"""
pyOptSparse_utils holds a minimal set of sparse-matrix type routines for pyOptSparse.
This is designed to replace the SciPy sparse matrix formats, which have no way to enforce
a constant sparsity structure as required by the optimizers.
We use a very simple dictionary format to represent the three most common forms of sparse matrices::
mat = {'coo':[row, col, data], 'shape':[nrow, ncols]} # A coo matrix
mat = {'csr':[rowp, colind, data], 'shape':[nrow, ncols]} # A csr matrix
mat = {'csc':[colp, rowind, data], 'shape':[nrow, ncols]} # A csc matrix
"""
# Standard Python modules
import importlib
import os
import sys
import types
from typing import Optional, Tuple, Union
import warnings
# External modules
import numpy as np
from numpy import ndarray
from scipy import sparse
from scipy.sparse import spmatrix
# Local modules
from .pyOpt_error import Error
from .types import ArrayType
# Define index mnemonics
IROW = 0
ICOL = 1
IROWP = 0
ICOLIND = 1
ICOLP = 0
IROWIND = 1
IDATA = 2
# Constants
INFINITY = 1e20
EPS = np.finfo(np.float64).eps
[docs]
def mapToCSR(mat: dict) -> Tuple[ndarray, ndarray, ndarray]:
"""
Given a pyoptsparse matrix definition, return a tuple containing a
map of the matrix to the CSR format.
Parameters
----------
mat : dict
A sparse matrix representation.
Returns
-------
tup : tuple of numpy arrays
tup[0] : numpy array (size=num_rows+1)
An array that holds the indices in col_idx and data at which each
row begins. The last index of contains the number of nonzero
elements in the sparse array.
tup[1] : numpy array (size=nnz)
An array of the column indices of each element in data.
tup[2] : numpy array (size=nnz)
An indexing array which maps the elements in the data array
to elements in the CSR data array.
"""
if "csr" in mat:
# First handle the trivial case CSR->CSR
row_p = mat["csr"][IROW]
col_idx = mat["csr"][ICOL]
idx_data = np.s_[:]
return row_p, col_idx, idx_data
num_rows = mat["shape"][0]
num_cols = mat["shape"][1]
if "csc" in mat:
# If given a CSC matrix, expand the column pointers so we
# effectively have a COO representation.
csc_colp = mat["csr"][ICOL]
rows = mat["csc"][IROW]
nnz = csc_colp[-1]
# Allocate the COO maps
cols = np.zeros(nnz, dtype="intc")
# We already have a full representation of the columns.
# We need to decompress the representation of the rows.
for j in range(num_cols):
cols[csc_colp[j] : csc_colp[j + 1]] = j
elif "coo" in mat:
rows = mat["coo"][IROW]
cols = mat["coo"][ICOL]
nnz = len(rows)
# Allocate the row pointer array
row_p = np.zeros(num_rows + 1, dtype="intc")
# Get the sort order that puts data in row-major form
idx_data = np.lexsort((cols, rows))
# Apply the row-major indexing to the COO column and row indices
col_idx = np.asarray(cols, dtype="intc")[idx_data]
rows_rowmaj = np.asarray(rows, dtype="intc")[idx_data]
# Now for i = 0 to num_rows-1, row_p[i] is the first occurrence
# of i in rows_rowmaj
row_p[:-1] = np.digitize(np.arange(num_rows), rows_rowmaj, right=True)
# By convention store nnz in the last element of row_p
row_p[-1] = nnz
return row_p, col_idx, idx_data
[docs]
def mapToCSC(mat: dict) -> Tuple[ndarray, ndarray, ndarray]:
"""
Given a pyoptsparse matrix definition, return a tuple containing a
map of the matrix to the CSC format.
Parameters
----------
mat : dict
A sparse matrix representation.
Returns
-------
tup : tuple of numpy arrays
tup[0] : numpy array (size=nnz)
An array that holds the row index of each element in the CSC
representation of the data.
tup[1] : numpy array (size=num_cols+1)
An array that holds the indices in the CSC representation
and data at which each column begins. The last index of
contains the number of nonzero elements in the sparse array.
tup[2] : numpy array
An indexing array which maps the elements in the data array
to elements in the CSC data array.
"""
if "csc" in mat:
# First handle the trivial case CSR->CSR
row_idx = mat["csc"][IROW]
col_p = mat["csc"][ICOL]
idx_data = np.s_[:]
return row_idx, col_p, idx_data
num_rows = mat["shape"][0]
num_cols = mat["shape"][1]
if "csr" in mat:
# If given a CSR matrix, expand the row pointers so we
# effectively have a COO representation.
csr_rowp = mat["csr"][IROW]
cols = mat["csr"][ICOL]
nnz = csr_rowp[-1]
# Allocate the COO maps
rows = np.zeros(nnz, dtype="intc")
# We already have a full representation of the columns.
# We need to decompress the representation of the rows.
for j in range(num_rows):
rows[csr_rowp[j] : csr_rowp[j + 1]] = j
# Now we have rows and cols, proceed as if we started with a COO matrix
elif "coo" in mat:
rows = mat["coo"][IROW]
cols = mat["coo"][ICOL]
nnz = len(rows)
else:
raise ValueError("Invalid matrix type")
# Allocate the new column pointer
col_p = np.zeros(num_cols + 1, dtype="intc")
# Get the sort order that puts data in column-major form
idx_data = np.lexsort((rows, cols))
# Apply the column-major indexing to the COO column and row indices
row_idx = np.asarray(rows, dtype="intc")[idx_data]
cols_colmaj = np.asarray(cols, dtype="intc")[idx_data]
# Now for i = 0 to num_cols-1, col_p[i] is the first occurrence
# of i in cols_colmaj
col_p[:-1] = np.digitize(np.arange(num_cols), cols_colmaj, right=True)
# By convention store nnz in the last element of col_p
col_p[-1] = nnz
return row_idx, col_p, idx_data
[docs]
def convertToCOO(mat: Union[dict, spmatrix, ndarray]):
"""
Take a pyoptsparse sparse matrix definition of a COO, CSR or
CSC matrix or numpy array or scipy sparse matrix and return
the same matrix in COO format.
Parameters
----------
mat : dict or numpy array
A sparse matrix representation or numpy array
Returns
-------
newMat : dict
A coo representation of the same matrix
"""
if isinstance(mat, dict):
if "coo" in mat:
return mat
if "csr" in mat:
return _csr_to_coo(mat)
elif "csc" in mat:
return _csc_to_coo(mat)
else:
# Try to do it with a scipy sparse matrix:
try:
if sparse.issparse(mat):
warnings.warn(
"Using scipy.sparse matrices with pyOptSparse is VERY STRONGLY discouraged. "
+ "Please use the simplified pyOptSparse format which allows for "
+ "fixed sparsity structure and explicit zeros in the matrix. "
+ "There is no way to guarantee a fixed sparsity structure with scipy matrices "
+ "which is what the underlying optimizers require. "
+ "Using scipy.sparse matrices may cause unexpected errors.",
stacklevel=2,
)
mat = mat.tocoo()
return {"coo": [mat.row, mat.col, mat.data], "shape": mat.shape}
except Exception:
pass
# Now try to do it with a numpy matrix:
try:
return _denseToCOO(np.atleast_2d(np.array(mat)))
except Exception:
raise Error(
"Unknown matrix format. "
+ "Must be a dense numpy array or a pyOptSparse sparse matrix format of COO, CSR or CSC. "
+ f"See documentation for correct format. Supplied Matrix is: {repr(mat)}"
)
[docs]
def convertToCSR(mat: Union[dict, spmatrix, ndarray]) -> dict:
"""
Take a pyoptsparse sparse matrix definition of a COO, CSR or
CSC matrix or numpy array and return the same matrix in CSR format
Parameters
----------
mat : dict or numpy array
A sparse matrix representation or numpy array
Returns
-------
newMat : dict
A coo representation of the same matrix
"""
if isinstance(mat, dict) and "csr" in mat:
return mat
mat = convertToCOO(mat)
n = mat["shape"][0]
m = mat["shape"][1]
rows = mat["coo"][IROW]
cols = mat["coo"][ICOL]
data = mat["coo"][IDATA]
rowp = np.zeros(n + 1, dtype="intc")
# Count up the number of times things are index
for row in rows:
rowp[row + 1] += 1
# Set up the array as a pointer
for i in range(1, n + 1):
rowp[i] += rowp[i - 1]
ncols = np.zeros(rowp[-1], dtype="intc")
ndata = np.zeros(rowp[-1], dtype=type(data[0]))
# Now, add all the values and the data
for i in range(len(rows)):
r = rows[i]
ncols[rowp[r]] = cols[i]
ndata[rowp[r]] = data[i]
rowp[r] += 1
# Readjust the pointer
for i in range(n, 0, -1):
rowp[i] = rowp[i - 1]
rowp[0] = 0
return {"csr": [rowp, ncols, ndata], "shape": [n, m]}
[docs]
def convertToCSC(mat: Union[dict, spmatrix, ndarray]) -> dict:
"""
Take a pyoptsparse sparse matrix definition of a COO, CSR or
CSC matrix or numpy array and return the same matrix in CSR format
Parameters
----------
mat : dict or numpy array
A sparse matrix representation or numpy array
Returns
-------
newMat : dict
A coo representation of the same matrix
"""
if "csc" in mat:
return mat
mat = convertToCSR(mat)
n = mat["shape"][0]
m = mat["shape"][1]
rowp = mat["csr"][IROWP]
cols = mat["csr"][ICOLIND]
data = mat["csr"][IDATA]
# Allocate the new arrays
colp = np.zeros(m + 1, "intc")
rows = np.zeros(len(cols), "intc")
# Count up the number of references to each column
for col in cols:
colp[col + 1] += 1
# Set colp so that it is now a pointer
for i in range(1, m):
colp[i] += colp[i - 1]
# Allocate data for the csc object
csc_data = np.zeros(len(data), dtype=type(data[0]))
# Scan through the CSR data structure
for i in range(n):
for jp in range(rowp[i], rowp[i + 1]):
# Set the new row location in the CSC data structure
j = cols[jp]
csc_data[colp[j]] = data[jp]
rows[colp[j]] = i
colp[j] += 1
# Reset the colp pointer
for j in range(m, 0, -1):
colp[j] = colp[j - 1]
colp[0] = 0
return {"csc": [colp, rows, csc_data], "shape": [n, m]}
[docs]
def convertToDense(mat: Union[dict, spmatrix, ndarray]) -> ndarray:
"""
Take a pyopsparse sparse matrix definition and convert back to a dense
format. This is typically the final step for optimizers with dense constraint
jacibians.
Parameters
----------
mat : dict
A sparse matrix representation. Should be in CSR format for best
efficiency
Returns
-------
newMat : array
A dense numpy array of the same matrix
"""
mat = convertToCSR(mat)
newMat = np.zeros(mat["shape"])
data = mat["csr"][IDATA]
colInd = mat["csr"][ICOLIND]
rowp = mat["csr"][IROWP]
for i in range(mat["shape"][0]):
for j in range(rowp[i], rowp[i + 1]):
newMat[i, colInd[j]] = data[j]
return newMat
[docs]
def scaleColumns(mat: dict, factor):
"""d=
Scale the columns of the matrix. Must be CSR format
"""
if not isinstance(mat, dict):
raise Error("mat for scaleColumbs must be pyoptsparse matrix format")
if "csr" not in mat:
raise Error("scaleColumns only works for CSR pyoptsparse matrix format")
if mat["shape"][1] != len(factor):
raise Error("Length of factor is incorrect")
for i in range(mat["shape"][0]):
iStart = mat["csr"][IROWP][i]
iEnd = mat["csr"][IROWP][i + 1]
mat["csr"][IDATA][iStart:iEnd] *= factor[mat["csr"][ICOLIND][iStart:iEnd]]
[docs]
def scaleRows(mat: dict, factor):
"""
Scale the rows of the matrix. Must be CSR format
"""
if not isinstance(mat, dict):
raise Error("mat for scaleRows must be pyoptsparse matrix format")
if "csr" not in mat:
raise Error("scaleRows only works for CSR pyoptsparse matrix format")
if mat["shape"][0] != len(factor):
raise Error("Length of factor is incorrect")
for i in range(mat["shape"][0]):
iStart = mat["csr"][IROWP][i]
iEnd = mat["csr"][IROWP][i + 1]
mat["csr"][IDATA][iStart:iEnd] *= factor[i]
def _denseToCOO(arr: ndarray) -> dict:
"""
Return a COO array that is a COO representation of the dense numpy
array, arr
Parameters
----------
arr : numpy array
Returns
-------
dict : mat
The pyoptsparse representation of a sparse matrix
"""
nRows = arr.shape[0]
nCols = arr.shape[1]
data = arr.flatten()
cols = np.mod(np.arange(nRows * nCols), nCols)
rows = np.arange(nRows * nCols) // nCols
return {"coo": [rows, cols, data], "shape": [nRows, nCols]}
def _csr_to_coo(mat: dict) -> dict:
"""
Convert the given CSR matrix to a COO format
Parameters
----------
mat : dict
pyoptsparse matrix definition
"""
# This is straight forward - just expand out the rows
rowp = mat["csr"][IROWP]
cols = mat["csr"][ICOLIND]
data = mat["csr"][IDATA]
coo_rows = np.zeros(len(cols), "intc")
coo_cols = np.array(cols, "intc")
for i in range(mat["shape"][0]):
coo_rows[rowp[i] : rowp[i + 1]] = i
coo_data = np.array(data)
return {"coo": [coo_rows, coo_cols, coo_data], "shape": mat["shape"]}
def _csc_to_coo(mat: dict) -> dict:
"""
Convert the given CSC matrix to a COO format
Parameters
----------
mat : dict
pyoptsparse matrix definition
"""
# This is straight forward - just expand out the rows
colp = mat["csc"][ICOLP]
rows = mat["csc"][IROWIND]
data = mat["csc"][IDATA]
# This is straight forward - just expand out the columns
coo_rows = np.array(rows, "intc")
coo_cols = np.zeros(len(rows), "intc")
for j in range(mat["shape"][1]):
coo_cols[colp[j] : colp[j + 1]] = j
coo_data = np.array(data)
return {"coo": [coo_rows, coo_cols, coo_data], "shape": mat["shape"]}
def _broadcast_to_array(name: str, value: ArrayType, n_values: int, allow_none: bool = False):
"""
Broadcast an input to an array with a specified length
Parameters
----------
name : str
The name of the input. This is only used in the error message emitted.
value : float, list[float], numpy array
The input value
n_values : int
The number of values
allow_none : bool, optional
Whether to allow `None` in the input/output, by default False
Returns
-------
NDArray
An array with the shape ``(n_values)``
Raises
------
Error
If either the input is not broadcastable, or if the input contains None and ``allow_none=False``.
Warnings
--------
Note that the default value for ``allow_none`` is False.
"""
try:
value = np.broadcast_to(value, n_values)
except ValueError:
raise Error(
f"The '{name}' argument is invalid. It must be None, a scalar, or a list/array or length {n_values}."
)
if not allow_none and any([i is None for i in value]):
raise Error(f"The {name} argument cannot be 'None'.")
return value
[docs]
def try_import_compiled_module_from_path(module_name: str, path: Optional[str] = None) -> Union[types.ModuleType, str]:
"""
Attempt to import a module from a given path.
Parameters
----------
module_name : str
The name of the module
path : Optional[str]
The path to import from. If None, the default ``sys.path`` is used.
Returns
-------
Union[types.ModuleType, str]
If importable, the imported module is returned.
If not importable, the error message is instead returned.
"""
orig_path = sys.path
if path is not None:
path = os.path.abspath(os.path.expandvars(os.path.expanduser(path)))
sys.path = [path]
try:
module = importlib.import_module(module_name)
except ImportError as e:
if path is not None:
warnings.warn(
f"{module_name} module could not be imported from {path}.",
stacklevel=2,
)
module = str(e)
finally:
sys.path = orig_path
return module