###### 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)
Exemple #2
0
    #### 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")