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analysis.py
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analysis.py
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import pandas as pd
import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import bisect
def f1(df, libraries):
'''
f1 is used to analyse LU of a dependency
'''
# library = "junit-junit-3.8.1"
date_time_start = datetime.datetime.fromisoformat('2019-01-01 00:00:00+00:00')
date_time_end = datetime.datetime.fromisoformat('2021-01-01 00:00:00+00:00')
for library in libraries:
l_df = df.loc[(df["dependencies"] == library)]
record = []
for index, row in l_df.iterrows():
# print(datetime.datetime.fromisoformat(row['start']))
start, end = datetime.datetime.fromisoformat(row['start']), datetime.datetime.fromisoformat(row['end'])
record.append({'date': start, 'label' : 1})
record.append({'date': end, 'label' : -1})
record = sorted(record, key=lambda parameter: parameter['date'])
x, y, lu = [], [], 0
for r in record:
if r['date'] < date_time_start:
continue
if r['date'] > date_time_end:
break
lu += r['label']
y.append(lu)
x.append(r['date'])
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter("%y-%m"))
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=300))
plt.plot(x, y, label = library)
plt.gcf().autofmt_xdate()
plt.legend()
plt.show()
def f2(df):
'''
f2 is to analyse the number of projects and dependencies
'''
print(len(df['project']))
print(len(np.unique(df['project'])))
print(len(np.unique(df['dependencies'])))
def f3(df):
'''
f3 is to analyse how many dependency a project has
'''
library_numbers = df.groupby(['project']).size()
plt.boxplot(library_numbers)
plt.show()
def f4(df):
'''
f4 is to analyse how many version does dependency have
'''
library_version_name = df['dependencies']
dependency_name = [x[:x.rfind('-')] for x in df['dependencies']]
dependency_version = [x[x.rfind('-')+1:] for x in df['dependencies']]
print(dependency_name[0], dependency_version[0])
df['name'] = dependency_name
df['version'] = dependency_version
name_version = df.groupby(['name', 'version']).size()
print(name_version.head(5))
names = name_version.groupby('name').size()
plt.boxplot(names)
plt.show()
def f5(df):
'''
f5 is to find the Peak LU of each dependency
'''
libraries = np.unique(df['dependencies'])
max_LU_list = []
for library in libraries:
l_df = df.loc[(df["dependencies"] == library)]
record = []
for index, row in l_df.iterrows():
start, end = datetime.datetime.fromisoformat(row['start']), datetime.datetime.fromisoformat(row['end'])
record.append({'date': start, 'label' : 1})
record.append({'date': end, 'label' : -1})
record = sorted(record, key=lambda parameter: parameter['date'])
LU, max_LU = 0, 0
for r in record:
LU += r['label']
max_LU = max(max_LU, LU)
max_LU_list.append(max_LU)
max_LU_list = sorted(max_LU_list)
# print(max_LU_list)
y = []
for i in range(200):
y.append(bisect.bisect_right(max_LU_list, i) / len(max_LU_list))
plt.plot(np.arange(200), y)
plt.xscale('log')
plt.hlines(0.75, 0, 200)
plt.vlines(2.8, 0, 1)
# plt.yticks(['{:,.2%}'.format(x) for x in y])
plt.show()
def f6(df):
'''
f5 is to calculate pre-peak
'''
libraries = np.unique(df['dependencies'])
days = []
for library in libraries:
l_df = df.loc[(df["dependencies"] == library)]
record = []
for index, row in l_df.iterrows():
start, end = datetime.datetime.fromisoformat(row['start']), datetime.datetime.fromisoformat(row['end'])
record.append({'date': start, 'label' : 1})
record.append({'date': end, 'label' : -1})
record = sorted(record, key=lambda parameter: parameter['date'])
LU, max_LU = 0, 0
init_date = record[0]['date']
for r in record:
LU += r['label']
if LU > max_LU:
peak_date = r['date']
max_LU = max(max_LU, LU)
post_peak = (peak_date - init_date).days
days.append(post_peak)
days = sorted([x for x in days if x != 0])
y = []
for i in range(3000):
y.append(bisect.bisect_right(days, i) / len(days))
plt.plot(np.arange(3000), y)
plt.hlines(0.75, 0, 3000)
plt.vlines(560, 0, 1)
plt.show()
# print(days)
def f7(df1, df2):
'''
f7 is to compare the Peak LU of each dependency
'''
libraries = np.unique(df1['dependencies'])
max_LU_list = []
for library in libraries:
l_df = df1.loc[(df1["dependencies"] == library)]
record = []
for index, row in l_df.iterrows():
start, end = datetime.datetime.fromisoformat(row['start']), datetime.datetime.fromisoformat(row['end'])
record.append({'date': start, 'label' : 1})
record.append({'date': end, 'label' : -1})
record = sorted(record, key=lambda parameter: parameter['date'])
LU, max_LU = 0, 0
for r in record:
LU += r['label']
max_LU = max(max_LU, LU)
max_LU_list.append(max_LU)
max_LU_list = sorted(max_LU_list)
# print(max_LU_list)
y = []
for i in range(200):
y.append(bisect.bisect_right(max_LU_list, i) / len(max_LU_list))
plt.plot(np.arange(200), y, label='2015')
libraries = np.unique(df2['dependencies'])
max_LU_list = []
for library in libraries:
l_df = df2.loc[(df2["dependencies"] == library)]
record = []
for index, row in l_df.iterrows():
start, end = datetime.datetime.fromisoformat(row['start']), datetime.datetime.fromisoformat(row['end'])
record.append({'date': start, 'label' : 1})
record.append({'date': end, 'label' : -1})
record = sorted(record, key=lambda parameter: parameter['date'])
LU, max_LU = 0, 0
for r in record:
LU += r['label']
max_LU = max(max_LU, LU)
max_LU_list.append(max_LU)
max_LU_list = sorted(max_LU_list)
# print(max_LU_list)
y = []
for i in range(200):
y.append(bisect.bisect_right(max_LU_list, i) / len(max_LU_list))
plt.plot(np.arange(200), y, label='2019')
plt.legend()
plt.xscale('log')
plt.hlines(0.75, 0, 200)
plt.vlines(1.34, 0, 1)
plt.vlines(2.8, 0, 1)
# plt.yticks(['{:,.2%}'.format(x) for x in y])
plt.show()
def f8(df1, df2):
'''
f8 is to compare two pre-peak
'''
libraries = np.unique(df1['dependencies'])
days = []
for library in libraries:
l_df = df1.loc[(df1["dependencies"] == library)]
record = []
for index, row in l_df.iterrows():
start, end = datetime.datetime.fromisoformat(row['start']), datetime.datetime.fromisoformat(row['end'])
record.append({'date': start, 'label' : 1})
record.append({'date': end, 'label' : -1})
record = sorted(record, key=lambda parameter: parameter['date'])
LU, max_LU = 0, 0
init_date = record[0]['date']
for r in record:
LU += r['label']
if LU > max_LU:
peak_date = r['date']
max_LU = max(max_LU, LU)
post_peak = (peak_date - init_date).days
days.append(post_peak)
days = sorted([x for x in days if x != 0])
y = []
for i in range(3000):
y.append(bisect.bisect_right(days, i) / len(days))
plt.plot(np.arange(3000), y, label='2015')
libraries = np.unique(df2['dependencies'])
days = []
for library in libraries:
l_df = df2.loc[(df2["dependencies"] == library)]
record = []
for index, row in l_df.iterrows():
start, end = datetime.datetime.fromisoformat(row['start']), datetime.datetime.fromisoformat(row['end'])
record.append({'date': start, 'label' : 1})
record.append({'date': end, 'label' : -1})
record = sorted(record, key=lambda parameter: parameter['date'])
LU, max_LU = 0, 0
init_date = record[0]['date']
for r in record:
LU += r['label']
if LU > max_LU:
peak_date = r['date']
max_LU = max(max_LU, LU)
post_peak = (peak_date - init_date).days
days.append(post_peak)
days = sorted([x for x in days if x != 0])
y = []
for i in range(3000):
y.append(bisect.bisect_right(days, i) / len(days))
plt.plot(np.arange(3000), y, label='2019')
plt.hlines(0.75, 0, 3000)
plt.vlines(560, 0, 1)
plt.vlines(163, 0, 1)
plt.legend()
plt.show()
if __name__ == "__main__":
df1 = pd.read_csv('project_dependency.csv')
df2 = pd.read_csv('project_dependency2.csv')
f7(df1, df2)
# f1(df2, ['junit-junit-4.11', 'junit-junit-4.12'])