clean up
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@ -12,7 +12,6 @@ class TestMechanics:
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@pytest.mark.parametrize('function',[mechanics.deviatoric_part,
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@pytest.mark.parametrize('function',[mechanics.deviatoric_part,
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mechanics.eigenvalues,
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mechanics.eigenvalues,
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mechanics.eigenvectors,
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mechanics.eigenvectors,
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mechanics.deviatoric_part,
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mechanics.left_stretch,
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mechanics.left_stretch,
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mechanics.maximum_shear,
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mechanics.maximum_shear,
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mechanics.Mises_strain,
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mechanics.Mises_strain,
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@ -42,12 +41,13 @@ class TestMechanics:
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assert np.allclose(mechanics.strain_tensor(F,t,m)[self.c],
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assert np.allclose(mechanics.strain_tensor(F,t,m)[self.c],
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mechanics.strain_tensor(F[self.c],t,m))
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mechanics.strain_tensor(F[self.c],t,m))
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@pytest.mark.parametrize('function',[mechanics.Cauchy,
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def test_Cauchy(self):
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mechanics.PK2,
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"""Ensure Cauchy stress is symmetrized 1. Piola-Kirchhoff stress for no deformation."""
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])
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def test_stress_measures(self,function):
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"""Ensure that all stress measures are equivalent for no deformation."""
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P = np.random.rand(self.n,3,3)
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P = np.random.rand(self.n,3,3)
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assert np.allclose(mechanics.Cauchy(P,np.broadcast_to(np.eye(3),(self.n,3,3))),
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assert np.allclose(function(P,np.broadcast_to(np.eye(3),(self.n,3,3))),mechanics.symmetric(P))
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mechanics.symmetric(P))
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def test_deviatoric_part(self):
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def test_deviatoric_part(self):
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I_n = np.broadcast_to(np.eye(3),(self.n,3,3))
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I_n = np.broadcast_to(np.eye(3),(self.n,3,3))
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@ -63,12 +63,6 @@ class TestMechanics:
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assert np.allclose(np.matmul(R,U),
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assert np.allclose(np.matmul(R,U),
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np.matmul(V,R))
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np.matmul(V,R))
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def test_PK2(self):
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"""Ensure 2. Piola-Kirchhoff stress is symmetrized 1. Piola-Kirchhoff stress for no deformation."""
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P = np.random.rand(self.n,3,3)
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assert np.allclose(mechanics.PK2(P,np.broadcast_to(np.eye(3),(self.n,3,3))),
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mechanics.symmetric(P))
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def test_strain_tensor_no_rotation(self):
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def test_strain_tensor_no_rotation(self):
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"""Ensure that left and right stretch give same results for no rotation."""
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"""Ensure that left and right stretch give same results for no rotation."""
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F = np.broadcast_to(np.eye(3),[self.n,3,3])*np.random.rand(self.n,3,3)
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F = np.broadcast_to(np.eye(3),[self.n,3,3])*np.random.rand(self.n,3,3)
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