import numpy as np
from pylops import LinearOperator
[docs]class Transpose(LinearOperator):
r"""Transpose operator.
Transpose axes of a multi-dimensional array. This operator works with
flattened input model (or data), which are however multi-dimensional in
nature and will be reshaped and treated as such in both forward and adjoint
modes.
Parameters
----------
dims : :obj:`tuple`, optional
Number of samples for each dimension
(``None`` if only one dimension is available)
axes : :obj:`tuple`, optional
Direction along which symmetrization is applied
dtype : :obj:`str`, optional
Type of elements in input array
Attributes
----------
shape : :obj:`tuple`
Operator shape
explicit : :obj:`bool`
Operator contains a matrix that can be solved explicitly
(``True``) or not (``False``)
Raises
------
ValueError
If ``axes`` contains repeated dimensions (or a dimension is missing)
Notes
-----
The Transpose operator reshapes the input model into a multi-dimensional
array of size ``dims`` and transposes (or swaps) its axes as defined
in ``axes``.
Similarly, in adjoint mode the data is reshaped into a multi-dimensional
array whose size is a permuted version of ``dims`` defined by ``axes``.
The array is then rearragned into the original model dimensions ``dims``.
"""
def __init__(self, dims, axes, dtype='float64'):
self.dims = list(dims)
self.axes = list(axes)
# find out if all axes are present only once in axes
ndims = len(self.dims)
if len(np.unique(self.axes)) != ndims:
raise ValueError('axes must contain each direction once')
# find out how axes should be transposed in adjoint mode
self.axesd = np.zeros(ndims, dtype=np.int)
self.dimsd = np.zeros(ndims, dtype=np.int)
self.axesd[self.axes] = np.arange(ndims, dtype=np.int)
self.dimsd[self.axesd] = self.dims
self.shape = (np.prod(self.dims), np.prod(self.dims))
self.dtype = np.dtype(dtype)
self.explicit = False
def _matvec(self, x):
y = x.reshape(self.dims)
y = np.transpose(y, self.axes)
return y.ravel()
def _rmatvec(self, x):
y = x.reshape(self.dimsd)
y = np.transpose(y, self.axesd)
return y.ravel()