def test_compare_scipy_Lognormal():
shape = 3.0
dist_cp = cp.Lognormal(mu=0.0, sigma=shape, scale=1.0, shift=0)
r0, r1 = dist_cp.range()
x = np.linspace(r0, r1, 300, endpoint=True)
pdf_sp = stats.lognorm.pdf(x, shape, scale=1.0, loc=0)
cdf_sp = stats.lognorm.cdf(x, shape, scale=1.0, loc=0)
np.testing.assert_allclose(pdf_sp, dist_cp.pdf(x))
np.testing.assert_allclose(cdf_sp, dist_cp.cdf(x))
import chaospy as cp
from ProblemSettings import *
parameters['allow_extrapolation'] = True
parameters["form_compiler"]["quadrature_degree"] = 4
parameters["form_compiler"]["cpp_optimize"] = True
parameters["form_compiler"]["representation"] = "uflacs"
timer = Timer("00: forward run")
model = problem()
# Define random distribution for each parameter
b_ff_dist = cp.Normal(mu=6.6, sigma=0.99)
b_xx_dist = cp.Normal(mu=4.0, sigma=0.6)
b_fx_dist = cp.Normal(mu=2.6, sigma=0.39)
CC_dist = cp.Lognormal(
mu=-0.049, sigma=0.538) #cp.Normal(mu = 1.1 , sigma = 0.165) #kPa
K_dist = cp.Lognormal(
mu=2.2964, sigma=0.11) #cp.Normal( mu = 10.0, sigma = 1.5) #kPa
fiber_rotation_epi_dist = cp.Normal(mu=50, sigma=7.5) #deg
fiber_rotation_end_dist = cp.Normal(mu=40, sigma=6.0) #deg
sheet_rotation_epi_dist = cp.Normal(mu=65, sigma=9.75) #deg
sheet_rotation_end_dist = cp.Normal(mu=25, sigma=3.75) #deg
print('Calling Chaospy')
# Define Joint probability density funtion
#joint = cp.J(b_ff_dist,b_xx_dist,b_fx_dist,CC_dist,K_dist,fiber_rotation_epi_dist)
joint = cp.J(b_ff_dist, b_xx_dist, b_fx_dist, CC_dist, K_dist,
fiber_rotation_epi_dist, fiber_rotation_end_dist,
sheet_rotation_epi_dist, sheet_rotation_end_dist)
import numpy as np import matplotlib.pyplot as plt # === The useful help function === # show help for uniform distributions cp.Uniform? # show help for sample generation cp.samplegen? # end help # === Distributions === # simple distributions rv1 = cp.Uniform(0, 1) rv2 = cp.Normal(0, 1) rv3 = cp.Lognormal(0, 1, 0.2, 0.8) print(rv1, rv2, rv3) # end simple distributions # joint distributions joint_distribution = cp.J(rv1, rv2, rv3) print(joint_distribution) # end joint distributions # creating iid variables X = cp.Normal() Y = cp.Iid(X, 4) print(Y) # end creating iid variables # === Sampling ===