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)