# %%
mc = MonteCarloSampling(distributions=[normal1, normal2],
nsamples=5,
random_state=RandomState(123))
mc.samples
# %% md
#
# To generate more samples on :code:`mc` after construction, we call :code:`mc.run` and once again specify
# :code:`nsamples`.
# %%
mc.run(nsamples=2, random_state=RandomState(23))
mc.samples
# %% md
#
# We can transform the samples onto the unit hypercube via applying the probability integral transformation on the
# samples to yield similar samples from the uniform distribution. We call :code:`mc.transform_u01`, from which results
# are stored in the :code:`samplesU01` attribute.
# %%
mc.transform_u01()
mc.samplesU01
import numpy as np
import pytest
from beartype.roar import BeartypeCallHintPepParamException
from UQpy.distributions import Normal, MultivariateNormal
from UQpy.sampling import MonteCarloSampling
dist1 = Normal(loc=0., scale=1.)
dist2 = Normal(loc=0., scale=1.)
x = MonteCarloSampling(distributions=dist1, nsamples=5, random_state=np.random.RandomState(123))
x.transform_u01()
y = MonteCarloSampling(distributions=[dist1, dist2])
y.run(nsamples=5, random_state=123)
y.transform_u01()
# Call run method multiple time, to cover lines where samples are append to existing ones
z1 = MonteCarloSampling(distributions=dist1, nsamples=2, random_state=123)
z1.run(nsamples=2)
z2 = MonteCarloSampling(distributions=[dist1, dist2], nsamples=2, random_state=np.random.RandomState(123))
z2.run(nsamples=2)
# Same object as z2, just to cover lines where, random_state is an integer
z3 = MonteCarloSampling(distributions=[dist1, dist2], nsamples=2, random_state=123)
z4 = MonteCarloSampling(distributions=[MultivariateNormal([0, 0])], nsamples=2,
random_state=np.random.RandomState(123))
z4.run(nsamples=2)
z4.transform_u01()
dist3 = Normal(loc=0., scale=1.)
del dist3.rvs
z5 = MonteCarloSampling(distributions=[dist3], random_state=np.random.RandomState(123))
plt.grid(True)
plt.tight_layout()
plt.show()
m = PythonModel(model_script='local_Resonance_pfn.py',
model_object_name="RunPythonModel")
model = RunModel(model=m)
# %% md
#
# Monte Carlo Simulation
# %%
x_mcs = MonteCarloSampling(distributions=[d1, d2])
x_mcs.run(nsamples=1000000)
model.run(samples=x_mcs.samples)
A = np.asarray(model.qoi_list) < 0
pf = np.shape(np.asarray(
model.qoi_list)[np.asarray(model.qoi_list) < 0])[0] / 1000000
print(pf)
ntrials = 1
pf_stretch = np.zeros((ntrials, 1))
cov1_stretch = np.zeros((ntrials, 1))
cov2_stretch = np.zeros((ntrials, 1))
m = np.ones(2)
m[0] = 5
m[1] = 125