def make_signal(sig_type, shape, indices, data):
dense = np.zeros(shape)
dense[indices[:, 0], indices[:, 1]] = data
if sig_type == "sparse_scipy":
m = scipy_sparse.csr_matrix((data, indices.T), shape=shape)
elif sig_type == "sparse_nengo":
m = nengo.transforms.SparseMatrix(data=data,
indices=indices,
shape=shape)
else:
m = dense
return Signal(m, name="MySignal"), dense
def allocate(self):
"""Return a `scipy.sparse.csr_matrix` or dense matrix equivalent.
We mark this data as readonly to be consistent with how other
data associated with signals are allocated. If this allocated
data is to be modified, it should be copied first.
"""
if self._allocated is not None:
return self._allocated
if scipy_sparse is None:
warnings.warn("Sparse operations require Scipy, which is not "
"installed. Using dense matrices instead.")
self._allocated = self.toarray().view()
else:
self._allocated = scipy_sparse.csr_matrix(
(self.data, self.indices.T), shape=self.shape)
self._allocated.data.setflags(write=False)
return self._allocated