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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os
import numpy as np
import pytest
from numpy.testing import assert_allclose
from mne import make_fixed_length_epochs
from mne._fiff.constants import FIFF
from mne._fiff.meas_info import _empty_info
from mne.datasets.testing import data_path, requires_testing_data
from mne.io import read_raw_nsx
from mne.io.nsx.nsx import _decode_online_filters, _read_header
from mne.io.tests.test_raw import _test_raw_reader
testing_path = data_path(download=False)
nsx_21_fname = os.path.join(testing_path, "nsx", "test_NEURALSG_raw.ns3")
nsx_22_fname = os.path.join(testing_path, "nsx", "test_NEURALCD_raw.ns3")
nsx_31_fname = os.path.join(testing_path, "nsx", "test_BRSMPGRP_raw.ns3")
nsx_test_fname = os.path.join(testing_path, "nsx", "Test_anonymized.ns3")
edf_test_fname = os.path.join(testing_path, "EDF", "test_reduced.edf")
def test_decode_online_filters():
"""Tests for online low/high-pass filter decoding."""
info = _empty_info(100.0)
highpass = np.array([0.0, 0.1])
lowpass = np.array([50, 50])
with pytest.warns(RuntimeWarning, match="different highpass filters"):
_decode_online_filters(info, highpass, lowpass)
assert info["highpass"] == 0.1
info = _empty_info(100.0)
highpass = np.array([0.0, 0.0])
lowpass = np.array([40, 50])
with pytest.warns(RuntimeWarning, match="different lowpass filters"):
_decode_online_filters(info, highpass, lowpass)
assert info["lowpass"] == 40
info = _empty_info(100.0)
highpass = np.array(["NaN", "NaN"])
lowpass = np.array(["NaN", "NaN"])
_decode_online_filters(info, highpass, lowpass)
assert info["highpass"] == 0.0
assert info["lowpass"] == 50.0
@requires_testing_data
def test_filetype_checks():
"""Tests for various error meaasges for wrong filetypes."""
with pytest.raises(ValueError, match="does not match"):
_read_header(edf_test_fname)
with pytest.raises(NotImplementedError, match="Only NSx files"):
read_raw_nsx(edf_test_fname)
@requires_testing_data
def test_nsx_ver_31():
"""Primary tests for BRSMPGRP reader."""
raw = read_raw_nsx(nsx_31_fname)
assert getattr(raw, "_data", False) is False
assert raw.info["sfreq"] == 2000
# Check info object
assert raw.info["meas_date"].day == 31
assert raw.info["meas_date"].year == 2023
assert raw.info["meas_date"].month == 1
assert raw.info["chs"][0]["cal"] == 0.6103515625
assert raw.info["chs"][0]["range"] == 0.001
# Check raw_extras
for r in raw._raw_extras:
assert r["orig_format"] == raw.orig_format
assert r["orig_nchan"] == 128
assert len(r["timestamp"]) == len(r["nb_data_points"])
assert len(r["timestamp"]) == len(r["offset_to_data_block"])
# Check annotations
assert raw.annotations[0]["onset"] * raw.info["sfreq"] == 101
assert raw.annotations[0]["duration"] * raw.info["sfreq"] == 49
# Ignore following RuntimeWarning in mne/io/base.py in _write_raw_data
# "Acquisition skips detected but did not fit evenly into output"
# "buffer_size, will be written as zeroes."
with pytest.warns(RuntimeWarning, match="skips detected"):
raw = _test_raw_reader(
read_raw_nsx,
input_fname=nsx_31_fname,
eog=None,
misc=None,
test_scaling=False,
test_rank=False,
)
raw_data, _ = raw[:]
n_channels, n_times = raw_data.shape
assert n_times == 300
assert n_channels == 128
# Check data
# There are two contiguous data packets (samples 0--100 and
# samples 150--300. Each data was generated as:
# ```data = np.ones((n_samples, ch_count))
# data[n_samples // 2] = np.arange(ch_count) + 10
# data[:, ch_count // 2] = np.arange(n_samples) + 100```
orig_data = raw_data / (raw.info["chs"][0]["cal"] * raw.info["chs"][0]["range"])
assert_allclose(sum(orig_data[:, 50] - 10 - np.arange(n_channels)), 76.0)
assert_allclose(orig_data[n_channels // 2, :100] - 100, np.arange(100))
assert_allclose(orig_data[n_channels // 2, 150:] - 100, np.arange(150))
data, times = raw.get_data(start=10, stop=20, return_times=True)
assert 128, 10 == data.shape
data, times = raw.get_data(start=50, stop=110, return_times=True)
assert 128, 50 == data.shape
data, times = raw.get_data(start=130, stop=170, return_times=True)
assert 128, 40 == data.shape
data, times = raw.get_data(start=200, stop=350, return_times=True)
assert 128, 100 == data.shape
data, times = raw.get_data(start=0, stop=300, return_times=True)
epochs = make_fixed_length_epochs(raw, duration=0.05, preload=False)
assert len(epochs.events) == 3
epochs = make_fixed_length_epochs(raw, duration=0.05, preload=True)
assert len(epochs) == 2
assert "BAD_ACQ_SKIP" in epochs.drop_log[1]
@requires_testing_data
def test_nsx_ver_22():
"""Primary tests for NEURALCD reader."""
raw = read_raw_nsx(
nsx_22_fname,
)
assert getattr(raw, "_data", False) is False
assert raw.info["sfreq"] == 2000
# Check info object
assert raw.info["meas_date"].day == 31
assert raw.info["meas_date"].year == 2023
assert raw.info["meas_date"].month == 1
assert raw.info["chs"][0]["cal"] == 0.6103515625
assert raw.info["chs"][0]["range"] == 0.001
# check raw_extras
for r in raw._raw_extras:
assert r["orig_format"] == raw.orig_format
assert r["orig_nchan"] == 128
assert len(r["timestamp"]) == len(r["nb_data_points"])
assert len(r["timestamp"]) == len(r["offset_to_data_block"])
# Check annotations
assert len(raw.annotations) == 0
raw = _test_raw_reader(
read_raw_nsx,
input_fname=nsx_22_fname,
eog=None,
misc=None,
test_scaling=False, # XXX this should be True
test_rank=False,
)
raw_data, _ = raw[:]
n_channels, n_times = raw_data.shape
assert n_times == 100
assert n_channels == 128
# Check data
# There is only one contiguous data packet, samples 0--100. Data
# was generated as:
# ```data = np.ones((n_samples, ch_count))
# data[n_samples // 2] = np.arange(ch_count) + 10
# data[:, ch_count // 2] = np.arange(n_samples) + 100```
orig_data = raw_data / (raw.info["chs"][0]["cal"] * raw.info["chs"][0]["range"])
assert_allclose(sum(orig_data[:, 50] - 10 - np.arange(n_channels)), 76.0)
assert_allclose(orig_data[n_channels // 2, :100] - 100, np.arange(100))
data, _ = raw.get_data(start=10, stop=20, return_times=True)
assert 128, 10 == data.shape
data, _ = raw.get_data(start=0, stop=300, return_times=True)
assert 128, 100 == data.shape
epochs = make_fixed_length_epochs(raw, duration=0.05, preload=True, id=1)
assert len(epochs) == 1
assert epochs.event_id["1"] == 1
with pytest.raises(ValueError, match="No events produced"):
_ = make_fixed_length_epochs(raw, duration=0.5, preload=True)
@requires_testing_data
def test_stim_eog_misc_chs_in_nsx():
"""Test stim/misc/eog channel assignments."""
raw = read_raw_nsx(nsx_22_fname, stim_channel="elec127", eog=["elec126"])
assert raw.info["chs"][127]["kind"] == FIFF.FIFFV_STIM_CH
assert raw.info["chs"][126]["kind"] == FIFF.FIFFV_EOG_CH
raw = read_raw_nsx(nsx_22_fname, stim_channel=["elec127"], eog=["elec126"])
assert raw.info["chs"][127]["kind"] == FIFF.FIFFV_STIM_CH
assert raw.info["chs"][126]["kind"] == FIFF.FIFFV_EOG_CH
raw = read_raw_nsx(nsx_22_fname, stim_channel=127, eog=["elec126"])
assert raw.info["chs"][127]["kind"] == FIFF.FIFFV_STIM_CH
assert raw.info["chs"][126]["kind"] == FIFF.FIFFV_EOG_CH
raw = read_raw_nsx(nsx_22_fname, stim_channel=[127], eog=["elec126"])
assert raw.info["chs"][127]["kind"] == FIFF.FIFFV_STIM_CH
assert raw.info["chs"][126]["kind"] == FIFF.FIFFV_EOG_CH
stims = [ch_info["kind"] == FIFF.FIFFV_STIM_CH for ch_info in raw.info["chs"]]
assert np.any(stims)
assert raw.info["chs"][126]["kind"] == FIFF.FIFFV_EOG_CH
with pytest.raises(ValueError, match="Invalid stim_channel"):
raw = read_raw_nsx(nsx_22_fname, stim_channel=["elec128", 129], eog=["elec126"])
with pytest.raises(ValueError, match="Invalid stim_channel"):
raw = read_raw_nsx(nsx_22_fname, stim_channel=("elec128",), eog=["elec126"])
raw = read_raw_nsx(nsx_22_fname, stim_channel="elec127", misc=["elec126", "elec1"])
assert raw.info["chs"][126]["kind"] == FIFF.FIFFV_MISC_CH
assert raw.info["chs"][1]["kind"] == FIFF.FIFFV_MISC_CH
@requires_testing_data
def test_nsx_ver_21():
"""Primary tests for NEURALSG reader."""
with pytest.raises(NotImplementedError, match="(= NEURALSG)*not supported"):
read_raw_nsx(nsx_21_fname)
@requires_testing_data
def test_nsx():
"""Tests for NEURALCD reader using real anonymized data."""
raw = read_raw_nsx(
nsx_test_fname,
)
assert getattr(raw, "_data", False) is False
assert raw.info["sfreq"] == 2000
# Check info object
assert raw.info["meas_date"].day == 13
assert raw.info["meas_date"].year == 2000
assert raw.info["meas_date"].month == 6
assert raw.info["lowpass"] == 1000
assert raw.info["highpass"] == 0.3
assert raw.info["chs"][0]["cal"] == 0.25
assert raw.info["chs"][0]["range"] == 1e-6
# check raw_extras
for r in raw._raw_extras:
assert r["orig_format"] == raw.orig_format
assert r["orig_nchan"] == 5
assert len(r["timestamp"]) == len(r["nb_data_points"])
assert len(r["timestamp"]) == len(r["offset_to_data_block"])
# Check annotations
assert len(raw.annotations) == 0
raw = _test_raw_reader(
read_raw_nsx,
input_fname=nsx_test_fname,
eog=None,
misc=None,
)
raw_data, times = raw[:]
n_channels, n_times = raw_data.shape
assert n_times == 100
assert times.shape[0] == n_times
assert n_channels == 5
# Check data
assert_allclose(
raw_data.mean(axis=-1),
np.array([-52.6375, 88.57, 70.5825, -22.055, -166.5]) * 1e-6, # uV
)
assert raw.first_time == 3.8
epochs = make_fixed_length_epochs(raw, duration=0.05, preload=True, id=1)
assert len(epochs) == 1
assert epochs.event_id["1"] == 1
with pytest.raises(ValueError, match="No events produced"):
_ = make_fixed_length_epochs(raw, duration=0.5, preload=True)
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