PyLops
pylops.signalprocessing.ConvolveND¶
- class pylops.signalprocessing.ConvolveND(dims, h, offset=None, axes=(-2, -1), method='fft', dtype='float64', name='C')[source]¶
ND convolution operator.
Apply n-dimensional convolution with a compact filter to model (and data) along the
axesof a n-dimensional array.- Parameters:
- dims
listorint Number of samples for each dimension
- h
numpy.ndarray nd compact filter to be convolved to input signal
- offset
tuple, optional Indices of the center of the compact filter
- axes
int, optional Added in version 2.0.0.
Axes along which convolution is applied
- method
str, optional Method used to calculate the convolution (
auto,directorfft) - seescipy.signal.convolvefor details.- dtype
str, optional Type of elements in input array.
- name
str, optional Added in version 2.0.0.
Name of operator (to be used by
pylops.utils.describe.describe)
- dims
- Attributes:
- nh
tuple Length of the filter
- convolve
callable Convolution function
- correlate
callable Correlation function
- dims
tuple Shape of the array after the adjoint, but before flattening.
For example,
x_reshaped = (Op.H * y.ravel()).reshape(Op.dims).- dimsd
tuple Shape of the array after the forward, but before flattening. In this case, same as
dims.- shape
tuple Operator shape.
- nh
Notes
The ConvolveND operator applies n-dimensional convolution between the input signal \(d(x_1, x_2, ..., x_N)\) and a compact filter kernel \(h(x_1, x_2, ..., x_N)\) in forward model. This is a straighforward extension to multiple dimensions of
pylops.signalprocessing.Convolve2Doperator.Methods
__init__(dims, h[, offset, axes, method, ...])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()
