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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>
# 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 SourceEstimate
from mne import make_field_map, pick_channels_evoked, read_evokeds
from mne.viz import (plot_sparse_source_estimates, plot_source_estimates,
plot_trans, mne_analyze_colormap)
from mne.datasets import sample
from mne.source_space import read_source_spaces
data_dir = sample.data_path(download=False)
base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
evoked_fname = op.join(base_dir, 'test-ave.fif')
subjects_dir = op.join(data_dir, 'subjects')
lacks_mayavi = False
try:
from mayavi import mlab
except ImportError:
try:
from enthought.mayavi import mlab
except ImportError:
lacks_mayavi = True
requires_mayavi = np.testing.dec.skipif(lacks_mayavi, 'Requires mayavi')
if not lacks_mayavi:
mlab.options.backend = 'test'
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
@sample.requires_sample_data
@requires_mayavi
def test_plot_sparse_source_estimates():
"""Test plotting of (sparse) source estimates
"""
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_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_data[(np.random.rand(20) * n_verts * n_time).astype(int)] = 1
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1)
colormap = mne_analyze_colormap(format='matplotlib')
# don't really need to test matplotlib method since it's not used now...
colormap = mne_analyze_colormap()
plot_source_estimates(stc, 'sample', colormap=colormap,
config_opts={'background': (1, 1, 0)},
subjects_dir=subjects_dir, colorbar=True)
assert_raises(TypeError, plot_source_estimates, stc, 'sample',
figure='foo', hemi='both')
# now do sparse version
vertices = sample_src[0]['vertno']
inds = [111, 333]
stc_data = np.zeros((len(inds), n_time))
stc_data[0, 1] = 1.
stc_data[1, 4] = 2.
vertices = [vertices[inds], np.empty(0, dtype=np.int)]
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_sparse_source_estimates(sample_src, stc, bgcolor=(1, 1, 1),
opacity=0.5, high_resolution=False)
@requires_mayavi
@sample.requires_sample_data
def test_plot_evoked_field():
"""Test plotting evoked field
"""
trans_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_raw-trans.fif')
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ['meg', None]:
maps = make_field_map(evoked, trans_fname=trans_fname,
subject='sample', subjects_dir=subjects_dir,
n_jobs=1, ch_type=t)
evoked.plot_field(maps, time=0.1)
@requires_mayavi
@sample.requires_sample_data
def test_plot_trans():
"""Test plotting of -trans.fif files
"""
trans_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_raw-trans.fif')
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
plot_trans(evoked.info, trans_fname=trans_fname, subject='sample',
subjects_dir=subjects_dir)
assert_raises(ValueError, plot_trans, evoked.info, trans_fname=trans_fname,
subject='sample', subjects_dir=subjects_dir,
ch_type='bad-chtype')
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