pylops_distributed.FirstDerivative¶
-
class
pylops_distributed.
FirstDerivative
(N, dims=None, dir=0, sampling=1.0, compute=(False, False), chunks=(None, None), todask=(False, 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
.- compute :
tuple
, optional Compute the outcome of forward and adjoint or simply define the graph and return a
dask.array.array
- chunks :
tuple
, optional Chunk size for model and data. If provided it will rechunk the model before applying the forward pass and the data before applying the adjoint pass
- todask :
tuple
, optional Apply
dask.array.from_array
to model and data before applying forward and adjoint respectively- dtype :
str
, optional Type of elements in input array.
Notes
Refer to
pylops.basicoperators.FirstDerivative
for implementation details.Attributes: Methods
__init__
(N[, dims, dir, sampling, compute, …])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])Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\). dot
(x)Matrix-vector multiplication. eigs
([neigs, symmetric, niter, uselobpcg])Most significant eigenvalues of linear operator. matmat
(X)Matrix-matrix multiplication. matvec
(x)Matrix-vector multiplication. rmatmat
(X)Adjoint matrix-matrix multiplication. rmatvec
(x)Adjoint Matrix-vector multiplication. todense
()Return dense matrix. tosparse
()Return sparse matrix. transpose
()Transpose this linear operator. - N :