def test_retrieve():
"""Test retrieval functionality."""
raw = _generate_raw(n_chan=1, iaf=11.25)
raw.add_events(np.array([[1, 0, 255], [10, 0, 255]]))
epochs = mne.Epochs(raw, mne.find_events(raw), tmin=0.0, preload=True)
# TODO: test multiple time windows
# TODO: test items
# TODO: test different summary functions
# TODO: test scale_times
windows = dict(onset=(-50, 50))
df1 = retrieve(epochs, windows)
df2 = pd.DataFrame({'channel': {0: '0', 1: 'STI 014', 2: '0', 3: 'STI 014'}, # noqa: E501
'condition': {0: '255', 1: '255', 2: '255', 3: '255'}, # noqa: E501
'epoch': {0: 0, 1: 0, 2: 1, 3: 1},
'mean': {0: 4.4267676108015275,
1: 39.23076923076923,
2: -9.852708759493767,
3: 19.615384615384617},
'win': {0: '-50..50', 1: '-50..50', 2: '-50..50', 3: '-50..50'}, # noqa: E501
'wname': {0: 'onset', 1: 'onset', 2: 'onset', 3: 'onset'}}) # noqa: E501
assert_true(np.all(df1.eq(df2)))
tmin=-0.2,
tmax=1.2, # epochs go from -200 to +1200ms
detrend=1, # linear detrending
baseline=None, # No baselining
reject=peak_to_peak, # peak-to-peak rejections
flat=flatline, # flatline rejection
on_missing=
'ignore', # ignore missing events -- not all ratings-cond combos occu (BT: the standard is to give Error)
preload=True)
epochs.drop_bad()
# absolute threshold rejection
bad_epoch_mask = abs_threshold(epochs.pick_types(eeg=True), threshold)
epochs.drop(bad_epoch_mask, reason="absolute threshold")
df = retrieve(
epochs, wins,
items=epochs.events[:, 1]) #BT: items are in the event structure
df.to_csv(csvout)
for c in codes:
erp = epochs[c].average()
ssavg[c].append(erp)
#BT: because he kept getting errors
for c in codes_with_ratings:
try:
erp = epochs[c].average()
ssavg[c].append(erp)
except KeyError: # this combination doesn't occur
pass
baseline=None, # No baselining
reject=peak_to_peak, # peak-to-peak rejections
flat=flatline, # flatline rejection
on_missing='ignore', # ignore missing events
preload=True)
epochs[c].drop_bad()
bad_epoch_mask = abs_threshold(epochs[c].pick_types(eeg=True), threshold)
epochs[c].drop(bad_epoch_mask,reason="absolute threshold")
#epochs = mne.concatenate_epochs([epochs[c] for c in epochs])
lang_erp = epochs['language']['lang/target'].average()
music_erp = epochs["music"]['music/target'].average()
lang_minus_music = mne.combine_evoked([lang_erp,music_erp], [1, -1])
# windows of interest for statistics
# units are *milliseconds* relative to time-locking event
wins = dict(baseline=(-200,0),
P100=(50,150),
N200=(150,250),
N400=(300,500))
for c in epochs:
df = retrieve(epochs[c], wins)
df.to_csv("{}.csv".format(c))
raw,
events=events,
event_id=codes,
tmin=-0.2,
tmax=1.2, # epochs go from -200 to +1200ms
detrend=1, # linear detrending
baseline=None, # No baselining
reject=peak_to_peak, # peak-to-peak rejections
flat=flatline, # flatline rejection
preload=True)
epochs.drop_bad()
# absolute threshold rejection
bad_epoch_mask = abs_threshold(epochs.pick_types(eeg=True), threshold)
epochs.drop(bad_epoch_mask, reason="absolute threshold")
df = retrieve(epochs, wins)
df.to_csv(csvout)
for c in codes:
erp = epochs[c].average()
ssavg[c].append(erp)
# visualizing grand averages
# non-overlapping 83% CIs of the ind. measures correspond to the 95% CI
# of the difference
mne.viz.plot_compare_evokeds(ssavg, picks=[15], invert_y=True, ci=.83)
# we can also look at different combinations of conditions and the
# the resulting difference waves
longfit = mne.grand_average(ssavg['long/fit'])
longfit.plot_joint()
preload=True,
flat=flatline,
reject=peak_to_peak,
reject_by_annotation=
False, #If True (default), epochs overlapping with segments whose description begins with 'bad' are rejected.
picks=ch_picks_epoch)
epochs = mne.epochs.combine_event_ids(
epochs, ['abs-min-yes-x', 'abs-max-yes-x'], {'abs-yes-x': 233243})
epochs = mne.epochs.combine_event_ids(epochs,
['abs-min-no-x', 'abs-max-no-x'],
{'abs-no-x': 238248})
#save epochs to file
epochs.save(evoked_out)
#retrieve time windows of interest
df = retrieve(epochs, wins, items=epochs.events[:, 1])
df.to_csv(csvout)
else:
print('epoch file "{}" exists, reading...'.format(evoked_out))
try:
epochs = mne.read_epochs(evoked_out)
print('Successfully read the file, yay!')
except FileNotFoundError:
raise FileNotFoundError(
"I can't find this damn file {}".format(evoked_out))
except:
raise IOError("Problem reading the file: {}".format(evoked_out))
print(epochs)
for c in newcodes:
erp = epochs[c].average()