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cleanup: removed unused python scripts

master
giomba 3 years ago
parent
commit
b152ec0627
  1. 5
      simulation/drawplot.sh
  2. 39
      simulation/plot.py
  3. 63
      simulation/pretty.py
  4. 10
      simulation/requirements.txt

5
simulation/drawplot.sh

@ -1,5 +0,0 @@
#!/bin/bash
python pretty.py | sort -n > pretty.csv
python plot.py

39
simulation/plot.py

@ -1,39 +0,0 @@
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as numpy
import pandas as pd
def getcolor(n):
return ['#aa0000', '#00aa00', '#0000aa', '#aaaa00', '#aa00aa'][int((i - 12) / 2)]
df = pd.read_csv('pretty.csv', delimiter='\s', engine='python')
x = df['k'].unique()
for i in df['i_min'].unique():
rows = df.loc[df['i_min'] == i]
plt.errorbar(
x,
rows['mean'],
yerr=rows['ci'],
marker='.',
capsize=4,
ms=3,
mec='r',
mfc='r',
linestyle='-',
linewidth=1,
color=getcolor(i),
ecolor='red'
)
handles = []
for i in df['i_min'].unique():
handles.append(mpatches.Patch(color=getcolor(i), label='Imin = ' + str(i)))
plt.ylim([0, 1400])
plt.legend(handles=handles)
plt.show()

63
simulation/pretty.py

@ -1,63 +0,0 @@
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import csv
import os
import re
import statistics
### PARAMETERS ###
RESULTSDIR='results/'
### FUNCTIONS ###
def compute_confidence_interval(samples): # 95%
stdev = statistics.stdev(samples)
ci = 1.96 * stdev / np.sqrt(len(samples))
return ci
### GLOBAL VARIABLES ###
all_route_discovery = dict()
### MAIN ###
for filename in os.listdir(RESULTSDIR):
df = pd.read_csv(RESULTSDIR + '/' + filename,
delimiter='\s|\t',
index_col=0,
nrows=4,
engine='python',
header=None).T
kappa = int(df['KAPPA'].to_string().split()[1])
i_min = int(df['I_MIN'].to_string().split()[1])
key = str(kappa) + ':' + str(i_min)
if (not key in all_route_discovery):
all_route_discovery[key] = { 'samples': [], 'k': kappa, 'i_min': i_min, 'count': 0 }
all_route_discovery[key]['count'] += 1
with open(RESULTSDIR + '/' + filename) as file:
for line in file:
if (re.match('ALL-ROUTE-DISCOVERY', line)):
t = int(line.split()[1]) / (10**6)
all_route_discovery[key]['samples'].append(t)
break
# now, for every k, i_min, i_max
# all_route_discovery contains an array with all samples of the measured value
print('k i_min count mean ci')
for simulation in all_route_discovery:
all_route_discovery[simulation]['mean'] = np.mean(all_route_discovery[simulation]['samples'])
all_route_discovery[simulation]['ci'] = compute_confidence_interval(all_route_discovery[simulation]['samples'])
print(
all_route_discovery[simulation]['k'],
all_route_discovery[simulation]['i_min'],
all_route_discovery[simulation]['count'],
all_route_discovery[simulation]['mean'],
all_route_discovery[simulation]['ci']
)
exit(0)

10
simulation/requirements.txt

@ -1,10 +0,0 @@
cycler==0.10.0
kiwisolver==1.1.0
matplotlib==3.1.2
numpy==1.17.4
pandas==0.25.3
pkg-resources==0.0.0
pyparsing==2.4.5
python-dateutil==2.8.1
pytz==2019.3
six==1.13.0
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