pylops.FirstDerivative¶
-
class
pylops.
FirstDerivative
(N, dims=None, dir=0, sampling=1.0, edge=False, dtype='float64')[source]¶ First derivative.
Apply second-order centered first derivative.
Parameters: - N :
int
Number of samples in model.
- dims :
tuple
, optional Number of samples for each dimension (
None
if only one dimension is available)- dir :
int
, optional Direction along which smoothing is applied.
- sampling :
float
, optional Sampling step
dx
.- edge :
bool
, optional Use reduced order derivative at edges (
True
) or ignore them (False
)- dtype :
str
, optional Type of elements in input array.
Notes
The FirstDerivative operator applies a first derivative to any chosen direction of a multi-dimensional array.
For simplicity, given a one dimensional array, the second-order centered first derivative is:
\[y[i] = (0.5x[i+1] - 0.5x[i-1]) / dx\]Attributes: Methods
__init__
(self, N[, dims, dir, sampling, …])Initialize this LinearOperator. adjoint
(self)Hermitian adjoint. apply_columns
(self, cols)Apply subset of columns of operator cond
(self, **kwargs_eig)Condition number of linear operator. conj
(self)Complex conjugate operator div
(self, y[, niter])Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\). dot
(self, x)Matrix-matrix or matrix-vector multiplication. eigs
(self[, neigs, symmetric, niter])Most significant eigenvalues of linear operator. matmat
(self, X)Matrix-matrix multiplication. matvec
(self, x)Matrix-vector multiplication. rmatmat
(self, X)Adjoint matrix-matrix multiplication. rmatvec
(self, x)Adjoint matrix-vector multiplication. todense
(self)Return dense matrix. transpose
(self)Transpose this linear operator. - N :