def test_add_epoch(epoch_df):
result = add_epoch(epoch_df, 'parent', 'child_a')
assert len(result) == (len(epoch_df) + 2)
m = result['name'] == 'parent_child_a'
assert (m.sum() == 2)
expected = [[1, 2], [30, 31]]
values = result.loc[m, ['start', 'end']].values
assert np.array_equal(expected, values)
def reformat_epochs(epochs, target_id):
new_epochs = []
new_epochs.append(epochs)
fg_samples = extract_sample_ids(epochs, 'fg')
bg_samples = extract_sample_ids(epochs, 'bg')
new_epochs.append(fg_samples.copy())
new_epochs.append(bg_samples.copy())
fg_samples['name'] = fg_samples['name'].apply(lambda x: f'fg_{x}')
bg_samples['name'] = bg_samples['name'].apply(lambda x: f'bg_{x}')
new_epochs.append(fg_samples.copy())
new_epochs.append(bg_samples.copy())
samples = pd.concat(new_epochs, ignore_index=True)
samples.sort_values('start', inplace=True)
target_id.epochs = samples
x = target_id.as_continuous()[0]
m = np.isfinite(x) & (x != 0)
targets = np.unique(x[m])
# Tag the epochs as target and whether it's current or not.
for i, target in enumerate(targets):
target_name = '{:02d}'.format(int(target))
m = samples['name'].str.match(target_name)
e = samples.loc[m].copy()
e['name'] = 'target_{}'.format(i)
new_epochs.append(e)
# extract all occurances of the target across all trials. find out what
# the actual (intended) target was for each particular trial.
x = target_id.extract_epoch(target_name)[:, 0, 0]
m = x == target
c = e.loc[m].copy()
c['name'] = 'current_target'
new_epochs.append(c)
c = e.loc[~m].copy()
c['name'] = 'other_target'
new_epochs.append(c)
repeating = extract_repeating(epochs)
new_epochs.append(repeating)
random = extract_random(epochs)
new_epochs.append(random)
stream_type = extract_stream_type(epochs)
new_epochs.append(stream_type)
epochs = pd.concat(new_epochs, ignore_index=True)
epochs.drop_duplicates(inplace=True)
epochs.sort_values('start', inplace=True)
# This fixes a bug in BAPHY->NEMS conversion of timestamps (not all trials
# from the same sequence are identical length and may be off by 0.001).
# This should be fine for RDT data because all relevant epochs are
# in units of 0.5, 0.15, 0.3, 0.25 sec.
epochs['start'] = epochs['start'].round(2)
epochs['end'] = epochs['end'].round(2)
epochs = add_epoch(epochs, 'random', 'single')
epochs = add_epoch(epochs, 'repeating', 'single')
epochs = add_epoch(epochs, 'random', 'dual')
epochs = add_epoch(epochs, 'repeating', 'dual')
for i in range(len(targets)):
target_name = f'target_{i}'
epochs = add_epoch(epochs, target_name, 'random_single')
epochs = add_epoch(epochs, target_name, 'repeating_single')
epochs = add_epoch(epochs, target_name, 'random_dual')
epochs = add_epoch(epochs, target_name, 'repeating_dual')
return epochs