def plot_visualize_mft_sources(fwdmag, stcdata, tmin, tstep, subject,
subjects_dir):
'''
Plot the MFT sources at time point of peak.
'''
print "##### Attempting to plot:"
# cf. decoding/plot_decoding_spatio_temporal_source.py
vertices = [s['vertno'] for s in fwdmag['src']]
if len(vertices) == 1:
vertices = [
fwdmag['src'][0]['vertno']
[fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] <= -0.],
fwdmag['src'][0]['vertno'][
fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] > -0.]
]
stc_feat = SourceEstimate(stcdata,
vertices=vertices,
tmin=-0.2,
tstep=tstep,
subject=subject)
for hemi in ['lh', 'rh']:
brain = stc_feat.plot(surface='white',
hemi=hemi,
subjects_dir=subjects_dir,
transparent=True,
clim='auto')
brain.show_view('lateral')
# use peak getter to move visualization to the time point of the peak
tmin = 0.095
tmax = 0.10
print "Restricting peak search to [%fs, %fs]" % (tmin, tmax)
if hemi == 'both':
vertno_max, time_idx = stc_feat.get_peak(hemi='rh',
time_as_index=True,
tmin=tmin,
tmax=tmax)
else:
vertno_max, time_idx = stc_feat.get_peak(hemi=hemi,
time_as_index=True,
tmin=tmin,
tmax=tmax)
if hemi == 'lh':
comax = fwdmag['src'][0]['rr'][vertno_max]
print "hemi=%s: vertno_max=%d, time_idx=%d fwdmag['src'][0]['rr'][vertno_max] = " %\
(hemi, vertno_max, time_idx), comax
elif len(fwdmag['src']) > 1:
comax = fwdmag['src'][1]['rr'][vertno_max]
print "hemi=%s: vertno_max=%d, time_idx=%d fwdmag['src'][1]['rr'][vertno_max] = " %\
(hemi, vertno_max, time_idx), comax
print "hemi=%s: setting time_idx=%d" % (hemi, time_idx)
brain.set_data_time_index(time_idx)
# draw marker at maximum peaking vertex
brain.add_foci(vertno_max,
coords_as_verts=True,
hemi=hemi,
color='blue',
scale_factor=0.6)
offsets = np.append([0], [s['nuse'] for s in fwdmag['src']])
if hemi == 'lh':
ifoci = [
np.nonzero([
stcdata[0:offsets[1], time_idx] >=
0.25 * np.max(stcdata[:, time_idx])
][0])
]
vfoci = fwdmag['src'][0]['vertno'][ifoci[0][0]]
cfoci = fwdmag['src'][0]['rr'][vfoci]
print "Coords of %d sel. vfoci: " % cfoci.shape[0]
print cfoci
print "vfoci: "
print vfoci
print "brain.geo['lh'].coords[vfoci] : "
print brain.geo['lh'].coords[vfoci]
elif len(fwdmag['src']) > 1:
ifoci = [
np.nonzero([
stcdata[offsets[1]:, time_idx] >=
0.25 * np.max(stcdata[:, time_idx])
][0])
]
vfoci = fwdmag['src'][1]['vertno'][ifoci[0][0]]
cfoci = fwdmag['src'][1]['rr'][vfoci]
print "Coords of %d sel. vfoci: " % cfoci.shape[0]
print cfoci
print "vfoci: "
print vfoci
print "brain.geo['rh'].coords[vfoci] : "
print brain.geo['rh'].coords[vfoci]
mrfoci = np.zeros(cfoci.shape)
invmri_head_t = invert_transform(fwdmag['info']['mri_head_t'])
mrfoci = apply_trans(invmri_head_t['trans'], cfoci, move=True)
print "mrfoci: "
print mrfoci
# Just some blops:
bloblist = np.zeros((300, 3))
for i in xrange(100):
bloblist[i, 0] = float(i)
bloblist[i + 100, 1] = float(i)
bloblist[i + 200, 2] = float(i)
mrblobs = apply_trans(invmri_head_t['trans'], bloblist, move=True)
brain.save_image('testfig_map_%s.png' % hemi)
brain.close()
def plot_visualize_mft_sources(fwdmag, stcdata, tmin, tstep,
subject, subjects_dir):
'''
Plot the MFT sources at time point of peak.
'''
print "##### Attempting to plot:"
# cf. decoding/plot_decoding_spatio_temporal_source.py
vertices = [s['vertno'] for s in fwdmag['src']]
if len(vertices) == 1:
vertices = [fwdmag['src'][0]['vertno'][fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] <= -0.],
fwdmag['src'][0]['vertno'][fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] > -0.]]
stc_feat = SourceEstimate(stcdata, vertices=vertices,
tmin=-0.2, tstep=tstep, subject=subject)
for hemi in ['lh', 'rh']:
brain = stc_feat.plot(surface='white', hemi=hemi, subjects_dir=subjects_dir,
transparent=True, clim='auto')
brain.show_view('lateral')
# use peak getter to move visualization to the time point of the peak
tmin = 0.095
tmax = 0.10
print "Restricting peak search to [%fs, %fs]" % (tmin, tmax)
if hemi == 'both':
vertno_max, time_idx = stc_feat.get_peak(hemi='rh', time_as_index=True,
tmin=tmin, tmax=tmax)
else:
vertno_max, time_idx = stc_feat.get_peak(hemi=hemi, time_as_index=True,
tmin=tmin, tmax=tmax)
if hemi == 'lh':
comax = fwdmag['src'][0]['rr'][vertno_max]
print "hemi=%s: vertno_max=%d, time_idx=%d fwdmag['src'][0]['rr'][vertno_max] = " %\
(hemi, vertno_max, time_idx), comax
elif len(fwdmag['src']) > 1:
comax = fwdmag['src'][1]['rr'][vertno_max]
print "hemi=%s: vertno_max=%d, time_idx=%d fwdmag['src'][1]['rr'][vertno_max] = " %\
(hemi, vertno_max, time_idx), comax
print "hemi=%s: setting time_idx=%d" % (hemi, time_idx)
brain.set_data_time_index(time_idx)
# draw marker at maximum peaking vertex
brain.add_foci(vertno_max, coords_as_verts=True, hemi=hemi, color='blue',
scale_factor=0.6)
offsets = np.append([0], [s['nuse'] for s in fwdmag['src']])
if hemi == 'lh':
ifoci = [np.nonzero([stcdata[0:offsets[1],time_idx]>=0.25*np.max(stcdata[:,time_idx])][0])]
vfoci = fwdmag['src'][0]['vertno'][ifoci[0][0]]
cfoci = fwdmag['src'][0]['rr'][vfoci]
print "Coords of %d sel. vfoci: " % cfoci.shape[0]
print cfoci
print "vfoci: "
print vfoci
print "brain.geo['lh'].coords[vfoci] : "
print brain.geo['lh'].coords[vfoci]
elif len(fwdmag['src']) > 1:
ifoci = [np.nonzero([stcdata[offsets[1]:,time_idx]>=0.25*np.max(stcdata[:,time_idx])][0])]
vfoci = fwdmag['src'][1]['vertno'][ifoci[0][0]]
cfoci = fwdmag['src'][1]['rr'][vfoci]
print "Coords of %d sel. vfoci: " % cfoci.shape[0]
print cfoci
print "vfoci: "
print vfoci
print "brain.geo['rh'].coords[vfoci] : "
print brain.geo['rh'].coords[vfoci]
mrfoci = np.zeros(cfoci.shape)
invmri_head_t = invert_transform(fwdmag['info']['mri_head_t'])
mrfoci = apply_trans(invmri_head_t['trans'],cfoci, move=True)
print "mrfoci: "
print mrfoci
# Just some blops:
bloblist = np.zeros((300,3))
for i in xrange(100):
bloblist[i,0] = float(i)
bloblist[i+100,1] = float(i)
bloblist[i+200,2] = float(i)
mrblobs = apply_trans(invmri_head_t['trans'], bloblist, move=True)
brain.save_image('testfig_map_%s.png' % hemi)
brain.close()
def plot_visualize_mft_sources(fwdmag, stcdata, tmin, tstep, subject,
subjects_dir):
"""
Plot the MFT sources at time point of peak.
Parameters
----------
fwdmag: forward solution
stcdata: stc with ||cdv|| (point sequence as in fwdmag['source_rr'])
tmin, tstep, subject: passed to mne.SourceEstimate()
"""
print("##### Attempting to plot:")
# cf. decoding/plot_decoding_spatio_temporal_source.py
vertices = [s['vertno'] for s in fwdmag['src']]
if len(vertices) == 1:
vertices = [
fwdmag['src'][0]['vertno']
[fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] <= -0.],
fwdmag['src'][0]['vertno'][
fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] > -0.]
]
elif len(vertices) > 2:
warnings.warn(
'plot_visualize_mft_sources(): Cannot handle more than two sources spaces'
)
return
stc_feat = SourceEstimate(stcdata,
vertices=vertices,
tmin=tmin,
tstep=tstep,
subject=subject)
itmaxsum = np.argmax(np.sum(stcdata, axis=0))
twmin = tmin + tstep * float(itmaxsum - stcdata.shape[1] / 20)
twmax = tmin + tstep * float(itmaxsum + stcdata.shape[1] / 20)
for ihemi, hemi in enumerate(['lh', 'rh', 'both']):
brain = stc_feat.plot(surface='white',
hemi=hemi,
subjects_dir=subjects_dir,
transparent=True,
clim='auto')
# use peak getter to move visualization to the time point of the peak
print("Restricting peak search to [%fs, %fs]" % (twmin, twmax))
if hemi == 'both':
brain.show_view('parietal')
vertno_max, time_idx = stc_feat.get_peak(hemi=None,
time_as_index=True,
tmin=twmin,
tmax=twmax)
else:
brain.show_view('lateral')
vertno_max, time_idx = stc_feat.get_peak(hemi=hemi,
time_as_index=True,
tmin=twmin,
tmax=twmax)
print("hemi=%s: setting time_idx=%d" % (hemi, time_idx))
brain.set_data_time_index(time_idx)
if hemi == 'lh' or hemi == 'rh':
# draw marker at maximum peaking vertex
brain.add_foci(vertno_max,
coords_as_verts=True,
hemi=hemi,
color='blue',
scale_factor=0.6)
if len(fwdmag['src']) > ihemi:
fwds = fwdmag['src'][ihemi]
comax = fwds['rr'][vertno_max]
print("hemi=%s: vertno_max=%d, time_idx=%d fwdmag['src'][%d]['rr'][vertno_max] = " % \
(hemi, vertno_max, time_idx, ihemi), comax)
offsets = np.append([0], [s['nuse'] for s in fwdmag['src']])
if hemi == 'lh':
ifoci = [
np.nonzero([
stcdata[0:offsets[1], time_idx] >=
0.25 * np.max(stcdata[:, time_idx])
][0])
]
elif len(fwdmag['src']) > 1:
ifoci = [
np.nonzero([
stcdata[offsets[1]:, time_idx] >=
0.25 * np.max(stcdata[:, time_idx])
][0])
]
vfoci = fwds['vertno'][ifoci[0][0]]
cfoci = fwds['rr'][vfoci]
print("Coords of %d sel. vfoci: " % cfoci.shape[0])
print(cfoci)
print("vfoci: ")
print(vfoci)
print("brain.geo[%s].coords[vfoci] : " % hemi)
print(brain.geo[hemi].coords[vfoci])
mrfoci = np.zeros(cfoci.shape)
invmri_head_t = invert_transform(fwdmag['info']['mri_head_t'])
mrfoci = apply_trans(invmri_head_t['trans'], cfoci, move=True)
print("mrfoci: ")
print(mrfoci)
# Just some blops along the coordinate axis:
# This will not yield reasonable results w an inflated brain.
# bloblist = np.zeros((300,3))
# for i in xrange(100):
# bloblist[i,0] = float(i)
# bloblist[i+100,1] = float(i)
# bloblist[i+200,2] = float(i)
# mrblobs = apply_trans(invmri_head_t['trans'], bloblist, move=True)
# brain.add_foci(mrblobs, coords_as_verts=False, hemi=hemi, color='yellow', scale_factor=0.3)
brain.save_image('testfig_map_%s.png' % hemi)
brain.close()
