# #plots.xlim(xlims)
# #leg = plots.legend(loc=0)
# #for legobj in leg.legendHandles:
# # legobj.set_linewidth(2.0)
# # This simply sets the x-axis data to diagonal so it fits better.
# f.autofmt_xdate()
#
# plt.ylabel("Fraction of occurence")
# plt.title("Level " + str(lev+1))
# #plt.tight_layout()
# plt.show()
# pdb.set_trace()
#Aggregate sound files
for date in dates:
print "Starting: Sound aggregation for day " + str(date)
has_video_array=has_video(disconnect_file_loc + sbj_id + "_" + str(date) + ".txt")
aggregate.aggregate(sbj_id, date, sound_loc + str(date) + "\\",
sound_loc + str(date) + "\\", has_video_array)
#Aggregate mvmt files
for date in dates:
print "Starting: Mvmt aggregation for day " + str(date)
has_video_array=has_video(disconnect_file_loc + sbj_id + "_" + str(date) + ".txt")
aggregate.aggregate(sbj_id, date, mvmt_loc,
mvmt_loc, has_video_array)
#Correlate clusters
for date in dates:
print "Starting: Correlations for day " + str(date)
best_corr_clusters.append(correlate_signals.correlate( sound_loc + str(date) + "\\" + sbj_id + "_" + str(date) + ".p",
else:
if printed == 0:
printed = 1
print("run out of videos at frame #" + str(f) +
" out of " + str(has_video_array.shape[0]) +
" for subject " + sbj_id + " on day " + str(day))
total_features[f] = -1
#plt.plot(total_features)
#plt.show()
pickle.dump(
total_features,
open(output_file_loc + sbj_id + "_" + str(day) + ".p", "wb"))
if __name__ == "__main__":
sbj_id = "fcb01f7a"
day = 13
sr = 30
input_file_loc = "E:\\mvmt\\%s\\" % sbj_id
output_file_loc = input_file_loc
has_video_array = has_video(
"C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\disconnect_times\\%s_%i.txt"
% (sbj_id, day),
samp_rate=sr)
aggregate(sbj_id, day, input_file_loc, output_file_loc, has_video_array)
# plt.plot(np.array(range(270*30*60))/(60*30.0), total_features[:270*30*60])
# #plt.ylim([10**11,6*10**15])
# plt.xlabel("Time (Min)")
# plt.ylabel("Speech sound level")
# plt.show()
for f in xrange(has_video_array.shape[0]):
if has_video_array[f] == 0:
total_features[f] = -1
else:
if feature_obj.has_next():
total_features[f] = feature_obj.next()
else:
if printed == 0:
printed = 1
print ("run out of videos at frame #" + str(f) + " out of " + str(has_video_array.shape[0])
+ " for subject " + sbj_id + " on day " + str(day))
total_features[f] = -1
#plt.plot(total_features)
#plt.show()
pickle.dump(total_features, open(output_file_loc + sbj_id + "_" + str(day) + ".p" , "wb"))
if __name__ == "__main__":
sbj_id = "e70923c4"
day = 5
sr=30
input_file_loc = "E:\\mvmt\\e70923c4\\"
output_file_loc = input_file_loc
has_video_array = has_video("C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\disconnect_times\\" \
+ sbj_id + "_" + str(day) + ".txt", samp_rate = sr)
aggregate(sbj_id, day, input_file_loc, output_file_loc, has_video_array)
# plt.plot(np.array(range(270*30*60))/(60*30.0), total_features[:270*30*60])
# #plt.ylim([10**11,6*10**15])
# plt.xlabel("Time (Min)")
# plt.ylabel("Speech sound level")
# plt.show()
# #plots.xlim(xlims)
# #leg = plots.legend(loc=0)
# #for legobj in leg.legendHandles:
# # legobj.set_linewidth(2.0)
# # This simply sets the x-axis data to diagonal so it fits better.
# f.autofmt_xdate()
#
# plt.ylabel("Fraction of occurence")
# plt.title("Level " + str(lev+1))
# #plt.tight_layout()
# plt.show()
# pdb.set_trace()
#Aggregate sound files
for date in dates:
print "Starting: Sound aggregation for day " + str(date)
has_video_array = has_video(disconnect_file_loc + sbj_id + "_" +
str(date) + ".txt")
aggregate.aggregate(sbj_id, date, sound_loc + str(date) + "\\",
sound_loc + str(date) + "\\", has_video_array)
#Aggregate mvmt files
for date in dates:
print "Starting: Mvmt aggregation for day " + str(date)
has_video_array = has_video(disconnect_file_loc + sbj_id + "_" +
str(date) + ".txt")
aggregate.aggregate(sbj_id, date, mvmt_loc, mvmt_loc, has_video_array)
#Correlate clusters
for date in dates:
print "Starting: Correlations for day " + str(date)
best_corr_clusters.append(
for f in xrange(has_video_array.shape[0]):
if has_video_array[f] == 0:
total_features[f] = -1
else:
if feature_obj.has_next():
total_features[f] = feature_obj.next()
else:
if printed == 0:
printed = 1
print ("run out of videos at frame #" + str(f) + " out of " + str(has_video_array.shape[0])
+ " for subject " + sbj_id + " on day " + str(day))
total_features[f] = -1
#plt.plot(total_features)
#plt.show()
pickle.dump(total_features, open(output_file_loc + sbj_id + "_" + str(day) + ".p" , "wb"))
if __name__ == "__main__":
sbj_id = "fcb01f7a"
day = 13
sr=30
input_file_loc = "E:\\mvmt\\%s\\" % sbj_id
output_file_loc = input_file_loc
has_video_array = has_video("C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\disconnect_times\\%s_%i.txt"
% (sbj_id, day), samp_rate = sr)
aggregate(sbj_id, day, input_file_loc, output_file_loc, has_video_array)
# plt.plot(np.array(range(270*30*60))/(60*30.0), total_features[:270*30*60])
# #plt.ylim([10**11,6*10**15])
# plt.xlabel("Time (Min)")
# plt.ylabel("Speech sound level")
# plt.show()
if feature_obj.has_next():
total_features[f] = feature_obj.next()
else:
if printed == 0:
printed = 1
print("run out of videos at frame #" + str(f) +
" out of " + str(has_video_array.shape[0]) +
" for subject " + sbj_id + " on day " + str(day))
total_features[f] = -1
#plt.plot(total_features)
#plt.show()
pickle.dump(
total_features,
open(output_file_loc + sbj_id + "_" + str(day) + ".p", "wb"))
if __name__ == "__main__":
sbj_id = "e70923c4"
day = 5
sr = 30
input_file_loc = "E:\\mvmt\\e70923c4\\"
output_file_loc = input_file_loc
has_video_array = has_video("C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\disconnect_times\\" \
+ sbj_id + "_" + str(day) + ".txt", samp_rate = sr)
aggregate(sbj_id, day, input_file_loc, output_file_loc, has_video_array)
# plt.plot(np.array(range(270*30*60))/(60*30.0), total_features[:270*30*60])
# #plt.ylim([10**11,6*10**15])
# plt.xlabel("Time (Min)")
# plt.ylabel("Speech sound level")
# plt.show()
