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