Copyright (c) Chair of Communication Networks, Technical University of Munich
'''
import numpy as np
import matplotlib
import scipy as sp
import scipy.stats
import matplotlib.pyplot as plt
import ci
from preprocessing import compile_results, remove_simdata
# --- throughput --- #
compile_results('fig5_throughput', 'Throughput', 'Fig5')
f = open("../export/fig5_throughput_data")
lines = f.read().split(' ')
# --- accesses --- #
compile_results('fig5_access', 'Access', 'Fig5')
f1 = open("../export/fig5_access_data")
lines1 = f1.read().split(' ')
# --- collisions --- #
compile_results('fig5_collisions', 'Collisions', 'Fig5')
f2 = open("../export/fig5_collisions_data")
lines2 = f2.read().split(' ')
# --- parameters --- #
n_s = [2*x for x in range(1, 21)]
import numpy as np
import matplotlib
import scipy as sp
import scipy.stats
import matplotlib.pyplot as plt
import ci
import os
from preprocessing import compile_results, remove_simdata
# variance_mean = []
# variance_ci = []
variance_box = []
# preprocess the data
compile_results("fig3", "errVar", "Fig3")
# open results of preprocessing
f = open("../export/fig3_data")
lines = f.read().split(" ")
# --- 2-24 subsystems --- #
n_s = [2 * x for x in range(1, 13)]
n_rep = 30
i = 0
fig, ax = plt.subplots(figsize=(8.5, 5))
for i_s in n_s:
Copyright (c) Chair of Communication Networks, Technical University of Munich
'''
import numpy as np
import scipy as sp
import scipy.stats
import matplotlib.pyplot as plt
import ci
import matplotlib
from preprocessing import compile_results, remove_simdata
# --- preprocessing --- #
compile_results('fig4', 'errVar', 'Fig4')
lines = []
with open("../export/fig4_data") as f:
for line in f:
lines = line.split(' ')
# --- setting parameters --- #
n_s = [10, 16, 20]
n_rep = 30
lmb = [(0.0+.1*x) for x in range(41)]
variance_nab_mean = []
variance_nab_ci = []
variance_nab_box = []
n_s = [4, 10, 14]
n_rep = 100
p_g = [0.1+float(x)/10 for x in range(10)]
fig, ax = plt.subplots(figsize=(8.5, 5))
p = [] # plots
for i_s in n_s:
# every number of subsystems
compile_results('fig8n' + str(i_s), 'errVar', 'Fig8N'+str(i_s))
f = open("../export/fig8n" + str(i_s) + '_data')
lines = f.read().split(' ')
i = 0
variance_mean = []
variance_na_mean = []
for value in [False, True]:
for p_g_i in p_g:
var = []
diff = []
# --- variables --- #
variance_mean = []
variance_ci = []
variance_box = []
variance_na_mean = []
variance_na_ci = []
variance_na_box = []
variance_nab_mean = []
variance_nab_ci = []
variance_nab_box = []
# --- preprocessing --- #
compile_results('fig7adapt', 'errVar', 'Fig7Adapt')
f = open("../export/fig7adapt_data")
# f = open("../export/fig7_adapt_data-1.csv")
lines = f.read().split(' ')
compile_results('fig7nadapt_a', 'errVar', 'Fig7NonAdaptA')
f1 = open("../export/fig7nadapt_a_data")
# f1 = open("../export/fig7_nadapt_a_data-1.csv")
lines1 = f1.read().split(' ')
compile_results('fig7nadapt_b', 'errVar', 'Fig7NonAdaptB')
f2 = open("../export/fig7nadapt_b_data")
# f2 = open("../export/fig7_nadapt_b_data-1.csv")
lines2 = f2.read().split(' ')
n_s = [4, 6, 8, 10, 12, 14, 16]