cleanup: removed unused python scripts
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#!/bin/bash
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python pretty.py | sort -n > pretty.csv
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python plot.py
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import matplotlib.pyplot as plt
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import matplotlib.patches as mpatches
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import numpy as numpy
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import pandas as pd
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def getcolor(n):
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return ['#aa0000', '#00aa00', '#0000aa', '#aaaa00', '#aa00aa'][int((i - 12) / 2)]
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df = pd.read_csv('pretty.csv', delimiter='\s', engine='python')
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x = df['k'].unique()
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for i in df['i_min'].unique():
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rows = df.loc[df['i_min'] == i]
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plt.errorbar(
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x,
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rows['mean'],
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yerr=rows['ci'],
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marker='.',
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capsize=4,
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ms=3,
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mec='r',
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mfc='r',
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linestyle='-',
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linewidth=1,
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color=getcolor(i),
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ecolor='red'
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)
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handles = []
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for i in df['i_min'].unique():
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handles.append(mpatches.Patch(color=getcolor(i), label='Imin = ' + str(i)))
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plt.ylim([0, 1400])
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plt.legend(handles=handles)
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plt.show()
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import csv
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import os
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import re
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import statistics
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### PARAMETERS ###
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RESULTSDIR='results/'
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### FUNCTIONS ###
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def compute_confidence_interval(samples): # 95%
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stdev = statistics.stdev(samples)
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ci = 1.96 * stdev / np.sqrt(len(samples))
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return ci
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### GLOBAL VARIABLES ###
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all_route_discovery = dict()
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### MAIN ###
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for filename in os.listdir(RESULTSDIR):
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df = pd.read_csv(RESULTSDIR + '/' + filename,
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delimiter='\s|\t',
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index_col=0,
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nrows=4,
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engine='python',
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header=None).T
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kappa = int(df['KAPPA'].to_string().split()[1])
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i_min = int(df['I_MIN'].to_string().split()[1])
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key = str(kappa) + ':' + str(i_min)
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if (not key in all_route_discovery):
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all_route_discovery[key] = { 'samples': [], 'k': kappa, 'i_min': i_min, 'count': 0 }
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all_route_discovery[key]['count'] += 1
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with open(RESULTSDIR + '/' + filename) as file:
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for line in file:
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if (re.match('ALL-ROUTE-DISCOVERY', line)):
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t = int(line.split()[1]) / (10**6)
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all_route_discovery[key]['samples'].append(t)
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break
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# now, for every k, i_min, i_max
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# all_route_discovery contains an array with all samples of the measured value
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print('k i_min count mean ci')
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for simulation in all_route_discovery:
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all_route_discovery[simulation]['mean'] = np.mean(all_route_discovery[simulation]['samples'])
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all_route_discovery[simulation]['ci'] = compute_confidence_interval(all_route_discovery[simulation]['samples'])
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print(
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all_route_discovery[simulation]['k'],
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all_route_discovery[simulation]['i_min'],
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all_route_discovery[simulation]['count'],
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all_route_discovery[simulation]['mean'],
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all_route_discovery[simulation]['ci']
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)
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exit(0)
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cycler==0.10.0
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kiwisolver==1.1.0
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matplotlib==3.1.2
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numpy==1.17.4
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pandas==0.25.3
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pkg-resources==0.0.0
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pyparsing==2.4.5
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python-dateutil==2.8.1
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pytz==2019.3
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six==1.13.0
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