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import numpy as np
import os.path as op
from numpy.testing import assert_array_almost_equal, assert_allclose
from scipy.signal import welch
import pytest
from mne import pick_types, Epochs, read_events
from mne.io import RawArray, read_raw_fif
from mne.utils import run_tests_if_main, requires_version
from mne.time_frequency import psd_welch, psd_multitaper, psd_array_welch
base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
raw_fname = op.join(base_dir, 'test_raw.fif')
event_fname = op.join(base_dir, 'test-eve.fif')
def test_psd_nan():
"""Test handling of NaN in psd_array_welch."""
n_samples, n_fft, n_overlap = 2048, 1024, 512
x = np.random.RandomState(0).randn(1, n_samples)
psds, freqs = psd_array_welch(
x[:n_fft + n_overlap], float(n_fft), n_fft=n_fft, n_overlap=n_overlap)
x[n_fft + n_overlap:] = np.nan # what Raw.get_data() will give us
psds_2, freqs_2 = psd_array_welch(
x, float(n_fft), n_fft=n_fft, n_overlap=n_overlap)
assert_allclose(freqs, freqs_2)
assert_allclose(psds, psds_2)
# 1-d
psds_2, freqs_2 = psd_array_welch(
x[0], float(n_fft), n_fft=n_fft, n_overlap=n_overlap)
assert_allclose(freqs, freqs_2)
assert_allclose(psds[0], psds_2)
def test_psd():
"""Tests the welch and multitaper PSD."""
raw = read_raw_fif(raw_fname)
picks_psd = [0, 1]
# Populate raw with sinusoids
rng = np.random.RandomState(40)
data = 0.1 * rng.randn(len(raw.ch_names), raw.n_times)
freqs_sig = [8., 50.]
for ix, freq in zip(picks_psd, freqs_sig):
data[ix, :] += 2 * np.sin(np.pi * 2. * freq * raw.times)
first_samp = raw._first_samps[0]
raw = RawArray(data, raw.info)
tmin, tmax = 0, 20 # use a few seconds of data
fmin, fmax = 2, 70 # look at frequencies between 2 and 70Hz
n_fft = 128
# -- Raw --
kws_psd = dict(tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,
picks=picks_psd) # Common to all
kws_welch = dict(n_fft=n_fft)
kws_mt = dict(low_bias=True)
funcs = [(psd_welch, kws_welch),
(psd_multitaper, kws_mt)]
for func, kws in funcs:
kws = kws.copy()
kws.update(kws_psd)
psds, freqs = func(raw, proj=False, **kws)
psds_proj, freqs_proj = func(raw, proj=True, **kws)
assert psds.shape == (len(kws['picks']), len(freqs))
assert np.sum(freqs < 0) == 0
assert np.sum(psds < 0) == 0
# Is power found where it should be
ixs_max = np.argmax(psds, axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs))
assert (np.abs(ixmax - ixtrue) < 2)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds, psds_proj)
# Array input shouldn't work
pytest.raises(ValueError, func, raw[:3, :20][0])
# test n_per_seg in psd_welch (and padding)
psds1, freqs1 = psd_welch(raw, proj=False, n_fft=128, n_per_seg=128,
**kws_psd)
psds2, freqs2 = psd_welch(raw, proj=False, n_fft=256, n_per_seg=128,
**kws_psd)
assert (len(freqs1) == np.floor(len(freqs2) / 2.))
assert (psds1.shape[-1] == np.floor(psds2.shape[-1] / 2.))
# tests ValueError when n_per_seg=None and n_fft > signal length
kws_psd.update(dict(n_fft=tmax * 1.1 * raw.info['sfreq']))
pytest.raises(ValueError, psd_welch, raw, proj=False, n_per_seg=None,
**kws_psd)
# ValueError when n_overlap > n_per_seg
kws_psd.update(dict(n_fft=128, n_per_seg=64, n_overlap=90))
pytest.raises(ValueError, psd_welch, raw, proj=False, **kws_psd)
# -- Epochs/Evoked --
events = read_events(event_fname)
events[:, 0] -= first_samp
tmin, tmax, event_id = -0.5, 0.5, 1
epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks_psd,
proj=False, preload=True, baseline=None)
evoked = epochs.average()
tmin_full, tmax_full = -1, 1
epochs_full = Epochs(raw, events[:10], event_id, tmin_full, tmax_full,
picks=picks_psd, proj=False, preload=True,
baseline=None)
kws_psd = dict(tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,
picks=picks_psd) # Common to all
funcs = [(psd_welch, kws_welch),
(psd_multitaper, kws_mt)]
for func, kws in funcs:
kws = kws.copy()
kws.update(kws_psd)
psds, freqs = func(
epochs[:1], proj=False, **kws)
psds_proj, freqs_proj = func(
epochs[:1], proj=True, **kws)
psds_f, freqs_f = func(
epochs_full[:1], proj=False, **kws)
# this one will fail if you add for example 0.1 to tmin
assert_array_almost_equal(psds, psds_f, 27)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds, psds_proj, 27)
# Is power found where it should be
ixs_max = np.argmax(psds.mean(0), axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs))
assert (np.abs(ixmax - ixtrue) < 2)
assert (psds.shape == (1, len(kws['picks']), len(freqs)))
assert (np.sum(freqs < 0) == 0)
assert (np.sum(psds < 0) == 0)
# Array input shouldn't work
pytest.raises(ValueError, func, epochs.get_data())
# Testing evoked (doesn't work w/ compute_epochs_psd)
psds_ev, freqs_ev = func(
evoked, proj=False, **kws)
psds_ev_proj, freqs_ev_proj = func(
evoked, proj=True, **kws)
# Is power found where it should be
ixs_max = np.argmax(psds_ev, axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs_ev))
assert (np.abs(ixmax - ixtrue) < 2)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds_ev, psds_ev_proj, 27)
assert (psds_ev.shape == (len(kws['picks']), len(freqs)))
@requires_version('scipy', '1.2.0')
@pytest.mark.parametrize('kind', ('raw', 'epochs', 'evoked'))
def test_psd_welch_average_kwarg(kind):
"""Test `average` kwarg of psd_welch()."""
raw = read_raw_fif(raw_fname)
picks_psd = [0, 1]
# Populate raw with sinusoids
rng = np.random.RandomState(40)
data = 0.1 * rng.randn(len(raw.ch_names), raw.n_times)
freqs_sig = [8., 50.]
for ix, freq in zip(picks_psd, freqs_sig):
data[ix, :] += 2 * np.sin(np.pi * 2. * freq * raw.times)
first_samp = raw._first_samps[0]
raw = RawArray(data, raw.info)
tmin, tmax = -0.5, 0.5
fmin, fmax = 0, np.inf
n_fft = 256
n_per_seg = 128
n_overlap = 0
event_id = 2
events = read_events(event_fname)
events[:, 0] -= first_samp
kws = dict(fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax, n_fft=n_fft,
n_per_seg=n_per_seg, n_overlap=n_overlap, picks=picks_psd)
if kind == 'raw':
inst = raw
elif kind == 'epochs':
inst = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks_psd,
proj=False, preload=True, baseline=None)
elif kind == 'evoked':
inst = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks_psd,
proj=False, preload=True, baseline=None).average()
else:
raise ValueError('Unknown parametrization passed to test, check test '
'for typos.')
psds_mean, freqs_mean = psd_welch(inst=inst, average='mean', **kws)
psds_median, freqs_median = psd_welch(inst=inst, average='median', **kws)
psds_unagg, freqs_unagg = psd_welch(inst=inst, average=None, **kws)
# Frequencies should be equal across all "average" types, as we feed in
# the exact same data.
assert_allclose(freqs_mean, freqs_median)
assert_allclose(freqs_mean, freqs_unagg)
# For `average=None`, the last dimension contains the un-aggregated
# segments.
assert psds_mean.shape == psds_median.shape
assert psds_mean.shape == psds_unagg.shape[:-1]
assert_allclose(psds_mean, psds_unagg.mean(axis=-1))
# SciPy's welch() function corrects the median PSD for its bias relative to
# the mean.
from scipy.signal.spectral import _median_bias
median_bias = _median_bias(psds_unagg.shape[-1])
assert_allclose(psds_median, np.median(psds_unagg, axis=-1) / median_bias)
@pytest.mark.slowtest
def test_compares_psd():
"""Test PSD estimation on raw for plt.psd and scipy.signal.welch."""
raw = read_raw_fif(raw_fname)
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', eeg=False, stim=False,
exclude=exclude)[:2]
tmin, tmax = 0, 10 # use the first 60s of data
fmin, fmax = 2, 70 # look at frequencies between 5 and 70Hz
n_fft = 2048
# Compute psds with the new implementation using Welch
psds_welch, freqs_welch = psd_welch(raw, tmin=tmin, tmax=tmax, fmin=fmin,
fmax=fmax, proj=False, picks=picks,
n_fft=n_fft, n_jobs=1)
# Compute psds with plt.psd
start, stop = raw.time_as_index([tmin, tmax])
data, times = raw[picks, start:(stop + 1)]
out = [welch(d, fs=raw.info['sfreq'], nperseg=n_fft, noverlap=0)
for d in data]
freqs_mpl = out[0][0]
psds_mpl = np.array([o[1] for o in out])
mask = (freqs_mpl >= fmin) & (freqs_mpl <= fmax)
freqs_mpl = freqs_mpl[mask]
psds_mpl = psds_mpl[:, mask]
assert_array_almost_equal(psds_welch, psds_mpl)
assert_array_almost_equal(freqs_welch, freqs_mpl)
assert (psds_welch.shape == (len(picks), len(freqs_welch)))
assert (psds_mpl.shape == (len(picks), len(freqs_mpl)))
assert (np.sum(freqs_welch < 0) == 0)
assert (np.sum(freqs_mpl < 0) == 0)
assert (np.sum(psds_welch < 0) == 0)
assert (np.sum(psds_mpl < 0) == 0)
run_tests_if_main()
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