Source code for pylops.basicoperators.MatrixMult

import logging

import numpy as np
import scipy as sp
from scipy.sparse.linalg import inv

from pylops import LinearOperator
from pylops.utils.backend import get_array_module

logging.basicConfig(format="%(levelname)s: %(message)s", level=logging.WARNING)

[docs]class MatrixMult(LinearOperator): r"""Matrix multiplication. Simple wrapper to :py:func:`` and :py:func:`numpy.vdot` for an input matrix :math:`\mathbf{A}`. Parameters ---------- A : :obj:`numpy.ndarray` or :obj:`scipy.sparse` matrix Matrix. dims : :obj:`tuple`, optional Number of samples for each other dimension of model (model/data will be reshaped and ``A`` applied multiple times to each column of the model/data). 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``) complex : :obj:`bool` Matrix has complex numbers (``True``) or not (``False``) """ def __init__(self, A, dims=None, dtype="float64"): ncp = get_array_module(A) self.A = A if isinstance(A, ncp.ndarray): self.complex = np.iscomplexobj(A) else: self.complex = np.iscomplexobj( if dims is None: self.reshape = False self.shape = A.shape self.explicit = True else: if isinstance(dims, int): dims = (dims,) self.reshape = True self.dims = np.array(dims, dtype=int) self.reshapedims = [ np.insert([], 0, self.A.shape[1]), np.insert([], 0, self.A.shape[0]), ] self.shape = ( A.shape[0] *, A.shape[1] *, ) self.explicit = False self.dtype = np.dtype(dtype) # Check dtype for correctness (upcast to complex when A is complex) if np.iscomplexobj(A) and not np.iscomplexobj(np.ones(1, dtype=self.dtype)): self.dtype = A.dtype logging.warning( "Matrix A is a complex object, dtype cast to %s" % self.dtype ) def _matvec(self, x): ncp = get_array_module(x) if self.reshape: x = ncp.reshape(x, self.reshapedims[0]) y = if self.reshape: return y.ravel() else: return y def _rmatvec(self, x): ncp = get_array_module(x) if self.reshape: x = ncp.reshape(x, self.reshapedims[1]) if self.complex: y = ( else: y = if self.reshape: return y.ravel() else: return y
[docs] def inv(self): r"""Return the inverse of :math:`\mathbf{A}`. Returns ---------- Ainv : :obj:`numpy.ndarray` Inverse matrix. """ if sp.sparse.issparse(self.A): Ainv = inv(self.A) else: ncp = get_array_module(self.A) Ainv = ncp.linalg.inv(self.A) return Ainv