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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
import pytest
import scipy.io as spio
from numpy.testing import assert_allclose, assert_array_equal, assert_array_less
from mne import pick_types
from mne.datasets import testing
from mne.io import read_raw_boxy
from mne.io.tests.test_raw import _test_raw_reader
data_path = testing.data_path(download=False)
boxy_0_40 = (
data_path / "BOXY" / "boxy_0_40_recording" / "boxy_0_40_notriggers_unparsed.txt"
)
p_pod_0_40 = (
data_path
/ "BOXY"
/ "boxy_0_40_recording"
/ "p_pod_10_6_3_loaded_data"
/ "p_pod_10_6_3_notriggers_unparsed.mat"
)
boxy_0_84 = (
data_path
/ "BOXY"
/ "boxy_0_84_digaux_recording"
/ "boxy_0_84_triggers_unparsed.txt"
)
boxy_0_84_parsed = (
data_path / "BOXY" / "boxy_0_84_digaux_recording" / "boxy_0_84_triggers_parsed.txt"
)
p_pod_0_84 = (
data_path
/ "BOXY"
/ "boxy_0_84_digaux_recording"
/ "p_pod_10_6_3_loaded_data"
/ "p_pod_10_6_3_triggers_unparsed.mat"
)
def _assert_ppod(raw, p_pod_file):
have_types = raw.get_channel_types(unique=True)
assert "fnirs_fd_phase" in raw, have_types
assert "fnirs_cw_amplitude" in raw, have_types
assert "fnirs_fd_ac_amplitude" in raw, have_types
ppod_data = spio.loadmat(p_pod_file)
# Compare MNE loaded data to p_pod loaded data.
map_ = dict(
dc="fnirs_cw_amplitude", ac="fnirs_fd_ac_amplitude", ph="fnirs_fd_phase"
)
for key, value in map_.items():
ppod = ppod_data[key].T
m = np.median(np.abs(ppod))
assert 1e-1 < m < 1e5, key # our atol is meaningful
atol = m * 1e-10
py = raw.get_data(value)
if key == "ph": # radians
assert_array_less(-np.pi, py)
assert_array_less(py, 3 * np.pi)
py = np.rad2deg(py)
assert_allclose(py, ppod, atol=atol, err_msg=key)
@testing.requires_testing_data
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
@testing.requires_testing_data
@pytest.mark.parametrize("fname", (boxy_0_84, boxy_0_84_parsed))
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]
@testing.requires_testing_data
@pytest.mark.parametrize("fname", (boxy_0_84, boxy_0_84_parsed))
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)
@testing.requires_testing_data
@pytest.mark.parametrize("fname", (boxy_0_40, boxy_0_84, boxy_0_84_parsed))
def test_raw_properties(fname):
"""Test raw reader properties."""
_test_raw_reader(read_raw_boxy, fname=fname, boundary_decimal=1)
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