pyepri.utils

This module contains standard operators usually involved in signal or image processing.

Functions

grad1d(u[, backend, notest])	Gradient (= forward finite differences) of a mono-dimensional array with Neumann boundary condition.
div1d(P[, backend, notest])	discrete divergence of a mono-dimensional array (opposite adjoint of grad1d).
grad2d(u[, backend, notest])	Gradient (= forward finite differences) of a 2-dimensional array with Neumann boundary condition.
div2d(P[, backend, notest])	discrete divergence of a 2D field vector (opposite adjoint of grad2d).
grad3d(u[, backend, notest])	Gradient (= forward finite differences) of a 3-dimensional array with Neumann boundary condition.
div3d(P[, backend, notest])	discrete divergence of a 3D field vector (opposite adjoint of grad3d).
_check_nd_inputs_(ndims, backend[, u, P])	Factorized consistency checks for functions in the pyepri.utils submodule.

Module Contents

pyepri.utils.grad1d(u, backend=None, notest=False)

Gradient (= forward finite differences) of a mono-dimensional array with Neumann boundary condition.

Parameters:

• u (array_like (with type backend.cls)) – Mono-dimensional array.

• backend (<class 'pyepri.backends.Backend'> or None, optional) –

  A numpy, cupy or torch backend (see pyepri.backends module).

  When backend is None, a default backend is inferred from the input array u.

• notest (bool, optional) – Set notest=True to disable consistency checks.

Returns:

G – Output array same shape as u corresponding to the forward finite differences of u.

Return type:

array_like (with type backend.cls)

See also

div1d

pyepri.utils.div1d(P, backend=None, notest=False)

discrete divergence of a mono-dimensional array (opposite adjoint of grad1d).

Parameters:

• P (array_like (with type backend.cls)) – Mono-dimensional input array.

• backend (<class 'pyepri.backends.Backend'> or None, optional) –

  A numpy, cupy or torch backend (see pyepri.backends module).

  When backend is None, a default backend is inferred from the input array P.

• notest (bool, optional) – Set notest=True to disable consistency checks.

Returns:

div – Mono-dimensional array with same shape as u corresponding to the discrete divergence (or opposite adjoint of the grad1d operator) of the input array P.

Return type:

array_like (with type backend.cls)

See also

grad2d

pyepri.utils.grad2d(u, backend=None, notest=False)

Gradient (= forward finite differences) of a 2-dimensional array with Neumann boundary condition.

Parameters:

• u (array_like (with type backend.cls)) – Two-dimensional array.

• backend (<class 'pyepri.backends.Backend'> or None, optional) –

  A numpy, cupy or torch backend (see pyepri.backends module).

  When backend is None, a default backend is inferred from the input array u.

• notest (bool, optional) – Set notest=True to disable consistency checks.

Returns:

G – Output array with shape (3,) + u.shape such that G[j] correspond to the forward finite differences of u along its j-th dimension (for j in range(2)).

Return type:

array_like (with type backend.cls)

See also

div2d

pyepri.utils.div2d(P, backend=None, notest=False)

discrete divergence of a 2D field vector (opposite adjoint of grad2d).

Parameters:

• P (array_like (with type backend.cls)) – Two-dimensional vector field array with shape (2, Ny, Nx).

• backend (<class 'pyepri.backends.Backend'>or None, optional) –

  A numpy, cupy or torch backend (see pyepri.backends module).

  When backend is None, a default backend is inferred from the input array P.

• notest (bool, optional) – Set notest=True to disable consistency checks.

Returns:

div – Two dimensional array with shape (Ny, Nx) corresponding to the discrete divergence (or opposite adjoint of the grad2d operator) of the input field vector array P.

Return type:

array_like (with type backend.cls)

See also

grad2d

pyepri.utils.grad3d(u, backend=None, notest=False)

Gradient (= forward finite differences) of a 3-dimensional array with Neumann boundary condition.

Parameters:

• u (array_like (with type backend.cls)) – Three-dimensional array.

• backend (<class 'pyepri.backends.Backend'> or None, optional) –

  A numpy, cupy or torch backend (see pyepri.backends module).

  When backend is None, a default backend is inferred from the input array u.

• notest (bool, optional) – Set notest=True to disable consistency checks.

Returns:

G – Output array with shape (3,) + u.shape such that G[j] correspond to the forward finite differences of u along its j-th dimension (for j in range(3)).

Return type:

array_like (with type backend.cls)

See also

div3d

pyepri.utils.div3d(P, backend=None, notest=False)

discrete divergence of a 3D field vector (opposite adjoint of grad3d).

Parameters:

• P (array_like (with type backend.cls)) – Three-dimensional vector field array with shape (3, Ny, Nx, Nz).

• backend (<class 'pyepri.backends.Backend'> or None, optional) –

  A numpy, cupy or torch backend (see pyepri.backends module).

  When backend is None, a default backend is inferred from the input array P.

• notest (bool, optional) – Set notest=True to disable consistency checks.

Returns:

div – Three dimensional array with shape (Ny, Nx, Nz) corresponding to the discrete divergence (or opposite adjoint of the grad3d operator) of the input field vector array P.

Return type:

array_like (with type backend.cls)

See also

grad3d

pyepri.utils._check_nd_inputs_(ndims, backend, u=None, P=None)

Factorized consistency checks for functions in the pyepri.utils submodule.