MRG, FIX: Check reject_by_annotation in ica.plot_properties

doc/changes/latest.inc

@@ -157,6 +157,10 @@ Changelog
 
 - Speed up :func:`mne.stats.summarize_clusters_stc` using Numba by `Yu-Han Luo`_
 
+- Add ``reject_by_annotation=True`` to :func:`mne.make_fixed_length_epochs` to reject bad epochs based on annotation by `Yu-Han Luo`_
+
+- Add ``reject_by_annotation=True`` to :meth:`mne.preprocessing.ICA.plot_properties` to reject bad data segments based on annotation by `Yu-Han Luo`_
+
 Bug
 ~~~
 
@@ -291,6 +295,8 @@ Bug
 
 - Fix bug in :meth:`mne.preprocessing.ICA.plot_properties` where time series plot doesn't start at the proper tmin by `Teon Brooks`_
 
+- Fix bug with :meth:`mne.preprocessing.ICA.plot_properties` where a :class:`mne.io.Raw` object with annotations would lead to an error by `Yu-Han Luo`_
+
 API
 ~~~
 

mne/cov.py

@@ -425,10 +425,7 @@ def compute_raw_covariance(raw, tmin=0, tmax=None, tstep=0.2, reject=None,
         method equals 'auto' or is a list of str. Defaults to False.
 
         .. versionadded:: 0.12
-    reject_by_annotation : bool
-        Whether to reject based on annotations. If True (default), epochs
-        overlapping with segments whose description begins with ``'bad'`` are
-        rejected. If False, no rejection based on annotations is performed.
+    %(reject_by_annotation_epochs)s
 
         .. versionadded:: 0.14
     %(rank_None)s

mne/epochs.py

@@ -2002,10 +2002,7 @@ class Epochs(BaseEpochs):
         warn, if 'ignore' it will proceed silently. Note.
         If none of the event ids are found in the data, an error will be
         automatically generated irrespective of this parameter.
-    reject_by_annotation : bool
-        Whether to reject based on annotations. If True (default), epochs
-        overlapping with segments whose description begins with ``'bad'`` are
-        rejected. If False, no rejection based on annotations is performed.
+    %(reject_by_annotation_epochs)s
     metadata : instance of pandas.DataFrame | None
         A :class:`pandas.DataFrame` specifying metadata about each epoch.
         If given, ``len(metadata)`` must equal ``len(events)``. The DataFrame
@@ -3277,8 +3274,8 @@ def average_movements(epochs, head_pos=None, orig_sfreq=None, picks=None,
 
 
 @verbose
-def make_fixed_length_epochs(raw, duration=1.,
-                             preload=False, verbose=None):
+def make_fixed_length_epochs(raw, duration=1., preload=False,
+                             reject_by_annotation=True, verbose=None):
     """Divide continuous raw data into equal-sized consecutive epochs.
 
     Parameters
@@ -3288,6 +3285,9 @@ def make_fixed_length_epochs(raw, duration=1.,
     duration : float
         Duration of each epoch in seconds. Defaults to 1.
     %(preload)s
+    %(reject_by_annotation_epochs)s
+
+        .. versionadded:: 0.21.0
     %(verbose)s
 
     Returns
@@ -3301,6 +3301,6 @@ def make_fixed_length_epochs(raw, duration=1.,
     """
     events = make_fixed_length_events(raw, 1, duration=duration)
     delta = 1. / raw.info['sfreq']
-    return Epochs(raw, events, event_id=[1], tmin=0.,
-                  tmax=duration - delta,
-                  verbose=verbose, baseline=None)
+    return Epochs(raw, events, event_id=[1], tmin=0, tmax=duration - delta,
+                  baseline=None, preload=preload,
+                  reject_by_annotation=reject_by_annotation, verbose=verbose)

mne/preprocessing/ecg.py

@@ -165,8 +165,7 @@ def find_ecg_events(raw, event_id=999, ch_name=None, tstart=0.0,
     return_ecg : bool
         Return ecg channel if synthesized. Defaults to False. If True and
         and ecg exists this will yield None.
-    reject_by_annotation : bool
-        Whether to omit data that is annotated as bad.
+    %(reject_by_annotation_all)s
 
         .. versionadded:: 0.18
     %(verbose)s
@@ -329,10 +328,7 @@ def create_ecg_epochs(raw, ch_name=None, event_id=999, picks=None, tmin=-0.5,
         When ECG is synthetically created (after picking), should it be added
         to the epochs? Must be False when synthetic channel is not used.
         Defaults to False.
-    reject_by_annotation : bool
-        Whether to reject based on annotations. If True (default), epochs
-        overlapping with segments whose description begins with ``'bad'`` are
-        rejected. If False, no rejection based on annotations is performed.
+    %(reject_by_annotation_epochs)s
 
         .. versionadded:: 0.14.0
     %(verbose)s

mne/preprocessing/eog.py

@@ -210,11 +210,7 @@ def create_eog_epochs(raw, ch_name=None, event_id=998, picks=None, tmin=-0.5,
         interval is used. If None, no correction is applied.
     preload : bool
         Preload epochs or not.
-    reject_by_annotation : bool
-        Whether to reject based on annotations. If True (default), segments
-        whose description begins with ``'bad'`` are not used for finding
-        artifacts and epochs overlapping with them are rejected. If False, no
-        rejection based on annotations is performed.
+    %(reject_by_annotation_epochs)s
 
         .. versionadded:: 0.14.0
     thresh : float

mne/preprocessing/ica.py

@@ -474,11 +474,7 @@ def fit(self, inst, picks=None, start=None, stop=None, decim=None,
         tstep : float
             Length of data chunks for artifact rejection in seconds.
             It only applies if ``inst`` is of type Raw.
-        reject_by_annotation : bool
-            Whether to omit bad segments from the data before fitting. If True,
-            annotated segments with a description that starts with 'bad' are
-            omitted. Has no effect if ``inst`` is an Epochs or Evoked object.
-            Defaults to True.
+        %(reject_by_annotation_raw)s
 
             .. versionadded:: 0.14.0
         %(verbose_meth)s
@@ -986,8 +982,7 @@ def score_sources(self, inst, target=None, score_func='pearsonr',
             Low pass frequency.
         h_freq : float
             High pass frequency.
-        reject_by_annotation : bool
-            If True, data annotated as bad will be omitted. Defaults to True.
+        %(reject_by_annotation_all)s
 
             .. versionadded:: 0.14.0
         %(verbose_meth)s
@@ -1196,8 +1191,7 @@ def find_bads_ecg(self, inst, ch_name=None, threshold=None, start=None,
             threshold components will be masked and the z-score will
             be recomputed until no supra-threshold component remains.
             Defaults to 'ctps'.
-        reject_by_annotation : bool
-            If True, data annotated as bad will be omitted. Defaults to True.
+        %(reject_by_annotation_all)s
 
             .. versionadded:: 0.14.0
         measure : 'zscore' | 'correlation'
@@ -1316,8 +1310,7 @@ def find_bads_ref(self, inst, ch_name=None, threshold=3.0, start=None,
             Low pass frequency.
         h_freq : float
             High pass frequency.
-        reject_by_annotation : bool
-            If True, data annotated as bad will be omitted. Defaults to True.
+        %(reject_by_annotation_all)s
         method : 'together' | 'separate'
             Method to use to identify reference channel related components.
             Defaults to ``'together'``. See notes.
@@ -1446,8 +1439,7 @@ def find_bads_eog(self, inst, ch_name=None, threshold=3.0, start=None,
             Low pass frequency.
         h_freq : float
             High pass frequency.
-        reject_by_annotation : bool
-            If True, data annotated as bad will be omitted. Defaults to True.
+        %(reject_by_annotation_all)s
 
             .. versionadded:: 0.14.0
         measure : 'zscore' | 'correlation'
@@ -1749,12 +1741,14 @@ def plot_components(self, picks=None, ch_type=None, res=64,
     @copy_function_doc_to_method_doc(plot_ica_properties)
     def plot_properties(self, inst, picks=None, axes=None, dB=True,
                         plot_std=True, topomap_args=None, image_args=None,
-                        psd_args=None, figsize=None, show=True, reject='auto'):
+                        psd_args=None, figsize=None, show=True, reject='auto',
+                        reject_by_annotation=True):
         return plot_ica_properties(self, inst, picks=picks, axes=axes,
                                    dB=dB, plot_std=plot_std,
                                    topomap_args=topomap_args,
                                    image_args=image_args, psd_args=psd_args,
-                                   figsize=figsize, show=show, reject=reject)
+                                   figsize=figsize, show=show, reject=reject,
+                                   reject_by_annotation=reject_by_annotation)
 
     @copy_function_doc_to_method_doc(plot_ica_sources)
     def plot_sources(self, inst, picks=None, start=None,

mne/tests/test_epochs.py

@@ -22,7 +22,7 @@
 from mne import (Epochs, Annotations, read_events, pick_events, read_epochs,
                  equalize_channels, pick_types, pick_channels, read_evokeds,
                  write_evokeds, create_info, make_fixed_length_events,
-                 combine_evoked)
+                 make_fixed_length_epochs, combine_evoked)
 from mne.baseline import rescale
 from mne.fixes import rfft, rfftfreq
 from mne.preprocessing import maxwell_filter
@@ -3019,4 +3019,19 @@ def test_pick_types_reject_flat_keys():
     assert sorted(epochs.flat.keys()) == ['grad', 'mag']
 
 
+@testing.requires_testing_data
+def test_make_fixed_length_epochs():
+    """Test dividing raw data into equal-sized consecutive epochs."""
+    raw = read_raw_fif(raw_fname, preload=True)
+    epochs = make_fixed_length_epochs(raw, duration=1, preload=True)
+    # Test Raw with annotations
+    annot = Annotations(onset=[0], duration=[5], description=['BAD'])
+    raw_annot = raw.set_annotations(annot)
+    epochs_annot = make_fixed_length_epochs(raw_annot, duration=1.0,
+                                            preload=True)
+    assert len(epochs) > 10
+    assert len(epochs_annot) > 10
+    assert len(epochs) > len(epochs_annot)
+
+
 run_tests_if_main()

mne/time_frequency/psd.py

@@ -206,11 +206,7 @@ def psd_welch(inst, fmin=0, fmax=np.inf, tmin=None, tmax=None, n_fft=256,
     proj : bool
         Apply SSP projection vectors. If inst is ndarray this is not used.
     %(n_jobs)s
-    reject_by_annotation : bool
-        Whether to omit bad segments from the data while computing the
-        PSD. If True, annotated segments with a description that starts
-        with 'bad' are omitted. Has no effect if ``inst`` is an Epochs or
-        Evoked object. Defaults to True.
+    %(reject_by_annotation_raw)s
 
         .. versionadded:: 0.15.0
     average : str | None

mne/utils/docs.py

@@ -72,6 +72,24 @@
     End time of the raw data to use in seconds (cannot exceed data duration).
 """
 
+
+# Reject by annotation
+docdict['reject_by_annotation_all'] = """
+reject_by_annotation : bool
+    Whether to omit bad segments from the data before fitting. If ``True``
+    (default), annotated segments whose description begins with ``'bad'`` are
+    omitted. If ``False``, no rejection based on annotations is performed.
+"""
+docdict['reject_by_annotation_epochs'] = """
+reject_by_annotation : bool
+    Whether to reject based on annotations. If ``True`` (default), epochs
+    overlapping with segments whose description begins with ``'bad'`` are
+    rejected. If ``False``, no rejection based on annotations is performed.
+"""
+docdict['reject_by_annotation_raw'] = docdict['reject_by_annotation_all'] + """
+    Has no effect if ``inst`` is not a :class:`mne.io.Raw` object."""
+
+
 # General plotting
 docdict["show"] = """
 show : bool

mne/viz/ica.py

@@ -258,7 +258,8 @@ def _get_psd_label_and_std(this_psd, dB, ica, num_std):
 @fill_doc
 def plot_ica_properties(ica, inst, picks=None, axes=None, dB=True,
                         plot_std=True, topomap_args=None, image_args=None,
-                        psd_args=None, figsize=None, show=True, reject='auto'):
+                        psd_args=None, figsize=None, show=True, reject='auto',
+                        reject_by_annotation=True):
     """Display component properties.
 
     Properties include the topography, epochs image, ERP/ERF, power
@@ -309,6 +310,9 @@ def plot_ica_properties(ica, inst, picks=None, axes=None, dB=True,
         If None, no rejection is applied. The default is 'auto',
         which applies the rejection parameters used when fitting
         the ICA object.
+    %(reject_by_annotation_raw)s
+
+        .. versionadded:: 0.21.0
 
     Returns
     -------
@@ -390,12 +394,18 @@ def plot_ica_properties(ica, inst, picks=None, axes=None, dB=True,
 
         # break up continuous signal into segments
         from ..epochs import make_fixed_length_epochs
-        inst_rejected = make_fixed_length_epochs(inst_rejected,
-                                                 duration=2.,
-                                                 verbose=False,
-                                                 preload=True)
-        inst = make_fixed_length_epochs(inst, duration=2., verbose=False,
-                                        preload=True)
+        inst_rejected = make_fixed_length_epochs(
+            inst_rejected,
+            duration=2,
+            preload=True,
+            reject_by_annotation=reject_by_annotation,
+            verbose=False)
+        inst = make_fixed_length_epochs(
+            inst,
+            duration=2,
+            preload=True,
+            reject_by_annotation=reject_by_annotation,
+            verbose=False)
         kind = "Segment"
     else:
         drop_inds = None

mne/viz/raw.py

@@ -604,11 +604,7 @@ def plot_raw_psd(raw, fmin=0, fmax=np.inf, tmin=None, tmax=None, proj=False,
     n_overlap : int
         The number of points of overlap between blocks. The default value
         is 0 (no overlap).
-    reject_by_annotation : bool
-        Whether to omit bad segments from the data while computing the
-        PSD. If True, annotated segments with a description that starts
-        with 'bad' are omitted. Has no effect if ``inst`` is an Epochs or
-        Evoked object. Defaults to True.
+    %(reject_by_annotation_raw)s
     %(plot_psd_picks_good_data)s
     ax : instance of Axes | None
         Axes to plot into. If None, axes will be created.

mne/viz/tests/test_ica.py

@@ -181,6 +181,25 @@ def test_plot_ica_properties():
         ica.plot_properties(epochs)
     plt.close('all')
 
+    # Test Raw with annotations
+    annot = Annotations(onset=[1], duration=[1], description=['BAD'])
+    raw_annot = _get_raw(preload=True).set_annotations(annot)
+
+    with pytest.warns(UserWarning, match='did not converge'):
+        ica.fit(raw_annot)
+    # drop bad data segments
+    ica.plot_properties(raw_annot)
+    # don't drop
+    ica.plot_properties(raw_annot, reject_by_annotation=False)
+    # fitting with bad data
+    with pytest.warns(UserWarning, match='did not converge'):
+        ica.fit(raw_annot, reject_by_annotation=False)
+    # drop bad data when plotting
+    ica.plot_properties(raw_annot)
+    # don't drop bad data when plotting
+    ica.plot_properties(raw_annot, reject_by_annotation=False)
+    plt.close('all')
+
 
 @requires_sklearn
 def test_plot_ica_sources():