ddg.optimize.ifs package

Submodules

• ddg.optimize.ifs.functionals module
  • flat_cubic_quad()
  • g_flat_cubic_quad()
  • flat_cubic_n_quad()
  • g_flat_cubic_n_quad()
  • flat_cubic_n2_quad()
  • g_flat_cubic_n2_quad()
  • flat_det_quad()
  • g_flat_det_quad()
  • circ_angle_quad()
  • g_circ_angle_quad()
  • dirichlet()
• ddg.optimize.ifs.utils module
  • log_history()
  • attribute_helper()
  • nflat_index()
  • zero_columns()
  • remove_columns()
  • initial_guess()

Module contents

ddg.optimize.ifs.wrap_functional(ifs, functional, gradient, cell_type, *attr_name, boundary=[])

Returns a functional and a gradient function over ifs, both taking a flat array as input.

The resulting function will accept an array of length #(non-boundary-cells)*dim(attribute) and the columns corresponding to the boundary vertices of the jacobian/gradient will be removed.

Parameters:

ifsIndexedFaceSet

IndexedFaceSet to apply the function on

functional: Callable

Functional to wrap into a flattened input format

gradient: Callable

Gradient of the functional

cell_type: {‘verts’, ‘edges’, ‘faces’}

Cell type of the attribute

*attr_name: tuple of str

Name of the indexed faceset attribute(s) to use The first entry is assumed to be the parameters

boundary: Iterable (default=[])

Vertices to ignore for applying new values

Returns:

wr_functionalCallable

Wrapped functional

wr_gradientCallable

Wrapped jacobian/gradient

See also

wrap_function_ignore, wrap_function_remove

ddg.optimize.ifs.flatten_functional_ignore(ifs, functional, cell_type, *attr_name, boundary=[])

Returns a functional over ifs that takes a flat array as input. The resulting function will accept an array of length #cells*dim(attribute) and will ignore the values for boundary-cells.

Parameters:

ifsIndexedFaceSet

IndexedFaceSet to apply the function on

functional: Callable

Functional to wrap into a flattened input format

attr_name: str

Name of the indexed faceset attribute to use

cell_type: {‘verts’, ‘edges’, ‘faces’}

Cell type of the attribute

boundary: Iterable (default=[])

Vertices to ignore for applying new values

Returns:

Callable

Wrapped Function

See also

wrap_function_remove, wrap_functional

ddg.optimize.ifs.flatten_functional(ifs, functional, cell_type, *attr_name, boundary=[])

Returns a functional over ifs that takes a flat array as input. The resulting function will accept an array of length #(non-boundary-cells)*dim(attribute).

Parameters:

ifsIndexedFaceSet

IndexedFaceSet to apply the function on

functional: Callable

Functional to wrap into a flattened input format

cell_type: {‘verts’, ‘edges’, ‘faces’}

Cell type of the attribute

attr_name: str

Name of the indexed faceset attribute to use

boundary: Iterable (default=[])

Vertices to ignore for applying new values

Returns:

Callable

Wrapped Function

See also

wrap_function_ignore, wrap_functional

ddg.optimize.ifs.minimize(ifs, functional, cell_type, *attr_name, boundary=[])

Quick access for scipy.optimize.minimize for IndexedFaceSet functionals.

Parameters:

ifsIndexedFaceSet

indexed face set to optimize on

functionalCallable

functional to optimize for

cell_type{‘verts’, ‘edges’, ‘faces’}

cell type of the parameter attribute

*attr_name

names of attributes to use in the functional. attr_name[0] is regarded as the only parameter attribute

boundarylist (default = [])

list of boundary cells to ignore

Returns:

resscipy.optimize.OptimizeResult

See also

least_squares, wrap_functional

Notes

Similar to wrap_functional values for the boundary cells are removed from the signature of the flattened functional before the optimization step.

ddg.optimize.ifs.least_squares(ifs, functional, cell_type, *attr_name, boundary=[])

Quick access for scipy.optimize.least_squares for IndexedFaceSet functionals.

Parameters:

ifsIndexedFaceSet

indexed face set to optimize on

functionalCallable

functional to optimize for

cell_type{‘verts’, ‘edges’, ‘faces’}

cell type of the parameter attribute

*attr_name

names of attributes to use in the functional. attr_name[0] is regarded as the only parameter attribute

boundarylist (default = [])

list of boundary cells to ignore

Returns:

resscipy.optimize.OptimizeResult

See also

minimize, wrap_functional

Notes

Similar to wrap_functional values for the boundary cells are removed from the signature of the flattened functional before the optimization step.

ddg.optimize.ifs.flatten_gradient(ifs, gradient, cell_type, *attr_name, boundary=[])

Returns a gradient function over ifs that takes a flat array as input. The resulting function will accept an array of length #(non-boundary-cells)*dim(attribute).

Parameters:

ifsIndexedFaceSet

IndexedFaceSet to apply the function on

gradient: Callable returning sparse matrices

Gradient to wrap into a flattened input format

cell_type: {‘verts’, ‘edges’, ‘faces’}

Cell type of the attribute

attr_name: str

Name of the indexed faceset attribute to use

boundary: Iterable (default=[])

Vertices to ignore for applying new values

Returns:

Callable

Wrapped Function

See also

flatten_gradient_ignore

ddg.optimize.ifs.flatten_gradient_ignore(ifs, gradient, cell_type, *attr_name, boundary=[])

Returns a gradient function over ifs that takes a flat array as input. The resulting function will accept an array of length #cells*dim(attribute) and will ignore the values for boundary-cells.

Parameters:

ifsIndexedFaceSet

IndexedFaceSet to apply the function on

gradient: Callable returning sparse matrices

Gradient to wrap into a flattened input format

cell_type: {‘verts, ‘edges’, ‘faces’}

Cell type of the attribute

attr_name: str

Name of the indexed faceset attribute to use

boundary: Iterable (default=[])

Vertices to ignore for applying new values

Returns:

Callable

Wrapped Function

See also

flatten_gradient