data_WF = pd.read_csv("input_files/stat/" file_name '.csv')

data_WF = data_WF[1:] #starting from first now; remove zero th row that is total

print(data_WF)

plt.figure()

plt.plot(data_WF['LOS_bin'], data_WF['Uni_Enct_N'], color='tab:blue', label='Encounter', marker='x', markersize=2,

linewidth=1) # graph edges number

plt.yscale('log') # use log scale

#plt.title('Number of edges of each ego network of C.diff')

plt.legend()

plt.xticks(np.arange(1, 27, 2)) #x axis labels

#plt.subplot(212) # for nodes

plt.plot(data_WF['LOS_bin'], data_WF['Uni_Pat_N'],color='tab:orange',label='Patient',marker='x',markersize=2, linewidth=1) # graph nodes number;

plt.legend()

plt.plot(data_WF['LOS_bin'], data_WF['Dia_N'],color='tab:green',label='Diagnose',marker='x',markersize=2, linewidth=1) # graph nodes number;

plt.legend()

plt.plot(data_WF['LOS_bin'], data_WF['Avg_Pat_Age'],color='tab:red',label='Average Age',marker='x',markersize=2, linewidth=1) # graph nodes number;

plt.legend()

plt.xlabel('Time window')

#plt.yscale('linear')

plt.subplots_adjust(top=0.95, bottom=0.09, left=0.06, right=0.98, hspace=0.25,

wspace=0.45)

plt.savefig(os.path.join("output_files/figures", file_name ".eps"), format="eps",dpi =1000)

data_wf = pd.read_csv("input_files/comorbidity_All_SCI/comorbidity_5/" filename '.csv')

#draw pie chart

less0_05 = data_wf[(data_wf['SCI'] < 0.05)].shape[0]

less0_1 = data_wf[(data_wf['SCI'] >= 0.05) & (data_wf['SCI'] <= 0.1)].shape[0]

greater0_1 = data_wf[(data_wf['SCI'] > 0.1)].shape[0]

my_data = [less0_05,less0_1,greater0_1]

my_labels = 'SCI: [0, 0.05)', 'SCI: [0.05, 0.1]', 'SCI: [0.1, 1]'

plt.pie(my_data, labels=my_labels, autopct='%1.1f%%')

#plt.title('My Title')

plt.axis('equal')

plt.subplots_adjust(top=0.99, bottom=0.01, left=0.01, right=0.99, hspace=0.25,

wspace=0.45)

plt.savefig(os.path.join("output_files/figures", filename "_pie.pdf"), format="pdf",dpi =1000)

#plt.show()

#draw histogram

df_hist = data_wf[(data_wf['SCI'] >= 0.05) & (data_wf['SCI'] <= 0.1)][['SCI']]

print(df_hist)

df_hist.plot.hist(grid = False,legend=False, bins = 50, rwidth = 0.98, color = '#33B5FF')

plt.xlabel('SCI')

plt.ylabel('Frequency')

plt.subplots_adjust(top=0.97, bottom=0.10, left=0.1, right=0.98, hspace=0.25,

wspace=0.45)

plt.savefig(os.path.join("output_files/figures", filename "_histogram.pdf"), format="pdf", dpi=1000)