scaled_data = pickle.load( open("save_scaled_data.p", "rb"))
print "plotting correlation 123"
one_list_data = list(itertools.chain.from_iterable(scaled_data))
print len(scaled_data)
print len(one_list_data)
print
print len(scaled_data[0])
print
print len(one_list_data[0]), one_list_data[0]
print len(zip(*one_list_data))
print len(zip(*one_list_data)[0])
# feat_list = zip(*one_list_data)
feature_names = FEATURE_NAMES
plot_correlation(one_list_data, feature_names, True)
plot_correlation(one_list_data, feature_names, False)
# plot_correlation(one_list_data)
data_new = pickle.load( open("save_scaled_data_new.p", "rb"))
annotations_new = pickle.load( open("save_an_new.p", "rb"))
print "..loaded"
print len(data)
print 0
d = list(itertools.chain.from_iterable(data))
d2 = list(itertools.chain.from_iterable(data_new))
print len(d)
print len(d[0])
print "---"
# assert 0
plot_correlation(d, FEATURE_NAMES, False)
plot_correlation(d, FEATURE_NAMES, False)
#===============================================================================
# print "removing candidates from data"
#
# data = data_new
# annotations = annotations_new
#===============================================================================
# # print map(sum, annotations)
# # # print filter_dead(data[0], )
# #
# # # print len([filter_dead(d,a) for d,a in zip(data,annotations) ])