Step 1: Plot the distribution of VT values for each time node

import numpy as np
import matplotlib.pyplot as plt
import os
import math
import time

path = '.\readvt\log\'
# path = '.\vt-32Mb-skyworth\'

path1 = '.\readvt\results\'

dir = []
abnorm = []
vrd = 6.0
files = os.listdir(path)
for i in files:
    if os.path.isdir(path + i):
        dir.append(i)

print(dir)

slotn = ['1']
# files = ['#1 2.log', '#3 4.log', '#5 6.log', '#7 8.log', '#9 10.log',]#,'3.log','4.log','5.log','6.log','7.log','8.log','9.log','10.log','11.log','12.log','13.log',
#  '14.log','15.log','16.log','17.log','18.log','19.log','20.log','21.log','22.log','23.log','24.log','25.log',
#  '26.log','27.log','28.log','29.log','30.log','31.log','32.log','33.log','34.log','35.log','36.log','37.log',
#  '38.log','39.log']#,'40.log']#,'41.log','42.log','43.log','44.log','45.log','46.log','47.log','48.log','49.log',
#  '50.log']#,'#101.log']
# files = ['1','2','3','4','5','6','7',
files = ['1.log','2.log','3.log','4.log','5.log','6.log','7.log','8.log','9.log','10.log','11.log','12.log','13.log','14.log','15.log','16.log','17.log'
       ,'18.log','19.log','20.log','21.log','22.log','23.log','24.log','25.log','26.log','27.log','28.log','29.log','30.log','31.log','32.log','33.log'
       ,'34.log','35.log','36.log','37.log','38.log','39.log','40.log','41.log','42.log','43.log','44.log','45.log','46.log','47.log','48.log','49.log'
       ,'50.log','61.log','62.log','63.log','64.log','65.log','66.log','67.log','68.log','69.log','70.log',
       '71.log','72.log','73.log','74.log','75.log','76.log','77.log','78.log','79.log','80.log',
       '81.log','82.log','83.log','84.log','85.log','86.log','87.log','88.log','89.log','90.log',
       '91.log','92.log','93.log','94.log','95.log','96.log','97.log','98.log','99.log','100.log']


## Step 2: Plot the distribution of VT values for each time node

plt.figure(figsize=(16,8),dpi=80)
for k in dir:
    files = os.listdir(path + k)
    # print(files,k)
    vt0_min = []  ### list store of min vt value of each dir
    slot_name = []
    vt_value_flt_wo0 = []
    for p in slotn:
        a = 'zero pattern vth worsest case:'
        for i in files:
            with open(path + k + '\' + i,'r',encoding='gb18030',errors='ignore') as file:
                data = file.readlines()
                for n in data:
                    if a in n:
                        #print(n)
                        vt_value_str = data[data.index(n)][31:-2]
                        #print(vt_value_str)
                        vt_value_flt_wo0.append(vt_value_str)
                        print(vt_value_flt_wo0,len(vt_value_flt_wo0))
                vt0_min = [float(i) for i in vt_value_flt_wo0]
            #slot_name.append(k+'\'+ str(i))
    #min_slot=slot_name[vt0_min.index(min(vt0_min))]
    print(vt0_min)
    x = np.sort(vt0_min)
    y = np.arange(1,len(x)+1)/len(x)
    plt.yticks(np.arange(-0.05,1.05,0.05)) # set yaxis label precision

    plt.plot(x,y,marker='.',linestyle=':',label='%s %d pcs min vt=%f'%(k,len(vt0_min),min(vt0_min)))
    plt.annotate(min(vt0_min),(min(vt0_min),1/len(vt0_min)))
#    plt.annotate('Vrd=%s'%vrd,(vrd+0.05,0.5))
    #plt.annotate(vt0_min[int(len(vt0_min)/2)],(vt0_min[int(len(vt0_min)/2)],0.5))#1/int(len(vt0_min)/2)))
    #plt.subplots_adjust(left=0.18, wspace=0.25, hspace=0.25,bottom=0.13, top=0.91)
#    plt.axvline(x=vrd,ls='-',c='red')
    plt.margins(x=0.5, y=0.05)
    plt.xlabel('vt value last not 0 count')
    plt.ylabel('cumulative probability')
    plt.legend(loc='lower right',fontsize='small')#'best'
    plt.tight_layout()
    plt.grid(b=bool)
    plt.title('vt distribution')
    #plt.show()
t = time.strftime('%Y%m%d-%H%M',time.localtime(time.time()))
plt.tight_layout()
plt.savefig(path1+'min_vt_value_distribution.png')
    #plt.savefig(path2+'vtdistr.png')
plt.close()

Step 3: Plot the trend of min vt value over time

plt.figure(figsize=(16,8),dpi=80)
vt0_min_all = []
for k in dir:
    files = os.listdir(path + k)
    vt0_min = []  ### list store of min vt value of each dir
    slot_name = []
    vt_value_flt_wo0 = []
    for p in slotn:
        a = 'zero pattern vth worsest case:'
        for i in files:
            with open(path + k + '\' + i,'r',encoding='gb18030',errors='ignore') as file:
                data = file.readlines()
                for n in data:
                    if a in n:
                        vt_value_str = data[data.index(n)][31:-2]
                        vt_value_flt_wo0.append(vt_value_str)
                vt0_min = [float(i) for i in vt_value_flt_wo0]
    vt0_min_all.append(min(vt0_min))
    plt.plot(vt0_min_all,marker='o',label='min vt value')
plt.xticks(np.arange(0,len(dir),1),dir,rotation=90)
plt.ylabel('min vt value')
plt.xlabel('time')
plt.title('Trend of min vt value over time')
plt.legend()
plt.tight_layout()
plt.grid(b=bool)
plt.savefig(path1+'min_vt_value_trend.png')
plt.close()