def test_drop_epochs():
"""Test dropping of epochs.
"""
raw, events, picks = _get_data()
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
events1 = events[events[:, 2] == event_id]
# Bound checks
assert_raises(IndexError, epochs.drop_epochs, [len(epochs.events)])
assert_raises(IndexError, epochs.drop_epochs, [-1])
assert_raises(ValueError, epochs.drop_epochs, [[1, 2], [3, 4]])
# Test selection attribute
assert_array_equal(epochs.selection,
np.where(events[:, 2] == event_id)[0])
assert_equal(len(epochs.drop_log), len(events))
assert_true(all(epochs.drop_log[k] == ['IGNORED']
for k in set(range(len(events))) - set(epochs.selection)))
selection = epochs.selection.copy()
n_events = len(epochs.events)
epochs.drop_epochs([2, 4], reason='d')
assert_equal(epochs.drop_log_stats(), 2. / n_events * 100)
assert_equal(len(epochs.drop_log), len(events))
assert_equal([epochs.drop_log[k]
for k in selection[[2, 4]]], [['d'], ['d']])
assert_array_equal(events[epochs.selection], events1[[0, 1, 3, 5, 6]])
assert_array_equal(events[epochs[3:].selection], events1[[5, 6]])
assert_array_equal(events[epochs['1'].selection], events1[[0, 1, 3, 5, 6]])
def test_drop_epochs():
"""Test dropping of epochs.
"""
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
events1 = events[events[:, 2] == event_id]
# Bound checks
assert_raises(IndexError, epochs.drop_epochs, [len(epochs.events)])
assert_raises(IndexError, epochs.drop_epochs, [-1])
assert_raises(ValueError, epochs.drop_epochs, [[1, 2], [3, 4]])
# Test selection attribute
assert_array_equal(epochs.selection,
np.where(events[:, 2] == event_id)[0])
assert_equal(len(epochs.drop_log), len(events))
assert_true(all(epochs.drop_log[k] == ['IGNORED']
for k in set(range(len(events))) - set(epochs.selection)))
selection = epochs.selection.copy()
n_events = len(epochs.events)
epochs.drop_epochs([2, 4], reason='d')
assert_equal(epochs.drop_log_stats(), 2. / n_events * 100)
assert_equal(len(epochs.drop_log), len(events))
assert_equal([epochs.drop_log[k]
for k in selection[[2, 4]]], [['d'], ['d']])
assert_array_equal(events[epochs.selection], events1[[0, 1, 3, 5, 6]])
assert_array_equal(events[epochs[3:].selection], events1[[5, 6]])
assert_array_equal(events[epochs['1'].selection], events1[[0, 1, 3, 5, 6]])
def update_log(log_file_path: str, epochs: Epochs, notes: str) -> pd.DataFrame:
"""
Documents the changes during preprocessing for an Epochs object.
Custom description can be added with the notes argument.
Parameters
----------
log_file_path
epochs
notes
Returns
----------
log data
"""
fid = epochs.info["temp"]
dropped_epochs_marker = ["FASTER", "USER", "AUTOREJECT"]
n_bad_epochs = len(
[drop for drop in epochs.drop_log if np.isin(dropped_epochs_marker, drop).any()]
)
stimuli = list(epochs.event_id.keys())
n_epochs_per_stimuli = [
(", ").join([f"{ind}: {len(epochs[ind])}" for ind in stimuli])
]
log = pd.DataFrame(
{
"fid": [fid],
"highpass": [epochs.info["highpass"]],
"lowpass": [epochs.info["lowpass"]],
"n_components": [np.NaN],
"n_bad_epochs": [n_bad_epochs],
"total_drop_percentage": [round(epochs.drop_log_stats(), 2)],
"n_epochs_per_stimuli": n_epochs_per_stimuli,
"stimuli": [list(epochs.event_id.keys())],
"t_min": [epochs.tmin],
"t_max": [epochs.tmax],
"n_interpolated": [np.NaN],
"average_ref_applied": [bool(epochs.info["custom_ref_applied"])],
"baseline": [epochs.baseline if epochs.baseline else np.NaN],
"notes": [notes],
"date_of_update": [datetime.utcnow().isoformat()],
"author": [settings["log"]["author"]],
}
)
description = epochs.info["description"]
if description is None:
logger.warning(
'Failed to update parameters from epochs.info["description"], \n'
"Returning log object, consider adding description field manually and rerunning this function.\n"
'Formatting should match this format: "n_components: 1, interpolated: AF7, Fp2, P3, CP5, Fp1"'
)
return log
if "n_components" in description:
n_components = [x for x in description.split(",")[0] if x.isdigit()][0]
log["n_components"].update(int(n_components))
if "interpolated" in description:
description_plain = description.replace(" ", "").replace(":", "")
interpolated_channels_str = description_plain.split("interpolated")[1]
n_interpolated = len(interpolated_channels_str.split(","))
log["n_interpolated"].update(n_interpolated)
log["interpolated"] = interpolated_channels_str
author_clean = re.sub("\W+", "", settings["log"]["author"])
log_file_name = f"{author_clean}_log.csv"
if os.path.isfile(os.path.join(log_file_path, log_file_name)):
log.to_csv(
os.path.join(log_file_path, log_file_name),
mode="a",
index=False,
header=False,
)
else:
log.to_csv(os.path.join(log_file_path, log_file_name), index=False)
return log
