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analysis.py
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#!/usr/bin/env python3
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
#
# See LICENSE for more details.
#
# Copyright: 2023 IBM
# Author: Misbah Anjum N <[email protected]>
'''
analysis.py script is aimed to help in analysis/comparison of avocado test runs
by generating a simple excel file (.xlsx). The results.json file which gets created
after avocado run is passed as input in command line while running this script and
depending on the flag/options provided, the excel analysis/omparison sheet will be
generated.
Prerequsites:-
pip3 install pandas[excel]
(or)
dnf install python3-pandas python3-numpy python3-openpyxl
flags/options:-
1. --new-analysis
2. --add-to-existing
3. --compare-two-results
python3 analysis.py --new-analysis <json_file>
python3 analysis.py --add-to-existing <xlsx_file> <json_file>
python3 analysis.py --compare-two-results <old_json_file> <new_json_file>
Check README.md for more explanation
'''
import sys
import json
import pandas as pd
from openpyxl.styles import Font, Border, Side, PatternFill, Alignment
def test_analysis(data):
'''
This function is used to generate an excel file which contains a summary of
avaocado based test runs. It reads results.json as input and generates a .xlsx
file as output.
'''
# Extract job name from debuglog attribute
job_name = data['debuglog'].split('/')[-2]
# Create a DataFrame
dataframe = pd.DataFrame(columns=['Name', 'Status', 'Fail Reason'])
# Add rows for job name and other attributes
dataframe.loc[0] = ['', 'Job Name', job_name]
dataframe.loc[1] = ['', 'Fail', data['failures']]
dataframe.loc[2] = ['', 'Error', data['errors']]
dataframe.loc[3] = ['', 'Skip', data['skip']]
dataframe.loc[4] = ['', 'Interrupt', data['interrupt']]
dataframe.loc[5] = ['', 'Cancel', data['cancel']]
dataframe.loc[6] = ['', 'Pass', data['pass']]
dataframe.loc[7] = ['', 'White-Board', data['tests'][0]['whiteboard']]
# Loop through the 'tests' list in the JSON data and add rows
for i, test in enumerate(data['tests']):
dataframe.loc[i + 8] = [test['name'], test['status'], test['fail_reason']]
# Save the DataFrame to a Excel file
dataframe.to_excel('Analysis.xlsx', index=False)
def Comparison_Analysis(excel, data):
'''
This function is used to generate an excel sheet which gives delta comparison
of two test avocado based test runs. Using excel sheet produced from the
function: test_analysis(data) and results.json as inputs, it generate a .xlsx
file as output.
'''
# store test_names in existing excel file
old_dataframe = pd.read_excel(excel)
test_names = old_dataframe[old_dataframe.columns[0]]
test_names = [test_names[x] for x in range(8, len(old_dataframe.index))]
# Extract job name from debuglog attribute
job_name = data['debuglog'].split('/')[-2]
# Create a DataFrame
new_dataframe = pd.DataFrame(columns=['Status', 'Fail Reason'])
# Add rows for job name and other attributes
new_dataframe.loc[0] = ['Job Name', job_name]
new_dataframe.loc[1] = ['Fail', data['failures']]
new_dataframe.loc[2] = ['Error', data['errors']]
new_dataframe.loc[3] = ['Skip', data['skip']]
new_dataframe.loc[4] = ['Interrupt', data['interrupt']]
new_dataframe.loc[5] = ['Cancel', data['cancel']]
new_dataframe.loc[6] = ['Pass', data['pass']]
new_dataframe.loc[7] = ['White-Board', data['tests'][0]['whiteboard']]
# Loop through the 'tests' list in the JSON data and add rows
for i, test in enumerate(data['tests']):
found = 0
for j in range(len(test_names)):
if test['name'] == test_names[j]:
new_dataframe.loc[j + 8] = [test['status'], test['fail_reason']]
found = 1
break
if found == 0:
new = [test['name']]
for i in range(len(old_dataframe.columns)-1):
new.append("")
old_dataframe.loc[len(old_dataframe.index)] = new
new_dataframe.loc[len(old_dataframe.index)-1] = [test['status'], test['fail_reason']]
final_res = pd.concat([old_dataframe, new_dataframe], axis=1)
final_res.to_excel(excel, index=False)
# Add the Result column to compare two results
if "--compare-two-results" in sys.argv:
dataframe = pd.read_excel(excel)
results = []
for i in range(len(dataframe.index)):
if dataframe.loc[i].iat[-2] == dataframe.loc[i].iat[-4]:
results.append("")
else:
if dataframe.loc[i].iat[-4] == "PASS" and not pd.isnull(dataframe.loc[i].iat[-2]):
results.append("REGRESSION")
elif dataframe.loc[i].iat[-2] == "PASS" and not pd.isnull(dataframe.loc[i].iat[-4]):
results.append("SOLVED")
else:
results.append("DIFF")
result_dataframe = pd.DataFrame(columns=['Result'])
for i in range(8, len(results)):
result_dataframe.loc[i] = results[i]
final_dataframe = pd.concat([dataframe, result_dataframe], axis=1)
final_dataframe.to_excel(excel, index=False)
def deco(excel):
'''
This function is used to conditionally format the xlsx file
Libraries used: ExcelWriter, openpyxl
'''
# Create a sample DataFrame
dataframe = pd.read_excel(excel)
# Create a Pandas ExcelWriter object and write to Excel file
excel_writer = pd.ExcelWriter(excel, engine='openpyxl')
dataframe.to_excel(excel_writer, sheet_name='Sheet1', index=False)
# Access the workbook and worksheet objects
workbook = excel_writer.book
worksheet = excel_writer.sheets['Sheet1']
# Column Width
worksheet.column_dimensions['A'].width = 60
worksheet.column_dimensions['B'].width = 20
worksheet.column_dimensions['C'].width = 80
worksheet.column_dimensions['D'].width = 20
worksheet.column_dimensions['E'].width = 80
worksheet.column_dimensions['F'].width = 20
# Apply styles to the entire sheet
for row in worksheet.iter_rows(min_row=2, max_row=len(dataframe) + 1):
for cell in row:
cell.font = Font(size=15)
cell.border = Border(left=Side(border_style='thin', color='000000'),
right=Side(border_style='thin', color='000000'),
top=Side(border_style='thin', color='000000'),
bottom=Side(border_style='thin', color='000000'))
cell.alignment = Alignment(wrap_text=True, vertical='center')
# Apply header formatting
for cell in worksheet[1]:
cell.font = Font(size=18, bold=True) # White text color
cell.fill = PatternFill(start_color='ADD8E6', end_color='ADD8E6', fill_type='solid') # Blue background color
# Conditional formatting for the "Result" column if present
try:
for idx, value in enumerate(dataframe['Result'], start=2):
cell = worksheet.cell(row=idx, column=6)
if value == 'DIFF':
cell.fill = PatternFill(start_color='FF0000', end_color='FF0000', fill_type='solid') # Red
elif value == 'SOLVED':
cell.fill = PatternFill(start_color='39E75F', end_color='39E75F', fill_type='solid') # Green
elif value == 'REGRESSION':
cell.fill = PatternFill(start_color='FFA500', end_color='FFA500', fill_type='solid') # Orange
except Exception as e:
pass
# Save the styled Excel file
workbook.save(excel)
def main():
try:
if "--new-analysis" in sys.argv:
with open(sys.argv[-1], 'r') as json_file:
data = json.load(json_file)
test_analysis(data)
deco("Analysis.xlsx")
elif "--add-to-existing" in sys.argv:
with open(sys.argv[-1], 'r') as json_file:
data = json.load(json_file)
excel = sys.argv[-2]
Comparison_Analysis(excel, data)
deco(excel)
elif "--compare-two-results" in sys.argv:
with open(sys.argv[-2], 'r') as json_file:
data = json.load(json_file)
test_analysis(data)
deco("Analysis.xlsx")
with open(sys.argv[-1], 'r') as json_file:
data = json.load(json_file)
Comparison_Analysis("Analysis.xlsx", data)
deco("Analysis.xlsx")
else:
raise Exception
except:
print("\nPay attention on the usage:\n"+usage())
sys.exit(1)
def usage():
return("python3 analysis.py --new-analysis <json_file>\n\
python3 analysis.py --add-to-existing <xlsx_file> <json_file>\n\
python3 analysis.py --compare-two-results <old_json_file> <new_json_file>\n")
if __name__ == '__main__':
main()