#time in mins
##### Extract RR intervals here #######
RR_sec_unclean = pr.get_RR_interval(rec_name, annotation, start_time,
end_time)
####DELETE THE FIRST RR_sec_unclean value#####
del RR_sec_unclean[0]
##### APPLY FILTERS TO CLEAN DATA #######
#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():
RR_sec_unclean=pr.get_RR_interval(rec_name,annotation,start_time,end_time)
print RR_sec_unclean
####DELETE THE FIRST RR_sec_unclean value#####
del RR_sec_unclean[0];
##### APPLY FILTERS TO CLEAN DATA #######
#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 #######
#if "n" in record:
####plot sodp and save fig ####
#fig_rr,plot_rr=graphs.plotScatter(rec_name,range(len(RR_sec)),RR_sec, "RR interval count", " RR interval (ms)", "Raw RR interval plot", 'b',xlim_lo=0, xlim_hi=80, ylim_lo=0.4, ylim_hi=1.0,axline=0)
#fig_rr.savefig(output_folder+"raw_rr_scatter_"+record+".pdf",format='pdf')
#if "p" in record:
####plot sodp and save fig ####
#fig_rr,plot_rr=graphs.plotScatter(rec_name,range(len(RR_sec)),RR_sec, "RR interval count", " RR interval (ms)", "Raw RR interval plot", 'r',xlim_lo=0, xlim_hi=80, ylim_lo=0.4, ylim_hi=1.0,axline=0)
#fig_rr.savefig(output_folder+"raw_rr_scatter_"+record+".pdf",format='pdf')
delta_RR_sec = pr.get_delta_rr(RR_sec);
total_min=30;
##### Calculating std of 5min features in all 6 intervals in 30min sodp features #################
##### Extract RR intervals here #######
RR_sec_unclean = pr.get_RR_interval(rec_name, annotation, start_time,
end_time)
####DELETE THE FIRST RR_sec_unclean value#####
del RR_sec_unclean[0]
##### APPLY FILTERS TO CLEAN DATA #######
#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;
##### Extract RR intervals here #######
RR_sec_unclean=pr.get_RR_interval(rec_name,annotation,start_time,end_time)
####DELETE THE FIRST RR_sec_unclean value#####
del RR_sec_unclean[0];
##### APPLY FILTERS TO CLEAN DATA #######
#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():
