def sparse_permute(A, rperm=(), cperm=(), safe=True):
"""
Permutes the rows and columns of a sparse CSR/CSC matrix or Qobj
according to the permutation arrays rperm and cperm, respectively.
Here, the permutation arrays specify the new order of the rows and
columns. i.e. [0,1,2,3,4] -> [3,0,4,1,2].
Parameters
----------
A : qobj, csr_matrix, csc_matrix
Input matrix.
rperm : array_like of integers
Array of row permutations.
cperm : array_like of integers
Array of column permutations.
safe : bool
Check structure of permutation arrays.
Returns
-------
perm_csr : csr_matrix, csc_matrix
CSR or CSC matrix with permuted rows/columns.
"""
rperm = np.asarray(rperm, dtype=np.int32)
cperm = np.asarray(cperm, dtype=np.int32)
nrows = A.shape[0]
ncols = A.shape[1]
if len(rperm)==0:
rperm = np.arange(nrows, dtype=np.int32)
if len(cperm)==0:
cperm = np.arange(ncols, dtype=np.int32)
if safe:
if len(np.setdiff1d(rperm, np.arange(nrows)))!=0:
raise Exception('Invalid row permutation array.')
if len(np.setdiff1d(cperm, np.arange(ncols)))!=0:
raise Exception('Invalid column permutation array.')
shp = A.shape
if A.__class__.__name__=='Qobj':
kind = 'csr'
dt = complex
data, ind, ptr = _sparse_permute(
A.data.data, A.data.indices, A.data.indptr,
nrows, ncols, rperm, cperm, 0)
else:
kind=A.getformat()
if kind=='csr':
flag = 0
elif kind=='csc':
flag = 1
else:
raise Exception('Input must be Qobj, CSR, or CSC matrix.')
data, ind, ptr = _sparse_permute(A.data, A.indices, A.indptr,
nrows, ncols, rperm, cperm, flag)
if kind=='csr':
return sp.csr_matrix((data, ind, ptr), shape=shp, dtype=data.dtype)
elif kind=='csc':
return sp.csc_matrix((data, ind, ptr), shape=shp, dtype=data.dtype)
def sparse_permute(A, rperm=(), cperm=(), safe=True):
"""
Permutes the rows and columns of a sparse CSR/CSC matrix or Qobj
according to the permutation arrays rperm and cperm, respectively.
Here, the permutation arrays specify the new order of the rows and
columns. i.e. [0,1,2,3,4] -> [3,0,4,1,2].
Parameters
----------
A : qobj, csr_matrix, csc_matrix
Input matrix.
rperm : array_like of integers
Array of row permutations.
cperm : array_like of integers
Array of column permutations.
safe : bool
Check structure of permutation arrays.
Returns
-------
perm_csr : csr_matrix, csc_matrix
CSR or CSC matrix with permuted rows/columns.
"""
rperm = np.asarray(rperm, dtype=np.int32)
cperm = np.asarray(cperm, dtype=np.int32)
nrows = A.shape[0]
ncols = A.shape[1]
if len(rperm) == 0:
rperm = np.arange(nrows, dtype=np.int32)
if len(cperm) == 0:
cperm = np.arange(ncols, dtype=np.int32)
if safe:
if len(np.setdiff1d(rperm, np.arange(nrows))) != 0:
raise Exception('Invalid row permutation array.')
if len(np.setdiff1d(cperm, np.arange(ncols))) != 0:
raise Exception('Invalid column permutation array.')
shp = A.shape
if A.__class__.__name__ == 'Qobj':
kind = 'csr'
dt = complex
data, ind, ptr = _sparse_permute(A.data.data, A.data.indices,
A.data.indptr, nrows, ncols, rperm,
cperm, 0)
else:
kind = A.getformat()
if kind == 'csr':
flag = 0
elif kind == 'csc':
flag = 1
else:
raise Exception('Input must be Qobj, CSR, or CSC matrix.')
data, ind, ptr = _sparse_permute(A.data, A.indices, A.indptr, nrows,
ncols, rperm, cperm, flag)
if kind == 'csr':
return sp.csr_matrix((data, ind, ptr), shape=shp, dtype=data.dtype)
elif kind == 'csc':
return sp.csc_matrix((data, ind, ptr), shape=shp, dtype=data.dtype)
