4.2.2.1. pde.grids.operators.cartesian module¶
This module implements differential operators on Cartesian grids
make a laplace operator on a Cartesian grid |
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make a gradient operator on a Cartesian grid |
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make a divergence operator on a Cartesian grid |
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make a vector gradient operator on a Cartesian grid |
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make a vector Laplacian on a Cartesian grid |
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make a tensor divergence operator on a Cartesian grid |
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make a operator that solves Poisson's equation |
- make_divergence(grid: CartesianGridBase, backend: str = 'auto') Callable[[numpy.ndarray, numpy.ndarray], None] [source]¶
make a divergence operator on a Cartesian grid
- Parameters
grid (
CartesianGridBase
) – The grid for which the operator is createdbackend (str) – Backend used for calculating the divergence operator. If backend=’auto’, a suitable backend is chosen automatically.
- Returns
A function that can be applied to an array of values
- make_gradient(grid: CartesianGridBase, backend: str = 'auto') Callable[[numpy.ndarray, numpy.ndarray], None] [source]¶
make a gradient operator on a Cartesian grid
- Parameters
grid (
CartesianGridBase
) – The grid for which the operator is createdbackend (str) – Backend used for calculating the gradient operator. If backend=’auto’, a suitable backend is chosen automatically.
- Returns
A function that can be applied to an array of values
- make_laplace(grid: CartesianGridBase, backend: str = 'auto') Callable[[numpy.ndarray, numpy.ndarray], None] [source]¶
make a laplace operator on a Cartesian grid
- Parameters
grid (
CartesianGridBase
) – The grid for which the operator is createdbackend (str) – Backend used for calculating the laplace operator. If backend=’auto’, a suitable backend is chosen automatically.
- Returns
A function that can be applied to an array of values
- make_poisson_solver(bcs: Boundaries, method: str = 'auto') Callable[[numpy.ndarray, numpy.ndarray], None] [source]¶
make a operator that solves Poisson’s equation
- Parameters
bcs (
Boundaries
) – {ARG_BOUNDARIES_INSTANCE}method (str) – Method used for calculating the tensor divergence operator. If method=’auto’, a suitable method is chosen automatically.
- Returns
A function that can be applied to an array of values
- make_tensor_divergence(grid: CartesianGridBase, backend: str = 'numba') Callable[[numpy.ndarray, numpy.ndarray], None] [source]¶
make a tensor divergence operator on a Cartesian grid
- Parameters
grid (
CartesianGridBase
) – The grid for which the operator is createdbackend (str) – Backend used for calculating the tensor divergence operator.
- Returns
A function that can be applied to an array of values
- make_vector_gradient(grid: CartesianGridBase, backend: str = 'numba') Callable[[numpy.ndarray, numpy.ndarray], None] [source]¶
make a vector gradient operator on a Cartesian grid
- Parameters
grid (
CartesianGridBase
) – The grid for which the operator is createdbackend (str) – Backend used for calculating the vector gradient operator.
- Returns
A function that can be applied to an array of values
- make_vector_laplace(grid: CartesianGridBase, backend: str = 'numba') Callable[[numpy.ndarray, numpy.ndarray], None] [source]¶
make a vector Laplacian on a Cartesian grid
- Parameters
grid (
CartesianGridBase
) – The grid for which the operator is createdbackend (str) – Backend used for calculating the vector laplace operator.
- Returns
A function that can be applied to an array of values