pylops.FirstDerivative

class pylops.FirstDerivative(dims, axis=-1, sampling=1.0, kind='centered', edge=False, order=3, dtype='float64', name='F')[source]

First derivative.

Apply a first derivative using a multiple-point stencil finite-difference approximation along axis.

Parameters:
dims : list or int

Number of samples for each dimension

axis : int, optional

New in version 2.0.0.

Axis along which derivative is applied.

sampling : float, optional

Sampling step \(\Delta x\).

kind : str, optional

Derivative kind (forward, centered, or backward).

edge : bool, optional

Use reduced order derivative at edges (True) or ignore them (False). This is currently only available

for centered derivative

order : int, optional

New in version 2.0.0.

Derivative order (3 or 5). This is currently only available for centered derivative

dtype : str, optional

Type of elements in input array.

name : str, optional

New in version 2.0.0.

Name of operator (to be used by pylops.utils.describe.describe)

Notes

The FirstDerivative operator applies a first derivative to any chosen direction of a multi-dimensional array using either a second- or third-order centered stencil or first-order forward/backward stencils.

For simplicity, given a one dimensional array, the second-order centered first derivative is:

\[y[i] = (0.5x[i+1] - 0.5x[i-1]) / \Delta x\]

while the first-order forward stencil is:

\[y[i] = (x[i+1] - x[i]) / \Delta x\]

and the first-order backward stencil is:

\[y[i] = (x[i] - x[i-1]) / \Delta x\]

Formulas for the third-order centered stencil can be found at this link.

Attributes:
shape : tuple

Operator shape

explicit : bool

Operator contains a matrix that can be solved explicitly (True) or not (False)

Methods

__init__(dims[, axis, sampling, kind, edge, …]) Initialize this LinearOperator.
adjoint() Hermitian adjoint.
apply_columns(cols) Apply subset of columns of operator
cond([uselobpcg]) Condition number of linear operator.
conj() Complex conjugate operator
div(y[, niter, densesolver]) Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\).
dot(x) Matrix-matrix or matrix-vector multiplication.
eigs([neigs, symmetric, niter, uselobpcg]) Most significant eigenvalues of linear operator.
matmat(X) Matrix-matrix multiplication.
matvec(x) Matrix-vector multiplication.
reset_count() Reset counters
rmatmat(X) Matrix-matrix multiplication.
rmatvec(x) Adjoint matrix-vector multiplication.
todense([backend]) Return dense matrix.
toimag([forw, adj]) Imag operator
toreal([forw, adj]) Real operator
tosparse() Return sparse matrix.
trace([neval, method, backend]) Trace of linear operator.
transpose() Transpose this linear operator.