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# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Cathy Nangini <cnangini@gmail.com>
# Mainak Jas <mainak@neuro.hut.fi>
#
# License: Simplified BSD
import os.path as op
import numpy as np
import pytest
from mne import (read_events, read_cov, read_source_spaces, read_evokeds,
read_dipole, SourceEstimate)
from mne.datasets import testing
from mne.filter import create_filter
from mne.io import read_raw_fif
from mne.minimum_norm import read_inverse_operator
from mne.viz import (plot_bem, plot_events, plot_source_spectrogram,
plot_snr_estimate, plot_filter, plot_csd)
from mne.utils import requires_nibabel, run_tests_if_main, requires_version
from mne.time_frequency import CrossSpectralDensity
# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
data_path = testing.data_path(download=False)
subjects_dir = op.join(data_path, 'subjects')
src_fname = op.join(subjects_dir, 'sample', 'bem', 'sample-oct-6-src.fif')
inv_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-4-meg-inv.fif')
evoked_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
dip_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_set1.dip')
base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
raw_fname = op.join(base_dir, 'test_raw.fif')
cov_fname = op.join(base_dir, 'test-cov.fif')
event_fname = op.join(base_dir, 'test-eve.fif')
def _get_raw():
"""Get raw data."""
return read_raw_fif(raw_fname, preload=True)
def _get_events():
"""Get events."""
return read_events(event_fname)
@requires_version('scipy', '0.16')
def test_plot_filter():
"""Test filter plotting."""
import matplotlib.pyplot as plt
l_freq, h_freq, sfreq = 2., 40., 1000.
data = np.zeros(5000)
freq = [0, 2, 40, 50, 500]
gain = [0, 1, 1, 0, 0]
h = create_filter(data, sfreq, l_freq, h_freq, fir_design='firwin2')
plot_filter(h, sfreq)
plt.close('all')
plot_filter(h, sfreq, freq, gain)
plt.close('all')
iir = create_filter(data, sfreq, l_freq, h_freq, method='iir')
plot_filter(iir, sfreq)
plt.close('all')
plot_filter(iir, sfreq, freq, gain)
plt.close('all')
iir_ba = create_filter(data, sfreq, l_freq, h_freq, method='iir',
iir_params=dict(output='ba'))
plot_filter(iir_ba, sfreq, freq, gain)
plt.close('all')
plot_filter(h, sfreq, freq, gain, fscale='linear')
plt.close('all')
def test_plot_cov():
"""Test plotting of covariances."""
import matplotlib.pyplot as plt
raw = _get_raw()
cov = read_cov(cov_fname)
with pytest.warns(RuntimeWarning, match='projection'):
fig1, fig2 = cov.plot(raw.info, proj=True, exclude=raw.ch_names[6:])
plt.close('all')
@testing.requires_testing_data
@requires_nibabel()
def test_plot_bem():
"""Test plotting of BEM contours."""
pytest.raises(IOError, plot_bem, subject='bad-subject',
subjects_dir=subjects_dir)
pytest.raises(ValueError, plot_bem, subject='sample',
subjects_dir=subjects_dir, orientation='bad-ori')
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='sagittal', slices=[25, 50])
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='coronal', slices=[25, 50],
brain_surfaces='white')
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='coronal', slices=[25, 50], src=src_fname)
def test_plot_events():
"""Test plotting events."""
import matplotlib.pyplot as plt
event_labels = {'aud_l': 1, 'aud_r': 2, 'vis_l': 3, 'vis_r': 4}
color = {1: 'green', 2: 'yellow', 3: 'red', 4: 'c'}
raw = _get_raw()
events = _get_events()
plot_events(events, raw.info['sfreq'], raw.first_samp)
plot_events(events, raw.info['sfreq'], raw.first_samp, equal_spacing=False)
# Test plotting events without sfreq
plot_events(events, first_samp=raw.first_samp)
with pytest.warns(RuntimeWarning, match='will be ignored'):
plot_events(events, raw.info['sfreq'], raw.first_samp,
event_id=event_labels)
with pytest.warns(RuntimeWarning, match='Color is not available'):
plot_events(events, raw.info['sfreq'], raw.first_samp,
color=color)
with pytest.warns(RuntimeWarning, match='event .* missing'):
plot_events(events, raw.info['sfreq'], raw.first_samp,
event_id=event_labels, color=color)
with pytest.warns(RuntimeWarning, match='event .* missing'):
pytest.raises(ValueError, plot_events, events, raw.info['sfreq'],
raw.first_samp, event_id={'aud_l': 1}, color=color)
pytest.raises(ValueError, plot_events, events, raw.info['sfreq'],
raw.first_samp, event_id={'aud_l': 111}, color=color)
plt.close('all')
@testing.requires_testing_data
def test_plot_source_spectrogram():
"""Test plotting of source spectrogram."""
import matplotlib.pyplot as plt
sample_src = read_source_spaces(op.join(subjects_dir, 'sample',
'bem', 'sample-oct-6-src.fif'))
# dense version
vertices = [s['vertno'] for s in sample_src]
n_times = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.ones((n_verts, n_times))
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_source_spectrogram([stc, stc], [[1, 2], [3, 4]])
pytest.raises(ValueError, plot_source_spectrogram, [], [])
pytest.raises(ValueError, plot_source_spectrogram, [stc, stc],
[[1, 2], [3, 4]], tmin=0)
pytest.raises(ValueError, plot_source_spectrogram, [stc, stc],
[[1, 2], [3, 4]], tmax=7)
plt.close('all')
@pytest.mark.slowtest
@testing.requires_testing_data
def test_plot_snr():
"""Test plotting SNR estimate."""
import matplotlib.pyplot as plt
inv = read_inverse_operator(inv_fname)
evoked = read_evokeds(evoked_fname, baseline=(None, 0))[0]
plot_snr_estimate(evoked, inv)
plt.close('all')
@testing.requires_testing_data
def test_plot_dipole_amplitudes():
"""Test plotting dipole amplitudes."""
import matplotlib.pyplot as plt
dipoles = read_dipole(dip_fname)
dipoles.plot_amplitudes(show=False)
plt.close('all')
def test_plot_csd():
"""Test plotting of CSD matrices."""
import matplotlib.pyplot as plt
csd = CrossSpectralDensity([1, 2, 3], ['CH1', 'CH2'],
frequencies=[(10, 20)], n_fft=1,
tmin=0, tmax=1,)
plot_csd(csd, mode='csd') # Plot cross-spectral density
plot_csd(csd, mode='coh') # Plot coherence
plt.close('all')
run_tests_if_main()
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