#RR_sec=dc.detrend_data(dc.quotient_filt(dc.square_filt(RR_sec_unclean)))
#RR_sec=dc.detrend_data((RR_sec_unclean)
RR_sec = dc.quotient_filt(dc.square_filt(RR_sec_unclean))
#RR_sec=dc.square_filt(RR_sec_unclean)
#RR_sec=RR_sec_unclean
##### Extract delta RR intervals here #######
delta_RR_sec = pr.get_delta_rr(RR_sec)
###### Calculating GOLBAL statistical features for RR_sec VALUES #################
###calculating AVG/mean of RR intervals ###
mean_global = np.mean(RR_sec) * 1000
mean_global_arr.append(mean_global) #do we need this?
feature_rec.append(mean_global)
global_vocab, index_of_features = cl.fill_global_vocab(
"mean_global", index_of_features, global_vocab)
# if "mean_global" not in global_vocab.keys():
# global_vocab["mean_global"]=index_of_features;
# index_of_features=index_of_features+1;
#print("mean_global is: " + str(mean_global))
#sdrr
sdrr_raw = np.std(RR_sec)
#print (" sdrr of RR_sec (raw) in sec is: " +str(sdrr_raw));
sdrr_raw_ms = sdrr_raw * 1000
std_dev_global = sdrr_raw_ms
feature_rec.append(std_dev_global)
global_vocab, index_of_features = cl.fill_global_vocab(
"std_dev_global", index_of_features, global_vocab)
#RR_sec=dc.detrend_data(dc.quotient_filt(dc.square_filt(RR_sec_unclean)))
#RR_sec=dc.detrend_data((RR_sec_unclean)
#RR_sec=dc.quotient_filt(dc.square_filt(RR_sec_unclean))
RR_sec = dc.square_filt(RR_sec_unclean)
#RR_sec=RR_sec_unclean
##### Extract delta RR intervals here #######
delta_RR_sec = pr.get_delta_rr(RR_sec)
###### Calculating GOLBAL statistical features for RR_sec VALUES #################
###calculating AVG/mean of RR intervals ###
mean_global = np.mean(RR_sec) * 1000
mean_global_arr.append(mean_global) #do we need this?
feature_rec.append(mean_global)
global_vocab, index_of_features = cl.fill_global_vocab(
"mean_global", index_of_features, global_vocab)
# if "mean_global" not in global_vocab.keys():
# global_vocab["mean_global"]=index_of_features;
# index_of_features=index_of_features+1;
#print("mean_global is: " + str(mean_global))
#sdrr
sdrr_raw = np.std(RR_sec)
#print (" sdrr of RR_sec (raw) in sec is: " +str(sdrr_raw));
sdrr_raw_ms = sdrr_raw * 1000
std_dev_global = sdrr_raw_ms
feature_rec.append(std_dev_global)
global_vocab, index_of_features = cl.fill_global_vocab(
"std_dev_global", index_of_features, global_vocab)
# ###### Calculating GOLBAL statistical features for RR_sec VALUES #################
# if "n" in record:
# ####plot sodp and save fig ####
# fig_std,plot_std_p=graphs.plot_simple(rec_name,range(len(std_dev_window_arr)), std_dev_window_arr, "Window number", "Standard Deviation", "SDW Series ", 'b',xlim_lo=0, xlim_hi=1, ylim_lo=0, ylim_hi=0.2)
# fig_std.savefig(output_folder+"std_dev_window_"+record+".pdf",format='pdf')
# if "p" in record:
# ####plot sodp and save fig ####
# fig_std,plot_std_p=graphs.plot_simple(rec_name,range(len(std_dev_window_arr)), std_dev_window_arr, "Window number", "Standard Deviation", "SDW Series ", 'r',xlim_lo=0, xlim_hi=1, ylim_lo=0, ylim_hi=0.2)
# fig_std.savefig(output_folder+"std_dev_window_"+record+".pdf",format='pdf')
###calculating AVG/mean of RR intervals ###
mean_global=np.mean(RR_sec)*1000;
mean_global_arr.append(mean_global) #do we need this?
feature_rec.append(mean_global)
global_vocab,index_of_features=cl.fill_global_vocab("mean_global", index_of_features, global_vocab)
# if "mean_global" not in global_vocab.keys():
# global_vocab["mean_global"]=index_of_features;
# index_of_features=index_of_features+1;
#print("mean_global is: " + str(mean_global))
#sdrr
sdrr_raw=np.std(RR_sec);
#print (" sdrr of RR_sec (raw) in sec is: " +str(sdrr_raw));
sdrr_raw_ms=sdrr_raw*1000;
std_dev_global=sdrr_raw_ms;
feature_rec.append(std_dev_global)
#print arr
#calc stats for 1 list and make 4 features
#print ('index_num is: ' + str(index_num))
size_val,min_max,mean_val,var_val,skewness_val,kurtosis_val=describe(arr)
min_val=min_max[0]
max_val=min_max[1]
feature_rec.append(size_val)
feature_rec.append(min_val)
feature_rec.append(max_val)
feature_rec.append(mean_val)
feature_rec.append(var_val)
feature_rec.append(skewness_val)
feature_rec.append(kurtosis_val)
for f_name in stat_feature_names:
global_vocab_ecg,index_of_features_ecg=cl.fill_global_vocab(f_name+'_'+all_feature_names[index_num], index_of_features_ecg, global_vocab_ecg)
index_num=index_num+1;
all_features.append(feature_rec)
print(' dict is: ' + str((global_vocab_ecg)))
print("all features is: ")
print all_features
print(len(all_features))
#write to all_features to file
rw.write_value(all_features,output_folder,"all_features_readable.txt",'w')
rw.write_features_to_file(all_features,output_folder,"all_features_pickle.txt")
rw.write_features_to_file(global_vocab_ecg,output_folder,"global_vocab_pickle.txt")
rw.write_features_to_file(rec_name_array, output_folder, "rec_name_array_pickle.txt")
exit()
#print ('index_num is: ' + str(index_num))
size_val, min_max, mean_val, var_val, skewness_val, kurtosis_val = describe(
arr)
min_val = min_max[0]
max_val = min_max[1]
feature_rec.append(size_val)
feature_rec.append(min_val)
feature_rec.append(max_val)
feature_rec.append(mean_val)
feature_rec.append(var_val)
feature_rec.append(skewness_val)
feature_rec.append(kurtosis_val)
for f_name in stat_feature_names:
global_vocab_ecg, index_of_features_ecg = cl.fill_global_vocab(
f_name + '_' + all_feature_names[index_num],
index_of_features_ecg, global_vocab_ecg)
index_num = index_num + 1
all_features.append(feature_rec)
print(' dict is: ' + str((global_vocab_ecg)))
print("all features is: ")
print all_features
print(len(all_features))
#write to all_features to file
rw.write_value(all_features, output_folder, "all_features_readable.txt", 'w')
rw.write_features_to_file(all_features, output_folder,
"all_features_pickle.txt")
rw.write_features_to_file(global_vocab_ecg, output_folder,
"global_vocab_pickle.txt")
# # #### APPLY FILTERS TO CLEAN DATA #######
# #
# # RR_sec=dc.detrend_data(dc.quotient_filt(dc.square_filt(RR_sec_unclean)))
# # RR_sec=dc.quotient_filt(dc.square_filt(RR_sec_unclean))
# # RR_sec=RR_sec_unclean
# # #### Extract delta RR intervals here #######
# # delta_RR_sec = pr.get_delta_rr(RR_sec);
###### Calculating GOLBAL statistical features for RR_sec VALUES #################
###calculating AVG/mean of RR intervals ###
mean_global=np.mean(RR_sec)*1000;
mean_global_arr.append(mean_global) #do we need this?
feature_rec.append(mean_global)
global_vocab,index_of_features=cl.fill_global_vocab("mean_global", index_of_features, global_vocab)
# if "mean_global" not in global_vocab.keys():
# global_vocab["mean_global"]=index_of_features;
# index_of_features=index_of_features+1;
#print("mean_global is: " + str(mean_global))
#sdrr
sdrr_raw=np.std(RR_sec);
#print (" sdrr of RR_sec (raw) in sec is: " +str(sdrr_raw));
sdrr_raw_ms=sdrr_raw*1000;
std_dev_global=sdrr_raw_ms;
feature_rec.append(std_dev_global)
