Source code for openmdao.matrices.matrix

"""Define the base Matrix class."""
from __future__ import division
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix, csc_matrix

from collections import OrderedDict

# scipy sparse types allowed to be subjacs
sparse_types = (csr_matrix, csc_matrix, coo_matrix)


[docs]class Matrix(object): """ Base matrix class. This class is used for global Jacobians. Attributes ---------- _comm : MPI.Comm or <FakeComm> communicator of the top-level system that owns the <Jacobian>. _matrix : object implementation-specific representation of the actual matrix. _submats : dict dictionary of sub-jacobian data keyed by (out_name, in_name). _metadata : dict implementation-specific data for the sub-jacobians. """
[docs] def __init__(self, comm): """ Initialize all attributes. Parameters ---------- comm : MPI.Comm or <FakeComm> communicator of the top-level system that owns the <Jacobian>. """ self._comm = comm self._matrix = None self._submats = OrderedDict() self._metadata = OrderedDict()
def _add_submat(self, key, info, irow, icol, src_indices, shape, factor=None): """ Declare a sub-jacobian. Parameters ---------- key : (str, str) Tuple of the form (output_var_name, input_var_name). info : dict sub-jacobian metadata. irow : int the starting row index (offset) for this sub-jacobian. icol : int the starting col index (offset) for this sub-jacobian. src_indices : ndarray indices from the source variable that an input variable connects to. shape : tuple Shape of the specified submatrix. factor : float or None Unit conversion factor. """ self._submats[key] = (info, (irow, icol), src_indices, shape, factor) def _build(self, num_rows, num_cols, in_ranges, out_ranges): """ Allocate the matrix. Parameters ---------- num_rows : int number of rows in the matrix. num_cols : int number of cols in the matrix. in_ranges : dict Maps input var name to column range. out_ranges : dict Maps output var name to row range. """ pass def _update_submat(self, key, jac): """ Update the values of a sub-jacobian. Parameters ---------- key : (str, str) the global output and input variable names. jac : ndarray or scipy.sparse or tuple the sub-jacobian, the same format with which it was declared. """ pass def _prod(self, vec, mode, ranges): """ Perform a matrix vector product. Parameters ---------- vec : ndarray[:] incoming vector to multiply. mode : str 'fwd' or 'rev'. ranges : (int, int, int, int) Min row, max row, min col, max col for the current system. Returns ------- ndarray[:] vector resulting from the product. """ pass def _pre_update(self): """ Do anything that needs to be done at the beginning of AssembledJacobian._update. """ pass def _post_update(self): """ Do anything that needs to be done at the end of AssembledJacobian._update. """ pass
[docs] def set_complex_step_mode(self, active): """ Turn on or off complex stepping mode. When turned on, the value in each subjac is cast as complex, and when turned off, they are returned to real values. Parameters ---------- active : bool Complex mode flag; set to True prior to commencing complex step. """ pass
def _compute_index_map(jrows, jcols, irow, icol, src_indices): """ Return row/column indices to map sub-jacobian to global jac. Parameters ---------- jrows : index array Array of row indices. jcols : index array Array of column indices. irow : int Row index for start of sub-jacobian. icol : int Column index for start of sub-jacobian. src_indices : index array Index array of which values to pull from a source into an input variable. Returns ------- tuple of (ndarray, ndarray, ndarray) Row indices, column indices, and indices of columns matching src_indices. """ icols = [] idxs = [] for i, idx in enumerate(src_indices): # pull out columns that match each index idxarr = np.nonzero(jcols == i)[0] idxs.append(idxarr) icols.append(np.full(idxarr.shape, idx, dtype=int)) idxs = np.hstack(idxs) icols = np.hstack(icols) + icol irows = jrows[idxs] + irow return (irows, icols, idxs)