def test3():
print('test3')
sim.Environment(random_seed=1234567)
print('string')
d = sim.Distribution('Exponential (1000)')
sample_and_print(d, 5)
sim.random_seed(1234567)
sample_and_print(d, 5)
sim.random_seed(None)
sample_and_print(d, 5)
print('triangular')
tr = sim.Triangular(1, 5, 3)
sample_and_print(tr, 5)
print('uniform')
u = sim.Uniform(1, 1.1)
sample_and_print(u, 5)
print('constant')
c = sim.Constant(10)
sample_and_print(c, 5)
print('normal')
n = sim.Normal(1, 2)
sample_and_print(n, 5)
sample_and_print(n, 5)
print('cdf')
cdf = sim.Cdf((1, 0, 2, 25, 2, 75, 3, 100))
sample_and_print(cdf, 5)
sample_and_print(cdf, 5)
print('pdf')
pdf = sim.Pdf((1, 2), 1)
sample_and_print(pdf, 5)
print('pdf 1')
pdf = sim.Pdf((sim.Uniform(10, 20), 10, sim.Uniform(20, 30), 80,
sim.Uniform(30, 40), 10))
sample_and_print(pdf, 5)
print('pdf 2')
pdf = sim.Pdf(
(sim.Uniform(10, 20), sim.Uniform(20, 30), sim.Uniform(30, 40)),
(10, 80, 10))
sample_and_print(pdf, 5)
print('pdf 3')
pdf = sim.Pdf(('red', 'green', 1000), (10, 1, 10))
sample_and_print(pdf, 5)
def test43():
def test(d):
d.print_info()
print('mean=', d.mean())
l=[d() for i in range(10000)]
print('one sample', d())
print('mean sampled =', sum(l)/(len(l)+1))
print('-' * 79)
env=sim.Environment()
test(sim.IntUniform(1,6))
test(sim.Weibull(2,1))
test(sim.Gamma(5,9))
test(sim.Erlang(2,scale=3))
test(sim.Exponential(rate=2))
test(sim.Beta(32,300))
test(sim.Normal(5,7))
test(sim.Normal(5,7,use_gauss=True))
test(sim.Distribution('Exponential(rate=2)'))
test(sim.Normal(5,5))
def test23():
sim.a=100
for d in ('uni(12,30)','n(12)','exponentia(a)','TRI(1)','(12)','12',' ( 12, 30) ','a'):
print(d)
print(sim.Distribution(d))
def test7():
print('test7')
class X1(sim.Component):
def process(self):
yield self.request(r1,5,r2,2,greedy=True,fail_at=5)
yield self.passivate()
class X2(sim.Component):
def process(self):
yield self.request(r1,8,r2)
yield self.passivate()
class X3(sim.Component):
def process(self):
yield self.request(r1,7)
yield self.passivate()
de=sim.Environment(trace=True)
x1=X1()
x2=X2()
x3=X3()
X4=sim.Component()
r1=sim.Resource(capacity=10,anonymous=True)
r2=sim.Resource()
r3=sim.Resource()
q={}
for i in range(1,5):
q[i]=sim.Queue()
x1.enter(q[1])
x1.enter(q[2])
x1.enter(q[3])
x2.enter(q[1])
x3.enter(q[1])
env.run(10)
r2.capacity(2)
env.run(20)
print(sim.default_env)
print(x1)
print(x2)
print(x3)
print (q[1])
print (q[2])
print (q[3])
print (q[4])
print(r1)
print(r2)
print(r3)
d=sim.Exponential(10)
print(d)
print(sim.Uniform(1,2))
print(sim.Triangular(40,150,55))
print(sim.Distribution('Constant(5)'))
groups = []
with sim.ItemFile('job shop.txt') as f:
job_select_method = f.read_item()
while True:
name = f.read_item()
if name == '//':
break
number_of_machines = f.read_item_int()
fraction = f.read_item_float()
color = f.read_item()
groups.append(Group(name=name, job_select_method=job_select_method,
fraction=fraction, number_of_machines=number_of_machines, color=color))
duration_dist = sim.Distribution(f.read_item())
inter_arrival_time_dist = sim.Distribution(f.read_item())
number_of_tasks_dist = sim.Distribution(f.read_item())
start_slack = f.read_item_float()
plant = sim.Queue('plant')
group_dist = sim.Pdf(groups, probabilities=[group.fraction for group in groups])
JobGenerator(inter_arrival_time_dist=inter_arrival_time_dist, number_of_tasks_dist=number_of_tasks_dist,
group_dist=group_dist, duration_dist=duration_dist)
scale_x = 1
animation()
env.run(100000)