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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>
# Mark Wronkiewicz <wronk.mark@gmail.com>
#
# License: Simplified BSD
import os.path as op
import numpy as np
import pytest
from mne import (make_field_map, pick_channels_evoked, read_evokeds,
read_trans, read_dipole, SourceEstimate, VectorSourceEstimate,
VolSourceEstimate, make_sphere_model, use_coil_def,
setup_volume_source_space, read_forward_solution)
from mne.io import read_raw_ctf, read_raw_bti, read_raw_kit, read_info
from mne.io.meas_info import write_dig
from mne.io.pick import pick_info
from mne.io.constants import FIFF
from mne.viz import (plot_sparse_source_estimates, plot_source_estimates,
snapshot_brain_montage, plot_head_positions,
plot_alignment, plot_volume_source_estimates)
from mne.viz.utils import _fake_click
from mne.utils import (requires_mayavi, requires_pysurfer, run_tests_if_main,
_import_mlab, requires_nibabel, check_version,
traits_test, requires_version)
from mne.datasets import testing
from mne.source_space import read_source_spaces
from mne.bem import read_bem_solution, read_bem_surfaces
# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
data_dir = testing.data_path(download=False)
subjects_dir = op.join(data_dir, 'subjects')
trans_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_trunc-trans.fif')
src_fname = op.join(data_dir, 'subjects', 'sample', 'bem',
'sample-oct-6-src.fif')
dip_fname = op.join(data_dir, 'MEG', 'sample', 'sample_audvis_trunc_set1.dip')
ctf_fname = op.join(data_dir, 'CTF', 'testdata_ctf.ds')
io_dir = op.join(op.abspath(op.dirname(__file__)), '..', '..', 'io')
base_dir = op.join(io_dir, 'tests', 'data')
evoked_fname = op.join(base_dir, 'test-ave.fif')
fwd_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_trunc-meg-vol-7-fwd.fif')
base_dir = op.join(io_dir, 'bti', 'tests', 'data')
pdf_fname = op.join(base_dir, 'test_pdf_linux')
config_fname = op.join(base_dir, 'test_config_linux')
hs_fname = op.join(base_dir, 'test_hs_linux')
sqd_fname = op.join(io_dir, 'kit', 'tests', 'data', 'test.sqd')
coil_3d = """# custom cube coil def
1 9999 1 8 3e-03 0.000e+00 "QuSpin ZFOPM 3mm cube"
0.1250 -0.750e-03 -0.750e-03 -0.750e-03 0.000 0.000 1.000
0.1250 -0.750e-03 0.750e-03 -0.750e-03 0.000 0.000 1.000
0.1250 0.750e-03 -0.750e-03 -0.750e-03 0.000 0.000 1.000
0.1250 0.750e-03 0.750e-03 -0.750e-03 0.000 0.000 1.000
0.1250 -0.750e-03 -0.750e-03 0.750e-03 0.000 0.000 1.000
0.1250 -0.750e-03 0.750e-03 0.750e-03 0.000 0.000 1.000
0.1250 0.750e-03 -0.750e-03 0.750e-03 0.000 0.000 1.000
0.1250 0.750e-03 0.750e-03 0.750e-03 0.000 0.000 1.000
"""
def test_plot_head_positions():
"""Test plotting of head positions."""
import matplotlib.pyplot as plt
info = read_info(evoked_fname)
pos = np.random.RandomState(0).randn(4, 10)
pos[:, 0] = np.arange(len(pos))
destination = (0., 0., 0.04)
with pytest.warns(None): # old MPL will cause a warning
plot_head_positions(pos)
if check_version('matplotlib', '1.4'):
plot_head_positions(pos, mode='field', info=info,
destination=destination)
else:
pytest.raises(RuntimeError, plot_head_positions, pos, mode='field',
info=info, destination=destination)
plot_head_positions([pos, pos]) # list support
pytest.raises(ValueError, plot_head_positions, ['pos'])
pytest.raises(ValueError, plot_head_positions, pos[:, :9])
pytest.raises(ValueError, plot_head_positions, pos, 'foo')
with pytest.raises(ValueError, match='shape'):
with pytest.warns(None): # old mpl no viridis warning
plot_head_positions(pos, axes=1.)
plt.close('all')
@testing.requires_testing_data
@requires_pysurfer
@requires_mayavi
@traits_test
def test_plot_sparse_source_estimates():
"""Test plotting of (sparse) source estimates."""
sample_src = read_source_spaces(src_fname)
# 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_size = stc_data.size
stc_data[(np.random.rand(stc_size // 20) * stc_size).astype(int)] = \
np.random.RandomState(0).rand(stc_data.size // 20)
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1)
colormap = 'mne_analyze'
plot_source_estimates(stc, 'sample', colormap=colormap,
background=(1, 1, 0),
subjects_dir=subjects_dir, colorbar=True,
clim='auto')
pytest.raises(TypeError, plot_source_estimates, stc, 'sample',
figure='foo', hemi='both', clim='auto',
subjects_dir=subjects_dir)
# 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)
@testing.requires_testing_data
@requires_mayavi
@traits_test
def test_plot_evoked_field():
"""Test plotting evoked field."""
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]:
with pytest.warns(RuntimeWarning, match='projection'):
maps = make_field_map(evoked, trans_fname, subject='sample',
subjects_dir=subjects_dir, n_jobs=1,
ch_type=t)
evoked.plot_field(maps, time=0.1)
@testing.requires_testing_data
@requires_mayavi
@traits_test
def test_plot_alignment(tmpdir):
"""Test plotting of -trans.fif files and MEG sensor layouts."""
# generate fiducials file for testing
tempdir = str(tmpdir)
fiducials_path = op.join(tempdir, 'fiducials.fif')
fid = [{'coord_frame': 5, 'ident': 1, 'kind': 1,
'r': [-0.08061612, -0.02908875, -0.04131077]},
{'coord_frame': 5, 'ident': 2, 'kind': 1,
'r': [0.00146763, 0.08506715, -0.03483611]},
{'coord_frame': 5, 'ident': 3, 'kind': 1,
'r': [0.08436285, -0.02850276, -0.04127743]}]
write_dig(fiducials_path, fid, 5)
mlab = _import_mlab()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
meg = ['helmet', 'sensors']
if system == 'KIT':
meg.append('ref')
plot_alignment(info, trans_fname, subject='sample',
subjects_dir=subjects_dir, meg=meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
mlab.close(all=True)
info = infos['Neuromag']
pytest.raises(TypeError, plot_alignment, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
pytest.raises(TypeError, plot_alignment, info, trans_fname,
subject='sample', subjects_dir=subjects_dir, src='foo')
pytest.raises(ValueError, plot_alignment, info, trans_fname,
subject='fsaverage', subjects_dir=subjects_dir,
src=sample_src)
sample_src.plot(subjects_dir=subjects_dir, head=True, skull=True,
brain='white')
mlab.close(all=True)
# no-head version
mlab.close(all=True)
# all coord frames
pytest.raises(ValueError, plot_alignment, info)
plot_alignment(info, surfaces=[])
for coord_frame in ('meg', 'head', 'mri'):
plot_alignment(info, meg=['helmet', 'sensors'], dig=True,
coord_frame=coord_frame, trans=trans_fname,
subject='sample', mri_fiducials=fiducials_path,
subjects_dir=subjects_dir, src=sample_src)
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog_seeg = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog_seeg.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog_seeg.set_channel_types({'EEG 001': 'ecog',
'EEG 002': 'seeg'})
with pytest.warns(RuntimeWarning, match='Cannot plot MEG'):
plot_alignment(evoked_eeg_ecog_seeg.info, subject='sample',
trans=trans_fname, subjects_dir=subjects_dir,
surfaces=['white', 'outer_skin', 'outer_skull'],
meg=['helmet', 'sensors'],
eeg=['original', 'projected'], ecog=True, seeg=True)
mlab.close(all=True)
sphere = make_sphere_model(info=evoked.info, r0='auto', head_radius='auto')
bem_sol = read_bem_solution(op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem-sol.fif'))
bem_surfs = read_bem_surfaces(op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem.fif'))
sample_src[0]['coord_frame'] = 4 # hack for coverage
plot_alignment(info, subject='sample', eeg='projected',
meg='helmet', bem=sphere, dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull',
'outer_skin'])
plot_alignment(info, trans_fname, subject='sample', meg='helmet',
subjects_dir=subjects_dir, eeg='projected', bem=sphere,
surfaces=['head', 'brain'], src=sample_src)
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info, trans_fname, subject='sample', meg=[],
subjects_dir=subjects_dir, bem=bem_sol, eeg=True,
surfaces=['head', 'inflated', 'outer_skull', 'inner_skull'])
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info, trans_fname, subject='sample',
meg=True, subjects_dir=subjects_dir,
surfaces=['head', 'inner_skull'], bem=bem_surfs)
sphere = make_sphere_model('auto', 'auto', evoked.info)
src = setup_volume_source_space(sphere=sphere)
plot_alignment(info, eeg='projected', meg='helmet', bem=sphere,
src=src, dig=True, surfaces=['brain', 'inner_skull',
'outer_skull', 'outer_skin'])
sphere = make_sphere_model('auto', None, evoked.info) # one layer
plot_alignment(info, trans_fname, subject='sample', meg=False,
coord_frame='mri', subjects_dir=subjects_dir,
surfaces=['brain'], bem=sphere, show_axes=True)
# 3D coil with no defined draw (ConvexHull)
info_cube = pick_info(info, [0])
info['dig'] = None
info_cube['chs'][0]['coil_type'] = 9999
with pytest.raises(RuntimeError, match='coil definition not found'):
plot_alignment(info_cube, meg='sensors', surfaces=())
coil_def_fname = op.join(tempdir, 'temp')
with open(coil_def_fname, 'w') as fid:
fid.write(coil_3d)
with use_coil_def(coil_def_fname):
plot_alignment(info_cube, meg='sensors', surfaces=(), dig=True)
# one layer bem with skull surfaces:
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['brain', 'head', 'inner_skull'], bem=sphere)
# wrong eeg value:
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir, eeg='foo')
# wrong meg value:
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir, meg='bar')
# multiple brain surfaces:
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['white', 'pial'])
pytest.raises(TypeError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=[1])
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['foo'])
mlab.close(all=True)
@testing.requires_testing_data
@requires_pysurfer
@requires_mayavi
@traits_test
def test_limits_to_control_points():
"""Test functionality for determining control points."""
sample_src = read_source_spaces(src_fname)
kwargs = dict(subjects_dir=subjects_dir, smoothing_steps=1)
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.random.RandomState(0).rand((n_verts * n_time))
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1, 'sample')
# Test for simple use cases
mlab = _import_mlab()
stc.plot(**kwargs)
stc.plot(clim=dict(pos_lims=(10, 50, 90)), **kwargs)
stc.plot(colormap='hot', clim='auto', **kwargs)
stc.plot(colormap='mne', clim='auto', **kwargs)
figs = [mlab.figure(), mlab.figure()]
stc.plot(clim=dict(kind='value', lims=(10, 50, 90)), figure=99, **kwargs)
pytest.raises(ValueError, stc.plot, clim='auto', figure=figs, **kwargs)
# Test for correct clim values
with pytest.raises(ValueError, match='monotonically'):
stc.plot(clim=dict(kind='value', pos_lims=[0, 1, 0]), **kwargs)
with pytest.raises(ValueError, match=r'.*must be \(3,\)'):
stc.plot(colormap='mne', clim=dict(pos_lims=(5, 10, 15, 20)), **kwargs)
with pytest.raises(ValueError, match='must be "value" or "percent"'):
stc.plot(clim=dict(pos_lims=(5, 10, 15), kind='foo'), **kwargs)
with pytest.raises(ValueError, match='must be "auto" or dict'):
stc.plot(colormap='mne', clim='foo', **kwargs)
with pytest.raises(TypeError, match='must be an instance of'):
plot_source_estimates('foo', clim='auto', **kwargs)
with pytest.raises(ValueError, match='hemi'):
stc.plot(hemi='foo', clim='auto', **kwargs)
with pytest.raises(ValueError, match='Exactly one'):
stc.plot(clim=dict(lims=[0, 1, 2], pos_lims=[0, 1, 2], kind='value'))
# Test handling of degenerate data: thresholded maps
stc._data.fill(0.)
with pytest.warns(RuntimeWarning, match='All data were zero'):
plot_source_estimates(stc, **kwargs)
mlab.close(all=True)
@testing.requires_testing_data
@requires_nibabel()
def test_stc_mpl():
"""Test plotting source estimates with matplotlib."""
import matplotlib.pyplot as plt
sample_src = read_source_spaces(src_fname)
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.ones((n_verts * n_time))
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1, 'sample')
with pytest.warns(RuntimeWarning, match='not included'):
stc.plot(subjects_dir=subjects_dir, time_unit='s', views='ven',
hemi='rh', smoothing_steps=2, subject='sample',
backend='matplotlib', spacing='oct1', initial_time=0.001,
colormap='Reds')
fig = stc.plot(subjects_dir=subjects_dir, time_unit='ms', views='dor',
hemi='lh', smoothing_steps=2, subject='sample',
backend='matplotlib', spacing='ico2', time_viewer=True,
colormap='mne')
time_viewer = fig.time_viewer
_fake_click(time_viewer, time_viewer.axes[0], (0.5, 0.5)) # change t
time_viewer.canvas.key_press_event('ctrl+right')
time_viewer.canvas.key_press_event('left')
pytest.raises(ValueError, stc.plot, subjects_dir=subjects_dir,
hemi='both', subject='sample', backend='matplotlib')
pytest.raises(ValueError, stc.plot, subjects_dir=subjects_dir,
time_unit='ss', subject='sample', backend='matplotlib')
plt.close('all')
@testing.requires_testing_data
@requires_nibabel()
def test_plot_dipole_mri_orthoview():
"""Test mpl dipole plotting."""
import matplotlib.pyplot as plt
dipoles = read_dipole(dip_fname)
trans = read_trans(trans_fname)
for coord_frame, idx, show_all in zip(['head', 'mri'],
['gof', 'amplitude'], [True, False]):
fig = dipoles.plot_locations(trans, 'sample', subjects_dir,
coord_frame=coord_frame, idx=idx,
show_all=show_all, mode='orthoview')
fig.canvas.scroll_event(0.5, 0.5, 1) # scroll up
fig.canvas.scroll_event(0.5, 0.5, -1) # scroll down
fig.canvas.key_press_event('up')
fig.canvas.key_press_event('down')
fig.canvas.key_press_event('a') # some other key
ax = plt.subplot(111)
pytest.raises(TypeError, dipoles.plot_locations, trans, 'sample',
subjects_dir, ax=ax)
plt.close('all')
@testing.requires_testing_data
@requires_mayavi
@traits_test
def test_snapshot_brain_montage():
"""Test snapshot brain montage."""
info = read_info(evoked_fname)
fig = plot_alignment(
info, trans=None, subject='sample', subjects_dir=subjects_dir)
xyz = np.vstack([ich['loc'][:3] for ich in info['chs']])
ch_names = [ich['ch_name'] for ich in info['chs']]
xyz_dict = dict(zip(ch_names, xyz))
xyz_dict[info['chs'][0]['ch_name']] = [1, 2] # Set one ch to only 2 vals
# Make sure wrong types are checked
pytest.raises(TypeError, snapshot_brain_montage, fig, xyz)
# All chs must have 3 position values
pytest.raises(ValueError, snapshot_brain_montage, fig, xyz_dict)
# Make sure we raise error if the figure has no scene
pytest.raises(TypeError, snapshot_brain_montage, fig, info)
@testing.requires_testing_data
@requires_nibabel()
@requires_version('nilearn', '0.4')
def test_plot_volume_source_estimates():
"""Test interactive plotting of volume source estimates."""
forward = read_forward_solution(fwd_fname)
sample_src = forward['src']
vertices = [s['vertno'] for s in sample_src]
n_verts = sum(len(v) for v in vertices)
n_time = 2
data = np.random.RandomState(0).rand(n_verts, n_time)
vol_stc = VolSourceEstimate(data, vertices, 1, 1)
for mode in ['glass_brain', 'stat_map']:
with pytest.warns(None): # sometimes get scalars/index warning
fig = vol_stc.plot(sample_src, subject='sample',
subjects_dir=subjects_dir,
mode=mode)
# [ax_time, ax_y, ax_x, ax_z]
for ax_idx in [0, 2, 3, 4]:
_fake_click(fig, fig.axes[ax_idx], (0.3, 0.5))
with pytest.raises(ValueError, match='must be one of'):
vol_stc.plot(sample_src, 'sample', subjects_dir, mode='abcd')
vertices.append([])
surface_stc = SourceEstimate(data, vertices, 1, 1)
with pytest.raises(ValueError, match='Only Vol'):
plot_volume_source_estimates(surface_stc, sample_src, 'sample',
subjects_dir)
with pytest.raises(ValueError, match='Negative colormap limits'):
vol_stc.plot(sample_src, 'sample', subjects_dir,
clim=dict(lims=[-1, 2, 3], kind='value'))
@testing.requires_testing_data
@requires_pysurfer
@requires_mayavi
@traits_test
def test_plot_vec_source_estimates():
"""Test plotting of vector source estimates."""
sample_src = read_source_spaces(src_fname)
vertices = [s['vertno'] for s in sample_src]
n_verts = sum(len(v) for v in vertices)
n_time = 5
data = np.random.RandomState(0).rand(n_verts, 3, n_time)
stc = VectorSourceEstimate(data, vertices, 1, 1)
stc.plot('sample', subjects_dir=subjects_dir)
with pytest.raises(ValueError, match='use "pos_lims"'):
stc.plot('sample', subjects_dir=subjects_dir,
clim=dict(pos_lims=[1, 2, 3]))
run_tests_if_main()
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