def test_normalize_adjacency_matrix(self):
adj = np.array([
[2., 1., 0.],
[1., 10., 5.],
[0., 5., 100.],
])
nadj = pre.normalize_adjacency_matrix(adj)
d_inv_sqrt = np.array([
[3.**-.5, 0., 0.],
[0., 16.**-.5, 0.],
[0., 0., 105.**-.5],
])
np.testing.assert_almost_equal(
d_inv_sqrt @ adj @ d_inv_sqrt, nadj.toarray())
def conversion_function(fem_data, data_directory):
adj = fem_data.calculate_adjacency_matrix_element()
nadj = prepost.normalize_adjacency_matrix(adj)
x_grad, y_grad, z_grad = \
fem_data.calculate_spatial_gradient_adjacency_matrices('elemental')
x_grad_2, y_grad_2, z_grad_2 = \
fem_data.calculate_spatial_gradient_adjacency_matrices(
'elemental', n_hop=2)
global_modulus = np.mean(
fem_data.elemental_data.get_attribute_data('modulus'), keepdims=True)
tensor_stress = fem_data.convert_array2symmetric_matrix(
fem_data.elemental_data.get_attribute_data('ElementalSTRESS'),
from_engineering=False)[:, :, :, None]
tensor_strain = fem_data.convert_array2symmetric_matrix(
fem_data.elemental_data.get_attribute_data('ElementalSTRAIN'),
from_engineering=True)[:, :, :, None]
tensor_gauss_strain1 = fem_data.convert_array2symmetric_matrix(
fem_data.elemental_data.get_attribute_data('GaussSTRAIN1'),
from_engineering=True)[:, :, :, None]
tensor_gauss_strain2 = fem_data.convert_array2symmetric_matrix(
fem_data.elemental_data.get_attribute_data('GaussSTRAIN2'),
from_engineering=True)[:, :, :, None]
tensor_gauss_strain3 = fem_data.convert_array2symmetric_matrix(
fem_data.elemental_data.get_attribute_data('GaussSTRAIN3'),
from_engineering=True)[:, :, :, None]
tensor_gauss_strain4 = fem_data.convert_array2symmetric_matrix(
fem_data.elemental_data.get_attribute_data('GaussSTRAIN4'),
from_engineering=True)[:, :, :, None]
return {
'adj': adj,
'nadj': nadj,
'global_modulus': global_modulus,
'x_grad': x_grad,
'y_grad': y_grad,
'z_grad': z_grad,
'x_grad_2': x_grad_2,
'y_grad_2': y_grad_2,
'z_grad_2': z_grad_2,
'tensor_stress': tensor_stress,
'tensor_strain': tensor_strain,
'tensor_gauss_strain1': tensor_gauss_strain1,
'tensor_gauss_strain2': tensor_gauss_strain2,
'tensor_gauss_strain3': tensor_gauss_strain3,
'tensor_gauss_strain4': tensor_gauss_strain4,
}
def conversion_function_rotation_thermal_stress(fem_data, raw_directory=None):
adj = fem_data.calculate_adjacency_matrix_node()
nadj = prepost.normalize_adjacency_matrix(adj)
nodal_grad_x, nodal_grad_y, nodal_grad_z = \
fem_data.calculate_spatial_gradient_adjacency_matrices(
'nodal', n_hop=2)
nodal_hess_xx = nodal_grad_x.dot(nodal_grad_x).tocoo()
nodal_hess_xy = nodal_grad_x.dot(nodal_grad_y).tocoo()
nodal_hess_xz = nodal_grad_x.dot(nodal_grad_z).tocoo()
nodal_hess_yx = nodal_grad_y.dot(nodal_grad_x).tocoo()
nodal_hess_yy = nodal_grad_y.dot(nodal_grad_y).tocoo()
nodal_hess_yz = nodal_grad_y.dot(nodal_grad_z).tocoo()
nodal_hess_zx = nodal_grad_z.dot(nodal_grad_x).tocoo()
nodal_hess_zy = nodal_grad_z.dot(nodal_grad_y).tocoo()
nodal_hess_zz = nodal_grad_z.dot(nodal_grad_z).tocoo()
frame_adjs = fem_data.calculate_frame_tensor_adjs(mode='nodal', n_hop=2)
nodal_frame_xx = frame_adjs[0][0]
nodal_frame_xy = frame_adjs[0][1]
nodal_frame_xz = frame_adjs[0][2]
nodal_frame_yx = frame_adjs[1][0]
nodal_frame_yy = frame_adjs[1][1]
nodal_frame_yz = frame_adjs[1][2]
nodal_frame_zx = frame_adjs[2][0]
nodal_frame_zy = frame_adjs[2][1]
nodal_frame_zz = frame_adjs[2][2]
filter_ = fem_data.filter_first_order_nodes()
node = fem_data.nodes.data[filter_]
nodal_mean_volume = fem_data.convert_elemental2nodal(
fem_data.calculate_element_volumes(), mode='mean')
nodal_concentrated_volume = fem_data.convert_elemental2nodal(
fem_data.calculate_element_volumes(), mode='effective')
initial_temperature = fem_data.nodal_data.get_attribute_data(
'INITIAL_TEMPERATURE')[filter_]
cnt_temperature = fem_data.nodal_data.get_attribute_data(
'CNT_TEMPERATURE')[filter_]
elemental_lte_array = fem_data.elemental_data.get_attribute_data(
'linear_thermal_expansion_coefficient_full')
nodal_lte_array = fem_data.convert_elemental2nodal(elemental_lte_array,
mode='mean')
global_lte_array = np.mean(elemental_lte_array, axis=0, keepdims=True)
elemental_lte_mat = fem_data.convert_array2symmetric_matrix(
elemental_lte_array, from_engineering=True)
nodal_lte_mat = fem_data.convert_array2symmetric_matrix(
nodal_lte_array, from_engineering=True)
global_lte_mat = np.mean(elemental_lte_mat, axis=0, keepdims=True)
elemental_strain_array = fem_data.elemental_data.get_attribute_data(
'ElementalSTRAIN')
nodal_strain_array = fem_data.nodal_data.get_attribute_data(
'NodalSTRAIN')[filter_]
elemental_strain_mat = fem_data.convert_array2symmetric_matrix(
elemental_strain_array, from_engineering=True)
nodal_strain_mat = fem_data.convert_array2symmetric_matrix(
nodal_strain_array, from_engineering=True)
dict_data = {
'nadj': nadj,
'nodal_grad_x': nodal_grad_x,
'nodal_grad_y': nodal_grad_y,
'nodal_grad_z': nodal_grad_z,
'nodal_hess_xx': nodal_hess_xx,
'nodal_hess_xy': nodal_hess_xy,
'nodal_hess_xz': nodal_hess_xz,
'nodal_hess_yx': nodal_hess_yx,
'nodal_hess_yy': nodal_hess_yy,
'nodal_hess_yz': nodal_hess_yz,
'nodal_hess_zx': nodal_hess_zx,
'nodal_hess_zy': nodal_hess_zy,
'nodal_hess_zz': nodal_hess_zz,
'nodal_frame_xx': nodal_frame_xx,
'nodal_frame_xy': nodal_frame_xy,
'nodal_frame_xz': nodal_frame_xz,
'nodal_frame_yx': nodal_frame_yx,
'nodal_frame_yy': nodal_frame_yy,
'nodal_frame_yz': nodal_frame_yz,
'nodal_frame_zx': nodal_frame_zx,
'nodal_frame_zy': nodal_frame_zy,
'nodal_frame_zz': nodal_frame_zz,
'node': node,
'nodal_strain_array': nodal_strain_array,
'elemental_strain_array': elemental_strain_array,
'nodal_strain_mat': nodal_strain_mat[..., None],
'elemental_strain_mat': elemental_strain_mat[..., None],
'nodal_mean_volume': nodal_mean_volume,
'nodal_concentrated_volume': nodal_concentrated_volume,
'initial_temperature': initial_temperature,
'cnt_temperature': cnt_temperature,
'elemental_lte_array': elemental_lte_array,
'nodal_lte_array': nodal_lte_array,
'global_lte_array': global_lte_array,
'elemental_lte_mat': elemental_lte_mat[..., None],
'nodal_lte_mat': nodal_lte_mat[..., None],
'global_lte_mat': global_lte_mat[..., None],
}
return dict_data
def conversion_function(fem_data, raw_directory=None):
adj = fem_data.calculate_adjacency_matrix_element()
nadj = prepost.normalize_adjacency_matrix(adj)
return {'adj': adj, 'nadj': nadj}