def test_stress():
from pymks.datasets import make_elastic_stress_random
from pymks import MKSHomogenizationModel, DiscreteIndicatorBasis
sample_size = 200
grain_size = [(5, 5), (6, 4), (4, 6), (2, 2)]
n_samples = [sample_size] * len(grain_size)
elastic_modulus = (410, 200)
poissons_ratio = (0.28, 0.3)
macro_strain = 0.001
size = (21, 21)
X, y = make_elastic_stress_random(n_samples=n_samples,
size=size,
grain_size=grain_size,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
macro_strain=macro_strain,
seed=0)
dbasis = DiscreteIndicatorBasis(n_states=2, domain=[0, 1])
model = MKSHomogenizationModel(basis=dbasis, n_components=3, degree=3)
model.fit(X, y)
test_sample_size = 1
n_samples = [test_sample_size] * len(grain_size)
X_new, y_new = make_elastic_stress_random(n_samples=n_samples,
size=size,
grain_size=grain_size,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
macro_strain=macro_strain,
seed=3)
y_result = model.predict(X_new)
assert np.allclose(np.round(y_new, decimals=2),
np.round(y_result, decimals=2))
def test_stress():
from pymks.datasets import make_elastic_stress_random
from pymks import MKSHomogenizationModel, DiscreteIndicatorBasis
sample_size = 200
grain_size = [(5, 5), (6, 4), (4, 6), (2, 2)]
n_samples = [sample_size] * len(grain_size)
elastic_modulus = (410, 200)
poissons_ratio = (0.28, 0.3)
macro_strain = 0.001
size = (21, 21)
X, y = make_elastic_stress_random(n_samples=n_samples, size=size,
grain_size=grain_size,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
macro_strain=macro_strain, seed=0)
dbasis = DiscreteIndicatorBasis(n_states=2, domain=[0, 1])
model = MKSHomogenizationModel(basis=dbasis, n_components=3, degree=3)
model.fit(X, y)
test_sample_size = 1
n_samples = [test_sample_size] * len(grain_size)
X_new, y_new = make_elastic_stress_random(
n_samples=n_samples, size=size, grain_size=grain_size,
elastic_modulus=elastic_modulus, poissons_ratio=poissons_ratio,
macro_strain=macro_strain, seed=3)
y_result = model.predict(X_new)
assert np.allclose(np.round(y_new, decimals=2),
np.round(y_result, decimals=2))
def test_make_elastic_stress_randome():
X, y = make_elastic_stress_random(n_samples=1, elastic_modulus=(1, 1),
poissons_ratio=(1, 1),
grain_size=(3, 3), macro_strain=1.0)
assert np.allclose(y, np.ones(y.shape))
X, y = make_elastic_stress_random(n_samples=1, grain_size=(1, 1),
elastic_modulus=(100, 200),
size=(2, 2), poissons_ratio=(1, 3),
macro_strain=1., seed=4)
X_result = np.array([[[1, 1],
[0, 1]]])
assert float(np.round(y, decimals=5)[0]) == 228.74696
assert np.allclose(X, X_result)
X, y = make_elastic_stress_random(n_samples=1, grain_size=(1, 1, 1),
elastic_modulus=(100, 200),
poissons_ratio=(1, 3), seed=5,
macro_strain=1., size=(2, 2, 2))
X_result = np.array([[[1, 1],
[1, 0]],
[[1, 1],
[0, 0]]])
assert np.allclose(X, X_result)
assert y.astype(int) == 145
macro_strain = 0.001
n_phases = 2
grain_size = [(40, 2), (10, 2), (2, 40), (2, 10)]
grain_size1 = (40, 2)
grain_size2 = (10, 2)
grain_size3 = (2, 40)
grain_size4 = (2, 10)
power = [1, 2, 5, 10]
# Create Samples and calculate stresses
dataset1, stresses1 = make_elastic_stress_random(n_samples=n_samples,
size=size,
grain_size=grain_size,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
macro_strain=macro_strain,
seed=0, power=power[0])
dataset2, stresses2 = make_elastic_stress_random(n_samples=n_samples,
size=size,
grain_size=grain_size,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,
macro_strain=macro_strain,
seed=0, power=power[1])
dataset3, stresses3 = make_elastic_stress_random(n_samples=n_samples,
size=size,
grain_size=grain_size,
elastic_modulus=elastic_modulus,
poissons_ratio=poissons_ratio,