diff --git a/CHANGES.md b/CHANGES.md index e7362de36..03ebd4d6a 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,3 +1,49 @@ +# Version 0.7.5 + +- Improved NeuroML conversion support + +- Make cfg.checkErrors = True by default, but set to False if using multiple cores or batch + +- Added methods to rename netParams dict keys in a nested format (needed for GUI) + +- Added analysis.plotSpikeStats() func to plot stats of cell rates, ISI CVs and synchronies + +- Added analysis.calculateRate() func to calculate avg and peak rate of pop subset at time range + +- Added analysis.plotRates() func to plot avg and peak rate of different pop subsets at time range + +- Added option to include list of pops or cells in 'include' arguments e.g. include=[['E4','E2'], [1,3]] + +- Added cfg.compactConnFormat option to replace conns dict format with compact list format + +- Added option to plotConn() and plot2Dnet() to load data from compact format json files + +- Adapted python2 code so conversion to python3 via 2to3 command works straight away + +- Added 'instantiate' argument to sim.load function + +- Added 'dpi' argument to analysis.plotSpikeHist() + +- Replaced init()/h.stdinit() with finitialize() so h.v_init and h.dt get set properly + +- Removed call to h.stdrun() but made v_init a global in cfg.hParams and initialized h.tstop to cfg.duration + +- Fixed bug setting globals that don't exist + +- Fixed issue setting global variables when loading from json + +- Fixed issue to make convergence+divergence connections randomization more robust and efficient (issue #254) + +- Fixed bug in colors of plotSpikeHist + +- Fixed bug in replaceDictODict() that lead to wrong results when importing cells + +- Fixed bug when using sim.gatherOnlySimData + +- Fixed bugs in saveLoadV1 example + +- Fixed bug when generating subConn with createNEURONObj=False + # Version 0.7.4 - Added polarity param to analysis.plotEPSPAmp() @@ -14,6 +60,7 @@ - Fixed bug that removed previously existing element from path during importCellParams() + # Version 0.7.3 - Option to create section lists based on y displacement from soma (addCellParamsSecList) diff --git a/doc/source/code/tut8_analysis.py b/doc/source/code/tut8_analysis.py new file mode 100644 index 000000000..10b853474 --- /dev/null +++ b/doc/source/code/tut8_analysis.py @@ -0,0 +1,209 @@ +""" +analysis.py + +Functions to read and plot figures from the batch simulation results. + +Contributors: salvadordura@gmail.com +""" + +import json +import pandas as pd +import seaborn as sb +import matplotlib.pyplot as plt +import pickle +import numpy as np +from pylab import * +from itertools import product +from pprint import pprint +from netpyne import specs + + +#-------------------------------------------------------------------- +# Function to read batch data +#-------------------------------------------------------------------- +def readBatchData(dataFolder, batchLabel, loadAll=False, saveAll=True, vars=None, maxCombs=None, listCombs=None): + # load from previously saved file with all data + if loadAll: + print '\nLoading single file with all data...' + filename = '%s/%s/%s_allData.json' % (dataFolder, batchLabel, batchLabel) + with open(filename, 'r') as fileObj: + dataLoad = json.load(fileObj, object_pairs_hook=specs.OrderedDict) + params = dataLoad['params'] + data = dataLoad['data'] + return params, data + + if isinstance(listCombs, basestring): + filename = str(listCombs) + with open(filename, 'r') as fileObj: + dataLoad = json.load(fileObj) + listCombs = dataLoad['paramsMatch'] + + # read the batch file and cfg + batchFile = '%s/%s_batch.json' % (dataFolder, batchLabel) + with open(batchFile, 'r') as fileObj: + b = json.load(fileObj)['batch'] + + # read params labels and ranges + params = b['params'] + + # reorder so grouped params come first + preorder = [p for p in params if 'group' in p and p['group']] + for p in params: + if p not in preorder: preorder.append(p) + params = preorder + + # read vars from all files - store in dict + if b['method'] == 'grid': + labelList, valuesList = zip(*[(p['label'], p['values']) for p in params]) + valueCombinations = product(*(valuesList)) + indexCombinations = product(*[range(len(x)) for x in valuesList]) + data = {} + print 'Reading data...' + missing = 0 + for i,(iComb, pComb) in enumerate(zip(indexCombinations, valueCombinations)): + if (not maxCombs or i<= maxCombs) and (not listCombs or list(pComb) in listCombs): + print i, iComb + # read output file + iCombStr = ''.join([''.join('_'+str(i)) for i in iComb]) + simLabel = b['batchLabel']+iCombStr + outFile = b['saveFolder']+'/'+simLabel+'.json' + try: + with open(outFile, 'r') as fileObj: + output = json.load(fileObj, object_pairs_hook=specs.OrderedDict) + # save output file in data dict + data[iCombStr] = {} + data[iCombStr]['paramValues'] = pComb # store param values + if not vars: vars = output.keys() + + for key in vars: + if isinstance(key, tuple): + container = output + for ikey in range(len(key)-1): + container = container[key[ikey]] + data[iCombStr][key[1]] = container[key[-1]] + + elif isinstance(key, basestring): + data[iCombStr][key] = output[key] + + except: + print '... file missing' + missing = missing + 1 + output = {} + else: + missing = missing + 1 + + print '%d files missing' % (missing) + + # save + if saveAll: + print 'Saving to single file with all data' + filename = '%s/%s_allData.json' % (dataFolder, batchLabel) + dataSave = {'params': params, 'data': data} + with open(filename, 'w') as fileObj: + json.dump(dataSave, fileObj) + + return params, data + +#-------------------------------------------------------------------- +# Function to convert data to Pandas +#-------------------------------------------------------------------- +def toPandas(params, data): + if 'simData' in data[data.keys()[0]]: + rows = [list(d['paramValues'])+[s for s in d['simData'].values()] for d in data.values()] + cols = [str(d['label']) for d in params]+[s for s in data[data.keys()[0]]['simData'].keys()] + else: + rows = [list(d['paramValues'])+[s for s in d.values()] for d in data.values()] + cols = [str(d['label']) for d in params]+[s for s in data[data.keys()[0]].keys()] + + df = pd.DataFrame(rows, columns=cols) + df['simLabel'] = data.keys() + + colRename=[] + for col in list(df.columns): + if col.startswith("[u'"): + colName = col.replace(", u'","_'").replace("[u","").replace("'","").replace("]","").replace(", ","_") + colRename.append(colName) + else: + colRename.append(col) + print colRename + df.columns = colRename + + return df + +#-------------------------------------------------------------------- +# Function to colors and style of figures +#-------------------------------------------------------------------- +def setPlotFormat(numColors=8): + plt.style.use('seaborn-whitegrid') + + plt.rcParams['font.size'] = 12 + plt.rcParams['axes.titlesize'] = 14 + plt.rcParams['axes.labelsize'] = 12 + plt.rcParams['legend.fontsize'] = 'large' + + NUM_COLORS = numColors + colormap = plt.get_cmap('nipy_spectral') + colorlist = [colormap(1.*i/NUM_COLORS) for i in range(NUM_COLORS)] + + plt.rc('axes', prop_cycle=(cycler('color', colorlist))) + + +#-------------------------------------------------------------------- +# Function to plot relation between parameters (tau2 and weight) and firing rate +#-------------------------------------------------------------------- +def plot2DRate(dataFolder, batchLabel, params, data, par1, par2, val, valLabel, graphType='matrix', saveFile=None): + df = toPandas(params, data) + # dfpop = dfPopRates(df1, 7) + + dfpop = df.iloc[:,0:5] # get param columns of all rows + # dfpop['simLabel'] = df['simLabel'] + for k in df.popRates[0].keys(): dfpop[k] = [r[k] for r in df.popRates] + #return dfpop + + print dfpop + # if not valLabel: valLabel = val + dfsubset = dfpop[[par1,par2,val]] + # dfgroup = dfsubset.groupby(by=[par1,par2]) + # if groupStat=='first': + # dfgroup2 = dfgroup.first() + # elif groupStat=='last': + # dfgroup2 = dfgroup.last() + # elif groupStat=='mean': + # dfgroup2 = dfgroup.mean() + # elif groupStat=='sum': + # dfgroup2 = dfgroup.sum() + # dffinal = pd.DataFrame(dfgroup2).reset_index() + + dfpiv = pd.pivot_table(dfsubset, index=par1, columns=par2, values=val) +# pandas.pivot_table(df,values='count',index='site_id',columns='week') + if graphType=='matrix': + sb.heatmap(dfpiv, square=True, cbar_kws={'label': valLabel}) + elif graphType=='line': + setPlotFormat(numColors = len(dfpiv.columns)) + #dfpiv = dfpiv[['IT2','IT4','IT5A','IT5B','PT5B','IT6','CT6']] + dfpiv.plot(marker='o') + try: + if saveFile: + plt.savefig(saveFile) + else: + plt.savefig(dataFolder+'/'+batchLabel+'_matrix_'+par1+'_'+par2+'_'+val+'.png') + except: + print 'Error saving figure...' + + plt.show() + +#-------------------------------------------------------------------- +# Function to read batch data and plot figure +#-------------------------------------------------------------------- +def readPlot(): + dataFolder = 'tut8_data/' + batchLabel = 'tauWeight' + + params, data = readBatchData(dataFolder, batchLabel, loadAll=0, saveAll=1, vars=None, maxCombs=None) + plot2DRate(dataFolder, batchLabel, params, data, 'synMechTau2', 'connWeight', 'M', "'M' pop rate (Hz)") + + +# Main code +if __name__ == '__main__': + readPlot() + diff --git a/doc/source/code/tut8_batch.py b/doc/source/code/tut8_batch.py new file mode 100644 index 000000000..4d7cc5036 --- /dev/null +++ b/doc/source/code/tut8_batch.py @@ -0,0 +1,31 @@ +from netpyne import specs +from netpyne.batch import Batch + +def batchTauWeight(): + # Create variable of type ordered dictionary (NetPyNE's customized version) + params = specs.ODict() + + # fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig) + params['synMechTau2'] = [3.0, 5.0, 7.0] + params['connWeight'] = [0.005, 0.01, 0.15] + + # create Batch object with paramaters to modify, and specifying files to use + b = Batch(params=params, cfgFile='tut8_cfg.py', netParamsFile='tut8_netParams.py',) + + # Set output folder, grid method (all param combinations), and run configuration + b.batchLabel = 'tauWeight' + b.saveFolder = 'tut8_data' + b.method = 'grid' + b.runCfg = {'type': 'mpi', + 'script': 'tut8_init.py', + 'skip': True} + + # Run batch simulations + b.run() + +# Main code +if __name__ == '__main__': + batchTauWeight() + + + diff --git a/doc/source/code/tut8_cfg.py b/doc/source/code/tut8_cfg.py new file mode 100644 index 000000000..1474d0589 --- /dev/null +++ b/doc/source/code/tut8_cfg.py @@ -0,0 +1,19 @@ +from netpyne import specs + +# Simulation options +cfg = specs.SimConfig() # object of class SimConfig to store simulation configuration + +cfg.duration = 1*1e3 # Duration of the simulation, in ms +cfg.dt = 0.025 # Internal integration timestep to use +cfg.verbose = False # Show detailed messages +cfg.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}} # Dict with traces to record +cfg.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc) +cfg.filename = 'tut8' # Set file output name +cfg.saveJson = True +cfg.printPopAvgRates = True +cfg.analysis['plotRaster'] = {'saveFig': True} # Plot a raster +cfg.analysis['plotTraces'] = {'include': [20], 'saveFig': True} # Plot recorded traces for this list of cells + +# Variable parameters (used in netParams) +cfg.synMechTau2 = 5 +cfg.connWeight = 0.01 \ No newline at end of file diff --git a/doc/source/code/tut8_data/tauWeight_0_0.json b/doc/source/code/tut8_data/tauWeight_0_0.json new file mode 100644 index 000000000..1ab6733c6 --- /dev/null +++ b/doc/source/code/tut8_data/tauWeight_0_0.json @@ -0,0 +1 @@ +{"netpyne_version": "0.7.4", "net": {"pops": {"S": {"cellModelClass": "", "tags": {"cellModel": "HH", "cellType": "PYR", "pop": "S", "numCells": 20}, "cellGids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}, "M": {"cellModelClass": "", "tags": {"cellModel": "HH", "cellType": "PYR", "pop": "M", "numCells": 20}, "cellGids": [20, 21, 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"cellType": "PYR", "pop": "S", "numCells": 20}, "M": {"cellModel": "HH", "cellType": "PYR", "pop": "M", "numCells": 20}}, "_labelid": 0, "stimTargetParams": {"bkg->PYR": {"loc": null, "conds": {"cellType": "PYR"}, "weight": 0.01, "delay": 5, "source": "bkg", "sec": null, "synMech": "exc"}}, "scaleConnWeightModels": {"__dict__": {}, "__methods__": {}, "__members__": {}}, "defaultDelay": 1, "stimSourceParams": {"bkg": {"rate": 10, "noise": 0.5, "type": "NetStim"}}, "propVelocity": 500.0, "subConnParams": {}}}, "netpyne_changeset": "g3580be7"}}, "params": [{"values": [3.0, 5.0, 7.0], "label": "synMechTau2"}, {"values": [0.005, 0.01, 0.15], "label": "connWeight"}]} \ No newline at end of file diff --git a/doc/source/code/tut8_data/tauWeight_batch.json b/doc/source/code/tut8_data/tauWeight_batch.json new file mode 100644 index 000000000..25aecbe29 --- /dev/null +++ b/doc/source/code/tut8_data/tauWeight_batch.json @@ -0,0 +1,33 @@ +{ + "batch": { + "batchLabel": "tauWeight", + "cfgFile": "tut8_cfg.py", + "initCfg": {}, + "method": "grid", + "netParamsFile": "tut8_netParams.py", + "params": [ + { + "label": "synMechTau2", + "values": [ + 3.0, + 5.0, + 7.0 + ] + }, + { + "label": "connWeight", + "values": [ + 0.005, + 0.01, + 0.15 + ] + } + ], + "runCfg": { + "script": "tut8_init.py", + "skip": true, + "type": "mpi" + }, + "saveFolder": "tut8_data" + } +} \ No newline at end of file diff --git a/doc/source/code/tut8_data/tauWeight_batchScript.py b/doc/source/code/tut8_data/tauWeight_batchScript.py new file mode 100644 index 000000000..a53ece25f Binary files /dev/null and b/doc/source/code/tut8_data/tauWeight_batchScript.py differ diff --git a/doc/source/code/tut8_data/tauWeight_matrix_synMechTau2_connWeight_M.png b/doc/source/code/tut8_data/tauWeight_matrix_synMechTau2_connWeight_M.png new file mode 100644 index 000000000..85d7cdc2e Binary files /dev/null and b/doc/source/code/tut8_data/tauWeight_matrix_synMechTau2_connWeight_M.png differ diff --git a/doc/source/code/tut8_data/tauWeight_netParams.py b/doc/source/code/tut8_data/tauWeight_netParams.py new file mode 100644 index 000000000..6a9a8d62a --- /dev/null +++ b/doc/source/code/tut8_data/tauWeight_netParams.py @@ -0,0 +1,36 @@ +from netpyne import specs, sim + +try: + from __main__ import cfg # import SimConfig object with params from parent module +except: + from tut8_cfg import cfg # if no simConfig in parent module, import directly from tut8_cfg module + +# Network parameters +netParams = specs.NetParams() # object of class NetParams to store the network parameters + +## Population parameters +netParams.popParams['S'] = {'cellType': 'PYR', 'numCells': 20, 'cellModel': 'HH'} +netParams.popParams['M'] = {'cellType': 'PYR', 'numCells': 20, 'cellModel': 'HH'} + +## Cell property rules +cellRule = {'conds': {'cellType': 'PYR'}, 'secs': {}} # cell rule dict +cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict +cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry +cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism +netParams.cellParams['PYRrule'] = cellRule # add dict to list of cell params + +## Synaptic mechanism parameters +netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': cfg.synMechTau2, 'e': 0} # excitatory synaptic mechanism + +# Stimulation parameters +netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 10, 'noise': 0.5} +netParams.stimTargetParams['bkg->PYR'] = {'source': 'bkg', 'conds': {'cellType': 'PYR'}, 'weight': 0.01, 'delay': 5, 'synMech': 'exc'} + +## Cell connectivity rules +netParams.connParams['S->M'] = { # S -> M label + 'preConds': {'pop': 'S'}, # conditions of presyn cells + 'postConds': {'pop': 'M'}, # conditions of postsyn cells + 'probability': 0.5, # probability of connection + 'weight': cfg.connWeight, # synaptic weight + 'delay': 5, # transmission delay (ms) + 'synMech': 'exc'} # synaptic mechanism diff --git a/doc/source/code/tut8_init.py b/doc/source/code/tut8_init.py new file mode 100644 index 000000000..f4f698f63 --- /dev/null +++ b/doc/source/code/tut8_init.py @@ -0,0 +1,8 @@ +from netpyne import sim + +# read cfg and netParams from command line arguments if available; otherwise use default +simConfig, netParams = sim.readCmdLineArgs(simConfigDefault='tut8_cfg.py', netParamsDefault='tut8_netParams.py') + +# Create network and run simulation +sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig) + diff --git a/doc/source/code/tut8_netParams.py b/doc/source/code/tut8_netParams.py new file mode 100644 index 000000000..6a9a8d62a --- /dev/null +++ b/doc/source/code/tut8_netParams.py @@ -0,0 +1,36 @@ +from netpyne import specs, sim + +try: + from __main__ import cfg # import SimConfig object with params from parent module +except: + from tut8_cfg import cfg # if no simConfig in parent module, import directly from tut8_cfg module + +# Network parameters +netParams = specs.NetParams() # object of class NetParams to store the network parameters + +## Population parameters +netParams.popParams['S'] = {'cellType': 'PYR', 'numCells': 20, 'cellModel': 'HH'} +netParams.popParams['M'] = {'cellType': 'PYR', 'numCells': 20, 'cellModel': 'HH'} + +## Cell property rules +cellRule = {'conds': {'cellType': 'PYR'}, 'secs': {}} # cell rule dict +cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict +cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry +cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism +netParams.cellParams['PYRrule'] = cellRule # add dict to list of cell params + +## Synaptic mechanism parameters +netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': cfg.synMechTau2, 'e': 0} # excitatory synaptic mechanism + +# Stimulation parameters +netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 10, 'noise': 0.5} +netParams.stimTargetParams['bkg->PYR'] = {'source': 'bkg', 'conds': {'cellType': 'PYR'}, 'weight': 0.01, 'delay': 5, 'synMech': 'exc'} + +## Cell connectivity rules +netParams.connParams['S->M'] = { # S -> M label + 'preConds': {'pop': 'S'}, # conditions of presyn cells + 'postConds': {'pop': 'M'}, # conditions of postsyn cells + 'probability': 0.5, # probability of connection + 'weight': cfg.connWeight, # synaptic weight + 'delay': 5, # transmission delay (ms) + 'synMech': 'exc'} # synaptic mechanism diff --git a/doc/source/code/tut_gap.py b/doc/source/code/tut_gap.py index c70fd60b4..984a3d60d 100644 --- a/doc/source/code/tut_gap.py +++ b/doc/source/code/tut_gap.py @@ -41,7 +41,8 @@ netParams.connParams['PYR1->PYR2'] = { 'preConds': {'pop': 'PYR1'}, 'postConds': {'pop': 'PYR2'}, # PYR1 -> PYR2 (gap junction) - 'weight': 200.0, + 'weight': 200.0, + 'delay': 0.1, 'synMech': 'esyn', 'gapJunction': True, 'sec': 'soma', diff --git a/doc/source/figs/spikestats.png b/doc/source/figs/spikestats.png new file mode 100644 index 000000000..633b8b3ed Binary files /dev/null and b/doc/source/figs/spikestats.png differ diff --git a/doc/source/figs/tut8_analysis.png b/doc/source/figs/tut8_analysis.png new file mode 100644 index 000000000..85d7cdc2e Binary files /dev/null and b/doc/source/figs/tut8_analysis.png differ diff --git a/doc/source/install.rst b/doc/source/install.rst index 926bfceee..708fe9607 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -14,12 +14,11 @@ Other useful links: * https://www.neuron.yale.edu/neuron/download/compile_linux * https://www.neuron.yale.edu/neuron/download/compilestd_osx -* [Neurosim lab wiki] Note: It is possible to use the NetPyNE package without NEURON, to convert model specifications into Python-based instantiated networks (hierarchical data structure of objects, dicts and lists), and export into different formats. -Install via pip ----------------- +Install via pip (latest released version) +----------------------------------------- To install the the package run ``pip install netpyne`` (Linux or Mac OS) or ``python -m pip install netpyne`` (Windows) @@ -29,3 +28,15 @@ If you need to install ``pip`` go to `this link `. + +Running batch simulations (Tutorial 8) +-------------------------------------------- + +Here we are going to illustrate how to run batch simulations using the simple network in Tutorial 2. By batch simulations we mean modifying some parameter values within a given range and automatically running simulations for all combinations of these parameter values (also known as grid parameter search). + +The first thing we need to do is break up the tut2.py code into three different files -- although not needed for such small models, this will be very helpful for more complex and large models. The file organization should be as follows (click on filename to download): + +* :download:`tut8_netParams.py ` : Defines the network model (netParams object). Includes "fixed" parameter values of cells, synapses, connections, stimulation, etc. Changes to this file should only be made for relatively stable improvements of the model. Parameter values that will be varied systematically to explore or tune the model should be included here by referencing the appropiate variable in the simulation configuration (cfg) module. Only a single netParams file is required for each batch of simulations. + +* :download:`tut8_cfg.py ` : Simulation configuration (simConfig object). Includes parameter values for each simulation run such as duration, dt, recording parameters etc. Also includes the model parameters that are being varied to explore or tune the network. When running a batch, NetPyNE will automatically create one cfg file for each parameter configuration (using this one as a starting point). + +* :download:`tut8_init.py ` : Sequence of commands to run a single simulation. Can be executed via 'python init.py'. When running a batch, NetPyNE will call init.py multiple times, pass a different cfg file for each of the parameter configurations explored. + +We will also need to add a new file to control the batch simulation: + +* :download:`tut8_batch.py ` : Defines the parameters and parameter values to be explored in a batch of simulations, the run configuration -- e.g. whether to use MPI+Bulletin Board (for multicore machines) or SLURM/PBS Torque (for HPCs) --, and the command to run the batch. + +In summary, **netParams.py** for fixed (network) parameters, **cfg.py** for variable (simulation) parameters, **init.py** to run a simulation, and **batch.py** to run a batch of simulations exploring combinations of parameter values. + +Lets say we want to explore how the connection weight and the synaptic decay time constant affect the firing rate of the motor population. The first step would be to modify ``tut8_netParams.py`` so these parameters are variable, i.e. change in each simulation. Therefore, we will change them from having fixed values (e.g. 5.0 and 0.01) to depending on a variable from simConfig: ``cfg.synMechTau2`` and ``cfg.connWeight`` (note that for simplicity we renamed ``simConfig`` to ``cfg``):: + + ... + + ## Synaptic mechanism parameters + netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': cfg.synMechTau2, 'e': 0} # excitatory synaptic mechanism + + ... + + ## Cell connectivity rules + netParams.connParams['S->M'] = { # S -> M label + 'preConds': {'pop': 'S'}, # conditions of presyn cells + 'postConds': {'pop': 'M'}, # conditions of postsyn cells + 'probability': 0.5, # probability of connection + 'weight': cfg.connWeight, # synaptic weight + 'delay': 5, # transmission delay (ms) + 'synMech': 'exc'} # synaptic mechanism + +Therefore, this also requires changing ``tut8_netParams.py`` to import the ``cfg`` module so that these 2 variables are available. The code will first attempt to load the ``cfg`` module from ``__main__``, which refers to the parent module which was initially executed by the user, in this case, ``tut8_init.py``. As mentioned before, ``tut8_init.py`` is responsible for loading both ``netParams`` and ``cfg`` and running the simulation. If that fails, the code will load ``cfg`` directly from the ``tut8_cfg.py`` file. To implement this we will add the following lines at the beginning of ``tut8_netParams.py``:: + + try: + from __main__ import cfg # import SimConfig object with params from parent module + except: + from tut8_cfg import cfg # if no simConfig in parent module, import directly from tut8_cfg module + +The next step is to add these two variables to the ``tut8_cfg.py`` so that they exist and can be used by netParams and modified in the batch simulation:: + + # Variable parameters (used in netParams) + cfg.synMechTau2 = 50 + cfg.connWeight = 0.01 + +Note we will also add to ``tut8_cfg.py`` the following line so that the average firing rates of each populations are printed in screen and saved to the output file:: + + cfg.printPopAvgRates = True + + +The ``tut8_init.py`` will contain only three lines, and just one new line compared to ``tut2.py``. This new line calls the method ``readCmdLineArgs`` which takes care of reading arguments from the command line. This is required to run the batch simulations, since NetPyNE will create multiple cfg files with the different parameter combinations and run them using command line arguments of the form: ``python tut8_init.py simConfig=filepath netParams=filepath``. You do not need to worry about this, since this is done behind the scenes by NetPyNE. The ``tut8_init.py`` should look like this:: + + from netpyne import sim + + # read cfg and netParams from command line arguments if available; otherwise use default + simConfig, netParams = sim.readCmdLineArgs(simConfigDefault='tut8_cfg.py', netParamsDefault='tut8_netParams.py') + + # Create network and run simulation + sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig) + +At this point you can run the simulation using e.g. ``python tut8_init.py`` and it should produce the same result as in Tutorial 2 (``tut2.py``). + +Now we will add the ``tut8_batch.py`` which contains all the information related to the batch simulations. We have defined a function ``batchTauWeight`` to explore this specific combination of parameters. We could later define other similar functions to create other batches. + +The first thing we do is create an ordered dictionary ``params`` -- this will be of a special NetPyNE type (``specs.ODict``) but it essentially behaves like an ordered dictionary. Next we add the parameters to explore as keys of this dictionary -- ``synMechTau2`` and ``connWeight`` -- and add the list of parameter values to try in the batch simulation as the dictionary keys -- ``[3.0, 5.0, 7.0]`` and ``[0.005, 0.01, 0.15]``. Note that parameter names should coincide with the variables defined in ``cfg``. + +We then create an object ``b`` of the NetPyNE class ``Batch`` and pass as arguments the parameters to explore, and the files containing the netParams and simConfig modules. Finally, we customize some attributes of the ``Batch`` object, including the the batch label (``'tauWeight'``), used to create the output file; the folder where to save the data (``'tut8_data'``), the method used to explore parameters (``'grid'``), meaning all combinations of the parameter values; and the run configuration indicating we want to use ``'mpi'`` (this uses MPI and NEURON's Bulletin Board; other options are available for supercomputers), the ``'tut8_init.py'`` to run each sims, and to ``'skip'`` runs if the output files already exist. + +At the end we just need to add the command to launch the batch simulation: ``b.run()``. + +The ``tut8_batch.py`` should look like this:: + + + from netpyne import specs + from netpyne.batch import Batch + + def batchTauWeight(): + # Create variable of type ordered dictionary (NetPyNE's customized version) + params = specs.ODict() + + # fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig) + params['synMechTau2'] = [3.0, 5.0, 7.0] + params['connWeight'] = [0.005, 0.01, 0.15] + + # create Batch object with paramaters to modify, and specifying files to use + b = Batch(params=params, cfgFile='tut8_cfg.py', netParamsFile='tut8_netParams.py',) + + # Set output folder, grid method (all param combinations), and run configuration + b.batchLabel = 'tauWeight' + b.saveFolder = 'tut8_data' + b.method = 'grid' + b.runCfg = {'type': 'mpi', + 'script': 'tut8_init.py', + 'skip': True} + + # Run batch simulations + b.run() + + # Main code + if __name__ == '__main__': + batchTauWeight() + +To run the batch simulations you will need to have MPI properly installed and NEURON configured to use MPI. Run the following command: ``mpiexec -np [num_cores] nrniv -python -mpi batch.py`` , where ``[num_cores]`` should be replaced with the number of processors you want to use. The minimum required is 2, since one will be uses to schedule the jobs (master node); e.g. if you select 4 processors, one will be used to schedule jobs, and the other 3 will run NEURON simulations with different parameter combinations. + +Once the simulations are completed you should have a new folder ``tut8_data`` with the following files: + +* **tauWeight_netParams.py**: a copy of the original netParams file used (``tut8_netParams.py``) + +* **tauWeight_batchScript.py**: a copy of the original batch file used (``tut8_batch.py``) + +* **tauWeight_batch.json**: a JSON file with the batch parameters and run option used. + +* For each combination of parameters (with x,y representing the indices of the parameter values): + + * **tauWeight_x_y_cfg.json**: JSON file with all the ``cfg`` variables copied from ``tut8_cfg.py`` but with the values of ``synMechTau2`` and ``connWeight`` for this specific combination of batch parameters. + + * **tauWeight_x_y.json**: JSON file with the output data for this combination of batch parameters; output data will contain by default the ``netParams``, ``net``, ``simConfig`` and ``simData``. + + * **tauWeight_x_y_raster.png** and **tauWeight_x_y_traces.png**: output figures for this combination of parameters. + + +To analyze the output data you can download :download:`tut8_analysis.py `. This file has functions to read and plot a matrix showing the results from the batch simulation results. This file requires the `Pandas `_ and `Seaborn `_ packages. IMPORTANT: The analysis functions (``tut8_analysis.py``) will be soon integrated into NetPyNE, and so we won't go into the details of the code. + +Running ``python tut8_analysis.py`` should produce a color plot showing the relation between the two parameter explored and the firing rate of the ``M`` populations: + +.. image:: figs/tut8_analysis.png + :width: 50% + +Notice how the rate initially increases as a function of connection weight, but then decreases due to depolarization blockade; and how the effect of the synaptic time decay constant (synMechTau2) depends on whether the cell is spiking normally or in blockade. Batch simulations and analyses facilitate exploration and understanding of these complex interactions. + +.. note:: For the more advanced users, this is what NetPyNE does under the hood when you run a batch: + + 1) Copy netParams.py (or whatever file is specified) to batch data folder + + 2) Load cfg (SimConfig object) from cfg.py (or whatever file is specified) + + 3) Iterate parameter combinations: + + 3a) Modify cfg (SimConfig object) with the parameter values + + 3b) Save cfg to .json file + + 3c) Run simulation by passing netParams.py and cfg.json files as arguments; this means the code in netParams.py is executed each time but cfg is just a set of fixed saved values. + +.. seealso:: The full description of options available in the Batch class will be available soon in the :ref:`package_reference`. + .. seealso:: For a comprehensive description of all the features available in NetPyNE see :ref:`package_reference`. \ No newline at end of file diff --git a/examples/NeuroMLImport/RS.mod b/examples/NeuroMLImport/RS.mod index 5b23714ef..f448c3708 100644 --- a/examples/NeuroMLImport/RS.mod +++ b/examples/NeuroMLImport/RS.mod @@ -3,9 +3,9 @@ TITLE Mod file for component: Component(id=RS type=izhikevich2007Cell) COMMENT This NEURON file has been generated by org.neuroml.export (see https://github.com/NeuroML/org.neuroml.export) - org.neuroml.export v1.5.2 - org.neuroml.model v1.5.2 - jLEMS v0.9.8.9 + org.neuroml.export v1.5.3 + org.neuroml.model v1.5.3 + jLEMS v0.9.9.0 ENDCOMMENT @@ -13,7 +13,7 @@ NEURON { POINT_PROCESS RS - NONSPECIFIC_CURRENT i : To ensure v of section follows vI + NONSPECIFIC_CURRENT i : To ensure v of section follows v_I RANGE v0 : parameter RANGE k : parameter RANGE vr : parameter @@ -71,6 +71,7 @@ ASSIGNED { copy_v (mV) + v_I (nA) iSyn (nA) : derived variable @@ -81,7 +82,6 @@ ASSIGNED { } STATE { - vI (nA) u (nA) } @@ -102,7 +102,7 @@ BREAKPOINT { copy_v = v - i = vI * C + i = v_I * C } NET_RECEIVE(flag) { @@ -114,7 +114,7 @@ NET_RECEIVE(flag) { v = c - vI = 0 : Setting rate of change of v to 0 + v_I = 0 : Setting rate of change of v to 0 u = u + d } @@ -140,7 +140,7 @@ PROCEDURE rates() { rate_v = iMemb / C ? Note units of all quantities used here need to be consistent! rate_u = a * ( b * (v- vr ) - u ) ? Note units of all quantities used here need to be consistent! - vI = -1 * rate_v + v_I = -1 * rate_v } diff --git a/examples/NeuroMLImport/poissonFiringSyn.mod b/examples/NeuroMLImport/poissonFiringSyn.mod index 412919d33..86bc67032 100644 --- a/examples/NeuroMLImport/poissonFiringSyn.mod +++ b/examples/NeuroMLImport/poissonFiringSyn.mod @@ -3,9 +3,9 @@ TITLE Mod file for component: Component(id=poissonFiringSyn type=poissonFiringSy COMMENT This NEURON file has been generated by org.neuroml.export (see https://github.com/NeuroML/org.neuroml.export) - org.neuroml.export v1.5.2 - org.neuroml.model v1.5.2 - jLEMS v0.9.8.9 + org.neuroml.export v1.5.3 + org.neuroml.model v1.5.3 + jLEMS v0.9.9.0 ENDCOMMENT diff --git a/examples/batchCell/cells/SPI6.py b/examples/batchCell/cells/SPI6.py deleted file mode 120000 index bf78c411c..000000000 --- a/examples/batchCell/cells/SPI6.py +++ /dev/null @@ -1 +0,0 @@ -/usr/site/nrniv/local/python/SPI6.py \ No newline at end of file diff --git a/examples/batchCell/cells/SPI6.py b/examples/batchCell/cells/SPI6.py new file mode 100644 index 000000000..873ed84ef --- /dev/null +++ b/examples/batchCell/cells/SPI6.py @@ -0,0 +1,207 @@ +# simplified corticospinal cell model (6 compartment) + +from neuron import h +from math import exp,log + +Vrest = -88.5366550238 +h.v_init = -75.0413649414 +h.celsius = 34.0 # for in vitro opt + +# geom properties +somaL = 48.4123467666 +somaDiam = 28.2149102762 +axonL = 594.292937602 +axonDiam = 1.40966286462 +apicL = 261.904636003 +apicDiam = 1.5831889597 +bdendL = 299.810775175 +bdendDiam = 2.2799248874 + +# passive properties +axonCap = 1.01280903702 +somaCap = 1.78829677463 +apicCap = 1.03418636866 +bdendCap = 1.89771901209 +rall = 114.510490019 +axonRM = 3945.2107187 +somaRM = 18501.7540916 +apicRM = 10751.193413 +bdendRM = 13123.00174 + +# Na, K reversal potentials calculated from BenS internal/external solutions via Nernst eq. +p_ek = -104.0 # these reversal potentials for in vitro conditions +p_ena = 42.0 +# h-current +h.erev_ih = -37.0 # global +gbar_h = 0.000140956438043 +h_gbar_tuft = 0.00565 # mho/cm^2 (based on Harnett 2015 J Neurosci) + +# d-current +gbar_kdmc = 0.000447365630734 +kdmc_gbar_axonm = 20 +# spiking currents +gbar_nax = 0.0345117294903 +nax_gbar_axonm = 5.0 +nax_gbar_somam = 1.0 +nax_gbar_dendm = 1.0 +gbar_kdr = 0.0131103978049 +kdr_gbar_axonm = 5.0 +# A few kinetic params changed vis-a-vis kdr.mod defaults: +kdr_vhalfn = 11.6427471384 +gbar_kap = 0.0898600246397 +kap_gbar_axonm = 5.0 +# A few kinetic params changed vis-a-vis kap.mod defaults: +kap_vhalfn = 32.7885075379 +kap_tq = -52.0967985869 +kap_vhalfl = -59.7867409796 # global!! + +# other ion channel parameters +cal_gcalbar = 4.41583533572e-06 +can_gcanbar = 4.60717910591e-06 +calginc = 1.0 +h_lambda = 325.0 +kBK_gpeak = 5.09733585163e-05 +kBK_caVhminShift = 43.8900261407 +cadad_depth = 0.119408607923 +cadad_taur = 99.1146852282 + +############################################################################### +# SPI6 Cell +############################################################################### +class SPI6 (): + "Simplified Corticospinal Cell Model" + def __init__(self,x=0,y=0,z=0,ID=0): + self.x,self.y,self.z=x,y,z + self.ID=ID + self.all_sec = [] + self.add_comp('soma') + self.set_morphology() + self.insert_conductances() + self.set_props() + + def add_comp(self, name): + self.__dict__[name] = h.Section(name=name)#,cell=self) + self.all_sec.append(self.__dict__[name]) + + def set_morphology(self): + self.add_comp('axon') + self.add_comp('Bdend') + self.add_comp('Adend1') + self.add_comp('Adend2') + self.add_comp('Adend3') + self.apic = [self.Adend1, self.Adend2, self.Adend3] + self.basal = [self.Bdend] + self.alldend = [self.Adend1, self.Adend2, self.Adend3, self.Bdend] + self.set_geom() + self.axon.connect(self.soma, 0.0, 0.0) + self.Bdend.connect(self.soma, 0.5, 0.0) # soma 0.5 to Bdend 0 + self.Adend1.connect(self.soma, 1.0, 0.0) + self.Adend2.connect(self.Adend1, 1.0, 0.0) + self.Adend3.connect(self.Adend2, 1.0, 0.0) + + def set_geom (self): + self.axon.L = axonL; self.axon.diam = axonDiam; + self.soma.L = somaL; self.soma.diam = somaDiam + for sec in self.apic: sec.L,sec.diam = apicL,apicDiam + self.Bdend.L = bdendL; self.Bdend.diam = bdendDiam + + def activeoff (self): + for sec in self.all_sec: sec.gbar_nax=sec.gbar_kdr=sec.gbar_kap=0.0 + + def set_axong (self): + axon = self.axon + axon.gbar_kdmc = gbar_kdmc * kdmc_gbar_axonm + axon.gbar_nax = gbar_nax * nax_gbar_axonm + axon.gbar_kdr = gbar_kdr * kdr_gbar_axonm + axon.gbar_kap = gbar_kap * kap_gbar_axonm + + def set_calprops (self,sec): + sec.gcalbar_cal = cal_gcalbar + sec.gcanbar_can = can_gcanbar + sec.gpeak_kBK = kBK_gpeak + sec.caVhmin_kBK = -46.08 + kBK_caVhminShift + sec.depth_cadad = cadad_depth + sec.taur_cadad = cadad_taur + + def set_somag (self): + sec = self.soma + sec.gbar_ih = gbar_h # Ih + self.set_calprops(sec) + sec.gbar_kdmc = gbar_kdmc + sec.gbar_nax = gbar_nax * nax_gbar_somam + + def set_bdendg (self): + sec = self.Bdend + sec.gbar_ih = gbar_h # Ih + self.set_calprops(sec) + sec.gbar_nax = gbar_nax * nax_gbar_dendm + + def set_apicg (self): + h.distance(0,0.5,sec=self.soma) # middle of soma is origin for distance + self.nexusdist = nexusdist = 300.0 + self.h_gbar_tuftm = h_gbar_tuftm = h_gbar_tuft / gbar_h + self.h_lambda = h_lambda = nexusdist / log(h_gbar_tuftm) + for sec in self.apic: + self.set_calprops(sec) + for seg in sec: + d = h.distance(seg.x,sec=sec) + if d <= nexusdist: seg.gbar_ih = gbar_h * exp(d/h_lambda) + else: seg.gbar_ih = h_gbar_tuft + sec.gbar_nax = gbar_nax * nax_gbar_dendm + self.apic[1].gcalbar_cal = cal_gcalbar * calginc # middle apical dend gets more iL + + # set properties + def set_props (self): + self.set_geom() + # cm - can differ across locations + self.axon.cm = axonCap + self.soma.cm = somaCap + self.Bdend.cm = bdendCap + for sec in self.apic: sec.cm = apicCap + # g_pas == 1.0/rm - can differ across locations + self.axon.g_pas = 1.0/axonRM + self.soma.g_pas = 1.0/somaRM + self.Bdend.g_pas = 1.0/bdendRM + for sec in self.apic: sec.g_pas = 1.0/apicRM + for sec in self.all_sec: + sec.ek = p_ek # K+ current reversal potential (mV) + sec.ena = p_ena # Na+ current reversal potential (mV) + sec.Ra = rall; sec.e_pas = Vrest # passive + sec.gbar_nax = gbar_nax # Na + sec.gbar_kdr = gbar_kdr # KDR + sec.vhalfn_kdr = kdr_vhalfn # KDR kinetics + sec.gbar_kap = gbar_kap # K-A + sec.vhalfn_kap = kap_vhalfn # K-A kinetics + sec.vhalfl_kap = kap_vhalfl + sec.tq_kap = kap_tq + self.set_somag() + self.set_bdendg() + self.set_apicg() + self.set_axong() + + def insert_conductances (self): + for sec in self.all_sec: + sec.insert('k_ion') + sec.insert('na_ion') + sec.insert('pas') # passive + sec.insert('nax') # Na current + sec.insert('kdr') # K delayed rectifier current + sec.insert('kap') # K-A current + for sec in [self.Adend3, self.Adend2, self.Adend1, self.Bdend, self.soma]: + sec.insert('ih') # h-current + sec.insert('ca_ion') # calcium channels + sec.insert('cal') # cal_mig.mod + sec.insert('can') # can_mig.mod + sec.insert('cadad') # cadad.mod - calcium decay + sec.insert('kBK') # kBK.mod - ca and v dependent k channel + for sec in [self.soma, self.axon]: sec.insert('kdmc') # K-D current in soma & axon only + +# +def prmstr (p,s,fctr=2.0,shift=5.0): + if p == 0.0: + print s,'=',str(p-shift),str(p+shift),str(p),'True' + elif p < 0.0: + print s, '=',str(p*fctr),str(p/fctr),str(p),'True' + else: + print s, ' = ' , str(p/fctr), str(p*fctr), str(p), 'True' + diff --git a/examples/batchCell/mod/HCN1.mod b/examples/batchCell/mod/HCN1.mod new file mode 120000 index 000000000..6846ffc02 --- /dev/null +++ b/examples/batchCell/mod/HCN1.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/HCN1.mod \ No newline at end of file diff --git a/examples/batchCell/mod/IC.mod b/examples/batchCell/mod/IC.mod new file mode 120000 index 000000000..e4d7e0f2e --- /dev/null +++ b/examples/batchCell/mod/IC.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/IC.mod \ No newline at end of file diff --git a/examples/batchCell/mod/MyExp2SynBB.mod b/examples/batchCell/mod/MyExp2SynBB.mod new file mode 120000 index 000000000..c6069f6a7 --- /dev/null +++ b/examples/batchCell/mod/MyExp2SynBB.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/MyExp2SynBB.mod \ No newline at end of file diff --git a/examples/batchCell/mod/MyExp2SynNMDABB.mod b/examples/batchCell/mod/MyExp2SynNMDABB.mod new file mode 120000 index 000000000..625c41104 --- /dev/null +++ b/examples/batchCell/mod/MyExp2SynNMDABB.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/MyExp2SynNMDABB.mod \ No newline at end of file diff --git a/examples/batchCell/mod/Nca.mod b/examples/batchCell/mod/Nca.mod new file mode 120000 index 000000000..e47012b5d --- /dev/null +++ b/examples/batchCell/mod/Nca.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/Nca.mod \ No newline at end of file diff --git a/examples/batchCell/mod/ar_traub.mod b/examples/batchCell/mod/ar_traub.mod new file mode 120000 index 000000000..40695443f --- /dev/null +++ b/examples/batchCell/mod/ar_traub.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/ar_traub.mod \ No newline at end of file diff --git a/examples/batchCell/mod/cadad.mod b/examples/batchCell/mod/cadad.mod new file mode 120000 index 000000000..687c2d0cd --- /dev/null +++ b/examples/batchCell/mod/cadad.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/cadad.mod \ No newline at end of file diff --git a/examples/batchCell/mod/cagk.mod b/examples/batchCell/mod/cagk.mod new file mode 120000 index 000000000..e45ba308a --- /dev/null +++ b/examples/batchCell/mod/cagk.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/cagk.mod \ No newline at end of file diff --git a/examples/batchCell/mod/cal_mh.mod b/examples/batchCell/mod/cal_mh.mod new file mode 120000 index 000000000..80a6394d8 --- /dev/null +++ b/examples/batchCell/mod/cal_mh.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/cal_mh.mod \ No newline at end of file diff --git a/examples/batchCell/mod/cal_mig.mod b/examples/batchCell/mod/cal_mig.mod new file mode 120000 index 000000000..8b8f90062 --- /dev/null +++ b/examples/batchCell/mod/cal_mig.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/cal_mig.mod \ No newline at end of file diff --git a/examples/batchCell/mod/can_mig.mod b/examples/batchCell/mod/can_mig.mod new file mode 120000 index 000000000..b4eb06550 --- /dev/null +++ b/examples/batchCell/mod/can_mig.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/can_mig.mod \ No newline at end of file diff --git a/examples/batchCell/mod/cat_mig.mod b/examples/batchCell/mod/cat_mig.mod new file mode 120000 index 000000000..3be489db0 --- /dev/null +++ b/examples/batchCell/mod/cat_mig.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/cat_mig.mod \ No newline at end of file diff --git a/examples/batchCell/mod/cat_traub.mod b/examples/batchCell/mod/cat_traub.mod new file mode 120000 index 000000000..2916312c5 --- /dev/null +++ b/examples/batchCell/mod/cat_traub.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/cat_traub.mod \ No newline at end of file diff --git a/examples/batchCell/mod/gabab.mod b/examples/batchCell/mod/gabab.mod new file mode 120000 index 000000000..350110487 --- /dev/null +++ b/examples/batchCell/mod/gabab.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/gabab.mod \ No newline at end of file diff --git a/examples/batchCell/mod/h_BS.mod b/examples/batchCell/mod/h_BS.mod new file mode 120000 index 000000000..f13cfa794 --- /dev/null +++ b/examples/batchCell/mod/h_BS.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/h_BS.mod \ No newline at end of file diff --git a/examples/batchCell/mod/h_harnett.mod b/examples/batchCell/mod/h_harnett.mod new file mode 120000 index 000000000..11f6e61ae --- /dev/null +++ b/examples/batchCell/mod/h_harnett.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/h_harnett.mod \ No newline at end of file diff --git a/examples/batchCell/mod/h_kole.mod b/examples/batchCell/mod/h_kole.mod new file mode 120000 index 000000000..3387f2140 --- /dev/null +++ b/examples/batchCell/mod/h_kole.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/h_kole.mod \ No newline at end of file diff --git a/examples/batchCell/mod/hsyn.mod b/examples/batchCell/mod/hsyn.mod new file mode 120000 index 000000000..6f27dc58f --- /dev/null +++ b/examples/batchCell/mod/hsyn.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/hsyn.mod \ No newline at end of file diff --git a/examples/batchCell/mod/ican_sidi.mod b/examples/batchCell/mod/ican_sidi.mod new file mode 120000 index 000000000..7f9d06904 --- /dev/null +++ b/examples/batchCell/mod/ican_sidi.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/ican_sidi.mod \ No newline at end of file diff --git a/examples/batchCell/mod/infot.mod b/examples/batchCell/mod/infot.mod new file mode 120000 index 000000000..191ec0996 --- /dev/null +++ b/examples/batchCell/mod/infot.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/infot.mod \ No newline at end of file diff --git a/examples/batchCell/mod/izhi2007a.mod b/examples/batchCell/mod/izhi2007a.mod new file mode 120000 index 000000000..eae209f8e --- /dev/null +++ b/examples/batchCell/mod/izhi2007a.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/izhi2007a.mod \ No newline at end of file diff --git a/examples/batchCell/mod/izhi2007b.mod b/examples/batchCell/mod/izhi2007b.mod new file mode 120000 index 000000000..2e5019fd5 --- /dev/null +++ b/examples/batchCell/mod/izhi2007b.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/izhi2007b.mod \ No newline at end of file diff --git a/examples/batchCell/mod/kBK.mod b/examples/batchCell/mod/kBK.mod new file mode 120000 index 000000000..aebb2a188 --- /dev/null +++ b/examples/batchCell/mod/kBK.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/kBK.mod \ No newline at end of file diff --git a/examples/batchCell/mod/kap_BS.mod b/examples/batchCell/mod/kap_BS.mod new file mode 120000 index 000000000..ade52edfb --- /dev/null +++ b/examples/batchCell/mod/kap_BS.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/kap_BS.mod \ No newline at end of file diff --git a/examples/batchCell/mod/kca.mod b/examples/batchCell/mod/kca.mod new file mode 120000 index 000000000..3eeeff092 --- /dev/null +++ b/examples/batchCell/mod/kca.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/kca.mod \ No newline at end of file diff --git a/examples/batchCell/mod/kdmc_BS.mod b/examples/batchCell/mod/kdmc_BS.mod new file mode 120000 index 000000000..8c5a6db04 --- /dev/null +++ b/examples/batchCell/mod/kdmc_BS.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/kdmc_BS.mod \ No newline at end of file diff --git a/examples/batchCell/mod/kdr_BS.mod b/examples/batchCell/mod/kdr_BS.mod new file mode 120000 index 000000000..860f3aae0 --- /dev/null +++ b/examples/batchCell/mod/kdr_BS.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/kdr_BS.mod \ No newline at end of file diff --git a/examples/batchCell/mod/km.mod b/examples/batchCell/mod/km.mod new file mode 120000 index 000000000..3e04eec2a --- /dev/null +++ b/examples/batchCell/mod/km.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/km.mod \ No newline at end of file diff --git a/examples/batchCell/mod/kv.mod b/examples/batchCell/mod/kv.mod new file mode 120000 index 000000000..786bc4578 --- /dev/null +++ b/examples/batchCell/mod/kv.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/kv.mod \ No newline at end of file diff --git a/examples/batchCell/mod/misc.mod b/examples/batchCell/mod/misc.mod new file mode 120000 index 000000000..05abe7261 --- /dev/null +++ b/examples/batchCell/mod/misc.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/misc.mod \ No newline at end of file diff --git a/examples/batchCell/mod/na2_mh.mod b/examples/batchCell/mod/na2_mh.mod new file mode 120000 index 000000000..6f5f5bf2f --- /dev/null +++ b/examples/batchCell/mod/na2_mh.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/na2_mh.mod \ No newline at end of file diff --git a/examples/batchCell/mod/nap_sidi.mod b/examples/batchCell/mod/nap_sidi.mod new file mode 120000 index 000000000..5e12484ea --- /dev/null +++ b/examples/batchCell/mod/nap_sidi.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/nap_sidi.mod \ No newline at end of file diff --git a/examples/batchCell/mod/nax_BS.mod b/examples/batchCell/mod/nax_BS.mod new file mode 120000 index 000000000..fa577e47e --- /dev/null +++ b/examples/batchCell/mod/nax_BS.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/nax_BS.mod \ No newline at end of file diff --git a/examples/batchCell/mod/naz.mod b/examples/batchCell/mod/naz.mod new file mode 120000 index 000000000..ede5c78cc --- /dev/null +++ b/examples/batchCell/mod/naz.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/naz.mod \ No newline at end of file diff --git a/examples/batchCell/mod/nstim.mod b/examples/batchCell/mod/nstim.mod new file mode 120000 index 000000000..4a5182497 --- /dev/null +++ b/examples/batchCell/mod/nstim.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/nstim.mod \ No newline at end of file diff --git a/examples/batchCell/mod/samnutils.mod b/examples/batchCell/mod/samnutils.mod new file mode 120000 index 000000000..c3406e2d7 --- /dev/null +++ b/examples/batchCell/mod/samnutils.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/samnutils.mod \ No newline at end of file diff --git a/examples/batchCell/mod/savedist.mod b/examples/batchCell/mod/savedist.mod new file mode 120000 index 000000000..6cd7d2156 --- /dev/null +++ b/examples/batchCell/mod/savedist.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/savedist.mod \ No newline at end of file diff --git a/examples/batchCell/mod/stats.mod b/examples/batchCell/mod/stats.mod new file mode 120000 index 000000000..467406c7d --- /dev/null +++ b/examples/batchCell/mod/stats.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/stats.mod \ No newline at end of file diff --git a/examples/batchCell/mod/vecst.mod b/examples/batchCell/mod/vecst.mod new file mode 120000 index 000000000..76cd2881d --- /dev/null +++ b/examples/batchCell/mod/vecst.mod @@ -0,0 +1 @@ +/usr/site/nrniv/local/mod/vecst.mod \ No newline at end of file diff --git a/examples/batchCell/netParams.py b/examples/batchCell/netParams.py index eaeef46f9..bba8c05b1 100644 --- a/examples/batchCell/netParams.py +++ b/examples/batchCell/netParams.py @@ -12,7 +12,7 @@ try: from __main__ import cfg # import SimConfig object with params from parent module except: - from cfg import cfg # if no simConfig in parent module, import directly from cfg module + from tut8_cfg import cfg # if no simConfig in parent module, import directly from tut8_cfg module ############################################################################### # diff --git a/examples/sandbox/sandbox.py b/examples/sandbox/sandbox.py index 8afd74b67..c5261803a 100644 --- a/examples/sandbox/sandbox.py +++ b/examples/sandbox/sandbox.py @@ -1,126 +1,82 @@ -""" -params.py - -netParams is a dict containing a set of network parameters using a standardized structure - -simConfig is a dict containing a set of simulation configurations using a standardized structure - -Contributors: salvadordura@gmail.com -""" - from netpyne import specs, sim -netParams = specs.NetParams() # object of class NetParams to store the network parameters -simConfig = specs.SimConfig() # object of class SimConfig to store the simulation configuration - - -############################################################################### -# -# MPI HH TUTORIAL PARAMS -# -############################################################################### +# Network parameters +netParams = specs.NetParams() # object of class NetParams to store the network parameters -############################################################################### -# NETWORK PARAMETERS -############################################################################### +netParams.sizeX = 100 # x-dimension (horizontal length) size in um +netParams.sizeY = 1000 # y-dimension (vertical height or cortical depth) size in um +netParams.sizeZ = 100 # z-dimension (horizontal length) size in um +netParams.propVelocity = 100.0 # propagation velocity (um/ms) +netParams.probLengthConst = 150.0 # length constant for conn probability (um) -netParams.probLengthConst = 200 -# Population parameters -netParams.popParams['PYR'] = {'cellModel': 'HH', 'cellType': 'PYR', 'numCells': 20, 'xnormRange': [0,0.2]} # add dict with params for this pop -netParams.popParams['PYR2'] = {'cellModel': 'HH', 'cellType': 'PYR', 'numCells': 20, 'xnormRange': [0.8,1.0]} # add dict with params for this pop -#netParams.popParams['PYR3'] = {'cellModel': 'HH', 'cellType': 'PYR', 'numCells': 5, 'xNormRange': [0.8,1.0]} # add dict with params for this pop -#netParams.popParams['INT'] = {'cellModel': 'HH', 'cellType': 'PYR', 'numCells': 100, 'xnormRange': [0.4,0.6]} # add dict with params for this pop +## Population parameters +netParams.popParams['E2'] = {'cellType': 'E', 'numCells': 3, 'yRange': [100,300], 'cellModel': 'HH'} +netParams.popParams['I2'] = {'cellType': 'I', 'numCells': 3, 'yRange': [100,300], 'cellModel': 'HH'} +netParams.popParams['E4'] = {'cellType': 'E', 'numCells': 3, 'yRange': [300,600], 'cellModel': 'HH'} +netParams.popParams['I4'] = {'cellType': 'I', 'numCells': 3, 'yRange': [300,600], 'cellModel': 'HH'} +netParams.popParams['E5'] = {'cellType': 'E', 'numCells': 3, 'ynormRange': [0.6,1.0], 'cellModel': 'HH'} +netParams.popParams['I5'] = {'cellType': 'I', 'numCells': 3, 'ynormRange': [0.6,1.0], 'cellModel': 'HH'} -# Cell parameters -## PYR cell properties -cellRule = {'conds': {'cellModel': 'HH', 'cellType': 'PYR'}, 'secs': {}} # cell rule dict -cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict -cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry -cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism -cellRule['secs']['soma']['vinit'] = -71 -netParams.cellParams['PYR'] = cellRule # add dict to list of cell params +## Cell property rules +cellRule = {'conds': {'cellType': 'E'}, 'secs': {}} # cell rule dict +cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict +cellRule['secs']['soma']['geom'] = {'diam': 15, 'L': 14, 'Ra': 120.0} # soma geometry +cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.13, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism +netParams.cellParams['Erule'] = cellRule # add dict to list of cell params +cellRule = {'conds': {'cellType': 'I'}, 'secs': {}} # cell rule dict +cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict +cellRule['secs']['soma']['geom'] = {'diam': 10.0, 'L': 9.0, 'Ra': 110.0} # soma geometry +cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.11, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism +netParams.cellParams['Irule'] = cellRule # add dict to list of cell params -# cellRule = {'conds': {'cellModel': 'HH', 'cellType': 'PYR2'}, 'secs': {}} # cell rule dict -# cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict -# cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry -# cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism -# cellRule['secs']['soma']['vinit'] = -71 -# netParams.cellParams['PYR2'] = cellRule # add dict to list of cell params -# Synaptic mechanism parameters -netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': 1.0, 'e': 0} +## Synaptic mechanism parameters +netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0} # NMDA synaptic mechanism +netParams.synMechParams['inh'] = {'mod': 'Exp2Syn', 'tau1': 0.6, 'tau2': 8.5, 'e': -75} # GABA synaptic mechanism # Stimulation parameters -netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 10, 'noise': 0.5, 'start': 1} -netParams.stimSourceParams['bkg2'] = {'type': 'NetStim', 'rate': 10, 'noise': 0.5, 'start': 1} - -#netParams.stimTargetParams['bkg->PYR1'] = {'source': 'bkg', 'conds': {'pop': 'PYR'}, 'weight': 0.1, 'delay': 'uniform(1,5)'} - - -# Connectivity parameters -netParams.connParams['PYR->ALL'] = { - 'preConds': {'pop': ['PYR']}, 'postConds': {'pop': ['PYR']}, - 'weight': 0.002, # weight of each connection - 'delay': '0.2+normal(13.0,1.4)', # delay min=0.2, mean=13.0, var = 1.4 - 'threshold': 10, # threshold - 'convergence': 12} - -# netParams.connParams['PYR->PYR2'] = { -# 'preConds': {'pop': ['PYR']}, 'postConds': {'pop': ['PYR2']}, -# 'weight': 0.002, # weight of each connection -# 'delay': '0.2+normal(13.0,1.4)', # delay min=0.2, mean=13.0, var = 1.4 -# 'threshold': 10, # threshold -# 'probability': 0.05} - - -# netParams.connParams['INT->PYR2'] = { -# 'preConds': {'pop': ['INT']}, 'postConds': {'pop': ['PYR2']}, -# 'weight': 0.002, # weight of each connection -# 'delay': '0.2+normal(13.0,1.4)', # delay min=0.2, mean=13.0, var = 1.4 -# 'threshold': 10, # threshold -# 'probability': 0.05, # '0.4*exp(-dist_3D/probLengthConst)', -# 'disynapticBias': 0.5} - - -############################################################################### -# SIMULATION PARAMETERS -############################################################################### - -# Simulation parameters -simConfig.duration = 0.1*1e3 # Duration of the simulation, in ms -simConfig.dt = 0.05 # Internal integration timestep to use -simConfig.seeds = {'conn': 1, 'stim': 1, 'loc': 1} # Seeds for randomizers (connectivity, input stimulation and cell locations) -simConfig.createNEURONObj = 1 # create HOC objects when instantiating network -simConfig.createPyStruct = 1 # create Python structure (simulator-independent) when instantiating network -simConfig.verbose = 0 # show detailed messages - -simConfig.checkErrors = 0 - -# Recording -simConfig.recordCells = [] # which cells to record from -simConfig.recordTraces = {'Vsoma':{'sec':'soma','loc':0.5,'var':'v','conds': {'cellType': 'PYR2'}}} -simConfig.recordStim = True # record spikes of cell stims -simConfig.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc) - -# Saving -simConfig.filename = 'HHTut' # Set file output name -simConfig.saveFileStep = 1000 # step size in ms to save data to disk -simConfig.savePickle = False # Whether or not to write spikes etc. to a .mat file -simConfig.saveMat = 0 - -# Analysis and plotting -simConfig.analysis['calculateDisynaptic'] = {'includePost': ['PYR2'], 'includePre': ['INT'], 'includePrePre': ['PYR']} -#simConfig.analysis['plotTraces'] = {'include': ['all'], 'oneFigPer':'trace'} -#simConfig.analysis['plot2Dnet'] = True -sim.createSimulateAnalyze() - - -#sim.analysis.calculateDisynaptic() - -#data=sim.loadSimData('HHTut.mat') - - +netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 20, 'noise': 0.3} +netParams.stimTargetParams['bkg->all'] = {'source': 'bkg', 'conds': {'cellType': ['E','I']}, 'weight': 0.01, 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'} + + +## Cell connectivity rules +netParams.connParams['E->all'] = { + 'preConds': {'cellType': 'E'}, 'postConds': {'y': [100,1000]}, # E -> all (100-1000 um) + 'probability': 0.1 , # probability of connection + 'weight': '0.005*post_ynorm', # synaptic weight + 'delay': 'dist_3D/propVelocity', # transmission delay (ms) + 'synMech': 'exc'} # synaptic mechanism + +netParams.connParams['I->E'] = { + 'preConds': {'cellType': 'I'}, 'postConds': {'pop': ['E2','E4','E5']}, # I -> E + 'probability': '0.4*exp(-dist_3D/probLengthConst)', # probability of connection + 'weight': 0.001, # synaptic weight + 'delay': 'dist_3D/propVelocity', # transmission delay (ms) + 'synMech': 'inh'} # synaptic mechanism + + +# Simulation options +simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration +simConfig.duration = 1*1e3 # Duration of the simulation, in ms +simConfig.dt = 0.025 # Internal integration timestep to use +simConfig.verbose = False # Show detailed messages +simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}} # Dict with traces to record +simConfig.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc) +simConfig.filename = 'tut5' # Set file output name +simConfig.savePickle = False # Save params, network and sim output to pickle file +simConfig.saveMat = False # Save params, network and sim output to pickle file +simConfig.saveJson=1 + +#simConfig.analysis['plotRaster'] = {'orderBy': 'y', 'orderInverse': True} # Plot a raster +simConfig.analysis['plotSpikeStats'] = {'include': [['E2','E4'], [15,16]]} # Plot recorded traces for this list of cells +#simConfig.analysis['plot2Dnet'] = True # plot 2D visualization of cell positions and connections +#simConfig.analysis['plotConn'] = True # plot connectivity matrix + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) + +# import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty diff --git a/examples/saveLoadV1/E4/manifest.json b/examples/saveLoadV1/E4/manifest.json index 3604dd019..a2a87c1c8 100644 --- a/examples/saveLoadV1/E4/manifest.json +++ b/examples/saveLoadV1/E4/manifest.json @@ -1,157 +1,156 @@ { "biophys": [ { - "model_type": "Biophysical - perisomatic", + "model_type": "Biophysical - perisomatic", "model_file": [ - "manifest.json", + "manifest.json", "320207387_fit.json" ] } - ], + ], "runs": [ { "sweeps": [ - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, 94 ] } - ], + ], "neuron": [ { "hoc": [ - "stdgui.hoc", - "import3d.hoc", - "cell_template.hoc" + "stdgui.hoc", + "import3d.hoc" ] } - ], + ], "manifest": [ { - "type": "dir", - "spec": ".", + "type": "dir", + "spec": ".", "key": "BASEDIR" - }, + }, { - "parent": "BASEDIR", - "type": "dir", - "spec": "work", + "parent": "BASEDIR", + "type": "dir", + "spec": "work", "key": "WORKDIR" - }, + }, { - "type": "file", - "spec": "Scnn1a-Tg3-Cre_Ai14-170426.03.01.01_491392845_m.swc", + "type": "file", + "spec": "Scnn1a-Tg3-Cre_Ai14-170426.03.01.01_491392845_m.swc", "key": "MORPHOLOGY" - }, + }, { - "type": "file", - "spec": "Scnn1a-Tg3-Cre_Ai14-170426.03.01.01_491392845_marker_m.swc", + "type": "file", + "spec": "Scnn1a-Tg3-Cre_Ai14-170426.03.01.01_491392845_marker_m.swc", "key": "MARKER" - }, + }, { - "type": "dir", - "spec": "modfiles", + "type": "dir", + "spec": "modfiles", "key": "MODFILE_DIR" - }, + }, { - "type": "file", - "spec": "320207381.nwb", - "key": "stimulus_path", + "type": "file", + "spec": "320207381.nwb", + "key": "stimulus_path", "format": "NWB" - }, + }, { - "key": "output_path", - "type": "file", - "spec": "320207381.nwb", - "parent_key": "WORKDIR", + "key": "output_path", + "type": "file", + "spec": "320207381.nwb", + "parent_key": "WORKDIR", "format": "NWB" } ] diff --git a/examples/saveLoadV1/E5/manifest.json b/examples/saveLoadV1/E5/manifest.json index 333715625..a24de4871 100644 --- a/examples/saveLoadV1/E5/manifest.json +++ b/examples/saveLoadV1/E5/manifest.json @@ -1,194 +1,193 @@ { "biophys": [ { - "model_type": "Biophysical - perisomatic", + "model_type": "Biophysical - perisomatic", "model_file": [ - "manifest.json", + "manifest.json", "314822529_fit.json" ] } - ], + ], "runs": [ { "sweeps": [ - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 131, - 132, - 133, - 134, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 98, + 99, + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 107, + 108, + 109, + 110, + 111, + 112, + 113, + 114, + 115, + 116, + 117, + 118, + 119, + 120, + 121, + 122, + 123, + 124, + 125, + 126, + 127, + 128, + 129, + 131, + 132, + 133, + 134, 137 ] } - ], + ], "neuron": [ { "hoc": [ - "stdgui.hoc", - "import3d.hoc", - "cell_template.hoc" + "stdgui.hoc", + "import3d.hoc" ] } - ], + ], "manifest": [ { - "type": "dir", - "spec": ".", + "type": "dir", + "spec": ".", "key": "BASEDIR" - }, + }, { - "parent": "BASEDIR", - "type": "dir", - "spec": "work", + "parent": "BASEDIR", + "type": "dir", + "spec": "work", "key": "WORKDIR" - }, + }, { - "type": "file", - "spec": "Rorb-IRES2-Cre-D_Ai14-168053.06.01.01_495334938_m.swc", + "type": "file", + "spec": "Rorb-IRES2-Cre-D_Ai14-168053.06.01.01_495334938_m.swc", "key": "MORPHOLOGY" - }, + }, { - "type": "file", - "spec": "Rorb-IRES2-Cre-D_Ai14-168053.06.01.01_495334938_marker_m.swc", + "type": "file", + "spec": "Rorb-IRES2-Cre-D_Ai14-168053.06.01.01_495334938_marker_m.swc", "key": "MARKER" - }, + }, { - "type": "dir", - "spec": "modfiles", + "type": "dir", + "spec": "modfiles", "key": "MODFILE_DIR" - }, + }, { - "type": "file", - "spec": "314822527.nwb", - "key": "stimulus_path", + "type": "file", + "spec": "314822527.nwb", + "key": "stimulus_path", "format": "NWB" - }, + }, { - "key": "output_path", - "type": "file", - "spec": "314822527.nwb", - "parent_key": "WORKDIR", + "key": "output_path", + "type": "file", + "spec": "314822527.nwb", + "parent_key": "WORKDIR", "format": "NWB" } ] diff --git a/examples/saveLoadV1/IF/manifest.json b/examples/saveLoadV1/IF/manifest.json index 16946cf96..97f9ff83a 100644 --- a/examples/saveLoadV1/IF/manifest.json +++ b/examples/saveLoadV1/IF/manifest.json @@ -1,161 +1,160 @@ { "biophys": [ { - "model_type": "Biophysical - perisomatic", + "model_type": "Biophysical - perisomatic", "model_file": [ - "manifest.json", + "manifest.json", "313861608_fit.json" ] } - ], + ], "runs": [ { "sweeps": [ - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 17, - 18, - 19, - 20, - 21, - 23, - 24, - 25, - 26, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 68, - 69, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 17, + 18, + 19, + 20, + 21, + 23, + 24, + 25, + 26, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 68, + 69, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, 105 ] } - ], + ], "neuron": [ { "hoc": [ - "stdgui.hoc", - "import3d.hoc", - "cell_template.hoc" + "stdgui.hoc", + "import3d.hoc" ] } - ], + ], "manifest": [ { - "type": "dir", - "spec": ".", + "type": "dir", + "spec": ".", "key": "BASEDIR" - }, + }, { - "parent": "BASEDIR", - "type": "dir", - "spec": "work", + "parent": "BASEDIR", + "type": "dir", + "spec": "work", "key": "WORKDIR" - }, + }, { - "type": "file", - "spec": "Pvalb-IRES-Cre_Ai14-165874.04.02.01_491119570_m.swc", + "type": "file", + "spec": "Pvalb-IRES-Cre_Ai14-165874.04.02.01_491119570_m.swc", "key": "MORPHOLOGY" - }, + }, { - "type": "file", - "spec": "Pvalb-IRES-Cre_Ai14-165874.04.02.01_491119570_marker_m.swc", + "type": "file", + "spec": "Pvalb-IRES-Cre_Ai14-165874.04.02.01_491119570_marker_m.swc", "key": "MARKER" - }, + }, { - "type": "dir", - "spec": "modfiles", + "type": "dir", + "spec": "modfiles", "key": "MODFILE_DIR" - }, + }, { - "type": "file", - "spec": "313861603.nwb", - "key": "stimulus_path", + "type": "file", + "spec": "313861603.nwb", + "key": "stimulus_path", "format": "NWB" - }, + }, { - "key": "output_path", - "type": "file", - "spec": "313861603.nwb", - "parent_key": "WORKDIR", + "key": "output_path", + "type": "file", + "spec": "313861603.nwb", + "parent_key": "WORKDIR", "format": "NWB" } ] diff --git a/examples/saveLoadV1/IL/manifest.json b/examples/saveLoadV1/IL/manifest.json index ce12afd66..f3ee353bd 100644 --- a/examples/saveLoadV1/IL/manifest.json +++ b/examples/saveLoadV1/IL/manifest.json @@ -1,161 +1,160 @@ { "biophys": [ { - "model_type": "Biophysical - perisomatic", + "model_type": "Biophysical - perisomatic", "model_file": [ - "manifest.json", + "manifest.json", "464212183_fit.json" ] } - ], + ], "runs": [ { "sweeps": [ - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 97, - 98, - 99, - 100, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 97, + 98, + 99, + 100, 102 ] } - ], + ], "neuron": [ { "hoc": [ - "stdgui.hoc", - "import3d.hoc", - "cell_template.hoc" + "stdgui.hoc", + "import3d.hoc" ] } - ], + ], "manifest": [ { - "type": "dir", - "spec": ".", + "type": "dir", + "spec": ".", "key": "BASEDIR" - }, + }, { - "parent": "BASEDIR", - "type": "dir", - "spec": "work", + "parent": "BASEDIR", + "type": "dir", + "spec": "work", "key": "WORKDIR" - }, + }, { - "type": "file", - "spec": "Sst-IRES-Cre_Ai14-165865.05.02.01_491119537_m.swc", + "type": "file", + "spec": "Sst-IRES-Cre_Ai14-165865.05.02.01_491119537_m.swc", "key": "MORPHOLOGY" - }, + }, { - "type": "file", - "spec": "Sst-IRES-Cre_Ai14-165865.05.02.01_491119537_marker_m.swc", + "type": "file", + "spec": "Sst-IRES-Cre_Ai14-165865.05.02.01_491119537_marker_m.swc", "key": "MARKER" - }, + }, { - "type": "dir", - "spec": "modfiles", + "type": "dir", + "spec": "modfiles", "key": "MODFILE_DIR" - }, + }, { - "type": "file", - "spec": "464212181.nwb", - "key": "stimulus_path", + "type": "file", + "spec": "464212181.nwb", + "key": "stimulus_path", "format": "NWB" - }, + }, { - "key": "output_path", - "type": "file", - "spec": "464212181.nwb", - "parent_key": "WORKDIR", + "key": "output_path", + "type": "file", + "spec": "464212181.nwb", + "parent_key": "WORKDIR", "format": "NWB" } ] diff --git a/examples/saveLoadV1/load_run.py b/examples/saveLoadV1/load_run.py index 249847b22..77b0dd8f9 100644 --- a/examples/saveLoadV1/load_run.py +++ b/examples/saveLoadV1/load_run.py @@ -17,10 +17,10 @@ 'V_dend6': {'sec': 'dend_6', 'loc': 0.5, 'var': 'v'}} # Dict with traces to record # Analysis -simConfig.addAnalysis('plotRaster', {'orderBy': 'y', 'orderInverse': True}) # Plot a raster -simConfig.addAnalysis('plotTraces', {'include': [('L2_E', 0), ('L5_IF', 0)]}) # Plot recorded traces for this list of cells -simConfig.addAnalysis('plot2Dnet', {'include': ['L2_E', 'L4_E']}) # plot 2D visualization of cell positions and connections -simConfig.addAnalysis('plotConn', True) # plot connectivity matrix +simConfig.analysis['plotRaster'] = {'orderBy': 'y', 'orderInverse': True} # Plot a raster +simConfig.analysis['plotTraces'] = {'include': [('L2_E', 0), ('L5_IF', 0)]} # Plot recorded traces for this list of cells +simConfig.analysis['plot2Dnet'] = {'include': ['L2_E', 'L4_E']} # plot 2D visualization of cell positions and connections +simConfig.analysis['plotConn'] = True # plot connectivity matrix ############################################################################### diff --git a/examples/saveLoadV1/params.py b/examples/saveLoadV1/params.py index e2c07c7df..1a33d9858 100644 --- a/examples/saveLoadV1/params.py +++ b/examples/saveLoadV1/params.py @@ -41,11 +41,6 @@ cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism netParams.cellParams['E2_simple'] = cellRule # add cell params rule -# Set vinit for all sections of all cells -for cellRule in netParams.cellParams.values(): - for sec in cellRule['secs'].values(): - sec['vinit'] = -90.0 - ## Synaptic mechanism parameters netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # excitatory synaptic mechanism @@ -85,15 +80,14 @@ netParams.subConnParams['all->E'] = { 'preConds': {'ynorm': [0,1]}, # presyn: all 'postConds': {'cellType': ['E2', 'E4', 'E5']}, # postsyn: E - 'sec': ['all'], # target all sections - 'somaPathDist': [0, 100.0], # path distance from soma - 'distribut': 'uniform'} # distribute syns uninformly + 'sec': 'apical', # target all sections # path distance from soma + 'density': 'uniform'} # distribute syns uninformly netParams.subConnParams['E->upperI'] = { 'preConds': {'cellType': ['E2', 'E4', 'E5']}, # presyn: all 'postConds': {'cellType': ['IF', 'IL'], 'ynorm': [0.2, 0.5]}, # postsyn: I, 0.2-0.5 - 'sec': ['basal', 'somatic'], # target all basal and somatic sections - 'dsitribute': 'random'} # distribute syns randomly + 'sec': 'basal', # target all basal and somatic sections + 'density': 'uniform'} # distribute syns randomly ## Stimulation parameters @@ -110,6 +104,7 @@ simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration simConfig.verbose = 0 # Show detailed messages +simConfig.hParams['v_init'] = -90 # Set v_init=-90 simConfig.createNEURONObj = 0 # create HOC objects when instantiating network simConfig.createPyStruct = True # create Python structure (simulator-independent) when instantiating network diff --git a/netpyne/.DS_Store b/netpyne/.DS_Store index de061bedc..35b216127 100644 Binary files a/netpyne/.DS_Store and b/netpyne/.DS_Store differ diff --git a/netpyne/__init__.py b/netpyne/__init__.py index ca554aa8a..b4188ddf6 100644 --- a/netpyne/__init__.py +++ b/netpyne/__init__.py @@ -1,3 +1,3 @@ -__version__ = '0.7.4' +__version__ = '0.7.5' __gui__ = True # global option to enable/disable graphics \ No newline at end of file diff --git a/netpyne/analysis.py b/netpyne/analysis.py index 7e94a0caf..39770aea8 100644 --- a/netpyne/analysis.py +++ b/netpyne/analysis.py @@ -17,24 +17,20 @@ from numbers import Number import math -import sim - import warnings warnings.filterwarnings("ignore") colorList = [[0.42,0.67,0.84], [0.90,0.76,0.00], [0.42,0.83,0.59], [0.90,0.32,0.00], [0.34,0.67,0.67], [0.90,0.59,0.00], [0.42,0.82,0.83], [1.00,0.85,0.00], [0.33,0.67,0.47], [1.00,0.38,0.60], [0.57,0.67,0.33], [0.5,0.2,0.0], - [0.71,0.82,0.41], [0.0,0.2,0.5], [0.70,0.32,0.10], - [0.42,0.67,0.84], [0.90,0.76,0.00], [0.42,0.83,0.59], [0.90,0.32,0.00], - [0.34,0.67,0.67], [0.90,0.59,0.00], [0.42,0.82,0.83], [1.00,0.85,0.00], - [0.33,0.67,0.47], [1.00,0.38,0.60], [0.57,0.67,0.33], [0.5,0.2,0.0], - [0.71,0.82,0.41], [0.0,0.2,0.5], [0.70,0.32,0.10]] + [0.71,0.82,0.41], [0.0,0.2,0.5], [0.70,0.32,0.10]]*3 ###################################################################################################################################################### ## Wrapper to run analysis functions in simConfig ###################################################################################################################################################### def plotData (): + import sim + ## Plotting if sim.rank == 0 and __gui__: sim.timing('start', 'plotTime') @@ -72,6 +68,8 @@ def _showFigure(): ## Save figure data ###################################################################################################################################################### def _saveFigData(figData, fileName=None, type=''): + import sim + if not fileName or not isinstance(fileName, basestring): fileName = sim.cfg.filename+'_'+type+'.pkl' @@ -154,24 +152,12 @@ def _smooth1d(x,window_len=11,window='hanning'): return y[(window_len/2-1):-(window_len/2)] -###################################################################################################################################################### -## Synchrony measure -###################################################################################################################################################### -def syncMeasure (): - t0=-1 - width=1 - cnt=0 - for spkt in sim.allSimData['spkt']: - if (spkt>=t0+width): - t0=spkt - cnt+=1 - return 1-cnt/(sim.cfg.duration/width) - - ###################################################################################################################################################### ## Get subset of cells and netstims indicated by include list ###################################################################################################################################################### def getCellsInclude(include): + import sim + allCells = sim.net.allCells allNetStimLabels = sim.net.params.stimSourceParams.keys() cellGids = [] @@ -207,6 +193,18 @@ def getCellsInclude(include): elif isinstance(condition[1], int): cellGids.extend([gid for i,gid in enumerate(cellsPop) if i==condition[1]]) + elif isinstance(condition, list): + for subcond in condition: + if isinstance(subcond, int): # cell gid + cellGids.append(subcond) + + elif isinstance(subcond, basestring): # entire pop + if subcond in allNetStimLabels: + netStimLabels.append(subcond) + else: + cellGids.extend([c['gid'] for c in allCells if c['tags']['pop']==subcond]) + + cellGids = list(set(cellGids)) # unique values cells = [cell for cell in allCells if cell['gid'] in cellGids] cells = sorted(cells, key=lambda k: k['gid']) @@ -217,40 +215,327 @@ def getCellsInclude(include): ###################################################################################################################################################### ## Get subset of cells and netstims indicated by include list ###################################################################################################################################################### -def getCellsIncludeTags(include, tags): +def getCellsIncludeTags(include, tags, tagsFormat=None): allCells = tags.copy() - if 'format' in allCells: allCells.pop('format') cellGids = [] - for condition in include: - if condition in ['all', 'allCells']: # all cells - cellGids = allCells.keys() - return cellGids + # using list with indices + if tagsFormat or 'format' in allCells: + if not tagsFormat: tagsFormat = allCells.pop('format') + popIndex = tagsFormat.index('pop') - elif isinstance(condition, int): # cell gid - cellGids.append(condition) - - elif isinstance(condition, basestring): # entire pop - cellGids.extend([gid for gid,c in allCells.iteritems() if c['pop']==condition]) - - elif isinstance(condition, tuple): # subset of a pop with relative indices - cellsPop = [gid for gid,c in allCells.iteritems() if c['pop']==condition[0]] - if isinstance(condition[1], list): - cellGids.extend([gid for i,gid in enumerate(cellsPop) if i in condition[1]]) - elif isinstance(condition[1], int): - cellGids.extend([gid for i,gid in enumerate(cellsPop) if i==condition[1]]) + for condition in include: + if condition in ['all', 'allCells']: # all cells + cellGids = allCells.keys() + return cellGids + + elif isinstance(condition, int): # cell gid + cellGids.append(condition) + + elif isinstance(condition, basestring): # entire pop + cellGids.extend([gid for gid,c in allCells.iteritems() if c[popIndex]==condition]) + + elif isinstance(condition, tuple): # subset of a pop with relative indices + cellsPop = [gid for gid,c in allCells.iteritems() if c[popIndex]==condition[0]] + if isinstance(condition[1], list): + cellGids.extend([gid for i,gid in enumerate(cellsPop) if i in condition[1]]) + elif isinstance(condition[1], int): + cellGids.extend([gid for i,gid in enumerate(cellsPop) if i==condition[1]]) + + # using dict with keys + else: + + for condition in include: + if condition in ['all', 'allCells']: # all cells + cellGids = allCells.keys() + return cellGids + + elif isinstance(condition, int): # cell gid + cellGids.append(condition) + + elif isinstance(condition, basestring): # entire pop + cellGids.extend([gid for gid,c in allCells.iteritems() if c['pop']==condition]) + + elif isinstance(condition, tuple): # subset of a pop with relative indices + cellsPop = [gid for gid,c in allCells.iteritems() if c['pop']==condition[0]] + if isinstance(condition[1], list): + cellGids.extend([gid for i,gid in enumerate(cellsPop) if i in condition[1]]) + elif isinstance(condition[1], int): + cellGids.extend([gid for i,gid in enumerate(cellsPop) if i==condition[1]]) cellGids = [int(x) for x in set(cellGids)] # unique values return cellGids +###################################################################################################################################################### +## Synchrony measure +###################################################################################################################################################### +def syncMeasure (): + import sim + + t0=-1 + width=1 + cnt=0 + for spkt in sim.allSimData['spkt']: + if (spkt>=t0+width): + t0=spkt + cnt+=1 + return 1-cnt/(sim.cfg.duration/width) + + +###################################################################################################################################################### +## Calculate avg and peak rate of different subsets of cells for specific time period +###################################################################################################################################################### +def calculateRate (include = ['allCells', 'eachPop'], peakBin = 5, timeRange = None): + ''' + Calculate avg and peak rate of different subsets of cells for specific time period + - include (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): List of data series to include. + Note: one line per item, not grouped (default: ['allCells', 'eachPop']) + - timeRange ([start:stop]): Time range of spikes shown; if None shows all (default: None) + - peakBin (int): Histogram bin size used to calculate peak firing rate; if None, peak rate not calculated (default: 5) + - Returns list with rates + ''' + + import sim + + print('Calculating avg and peak firing rates ...') + + # Replace 'eachPop' with list of pops + if 'eachPop' in include: + include.remove('eachPop') + for pop in sim.net.allPops: include.append(pop) + + # time range + if timeRange is None: + timeRange = [0,sim.cfg.duration] + + avg, peak, histData = [], [], [] + + # Plot separate line for each entry in include + for iplot,subset in enumerate(include): + cells, cellGids, netStimLabels = getCellsInclude([subset]) + numNetStims = 0 + + # Select cells to include + if len(cellGids) > 0: + try: + spkinds,spkts = zip(*[(spkgid,spkt) for spkgid,spkt in zip(sim.allSimData['spkid'],sim.allSimData['spkt']) if spkgid in cellGids]) + except: + spkinds,spkts = [],[] + else: + spkinds,spkts = [],[] + + # Add NetStim spikes + spkts, spkinds = list(spkts), list(spkinds) + numNetStims = 0 + if 'stims' in sim.allSimData: + for netStimLabel in netStimLabels: + netStimSpks = [spk for cell,stims in sim.allSimData['stims'].iteritems() \ + for stimLabel,stimSpks in stims.iteritems() for spk in stimSpks if stimLabel == netStimLabel] + if len(netStimSpks) > 0: + lastInd = max(spkinds) if len(spkinds)>0 else 0 + spktsNew = netStimSpks + spkindsNew = [lastInd+1+i for i in range(len(netStimSpks))] + spkts.extend(spktsNew) + spkinds.extend(spkindsNew) + numNetStims += 1 + + if peakBin: + histo = np.histogram(spkts, bins = np.arange(timeRange[0], timeRange[1], peakBin)) + histoT = histo[1][:-1]+peakBin/2 + histoCount = histo[0] + + histData.append(histoCount) + + histoCount = histoCount * float((1000.0 / peakBin)) / float((len(cellGids)+numNetStims)) # convert to firing rate + peak.append(float(max(histoCount))) + + spktsRange = [spkt for spkt in spkts if timeRange[0] <= spkt <= timeRange[1]] + avg.append(float(len(spktsRange)) / float((len(cellGids)+numNetStims)) / float((timeRange[1]-timeRange[0])) * 1000.0) + + return include, avg, peak + + +###################################################################################################################################################### +## Plot avg and peak rates at different time periods +###################################################################################################################################################### +def plotRates (include =['allCells', 'eachPop'], peakBin = 5, timeRanges = None, timeRangeLabels = None, colors = None, figSize = ((5,5)), saveData = None, + saveFig = None, showFig = True): + ''' + Calculate avg and peak rate of different subsets of cells for specific time period + - include (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): List of data series to include. + Note: one line per item, not grouped (default: ['allCells', 'eachPop']) + - timeRanges ([[start1:stop1], [start2:stop2]]): List of time range of spikes shown; if None shows all (default: None) + - timeRangeLabels (['preStim', 'postStim']): List of labels for each time range period (default: None) + - peakBin (int): Histogram bin size used to calculate peak firing rate; if None, peak rate not calculated (default: 5) + - figSize ((width, height)): Size of figure (default: (10,8)) + - saveData (None|True|'fileName'): File name where to save the final data used to generate the figure; + if set to True uses filename from simConfig (default: None) + - saveFig (None|True|'fileName'): File name where to save the figure (default: None) + if set to True uses filename from simConfig (default: None) + - showFig (True|False): Whether to show the figure or not (default: True) + + - Returns figs + ''' + import sim + + if not colors: colors = colorList + + avgs = [] + peaks = [] + if not timeRangeLabels: + timeRangeLabels = ['%f-%f ms'%(t[0], t[1]) for t in timeRanges] #['period '+i for i in range(len(timeRanges))] + + for i, timeRange in enumerate(timeRanges): + labels, avg, peak = sim.analysis.calculateRate(include=include, peakBin=peakBin, timeRange=timeRange) + avgs.append(avg) + peaks.append(peak) + + fig1,ax1 = plt.subplots(figsize=figSize) + + # avg + fontsiz=14 + ax1.set_color_cycle(colors) + ax1.plot(avgs, marker='o') + #ax1.set_xlabel('Time period', fontsize=fontsiz) + ax1.set_ylabel('Avg firing rate', fontsize=fontsiz) + ax1.set_xticks(range(len(timeRangeLabels))) + ax1.set_xticklabels(timeRangeLabels) + ax1.set_xlim(-0.5, len(avgs)-0.5) + ax1.legend(include) + + try: + plt.tight_layout() + except: + pass + + # save figure + if saveFig: + if isinstance(saveFig, basestring): + filename = saveFig + else: + filename = sim.cfg.filename+'_'+'avgRates.png' + plt.savefig(filename) + + # show fig + if showFig: _showFigure() + + # peak + fig2,ax2 = plt.subplots(figsize=figSize) + ax2.set_color_cycle(colors) + ax2.plot(peaks, marker='o') + #ax2.set_xlabel('Time period', fontsize=fontsiz) + ax2.set_ylabel('Peak firing rate', fontsize=fontsiz) + ax2.set_xticks(range(len(timeRangeLabels))) + ax2.set_xticklabels(timeRangeLabels) + ax2.set_xlim(-0.5, len(peaks)-0.5) + ax2.legend(include) + + try: + plt.tight_layout() + except: + pass + + # save figure + if saveFig: + if isinstance(saveFig, basestring): + filename = saveFig + else: + filename = sim.cfg.filename+'_'+'peakRates.png' + plt.savefig(filename) + + # show fig + if showFig: _showFigure() + + + # save figure data + if saveData: + figData = {'includeList': includeList, 'timeRanges': timeRanges, 'avgs': avgs, 'peaks': peaks} + + _saveFigData(figData, saveData, 'raster') + + return fig1, fig2, avgs, peaks + + + + +###################################################################################################################################################### +## Plot sync at different time periods +###################################################################################################################################################### +def plotSyncs (include =['allCells', 'eachPop'], timeRanges = None, timeRangeLabels = None, colors = None, figSize = ((5,5)), saveData = None, + saveFig = None, showFig = True): + ''' + Calculate avg and peak rate of different subsets of cells for specific time period + - include (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): List of data series to include. + Note: one line per item, not grouped (default: ['allCells', 'eachPop']) + - timeRanges ([[start1:stop1], [start2:stop2]]): List of time range of spikes shown; if None shows all (default: None) + - timeRangeLabels (['preStim', 'postStim']): List of labels for each time range period (default: None) + - figSize ((width, height)): Size of figure (default: (10,8)) + - saveData (None|True|'fileName'): File name where to save the final data used to generate the figure; + if set to True uses filename from simConfig (default: None) + - saveFig (None|True|'fileName'): File name where to save the figure (default: None) + if set to True uses filename from simConfig (default: None) + - showFig (True|False): Whether to show the figure or not (default: True) + + - Returns figs + ''' + import sim + + if not colors: colors = colorList + + syncs = [] + if not timeRangeLabels: + timeRangeLabels = ['%f-%f ms'%(t[0], t[1]) for t in timeRanges] #['period '+i for i in range(len(timeRanges))] + + for i, timeRange in enumerate(timeRanges): + print timeRange + _, sync = sim.analysis.plotSpikeStats (include = include, timeRange = timeRange, stats = ['sync'], saveFig = False, showFig =False) + print sync + sync = [s[0] for s in sync] + syncs.append(sync) + + fig1,ax1 = plt.subplots(figsize=figSize) + + # avg + fontsiz=14 + ax1.set_color_cycle(colors) + ax1.plot(syncs, marker='o') + ax1.set_xlabel('Time period', fontsize=fontsiz) + ax1.set_ylabel('Spiking synchrony', fontsize=fontsiz) + ax1.set_xticks(range(len(timeRangeLabels))) + ax1.set_xticklabels(timeRangeLabels) + ax1.set_xlim(-0.5, len(syncs)-0.5) + ax1.legend(include) + + # save figure + if saveFig: + if isinstance(saveFig, basestring): + filename = saveFig + else: + filename = sim.cfg.filename+'_'+'sync.png' + plt.savefig(filename) + + # show fig + if showFig: _showFigure() + + # save figure data + if saveData: + figData = {'includeList': includeList, 'timeRanges': timeRanges, 'syncs': syncs} + + _saveFigData(figData, saveData, 'raster') + + + + return fig1, syncs + + ###################################################################################################################################################### ## Raster plot ###################################################################################################################################################### def plotRaster (include = ['allCells'], timeRange = None, maxSpikes = 1e8, orderBy = 'gid', orderInverse = False, labels = 'legend', popRates = False, - spikeHist = None, spikeHistBin = 5, syncLines = False, lw = 2, marker = '|', popColors = None, figSize = (10,8), dpi = 100, saveData = None, saveFig = None, + spikeHist = None, spikeHistBin = 5, syncLines = False, lw = 2, marker = '|', markerSize=5, popColors = None, figSize = (10,8), dpi = 100, saveData = None, saveFig = None, showFig = True): ''' Raster plot of network cells @@ -278,6 +563,7 @@ def plotRaster (include = ['allCells'], timeRange = None, maxSpikes = 1e8, order - Returns figure handle ''' + import sim print('Plotting raster...') @@ -382,7 +668,7 @@ def plotRaster (include = ['allCells'], timeRange = None, maxSpikes = 1e8, order gs = gridspec.GridSpec(2, 1,height_ratios=[2,1]) ax1=plt.subplot(gs[0]) - ax1.scatter(spkts, spkinds, 10, lw=lw, marker=marker, color = spkgidColors) # Create raster + ax1.scatter(spkts, spkinds, lw=lw, s=markerSize, marker=marker, color = spkgidColors) # Create raster ax1.set_xlim(timeRange) # Plot stats @@ -450,7 +736,7 @@ def plotRaster (include = ['allCells'], timeRange = None, maxSpikes = 1e8, order else: finalty = tyOffset + ty/2.0 - 0.01 plt.text(tx, finalty, label, transform=ax.transAxes, fontsize=fontsiz, color=popColors[popLabel]) - maxLabelLen = max([len(l) for l in labels]) + maxLabelLen = min(6, max([len(l) for l in labels])) plt.subplots_adjust(right=(1.0-0.011*maxLabelLen)) # Plot spike hist @@ -494,7 +780,7 @@ def plotRaster (include = ['allCells'], timeRange = None, maxSpikes = 1e8, order ## Plot spike histogram ###################################################################################################################################################### def plotSpikeHist (include = ['allCells', 'eachPop'], timeRange = None, binSize = 5, overlay=True, graphType='line', yaxis = 'rate', - popColors = None, figSize = (10,8), saveData = None, saveFig = None, showFig = True): + popColors = [], dpi = 100, figSize = (10,8), saveData = None, saveFig = None, showFig = True): ''' Plot spike histogram - include (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): List of data series to include. @@ -515,6 +801,8 @@ def plotSpikeHist (include = ['allCells', 'eachPop'], timeRange = None, binSize - Returns figure handle ''' + import sim + print('Plotting spike histogram...') # Replace 'eachPop' with list of pops @@ -576,7 +864,7 @@ def plotSpikeHist (include = ['allCells', 'eachPop'], timeRange = None, binSize if yaxis=='rate': histoCount = histoCount * (1000.0 / binSize) / (len(cellGids)+numNetStims) # convert to firing rate - color = popColors[subset] if subset in popColors else colorList[i%len(colorList)] + color = popColors[subset] if subset in popColors else colorList[iplot%len(colorList)] if not overlay: plt.subplot(len(include),1,iplot+1) # if subplot, create new subplot @@ -586,7 +874,8 @@ def plotSpikeHist (include = ['allCells', 'eachPop'], timeRange = None, binSize if graphType == 'line': plt.plot (histoT, histoCount, linewidth=1.0, color = color) elif graphType == 'bar': - plt.bar(histoT, histoCount, width = binSize, color = color) + #plt.bar(histoT, histoCount, width = binSize, color = color, fill=False) + plt.plot (histoT, histoCount, linewidth=1.0, color = color, ls='steps') if iplot == 0: plt.xlabel('Time (ms)', fontsize=fontsiz) @@ -622,7 +911,7 @@ def plotSpikeHist (include = ['allCells', 'eachPop'], timeRange = None, binSize filename = saveFig else: filename = sim.cfg.filename+'_'+'spikeHist.png' - plt.savefig(filename) + plt.savefig(filename, dpi=dpi) # show fig if showFig: _showFigure() @@ -634,7 +923,207 @@ def plotSpikeHist (include = ['allCells', 'eachPop'], timeRange = None, binSize ###################################################################################################################################################### ## Plot spike histogram ###################################################################################################################################################### -def plotRatePSD (include = ['allCells', 'eachPop'], timeRange = None, binSize = 5, Fs = 200, smooth = 0, overlay=True, +def plotSpikeStats (include = ['allCells', 'eachPop'], timeRange = None, graphType='boxplot', stats = ['rate', 'isicv'], + popColors = [], xlim = None, figSize = (6,8), saveData = None, saveFig = None, showFig = True): + ''' + Plot spike histogram + - include (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): List of data series to include. + Note: one line per item, not grouped (default: ['allCells', 'eachPop']) + - timeRange ([start:stop]): Time range of spikes shown; if None shows all (default: None) + - graphType ('boxplot'): Type of graph to use (default: 'boxplot') + - stats (['rate', |'isicv'| 'sync'| 'pairsync']): Measure to plot stats on (default: ['rate', 'isicv']) + - popColors (dict): Dictionary with color (value) used for each population (key) (default: None) + - figSize ((width, height)): Size of figure (default: (10,8)) + - saveData (None|True|'fileName'): File name where to save the final data used to generate the figure; + if set to True uses filename from simConfig (default: None) + - saveFig (None|True|'fileName'): File name where to save the figure; + if set to True uses filename from simConfig (default: None) + - showFig (True|False): Whether to show the figure or not (default: True) + + - Returns figure handle and statData + ''' + + import sim + + print('Plotting spike stats...') + + # Set plot style + colors = [] + params = { + 'axes.labelsize': 14, + 'text.fontsize': 14, + 'legend.fontsize': 14, + 'xtick.labelsize': 14, + 'ytick.labelsize': 14, + 'text.usetex': False, + } + plt.rcParams.update(params) + + # Replace 'eachPop' with list of pops + if 'eachPop' in include: + include.remove('eachPop') + for pop in sim.net.allPops: include.append(pop) + + # time range + if timeRange is None: + timeRange = [0,sim.cfg.duration] + + for stat in stats: + # create fig + fig,ax1 = plt.subplots(figsize=figSize) + fontsiz = 16 + + statData = [] + + # Calculate data for each entry in include + for iplot,subset in enumerate(include): + + cells, cellGids, netStimLabels = getCellsInclude([subset]) + numNetStims = 0 + + # Select cells to include + if len(cellGids) > 0: + try: + spkinds,spkts = zip(*[(spkgid,spkt) for spkgid,spkt in zip(sim.allSimData['spkid'],sim.allSimData['spkt']) if spkgid in cellGids]) + except: + spkinds,spkts = [],[] + else: + spkinds,spkts = [],[] + + # Add NetStim spikes + spkts, spkinds = list(spkts), list(spkinds) + numNetStims = 0 + if 'stims' in sim.allSimData: + for netStimLabel in netStimLabels: + netStimSpks = [spk for cell,stims in sim.allSimData['stims'].iteritems() \ + for stimLabel,stimSpks in stims.iteritems() for spk in stimSpks if stimLabel == netStimLabel] + if len(netStimSpks) > 0: + lastInd = max(spkinds) if len(spkinds)>0 else 0 + spktsNew = netStimSpks + spkindsNew = [lastInd+1+i for i in range(len(netStimSpks))] + spkts.extend(spktsNew) + spkinds.extend(spkindsNew) + numNetStims += 1 + + spkts,spkinds = zip(*[(spkt, spkind) for spkt, spkind in zip(spkts, spkinds) if timeRange[0] <= spkt <= timeRange[1]]) + + # rate stats + if stat == 'rate': + toRate = 1e3/(timeRange[1]-timeRange[0]) + rates = [spkinds.count(gid)*toRate for gid in set(spkinds)] #cellGids] #set(spkinds)] + statData.insert(0, rates) + xlabel = 'Rate (Hz)' + + # Inter-spike interval (ISI) coefficient of variation (CV) stats + elif stat == 'isicv': + xlabel = 'Irregularity (ISI CV)' + spkmat = [[spkt for spkind,spkt in zip(spkinds,spkts) if spkind==gid] for gid in set(spkinds)] + isimat = [[t - s for s, t in zip(spks, spks[1:])] for spks in spkmat] + isicv = [np.std(x) / np.mean(x) for x in isimat if len(x)>0] + statData.insert(0, isicv) + + # synchrony + elif stat in ['sync', 'pairsync']: + try: + import pyspike + except: + print "Error: plotSpikeStats() requires the PySpike python package to calculate synchrony (try: pip install pyspike)" + return 0 + + + spkmat = [pyspike.SpikeTrain([spkt for spkind,spkt in zip(spkinds,spkts) if spkind==gid], timeRange) for gid in set(spkinds)] + if stat == 'sync': + xlabel = 'Synchrony'# (SPIKE-Sync measure)' # see http://www.scholarpedia.org/article/Measures_of_spike_train_synchrony + syncMat = [pyspike.spike_sync(spkmat)] + #graphType = 'bar' + elif stat == 'pairsync': + xlabel = 'Pairwise synchrony'# (SPIKE-Sync measure)' # see http://www.scholarpedia.org/article/Measures_of_spike_train_synchrony + syncMat = np.mean(pyspike.spike_sync_matrix(spkmat), 0) + + + statData.insert(0, syncMat) + + colors.insert(0, popColors[subset] if subset in popColors else colorList[iplot%len(colorList)]) + + # plotting + if include[0] == 'allCells': + colors.insert(len(include), (0.5,0.5,0.5)) # if allCells is at top make its color=black + del colors[0] + + if graphType == 'boxplot': + meanpointprops = dict(marker=(5,1,0), markeredgecolor='black', markerfacecolor='white') + bp=plt.boxplot(statData, labels=include[::-1], notch=False, sym='k+', meanprops=meanpointprops, + whis=1.5, widths=0.6, vert=False, showmeans=True, patch_artist=True) + plt.xlabel(xlabel, fontsize=fontsiz) + plt.ylabel('Population', fontsize=fontsiz) + + icolor=0 + borderColor = 'k' + for i in range(0, len(bp['boxes'])): + icolor = i + bp['boxes'][i].set_facecolor(colors[icolor]) + bp['boxes'][i].set_linewidth(2) + # we have two whiskers! + bp['whiskers'][i*2].set_color(borderColor) + bp['whiskers'][i*2 + 1].set_color(borderColor) + bp['whiskers'][i*2].set_linewidth(2) + bp['whiskers'][i*2 + 1].set_linewidth(2) + bp['medians'][i].set_color(borderColor) + bp['medians'][i].set_linewidth(3) + # for f in bp['fliers']: + # f.set_color(colors[icolor]) + # print f + # and 4 caps to remove + for c in bp['caps']: + c.set_color(borderColor) + c.set_linewidth(2) + + ax = plt.gca() + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.spines['bottom'].set_visible(False) + ax.get_xaxis().tick_bottom() + ax.get_yaxis().tick_left() + ax.tick_params(axis='x', length=0) + ax.tick_params(axis='y', direction='out') + ax.grid(axis='x', color="0.9", linestyle='-', linewidth=1) + ax.set_axisbelow(True) + if xlim: ax.set_xlim(xlim) + + # elif graphType == 'bar': + # print range(1, len(statData)+1), statData + # plt.bar(range(1, len(statData)+1), statData, tick_label=include[::-1], orientation='horizontal', colors=colors) + + try: + plt.tight_layout() + except: + pass + + # save figure data + if saveData: + figData = {'include': include, 'statData': statData, 'timeRange': timeRange, 'saveData': saveData, 'saveFig': saveFig, 'showFig': showFig} + + _saveFigData(figData, saveData, 'spikeStats_'+stat) + + # save figure + if saveFig: + if isinstance(saveFig, basestring): + filename = saveFig+'_'+'spikeStat_'+stat+'.png' + else: + filename = sim.cfg.filename+'_'+'spikeStat_'+stat+'.png' + plt.savefig(filename) + + # show fig + if showFig: _showFigure() + + return fig, statData + + + +###################################################################################################################################################### +## Plot spike histogram +###################################################################################################################################################### +def plotRatePSD (include = ['allCells', 'eachPop'], timeRange = None, binSize = 5, Fs = 200, smooth = 0, overlay=True, ylim = None, popColors = None, figSize = (10,8), saveData = None, saveFig = None, showFig = True): ''' Plot firing rate power spectral density (PSD) @@ -658,6 +1147,8 @@ def plotRatePSD (include = ['allCells', 'eachPop'], timeRange = None, binSize = - Returns figure handle ''' + import sim + print('Plotting firing rate power spectral density (PSD) ...') # Replace 'eachPop' with list of pops @@ -675,6 +1166,7 @@ def plotRatePSD (include = ['allCells', 'eachPop'], timeRange = None, binSize = fig,ax1 = plt.subplots(figsize=figSize) fontsiz = 12 + allPower, allSignal, allFreqs=[], [], [] # Plot separate line for each entry in include for iplot,subset in enumerate(include): cells, cellGids, netStimLabels = getCellsInclude([subset]) @@ -728,12 +1220,17 @@ def plotRatePSD (include = ['allCells', 'eachPop'], timeRange = None, binSize = signal = 10*np.log10(power[0]) freqs = power[1] + allFreqs.append(freqs) + allPower.append(power) + allSignal.append(signal) + plt.plot(freqs, signal, linewidth=1.5, color=color) plt.xlabel('Frequency (Hz)', fontsize=fontsiz) plt.ylabel('Power Spectral Density (dB/Hz)', fontsize=fontsiz) # add yaxis in opposite side plt.xlim([0, (Fs/2)-1]) + if ylim: plt.ylim(ylim) if len(include) < 5: # if apply tight_layout with many subplots it inverts the y-axis try: @@ -746,7 +1243,7 @@ def plotRatePSD (include = ['allCells', 'eachPop'], timeRange = None, binSize = for i,subset in enumerate(include): color = popColors[subset] if subset in popColors else colorList[i%len(colorList)] plt.plot(0,0,color=color,label=str(subset)) - plt.legend(fontsize=fontsiz, bbox_to_anchor=(1.04, 1), loc=2, borderaxespad=0.) + plt.legend(fontsize=fontsiz, loc=1)#, bbox_to_anchor=(1.04, 1), loc=2, borderaxespad=0.) maxLabelLen = min(10,max([len(str(l)) for l in include])) plt.subplots_adjust(right=(0.9-0.012*maxLabelLen)) @@ -769,14 +1266,14 @@ def plotRatePSD (include = ['allCells', 'eachPop'], timeRange = None, binSize = # show fig if showFig: _showFigure() - return fig, power + return fig, allSignal, allPower, allFreqs ###################################################################################################################################################### ## Plot recorded cell traces (V, i, g, etc.) ###################################################################################################################################################### -def plotTraces (include = None, timeRange = None, overlay = False, oneFigPer = 'cell', rerun = False, colors = None, ylim = None, +def plotTraces (include = None, timeRange = None, overlay = False, oneFigPer = 'cell', rerun = False, colors = None, ylim = None, axis='on', figSize = (10,8), saveData = None, saveFig = None, showFig = True): ''' Plot recorded traces @@ -798,13 +1295,16 @@ def plotTraces (include = None, timeRange = None, overlay = False, oneFigPer = ' - Returns figure handles ''' + import sim print('Plotting recorded cell traces ...') if include is None: include = [] # If not defined, initialize as empty list global colorList if isinstance(colors, list): - colorList = colors + colorList2 = colors + else: + colorList2 = colorList # rerun simulation so new include cells get recorded from if rerun: @@ -833,18 +1333,18 @@ def plotTraces (include = None, timeRange = None, overlay = False, oneFigPer = ' def plotFigPerTrace(subGids): for itrace, trace in enumerate(tracesList): figs['_trace_'+str(trace)] = plt.figure(figsize=figSize) # Open a new figure - fontsiz = 12 + fontsiz = 2 for igid, gid in enumerate(subGids): if 'cell_'+str(gid) in sim.allSimData[trace]: data = sim.allSimData[trace]['cell_'+str(gid)][int(timeRange[0]/recordStep):int(timeRange[1]/recordStep)] t = np.arange(timeRange[0], timeRange[1]+recordStep, recordStep) tracesData.append({'t': t, 'cell_'+str(gid)+'_'+trace: data}) - color = colorList[igid%len(colorList)] + color = colorList2[igid%len(colorList2)] if not overlay: plt.subplot(len(subGids),1,igid+1) - color = 'blue' plt.ylabel(trace, fontsize=fontsiz) plt.plot(t[:len(data)], data, linewidth=1.5, color=color, label='Cell %d, Pop %s '%(int(gid), gidPops[gid])) + plt.axis(axis) plt.xlabel('Time (ms)', fontsize=fontsiz) plt.xlim(timeRange) if ylim: plt.ylim(ylim) @@ -872,11 +1372,12 @@ def plotFigPerTrace(subGids): lenData = len(data) t = np.arange(timeRange[0], timeRange[1]+recordStep, recordStep) tracesData.append({'t': t, 'cell_'+str(gid)+'_'+trace: data}) - color = colorList[itrace%len(colorList)] + color = colorList2[itrace%len(colorList2)] if not overlay: plt.subplot(len(tracesList),1,itrace+1) color = 'blue' plt.plot(t[:lenData], data, linewidth=1.5, color=color, label=trace) + plt.axis(axis) plt.xlabel('Time (ms)', fontsize=fontsiz) plt.ylabel(trace, fontsize=fontsiz) plt.xlim(timeRange) @@ -897,6 +1398,7 @@ def plotFigPerTrace(subGids): for popLabel, popGids in allPopGids.iteritems(): plotFigPerTrace(popGids) + try: plt.tight_layout() except: @@ -965,6 +1467,7 @@ def plotShape (includePost = ['all'], includePre = ['all'], showSyns = False, sy - Returns figure handles ''' + import sim from neuron import h, gui if not iv: # plot using Python instead of interviews @@ -1055,7 +1558,7 @@ def plotShape (includePost = ['all'], includePre = ['all'], showSyns = False, sy if not ivprops: ivprops = {'colorSecs': 1, 'colorSyns':2 ,'style': 'o', 'siz':2} - for cell in [c for c in sim.net.cells if c.tags['pop'] in includePost]: + for cell in [c for c in sim.net.cells if c.gid in includePost or c.tags['pop'] in includePost]: for sec in cell.secs.values(): if 'axon' in sec['hSec'].hname() and not includeAxon: continue sec['hSec'].push() @@ -1091,6 +1594,8 @@ def plotShape (includePost = ['all'], includePre = ['all'], showSyns = False, sy ## Plot LFP (time-resolved or power spectra) ###################################################################################################################################################### def plotLFP (): + import sim + print('Plotting LFP power spectral density...') colorspsd=array([[0.42,0.67,0.84],[0.42,0.83,0.59],[0.90,0.76,0.00],[0.90,0.32,0.00],[0.34,0.67,0.67],[0.42,0.82,0.83],[0.90,0.59,0.00],[0.33,0.67,0.47],[1.00,0.85,0.00],[0.71,0.82,0.41],[0.57,0.67,0.33],[1.00,0.38,0.60],[0.5,0.2,0.0],[0.0,0.2,0.5]]) @@ -1121,46 +1626,52 @@ def plotLFP (): h=plt.axes() h.set_yticklabels([]) + plt.show() def _roundFigures(x, n): """Returns x rounded to n significant figures.""" return round(x, int(n - math.ceil(math.np.log10(abs(x))))) + ###################################################################################################################################################### -## Plot connectivity +## Support function for plotConn() - calculate conn using data from sim object ###################################################################################################################################################### -def plotConn (includePre = ['all'], includePost = ['all'], feature = 'strength', orderBy = 'gid', figSize = (10,10), groupBy = 'pop', groupByInterval = None, - graphType = 'matrix', synOrConn = 'syn', synMech = None, saveData = None, saveFig = None, showFig = True): - ''' - Plot network connectivity - - includePre (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): Cells to show (default: ['all']) - - includePost (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): Cells to show (default: ['all']) - - feature ('weight'|'delay'|'numConns'|'probability'|'strength'|'convergence'|'divergence'): Feature to show in connectivity matrix; - the only features applicable to groupBy='cell' are 'weight', 'delay' and 'numConns'; 'strength' = weight * probability (default: 'strength') - - groupBy ('pop'|'cell'|'y'|: Show matrix for individual cells, populations, or by other numeric tag such as 'y' (default: 'pop') - - groupByInterval (int or float): Interval of groupBy feature to group cells by in conn matrix, e.g. 100 to group by cortical depth in steps of 100 um (default: None) - - orderBy ('gid'|'y'|'ynorm'|...): Unique numeric cell property to order x and y axes by, e.g. 'gid', 'ynorm', 'y' (requires groupBy='cells') (default: 'gid') - - graphType ('matrix','bar','pie'): Type of graph to represent data (default: 'matrix') - - synOrConn ('syn'|'conn'): Use synapses or connections; note 1 connection can have multiple synapses (default: 'syn') - - figSize ((width, height)): Size of figure (default: (10,10)) - - synMech (['AMPA', 'GABAA',...]): Show results only for these syn mechs (default: None) - - saveData (None|True|'fileName'): File name where to save the final data used to generate the figure; - if set to True uses filename from simConfig (default: None) - - saveFig (None|True|'fileName'): File name where to save the figure; - if set to True uses filename from simConfig (default: None) - - showFig (True|False): Whether to show the figure or not (default: True) - - Returns figure handles - ''' +def __plotConnCalculateFromSim__(includePre, includePost, feature, orderBy, groupBy, groupByInterval, synOrConn, synMech): - print('Plotting connectivity matrix...') + import sim def list_of_dict_unique_by_key(seq, key): seen = set() seen_add = seen.add return [x for x in seq if x[key] not in seen and not seen_add(x[key])] - + + # adapt indices/keys based on compact vs long conn format + if sim.cfg.compactConnFormat: + connsFormat = sim.cfg.compactConnFormat + + # set indices of fields to read compact format (no keys) + missing = [] + preGidIndex = connsFormat.index('preGid') if 'preGid' in connsFormat else missing.append('preGid') + synMechIndex = connsFormat.index('synMech') if 'synMech' in connsFormat else missing.append('synMech') + weightIndex = connsFormat.index('weight') if 'weight' in connsFormat else missing.append('weight') + delayIndex = connsFormat.index('delay') if 'delay' in connsFormat else missing.append('delay') + preLabelIndex = connsFormat.index('preLabel') if 'preLabel' in connsFormat else -1 + + if len(missing) > 0: + print " Error: cfg.compactConnFormat missing:" + print missing + return None, None, None + else: + # using long conn format (dict) + preGidIndex = 'preGid' + synMechIndex = 'synMech' + weightIndex = 'weight' + delayIndex = 'delay' + preLabelIndex = 'preLabel' + + # Calculate pre and post cells involved cellsPre, cellGidsPre, netStimPopsPre = getCellsInclude(includePre) if includePre == includePost: cellsPost, cellGidsPost, netStimPopsPost = cellsPre, cellGidsPre, netStimPopsPre @@ -1210,21 +1721,23 @@ def list_of_dict_unique_by_key(seq, key): if synOrConn=='syn': cellConns = cell['conns'] # include all synapses else: - cellConns = list_of_dict_unique_by_key(cell['conns'], 'preGid') + cellConns = list_of_dict_unique_by_key(cell['conns'], preGidIndex) if synMech: - cellConns = [conn for conn in cellConns if conn['synMech'] in synMech] + cellConns = [conn for conn in cellConns if conn[synMechIndex] in synMech] for conn in cellConns: - if conn['preGid'] != 'NetStim' and conn['preGid'] in cellIndsPre: + if conn[preGidIndex] != 'NetStim' and conn[preGidIndex] in cellIndsPre: if feature in ['weight', 'delay']: - if conn['preGid'] in cellIndsPre: - connMatrix[cellIndsPre[conn['preGid']], cellIndsPost[cell['gid']]] += conn[feature] - countMatrix[cellIndsPre[conn['preGid']], cellIndsPost[cell['gid']]] += 1 + if conn[preGidIndex] in cellIndsPre: + connMatrix[cellIndsPre[conn[preGidIndex]], cellIndsPost[cell['gid']]] += conn[feature] + countMatrix[cellIndsPre[conn[preGidIndex]], cellIndsPost[cell['gid']]] += 1 if feature in ['weight', 'delay']: connMatrix = connMatrix / countMatrix elif feature in ['numConns']: connMatrix = countMatrix + pre, post = cellsPre, cellsPost + # Calculate matrix if grouped by pop elif groupBy == 'pop': @@ -1282,24 +1795,26 @@ def list_of_dict_unique_by_key(seq, key): if synOrConn=='syn': cellConns = cell['conns'] # include all synapses else: - cellConns = list_of_dict_unique_by_key(cell['conns'], 'preGid') + cellConns = list_of_dict_unique_by_key(cell['conns'], preGidIndex) if synMech: - cellConns = [conn for conn in cellConns if conn['synMech'] in synMech] + cellConns = [conn for conn in cellConns if conn[synMechIndex] in synMech] for conn in cellConns: - if conn['preGid'] == 'NetStim': - prePopLabel = conn['preLabel'] + if conn[preGidIndex] == 'NetStim': + prePopLabel = conn[preLabelIndex] if preLabelIndex in conn else 'NetStim' else: - preCell = next((cell for cell in cellsPre if cell['gid']==conn['preGid']), None) + preCell = next((cell for cell in cellsPre if cell['gid']==conn[preGidIndex]), None) prePopLabel = preCell['tags']['pop'] if preCell else None if prePopLabel in popIndsPre: if feature in ['weight', 'strength']: - weightMatrix[popIndsPre[prePopLabel], popIndsPost[cell['tags']['pop']]] += conn['weight'] + weightMatrix[popIndsPre[prePopLabel], popIndsPost[cell['tags']['pop']]] += conn[weightIndex] elif feature == 'delay': - delayMatrix[popIndsPre[prePopLabel], popIndsPost[cell['tags']['pop']]] += conn['delay'] + delayMatrix[popIndsPre[prePopLabel], popIndsPost[cell['tags']['pop']]] += conn[delayIndex] countMatrix[popIndsPre[prePopLabel], popIndsPost[cell['tags']['pop']]] += 1 + + pre, post = popsPre, popsPost # Calculate matrix if grouped by numeric tag (eg. 'y') elif groupBy in sim.net.allCells[0]['tags'] and isinstance(sim.net.allCells[0]['tags'][groupBy], Number): @@ -1365,16 +1880,16 @@ def list_of_dict_unique_by_key(seq, key): if synOrConn=='syn': cellConns = cell['conns'] # include all synapses else: - cellConns = list_of_dict_unique_by_key(cell['conns'], 'preGid') + cellConns = list_of_dict_unique_by_key(cell['conns'], preGidIndex) if synMech: - cellConns = [conn for conn in cellConns if conn['synMech'] in synMech] + cellConns = [conn for conn in cellConns if conn[synMechIndex] in synMech] for conn in cellConns: - if conn['preGid'] == 'NetStim': + if conn[preGidIndex] == 'NetStim': prePopLabel = -1 # maybe add in future else: - preCell = next((c for c in cellsPre if cell['gid']==conn['preGid']), None) + preCell = next((c for c in cellsPre if cell['gid']==conn[preGidIndex]), None) if preCell: preGroup = _roundFigures(groupByInterval * np.floor(preCell['tags'][groupBy] / groupByInterval), 3) else: @@ -1385,10 +1900,12 @@ def list_of_dict_unique_by_key(seq, key): #print groupInds if preGroup in groupIndsPre: if feature in ['weight', 'strength']: - weightMatrix[groupIndsPre[preGroup], groupIndsPost[postGroup]] += conn['weight'] + weightMatrix[groupIndsPre[preGroup], groupIndsPost[postGroup]] += conn[weightIndex] elif feature == 'delay': - delayMatrix[groupIndsPre[preGroup], groupIndsPost[postGroup]] += conn['delay'] - countMatrix[groupIndsPre[preGroup], groupIndsPost[postGroup]] += 1 + delayMatrix[groupIndsPre[preGroup], groupIndsPost[postGroup]] += conn[delayIndex] + countMatrix[groupIndsPre[preGroup], groupIndsPost[postGroup]] += 1 + + pre, post = groupsPre, groupsPost # no valid groupBy else: @@ -1411,6 +1928,227 @@ def list_of_dict_unique_by_key(seq, key): elif feature == 'divergence': connMatrix = countMatrix / maxPreConnMatrix + return connMatrix, pre, post + + +###################################################################################################################################################### +## Support function for plotConn() - calculate conn using data from files with short format (no keys) +###################################################################################################################################################### + +def __plotConnCalculateFromFile__(includePre, includePost, feature, orderBy, groupBy, groupByInterval, synOrConn, synMech, connsFile, tagsFile): + + import sim + import json + from time import time + + def list_of_dict_unique_by_key(seq, index): + seen = set() + seen_add = seen.add + return [x for x in seq if x[index] not in seen and not seen_add(x[index])] + + # load files with tags and conns + start = time() + tags, conns = None, None + if tagsFile: + print 'Loading tags file...' + with open(tagsFile, 'r') as fileObj: tagsTmp = json.load(fileObj)['tags'] + tagsFormat = tagsTmp.pop('format', []) + tags = {int(k): v for k,v in tagsTmp.iteritems()} # find method to load json with int keys? + del tagsTmp + if connsFile: + print 'Loading conns file...' + with open(connsFile, 'r') as fileObj: connsTmp = json.load(fileObj)['conns'] + connsFormat = connsTmp.pop('format', []) + conns = {int(k): v for k,v in connsTmp.iteritems()} + del connsTmp + + print 'Finished loading; total time (s): %.2f'%(time()-start) + + # find pre and post cells + if tags and conns: + cellGidsPre = getCellsIncludeTags(includePre, tags, tagsFormat) + if includePre == includePost: + cellGidsPost = cellGidsPre + else: + cellGidsPost = getCellsIncludeTags(includePost, tags, tagsFormat) + else: + print 'Error loading tags and conns from file' + return None, None, None + + + # set indices of fields to read compact format (no keys) + missing = [] + popIndex = tagsFormat.index('pop') if 'pop' in tagsFormat else missing.append('pop') + preGidIndex = connsFormat.index('preGid') if 'preGid' in connsFormat else missing.append('preGid') + synMechIndex = connsFormat.index('synMech') if 'synMech' in connsFormat else missing.append('synMech') + weightIndex = connsFormat.index('weight') if 'weight' in connsFormat else missing.append('weight') + delayIndex = connsFormat.index('delay') if 'delay' in connsFormat else missing.append('delay') + preLabelIndex = connsFormat.index('preLabel') if 'preLabel' in connsFormat else -1 + + if len(missing) > 0: + print "Missing:" + print missing + return None, None, None + + if isinstance(synMech, basestring): synMech = [synMech] # make sure synMech is a list + + # Calculate matrix if grouped by cell + if groupBy == 'cell': + print 'plotConn from file for groupBy=cell not implemented yet' + return None, None, None + + # Calculate matrix if grouped by pop + elif groupBy == 'pop': + + # get list of pops + print ' Obtaining list of populations ...' + popsPre = list(set([tags[gid][popIndex] for gid in cellGidsPre])) + popIndsPre = {pop: ind for ind,pop in enumerate(popsPre)} + netStimPopsPre = [] # netstims not yet supported + netStimPopsPost = [] + + if includePre == includePost: + popsPost = popsPre + popIndsPost = popIndsPre + else: + popsPost = list(set([tags[gid][popIndex] for gid in cellGidsPost])) + popIndsPost = {pop: ind for ind,pop in enumerate(popsPost)} + + # initialize matrices + if feature in ['weight', 'strength']: + weightMatrix = np.zeros((len(popsPre), len(popsPost))) + elif feature == 'delay': + delayMatrix = np.zeros((len(popsPre), len(popsPost))) + countMatrix = np.zeros((len(popsPre), len(popsPost))) + + # calculate max num conns per pre and post pair of pops + print ' Calculating max num conns for each pair of population ...' + numCellsPopPre = {} + for pop in popsPre: + if pop in netStimPopsPre: + numCellsPopPre[pop] = -1 + else: + numCellsPopPre[pop] = len([gid for gid in cellGidsPre if tags[gid][popIndex]==pop]) + + if includePre == includePost: + numCellsPopPost = numCellsPopPre + else: + numCellsPopPost = {} + for pop in popsPost: + if pop in netStimPopsPost: + numCellsPopPost[pop] = -1 + else: + numCellsPopPost[pop] = len([gid for gid in cellGidsPost if tags[gid][popIndex]==pop]) + + maxConnMatrix = np.zeros((len(popsPre), len(popsPost))) + if feature == 'convergence': maxPostConnMatrix = np.zeros((len(popsPre), len(popsPost))) + if feature == 'divergence': maxPreConnMatrix = np.zeros((len(popsPre), len(popsPost))) + for prePop in popsPre: + for postPop in popsPost: + if numCellsPopPre[prePop] == -1: numCellsPopPre[prePop] = numCellsPopPost[postPop] + maxConnMatrix[popIndsPre[prePop], popIndsPost[postPop]] = numCellsPopPre[prePop]*numCellsPopPost[postPop] + if feature == 'convergence': maxPostConnMatrix[popIndsPre[prePop], popIndsPost[postPop]] = numCellsPopPost[postPop] + if feature == 'divergence': maxPreConnMatrix[popIndsPre[prePop], popIndsPost[postPop]] = numCellsPopPre[prePop] + + # Calculate conn matrix + print ' Calculating weights, strength, prob, delay etc matrices ...' + for postGid in cellGidsPost: # for each postsyn cell + print ' cell %d'%(int(postGid)) + if synOrConn=='syn': + cellConns = conns[postGid] # include all synapses + else: + cellConns = list_of_dict_unique_by_index(conns[postGid], preGidIndex) + + if synMech: + cellConns = [conn for conn in cellConns if conn[synMechIndex] in synMech] + + for conn in cellConns: + if conn[preGidIndex] == 'NetStim': + prePopLabel = conn[preLabelIndex] if preLabelIndex >=0 else 'NetStims' + else: + preCellGid = next((gid for gid in cellGidsPre if gid==conn[preGidIndex]), None) + prePopLabel = tags[preCellGid][popIndex] if preCellGid else None + + if prePopLabel in popIndsPre: + if feature in ['weight', 'strength']: + weightMatrix[popIndsPre[prePopLabel], popIndsPost[tags[postGid][popIndex]]] += conn[weightIndex] + elif feature == 'delay': + delayMatrix[popIndsPre[prePopLabel], popIndsPost[tags[postGid][popIndex]]] += conn[delayIndex] + countMatrix[popIndsPre[prePopLabel], popIndsPost[tags[postGid][popIndex]]] += 1 + + pre, post = popsPre, popsPost + + # Calculate matrix if grouped by numeric tag (eg. 'y') + elif groupBy in sim.net.allCells[0]['tags'] and isinstance(sim.net.allCells[0]['tags'][groupBy], Number): + print 'plotConn from file for groupBy=[arbitrary property] not implemented yet' + return None, None, None + + # no valid groupBy + else: + print 'groupBy (%s) is not valid'%(str(groupBy)) + return + + if groupBy != 'cell': + if feature == 'weight': + connMatrix = weightMatrix / countMatrix # avg weight per conn (fix to remove divide by zero warning) + elif feature == 'delay': + connMatrix = delayMatrix / countMatrix + elif feature == 'numConns': + connMatrix = countMatrix + elif feature in ['probability', 'strength']: + connMatrix = countMatrix / maxConnMatrix # probability + if feature == 'strength': + connMatrix = connMatrix * weightMatrix # strength + elif feature == 'convergence': + connMatrix = countMatrix / maxPostConnMatrix + elif feature == 'divergence': + connMatrix = countMatrix / maxPreConnMatrix + + print ' plotting ...' + return connMatrix, pre, post + + +###################################################################################################################################################### +## Plot connectivity +###################################################################################################################################################### +def plotConn (includePre = ['all'], includePost = ['all'], feature = 'strength', orderBy = 'gid', figSize = (10,10), groupBy = 'pop', groupByInterval = None, + graphType = 'matrix', synOrConn = 'syn', synMech = None, connsFile = None, tagsFile = None, clim = None, saveData = None, saveFig = None, showFig = True): + ''' + Plot network connectivity + - includePre (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): Cells to show (default: ['all']) + - includePost (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): Cells to show (default: ['all']) + - feature ('weight'|'delay'|'numConns'|'probability'|'strength'|'convergence'|'divergence'): Feature to show in connectivity matrix; + the only features applicable to groupBy='cell' are 'weight', 'delay' and 'numConns'; 'strength' = weight * probability (default: 'strength') + - groupBy ('pop'|'cell'|'y'|: Show matrix for individual cells, populations, or by other numeric tag such as 'y' (default: 'pop') + - groupByInterval (int or float): Interval of groupBy feature to group cells by in conn matrix, e.g. 100 to group by cortical depth in steps of 100 um (default: None) + - orderBy ('gid'|'y'|'ynorm'|...): Unique numeric cell property to order x and y axes by, e.g. 'gid', 'ynorm', 'y' (requires groupBy='cells') (default: 'gid') + - graphType ('matrix','bar','pie'): Type of graph to represent data (default: 'matrix') + - synOrConn ('syn'|'conn'): Use synapses or connections; note 1 connection can have multiple synapses (default: 'syn') + - figSize ((width, height)): Size of figure (default: (10,10)) + - synMech (['AMPA', 'GABAA',...]): Show results only for these syn mechs (default: None) + - saveData (None|True|'fileName'): File name where to save the final data used to generate the figure; + if set to True uses filename from simConfig (default: None) + - saveFig (None|True|'fileName'): File name where to save the figure; + if set to True uses filename from simConfig (default: None) + - showFig (True|False): Whether to show the figure or not (default: True) + + - Returns figure handles + ''' + + import sim + + print('Plotting connectivity matrix...') + + if connsFile and tagsFile: + connMatrix, pre, post = __plotConnCalculateFromFile__(includePre, includePost, feature, orderBy, groupBy, groupByInterval, synOrConn, synMech, connsFile, tagsFile) + else: + connMatrix, pre, post = __plotConnCalculateFromSim__(includePre, includePost, feature, orderBy, groupBy, groupByInterval, synOrConn, synMech) + + + if connMatrix is None: + print "Error calculating connMatrix in plotConn()" + return None + # matrix plot if graphType == 'matrix': # Create plot @@ -1425,6 +2163,8 @@ def list_of_dict_unique_by_key(seq, key): # Plot grid lines plt.hold(True) if groupBy == 'cell': + cellsPre, cellsPost = pre, post + # Make pretty stepy = max(1, int(len(cellsPre)/10.0)) basey = 100 if stepy>100 else 10 @@ -1440,9 +2180,10 @@ def list_of_dict_unique_by_key(seq, key): h.xaxis.set_ticks_position('top') plt.xlim(-0.5,len(cellsPost)-0.5) plt.ylim(len(cellsPre)-0.5,-0.5) - plt.clim(np.nanmin(connMatrix),np.nanmax(connMatrix)) elif groupBy == 'pop': + popsPre, popsPost = pre, post + for ipop, pop in enumerate(popsPre): plt.plot(array([0,len(popsPre)])-0.5,array([ipop,ipop])-0.5,'-',c=(0.7,0.7,0.7)) for ipop, pop in enumerate(popsPost): @@ -1456,9 +2197,10 @@ def list_of_dict_unique_by_key(seq, key): h.xaxis.set_ticks_position('top') plt.xlim(-0.5,len(popsPost)-0.5) plt.ylim(len(popsPre)-0.5,-0.5) - plt.clim(np.nanmin(connMatrix),np.nanmax(connMatrix)) else: + groupsPre, groupsPost = pre, post + for igroup, group in enumerate(groupsPre): plt.plot(array([0,len(groupsPre)])-0.5,array([igroup,igroup])-0.5,'-',c=(0.7,0.7,0.7)) for igroup, group in enumerate(groupsPost): @@ -1472,8 +2214,9 @@ def list_of_dict_unique_by_key(seq, key): h.xaxis.set_ticks_position('top') plt.xlim(-0.5,len(groupsPost)-0.5) plt.ylim(len(groupsPre)-0.5,-0.5) - plt.clim(np.nanmin(connMatrix),np.nanmax(connMatrix)) + if not clim: clim = [np.nanmin(connMatrix), np.nanmax(connMatrix)] + plt.clim(clim[0], clim[1]) plt.colorbar(label=feature, shrink=0.8) #.set_label(label='Fitness',size=20,weight='bold') plt.xlabel('post') h.xaxis.set_label_coords(0.5, 1.06) @@ -1483,6 +2226,8 @@ def list_of_dict_unique_by_key(seq, key): # stacked bar graph elif graphType == 'bar': if groupBy == 'pop': + popsPre, popsPost = pre, post + from netpyne.support import stackedBarGraph SBG = stackedBarGraph.StackedBarGrapher() @@ -1524,7 +2269,8 @@ def list_of_dict_unique_by_key(seq, key): ###################################################################################################################################################### ## Plot 2D representation of network cell positions and connections ###################################################################################################################################################### -def plot2Dnet (include = ['allCells'], figSize = (12,12), view = 'xy', showConns = True, popColors = None, saveData = None, saveFig = None, showFig = True): +def plot2Dnet (include = ['allCells'], figSize = (12,12), view = 'xy', showConns = True, popColors = None, + tagsFile = None, saveData = None, saveFig = None, showFig = True): ''' Plot 2D representation of network cell positions and connections - include (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): Cells to show (default: ['all']) @@ -1540,34 +2286,76 @@ def plot2Dnet (include = ['allCells'], figSize = (12,12), view = 'xy', showConns - Returns figure handles ''' + import sim print('Plotting 2D representation of network cell locations and connections...') fig = plt.figure(figsize=figSize) - # colorList = [[0.42,0.67,0.84], [0.90,0.76,0.00], [0.42,0.83,0.59], [0.90,0.32,0.00], - # [0.34,0.67,0.67], [0.90,0.59,0.00], [0.42,0.82,0.83], [1.00,0.85,0.00], - # [0.33,0.67,0.47], [1.00,0.38,0.60], [0.57,0.67,0.33], [0.5,0.2,0.0], - # [0.71,0.82,0.41], [0.0,0.2,0.5]] - - cells, cellGids, _ = getCellsInclude(include) - selectedPops = [cell['tags']['pop'] for cell in cells] - popLabels = [pop for pop in sim.net.allPops if pop in selectedPops] # preserves original ordering - popColorsTmp = {popLabel: colorList[ipop%len(colorList)] for ipop,popLabel in enumerate(popLabels)} # dict with color for each pop - if popColors: popColorsTmp.update(popColors) - popColors = popColorsTmp - cellColors = [popColors[cell['tags']['pop']] for cell in cells] - # front view if view == 'xy': ycoord = 'y' elif view == 'xz': ycoord = 'z' - posX = [cell['tags']['x'] for cell in cells] # get all x positions - posY = [cell['tags'][ycoord] for cell in cells] # get all y positions + if tagsFile: + print 'Loading tags file...' + import json + with open(tagsFile, 'r') as fileObj: tagsTmp = json.load(fileObj)['tags'] + tagsFormat = tagsTmp.pop('format', []) + tags = {int(k): v for k,v in tagsTmp.iteritems()} # find method to load json with int keys? + del tagsTmp + + # set indices of fields to read compact format (no keys) + missing = [] + popIndex = tagsFormat.index('pop') if 'pop' in tagsFormat else missing.append('pop') + xIndex = tagsFormat.index('x') if 'x' in tagsFormat else missing.append('x') + yIndex = tagsFormat.index('y') if 'y' in tagsFormat else missing.append('y') + zIndex = tagsFormat.index('z') if 'z' in tagsFormat else missing.append('z') + if len(missing) > 0: + print "Missing:" + print missing + return None, None, None + + # find pre and post cells + if tags: + cellGids = getCellsIncludeTags(include, tags, tagsFormat) + popLabels = list(set([tags[gid][popIndex] for gid in cellGids])) + + # pop and cell colors + popColorsTmp = {popLabel: colorList[ipop%len(colorList)] for ipop,popLabel in enumerate(popLabels)} # dict with color for each pop + if popColors: popColorsTmp.update(popColors) + popColors = popColorsTmp + cellColors = [popColors[tags[gid][popIndex]] for gid in cellGids] + + # cell locations + posX = [tags[gid][xIndex] for gid in cellGids] # get all x positions + if ycoord == 'y': + posY = [tags[gid][yIndex] for gid in cellGids] # get all y positions + elif ycoord == 'z': + posY = [tags[gid][zIndex] for gid in cellGids] # get all y positions + else: + print 'Error loading tags from file' + return None + + else: + cells, cellGids, _ = getCellsInclude(include) + selectedPops = [cell['tags']['pop'] for cell in cells] + popLabels = [pop for pop in sim.net.allPops if pop in selectedPops] # preserves original ordering + + # pop and cell colors + popColorsTmp = {popLabel: colorList[ipop%len(colorList)] for ipop,popLabel in enumerate(popLabels)} # dict with color for each pop + if popColors: popColorsTmp.update(popColors) + popColors = popColorsTmp + cellColors = [popColors[cell['tags']['pop']] for cell in cells] + + # cell locations + posX = [cell['tags']['x'] for cell in cells] # get all x positions + posY = [cell['tags'][ycoord] for cell in cells] # get all y positions + + plt.scatter(posX, posY, s=60, color = cellColors) # plot cell soma positions - if showConns: + if showConns and not tagsFile: for postCell in cells: for con in postCell['conns']: # plot connections between cells if not isinstance(con['preGid'], basestring) and con['preGid'] in cellGids: @@ -1578,13 +2366,13 @@ def plot2Dnet (include = ['allCells'], figSize = (12,12), view = 'xy', showConns color = 'blue' width = 0.1 #50*con['weight'] plt.plot([posXpre, posXpost], [posYpre, posYpost], color=color, linewidth=width) # plot line from pre to post + plt.xlabel('x (um)') plt.ylabel(ycoord+' (um)') plt.xlim([min(posX)-0.05*max(posX),1.05*max(posX)]) plt.ylim([min(posY)-0.05*max(posY),1.05*max(posY)]) fontsiz = 12 - for popLabel in popLabels: plt.plot(0,0,color=popColors[popLabel],label=popLabel) plt.legend(fontsize=fontsiz, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.) @@ -1619,6 +2407,7 @@ def calculateDisynaptic(includePost = ['allCells'], includePre = ['allCells'], i import json from time import time + import sim numDis = 0 totCon = 0 @@ -1636,6 +2425,7 @@ def calculateDisynaptic(includePost = ['allCells'], includePre = ['allCells'], i del connsTmp print ' Calculating disynaptic connections...' + # loading from json files if tags and conns: cellsPreGids = getCellsIncludeTags(includePre, tags) cellsPrePreGids = getCellsIncludeTags(includePrePre, tags) @@ -1652,16 +2442,26 @@ def calculateDisynaptic(includePost = ['allCells'], includePre = ['allCells'], i numDis += 1 else: + if sim.cfg.compactConnFormat: + if 'preGid' in sim.cfg.compactConnFormat: + preGidIndex = sim.cfg.compactConnFormat.index('preGid') # using compact conn format (list) + else: + print ' Error: cfg.compactConnFormat does not include "preGid"' + return -1 + else: + preGidIndex = 'preGid' # using long conn format (dict) + _, cellsPreGids, _ = getCellsInclude(includePre) _, cellsPrePreGids, _ = getCellsInclude(includePrePre) cellsPost, _, _ = getCellsInclude(includePost) for postCell in cellsPost: - preGidsAll = [conn['preGid'] for conn in postCell['conns'] if isinstance(conn['preGid'], Number) and conn['preGid'] in cellsPreGids+cellsPrePreGids] + print postCell['gid'] + preGidsAll = [conn[preGidIndex] for conn in postCell['conns'] if isinstance(conn[preGidIndex], Number) and conn[preGidIndex] in cellsPreGids+cellsPrePreGids] preGids = [gid for gid in preGidsAll if gid in cellsPreGids] for preGid in preGids: preCell = sim.net.allCells[preGid] - prePreGids = [conn['preGid'] for conn in preCell['conns'] if conn['preGid'] in cellsPrePreGids] + prePreGids = [conn[preGidIndex] for conn in preCell['conns'] if conn[preGidIndex] in cellsPrePreGids] totCon += 1 if not set(prePreGids).isdisjoint(preGidsAll): numDis += 1 @@ -1696,6 +2496,7 @@ def nTE(cells1 = [], cells2 = [], spks1 = None, spks2 = None, timeRange = None, from neuron import h import netpyne + import sim import os root = os.path.dirname(netpyne.__file__) @@ -1815,6 +2616,7 @@ def granger(cells1 = [], cells2 = [], spks1 = None, spks2 = None, label1 = 'spkT fig: Figure handle ''' + import sim import numpy as np from netpyne.support.bsmart import pwcausalr @@ -1925,6 +2727,8 @@ def granger(cells1 = [], cells2 = [], spks1 = None, spks2 = None, label1 = 'spkT ###################################################################################################################################################### def plotEPSPAmp(include=None, trace=None, start=0, interval=50, number=2, amp='absolute', polarity='exc', saveFig=False, showFig=True): + import sim + print('Plotting EPSP amplitudes...') if include is None: include = [] # If not defined, initialize as empty list @@ -1991,80 +2795,3 @@ def plotEPSPAmp(include=None, trace=None, start=0, interval=50, number=2, amp='a return peaks, fig - - -###################################################################################################################################################### -## Plot weight changes -###################################################################################################################################################### -def plotWeightChanges(): - print('Plotting weight changes...') - - if sim.usestdp: - # create plot - figh = plt.figure(figsize=(1.2*8,1.2*6)) - figh.subplots_adjust(left=0.02) # Less space on left - figh.subplots_adjust(right=0.98) # Less space on right - figh.subplots_adjust(top=0.96) # Less space on bottom - figh.subplots_adjust(bottom=0.02) # Less space on bottom - figh.subplots_adjust(wspace=0) # More space between - figh.subplots_adjust(hspace=0) # More space between - h = plt.axes() - - # create data matrix - wcs = [x[-1][-1] for x in sim.allweightchanges] # absolute final weight - wcs = [x[-1][-1]-x[0][-1] for x in sim.allweightchanges] # absolute weight change - pre,post,recep = zip(*[(x[0],x[1],x[2]) for x in sim.allstdpconndata]) - ncells = int(max(max(pre),max(post))+1) - wcmat = np.zeros([ncells, ncells]) - - for iwc,ipre,ipost,irecep in zip(wcs,pre,post,recep): - wcmat[int(ipre),int(ipost)] = iwc *(-1 if irecep>=2 else 1) - - # plot - plt.imshow(wcmat,interpolation='nearest',cmap=_bicolormap(gap=0,mingreen=0.2,redbluemix=0.1,epsilon=0.01)) - plt.xlabel('post-synaptic cell id') - plt.ylabel('pre-synaptic cell id') - h.set_xticks(sim.popGidStart) - h.set_yticks(sim.popGidStart) - h.set_xticklabels(sim.popnames) - h.set_yticklabels(sim.popnames) - h.xaxif.set_ticks_position('top') - plt.xlim(-0.5,ncells-0.5) - plt.ylim(ncells-0.5,-0.5) - plt.clim(-abs(wcmat).max(),abs(wcmat).max()) - plt.colorbar() - _showFigure() - - - -###################################################################################################################################################### -## Create colormap -###################################################################################################################################################### -def _bicolormap(gap=0.1,mingreen=0.2,redbluemix=0.5,epsilon=0.01): - from matplotlib.colors import LinearSegmentedColormap as makecolormap - - mng=mingreen; # Minimum amount of green to add into the colors - mix=redbluemix; # How much red to mix with the blue an vice versa - eps=epsilon; # How much of the center of the colormap to make gray - omg=1-gap # omg = one minus gap - - cdict = {'red': ((0.00000, 0.0, 0.0), - (0.5-eps, mix, omg), - (0.50000, omg, omg), - (0.5+eps, omg, 1.0), - (1.00000, 1.0, 1.0)), - - 'green': ((0.00000, mng, mng), - (0.5-eps, omg, omg), - (0.50000, omg, omg), - (0.5+eps, omg, omg), - (1.00000, mng, mng)), - - 'blue': ((0.00000, 1.0, 1.0), - (0.5-eps, 1.0, omg), - (0.50000, omg, omg), - (0.5+eps, omg, mix), - (1.00000, 0.0, 0.0))} - cmap = makecolormap('bicolormap',cdict,256) - - return cmap diff --git a/netpyne/batch.py b/netpyne/batch.py index d2c0217b4..a519baf30 100644 --- a/netpyne/batch.py +++ b/netpyne/batch.py @@ -9,7 +9,7 @@ import datetime from itertools import izip, product -from popen2 import popen2 +from subprocess import Popen, PIPE from time import sleep import imp from netpyne import specs @@ -20,8 +20,7 @@ # function to run single job using ParallelContext bulletin board (master/slave) # func needs to be outside of class def runJob(script, cfgSavePath, netParamsSavePath): - from subprocess import Popen, PIPE - + print '\nJob in rank id: ',pc.id() command = 'nrniv %s simConfig=%s netParams=%s' % (script, cfgSavePath, netParamsSavePath) print command+'\n' @@ -125,6 +124,7 @@ def run(self): cfgModuleName = os.path.basename(self.cfgFile).split('.')[0] cfgModule = imp.load_source(cfgModuleName, self.cfgFile) self.cfg = cfgModule.cfg + self.cfg.checkErrors = False # avoid error checking during batch # set initial cfg initCfg if len(self.initCfg) > 0: @@ -224,23 +224,30 @@ def run(self): command = '%s -np %d nrniv -python -mpi %s simConfig=%s netParams=%s' % (mpiCommand, numproc, script, cfgSavePath, netParamsSavePath) - output, input = popen2('qsub') # Open a pipe to the qsub command. jobString = """#!/bin/bash - #PBS -N %s - #PBS -l walltime=%s - #PBS -q %s - #PBS -l %s - #PBS -o %s.run - #PBS -e %s.err - cd $PBS_O_WORKDIR - echo $PBS_O_WORKDIR - %s""" % (jobName, walltime, queueName, nodesppn, jobName, jobName, command) +#PBS -N %s +#PBS -l walltime=%s +#PBS -q %s +#PBS -l %s +#PBS -o %s.run +#PBS -e %s.err +cd $PBS_O_WORKDIR +echo $PBS_O_WORKDIR +%s + """ % (jobName, walltime, queueName, nodesppn, jobName, jobName, command) - # Send job_string to qsub - input.write(jobString) + # Send job_string to qsub + print 'Submitting job ',jobName print jobString+'\n' - input.close() + + batchfile = '%s.pbs'%(jobName) + with open(batchfile, 'w') as text_file: + text_file.write("%s" % jobString) + + proc = Popen(['qsub', batchfile], stderr=PIPE, stdout=PIPE) # Open a pipe to the qsub command. + (output, input) = (proc.stdin, proc.stdout) + # hpc torque job submission elif self.runCfg.get('type',None) == 'hpc_slurm': @@ -257,6 +264,12 @@ def run(self): script = self.runCfg.get('script', 'init.py') mpiCommand = self.runCfg.get('mpiCommand', 'ibrun') walltime = self.runCfg.get('walltime', '00:30:00') + reservation = self.runCfg.get('reservation', None) + if reservation: + res = '#SBATCH --res=%s'%(reservation) + else: + res = '' + numproc = nodes*coresPerNode command = '%s -np %d nrniv -python -mpi %s simConfig=%s netParams=%s' % (mpiCommand, numproc, script, cfgSavePath, netParamsSavePath) @@ -270,28 +283,26 @@ def run(self): #SBATCH -e %s.err #SBATCH --mail-user=%s #SBATCH --mail-type=end +%s source ~/.bashrc cd %s %s wait - """ % (jobName, allocation, walltime, nodes, coresPerNode, jobName, jobName, email, folder, command) + """ % (simLabel, allocation, walltime, nodes, coresPerNode, jobName, jobName, email, res, folder, command) # Send job_string to qsub - # output, input = popen2('sbatch') # Open a pipe to the qsub command. - # input.write(jobString) print 'Submitting job ',jobName print jobString+'\n' - # input.close() batchfile = '%s.sbatch'%(jobName) with open(batchfile, 'w') as text_file: text_file.write("%s" % jobString) #subprocess.call - output, pinput = popen2('sbatch '+batchfile) # Open a pipe to the qsub command. - pinput.close() - + proc = Popen(['sbatch',batchfile], stdin=PIPE, stdout=PIPE) # Open a pipe to the qsub command. + (output, input) = (proc.stdin, proc.stdout) + # pc bulletin board job submission (master/slave) via mpi # eg. usage: mpiexec -n 4 nrniv -mpi batch.py elif self.runCfg.get('type',None) == 'mpi': diff --git a/netpyne/cell.py b/netpyne/cell.py index 832392b99..c7df538fd 100644 --- a/netpyne/cell.py +++ b/netpyne/cell.py @@ -12,7 +12,6 @@ from neuron import h # Import NEURON from specs import Dict import numpy as np -import sim ############################################################################### @@ -32,6 +31,8 @@ def __init__ (self, gid, tags): def recordStimSpikes (self): + import sim + sim.simData['stims'].update({'cell_'+str(self.gid): Dict()}) for conn in self.conns: if conn['preGid'] == 'NetStim': @@ -84,6 +85,8 @@ def _shapeStim(self, isi=1, variation=0, width=0.05, weight=10, start=0, finish= def addNetStim (self, params, stimContainer=None): + import sim + if not stimContainer: self.stims.append(Dict(params.copy())) # add new stim to Cell object stimContainer = self.stims[-1] @@ -118,6 +121,8 @@ def addNetStim (self, params, stimContainer=None): def __getstate__ (self): ''' Removes non-picklable h objects so can be pickled and sent via py_alltoall''' + import sim + odict = self.__dict__.copy() # copy the dict since we change it odict = sim.copyReplaceItemObj(odict, keystart='h', newval=None) # replace h objects with None so can be pickled odict = sim.copyReplaceItemObj(odict, keystart='NeuroML', newval='---Removed_NeuroML_obj---') # replace NeuroML objects with str so can be pickled @@ -125,6 +130,8 @@ def __getstate__ (self): def recordTraces (self): + import sim + # set up voltagse recording; recdict will be taken from global context for key, params in sim.cfg.recordTraces.iteritems(): @@ -156,33 +163,34 @@ def recordTraces (self): ptr = None if 'loc' in params: if 'mech' in params: # eg. soma(0.5).hh._ref_gna - ptr = self.secs[params['sec']]['hSec'](params['loc']).__getattribute__(params['mech']).__getattribute__('_ref_'+params['var']) + ptr = getattr(getattr(self.secs[params['sec']]['hSec'](params['loc']), params['mech']), '_ref_'+params['var']) + elif 'synMech' in params: # eg. soma(0.5).AMPA._ref_g sec = self.secs[params['sec']] synMech = next((synMech for synMech in sec['synMechs'] if synMech['label']==params['synMech'] and synMech['loc']==params['loc']), None) - ptr = synMech['hSyn'].__getattribute__('_ref_'+params['var']) + ptr = getattr(synMech['hSyn'], '_ref_'+params['var']) else: # eg. soma(0.5)._ref_v - ptr = self.secs[params['sec']]['hSec'](params['loc']).__getattribute__('_ref_'+params['var']) + ptr = getattr(self.secs[params['sec']]['hSec'](params['loc']), '_ref_'+params['var']) elif 'synMech' in params: # special case where want to record from multiple synMechs if 'sec' in params: sec = self.secs[params['sec']] synMechs = [synMech for synMech in sec['synMechs'] if synMech['label']==params['synMech']] - ptr = [synMech['hSyn'].__getattribute__('_ref_'+params['var']) for synMech in synMechs] + ptr = [getattr(synMech['hSyn'], '_ref_'+params['var']) for synMech in synMechs] secLocs = [params.sec+str(synMech['loc']) for synMech in synMechs] else: ptr = [] secLocs = [] for secName,sec in self.secs.iteritems(): synMechs = [synMech for synMech in sec['synMechs'] if synMech['label']==params['synMech']] - ptr.extend([synMech['hSyn'].__getattribute__('_ref_'+params['var']) for synMech in synMechs]) + ptr.extend([getattr(synMech['hSyn'], '_ref_'+params['var']) for synMech in synMechs]) secLocs.extend([secName+'_'+str(synMech['loc']) for synMech in synMechs]) else: if 'pointp' in params: # eg. soma.izh._ref_u if params['pointp'] in self.secs[params['sec']]['pointps']: - ptr = self.secs[params['sec']]['pointps'][params['pointp']]['hPointp'].__getattribute__('_ref_'+params['var']) + ptr = getattr(self.secs[params['sec']]['pointps'][params['pointp']]['hPointp'], '_ref_'+params['var']) elif 'var' in params: # point process cell eg. cell._ref_v - ptr = self.hPointp.__getattribute__('_ref_'+params['var']) + ptr = getattr(self.hPointp, '_ref_'+params['var']) if ptr: # if pointer has been created, then setup recording if isinstance(ptr, list): @@ -222,6 +230,8 @@ def __init__ (self, gid, tags, create=True, associateGid=True): def create (self): + import sim + for propLabel, prop in sim.net.params.cellParams.iteritems(): # for each set of cell properties conditionsMet = 1 for (condKey,condVal) in prop['conds'].iteritems(): # check if all conditions are met @@ -250,6 +260,8 @@ def create (self): def modify (self, prop): + import sim + conditionsMet = 1 for (condKey,condVal) in prop['conds'].iteritems(): # check if all conditions are met if condKey=='label': @@ -370,6 +382,8 @@ def initV (self): def createNEURONObj (self, prop): + import sim + # set params for all sections for sectName,sectParams in prop['secs'].iteritems(): # create section @@ -406,7 +420,7 @@ def createNEURONObj (self, prop): if type(mechParamValue) in [list]: mechParamValueFinal = mechParamValue[iseg] if mechParamValueFinal is not None: # avoid setting None values - seg.__getattribute__(mechName).__setattr__(mechParamName,mechParamValueFinal) + setattr(getattr(seg, mechName), mechParamName,mechParamValueFinal) # add ions if 'ions' in sectParams: @@ -420,12 +434,12 @@ def createNEURONObj (self, prop): if type(ionParamValue) in [list]: ionParamValueFinal = ionParamValue[iseg] if ionParamName == 'e': - seg.__setattr__(ionParamName+ionName,ionParamValueFinal) + setattr(seg, ionParamName+ionName, ionParamValueFinal) elif ionParamName == 'o': - seg.__setattr__('%so'%ionName,ionParamValueFinal) + setattr(seg, '%so'%ionName, ionParamValueFinal) h('%so0_%s_ion = %s'%(ionName,ionName,ionParamValueFinal)) # e.g. cao0_ca_ion, the default initial value elif ionParamName == 'i': - seg.__setattr__('%si'%ionName,ionParamValueFinal) + setattr(seg, '%si'%ionName, ionParamValueFinal) h('%si0_%s_ion = %s'%(ionName,ionName,ionParamValueFinal)) # e.g. cai0_ca_ion, the default initial value #if sim.cfg.verbose: print("Updated ion: %s in %s, e: %s, o: %s, i: %s" % \ @@ -497,20 +511,26 @@ def addStimsNEURONObj(self): def addConnsNEURONObj(self): # Note: loading connections to point process (eg. Izhi2007a) not yet supported # Note: assumes weight is in index 0 (netcon.weight[0]) + + import sim + # assumes python structure exists for conn in self.conns: # set postsyn target synMech = next((synMech for synMech in self.secs[conn['sec']]['synMechs'] if synMech['label']==conn['synMech'] and synMech['loc']==conn['loc']), None) + if not synMech: synMech = self.addSynMech(conn['synMech'], conn['sec'], conn['loc']) #continue # go to next conn + try: postTarget = synMech['hSyn'] except: print '\nError: no synMech available for conn: ', conn print ' cell tags: ',self.tags print ' cell synMechs: ',self.secs[conn['sec']]['synMechs'] - exit() + import sys + sys.exit() # create NetCon if conn['preGid'] == 'NetStim': @@ -533,6 +553,8 @@ def addConnsNEURONObj(self): def associateGid (self, threshold = None): + import sim + if self.secs: if sim.cfg.createNEURONObj: sim.pc.set_gid2node(self.gid, sim.rank) # this is the key call that assigns cell gid to a particular node @@ -547,7 +569,7 @@ def associateGid (self, threshold = None): if 'pointps' in sec: # if no syns, check if point processes with 'vref' (artificial cell) for pointpName, pointpParams in sec['pointps'].iteritems(): if 'vref' in pointpParams: - nc = h.NetCon(sec['pointps'][pointpName]['hPointp'].__getattribute__('_ref_'+pointpParams['vref']), None, sec=sec['hSec']) + nc = h.NetCon(getattr(sec['pointps'][pointpName]['hPointp'], '_ref_'+pointpParams['vref']), None, sec=sec['hSec']) break if not nc: # if still haven't created netcon nc = h.NetCon(sec['hSec'](loc)._ref_v, None, sec=sec['hSec']) @@ -561,6 +583,8 @@ def associateGid (self, threshold = None): def addSynMech (self, synLabel, secLabel, loc): + import sim + synMechParams = sim.net.params.synMechParams.get(synLabel) # get params for this synMech sec = self.secs.get(secLabel, None) # add synaptic mechanism to python struct @@ -607,6 +631,8 @@ def addSynMech (self, synLabel, secLabel, loc): def modifySynMechs (self, params): + import sim + conditionsMet = 1 if 'cellConds' in params: if conditionsMet: @@ -658,6 +684,8 @@ def modifySynMechs (self, params): def addConn (self, params, netStimParams = None): + import sim + threshold = params.get('threshold', sim.net.params.defaultThreshold) # if no threshold specified, set default if params.get('weight') is None: params['weight'] = sim.net.params.defaultWeight # if no weight, set default if params.get('delay') is None: params['delay'] = sim.net.params.defaultDelay # if no delay, set default @@ -819,6 +847,8 @@ def addConn (self, params, netStimParams = None): def modifyConns (self, params): + import sim + for conn in self.conns: conditionsMet = 1 @@ -877,6 +907,8 @@ def modifyConns (self, params): def modifyStims (self, params): + import sim + conditionsMet = 1 if 'cellConds' in params: if conditionsMet: @@ -942,6 +974,8 @@ def modifyStims (self, params): def addStim (self, params): + import sim + if not params['sec'] or (isinstance(params['sec'], basestring) and not params['sec'] in self.secs.keys()+self.secLists.keys()): if sim.cfg.verbose: print ' Warning: no valid sec specified for stim on cell gid=%d so using soma or 1st available. Existing secs: %s; params: %s'%(self.gid, self.secs.keys(),params) if 'soma' in self.secs: @@ -1019,7 +1053,12 @@ def addStim (self, params): if sim.cfg.verbose: print(' originalFormat: %s'%(params['originalFormat'])) rand = h.Random() - sim._init_stim_randomizer(rand, params['type'], params['stim_count'], sim.cfg.seeds['stim']) + stim_ref = params['label'][:params['label'].rfind(self.tags['pop'])] + + # e.g. Stim3_2_popPyrS_2_soma_0_5 -> 2 + index_in_stim = int(stim_ref.split('_')[-2]) + stim_id = stim_ref.split('_')[0] + sim._init_stim_randomizer(rand, stim_id, index_in_stim, sim.cfg.seeds['stim']) rand.negexp(1) stim.noiseFromRandom(rand) params['h%s'%params['originalFormat']] = rand @@ -1035,6 +1074,8 @@ def addStim (self, params): def _setConnSections (self, params): + import sim + # if no section specified or single section specified does not exist if not params.get('sec') or (isinstance(params.get('sec'), basestring) and not params.get('sec') in self.secs.keys()+self.secLists.keys()): if sim.cfg.verbose: print ' Warning: no valid sec specified for connection to cell gid=%d so using soma or 1st available'%(self.gid) @@ -1071,6 +1112,8 @@ def _setConnSections (self, params): def _setConnWeights (self, params, netStimParams, secLabels): + import sim + if netStimParams: scaleFactor = sim.net.params.scaleConnWeightNetStims elif sim.net.params.scaleConnWeightModels.get(self.tags['cellModel'], None) is not None: @@ -1088,6 +1131,8 @@ def _setConnWeights (self, params, netStimParams, secLabels): def _setConnPointP(self, params, secLabels, weightIndex): + import sim + # Find if any point process with V not calculated in section (artifical cell, eg. Izhi2007a) pointp = None if len(secLabels)==1 and 'pointps' in self.secs[secLabels[0]]: # check if point processes with 'vref' (artificial cell) @@ -1109,6 +1154,8 @@ def _setConnPointP(self, params, secLabels, weightIndex): def _setConnSynMechs (self, params, secLabels): + import sim + synsPerConn = params['synsPerConn'] if not params.get('synMech'): if sim.net.params.synMechParams: # if no synMech specified, but some synMech params defined @@ -1144,6 +1191,8 @@ def _setConnSynMechs (self, params, secLabels): def _distributeSynsUniformly (self, secList, numSyns): + import sim + from numpy import cumsum if 'L' in self.secs[secList[0]]['geom']: secLengths = [self.secs[s]['geom']['L'] for s in secList] @@ -1167,6 +1216,8 @@ def _distributeSynsUniformly (self, secList, numSyns): def _addConnPlasticity (self, params, sec, netcon, weightIndex): + import sim + plasticity = params.get('plast') if plasticity and sim.cfg.createNEURONObj: try: @@ -1202,6 +1253,8 @@ class PointCell (Cell): ''' def __init__ (self, gid, tags, create=True, associateGid=True): + import sim + super(PointCell, self).__init__(gid, tags) self.hPointp = None if 'params' in self.tags: @@ -1213,6 +1266,8 @@ def __init__ (self, gid, tags, create=True, associateGid=True): def createNEURONObj (self): + import sim + # add point processes try: self.hPointp = getattr(h, self.tags['cellModel'])() @@ -1391,10 +1446,12 @@ def createNEURONObj (self): def associateGid (self, threshold = None): + import sim + if sim.cfg.createNEURONObj: sim.pc.set_gid2node(self.gid, sim.rank) # this is the key call that assigns cell gid to a particular node if 'vref' in self.tags: - nc = h.NetCon(self.hPointp.__getattribute__('_ref_'+self.tags['vref']), None) + nc = h.NetCon(getattr(self.hPointp, '_ref_'+self.tags['vref']), None) else: nc = h.NetCon(self.hPointp, None) threshold = threshold if threshold is not None else sim.net.params.defaultThreshold @@ -1406,6 +1463,8 @@ def associateGid (self, threshold = None): def _setConnWeights (self, params, netStimParams): + import sim + if netStimParams: scaleFactor = sim.net.params.scaleConnWeightNetStims elif sim.net.params.scaleConnWeightModels.get(self.tags['cellModel'], None) is not None: @@ -1422,6 +1481,8 @@ def _setConnWeights (self, params, netStimParams): def addConn (self, params, netStimParams = None): + import sim + #threshold = params.get('threshold', sim.net.params.defaultThreshold) # if no threshold specified, set default if params.get('weight') is None: params['weight'] = sim.net.params.defaultWeight # if no weight, set default if params.get('delay') is None: params['delay'] = sim.net.params.defaultDelay # if no delay, set default @@ -1463,7 +1524,7 @@ def addConn (self, params, netStimParams = None): # NEURON objects if sim.cfg.createNEURONObj: if 'vref' in self.tags: - postTarget = self.hPointp.__getattribute__('_ref_'+self.tags['vref']) # local point neuron + postTarget = getattr(self.hPointp, '_ref_'+self.tags['vref']) # local point neuron else: postTarget = self.hPointp @@ -1564,6 +1625,7 @@ class NML2SpikeSource (CompartCell): ''' def associateGid (self, threshold = 10.0): + import sim if sim.cfg.createNEURONObj: sim.pc.set_gid2node(self.gid, sim.rank) # this is the key call that assigns cell gid to a particular node @@ -1577,6 +1639,8 @@ def associateGid (self, threshold = 10.0): sim.net.lid2gid.append(self.gid) # index = local id; value = global id def initRandom(self): + import sim + rand = h.Random() self.stims.append(Dict()) # add new stim to Cell object randContainer = self.stims[-1] @@ -1585,7 +1649,7 @@ def initRandom(self): seed = sim.cfg.seeds['stim'] randContainer['seed'] = seed self.secs['soma']['pointps'][self.tags['cellType']].hPointp.noiseFromRandom(rand) # use random number generator - sim._init_stim_randomizer(rand, randContainer['type'], self.gid, seed) + sim._init_stim_randomizer(rand, self.tags['pop'], self.tags['cellLabel'], seed) randContainer['hRandom'].negexp(1) #print("Created Random: %s with %s (%s)"%(rand,seed, sim.cfg.seeds)) diff --git a/netpyne/network.py b/netpyne/network.py index 7b3a785c2..cb63dc3ad 100644 --- a/netpyne/network.py +++ b/netpyne/network.py @@ -13,7 +13,6 @@ from copy import copy from specs import ODict from neuron import h # import NEURON -import sim class Network (object): @@ -55,6 +54,8 @@ def setParams (self, params): # Instantiate network populations (objects of class 'Pop') ############################################################################### def createPops (self): + import sim + for popLabel, popParam in self.params.popParams.iteritems(): # for each set of population paramseters self.pops[popLabel] = sim.Pop(popLabel, popParam) # instantiate a new object of class Pop and add to list pop return self.pops @@ -64,6 +65,8 @@ def createPops (self): # Create Cells ############################################################################### def createCells (self): + import sim + sim.pc.barrier() sim.timing('start', 'createTime') if sim.rank==0: @@ -85,6 +88,8 @@ def createCells (self): # Add stims ############################################################################### def addStims (self): + import sim + sim.timing('start', 'stimsTime') if self.params.stimSourceParams and self.params.stimTargetParams: if sim.rank==0: @@ -207,6 +212,8 @@ def _addCellStim (self, stimParam, postCell): # Convert stim param string to function ############################################################################### def _stimStrToFunc (self, postCellsTags, sourceParams, targetParams): + import sim + # list of params that have a function passed in as a string #params = sourceParams+targetParams params = sourceParams.copy() @@ -338,6 +345,8 @@ def _interpolateSegmentSigma(self, cell, secList, gridX, gridY, gridSigma): # Subcellular connectivity (distribution of synapses) ############################################################################### def subcellularConn(self, allCellTags, allPopTags): + import sim + sim.timing('start', 'subConnectTime') print(' Distributing synapses based on subcellular connectivity rules...') @@ -490,6 +499,8 @@ def subcellularConn(self, allCellTags, allPopTags): # Connect Cells ############################################################################### def connectCells (self): + import sim + # Instantiate network connections based on the connectivity rules defined in params sim.timing('start', 'connectTime') if sim.rank==0: @@ -544,11 +555,12 @@ def connectCells (self): sim.cfg.createNEURONObj = origCreateNEURONObj # set to original value sim.cfg.addSynMechs = origAddSynMechs # set to original value cellsUpdate = [c for c in sim.net.cells if c.tags['cellModel'] not in ['NetStim', 'VecStim']] - for cell in cellsUpdate: - # Add synMechs, stim and conn NEURON objects - cell.addStimsNEURONObj() - #cell.addSynMechsNEURONObj() - cell.addConnsNEURONObj() + if sim.cfg.createNEURONObj: + for cell in cellsUpdate: + # Add synMechs, stim and conn NEURON objects + cell.addStimsNEURONObj() + #cell.addSynMechsNEURONObj() + cell.addConnsNEURONObj() nodeSynapses = sum([len(cell.conns) for cell in sim.net.cells]) nodeConnections = sum([len(set([conn['preGid'] for conn in cell.conns])) for cell in sim.net.cells]) @@ -590,6 +602,8 @@ def _findCellsCondition(self, allCellTags, conds): # Find pre and post cells matching conditions ############################################################################### def _findPrePostCellsCondition(self, allCellTags, preConds, postConds): + import sim + #try: preCellsTags = dict(allCellTags) # initialize with all presyn cells (make copy) postCellsTags = None @@ -745,7 +759,7 @@ def _disynapticBiasProb2(self, probMatrix, allRands, bias, prePreGids, postPreGi disynNotconn = sorted(disynNotConn, key=lambda k: probMatrix[k], reverse=True) # replaced nonDisynConn with disynNotConn - for i in range(disynAdd): + for i in range(min(disynAdd, nonDisynConn, disynNotConn)): connCreate[nonDisynConn[i]] = False connCreate[disynNotConn[i]] = True @@ -757,6 +771,8 @@ def _disynapticBiasProb2(self, probMatrix, allRands, bias, prePreGids, postPreGi ### Full connectivity ############################################################################### def fullConn (self, preCellsTags, postCellsTags, connParam): + import sim + ''' Generates connections between all pre and post-syn cells ''' if sim.cfg.verbose: print 'Generating set of all-to-all connections (rule: %s) ...' % (connParam['label']) @@ -778,6 +794,8 @@ def fullConn (self, preCellsTags, postCellsTags, connParam): ### Probabilistic connectivity ############################################################################### def probConn (self, preCellsTags, postCellsTags, connParam): + import sim + ''' Generates connections between all pre and post-syn cells based on probability values''' if sim.cfg.verbose: print 'Generating set of probabilistic connections (rule: %s) ...' % (connParam['label']) @@ -785,7 +803,8 @@ def probConn (self, preCellsTags, postCellsTags, connParam): # get list of params that have a lambda function paramsStrFunc = [param for param in [p+'Func' for p in self.connStringFuncParams] if param in connParam] - if isinstance(connParam.get('disynapticBias', None), Number): # calculate the conn preGids of the each pre and post cell + # probabilistic connections with disynapticBias + if isinstance(connParam.get('disynapticBias', None), Number): allPreGids = sim._gatherAllCellConnPreGids() prePreGids = {gid: allPreGids[gid] for gid in preCellsTags} postPreGids = {gid: allPreGids[gid] for gid in postCellsTags} @@ -801,9 +820,9 @@ def probConn (self, preCellsTags, postCellsTags, connParam): connParam[paramStrFunc+'Args'] = {k:v if isinstance(v, Number) else v(preCellsTags[preCellGid],postCellsTags[postCellGid]) for k,v in connParam[paramStrFunc+'Vars'].iteritems()} self._addCellConn(connParam, preCellGid, postCellGid) # add connection - - # disynCounter = 0 # counter for disynaptic connections modified, to keep balance (keep same avg prob) + # standard probabilistic conenctions else: + # calculate the conn preGids of the each pre and post cell for postCellGid,postCellTags in postCellsTags.iteritems(): # for each postsyn cell if postCellGid in self.lid2gid: # check if postsyn is in this node for preCellGid, preCellTags in preCellsTags.iteritems(): # for each presyn cell @@ -814,12 +833,26 @@ def probConn (self, preCellsTags, postCellsTags, connParam): self._addCellConn(connParam, preCellGid, postCellGid) # add connection + ############################################################################### + ### Generate random unique integers + ############################################################################### + def randUniqueInt(self, r, N, vmin, vmax): + import sim + + r.discunif(vmin,vmax) + out = [] + while len(out) 1: sim.cfg.checkErrors = False # turn of error chceking if using multiple cores + if hasattr(sim.cfg, 'checkErrors') and sim.cfg.checkErrors: # whether to validate the input parameters + try: + simTestObj = sim.SimTestObj(sim.cfg.checkErrorsVerbose) + simTestObj.simConfig = sim.cfg + simTestObj.netParams = sim.net.params + simTestObj.runTests() + except: + print("\nAn exception occurred during the error checking process...") + sim.timing('stop', 'initialTime') @@ -74,6 +81,7 @@ def initialize (netParams = None, simConfig = None, net = None): # Set network object to use in simulation ############################################################################### def setNet (net): + import sim sim.net = net @@ -81,6 +89,8 @@ def setNet (net): # Set network params to use in simulation ############################################################################### def setNetParams (params): + import sim + if params and isinstance(params, specs.NetParams): paramsDict = replaceKeys(params.todict(), 'popLabel', 'pop') # for backward compatibility sim.net.params = specs.NetParams(paramsDict) # convert back to NetParams obj @@ -94,6 +104,8 @@ def setNetParams (params): # Set simulation config ############################################################################### def setSimCfg (cfg): + import sim + if cfg and isinstance(cfg, specs.SimConfig): sim.cfg = cfg # set elif cfg and isinstance(cfg, dict): @@ -109,6 +121,8 @@ def setSimCfg (cfg): # Create parallel context ############################################################################### def createParallelContext (): + import sim + sim.pc = h.ParallelContext() # MPI: Initialize the ParallelContext class sim.pc.done() sim.nhosts = int(sim.pc.nhost()) # Find number of hosts @@ -135,20 +149,42 @@ def loadNetParams (filename, data=None, setLoaded=True): pass +############################################################################### +# Convert compact (list-based) to long (dict-based) conn format +############################################################################### +def compactToLongConnFormat(cells, connFormat): + + formatIndices = {key: connFormat.index(key) for key in connFormat} + try: + for cell in cells: + for iconn, conn in enumerate(cell['conns']): + cell['conns'][iconn] = {key: conn[index] for key,index in formatIndices.iteritems()} + return cells + except: + print "Error converting conns from compact to long format" + return cells + + ############################################################################### # Load cells and pops from file and create NEURON objs ############################################################################### -def loadNet (filename, data=None, instantiate=True): +def loadNet (filename, data=None, instantiate=True, compactConnFormat=False): + import sim + if not data: data = _loadFile(filename) if 'net' in data and 'cells' in data['net'] and 'pops' in data['net']: if sim.rank == 0: sim.timing('start', 'loadNetTime') print('Loading net...') + if compactConnFormat: compactToLongConnFormat(data['net']['cells'], compactConnFormat) sim.net.allPops = data['net']['pops'] sim.net.allCells = data['net']['cells'] if instantiate: # calculate cells to instantiate in this node - cellsNode = [data['net']['cells'][i] for i in xrange(int(sim.rank), len(data['net']['cells']), sim.nhosts)] + if isinstance(instantiate, list): + cellsNode = [data['net']['cells'][i] for i in xrange(int(sim.rank), len(data['net']['cells']), sim.nhosts) if i in instantiate] + else: + cellsNode = [data['net']['cells'][i] for i in xrange(int(sim.rank), len(data['net']['cells']), sim.nhosts)] if sim.cfg.createPyStruct: for popLoadLabel, popLoad in data['net']['pops'].iteritems(): pop = sim.Pop(popLoadLabel, popLoad['tags']) @@ -224,6 +260,8 @@ def loadSimCfg (filename, data=None, setLoaded=True): # Load netParams from cell ############################################################################### def loadSimData (filename, data=None): + import sim + if not data: data = _loadFile(filename) print('Loading simData...') if 'simData' in data: @@ -237,11 +275,14 @@ def loadSimData (filename, data=None): ############################################################################### # Load all data in file ############################################################################### -def loadAll (filename, data=None): +def loadAll (filename, data=None, instantiate=True): + import sim + if not data: data = _loadFile(filename) loadSimCfg(filename, data=data) loadNetParams(filename, data=data) - loadNet(filename, data=data) + if hasattr(sim.cfg, 'compactConnFormat'): connFormat = sim.cfg.compactConnFormat + loadNet(filename, data=data, instantiate=instantiate, compactConnFormat=connFormat) loadSimData(filename, data=data) @@ -310,6 +351,7 @@ def _mat2dict(obj): # Load data from file ############################################################################### def _loadFile (filename): + import sim import os if hasattr(sim, 'cfg') and sim.cfg.timing: sim.timing('start', 'loadFileTime') @@ -393,6 +435,8 @@ def _loadFile (filename): # Clear all sim objects in memory ############################################################################### def clearAll (): + import sim + # clean up sim.pc.barrier() sim.pc.gid_clear() # clear previous gid settings @@ -438,6 +482,8 @@ def id32 (obj): # Initialize the stim randomizer ############################################################################### def _init_stim_randomizer(rand, stimType, gid, seed): + import sim + rand.Random123(sim.id32(stimType), gid, seed) @@ -574,39 +620,22 @@ def replaceNoneObj (obj): def replaceDictODict (obj): if type(obj) == list: for item in obj: - if isinstance(item, Dict): + if type(item) == Dict: item = item.todict() - elif isinstance(item, ODict): + elif type(item) == ODict: item = item.toOrderedDict() - if isinstance(item, (list, dict, OrderedDict)): + if type(item) in [list, dict, OrderedDict]: replaceDictODict(item) - elif isinstance(obj, (dict, OrderedDict, Dict, ODict)): + elif type(obj) in [dict, OrderedDict, Dict, ODict]: for key,val in obj.iteritems(): - if isinstance(val, Dict): + if type(val) == Dict: obj[key] = val.todict() - elif isinstance(val, ODict): + elif type(val) == ODict: obj[key] = val.toOrderedDict() - if isinstance(val, (list, dict, OrderedDict)): + if type(val) in [list, dict, OrderedDict]: replaceDictODict(val) - # elif type(obj) == Dict: - # obj = obj.todict() - # for key,val in obj.iteritems(): - # if isinstance(item, (dict, Dict))(val, (list, dict, Dict, ODict)): - # replaceDictODict(val) - - # elif type(obj) == ODict: - # print obj.keys() - # obj = obj.toOrderedDict() - # for key,val in obj.iteritems(): - # if isinstance(item, (dict, Dict))(val, (list, dict, Dict, ODict)): - # replaceDictODict(val) - - # elif type(obj) == dict: - # for key,val in obj.iteritems(): - # if isinstance(item, (dict, Dict))(val, (list, dict, Dict, ODict)): - # replaceDictODict(val) return obj ############################################################################### @@ -675,6 +704,8 @@ def _dict2utf8 (obj): ### Convert dict strings to utf8 so can be saved in HDF5 format ############################################################################### def cellByGid (gid): + import sim + cell = next((c for c in sim.net.cells if c.gid==gid), None) return cell @@ -683,6 +714,7 @@ def cellByGid (gid): ### Read simConfig and netParams from command line arguments ############################################################################### def readCmdLineArgs (simConfigDefault='cfg.py', netParamsDefault='netParams.py'): + import sim import imp, __main__ if len(sys.argv) > 1: @@ -730,6 +762,8 @@ def readCmdLineArgs (simConfigDefault='cfg.py', netParamsDefault='netParams.py') ### Setup Recording ############################################################################### def setupRecording (): + import sim + timing('start', 'setrecordTime') # spike recording @@ -804,6 +838,8 @@ def setupRecording (): ### Get cells list for recording based on set of conditions ############################################################################### def getCellsList (include): + import sim + if sim.nhosts > 1 and any(isinstance(cond, tuple) or isinstance(cond,list) for cond in include): # Gather tags from all cells allCellTags = sim._gatherAllCellTags() else: @@ -839,30 +875,34 @@ def getCellsList (include): ### Get cells list for recording based on set of conditions ############################################################################### def setGlobals (): + import sim + hParams = sim.cfg.hParams # iterate globals dic in each cellParams cellGlobs = {k:v for k,v in hParams.iteritems()} for cellRuleName, cellRule in sim.net.params.cellParams.iteritems(): - for k,v in getattr(cellRule, 'globals', {}).iteritems(): + for k,v in cellRule.get('globals', {}).iteritems(): if k not in cellGlobs: cellGlobs[k] = v - elif k in ['celsius', 'v_init', 'clamp_resist'] and cellGlobs[k] != v: # exception + elif cellGlobs[k] != v and sim.cfg.verbose: if k == 'v_init': - wrongVinit = [s['v_init'] for s in cellRule['secs'] if 'v_init' in s and s['v_init'] != v] # check if set inside secs - if len(wrongVinit) > 0: - print '\nWarning: global variable %s=%s, but cellParams rule %s requires %s=%s' % (k, str(cellGlobs[k]), cellRuleName, k, str(v)) + wrongVinit = [s['vinit'] for s in cellRule['secs'].values() if 'vinit' in s and s['vinit'] == v and s['vinit'] != cellGlobs[k]] # check if set inside secs (set by default during import) + if len(wrongVinit) == len(cellRule['secs']): + print "\nWarning: global variable %s=%s differs from that set for each section in cellParams rule %s: %s" % (k, str(cellGlobs[k]), cellRuleName, str(v)) + else: # no need since v_inits set in each sec during import + print "\nWarning: global variable %s=%s differs from that defined (not used) in the 'globals' of cellParams rule %s: %s" % (k, str(cellGlobs[k]), cellRuleName, str(v)) else: - print '\nWarning: global variable %s=%s, but cellParams rule %s requires %s=%s' % (k, str(cellGlobs[k]), cellRuleName, k, str(v)) - elif k in cellGlobs and cellGlobs[k] != v: - print '\nError: global variable %s has different values (%s vs %s) in two cellParams rules' % (k, str(v), str(cellGlobs[k])) - sys.exit() + print "\nWarning: global variable %s=%s differs from that defined (not used) in the 'globals' of cellParams rule %s: %s" % (k, str(cellGlobs[k]), cellRuleName, str(v)) + + # add tstop as global (for ease of transition with standard NEURON) + cellGlobs['tstop'] = float(sim.cfg.duration) # h global params if sim.cfg.verbose and len(cellGlobs) > 0: print '\nSetting h global variables ...' for key,val in cellGlobs.iteritems(): try: - setattr(h, key, val) # set other h global vars (celsius, clamp_resist) + h('%s=%s'%(key,val)) if sim.cfg.verbose: print(' h.%s = %s' % (key, str(val))) except: print '\nError: could not set global %s = %s' % (key, str(val)) @@ -872,6 +912,8 @@ def setGlobals (): ### Commands required just before running simulation ############################################################################### def preRun (): + import sim + # set initial v of cells sim.fih = [] for cell in sim.net.cells: @@ -897,9 +939,11 @@ def preRun (): # set h global params sim.setGlobals() - # time vars + # set h.dt h.dt = sim.cfg.dt - h.tstop = sim.cfg.duration + + # set v_init if doesn't exist + if 'v_init' not in sim.cfg.hParams: sim.cfg.hParams['v_init'] = -65.0 # parallelcontext vars sim.pc.set_maxstep(10) @@ -929,24 +973,28 @@ def printRunTime(): if 'originalFormat' in pop.tags and pop.tags['originalFormat'] == 'NeuroML2_SpikeSource': if sim.cfg.verbose: print("== Setting random generator in NeuroML spike generator") cell.initRandom() - for stim in cell.stims: - if 'hRandom' in stim: - #stim['hRandom'].Random123(sim.id32(stim['source']), cell.gid, stim['seed']) - _init_stim_randomizer(stim['hRandom'], stim['type'], cell.gid, stim['seed']) - stim['hRandom'].negexp(1) - # Check if noiseFromRandom is in stim['hNetStim']; see https://github.com/Neurosim-lab/netpyne/issues/219 - if not isinstance(stim['hNetStim'].noiseFromRandom, dict): - stim['hNetStim'].noiseFromRandom(stim['hRandom']) + else: + for stim in cell.stims: + if 'hRandom' in stim: + #stim['hRandom'].Random123(sim.id32(stim['source']), cell.gid, stim['seed']) + _init_stim_randomizer(stim['hRandom'], stim['type'], cell.gid, stim['seed']) + stim['hRandom'].negexp(1) + # Check if noiseFromRandom is in stim['hNetStim']; see https://github.com/Neurosim-lab/netpyne/issues/219 + if not isinstance(stim['hNetStim'].noiseFromRandom, dict): + stim['hNetStim'].noiseFromRandom(stim['hRandom']) ############################################################################### ### Run Simulation ############################################################################### def runSim (): + import sim + sim.pc.barrier() timing('start', 'runTime') preRun() - init() + + h.finitialize(float(sim.cfg.hParams['v_init'])) if sim.rank == 0: print('\nRunning simulation for %s ms...'%sim.cfg.duration) sim.pc.psolve(sim.cfg.duration) @@ -962,6 +1010,8 @@ def runSim (): ### Run Simulation ############################################################################### def runSimWithIntervalFunc (interval, func): + import sim + sim.pc.barrier() timing('start', 'runTime') preRun() @@ -983,6 +1033,8 @@ def runSimWithIntervalFunc (interval, func): ### Gather tags from cells ############################################################################### def _gatherAllCellTags (): + import sim + data = [{cell.gid: cell.tags for cell in sim.net.cells}]*sim.nhosts # send cells data to other nodes gather = sim.pc.py_alltoall(data) # collect cells data from other nodes (required to generate connections) sim.pc.barrier() @@ -1007,6 +1059,8 @@ def _gatherAllCellTags (): ### Gather tags from cells ############################################################################### def _gatherAllCellConnPreGids (): + import sim + data = [{cell.gid: [conn['preGid'] for conn in cell.conns] for cell in sim.net.cells}]*sim.nhosts # send cells data to other nodes gather = sim.pc.py_alltoall(data) # collect cells data from other nodes (required to generate connections) sim.pc.barrier() @@ -1031,6 +1085,8 @@ def _gatherAllCellConnPreGids (): ### Gather data from nodes ############################################################################### def gatherData (): + import sim + timing('start', 'gatherTime') ## Pack data from all hosts if sim.rank==0: @@ -1046,6 +1102,10 @@ def gatherData (): if not sim.cfg.saveCellConns: for cell in sim.net.cells: cell.conns = [] + + # Store conns in a compact list format instead of a long dict format (cfg.compactConnFormat contains list of keys to include) + elif sim.cfg.compactConnFormat: + sim.compactConnFormat() simDataVecs = ['spkt','spkid','stims']+sim.cfg.recordTraces.keys() singleNodeVecs = ['t'] @@ -1088,13 +1148,13 @@ def gatherData (): elif key not in singleNodeVecs: sim.allSimData[key].update(val) # update simData dicts which are not Vectors - if len(sim.allSimData['spkt']) > 0: - sim.allSimData['spkt'], sim.allSimData['spkid'] = zip(*sorted(zip(sim.allSimData['spkt'], sim.allSimData['spkid']))) # sort spks + if len(sim.allSimData['spkt']) > 0: + sim.allSimData['spkt'], sim.allSimData['spkid'] = zip(*sorted(zip(sim.allSimData['spkt'], sim.allSimData['spkid']))) # sort spks - sim.net.allPops = ODict() # pops - for popLabel,pop in sim.net.pops.iteritems(): sim.net.allPops[popLabel] = pop.__getstate__() # can't use dict comprehension for OrderedDict + sim.net.allPops = ODict() # pops + for popLabel,pop in sim.net.pops.iteritems(): sim.net.allPops[popLabel] = pop.__getstate__() # can't use dict comprehension for OrderedDict - sim.net.allCells = [c.__dict__ for c in sim.net.cells] + sim.net.allCells = [c.__dict__ for c in sim.net.cells] # gather cells, pops and sim data else: @@ -1194,7 +1254,11 @@ def gatherData (): print('\nAnalyzing...') sim.totalSpikes = len(sim.allSimData['spkt']) sim.totalSynapses = sum([len(cell['conns']) for cell in sim.net.allCells]) - sim.totalConnections = sum([len(set([conn['preGid'] for conn in cell['conns']])) for cell in sim.net.allCells]) + if sim.cfg.compactConnFormat: + preGidIndex = sim.cfg.compactConnFormat.index('preGid') if 'preGid' in sim.cfg.compactConnFormat else 0 + sim.totalConnections = sum([len(set([conn[preGidIndex] for conn in cell['conns']])) for cell in sim.net.allCells]) + else: + sim.totalConnections = sum([len(set([conn['preGid'] for conn in cell['conns']])) for cell in sim.net.allCells]) sim.numCells = len(sim.net.allCells) if sim.totalSpikes > 0: @@ -1228,6 +1292,8 @@ def gatherData (): ### Calculate and print avg pop rates ############################################################################### def popAvgRates (trange = None, show = True): + import sim + if not hasattr(sim, 'allSimData') or 'spkt' not in sim.allSimData: print 'Error: sim.allSimData not available; please call sim.gatherData()' return None @@ -1255,6 +1321,8 @@ def popAvgRates (trange = None, show = True): ### Calculate and print load balance ############################################################################### def loadBalance (): + import sim + computation_time = sim.pc.step_time() max_comp_time = sim.pc.allreduce(computation_time, 2) min_comp_time = sim.pc.allreduce(computation_time, 3) @@ -1276,6 +1344,8 @@ def loadBalance (): ### Gather data from nodes ############################################################################### def _gatherCells (): + import sim + ## Pack data from all hosts if sim.rank==0: print('\nUpdating sim.net.allCells...') @@ -1314,6 +1384,7 @@ def _gatherCells (): ### Save data ############################################################################### def saveData (include = None): + import sim if sim.rank == 0 and not getattr(sim.net, 'allCells', None): needGather = True else: needGather = False @@ -1468,6 +1539,7 @@ def saveData (include = None): ############################################################################### def ijsonLoad(filename, tagsGidRange=None, connsGidRange=None, loadTags=True, loadConns=True, tagFormat=None, connFormat=None, saveTags=None, saveConns=None): # requires: 1) pip install ijson, 2) brew install yajl + import sim import ijson.backends.yajl2_cffi as ijson import json from time import time @@ -1500,15 +1572,15 @@ def ijsonLoad(filename, tagsGidRange=None, connsGidRange=None, loadTags=True, lo elif loadTags: print 'Storing tags ...' if tagFormat: - tags = {int(cell['gid']): {param: cell['tags'][param] for param in tagFormat} for cell in objs if tagsGidRange==None or cell['gid'] in tagsGidRange} + tags.update({int(cell['gid']): [cell['tags'][param] for param in tagFormat] for cell in objs if tagsGidRange==None or cell['gid'] in tagsGidRange}) else: - tags = {int(cell['gid']): cell['tags'] for cell in objs if tagsGidRange==None or cell['gid'] in tagsGidRange} + tags.update({int(cell['gid']): cell['tags'] for cell in objs if tagsGidRange==None or cell['gid'] in tagsGidRange}) elif loadConns: print 'Storing conns...' if connFormat: - conns = {int(cell['gid']): [[conn[param] for param in connFormat] for conn in cell['conns']] for cell in objs if connsGidRange==None or cell['gid'] in connsGidRange} + conns.update({int(cell['gid']): [[conn[param] for param in connFormat] for conn in cell['conns']] for cell in objs if connsGidRange==None or cell['gid'] in connsGidRange}) else: - conns = {int(cell['gid']): cell['conns'] for cell in objs if connsGidRange==None or cell['gid'] in connsGidRange} + conns.update({int(cell['gid']): cell['conns'] for cell in objs if connsGidRange==None or cell['gid'] in connsGidRange}) print 'time ellapsed (s): ', time() - start @@ -1527,10 +1599,29 @@ def ijsonLoad(filename, tagsGidRange=None, connsGidRange=None, loadTags=True, lo return tags, conns +############################################################################### +### Convet connections in long dict format to compact list format +############################################################################### +def compactConnFormat(): + import sim + + if type(sim.cfg.compactConnFormat) is not list: + sim.cfg.compactConnFormat = ['preGid', 'sec', 'loc', 'synMech', 'weight', 'delay'] + + connFormat = sim.cfg.compactConnFormat + for cell in sim.net.cells: + newConns = [[conn[param] for param in connFormat] for conn in cell.conns] + del cell.conns + cell.conns = newConns + + + ############################################################################### ### Timing - Stop Watch ############################################################################### def timing (mode, processName): + import sim + if sim.rank == 0 and sim.cfg.timing: if mode == 'start': sim.timingData[processName] = time() @@ -1542,6 +1633,7 @@ def timing (mode, processName): ### Print netpyne version ############################################################################### def version (show=True): + import sim from netpyne import __version__ if show: print(__version__) @@ -1552,6 +1644,7 @@ def version (show=True): ### Print github version ############################################################################### def gitChangeset (show=True): + import sim import netpyne, os, subprocess currentPath = os.getcwd() try: @@ -1570,6 +1663,8 @@ def gitChangeset (show=True): ### Print github version ############################################################################### def checkMemory (): + import sim + # print memory diagnostic info if sim.rank == 0: # and checkMemory: import resource diff --git a/netpyne/specs.py b/netpyne/specs.py index 1bf5cf27f..728880128 100644 --- a/netpyne/specs.py +++ b/netpyne/specs.py @@ -16,6 +16,7 @@ class Dict(dict): __slots__ = [] + def __init__(*args, **kwargs): self = args[0] args = args[1:] @@ -61,6 +62,7 @@ def __delattr__(self, k): else: object.__delattr__(self, k) + def todict(self): return self.undotify(self) @@ -195,6 +197,30 @@ def undotify(self, x): else: return x + def __rename__(self, old, new, label=None): + ''' + old (string): old dict key + new (string): new dict key + label (list/tuple of strings): nested keys pointing to dict with key to be replaced; + e.g. ('PYR', 'secs'); use None to replace root key; defaults to None + + returns: True if successful, False otherwse + ''' + obj = self + if isinstance(label, (tuple, list)): + for ip in range(len(label)): + try: + obj = obj[label[ip]] + except: + return False + + if old in obj: + obj[new] = obj.pop(old) # replace + return True + else: + return False + + def __getstate__ (self): return self.toOrderedDict() @@ -216,6 +242,9 @@ def setParam(self, label, param, value): d[param] = value return True + + def rename(self, old, new, label=None): + return self.__rename__(old, new, label) class CellParams (ODict): def setParam(self, label, param, value): @@ -228,6 +257,21 @@ def setParam(self, label, param, value): return True + def rename(self, old, new, label=None): + success = self.__rename__(old, new, label) + + try: + # special case: renaming cellParams[x]['secs'] requires updating topology + if isinstance(label, (list, tuple)) and 'secs' in self[label[0]]: + d = self[label[0]] + for sec in d['secs'].values(): # replace appearences in topol + if sec['topol'].get('parentSec') == old: + sec['topol']['parentSec'] = new + return success + except: + return False + + class ConnParams (ODict): def setParam(self, label, param, value): if label in self: @@ -239,6 +283,9 @@ def setParam(self, label, param, value): return True + def rename(self, old, new, label=None): + return self.__rename__(old, new, label) + class SynMechParams (ODict): def setParam(self, label, param, value): @@ -251,6 +298,10 @@ def setParam(self, label, param, value): return True + def rename(self, old, new, label=None): + return self.__rename__(old, new, label) + + class SubConnParams (ODict): def setParam(self, label, param, value): if label in self: @@ -262,6 +313,9 @@ def setParam(self, label, param, value): return True + def rename(self, old, new, label=None): + return self.__rename__(old, new, label) + class StimSourceParams (ODict): def setParam(self, label, param, value): @@ -274,6 +328,9 @@ def setParam(self, label, param, value): return True + def rename(self, old, new, label=None): + return self.__rename__(old, new, label) + class StimTargetParams (ODict): def setParam(self, label, param, value): @@ -286,6 +343,9 @@ def setParam(self, label, param, value): return True + def rename(self, old, new, label=None): + return self.__rename__(old, new, label) + ############################################################################### # NETWORK PARAMETERS CLASS @@ -407,6 +467,22 @@ def addStimTargetParams(self, label=None, params=None): self._labelid += 1 self.stimTargetParams[label] = Dict(params) + # def rename(self, attr, old, new): + # try: + # obj = getattr(self, attr) + # except: + # print 'Error renaming: netParams does not contain %s' % (attr) + # return False + + # if old not in obj: + # print 'Error renaming: netParams.%s rule does not contain %s' % (attribute, old) + # return False + + # obj[new] = obj.pop(old) # replace + + # return True + + def importCellParams(self, label, conds, fileName, cellName, cellArgs=None, importSynMechs=False, somaAtOrigin=False, cellInstance=False): if cellArgs is None: cellArgs = {} if not label: @@ -480,19 +556,7 @@ def addCellParamsSecList(self, label, secListName, somaDist=None, somaDistY=None def renameCellParamsSec(self, label, oldSec, newSec): - if label in self.cellParams: - cellRule = self.cellParams[label] - else: - print 'Error renaming section: netParams.cellParams does not contain %s' % (label) - return - - if oldSec not in cellRule['secs']: - print 'Error renaming section: cellRule does not contain section %s' % (label) - return - - cellRule['secs'][newSec] = cellRule['secs'].pop(oldSec) # replace sec name - for sec in cellRule['secs'].values(): # replace appearences in topol - if sec['topol'].get('parentSec') == oldSec: sec['topol']['parentSec'] = newSec + self.cellParams.rename(oldSec, newSec, (label, 'secs')) def addCellParamsWeightNorm(self, label, fileName, threshold=1000): @@ -552,16 +616,19 @@ def __init__(self, simConfigDict = None): # Simulation parameters self.duration = self.tstop = 1*1e3 # Duration of the simulation, in ms self.dt = 0.025 # Internal integration timestep to use - self.hParams = Dict({'celsius': 6.3, 'clamp_resist': 0.001}) # parameters of h module + self.hParams = Dict({'celsius': 6.3, 'v_init': -65.0, 'clamp_resist': 0.001}) # parameters of h module self.cache_efficient = False # use CVode cache_efficient option to optimize load when running on many cores self.cvode_active = False # Use CVode variable time step self.cvode_atol = 0.001 # absolute error tolerance self.seeds = Dict({'conn': 1, 'stim': 1, 'loc': 1}) # Seeds for randomizers (connectivity, input stimulation and cell locations) - self.createNEURONObj = True # create HOC objects when instantiating network + self.createNEURONObj = True # create runnable network in NEURON when instantiating netpyne network metadata self.createPyStruct = True # create Python structure (simulator-independent) when instantiating network self.addSynMechs = True # whether to add synaptich mechanisms or not self.includeParamsLabel = True # include label of param rule that created that cell, conn or stim self.gatherOnlySimData = False # omits gathering of net+cell data thus reducing gatherData time + self.compactConnFormat = False # replace dict format with compact list format for conns (need to provide list of keys to include) + self.saveCellSecs = True # save all the sections info for each cell (False reduces time+space; available in netParams; prevents re-simulation) + self.saveCellConns = True # save all the conns info for each cell (False reduces time+space; prevents re-simulation) self.timing = True # show timing of each process self.saveTiming = False # save timing data to pickle file self.printRunTime = False # print run time at interval (in sec) specified here (eg. 0.1) @@ -589,11 +656,9 @@ def __init__(self, simConfigDict = None): self.saveHDF5 = False # save to HDF5 file self.saveDat = False # save traces to .dat file(s) self.backupCfgFile = [] # copy cfg file, list with [sourceFile,destFolder] (eg. ['cfg.py', 'backupcfg/']) - self.saveCellSecs = True # save all the sections info for each cell (False reduces time+space; available in netParams; prevents re-simulation) - self.saveCellConns = True # save all the conns info for each cell (False reduces time+space; prevents re-simulation) # error checking - self.checkErrors = False # whether to validate the input parameters + self.checkErrors = False # whether to validate the input parameters (will be turned off if num processors > 1) self.checkErrorsVerbose = False # whether to print detailed errors during input parameter validation # self.exitOnError = False # whether to hard exit on error diff --git a/netpyne/tests/tests.py b/netpyne/tests/tests.py index 73d98d909..a79718b48 100644 --- a/netpyne/tests/tests.py +++ b/netpyne/tests/tests.py @@ -16,13 +16,13 @@ from netpyne import utils VALID_SHAPES = ['cuboid', 'ellipsoid', 'cylinder'] -POP_NUMCELLS_PARAMS = ['density', 'numCells', 'gridSpacing'] +POP_NUMCELLS_PARAMS = ['density', 'numCells', 'gridSpacing', 'cellsList'] VALID_GEOMETRIES = ['cm', 'L', 'diam', 'Ra', 'pt3d', 'nseg'] VALID_GEOMETRIES_SUBSET = ['L', 'diam', 'Ra'] PT_3D = 'pt3d' VALID_TOPOLOGY_PARAMS = ['parentSec', 'parentX','childX'] PULSE_KEYS = ['start','end','rate','noise'] -POP_STIM_KEYS = ['start','end','rate','noise','spkTimes','pulses'] +POP_STIM_KEYS = ['seeds','start','end','rate','noise','spkTimes','pulses'] MESSAGE_TYPE_WARNING = "WARNING" MESSAGE_TYPE_ERROR = "ERROR" @@ -62,6 +62,7 @@ TEST_TYPE_SPECIAL = "Special" # special method, method name provided TEST_TYPE_EXISTS_IN_ALL_DICTS = "Exists in all dicts" TEST_TYPE_DICT_KEY_VALID_VALUE = "Dict key is valid value" + TEST_TYPE_VALID_GEOMETRIES = "Valid geometries" TEST_TYPE_VALID_TOPOLOGIES = "Valid topologies" TEST_TYPE_VALID_MECHS = "Valid mechs" @@ -73,7 +74,7 @@ TEST_TYPE_VALID_SYN_MECHS = "Valid synaptic mechanisms" TEST_TYPE_ARRAY_IN_RANGE = "Array elements in range" -TEST_TYPE_EXISTS_IN_POP_LABELS = "Exists in pop labels" +TEST_TYPE_EXISTS_IN_POP = "Exists in pop labels" TEST_TYPE_VALID_SEC_LIST = "Valid sec list for conn" TEST_TYPE_CONN_PARM_HIERARCHY = "Check for parameter hierarchy" TEST_TYPE_CONN_SHAPE = "Check for shape in conn params" @@ -136,17 +137,17 @@ def testExistsInAllDicts(self, paramValues,paramKey, dictKey): e.args += (val,) raise - def testExistsInPopLabels(self, val,paramValues, popLabels): + def testExistsInPop(self, val,paramValues, pops): try: - existsInPopLabels = False - popLabelsSpecified = False - paramPopLabel = '' + existsInPops = False + popsSpecified = False + paramPop = '' errorMessage = '' - if isinstance (paramValues[val], dict ) and 'popLabel' in paramValues[val]: - popLabelsSpecified = True + if isinstance (paramValues[val], dict ) and 'pop' in paramValues[val]: + popsSpecified = True #print( " LLL **** ") - if isinstance (popLabels, dict) and paramValues[val]['popLabel'] not in popLabels.keys(): - errorMessage = "ConnParams->'popLabel': Pop label specified in conn params is: " + str(paramValues[val]['popLabel']) + ". This does not exist in list of pop labels = " + str(popLabels.keys()) + "." + if isinstance (pops, dict) and paramValues[val]['pop'] not in pop.keys(): + errorMessage = "ConnParams->'pop': Pop specified in conn params is: " + str(paramValues[val]['pop']) + ". This does not exist in list of pop keys = " + str(pop.keys()) + "." return errorMessage except Exception as e: # e.args += (e,) @@ -431,7 +432,7 @@ def testValidGeometries(self,paramValues): # TEST_TYPE_VALUE_LIST #assert geomValid is True if PT_3D in values['geom']: - if not isinstance ( values['geom'][PT_3D] , list ): + if not isinstance ( values['geom'][PT_3D] , list ) and not isinstance ( values['geom'][PT_3D] , tuple ): errorMessage = "cellParams -> secs ('" + str(key) + "') -> 'geom': pt3D must be an array with each array element being a 4-element list or array of floats." geomValid = False elif len(values['geom'][PT_3D]) == 0: @@ -443,7 +444,7 @@ def testValidGeometries(self,paramValues): # TEST_TYPE_VALUE_LIST #assert geomValid is True for elem in values['geom'][PT_3D]: - if not isinstance ( elem , list ): + if not isinstance ( elem , list ) and not isinstance ( elem , tuple ): errorMessage = "cellParams -> secs ('" + str(key) + "') -> 'geom' -> 'pt3D':Type error. pt3D must be an array with each array element being a 4-element list or array of floats.Value specified is: '" + str(elem) + "'." geomValid = False elif len(elem) != 4: @@ -1050,7 +1051,7 @@ def testValidStimSource(self,paramValues): # TEST_TYPE_STIM_SOURCE_TEST else: allowedValues = mechVarList['pointps'][simType] + ['rate'] if any([x not in allowedValues for x in allKeys]): - errorMessage = "StimSourceParams -> 'simType': Invalid parameter specified. Values specified are " + str(allKeys) + ", while allowed values are: " + str(allowedValues) + errorMessage = "StimSourceParams: Invalid parameter specified. Values specified are " + str(allKeys) + ", while allowed values are: " + str(allowedValues) errorMessages.append(errorMessage) except Exception as e: @@ -1110,16 +1111,16 @@ def testValidAnalysis(self, simConfig): # TEST_TYPE_VALID_ANALYSIS plotRaster = analysis['plotRaster'] - if not isinstance ( plotRaster, dict): - errorMessages.append("SimConfig->'analysis'->'plotRaster': Must be a dict. Value provided is " + str(plotRaster) + ".") + if not isinstance ( plotRaster, dict) and not isinstance ( plotRaster, bool): + errorMessages.append("SimConfig->'analysis'->'plotRaster': Must be a dict or bool. Value provided is " + str(plotRaster) + ".") - else: + elif isinstance ( plotRaster, dict): #print ( " in plot raster 2 " + str(plotRaster.keys())) validList = ['include', 'timeRange', 'maxSpikes', 'orderBy', 'orderInverse', 'labels', 'popRates', 'spikeHist', 'spikeHistBin', 'syncLines', 'figSize', 'saveData', 'saveFig', 'showFig'] if not all(x in validList for x in plotRaster.keys()): - errorMessages.append("SimConfig->'analysis'->'plotRaster': plotRaster must be a dict with keys in list " + str(validList) + ". Keys supplied are " + str(plotRaster.keys()) + ".") + errorMessages.append("SimConfig->'analysis'->'plotRaster': plotRaster must be a bool or dict with keys in list " + str(validList) + ". Keys supplied are " + str(plotRaster.keys()) + ".") if 'include' in plotRaster and not isinstance( plotRaster['include'], dict): errorMessages.append("SimConfig->'analysis'->'plotRaster'->'include': Must be a list. Value provided is " + str(plotRaster['include']) + ".") @@ -1179,54 +1180,56 @@ def testValidAnalysis(self, simConfig): # TEST_TYPE_VALID_ANALYSIS plotSpikeHist = analysis['plotSpikeHist'] - if not isinstance ( plotSpikeHist, dict): - errorMessages.append("SimConfig->'analysis'->'plotSpikeHist': Must be a dict. Value provided is " + str(plotSpikeHist) + ".") + if not isinstance ( plotSpikeHist, dict) and not isinstance ( plotSpikeHist, bool): + errorMessages.append("SimConfig->'analysis'->'plotSpikeHist': Must be a dict or bool. Value provided is " + str(plotSpikeHist) + ".") - if 'include' in plotSpikeHist and not isinstance( plotSpikeHist['include'], list): - errorMessages.append("SimConfig->'analysis'->'plotSpikeHist'->'include': Must be a list. Value provided is " + str(plotSpikeHist['include']) + ".") + elif isinstance ( plotSpikeHist, dict): - # if 'timeRange' in plotRaster and not isinstance( plotRaster['timeRange'], dict): - # errorMessages.append("SimConfig->'analysis'->'plotRaster'->'timeRange': Must be a list. Value provided is " + str(plotRaster['timeRange']) + ".") + if 'include' in plotSpikeHist and not isinstance( plotSpikeHist['include'], list): + errorMessages.append("SimConfig->'analysis'->'plotSpikeHist'->'include': Must be a list. Value provided is " + str(plotSpikeHist['include']) + ".") - if 'orderInverse' in plotRaster: + # if 'timeRange' in plotRaster and not isinstance( plotRaster['timeRange'], dict): + # errorMessages.append("SimConfig->'analysis'->'plotRaster'->'timeRange': Must be a list. Value provided is " + str(plotRaster['timeRange']) + ".") - if not isinstance( plotRaster['orderInverse'], bool): - errorMessages.append("SimConfig->'analysis'->'plotRaster'->'orderInverse': Must be boolean. Value provided is " + str(plotRaster['orderInverse']) + ".") + if 'orderInverse' in plotSpikeHist: - if 'overlay' in plotRaster: + if not isinstance( plotSpikeHist['orderInverse'], bool) and not isinstance ( plotSpikeHist, dict): + errorMessages.append("SimConfig->'analysis'->'plotSpikeHist'->'orderInverse': Must be boolean. Value provided is " + str(plotSpikeHist['orderInverse']) + ".") - if not isinstance( plotRaster['overlay'], bool): - errorMessages.append("SimConfig->'analysis'->'plotRaster'->'overlay': Must be boolean. Value provided is " + str(plotRaster['overlay']) + ".") + if 'overlay' in plotSpikeHist: - if 'graphType' in plotRaster and plotRaster['graphType'] not in ['line','bar']: - errorMessages.append("SimConfig->'analysis'->'plotRaster'->'graphType': GraphType must be in " + str(['line','bar']) + ". Value provided is " + str(plotRaster['graphType']) + ".") + if not isinstance( plotSpikeHist['overlay'], bool): + errorMessages.append("SimConfig->'analysis'->'plotSpikeHist'->'overlay': Must be boolean. Value provided is " + str(plotSpikeHist['overlay']) + ".") - if 'yaxis' in plotRaster and plotRaster['yaxis'] not in ['rate','count']: - errorMessages.append("SimConfig->'analysis'->'plotRaster'->'yaxis': yaxis must be in " + str(['rate','count']) + ". Value provided is " + str(plotRaster['yaxis']) + ".") + if 'graphType' in plotSpikeHist and plotSpikeHist['graphType'] not in ['line','bar']: + errorMessages.append("SimConfig->'analysis'->'plotSpikeHist'->'graphType': GraphType must be in " + str(['line','bar']) + ". Value provided is " + str(plotSpikeHist['graphType']) + ".") - if 'figSize' in plotRaster and not isinstance (plotRaster['figSize'], tuple): - errorMessages.append("SimConfig->'analysis'->'plotRaster'->'figSize': figSize must be tuple if specified. Value provided is " + str(plotRaster['figSize']) + ".") + if 'yaxis' in plotSpikeHist and plotSpikeHist['yaxis'] not in ['rate','count']: + errorMessages.append("SimConfig->'analysis'->'plotSpikeHist'->'yaxis': yaxis must be in " + str(['rate','count']) + ". Value provided is " + str(plotSpikeHist['yaxis']) + ".") - if 'binSize' in plotRaster and not isinstance( plotRaster['binSize'], int): - errorMessages.append("SimConfig->'analysis'->'plotRaster'->'binSize': Must be an integer. Value provided is " + str(plotRaster['binSize']) + ".") + if 'figSize' in plotSpikeHist and not isinstance (plotSpikeHist['figSize'], tuple): + errorMessages.append("SimConfig->'analysis'->'plotSpikeHist'->'figSize': figSize must be tuple if specified. Value provided is " + str(plotSpikeHist['figSize']) + ".") - if 'showFig' in plotRaster: + if 'binSize' in plotSpikeHist and not isinstance( plotSpikeHist['binSize'], int): + errorMessages.append("SimConfig->'analysis'->'plotSpikeHist'->'binSize': Must be an integer. Value provided is " + str(plotSpikeHist['binSize']) + ".") - if not isinstance( plotRaster['showFig'], bool): - errorMessages.append("SimConfig->'analysis'->'plotRaster'->'showFig': Must be boolean. Value provided is " + str(plotRaster['showFig']) + ".") + if 'showFig' in plotSpikeHist: + + if not isinstance( plotSpikeHist['showFig'], bool): + errorMessages.append("SimConfig->'analysis'->'plotSpikeHist'->'showFig': Must be boolean. Value provided is " + str(plotSpikeHist['showFig']) + ".") if 'plotSpikePSD' in analysis: plotSpikePSD = analysis['plotSpikePSD'] - if not isinstance ( plotSpikePSD, dict): - errorMessages.append("SimConfig->'analysis'->'plotSpikePSD': Must be a dict. Value provided is " + str(plotSpikePSD) + ".") + if not isinstance ( plotSpikePSD, dict) and not isinstance ( plotSpikePSD, bool): + errorMessages.append("SimConfig->'analysis'->'plotSpikePSD': Must be a dict or bool. Value provided is " + str(plotSpikePSD) + ".") - else: + elif isinstance ( plotSpikePSD, dict): validList = ['include', 'timeRange', 'binSize', 'Fs', 'spikeHist', 'overlay', 'yaxis', 'figSize', 'saveData', 'saveFig' , 'showFig'] if not all(x in validList for x in plotSpikePSD.keys()): - errorMessages.append("SimConfig->'analysis'->'plotSpikePSD': plotSpikePSD must be a dict with keys in list " + str(validList) + ". Keys supplied are " + str(plotSpikePSD.keys()) + ".") + errorMessages.append("SimConfig->'analysis'->'plotSpikePSD': plotSpikePSD must be a bool or dict with keys in list " + str(validList) + ". Keys supplied are " + str(plotSpikePSD.keys()) + ".") if 'include' in plotSpikePSD and not isinstance( plotSpikePSD['include'], list): errorMessages.append("SimConfig->'analysis'->'plotSpikePSD'->'include': Must be a list. Value provided is " + str(plotSpikePSD['include']) + ".") @@ -1256,15 +1259,15 @@ def testValidAnalysis(self, simConfig): # TEST_TYPE_VALID_ANALYSIS if 'plotTraces' in analysis: plotTraces = analysis['plotTraces'] - if not isinstance ( plotTraces, dict): - errorMessages.append("SimConfig->'analysis'->'plotTraces': Must be a dict. Value provided is " + str(plotTraces) + ".") + if not isinstance ( plotTraces, dict) and not isinstance ( plotTraces, bool): + errorMessages.append("SimConfig->'analysis'->'plotTraces': Must be a dict or bool. Value provided is " + str(plotTraces) + ".") - else: + elif isinstance ( plotTraces, dict): validList = ['include', 'timeRange','overlay', 'oneFigPer', 'rerun', 'figSize', 'saveData' , 'showFig'] if not all(x in validList for x in plotTraces.keys()): - errorMessages.append("SimConfig->'analysis'->'plotTraces': plotTraces must be a dict with keys in list " + str(validList) + ". Keys supplied are " + str(plotTraces.keys()) + ".") + errorMessages.append("SimConfig->'analysis'->'plotTraces': plotTraces must be a bool or dict with keys in list " + str(validList) + ". Keys supplied are " + str(plotTraces.keys()) + ".") if 'include' in plotTraces and not isinstance( plotTraces['include'], list): errorMessages.append("SimConfig->'analysis'->'plotTraces'->'include': Must be a list. Value provided is " + str(plotTraces['include']) + ".") @@ -1293,14 +1296,14 @@ def testValidAnalysis(self, simConfig): # TEST_TYPE_VALID_ANALYSIS if 'plotShape' in analysis: plotShapes = analysis['plotShapes'] - if not isinstance ( plotShapes, dict): - errorMessages.append("SimConfig->'analysis'->'plotShapes': Must be a dict. Value provided is " + str(plotShapes) + ".") - else: + if not isinstance ( plotShapes, dict) and not isinstance ( plotShapes, bool): + errorMessages.append("SimConfig->'analysis'->'plotShapes': Must be a dict or bool. Value provided is " + str(plotShapes) + ".") + elif isinstance ( plotShapes, dict): validList = ['showSyns', 'include', 'style', 'siz', 'figSize', 'saveData', 'saveFig', 'showFig'] if not all(x in validList for x in plotShape.keys()): - errorMessages.append("SimConfig->'analysis'->'plotShape': plotShape must be a dict with keys in list " + str(validList) + ". Keys supplied are " + str(plotShape.keys()) + ".") + errorMessages.append("SimConfig->'analysis'->'plotShape': plotShape must be a bool or dict with keys in list " + str(validList) + ". Keys supplied are " + str(plotShape.keys()) + ".") if 'showSyns' in plotShapes and not isinstance( plotShapes['showSyns'], bool): errorMessages.append("SimConfig->'analysis'->'plotShapes'->'showSyns': Must be boolean. Value provided is " + str(plotShapes['showSyns']) + ".") @@ -1312,15 +1315,15 @@ def testValidAnalysis(self, simConfig): # TEST_TYPE_VALID_ANALYSIS plotConn = analysis['plotConn'] - if not isinstance ( plotConn, dict): - errorMessages.append("SimConfig->'analysis'->'plotConn': Must be a dict. Value provided is " + str(plotConn) + ".") + if not isinstance ( plotConn, dict) and not isinstance ( plotConn, bool): + errorMessages.append("SimConfig->'analysis'->'plotConn': Must be a dict or bool. Value provided is " + str(plotConn) + ".") - else: + elif isinstance ( plotConn, dict): validList = ['include', 'feature', 'orderBy', 'figSize', 'groupBy', 'saveData', 'saveFig', 'showFig'] if not all(x in validList for x in plotConn.keys()): - errorMessages.append("SimConfig->'analysis'->'plotConn': plotConn must be a dict with keys in list " + str(validList) + ". Keys supplied are " + str(plotConn.keys()) + ".") + errorMessages.append("SimConfig->'analysis'->'plotConn': plotConn must be a bool or dict with keys in list " + str(validList) + ". Keys supplied are " + str(plotConn.keys()) + ".") if 'include' in plotConn and not isinstance( plotConn['include'], list): errorMessages.append("SimConfig->'analysis'->'plotConn'->'include': Must be a list. Value provided is " + str(plotConn['include']) + ".") @@ -1344,14 +1347,14 @@ def testValidAnalysis(self, simConfig): # TEST_TYPE_VALID_ANALYSIS plot2DNet = analysis['plot2DNet'] - if not isinstance ( plot2DNet, dict): - errorMessages.append("SimConfig->'analysis'->'plot2DNet': Must be a dict. Value provided is " + str(plot2DNet) + ".") - else: + if not isinstance ( plot2DNet, dict) and not isinstance ( plot2DNet, bool): + errorMessages.append("SimConfig->'analysis'->'plot2DNet': Must be a dict or bool. Value provided is " + str(plot2DNet) + ".") + elif isinstance ( plot2DNet, dict): validList = ['include', 'feature', 'orderBy', 'figSize', 'groupBy', 'saveData', 'saveFig', 'showFig'] if not all(x in validList for x in plot2DNet.keys()): - errorMessages.append("SimConfig->'analysis'->'plot2DNet': plot2DNet must be a dict with keys in list " + str(validList) + ". Keys supplied are " + str(plot2DNet.keys()) + ".") + errorMessages.append("SimConfig->'analysis'->'plot2DNet': plot2DNet must be a bool or dict with keys in list " + str(validList) + ". Keys supplied are " + str(plot2DNet.keys()) + ".") if 'include' in plot2DNet and not isinstance( plot2DNet['include'], list): errorMessages.append("SimConfig->'analysis'->'plot2DNet'->'include': Must be a list. Value provided is " + str(plot2DNet['include']) + ".") @@ -1374,13 +1377,13 @@ def testValidAnalysis(self, simConfig): # TEST_TYPE_VALID_ANALYSIS nTE = analysis['nTE'] - if not isinstance ( nTE, dict): - errorMessages.append("SimConfig->'analysis'->'nTE': Must be a dict. Value provided is " + str(nTE) + ".") - else: + if not isinstance ( nTE, dict) and not isinstance ( nTE, bool): + errorMessages.append("SimConfig->'analysis'->'nTE': Must be a dict or bool. Value provided is " + str(nTE) + ".") + elif isinstance ( nTE, dict): validList = ['cells1', 'cells2', 'spks1', 'spks2', 'timeRange', 'binSize', 'numShuffle'] if not all(x in validList for x in nTE.keys()): - errorMessages.append("SimConfig->'analysis'->'nTE': nTE must be a dict with keys in list " + str(validList) + ". Keys supplied are " + str(nTE.keys()) + ".") + errorMessages.append("SimConfig->'analysis'->'nTE': nTE must be a bool or dict with keys in list " + str(validList) + ". Keys supplied are " + str(nTE.keys()) + ".") if 'cells1' in nTE and not isinstance( nTE['cells1'], list): errorMessages.append("SimConfig->'analysis'->'nTE'->'cells1': Must be a list. Value provided is " + str(nTE['cells1']) + ".") @@ -1404,14 +1407,14 @@ def testValidAnalysis(self, simConfig): # TEST_TYPE_VALID_ANALYSIS granger = analysis['granger'] - if not isinstance ( granger, dict): - errorMessages.append("SimConfig->'analysis'->'granger': Must be a dict. Value provided is " + str(granger) + ".") - else: + if not isinstance ( granger, dict) and not isinstance ( granger, bool): + errorMessages.append("SimConfig->'analysis'->'granger': Must be a dict or bool. Value provided is " + str(granger) + ".") + elif isinstance ( granger, dict): validList = ['cells1', 'cells2', 'spks1', 'spks2','label1', 'label2', 'timeRange', 'binSize', 'plotFig', 'saveData', 'saveFig', 'showFig'] if not all(x in validList for x in granger.keys()): - errorMessages.append("SimConfig->'analysis'->'granger': granger must be a dict with keys in list " + str(validList) + ". Keys supplied are " + str(granger.keys()) + ".") + errorMessages.append("SimConfig->'analysis'->'granger': granger must be a bool or dict with keys in list " + str(validList) + ". Keys supplied are " + str(granger.keys()) + ".") if 'cells1' in granger and not isinstance( granger['cells1'], list): errorMessages.append("SimConfig->'analysis'->'granger'->'cells1': Must be a list. Value provided is " + str(granger['cells1']) + ".") @@ -1651,7 +1654,7 @@ def loadSimConfigTests(self): testObj.testName = "printRunTimeTest" testObj.testParameterType = "string" testObj.testParameterValue = "self.simConfig.printRunTime" - testObj.testTypes = [TEST_TYPE_IS_BOOL] + testObj.testTypes = [TEST_TYPE_IS_FLOAT] testObj.messageText = ["simConfig->'printRunTime':printRunTime is not a boolean."] testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR] @@ -1747,7 +1750,7 @@ def loadSimConfigTests(self): testObj.testParameterType = "string" testObj.testParameterValue = "self.simConfig.saveDataInclude" testObj.testTypes = [TEST_TYPE_IS_LIST, TEST_TYPE_ALL_VALUE_LIST] - testObj.testValueList = ['netParams', 'netCells', 'netPops', 'simConfig', 'simData'] + testObj.testValueList = ['net','netParams', 'netCells', 'netPops', 'simConfig', 'simData'] testObj.messageText = ["simConfig->'saveDataInclude':saveDataInclude is not a dict.","SimConfig->'saveDataInclude':is not a valid value. Valid values are 'netParams', 'netCells', 'netPops', 'simConfig', 'simData'." ] testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR, MESSAGE_TYPE_ERROR] @@ -1934,7 +1937,7 @@ def loadPopTests(self): testObj.testParameterType = "string" testObj.testParameterValue = "cellModel" testObj.testTypes = [TEST_TYPE_EXISTS] - testObj.messageText = ["popParams->'cellModel': No cellModel specified in population paramters."] + testObj.messageText = ["popParams->'cellModel': No cellModel specified in population parameters."] testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR] self.testParamsMap["pop"]["cellModelTest"] = testObj @@ -1945,7 +1948,7 @@ def loadPopTests(self): testObj.testParameterType = "list" testObj.testParameterValueList = ['density','numCells','gridSpacing'] testObj.testTypes = [TEST_TYPE_EXISTS_IN_LIST] - testObj.messageText = ["popParams->'volumeParams': One of the following must be specified in parameters: " + str(testObj.testParameterValueList)] + testObj.messageText = ["popParams: One of the following must be specified in parameters: " + str(testObj.testParameterValueList)] testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR] self.testParamsMap["pop"]["volumeParamsTest"] = testObj @@ -1955,10 +1958,10 @@ def loadPopTests(self): testObj.testName = "xNormRangeTest" testObj.testParameterType = "string" testObj.testParameterValue = "xnormRange" - testObj.testTypes = [TEST_TYPE_IS_VALID_RANGE, TEST_TYPE_IN_RANGE] + testObj.testTypes = [TEST_TYPE_IS_VALID_RANGE] testObj.testValueRange = "[0,1]" - testObj.messageText = ["popParams->'xnormRange': XNormRange invalid range.","popParams->'xnormRange': XNormRange not in range."] - testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR, MESSAGE_TYPE_ERROR] + testObj.messageText = ["popParams->'xnormRange': XNormRange invalid range."] + testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR] self.testParamsMap["pop"]["xNormRangeTest"] = testObj @@ -1967,10 +1970,10 @@ def loadPopTests(self): testObj.testName = "yNormRangeTest" testObj.testParameterType = "string" testObj.testParameterValue = "ynormRange" - testObj.testTypes = [TEST_TYPE_IS_VALID_RANGE, TEST_TYPE_IN_RANGE] + testObj.testTypes = [TEST_TYPE_IS_VALID_RANGE] testObj.testValueRange = "[0,1]" - testObj.messageText = ["popParams->'ynormRange': YNormRange invalid.","popParams->'ynormRange': YNormRange not in range."] - testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR, MESSAGE_TYPE_ERROR] + testObj.messageText = ["popParams->'ynormRange': YNormRange invalid."] + testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR] self.testParamsMap["pop"]["yNormRangeTest"] = testObj @@ -1979,10 +1982,10 @@ def loadPopTests(self): testObj.testName = "zNormRangeTest" testObj.testParameterType = "string" testObj.testParameterValue = "znormRange" - testObj.testTypes = [TEST_TYPE_IS_VALID_RANGE, TEST_TYPE_IN_RANGE] + testObj.testTypes = [TEST_TYPE_IS_VALID_RANGE] testObj.testValueRange = "[0,1]" - testObj.messageText = ["popParams->'znormRange': ZNormRange invalid.","popParams->'znormRange': ZNormRange not in range."] - testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR, MESSAGE_TYPE_ERROR] + testObj.messageText = ["popParams->'znormRange': ZNormRange invalid."] + testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR] self.testParamsMap["pop"]["zNormRangeTest"] = testObj @@ -2045,9 +2048,9 @@ def loadNetTests(self): testObj.testName = "sizeXTest" testObj.testParameterType = "string" testObj.testParameterValue = "self.netParams.sizeX" - testObj.testTypes = [TEST_TYPE_IS_INT, TEST_TYPE_GT_ZERO] + testObj.testTypes = [TEST_TYPE_IS_FLOAT, TEST_TYPE_GT_ZERO] testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR, MESSAGE_TYPE_ERROR] - testObj.messageText = ["NetParams->'sizeX': Value should be an int.","NetParams->'sizeX': sizeX is not greater than 0."] + testObj.messageText = ["NetParams->'sizeX': Value should be a float.","NetParams->'sizeX': sizeX is not greater than 0."] self.testParamsMap["net"]["sizeXTest"] = testObj @@ -2056,9 +2059,9 @@ def loadNetTests(self): testObj.testName = "sizeYTest" testObj.testParameterType = "string" testObj.testParameterValue = "self.netParams.sizeY" - testObj.testTypes = [TEST_TYPE_IS_INT, TEST_TYPE_GT_ZERO] + testObj.testTypes = [TEST_TYPE_IS_FLOAT, TEST_TYPE_GT_ZERO] testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR, MESSAGE_TYPE_ERROR] - testObj.messageText = ["NetParams->'sizeY': Value should be an int.","NetParams->'sizeY': sizeY is not greater than 0."] + testObj.messageText = ["NetParams->'sizeY': Value should be a float.","NetParams->'sizeY': sizeY is not greater than 0."] self.testParamsMap["net"]["sizeYTest"] = testObj @@ -2067,9 +2070,9 @@ def loadNetTests(self): testObj.testName = "sizeZTest" testObj.testParameterType = "string" testObj.testParameterValue = "self.netParams.sizeZ" - testObj.testTypes = [TEST_TYPE_IS_INT, TEST_TYPE_GT_ZERO] + testObj.testTypes = [TEST_TYPE_IS_FLOAT, TEST_TYPE_GT_ZERO] testObj.errorMessageLevel = [MESSAGE_TYPE_ERROR, MESSAGE_TYPE_ERROR] - testObj.messageText = ["NetParams->'sizeZ': Value should be an int.","NetParams->'sizeZ': sizeZ is not greater than 0."] + testObj.messageText = ["NetParams->'sizeZ': Value should be a float.","NetParams->'sizeZ': sizeZ is not greater than 0."] self.testParamsMap["net"]["sizeZTest"] = testObj @@ -2230,25 +2233,25 @@ def loadConnTests(self): self.testParamsMap["conn"] = {} - # pop Labels test - testObj = TestObj() - testObj.testName = "popLabelsTest" - testObj.testParameterType = "string" - testObj.testParameterValue = "preConds" - testObj.testTypes = [TEST_TYPE_EXISTS_IN_POP_LABELS] - testObj.messageText = ["ConnParams->'popLabel': Pop label specified for preConds not listed in pop parameters."] - testObj.errorMessageLevel = [MESSAGE_TYPE_WARNING] - self.testParamsMap["conn"]["preCondsPopLabelsTest"] = testObj - - # pop Labels test - testObj = TestObj() - testObj.testName = "popLabelsTest" - testObj.testParameterType = "string" - testObj.testParameterValue = "postConds" - testObj.testTypes = [TEST_TYPE_EXISTS_IN_POP_LABELS] - testObj.messageText = ["ConnParams->'popLabel': Pop label specified for postConds not listed in pop parameters."] - testObj.errorMessageLevel = [MESSAGE_TYPE_WARNING] - self.testParamsMap["conn"]["postCondsPopLabelsTest"] = testObj + # # pop Labels test + # testObj = TestObj() + # testObj.testName = "popLabelsTest" + # testObj.testParameterType = "string" + # testObj.testParameterValue = "preConds" + # testObj.testTypes = [TEST_TYPE_EXISTS_IN_POP] + # testObj.messageText = ["ConnParams->'pop': Pop specified for preConds not listed in pop parameters."] + # testObj.errorMessageLevel = [MESSAGE_TYPE_WARNING] + # self.testParamsMap["conn"]["preCondsPopTest"] = testObj + # + # # pop Labels test + # testObj = TestObj() + # testObj.testName = "popLabelsTest" + # testObj.testParameterType = "string" + # testObj.testParameterValue = "postConds" + # testObj.testTypes = [TEST_TYPE_EXISTS_IN_POP] + # testObj.messageText = ["ConnParams->'pop': Pop specified for postConds not listed in pop parameters."] + # testObj.errorMessageLevel = [MESSAGE_TYPE_WARNING] + # self.testParamsMap["conn"]["postCondsPopTest"] = testObj # condsTest test testObj = TestObj() @@ -2487,7 +2490,7 @@ def execRunTests(self, testObj, params): except Exception as e: if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType)+ " value: " + str(paramValues)) - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + ".Values provided are: " + str(paramValues) ) else: try: @@ -2499,7 +2502,7 @@ def execRunTests(self, testObj, params): except Exception as e: if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType)+ " value: " + str(testObj.testParameterValue)) - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + ".Values provided are: " + str(paramValues)) elif testType == TEST_TYPE_EXISTS_IN_LIST: @@ -2515,7 +2518,7 @@ def execRunTests(self, testObj, params): except Exception as e: if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType)+ " value: " + str(paramValues)) - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + ".Values provided are: " + str(paramValues)) else: @@ -2528,7 +2531,7 @@ def execRunTests(self, testObj, params): except Exception as e: if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType)+ " value: " + str(testObj.testParameterValue)) - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + ".Values provided are: " + str(paramValues)) elif testType == TEST_TYPE_EXISTS_IN_DICT: @@ -2547,7 +2550,7 @@ def execRunTests(self, testObj, params): #traceback.print_exc(file=sys.stdout) if self.verboseFlag: print ( "Test " + testObj.testParameterValue + " for: " + str(testType)+ " value: " + str(eval(testObj.compareDict))) - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + ". Value provided is " + paramValues[testObj.testParameterValue][testObj.testParameterValue1] + ".") + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + " Value provided is '" + paramValues[testObj.testParameterValue][testObj.testParameterValue1] + "'.") elif testType == TEST_TYPE_IN_RANGE: @@ -2564,7 +2567,7 @@ def execRunTests(self, testObj, params): except Exception as e: if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType) + " value: " + str(paramValues)) - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + ".Values provided are: " + str(paramValues)) else: try: @@ -2576,7 +2579,7 @@ def execRunTests(self, testObj, params): except Exception as e: if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType)+ " value: " + str(testObj.testParameterValue)) - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + " Values provided are: " + str(paramValues)) elif testType == TEST_TYPE_ARRAY_IN_RANGE: @@ -2615,7 +2618,7 @@ def execRunTests(self, testObj, params): except Exception as e: if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType)+ " value: " + str(paramValues)) - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + ".Values provided are: " + str(paramValues)) else: @@ -2628,7 +2631,7 @@ def execRunTests(self, testObj, params): except Exception as e: if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType)+ " value: " + str(paramValues)) - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + ".Values provided are: " + str(paramValues)) elif testType == TEST_TYPE_IS_INT: @@ -2646,7 +2649,7 @@ def execRunTests(self, testObj, params): except Exception as e: if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType)+ " value: " + str(paramValues)) - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + ".Values provided are: " + str(paramValues)) else: @@ -2663,7 +2666,7 @@ def execRunTests(self, testObj, params): if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType)+ " value: " + str(paramName)) try: - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + " Value specified is " + str(paramName) + "." + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + " Value specified is " + str(paramName) + "." + ".Values provided are: " + str(paramValues) except: pass @@ -2683,7 +2686,7 @@ def execRunTests(self, testObj, params): except Exception as e: if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType)+ " value: " + str(paramValues)) - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex] + ".Values provided are: " + str(paramValues)) else: @@ -2705,7 +2708,7 @@ def execRunTests(self, testObj, params): if self.verboseFlag: print ( "Test: " + str(testObj.testParameterValue) + " for: " + str(testType)+ " value: " + str(testObj.testParameterValue) + ".") try: - print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + " Value specified is " + str(paramName) + "." + print str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + " Value specified is " + str(paramName) + "." + ".Values provided are: " + str(paramValues) except: pass @@ -3111,12 +3114,12 @@ def execRunTests(self, testObj, params): #print ( "paramvalues = " + str(paramValues)) print (str(MESSAGE_TYPE_ERROR) + ": " + str(e)) - elif testType == TEST_TYPE_EXISTS_IN_POP_LABELS: + elif testType == TEST_TYPE_EXISTS_IN_POP: if isinstance(params, dict): for paramLabel, paramValues in params.items(): try: - errorMessage = self.testTypeObj.testExistsInPopLabels(testObj.testParameterValue, paramValues, self.netParams.popParams) + errorMessage = self.testTypeObj.testExistsInPop(testObj.testParameterValue, paramValues, self.netParams.popParams) if errorMessage != '': diff --git a/netpyne/tests/validate_tests.py b/netpyne/tests/validate_tests.py index cf0ef8f8f..3ddc7c668 100644 --- a/netpyne/tests/validate_tests.py +++ b/netpyne/tests/validate_tests.py @@ -35,41 +35,41 @@ def loadSimConfigTests(self): # print ( " loading tests ") self.paramsMap["simConfig"] = {} - # duration - self.paramsMap["simConfig"]["durationTest"] = [] - simConfigParams = ParamsObj() - - # Simulation parameters - simConfigParams.simConfig.duration = simConfigParams.simConfig.tstop = 100.0 # Duration of the simulation, in ms - simConfigParams.simConfig.dt = "a" # Internal integration timestep to use - - simConfigParams.simConfig.seeds = {'con': 0, 'stim': 0, 'loc': 0} - - simConfigParams.simConfig.createNEURONObj = 1 # create HOC objects when instantiating network - simConfigParams.simConfig.createPyStruct = 1 # create Python structure (simulator-independent) when instantiating network - simConfigParams.simConfig.verbose = 0 # show detailed messages - - # Recording - simConfigParams.simConfig.recordCells = ['all'] - - # Column: v_pop_pre_0_RS_v: Pop: pop_pre; cell: 0; segment id: $oc.segment_id; segment name: soma; Neuron loc: soma(0.5); value: v (v) - simConfigParams.simConfig.recordTraces['Volts_file__pop_pre_pop_pre_0_soma_v'] = {'bla':1,'sec':'soma','loc':0.5,'var':'v','conds':{'pop':'pop_pre'}}#,'cellLabel':0}} - # Column: v_pop_pre_1_RS_v: Pop: pop_pre; cell: 1; segment id: $oc.segment_id; segment name: soma; Neuron loc: soma(0.5); value: v (v) - simConfigParams.simConfig.recordTraces['Volts_file__pop_pre_pop_pre_1_soma_v'] = {'sec':'soma','loc':0.5,'var':'v','conds':{'pop':'pop_pre'}}#, 'cellLabel':1}} - # Column: v_pop_post_0_RS_v: Pop: pop_post; cell: 0; segment id: $oc.segment_id; segment name: soma; Neuron loc: soma(0.5); value: v (v) - simConfigParams.simConfig.recordTraces['Volts_file__pop_post_pop_post_0_soma_v'] = {'sec':'soma','loc':0.5,'var':'v','conds':{'pop':'pop_post'}}#, 'cellLabel':0}} - - simConfigParams.simConfig.recordStim = True # record spikes of cell stims - simConfigParams.simConfig.recordStep = simConfigParams.simConfig.dt # Step size in ms to save data (eg. V traces, LFP, etc) - - # Analysis and plottingsimConfig.plotRaster = True # Whether or not to plot a raster - simConfigParams.simConfig.analysis.plotTraces = {'include': ['all']} - - # Saving - simConfigParams.simConfig.saveJson=1 - simConfigParams.simConfig.saveFileStep = simConfigParams.simConfig.dt # step size in ms to save data to disk - - self.paramsMap["simConfig"]["durationTest"].append(simConfigParams) + # # duration + # self.paramsMap["simConfig"]["durationTest"] = [] + # simConfigParams = ParamsObj() + # + # # Simulation parameters + # simConfigParams.simConfig.duration = simConfigParams.simConfig.tstop = 100.0 # Duration of the simulation, in ms + # simConfigParams.simConfig.dt = "a" # Internal integration timestep to use + # + # simConfigParams.simConfig.seeds = {'con': 0, 'stim': 0, 'loc': 0} + # + # simConfigParams.simConfig.createNEURONObj = 1 # create HOC objects when instantiating network + # simConfigParams.simConfig.createPyStruct = 1 # create Python structure (simulator-independent) when instantiating network + # simConfigParams.simConfig.verbose = 0 # show detailed messages + # + # # Recording + # simConfigParams.simConfig.recordCells = ['all'] + # + # # Column: v_pop_pre_0_RS_v: Pop: pop_pre; cell: 0; segment id: $oc.segment_id; segment name: soma; Neuron loc: soma(0.5); value: v (v) + # simConfigParams.simConfig.recordTraces['Volts_file__pop_pre_pop_pre_0_soma_v'] = {'bla':1,'sec':'soma','loc':0.5,'var':'v','conds':{'pop':'pop_pre'}}#,'cellLabel':0}} + # # Column: v_pop_pre_1_RS_v: Pop: pop_pre; cell: 1; segment id: $oc.segment_id; segment name: soma; Neuron loc: soma(0.5); value: v (v) + # simConfigParams.simConfig.recordTraces['Volts_file__pop_pre_pop_pre_1_soma_v'] = {'sec':'soma','loc':0.5,'var':'v','conds':{'pop':'pop_pre'}}#, 'cellLabel':1}} + # # Column: v_pop_post_0_RS_v: Pop: pop_post; cell: 0; segment id: $oc.segment_id; segment name: soma; Neuron loc: soma(0.5); value: v (v) + # simConfigParams.simConfig.recordTraces['Volts_file__pop_post_pop_post_0_soma_v'] = {'sec':'soma','loc':0.5,'var':'v','conds':{'pop':'pop_post'}}#, 'cellLabel':0}} + # + # simConfigParams.simConfig.recordStim = True # record spikes of cell stims + # simConfigParams.simConfig.recordStep = simConfigParams.simConfig.dt # Step size in ms to save data (eg. V traces, LFP, etc) + # + # # Analysis and plottingsimConfig.plotRaster = True # Whether or not to plot a raster + # simConfigParams.simConfig.analysis.plotTraces = {'include': ['all']} + # + # # Saving + # simConfigParams.simConfig.saveJson=1 + # simConfigParams.simConfig.saveFileStep = simConfigParams.simConfig.dt # step size in ms to save data to disk + # + # self.paramsMap["simConfig"]["durationTest"].append(simConfigParams) # simConfigParams.simConfig.duration = 0.5*1e3 # Duration of the simulation, in ms # simConfigParams.simConfig.dt = 0.025 # Internal integration timestep to use @@ -258,73 +258,73 @@ def loadTestsWithParams(self): cellModelParams = ParamsObj() cellModelParams.netParams.popParams['invalidCellModelParams'] = {'cellType': 'PYR', 'numCells': 50} # add dict with params for this pop self.paramsMap["pop"]["cellModelTest"].append(cellModelParams) - - self.paramsMap["pop"]["volumeParamsTest"] = [] - - volumeParams = ParamsObj() - volumeParams.netParams.popParams['validVolumeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop - self.paramsMap["pop"]["volumeParamsTest"].append(volumeParams) - - volumeParams = ParamsObj() - volumeParams.netParams.popParams['invalidVolumeParams'] = {'cellType': 'PYR', 'cellModel': 'HH'} # add dict with params for this pop - self.paramsMap["pop"]["volumeParamsTest"].append(volumeParams) - - self.paramsMap["pop"]["xNormRangeParamsTest"] = [] - - params = ParamsObj() - params.netParams.popParams['validxNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'xnormRange' : [0.6,0.9]} # add dict with params for this pop - self.paramsMap["pop"]["xNormRangeParamsTest"].append(params) - - params = ParamsObj() - params.netParams.popParams['invalidxNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'xnormRange' : 0.6} # add dict with params for this pop - self.paramsMap["pop"]["xNormRangeParamsTest"].append(params) - - params = ParamsObj() - params.netParams.popParams['invalidxNormRangeParams1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'xnormRange' : [6,10]} # add dict with params for this pop - self.paramsMap["pop"]["xNormRangeParamsTest"].append(params) - - self.paramsMap["pop"]["yNormRangeParamsTest"] = [] - - params = ParamsObj() - params.netParams.popParams['validyNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'ynormRange' : [0.6,0.9]} # add dict with params for this pop - self.paramsMap["pop"]["yNormRangeParamsTest"].append(params) - - params = ParamsObj() - params.netParams.popParams['invalidyNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'ynormRange' : 0.6} # add dict with params for this pop - self.paramsMap["pop"]["yNormRangeParamsTest"].append(params) - - params = ParamsObj() - params.netParams.popParams['invalidyNormRangeParams1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'ynormRange' : [6,10]} # add dict with params for this pop - self.paramsMap["pop"]["yNormRangeParamsTest"].append(params) - - self.paramsMap["pop"]["zNormRangeParamsTest"] = [] - - params = ParamsObj() - params.netParams.popParams['validzNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : [0.6,0.9]} # add dict with params for this pop - self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) - - params = ParamsObj() - params.netParams.popParams['invalidzNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : 0.6} # add dict with params for this pop - self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) - - params = ParamsObj() - params.netParams.popParams['invalidzNormRangeParams1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : [6,10]} # add dict with params for this pop - self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) - - self.paramsMap["pop"]["zNormRangeParamsTest"] = [] - - params = ParamsObj() - params.netParams.popParams['validzNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : [0.6,0.9]} # add dict with params for this pop - self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) - - params = ParamsObj() - params.netParams.popParams['invalidzNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : 0.6} # add dict with params for this pop - self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) - - params = ParamsObj() - params.netParams.popParams['invalidzNormRangeParams1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : [6,10]} # add dict with params for this pop - self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) - + # + # self.paramsMap["pop"]["volumeParamsTest"] = [] + # + # volumeParams = ParamsObj() + # volumeParams.netParams.popParams['validVolumeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop + # self.paramsMap["pop"]["volumeParamsTest"].append(volumeParams) + # + # volumeParams = ParamsObj() + # volumeParams.netParams.popParams['invalidVolumeParams'] = {'cellType': 'PYR', 'cellModel': 'HH'} # add dict with params for this pop + # self.paramsMap["pop"]["volumeParamsTest"].append(volumeParams) + # + # self.paramsMap["pop"]["xNormRangeParamsTest"] = [] + # + # params = ParamsObj() + # params.netParams.popParams['validxNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'xnormRange' : [0.6,0.9]} # add dict with params for this pop + # self.paramsMap["pop"]["xNormRangeParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.popParams['invalidxNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'xnormRange' : 0.6} # add dict with params for this pop + # self.paramsMap["pop"]["xNormRangeParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.popParams['invalidxNormRangeParams1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'xnormRange' : [6,10]} # add dict with params for this pop + # self.paramsMap["pop"]["xNormRangeParamsTest"].append(params) + # + # self.paramsMap["pop"]["yNormRangeParamsTest"] = [] + # + # params = ParamsObj() + # params.netParams.popParams['validyNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'ynormRange' : [0.6,0.9]} # add dict with params for this pop + # self.paramsMap["pop"]["yNormRangeParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.popParams['invalidyNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'ynormRange' : 0.6} # add dict with params for this pop + # self.paramsMap["pop"]["yNormRangeParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.popParams['invalidyNormRangeParams1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'ynormRange' : [6,10]} # add dict with params for this pop + # self.paramsMap["pop"]["yNormRangeParamsTest"].append(params) + # + # self.paramsMap["pop"]["zNormRangeParamsTest"] = [] + # + # params = ParamsObj() + # params.netParams.popParams['validzNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : [0.6,0.9]} # add dict with params for this pop + # self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.popParams['invalidzNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : 0.6} # add dict with params for this pop + # self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.popParams['invalidzNormRangeParams1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : [6,10]} # add dict with params for this pop + # self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) + # + # self.paramsMap["pop"]["zNormRangeParamsTest"] = [] + # + # params = ParamsObj() + # params.netParams.popParams['validzNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : [0.6,0.9]} # add dict with params for this pop + # self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.popParams['invalidzNormRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : 0.6} # add dict with params for this pop + # self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.popParams['invalidzNormRangeParams1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'znormRange' : [6,10]} # add dict with params for this pop + # self.paramsMap["pop"]["zNormRangeParamsTest"].append(params) + # self.paramsMap["pop"]["xRangeParamsTest"] = [] params = ParamsObj() @@ -332,77 +332,77 @@ def loadTestsWithParams(self): params.netParams.popParams['validxRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'xRange' : [30,60]} # add dict with params for this pop self.paramsMap["pop"]["xRangeParamsTest"].append(params) - params = ParamsObj() - params.netParams.sizeX = 70 # max size for network - params.netParams.popParams['invalidxRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'xRange' : [40,90]} # add dict with params for this pop - self.paramsMap["pop"]["xRangeParamsTest"].append(params) - - self.paramsMap["pop"]["yRangeParamsTest"] = [] - - params = ParamsObj() - params.netParams.sizeY = 70 # max size for network - params.netParams.popParams['validyRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'yRange' : [30,60]} # add dict with params for this pop - self.paramsMap["pop"]["yRangeParamsTest"].append(params) - - params = ParamsObj() - params.netParams.sizeY = 70 # max size for network - params.netParams.popParams['invalidyRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'yRange' : [40,90]} # add dict with params for this pop - self.paramsMap["pop"]["yRangeParamsTest"].append(params) - - self.paramsMap["pop"]["zRangeParamsTest"] = [] - - params = ParamsObj() - params.netParams.sizeZ = 70 # max size for network - params.netParams.popParams['validzRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'zRange' : [30,60]} # add dict with params for this pop - self.paramsMap["pop"]["zRangeParamsTest"].append(params) - - params = ParamsObj() - params.netParams.sizeZ = 70 # max size for network - params.netParams.popParams['invalidzRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'zRange' : [40,90]} # add dict with params for this pop - self.paramsMap["pop"]["zRangeParamsTest"].append(params) - - self.paramsMap["pop"]["popStimParamsTest"] = [] - - params = ParamsObj() - params.netParams.popParams['validPopStimParams1'] = {'cellModel': 'IntFire2', 'taum': 100, 'noise': 0.5, 'numCells': 100} # Intfire2 - self.paramsMap["pop"]["popStimParamsTest"].append(params) - - params = ParamsObj() - params.netParams.popParams['validPopStimParams2'] = {'cellModel': 'NetStim', 'rate': 100, 'noise': 0.5, 'numCells': 100} # NetsStim - self.paramsMap["pop"]["popStimParamsTest"].append(params) - - params = ParamsObj() - # create custom list of spike times - spkTimes = range(0,1000,20) + [138, 155,270] - # create list of pulses (each item is a dict with pulse params) - pulses = [{'start': 10, 'end': 100, 'rate': 200, 'noise': 0.5},{'start': 400, 'end': 500, 'rate': 1, 'noise': 0.0}] - params.netParams.popParams['validPopStimParams3'] = {'cellModel': 'VecStim', 'numCells': 100, 'spkTimes': spkTimes, 'pulses': pulses} # VecStim with spike times - self.paramsMap["pop"]["popStimParamsTest"].append(params) - - params = ParamsObj() - params.netParams.popParams['invalidPopStimParams1'] = {'cellModel': 'IntFire2', 'taum': 100, 'noise': 2, 'numCells': 100} # Intfire2 - self.paramsMap["pop"]["popStimParamsTest"].append(params) - - params = ParamsObj() - params.netParams.popParams['invalidPopStimParams2'] = {'cellModel': 'NetStim', 'rate': '2', 'noise': 0.5, 'numCells': 100} # NetsStim - self.paramsMap["pop"]["popStimParamsTest"].append(params) - - params = ParamsObj() - # create custom list of spike times - spkTimes = 1000 - # create list of pulses (each item is a dict with pulse params) - pulses = [{'start': 10, 'end': 100, 'rate': 200, 'noise': 0.5},{'start': 400, 'end': 500, 'rate': 1, 'noise': 0.0}] - params.netParams.popParams['invalidPopStimParams3'] = {'cellModel': 'VecStim', 'numCells': 100, 'spkTimes': spkTimes, 'pulses': pulses} # VecStim with spike times - self.paramsMap["pop"]["popStimParamsTest"].append(params) - - params = ParamsObj() - # create custom list of spike times - spkTimes = range(0,1000,20) + [138, 155,270] - # create list of pulses (each item is a dict with pulse params) - pulses = [{'start': 10, 'end ': 100, 'rate': 200, 'noise': 0.5}, {'start': 400, 'end': 500, 'rate': 1, 'noise': 0.0}] - params.netParams.popParams['invalidPopStimParams4'] = {'cellModel': 'VecStim', 'numCells': 100, 'spkTimes': spkTimes, 'pulses': pulses} # VecStim with spike times - self.paramsMap["pop"]["popStimParamsTest"].append(params) - + # params = ParamsObj() + # params.netParams.sizeX = 70.0 # max size for network + # params.netParams.popParams['invalidxRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'xRange' : [40,90]} # add dict with params for this pop + # self.paramsMap["pop"]["xRangeParamsTest"].append(params) + # + # self.paramsMap["pop"]["yRangeParamsTest"] = [] + # + # params = ParamsObj() + # params.netParams.sizeY = 70 # max size for network + # params.netParams.popParams['validyRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'yRange' : [30,60]} # add dict with params for this pop + # self.paramsMap["pop"]["yRangeParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.sizeY = 70 # max size for network + # params.netParams.popParams['invalidyRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'yRange' : [40,90]} # add dict with params for this pop + # self.paramsMap["pop"]["yRangeParamsTest"].append(params) + # + # self.paramsMap["pop"]["zRangeParamsTest"] = [] + # + # params = ParamsObj() + # params.netParams.sizeZ = 70 # max size for network + # params.netParams.popParams['validzRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'zRange' : [30,60]} # add dict with params for this pop + # self.paramsMap["pop"]["zRangeParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.sizeZ = 70 # max size for network + # params.netParams.popParams['invalidzRangeParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50, 'zRange' : [40,90]} # add dict with params for this pop + # self.paramsMap["pop"]["zRangeParamsTest"].append(params) + # + # self.paramsMap["pop"]["popStimParamsTest"] = [] + # + # params = ParamsObj() + # params.netParams.popParams['validPopStimParams1'] = {'cellModel': 'IntFire2', 'taum': 100, 'noise': 0.5, 'numCells': 100} # Intfire2 + # self.paramsMap["pop"]["popStimParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.popParams['validPopStimParams2'] = {'cellModel': 'NetStim', 'rate': 100, 'noise': 0.5, 'numCells': 100} # NetsStim + # self.paramsMap["pop"]["popStimParamsTest"].append(params) + # + # params = ParamsObj() + # # create custom list of spike times + # spkTimes = range(0,1000,20) + [138, 155,270] + # # create list of pulses (each item is a dict with pulse params) + # pulses = [{'start': 10, 'end': 100, 'rate': 200, 'noise': 0.5},{'start': 400, 'end': 500, 'rate': 1, 'noise': 0.0}] + # params.netParams.popParams['validPopStimParams3'] = {'cellModel': 'VecStim', 'numCells': 100, 'spkTimes': spkTimes, 'pulses': pulses} # VecStim with spike times + # self.paramsMap["pop"]["popStimParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.popParams['invalidPopStimParams1'] = {'cellModel': 'IntFire2', 'taum': 100, 'noise': 2, 'numCells': 100} # Intfire2 + # self.paramsMap["pop"]["popStimParamsTest"].append(params) + # + # params = ParamsObj() + # params.netParams.popParams['invalidPopStimParams2'] = {'cellModel': 'NetStim', 'rate': '2', 'noise': 0.5, 'numCells': 100} # NetsStim + # self.paramsMap["pop"]["popStimParamsTest"].append(params) + # + # params = ParamsObj() + # # create custom list of spike times + # spkTimes = 1000 + # # create list of pulses (each item is a dict with pulse params) + # pulses = [{'start': 10, 'end': 100, 'rate': 200, 'noise': 0.5},{'start': 400, 'end': 500, 'rate': 1, 'noise': 0.0}] + # params.netParams.popParams['invalidPopStimParams3'] = {'cellModel': 'VecStim', 'numCells': 100, 'spkTimes': spkTimes, 'pulses': pulses} # VecStim with spike times + # self.paramsMap["pop"]["popStimParamsTest"].append(params) + # + # params = ParamsObj() + # # create custom list of spike times + # spkTimes = range(0,1000,20) + [138, 155,270] + # # create list of pulses (each item is a dict with pulse params) + # pulses = [{'start': 10, 'end ': 100, 'rate': 200, 'noise': 0.5}, {'start': 400, 'end': 500, 'rate': 1, 'noise': 0.0}] + # params.netParams.popParams['invalidPopStimParams4'] = {'cellModel': 'VecStim', 'numCells': 100, 'spkTimes': spkTimes, 'pulses': pulses} # VecStim with spike times + # self.paramsMap["pop"]["popStimParamsTest"].append(params) + # # #net params test self.paramsMap["net"]["sizeXParamsTest"] = [] @@ -411,1747 +411,1747 @@ def loadTestsWithParams(self): self.paramsMap["net"]["sizeXParamsTest"].append(params) params = ParamsObj() - params.netParams.sizeX = 3.53 # max size for network + params.netParams.sizeX = "abc" # max size for network self.paramsMap["net"]["sizeXParamsTest"].append(params) params = ParamsObj() params.netParams.sizeX = -44 # max size for network self.paramsMap["net"]["sizeXParamsTest"].append(params) - - self.paramsMap["net"]["shapeTest"] = [] - - params = ParamsObj() - params.netParams.shape = "cuboid" # max size for network - self.paramsMap["net"]["shapeTest"].append(params) - - params = ParamsObj() - params.netParams.shape = "ellipsoid" # max size for network - self.paramsMap["net"]["shapeTest"].append(params) - - params = ParamsObj() - params.netParams.shape = "cylinder" # max size for network - self.paramsMap["net"]["shapeTest"].append(params) - - params = ParamsObj() - params.netParams.shape = "sphere" # max size for network - self.paramsMap["net"]["shapeTest"].append(params) - + # + # self.paramsMap["net"]["shapeTest"] = [] + # + # params = ParamsObj() + # params.netParams.shape = "cuboid" # max size for network + # self.paramsMap["net"]["shapeTest"].append(params) + # + # params = ParamsObj() + # params.netParams.shape = "ellipsoid" # max size for network + # self.paramsMap["net"]["shapeTest"].append(params) + # + # params = ParamsObj() + # params.netParams.shape = "cylinder" # max size for network + # self.paramsMap["net"]["shapeTest"].append(params) + # + # params = ParamsObj() + # params.netParams.shape = "sphere" # max size for network + # self.paramsMap["net"]["shapeTest"].append(params) + # + # # + # # # cell params test + # self.paramsMap["cell"]["condsTest"] = [] + # + # # valid cell conds rule + # params = ParamsObj() + # cellRule = {'conds': {'cellType': 'E2', 'cellModel': 'simple'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['validConds'] = cellRule # add dict with params for this pop + # #print ( str(cellRule["conds"]) ) + # self.paramsMap["cell"]["condsTest"].append(params) + # + # # valid cell conds rule + # params = ParamsObj() + # cellRule = {'conds': 'test', 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['inValidConds1'] = cellRule # add dict with params for this pop + # #print ( str(cellRule["conds"]) ) + # self.paramsMap["cell"]["condsTest"].append(params) + # + # # invalid cell conds rule + # params = ParamsObj() + # cellRule = { 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['inValidConds2'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["condsTest"].append(params) + # + # # # cell params test + # self.paramsMap["cell"]["secsTest"] = [] + # + # # invalid sec type rule + # params = ParamsObj() + # cellRule = { 'secs': 'test'} # cell rule dict + # params.netParams.cellParams['inValidSecs1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["secsTest"].append(params) + # + # # cell types test + # self.paramsMap["cell"]["cellTypesTest"] = [] + # + # # valid cell type rule + # params = ParamsObj() + # params.netParams.popParams['validCellModelParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'numCells': 50} # add dict with params for this pop + # cellRule = {'conds': {'cellType': 'PYR'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['validCellTypes'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["cellTypesTest"].append(params) + # + # # invalid cell type rule + # params = ParamsObj() + # params.netParams.popParams['validCellModelParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'numCells': 50} # add dict with params for this pop + # cellRule = { 'conds': {'cellType': 'PY1'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['inValidCellTypes'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["cellTypesTest"].append(params) # # # cell params test - self.paramsMap["cell"]["condsTest"] = [] - - # valid cell conds rule - params = ParamsObj() - cellRule = {'conds': {'cellType': 'E2', 'cellModel': 'simple'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['validConds'] = cellRule # add dict with params for this pop - #print ( str(cellRule["conds"]) ) - self.paramsMap["cell"]["condsTest"].append(params) - - # valid cell conds rule - params = ParamsObj() - cellRule = {'conds': 'test', 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['inValidConds1'] = cellRule # add dict with params for this pop - #print ( str(cellRule["conds"]) ) - self.paramsMap["cell"]["condsTest"].append(params) - - # invalid cell conds rule - params = ParamsObj() - cellRule = { 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['inValidConds2'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["condsTest"].append(params) - - # # cell params test - self.paramsMap["cell"]["secsTest"] = [] - - # invalid sec type rule - params = ParamsObj() - cellRule = { 'secs': 'test'} # cell rule dict - params.netParams.cellParams['inValidSecs1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["secsTest"].append(params) - - # cell types test - self.paramsMap["cell"]["cellTypesTest"] = [] - - # valid cell type rule - params = ParamsObj() - params.netParams.popParams['validCellModelParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'numCells': 50} # add dict with params for this pop - cellRule = {'conds': {'cellType': 'PYR'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['validCellTypes'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["cellTypesTest"].append(params) - - # invalid cell type rule - params = ParamsObj() - params.netParams.popParams['validCellModelParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'numCells': 50} # add dict with params for this pop - cellRule = { 'conds': {'cellType': 'PY1'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['inValidCellTypes'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["cellTypesTest"].append(params) - - # cell params test - self.paramsMap["cell"]["cellModelsTest"] = [] - - # valid cell model rule - params = ParamsObj() - params.netParams.popParams['validCellModelParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'numCells': 50} # add dict with params for this pop - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['validCellModel'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["cellModelsTest"].append(params) - - # invalid cell model rule - params = ParamsObj() - params.netParams.popParams['validCellModelParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'numCells': 50} # add dict with params for this pop - cellRule = { 'conds': {'cellModel': 'H1' }, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['inValidCellModel'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["cellModelsTest"].append(params) + # self.paramsMap["cell"]["cellModelsTest"] = [] + # + # # valid cell model rule + # params = ParamsObj() + # params.netParams.popParams['validCellModelParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'numCells': 50} # add dict with params for this pop + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['validCellModel'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["cellModelsTest"].append(params) + # + # # invalid cell model rule + # params = ParamsObj() + # params.netParams.popParams['validCellModelParams'] = {'cellType': 'PYR', 'cellModel': 'HH', 'numCells': 50} # add dict with params for this pop + # cellRule = { 'conds': {'cellModel': 'H1' }, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['inValidCellModel'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["cellModelsTest"].append(params) + # # + # # geom test + # self.paramsMap["cell"]["geomTest"] = [] + # # + # # # valid geom rule + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['validGeom'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) + # + # # invalid geom rule + # params = ParamsObj() + # cellRule = { 'conds': {'cellModel': 'H1' }, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = { 'mechs': {}} # soma params dict + # params.netParams.cellParams['inValidGeom'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) # - # geom test - self.paramsMap["cell"]["geomTest"] = [] + # #valid geom rule + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['validGeom1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) # # # valid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['validGeom'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # invalid geom rule - params = ParamsObj() - cellRule = { 'conds': {'cellModel': 'H1' }, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = { 'mechs': {}} # soma params dict - params.netParams.cellParams['inValidGeom'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - #valid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['validGeom1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # valid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0, 'pt3d' : [] } # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['validGeom2'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # valid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'pt3d' : [] } # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['validGeom3'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # valid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'pt3d' : [[1,2,3,4],[3,4,5,6]] } # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['validGeom4'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # invalid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'pt3d' : 2.3 } # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['invalidGeom1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # invalid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam' : 2.3 } # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['invalidGeom2'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # invalid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'xy' : 2.3 } # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['invalidGeom3'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # invalid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['invalidGeom4'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # invalid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'pt3d':[2,3,4]} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['invalidGeom5'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # invalid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'pt3d':[[2,3,4],[3,4,5]]} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['invalidGeom6'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # invalid geom rule - params = ParamsObj() - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'pt3d':[[2,3,4,4],[3,4,"a",3]]} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - params.netParams.cellParams['invalidGeom7'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["geomTest"].append(params) - - # # topology test - self.paramsMap["cell"]["topologyTest"] = [] - - # valid topology rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['validTopology1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["topologyTest"].append(params) - - # invalid topology rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['invalidTopology1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["topologyTest"].append(params) - - # invalid topology rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['invalidTopology2'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["topologyTest"].append(params) - - # invalid topology rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['invalidTopology3'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["topologyTest"].append(params) - - # invalid topology rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma1', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['invalidTopology4'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["topologyTest"].append(params) - - # invalid topology rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 2.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['invalidTopology5'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["topologyTest"].append(params) - - # invalid topology rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 2.0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['invalidTopology6'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["topologyTest"].append(params) - - # mechs test - self.paramsMap["cell"]["mechsTest"] = [] - - # valid mechs rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['validMechs1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["mechsTest"].append(params) - - # invalid mechs rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['invalidMechs1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["mechsTest"].append(params) - - # invalid mechs rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357} # dend mechanisms - - params.netParams.cellParams['invalidMechs2'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["mechsTest"].append(params) - - # invalid mechs rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl1': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['invalidMechs3'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["mechsTest"].append(params) + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0, 'pt3d' : [] } # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['validGeom2'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) + # + # # valid geom rule + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'pt3d' : [] } # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['validGeom3'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) + # + # # valid geom rule + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'pt3d' : [[1,2,3,4],[3,4,5,6]] } # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['validGeom4'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) + # + # # invalid geom rule + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'pt3d' : 2.3 } # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['invalidGeom1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) + # + # # invalid geom rule + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam' : 2.3 } # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['invalidGeom2'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) + # + # # invalid geom rule + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'xy' : 2.3 } # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['invalidGeom3'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) + # + # # invalid geom rule + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['invalidGeom4'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) + # + # # invalid geom rule + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'pt3d':[2,3,4]} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['invalidGeom5'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) + # + # # invalid geom rule + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'pt3d':[[2,3,4],[3,4,5]]} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['invalidGeom6'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) + # + # # invalid geom rule + # params = ParamsObj() + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'pt3d':[[2,3,4,4],[3,4,"a",3]]} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # params.netParams.cellParams['invalidGeom7'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["geomTest"].append(params) + # + # # # topology test + # self.paramsMap["cell"]["topologyTest"] = [] + # + # # valid topology rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['validTopology1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["topologyTest"].append(params) + # + # # invalid topology rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidTopology1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["topologyTest"].append(params) + # + # # invalid topology rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidTopology2'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["topologyTest"].append(params) + # + # # invalid topology rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidTopology3'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["topologyTest"].append(params) + # + # # invalid topology rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma1', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidTopology4'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["topologyTest"].append(params) + # + # # invalid topology rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 2.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidTopology5'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["topologyTest"].append(params) + # + # # invalid topology rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 2.0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidTopology6'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["topologyTest"].append(params) + # + # # mechs test + # self.paramsMap["cell"]["mechsTest"] = [] + # + # # valid mechs rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['validMechs1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["mechsTest"].append(params) + # + # # invalid mechs rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidMechs1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["mechsTest"].append(params) + # + # # invalid mechs rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357} # dend mechanisms + # + # params.netParams.cellParams['invalidMechs2'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["mechsTest"].append(params) + # + # # invalid mechs rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl1': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidMechs3'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["mechsTest"].append(params) + # # + # # # invalid mechs rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e1': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidMechs4'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["mechsTest"].append(params) + # + # # ions test + # self.paramsMap["cell"]["ionsTest"] = [] + # + # # valid ions rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['soma']['mechs']['k_ion'] = {'i':10,'e':20,'o':30} # potassium ions + # cellRule['secs']['soma']['mechs']['na_ion'] = {'o':3,'i':4,'e':5} # sodium ions + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['validIons1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["ionsTest"].append(params) + # + # # invalid ions rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['soma']['mechs']['k_ion'] = {'x':10} # potassium ions + # cellRule['secs']['soma']['mechs']['na_ion'] = {'y':3} # sodium ions + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidIons1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["ionsTest"].append(params) + # + # # invalid mechs rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['soma']['mechs']['k_ion'] = {'i':10} # potassium ions + # cellRule['secs']['soma']['mechs']['na_ion'] = {'o':3} # sodium ions + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'e': 0.0000357, 'g':0.3} # dend mechanisms + # + # params.netParams.cellParams['invalidIons2'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["ionsTest"].append(params) + # + # # invalid mechs rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl1': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['soma']['mechs']['mg_ion'] = {'mg1':10} # mg ions + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidIons3'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["ionsTest"].append(params) + # + # # invalid mechs rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e1': -70} # dend mechanisms + # + # params.netParams.cellParams['invalidIons4'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["ionsTest"].append(params) + # + # # pointps test + # self.paramsMap["cell"]["pointpsTest"] = [] # - # # invalid mechs rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e1': -70} # dend mechanisms - - params.netParams.cellParams['invalidMechs4'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["mechsTest"].append(params) - - # ions test - self.paramsMap["cell"]["ionsTest"] = [] - - # valid ions rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['soma']['mechs']['k_ion'] = {'i':10,'e':20,'o':30} # potassium ions - cellRule['secs']['soma']['mechs']['na_ion'] = {'o':3,'i':4,'e':5} # sodium ions - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['validIons1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["ionsTest"].append(params) - - # invalid ions rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['soma']['mechs']['k_ion'] = {'x':10} # potassium ions - cellRule['secs']['soma']['mechs']['na_ion'] = {'y':3} # sodium ions - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['invalidIons1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["ionsTest"].append(params) - - # invalid mechs rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['soma']['mechs']['k_ion'] = {'i':10} # potassium ions - cellRule['secs']['soma']['mechs']['na_ion'] = {'o':3} # sodium ions - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'e': 0.0000357, 'g':0.3} # dend mechanisms - - params.netParams.cellParams['invalidIons2'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["ionsTest"].append(params) - - # invalid mechs rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl1': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['soma']['mechs']['mg_ion'] = {'mg1':10} # mg ions - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - params.netParams.cellParams['invalidIons3'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["ionsTest"].append(params) - - # invalid mechs rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e1': -70} # dend mechanisms - - params.netParams.cellParams['invalidIons4'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["ionsTest"].append(params) - - # pointps test - self.paramsMap["cell"]["pointpsTest"] = [] - - # valid pointps rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} - - params.netParams.cellParams['validPointPs1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["pointpsTest"].append(params) - - # invalid pointps rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['pointps']['Izhi'] = { 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} - - params.netParams.cellParams['invalidPointPs1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["pointpsTest"].append(params) - - # invalid pointps rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1,'synList' :'q'} - - params.netParams.cellParams['invalidPointPs2'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["pointpsTest"].append(params) - - # invalid pointps rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1,'loc' :4} - - params.netParams.cellParams['invalidPointPs2'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["pointpsTest"].append(params) - - # secList test - self.paramsMap["cell"]["secListTest"] = [] - - # valid pointps rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} - - cellRule['secList'] = {'apicdend': ['soma','dend'], 'basaldend':['dend']} - - params.netParams.cellParams['validSecList'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["secListTest"].append(params) - - # valid pointps rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} - - cellRule['secList'] = {'apicdend': ['somax','dend'], 'basaldend':['dend']} - - params.netParams.cellParams['invalidSecList1'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["secListTest"].append(params) - - # valid pointps rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} - - cellRule['secList'] = {'apicdend': 'soma', 'basaldend':['dend']} - - params.netParams.cellParams['invalidSecList2'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["secListTest"].append(params) - - # secList test - self.paramsMap["cell"]["spikeGenLocTest"] = [] - # # valid pointps rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} - - cellRule['secs']['axon'] = {'geom': {}, 'topol': {}, 'mechs': {}} - cellRule['secs']['axon']['spikeGenLoc'] = 0.7 - - params.netParams.cellParams['validSpikeGneLoc'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["spikeGenLocTest"].append(params) - - # valid pointps rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} - - cellRule['secs']['axon'] = {'geom': {}, 'topol': {}, 'mechs': {}} - cellRule['secs']['axon']['spikeGenLoc'] = 1.7 - - params.netParams.cellParams['invalidSpikeGneLoc'] = cellRule # add dict with params for this pop - self.paramsMap["cell"]["spikeGenLocTest"].append(params) + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} + # + # params.netParams.cellParams['validPointPs1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["pointpsTest"].append(params) + # + # # invalid pointps rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['pointps']['Izhi'] = { 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} + # + # params.netParams.cellParams['invalidPointPs1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["pointpsTest"].append(params) + # + # # invalid pointps rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1,'synList' :'q'} + # + # params.netParams.cellParams['invalidPointPs2'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["pointpsTest"].append(params) + # + # # invalid pointps rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1,'loc' :4} + # + # params.netParams.cellParams['invalidPointPs2'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["pointpsTest"].append(params) + # + # # secList test + # self.paramsMap["cell"]["secListTest"] = [] # + # # valid pointps rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + # + # cellRule['secList'] = {'apicdend': ['soma','dend'], 'basaldend':['dend']} + # + # params.netParams.cellParams['validSecList'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["secListTest"].append(params) + # + # # valid pointps rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + # + # cellRule['secList'] = {'apicdend': ['somax','dend'], 'basaldend':['dend']} + # + # params.netParams.cellParams['invalidSecList1'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["secListTest"].append(params) + # + # # valid pointps rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + # + # cellRule['secList'] = {'apicdend': 'soma', 'basaldend':['dend']} + # + # params.netParams.cellParams['invalidSecList2'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["secListTest"].append(params) + # + # # secList test + # self.paramsMap["cell"]["spikeGenLocTest"] = [] + # + # # # valid pointps rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + # + # cellRule['secs']['axon'] = {'geom': {}, 'topol': {}, 'mechs': {}} + # cellRule['secs']['axon']['spikeGenLoc'] = 0.7 + # + # params.netParams.cellParams['validSpikeGneLoc'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["spikeGenLocTest"].append(params) + # + # # valid pointps rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'pointps': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['pointps']['Izhi'] = {'mod':'Izhi2007b', 'C':1, 'k':0.7, 'vr':-60, 'vt':-40, 'vpeak':35, 'a':0.03, 'b':-2, 'c':-50, 'd':100, 'celltype':1} + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + # + # cellRule['secs']['axon'] = {'geom': {}, 'topol': {}, 'mechs': {}} + # cellRule['secs']['axon']['spikeGenLoc'] = 1.7 + # + # params.netParams.cellParams['invalidSpikeGneLoc'] = cellRule # add dict with params for this pop + # self.paramsMap["cell"]["spikeGenLocTest"].append(params) + # # + # # + # # # conn test + # self.paramsMap["conn"]["preCondsTest"] = [] + # + # # valid mechs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'delay': 5} # delay + # + # params.netParams.connParams['validPreConds1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["preCondsTest"].append(params) + # + # # invalid conds rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': 2.3 , 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'delay': 5} # delay + # + # params.netParams.connParams['invalidPreConds1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["preCondsTest"].append(params) # # # conn test - self.paramsMap["conn"]["preCondsTest"] = [] - - # valid mechs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'delay': 5} # delay - - params.netParams.connParams['validPreConds1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["preCondsTest"].append(params) - - # invalid conds rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': 2.3 , 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'delay': 5} # delay - - params.netParams.connParams['invalidPreConds1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["preCondsTest"].append(params) - - # conn test - self.paramsMap["conn"]["postCondsTest"] = [] - - # invalid conds rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'delay': 5} # delay - - params.netParams.connParams['validPostConds1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["postCondsTest"].append(params) - - # invalid conds rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': 2.3, - 'weight': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'delay': 5} # delay - - params.netParams.connParams['invalidPostConds1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["postCondsTest"].append(params) - - # loc (optional) - Location of target synaptic mechanism (e.g. 0.3) - # If omitted, defaults to 0.5. - # If have list of synMechs, can have single loc for all, or list of locs (one per synMech, e.g. for 2 synMechs: [0.4, 0.7]). - # If have synsPerConn > 1, can have single loc for all, or list of locs (one per synapse, e.g. if synsPerConn = 3: [0.4, 0.5, 0.7]) - # If have both a list of synMechs and synsPerConn > 1, can have a 2D list for each synapse of each synMech (e.g. for 2 synMechs and synsPerConn = 3: [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]]) - - # conn test - self.paramsMap["conn"]["connsLocTest"] = [] - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'loc' : 1, - 'weight': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validConnsLoc0'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsLocTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'loc' : [0.5,0.7], - 'weight': 0.0, # weight of each connection - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validConnsLoc1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsLocTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'weight': 0.0, # weight of each connection - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 3, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validConnsLoc2'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsLocTest"].append(params) - + # self.paramsMap["conn"]["postCondsTest"] = [] + # + # # invalid conds rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'delay': 5} # delay + # + # params.netParams.connParams['validPostConds1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["postCondsTest"].append(params) + # + # # invalid conds rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': 2.3, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'delay': 5} # delay + # + # params.netParams.connParams['invalidPostConds1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["postCondsTest"].append(params) + # + # # loc (optional) - Location of target synaptic mechanism (e.g. 0.3) + # # If omitted, defaults to 0.5. + # # If have list of synMechs, can have single loc for all, or list of locs (one per synMech, e.g. for 2 synMechs: [0.4, 0.7]). + # # If have synsPerConn > 1, can have single loc for all, or list of locs (one per synapse, e.g. if synsPerConn = 3: [0.4, 0.5, 0.7]) + # # If have both a list of synMechs and synsPerConn > 1, can have a 2D list for each synapse of each synMech (e.g. for 2 synMechs and synsPerConn = 3: [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]]) + # + # # conn test + # self.paramsMap["conn"]["connsLocTest"] = [] + # # # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'weight': 0.0, # weight of each connection - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['invalidConnsLoc1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsLocTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 3, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['invalidConnsLoc2'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsLocTest"].append(params) - - # invalid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'loc' : 1.5, - 'weight': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['invalidConnsLoc3'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsLocTest"].append(params) - - # conn test - self.paramsMap["conn"]["connsWeightTest"] = [] - - # valid weights rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight' : 1, - 'loc': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validConnsWeight0'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsWeightTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight' : [0.5,0.7], - 'loc': 1.0, - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validConnsWeight1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsWeightTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'loc': 0.0, # weight of each connection - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 3, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validConnsWeight2'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsWeightTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'loc': 0.0, # weight of each connection - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['invalidConnsWeight1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsWeightTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'loc': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 3, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['invalidConnsWeight2'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsWeightTest"].append(params) - - # invalid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight' : 1.5, - 'loc': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['invalidConnsWeight3'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsWeightTest"].append(params) - # conn test - self.paramsMap["conn"]["connsDelayTest"] = [] - - # valid weights rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'delay' : 1, - 'loc': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'synsPerConn': 1, - } # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validconnsDelay0'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsDelayTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'delay' : [0.5,0.7], - 'loc': 1.0, - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 1, - } # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validconnsDelay1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsDelayTest"].append(params) + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'loc' : 1, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validConnsLoc0'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsLocTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'loc' : [0.5,0.7], + # 'weight': 0.0, # weight of each connection + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validConnsLoc1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsLocTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'weight': 0.0, # weight of each connection + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 3, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validConnsLoc2'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsLocTest"].append(params) + # + # # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'weight': 0.0, # weight of each connection + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['invalidConnsLoc1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsLocTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 3, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['invalidConnsLoc2'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsLocTest"].append(params) + # + # # invalid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'loc' : 1.5, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['invalidConnsLoc3'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsLocTest"].append(params) + # + # # conn test + # self.paramsMap["conn"]["connsWeightTest"] = [] + # + # # valid weights rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight' : 1, + # 'loc': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validConnsWeight0'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsWeightTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight' : [0.5,0.7], + # 'loc': 1.0, + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validConnsWeight1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsWeightTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'loc': 0.0, # weight of each connection + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 3, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validConnsWeight2'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsWeightTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'loc': 0.0, # weight of each connection + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['invalidConnsWeight1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsWeightTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'loc': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 3, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['invalidConnsWeight2'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsWeightTest"].append(params) + # + # # invalid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight' : 1.5, + # 'loc': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['invalidConnsWeight3'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsWeightTest"].append(params) + # # conn test + # self.paramsMap["conn"]["connsDelayTest"] = [] + # + # # valid weights rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'delay' : 1, + # 'loc': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'synsPerConn': 1, + # } # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validconnsDelay0'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsDelayTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'delay' : [0.5,0.7], + # 'loc': 1.0, + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 1, + # } # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validconnsDelay1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsDelayTest"].append(params) + # # + # # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'delay' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'loc': 0.0, # delay of each connection + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 3, + # } # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validconnsDelay2'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsDelayTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'delay' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'loc': 0.0, # delay of each connection + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 1, + # } # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['invalidconnsDelay1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsDelayTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'delay' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'loc': 0.0, # delay of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 3, + # } # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['invalidconnsDelay2'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsDelayTest"].append(params) + # + # # invalid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'delay' : 1.5, + # 'loc': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'synsPerConn': 1, + # } # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['invalidconnsDelay3'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsDelayTest"].append(params) + # + # + # # conn test + # self.paramsMap["conn"]["synMechsTest"] = [] + # + # # valid locs rule + # params = ParamsObj() + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'loc' : [0.5,0.7], + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # params.netParams.connParams['validSynMechs1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["synMechsTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'weight': 0.0, # weight of each connection + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 3, + # 'delay': 5} # delay + # + # params.netParams.connParams['validSynMechs1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["synMechsTest"].append(params) # # # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'delay' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'loc': 0.0, # delay of each connection - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 3, - } # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validconnsDelay2'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsDelayTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'delay' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'loc': 0.0, # delay of each connection - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 1, - } # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['invalidconnsDelay1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsDelayTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'delay' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'loc': 0.0, # delay of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 3, - } # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['invalidconnsDelay2'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsDelayTest"].append(params) - - # invalid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'delay' : 1.5, - 'loc': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'synsPerConn': 1, - } # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['invalidconnsDelay3'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsDelayTest"].append(params) - - - # conn test - self.paramsMap["conn"]["synMechsTest"] = [] - - # valid locs rule - params = ParamsObj() - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'loc' : [0.5,0.7], - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - params.netParams.connParams['validSynMechs1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["synMechsTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'weight': 0.0, # weight of each connection - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 3, - 'delay': 5} # delay - - params.netParams.connParams['validSynMechs1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["synMechsTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'weight': 0.0, # weight of each connection - 'synMech': 'XYZ', # target inh synapse - 'synsPerConn': 3, - 'delay': 5} # delay - - params.netParams.connParams['invalidSynMechs1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["synMechsTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], - 'weight': 0.0, # weight of each connection - 'synMech': ['XYZ','ABC'], # target inh synapse - 'synsPerConn': 3, - 'delay': 5} # delay - - params.netParams.connParams['invalidSynMechs2'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["synMechsTest"].append(params) - - # conn test - self.paramsMap["conn"]["popLabelsTest"] = [] - - # valid pop labels rule - params = ParamsObj() - - params.netParams.popParams['popLabel1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop - params.netParams.popParams['popLabel2'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'popLabel1'}, 'postConds': {'popLabel': 'popLabel2'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'delay': 5} # delay - - params.netParams.connParams['validPopLabels1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["popLabelsTest"].append(params) - - # valid pop labels rule - params = ParamsObj() - - params.netParams.popParams['popLabel1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop - params.netParams.popParams['popLabel2'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'popLabel2'}, 'postConds': {'popLabel': 'popLabel3'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'delay': 5} # delay - - params.netParams.connParams['invalidPopLabels1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["popLabelsTest"].append(params) - # conn test - self.paramsMap["conn"]["popLabelsTest"] = [] - - # valid pop labels rule - params = ParamsObj() - - params.netParams.popParams['popLabel1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop - params.netParams.popParams['popLabel2'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'popLabel1'}, 'postConds': {'popLabel': 'popLabel2'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'delay': 5} # delay - - params.netParams.connParams['validPopLabels1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["popLabelsTest"].append(params) - - # conn test - self.paramsMap["conn"]["secListTest"] = [] - - # valid pop labels rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl1': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - cellRule['secList'] = {'apicdend': ['soma','dend'], 'basaldend':['dend']} - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'popLabel2'}, 'postConds': {'popLabel': 'popLabel3'}, - 'sec': 'apicdend', - 'weight': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - params.netParams.cellParams["cellParams1"] = cellRule - - params.netParams.connParams['validSecList1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["secListTest"].append(params) - - # valid pop labels rule - params = ParamsObj() - - cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict - cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict - cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry - cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl1': 0.003, 'el': -70} # soma hh mechanism - - cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict - cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry - cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology - cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms - - cellRule['secList'] = {'apicdend': ['soma','dend'], 'basaldend':['dend']} - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'popLabel2'}, 'postConds': {'popLabel': 'popLabel3'}, - 'sec': 'apicdend1', - 'weight': 0.0, # weight of each connection - 'synMech': 'inh', # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - params.netParams.cellParams["cellParams1"] = cellRule - - params.netParams.connParams['invalidSecList1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["secListTest"].append(params) + # params = ParamsObj() + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'weight': 0.0, # weight of each connection + # 'synMech': 'XYZ', # target inh synapse + # 'synsPerConn': 3, + # 'delay': 5} # delay + # + # params.netParams.connParams['invalidSynMechs1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["synMechsTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'loc' : [[0.2, 0.3, 0.5], [0.5, 0.6, 0.7]], + # 'weight': 0.0, # weight of each connection + # 'synMech': ['XYZ','ABC'], # target inh synapse + # 'synsPerConn': 3, + # 'delay': 5} # delay + # + # params.netParams.connParams['invalidSynMechs2'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["synMechsTest"].append(params) # # # conn test - self.paramsMap["conn"]["connListTest"] = [] - - # valid pop labels rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'connList' : [[0,1],[2,1]], - 'weight': 0.0, # weight of each connection - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validConnsLoc1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connListTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'connList' : [[[0.1,0.2], [0.1,0.3], [0.1,0.5]], [[0.5,0.1], [0.1,0.6], [0.1,0.7]]], - 'weight': 0.0, # weight of each connection - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 3, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validConnsLoc2'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsLocTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'connList' : [0.1,0.2], - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5} # delay - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['validConnsLoc3'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsLocTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'connList' : [[[0.1,0.2], [0.1,0.3], [0.1,0.5]], [[0.5,0.1], [0.1,0.6], [0.1,0.7]]], - 'weight': 0.0, # weight of each connection - 'synMech': ['AMPA','NMDA'], # target inh synapse - 'synsPerConn': 1, - 'delay': 5} - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['invalidConnsLoc1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsLocTest"].append(params) - - # valid locs rule - params = ParamsObj() - - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'connList' : [[0.1,0.2], [0.1,0.3]], - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5} - - # Synaptic mechanism parameters - params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA - params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA - params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA - params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB - - params.netParams.connParams['invalidConnsLoc2'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsLocTest"].append(params) - - self.paramsMap["conn"]["connsHierarchyTest"] = [] - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'probability':0.5, - 'shape': {'switchOnOff': [200, 800], 'pulseType': 'square', 'pulsePeriod': 100, 'pulseWidth': 50}, - } - - params.netParams.connParams['validHierarchy1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsHierarchyTest"].append(params) - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'probability':0.5, - 'convergence': 0.5, - } - - params.netParams.connParams['invalidHierarchy1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsHierarchyTest"].append(params) - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'convergence': 0.5, - 'divergence':0.5, - } - - params.netParams.connParams['invalidHierarchy2'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsHierarchyTest"].append(params) - - self.paramsMap["conn"]["connsShapeTest"] = [] - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'shape': {'switchOnOff': [200, 800], 'pulseType': 'square', 'pulsePeriod': 100, 'pulseWidth': 50}, - } - - params.netParams.connParams['validShape1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsShapeTest"].append(params) - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'shape': {'switchOnOff': 200, 'pulseType': 'square', 'pulsePeriod': 100, 'pulseWidth': 50}, - } - - params.netParams.connParams['invalidShape1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsShapeTest"].append(params) - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'shape': {'switchOnOff': ['200','300'], 'pulseType': 'square', 'pulsePeriod': 100, 'pulseWidth': 50}, - } - - params.netParams.connParams['invalidShape2'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsShapeTest"].append(params) - - self.paramsMap["conn"]["connsPlasticityTest"] = [] - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'plasticity': {'mech': 'STDP', 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, - } - - params.netParams.connParams['validPlasticity1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsPlasticityTest"].append(params) - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, - } - - params.netParams.connParams['invalidPlasticity1'] = connRule # add dict with params for this pop - self.paramsMap["conn"]["connsPlasticityTest"].append(params) - - self.paramsMap["stimSource"]["stimSourceTest"] = [] - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, - } - - params.netParams.stimSourceParams['Input_1'] = {'type': 'IClamp', 'delay': 10, 'dur': 800, 'amp': 'uniform(0.05,0.5)'} - params.netParams.stimSourceParams['Input_2'] = {'type': 'VClamp', 'dur':[0,1,1], 'amp':[1,1,1],'gain':1, 'rstim':0, 'tau1':1, 'tau2':1, 'i':1} - params.netParams.stimSourceParams['Input_3'] = {'type': 'AlphaSynapse', 'onset': 'uniform(1,500)', 'tau': 5, 'gmax': 'post_ynorm', 'e': 0} - params.netParams.stimSourceParams['Input_4'] = {'type': 'NetStim', 'interval': 'uniform(20,100)', 'number': 1000, 'start': 5, 'noise': 0.1} - - # Stimulation mapping parameters - params.netParams.stimTargetParams['Input1->PYR'] = { - 'source': 'Input_1', - 'sec':'soma', - 'loc': 0.5, - 'conds': {'pop':'PYR', 'cellList': range(8)}} - params.netParams.stimTargetParams['Input3->Basket'] = { - 'source': 'Input_3', - 'sec':'soma', - 'loc': 0.5, - 'conds': {'cellType':'Basket'}} - params.netParams.stimTargetParams['Input4->PYR3'] = { - 'source': 'Input_4', - 'sec':'soma', - 'loc': 0.5, - 'weight': '0.1+gauss(0.2,0.05)', - 'delay': 1, - 'conds': {'pop':'PYR3', 'cellList': [0,1,2,5,10,14,15]}} - - params.netParams.connParams['validStimSource1'] = connRule # add dict with params for this pop - self.paramsMap["stimSource"]["stimSourceTest"].append(params) - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, - } - - params.netParams.stimSourceParams['Input_1'] = {'type': 'XYClamp', 'delay': 10, 'dur': 800, 'amp': 'uniform(0.05,0.5)'} - params.netParams.stimSourceParams['Input_2'] = {'type': 'VClamp', 'dur':[0,1,1], 'amp':[1,1,1],'gain':1, 'rstim':0, 'tau1':1, 'tau2':1, 'i':1} - params.netParams.stimSourceParams['Input_3'] = {'type': 'AlphaSynapse', 'onset': 'uniform(1,500)', 'tau': 5, 'gmax': 'post_ynorm', 'e': 0} - params.netParams.stimSourceParams['Input_4'] = {'type': 'NetStim', 'interval': 'uniform(20,100)', 'number': 1000, 'start': 5, 'noise': 0.1} - - # Stimulation mapping parameters - params.netParams.stimTargetParams['Input1->PYR'] = { - 'source': 'Input_1', - 'sec':'soma', - 'loc': 0.5, - 'conds': {'pop':'PYR', 'cellList': range(8)}} - params.netParams.stimTargetParams['Input3->Basket'] = { - 'source': 'Input_3', - 'sec':'soma', - 'loc': 0.5, - 'conds': {'cellType':'Basket'}} - params.netParams.stimTargetParams['Input4->PYR3'] = { - 'source': 'Input_4', - 'sec':'soma', - 'loc': 0.5, - 'weight': '0.1+gauss(0.2,0.05)', - 'delay': 1, - 'conds': {'pop':'PYR3', 'cellList': [0,1,2,5,10,14,15]}} - - params.netParams.connParams['invalidStimSource1'] = connRule # add dict with params for this pop - self.paramsMap["stimSource"]["stimSourceTest"].append(params) + # self.paramsMap["conn"]["popLabelsTest"] = [] # - self.paramsMap["stimTarget"]["stimTargetTest"] = [] - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, - } - - params.netParams.stimSourceParams['Input_1'] = {'type': 'IClamp', 'delay': 10, 'dur': 800, 'amp': 'uniform(0.05,0.5)'} - params.netParams.stimSourceParams['Input_2'] = {'type': 'VClamp', 'dur':[0,1,1], 'amp':[1,1,1],'gain':1, 'rstim':0, 'tau1':1, 'tau2':1, 'i':1} - params.netParams.stimSourceParams['Input_3'] = {'type': 'AlphaSynapse', 'onset': 'uniform(1,500)', 'tau': 5, 'gmax': 'post_ynorm', 'e': 0} - params.netParams.stimSourceParams['Input_4'] = {'type': 'NetStim', 'interval': 'uniform(20,100)', 'number': 1000, 'start': 5, 'noise': 0.1} - - # Stimulation mapping parameters - params.netParams.stimTargetParams['Input1->PYR'] = { - 'source': 'Input_1', - 'sec':'soma', - 'loc': 0.5, - 'conds': {'pop':'PYR', 'cellList': range(8)}} - params.netParams.stimTargetParams['Input3->Basket'] = { - 'source': 'Input_3', - 'sec':'soma', - 'loc': 0.5, - 'conds': {'cellType':'Basket'}} - params.netParams.stimTargetParams['Input4->PYR3'] = { - 'source': 'Input_4', - 'sec':'soma', - 'loc': 0.5, - 'weight': '0.1+gauss(0.2,0.05)', - 'delay': 1, - 'conds': {'pop':'PYR3', 'cellList': [0,1,2,5,10,14,15]}} - - params.netParams.connParams['validStimTarget1'] = connRule # add dict with params for this pop - self.paramsMap["stimTarget"]["stimTargetTest"].append(params) - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, - } - - params.netParams.stimSourceParams['Input_1'] = {'type': 'IClamp', 'delay': 10, 'dur': 800, 'amp': 'uniform(0.05,0.5)'} - params.netParams.stimSourceParams['Input_2'] = {'type': 'VClamp', 'dur':[0,1,1], 'amp':[1,1,1],'gain':1, 'rstim':0, 'tau1':1, 'tau2':1, 'i':1} - params.netParams.stimSourceParams['Input_3'] = {'type': 'AlphaSynapse', 'onset': 'uniform(1,500)', 'tau': 5, 'gmax': 'post_ynorm', 'e': 0} - params.netParams.stimSourceParams['Input_4'] = {'type': 'NetStim', 'interval': 'uniform(20,100)', 'number': 1000, 'start': 5, 'noise': 0.1} - - # Stimulation mapping parameters - params.netParams.stimTargetParams['Input1->PYR'] = { - 'source': 'Input_1', - 'sec':'soma', - 'loc': 0.5, - 'conds': {'pop':'PYR', 'cellList': range(8)}} - params.netParams.stimTargetParams['Input3->Basket'] = { - 'source': 'Input_3', - 'sec':'soma', - 'loc': 0.5, - 'conds': {'cellType':'Basket'}} - params.netParams.stimTargetParams['Input4->PYR3'] = { - 'source': 'Input_4', - 'sec':'soma', - 'loc': 0.5, - 'weight': '0.1+gauss(0.2,0.05)', - 'delay': 1, - 'conds': {'pop':'PYR3', 'cellList': [0,1,2,5,10,14,15]}} - - params.netParams.connParams['validStimTarget1'] = connRule # add dict with params for this pop - self.paramsMap["stimTarget"]["stimTargetTest"].append(params) - - params = ParamsObj() - # Connectivity parameters - connRule = { - 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, - 'weight': 0.0, # weight of each connection - 'synMech': 'AMPA', # target inh synapse - 'synsPerConn': 1, - 'delay': 5, - 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, - } - - params.netParams.stimSourceParams['Input_1'] = {'type': 'IClamp', 'delay': 10, 'dur': 800, 'amp': 'uniform(0.05,0.5)'} - params.netParams.stimSourceParams['Input_2'] = {'type': 'VClamp', 'dur':[0,1,1], 'amp':[1,1,1],'gain':1, 'rstim':0, 'tau1':1, 'tau2':1, 'i':1} - params.netParams.stimSourceParams['Input_3'] = {'type': 'AlphaSynapse', 'onset': 'uniform(1,500)', 'tau': 5, 'gmax': 'post_ynorm', 'e': 0} - params.netParams.stimSourceParams['Input_4'] = {'type': 'NetStim', 'interval': 'uniform(20,100)', 'number': 1000, 'start': 5, 'noise': 0.1} - - # Stimulation mapping parameters - params.netParams.stimTargetParams['Input1->PYR'] = { - 'source': 'Input_11', - 'sec':'soma', - 'loc': 0.5, - 'conds': {'pop':'PYR', 'cellList': range(8)}} - params.netParams.stimTargetParams['Input3->Basket'] = { - 'source': 'Input_3', - 'sec':'soma', - 'loc': 0.5, - 'conds': {'cellType':'Basket'}} - params.netParams.stimTargetParams['Input4->PYR3'] = { - 'source': 'Input_4', - 'sec':'soma', - 'loc': 0.5, - 'weight': '0.1+gauss(0.2,0.05)', - 'delay': 1, - 'conds': {'pop':'PYR3', 'cellList': [0,1,2,5,10,14,15]}} - - params.netParams.connParams['invalidStimTarget1'] = connRule # add dict with params for this pop - self.paramsMap["stimTarget"]["stimTargetTest"].append(params) + # # valid pop labels rule + # params = ParamsObj() + # + # params.netParams.popParams['popLabel1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop + # params.netParams.popParams['popLabel2'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'popLabel1'}, 'postConds': {'popLabel': 'popLabel2'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'delay': 5} # delay + # + # params.netParams.connParams['validPopLabels1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["popLabelsTest"].append(params) + # + # # valid pop labels rule + # params = ParamsObj() + # + # params.netParams.popParams['popLabel1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop + # params.netParams.popParams['popLabel2'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'popLabel2'}, 'postConds': {'popLabel': 'popLabel3'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'delay': 5} # delay + # + # params.netParams.connParams['invalidPopLabels1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["popLabelsTest"].append(params) + # # conn test + # self.paramsMap["conn"]["popLabelsTest"] = [] + # + # # valid pop labels rule + # params = ParamsObj() + # + # params.netParams.popParams['popLabel1'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop + # params.netParams.popParams['popLabel2'] = {'cellType': 'PYR', 'cellModel': 'HH', 'density' : 0.8, 'numCells': 50} # add dict with params for this pop + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'popLabel1'}, 'postConds': {'popLabel': 'popLabel2'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'delay': 5} # delay + # + # params.netParams.connParams['validPopLabels1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["popLabelsTest"].append(params) + # + # # conn test + # self.paramsMap["conn"]["secListTest"] = [] + # + # # valid pop labels rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl1': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # cellRule['secList'] = {'apicdend': ['soma','dend'], 'basaldend':['dend']} + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'popLabel2'}, 'postConds': {'popLabel': 'popLabel3'}, + # 'sec': 'apicdend', + # 'weight': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # params.netParams.cellParams["cellParams1"] = cellRule + # + # params.netParams.connParams['validSecList1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["secListTest"].append(params) + # + # # valid pop labels rule + # params = ParamsObj() + # + # cellRule = {'conds': {'cellModel': 'HH'}, 'secs': {}} # cell rule dict + # cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}} # soma params dict + # cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0} # soma geometry + # cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl1': 0.003, 'el': -70} # soma hh mechanism + # + # cellRule['secs']['dend'] = {'geom': {}, 'topol': {}, 'mechs': {}} # dend params dict + # cellRule['secs']['dend']['geom'] = {'diam': 5.0, 'L': 150.0, 'Ra': 150.0, 'cm': 1} # dend geometry + # cellRule['secs']['dend']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # dend topology + # cellRule['secs']['dend']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} # dend mechanisms + # + # cellRule['secList'] = {'apicdend': ['soma','dend'], 'basaldend':['dend']} + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'popLabel2'}, 'postConds': {'popLabel': 'popLabel3'}, + # 'sec': 'apicdend1', + # 'weight': 0.0, # weight of each connection + # 'synMech': 'inh', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # params.netParams.cellParams["cellParams1"] = cellRule + # + # params.netParams.connParams['invalidSecList1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["secListTest"].append(params) + # # + # # # conn test + # self.paramsMap["conn"]["connListTest"] = [] + # + # # valid pop labels rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'connList' : [[0,1],[2,1]], + # 'weight': 0.0, # weight of each connection + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validConnsLoc1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connListTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'connList' : [[[0.1,0.2], [0.1,0.3], [0.1,0.5]], [[0.5,0.1], [0.1,0.6], [0.1,0.7]]], + # 'weight': 0.0, # weight of each connection + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 3, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validConnsLoc2'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsLocTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'connList' : [0.1,0.2], + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} # delay + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['validConnsLoc3'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsLocTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'connList' : [[[0.1,0.2], [0.1,0.3], [0.1,0.5]], [[0.5,0.1], [0.1,0.6], [0.1,0.7]]], + # 'weight': 0.0, # weight of each connection + # 'synMech': ['AMPA','NMDA'], # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['invalidConnsLoc1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsLocTest"].append(params) + # + # # valid locs rule + # params = ParamsObj() + # + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'connList' : [[0.1,0.2], [0.1,0.3]], + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5} + # + # # Synaptic mechanism parameters + # params.netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} # AMPA + # params.netParams.synMechParams['NMDA'] = {'mod': 'Exp2Syn', 'tau1': 0.15, 'tau2': 15, 'e': 0} # NMDA + # params.netParams.synMechParams['GABAA'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAA + # params.netParams.synMechParams['GABAB'] = {'mod': 'Exp2Syn', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} # GABAB + # + # params.netParams.connParams['invalidConnsLoc2'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsLocTest"].append(params) + # + # self.paramsMap["conn"]["connsHierarchyTest"] = [] + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'probability':0.5, + # 'shape': {'switchOnOff': [200, 800], 'pulseType': 'square', 'pulsePeriod': 100, 'pulseWidth': 50}, + # } + # + # params.netParams.connParams['validHierarchy1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsHierarchyTest"].append(params) + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'probability':0.5, + # 'convergence': 0.5, + # } + # + # params.netParams.connParams['invalidHierarchy1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsHierarchyTest"].append(params) + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'convergence': 0.5, + # 'divergence':0.5, + # } + # + # params.netParams.connParams['invalidHierarchy2'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsHierarchyTest"].append(params) + # + # self.paramsMap["conn"]["connsShapeTest"] = [] + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'shape': {'switchOnOff': [200, 800], 'pulseType': 'square', 'pulsePeriod': 100, 'pulseWidth': 50}, + # } + # + # params.netParams.connParams['validShape1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsShapeTest"].append(params) + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'shape': {'switchOnOff': 200, 'pulseType': 'square', 'pulsePeriod': 100, 'pulseWidth': 50}, + # } + # + # params.netParams.connParams['invalidShape1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsShapeTest"].append(params) + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'shape': {'switchOnOff': ['200','300'], 'pulseType': 'square', 'pulsePeriod': 100, 'pulseWidth': 50}, + # } + # + # params.netParams.connParams['invalidShape2'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsShapeTest"].append(params) + # + # self.paramsMap["conn"]["connsPlasticityTest"] = [] + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'plasticity': {'mech': 'STDP', 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, + # } + # + # params.netParams.connParams['validPlasticity1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsPlasticityTest"].append(params) + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, + # } + # + # params.netParams.connParams['invalidPlasticity1'] = connRule # add dict with params for this pop + # self.paramsMap["conn"]["connsPlasticityTest"].append(params) + # + # self.paramsMap["stimSource"]["stimSourceTest"] = [] + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, + # } + # + # params.netParams.stimSourceParams['Input_1'] = {'type': 'IClamp', 'delay': 10, 'dur': 800, 'amp': 'uniform(0.05,0.5)'} + # params.netParams.stimSourceParams['Input_2'] = {'type': 'VClamp', 'dur':[0,1,1], 'amp':[1,1,1],'gain':1, 'rstim':0, 'tau1':1, 'tau2':1, 'i':1} + # params.netParams.stimSourceParams['Input_3'] = {'type': 'AlphaSynapse', 'onset': 'uniform(1,500)', 'tau': 5, 'gmax': 'post_ynorm', 'e': 0} + # params.netParams.stimSourceParams['Input_4'] = {'type': 'NetStim', 'interval': 'uniform(20,100)', 'number': 1000, 'start': 5, 'noise': 0.1} + # + # # Stimulation mapping parameters + # params.netParams.stimTargetParams['Input1->PYR'] = { + # 'source': 'Input_1', + # 'sec':'soma', + # 'loc': 0.5, + # 'conds': {'pop':'PYR', 'cellList': range(8)}} + # params.netParams.stimTargetParams['Input3->Basket'] = { + # 'source': 'Input_3', + # 'sec':'soma', + # 'loc': 0.5, + # 'conds': {'cellType':'Basket'}} + # params.netParams.stimTargetParams['Input4->PYR3'] = { + # 'source': 'Input_4', + # 'sec':'soma', + # 'loc': 0.5, + # 'weight': '0.1+gauss(0.2,0.05)', + # 'delay': 1, + # 'conds': {'pop':'PYR3', 'cellList': [0,1,2,5,10,14,15]}} + # + # params.netParams.connParams['validStimSource1'] = connRule # add dict with params for this pop + # self.paramsMap["stimSource"]["stimSourceTest"].append(params) + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, + # } + # + # params.netParams.stimSourceParams['Input_1'] = {'type': 'XYClamp', 'delay': 10, 'dur': 800, 'amp': 'uniform(0.05,0.5)'} + # params.netParams.stimSourceParams['Input_2'] = {'type': 'VClamp', 'dur':[0,1,1], 'amp':[1,1,1],'gain':1, 'rstim':0, 'tau1':1, 'tau2':1, 'i':1} + # params.netParams.stimSourceParams['Input_3'] = {'type': 'AlphaSynapse', 'onset': 'uniform(1,500)', 'tau': 5, 'gmax': 'post_ynorm', 'e': 0} + # params.netParams.stimSourceParams['Input_4'] = {'type': 'NetStim', 'interval': 'uniform(20,100)', 'number': 1000, 'start': 5, 'noise': 0.1} + # + # # Stimulation mapping parameters + # params.netParams.stimTargetParams['Input1->PYR'] = { + # 'source': 'Input_1', + # 'sec':'soma', + # 'loc': 0.5, + # 'conds': {'pop':'PYR', 'cellList': range(8)}} + # params.netParams.stimTargetParams['Input3->Basket'] = { + # 'source': 'Input_3', + # 'sec':'soma', + # 'loc': 0.5, + # 'conds': {'cellType':'Basket'}} + # params.netParams.stimTargetParams['Input4->PYR3'] = { + # 'source': 'Input_4', + # 'sec':'soma', + # 'loc': 0.5, + # 'weight': '0.1+gauss(0.2,0.05)', + # 'delay': 1, + # 'conds': {'pop':'PYR3', 'cellList': [0,1,2,5,10,14,15]}} + # + # params.netParams.connParams['invalidStimSource1'] = connRule # add dict with params for this pop + # self.paramsMap["stimSource"]["stimSourceTest"].append(params) + # # + # self.paramsMap["stimTarget"]["stimTargetTest"] = [] + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, + # } + # + # params.netParams.stimSourceParams['Input_1'] = {'type': 'IClamp', 'delay': 10, 'dur': 800, 'amp': 'uniform(0.05,0.5)'} + # params.netParams.stimSourceParams['Input_2'] = {'type': 'VClamp', 'dur':[0,1,1], 'amp':[1,1,1],'gain':1, 'rstim':0, 'tau1':1, 'tau2':1, 'i':1} + # params.netParams.stimSourceParams['Input_3'] = {'type': 'AlphaSynapse', 'onset': 'uniform(1,500)', 'tau': 5, 'gmax': 'post_ynorm', 'e': 0} + # params.netParams.stimSourceParams['Input_4'] = {'type': 'NetStim', 'interval': 'uniform(20,100)', 'number': 1000, 'start': 5, 'noise': 0.1} + # + # # Stimulation mapping parameters + # params.netParams.stimTargetParams['Input1->PYR'] = { + # 'source': 'Input_1', + # 'sec':'soma', + # 'loc': 0.5, + # 'conds': {'pop':'PYR', 'cellList': range(8)}} + # params.netParams.stimTargetParams['Input3->Basket'] = { + # 'source': 'Input_3', + # 'sec':'soma', + # 'loc': 0.5, + # 'conds': {'cellType':'Basket'}} + # params.netParams.stimTargetParams['Input4->PYR3'] = { + # 'source': 'Input_4', + # 'sec':'soma', + # 'loc': 0.5, + # 'weight': '0.1+gauss(0.2,0.05)', + # 'delay': 1, + # 'conds': {'pop':'PYR3', 'cellList': [0,1,2,5,10,14,15]}} + # + # params.netParams.connParams['validStimTarget1'] = connRule # add dict with params for this pop + # self.paramsMap["stimTarget"]["stimTargetTest"].append(params) + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, + # } + # + # params.netParams.stimSourceParams['Input_1'] = {'type': 'IClamp', 'delay': 10, 'dur': 800, 'amp': 'uniform(0.05,0.5)'} + # params.netParams.stimSourceParams['Input_2'] = {'type': 'VClamp', 'dur':[0,1,1], 'amp':[1,1,1],'gain':1, 'rstim':0, 'tau1':1, 'tau2':1, 'i':1} + # params.netParams.stimSourceParams['Input_3'] = {'type': 'AlphaSynapse', 'onset': 'uniform(1,500)', 'tau': 5, 'gmax': 'post_ynorm', 'e': 0} + # params.netParams.stimSourceParams['Input_4'] = {'type': 'NetStim', 'interval': 'uniform(20,100)', 'number': 1000, 'start': 5, 'noise': 0.1} + # + # # Stimulation mapping parameters + # params.netParams.stimTargetParams['Input1->PYR'] = { + # 'source': 'Input_1', + # 'sec':'soma', + # 'loc': 0.5, + # 'conds': {'pop':'PYR', 'cellList': range(8)}} + # params.netParams.stimTargetParams['Input3->Basket'] = { + # 'source': 'Input_3', + # 'sec':'soma', + # 'loc': 0.5, + # 'conds': {'cellType':'Basket'}} + # params.netParams.stimTargetParams['Input4->PYR3'] = { + # 'source': 'Input_4', + # 'sec':'soma', + # 'loc': 0.5, + # 'weight': '0.1+gauss(0.2,0.05)', + # 'delay': 1, + # 'conds': {'pop':'PYR3', 'cellList': [0,1,2,5,10,14,15]}} + # + # params.netParams.connParams['validStimTarget1'] = connRule # add dict with params for this pop + # self.paramsMap["stimTarget"]["stimTargetTest"].append(params) + # + # params = ParamsObj() + # # Connectivity parameters + # connRule = { + # 'preConds': {'popLabel': 'hop'}, 'postConds': {'popLabel': 'hop'}, + # 'weight': 0.0, # weight of each connection + # 'synMech': 'AMPA', # target inh synapse + # 'synsPerConn': 1, + # 'delay': 5, + # 'plasticity': { 'params': {'hebbwt': 0.01, 'antiwt':-0.01, 'wmax': 50, 'RLon': 1 ,'tauhebb': 10}}, + # } + # + # params.netParams.stimSourceParams['Input_1'] = {'type': 'IClamp', 'delay': 10, 'dur': 800, 'amp': 'uniform(0.05,0.5)'} + # params.netParams.stimSourceParams['Input_2'] = {'type': 'VClamp', 'dur':[0,1,1], 'amp':[1,1,1],'gain':1, 'rstim':0, 'tau1':1, 'tau2':1, 'i':1} + # params.netParams.stimSourceParams['Input_3'] = {'type': 'AlphaSynapse', 'onset': 'uniform(1,500)', 'tau': 5, 'gmax': 'post_ynorm', 'e': 0} + # params.netParams.stimSourceParams['Input_4'] = {'type': 'NetStim', 'interval': 'uniform(20,100)', 'number': 1000, 'start': 5, 'noise': 0.1} + # + # # Stimulation mapping parameters + # params.netParams.stimTargetParams['Input1->PYR'] = { + # 'source': 'Input_11', + # 'sec':'soma', + # 'loc': 0.5, + # 'conds': {'pop':'PYR', 'cellList': range(8)}} + # params.netParams.stimTargetParams['Input3->Basket'] = { + # 'source': 'Input_3', + # 'sec':'soma', + # 'loc': 0.5, + # 'conds': {'cellType':'Basket'}} + # params.netParams.stimTargetParams['Input4->PYR3'] = { + # 'source': 'Input_4', + # 'sec':'soma', + # 'loc': 0.5, + # 'weight': '0.1+gauss(0.2,0.05)', + # 'delay': 1, + # 'conds': {'pop':'PYR3', 'cellList': [0,1,2,5,10,14,15]}} + # + # params.netParams.connParams['invalidStimTarget1'] = connRule # add dict with params for this pop + # self.paramsMap["stimTarget"]["stimTargetTest"].append(params) def runTestsWithParams(self): diff --git a/netpyne/utils.py b/netpyne/utils.py index c4cd5f87b..6c75c2e40 100644 --- a/netpyne/utils.py +++ b/netpyne/utils.py @@ -8,9 +8,9 @@ import os, sys from numbers import Number from neuron import h -h.load_file("stdrun.hoc") - +import importlib +#h.load_file("stdrun.hoc") def getSecName (sec, dirCellSecNames = None): if dirCellSecNames is None: dirCellSecNames = {} @@ -43,7 +43,7 @@ def importCellParams (fileName, labels, values, key = None): else: removeFilePath = False moduleName = fileNameOnly.split('.py')[0] # remove .py to obtain module name - exec('import '+ moduleName + ' as tempModule') in locals() # import module dynamically + tempModule = importlib.import_module(moduleName) modulePointer = tempModule paramLabels = getattr(modulePointer, labels) # tuple with labels paramValues = getattr(modulePointer, values) # variable with paramValues @@ -173,7 +173,7 @@ def importCell (fileName, cellName, cellArgs = None, cellInstance = False): else: removeFilePath = False moduleName = fileNameOnly.split('.py')[0] # remove .py to obtain module name - exec('import ' + moduleName + ' as tempModule') in globals(), locals() # import module dynamically + tempModule = importlib.import_module(moduleName) modulePointer = tempModule if isinstance(cellArgs, dict): cell = getattr(modulePointer, cellName)(**cellArgs) # create cell using template, passing dict with args @@ -205,7 +205,7 @@ def importCell (fileName, cellName, cellArgs = None, cellInstance = False): def importCellsFromNet (netParams, fileName, labelList, condsList, cellNamesList, importSynMechs): h.initnrn() - + ''' Import cell from HOC template or python file into framework format (dict of sections, with geom, topol, mechs, syns)''' if fileName.endswith('.hoc') or fileName.endswith('.tem'): print 'Importing from .hoc network not yet supported' @@ -228,7 +228,7 @@ def importCellsFromNet (netParams, fileName, labelList, condsList, cellNamesList print '\nRunning network in %s to import cells into NetPyNE ...\n'%(fileName) from neuron import load_mechanisms load_mechanisms(filePath) - exec('import ' + moduleName + ' as tempModule') in globals(), locals() # import module dynamically + tempModule = importlib.import_module(moduleName) modulePointer = tempModule if removeFilePath: sys.path.remove(filePath) else: @@ -239,14 +239,16 @@ def importCellsFromNet (netParams, fileName, labelList, condsList, cellNamesList print '\nImporting %s from %s ...'%(cellName, fileName) exec('cell = tempModule' + '.' + cellName) #cell = getattr(modulePointer, cellName) # get cell object - secs, secLists, synMechs = getCellParams(cell) + varList = mechVarList() + origGlob = getGlobals(varList['mechs'].keys()+varList['pointps'].keys()) + secs, secLists, synMechs = getCellParams(cell, varList, origGlob) cellRule = {'conds': conds, 'secs': secs, 'secLists': secLists} netParams.addCellParams(label, cellRule) if importSynMechs: for synMech in synMechs: netParams.addSynMechParams(synMech.pop('label'), synMech) -def getCellParams(cell, varList, origGlob): +def getCellParams(cell, varList={}, origGlob={}): dirCell = dir(cell) if 'all_sec' in dirCell: @@ -489,6 +491,6 @@ def importConnFromExcel (fileName, sheetName): line = line + ",\n'weight': " + str(weight) # write prob line = line + "})" # add closing brackets line = line + '\n\n' # new line after each conn rule - sim.write(line) # write to file + f.write(line) # write to file diff --git a/netpyne/wrappers.py b/netpyne/wrappers.py index 03c1301e9..8f6bdd4c7 100644 --- a/netpyne/wrappers.py +++ b/netpyne/wrappers.py @@ -11,13 +11,13 @@ __all__.extend(['create', 'simulate', 'analyze', 'createSimulate', 'createSimulateAnalyze', 'load', 'loadSimulate', 'loadSimulateAnalyze', \ 'createExportNeuroML2','importNeuroML2SimulateAnalyze']) # wrappers -import sim ############################################################################### # Wrapper to create network ############################################################################### def create (netParams=None, simConfig=None, output=False): ''' Sequence of commands to create network ''' + import sim import __main__ as top if not netParams: netParams = top.netParams if not simConfig: simConfig = top.simConfig @@ -37,6 +37,7 @@ def create (netParams=None, simConfig=None, output=False): ############################################################################### def simulate (): ''' Sequence of commands to simulate network ''' + import sim sim.runSim() # run parallel Neuron simulation sim.gatherData() # gather spiking data and cell info from each node @@ -46,6 +47,7 @@ def simulate (): ############################################################################### def analyze (): ''' Sequence of commands to simulate network ''' + import sim sim.saveData() # run parallel Neuron simulation sim.analysis.plotData() # gather spiking data and cell info from each node @@ -55,6 +57,7 @@ def analyze (): ############################################################################### def createSimulate (netParams=None, simConfig=None, output=False): ''' Sequence of commands create, simulate and analyse network ''' + import sim (pops, cells, conns, stims, simData) = sim.create(netParams, simConfig, output=True) sim.simulate() @@ -66,6 +69,7 @@ def createSimulate (netParams=None, simConfig=None, output=False): ############################################################################### def createSimulateAnalyze (netParams=None, simConfig=None, output=False): ''' Sequence of commands create, simulate and analyse network ''' + import sim (pops, cells, conns, stims, simData) = sim.create(netParams, simConfig, output=True) sim.simulate() sim.analyze() @@ -76,12 +80,13 @@ def createSimulateAnalyze (netParams=None, simConfig=None, output=False): ############################################################################### # Wrapper to load all, ready for simulation ############################################################################### -def load (filename, simConfig=None, output=False): +def load (filename, simConfig=None, output=False, instantiate=True): ''' Sequence of commands load, simulate and analyse network ''' + import sim sim.initialize() # create network object and set cfg and net params - sim.loadAll(filename) + sim.loadAll(filename, instantiate=instantiate) if simConfig: sim.setSimCfg(simConfig) - if len(sim.net.cells) == 0: + if len(sim.net.cells) == 0 and instantiate: pops = sim.net.createPops() # instantiate network populations cells = sim.net.createCells() # instantiate network cells based on defined populations conns = sim.net.connectCells() # create connections between cells based on params @@ -98,6 +103,7 @@ def load (filename, simConfig=None, output=False): # Wrapper to load net and simulate ############################################################################### def loadSimulate (filename, simConfig=None, output=False): + import sim sim.load(filename, simConfig) sim.simulate() @@ -108,6 +114,7 @@ def loadSimulate (filename, simConfig=None, output=False): # Wrapper to load net and simulate ############################################################################### def loadSimulateAnalyze (filename, simConfig=None, output=False): + import sim sim.load(filename, simConfig) sim.simulate() sim.analyze() @@ -120,6 +127,7 @@ def loadSimulateAnalyze (filename, simConfig=None, output=False): ############################################################################### def createExportNeuroML2 (netParams=None, simConfig=None, reference=None, connections=True, stimulations=True, output=False): ''' Sequence of commands to create and export network to NeuroML2 ''' + import sim import __main__ as top if not netParams: netParams = top.netParams if not simConfig: simConfig = top.simConfig @@ -139,5 +147,6 @@ def createExportNeuroML2 (netParams=None, simConfig=None, reference=None, connec # Wrapper to import network from NeuroML2 ############################################################################### def importNeuroML2SimulateAnalyze(fileName, simConfig): + import sim - return sim.importNeuroML2(fileName, simConfig) \ No newline at end of file + return sim.importNeuroML2(fileName, simConfig, simulate=True, analyze=True) \ No newline at end of file diff --git a/sdnotes.org b/sdnotes.org index 976bbbe83..4e74f26de 100644 --- a/sdnotes.org +++ b/sdnotes.org @@ -6963,6 +6963,344 @@ sam: need to recompile Neuron and specify python3 - netParams conceptual (eg. label doesn't exist) - error check - netpyne bug +* 17Oct17 Fixing issue importing cell +** problem +Importing cell works in Penny's computer but not mine or Sergios +works = produces same outptu in NEURON/Py than in NetPyNE + +** same code (penny+me) +*** NEURON/Py code +from CA229_PFC import * +import matplotlib.pyplot as plt +from neuron import h +import numpy as np +#import json +#import time +#import pdb # For python debugging + + + + +cell = CA229() +cell.init_once() + +Stim = h.IClamp(cell.soma[2](0.5)) +Stim.dur = 300 +Stim.delay = 200 +Stim.amp = 0.2 + +t_vec = h.Vector() +t_vec.record(h._ref_t) +v_vec_soma = h.Vector() +v_vec_dend1 = h.Vector() +v_vec_dend2 = h.Vector() + + +v_vec_soma.record(cell.soma[2](0.5)._ref_v) +#v_vec_dend1.record(cell.basal[8](0.9)._ref_v) +v_vec_dend2.record(cell.basal[34](0.8)._ref_v) + + +h.tstop = 500 +h.v_init = -67.3 +h.celsius = 32 +h.run() # plot voltage vs time + + +plt.figure() # Default figsize is (8,6) +plt.plot(t_vec, v_vec_soma, label = 'Soma[0](0.5)', color = 'blue') +#plt.plot(t_vec, v_vec_dend1, label = 'Basal[8](0.9)', color = 'green') +plt.plot(t_vec, v_vec_dend2, label = 'Basal[34](0.8)', color = 'red') +plt.ylim([-80, 60]) +plt.xlim([0,500]) +plt.legend(loc = 'best') +plt.show() + +*** netpyne code +""" +params.py + +netParams is a dict containing a set of network parameters using a standardized structure + +simConfig is a dict containing a set of simulation configurations using a standardized structure + +Contributors: salvadordura@gmail.com +""" + +#import matplotlib; matplotlib.use('Agg') + +from netpyne import specs +# from netpyne import sim +# sim.version() +# sim.gitChangeset() + +netParams = specs.NetParams() # object of class NetParams to store the network parameters +simConfig = specs.SimConfig() # object of class SimConfig to store the simulation configuration + +#netParams.scaleConnWeightNetStims = 1.0 #0.5 # scale conn weight factor for NetStims +############################################################################### +# +# MPI HH TUTORIAL PARAMS +# +############################################################################### + +############################################################################### +# NETWORK PARAMETERS +############################################################################### + +# Population parameters +########## + +cellRule = netParams.importCellParams(label='PFC_full',conds={'cellType': 'PFC', 'cellModel': 'PFC_full'}, + fileName='CA229_PFC.py', cellName='MakeCA229') + +netParams.cellParams['PFC_cell_rule'] = cellRule + + +data = netParams.cellParams['PFC_cell_rule'] + +#import json +#with open('CellRule.json','w') as output: +# json.dump(data, output) +########## secName is the compartment name: soma_0, soma_1, basal_1, basal_2 +#for secName,sec in cellRule['secs'].iteritems(): sec['v_init'] = -67.59 + +netParams.popParams['PFC'] = {'cellType': 'PFC', 'cellModel': 'PFC_full', 'numCells': 1} + + +#netParams.synMechParams['AMPA'] = {'mod':'GLU'} +#netParams.synMechParams['NMDA'] = {'mod':'NMDA','Beta': 0.01} + + +netParams.stimSourceParams['Stim'] = {'type':'IClamp','delay':200, 'amp': 0.2, 'dur':300} +netParams.stimTargetParams['Stim->PFC'] = {'source': 'Stim','conds': {'pop':'PFC'}, 'sec': 'soma_2', 'loc': 0.5} +#netParams.popParams['ST1'] = {'cellModel': 'NetStim', 'numCells': 1, 'rate':50, 'noise': 0, 'start':50, 'number': 1} +#netParams.popParams['ST1_1'] = {'cellModel': 'NetStim', 'numCells': 1, 'rate': 50, 'noise': 0, 'start':50, 'number': 1} +#netParams.popParams['ST2'] = {'cellModel': 'NetStim', 'numCells': 1, 'rate':50, 'noise': 0, 'start':50, 'number': 1} +#import itertools + +#w_AMPA = list(itertools.repeat(0.00058, 20)) +#loc_AMPA = list(itertools.repeat(0.6, 20)) +###### Connectivity parameters +#netParams.connParams['ST1'] = { +# 'preConds': {'pop': 'ST1'}, +# 'postConds': {'pop': 'PFC', 'cellModel':'PFC_full'}, +# 'synMech': 'AMPA', +# 'weight': 0.00058, # weight of each connection +# 'delay': 10, +# 'synsPerConn': 10, +# 'sec': 'basal_34', +# 'loc': 0.6, +# 'threshold': -20}# +# + +#netParams.connParams['ST2'] = { +# 'preConds': {'pop': 'ST2'}, +# 'postConds': {'pop': 'PFC', 'cellModel':'PFC_full'}, +# 'synMech': 'NMDA', +# 'weight': 0.02, # weight of each connection +# 'delay': 10, +# 'synsPerConn': 2, +# 'sec': 'basal_34', +# 'loc': 0.6, +# 'threshold': -20} + + + +############################################################################### +# SIMULATION PARAMETERS +############################################################################### + +# Simulation parameters +#simConfig.hParams = {'celsius': 6} +simConfig.duration = 500 # Duration of the simulation, in ms +simConfig.dt = 0.025 # Internal integration timestep to use +simConfig.seeds = {'conn': 1, 'stim': 1, 'loc': 1} # Seeds for randomizers (connectivity, input stimulation and cell locations) +simConfig.createNEURONObj = 1 # create HOC objects when instantiating network +simConfig.createPyStruct = 1 # create Python structure (simulator-independent) when instantiating network +simConfig.verbose = False # show detailed messages +simConfig.hParams = {'celsius': 32, 'v_init': -67.3} + +#'celsius': 32, +# Recording +simConfig.recordCells = ['all'] # which cells to record from +simConfig.recordTraces = {'V_soma':{'sec':'soma_2','loc':0.5,'var':'v'}} +simConfig.recordTraces['Bdend1'] = {'sec':'basal_34', 'loc': 0.8, 'var': 'v'} +simConfig.recordTraces['Bdend2'] = {'sec':'basal_34', 'loc': 0.5, 'var': 'v'} +simConfig.recordTraces['Bdend3'] = {'sec':'basal_34', 'loc': 0.3, 'var': 'v'} +#simConfig.recordTraces['Bdend'] = {'sec':'basal_34', 'loc': 0.8, 'var': 'v'} +simConfig.recordStim = False # record spikes of cell stims +simConfig.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc) + +# Saving +simConfig.filename = 'HHTut' # Set file output name +#simConfig.Label = 'sim1' +simConfig.saveFileStep = 1000 # step size in ms to save data to disk +simConfig.savePickle = False # Whether or not to write spikes etc. to a .mat file +simConfig.recordStim = True +simConfig.saveJson = True # save to json file + +# Analysis and plotting +simConfig.analysis['plotRaster'] = False # Plot raster +simConfig.analysis['plotTraces'] = {'include': [0], 'overlay': True, 'saveFig': True, 'showFig': False} # Plot raster +simConfig.analysis['plot2Dnet'] = False # Plot 2D net cells and connections + +from netpyne import sim +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) + +import pylab; pylab.show() + +** same/similar NEURON version +penny: NEURON -- VERSION 7.5 master (8c17845) 2017-06-22 +me: NEURON -- VERSION 7.5 master (0388d94) 2017-08-09 + +** python version +penny: Python 2.7.13 |Continuum Analytics, Inc.| (default, Dec 20 2016, 23:05:08) +me: Python 2.7.13 |Anaconda custom (x86_64)| (default, Dec 20 2016, 23:05:08) + +** netpyne version! +difference in netpyne: sim.replaceDictODict() +- due to an obscure bug resulting from using `isinstance()` instead of `type()` (isinstance takes into account inherited +classes, so was applied to wrong objects and messed up imported structures) + +* 17Oct18 Implementing HNN's dipole +** chat with sam +samn [8:58 AM] +looks like two components dipole.mod (non point process with pointer) dipole_pp.mod (point process) + + +salvadord [11:48 AM] +not familiar with non-point process with pointer — any doc/notes on that ? + + +samn [11:49 AM] not much - stephanie developed that with michael hines a long time ago and then shane lee translated it to +python with michael's help + + +[11:49] +maybe i should email steph/michael and cc you + + +[11:49] +dipole_insert in cell.py has a few comments + + +salvadord [11:50 AM] +I’ll check the code and if can’t figure out will let u know + + +samn [11:50 AM] +mainly what dipole needs is the axial resistance and voltage in different locations so that it can calculate the axial current flow + + +salvadord [11:50 AM] +ic + + +samn [11:51 AM] +wonder if mh could add something for getting axial current in an easier way; similar to new feature of transmembrane current + +* 17Oct18 Automating conversion from py2 to py3 +** steps required and which can be included in py2 +*** 2to3 +convert everything, including examples and docs +*** DONE - replace global import sim with local in each func +- can probably be done now in py2 +*** DONE fix import neuromlFuncs and analysis +- use py3 format +from . import x +import .x +*** DONE id32() encode utf-8 +- use in py2 (error?) +return int(hashlib.md5(obj.encode('utf-8')).hexdigest()[0:8],16) +*** DONE replace popen2 with Popen in batch.py +- not sure if Popen available in py2? + +py2: output, input = popen2('qsub') # Open a pipe to the qsub command. + +py3: proc = Popen(command.split(' '), stdin=PIPE, stdout=PIPE) # Open a pipe to the qsub command. + (output, input) = (proc.stdin, proc.stdout) +*** DONE replaced exec to import with importlib.import_module +- test in py2 - looks like more elegant solution + + # tempModule=None + # exec(('import '+ moduleName + ' as tempModule'), locals()) # import module dynamically + import importlib + tempModule = importlib.import_module(moduleName) + +*** DONE replaced x.setattr(...) with setattr(x,...) +- 4 occur in cell.py - more stable, use in py2 + + +** create script to convert py2 to 3 +# py2to3.py - convert netpyne py2 to py3 +from subprocess import call + +py2_root = '../netpyne_repo' +folders = ['netpyne', 'doc', 'examples'] +files = ['CHANGES.md', 'README.md', 'sdnotes.org', '.gitignore'] + +for file in files: + call(('rm %s'%(file)).split(' ')) + call(('cp %s/%s .'%(py2_root, file)).split(' ')) + +for folder in folders: + call(('rm -r %s'%(folder)).split(' ')) + call(('2to3 --output-dir=./%s -W -n %s/%s'%(folder, py2_root, folder)).split(' ')) + +** error checking error in py3 +Traceback (most recent call last): + File "/usr/site/python3/netpyne/netpyne/tests/tests.py", line 2849, in execRunTests + self.testTypeObj.testIsBoolean(paramName) + File "/usr/site/python3/netpyne/netpyne/tests/tests.py", line 257, in testIsBoolean + assert (isinstance (val,bool) or val in [0,1]), "Value specified is " + str(val) + "." +AssertionError: Value specified is 0.1. + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "init.py", line 8, in + sim.createSimulateAnalyze(simConfig = cfg, netParams = netParams) + File "/usr/site/python3/netpyne/netpyne/wrappers.py", line 73, in createSimulateAnalyze + (pops, cells, conns, stims, simData) = sim.create(netParams, simConfig, output=True) + File "/usr/site/python3/netpyne/netpyne/wrappers.py", line 25, in create + sim.initialize(netParams, simConfig) # create network object and set cfg and net params + File "/usr/site/python3/netpyne/netpyne/simFuncs.py", line 72, in initialize + simTestObj.runTests() + File "/usr/site/python3/netpyne/netpyne/tests/tests.py", line 1520, in runTests + self.runSimConfigTests() # load simConfig tests + File "/usr/site/python3/netpyne/netpyne/tests/tests.py", line 2465, in runSimConfigTests + self.execRunTests(simConfigTestObj, simConfigParams) + File "/usr/site/python3/netpyne/netpyne/tests/tests.py", line 2857, in execRunTests + print(str(testObj.errorMessageLevel[testIndex]) + ": " + str(testObj.messageText[testIndex]) + str(e[0])) +TypeError: 'AssertionError' object is not subscriptable + +* 17Nov01 Issue reproducing PT cell results +** problem +- when run vs load from saved get different result +- also in old vs new version of netpyne where the way to convert netParams to dict changed +- saved netParams and net in json are identical in good vs bad version +** possible explanations/solutions +- order of mechs saved (order of instnatiation affects since dependent global variables?) +- global variables not saved properly + +** solved +was due to using getattr() instead of x.get() when setting global variables + +* 17Nov22 MPI error during gathering +** forum link +https://www.neuron.yale.edu/phpBB/viewtopic.php?f=45&t=3770&sid=7945c6bfcb495b22a4b3e72c0850d885 +** testing +tried on zn +- 24 nodes gave error +- trying with: +simConfig.gatherOnlySimData = True +simConfig.saveCellSecs = False +simConfig.saveCellConns = False + +only a couple traces actually recorded so small simData -- maybe bug because empty? +WORKED OK! + * Netpyne Models/Users ** Github examples folder ** Documentation tutorials @@ -6993,15 +7331,18 @@ Ohio Wesleyan University ** Learning, dendritic computations (Birgit Kriener, Einevoll lab, University of Oslo) ** Thalamocortical epilespy (Andrew Knox, Cincinatti) ** Cortex, Schizophrenia (Cristoph Metzner, Hertfordshire) + ** Spinal cord circuits (Vittorio Caggiano, IBM Watson) ** Convert hoc models (Ben Latimer, Missouri) ** Thalamocortical stimulation (Brenyn Jungmann, Nahir lab,Missouri) ** Fear circuits (Aliyah Taylor, Princeton/Missouri) ** Macaque M1 TMS/tDCS (Aman Aberra, Duke) -** CA1/CA3 (Mariane Bezaire, Boston University) +** CA1/CA3 (Mariane Bezaire, Boston University; Andras Ecker, HBP) ** EEG (Sam Neymotin, Boston) +** Cerebellum (Sergio Solinas) +** Cerebellum (Stefano Masoli, Univ Pavia) ** EMAIL LIST salvadordura@gmail.com billl@neurosim.downstate.edu @@ -7034,6 +7375,8 @@ bwjmbb@mail.missouri.edu ali.taylor331@gmail.com aman.aberra@duke.edu marianne.bezaire@gmail.com +smgsolinas@gmail.com +bremen@fastwebnet.it * Related tools ** NEURON