138 lines
3.2 KiB
Python
138 lines
3.2 KiB
Python
|
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])
|
||
|
|
i_max = int(df['I_MAX'].to_string().split()[1])
|
||
|
|
key = str(kappa) + ':' + str(i_min) + ':' + str(i_max)
|
||
|
|
|
||
|
|
if (not key in all_route_discovery):
|
||
|
|
all_route_discovery[key] = { 'samples': [], 'k': kappa, 'i_min': i_min }
|
||
|
|
|
||
|
|
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 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(
|
||
|
|
#simulation,
|
||
|
|
all_route_discovery[simulation]['k'],
|
||
|
|
all_route_discovery[simulation]['i_min'],
|
||
|
|
all_route_discovery[simulation]['mean'],
|
||
|
|
all_route_discovery[simulation]['ci']
|
||
|
|
)
|
||
|
|
|
||
|
|
exit(0)
|
||
|
|
|
||
|
|
'''
|
||
|
|
d = []
|
||
|
|
f = []
|
||
|
|
|
||
|
|
# compute fd in our interval of interest
|
||
|
|
d = np.arange(0, (M * 2**0.5 / 2))
|
||
|
|
for i in d:
|
||
|
|
f.append(fd(i))
|
||
|
|
|
||
|
|
# compute integral of fd
|
||
|
|
integral = []
|
||
|
|
last = 0
|
||
|
|
for i in range(0, len(f)):
|
||
|
|
part = f[i]
|
||
|
|
last = last + part
|
||
|
|
integral.append(last)
|
||
|
|
|
||
|
|
print("normalization fd: ", np.trapz(f))
|
||
|
|
|
||
|
|
# make plots
|
||
|
|
fig, ax1 = plt.subplots()
|
||
|
|
|
||
|
|
color = 'tab:red'
|
||
|
|
ax1.set_xlabel('d [m]')
|
||
|
|
ax1.set_ylabel('fD(d)')
|
||
|
|
ax1.plot(d, f, 'c,', label='fD')
|
||
|
|
|
||
|
|
ax2 = ax1.twinx()
|
||
|
|
|
||
|
|
color = 'tab:blue'
|
||
|
|
ax2.set_ylabel('FD(d)')
|
||
|
|
ax2.plot(d, integral, 'b,', label="FD")
|
||
|
|
|
||
|
|
plt.legend()
|
||
|
|
plt.show()
|
||
|
|
|
||
|
|
def fs(s):
|
||
|
|
if s >= 0 and s <= (T * M**2 ) / 4:
|
||
|
|
return np.pi / ( T * M**2 )
|
||
|
|
elif s >= (T * M**2 ) / 4 and s <= (T * M**2) / 2:
|
||
|
|
return (np.pi / (T * M**2)) - (4 / (T * M**2)) * np.arccos(M/2 * ((T / s)**0.5))
|
||
|
|
else:
|
||
|
|
return 0
|
||
|
|
|
||
|
|
### Distribution of Service Time
|
||
|
|
s = T * d**2
|
||
|
|
f = []
|
||
|
|
for i in s:
|
||
|
|
f.append(fs(i))
|
||
|
|
|
||
|
|
print("normalization fs: ", np.trapz(f, s))
|
||
|
|
|
||
|
|
# compute integral of fs
|
||
|
|
integral = [ 0 ]
|
||
|
|
last = 0
|
||
|
|
for i in range(1, len(f)):
|
||
|
|
part = f[i] * (s[i] - s[i - 1])
|
||
|
|
last = last + part
|
||
|
|
integral.append(last)
|
||
|
|
|
||
|
|
# make plots
|
||
|
|
fig, ax1 = plt.subplots()
|
||
|
|
color = 'tab:red'
|
||
|
|
ax1.set_xlabel('s [s]')
|
||
|
|
ax1.set_ylabel('fS(s)')
|
||
|
|
ax1.plot(s, f, 'c,', label='fS')
|
||
|
|
|
||
|
|
ax2 = ax1.twinx()
|
||
|
|
|
||
|
|
color = 'tab:blue'
|
||
|
|
ax2.set_ylabel('FD(d)')
|
||
|
|
ax2.plot(s, integral, 'b,', label='FS')
|
||
|
|
|
||
|
|
plt.legend()
|
||
|
|
plt.show()
|
||
|
|
'''
|