def fromsp(cls, geometry, P, S=None):
""" Read and return the object with possible overlap """
# Calculate maximum number of connections per row
nc = 0
# Ensure list of csr format (to get dimensions)
if isspmatrix(P):
P = [P]
# Number of dimensions
dim = len(P)
# Sort all indices for the passed sparse matrices
for i in range(dim):
P[i] = P[i].tocsr()
P[i].sort_indices()
P[i].sum_duplicates()
# Figure out the maximum connections per
# row to reduce number of re-allocations to 0
for i in range(P[0].shape[0]):
nc = max(nc, P[0][i, :].getnnz())
# Create the sparse object
p = cls(geometry, dim, P[0].dtype, nc, orthogonal=S is None)
if p._size != P[0].shape[0]:
raise ValueError(cls.__name__ + '.fromsp cannot create a new class, the geometry ' + \
'and sparse matrices does not have coinciding dimensions size != sp.shape[0]')
for i in range(dim):
ptr = P[i].indptr
col = P[i].indices
D = P[i].data
# loop and add elements
for r in range(p.shape[0]):
sl = slice(ptr[r], ptr[r+1], None)
p[r, col[sl], i] = D[sl]
if not S is None:
S = S.tocsr()
S.sort_indices()
S.sum_duplicates()
ptr = S.indptr
col = S.indices
D = S.data
# loop and add elements
for r in range(p.shape[0]):
sl = slice(ptr[r], ptr[r+1], None)
p.S[r, col[sl]] = D[sl]
return p
def fromsp(cls, geometry, P, S=None, **kwargs):
r""" Create a sparse model from a preset `Geometry` and a list of sparse matrices
The passed sparse matrices are in one of `scipy.sparse` formats.
Parameters
----------
geometry : Geometry
geometry to describe the new sparse geometry
P : list of scipy.sparse or scipy.sparse
the new sparse matrices that are to be populated in the sparse
matrix
S : scipy.sparse, optional
if provided this refers to the overlap matrix and will force the
returned sparse matrix to be non-orthogonal
**kwargs : optional
any arguments that are directly passed to the ``__init__`` method
of the class.
Returns
-------
SparseGeometry
a new sparse matrix that holds the passed geometry and the elements of `P` and optionally being non-orthogonal if `S` is not none
"""
# Ensure list of csr format (to get dimensions)
if isspmatrix(P):
P = [P]
if isinstance(P, tuple):
P = list(P)
# Number of dimensions, before S!
dim = len(P)
if not S is None:
P.append(S)
p = cls(geometry, dim, P[0].dtype, 1, orthogonal=S is None, **kwargs)
p._csr = p._csr.fromsp(*P, dtype=kwargs.get("dtype"))
if p._size != P[0].shape[0]:
raise ValueError(
f"{cls.__name__}.fromsp cannot create a new class, the geometry "
"and sparse matrices does not have coinciding dimensions size != P[0].shape[0]"
)
return p
def fromsp(cls, geometry, P, S=None, **kwargs):
r""" Create a sparse model from a preset `Geometry` and a list of sparse matrices
The passed sparse matrices are in one of `scipy.sparse` formats.
Parameters
----------
geometry : Geometry
geometry to describe the new sparse geometry
P : list of scipy.sparse or scipy.sparse
the new sparse matrices that are to be populated in the sparse
matrix
S : scipy.sparse, optional
if provided this refers to the overlap matrix and will force the
returned sparse matrix to be non-orthogonal
**kwargs : optional
any arguments that are directly passed to the ``__init__`` method
of the class.
Returns
-------
SparseGeometry
a new sparse matrix that holds the passed geometry and the elements of `P` and optionally being non-orthogonal if `S` is not none
"""
# Ensure list of csr format (to get dimensions)
if isspmatrix(P):
P = [P]
if isinstance(P, tuple):
P = list(P)
# Number of dimensions
dim = len(P)
nnzpr = 1
# Sort all indices for the passed sparse matrices
for i in range(dim):
P[i] = P[i].tocsr()
P[i].sort_indices()
P[i].sum_duplicates()
nnzpr = max(nnzpr, P[i].nnz // P[i].shape[0])
# Create the sparse object
p = cls(geometry,
dim,
P[0].dtype,
nnzpr,
orthogonal=S is None,
**kwargs)
if p._size != P[0].shape[0]:
raise ValueError(cls.__name__ + '.fromsp cannot create a new class, the geometry ' + \
'and sparse matrices does not have coinciding dimensions size != P[0].shape[0]')
for i in range(dim):
ptr = P[i].indptr
col = P[i].indices
D = P[i].data
# loop and add elements
for r in range(p.shape[0]):
sl = slice(ptr[r], ptr[r + 1], None)
p[r, col[sl], i] = D[sl]
if not S is None:
S = S.tocsr()
S.sort_indices()
S.sum_duplicates()
ptr = S.indptr
col = S.indices
D = S.data
# loop and add elements
for r in range(p.shape[0]):
sl = slice(ptr[r], ptr[r + 1], None)
p.S[r, col[sl]] = D[sl]
return p