def _compute_sparsity(self, ordered_of_info, ordered_wrt_info,
num_full_jacs, tol, orders):
"""
Compute a dense sparsity matrix for this jacobian using saved absolute summations.
The sparsity matrix will contain only those columns that match the wrt variables in
wrt_matches, but will contain rows for all outputs in the given system.
Parameters
----------
ordered_of_info : list of (name, offset, end, idxs)
Name, offset, etc. of row variables in the order that they appear in the jacobian.
ordered_wrt_info : list of (name, offset, end, idxs)
Name, offset, etc. of column variables in the order that they appear in the jacobian.
num_full_jacs : int
Number of times to compute partial jacobian when computing sparsity.
tol : float
Tolerance used to determine if an array entry is zero or nonzero.
orders : int
Number of orders +/- for the tolerance sweep.
Returns
-------
ndarray
Boolean sparsity matrix.
"""
from openmdao.utils.coloring import _tol_sweep
subjacs = self._subjacs_info
summ = self._jac_summ
rend = ordered_of_info[-1][2]
cend = ordered_wrt_info[-1][2]
J = np.zeros((rend, cend))
for of, roffset, rend, _ in ordered_of_info:
for wrt, coffset, cend, _ in ordered_wrt_info:
key = (of, wrt)
if key in subjacs:
meta = subjacs[key]
if meta['rows'] is not None:
rows = meta['rows'] + roffset
cols = meta['cols'] + coffset
J[rows, cols] = summ[key]
elif issparse(summ[key]):
raise NotImplementedError(
"{}: scipy sparse arrays are not "
"supported yet.".format(self.msginfo))
else:
J[roffset:rend, coffset:cend] = summ[key]
J *= (1.0 / np.max(J))
tol_info = _tol_sweep(J, tol, orders)
boolJ = np.zeros(J.shape, dtype=bool)
boolJ[J > tol_info['good_tol']] = True
return boolJ, tol_info
def _compute_sparsity(self, ordered_of_info, ordered_wrt_info, tol,
orders):
"""
Compute a dense sparsity matrix for this jacobian using saved absolute summations.
The sparsity matrix will contain only those columns that match the wrt variables in
wrt_matches, but will contain rows for all outputs in the given system.
Parameters
----------
ordered_of_info : list of (name, offset, end, idxs)
Name, offset, etc. of row variables in the order that they appear in the jacobian.
ordered_wrt_info : list of (name, offset, end, idxs)
Name, offset, etc. of column variables in the order that they appear in the jacobian.
tol : float
Tolerance used to determine if an array entry is zero or nonzero.
orders : int
Number of orders +/- for the tolerance sweep.
Returns
-------
coo_matrix
Boolean sparsity matrix.
"""
from openmdao.utils.coloring import _tol_sweep
subjacs = self._subjacs_info
summ = self._jac_summ
Jrows = []
Jcols = []
Jdata = []
for of, roffset, rend, _ in ordered_of_info:
for wrt, coffset, cend, _, _ in ordered_wrt_info:
key = (of, wrt)
if key in summ:
subsum = summ[key]
meta = subjacs[key]
if meta['rows'] is not None:
Jrows.append(meta['rows'] + roffset)
Jcols.append(meta['cols'] + coffset)
Jdata.append(subsum)
elif issparse(subsum):
raise NotImplementedError(
"{}: scipy sparse arrays are not "
"supported yet.".format(self.msginfo))
else: # dense
for i, r in enumerate(range(roffset, rend)):
Jrows.append([r] * (cend - coffset))
Jcols.append(np.arange(coffset, cend))
Jdata.append(subsum[i, :])
summ = self._jac_summ = None # free up some memory
Jrows = np.hstack(Jrows)
Jcols = np.hstack(Jcols)
Jdata = np.hstack([d.flat for d in Jdata])
shape = (rend, cend)
Jdata *= (1.0 / np.max(Jdata))
tol_info = _tol_sweep(Jdata, tol, orders)
mask = Jdata > tol_info['good_tol']
size = np.count_nonzero(mask)
boolJ = coo_matrix(
(np.ones(size, dtype=bool), (Jrows[mask], Jcols[mask])),
shape=shape)
return boolJ, tol_info
