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,
# ======================= #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)