def arrival_rate(t):
# print("Possion",qt.poisson_random_measure(t, rate, 100)) # Poission Distribution
# print("===")
# print(qt.poisson_random_measure(t, rate, 100))
# return qt.poisson_random_measure(t, rate, 100)
# print(qt.poisson_random_measure(t, rate, 200))
return qt.poisson_random_measure(t, rate, 200)
def observed_new_arrival():
rate = lambda t: 2 + 16 * np.sin(np.pi * t / 8)**2
arr = lambda t: qt.poisson_random_measure(t, rate, 18)
ser = lambda t: t + np.random.gamma(4, 0.1)
q = qt.QueueServer(5, arrival_f=arr, service_f=ser, seed=54)
q.set_active()
nA0, nD0 = q.num_arrivals[1], q.num_departures
q.simulate(nA=1000)
result = q.num_departures - nD0, q.num_arrivals[1] - nA0,
print(result)
def time_simulation():
rate = lambda t: 2 + 16 * np.sin(np.pi * t / 8)**2
arr = lambda t: qt.poisson_random_measure(t, rate, 18)
ser = lambda t: t + np.random.gamma(4, 0.1)
q = qt.QueueServer(5, arrival_f=arr, service_f=ser, seed=54)
q.set_active()
num_events = q.num_arrivals[0] + q.num_departures
t0 = q.time
q.simulate(t=175)
round(float(q.time - t0), 1)
result = q.num_arrivals[1] + q.num_departures - num_events
print(result)
def event_simulation():
rate = lambda t: 2 + 16 * np.sin(np.pi * t / 8)**2
arr = lambda t: qt.poisson_random_measure(t, rate, 18)
ser = lambda t: t + np.random.gamma(4, 0.1)
q = qt.QueueServer(5, arrival_f=arr, service_f=ser, seed=54)
q.set_active()
q.collect_data = True
q.simulate(100)
data = q.fetch_data()
print(data)
num_events = q.num_arrivals[0] + q.num_departures
print("the number of job arrival: ", q.num_arrivals[0])
print("the number of job departure: ", q.num_departures)
print("the total number of jobs ", num_events)
import numpy as np
# Queueing parameters
a, b = 158, 50
h = 13.0
nc = 8
mu = 30.0
# Data collection variables and parameters
N = 10 # 500
w0 = np.array([] )
s0 = np.zeros(N)
# Queueing process functions
rate = lambda t: a + b - b * np.cos(2 * np.pi * t / h)
arr = lambda t: qt.poisson_random_measure(t, rate, a + 2 * b)
ser = lambda t: t + np.random.exponential(1 / mu)
# The Queue
q = qt.QueueServer(nServers=nc, deactive_t=h,
arrival_f=arr, service_f=ser)
for k in range(N):
q.collect_data = True
q.set_active()
q.simulate(t=h+5)
data = q.fetch_data()
s0[k] = np.std(data[:,1] - data[:, 0] )
w0 = np.hstack((w0, data[:,1] - data[:, 0] ) )
q.clear()
def arr(t):
return qt.poisson_random_measure(t, rate, 18)
def arrival_rate(t):
print("Possion", qt.poisson_random_measure(t, rate,
100)) # Poission Distribution
return qt.poisson_random_measure(t, rate, 100)
def arr_f(t):
# precinctPop = 500
if t<=1:
return qt.poisson_random_measure(t,arrival_rate,0.156*precinctPop)
elif t<=2:
return qt.poisson_random_measure(t,arrival_rate,0.087*precinctPop)
elif t<=3:
return qt.poisson_random_measure(t,arrival_rate,0.095*precinctPop)
elif t<=4:
return qt.poisson_random_measure(t,arrival_rate,0.112*precinctPop)
elif t<=5:
return qt.poisson_random_measure(t,arrival_rate,0.087*precinctPop)
elif t<=6:
return qt.poisson_random_measure(t,arrival_rate,0.054*precinctPop)
elif t<=7:
return qt.poisson_random_measure(t,arrival_rate,0.072*precinctPop)
elif t<=8:
return qt.poisson_random_measure(t,arrival_rate,0.067*precinctPop)
elif t<=9:
return qt.poisson_random_measure(t,arrival_rate,0.063*precinctPop)
elif t<=10:
return qt.poisson_random_measure(t,arrival_rate,0.067*precinctPop)
elif t<=11:
return qt.poisson_random_measure(t,arrival_rate,0.068*precinctPop)
elif t<=12:
return qt.poisson_random_measure(t,arrival_rate,0.053*precinctPop)
else:
return qt.poisson_random_measure(t,arrival_rate,0.02*precinctPop)