6lSrYX8pgNQM5juUIvs0BE7yeihUS0sKurOFtzJHCBhpEk7NS8UttKzYMoi9NmQTZhjjwKLBXOLA/zYJlZBrPjqGNLzSMSZCPdUJYFI5anGePlwCoFYiwPNUlU5k6ShRdTiyRJUiHXRFsBEH5b5TIzRFETeR6N3FbmnDIybKAmS68o62mmE13TLiwP220V5EaL9Zgxo9NYHlWs7a1ip0ktqY/eHMdkf+80qbWpMtfPdvVhj5kd6f0uGskDyj2xoa+G7oE6praX03vRVg4xFCW46K7n8JNbVN7CrZ98JVpKAZ54wRbujeYRJ4xe0UFECatgANHhFQn3Bh2iYy0iDxdyzhUAmDejPSdWz+yoYKgea8ujYlkehjxqUZK6VmRNp0YwVlqllFVdMOhsKSFh4Ps3L06/d/VWUSkFeMNhswtrfbVody1gJ+ntpfWBHmF5yHBlN4qpV5PHcPXEikrQzJvRgXrMFjFNLtDcDJ5dO7zwPViP0/dEkkczIdtbA9sLeRTFsOXGhcz8M2Y+kpmPnDVr1madyC6ZnrcYrCipxC43kiUJ2haJnM9DCvI5wVx0+iGRs30WzlqPC7YXEUn5aCvbbdWiBXPTxqpFKtm1WYUXRe6JecFayoGYoMoQlxLA03kGRKiuNNeL8jxkKZXeoXoadmuIa6oTi99aCbNyD8Il11IKc6PFepykI8X1fTV09VYxa1JL2nmaazK1laa2lwtDH+/51AnW99U9Q9htavbiuoKxwW5T27C+v4ZNgzVMaSun19xWztraMxRhz5kdmDejA63l0NIYSoEKl+7Qls3anszFFSeMya1lq9Moh41fbdnGRmQn4RIFc76K6/TOCqr1JNU8DPH3VSN06HNIwXx5ExV5zcRgZVHix70GM+c9s0runNXZAiKyCNKgHFJK2jLPYra2rLp6q2kI8/q+arqtOxDp0eRhns8iFBW/nD9DWafLxLVPHoaAmomaGqzHmN5RwW5TMuvwufUD4zIr5HAZ5r1E1FPw10tEPY32GyOsgKqtZTAHYzSXiAnPtkJy2dEwrJBc6OVOnkfB9pblERCMG1dGSRn3VKg1jMIkQVHDKo1UCrOkPxnCK2tSWeVGxHJZAbgUZGTQSDCXeRvuvOppYcQCy0OOhs2jLTUPUxk4SRh91Sh9sWREmUR7OcyVMm8pKQ3B7fijhDG9M9M81vYOactDneNy7d83ZUtaynnrBYA1wmspqdBL6apqRB4zOivY0F/Vlkcl/d1cjcRNHpTnksdfK4glilllz4toq2qD0SwAtJWbtzzeevRcnHaoHc6bMOcmNZrWXsFAPcLGfkUepr0JA+2p5pGFZa/Y2Lg8ykvnqyrFRvOoFGge7n0erMdYqwcEQHEUWX81To8ja0vtqn/Tr1z9BLp0GHTPUISZmjTzgnkdlZCGtTyu1pWHJebNULqYDAMezkJc01NtmHUuccyeM7CTcEXet3QDLrrruZF33EIMF201iZknF/xNYubJY94yG/cDeAkR7UFEFQBnALhqLE6Uua2AomTAILDLkxS5odw8D5k7YWWeF2ZBfCAAACAASURBVAnmJCyPxC5zAijxsFGoblrDKkrSUFk7VDezMOSsfaYsigwrjpN8ngegy1CLjHFJKmmbAml5FGseMsKkUrKJrr8WIeEs/NUQoetHbhOWRzW1qoJCt1WcMDoqilgUeaiOxnRCP7rlWazqHsyIMQzx7uP3wHCIdNiyHL23NuiMp3e0YIOuWjtVWB7u9hl52MuNhmBG1HKCq4QZnS0l3LF4XTqi7x3G7y073kZkZ/D2o+fl2mJCtiUmtZawtqeqSFpYHkDWkT++qid9LoYjj++fqZItjduqKNqqzdGWBmpRak0CmUYoMViL0+PIgfnUtkphoMOU9gpKAWHFxgH846lM3K9GCcqloFCUHw57zurAnGlt1rVXhiGPoXqcs6BmduajyA6ZMyUN+QaAz/3b/jh2wYxRtW1z0LTbioh2IqK55m8sG+WCmSMAH4YqmfIEgN8z82Njca5Ct5UzfWzQyJJoWJ5EHEeE3sqOmnV4b+aGolymOmA0j6JQXTEXRpSkdf7tUF27PEmaVBjbIcLm2MVuq8RyQ7luq0hrHmp+k3x5EoN0VkHLbRUgjjmrT9RWstpkyOMX/3kkACfaKnW9hWgpZ0EFsg8hIszoqGB1zxC6B+rYaVKr1fFHMaftqpQC7L/bZFz5wePQCLG2kGQH3EgnmdFR0UJ9HVOk5uF03kZId4V31/JYI9xWhsQAFaILNM5FcM8pM8b/7eBdsc/O9mwLckRrUJTM1loK0+TFeTM6rBF1u54V8g+LVqS/7Yruxm4rM4tkqnmEec3DtawGajG6+pSOlbZJP7v/feJLcOohu+E/Xrp7+pxLtLeEheVJJrWWUA4D/H7hCrzrV/Y8G+UwGNY1WISWUoj/PHa+tWw48qhGcc4yPHR3ZZWZoIf2SohSGGCanqKgpRTgPS/bA/vuMvbj+xGvnohOI6JnACwFcCuA5wBcO8btyoGZr2HmvZl5ATOfN1bnOXT3qfjQKxeoKrlWiK1aLy2GRM+1YZanbih2y5OIWlWWwJ4P4XXJIy3JLtxTdZ13wpx16rJ8fDWK05e3FIpQXT3Ht5kaV9YTKgc2ScTabWVIyDzk3YP1LGlNCOay0qjp7FtKYU7zuPR9R6MSBqkVVI8EeejIMOO7Ty2PMKv/1FoOcMJ+OwPI9II4ySoAGx3GWB4l5wWf3lFJ8wemtpetjv+0H96RZpeb63V97S56hiKrM/7n8yr5zo32md5RQe9QhK6+Kqa0ldMRpes3N6PZ3Z0IJ/N7muNKzSNJGJ953X4AsuenrxphzrQ2fPn0A3Jtlu2VndMP3no49trZTi6b3p4f6cYF/nQZ3bX3zp0WeRS5xkyhxSIY4jR6RjkMchNPuaXWB2oxNvTXLHHfPGPzZrTj+2cehilt5Zz7C1BRhkXkMVnMXOiiMgri+OabD8Z9n1Za2TRHPxruODJoxGCPme2451Mn4GtvPAgAsLuO8puqf6fxnI62mTvwZQDHAHiamfeASha8c0xbNYE4es8Z+J+T97UiPFRIrkgSHCF6yirJbkVb2UmCUjCX0Vnqf2BNZxs4mocJv5WkkmobUWJ1ftbMg2K5nEq3XAqs40VJgqrI8j5q/nR0VEL89p5lFhmkUViRdKVpK6kUpEmCoRZ8j1swE+86fn5KQJbmoQktq4xqC+YDtcga7ZmOf7AeW7qKIS0gX2xuekcFq7qH0m0leXQP1PGXB1dZUwEPNzJM2yE0hHNO2Rc7T27Br99zFN58RDaL83ThP5/aVsHsqW34zXuOwmsO2AVnv2qvdDsTr7/79HbrHIbETccq3VZRwjhk96mY0lZOrcu+aoTD507DwXOmptvtOasD++4yCZ3CqjG/r2mf25kZK/vhc09K8xqKxFhDEK3lALtPa7c6vaIR+nBVYF0rQ76LreUAl7z3aLz+sNk4rsA1s/v0fFixGa03aou0PE4X+s6k1nLD+UMef6F52bejUsJOmtTMJGYA8NU3HIRKqbHbsG8oyrnTdprUil2mtKbvoAmjLiL5sUYz5FFn5vUAAiIKmPkfAA4d43ZtE7BCckeIwpLRVnaorghzjR2B3Tp+ttzsZxVSDDLyMKNtIBsZm+gpNQ9HIiyPzMIYEP75ULuIVLts95dpa00InNM6Kjhmzxl4ek2fFXprSEdOzZlZHsoCqIk5O+RyM41vxcqq5zTiyQiShmQHarF1HBkpVa2r+aorujSKWy9ov12VGT+jo5KWTK+Ugpzv3Cw32HeXSfjS6QfgwNmT06zpv539MksPkQR0+qGzce+nT8Thc6fh2/9+SLpcRiyZrPKXv2QWKqUAHz9pH+ymRVtjWbhirNGwjEtnjShA+Iq91QzRreXMousdqqOztZTuBwCvP3Q2/v7fr9AuVuh7S/jeGYfiL//f8bn7IF08k0XWdsLApe87Gh894SXpetPJ7TmzE0Fg1/iqlAJc+t6jc8cH1P0dCSWhxR21xwwcv9dMhAHhPS/La1Im30bCRDm512TQXg4xd7oSs2d2tqT3ZjhBvEh7aNh+cc6popN/69Fzhx2c9FajnNVm3IjH7TUTx+w5PbU4p00AeTSj+HQTUSeA2wBcQkRrAYz/hLkTAMuSEJpHkeUh3VAJwwrhNa6TKEksUmEIt5WTz2HcUG60VaWkNA+jMYROp28EbTNSNcUGmRmDtSiXeAcoopART0CWRCU7gZaysnoMUbSUAqt8PGCy24O0rcbyqFjHCcGszmu7rTLXGJBlTpuXb7AWWyNH0/EP1mKt86gZCVtLSkhP9L045cBd8PU3HgxACdcGlTAsjMqRbSUivPPY+Xin8FUfOHuKNSd2M+Gu0wvIQ8L8/p3a2trTqU1k8m1MZvpiHcZ57UdfnpanaC2HGIpiVKMYmwbrmNJWtshWdmJl7dorhYTTD81SpqRV0eKMig0RJcw4bsFMHLdgJl6ycyeimNNaTiaXRloepYByOTqAGjX/4qyX4r0XL8QR86bit/c8n9sGsCtcy9+rKBxXJjT+6G2HY31/zapW67oxzbKj9piGK/+5Amt6htJ704g83nnsPLz/FXsWriuCdH26nfxw5NE3FOVE+Z0mqUHG5NYyLn//senyqR2Nw4bHCs2Qx+kAhqCmn30bgClQswru8LCjrfSywK6Ga+d5qG3cMiRyNG+RSlC8PQCEZIf8ZpaHckOZTGy34zX+f9PplwXRDdbj1MUiNQ9rQqYg66jN+QwqYUYG5nu5ZEdb1ZMs6a+lFKAaJ+l8Ggambaqjsye0ArLCfCZU11zjQC1OzwcIy6MeW0ECk1pL6B2KUpI7aM4UTNEd9gwxYqyUglTMlWjGn10usIAa4ZDdp1rnNQQgYSzSTt2eUw/eFa2lADc+sQa/X7hCWB4l7DqlFS9oy2OfnSelrqW2cog1PUPY57N/BwAcMXeapUVI3cDoQq4GIB1SjcKFpQ7wbwcrN8+Fty0BkBG+HHSUwsDq6M946e741Gv3QxQnmNHZgms/+nIAaEgeQJbnJK28oixy07kCwGsP2jW33oSzm4GNwesO3g3XPLIaH/iXBbj1qS5NHvkO+dOv3Rfvf8WC9PsvzzoST63uy5XHl5CeU9e95D5rX3vjQfjnso0quKBax8xJLThmz+m4RxfMnNHA4plUcC/GGiO+Jczcz8wxM0fMfDEzX6DdWDs8LAsj4ZQcpBZiu6GKQ3Vl4l2j2lYuSZis6qhgeZQMZ3movArT2YQixFa6rVTJd+U6siKeUn1BRx2Jh9u8cNVIkU0QZJWHayJUN9MLVIHCauS4rXSH1jNYR5Rw2iGYazDzh3e22tFWg3XX8jCkEmlxX32frMusm7ko5AhaWgAVrY9c+j7bpdKMziG3GS7c9emvnIIrP3CsZfHsXuBaMRasmUaXiHDSAbukGod0exlLo6MSWiGpLeUQ/3w+Kz937IIZTicuLA8xfbGElDNylof+XpR/Zu6HIXxL8wgop7NMaStjRgGJGhy/1wzMm9GOH+o5Ukw7JdkXkYerl7gwetqr99/ZWt7ZUsLF7z4KB86ekl5LkeXhaiav2ndnnPHS3XPbSZDIcXaP6Qryu0xuxeHzlEazfMMg2soBLn//sThNl87feZKd7Z+egwgHzZ6Cz2o31nhgRLoiojcC+AaAnaAyvQkAT0Cux7hDWhgxy2xupMuLBHO3JLtVnVdmqnOR24r0fvbytCR7yXYrmZdK5pKozlrnZxi3kg7nNB1dq3Yd1WI1D7ObiDeoS5aUSw556FBdQwZy3o6V3YOqMrBoq9ledmJmdG3mCTfup5KwMOSxM8sjssx+k+xmLA/TYZkOzCR8yRG0RR76uG756pEirNS+wgIahjzM7yWz43edmu8ATCfiuinMIGAnEUW0YFYnbn9mXc5t0ypG05993X7oaClZM+nJ68oKajYmykaWR9HsfKZYoxmty9+77Fh4LQU5FQDwjmPmpW6nS957jLXOPJcyKsq9flOccjiYZ2mPGY23NdtIy2PPWR1Y0tVfKLiXRxhsSMvDzT9xy5i42fTmmf7Gmw7Gh/51QWpBF+GvZ79s2HZsbTRj63wTwKnM/MRYN2ZbgxttZchBurPsWlhqP7ckOxHpyZQyC4Moe6hcQR5QFsZgHGcWSRptZbuVZOl1QGse0vIQxDVQi1N/u3koh+qJStQLs/MCxZaHmgLX1jDMy/S9G5/B/1zxsNWmllKAaj1GrRRYI/W0xIbu3Dscy6O/GqX6BWBbQztNkpZHJphvGqynxGheMEMecgTdIUbApk1uOGRRKKoLO+pr5NdIdhpFHZD5nd3RtCGDWWKUPldHYrmRQJLETDl5ee0l57eU5zWQ31zLw9ynIvKQc4ebc5mpi0vaQjWldVobRBh9+fUHFi4HMreoDG1279Xph+TK3eVw8gG74BtvOghvOGxOw3kyiiyPvWZ1YklXfy5EGBjZzenOEPiNNx2UalrsVFkqhZRGAwKZ1tVWCccld2M0aIY81rwYiQPIR1tlkVDF0VYk3FCynAmQuaHc6Kl0+5zloV68opkEgbzlYToGI2jLaCtAWx71OB3ly7mzzQROcvuBepYsZ1DRGoYkD7P9SjF3tdQ8+qqR1R5A5CrocFMp4gM6qkoWURQ6jHSzGLLYNFjHomUbcZJ2RZgOxtQGsnIOKnntxdUsGk0hKtGs26pZmN/fdWuYkiNyxL2bLpHuhruajkZmeMv7JUe0h86dipXdgzkX3XRNvFPayrjgTLvkuCw54sL48ueJEX1LKUAkghzUnDP53IVmYAYzk8V9aKuE+OabDsaxC2ZgXV8Vh+4+tdHuKYKA8JaXDp/jbO6ZrDulOvs1aaRe0fYNz+mslud/w2FzsLJ7CFcsXI5Vm5RY/2+H7Irv3vg0gOaCMSYKzZDHQiL6HYA/A0jvHDP/ccxatY1AWhhxIjp24c6SbitZ8ypx3E0m30KWdk8tj4KoqlJA+rzF5GHi+bMkQVuryDLMs2iogVqURqvIMNd6lM8wzwRz0dkawVxEcxWNok1bWkoB7l3agz1mdKTZ4kDmmjGJbmbknloetch2e6TRaowiofpjv38IAHDUHtMBZB3MF//6OKa0lXHY3KxTkaPpzHqy3+6iuSVc2NpLcy/4Je89uiHRmOfFXW8SJuX9M64d1wAwA4JdhItLjnplm7/95kNw1nHzcxVz//eUfTFvRgfOOm5+zsWSRlsV3J93HT8fc6a14eQDdxHbh+i3yIOsdo4Gxr3jJlX+h9Yb3LyYZnDjx16RWskSRW6r2drVKKvtGriWxXELZuB7ZxyGt154D55Z22dpHi4qpQAfe/Xe+PujLwCb1LO4YFYnPnHS3vj29U+jwNDZZtAMeUwGMADgJLGMAezw5OGG5JofMnNnZZYHWbWqkCeDUJUPke6pougsSVBRgUViTOSB1G1ld/r9qUjsurMSSzCXkUr1JEFn2c2piKzzAdlL1adn83PXG5hzGoH9qTW9OH6vLKHL+Kq7+gx52EQ3UIvtTt4R7Q3cUfqJ+9mWBwBc+cHjrNh/2dGb47ovfxQXJ4ZJbI7lcfxeMxuuu+S9x+CKRctzrhhzbCkuN5qcyVzbbgWaCmBbu22VEC+dPz23TXulhHcX5E8AGfEePndabl0pDHCKE93U6lin5jZvmeWx9aKK9tqpOMekRbit5k5vx/MbBvDmI3bHIys34UP/uqBwH4kwIMya1IKZnS2KPJogANerYfJBzCBuW8SIvwQzv2s8GrItwrUwinIqWFgeqYtICOOphhEGeuZBtTwMqHCGQXmOJMmH8JpQ1UZuKyNcysKI5hyWYF4xmkecK4sOZC+rK5gDKv48E7Pzb0aR4CwJwIQVGk2iPee2iqzOee+dJ6kwYdFOwCaJT5y0d1r6QXYwJjLJQLqoGkVVNWN5yOsZacrTZnDEvGk4Yl6+U/7iaQfgyHnTcKRYV5QnAmSdu6z8KzGSe2UkhAHhqg8fj/kzRxamgSyqzpx3t6lt6FndO2wp80ZIyWMz9h0tpOVx9Udelg66vvnmQ0bYU8HoP2efsBceWtGNA/UEVMNBBtsAmWt3vKaU3Rw0E211QcHiTQAWMvNftn6Tth3YFkZW8VaG5MoaVtmEScLC0H1MOVCWh9RCZEl2182lChdm09xm09BqzUOThEtog47QneoF9dgKizX+8cF6jEhMZ2uEf/c4QNbZ9ovOPQyU+026UEK9z8b+em5fILM81va6bitteVSdTPJKiCPnT8Ndz663OkDpVpGZu8N1MM2QR6N6RhKSxDbHDdMsprZX8A6nmB4R4YunHWBNJwtklseuU4otk6K6TqOFLHcyEtJcI32vvvuWQ/H0mt5cmGwzGEijucY+n6FSUkU9OyohiKgw32M4mBDx4xbMxONfOrmpfcxP47ovB2vbbj52M09TK1Q5kmf038EApgN4DxGdP4Ztm3DIOczjJHNv2BaD2tYugJjka1XpjO6iEN5Y1sIS5mvCmXibE8wdTUIWxAPkqE+tN8KrEczbhOVRixPLwigFQRZtJZLypOVhOgYiyhWtc/M11L52GXAioEvPSWFeFHkNbsd+wG4q0qRRaKkcjbdXQrx6/51x4TuPzG1nua22xPJwstDHG/953Pxc2W0zINjVsTzMvXHLmo81zDNo3ov9dp2M0w+d3VQejYsiwXysUA4DdLaURv27mgFP0SyCI8HoVOZemWTA8bC0NhfN0PheAF6ly6KDiH4M4HoArwbwyBi2bcKRahI62iorHWJrIYDy52Zuq3wp9XKgSnsUlmpP8vN2uJZHmudhNI96seaRCeY2qfRq4bXd0TyG6okqjChzAEJK3WImXwTIiKi3GqWzr6nlBGldm3OuF+KitCSIVNKYsTxMh26iW9b319JwVAMzT4M7MY+BzP8gokLiAOzIK9mm7595GHqG6vjMnx5tSvPYUhfQWMBYQK7lMbWtjO6B+qhLiG9xexoEJGwOkgbRaGMBNcXs6Drthz5/EmJmHP7lG3DCfjuN+pzf/vdDcO2jq7G/HiQdPncavvbGgwqz5LcVNPNLzAbQAeWqgv68GzPHRJSPW9uB4E4GVeS2klVy0/LkSZLP2zCCucj/KJ5UKhPMZQa7nEkQyJcPKTmjnhbnxe1xyMN0NIM1W/Mw1zKQJglmL35LgeWhtgkg2cMQmezo3cSwjpZSOmGRyb0wM/3FCedGp4Y8TNFEF0UltYsgR5OSPE49ZDes66viM396tDAU1cVoSnKPF9orxZbHlPYKsH6gMEdhLOFav1uCP/zXcbjhiTW53JOxwIGzJ6cFKJuFyS26/zMnNtSkhsPU9grOPCoL4SUi6/u2iGaTBB8kolugsstfAeCrRNQB4MYxbNuEw7IMmAvcVsVzlUexXZJd/Q+svA0Sob1RnBQK5lGSpLV40uUjJAm6obpmfV/VXm5FW4mS7IB62QvLk4gck4qzvYQhst++52ic+oM7cscBsuqwRBmRzZBFCx3yMPWgGpXydudJaAZuKGpHE8l+BpvjehlrnHzgrqjHjHkzbKvNZLcP1sfXf+5GW20JDpozBQfNGVl43hr47xP33ux9ZxUUgNxRMeIbwMy/AHAcVJ7HnwG8jJl/rmtefbKZk5DCBUS0mIgeJqLDC7ZpJ6KriehJInqMiP7/9s4+WK6yvuPf7+7dm5vcJIS8SEISFCHyImCQK1hRlDdLggUdZlodKoMWM9YginWsKbbTdmxLZ6xVpwzTaO1Iq6gzWFurrViLr4VCwAhqkEaLQwSaAAIJSe7L7q9/nOfZ85znnLN7l9x9ufd+PzN3dvfs2XOec5N7vvt7vzF472qS+0judD/XTP8Snz+ZbKugZ1Px0KdsoV492A6k8zbCHlYkXRZRvg1JpcJsym8U8zjYrPPIpvg9626u3hXk3VEHI4skl20V3EirFRbWeQyXuHzC52uXLWxOozt93VHYfPrqzHk9PptkYa3aFOWjF9WaaY3x/v6P8ulD+Tx7INv+4/niRezoaXxz7LULaDqsWrIAb3/18Tlfvfef728xXbAbzKTlIQaP0q9aJE82sweDG/0j7nE1ydVmdl8H59kEYIP7OQfAze4x5iNmdoebU/5NkpvMzE8t/IKZXdvBOY+YuJ6jaUUUdNv1LUiANIW3wtRN4sfEhm4rwIlKvZFrQ5IMRmqgHu3vb6rxzd2LiK+d8BW/cfFgsyWHs0B+tu8AnjmU9YfXKmm79qJiPSA7Q9pf99plC/H9D16Q+R3GkwjTz/vhRtn2GUcvGsZTz03khuT49hxPR26rT189hn+48xczUuVNJrMtzlyfT5mNGUTLo4wPXXoqRmpV/PpLV7ffeQaJs63E3KKVnf4+AFsA/FXBewbggoLtZVwO4BZLAgR3kVxGco2ZPdY8oNlBAHe45xMk7wOwrvhwvSGTDVVQJFgPRCLeXg/amQDJjTG0SPxbtSG3PbIwmjPMo5Rf/wfpYxupOyvZ7qu2ffwgbTboiv6CWEi1Qtx69yOZ/YC0E2+yX7HlEbZu8J8tuqHGxY0e7zaLq7OXjzrxiPb3AfFLz8gGEC84+RhccHLnqZ9lhLMtWjFUIUaHq80BUYPM8tFh/PmbTu/5eb0lN51Gk2L2USoeZrbFPZ4/A+dZi9RyAYA9bttjRTuTXAbgNwB8PNh8BcnzADwE4Hoze6Tgc1uQCB6OO+7Ig02ZgHnDMmm0QBroDjOqgNRtFfrUhyrEhJuel7VIIrcVs+LhLYChZrPB5GbriwHjtiJ79x/GcLWSazYYxzBIYmGt2kztDTNiMnMfCooEgWzKZGz9ZH+H+c8CaW1HHGdYMTqM3QX7VyrEzj+6uHAAUD8giR9PM4d/vtKqhY2Y/ZT+q5J8BcnVweurSP6zi13k+xq0puirR2FOC8khALcC+ISZ/dxt/gqAF5nZGUiC9J8p+qyZbTezMTMbW7VqVYdLzFMmEnF9RjNDyhXY+TYk1djyKLBIht1wp0LLI9MjK9nff5vz/uu4lfre/eNYPjqcBvdL3FbhsYBsIDpsY1EriW2EKZO+I2/RTcL39SlzW8WWh584F8c8gCQjRTei2cNI1OVAzC1a/SX+LYAJAHDf+G8EcAuSlN3t7Q5McqsPcAN4FEA4MWWd21bEdgD/Y2bNAkQze9LMfFrwJwGc1e78M0Ezq8oVCTaD2UGKrQXuLMCLRCMTIwF8hXmyPdOee6hSmG1VrSSpur4VdS2yPPzNPs6l3394KjOzoha5rcrE4P+eTdtAZ1t354sEgax4+GO2mmuQE49aPuYBpMVSReIxUwxijcZcpBnvkuDPSVr5AKpm9pR7/lsAtpvZbQBuc4LQEjO7CcBNAEDyUgDXkvw8kkD5M2G8w0Pyw0jG3F4TbQ/jI5cB6EmLeLr/8+bqOeIiwWb9R04kEgsjKyoMLJh0e62azCSPW7VXmWRnTdXNtTJxAXP3bc5nVfmMlrA7aDiqMt9tt9gN9fizacmOv7kOVyuZzJ1sf6q826pW5LYquW+kxYrZ/4JePMZLigFngh03XIzJRveOLxJkecxtWooHySFXWX4hXCxhGp8r4msANgPYjaRD79v8GyR3mtlGkusA3ADgQQD3uZvW35jZpwBcR/IyAFMAngJwdYfnf15k2pCEbqtMw0TkAuNT9QYMWctjqJIGxjPurAoxUW+UtieZDGZtAOnNP7Y8wnhDWG3thaDIbRVaIb/72rRbqD9HbC2UWR5DLdxW3mOZbx9e7LbywlfU+nqmaDWNTcwcRy2soUJgUW0w4lRiZmn1r3orgG+TfALAIQDfBQCSJyKtNp8WLstqa8l7G93jHhTHRmBm2wBs6+ScM0E2tmGZ2AaQWh7MWBLJxMAKEcU80t5WodtqOMq2qkTWzfhktgaDZDKhb6qRzEePYh5Atto6F/MoyJ7adNrq7ByGZqAz+88RVs6G7RuGW7it/K8gDnB5yyM2VnyxX9gXS8xOLt+4FhuOWSKxnqO0yrb6M5LfBLAGwO1mze+OFQDv7sXi+k123Gz2Bu0D2hZbHpXE8qhWsi6fZHt2IiGQfFufKnJbefFwIhHixSMzIS/oFhtmJMVdeIssj9hi8PvErSDCcauFMY8W2VYWmR5ldRnhuFUxuxmpVQtnf4i5QUt70szuKtj2UPeWM1ik2VZwlkfwHunak0TbXYEdkcZIgKDCPA6kV73bKlskmIpHPXdzH6lV8ezhqcxQncXBzXxx0JfHH+fhJw8CKE69jY/frEJv0Wo87PZZKzkOkGZblbmtYl567FJ84JKT8KYzp1dvIYToD0qDaEHYVTeMeQCJe8nP88haEi5gbtnYRrWSbs8HzPPdc4cCt1UccPRB87Lq74zlUdIuHUjdTWHb9fC4RTf46y7cACArVt691SrbKrY8vKjFtgpJvOt1J5bOpBBCDAYSjxbQjYptNHy2Vd4NFc75AMKJgdnYhhcJy4lNVGEedNUFEssjHuLjW4uUpbOG4lGNhCeTPVViMTTdVgXicf1FG/C/f7E5b88apAAAEaVJREFU87totqEocFu9/dXHY/3yhbnW0l5L+jELQwhx5CgNog0+tlFvGBYE39ArTFuyh/fMoQoxWTcMV6P2JM6dFRcJNi2VuKuuu+kfnmzkAtep5VHs+lkSiEer1hDDJTGPBS3Eqehm7zveFr13/MpRfPcD+U420+h6LoQYYGR5tCGZq2G54r5m76m4zsOl6tbjAHvVi0e+gnuiwG0VWh5xwNxbHmUxidFpikdZrKKV26oIPzP7mZKOt0X4VcXXJoSYHcjyaEMoEnFgvLjOw6XkRgH2msvCMrNM4VzqtnLHDbrqAkm2Ve7m3sbyyLitKmxeQ0xaDJi9gaedd6f33cIPH3riwPTF49Iz1uCeh5/C771+8BsLCiHyyPJoQ4V0M8yzAfDUIonqPIJxs7GFkRT95d1Wk1OWG/rkLZDCgLl3K5VYHosD8SCJO7cVN0COpxPGx28VAA9ZvTQJbj/53PQHS47UqrjxijPm1fAcIeYSEo82+NhGmdsqV+cRxDAyIjHkYxj1jAjVXIA919vK7XO4KGBekG0Vsjia87y0ZB5z2BY+xIvSdB1KTctjvwr7hJgvyG3VhnCuRuiGqjDteltU5xGn8Prg9OHJelSRXsHEVH7ok7c2xosC5k3LI+u2IpMspsVRm/N2sYtctlXQtn06LFtUw7qjF+L6IxjfKYSYXUg82tDMtorcUEnX23zMI7Qkwv19HOHwZD1XJDhZzw99ahkwL7E8Xnn8Ctz58yexaMH0At3Ndukl2VzTjWWTxPd+v9g1JoSYm0g82hDGNkKRSEQlqQHJdM+tpONm42JAAHhuPFsx3gyYR+1JWgXMj1s+mrw3me0Mu/2qs/CLJw92PPOiWimxPDo6ihBiPqGYRxuybqs45tEoqDBPA+aZBoi+x9RkPZqXUWnWfwDZwVJAYqnE6banrV0KIJk/HrJkpIbT1h417Wsr80pVpumuEkLMX2R5tMHHNnJuKzIX5AbSVN04O8sHpZ8bn8LKYN6Gd2eNT9Yzx/KFfg3LDmcCgJcemwiEHyE7He7admFutkaZRjQ7YEpEhBAlSDza0IxtNLI320qFrjUJirvnllgeByey2VPeqjjshh95wfGFd0C+Nfry0WF86NJT8OoNK6d9HeHxYiyq9272oZJ2CCFKkHi0oRnbyDU6dB11Ecc2kpnkjUZ2ip5vPvjcxFQmNdbHJ7zl4QUnbAwYxyQA4JrXvPiIr61MHVLtkHoIIYrpa8yDCZ8guZvk/SRfXrLft0j+1M9EJ/mCXq3RN0aMi/6826qswjx2cw1Xkwwos2wDwVozCys7t2PhcLU5fKmo4eBMErdL95aIvFZCiDL6bXlsArDB/ZwD4Gb3WMSVZrajVwvzJBXm+e65lUqynVFWlR83O1VvYGhBvm158jyYqeEbIE5liweBZJ730wcnuzYDukwcTlqdBORfdcKKrpxXCDH76bd4XA7gFjel8C6Sy0iuMbPH+ryuJtkGiMF2Z3mQjGIeyfaJumVFInBVhWLg4x+HJuq5gPaao0bw4OP7cwHzmSbuerVx/TLcfcOFeMGS8jiJEGJ+0+9U3bUAHgle73Hbivh757L6Q5aUPpPcQnIHyR379u2bkQV6yyOOeVRKKs+H3FjZyXqjdFhTOEfcu63Gpxq5NiRHL0qysrrltjr/pMT7t3Hdstx7Eg4hRCv6LR5Fd8WiUQ9XmtnpAF7jft5adDAz225mY2Y2tmrVqhlZoLc8vJXR3B6ISq7RYaOByXq2rciCEsuj6baarOcqupcurLk1dOef6eJTj8GuP70Ep6+bfm2IEEIAfRAPklt94BvAowDWB2+vc9symNkv3eN+AJ8DcHYv1go4C8NVksf1HM1W7dGEQbNEDOJK8vSz+e1PHBjHkqiB4VFOPLoZL184PL1WJkIIEdJz8TCzm8xso5ltBPBlAFe5rKtXAngmjneQHCK50j2vAXgDgB/1ar3VZrZVthiwksm2Svf3VsXBiXo2JXeo2G3lhWTPrw7hmKXZ9uRePA65NF4hhBgU+h0w/xqAzQB2AzgI4G3+DZI7ncAsAPB1JxxVAP8B4JO9WmDTbRXN7fDdc4fNUKmElkQqHsMlcY6w3Yjff3yqkSvk826rgxMSDyHEYNFX8XBZVltL3tvoHp8DcFYv1xXi25NM1hs5MUh6WFWjWeXJPvWGZWIeRUHyeHscpF7suuN20oZECCF6Qb8D5gNPtUKMTzVglr3RDw8lczhykwRDwRiqFD7PFAkGxzxmaVY8Frm5HAcnJB5CiMFC4tGGaoU47NxG4djXBU3xQNRttzhIXlYkGGZexTEPP4v8uXG5rYQQg4XEow0kmwHr2PIYn2oU1HmUiUTx8/CY8bhY3313TYumhkII0Q/6HTAfeKpMs53Csa8LhqpNt1Vc5+EZLqkML7NC4rTZF64YxaeuGsPZL15+ZBchhBAzjMSjDdUKcWiiheVhyNV5eOJW6un24uD5SC0vNhedeszzX7wQQnQJua3aUAncVrmYRz3vtspYFUPFv96Mayv48IIhFewJIWYHEo82+DoPIG95AEk33KJU3Xj/kLLK85GaxEMIMTuQeLQhdEllYx5+Dkc9N8/DMxxZHqMuplFmnRS5rYQQYhDR3aoNYSfd0JJYEAxxytZ5FFsVAJq9q8pSeGV5CCFmCxKPNlQzlkfebXUotjxKCgABYOnCIbdPWDAot5UQYvYh8WhDpdTySG70E1ONFnUe2Wwrb3n4eeZA1i02UhJgF0KIQUN3qzaExsNIgeWR7BM2PSwPmC8dSSyPRqP4XN2eGCiEEDOF7lZtyFoe+YA5AKxwleBA64C5tzz2j0/O+DqFEKKXSDzaUGkT8wCAFaOpeLQKmK9cnPSumpgqMT2EEGKWoArzNpRnW6VWyIrFaUPDVgHz973+JagNEW88s2xMuxBCzA4kHm1ol20FZN1WtUwhYTZgvnjBELZtOqUbyxRCiJ7SE7eVGzP7CZK7Sd5P8uUF+yzxs83dzxMkP+beu5rkvuC9a3qxbqBVtlX6fFVoeZQ0PRRCiLlEryyPTQA2uJ9zANzsHpuY2X4AG/1rkvcC+FKwyxfM7NruLzWLv/9XK8y2Ui8LmAfZVn4ehxBCzDV69dX4cgC3WMJdAJaRXFO2M8kNAF4A4Ls9Wl8pPmAep92Glsfy0VA8Ustj+aJhCCHEXKRXX43XAngkeL3HbXusZP+3ILE0LNh2BcnzADwE4HozeyT+EMktALYAwHHHHTcT624GzOO028XOqjh1zdJM8Dx0W4WZWq24YfMpWDwiK0UIMXvo1R2r6C5qBds8bwbw1uD1VwDcambjJN8J4DMALsgd0Gw7gO0AMDY21ur408YHzOP4xbJFw/jcO87BmeuPzmx/PnGOd5z34ue/QCGE6ANdc1uR3OoD3AAeBbA+eHud21b0uZcBGDKze/02M3vSzMbdy08COKtLy87hA+ZFg51edcLK3PS/oWlaG0IIMZvpmniY2U1mttHMNgL4MoCrXNbVKwE8Y2atXFa3hhui+MhlAHZ1ZdEFePEIs65a4S2VdUcv7NqahBCi3/TKbfU1AJsB7AZwEMDb/BskdzqB8fym2zfkOpKXAZgC8BSAq7u62oAw22o6kMRnrzkHJ61e0sVVCSFEf+mJeLjA99aS9zZGr3MBADPbBmBbd1bXGh/07sQbde6JK7u0GiGEGAxUxdYGn2013cwpIYSYD0g82lCtdBbzEEKI+YDEow1pwLzPCxFCiAFC4tEGWR5CCJFH4tGGisRDCCFySDzakAbM+7wQIYQYIHRLbEOzzkOWhxBCNJF4tIFK1RVCiBwSjzZUO2xPIoQQ8wGJRxt8tpXcVkIIkSLxaIN3V0k7hBAiReLRBm9xTLcxohBCzAckHm3w2VaKeQghRIrEow1eNKQdQgiRIvFoQzNgLreVEEI0mRXiQfJkkneSHCf5/n6sQW4rIYRI6dUkwSPlKQDXAXhjr09cbxgAiYcQQoTMCsvDzPaa2T0AJnt97oYl4lGdFb8pIYToDboltsEZHrI8hBAiYE6JB8ktJHeQ3LFv374ZOabcVkIIkWdgxYPkVpI73c+x0/mMmW03szEzG1u1atWMrMO7rdQYUQghUgY2YG5mNwG4qf/rSB6lHUIIkTKw4hFCcjWAHQCWAmiQfC+AU83s2W6fW24rIYTIMyvEw8weB7CuH+f2xYEjtWo/Ti+EEAPJrBCPfnLpGWuw6/Fn8a7XndjvpQghxMAg8WhDrVrBtk2n9HsZQggxUAxstpUQQojBReIhhBCiYyQeQgghOkbiIYQQomMkHkIIITpG4iGEEKJjJB5CCCE6RuIhhBCiY2i+898cg+Q+AL94nh9fCeCJGVzObEDXPD/QNc8PjuSaX2hmbduSz1nxOBJI7jCzsX6vo5fomucHuub5QS+uWW4rIYQQHSPxEEII0TESj2K293sBfUDXPD/QNc8Pun7NinkIIYToGFkeQgghOkbiIYQQomMkHhEkLyH5U5K7SX6w3+vpNiQ/TXIvyR/1ey29guR6kneQ3EXyxyTf0+81dRuSIyTvJvlDd81/0u819QqSVZI/IPmv/V5LLyD5MMkHSO4kuaNr51HMI4VkFcBDAC4GsAfAPQDeYmY/6evCugjJ8wAcAHCLmZ3W7/X0ApJrAKwxs/tILgFwL4A3zvF/ZwIYNbMDJGsAvgfgPWZ2V5+X1nVIvg/AGIClZvaGfq+n25B8GMCYmXW1MFKWR5azAew2s5+b2QSAzwO4vM9r6ipm9h0AT/V7Hb3EzB4zs/vc8/0AdgFY299VdRdLOOBe1tzPnP/mSHIdgEsBfKrfa5lrSDyyrAXwSPB6D+b4TWW+Q/JFAM4E8N/9XUn3ce6bnQD2AviGmc35awbwMQAfANDo90J6iAG4neS9JLd06yQSjyws2Dbnv53NV0guBnAbgPea2bP9Xk+3MbO6mW0EsA7A2STntJuS5BsA7DWze/u9lh5zrpm9HMAmAFuda3rGkXhk2QNgffB6HYBH+7QW0UWc3/82AJ81sy/1ez29xMyeBvAtAJf0eSnd5lwAl7kYwOcBXEDyH/u7pO5jZo+6x70A/gmJO37GkXhkuQfABpLHkxwG8GYA/9LnNYkZxgWP/w7ALjP7aL/X0wtIriK5zD1fCOAiAA/2d1Xdxcy2mdk6M3sRkr/l/zSz3+7zsroKyVGXBAKSowBeD6ArmZQSjwAzmwJwLYCvIwmiftHMftzfVXUXkrcCuBPASST3kPydfq+pB5wL4K1IvonudD+b+72oLrMGwB0k70fyJekbZjYvUlfnGccA+B7JHwK4G8BXzezfu3EipeoKIYToGFkeQgghOkbiIYQQomMkHkIIITpG4iGEEKJjJB5CCCE6RuIhRBtIrghSeh8n+cvg9X916Zxnkiztx+TqNrqSginEdBjq9wKEGHTM7EkAGwGA5B8DOGBmH+nyaf8AwIdbrGkfycdInmtm3+/yWoTIIctDiCOA5AH3+DqS3yb5RZIPkbyR5JVuhsYDJE9w+60ieRvJe9zPuQXHXALgDDP7oXv92sDS+YGvIAbwZQBX9uhShcgg8RBi5ngZgPcAOB1JBftLzOxsJO3A3+32+TiAvzazVwC4AsWtwseQbSnxfgBbXVPD1wA45LbvcK+F6DlyWwkxc9xjZo8BAMmfAbjdbX8AwPnu+UUATk3aawEAlpJc4uaKeNYA2Be8/j6Aj5L8LIAvmdket30vgGNn/jKEaI/EQ4iZYzx43gheN5D+rVUA/JqZHUI5hwCM+BdmdiPJrwLYDOAukheZ2YNun1bHEaJryG0lRG+5HUnzTQAAyY0F++wCcGKwzwlm9oCZ/SUSV9XJ7q2XoEsdU4Voh8RDiN5yHYAxkveT/AmAd8Y7OKviqCAw/l6SP3KdUg8B+De3/XwAX+3FooWIUVddIQYQktcD2G9mrWo9vgPgcjP7Ve9WJkSCLA8hBpObkY2hZCC5CsBHJRyiX8jyEEII0TGyPIQQQnSMxEMIIUTHSDyEEEJ0jMRDCCFEx0g8hBBCdMz/A3HHRRpAhluCAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -291,7 +291,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 270\n"
+      "Starting experimental run with id: 316\n"
      ]
     }
    ],
@@ -314,7 +314,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -347,7 +347,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 271\n"
+      "Starting experimental run with id: 317\n"
      ]
     }
    ],
@@ -374,7 +374,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -407,7 +407,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 272\n"
+      "Starting experimental run with id: 318\n"
      ]
     }
    ],
@@ -435,7 +435,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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cgdzMhDEIgiAToyMzf3nlRIzGMFEQBEFCW3vrDhNFzyAIgiClr6+/bAmlYIiR8E0UBEGQ0NNRKVtCKZiRxx2FW0r/hM0c7CEIgsdp5RjIRW06a2ZKNwY0cbCHIAgeZ/m8LWVLyM3q+knqYhD7DKaDZg72EATB46y+d1nZEkojJpCDIGgIR517ftkSctM2u7tsCaVgKILbNAuSzgLOAli2rHVbJ8HMYXC/0bIl5Eb+JBdG9AyahDRS0EUAK1eubM3FzsGMYu49/iqXwQVlKyiHJLhNuKMIgqABeGzR9GwqW0E5GLEDeVpo5mAPQdAounaUrSA/LbDUfkIi0tk00MzBHoKgUbisWFt0zsBM0TMIgqAxjDr85VV6ylZQHrHPIAiChjCwsGwF+8DMrw9rYhQX9rKZCWMQBCXQt9bfFHL/oplfIdbCEMOjsZooCIIGsHVFa1asXol9BkEQBC1O7EAOgqBhDC8cKVtCbtrmDpUtoTRGo2cQBEFDaPM3Z7Df/J1lS8jNfQXkYUYEtwmCoDG09w2XLSHIiCEqMYEcBEEjOGDh9rIl5MZaoHU8EbEDOQiChrBpR2/ZEnIzb3arxkAmJpCDIGgMHe3+JpA3bZlTtoSSCHcUQRA0iIVzdpctITfrdrSuP4rYgRwEQUNYu2ZR2RKCjMRqoiAIGkb3I51lS8iPv9WwhRCriYIgaBjyN2XASE+LWgNimCgIggbRs7FsBfmx9plfIdYiVhMFQdAwuna0bivbI7GaKAiChjDqsZXtUHIhWDiqC4KgQcze6G/SYLh35reOaxHBbYIgaBibj/K3OqV7S9kKysGAymgxhlDSycAFQDtwsZmdN+78GcDHgfXpW/9uZhcXcvM6hDEIghJoc+inbrhVNyBTzASypHbgQuCFwDpglaQrzOy2cUm/aWZvn/INcxLGIAhKYPfB/iaQrdOf5iIoMLjNccDdZnYvgKRvAK8AxhuDUghjEAQloIq/MejR7tY0BlDYnMESYG3V8Trg+Brp/lzS84A/AO80s7U10hROGIMgKIHKgkrZEvIzy9+kdyFYrmGiRZJWVx1fZGYXpa9rZTLewv4AuNzMBiW9Bfgy8Ke59O4jYQyCoAQ6FwyWLSE3w7scutAogJwTyBvNbOUE59YBS6uODwEe3ONeZpuqDr8AfDTrjadKGIMgKIFD9vO3NGf+wQNlS8jNmgLyKHDOYBWwQtJhJKuFXgu8vjqBpMVm9lB6eCpwexE3zkIYgyAogU27/AW3mdM5VLaE0igiypuZVSS9HbiKZGnpJWZ2q6RzgdVmdgVwtqRTgQqwGThjyjfOSBiDICiBwxZsLltCbrYPdZctoTSK2nRmZlcCV45774NVr98LvLeQm+WkocYgwwaLZSQTJPPTNO9JCysIZjT3bt6vbAm5WTRnZ9kSSsHyTSC7pWHGIOMGiw8A3zKzz0p6EonFXN4oTUHQLEj+lmmu3zy/bAmlUcQwUbPTyJ5Blg0WBsxNX89j3Mx6EMxUdjoMITm6tatsCSUhRgpyR9HMNNIYZNlgcQ5wtaR3AL3ASbUyknQWcBbAsmXLChca+OfQL36sbAm56Njizxj486ZUDBHPYOpk2WDxOuBSM/ukpD8GviLpGDMb3eOiZNPGRQArV670178OGs7s+32tgffom8hadbmJJfMGM51Gfr11N1gAZwInA5jZLyX1AIuARxuoK5iBdG0vW0E+BhaVrSA/nc7KuEjChfXUqLvBAngAeAFwqaQnAj3AhgZqCmYolVllK8jHLIfNnTaHHjSKwIgJ5CmRcYPF3wFfkPROkjI/w6wVOmRB0bQ72w/VucvfY94C9eEERKSzKZNhg8VtwLMbqSFoDXo2+6pcK7P8VS49m0brJ5qhjI76+77y0qpTQsEMY6jP14/V27AWwGj7zF9eWQuzGCYKAjd4q1zlsJHdqnMGEEtLg8AN3kIyjvpaCZvg0IAVRSvMZIYxCGYEw/NbuKaaJvo7Zn7reCJimCgInKADfAWLGen399Mb7StbQTkUGM+gqfH3RAZBDXp7fQVeaevzN+6wdYOzsbiiiAnkIPDDMw6alpjhhXHLxsVlS8jNkw9fX7aE3BQR6QzY25HODCSMQTAjuHWTr8p12bytZUvIzSO7W7RnQPQMgsANT13ky/v54Ii/n94xCx6qn6jJuL6gfJp9NZGkQ0hc/jwXOBjoB24B/gv40Xjnn7Xw90QGQQ3u2e4rctjsDn9uSx/cPbd+ohlIs/smkvQlkpABPwQ+SuLoswc4ksQR6PslvcfMrpssnzAGwYxg/RZfUbhmdTtzpgTs2t2iMZANrLndUXzSzG6p8f4twHcldQF1A8GEMQhmBMMDvh7l9jZ/+yJGK63pjgJo9gnkkyVtNbN1tU6a2RBwd71M6v6C0qAzp5OMRS1mz7Gor5rZtjyqg6AhbPYVkrHygL9IZ6PzR8qWUBJq6mEikiGiX0q6D7gc+LaZbcybyaTGQNKPSALSfB/4CHuORZ0IfF/Sp1J31EFQGj0bfLVaBxc2d1OzFr0PtGrgS5q6Z2Bm75T0LuB5JJPI/yjpZhLD8J9mtiNLPvV6Bm+oYWF2Ar9J/z4pyWHMpmCm0ZnpcW8e2geauqVZky5nZVwYDjadpXFgrgWuTePInAScB3wOmJ0lj0mNwZghkNQL9JvZqKQjgaNJlisN70t3JAiKxpvjt3Zf3jMAMF+dr2JpcmMwhqSnkPQOXgNsAt6X9dqss27XAc+VtAD4CbA6vdlp+aQGQWPo6C9bQT72u9WX+wyAwQXOLG6RNPEwkaQVJAbgdcAI8A3gRWZ2b558shoDmdluSWcC/2ZmH5P021yKg6CBdG1v4l9rDXYs87dMs5XjGTSzMSAJLXw58Boz+/2+ZpLZGKSrik4Dzsx5bRA0nDZnC1002ty1Sy1GunwMlRSO0ezDREfU22EsSfXiy2et0P8P8F6SmelbJR0O/DTjtUHQcLq2+7IGW1b4a0t5jM5WFE3ujuJ/JH0H+L6ZPTD2ZrrZ7DnAX5LU15dOlkmmJzLdxnxd1fG9wNn5NQdBY/BWuQ4uLFtBfjp2la2gRJrbGJwM/BVwuaTDgK0kWwDagauB883spnqZ1NtncBHJHMFe41DpCqPXAINm9rX8+oOgOIbmla0gH0MH+/NNxEOtO4GsJnZHYWYDwGeAz0jqBBaRrP7M5Rq3XnPqMyQbGJ5Csut4A4nFWQHMBS4BwhAEpVOZ09xNt71o9zfmMrTU4XrYIjCavWfwGGY2DOyTe9l6+wxuAl4taQ6wksfdUdxuZnfuyw2DoBEsfurDZUvIxe4hX+4zALbv9OdCoxjU7BPIhZB1zmAncE1jpQTBvnP0/EfLlpCLtbt8eVkFOHCOvy3I9xSVkZOewVTwNesWBBOw+pGlZUvIxVELN5QtITebB2eVLaE8whgEgQ/mdPsaz17Q1cpLcxzSxMZA0g5qKxSJ26JMUYlyGQNJvWaW+SmWdDJwAckSp4vN7LwaaV4NnEPyYW42s9fn0RQEAItn+xrCuG3rQWVLyM2Cbmc+P4rCiltNlKVOzIuZ9U1ZGBmNgaQ/AS4G5gDLJP0R8Ndm9tZJrmkHLgReCKwDVkm6wsxuq0qzgmQz27PNbIukA/b9owStzIb+3rIl5GKo4q9TvskyOb+cmRTQM8hSJxZBWo8+NttfvRFtMrI+kecDLwauSDO/WdLz6lxzHHD3mLMkSd8AXgFUf/A3Axea2ZY0X1+zgEHT8MCjvnZxzZrtMOzltlZdTVQYWerEfUbSqcAngYNJYs8cCtwOPDnL9ZmbJ2a2Vtqjq1Rv//8SYG3V8Trg+HFpjgSQdD1Jt+kcM/vv8RlJOgs4C2DZsrqhPIMpcvSHzi9bQm5skS/Hb7vlSy9A19bW9WGtYuYMstSJU+GfgWcBPzazYyWdSOLJNBNZjcHadKjIUn8XZ5NYnMmoNcg2vkg7SDawnQAcAvxM0jHjd86Z2UXARQArV65s4qmcoCy6N/qqqAYO8uVLCaDN4abpwsi+z2CRpNVVxxel9RdkqxOnwrCZbZLUJqnNzH4q6aNZL85qDN5CMumxhMSaXQ28rc4164Dq9X6HkITQHJ/mhnTX3H2S7iQxDqsy6goaQNf2shXkZ9cSX22E3jUtHELSGwZk3zC+0cxWTnAuS504FbamG4SvA74m6VEgs+PxrJvONpI/kM0qYEXqOGk9SfCF8SuFvkfSjbk0DZ95JJArIENQPB6jcM27x9cO0Z5N/txRDCzw1fsqkoKGibLUiVPhFcAA8E6S+noecG7Wi7OuJjoMeAewvPoaMzt1omvMrJLG4ryKZD7gktT99bnAajO7Ij33Ikm3kcxBvNvMNmUVHzSGrh2+WtlQ2I912uja4W+YqH//1jUGRQzmTFQnTj3nx/KvXvb/5bzXZx0m+h7wReAH5OgwmdmVwJXj3vtg1WsD3pX+BU2CORzBaBvyZQ08urrpW9fCoc4Kerxq1YlFIenPgI8CB5DMTzRk09mAmf2/fZMYeKPdWcUKMDjPV6vV45BL+6C/56IIZG56nh8DXm5m9Rb31CSrMbhA0odIJo4fG1E2s9/sy02D5kb+RjCQs0areQwN4KNCbAw+unKP7KshgOzG4CnAG4A/5fFhIkuPgxnGI8e5ePD3YPb6shXkw/xtQEY+KsSG4CTk52pJ3yQZ1q9utH83y8VZH8n/BRxuZv62TQa5Ge3y1wTc8QRfmtsH/FWsu5b6KuNC8fHR5wK7gRdVvWdAocbgZmA+yRbnYIZj3T6aQdVotq9xIlvoo3appq3Nn+ZCcDJnYGZvnMr1WY3BgcAdklaxZ/djwqWlgV/mLvblARRgZNTXhOzTD1pXtoTc/HLN8pIVlIgDYyCp1iKfbSRL+b9f7/qsxuBDuVQFrlk6L1cc7aZgkzOvpQ/uzrTar6k4aKG/rektFumsBzga+HZ6/OfArcCZkk40s7+d7OKsO5CvnZLEwBUHzPLXM/CmubvN17AWwIbBQtzmu8TDMBFwBPCnZlYBkPRZkhWgLwR+X+/iSY2BpJ+b2XNqRNLJtZkh8EVl1N+us77OgbIl5GJXxZ/X0v27fRncQvFhDJYAvSRDQ6SvDzazEUl1nczU6xn0QnGRdAIfPNzv7+te1L2zbAm5WD80v2wJuemd7dBpVRE4mUAm2XR2k6RrSBrszwP+RVIv8ON6F9czBj6KICiUUYfryR/s91W5PrLLn8Fds21B2RLKw0FNaGZflHQlSRAdAe8zszGvqO+ud309Y3CApAn9BpnZpzIrDdywcZevyViA+x/dr2wJubCCYupOJwfu528CuTCa2BhIOtrM7pD09PStsQA6B0k6KKuniHrGoJ0k7rG/JzfYZ4Z+5SuEJMDI4b4ir2inv3mZRx9qzRDloumHid5FEgnykzXOZfYUUc8YPGRmmf1hBzOD0a6yFeSn9w5fzn5GZpWtID/dW8pWUBLW3O4ozOys9P+JU8mnnjGIHkELMu/u5m4G1WK419ej2u/QGFRml62gRJr4JyHpmcBaM3s4Pf4Lkj0Ga0jiym/Okk89Y/CCKakMXDLqq5ENQO/Dvlyt9mz1ZbwAhmf701wYTWwMgM8DJwFIeh5wHkkwsqeRxI5/VZZMJjUGWS1KMLPwGPayMstXRdW1o4nHHSZgaI5DV6sF0eRzBu1VdfVrgIvM7DvAdyTdlDWT1v12gwnp3u6rle2R0Q5fxgtgzkP+dk0XRpMbA0kd6c7jF5BMJo+RuY4PYxDsxfBsX07fwF8UruE5/sq4MuKrjAvDaHZjcDlwraSNQD/wMwBJR/D4buS6hDEI9mJwvr+Kqsl/rHvjTS9QaeE5gyZfTfQRST8BFgNXp7HlAdpI5g4yEcYg2Ishf5tj6XLmNmfA1x45ANr7y1ZQHk0+Z4CZ3VDjvT/kySOMQbAXOw9t4mbQBMy701dvZtBhcBvr9Ke5MFrgo4cxCPaimbvEE7Htic4mvdv91S5tu/ztmi6E5p8zKIQwBsFezDvcX3CbXbt9uYSWwxCSlb7WNAaiNXbfhjEI9uIpix4qW0JuvMUz2DLkbzvvvE5/kwafLyojf7Y7N2EMgr3wVrECHD5rQ9kScvFwuy+X2wBbhx360CgIj0OneQljEOzF0Ki/x+JXlLGrAAAWqElEQVTajUeWLSEXszt8eVkF6HIYqrMwomcwNSSdDFxA4gr7YjM7b4J0ryIJ4vxMM1vdSE1Bfdbs9BfE5LA5vjynzGofKltCbrZXesqWUA5+Ip1NiYYZA0ntwIUkwZjXAaskXWFmt41L1wecDfyqUVqCfDxp3sNlS8jNH3b48rU/UPHX+xoa8ae5MMIYTInjgLvN7F4ASd8AXgHcNi7dP5PE7vz7BmoJcvCD259StoTczJ3na3Jz2xZ/E8hds/0NbRVF9AymxhIeD78GSe/g+OoEko4FlprZDyVNaAwknUXqfGnZsmUNkNo4jvi4v8igHQ4nCrce7GvZo8ewlyMP+Vq+WyQxgTw1aj3tj9lXSW3A+cAZ9TIys4tI/HKzcuVKVza6vd/fj9581asAdDzqKzxbt68pjgR/j3IxxKazKbMOWFp1fAjwYNVxH3AMcI0kgIOAKySdOpMmkT3GBujcWbaC/HT4GiWif1HZCvLjbPVusYQxmBKrgBWSDgPWA68FXj920sy2AY/9JCRdA/z9TDIEAD0OW4DDvWUryM+wL9dE9K31V7t09LfAWEkNRMwZTAkzq0h6O3AVydLSS8zsVknnAqvN7IpG3buZ6Nnk7wfUudPfeMCc9b66YG1D/p6L0S5nFrdIwhhMDTO7Erhy3HsfnCDtCY3UUhazH/a3m3d4jr8gyMPOQjJ2bfW3z2C4z1cZF4ls5luD1v12p4mBRf5WYLQP+Gu1emtpDy3wNeENMNSijuqw4lcTKZkovQA4BdgNnGFmv6mR7hqSoDVjs2IvMrNHi1WTEMagwYz0+Bty8TjpPdrtawjDY8XaudOZm/AiKb5j8BJgRfp3PPBZxi29r+K06ZhLDWPQYAYW+KqkAHYu9qfZW6SzwXllK8hPR3/rVhcNmEB+BXBZGqLyBknzJS02s9JcBrfutztNDCwsW0F+RvztOWPIWeXasatsBfkZmlu2ghIp3hjU2pS7BKhlDL4kaQT4DvDhqhjHhRLGoMH0H+Kva20Oo3B1bPc17DJwsL/nQt3+NBdCPkd1iyRVD+lclG6aHc+km3KrOM3M1qc+3L4DvAG4LLOaHIQxaDQ9Dn9A3hbtA/OO9rWhY16Ps11yQP+wv0nvNQXkIXJNIG80s5U185HeBrw5PVzF5JtyATCz9en/HZK+TuLzLYyBR4494oGyJeRmd8Xf0tL5Xb6W8G4f9rfK7Kh5DVnE0lAKc4VcwMiMmV1I4skZSS8F3p468Dwe2DZ+vkBSBzDfzDZK6gReBvx4ykImIIxBg3nCnI1lS2gJhkd99Wa62vxt857T4W9vRFE0YAL5SpJlpXeTLC1942P3km4ys6cB3cBVqSFoJzEEXyhcSUoYgwazYXBO2RJyUzFfFSvAsXN99cA2Dvl7LnZW/A0TFUIDHNWlk8Bvm+Dc09L/u4BnFHvniQlj0GDmdvgavgBY1+8vPu9aZ8u2+hw+FzdvWlK2hNIIF9bBlLlju68IXACP7vTXaj1k1tayJeRiV8XfnMEBsx26sy0KfwvschPGoMHcddfBZUvIzdyDnO3gArY6C8jz+42Ly5aQmycs2FS2hHIw0OjMtwZhDBpM53x/vh129/sbG/7Z7SvKlpCLrjn+JmN/vebwsiWURriwDqZM583+Vo0M+xsloq3b16+153f+DG5lma8yLpQW+OhhDBpMj8PoULP8LSen4swhYPc2j7WLrzIuighuExRC9w5/yxBGuvz96K3dl2aPlcuc9f6e5UIwK2TTWbMTxqDBdDk0Bu27/bnQ8BaFq33AXxlrxN+zXBQejXdewhg0GFX8PUUjPb4qVoDKLF+aHe7rw9pbt7qIfQbBlGmr+HuKrM3XkAsA8qV5eI4vL6vQGhViTQyIpaXBVBlY4M/pW2e/v19999bhsiXkYttyf5vOeh+tlC2hPGa+LQhj0Gh2LPM3HtDm0IV1+5CvR3nUl1wAdh7sUHRBxJxBMGUGF5StID8du8tWkJ+Ks0brsLPIbACd28tWUCKxmiiYKkOL/K0aYaO/8eyhpb529NqwrzkOgMpyZxa3KKw15kvCGDSY5Uc8XLaE3Kxb4M9raV+PrzmDZfO3lC0hNw/tbM0gyMmms+gZBFNkyZxtZUvIzYsX31a2hNzct3v/siXkYmHXrrIl5GZOp6/eF8Bvi8ooegbBVOlohf5lE9Dd5msIY3GXL5fbALdu8+dptSiiZxBMmVctWlW2hNx0yVfFCnDHTl8V1XqHKwsOb9UQrg2IdNaMNNQYSDoZuIAkfufFZnbeuPPvAt4EVIANwF+Z2ZpGappufrbjqLIl5GZ2u7/hgAO7fS11eXbfXWVLyM36YX8GrBjCN9GUkNQOXAi8EFgHrJJ0hZlVD0j/FlhpZrsl/Q3wMeA1jdJUBg8N+FtDuKLXn9vSUXztjbh4/XPLlpCbJ897qGwJpRHBbabGccDdZnYvgKRvAK8AHjMGZvbTqvQ3AKc3UE8pjDp0+3vthiPKlpCbwRFfI54e55K+ffuxZUvYB74z9SxiaemUWQKsrTpeBxw/SfozgR/Vy/SWhx/hiI9/aorSpo9Kn79hou4D/O06G9jsK+ylOv3VLgcc6G9l3L1FZRTDRFOiVpO4ZolKOh1YCTx/gvNnAWcBtC+cT2Wen41c7dsdbuAa9hedbdZmX8NEw3P8VS7bHvC1fLdQ/H1duWmkMVgHLK06PgR4cHwiSScB7weeb2Y1Awab2UXARQC9+y+1/Vb5GRJoc+jCevcBvipWAG9z3h4DCHX72ydXGLG0dGqsAlZIOgxYD7wWeH11AknHAp8HTjazTLOW7YPGvHv9BJn3FnQFoGPAX2+mZ5Ov5bBDa/2V8XCvv2e5MMIY7DtmVpH0duAqkqWll5jZrZLOBVab2RXAx4E5wLeV+KN/wMxOnSxfDVXoXrOpUbILZ/Cw/cqWkJvZD/mqWAGs3VdF1Xe7v2b26NyesiWUgszQSBiDKWFmVwJXjnvvg1WvT8qdZ3s7Iwv7ClA3PbQN+pnfGGNklp9huDF6/vBI2RJyUVm8sGwJudGov0nvwoieQRMisC4/XezOtX56MWN0dnWVLSE3Iwf52hDVNuSv9zW00NeKrUIJYxBMlZED/XkAbev35QEUYGB/X0MYbUP+WtmdO/w9F4VghKO6ZsQ6xMAiPy3X7Ye6K2J6H/b35Hc4C9W5a7G/cKhtB/jTzC+KySZWEzUhlVli05P9yG5z2Jja+FRfk7EAbRVfmnvXl60gP1v97Z8sCIMWmC/xU6umjHbB7mWOJmXb/LUo2nb7mZMZY3SWo2cCqMx2WMbd/p7lQjBizqAZ6e4Z4glH7rV3rWlZv8XfnEHPwf66M4fO87VUc8CZLyWA7YO+5mUA7isqo5nfMfBnDGZ3DPOMhWvrJ2wSjl3obzzgAIeRz79y93FlS8jFysUPlC0hN4t6/EVnu6GgfGLOoAnpa+vnhLm3ly0jM6t3HV62hNwc2OnPIdkbjrixbAm52Fzx5/+pr32gbAnlEcag+ajQzqbKnLJlZObQbn/RoVZ0PVy2hNws7/RVzptG/BmD3jY/bmAKxYCIZ9B8DIx2cmv/IWXLyMwhXZvLlpCba3cdXbaE3Kwd8LWj9/T9ClrzOI2sr/ja2Fccxa8mknQ08CXg6cD7zewThd5gH3BnDLpUYVm3n129t+xaUraElmBXpbtsCbl4z91/XraE3Dxr0f1lS9gHflNMNsUPE20GzgZeWXTG+4o7Y7C1MpsfPPLUsmVkZseQr0oKoH/Y3+aioxf6CtU5r8vf+PuvNy+tn2gm0oBhotRL86OSXlpoxlPAnTEY2NXFH248tGwZmWk71F/UsNERXxu4AH476KsHtnuHv2WabPfXSCgGA5v5a0vdGQPajJG5fjYYdd7ib6JwZIG/ybKhIV9O1Gb7W73LqL/aojiyDxMtkrS66viiNDhX0+Pu620fEHNv9yO7zZ9zSvr8LYFn2M8CMwA6dvszuC1LvmGijWa2stYJSW8D3pwenmJmTbV71k+tmtI2DHMe9NNlm/WIv+V4o93+XCV4Y7jXXxm3D/j53RVOAauJzOxC4MKpi2kM7oxB++4K827aULaMzFT29xOIZ4yuNb4CxQDgLAZDd4+/8ffh/WaXLaEkrPDVRJIOAlYDc4FRSX8LPMnMShtAdGcMvAW36bjbnzsK28/fenJv7gK03Z9rh84kNG3rYRS+z8DMHgaaasOUP2PQJqzbT6tKs/ytGtHu/rIlzHiGDtu/bAm56djub8izMJw1NvYFd8bAJKzDz9LH4WWLypaQm/bd/ryWtm3ztYS3Y8dQ2RJyM9rlrroojjAGTYhgpMeP7Eqfn17MGNue4K8301bxNTfTPuCvchmc56cR9hi/LCAPM2zEz3L2fcVPrZpSmdXGlif6WVM+sF/ZCloEZwtd2gf8jb+3OSvjQglHdc3HyCzY/FQ/Vrptvr/hANvoz4VG27CvytWjMRha4Od3VzgxTNSEdIzSvtDPRNa8Pn+TsUuXNtVemEzcfNeysiXkwrr97Uac2+fPn1IhWMRAbkr6ugZ53uF3ly0jM7Pa/U3GPjIwt2wJuXnKEX6i3wEcOGtH2RJyMzzqZ0n3GL8vKqPoGTQfnW0jHNztJxLX7HZ/w0TLe/y4CB9jcaevGMg9bf4aCbtH/Q0fXlZQPhY9g+ajr22A5/XdUbaMzDxa8dfK3jHqZ4J+jGHz9SiPjvpbmTMw6m9lXCGYwUgYg6ajQyPs376zbBmZ6ZS/Sbe1w/4qqsM7fcUzeHhkXtkScvOGPl+hRQHeUlRG4cJ6epB0MnAB0A5cbGbnTZR252gPP9t95LRpmyoe48bucjgc8MvhFWVLyMXCDj8NmjEu2e7RN9FdU87BAIulpY1HUjuJJ78XAuuAVZKuMLPbaqXvH+3k9l2Lp1PilNgw6GszFMCcDn8GbJazuZmNHc58bgNbhz0ag59PPQuL4DbTxXHA3WZ2L4CkbwCvAGoag13DXdz4iJ9IZ6Pmbz351rX+hjC69ve1hHdk2N/KnBZYUDMh0TOYHpYA1esC1wHHVyeQdBZwVnq487cv/cidDdKyCPA0MOpNL/jT7E0vhOZqptxy3MGWq348+q2sTsa8lftjNIMxqNV03sMMp2HjGh46TtLqiaIUNSPe9II/zd70QmguGjM7uWwN00EzLBtZByytOj4E8LcFNgiCwDHNYAxWASskHSapC3gtcEXJmoIgCFqK0oeJzKwi6e3AVSRLSy8xs1tLktPwoaiC8aYX/Gn2phdCc7APyFp5iUAQBEEANMcwURAEQVAyYQyCIAiC1jMGkk6WdKekuyW9p8b5d0m6TdLvJP1EUuk73Opprkr3KkkmqdQleln0Snp1Ws63Svr6dGusoafec7FM0k8l/TZ9Nk4pQ+dkSLpE0qOSbilbSy3q6ZN0Wlq2v5P0C0l/NN0aWxoza5k/kgnqe4DDgS7gZuBJ49KcCMxOX/8N8M1m15ym6wOuA24AVjazXmAF8FtgQXp8QLOXMckE59+kr58E3F+m5gk+x/OApwO3lK1lX/QBf1L1TLwE+FXZmlvpr9V6Bo+5vjCzIWDM9cVjmNlPzWx3engDyb6HMqmrOeWfgY8BZYejyqL3zcCFZrYFwMzKdjmaRbMBY/7I59GEe2HM7Dpgc9k6JqKePjP7xdgzQXP89lqKVjMGtVxfLJkk/ZnAjxqqqD51NUs6FlhqZj+cTmETkKWMjwSOlHS9pBtSr7VlkkXzOcDpktYBVwLvmB5pLUsz/PZaitL3GUwzdV1fPJZQOh1YCTy/oYrqM6lmSW3A+cAZ0yWoDlnKuINkqOgEktbfzyQdY2ZbG6xtIrJofh1wqZl9UtIfA19JNc98d5bTjKQTSYzBc8rW0kq0Ws8gk+sLSScB7wdONbOy/TnX09wHHANcI+l+4FnAFSVOImcp43XA981s2MzuA+4kMQ5lkUXzmcC3AMzsl0APiXO1oEAkPRW4GHiFmfmLv+qYVjMGdV1fpEMunycxBGWPZUMdzWa2zcwWmdlyM1tOMtZ6qpmtLkduJvci3yOZqEfSIpJho3unVeWeZNH8APACAElPJDEGG6ZV5QxH0jLgu8AbzOwPZetpNVrKGJhZBRhzfXE78C0zu1XSuZJOTZN9HJgDfFvSTZJK9ZOUUXPTkFHvVcAmSbcBPwXeXWYrMKPmvwPeLOlm4HLgDDNrqu37ki4HfgkcJWmdpDPL1lRNLX2S3iJpLDrlB4H9gM+kv72yGjQtSbijCIIgCFqrZxAEQRDUJoxBEARBEMYgCIIgCGMQBEEQEMYgCIIgIIxBEARBQBiDICgdSU+U9DlJ/yHpb8rWE7QmYQxmOGl8g09WHf+9pHMKyHf5dPnNl3S2pNslfa2AvH5R5/x8SW/dh3xnSbpWUvtkfvtrxU0ws9vN7C3Aq0n8YY2/pkvSdZJazZdYMI2EMZj5DAJ/lrp9aBqUkPX5eytwipmdNtX7mtmf1EkyP71fXv4K+K6ZjQCXAnt5YpXUDlxI4qv/ScDrJD0pPXcq8HPgJzU0D6Xvv2YfdAVBJsIYzHwqJIFZ3ln95viW/ViPIX3/DkkXS7pF0tcknZS6m75L0nFV2XRI+nIameo/JM1O8zpd0o2pS4HPp5Xg2D1vl/QZ4Dfs6RxuLMrcLenf36bvfY4k6MwVkmp9hjsm0LBXXun7O8dp+YKSaGtXS5oFnAc8IdX+cUm9kv5L0s1pXhNVyKcB34dJ/fZPGDfBzK5IDdVEBu97k5wLgikTxqA1uBA4TdK8jOmPAC4AngocDbyexJ3w3wPvq0p3FHCRmT0V2A68NXXi9hrg2Wb2NGCEPSuxo4DLzOxYM1sz9qakZwBvBI4n8bz6ZknHpsMnDwInmtn5NbTW0lAzrxrXriAJsvNkYCvw58B7gHvM7Glm9m6SFv6DZvZHZnYM8N/jM0md2x1uZvfXKswqasZNkHSCpP8n6fMksRJqcQvwzDr5B8E+E2OQLYCZbZd0GXA20J/hkvvM7PcAkm4FfmJmJun3wPKqdGvN7Pr09VfT/AeAZwCrJAHMAqq9v64xsxtq3PM5wH+a2a70vt8FnksSHnMyamkYzpjXfWZ2U/r61+ln+/m4NL8HPiHpo8APzexnNTQsIjEm9agZN8HMrgGumexCMxuRNCSpz8x2ZLhXEOQijEHr8GmSoZkvpccV9uwZ9lS9ro7hMFp1PMqez8x4L4dGUuF92czeO4GOXRO8X6uizMJEGrJQ/TlHSAzXnpmZ/SHtaZwC/Kukq83s3HHJ+tmz/CYiUzyNSeim/LCmwQwlholaBDPbTBKcZcyt8SPAAZL2k9QNvGwfsl2mJOoXJJHAxiZAXyXpAABJCyUdmiGv64BXSpotqRf4X0CtVngWDfuaF8AOkoBBpPoPBnab2VeBT5AEdN+DNG5vu6R6BiFL3ISaSNoP2GBmw9k+RhDkI4xBa/FJ0uhcaaVyLvAr4IfAHfuQ3+3AX0r6HbAQ+KyZ3QZ8ALg6ff//AxbXy8jMfkOyCufGVNPFZlZviGgiDfuaF2lchevTyeKPA08BbpR0E0n0uw9PcOnVpGEaNUFcgYniJmTRRRIMaKL5hCCYMhHPIHCLpOUk4/jHlCxlLELeu8zsDQ3K/7vAe83szkbkHwTRMwiCAkh7Hj8dW0ZbJOmQ0vfCEASNJHoGQRAEQfQMgiAIgjAGQRAEAWEMgiAIAsIYBEEQBIQxCIIgCAhjEARBEBDGIAiCICCMQRAEQUAYgyAIggD4/wGlrtPoWsMpCQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
diff --git a/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb b/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
index 9565fb9b818..230c5782ee5 100644
--- a/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
+++ b/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
@@ -123,15 +123,32 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Object `a.spectrum` not found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "a.spectrum?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#94@C:\\Users\\jenielse/experiments.db\n",
-       "----------------------------------------------------------"
+       "tutorial_exp#no sample#103@C:\\Users\\jenielse/experiments.db\n",
+       "-----------------------------------------------------------"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -143,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "scrolled": true
    },
@@ -188,7 +205,7 @@
        "501"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -200,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +226,7 @@
        "array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -236,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "scrolled": true
    },
@@ -263,7 +280,7 @@
        "501"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -282,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,16 +331,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(<qcodes.instrument.parameter.GeneratedSetPoints: freq_axis at 2910773430144>,)"
+       "(<qcodes.instrument.parameter.GeneratedSetPoints: freq_axis at 2409471349424>,)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -341,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,14 +381,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 273\n"
+      "Starting experimental run with id: 319\n"
      ]
     }
    ],
@@ -394,22 +411,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x2a5b7a25390>], [None])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x230ffb1c390>], [None])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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yQW3YUu9aGxKGGk0sWbt5WNvcFjFaGMr1ALSn1qlItsfU509Jvb1eB2BdqhK7GcA70j2Zd0WyTebNw010WTTTrQIqldjdS100WlCXdQKPLFgLAJi1dMOItF8W0+evxqevmNa1/qpVk3dYb+T1kyg4F+P9P7kPb/nBnca5LUNN/OCWZ7z33P9cshtyqxNvs6lw0Bk34ewbym22+eZz78SHL57SUpsx2HFcH4DhZyjf/MuT+Idzbse6/uGxiW2rGAm34auQ7F99KBEtIqJPATgHwNuJaDaAt6fHAPBXAHOQ7OP9cwCfBwCl1GoAZwN4KP33rfTcqIa2nbTBTzqyIm4Fetuc6J28Rxif+/XDuO2pZVhhqYo6jcEReh+aoXkZZgEfraf3/WrqvA5S1T52yCSU4Z3Y75iVSN4bBjrT7k1PLMXE02/E4jakno9cPAWX3ju3I/QMF0bCy+tkpdReSqk+pdQ+SqlLlVKrlFLHKqUOTv+uTssqpdS/KqUOVEq9Uik1jdXzS6XUQem/y4b5GfCrqfOxcaDcKqqRzsrVNjjKiDGUohnKwqevmIbjf3pfdPlbZy7Dv131SFmyvNDU+hhgvdEs/f6M+tMGhhpNjMQeddX0weoWQ4ll+FpablWq6hbGpKrE9cMsoWiUfZe/vHcuJp5+I/oHTXr/MH0RAOCpJetbpuWBuatx9g0zW75/JDBaVF5bFabPX4NvXDcDZ/wpTu+s0cxW+eU/Ys2Dhhojo/JSJWm/7alleGzh2uj6p81bjZueWBpd/m8znsfE02/E8vVbguV81H7p6kfxijNb15JmDKWeM/jhlN602rTpmQGLFgDZbaOLn2R0D7fKq1VoCWL1pkHx+tYi0XcKPYbSAvQgWbC6nNeTVnlVWxhltYppBG42FW6buQzDvYVzt74PhWS1Hfs8v56abM8w63nZplNUzY2PxzMvCXoiHykGr8eQrfKKfT+NTEIZPbjj6eX45OWJEmK4VV7twh5vL9St1XsMpQVsPzbR824qqTJptqHy0vdohnLFlHn49JXTcP1jw+vc1q0Vl2a2tgrHBz2R+vtSi1Thelr98PVd3IbSqTkkhib93K06HWQqr1HEUf7lyunZ75CE8uDc1XhgzqrhIKltjKb+HQ70GEoLqKUf86aBchG9+uNvZZBpI6xeEWsXx+fXhVU+nUK3V1y69nrkip9PiEeefSvOuWlWS+3GMjBf+0Ch/bs0Yrq6yChfVEfuIDI6Z7yQhPLhi6fgI5dM7Wh7uhs6tijoTDVbHXoMpQ2UFcv1YG1N5WVKKNqWMVwDt9sq90yF1IxzOtB9WSHC6k2DuOiu58TrRUbnWAbmax/IJ+dOzc0xFFU8RvlYZIublu7uPobTO/7cm2dh0ZruxKCMNqeHbqPHUFqAnkxa9fJqZVVYtWwouobhVtW24lAQA/0cZSUUn8qryMtLox7JwJz6uYTS4XfgM7Rz1AqM8sVtJH+79T63Jvz0jueKC5XEC9SE0mMorUCPlbKrqHYCG3MJxbQNlHXnbRXd/kD0BF2PdIvO+pJ15WC9ieUbthj1+aAZUcsSikBLpxBTXcVDfyyDyN2GRxFGATGh72nSt2/Dd28yA0F93Z3VMgqeaTjRYygtoNX5I/PyaqHXbaO8lnKGayWkP7RuLWj1YwxFcmlphf3lPzyGo74zOcpQrdWOsSo2t33l/CYADy9YU8redMPjrlNFzCLB5+Xlw6aBOr59w8wsk29TmQuTHoqxcuMALr6rXB6+F1r39hhKC2hVKqi34TbcVzUZSq7yGl4Jpds2lEZJlRdXH/4l9XhTKn9Dvu5p10vKsKGkv5es24ITfnY/rn5ooXyThZUbB/CF37rBnDGvNFtgeBiiXcdFdz2HX9w7F1dOmQcgf+7RapT3YaDe3dT2rX5OPbfhBD2G0gJallC0Ibktt+GRcffMRfju2lBiJQZJ5ZXVxerzMf9amyovw8vLGhA6kefdz6zwqvCmPLcKk759m1lnOkDKMBRH5eUpr8eNXtTobt7K+Elpz0oJ98xegYmn3yhma+a9OWPxOvyuIAtzUf+90GxUPYbSJaztH8RCK/CxHVfNLLCxriWUYVZ5dV1CSf5GG+XTCVHqS6NPPNVlNogOSigcdz+zAqf88kFceKds8L1TSM+uY1pibDI5Q/FIKAX3j0obSgQ2diCC/tqHFwMAHhYyMvPFwXt/fC9Oj8zC3EOCHkNpATGT+JvPvRNv/P4dxrl2Ahuz7LZN7Tac0lK6plbRbckodRtuNHHXMyuc3Eg2iiZdPTH4StUKJuQimAzFlRL0/iVzV8nbDkiMcCBdLMS801zlZRvlI24Gi5Qf5SvoL/z2YUw8/cbsuJ38axqacetviqNVd2WfJDy6e7fz6DGUFhBjQ5G2hs0/4vJt6glw0FrBD7+EQpjy3CrMer71pHeh+p9dvhGn/vJBnPEn/2ZV/3H1o1nKFen5+fvx9U/bEgprI8jcfO0LY2CwHi+htM8QXQllNOr9b7BS5HSCoWg7XZ/gHVO2D3xxJu125Wh8FzHoMZQW0K7hrhWjvK3iyIzyw+w2TASc/POpeNcF93S0fj2JrtyYrOznrNjoLfunRxY793EoVbzKb9+GYrZXFpKEolfO7bgN5zSFK2kINpSRmMOue2QxPnhh/E6kZdMdSdBSfk3g6p0KqGzXK3Ir5Sc9hjKcaMezpmbvEDjMbsMa3RDhB+t5Cnit9olVxXgfv8AoX+QlVdiu4DbMUUS9yFC0yivGhtJipLyuOk8DlNMh1bRhyxC+cd0MbB7sjnfVv1/9KKbPj3e13pAylDGt+N6nGApJKB1eoLUaKd/p2KbhQo+htIkyomknAhu1yiuXULZuTHluFV71zZuxJt3KVk+qsV1UJKFsGmhkUg9H+8kV899OxuEI2sMMpfj+TGKNZIh2c5JRXhrLl9w9B7+aOh9XTJkX1U6rGKjHPYd2ShlTa33q0n32icsfwhX3zzOutbi+ENyGW6sno2Mr/bB7DKUF8MFSZuC0s2OjrfLKJqThikPpEutavHYztgw1s/0k9MQS67jQFL48hTwN/nt/fI/jnsvrb33DsrzdkB3j2kcW43//7NqDpMfTMRZlvLzc9PklI+WJn/OXG4qc8MtCL5RiGUoj8A09t2Ijps8v3riV99lVlltwWclA95/vvpZVXlvpUrHHUFpAtEHWQrZjY0uBjVYur2wgl66qJXAbSiehGYL+xjen3l2xakFJwuCvZMuQPFGVjTS3wW+T6mgwIq6cMt+5Lkmpg2W8vMhcYPiwcHU/nl3u7hmTZRpgDEiaxHQOuUaXFi7j+qoAgIGhOJVaFhws9N+x592FD15YvN996J1n8UuBOCMJ1z26BGdd/6RTT6vYSjVePYbSCopiEHzoaGCjpmW4jPLp305nT9UMWTOWjWngWjRDkVReKJ6U287lxVVewiAoknxCKq8oCSVzI/cY5dO/b/z+HXjb/93tvDVpKwVT8la4/L65WJuqIltlvEUYm6qufIzfRqNZToKVEGLC+nvi9MQ8+o8mz8bllvqsDN7743twws/yLbO3VoZSG2kCtkbwd11GQmknsDHLPWVJKMPtNlyWnyil8OSS9XjF3juL1zVD0BOW9uKJV3nF08EN0O2qvPh7bwhEDDIVjqTvF92GNS0xNhSS6dePuHFLHbOXybtZAnLqGo7ZyzfirL/k+5l3naEUpFRpNhUqFWKZIlpnKFzlZdejH3PDQO7232gq73j0UaEZU+z8MGOx6YbfM8q/gFDk4eNDHtjYetsjtx+Ka8T1YTqLQL5m+iK898f34raZy8SymsnqvtmUqrzKBugZtCrlMFrb1lBr0yhvSChO+hMyGMo4gaFIE5SmpQxFPvq//IfH8Pbz7/beJ9qe2KlBy6bRCYayZaiB+59daZwbk0koYYZiLzwkl99YhBwZ9DjkEfntTO5lb9XedD2G8gJFme+sZfsv8gndnrzaHXdn3zATb/3BndnxpoE6fj9toas3zmwoxR8yjyvQAYhS3iQg7z89UeitX9szyruqQHsF7Is0j4UyJBS3DoOhpHYCDqkf6xZzDbbvGQ9FvabploJseZ/Z5HWCoZzxpxn46C8eMGKMxtaSvilSeen2621I+RpczWnXYruvA61N7vqWsvc+l/bN1slOegylJbSs8tLpQFoYLfqewbql8mpz6F1679wsmSEAfOWax/GVax7Hk0tMEbxFjVcG3/evJwo9wWmVV7tGebuP7RVw7jbcYhwK+y2pzfhe82P74lReeqIrGh/r+oewbP1Aek97qWOKvLwy2jrAUGanzgF8v3jdN4USih4nnbChNP2MUzNcc3sCf12+BZay/sZC5/9T3XGq6zp6NpQWYBgvS7z4jKEA+Pp1T2DB6s248pNHRd5r1pEZxzu8lHlkQaKusvX+SljRdgK2Ub40QxFm3xmL1zluqAPWCtjvdhuHZpGE0uAqL1dCkZ5Pq2KKFimvP2cy+lPVSOxE75M4DC8v1q7tfNEtFYy2oRS5DdctCaVbRnndnfxx25LOIm7lUnaez23rlFF6EkpLaM2GkunIFfDrqQtw9zMrSrepm6t4+MmKDQM45IybovzxJdqWrEt2POyUX31R92T7oGjddcZQ4uqXVF4f+8UDzjm/hNK+DcXdNbFY5SU9Hx8fIfSzqPVWnQry7ajzc6886xYsW5+8/26ovCTkKq84CaVVrzwOOx8eh6RFaCmvVgmVF1fH5rFI5ZscDegxlBZgrF5KqbzS+xkbiE12p6x7szgUa+TNWLwOg40mzr91djRdALDZ+qA78eHGQM+HWvOk+yP0IfJJMLb/bR29jq+QVqvL1m/Bun43uScHp0+SEkyG4n5mog0lUuUl3VMEN5JbjyOTjplL5aSf3XcbDjMULb3VM6bbOj0ho7xk+2hLQIm4ly8QyriOj0b0GEqbaMVtmN8yz2Os9rVjB6TZre+xw1gAfiO4D09ZE4k9gfie8vZZyzB1zqpSbXFkEkrTfL7QKpKrO2InOtsoXwuovF77/ybjtd91o+s5+F3SBFUsofiN8mXUHTYz80mQQ1biySwlnKfeYZNQtA2lQOVl21DaIadhuA2b16RtD0LP3gm34f4BLqHEp98ZjegxlBbA37V+8QtW9eMtP7gTy1OVQX5dVo/tul0fABgG8Zg2bWOq64yVnFi8dnNUvRofumiKQbs9SfoG+Ccvn4aTLpnqrbdocswj5c1yoVQffDKOZej2CljX4ZssiryO+HuVpATDKB+Zd6qVyTI2l5ftBly0p7xdbScYivSqdJJHKVJe6uNWmK6NUEJQ21Zp01EWMXf2D+VaioESCUJHI3oMpQWYkfLJweX3z8PclZtwfbqveX49/60nTaUUdhqfMJQNW8KqFbsee6DZH1Y745BPgvYkaTO0svB5w+g+sVV3oYnSlFDi2rcZRCahtJxtOP8tTbZDhpeXK6FIjHCokY+PWMSqvLLU+Lr9zCgvw6avk6lX+FAwbCiOWo61n0ko5dWCNkJ9JtlQ/jB9kWirC6lFled7lcC3Nc5VXoW3jUr0GEoLMKWOcFlzpZP/LetCrKzyvgm6rO61j+1ap7cZBgSVV4suz8VG+bQ9q2C8yiuOIQx44lAaLXt55b8dtRNMiWC8wFCkicZW+8XAfk++1Di2F5Xu73WbZRuew1C6NMPpXRMlLy9TrZgcDbVgZ7JhuA1b/ZUvnPIy5978NKbONdW6dz+zAkd86xavhsFegPUP1nHo12/CrUKAL98aIGf8WydH6TGUFsBftbRy4ZDcSxVylULZYWNHrHviD6PBXVr5ILYnSWX9LQvvSthaeWqEVF6coRTNc5rv2hJKpYOBjZJhn0+QkspLajbv83iaYhcQrsor+Sul9gfc92EfD9abbWRqzqFrlYzypmFcj5PuBmjYtkoNLkUAwGML1wbrsW0x81b2Y6DexHm3PO2U5dtda/f2noTSARDRfxDRk0Q0g4iuIqJxRPRSInqAiGYT0dVENCYtOzY9fja9PnG46OTf8PPrt2DRmv6osqJUEjkhOEZ5zwxdduXG1THBDLolRPgyaEXlVSthlNdZmu0JSz9Hq4GBvFmJKfEJXHpXUj9qWsolHLUkFM+44PTMWLwOX7zqEbHQDu6TAAAgAElEQVScvt2mwe7nQ75+E47+3u3Z8f3PrhTT9BdBky9JKIYnnW1D6ZKNIZfEZQldY4dx4RC+TLWYMRY/vYaXV6PhtLc12VNGDUMhor0BfBHAJKXUKwBUAZwE4HsAzldKHQxgDYBPpbd8CsAapdRBAM5Pyw07PnTRFBz9vTu816VVlv07BlxdBuRGZXewlauXu7TySd2VUMwVV6dgx6FohAIOuVG+iKGM9TGUyPv94JKnOxny1bv0qkMSSpmhEUs+t4/FeOXFqLx0tD4AfPQXD4hp+ouhF0pu/UEbSgstxUC/yqJ+3X5sXEy4/d1Kqmo+NqVN1rYmaWXUMJQUNQDjiagGYDsASwG8FcA16fUrAHwg/X18eoz0+rHUTgrSEiij3zR07Vr/izx5YWxNtpHPtx9K2cE3zpBQwmqchIBy9Wv43kyWHNJqzlbRcHCV15lsDwoJfb5I7PQ5Wo+Uz39LgY0rNg7g6IP24E1Z97tn88kynqZYhsgzBcQkVnRUXl2TCJK/0mPwJu04lJgF2Xm3PI0Zi9cFyzhuw9nfcP07eBiKbWt0JEjhHv3s4/oq2Tg145y2njwso4ahKKUWA/gBgAVIGMk6ANMBrFVKaSXjIgB7p7/3BrAwvbeelt/drpeIPkNE04ho2ooVZSLTQ8TGF5UCpGKN8gP1RuZJwtO2AP79UMp898+v22IYq0OG5pwBhhsoK55ngY2OhBLnNlwEXdL1WtMqlPa9vOy+Wr1pEAtXb8YbD94De+wwVnwn0jntcVaGpKZSeHb5Rnzpd48E+4zHoRSlLbn7mRWYv8o0NnfLKG+v4Dkk+2O94a7gJdQbTfz49mdx/E/vy45jskf4vku7Od+e9q10k25zXF9VllC2Hn4yehgKEe2KROp4KYCXANgewHFCUXtOla7lJ5S6RCk1SSk1acKECR2hNXbFCcDI9cWZQrYSCnwZH/v5AzjiW7cYbWblM5UX8LcZz2cp48tM6K/77mQsXJ3Hq/BJw/XyMv/64HyIBTf4VF6hHFVl8jgVrThbjUgOSXOPLUxWxUfsu0v6mtw2RAmFSbBl6PjyHx7Dnx9dgicCq3HuPVS0wdspv3wQX/3jE4X0dgKKvSEbUjaCWJWXne7+B7c8gw9eOAVPLApLLLHejL7L9sLPZlAhe9q4WlWMlO+WdNgNjBqGAuBtAOYqpVYopYYAXAvgHwDskqrAAGAfADrQYxGAfQEgvb4zgHIJrFqE9H59KVTEFB0q3/M8NFSmsX1F8iyoyXEuoQCf/fX0LGV8O0MvlE4k1obiu14U72D3acjLq8xeGD6vnZCqJQb8NruvlqxLmPTE3bcHQR4vZWwoIabcbLIEi0NNr2qxrMrLRqyE0qoBWVqFS04isXYmm16dUmblJtmrzW7TZqB2ez4GW3SfxuxlG/DNvzwJpXKWylVe/LZuSYfdwGhiKAsAvI6ItkttIccCmAngDgAnpmVOBfDn9Pf16THS67erEXSHWL9ZDnIyVF5sdeVb8U95bhV++8ACpx6fzSV2oMfAlFDkCb1YQinXvo/ewZDKqwxD8XgF2TapsghFyus2qxUCkdxnkhSi1W/uO/XT0VCKZez158Pi6rBW9hKJZyjl6g2qUtkpW0IpWtr44qjsZ/fth1K4cPJpJJT5o2k9n27+tMsewmX3zcOiNZuz8WKqvPzagtGMUZO+Xin1ABFdA+BhAHUAjwC4BMCNAH5HRN9Oz12a3nIpgF8R0bNIJJOTho1WYbit8zKU/DefMGzRWOPknydpTD762v2sNnV95sB0aogce9IHEZRQPCs3t45y5Pj4RlDlVWJC9DHuLNdSi/rpkA1FX6tWyBtoKHVjHthoMahAnyulsmjzUAp4fq2V1O+xafLLTn3W/GugqZKtdxtNlU26sQk07Uk43/LYKuhsASy/A/vJfN1hMyT7O8ty8LHvWNc1tq+KAcF1vMdQWoRS6kwAZ1qn5wBwNg1RSm0B8KHhoMtt2z3nYyjSSkNxzXHkkq5pjVT/RBkHKVcVn1y9qVcK6vXp/72R/Z6PpdFU3r28y0yIDc8EEcsgfQh54diTl9Qn0nNnUeCBtlw68gSLA/VmVKR8KwwlNoixqRSqJbZhC6kem0qhQkADLdhQbIaSkl8kneUq2KKFk3zdGWf6r6c6IspVXrVKNo8YRvlt3YZCRNsTkZtP4gUC6fX6VV7570G2uiqKsHfatERn30CNHXubBl2bT4yEUtRA2bEf+lh8k1iZCdFrQ8muR1dl3h+QULK9RgIqL6ldW0LxJQC178ltKH6VF58gQ5Oqj/EP1SMllJL9mY9n90YFnsQzGQuZJ1xBQz4JxX48r8rLqj7WPd+2Cdq20nynVd5mcjSur5qpevnzzVy6Hhfc9ozcIKtjzabBYJnhQBRDIaIKEX2UiG4kouUAZgFYmka1n0tEB3eXzNEFafWyfkuxUX6IGdwi5+e8TV2fE05hr4jiKuwfcCcf7k3i2lDiVoZlGVzIg2Wo0cT5tz7jpPgvZ0NJ6fC4V3fDy0tPNhVK5AWpBaldv1HeTwdXeW0eavizB7NKWonWik2iWbo/PRO4rkszFEdCKTmuMjVkwcPnaefN8+4CyyehWPVZx47GjZUZW6uIbsOfuOwhXHDbbCNFi43L75+HV599Kxas8mftGA7ESih3ADgQwNcAvFgpta9Sak8AbwQwFcA5RPRPXaJx1EEaSr54Bv6BDTIf+qAxUmozW+mYX2CrgY2SV1owUj7w4RvlvCovuXxIUpu/qh8/nDwbn7ziIeN8tcSE6I/3MaWBsuB3+QzAVaJEpSE0EUq9kunX0/MhGrlR3s43xWHGQHmL+RG78GmNn4j9oZS7s2Y9MiOz4yjBpMYQsgWIVb+9wPI1b3+neb97JH5G2/gxVTGwMS/np/t3Dy4EAKyPzF7eLcTaUN6WuvIaUEqtBvBHAH8kor6OUraVgb9/Mw9Pfn6IxQKUzd5r65p9Ek6sTYavdrTh0/Dy8kSQlzfKlysPJO6TW4aa2f7yTkbdEktsn048pLuPAa/PjrbPXLsDNhR5USJLgUEbSjPPGB1awXInkLLqVokmf7lydYfc55tKZX2oGUSsDcXn2FA0cnwqUh+Dcu83j53+sL3MmFGex6FI1YcyY+v9j6TdQYcTUa1LzKSVMtsMxBVn/ptPgIaEUs/0L9FGbrseW1JwVV5x2MQS0mWrwJANJf1b5HHiY2i+D1lSeW03Jlnn9Kc2ATtuotQumRbjaDSV4fvfioSyetMgvnFdnvLFl5lXuw1LLyWUesWe8ENd3lQqe1fB7aQ94zMW8c4jJesN3KeUuxFaPTJNtz1+7YWYhrtjo00ZjPbt+mzY36mvXaPN9GpfjXIpVbgjpHbU736k7felvLyIaBKAMwDsn96bqACVelUXaBu1EL12MqMfGZMkH3hDbJOj8vuh5K0nx56RGllfP5t8tF7ZNDTbIn4cvWXHs7Ra1vuHbEmZns4Y/KdHFuE/rn4MB0zYvmQreX8f+D9/xUcm7cuep/wXeM5NTxlxMj7ngQqlRnmRHvccHx9ALokFAxtVLllKdjFeTqOVyOtYRhGiVZTUsgWS/E3ptUQ9YyiREopHDVm4IIq0oRR5eWWXrWMpZZK+NqZazTJXh5w2wvSPLMq6Df8GwH8DeALAVpRhprMQdeLsN5+LJQnlkrvnsPvihoC9kvNJOLErbr6arVl6asAvoRTVr2yngQJypPrGj0kYSr/FUH5+91wAyXbLB07YHs+tiNs+2abj6mkLccyhSRqeVuJQ7O/a96FXKIk7KIr5sespY5RvNHMJZdNgPZCRgP0OTEy++2PHqa/qGYvXYcbi9UK95l/jmnKl52gbikeiiFXZFmVbLgxs1MfWdUki0nVxCUWiM8Z1e6uSUACsUEpd3xVKtiIUGVkNCSUdiL+ftlD0BIuXUJT1123Xrk8p5bU38D0YqlVX5WXra/WlotWtd+LxbQEsfCNaQtE2gZplhVcoH+2tlIJk22pltW7bdaX9UIgSCaPMvjX2Xh8xRnmlcmN+/2AD242RvfmlJKVlUHac2njvj+8NlldKOdysycZvI33G2F0tfWrIogWET3Pgy21nw1Yc+Jh3rhJTeWBjtYKmSu4Rx0f6Tc5ZsRHbjanhxTuPc+sdYRmlLEM5k4h+AWAygCwpjlLq2o5SNcohrqbYb9OGkvz9yjWPt9dmNgDN9mxa+LFSfu8qnqZDSyjh/VBMOnzwxXv4IE1AOUNJJZSKaerTEdRl0FTmM+XP0wpDMduW0tTwMlIL0kRTt1ReGiHvrQazoYSi2Q0bXwvPXF41G1cmNK6UytWxrsor3JAvDqUonXyshFJkQ9EoHv/5PWNSb72hZlP2AkzH2VvPuwsAMO+c94j1jSTKMpRPADgMQB9ylZdCksjxBQ3+IpsGQylY0RurZt9qRhleOr52nfoC7fIPQk/O/JxPjVPWKF80wKXJTau8tJeXllD45FOWoSiYXmx2ss0ysJm07QEE5BOhLzmkQrKnRl+VsCbdosAX2Pimc+/w0sJtKM2mXyLNMy2olry8YhGjclXCgewmm9tQ8g224tLX++JQCunzXC9vQ7HHl3+BpqvSqt16Q2aXMXv3bG0M5Qil1Cu7QslWhKJVreEt1VD46R3PBurKf/tWmEq5K2qfuyWvIilTMMEAqKUSQNCGEm3UtGlPJ0fr/JpNg/jghfeLe4lrtc3GdGVeE/aeKKvyaipZB92Kl5c9aUvvTRfhqTXsdh3G5LGhcPx+2kKznmbO0DYPNUD9crS0tLeIBN+VWEkuppS56PE/c1PlcSOZhOJJT2MjVkKR2pTKuXEo/m9VKqfPO5H5LISgxhiKxPTddDLuVgRbm8prKhG9TCk1syvUbCUoemV8MNzw+BJczIzwobp8yf0U3IGZXXMGdpyEwm/TGiUzg64v23DRBxm3kps8aznmrJSN6uMzhpKs3Psq+Wo/p7m8DcWUUDR9papJ2raaliboaoVLKO51pVymqCfLPGbCfUZbdZq4DSfvajrb7sCGYZQPvEPftdhuKiuh2DYHo5xikfLWnvIhgux33WzmEr6tnbQXBz4bis/Ib6MofT1ZHpVNlTJOyuOJhprNoBegxrINW7DXzuOD7Q03ykbBHA3gUSJ6mogeJ6IniKg948BWiDLiNjd+F9XFczHxSaip+JbB5oB3JQIY9/nAmV4moUSsYn36d58bs4+EEDvQNhRtO5g8azkenGtudVMmWl7TwVUGebbh8l+gwwhCNhSv27ByGFOWjboELXZAahEUCiZ9z6Wu2VD0sJGYLvhW18n1wUCcBq+T98lgoxnthGHHLWn4JB7f/c736qU1SRVbIcq+w0Tl5d5hMzUpzcpIM5SyEsq7ukLFVofQYLZWwgXTA7++hUko9kenD53cVPYEbtDib1eyoYRVXu59dn1Vcq/r4zIaKs1QuGvzhy+egpfttZNDcyya9nvxqDZiYLcs2VAqxMoKTWgPJjEbtU8/IsB2NijCUENh7kp/vicfc4pPERRVyqlXVnkpY+E01GhG5fLiwZ5AsrlYbBZhX3ySN1mq0LZUTi9cJCeApkq+D20rHGIMkGOo3jQWQNJidatSeSml5uvfRLQHgFUjuanVaMRVDy7A3+27S3Zc9K37JBTTzTPXs06ZswqfuvwhHPXS3ZL7rQEUO0Hy+zIvr4CEUrSi16nmY5NVhhiMTsduR37zmsq7DZsqAx9DiRnOMTYUrZJLbCjC6jtVczTTKaZWoTx9fYkvSiklMjQfLrrrueB1r8orsokolZckoUh1NfnCSWX2trG1iqP+sZ1R+DgdqDey79DW5MZmG46VUHy5vNyFlcr+KpWME63yqnvchj/6iweMY2ncjfRsHJtt+HVEdCcRXUtEryaiGQBmAFhGRC84qSX00p5bsQmnXfYQK1skoeTgNhRbNOdjZ/Ks5V53S+ljlSB6efH9UJpNXH7fXDy8QO9Vr+/zifryZJgvtt0cRj5o0X9TIJVIK27DkuTouDlHfJCu7cNVeRV5eTWVyZjG1CqZyiubfIpJSd2GOxdj7Hu/yzcMYOLpNxbeHzOfKeG3z8tLP9pAo5ktMLYbUy1wRjEn2zqzodh9ZY9Dn/He2R8oIKkb5TKVl59Rq1T9mau8mlGMWXJXH+nVfayE8hMA/4Nk3/bbARynlJpKRIcBuArA37pE36hE0UvjK+vCb50NnAGPyqupXJ2qbyVlfqx+Sjmjqwm5vBpNhbP+kvhezDvnPYUqL99ug/7cXv7pUvvjh3JTlXYbtiZen+4+5kN2bR/uPZpZ+AMb882jAL3qdtUthU4QJW0oRYjcR8uLGJuU9EhF3X7xXXNw8V2Jc8v4vmrmaq1x68xleV2Wi3iDrfhjvbzsUvbkXSTJ+f7mOzbm7TWVAiGXUIYaKpKh6Lrjx0u3EWuUrymlblFK/QHA80qpqQCglJrVPdJGL0qpJAptKDkGGUNpGoPEz5hcRmPe5wOvX6tnQhl0dTtFxvoQg+MISSj6w7IlFMPLq6zKC3JgY9lATCAuDoUzHZ99oGJJKHy/HI0iZpG4Q3duEmk1nb9GzO3SGPVKKML5cUI2gM/+erpRp52gNZNQCvoql1zNcnY2hHgvL/m74O1p9WcmoTSbUeOwLsTkjLSEEstQ+HS22bo20s8w7Chj+Cr6wPh1KcI++e1W4lPZmGX84PflubxkWjid3o2FPITEBEva0B+WLaFsqbsZkmPRtG0NSqYpTkKxI+Xde3K3YdmGkriKmgxl0N4PhWK8klqXUN52+IvE+tpBzLdhL1V8aCr5+9FOG6FYEFvajo1D8dpQPAssl+aUgWTH5vkH563G1DmrDCcbrf6sMQklRtKQtAJbhQ0FwBFEtJ6INgB4FRFtYMcvuEDHMi+t0CjPBqZtiM/LuB+Pb+D76nDpyq9J6esdo2d2X35uzoqN2e8ilddNM5biqO/cZqR88aFPp6CwPmK+zXIrgY1cQhlqmpN3Tm9xXbZRXkorXmEqL5+EwjG2Vs2zDXO+V0APTw5ZFlIXtstQYkiRns/XR2Ly0IyheOqHMlRUPF+W3Ve26lUvjFr18vLZEPlznHTJVINWhSTINY+Ul728bMh7w+RHC1f34zcPzMdwIsqGopR6we4f3y6K3RTz33YwVlamGfpQ/QM92LQhoVScNgdtZbpe0bNKdU4hft73QS1euxmrNg1iy2Az27LWhz6P9LGW6c2F4PkgEm+o/Jm2DJkGcA073kWCTZ7Uzzz9mH356ec34NqHF2Pf3fKgtDFpYsBkNZ2XLU514w9CbQVlq7ITkMasrI3U7elfUQpXsvyiA199Ld0+a7klbedju8jGo6y/Gk7ArlciNxmSL2VS1l4qhSVxKLmXVwxjllyo+e8PXng/lm8YwImv2afwm+sUohgKEf1n6LpS6v86Q87WgTJruKKy/LpvHxW9ijHuK5jAixrnH4ieD/jkpSdcTgMQ+JCabr3GcbZCbKZt+iUMKdVKcq8rVcVCWRKKdkHl9K7tH8Qpv3zQufexhWsxY8k6fOy1+6e0F7eXSyjuFsAnXnQ/AGCorrL1sXaVHmo0jXddpPJqtKHyipGcYurg/RFFijEByuNYX5MY1DhD5eW+jC/89hH89zsPzY65yqtImssj6sMSSigeC3AZU0g9p9Pw6HE/VODlVSFT4pYYNJB45uk2hguxXl47pn8PBfD3AHQK+/cBuLvTRI12lPGkiNXZAuYgttON+xiHXbvJT/xtSx8Eb9NWTelLhUZ5mx5L5NcfQWhOTgyUFPz4y2wDnNBl2lA2D2qGkpfxpb45/qf3AUDOUCIcernbsN0rUjtj0slkoJ6n3SBQ4YraDuIrB/e+spkDGkqhAspUe3ESivvbZ2eSqtO53kKkbh40Y7qavvHruA2bfzVivbwc93nvwi+XYLSEksWhNOQ4FI1qhdBknmBlVKTdRqzK65sAQES3ADhSKbUhPT4LwB+6Rt02gDI2FN/WwUq5IrBv5RNroPN51WgMOBKKLuOpTxC/k/tMRpMxFGFOPmDC9pizYhOOeulu6KtWUG/67S3VsjaUphmDoFV6raVeKS6TJ4d0+0TfzqWPsemqO4mSlseEhE6rvMp2R6Op0Me0KVECijABSu3yLNscmQ0l0BpnslxCic2WbZdy41B8EkfOKJK/MP5m5djfxG3Y8vIKkFlJ95WWPNZ8eeOGC2Vzee0HgKczHQQwsWPUbCUo84L4C951uz7sul2fVSD/6WMGyWC0B7Rzu3PCR+aXf/8YLrtvXnacbWLU1MeuhFKUusJJG2LQztKPN7SE4s7Krztgd8w75z3YZ9ftstWaD+16eUleP7E8KkY6yry8hFxeWX4qNsuMrTGVl8rLxexd4ti7ItEplRcgbwY21GjineffjdtnLTPvkVQ0Ii3yGB5XYJS36WiofMVfJM1l35VVuZs5wteu2b7PDTlvT2VeXmYcip/GSva9ChKK1MYwOuKWZSi/AvAgEZ1FRGcCeADAFZ0na9sBf9n/8fZDnMmIv2o+L5heXpKEEpYIkmvyQPrjw4vE87rNvkrFsaFkjMHHUApUXvkHrW0obh38VF+B1b2sl9ctM5fhG3+ekR1nRlql8PiitfjOjTOjVFlASRtKoE7el9nmSnXTXhYTBO9T1bWCsgzFt+8IAKzcOICnl23A6X98wlvGXtGb5eQ07uM9u1IadFkOLj7biI34bMM+BqHb1OXM8065dLFYYTaUJA7FT6ftlSntT2/S6q2q4yjFUJRS30GyydYaAGsBfEIp9d1uEDaaYb/s0GLZWAFLdVkrqex30xwkXhHbqc+89ujCtcH9WAxa0zZrVcokFP1sRRtS8QnapMc8r59L6gvOJIoYSlkvLwBYum4Loyv/e/us5fj5PXO9tIfo9L17Y8dGqx7NZBqChDLYaGaTESFOQonZa1yCVHNZA7/uM71QkvLB1Sr+RVTonE9CKXIb5m3r3z6jvP36bAlDqo+Xc2k2JXWfxJMjSS9DlPdTsYRi0mQ78DgtDKPOq2y2YSilHgbwcBdo2Wrg6MR9wQawVSru7GOotiwmwn/7V/7+gd5UCjc9sRSX3TcP//qWg0T6AFenX6sQ+gf174rxHN7kkD7jo77elD9ogw7WPUUqrbIqLxsNxuh86pCmktPk86YrROJqVbsNh1Re/DZD5cVpiJjgB1uUUKSJpuxq1s6iy6vUKsaq1YlSpLycvl42TmtpLja1EHfFLnaSkSVtOwdYkVG+aT2Xo2Fg53X6emPHxpqfzkrFXJAUZccYRgGltMqrB/iNrBL4QEr4jrUKZr/5hGYHKNoTiz4KqbygtBdQ3ISjm+DSgZ4YfW7BGr604vYHdt4tT+PDF00R6zBW/gUjs6zKywanS78T27j9o8mzjeM8gp3TKdORq7wCRnlDQsmN8nzVH6OC8jGBVnhuWScFd6LMf+vMBjXrZSrjt1/ybSp5vFUEhmzDkPZV7n5caEPJ2g5LKL62c1WWyZhCySGbKo1DybINxxjluduw2/4V98/Lz3VOI1qIHkNpAb4Vp1iWFZYmQZ+EYoqxkvFdDyb/QFfQH2XcRMFVXhqOhFKkO/ZITPr8bU8tx4PzVhfaIYoYRrsSCncW0L/tyPwfWgwlU9cZEopcf77BlrsFsKQeGsMkFH5DO4kfffE8GlLNpW0oVp/w+7UdzukjPkaVc8qgxSZnp3G1TGUYotVVeaV12iov223YszBypFfPe4mVUHh5brsEipND5ttxS7m8koMzr3/SOTccGFUMhYh2IaJriGgWET1FRK8not2I6FYimp3+3TUtS0T0IyJ6Nt098sjhotOnEy8qWxHUH74gNkNCabofVraSkgPa07bzemJUTbnKKx8WeuLW7fvU9d6NmbyMyO0z0zYRZhjtSiiarjX9g1i5MQkAK5LkdP/wtn3uy3yDLW/+M5XvK6/jUAZto3wb+m9fxoEQYmw2HCGJW+/vYzN/0YgsqbyUObGe8Oq98fhZ78xVhgG6bI8+n8rVSb1iMQKNsl5eNiMJ7kGvEok8k1AKUq/oS9m3yMp+/NIHcd+zK3HghO3NNoYJhQyFiD5LRD8nopOI6AYi+lwX6fkhgL8ppQ4DcASApwCcDmCyUupgAJPTYwA4DsDB6b/PALiwi3QFEZrbTKO8X0K55O7n8LVrc28YR8XlGeDOhMP1qUwHHbPS1W1yl139u9BtuECCKVoZ2ufalWCKoPujf7CB3z20EEBxJlrNb2wbigTuNmxDVnnxKOm8XDshJjonmg/Sq2olsBGAKDXoHUirtspLWFHLcSjmxO14SAZINfZDYe+1OHuzTI89NoptKKak41sQaqZJ4CqvsISi31EmoVjs7YeTZ2Pf3bZz2hoOxEgob0UyYX9BKfVeJBN9x0FEOwF4E4BLAUApNaiUWgvgeOSuyVcA+ED6+3gAV6oEUwHsQkR7dYM2G64KI65sqNyl9841jm3jut1m3SOaG6oyQ0Lxz0x2DAFXlegJM1sVFTCMIhuK3SZHzEQtlW0F0rxS5C01WG/ijD89YXiL+Wwo2X4oECa+TD2Un9Iqr8G6uTqNVXntscMY55xtu7Ah1cybi1ErNjj3g7ng0OltHC8v5f6W1DID9aZh18q1iMUiCn+XPE4ntj/tdxZvQzHHu1dVrPI/Csl4zlVe4fT1+ppvDrAlnHYTfpZBjJfXKqWUIqLvpccDXaLlAAArAFxGREcAmA7gSwBepJRaCgBKqaVEtGdafm8AC9n9i9JzS3mlRPQZJAwR++23X2codSZH/4f39PMbOC2CP7qrRgHMQcAZg4ZeMblxAFxCcd11Q9DfHZ8A7O2BvalXBAOhpp3fryF5vBnG7iIJpV2OIkDaV4Sv2O+evQK/eWCBcd036WapV4QtgKU7TAkln6RjJ4MX7zwOKzcOGueKgkMl8Pb23HGswTwlBATkjKHY70oJv6X1zud/YzqTcjVickf/H48AACAASURBVK9fauYSBc/64Ki8bBuKhwG4O6j6Flby9ZARXxvlKxVChXTqlcB719+U55uzJZxRpfJCooaCUuov6fG1XaKlBuBIABcqpV4NYBNy9ZYE6Wtxuk4pdYlSapJSatKECRM6QqgzQQS+W2M1K5RT2TU/Q5GMk1rcDel2ecqWmE2YMuMgk1C0y6dPdM/o8XyIXoYi1GEau8OTYefZiZzChO/BEvI4cs5rt2G4fSYxU516ZdBancauqF+803jnXK2AoYhuw6y97ccWrzftuCJOrp7IXQmFiyjGnyD0ws12u5bGJJfIeeBndGBjevz9E18FIH7fHH3WHvfOd8HqaSqVdWCtWsFQs4kQma6EYhb2bY43HChkKHpXRiLaIz2+q0u0LAKwSCn1QHp8DRIGs0yrstK/y1n5fdn9+wBY0iXaDDgTROR9ottweugYLq1vTlqFAOGVkFLcI8Q/qGyvIz4RVa1rvnr09+tVeUXYArikV5TepGxyyBjYu/IBZpJBaQ7xMT6+H4oN81xyMKaae/hk7smIl1D22nmcc64oOFQCX4nH5EsLBYPq4Fi7HlNC8UsZNjSTttWwUh/xSZWnESrONoyMHiLgw5P2xSEv2kGIQ/Hdb36XuUpPhkr/08/UVyE0CpJD2t+0TcuQpfIabRKKxi+7RgUApdTzABYSkc47fSyAmUgyG5+anjsVwJ/T39cDOCX19nodgHVaNTbciDUQi27D6VCzGYoZKe+KwJnKK7ByUiqfyGNiUXRdhoRiqby8cSgeo33oo7dh2lDCZbvATzwSCtuHXpgWihgKEPdBG+nrWflYhrLb9q4Npa8omEcAH06VCuHZ7xyHLwSCYvOYGfd+7TYcWiyFzrkwmbTPPgf4t2IoDmzM/+p3WCFyd2z0VOMmhwwzTC2h6B6qplm2g27D6d9czWzPDU1nHhgulImU74aWwca/AfgNEY0BMAdJmpcKgN8T0acALADwobTsXwG8G8CzAPrTssMC5/1E9oy0ql63uY6vXPNYpm/WsAeEY3jLPD1CAy9uL23bKN8nxaF4JJCMXq8NJSzyG3Sw7ikyCHfBhCIa5bmEMlR3qfbRmev742woY4S9MIgo2svLVisBrdmZuEpI55cKpxZK/mrpUjLKu33kTnYxahnHhmKplTj4uwxJKPYnycerPclL5Wzkxvi0nHXegUra0syrVq2kgY2B7zq91LAbSTFkSTjDqfIqw1C6TpVS6lEAk4RLxwplFYB/7TZNElpWecHtxEcWrMGiNZudsqaXl2AkbOodB/0rJ23wA4pF/aTOpIwch5IyMJ+EUuB15kZUh1f7RSqtdt2GJUh9xCejLcL2xWM8rrlVNvuVsqHUm8azxdhQqhUSmUeRxkt6lXw85c/g7+uQDU/qL7tdJZzzISMjU3nJYw6wGQr38gpzaC716PdQrVC0DaWZc0iDNru4vR+KfjbdVqg79FUpUj45byaXHK0qr+GQULYKuEb51lVeRQGBuj271JBHQrFTg8fYUDR0ES6h5CovXcbDULxGec/zCed47xSqvMKXW4IkxfH5h0srGmM8s7bhNmxfE8pzGwpXI23YMiSUNjG2VhHHVpENxLepVUYnewYfQpHy2ihvS3681VC2YRsVm56ghKIyzznDy6so1ohJTPqZKuRKKH4biklT0fPpRZ/u674Kod6QsyzzewB//jx7g67hdBsuw1C+1jUqtjK4K864+0iYXbxGbkvSsNvUOl2znBKOkxOhGAvtzdMMSChFySHt7KrSc5jl3XOm2/AIGOWFPuIf4+ahEhIKN8oHxksWKc/iUDTW9g/hM7+aXkj32FpFlEZaSU/Dx6NeV4S6+taZ5l4nhg0llVBcdRH7bf0NwRJQgva5wXozZygBTz3b5Z9LFLbUwOFnEKbUkNcnS3JJPSpbQFWrERKKZigN3ZZZ2rHDNYGZS9Zj1cZuRXzkiGYoSqkZxaVeGLBfdrxR3r23UHT2lNFGdj4JNpX7servQGJch7xoB3z1XYfhX958gNFOLRAp72MQmgwnt5hcXLwSkyNLKtspSCqvRosMJXcbdm0o0pqfx/tI7/uEI/f2kY2xtaosoRR0YqzKKxRnpfOd5XaN/P4tXgnFVcfEqbxMenIbilt2qNHM1IhbAnEoNv748CLMen49FLNrVIkcpxavLdEjmdjFuWaMq9dqlUpqlPfT6Ki8rLJDDVOn0T9Ux7t/dI93H6ROIpqhENFhRPTVNH/WD9Pfh3eTuNGEgXoD37huBtZsyoPHvnvCK3HHfx1TQv0Sr/IyEkUKwoUeTKsZPUlEPf9Y/ftAAMlk+LljDsx2wNPaANnLK29DpNcjofi+PElgGmkbiiihcG+hEiov7jYcI9FWSO/LLruMhtRX4/pklVcrfWSkCsq8nIrv02U57dp24cRFSBJKBEfJVVD6XnPy5hhsNDGuz5VQYlS/N89Ylqih0uNKRYqU93236V/GMJLzIZWXMqShWKO8j1ltHmrgoXlrsmOt5uvGN2MjiqEQ0VcB/A5JHz8I4KH091VEFAo+3GZw/aNL8Kup8/H9m5/O3ujbDn8RXrrH9tGr5Qq5A9G3YuKnJUM4z0WV32N5dzCJRfqQskAxfX+m8nJtKNkWvl56y6m8pGcqY0PpjpeXSxOnv5SEwhlKRNtEem8VuXwoSHFsrSpKI61IKJynGmq7SJi7TaaLGY8NZfmGLVixfotxLgQ7tke/m00Co09UXslCyTTKF7e0505jXbfhSBuKgvmN+yb9rHxmlNcSCjk2EKkNgMWiFTzTcBrlY728PgXg5Uopw0JIRP8H4EkA53SasNEGPtnoX/mH1rpRvigNNiB/BJJxMWEgJp2527C7+tYff76nvKvy0jQXxaHY11+001gsWz/gdVmUvG1KRcp3w8tL6KMPX5zv3bJ5yL0+tsDLS1IXSZQnDMWv8gr1RyKh+GnwoWh3vzzBZXFf54sSt36fy+1R35mcnYsxHGc2FOaivGrjAN5wzu0A0sk4bWuowW0ofoYiPdqO42pG9DqRy0BC34FxKaCW05e5i3IttaEE41AyR5s4n3IpS3a3EKvyagJ4iXB+r/TaNg/98qqVnOPbRsIiSKtVb7LFAhuKxGQkCSWk8srostrhRnlblVEYKZ8ev/uVexn32ZAYYjmjfPBySyjSr28erDvnfBIKp8/Z7kCgnUBJ3IlH5SXFmWiMrVU9bsPtGeVb6WNj3KZjImaL4phVdPaMWuWlgOUbckPzpIm7Zr+5lxeP8YphXLoPuIQSS69SJpv2qfTyLMu6reR8NbWhhKjU13yu+jby5wmX6wRiJZR/BzCZiGYjT8i4H4CDkAQjbvPQk02VKBscZXTMSTlJQpHL8kEiTeJDwo22uiQJbPTXkTHGTIXgSii227Fv8NqR8nnKFk95SQUneD/5EDIUt4qirXRDKq8x1YqR1Zari+wnNVLMpH8rFT22ZP28nQKeY2xfRbSxFLoNSyovUUIJVmPWafzOpYWidqOM8tZfwPym+ELIp/KKicfSrtt5GI7w3XoXgvJi0M+ArMDGSoyEkvz1RcrbyBjKMHCUKIailPobER0C4CgkGX0JSS6th5RScvTSNgb9UviHbYvghWDFDpiwPfoHGljhceXjE65PQqlYorgtoSxZuxmL06BJaZWYS1rm5C/lgCpSedluw3ZApEN/gVqn2+nrJRSlp+kXdPV6FTy2z2QoodQrPgmlQhA3UwOKbSiiUT7QSScftR/mrNjonOdtZ0wxZnxnUgMft8nfotgPfV9RK5mXF5OaDTUpe97BRjNLZzMwVM4or1OXhOJwfPO9pCXQ56X7m0qh2TRdlG23Xy+dJSWUbqiJbZRxG24qpaYqpf6olLom/d0gok90k8DRAj0gqhVhxel5T9odV6NCefr6f3/bIaJ/u90eIEsxQw3lTCLHnneXsVr55OXTMHPpegBFySFhlOHqlXtmr8QrzrzZCaayYbsNS14/ZnlBQmG/i43y3bChFKm8XIaiV8XaU04jcxsmdwtgCRVmlJdeVUh9NbavIqu8PH105SePwndPeKVIl+TlVUpCESbTkNuwxpJ1WwxJQoKmQ5OjWPwGkMfNaOS7YPoDG/kkq/t4KGXqum6p60tLKFY5W5IjS0KJ8XrzpTvy0TocKq9ObAH8zQ7UMepRZxKKrSryvae9dzFTivMXSgh/qMVeXk1nElmxYcCrYoraAjgzypvDYuNAPY+E93zzdlZhXYU3sl6gJySh2H3VjcVWUYp/O98ap2O8zVD46tbqA5+6j8h1/dYI21A8RnmPVJP1raTy4oGNLAV/CFy6OPuGmdkErifFUGBjGWQTfIXX4zIEjb40D5lmVGNqlaAqSd9fz3bN9NtQ/AG71rNm5f0LR+6inOTyCsehaPjS19u47akk+LQbamIbUSovInrcdwnAizpHzuhFM2MozMsry35a8OGy8nqy0HEHPhiqA8nLq6nkFYdncAV3bLQ8uaS9yIuyptoRwnaKcRtFNhSbWY6pVowVbFe8vApUXpINRVOhYx40Qm7DsrRIqFYom2BshCQyb2Cjd1x6qzImvlAKfvOe/H0sWbcFf3lsCT74mn2y546xocQgd3PPx2tol89aNelTvRAYW6sEF1a1CmEQ+v1wG4pb1h+HYqu8PGophey8UipTpecSipfMDL5N7Wxcdt88AKPLKP8iAO8EsMY6TwDu7yhFoxSNbOVdyQdTwQuyV0yGhELhFYMhoUgR3E1X5QX4B5foZqxpsdqR9iIvVnmZDMdmUr7yHKEdG8fWLIYi1toeiiQUSeWV5WCypLrcbdidTKRJLVd5KXHGDam8dhonf8a+ezJ1pJTLi839lYo5gftgv8t8n47kr7vhU2uwJ3gFyyhvSWS1ShLwqcfN2FrVWZyRUZ4yerldox2jvP7pX4gl/7TUVa0Qnli8Dus2F+dwi/Xy0hgOt+FYhnIDgB3SbMAGiOjOjlI0SpG5DbOXkkXuehSHzgddSuWVj5Lcj9w0dEor0DI2i3zyN+8NqVd8C7w8kj75q2nz0SNNqqYNxZJQalUAdXbdS2LLkOJQONZvcd2GNex3zScje+L2MVMqaUOZsONY/OOr98YXjz3YyakFhPZq8T2FvMFWsYSiPKt4uXyMfUCE1YhjlLeu96USio6UL5JQ9KKg3mim9hmmtrQQDGy0aAyVz/dDyW0oALBgdb+XTg17EVeEYeAn0V5enwpc+2jnyBm9yG0o+TnK/spvyp7wuVGeKLzuM43ybNWf6aVdG4p9n0S/BP3h6MkktNNfcSBmzvyA4j3oTTpcmjTsAMJuuEDGuJT6YK9iq4HJSIr1SCSUVGUSaUMZU63gf959uFGPdA8bNklbmQeeew+fnIq6WEuNIY8nCe1LKJowBe4bZjPdWpVQJUJ/KiGNqVWCruHcKM/tGmU0ATzdERDhHZn+415esfClr9dw3vto8fKiCIV1TJmtGdyXO8YNFBBWrdY9oS6TJBReutFU4uDzq7wCNhRWJxB2US2OlE+O9aRVhqHw/rC7Zqxlo+iOyqt8jC5nCByc4dld5kuDE7KhtJJGRdNgM6N8OnbBactUXp5x+tI9tk/usVx+Czd0atuGklZjqZfsBVytYnq/ja1VCr0dk9QnptuwpIEI7cBo2FAYrcb9LA9ZU+WTfZltm4tUXjYDaWEDz9KIbeIOIvo3ItqPnySiMUT0ViK6Avk2vdsk+MuzXWN9n7q9iq5UchfSSpGEwuY2/Q3w8ZG4Dbv3lVExZSDdTiqhBEaeP1DRbD8zypfy8uK/LZWX9aHxSe6Pn3u9l94yiGEo++22nUmHxxMolBxSG/+/+f6X5/VkNhT5HYbUkD7oLnM95vx1cXOHnqB9TfO0PGXsDK3uICjZUCTJS0OrvDTG1iqODZCTrVSymKqnEcI8Y7SNkA3FSH9U6MySesm1JKGk3nSe/pQ0JN1GLEN5F4AGkmSQS4hoJhHNATAbwMkAzldKXd4lGkcFcrfZfAWSqbw8L8qeBGy34RBHkVReBj1N+SP2584KrMyY1wxQIKEUqLx0+3oy8xrxpfMBo7xNE3/01+y/m5feQBMOYgLw3n+EmYHIK6Fkx2Tp1JMV6ZeOPRin/sNEo56Q23BVWLnam7A597DYBok2aZIzVV7hBZN+Jyoi20PM+SLkfZ2r7Di99nMm2xfnjL1WDUsoQLKY0tsw516cbrmQ+7zp5ZX+tcpx24piEkqZhYN2dvBKKMMgkThtxhRSSm1RSv1MKfUGAPsj2ZL3SKXU/kqpf5aM9dsasmy7Kv90c8OrfI+7IjBVOqGhwwdJFunK7qg3m+L9F981R6xPklBye45uJ/lrx6Fw+BhEvv9DcpyvXj31lJRQahXZLdeHE14t7x8SSkcyVDDZAP5kkI4NxbBfMPWlEDyq79c2tk5JKHrF7nobxqk0i1ReWSZqW+WVTZadtaHYGoGmMtmoI6FUKFvY9FWS9DRFjhd8g6s8sDFeQvnJ7c9G2VAyVRi0UT5tvwxDyeJ95Os+qbmbKM3DlFJDSqmlSqm13SBotEKrQwyDqqXTtSG6DbMJN6x6cG0ovKF6Q/as8SG0+s510lrlFaDLaxMx62hF5WXkuBI8djiKvrsTJ+0jng8Z84smG/v+H5386jypn0etZLeWOXcIz1MheKOki/JySWoZzYTsBUIoRkgMbPQ0zTcFk9AtCYV7JQZtKNU8x1mtStg81HA89WxaapVKmsuLvUPr+X171gBJep7FazfnZdO//kBI7a1WTkKpVggDja1X5fWCx2A9lVCEoKNYlZdRrkBCEb282PV6U4mTiA9SqmseZJmU0Sov/7CI9ehp38vLvGZLKEXP7vt4QhMzZ7rvSbMl++od31fF+494CY4+eAIA4KiX7maVS+kkWdqU6KiEAhsLHDCkScUnofAJ2YYRh1JkIzRsKOa1Bav6saZ/ULirHRuKbkQzRPNblCLlNePuq1Zw4mvcRYZNSV+V8NTS9egfqDsqNo3ExBL3DPpdhlyoeYBm6NvjGFOtpDm//MzNseF2n5/0GEosdOK/BtNxZysmzz3SC42NlOe2iszLq40BIalzHJWX4E3m0FXo5aVtKOVVXvz5pKhnX1kJPtVBaAVoJHf0lMvsD+l7fPMhE/DUt97lMBSeWJFPPg2rf8y6O6vy0jSUWalKkfK+ztbvJGFCZpk3nXsHnlnmJp8E2pBQ9F+PhCLGoWTeU4RjD99ToMVeCBEeXbgWk2ctd9rj94S0o1y9XGiUhxlPE6vyGtuXpICqB6LqnTC4YZBQYgMbX/AYTIOjGsJual63YVsNYqUtD62yjWzDntFbZnxEGeXTMiGPsKL9W3ImFVaHiIGN3ChvLXVsd0r74zjnhFfioXlrsn2zvZ5JIZUXW577/BKkyXj8GDf1SW5/sCSUhmxD0TT7NtiS3nXRxFz1SChZLizhHjF9vbf+pCKffcCHVm0oksTEm3Yj5Ym5TsvbJNsYEjJG29+pgjyu995lPBav3Sx+r/aZzOaYxh3ZWoIiaK/Hky6ZipWejOWuDSWq6rZQiqEQ0SQAZyAxzNeANLOEUq/qAm2jCjogin9wPpFYw54UDY0XFXgccQlFMMonx/EIejClFeXR7v6yvkv6O8wi5bWE4lV5CTs28t8eI7eG/XGcdNR+OHL/XRlD8ai8gjYUPpmGN86y+8Fn+LbJyANkzclKe/r4JpSi1aX0XrwMJSih8HK6bbmsnr+fWrremNQKN7GKEFFsRszp8GWydqVa04YSw1CkLQjsIZNIRu69e++aMJQTL8p3+WwyxiEhYU7cMcacX3xdpeOyps+3s2HlcDUko09C+Q2A/wbwBPDC2KlRQ7voNZnRtEiPH4qUL3q55n4oyV/7ljIirGxDMettREgoPtiifWZDKSGhmNmGzWsxahsquB46b9PkU2X7DNp2eU4/L5ulEveovOxcUBoS1UV6/CK3YWm2MtyGLabn1J8y3U9dMc04zyfPROqy6S5GhUiIGTEnePv57XfA41B05mEbdhcMGglIzb85/fGOE9w92KwjP8/jeAynCCLUPd+PHZclwXUUKbylbZRlKCuUUtd3hZJRjpCEUuRWaZcHUpVX5OTWzBhY6wi5xObzS1LmZXvtWLp+e9+HzGDr89cvsKHYH0OoL8X7PZJI6DvkXl6++/PJ2D4v00vId/g85tw78PoDd0+ui0b53EjL8ZOPvlqk5WvHHZ79lvrDZ5TPjdoujEh5j5Sl4bPr8GSQY2oVbBkqn21Yqlmf0/TYQ0iMlGc2lKK4LQWboWgGZqm8PExfit+y47Oc9pVKgig1Q7EZuue71btRStC3ubFRo09COZOIfgFgMoBMxlVKXdtRqkYhBpjbsCOKe+6RRM5cKgjLN/zD9u24VsqGElB52YN5z53GYd4578E/XzlNTDoo05v8zbYALnAplcjhE5/9rHZfysyY3y/TaXuLcfCJ0K/GjF086NVGPnHPW9WPeav6vfVUiDB51nKnrom7b4/5q8xkgZ8/5kB8gMXalFN5iY8AwF0hp48gwsd0eR1jqhJDac2KkqngMjWhLaGY77bGJJSEubh12gsevpDLGZjLUKRHCO09b5fn53kcCv9OQx6JYzzxUJqOplLCN+O9pWMoy1A+AeAwAH3IVV4KwDbPUHQQkQ56AtwVEweRO8ASnajKfkfbUDwfYJkBIgc25rQALF9ZwUQiwTbKc5dSCZIKjq+67GfzR6Ln4PfE2LWO2GdnPLZoXXY8FCGh+FK/OxJKzk9EUUBU2XkMs0XSGCDngMq8vDy6dOnV8HPcsUCCr4/4WLOzRAPtBzbqgalg0uuqvCpM5SXbUIwdKq1r+ULOPJ8EI7r0ibn1Msbhs6GYQZT8Wy/aVM0HLdmUsZ11CmXdho9QSk1SSp2qlPpE+u+TXaFslEF7ADWarieTuJ+3IIHw95uovPztcZVQ0zOwy8ShSJtH5YxRT/45bQm98fXf9tQyTDz9RqzYkAiulYpZpw3J+Dx+jF+Md5U2woTMfntVXuyZJk3cDf/z7sOy47qg7rGRpy2x6nU8qfRkLG8BLAUNhjzLit79ca94Md758nyvu2qFAhKK/70aKhc9f3vK+yY8rjocIzxUq27DuYTC63FtPpw+bWuoVeVtkjktNllrNw8a7fJ7lHJz6YUYViiwMYlDcRcTvh03AXfLaY6qZ14ajQxlKhG9rCuUjHJoD6BGs5nHoaTXxBUkJAmFJYeskDgpvv1lyaTAJzf9u53hMNRo4uwbZmLBKnefBa+EUqLBRWuS6OAZS9aldYTLiwwl8JHsPL7POJYlFApeB8xJx35HMRKK3yjvvmvdhrQ61fVc8vFJ+MDfvQQv3mlc0JHAYajWiVq1gv98+6HZ8ZPffKeXoeh7X7P/rk5bUuS571X61H+mhCLkIPPUJ9EoneTp6/kwslVEfdVK1n4iobhVhjzS+tMN1RwbSnqfrT6VGGyWA9Drbp+q3dJb64LKUUJI5cXT/nCMxsDGowE8RkRPE9HjRPREYHvgbQpDWkJRXEJJ/wrl5VWl+du+ftiLd8SFHzsyaYdJFHqic2Mx4ulfsLofl947FydexDbYtMZ401KBtbKgacWLTUNiKP/1jkPwxFnvwI7jTIZSRJuvff7RJ2rHsgwl+SsFxHHwzamkqUSXf+U+O+OCk16NSsXv1iqpR+Uxl5cf11f1pq/X5894z+H4RyvnmeGhxaQsCT4Jhb9bSRXXvg1F12OrvKx3UCHGUOQ4lBBD0VsH23dlKeetR5PGjO5PXyvf+9ssLF67mTmx+CUujpDKSz+mLx1QN1HWhvKurlDBQERVANMALFZKvZeIXgrgdwB2A/AwgI8rpQaJaCyAKwG8BsAqAB9RSs3rFl115jbMaNU/hOeQVgjhDbaIcjUFX6n89I7nAAjR4i3Qv3yDGwSVec1YqrUyKjWNTAIrGLySTcfelx1IDK07jusTVldCn7PfMW7DSUJGRlOEUT7Wo49Pfkq5Xm3S5OPrsqSozVGk+1PpKW3Kr/pI/vZVKzj0xaZHn6nyCksoPqY7xBZDoZV0WdhbBWhJwUdPhZjKy8OwQx7y2knDMcrDVFNl7QUklCImqu80g2tNiZvTGiOhxHhGdhplGcqpnvPfapcQhi8BeArATunx95Ckx/8dEV0E4FMALkz/rlFKHUREJ6XlPtJBOgzoCZAb5TV878kJRCTrt72CgN8wCwj5rEqMEL3akmB7eYXSdhfBjkPxQXo+SS/s0+MXTcgxDIFgvru24lA8k4veAthmoD67m4/mMhKKRp4c0p1offVwU1tRckgvQ6kXqLwiBJRkDNp9ZtLTbKoChpK3X6tW5GwDxaS4RvlmcqMjEQn1ZwG/BVF7uQ2F1cfqr1YITbbgCbkN59Kx/713C2WXD5vYvwaA4wBM7BQxRLQPgPcA+EV6TADeCuCatMgVAD6Q/j4+PUZ6/dhu7hqpDY11vYGBQbd8jy0S2x+yfZsuXyF5Bd+OhDIgbH1qe6vpQV9kjA0hWx0XcBTp+SSjfB6tbDMUt04zW7HcLlkHXjdgoYKD9tzBa5T3vWtdi81ApWa9nmU23ZCfz7ditt1pnYUNg7gfSkmGovdwBzwqrxb9vGwVs2WTF22Wun2fl1eM+s21oSSMzFWxuc+aq7zC7ehbuZMIr86mIazy0t+M1cZok1CUUufxYyL6AYBOBjpeAOArALQcvjuAtUop7Xe4CIBW+u4NYGFKV52I1qXlV1o0fgbAZwBgv/2MDSdLIVN5qWRoFK2Glec8v8dddeaTpxT45+isSwwQSULJ3YaTiprWcSvjjzsdhCA9nySh6D5yHt2jZtTw2kDYd0jwbyFg03/jF4/G3ruMx51PrxDLu5NL/lsp18uunMrLpVP0crNOaRrcSHm+sDGvySovmTCfDYUHB0oTX6teXo7bsDLVQMU2FLfOwjQxkL28mkoKvnXvzY3yRa0kdR0wsb5AtgAAIABJREFUYQec+vr9ccWU+Ub9tQqBK6tDDEXT4Y2N6iLaTQ65HYADOkEIEb0XwHKl1HQiOkafForaC2vpWn5CqUsAXAIAkyZNanEoM6N8GtjIGxdfk3LPGx5GJFzPJk8SV/COUT6K8gR2cJlUT8NSV7Wn8iovoYwTPpJ8X+9y4nuRl1ZSt3/VZk+WL3/Jztk9Yns+9QIlE5ArobgVlUkXEyWheKQ7Y+xa9UgqF7+EIk9qg4bbcGteXtLgpuwvpfWYu1u67yCfeH3JITmfv+y0v0ejqTB9/hp864aZebs+G4rAwDhqlXxhWMS3jJACYczbbQUZSoHtrJsopfLSXl3pvycBPA3ghx2i5Q0A3k9E85AY4d+KRGLZhYg049sHwJL09yIA+6Z01QDsDGB1h2hxkLsNu4Y6WTfrbtHLX6i46mQqBtGG4qRwz4+POXRCkH6uhshp1PWkx8xhQNNYFllyyIJ75dQVfo8gZ/VdMMlGqbwQmMQjGFKovL26d2woQv2+D15afEhFXQklORGSUGyYKi9vMbFejYGhsFG+rISy727jU3osBlcgoRCR4TYsPbYeh6cfdxhesffOOGLfXfDR15qaDEktWG8oh1lKElK0Ud6aGwA4EgpHMFK+or9f6/xosqGk9ol/BfC+9N87ALxEKfWTThCilPqaUmofpdREACcBuF0p9TEAdwA4MS12KoA/p7+vR+4kcGJavmUJpIA2yyhv2VCEz5tnEPWVcyaJ9ES1IksoobQhxRO4/5pPFG5l+OVeXuFyMfu3c8Sstvhz+PojZJTn8N3vzWLskQ6IUjdT28tLqMcfnS+MpQgJRU9CocBG+907uaSEMvZ1G1xCERcJJWwoZ77vZdh9+7EpHSnNWT3mRC0Z5fVOnzVfLi+9iGLn7NW/FIcyUG9kGX+z9q1yfWwP+yLVmpQY1TbKc4RzeRW/924hmqGkk/X5Sqn56b/FzLbRTXwVwH8S0bNIbCSXpucvBbB7ev4/AZzeLQLsNCi2ykualZTkVmitoO33y1UUUmoSextcflRks2gJLVRp22V8KPrAbOYbY0Mxy/sYAq8jwCAK4lCc8x4bCiGZgFwvL7cO78Qt2dsiVGavP2AP/PMbX4rDLNdgMuxIJjjjKwpsjLGhVMgtN2vpBvzpkUWeWk0YY9xSBdlxKJIKakw1mXh9CwQ79ir5HV74NZXCYL2JcdakLrWfpV4RW2f3coYiLAQco7zgYs/ble4ZBn5S2oYylYj+Xin1UFeoSaGUuhPAnenvOQCOEspsAfChbtKhwVfTzaZklHfvaQo2FBj3uJHy+ohIXsG7bsP8WvnRUiTPtRKHotGKl1cIURIKvx5jQ4F/18xQkKEE33bPiYQi2FBKqLyksRJD287b9eGM97wMF9/1nFOf7x5jgrZVTBZ879hkKIRa1ZS4r39sCa5/bIl0a05XRp9Lqz6TOMiEbCi5yss32nKDrL9/nbGgEinMjptysx0Tk1C81TvQ1dSCEkooOaRM92hkKG8B8C9ENB+J6zAB2/4GWzxQq54Z5c2JSUIolw4RHI7DJZSY9Ni83VYklEJXRmdVXMyE8kj5cLnYnenytt3Vn1sm/x2yR/DfZSWU+MBGvbqX41B8WwBLqAhjRSoayxwNKc0qK6q8POM7xm2YkKh+Qk4hEjTJFeK2PpPBKchxM7wOzVCKtq4OTbb2tYZSGGooxyuxHRuKqfIyJTH7OiC7Y9t0hMIWuoWyDOW4rlAxymFIKHpVZE1MElwbSg4pPxPYRzQkSCih1CtFNpRW4Bh5yb/hj0aWD6yAo5RlKJLK68YvHm2eg/8D5Pfl5d2PLru/rFHeYXi6PS2hNK3y8W2CJKN8PEPyqValNo3khCwuSoLXKG/tKRKa/AphvS/+SylzSWR7nSWR8klZ73CLGIb2IkI7HdhSgn2cMNJGSmtRG/lvScII7f5qw+fdNxoZyueVUl/lJ4joe0jsHNssePbUfDveHLEMxQlsdK4nf6sVeSvYkFqrSMUkoazKy+cswJG7HpdjKFLaFQ5pla1deTN6yV+e38dv8K6+Y+7n5QMqL8B0xw3RJ7cZlsaK6gypPuw7OqLyanCG0po6NqfBT492383pcenL09GEJRQbvzhlUhZoa5OvGaaWUPbffTv85OQj0T9Yx49uf9Zovyg5ZP5s/Dd5z2loda1Urd8oHyShIyi7dHi7cG6bl1qc/d2tl9iqysu1oegPRmYoroRSvCK3EbN1KKeRQ4q2t6Hp/v/tfXmYH0Wd9+f7O+aeTJLJnclJAjkgCSEhCYEAgcQAahRBYFFAUDxwUZdVQRTRfVVcX0VZ2X1kvRB9XVcRQZd9FcFddBU5RUDkEEE5BJRDCOSYTO0f3dVdVV1VXX38jpnU53nmmV93V1d9q7u6vvU9K01iEvt35wWbceeHN1vLu7hAShKgoZukrHPNk6WJDjdGI/Y/MMqnBzaaVsv6wMZ0GkznpT4o9eoCG01meRe34cDTKr+Eost+EJ1RjfKaRUeUKsfwbOPtteV7j1wyFesXTErQAMRBwpyhEIIkn4k4lGr8HafHoYj9JOk/b0OEi7pWlzmg0XCSUIjo7QDeAWC+kl24H8D/NIKwdoKo8tqtMcq7SiiymixpEBZTr+gYSmIVLPx2XQV21CrSCtKGPANwOFJ5uZUDAo8VmxtkSIx0qJ2QpZW3YeIXPZwsH2VWo7w5bxI5BzaabFoE/YSSLGfqs2Vho5TV5cYy2mZcJBSQdmvcNOjyyfHmxE3ObG7DYvJPM0NJahxUqN2MGYps8FfHgBjYmBqRr+unSIPBiUOXoc8Uh9IEfuKs8vp/AP4TwCcgu+e+wBhrWDBhu0A0ygduw0xZOblKKPFv3epY1H3qNsRKBjbGv22b8YhQXY9tyDMAua0gTWKyJfXTweXjMOXyElUDqjNFdrdht4dSEWwocEwOaZpzbGl6pHMmqcwi3aj1yiv+ZHkRLm7DVFBCkZuQJ0qmKAt0Xl78lGlCV7ei0NKg9JNL6uoiKCGlVirYtnM3rrjpkVQvL51R3nQ9oJdLrcmKTV5ebWNDYYw9D+B5ACc1lpz2BJdQ6tVgxcGUoEXTa0quKuVBY1OV6eL+bAPC1SgvftwZFk3O4M8q1W1Y3HvEgfasH4e0uqNYl60yGhdvsCztquUIerdh23axCVoqGvWohgzXoEvXeSW2WZiYrp5RSF5eRIVsKLKjhXxuhNkXJlVBVWia0F0kFBVcQlFjQZKeWMHxh753N45cPMVap2wvsV/nx0Z73mixoRDR5UQ0XjieQERfLp+s9gLfeKmrVtXunuiuBpHvMUkoRPKEq4J/oJKE4jhasqwWTf063JLmJd710V63+IG7THBONhTp+YqSiPm88d0ZmX0apQFEdRFDMq5IH7ukn/V0EkoW2nQeci6I+mC8rj9fhoQS2Us075SfY2GQsUqvWAc/Y1InqglRdVDHWmSUDyUUk5Qj0pPmzKLTeMjqPlXKNEvXscqr+RJK1je9jDH2HD9gjD0LYP9ySWo/8MHQWa9EgY0iTK8p8SGnxK7w923y8uIDl3+gYh2uDMUWEKXCNABt+1nHdKdIKCOya2kaknEomjKGe3VpLYI6zW27LhJMEFfTwQ5/6YGNpimHYJd24zbNEm8epHt56ceSOHQrlFTVZgEZ3h2Q9PJK2Ioq8aSbbpQ302CyoXRGNhS9Z6Mome1IicOxeXQF9CWZZZpEqj6PZthQsjKUChFFG1ET0UQUz1jc9uDSQmetGqdeSdF5AvpBIP42GXKD1CsahhIOXP6BFpVQXHeRU2FjSg/9eVuCNh2yx6Goz9L+0ZkkSFVayTrZukooUqQ8dKlXNAzFYkNJ1K9t00SLjVIzKhpJWISLKotAqKd5aGjvk/+LdMQSinyPLtuwKM3o8LRmB9MELSpDUSQUUznxm9QlZzXd7KLy0i0y1HZdpPqykfVNfxrAL4joH4joowB+DuAfyyervcCDDDtrlSg5pGnCEmF7oaI4Hp0TrtlE5JpGFeE6WOo1e7kr375OoFFfVjVGaqPWU+goGtiYNsmKlw/dO1bRuaRwsdLheIOYB0sX2Ki3Gxm8vDSzh+52V/d1V6g2izz1Vih9zFnvl7zy+DPl9dl3bKwSYfXciQCAY1cOWduxUZhQeSluw6Zy4uItLVOAGIflZpS3zDvhczBuqdBAZN1g62tEdCuC1PIE4FjG2G9Sbhv14OqZrnoVf9m2M1gZSe/GIKFYDKlazx1BQnnZsmVvvSq5EAEw67ON9xowbaA7tQ5dltXdie1ak8+kXqWIOafxE5N9KT623yMyw/cftQj7zRzAp6+7X5ZiLGqDib0d2vOuDEg1aCdsKJrXYHomOgcOHRlGG0pOJ6toQnKUUE5dNwcvbB/Gd+94LDoXGOVLikOJ6gz+q8khdW7Dsyb24OGLjnFoyH2yjQMbKxEduiqySCjju+PxpiMl+W7JuLiJn5N5/mkUshrlCcBKABMZY/8E4EUiSiRuHGvgk0FfZy0aGC4SiurGmSqhEC9nH4CxDSWGq5eXGNio23JXnCRMk62q8nLdfbDQxOIkoejp7ahWMBTuqSGrvzQvAcDPz92IboOdyNmGIu3YyNziUAxqGd1qtGwbyrKhAQxNkBcTFY0kLEJ1VScirWE8i6u6CnURJv4PbCgCPQW8mhohoYjjPU1CGd9TN9YDZLOh8H7rcps1Glm/8H8GsA6x+/ALAC4tlaI2BFc/9XZWsX3XSBCHYtDXi0gwDOl3MvpZ9B/XGvHCj6cW5ScSxX23V8mT5c2f3Iuvvim5FhA/StMAVFVeOl26bnIvMrEkJJSUwEbptPDxqQsBk7eYWY3pyFCEcgxyBDqgZ8J2CSVJowqzDcVMM6956YxxmNAjS2WpbsOa87oEpsUWEsnxyP+PMDmwsYhXk61owig/rM/lpZarZZBQBrpFhpJOA8Ey74SdacV+KFkN6msYYyuJ6A4g8PIiIr1uYAyBG+X7uoKXrkaa58mhVNEsjsUV2A6N2zD/dPhAteUxMoG38c7DF2DWxJ7EdSlliGGCS0goGkaheyS2XebSkFXlJcK0rwSZ6tEwe1u7tjaJADhHyusRGJfViTqJojQnVTb2+5Mp+5NSaGBDyeM2zL8FsS55UZCm8sqSgduWvl59rmrqFXWn04ge4btI8/KSGIo2Kj4pDaa9b3P2hsYh65veRURVhGOfiCYDyJaXehRi1whXeQUDaPuukYTHlg5WzyStGiO8ryL78nOcdfhe2GtyL45cMhWAmrrb7VXGg01/3SXiPm2nun/YulR736S+YOc9Ue321kPnp7YHFPNYEXNhuaRe0akjs7YruQ3DLQ7FrPJyi0Mx0+J2s2kCck1fTzCovEoKbOS/IhsK5PgSXfCfczsZJBTOHNSEpmoVdUlCcVd5uagzbYGNkYTisAgrG1kZyiUArgIwlYg+BuBnAD5eOlVthkhC6QwEupd37nYMbDTXGQwQPcMxTQALpvTj+nMOw2BoMM4joZgSx0XXHSYf1bCvTiJ7TenT9n3h1H784ryN2BQyxN6OKs47arEL2U6rLRcmoMYCaVVHhvoDOlJJldok0ttQskTKF4WzhJK4T5CyNNClAtLtX58vl1dcZ1w/SVeD+J74ehEVj61kQkIJ1VfJ1CuKhCKsXtLy5+lUXqZUQvzYOEaVeuJ72kxCYYx9A8D7EDCRxwG8hjH27UYQ1k7gq8vekKFsH96t2FCyTz6kuR5/RPYXzwdqHhuKLpOpXHd83pas0HQPEKg9tHEiAKYPdGPaQJeVBh1cVp82FUC0+lckS32AqSUK2ULze1+xT6IcwRCHog1sNHOUIlOBy3NW0wkBLpHy8phjTLOHPOn3lXeFGogq/udtAsC1Zx+SyKRdlg1FvWbaD8UW2JgGvljV1aM7F0TK6+uKNR1tLqEQUReAowEcicB1eEt4bkyDJ4fkL337LllCMRqDU/SyphWh7sVfclKckIAv+KTVmfPKWf6vwuUjUKe95MpM33NebHrIUF7cMZzalqmNLBKK5GiQqDdZniifUf6swxfgkIWTpDYDCUW3wVY2CSXL4lJVI9polu168cGRi6diwZS+sFDwb0JPHecfHUuUqjTLwJJ2FVAhlRdA0YPR2VD4ompSX0fi5RbwBZDr0Ugo9WrSbTfBkDMlYo3Lao3yal/ItoAi6b+ujUYh6yP/GoClCFRfnwewGMAVZRPVbpjS34V18wcxPvSCUV0ATa/J9h1VNJMWP9apnV69fEb0m09WWTP2Bu3yj9JBQjFMcOp5VaVRr+pVSbztng53X5BoS2GH7UyNTEDyXJN/6yQFVXqUV492etU8TETBFsAJlVdGhuIqozx80TEJNaLL0Ai2tY7x8WP3Ffb7iKXajUKSQ3XMMaZ33c2VbViz8IlW3uEPcUf5YIGWvujgeP+WRUpzFqarHO/YNYLOWjXV886VkV58wnKlHhcJxcwwedlWJIfM6uW1D2NM7P1PiOjOMglqR2xaMhWblkzFjfc/DSCUUAwrOxFp7pqmDyBNVI9UXsIk5erRIn6MWrpyrGLUybFa0auMKLqeuYnks9Lm8rKv2HT0mFRnYl03n3+EdM1Op9wmP1ZVXrpqbHtmqOWzmFtsY0O8ItkrLPp7Dp1tRHUNJyqm8tKt3PmpkZFYlRmoL2XYvqNXLpuO+/70V3zvV4+HlZppUOv501+3o6NWib8lwy6lrmro1+4vR/G7xaFY0gYpz8lWb9nI+qbvIKK1/ICI1mAP2GCLg+tMA7fB9A/ONsdTxWZDkc/vNblXOubf527GrFINh+xBEvzPmPkEAHDMsulYNK0/MZnpbCi2DbBcBnba6i+LhGJ6NmRQG1RIZliiRJUuoYRtKpKek1HeVq+9WSvyeHnJzEX/W1evbl/3QoGNmiPOoIZHRvCHv7xkpKWswEa16t//eRs6a5XUMZm33zq61aoIZpqjhVuCCeUiJxMyx6EAOIWI/hAezwZwLxHdBYAxxpaVSl2bgasAXlYkFOOLtUoo5vLiwPz6GWuwdMY4qRy/LrsNm9ua2t+F517aJd2blhhSh08fvxxd9Sq++NOHpPOJHEoV0qo5TKK4DvsNBfvFL5zaJ92r1uUC2+RoirjPI3WK9UfFSG+U1zI5qw0l/2zgHDsjMhRD2+Jv1V7CmM6G4r5SV+8DZMbOq+bG98t//gh+88Rfo/KqH5X1+yN1CwVb2fhaf1cNL2wfRketotEwyPe5qqFVuObySnMcGQ2BjVsaQsUoAY+/eHnnbintQp6PXRdbEBnMhY9o5ZzxCZsDHyjBzpHBPGQbvFPGdeK+J1+Q2kjdklRLs/68qvqoVUi7d320cnL40LaumIn9Zg5g/mQ9Q8nyyE3t6RwjOKGm6l1tKHFySNIGNur5SWP8hl0nkn1njsMvHvoLAL20oq6KEzYUzTmifG7D0f2aN8G/Q85MgKCPaXuOqHSJ34CNQrFLi6b145aHn0VHNZZQolqUSvJuLKZ7XcnAVnNskmi/E9E2Ki8iWk1E0xhjjzDGHgFwKALD/DkItgHm58c0eLrq7cMjxlWvK4JIef0kKb543Uqff7S7hRQwtklanNx10o0JqhTjGrtSq+rVHLr+2cCZSXCP0qbOmG6otkqkNXjbPsg0DxoTSClHhIRRXhf5DtjVkGrpLOsB13nk8EWxwb1iGN9iXSqjYExvQ9EtLlyho127WKlkY8gE+RnanpH4zgfCJI6d9XSVV17bkYs6t2KRUMQytuNGwLXHXwCwEwCIaAOAixB4fD0P4LLGkNZ+4CsjNYo9l4SiWQbrjPK6VU7s5ZW8VwfxM+O05pFQTG2oKVVMNpQsKi8VboGNJknE8Bv6icR0Xr3fdl1MrKjaUEz9t6khiywuXRn4nMHYVmcyystb8qoSCtPaUIpusCV6cgH6/XiythCovJh0bCvL0RtmywgkFPuYLFflpRKVPiaSNpTGcxRXlVeVMfZM+PsEAJcxxq4EcCUR/aoxpLUfxA11XLxgbCCNWiVewQf/axV9vh4+ULjKi5e1obNWwY7hkahuF36SZnznULOuVg10Z5VQRLgFNurvNX5IFikkT2BjWKlEXyChyDYUEz12o3z+ycDleTMweeUvMeFwvEF+ZDq3YZ0NJW2lfvCCSbjn8efxbGjnE9vUUU4UqFTF6POsY6qSUHmZ7xffF1c/i27DkWu7UkWaUX7FrPFR3JJMG283PqdLc5MqoTRDJFHbdCxXJSLOfI4AcINwbczv2Mgh5rAqqvLSqVVUo7xpZccHisuqFwDGddVw24c24dcXbo5VXgXyfKi3umQfBgT7Qo6Bngwi00koZujUIabyOmYf0ZGm8govx1HmwRbAolRr8jorK7BRhdXbULiWdXwnjPJIjtlKJT2wcXJ/Jy49eaWWLtPzVqUUouQ4tIHg7ukokt8bbvnQofHySiSHTHFG+OeTV+KczfskzusXY+r4T5c4mmEzSbTpWO6bAP6biK4G8DKAnwIAES1AoPbaI9ApSSgxXN7bAx87SjpORqHEdfKBYgoIi7MNx+dMk/TeU/vwka37oq+zhnFd9cjgn8dtmEOdnBPJIg2MkJ/No1p24UFZRXqj4GJZ/aUa5aNyFLXBIKcvd1F5pXUli5ee+Fxeu/9MYzlRQpHHt15acAlsBNIllEDSNi1CDLRq0p4MdNdx9VnrrW3FFbs/b5G2npCh2GyEHGlaA9M40EbKqwwF6d9RAdNVbjhJF4yxjxHR9QCmA/gRi99EBcDfNoq4dkPgDhvsOuiSy0uEyhzESHkuvqupV0wMpRIxlDgOxSTevvng+VLiuSw2FNc5S91b2yyhBP/zrJxcmIVLrdJEaZnEMqvPhHul/wieo5ht1lSF+LgrRNIeKmVIKKvmTMDFJ6wwlhNX/XJCzRiSUT6xAmea9PVucSimudfUb50dBQCWzxqf2hYQvPs8EkpPmDVB1A7wBVZWG0oWtapalS4zQHQtpf5GwpmHMcZuYoxdxRjbJpy7nzF2exmEENEsIvoJEd1LRPcQ0bvC8xOJ6DoieiD8PyE8T0R0CRE9SES/JqKV9hbKgc7DJE/OINFllX8cqrrENDFzlcnISLyyswXvSbSGxy4rXJPXjHqrmsbbtKGSayYA2702ZK3WZloxXUuXUJT3Ef4XGYpZQol/J7ylSrChpDsUCExEVHmRvoyLhEKUnnqFwcyojbtwpiRmTEPSKG++XzLKhxLK8EhSqspqQzFKKJrHpXObN+0qarqnGWiBUGTEMIBzGGOLAawFcBYRLQFwLoDrGWMLAVwfHgPAUQAWhn9nAviXZhCp3ywozwQZD2L+cagTbprKS1zBptkt4nbDewvovNQ7VaO8kZbwfy4bSsFvQ+s2bChrV3m5SShqOb5trK2OY1fG6qg0V9tMqVcilZX7Q1TT/Me/YyQDG5OTaIXSVT+6TMf80FXllXkxASWw0VZWY5TXSfhZU6/Y4qOS55RjAJ89cQVOOnCWsf4W2OTbh6Ewxp7g0g5j7AUA9wKYCWArgMvDYpcDeE34eyuAr7EANwEYT0TTG00n/9BNKzhXiBJKhyKh8P9pRvkRhuhLEFVe+80ciMsmJBTh3pKgSigm9ZsuE4Ar3CSUbGUC43ty9W6XUBwZiuA2DMTbxtrqOO2gufjk6/YDAHQoasQii83o3gx16Ma3+lzU9xy4DSclK5fkkCbSzEZ5+14kqe0ROdtQKhJDCdp1WZDlt6Ekz+sklBnju3GuZT+hvG7LRdA2DEUEEc0FsD+AXwKYyhh7AgiYDgAefTUTwB+F2x4NzzUUdR1DyVtZeGPEUCKXU7uEokbKi+dqFcK337YuKmuKMHfhJ67ZhlUbigmqSi8LGiG9B7spBp0RP36CeYKilC9G3aeCV7N9l2iUN9xLFK1qdV5MIrI46dn2NeFjrozNvQK34STdaXEoqjuyer8OCQnFlUihvLPbsPC7S8NQYrdhVUJJYShGKTj9nIvHZDu7DTcNRNQH4EoA72aM/dVWVHMu8VkQ0ZlEdCsR3fr0008Xpq9TmfyDNvLVxevojFRekP6bVjiilJFm6FZPc71rnn1PTHWqKi8TIvtCLi+vYh+HdsIUqpT2TCFzWotUOkjdfyX47aLyEpGcMPP332pDMSwwJKO8tHiy05HcxTFdQmFMdnLh9wV0JEgFkM5w00AkZ4uwSihiotB6zFBM9klXmowqL80z1nl5AfrvuIgmoCjaKoaEiOoImMk3GGPfDU8/SUTTGWNPhCqtp8LzjwIQFYhDCHaRlMAYuwxhNP+qVasKr8N0EkreF8dvU1VefGWRakMZYZHOWlphSytkmbZ3blwABuBEi+41DW9cOwe/e+pFXHfvk3jupV0JlZeK2DMuOG6UyssFpPzWOTXo0uKI19Lq1xm3xT10nBhKtdiEKSIKlMtiQzEwEZWOWiXOocWgD2ycGG5ZbW0v45WkDSXbA1JzeaWV5eA21N2aW5OTfoqEkjKYbN+xi4Tisp132WgbCYWCJ/QlAPcyxj4jXLoGwKnh71MBXC2cPyX09loL4HmuGmsk1IEMFFB58TqreqO8SVWgWwHrzulo6+mo4f1bFmUKAlPR21nDp45fHrkjp9VVr8oSWCuM8jqIH6m6CZepvXQbCmmz9m53iEMRUVSlo9KU+R7pfjMd4qLH5OW199R+fEdQw6rQeXnxIxPpJrdhVxApubxsZYXf/N2KO3DGqWHk+9Jyi2UZ0wnpR6FHhxbwk7aSUNYDeCOAu4R0Lh9AkDfs34noDAB/AHB8eO1aBNsRPwjgJQBvagaRXCKQP7h8b45vkBVNyNEKnrdld78VIW45W4b0BKTr1XnNamCjilpEW35RvOjHkaLxSq6sHVWIujrTbCguE4lu4SIim5eXTIsL9C7ElJh5O2oVvBz2jYEZx2y0nbAOLLtRXnVayAqCey4vkYYoj95IkuY8UpIrTLbQSoUSzDEvPWV0BCu8AAAZtklEQVSgbRgKY+xnMI+rI9QTYXDlWQ0lSoNY5VX8ZXG3Xz4hJ92G0yUU3TnT6rJsxMn67B937HQQoIzkkFkxNKEbALBoej9wB68TmNgXqGPW7TWIa+/6U1TeNAm7eHnpthyWVF6W/vMrRTalUuEah6KjI/itl34BhfEZIuV194lgSNojdHSIKC6hqCovt5X+4unjcOLqWXjzIfMxLpTQ33zwvEK0uCApocQnapVApRxfk/83E23DUEYL1MkRyPahfub1y/GtWwLnNO4pErkiR/WFKi+DH7tsQIZ8rxJD0UhHD151R83eiMqE86m8inVk7fxBXPPO9dh3xgA+fu1vQ3qAmeO7ccM5h6KrXpUYSpH09boyUuoVh76oK/0ixr9ccSiSlGumQrT16GwoUX2W+T/Yzz6+b+GUPjyzbWfYtklCKchQoG6wZS6rSigXvS7eR/Dhi44pRIcrdLm8OAL6xMj91sEzlIyIPiBRraF8qP//3Yegv6sOHY5dOYRjVwZ7SPMVUjKwMSib5jYsktFZq+KkA2fjuANmKh9HkYnYTefVUXWzoRQzyme+JYFlQ3JaDs6w50+OJzAOs5eXvQ2T/SWrUb7oClymSf6vg6oycYmaB2Q6GUvGoUT3WegTAxuHJnTj+397MNZfdIOV5iJ7rPB6pTiUlLKthm3M1CqEHU2kxQbPUDJCtzJSv6FF08YlyuiwW7GhqEZrk21Ct8IlAj5x7H6ptJlwwzmHZl71xRJKupeXWL4VEooOolpJfabxql5Gai4v6CVIqe4cRvkicFGx2aFXpwKKUR5JqZrP2WnPjfd3xkA3uupViQmedtBc/N2/34k5gz1R+TS7XRqy5PLK67I9NKEnvZAjVP4pPs5kMGnr0DZeXqMF9WoxlZcIvp1DNHkoNpROk4RSFScse+NZdkdUP4BUo3xYd9rk1xExzFDl1QKjvA7iZKhmSM7dHCmre6GmDsXbzZU2HbJkG47tQfl6JRrl1To6JAkF2GXYCtQqoYBh4ZQ+nH/0Ynz+b/ZX7iMcu3IID190DMb3xO7HS6a7LdpMIJKN8jbmklc6njepFz8/d2O+mxM0qONTsKG0Iq2wAV5CyYg4ZsR9UjeBD2g1sJFXZ5qodW6pRqNmE5YrJvUD9z7p4BIKV3m1ILBRB/FDNEooWT13oLgNC7d31YOs0i4SWmI3xAKK8aLPjgy/AYWhAOjv1E8p1h1FWfCc37JhfqIl022H7T1Ff8ERAUOJj20xKXkjzisUpEc5Zd0cbN+1G1fd8ZhkPDdB626skCB+Q60IYDShfVjbKIFWQslZV6zy0qdeMTEUaUJOWX02w204TW/eDkZ5HcSNn3TxE3kwoaeO8T3CdgHCte4O9ywFKrLsl65CddzIfr9ZhScZ5RnDlHFd+On7Dscp6+ZoacgK03sf6KnjcyeaU/GngW98xmFjKEWf20e37ot/PG55ZruPzbtO9fLSoRXGec9QMkJdbQcH+epKGuUh/TcZZrU2FFMjJc3DujHLyWBguOCVSzTXgwI11YbSIqO8CjFzdFkM5T2b9sYVZ6zR1hOnvWnuZ5fFbXj2xKTe33abaNfgE9isiT1Rm0454yzXbDS/atkMh9rN9TJJQrGVzS+hNAo2G0orjSieoWSEmsgRyL96TrgNRyohu4QiTkhpKq9CEorwqevqERMLnq7xxf/A0UEm1ISXVwviUHQQV3amrMxZ0dtZw+T+zuhYHCc855ktWWIjtBecgbtUfdU7DsKVb5ej2iUvL6WWD79qKTYtmRocaNxwuRSgPs8+QTWmD8pDeJ+Z1iLJDwny9g82m1TeZtRnVURiUMeFeJhgKC30G/YMJSN0xtLCKq+6YpcJB4TJHVdc4Mbbs9rVTkWhZSjRpKG/54yD50l++kWM8g2RUMQtbxukhxar5ZOoTeXnmuE5C1zsQXzxMNjXiQPmTJSuSSn+lSq6O6rYuiJdUlCbHtflar5t1HshxShvUXkVdmYojpNWzzbW7aJCvfG9h5dHjAWeoWQElxrE1XvegROpvJRcXtxwZzTKawZQ3txTrrDGMITP4p6PvAL3fOQV5jo4Te1iQ7HotGsVwvxJvfjM65eX1l5vyFDy2FCKgAcV6lp120NG/1uFzc6j3ibHadkmcztteUFQVF565zQA8beV9b2ZaBfVhK6YPdiDX35ATBhitv/pXvTsHG3mgffyygg+CQ1LqQ4KqrxCt1o+AHeF/sQmG4p2cjWQUOSDFD84vYRCUrleg4ePSku7GOXtqifCDX9/WKnt9XZylZeZkTViAs2TesUELVOCPA7Ec9Gx0vgHX7kYb/zSzYn71HYa5cGUdBu2McOABpeNwnT3cfAmrnrH+gwSmlif8NtmQ4naa77uy0soGcEnedHfPnccCldtKUb54bBusw0lbjD24DGovIowFOG3btCeEdpNZjkGcEVeXnniUBowUutNMI7rto+1rXSP3m86tq6YgXOPWmStN8tc4cS/rUZp8Xd21Sen4XVhhggAWDVnIv71lFWpZDVKllNVXjbaef+y5lczPfcqkXVRMakvsMEtmtavEKL92ZKdGU3wEkpGaCWUvHEoBrdhXrfJzVA3gIxxKCV9krr6jztgCMcdMJS8kFJHnsfVbAnFhtVzJ+Cofd12mxZb6O1Il1C66lV87sT9E+fLiUPRjBuH++VATc318L9V5UWET79+Oa68/VEAwWIpvs+MRsZYiGouaxxKihu/CcZ5IaVLi6ePw5VvPwjLhgbwxZ/9XrhNXEjGv5utQrXBM5SM4IOKq6WA4kZ57v3DbQt8wyJjfIcusNHQRlljrYx4Ft3eLe7t527eiKwqDI5vv+0g57LiY+sJVYJ1x858+bRV0bMrJQ4l5zNMz18W/M+6LbELPY2M2RNVQrutRvngf9bxYnpuLn06YM4Ea33i72TqldYxGM9QMoIPqh3DMUPJu0Lgq6IJPXX8n9fsG7lf7gyZVd1hRZSaeqXATCzpxEsYo1GusjwqrwZ8JGWmiDdBbMHFy0vExkVTS6HBlJfMFenbXYc2FPFMhsZ0uv5mBH/LkfLmcvF2EsUkFL4oyL/Dq7iQlI3yYlxNkcVHUXiGkhFcPbVTYChLZ+bLK7RbkETesDaOLB7mDCUDMzANoSLfZVocSlYUi0Mp3HwCzciBJNtQ0uNQGgEXo7xrcKGOsdskFBfbhH7zs+BikQVRGmQbSrqEUtZ7y8/Yhd+KhEJobdp6Ds9QMoKrp3YKKq/VcyeaiuPy0w+MdOcquJitRk5zG4rLhBfpoQ2jqaz4iiLfNWdMrRTFdWiKhKJRTeSJlFffb5ZVaJQjroTnb3cwzDaludBT9htaNWcCbn3kWQCKhGIRUdJsmlmRX/Wov7FWqQTfOWv9d+YZSkZ0hUGI4gfe31XHe47cGxf/+P5E+UP3nmysa8RgK9k1whmKwwcXrfL0H0RZK/sijOmBJ18EAEwb6MpdRyM8WZri5SX85hNTnr7053AzjWiwSChZXqupqOo+nhUuUkxZuPz0A/HnF3eE7bplG+b20rw2Nw7VlpgZoqQo2lMqwR48u5XiPpfXKIBpu9t3Hbkwc12RhKIwjljl5fJ69IMzzgtWSLRI1JcHT70QfMAbLMw1DfVqBT9894b8RGjQSHVKBO4qXaHIHTyPzW3OYC++fsYanLxmdnphDcrqqk1CYZpzLnXZJr6yQyl6O2uYM9gLwD0OJWYoJam88vITiaHIXl7tIv17CSUjugpu7COCuy2qK9ZY5ZXFsikf1qsV7BgeaWjqFVd86rhluP/JFzDQrd/F0hX7qH75owjd9WrkvZfXdnPwwkn47/ufynVvhfReVXtN7gMArJqb9CpyhZq3S0SaK7HpPhdmUxSiVGJjXDtLklBc2rKBDL+rFWpcwE5GeIaSEdyGUga4UV5dsfKgSZcVUXeHfpBzhlKWH3+Reo5fNasUGrLi6rPWY1xBJlYU/Kl11avYvVv/vvMg66RUIf0qdvms8fifczdihqM60mqU15yzYc28iThsn8n44DHJTNVFMUVI0GmCq4Syz9RgIfOWQ+Yby7igu6MqeYdmhfgNqrm8TAb7ZsMzlIzoMqi8AOA/zj4Yz7+0y7kurvIySigOKq8rTl+Da+58XMpwCwjb7paj8WrpIBVx8IJJib6asHzW+PRCDQZ/bl31SmQbK2IPyp1KvQLjKnbm+G7rvWnu47rUKy7oqlfx1TcdmO0mB9x14Wanb0ek1xaHMtjXKSU5zYvvvG0dfvSbJ6N9cbJCTMUkZTt3jOlpBjxDyQjbXtZLZwxkqmtkRM84DpgzAXc99jwmWSbOq89aj0n9nZg5vhtnH5G036gp4/NgzbyJuOqOxwC0z65wX3/zmvRCbQT+4fd0VLE7g+RZNgIJpRjIpFmxqKfUeXr6QBcG+zo0JbVV5oKceNIMrmrbsnQaTlk3t0CLblgwpR8LpuRX24qq0kpCQjE/MZ7KpRnwDCUjbBJKVpgklPOPWYw3rJ1tXTmmrb7rSgbjPDhh9SzMGezFSf96U0M3CxrL4I+/t7MmeHnl18VvWDgZl934EFbPM7uq6xDYUMpwG9aovML/oi3E1NYvzjtCe96ERiY45N/fZ05YHuVZGzVQ41AMr/am847ILRHlwSh7iq2HTULJCpMNpV6tFFrJBHUUV3kRUaReahcJZbSBP7Xejlq0LUERCeXghZPwwMeOyhG13Ti77fxJgWF/85JkZH9edtCo/WlEfOW0A/H1mx6JdtIcTZAi5U3SJyvmqp8HnqFkhCmlfB5EcSgNUIFw8bioooOvED0/yQf+3ESVV1GjfB5vozIWBIwFqpbD9pks7Rk/e7AHd124WdqFsSjmTerFY8+9nDkhYxas22sQ6/YabFj9jYRklK+SbLBvAT0RLS1se1SizJVTHCnfgKC9ajKJZR5w10ovoRRDX2ctTvrZhJQvKipUPDlkb2cNRKQ1pLvaLVxx6ckrcfsjz2JKf3NX2GVhYm+6nagIvNuwRwI8DqURk3VHKPUUZyjFEtq1AntP7Ws1CRG4m2hPZxW7hhu3gEhDEaP8lHFdeP+WRThmP7eU/UDx+W2gu47DF00pWEvrcPuHNjW0fnFh21WrlpYWpig8Q2khJvd34rHnXm5MWhG+b4stp4QDRkaZyuvOCzaXaucqihd3DAMIV/edwUOc0NP82BgqaJR/+2F75bqvBZsGjmnUq4Rdu5nEsM84ZB6OXDIVJ152U8vo4vAMpYX41lvX4ubfP1NaBK4IHmW/q0AgFRBPCKNFQhlowWRtw7aQofR11PCWDfMxd7AHr1g6rel0tJFWZEzj6rPWN9TuM66rjr9s2yntGDt9oBvTB+yxRM2CZygtxNCEHgw5bp+bFVuWTsNNDz2DmROKDbTZgwF9b8u5Qm1HfO7EFU1ra9uOIGVfT2cNXfUqTjwwXy6uoqg00s2rhbj9Q5ui3HftgEYH037q+GX4+LW/xfjupI1mUl8Hduxq7bMY1QyFiLYA+ByAKoAvMsYuajFJbYNTD5qL1+4/VHjFPq6rXkqUcDth64qZ2vNr5k3EbWFq87LAJRTTFgbNQiChNI+jvHrFDHzhxoewaUlj7SCNNn63GzYummrceO2XHzgSjDGc9927ALRmo61Ry1CIqArgUgCbADwK4BYiuoYx9pvWUtYeIKK2U/+0O7711nWl1/nSzkBC6S3RpTYPTlg9u6nJNZfOGBhzC5F2R2CLbW0allHLUAAcCOBBxthDAEBE/wZgK4CGM5Q7P7y50U2Mefz47zagozr6AsqyYq/JQar0OYONUW26Is/2Ch6txydftx+e2eaeHxBAZMMpkpEhL0YzQ5kJ4I/C8aMAEomeiOhMAGcCwOzZ5eivi6Zh90DhTACjBWcfsRAbF0/FsqHWJ6r0GH04YXX2Oet9Wxaht7OGrStmNIAiO9rHvzI7dIJdQmnIGLuMMbaKMbZq8uT8Gzx5eORBrVrBijbIeuyx52BcVx3nHbW4Id6jaRjNDOVRAOJGG0MAHm8RLR4eHh57PEYzQ7kFwEIimkdEHQBOBHBNi2ny8PDw2GMxam0ojLFhInongB8icBv+MmPsnhaT5eHh4bHHYtQyFABgjF0L4NpW0+Hh4eHhMbpVXh4eHh4ebQTPUDw8PDw8SoFnKB4eHh4epcAzFA8PDw+PUkBsD9qwgIieBvBIztsnAfhzieSMBvg+7xnwfd4zUKTPcxhjqZHhexRDKQIiupUxtqrVdDQTvs97Bnyf9ww0o89e5eXh4eHhUQo8Q/Hw8PDwKAWeobjjslYT0AL4Pu8Z8H3eM9DwPnsbioeHh4dHKfASioeHh4dHKfAMxcPDw8OjFHiG4gAi2kJE9xHRg0R0bqvpKQtE9GUieoqI7hbOTSSi64jogfD/hPA8EdEl4TP4NRGtbB3l+UBEs4joJ0R0LxHdQ0TvCs+P5T53EdHNRHRn2OePhOfnEdEvwz5/K9wCAkTUGR4/GF6f20r6i4CIqkR0BxH9IDwe030mooeJ6C4i+hUR3Rqea+rY9gwlBURUBXApgKMALAFwEhEtaS1VpeGrALYo584FcD1jbCGA68NjIOj/wvDvTAD/0iQay8QwgHMYY4sBrAVwVvgux3KfdwDYyBhbDmAFgC1EtBbAJwFcHPb5WQBnhOXPAPAsY2wBgIvDcqMV7wJwr3C8J/T5cMbYCiHepLljmzHm/yx/ANYB+KFwfB6A81pNV4n9mwvgbuH4PgDTw9/TAdwX/v4CgJN05UbrH4CrAWzaU/oMoAfA7QDWIIiYroXnozGOYH+hdeHvWliOWk17jr4OIZhANwL4AYItw8d6nx8GMEk519Sx7SWUdMwE8Efh+NHw3FjFVMbYEwAQ/p8Snh9TzyFUa+wP4JcY430OVT+/AvAUgOsA/A7Ac4yx4bCI2K+oz+H15wEMNpfiUvBZAO8DMBIeD2Ls95kB+BER3UZEZ4bnmjq2R/UGW00Cac7tib7WY+Y5EFEfgCsBvJsx9lciXdeCoppzo67PjLHdAFYQ0XgAVwFYrCsW/h/1fSaiVwJ4ijF2GxEdxk9rio6ZPodYzxh7nIimALiOiH5rKduQPnsJJR2PApglHA8BeLxFtDQDTxLRdAAI/z8Vnh8Tz4GI6giYyTcYY98NT4/pPnMwxp4D8F8I7EfjiYgvKMV+RX0Orw8AeKa5lBbGegCvJqKHAfwbArXXZzG2+wzG2OPh/6cQLBwORJPHtmco6bgFwMLQQ6QDwIkArmkxTY3ENQBODX+fisDOwM+fEnqHrAXwPBelRwsoEEW+BOBexthnhEtjuc+TQ8kERNQN4EgEhuqfADguLKb2mT+L4wDcwEIl+2gBY+w8xtgQY2wugu/1BsbYyRjDfSaiXiLq578BbAZwN5o9tlttSBoNfwCOBnA/At3z+a2mp8R+fRPAEwB2IVixnIFAd3w9gAfC/xPDsoTA2+13AO4CsKrV9Ofo78EIxPpfA/hV+Hf0GO/zMgB3hH2+G8AF4fn5AG4G8CCAbwPoDM93hccPhtfnt7oPBft/GIAfjPU+h327M/y7h89TzR7bPvWKh4eHh0cp8CovDw8PD49S4BmKh4eHh0cp8AzFw8PDw6MUeIbi4eHh4VEKPEPx8PDw8CgFnqF47PEgot1hhlb+N7eBbd1JRN90LPvFLIlIiejdRHRK+PurRHSccv1Fy70dRHSjEPjn4ZEZfvB4eAAvM8ZWmC4SUY3FOaByg4gWI1jEbSCiXsbYNlt5xtibM9RdA3A6gFxpyBljO4noegAnAPhGnjo8PLyE4uGhARGdRkTfJqLvA/hReO69RHRLuH/ER4Sy51OwX86PieibRPT3hmr/BsAVYX2vDu+thXUeFh5/gog+Fv7+LyJaFSZ3/CoR3R3ud/EeTd0bAdzuwviI6KOCNPYYEX0lvPQ9ACe7PB8PDx28hOLhAXSH2XgB4PeMsdeGv9cBWMYYe4aINiPYO+JABFHG1xDRBgDbEKT32B/B93Q7gNsM7ZyAIF3+PgDeCeCbjLFhIjoNwHeI6GwE+9OsUe5bAWAmY2xfAOCpVBSs17T7KSL6oFqQMXYBgAuIaADATwF8Prx0N4DVBto9PFLhGYqHh1nldR1jjCcJ3Bz+3REe9yFgMP0ArmKMvQQARKTN80ZEqwE8zRh7hIgeBfBlIprAGHuWMXYPEV0B4PsI9uXYqdz+EID5RPRPAP4DocSkYDrkzaQA4L2Mse8INLwo/CYEqq2LGWO3AUFWYiLaSUT9jLEXdP3w8LDBq7w8PMwQbRwE4BMs2A1vBWNsAWPsS+E1l/xFJwFYFGbA/R2AcQBeJ1zfD8BzAKaqNzLGngWwHEGm4LMAfFFT/8sIclK54kIAjzLGvqKc7wSwPUM9Hh4RPEPx8HDDDwGcHu6lAiKaGe47cSOA1xJRd5jt9VXqjURUAXA8AvXZXBZkwd2KgMmAiI5FkMRvA4BLVJUWEU0CUGGMXQngQ9Ab3u8FsMClI+F+IZsAnK2cH0QgRe1yqcfDQ4VXeXl4OIAx9qPQS+sX4YZcLwJ4A2PsdiL6FoLMxY8gsEmo2ADgMcbYY8K5GwEsIaJZAC4CcARj7I9E9HkAn0OcchwIdtL7SsiYgGAbahX/icDg74JzAMwAcHPYl2tCu8rhAK51rMPDIwGfbdjDo0QQ0YUAXmSM/d8WtH0VgPcxxh7Ief93AZzHGLuvXMo89hR4lZeHx9jBuQiM85kRbh73Pc9MPIrASygeHh4eHqXASygeHh4eHqXAMxQPDw8Pj1LgGYqHh4eHRynwDMXDw8PDoxR4huLh4eHhUQr+F8Z7X4W6qaqyAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]

From 8859dcae39e618cbf2779a01e3ed2aeae3dfa1a4 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 13:32:15 +0100
Subject: [PATCH 633/719] Correct types to reflect validation

---
 qcodes/instrument/parameter.py | 2 +-
 qcodes/utils/validators.py     | 9 ++++++---
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 74a536eeac2..b7ea3438f26 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1002,7 +1002,7 @@ def validate_consistent_shape(self) -> None:
                              f"with Arrays validator. {self.name} does "
                              f"not have an Arrays validator.")
         output_shape = self.vals.shape
-        setpoints_shape_list: List[Optional[int]] = []
+        setpoints_shape_list: List[Optional[Union[int, Callable[[], int]]]] = []
         for sp in self.setpoints:
             if not isinstance(sp.vals, Arrays):
                 raise ValueError(f"Can only validate shapes for parameters "
diff --git a/qcodes/utils/validators.py b/qcodes/utils/validators.py
index c20f42a2ea2..3c1675133e6 100644
--- a/qcodes/utils/validators.py
+++ b/qcodes/utils/validators.py
@@ -14,7 +14,7 @@
 BIGINT = int(1e18)
 
 numbertypes = Union[float, int, np.floating, np.integer]
-
+shape_tuple_type = Optional[Tuple[Union[int, TCallable[[], int]], ...]]
 
 def validate_all(*args, context: str = '') -> None:
     """
@@ -518,7 +518,10 @@ def __init__(self, min_value: numbertypes = -float("inf"),
                           collections.abc.Sequence) and shape is not None:
             raise ValueError(f"Shape must be a sequence (List, Tuple ...) "
                              f"got a {type(shape)}")
-        self._shape = shape
+        self._shape: shape_tuple_type = None
+        if shape is not None:
+            self._shape = tuple(shape)
+
 
     @property
     def valid_values(self) -> Tuple[np.ndarray]:
@@ -531,7 +534,7 @@ def valid_values(self) -> Tuple[np.ndarray]:
             return (val_arr,)
 
     @property
-    def shape(self) -> Optional[Tuple[int, ...]]:
+    def shape(self) -> shape_tuple_type:
         return self._shape
 
     @property

From 5479d931c0729c9614b110f2db10d7a7a35a9e9f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 13:44:52 +0100
Subject: [PATCH 634/719] remove no longer used method

---
 qcodes/dataset/measurements.py | 19 -------------------
 1 file changed, 19 deletions(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index 31884124dd4..d2bcc5fa59c 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -293,25 +293,6 @@ def _unbundle_arrayparameter(self,
                                             parameter.setpoints,
                                             res, found_parameters)
 
-    def _unbundle_parameter_with_setpoints(self,
-                                           parameter: ParameterWithSetpoints,
-                                           res: List[res_type],
-                                           found_parameters: List[str]) -> None:
-        setpoint_names = []
-        setpoint_data = []
-        for setpointparam in parameter.setpoints:
-            these_setpoints = setpointparam.get()
-            setpoint_names.append(setpointparam.full_name)
-            setpoint_data.append(these_setpoints)
-            found_parameters.append(setpointparam.full_name)
-            if setpointparam.full_name not in self.parameters.keys():
-                raise RuntimeError(f'{setpointparam.full_name} '
-                                   f'which is a setpoint parameter for '
-                                   f'{parameter.full_name} is not registered!')
-        output_grids = np.meshgrid(*setpoint_data, indexing='ij')
-        for name, grid in zip(setpoint_names, output_grids):
-            res.append((name, grid))
-
     def _unbundle_setpoints_from_param(self, parameter: _BaseParameter,
                                        sp_names: Sequence[str],
                                        fallback_sp_name: str,

From e0b3e99f310b3569d0a1805d3b76082a954dea7c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 21 Jan 2019 14:28:13 +0100
Subject: [PATCH 635/719] Add docs to helper function

---
 docs/api/public.rst            |  1 +
 qcodes/instrument/parameter.py | 20 +++++++++-----------
 2 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/docs/api/public.rst b/docs/api/public.rst
index ca634cef9cb..a0ce1610fef 100644
--- a/docs/api/public.rst
+++ b/docs/api/public.rst
@@ -115,6 +115,7 @@ Instrument
    SweepValues
    combine
    CombinedParameter
+   qcodes.instrument.parameter.expand_setpoints_helper
 
 
    Instrument
diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index b7ea3438f26..4a0626b8a04 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1933,18 +1933,19 @@ def set_raw(self, value: Union[int, float]) -> None:
         self._wrapped_parameter.set(instrument_value)
 
 
-def expand_setpoints_helper(parameter: ParameterWithSetpoints,
-                            update_setpoints_from_inst: bool = True) -> List[
+def expand_setpoints_helper(parameter: ParameterWithSetpoints) -> List[
         Tuple[_BaseParameter, numpy.ndarray]]:
     """
-
-
+    A helper function that takes a :class:`.ParameterWithSetpoints` and
+    acquires the parameter along with it's setpoints. The data is returned
+    in a format prepared to insert into the dataset.
 
     Args:
-        parameter: A ParameterWithSetpoints to be acquired
-        update_setpoints_from_inst:
-    Returns:
+        parameter: A ParameterWithSetpoints to be acquired and expanded
 
+    Returns:
+        A list of tuples of parameters and values for the specified parameter
+        and its setpoints.
     """
     if not isinstance(parameter, ParameterWithSetpoints):
         raise TypeError(
@@ -1956,10 +1957,7 @@ def expand_setpoints_helper(parameter: ParameterWithSetpoints,
     setpoint_names = []
     setpoint_data = []
     for setpointparam in parameter.setpoints:
-        if update_setpoints_from_inst:
-            these_setpoints = setpointparam.get()
-        else:
-            these_setpoints = setpointparam.get_latest()
+        these_setpoints = setpointparam.get()
         setpoint_names.append(setpointparam.full_name)
         setpoint_params.append(setpointparam)
         setpoint_data.append(these_setpoints)

From 6c3d327b6bab9145b22a63e902b7814a127ca263 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Tue, 22 Jan 2019 11:53:55 +1100
Subject: [PATCH 636/719] Add tests for mock instruments

---
 qcodes/tests/instrument_mocks.py | 24 ++++++++++------
 qcodes/tests/test_instrument.py  | 48 ++++++++++++++++++++++++++++----
 2 files changed, 57 insertions(+), 15 deletions(-)

diff --git a/qcodes/tests/instrument_mocks.py b/qcodes/tests/instrument_mocks.py
index c47ea7c5fd8..a345ac14969 100644
--- a/qcodes/tests/instrument_mocks.py
+++ b/qcodes/tests/instrument_mocks.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from qcodes.instrument.base import Instrument
+from qcodes.instrument.base import Instrument, InstrumentBase
 from qcodes.utils.validators import Numbers
 from qcodes.instrument.parameter import MultiParameter, Parameter, ArrayParameter
 from qcodes.instrument.channel import InstrumentChannel, ChannelList
@@ -56,25 +56,31 @@ def _measure_skewed_parabola(self):
         Adds an -x term to add a corelation between the parameters.
         '''
         return ((self.x.get()**2 + self.y.get()**2 +
-                self.z.get()**2)*(1 + abs(self.y.get()-self.x.get())) +
-                self.noise.get()*np.random.rand(1))
+                 self.z.get()**2)*(1 + abs(self.y.get()-self.x.get())) +
+                 self.noise.get()*np.random.rand(1))
 
 
-class MockMetaParabola(Instrument):
+class MockMetaParabola(InstrumentBase):
     '''
     Test for a meta instrument, has a tunable gain knob
+
+    Unlike a MockParabola, a MockMetaParabola does not have a connection, or
+    access to ask_raw/write_raw, i.e. it would not be connected to a real instrument.
+
+    It is also not tracked in the global _all_instruments list, but is still
+    snapshottable in a station.
     '''
 
     def __init__(self, name, mock_parabola_inst, **kw):
+        """
+        Create a new MockMetaParabola, connected to an existing MockParabola instance.
+        """
         super().__init__(name, **kw)
         self.mock_parabola_inst = mock_parabola_inst
 
         # Instrument parameters
         for parname in ['x', 'y', 'z']:
-            self.add_parameter(parname, unit='a.u.',
-                               parameter_class=Parameter,
-                               vals=Numbers(), initial_value=0,
-                               get_cmd=None, set_cmd=None)
+            self.parameters[parname] = getattr(mock_parabola_inst, parname)
         self.add_parameter('gain', parameter_class=Parameter,
                            initial_value=1,
                            get_cmd=None, set_cmd=None)
@@ -95,7 +101,7 @@ def _get_skew_parabola(self):
 
 class DummyInstrument(Instrument):
 
-    def __init__(self, name='dummy', gates=['dac1', 'dac2', 'dac3'], **kwargs):
+    def __init__(self, name='dummy', gates=('dac1', 'dac2', 'dac3'), **kwargs):
 
         """
         Create a dummy instrument that can be used for testing
diff --git a/qcodes/tests/test_instrument.py b/qcodes/tests/test_instrument.py
index 0ddc652b142..439870d4974 100644
--- a/qcodes/tests/test_instrument.py
+++ b/qcodes/tests/test_instrument.py
@@ -1,12 +1,17 @@
 """
 Test suite for  instument.*
 """
+
+import gc
 import weakref
+import io
+import contextlib
+
 from unittest import TestCase
-from qcodes.instrument.base import Instrument, InstrumentBase, find_or_create_instrument
-from .instrument_mocks import DummyInstrument, MockParabola
-from qcodes.instrument.parameter import Parameter
-import gc
+from ..instrument.base import Instrument, InstrumentBase, find_or_create_instrument
+from ..instrument.parameter import Parameter
+from .instrument_mocks import DummyInstrument, MockParabola, MockMetaParabola
+
 
 
 class TestInstrument(TestCase):
@@ -65,10 +70,10 @@ def test_repr(self):
 
     def test_add_remove_f_p(self):
         with self.assertRaises(KeyError):
-                self.instrument.add_parameter('dac1', get_cmd='foo')
+            self.instrument.add_parameter('dac1', get_cmd='foo')
         self.instrument.add_function('function', call_cmd='foo')
         with self.assertRaises(KeyError):
-                self.instrument.add_function('function', call_cmd='foo')
+            self.instrument.add_function('function', call_cmd='foo')
 
         self.instrument.add_function('dac1', call_cmd='foo')
         # test custom __get_attr__
@@ -125,6 +130,37 @@ def test_snapshot_value(self):
                          snapshot['parameters']['has_snapshot_value']['value'])
         self.assertNotIn('value', snapshot['parameters']['no_snapshot_value'])
 
+    def test_meta_instrument(self):
+        mock_instrument = MockMetaParabola("mock_parabola", self.instrument2)
+
+        # Check that the mock instrument can return values
+        self.assertEqual(mock_instrument.parabola(), self.instrument2.parabola())
+        mock_instrument.x(1)
+        mock_instrument.y(2)
+        self.assertEqual(mock_instrument.parabola(), self.instrument2.parabola())
+        self.assertNotEqual(mock_instrument.parabola(), 0)
+
+        # Add a scaling factor
+        mock_instrument.gain(2)
+        self.assertEqual(mock_instrument.parabola(), self.instrument2.parabola()*2)
+
+        # Check snapshots
+        snap = mock_instrument.snapshot(update=True)
+        self.assertIn("parameters", snap)
+        self.assertIn("gain", snap["parameters"])
+        self.assertEqual(snap["parameters"]["gain"]["value"], 2)
+
+        # Check printable snapshot
+        f = io.StringIO()
+        with contextlib.redirect_stdout(f):
+            mock_instrument.print_readable_snapshot()
+        readable_snap = f.getvalue()
+
+        # Line length satisfied
+        self.assertTrue(all(len(line) <= 80 for line in readable_snap.splitlines()))
+        # Gain is included in output with correct value
+        self.assertRegex(readable_snap, r"gain[ \t]+:[ \t]+2")
+
 
 class TestFindOrCreateInstrument(TestCase):
     """Tests for find_or_create_instrument function"""

From 27173c4c2719a7a5fe4a81cfdac3879e7551e5c7 Mon Sep 17 00:00:00 2001
From: Sebastian Pauka <spauka@gmail.com>
Date: Tue, 22 Jan 2019 13:35:20 +1100
Subject: [PATCH 637/719] Test parameter error conditions

---
 qcodes/tests/test_parameter.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/qcodes/tests/test_parameter.py b/qcodes/tests/test_parameter.py
index bf71c849e8f..0335a2003c4 100644
--- a/qcodes/tests/test_parameter.py
+++ b/qcodes/tests/test_parameter.py
@@ -259,6 +259,12 @@ def test_step_ramp(self):
         p(44.5)
         self.assertListEqual(p.set_values, [42, 43, 44, 44.5])
 
+        # Test error conditions
+        with self.assertLogs(level='WARN'):
+            self.assertEqual(p.get_ramp_values("A", 1), [])
+        with self.assertRaises(RuntimeError):
+            p.get_ramp_values((1, 2, 3), 1)
+
     def test_scale_raw_value(self):
         p = Parameter(name='test_scale_raw_value', set_cmd=None)
         p(42)

From 6158bc7ee1a0f153559a78e53465c68a56c1ac46 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 22 Jan 2019 13:15:37 +0100
Subject: [PATCH 638/719] Add a few more points to basic example

---
 ...le-Example-of-ParameterWithSetpoints.ipynb | 320 ++++++++++++++++--
 1 file changed, 290 insertions(+), 30 deletions(-)

diff --git a/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb b/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
index 230c5782ee5..f441d73557a 100644
--- a/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
+++ b/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
@@ -45,7 +45,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qcodes.instrument.parameter import ParameterWithSetpoints, GeneratedSetPoints\n",
+    "from qcodes.instrument.parameter import ParameterWithSetpoints, GeneratedSetPoints, Parameter\n",
     "from qcodes.instrument.parameter import expand_setpoints_helper"
    ]
   },
@@ -56,7 +56,11 @@
     "Here we define an dummy instrument that returns something like a frequency spectrum from a starting frequency give by `f_start` to a stop frequency given by `f_stop`\n",
     "in `n_points` steps. \n",
     "\n",
-    "The functionality of the ParameterWithSetpoints is implemented only by having a reference to it's setpoints which is consumed by the dataset context manager. To do this we only have to define the parameter for the setpoints and the spectrum and let the spectrum parameter know that the frequency axis is the setpoints axis of the spectrum."
+    "The extra functionality of the ParameterWithSetpoints is implemented by giving it a reference to one or more parameters that acts like its setpoints. To setup a `ParameterWithSetpoints` we have to do two things in addition to what we do for a normal parameter.\n",
+    "\n",
+    "* Define one or more parameter for the setpoints (one for each dimmension of the array and let the `ParameterWithSetpoints` know that these are the setpoints.\n",
+    "* Give both the setpoints parameter(s) and the `ParameterWithSetpoints` a validator of type `Arrays` with a shape. The shapes should be such that the combined shape of the setpoints matches the shape of the ParameterWithSetpoints. Note that the shape may change with setting of the instrument the shape can be defined by another parameter as shown below. \n",
+    "\n"
    ]
   },
   {
@@ -68,7 +72,7 @@
     "class DummyArray(ParameterWithSetpoints):\n",
     "    \n",
     "    def get_raw(self):\n",
-    "        npoints = self.root_instrument.n_points()\n",
+    "        npoints = self.root_instrument.n_points.get_latest()\n",
     "        return np.random.rand(npoints)\n",
     "    \n",
     "\n",
@@ -108,47 +112,50 @@
     "                           startparam=self.f_start,\n",
     "                           stopparam=self.f_stop,\n",
     "                           numpointsparam=self.n_points,\n",
-    "                           vals=Arrays(shape=(self.n_points,)))\n",
+    "                           vals=Arrays(shape=(self.n_points.get_latest,)))\n",
     "                           \n",
     "        self.add_parameter('spectrum',\n",
     "                   unit='dBm',\n",
     "                   setpoints=(self.freq_axis,),\n",
     "                   label='Spectrum',\n",
     "                   parameter_class=DummyArray,\n",
-    "                   vals=Arrays(shape=(self.n_points,)))\n",
+    "                   vals=Arrays(shape=(self.n_points.get_latest,)))\n",
     "                "
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 6,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Object `a.spectrum` not found.\n"
-     ]
-    }
-   ],
    "source": [
-    "a.spectrum?"
+    "In the above exampel the shape is defined by the parameter `n_points` that defines how many samples our \"SpectrumAnalyzer\" returns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This means that the validation will call the function to get `n_points` twice for each get of the parameter. This may be too slow however if you (as the driver writer) can guarantee that the cached value for `n_points` will never be out of sync with the value stored in the instrument `n_points` may be replaced by it's latest known value such that the validator reads. \n",
+    "```python\n",
+    "vals=Arrays(shape=(self.n_points.get_latest))\n",
+    "```\n",
+    "\n",
+    "This avoids any additional call to the instrument. In the same way the arguments to the `freq_axis` parameter that defines the start, stop and number of points can be replaced by their latest value if the instrument allows it.\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#103@C:\\Users\\jenielse/experiments.db\n",
+       "tutorial_exp#no sample#113@C:\\Users\\jenielse/experiments.db\n",
        "-----------------------------------------------------------"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,11 +199,31 @@
     "And we can grab the frequency axis"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "501"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.n_points()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
@@ -264,7 +291,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Naturally we can also grap the spectrum."
+    "Naturally we can also grap the spectrum. Getting the spectrum will automatically perform validation both for the shape of the parameter it self and the relation to the setpoint parameter(s)"
    ]
   },
   {
@@ -337,7 +364,7 @@
     {
      "data": {
       "text/plain": [
-       "(<qcodes.instrument.parameter.GeneratedSetPoints: freq_axis at 2409471349424>,)"
+       "(<qcodes.instrument.parameter.GeneratedSetPoints: freq_axis at 2390766145264>,)"
       ]
      },
      "execution_count": 16,
@@ -376,7 +403,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also directly consume the parameter in a measurement. This implicitly calls get on the setpoint parameter(s) when the data is added to the datasaver."
+    "We can also directly consume the parameter in a measurement."
    ]
   },
   {
@@ -388,7 +415,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 319\n"
+      "Starting experimental run with id: 327\n"
      ]
     }
    ],
@@ -397,7 +424,8 @@
     "meas.register_parameter(a.spectrum)\n",
     "\n",
     "with meas.run() as datasaver:\n",
-    "    datasaver.add_result(*expand_setpoints_helper(a.spectrum))\n",
+    "    datasaver.add_result((a.freq_axis, a.freq_axis()),\n",
+    "                         (a.spectrum, a.spectrum()))\n",
     "    \n",
     "    dataid = datasaver.run_id"
    ]
@@ -417,7 +445,7 @@
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x230ffb1c390>], [None])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x22ca4c55f98>], [None])"
       ]
      },
      "execution_count": 19,
@@ -426,7 +454,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -440,6 +468,238 @@
    "source": [
     "plot_by_id(dataid)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To reduce the amount of typing a simple convenicen function is provided that will take a `ParameterWithSetpoints` and get both its values and the setpoints values and return them as tuples for direct insertion into the Dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(<qcodes.instrument.parameter.GeneratedSetPoints: freq_axis at 2390766145264>,\n",
+       "  array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
+       "          11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
+       "          22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,\n",
+       "          33.,  34.,  35.,  36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,\n",
+       "          44.,  45.,  46.,  47.,  48.,  49.,  50.,  51.,  52.,  53.,  54.,\n",
+       "          55.,  56.,  57.,  58.,  59.,  60.,  61.,  62.,  63.,  64.,  65.,\n",
+       "          66.,  67.,  68.,  69.,  70.,  71.,  72.,  73.,  74.,  75.,  76.,\n",
+       "          77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,\n",
+       "          88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,  96.,  97.,  98.,\n",
+       "          99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109.,\n",
+       "         110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.,\n",
+       "         121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,\n",
+       "         132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142.,\n",
+       "         143., 144., 145., 146., 147., 148., 149., 150., 151., 152., 153.,\n",
+       "         154., 155., 156., 157., 158., 159., 160., 161., 162., 163., 164.,\n",
+       "         165., 166., 167., 168., 169., 170., 171., 172., 173., 174., 175.,\n",
+       "         176., 177., 178., 179., 180., 181., 182., 183., 184., 185., 186.,\n",
+       "         187., 188., 189., 190., 191., 192., 193., 194., 195., 196., 197.,\n",
+       "         198., 199., 200., 201., 202., 203., 204., 205., 206., 207., 208.,\n",
+       "         209., 210., 211., 212., 213., 214., 215., 216., 217., 218., 219.,\n",
+       "         220., 221., 222., 223., 224., 225., 226., 227., 228., 229., 230.,\n",
+       "         231., 232., 233., 234., 235., 236., 237., 238., 239., 240., 241.,\n",
+       "         242., 243., 244., 245., 246., 247., 248., 249., 250., 251., 252.,\n",
+       "         253., 254., 255., 256., 257., 258., 259., 260., 261., 262., 263.,\n",
+       "         264., 265., 266., 267., 268., 269., 270., 271., 272., 273., 274.,\n",
+       "         275., 276., 277., 278., 279., 280., 281., 282., 283., 284., 285.,\n",
+       "         286., 287., 288., 289., 290., 291., 292., 293., 294., 295., 296.,\n",
+       "         297., 298., 299., 300., 301., 302., 303., 304., 305., 306., 307.,\n",
+       "         308., 309., 310., 311., 312., 313., 314., 315., 316., 317., 318.,\n",
+       "         319., 320., 321., 322., 323., 324., 325., 326., 327., 328., 329.,\n",
+       "         330., 331., 332., 333., 334., 335., 336., 337., 338., 339., 340.,\n",
+       "         341., 342., 343., 344., 345., 346., 347., 348., 349., 350., 351.,\n",
+       "         352., 353., 354., 355., 356., 357., 358., 359., 360., 361., 362.,\n",
+       "         363., 364., 365., 366., 367., 368., 369., 370., 371., 372., 373.,\n",
+       "         374., 375., 376., 377., 378., 379., 380., 381., 382., 383., 384.,\n",
+       "         385., 386., 387., 388., 389., 390., 391., 392., 393., 394., 395.,\n",
+       "         396., 397., 398., 399., 400., 401., 402., 403., 404., 405., 406.,\n",
+       "         407., 408., 409., 410., 411., 412., 413., 414., 415., 416., 417.,\n",
+       "         418., 419., 420., 421., 422., 423., 424., 425., 426., 427., 428.,\n",
+       "         429., 430., 431., 432., 433., 434., 435., 436., 437., 438., 439.,\n",
+       "         440., 441., 442., 443., 444., 445., 446., 447., 448., 449., 450.,\n",
+       "         451., 452., 453., 454., 455., 456., 457., 458., 459., 460., 461.,\n",
+       "         462., 463., 464., 465., 466., 467., 468., 469., 470., 471., 472.,\n",
+       "         473., 474., 475., 476., 477., 478., 479., 480., 481., 482., 483.,\n",
+       "         484., 485., 486., 487., 488., 489., 490., 491., 492., 493., 494.,\n",
+       "         495., 496., 497., 498., 499., 500.])),\n",
+       " (<__main__.DummyArray: spectrum at 2390766145376>,\n",
+       "  array([9.99565739e-01, 3.41410534e-01, 3.99354826e-02, 1.40788221e-01,\n",
+       "         3.15613693e-01, 7.28015631e-01, 6.33773056e-01, 4.25418735e-01,\n",
+       "         7.94719526e-01, 9.61731268e-01, 9.75351491e-01, 6.22680081e-01,\n",
+       "         7.30689866e-01, 7.44535004e-01, 1.81936308e-01, 4.97046986e-01,\n",
+       "         8.84645545e-01, 3.36639293e-01, 9.63264159e-01, 7.75776116e-01,\n",
+       "         9.30999126e-01, 5.09147388e-02, 8.07309008e-01, 5.83048231e-01,\n",
+       "         8.30254178e-01, 3.55403452e-01, 9.29724922e-01, 7.55007191e-01,\n",
+       "         7.42848906e-01, 2.39841281e-01, 2.53890657e-02, 2.98199498e-01,\n",
+       "         2.17006527e-01, 5.45356014e-01, 6.19503804e-01, 5.52957884e-01,\n",
+       "         3.71367390e-01, 1.74369637e-01, 9.67179968e-01, 5.57432914e-01,\n",
+       "         1.81509404e-02, 1.96939687e-01, 4.47661103e-01, 6.82216425e-01,\n",
+       "         8.38756146e-02, 1.48217835e-01, 4.22221137e-01, 5.70910256e-01,\n",
+       "         7.91652764e-02, 2.64279322e-01, 2.13251130e-01, 5.47825630e-01,\n",
+       "         6.26465259e-01, 3.44912193e-01, 8.74813028e-01, 3.76769481e-01,\n",
+       "         6.68953633e-01, 3.34877444e-01, 6.12556740e-01, 4.55317971e-01,\n",
+       "         7.36250566e-02, 2.29914116e-01, 5.67923192e-01, 2.54977559e-02,\n",
+       "         2.81176076e-01, 3.11385813e-02, 7.55621595e-01, 3.45681008e-01,\n",
+       "         1.91843692e-01, 5.02846865e-01, 7.93729828e-01, 9.72051900e-01,\n",
+       "         7.05178455e-01, 4.21497225e-01, 5.36699812e-01, 4.14430358e-01,\n",
+       "         7.28965120e-01, 1.37664071e-01, 4.97820347e-01, 1.19387504e-01,\n",
+       "         7.35322096e-01, 1.97371125e-01, 4.93550132e-01, 9.07098820e-01,\n",
+       "         5.24590272e-01, 8.93539567e-01, 9.91839182e-01, 7.94509547e-01,\n",
+       "         2.60763277e-01, 3.12911177e-01, 9.37775679e-01, 7.94952432e-01,\n",
+       "         8.90915205e-01, 4.52876587e-01, 2.22377019e-01, 8.19277381e-01,\n",
+       "         1.04698600e-01, 8.29247305e-01, 7.25398842e-01, 4.30217234e-01,\n",
+       "         2.02351666e-01, 6.02480394e-01, 7.63139009e-01, 4.59794785e-01,\n",
+       "         4.48857895e-01, 3.09285048e-01, 3.15328615e-01, 2.48970026e-01,\n",
+       "         7.82595438e-01, 3.43069627e-01, 2.46129472e-01, 9.27906688e-01,\n",
+       "         3.62496903e-01, 9.36183165e-01, 5.30122696e-01, 4.71887824e-01,\n",
+       "         8.98885207e-01, 1.99440087e-01, 9.30878418e-01, 5.80235636e-03,\n",
+       "         4.85949268e-01, 1.21253358e-01, 1.05663840e-01, 4.45995909e-01,\n",
+       "         1.32285697e-01, 1.88051449e-01, 5.66314432e-01, 1.96391013e-01,\n",
+       "         6.44853441e-01, 7.44730617e-01, 9.33214530e-01, 3.65254472e-01,\n",
+       "         5.34042118e-01, 4.38401666e-01, 8.00591221e-01, 4.53647268e-01,\n",
+       "         9.53921476e-01, 2.99292368e-01, 3.58211243e-01, 8.88666696e-02,\n",
+       "         7.31102742e-01, 2.74750012e-01, 2.08909460e-01, 1.34151145e-01,\n",
+       "         5.40630994e-01, 5.33618975e-01, 5.85910554e-01, 6.62181534e-02,\n",
+       "         4.39061197e-01, 5.43240704e-01, 2.83730296e-01, 6.53141042e-01,\n",
+       "         5.13939243e-01, 4.01024990e-01, 9.90300493e-01, 9.59141680e-01,\n",
+       "         1.46507223e-01, 2.71011309e-01, 5.53086821e-01, 3.02678580e-01,\n",
+       "         6.67713948e-01, 4.19330128e-01, 7.97884870e-01, 6.22200535e-01,\n",
+       "         6.41336196e-01, 5.32021719e-01, 9.69912009e-01, 5.33322177e-01,\n",
+       "         9.31792556e-01, 1.31678130e-01, 2.97724981e-01, 5.84659648e-01,\n",
+       "         7.74580017e-01, 9.55835743e-01, 7.26774085e-01, 3.44820833e-01,\n",
+       "         9.11520346e-01, 3.09289513e-01, 4.48603620e-01, 7.38350720e-01,\n",
+       "         7.15318239e-01, 6.31576295e-02, 9.53652028e-01, 8.42154221e-01,\n",
+       "         6.15929309e-01, 9.65018511e-01, 8.27526534e-01, 8.60006983e-01,\n",
+       "         9.65526594e-01, 2.11791838e-01, 3.71902870e-01, 9.83151064e-01,\n",
+       "         8.26111626e-01, 2.22077570e-01, 4.95228585e-02, 5.04174503e-01,\n",
+       "         8.15431883e-01, 1.50511340e-02, 1.03113214e-01, 8.62051326e-01,\n",
+       "         9.37253043e-01, 6.45135686e-01, 8.13276210e-01, 9.04238921e-01,\n",
+       "         3.46383319e-01, 3.83622610e-01, 4.39520093e-02, 6.02628857e-01,\n",
+       "         1.37431584e-01, 9.75922110e-01, 4.02342098e-01, 9.72736764e-01,\n",
+       "         3.46422493e-02, 8.15245076e-01, 7.02859924e-02, 8.41651799e-01,\n",
+       "         7.35915944e-02, 2.24614090e-01, 7.94129711e-01, 3.95945647e-01,\n",
+       "         4.72410498e-01, 6.54710352e-01, 8.00370576e-01, 5.99576342e-01,\n",
+       "         6.11392722e-01, 1.38540389e-01, 5.36475042e-01, 4.29769602e-01,\n",
+       "         3.63185576e-02, 4.39795715e-01, 1.51145805e-01, 7.85529960e-01,\n",
+       "         2.09436292e-01, 1.62671340e-01, 7.77973688e-01, 3.42470450e-01,\n",
+       "         6.36011358e-01, 5.65356534e-01, 7.06493270e-01, 1.25917957e-01,\n",
+       "         2.34300213e-01, 6.52203562e-01, 5.68346343e-01, 2.85112367e-01,\n",
+       "         4.04118591e-01, 3.12736602e-01, 5.13240793e-01, 5.92217830e-01,\n",
+       "         1.38846864e-01, 5.98383214e-01, 4.93381466e-01, 1.58642649e-01,\n",
+       "         4.47102143e-01, 2.96732893e-01, 2.76475495e-02, 5.23742750e-01,\n",
+       "         8.29322816e-01, 9.04275946e-01, 8.99065887e-01, 5.10491632e-01,\n",
+       "         2.28652128e-02, 9.74347293e-01, 5.40687677e-01, 5.35116990e-01,\n",
+       "         8.99989930e-01, 3.80631425e-01, 1.84888208e-01, 5.28368299e-01,\n",
+       "         4.83096250e-01, 8.52747960e-01, 7.45884962e-01, 7.57777901e-01,\n",
+       "         8.44041988e-01, 5.60282248e-01, 2.56056230e-01, 3.12732884e-01,\n",
+       "         4.98863229e-01, 8.88598038e-01, 5.23785822e-01, 9.69375963e-01,\n",
+       "         4.56801666e-02, 3.33066786e-02, 7.19046639e-01, 6.61791910e-01,\n",
+       "         8.29852907e-01, 8.99954459e-01, 9.42031477e-01, 6.24357383e-02,\n",
+       "         6.36244850e-01, 6.85687687e-01, 9.43497015e-01, 7.80660161e-01,\n",
+       "         5.40653727e-02, 8.31770798e-01, 5.63136226e-01, 9.42273672e-01,\n",
+       "         3.99474067e-01, 1.56019596e-01, 4.93322139e-01, 2.55309361e-01,\n",
+       "         3.29871694e-02, 2.91325615e-01, 4.00268302e-01, 8.85430666e-01,\n",
+       "         7.05016503e-01, 7.73273740e-01, 4.99769528e-01, 7.12745196e-01,\n",
+       "         9.84614743e-01, 1.80047561e-01, 1.89594444e-01, 2.12153721e-01,\n",
+       "         1.86603275e-01, 5.51526333e-01, 7.77398967e-01, 5.38552491e-01,\n",
+       "         3.17530045e-01, 7.53669384e-01, 2.67014370e-01, 1.78044634e-01,\n",
+       "         3.07581486e-01, 8.80043188e-01, 5.87604233e-01, 5.21717066e-01,\n",
+       "         9.11419690e-01, 7.50522873e-01, 4.06413383e-01, 4.69524466e-01,\n",
+       "         3.31204756e-01, 1.86158271e-01, 6.94798146e-01, 6.89873564e-01,\n",
+       "         2.93527570e-01, 6.65736905e-01, 4.59224702e-01, 6.24971805e-01,\n",
+       "         8.05249807e-01, 1.41016520e-01, 3.36891598e-01, 1.60068945e-04,\n",
+       "         4.46217609e-02, 5.00118941e-01, 2.24513027e-01, 7.57107689e-01,\n",
+       "         2.73578667e-01, 5.50661065e-01, 6.34878404e-01, 6.14719915e-01,\n",
+       "         4.48753943e-01, 3.69149028e-01, 8.78733083e-01, 2.06124283e-01,\n",
+       "         8.07536354e-01, 1.47461713e-01, 9.21496176e-02, 9.36120310e-02,\n",
+       "         7.23257819e-01, 4.83356676e-01, 8.10933785e-01, 4.86625441e-01,\n",
+       "         3.25327354e-01, 3.15575516e-01, 7.51404423e-01, 6.32419671e-01,\n",
+       "         9.98517225e-01, 4.66755026e-01, 4.71491101e-01, 9.03844024e-01,\n",
+       "         2.85615622e-01, 1.58447740e-01, 9.44181381e-01, 8.52534251e-01,\n",
+       "         3.20851872e-01, 9.81898783e-01, 6.76955086e-02, 7.04578525e-01,\n",
+       "         5.50502958e-01, 6.43058346e-01, 1.05994915e-01, 8.38242737e-01,\n",
+       "         7.32299423e-01, 6.80098191e-01, 2.82491453e-01, 1.17841353e-02,\n",
+       "         9.32406843e-02, 1.69652650e-02, 1.96996814e-03, 4.61902088e-01,\n",
+       "         5.08387117e-01, 7.63085205e-01, 8.69900900e-01, 5.30100068e-01,\n",
+       "         3.86444567e-01, 6.98676647e-01, 4.10054942e-01, 3.23854673e-01,\n",
+       "         9.78018718e-01, 4.03827092e-01, 9.71707077e-01, 8.21548158e-01,\n",
+       "         4.53221507e-01, 2.15674959e-01, 7.36859777e-01, 9.89141311e-01,\n",
+       "         4.71907900e-01, 5.55581679e-01, 7.72265047e-01, 4.04337572e-02,\n",
+       "         1.93989802e-01, 8.06078413e-01, 1.80137085e-01, 2.54887847e-01,\n",
+       "         3.87938987e-01, 4.07401628e-01, 5.69662950e-01, 3.14091850e-02,\n",
+       "         8.41129149e-02, 7.85351895e-02, 4.76903631e-01, 8.36711074e-01,\n",
+       "         6.33082371e-01, 9.24847168e-01, 4.15136143e-01, 9.81838838e-01,\n",
+       "         4.45930210e-01, 3.60801272e-01, 1.71282850e-01, 9.38710953e-01,\n",
+       "         4.36674624e-01, 1.86638477e-02, 4.17193024e-01, 3.24773079e-01,\n",
+       "         8.56446942e-01, 8.55979425e-01, 2.43593387e-01, 5.67727569e-03,\n",
+       "         3.37346043e-01, 7.61597656e-01, 1.75936838e-01, 5.64869495e-01,\n",
+       "         3.19844051e-01, 5.50864248e-02, 8.41955884e-01, 9.00095568e-01,\n",
+       "         8.47223469e-01, 2.95250706e-01, 4.42064643e-01, 2.05838150e-01,\n",
+       "         7.97738189e-01, 4.74754482e-01, 8.05965198e-01, 2.28721142e-02,\n",
+       "         1.97183650e-01, 3.62138667e-01, 1.96555145e-01, 9.30542505e-01,\n",
+       "         4.06188646e-02, 7.88223486e-01, 2.45454171e-01, 3.20629459e-01,\n",
+       "         9.28427922e-01, 5.96984264e-01, 1.00474217e-01, 1.74115023e-01,\n",
+       "         3.05847269e-01, 2.75380202e-01, 7.72199193e-01, 1.42540362e-02,\n",
+       "         3.24757775e-01, 1.56542894e-01, 4.05483346e-01, 7.69122902e-01,\n",
+       "         1.40467431e-01, 7.59219506e-01, 1.40852631e-01, 4.66496589e-01,\n",
+       "         1.91075180e-01, 1.80290753e-01, 4.80687931e-01, 9.37127278e-01,\n",
+       "         7.18769072e-01, 6.31174172e-01, 5.64695986e-01, 3.27438447e-01,\n",
+       "         3.37798596e-01, 2.43042117e-01, 9.61556179e-01, 7.98997328e-01,\n",
+       "         7.78154571e-01, 6.84901123e-02, 6.81428228e-01, 3.56675924e-01,\n",
+       "         5.76521790e-02, 4.76279546e-01, 9.41579081e-01, 3.38922558e-01,\n",
+       "         5.62890699e-01, 8.80744049e-01, 1.07252691e-01, 5.21412295e-01,\n",
+       "         1.27897017e-01]))]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expand_setpoints_helper(a.spectrum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 328\n"
+     ]
+    }
+   ],
+   "source": [
+    "meas = Measurement()\n",
+    "meas.register_parameter(a.spectrum)\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result(*expand_setpoints_helper(a.spectrum))\n",
+    "    \n",
+    "    dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From da260350f3e13312f7d6ae57e3c1aa63348c93b8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 22 Jan 2019 13:55:41 +0100
Subject: [PATCH 639/719] rename to reflect new parametername

---
 ...etpoints-Example-with-Dual-Setpoints.ipynb | 482 ++++++++++++++++++
 ...arameter-Example-with-Dual-Setpoints.ipynb | 482 ------------------
 2 files changed, 482 insertions(+), 482 deletions(-)
 create mode 100644 docs/examples/writing_drivers/A-ParameterWithSetpoints-Example-with-Dual-Setpoints.ipynb
 delete mode 100644 docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb

diff --git a/docs/examples/writing_drivers/A-ParameterWithSetpoints-Example-with-Dual-Setpoints.ipynb b/docs/examples/writing_drivers/A-ParameterWithSetpoints-Example-with-Dual-Setpoints.ipynb
new file mode 100644
index 00000000000..f5531d9e635
--- /dev/null
+++ b/docs/examples/writing_drivers/A-ParameterWithSetpoints-Example-with-Dual-Setpoints.ipynb
@@ -0,0 +1,482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A ParameterWithSetpoints Example with Dual Setpoints\n",
+    "\n",
+    "In this example we consider a dummy instrument that can return a time trace or the DFT (magnitude square) of that trace. The setpoints are accounted for in an easy way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from qcodes.instrument.base import Instrument, Parameter\n",
+    "from qcodes.instrument.parameter import ParameterWithSetpoints, expand_setpoints_helper\n",
+    "from qcodes.dataset.measurements import Measurement\n",
+    "from qcodes.dataset.plotting import plot_by_id, load_by_id\n",
+    "import qcodes.utils.validators as vals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.dataset.database import initialise_database\n",
+    "from qcodes.dataset.experiment_container import new_experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def timetrace(npts: int, dt: float) -> np.ndarray:\n",
+    "    \"\"\"\n",
+    "    A very realistic-looking signal\n",
+    "    \"\"\"\n",
+    "    #freq = 10/(dt*npts)\n",
+    "    #decay = 1/(dt*npts)\n",
+    "    freq = 10\n",
+    "    decay = 1\n",
+    "    time = np.linspace(0, npts*dt, npts, endpoint=False)\n",
+    "    signal = np.exp(-decay*time)*np.sin(2*np.pi*freq*time)\n",
+    "    noise = 0.1*np.random.randn(npts)\n",
+    "    return signal + noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "class TimeTrace(ParameterWithSetpoints):\n",
+    "    \n",
+    "    def get_raw(self):\n",
+    "        npts = self.root_instrument.npts()\n",
+    "        dt = self.root_instrument.dt()\n",
+    "        \n",
+    "        return timetrace(npts, dt)\n",
+    "    \n",
+    "\n",
+    "class Periodogram(ParameterWithSetpoints):\n",
+    "    \n",
+    "    def get_raw(self):\n",
+    "        npts = self.root_instrument.npts()\n",
+    "        dt = self.root_instrument.dt()\n",
+    "        \n",
+    "        tt = self.root_instrument.trace()\n",
+    "        \n",
+    "        return np.abs(np.fft.fft(tt))**2\n",
+    "        \n",
+    "        \n",
+    "class TimeAxis(Parameter):\n",
+    "\n",
+    "    def get_raw(self):\n",
+    "        npts = self.root_instrument.npts()\n",
+    "        dt = self.root_instrument.dt()\n",
+    "        return np.linspace(0, dt*npts, npts, endpoint=False)\n",
+    "\n",
+    "    \n",
+    "class FrequencyAxis(Parameter):\n",
+    "    \n",
+    "    def get_raw(self):\n",
+    "        npts = self.root_instrument.npts()\n",
+    "        dt = self.root_instrument.dt()\n",
+    "\n",
+    "        return np.linspace(0, 1/dt, npts)\n",
+    "    \n",
+    "        \n",
+    "class OzzyLowScope(Instrument):\n",
+    "    \n",
+    "    def __init__(self, name, **kwargs):\n",
+    "        \n",
+    "        super().__init__(name, **kwargs)\n",
+    "        \n",
+    "        self.add_parameter(name='npts',\n",
+    "                           initial_value=500,\n",
+    "                           label='Number of points',\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "        \n",
+    "        self.add_parameter(name='dt',\n",
+    "                           initial_value=1e-3,\n",
+    "                           label='Time resolution',\n",
+    "                           unit='s',\n",
+    "                           get_cmd=None,\n",
+    "                           set_cmd=None)\n",
+    "        \n",
+    "        self.add_parameter(name='time_axis',\n",
+    "                           label='Time',\n",
+    "                           unit='s',\n",
+    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
+    "                           parameter_class=TimeAxis)\n",
+    "        \n",
+    "        self.add_parameter(name='freq_axis',\n",
+    "                           label='Frequency',\n",
+    "                           unit='Hz',\n",
+    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
+    "                           parameter_class=FrequencyAxis)\n",
+    "        \n",
+    "        self.add_parameter(name='trace',\n",
+    "                           label='Signal',\n",
+    "                           unit='V',\n",
+    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
+    "                           setpoints=(self.time_axis,),\n",
+    "                           parameter_class=TimeTrace)\n",
+    "        \n",
+    "        self.add_parameter(name='periodogram',\n",
+    "                           label='Periodogram',\n",
+    "                           unit='V^2/Hz',\n",
+    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
+    "                           setpoints=(self.freq_axis,),\n",
+    "                           parameter_class=Periodogram)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "osc = OzzyLowScope('osc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tutorial_exp#no sample#115@C:\\Users\\jenielse/experiments.db\n",
+       "-----------------------------------------------------------"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "initialise_database()\n",
+    "new_experiment(name='tutorial_exp', sample_name=\"no sample\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Measurement 1: Time Trace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 329\n"
+     ]
+    }
+   ],
+   "source": [
+    "timemeas = Measurement()\n",
+    "timemeas.register_parameter(osc.trace)\n",
+    "\n",
+    "osc.dt(0.001)\n",
+    "\n",
+    "with timemeas.run() as datasaver:\n",
+    "    datasaver.add_result(*expand_setpoints_helper(osc.trace))\n",
+    "    \n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 330\n"
+     ]
+    }
+   ],
+   "source": [
+    "osc.dt(0.01)  # make the trace 10 times longer\n",
+    "\n",
+    "with timemeas.run() as datasaver:\n",
+    "    datasaver.add_result(*expand_setpoints_helper(osc.trace))\n",
+    "    \n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Measurement 2: Periodogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 331\n"
+     ]
+    }
+   ],
+   "source": [
+    "freqmeas = Measurement()\n",
+    "freqmeas.register_parameter(osc.periodogram)\n",
+    "\n",
+    "osc.dt(0.01)\n",
+    "\n",
+    "with freqmeas.run() as datasaver:\n",
+    "    datasaver.add_result(*expand_setpoints_helper(osc.periodogram))\n",
+    "    \n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZmbFwCNvbK/N97lbChIimAvgAwCgiWklElzLGUgCuBvAagIUAnmSMLQhwzfK0TMwJ17suri0TDY0yBxcmfRviFStMulXMhDF2nsv2l+ESZPdxzbKMmWw3LZM+9XEdM9HQKHNwN1e/hips026u8kS5WyZ96uNo09lcGhplDcsyqa9cy6TihUm5x0z61MUrNvtDQ2NnQVsyjVg4hJ61MR2A1+ha7OhIoToaRn1VRMdMNDTKHG2JNKpjxvu8rU1bJmWJcnVzbW9Poa4qgupYuGKLnDQ0dha0JgzlsKEqikQ6U5FV8BUvTMrZzVUfj6AmGkYinUEqrQWKhka5oi2ZQU0sjIYqI+epEoPwFS9MyhU72pOWZQJUbtWshsbOgLZEClXRMOqrogBQkUF4LUy6Kba3p1Abi6AqqoWJhka5oy2ZRo0ZMwG0MClLlGvMZGtbEj2qo6jhlokOwmtolC1azQB8Q7VhmWxr026uskO5xky4MKk2LRO9QJaGRvmiLZG2sjMBbZlodCG2tiXRoyZqxUy0MNHQKF9wN1eDGTPRAXiNLkF7Mo2OVMZ0cxmajHZzaWiUL1qFOhOgMjsHV7wwKceYCfenNggxE71AloZG+aI9kUZ1NILaWARE2s1lgYhqzbXZuz3KMWay1RQmPaqjOjVYQ6PMwRhDazKN6lgIoRChPh7ZeQPwRBQiovOJ6CUiWg/gMwBriGgBEf2aiEYWl8ydC6IwqTXdXDpmoqFRnkikM0hnmOWyrq+K7tSWyTQAuwH4EYD+jLEhjLG+AA4DMB3AnUT0jSLRuNNBZZloYaKhUZ5oTxjdK3hmZn1VBNsqUJj4Xc/kGMaYwy5jjG0G8C8A/yKiaEEp24mxuSUBALY6k9aOypt8Gho7A1rNlVK5YthQHUVza6KUJBUFviwTLkiI6E0iOkncR0QPiMdodB5zVmxBXTyCIT2rEQ2HEA0TWnXMREOjLMEzMbliOGZAAz5dvQ2JVGX12wsagN8FwA1EdLOwbWIB6Sk4yjGba/qSTdhveE9EwsbjqY6GdWqwhkaZgruoeWukA3dtQlsyjXkrt5SSrIIjqDDZAuBoAP2I6AUi6vYpUuWWzZXJMCzZ0IKxg7L01sYjOjVYQ6NMwdvNc8tkr0ENAIDF63eUjKZiIKgwIcZYijH2LRixkvcA9C08WTsveApwXVU2nFUdC+sAvIZGmaJVcnPVx6O27ZUCvwF4jj/zD4yxR4joEwDfLixJOzf4BKuOZR9NjRYmGhplC9nNZdWOVZi3IZAwYYzdL32fBeD/CkrRTg7uzqqNZWtCa6LazaWhUa7IurkMdhuLGEk1LRWmIPoSJkT0ewDMbT9j7NqCUbSTo6XDPvEAoCYexqYdlZdKqKGxM6DFVARrRAUxFqm4dH+/lslM4fOtAG52O1Cjc2hVTLyeNTF8uaGygnUaGjsLtrRmi5A5amPhndMyYYw9yj8T0XfE76UCEZ0G4GQYCQB/ZIy9XmKSCgLuX62N24VJc4su49HQKEdsbkmgNha2YiaAETeptHT/fBo9urq7OgsieoiI1hPRfGn7CUS0iIgWE9EUAGCMPcsYuxzAxQDOKRZNXY2sZZKV8z1rotjRkaq4IicNjZ0BzS0JNNbEbNtq4xHL/VUp6G4t6B8BcIK4wexO/EcAJwIYA+A8IhojHPITc39FIBszESyTWmMi/vS5+cpzNDQ0uif+t3gjnpmzCr1q7cKkJhZGa8dOaJkQ0XYi2kZE2wGMMz9v49sLRQxj7B0Am6XN+wNYzBhbwhhLAHgCwKlk4JcAXmGMzXah+woimklEMzds2FAoMouK1qQzAN/T1Gqe+GiFtk40NMoIF/zlQwCwFsXiqI1FrJ5dlQK/lsnxABoZY/WMsQhjrMH8q2eMNRSTQACDAKwQvq80t10D4BgAZxHRlaoTGWMPMMYmMsYm9unTp8hkFgY8w8MWM6nNBu46UpWlzWho7AzgQXiO6p3VMgFwEYCZRPQEEV1MRP2LSZQEUmxjjLHfMcb2ZYxdyRj7s+IY4+Qy683FMzyqIllh0lidNZHbk9oy0dAoFwxqrAYArN/ebtteG9tJYyYmw94HwC0AegJ4hIg+IKLbiejwIq+6uBLAEOH7YACr/Z5cbr25WjtSqImFEQplZWi/hrj1WVsmGhrlg0E9DWFy5j6Dbdtr4juvZQIAYIx9xhi7hzF2AoCjYPTmOhvAh8UgzsRHAEYS0S5EFANwLoDn/Z5cbpZJazJti5cAQFNdHLefPhaAtkw0NMoJmQzDhCGNuOGE0bbtNbEwWpNpMFa05Nguh29hQkSjiehoIqoDAMZYG2PsZQAvMcYK0oaeiKYC+ADAKCJaSUSXMsZSAK4G8BqAhQCeZIwt8HvNcrRMxHgJB88G0ZaJhkb5oCOVQc+aqM3TABgJNukMQ0cFJdT4badyLYyGjgsB/JWIrmOMPWfuvh3Aq4UghjF2nsv2lwG8nM81iWgygMkjRozoDGldhpZE2lreU0RV1JD72jLR0CgftCfTtmJFjlphOW7V/nKEX8vkcgD7MsZOAzAJwE1EdJ25TxUg7zYoN8ukLZFGbdwp4+NmQL4jlcbs5c268aOGRjcGf0c7UhnEI042y13ZlfQe++3NFWaM7QAAxthSIpoE4GkiGoZuLkzKzzJJoU4hTLhlsmZLO77/1DycuFd//Okb+3Y1eRoaGjmwtTWJM+57H8fs0c/VMqmJZy2TSoFfy2QtEU3gX0zBcgqA3gDGFoOwQqEcLJMWoXtoa0faVv3OwS2T9ds7AACfrilYraiGhkYBkcwYruhZyza7Wia1pmXC3/32ZBrJdHm7sIPUmawVN5grLl4E4PCCU7UT4YMvN2HPm1/D+4s3AgBakylroonglsm2dqP4KRbubp1wNDQ0AIAnaCVSGXfLxFogy7BMRt/0Kr5+/wddRmMx4LfOZCVjbK3Lvv8VlqTCorunBs9aZnSPeZcLk460ZQKLiJsTcltb0vyuhYmGRndEOmNIk/ZUJmfMRGxDP2f5lq4hsEgIxJGIqL5YhBQL3d3NxbUWvhpbSyLlqDMBgKoIt0wMs1hbJhoa3RNp0zThQiXuGTNJWceVO4LUmQwC8GIRadkpIQqTdIahPZlRx0zM47a0GisuxhTajoaGRumRTtuFg3fMJI2tbZWxVpHfrsF7wqgluaG45BQe3d3NxdGezKDNtE6UMRNzQjabwiSqLRMNjW6JVMYeSFfFTKpjWcuEv9PlDr8caRqAKxhj04tJTDHQ3d1c3L317zmr8O7nRpv8aoVlEgmHEA6RteKiStvR0NAoPWS3lTpmko2BXviXYnaj6jr45UgfATizmITsrBCX7rzqMWNZFlU7FcCwTpq1m0tDo1sjLfXbUlkm0XAIsUgIn67ZhtVb2x37yxF+OdLXADQQ0a+KSczOCO7aElEdVdeSxqNhq8gpHqmMFgwawPVPz8PwKS+VmgyNAiHlI2YCGC1Vvli/oytI6hL4TQ1OM8auAFB2v7y7x0xUwmRorxrlsWLPLp3NVTl4cubKUpOgUUDIbi6V2xoABveswbJNrV1BUpcgaAv6nxWLkGKhK2Mmi9fvwJzlzYHO4TGTYU2GAIlHQti9X53y2MaaqHK7RvfBis2tefdbCtqOfPH67RWTVlpJSEnPhC+7LWPc4CxPGt5UA6Jgc2BraxJvfLouPyKLAL/ZXH8gokOKTUy545i738bp970f6Jy2RBrDmmpwlrl4zqj+9Yi4WB3ipJQnrEb3wGG/moZLH5mZ17lBnuni9dtxzN3v4N7/fJ7XvTSKB1nAuymB4wc3AjCC8WdPHALGEKgl/XefnIvL/zYTa7a25U9sAeHXMvkCwG+IaCkR/VLs06XRObSaLedPHjcAZ+4zGNcfP9r12J61WWGSznS+j09HKo3rnpiDJRvKw3vZnkzjzlc+67adVjkT+WDJprzOD9Kbifdom7F0c1736go8NXMFZnZj+kQwxnDL8wvw/pcbO30tWZj0qlVbJsfv2R8XHjgMN08eY7mw2xVubzes3mIIEXl9+VLBb8zkXsbYQQCOALAZwMNEtJCIfkpEuxeVwgrGK5+swbrtHaiKhrFrnzrc9fXxOHRkb9fjewoajkqLbelIIRWAIS3d2Irn5q4ObE2VCv+Yvgx/fvtL3P/2klKTokSikwsdBTk/EjJeXTnY253ww6c/xll/Lo9+U5+s2opH3l+Knz7ne909AMDWtqTDNeWImbisV9KjJoqfn7YXztlvqBVXeeHjNdi4o8PXvWuEWpXugKAxk2WMsV8yxvYGcD6A02EsmKUREEs27MBVj83GvBVbXCebDNHNlVH4Vve8+TVc+Y/Zju2MMcxdscUx6clcPKBcKnB5Jpvqt3cHdKkwCRsPT7s7C4NX5hutBycMaXTsW76pFZtbnIWFn6/bjvG3vo6nZ9kTKOSiRaLcq3RwHnDTs/PxDZ91J3zdox3dZC35oL25omZ21GMAXgHwObp5/Ul3zebaJExOt2wPGT2qBcvE1Ej/NWslhk95yZrs/1noDMi98PEanPbH/+H5eatt20UNKlNEpjR7eXMgi8kN/BpcK1dhW3sSw6e8hL9PX9bp+8lYsmEHnpix3HU/X1I5EspviZ9EgDHi9/AKwC9YvRXPzV2VFy1eSKYzGD7lJfz+zS8Kfm03fLZ2W1GVns07jPdHtZbQ4b+ehkN/+V/H9tnLjGSbGV9txpl/eh9H/uYtAN7PxA1iLcpna7f7OodbJjvay8gyIaJjieghACsBXAFjCd3dGGPnMMaeLSaBnUVXZXP5ZZYvzFuN1kTKNgH8WiZiGjGfsH8zmeZXG1tcz+MxkS+lnHZx0hdrLeoFq7fijPvex69fX9TpayVNerlWrgL3I//jg8ILk+PueQdTnvnENeOGj2E4X2GSxzPwirOc/Lv3cN0TcwNnieUCn4f3vfVlQa/rhRN++y4u+EvxGnDw3yRbFRyqRay4QtirLoZZy5qtdzAfa1GlUL79+Qas2+Ze0MgbwnYXz4Jfy+THAD4AsAdjbDJj7DHGmDv32gmxXRAOJ//uXfzomU8cx8xa1oxrps7Bz19ciO3Cglh+W6OMGdBgfeYTNqrQUOcsb8avXv3M+k4ui2GK56jqXQqBjabGt2BV5xfz4vR6af7cYsuXoXuBj7mbBdFZyyQZIP7Bx8IP41q3zZ8P3i+SptBzY7yFBheY8wswh9zA578fnfC9Lzbij9MWY5M5t5ukALto5Y/q56/RepXEAxhj+OZDM3DaH50rfCxcsw3Dp7xkZXHxNY5KDb8B+CMZYw8yxsojNaMEEB/ogtXbMFXhDuGBtfXb2rFdON5v08YjR/fFjB8fjX2GNmYZK/edC2/B6fe9r9QaZbYjMqJiBfE4W2WOuwcHZypuqdPiMVEP66WzcLMguGXiRV8+11UhI7U5V6GhytBcl24qrN7HhWkQ4dcZdMXStrytkZ8syW/89UP8+rVF2NxivM+i+xnIvlfPfOtgPPttfxUV8pzhY7tG0WrlxY8Nd/X0JQY73lZmlolGDmxry82MObOIR0O2dD4vt42Mvg1ViIRCllbIBdHv/7vYcSzXkNzif2IgO0hKYhDwexfC08KtDi9BwTXMQlsmojsppzDJO2bi/xnwsfCyDgY2VgMAlnq4QPNBZxMNgqJYc1NEEMuEg7u5np2jjkX2rIn5jofKc9rLUyDHDMvNzaWRA34eKH8p4pGwtS4JELydfDhE4DyEM01VbUMuF4jNzZUoDoPgjIcxw/qZ+Is38Nai9XldizNOL0HBNUyvIH0+EH3Xrm6uZGdjJgHcXNwy8bAOetfFAQArmwtb1NYVwiSdYTjkzv/i2TmrutQyCZIpyN1c8ruX8uGOlSHzAC8BKguebeUUgNfIDT/a0w4zTvLc3FV4a9EGa3tQl0wkTBZj9WKastYqvyfpLnBztZsMloFhyYYWbNyRwC9fzS8Yz7Vxr5eUM54g1p4f7BBiXO6WiXHvfNeaCZLNxR9t0kNh4M9fpL0Q8JOs0dmgf2sihVVb2nD90x/bOmsXC9kAvH+63WIV3FUWCiRM7Mf+7IVPXY+VXWJdYbn5Qc5ZT0RXEtGDRHQuEb1IRFd1BWHlgh//+xN8759zfQUjuWsrw2DrFhpg+nn2AAAgAElEQVTUxx4Okb9gNHdzmd/luEUxAvBfbWzBzc/Nt67Nr8uYvwC6Gza3JPDix2sAuLsiZi3bjGumzgFQeDeXqB27CZNEHtlcr5r1DQDwm9cWWQIpF/h888oi5PQUmhmLQu/qx511TYAxxzlmfBU81CpmV7UlncLwpY/XKOOS+cKyTAIIE7dMOr45yDyXlcKXPlnjcaz9uql0Bu9/uRF7/+x1Wyy2q+GHix0FIx34asbYKQDGF5ckfyCiXYnor0T0dCnpePzD5XhmziokFO6GZ+eswvApL1kajJsrLKgmGwkR5q3ciq82tiDsoYFzF4hbzEQUJm7azdWPz/bVHr09mcbtLy/EtVPn4NEPlmHuii0ABGEi3CMcIqzb1o4v1vnLpweAbz82O+vXdtF6/zU7W1ORb9zCDa1CYZibZp4NwPu7dyqdwZX/mGV9/2TVVjw23R+D5O4YL02aM/2WAludojDlAl6GOLe+fr//KvgdHSnMWd6MdtPtmmHqAPy3H5+NHz3zCZ6cuQIf+mhfc8crCzF8ykuuFlOu1GAVVPMgnWF4fp4xD4MoFVEfGZ1X/G0mTrr3XcdaRsk0w73/+QLNrUl8srJ09XR+uNgmZjyBX5rfC5tnKICIHiKi9UQ0X9p+AhEtIqLFRDQFABhjSxhjlxaLFjesbG5VMl6VhniP2YRvg9lHaYvL8pzRgIwvZEqHI3/zli/LhMPbzWX/Ta/OX4vxt77uyixkPPr+UjzwzhJ8ssqYzFvbjN/awceKZd0tkRDhgNvfxLH3vOPr2gCwWmhml3bRCMWxyDejyg0iQ3ZPDQ4WgJ+5zNlh+p0vNiiOdIKT4JXNxZl+oWMOfnqI5dvN+AdPzsPp972PVVuyz9vLsrr+6Y9xzgO56094C55bnl+Aix+e4difzebyT6uKDzz47hIryypQzMTjWH6f1z9dh0/XbHNYMcl0xgr0ix6GpRtburSrtJ837l7AKP4zv/+reOTgEQAniBuIKAzgjwBOBDAGwHlENKaINLgik2E49JfTLFeKCNULxgsT3/18A+5+fRG2CJbJZYfugt51Rn56UMYnar5erumWjhT2/tnreGOhOuAtaviym+vOVxbaLCk3ja65JYF129qt2AjHZnN5Yf6SMjCrFicfF5RYi+MWc1bFj6Yv2YT9b/sPhk95KS93C4cYU/r37FXK8eAuqrDP4L+q0NRvX6a05eZSD8bHK7fg83WGK7WlkzGTsTe/huFTXsJNzxo6np8AvJv1mAuLzQLb5ZuzYyPPTdXYJ1IZfOmjYemjHyyzxSsBQxHkCoIcgBfvtf9t/7F1FJDnPAB8LlSvB7JMBB5w19l2588bn67DHS9nu1bJlm8ynRH6dBljtXxTKyb95i38tgu7Suec9YyxzwCAiEYT0Q0AziKie4noBiLao5DEMMbegdFIUsT+ABablkgCwBMATvVzPSK6gohmEtHMDRv8aXxe4O6qNz5dh0yG2QSIKueeFybe8sKn+N1/F9tcJeOGNFqCIGgAXmRWXi/2iuZWNLcmMc90OckU2rO57C+svNSom2vnwDvexAG3v+l4CXn2E2cEiTSzxiOf4LhIj1stgOii4ONy9eOzrQ67/56Tf2uRFuHZ/X36Mry2YK3jGH5Pv89TpYD41Yz5cW5uma/9IVvs1lnLhD833qJGnnMq5p6vRsw77IoZaDL9qj5ZP3n2Exx919toVuzLhXbh98jWvPh1/fYO3Py8dyNIMSgfRJiI78SgntW2fddMnYP738k2N5UtomSaWe9HayKFZDqDDTuM9+/dLzrfBdkv/LZTuQEGEycAM2CsCU8ApnK3UxExCMAK4ftKAIOIqImI/gxgbyL6kepExtgDAG4FMDsWU7eBDoJmM4BOBNz47HyMvPEVa5+KMcgvXYdwTL3QAyifmInXfTlyKYdewiQuCRM37bbDSv2132ytWWzFtbf2RNoKDvrV3G30iJaJy09uFmp33v58A+at2GJV4APusSOO5+auwqifvILZy5vx6ny7e0/OdtuuSMfklpxfJqJSBPy2oedCJMNyM245ZrJicyue/GgFbn5uPs76U7CO0S0dKdwr9eRSJW/INPnN7mpSCBN5bqoK+f77maEs5tMWSHy2cgBe/h25XFdimm6+lkkuZWSt9PtFy+SGf32CkTe+YlmsXenmUi827sSlAPZkjNkiyER0N4AFAO4sNGHibRTbGGNsE4Arc51suudemDhx4uWdJaTZjHmEiRyZJH5agbcJk7auKjv0QTX1kMAVvdJJc71YtphJMo2/f7AUJ40dgKa6uKPFS2sijSava8nCRLJMWpPZfmTteWjKfiwTOSZ1qqIVhRdufeFTdKQyOMNsyb/0zpOtfaqY0tF79LM06c/Xbcdv/2MwWb++cpU167eGQ7QEf/jUPNx9TnaJIbm5o8yMf/rcfExblJ+lfucrnzkaEW5pTVp9ojhkJpZIZxCP5C7g62kJk+xytqKwYoxh/XanMOkImI2YyTCEQoSnZ63EwMYqa7ts6cm/I5eAEKvRg9Q6icIk13lydwtDmNjHf4UpjLtbzAQAMgAGKrYPMPcVEysBDBG+Dwaw2uVYBwrZNZgzK9WE8lMjIGqzdfGI5XYKapmIzNTLjN0k+d+9AvALVm/DTc8twL6/+A/ueGWhw801b+UWz2aSD/9vqe07Z2DcJG9LpK3fv3xzllGkMwx//2Bpzlx5P5ZJZxcJkq2PU//wHi56aIa5z07fm5+tx1VCJhZ3JYpgjHmugqeyQrwsk61tSSuJQTzsGcF9t609ieuemGs7j1uV732xEcOnvKRIzPDPcD5e5XyPmhWJJTITU8UXRPz3s3VYvH671ZpEnCOtUvJDi6LlOnfFyeOXTGcwTVEku3Ct0d/qB0/Nw/kPZlu+y3qKrCTles1FQRskNBi2JY/YT6zJUUWfyjCHAvPJSmM+dkdh8h0AbxLRK0T0gPn3KoA3zX3FxEcARhLRLkQUA3AugOf9nlzIrsHNLe5uDD/uCVFrqYtHrJc4aMzEb0+kKYpmkyLEF0Wk7f63l0Am6erH51gttlWQmW17Mo0Vm1utOEVbIm0xwrVCNfnjHy7DTc8twD9ytIwXXwo3y6S5NYHJ4wdi0qg+rtdhjKEjlVb63WWGN2/lVrzzuaHBq9x8PEtvyYYd+OHTH1vbObN+cuYKHHTHf/HxSqegAYILk/G3vo6D7ngTgHMMuKXMFKe3JtJgjFnHLJa6R8tuqvZk2oo9yIJGXJXz3nMNa4i/FyJkJpzLcvi/R2bimLvfsdwQYnNKMRHk0fe9FQ9ZUN7zxue45OGPHMe5rZ3uFB72734TJAB/65ioICuXoRzXSaYyjt/NsyrzTYTIB77cXIyxV80VFfeHEcMgGBbDR4yxguUdEtFUAJMA9CailQBuZoz9lYiuBvAagDCAhxhjvpdDI6LJACaPGDGi0/RxDUwV0PTj5moRzqsX3VwBYwgyw5kwpNGq6/CCW9FiOEQWY+TobBHjzGXNOOxX06zvHamMMs7AO8FGQoS2RBpEzuA/YGf0qhdkxlebsbK5Dd88aDjeXay21h7/cDke/zDrnhTdWLnQmkijKhqy0cHf8QffXWI7lvvdp5l+/KWbWjFusHPRJZVSkMs1ycdQljk/euYTnLf/UMfx1dEw2pJpJNIZ63nLw9csuakuemgGZny1GYMaq20puuL9AWDvIT0Ri4TwzJyVjhVC5TYvKsuEMYYtrUn7ctSKZysK/ttf/syx33ZfScguEywcESptPxIiB1P2q9nvPbQRc5bnfgf9QFZWc3UwSErJQEBWmBRiHSG/8M3FGGMZxth0xti/GGNPm5/TRHRJoYhhjJ3HGBvAGIsyxgYzxv5qbn+ZMbY7Y2w3xthtAa9ZMMvEy42i0ihrPczT2k64uYKsFW6Di5urOhrGBknjKnRtQpoxZebRMjMFNBIOYY+fvop9f/6G8vx2oTJcVag3bdF6hEOECw8aVpT2Ei0dKdtKl0BW89y1d521bWivGoshcoHs5u4IapmI4Pc4Zo++yu0cjeZSz60daWvc5Mw7OQOKp1DLgkTGwMYqTB430JFqq6KjXVHZP3XGCuz98zewcE22tbzKAszlIhMhC2g3rb5Dcc3qaDhnAN4NVT7iQX4R9VAuVYt3JdMZhzLLx6ErV2EsRGXXrQW4RtFQ0JhJm/2l+/15e1sWhkrLrFE8+Fg4hF161wbK3pAh3yvfha04U6mOhW2B36baWKdrE0RMHj8QjKkZ5YrNBsPifvEWFyHWnszgmD36oSbmfOEBw9SvioRQFQ0HDsb6QVsy7Wg1zoUE51dTLz8Qo/rXWy82FyZuHaWT6Qx61kSx9M6TsfTOk3H1kSNcA/CihtmeTFuFmz8/bS8AwK59agE4mR+vlm5Npq3nLY+eKubhB5FwCD2qo9b6JiK4hXDahIEWzTJ4w0+xavuzNc6uCB2pNPrWx7Fr79qcNMlM1U2QNysUw+pY2DF+fhs/xqOFK5KNhAk3nDAaMYWSqbKoDDeXMd5TLz8QA3tkEwq2tCYKvjiaG/ymBn/s8vcJgH5FprFTKKRl0ipJ+YGNVbjs0F0BQNlTSWWZXHjQMEz7wSTbtqBFi5wpn73vYOPeeTJP/uLJE7SpLpbTMlmweqtvrW2ImTev0gZ5Zo4qqCqiI2m4mcLkdEUAxpjwcQyiyfpFIpVxaIVc6+UMZ+zgHggTWd85A5WVEJFmUamIRUKuqb5iyunare1W4WZNLIKTxw2w9rkxDtGvrnJz5YtomJTJJ3wTV6hUz4QLYdGNs1hReNiRzCAeDWHMwAbHPhmpjFG8yJUhN8tE9UxqFMLEb+NHbpnkGSaxIRImXDVpN7x07aGOfbUqyyTDkEwzDOxRhYN2a7K1vU+Z+7oCflOD+wE4HoDc/4EABEtUL2PIpnp1NGJlXqhaPsjpeoDdpWUVLQasCOcvb39TA/FrmYhT6j+frrNy+eVlg+uroo5cdo5129rxw6c/xjufb8CFBw7zdV8urFRMh0/0XJZQezKNqmgY4TApLZNEmlljywX7YSN7Y2ivGjz24XLH8YDBeLmrar4iS0lEKsNQFVUHRvnPChPZmnByjX+rC7NOpJhkoRqfk+kMwiH7MxHTntdsbbfGIBwiQ4CZ32X3Ev+aymSsY2Rt+70vNuBr41XJmnZMGNKI/Yb3xIPvfmWjWWVxck25zhImzveDrwAqPnuVKzmRziASCjl6UqnQ0pHG6WZq973nTnBl7qpnUh2LIJFK45nZK3H8nv1RG4/4bvzILZOaaNjVuvYL7uZSub+Vlkk6g5SgTMkCpz2V9jV2nYXfO7wIoI4xtkz6WwrgraJRVwAU0s0lC4zaeNhyUakC1qoHLz5UK5sr4IPmL2+/BkOY5BMjuOxvM/HQ/76y0dmjOopJo/oglc64WibvfbHRynD6e44MLA5eX6CyTDjEF1DVWLI9lclpmcTMZ8G14NtPH2vT2mWIlxErjFUw0i8lYRLi12HW93CIsLUtiX1+/oblwnOLtSXTGdt84HNJpRyIrXjOe3A6npxp1PFaAsxl5UWedJHKZGNWsivtqVkrfbWpH9ZUgxtPtncyiobV1hQPj/G5Jc/RW19YgFfNLgK57t2RzCAcIl/LW//tg6XW58emL/ewTIzxPHVCVojWxML4ckMLvvfkPKvSPWjMRJU8EhRcQVUJgFqFgsqYMWf4ebJymE9dVz7wu2zvpYyx91z2nV9YkgqLQrq5ZIFRHQtbDEbFfFWaRUwRHwna4Za7p/rUG4sf+bZMuM9c0ky5BdVQHUE0HMKOjpSreZ/P2hg8O8WtvXo8EvJnmUTCxsJgjCGTYfj79GVo6Ujhq40tSKUzllDmFmSN8HxUEDX0He1JazEpGWf+6X1D85OeE9esraw4k7Fv3JGwZSCJLpVVW9pw/9tfgjEjA0e8JmeWyXQGHy7ZhJeFNuSyJs2XMAiHCCHKLpYm5zjw73/7YJnVgFBk7H3q48bCZT6eqyolPhoxtq3f3o7mlgReW7AWSzbscFom0hwV65L4nHITFh2pNCIhUsYQZLwupPxu70h5xEyMZyI+c1H54ynAft1c3DIpiDDxskzi6uu3JdOWRSMrsYVaXiIXfLm5iIhYjiiOn2PKHe3JDHpUR62895pYxNMyUVW229xcim1+wN1FvFGkX8uEeyPk94P7WBuqoohFQtjqsQSxSpjsN7wnPlqa9YDGwiGbSysiaNx18YjjGn0b4o6CwRfmrUZbIo2v7zcEjLGsmytESKUZ5qzYgpuenW81HxzRt84axz+ctw/uf+dL9KyJIRxSp4Ya48HA3/32ZAaNNVFlHcGsZc0Y1a/e8Tw5oxJTrGVNuEpaovnCv3yIJRtbcPreg5BMq91ciVTG6oTL05fd4i7hECEcytIgu7lO3Ks//vLeV7aUaJFBNlZHsWF7h684Exd8Pzt1Tywyi/M4gz/ojv9ax+3Suxa/OXscgKyiwufois2tjlRqPh/61MdtbVSiYUIyzZBIGRXecosfEWMH9bDSYTm2tyddLROeFCEKE1Gj57/VbwCej3+uAkM/sCwTBV9QWSaAoczy83icivOqYsQQVfDLxaYR0TVEZEtkJ6IYER1FRI8C+GbhyeseaE+m0ZpIoT2ZRr8G++Tjfsq2RNqhBSk1OcUEyTc1uFetQcvpew/ydV7GxRXCX4C6eASxcAg7OtwDsiph0lRr1+h71Niznvg4JFIZ9K13av/18ajjutdMnYPr/2UUAibTDBlmMGbu0pH1lhWbWy0GcOjI3vj7pQcgFCJPq09kFB2pNBqlbC0RyYzhk57702OtbSQE4ImM7/L9xg9utNVmLDG7COx/+5t4+/P1NhcnnwcH35llzHxcdrgszRoiuLq5fnTiaBzhUcAJwMpQ87MoF++pdtFBw3Hb6WMBqK3qEGWt59o4d3Ea17/68dn42wd29yj/bQ1V9vHngoi7cLwsE7Fuy7puR8q1cJD3ietVm72nKAi4deDXzdVkCiXRbZYv+Jhyq0+EW9ZYWyJt8aIaUyhyXtVVlolfLnYCgDSMxo6riehTIloC4AsA5wG4hzH2SJFo7BQ6GzNpbklgj5++iqkzVqAtmUbf+mzaXVhgVq2JlMNXqXrRVPGRoL25jt2jPwCjw+q8m4/D7WeM9XXeI+8vxfxV9iysEGXdC/VVhqXlpcmomJr8IveUhElEECZ9FMKkoTrimT3GGV3cdHOlM8xBY4YxpY/Za2xFPsGtTje0JQxXi5h5J1omYZNpiUu1jh/cA71qY66tdpJpZnN7qujnFefclXn/hfta+8IhAhF3czmD64bV4j23qq2YRm7tVZXCrprPo/s3WMKtNmbP5tqiWCCOd9ptqLbPI54NmUgZMROvILLKRba9PeUagOfj2SjUDolZUDzR48Z/e3eR4OjfUIU5Nx2Lbx85wqZw5gMuAFVKJilbFXI3l7GPz1HOq7pqWV+/MZN2xth9jLFDAAwDcDSAfRhjwxhjlzPG5ua4RMnQ2ZhJY00UdbEIlm9qQXsy7WCG/IG3JzO2yQioLRNbzMTK5gpmmfz4pNH46MZj0KM6ih7V0UCWzfkPTrcVDxqBTYPu2ngk57VUlkm9pFE2SsV9XKPtSGWUcYn6KqdlwpHJsOxyv+FstpSsbckuIw6vmIltpclUGlUeLoodHSlEQiGbgsBf+jRjlhARSbj+hNGIR0JWwFuVGaRyc4n4UhImk0b1wWAz1ZoLMDfLJBKinM+TP3tVUaEMv5Z2Rypbbc8FAKdPFRvjbkB5HvH3qSNlxJa8hYnz2aUzzDPpIxIimyJk648VIizZ2ILZPqvawyGjSSURYdoPJmHezcf5Os8LQWKpbYKbi9f4cC9At4qZiDA7B/tbfq8CQEQY2lSDZZtb0ZZwFq7xB9iaSDlS8lSMzJbNZf5XmbNeiIRDSg3f77likDZE2Ze0Nh6x0RciZ3xFdNkQGZkk8ksu12NYlkk6o9RuG6qitt5gIra2JbPZUmSkwRqWiSrhwXltL81cdJV1JDOeVcwtHSlEw3ZNf9ayZjw3dxUygmUiPnPOALkwUWnl9joTJ61rtxoxHH6NWDhkncNpCZljsnZrO64VFm6LhEM5GRLX6P1or6prqVxPHam0JUw4rfy7SmngdS710rzh71MibVom0r3E9jZugkYlvMR5K87VMInCJBSomFh0p6lKAvKBykUnt0TiaE2krLHmMTHepqZbZXPt7BjWVIO3Fm3AtvaUI1uDM4+2ZNrh5gqHCCePG2CrxwiqPRca0TDZLJOIkHLJYyYcqmCfGE+pM/fLTEauxxAZcDgUwlWTdsOeQgFar9ooNrksarSppcMSaKEQCW4uf9lzXszUZpkk055VzBlmd2tyXPfEXMxbsdXG2C16IkZtBHdzqQSmyLBiYacw4y6gRDqDWDhki8uEhf8tHSlM+s00K8sLMH57rrkVs4RJbjeXah0alRuxPZkWLBNjPxfcqvtsaumwWdg9a6I4anRfXHnEbsJvcdaZiO+buE8UEPJaLkB2XkfDIZsCGJYUgSAWf65mjJ3F94/dHbWxMI7YXR0DazXdsEB2XvPf9uqCtV3SPbjihUkh6kxsflVJYESF2gbZzRUJEf54/j5WywvjeEWdSR4rD+aLaDhky/gRg9TV0bCNPlUaoqhZcreQzFBkDTJiEybADSeMtgUqe9fFXRfyWrWlHb8zF2Oygs0Z5kg15b9NhpdlIo4DTz32QtRk5jJmLN1sxU/EsYiGQoiFs61qxFX4VDSr5gEXQB3JbE0KP4ff00iXdjLqSDiUMx7XWctENeZtSadl8uqCtZi1TL1sMmNG1wXuKuxVG8NDF+9niz2oYiaiYifGTMRAusoS4kkB0bBkmQiXj4SdmXleyGMl6kA4ZGRvLPjZCejfUKXcbyQp2C0Tbuk9N3c1fpyjg3ghUPHCpBB1JqeMzRa+yVq3l89bZGRRj3S/fNZEzxexcMjhV08K/m3xhVVZJmLbE/4Cy0xG9l+HJMsEsGtybvUdgFHcxosjw6ZWnmZMabqrmLHMTHkPK8DeVsQqivR4Fl5WjsoyiYQNBsgTCFQ9umzzR+Gq4W1UEum0IEzsgVY3pmdo1+p9fEXDw01N148wUccAnTS3JzMOYTJ/1Tac+acPXK/duzZuMfOQFQuyWwpykF1U7PbfpZf1WbQ2VJXufF7HI96Wid+0YJHmYoG7172UA/4sBjcaMbXBwvK/Hy3dnH+DWJ8IJEyIaCIR/ZuIZvPeXET0ce4zyxsHj+iN4/c0WpBVx8J45brD8Pp3Dwdgf7j9e1Th3nMn4JsHGW6tkMKPKjKPY8f0c2wrNqLhkK0ZXjhEVqM+2bRX9QESYyaWMAmH8Mktx2H3fkb33P497NqTyIQjCqbbVOe+pPLKzdm6g1CIEAro5pIZoGh9cIZ3ycMzkM4Y62gvvu1ETL38QCUtXj3Usi4nOz2xSMhIbc6wnJaJijFzyySRylj7ZSHiRlbYw8112Mje+OqOkzBxWE8AwA+f/thz8TPAv2XSnkxbVl8kRL609qa6mC2hQL6fXAEfM5t6AsDfL93f5v4RhYyqiWWNZZmQlUZr3APC55BLdEKNQsiSQY3Vrvt42rr43vz+vL1xzznjre+cF33vuN3x52/sgyNHZztKv/qdw4vOZ4Je/TEADwM4E8BkAKeY/ysejdUGwyMi7DGgAbv3qwcgaZYhwqkTBik1bW56i5rir84aj/enHFWQqlm/iITtGleIyNJYomF70FFVgLVdYIjcAjGyYqKW8Jk8fgB+cNzu1nFhiSkY981e08syEdNqQ9wyyTC0p9IIhwhvfv8Iq826n3iUGBfh48CXsK2KGm4sN23eyzJRadPRcJYBJtIZZcxEDLqrgshWzCSVsWjn9PGfG3bhZHLCgO23mC47sRDwKbNFixvCOSy/KSeOxnn7D7HFTEKKQk4VetfFLQuWJKHC7xMTLOE3v3eE5Vbu11BlE/TiM96qGHNRsbNZzZJV6bcvF1AYy+S17x6OmT85RrnPskyE+TV5/ED0b8gKIL4vHgnjhL0GOIRvsRH0DhsYY88zxr4Se3QVhbJuhkazuGmrpOnYtG6uMVovRfY4zphj0gMe6KGNFAPRcMhWAS26uaKRkG0CqiwTsXVLTHJz8RTnqmgYX98vu9Ky+AKIQWMOL8tERDhkvLQps86kKhLCbn3qLD+yn2wu0TKRW49woe5mgXi5GKzfRfb5wMfzlflrMH3JJsd5uSyTzS0J/OXdJdjRkbb283P4uIZcBEY4FHLVRvlYiW5bOR4oI5dlcuCuTaiORmxurohpTeZCU11MSCzg9Nvdo5xhHzyiN4b0qrFoj0iJEeJnlTzgv1NmsKIiQHB2V/ZCIYRJXTziqljxOSnPZ1EZked/vis95ougOWw3E9FfYCzXa/WdYIw9U1CquiH6mA9ZbpInvkzjBhtxmbBSmBQmXTAorjh8VzwgNDGMhUM2jSskuLliYQJj3paJCNHNZfzPBvJVAgTIvujiRO9V60+YhIgQCRM6khm0me1VgKzQ85PNJTKQDGO2dUKqBEtLBS83gcrlFBW06e/+c555nJ3B5YqZLNvUil+8tBAArAw4y1UoxRhkGEWWbvuMk0UBlstCVmVziefXxcNmum52IS6ezp0LTXVxYTExp8IRJuc6LFwoRH2kQIuoitqFsnUP4Ws6wxxpuPsO64nm1gSWbHC6A7sq7Ck/zxF96hGPhGyNHkuFoJbJJQAmwKiIn4ysq6vbolBdg79x4DBcduguuOzwXW3bxQfI25pkXx4nY1a1qi8mJkmphNEIOSyTA3dtAgDsMaDBHjPJIQDjUbvrjguV6ljY5moQx0hlmcQjYWU7DBkhs9o7lcn26gKyFfh+Yiai5ZVmzNatOO7CZNyupdpnc3OFQg5ro6E6avON27K/JGYgr6IYk4S3HGOQEQm7t5Ph9xWFul8CAWsAACAASURBVFsNg3WOyjIRNOP6qiiqo2GkMsyyYMM+Yya9a7MxEy5MIpJlIiNrSeau9BfB3bPis2msidrukcowh1XTpy6O8/ZzLo0MuFuHhYb8O3vURK3O2Kr42G2n74WnrjyoS2gLKkzGM8YmMsa+yRi7xPz7v6JQViAUqmtwVTSMn5wyxtE/SHyAvIJX5eY6e6Lh9hnaVNMpOnLhrrONOAyHrO1GpWyuEBHO2GcQZtx4NMYNbrRp7nKqswzOmPlLyNs5VEXCthdVZZnI714fj7iJeB2eZdORzFgaJvcnK7O5HDUwopuL2YraLCYmXOeig7I1Qn6yueT0UtmVUl8VwbvXH2l9F8cpLtSZ3HHGWOw9tKftXH5sTHJ5uFkm4ZC9/cvFBw+3PqsEZq4lXlUMU5z/Paqj1vjyLsT+3VxxRzzNMW/kBqVcmITUKdtu4POGP5v5tx6P96cc5bRMJD+X0b7G+PzNg4bh+asPsfYVK5urf0OVLUVaNQf5PJGX3gaACw4Yhv2G93JsLwaC+l6mE9EYxtinRaGmDJENhtpNcgC2Ljpn7TsYp04YWPSMigGNVbY4jDz5ZGGSNheI4n18giwnzF9GscYiFrEHNY028NnvIRcG2FQXs5oguoHXmaTSdsskK0yCWSYZZq+Q5p2LxfY2uwhLxXq7ufh/0dIIOdKk45GwbXxiNjeX3YKTU2Gzlol9zrmRJbd/6ddQlXWJKJhSrmUAVDJBdpNxRm2tdOgzAN9UF7OOUwXgw2GyLEfe+60q6kxq8QP+TPh5vNZEpDPNnJaJ+FuICOMGN2b3FckweX/KUTYZqrLAxgwwkoEWr3OuUtmVCCpMDgXwTSL6CkbMxIhTMTau4JSVCfjDPVpIw1NZJkDXpADLL658z5gUgJfXzI7ZGJo3vZyR8MtFwiFbEPfecydg/OBGWxWyKjUYcHYeViFkrheSYczWcYC7uVQMUtZYRcsknWFoE2ImiXS2BxiHvQ5BbfmkMkzpmuGpwfLxIlRdg43PTtdNNnvOnhrsGjOR3FxiHY0qyUBeBsAP5FZAfHy5lRMJ+XNB9a6NO8ZGtkwO2rUJN08egzP2MZarro55J0y4wS1mIt4/nWaQTSGuzADO1vTFskxkq07lyhrV34il+Y09FgtBhckJRaGijDG4Zw3+ctFEHDKit7WNzzO3Dp/FRC5hIqcGp6SUJrHZXq53VF4DPRoimzA5dYIRQ/ps7TbhmuqsFD8ZXbwLLo+Z8KQGq1W5j6IsOQDPNeixg3rg7H0NJmUTJkLcSKUBxyMhpBJpi+3IadDOjCH7NUTNXo4ROItBuXvG7o5zjZlIjFy0ksSlon9z9nj84Kl5Od1cKsjzy3JzJfxbJtEwoaE6YjFOZtWo2F2lRIRLDtnFca+gi8txmuXGkuJYqSyTMGXjP7Iw6arEKdWzrotH8PhlB2BU//quIcIFgYQJY2wZEfUEMBKAWJm2rKBUlRmOMYsPOfjL0MWZeQCy5vYPjx+FvQb1cGR4yEWLsmUyXjDdc1km/F789544dgB261PnOE6VtimPTZOPmIlhmRjZaB2pDHrWGPQFSW6IuwiTO84Ym3WbSLUiWdqd4xE31/zmYyBrknIA3pnaKaSjii4yRawhm4rtLwDPGTBHVTRkKTqiNn/WvoPx8P++8rXaogy54zUfX57Y4Kdosak2DiKnBaOKtYk4clQfNLckXFdoFJtsiliw2lBuDhnRZNsujnc6wxQCg3DmvoMxZ/kWfPeY3e3ndtHL7iY4DxaU2VIhkDAhossAXAdgMIC5AA4E8AGAo7zO29nAp2AJZInFPL595AgAxqJRIuSYidxgMRYJ4ajRfbFhe4fFrA4b2RvvfrHRca+sZWJ8P37P/jh+z/6O40ShlG3Vbh+dPj4sk1CIECZjgalhTTUWc+XCpMWHZi27ubgLTnRniQLY7TMHZ2R8RL1SkQFvYSJC1fE3WwFvH0PRxSa6MGWroToazlqR0m+pjTlXwJShqruQ3Vz89ywyrdGQQkjI4O4ZPt/UVp5znPYe2tORpGC7bk0Ma7e1AzAK/F6YtxoAcNreA/H25xtw9B52JVAc71SGOX5viAwr+O5zJjju1VXCRFU42l0Q1Il/HYD9ACxjjB0JYG8AGwpOlQ8QUS0RPUpEDxLRBaWgIRe6umgIcAYCnQyQ4Q/TvvC8xl+/OREvXHOo5eYaO6gHfn/e3s57ufiPZeRqpwIYVs2EIY3wQpiyC3ct29RqMa5qaWlYL8gBeL7YV63Q1FJkwvZ+TQrLhF+PdzaWfpesNbu5rmQYLip1AF5uQZ+Ng7hr9oDh1nETerXxcE5hooIjJmfS+Pk6+xr1Xvi+2S0h6+Zy0hjUlQVkW7AfOqI3fnzSaGv76XsPxld3nORYTkKsln9h3mrMl5YB9hKKxQrAy8hnHLoKQYVJO2OsHQCIKM4Y+wzAqEIRQ0QPEdF6IpovbT+BiBYR0WIimmJuPgPA04yxywF8rVA0FAJBKmcLjVwxk6kzVmD6EnX3Vg4uBEnQFN2WZwXUiz6JULdTsbu7etfF8a1Ju9nOq5VSk0MEXCkcwzX1GslP7wXRMtnWnsRNzy0AYG9b7sbE1JaJcT0+AnLhnyws5OfjthStUTsh34sLE7Ldm2v0sstJvld9VcQiVA5aJ9IZLFi9DU/maKnioNNDOI4b3MNsWWI/R17vhlsI/Fpu8aeg4FlfkbCzcFKl6K3e0m77/sOn7W0HvZTDrlIcu7IpbFAEFSYriagRwLMA3iCi5wCsLiA9j0AK8hNRGMAfAZwIYAyA84hoDAxXG5/5XVsJmAMllCVOYeIR9/h/h++K+y7Yx9d1VZM4m9nifa7aMjG+i1eVryNrjqEQYcKQRkvI8EyosWbngbP2HYJcEJnd8k1ZF6CYOOAWRFcG4KM8o80gvlFastiRzSW363exTMIelgm3kHrU2Oua5GvL1NZVRaxeZ3J3gyNHGdmIs5c1K+lxA2eifO0RUXm5/LBdjd8izUm35ZHlGJF9sargTFTM8vNT6/KtSbvhskN3wci+zrgf4J2Q0lVMvivXPgoK3zETMmbNtYyxLQBuIaJpAHoAeLVQxDDG3iGi4dLm/QEsZowtMel4AsCpAFYiG7tRjjARXQHgCgAYOlRduVoMlDIAL9/Tq8XCqRMGYYywSJUbGFOnNV9+2K6YtawZp4wboDgrC/FFc4uZAPb28IBRLb56a1Zb5IIyFgmhJZHtVdWvoQpL7zw55+8A7E0AuT/96SsPsmmWJDGx6mjYXIfc3c3FrdGe0pLFXv2fVPs5VC1CZKHWYDJL/mhk7ViW8WKXAVnoXXbYrnh8xnJbV2i/EMde7j2noquhOopVW4xu0IcLHRos4aFIZvAjDC45ZDgG9qjG65+uBZDN1gqH/LV06dtQhZ+cMgYf/v495X4vd11XGQzd2DDxb5kwg0M+K3x/22z6qF4ir3AYhKwFAhhCZBCAZwCcSUR/AvCC6kTG2ANmxf7EPn3UK5RVGnK5uUTk6uUjXkrVBmVIrxq8dO1hOTOx5KVsRTpFRrN7v3pbVbHcaJL/FO7ecYs3eEFczXCdKai8lkAWa2eiijc5ZgXg7UulZu/nHTNxc3PxNGjVvThj5WnL1li6/goD9fGsAFFZB33q4tiw3VlFHQS2in4uTKRjuBC8+ODhePCifa3tXpq/H8vk5sl74nKh3RF3p0VCoUDtTtxkhpcw6So3VynisH4R9G2cTkT7FYUSd6hGjzHGWsx2Llcxxh5zPblAvbnyQUnqTKQn6lUd7Nc0Z2DWyyzHMYLSZMVMXO49oEe2el9mtKJlIv4PAlGArttuCBOvFviREGW705r0XHvUCGu/FTOxLBOp3Y7Lb+BwtUxCIYc2zV2WfGGwOssyscefAMPyGNhoX1dG7BCsFCb1cWVLDg4/fEz8PXL1Pwe3kHrXxWzH8LFReU3zcSM1COMT5Hy3JW67QwC+OyPo23gkgA+I6MsuXBxrJQDRGT4YAeI0herNFQRW0WIJJpjMrERNZoC0aFUu098Shiz7Iqna0udCRJERpYqZAFJdhyQI5SpzN63eC+L4rN3ajppY2PM3hUNkBdX5GHzvuFFWmxXZzSW3cc9pmXjFTBw1Qsb37WbWFde8VY1Fp31/koOZi3OhwU2YbO/o1LxVublk8CJTWcvmz1uVwJJPzKROiJn4cXNxyMKE16N4kdBVqcHdGUE5w4lFocIbHwEYSUS7AFgF4FwA5/s9mYgmA5g8YsSInMcWCtzlUYrp5XVPeV8QbY2/6DWxMO46e7xtXZNcsAe0zf9SNpfq2CAFf34hXmH9tg5XF1csHEIibfSwyja8zDIZHheTXW0yg5QtQ1lAuLnqooqOvzzhgKczWz2lFJZJLreOm2XilR48un/u+JrYrNLtt3HhLRfMZi0TpzQJB1Ac7jp7Av4w7QscsXtf3P7yZ0Z6coCpIgsTLty8xlQLk4CWibggVjEWxyKiqTCKIEcR0UoiupQxlgJwNYDXACwE8CRjbEEAmncqyyRI+mIuYWLFQ4Vza+MRnLnvYJx/gP+EBlUrcSv9GDLzDSk/A8ILa/7zK0zm33q89VlkE9s7Uo6AOQevOwmHsi1i2hJZAcqvk3Vz+XONOGMmaleQyjXDx4MzfMvNpYiZ5Hq2KhdUk0dvpxk3Ho19h7kXCHKIz4S71eSRqTPHNpG2J2F60RzEMhnaVINfnTXeMzXYC67CxPPd8n35ikXQCvjvKTZvBTCLMTa3s8Qwxs5z2f4ygJfzuWZpLBPr3l12Tw6vd86R6ZVLmAifuZ8+14JZSpoUqcFe/aQ45Pb5XLvkR/htnCnXNfjZVxuPoLk1CSKyVnJkNsvE+B9XMMyhwiqADgHuN2YSdsZMuKV2yriBeGX+Wqv1jSpmEoR5cni5+3hX6VwQLTE3QcnvI7c6sSrgFXI5n5hJ1s0VCnR+ShYm5tP1GlNtmQR3c000/3j21Mkw3FBXEtFTjLFfFZK4QoAx9gKAFyZOnHh5193T+F+K6eWdvhjMMuFgwiJSuRbMygXVGvBuNDlcRFIGWBA3118umohZy501FG7CkQuZ1kQKt5y6J0YPqMchuwnNPGF3c4kM8B1hvRIZfl11xlok6pjCyeMG4ORx2XRcy80lzDjRrfPYZQf4YnYqwfrEFQfipY/X5Dw3S6MQgI+qf1udizDxciPlEzOpjoYtCy+IYudumbif043LP7oMQTlDE4B9GGM7AICIbgbwNIDDAcwC0O2ESSkskwN3NRajOXz3rk9HDpIL79fNBQATh/VEU20M1xw9sjPkObO5JBLEl16OmZDkzgmSGnzMmH44Zkw//OfTdbbtbtr4HWeMxS9eWojd+tShKhrGFYfbq/M5g5FTg3PBb9GiUQFv3+dmiVlD6WKZiB2trz9hFLa0ZtuGiFAJkwN3bbJW4gwKt+fDExoS6eJaJkSEE/fq78s9J8Itm8tLIOVjCXYGcpPK7oCgwmQoALGuJAlgGGOsjYg6l6BeJJTCMtl7aE8svu3EwOssFAKqOd1QFcGhI3tj4Zrttu3+LROjfmLWTcd2mj5HOxWPY916TfHfmE82lww3y2TvoT3xr6sOdj3PcnNJqcG54NWCXoS8sJXXsUFiJt+a5K5U5ZOp5wU3QcmFjJzEwZMTVEOZb+X3H8731+FBhOzmOnZMP3z41WYcIzWGFNGVLu0vbz+pJF6PXAg6ex6HUWvyHIy5ewqAqURUC0CvviigFIIEULsKPr7FCEAfdddbtu05LRNzyuZqlxIEVszEJZtLPE5mIH7jDV6Qf0o+MSARQQsnHbUjLnVAkbCzQaKrZWIJ2ezx+TA3r9hSPuDCT05O4GvXyMkPXtp9V/akkhuXjh/SmLPDQlfWmXTX/lxB1zP5ORG9DGPFRQJwJWNsprm7u3bu7XI3VynhGYCXvpdiUmbdXO7HPHzxfhjRtw63v7zQtj2bzBU8ZuKGmjxjQHJqsF95K7uu3Jh+RBEzcRM8hXqOhbZM3BSqI0f1xa/OGoevjR9o285/hyozTm7/UkykJPebn9HVAfjgRYsAkAKQMf+rna/dCKVIDS4lAgXgcxUtmrv9xgO8MNAsmOT+6Kyby0nDkaP7YkivGsxfrW4B3hk3l3w3sfV8EFipwdFgbq5cLWys4xQZSG7MWe7AnC/yHYugICJ8feIQR4dlr7m791Dv5QkKCTlm4sfK08IkoDAhousAPAagN4C+AP5BRNcUgzCN/OA1p4NmcxXSD3zTKWMAAP1NoeJHm77j9HE2P7Wj7iIPy+TA3ZpsWm7+lonxP+vm8idN/DIdVdW2Vx8voPPCJB4J26yf3ynWsMkHfGQO2KUXLj54uOtxqjmx16AG7DWowbU1SzEgx0z8GH5algSPmVwK4ADGWAsAENEvYRQZ/r7QhBUKO5+by31Wy7v8CotCrM9y4tgBWHL7SZZ/X5WBJOPQkb1x6MjeGD7lJduxVmpwHpZJXTyCV687HAfe8SaAzlgm3M0V0DIxf/jdXx+Pj5Y615V5+JL98MSM5caqkg7h6eLm8rDygqI2HsGW1iS+c8xIhxuqszj/gKE4dcIg1/1ZN1d22wtXH9rl6wOpluvNhSCNJCsVQYUJwb52SBqlKafwjVJkc5UShexsWugHa2sp7rPTrQhV1lJedAgXyNcyyUiWif+YiXHzM/YZjDP2GezYf+SovtbaIs6YiVsA3vhfCO24JhrGFiSL4rbJZY1awkQYTSLqcq0/H8tEy5LgwuRhAB8S0b9hvNOnAnio4FRp5I0gAfhcOHviYHywZJO1nnwhoWpB7/8c43u+Gqso1PLN5nJUwPskJkiw3GGZuGQtWG4u31d2R1XM3tSykMgVoyt2rca3j9wNYwfljr3Ij9KPxadjJsGzue4mordgZHMBwCWMsTkFp0ojb3gx56Cp+vVVUTx40cROUqRGPsyKC4F+DVVYsHpb3tlcItPK1zKB7ObK8ypecKQR53JzFYChVZm/pxjMMZcriM/PYrm1fnj86NwHKeBnKLQw8SlMXHpyAcCRRHQkY+zuAtJUUOx8MRP3faVYX8UNUs9GXwgL8YbXFqzFqP71ed1bfPHzjpnIbq4iMEC5zsbNzSV3BugMeE+xzhgmz377ELQns95wPjY5LRPLzdW94E+YFJ+O7g6/ql29+TcRwFUwVjocBOBKGOuyd1vo1GBxXxcSkgOWZRKAJn5KY00M5+yX/zLMIo/Ot9cYZ3hWOxWf0iSQ8PQZM7GoKcDz5ZZWZ9xcE4Y0Kluw5IyZWO1USitOdpOWj/ZjdWjLxKdlwhi7FQCI6HUYvbm2m99vAfBU0ajTCAw/AfjTJgzExYfs0lUkKZFPAL5QL2zI5ubK1zIxGF7UowVIZ+G3nUohG4vGXbodFwK53FzdhR8/+f8Owpufrcf1Txvr/mlh4g9Bnc5yb64EgOEFo0aj0/Ca03xfvx5VmDCk64rAVMjn5StUUFi8Tr5V31x4WBXtRZAm8hi5FTwWcskD7rbzWVsZCH4fX6ndXE11cew3vJf13c+wku4aHFiY/B3ADCK6xbRKPgTwaMGp0sgbfirgu4MWla0zCZ7N1fl7Z68TtLcWx9GjjWJKbtl0hWXi1oZdtEzcVo70i0K4udyQO1ur9POSQ/z5/lKDuw/tpULQbK7biOgVAIfBeH90Nlc3g5/U4O4QO8nn5SsU3eJ18tXm7zhjLL533O6WZeNXKAW5newWcqOVb66KhvHfbx0caEllGfx3FNLNxetGfBf2ldo0gTw//bi5ikdLuSAfGz8NozcXM/93a+x82VzlYZlkXTP+zykUgyuE1h2LhDCosRoA8P1jd8eJY/t3+pr5YmTfOlx95Aics98Q1FdFkV+OmwEeM5GrwAuBIMtElxqkLZPAqPjeXDtbNpfnnM7DtVQs8AB2KSgp9O+/5uiRGNHXnYUXW2slIvzg+FEY0qum09fidSYdycLribkYLt9b6mwuwE6rn/nSDV6pkqPie3PtbPCa+N3JzcU7xh5RgtUouxpEVLxKvAKDWyYdqXSOI4Mjl2XSo9powCmuDFkqiMLEz/vS1SstdkdUfG8uDSe6g0leG4/g3euPRN+GzgWMywE9a2LYuMNYiLQ7WIVe4AH49qJYJt77m+riePuHkzDQdB+WEvYAvE4N9oOgqSy8NxfP5poO4K8Fp0qjKOC6cXewTABgSK+aLm0tXio8feVB1iqG+bpw7j13QiFJckV2Sd3CWyZ+GO6wplqP4syuQ+CmqN3knSolAj01s23K/wHYDKAZRjbXb4tBmEbx0N2140rD8N61uPCgYZ26hlfr9kKCC5NCWiZWO5XuosX4gM0y8UG3fqfyyOZijM0CMKsItGgUG+ZLrU3y0qG7Mx0eyypFzKQ7wRaALyEd5QS/jR7fY/+/vfsPtqK87zj+/gRyIwaCRsBREFHBX2lU9IKaMA5V42giYjpEcGqjQqW2MY22SUZjOmlm6tBOnTSlGhoLRJ04/qwm2LFVY2K11ugFNAkWnTL4g1uomCaiUVolfvvH7sk9nPvrHHaXvWfP5zVzh7N79+z5Ls/d893neXafJ2KOpDfZ/c49ARERHyokOitEG53ThZqyf/lt8yPNoQck41IdPnFsbvuMNryI2b0Dvn3iLlOzY3PNUXJJ9ZGIeKXgmJoi6XDgWmB8RCwoO5524pMDfvAnpzFhbPU7/1t16hEHcO8ffYwTpuQ/3E471Uzqh0dpo7BL1XSfSSQ9h/fl8aGSVkvaLmlDw/qzJb0gaZOkq4eJZ3NELMkjnk5RexLZuQSmTxrHfvt27bXPOzodLv+IPbjirw0Lv7ecOHX/Qqahbacv5d0uuNoo7jK12mfyY0mzIqIn4+feDNwA3FpbIWkUcCPwCaAX6JG0BhgFLGt4/+KI2J4xhkq5+dJZPLHp501t65rJ3nfe8Qdz5IHjOOag1lqEn7zmdMa8vxp3vLXT312ztwZ/d8nJPPL8q3shopGv1WTy28Dlkl4C3qKvz+S4VnYSEY9JmtawejawKSI2A0i6A5gfEcuAc1uMk3QfS4GlAFOn7vn8F+1g7lGTmJvOHT6YvrbrvRCQ7UZSy4kE4KDx7tcpQ7Md8HNmTGDOjPIfshwJWk0m5xQSRWIysKVuuRc4ebCNJR0AXAfMlHRNmnR2ExE3SdoGzOvq6jop74DbVRFNGGZVoiZrJtan1cbYV0hGDL44Il4mubPrwJxiGajEBn3CKyL+JyIuj4gjBkokddt11NhcQ8lz3guzKtt9bK4SA2kjrSaTbwGnAhemy2+S9HPkoRc4pG55CrA1604lzZN0044dO7LuqjJcMbEytNOXcqsDPVrryeTkiPgc8L8AEfFLIK9bYnqAGZIOk9QFLALWZN2payb9udpuNrRWJ8ey1vtM3k3vugoASRPZgzlNJN0OzAUmSOoFvhYRqyRdATxIcgfX6oh4rtV9D/BZHTWfyVBq40K128nxjQuOZ+vrO8sOw/bQ3190Eqv+bXNb3Uwg10xa1moyWU7yrMkkSdcBC4CvtvqhEXHhIOsfAB5odX/DfNb9wP3d3d2X5bnfdtZuJ8fvnDil7BAsg49OGc83F80sO4w91m4XX2Vpddre2yStA84g6TA/PyI2FhJZTlwz6dM3arDPDrNmyU8tNqXZsbn2AS4HpgM/A74dEbuKDCwvrpn05ysts+b52qs5zXbA3wJ0kySSc4DrC4vICtOOA+6Zlc2nS3OabeY6NiI+CiBpFfB0cSHly81c/fnkMGueL76a02zN5N3ai3Zp3qrxrcH9+eQwa57Pl+Y0WzM5XtIb6WsBY9Jlz2fSRtwBb9Y6ny3NaXY+k2oMW2qAO+DNWuFrr+bs3YkSSuDhVOpEbT4Tnx1mzfL50pzKJxP3mfTnmomZ5a3yycT6uM/EzIriZNKB3udSN7OcVf5rxX0m/Xl4CDPLW+WTiftM+tSegHcrl5nlrfLJxPpzn4mZ5c3JpAM5mZhZ3pxMOpBvDTazvDmZdCA/hGVmeat8MvHdXH2C9py218xGvsonE9/N1d/7nE3MLGeVTybWp29yrHLjMLPqcTLpQO4zMbO8OZl0EE/ba2ZFcTLpQG7mMrO8OZl0INdMzCxvbZtMJJ0v6R8kfV/SWWXH0w5i+E3MzPZIKclE0mpJ2yVtaFh/tqQXJG2SdPVQ+4iI70XEZcAlwMICw60cV0zMLG9NzQFfgJuBG4BbayskjQJuBD4B9AI9ktYAo4BlDe9fHBHb09dfTd9nw4hw3cTMilFKMomIxyRNa1g9G9gUEZsBJN0BzI+IZcC5jftQcn/rXwL/HBHrB/ocSUuBpQBTp07NLf525/lMzCxvI6nPZDKwpW65N103mM8DZwILJF0+0AYRcVNEdEdE98SJE/OL1MzMdlNWM9dABrpcHrRdJiKWA8uH3ak0D5g3ffr0DKFVi/tMzCxvI6lm0gscUrc8Bdiadacem8vMrHgjKZn0ADMkHSapC1gErMm6U48a3Mf972ZWlLJuDb4deBI4SlKvpCURsQu4AngQ2AjcFRHPZf0s10z6czOXmeWtrLu5Lhxk/QPAA3l+lvtM+oQfWzSzgoykZq5CuGbSn28NNrO8VT6ZuM+kj/tMzKwolU8mrpn05z4TM8tb5ZOJ9RnTNQoY+IEeM7MsRtJDi4VwB3yfFRedxN1rtzB90tiyQzEb8W5dPJsdO98tO4y2oU4Z/K+7uzvWrl1bdhhmZm1F0rqI6B5uOzdzmZlZZpVPJr6by8yseJVPJr6by8yseJVPJmZmVjwnEzMzy6zyycR9JmZmxat8MnGfiZlZ8SqfTMzMrHgd89CipNeAlzPsYgLw85zCaReddsyddrzgY+4UWY750IiYONxGHZNMspK0tpmnQKuk0465044XfMydYm8cs5u5zMwsMycTMzPLzMmkeTeVHUAJOu2YO+14wcfcKQo/ZveZmJlZZq6ZmJlZZk4mZmaWmZPJysrp6QAABr9JREFUMCSdLekFSZskXV12PEWQdIikH0naKOk5SV9I139Y0sOS/jP9d/+yY82bpFGSnpH0T+nyYZKeSo/5TkldZceYJ0n7SbpH0vNpeZ9a9XKWdFX6d71B0u2S9qlaOUtaLWm7pA116wYsVyWWp99pP5V0Yh4xOJkMQdIo4EbgHOBY4EJJx5YbVSF2AX8aEccApwCfS4/zauCRiJgBPJIuV80XgI11y38F/E16zL8ElpQSVXH+FviXiDgaOJ7k2CtbzpImA38MdEfEbwGjgEVUr5xvBs5uWDdYuZ4DzEh/lgIr8gjAyWRos4FNEbE5It4B7gDmlxxT7iJiW0SsT1+/SfIFM5nkWG9JN7sFOL+cCIshaQrwKWBluizgdOCedJNKHbOkDwGnAasAIuKdiHidipczMBoYI2k0sC+wjYqVc0Q8BvyiYfVg5TofuDUSPwb2k3RQ1hicTIY2GdhSt9ybrqssSdOAmcBTwIERsQ2ShANMKi+yQnwT+DLwXrp8APB6ROxKl6tW3ocDrwHfSZv2Vkr6IBUu54j4L+B64BWSJLIDWEe1y7lmsHIt5HvNyWRoGmBdZe+lljQW+Efgyoh4o+x4iiTpXGB7RKyrXz3AplUq79HAicCKiJgJvEWFmrQGkvYTzAcOAw4GPkjSzNOoSuU8nEL+zp1MhtYLHFK3PAXYWlIshZL0fpJEcltE3JuufrVW/U3/3V5WfAX4OHCepJdImi9PJ6mp7Jc2h0D1yrsX6I2Ip9Lle0iSS5XL+UzgxYh4LSLeBe4FPka1y7lmsHIt5HvNyWRoPcCM9M6PLpKOuzUlx5S7tK9gFbAxIr5R96s1wMXp64uB7+/t2IoSEddExJSImEZSrj+MiN8FfgQsSDer2jH/N7BF0lHpqjOA/6DC5UzSvHWKpH3Tv/PaMVe2nOsMVq5rgM+md3WdAuyoNYdl4SfghyHpkyRXrKOA1RFxXckh5U7SHOBx4Gf09R98haTf5C5gKslJ+ZmIaOzka3uS5gJfjIhzJR1OUlP5MPAMcFFE/F+Z8eVJ0gkkNxx0AZuBS0kuKitbzpK+DiwkuWvxGeD3SfoIKlPOkm4H5pIMNf8q8DXgewxQrmlSvYHk7q+3gUsjYm3mGJxMzMwsKzdzmZlZZk4mZmaWmZOJmZll5mRiZmaZOZmYmVlmTibWcST9WtKzdT/Tyo4pT5JmSqqNN3aJpBsafv+opO4h3n+HpBlFx2nVMnr4TcwqZ2dEnDDYLyWNrhu3qR19BfiLDO9fQTJm2WX5hGOdwDUTM35zBX+3pPuBh9J1X5LUk8758PW6ba9VMsfND9L5Mb6Yrv/NFb+kCelQLbU5U/66bl9/kK6fm76nNr/IbekDZUiaJenfJf1E0tOSxkl6PH3osBbHE5KOaziOccBxEfGTJo75vLra2QuSXkx/9ThwZt1wI2bD8h+LdaIxkp5NX78YEZ9OX59K8kX8C0lnkcz3MJtkYLw1kk4jGRxxEcnIyqOB9SSj0A5lCcmQFbMkfQB4QtJD6e9mAh8hGRvpCeDjkp4G7gQWRkRPOnT8TpIn1y8BrpR0JPCBiPhpw2d1Axsa1i1MRzmomQ4QEWtIhweSdBfwr+n69yRtIpnvZLhjMwOcTKwzDdbM9XDdMCJnpT/PpMtjSZLLOOC+iHgbQFIzY7WdBRwnqTYW1Ph0X+8AT0dEb7qvZ4FpJMOkb4uIHoDaCM6S7gb+TNKXgMUkEyI1OohkmPl6d0bEFbUFSY/W/1LSl0n+T26sW72dZJRdJxNripOJWZ+36l4LWBYR367fQNKVDD5c9y76mo73adjX5yPiwYZ9zQXqx4P6Nck5qYE+IyLelvQwyZDqF5DUQhrtbPjsIUk6A/gMyaRZ9fZJ92XWFPeZmA3sQWBxOscLkiZLmgQ8Bnxa0pi0f2Je3XteAk5KXy9o2NcfpsP8I+nIdFKqwTwPHCxpVrr9uLr+i5XAcqBnkMEYN5I2Yw1H0qHAt4ALIqIxcRwJPNfMfszANROzAUXEQ5KOAZ5M+8R/RTKy7HpJdwLPAi+TdFbXXA/cJen3gB/WrV9J0ny1Pu1gf40hpomNiHckLQT+TtIYkhrCmcCvImKdpDeA7wzy3ucljZc0Lp2CeSiXkMwueV96jFsj4pOSDiRp9so8LLl1Do8abJaBpD8n+ZK/fi993sHAo8DREfHeINtcBbwZESv38DOuAt6IiFV7HKh1HDdzmbUJSZ8lmWPm2sESSWoFu/fFtOp14JYM77cO5JqJmZll5pqJmZll5mRiZmaZOZmYmVlmTiZmZpaZk4mZmWX2//Rn+vcY+4qqAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs, cbax = plot_by_id(dataid)\n",
+    "aa = axs[0]\n",
+    "aa.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just for the fun of it, let's make a measurement with the averaged periodogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 332\n"
+     ]
+    }
+   ],
+   "source": [
+    "no_of_avgs = 100\n",
+    "\n",
+    "with freqmeas.run() as datasaver:\n",
+    "    \n",
+    "    temp_per = osc.periodogram()\n",
+    "    \n",
+    "    for _ in range(no_of_avgs-1):\n",
+    "        temp_per += osc.periodogram()\n",
+    "        \n",
+    "    datasaver.add_result((osc.periodogram, temp_per/no_of_avgs),\n",
+    "                         (osc.freq_axis, osc.freq_axis.get()))\n",
+    "\n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs, cbax = plot_by_id(dataid)\n",
+    "aa = axs[0]\n",
+    "aa.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Measurement 3: 2D Sweeping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 333\n"
+     ]
+    }
+   ],
+   "source": [
+    "meas = Measurement()\n",
+    "meas.register_parameter(osc.npts)\n",
+    "meas.register_parameter(osc.trace, setpoints=[osc.npts])\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "\n",
+    "    osc.dt(0.001)\n",
+    "    \n",
+    "    for npts in [200, 400, 600, 800, 1000, 1200]:\n",
+    "        osc.npts(npts)\n",
+    "        datasaver.add_result(*expand_setpoints_helper(osc.trace),\n",
+    "                             (osc.npts, osc.npts()))\n",
+    "        \n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb b/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb
deleted file mode 100644
index c13a27e5f85..00000000000
--- a/docs/examples/writing_drivers/An-ArrayParameter-Example-with-Dual-Setpoints.ipynb
+++ /dev/null
@@ -1,482 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# An ArrayParameter Example with Dual Setpoints\n",
-    "\n",
-    "In this example we consider a dummy instrument that can return a time trace or the DFT (magnitude square) of that trace. The setpoints are accounted for in an easy way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "from qcodes.instrument.base import Instrument, Parameter\n",
-    "from qcodes.instrument.parameter import ParameterWithSetpoints, expand_setpoints_helper\n",
-    "from qcodes.dataset.measurements import Measurement\n",
-    "from qcodes.dataset.plotting import plot_by_id, load_by_id\n",
-    "import qcodes.utils.validators as vals"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.dataset.database import initialise_database\n",
-    "from qcodes.dataset.experiment_container import new_experiment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def timetrace(npts: int, dt: float) -> np.ndarray:\n",
-    "    \"\"\"\n",
-    "    A very realistic-looking signal\n",
-    "    \"\"\"\n",
-    "    #freq = 10/(dt*npts)\n",
-    "    #decay = 1/(dt*npts)\n",
-    "    freq = 10\n",
-    "    decay = 1\n",
-    "    time = np.linspace(0, npts*dt, npts, endpoint=False)\n",
-    "    signal = np.exp(-decay*time)*np.sin(2*np.pi*freq*time)\n",
-    "    noise = 0.1*np.random.randn(npts)\n",
-    "    return signal + noise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "class TimeTrace(ParameterWithSetpoints):\n",
-    "    \n",
-    "    def get_raw(self):\n",
-    "        npts = self.root_instrument.npts()\n",
-    "        dt = self.root_instrument.dt()\n",
-    "        \n",
-    "        return timetrace(npts, dt)\n",
-    "    \n",
-    "\n",
-    "class Periodogram(ParameterWithSetpoints):\n",
-    "    \n",
-    "    def get_raw(self):\n",
-    "        npts = self.root_instrument.npts()\n",
-    "        dt = self.root_instrument.dt()\n",
-    "        \n",
-    "        tt = self.root_instrument.trace()\n",
-    "        \n",
-    "        return np.abs(np.fft.fft(tt))**2\n",
-    "        \n",
-    "        \n",
-    "class TimeAxis(Parameter):\n",
-    "\n",
-    "    def get_raw(self):\n",
-    "        npts = self.root_instrument.npts()\n",
-    "        dt = self.root_instrument.dt()\n",
-    "        return np.linspace(0, dt*npts, npts, endpoint=False)\n",
-    "\n",
-    "    \n",
-    "class FrequencyAxis(Parameter):\n",
-    "    \n",
-    "    def get_raw(self):\n",
-    "        npts = self.root_instrument.npts()\n",
-    "        dt = self.root_instrument.dt()\n",
-    "\n",
-    "        return np.linspace(0, 1/dt, npts)\n",
-    "    \n",
-    "        \n",
-    "class OzzyLowScope(Instrument):\n",
-    "    \n",
-    "    def __init__(self, name, **kwargs):\n",
-    "        \n",
-    "        super().__init__(name, **kwargs)\n",
-    "        \n",
-    "        self.add_parameter(name='npts',\n",
-    "                           initial_value=500,\n",
-    "                           label='Number of points',\n",
-    "                           get_cmd=None,\n",
-    "                           set_cmd=None)\n",
-    "        \n",
-    "        self.add_parameter(name='dt',\n",
-    "                           initial_value=1e-3,\n",
-    "                           label='Time resolution',\n",
-    "                           unit='s',\n",
-    "                           get_cmd=None,\n",
-    "                           set_cmd=None)\n",
-    "        \n",
-    "        self.add_parameter(name='time_axis',\n",
-    "                           label='Time',\n",
-    "                           unit='s',\n",
-    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
-    "                           parameter_class=TimeAxis)\n",
-    "        \n",
-    "        self.add_parameter(name='freq_axis',\n",
-    "                           label='Frequency',\n",
-    "                           unit='Hz',\n",
-    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
-    "                           parameter_class=FrequencyAxis)\n",
-    "        \n",
-    "        self.add_parameter(name='trace',\n",
-    "                           label='Signal',\n",
-    "                           unit='V',\n",
-    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
-    "                           setpoints=(self.time_axis,),\n",
-    "                           parameter_class=TimeTrace)\n",
-    "        \n",
-    "        self.add_parameter(name='periodogram',\n",
-    "                           label='Periodogram',\n",
-    "                           unit='V^2/Hz',\n",
-    "                           vals=vals.Arrays(shape=(self.npts,)),\n",
-    "                           setpoints=(self.freq_axis,),\n",
-    "                           parameter_class=Periodogram)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "osc = OzzyLowScope('osc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tutorial_exp#no sample#102@C:\\Users\\jenielse/experiments.db\n",
-       "-----------------------------------------------------------"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "initialise_database()\n",
-    "new_experiment(name='tutorial_exp', sample_name=\"no sample\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Measurement 1: Time Trace"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 314\n"
-     ]
-    }
-   ],
-   "source": [
-    "timemeas = Measurement()\n",
-    "timemeas.register_parameter(osc.trace)\n",
-    "\n",
-    "osc.dt(0.001)\n",
-    "\n",
-    "with timemeas.run() as datasaver:\n",
-    "    datasaver.add_result(*expand_setpoints_helper(osc.trace))\n",
-    "    \n",
-    "dataid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 315\n"
-     ]
-    }
-   ],
-   "source": [
-    "osc.dt(0.01)  # make the trace 10 times longer\n",
-    "\n",
-    "with timemeas.run() as datasaver:\n",
-    "    datasaver.add_result(*expand_setpoints_helper(osc.trace))\n",
-    "    \n",
-    "dataid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Measurement 2: Periodogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 316\n"
-     ]
-    }
-   ],
-   "source": [
-    "freqmeas = Measurement()\n",
-    "freqmeas.register_parameter(osc.periodogram)\n",
-    "\n",
-    "osc.dt(0.01)\n",
-    "\n",
-    "with freqmeas.run() as datasaver:\n",
-    "    datasaver.add_result(*expand_setpoints_helper(osc.periodogram))\n",
-    "    \n",
-    "dataid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "axs, cbax = plot_by_id(dataid)\n",
-    "aa = axs[0]\n",
-    "aa.set_yscale('log')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just for the fun of it, let's make a measurement with the averaged periodogram."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 317\n"
-     ]
-    }
-   ],
-   "source": [
-    "no_of_avgs = 100\n",
-    "\n",
-    "with freqmeas.run() as datasaver:\n",
-    "    \n",
-    "    temp_per = osc.periodogram()\n",
-    "    \n",
-    "    for _ in range(no_of_avgs-1):\n",
-    "        temp_per += osc.periodogram()\n",
-    "        \n",
-    "    datasaver.add_result((osc.periodogram, temp_per/no_of_avgs),\n",
-    "                         (osc.freq_axis, osc.freq_axis.get()))\n",
-    "\n",
-    "dataid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "axs, cbax = plot_by_id(dataid)\n",
-    "aa = axs[0]\n",
-    "aa.set_yscale('log')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Measurement 3: 2D Sweeping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 318\n"
-     ]
-    }
-   ],
-   "source": [
-    "meas = Measurement()\n",
-    "meas.register_parameter(osc.npts)\n",
-    "meas.register_parameter(osc.trace, setpoints=[osc.npts])\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "\n",
-    "    osc.dt(0.001)\n",
-    "    \n",
-    "    for npts in [200, 400, 600, 800, 1000, 1200]:\n",
-    "        osc.npts(npts)\n",
-    "        datasaver.add_result(*expand_setpoints_helper(osc.trace),\n",
-    "                             (osc.npts, osc.npts()))\n",
-    "        \n",
-    "dataid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 8deddb11c0534bcac4bc5612113bc02e16a08bf1 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 22 Jan 2019 14:13:28 +0100
Subject: [PATCH 640/719] add unit tests for expanding

---
 qcodes/tests/test_parameter_with_setpoints.py | 57 ++++++++++++++++++-
 1 file changed, 56 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index def67b65823..fa0a9c18ed0 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -1,7 +1,9 @@
+import numpy as np
 from numpy.random import rand
 import pytest
 
-from qcodes.instrument.parameter import ParameterWithSetpoints, Parameter
+from qcodes.instrument.parameter import ParameterWithSetpoints, Parameter,\
+    expand_setpoints_helper
 import qcodes.utils.validators as vals
 
 
@@ -304,3 +306,56 @@ def test_validation_one_sp_dim_missing():
         param_sp_without_shape.validate_consistent_shape()
     with pytest.raises(ValueError, match=expected_err_msg):
         param_sp_without_shape.validate(param_sp_without_shape.get())
+
+
+def test_expand_setpoints():
+    """
+    Test that the setpoints expannder helper function works correctly
+    """
+
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(20)
+
+    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
+                            vals=vals.Arrays(shape=(n_points_1,)))
+    setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: rand(n_points_2()),
+                            vals=vals.Arrays(shape=(n_points_2,)))
+
+    param_with_setpoints_1 = ParameterWithSetpoints('param_1',
+                                                    get_cmd=lambda:
+                                                    rand(n_points_1()),
+                                                    setpoints=(setpoints_1,),
+                                                    vals=vals.Arrays(
+                                                        shape=(n_points_1,)))
+
+    data = expand_setpoints_helper(param_with_setpoints_1)
+
+    assert len(data) == 2
+    assert len(data[0][1]) == len(data[1][1])
+
+    param_with_setpoints_2 = ParameterWithSetpoints('param_2',
+                                                    get_cmd=lambda:
+                                                    rand(n_points_1(),
+                                                         n_points_2()),
+                                                    vals=vals.Arrays(
+                                                        shape=(n_points_1,
+                                                               n_points_2)))
+    param_with_setpoints_2.setpoints = (setpoints_1, setpoints_2)
+
+    data = expand_setpoints_helper(param_with_setpoints_2)
+
+    assert len(data) == 3
+    assert data[0][1].shape == data[1][1].shape
+    assert data[0][1].shape == data[2][1].shape
+
+    sp1 = data[0][1]
+    sp2 = data[1][1]
+    # the first set of setpoints should be repeated along the second axis
+    for i in range(sp1.shape[1]):
+        np.testing.assert_array_equal(sp1[:, 0], sp1[:, i])
+    # the second set of setpoints should be repeated along the first axis
+    for i in range(sp2.shape[0]):
+        np.testing.assert_array_equal(sp2[0, :], sp2[i, :])

From 25ca8488355200fd294f34c3e04a40893482f85e Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 22 Jan 2019 14:52:43 +0100
Subject: [PATCH 641/719] Test actual content

---
 qcodes/tests/test_parameter_with_setpoints.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index fa0a9c18ed0..e48513625a2 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -319,9 +319,9 @@ def test_expand_setpoints():
     n_points_1.set(10)
     n_points_2.set(20)
 
-    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: rand(n_points_1()),
+    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: np.arange(n_points_1()),
                             vals=vals.Arrays(shape=(n_points_1,)))
-    setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: rand(n_points_2()),
+    setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: np.arange(n_points_2()),
                             vals=vals.Arrays(shape=(n_points_2,)))
 
     param_with_setpoints_1 = ParameterWithSetpoints('param_1',
@@ -355,7 +355,7 @@ def test_expand_setpoints():
     sp2 = data[1][1]
     # the first set of setpoints should be repeated along the second axis
     for i in range(sp1.shape[1]):
-        np.testing.assert_array_equal(sp1[:, 0], sp1[:, i])
+        np.testing.assert_array_equal(sp1[:, i], np.arange(sp1.shape[0]))
     # the second set of setpoints should be repeated along the first axis
     for i in range(sp2.shape[0]):
-        np.testing.assert_array_equal(sp2[0, :], sp2[i, :])
+        np.testing.assert_array_equal(sp2[i, :], np.arange(sp1.shape[1]))

From 98ace9a5bb973642959fdbd3cb8e58818808102d Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 22 Jan 2019 14:58:48 +0100
Subject: [PATCH 642/719] Improve test by adding a 3 dim example too

---
 qcodes/tests/test_parameter_with_setpoints.py | 36 +++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index e48513625a2..184b3d099f0 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -315,14 +315,19 @@ def test_expand_setpoints():
 
     n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
     n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+    n_points_3 = Parameter('n_points_3', set_cmd=None, vals=vals.Ints())
 
     n_points_1.set(10)
     n_points_2.set(20)
+    n_points_3.set(15)
 
     setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: np.arange(n_points_1()),
                             vals=vals.Arrays(shape=(n_points_1,)))
     setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: np.arange(n_points_2()),
                             vals=vals.Arrays(shape=(n_points_2,)))
+    setpoints_3 = Parameter('setpoints_3', get_cmd=lambda: np.arange(n_points_3()),
+                            vals=vals.Arrays(shape=(n_points_3,)))
+
 
     param_with_setpoints_1 = ParameterWithSetpoints('param_1',
                                                     get_cmd=lambda:
@@ -359,3 +364,34 @@ def test_expand_setpoints():
     # the second set of setpoints should be repeated along the first axis
     for i in range(sp2.shape[0]):
         np.testing.assert_array_equal(sp2[i, :], np.arange(sp1.shape[1]))
+
+
+    param_with_setpoints_3 = ParameterWithSetpoints('param_2',
+                                                    get_cmd=lambda:
+                                                    rand(n_points_1(),
+                                                         n_points_2(),
+                                                         n_points_3()),
+                                                    vals=vals.Arrays(
+                                                        shape=(n_points_1,
+                                                               n_points_2,
+                                                               n_points_3)))
+    param_with_setpoints_3.setpoints = (setpoints_1, setpoints_2, setpoints_3)
+    data = expand_setpoints_helper(param_with_setpoints_3)
+    assert len(data) == 4
+    assert data[0][1].shape == data[1][1].shape
+    assert data[0][1].shape == data[2][1].shape
+    assert data[0][1].shape == data[3][1].shape
+
+    sp1 = data[0][1]
+    for i in range(sp1.shape[1]):
+        for j in range(sp1.shape[2]):
+            np.testing.assert_array_equal(sp1[:, i, j], np.arange(sp1.shape[0]))
+    sp2 = data[1][1]
+    for i in range(sp2.shape[0]):
+        for j in range(sp2.shape[2]):
+            np.testing.assert_array_equal(sp2[i, :, j], np.arange(sp2.shape[1]))
+
+    sp3 = data[2][1]
+    for i in range(sp3.shape[0]):
+        for j in range(sp3.shape[1]):
+            np.testing.assert_array_equal(sp3[i, j, :], np.arange(sp3.shape[2]))

From 7cb8f23388da8b94606d37ac2fa88dea7225a25c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 22 Jan 2019 15:29:39 +0100
Subject: [PATCH 643/719] Remove helper parameter from parameter class

---
 qcodes/instrument/parameter.py   | 16 ----------------
 qcodes/tests/instrument_mocks.py | 18 +++++++++++++++++-
 2 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 4a0626b8a04..e96d908ba12 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1043,22 +1043,6 @@ def validate(self, value: ParamDataType) -> None:
         super().validate(value)
 
 
-class GeneratedSetPoints(Parameter):
-    """
-    A parameter that generates a setpoint array from start, stop and num points
-    parameters.
-    """
-    def __init__(self, startparam, stopparam, numpointsparam, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._startparam = startparam
-        self._stopparam = stopparam
-        self._numpointsparam = numpointsparam
-
-    def get_raw(self):
-        return numpy.linspace(self._startparam(), self._stopparam(),
-                              self._numpointsparam())
-
-
 class ArrayParameter(_BaseParameter):
     """
     A gettable parameter that returns an array of values.
diff --git a/qcodes/tests/instrument_mocks.py b/qcodes/tests/instrument_mocks.py
index 8a02ffebaed..9f942559bda 100644
--- a/qcodes/tests/instrument_mocks.py
+++ b/qcodes/tests/instrument_mocks.py
@@ -7,7 +7,7 @@
 from qcodes.instrument.base import Instrument
 from qcodes.utils.validators import Numbers, Arrays
 from qcodes.instrument.parameter import MultiParameter, Parameter, \
-    ArrayParameter, ParameterWithSetpoints, GeneratedSetPoints
+    ArrayParameter, ParameterWithSetpoints
 from qcodes.instrument.channel import InstrumentChannel, ChannelList
 import random
 
@@ -355,6 +355,22 @@ def get_raw(self):
         return item
 
 
+class GeneratedSetPoints(Parameter):
+    """
+    A parameter that generates a setpoint array from start, stop and num points
+    parameters.
+    """
+    def __init__(self, startparam, stopparam, numpointsparam, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._startparam = startparam
+        self._stopparam = stopparam
+        self._numpointsparam = numpointsparam
+
+    def get_raw(self):
+        return np.linspace(self._startparam(), self._stopparam(),
+                              self._numpointsparam())
+
+
 class DummyParameterWithSetpoints1D(ParameterWithSetpoints):
     """
     Dummy parameter that returns data with a shape based on the

From 055fd9be7623f9f90aaa7c88d074d9ad8dcc57b4 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 22 Jan 2019 15:30:26 +0100
Subject: [PATCH 644/719] Update notebook to not use removed parameter class

---
 ...le-Example-of-ParameterWithSetpoints.ipynb | 260 +++++++++---------
 1 file changed, 125 insertions(+), 135 deletions(-)

diff --git a/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb b/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
index f441d73557a..b9d73f4ce55 100644
--- a/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
+++ b/docs/examples/writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb
@@ -45,7 +45,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qcodes.instrument.parameter import ParameterWithSetpoints, GeneratedSetPoints, Parameter\n",
+    "from qcodes.instrument.parameter import ParameterWithSetpoints, Parameter\n",
     "from qcodes.instrument.parameter import expand_setpoints_helper"
    ]
   },
@@ -69,6 +69,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "class GeneratedSetPoints(Parameter):\n",
+    "    \"\"\"\n",
+    "    A parameter that generates a setpoint array from start, stop and num points\n",
+    "    parameters.\n",
+    "    \"\"\"\n",
+    "    def __init__(self, startparam, stopparam, numpointsparam, *args, **kwargs):\n",
+    "        super().__init__(*args, **kwargs)\n",
+    "        self._startparam = startparam\n",
+    "        self._stopparam = stopparam\n",
+    "        self._numpointsparam = numpointsparam\n",
+    "\n",
+    "    def get_raw(self):\n",
+    "        return np.linspace(self._startparam(), self._stopparam(),\n",
+    "                              self._numpointsparam())\n",
+    "\n",
     "class DummyArray(ParameterWithSetpoints):\n",
     "    \n",
     "    def get_raw(self):\n",
@@ -151,7 +166,7 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#113@C:\\Users\\jenielse/experiments.db\n",
+       "tutorial_exp#no sample#117@C:\\Users\\jenielse/experiments.db\n",
        "-----------------------------------------------------------"
       ]
      },
@@ -364,7 +379,7 @@
     {
      "data": {
       "text/plain": [
-       "(<qcodes.instrument.parameter.GeneratedSetPoints: freq_axis at 2390766145264>,)"
+       "(<__main__.GeneratedSetPoints: freq_axis at 2072579745664>,)"
       ]
      },
      "execution_count": 16,
@@ -415,7 +430,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 327\n"
+      "Starting experimental run with id: 336\n"
      ]
     }
    ],
@@ -445,7 +460,7 @@
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x22ca4c55f98>], [None])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1e28f64dc50>], [None])"
       ]
      },
      "execution_count": 19,
@@ -454,7 +469,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -486,7 +501,7 @@
     {
      "data": {
       "text/plain": [
-       "[(<qcodes.instrument.parameter.GeneratedSetPoints: freq_axis at 2390766145264>,\n",
+       "[(<__main__.GeneratedSetPoints: freq_axis at 2072579745664>,\n",
        "  array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
        "          11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
        "          22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,\n",
@@ -533,133 +548,108 @@
        "         473., 474., 475., 476., 477., 478., 479., 480., 481., 482., 483.,\n",
        "         484., 485., 486., 487., 488., 489., 490., 491., 492., 493., 494.,\n",
        "         495., 496., 497., 498., 499., 500.])),\n",
-       " (<__main__.DummyArray: spectrum at 2390766145376>,\n",
-       "  array([9.99565739e-01, 3.41410534e-01, 3.99354826e-02, 1.40788221e-01,\n",
-       "         3.15613693e-01, 7.28015631e-01, 6.33773056e-01, 4.25418735e-01,\n",
-       "         7.94719526e-01, 9.61731268e-01, 9.75351491e-01, 6.22680081e-01,\n",
-       "         7.30689866e-01, 7.44535004e-01, 1.81936308e-01, 4.97046986e-01,\n",
-       "         8.84645545e-01, 3.36639293e-01, 9.63264159e-01, 7.75776116e-01,\n",
-       "         9.30999126e-01, 5.09147388e-02, 8.07309008e-01, 5.83048231e-01,\n",
-       "         8.30254178e-01, 3.55403452e-01, 9.29724922e-01, 7.55007191e-01,\n",
-       "         7.42848906e-01, 2.39841281e-01, 2.53890657e-02, 2.98199498e-01,\n",
-       "         2.17006527e-01, 5.45356014e-01, 6.19503804e-01, 5.52957884e-01,\n",
-       "         3.71367390e-01, 1.74369637e-01, 9.67179968e-01, 5.57432914e-01,\n",
-       "         1.81509404e-02, 1.96939687e-01, 4.47661103e-01, 6.82216425e-01,\n",
-       "         8.38756146e-02, 1.48217835e-01, 4.22221137e-01, 5.70910256e-01,\n",
-       "         7.91652764e-02, 2.64279322e-01, 2.13251130e-01, 5.47825630e-01,\n",
-       "         6.26465259e-01, 3.44912193e-01, 8.74813028e-01, 3.76769481e-01,\n",
-       "         6.68953633e-01, 3.34877444e-01, 6.12556740e-01, 4.55317971e-01,\n",
-       "         7.36250566e-02, 2.29914116e-01, 5.67923192e-01, 2.54977559e-02,\n",
-       "         2.81176076e-01, 3.11385813e-02, 7.55621595e-01, 3.45681008e-01,\n",
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-       "         7.05178455e-01, 4.21497225e-01, 5.36699812e-01, 4.14430358e-01,\n",
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-       "         7.35322096e-01, 1.97371125e-01, 4.93550132e-01, 9.07098820e-01,\n",
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-       "         2.02351666e-01, 6.02480394e-01, 7.63139009e-01, 4.59794785e-01,\n",
-       "         4.48857895e-01, 3.09285048e-01, 3.15328615e-01, 2.48970026e-01,\n",
-       "         7.82595438e-01, 3.43069627e-01, 2.46129472e-01, 9.27906688e-01,\n",
-       "         3.62496903e-01, 9.36183165e-01, 5.30122696e-01, 4.71887824e-01,\n",
-       "         8.98885207e-01, 1.99440087e-01, 9.30878418e-01, 5.80235636e-03,\n",
-       "         4.85949268e-01, 1.21253358e-01, 1.05663840e-01, 4.45995909e-01,\n",
-       "         1.32285697e-01, 1.88051449e-01, 5.66314432e-01, 1.96391013e-01,\n",
-       "         6.44853441e-01, 7.44730617e-01, 9.33214530e-01, 3.65254472e-01,\n",
-       "         5.34042118e-01, 4.38401666e-01, 8.00591221e-01, 4.53647268e-01,\n",
-       "         9.53921476e-01, 2.99292368e-01, 3.58211243e-01, 8.88666696e-02,\n",
-       "         7.31102742e-01, 2.74750012e-01, 2.08909460e-01, 1.34151145e-01,\n",
-       "         5.40630994e-01, 5.33618975e-01, 5.85910554e-01, 6.62181534e-02,\n",
-       "         4.39061197e-01, 5.43240704e-01, 2.83730296e-01, 6.53141042e-01,\n",
-       "         5.13939243e-01, 4.01024990e-01, 9.90300493e-01, 9.59141680e-01,\n",
-       "         1.46507223e-01, 2.71011309e-01, 5.53086821e-01, 3.02678580e-01,\n",
-       "         6.67713948e-01, 4.19330128e-01, 7.97884870e-01, 6.22200535e-01,\n",
-       "         6.41336196e-01, 5.32021719e-01, 9.69912009e-01, 5.33322177e-01,\n",
-       "         9.31792556e-01, 1.31678130e-01, 2.97724981e-01, 5.84659648e-01,\n",
-       "         7.74580017e-01, 9.55835743e-01, 7.26774085e-01, 3.44820833e-01,\n",
-       "         9.11520346e-01, 3.09289513e-01, 4.48603620e-01, 7.38350720e-01,\n",
-       "         7.15318239e-01, 6.31576295e-02, 9.53652028e-01, 8.42154221e-01,\n",
-       "         6.15929309e-01, 9.65018511e-01, 8.27526534e-01, 8.60006983e-01,\n",
-       "         9.65526594e-01, 2.11791838e-01, 3.71902870e-01, 9.83151064e-01,\n",
-       "         8.26111626e-01, 2.22077570e-01, 4.95228585e-02, 5.04174503e-01,\n",
-       "         8.15431883e-01, 1.50511340e-02, 1.03113214e-01, 8.62051326e-01,\n",
-       "         9.37253043e-01, 6.45135686e-01, 8.13276210e-01, 9.04238921e-01,\n",
-       "         3.46383319e-01, 3.83622610e-01, 4.39520093e-02, 6.02628857e-01,\n",
-       "         1.37431584e-01, 9.75922110e-01, 4.02342098e-01, 9.72736764e-01,\n",
-       "         3.46422493e-02, 8.15245076e-01, 7.02859924e-02, 8.41651799e-01,\n",
-       "         7.35915944e-02, 2.24614090e-01, 7.94129711e-01, 3.95945647e-01,\n",
-       "         4.72410498e-01, 6.54710352e-01, 8.00370576e-01, 5.99576342e-01,\n",
-       "         6.11392722e-01, 1.38540389e-01, 5.36475042e-01, 4.29769602e-01,\n",
-       "         3.63185576e-02, 4.39795715e-01, 1.51145805e-01, 7.85529960e-01,\n",
-       "         2.09436292e-01, 1.62671340e-01, 7.77973688e-01, 3.42470450e-01,\n",
-       "         6.36011358e-01, 5.65356534e-01, 7.06493270e-01, 1.25917957e-01,\n",
-       "         2.34300213e-01, 6.52203562e-01, 5.68346343e-01, 2.85112367e-01,\n",
-       "         4.04118591e-01, 3.12736602e-01, 5.13240793e-01, 5.92217830e-01,\n",
-       "         1.38846864e-01, 5.98383214e-01, 4.93381466e-01, 1.58642649e-01,\n",
-       "         4.47102143e-01, 2.96732893e-01, 2.76475495e-02, 5.23742750e-01,\n",
-       "         8.29322816e-01, 9.04275946e-01, 8.99065887e-01, 5.10491632e-01,\n",
-       "         2.28652128e-02, 9.74347293e-01, 5.40687677e-01, 5.35116990e-01,\n",
-       "         8.99989930e-01, 3.80631425e-01, 1.84888208e-01, 5.28368299e-01,\n",
-       "         4.83096250e-01, 8.52747960e-01, 7.45884962e-01, 7.57777901e-01,\n",
-       "         8.44041988e-01, 5.60282248e-01, 2.56056230e-01, 3.12732884e-01,\n",
-       "         4.98863229e-01, 8.88598038e-01, 5.23785822e-01, 9.69375963e-01,\n",
-       "         4.56801666e-02, 3.33066786e-02, 7.19046639e-01, 6.61791910e-01,\n",
-       "         8.29852907e-01, 8.99954459e-01, 9.42031477e-01, 6.24357383e-02,\n",
-       "         6.36244850e-01, 6.85687687e-01, 9.43497015e-01, 7.80660161e-01,\n",
-       "         5.40653727e-02, 8.31770798e-01, 5.63136226e-01, 9.42273672e-01,\n",
-       "         3.99474067e-01, 1.56019596e-01, 4.93322139e-01, 2.55309361e-01,\n",
-       "         3.29871694e-02, 2.91325615e-01, 4.00268302e-01, 8.85430666e-01,\n",
-       "         7.05016503e-01, 7.73273740e-01, 4.99769528e-01, 7.12745196e-01,\n",
-       "         9.84614743e-01, 1.80047561e-01, 1.89594444e-01, 2.12153721e-01,\n",
-       "         1.86603275e-01, 5.51526333e-01, 7.77398967e-01, 5.38552491e-01,\n",
-       "         3.17530045e-01, 7.53669384e-01, 2.67014370e-01, 1.78044634e-01,\n",
-       "         3.07581486e-01, 8.80043188e-01, 5.87604233e-01, 5.21717066e-01,\n",
-       "         9.11419690e-01, 7.50522873e-01, 4.06413383e-01, 4.69524466e-01,\n",
-       "         3.31204756e-01, 1.86158271e-01, 6.94798146e-01, 6.89873564e-01,\n",
-       "         2.93527570e-01, 6.65736905e-01, 4.59224702e-01, 6.24971805e-01,\n",
-       "         8.05249807e-01, 1.41016520e-01, 3.36891598e-01, 1.60068945e-04,\n",
-       "         4.46217609e-02, 5.00118941e-01, 2.24513027e-01, 7.57107689e-01,\n",
-       "         2.73578667e-01, 5.50661065e-01, 6.34878404e-01, 6.14719915e-01,\n",
-       "         4.48753943e-01, 3.69149028e-01, 8.78733083e-01, 2.06124283e-01,\n",
-       "         8.07536354e-01, 1.47461713e-01, 9.21496176e-02, 9.36120310e-02,\n",
-       "         7.23257819e-01, 4.83356676e-01, 8.10933785e-01, 4.86625441e-01,\n",
-       "         3.25327354e-01, 3.15575516e-01, 7.51404423e-01, 6.32419671e-01,\n",
-       "         9.98517225e-01, 4.66755026e-01, 4.71491101e-01, 9.03844024e-01,\n",
-       "         2.85615622e-01, 1.58447740e-01, 9.44181381e-01, 8.52534251e-01,\n",
-       "         3.20851872e-01, 9.81898783e-01, 6.76955086e-02, 7.04578525e-01,\n",
-       "         5.50502958e-01, 6.43058346e-01, 1.05994915e-01, 8.38242737e-01,\n",
-       "         7.32299423e-01, 6.80098191e-01, 2.82491453e-01, 1.17841353e-02,\n",
-       "         9.32406843e-02, 1.69652650e-02, 1.96996814e-03, 4.61902088e-01,\n",
-       "         5.08387117e-01, 7.63085205e-01, 8.69900900e-01, 5.30100068e-01,\n",
-       "         3.86444567e-01, 6.98676647e-01, 4.10054942e-01, 3.23854673e-01,\n",
-       "         9.78018718e-01, 4.03827092e-01, 9.71707077e-01, 8.21548158e-01,\n",
-       "         4.53221507e-01, 2.15674959e-01, 7.36859777e-01, 9.89141311e-01,\n",
-       "         4.71907900e-01, 5.55581679e-01, 7.72265047e-01, 4.04337572e-02,\n",
-       "         1.93989802e-01, 8.06078413e-01, 1.80137085e-01, 2.54887847e-01,\n",
-       "         3.87938987e-01, 4.07401628e-01, 5.69662950e-01, 3.14091850e-02,\n",
-       "         8.41129149e-02, 7.85351895e-02, 4.76903631e-01, 8.36711074e-01,\n",
-       "         6.33082371e-01, 9.24847168e-01, 4.15136143e-01, 9.81838838e-01,\n",
-       "         4.45930210e-01, 3.60801272e-01, 1.71282850e-01, 9.38710953e-01,\n",
-       "         4.36674624e-01, 1.86638477e-02, 4.17193024e-01, 3.24773079e-01,\n",
-       "         8.56446942e-01, 8.55979425e-01, 2.43593387e-01, 5.67727569e-03,\n",
-       "         3.37346043e-01, 7.61597656e-01, 1.75936838e-01, 5.64869495e-01,\n",
-       "         3.19844051e-01, 5.50864248e-02, 8.41955884e-01, 9.00095568e-01,\n",
-       "         8.47223469e-01, 2.95250706e-01, 4.42064643e-01, 2.05838150e-01,\n",
-       "         7.97738189e-01, 4.74754482e-01, 8.05965198e-01, 2.28721142e-02,\n",
-       "         1.97183650e-01, 3.62138667e-01, 1.96555145e-01, 9.30542505e-01,\n",
-       "         4.06188646e-02, 7.88223486e-01, 2.45454171e-01, 3.20629459e-01,\n",
-       "         9.28427922e-01, 5.96984264e-01, 1.00474217e-01, 1.74115023e-01,\n",
-       "         3.05847269e-01, 2.75380202e-01, 7.72199193e-01, 1.42540362e-02,\n",
-       "         3.24757775e-01, 1.56542894e-01, 4.05483346e-01, 7.69122902e-01,\n",
-       "         1.40467431e-01, 7.59219506e-01, 1.40852631e-01, 4.66496589e-01,\n",
-       "         1.91075180e-01, 1.80290753e-01, 4.80687931e-01, 9.37127278e-01,\n",
-       "         7.18769072e-01, 6.31174172e-01, 5.64695986e-01, 3.27438447e-01,\n",
-       "         3.37798596e-01, 2.43042117e-01, 9.61556179e-01, 7.98997328e-01,\n",
-       "         7.78154571e-01, 6.84901123e-02, 6.81428228e-01, 3.56675924e-01,\n",
-       "         5.76521790e-02, 4.76279546e-01, 9.41579081e-01, 3.38922558e-01,\n",
-       "         5.62890699e-01, 8.80744049e-01, 1.07252691e-01, 5.21412295e-01,\n",
-       "         1.27897017e-01]))]"
+       " (<__main__.DummyArray: spectrum at 2072579987216>,\n",
+       "  array([0.86536747, 0.29859039, 0.82084698, 0.91368057, 0.61455324,\n",
+       "         0.18364267, 0.45377498, 0.35979627, 0.62712436, 0.89225127,\n",
+       "         0.56338498, 0.95169498, 0.82175451, 0.74232618, 0.32913868,\n",
+       "         0.4578325 , 0.59966019, 0.03037177, 0.57714132, 0.15786797,\n",
+       "         0.2219711 , 0.44814167, 0.88315066, 0.13859247, 0.15520284,\n",
+       "         0.20253144, 0.64010047, 0.75656851, 0.17718142, 0.45524419,\n",
+       "         0.54346346, 0.04026371, 0.34179898, 0.70339426, 0.57158973,\n",
+       "         0.16625723, 0.5396359 , 0.67015621, 0.90118115, 0.62925947,\n",
+       "         0.45778511, 0.80610395, 0.68532727, 0.54664685, 0.14305383,\n",
+       "         0.13837366, 0.95450493, 0.44825993, 0.81401483, 0.5602484 ,\n",
+       "         0.05639603, 0.91964763, 0.95340865, 0.67262527, 0.57929841,\n",
+       "         0.92678493, 0.91057785, 0.60835531, 0.64508078, 0.46733969,\n",
+       "         0.67270952, 0.76534444, 0.12477741, 0.39255348, 0.74525787,\n",
+       "         0.04693258, 0.9077846 , 0.92526121, 0.39263387, 0.04583623,\n",
+       "         0.2141725 , 0.63754963, 0.24370497, 0.74339238, 0.92659933,\n",
+       "         0.85174213, 0.12495432, 0.00990371, 0.12062237, 0.88099688,\n",
+       "         0.82721913, 0.65281902, 0.0367411 , 0.71886092, 0.35385878,\n",
+       "         0.70060947, 0.95994385, 0.43580015, 0.6963726 , 0.82638215,\n",
+       "         0.80941677, 0.43222015, 0.10094638, 0.86046065, 0.98778208,\n",
+       "         0.61608976, 0.62403534, 0.20869156, 0.40583382, 0.49355553,\n",
+       "         0.06361045, 0.05998159, 0.3639887 , 0.78324704, 0.21742799,\n",
+       "         0.24780452, 0.6811572 , 0.23187278, 0.29098442, 0.58719475,\n",
+       "         0.02644414, 0.40015752, 0.55789337, 0.63657434, 0.08839236,\n",
+       "         0.68377633, 0.4886613 , 0.09587301, 0.17681394, 0.49222955,\n",
+       "         0.35228825, 0.62230727, 0.89546786, 0.30719343, 0.61006545,\n",
+       "         0.12746545, 0.60452389, 0.87510249, 0.67984838, 0.41769204,\n",
+       "         0.77700396, 0.19352418, 0.90023443, 0.59682864, 0.18527968,\n",
+       "         0.88034446, 0.62875055, 0.18376827, 0.45533057, 0.86826886,\n",
+       "         0.37840521, 0.68898267, 0.98057476, 0.32008047, 0.6090082 ,\n",
+       "         0.60187326, 0.06323076, 0.63471748, 0.16353518, 0.92128275,\n",
+       "         0.84173413, 0.37044226, 0.53208976, 0.1483618 , 0.63039749,\n",
+       "         0.39664339, 0.36097958, 0.11188719, 0.46465467, 0.252853  ,\n",
+       "         0.39961396, 0.19482905, 0.33277628, 0.21982179, 0.17046042,\n",
+       "         0.38644222, 0.8387503 , 0.44132958, 0.81586627, 0.1572804 ,\n",
+       "         0.10499212, 0.16345951, 0.652634  , 0.45524206, 0.14401507,\n",
+       "         0.1486991 , 0.7562868 , 0.01146383, 0.09177292, 0.60800914,\n",
+       "         0.43472758, 0.54981235, 0.65335513, 0.32328374, 0.29088797,\n",
+       "         0.56010578, 0.66517807, 0.3118759 , 0.29057539, 0.78580382,\n",
+       "         0.34755223, 0.62184899, 0.27581073, 0.68719329, 0.53713185,\n",
+       "         0.52915006, 0.70674643, 0.23495171, 0.08467213, 0.18172241,\n",
+       "         0.05831049, 0.14010368, 0.95477364, 0.27427922, 0.49432461,\n",
+       "         0.94830211, 0.65350994, 0.97720952, 0.38793503, 0.43078532,\n",
+       "         0.60055533, 0.45612298, 0.43525104, 0.45292133, 0.07611186,\n",
+       "         0.54250503, 0.82790802, 0.00107829, 0.74350025, 0.92980432,\n",
+       "         0.82130183, 0.46705448, 0.90780987, 0.39793994, 0.3632405 ,\n",
+       "         0.51481468, 0.53859361, 0.31412174, 0.79974061, 0.24121438,\n",
+       "         0.53013962, 0.72108407, 0.6190634 , 0.2339466 , 0.59343837,\n",
+       "         0.53138341, 0.85308342, 0.94709494, 0.79212733, 0.37422848,\n",
+       "         0.20375092, 0.10770356, 0.10456515, 0.27375365, 0.33802088,\n",
+       "         0.54779838, 0.11996523, 0.12528882, 0.42315329, 0.56398724,\n",
+       "         0.82320195, 0.19436543, 0.27056648, 0.84419055, 0.74141736,\n",
+       "         0.47401059, 0.66412291, 0.91492523, 0.8030796 , 0.38259619,\n",
+       "         0.15071856, 0.74813237, 0.63873298, 0.15212901, 0.72034765,\n",
+       "         0.01065614, 0.11741044, 0.98735841, 0.07387175, 0.81615834,\n",
+       "         0.87307196, 0.63600421, 0.55919491, 0.15403717, 0.53301984,\n",
+       "         0.9927068 , 0.98842399, 0.91134694, 0.26283517, 0.70691037,\n",
+       "         0.2071561 , 0.67756875, 0.65668211, 0.96703373, 0.41125681,\n",
+       "         0.44333588, 0.60419397, 0.33882376, 0.45339681, 0.0497404 ,\n",
+       "         0.06967122, 0.11909174, 0.91881959, 0.15660234, 0.22618712,\n",
+       "         0.24640627, 0.7998274 , 0.96122471, 0.62562886, 0.06707783,\n",
+       "         0.67796038, 0.08845965, 0.28573045, 0.3123472 , 0.81742697,\n",
+       "         0.53646303, 0.77828274, 0.46482159, 0.72699887, 0.91701853,\n",
+       "         0.05253901, 0.57545759, 0.25048138, 0.48379325, 0.78162697,\n",
+       "         0.58398544, 0.41698415, 0.6284954 , 0.94637367, 0.64842507,\n",
+       "         0.81973645, 0.04179437, 0.99382408, 0.23659296, 0.35950726,\n",
+       "         0.83701923, 0.37452202, 0.01473713, 0.44214445, 0.85291473,\n",
+       "         0.5641682 , 0.56499693, 0.37936776, 0.23570418, 0.44752636,\n",
+       "         0.8568403 , 0.55616941, 0.70843247, 0.08543703, 0.28762696,\n",
+       "         0.6713547 , 0.56511386, 0.31827232, 0.73545876, 0.57949861,\n",
+       "         0.4178843 , 0.74006806, 0.79989244, 0.70625507, 0.7194835 ,\n",
+       "         0.17096816, 0.4111622 , 0.3359209 , 0.25933937, 0.74188318,\n",
+       "         0.29583468, 0.15370529, 0.38448631, 0.26009102, 0.36529022,\n",
+       "         0.44504132, 0.89064145, 0.68751826, 0.96864814, 0.07699793,\n",
+       "         0.04650828, 0.44275895, 0.63232553, 0.90421849, 0.00306439,\n",
+       "         0.68610544, 0.24489545, 0.45530468, 0.29433935, 0.90384657,\n",
+       "         0.32905761, 0.44558471, 0.28541469, 0.03609067, 0.74669424,\n",
+       "         0.62205441, 0.903832  , 0.77220628, 0.38832139, 0.366569  ,\n",
+       "         0.68272105, 0.9922684 , 0.7013004 , 0.55940211, 0.08119379,\n",
+       "         0.23006435, 0.50410485, 0.22791847, 0.06294831, 0.48371637,\n",
+       "         0.75252168, 0.06348189, 0.60715702, 0.90961961, 0.56000527,\n",
+       "         0.67157972, 0.05222022, 0.59808924, 0.25884604, 0.02088173,\n",
+       "         0.71407818, 0.62914133, 0.83651805, 0.56325101, 0.63452185,\n",
+       "         0.57889954, 0.95608732, 0.30560947, 0.08407796, 0.15896502,\n",
+       "         0.4146791 , 0.85217107, 0.3462493 , 0.91028986, 0.62092398,\n",
+       "         0.7435559 , 0.50523086, 0.21044469, 0.83365079, 0.94989624,\n",
+       "         0.51238926, 0.80846891, 0.05752002, 0.84009213, 0.52091394,\n",
+       "         0.23221664, 0.23068129, 0.25806962, 0.95441725, 0.55393211,\n",
+       "         0.76450718, 0.68608248, 0.94106054, 0.53812395, 0.82403649,\n",
+       "         0.55135986, 0.61844135, 0.23139451, 0.87328949, 0.1879685 ,\n",
+       "         0.5336624 , 0.01587257, 0.83236641, 0.74359308, 0.48695279,\n",
+       "         0.94892595, 0.53603039, 0.83163792, 0.02827259, 0.81216119,\n",
+       "         0.34430425, 0.25886725, 0.25800136, 0.7950081 , 0.84169572,\n",
+       "         0.4912698 , 0.35440899, 0.02880035, 0.19655135, 0.93784968,\n",
+       "         0.04121133, 0.35014399, 0.92349564, 0.47462598, 0.40827229,\n",
+       "         0.54059349, 0.75617674, 0.07043407, 0.51481538, 0.44201303,\n",
+       "         0.38643611, 0.87693167, 0.18692534, 0.26012371, 0.63277901,\n",
+       "         0.1216675 , 0.62759165, 0.72515053, 0.95134081, 0.84278708,\n",
+       "         0.7225574 , 0.24811009, 0.65825326, 0.37135144, 0.94878294,\n",
+       "         0.85332332, 0.98069609, 0.16651845, 0.82405489, 0.72917224,\n",
+       "         0.33358628, 0.76275719, 0.48205808, 0.12139253, 0.72872294,\n",
+       "         0.60048868]))]"
       ]
      },
      "execution_count": 20,
@@ -680,7 +670,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 328\n"
+      "Starting experimental run with id: 337\n"
      ]
     }
    ],

From c3c2aba634bf1751e9887d2aec0c298d1682ff8b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 22 Jan 2019 16:03:02 +0100
Subject: [PATCH 645/719] Add comment

---
 qcodes/instrument/parameter.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index afa08c4772a..7881f64f0ef 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -268,6 +268,9 @@ def __init__(self, name: str,
         # Specify time of last set operation, used when comparing to delay to
         # check if additional waiting time is needed before next set
         self._t_last_set = time.perf_counter()
+        # should we call validate when getting data. default to False
+        # intended to be changed in a subclass if you want the subclass
+        # to perform a validation on get
         self._validate_on_get = False
 
     @abstractmethod

From 81a835a740a347044beec5ef37da4b25bfd24d4d Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 24 Jan 2019 09:46:46 +0100
Subject: [PATCH 646/719] Update qcodes/dataset/measurements.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/dataset/measurements.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index d2bcc5fa59c..21147c2545b 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -25,7 +25,7 @@
 scalar_res_types = Union[str, int, float, np.dtype]
 res_type = Tuple[Union[_BaseParameter, str],
                  Union[scalar_res_types, np.ndarray,
-                 Sequence[scalar_res_types]]]
+                       Sequence[scalar_res_types]]]
 setpoints_type = Sequence[Union[str, _BaseParameter]]
 numeric_types = Union[float, int]
 

From 3550efb8f756410ad1897d9f78472a1b4e7a0992 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 24 Jan 2019 09:48:30 +0100
Subject: [PATCH 647/719] Update qcodes/tests/test_parameter_with_setpoints.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/tests/test_parameter_with_setpoints.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index 184b3d099f0..dacdec55466 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -310,7 +310,7 @@ def test_validation_one_sp_dim_missing():
 
 def test_expand_setpoints():
     """
-    Test that the setpoints expannder helper function works correctly
+    Test that the setpoints expander helper function works correctly
     """
 
     n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())

From 3b232ffdf07c0961a88f145a1317837f8d918808 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 24 Jan 2019 09:56:01 +0100
Subject: [PATCH 648/719] Update qcodes/dataset/measurements.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/dataset/measurements.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index 21147c2545b..3c33a04558e 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -762,7 +762,7 @@ def _register_parameter_with_setpoints(self,
 
             self.parameters[spname] = spparamspec
 
-            my_setpoints += [spname]
+            my_setpoints.append(spname)
 
         self._register_parameter(name,
                                  parameter.label,

From 908c8410e09fb1bdf8a9b53c941f3ffdc784a0bd Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 24 Jan 2019 10:26:05 +0100
Subject: [PATCH 649/719] Update qcodes/instrument/parameter.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 7881f64f0ef..af487cd5eb5 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -562,7 +562,7 @@ def step(self) -> Optional[Number]:
         return self._step
 
     @step.setter
-    def step(self, step: Number):
+    def step(self, step: Optional[Number]):
         """
         Configure whether this Parameter uses steps during set operations.
         If step is a positive number, this is the maximum value change

From 2c18017c5fa5b773fe2e64c43bf5805b90f21935 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 10:32:21 +0100
Subject: [PATCH 650/719] Clarify docstring

---
 qcodes/instrument/parameter.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index af487cd5eb5..362367e1a42 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -568,17 +568,18 @@ def step(self, step: Optional[Number]):
         If step is a positive number, this is the maximum value change
         allowed in one hardware call, so a single set can result in many
         calls to the hardware if the starting value is far from the target.
+        If None stepping will not be used.
 
         Args:
-            step (Union[int, float]): A positive number, the largest change
+            step: A positive number or None, the largest change
                 allowed in one call. All but the final change will attempt to
                 change by +/- step exactly
 
         Raises:
-            TypeError: if step is not numeric
+            TypeError: if step is not numeric or None
             ValueError: if step is negative
-            TypeError:  if step is not integer for an integer parameter
-            TypeError: if step is not a number
+            TypeError:  if step is not integer or None for an integer parameter
+            TypeError: if step is not a number on None
         """
         if step is None:
             self._step: Optional[Number] = step

From d5ebf3e45eaa8057238d37eb5bbf64dce246d207 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 10:32:52 +0100
Subject: [PATCH 651/719] More clarification of docstring

---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 362367e1a42..03c5700eac3 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -568,7 +568,7 @@ def step(self, step: Optional[Number]):
         If step is a positive number, this is the maximum value change
         allowed in one hardware call, so a single set can result in many
         calls to the hardware if the starting value is far from the target.
-        If None stepping will not be used.
+        If step is None stepping will not be used.
 
         Args:
             step: A positive number or None, the largest change

From f94e3f637e578f652b810ea77bfd2d5e79487748 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 10:37:54 +0100
Subject: [PATCH 652/719] Replace shape_evaluated with shape_unevalued to not
 change default behaviour

---
 qcodes/instrument/parameter.py | 6 +++---
 qcodes/utils/validators.py     | 8 ++++----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 03c5700eac3..e1013be348d 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1005,7 +1005,7 @@ def validate_consistent_shape(self) -> None:
             raise ValueError(f"Can only validate shapes for parameters "
                              f"with Arrays validator. {self.name} does "
                              f"not have an Arrays validator.")
-        output_shape = self.vals.shape
+        output_shape = self.vals.shape_unevaluated
         setpoints_shape_list: List[Optional[Union[int, Callable[[], int]]]] = []
         for sp in self.setpoints:
             if not isinstance(sp.vals, Arrays):
@@ -1014,9 +1014,9 @@ def validate_consistent_shape(self) -> None:
                                  f"a setpoint vector but does not have an "
                                  f"Arrays validator")
             if sp.vals.shape is not None:
-                setpoints_shape_list.extend(sp.vals.shape)
+                setpoints_shape_list.extend(sp.vals.shape_unevaluated)
             else:
-                setpoints_shape_list.append(sp.vals.shape)
+                setpoints_shape_list.append(sp.vals.shape_unevaluated)
         setpoints_shape = tuple(setpoints_shape_list)
 
         if output_shape is None:
diff --git a/qcodes/utils/validators.py b/qcodes/utils/validators.py
index 3c1675133e6..2003647a4f3 100644
--- a/qcodes/utils/validators.py
+++ b/qcodes/utils/validators.py
@@ -529,16 +529,16 @@ def valid_values(self) -> Tuple[np.ndarray]:
         if shape is None:
             return (np.array([self._min_value]),)
         else:
-            val_arr = np.empty(self.shape_evaluated)
+            val_arr = np.empty(self.shape)
             val_arr.fill(self._min_value)
             return (val_arr,)
 
     @property
-    def shape(self) -> shape_tuple_type:
+    def shape_unevaluated(self) -> shape_tuple_type:
         return self._shape
 
     @property
-    def shape_evaluated(self) -> Optional[Tuple[int, ...]]:
+    def shape(self) -> Optional[Tuple[int, ...]]:
         if self._shape is None:
             return None
         shape_array = []
@@ -560,7 +560,7 @@ def validate(self, value: np.ndarray, context: str = '') -> None:
             raise TypeError(
                 '{} is not an int or float; {}'.format(repr(value), context))
         if self.shape is not None:
-            shape = self.shape_evaluated
+            shape = self.shape
             if np.shape(value) != shape:
                 raise ValueError(
                     f'{repr(value)} does not have expected shape {shape},'

From d84ea93273252630f9f4e4441036fbd0c0d1f112 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 10:45:16 +0100
Subject: [PATCH 653/719] Use unevaluated in repr and update comment

---
 qcodes/utils/validators.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/utils/validators.py b/qcodes/utils/validators.py
index 2003647a4f3..bf956038681 100644
--- a/qcodes/utils/validators.py
+++ b/qcodes/utils/validators.py
@@ -590,9 +590,9 @@ def __repr__(self) -> str:
         minv = self._min_value if math.isfinite(self._min_value) else None
         maxv = self._max_value if math.isfinite(self._max_value) else None
         # we don't want the repr to execute any deferred shape argument
-        # so we directly use _shape
+        # so we use shape_unevaluated
         return '<Arrays{}, shape: {}>'.format(range_str(minv, maxv, 'v'),
-                                              self.shape)
+                                              self.shape_unevaluated)
 
 
 

From ce070e19dbacfba8b885a8c789939853c5da5bb0 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 10:49:05 +0100
Subject: [PATCH 654/719] Missed one change to unevaluated

---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index e1013be348d..a3ebb91535d 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1013,7 +1013,7 @@ def validate_consistent_shape(self) -> None:
                                  f"with Arrays validator. {sp.name} is "
                                  f"a setpoint vector but does not have an "
                                  f"Arrays validator")
-            if sp.vals.shape is not None:
+            if sp.vals.shape_unevaluated is not None:
                 setpoints_shape_list.extend(sp.vals.shape_unevaluated)
             else:
                 setpoints_shape_list.append(sp.vals.shape_unevaluated)

From 72d0cdef56b3825e702aceed49d42a91e88abcd5 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 10:50:57 +0100
Subject: [PATCH 655/719] Clarify types a bit

---
 qcodes/utils/validators.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/utils/validators.py b/qcodes/utils/validators.py
index bf956038681..d320e775245 100644
--- a/qcodes/utils/validators.py
+++ b/qcodes/utils/validators.py
@@ -14,7 +14,8 @@
 BIGINT = int(1e18)
 
 numbertypes = Union[float, int, np.floating, np.integer]
-shape_tuple_type = Optional[Tuple[Union[int, TCallable[[], int]], ...]]
+shape_type = Union[int, TCallable[[], int]]
+shape_tuple_type = Optional[Tuple[shape_type, ...]]
 
 def validate_all(*args, context: str = '') -> None:
     """
@@ -500,8 +501,7 @@ class Arrays(Validator):
 
     def __init__(self, min_value: numbertypes = -float("inf"),
                  max_value: numbertypes = float("inf"),
-                 shape: TSequence[
-                     Union[int, TCallable[[], int]]] = None) -> None:
+                 shape: TSequence[shape_type] = None) -> None:
 
         if isinstance(min_value, self.validtypes):
             self._min_value = min_value

From 477a2e75c4e187dbb9e4678669c70d8be736e159 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 24 Jan 2019 10:51:57 +0100
Subject: [PATCH 656/719] Update
 qcodes/instrument_drivers/signal_hound/USB_SA124B.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/instrument_drivers/signal_hound/USB_SA124B.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
index ac7cf6254cd..b4a31e20449 100644
--- a/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
+++ b/qcodes/instrument_drivers/signal_hound/USB_SA124B.py
@@ -392,7 +392,8 @@ def _get_npts(self) -> int:
         if not self._parameters_synced:
             self.sync_parameters()
         sweep_info = self.QuerySweep()
-        return sweep_info[0]
+        sweep_len = sweep_info[0]
+        return sweep_len
 
     def _update_trace(self) -> None:
         """

From 77fc64b1fd83a52a96132cfe1e673ef6ea105ea8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 10:55:17 +0100
Subject: [PATCH 657/719] Put comment in bold

---
 docs/examples/Creating Instrument Drivers.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/examples/Creating Instrument Drivers.ipynb b/docs/examples/Creating Instrument Drivers.ipynb
index cc912af7403..2d54bde0d97 100644
--- a/docs/examples/Creating Instrument Drivers.ipynb	
+++ b/docs/examples/Creating Instrument Drivers.ipynb	
@@ -474,7 +474,7 @@
    "source": [
     "`ParameterWithSetpoints` is specifically designed to handle the situations where the instrument returns an array of data with assosiated setpoints. An example of how to use it can be found in the notebook [Simple Example of ParameterWithSetpoints](writing_drivers/Simple-Example-of-ParameterWithSetpoints.ipynb)\n",
     "\n",
-    "`ArrayParameter` is an older alternative that does the same thing. However, it is significantly less flexible and much harder to use correct but used in a significant number of drivers. It is not recommended for any new driver.\n",
+    "`ArrayParameter` is an older alternative that does the same thing. However, it is significantly less flexible and much harder to use correct but used in a significant number of drivers. **It is not recommended for any new driver.**\n",
     "\n",
     "`MultiParameter` is designed to for the situation where multiple different types of data is captured from the same instrument command."
    ]

From 3abdb5a5fdf35ede565004563658f8618e2c30ac Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 24 Jan 2019 10:55:44 +0100
Subject: [PATCH 658/719] Update qcodes/instrument/parameter.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index a3ebb91535d..6e79669a28a 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -951,7 +951,7 @@ class ParameterWithSetpoints(Parameter):
 
     In most cases this will probably be a parameter that returns an array.
     It is expected that the setpoint arrays are 1D arrays such that the
-    combined shape of the parameter. E.G if parameter is of shape (m,n)
+    combined shape of the parameter e.g. if parameter is of shape (m,n)
     self.setpoints is a list of parameters of shape (m,) and (n,)
 
     In all other ways this is identical to  :class:`Parameter` See the

From 6d2b656503511606215934702e4e24669a4a15e7 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 24 Jan 2019 10:56:03 +0100
Subject: [PATCH 659/719] Update qcodes/instrument/parameter.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 6e79669a28a..6e322330687 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -952,7 +952,7 @@ class ParameterWithSetpoints(Parameter):
     In most cases this will probably be a parameter that returns an array.
     It is expected that the setpoint arrays are 1D arrays such that the
     combined shape of the parameter e.g. if parameter is of shape (m,n)
-    self.setpoints is a list of parameters of shape (m,) and (n,)
+    `setpoints` is a list of parameters of shape (m,) and (n,)
 
     In all other ways this is identical to  :class:`Parameter` See the
     documentation of :class:`Parameter` for more details.

From 51d72d60700bb2dbf7609c91648309550cb37ce7 Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 24 Jan 2019 10:57:10 +0100
Subject: [PATCH 660/719] Update qcodes/tests/instrument_mocks.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/tests/instrument_mocks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/tests/instrument_mocks.py b/qcodes/tests/instrument_mocks.py
index c8c389a345a..c752805d520 100644
--- a/qcodes/tests/instrument_mocks.py
+++ b/qcodes/tests/instrument_mocks.py
@@ -380,7 +380,7 @@ def get_raw(self):
 class DummyParameterWithSetpoints1D(ParameterWithSetpoints):
     """
     Dummy parameter that returns data with a shape based on the
-    `n_points` parameter in the instrument.
+    `dummy_n_points` parameter in the instrument.
     """
 
     def get_raw(self):

From ef06ae71cf79ff7f833134f2a32b240adbc3b31f Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 24 Jan 2019 11:01:01 +0100
Subject: [PATCH 661/719] Update qcodes/instrument/parameter.py

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 6e322330687..87de8304170 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1937,7 +1937,7 @@ def expand_setpoints_helper(parameter: ParameterWithSetpoints) -> List[
     """
     if not isinstance(parameter, ParameterWithSetpoints):
         raise TypeError(
-            f"Expanding setpoints only works for ParameterWithSetpoints."
+            f"Expanding setpoints only works for ParameterWithSetpoints. "
             f"Supplied a {type(parameter)}")
     res = []
     found_parameters = []

From 25a5f3a1be077087e483dbbb1cf22bbe360e9d9d Mon Sep 17 00:00:00 2001
From: Mikhail Astafev <astafan8@gmail.com>
Date: Thu, 24 Jan 2019 11:09:24 +0100
Subject: [PATCH 662/719] Apply suggestions from code review

Co-Authored-By: jenshnielsen <jenshnielsen@gmail.com>
---
 qcodes/tests/dataset/test_measurement_context_manager.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 06981122d15..12ebd63348e 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -1009,7 +1009,7 @@ def test_datasaver_parameter_with_setpoints(channel_array_instrument,
         datasaver.add_result(*expand_setpoints_helper(param))
     assert datasaver.points_written == n
 
-    expected_params = ('dummy_channel_inst_ChanA_dummy_sp_axis',
+    expected_params = (dependency_name,
                        'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints')
     ds = load_by_id(datasaver.run_id)
     for param in expected_params:
@@ -1020,7 +1020,7 @@ def test_datasaver_parameter_with_setpoints(channel_array_instrument,
     assert len(datadict) == 1
     subdata = datadict[
         'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints']
-    assert_allclose(subdata['dummy_channel_inst_ChanA_dummy_sp_axis'],
+    assert_allclose(subdata[dependency_name],
                     np.linspace(chan.dummy_start(),
                                 chan.dummy_stop(),
                                 chan.dummy_n_points()))

From a084e1b29f3ac7da402abaafc1166e47968bf0d3 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 11:23:56 +0100
Subject: [PATCH 663/719] remove unused variables

---
 qcodes/instrument/parameter.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 6e322330687..07aa6fb338c 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -1940,16 +1940,12 @@ def expand_setpoints_helper(parameter: ParameterWithSetpoints) -> List[
             f"Expanding setpoints only works for ParameterWithSetpoints."
             f"Supplied a {type(parameter)}")
     res = []
-    found_parameters = []
     setpoint_params = []
-    setpoint_names = []
     setpoint_data = []
     for setpointparam in parameter.setpoints:
         these_setpoints = setpointparam.get()
-        setpoint_names.append(setpointparam.full_name)
         setpoint_params.append(setpointparam)
         setpoint_data.append(these_setpoints)
-        found_parameters.append(setpointparam.full_name)
     output_grids = numpy.meshgrid(*setpoint_data, indexing='ij')
     for param, grid in zip(setpoint_params, output_grids):
         res.append((param, grid))

From b85e08e28d525cdae091305472eea310cb143b99 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 12:29:34 +0100
Subject: [PATCH 664/719] Split tests in 3

---
 qcodes/tests/test_parameter_with_setpoints.py | 51 +++++++++++++------
 1 file changed, 35 insertions(+), 16 deletions(-)

diff --git a/qcodes/tests/test_parameter_with_setpoints.py b/qcodes/tests/test_parameter_with_setpoints.py
index dacdec55466..5f14ea90776 100644
--- a/qcodes/tests/test_parameter_with_setpoints.py
+++ b/qcodes/tests/test_parameter_with_setpoints.py
@@ -7,6 +7,27 @@
 import qcodes.utils.validators as vals
 
 
+@pytest.fixture()
+def parameters():
+    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
+    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
+    n_points_3 = Parameter('n_points_3', set_cmd=None, vals=vals.Ints())
+
+    n_points_1.set(10)
+    n_points_2.set(20)
+    n_points_3.set(15)
+
+    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: np.arange(n_points_1()),
+                            vals=vals.Arrays(shape=(n_points_1,)))
+    setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: np.arange(n_points_2()),
+                            vals=vals.Arrays(shape=(n_points_2,)))
+    setpoints_3 = Parameter('setpoints_3', get_cmd=lambda: np.arange(n_points_3()),
+                            vals=vals.Arrays(shape=(n_points_3,)))
+    yield (n_points_1, n_points_2, n_points_3,
+           setpoints_1, setpoints_2, setpoints_3)
+
+
+
 def test_validation_shapes():
     """
     Test that various parameters with setpoints and shape combinations
@@ -308,26 +329,13 @@ def test_validation_one_sp_dim_missing():
         param_sp_without_shape.validate(param_sp_without_shape.get())
 
 
-def test_expand_setpoints():
+def test_expand_setpoints_1c(parameters):
     """
     Test that the setpoints expander helper function works correctly
     """
 
-    n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
-    n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
-    n_points_3 = Parameter('n_points_3', set_cmd=None, vals=vals.Ints())
-
-    n_points_1.set(10)
-    n_points_2.set(20)
-    n_points_3.set(15)
-
-    setpoints_1 = Parameter('setpoints_1', get_cmd=lambda: np.arange(n_points_1()),
-                            vals=vals.Arrays(shape=(n_points_1,)))
-    setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: np.arange(n_points_2()),
-                            vals=vals.Arrays(shape=(n_points_2,)))
-    setpoints_3 = Parameter('setpoints_3', get_cmd=lambda: np.arange(n_points_3()),
-                            vals=vals.Arrays(shape=(n_points_3,)))
-
+    n_points_1, n_points_2, n_points_3, \
+    setpoints_1, setpoints_2, setpoints_3 = parameters
 
     param_with_setpoints_1 = ParameterWithSetpoints('param_1',
                                                     get_cmd=lambda:
@@ -341,6 +349,12 @@ def test_expand_setpoints():
     assert len(data) == 2
     assert len(data[0][1]) == len(data[1][1])
 
+
+def test_expand_setpoints_2d(parameters):
+
+    n_points_1, n_points_2, n_points_3, \
+    setpoints_1, setpoints_2, setpoints_3 = parameters
+
     param_with_setpoints_2 = ParameterWithSetpoints('param_2',
                                                     get_cmd=lambda:
                                                     rand(n_points_1(),
@@ -366,6 +380,11 @@ def test_expand_setpoints():
         np.testing.assert_array_equal(sp2[i, :], np.arange(sp1.shape[1]))
 
 
+def test_expand_setpoints_3d(parameters):
+
+    n_points_1, n_points_2, n_points_3, \
+        setpoints_1, setpoints_2, setpoints_3 = parameters
+
     param_with_setpoints_3 = ParameterWithSetpoints('param_2',
                                                     get_cmd=lambda:
                                                     rand(n_points_1(),

From ec40bab24d2c15281d211dfa64b7c7932545c358 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 12:47:09 +0100
Subject: [PATCH 665/719] Add test that verifies that registered but missing
 parameter raises correctly

---
 .../test_measurement_context_manager.py       | 36 +++++++++++++++++--
 1 file changed, 34 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 12ebd63348e..7aba78f3152 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -1035,8 +1035,8 @@ def test_datasaver_parameter_with_setpoints_missing_reg_raises(
         channel_array_instrument,
         DAC):
     """
-    Test that if for whatever reason the setpoints are changed between
-    registering and adding this raises correctly
+    Test that if for whatever reason new setpoints are added after
+    registering but before adding this raises correctly
     """
     chan = channel_array_instrument.A
     param = chan.dummy_parameter_with_setpoints
@@ -1059,6 +1059,38 @@ def test_datasaver_parameter_with_setpoints_missing_reg_raises(
             datasaver.add_result(*expand_setpoints_helper(param))
 
 
+@pytest.mark.usefixtures("experiment")
+def test_datasaver_parameter_with_setpoints_reg_but_missing(
+        channel_array_instrument,
+        DAC):
+    """
+    Test that if for whatever reason the setpoints are removed between
+    registering and adding this raises correctly
+    """
+    chan = channel_array_instrument.A
+    param = chan.dummy_parameter_with_setpoints
+    chan.dummy_n_points(11)
+    chan.dummy_start(0)
+    chan.dummy_stop(10)
+
+    meas = Measurement()
+    meas.register_parameter(param)
+
+    param.setpoints = ()
+
+    with meas.run() as datasaver:
+        with pytest.raises(ValueError, match=r"Shape of output is not"
+                                             r" consistent with setpoints."
+                                             r" Output is shape "
+                                             r"\(<qcodes.instrument.parameter."
+                                             r"Parameter: dummy_n_points at "
+                                             r"[0-9]+>,\) and setpoints are "
+                                             r"shape \(\)', 'getting dummy_"
+                                             r"channel_inst_ChanA_dummy_"
+                                             r"parameter_with_setpoints"):
+            datasaver.add_result(*expand_setpoints_helper(param))
+
+
 @settings(max_examples=5, deadline=None)
 @given(N=hst.integers(min_value=5, max_value=500))
 @pytest.mark.usefixtures("experiment")

From 82bf6a86006377ccb53c5fd158b05cbccd61b87f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 13:00:28 +0100
Subject: [PATCH 666/719] Add alternative test that verifies that missing 
 setpoints are handled correctly

---
 .../test_measurement_context_manager.py       | 43 ++++++++++++++++++-
 1 file changed, 41 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 7aba78f3152..703bf9f973c 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -1060,12 +1060,13 @@ def test_datasaver_parameter_with_setpoints_missing_reg_raises(
 
 
 @pytest.mark.usefixtures("experiment")
-def test_datasaver_parameter_with_setpoints_reg_but_missing(
+def test_datasaver_parameter_with_setpoints_reg_but_missing_validator(
         channel_array_instrument,
         DAC):
     """
     Test that if for whatever reason the setpoints are removed between
-    registering and adding this raises correctly
+    registering and adding this raises correctly. This tests tests that
+    the parameter validator correctly asserts this.
     """
     chan = channel_array_instrument.A
     param = chan.dummy_parameter_with_setpoints
@@ -1091,6 +1092,44 @@ def test_datasaver_parameter_with_setpoints_reg_but_missing(
             datasaver.add_result(*expand_setpoints_helper(param))
 
 
+@pytest.mark.usefixtures("experiment")
+def test_datasaver_parameter_with_setpoints_reg_but_missing(
+        channel_array_instrument,
+        DAC):
+    """
+    Test that if for whatever reason the setpoints are removed between
+    registering and adding this raises correctly. This tests that
+    the add parameter logic correctly notices a missing dependency
+    """
+    chan = channel_array_instrument.A
+    param = chan.dummy_parameter_with_setpoints
+    chan.dummy_n_points(11)
+    chan.dummy_start(0)
+    chan.dummy_stop(10)
+
+    someparam = Parameter('someparam', vals=Arrays(shape=(10,)))
+    old_setpoints = param.setpoints
+    param.setpoints = (old_setpoints[0], someparam)
+
+    meas = Measurement()
+    meas.register_parameter(param)
+
+    param.setpoints = old_setpoints
+    with meas.run() as datasaver:
+        with pytest.raises(ValueError, match=r"Can not add this result; "
+                                             r"missing setpoint values for "
+                                             r"dummy_channel_inst_ChanA_dummy_"
+                                             r"parameter_with_setpoints: "
+                                             r"\{'dummy_channel_inst_ChanA_"
+                                             r"dummy_sp_axis', 'someparam'\}"
+                                             r". Values only given for \['"
+                                             r"dummy_channel_inst_ChanA_dummy_sp_axis', "
+                                             r"'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints', "
+                                             r"'dummy_channel_inst_ChanA_dummy_sp_axis', "
+                                             r"'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints'\]."):
+            datasaver.add_result(*expand_setpoints_helper(param))
+
+
 @settings(max_examples=5, deadline=None)
 @given(N=hst.integers(min_value=5, max_value=500))
 @pytest.mark.usefixtures("experiment")

From 5bf4485ea6775ed0cdd5b577769525cf47fdd496 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 13:17:02 +0100
Subject: [PATCH 667/719] Don't force a shape evaluation in construct

This may be too early for the instrument to be setup
---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 0bc970c4387..6566a1d6643 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -968,7 +968,7 @@ def __init__(self, name: str, *,
         if not isinstance(vals, Arrays):
             raise ValueError(f"A ParameterWithSetpoints must have an Arrays "
                              f"validator got {type(vals)}")
-        if vals.shape is None:
+        if vals.shape_unevaluated is None:
             raise RuntimeError("A ParameterWithSetpoints must have a shape "
                                "defined for its validator.")
 

From 8952eb95010f504e5ed2d2b8a394dc4ac455c0b0 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 13:49:08 +0100
Subject: [PATCH 668/719] Link signal hound notebooks

---
 ...nd USB-SA124B ParameterWithSetpoints.ipynb |  363 ----
 ...example with Signal Hound USB-SA124B.ipynb | 1860 ----------------
 ...nd-USB-SA124B-ParameterWithSetpoints.ipynb |  363 ++++
 ...example-with-Signal-Hound-USB-SA124B.ipynb | 1869 +++++++++++++++++
 4 files changed, 2232 insertions(+), 2223 deletions(-)
 delete mode 100644 docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints.ipynb
 delete mode 100644 docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B.ipynb
 create mode 100644 docs/examples/driver_examples/Qcodes-example-with-Signal-Hound-USB-SA124B-ParameterWithSetpoints.ipynb
 create mode 100644 docs/examples/driver_examples/Qcodes-example-with-Signal-Hound-USB-SA124B.ipynb

diff --git a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints.ipynb b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints.ipynb
deleted file mode 100644
index 8c0fa438b1f..00000000000
--- a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints.ipynb	
+++ /dev/null
@@ -1,363 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import qcodes as qc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.instrument_drivers.signal_hound.USB_SA124B import SignalHound_USB_SA124B\n",
-    "from qcodes.dataset.measurements import Measurement\n",
-    "from qcodes.dataset.plotting import plot_by_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connected to: Signal Hound sa124B (serial:17172185, firmware:Version 3.13) in 5.93s\n"
-     ]
-    }
-   ],
-   "source": [
-    "mysa = SignalHound_USB_SA124B('mysa', dll_path='C:\\\\Program Files\\\\Signal Hound\\\\Spike\\\\sa_api.dll')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'vendor': 'Signal Hound',\n",
-       " 'model': 'sa124B',\n",
-       " 'serial': '17172185',\n",
-       " 'firmware': 'Version 3.13'}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mysa.get_idn()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mysa.frequency(2e9)\n",
-    "mysa.span(0.5e6)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 309\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1da84b9e4a8>], [None])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.avg(1)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
-    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 310\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1da84cb27b8>], [None])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.avg(10)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
-    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 311\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1da84cb96a0>], [None])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.avg(100)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
-    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    \n",
-    "    dataid = datasaver.run_id\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 312\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1da8607e2e8>], [None])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.frequency(3e9)\n",
-    "mysa.span(1e6)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
-    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    dataid = datasaver.run_id\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 313\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "During call of saInitiate the followingWarning: saBandwidthClamped was raised\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1da86131198>], [None])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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BeyU1I5gPYk/gEmBnoAtwWqoNmNmDZtbXzPq2bdu2KIl20XhJIboFy1bzrw+ncMJDn6dcPmTCz7w+NrizH3DXMH593yd5T8PadWXc8953PP/FjIwX+jE/LOKXFevvio/+16ccfs/HQOUbdI+9f3jO7/nHkG854/GRfPzdvArLXhw9ky+mL+T8Z6NneivXrGPNuswllGP+/SnH/PvTnNMaU1ZmkX8X3a9+m4HPjGbg06UrUSYr2EQ9kgYCZ4dPFwLXWnCmJkuaBvQAZgJfmtnU8D2vALsCjxQqXa5wClFyKCszZv+ygs1bNsr7tktpXVnqC9NZT44E4IgdOuRtX8lVPC+Omskdg9c3BqeaFGZdmXHkfZ+wY6cWGbctqYq9fta/ecrcpWzWoiEN6tZm9doyJvy4mN4dWzBjQTBWYv6yVRXe/ZcXx6Xfcpp09bjmHbZu15R3L96rKgnPqMuVbzGgZzsePLVvpPXf/uon6tRK/P0MnvAzmzZrwPabNy9EEjMqWInBzO4zs95m1huYBOwPIKkdsDUwFfgCaCkpVgTYD5hQqDS5mufeDyazx98+2GBCbpSi2u2K/41PeL4ywkDE2MC1sTOjVx1V5dhWrSlj/zuGctFzYwC48Y0JHHnfJ0ydm5/qmk+nzGN63HfomwhVeWlFzAkHTVhfvbVmXRmdL3+TZz6fEXk3Zz85kiPu/Tjn5OVDsaqSbgR2lzQeeA+4zMzmmdk6gmqk98JlAh4qUppcDRAbVf3TL9U/JEMuqnvl2563fQCkrybMV/pjm18dVu18OiX4vMeHVVyLVkRr4E3OkoZM+DnhwnziQ5+zz98/TFjnna9+zD3B8fvMISNcujIIeXLbu5OqtM9iKcqcz2Y2GxiQZtlgoFcx0rGh+dMLY3jly1lMvaW088MW09wlqzAzNmnWoNRJqZTYpSRfzTKjZyxk+JT5bNO+afp9Jl2/8pkpxW86W516qnhQ6d4Re/3S/46lXYTPOvkiHauSy+Spz77ngG3aUad25e6P8922VlaN2uqKkjG4wnhp9KxSJ6Hgkn8qO98cxBQq5mTpy1at5cdfVjBi2kLGz1rELUcn3sfMW7qK+UtXs/Wm6S/OMVFvMssijCiGoEG4kKKkIuox9bz23YrbT7oYll/gw9enzF3GlLnZqxErU4n1yeT5XP/619x05PZ8OmUePds3o0Wjetm7ocYdsJmxeOVamjesm/EtUc5jxI+8KDwkhqvewh9LIarm97rtA654aXzW9U5//AsOuHMYV748nmdH/FD++nc/L+HGNyaw798/5KC4qJ5RZLvbfGL49PLHN7z+Nc+NSKyb/uanJXS+/M1I265K1Nt0yXxz3I9Z18nFyOkLEvebYp3k7qGfTq7YSym19F+ed776iZVr1nHiQ5/T+6+DmbN4JXcPyRK+I+6AH/l4GjvcMIgfFqQefvX9/GUJPaBqSsduzxhctRYbvVqIH9SMBct5dkT2xsAR0xIvWp0vf5N1ZcbvHh3BIx9PY8nKaBdesT5MQ7Zr6ffz119oHvtkOpe/NJ7Ol7/Jvz4M+tC/NT56/fjMhfmPgPp4ODtafIYdX50zZe5SOl/+JsOnzM+4ndh5uCypgXxcikbvf74/OSEzvDccTxDsPFq6U4mP9zR5zlLWRbx1l8TgsB0j3Tne+/YPufnNiTmnacGy9OM6isEzBldlxSgBp2roW5ulL3ohTfppcc7HbVDlHO6p4d8DsDJNXKKYOYtX8s5XhR8wla60EMsQXs8y2jr5/VL0uvt8lFTMqvaRREnC8Cnzy49p4fI1fD9/Gfd9MDljT7tjqzCGIh88Y3B5E/UH9u3PSzj10RGRuk2mM+zbuXS96u2MI4PnL11F58vf5IWRP3Dhc1/mtL9lq9ZmnGugygHaqnhRe2Do1PSbNjj+oc847z+jKoSaSHUneuy/P2XC7PWjs2PhKtLJJR5TZS7elXlPVT6NKlVTVqKqc+/bP+T2d7/hpIc+S7vO1BJ3z/aMweVN1N/zta9+xbBv50aKrWNpfngffhNcnL4I66ZXrF6XMEoXYFzY5fEvL47j1TGzy4v9ZpYQBjrVKNVj7x/O7re+H/GIUkvVgBw7jiWr1jI+hzECMVEvmjPDQWHJgeR2vHFwhXVHfr+QQ//5UXk//1gDfzpPhtVI5WlKsU6UC+W970+uMAJ5XZlx1SvZ230gsdRUlTEUCZl8jpuJnd/jH/ws5yCJKwsYH6qqPGNwVVbIBrXYRWdNmh/RnCWreObzGQz4x1B2uGFQ6jcnmbtkVUIY6C5XvsWWV7zFwx+tvwufGBffKJVTH/2cH7OMrbhzcGKoaZF4ro649+PyjC2qnxavzHpHH2/rq9+JvG5yP/8oUl2PX/jiB64qj7SaPie7Y/C3FSYoWrJybUIDfyZfzshPHKlMecq8pZnP9RfT12cGTyRlmDFWA+O8esawkVtXZpWq0pk8Z0n5HXEhv/SxO/kTH/6cV8dU7J774LCpXPnyeH5YEL2BNV16b8qhkXDe0opVMrMWrWDxyjXlPWz+N3pm1u3E9+5ZGLHBMTYILJ1sn8fiFfmdXzr5uvpQXAZbrK758Rf36VWohkmuIux705AKJdGY+BuJDY1nDBu5c58aSY9rEu8qR05fwIffpJ8W4+vZv3DAncP499ApCa+LoErno+/yE/125ZrE6qHBE3KLoJl8n1boa1T/W9+n1/WDOPb+4SkHc83+ZWXG8NLXvfZ1IZNX7q4hxZ80p5imzU/MGCbPiR7+IlXpYUmacQ1vjIveM6wqGWRVgvlVlmcM1dyM+cs58M6hWYu0lTVkYsUM4Nj7h3PaY1+kfc/0eUFXylRF+atf+YpTHhnB5DQhif/6+gQ+m5q++mT12jKWhf3uj73/00iDm3KVreorvgQ1pZKxetJNeTk/Q6kgfr+ZeuakKq3Eu3/olPIQE7n653u5TcH5xfSFfDY1sUtq/MW1WCWGRcvTD0rLNHtcmRkvjIxWdRWTr0NasGw142Zmrw4b9f1Cbn27uKE0PGOo5h7+eCrfzVmaUOVQLCtWr+P/3ppYoappYPmEMxV/IpPDC2m6u6xHP5mWcZ+/eWA42173LivXrOOrWZnr+XOlCg9Sey8us5wxP7/zRg24K/1AuPjYPk+E3VJTyTZ/8u3vfpN7wkLJ7SKpLEkaMHfNq0FJJ9XNwKSqBKsrgoXL13Dtq+tLastWra10qIt0bRWZNvere6OFVb8/qXReaB4Sw6X18EdTeXDYVJo3rMvAfbsWZZ+x7qfJ1Vv5UJmfe61alWtaF2RtnAYiD6aKtzDD3XExJA/4izc/qWSbr4mGiuXMJyrGWFq7zli9tow5S1ZmDP9enSbaqSrPGKq5UsbVinUlXLsuTYTNHNM2K8O4gFz9vDj7RTfdiORcxiB881PlSi1Re8w89Vn6kkFNVB1637zyZfoYYqc+OiLn7Z3++Bf07tiCl8Pt1k5zs7ByTRk/LFjOhwWad3vNujLqVjLgX648Y6ghSjF7ZrYfea4Xgf5VHBcQv783I4SEuDCM7Z8s07k0s4RqsDZN6kdNXoJ8zSPgcvfqmPSjrYdV4qI9bd6yhFHKmUp5sXDl8b6bszQvJacj7vmYp87chbZNK/edzEXk7EdS43TzMbvCqUqsoLuHfMeVL0cbLFS+vxTFgEwX0tMeG5Gy4S+2lclzlvDTL6n73t/3weSUXVALLdO5fHL491weF1ivsndo17++cc43FR8Pyq0XJQx4NpN+WsIFOUxhWhVpv/WSakk6UdKbkuYQzML2o6SvJd0uqVtRUphHZzz+Bc9/UTFo2pp1ZawrMwZP+JkdbhiUU7/+y14cxzYR6sOf/2JGpJG+ycqv05UoMtw15Fue+XwG73z1I1+G+15XZjwwdEraY3xt7Pq7rWxVRWZWPgI5JjmVB9w5jF1veS/laNpPJs9Pe1dfSJlGySZ3ic1lLmEXjESv0uxoLqOsIcHzJFNV0gfAEOAK4CszKwOQ1ArYF7hV0stm9p/CJzM/3p80h/cnzeG4nTslvN7tqrfZbrNmLF8V9JuftWgFW7VtEmmbz4dd3crKLGNDZSx6ZK7zCOSjzva8/wS9iO46bgfWrjNueXsSC5ev4fJDelRYN74XTq6llUJXd82KECX0mc9ncN8Hk3nqzH5p11mWIQx1zRujWr3EvmuuML5L0w083zKVkw8wsxvNbFwsUwAwswVm9j8zOwZ4vvBJLI6oXSOXrlqbcpBSLBxyNk98Oj1jcLZ08nHNvfj5seUhCDJdHGPi4xStKwviC8XH9k++hFa2oXxdmfGXF8fybZY7zTER6mmvfHk8sxat4G/vVOz3Hav3/ylDw3U1mkTLuQoyDZDMp7QZg5mVl1kk7SHp9PBxW0lbJq+zsdjuunc5MUVUxEEZRuX+b9T60AjXvfY1pz0WvWdEuiByiesY/x35Q4WugunWzbS9VK9L4q3xP/L3Qd9y69u5x5bPZtq8pbwwcia//8+ovG3z3a8rfh7/CCdgyZTJesbgXITGZ0nXAZcRVCkB1AVqTPVRIYzMMYrin/87NuF5qlg1r46ZxffzU43yjVXnpL+cvTZ2Npe+OI6dbsocFTNeuq2l63ARu1NZvmp920RyZExILEVEKZWUQlUicTq3MYjS5eIo4FfAMgAzmw1kn9w2jqTmkl6XNDZsvI6VPvaVNCbub6WkI3M9iHwp5c3ihc+N4bB/flzh9VR3sI9/Mi0h+md88LVsQ+ezHeODw+ICoIX/3xr/Y3kc//j3fzI5/excK9esY9vrKs7xm8oNYQ+eYpz/Jz6dnrXKyrmNXZRxDKvNzCQFE1BJjSuxn4HABDM7QlJb4BtJT5vZB0DvcLutgMnAoAzbqbbSVUGk6s+e7oZ16aq1zFu6KmXf+dh7Fi5bnbEr5P1Dp7Bi9dqMIRWC7SUmYv7SVex2y/sJMXZix/T17MV8PTt7G4y0viRy4kOfZ10/5qPvos7dW3XXvfY1m7dsmHb5iBzDYDu3IYpSYnhB0gNAC0lnE/RUeijH/RjQVMHVqAmwAEiuZzgWeNvM8hucJgeFqGDIdbRv35uG8OKo9OGay1LkQIuTRvhmyxRSGTFtQYXAa7n20MkW3C2rIhXZMs2BXJkQFc5taLKWGMzs75IOBBYDWwPXmlnFaaAyuxd4DYhVQx0X39MpdDxwZ7oNSDoHOAegU6dO6VYrmN89OiLlRTmdlWvWsSpND4JsGdBnU+czde5Syiyu8TnD+lECn8VkikIZRaYAY0fc8zFbb5pTLaNzrhqKFBIjzAhyzQziHQSMAfYDtgIGS/rIzBYDSGoPbA+krZQ2sweBBwH69u1bkNu6TBvNNs/t3CWr2ONv7/PUmbuwZZvGHPfgZ1UaBv+vD4Noir/tuzmwviqpqgd+dw5hlVPNK5xp/yuqMIezc676yDTyeYmkxen+sm1Y0sBYozJBG8NLFpgMTAPiR1f9Fni5WN1fs40jiFKlZGYJ1Q4/LV7JzIUryqf3y5QpSGLmwmg1ZoWKpJnczvFJllnBisErcZyrHtKWGMysKYCkvwI/AU8RXDNPIkKvJDO7D7gv3Ma/gf2BjyS1I6iSir8dPYH13WEL7ssZi+jQIn0DZBQ3vzmRhz/OPLdAOrMWrWCPv31Av86tGDF9AeOvH5B23ViIhnzHn3nsk+kM6Llp+fP/fFYxVEgq76eY2CdfplVhSkbnXP5EqUo6yMx2iXv+b0mfA7flsJ8bgccljSfIXC4zs3kAkjoDHYGhOWyvIGIXptid630fTKZ+nVqctWeXCuumyxTGzVzEouXRGmFjPWDmRJncvQAt45WJA5Q8SUuyKKOTnXPVW5SMYZ2kk4DnCK6ZJwA5VSaHYx9S3hab2XRgs1y2VyyxmbBSZQzpjJ6xiOMeqDgyOpPYiNxM/vLiOLbt0Ix2zRrktG3nnMtVlIzhRODu8A/g4/C1DVZVb85zjS75+tj08ePjPf35DDarYhWYc85lE6W76nTg14VPSvEMfGY0//dWQz6+bN9qGR4hXY/QZz6P1g4QVTU8dOdcNRAlVtLmkl6WNEfSz5L+J2nzYiSukGYtWsGWV7zFlI14pq1Uk+c451yUkc+PEQxO60DQFvB6+NoG4bOpFeP9fDF9Qd7vznNSp5NqAAAgAElEQVThd/LOuVKK0sbQ1sziM4LHJV1UqAQV27c/VWwPiE2q45xzG6MoJYZ5kk6WVDv8OxlIH1azhqlMXCHnnNuQRckYziAYmfwT8CNBsLszCpko55xzpROlV9IMgvkYNlovjU4f7bQQyjzCp3OuhLJmDOE0nucDnePXN7ONJrP40wtjs6+URy99Oauo+3POuXhRGp9fAR4h6I1UnJmonXPOlUyUjGGlmf2z4ClxzjlXLUTJGO6WdB3BlJvlI6LMbHTBUuWcc65komQM2wOnEEyyE6tKsvC5c865DUyUjOEooIuZVXFCX+ecczVBlHEMY4EWhU6Ic8656iFKiaEdMEnSFyS2MWw03VWdc25jEiVjuK7gqXDOOVdtRBn5XPIpN51zzhVPlDYG55xzGxHPGJxzziXwjME551yCKFN79pc0WNK3kqZKmiZpai47kdRc0uuSxkr6WtLpcctuC1+bKOmfqo6TMDvn3EYkSq+kR4CLgVHAukruZyAwwcyOkNQW+EbS00BfoD/QK1zvY2Bv4MNK7sc551wVRckYfjGzt6u4HwOahqWBJsACYG34egOgHiCgLvBzFfflnHOuCqJkDB9Iuh14icoH0bsXeA2YDTQFjjOzMmC4pA8IZoYTcK+ZTUy1AUnnAOcAdOrUKYddO+ecy0WUjGGX8H/fuNdyDaJ3EDAmfM9WwGBJHwGbANsAm4frDZa0l5kNS96AmT0IPAjQt29fn+LMOecKJMoAt30rs2FJA4Gzw6cLgWvNzIDJkqYBPQjaEz4zs6Xhe94GdgUqZAzOOeeKI0qvpOaS7pQ0Mvy7Q1LzbO8zs/vMrLeZ9QYmAfuH22sHbA1MBWYAe0uqI6kuQUaRsirJOedccUQZx/AosAT4bfi3GHgsx/3cCOwuaTzwHnCZmc0DXgSmAOMJoriONbPXc9y2c865PIrSxrCVmR0T9/wGSWNy2YmZzQYGpHh9HXBuLttyzjlXWFFKDCsk7RF7Iqk/sKJwSXLOOVdKUUoM5wFPxrUrLAR+V7gkOeecK6UovZLGAjtIahY+X1zwVDnnnCuZKCUGwDME55zbWHh0Veeccwk8Y3DOOZcgygC330hqGj6+WtJLknYsfNKcc86VQpQSwzVmtiTssnoQ8ATw78ImyznnXKlEyRhiczAcBvzbzF4lCJPtnHNuAxQlY5gl6QGCcBhvSaof8X3OOedqoCgX+N8C7wIHm9kioBVwaUFT5ZxzrmTSjmOQ1Cru6Ydxr60CRhY2Wc4550ol0wC3UQQT8gjoRBAKQ0ALgnDZWxY8dc4554oubVWSmW1pZl0IqpGOMLM2ZtYaOJxgmk/nnHMboChtDDub2VuxJ2b2NsGEOs455zZAUWIlzZN0NfAfgqqlk4H5BU2Vc865kolSYjgBaAu8DLwCbBK+5pxzbgMUJez2AuDCIqTFOedcNZA1Y5DUHbgE6By/vpntV7hkOeecK5UobQz/Be4HHmZ9eAznnHMbqCgZw1ozq1LQvHBa0P8QjIeoA/zdzB4Ll/2NIA4TwI1m9nxV9uWcc65qomQMr0v6A0Hj86rYi2HbQ1QDgQlmdoSktsA3kp4GDgR2BHoD9YGhkt722eKcc650omQMvwv/x8dHMqBLDvsxoKkkAU2ABcBaoCcw1MzWAmsljQUOBl7IYdvOOefyKEqvpHyEvrgXeA2YDTQFjjOzsjAjuE7SnUAjYF9gQqoNSDoHOAegU6dOeUiSc865VKLM4NYonLntwfB5N0mH57ifg4AxQAeCaqN7JTUzs0HAW8CnwLPAcIKSRAVm9qCZ9TWzvm3bts1x984556KKMsDtMWA1sHv4fCZwU7Y3SRooaYykMQRtDC9ZYDIwDegBYGY3m1lvMzuQIEjfd5U4Duecc3kSJWPYysxuA9YAmNkKggt4RmZ2X3jB7w1MAvYHkNQO2BqYKqm2pNbh672AXsCgSh2Jc865vIjS+LxaUkOCBmQkbUVc76SIbgQelzSeIFO5zMzmSWoAfBS0SbMYODlsiHbOOVciUTKG64B3gI5hF9P+wGm57MTMZgMDUry+kqBnknPOuWoiSq+kwZJGA7sS3O1faGbzCp4y55xzJRGlxADB/At7EFQn1SUY7Oacc24DFKW76r+A84DxwFfAuZLuK3TCnHPOlUaUEsPewHZmFmt8foIgk3DOObcBitJd9RuC4HcxHYFxhUmOc865UotSYmgNTJQ0Iny+MzBc0msAZvarQiXOOedc8UXJGK4teCqcc85VG1G6qw6VtAXQzcyGhIPd6pjZksInzznnXLFF6ZV0NvAi8ED40ubAK4VMlHPOudKJ0vg8kGC082IAM/sO2KSQiXLOOVc6UTKGVWa2OvZEUh3CuEnOOec2PFEyhqGSrgQaSjoQ+C/wemGT5ZxzrlSiZAyXA3MJBrWdSzCxztWFTJRzzrnSidIrqQx4KPxzzjm3gUubMYRzJ6RtSzCzXgVJkXPOuZLKVGKIzes8MPz/VPj/JGB5wVLknHOupNJmDGb2PYCk/mbWP27R5ZI+Af5a6MQ555wrviiNz40l7RF7Iml3oHHhkuScc66UosRKOhN4VFJzgjaHX4AzCpoq55xzJZO1xGBmo8xsB6AX0NvMepvZ6MInzTlXWdtt1qzUSSip3++zVamTUKOlzRgknSypfLmZLTazX+KWbxVfxeScqz4O2a59qZNQssxpn63bcsF+3Uqy7w1Fpqqk1sCXkkYBowgGuTUAuhLM6jaPYPBbVpJaAo8CWwErgTPM7Ktw2cHA3UBt4GEzu7Vyh+KcS1avdi1WrysrdTKKqtsmTWhYr3apk1GjpS0xmNndwI7As0BbYP/w+SzgFDM7JgyoF8WVwJhw7MOpBBkBkmoD9wGHAD2BEyT1rOSxOLfReOKMfhmXb9U26B9yxh5bFiM5KZnBhftXvHM/YocOJUhN4bRsVLfUSci7jG0MZrbOzAab2fVmdq6ZXWRmD5jZjBz30xN4L9zmJKCzpHZAP2CymU0NA/U9B/y6Esfh3EZl7+5teeP89DW5+2/TjjfO34PLDt6aG361bRFTtp4ZXHxgd9o3b1D+2hE7dOCfx/cu6H4lFXT7yQ7dPn213ZA/7V3ElORPlO6q+TAWOBpAUj9gC4J5HTYDfohbb2b4WgWSzpE0UtLIuXPnFji5zlXeBSnukguh6yZNMi7fbrPmSMIsezDkvxy8NQds0y5fSQPWh02Iv0yL4l+4SynbZwQwcN+t2KNrmyKkJrpiZQy3Ai0ljQHOB74E1pL4nYlJ+S02swfNrK+Z9W3btm3hUuo2WIduv2lR9nPyrp2Ksp/6dfL38z1rjy5521ahbN6yIf06t4q8/gn9Kn4OzRvmv9qnqvnchft3p1at6pVZZvxmSaol6beV2bCkgZLGhJlBEzM73cx6E7QxtAWmEZQQOsa9bXNgdmX251w2mzRtkH2lPFDK+538qFt7/bYz3XnHL9l602i9g2rnmM8M6FmxhLFX9/U3bbGSSiydTRvU4YL9u6bdXtum9Su8Fn+IEjx5ZsW2lftP3inxPeH/W47enum3HsY9J/RJu89bjt6eAT3bcVzfjmnXiRnyp71Svl7IzzvZJ5fvV5T9ZGtjKAP+WJkNm9l94ZiH3sBySfXCRWcBw8xsMfAF0E3SluHy44HXKrM/57I5M48Nsefslf4OOx81JTtt0TLl60P+tDcPndq3/Pme3bJXQey2Veus69SuJW48crvI6evdsQX1UpRYGmQoxbx94Z503aRp2uVvXlCxzWTaLYclbr9uxd5GyRnKVhGqb+I9eGpf/nZs9pigjevXoUn9KGOCE7VrVjHDq4ydO7dksxYN87KtbKLcIwyWdImkjpJaxf5y3M82wNeSJhH0QLoQwMzWEmQ87wITgRfM7Osct+1cJLVzKK53bt0o4/KqXPsP3jZ7ldZlB/dI+foWrRtzYNydeq2IuVCbJvVSvr7TFi0ZceX+1K6llCWqPx3YneuPqNhR0MjeVpCpaaNhigt8thJdujvz5DaU3+y0efpt5PjB7dcj+yzG2bbZvnn2i3m6TVx5aA/uO3FHAPptmetlt/KiZAxnEERYHUYwnmEUMDKXnZjZcDPrZmY9zOxoM1sYt+wtM+tuZluZ2c25bNdtfG6PcGeXTj7no43dOf5uty0qLMt27fnXSTvmMSWJcsn8IKia2qRZ+gtyt02acFr/ypW0LOmMx1+/K1OqSnfnXZb0wSZnWB1bpc/ks7XLH9lnfV8YpcmaurTJHDru8F7ZBxumOx/n7LUVh/Vqz4eX7MOfDtw663byJUpIjC1T/FX/liq3QYp6h1xV2e6Gz9m7C1cftg3XHN6To/okdqRL9d7R1xxY/riyDY1/O2b7jMt369I6Ug+kqC4Z0J0BSaWb3/YN7sZ7tm9afpH866+35eIDuudtv+kktyXElGU55t4dW/DcObsC0Ut6bZpkr/7ZuXNLXjh3N363e+eM6525x5YZ7/al7Onq3KZxzpl+VWTNGCQ1knS1pAfD590kHZ7tfc7lS3y9qgGN6tUu+SCp+nVqc9aeXahTu1Z5ZrVjpxa8dcGeKddv1TixKue9P+fWv33nzi05bueKvWziuznee+L6RtaoXULTVc9ccUgP/rhftwoXo1uP7sVVhwYZYmwXzRrUZetN17cd3HJ0kIHt2KllmJZISQHg7D3Tl05ap7lYx+cL6dpJurdL3bYRX6q59vCgymz4FfuxS5fUF/LD4u7+hei3Zaus51pSyqqz6ixKVdJjwGpg9/D5TOCmgqXIuQzMjAl/PTjr3XMmHZpn752U6aceq/ONufLQHpy4SyeePWdXenZoFumudKu2uTWQphO7E+21efO0F05Yf5GO6ty9Uwehq1VLnL1XFxrVS98Ie0K/Trxz0Z7c8Ov0A+vS3eRfdVj2wAfTb01skI5d3Hft0opTdq1YtRfsL3tJ6ow9tmT6rYcltAkIePqsXdixUwvaNKmXtlNANu2zfOeq29iOKBnDVmZ2G7AGwMxWULW2N+dKKvkS8enl+1W42HTdpAlfXHUAwy7dt8L7+3RqkfC8dZP6/N9R21O/TnBXmPwbj/qbzzSSefvNWqRdFsXdx/dJ2esnao+Z0dccyIgr90+5LLktAaDHps3Kz0f5epWs5UrXcJ683SjVjJW5APfv2oaX/tCfOrVrJb4/h01dd8S27B125f1VDQgJEqXv1WpJDQl/T5K2AlYVNFWuxujZvhkTflxctP3FukhW5iKT7Xf8zxP6sGzVWjq1asQOHVtUqmtisJ/EPR0aMdLpdps1B4KRsMmuODR1L6Vk6U5Lw3q12bZDc4Zdui973f4BEIzQPjep2+3RfTajUf2K1R7JVWEQ/bqY67X4/P260rxhXfbZOriQDvvLvqxZl/4DLysfL5Hbfvbu3jb9RTrN7ip7R9ywXm1+3bsDQ7+dS6qmgup2px3lm3898A7QUdLTQH/gtAKmydUgfTq1KGrGcHiv/NxtvTqwP7++75OE16LeyeWaJzVrGD2DiZVcRkxbkPB63RxHn6W70HRq3YjOrRsxff5yjuqzGY2TMr87jytsHKMo/jwgsfdNpmoriFZiaNawLvVq1+KKQ3pw6YvjgOyBCCFzZnNiipHVMYMv3ouvZyf+LlJt6/6Td6Ju7Vp5GfuST1F6JQ0iiHN0GkGk1b5m9mFhk+Xcenf8dofyx7HG0Kr+kAoRGiG9zIlt37xBUQPdxTLAVo0yV9Fks18YW2mb9plHVrdvFtTZ16ldmKtfWdII61Tq1q7Ftzcfwm/6dmT4Ffsx6OLUo5hjUlWPAbSIi6R6ZJ+UYd0A6NauacblMQdvl9jrK1VpsRSy3spIeopgDMNHYWRU58pVptr4j/t25d4PJkdef9cuFUfu5rFXZv7leP0bfkXFuvuoF9HYhWrbDs0j7++iA7pzzt5bVbqqLOZXO3TgwG3a0bBebabPW552vftP2Ylh386lQ0Lvsvx9gN3CHke/jljia9+8Ie0jnq7kasEoA97S6d0xaLg+vFcHXhmTHPmnehUZovZKag/cI2mKpP9JurDA6aoxjuxd/RuSskk1SCuqbBfo+Ng5EMwTcMlB2QfqXHFID64+bBueD/uf51v9uoWLH5mPaoE+HVvwl4Ozn6ctWjfm1YH9uf5X0acxqVVLVc4UYqJMiNOqcb1Id8+VtVmLhky75VCOyTDiOV+q0ntoyzaNmX7rYRyQIsZUTHW54YlSlfQ+cDNwDfAw0Bf4fYHTVWPUyTXyWA6ujNjgWBl/iJsTt5AjKndPitMT5Ye179ZtOX7nTpy1Zxd2SVFaSHb1YdtESkvLsOrkzD22jBSmIObR0/omPM92BMnLK3MtkcQf9kkfcC7eDh1blPcAqi4XlkL78JJ9ePkPu/PuRUGVUL67e27ROhjNnKrRvRBiya8uH1+UAW7vAZ8AxwHfADubWeGuWDVMIX+ILatYBxzz9Fm7sEPHxO6O8XfyzSPOQNW9XX763mfz2On9KqRp584t00bA3G6z5oy/fkCF1x8+tS/vXLR+wFnDerWZfuthnLVnbgP39+uR2zwFxRqdnUk1SEIkyd/LqDq3aUyfTi0TBtbl058O7M5jp+8cKQBhPsQ+ruqSsUe53R1HMMBtO6AXsF3YfXWj98K5uxV0+5lmhspF/65tEu5iU/VnjyL13VPmb3K669PQS/fJad//PW/3hAiYyXtt2qBi5nZAz3b0iBhyOp+Se/q49J47e1c+u2J/3r4w9YjxUqlbuxb7bl359oRcrS8xVI+cIUpV0sVmthdwFDCfoM1hUaETVhN03aRJQT/IfF5g4lOZS0NlvHzEnf/bMcHFfYvWjbn8kB7cddwOWd6RXVVHERfiE0wMneCSxd8Zb9q8QdaeTRuN6pEvRKpK+qOk54ExwJHAowShsx1k/CCfSjGpSOsi1VlWF/G9OPbu3jYhpMB5e2/FUX2q3mAYi8d/81HbJcxVEPPCubtxx28qZkCxSdyjhMjIh8sO7sFB2+Z3+syarqZUeWVz/8k7cv/JlY+aW8zJfqKIckvaELgTGBXOn+BC2eKv7NktsUfOGf23ZMu2jbnmla8qtb+X/rA7R//r00q9N2rlZfd2Tfj256Xlzy89aGtuf/cbIKiaGT51Pk3r12HJqmhfhW5pgpfF27t7W2YuXM6UucsibTOdk3ZJ3buq35atUka3/Piy/ZizZFXB49TE2jR+v0/h+6g/c9YuPD/yh+wr5lnTBsGlZJM8TUpT0xy8XXuWr6765bGaFBgiVSXdDqwEzgtLD1Uv+2+A0hWFo8ywFVWugdAySW4gffKMfpy8aydeGdifEVet71c/cN/1PWPO6N+ZsdcOYLOW65uY8jES+Ykz+qUNflZIjevXYcsssfSr6p4T+hR8H/F279qGu4/vU/SgbLtv1Zo7frMDVx0avdtsTbVlm8ZskWUip1yVtzFUk9bnKFVJFwBPA5uEf/+RdH6hE1ZdZLvLizXIXrh/t5STjz9wyk4pXy+1vklRIvfq3pabjtyeRvXqpJ1JS1JCb6G3LtiT/l3zk/HtnOPsVJnCGD96Wl/+UeLQDoeEI1p7dtg46s4lccxOm0ca1wDRxj9UVx9csg9DUwRXrIpYxpApQm4xRalKOgvYxcyWAUj6GzAcuKeQCSumw3u1541xP1Z4/YZfbcvvdu/MS6Nn8vPiinEDJXHJQVvTpW0TDtq2HcO+m1u+bHA45L5RvTr0CLvU1a1T+Lu4Pbu14aPv5mVdr7KTxVRFppvYXBvEM01akmv30kI4vFcHDt5206zjXGpanP58efG83Rg04eeUczhvDN44f4+Uv4fNWzZkyJ/25oA7hxY/UXGiZAwC1sU9X8cG1tHi3hN35I1xb1Z4PUppvEHd2py4S1Ai6NOxBc98PgNIrFs/vl9HZi9awQX7deOlL2fllLbmDevyy4o1NMgyUjfWFtA2T3ccR++4GavWlgHBQLu1yfMnRrB30qjnbKXkQRfvxVezfsl5P9VVtkzhuXN2pVOGaSc3ZF03aUrXTQozBqEmiEXSjYlvfO66SXHGC2USJWN4DPhc0svh8yOBRwqXpOqnU6tGKUsMyY7dafPyyI3x6tepzRWHRhudG/Pp5fsBMORPe7PzzUPS9loYdPFefD9/OYtXrMm4vVTX5L8dsz1T56Vu8L3zt+urYs7ZK7E67bJDenDhs1/SuU3mi1qsh1DTBnVYsnItrbPE1e/ermnambaqi+YN62Y9jqhSxYByG6demzfnzfE/JsxWWEpRGp/vBE4HFgALgdPN7B+57ERSS0kvSxonaYSk7eKWPSppjqTKddUpggdP6Zt2vtl4lW3wi7VT/DtukvhYwLFYXWy6TXdv15QDe7Yrn/A8l/7gx+3ciSsOyS3DAth3600Yd/1BWcMhx+ZOuHD/bgC0aFjzu+qOvW5AhQloCul/v9+9YPGiXH7FOnT0rsRo7rP37MK7F+1Fnzx2MKmKtL9sSQ2A84CuwHjgX1XornolMMbMjpLUA7gPiHV9eRy4F3iyktsuuJaN61UIj5tP223WnCfTxIaP9VKI5QudWjVixoKKkSz7bdmKNy/Yg202bcbNb01Mu6//nle40dq9O7ZgzA8+9jGfKjuVpCu+BnVr8+zZu7JN+9xLvbVqKSG8x4vn7VbS9pdMJYYnCALmjScY0Pb3KuynJ/AeQBi6u7OkduHzYQSlkYLrkSKuym8yRGTcI6nHTXIM/2I0tMTuQto0zd52sG2H5lkblXOd8CUX6S5isdfz2XXXuepot61a0yIPMc76dm5VoR2imDLVBfQ0s+0BJD0CjKjCfsYSTPbzsaR+wBbA5sDPUTcg6RzgHIBOnSrX/fPps3Zhwo+LOeWR4FAGXbxXxjrtLkmhFoZdui/LVq/lwDuHsmz1ujTvyl2mS3nj+nW47dhe5ZnU2XtuyTWvfp1xe7GAcic+9DnjkxpzC5mZpdt2n04tmXTjwRttDxTnappMt4/lrZl5GPF8K9BS0hjgfOBLIKdtmtmDZtbXzPq2bds2+xtSaN2kfsJo5FwbOps3qkuHFg2pW6fyd91H9dmMAzPEY0/lt307lrc5nLJb5woT1ydr2qAuTRvU5emzdykPS1zqcTOeKThXc2S6wu0gaXH4twToFXssKeskv5IGShoTZgZNzOx0M+sNnAq0Babl5xBqlib161SI51OoQarNGtStEJa4kANiT+vfOWV1nXOuZkmbMZhZbTNrFv41NbM6cY+zdn0xs/vMrHeYGSyXFKt4OwsYZmbFm0E+z3bYPOh1kKrk8KcDu0eKiTP22gGRejrlWyFLDpu3bMQ7F2WeS7emevkPu3NphJnnnNsQFCtw/DbAk5LWAROAM2MLJD0L7AO0kTQTuM7MqvU4iX+dtCOT5yxNOT3iBWHXzGyaN6pLvXAk9AY1WnAD1adTy2rTldC5QitKxmBmw4GUV0wzO6EYacjmtmN60al1I859ahS/ZBks1rh+nUrPPFVqG0qYY+dc4RSu72IN89udO7Jrl9a89sf+RdtnMRuEq8vMUPmULUyIc65yNso5CLdo3YgWDVPPc7xF68bs2qUVR+dhApmo4kdMP3xqXzZvVbhh8cWYEOQ/Z+7CmB8WFnw/b1+4F1/OKPx+nNvYbJQZQ7aQuc+dU9i5nGNSlRgOyLEra1SN6gYfdTGqkvbo1oY9ijCYbcs2jYs614FzG4uNMmOobopR7f/PE/rw/Bc/sO1GMj+Ac67yPGPYSGzavAEXHhCtx5RzbuPmGUMJ7dGtDfv32IRrDq/Z0yF2b9ckL/FhnHPVg2cMJdSgbm0eOW3nUiejygZdvHepk+CcyyPv7+eccy6BZwzOOecSeMbgnHMugWcMzjnnEnjG4JxzLoFnDM455xJ4xuCccy6BZwzOOecSeMbgnHMugWcMzjnnEnjG4JxzLoFnDM455xJ4xuCccy6BZww1nBVz4mjn3EahKBmDpJaSXpY0TtIISduFr3eU9IGkiZK+lnRhMdLjnHMuvWKVGK4ExphZL+BU4O7w9bXAn81sG2BXYKCkmj1rjXPO1XDFyhh6Au8BmNkkoLOkdmb2o5mNDl9fAkwENitSmpxzzqVQrIxhLHA0gKR+wBbA5vErSOoM9AE+T7UBSedIGilp5Ny5cwuaWOec25gVK2O4FWgpaQxwPvAlQTUSAJKaAP8DLjKzxak2YGYPmllfM+vbtm3bYqTZOec2SgWb81nSQODs8OmhZnZ6+LqAaeEfkuoSZApPm9lLhUrPhsoMpFKnwjm3ISlYicHM7jOz3mbWG1guqV646CxgmJktDjOJR4CJZnZnodKyIfLMwDlXKMWqStoG+FrSJOAQINYttT9wCrCfpDHh36FFSpNzzrkUClaVFM/MhgPdUrz+MeD3vpXQuF4dlq5aiw9vc87lW1EyBpd/rwzcnfcnzaF2Lc9XnXP55RlDDdV1k6Z03aRpqZPhnNsAeawk55xzCTxjcM45l8AzBueccwk8Y3DOOZfAMwbnnHMJPGNwzjmXwDMG55xzCTxjcM45l0A1cc5gSXOB70udjhy1AeaVOhFF5se8cfBjrhm2MLNIcxbUyIyhJpI00sz6ljodxeTHvHHwY97weFWSc865BJ4xOOecS+AZQ/E8WOoElIAf88bBj3kD420MzjnnEniJwTnnXALPGJxzziXwjKGKJHWU9IGkiZK+lnRhinVaSnpZ0jhJIyRtF7eshaQXJU0Kt7FbcY8gd3k45ovD930l6VlJDYp7BPknqUF4nGPDY7uh1GmqqijHJKm+pOclTZb0uaTO4esHSholaXz4f79ipz9XVTneuOWdJC2VdEmx0l0QZuZ/VfgD2gM7ho+bAt8CPZPWuR24LnzcA3gvbtkTwFnh43pAi1IfUyGPGdgMmAY0DJ+/AJxW6mPKwzkR0CR8XBf4HNi11Okq9DEBfwDuDx8fDzwfPu4DdAgfbwfMKvXxFPJ440c/E2oAAAatSURBVJb/D/gvcEmpj6cqf15iqCIz+9HMRoePlwATCS5+8XoC74XrTAI6S2onqRmwF/BIuGy1mS0qWuIrqSrHHC6rAzSUVAdoBMwuSsILyAJLw6d1w78a3bMj4jH9muDmBuBFYH9JMrMvzSz2uX4NNJBUv+CJroKqHC+ApCOBqQTHW6N5xpBHYbGyD8GdRryxwNHhOv2ALYDNgS7AXOAxSV9KelhS46IlOA9yPWYzmwX8HZgB/Aj8YmaDipXeQpJUW9IYYA4w2MySz0mNE+GYNgN+ADCztcAvQOukdY4BvjSzVYVOb1VV9njD3+1lQI2vQgTPGPJGUhOCYuRFZrY4afGtQMvwC3c+8CWwluDOeUfg32bWB1gGXF68VFdNZY5ZUkuCu64tgQ5AY0knFzHZBWNm68ysN0Gm3y++XaWminBMSvW28oXStsDfgHMLl8r8qcLx3gDcFVfiqNHqlDoBGwJJdQkukE+b2UvJy8OL5unhuiKoY59GUI0yM+6u5EVqSMZQhWM+CJhmZnPDZS8BuwP/KVLSC87MFkn6EDgY+KrEycmLDMc0E+gIzAyrBpsDCwAkbQ68DJxqZlOKm+KqqcTx7gIcK+k2oAVQJmmlmd1b3JTnh5cYqii86D0CTDSzO9Os00JSvfDpWcAwM1tsZj8BP0jaOly2PzCh4ImuoqocM0EV0q6SGoXb2Z+gjaJGk9RWUovwcUPgAGBSaVNVNRGP6TXgd+HjY4H3zczC970JXGFmnxQrzVVRleM1sz3NrLOZdQb+AfxfTc0UwEsM+dAfOAUYH1abAFwJdAIws/uBbYAnJa0juPCfGff+84Gnw4voVMK77Gqu0sdsZp9LehEYTVCd9iUbRniB9sATkmoT3HC9YGZvlDhNVZXymCT9FRhpZq8R3CA8JWkywZ3z8eF7/wh0Ba6RdE342gAzm1PcQ8hJVY53g+IhMZxzziXwqiTnnHMJPGNwzjmXwDMG55xzCTxjcM45l8AzBueccwk8Y3A1hqR1ksbE/XUudZrySVIfSQ/HPT84jPY5KTze5yV1Cpc9LunYpPenHXUrqZ6kYeGgLOcy8i+Jq0lWhOEKUpJUJ4xfU1NdCdwEEIZiuAf4lZlNDF/7FdCZYJBgTsxstaT3gOOAp/OVYLdh8hKDq9EknSbpv5JeBwaFr10q6QsFc0HcELfuVZK+kTREwTwQl4Svfyipb/i4jaTp4ePakm6P29a54ev7hO+JzaPxdDiKG0k7S/pUQUz/EZKaSvpIUu+4dHwiqVfScTQFepnZ2PClywhGz5aPCjez18xsWIRz8te4UtUsSY+Fi14BTsrpBLuNkpcYXE3SMG6k9TQzOyp8vBvBRXWBpAFAN6AfQcCz1yTtRRCg8HiCSLB1CEZej8qyvzMJor/urCBk9CeSYpFg+wDbEoQM/wToL2kE8DxwnJl9oSCs+grgYeA04CJJ3YH6ZjYuaV99SYzJsy1BFNpMbpd0dfKLZnYtcK2k5sBHQCw0w1fAzlm26ZxnDK5GSVeVNNjMFoSPB4R/X4bPmxBkFE2Bl81sOYCk1yLsbwDQK64uv3m4rdXACDObGW5rDEEVzy/Aj2b2BZQHEkTSfwlCQ1wKnAE8nmJf7QlCsFcgqTXB3BaNgAfNLJZhXGpmL8attzTusQiqjO4ys1FhetZJWi2paTiPhnMpecbgNgTL4h4LuMXMHohfQdJFpJ84Zy3rq1XjpxkVcL6ZvZu0rX2A+LkF1hH8lpRqH2a2XNJggnDjvyUoHSRbkbTvrwlCso81s/lA77Dqq0maY0h2PUHk3seSXq8PrIy4DbeR8jYGt6F5FzhDwVwRSNpM0ibAMOAoSQ3D+vwj4t4zHdgpfHxs0rZ+ryDEOJK6K/NESpOADpJ2DtdvGtcL6GHgn8AXcaWbeBMJgs7F3AZcJWmbuNcaZdh3OUmHAwcCFyS93hqYa2ZromzHbby8xOA2KGY2KLyYDg/bg5cCJ5vZaEnPA2OA7wnq3mP+Drwg6RTg/bjXHyaoIhodVs3MBY7MsO/Vko4D7lEQtnkFQejmpWY2StJiIPkOPvbeSZKax6p5zGy8pAsJItQ2BeYT9Ea6LsJp+DPBJEgjwnPwWtjusC/wVoT3u42cR1d1GyVJ1xNcsLM18OZrfx2AD4EeZlaWZp2LgSVm9nCq5XlIw0sE8yN8U4jtuw2HVyU5V2CSTiWYE/uqdJlC6N8ktl3kMw31gFc8U3BReInBOedcAi8xOOecS+AZg3POuQSeMTjnnEvgGYNzzrkEnjE455xL8P9qMZC4QiBZ2AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mysa.frequency(3e9)\n",
-    "mysa.span(1e8)\n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(mysa.freq_sweep)\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
-    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
-    "    dataid = datasaver.run_id\n",
-    "plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mysa.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  },
-  "nbsphinx": {
-   "execute": "never"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B.ipynb b/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B.ipynb
deleted file mode 100644
index 2eefb4519ac..00000000000
--- a/docs/examples/driver_examples/Qcodes example with Signal Hound USB-SA124B.ipynb	
+++ /dev/null
@@ -1,1860 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Qcodes example with Signal Hound USB-SA124B"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.dataset.measurements import Measurement\n",
-    "from qcodes.dataset.plotting import plot_by_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.instrument_drivers.signal_hound.USB_SA124B import SignalHound_USB_SA124B"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connected to: Signal Hound sa124B (serial:17172185, firmware:Version 3.13) in 6.03s\n"
-     ]
-    }
-   ],
-   "source": [
-    "sh = SignalHound_USB_SA124B('mysignalhound')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Frequency trace"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The primary functionality of the Signal Hound driver is to capture a frequency trace. \n",
-    "The frequency trace is defined by the center frequency and span. After changing any parameter on the Signal Hound \n",
-    "is is important to sync the parameters to the device or you will get a runtime error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sh.frequency(1e9)\n",
-    "sh.span(10e6)\n",
-    "sh.configure()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now capture a trace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 741\n"
-     ]
-    }
-   ],
-   "source": [
-    "meas = Measurement()\n",
-    "meas.register_parameter(sh.trace)\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((sh.trace, sh.trace(),))\n",
-    "    runid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "<img 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\" width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d551d8c2e8>], [None])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_by_id(runid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this case we are not measuring any signal so as expected we see noise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Averaging"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The driver supports averaging over multiple traces simply by setting the number of averages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sh.avg(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 742\n"
-     ]
-    }
-   ],
-   "source": [
-    "meas = Measurement()\n",
-    "meas.register_parameter(sh.trace)\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((sh.trace, sh.trace(),))\n",
-    "    runid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1d552f82780>], [None])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_by_id(runid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We note that the spread of the noise level has gone down compared to a single measurement"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Power"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Spectrum Analyzer also supports measuring the power at a specific frequency. This works by capturing a trace with a span of 250 KHz (The minimum supported)around the center frequency and returning the maximum value in that range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-97.48905792236329"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sh.power()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.7"
-  },
-  "nbsphinx": {
-   "execute": "never"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/examples/driver_examples/Qcodes-example-with-Signal-Hound-USB-SA124B-ParameterWithSetpoints.ipynb b/docs/examples/driver_examples/Qcodes-example-with-Signal-Hound-USB-SA124B-ParameterWithSetpoints.ipynb
new file mode 100644
index 00000000000..9288880a011
--- /dev/null
+++ b/docs/examples/driver_examples/Qcodes-example-with-Signal-Hound-USB-SA124B-ParameterWithSetpoints.ipynb
@@ -0,0 +1,363 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example build on top of the example in [Qcodes example with Signal Hound USB-SA124B.ipynb](Qcodes-example-with-Signal-Hound-USB-SA124B.ipynb) and shows how this driver can be used with a ParameterWithSetpoints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import qcodes as qc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument_drivers.signal_hound.USB_SA124B import SignalHound_USB_SA124B\n",
+    "from qcodes.dataset.measurements import Measurement\n",
+    "from qcodes.dataset.plotting import plot_by_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected to: Signal Hound sa124B (serial:17172185, firmware:Version 3.13) in 6.19s\n"
+     ]
+    }
+   ],
+   "source": [
+    "mysa = SignalHound_USB_SA124B('mysa', dll_path='C:\\\\Program Files\\\\Signal Hound\\\\Spike\\\\sa_api.dll')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'vendor': 'Signal Hound',\n",
+       " 'model': 'sa124B',\n",
+       " 'serial': '17172185',\n",
+       " 'firmware': 'Version 3.13'}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mysa.get_idn()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mysa.frequency(2e9)\n",
+    "mysa.span(0.5e6)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 359\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x23d0ac3ed30>], [None])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.avg(1)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
+    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 360\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x23d0ac4f9b0>], [None])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.avg(10)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
+    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 361\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x23d0ac4f2e8>], [None])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.avg(100)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
+    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    \n",
+    "    dataid = datasaver.run_id\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 362\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x23d0c1ab4a8>], [None])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.frequency(3e9)\n",
+    "mysa.span(1e6)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
+    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    dataid = datasaver.run_id\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 363\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "During call of saInitiate the followingWarning: saBandwidthClamped was raised\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x23d0c1a92b0>], [None])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mysa.frequency(3e9)\n",
+    "mysa.span(1e8)\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(mysa.freq_sweep)\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mysa.frequency_axis, mysa.frequency_axis.get()),\n",
+    "                         (mysa.freq_sweep, mysa.freq_sweep.get(),))\n",
+    "    dataid = datasaver.run_id\n",
+    "plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mysa.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  },
+  "nbsphinx": {
+   "execute": "never"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/driver_examples/Qcodes-example-with-Signal-Hound-USB-SA124B.ipynb b/docs/examples/driver_examples/Qcodes-example-with-Signal-Hound-USB-SA124B.ipynb
new file mode 100644
index 00000000000..69600e16063
--- /dev/null
+++ b/docs/examples/driver_examples/Qcodes-example-with-Signal-Hound-USB-SA124B.ipynb
@@ -0,0 +1,1869 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Qcodes example with Signal Hound USB-SA124B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Please also note the extensions to this driver in [Qcodes example with Signal Hound USB-SA124B ParameterWithSetpoints](Qcodes-example-with-Signal-Hound-USB-SA124B-ParameterWithSetpoints.ipynb) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.dataset.measurements import Measurement\n",
+    "from qcodes.dataset.plotting import plot_by_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.instrument_drivers.signal_hound.USB_SA124B import SignalHound_USB_SA124B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected to: Signal Hound sa124B (serial:17172185, firmware:Version 3.13) in 6.21s\n"
+     ]
+    }
+   ],
+   "source": [
+    "sh = SignalHound_USB_SA124B('mysignalhound')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Frequency trace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The primary functionality of the Signal Hound driver is to capture a frequency trace. \n",
+    "The frequency trace is defined by the center frequency and span. After changing any parameter on the Signal Hound \n",
+    "is is important to sync the parameters to the device or you will get a runtime error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sh.frequency(1e9)\n",
+    "sh.span(10e6)\n",
+    "sh.configure()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now capture a trace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 357\n"
+     ]
+    }
+   ],
+   "source": [
+    "meas = Measurement()\n",
+    "meas.register_parameter(sh.trace)\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((sh.trace, sh.trace(),))\n",
+    "    runid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x158d5573d68>], [None])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot_by_id(runid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case we are not measuring any signal so as expected we see noise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Averaging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The driver supports averaging over multiple traces simply by setting the number of averages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sh.avg(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 358\n"
+     ]
+    }
+   ],
+   "source": [
+    "meas = Measurement()\n",
+    "meas.register_parameter(sh.trace)\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((sh.trace, sh.trace(),))\n",
+    "    runid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x158d68410b8>], [None])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot_by_id(runid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We note that the spread of the noise level has gone down compared to a single measurement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Power"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Spectrum Analyzer also supports measuring the power at a specific frequency. This works by capturing a trace with a span of 250 KHz (The minimum supported)around the center frequency and returning the maximum value in that range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-98.23629913330078"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sh.power()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sh.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  },
+  "nbsphinx": {
+   "execute": "never"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From f5787f4f38bbf0ebcd42b9671d7f882346c2c870 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 15:17:57 +0100
Subject: [PATCH 669/719] Ensure stable error message that we can test against

---
 qcodes/dataset/measurements.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/measurements.py b/qcodes/dataset/measurements.py
index 3c33a04558e..e9bcf907b62 100644
--- a/qcodes/dataset/measurements.py
+++ b/qcodes/dataset/measurements.py
@@ -214,9 +214,9 @@ def add_result(self, *res_tuple: res_type) -> None:
                 if not stuffweneed.issubset(stuffwehave):
                     raise ValueError('Can not add this result; missing '
                                      f'setpoint values for {paramstr}:'
-                                     f' {stuffweneed}.'
+                                     f' {sorted(stuffweneed)}.'
                                      f' Values only given for'
-                                     f' {found_parameters}.')
+                                     f' {sorted(stuffwehave)}.')
 
         if inserting_unrolled_array and inserting_as_arrays:
             raise RuntimeError("Trying to insert multiple data values both "

From 4222b85fd1025c71dec012a9c25de47a2ec27d43 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 15:18:32 +0100
Subject: [PATCH 670/719] Update output to reflect new error output

---
 .../tests/dataset/test_measurement_context_manager.py  | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 703bf9f973c..29fd05b1d15 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -1120,13 +1120,11 @@ def test_datasaver_parameter_with_setpoints_reg_but_missing(
                                              r"missing setpoint values for "
                                              r"dummy_channel_inst_ChanA_dummy_"
                                              r"parameter_with_setpoints: "
-                                             r"\{'dummy_channel_inst_ChanA_"
-                                             r"dummy_sp_axis', 'someparam'\}"
-                                             r". Values only given for \['"
-                                             r"dummy_channel_inst_ChanA_dummy_sp_axis', "
+                                             r"\['dummy_channel_inst_ChanA_"
+                                             r"dummy_sp_axis', 'someparam'\]"
+                                             r". Values only given for \["
                                              r"'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints', "
-                                             r"'dummy_channel_inst_ChanA_dummy_sp_axis', "
-                                             r"'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints'\]."):
+                                             r"'dummy_channel_inst_ChanA_dummy_sp_axis'\]"):
             datasaver.add_result(*expand_setpoints_helper(param))
 
 

From fe1f859ca11489a26005154bab78dea1db424f8f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 24 Jan 2019 15:19:05 +0100
Subject: [PATCH 671/719] consistent assert on setpoint type

---
 qcodes/instrument/parameter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index 6566a1d6643..7f0b63a50d9 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -988,7 +988,7 @@ def setpoints(self) -> Sequence[_BaseParameter]:
     @setpoints.setter
     def setpoints(self, setpoints: Sequence[_BaseParameter]):
         for setpointarray in setpoints:
-            if not isinstance(setpointarray, _BaseParameter):
+            if not isinstance(setpointarray, Parameter):
                 raise TypeError(f"Setpoints is of type {type(setpointarray)}"
                                 f" expcected a QCoDeS parameter")
         self._setpoints = setpoints

From cf0e532ad37244dfc0fec421e0c2fa049ad7aa08 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 25 Jan 2019 15:06:05 +0100
Subject: [PATCH 672/719] Don't allow connection to db that is newer than the
 one we support

This can happen if you go back to an old git version and is dangerous
as it can result in inconsistent dbs
---
 qcodes/dataset/sqlite_base.py                     | 10 +++++++++-
 .../test_database_creation_and_upgrading.py       | 15 +++++++++++++++
 2 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index fee22744eac..f62bdac439a 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -322,7 +322,7 @@ def many_many(curr: sqlite3.Cursor, *columns: str) -> List[List[Any]]:
 
 
 def connect(name: str, debug: bool = False,
-            version: int=-1) -> ConnectionPlus:
+            version: int = -1) -> ConnectionPlus:
     """
     Connect or create  database. If debug the queries will be echoed back.
     This function takes care of registering the numpy/sqlite type
@@ -350,6 +350,14 @@ def connect(name: str, debug: bool = False,
     sqlite3_conn = sqlite3.connect(name, detect_types=sqlite3.PARSE_DECLTYPES)
     conn = ConnectionPlus(sqlite3_conn)
 
+    latest_supported_version = _latest_available_version()
+    db_version = get_user_version(conn)
+
+    if db_version > latest_supported_version:
+        raise RuntimeError(f"Database {name} is version {db_version} but this "
+                           f"version of QCoDeS supports up to "
+                           f"version {latest_supported_version}")
+
     # sqlite3 options
     conn.row_factory = sqlite3.Row
 
diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index 4b40ef87dc2..5aac5f7d7af 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -23,6 +23,7 @@
                                         update_GUIDs,
                                         get_db_version_and_newest_available_version,
                                         get_user_version,
+                                        set_user_version,
                                         atomic_transaction,
                                         perform_db_upgrade_0_to_1,
                                         perform_db_upgrade_1_to_2,
@@ -355,6 +356,20 @@ def test_update_existing_guids(caplog):
         assert guid_comps_5['work_station'] == old_ws
 
 
+@pytest.mark.usefixtures("empty_temp_db")
+def test_cannot_connect_to_newer_db():
+    conn = connect(qc.config["core"]["db_location"],
+                   qc.config["core"]["db_debug"])
+    current_version = get_user_version(conn)
+    set_user_version(conn, current_version+1)
+    conn.close()
+    err_msg = f'is version {current_version + 1} but this version of QCoDeS ' \
+        f'supports up to version {current_version}'
+    with pytest.raises(RuntimeError, match=err_msg):
+        conn = connect(qc.config["core"]["db_location"],
+                       qc.config["core"]["db_debug"])
+
+
 def test_latest_available_version():
     assert 3 == _latest_available_version()
 

From a6ac9e2a49b1f9f891cf9129c4a57b5f6fdd828c Mon Sep 17 00:00:00 2001
From: Pieter Eendebak <pieter.eendebak@tno.nl>
Date: Tue, 29 Jan 2019 14:11:29 +0100
Subject: [PATCH 673/719] remove invalid test

---
 qcodes/tests/test_hdf5formatter.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/qcodes/tests/test_hdf5formatter.py b/qcodes/tests/test_hdf5formatter.py
index d32899030dd..961e4f1f2f3 100644
--- a/qcodes/tests/test_hdf5formatter.py
+++ b/qcodes/tests/test_hdf5formatter.py
@@ -244,11 +244,7 @@ def test_dataset_finalize_closes_file(self):
         # closing before file is written should not raise error
         self.formatter.write(data, flush=False)
         fp = data._h5_base_group.filename
-        fp2 = fp[:-5]+'_2.hdf5'
-        copy(fp, fp2)
-        # Raises an error because the file was still open
-        with self.assertRaises(OSError):
-            F2 = h5py.File(fp2)
+
         # Attaching the formatter like this should not be neccesary
         data.formatter = self.formatter
         data.finalize()

From d30104599e4e38098f21c8ad6fba6887c786f77a Mon Sep 17 00:00:00 2001
From: Pieter Eendebak <pieter.eendebak@tno.nl>
Date: Tue, 29 Jan 2019 18:18:24 +0100
Subject: [PATCH 674/719] remove invalid test

---
 qcodes/tests/test_hdf5formatter.py | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/qcodes/tests/test_hdf5formatter.py b/qcodes/tests/test_hdf5formatter.py
index 961e4f1f2f3..7fbe0af7dd3 100644
--- a/qcodes/tests/test_hdf5formatter.py
+++ b/qcodes/tests/test_hdf5formatter.py
@@ -218,16 +218,10 @@ def test_dataset_closing(self):
         data = DataSet1D(location=self.loc_provider, name='test_closing')
         self.formatter.write(data, flush=False)
         fp = data._h5_base_group.filename
-        fp2 = fp[:-5]+'_2.hdf5'
-        copy(fp, fp2)
-        # Raises an error because the file was open and not flushed
-        # This tests if this way of testing works
-        with self.assertRaises(OSError):
-            F2 = h5py.File(fp2)
         self.formatter.close_file(data)
         fp3 = fp[:-5]+'_3.hdf5'
         copy(fp, fp3)
-        # Should now not raise an error because the file was properly closed
+        # Should not raise an error because the file was properly closed
         F3 = h5py.File(fp3)
 
     def test_dataset_flush_after_write(self):

From 506756bbd0dc150c30e60a9d765bb46b343c6aab Mon Sep 17 00:00:00 2001
From: ectof <eoofarre@microsoft.com>
Date: Thu, 31 Jan 2019 12:28:20 +0100
Subject: [PATCH 675/719] replaced array.name for array.array_id, array.name
 was often non-unique, for a 2d sweep of e.g. gates both parameters would have
 name \'v\'

---
 qcodes/dataset/legacy_import.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/qcodes/dataset/legacy_import.py b/qcodes/dataset/legacy_import.py
index debab3e07f0..7f453ac8c5f 100644
--- a/qcodes/dataset/legacy_import.py
+++ b/qcodes/dataset/legacy_import.py
@@ -19,8 +19,8 @@ def setup_measurement(dataset: OldDataSet) -> Measurement:
         if array.is_setpoint:
             setarrays = None
         else:
-            setarrays = [setarray.name for setarray in array.set_arrays]
-        meas.register_custom_parameter(name=array.name,
+            setarrays = [setarray.array_id for setarray in array.set_arrays]
+        meas.register_custom_parameter(name=array.array_id,
                                        label=array.label,
                                        unit=array.unit,
                                        setpoints = setarrays
@@ -33,13 +33,13 @@ def store_array_to_database(datasaver, array):
     if dims == 2:
         for index1, i in enumerate(array.set_arrays[0]):
             for index2, j in enumerate(array.set_arrays[1][index1]):
-                datasaver.add_result((array.set_arrays[0].name, i),
-                                     (array.set_arrays[1].name, j),
-                                     (array.name, array[index1,index2]))
+                datasaver.add_result((array.set_arrays[0].array_id, i),
+                                     (array.set_arrays[1].array_id, j),
+                                     (array.array_id, array[index1,index2]))
     elif dims == 1:
         for index, i in enumerate(array.set_arrays[0]):
-            datasaver.add_result((array.set_arrays[0].name, i),
-                                 (array.name, array[index]))
+            datasaver.add_result((array.set_arrays[0].array_id, i),
+                                 (array.array_id, array[index]))
     else:
         raise NotImplementedError('The exporter only currently handles 1 and 2 Dimentional data')
     return datasaver.run_id
@@ -52,14 +52,14 @@ def store_array_to_database_alt(meas, array):
         with meas.run() as datasaver:
             for index1, i in enumerate(array.set_arrays[0]):
                 outer_data[:] = i
-                datasaver.add_result((array.set_arrays[0].name, outer_data),
-                                     (array.set_arrays[1].name, array.set_arrays[1][index1,:]),
-                                     (array.name, array[index1,:]))
+                datasaver.add_result((array.set_arrays[0].array_id, outer_data),
+                                     (array.set_arrays[1].array_id, array.set_arrays[1][index1,:]),
+                                     (array.array_id, array[index1,:]))
     elif dims == 1:
         with meas.run() as datasaver:
             for index, i in enumerate(array.set_arrays[0]):
-                datasaver.add_result((array.set_arrays[0].name, i),
-                                     (array.name, array[index]))
+                datasaver.add_result((array.set_arrays[0].array_id, i),
+                                     (array.array_id, array[index]))
     else:
         raise NotImplementedError('The exporter only currently handles 1 and 2 Dimentional data')
     return datasaver.run_id

From ee5fedc31dd99eb29c016c5c890b1e34879f3046 Mon Sep 17 00:00:00 2001
From: Pieter Eendebak <pieter.eendebak@tno.nl>
Date: Thu, 31 Jan 2019 14:34:39 +0100
Subject: [PATCH 676/719] update docstring

---
 qcodes/instrument_drivers/QuTech/D5a.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/QuTech/D5a.py b/qcodes/instrument_drivers/QuTech/D5a.py
index 1d0008cf12a..9f54adfadfd 100644
--- a/qcodes/instrument_drivers/QuTech/D5a.py
+++ b/qcodes/instrument_drivers/QuTech/D5a.py
@@ -65,7 +65,8 @@ def __init__(self, name, spi_rack, module, inter_delay=0.1, dac_step=10e-3,
 
         self._span_get_map = {v: k for k, v in self._span_set_map.items()}
 
-        self.add_function('set_dacs_zero', call_cmd=self._set_dacs_zero)
+        self.add_function('set_dacs_zero', call_cmd=self._set_dacs_zero,
+                          docstring='Reset all dacs to zero voltage. no ramping is performed.')
 
         if self._mV:
             self._gain = 1e3

From fad91e19a01534f3c3ad6b2d564586a2015a1a43 Mon Sep 17 00:00:00 2001
From: Pieter Eendebak <pieter.eendebak@tno.nl>
Date: Thu, 31 Jan 2019 14:36:38 +0100
Subject: [PATCH 677/719] update docstring

---
 qcodes/instrument_drivers/QuTech/D5a.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/QuTech/D5a.py b/qcodes/instrument_drivers/QuTech/D5a.py
index 9f54adfadfd..950167a9c08 100644
--- a/qcodes/instrument_drivers/QuTech/D5a.py
+++ b/qcodes/instrument_drivers/QuTech/D5a.py
@@ -66,7 +66,7 @@ def __init__(self, name, spi_rack, module, inter_delay=0.1, dac_step=10e-3,
         self._span_get_map = {v: k for k, v in self._span_set_map.items()}
 
         self.add_function('set_dacs_zero', call_cmd=self._set_dacs_zero,
-                          docstring='Reset all dacs to zero voltage. no ramping is performed.')
+                          docstring='Reset all dacs to zero voltage. No ramping is performed.')
 
         if self._mV:
             self._gain = 1e3
@@ -89,7 +89,8 @@ def __init__(self, name, spi_rack, module, inter_delay=0.1, dac_step=10e-3,
 
             self.add_parameter('stepsize{}'.format(i + 1),
                                get_cmd=partial(self.d5a.get_stepsize, i),
-                               unit='V')
+                               unit='V',
+                               docstring='Returns the smallest voltage step of the DAC.')
 
             self.add_parameter('span{}'.format(i + 1),
                                get_cmd=partial(self._get_span, i),

From a03c90d7fa9d304607e25599cef94339d7514e4a Mon Sep 17 00:00:00 2001
From: Pieter Eendebak <pieter.eendebak@tno.nl>
Date: Thu, 31 Jan 2019 14:42:43 +0100
Subject: [PATCH 678/719] fix: qcodes standards do not allow changing
 parameters on overridden method

---
 qcodes/data/hdf5_format.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/qcodes/data/hdf5_format.py b/qcodes/data/hdf5_format.py
index 73d130b56de..29ff079dc48 100644
--- a/qcodes/data/hdf5_format.py
+++ b/qcodes/data/hdf5_format.py
@@ -272,7 +272,7 @@ def _create_dataarray_dset(self, array, group):
 
         return dset
 
-    def write_metadata(self, data_set, io_manager=None, location=None, read_first=True, flush = True):
+    def write_metadata(self, data_set, io_manager=None, location=None, read_first=True):
         """
         Writes metadata of dataset to file using write_dict_to_hdf5 method
 
@@ -292,8 +292,7 @@ def write_metadata(self, data_set, io_manager=None, location=None, read_first=Tr
 
         # flush ensures buffers are written to disk
         # (useful for ensuring openable by other files)
-        if flush:
-            data_set._h5_base_group.file.flush()
+        data_set._h5_base_group.file.flush()
 
     def _read_list_group(self, entry_point, list_type):
         d = {}

From 42886118669d84654f0aeb9defb1705e8bb4155a Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Thu, 31 Jan 2019 10:06:40 -0800
Subject: [PATCH 679/719] Update tests to reflect new names

---
 .../dataset/test_measurement_context_manager.py      | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/qcodes/tests/dataset/test_measurement_context_manager.py b/qcodes/tests/dataset/test_measurement_context_manager.py
index 29fd05b1d15..bde7132ecec 100644
--- a/qcodes/tests/dataset/test_measurement_context_manager.py
+++ b/qcodes/tests/dataset/test_measurement_context_manager.py
@@ -1554,12 +1554,12 @@ def test_load_legacy_files_2D():
     run_ids = import_dat_file(full_location)
     run_id = run_ids[0]
     data = load_by_id(run_id)
-    assert data.parameters == 'ch1,ch2,voltage'
+    assert data.parameters == 'dac_ch1_set,dac_ch2_set,dmm_voltage'
     assert data.number_of_results == 36
-    expected_names = ['ch1', 'ch2', 'voltage']
+    expected_names = ['dac_ch1_set', 'dac_ch2_set', 'dmm_voltage']
     expected_labels = ['Gate ch1', 'Gate ch2', 'Gate voltage']
     expected_units = ['V', 'V', 'V']
-    expected_depends_on = ['', '', 'ch1, ch2']
+    expected_depends_on = ['', '', 'dac_ch1_set, dac_ch2_set']
     for i, parameter in enumerate(data.get_parameters()):
         assert parameter.name == expected_names[i]
         assert parameter.label == expected_labels[i]
@@ -1580,12 +1580,12 @@ def test_load_legacy_files_1D():
     run_ids = import_dat_file(full_location)
     run_id = run_ids[0]
     data = load_by_id(run_id)
-    assert data.parameters == 'ch1,voltage'
+    assert data.parameters == 'dac_ch1_set,dmm_voltage'
     assert data.number_of_results == 201
-    expected_names = ['ch1', 'voltage']
+    expected_names = ['dac_ch1_set', 'dmm_voltage']
     expected_labels = ['Gate ch1', 'Gate voltage']
     expected_units = ['V', 'V']
-    expected_depends_on = ['', 'ch1']
+    expected_depends_on = ['', 'dac_ch1_set']
     for i, parameter in enumerate(data.get_parameters()):
         assert parameter.name == expected_names[i]
         assert parameter.label == expected_labels[i]

From ed9d02225a43efa2cc89a57e86bdcbb731a164fe Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 23 Jan 2019 13:32:48 +0100
Subject: [PATCH 680/719] Validate that infered_from adn depends_on are not
 strings but sequences

---
 qcodes/dataset/param_spec.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/qcodes/dataset/param_spec.py b/qcodes/dataset/param_spec.py
index beb7183ec66..f425fc744a7 100644
--- a/qcodes/dataset/param_spec.py
+++ b/qcodes/dataset/param_spec.py
@@ -43,10 +43,18 @@ def __init__(self, name: str,
         inferred_from = [] if inferred_from is None else inferred_from
         depends_on = [] if depends_on is None else depends_on
 
+        if isinstance(inferred_from, str):
+            raise ValueError(f"ParamSpec {self.name} got "
+                             f"string {inferred_from} it needs a "
+                             f"Sequence of ParamSpecs or strings")
         self._inferred_from.extend(
             p.name if isinstance(p, ParamSpec) else p
             for p in inferred_from)
 
+        if isinstance(depends_on, str):
+            raise ValueError(f"ParamSpec {self.name} got "
+                             f"string {depends_on} it needs a "
+                             f"Sequence of ParamSpecs or strings")
         self._depends_on.extend(
             p.name if isinstance(p, ParamSpec) else p
             for p in depends_on)

From 234843a9633f716632ef862836f1bc39ad4f506b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 23 Jan 2019 13:48:17 +0100
Subject: [PATCH 681/719] Correct upgrade path

---
 qcodes/dataset/sqlite_base.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index f62bdac439a..c6e743cbe45 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -617,6 +617,8 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
 
         # first possibility: another parameter depends on this parameter
         if layout_id in indeps:
+            inferred_from = inferred_from.split(', ')
+
             paramspec = ParamSpec(name=name, paramtype=paramtype,
                                   label=label, unit=unit,
                                   inferred_from=inferred_from)
@@ -627,6 +629,7 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
 
             setpoints = dependencies[layout_id]
             depends_on = [paramspecs[idp].name for idp in setpoints]
+            inferred_from = inferred_from.split(', ')
 
             paramspec = ParamSpec(name=name,
                                   paramtype=paramtype,

From c5108f5a40513d14e1c7d0b39a46b2f604ba5ea5 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 23 Jan 2019 14:34:34 +0100
Subject: [PATCH 682/719] Mypy fix

---
 qcodes/dataset/sqlite_base.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index c6e743cbe45..e317d93ab7c 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -617,11 +617,11 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
 
         # first possibility: another parameter depends on this parameter
         if layout_id in indeps:
-            inferred_from = inferred_from.split(', ')
+            inferred_from_list = inferred_from.split(', ')
 
             paramspec = ParamSpec(name=name, paramtype=paramtype,
                                   label=label, unit=unit,
-                                  inferred_from=inferred_from)
+                                  inferred_from=inferred_from_list)
             paramspecs[layout_id] = paramspec
 
         # second possibility: this parameter depends on another parameter
@@ -629,13 +629,13 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
 
             setpoints = dependencies[layout_id]
             depends_on = [paramspecs[idp].name for idp in setpoints]
-            inferred_from = inferred_from.split(', ')
+            inferred_from_list = inferred_from.split(', ')
 
             paramspec = ParamSpec(name=name,
                                   paramtype=paramtype,
                                   label=label, unit=unit,
                                   depends_on=depends_on,
-                                  inferred_from=inferred_from)
+                                  inferred_from=inferred_from_list)
             paramspecs[layout_id] = paramspec
 
         # third possibility: no dependencies

From 3d230ae636099718b45a006dfbacbaf722ca0e69 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 25 Jan 2019 15:52:58 +0100
Subject: [PATCH 683/719] Add another db to version 3 generation

---
 .../generate_version_3.py                     | 60 ++++++++++++++++++-
 1 file changed, 59 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py b/qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py
index 7d67cd50628..f080819c647 100644
--- a/qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py
+++ b/qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py
@@ -29,6 +29,63 @@ def generate_empty_DB_file():
     sqlite_base.connect(path)
 
 
+def generate_DB_file_with_some_runs():
+    """
+    Generate a .db-file with a handful of runs with some interdependent
+    parameters
+    """
+
+    # This function will run often on CI and re-generate the .db-files
+    # That should ideally be a deterministic action
+    # (although this hopefully plays no role)
+    np.random.seed(0)
+
+    v3fixturepath = os.path.join(fixturepath, 'version3')
+    os.makedirs(v3fixturepath, exist_ok=True)
+    path = os.path.join(v3fixturepath, 'some_runs.db')
+
+    if os.path.exists(path):
+        os.remove(path)
+
+    from qcodes.dataset.sqlite_base import connect
+    from qcodes.dataset.measurements import Measurement
+    from qcodes.dataset.experiment_container import Experiment
+    from qcodes import Parameter
+
+    connect(path)
+    exp = Experiment(path_to_db=path,
+                     name='experiment_1',
+                     sample_name='no_sample_1')
+
+    # Now make some parameters to use in measurements
+    params = []
+    for n in range(5):
+        params.append(Parameter(f'p{n}', label=f'Parameter {n}',
+                                unit=f'unit {n}', set_cmd=None, get_cmd=None))
+
+    # Set up an experiment
+
+    meas = Measurement(exp)
+    meas.register_parameter(params[0])
+    meas.register_parameter(params[1])
+    meas.register_parameter(params[2], basis=(params[0],))
+    meas.register_parameter(params[3], basis=(params[1],))
+    meas.register_parameter(params[4], setpoints=(params[2], params[3]))
+
+    # Make a number of identical runs
+
+    for _ in range(10):
+
+        with meas.run() as datasaver:
+
+            for x in np.random.rand(10):
+                for y in np.random.rand(10):
+                    z = np.random.rand()
+                    datasaver.add_result((params[2], x),
+                                         (params[3], y),
+                                         (params[4], z))
+
+
 def generate_DB_file_with_some_runs_having_not_run_descriptions():
     """
     Generate a .db-file with a handful of runs some of which lack run
@@ -133,7 +190,8 @@ def generate_DB_file_with_some_runs_having_not_run_descriptions():
 if __name__ == '__main__':
 
     gens = (generate_empty_DB_file,
-            generate_DB_file_with_some_runs_having_not_run_descriptions)
+            generate_DB_file_with_some_runs_having_not_run_descriptions,
+            generate_DB_file_with_some_runs)
 
     # pylint: disable=E1101
     utils.checkout_to_old_version_and_run_generators(version=3, gens=gens)

From 37d89f093e6498389454fc536601c5d4c5fefe66 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sat, 26 Jan 2019 18:07:57 +0100
Subject: [PATCH 684/719] Add logic to generate v3 db from upgraded v2

---
 .../legacy_DB_generation/generate_version_3.py | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py b/qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py
index f080819c647..76822e67cc0 100644
--- a/qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py
+++ b/qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py
@@ -1,6 +1,7 @@
 # Generate version 3 database files for qcodes' test suite to consume
 
 import os
+import shutil
 import numpy as np
 
 # NB: it's important that we do not import anything from qcodes before we
@@ -187,11 +188,26 @@ def generate_DB_file_with_some_runs_having_not_run_descriptions():
     conn.commit()  # just to be sure
 
 
+def generate_upgraded_v2_runs():
+    """
+    Generate some runs by upgradeing from v2 db. This
+    is needed since the bug we want to test against is in
+    the v2 to v3 upgrade and not in v3 it self.
+    This requires the v2 generation to be run before this one
+    """
+    import qcodes.dataset.sqlite_base as sqlite_base
+    v2fixture_path = os.path.join(fixturepath, 'version2', 'some_runs.db')
+    v3fixturepath = os.path.join(fixturepath, 'version3', 'some_runs_upgraded_2.db')
+    shutil.copy2(v2fixture_path, v3fixturepath)
+    sqlite_base.connect(v3fixturepath)
+
+
 if __name__ == '__main__':
 
     gens = (generate_empty_DB_file,
             generate_DB_file_with_some_runs_having_not_run_descriptions,
-            generate_DB_file_with_some_runs)
+            generate_DB_file_with_some_runs,
+            generate_upgraded_v2_runs)
 
     # pylint: disable=E1101
     utils.checkout_to_old_version_and_run_generators(version=3, gens=gens)

From e382dffac4441de7d344311b171252eae244965c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Fri, 25 Jan 2019 15:31:17 +0100
Subject: [PATCH 685/719] Actually test all parameters in db upgrade

---
 .../test_database_creation_and_upgrading.py    | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index 5aac5f7d7af..5844c101db5 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -263,6 +263,24 @@ def test_perform_actual_upgrade_2_to_3_some_runs():
         assert p0.label == "Parameter 0"
         assert p0.unit == "unit 0"
 
+        p1 = [p for p in idp.paramspecs if p.name == 'p1'][0]
+        assert p1.depends_on == ''
+        assert p1.inferred_from == ''
+        assert p1.label == "Parameter 1"
+        assert p1.unit == "unit 1"
+
+        p2 = [p for p in idp.paramspecs if p.name == 'p2'][0]
+        assert p2.depends_on == ''
+        assert p2.inferred_from == 'p0'
+        assert p2.label == "Parameter 2"
+        assert p2.unit == "unit 2"
+
+        p3 = [p for p in idp.paramspecs if p.name == 'p3'][0]
+        assert p3.depends_on == ''
+        assert p3.inferred_from == 'p1'
+        assert p3.label == "Parameter 3"
+        assert p3.unit == "unit 3"
+
         p4 = [p for p in idp.paramspecs if p.name == 'p4'][0]
         assert p4.depends_on == 'p2, p3'
         assert p4.inferred_from == ''

From a18eab6ff820843ee8eea36ada915b24d89f67e8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sat, 26 Jan 2019 18:24:15 +0100
Subject: [PATCH 686/719] correct v3 hash

---
 qcodes/tests/dataset/legacy_DB_generation/utils.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/qcodes/tests/dataset/legacy_DB_generation/utils.py b/qcodes/tests/dataset/legacy_DB_generation/utils.py
index ba01ffa04ad..d5d5afac1d4 100644
--- a/qcodes/tests/dataset/legacy_DB_generation/utils.py
+++ b/qcodes/tests/dataset/legacy_DB_generation/utils.py
@@ -24,10 +24,7 @@
 GIT_HASHES: Dict[int, str] = {0: '78d42620fc245a975b5a615ed5e33061baac7846',
                               1: '056d59627e22fa3ca7aad4c265e9897c343f79cf',
                               2: '5202255924542dad6841dfe3d941a7f80c43956c',
-                              # NOTE: commit hash associated with version 3
-                              # will need to be updated once we move to
-                              # version 4
-                              3: 'a5d8edae05898df0cbfb5d9048565df91efea692'}
+                              3: '01cb405d47b72b1a5bab227dcf4b69866ecebb0f'}
 
 gitrepopath = os.sep.join(os.path.realpath(__file__).split(os.sep)[:-5])
 repo = Repo(gitrepopath)

From 90132bafa257f97309003aaa20c4813d117d77cf Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sat, 26 Jan 2019 18:39:17 +0100
Subject: [PATCH 687/719] add test that performing v3 to v4

Make sure that it fixes the issue observed in v3
---
 .../test_database_creation_and_upgrading.py   | 98 +++++++++++++++++++
 1 file changed, 98 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index 5844c101db5..b51ba291b34 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -28,6 +28,7 @@
                                         perform_db_upgrade_0_to_1,
                                         perform_db_upgrade_1_to_2,
                                         perform_db_upgrade_2_to_3,
+                                        perform_db_upgrade_3_to_4,
                                         _latest_available_version)
 
 from qcodes.dataset.guids import parse_guid
@@ -288,6 +289,103 @@ def test_perform_actual_upgrade_2_to_3_some_runs():
         assert p4.unit == "unit 4"
 
 
+def test_perfrom_upgrade_v3_to_v4_fixes():
+
+    v3fixpath = os.path.join(fixturepath, 'db_files', 'version3')
+
+    dbname_old = os.path.join(v3fixpath, 'some_runs_upgraded_2.db')
+
+    if not os.path.exists(dbname_old):
+        pytest.skip("No db-file fixtures found. You can generate test db-files"
+                    " using the scripts in the legacy_DB_generation folder")
+
+    with temporarily_copied_DB(dbname_old, debug=False, version=3) as conn:
+
+        assert get_user_version(conn) == 3
+
+        sql = f"""
+              SELECT run_description
+              FROM runs
+              WHERE run_id == 1
+              """
+        c = atomic_transaction(conn, sql)
+        json_str = one(c, 'run_description')
+
+        desc = RunDescriber.from_json(json_str)
+        idp = desc.interdeps
+        assert isinstance(idp, InterDependencies)
+
+        p0 = [p for p in idp.paramspecs if p.name == 'p0'][0]
+        assert p0.depends_on == ''
+        assert p0.inferred_from == ''
+        assert p0.label == "Parameter 0"
+        assert p0.unit == "unit 0"
+
+        p1 = [p for p in idp.paramspecs if p.name == 'p1'][0]
+        assert p1.depends_on == ''
+        assert p1.inferred_from == ''
+        assert p1.label == "Parameter 1"
+        assert p1.unit == "unit 1"
+
+        p2 = [p for p in idp.paramspecs if p.name == 'p2'][0]
+        assert p2.depends_on == ''
+        assert p2.inferred_from == 'p, 0'
+        assert p2.label == "Parameter 2"
+        assert p2.unit == "unit 2"
+
+        p3 = [p for p in idp.paramspecs if p.name == 'p3'][0]
+        assert p3.depends_on == ''
+        assert p3.inferred_from == 'p, 1'
+        assert p3.label == "Parameter 3"
+        assert p3.unit == "unit 3"
+
+        p4 = [p for p in idp.paramspecs if p.name == 'p4'][0]
+        assert p4.depends_on == 'p2, p3'
+        assert p4.inferred_from == ''
+        assert p4.label == "Parameter 4"
+        assert p4.unit == "unit 4"
+
+        perform_db_upgrade_3_to_4(conn)
+
+        c = atomic_transaction(conn, sql)
+        json_str = one(c, 'run_description')
+
+        desc = RunDescriber.from_json(json_str)
+        idp = desc.interdeps
+        assert isinstance(idp, InterDependencies)
+
+        p0 = [p for p in idp.paramspecs if p.name == 'p0'][0]
+        assert p0.depends_on == ''
+        assert p0.inferred_from == ''
+        assert p0.label == "Parameter 0"
+        assert p0.unit == "unit 0"
+
+        p1 = [p for p in idp.paramspecs if p.name == 'p1'][0]
+        assert p1.depends_on == ''
+        assert p1.inferred_from == ''
+        assert p1.label == "Parameter 1"
+        assert p1.unit == "unit 1"
+
+        p2 = [p for p in idp.paramspecs if p.name == 'p2'][0]
+        assert p2.depends_on == ''
+        assert p2.inferred_from == 'p0'
+        assert p2.label == "Parameter 2"
+        assert p2.unit == "unit 2"
+
+        p3 = [p for p in idp.paramspecs if p.name == 'p3'][0]
+        assert p3.depends_on == ''
+        assert p3.inferred_from == 'p1'
+        assert p3.label == "Parameter 3"
+        assert p3.unit == "unit 3"
+
+        p4 = [p for p in idp.paramspecs if p.name == 'p4'][0]
+        assert p4.depends_on == 'p2, p3'
+        assert p4.inferred_from == ''
+        assert p4.label == "Parameter 4"
+        assert p4.unit == "unit 4"
+
+
+
 @pytest.mark.usefixtures("empty_temp_db")
 def test_update_existing_guids(caplog):
 

From 0ee04630647a5300f207b877fac5fb14675fa831 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sat, 26 Jan 2019 18:42:11 +0100
Subject: [PATCH 688/719] Add stupid version of v3-v4 upgrade

This should either be refactored to reduce duplication
or simplified to only generate new desc when needed
---
 qcodes/dataset/sqlite_base.py | 68 +++++++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index e317d93ab7c..42017f6e42b 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -723,6 +723,74 @@ def perform_db_upgrade_2_to_3(conn: ConnectionPlus) -> None:
             log.debug(f"Upgrade in transition, run number {run_id}: OK")
 
 
+@upgrader
+def perform_db_upgrade_3_to_4(conn: ConnectionPlus) -> None:
+    """
+    Perform the upgrade from version 3 to version 4. This really
+    repeats the version 3 upgrade as it originally had a bug in
+    the inferred annotation. This has since been fixes so rerun
+    the upgrade.
+    """
+
+    no_of_runs_query = "SELECT max(run_id) FROM runs"
+    no_of_runs = one(atomic_transaction(conn, no_of_runs_query), 'max(run_id)')
+    no_of_runs = no_of_runs or 0
+
+    # If one run fails, we want the whole upgrade to roll back, hence the
+    # entire upgrade is one atomic transaction
+
+    with atomic(conn) as conn:
+
+        result_tables = _2to3_get_result_tables(conn)
+        layout_ids_all = _2to3_get_layout_ids(conn)
+        indeps_all = _2to3_get_indeps(conn)
+        deps_all = _2to3_get_deps(conn)
+        layouts = _2to3_get_layouts(conn)
+        dependencies = _2to3_get_dependencies(conn)
+
+        pbar = tqdm(range(1, no_of_runs+1))
+        pbar.set_description("Upgrading database")
+
+        for run_id in pbar:
+
+            if run_id in layout_ids_all:
+
+                result_table_name = result_tables[run_id]
+                layout_ids = list(layout_ids_all[run_id])
+                if run_id in indeps_all:
+                    independents = tuple(indeps_all[run_id])
+                else:
+                    independents = ()
+                if run_id in deps_all:
+                    dependents = tuple(deps_all[run_id])
+                else:
+                    dependents = ()
+
+                paramspecs = _2to3_get_paramspecs(conn,
+                                                  layout_ids,
+                                                  layouts,
+                                                  dependencies,
+                                                  dependents,
+                                                  independents,
+                                                  result_table_name)
+
+                interdeps = InterDependencies(*paramspecs.values())
+                desc = RunDescriber(interdeps=interdeps)
+                json_str = desc.to_json()
+
+            else:
+
+                json_str = RunDescriber(InterDependencies()).to_json()
+
+            sql = f"""
+                   UPDATE runs
+                   SET run_description = ?
+                   WHERE run_id == ?
+                   """
+            cur = conn.cursor()
+            cur.execute(sql, (json_str, run_id))
+            log.debug(f"Upgrade in transition, run number {run_id}: OK")
+
 def _latest_available_version() -> int:
     """Return latest available database schema version"""
     return len(_UPGRADE_ACTIONS)

From f68c853add830be6fbfe952a7b1876a24b13e6a3 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Sat, 26 Jan 2019 19:11:11 +0100
Subject: [PATCH 689/719] adapt tests to new latest version

---
 qcodes/tests/dataset/test_database_creation_and_upgrading.py | 2 +-
 qcodes/tests/dataset/test_database_extract_runs.py           | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index b51ba291b34..8b05880ed0d 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -487,7 +487,7 @@ def test_cannot_connect_to_newer_db():
 
 
 def test_latest_available_version():
-    assert 3 == _latest_available_version()
+    assert 4 == _latest_available_version()
 
 
 @pytest.mark.parametrize('version', VERSIONS)
diff --git a/qcodes/tests/dataset/test_database_extract_runs.py b/qcodes/tests/dataset/test_database_extract_runs.py
index e0131649674..7977cf3cc7f 100644
--- a/qcodes/tests/dataset/test_database_extract_runs.py
+++ b/qcodes/tests/dataset/test_database_extract_runs.py
@@ -443,7 +443,7 @@ def test_old_versions_not_touched(two_empty_temp_db_connections,
         with pytest.warns(UserWarning) as warning:
             extract_runs_into_db(fixturepath, target_path, 1)
             expected_mssg = ('Source DB version is 2, but this '
-                             'function needs it to be in version 3. '
+                             'function needs it to be in version 4. '
                              'Run this function again with '
                              'upgrade_source_db=True to auto-upgrade '
                              'the source DB file.')
@@ -465,7 +465,7 @@ def test_old_versions_not_touched(two_empty_temp_db_connections,
         with pytest.warns(UserWarning) as warning:
             extract_runs_into_db(source_path, fixturepath, 1)
             expected_mssg = ('Target DB version is 2, but this '
-                             'function needs it to be in version 3. '
+                             'function needs it to be in version 4. '
                              'Run this function again with '
                              'upgrade_target_db=True to auto-upgrade '
                              'the target DB file.')

From c719e85c5d4c98a6c8349d6ca34bb2c904bb372d Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 11:20:29 +0100
Subject: [PATCH 690/719] Use test generators from other repo

---
 .travis.yml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 4a467ab86f5..84ba10709c6 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -41,11 +41,14 @@ script:
     - |
       pip uninstall -y qcodes
       pip install -e .
-      cd qcodes/tests/dataset/legacy_DB_generation
+      cd ..
+      git clone https://github.com/QCoDeS/qcodes_generate_test_db.git
+      cd qcodes_generate_test_db
       python generate_version_0.py
       python generate_version_1.py
       python generate_version_2.py
       python generate_version_3.py
+      cd $TRAVIS_BUILD_DIR
       cd ../../../..
       pip uninstall -y qcodes
       pip install .

From 70511e243c7b20bbe4af2299f48cba58e4525862 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 11:24:40 +0100
Subject: [PATCH 691/719] Correct path

---
 .travis.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 84ba10709c6..00122bee005 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -49,7 +49,6 @@ script:
       python generate_version_2.py
       python generate_version_3.py
       cd $TRAVIS_BUILD_DIR
-      cd ../../../..
       pip uninstall -y qcodes
       pip install .
     - cd qcodes

From 3fa7e6cfebe47c6c46a633a4c65594a840427ee8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 12:25:38 +0100
Subject: [PATCH 692/719] Update azure build

---
 azure-pipelines.yml | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 32969c49a28..ba15fad080d 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -29,11 +29,13 @@ jobs:
           mypy qcodes
         displayName: "mypy"
       - script: |
-          cd qcodes
-          python tests\dataset\legacy_DB_generation\generate_version_0.py
-          python tests\dataset\legacy_DB_generation\generate_version_1.py
-          python tests\dataset\legacy_DB_generation\generate_version_2.py
-          python tests\dataset\legacy_DB_generation\generate_version_3.py
+          cd ..
+          git clone https://github.com/QCoDeS/qcodes_generate_test_db.git
+          cd qcodes_generate_test_db
+          python generate_version_0.py
+          python generate_version_1.py
+          python generate_version_2.py
+          python generate_version_3.py
         displayName: "Generate db fixtures"
       - script: |
           cd qcodes

From 4abcd6e999b55ea5f363e665a3dc8600ccd67446 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 12:41:42 +0100
Subject: [PATCH 693/719] legacy generation has been moved to it's own repo

---
 .../dataset/legacy_DB_generation/README.md    |  26 ---
 .../dataset/legacy_DB_generation/__init__.py  |   0
 .../generate_version_0.py                     |  37 ---
 .../generate_version_1.py                     |  36 ---
 .../generate_version_2.py                     | 146 ------------
 .../generate_version_3.py                     | 213 ------------------
 .../dataset/legacy_DB_generation/utils.py     |  85 -------
 7 files changed, 543 deletions(-)
 delete mode 100644 qcodes/tests/dataset/legacy_DB_generation/README.md
 delete mode 100644 qcodes/tests/dataset/legacy_DB_generation/__init__.py
 delete mode 100644 qcodes/tests/dataset/legacy_DB_generation/generate_version_0.py
 delete mode 100644 qcodes/tests/dataset/legacy_DB_generation/generate_version_1.py
 delete mode 100644 qcodes/tests/dataset/legacy_DB_generation/generate_version_2.py
 delete mode 100644 qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py
 delete mode 100644 qcodes/tests/dataset/legacy_DB_generation/utils.py

diff --git a/qcodes/tests/dataset/legacy_DB_generation/README.md b/qcodes/tests/dataset/legacy_DB_generation/README.md
deleted file mode 100644
index 1f34eed61cb..00000000000
--- a/qcodes/tests/dataset/legacy_DB_generation/README.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# Legacy database file generation
-
-## Why have old database files?
-
-When the database schema (the database user version number) changes, the API in the core QCoDeS repository also changes to produce output matching the new version. That, in turn, makes the official API incapable of generating databases of older versions. To the end of testing the upgrade functions, we would like to be able to generate old version databases. The purpose of the `legacy_DB_generation` folder is contain scripts that generate old-version databases for the tests to consume.
-
-When a new corner-case that we want to test for is discovered, we extend the scripts to produce a .db-file covering that case. When a new version upgrade is launched, we make a new script to generate .db-files for that version upgrade to test itself on.
-
-## How to run the scripts?
-
-The scripts use `gitpython` to roll QCoDeS back to a relevant old commit and create database files with the ancient source code.
-
-The scripts should *not* be run as a part of the QCoDeS test suite, but prior to test execution in a different process. The scripts have some dependencies and will run in the normal QCoDeS environment **PROVIDED** that QCoDeS was installed with the editable flag (i.e. `pip install -e <path-to-qcodes>`).
-
-## How do I write my own script?
-
-First, please check if there is already a script producing .db-files of your desired version. If so, simply extend that script to produce a .db-file covering your particular test case. If not, follow this checklist:
-
- * Identify the database version of which you would like to create one or more .db-files. Let's call that "your version".
- * Check the variable `GIT_HASHES` in `utils.py` to see if "your version" already has a recorded commit hash.
-   * If not, search through the `git log` of `master` to find the merge commit *just* before the merge commit that introduces the *next* version after "your version". Put that first commit into `GIT_HASHES` along with the version number of "your version".
- * Make a script called `generate_version_<your_version>.py`. Copy the general structure of `generate_version_0.py`. Make your generating functions take *ZERO* arguments and do all their imports inside their own scope.
-
-## Anything else?
-
-Remember to update the tests to use your newly generated fixtures. A test must **skip** (not fail) if the fixture is not present on disk. Also make sure that the CI runs your fixture-generating script.
diff --git a/qcodes/tests/dataset/legacy_DB_generation/__init__.py b/qcodes/tests/dataset/legacy_DB_generation/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/qcodes/tests/dataset/legacy_DB_generation/generate_version_0.py b/qcodes/tests/dataset/legacy_DB_generation/generate_version_0.py
deleted file mode 100644
index 7692ef15336..00000000000
--- a/qcodes/tests/dataset/legacy_DB_generation/generate_version_0.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# Generate version 0 database files for qcodes' test suite to consume
-
-import os
-
-# NB: it's important that we do not import anything from qcodes before we
-# do the git magic (which we do below), hence the relative import here
-import utils as utils
-
-
-fixturepath = os.sep.join(os.path.realpath(__file__).split(os.sep)[:-2])
-fixturepath = os.path.join(fixturepath, 'fixtures', 'db_files')
-
-
-def generate_empty_DB_file():
-    """
-    Generate the bare minimal DB file with no runs
-    """
-
-    import qcodes.dataset.sqlite_base as sqlite_base
-
-    v0fixturepath = os.path.join(fixturepath, 'version0')
-    os.makedirs(v0fixturepath, exist_ok=True)
-    path = os.path.join(v0fixturepath, 'empty.db')
-
-    if os.path.exists(path):
-        os.remove(path)
-
-    conn = sqlite_base.connect(path)
-    sqlite_base.init_db(conn)
-
-
-if __name__ == '__main__':
-
-    gens = (generate_empty_DB_file,)
-
-    # pylint: disable=E1101
-    utils.checkout_to_old_version_and_run_generators(version=0, gens=gens)
diff --git a/qcodes/tests/dataset/legacy_DB_generation/generate_version_1.py b/qcodes/tests/dataset/legacy_DB_generation/generate_version_1.py
deleted file mode 100644
index 1dd6a6d9d6c..00000000000
--- a/qcodes/tests/dataset/legacy_DB_generation/generate_version_1.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# Generate version 1 database files for qcodes' test suite to consume
-
-import os
-
-# NB: it's important that we do not import anything from qcodes before we
-# do the git magic (which we do below), hence the relative import here
-import utils as utils
-
-
-fixturepath = os.sep.join(os.path.realpath(__file__).split(os.sep)[:-2])
-fixturepath = os.path.join(fixturepath, 'fixtures', 'db_files')
-
-
-def generate_empty_DB_file():
-    """
-    Generate the bare minimal DB file with no runs
-    """
-
-    import qcodes.dataset.sqlite_base as sqlite_base
-
-    v0fixturepath = os.path.join(fixturepath, 'version1')
-    os.makedirs(v0fixturepath, exist_ok=True)
-    path = os.path.join(v0fixturepath, 'empty.db')
-
-    if os.path.exists(path):
-        os.remove(path)
-
-    sqlite_base.connect(path)
-
-
-if __name__ == '__main__':
-
-    gens = (generate_empty_DB_file,)
-
-    # pylint: disable=E1101
-    utils.checkout_to_old_version_and_run_generators(version=1, gens=gens)
diff --git a/qcodes/tests/dataset/legacy_DB_generation/generate_version_2.py b/qcodes/tests/dataset/legacy_DB_generation/generate_version_2.py
deleted file mode 100644
index ac68dfadbed..00000000000
--- a/qcodes/tests/dataset/legacy_DB_generation/generate_version_2.py
+++ /dev/null
@@ -1,146 +0,0 @@
-# Generate version 2 database files for qcodes' test suite to consume
-
-import os
-import numpy as np
-
-# NB: it's important that we do not import anything from qcodes before we
-# do the git magic (which we do below), hence the relative import here
-import utils as utils
-
-
-fixturepath = os.sep.join(os.path.realpath(__file__).split(os.sep)[:-2])
-fixturepath = os.path.join(fixturepath, 'fixtures', 'db_files')
-
-
-def generate_empty_DB_file():
-    """
-    Generate the bare minimal DB file with no runs
-    """
-
-    import qcodes.dataset.sqlite_base as sqlite_base
-
-    v2fixturepath = os.path.join(fixturepath, 'version2')
-    os.makedirs(v2fixturepath, exist_ok=True)
-    path = os.path.join(v2fixturepath, 'empty.db')
-
-    if os.path.exists(path):
-        os.remove(path)
-
-    sqlite_base.connect(path)
-
-
-def generate_DB_file_with_some_runs():
-    """
-    Generate a .db-file with a handful of runs with some interdependent
-    parameters
-    """
-
-    # This function will run often on CI and re-generate the .db-files
-    # That should ideally be a deterministic action
-    # (although this hopefully plays no role)
-    np.random.seed(0)
-
-    v2fixturepath = os.path.join(fixturepath, 'version2')
-    os.makedirs(v2fixturepath, exist_ok=True)
-    path = os.path.join(v2fixturepath, 'some_runs.db')
-
-    if os.path.exists(path):
-        os.remove(path)
-
-    from qcodes.dataset.sqlite_base import connect
-    from qcodes.dataset.measurements import Measurement
-    from qcodes.dataset.experiment_container import Experiment
-    from qcodes import Parameter
-
-    connect(path)
-    exp = Experiment(path)
-    exp._new(name='experiment_1', sample_name='no_sample_1')
-
-    # Now make some parameters to use in measurements
-    params = []
-    for n in range(5):
-        params.append(Parameter(f'p{n}', label=f'Parameter {n}',
-                                unit=f'unit {n}', set_cmd=None, get_cmd=None))
-
-    # Set up an experiment
-
-    meas = Measurement(exp)
-    meas.register_parameter(params[0])
-    meas.register_parameter(params[1])
-    meas.register_parameter(params[2], basis=(params[0],))
-    meas.register_parameter(params[3], basis=(params[1],))
-    meas.register_parameter(params[4], setpoints=(params[2], params[3]))
-
-    # Make a number of identical runs
-
-    for _ in range(10):
-
-        with meas.run() as datasaver:
-
-            for x in np.random.rand(10):
-                for y in np.random.rand(10):
-                    z = np.random.rand()
-                    datasaver.add_result((params[2], x),
-                                         (params[3], y),
-                                         (params[4], z))
-
-
-def generate_DB_file_with_empty_runs():
-    """
-    Generate a DB file that holds empty runs and runs with no interdependencies
-    """
-
-    v2fixturepath = os.path.join(fixturepath, 'version2')
-    os.makedirs(v2fixturepath, exist_ok=True)
-    path = os.path.join(v2fixturepath, 'empty_runs.db')
-
-    if os.path.exists(path):
-        os.remove(path)
-
-    from qcodes.dataset.sqlite_base import connect
-    from qcodes.dataset.measurements import Measurement
-    from qcodes.dataset.experiment_container import Experiment
-    from qcodes import Parameter
-    from qcodes.dataset.data_set import DataSet
-
-    conn = connect(path)
-    exp = Experiment(path)
-    exp._new(name='experiment_1', sample_name='no_sample_1')
-
-    # Now make some parameters to use in measurements
-    params = []
-    for n in range(5):
-        params.append(Parameter(f'p{n}', label=f'Parameter {n}',
-                                unit=f'unit {n}', set_cmd=None, get_cmd=None))
-
-    # truly empty run, no layouts table, no nothing
-    dataset = DataSet(path, conn=conn)
-    dataset._new('empty_dataset', exp_id=exp.exp_id)
-
-    # empty run
-    meas = Measurement(exp)
-    with meas.run() as datasaver:
-        pass
-
-    # run with no interdeps
-    meas = Measurement(exp)
-    for param in params:
-        meas.register_parameter(param)
-
-    with meas.run() as datasaver:
-        pass
-
-    with meas.run() as datasaver:
-        for _ in range(10):
-            res = tuple((p, 0.0) for p in params)
-            datasaver.add_result(*res)
-
-
-if __name__ == '__main__':
-
-    gens = (generate_empty_DB_file,
-            generate_DB_file_with_some_runs,
-            generate_DB_file_with_empty_runs)
-
-    # pylint: disable=E1101
-    utils.checkout_to_old_version_and_run_generators(version=2, gens=gens)
diff --git a/qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py b/qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py
deleted file mode 100644
index 76822e67cc0..00000000000
--- a/qcodes/tests/dataset/legacy_DB_generation/generate_version_3.py
+++ /dev/null
@@ -1,213 +0,0 @@
-# Generate version 3 database files for qcodes' test suite to consume
-
-import os
-import shutil
-import numpy as np
-
-# NB: it's important that we do not import anything from qcodes before we
-# do the git magic (which we do below), hence the relative import here
-import utils as utils
-
-
-fixturepath = os.sep.join(os.path.realpath(__file__).split(os.sep)[:-2])
-fixturepath = os.path.join(fixturepath, 'fixtures', 'db_files')
-
-
-def generate_empty_DB_file():
-    """
-    Generate the bare minimal DB file with no runs
-    """
-
-    import qcodes.dataset.sqlite_base as sqlite_base
-
-    v3fixturepath = os.path.join(fixturepath, 'version3')
-    os.makedirs(v3fixturepath, exist_ok=True)
-    path = os.path.join(v3fixturepath, 'empty.db')
-
-    if os.path.exists(path):
-        os.remove(path)
-
-    sqlite_base.connect(path)
-
-
-def generate_DB_file_with_some_runs():
-    """
-    Generate a .db-file with a handful of runs with some interdependent
-    parameters
-    """
-
-    # This function will run often on CI and re-generate the .db-files
-    # That should ideally be a deterministic action
-    # (although this hopefully plays no role)
-    np.random.seed(0)
-
-    v3fixturepath = os.path.join(fixturepath, 'version3')
-    os.makedirs(v3fixturepath, exist_ok=True)
-    path = os.path.join(v3fixturepath, 'some_runs.db')
-
-    if os.path.exists(path):
-        os.remove(path)
-
-    from qcodes.dataset.sqlite_base import connect
-    from qcodes.dataset.measurements import Measurement
-    from qcodes.dataset.experiment_container import Experiment
-    from qcodes import Parameter
-
-    connect(path)
-    exp = Experiment(path_to_db=path,
-                     name='experiment_1',
-                     sample_name='no_sample_1')
-
-    # Now make some parameters to use in measurements
-    params = []
-    for n in range(5):
-        params.append(Parameter(f'p{n}', label=f'Parameter {n}',
-                                unit=f'unit {n}', set_cmd=None, get_cmd=None))
-
-    # Set up an experiment
-
-    meas = Measurement(exp)
-    meas.register_parameter(params[0])
-    meas.register_parameter(params[1])
-    meas.register_parameter(params[2], basis=(params[0],))
-    meas.register_parameter(params[3], basis=(params[1],))
-    meas.register_parameter(params[4], setpoints=(params[2], params[3]))
-
-    # Make a number of identical runs
-
-    for _ in range(10):
-
-        with meas.run() as datasaver:
-
-            for x in np.random.rand(10):
-                for y in np.random.rand(10):
-                    z = np.random.rand()
-                    datasaver.add_result((params[2], x),
-                                         (params[3], y),
-                                         (params[4], z))
-
-
-def generate_DB_file_with_some_runs_having_not_run_descriptions():
-    """
-    Generate a .db-file with a handful of runs some of which lack run
-    description or have it as empty object (based on a real case).
-
-    Generated runs:
-        #1: run with parameters and correct run description
-        #2: run with parameters but run description is NULL
-        #3: run with parameters but run description is empty RunDescriber
-        #4: run without parameters but run description is NULL
-    """
-    v3fixturepath = os.path.join(fixturepath, 'version3')
-    os.makedirs(v3fixturepath, exist_ok=True)
-    path = os.path.join(v3fixturepath, 'some_runs_without_run_description.db')
-
-    if os.path.exists(path):
-        os.remove(path)
-
-    from qcodes.dataset.measurements import Measurement
-    from qcodes.dataset.experiment_container import Experiment
-    from qcodes import Parameter
-    from qcodes.dataset.descriptions import RunDescriber
-    from qcodes.dataset.dependencies import InterDependencies
-
-    exp = Experiment(path_to_db=path,
-                     name='experiment_1',
-                     sample_name='no_sample_1')
-    conn = exp.conn
-
-    # Now make some parameters to use in measurements
-    params = []
-    for n in range(5):
-        params.append(Parameter(f'p{n}', label=f'Parameter {n}',
-                                unit=f'unit {n}', set_cmd=None, get_cmd=None))
-
-    # Set up a measurement
-
-    meas = Measurement(exp)
-    meas.register_parameter(params[0])
-    meas.register_parameter(params[1])
-    meas.register_parameter(params[2], basis=(params[0],))
-    meas.register_parameter(params[3], basis=(params[1],))
-    meas.register_parameter(params[4], setpoints=(params[2], params[3]))
-
-    # Initially make 3 correct runs
-
-    run_ids = []
-
-    for _ in range(3):
-
-        with meas.run() as datasaver:
-
-            for x in np.random.rand(10):
-                for y in np.random.rand(10):
-                    z = np.random.rand()
-                    datasaver.add_result((params[2], x),
-                                         (params[3], y),
-                                         (params[4], z))
-
-        run_ids.append(datasaver.run_id)
-
-    assert [1, 2, 3] == run_ids, 'Run ids of generated runs are not as ' \
-                                 'expected after generating runs #1-3'
-
-    # Formulate SQL query for adjusting run_description column
-
-    set_run_description_sql = f"""
-               UPDATE runs
-               SET run_description = ?
-               WHERE run_id == ?
-               """
-
-    # Make run_description of run #2 NULL
-
-    conn.execute(set_run_description_sql, (None, run_ids[1]))
-    conn.commit()  # just to be sure
-
-    # Make run_description of run #3 equivalent to an empty RunDescriber
-
-    empty_run_description = RunDescriber(InterDependencies()).to_json()
-    conn.execute(set_run_description_sql, (empty_run_description, run_ids[2]))
-    conn.commit()  # just to be sure
-
-    # Set up a measurement without parameters, and create run #4 out of it
-
-    meas_no_params = Measurement(exp)
-
-    with meas_no_params.run() as datasaver:
-        pass
-
-    run_ids.append(datasaver.run_id)
-
-    assert [1, 2, 3, 4] == run_ids, 'Run ids of generated runs are not as ' \
-                                    'expected after generating run #4'
-
-    # Make run_description of run #4 NULL
-
-    conn.execute(set_run_description_sql, (None, run_ids[3]))
-    conn.commit()  # just to be sure
-
-
-def generate_upgraded_v2_runs():
-    """
-    Generate some runs by upgradeing from v2 db. This
-    is needed since the bug we want to test against is in
-    the v2 to v3 upgrade and not in v3 it self.
-    This requires the v2 generation to be run before this one
-    """
-    import qcodes.dataset.sqlite_base as sqlite_base
-    v2fixture_path = os.path.join(fixturepath, 'version2', 'some_runs.db')
-    v3fixturepath = os.path.join(fixturepath, 'version3', 'some_runs_upgraded_2.db')
-    shutil.copy2(v2fixture_path, v3fixturepath)
-    sqlite_base.connect(v3fixturepath)
-
-
-if __name__ == '__main__':
-
-    gens = (generate_empty_DB_file,
-            generate_DB_file_with_some_runs_having_not_run_descriptions,
-            generate_DB_file_with_some_runs,
-            generate_upgraded_v2_runs)
-
-    # pylint: disable=E1101
-    utils.checkout_to_old_version_and_run_generators(version=3, gens=gens)
diff --git a/qcodes/tests/dataset/legacy_DB_generation/utils.py b/qcodes/tests/dataset/legacy_DB_generation/utils.py
deleted file mode 100644
index d5d5afac1d4..00000000000
--- a/qcodes/tests/dataset/legacy_DB_generation/utils.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# General utilities for the database generation and loading scheme
-from typing import Dict, List, Tuple
-from contextlib import contextmanager
-import os
-
-from git import Repo
-
-# A brief overview of what each version introduces:
-#
-# Version 0: the original table schema, runs, experiments, layouts,
-# dependencies, result-tables
-#
-# Version 1: a GUID column is added to the runs table
-#
-# Version 2: indices are added to runs; GUID and exp_id
-#
-# Version 3: run_description column is added to the runs table
-#
-
-# NOTE that each hash is supposed to be representing a commit JUST before the
-# "next" version is being introduced.
-#
-
-GIT_HASHES: Dict[int, str] = {0: '78d42620fc245a975b5a615ed5e33061baac7846',
-                              1: '056d59627e22fa3ca7aad4c265e9897c343f79cf',
-                              2: '5202255924542dad6841dfe3d941a7f80c43956c',
-                              3: '01cb405d47b72b1a5bab227dcf4b69866ecebb0f'}
-
-gitrepopath = os.sep.join(os.path.realpath(__file__).split(os.sep)[:-5])
-repo = Repo(gitrepopath)
-
-
-@contextmanager
-def leave_untouched(repo):
-    """
-    Leave a git repository untouched by whatever fiddling around we need to do
-    We support both the case of the repo being initially in a detached head
-    state (relevant for Travis) and the -hopefully- normal case for users of
-    being at the tip of a branch
-    """
-
-    if repo.is_dirty():
-        raise ValueError('Git repository is dirty. Can not proceed.')
-
-    was_detached = repo.head.is_detached
-
-    if not was_detached:
-        current_branch = repo.active_branch
-    current_commit = repo.head.commit
-
-    try:
-        yield
-
-    finally:
-        repo.git.reset('--hard', current_commit)
-        if not was_detached:
-            repo.git.checkout(current_branch)
-
-
-def checkout_to_old_version_and_run_generators(version: int,
-                                               gens: Tuple) -> None:
-    """
-    Check out the repo to an older version and run the generating functions
-    supplied.
-    """
-
-    with leave_untouched(repo):
-
-        repo.git.checkout(GIT_HASHES[version])
-
-        # If QCoDeS is not installed in editable mode, it makes no difference
-        # to do our git magic, since the import will be from site-packages in
-        # the environment folder, and not from the git-managed folder
-        import qcodes
-        qcpath = os.sep.join(qcodes.__file__.split(os.sep)[:-2])
-
-        # Windows and paths... There can be random un-capitalizations
-        if qcpath.lower() != gitrepopath.lower():
-            raise ValueError('QCoDeS does not seem to be installed in editable'
-                             ' mode, can not proceed. To use this script, '
-                             'uninstall QCoDeS and reinstall it with pip '
-                             'install -e <path-to-qcodes-folder>')
-
-        for generator in gens:
-            generator()

From 1c1ef1c01542b2b37dc18d0957a170dd9e3aff5e Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 12:53:45 +0100
Subject: [PATCH 694/719] update tests to reflect that there are now 2 inferred
 params

---
 .../tests/dataset/test_database_creation_and_upgrading.py   | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index 8b05880ed0d..8050c52171c 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -278,7 +278,7 @@ def test_perform_actual_upgrade_2_to_3_some_runs():
 
         p3 = [p for p in idp.paramspecs if p.name == 'p3'][0]
         assert p3.depends_on == ''
-        assert p3.inferred_from == 'p1'
+        assert p3.inferred_from == 'p1, p0'
         assert p3.label == "Parameter 3"
         assert p3.unit == "unit 3"
 
@@ -335,7 +335,7 @@ def test_perfrom_upgrade_v3_to_v4_fixes():
 
         p3 = [p for p in idp.paramspecs if p.name == 'p3'][0]
         assert p3.depends_on == ''
-        assert p3.inferred_from == 'p, 1'
+        assert p3.inferred_from == 'p, 1, ,,  , p, 0'
         assert p3.label == "Parameter 3"
         assert p3.unit == "unit 3"
 
@@ -374,7 +374,7 @@ def test_perfrom_upgrade_v3_to_v4_fixes():
 
         p3 = [p for p in idp.paramspecs if p.name == 'p3'][0]
         assert p3.depends_on == ''
-        assert p3.inferred_from == 'p1'
+        assert p3.inferred_from == 'p1, p0'
         assert p3.label == "Parameter 3"
         assert p3.unit == "unit 3"
 

From bac7d43f1f58247851802ae7c79bc4930e540697 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 14:34:39 +0100
Subject: [PATCH 695/719] Handle empty inferred_from correctly

---
 qcodes/dataset/sqlite_base.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 42017f6e42b..38c71ebaba1 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -615,9 +615,11 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
                 paramtype = row['type']
                 break
 
+        inferred_from_list: List[str] = []
         # first possibility: another parameter depends on this parameter
         if layout_id in indeps:
-            inferred_from_list = inferred_from.split(', ')
+            if inferred_from != '':
+                inferred_from_list = inferred_from.split(', ')
 
             paramspec = ParamSpec(name=name, paramtype=paramtype,
                                   label=label, unit=unit,
@@ -629,7 +631,8 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
 
             setpoints = dependencies[layout_id]
             depends_on = [paramspecs[idp].name for idp in setpoints]
-            inferred_from_list = inferred_from.split(', ')
+            if inferred_from != '':
+                inferred_from_list = inferred_from.split(', ')
 
             paramspec = ParamSpec(name=name,
                                   paramtype=paramtype,
@@ -644,7 +647,7 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
                                   paramtype=paramtype,
                                   label=label, unit=unit,
                                   depends_on=[],
-                                  inferred_from=[])
+                                  inferred_from=inferred_from_list)
             paramspecs[layout_id] = paramspec
 
     return paramspecs

From a52d6ec0bad616452fd0474215470f53441fa258 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 14:39:39 +0100
Subject: [PATCH 696/719] Add more asserts to tests

---
 .../test_database_creation_and_upgrading.py   | 38 +++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index 8050c52171c..e4ebec039a6 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -260,36 +260,50 @@ def test_perform_actual_upgrade_2_to_3_some_runs():
 
         p0 = [p for p in idp.paramspecs if p.name == 'p0'][0]
         assert p0.depends_on == ''
+        assert p0.depends_on_ == []
         assert p0.inferred_from == ''
+        assert p0.inferred_from_ == []
         assert p0.label == "Parameter 0"
         assert p0.unit == "unit 0"
 
         p1 = [p for p in idp.paramspecs if p.name == 'p1'][0]
         assert p1.depends_on == ''
+        assert p1.depends_on_ == []
         assert p1.inferred_from == ''
+        assert p1.inferred_from_ == []
         assert p1.label == "Parameter 1"
         assert p1.unit == "unit 1"
 
         p2 = [p for p in idp.paramspecs if p.name == 'p2'][0]
         assert p2.depends_on == ''
+        assert p2.depends_on_ == []
         assert p2.inferred_from == 'p0'
+        assert p2.inferred_from_ == ['p0']
         assert p2.label == "Parameter 2"
         assert p2.unit == "unit 2"
 
         p3 = [p for p in idp.paramspecs if p.name == 'p3'][0]
         assert p3.depends_on == ''
+        assert p3.depends_on_ == []
         assert p3.inferred_from == 'p1, p0'
+        assert p3.inferred_from_ == ['p1', 'p0']
         assert p3.label == "Parameter 3"
         assert p3.unit == "unit 3"
 
         p4 = [p for p in idp.paramspecs if p.name == 'p4'][0]
         assert p4.depends_on == 'p2, p3'
+        assert p4.depends_on_ == ['p2', 'p3']
         assert p4.inferred_from == ''
+        assert p4.inferred_from_ == []
         assert p4.label == "Parameter 4"
         assert p4.unit == "unit 4"
 
 
 def test_perfrom_upgrade_v3_to_v4_fixes():
+    """
+    Test that a db that was upgraded from v2 to v3 with a buggy
+    version will be corrected when upgraded to v4.
+    """
 
     v3fixpath = os.path.join(fixturepath, 'db_files', 'version3')
 
@@ -317,31 +331,45 @@ def test_perfrom_upgrade_v3_to_v4_fixes():
 
         p0 = [p for p in idp.paramspecs if p.name == 'p0'][0]
         assert p0.depends_on == ''
+        assert p0.depends_on_ == []
         assert p0.inferred_from == ''
+        assert p0.inferred_from_ == []
         assert p0.label == "Parameter 0"
         assert p0.unit == "unit 0"
 
         p1 = [p for p in idp.paramspecs if p.name == 'p1'][0]
         assert p1.depends_on == ''
+        assert p1.depends_on_ == []
         assert p1.inferred_from == ''
+        assert p1.inferred_from_ == []
         assert p1.label == "Parameter 1"
         assert p1.unit == "unit 1"
 
         p2 = [p for p in idp.paramspecs if p.name == 'p2'][0]
         assert p2.depends_on == ''
+        assert p2.depends_on_ == []
+        # the 2 lines below are wrong due to the incorrect upgrade from
+        # db version 2 to 3
         assert p2.inferred_from == 'p, 0'
+        assert p2.inferred_from_ == ['p', '0']
         assert p2.label == "Parameter 2"
         assert p2.unit == "unit 2"
 
         p3 = [p for p in idp.paramspecs if p.name == 'p3'][0]
         assert p3.depends_on == ''
+        assert p3.depends_on_ == []
+        # the 2 lines below are wrong due to the incorrect upgrade from
+        # db version 2 to 3
         assert p3.inferred_from == 'p, 1, ,,  , p, 0'
+        assert p3.inferred_from_ == ['p', '1', ',', ' ', 'p', '0']
         assert p3.label == "Parameter 3"
         assert p3.unit == "unit 3"
 
         p4 = [p for p in idp.paramspecs if p.name == 'p4'][0]
         assert p4.depends_on == 'p2, p3'
+        assert p4.depends_on_ == ['p2', 'p3']
         assert p4.inferred_from == ''
+        assert p4.inferred_from_ == []
         assert p4.label == "Parameter 4"
         assert p4.unit == "unit 4"
 
@@ -356,31 +384,41 @@ def test_perfrom_upgrade_v3_to_v4_fixes():
 
         p0 = [p for p in idp.paramspecs if p.name == 'p0'][0]
         assert p0.depends_on == ''
+        assert p0.depends_on_ == []
         assert p0.inferred_from == ''
+        assert p0.inferred_from_ == []
         assert p0.label == "Parameter 0"
         assert p0.unit == "unit 0"
 
         p1 = [p for p in idp.paramspecs if p.name == 'p1'][0]
         assert p1.depends_on == ''
+        assert p1.depends_on_ == []
         assert p1.inferred_from == ''
+        assert p1.inferred_from_ == []
         assert p1.label == "Parameter 1"
         assert p1.unit == "unit 1"
 
         p2 = [p for p in idp.paramspecs if p.name == 'p2'][0]
         assert p2.depends_on == ''
+        assert p2.depends_on_ == []
         assert p2.inferred_from == 'p0'
+        assert p2.inferred_from_ == ['p0']
         assert p2.label == "Parameter 2"
         assert p2.unit == "unit 2"
 
         p3 = [p for p in idp.paramspecs if p.name == 'p3'][0]
         assert p3.depends_on == ''
+        assert p3.depends_on_ == []
         assert p3.inferred_from == 'p1, p0'
+        assert p3.inferred_from_ == ['p1', 'p0']
         assert p3.label == "Parameter 3"
         assert p3.unit == "unit 3"
 
         p4 = [p for p in idp.paramspecs if p.name == 'p4'][0]
         assert p4.depends_on == 'p2, p3'
+        assert p4.depends_on_ == ['p2', 'p3']
         assert p4.inferred_from == ''
+        assert p4.inferred_from_ == []
         assert p4.label == "Parameter 4"
         assert p4.unit == "unit 4"
 

From 67545d1381edbe5a7dad3daf78cda000e4d25161 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 14:50:44 +0100
Subject: [PATCH 697/719] simplify upgrade logic

---
 qcodes/dataset/sqlite_base.py | 28 ++++++++--------------------
 1 file changed, 8 insertions(+), 20 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 38c71ebaba1..defde46e1d6 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -616,16 +616,12 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
                 break
 
         inferred_from_list: List[str] = []
+        depends_on: List[str] = []
         # first possibility: another parameter depends on this parameter
         if layout_id in indeps:
             if inferred_from != '':
                 inferred_from_list = inferred_from.split(', ')
 
-            paramspec = ParamSpec(name=name, paramtype=paramtype,
-                                  label=label, unit=unit,
-                                  inferred_from=inferred_from_list)
-            paramspecs[layout_id] = paramspec
-
         # second possibility: this parameter depends on another parameter
         elif layout_id in deps:
 
@@ -633,22 +629,14 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
             depends_on = [paramspecs[idp].name for idp in setpoints]
             if inferred_from != '':
                 inferred_from_list = inferred_from.split(', ')
-
-            paramspec = ParamSpec(name=name,
-                                  paramtype=paramtype,
-                                  label=label, unit=unit,
-                                  depends_on=depends_on,
-                                  inferred_from=inferred_from_list)
-            paramspecs[layout_id] = paramspec
-
         # third possibility: no dependencies
-        else:
-            paramspec = ParamSpec(name=name,
-                                  paramtype=paramtype,
-                                  label=label, unit=unit,
-                                  depends_on=[],
-                                  inferred_from=inferred_from_list)
-            paramspecs[layout_id] = paramspec
+
+        paramspec = ParamSpec(name=name,
+                              paramtype=paramtype,
+                              label=label, unit=unit,
+                              depends_on=depends_on,
+                              inferred_from=inferred_from_list)
+        paramspecs[layout_id] = paramspec
 
     return paramspecs
 

From 6ea4cee6fc08644ef46212c081fac733c350378c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 15:19:50 +0100
Subject: [PATCH 698/719] test param that is neither dep/dependent

---
 .../test_database_creation_and_upgrading.py       | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index e4ebec039a6..6b4c1ffb9f5 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -298,6 +298,13 @@ def test_perform_actual_upgrade_2_to_3_some_runs():
         assert p4.label == "Parameter 4"
         assert p4.unit == "unit 4"
 
+        p5 = [p for p in idp.paramspecs if p.name == 'p5'][0]
+        assert p5.depends_on == ''
+        assert p5.depends_on_ == []
+        assert p5.inferred_from == 'p0'
+        assert p5.inferred_from_ == ['p0']
+        assert p5.label == "Parameter 5"
+        assert p5.unit == "unit 5"
 
 def test_perfrom_upgrade_v3_to_v4_fixes():
     """
@@ -422,6 +429,14 @@ def test_perfrom_upgrade_v3_to_v4_fixes():
         assert p4.label == "Parameter 4"
         assert p4.unit == "unit 4"
 
+        p5 = [p for p in idp.paramspecs if p.name == 'p5'][0]
+        assert p5.depends_on == ''
+        assert p5.depends_on_ == []
+        assert p5.inferred_from == 'p0'
+        assert p5.inferred_from_ == ['p0']
+        assert p5.label == "Parameter 5"
+        assert p5.unit == "unit 5"
+
 
 
 @pytest.mark.usefixtures("empty_temp_db")

From 3fe5858d67a006747ad24dbe9bcb0f40def59ccb Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 15:25:31 +0100
Subject: [PATCH 699/719] test that this is wrong before upgrade

---
 .../dataset/test_database_creation_and_upgrading.py    | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index 6b4c1ffb9f5..5dcb5999b8c 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -380,6 +380,16 @@ def test_perfrom_upgrade_v3_to_v4_fixes():
         assert p4.label == "Parameter 4"
         assert p4.unit == "unit 4"
 
+        p5 = [p for p in idp.paramspecs if p.name == 'p5'][0]
+        assert p5.depends_on == ''
+        assert p5.depends_on_ == []
+        # the 2 lines below are wrong due to the incorrect upgrade from
+        # db version 2 to 3. Here the interdep is missing
+        assert p5.inferred_from == ''
+        assert p5.inferred_from_ == []
+        assert p5.label == "Parameter 5"
+        assert p5.unit == "unit 5"
+
         perform_db_upgrade_3_to_4(conn)
 
         c = atomic_transaction(conn, sql)

From fefc025c314484bfe37ddb668ed46111966f50f5 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 15:26:22 +0100
Subject: [PATCH 700/719] also handle inferred_from when not dep/dependent

---
 qcodes/dataset/sqlite_base.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index defde46e1d6..3dcd568a397 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -630,7 +630,9 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
             if inferred_from != '':
                 inferred_from_list = inferred_from.split(', ')
         # third possibility: no dependencies
-
+        else:
+            if inferred_from != '':
+                inferred_from_list = inferred_from.split(', ')
         paramspec = ParamSpec(name=name,
                               paramtype=paramtype,
                               label=label, unit=unit,

From 64af8e82543e5b1de4ec7de3e907898747d783b9 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 15:35:16 +0100
Subject: [PATCH 701/719] simplifty upgrade logic

---
 qcodes/dataset/sqlite_base.py | 19 ++++++-------------
 1 file changed, 6 insertions(+), 13 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 3dcd568a397..49c3239808f 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -617,22 +617,15 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
 
         inferred_from_list: List[str] = []
         depends_on: List[str] = []
-        # first possibility: another parameter depends on this parameter
-        if layout_id in indeps:
-            if inferred_from != '':
-                inferred_from_list = inferred_from.split(', ')
-
-        # second possibility: this parameter depends on another parameter
-        elif layout_id in deps:
 
+        # this parameter depends on another parameter
+        if layout_id in deps:
             setpoints = dependencies[layout_id]
             depends_on = [paramspecs[idp].name for idp in setpoints]
-            if inferred_from != '':
-                inferred_from_list = inferred_from.split(', ')
-        # third possibility: no dependencies
-        else:
-            if inferred_from != '':
-                inferred_from_list = inferred_from.split(', ')
+
+        if inferred_from != '':
+            inferred_from_list = inferred_from.split(', ')
+
         paramspec = ParamSpec(name=name,
                               paramtype=paramtype,
                               label=label, unit=unit,

From 959a79f4b22cfe67317e607a454369553f8557e2 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 15:58:29 +0100
Subject: [PATCH 702/719] docstring

---
 qcodes/dataset/sqlite_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 49c3239808f..87b04e4966a 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -713,7 +713,7 @@ def perform_db_upgrade_2_to_3(conn: ConnectionPlus) -> None:
 def perform_db_upgrade_3_to_4(conn: ConnectionPlus) -> None:
     """
     Perform the upgrade from version 3 to version 4. This really
-    repeats the version 3 upgrade as it originally had a bug in
+    repeats the version 3 upgrade as it originally had two bugs in
     the inferred annotation. This has since been fixes so rerun
     the upgrade.
     """

From 62e416099a7c42d329ac0106cba17a9065fcd6c0 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 16:04:29 +0100
Subject: [PATCH 703/719] add a direct v3->v4 upgrade test

---
 .../test_database_creation_and_upgrading.py   | 83 ++++++++++++++++++-
 1 file changed, 82 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index 5dcb5999b8c..ce68d276e17 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -306,7 +306,8 @@ def test_perform_actual_upgrade_2_to_3_some_runs():
         assert p5.label == "Parameter 5"
         assert p5.unit == "unit 5"
 
-def test_perfrom_upgrade_v3_to_v4_fixes():
+
+def test_perform_upgrade_v2_v3_to_v4_fixes():
     """
     Test that a db that was upgraded from v2 to v3 with a buggy
     version will be corrected when upgraded to v4.
@@ -448,6 +449,86 @@ def test_perfrom_upgrade_v3_to_v4_fixes():
         assert p5.unit == "unit 5"
 
 
+def test_perform_upgrade_v3_to_v4_fixes():
+    """
+    Test that a db that was upgraded from v2 to v3 with a buggy
+    version will be corrected when upgraded to v4.
+    """
+
+    v3fixpath = os.path.join(fixturepath, 'db_files', 'version3')
+
+    dbname_old = os.path.join(v3fixpath, 'some_runs_upgraded_2.db')
+
+    if not os.path.exists(dbname_old):
+        pytest.skip("No db-file fixtures found. You can generate test db-files"
+                    " using the scripts in the legacy_DB_generation folder")
+
+    with temporarily_copied_DB(dbname_old, debug=False, version=3) as conn:
+
+        assert get_user_version(conn) == 3
+
+        sql = f"""
+              SELECT run_description
+              FROM runs
+              WHERE run_id == 1
+              """
+
+        perform_db_upgrade_3_to_4(conn)
+
+        c = atomic_transaction(conn, sql)
+        json_str = one(c, 'run_description')
+
+        desc = RunDescriber.from_json(json_str)
+        idp = desc.interdeps
+        assert isinstance(idp, InterDependencies)
+
+        p0 = [p for p in idp.paramspecs if p.name == 'p0'][0]
+        assert p0.depends_on == ''
+        assert p0.depends_on_ == []
+        assert p0.inferred_from == ''
+        assert p0.inferred_from_ == []
+        assert p0.label == "Parameter 0"
+        assert p0.unit == "unit 0"
+
+        p1 = [p for p in idp.paramspecs if p.name == 'p1'][0]
+        assert p1.depends_on == ''
+        assert p1.depends_on_ == []
+        assert p1.inferred_from == ''
+        assert p1.inferred_from_ == []
+        assert p1.label == "Parameter 1"
+        assert p1.unit == "unit 1"
+
+        p2 = [p for p in idp.paramspecs if p.name == 'p2'][0]
+        assert p2.depends_on == ''
+        assert p2.depends_on_ == []
+        assert p2.inferred_from == 'p0'
+        assert p2.inferred_from_ == ['p0']
+        assert p2.label == "Parameter 2"
+        assert p2.unit == "unit 2"
+
+        p3 = [p for p in idp.paramspecs if p.name == 'p3'][0]
+        assert p3.depends_on == ''
+        assert p3.depends_on_ == []
+        assert p3.inferred_from == 'p1, p0'
+        assert p3.inferred_from_ == ['p1', 'p0']
+        assert p3.label == "Parameter 3"
+        assert p3.unit == "unit 3"
+
+        p4 = [p for p in idp.paramspecs if p.name == 'p4'][0]
+        assert p4.depends_on == 'p2, p3'
+        assert p4.depends_on_ == ['p2', 'p3']
+        assert p4.inferred_from == ''
+        assert p4.inferred_from_ == []
+        assert p4.label == "Parameter 4"
+        assert p4.unit == "unit 4"
+
+        p5 = [p for p in idp.paramspecs if p.name == 'p5'][0]
+        assert p5.depends_on == ''
+        assert p5.depends_on_ == []
+        assert p5.inferred_from == 'p0'
+        assert p5.inferred_from_ == ['p0']
+        assert p5.label == "Parameter 5"
+        assert p5.unit == "unit 5"
 
 @pytest.mark.usefixtures("empty_temp_db")
 def test_update_existing_guids(caplog):

From 82aeb96a7a17ce9210565b1f6a01661306fd3ff9 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 16:25:04 +0100
Subject: [PATCH 704/719] More clear error message

---
 qcodes/dataset/param_spec.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/param_spec.py b/qcodes/dataset/param_spec.py
index f425fc744a7..3bd8c7132c9 100644
--- a/qcodes/dataset/param_spec.py
+++ b/qcodes/dataset/param_spec.py
@@ -45,7 +45,8 @@ def __init__(self, name: str,
 
         if isinstance(inferred_from, str):
             raise ValueError(f"ParamSpec {self.name} got "
-                             f"string {inferred_from} it needs a "
+                             f"string {inferred_from} as inferred_from. "
+                             f"It needs a "
                              f"Sequence of ParamSpecs or strings")
         self._inferred_from.extend(
             p.name if isinstance(p, ParamSpec) else p
@@ -53,7 +54,7 @@ def __init__(self, name: str,
 
         if isinstance(depends_on, str):
             raise ValueError(f"ParamSpec {self.name} got "
-                             f"string {depends_on} it needs a "
+                             f"string {depends_on} as depends_on. It needs a "
                              f"Sequence of ParamSpecs or strings")
         self._depends_on.extend(
             p.name if isinstance(p, ParamSpec) else p

From 5e317fa439b2ae1a790c8d397da5fe52f55019ba Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 4 Feb 2019 16:28:42 +0100
Subject: [PATCH 705/719] Add tests for invalid paramspecs

---
 qcodes/tests/dataset/test_paramspec.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/qcodes/tests/dataset/test_paramspec.py b/qcodes/tests/dataset/test_paramspec.py
index 74a0f5d7a34..30cbe2ba9eb 100644
--- a/qcodes/tests/dataset/test_paramspec.py
+++ b/qcodes/tests/dataset/test_paramspec.py
@@ -47,7 +47,7 @@ def version_0_serializations():
                  'paramtype': 'array',
                  'label': 'My Array ParamSpec',
                  'unit': 'Ars',
-                 'inferred_from': ['p1', 'p2' ],
+                 'inferred_from': ['p1', 'p2'],
                  'depends_on': []})
     return sers
 
@@ -141,6 +141,11 @@ def test_depends_on(name1, name2, name3):
     assert ps1.depends_on == f"{ps2.name}, {ps3.name}, foo"
     assert ps1.depends_on_ == [ps2.name, ps3.name, "foo"]
 
+    with pytest.raises(ValueError,
+                       match=f"ParamSpec {name1} got string foo as depends_on. "
+                       "It needs a Sequence of ParamSpecs or strings"):
+        ParamSpec(name1, "numeric", depends_on='foo')
+
 
 @given(
     name1=hst.text(min_size=4, alphabet=alphabet),
@@ -156,6 +161,12 @@ def test_inferred_from(name1, name2, name3):
     assert ps1.inferred_from == f"{ps2.name}, {ps3.name}, bar"
     assert ps1.inferred_from_ == [ps2.name, ps3.name, "bar"]
 
+    with pytest.raises(ValueError,
+                       match=f"ParamSpec {name1} got string foo as "
+                       f"inferred_from. "
+                       "It needs a Sequence of ParamSpecs or strings"):
+        ParamSpec(name1, "numeric", inferred_from='foo')
+
 
 @given(
     name1=hst.text(min_size=4, alphabet=alphabet),

From 24f3fe9b274f44d4bdef5da2e4a9c3abca5a64ee Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 5 Feb 2019 09:12:54 +0100
Subject: [PATCH 706/719] Document the errors fixed

---
 qcodes/dataset/sqlite_base.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 87b04e4966a..6eaca9948c3 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -714,8 +714,11 @@ def perform_db_upgrade_3_to_4(conn: ConnectionPlus) -> None:
     """
     Perform the upgrade from version 3 to version 4. This really
     repeats the version 3 upgrade as it originally had two bugs in
-    the inferred annotation. This has since been fixes so rerun
-    the upgrade.
+    the inferred annotation. inferred_from was passed incorrectly
+    resulting in the parameter being marked inferred_from for each char
+    in the inferred_from variable and inferred_from was not handled
+    correctly for parameters that were neither dependencies nor dependent on
+    other parameters. Both have since been fixes so rerun the upgrade.
     """
 
     no_of_runs_query = "SELECT max(run_id) FROM runs"

From 98551a88a20a845f9436001a41787dc441a0dff3 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 5 Feb 2019 11:15:38 +0100
Subject: [PATCH 707/719] Add export docs to dataset

---
 .../DataSet/Dataset Context Manager.ipynb     | 7034 ++---------------
 1 file changed, 810 insertions(+), 6224 deletions(-)

diff --git a/docs/examples/DataSet/Dataset Context Manager.ipynb b/docs/examples/DataSet/Dataset Context Manager.ipynb
index 366fce154b0..7e0d985eebf 100644
--- a/docs/examples/DataSet/Dataset Context Manager.ipynb	
+++ b/docs/examples/DataSet/Dataset Context Manager.ipynb	
@@ -14,7 +14,22 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logging hadn't been started.\n",
+      "Activating auto-logging. Current session state plus future input saved.\n",
+      "Filename       : C:\\Users\\jenielse\\.qcodes\\logs\\command_history.log\n",
+      "Mode           : append\n",
+      "Output logging : True\n",
+      "Raw input log  : False\n",
+      "Timestamping   : True\n",
+      "State          : active\n"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib notebook\n",
     "from collections import OrderedDict\n",
@@ -40,8 +55,7 @@
     "from qcodes.dataset.plotting import plot_by_id\n",
     "from qcodes.dataset.data_export import get_shaped_data_by_runid\n",
     "\n",
-    "# import logging\n",
-    "# logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n"
+    "qc.logger.start_all_logging()"
    ]
   },
   {
@@ -137,8 +151,8 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#15@./exp_container_tutorial.db\n",
-       "-----------------------------------------------------"
+       "tutorial_exp#no sample#128@C:\\Users\\jenielse/experiments.db\n",
+       "-----------------------------------------------------------"
       ]
      },
      "execution_count": 6,
@@ -164,7 +178,7 @@
      "text": [
       "Starting the measurement\n",
       "Doing stuff with the following two instruments: <DummyInstrument: dmm>, <DummyInstrument: dac>\n",
-      "Starting experimental run with id: 68\n",
+      "Starting experimental run with id: 432\n",
       "End of experiment\n"
      ]
     }
@@ -981,7 +995,7 @@
     {
      "data": {
       "text/html": [
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9u3bZfDgwcosjnz58vlcGdsavIYyAExOTjYfwzrLy9fK3VlX6D5//rw0aNBAef/Kly8vDz30kPTr10/uuOMOJQiPiYmRH374wedYrZ/Lxx9/XCpUqCCGcSUsufvuu6V///7y4IMPSqVKlYIOADdv3mzuU7x4cbntttuke/fuMnDgQHn44YelefPmEh8fr4x11apV2Xpdw8H6h43MGnDvvfdKYmKi9OzZU66//nrl9nvuuUfcbrfXY23YsMG8RIW/1+zJJ5809ylTpoz8/fffXvez1pJXXnnFnKVZsmRJ6dixo/Tv319atWqlPKZhGPLee++F5LX58ssvPWpUIKGsFVaPPPKI8hyLFCki9913n/Tv31/uu+8+5XNmGIEDLDtSUlKU55M/f35p2LCh9OjRQx555BHp1auXtGjRQqmlvn4TZO4THx8vN910kzz44IOSmJgo/fv3l3vuuUc5hmEY8uqrr/odm3Vc77zzjvm7pE6dOtKzZ0/p27evx7/dQYMGicj/frcWKFBAbr31Vunbt690795drrnmGmX/L7/80ufjh+O7PpgAMJSfW6tjx46Z9ylatKit0BtAZCEABBCUYALA9PR0ZSXgQKekhjsAzAwaRo8e7bGQxYkTJ5TnVqNGjYCPb1duBYDz5s1TmqbDhw8rDZK3rWzZsrJw4UK/43e5XNKiRQvzPoUKFZIRI0bI+vXrJSUlRU6dOiVr166Vxx57zGy04uLi/P44DodwXQNw1apVSjP23XffKbcfO3bM9sIn1qYg87Vq3bq1HD16VNkvNTVVhgwZojyfTz75xOdxu3XrZu5XrVo1r7OIMjIy5O233zYft0CBArJ69Wqvx7P+uylYsKDky5dPJk+e7DFL4NKlS8r/DzYAjI2Nlbi4OJk2bZpH8/HVV1+ZzbFhXAkL8+fPL5UqVfIISDIyMpTLDRQtWtTnYjUZGRlyxx13mPs2bNjQ62lQqampymzDIkWK+GzCrc87Li5OihQpIjNmzPDY748//lCaSH8LI0XaNQBDGQBaZecagImJicqx33rrLY9GdPv27cqsooSEBNm/f7/X41k/l5mf9YceekhOnDih7OdyuZTPgJ33aPfu3TJ06FBJSkry2SynpKQo/35r167tsxnPzQDwvffeMx/r6quvlvfff9/rTKGFCxcq/679hTOvvPKK8jnds2ePx7GsM3x9rWAv4llLDMOQJ5980qMuHTp0SPl+j4+Pl507dwb5aniyvmcjR460dZ9w1IoZM2Yo3xUPP/ywnD17VtknJSVFunbtquw3c+bM4J5wFi+//LJ5rLp16/p8Td1ut6xevVoGDhwohw8f9rrPwIEDZe7cuZKamur19vT0dPnwww+lcOHC5vvt6/Msor7OsbGxUqxYMZk9e7bHfq+99pq5X0xMjPmcbrjhBvnzzz+VfVNTU+XBBx9Uvmt9Ccd3vd0AMByfWyvr752tW7faug+AyEEACCAowQSA1tk6hmEEPA0r3AGgYfg/JfXIkSPmdfcMw5C1a9cGHEN2/fjjj+bjZJ1t6HK5zNOYHnroIdvHfP/995UfpvXr1zf/f+XKlaVbt27St29fadKkicfsmEDvzcWLF22dbpc/f35p1aqVz2ApnLIGgL5mE3nbvF1fysp6bcUSJUqY/z7dbrdyynODBg38nkpjbQoyw6eszaqVdQZtxYoVvZ7aumjRIuV99jVbJpP138m9997rdZ+swXGgGXWZgg0ADcOQzz77zOfxrAvKGMaVmTC+ruOVkZGhzJrzNYt32rRpyvsV6Bpx1ssYPPbYYwGfd758+WTBggU+jzdz5kwllPIVChEAerdjxw4lIPJ3SueJEyeUP0L5ClGyfi7btm1ra2ZLqN+j/v37m8f75ZdfvO6TWwFgSkqKOTO+YMGCsnz5cr/7b9682fwjW+nSpX0G8G63W1q1amU+h6ZNm5p17cSJE8pM5UCLTmWtJb4+nyJXZo1aZz0FmlVtx1133WUeb/r06bbuE+pakZGRIZUqVTL3e/DBB30ez+Vyyb333mvuW6NGDVuzvny5//77zWNl53q12fHZZ5+ZjzlmzBif+2Wdmfjbb7/53Nf6PhqGIeXKlfMI/zOdOXNGuWSFr9+J4fiutxMAhutza2XtA7yF1wAiGwEggKAECgDPnDkjv/32m3Tp0kX58fPEE08EPHa4A8CyZcua13jxpUOHDub+U6dODTiG7Bo2bJjZACQnJyu3rVu3zlZzm9VLL73kEcgVKFBA3nnnHY8f+evWrZMqVaqY+5UuXVpOnToV8DGWL18u9erV83iczO3666+XDz/80Pb1ZEIp2OuKWbdAgYbL5VJmjd15553icrnk9ddfN/9b4cKFZfv27X6Pk7UpWLp0qd/9T548qYTS3mYwWBs6f6epZnK73VK9enXz35/1NM9M1sa6UqVKtq6pJxJ8ANioUSO/x/vll1+U18vf7EoRNazzNSPHulCLv9PZM124cMFsqEqUKOG1YbY+7wceeMDv8Vwul5QuXdrcf9u2bV73IwD0bvjw4eb+N910U8AAwzo7qlChQnLu3DmPfbJ+Lnfs2BFwHCKhf49WrFgR8N96bgWA1pl6AwcOtHUfa2A/a9Ysn/sdOXJESpUq5fFbon379uZ/q1u3rs/ZYJmstSQhIcFj1ltW1lnysbGxXq/TGgzrpQf8BXlWoa4VP/30k3l7XFxcwD8A7d+/X5nRHugMAH+sC4QFWuAjVNLT083vxJtvvtnnftbXuXPnzn6Paf2jmJ3fXdYZ975+J4bju95OABjOz20m6/N/7rnnbD0GgMhBAAggKNYA0M5WsmRJv9caswp3ADhgwICAx7TO9LJ7Sk92ZF7gvG7duh63WU9JCeb0ivHjx3u8/q+99prP/Xft2mWeTmMY/hcUcLvdMmXKFPN0kbi4OGnRooX069dP+vTpI02aNFFO17z++uvDsuKiP+EMAEWuNE7Wv/z369dPuaj4+++/H/AY1qbg2muvtfW8rKccDR8+XLnt8uXLZtMQGxvrd4aBlfUUyjlz5njcbm2shw0bZuuYIsEHgIFmFh45ckR5nwLNLLWu3OxtJszBgwfN2ytXrmz7eVlnLHn7TFqf9+effx7weNYZJ74u8E8A6J31tN5AM3dFrnxGrAtDeAtqrJ/Lm266KeAxMwX7HqWlpcnSpUtlypQpMm7cOBk6dKgyE7lHjx7m8e6++26vx8itANB62Qe7s7usYeuTTz7pd99Zs2Ypz2PgwIHm/4+Li5NNmzYFfDxrLenTp0/A/V0ul3kdYMMwbK3m6o/1unrr16+3dZ9Q1wrraciBLrOS6Z///Kd5n7Fjx9q6jzfWBcIyr58XChs3bpTp06fLM888I8OGDfOYsZ/5usfFxfk8hvV1DnQ5kpUrVyrfM8eOHfO7/+TJk819fQX1of6uF7EXAIb7cysi8vjjj5v7+5t1CyAyEQACCEowAWCBAgXkww8/tH3scAeAdprFd99919z/kUcesT32YKSkpJin4Hr78ZR5Wk2pUqWCOj3n+eefV17/ChUqBJy59cQTT5j7X3/99V73ycjIkM6dOyvBirdZBjt37lQWoqhUqZKtWYWhEq5rAFp99dVXXv+tt2/f3tb9rU1Bly5dbN3HOsuwRYsWym2rV682bytatKjtU56tK2z+5z//8XhMa2MdzCk+wQaAgRrw1NRU5XX2NnvLyjrDp02bNh63W9+/ChUq2H69qlWrZt7P28Ib1udtXSTCF2uj56s5JQD0lJGRofyhwW7o0rJlS/M+3v4gZf1c+rvWWlZ236MLFy7I6NGjPRYy8Lf5CiJzIwB0u93KH4d69+5t63Ny3333BVXfBg0a5PW5v/HGG7bGaa0l3uqYN9bTVidOnGjrPr5YT0X3NZM3q1DXiubNm5u3+/uDn9Vzzz1n3sfu4iXezJ07V3nfGjRoIG+++WbAmfDeuN1umTZtmjk73e7ma9ZnMK/ztm3bzH2zrmzvzdSpUwP+Tgz1d71I4AAwtz63I0eONPd/+OGHbT03AJGDABBAUKwBYKNGjZQfEb1795Y777xTmRVlGP6v02IV7gDQ3yIKmawLdvha8TWn/F3/z+12m01ihw4dgjru22+/rbzujz76aMD7/Pbbb+b+vk4Htc6KbN26td9rY507d065DlugaziFUm4EgCLqtXoMw//1grKyNgWBTmfN9O2335r3qVevnnLbDz/8EFSz5G17/vnnPR7T2lj7uhaZN8EGgIFOi7KGHfnz5w/4+IFCKmuDld3N22nW1ud94MCBgOMMtnYRAF6RnJysvBd2T+G0XlvP2wwX6+fS7veViL336MSJE8r1WO1u1atX93q83AgAT58+nePPSevWrQM+zsWLF6VOnTrK/dq0aWP7D1/WWuJvsRAr6+qoOf1+sgaAdkOvUNcK63UNsy5Q5Yv1OnqBLsMQSNYFLDK30qVLS/v27eXNN98M+Ll2uVzSs2fPbP0787WoSDCvs/W3Q9WqVQM+Zzu/E0P9XZ91nN4CwNz63P773/829ycABPIeAkAAQbGzCMjRo0c9VpuzsyJsuANAO010KAPA7du3e/1ra+PGjc3H6NGjh3KbNVzKGrAOHjzY72ws6+mPhmHIlClTAo4xa0OdddU76/XPDMOQNWvWBDzmxx9/bO6fkJBg+/pxOZVbAaD1r9+GEdxCLdam4JlnnrF1H+ustqynEn300Uc5/sHv7dRva2O9bNky288v2AAw0LGDDTsChVRZFybKzuatjkRC7QpGXg0A9+7dq7wXdk95Hzp0qHkfbzN2rJ9Lf5dCyMrOe2SdPR0fHy+DBg2Sn376SXbt2iXnzp1T6qOdU/xyIwDct29fjj8ndv6NZGRkSNOmTZX7BXPtXWstWbJkia37WOu3ndOG/cnpKcChqBWVK1c2b/c2O9kb6x+OatWqZes+/sycOVOZ/Z91y5cvn3Tq1MlnEPfOO+8o+7dr104+++wz2bp1q5w+fdrj2s12XsNgXme7q+tmCjYADMV3vZ1x5tbnllOAgbyNABBAUOyuAuxyuaR169bmviVKlAh4OmiwP4ytIWMkBoDWhjlUm78xrV+/Xtn3o48+CjjGrKdYZr3G2sKFC83brr76alszM6zXWTMM+zMjcio3AsDFixcrKyhnbr5WnM0q1LMCvvnmG/O2hg0bBv18fHFqAPjyyy+bt3fq1Mnek7KBANDT5cuXbT1GpM0ADGUAaG3I7azIaa3hOgPArK/z+fPnw/I4EyZM8KilRYsWlT179ti6v+4ZgNddd515LLszpUNdK3TPALQ6ePCgfPrppzJgwACpXbu2x3tbunRp+euvvzzuZ12QzM41o61/lMwLAWBuzQDMrc+t9bf3pEmTwvIYAMKHABBAUOwGgCJXfgxa/0I+YsQIv/tfe+215r579+4NOJa2bduGvInOywFgamqqcn0sO9c8PH78uHL8rGGddTaf3UUTLl68qBwz0MINoRLuAPDUqVNSoUIF8/i1atUy/3eJEiXk0KFDAY+RnesCWReFufPOO5Xbli5dat5WvHjxoK4Z6Y9TA8BPPvnEvP2GG26w96RsiIYAcMmSJeZ9mjVrFnB/a20J1zUAN2zYYGvs1kVcAl0DMJQB4LRp08zbA634KqLOzNIZALpcLmWlWLuvczB+//135TFq1qxp/u+mTZvamjmenWsAtmvXzrzPhAkTcvQcrAt0TJs2zdZ9Ql0rrNcAfP31122NYdKkSeZ9cnINwEAOHDggEydOVK5Ld//99yv77Nmzx7ytVKlSAd/3U6dO2fqej6QAMBTf9XbGmRufWxGRO+64w3yMYK4RDCAyEAACCEowAaCI+iMoPj5ejh496nPfevXqmfuuXbs24LErVqwY8iY63NcAtDbR3q4llrnK7j333JOt499+++3m8e2symcdT4ECBTwuqP3111+btxctWjRbMwB37NiRrecSrHAHgNZT+W644Qa5cOGC0oC2aNHC7/URRUK/MuD58+clJibGvH3VqlXZfn5WToSREQ0AACAASURBVA0Ad+zYYd6eL18+OXLkiL0nFkA4AkDrirChDgAzMjKCDpHWrVtn3sfb6uVZ/frrr7YewxqqB7sK8DvvvBNw/7S0NGX17kCrAIcyALQutjBu3LiAx7Ou6KozABQR5ZTOQKt1B+vs2bNStWpV8/iPPPKIHD58WEqUKGH+NzunTVprSe/evQPuH+pVgIcNG2Yea+TIkbbuE+paYf0307lzZ1tjsP7xNCerANtl/cNLXFycpKWlmbctX77cvO22224LeCzr6tF5JQDMzVWAw/m5zVSmTBnzMbZu3RqWxwAQPgSAAIISbAB46tQpufrqq837DBs2zOe+1r/MB1o9+Pfff1d+BOaVAHDixInm8bPOGLP+uMvuDzfrIgd2VgG2XhvL2ymkq1atUl7nYK8BGBsbKxcvXszWcwlWOANA60ye+Ph42bJli4hcuSaZ9XSkQKcvWZsCwwi8CMaJEyekUKFC5v6zZ8/22Mc6uymY6xH649QAUESUVSbtNu2BhCMAtJ62and2UTCsM0UCBdci6syb2NhYuXDhgt/9Bw4caOu9s66wbOfUz+HDh5v72zl90bryc6FChbyuJB2uAHDy5Mm2vvtEriygVKpUqYgJAK2zxCpVquRxHbac6NWrl3nsmjVrmv+WvvvuO+W5rVy50u9xrLUkISHB54qwmayr1sbGxto+hdyXL7/80jyet1XHvQl1rfjpp5+Uf9/Jycl+j3fo0CHls//rr7/aGndOZJ21d/z4cfO2lStXmv/d18rXVm3atMlzAWCovuvtjDOcn1sRkWPHjpnHL1q0qK3vDgCRhQAQQFCCDQBFRMaMGWPep3Dhwj5/oFpnS/j7S3BaWprccssteTIAbNmypRiGIVWqVPG4zRoyBWp8fDl58qQy2+W1117zue+uXbuUU3O8LRqSkZGhHC/QCo1ZVwFu2bJltp5HdoQrAPzrr7+kSJEi5nGznlr96aefKk3lunXrfB4ra1PQoEEDvwsZWBeF8RXoWq/TaPffeSZfM3KdHAB++OGH5j4xMTGyaNGiwE/s//h6vcIRAFoXKwgmlLKrbNmyQX9WrGGdvz/SrFmzRpmZ6u+9s9ZyO//WduzYoay+6m8cp0+fVi4tkZiY6HW/cAWA1oCoRo0afv8gYw18IyEAPHXqlPLHjf79+9u+xMDx48d9BgPW1yQmJsajXvbr18+8vUqVKn5DPWstMQz/CxJcuHBBuV6e3dMy/UlOTjavCZuQkGArDAl1rcjIyJBKlSqZ+/To0cPnsdxutzzwwAPmvjVr1szRZSMChY2ZNm7caD5mwYIFJT093bzNGigVKFDA7+VfrNcuzGsBYCi+6+2MM1yf20zW6w536NDB1nEBRBYCQABByU4AeOLECSVAGT16tNf9du/erTR2Y8aM8fgRtH//fjNEi4uLy1MBYHp6uvk69OrVy+P2hx9+WAzjyl/xrafIBMu60EGBAgXk3Xff9fgBuH79euXC29WqVZPU1FSvx3v66aeVH7IPPvig/P333x777dy502MlQH8XRre+N3YWFQgkHAFgenq63HzzzeYxfV0vqVu3buY+tWrV8jk7ytoUxMbGimEY0rp1azl27JiyX2pqqrLSnmH4X9TF+lrmz59fRo4c6XN2S2pqqvzwww/Srl07adKkidd9nBwApqenK3UsLi5OXnrpJZ/v2dmzZ+Wzzz6TZs2a+QwNwhEAWpvdBg0a5KgmeJNZRw3DkFdffdXWfay1oHjx4rJixQqPfX766ScpUaKEUp/9vXfW4MvuogyJiYlKiDR16lSPGrdr1y5p2LChuV9CQoLs37/f6/HCFQAmJydLfHy8uU/Pnj0lJSVF2SclJUX69u0rhmEo18zVHQCKqN+HhmHIfffd5/OSDm63W5YvXy6DBg2SQoUKef0+2b9/v/IHJW8z3c+fP6/M0u3Zs6fP8VlrSWY9/fe//+0x6+nQoUPKtfLi4+NDtjiVdRXjpKSkgPuHo1bMmDFDeZ8GDBjgMdP1zJkzysxLwzBk5syZ9p6kD1WqVJFu3brJ3Llzfc4027Jli9xwww1+v0Otn9MGDRrIzp07ldszMjLkzTfflIIFC0qBAgWU2pIXAsBQfdfbHWeoP7dWjz76qHnccMxMBxB+BIAAgpKdAFBEnc1y9dVX+1wReMCAAcoPl2uvvVZ69Ogh/fv3l+bNm5uzSlq3bi1dunQJ+MM4kgLA1atXm8f+4IMPPG7PnF3j7eLPwXC5XNKpUyeP17F79+7Sr18/ufXWW5WVbIsWLSobN270ebyLFy96BHvx8fHSokUL6d+/vzz88MPSpEkTj9VxH3/8cb/jDHcA2KdPHxk8eLDt7YcffvA4pnX2aunSpT1+vGdKSUmRypUrm/sOHDjQ637WpmDYsGHmfeLi4qRNmzaSmJgoHTt2lOLFiyvP5cEHH/T73FNTU5UL0mce87bbbpNevXrJgAED5KGHHpKGDRsqzdMtt9zi9XhODgBFrgQz1qY0M3y56667pE+fPpKYmCidO3eW+vXrK6fL+TrFOhxN/alTp5Tw6LrrrpN+/frJsGHDZPjw4TJ8+HBZuHBhwMfyZerUqeax8+XLJy1atJDHHnvMPPbw4cM9wqpTp04p11DLnz+/3H777dK/f3/p3r27Mvt3+vTptt67efPmKe9D48aN5dFHH1XGkXVW0Pnz56VBgwbK/SpUqCBdu3Y1vyusi4XExMTI999/73MM4QoARdQaYhiGlCxZUtq2bSv9+/eXtm3bmrOwCxYsKB999FHABj83A0ARkdGjRyvjz5cvn9SvX1+6desmAwcOlO7du0vz5s2VYM8wDI8gweVyKYsHNG/e3Odso6wzSL1dL1dErSWvvPKKeZ+SJUtKp06dJDExUe6++24zgMnc7Fw70q533nnHPO6YMWMC7h+OWiGinnKf+b1+//33S2JiorRr1075I6xhBF6UzQ7rcylUqJA0btzY/HfRuXNnuemmm5THLFy4sHn5DKv58+crf/yNjY2V5s2bS9++faVz587KbOUXX3zR1msYSQFgqL7rgxlnqD63Vm6323xd4+Pj5cyZMwFfKwCRhwAQQFCyGwAeP35cmd3g674XL16U1q1bKz9Ism7333+/nDlzJiwraYYzALTOzMs6++Do0aPZel19uXTpkgwePFj5Ue1tq1Wrltcf5FmlpKQor6W/LTY2ViZNmhTwVBLrrLm77747x885awAY7Jb1ottLly5VQs1AF4xftmyZEjrMmjXLY5+sQcPWrVuV0MTb1rt3b1uzvzIyMmTMmDHKdYT8bTExMTJ06FCvx3J6AChy5ZTAAQMGKAGfv61w4cLy0ksvZft5W9mtS1OnTvX7Gc7JqcFpaWnKokHeNm/PJSkpSUqWLOnzPnFxcfKf//wnqPeua9eufsfh7d/J2bNnpWPHjgHft3Llysn8+fP9Pn44A8CMjIyAtbN48eLy448/2mrwczsAFBH5/PPPzQWq7GyNGzdWTvMUUS/xUaxYMTlw4IDfx7Rey6xYsWJeZ29mrSXff/+9cs1hb/82s17CIadOnTplhrjVqlULuH+4aoWIyDPPPOMRdmbdChUqFPBatXbVqlXL9r+J6667zu8iVVOmTFG+P7NuBQoUkAkTJighlL/XMJICwFB91wc7zlB8bq2sC7Z079494OMDiEwEgACCkt0AUERkxIgRyg96X389dLvd8umnn0qrVq2kVKlSEhMTIxUqVJB77rlHvv32W3O/vBYAZi5yUqZMGY/brKvthvKi3GvXrpUhQ4ZInTp1JCEhQeLi4qRixYrSvn17+eSTTwIuEpLVpk2bZMSIEdKkSRMpU6aMxMbGSnx8vJQrV05atmwpzz33nO2VVa2rPk+fPj0bz04VygAw64w+Oysqi4iMGzfOvE+pUqU8XgtvQcPZs2fljTfekKZNm5qvacWKFaVLly5BXZ8u07Fjx+Sll16SNm3aSMWKFaVw4cISExMjJUuWlAYNGkif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width=\"640\">"
+       "<div id='8afbcf1a-7115-4999-a515-8d5ad8f14b23'></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -995,6 +1009,217 @@
     "ax, cbax = plot_by_id(dataid)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exporting data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset implements a number of methods for accessing the data of a given dataset. Here we will concentrate on the two most user friendly methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`get_parameter_data` returns the data as a dict of numpy arrays. The dict is indexed by the measured (dependent) parameter in the outermost level and and the names of the dependent and independent parameters in the innermost level. The first parameter in the innermost level is always the dependent parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'dmm_v1': {'dmm_v1': array([ 5.10277843,  2.9413283 ,  1.60122129,  1.19706838,  0.46107292,\n",
+       "          0.30048698,  0.16128984,  0.24378436,  0.10619672, -0.15406753]),\n",
+       "  'dac_ch1': array([ 0.        ,  2.77777778,  5.55555556,  8.33333333, 11.11111111,\n",
+       "         13.88888889, 16.66666667, 19.44444444, 22.22222222, 25.        ])}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datasaver.dataset.get_parameter_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default `get_parameter_data` returns all data stored in the dataset but data specific to one or more measured parameters can be returned by passing the parameter name(s) or `ParamSpec` to the method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'dmm_v1': {'dmm_v1': array([ 5.10277843,  2.9413283 ,  1.60122129,  1.19706838,  0.46107292,\n",
+       "          0.30048698,  0.16128984,  0.24378436,  0.10619672, -0.15406753]),\n",
+       "  'dac_ch1': array([ 0.        ,  2.77777778,  5.55555556,  8.33333333, 11.11111111,\n",
+       "         13.88888889, 16.66666667, 19.44444444, 22.22222222, 25.        ])}}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datasaver.dataset.get_parameter_data('dmm_v1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also simply fetch the data for one or more dependent parameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'dac_ch1': {'dac_ch1': array([ 0.        ,  2.77777778,  5.55555556,  8.33333333, 11.11111111,\n",
+       "         13.88888889, 16.66666667, 19.44444444, 22.22222222, 25.        ])}}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datasaver.dataset.get_parameter_data('dac_ch1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data can also be exported as one or more [Pandas](https://pandas.pydata.org/) dataframes. The dataframes are returns as a dict from measured parameters to dataframes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dmm_v1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dac_ch1</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.000000</th>\n",
+       "      <td>5.102778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.777778</th>\n",
+       "      <td>2.941328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.555556</th>\n",
+       "      <td>1.601221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.333333</th>\n",
+       "      <td>1.197068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11.111111</th>\n",
+       "      <td>0.461073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13.888889</th>\n",
+       "      <td>0.300487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16.666667</th>\n",
+       "      <td>0.161290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19.444444</th>\n",
+       "      <td>0.243784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.222222</th>\n",
+       "      <td>0.106197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25.000000</th>\n",
+       "      <td>-0.154068</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             dmm_v1\n",
+       "dac_ch1            \n",
+       "0.000000   5.102778\n",
+       "2.777778   2.941328\n",
+       "5.555556   1.601221\n",
+       "8.333333   1.197068\n",
+       "11.111111  0.461073\n",
+       "13.888889  0.300487\n",
+       "16.666667  0.161290\n",
+       "19.444444  0.243784\n",
+       "22.222222  0.106197\n",
+       "25.000000 -0.154068"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1']"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1006,7 +1231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1015,7 +1240,7 @@
      "text": [
       "Starting the measurement\n",
       "Doing stuff with the following two instruments: <DummyInstrument: dmm>, <DummyInstrument: dac>\n",
-      "Starting experimental run with id: 69\n",
+      "Starting experimental run with id: 433\n",
       "End of experiment\n"
      ]
     }
@@ -1042,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1828,7 +2053,7 @@
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\" width=\"640\">"
+       "<div id='aac4799b-f454-45dd-a40c-9371c25b406f'></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1844,7 +2069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1853,7 +2078,7 @@
      "text": [
       "Starting the measurement\n",
       "Doing stuff with the following two instruments: <DummyInstrument: dmm>, <DummyInstrument: dac>\n",
-      "Starting experimental run with id: 70\n",
+      "Starting experimental run with id: 434\n",
       "End of experiment\n"
      ]
     }
@@ -1884,7 +2109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -2670,7 +2895,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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width=\"640\">"
+       "<div id='a11d16be-6dc9-4fea-8d85-a88407fbe7e8'></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2698,9 +2923,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<qcodes.dataset.measurements.Measurement at 0x1fe2338ae80>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# For the 2D, we'll need a new batch of parameters, notably one with two \n",
     "# other parameters as setpoints. We therefore define a new Measurement\n",
@@ -2714,7 +2950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2733,7 +2969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2752,14 +2988,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 71\n"
+      "Starting experimental run with id: 435\n"
      ]
     }
    ],
@@ -2780,6215 +3016,565 @@
     "    dataid = datasaver.run_id"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When exporting a two or higher dimensional dataset as a Pandas dataframe a [MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html) is used to index the measured parameter based on all the dependencies"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1'][0:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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AgAAAAAAACHGAAgAAAAAAACEGAMgAAAAAAAAEGIMgAAAAAAAAECIMQACAAAAAAAAIcYACAAAAAAAAIQYAyAAAAAAAAAQYgyAAAAAAAAAQIgxAAIAAAAAAAAhxgAIAAAAAAAAhBgDIAAAAAAAABBi/x/1Bw7NQGzEVQAAAABJRU5ErkJggg==\" width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax, abax = plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 72\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Looking at the above picture, we may decide to sample more finely in the central\n",
-    "# region\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "\n",
-    "    v1points = np.concatenate((np.linspace(-1, -0.5, 5),\n",
-    "                               np.linspace(-0.51, 0.5, 200),\n",
-    "                               np.linspace(0.51, 1, 5)))\n",
-    "    v2points = np.concatenate((np.linspace(-1, -0.25, 5),\n",
-    "                               np.linspace(-0.26, 0.5, 200),\n",
-    "                               np.linspace(0.51, 1, 5)))\n",
-    "    \n",
-    "    for v1 in v1points:\n",
-    "        for v2 in v2points:\n",
-    "            dac.ch1(v1)\n",
-    "            dac.ch2(v2)\n",
-    "            val = dmm.v1.get()\n",
-    "            datasaver.add_result((dac.ch1, v1),\n",
-    "                                 (dac.ch2, v2),\n",
-    "                                 (dmm.v1, val))\n",
-    "\n",
-    "    dataid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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\" width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax, cbax = plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 73\n",
-      "130 46 0.25089416830953565\n",
-      "----------\n"
-     ]
-    }
-   ],
-   "source": [
-    "# or even perform an adaptive sweep... ooohh...\n",
-    "#\n",
-    "# This example is a not-very-clever toy model example,\n",
-    "# but it nicely shows a semi-realistic measurement that the old qc.Loop\n",
-    "# could not handle\n",
-    "\n",
-    "v1_points = np.linspace(-1, 1, 250)\n",
-    "v2_points = np.linspace(1, -1, 250)\n",
-    "\n",
-    "threshold = 0.25\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "    \n",
-    "    # Do normal sweeping until the peak is detected\n",
-    "    \n",
-    "    for v2ind, v2 in enumerate(v2_points):\n",
-    "        for v1ind, v1 in enumerate(v1_points):\n",
-    "            dac.ch1(v1)\n",
-    "            dac.ch2(v2)\n",
-    "            val = dmm.v1.get()\n",
-    "            datasaver.add_result((dac.ch1, v1),\n",
-    "                                 (dac.ch2, v2),\n",
-    "                                 (dmm.v1, val))\n",
-    "            if val > threshold:\n",
-    "                break\n",
-    "        else:\n",
-    "            continue\n",
-    "        break\n",
-    "        \n",
-    "    print(v1ind, v2ind, val)\n",
-    "    print('-'*10)\n",
-    "        \n",
-    "    # now be more clever, meandering back and forth over the peak\n",
-    "    doneyet = False\n",
-    "    rowdone = False\n",
-    "    v1_step = 1\n",
-    "    while not doneyet:\n",
-    "            v2 = v2_points[v2ind]\n",
-    "            v1 = v1_points[v1ind+v1_step-1]\n",
-    "            dac.ch1(v1)\n",
-    "            dac.ch2(v2)\n",
-    "            val = dmm.v1.get()\n",
-    "            datasaver.add_result((dac.ch1, v1),\n",
-    "                                 (dac.ch2, v2),\n",
-    "                                 (dmm.v1, val))\n",
-    "            if val < threshold:\n",
-    "                if rowdone:\n",
-    "                    doneyet = True\n",
-    "                v2ind += 1\n",
-    "                v1_step *= -1\n",
-    "                rowdone = True\n",
-    "            else:\n",
-    "                v1ind += v1_step\n",
-    "                rowdone = False\n",
-    "                \n",
-    "dataid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax, cbax = plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## Random sampling "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We may also chose to sample completly randomly across the phase space"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gauss = gauss_model(0.1, 0.2, 0.25)\n",
-    "next(gauss)\n",
-    "\n",
-    "def measure_gauss(x, y):\n",
-    "    val = gauss.send((x, y))\n",
-    "    next(gauss)\n",
-    "    return val\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 74\n"
-     ]
-    }
-   ],
-   "source": [
-    "v1_points = np.linspace(-1, 1, 250)\n",
-    "v2_points = np.linspace(1, -1, 250)\n",
-    "\n",
-    "threshold = 0.25\n",
-    "\n",
-    "npoints = 5000\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "    for i in range(npoints):\n",
-    "        x = 2*(np.random.rand()-.5)\n",
-    "        y = 2*(np.random.rand()-.5)\n",
-    "        z = measure_gauss(x,y)\n",
-    "        datasaver.add_result((dac.ch1, x),\n",
-    "                     (dac.ch2, y),\n",
-    "                     (dmm.v1, z))\n",
-    "dataid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "<img 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\" width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax, cbax = plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Optimiser"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An example to show that the algorithm is flexible enough to be used with completly unstructured data such as the output of an downhill simplex optimization. The downhill simplex is somewhat more sensitive to noise and it is important that 'fatol' is set to match the expected noise."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.optimize import minimize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "noise = 0.0005\n",
-    "\n",
-    "gauss = gauss_model(0.1, 0.2, 0.25, noise=noise)\n",
-    "next(gauss)\n",
-    "\n",
-    "def measure_gauss(x, y):\n",
-    "    val = gauss.send((x, y))\n",
-    "    next(gauss)\n",
-    "    return val\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 75\n"
-     ]
-    }
-   ],
-   "source": [
-    "x0 = [np.random.rand(), np.random.rand()]\n",
-    "with meas.run() as datasaver:\n",
-    "    def mycallback(xk):\n",
-    "        datasaver.add_result((dac.ch1, xk[0]),\n",
-    "                     (dac.ch2, xk[1]),\n",
-    "                     (dmm.v1, measure_gauss(xk[0], xk[1])))\n",
-    "    \n",
-    "    res = minimize(lambda x: -measure_gauss(*x), x0, method='Nelder-Mead', tol=1e-10, \n",
-    "                   callback=mycallback, options={'fatol': noise})\n",
-    "    \n",
-    "    run_id = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       " final_simplex: (array([[0.1, 0.2],\n",
-       "       [0.1, 0.2],\n",
-       "       [0.1, 0.2]]), array([-1.13314845, -1.13314845, -1.13314845]))\n",
-       "           fun: -1.1331484530668263\n",
-       "       message: 'Optimization terminated successfully.'\n",
-       "          nfev: 163\n",
-       "           nit: 81\n",
-       "        status: 0\n",
-       "       success: True\n",
-       "             x: array([0.1, 0.2])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax, cbax = plot_by_id(run_id)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## Subscriptions\n",
-    "\n",
-    "The Measurement object can also handle subscriptions to the dataset. Subscriptions are really, under the hood, triggers in the underlying SQLite database. Therefore, the subscribers are only called when data is written to the database (which happens every `write_period`).\n",
-    "\n",
-    "When making a subscription, two things must be supplied, a function and a mutable state object. The function **MUST** have a call signature of `f(result_list, length, state, **kwargs)`, where result_list is a list of tuples of parameter values inserted in the dataset, length is an integer (the step number of the run), and state is the mutable state object. The function does not need to actually use these arguments, but the call signature must match this.\n",
-    "\n",
-    "We show two examples of subscribtions here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Subscription example 1: simple printing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 76\n",
-      "Added points to measurement, step 0.\n",
-      "Added points to measurement, step 1.\n",
-      "The run now holds 1 rows\n",
-      "Added points to measurement, step 2.\n",
-      "Added points to measurement, step 3.\n",
-      "Added points to measurement, step 4.\n",
-      "The run now holds 6 rows\n",
-      "Added points to measurement, step 5.\n",
-      "Added points to measurement, step 6.\n",
-      "Added points to measurement, step 7.\n",
-      "The run now holds 9 rows\n",
-      "Added points to measurement, step 8.\n",
-      "Added points to measurement, step 9.\n",
-      "The run now holds 10 rows\n",
-      "The run now holds 10 rows\n",
-      "The run now holds 10 rows\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "def print_which_step(results_list, length, state):\n",
-    "    \"\"\"\n",
-    "    This subscriber does not use results_list nor state; it simply\n",
-    "    prints how many results we have added to the database\n",
-    "    \"\"\"\n",
-    "    print(f'The run now holds {length} rows')\n",
-    "    \n",
-    "    \n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(dac.ch1)\n",
-    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1,))\n",
-    "\n",
-    "meas.write_period = 1  # We write to the database every 1 second\n",
-    "\n",
-    "meas.add_subscriber(print_which_step, state=[])\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "    for n in range(10):\n",
-    "        datasaver.add_result((dac.ch1, n), (dmm.v1, n**2))\n",
-    "        print(f'Added points to measurement, step {n}.')\n",
-    "        sleep(0.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Subscription example 2: using the state\n",
-    "\n",
-    "We add two subscribers now."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 77\n",
-      "Added points to measurement, step 0.\n",
-      "First parameter value list: []\n",
-      "Added points to measurement, step 1.\n",
-      "First parameter value list: []\n",
-      "The run now holds 2 rows\n",
-      "Added points to measurement, step 2.\n",
-      "First parameter value list: [0, 1, 2]\n",
-      "Added points to measurement, step 3.\n",
-      "First parameter value list: [0, 1, 2]\n",
-      "Added points to measurement, step 4.\n",
-      "First parameter value list: [0, 1, 2]\n",
-      "The run now holds 5 rows\n",
-      "Added points to measurement, step 5.\n",
-      "First parameter value list: [0, 1, 2, 3, 4, 5]\n",
-      "Added points to measurement, step 6.\n",
-      "First parameter value list: [0, 1, 2, 3, 4, 5]\n",
-      "Added points to measurement, step 7.\n",
-      "First parameter value list: [0, 1, 2, 3, 4, 5]\n",
-      "The run now holds 7 rows\n",
-      "Added points to measurement, step 8.\n",
-      "First parameter value list: [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
-      "Added points to measurement, step 9.\n",
-      "First parameter value list: [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
-      "The run now holds 10 rows\n",
-      "The run now holds 10 rows\n",
-      "The run now holds 10 rows\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "def get_list_of_first_param(results_list, lenght, state):\n",
-    "    \"\"\"\n",
-    "    Modify the state (a list) to hold all the values for\n",
-    "    the first parameter\n",
-    "    \"\"\"\n",
-    "    param_vals = [parvals[0] for parvals in results_list]\n",
-    "    state += param_vals\n",
-    "    \n",
-    "meas = Measurement()\n",
-    "meas.register_parameter(dac.ch1)\n",
-    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1,))\n",
-    "\n",
-    "meas.write_period = 1  # We write to the database every 1 second\n",
-    "\n",
-    "first_param_list = []\n",
-    "\n",
-    "meas.add_subscriber(print_which_step, state=[])\n",
-    "meas.add_subscriber(get_list_of_first_param, state=first_param_list)\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "    for n in range(10):\n",
-    "        datasaver.add_result((dac.ch1, n), (dmm.v1, n**2))\n",
-    "        print(f'Added points to measurement, step {n}.')\n",
-    "        print(f'First parameter value list: {first_param_list}')\n",
-    "        sleep(0.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## QCoDeS Array and MultiParameter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Measurement object supports automatic handling of Array and MultiParameters. When registering these parameters \n",
-    "the individual components are unpacked and added to the dataset as if they were separate parameters. Lets consider a MultiParamter with array components as the most general case.\n",
-    "\n",
-    "First lets use a dummy instrument that produces data as Array and MultiParameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qcodes.tests.instrument_mocks import DummyChannelInstrument"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mydummy = DummyChannelInstrument('MyDummy')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This instrument produces 2 Arrays with the names, shapes and setpoints given below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('this', 'that')"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mydummy.A.dummy_2d_multi_parameter.names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((5, 3), (5, 3))"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mydummy.A.dummy_2d_multi_parameter.shapes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(('this_setpoint', 'that_setpoint'), ('this_setpoint', 'that_setpoint'))"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mydummy.A.dummy_2d_multi_parameter.setpoint_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('MyDummy_ChanA_this_setpoint',\n",
-       "              ParamSpec('MyDummy_ChanA_this_setpoint', 'numeric', 'this setpoint', 'this setpointunit', inferred_from=[], depends_on=[])),\n",
-       "             ('MyDummy_ChanA_that_setpoint',\n",
-       "              ParamSpec('MyDummy_ChanA_that_setpoint', 'numeric', 'that setpoint', 'that setpointunit', inferred_from=[], depends_on=[])),\n",
-       "             ('this',\n",
-       "              ParamSpec('this', 'numeric', 'this label', 'this unit', inferred_from=[], depends_on=['MyDummy_ChanA_this_setpoint', 'MyDummy_ChanA_that_setpoint'])),\n",
-       "             ('that',\n",
-       "              ParamSpec('that', 'numeric', 'that label', 'that unit', inferred_from=[], depends_on=['MyDummy_ChanA_this_setpoint', 'MyDummy_ChanA_that_setpoint']))])"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "meas = Measurement()\n",
-    "\n",
-    "meas.register_parameter(mydummy.A.dummy_2d_multi_parameter)\n",
-    "meas.parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When adding the MultiParameter to the measurement we can see that we add each of the individual components as a \n",
-    "separate parameter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting experimental run with id: 78\n"
-     ]
-    }
-   ],
-   "source": [
-    "with meas.run() as datasaver:\n",
-    "    datasaver.add_result((mydummy.A.dummy_2d_multi_parameter, mydummy.A.dummy_2d_multi_parameter()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And when adding the result of a MultiParameter it is automatically unpacked into its components."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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\" width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x1a6e89b0c50>,\n",
-       "  <matplotlib.axes._subplots.AxesSubplot at 0x1a6e7a0de80>],\n",
-       " [<matplotlib.colorbar.Colorbar at 0x1a6e7cd1710>,\n",
-       "  <matplotlib.colorbar.Colorbar at 0x1a6e8c7f550>])"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "source": [
+    "If your data is on a regular grid it may make sense to view the data as an [XArray](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html) Dataset. The Pandas dataframe can be directly exported to xarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1'].to_xarray()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note however, that XArray is only suited for data that is on regular grid with few or no missing values. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax, abax = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Looking at the above picture, we may decide to sample more finely in the central\n",
+    "# region\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "\n",
+    "    v1points = np.concatenate((np.linspace(-1, -0.5, 5),\n",
+    "                               np.linspace(-0.51, 0.5, 200),\n",
+    "                               np.linspace(0.51, 1, 5)))\n",
+    "    v2points = np.concatenate((np.linspace(-1, -0.25, 5),\n",
+    "                               np.linspace(-0.26, 0.5, 200),\n",
+    "                               np.linspace(0.51, 1, 5)))\n",
+    "    \n",
+    "    for v1 in v1points:\n",
+    "        for v2 in v2points:\n",
+    "            dac.ch1(v1)\n",
+    "            dac.ch2(v2)\n",
+    "            val = dmm.v1.get()\n",
+    "            datasaver.add_result((dac.ch1, v1),\n",
+    "                                 (dac.ch2, v2),\n",
+    "                                 (dmm.v1, val))\n",
+    "\n",
+    "    dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax, cbax = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# or even perform an adaptive sweep... ooohh...\n",
+    "#\n",
+    "# This example is a not-very-clever toy model example,\n",
+    "# but it nicely shows a semi-realistic measurement that the old qc.Loop\n",
+    "# could not handle\n",
+    "\n",
+    "v1_points = np.linspace(-1, 1, 250)\n",
+    "v2_points = np.linspace(1, -1, 250)\n",
+    "\n",
+    "threshold = 0.25\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    \n",
+    "    # Do normal sweeping until the peak is detected\n",
+    "    \n",
+    "    for v2ind, v2 in enumerate(v2_points):\n",
+    "        for v1ind, v1 in enumerate(v1_points):\n",
+    "            dac.ch1(v1)\n",
+    "            dac.ch2(v2)\n",
+    "            val = dmm.v1.get()\n",
+    "            datasaver.add_result((dac.ch1, v1),\n",
+    "                                 (dac.ch2, v2),\n",
+    "                                 (dmm.v1, val))\n",
+    "            if val > threshold:\n",
+    "                break\n",
+    "        else:\n",
+    "            continue\n",
+    "        break\n",
+    "        \n",
+    "    print(v1ind, v2ind, val)\n",
+    "    print('-'*10)\n",
+    "        \n",
+    "    # now be more clever, meandering back and forth over the peak\n",
+    "    doneyet = False\n",
+    "    rowdone = False\n",
+    "    v1_step = 1\n",
+    "    while not doneyet:\n",
+    "            v2 = v2_points[v2ind]\n",
+    "            v1 = v1_points[v1ind+v1_step-1]\n",
+    "            dac.ch1(v1)\n",
+    "            dac.ch2(v2)\n",
+    "            val = dmm.v1.get()\n",
+    "            datasaver.add_result((dac.ch1, v1),\n",
+    "                                 (dac.ch2, v2),\n",
+    "                                 (dmm.v1, val))\n",
+    "            if val < threshold:\n",
+    "                if rowdone:\n",
+    "                    doneyet = True\n",
+    "                v2ind += 1\n",
+    "                v1_step *= -1\n",
+    "                rowdone = True\n",
+    "            else:\n",
+    "                v1ind += v1_step\n",
+    "                rowdone = False\n",
+    "                \n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax, cbax = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Random sampling "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We may also chose to sample completely randomly across the phase space"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gauss = gauss_model(0.1, 0.2, 0.25)\n",
+    "next(gauss)\n",
+    "\n",
+    "def measure_gauss(x, y):\n",
+    "    val = gauss.send((x, y))\n",
+    "    next(gauss)\n",
+    "    return val\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v1_points = np.linspace(-1, 1, 250)\n",
+    "v2_points = np.linspace(1, -1, 250)\n",
+    "\n",
+    "threshold = 0.25\n",
+    "\n",
+    "npoints = 5000\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    for i in range(npoints):\n",
+    "        x = 2*(np.random.rand()-.5)\n",
+    "        y = 2*(np.random.rand()-.5)\n",
+    "        z = measure_gauss(x,y)\n",
+    "        datasaver.add_result((dac.ch1, x),\n",
+    "                     (dac.ch2, y),\n",
+    "                     (dmm.v1, z))\n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax, cbax = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1'][0:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unlike the data measured on a grid above all the measured data points have an unique combination of the two dependent parameters. When exporting to XArray NaN's will therefore replace all the missing combinations of `dac_ch1` and `dac_ch2` and the data is unlikely to be useful in this format. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1'][0:10].to_xarray()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optimiser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example to show that the algorithm is flexible enough to be used with completely unstructured data such as the output of an downhill simplex optimization. The downhill simplex is somewhat more sensitive to noise and it is important that 'fatol' is set to match the expected noise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.optimize import minimize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "noise = 0.0005\n",
+    "\n",
+    "gauss = gauss_model(0.1, 0.2, 0.25, noise=noise)\n",
+    "next(gauss)\n",
+    "\n",
+    "def measure_gauss(x, y):\n",
+    "    val = gauss.send((x, y))\n",
+    "    next(gauss)\n",
+    "    return val\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x0 = [np.random.rand(), np.random.rand()]\n",
+    "with meas.run() as datasaver:\n",
+    "    def mycallback(xk):\n",
+    "        datasaver.add_result((dac.ch1, xk[0]),\n",
+    "                     (dac.ch2, xk[1]),\n",
+    "                     (dmm.v1, measure_gauss(xk[0], xk[1])))\n",
+    "    \n",
+    "    res = minimize(lambda x: -measure_gauss(*x), x0, method='Nelder-Mead', tol=1e-10, \n",
+    "                   callback=mycallback, options={'fatol': noise})\n",
+    "    \n",
+    "    run_id = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax, cbax = plot_by_id(run_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Subscriptions\n",
+    "\n",
+    "The Measurement object can also handle subscriptions to the dataset. Subscriptions are really, under the hood, triggers in the underlying SQLite database. Therefore, the subscribers are only called when data is written to the database (which happens every `write_period`).\n",
+    "\n",
+    "When making a subscription, two things must be supplied, a function and a mutable state object. The function **MUST** have a call signature of `f(result_list, length, state, **kwargs)`, where result_list is a list of tuples of parameter values inserted in the dataset, length is an integer (the step number of the run), and state is the mutable state object. The function does not need to actually use these arguments, but the call signature must match this.\n",
+    "\n",
+    "We show two examples of subscriptions here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Subscription example 1: simple printing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def print_which_step(results_list, length, state):\n",
+    "    \"\"\"\n",
+    "    This subscriber does not use results_list nor state; it simply\n",
+    "    prints how many results we have added to the database\n",
+    "    \"\"\"\n",
+    "    print(f'The run now holds {length} rows')\n",
+    "    \n",
+    "    \n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(dac.ch1)\n",
+    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1,))\n",
+    "\n",
+    "meas.write_period = 1  # We write to the database every 1 second\n",
+    "\n",
+    "meas.add_subscriber(print_which_step, state=[])\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    for n in range(10):\n",
+    "        datasaver.add_result((dac.ch1, n), (dmm.v1, n**2))\n",
+    "        print(f'Added points to measurement, step {n}.')\n",
+    "        sleep(0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Subscription example 2: using the state\n",
+    "\n",
+    "We add two subscribers now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def get_list_of_first_param(results_list, lenght, state):\n",
+    "    \"\"\"\n",
+    "    Modify the state (a list) to hold all the values for\n",
+    "    the first parameter\n",
+    "    \"\"\"\n",
+    "    param_vals = [parvals[0] for parvals in results_list]\n",
+    "    state += param_vals\n",
+    "    \n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(dac.ch1)\n",
+    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1,))\n",
+    "\n",
+    "meas.write_period = 1  # We write to the database every 1 second\n",
+    "\n",
+    "first_param_list = []\n",
+    "\n",
+    "meas.add_subscriber(print_which_step, state=[])\n",
+    "meas.add_subscriber(get_list_of_first_param, state=first_param_list)\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    for n in range(10):\n",
+    "        datasaver.add_result((dac.ch1, n), (dmm.v1, n**2))\n",
+    "        print(f'Added points to measurement, step {n}.')\n",
+    "        print(f'First parameter value list: {first_param_list}')\n",
+    "        sleep(0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## QCoDeS Array and MultiParameter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Measurement object supports automatic handling of Array and MultiParameters. When registering these parameters \n",
+    "the individual components are unpacked and added to the dataset as if they were separate parameters. Lets consider a MultiParamter with array components as the most general case.\n",
+    "\n",
+    "First lets use a dummy instrument that produces data as Array and MultiParameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qcodes.tests.instrument_mocks import DummyChannelInstrument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mydummy = DummyChannelInstrument('MyDummy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This instrument produces 2 Arrays with the names, shapes and setpoints given below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mydummy.A.dummy_2d_multi_parameter.names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mydummy.A.dummy_2d_multi_parameter.shapes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mydummy.A.dummy_2d_multi_parameter.setpoint_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meas = Measurement()\n",
+    "\n",
+    "meas.register_parameter(mydummy.A.dummy_2d_multi_parameter)\n",
+    "meas.parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When adding the MultiParameter to the measurement we can see that we add each of the individual components as a \n",
+    "separate parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with meas.run() as datasaver:\n",
+    "    datasaver.add_result((mydummy.A.dummy_2d_multi_parameter, mydummy.A.dummy_2d_multi_parameter()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And when adding the result of a MultiParameter it is automatically unpacked into its components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plot_by_id(datasaver.run_id)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasaver.dataset.get_data_as_pandas_dataframe()['that']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasaver.dataset.get_data_as_pandas_dataframe()['that'].to_xarray()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -9013,7 +3599,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.8"
   },
   "toc": {
    "base_numbering": 1,

From 4ca776ac603eadbaea9fa7a8975e04ec03f05148 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 5 Feb 2019 11:19:27 +0100
Subject: [PATCH 708/719] rerun notebook

---
 .../DataSet/Dataset Context Manager.ipynb     | 6613 ++++++++++++++++-
 1 file changed, 6362 insertions(+), 251 deletions(-)

diff --git a/docs/examples/DataSet/Dataset Context Manager.ipynb b/docs/examples/DataSet/Dataset Context Manager.ipynb
index 7e0d985eebf..933f8b76c34 100644
--- a/docs/examples/DataSet/Dataset Context Manager.ipynb	
+++ b/docs/examples/DataSet/Dataset Context Manager.ipynb	
@@ -151,7 +151,7 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#128@C:\\Users\\jenielse/experiments.db\n",
+       "tutorial_exp#no sample#129@C:\\Users\\jenielse/experiments.db\n",
        "-----------------------------------------------------------"
       ]
      },
@@ -178,7 +178,7 @@
      "text": [
       "Starting the measurement\n",
       "Doing stuff with the following two instruments: <DummyInstrument: dmm>, <DummyInstrument: dac>\n",
-      "Starting experimental run with id: 432\n",
+      "Starting experimental run with id: 443\n",
       "End of experiment\n"
      ]
     }
@@ -995,7 +995,7 @@
     {
      "data": {
       "text/html": [
-       "<div id='8afbcf1a-7115-4999-a515-8d5ad8f14b23'></div>"
+       "<img 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\" 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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1038,8 +1038,8 @@
     {
      "data": {
       "text/plain": [
-       "{'dmm_v1': {'dmm_v1': array([ 5.10277843,  2.9413283 ,  1.60122129,  1.19706838,  0.46107292,\n",
-       "          0.30048698,  0.16128984,  0.24378436,  0.10619672, -0.15406753]),\n",
+       "{'dmm_v1': {'dmm_v1': array([ 5.02348283,  2.76737943,  1.54907291,  0.82583077,  0.40845978,\n",
+       "          0.12302513,  0.1933247 ,  0.21199432,  0.04990785, -0.11080111]),\n",
        "  'dac_ch1': array([ 0.        ,  2.77777778,  5.55555556,  8.33333333, 11.11111111,\n",
        "         13.88888889, 16.66666667, 19.44444444, 22.22222222, 25.        ])}}"
       ]
@@ -1068,8 +1068,8 @@
     {
      "data": {
       "text/plain": [
-       "{'dmm_v1': {'dmm_v1': array([ 5.10277843,  2.9413283 ,  1.60122129,  1.19706838,  0.46107292,\n",
-       "          0.30048698,  0.16128984,  0.24378436,  0.10619672, -0.15406753]),\n",
+       "{'dmm_v1': {'dmm_v1': array([ 5.02348283,  2.76737943,  1.54907291,  0.82583077,  0.40845978,\n",
+       "          0.12302513,  0.1933247 ,  0.21199432,  0.04990785, -0.11080111]),\n",
        "  'dac_ch1': array([ 0.        ,  2.77777778,  5.55555556,  8.33333333, 11.11111111,\n",
        "         13.88888889, 16.66666667, 19.44444444, 22.22222222, 25.        ])}}"
       ]
@@ -1154,43 +1154,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0.000000</th>\n",
-       "      <td>5.102778</td>\n",
+       "      <td>5.023483</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2.777778</th>\n",
-       "      <td>2.941328</td>\n",
+       "      <td>2.767379</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5.555556</th>\n",
-       "      <td>1.601221</td>\n",
+       "      <td>1.549073</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8.333333</th>\n",
-       "      <td>1.197068</td>\n",
+       "      <td>0.825831</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11.111111</th>\n",
-       "      <td>0.461073</td>\n",
+       "      <td>0.408460</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13.888889</th>\n",
-       "      <td>0.300487</td>\n",
+       "      <td>0.123025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16.666667</th>\n",
-       "      <td>0.161290</td>\n",
+       "      <td>0.193325</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19.444444</th>\n",
-       "      <td>0.243784</td>\n",
+       "      <td>0.211994</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22.222222</th>\n",
-       "      <td>0.106197</td>\n",
+       "      <td>0.049908</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25.000000</th>\n",
-       "      <td>-0.154068</td>\n",
+       "      <td>-0.110801</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1199,16 +1199,16 @@
       "text/plain": [
        "             dmm_v1\n",
        "dac_ch1            \n",
-       "0.000000   5.102778\n",
-       "2.777778   2.941328\n",
-       "5.555556   1.601221\n",
-       "8.333333   1.197068\n",
-       "11.111111  0.461073\n",
-       "13.888889  0.300487\n",
-       "16.666667  0.161290\n",
-       "19.444444  0.243784\n",
-       "22.222222  0.106197\n",
-       "25.000000 -0.154068"
+       "0.000000   5.023483\n",
+       "2.777778   2.767379\n",
+       "5.555556   1.549073\n",
+       "8.333333   0.825831\n",
+       "11.111111  0.408460\n",
+       "13.888889  0.123025\n",
+       "16.666667  0.193325\n",
+       "19.444444  0.211994\n",
+       "22.222222  0.049908\n",
+       "25.000000 -0.110801"
       ]
      },
      "execution_count": 12,
@@ -1240,7 +1240,7 @@
      "text": [
       "Starting the measurement\n",
       "Doing stuff with the following two instruments: <DummyInstrument: dmm>, <DummyInstrument: dac>\n",
-      "Starting experimental run with id: 433\n",
+      "Starting experimental run with id: 444\n",
       "End of experiment\n"
      ]
     }
@@ -2053,7 +2053,7 @@
     {
      "data": {
       "text/html": [
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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2078,7 +2078,7 @@
      "text": [
       "Starting the measurement\n",
       "Doing stuff with the following two instruments: <DummyInstrument: dmm>, <DummyInstrument: dac>\n",
-      "Starting experimental run with id: 434\n",
+      "Starting experimental run with id: 445\n",
       "End of experiment\n"
      ]
     }
@@ -2895,7 +2895,7 @@
     {
      "data": {
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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2929,7 +2929,7 @@
     {
      "data": {
       "text/plain": [
-       "<qcodes.dataset.measurements.Measurement at 0x1fe2338ae80>"
+       "<qcodes.dataset.measurements.Measurement at 0x2116b749860>"
       ]
      },
      "execution_count": 17,
@@ -2969,7 +2969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2988,14 +2988,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 435\n"
+      "Starting experimental run with id: 446\n"
      ]
     }
    ],
@@ -3025,11 +3025,107 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>dmm_v1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dac_ch1</th>\n",
+       "      <th>dac_ch2</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"10\" valign=\"top\">-1.0</th>\n",
+       "      <th>-1.000000</th>\n",
+       "      <td>-0.000392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.989950</th>\n",
+       "      <td>0.000483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.979899</th>\n",
+       "      <td>0.000108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.969849</th>\n",
+       "      <td>-0.000007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.959799</th>\n",
+       "      <td>0.000011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.949749</th>\n",
+       "      <td>-0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.939698</th>\n",
+       "      <td>-0.000001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.929648</th>\n",
+       "      <td>-0.000001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.919598</th>\n",
+       "      <td>0.000001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.909548</th>\n",
+       "      <td>-0.000002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     dmm_v1\n",
+       "dac_ch1 dac_ch2            \n",
+       "-1.0    -1.000000 -0.000392\n",
+       "        -0.989950  0.000483\n",
+       "        -0.979899  0.000108\n",
+       "        -0.969849 -0.000007\n",
+       "        -0.959799  0.000011\n",
+       "        -0.949749 -0.000003\n",
+       "        -0.939698 -0.000001\n",
+       "        -0.929648 -0.000001\n",
+       "        -0.919598  0.000001\n",
+       "        -0.909548 -0.000002"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1'][0:10]"
    ]
@@ -3043,9 +3139,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (dac_ch1: 200, dac_ch2: 200)\n",
+       "Coordinates:\n",
+       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
+       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
+       "Data variables:\n",
+       "    dmm_v1   (dac_ch1, dac_ch2) float64 -0.0003923 0.0004831 ... 1.039e-05"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1'].to_xarray()"
    ]
@@ -3059,195 +3172,3498 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ax, abax = plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Looking at the above picture, we may decide to sample more finely in the central\n",
-    "# region\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "\n",
-    "    v1points = np.concatenate((np.linspace(-1, -0.5, 5),\n",
-    "                               np.linspace(-0.51, 0.5, 200),\n",
-    "                               np.linspace(0.51, 1, 5)))\n",
-    "    v2points = np.concatenate((np.linspace(-1, -0.25, 5),\n",
-    "                               np.linspace(-0.26, 0.5, 200),\n",
-    "                               np.linspace(0.51, 1, 5)))\n",
-    "    \n",
-    "    for v1 in v1points:\n",
-    "        for v2 in v2points:\n",
-    "            dac.ch1(v1)\n",
-    "            dac.ch2(v2)\n",
-    "            val = dmm.v1.get()\n",
-    "            datasaver.add_result((dac.ch1, v1),\n",
-    "                                 (dac.ch2, v2),\n",
-    "                                 (dmm.v1, val))\n",
-    "\n",
-    "    dataid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ax, cbax = plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# or even perform an adaptive sweep... ooohh...\n",
-    "#\n",
-    "# This example is a not-very-clever toy model example,\n",
-    "# but it nicely shows a semi-realistic measurement that the old qc.Loop\n",
-    "# could not handle\n",
-    "\n",
-    "v1_points = np.linspace(-1, 1, 250)\n",
-    "v2_points = np.linspace(1, -1, 250)\n",
-    "\n",
-    "threshold = 0.25\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "    \n",
-    "    # Do normal sweeping until the peak is detected\n",
-    "    \n",
-    "    for v2ind, v2 in enumerate(v2_points):\n",
-    "        for v1ind, v1 in enumerate(v1_points):\n",
-    "            dac.ch1(v1)\n",
-    "            dac.ch2(v2)\n",
-    "            val = dmm.v1.get()\n",
-    "            datasaver.add_result((dac.ch1, v1),\n",
-    "                                 (dac.ch2, v2),\n",
-    "                                 (dmm.v1, val))\n",
-    "            if val > threshold:\n",
-    "                break\n",
-    "        else:\n",
-    "            continue\n",
-    "        break\n",
-    "        \n",
-    "    print(v1ind, v2ind, val)\n",
-    "    print('-'*10)\n",
-    "        \n",
-    "    # now be more clever, meandering back and forth over the peak\n",
-    "    doneyet = False\n",
-    "    rowdone = False\n",
-    "    v1_step = 1\n",
-    "    while not doneyet:\n",
-    "            v2 = v2_points[v2ind]\n",
-    "            v1 = v1_points[v1ind+v1_step-1]\n",
-    "            dac.ch1(v1)\n",
-    "            dac.ch2(v2)\n",
-    "            val = dmm.v1.get()\n",
-    "            datasaver.add_result((dac.ch1, v1),\n",
-    "                                 (dac.ch2, v2),\n",
-    "                                 (dmm.v1, val))\n",
-    "            if val < threshold:\n",
-    "                if rowdone:\n",
-    "                    doneyet = True\n",
-    "                v2ind += 1\n",
-    "                v1_step *= -1\n",
-    "                rowdone = True\n",
-    "            else:\n",
-    "                v1ind += v1_step\n",
-    "                rowdone = False\n",
-    "                \n",
-    "dataid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ax, cbax = plot_by_id(dataid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## Random sampling "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We may also chose to sample completely randomly across the phase space"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gauss = gauss_model(0.1, 0.2, 0.25)\n",
-    "next(gauss)\n",
-    "\n",
-    "def measure_gauss(x, y):\n",
-    "    val = gauss.send((x, y))\n",
-    "    next(gauss)\n",
-    "    return val\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v1_points = np.linspace(-1, 1, 250)\n",
-    "v2_points = np.linspace(1, -1, 250)\n",
-    "\n",
-    "threshold = 0.25\n",
-    "\n",
-    "npoints = 5000\n",
-    "\n",
-    "with meas.run() as datasaver:\n",
-    "    for i in range(npoints):\n",
-    "        x = 2*(np.random.rand()-.5)\n",
-    "        y = 2*(np.random.rand()-.5)\n",
-    "        z = measure_gauss(x,y)\n",
-    "        datasaver.add_result((dac.ch1, x),\n",
-    "                     (dac.ch2, y),\n",
-    "                     (dmm.v1, z))\n",
-    "dataid = datasaver.run_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax, abax = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 447\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Looking at the above picture, we may decide to sample more finely in the central\n",
+    "# region\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "\n",
+    "    v1points = np.concatenate((np.linspace(-1, -0.5, 5),\n",
+    "                               np.linspace(-0.51, 0.5, 200),\n",
+    "                               np.linspace(0.51, 1, 5)))\n",
+    "    v2points = np.concatenate((np.linspace(-1, -0.25, 5),\n",
+    "                               np.linspace(-0.26, 0.5, 200),\n",
+    "                               np.linspace(0.51, 1, 5)))\n",
+    "    \n",
+    "    for v1 in v1points:\n",
+    "        for v2 in v2points:\n",
+    "            dac.ch1(v1)\n",
+    "            dac.ch2(v2)\n",
+    "            val = dmm.v1.get()\n",
+    "            datasaver.add_result((dac.ch1, v1),\n",
+    "                                 (dac.ch2, v2),\n",
+    "                                 (dmm.v1, val))\n",
+    "\n",
+    "    dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax, cbax = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 448\n",
+      "130 46 0.25089416830953565\n",
+      "----------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# or even perform an adaptive sweep... ooohh...\n",
+    "#\n",
+    "# This example is a not-very-clever toy model example,\n",
+    "# but it nicely shows a semi-realistic measurement that the old qc.Loop\n",
+    "# could not handle\n",
+    "\n",
+    "v1_points = np.linspace(-1, 1, 250)\n",
+    "v2_points = np.linspace(1, -1, 250)\n",
+    "\n",
+    "threshold = 0.25\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    \n",
+    "    # Do normal sweeping until the peak is detected\n",
+    "    \n",
+    "    for v2ind, v2 in enumerate(v2_points):\n",
+    "        for v1ind, v1 in enumerate(v1_points):\n",
+    "            dac.ch1(v1)\n",
+    "            dac.ch2(v2)\n",
+    "            val = dmm.v1.get()\n",
+    "            datasaver.add_result((dac.ch1, v1),\n",
+    "                                 (dac.ch2, v2),\n",
+    "                                 (dmm.v1, val))\n",
+    "            if val > threshold:\n",
+    "                break\n",
+    "        else:\n",
+    "            continue\n",
+    "        break\n",
+    "        \n",
+    "    print(v1ind, v2ind, val)\n",
+    "    print('-'*10)\n",
+    "        \n",
+    "    # now be more clever, meandering back and forth over the peak\n",
+    "    doneyet = False\n",
+    "    rowdone = False\n",
+    "    v1_step = 1\n",
+    "    while not doneyet:\n",
+    "            v2 = v2_points[v2ind]\n",
+    "            v1 = v1_points[v1ind+v1_step-1]\n",
+    "            dac.ch1(v1)\n",
+    "            dac.ch2(v2)\n",
+    "            val = dmm.v1.get()\n",
+    "            datasaver.add_result((dac.ch1, v1),\n",
+    "                                 (dac.ch2, v2),\n",
+    "                                 (dmm.v1, val))\n",
+    "            if val < threshold:\n",
+    "                if rowdone:\n",
+    "                    doneyet = True\n",
+    "                v2ind += 1\n",
+    "                v1_step *= -1\n",
+    "                rowdone = True\n",
+    "            else:\n",
+    "                v1ind += v1_step\n",
+    "                rowdone = False\n",
+    "                \n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax, cbax = plot_by_id(dataid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Random sampling "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We may also chose to sample completely randomly across the phase space"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "gauss = gauss_model(0.1, 0.2, 0.25)\n",
+    "next(gauss)\n",
+    "\n",
+    "def measure_gauss(x, y):\n",
+    "    val = gauss.send((x, y))\n",
+    "    next(gauss)\n",
+    "    return val\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 449\n"
+     ]
+    }
+   ],
+   "source": [
+    "v1_points = np.linspace(-1, 1, 250)\n",
+    "v2_points = np.linspace(1, -1, 250)\n",
+    "\n",
+    "threshold = 0.25\n",
+    "\n",
+    "npoints = 5000\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "    for i in range(npoints):\n",
+    "        x = 2*(np.random.rand()-.5)\n",
+    "        y = 2*(np.random.rand()-.5)\n",
+    "        z = measure_gauss(x,y)\n",
+    "        datasaver.add_result((dac.ch1, x),\n",
+    "                     (dac.ch2, y),\n",
+    "                     (dmm.v1, z))\n",
+    "dataid = datasaver.run_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ax, cbax = plot_by_id(dataid)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>dmm_v1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dac_ch1</th>\n",
+       "      <th>dac_ch2</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>-0.209503</th>\n",
+       "      <th>0.628723</th>\n",
+       "      <td>0.121980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.744003</th>\n",
+       "      <th>0.431778</th>\n",
+       "      <td>0.002514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.773814</th>\n",
+       "      <th>0.384253</th>\n",
+       "      <td>0.022878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.072686</th>\n",
+       "      <th>-0.986115</th>\n",
+       "      <td>0.000041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.747001</th>\n",
+       "      <th>0.343167</th>\n",
+       "      <td>0.003133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.762881</th>\n",
+       "      <th>-0.322906</th>\n",
+       "      <td>0.000334</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.947456</th>\n",
+       "      <th>0.192068</th>\n",
+       "      <td>0.003616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.670000</th>\n",
+       "      <th>-0.532965</th>\n",
+       "      <td>0.000132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.403736</th>\n",
+       "      <th>-0.118555</th>\n",
+       "      <td>0.066083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.278310</th>\n",
+       "      <th>0.885972</th>\n",
+       "      <td>0.020366</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       dmm_v1\n",
+       "dac_ch1   dac_ch2            \n",
+       "-0.209503  0.628723  0.121980\n",
+       "-0.744003  0.431778  0.002514\n",
+       " 0.773814  0.384253  0.022878\n",
+       " 0.072686 -0.986115  0.000041\n",
+       "-0.747001  0.343167  0.003133\n",
+       "-0.762881 -0.322906  0.000334\n",
+       " 0.947456  0.192068  0.003616\n",
+       "-0.670000 -0.532965  0.000132\n",
+       "-0.403736 -0.118555  0.066083\n",
+       " 0.278310  0.885972  0.020366"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1'][0:10]"
    ]
@@ -3261,9 +6677,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (dac_ch1: 5000, dac_ch2: 5000)\n",
+       "Coordinates:\n",
+       "  * dac_ch1  (dac_ch1) float64 -0.9991 -0.9979 -0.9976 ... 0.9982 0.9985 0.9987\n",
+       "  * dac_ch2  (dac_ch2) float64 -0.999 -0.9989 -0.9987 ... 0.9968 0.9971 0.9977\n",
+       "Data variables:\n",
+       "    dmm_v1   (dac_ch1, dac_ch2) float64 nan nan nan nan nan ... nan nan nan nan"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1'][0:10].to_xarray()"
    ]
@@ -3284,7 +6717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3293,7 +6726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3310,9 +6743,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 450\n"
+     ]
+    }
+   ],
    "source": [
     "x0 = [np.random.rand(), np.random.rand()]\n",
     "with meas.run() as datasaver:\n",
@@ -3329,18 +6770,831 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       " final_simplex: (array([[0.99812801, 0.64445492],\n",
+       "       [0.99812801, 0.64445492],\n",
+       "       [0.99812801, 0.64445492]]), array([-0.00083562, -0.00036764, -0.00036764]))\n",
+       "           fun: -0.0008356242479310768\n",
+       "       message: 'Optimization terminated successfully.'\n",
+       "          nfev: 123\n",
+       "           nit: 33\n",
+       "        status: 0\n",
+       "       success: True\n",
+       "             x: array([0.99812801, 0.64445492])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ax, cbax = plot_by_id(run_id)"
    ]
@@ -3369,9 +7623,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 451\n",
+      "Added points to measurement, step 0.\n",
+      "Added points to measurement, step 1.\n",
+      "The run now holds 3 rows\n",
+      "Added points to measurement, step 2.\n",
+      "Added points to measurement, step 3.\n",
+      "Added points to measurement, step 4.\n",
+      "The run now holds 6 rows\n",
+      "Added points to measurement, step 5.\n",
+      "Added points to measurement, step 6.\n",
+      "The run now holds 8 rows\n",
+      "Added points to measurement, step 7.\n",
+      "Added points to measurement, step 8.\n",
+      "Added points to measurement, step 9.\n",
+      "The run now holds 10 rows\n",
+      "The run now holds 10 rows\n",
+      "The run now holds 10 rows\n"
+     ]
+    }
+   ],
    "source": [
     "\n",
     "\n",
@@ -3409,9 +7687,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 452\n",
+      "Added points to measurement, step 0.\n",
+      "First parameter value list: []\n",
+      "Added points to measurement, step 1.\n",
+      "First parameter value list: []\n",
+      "The run now holds 1 rows\n",
+      "Added points to measurement, step 2.\n",
+      "First parameter value list: [0, 1, 2]\n",
+      "Added points to measurement, step 3.\n",
+      "First parameter value list: [0, 1, 2]\n",
+      "Added points to measurement, step 4.\n",
+      "First parameter value list: [0, 1, 2]\n",
+      "The run now holds 4 rows\n",
+      "Added points to measurement, step 5.\n",
+      "First parameter value list: [0, 1, 2, 3, 4, 5]\n",
+      "Added points to measurement, step 6.\n",
+      "First parameter value list: [0, 1, 2, 3, 4, 5]\n",
+      "The run now holds 7 rows\n",
+      "Added points to measurement, step 7.\n",
+      "First parameter value list: [0, 1, 2, 3, 4, 5, 6, 7]\n",
+      "Added points to measurement, step 8.\n",
+      "First parameter value list: [0, 1, 2, 3, 4, 5, 6, 7]\n",
+      "The run now holds 9 rows\n",
+      "Added points to measurement, step 9.\n",
+      "First parameter value list: [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
+      "The run now holds 10 rows\n",
+      "The run now holds 10 rows\n"
+     ]
+    }
+   ],
    "source": [
     "\n",
     "\n",
@@ -3461,7 +7773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3470,7 +7782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3486,36 +7798,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('this', 'that')"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mydummy.A.dummy_2d_multi_parameter.names"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((5, 3), (5, 3))"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mydummy.A.dummy_2d_multi_parameter.shapes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(('this_setpoint', 'that_setpoint'), ('this_setpoint', 'that_setpoint'))"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mydummy.A.dummy_2d_multi_parameter.setpoint_names"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OrderedDict([('MyDummy_ChanA_this_setpoint',\n",
+       "              ParamSpec('MyDummy_ChanA_this_setpoint', 'numeric', 'this setpoint', 'this setpointunit', inferred_from=[], depends_on=[])),\n",
+       "             ('MyDummy_ChanA_that_setpoint',\n",
+       "              ParamSpec('MyDummy_ChanA_that_setpoint', 'numeric', 'that setpoint', 'that setpointunit', inferred_from=[], depends_on=[])),\n",
+       "             ('this',\n",
+       "              ParamSpec('this', 'numeric', 'this label', 'this unit', inferred_from=[], depends_on=['MyDummy_ChanA_this_setpoint', 'MyDummy_ChanA_that_setpoint'])),\n",
+       "             ('that',\n",
+       "              ParamSpec('that', 'numeric', 'that label', 'that unit', inferred_from=[], depends_on=['MyDummy_ChanA_this_setpoint', 'MyDummy_ChanA_that_setpoint']))])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "meas = Measurement()\n",
     "\n",
@@ -3533,9 +7896,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 453\n"
+     ]
+    }
+   ],
    "source": [
     "with meas.run() as datasaver:\n",
     "    datasaver.add_result((mydummy.A.dummy_2d_multi_parameter, mydummy.A.dummy_2d_multi_parameter()))"
@@ -3550,27 +7921,1767 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x2116bef21d0>,\n",
+       "  <matplotlib.axes._subplots.AxesSubplot at 0x2116bf18be0>],\n",
+       " [<matplotlib.colorbar.Colorbar at 0x2116bf5e550>,\n",
+       "  <matplotlib.colorbar.Colorbar at 0x2116bf254e0>])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "plot_by_id(datasaver.run_id)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>that</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MyDummy_ChanA_this_setpoint</th>\n",
+       "      <th>MyDummy_ChanA_that_setpoint</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">5</th>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">7</th>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">9</th>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                         that\n",
+       "MyDummy_ChanA_this_setpoint MyDummy_ChanA_that_setpoint      \n",
+       "5                           9                               1\n",
+       "                            10                              1\n",
+       "                            11                              1\n",
+       "6                           9                               1\n",
+       "                            10                              1\n",
+       "                            11                              1\n",
+       "7                           9                               1\n",
+       "                            10                              1\n",
+       "                            11                              1\n",
+       "8                           9                               1\n",
+       "                            10                              1\n",
+       "                            11                              1\n",
+       "9                           9                               1\n",
+       "                            10                              1\n",
+       "                            11                              1"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "datasaver.dataset.get_data_as_pandas_dataframe()['that']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                      (MyDummy_ChanA_that_setpoint: 3, MyDummy_ChanA_this_setpoint: 5)\n",
+       "Coordinates:\n",
+       "  * MyDummy_ChanA_this_setpoint  (MyDummy_ChanA_this_setpoint) int64 5 6 7 8 9\n",
+       "  * MyDummy_ChanA_that_setpoint  (MyDummy_ChanA_that_setpoint) int64 9 10 11\n",
+       "Data variables:\n",
+       "    that                         (MyDummy_ChanA_this_setpoint, MyDummy_ChanA_that_setpoint) int32 1 ... 1"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "datasaver.dataset.get_data_as_pandas_dataframe()['that'].to_xarray()"
    ]

From 38815695d61317d01399a36a2a2e33e7bf3f57bb Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 5 Feb 2019 11:28:41 +0100
Subject: [PATCH 709/719] Add xarray to docs requirements

---
 docs_requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs_requirements.txt b/docs_requirements.txt
index a811e5d86bb..c30fc07d3f5 100644
--- a/docs_requirements.txt
+++ b/docs_requirements.txt
@@ -6,3 +6,4 @@ ipython
 jupyter
 scipy
 pandas
+xarray
\ No newline at end of file

From 5840f221277e241c5f2c7460d386cfeeca99a5e1 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 5 Feb 2019 12:38:17 +0100
Subject: [PATCH 710/719] increase timeout of notebook

---
 docs/examples/DataSet/Dataset Context Manager.ipynb | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/docs/examples/DataSet/Dataset Context Manager.ipynb b/docs/examples/DataSet/Dataset Context Manager.ipynb
index 933f8b76c34..1935382f8d0 100644
--- a/docs/examples/DataSet/Dataset Context Manager.ipynb	
+++ b/docs/examples/DataSet/Dataset Context Manager.ipynb	
@@ -9712,6 +9712,9 @@
    "pygments_lexer": "ipython3",
    "version": "3.6.8"
   },
+  "nbsphinx": {
+   "timeout": 60
+  },
   "toc": {
    "base_numbering": 1,
    "nav_menu": {},

From 39a59bf58012a2399126231f33169cc9f91ba9e2 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Tue, 5 Feb 2019 15:17:13 +0100
Subject: [PATCH 711/719] Add WIP notebook

---
 .../Working-With-Pandas-and-XArray.ipynb      | 3353 +++++++++++++++++
 1 file changed, 3353 insertions(+)
 create mode 100644 docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb

diff --git a/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb b/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb
new file mode 100644
index 00000000000..c5a53f66204
--- /dev/null
+++ b/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb
@@ -0,0 +1,3353 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Working with Pandas and XArray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logging hadn't been started.\n",
+      "Activating auto-logging. Current session state plus future input saved.\n",
+      "Filename       : C:\\Users\\jenielse\\.qcodes\\logs\\command_history.log\n",
+      "Mode           : append\n",
+      "Output logging : True\n",
+      "Raw input log  : False\n",
+      "Timestamping   : True\n",
+      "State          : active\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib notebook\n",
+    "import pandas as pd\n",
+    "from functools import partial\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import qcodes as qc\n",
+    "from qcodes.dataset.experiment_container import new_experiment\n",
+    "from qcodes.dataset.database import initialise_database\n",
+    "from qcodes.tests.instrument_mocks import DummyInstrument\n",
+    "from qcodes.dataset.measurements import Measurement\n",
+    "\n",
+    "qc.logger.start_all_logging()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we borrow an example from the Dataset notebook to have some data to work with. We split the measurement in two so we can try merging it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preparatory mocking of physical setup\n",
+    "dac = DummyInstrument('dac', gates=['ch1', 'ch2'])\n",
+    "dmm = DummyInstrument('dmm', gates=['v1', 'v2'])\n",
+    "station = qc.Station(dmm, dac)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tutorial_exp#no sample#134@C:\\Users\\jenielse/experiments.db\n",
+       "-----------------------------------------------------------"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "initialise_database()\n",
+    "new_experiment(name='tutorial_exp', sample_name=\"no sample\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<qcodes.dataset.measurements.Measurement at 0x1c06c6222e8>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For the 2D, we'll need a new batch of parameters, notably one with two \n",
+    "# other parameters as setpoints. We therefore define a new Measurement\n",
+    "# with new parameters\n",
+    "\n",
+    "meas = Measurement()\n",
+    "meas.register_parameter(dac.ch1)  # register the first independent parameter\n",
+    "meas.register_parameter(dac.ch2)  # register the second independent parameter\n",
+    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1, dac.ch2))  # now register the dependent oone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# and we'll make a 2D gaussian to sample from/measure\n",
+    "def gauss_model(x0: float, y0: float, sigma: float, noise: float=0.0005):\n",
+    "    \"\"\"\n",
+    "    Returns a generator sampling a gaussian. The gaussian is\n",
+    "    normalised such that its maximal value is simply 1\n",
+    "    \"\"\"\n",
+    "    while True:\n",
+    "        (x, y) = yield\n",
+    "        model = np.exp(-((x0-x)**2+(y0-y)**2)/2/sigma**2)*np.exp(2*sigma**2)\n",
+    "        noise = np.random.randn()*noise\n",
+    "        yield model + noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# and finally wire up the dmm v1 to \"measure\" the gaussian\n",
+    "\n",
+    "gauss = gauss_model(0.1, 0.2, 0.25)\n",
+    "next(gauss)\n",
+    "\n",
+    "def measure_gauss(dac):\n",
+    "    val = gauss.send((dac.ch1.get(), dac.ch2.get()))\n",
+    "    next(gauss)\n",
+    "    return val\n",
+    "\n",
+    "dmm.v1.get = partial(measure_gauss, dac)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First half:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 474\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run a 2D sweep\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "\n",
+    "    for v1 in np.linspace(-1, 0, 200, endpoint=False):\n",
+    "        for v2 in np.linspace(-1, 1, 201):\n",
+    "            dac.ch1(v1)\n",
+    "            dac.ch2(v2)\n",
+    "            val = dmm.v1.get()\n",
+    "            datasaver.add_result((dac.ch1, v1),\n",
+    "                                 (dac.ch2, v2),\n",
+    "                                 (dmm.v1, val))\n",
+    "            \n",
+    "    dataid = datasaver.run_id\n",
+    "df1 = datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Second half:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting experimental run with id: 475\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run a 2D sweep\n",
+    "\n",
+    "with meas.run() as datasaver:\n",
+    "\n",
+    "    for v1 in np.linspace(0, 1, 201):\n",
+    "        for v2 in np.linspace(-1, 1, 201):\n",
+    "            dac.ch1(v1)\n",
+    "            dac.ch2(v2)\n",
+    "            val = dmm.v1.get()\n",
+    "            datasaver.add_result((dac.ch1, v1),\n",
+    "                                 (dac.ch2, v2),\n",
+    "                                 (dmm.v1, val))\n",
+    "            \n",
+    "    dataid = datasaver.run_id\n",
+    "df2 = datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets first inspect the Pandas dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>dmm_v1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dac_ch1</th>\n",
+       "      <th>dac_ch2</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"10\" valign=\"top\">-1.0</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>-0.000372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>0.000267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>0.000217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>0.000015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.96</th>\n",
+       "      <td>0.000023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.95</th>\n",
+       "      <td>-0.000037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.94</th>\n",
+       "      <td>-0.000004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.93</th>\n",
+       "      <td>0.000004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.92</th>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.91</th>\n",
+       "      <td>-0.000002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   dmm_v1\n",
+       "dac_ch1 dac_ch2          \n",
+       "-1.0    -1.00   -0.000372\n",
+       "        -0.99    0.000267\n",
+       "        -0.98    0.000217\n",
+       "        -0.97    0.000015\n",
+       "        -0.96    0.000023\n",
+       "        -0.95   -0.000037\n",
+       "        -0.94   -0.000004\n",
+       "        -0.93    0.000004\n",
+       "        -0.92    0.000003\n",
+       "        -0.91   -0.000002"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also reset the index to return a simpler view where all datapoints are simply indexed by a running counter. This will come in handy just below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dac_ch1</th>\n",
+       "      <th>dac_ch2</th>\n",
+       "      <th>dmm_v1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.00</td>\n",
+       "      <td>-0.000372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.99</td>\n",
+       "      <td>0.000267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.98</td>\n",
+       "      <td>0.000217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.97</td>\n",
+       "      <td>0.000015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.96</td>\n",
+       "      <td>0.000023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.95</td>\n",
+       "      <td>-0.000037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.94</td>\n",
+       "      <td>-0.000004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.93</td>\n",
+       "      <td>0.000004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.92</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.91</td>\n",
+       "      <td>-0.000002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   dac_ch1  dac_ch2    dmm_v1\n",
+       "0     -1.0    -1.00 -0.000372\n",
+       "1     -1.0    -0.99  0.000267\n",
+       "2     -1.0    -0.98  0.000217\n",
+       "3     -1.0    -0.97  0.000015\n",
+       "4     -1.0    -0.96  0.000023\n",
+       "5     -1.0    -0.95 -0.000037\n",
+       "6     -1.0    -0.94 -0.000004\n",
+       "7     -1.0    -0.93  0.000004\n",
+       "8     -1.0    -0.92  0.000003\n",
+       "9     -1.0    -0.91 -0.000002"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.reset_index()[0:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas has build in support for various forms of plotting. This does however, not support MultiIndex at the moment but we can use `reset_index` to make the data available for plotting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c06cb3f1d0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.reset_index().plot.scatter('dac_ch1', 'dac_ch2', c='dmm_v1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Merging two dataframes with the same labels is fairly simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat([df1, df2], sort=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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Pfn5Tp7j7XPI8OfEIQy08jBtL5BvM/wFntwEwIiICKhfv77tExERwde3IBcDoAMxAAaffOGk+LrUG2iwpwItbwBn9fzrX//y+WuadQx91TvoKxh0ywVk+V8MgKxAEgOgAzEABp/chD+3ANAX4OcPGEPQM3MCFQadQqA/giLsBgau3ATAyMhIaNCgge0TGRnJ17cgV54BwIyMDKhRowaEhYVBVFQUnDlzRve5sbGxyjh69+7dxXMSEhI8Hm/RooWlz8QAGFxyC/7cDHe4BX9m4M0s7Nl9DdVzAgkI7QZHVDBopUTsxBFkIPSt3ATA+vXrwwMPPGD71K9fn69vQa48AYC7d++G0NBQyMzMhJycHEhKSoLixYuLAZOyfvrpJ/j222/Fef/996FgwYKQlZUlnpOQkABxcXGa5/3000+WPhcDYHDIzXKvNwDEi79V4PNFideXjp3Z889//lMcK4/5AiadwKAb/YK+7BPk8nBgyE0AbNCgATRq1Mj2adCgAV/fglx5AgCbN28OEyZM0NwXEREBqamppr5/9erVUKJECfjHP/4h7ktISIA+ffo4+lwMgMGjvFDylZ0kMwDoi7KuN5iyAnLyc/C26jny841exyw02nUIjYIk/ugR5N7A4BQDICuQFPAA+Mcff0DBggVh3759mvsTExOhbdu2pl6jQYMGMG7cOM19CQkJUKpUKShXrhyEh4fD2LFj4ebNm4avc+fOHbh9+7Y4N27c4F+QPKxAgT43+vsoxFiBPbOunzeAs3r+8Y9/+OR1/OESWnEG6d+Tv4Ii7ALmHbkJgA0bNoTGjRvbPg0bNuTrW5Ar4AHw66+/hpCQEDh37pzm/kWLFkHdunW9fv/FixchJCQELl68qLl/9+7dcPjwYXjvvffg4MGD0KhRI6hfvz7cuXNH97XS0tKUvYX8C5L35FbZ118AaLXU69TtMwttgXTswqHTnkGrZWF/wx87gYEjNwGwUaNGEBUVZfs0atSIr29BrjwDgOfPn9fcv3DhQqhXr57X7x8/fjw0aNDA6/O++eYbCA0Nhb179+o+hx3A4JBb/X6+TvZSiPAV+PnK3fvHP/4Bv/32myGQeXvc7GvQ56ieT59j1yXUc0PNwqDdHkFVUEQVDKFf200JMwC6LwZAViAp4AHQSQn4n//8J5QsWRLWrFlj6r3q1KkDS5cuNf3ZuAcwbypQAdCXSV5/wJ4ZeLN6fv31V/j1119tfa+34w930CoMOk0Nq0BQzx20mhC2A4MsZ3ITABs3bgxNmza1fRo3bszXtyBXwAMgwH9DIBMnTtTcFxkZ6TUEkpWVBWFhYfDjjz96fY8ff/wRwsLCIDs72/TnYgDMewpE8KMAKIOgLxO9ZsMZetCnB1V6MIe38X569O7Xe67q+UZAiY+bcRmtuoROEsQy/BlBYKD2BdLfJZY1uQmAUVFR0KxZM9snKiqKr29BrjwBgDgGZtOmTZCTkwPJyclQvHhxuH79OgAAjBw5UgmDMTExMHjwYI/7f/vtN5g+fTqcP38erl27BidPnoRWrVpBlSpV4NdffzX9uRgA855yq9/PTsjDV4leq26fVYfPCtBh+4T8tZVjFhyNnEU77qCeC6gHhmZHycjl/dzoEbQKgAx/9sQAyAok5QkABPjvIOjq1atD4cKFISoqCk6fPi0ei42NhYSEBM3zP/roIwgJCYFjx455vNa//vUv6NKlC5QrVw5CQ0OhWrVqkJCQAF9++aWlz8QAmHfk77KvL0MeZl0/q9BnFvbMlGyN4M7suXXrlvjT6NDn+hIWzcKhLwIkVsIigQ6ADIP25SYANm3aFJo3b277NG3alK9vQa48A4CBKAbAvKPcAD87vX5WnD+zZV5vACiHKuxAH4U1FeiZPb/88ovXx+Xn6L2fEQTevn3bKwB6KxXLP1+rIGg3JBKIw6JZ5uQmAEZHR0PLli1tn+joaL6+BbkYAB2IATDwlVuun5Wyr9mAh1uOn1mHzyzQUWijX//yyy/w888/w88//+xxv9nnmPkMVt1BM+Vis0liM72CdoMiDIF5TwyArEASA6ADMQAGvvwBf76e6+dLx0+GPxlajMIb3qCPwpQe5HkDPV8eM++rAkH5PjN9hGZ7BlVjZPC21bKw3S0ielBoJiVMfwcY/nwvNwGwWbNm0KpVK9unWbNmfH0LcjEAOhADYOAq0Jw/X4Of2WSvXM404/aZ7dOT3TkZzH766SfN+fnnn+Gnn36CH3/8Udz3448/ag69T/Uc+fWMwJDetuIQ2nEFvaWG7QRFnIKgHSeQXUD/yk0AbN68OTz44IO2T/Pmzfn6FuRiAHQgBsDAVG7Bn9Ogh1nwMwt9qhKv3dKuFUePQpoMbT/++CP88MMP4uDXMgiaeY4KDK26hio49AaEVmDQbGnY6mo5J04ggqA3IGQA9L3cBMCWLVtCTEyM7dOyZUu+vgW5GAAdiAEw8OTEvfA1ADoZ72Il4GEW/mQANFvaVUEUBT09IJNBDs/3338P33//vea23lE9R349u2DoDQKNgiTyCBw5NOKkR9BbUMQXAGjGFWQA9L0YAFmBJAZAB2IADDy5DX5W+v3shjzMBDysQp+e22fk8MklXdmhU52bN2/CzZs3PWAO7//uu+/gu+++0zwPb+s9xwgczbiGKih04grqOYJWZgnaCYn4CgR9HRBh6ctNAGzVqhW0adPG9mnVqhVf34JcDIAOxAAYWPKn++eLMS8UAn0R8jCzlUMevizDn1Hq1pvLJzt6FN5UIEdhzslRvS59Tz23UA8GVe6mCgiN0sRmhkzrBUXszg402xuYG+lg/H1kaeUmALZu3RpiY2Ntn9atW/P1LcjFAOhADICBpUB0/nw53sVun58qzCEHJWToU8ESdfGou0fh7ttvvxWg9u233yrPN998o7ltdFTPwe+lUEhfHyHxu+++M+UUqsIq8s/FriNoJihidaOInZmBbo+JYanFAMgKJDEAOhADYODITdfPl0lfqyEPO6Ves2EOGfzk8q7s6lHQoiDmDeK+/vprSwe/xwwoquBQdg+NHMIffvhBt2/Q23gZOyBoZqNIXncCWf+TmwAYExMD7dq1s31iYmL4+hbkYgB0IAbAwFFulnztOH/eXD9524Q3ALQCf0blXQQhVVlXhinZ1ZPh7quvvvL4Wj43btwwPKrvweMNGhEI6efVg0G9UrHKFaQASMvDt27d8jpc2mx/oJmSsJVh0U7HwzgZEYO/nyx3AbBNmzbQvn1726dNmzZ8fQtyMQA6EANg7ssfzp8VADST9PXW7+ct5GHG9bMS7FDN3JP7+eR+O5XDJzt0FPIQ4L788ksBchTsvvzyS/jyyy/hiy++gC+++EJ8jfepvjYCRfo+MhzKn1flEMq9hHogqIJBCoT+dAN9PS/QTQhkAPyvGABZgSQGQAdiAMx95Xbfny+Svt62eNCdvUbgp+rxM1PmlZ0+Gfi8OXwy8KngDm/L5/r167qHPkfv++n74ZHdQ9mNlHsJKRBSCFT1C/7444/i50h/rt5Wz3kLihj1BlodE2MmIeytFMzjYfwjNwEwNjYWOnbsaPvExsby9S3IxQDoQAyAuSs3+/7s9Px56/ez4/zpBTxU2zlk6MM/5UCEqrxLgQ//VLl7FLoorF2/fh2uXbvm9Xz++eeGhz7n2rVr4nVlWKT3qcBQdgllh1AOqqhgUAZB6gha6Q/UGxmjSgq7MS+QXUD35CYAtmvXDjp16mT7tGvXjq9vQS4GQAdiAMxd5Xbfn9PAh5n9vbLrZzTHz2iMizyIWc/tk8Maqn49dPRkZ47CGIU3Fex99tlnXs+nn36q+dobKOKhECp/TrlUTF1Co5SxmfKwXp+gNxA0UxJWjYyx4gTanQ+oB4UMf/bEAMgKJDEAOhADYO7Izb4/K3P+nIIfBUCrvX4U/ozKvNTx81beRejTK93qOXl6APfpp5/CJ598Ap9++qk4H3/8MXzyySfifPzxxx73yd+DXxvBo+waqkrL1B2UewdVMOjNEcTysLc5glYGSFtdI+cLF9BKbyBDoDW5CYAdOnSALl262D4dOnTg61uQiwHQgRgA3Vdul33NpH2t9PxZKfmaAT+jYAcFP3k8C8KPUS+f7PDJsIWQh5CGQIfARgHv448/ho8++gg+/PBD+OijjzT3yQefI7+mDI4qMJTB9PPPPzd0BikQ0hK4XnlYrywsl4ZVbqDZgIjTMTEqJ1AFhE7GxDAEmpObANixY0fo2rWr7dOxY0e+vgW5GAAdiAHQfeUmADpN+1L4U40BofCnGiWi2uChF/DQK/XKjp9eX5/K6ZNhCmFLBj4Z5ujtDz/8UHP+/ve/6x75OfL30tem70sBUQ8IVSVjOW2scgX1RskY9QciCMoQiH+nZkfFGDmBVkfE5Bb8MQC6B4CdOnWCuLg426dTp058fQtyMQA6EAOgu3IL/uw4f/7o9zNa3YZQYRTw0EvzyuBnBvpkdw/LtypHzwjucnJyNOeDDz6wdOj3qmARwZDCp+wYqqDQKEgiB0dUfYJGQREjCDQzLsYsCNoZEePLbSEMgd7FAMgKJDEAOhADoLvKLQC0uuHDqOzrpOSrWt2G4KcKeKj6/PQCHRT8VL18FP5kh08GPj24e//998V57733xJ965+rVq+J5ekcGQyO3EIFQ7in89NNPhSuIMKhKE1NX0GiMjF5QRBUSsZIStjMw2i0nkF1Ac3ITADt37gzdu3e3fTp37szXtyAXA6ADMQC6p9zs+3OS9nXi/Mn7e/U2eOj1+VHHj7p+suOncvtoaVfuzVMBnwr0EOAQ5q5cuQJXrlzR3JbP5cuX4fLly+I2fUz+Pvr6KlBEMNRzCOVSMTqDcolYL0GscgTltDD+abcvUG9eoJUxMb6CQC4FO5ebANilSxfo0aOH7dOlSxe+vgW5GAAdiAHQPfkS/nzV92dlr69Z589oqDPd4qHq9ZNn+Klcvy+//NJjfp5c5tXr5UPoow6f7OrJkEZh7vLly3Dp0iW4dOkSvPvuu5qD96seo8+RX+fSpUua96HOIYVC6hSq+ggpDBoliREGVWVhVWpYdgNlEDRTEtYbE4MgaGVOIP132Fsp2M62EAZAYzEAsgJJDIAOxADojvJi3583589ozIveNg+jkIe3gAd1/ajjp5fcVUGf7PLJ7p4MZghtCHDvvPMOvP3225rz1ltvaf7E2/LXePudd94Rr4O36XshJKpcQr2SsQoEqSP42WefiZ+ZXn8gnSWoKgl///33HuNiaEnfzKgYIyeQ/ntn5ADqOYG+2Blsp0Ujv4GgmwAYFxcHvXr1sn3i4uL4+hbkYgB0IAZAd5QbpV8riV9f9vzpbfWgA51p0EMv4KEX7rh+/bpumffjjz/WLe9Shw+PDH0IeXgQ5N58803NbTwXL16EN954Ay5evGjq4PfJr0OB8Z133lG6hhQIr169qiwT01IxHTtDS8O0PKwXFKFuoN7IGKMtInb7Aq2Ugq3MCfR3KTg/QaCbANitWzfo3bu37dOtWze+vgW5GAAdiAHQ/8ot989J6MNozp9R0ldvowd1/uS9vdT5k/fy6oU79Bw/ldtH+/nkUq4MfOjWUcBDyMNz4cIFzTl//rw48tfyYxcuXNC8Fh6EQ9k5lIGQwiCCILqD1BlEEJTHysg9gnrjY/TSwnolYb0NImYB0NuwaAbAwBEDICuQxADoQAyA/pev4M8o3WgF/qz0/RmVfo3m+6nAT4Y/6vzRDR6qcq+8ek1O8qr6+mh5lwKfXM5F4KNOnR7onT17Fs6dOwfnzp2Ds2fPwtmzZ+H1118Xt/FrvE/+k37fuXPnNGAowyF1DSkU0n8G6gzKZWL8edDysCosgjBoJiRiJiUsA6DemBgrw6LNDom2Ugr2Rzk4P8hNAOzRowf07dvX9unRowdf34JcDIAOxADoX/nS/bOb+nXq/JmZ8SenfFUjXhD86OgR6vzRsS56AQ90/Oh2jQ8//FBZ5kWnT3b7aClXdvgQ9BDUKLBRuDtz5gycOSQBMj0AACAASURBVHMGTp8+LQ5+Ld9PD/1eCor0oGOInwc/Gy0XU2dQ7huk5WEZAml6mI6P0XMEVSNjjMbFqFLCVkrBViBQLx3sq1Vx3A+olpsA2LNnT4iPj7d9evbsyde3IBcDoAMxAPpXgez+me37k0u/RgOevW30MNvvh/AnBzxonx/2vGHpk6Z3ZehDp0/P5aPQh3BGgQ5vnzp1Ck6ePKk5f/vb3zz+PHHihPiaPiafU6dOecCjDIboEho5gxQGKQhSR9AoLKIqC+v1BtJxMbSXk/YGmkkIG6WDzWwL8eYCmh0SbSZxz/D3P7kJgL169YJ+/frZPr169eLrW5CLAdCBGAD9J3+7f1ZTv062fHgLfKDjI5d8f/jhB02vH454kUu+ql4/o4CHqtQrl3lpeRfhT+7bQ1ePQt+pU6fE+dvf/ibOiRMn4MSJE/Daa6/B8ePH4bXXXtPcPn78uDjHjh0Tt+Xn42vg6+Hrq6CQAiGWnikQqkrEFAZlEJSTw6rRMdcUaWEVBOqtktObF2gUDDE7LNqKC+jLGYHsAv5PDICsQBIDoAMxAPpPbpd+ncz7M0r8Wgl8mHH+VONdEP5UIQ9VwENO9F6+fNljTAtN6aLbJ/fu0XItunsU9mSYO3bsGLz66qvwyiuviPPyyy/Dyy+/rPn66NGjcPToUY/HXn31VTh27Jh4HfwaAZECJ3UKqTuoAkEsEcuOoJwellPDejMEzUKgqi/QyAn0NidQb2WcmZ3BeqviKAS6sSUk2OUmAPbu3Rv69+9v+/Tu3Zuvb0EuBkAHYgD0j3LD/fNH359ZANTr99MLe8gr3OReP7qnV072ygObL126pEzwUvCjwEehDwELYU8GPYQ3BLkjR47A0aNH4ciRI3D48GE4fPgwHDp0SHMOHDgAhw4dgoMHD8LBgwfF/UeOHNEcCokIiAiDFApPnDghnMFTp05p3Eo5TKLqFaSlYbksrHIDVSVhBEE9J5CWhI3GxBilg83MCLQLgW5vCQlmuQmAffr0gQEDBtg+ffr04etbkCvPAGBGRgbUqFEDwsLCICoqCs6cOaP73KysLAgJCfE4v//+u+3XVIkB0PfKjeCH3dSvmVl/3jZ86M34QwCU+/3QTZLLvqrVbTL8qVK9co+fN7dPz+VDRw5Bj0Iegt7BgwfhwIEDcODAAXjppZdg//79sH//fnH7pZdeEmffvn2ar/fv3y+gkJ5Dhw6J9zh69KhwCuk5fvy4pmRMgyeqfkE6c1DVI6jnBsppYQRB2QnUA0EVBNIRMbITiCBod1uIHQi06wIyBP5XDICsQFKeAMDdu3dDaGgoZGZmQk5ODiQlJUHx4sXhiy++UD4/KysLSpYs6ZGYdPKaKjEA+l550f0zs+VDDnyoQh+qsq9e2EOv30/e4EHBj8KfHvhhoAN752h5l/bwUfBDNw5BjIIeQt6+fftg37598OKLL2rOCy+8oLn9wgsvwPPPPy9uy8/Zu3evOAiJ+/fvhwMHDggoPHz4sMYpRHfw2LFjwhXEUjF1BfUcwbfeekuzdcRMSES1W/j69evi78/bmBjqAnpLB9sdEm3UC2iUDOYysH25CYB9+/aFgQMH2j59+/bl61uQK08AYPPmzWHChAma+yIiIiA1NVX5/KysLChVqpRPX1MlBkDfKpDdP7ODnr2VfenWB7y4U/hD8MPAB+33u3HjhgA/Cn90oLNerx91/bDHj/b5UfCTU7sIfrSHD3v1jhw5Itw9dPH27dsnII2C3Z49e2D37t3i7Nq1C3bu3Am7du0yPPic3bt3i9fYs2ePAEWEQ4RMCoToENJy8auvvqoJlahKxBQEValhGQTpnmHaGyg7gXp9gXJPoKofEI9RMtgXm0ICwQUMVrkJgPHx8TBo0CDbJz4+nq9vQa6AB8A//vgDChYsCPv27dPcn5iYCG3btlV+T1ZWFhQsWBCqVasGVapUgR49esC7777r6DVVYgD0rQLV/XMy6Fmv7OvN+aOBD9rzJ8Mf9vt5m+lHy720x08e4YLgR9O7tKePlnexZw8dvr179woHD0ENwW3Xrl2wY8cO2L59O2zbtk38iWf79u3ivq1bt4pDH9+xY4fmyGBIgVB2CBEEqSuIIRIMkCAIyoERTD9fvHjRozdQlRSmARFvo2L0ysF642FU5WBvMwL9sSWEXUB7YgBkBZICHgC//vprCAkJgXPnzmnuX7RoEdStW1f5PRcuXIBt27bB5cuX4cyZM9C/f38oWrQofPzxx7ZfEwDgzp07mv6bGzdu8C+Ij5Tb8OfE/dPr+1ONe1HBn+z+ofMnBz4Q/uSNHnLJl65uU23toOVeGfywx4+WeV9++WWPPj6EPnTfKPDt3LlTwB6Fui1btkB2djZkZWVBVlYWbNmyBTZv3iy+3rx5M2zatAk2b97scbZs2aI52dnZAhDxfXbu3CnO7t27NUCIvYS0dxBdQewTxF7Bk9I4GXmeoJ4baFQSxoSwDIH494sDoxEC9ZLBCILyjEB/QKBRGEQPArkP0FhuAmC/fv1g8ODBtk+/fv34+hbkyjMAeP78ec39CxcuhHr16pl6jb/++gsaNWoEU6ZMcfSaaWlpynAJ/4I4l6/gzywAmt31683987bpQ7Xlg5Z+ZecPnSAs/RoNd5adP1XJl7p+OMuPBjxouIOWemmZF/v6ZKdPBX0Ie9nZ2bBlyxYPuNu4cSNkZmZCZmYmbNy4ETZs2KA8zz33HDz33HPi9saNG2Hjxo2wadMmzaFwuHXrVo1TSB1ChEG5ZxBdQQTBY8eOAR1ETSGQgiDtDdRzA/XmBeo5gfh3/80332jmBJrZFqJaF0ch0GhLCEKgygW0uirO2+9mfi8DuwmA/fv3hyFDhtg+/fv35+tbkCvgAdBX5dqxY8dCXFyco9dkB9A/ysvun9lNH3pbPui4F1XgQx7wLMMfdf3ee+89zSYPhD855IHgd+bMGeH4YbgDS73Y3ye7fbS8i24bdfhk2EPgQ6Bbv349rF+/HtatWwfr16+HjIwMWLduHaxbtw4yMjIgIyMDnnnmGXEb73/22Wdh/fr18Oyzz8Kzzz4r4HDDhg3ifdBNRIcwOzsbtm3bBjt27BD9hDRcQvsFEQTpOBkEQTO9gegEqlLCcjiE9gRek/YIYzCE9gNSN1AOhZgZEm00HNqKC2h2RZyvHMBghEAGQFYgKeABEOC/gY2JEydq7ouMjDQd2PjPf/4D0dHRMHr0aJ+9JgD3APpKgRT8MOP+2dn0oTfoGZ0euecPR73IPX+qrR6qfj9a8lWta6Pl3tdee00DfjTUQR0/2ssnO32bN2/WOHoIahT4MjIy4Omnn4a1a9eK89RTT8FTTz2lvA/PmjVrxPc988wz8PTTT8MzzzwjwBHfA6EwMzNTOIQIhFguRldwz549ShDE0jCCoBwUkUFQTgrTAdK0L9BMOZimg+WNIUbbQsyGQnzhAprdFcwuoFpuAuDAgQNh2LBhts/AgQP5+hbkyhMAiCNbNm3aBDk5OZCcnAzFixeH69evAwDAyJEjNeCWnp4Or7zyCnz22Wdw6dIlGD16NBQqVAguXrxo+jXNiAHQN8pL7p9e6tfMoGdv4170yr407UudPwp/6PzJ8EeDHrLrRwMeCH6HDh3SOH5Y6t25c6fo56PlXXT6EPqok6eCvdWrV8OqVavEefLJJ+HJJ5+EVatWicfwPrwfH1u9ejWsWbNGHHxNBMKMjAzhEqLjiDBIQRDLxAiCzz//vOgTxMAIuoFyUAR7AxEC6Vo57AukO4VpQpiGQ/SCIQj/ZmYE/vTTT6YB0NuWECt7gs0CoB4I5mcX0E0AHDRoEAwfPtz2GTRokK3PanW+7urVq6Fu3bpQpEgRuO+++yA5OdljZi/LP8oTAAjw33+pqlevDoULF4aoqCg4ffq0eCw2NhYSEhLE18nJyVCtWjUoXLgwlCtXDrp06eLR7+ftNc2IAdC5fFX+9efYFyMANOr7owBotOIN+77onL9rJPDhrecPN3rII16w3w9n+qnGurzyyiua2X0vvfSSpscPHT90+yj0YWkXS7nozqFzh9CGULdixQpYuXIlLF++HJYvXw4rVqyAZcuWia+XL18OS5cuheXLl2vuX7ZsGaxYsUJzVq5cqQHHNWvWaJxEdAgRCLHnkJaJsTy8c+dOjSMou4Evv/yyR1oYIfD111/3cAIxJYzlYCwJUyeQpoNxfRzOCcRkMA2FyMEQKy6gnV3BgVQKDia5DYAjRoywfewAoNX5utu3b4ewsDDYsWMHXLt2DV599VWoVKkSJCcn++rHwTJQngHAQBQDoHP5EwB9NfTZat+f0Zo3o74/1Xo3b84fLft6S/lS1w9DHgh/CH7Y4yeXehH8EPqeffZZDfTJDt+TTz6pgT2Eu6VLl8KSJUtg8eLF4k/5LFq0SBz8esmSJbBkyRLxGkuXLoVly5bBsmXLYOXKlbBy5UpYtWqVcAcRBtEdpCCI4ZGsrCxNnyCOk5HdwMOHD3uEROSAiNwXKEOg3qxA1ZxAvRmBRjuD5VSwCgL1XECEQHnrjWpAtK/KwFZ/74MFBIMdAK3O1508eTJ06NBBc9+0adMgJibG+j84y7IYAB2IAdCZ3A5/+Mr9M1r35m3kC13xZjTrTy/wQde6ybt8adIX4Y+ub8OgB+31O3DggCbggYleLPdiqEN2+2iJF52+lStXwooVK4RrR4Fv0aJFsHDhQnjiiSfEWbBgAcyfPx8WLFggbs+fPx/S09PFbXyMft/ChQth4cKFAhDxfdBVlN1BWiamJWJaHsZxMjhChvYHYkgEewNVAREZAmUnkJaDaTpY7glU9QNiOVjuB9SbD+jNBZRHw5jZDqJyAc3sCeYwiFZuAuDgwYNh5MiRts/gwYMhJCQEbty4oQka3blzR/m+dsKVu3btglKlSon2rM8++wwiIiJgyZIlvv2hsJRiAHQgBkBn8hX82QFAf7t/MvzhYF+j0Aft+UMAlOf84Yw/2fnDlW4IfnSNG5Z8Meghj3XBgIec6KXBDnT8sMS7Zs0a4fYh+C1btky4ejLwLViwANLT0yE9PR3S0tIgLS0N5s6dqzxz5szx+HrevHmQlpYmXgMhkcIhuooIg8uXLxcwiGViWh5GEKSlYewRpEERhEAcG4NuoF5K2IwTSOcE0nKwqh8Qy8HUCVS5gHqr4sy4gCoA9OYCOpkNyA6g/wFwyJAh8NBDD9k+Q4YMUY49S0tLU76v3fm6a9euhdDQUChUqBCEhIR4hDNZ/hMDoAMxADqTP90/GQCduH9m172p3D8zO35Vg55xty8mfmXnjwY+qPOn2uhBx7scOXJElHwR/vbs2ePR54c9fpjkfeaZZzxKvOj2UZcPYWz+/PmQlpYG8+bNE2fu3Lkwe/ZsePzxx8Wfs2bNglmzZsHjjz8uvk5NTYXU1FRxP/45Z84czcHXTUtL08AgOoOLFy8WZeIVK1YoHUEcMbNhwwbhBmZnZ2vcwOeffx727t0rBkljQOT48ePKvkAKgSonUB4WTdfGIfxTCFQlg+WdwUZbQuQ0sGo0jJ4L6G1FnJPtIAyA/n0PXwGgWQfQznzdkydPQoUKFSAzMxOuXr0K+/btg6pVq8KCBQt8/nNheYoB0IEYAJ0pt9w/PQA0G/wwSv4alX5V8IelXzroWR73QuFPHvUiJ31xly86f3K/Hw16YMl3+/btItlL+/yw3Lt27VpYs2aN0u1D8KPlWwS/OXPmwOzZswXsIdilpKSIM3PmTPHnzJkzYfr06eI2fSwlJQUee+wxcSg0zp4928MlnD9/vgYGsWcQS8SrVq0SfYJyjyAOl966datIC9OksOwEHj9+3KMcrIJAOiyaro2jw6LlZLDeujhVKtioFGx1T7AZCDQbBmEI/J/cBMChQ4dCQkKC7TN06FBLn9VOCTgmJgZmzJihuW/btm1QtGhR+Ouvv+z9AFimxQDoQAyA9uVm758RANrp/aPun5ngB13zRgFQBX96fX+y8/fOO++Isi9N+6Lrd+LECeH84TYP7PeTZ/plZ2eLci8GPGiid/Xq1fDkk0+K3j5012S3D8u1CGapqakC2BDkZsyYAdOnT4dp06aJM3XqVI+TnJwMycnJ4vFp06bB9OnTYcaMGRo4TElJEWCJLuHcuXM1MLhgwQIRKqG9grQ0/PTTT4sxMgiB1A3EkjD2BaITSBPCNByicgL1hkWrSsEIgbQfkJaCrbiAt2/fVrqARr2AegOi7bqADIH/lZsAOGzYMBg1apTtM2zYMFshECvzdaOioiAlJUVz386dO6FIkSLw73//2/o/PMuSGAAdiAHQvgIt/GF17Iuq/Ott4LO3eX8IgKotH9T5w74/Gf7kzR50vh8Ne2DKF52/zZs3i5Vr69atE+Xep556SlPqpWEODG1Q8MPyLYLZjBkzNMA3depUSEpKgqSkJEhMTPQ4U6ZMgSlTpsCjjz4qbuNj+H1JSUmQnJwsoHDatGnifdAlRBicPXs2zJ07V1MiXrhwoegVpKVh2h+4fv16sWFk8+bNmpKwXA7GcAhuD0EnEEfEyBBI18bRQdE4IxAh0MyQaNkFlPsBve0JtuICOtkPzCNh/qdgB0CrM3vT0tKgRIkSsGvXLvj888/h2LFjULt2bRg0aJDPfy4sTzEAOhADoD1Z/T9+X87+s7vyzZv7p1f6xd4/b6Vfo9AHdf/Q+aOlX7ns6835w/EuGPTIzMxUlnvR9Vu6dKlw/DDMQUu8FPwQ+NDFk4Hv0UcfhcmTJ8OkSZNg0qRJMHHiRJg4caLH1/KZNGkSTJ48GR599FEBiBQO0S1Eh5CWirFEPHfuXOEIUhBcvny5SA2jG7hu3TqRFEYIpONisByMCWEsB9OB0TQdjHMCzSSD9UrB3lxAFQCqXEBVP6AbLiBDoLsAOHz4cBg9erTtM3z4cNuDoM3O7P2///s/SE9Ph9q1a0ORIkWgatWqMGnSJPjll1989eNgGYgB0IEYAO0p0MIfeu4fAqBR8MOb+0fHvui5f/K8P4Q/nPenmvVHE78IfzTwoYI/3OiBKV/s99Pr9aOu3xNPPCHAD8McMvhNmzZNQB+FPTwTJ06ECRMmwCOPPCLO+PHjYdy4cTB+/HjN13gfHnz+hAkTYMKECR5QiECIIDh9+nSYPn26cAVlRxADIwsXLhRuIJ0liCVhGhDB4dHoBFIIxKHRMgS+/vrrhhAoJ4Pl+YDX/n8wSG9LCP77JW8IseoC5hUAzOtyEwBHjBgBY8aMsX1GjBjB17cgFwOgAzEA2lNulX99sfNXte/X28w/1cBnuu1D5f6pSr96iV+EP1r2RfjDsAeOeaFDnTds2ADr16/36PWjIY+FCxfC/PnzDcFv+vTpGpcPHT4EPoS+cePGwdixY2Hs2LHw8MMPw8MPP+xx0Rk9erTHffhc/N6xY8d6QOHEiRM1MCi7gioQxLAIuoG4fQR7A5966imPkjDODKTzAumsQOwJNNsPKIdCqAtIS8EUAvVcQNVYGDM7gvU2g+gBoLdSMPcA6osBkBVIYgB0IAZAe3Kr/OuLwc9W3T86948mf70NfP7kk0/gww8/VI58wWHPdMsH9v3hoGe55+/gwYMeZV+EPxzvgmEPHOa8cuVKDfjRPj8Mdzz22GMihIGBjaSkJJgyZYoAPz3gQ8AbNWoUJCQkiD/lQ0dR4NejRo3SlKcQDMeOHSscwwkTJghXcPLkyR6u4IwZMzQJYkwOp6ena+YI6kEgzgykEEiDIXRgNM4JRAhUJYMpBMouIPYDyi4gpoL1XEC9NXEUAL1BoJ4LaASAFALtBEHyiwvoJgCOHDlS/M+TnTNy5Ei+vgW5GAAdiAHQuvzZ/+eLzR9m3D86E0s19Fk198/MzD8EQL1NH3TLhwx/x44d85jz9+KLL8Lu3bth+/btAv4w6Utn+61atUq4fjjEGeEPXT+51IvgRx2/CRMmwPjx4wX0UVdv1KhR8NBDD4ktA7huSl5AP2zYMM3B+/F78PsRDqljiCAou4LoCE6dOlWMmcGwyOzZszUQiGvncIj0qlWrPMIhtBws9wTKcwJlCJRLwSoI9DYgGvsB/eUC4u+BkQPobSwMu4BquQmADz30kMY1t3oeeughvr4FuRgAHYgB0Lpyq/zrZPSLmaHPqt4/Ofkr7/ql8Cfv+b169SpcuXJFwB+mfuVZf3/729/ErL8jR454pH1p2Rfhj270wH4/hD8c64IhDxzngjP6pk6dKhK7KvCTXT6EPoS9YcOGwdChQ2HIkCEwZMgQGDx4sMcZNGiQ5gwePFg8X4ZDFQw+/PDDGhik5WEEQRoWoW4gloRpXyAdHo1OoNwTSHcIHzp0SAOBqlCIPCSaloJVW0LwfxjobEBVIpiOhbHrAqocQJULaFQKZhdQLTcBMCEhQfwe2DkJCQl8fQtyMQA6EAOgdfkC/pz2/1lN/9qZ+ye7f3Lp95pi7AvCH3X/KPzJmz5OnjwJx48fF/B36NAh4fw9//zzYrUbwp+q50+GP5rwnTVrlij5YsADx7RQ8Bs3bpwo8VLoo64ewt6gQYNgwIABMHDgQBgwYAAMGDAA+vfvD/379xe3+/XrJw7ej99DwXDIkCEwdOhQGD58uHAHsbRMQXD8+PEiNEIhUC4L60HgihUrPMrBuEcY08F0RMz+/fsNQyFmS8HYD0hnA9pxAeU0MB0OrbcZRG87iFkHUA8E83sSmAGQFUhiAHQgBkDr8hcAehv+7LT8qzf65ZdffjGEPzr02WjmHy39Xr16VZR+Ef7eeOMNj76/48ePw7FjxzSz/hD+cM6fHPjIyMgQe3wR/rDkm56erin5PvbYYzBjxgwP10/u8Rs9ejQkJCQIp4+6fBT6EOr69u0rTp8+fcTp3bu3x8HH+vbtC/Hx8RAfH68Bw4EDBwqHEJ3BESNGaPoGZRBUuYF0ZMzcuXPFJhEMh1AnEAdGYzmYDot+4YUXYO/evZptITQUgjuDaSkYIZAOiMZAiN5sQJULqBoO/dNPPynXw+ntCLaSBvZ1GIQdQN++x6hRozRJeqtn1KhRfH0LcjEAOhADoDXlVvnXSfrXSvnXW+/f9evXlQCIwQ+66o26f3TeH+74xXl/r7zyChw+fFiEPuT1bnTOH/b80eHO6PzhQGeEPyz5Tps2DZKSkjSuHwU/7O3DEi9CHzp8CH3x8fHQt29f6N27N/Tq1Qt69uwJPXr0gB49ekD37t01t7t37w7dunWDbt26ift79OgBvXr10oAhQiE6hCpXEMvD6AjKbmBiYqLoDZT7AlUQiCvkcERMZmYmZGVlwdatW2H79u2aZDDtB9SbD6haFUcHRKvGwtA9wTdu3LDlAqrKwLID6NQFtAOAZiEwr8pNABw9erRm5JLVM3r0aL6+BbkYAB2IAdCaAgEAzbp/Vsq/Rr1/MgCq3D+67k3l/tE9v6dOndKkflWhD9r3J8MfOn844w/LvnPnzvXo95s2bZoIeUycOFFT7kXX76GHHoLhw4fD0KFDBfhhGRddvt69ewvg69atG8TFxUHXrl2ha9eu0KVLF/Fn586doVOnTuJ07twZunTpIp6DUIhg2KtXL+jVq5d4H5UrOHz4cE2PILqBtDeQQmBKSoqHE0jHxDz55JMecwIpBGI/IJaCDx48qCkF09Ew58+fV5aCrbiA8nYQhEC9MIjeXEAzLqA3B9BpCZgdQN++BwMgy5sYAB2IAdCa8hIAGqV/5dl/3ty/GzduaDZ+qNw/vZl/mPrF3j+c94el38OHD8P+/fvFvD867mXTpk0i9CGPepEDH9T5Q9cPS74IfxjyoOA3bNgwUebt37+/xumToY9CXocOHaBDhw7Qrl076NChA7Rv3x7atWsH7dq1g9jYWIiNjYV27dpB+/btoX379tCxY0cPMIyLi9O4hH369BElYgRBLA1jYAT7A8eOHSvSwjQpPG3aNJg5c6aAQFwjhxBIR8TQjSEbN26ELVu2iFLw888/7zEfEFPB1AU8d+6cMhBCXUC5F1B2AVW9gDgSxh8uoFEamCHQWG4C4JgxY8QcTjtnzJgxfH0LcjEAOhADoDX5CwCN+v/0yr8UAL2tfjMb/rDi/uHWD5r8pWNf9Nw/3PbxyiuvwNGjR0XqF4c9Y+kX+/7WrVsHTz/9tBjyjM4fHfA8Z84cjfOHYQ/s90P4Gz16tCbgQcu96Phhebd79+4QFxcnwA+hDwGvbdu20KZNG82JiYmB1q1bixMTEyMea9u2LcTGxgogbN++vQDCrl27QlxcnHAFZRAcNGiQKAvLEDh+/HgPCJSdwHnz5on1cUuXLhUQiE4gJoM3b94MW7duFaNhjErB3gZE44YQo+HQFAC//vprAYDfffedpgyM/6NitB7OTBhEtRvY1wAY7EEQNwHw4Ycf1l2taOY8/PDDfH0LcjEAOhADoDXlRffPCACxyd6b+4cAqLfyDcMfsvsn7/o9KQ18VpV+s7OzISsrS5P4XbNmjej7o0OeZeePwh9u8hg/fjw8/PDDml4/DHhguTc+Pl44fgh+nTt3ho4dO0LHjh2hffv2EBsbCzExMQLyHnzwQWjVqhW0bNkSWrVqBS1atIAWLVpA8+bNxe0WLVqIx1u1agUPPvigeI02bdoIlxDdwc6dO0NcXBx0794devbsKUCwf//+YpwMDYlgSViGwOTkZDEvENPBuD5u0aJFsHTpUs3auGeeeQaeffZZ4QJu3boVdu3aJUrBOBqGBkJkF5DOBqQAaLQijoZBZBcQARAHQ6tmAlpNA9sBQH/1ADIAen8PBkCWNzEAOhADoDUFCwDquX/eBj/L7p9c/pXdP9z4gTP/Tpw4oZz5h8EPOvLlueeeg3Xr1mlKv0uXLoVFixaJ7R5z586FWbNmQUpKimbMy6RJkzxSvhT+Bg0aBAMHDhTl3l69eolSb5cuXTTQh05fTEwMPPjgg9CyZUto3rw5NGvWDJo1awbR0dHQH/jHrQAAIABJREFUtGlTjxMVFQVRUVHQtGlTiI6OhmbNmkHz5s01QIguIS0XY79gXFwc9OzZU/QIYn8gQiDOD6R9gRgOmTJlikgHoxM4Z84cSE9PFxBIk8Fr164VpWCcD0hTwVgK9uYCYiKY7gmWwyC4HQRHwly/fl0DgN98840yDKLnAOr1Aaog0AgAEQJVDqAMggyA7gDg2LFjYdKkSbbP2LFj+foW5GIAdCAGQPMKhv4/vfKvavTLV199pQuANPwhj35RuX/yxg/q/tFVb1j6xeAHun96s/5wtZtc9tVz/oYMGaIZ6dKnTx/o1asXdOvWDbp27aop87Zt21aUctHha9asmQC7Jk2aQJMmTaBx48bQqFEjcR544AHNadSoETRu3BiaNGkivheBEB1CBMG2bduKnkIsDXfr1k2UhbEkbOQETpw4UaSDk5OTYcaMGZCamgqPP/642B+MMwJpKZjOB1SVgtEFPHr0qKYXkK6Je+ONN5RlYOoCyttB6ExA6gLS9XBmN4PouYCqIIiVMAiPgvmf3ATAcePGibWIds64ceP4+hbkYgB0IAZA8/IFAPpj/Isvhj/rASCd/Ud3/uqFP7y5f6+99hq8/PLLcOjQIU3wgw58xtIvXfMm9/3Je31xzh/t+RszZoxH2ZcGPXr37g09evQQPX4dO3aEdu3aQZs2bUSJF0u6zZo1g6ZNm0KTJk0E5DVs2BAaNGgA9evXh/r168P9998v/pRP/fr1oWHDhgIKKRBGR0eLsjGWiNu2bSuCI507dxZBkd69e4uSMEIgpoTlcjDOCcRQiDweBvsBcUj02rVrISMjw2NfsOwCHj58WCSCEQJlFxDDINQFVAEgHQwtA6AqDOLEBVQBoB4EchBEXwyArEASA6ADMQCaV24DoL/Lv3rhDwRAPfePAiC6fxj+OHv2rHD/sPyLQ5/379+v2fW7ZcsWzcBn6v5h6XfBggWQlpYmQh9Y+sU5fzTtS8u+w4YNE/BHE77Y64euH5Z5sX8PS7kIfg0bNoT69etDZGSkOBEREVCvXj1x6tatqzn16tWDiIgIiIiI0ABhgwYNhDsYFRUlSsRYGsYewQ4dOojewJ49ewoIRCdw6NChMGLECDEmho6IoclgWgrGfsDFixfD0qVLPQIhGzZsgM2bN0N2drbHrmB0AeXh0GfPnrU1EkaVBpbLwNQB1FsNp3IAVavh9ACQN4KYk5sAOH78eHj00Udtn/Hjx/P1LcjFAOhADIDmlZ8AkLp/tPyL7h8CoLz27a233hLlX73k7+HDh+HAgQMa92/r1q1i4POzzz7r4f7R0i+6fykpKaL0i/D3yCOP6Pb8Ifz16tULunfvLkq+7du3h7Zt20JMTIwo9SL4IfSh04ewh3AXHh4OderUgTp16kDt2rWhVq1aULNmTahVqxbUrl0bateuLR4PDw/XQGFkZKQAQXQFsV+wefPmHm5g586doWvXrsIJ7Nu3ryYhjGvkxowZ4wGBWArGvcHyfMDly5d7DIimLiCuicNEMHUB6Yo43A6iNxhaDoPQNDBCIM4EVKWB5XEwFAKN5gG6vRaOAdA37/HII4/AlClTbJ9HHnmEr29BLgZAB2IANK9g7f/zBoA4/sVM+Ze6f1j+ld2/gwcPCvdvz549sGPHDtiyZQts3rxZzPyjA5+p+4d9fykpKZoVb1j6HTdunJjzR4c7DxgwAOLj4wX84VgXDHkg/DVv3hyaNm0KjRs3hgceeEDj9iH4ybBHT40aNaB69epQvXp1qFGjBtSoUUPcj1BIYRDdQQRBLA0jBKIbSEvCOC4GE8JyMIRCICaDcVC0XAqWAyFr1qyBtWvXalxA1XBoTAQfP35cuSNYXg+n2gxCwyAUAPX2A5sdCp3XXMC8JrcBMDEx0fZhAAx+MQA6EAOgOQVSAMRo/ZsV908FgHrlXwqAcviDzv6T3b/Tp0+Lwc906wcmf2nvnzf3D0u/mPqdPn06JCUlidAHbvjAvr9hw4bBkCFDBPz17t1bOH+Y8kX4a9mypQh4NG7cWJR6qeOH4Idgh6BXtWpVqFatGlSrVg2qVq0K9913H1SpUgXuu+8+8VjVqlU9oBBhsG7duhAREQGRkZEaNzAqKkoJgVgORiewX79+YmC0qh8QQyE4H5C6gOnp6WJVnOwC0uHQtBfwpZde0mwHOXHihEgEy+vhvM0ElIdCYxjE7FYQOg5GngcoD4V2MwjCDqBv3oMBkOVNDIAOxABoTv4CQCP4swKARuVf6gDq7f416v+79v+3f8jlX9XsPxr+0Cv/0rl/uPUD3b/169cL+FuxYoVm7Iuc+qX7fVWlX3T++vXrJwIfWPbFlC/O8UP4a9SoETRo0ED09tWtW1eUcmvVqiXAr2rVqlClShWoUqUKVK5cGSpXrixuV6pUSRx8DB9HKEQYxHIxuoLYJ9iwYUNNb2CLFi1EX2C7du00PYGYDh44cKDoB0QIHDt2rGY+ILqACIHz5s2DJ554QjMbUO4FpGVgdAExDPLKK6/AsWPHNCNhsAxMwyBYBv7ggw90V8NRANQrA6v6AOUysAyAuRUEYQB0/h4TJkyApKQk22fChAl8fQtyMQA6EAOgOeVG/x8FQL3yrxkA9Nb/JwOgqvxLAdBo9RsNf8iz/+TBz7t27RLhj40bN8Kzzz6rKf/Soc+y+0d3/Mql3xEjRsDw4cNF3x8d9UJ7/lq3bi3gr2nTphr4w/4+LPNSt+++++7TgF7FihWhQoUK4s8KFSpA+fLloXz58lCxYkXNkUGwWrVqAgQRAuvVq6cJiSAE4riYtm3batLBPXv2FHMCBw8eDMOHD4cRI0aIbSHoAk6ePNljSwjOBsSxMKpE8KZNmyA7Oxu2b98uwiC0DEzDIK+//rpHGISmgVVDoY36AHEcDJaAvY2DMdoKorcX2GwJ2F+jYPIaBLoJgBMnToTk5GTbZ+LEiXx9C3IxADoQA6A55XUAtNr/J5d/MQDy0UcfaQBQXv2G5V+a/qXlXxr+wLl/WP7FlW907p/K/ZsxY4YIfqD7h6XfkSNHitIvjntB+OvSpQt06NDBA/7Q+cOSL7p+KscPga9ixYoC8sqVKydO2bJl4d5774WyZctC2bJlNY/JUFi5cmWNI0jdwLp164qSMEIgloNxcHSHDh3EnECaDKal4FGjRomdwXRAtKoXkO4Jfuqpp4QLmJmZqRwJg+vhMAxC08CqMvCVK1d0+wCvmdwN7K0PUA8AvfUBchnYvBgAWYEkBkAHYgA0p9wAQF8mgL3N/zMa/4Lz/1TlX7n/j+79xf4/Wv7dv38/vPDCCyL8kZ2drQl/rF27VvT+Yfk3PT3dY+MHbvugM//Q/cMVb/Hx8dCnTx9N6RfXuSH80fEu6PzVqVNHwB/t6atUqZJw9yjs3XvvvXDPPfdobssHH0MQRMewUqVKGkcQIRCdQAqBTZs2hWbNmgkIxF3CmAzu27cv9OvXT6SCcTQMdQHpbEA5EUzLwHQ7CIZBtm3bBjt27FCWgelmEHk1HAVA2gdIy8B6AEjHwagAUB4Ho9cHmBtBEAZA5+8xadIkmDp1qu0zadIkvr4FuRgAHYgB0JwCBQCNAiCqAdAyAFL40wNAuvsXy78yAKrKv2+++SZcuHBBlH9Vs/9w8wct/6rCHytWrNCEP3DfL7p/uPFDdv+w9DtgwAAx8iUuLk6Uftu0aSPSvtHR0SLwcf/990O9evWgTp06IrVbrVo1TV9fhQoVoFy5chrQK1OmDJQuXRpKly4tbpcqVQpKlSol7qeP4/eiO1ihQgUNBGJJGBPD9erV0/QERkdHi4HRbdq0EaEQ2QWkqWDaCzhp0iRlIjgtLQ2eeOIJUQbGMIi8I1ivDEw3g2AaWO4DxDSwaiuItz5AVRnYigNoNggiO/AyBKp+h/UgkAHQ+XtMnjwZpk2bZvtMnjyZr29BrjwDgBkZGVCjRg0ICwuDqKgoOHPmjO5zN2zYADExMeLi0bFjR7h48aLmOQkJCRASEqI5LVq0sPSZGADNyY0AiF0ApGUuOwEQCoDY/6da/yaXf+n4F+z/k8u/J06c8Ej/7tmzR8z+o5s/sPxLe/9o+ZcOfUb3T+79Gzp0KAwcOBD69eunGfnSsWNHUfpt0aIFREdHQ5MmTUTPH8JfrVq1NCXfypUre7h+99xzj/i9LFWqFJQoUQJKlCgBJUuWFLfpwftLliwpQBBdwbJly0L58uVFYKRKlSpQtWpVAYF16tQREIjp4OjoaGjZsqUoBXfs2BG6dOkiRsNgIAQBcNSoUcpEMLqAqpEw8mBonAmIAEhnAh45csRRHyAtAyMAyn2ACIEIgLIDaDYIwqNgnIsBkBVIyhMAuHv3bggNDYXMzEzIycmBpKQkKF68OHzxxRfK5w8bNgwyMjLg0qVL8Pe//x1Gjx4NpUqVgq+++ko8JyEhAeLi4uDbb78V56effrL0uRgAzSk3AdBJ/58VAMTyrxwAoQAob//A8i8NgMjjX7D8e/DgQdi7d68GADdv3gwbNmwQ5V8MfyxduhQWLlyoKf/SlW+q5C9u+8CxL7jpA90/nPVHS7/Y9xceHg61a9dWwl+FChVEubdMmTJQqlQpDezdfffd4hQvXhyKFy8OxYoVE7fvvvtuDQyiO4ggiBBI+wKpE1inTh0xK7BRo0bQpEkTMShaTgX36tVLsy94+PDhogyMvYAYBqHbQehgaL00sGomoLc+wDfeeAPefvttMQ9Q3gqCQRDaB4gAKO8FVg2E1tsIIruAKgdQbyA0O4DGchMAH330UZg+fbrt8+ijj/L1LciVJwCwefPmMGHCBM19ERERkJqaaur7//3vf0OJEiUgOztb3JeQkAB9+vRx9LkYAL3LF+VfJyNgnAKgUQDELACq5v8hAL799tuaAIg8/uXll18W2z/k9O+mTZs0w5+x/48OflaVf9H9o+VfdP8w+YvuHwY/cMUbJn4bNmyogT9a9sWwB+31K1OmDJQsWVLAHwJfsWLFNKdIkSJQpEgRKFq0qOZ+CoMIglgW1oNAnBdYt25dUQpu1KiRKAW3bt3awwXERDCOhaFlYBoGwe0gqampyjQwnQlIAVDuAzx06BC88sorYig0nQcobwXRWwtnJghiBIC3bt1SAqBRCMROEpgdQHcBMDExEWbOnGn7JCYm8vUtyBXwAPjHH39AwYIFYd++fZr7ExMToW3btqZe49dff4UiRYrAoUOHxH0JCQlQqlQpKFeuHISHh8PYsWPh5s2blj4bA6B35WUAtJIANgqAIABi+RcDILT/74033tAd/4K7f1944QUx/FnV//fkk08K9w/3/tLZf5j+RfdvzJgxMGrUKI+5f7T3r0OHDqL3D92/Bx54AO6//34R+sBRL1WrVoXKlSsL+EPww3Ivwh+CX9GiRaFo0aIC+vCEhYVBWFiY5j4EQhkCS5curesEyqXg+vXrwwMPPCBSwQ8++CC0bdtW7AvGXkB5LqCqDEwBkPYBLl68WJSB6Wq4TZs26fYBUgCUt4Ko1sLJ8wCdACBCoJ4DSF1AIwBECDRyABkAGQBZgaWAB8Cvv/4aQkJC4Ny5c5r7Fy1aBHXr1jX1GpMmTYLatWvD77//Lu7bvXs3HD58GN577z04ePCgKGnduXNH93Xu3LmjmZ5/48YN/gXxotwIgLgJgDdv3jQcAK0CQFUAhAKgHADB+X8YAKHlXxz/Iq9+mz9/vkf6l87+w/Ivjn6h+3579uwpNn7ExsaK3j9c84a9f9T9w9IvjnhB54/2+t19990eTh/CXuHChZWHwiBCILqBshOIKWGEQEwGowsYGRkpXMCmTZtCixYtICYmBtq1awedOnUSZeD4+Hjo378/DBo0SIyEoevh6GaQlJQUMRQa+wCXLl3q0QdIx8Hs3r3bYysI9gGeOnUKzpw5owyCUAA0CoIgAGIfIP57atUBdDoLkEfBeMpNAExKSoKUlBTbJykpia9vQa48A4Dnz5/X3L9w4UKoV6+e1+9ftmwZlClTBq5cuWL4vG+++QZCQ0Nh7969us9JS0vzCI7wL4ix8joAIgTiBgV5BAxdAScHQCgAehsAfeHCBU3/HwKgvP6Njn9R9f9h+ReHP6empor+Pxz/YhT+wNEvquRvVFSUcP/q1asHtWvXhpo1a0K1atXEgGea9r3nnns0/X7o/CH4FS5cGEJDQ6FQoUJQqFAhzW16H4XBIkWKCAhUlYMxHYwuYPXq1ZWBEARA6gJ26dIFunfvDn369BFl4GHDhmk2g8gzAVV9gLQMjLuB5XmAGAQ5cOCAAMDXXntNACANghglgTEIYgSAqlEwmATWGwXjr4HQMgTmt2HQbgJgcnIyPPbYY7ZPcnIyX9+CXAEPgE5KwCtWrIBSpUrBW2+9Zeq96tSpA0uXLtV9nB1A68rrAEhHwPz8888eI2AQAFUJYDoCxlsCGEfAyPt/MQBCE8DZ2dm6/X/exr9MmjRJOfsPwx+Y/u3cuTO0a9dOM/ePun9169YV8/6w94+6f1j6LVmypKbfT4Y/hL6CBQsqD0IggqAMgegEqkrBNBVcu3ZtCA8PFwDYpEkTiI6OhlatWmlGwnTv3t1rGVhvKDQtA1MAlPsAMQgiD4TGJLARAGIfoLdRMDQJLI+CsTMMmh1A38hNAJw6dSqkpqbaPlOnTuXrW5Ar4AEQ4L8hkIkTJ2rui4yMNAyBLF++HEqWLAkXLlww9R4//vgjhIWFaYIi3sQ9gN4VqABoNARaLwFsFgBVMwA//PBDTQIYAfCtt97SJIBV+3/3798Pe/fuFft/KQDS+X8IgBgAUfX/TZw4UZP+HT58uOj/w/Rvt27dNOXf5s2ba9a9RUREiN4/dP9w1h8t/crwR8u9MvgVKFAA7rrrLnEKFCgABQoUEI/rQSD2BGI/oDcXkCaCmzZtKkbCYBqYrofDzSB6fYBJSUm6fYDLly/3AMBNmzZ5JIEPHDgAR44c0QyE1tsIgg4gBUDVSjjVKBg9ADQ7DNpfPYAMgP57DwZAljflCQDEMTCbNm2CnJwcSE5OhuLFi8P169cBAGDkyJEaGFy2bBkULlwYXnzxRc2Yl99++w0AAH777TeYPn06nD9/Hq5duwYnT56EVq1aQZUqVeDXX381/bkYAL0rmABQNQRaBkB5BzACoN4IGKsAuH37dl0ApOvf5s6dqwHApKQkePTRR2HChAki/UsBEPv/evToAXFxcZqdv7T8W79+fVH+lde80d4/7PujgQ/a61eoUCEBeRT8VAdBUIbAokWLeriAtBdQlQjGMAjtA6RpYLoZRDUORtUHSNfC0SCInASmALh7925dAKQbQeSVcKokMAVAOgrGCAD1RsEwAPpXbgLgtGnTYNasWbbPtGnT+PoW5MoTAAjw30HQ1atXh8KFC0NUVBScPn1aPBYbGwsJCQni6+rVqyt79dLS0gAA4F//+hd06dIFypUrB6GhoVCtWjVISEiAL7/80tJnYgD0rmAEQDoCxggAcQag7ADiCjgcAYMAePbsWQGAx44dEyNgZAdwy5YtsHHjRo8EMALgggULPAIgSUlJmgAIHf8yaNAgTf9f165dlelfXPmGAIjhD1XyVwZAueyrcv3k31fZDZTLwdQFxF5AHBJdrlw5sSWEhkFoHyDOBGzdujW0bdtWA4DYB0iDIKNHjxYAiPMAp0+fLtbCyQCISWDcCLJp0ybIzs6GHTt2CADcv3+/4Uo4bwBoNAvQaBsIOtp6swB9tQ2EAVArNwFw+vTp8Pjjj9s+06dP5+tbkCvPAGAgigHQu/I6AN6+fdsrAHobAk0dQLoDmM4APH/+vBgBQ2cA4ggYHAKNI2AoAK5Zs0YAIG4AQQdQlQCm8/+GDRsmAiC9e/eGHj16QJcuXaBdu3YaAJT7/9ABVIU/sPxbokQJ3dAHdf9U/7OmB4IyAKILSLeFIADKfYC1atXSpIHpUGg5CIJ9gDIA0iBIYmIizJgxQwOANAmMA6HpKJjs7GzNKJiXXnpJdxYgJoGtzgJUDYPGUTB0HVyg9wFa+W9HXoFABkBWIIkB0IEYAL0rrwOgrx1AIwDEErBqBzAtAeMMQLoCzpsDKI+AQQAcMmSIBgBxADQCYMuWLTXjX+TNH1j+VQEg9v/JAIjunxUAlF1A2guoVwamfYAqAMTVcK1atYK2bdtC+/btPZLAgwYN8kgCUweQbgRRrYSTdwJTAKQOIO4ERgCkO4HtOoAIgNQBNLsNhB1A/8hNAJwxYwbMnj3b9pkxYwZf34JcDIAOxADoXQyA1h1AFQC++OKLsHv3bs0QaD0ATE9Phzlz5nisgFM5gHQEDAXA2NhYDQDSAAgCIN38Ic/+QwDEQc8qADQDfzIEYi+gKgyiAsBKlSppADA8PFwXADEI4g0AcSVcYmKix0o41U5gPQDEYdAUAE+cOGEJALEH0Nfr4FQ9gGb2ATMAGstNAJw5cybMmTPH9pk5cyZf34JcDIAOxADoXQyA9gDw+PHjGgB84YUXNACIJWCnACiXgFUAGB0drQRAvQBIIABg2bJlNQCISWAKgE2aNIFmzZo5BkAsAeMsQBkAMzIy4LnnnvMAwBdeeEG3B5ABkAHQ6XswALK8iQHQgRgAvYsB0DclYHQAzZSA58+f7zEE2m4JmO7/1XMAVds/zAKgWQi0C4DyKBhvDqCbJWAKgFwCZgD09XukpKTA3LlzbZ+UlBS+vgW5GAAdiAHQu/I6AOZGCAQdQAyBIACaDYFQB9BqCETPAVT1AMolYFUIxBcAqBoHQ0MgxYoVU4ZAEACrVq2qAcCIiAilA0h7AGkIRG8bSGJiouE6OG8hEFwH52YIxAgAOQTif7kJgI899hjMmzfP9nnsscf4+hbkYgB0IAZA78rrABgoDqCdEIgTBxDXwKlCIJgCrlGjhhgCrQJADIHQGYCqFLARBKoGQztJAcsOIE0B2wmByGNg9BxAVQlYDoGoxsCwAxg88AfAAMgKLDEAOhADoHcFIwCaHQRt1gHEOYB0DAzOAaRjYFQ9gLIDSAdBW3UAcQ6gagwMzgGke4D1xsDQHcAqADTjAqqGQdMUsGobCAKgah0czgHEFLBqDiCOgaFzAAcPHiwAkA6CtjIHEHsAjeYA2gVAox5A1SDoH374wXAQNPcA+lduAmBqaiqkpaXZPqmpqXx9C3IxADoQA6B3BRMAmlkF5w0AjQZBy5tAVACIDqAZALQ6B5AOgsZNIA8++CA0b95cCYA4CBrLwBQAcRD03XffLWYByvt/VZtAVACoNwIGy7/yIGgEQAyAyJtAcBB0VFSU7iaQPn36GG4CQQCkq+CMAJBuAqEAeODAAU0I5OTJkwIA5VVwly5dEqvgcnJyNKvgzAKgt00gDID+lZsAOGvWLJg/f77tM2vWLL6+BbkYAB2IAdC7AhUAEQJVAIgQiAB469YtWwCotwsYAVBeBUc3gdBVcAcOHBAAuHPnTuUmELoL+IknnlBuAsFdwOPGjYPRo0cLABw0aJBYBdezZ0+Ii4uDjh07agCQroKju4C9JYFVw6DlXcB66+DkfcCFChUS32+0C5gmgGn/X506dZSr4HAXcKdOnSAuLk65CzghIcHWLmDZAVTtApYB0GgXMDqA8i5gBEDVLmB5EwgCoGoXMJeA/Ss3AfDxxx+HBQsW2D6PP/54nr2+ffrpp9C+ffvc/hgBLwZAB2IA9K68DoAIgWYB8Msvv4QvvvjCEACvXr0Kly9fNtwF/Nprr3kA4J49e2DHjh1iF/CGDRsgIyNDA4DYAzhv3jyxC3jGjBkaABw7diyMHj1a7AIeMmSIBgC7desGHTt2hNjYWIiJidGMgpEBkJaB6Tq40qVLe7iAGAiRnUA6GBphj35NV8Bh6dcIALH8iwEQ7P+rU6cOREREQIMGDTQJ4DZt2ggA7NatG/Ts2RPi4+NhwIABMHToUBg5ciSMGjXKAwCTk5Nh5syZkJqaCnPmzBEAuGTJEgGAa9euhXXr1sGGDRtg8+bNHgC4f/9+OHLkCLz66qumABAdQAqAn3zyCci7gL/66isNAKo2gcgAePv2bfHvP4dA/CMGQHd0+fJlKFCgQG5/jICXaQDMyMiAjh07wsCBA+HEiROax3744QeoWbOmzz9coIsB0LvyOgCadQAxCawCwE8++QQ++ugjyMnJ8QDAt99+Gy5evAgXLlyAs2fPwunTpz0A8ODBg7Bv3z7Ys2cP7Ny5E7Kzs2Hjxo3w3HPPaQBwxYoVwgFMT0+H2bNnQ2pqKsyYMQOSk5NhypQpMGnSJBg/frwoA48YMQKGDBkCAwYMEGXg7t27Q+fOnTVBkOjoaE0ZGIMgKhdQlQbGXkDZCaTJYPnQki91/mjpF3v/ypQpI8q/dP4fLf+Gh4fD/fffb3oN3MCBA2HYsGEwcuRIjwTwlClTYOrUqTBz5kyYNWsWzJkzB+bPnw8LFy4UALh69WoBgJmZmZCVlQXbtm2DXbt2wfPPPw/79u2DgwcPwpEjR+DYsWPw2muvwalTpwQAXrhwAS5evAhvv/02vPvuu3DlyhW4evUqfPDBBwIAP/nkE/j0008FAKIDKAOg7ABi+Vd2AAMF/tgBdP4es2fPhieeeML2mT17dsBe35566inDk5KSwgBoQqYA8KmnnoJixYrB5MmTYcSIERAWFgaLFy8Wj3/33Xf58ofNAOhdvgBAFQTmFgDSJDAC4LfffusVAD/88EMPAHz33Xfh7bffhjfffBMuXLgAr7/+ugDAEydOwKuvvgpHjhzRAKDsAGISePXq1bBixQpYsmSJAEDVKBgZAFVlYATADh06iDIwnQdYv3590QeocgHLly+v6QVECFQ5gdQNlGEPH5Phj6Z+S5UqJXr/qPtXpUoVqFatmsb9w/4/Wv5t3bo1tGvXDjp27GiYAB4zZozHCBicAUgTwAiAK1asgNWrV8PTTz9tCICHDh2Co0ePwrFjx+DEiRNw6tQpMQNQBYDvvffousJNAAAgAElEQVQefPDBB5oRMDIA0gSwUwCkPYBGAMg9gObkJgDOmTMHFi5caPvMmTMnYK9vd911F1SuXBlq1KihPJUrV86XTGJVpgDw/vvvhx07doivz58/D+XLl4e5c+cCAANgIP6CBIryMgD++uuvAgBVo2Bu3rwJN2/eNAWA1AF87733PADwjTfegLNnz8KZM2fg5MmTYhQMAiBuA9m5cyds27YNsrKyNACIQZClS5fCwoULlUlgLAPjKJgxY8bAQw89pAHA+Ph46NWrF3Tt2hU6deoE7dq1g5iYGGjRooWYB6hyAelMQJoILlOmjMdYGIRA6gjKriDeh+VePeevdOnSwvmT17+h+0fhTy7/xsTEQPv27ZXlX28BENwCggEQb2vgtmzZopwBaDQE+s033xQzAO0AIM4AvHnzpu4ImFu3bmn6/9wEQKPfeQZAZ+8RzABYo0YN2LNnj+7jly5dypdMYlWmALBo0aJw7do1zX3vv/8+VKhQAVJTUxkAA/AXJJDkBgDKEOgrADQzC1AFgHIS+KOPPoK///3vAgCvXLkiABBnAZ47d04DgDQJfODAAc0+4KysLGUQZNmyZbB48WKYP3++Rx8gloExCDJmzBhISEiAESNGaHYC9+nTB7p16wadO3fWjIPBMAjdCiK7gCoIxH5A6gQWL15cwCACHgU+BEQ8xYoVU/b8ofNXrlw5Ze8fjn6JiIiA+vXra8a/YPmXun+0/DtkyBBR/n344Yc15d/k5GRNACQ9Pd2j/48GQHAG4I4dO0T/nx4AYv+fDIBXr16F9957z2MEDAVAvTVwdgGQS8C+lZsAOHfuXFi0aJHtM3fu3IC9vvXv3x9SUlJ0H798+TLcddddLn6ivClTAFi1alU4c+aMx/0ffPABVKhQAUaOHMkAyNJVbgIghUAjAEQIdAqAeqNgEABpEEQFgHIQhAIgHQWDZeDnnntOCYByGVgGQCwDYxgEARDLwDgPsFOnThAbGwutW7cWLmCjRo2gYcOGEBkZqXEBaSlY7gdEJxCDIQiCMgzSg/fj8+i6t9KlS0OZMmU08FepUiWoUqWKWP0mu38NGzaERo0aQXR0NLRs2RLatGkDsbGx0KlTJ+jatatI/xrtAKYAiP1/uAOYln8RALH8iwEQCoDeEsDetoBgAMTMFhAZAGn5V1UCRgg0SgF7g0DV76gMgb5wAPOS3ATAefPmweLFi22fefPmBez17YMPPoC33npL9/E///wTrl+/7uInypsyBYBDhw6FpKQk5WPvv/8+lCtXjgGQpavcCIKYBUDZBdQDQOwD1ANAs9tAaB8gjoLBIIg8CgaDIFgGxiTwzp07YevWrbB582bYsGEDrFu3TpSBV65cCUuWLNFdCZeUlCTmAeI4GJoGRhewV69eGhdQ7gXEzSCRkZFQp04dqFWrFtSoUQOqVaumCYWgE3jPPfdAmTJloEyZMqJvr2TJkh6uIIIfOn1Y6sWDzh+FvwoVKgjnTw5+1K1bV5R+GzVqJGb/6YU/4uPjYeDAgTB48GAYMWKEJv2L5V+9/j+6As5M/x8GQGgC+PXXX/cIgNAEMA2AmJkBqLcFxGgETCA4gMHo/gEwALICS6YA8MqVK7B582bdx99//31IT0/32YfKK2IANKdAAUArw6CtJIEpAOr1AaoAkPYBXrx4Ec6dOyeSwLQP8OWXX/ZIAqvKwLQPkI6DoWlgCoB6YRBcC0ddQG+bQcLDw6FWrVqiH1AOhZQrV07MCEQYpBAoQ54MiaVKlRIAif1+WPKtVKkSVK5cWQQ/qPMXGRmpKf3i7t82bdqI3j+c/denTx/N/l8c/4Lr3+j8P9wAQuf/LVmyxGv/nwyAZgMgVgBQDoDIe4D1AiC+BkBv7l9+K/8CuAuAaWlpsGTJEtsnLS0t4K9v6enp8Omnn+b2x8iz4jmADsQAaE65AYD+SAJjGVgFgN6CIB9//LGmDKyXBKZlYAqAuBEE+wBVG0HoPEBMA6elpQkApHuBJ02apNkLjC7g4MGDYeDAgRAfHw+9e/eGbt26QZcuXUQiuHXr1tCyZUto1qwZNG7c2GM4NJaDcUsI7gpGJ5CCIIVBLBHT8i6OdsGD30NXvaHrhwOfse+PzvzDrR/R0dGazR/y6rd+/fppwh+q9W+JiYli/AvO/9Mr/8oDoLH8++KLL8L+/ft1+//kAAhNANP+PysBEDoDUO7/8zYCRgWAqgCINwDM7/1/AO4CYHp6OixdutT2SU9PD/jrW8OGDaFAgQLQokULePrpp+H777/P7Y+Up2QZAH/55Rd49dVXYdu2bZCdnS3O1q1b/fH5AloMgOaU1wHQqA/QaBQMLQNjEli1ExgHQuv1AeI4GLkPEMvAmZmZogy8evVqTRmYroV77LHHYPr06R5lYAyDjBw5EoYOHQqDBw+G/v37a1bDderUScwFxFJwdHQ0PPDAA6IUjE5g7dq1oVatWlC9enWoWrWq6AmsWLEiVKhQASpUqKB0BRHwKOjRg64fJn1xz2+1atUE/NWqVQvCw8M1Gz+aNGkC0dHR0Lx5c43717lzZ+jWrRv06tUL4uPjRe8fbv/A8q/c+yeXf+fPn69Z/7ZmzRrNAOisrCzYunWrh/uH/X+qAAgOgKYJYL3+PxkA6Qo4XwCg3TVwMgSqfoe5BOy/98gPAAjw3wrkrFmzoGbNmhAaGgrdunWDHTt2wD//+c/c/mgBL0sAePDgQShRogQUKFBA9OLQ/2PPb2IANCdfAKCTUTBmgyB2ksAUAI36AD/5f+ydeXgUVdb/GzEJSxbX+YEo4EgI0aCIyOJCxg0iojgyosg2qCiogMuMyvCOEUkgwYjsKDEEhEDY911AliwIEjWYgOwEUMSwqoAi398fvqfeW7eruqurqiuVzvk8z30klU73TZtQH86555zdu73SwFoCSOcAqRpYFEBKA8+ePRszZ87EtGnTMGXKFGRmZnq1gxGLQeQooNgUWisK2LVrV/zjH/9QooAdOnRQxsNRWxi5Krhp06ZKf0BZAulcoFwlTCIoL4oUitfonJ9Y6EHyR+JHzZ6p4pcaPlPkj9q+UOFHUlKSKvr35JNPomvXrpqtX6j3nxz9o+pfGv9G83/Hjh2rpH+nTp2qpH+p/Yt4/m/t2rVeBSDyDGB/I+AOHDjgUwB9VQBbHQMXjBQwF4FYf40hQ4YgPT3d9BoyZEilu79t3rwZL730Eq699lpERUVV9HZcT0ACGBsbi4EDB7JZ/y8sgMYIlgDaXQgSaARQ7xwgCaBeGtjXOUCaCCL3A1y5ciWWLFmCBQsWYO7cuZg5c6ZXNfCECRMwZswYZSqIvyggjYZ74YUX8Oyzz3qlgv/xj3+oxsO1a9dONSJOqzUMtYdp3Lixci5QLA4RRZAigxQdJNETZY/Wddddp5zzE8XvxhtvVKJ+1O5FnPZx5513esmf3PZF7vsnjn6To39vvPGGV/RPLv4YM2aMkv7Vqv6l9O/y5cuV6J94/k9O/2qd/9ObACIXgBgdAWclAqgnf/4igPQ7zRHA4LzGe++9hxEjRphe7733nqm9jh8/Hg0bNkRERASaN2+u2UFE5OTJk3jppZdQp04dREREoEmTJli2bJmp772oqAhvvPEG6tWrhxo1aph6jqpEQAJYq1Yt7N27N1h7qXSwABrHLQIYSCGIXiWwKIGyAGqlgfXOAcr9AEkAKQ1MU0HWrFmjjIVbuHChqhqYikHEKCClgdPS0ryigHJPQIoC9unTB88995zSF1CsCH788ceVquD27durJoS0bt0aLVu2VEbFkQhSixg6F0gFIg0bNlSigiSCWuv6669XfV4+50fTPei8X+PGjREfH6+q9r3zzjvRunVrtG7dGnfffTfatm2L++67D+3bt/eSvy5duqBr167o3r27KvonFn5Q9E+r+IN6/40aNQpjx47FxIkTlfSvGP2bP3++qv2Lv/5/WhNAKP3rawScXvqXBFCM/tklgBXRAoYFUP81UlJSkJGRYXqlpKQEvNfc3FyEhYUhMzMTJSUlGDhwIGrXro2DBw9qPv7ChQto0aIFOnTogM2bN+PAgQPYtGkTvvrqK8OvuW/fPqSkpCA+Ph7Vq1fHfffdh8zMTJw6dcrwc1RVAhLAv//97z67b1c1WACNUxHnAO2sBCYJ9DcSjqKA8jlAmghCaWASQLEdzNatW1XtYOSxcMuXL1dNBaE0cHZ2thIFFGcDjxgxAsOGDTMUBXzxxRfx/PPPo3fv3ujVq5dXW5hOnTqpIoH333+/KhJI7WFIAqlCmKKBsgjSfxs0aOCVJibRExeNeKKvJelr0qQJ4uPjlVYvVO1Lad+77rpL6fd3//33K+f+xNSvKH89e/ZUKn/1Gj/Lvf/S0tKU0W909o+aP0+ZMgU5OTnK9I+FCxdi8eLFWLFihdL+heb/itW/YvpXPP9H6V+5/x8JoNn0b7ALQLgC+E9CXQBbtmyJvn37qq41adIEb7/9tubjJ06ciL/+9a/47bffTH2vrVu3xmWXXYbbbrsNI0aMwOHDh009T1XFrwAuWrRIWZ988gnq16+P5ORkzJ07V/W5RYsWObFfV8ECaJxQEcBAzwFSGljvHKBWFFA8B0jFIDQVZOnSpUoaODc3V+kJKEYBx4wZo4oCpqamKlFA6gsongV8+eWXdZtDd+nSRTkP2KlTJyUSSBJ477334u6771Yk8M4771REkApEmjRpgri4OOV8YGxsrJcQitFBkjw610f/pWgfFXlQi5eEhAQ0bdoUzZo1U+SPon733nuvMuv3oYceUp37o6IPWf6effZZvPDCC15tX958800MGjRIGf0mnv2jyt9x48Yp0b/Jkydj6tSpmDFjBmbPno158+apij9Wr16tpH9JAMXmz9u3b/c6/7dz506v6J/W+T+t9i9mBNBM9I8rgPVxUgBTU1PxwQcfmF6pqanweDwoKytT/g48ffo0zp8/r/m6Fy5cQPXq1TF//nzV9QEDBqBt27aaX/Pwww+jW7du6NOnD/7yl7/glltuQWpqKi5evGjoex00aBB27NgR2BvEKPgVwGrVqhla3Aia8UVFC6DeOUC6wflqCC0KoFZDaHEmMFUDawmgnAYWBbCoqEg1F1irKfSqVauwdOlSpRhkzpw5mqPhKAr44Ycf+j0L+OqrryptYV588UWvquBnnnlGiQSSBFIk8KGHHsJ9992n9AikFjGUEhYLRBISEnDLLbcoUcG4uDg0btxYOSsorkaNGimLHtO4cWPExcUpZ/yoyOPWW29VUr533HGHkvallC/JX7t27bwifzTujSZ+kPzJM39fe+011di3QKJ/4uxfrebPWulfufpXPP+nl/4VBVBO/+oJoJj+lQtAxBnAsgTqRQD9pYCregEI4KwADhs2DCNHjjS9hg0bBo/H47WSk5M1X/fIkSPweDzIy8tTXU9NTUXjxo01vyYuLg4RERF49tlnsW3bNsycORNXXXUVhgwZYut7EhUVxcfXNOA+gBZgATSOmwpBgnUO0Fc7mH379qnawVAaWCwGoakgWtXAVAyyfPlyVTHIrFmzkJOTg2nTpimTQfQqgsXpIP/5z39U00Hk84AkgVQVTEUh8plAigTStJB77rkHd911l1IgIqaFb731VkUGExIScPPNN+Pmm29GfHy8kiomyYuPj1cWPYbETzznd8cdd6BFixaK+LVp00aRv/vvv1+p9n344YeVZs9i0YfY8PnZZ59Vmj7Lqd+33noL//nPf/Df//5XOfunFf376KOPlNFvcvRv4cKFWLp0KVasWKFq/iymf+nsH03/0Br/RtW/+/btU6V/tc7/yfJ34sQJw9W/WhFAN8kfC6Dv17BLAI1GAEkA8/PzVddTUlIQFxen+TWxsbG44YYbVBG/Dz74AHXq1LHvDQEQGRnJAqgBC6AFWAADozIKoJU0MEmgKIC+mkKTAMpNoTds2IC1a9cqo+GWLl2KRYsWqaKA06dP9xkFTE9PR2pqqioKSG1h6DygPCeYqoK7d++uigTSmcBHHnlEKQx56KGHlDYx1DCaJPDOO+9EixYtcMcdd6B58+Zo1qyZ0kSaGkmTFFI6lxad66NIX7NmzZSmzi1btkTLli0V8bvnnnuUQg+K+iUlJaFDhw6qSR8U+aNmz//85z+Veb8vvPACXnrpJbzyyit47bXXlIkf//nPf5TUL1X+pqenIyMjQ1X4QdE/Kv7Izc3F3LlzsWDBAixZskQV/Vu7di02bNiATZs2eUX/qPr3m2++8Zr+sXfvXp/tX0j+gi2AcoTdrgIQjgDa8xrDhw/Hhx9+aHoNHz48oL2aSQG3bdsWDzzwgOra8uXL4fF4cOHCBXNvgAYsgNoEJID9+/fH6NGjva6PHTtWd1ZwKMMCGBihLIBiFNDfWDitptC+ikHoLKCvKCBVBFNj6IkTJyrj4cS2MCSB4og4mhBCRSEvvfQSXnjhBVVRSPfu3ZXCkC5duqBz585efQIfeughLxGkIhFKDd95550qIaS5wiR31Lj59ttvR/PmzZX/0qI0LxV40Dm/e++9VxX1o5SvGPXr3LmzMuf3mWee8ar4ffHFF9GvXz9V6pd6/lHVr97UD3HsGzV+njFjhqr1C0X/1qxZ49X7Ty7++Oqrr7yqf6n9i9z82Vf1r5n0ryx/TgtgqMof4KwApqWlYdSoUaZXWlqaqSKQfv36qa7Fx8frFoEMGjQIDRo0wB9//KFcGzVqFOrWrWvum9eBBVCbgATwuuuuw7Zt27yuf/nll6hXr55tm6ossAAGRrDSwFo3G3/nAEUJ1DsHKKaB5XOAsgTqpYHFptCiAGoVg1AUkARQqyXM2rVrlWIQigJSQUhOTo7SF5CigGPGjFFSwTQijlLBgwcPxqBBg/DWW295SWC/fv2UVDBJYI8ePZSUMBWHUGFIx44dFRGkySGUGqbzgXfddZeyWrdujVatWinnBUkGKZ1Li6J8LVu2RKtWrdC6dWvcddddSrQvMTER9913H+677z6lypeifo8++igef/xxZcQbyZ/Y609u9vzyyy+jf//+Xuf+KPJHqd+0tDTVzN+JEyciMzMT2dnZXmf/qPGzOPfXV+8/8eyfOP6Nzv/JxR9aAkjyZ4cAOlkAwgJo32tUhABSG5isrCyUlJTg1VdfRe3atXHgwAEAQI8ePVQyeOjQIURGRuKVV17Brl27sHTpUvzlL39BSkqKre8JC6A2AQlgREQEdu/e7XV99+7diIiIsG1TlQUWwMAIhXOA/tLAWk2hKQ0sFoPojYajamCSwLy8PK8oIFUEL1myRBkPN2vWLMycOROffvqpakaw2BZGrAp+7733lKpgOg/4r3/9S5kVLFYGP//886rCEFkCn3jiCSUaSCLYvn17tGvXThFBKhZJTExUIoN33323IoZt2rRBmzZtVH+m83z0mHvuuUdp6fK3v/0N999/v1Ld+9BDD3lF/R5//HFV1K9r166qM3/PPfecqt3LK6+8ggEDBqhavlDRhzjxQ6/pc1ZWFqZOnYpp06apxr4tWrRINfdXPPvna/RbIM2fxfm//qJ/WulfEkCt9K9WBFD+hxUXgBjDSQFMT0/H6NGjTa/09HTTjaAbNGiA8PBwNG/eHBs2bFA+l5iYiF69eqken5+fj1atWiEiIgJ//etfA6oCNgoXgWgTkADecsstGDt2rNf1MWPGID4+3rZNVRZYAAOjsgqgrzTwiRMnNKeCmIkCimcBt27dqjoLKPcFXLlypTIebsGCBYoE5uTkKG1hPv74Y6UghM4DyhJIqWCKBJIEUiSwb9++KgmkFjF0LlAsDvn73/+Oxx9/XKkU7tChg1Io8uCDD+KBBx5QliiEiYmJSpSQJI+ie/KiSB+leanAo0OHDnjkkUfw6KOPek33IPGTW71Q2pfkb+DAgYr80bQPcdwbnfvzlfqdNm2aV98/mvpB0b/169erzv6JxR/i5A+92b/y2T+94g8SQPo5NTP9w470byDyxwJo32ukp6djzJgxppdZAXQjHAHUJiABzMrKQs2aNfHOO+/g888/x+eff47//ve/qFWrFiZNmhSsPSoEOmJm7ty5iI+PR3h4OOLj470Op166dAnJycmoW7cuatSogcTExIB6CrEABoYbBNDoWDitdjByNTBJoCyAYjGI2BJGjgL6Gw2ndRaQKoJpOsiyZcuwaNEiLFiwwGtGcFZWFiZNmoQJEyZg3LhxGD16tJIKFhtEy5XBYlEIRQJFEZQlkKqExYjg448/jsceewyPPPIIOnTooKSHKUUsSuH999+vRAnpzw888IBKGh988EE89NBDaN++vSJ9jzzyiEr6xHRvly5dlBYv3bp1U+RPLPagVi/9+/dX5vy+/vrreOutt5Rmz2Lkj8796aV+xZm/YvRPLvzQi/7JZ//E5s9i9O/AgQM+BVCM/ukJoFb0LxgCGGj6lwXQvtdgAfw/Nm3apFu9XJUJuAp4woQJqFevntL/78Ybb8TUqVODsTcVgY6Yyc/PR/Xq1TFs2DCUlpZi2LBhuPzyy1FYWKg8Ji0tDVFRUZg3bx6Ki4vx1FNPoW7dujhz5oyhPbEABo4dAhjoOUC7o4B65wCpGEQWQF+TQagljJYEUhSQ2sLQWUCSQJoOQqng+fPnqwpCKBVMc4IpEiifB6SiEJJAOhNILWLoTGDfvn1VvQLFKuGuXbvi6aefVomgWChC6WGSNooOikJIq3379irRI3GkSF/Hjh3x6KOPKmneJ554Ak888YQq3UtVvlToQVG/5557Di+88ILmjN9///vfSrPn//mf/8E777yD9957T5n1S5E/qvqlnn9ZWVmYMmWKKvUrFn5oRf/0xr5ptX4Riz/0zv7Zmf4Vxc9q+pfP/6lxUgBHjBiBsWPHml4jRoxw/f3t0qVLmD17Nvr166f8fSMuxjem28D8+OOPOHv2rObnNm/ebLttBzpipkuXLkhKSlJda9++PZ5++mkAf/7g1KlTB2lpacrnz58/j5iYGHz00UeG9sQCGDjBEkA708CBNIUWo4ByNbDWZBC9KKDcEkaOAhYWFioFIWIUUJTAhQsXKgUh06ZNw9SpU5XWMKIEjho1Cu+//74yKzglJcXrTODbb7+NN954QzkT+MorrygVwvLs4J49e6JHjx5KpbAog9Q2RvxLuVOnTujUqRMee+wxPProo6ooIUX05EXSJ0b6OnfurET7unTpooifWOEr9vejyB9V+ory9+abbypj3uR2L6L8ief+KPVL8kc9/8TCj2XLlmH16tX47LPPVKnfvLw8r+ifOPWDzv7Jvf+0zv7J7V9o9m95ebllAdQqANETQE7/+sdJAXz//fcxbtw40+v99993/f2tf//+iIiIQFJSkvL7Li7GN0HpA2j3gUsz/YVuuOEGjBw5UnVt5MiRqF+/PgBg79698Hg82L59u+oxjz32GHr27Kn5nOfPn1c1xCwrK3P9L4jbqKg0sCiBegJoJQ3sLwpIxSDUEkYrCig3htaKAoqpYHlE3OLFizFv3jxlTvD06dPx6aefKq1hPvroI0yYMEF1HjA9PV2JBNKZQBoXJ0YCaWKIKIGUEqbxcWJEsFu3bkrvQBK0J598Ek8++SQ6d+6Mzp07q84N0qKP5X/JUxsXkr4nn3xSkT464yef8+vduzeee+455axf3759lfFulPIV5Y/O/ImRv+HDh2PEiBFK2pemfcgtX3JycjBr1izMnTtXNfFj5cqVSupXbPsiR/+Kiop8Rv/E9K9e9M/o6Ddf5//EfwTpVQDrRf9kCdT6Ha3qEuikAGZkZGD8+PGmV0ZGhuvvb1deeSWWLVtW0duotARFAO0+cGlmxExYWBhycnJU13JychAeHg4AyMvLg8fjwZEjR1SP6dOnD9q1a6f5nMnJyZqjcdz8C+I2AhFAXxJoRQDtrAaWJ4PYEQUUx8PpFYSII+JWr16tSODChQuVBtGUCp46dapKAqk9jNgkmnoEDh06FO+++65qZBw1ixZF8OWXX1bGx5EEUpHIP//5T6ViWGwi/dRTTylROkoVU7qYUsb0Z5JFEj3xz3Kkr3v37ujRowd69uzpFfUj8RNbvFCxhzzfl6Z8iL3+ZPmjyJ847UMs+hCrfvVSvzT1Y8uWLbpTP8TGz2L078CBA5qVv76if1ry5y/6pxcBNCp/HAHUhgXQXho2bIjS0tKK3kalpVIJYCAjZsLCwjBjxgzVtenTpyvtakgAjx49qnrM888/j/bt22s+J0cA7SFYAihLoBkBlCVQ6xygLIHyaDhRArVawohRQHk8nNgXUJZArbYw4pzgZcuWYcmSJUpvQGoNM336dCUdTEUhYmUwnQnUaxFD0UAqEJElsF+/fnjxxRfxwgsvoE+fPkrFMImgmCKmRRFCKiB5+umnldW1a1cljSx+LBZ09OjRA7169VKWeMZPjPi99NJLSpWvfN5PjPpRwYfWmb/Ro0dj7NixStHHxx9/rMif2O9v3rx5mhM/KPK3adMmzcIPavsiyp8c/dsvVf6Kkz98Rf/0BNBf8YeR9K/8+8UFIP5xUgBHjhyJiRMnml4jR450/f1typQpePrpp/Hrr79W9FYqJZVCAN2SApbhM4DmqIzVwEZ6AhqNAop9AeXpIHoFITQiTkwFb9iwQVcC6TwgFYVQOpgqg+VI4AcffKBEA8UKYbE45O2331ZFBMW0cL9+/VSFImJ6WJZBkjb57KD43x49emguOcr33HPPKf38+vTpoxR4iOf8KOL3+uuvq+SPij1I/FJSUpQ+f++//74y5k1M+2ZmZqr6/YmzfsXI38qVK7FmzRql4bNe6tdX4YeV6J+v1i+BNH42Gv3j9K8xnBTADz/8EB999JHp9eGHH7r+/vbLL7+gffv2iIyMREJCgjJFiBbjm0ohgEDgI2a6dOmChx9+WHUtKSnJqwgkPT1d+fyFCxe4CMQBgimAZqKAegJoNQpIjaFJAn1FARFZy8YAACAASURBVCkVLEcBtSQwPz9fKQgRI4HUGkaeEkLtYWhSyOTJk5WikHHjxinTQqhZNKWE33vvPUUC//vf/2Lw4MGKCMrRwP79+yuFIhQZpGIREkFxPfvssyoxlKN5vXv3VpZ4qLt3795KelcUPjnVS3396Jzfv/71L/z73/9WWrzIDZ7pvF96ejpGjBihWe1LaV+9il9R/ij160v+xOifOPLtu+++U+RPPPt36NAh5eyfKH96AhjI2T8zxR92CmCgR0MqKyyA9vLkk0/immuuQd++fZGcnIx3331XtRjfVIoiECDwETN5eXmoXr060tLSUFpairS0NM02MDExMZg/fz6Ki4vRtWtXbgPjAME8BxjMamCjUUAjFcF6UUBRAqkqWCsVLDeIFlvDiEUh4rxgSgeLPQKpMISqg0kCqU0MnQukAhESQbFIhM4HUgNpkkGxh6AcHezTp4+yRDGkal1xkUTKwkdRR5I+MeJHPf2oqTNF/CjlS2f9xOkeFPUj+Rs3bpwy5eOTTz5R0r6y/IkVv6L86Z37k1O/JH9y02cjUz+05v4GEv0LRABFCZTlT5RAvd9LOyWwsuKkAI4aNQoff/yx6TVq1CjX399q1aqFTZs2VfQ2Ki2VJgIIBD5iZs6cOYiLi0NYWBiaNGmCefPmqT5/6dKfjaDr1KmDiIgItG3bFsXFxYb3wwJonopKAwcaBRRHY/mLAvoaD+drRrAYBZRHxFEqWJZAMRUs9weUR8XRpJDZs2d7zQwWq4OpWbR4LpAkkKqEhwwZopwPHDx4sBIN/Ne//oU33nhDVSwiyyCdx6Nzg7RI6ORoHi1RHuljkj1aAwYMUFK9NMdXrO4dPHiwUuGbnJyspHwp6peRkYEPPvgAH374oXLmjyJ/YrWv2Oh57ty5qjm/lPb97LPPsG7dOlXLF1n+xNSvnvzJqV+t6B/9fJnp+6dV/auX/vUnf1bTv1VB/gBnBXD06NGYNGmS6TV69GjX39/i4uLw9ddfV/Q2Ki0BCeC+ffvw3XffeV3/7rvvsH//frv2VGlgATSPkwJoZ0sYo+PhSAL1ooBic2itKCBJIKWDv/76a2VMnHwekNLB8nnAFStWKOPiFi5cqEigWB1MfQIpGkgpYflc4PDhwzFs2DCkpqYqEiimhQcNGqScEaT0MIkgyeCAAQOUJaaMxQgeLZJG+RrJHoke/Vc83/fWW2+pCjxI/Ki9i3jWj6p8qcefON5t0qRJSspXlL9Zs2Z5pX1XrVqlRP7o3J+W/Inn/qjwg3r+kfxR02c5+idO/NCL/pWXl+PkyZOOR/+MCKCeBFaV9C/AAmg3S5cuRfv27aukf9hBQALYtm1bTJkyxev6tGnTkJiYaNeeKg0sgNawQwArQxTQaFuYffv2eVUFy61hxPOA1CBajARqtYehwpBFixZpRgKzs7OVaKA8NUSMBqanp3s1j3733XdVhSK05IIRaipNqWJfcjhw4EAlmic+hlK78rm+N998U1lyM+d3331XSfUOHTpUifpRypdavND0A7HNi9zkWaz2XbhwoVfaV2z2TFW/voo+jKR+5ZFvvlK/cvTPV9uXQOWPBFBL/EQBDHbqlwXQ+GuMHTsWn3zyiek1duxY19/frrjiCoSHh+Oyyy5DZGQkrrzyStVifBOQAEZFRWH37t1e13fv3o2YmBjbNlVZYAG0htuKQfQkUBZAX82htaKAvkbE+esNKJ4HJAkUp4RoNYlet26dkgpevXo1Vq5cqZoWIo6Mmz59ujI2bvLkyUo0UJZAOhtIEUGxZQzNE05OTlYibWKKmKKC4vr3v/+tpI1J6sRIHi0xtSyOa6MoH53rE9O8Yj8/KvCgWb4U9cvIyFCle8ePH6+kfMXIH8nfrFmzlD5/8pm/NWvWqM78kfzl5eVpyh+d+ysuLtYc9ybKH6V/9SZ+BBr9MyuAVgo/OP37fzgpgOPGjUNWVpbpNW7cONff36ZMmeJzMb4JSACjo6O92qYAwLZt2xAZGWnbpioLLIDWsEsA7WwJ468xdDCigHJBiNZ5QLE1jHgeUCsSKFYGr127VpFAsUUMTQyh4hAxJSy3ipErhfVSw+I5QTEyOHjwYGVRuphSxiSIJInif0nyxD9TdI9kj4SPzvaJLV2osjctLU2p7hWLPOTefhT1y87OVho8k/zRhA9q9SLKn3jmT6z4lZs907SP4uJinw2fqerXSNsXu6J/elM/gn3+L9DoX2WXQBZAxk0EJICPPPIInnzySVy8eFG5dvHiRXTu3Nlr7m5VgAXQGoH+xe9EGtjsWUCrUUBf5wEpFazXH9DXuLi1a9fis88+U1rEUIUwpYPnzJmD3NxcpU3Mp59+6jU5ZMKECV4iKKeGSQR9ySCJGv2ZBE4UQ4rm0efEx4gRPjG9O2TIELz33nuqNO+wYcOQnp6uVPbSOb9Ro0Zh9OjRqqkekyZNQlZWluZ5P7HSV+7zJ575o4IPUf6++OILr4pfedYvyR9F//b/b2sgrXm//uRPTwD1Cj+cOvvnS/6qQu8/EScFcPz48Zg8ebLpNX78+Epzfzt27JiSIREX45uABPDbb7/F1VdfjZtuuknpy3XTTTfh2muvDah6NlRgAbSOXRJophikoqOAVBVMkx3k84DUB06UQLk/oBwJFNvDbNy4UakOXrdunRIJXLp0qVeFMEUDc3JyVClhigZ+/PHHqpYxYmr4gw8+QEZGhtI+RhTClJQUpKSkKK1kxJWcnKykjcX0MUXy6PP0OVn2SDTFFK8Y6SPpE8WPqnu1xI9avNBcX0r50nm/pUuXqiJ/69atw3phvq/emDex3Qud+xOnfdD/Z5I/rYkf9POil/ol+Tt58mTQon/+Rr8FM/UbKhLopABOmDAB2dnZpteECRNcf3/btm0bbrnlFlx22WWoVq2aal122WUVvT3XE3AbmCNHjmDQoEHo0KEDOnfujCFDhqC8vDwYe3M9LIDWCaYAVmQUUEsC9aqCtc4DyqlgeV6wWBm8fft21cxgrWkhJIFyhbAogdQ0Ojc3VzkfOGXKFKVSODMzU9U7UI4IkgySEGqliWnRxxS1I6mTI3kkj+I1+noxtUuLRJSkb8yYMcoZv/Hjxyvp3kmTJilFHhTxmzFjhlLlSylfsdJ3xYoVWLVqlerMHzV6JvGjM39yxa/enF+taR9y1a9W9I9+nvRGvvmTPz0B1BM/M3N/7S78YAEM7DUmTpzo94ycrzVx4kTX39+aNm2Kv//97ygsLFSdm6XF+CYofQCrCiyA9uBkGtisBOpFAX01hzbaG1CUQK2qYFkC5Ukh8pnAwsJCVSRQnhjy2WefKYUhS5cuVVLCetHATz/9VBFBsVCE2sZQBe2YMWMUIZSbSlNkkP5LS0wdi4Ua4ho+fLiySPootUuyR4sKOyjFO2HCBEX66JxfVlaWcs5v+vTpyMnJQW5uLmbNmuXV32/p0qUq8fvss89UBR9yqxcq+Ni6davfMW968iemfbUKP0T5C+bZP7cJYCjAAmgvkZGRmoWpjDFMCeAvv/yC0tLSKp9vZwG0h2AKYKBRQLMzggOJAmqdB5SLQnxFAsX2MOKZwKKiIq8zgQUFBZoSSFXCq1atUtLCixcv9hohR0UiJINUKCKKIKWHJ06ciHHjxilSKEYH5QihHCmkRR+T3Mkfy2ld8UyfGO2jgfai9FG6V0z1UsSPzvqJKd/ly5crPf7kqJ844UNs9SKe+RPlT2z3QhW/epE/seiDCj/khs9mmj5bif6JAqglf1oSyJW/3jgpgB9//DE+/fRT0+vjjz92/f2tU6dOmDt3bkVvo9ISkAD++OOPeOSRR3DZZZdprqoGC6A9OF0MEqwooFZzaKNj4nxNCdGSQLEoRC8SKFYH5+XlIS8vT3UukIpDSAKXL1/u1TharBSeOXOmkhqmqCAVi5AIUmRw4sSJXsUjo0eP9lqyINKfSe7Ez9Mi4aNRbbQmTJignO8Tx7dlZ2erzviJUb85c+ao0r2LFi1SnfWj/n503k9u8yJH/vTkj878yZM+5IpfahIuN3wWz/1pRf5k+ZObPmuJn578+Tr7Z0T8SP7savwcShLopABOmjQJ06ZNM70mTZrk+vvb8ePH0aFDB7z77ruYO3cuFi1apFqMbwISwGeeeQZ33XUXvvjiC9SuXRurV6/GtGnTEBcXh6VLlwZrj66FBdA+7BJAOyqCjUQBSQR9RQH9VQVTOthfZbAYCRRTwVqRQOoTSBJIs4MLCwt1i0OoUlgsEiEJFM8HzpkzB7Nnz1ZmCsvpYYoMyoUjohCKUihGCimNTK1ZxNSyeF1O65Lsief6ROmjaAaJH1X2UkPnBQsWKOK3ZMkSLF26FCtXrsSqVatU4kdRP7nSVyz2MFrt62/SB6V9xXN/WpE/LQE8ffq08rMYiABqjXyzKoCc/vWGBdBeFi1ahOjoaK8CEC4CMUZAAlinTh1s2bIFwJ9NoXft2gXgz/8Jd999t/27czksgPZR2aKARlPBogQa7Q9IlcGiAPpqFC1HA8WxcVu3blVJIJ0NlFPCdDaQikSobyClhRcsWID58+crMkhRQTEiSGvq1KmqKuLMzEyvKCEtStWS1FEET1wTJ05U/iye5fvkk0+UtK5YzatV2JGbm4vc3FzljB/Jn1jdu2LFCiXqJzd3FsVPlD95ti9V+8ppX3nKh16vPy3505r1a+e5PyvyR79bLH/GcFoAp0+fbnpVBgFs0KABXn75Zfzwww8VvZVKScCTQPb/78y9Bg0aYPPmzQCAffv2oWbNmrZvzu2wANpLRUug2Sig3BbGSGsYLQk8duyY13lArUigmAqmM4E0MUQ+EyhWCJMIyucCN2zYoIoIymPkqG0MyaCv9PCMGTOQk5OjSKEYGaTInNhrjM4R0hLP69ESx1NpiR5Jp5jeFc/2UZqXijvojJ/Y049m+WpV+Ipn/QoKCnz2+CP527Fjh2baV57yIRd9iO1e5MIPrbSvmZ5/dgsgR/+M46QAZmZmIicnx/TKzMx0/f0tMjISe/bsqehtVFoCEsAWLVpg5cqVAP48fNmjRw8cPnwYb775Jv76178GZYNuhgXQXoItgHa1hfEVBTRaFSyeB/QXCfR3JnDXrl2qsXG+zgVqRQNJBsWWMWJaWIwIijJIY+XmzZunEkKKtokySGkl+aC5WHU4depUJXpIf5Y/pnQuyR5Jpxjpox5+dLaPmjgvWrRIJX5yWxcSPznqp9fiheSvqKhIc7avmPbVOvMny5/epA+K/vlK+2qd+7NT/ipCAEMRFkB76dmzJzIzMyt6G5WWgARw+vTpyM7OBgBs374d1157LapVq4aIiAjk5uYGY3+uhgXQftwaBbQjFXz69GnN84ByJFAuCtGbFqLVIkZuGE3pYL2m0SSBeiJIhSKiCIqRQYoIzp8/X1nymUGaOyyKmhwtJEmkCJ74efEx9Dkxwkdrzpw5ivjNmzdPFemjNC+Jn1aBB53zE4s8xApfKvSg/n7yeT/q8Sef+RMjf3Tmz0ivPyMVv4HO+tUTQH/yZ0QA/Z3NDUQAQxUnBTArK0uJzptZWVlZrr+/paSk4JprrkGvXr2QkZHhVWTG+MZSH8BffvkFX375JY4fP27XfioVLID2U1migGaqgrXOA+pVBvuLBMrRQFkCS0pKlOIQcXKIOD5ObBej1TJGTAtrtY6hqmGKCoppYvHMoCiEtEgMKWJIf6ZFn6fP0fk9MaVLz03tWyjS50/6KOJnRPzonB8t8byfHPUTp3uI493EYg95yofWjF8686c351eUPz0B1Gr5Iv/8+mr94k/+Ah37xgL4J04K4OTJk5VovJk1efJk19/fGjZsqLtuvPHGit6e6/ErgK+99prhVdVgAQwOlSUKaLUoRC8SKKeC/c0N9jU1hM6j0RgyUQTF6SGUFtbrHUipUfGcIMngihUrlJ55shRShJAqisUlC6K4SOrEx4jXqHKXhI8WiZ+Y4iXpoyIXkj+5slc850fvhVZ7F/m8nyh/YtRPPvOnNd9XPvfna9KH0aIPEkAjaV8j8md05q9d6d9QhgWQcRN+BfBvf/ubakVFRaFWrVq4/fbbcfvtt6N27dqIjo7Gfffd58R+XQULYPBwaxQwkAbRRvoDyoUhgUQC9c4FalUIf/vtt/j2228VCRQLRMTm0SSDJIGiCGoVjFB6WFwrV65UTRqh6CDJmVZRCUmhltjRY/Sie+LSkj6K9IkRP4r2yZW9vvr6kfjp9fczIn9inz+tKR9i1E8r+ifLn5HIn1UB5OiffTgpgNnZ2V6R9UBWdna2K+9vRgNSr7/+ekVv1fUElAL+4IMP8Oijj+LEiRPKtRMnTqBTp07IyMiwfXNuhwUweFR0FNBMKthIFDCQHoE0LcRIn0AxGkiRQCoQoXNpclpYPB8oiqA8SaSgoECZLbxp0ybdFDFFBrUKSEQpXLFihbIoWkhCRx/Tn2mJgkdLrN4VR7WR9GmleKmqV474kfTR90/n/Hy1dyH5EyN/lPL1F/nTKviQBVCv6ENP/gLt9yfLn1bkL5DoH0cA/eOkAE6ZMkV1RjbQNWXKFFfe3zgoZR8BCeB1112HHTt2eF0vLi5G3bp1bdtUZYEFMLg4HQW0IoFGRsUZmRSi1x5Gb2KIVq9AuVWMWCUs9gykKmGxZYwYFRTPB1KUjKaK+EoRi+Pm6NygGCGkjymFLBaXkCiS2JHciUuM7tFrUFpXFj+SPjHNqyd+YkNnUfzkcW4kf/S+ylW++/+32IN6/GkVe9glf1oVv2aif1blj1u/GMNJAZw6darq3G2ga+rUqa6/v3FQyhoBCWBkZCTWrl3rdX3t2rWIjIy0bVOVBRbA4FIRBSFOnwekymC9HoF6kUA5GqhXHELpSIoGymlhOiMotowRJVBMD4vtY0QRlFPFVEWsdXZQLCiRlyx08pIFT5ZOUfjEKJ/W+T5R+kT506rupfS5XOghp3v99fgTZ/vS/095yoec9g303F8g8mdHyxd/Ahjo7zALoH2vURUEkINS1ghIAHv06IH69etjzpw5ylmkOXPmoGHDhujZs2ew9uhaWACDT7AFMNjnAQONBJIIikUheucC5TYxWgUiJChiWpiaR4vRQLFQRIwKaqWGqZcgnRWUZVCMEGpJIZ0j1BNFUejEx4hLrNqVl15BB0X7xJYuYpqXIn5aUT9K9VKFr15/P0r3ypE/LfELJPJnpN2LL/nzdebPiPz56vmnJ4Asf944KYDTpk1TFVAFuqZNm+b6+xsHpawRkAD+8ssv6NevHyIiInDZZZfhsssuQ3h4OPr164eff/45WHt0LSyAwcftUUAj/QGNTArxNS1ETAmLAiieC9SKBlIqkqKBes2j9QpFxP6BYnpYq6m0WEFMUkjnBuUooVbEkCJ3YspWFkg9ySMB1avileVV7OMnSx+Jn1jdK0uf1jxf+ayfv9FuevInT/nwN+PXKQHk1K89OCmA06dPV/XnDHRNnz7d9fc3DkpZw1QfwJ9//lmJElRF8SNYAJ3BLgG0UhVs9Tyg0fYwenOD9aKBYjpYlECxUni/TrsYrQbSWqlh+aygVgWxKFkUHRRTxrIYitImp2nF6/ISv16O7tF/5d59YnpXFD+xlx99v1oFHpTu1TrrJ6Z7tQo9SP70mjyT6NsZ+fMlf2aaPZtp+mwm+lcVJJAF0F44KGUNS42gqzosgM5gZxTQrqpgK5FAf2cC5cIQvV6BcnGIOEf48OHDmgUi+/fv9+oZqFcoIo+V05JB+bygeG5Qq8egLIdaRSai2IkCSX8Wn4fSubLwyWf6tFK8Yh8/sbhDK+pHEVStqJ9Wixd/4ienfANp9aLX6y9Q+fM351cv+hcM+asqOCmAOTk5Xr03A1k5OTmV5v7GQSlzsABagAXQOewUwGC0hgm0MthoJPDUqVM+i0NIBPWKQ8RqYblIRK9QhERQKz0sC6E8ao7ODcpFJFqCKJ7HkyVO7/Naz6MnfCR7YnpXq5WLHO2TCzy0WrtQhS+9x7L8kQDKkz20In9isYdRATRS8GFX9I8F0D6cFMAZM2YoTdjNrBkzZvD9LcRhAbQAC6BzOBEFtCMSGEhlsK8+geIZML0qYVEAjRaIaPUOJMkRRVCMDJIEipFBWQZF2aJFYiieHRRTsfKZQlHkxMdqfUyiR4teS4z0ycInVvPSkke3ialerXSvVpGHVmNnudhDb65voOJnRv78iZ+vZs/BTv1WNVgAGTfBAmgBFkBnMRNZcOI8oCiBgZ4HNBIJFEVQqzjEXzRQlkE5GrhfKBYRK4dJAuUUMUUGxbOC8hLFUEvOxI9lmdMSOjmVK0/moNeiSmaxkENeYqRPFD+SX3GKh1aqV6+ps5ju1WrxopXy1evzRz8PRqZ8WGn3UlHTPlgAgy+Aubm5qjGJga7c3Fy+v4U4LIAWYAF0FrsFMJBUsJXK4EAigUaLQ7Qmh/gbIycXiVA0kApFZBHUKhgRZVCcMiKnikU5JCkTU8fyNVHmZLHT+jq5eENcYrRS7NsnLvp+5MIOUfzofdGr7tXq7SdG/MymfP1V+7pV/gIVwKqIkwI4a9Ys1RjGQNesWbP4/hbisABagAXQeSpSAq2kgo2cCdSbHWxkcojcLkYuEJFHycmTRMTZwnrNpLX6CcpRQRIurfFzcipWPlcoR+98RRjlVK54nk8e0SYvvRSvGPHTquw1UuQhp3uNpHxFATTb5qWix7yx/BmDBZBxEyyAFmABdB6nBDAYlcF2RwKNRgONpobliSKiCGqliMUzc3oRQhIxeWmlZkWBE88byulbsWBDLt7QOs/n61yfLH3iBA852qcX8ZPlTy/qJxb2yAJoJPJn15SPQCN/HP2zDycFcPbs2cqcbTNr9uzZfH8LcVgALcACWDGEogQaLQ7x1ypGrhYWBVAvNSxLoFwwIoqSmCaWm0trLVEKtVKxehFELckTl1ZjZvH1xNSu2L5FTvNqFXbIZ/y0JnnI8ief9ZPP++lJn5FiDzOzfQOt9uXonzM4KYBz5szBsmXLTK85c+bw/S3Ecb0AnjhxAt27d0d0dDSio6PRvXt3nDx5Uvfx5eXleOWVV9C4cWPUrFkTN9xwA/r3749Tp06pHufxeLzWxIkTA9obC2DF4ZQAuiESaKRVjJFooFwoIvcPlKOCogTqpYjFJcqWHCWUJVGsNhYjd3KqVhRIuWBDrF4Wp3NonecTI5lyUYcY7aM/a6V55QIPX339/M3z1RJALfFzgwBy9M8+nBTAuXPnYvny5abX3Llz+f4W4rheAJOSkpCQkKA0iU1ISEDHjh11H19cXIwnnngCixcvxp49e7B27VrExsaic+fOqsd5PB5kZ2erboS//vprQHtjAaxY3BYJNDs3OJD5wVqRQK20sBgNFEXQV6GIuPSKRkQhFNOmBw4c8BJCWQ7F9LEsimIFrih48uPFr5OjelrSJ6d2aYnfDwmfmOLVkz+tAg+9Ig9f6V69lK/ZSl87p3wYlUDx94nlzxgsgIybcLUAlpSUwOPxoLCwULlWUFAAj8eDnTt3Gn6e2bNnIzw8HL///rtyzePxYMGCBZb2xwJY8YRSJNDs2UCtvoFyWlivUEQWQb2pIr56CuoJoV6bGflMoSx3chRPS/S00tKy9Gmldo00b5aLO/wVeOiNciPx02rxEmjK15cAak34MDLf1+mqXxZAZwVw3rx5WLlypek1b948vr+FOK4WwKysLMTExHhdj4mJweTJkw0/T2ZmJq655hrVNY/Hg3r16uHqq69GixYtMHHiRPzxxx8+n+f8+fOqm25ZWRn/glQwwYgCBhIJDKRHoJlIIImglgCKqUStxtF64+S0IoJaUUGtyKDWeUH57KCWEIqiKDai1ovW6X1e63nk1xAlT94f7V8UPl/RPjni56utixz189fc2Y42L/5GvAU65SNY4scC+CdOCuD8+fOxatUq02v+/Pl8fwtxXC2AqampiI2N9boeGxuLYcOGGXqOn376CfXr18fgwYNV14cOHYr8/HwUFRUhIyMDtWrVwtChQ30+V3JysubZQf4FqVjcngo22ibGnwQaLRDRaxmjVShCE0XkM4LyWUFZBPXSxFpCKEoh/VmMyMkyJ0ue1hKjefQ64n+1Kngp0ufrfJ/4Hvhq6aI1wzfQdK/WeT8ro92MVvuS+Dk15o3l7/9gAWTcRIUIoJ5IiWvr1q1ITU1F48aNvb6+UaNGGD58uN/XOX36NFq1aoWkpCT89ttvPh+bkZGB6Ohon4/hCKA7MXtDcloCA5kdrCWBetFAf6Pk/I2Uk9PDWtXDvqKDoljptZcRo29ydE5Oz/oSSa30rfhcek2axf3K6V0x2qeV6tWSPl8FHqKQaxV5GEn72hX5C7TogwUwuDgpgAsWLMDq1atNrwULFvD9LcSpEAE8fvy4V88veZ07d85SCvjMmTNo06YNHnjgAZw7d87vnjZv3gyPx4MffvjB8PfBZwDdRUWmgo2eCQy0YbSZaKC/84F6PQS1RFArMii2QpGFUC6kkCVRljdRFmVBlB9Hj9XqyycvMbUr71dL/PSKOvwVd/hq7WKk0MOI+Nkd+XPyzB8LoBonBXDhwoVYs2aN6bVw4UK+v4U4rk4BUxHIli1blGuFhYV+i0BOnz6N1q1bIzExEb/88ouh1xo7dixq1KiB8+fPG94fC6C7CNZ5QD0RdFICffUMNNI2xl+xiJGiEXlpCaFYRKFVVOIrlayVrvW19F5LT/b8SZ9eDz9faV65wENL+ozM8tUSQDnipyWAehM+7J7ywef+7IEFkHETrhZA4M82MLfeeisKCgpQUFCApk2bqtrAHD58GHFxcYoknjlzBq1atULTpk2xZ88e1Y3h4sWLAIDFixdj0qRJKC4uxp49e5CZmYno6GgMGDAgoL2xALqPYEmg1Uig2ZRwoI2jfRWKaKWG5QIG+ZyglgzK0UGSQbmQRCvy5k8U/cmk1nNpvY6W3zmMMQAAIABJREFUsOq1b5EjflopXnlpTfGQ5c/OqJ+VCR8VJYCMN04K4OLFi7F27VrTa/HixXx/C3FcL4Dl5eXo1q0boqKiEBUVhW7duqkaQe/fvx8ejwfr168HAKxfv173XOH+/fsBACtWrECzZs0QGRmJWrVqISEhAaNGjVK1iTECC6D7sJKickIC7RJBozLoKzWslx4WBUgrRWw0QqhXWKIVodOTOa3H+IrqyfInR/iMRvt8pXl9RfysFnnYfdaP5c9dOCmAS5Yswbp160yvJUuW8P0txHG9ALoZFkB34rQAWo0EGk0JmykSMTpRRK+xtFbxAy09sRKXVgTOlyD6k0ittixabVr0zvL5i/LpNXDWiqj6K/Dw1drFStTPTgEMVuqX0YYFkHETLIAWYAF0L8GSwGCKoNbN32qRiL+IoK+CEaMyKKeK9dLHWj0ItZZe/71ARU+vUbPW8pXi9VfcEUjUz2rkz+4JH8Go/GX0YQFk3AQLoAVYAN1NRQigLwmURVDvBm8mGmhGBkkItdqXaEUEjTSb1pNDUciMRA79Lb2Inq92LXKkT0/2xDOT/oRPq8DDaJEHSZ8/ARR/VuySP077VgxOCuDSpUuxfv1602vp0qWm9jp+/Hg0bNgQERERaN68OTZu3Gjo62bOnAmPx4NOnTqZ+bYZE7AAWoAF0P1UxkhgIOcCjaaFfaWHRcnRkkCx2tVfEYnR9LG/1KzRx+iJnrwXf4UcgUT7tFK+wYz6GanyrcjxbiyAxnFSAJctW4bPP//c9Fq2bFnAe83NzUVYWBgyMzNRUlKCgQMHonbt2jh48KDPrztw4ADq1auHe++9lwXQQVgALcACWDmoKAkMpGm0UREMpEjETETQSOGIr0ISfxXGeuLmSxa1HmMkjWtG9vyld/WEz2iBh9Vzfm6q8mUBDJxQF8CWLVuib9++qmtNmjTB22+/rfs1Fy9exN13341PPvkEvXr1YgF0EBZAC7AAVg6s3NSclEAjKWF/0UCtiKCRqmH5Yy0x0hNDX6Kll0L2F0U0+nlfEipLnpb4acmvvzYuRnr6BSJ/oSCAjDGcFMDly5djw4YNptfy5csD2uuFCxdQvXp1zJ8/X3V9wIABaNu2re7XvfPOO3j88ccBgAXQYVgALcACWLkIpgTamRK2Ixror21MoFFBf1LoL0roK1JoZRl5Xb09632fZiJ+/qJ+FZHudTL6xxJoDCcFcMWKFdi4caPptWLFCng8HpSVlal+n/SGJRw5cgQejwd5eXmq63ojXYE/J3DVq1cPx48fB8AC6DQsgBZgAaxcBDsS6EQ00I5CEVkItSJcRqVQPjdohyRakTp5L7727a91i5FZvUZm95qt8PUlfXbKH5/7c47KKIDySk5O1nxdEsD8/HzV9ZSUFMTFxXk9/syZM2jYsCGWL1+uXGMBdBYWQAuwAFZO3BgJDHY00OgZQT0BNCqCRtLHZ8+e9Zua9SeVRl/HnwBqfb9GK3oDjfgF0trFqARWlPwx5nBSAFeuXIlNmzaZXitXrgwoAhhoCrioqAgejwfVq1dXVrVq1VCtWjVUr14de/bsCcr7w/wfLIAWYAGsnLg1EmjXGUErQhhIqtioGBpZVkQu0Aifr7SuFeGzS/qMNHXWk79ApI/lz3mcFMBVq1Zh8+bNpteqVatMFYH069dPdS0+Pl6zCOTcuXMoLi5WrU6dOuH+++9HcXExLly4YPm9YHzDAmgBFsDKi9slMJBooJ0RQX8S6O/soD8xNCKPRuTS6GN87VdL9swIoJ6AByPqV9EpXxZAa4S6AFIbmKysLJSUlODVV19F7dq1ceDAAQBAjx49fFYEcwrYWVgALcACWLlxuwT6EkG7ZdAOKfQXcXN6GZFYK9E+JyJ+dkqfKH4c/asYnBTA1atXIy8vz/RavXq16UbQDRo0QHh4OJo3b44NGzYon0tMTESvXr10v5YF0FlYAC3AAlj5cUICnY4GGkkP++snaFQKRTn0J11GpMyOx/iK8PmL9JkRPiPy52uKh9nWLk5W+LL82UNVEECm8sACaAEWwNDA6o3RLgk0K4L+ZNDsGUEzEmgkUmg0xWx1Gdmz0SifP/kzE/WzWuThVMqXBdA+WAAZN8ECaAEWwNCisohgsIQwUCkMVBArapn5fuyK8plJ9/qTP/FniOWvcuGkAK5Zswb5+fmm15o1a/j+FuKwAFqABTC0cEoA7ZLAYAigWRGsaMmzQ/zE7ztYAmg14mf2zB/LnztwUgA/++wzFBQUmF6fffYZ399CHBZAC7AAhh52nZdyiwjacVbQanTQDnEMxmsbOdPnT/gqQvqckEDxd4GxDycFcO3atSgsLDS91q5dy/e3EIcF0AIsgKGJkwIYDAnUkkJZWPxJoJFUcTDk0CnZ05M/+f3wJ3t6AuivwMPu6l67pY/FLziwADJuggXQAiyAoY2TEmhUBO04H2hnVFBLqoIlcMGK7hmN9Nlxxi+QqJ/T6V4m+DgtgFu2bDG9WABDHxZAC7AAhj5OS2BFyGAgUmhGDM0uoxIXyDL6PRqVvUCjfYHInxEBtOvIAuMMTgrgunXr8MUXX5he69at4/tbiMMCaAEWwKqB0ynhQASwoiTQSRG0S/iCLYB2RPy40CO0YQFk3AQLoAVYAKsWdkVbgiGDcgsRu4TQjBg6KYtW9xWI6AUifGbTvBUhfyyAzuGkAK5fvx5bt241vdavX8/3txCHBdACLIBVj4oQwEAjgnZWEPsSQDOpY6uPsSJ+Roo3nJQ/p4s8WPwqHicF8PPPP8e2bdtMr88//5zvbyEOC6AFWACrLhUVDTQjg8GKCtoZIQzGsvL9BCJ7ZlK8ZgSQpa/ywwLIuAkWQAuwAFZtKlICg3lW0G45tCKVdjzGLsmzQ/YClT/554TFr3LDAsi4CRZAC7AAMnZJoBURDFZE0A0SGOzltABWVNSPBdAdOCmAGzZswJdffml6bdiwge9vIQ4LoAVYABnAXgk0K4NmzwhalUI3C6Id35P4/jglfZzyDV1YABk3wQJoARZARqaio4F2pojNiKEvCfMnakZELpDHWI3umY32WZU/u/8xwbgHJwVw48aN2L59u+m1ceNGvr+FOCyAFmABZLSo6GigG6ODbl9W3yO3SR+LnztxUgA3bdqEoqIi02vTpk18fwtxWAAtwALI6OE2CbRDBLWEsLJJYkWLHktf1cZJAdy8eTO++uor02vz5s18fwtxWAAtwALI+CIYN3g3iWBliRLa/b2yADJmYQFk3ITrBfDEiRPo3r07oqOjER0dje7du+PkyZM+vyYxMREej0e1nnrqKcvPK8MCyPgjGDd5u4UwGBFCq48xIm/BEjwt0bMqgPL/K5a+qomTApiXl4evv/7a9MrLy+P7W4jjegFMSkpCQkIC8vPzkZ+fj4SEBHTs2NHn1yQmJqJPnz74/vvvlXXq1CnLzyvDAsgESmWSwGBGDN26xO/ZjugeR/wYEScFMD8/H998843plZ+fz/e3EMfVAlhSUgKPx4PCwkLlWkFBATweD3bu3Kn7dYmJiRg4cKDtzyvDAsiYobJEBJ1IHbthBev9Cnb0l6l8sAAybsLVApiVlYWYmBiv6zExMZg8ebLu1yUmJuKaa67B1VdfjZtvvhlvvPEGzpw5Y/l5ZVgAGSsEWxCCJYS+5KmiZU5rX07JnhPyJ/7cMJUPJwWwoKAAxcXFphcFRfj+Frq4WgBTU1MRGxvrdT02NhbDhg3T/bpJkyZhzZo1KC4uxsyZM9GwYUM8+OCDlp/3/PnzOH36tLLKysr4F4SxRGWVQLdHCp3+np34/8hUfpwUwMLCQuzYscP0Kiws5PtbiFMhApicnOxVpCGvrVu3IjU1FY0bN/b6+kaNGmH48OGGX2/btm3weDz48ssvAcD08+rtm39BGCs4IQ8VIYJmo4haEunrMW6QvWBJIBNasAAybqJCBPD48eMoLS31uc6dO2dbqvbSpUsICwtDbm4uAPMpYI4AMk5RFaWwsiyn/t8woQcLIOMmXJ0CpmKNLVu2KNfohzKQYo3i4mJlNqKdz8tnAJlg4pRosBBWnOSx+FUtnBTALVu24NtvvzW9tmzZwve3EMfVAgj82a7l1ltvRUFBAQoKCtC0aVNVu5bDhw8jLi5Okbk9e/ZgyJAh2Lp1K/bv349ly5ahSZMmuP3223Hx4kXDz2sEFkDGCSpKAkNZEPW+J5Y9JpiwADJuwvUCWF5ejm7duiEqKgpRUVHo1q2bqmHz/v374fF4sH79egDAoUOH0LZtW1x11VUIDw/HTTfdhAEDBqC8vDyg5zUCCyDjJBUZlQo1Cazo95AFsGripAB+8cUXKCkpMb2++OILvr+FOK4XQDfDAshUFBUtLr4kSku0Kkrq3Ch9LH5VFycFcOvWrX7P2vtaW7du5ftbiMMCaAEWQKaiqGiBMSOM/sTN7GMq+ntj+WOM4qQAbtu2DTt37jS9qHsG399CFxZAC7AAMm6hoqWGF8se4x8WQMZNsABagAWQcROydFS0/FTlxTBaOCmAX375JXbt2mV6ffnll3x/C3FYAC3AAsi4nYoWoaq2GMYXTgrg9u3b8d1335le27dv5/tbiMMCaAEWQKYyUdFyFEpL771lGF+wADJuggXQAiyATGWnokWqMi6GMQsLIOMmWAAtwALIhBIVLVZuWwxjN04L4O7du00vFsDQhwXQAiyATKjhT4AqWsqCIXcsfYxTsAAyboIF0AIsgExVpKLljaN7TGXFSQEsKirCnj17TK+ioiK+v4U4LIAWYAFkqiq+JMpIpM0poWPpY9wECyDjJlgALcACyDDG8SdvRgSPI3lMZYYFkHETLIAWYAFkGIZhjOKkAH711VfYu3ev6fXVV1/x/S3EYQG0AAsgwzAMYxQnBfDrr7/Gvn37TK+vv/6a728hDgugBVgAGYZhGKM4KYDffPMN9u/fb3p98803fH8LcVgALcACyDAMwxiFBZBxEyyAFmABZBiGYYzCAsi4CRZAC7AAMgzDMEZxUgCLi4tx4MAB06u4uJjvbyEOC6AFWAAZhmEYo7AAMm6CBdACLIAMwzCMUZwUwB07duDgwYOm144dO/j+FuKwAFqABZBhGIYxCgsg4yZYAC3AAsgwDMMYhQWQcRMsgBZgAWQYhmGM4qQAfvvttzh06JDp9e233/L9LcRhAbQACyDDMAxjFCcFsKSkBGVlZaZXSUkJ399CHBZAC7AAMgzDMEZhAWTcBAugBVgAGYZhGKM4KYClpaU4fPiw6VVaWsr3txCHBdACLIAMwzCMUaqCAI4fPx4NGzZEREQEmjdvjo0bN+o+dtKkSbjnnntwxRVX4IorrsADDzyALVu2WH0LGIOwAFqABZBhGIYxipMCuHPnThw5csT02rlzZ8B7zc3NRVhYGDIzM1FSUoKBAweidu3aOHjwoObjn3nmGYwfPx5FRUUoLS1F7969ERMTg8OHD9v1djA+YAG0AAsgwzAMY5RQF8CWLVuib9++qmtNmjTB22+/bejrL168iKioKEydOjWg75kxBwugBVgAGYZhGKM4KYC7du3C0aNHTa9du3YFtNcLFy6gevXqmD9/vur6gAED0LZtW0PPcebMGdSoUQNLliwJ+PtmAocF0AIsgAzDMIxRnBTA7777Dt9//73p9d1338Hj8aCsrAynT59W1vnz5zVf98iRI/B4PMjLy1NdT01NRePGjQ3t/aWXXsJNN92Ec+fOWX4fGP+wAFqABZBhGIYxSmUUQHklJydrvi4JYH5+vup6SkoK4uLi/O47PT0dV155Jb7++ms73gbGAK4XwBMnTqB79+6Ijo5GdHQ0unfvjpMnT+o+fv/+/Zo/tB6PB7Nnz1Yep/X5iRMnBrQ3FkCGYRjGKJVRAI1GAK2kgN9//33ExMRg69attr0PjH9cL4BJSUlISEhAfn4+8vPzkZCQgI4dO+o+/uLFi14/yEOGDEHt2rVx9uxZ5XEejwfZ2dmqx/36668B7Y0FkGEYhjGKkwK4e/du/PDDD6bX7t27TRWB9OvXT3UtPj7eZxHIiBEjEB0djYKCAtPfM2MOVwsgdSIvLCxUrhUUFCgVTkZp1qwZnn32WdU1j8eDBQsWWNofCyDDMAxjlFAXQGoDk5WVhZKSErz66quoXbs2Dhw4AADo0aOHSgbT09MRHh6OuXPnqoIxYrCGCR6uFsCsrCzExMR4XY+JicHkyZMNPce2bds0D6Z6PB7Uq1cPV199NVq0aIGJEyfijz/+CGh/LIAMwzCMUZwUwD179uDYsWOm1549e0w3gm7QoAHCw8PRvHlzbNiwQflcYmIievXqpXzcoEGDgM4ZMvbiagFMTU1FbGys1/XY2FgMGzbM0HP069cP8fHxXteHDh2K/Px8FBUVISMjA7Vq1cLQoUN9Ptf58+dVZyHKyspYABmGYRhDVAUBZCoPFSKAycnJuoUatLZu3apbPt6oUSMMHz7c7+v8+uuviImJQUZGht/HZmRkIDo62tS++ReEYRiG8QcLIOMmKkQAjx8/jtLSUp/r3LlzllPAn376KcLCwvDjjz/6fezmzZvh8Xjwww8/6D6GI4AMwzCMWZwUwL179+LHH380vfbu3cv3txDH1SlgKgIRh0MXFhYaLgJJTExE586dDb3W2LFjUaNGDd0Sdy34DCDDMAxjFCcFcN++fTh+/LjptW/fPr6/hTiuFkDgzzYwt956KwoKClBQUICmTZuq2sAcPnwYcXFxKkkEgN27d6NatWpYsWKF13MuXrwYkyZNQnFxMfbs2YPMzExER0djwIABAe2NBZBhGIYxipMCuH//fvz000+mF/XU5ftb6OJ6ASwvL0e3bt0QFRWFqKgodOvWTdUImn5I169fr/q6QYMG4frrr9es7F2xYgWaNWuGyMhI1KpVCwkJCRg1ahR+//33gPbGAsgwDMMYhQWQcROuF0A3wwLIMAzDGIUFkHETLIAWYAFkGIZhjOK0AJaXl5teLIChDwugBVgAGYZhGKOwADJuggXQAiyADMMwjFGcFMADBw7gxIkTpteBAwf4/hbisABagAWQYRiGMQoLIOMmWAAtwALIMAzDGIUFkHETLIAWYAFkGIZhjOKkAB48eBAnT540vQ4ePMj3txCHBdACLIAMwzCMUZwUwEOHDuHUqVOm16FDh/j+FuKwAFqABZBhGIYxCgsg4yZYAC3AAsgwDMMYhQWQcRMsgBZgAWQYhmGM4qQAlpWV4fTp06ZXWVkZ399CHBZAC7AAMgzDMEZhAWTcBAugBVgAGYZhGKOwADJuggXQAiyADMMwjFGcFMDDhw/jzJkzptfhw4f5/hbisABagAWQYRiGMQoLIOMmWAAtwALIMAzDGIUFkHETLIAWYAFkGIZhjOKkAB45cgRnz541vY4cOcL3txCHBdACLIAMwzCMUVgAGTfBAmgBFkCGYRjGKCyAjJtgAbQACyDDMAxjFCcF8OjRo/j5559Nr6NHj/L9LcRhAbQACyDDMAxjFBZAxk2wAFqABZBhGIYxipMC+P333+OXX34xvb7//nu+v4U4LIAWYAFkGIZhjMICyLgJFkALsAAyDMMwRmEBZNwEC6AFWAAZhmEYozgpgD/88AN+/fVX0+uHH37g+1uIwwJoARZAhmEYxigsgIybYAG0AAsgwzAMYxQWQMZNsABagAWQYRiGMYqTAnjs2DGcO3fO9Dp27Bjf30IcFkALsAAyDMMwRmEBZNwEC6AFWAAZhmEYo7AAMm6CBdACLIAMwzCMUVgAGTfBAmgBFkCGYRjGKCyAjJtwvQCmpKSgTZs2qFmzJmJiYgx9zaVLl5CcnIy6deuiRo0aSExMxI4dO1SPOXHiBLp3747o6GhER0eje/fuOHnyZEB7YwFkGIZhjOKkAP744484f/686fXjjz/y/S3Ecb0AvvPOOxg5ciRef/11wwKYlpaGqKgozJs3D8XFxXjqqadQt25dnDlzRnlMUlISEhISkJ+fj/z8fCQkJKBjx44B7Y0FkGEYhjEKCyDjJlwvgER2drYhAbx06RLq1KmDtLQ05dr58+cRExODjz76CABQUlICj8eDwsJC5TEFBQXweDzYuXOn4T2xADIMwzBGcVIAjx8/jgsXLphex48f5/tbiBNyArh37154PB5s375ddf2xxx5Dz549AQBZWVmazxUTE4PJkyfrPvf58+dx+vRpZZWVlfEvCMMwDGMIFkDGTYScAObl5cHj8eDIkSOq63369EG7du0AAKmpqYiNjfX62tjYWAwbNkz3uZOTk+HxeLwW/4IwDMMw/mABZNxEhQignkiJa+vWraqvCVQAjx49qrr+/PPPo3379gD+FMDGjRt7fW2jRo0wfPhw3efmCCDDMAxjFicF8KeffsJvv/1mev300098fwtxKkQAjx8/jtLSUp/r3Llzqq9xQwpYhs8AMgzDMEZhAWTcRMilgKkIJD09Xbl24cIFzSKQLVu2KI8pLCzkIhCGYRgmaLAAMm7C9QJ48OBBFBUVYciQIYiMjERRURGKiopw9uxZ5TFxcXGYP3++8nFaWhpiYmIwf/58FBcXo2vXrpptYG699VYUFBSgoKAATZs25TYwDMMwTNBwUgDLy8vx+++/m17l5eV8fwtxXC+AvXr10jwjuH79euUxHo8H2dnZysfUCLpOnTqIiIhA27ZtUVxcrHre8vJydOvWDVFRUYiKikK3bt24ETTDMAwTNFgAGTfhegF0MyyADMMwjFFYABk3wQJoARZAhmEYxihOCuCJEydw8eJF0+vEiRN8fwtxWAAtwALIMAzDGIUFkHETLIAWYAFkGIZhjMICyLgJFkALsAAyDMMwRnFSAE+ePIk//vjD9Dp58iTf30IcFkALsAAyDMMwRmEBZNwEC6AFWAAZhmEYo7AAMm6CBdACLIAMwzCMUZwUwFOnTuHSpUum16lTp/j+FuKwAFqABZBhGIYxSlUQwPHjx6Nhw4aIiIhA8+bNsXHjRp+Pnzt3LuLj4xEeHo74+HjVVC8muLAAWoAFkGEYhjFKqAtgbm4uwsLCkJmZiZKSEgwcOBC1a9fGwYMHNR+fn5+P6tWrY9iwYSgtLcWwYcNw+eWXo7Cw0K63g/EBC6AFWAAZhmEYozgpgFZfw8zztGzZEn379lVda9KkCd5++23Nx3fp0gVJSUmqa+3bt8fTTz8d+IaZgGEBtAD9C6msrAynT5/mxYsXL168dFdZWZkSnQsWp0+ftuW+RHuVn+f8+fOar3vhwgVUr17dK4U7YMAAtG3bVvNrbrjhBowcOVJ1beTIkahfv749bwbjExZAC9AvCC9evHjx4mV0lZWVBe2+dO7cOdSpU8eWfUZGRnpdS05O1nzdI0eOwOPxIC8vT3U9NTUVjRs31vyasLAw5OTkqK7l5OQgPDzclveC8Q0LoAX++OMPlJWV4dSpU7b9y7AyRRN5z7znUNgv75n37NSeT506hbKyMvzxxx9BvTedO3fOlu9d696mFwEkAczPz1ddT0lJQVxcnObXhIWFYcaMGapr06dPR0REhD1vBOMTFkCXcPp05TtPyHt2hsq258q2X4D37BS859CFU8CVDxZAl1AZ/5LhPTtDZdtzZdsvwHt2Ct5zaNOyZUv069dPdS0+Pt5nEcjDDz+supaUlMRFIA7BAugSKuNfMrxnZ6hse65s+wV4z07Bew5tqA1MVlYWSkpK8Oqrr6J27do4cOAAAKBHjx4qGczLy0P16tWRlpaG0tJSpKWlcRsYB2EBdAnnz59HcnKy7vkKN8J7dobKtufKtl+A9+wUvOfQZ/z48WjQoAHCw8PRvHlzbNiwQflcYmIievXqpXr8nDlzEBcXh7CwMDRp0gTz5s1zeMdVFxZAhmEYhmGYKgYLIMMwDMMwTBWDBZBhGIZhGKaKwQLIMAzDMAxTxWABZBiGYRiGqWKwADpESkoK2rRpg5o1ayImJsbQ11y6dAnJycmoW7cuatSogcTEROzYsUP1mBMnTqB79+6Ijo5GdHQ0unfvjpMnT9qy50Cfe//+/bojhWbPnq08TuvzEydOrJA9A39Wpsn7eeqppyw/b7D2XF5ejldeeQWNGzdGzZo1ccMNN6B///5e80XtfJ/Hjx+Phg0bIiIiAs2bN8fGjRt9Pn7u3LmIj49HeHg44uPjvZrDGvnZtkoge540aRLuueceXHHFFbjiiivwwAMPYMuWLarH9OrVy+v9bNWqVYXtOTs7W/P/8blz50w/Z7D3rPW75vF40KFDB+UxwXyfN2zYgI4dO6Ju3brweDxYsGCB36/5/PPP0bx5c0RERODGG2/U/B0K9nvMMMGABdAh3nnnHYwcORKvv/66YQFMS0tDVFQU5s2bh+LiYjz11FOoW7cuzpw5ozwmKSkJCQkJyM/PR35+PhISEtCxY0db9hzoc1+8eBHff/+9ag0ZMgS1a9fG2bNnlcd5PB5kZ2erHvfrr79WyJ6BP29Kffr0Ue1Hlik3vc/FxcV44oknsHjxYuzZswdr165FbGwsOnfurHqcXe8z9fbKzMxESUkJBg4ciNq1a+PgwYOaj8/Pz0f16tUxbNgwlJaWYtiwYV69vYz8bFsh0D0/88wzGD9+PIqKilBaWorevXsjJiYGhw8fVh7Tq1cvJCUlqd7P8vJyW/ZrZs/Z2dmIjo72+p2z8pzB3nN5eblqrzt27ED16tWRnZ2tPCaY7/Py5csxePBgzJs3z5AA7tu3D7Vq1cLAgQNRUlKCzMxMhIWFYe7cuabfA4ZxCyyADpOdnW1IAC9duoQ6deogLS1NuXb+/HnExMTgo48+AgCUlJTA4/GobqwFBQXweDzYuXOnpX3a9dzNmjXDs88+q7pm9F/egWJ2z4mJiRg4cKDtzxvMPcvMnj0b4eHh+P3335Vrdr3PLVu2RN++fVXXmjRp4rO7f1JSkupa+/btle7+Rn62nd6zzMWLFxEVFYWpU6cm9KnsAAALoElEQVQq13r16oVOnTrZsj8tAt2zkb9LrL4P/rD6/B9++CGioqLw888/K9eC/T4TRn4/3nzzTTRp0kR17cUXX0Tr1q2Vj4P9HjNMsGABdBijArh37154PB5s375ddf2xxx5Dz549AQBZWVmazxUTE4PJkydb2qcdz71t2zZ4PB7k5eWprns8HtSrVw9XX301WrRogYkTJ9oyHN3snhMTE3HNNdfg6quvxs0334w33nhDFYly+/sMAJmZmbjmmmtU1+x4n4Mx39PIz7YVzOxZ5syZM6hRowaWLFmiXOvVqxdiYmJw7bXXIjY2Fs8//zyOHTtmeb9m95ydnY3q1aujfv36qFevHh555BHVe2rH+2D3nmUSEhLQp08f1bVgvs8iRgTw3nvvxYABA1TX5s+fj8svvxy//fZb0N9jhgkmLIAOY1QA8/Ly4PF4cOTIEdX1Pn36oF27dgCA1NRUxMbGen1tbGwshg0bZmmfdjx3v379EB8f73V96NChyM/PR1FRETIyMlCrVi0MHTrU0n6t7HnSpElYs2YNiouLMXPmTDRs2BAPPvig5ecN5p5FfvrpJ9SvXx+DBw9WXbfjfT5y5IimxKempqJx48aaXxMWFoacnBzVtZycHISHhwMw9rNtBTN7lnnppZdw0003qc7T5ebmYunSpSguLsbixYtx22234ZZbbrFlQoSZPRcUFGDatGn46quvsHHjRnTu3Bk1a9bEd999Z/o5g71nkS1btsDj8XidtQzm+yxiRABjY2ORmpqqukY/v0ePHg36e8wwwYQF0ALJycm6RQ+0tm7dqvqaQAXw6NGjquvPP/882rdvD0D/L5lGjRph+PDhlvZs5rlFfv31V8TExCAjI8PvYzMyMhAdHa37eaf2TFDk8ssvvwTg7vf59OnTaNWqFZKSkvDbb7/5fKy/91kLusHl5+errqekpCAuLk7za8LCwjBjxgzVtenTpyMiIgKAsZ9tK5jZs0h6ejquvPJKfP311z4fd/ToUYSFhdkyusrqngHgjz/+wG233Yb+/fvb9pzB3PMLL7yAhIQEv4+z830WMSqA8j/ENm/eDI/Hg++//z7o7zHDBBMWQAscP34cpaWlPpdckVfRKWCje7aamvz0008RFhaGH3/80e9j6S/UH374oUL3TFy6dAlhYWHIzc0F4N73+cyZM2jTpg0eeOABr58zLfy9z1pUtRTw+++/j5iYGK9/uOnRqFEj1VlGs9iVSnz++eeV85duTgH/8ssviI6OxqhRowy9ll3vswingJmqDgugwwRaBJKenq5cu3DhgmYRiJhCKSwstLU4wexzJyYmelWl6jF27FjUqFHDcorHrvejuLgYHo9HGWLuxvf59OnTaN26NRITE/HLL78Yei2z73PLli3Rr18/1bX4+HifRSAPP/yw6lpSUpJXEYivn22rBLpnABgxYgSio6NRUFBg6DV++uknREREqApFrGBmzyKXLl1CixYt0Lt3b9ue0x9mnz87OxsRERH46aef/L6G3e8zYbQIRD7G0rdvX68ikGC+xwwTLFgAHeLgwYMoKirCkCFDEBkZiaKiIhQVFanao8TFxan+JZmWloaYmBjMnz8fxcXF6Nq1q2YbmFtvvRUFBQUoKChA06ZNbW1P4uu5Dx8+jLi4OK8zPLt370a1atWwYsUKr+dcvHgxJk2ahOLiYuzZsweZmZmIjo72+le2U3ves2cPhgwZgq1bt2L//v1YtmwZmjRpgttvvx0XL140/LxO7vnMmTNo1aoVmjZtij179qjaZdCe7Xyfqc1FVlYWSkpK8Oqrr6J27do4cOAAAKBHjx6qm11eXh6qV6+OtLQ0lJaWIi0tTbMNjL+fbSsEuuf09HSEh4dj7ty5qveTfj/Pnj2LN954A/n5+di/fz/Wr1+PNm3aoF69ehW253fffRcrV67E3r17UVRUhN69e+Pyyy9X/T76e06n90zcc889Xr02geC/z2fPnlX+7vV4PBg5ciSKioqUli1vv/02evTooTye2sC89tprKCkpQVZWlm4bmGC9xwwTLFgAHUKruanH48H69euVx1DfNoKa5dapUwcRERFo27YtiouLVc9bXl6Obt26ISoqClFRUejWrZttDYr9PTc1fha/BwAYNGgQrr/+es2K0xUrVqBZs2aIjIxErVq1kJCQgFGjRqnalzi550OHDqFt27a46qqrEB4ejptuugkDBgzw6jvmpvd5/fr1uucK9+/fD8D+93n8+PFo0KABwsPD0bx5cyU6CvwZ7e3Vq5fq8XPmzEFcXBzCwsLQpEkTr/NbRn62rRLInhs0aKD5fiYnJwP480xru3btcO211yIsLAz169dHr169cOjQoQrb86uvvor69esjPDwc1157Ldq1a+d1Fs3fczq9ZwDYtWsXPB4PVq9e7fVcwX6f9X53aI+9evVCYmKi6ms+//xz3H777QgPD0fDhg11G0EH8z1mmGDAAsgwDMMwDFPFYAFkGIZhGIapYrAAMgzDMAzDVDFYABmGYRiGYaoYLIAMwzAMwzBVDBZAhmEYhmGYKgYLIMMwDMMwTBWDBZBhGIZhGKaKwQLIMAzDMAxTxWABZJgqTGJiIgYOHFjR2zA0l5VhGIaxDxZAhqnCVCYBHDBgAJo3b47w8HDcdtttDu2MYRgmNGEBZJgqTGUSwP79+2PcuHHo0aMHCyDDMIxFWAAZporw888/o0ePHqhduzbq1KmDjIwMlQBOmzYNd9xxByIjI/H//t//Q9euXXHs2DHVc+zYsQMdOnRAVFQUIiMjcc8992DPnj2GXj8rKws333wzwsPDUadOHbz88svK5zweDzIzM/H444+jZs2aaNSoERYtWqT5PMnJySyADMMwFmEBZJgqQr9+/XD99ddj9erV+Oabb9CxY0dERkYqApiVlYXly5dj7969KCgoQOvWrfHwww8rX3/48GFcddVVeOKJJ7B161bs2rULkydPxs6dO/2+9oQJE1CjRg2MGjUKu3btwhdffIEPP/xQ+bzH48H111+PGTNmYPfu3RgwYAAiIyNRXl7u9VwsgAzDMNZhAWSYKsDZs2cRHh6O3Nxc5Vp5eTlq1qypmwL+4osv4PF4cPbsWQDAoEGDcOONN+K3334L+PWvu+46DB48WPfzHo8H//M//6N8/PPPP6NatWpYsWKF12NZABmGYazDAsgwVYCvvvoKHo8HBw8eVF1v1qyZIoDbt2/HY489hvr16yMyMhK1atWCx+PBt99+CwB4+OGH0bNnz4Bf+9ixY/B4PFi3bp3uYzweD2bPnq26Fh0djalTp3o9lgWQYRjGOiyADFMFKCoq8imAP//8M6655ho888wz2Lhx4/9v545djosCOI7f5aIrGQyyyMTfYHiyGQ1mO4tMdrONyUaUMrIpk5luMqh7kzJKBkqZfu+meHp6xdv7DOf7qbPcc+uezvSt07nabDaaTqeyLEuu60qSisXiWwF4Pp9fCsDnSyDRaFTdbvfbuwQgAHyOAAQMcLlcZNu2RqPR/dnpdJLjOKrValosFrIsS/v9/j4/GAweArDRaLx9BJxKpf56BEwAAsD/QwAChqhUKkomk5rNZlqv1yoUCvdLIIfDQYFAQPV6XdvtVuPxWOl0+iEAj8ejYrHY/RKI53nq9/svXQLp9XoKhUJqtVryPE/L5VLtdvs+/0oA+r4v13VVLpeVTqfluq5c19Xtdvs3GwQABiEAAUNcLheVSiU5jqN4PK5ms/nwG5jhcKhUKqVgMKhsNqvJZPIQgJK0Wq2Uz+flOI4ikYi+vr603W5f+n6n01Emk5Ft20okEqpWq/e5VwIwl8vJsqxvY7fbvb8pAGAoAhAAAMAwBCAAAIBhCEAAHwuHwz+O+Xz+28sDADwhAAF8zPf9H8f1ev3t5QEAnhCAAAAAhiEAAQAADEMAAgAAGIYABAAAMAwBCAAAYBgCEAAAwDAEIAAAgGEIQAAAAMP8AVSLOa7341qFAAAAAElFTkSuQmCC\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c06d7d73c8>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.reset_index().plot.scatter('dac_ch1', 'dac_ch2', c='dmm_v1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to select a subset of data from the datframe based on the x and y values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>dmm_v1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dac_ch1</th>\n",
+       "      <th>dac_ch2</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">-1.000</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>-3.724157e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>2.674091e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>2.168524e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>1.543205e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">-0.995</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>7.680241e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>9.298480e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>1.123969e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>1.356443e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">-0.990</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>8.381701e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>1.014774e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>1.226624e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>1.480331e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">-0.985</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>9.143570e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>1.107013e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>1.338120e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>1.614888e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">-0.980</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>9.970701e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>1.207154e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>1.459167e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>1.760972e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">-0.975</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>1.086831e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>1.315828e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>1.590528e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>1.919502e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">-0.970</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>1.184198e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>1.433710e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>1.733020e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>2.091467e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">-0.965</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>1.289772e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>1.561530e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>1.887524e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>2.277927e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">-0.960</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>1.404197e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>1.700064e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>2.054979e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>2.480018e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">-0.955</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>1.528162e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>1.850148e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>2.236396e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>2.698958e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">-0.950</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>1.662406e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>2.012677e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>2.432856e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>2.936052e-09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       dmm_v1\n",
+       "dac_ch1 dac_ch2              \n",
+       "-1.000  -1.00   -3.724157e-04\n",
+       "        -0.99    2.674091e-04\n",
+       "        -0.98    2.168524e-04\n",
+       "        -0.97    1.543205e-05\n",
+       "-0.995  -1.00    7.680241e-10\n",
+       "        -0.99    9.298480e-10\n",
+       "        -0.98    1.123969e-09\n",
+       "        -0.97    1.356443e-09\n",
+       "-0.990  -1.00    8.381701e-10\n",
+       "        -0.99    1.014774e-09\n",
+       "        -0.98    1.226624e-09\n",
+       "        -0.97    1.480331e-09\n",
+       "-0.985  -1.00    9.143570e-10\n",
+       "        -0.99    1.107013e-09\n",
+       "        -0.98    1.338120e-09\n",
+       "        -0.97    1.614888e-09\n",
+       "-0.980  -1.00    9.970701e-10\n",
+       "        -0.99    1.207154e-09\n",
+       "        -0.98    1.459167e-09\n",
+       "        -0.97    1.760972e-09\n",
+       "-0.975  -1.00    1.086831e-09\n",
+       "        -0.99    1.315828e-09\n",
+       "        -0.98    1.590528e-09\n",
+       "        -0.97    1.919502e-09\n",
+       "-0.970  -1.00    1.184198e-09\n",
+       "        -0.99    1.433710e-09\n",
+       "        -0.98    1.733020e-09\n",
+       "        -0.97    2.091467e-09\n",
+       "-0.965  -1.00    1.289772e-09\n",
+       "        -0.99    1.561530e-09\n",
+       "        -0.98    1.887524e-09\n",
+       "        -0.97    2.277927e-09\n",
+       "-0.960  -1.00    1.404197e-09\n",
+       "        -0.99    1.700064e-09\n",
+       "        -0.98    2.054979e-09\n",
+       "        -0.97    2.480018e-09\n",
+       "-0.955  -1.00    1.528162e-09\n",
+       "        -0.99    1.850148e-09\n",
+       "        -0.98    2.236396e-09\n",
+       "        -0.97    2.698958e-09\n",
+       "-0.950  -1.00    1.662406e-09\n",
+       "        -0.99    2.012677e-09\n",
+       "        -0.98    2.432856e-09\n",
+       "        -0.97    2.936052e-09"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[(slice(-1, -0.95), slice(-1, -0.97)), :]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## XArray"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In many cases when working with data on a grid it may be more convenient to export the data to a [XArray](http://xarray.pydata.org) Dataset or DataArray"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Pandas DataSet can be directly converted to a XArray DataSet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xaDataSet = df.to_xarray()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (dac_ch1: 401, dac_ch2: 201)\n",
+       "Coordinates:\n",
+       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.995 -0.99 -0.985 ... 0.985 0.99 0.995 1.0\n",
+       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
+       "Data variables:\n",
+       "    dmm_v1   (dac_ch1, dac_ch2) float64 -0.0003724 0.0002674 ... 1.039e-05"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xaDataSet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, in many cases it is more convenient to work with a XArray DataArray. The DataArray can only contain a single dependent variable and can be obtained from the Dataset by indexing using the parameter name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xaDataArray = xaDataSet['dmm_v1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray 'dmm_v1' (dac_ch1: 401, dac_ch2: 201)>\n",
+       "array([[-3.724157e-04,  2.674091e-04,  2.168524e-04, ...,  5.451526e-07,\n",
+       "         4.808069e-07,  4.233782e-07],\n",
+       "       [ 7.680241e-10,  9.298480e-10,  1.123969e-09, ...,  5.951812e-07,\n",
+       "         5.249305e-07,  4.622315e-07],\n",
+       "       [ 8.381701e-10,  1.014774e-09,  1.226624e-09, ...,  6.495409e-07,\n",
+       "         5.728740e-07,  5.044485e-07],\n",
+       "       ...,\n",
+       "       [ 1.991485e-08,  2.411094e-08,  2.914449e-08, ...,  1.543304e-05,\n",
+       "         1.361144e-05,  1.198566e-05],\n",
+       "       [ 1.854251e-08,  2.244944e-08,  2.713612e-08, ...,  1.436954e-05,\n",
+       "         1.267347e-05,  1.115972e-05],\n",
+       "       [ 1.725783e-08,  2.089408e-08,  2.525605e-08, ...,  1.337397e-05,\n",
+       "         1.179541e-05,  1.038654e-05]])\n",
+       "Coordinates:\n",
+       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.995 -0.99 -0.985 ... 0.985 0.99 0.995 1.0\n",
+       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xaDataArray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2,2)\n",
+    "xaDataArray.plot(ax=ax[0,0])\n",
+    "xaDataArray.mean(dim='dac_ch1').plot(ax=ax[1,0])\n",
+    "xaDataArray.mean(dim='dac_ch2').plot(ax=ax[0,1])\n",
+    "xaDataArray[[200]].plot(ax=ax[1,1])\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 8f18a331962fafa84f2920480ea17a5e39ba64a6 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 6 Feb 2019 13:07:04 +0100
Subject: [PATCH 712/719] Update notebooks with more text

---
 .../DataSet/Dataset Context Manager.ipynb     | 329 ++++++++++--------
 .../Working-With-Pandas-and-XArray.ipynb      | 205 ++++++-----
 2 files changed, 295 insertions(+), 239 deletions(-)

diff --git a/docs/examples/DataSet/Dataset Context Manager.ipynb b/docs/examples/DataSet/Dataset Context Manager.ipynb
index 1935382f8d0..07468f95aa5 100644
--- a/docs/examples/DataSet/Dataset Context Manager.ipynb	
+++ b/docs/examples/DataSet/Dataset Context Manager.ipynb	
@@ -151,7 +151,7 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#129@C:\\Users\\jenielse/experiments.db\n",
+       "tutorial_exp#no sample#137@C:\\Users\\jenielse/experiments.db\n",
        "-----------------------------------------------------------"
       ]
      },
@@ -178,7 +178,7 @@
      "text": [
       "Starting the measurement\n",
       "Doing stuff with the following two instruments: <DummyInstrument: dmm>, <DummyInstrument: dac>\n",
-      "Starting experimental run with id: 443\n",
+      "Starting experimental run with id: 480\n",
       "End of experiment\n"
      ]
     }
@@ -995,7 +995,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1038,8 +1038,8 @@
     {
      "data": {
       "text/plain": [
-       "{'dmm_v1': {'dmm_v1': array([ 5.02348283,  2.76737943,  1.54907291,  0.82583077,  0.40845978,\n",
-       "          0.12302513,  0.1933247 ,  0.21199432,  0.04990785, -0.11080111]),\n",
+       "{'dmm_v1': {'dmm_v1': array([4.84812039, 2.85192992, 1.77006663, 1.16772991, 0.70324665,\n",
+       "         0.18518276, 0.25720134, 0.07539374, 0.01103977, 0.02022141]),\n",
        "  'dac_ch1': array([ 0.        ,  2.77777778,  5.55555556,  8.33333333, 11.11111111,\n",
        "         13.88888889, 16.66666667, 19.44444444, 22.22222222, 25.        ])}}"
       ]
@@ -1068,8 +1068,8 @@
     {
      "data": {
       "text/plain": [
-       "{'dmm_v1': {'dmm_v1': array([ 5.02348283,  2.76737943,  1.54907291,  0.82583077,  0.40845978,\n",
-       "          0.12302513,  0.1933247 ,  0.21199432,  0.04990785, -0.11080111]),\n",
+       "{'dmm_v1': {'dmm_v1': array([4.84812039, 2.85192992, 1.77006663, 1.16772991, 0.70324665,\n",
+       "         0.18518276, 0.25720134, 0.07539374, 0.01103977, 0.02022141]),\n",
        "  'dac_ch1': array([ 0.        ,  2.77777778,  5.55555556,  8.33333333, 11.11111111,\n",
        "         13.88888889, 16.66666667, 19.44444444, 22.22222222, 25.        ])}}"
       ]
@@ -1115,7 +1115,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The data can also be exported as one or more [Pandas](https://pandas.pydata.org/) dataframes. The dataframes are returns as a dict from measured parameters to dataframes."
+    "The data can also be exported as one or more [Pandas](https://pandas.pydata.org/) DataFrames. The DataFrames are returns as a dict from measured parameters to DataFrames."
    ]
   },
   {
@@ -1154,43 +1154,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0.000000</th>\n",
-       "      <td>5.023483</td>\n",
+       "      <td>4.848120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2.777778</th>\n",
-       "      <td>2.767379</td>\n",
+       "      <td>2.851930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5.555556</th>\n",
-       "      <td>1.549073</td>\n",
+       "      <td>1.770067</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8.333333</th>\n",
-       "      <td>0.825831</td>\n",
+       "      <td>1.167730</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11.111111</th>\n",
-       "      <td>0.408460</td>\n",
+       "      <td>0.703247</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13.888889</th>\n",
-       "      <td>0.123025</td>\n",
+       "      <td>0.185183</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16.666667</th>\n",
-       "      <td>0.193325</td>\n",
+       "      <td>0.257201</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19.444444</th>\n",
-       "      <td>0.211994</td>\n",
+       "      <td>0.075394</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22.222222</th>\n",
-       "      <td>0.049908</td>\n",
+       "      <td>0.011040</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25.000000</th>\n",
-       "      <td>-0.110801</td>\n",
+       "      <td>0.020221</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1199,16 +1199,16 @@
       "text/plain": [
        "             dmm_v1\n",
        "dac_ch1            \n",
-       "0.000000   5.023483\n",
-       "2.777778   2.767379\n",
-       "5.555556   1.549073\n",
-       "8.333333   0.825831\n",
-       "11.111111  0.408460\n",
-       "13.888889  0.123025\n",
-       "16.666667  0.193325\n",
-       "19.444444  0.211994\n",
-       "22.222222  0.049908\n",
-       "25.000000 -0.110801"
+       "0.000000   4.848120\n",
+       "2.777778   2.851930\n",
+       "5.555556   1.770067\n",
+       "8.333333   1.167730\n",
+       "11.111111  0.703247\n",
+       "13.888889  0.185183\n",
+       "16.666667  0.257201\n",
+       "19.444444  0.075394\n",
+       "22.222222  0.011040\n",
+       "25.000000  0.020221"
       ]
      },
      "execution_count": 12,
@@ -1220,6 +1220,13 @@
     "datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1']"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more details about using Pandas and XArray see [Working With Pandas and XArray](./Working-With-Pandas-and-XArray.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1240,7 +1247,7 @@
      "text": [
       "Starting the measurement\n",
       "Doing stuff with the following two instruments: <DummyInstrument: dmm>, <DummyInstrument: dac>\n",
-      "Starting experimental run with id: 444\n",
+      "Starting experimental run with id: 481\n",
       "End of experiment\n"
      ]
     }
@@ -2053,7 +2060,7 @@
     {
      "data": {
       "text/html": [
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\" 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2078,7 +2085,7 @@
      "text": [
       "Starting the measurement\n",
       "Doing stuff with the following two instruments: <DummyInstrument: dmm>, <DummyInstrument: dac>\n",
-      "Starting experimental run with id: 445\n",
+      "Starting experimental run with id: 482\n",
       "End of experiment\n"
      ]
     }
@@ -2895,7 +2902,7 @@
     {
      "data": {
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\" 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2929,7 +2936,7 @@
     {
      "data": {
       "text/plain": [
-       "<qcodes.dataset.measurements.Measurement at 0x2116b749860>"
+       "<qcodes.dataset.measurements.Measurement at 0x1fc7e983358>"
       ]
      },
      "execution_count": 17,
@@ -2995,7 +3002,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 446\n"
+      "Starting experimental run with id: 483\n"
      ]
     }
    ],
@@ -3020,7 +3027,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When exporting a two or higher dimensional dataset as a Pandas dataframe a [MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html) is used to index the measured parameter based on all the dependencies"
+    "When exporting a two or higher dimensional datasets as a Pandas DataFrame a [MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html) is used to index the measured parameter based on all the dependencies"
    ]
   },
   {
@@ -3064,43 +3071,43 @@
        "    <tr>\n",
        "      <th rowspan=\"10\" valign=\"top\">-1.0</th>\n",
        "      <th>-1.000000</th>\n",
-       "      <td>-0.000392</td>\n",
+       "      <td>0.000369</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.989950</th>\n",
-       "      <td>0.000483</td>\n",
+       "      <td>-0.000470</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.979899</th>\n",
-       "      <td>0.000108</td>\n",
+       "      <td>-0.000244</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.969849</th>\n",
-       "      <td>-0.000007</td>\n",
+       "      <td>0.000091</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.959799</th>\n",
-       "      <td>0.000011</td>\n",
+       "      <td>-0.000067</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.949749</th>\n",
-       "      <td>-0.000003</td>\n",
+       "      <td>0.000061</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.939698</th>\n",
-       "      <td>-0.000001</td>\n",
+       "      <td>0.000071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.929648</th>\n",
-       "      <td>-0.000001</td>\n",
+       "      <td>-0.000088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.919598</th>\n",
-       "      <td>0.000001</td>\n",
+       "      <td>0.000148</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.909548</th>\n",
-       "      <td>-0.000002</td>\n",
+       "      <td>-0.000252</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3109,16 +3116,16 @@
       "text/plain": [
        "                     dmm_v1\n",
        "dac_ch1 dac_ch2            \n",
-       "-1.0    -1.000000 -0.000392\n",
-       "        -0.989950  0.000483\n",
-       "        -0.979899  0.000108\n",
-       "        -0.969849 -0.000007\n",
-       "        -0.959799  0.000011\n",
-       "        -0.949749 -0.000003\n",
-       "        -0.939698 -0.000001\n",
-       "        -0.929648 -0.000001\n",
-       "        -0.919598  0.000001\n",
-       "        -0.909548 -0.000002"
+       "-1.0    -1.000000  0.000369\n",
+       "        -0.989950 -0.000470\n",
+       "        -0.979899 -0.000244\n",
+       "        -0.969849  0.000091\n",
+       "        -0.959799 -0.000067\n",
+       "        -0.949749  0.000061\n",
+       "        -0.939698  0.000071\n",
+       "        -0.929648 -0.000088\n",
+       "        -0.919598  0.000148\n",
+       "        -0.909548 -0.000252"
       ]
      },
      "execution_count": 21,
@@ -3134,7 +3141,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If your data is on a regular grid it may make sense to view the data as an [XArray](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html) Dataset. The Pandas dataframe can be directly exported to xarray"
+    "If your data is on a regular grid it may make sense to view the data as an [XArray](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html) Dataset. The Pandas DataFrame can be directly exported to a XArray Dataset."
    ]
   },
   {
@@ -3151,7 +3158,7 @@
        "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
        "  * dac_ch2  (dac_ch2) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
        "Data variables:\n",
-       "    dmm_v1   (dac_ch1, dac_ch2) float64 -0.0003923 0.0004831 ... 1.039e-05"
+       "    dmm_v1   (dac_ch1, dac_ch2) float64 0.0003689 -0.0004697 ... 1.039e-05"
       ]
      },
      "execution_count": 22,
@@ -3167,7 +3174,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note however, that XArray is only suited for data that is on regular grid with few or no missing values. "
+    "Note however, that XArray is only suited for data that is on rectangular grid with few or no missing values. "
    ]
   },
   {
@@ -3958,7 +3965,7 @@
     {
      "data": {
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\" 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3981,7 +3988,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 447\n"
+      "Starting experimental run with id: 484\n"
      ]
     }
    ],
@@ -4798,7 +4805,7 @@
     {
      "data": {
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\" 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -4821,7 +4828,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 448\n",
+      "Starting experimental run with id: 485\n",
       "130 46 0.25089416830953565\n",
       "----------\n"
      ]
@@ -5674,7 +5681,7 @@
     {
      "data": {
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\" 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -5729,7 +5736,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 449\n"
+      "Starting experimental run with id: 486\n"
      ]
     }
    ],
@@ -6540,7 +6547,7 @@
     {
      "data": {
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\" 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+       "<img 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -6591,54 +6598,54 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>-0.209503</th>\n",
-       "      <th>0.628723</th>\n",
-       "      <td>0.121980</td>\n",
+       "      <th>-0.257408</th>\n",
+       "      <th>-0.880324</th>\n",
+       "      <td>0.000129</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-0.744003</th>\n",
-       "      <th>0.431778</th>\n",
-       "      <td>0.002514</td>\n",
+       "      <th>-0.708778</th>\n",
+       "      <th>-0.054650</th>\n",
+       "      <td>0.003752</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0.773814</th>\n",
-       "      <th>0.384253</th>\n",
-       "      <td>0.022878</td>\n",
+       "      <th>-0.317528</th>\n",
+       "      <th>-0.409047</th>\n",
+       "      <td>0.014472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0.072686</th>\n",
-       "      <th>-0.986115</th>\n",
-       "      <td>0.000041</td>\n",
+       "      <th>0.754722</th>\n",
+       "      <th>-0.727091</th>\n",
+       "      <td>0.000023</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-0.747001</th>\n",
-       "      <th>0.343167</th>\n",
-       "      <td>0.003133</td>\n",
+       "      <th>0.076441</th>\n",
+       "      <th>-0.934705</th>\n",
+       "      <td>0.000027</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-0.762881</th>\n",
-       "      <th>-0.322906</th>\n",
-       "      <td>0.000334</td>\n",
+       "      <th>0.026166</th>\n",
+       "      <th>0.463654</th>\n",
+       "      <td>0.622060</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0.947456</th>\n",
-       "      <th>0.192068</th>\n",
-       "      <td>0.003616</td>\n",
+       "      <th>0.796483</th>\n",
+       "      <th>0.268140</th>\n",
+       "      <td>0.022531</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-0.670000</th>\n",
-       "      <th>-0.532965</th>\n",
-       "      <td>0.000132</td>\n",
+       "      <th>-0.378744</th>\n",
+       "      <th>-0.525162</th>\n",
+       "      <td>0.002697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-0.403736</th>\n",
-       "      <th>-0.118555</th>\n",
-       "      <td>0.066083</td>\n",
+       "      <th>0.458012</th>\n",
+       "      <th>-0.216361</th>\n",
+       "      <td>0.101547</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0.278310</th>\n",
-       "      <th>0.885972</th>\n",
-       "      <td>0.020366</td>\n",
+       "      <th>-0.642956</th>\n",
+       "      <th>-0.386786</th>\n",
+       "      <td>0.000871</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -6647,16 +6654,16 @@
       "text/plain": [
        "                       dmm_v1\n",
        "dac_ch1   dac_ch2            \n",
-       "-0.209503  0.628723  0.121980\n",
-       "-0.744003  0.431778  0.002514\n",
-       " 0.773814  0.384253  0.022878\n",
-       " 0.072686 -0.986115  0.000041\n",
-       "-0.747001  0.343167  0.003133\n",
-       "-0.762881 -0.322906  0.000334\n",
-       " 0.947456  0.192068  0.003616\n",
-       "-0.670000 -0.532965  0.000132\n",
-       "-0.403736 -0.118555  0.066083\n",
-       " 0.278310  0.885972  0.020366"
+       "-0.257408 -0.880324  0.000129\n",
+       "-0.708778 -0.054650  0.003752\n",
+       "-0.317528 -0.409047  0.014472\n",
+       " 0.754722 -0.727091  0.000023\n",
+       " 0.076441 -0.934705  0.000027\n",
+       " 0.026166  0.463654  0.622060\n",
+       " 0.796483  0.268140  0.022531\n",
+       "-0.378744 -0.525162  0.002697\n",
+       " 0.458012 -0.216361  0.101547\n",
+       "-0.642956 -0.386786  0.000871"
       ]
      },
      "execution_count": 31,
@@ -6686,8 +6693,8 @@
        "<xarray.Dataset>\n",
        "Dimensions:  (dac_ch1: 5000, dac_ch2: 5000)\n",
        "Coordinates:\n",
-       "  * dac_ch1  (dac_ch1) float64 -0.9991 -0.9979 -0.9976 ... 0.9982 0.9985 0.9987\n",
-       "  * dac_ch2  (dac_ch2) float64 -0.999 -0.9989 -0.9987 ... 0.9968 0.9971 0.9977\n",
+       "  * dac_ch1  (dac_ch1) float64 -0.9997 -0.9995 -0.9994 ... 0.9991 0.9992 0.9998\n",
+       "  * dac_ch2  (dac_ch2) float64 -0.9999 -0.9993 -0.9993 ... 0.999 0.9999 1.0\n",
        "Data variables:\n",
        "    dmm_v1   (dac_ch1, dac_ch2) float64 nan nan nan nan nan ... nan nan nan nan"
       ]
@@ -6750,7 +6757,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 450\n"
+      "Starting experimental run with id: 487\n"
      ]
     }
    ],
@@ -6776,16 +6783,16 @@
     {
      "data": {
       "text/plain": [
-       " final_simplex: (array([[0.99812801, 0.64445492],\n",
-       "       [0.99812801, 0.64445492],\n",
-       "       [0.99812801, 0.64445492]]), array([-0.00083562, -0.00036764, -0.00036764]))\n",
-       "           fun: -0.0008356242479310768\n",
+       " final_simplex: (array([[0.1, 0.2],\n",
+       "       [0.1, 0.2],\n",
+       "       [0.1, 0.2]]), array([-1.13314845, -1.13314845, -1.13314845]))\n",
+       "           fun: -1.1331484530668263\n",
        "       message: 'Optimization terminated successfully.'\n",
-       "          nfev: 123\n",
-       "           nit: 33\n",
+       "          nfev: 153\n",
+       "           nit: 75\n",
        "        status: 0\n",
        "       success: True\n",
-       "             x: array([0.99812801, 0.64445492])"
+       "             x: array([0.1, 0.2])"
       ]
      },
      "execution_count": 36,
@@ -7585,7 +7592,7 @@
     {
      "data": {
       "text/html": [
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\" 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -7593,6 +7600,18 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2019-02-06 12:40:45,288 ¦ py.warnings ¦ WARNING ¦ warnings ¦ _showwarnmsg ¦ 99 ¦ c:\\users\\jenielse\\source\\repos\\qcodes\\qcodes\\dataset\\data_export.py:131: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  remainders = np.mod(steps[1:]/steps[0], 1)\n",
+      "\n",
+      "2019-02-06 12:40:45,296 ¦ py.warnings ¦ WARNING ¦ warnings ¦ _showwarnmsg ¦ 99 ¦ c:\\users\\jenielse\\source\\repos\\qcodes\\qcodes\\dataset\\data_export.py:131: RuntimeWarning: invalid value encountered in remainder\n",
+      "  remainders = np.mod(steps[1:]/steps[0], 1)\n",
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -7630,21 +7649,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 451\n",
+      "Starting experimental run with id: 488\n",
       "Added points to measurement, step 0.\n",
       "Added points to measurement, step 1.\n",
       "The run now holds 3 rows\n",
       "Added points to measurement, step 2.\n",
       "Added points to measurement, step 3.\n",
+      "The run now holds 5 rows\n",
       "Added points to measurement, step 4.\n",
-      "The run now holds 6 rows\n",
       "Added points to measurement, step 5.\n",
+      "The run now holds 6 rows\n",
       "Added points to measurement, step 6.\n",
-      "The run now holds 8 rows\n",
       "Added points to measurement, step 7.\n",
       "Added points to measurement, step 8.\n",
-      "Added points to measurement, step 9.\n",
       "The run now holds 10 rows\n",
+      "Added points to measurement, step 9.\n",
       "The run now holds 10 rows\n",
       "The run now holds 10 rows\n"
      ]
@@ -7694,7 +7713,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 452\n",
+      "Starting experimental run with id: 489\n",
       "Added points to measurement, step 0.\n",
       "First parameter value list: []\n",
       "Added points to measurement, step 1.\n",
@@ -7704,21 +7723,22 @@
       "First parameter value list: [0, 1, 2]\n",
       "Added points to measurement, step 3.\n",
       "First parameter value list: [0, 1, 2]\n",
-      "Added points to measurement, step 4.\n",
-      "First parameter value list: [0, 1, 2]\n",
       "The run now holds 4 rows\n",
+      "Added points to measurement, step 4.\n",
+      "First parameter value list: [0, 1, 2, 3, 4]\n",
       "Added points to measurement, step 5.\n",
-      "First parameter value list: [0, 1, 2, 3, 4, 5]\n",
+      "First parameter value list: [0, 1, 2, 3, 4]\n",
+      "The run now holds 6 rows\n",
       "Added points to measurement, step 6.\n",
-      "First parameter value list: [0, 1, 2, 3, 4, 5]\n",
-      "The run now holds 7 rows\n",
+      "First parameter value list: [0, 1, 2, 3, 4, 5, 6]\n",
       "Added points to measurement, step 7.\n",
-      "First parameter value list: [0, 1, 2, 3, 4, 5, 6, 7]\n",
+      "First parameter value list: [0, 1, 2, 3, 4, 5, 6]\n",
+      "The run now holds 8 rows\n",
       "Added points to measurement, step 8.\n",
       "First parameter value list: [0, 1, 2, 3, 4, 5, 6, 7]\n",
-      "The run now holds 9 rows\n",
       "Added points to measurement, step 9.\n",
-      "First parameter value list: [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
+      "First parameter value list: [0, 1, 2, 3, 4, 5, 6, 7]\n",
+      "The run now holds 10 rows\n",
       "The run now holds 10 rows\n",
       "The run now holds 10 rows\n"
      ]
@@ -7903,7 +7923,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 453\n"
+      "Starting experimental run with id: 490\n"
      ]
     }
    ],
@@ -8707,7 +8727,7 @@
     {
      "data": {
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\" 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+       "<img 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\" 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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -9499,7 +9519,7 @@
     {
      "data": {
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\" 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -9511,10 +9531,10 @@
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axes._subplots.AxesSubplot at 0x2116bef21d0>,\n",
-       "  <matplotlib.axes._subplots.AxesSubplot at 0x2116bf18be0>],\n",
-       " [<matplotlib.colorbar.Colorbar at 0x2116bf5e550>,\n",
-       "  <matplotlib.colorbar.Colorbar at 0x2116bf254e0>])"
+       "([<matplotlib.axes._subplots.AxesSubplot at 0x1fc014ab0f0>,\n",
+       "  <matplotlib.axes._subplots.AxesSubplot at 0x1fc01497f28>],\n",
+       " [<matplotlib.colorbar.Colorbar at 0x1fc012f8828>,\n",
+       "  <matplotlib.colorbar.Colorbar at 0x1fc012c96d8>])"
       ]
      },
      "execution_count": 47,
@@ -9528,7 +9548,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'that': {'that': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n",
+       "  'MyDummy_ChanA_this_setpoint': array([5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9]),\n",
+       "  'MyDummy_ChanA_that_setpoint': array([ 9, 10, 11,  9, 10, 11,  9, 10, 11,  9, 10, 11,  9, 10, 11])}}"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datasaver.dataset.get_parameter_data('that')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -9651,7 +9693,7 @@
        "                            11                              1"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9662,7 +9704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -9677,7 +9719,7 @@
        "    that                         (MyDummy_ChanA_this_setpoint, MyDummy_ChanA_that_setpoint) int32 1 ... 1"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9685,13 +9727,6 @@
    "source": [
     "datasaver.dataset.get_data_as_pandas_dataframe()['that'].to_xarray()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb b/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb
index c5a53f66204..f8145fbb8fb 100644
--- a/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb
+++ b/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb
@@ -7,6 +7,27 @@
     "# Working with Pandas and XArray"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook is demonstrates how Pandas and XArray can be used to work with the QCoDeS Dataset. It is not meant as a general introduction to Pandas and XAarray. We refer to the official documentation for [Pandas](https://pandas.pydata.org/) and [XArray](http://xarray.pydata.org/en/stable/) for this. This notebook requires that both Pandas and XArray is installed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we borrow an example from the Dataset notebook to have some data to work with. We split the measurement in two so we can try merging it with Pandas."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -43,13 +64,6 @@
     "qc.logger.start_all_logging()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we borrow an example from the Dataset notebook to have some data to work with. We split the measurement in two so we can try merging it."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -70,7 +84,7 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#134@C:\\Users\\jenielse/experiments.db\n",
+       "tutorial_exp#no sample#136@C:\\Users\\jenielse/experiments.db\n",
        "-----------------------------------------------------------"
       ]
      },
@@ -92,7 +106,7 @@
     {
      "data": {
       "text/plain": [
-       "<qcodes.dataset.measurements.Measurement at 0x1c06c6222e8>"
+       "<qcodes.dataset.measurements.Measurement at 0x1e4b47ae278>"
       ]
      },
      "execution_count": 4,
@@ -153,7 +167,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First half:"
+    "We then perform a very basic experiment. To be able to demonstrate merging of datasets in Pandas we will perform the measurement in two parts."
    ]
   },
   {
@@ -165,7 +179,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 474\n"
+      "Starting experimental run with id: 478\n"
      ]
     }
    ],
@@ -187,13 +201,6 @@
     "df1 = datasaver.dataset.get_data_as_pandas_dataframe()['dmm_v1']"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Second half:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -203,7 +210,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 475\n"
+      "Starting experimental run with id: 479\n"
      ]
     }
    ],
@@ -229,7 +236,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lets first inspect the Pandas dataframe"
+    "`get_data_as_pandas_dataframe` returns the data as a dict from measured (dependent) parameters to DataFrames. Here we are only interested in the dataframe of a single parameter so we selec that from the dict."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets first inspect the Pandas DataFrame. Note how both dependent variables are used for the index. Pandas refers to this as a [MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html)"
    ]
   },
   {
@@ -271,61 +292,61 @@
        "    <tr>\n",
        "      <th rowspan=\"10\" valign=\"top\">-1.0</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>-0.000372</td>\n",
+       "      <td>-5.073949e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>0.000267</td>\n",
+       "      <td>7.058781e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>0.000217</td>\n",
+       "      <td>9.828259e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>0.000015</td>\n",
+       "      <td>-1.795980e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.96</th>\n",
-       "      <td>0.000023</td>\n",
+       "      <td>-3.483247e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.95</th>\n",
-       "      <td>-0.000037</td>\n",
+       "      <td>-7.151994e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.94</th>\n",
-       "      <td>-0.000004</td>\n",
+       "      <td>3.202385e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.93</th>\n",
-       "      <td>0.000004</td>\n",
+       "      <td>-8.596531e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.92</th>\n",
-       "      <td>0.000003</td>\n",
+       "      <td>1.450387e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.91</th>\n",
-       "      <td>-0.000002</td>\n",
+       "      <td>4.319205e-07</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                   dmm_v1\n",
-       "dac_ch1 dac_ch2          \n",
-       "-1.0    -1.00   -0.000372\n",
-       "        -0.99    0.000267\n",
-       "        -0.98    0.000217\n",
-       "        -0.97    0.000015\n",
-       "        -0.96    0.000023\n",
-       "        -0.95   -0.000037\n",
-       "        -0.94   -0.000004\n",
-       "        -0.93    0.000004\n",
-       "        -0.92    0.000003\n",
-       "        -0.91   -0.000002"
+       "                       dmm_v1\n",
+       "dac_ch1 dac_ch2              \n",
+       "-1.0    -1.00   -5.073949e-04\n",
+       "        -0.99    7.058781e-04\n",
+       "        -0.98    9.828259e-05\n",
+       "        -0.97   -1.795980e-04\n",
+       "        -0.96   -3.483247e-04\n",
+       "        -0.95   -7.151994e-05\n",
+       "        -0.94    3.202385e-05\n",
+       "        -0.93   -8.596531e-07\n",
+       "        -0.92    1.450387e-06\n",
+       "        -0.91    4.319205e-07"
       ]
      },
      "execution_count": 9,
@@ -341,7 +362,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also reset the index to return a simpler view where all datapoints are simply indexed by a running counter. This will come in handy just below."
+    "We can also reset the index to return a simpler view where all data points are simply indexed by a running counter. As we shall see below this can be needed in some situations."
    ]
   },
   {
@@ -380,78 +401,78 @@
        "      <th>0</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-1.00</td>\n",
-       "      <td>-0.000372</td>\n",
+       "      <td>-5.073949e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.99</td>\n",
-       "      <td>0.000267</td>\n",
+       "      <td>7.058781e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.98</td>\n",
-       "      <td>0.000217</td>\n",
+       "      <td>9.828259e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.97</td>\n",
-       "      <td>0.000015</td>\n",
+       "      <td>-1.795980e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.96</td>\n",
-       "      <td>0.000023</td>\n",
+       "      <td>-3.483247e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.95</td>\n",
-       "      <td>-0.000037</td>\n",
+       "      <td>-7.151994e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.94</td>\n",
-       "      <td>-0.000004</td>\n",
+       "      <td>3.202385e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.93</td>\n",
-       "      <td>0.000004</td>\n",
+       "      <td>-8.596531e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.92</td>\n",
-       "      <td>0.000003</td>\n",
+       "      <td>1.450387e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>-1.0</td>\n",
        "      <td>-0.91</td>\n",
-       "      <td>-0.000002</td>\n",
+       "      <td>4.319205e-07</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   dac_ch1  dac_ch2    dmm_v1\n",
-       "0     -1.0    -1.00 -0.000372\n",
-       "1     -1.0    -0.99  0.000267\n",
-       "2     -1.0    -0.98  0.000217\n",
-       "3     -1.0    -0.97  0.000015\n",
-       "4     -1.0    -0.96  0.000023\n",
-       "5     -1.0    -0.95 -0.000037\n",
-       "6     -1.0    -0.94 -0.000004\n",
-       "7     -1.0    -0.93  0.000004\n",
-       "8     -1.0    -0.92  0.000003\n",
-       "9     -1.0    -0.91 -0.000002"
+       "   dac_ch1  dac_ch2        dmm_v1\n",
+       "0     -1.0    -1.00 -5.073949e-04\n",
+       "1     -1.0    -0.99  7.058781e-04\n",
+       "2     -1.0    -0.98  9.828259e-05\n",
+       "3     -1.0    -0.97 -1.795980e-04\n",
+       "4     -1.0    -0.96 -3.483247e-04\n",
+       "5     -1.0    -0.95 -7.151994e-05\n",
+       "6     -1.0    -0.94  3.202385e-05\n",
+       "7     -1.0    -0.93 -8.596531e-07\n",
+       "8     -1.0    -0.92  1.450387e-06\n",
+       "9     -1.0    -0.91  4.319205e-07"
       ]
      },
      "execution_count": 10,
@@ -467,14 +488,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pandas has build in support for various forms of plotting. This does however, not support MultiIndex at the moment but we can use `reset_index` to make the data available for plotting."
+    "Pandas has build in support for various forms of plotting. This does however, not support MultiIndex at the moment so we use `reset_index` to make the data available for plotting."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
@@ -1260,7 +1281,7 @@
     {
      "data": {
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\" 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1272,7 +1293,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c06cb3f1d0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4b4cfe1d0>"
       ]
      },
      "execution_count": 11,
@@ -2090,7 +2111,7 @@
     {
      "data": {
       "text/html": [
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\" 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+       "<img 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2102,7 +2123,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c06d7d73c8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4b5914588>"
       ]
      },
      "execution_count": 13,
@@ -2118,7 +2139,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is also possible to select a subset of data from the datframe based on the x and y values"
+    "It is also possible to select a subset of data from the datframe based on the x and y values."
    ]
   },
   {
@@ -2160,19 +2181,19 @@
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-1.000</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>-3.724157e-04</td>\n",
+       "      <td>-5.073949e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>2.674091e-04</td>\n",
+       "      <td>7.058781e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>2.168524e-04</td>\n",
+       "      <td>9.828259e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>1.543205e-05</td>\n",
+       "      <td>-1.795980e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.995</th>\n",
@@ -2351,10 +2372,10 @@
       "text/plain": [
        "                       dmm_v1\n",
        "dac_ch1 dac_ch2              \n",
-       "-1.000  -1.00   -3.724157e-04\n",
-       "        -0.99    2.674091e-04\n",
-       "        -0.98    2.168524e-04\n",
-       "        -0.97    1.543205e-05\n",
+       "-1.000  -1.00   -5.073949e-04\n",
+       "        -0.99    7.058781e-04\n",
+       "        -0.98    9.828259e-05\n",
+       "        -0.97   -1.795980e-04\n",
        "-0.995  -1.00    7.680241e-10\n",
        "        -0.99    9.298480e-10\n",
        "        -0.98    1.123969e-09\n",
@@ -2410,21 +2431,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## XArray"
+    "## Working with XArray"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In many cases when working with data on a grid it may be more convenient to export the data to a [XArray](http://xarray.pydata.org) Dataset or DataArray"
+    "In many cases when working with data on a rectangular grids it may be more convenient to export the data to a [XArray](http://xarray.pydata.org) Dataset or DataArray"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The Pandas DataSet can be directly converted to a XArray DataSet:"
+    "The Pandas DataSet can be directly converted to a XArray [Dataset](http://xarray.pydata.org/en/stable/data-structures.html?#dataset):"
    ]
   },
   {
@@ -2450,7 +2471,7 @@
        "  * dac_ch1  (dac_ch1) float64 -1.0 -0.995 -0.99 -0.985 ... 0.985 0.99 0.995 1.0\n",
        "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
        "Data variables:\n",
-       "    dmm_v1   (dac_ch1, dac_ch2) float64 -0.0003724 0.0002674 ... 1.039e-05"
+       "    dmm_v1   (dac_ch1, dac_ch2) float64 -0.0005074 0.0007059 ... 1.039e-05"
       ]
      },
      "execution_count": 16,
@@ -2466,7 +2487,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "However, in many cases it is more convenient to work with a XArray DataArray. The DataArray can only contain a single dependent variable and can be obtained from the Dataset by indexing using the parameter name."
+    "However, in many cases it is more convenient to work with a XArray [DataArray](http://xarray.pydata.org/en/stable/data-structures.html?#dataarray). The DataArray can only contain a single dependent variable and can be obtained from the Dataset by indexing using the parameter name."
    ]
   },
   {
@@ -2487,7 +2508,7 @@
      "data": {
       "text/plain": [
        "<xarray.DataArray 'dmm_v1' (dac_ch1: 401, dac_ch2: 201)>\n",
-       "array([[-3.724157e-04,  2.674091e-04,  2.168524e-04, ...,  5.451526e-07,\n",
+       "array([[-5.073949e-04,  7.058781e-04,  9.828259e-05, ...,  5.451526e-07,\n",
        "         4.808069e-07,  4.233782e-07],\n",
        "       [ 7.680241e-10,  9.298480e-10,  1.123969e-09, ...,  5.951812e-07,\n",
        "         5.249305e-07,  4.622315e-07],\n",
@@ -2516,7 +2537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -3302,7 +3323,7 @@
     {
      "data": {
       "text/html": [
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\" 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\" width=\"640\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3317,16 +3338,16 @@
     "xaDataArray.plot(ax=ax[0,0])\n",
     "xaDataArray.mean(dim='dac_ch1').plot(ax=ax[1,0])\n",
     "xaDataArray.mean(dim='dac_ch2').plot(ax=ax[0,1])\n",
-    "xaDataArray[[200]].plot(ax=ax[1,1])\n",
+    "xaDataArray[200,:].plot(ax=ax[1,1])\n",
     "fig.tight_layout()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Above we demonstrate a few ways to index the data from a DataArray. For instance the DataArray can be directly plotted, the mean extracted or a specific row/column selected."
+   ]
   }
  ],
  "metadata": {

From 863de65011ed1c6280e76315c11bb4ca13a5c4ea Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 6 Feb 2019 13:23:25 +0100
Subject: [PATCH 713/719] Update test docstrings

---
 .../test_database_creation_and_upgrading.py   | 27 ++++++++++++-------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/qcodes/tests/dataset/test_database_creation_and_upgrading.py b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
index ce68d276e17..d2cc5e2b76a 100644
--- a/qcodes/tests/dataset/test_database_creation_and_upgrading.py
+++ b/qcodes/tests/dataset/test_database_creation_and_upgrading.py
@@ -125,7 +125,8 @@ def test_perform_actual_upgrade_0_to_1():
 
     if not os.path.exists(dbname_old):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
-                    " using the scripts in the legacy_DB_generation folder")
+                    " using the scripts in the "
+                    "https://github.com/QCoDeS/qcodes_generate_test_db/ repo")
 
     with temporarily_copied_DB(dbname_old, debug=False, version=0) as conn:
 
@@ -183,7 +184,8 @@ def test_perform_actual_upgrade_2_to_3_empty():
 
     if not os.path.exists(dbname_old):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
-                    " using the scripts in the legacy_DB_generation folder")
+                    " using the scripts in the "
+                    "https://github.com/QCoDeS/qcodes_generate_test_db/ repo")
 
     with temporarily_copied_DB(dbname_old, debug=False, version=2) as conn:
 
@@ -212,7 +214,8 @@ def test_perform_actual_upgrade_2_to_3_empty_runs():
 
     if not os.path.exists(dbname_old):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
-                    " using the scripts in the legacy_DB_generation folder")
+                    " using the scripts in the "
+                    "https://github.com/QCoDeS/qcodes_generate_test_db/ repo")
 
     with temporarily_copied_DB(dbname_old, debug=False, version=2) as conn:
 
@@ -227,7 +230,8 @@ def test_perform_actual_upgrade_2_to_3_some_runs():
 
     if not os.path.exists(dbname_old):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
-                    " using the scripts in the legacy_DB_generation folder")
+                    " using the scripts in the"
+                    "https://github.com/QCoDeS/qcodes_generate_test_db/ repo")
 
     with temporarily_copied_DB(dbname_old, debug=False, version=2) as conn:
 
@@ -319,7 +323,8 @@ def test_perform_upgrade_v2_v3_to_v4_fixes():
 
     if not os.path.exists(dbname_old):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
-                    " using the scripts in the legacy_DB_generation folder")
+                    " using the scripts in the"
+                    " https://github.com/QCoDeS/qcodes_generate_test_db/ repo")
 
     with temporarily_copied_DB(dbname_old, debug=False, version=3) as conn:
 
@@ -449,10 +454,9 @@ def test_perform_upgrade_v2_v3_to_v4_fixes():
         assert p5.unit == "unit 5"
 
 
-def test_perform_upgrade_v3_to_v4_fixes():
+def test_perform_upgrade_v3_to_v4():
     """
-    Test that a db that was upgraded from v2 to v3 with a buggy
-    version will be corrected when upgraded to v4.
+    Test that a db upgrade from v2 to v4 works correctly.
     """
 
     v3fixpath = os.path.join(fixturepath, 'db_files', 'version3')
@@ -461,7 +465,8 @@ def test_perform_upgrade_v3_to_v4_fixes():
 
     if not os.path.exists(dbname_old):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
-                    " using the scripts in the legacy_DB_generation folder")
+                    " using the scripts in the "
+                    "https://github.com/QCoDeS/qcodes_generate_test_db/ repo")
 
     with temporarily_copied_DB(dbname_old, debug=False, version=3) as conn:
 
@@ -530,6 +535,7 @@ def test_perform_upgrade_v3_to_v4_fixes():
         assert p5.label == "Parameter 5"
         assert p5.unit == "unit 5"
 
+
 @pytest.mark.usefixtures("empty_temp_db")
 def test_update_existing_guids(caplog):
 
@@ -643,7 +649,8 @@ def test_getting_db_version(version):
 
     if not os.path.exists(dbname):
         pytest.skip("No db-file fixtures found. You can generate test db-files"
-                    " using the scripts in the legacy_DB_generation folder")
+                    " using the scripts in the "
+                    "https://github.com/QCoDeS/qcodes_generate_test_db/ repo")
 
     (db_v, new_v) = get_db_version_and_newest_available_version(dbname)
 

From 6824df549e753847b15a34dc4ee4496b0cf7b05e Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 6 Feb 2019 13:23:44 +0100
Subject: [PATCH 714/719] Correct docstring

---
 qcodes/dataset/sqlite_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 6eaca9948c3..8cf48f6c971 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -718,7 +718,7 @@ def perform_db_upgrade_3_to_4(conn: ConnectionPlus) -> None:
     resulting in the parameter being marked inferred_from for each char
     in the inferred_from variable and inferred_from was not handled
     correctly for parameters that were neither dependencies nor dependent on
-    other parameters. Both have since been fixes so rerun the upgrade.
+    other parameters. Both have since been fixed so rerun the upgrade.
     """
 
     no_of_runs_query = "SELECT max(run_id) FROM runs"

From 778de4dc1d83e7bd0142ad99f7aa4fadf03b3fec Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Wed, 6 Feb 2019 13:26:24 +0100
Subject: [PATCH 715/719] More consistent variable names

---
 qcodes/dataset/sqlite_base.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/qcodes/dataset/sqlite_base.py b/qcodes/dataset/sqlite_base.py
index 8cf48f6c971..90d2721e8e6 100644
--- a/qcodes/dataset/sqlite_base.py
+++ b/qcodes/dataset/sqlite_base.py
@@ -606,7 +606,7 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
     # depend, then the dependent ParamSpecs and finally the rest
 
     for layout_id in list(indeps) + list(deps) + list(the_rest):
-        (name, label, unit, inferred_from) = layouts[layout_id]
+        (name, label, unit, inferred_from_str) = layouts[layout_id]
         # get the data type
         sql = f'PRAGMA TABLE_INFO("{result_table_name}")'
         c = transaction(conn, sql)
@@ -615,7 +615,7 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
                 paramtype = row['type']
                 break
 
-        inferred_from_list: List[str] = []
+        inferred_from: List[str] = []
         depends_on: List[str] = []
 
         # this parameter depends on another parameter
@@ -623,14 +623,14 @@ def _2to3_get_paramspecs(conn: ConnectionPlus,
             setpoints = dependencies[layout_id]
             depends_on = [paramspecs[idp].name for idp in setpoints]
 
-        if inferred_from != '':
-            inferred_from_list = inferred_from.split(', ')
+        if inferred_from_str != '':
+            inferred_from = inferred_from_str.split(', ')
 
         paramspec = ParamSpec(name=name,
                               paramtype=paramtype,
                               label=label, unit=unit,
                               depends_on=depends_on,
-                              inferred_from=inferred_from_list)
+                              inferred_from=inferred_from)
         paramspecs[layout_id] = paramspec
 
     return paramspecs

From c9f6bbdb50ab39e98f1dba66ccc10ad3c0cff4af Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 6 Feb 2019 14:32:32 +0100
Subject: [PATCH 716/719] Fix a few typos

---
 .../Working-With-Pandas-and-XArray.ipynb      | 1321 ++++++++++++++---
 1 file changed, 1074 insertions(+), 247 deletions(-)

diff --git a/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb b/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb
index f8145fbb8fb..b5fb736da4f 100644
--- a/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb
+++ b/docs/examples/DataSet/Working-With-Pandas-and-XArray.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This notebook is demonstrates how Pandas and XArray can be used to work with the QCoDeS Dataset. It is not meant as a general introduction to Pandas and XAarray. We refer to the official documentation for [Pandas](https://pandas.pydata.org/) and [XArray](http://xarray.pydata.org/en/stable/) for this. This notebook requires that both Pandas and XArray is installed."
+    "This notebook demonstrates how Pandas and XArray can be used to work with the QCoDeS Dataset. It is not meant as a general introduction to Pandas and XArray. We refer to the official documentation for [Pandas](https://pandas.pydata.org/) and [XArray](http://xarray.pydata.org/en/stable/) for this. This notebook requires that both Pandas and XArray are installed."
    ]
   },
   {
@@ -25,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First we borrow an example from the Dataset notebook to have some data to work with. We split the measurement in two so we can try merging it with Pandas."
+    "First we borrow an example from the measurement notebook to have some data to work with. We split the measurement in two so we can try merging it with Pandas."
    ]
   },
   {
@@ -39,7 +39,7 @@
      "text": [
       "Logging hadn't been started.\n",
       "Activating auto-logging. Current session state plus future input saved.\n",
-      "Filename       : C:\\Users\\jenielse\\.qcodes\\logs\\command_history.log\n",
+      "Filename       : C:\\Users\\wihpniel\\.qcodes\\logs\\command_history.log\n",
       "Mode           : append\n",
       "Output logging : True\n",
       "Raw input log  : False\n",
@@ -56,7 +56,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "import qcodes as qc\n",
-    "from qcodes.dataset.experiment_container import new_experiment\n",
+    "from qcodes.dataset.experiment_container import load_or_create_experiment\n",
     "from qcodes.dataset.database import initialise_database\n",
     "from qcodes.tests.instrument_mocks import DummyInstrument\n",
     "from qcodes.dataset.measurements import Measurement\n",
@@ -84,8 +84,10 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#136@C:\\Users\\jenielse/experiments.db\n",
-       "-----------------------------------------------------------"
+       "working_with_pandas#no sample#7@C:\\Users\\wihpniel\\src\\Qcodes\\docs\\examples\\DataSet/db_files/mvmhqlmnfs.db\n",
+       "---------------------------------------------------------------------------------------------------------\n",
+       "86-results-1-dac_ch1,dac_ch2,dmm_v1-40200\n",
+       "87-results-2-dac_ch1,dac_ch2,dmm_v1-40401"
       ]
      },
      "execution_count": 3,
@@ -95,7 +97,8 @@
    ],
    "source": [
     "initialise_database()\n",
-    "new_experiment(name='tutorial_exp', sample_name=\"no sample\")"
+    "load_or_create_experiment(experiment_name='working_with_pandas',\n",
+    "                          sample_name=\"no sample\")"
    ]
   },
   {
@@ -106,7 +109,7 @@
     {
      "data": {
       "text/plain": [
-       "<qcodes.dataset.measurements.Measurement at 0x1e4b47ae278>"
+       "<qcodes.dataset.measurements.Measurement at 0x17e85a7c828>"
       ]
      },
      "execution_count": 4,
@@ -115,14 +118,10 @@
     }
    ],
    "source": [
-    "# For the 2D, we'll need a new batch of parameters, notably one with two \n",
-    "# other parameters as setpoints. We therefore define a new Measurement\n",
-    "# with new parameters\n",
-    "\n",
     "meas = Measurement()\n",
     "meas.register_parameter(dac.ch1)  # register the first independent parameter\n",
     "meas.register_parameter(dac.ch2)  # register the second independent parameter\n",
-    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1, dac.ch2))  # now register the dependent oone"
+    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1, dac.ch2))  # register the dependent one"
    ]
   },
   {
@@ -179,7 +178,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 478\n"
+      "Starting experimental run with id: 88\n"
      ]
     }
    ],
@@ -210,7 +209,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting experimental run with id: 479\n"
+      "Starting experimental run with id: 89\n"
      ]
     }
    ],
@@ -236,7 +235,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`get_data_as_pandas_dataframe` returns the data as a dict from measured (dependent) parameters to DataFrames. Here we are only interested in the dataframe of a single parameter so we selec that from the dict."
+    "`get_data_as_pandas_dataframe` returns the data as a dict from measured (dependent) parameters to DataFrames. Here we are only interested in the dataframe of a single parameter, so we select that from the dict."
    ]
   },
   {
@@ -250,13 +249,134 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lets first inspect the Pandas DataFrame. Note how both dependent variables are used for the index. Pandas refers to this as a [MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html)"
+    "Lets first inspect the Pandas DataFrame. Note how both dependent variables are used for the index. Pandas refers to this as a [MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html). For visual clarity, we just look at the first N points of the dataset."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>dmm_v1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dac_ch1</th>\n",
+       "      <th>dac_ch2</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"10\" valign=\"top\">-1.0</th>\n",
+       "      <th>-1.00</th>\n",
+       "      <td>-0.000271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.99</th>\n",
+       "      <td>-0.000081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.98</th>\n",
+       "      <td>0.000111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.97</th>\n",
+       "      <td>0.000140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.96</th>\n",
+       "      <td>-0.000240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.95</th>\n",
+       "      <td>-0.000176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.94</th>\n",
+       "      <td>0.000081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.93</th>\n",
+       "      <td>-0.000004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.92</th>\n",
+       "      <td>0.000008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.91</th>\n",
+       "      <td>-0.000002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   dmm_v1\n",
+       "dac_ch1 dac_ch2          \n",
+       "-1.0    -1.00   -0.000271\n",
+       "        -0.99   -0.000081\n",
+       "        -0.98    0.000111\n",
+       "        -0.97    0.000140\n",
+       "        -0.96   -0.000240\n",
+       "        -0.95   -0.000176\n",
+       "        -0.94    0.000081\n",
+       "        -0.93   -0.000004\n",
+       "        -0.92    0.000008\n",
+       "        -0.91   -0.000002"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[:N]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also reset the index to return a simpler view where all data points are simply indexed by a running counter. As we shall see below this can be needed in some situations. Note that calling `reset_index` leaves the original dataframe untouched."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -267,236 +387,943 @@
        "        vertical-align: middle;\n",
        "    }\n",
        "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dac_ch1</th>\n",
+       "      <th>dac_ch2</th>\n",
+       "      <th>dmm_v1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.00</td>\n",
+       "      <td>-0.000271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.99</td>\n",
+       "      <td>-0.000081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.98</td>\n",
+       "      <td>0.000111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.97</td>\n",
+       "      <td>0.000140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.96</td>\n",
+       "      <td>-0.000240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.95</td>\n",
+       "      <td>-0.000176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.94</td>\n",
+       "      <td>0.000081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.93</td>\n",
+       "      <td>-0.000004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.92</td>\n",
+       "      <td>0.000008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.91</td>\n",
+       "      <td>-0.000002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   dac_ch1  dac_ch2    dmm_v1\n",
+       "0     -1.0    -1.00 -0.000271\n",
+       "1     -1.0    -0.99 -0.000081\n",
+       "2     -1.0    -0.98  0.000111\n",
+       "3     -1.0    -0.97  0.000140\n",
+       "4     -1.0    -0.96 -0.000240\n",
+       "5     -1.0    -0.95 -0.000176\n",
+       "6     -1.0    -0.94  0.000081\n",
+       "7     -1.0    -0.93 -0.000004\n",
+       "8     -1.0    -0.92  0.000008\n",
+       "9     -1.0    -0.91 -0.000002"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.reset_index()[0:N]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas has built-in support for various forms of plotting. This does not, however, support MultiIndex at the moment so we use `reset_index` to make the data available for plotting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
+       "}\n",
        "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
        "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>dmm_v1</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dac_ch1</th>\n",
-       "      <th>dac_ch2</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"10\" valign=\"top\">-1.0</th>\n",
-       "      <th>-1.00</th>\n",
-       "      <td>-5.073949e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-0.99</th>\n",
-       "      <td>7.058781e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-0.98</th>\n",
-       "      <td>9.828259e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-0.97</th>\n",
-       "      <td>-1.795980e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-0.96</th>\n",
-       "      <td>-3.483247e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-0.95</th>\n",
-       "      <td>-7.151994e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-0.94</th>\n",
-       "      <td>3.202385e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-0.93</th>\n",
-       "      <td>-8.596531e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-0.92</th>\n",
-       "      <td>1.450387e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-0.91</th>\n",
-       "      <td>4.319205e-07</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
       ],
       "text/plain": [
-       "                       dmm_v1\n",
-       "dac_ch1 dac_ch2              \n",
-       "-1.0    -1.00   -5.073949e-04\n",
-       "        -0.99    7.058781e-04\n",
-       "        -0.98    9.828259e-05\n",
-       "        -0.97   -1.795980e-04\n",
-       "        -0.96   -3.483247e-04\n",
-       "        -0.95   -7.151994e-05\n",
-       "        -0.94    3.202385e-05\n",
-       "        -0.93   -8.596531e-07\n",
-       "        -0.92    1.450387e-06\n",
-       "        -0.91    4.319205e-07"
+       "<IPython.core.display.Javascript object>"
       ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1[:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also reset the index to return a simpler view where all data points are simply indexed by a running counter. As we shall see below this can be needed in some situations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dac_ch1</th>\n",
-       "      <th>dac_ch2</th>\n",
-       "      <th>dmm_v1</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.00</td>\n",
-       "      <td>-5.073949e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-0.99</td>\n",
-       "      <td>7.058781e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-0.98</td>\n",
-       "      <td>9.828259e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-0.97</td>\n",
-       "      <td>-1.795980e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-0.96</td>\n",
-       "      <td>-3.483247e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-0.95</td>\n",
-       "      <td>-7.151994e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-0.94</td>\n",
-       "      <td>3.202385e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-0.93</td>\n",
-       "      <td>-8.596531e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-0.92</td>\n",
-       "      <td>1.450387e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-0.91</td>\n",
-       "      <td>4.319205e-07</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+       "<img 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\" width=\"639.8333333333334\">"
       ],
       "text/plain": [
-       "   dac_ch1  dac_ch2        dmm_v1\n",
-       "0     -1.0    -1.00 -5.073949e-04\n",
-       "1     -1.0    -0.99  7.058781e-04\n",
-       "2     -1.0    -0.98  9.828259e-05\n",
-       "3     -1.0    -0.97 -1.795980e-04\n",
-       "4     -1.0    -0.96 -3.483247e-04\n",
-       "5     -1.0    -0.95 -7.151994e-05\n",
-       "6     -1.0    -0.94  3.202385e-05\n",
-       "7     -1.0    -0.93 -8.596531e-07\n",
-       "8     -1.0    -0.92  1.450387e-06\n",
-       "9     -1.0    -0.91  4.319205e-07"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 10,
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x17e85f41d30>"
+      ]
+     },
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df1.reset_index()[0:10]"
+    "df1.reset_index().plot.scatter('dac_ch1', 'dac_ch2', c='dmm_v1')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pandas has build in support for various forms of plotting. This does however, not support MultiIndex at the moment so we use `reset_index` to make the data available for plotting."
+    "Similarly, for the other dataframe:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1281,7 +2108,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" 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+       "<img 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\" width=\"639.8333333333334\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1293,16 +2120,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4b4cfe1d0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x17e86fe1da0>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df1.reset_index().plot.scatter('dac_ch1', 'dac_ch2', c='dmm_v1')"
+    "df2.reset_index().plot.scatter('dac_ch1', 'dac_ch2', c='dmm_v1')"
    ]
   },
   {
@@ -1314,7 +2141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1323,7 +2150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {
     "scrolled": false
    },
@@ -2111,7 +2938,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" 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+       "<img 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\" width=\"639.8333333333334\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2123,10 +2950,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4b5914588>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x17e863db780>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2144,7 +2971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -2181,19 +3008,19 @@
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-1.000</th>\n",
        "      <th>-1.00</th>\n",
-       "      <td>-5.073949e-04</td>\n",
+       "      <td>-2.714239e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.99</th>\n",
-       "      <td>7.058781e-04</td>\n",
+       "      <td>-8.128461e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.98</th>\n",
-       "      <td>9.828259e-05</td>\n",
+       "      <td>1.112536e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-0.97</th>\n",
-       "      <td>-1.795980e-04</td>\n",
+       "      <td>1.397232e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">-0.995</th>\n",
@@ -2372,10 +3199,10 @@
       "text/plain": [
        "                       dmm_v1\n",
        "dac_ch1 dac_ch2              \n",
-       "-1.000  -1.00   -5.073949e-04\n",
-       "        -0.99    7.058781e-04\n",
-       "        -0.98    9.828259e-05\n",
-       "        -0.97   -1.795980e-04\n",
+       "-1.000  -1.00   -2.714239e-04\n",
+       "        -0.99   -8.128461e-05\n",
+       "        -0.98    1.112536e-04\n",
+       "        -0.97    1.397232e-04\n",
        "-0.995  -1.00    7.680241e-10\n",
        "        -0.99    9.298480e-10\n",
        "        -0.98    1.123969e-09\n",
@@ -2418,7 +3245,7 @@
        "        -0.97    2.936052e-09"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2450,7 +3277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2459,7 +3286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -2471,10 +3298,10 @@
        "  * dac_ch1  (dac_ch1) float64 -1.0 -0.995 -0.99 -0.985 ... 0.985 0.99 0.995 1.0\n",
        "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
        "Data variables:\n",
-       "    dmm_v1   (dac_ch1, dac_ch2) float64 -0.0005074 0.0007059 ... 1.039e-05"
+       "    dmm_v1   (dac_ch1, dac_ch2) float64 -0.0002714 -8.128e-05 ... 1.039e-05"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2492,7 +3319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2501,14 +3328,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "<xarray.DataArray 'dmm_v1' (dac_ch1: 401, dac_ch2: 201)>\n",
-       "array([[-5.073949e-04,  7.058781e-04,  9.828259e-05, ...,  5.451526e-07,\n",
+       "array([[-2.714239e-04, -8.128461e-05,  1.112536e-04, ...,  5.451526e-07,\n",
        "         4.808069e-07,  4.233782e-07],\n",
        "       [ 7.680241e-10,  9.298480e-10,  1.123969e-09, ...,  5.951812e-07,\n",
        "         5.249305e-07,  4.622315e-07],\n",
@@ -2526,7 +3353,7 @@
        "  * dac_ch2  (dac_ch2) float64 -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2537,7 +3364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -3323,7 +4150,7 @@
     {
      "data": {
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\" 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\" width=\"639.8333333333334\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3366,7 +4193,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,

From 7b4d1791a8c2ac5eb61cc7d9740c731238532292 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen <whpn@mailbox.org>
Date: Wed, 6 Feb 2019 14:35:13 +0100
Subject: [PATCH 717/719] Remove use of new_experiment

---
 docs/examples/DataSet/Dataset Context Manager.ipynb | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/docs/examples/DataSet/Dataset Context Manager.ipynb b/docs/examples/DataSet/Dataset Context Manager.ipynb
index 07468f95aa5..28b262b819f 100644
--- a/docs/examples/DataSet/Dataset Context Manager.ipynb	
+++ b/docs/examples/DataSet/Dataset Context Manager.ipynb	
@@ -21,7 +21,7 @@
      "text": [
       "Logging hadn't been started.\n",
       "Activating auto-logging. Current session state plus future input saved.\n",
-      "Filename       : C:\\Users\\jenielse\\.qcodes\\logs\\command_history.log\n",
+      "Filename       : C:\\Users\\wihpniel\\.qcodes\\logs\\command_history.log\n",
       "Mode           : append\n",
       "Output logging : True\n",
       "Raw input log  : False\n",
@@ -47,7 +47,7 @@
     "from qcodes import Station\n",
     "from qcodes.dataset.experiment_container import (Experiment,\n",
     "                                                 load_last_experiment,\n",
-    "                                                 new_experiment)\n",
+    "                                                 load_or_create_experiment)\n",
     "from qcodes.dataset.database import initialise_database\n",
     "from qcodes.tests.instrument_mocks import DummyInstrument\n",
     "from qcodes.dataset.param_spec import ParamSpec\n",
@@ -151,8 +151,8 @@
     {
      "data": {
       "text/plain": [
-       "tutorial_exp#no sample#137@C:\\Users\\jenielse/experiments.db\n",
-       "-----------------------------------------------------------"
+       "dataset_context_manager#no sample#8@C:\\Users\\wihpniel\\src\\Qcodes\\docs\\examples\\DataSet/db_files/mvmhqlmnfs.db\n",
+       "-------------------------------------------------------------------------------------------------------------"
       ]
      },
      "execution_count": 6,
@@ -162,7 +162,8 @@
    ],
    "source": [
     "initialise_database()\n",
-    "new_experiment(name='tutorial_exp', sample_name=\"no sample\")"
+    "load_or_create_experiment(experiment_name='dataset_context_manager',\n",
+    "                          sample_name=\"no sample\")"
    ]
   },
   {
@@ -9745,7 +9746,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.7"
   },
   "nbsphinx": {
    "timeout": 60

From 83d4b25e0641e1c5598f5c96cf7604fd0012a2f8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 11 Feb 2019 11:03:15 +0100
Subject: [PATCH 718/719] This workaround is no longer needed with mypy=0.670

---
 qcodes/logger/logger.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/logger/logger.py b/qcodes/logger/logger.py
index fee0ea9c64a..21d3eee4fef 100644
--- a/qcodes/logger/logger.py
+++ b/qcodes/logger/logger.py
@@ -89,7 +89,7 @@ def get_level_code(level: Union[str, int]) -> int:
         # bug:
         # >>> import logging
         # >>> print(logging.getLevelName('DEBUG'))
-        return logging.getLevelName(level)  # type: ignore
+        return logging.getLevelName(level)
     else:
         raise RuntimeError('get_level_code: '
                            f'Cannot to convert level {level} of type '

From 258ecfe7eee0bc688c2d80a4e5081bceb36d6483 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" <Jens.Nielsen@microsoft.com>
Date: Mon, 11 Feb 2019 11:04:05 +0100
Subject: [PATCH 719/719] Require the latest version of mypy

fix this to a specific version to avoid failures in the future
---
 test_requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test_requirements.txt b/test_requirements.txt
index ef898e0b2d1..b885df50d2f 100644
--- a/test_requirements.txt
+++ b/test_requirements.txt
@@ -3,7 +3,7 @@ pytest-cov
 pytest
 codacy-coverage
 hypothesis!=3.69.11 # due to bug in log capture
-mypy>=0.630
+mypy==0.670
 git+https://github.com/QCoDeS/pyvisa-sim.git
 lxml
 codecov