mne pythonで作ったcon (connectivityのデータ)とlabelからconnectivityの図の動画を作るfunction

func_make_movie.py

#!/usr/bin/env python
# _*_ coding: utf-8 -*-

def make_movie_circle_plot(con,n_seed_label,labels, times, con_method, vmin, vmax):
	"""
	mne pythonで作ったcon(connectivity)のデータから時系列でfigureを作る
	# all-to-allかseed basedのデータにするか
	#n_seed_label = 9 # number or None: e.g. labels[9].name=entorhinal-rh
	#n_seed_label = None
	"""

	import numpy as np
	if n_seed_label is None:
		# pattern 1: all to all
		con_res = con # 特に意味なし。チュートリアルではmethodを2つ計算しており、それを格納するのに使っている
		sTitle = 'All-to-All Connectivity '
		#vmin,vmax=0.9,1.0
		movie_name_part = 'all2all_'
	else:
		## pattern 2: 1 to all
		con_res = np.zeros(shape=con.shape)
		con_res[n_seed_label,:,0,:]=con[n_seed_label,:,0,:] 
		con_res[:,n_seed_label,0,:]=con[:,n_seed_label,0,:]
		sTitle =  'seed-to-All Connectivity '+ labels[n_seed_label].name+ ' '
		#vmin,vmax=0.5,1.0
		movie_name_part = labels[n_seed_label].name+'_'

	label_names = [label.name for label in labels]
	label_colors = [label.color for label in labels]

	lh_labels = [name for name in label_names if name.endswith('lh')]

	# Get the y-location of the label
	label_ypos = list()
	for name in lh_labels:
		idx = label_names.index(name)
		ypos = np.mean(labels[idx].pos[:, 1])
		label_ypos.append(ypos)

	# Reorder the labels based on their location
	lh_labels = [label for (ypos, label) in sorted(zip(label_ypos, lh_labels))]

	# For the right hemi
	rh_labels = [label[:-2] + 'rh' for label in lh_labels]

	# Save the plot order and create a circular layout
	node_order = list()
	node_order.extend(lh_labels[::-1])  # reverse the order
	node_order.extend(rh_labels)

	from mne.viz import circular_layout, plot_connectivity_circle

	node_angles = circular_layout(label_names, node_order, start_pos=90,
		                          group_boundaries=[0, len(label_names) / 2])

	# Plot the graph using node colors from the FreeSurfer parcellation. We only
	# show the 300 strongest connections.
	import matplotlib.pyplot as plt
	#times_title = evoked.times*1000
	times_title = times*1000
	import datetime
	png_dir = 'png'+ datetime.datetime.now().strftime('%y%m%d%H%M%S')

	## movieのためのpngをたくさん作る
	import os
	os.mkdir(png_dir)
	os.chdir(png_dir)
	# plot
	for ii in np.arange(len(times_title)):
		print(ii)
		fig=plt.figure(num=None, figsize=(8,8),facecolor='black')
		plot_connectivity_circle(con_res[:,:,0,ii], label_names, 
							n_lines=300,
		                    node_angles=node_angles, node_colors=label_colors,
		                    title= sTitle + con_method +' '+ str(times_title[ii]),
							vmin = vmin, vmax =vmax,
							fig=fig)
		plt.savefig('%s%04d%s' %('connectivity_',ii,'.png'), facecolor='black')
		fig.clf()
		plt.close()

	## ffmpeg: png->mp4
	#import os
	movie_fname = 'movie_'+ movie_name_part +fname+'_'+ str(event_time)+'_'+str(fmin)+'_'+str(fmax)+'Hz.mp4'
	os.chdir('../')
	os.system('ffmpeg -framerate 10 -i png/connectivity_%04d.png '+ movie_fname)  #https://goo.gl/GW6LXi
	os.system('rm -rf '+png_dir)
	# subprocess.call('ffmpeg', shell=True) #is also possible

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