def test_text():
"""Test plotting of text
"""
mlab.options.backend = 'test'
brain = Brain(*std_args)
brain.add_text(0.1, 0.1, 'Hello', 'blah')
brain.close()
def test_text():
"""Test plotting of text
"""
mlab.options.backend = 'test'
brain = Brain(*std_args)
brain.add_text(0.1, 0.1, 'Hello', 'blah')
brain.close()
def test_text():
"""Test plotting of text."""
_set_backend('test')
brain = Brain(*std_args)
brain.add_text(0.1, 0.1, 'Hello', 'blah')
brain.close()
views=['lat'],
config_opts=dict(background="white", colorbar="False"))
for r in sorted(rois):
# Name of ROI
name_roi = os.path.splitext(os.path.basename(r))[0]
#save_png= os.path.join(datadir, "{h}_{name_roi}".format(h=hemi, name_roi=name_roi))
# Manage the color
color = [val / 255. for val in color_list[cpt_color]]
# Add a title
pos_y += 0.05
text = brain.add_text(0.1,
pos_y,
color=tuple(color),
text=name_roi,
name=name_roi)
text = brain.texts[name_roi]
text.width = 0.2
# Projection part
# If reg_file if needed:
# surf_data = io.project_volume_data(r, "lh", reg_file)
# Projection parameters, see the docstring for more information
projmeth = "frac" # frac' method, projection on a fraction of thickness
projsum = "max" # the max value is projected
projarg = [0, 1, 0.1] # from 0 to 1 thickness fract every 0.1 step
smooth_fwhm = 0 # no smoothing
surf_data = io.project_volume_data(r,
hemi,
# Surface used to project 'white', 'inflated' or other
surf = "inflated"
brain = Brain("fsaverage", hemi, surf, views=['lat'],
config_opts=dict(background="white", colorbar="False"))
for r in sorted(rois):
# Name of ROI
name_roi = os.path.splitext(os.path.basename(r))[0]
#save_png= os.path.join(datadir, "{h}_{name_roi}".format(h=hemi, name_roi=name_roi))
# Manage the color
color = [val/255. for val in color_list[cpt_color]]
# Add a title
pos_y += 0.05
text = brain.add_text(0.1, pos_y, color=tuple(color),
text=name_roi, name=name_roi)
text = brain.texts[name_roi]
text.width = 0.2
# Projection part
# If reg_file if needed:
# surf_data = io.project_volume_data(r, "lh", reg_file)
# Projection parameters, see the docstring for more information
projmeth = "frac" # frac' method, projection on a fraction of thickness
projsum = "max" # the max value is projected
projarg = [0, 1, 0.1] # from 0 to 1 thickness fract every 0.1 step
smooth_fwhm = 0 # no smoothing
surf_data = io.project_volume_data(r, hemi,
projmeth=projmeth,
projsum=projsum,
projarg=projarg,
def test_text():
"""Test plotting of text."""
_set_backend('test')
brain = Brain(*std_args)
brain.add_text(0.1, 0.1, 'Hello', 'blah')
brain.close()
# =======================
#set time window to find peak within certain times
tmin=.00
tmax=.1
vertno_max, time_max = stc.get_peak(hemi='lh',tmin=tmin,tmax=tmax)
#also get indices
vertno_max_i, time_max_i = stc.get_peak(hemi='lh',tmin=tmin,tmax=tmax,vert_as_index=True, time_as_index=True)
#show peak on brain
surfer_kwargs = dict(
subjects_dir=shared_dir, views='lateral',
initial_time=time_max, time_unit='s', size=(800, 800), smoothing_steps=5)
brain = stc.plot(**surfer_kwargs)
brain.add_foci(vertno_max, coords_as_verts=True, hemi='lh', color='black',
scale_factor=1, alpha=0.9)
brain.add_text(0.1, 0.9, 'Peak between '+str(tmin*1000)+' and '+str(tmax*1000)+' ms: '+str(round(time_max*1000))+' ms', 'title',font_size=14)
#take activation at only max time and see which sources should be included in peak, based on a cutoff
lh_data=stc.data[:len(stc.vertices[0])]; #right: stc.data[len(stc.vertices[1]:)]
max_data=lh_data[:,time_max_i]
#max_data=abs(max_data)
#take absolute? i guess not because I would just canel stuff out?
# so we have to distinguish if the max is a peak or a trough
#if lh_data[vertno_max_i,time_max_i]<0:
# #max is trough
# midway_in_value=max_data.min()+abs(max_data.min()-max_data.max())/2
# midway_in_value_from0=max_data.min()-max_data.min()/2
# cutoff=max_data.max()#midway_in_value_from0
# #now find all sources where its above cutoff
# source_mask=[]
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',
initial_time=time_max, time_unit='s', size=(800, 800), smoothing_steps=5)
brain = stc.plot(**surfer_kwargs)
brain.add_foci(vertno_max, coords_as_verts=True, hemi='rh', color='blue',
scale_factor=0.6, alpha=0.5)
brain.add_text(0.1, 0.9, 'dSPM (plus location of maximal activation)', 'title',
font_size=14)
stc_vec = mne.minimum_norm.apply_inverse(erp, inverse_operator,
method='MNE', pick_ori='vector')
brain = stc_vec.plot(**surfer_kwargs)
brain.add_text(0.1, 0.9, 'Vector solution', 'title', font_size=20)
