Add solution for rep-17

2021-05-14 19:02:02 +02:00 · 2021-05-14 19:02:02 +02:00 · a8d6910591
commit a8d6910591
parent aac0e096f7
19 changed files with 4600 additions and 0 deletions
--- a/ex3/csv/global_last10years.csv
+++ b/ex3/csv/global_last10years.csv
--- a/ex3/csv/team13_monthly.csv
+++ b/ex3/csv/team13_monthly.csv
@ -0,0 +1,745 @@
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 1580425200,34759727.033333,1615677694.8333,273645.1,10458603.066667
 1580428800,43265289.2,2041413915.0667,282272.21666667,10872788.816667
 1580432400,41937818.866667,2217530309.55,299454.83333333,10822880.016667
 1580436000,41940494.883333,2272927915.2333,311407.61666667,10827833.583333
 1580439600,42118512.55,2179319404.9167,317342.26666667,10792689.083333
 1580443200,43964426.316667,2042974369.3833,328238.26666667,10882835.516667
 1580446800,43827728.75,1969095007.8667,335445.11666667,10878141.466667
 1580450400,43632441.2,2067886301.4333,340876.76666667,10875689.583333
 1580454000,42968033.016667,2083181906.2667,346739.13333333,10860828.25
 1580457600,43180374.05,2034245945.2167,347601.95,10873718.1
 1580461200,42012815.1,2064779816.5,343095.26666667,10840513.516667
 1580464800,42226677.666667,2071001379.4333,335970.61666667,10840093
 1580468400,42468691.516667,1973168368.1833,335259.88333333,10841118.85
 1580472000,42287528.25,2151081223.0833,338006.28333333,10844260.95
 1580475600,42211658.35,2190771788.8167,341029.35,10794353.966667
 1580479200,43991405.65,1955637358.3833,345590.03333333,10859992.533333
 1580482800,44013065.983333,1962850229.8833,344741.53333333,10853878.466667
 1580486400,44305771.333333,2009047567.6833,335953.56666667,10852817.716667
 1580490000,44202954.683333,2054486485.3667,321305.55,10860727.333333
 1580493600,44710511.466667,1954138708.4,306653.43333333,10875344.9
 1580497200,44699242.016667,1999524647.05,292438.7,10880469.05
 1580500800,43665711.866667,2064647469.1667,282062.51666667,10860654.433333
 1580504400,43623867.05,2002547919.0167,272671.06666667,10810174.866667
 1580508000,44358857.8,2094223567.2667,268465,10865654.85
--- a/ex3/figures/bytes_daily_boxplot.png
+++ b/ex3/figures/bytes_daily_boxplot.png
--- a/ex3/figures/bytes_daily_hist.png
+++ b/ex3/figures/bytes_daily_hist.png
--- a/ex3/figures/bytes_hourly_boxplot.png
+++ b/ex3/figures/bytes_hourly_boxplot.png
--- a/ex3/figures/bytes_hourly_hist.png
+++ b/ex3/figures/bytes_hourly_hist.png
--- a/ex3/figures/packets_daily_boxplot.png
+++ b/ex3/figures/packets_daily_boxplot.png
--- a/ex3/figures/packets_daily_hist.png
+++ b/ex3/figures/packets_daily_hist.png
--- a/ex3/figures/packets_hourly_boxplot.png
+++ b/ex3/figures/packets_hourly_boxplot.png
--- a/ex3/figures/packets_hourly_hist.png
+++ b/ex3/figures/packets_hourly_hist.png
--- a/ex3/figures/uIPd_daily_boxplot.png
+++ b/ex3/figures/uIPd_daily_boxplot.png
--- a/ex3/figures/uIPd_daily_hist.png
+++ b/ex3/figures/uIPd_daily_hist.png
--- a/ex3/figures/uIPd_hourly_boxplot.png
+++ b/ex3/figures/uIPd_hourly_boxplot.png
--- a/ex3/figures/uIPd_hourly_hist.png
+++ b/ex3/figures/uIPd_hourly_hist.png
--- a/ex3/figures/uIPs_daily_boxplot.png
+++ b/ex3/figures/uIPs_daily_boxplot.png
--- a/ex3/figures/uIPs_daily_hist.png
+++ b/ex3/figures/uIPs_daily_hist.png
--- a/ex3/figures/uIPs_hourly_boxplot.png
+++ b/ex3/figures/uIPs_hourly_boxplot.png
--- a/ex3/figures/uIPs_hourly_hist.png
+++ b/ex3/figures/uIPs_hourly_hist.png
--- a/ex3/rep-17.py
+++ b/ex3/rep-17.py
@ -0,0 +1,169 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np
 dataset_hourly = pd.read_csv('data/team13_monthly.csv', index_col=0).dropna()
 dataset_daily = pd.read_csv('data/global_last10years.csv', index_col=0).dropna()
 table_a_info = {
    'total sum': dataset_hourly.sum(),
    'mean': dataset_hourly.mean(),
    'median': dataset_hourly.median(),
    'standard deviation': dataset_hourly.std(),
 }
 table_a = round((pd.DataFrame(table_a_info).set_index(dataset_hourly.columns)) / 1000000, 1)
 print(table_a)
 # Filter daily dataset by timestamp range of hourly dataset
 dataset_daily_filtered = dataset_daily[(dataset_daily.index >= dataset_hourly.index[0]) &
                                       (dataset_daily.index <= dataset_hourly.index[-1])]
 table_b_info = {
    'total sum': dataset_daily_filtered.sum(),
    'mean': dataset_daily_filtered.mean(),
    'median': dataset_daily_filtered.median(),
    'standard deviation': dataset_daily_filtered.std(),
 }
 table_b = round((pd.DataFrame(table_b_info).set_index(dataset_daily.columns) / 1000000), 1)
 print(table_b)
 # Plotting hourly bytes histogram
 plt.hist(dataset_hourly['#bytes'] / 1000000000, bins=30)
 plt.xlabel('GB/h')
 plt.ylabel('Frequency')
 plt.xticks(np.arange(1, 2.6, 0.1))
 plt.grid()
 plt.title('Histogram of gigabytes per hour during one month')
 plt.savefig('figures/bytes_hourly_hist.png')
 plt.show()
 # Plotting daily bytes histogram
 plt.hist(dataset_daily['# Bytes'] / 1000000000, bins=40)
 plt.xlabel('GB/day')
 plt.ylabel('Frequency')
 plt.xticks(np.arange(0, 5.5, 0.2))
 plt.grid()
 plt.title('Histogram of gigabytes per day during 10 years')
 plt.savefig('figures/bytes_daily_hist.png')
 plt.show()
 # Plotting hourly packets histogram
 plt.hist(dataset_hourly['#packets'] / (1*10**6), bins=30)
 plt.xlabel('#packets/h in millions')
 plt.ylabel('Frequency')
 plt.grid()
 plt.title('Histogram of packets per hour during one month')
 plt.savefig('figures/packets_hourly_hist.png')
 plt.show()
 # Plotting daily packets histogram
 plt.hist(dataset_daily['# Packets'] / (1*10**6), bins=30)
 plt.xlabel('#packets/day in millions')
 plt.ylabel('Frequency')
 plt.grid()
 plt.title('Histogram of packets per day during 10 years')
 plt.savefig('figures/packets_daily_hist.png')
 plt.show()
 # Plotting hourly unique source IPs histogram
 plt.hist(dataset_hourly['#unique_IP_sources'] / (1*10**3), bins=30)
 plt.xlabel('#uIPs/h in thousands')
 plt.ylabel('Frequency')
 plt.grid()
 plt.title('Histogram of unique source IPs per hour during one month')
 plt.savefig('figures/uIPs_hourly_hist.png')
 plt.show()
 # Plotting daily unique source IPs histogram
 plt.hist(dataset_daily['# Unique Source IPs'] / (1*10**3), bins=30)
 plt.xlabel('#uIPs/day in thousands')
 plt.ylabel('Frequency')
 plt.grid()
 plt.title('Histogram of unique source IPs per day during 10 years')
 plt.savefig('figures/uIPs_daily_hist.png')
 plt.show()
 # Plotting hourly unique destination IPs histogram
 plt.hist(dataset_hourly['#unique_IP_destinations'] / (1*10**6), bins=30)
 plt.xlabel('#uIPd/h in millions')
 plt.ylabel('Frequency')
 plt.grid()
 plt.title('Histogram of unique destination IPs per hour during one month')
 plt.savefig('figures/uIPd_hourly_hist.png')
 plt.show()
 # Plotting daily unique destination IPs histogram
 plt.hist(dataset_daily['# Unique Destination IPs'] / (1*10**6), bins=30)
 plt.xlabel('#uIPs/day in millions')
 plt.ylabel('Frequency')
 plt.grid()
 plt.title('Histogram of unique destination IPs per day during 10 years')
 plt.savefig('figures/uIPd_daily_hist.png')
 plt.show()
 # Plotting hourly bytes boxplot
 plt.boxplot(dataset_hourly['#bytes'] / (1*10**9))
 plt.ylabel('GB/h')
 plt.grid()
 plt.title('Boxplot of gigabytes per hour during one month')
 plt.show()
 plt.savefig('figures/bytes_hourly_boxplot.png')
 # Plotting daily bytes boxplot
 plt.boxplot(dataset_daily['# Bytes'] / (1*10**9))
 plt.ylabel('GB/day')
 plt.grid()
 plt.title('Boxplot of gigabytes per day during 10 years')
 plt.show()
 plt.savefig('figures/bytes_daily_boxplot.png')
 # Plotting hourly packets boxplot
 plt.boxplot(dataset_hourly['#packets'] / (1*10**6))
 plt.xlabel('')
 plt.ylabel('#packets/h in millions')
 plt.grid()
 plt.title('Boxplot of packets per hour during one month')
 plt.show()
 plt.savefig('figures/packets_hourly_boxplot.png')
 # Plotting daily packets boxplot
 plt.boxplot(dataset_daily['# Packets'] / (1*10**6))
 plt.ylabel('#packets/day in millions')
 plt.grid()
 plt.title('Boxplot of packets per day during 10 years')
 plt.show()
 plt.savefig('figures/packets_daily_boxplot.png')
 # Plotting hourly unique source IPs boxplot
 plt.boxplot(dataset_hourly['#unique_IP_sources'] / (1*10**3))
 plt.ylabel('#uIPs/h in thousands')
 plt.grid()
 plt.title('Boxplot of unique source IPs per hour during one month')
 plt.show()
 plt.savefig('figures/uIPs_hourly_boxplot.png')
 # Plotting daily unique source IPs boxplot
 plt.boxplot(dataset_daily['# Unique Source IPs'] / (1*10**3))
 plt.ylabel('#uIPs/day in thousands')
 plt.grid()
 plt.title('Boxplot of unique source IPs per day during 10 years')
 plt.show()
 plt.savefig('figures/uIPs_daily_boxplot.png')
 # Plotting hourly unique destination IPs boxplot
 plt.boxplot(dataset_hourly['#unique_IP_destinations'] / (1*10**6))
 plt.ylabel('#uIPd/h in millions')
 plt.grid()
 plt.title('Boxplot of unique destination IPs per hour during one month')
 plt.show()
 plt.savefig('figures/uIPd_hourly_boxplot.png')
 # Plotting daily unique destination IPs boxplot
 plt.boxplot(dataset_daily['# Unique Destination IPs'] / (1*10**6))
 plt.ylabel('#uIPd/day in millions')
 plt.grid()
 plt.title('Boxplot of unique destination IPs per day during 10 years')
 plt.show()
 plt.savefig('figures/uIPd_daily_boxplot.png')