def __init__(self,
arrival_rate: float,
service_rate: float,
precision: float = 1e-9):
arrival = Poisson(arrival_rate)
service = Poisson(service_rate)
super().__init__(arrival, service, precision=precision)
def __init__(self,
arrival_rate: float,
service_rate: float,
queue_capacity: int,
precision: float = 1e-9):
if abs(np.math.modf(queue_capacity)[0]) > 1e-12 or queue_capacity <= 0:
raise ValueError(f"positive integer expected, "
f"but {queue_capacity} found")
arrival = Poisson(arrival_rate)
service = Poisson(service_rate)
super().__init__(arrival,
service,
queue_capacity=queue_capacity,
precision=precision)
def test_moments_poisson_arrival(self):
source = Poisson(3.0)
self.assertIsInstance(stats.moment(source, 1), np.ndarray)
self.assertIsInstance(stats.moment(source, 2), np.ndarray)
assert_almost_equal(stats.moment(source, 1), [1 / 3], 10)
assert_almost_equal(stats.moment(source, 2), [1 / 3, 2 / 9], 10)
arrival_avg: Sequence[float]
response_time_avg: Sequence[float]
wait_time_avg: Sequence[float]
delivery_delay_avg: Sequence[float]
# Test parameters:
tol: float = 1e-1
max_packets: int = int(1.5e5)
# noinspection DuplicatedCode
@pytest.mark.parametrize(
'props',
[
TandemProps(
arrival=Poisson(2),
service=[Poisson(5)],
queue_capacity=4,
# System and queue sizes:
system_size_avg=[0.642],
system_size_std=[0.981],
queue_size_avg=[0.2444],
queue_size_std=[0.6545],
busy_avg=[0.3975],
busy_std=[0.4894],
# Scalar probabilities and rates:
drop_prob=[0.0062],
delivery_prob=[0.9938],
utilization=[0.3975],
# Intervals:
departure_avg=[0.5031],
from numpy.testing import assert_allclose
from pyqumo import stats
from pyqumo.arrivals import MarkovArrival, Poisson, \
GIProcess
#
# POISSON PROCESS
# #######################
from pyqumo.random import Const, Uniform
@pytest.mark.parametrize('proc, m1, m2, m3, l1, string', [
# Poisson process:
(Poisson(1.0), 1, 2, 6, 0.0, '(Poisson: r=1)'),
(Poisson(2.5), 0.4, 0.32, 0.384, 0.0, '(Poisson: r=2.5)'),
# GI with uniform or constant distributions:
(GIProcess(Const(3)), 3, 9, 27, 0, '(GI: f=(Const: value=3))'),
(GIProcess(Uniform(2, 10)), 6, 124 / 3, 312, 0,
'(GI: f=(Uniform: a=2, b=10))'),
# MAP variants of Poisson or Erlang processes:
(
MarkovArrival.poisson(2.5),
0.4, 0.32, 0.384, 0.0,
'(MAP: d0=[[-2.5]], d1=[[2.5]])'
), (
MarkovArrival.erlang(3, rate=4.2),
0.714286, 0.680272, 0.809848, 0.0,
'(MAP: d0=[[-4.2, 4.2, 0], [0, -4.2, 4.2], [0, 0, -4.2]], '
'd1=[[0, 0, 0], [0, 0, 0], [4.2, 0, 0]])'
def departure(self):
return Poisson(self.lambda_)
utilization: float
# Departure process properties:
departure_rate: float
# Response and wait time:
response_time_avg: float
wait_time_avg: float
# Test parameters:
tol: float = 1e-1
max_packets: int = int(1e5)
@pytest.mark.parametrize('props', [
GG1Props(arrival=Poisson(2),
service=Poisson(5),
queue_capacity=4,
system_size_avg=0.642,
system_size_std=0.981,
queue_size_avg=0.2444,
queue_size_std=0.6545,
loss_prob=0.0062,
utilization=0.3975,
departure_rate=1.9877,
response_time_avg=0.323,
wait_time_avg=0.123),
GG1Props(arrival=Exponential(42),
service=Exponential(34),
queue_capacity=7,
system_size_avg=5.3295,
arrival_avg: Sequence[float]
response_time_avg: Sequence[float]
wait_time_avg: Sequence[float]
delivery_delay_avg: Sequence[float]
# Test parameters:
tol: float = 1e-1
max_packets: int = int(1.5e5)
# noinspection DuplicatedCode
@pytest.mark.parametrize(
'props',
[
TandemProps(
arrival=Poisson(2),
service=Poisson(5),
queue_capacity=4,
num_stations=1,
# System and queue sizes:
system_size_avg=[0.642],
system_size_std=[0.981],
queue_size_avg=[0.2444],
queue_size_std=[0.6545],
busy_avg=[0.3975],
busy_std=[0.4894],
# Scalar probabilities and rates:
drop_prob=[0.0062],
delivery_prob=[0.9938],
utilization=[0.3975],
# Intervals:
# Departure process properties:
departure_rate: float
# Response and wait time:
response_time_avg: float
wait_time_avg: float
# Test parameters:
tol: float = 1e-1
max_packets: int = int(1e5)
@pytest.mark.parametrize('props', [
GG1Props(
arrival=Poisson(2), service=Poisson(5), queue_capacity=4,
system_size_avg=0.642, system_size_std=0.981,
queue_size_avg=0.2444, queue_size_std=0.6545,
loss_prob=0.0062, utilization=0.3975, departure_rate=1.9877,
response_time_avg=0.323, wait_time_avg=0.123),
GG1Props(
arrival=Exponential(42), service=Exponential(34),
queue_capacity=7,
system_size_avg=5.3295, system_size_std=5.6015**0.5,
queue_size_avg=4.3708, queue_size_std=5.2010**0.5,
loss_prob=0.2239, utilization=0.9587, departure_rate=32.5959,
response_time_avg=0.163, wait_time_avg=0.134,
max_packets=int(1e5)
),
GG1Props(
arrival=Poisson(1), service=Exponential(2),