array_utils.py¶
Utils for dealing with arrays.

openmdao.utils.array_utils.
abs_complex
(x)[source]¶ Compute the absolute value of a complexstepped vector.
Rather than taking a Euclidian norm, simply negate the values that are less than zero.
 Parameters
 xndarray
Input array.
 Returns
 ndarray
Complexstep absolute value of the array.

openmdao.utils.array_utils.
array_connection_compatible
(shape1, shape2)[source]¶ Return True if the two arrays shapes are compatible.
Array shapes are compatible if the underlying data has the same size and is stored in the same contiguous order for the two shapes.
 Parameters
 shape1tuple of int
Shape of the first array.
 shape2tuple of int
Shape of the second array.
 Returns
 bool
True if the two shapes are compatible for connection, else False.

openmdao.utils.array_utils.
array_viz
(arr, prob=None, of=None, wrt=None, stream=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf8'>)[source]¶ Display the structure of a boolean array in a compact form.
If prob, of, and wrt are supplied, print the name of the response alongside each row and print the names of the design vars, aligned with each column, at the bottom.
 Parameters
 arrndarray
Array being visualized.
 probProblem or None
Problem object.
 oflist of str or None
Names of response variables used in derivative calculation.
 wrtlist of str or None
Names of design variables used in derivative calculation.
 streamfilelike
Stream where output will be written.

openmdao.utils.array_utils.
convert_neg
(arr, size)[source]¶ Convert any negative indices into their positive equivalent.
This only works for a 1D array.
 Parameters
 arrndarray
Array having negative indices converted.
 sizeint
Dimension of the array.
 Returns
 ndarray
The converted array.

openmdao.utils.array_utils.
dv_abs_complex
(x, x_deriv)[source]¶ Compute the complexstep derivative of the absolute value function and its derivative.
 Parameters
 xndarray
Input array, used for determining which elements to negate.
 x_derivndarray
Incominng partial derivative array, may have one additional dimension.
 Returns
 ndarray
Absolute value applied to x.
 ndarray
Absolute value applied to x_deriv.

openmdao.utils.array_utils.
evenly_distrib_idxs
(num_divisions, arr_size)[source]¶ Return evenly distributed entries for the given array size.
Given a number of divisions and the size of an array, chop the array up into pieces according to number of divisions, keeping the distribution of entries as even as possible.
 Parameters
 num_divisionsint
Number of parts to divide the array into.
 arr_sizeint
Number of entries in the array.
 Returns
 tuple
a tuple of (sizes, offsets), where sizes and offsets contain values for all divisions.

openmdao.utils.array_utils.
get_input_idx_split
(full_idxs, inputs, outputs, use_full_cols, is_total)[source]¶ Split an array of indices into vec outs + ins into two arrays of indices into outs and ins.
 Parameters
 full_idxsndarray
Indices into the full array (which could be outs + ins or just ins)
 inputsVector
Inputs vector.
 outputsVector
Outputs vector.
 use_full_colsbool
If True, full idxs are into the full outs + ins vector.
 is_totalbool
If True, total derivatives are being computed and wrt vector is the outputs vector.
 Returns
 list of tuples
Each tuple is of the form (array, idxs).

openmdao.utils.array_utils.
shape_to_len
(shape)[source]¶ Compute length given a shape tuple.
For realisticdimension arrays, looping over the shape tuple is much faster than np.prod.
 Parameters
 shapetuple
Numpy shape tuple.
 Returns
 int
Length of multidimensional array.

openmdao.utils.array_utils.
sizes2offsets
(size_array)[source]¶ For a given array of sizes, return an array of offsets.
Offsets will be computed using a flattened version of size_array and then reshaped to match the shape of size_array.
 Parameters
 size_arrayndarray
Array of sizes.
 Returns
 ndarray
Array of offsets.

openmdao.utils.array_utils.
sub2full_indices
(all_names, matching_names, sizes, idx_map=())[source]¶ Return the given indices converted into indices into the full vector.
This routine is used to compute how column indices computed during coloring of a subset of the jacobian map to column indices corresponding to the full jacobian.
 Parameters
 all_namesordered iter of str
An ordered list of variable names containing all variables of the appropriate type.
 matching_namesset of str
Subset of all_names that make up the reduced index set.
 sizesndarray of int
Array of variable sizes.
 idx_mapdict
Mapping of var name to some subset of its full indices.
 Returns
 ndarray
Full array indices that map to the provided subset of variables.

openmdao.utils.array_utils.
take_nth
(rank, size, seq)[source]¶ Iterate returning every nth value.
Return an iterator over the sequence that returns every nth element of seq based on the given rank within a group of the given size. For example, if size = 2, a rank of 0 returns even indexed elements and a rank of 1 returns odd indexed elements.
 Parameters
 rankint
MPI rank of this process.
 sizeint
Size of the array we’re taking nth entries from.
 seqiter
Iterator containing the values being returned.

openmdao.utils.array_utils.
tile_sparse_jac
(data, rows, cols, nrow, ncol, num_nodes)[source]¶ Assemble arrays necessary to define a COO sparse jacobian for a vectorized component.
These arrays can also be passed to csc_matrix or csr_matrix to create CSC and CSR sparse matrices.
 Parameters
 datandarray
Array of values
 rowsindex array
Array of row indices.
 colsindex array
Array of column indices.
 nrowint
Number of rows in sub jacobian.
 ncolint
Number of columns in sub jacobian.
 num_nodesint
Number of vectorized copies to tile.
 Returns
 ndarray, ndarray, ndarray
Arrays to define a COO sparse jacobian of size num_nodes*nrow by num_nodes*ncol