def plot(cur_lf0, o): print o cur_lf0[cur_lf0 < 0] = np.nan Utility.plot_graph(np.exp(cur_lf0), o) pass
tone_stress = np.intersect1d(np.where( np.array(tone) == target_tone)[0] , np.where( np.array(stress) == 'Stress')[0] ).astype(int) y_tone = y[tone_stress][:, :-1] # y_tone_means = np.mean(y_tone, axis=1) # print y_tone.shape # print y_tone_means # y_temp = [] # for idx, yt in enumerate(y_tone): # # print yt, y_tone_means[idx], yt-y_tone_means[idx] # y_temp.append(yt-y_tone_means[idx]) # y_tone = np.array(y_temp) out_object = '/home/h1/decha/Dropbox/Inter_speech_2016/Syllable_object/Typical_contour/50dims/tone_{}.pickle'.format(target_tone) # y_means = Utility.load_obj(out_object) # y_means = np.mean(y_tone, axis=0) x = np.arange(len(y_means)) # print y_means, len(y_means), x outname = '/home/h1/decha/Dropbox/Inter_speech_2016/temporary_output/tone_{}_typical.eps'.format(target_tone) Utility.plot_graph(x, y_means, outname) # out_object = '/home/h1/decha/Dropbox/Inter_speech_2016/Syllable_object/Typical_contour/50dims/tone_{}.pickle'.format(target_tone) Utility.save_obj(y_means, out_object)