74 lines
2.7 KiB
Python
74 lines
2.7 KiB
Python
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import pytest
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import numpy as np
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from damask import tensor
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def eigenvalues(T_sym):
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return np.linalg.eigvalsh(symmetric(T_sym))
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def eigenvectors(T_sym,RHS=False):
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(u,v) = np.linalg.eigh(symmetric(T_sym))
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if RHS:
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if np.linalg.det(v) < 0.0: v[:,2] *= -1.0
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return v
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def symmetric(T):
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return (T+transpose(T))*0.5
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def transpose(T):
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return T.T
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class TestTensor:
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n = 1000
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c = np.random.randint(n)
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@pytest.mark.parametrize('vectorized,single',[(tensor.eigenvalues , eigenvalues ),
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(tensor.eigenvectors , eigenvectors ),
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(tensor.symmetric , symmetric ),
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(tensor.transpose , transpose ),
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])
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def test_vectorize_1_arg(self,vectorized,single):
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epsilon = np.random.rand(self.n,3,3)
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epsilon_vec = np.reshape(epsilon,(self.n//10,10,3,3))
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for i,v in enumerate(np.reshape(vectorized(epsilon_vec),vectorized(epsilon).shape)):
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assert np.allclose(single(epsilon[i]),v)
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def test_symmetric(self):
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"""Ensure that a symmetric tensor is half of the sum of a tensor and its transpose."""
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x = np.random.rand(self.n,3,3)
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assert np.allclose(tensor.symmetric(x)*2.0,tensor.transpose(x)+x)
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def test_transpose(self):
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"""Ensure that a symmetric tensor equals its transpose."""
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x = tensor.symmetric(np.random.rand(self.n,3,3))
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assert np.allclose(tensor.transpose(x),x)
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def test_eigenvalues(self):
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"""Ensure that the characteristic polynomial can be solved."""
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A = tensor.symmetric(np.random.rand(self.n,3,3))
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lambd = tensor.eigenvalues(A)
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s = np.random.randint(self.n)
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for i in range(3):
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assert np.allclose(np.linalg.det(A[s]-lambd[s,i]*np.eye(3)),.0)
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def test_eigenvalues_and_vectors(self):
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"""Ensure that eigenvalues and -vectors are the solution to the characteristic polynomial."""
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A = tensor.symmetric(np.random.rand(self.n,3,3))
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lambd = tensor.eigenvalues(A)
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x = tensor.eigenvectors(A)
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s = np.random.randint(self.n)
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for i in range(3):
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assert np.allclose(np.dot(A[s]-lambd[s,i]*np.eye(3),x[s,:,i]),.0)
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def test_eigenvectors_RHS(self):
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"""Ensure that RHS coordinate system does only change sign of determinant."""
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A = tensor.symmetric(np.random.rand(self.n,3,3))
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LRHS = np.linalg.det(tensor.eigenvectors(A,RHS=False))
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RHS = np.linalg.det(tensor.eigenvectors(A,RHS=True))
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assert np.allclose(np.abs(LRHS),RHS)
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