queuing-system-simulation/plot.py at main · denniemok/queuing-system-simulation · GitHub

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import random
import sys
import numpy as np
import math
import matplotlib.pyplot as plt

s = sys.argv[1]

para = np.loadtxt(f"config/para_{s}.txt")

interarrival = np.loadtxt(f"config/interarrival_{s}.txt")

with open(f"config/service_{s}.txt", "r") as f:
    lines = f.readlines()
    service = [line.split() for line in lines]

time_end = 1000

lamb = interarrival[0]
a_2l = interarrival[1]
a_2u = interarrival[2]

p_0 = float(service[0][0])
alpha_0 = float(service[1][0])
beta_0 = float(service[1][1])
eta_0 = float(service[1][2])
gamma_0 = math.pow(alpha_0, -eta_0) - math.pow(beta_0, -eta_0)
alpha_1 = float(service[2][0])
eta_1 = float(service[2][1])
gamma_1 = math.pow(alpha_1, -eta_1)

a = []
b = []
c = []

for i in range(time_end):

	d = random.uniform(a_2l, a_2u)
	e = random.expovariate(lamb)

	a.append(d)
	b.append(e)
	c.append(d * e)

figure1, axis = plt.subplots(1, 3)
axis[0].hist(b, bins=50)
axis[0].set_title("a_1k")
axis[1].hist(a, bins=50)
axis[1].set_title("a_2k")
axis[2].hist(c, bins=50)
axis[2].set_title("a_k")
print(np.mean(b), np.mean(a), np.mean(c))


f = []

for i in range(time_end):
	if random.random() < p_0:
	    f.append(0)
	else:
	    f.append(1)

figure2, axis = plt.subplots(1, 1)
axis.bar([0,1], [f.count(0), f.count(1)], width = 0.4)
axis.set_xticks([0,1], [0,1])
axis.set_title("Group Distribution")
print(f.count(0), f.count(1))


h = []
j = []
m = []
n = []

for i in np.arange(alpha_0, beta_0+0.1, 0.1):
	j.append(eta_0 / (gamma_0 * math.pow(i, eta_0 + 1)))
	h.append((math.pow(alpha_0, -eta_0) - math.pow(i, -eta_0)) / gamma_0)

haha = math.exp(math.log(math.pow(alpha_1, -eta_1) - gamma_1 * 0.9995) / -eta_1)
for i in np.arange(alpha_1, haha, 0.1):
	n.append(eta_1 / (gamma_1 * math.pow(i, eta_1 + 1)))
	m.append((math.pow(alpha_1, -eta_1) - math.pow(i, -eta_1)) / gamma_1)

figure3, axis = plt.subplots(1, 2)
axis[0].plot(np.arange(alpha_0, beta_0+0.1, 0.1), j)
axis[0].set_title("Class 0")
axis[1].plot(np.arange(alpha_1, haha, 0.1), n)
axis[1].set_title("Class 1")

figure4, axis = plt.subplots(1, 2)
axis[0].plot(np.arange(alpha_0, beta_0+0.1, 0.1), h)
axis[0].set_title("Class 0")
axis[1].plot(np.arange(alpha_1, haha, 0.1), m)
axis[1].set_title("Class 1")

g = []
k = []

for i in range(time_end):
	g.append(math.exp(math.log(math.pow(alpha_0, -eta_0) - gamma_0 * random.random()) / -eta_0))
	k.append(math.exp(math.log(math.pow(alpha_1, -eta_1) - gamma_1 * random.random()) / -eta_1))

figure5, axis = plt.subplots(1, 2)
axis[0].hist(g, bins=50)
axis[0].set_title("Class 0")
axis[1].hist(k, bins=50)
axis[1].set_title("Class 1")

plt.show()
plt.clf()