###### Calculating window based statistical features for RAW SDRR VALUES #################
window_size=50;
std_dev_window_arr=[]
std_dev_window_arr=pr.get_window_std_dev(RR_sec, window_size)
window_number=1;
# for val in std_dev_window_arr:
# feature_rec.append(val)
# global_vocab,index_of_features=cl.fill_global_vocab("std_window_"+str(window_number), index_of_features, global_vocab)
# window_number=window_number+1;
###plot window std_dev and save fig ####
# fig_std_p,plot_std_p=graphs.plot_simple(rec_name,range(len(std_dev_window_arr)), std_dev_window_arr, "window std dev for window size: %s" % window_size, "std_dev", "window std dev", "r", 0, 0,-0.1, 0.5)
# fig_std_p.savefig(output_folder+"std_dev_window_"+record+".png")
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 variation in the std_dev_window_arr and save as feature
var_std_dev_window_arr=np.std(std_dev_window_arr);
#print("type of np.std elemnt is : " + str(type(var_std_dev_window_arr()))
#print("std of std array is " +str(var_std_dev_window_arr))
#var_std_dev_window_arr=np.nan_to_num(var_std_dev_window_arr)
#print "standard deviation of var_std_dev_window_arr is" +str(var_std_dev_window_arr)
feature_rec.append(np.float64(var_std_dev_window_arr));
global_vocab,index_of_features=cl.fill_global_vocab("var_std_dev_window_arr", index_of_features, global_vocab)
#### calculating skewness and kurtosis #####
#skewness_global=scipy.stats.skew(RR_sec)
#print("skewness: "+ str(skewness_global))
#kurtosis_global=scipy.stats.kurtosis(RR_sec, fisher='false')
#print("kurtosis_globl is : " +str(kurtosis_global))
#print("answer for kurtosis test is: "+str(scipy.stats.kurtosistest(RR_sec)))
###### Calculate nn50, pnn50 and rmSSD or diff_rr_std_dev ######
std_dev_diff = np.std(delta_RR_sec)
feature_rec.append(std_dev_diff)
global_vocab, index_of_features = cl.fill_global_vocab(
"std_dev_diff", index_of_features, global_vocab)
###### Calculating window based statistical features for RAW SDRR VALUES #################
window_size = 50
std_dev_window_arr = []
std_dev_window_arr = pr.get_window_std_dev(RR_sec, window_size)
window_number = 1
# for val in std_dev_window_arr:
# feature_rec.append(val)
# global_vocab,index_of_features=cl.fill_global_vocab("std_window_"+str(window_number), index_of_features, global_vocab)
# window_number=window_number+1;
###plot window std_dev and save fig ####
fig_std_p, plot_std_p = graphs.plot_simple(
rec_name, range(len(std_dev_window_arr)), std_dev_window_arr,
"quo_filt window std dev for window size: %s" % window_size,
"std_dev", "window std dev", "r", 0, 0, -0.1, 0.5)
fig_std_p.savefig(output_folder + "std_dev_window_" + record + ".png")
global_vocab,index_of_features=cl.fill_global_vocab("std_dev_global", index_of_features, global_vocab)
# print (" sdrr of RR_sec (raw) in ms is: " +str(sdrr_raw_ms));
#### calculating skewness and kurtosis #####
#skewness_global=scipy.stats.skew(RR_sec)
#print("skewness: "+ str(skewness_global))
#kurtosis_global=scipy.stats.kurtosis(RR_sec, fisher='false')
#print("kurtosis_globl is : " +str(kurtosis_global))
#print("answer for kurtosis test is: "+str(scipy.stats.kurtosistest(RR_sec)))
###### Calculate nn50, pnn50 and rmSSD or diff_rr_std_dev ######
std_dev_diff=np.std(delta_RR_sec);
feature_rec.append(std_dev_diff);
global_vocab,index_of_features=cl.fill_global_vocab("std_dev_diff", index_of_features, global_vocab)
###### Calculating window based statistical features for RAW SDRR VALUES #################
window_size=50;
std_dev_window_arr=[]
std_dev_window_arr=pr.get_window_std_dev(RR_sec, window_size)
window_number=1;
# for val in std_dev_window_arr:
# feature_rec.append(val)
# global_vocab,index_of_features=cl.fill_global_vocab("std_window_"+str(window_number), index_of_features, global_vocab)
# window_number=window_number+1;
###plot window std_dev and save fig ####
fig_std_p,plot_std_p=graphs.plot_simple(rec_name,range(len(std_dev_window_arr)), std_dev_window_arr, "quo_filt window std dev for window size: %s" % window_size, "std_dev", "window std dev", "r", 0, 0,-0.1, 0.5)
fig_std_p.savefig(output_folder+"std_dev_window_"+record+".png")