DataView.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.read_excel('/Users/rhjiang/IdeaProjects/nlp2model/out/res2.xlsx', sheet_name='Sheet1')
print("Column headings:")
print(df.columns)

tcons = []
tcode = []
candidate = []
valid = []
time = []
for i in df.index:
    if df['#Candidate'][i] < 200000:
        candidate.append(df['#Candidate'][i])
    else:
        candidate.append(200001)
    valid.append(df['#Valid'][i])
    time.append(df['Time (ms)'][i]/1000)
    if df['isSubject'][i] and df['#Tcons'][i] != 0 and df['#Tcode'][i] != 0 and df['Correct'][i]:
        tcons.append(df['#Tcons'][i])
        tcode.append(df['#Tcode'][i])
print(tcons)
print(tcode)
print(time)

fig, axes = plt.subplots(nrows=4)

# the histogram of the data
ax0, ax1, ax2, ax3 = axes.flatten()
_, _, patches = ax0.hist([tcons,tcode], 10, histtype='bar', align="mid", color = ['black', 'gray'], label=['#TCons','#TCode'])
ax0.set_xlabel('#T')
ax0.set_ylabel('#Correct')
ax0.set_title('Test Case Number Distributions')
fig.tight_layout()
for i in range(0,2):
    for b in patches[i]:
        h = b.get_height()
        if h > 0:
            ax0.text(b.get_x()+b.get_width()/2, h, '%d'%int (h),ha='center',va='bottom')
ax0.legend()
plt.sca(ax0)
plt.xticks(np.arange(0, 101, 10))

_, _, patches = ax1.hist(candidate, 20, histtype='bar', align="mid", color='black', label='#Candidate')
ax1.set_xlabel('#Candidate')
ax1.set_ylabel('#TM')
ax1.set_title('Candidate OCL Distributions')
for b in patches:
    h = b.get_height()
    if h > 0:
        ax1.text(b.get_x()+b.get_width()/2, h, '%d'%int (h),ha='center',va='bottom')
plt.sca(ax1)
plt.xticks(np.arange(0, 200000, 25000))

_, _, patches = ax2.hist(valid, np.arange(0,35,1), histtype='bar', align="mid", color='black', label='#Valid')
ax2.set_xlabel('#Valid')
ax2.set_ylabel('#TM')
ax2.set_title('Valid OCL Distribution')
for b in patches:
    h = b.get_height()
    if h > 0:
        ax2.text(b.get_x()+b.get_width()/2, h, '%d'%int (h),ha='center',va='bottom')
plt.sca(ax2)
plt.xticks(np.arange(0, 36, 5))

_, _, patches = ax3.hist(time, np.arange(0,250,12.5), histtype='bar', align="mid", color='black', label='Time(s)')
ax3.set_xlabel('Time(s)')
ax3.set_ylabel('#TM')
ax3.set_title('Generating Time')
for b in patches:
    h = b.get_height()
    if h > 0:
        ax3.text(b.get_x()+b.get_width()/2, h, '%d'%int (h),ha='center',va='bottom')
plt.sca(ax3)
plt.xticks(np.arange(0, 250, 25))

plt.show()