]
# all_feature_names_s1=['s1_wave_dur_time','s1_wave_ini_time','s1_wave_ter_time','s1_wave_asy_time','s1_pr_on_time','s1_pr_peak_time','s1_pr_off_time','s1_pp_on_time']
index_num = 0
for arr in all_time_features:
index_num = index_num + 1
trend_s0 = pecg.extract_trend_frm_list(arr)
# trend_s1=pecg.extract_trend_frm_list(s1_wave_ini_time)
###plot pwave points and save fig ####
fig_pwave, plot_pwave = graphs.plotScatter(
record,
range(len(trend_s0)),
trend_s0,
"Number of 10 P wave-length intervals",
" P dur variability (samples) ",
"P dur variability",
"b",
xlim_lo=0,
xlim_hi=140,
ylim_lo=0,
ylim_hi=140,
axline=0,
)
plt.plot(
range(len(trend_s0)), np.poly1d(np.polyfit(range(len(trend_s0)), trend_s0, 1))(range(len(trend_s0)))
)
plt.xlim(0, 100)
plt.ylim(0, 140)
# fig_pwave,plot_pwave=graphs.plot_simple(rec_name,range(len(p_wave_times_0)),p_wave_times_0, "serial num", " pwave duration (ms) ", "s0 p wave duration ", 'g',xlim_lo=0, xlim_hi=0, ylim_lo=0, ylim_hi=0)
fig_pwave.savefig(output_folder + "trend_s0_pwave_ini_" + record + ".pdf", format="pdf")
plt.show()
print ("done")
for sig_num in signals: # this for loop generates feature arrays for both signals 0 and 1
all_time_features=pecg.extract_wave_times(output_folder,record,rec_name,annotation,start_time,end_time,sig_num)
# all_feature_names=['wave_dur_time','wave_ini_time','wave_ter_time','wave_asy_time','pr_on_time','pr_peak_time','pr_off_time']
# stat_feature_names=[sig_num+'_'+'size_val',sig_num+'_'+'min',sig_num+'_'+'max',sig_num+'_'+'mean_val',sig_num+'_'+'var_val',sig_num+'_'+'skewness_val',sig_num+'_'+'kurtosis_val']
all_feature_names=['wave_dur_time']
stat_feature_names=[sig_num+'_'+'size_val',sig_num+'_'+'min',sig_num+'_'+'max',sig_num+'_'+'mean_val',sig_num+'_'+'var_val',sig_num+'_'+'skewness_val',sig_num+'_'+'kurtosis_val']
#all_feature_names_s1=['s1_wave_dur_time','s1_wave_ini_time','s1_wave_ter_time','s1_wave_asy_time','s1_pr_on_time','s1_pr_peak_time','s1_pr_off_time','s1_pp_on_time']
index_num=0;
for arr in all_time_features:
index_num=index_num+1;
trend_s0=pecg.extract_trend_frm_list(arr)
#trend_s1=pecg.extract_trend_frm_list(s1_wave_ini_time)
###plot pwave points and save fig ####
fig_pwave,plot_pwave=graphs.plotScatter(record,range(len(trend_s0)),trend_s0, "Number of 10 P wave-length intervals", " P dur variability (samples) ", "P dur variability", 'b',xlim_lo=0, xlim_hi=140, ylim_lo=0, ylim_hi=140,axline=0)
plt.plot(range(len(trend_s0)), np.poly1d(np.polyfit(range(len(trend_s0)), trend_s0, 1))(range(len(trend_s0))))
plt.xlim(0,100);
plt.ylim(0,140);
#fig_pwave,plot_pwave=graphs.plot_simple(rec_name,range(len(p_wave_times_0)),p_wave_times_0, "serial num", " pwave duration (ms) ", "s0 p wave duration ", 'g',xlim_lo=0, xlim_hi=0, ylim_lo=0, ylim_hi=0)
fig_pwave.savefig(output_folder+"trend_s0_pwave_ini_"+record+".pdf",format='pdf')
plt.show()
print("done")
exit()
#all_features.append(feature_rec)
#pwave_time_patient,pwave_time_normal=pecg.separate_p_n_pwave(record,db_name,p_wave_times,pwave_time_patient,pwave_time_normal) #i dont thing we need this
# feature_rec.append(p_wave_times_0)
# global_vocab_ecg,index_of_features_ecg=cl.fill_global_vocab("pwave_time_0", index_of_features_ecg, global_vocab_ecg)
def calc_std_5min_sodp_measures(rec_name, annotation, total_min, radius_array):
#this functions calculates features from 5 min of RR intervals for record , and calculates std of each feature for all 5min intervlas in a 30min record.
#it returns all_features_6_intervals : each list corresponds to one feature and values within the list are denote the feature value per interval
#it returns: std_dev_all_features :each value is the std dev of the feature in 6 intervals
#it also returns feature_name_overall : names of features whose std dev can be found in std_dev_all_Features
feature_list_5min = []
num_5min_interval = 1 #keeps track of which chunck of 5 min interval it is
feature_name = []
start_time_arr = range(0, total_min, 5)
end_time_arr = range(5, total_min + 5, 5)
# everytime the loop runs it extracts feauters for those 5 mins
for start_time, end_time in zip(start_time_arr, end_time_arr):
error_rec_names = []
count_quad1 = 0
count_quad2 = 0
count_quad3 = 0
count_quad4 = 0
count_quad11 = 0
count_quad12 = 0
count_quad13 = 0
count_quad14 = 0
count_quad21 = 0
count_quad22 = 0
count_quad23 = 0
count_quad24 = 0
count_quad31 = 0
count_quad32 = 0
count_quad33 = 0
count_quad34 = 0
count_quad41 = 0
count_quad42 = 0
count_quad43 = 0
count_quad44 = 0
points_at_origin = 0
quad = 0
print("calculating std of 5 min sodp features")
print("start time is: " + str(start_time))
print("end time is: " + str(end_time))
##### 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]
RR_sec = RR_sec_unclean
#print("len of 5min RR_sec is: " + str(len(RR_sec)))
num_RR_5min = len(RR_sec)
#print("num_RR_5min_"+str(num_5min_interval))
x_val_sodp, y_val_sodp, ctm_array, ctm_feature_name, distance_array, dist_feature_name = calc_sodp_measures(
rec_name, RR_sec, radius_array)
## calculate the number of poitns in each quadrant
num_points_on_graph = len(x_val_sodp)
####plot sodp and save fig ####
record_name = "n43"
output_folder = "/home/ubuntu/Documents/Thesis_work/results/thesis_images/chapter_5/"
fig_sodp, plot_sodp = graphs.plotScatter(
record_name,
x_val_sodp,
y_val_sodp,
"x[n+1]-x[n]",
" x[n+2]-x[n+1] ",
"SODP plot for 5min segment " + str(num_5min_interval),
'b',
xlim_lo=-1,
xlim_hi=1,
ylim_lo=-1,
ylim_hi=1,
axline=1)
fig_sodp.savefig(output_folder + "sodp_plot_" + record_name + "_" +
str(num_5min_interval) + ".pdf",
format='pdf')
for x_val, y_val in zip(x_val_sodp, y_val_sodp):
if x_val < -1 or x_val > 1 or y_val < -1 or y_val > 1:
print("x_val is: " + str(x_val) + "y_val is: " + str(y_val))
print("errorneous value in: " + str(rec_name) + "_" +
str(num_5min_interval))
error_rec_names.append(rec_name)
elif x_val > 0 and y_val > 0:
quad = 1
count_quad1 = count_quad1 + 1
sub_quad = count_points_in_16quad(x_val, y_val, quad)
if sub_quad is 11:
count_quad11 = count_quad11 + 1
elif sub_quad is 12:
count_quad12 = count_quad12 + 1
elif sub_quad is 13:
count_quad13 = count_quad13 + 1
elif sub_quad is 14:
count_quad14 = count_quad14 + 1
elif x_val < 0 and y_val > 0:
quad = 2
count_quad2 = count_quad2 + 1
sub_quad = count_points_in_16quad(x_val, y_val, quad)
if sub_quad is 21:
count_quad21 = count_quad21 + 1
elif sub_quad is 22:
count_quad22 = count_quad22 + 1
elif sub_quad is 23:
count_quad23 = count_quad23 + 1
elif sub_quad is 24:
count_quad24 = count_quad24 + 1
elif x_val < 0 and y_val < 0:
quad = 3
count_quad3 = count_quad3 + 1
sub_quad = count_points_in_16quad(x_val, y_val, quad)
if sub_quad is 31:
count_quad31 = count_quad31 + 1
elif sub_quad is 32:
count_quad32 = count_quad32 + 1
elif sub_quad is 33:
count_quad33 = count_quad33 + 1
elif sub_quad is 34:
count_quad34 = count_quad34 + 1
elif x_val > 0 and y_val < 0:
quad = 4
count_quad4 = count_quad4 + 1
sub_quad = count_points_in_16quad(x_val, y_val, quad)
if sub_quad is 41:
count_quad41 = count_quad41 + 1
elif sub_quad is 42:
count_quad42 = count_quad42 + 1
elif sub_quad is 43:
count_quad43 = count_quad43 + 1
elif sub_quad is 44:
count_quad44 = count_quad44 + 1
else:
points_at_origin = points_at_origin + 1
ratio_points_at_origin = float(points_at_origin) / num_points_on_graph
#store the quad count values in features list
feature_list_5min.append(ratio_points_at_origin)
feature_name.append("ratio_points_at_origin_5min_" +
str(num_5min_interval))
#calculate quad_ratio
quad1_ratio = float(count_quad1) / num_points_on_graph
quad2_ratio = float(count_quad2) / num_points_on_graph
quad3_ratio = float(count_quad3) / num_points_on_graph
quad4_ratio = float(count_quad4) / num_points_on_graph
quad_11_ratio = float(count_quad11) / num_points_on_graph
quad_12_ratio = float(count_quad12) / num_points_on_graph
quad_13_ratio = float(count_quad13) / num_points_on_graph
quad_14_ratio = float(count_quad14) / num_points_on_graph
quad_21_ratio = float(count_quad21) / num_points_on_graph
quad_22_ratio = float(count_quad22) / num_points_on_graph
quad_23_ratio = float(count_quad23) / num_points_on_graph
quad_24_ratio = float(count_quad24) / num_points_on_graph
quad_31_ratio = float(count_quad31) / num_points_on_graph
quad_32_ratio = float(count_quad32) / num_points_on_graph
quad_33_ratio = float(count_quad33) / num_points_on_graph
quad_34_ratio = float(count_quad34) / num_points_on_graph
quad_41_ratio = float(count_quad41) / num_points_on_graph
quad_42_ratio = float(count_quad42) / num_points_on_graph
quad_43_ratio = float(count_quad43) / num_points_on_graph
quad_44_ratio = float(count_quad44) / num_points_on_graph
#store the quad ratio values in features list
feature_list_5min.append(quad1_ratio)
feature_name.append("std_quad1_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad2_ratio)
feature_name.append("std_quad2_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad3_ratio)
feature_name.append("std_quad3_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad4_ratio)
feature_name.append("std_quad4_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_11_ratio)
feature_name.append("std_quad11_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_12_ratio)
feature_name.append("std_quad12_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_13_ratio)
feature_name.append("std_quad13_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_14_ratio)
feature_name.append("std_quad14_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_21_ratio)
feature_name.append("std_quad21_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_22_ratio)
feature_name.append("std_quad22_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_23_ratio)
feature_name.append("std_quad23_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_24_ratio)
feature_name.append("std_quad24_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_31_ratio)
feature_name.append("std_quad31_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_32_ratio)
feature_name.append("std_quad32_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_33_ratio)
feature_name.append("std_quad33_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_34_ratio)
feature_name.append("std_quad34_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_41_ratio)
feature_name.append("std_quad41_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_42_ratio)
feature_name.append("std_quad42_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_43_ratio)
feature_name.append("std_quad43_ratio_5min_" + str(num_5min_interval))
feature_list_5min.append(quad_44_ratio)
feature_name.append("std_quad44_ratio_5min_" + str(num_5min_interval))
#print ("feature list for this 5 min interval is: " + str(feature_list_5min))
#print("indexes are: " + str(range(len(feature_list_5min))))
num_5min_interval = num_5min_interval + 1
print("rec_names with errornos records are : " + str(error_rec_names))
dist_bw_feature = 21
start_ind = 0
end_ind = 105
feature_name_overall = []
#print (len(feature_list_5min))
all_features_6_intervals = []
for i in range(0, 21):
name = str(feature_name[i])
feature_name_overall.append(name[:-2])
#print("feature_name is: " + str(name[:-2]))
feature_index_5min = range(start_ind, end_ind + 21, dist_bw_feature)
one_feature_all_intervals = []
for val in feature_index_5min:
#print("val is : "+str(val))
feature_val = feature_list_5min[val]
#print("feature_val at index : " + str(val)+" is: "+str(feature_val))
one_feature_all_intervals.append(feature_val)
all_features_6_intervals.append(one_feature_all_intervals)
start_ind = start_ind + 1
end_ind = end_ind + 1
feature_index_5min = range(start_ind, end_ind + 21, dist_bw_feature)
#print ("new indexes for feature index is: " + str(feature_index_5min))
std_dev_all_features = []
for list_feature_vals in all_features_6_intervals:
std_dev_5min_feature = np.std(list_feature_vals)
std_dev_all_features.append(std_dev_5min_feature)
#for name,val in zip(feature_name_overall,std_dev_all_features):
#print("name of feature is : " + str(name))
#print("val of feature is : " + str(val))
return all_features_6_intervals, std_dev_all_features, feature_name_overall
global_vocab,index_of_features=cl.fill_global_vocab(name, index_of_features, global_vocab)
# if "n" in record:
# ####plot sodp and save fig ####
# fig_sodp,plot_sodp=graphs.plotScatter(rec_name,x_val_sodp,y_val_sodp, "x[n+1]-x[n]", " x[n+2]-x[n+1] ", "SODP plot ", 'b',xlim_lo=-1, xlim_hi=1, ylim_lo=-1, ylim_hi=1,axline=1)
# fig_sodp.savefig(output_folder+"sodp_plot_"+record+".pdf",format='pdf')
# if "p" in record:
# ####plot sodp and save fig ####
# fig_sodp,plot_sodp=graphs.plotScatter(rec_name,x_val_sodp,y_val_sodp, "x[n+1]-x[n]", " x[n+2]-x[n+1] ", "SODP plot ", 'r',xlim_lo=-1, xlim_hi=1, ylim_lo=-1, ylim_hi=1,axline=1)
# fig_sodp.savefig(output_folder+"sodp_plot_"+record+".pdf",format='pdf')
############## showing 16 quadrants
if "p" in record:
####plot sodp and save fig ####
fig_sodp,plot_sodp=graphs.plotScatter(rec_name,x_val_sodp,y_val_sodp, "x[n+1]-x[n]", " x[n+2]-x[n+1] ", "16 Quadrants in the SODP plot ", 'r',xlim_lo=-1, xlim_hi=1, ylim_lo=-1, ylim_hi=1,axline=1)
fig_sodp.savefig(output_folder+"quad_sodp_plot_"+record+".pdf",format='pdf')
###################################3
# ##### Calculating 30 min quadrant points ratio features #################
#end_time=1;
feature_list_30min,feature_name=nlm.calc_30min_sodp_measures(rec_name,annotation, start_time,end_time, x_val_sodp,y_val_sodp)
##add value to feature array and name to global vocab
for val,name in zip(feature_list_30min,feature_name):
feature_rec.append(val)
#print name
#print val
#
# if "n" in record:
#
# ####plot sodp and save fig ####
# fig_sodp,plot_sodp=graphs.plotScatter(rec_name,x_val_sodp,y_val_sodp, "x[n+1]-x[n]", " x[n+2]-x[n+1] ", "SODP plot for "+str(start_time)+"to"+str(end_time), 'b',xlim_lo=-1, xlim_hi=1, ylim_lo=-1, ylim_hi=1,axline=1)
# fig_sodp.savefig(output_folder+"sodp_plot_"+record+"_"+str(start_time)+"to"+str(end_time)+".png")
#
# elif "p" in record:
# ####plot sodp and save fig ####
# fig_sodp,plot_sodp=graphs.plotScatter(rec_name,x_val_sodp,y_val_sodp, "x[n+1]-x[n]", " x[n+2]-x[n+1] ", "SODP plot for "+str(start_time)+"to"+str(end_time), 'r',xlim_lo=-1, xlim_hi=1, ylim_lo=-1, ylim_hi=1,axline=1)
# fig_sodp.savefig(output_folder+"sodp_plot_"+record+"_"+str(start_time)+"to"+str(end_time)+".png")
#
######### PLOTTING SODP for afib plots array ################
####plot sodp and save fig ####
fig_sodp, plot_sodp = graphs.plotScatter(
rec_name,
x_val_sodp,
y_val_sodp,
"x[n+1]-x[n]",
" x[n+2]-x[n+1] ",
"SODP plot for " + str(start_time) + "to" + str(end_time),
'b',
xlim_lo=-1,
xlim_hi=1,
ylim_lo=-1,
ylim_hi=1,
axline=1)
fig_sodp.savefig(output_folder + "sodp_plot_" + record + "_" +
str(start_time) + "to" + str(end_time) + ".png")
def calc_std_5min_sodp_measures(rec_name,annotation, total_min, radius_array):
#this functions calculates features from 5 min of RR intervals for record , and calculates std of each feature for all 5min intervlas in a 30min record.
#it returns all_features_6_intervals : each list corresponds to one feature and values within the list are denote the feature value per interval
#it returns: std_dev_all_features :each value is the std dev of the feature in 6 intervals
#it also returns feature_name_overall : names of features whose std dev can be found in std_dev_all_Features
feature_list_5min=[]
num_5min_interval=1 #keeps track of which chunck of 5 min interval it is
feature_name=[]
start_time_arr=range(0,total_min,5)
end_time_arr=range(5,total_min+5,5)
# everytime the loop runs it extracts feauters for those 5 mins
for start_time, end_time in zip(start_time_arr,end_time_arr):
error_rec_names=[]
count_quad1=0;
count_quad2=0;
count_quad3=0;
count_quad4=0;
count_quad11=0;
count_quad12=0
count_quad13=0
count_quad14=0
count_quad21=0;
count_quad22=0
count_quad23=0
count_quad24=0
count_quad31=0;
count_quad32=0
count_quad33=0
count_quad34=0
count_quad41=0;
count_quad42=0
count_quad43=0
count_quad44=0
points_at_origin=0;
quad=0;
print("calculating std of 5 min sodp features")
print ("start time is: " +str(start_time));
print ("end time is: " +str(end_time));
##### 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];
RR_sec=RR_sec_unclean
#print("len of 5min RR_sec is: " + str(len(RR_sec)))
num_RR_5min=len(RR_sec)
#print("num_RR_5min_"+str(num_5min_interval))
x_val_sodp,y_val_sodp,ctm_array,ctm_feature_name,distance_array,dist_feature_name=calc_sodp_measures(rec_name,RR_sec, radius_array);
## calculate the number of poitns in each quadrant
num_points_on_graph=len(x_val_sodp)
####plot sodp and save fig ####
record_name="n43"
output_folder="/home/ubuntu/Documents/Thesis_work/results/thesis_images/chapter_5/"
fig_sodp,plot_sodp=graphs.plotScatter(record_name,x_val_sodp,y_val_sodp, "x[n+1]-x[n]", " x[n+2]-x[n+1] ", "SODP plot for 5min segment "+str(num_5min_interval) , 'b',xlim_lo=-1, xlim_hi=1, ylim_lo=-1, ylim_hi=1,axline=1)
fig_sodp.savefig(output_folder+"sodp_plot_"+record_name+"_"+str(num_5min_interval)+".pdf",format='pdf')
for x_val,y_val in zip(x_val_sodp,y_val_sodp):
if x_val <-1 or x_val>1 or y_val<-1 or y_val>1:
print("x_val is: " +str(x_val) + "y_val is: " + str(y_val))
print("errorneous value in: " + str(rec_name) +"_"+str(num_5min_interval))
error_rec_names.append(rec_name)
elif x_val > 0 and y_val >0:
quad=1;
count_quad1=count_quad1+1;
sub_quad=count_points_in_16quad(x_val,y_val,quad)
if sub_quad is 11:
count_quad11=count_quad11+1;
elif sub_quad is 12:
count_quad12=count_quad12+1;
elif sub_quad is 13:
count_quad13=count_quad13+1;
elif sub_quad is 14:
count_quad14=count_quad14+1;
elif x_val <0 and y_val >0:
quad=2;
count_quad2=count_quad2+1;
sub_quad=count_points_in_16quad(x_val,y_val,quad)
if sub_quad is 21:
count_quad21=count_quad21+1
elif sub_quad is 22:
count_quad22=count_quad22+1
elif sub_quad is 23:
count_quad23=count_quad23+1
elif sub_quad is 24:
count_quad24=count_quad24+1
elif x_val <0 and y_val <0:
quad=3;
count_quad3=count_quad3+1;
sub_quad=count_points_in_16quad(x_val,y_val,quad)
if sub_quad is 31:
count_quad31=count_quad31+1
elif sub_quad is 32:
count_quad32=count_quad32+1
elif sub_quad is 33:
count_quad33=count_quad33+1
elif sub_quad is 34:
count_quad34=count_quad34+1
elif x_val>0 and y_val<0:
quad=4;
count_quad4=count_quad4+1;
sub_quad=count_points_in_16quad(x_val,y_val,quad)
if sub_quad is 41:
count_quad41=count_quad41+1
elif sub_quad is 42:
count_quad42=count_quad42+1
elif sub_quad is 43:
count_quad43=count_quad43+1
elif sub_quad is 44:
count_quad44=count_quad44+1
else:
points_at_origin=points_at_origin+1
ratio_points_at_origin=float(points_at_origin)/num_points_on_graph
#store the quad count values in features list
feature_list_5min.append(ratio_points_at_origin)
feature_name.append("ratio_points_at_origin_5min_"+str(num_5min_interval))
#calculate quad_ratio
quad1_ratio=float(count_quad1)/num_points_on_graph;
quad2_ratio=float(count_quad2)/num_points_on_graph;
quad3_ratio=float(count_quad3)/num_points_on_graph;
quad4_ratio=float(count_quad4)/num_points_on_graph;
quad_11_ratio=float(count_quad11)/num_points_on_graph;
quad_12_ratio=float(count_quad12)/num_points_on_graph;
quad_13_ratio=float(count_quad13)/num_points_on_graph;
quad_14_ratio=float(count_quad14)/num_points_on_graph;
quad_21_ratio=float(count_quad21)/num_points_on_graph;
quad_22_ratio=float(count_quad22)/num_points_on_graph;
quad_23_ratio=float(count_quad23)/num_points_on_graph;
quad_24_ratio=float(count_quad24)/num_points_on_graph;
quad_31_ratio=float(count_quad31)/num_points_on_graph;
quad_32_ratio=float(count_quad32)/num_points_on_graph;
quad_33_ratio=float(count_quad33)/num_points_on_graph;
quad_34_ratio=float(count_quad34)/num_points_on_graph;
quad_41_ratio=float(count_quad41)/num_points_on_graph;
quad_42_ratio=float(count_quad42)/num_points_on_graph;
quad_43_ratio=float(count_quad43)/num_points_on_graph;
quad_44_ratio=float(count_quad44)/num_points_on_graph;
#store the quad ratio values in features list
feature_list_5min.append(quad1_ratio)
feature_name.append("std_quad1_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad2_ratio)
feature_name.append("std_quad2_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad3_ratio)
feature_name.append("std_quad3_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad4_ratio)
feature_name.append("std_quad4_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_11_ratio)
feature_name.append("std_quad11_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_12_ratio)
feature_name.append("std_quad12_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_13_ratio)
feature_name.append("std_quad13_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_14_ratio)
feature_name.append("std_quad14_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_21_ratio)
feature_name.append("std_quad21_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_22_ratio)
feature_name.append("std_quad22_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_23_ratio)
feature_name.append("std_quad23_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_24_ratio)
feature_name.append("std_quad24_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_31_ratio)
feature_name.append("std_quad31_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_32_ratio)
feature_name.append("std_quad32_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_33_ratio)
feature_name.append("std_quad33_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_34_ratio)
feature_name.append("std_quad34_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_41_ratio)
feature_name.append("std_quad41_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_42_ratio)
feature_name.append("std_quad42_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_43_ratio)
feature_name.append("std_quad43_ratio_5min_"+str(num_5min_interval))
feature_list_5min.append(quad_44_ratio)
feature_name.append("std_quad44_ratio_5min_"+str(num_5min_interval))
#print ("feature list for this 5 min interval is: " + str(feature_list_5min))
#print("indexes are: " + str(range(len(feature_list_5min))))
num_5min_interval=num_5min_interval+1;
print ("rec_names with errornos records are : " + str(error_rec_names))
dist_bw_feature=21
start_ind=0
end_ind=105
feature_name_overall=[]
#print (len(feature_list_5min))
all_features_6_intervals=[]
for i in range(0,21):
name=str(feature_name[i])
feature_name_overall.append(name[:-2])
#print("feature_name is: " + str(name[:-2]))
feature_index_5min=range(start_ind,end_ind+21,dist_bw_feature)
one_feature_all_intervals=[]
for val in feature_index_5min:
#print("val is : "+str(val))
feature_val=feature_list_5min[val]
#print("feature_val at index : " + str(val)+" is: "+str(feature_val))
one_feature_all_intervals.append(feature_val)
all_features_6_intervals.append(one_feature_all_intervals)
start_ind=start_ind+1
end_ind=end_ind+1
feature_index_5min=range(start_ind,end_ind+21,dist_bw_feature)
#print ("new indexes for feature index is: " + str(feature_index_5min))
std_dev_all_features=[]
for list_feature_vals in all_features_6_intervals:
std_dev_5min_feature=np.std(list_feature_vals)
std_dev_all_features.append(std_dev_5min_feature)
#for name,val in zip(feature_name_overall,std_dev_all_features):
#print("name of feature is : " + str(name))
#print("val of feature is : " + str(val))
return all_features_6_intervals,std_dev_all_features,feature_name_overall
ctm_array=[];
dist_array=[];
x_val_sodp,y_val_sodp,ctm_array,dist_array=nlm.calc_sodp_measures(rec_name,RR_sec, radius_array);
ctm_list_list.append(ctm_array)
dist_list_list.append(dist_array)
# ######### PLOTTING SODP for afib plots array ################
#
# if "n" in record:
#
# ####plot sodp and save fig ####
# fig_sodp,plot_sodp=graphs.plotScatter(rec_name,x_val_sodp,y_val_sodp, "x[n+1]-x[n]", " x[n+2]-x[n+1] ", "SODP plot for "+str(start_time)+"to"+str(end_time), 'b',xlim_lo=-1, xlim_hi=1, ylim_lo=-1, ylim_hi=1,axline=1)
# fig_sodp.savefig(output_folder+"sodp_plot_"+record+"_"+str(start_time)+"to"+str(end_time)+".png")
#
# elif "p" in record:
# ####plot sodp and save fig ####
# fig_sodp,plot_sodp=graphs.plotScatter(rec_name,x_val_sodp,y_val_sodp, "x[n+1]-x[n]", " x[n+2]-x[n+1] ", "SODP plot for "+str(start_time)+"to"+str(end_time), 'r',xlim_lo=-1, xlim_hi=1, ylim_lo=-1, ylim_hi=1,axline=1)
# fig_sodp.savefig(output_folder+"sodp_plot_"+record+"_"+str(start_time)+"to"+str(end_time)+".png")
#
######### PLOTTING SODP for afib plots array ################
####plot sodp and save fig ####
fig_sodp,plot_sodp=graphs.plotScatter(rec_name,x_val_sodp,y_val_sodp, "x[n+1]-x[n]", " x[n+2]-x[n+1] ", "SODP plot for "+str(start_time)+"to"+str(end_time), 'b',xlim_lo=-1, xlim_hi=1, ylim_lo=-1, ylim_hi=1,axline=1)
fig_sodp.savefig(output_folder+"sodp_plot_"+record+"_"+str(start_time)+"to"+str(end_time)+".png")
