def setup(self):
self.surface = surface = self.options['surface']
mesh = surface['mesh']
nx = self.nx = mesh.shape[0]
ny = self.ny = mesh.shape[1]
self.add_input('mesh', val=np.ones((self.nx, self.ny, 3)), units='m')
self.add_input('disp', val=np.ones((self.ny, 6)), units='m')
self.add_input('transformation_matrix', val=np.ones((self.ny, 3, 3)))
self.add_input('nodes', val=np.ones((self.ny, 3)), units='m')
self.add_output('def_mesh',
val=np.random.random_sample((self.nx, self.ny, 3)),
units='m')
# Create index arrays for each relevant input and output.
# This allows us to set up the rows and cols for the sparse Jacobians.
disp_indices = get_array_indices(self.ny, 6)
nodes_indices = get_array_indices(self.ny, 3)
mesh_indices = get_array_indices(self.nx, self.ny, 3)
transform_indices = get_array_indices(self.ny, 3, 3)
mesh_disp_indices = get_array_indices(self.nx, self.ny, 3)
# Set up the rows and cols for `def_mesh` wrt `disp`
rows = mesh_disp_indices.flatten()
cols = np.einsum('i,jk->ijk', np.ones(self.nx),
disp_indices[:, :3]).flatten()
self.declare_partials('def_mesh', 'disp', val=1., rows=rows, cols=cols)
# Set up the rows and cols for `def_mesh` wrt `nodes`
rows = np.einsum('ijk,l->ijkl', mesh_disp_indices,
np.ones(3, int)).flatten()
cols = np.einsum('ik,jl->ijkl', np.ones((self.nx, 3), int),
nodes_indices).flatten()
self.declare_partials('def_mesh', 'nodes', rows=rows, cols=cols)
# Set up the rows and cols for `def_mesh` wrt `mesh`
rows = np.einsum('ijk,l->ijkl', mesh_disp_indices,
np.ones(3, int)).flatten()
cols = np.einsum('ijl,k->ijkl', mesh_indices, np.ones(3,
int)).flatten()
self.declare_partials('def_mesh', 'mesh', rows=rows, cols=cols)
# Set up the rows and cols for `def_mesh` wrt `transformation_matrix`
rows = np.einsum('ijl,k->ijkl', mesh_disp_indices,
np.ones(3, int)).flatten()
cols = np.einsum('jlk,i->ijkl', transform_indices,
np.ones(self.nx, int)).flatten()
self.declare_partials('def_mesh',
'transformation_matrix',
rows=rows,
cols=cols)
def setup(self):
self.surface = surface = self.options['surface']
mesh=surface['mesh']
nx = self.nx = mesh.shape[0]
ny = self.ny = mesh.shape[1]
self.add_input('mesh', val=np.ones((self.nx, self.ny, 3)), units='m')
self.add_input('disp', val=np.ones((self.ny, 6)), units='m')
self.add_input('transformation_matrix', val=np.ones((self.ny, 3, 3)))
self.add_input('nodes', val=np.ones((self.ny, 3)), units='m')
self.add_output('def_mesh', val=np.random.random_sample((self.nx, self.ny, 3)), units='m')
# Create index arrays for each relevant input and output.
# This allows us to set up the rows and cols for the sparse Jacobians.
disp_indices = get_array_indices(self.ny, 6)
nodes_indices = get_array_indices(self.ny, 3)
mesh_indices = get_array_indices(self.nx, self.ny, 3)
transform_indices = get_array_indices(self.ny, 3, 3)
mesh_disp_indices = get_array_indices(self.nx, self.ny, 3)
# Set up the rows and cols for `def_mesh` wrt `disp`
rows = mesh_disp_indices.flatten()
cols = np.einsum('i,jk->ijk', np.ones(self.nx), disp_indices[:, :3]).flatten()
self.declare_partials('def_mesh', 'disp', val=1., rows=rows, cols=cols)
# Set up the rows and cols for `def_mesh` wrt `nodes`
rows = np.einsum('ijk,l->ijkl', mesh_disp_indices, np.ones(3, int)).flatten()
cols = np.einsum('ik,jl->ijkl', np.ones((self.nx, 3), int), nodes_indices).flatten()
self.declare_partials('def_mesh', 'nodes', rows=rows, cols=cols)
# Set up the rows and cols for `def_mesh` wrt `mesh`
rows = np.einsum('ijk,l->ijkl', mesh_disp_indices, np.ones(3, int)).flatten()
cols = np.einsum('ijl,k->ijkl', mesh_indices, np.ones(3, int)).flatten()
self.declare_partials('def_mesh', 'mesh', rows=rows, cols=cols)
# Set up the rows and cols for `def_mesh` wrt `transformation_matrix`
rows = np.einsum('ijl,k->ijkl', mesh_disp_indices, np.ones(3, int)).flatten()
cols = np.einsum('jlk,i->ijkl', transform_indices, np.ones(self.nx, int)).flatten()
self.declare_partials('def_mesh', 'transformation_matrix', rows=rows, cols=cols)
def setup(self):
self.surface = surface = self.options['surface']
mesh=surface['mesh']
self.nx = mesh.shape[0]
self.ny = mesh.shape[1]
self.add_input('disp', val=np.zeros((self.ny, 6)), units='m')
self.add_output('transformation_matrix', shape=(self.ny, 3, 3))
# Create index arrays for each relevant input and output.
# This allows us to set up the rows and cols for the sparse Jacobians.
disp_indices = get_array_indices(self.ny, 6)
transform_indices = get_array_indices(self.ny, 3, 3)
# Set up the rows and cols for `transformation_matrix` wrt `disp`
rows = np.einsum('ijk,l->ijkl',
transform_indices,
np.ones(3, int)).flatten()
cols = np.einsum('il,jk->ijkl',
get_array_indices(self.ny, 6)[:, 3:],
np.ones((3, 3), int)).flatten()
self.declare_partials('transformation_matrix', 'disp', rows=rows, cols=cols)
def setup(self):
self.surface = surface = self.options['surface']
mesh=surface['mesh']
self.nx = mesh.shape[0]
self.ny = mesh.shape[1]
self.add_input('disp', val=np.zeros((self.ny, 6)), units='m')
self.add_output('transformation_matrix', shape=(self.ny, 3, 3))
# Create index arrays for each relevant input and output.
# This allows us to set up the rows and cols for the sparse Jacobians.
disp_indices = get_array_indices(self.ny, 6)
transform_indices = get_array_indices(self.ny, 3, 3)
# Set up the rows and cols for `transformation_matrix` wrt `disp`
rows = np.einsum('ijk,l->ijkl',
transform_indices,
np.ones(3, int)).flatten()
cols = np.einsum('il,jk->ijkl',
get_array_indices(self.ny, 6)[:, 3:],
np.ones((3, 3), int)).flatten()
self.declare_partials('transformation_matrix', 'disp', rows=rows, cols=cols)
