def test_meg_inverse():
"""Test plotting of MEG inverse solution."""
_set_backend()
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
vertices = stc['vertices']
colormap = 'hot'
data = stc['data']
data_full = (brain.geo['lh'].nn[vertices][..., np.newaxis] *
data[:, np.newaxis])
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1], endpoint=False)
def time_label(t):
return 'time=%0.2f ms' % (1e3 * t)
for use_data in (data, data_full):
brain.add_data(use_data, colormap=colormap, vertices=vertices,
smoothing_steps=1, time=time, time_label=time_label)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
assert brain.data_dict['lh']['time_idx'] == 0
brain.set_time(.1)
assert brain.data_dict['lh']['time_idx'] == 2
# viewer = TimeViewer(brain)
# multiple data layers
pytest.raises(ValueError, brain.add_data, data, vertices=vertices,
time=time[:-1])
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=1, time=time, time_label=time_label,
initial_time=.09)
assert brain.data_dict['lh']['time_idx'] == 1
data_dicts = brain._data_dicts['lh']
assert len(data_dicts) == 3
assert data_dicts[0]['time_idx'] == 1
assert data_dicts[1]['time_idx'] == 1
# shift time in both layers
brain.set_data_time_index(0)
assert data_dicts[0]['time_idx'] == 0
assert data_dicts[1]['time_idx'] == 0
brain.set_data_smoothing_steps(2)
# add second data-layer without time axis
brain.add_data(data[:, 1], colormap=colormap, vertices=vertices,
smoothing_steps=2)
brain.set_data_time_index(2)
assert len(data_dicts) == 4
# change surface
brain.set_surf('white')
# remove all layers
brain.remove_data()
assert brain._data_dicts['lh'] == []
brain.close()
def plot_overlays_Fgroup(condition,modality,hemi,azimuth):
brain = Brain(subject_id='fsaverage', hemi=hemi,surf='pial',cortex = 'low_contrast', size=(600, 600))
stc_fname = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/Plot_STATS/'
+"_vs_".join(condition) +'/fmap'+ modality+ '_'
+"_vs_".join(condition)+ '-' + hemi+'.stc')
stc = read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
brain.add_data(data, thresh = 3.259,colormap='hot',alpha=1, vertices=vertices,
smoothing_steps=3,hemi=hemi)
brain.set_data_time_index(0)
brain.scale_data_colormap(fmin=3.26, fmid=5.84, fmax= 8.42, transparent=False)
brain.show_view(dict(azimuth=azimuth,elevation=None, distance=None))
# mayavi.mlab.view(azimuth=0, elevation=None, distance=None, focalpoint=None,
# roll=None, reset_roll=True, figure=None)
PlotDir = []
PlotDir = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/Plot_STATS/'
+ "_vs_".join(condition))
if not os.path.exists(PlotDir):
os.makedirs(PlotDir)
brain.save_image(PlotDir + '/Fmap_IcaCorr_' + modality + '_' + 'dSPM' + '_'
+ '_' + "_vs_".join(condition) + '_'
+ hemi + '_'+ str(azimuth)+ '_ico-5-fwd-fsaverage-'+'.png')
def test_meg_inverse():
"""Test plotting of MEG inverse solution."""
_set_backend()
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
vertices = stc['vertices']
colormap = 'hot'
data = stc['data']
data_full = (brain.geo['lh'].nn[vertices][..., np.newaxis] *
data[:, np.newaxis])
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1], endpoint=False)
def time_label(t):
return 'time=%0.2f ms' % (1e3 * t)
for use_data in (data, data_full):
brain.add_data(use_data, colormap=colormap, vertices=vertices,
smoothing_steps=1, time=time, time_label=time_label)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
assert_equal(brain.data_dict['lh']['time_idx'], 0)
brain.set_time(.1)
assert_equal(brain.data_dict['lh']['time_idx'], 2)
# viewer = TimeViewer(brain)
# multiple data layers
assert_raises(ValueError, brain.add_data, data, vertices=vertices,
time=time[:-1])
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=1, time=time, time_label=time_label,
initial_time=.09)
assert_equal(brain.data_dict['lh']['time_idx'], 1)
data_dicts = brain._data_dicts['lh']
assert_equal(len(data_dicts), 3)
assert_equal(data_dicts[0]['time_idx'], 1)
assert_equal(data_dicts[1]['time_idx'], 1)
# shift time in both layers
brain.set_data_time_index(0)
assert_equal(data_dicts[0]['time_idx'], 0)
assert_equal(data_dicts[1]['time_idx'], 0)
brain.set_data_smoothing_steps(2)
# add second data-layer without time axis
brain.add_data(data[:, 1], colormap=colormap, vertices=vertices,
smoothing_steps=2)
brain.set_data_time_index(2)
assert_equal(len(data_dicts), 4)
# change surface
brain.set_surf('white')
# remove all layers
brain.remove_data()
assert_equal(brain._data_dicts['lh'], [])
brain.close()
def test_meg_inverse():
"""Test plotting of MEG inverse solution."""
mlab.options.backend = 'test'
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1], endpoint=False)
colormap = 'hot'
def time_label(t):
return 'time=%0.2f ms' % (1e3 * t)
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=10, time=time, time_label=time_label)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
assert_equal(brain.data_dict['lh']['time_idx'], 0)
brain.set_time(.1)
assert_equal(brain.data_dict['lh']['time_idx'], 2)
# viewer = TimeViewer(brain)
# multiple data layers
assert_raises(ValueError, brain.add_data, data, vertices=vertices,
time=time[:-1])
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=10, time=time, time_label=time_label,
initial_time=.09)
assert_equal(brain.data_dict['lh']['time_idx'], 1)
data_dicts = brain._data_dicts['lh']
assert_equal(len(data_dicts), 2)
assert_equal(data_dicts[0]['time_idx'], 1)
assert_equal(data_dicts[1]['time_idx'], 1)
# shift time in both layers
brain.set_data_time_index(0)
assert_equal(data_dicts[0]['time_idx'], 0)
assert_equal(data_dicts[1]['time_idx'], 0)
# remove all layers
brain.remove_data()
assert_equal(brain._data_dicts['lh'], [])
brain.close()
def test_meg_inverse():
"""Test plotting of MEG inverse solution
"""
mlab.options.backend = 'test'
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
time = 1e3 * np.linspace(stc['tmin'],
stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1])
colormap = 'hot'
time_label = 'time=%0.2f ms'
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=10, time=time, time_label=time_label)
brain.set_data_time_index(2)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
def plot_overlays_diff_singlesubj(subject,condition,method,modality,hemi,indextime, azimuth):
subject_id, surface = 'fsaverage', 'inflated'
hemi = hemi
brain = Brain(subject_id, hemi, surface, size=(600, 600))
stc_fname = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/' + subject + '/mne_python/STCS_diff/IcaCorr_'
+ condition[0] + '-' + condition[1]
+ '/' + modality + '_' + method + '_' + subject
+ '_' + condition[0] + '-' + condition[1]
+ '_' + '_ico-5-fwd-fsaverage-.stc-'+hemi+'.stc')
stc = read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1])
colormap = 'hot'
time_label = lambda t: 'time=%0.2f ms' % (t * 1e3)
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=4, time=time, time_label=time_label,
hemi=hemi)
brain.set_data_time_index(indextime)
brain.scale_data_colormap(fmin=0, fmid=2.5, fmax=5, transparent=True)
brain.show_view(dict(azimuth=azimuth,elevation=None, distance=None))
# mayavi.mlab.view(azimuth=0, elevation=None, distance=None, focalpoint=None,
# roll=None, reset_roll=True, figure=None)
realtime = stc['tmin'] + stc['tstep']*indextime
PlotDir = []
PlotDir = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/' + subject + '/mne_python/BrainMaps/IcaCorr_' +
+ condition[0] + '-' + condition[1])
if not os.path.exists(PlotDir):
os.makedirs(PlotDir)
brain.save_image(PlotDir + '/IcaCorr_' + modality + '_' + method + '_' + subject
+ '_' + condition[0] + '-' + condition[1] + '_' + str(realtime) + hemi
+ '_'+ str(azimuth)+ '_ico-5-fwd-fsaverage-'
+'.png')
def test_meg_inverse():
"""Test plotting of MEG inverse solution
"""
mlab.options.backend = 'test'
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
time = 1e3 * np.linspace(
stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'], data.shape[1])
colormap = 'hot'
time_label = 'time=%0.2f ms'
brain.add_data(data,
colormap=colormap,
vertices=vertices,
smoothing_steps=10,
time=time,
time_label=time_label)
brain.set_data_time_index(2)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
# viewer = TimeViewer(brain)
brain.close()
def plot_overlays_diff_group(condition,method,modality,hemi,indextime,azimuth):
hemi = hemi
brain = Brain(subject_id='fsaverage', hemi=hemi, surface='pial', size=(600, 600))
stc_fname = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/BrainMaps/IcaCorr_'
+ modality + '_' + condition[0] + '-' + condition[1] + '_pick_oriNone_' + method
+ '_ico-5-fwd-fsaverage.stc-'+ hemi +'.stc')
stc = read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1])
# colormap = 'seismic'
colormap = mne.viz.mne_analyze_colormap(limits=[-3,-1.81,-1.80, 1.80,1.81, 3], format='mayavi')
time_label = lambda t: 'time=%0.2f ms' % (t * 1e3)
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=20, time=time, time_label=time_label,
hemi=hemi)
brain.set_data_time_index(indextime)
brain.scale_data_colormap(fmin=-1.82, fmid=0, fmax= 1.82, transparent=False)
brain.show_view(dict(azimuth=azimuth,elevation=None, distance=None))
# mayavi.mlab.view(azimuth=0, elevation=None, distance=None, focalpoint=None,
# roll=None, reset_roll=True, figure=None)
realtime = stc['tmin'] + stc['tstep']*indextime
PlotDir = []
PlotDir = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/Plots/IcaCorr_'
+ condition[0] + '-' + condition[1] )
if not os.path.exists(PlotDir):
os.makedirs(PlotDir)
brain.save_image(PlotDir + '/IcaCorr_' + modality + '_' + method + '_'
+ '_' + condition[0] + '-' + condition[1] + '_' + str(realtime)
+ hemi + '_'+ str(azimuth)+ '_ico-5-fwd-fsaverage-'+'.png')
vertices = stc['vertices']
"""
time points in milliseconds
"""
time = 1e3 * np.linspace(
stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'], data.shape[1])
"""
colormap to use
"""
colormap = 'hot'
"""
label for time annotation
"""
time_label = 'time=%0.2f ms'
brain.add_data(data,
colormap=colormap,
vertices=vertices,
smoothing_steps=10,
time=time,
time_label=time_label)
"""
scale colormap and set time (index) to display
"""
brain.set_data_time_index(2)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
"""
uncomment this line to use the interactive TimeViewer GUI
"""
#viewer = TimeViewer(brain)
time points in milliseconds
"""
time = 1e3 * np.linspace(stc['tmin'],
stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1])
"""
colormap to use
"""
colormap = 'hot'
"""
label for time annotation
"""
time_label = 'time=%0.2f ms'
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=10, time=time, time_label=time_label,
hemi=hemi)
"""
scale colormap and set time (index) to display
"""
brain.set_data_time_index(2)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
"""
uncomment these lines to use the interactive TimeViewer GUI
"""
#from surfer import TimeViewer
#viewer = TimeViewer(brain)
brain = Brain(subject_id=mri_partic,subjects_dir=shared_dir,surf='orig',hemi='both', background='white', size=(800, 600))
brain.add_annotation(parc)
brain = stc.plot(surface='inflated', hemi='lh', subjects_dir=shared_dir)
brain.set_data_time_index(300) # 221 for S2
brain.scale_data_colormap(fmin=-1e-12, fmid=1e-12, fmax=50e-12, transparent=True)
brain.show_view('lateral')
vertno_max, time_max = stc.get_peak(hemi='rh')
surfer_kwargs = dict(
subjects_dir=shared_dir,
clim=dict(kind='value', lims=[8, 12, 15]), views='lateral',
