def test_boxy_filetypes(fname):
"""Test reading parsed and unparsed BOXY data files."""
# BOXY data files can be saved in two formats (parsed and unparsed) which
# mostly determines how the data is organised.
# For parsed files, each row is a single timepoint and all
# source/detector combinations are represented as columns.
# For unparsed files, each row is a source and each group of n rows
# represents a timepoint. For example, if there are ten sources in the raw
# data then the first ten rows represent the ten sources at timepoint 1
# while the next set of ten rows are the ten sources at timepoint 2.
# Detectors are represented as columns.
# Since p_pod is designed to only load unparsed files, we will first
# compare MNE and p_pod loaded data from an unparsed data file. If those
# files are comparable, then we will compare the MNE loaded data between
# parsed and unparsed files.
raw = read_raw_boxy(fname, verbose=True)
assert raw.info['sfreq'] == 79.4722
_assert_ppod(raw, p_pod_0_84)
# Grab our different data types.
unp_dc = raw.copy().pick('fnirs_cw_amplitude')
unp_ac = raw.copy().pick('fnirs_fd_ac_amplitude')
unp_ph = raw.copy().pick('fnirs_fd_phase')
# Check channel names.
chans = [
'S1_D1', 'S2_D1', 'S3_D1', 'S4_D1', 'S5_D1', 'S6_D1', 'S7_D1', 'S8_D1'
]
assert unp_dc.info['ch_names'] == [i_chan + ' ' + 'DC' for i_chan in chans]
assert unp_ac.info['ch_names'] == [i_chan + ' ' + 'AC' for i_chan in chans]
assert unp_ph.info['ch_names'] == [i_chan + ' ' + 'Ph' for i_chan in chans]
def test_boxy_digaux(fname):
"""Test reading BOXY files and generating annotations from digaux."""
srate = 79.4722
raw = read_raw_boxy(fname, verbose=True)
# Grab our different data types.
picks_dc = pick_types(raw.info, fnirs='fnirs_cw_amplitude')
picks_ac = pick_types(raw.info, fnirs='fnirs_fd_ac_amplitude')
picks_ph = pick_types(raw.info, fnirs='fnirs_fd_phase')
assert_array_equal(picks_dc, np.arange(0, 8) * 3 + 0)
assert_array_equal(picks_ac, np.arange(0, 8) * 3 + 1)
assert_array_equal(picks_ph, np.arange(0, 8) * 3 + 2)
# Check that our event order matches what we expect.
event_list = ['1.0', '2.0', '3.0', '4.0', '5.0']
assert_array_equal(raw.annotations.description, event_list)
# Check that our event timings are what we expect.
event_onset = [
i_time * (1.0 / srate) for i_time in [105, 185, 265, 344, 424]
]
assert_allclose(raw.annotations.onset, event_onset, atol=1e-6)
# Now let's compare parsed and unparsed events to p_pod loaded digaux.
# Load our p_pod data.
ppod_data = spio.loadmat(p_pod_0_84)
ppod_digaux = np.transpose(ppod_data['digaux'])[0]
# Now let's get our triggers from the p_pod digaux.
# We only want the first instance of each trigger.
prev_mrk = 0
mrk_idx = list()
duration = list()
tmp_dur = 0
for i_num, i_mrk in enumerate(ppod_digaux):
if i_mrk != 0 and i_mrk != prev_mrk:
mrk_idx.append(i_num)
if i_mrk != 0 and i_mrk == prev_mrk:
tmp_dur += 1
if i_mrk == 0 and i_mrk != prev_mrk:
duration.append((tmp_dur + 1) * (1.0 / srate))
tmp_dur = 0
prev_mrk = i_mrk
onset = np.asarray([i_mrk * (1.0 / srate) for i_mrk in mrk_idx])
description = np.asarray(
[str(float(i_mrk)) for i_mrk in ppod_digaux[mrk_idx]])
assert_array_equal(raw.annotations.description, description)
assert_allclose(raw.annotations.onset, onset, atol=1e-6)
def test_boxy_load():
"""Test reading BOXY files."""
raw = read_raw_boxy(boxy_0_40, verbose=True)
assert raw.info['sfreq'] == 62.5
_assert_ppod(raw, p_pod_0_40)
# Grab our different data types.
mne_ph = raw.copy().pick(picks='fnirs_fd_phase')
mne_dc = raw.copy().pick(picks='fnirs_cw_amplitude')
mne_ac = raw.copy().pick(picks='fnirs_fd_ac_amplitude')
# Check channel names.
first_chans = [
'S1_D1', 'S2_D1', 'S3_D1', 'S4_D1', 'S5_D1', 'S6_D1', 'S7_D1', 'S8_D1',
'S9_D1', 'S10_D1'
]
last_chans = [
'S1_D8', 'S2_D8', 'S3_D8', 'S4_D8', 'S5_D8', 'S6_D8', 'S7_D8', 'S8_D8',
'S9_D8', 'S10_D8'
]
assert mne_dc.info['ch_names'][:10] == [
i_chan + ' ' + 'DC' for i_chan in first_chans
]
assert mne_ac.info['ch_names'][:10] == [
i_chan + ' ' + 'AC' for i_chan in first_chans
]
assert mne_ph.info['ch_names'][:10] == [
i_chan + ' ' + 'Ph' for i_chan in first_chans
]
assert mne_dc.info['ch_names'][70::] == [
i_chan + ' ' + 'DC' for i_chan in last_chans
]
assert mne_ac.info['ch_names'][70::] == [
i_chan + ' ' + 'AC' for i_chan in last_chans
]
assert mne_ph.info['ch_names'][70::] == [
i_chan + ' ' + 'Ph' for i_chan in last_chans
]
# Since this data set has no 'digaux' for creating trigger annotations,
# let's make sure our Raw object has no annotations.
assert len(raw.annotations) == 0