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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 warnings
import numpy as np
from numpy.testing import assert_raises
from mne import io, read_events, read_cov, read_source_spaces
from mne import SourceEstimate
from mne.datasets import sample
from mne.viz import plot_cov, plot_bem, plot_events
from mne.viz import plot_source_spectrogram
warnings.simplefilter('always') # enable b/c these tests throw warnings
# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
import matplotlib.pyplot as plt
data_dir = sample.data_path(download=False)
subjects_dir = op.join(data_dir, 'subjects')
ecg_fname = op.join(data_dir, 'MEG', 'sample', 'sample_audvis_ecg_proj.fif')
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_name = op.join(base_dir, 'test-eve.fif')
def _get_raw():
return io.Raw(raw_fname, preload=True)
def _get_events():
return read_events(event_name)
def test_plot_cov():
"""Test plotting of covariances
"""
raw = _get_raw()
cov = read_cov(cov_fname)
fig1, fig2 = plot_cov(cov, raw.info, proj=True, exclude=raw.ch_names[6:])
plt.close('all')
@sample.requires_sample_data
def test_plot_bem():
"""Test plotting of BEM contours
"""
assert_raises(IOError, plot_bem, subject='bad-subject',
subjects_dir=subjects_dir)
assert_raises(ValueError, plot_bem, subject='sample',
subjects_dir=subjects_dir, orientation='bad-ori')
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='sagittal', slices=[50, 100])
def test_plot_events():
"""Test plotting events
"""
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)
warnings.simplefilter('always', UserWarning)
with warnings.catch_warnings(record=True):
plot_events(events, raw.info['sfreq'], raw.first_samp,
event_id=event_labels)
plot_events(events, raw.info['sfreq'], raw.first_samp,
color=color)
plot_events(events, raw.info['sfreq'], raw.first_samp,
event_id=event_labels, color=color)
assert_raises(ValueError, plot_events, events, raw.info['sfreq'],
raw.first_samp, event_id={'aud_l': 1}, color=color)
assert_raises(ValueError, plot_events, events, raw.info['sfreq'],
raw.first_samp, event_id={'aud_l': 111}, color=color)
@sample.requires_sample_data
def test_plot_source_spectrogram():
"""Test plotting of source spectrogram
"""
sample_src = read_source_spaces(op.join(data_dir, 'subjects', '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]])
assert_raises(ValueError, plot_source_spectrogram, [], [])
assert_raises(ValueError, plot_source_spectrogram, [stc, stc],
[[1, 2], [3, 4]], tmin=0)
assert_raises(ValueError, plot_source_spectrogram, [stc, stc],
[[1, 2], [3, 4]], tmax=7)
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