# 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 : 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.

## Examples using pylops.FirstDerivative¶ Causal Integration Derivatives Operators concatenation Total Variation (TV) Regularization 05. Image deblurring 16. CT Scan Imaging