def brain_plot(r_name, viewtimes, brain):
views = {'rh': ['medial', 'parietal'], 'lh': ['parietal', 'medial']}
if type(brain) is str:
hemi = brain
brain = Brain('fsaverage',
hemi,
'inflated',
cortex='low_contrast',
subjects_dir=sv.subjects_dir,
background='w',
foreground='k')
else:
hemi = brain.geo.keys()[0]
sv.show_labels_as_data(src_df_masked.loc[r_name], show_measure, brain,
**colorinfo)
filepat = os.path.join(
figdir, filepat_base + '_{}_{}_%02d.png'.format(hemi, r_name))
brain.save_image_sequence([times.get_loc(t) for t in viewtimes],
filepat,
montage=views[hemi])
return brain
def make_brain_figures(srcdf, measure, cinfo, filepat_base, top=5):
views = {'rh': ['medial', 'lateral'],
'lh': ['lateral', 'medial']}
toplabels = {}
for hemi in ['lh', 'rh']:
brain = Brain('fsaverage', hemi, 'inflated', cortex='low_contrast',
subjects_dir=sv.subjects_dir, background='w',
foreground='k')
sv.show_labels_as_data(srcdf, measure, brain,
time_label=None, parc=parc, **cinfo)
if top:
ind = srcdf.index.get_level_values('label').map(
lambda x: x.startswith(hemi[0].upper()))
topl = srcdf[ind].abs().sort_values(
measure, ascending=False).head(top)
topl = (
topl[(topl[measure] != 0)
& topl[measure].notna()]
.index.droplevel('time'))
toplabels[hemi] = topl
alllabels = mne.read_labels_from_annot(
'fsaverage', parc=parc, hemi=hemi)
brain.remove_labels()
for label in alllabels:
if label.name in topl:
brain.add_label(label, borders=1, hemi=hemi, alpha=0.8,
color='k')
# increase font size of colorbar - this only works by increasing the
# colorbar itself and setting the ratio of colorbar to text
brain.data['colorbar'].scalar_bar.bar_ratio = 0.35
brain.data['colorbar'].scalar_bar_representation.position = [0.075, 0.01]
brain.data['colorbar'].scalar_bar_representation.position2 = [0.85, 0.12]
filepat = os.path.join(figdir, filepat_base + '_{}.png'.format(hemi))
brain.save_montage(filepat, views[hemi], colorbar=0)
brain.close()
infiles = [
os.path.join(figdir, filepat_base + '_{}.png'.format(hemi))
for hemi in ['lh', 'rh']]
outfile = os.path.join(figdir, filepat_base + '.png')
os.system("montage -tile 2x1 -geometry +0+0 {} {}".format(
' '.join(infiles), outfile))
return toplabels
def plot(r_name, measure, mask=None, clim=[0, 3., 5], perm=0):
src_df = second_level.loc[perm].xs(r_name, axis=1, level='regressor')
if mask:
insig = pd.np.logical_not(second_level.loc[perm,
('significant', r_name)])
src_df[insig.values] = 0
src_df['time'] = 0
src_df.set_index('time', append=True, inplace=True)
if measure in ['tval', 'mean']:
labels = sv.show_labels_as_data(src_df,
measure,
brain,
colormap='divtrans',
clim=clim)
else:
labels = sv.show_labels_as_data(src_df, measure, brain)
return labels
#%% load specific regressor
r_name = 'dot_x'
show_measure = 'mu_mean'
src_df_masked = ss.load_src_df(basefile, r_name, clusters)
brain = Brain('fsaverage',
'both',
'inflated',
cortex='low_contrast',
subjects_dir=sv.subjects_dir,
background='w',
foreground='k')
labels = sv.show_labels_as_data(src_df_masked,
show_measure,
brain,
transparent=True)
#brain.scale_data_colormap(src_df_masked[show_measure].min(),
# src_df_masked[show_measure].median(),
# src_df_masked[show_measure].max(), True)
brain.scale_data_colormap(0.01, 0.025, 0.06, True)
#labels = sv.show_labels_as_data(src_df_masked, 'mu_mean', brain,
# transparent=True,
# region_aggfun=lambda a: np.max(a, axis=0))
#tv = TimeViewer(brain)
srcdf_normed = srcdf.copy()
if normalise:
for wname in winnames:
normed, _, _ = helpers.normalise_magnitudes(
srcdf.loc[(slice(None), wname), :].dropna())
srcdf_normed.loc[normed.index] = normed
if interactive:
hemi = 'lh'
wname = 'build-up'
brain = Brain('fsaverage', hemi, 'inflated', cortex='low_contrast',
subjects_dir=sv.subjects_dir, background='w', foreground='k')
sv.show_labels_as_data(srcdf_normed.loc[(slice(None), wname), :],
show_measure, brain, time_label=None, parc=parc,
**get_colorinfo(srcdf, show_measure))
else:
toplstrs = []
for wname in winnames:
filepat_base = 'win_brain_{}_{}_{}'.format(r_name, show_measure, wname)
df = srcdf_normed.loc[(slice(None), wname), :]
if normalise:
cinfo = get_colorinfo(srcdf_normed, show_measure)
else:
cinfo = get_colorinfo(df, show_measure)
toplabels = make_brain_figures(df, show_measure, cinfo, filepat_base)
toplstrs.append('{}: left - {}; right - {}'.format(
def brain_plot(
wname,
brain,
toplabels=None,
save=False,
):
views = {
'rh':
['medial',
'lateral'], #{'azimuth': -20, 'elevation': 62, 'roll': -68}],
'lh': ['lateral', 'medial']
}
if type(brain) is str:
hemi = brain
brain = Brain('fsaverage',
hemi,
'inflated',
cortex='low_contrast',
subjects_dir=sv.subjects_dir,
background='w',
foreground='k')
else:
hemi = brain.geo.keys()[0]
srcdf = pd.DataFrame(xma_win[wname].values.copy(),
index=pd.MultiIndex.from_product(
[labels, [0]], names=['label', 'time']),
columns=[measure])
# get colorinfo based on distribution of differences before nulling
cinfo = get_colorinfo(srcdf)
# set all areas to 0 for which there is insufficient evidence that they
# represent evidence; for positive differences supporting dot_x only
# consider significant dot_x areas and equally for accev and negative
for reg in regressors:
if reg == 'accev':
siglabels = srcdf[srcdf[measure] < 0].xs(0, level='time').index
else:
siglabels = srcdf[srcdf[measure] > 0].xs(0, level='time').index
siglabels = is_significant.xs(
reg, level='regressor').loc[list(siglabels), wname]
siglabels = siglabels[~siglabels].index
srcdf.loc[(list(siglabels), 0), measure] = 0
sv.show_labels_as_data(srcdf,
measure,
brain,
time_label=None,
parc=parc,
**cinfo)
# if toplabels is not None:
# alllabels = mne.read_labels_from_annot(
# 'fsaverage', parc=parc, hemi=hemi)
# for label in alllabels:
# if label.name in toplabels[hemi]:
# brain.add_label(label, borders=1, hemi=hemi, alpha=0.8,
# color='k')
# increase font size of colorbar - this only works by increasing the
# colorbar itself and setting the ratio of colorbar to text
brain.data['colorbar'].scalar_bar.bar_ratio = 0.35
brain.data['colorbar'].scalar_bar_representation.position = [
0.075, 0.01
]
brain.data['colorbar'].scalar_bar_representation.position2 = [
0.85, 0.12
]
if save:
filepat = os.path.join(
figdir, filepat_base + '_{}_{}.png'.format(hemi, wname))
brain.save_montage(filepat, views[hemi], colorbar=0)
return brain, srcdf[srcdf[measure].abs() > cinfo['fmid']].xs(
0, level='time')
def brain_plot(brain,
wname,
toplabels=False,
save=False,
backface_culling=True):
if r_name in ['dot_x', 'dot_y', 'dot_x_sign']:
views = {
'rh':
['medial',
'parietal'], #{'azimuth': -20, 'elevation': 62, 'roll': -68}],
'lh': ['parietal', 'medial']
}
elif r_name in ['sum_dot_x', 'response']:
views = {'rh': ['medial', 'lateral'], 'lh': ['lateral', 'medial']}
if type(brain) is str:
hemi = brain
brain = Brain('fsaverage',
hemi,
'inflated',
cortex='low_contrast',
subjects_dir=sv.subjects_dir,
background='w',
foreground='k')
else:
hemi = brain.geo.keys()[0]
if common_color_scale:
cinfo = get_colorinfo(avsrcdf)
else:
cinfo = get_colorinfo(results[wname]['avsrcdf'])
sv.show_labels_as_data(results[wname]['avsrcdf'],
show_measure,
brain,
time_label=None,
parc=parc,
**cinfo)
if backface_culling:
brain.data['surfaces'][0].actor.property.backface_culling = True
if toplabels:
brain.remove_labels()
alllabels = mne.read_labels_from_annot('fsaverage',
parc=parc,
hemi=hemi)
for label in alllabels:
if label.name in results[wname]['toplabels'][hemi]:
brain.add_label(label,
borders=1,
hemi=hemi,
alpha=0.8,
color='k')
if backface_culling:
for ldict in brain._label_dicts.values():
ldict['surfaces'][0].actor.property.backface_culling = True
# increase font size of colorbar - this only works by increasing the
# colorbar itself and setting the ratio of colorbar to text
brain.data['colorbar'].scalar_bar.bar_ratio = 0.35
brain.data['colorbar'].scalar_bar_representation.position = [0.075, 0.01]
brain.data['colorbar'].scalar_bar_representation.position2 = [0.85, 0.12]
if save:
filepat = os.path.join(figdir,
filepat_base + '_{}_{}.png'.format(wname, hemi))
brain.save_montage(filepat, views[hemi], colorbar=0)
return brain