def tobsr(self, blocksize=None, copy=True):
from scipy.sparse.bsr import bsr_matrix
if blocksize is None:
from scipy.sparse.spfuncs import estimate_blocksize
return self.tobsr(blocksize=estimate_blocksize(self))
elif blocksize == (1, 1):
arg1 = (self.data.reshape(-1, 1, 1), self.indices, self.indptr)
return bsr_matrix(arg1, shape=self.shape, copy=copy)
else:
R, C = blocksize
M, N = self.shape
if R < 1 or C < 1 or M % R != 0 or N % C != 0:
raise ValueError('invalid blocksize %s' % blocksize)
blks = csr_count_blocks(M, N, R, C, self.indptr, self.indices)
idx_dtype = get_index_dtype((self.indptr, self.indices),
maxval=max(N // C, blks))
indptr = np.empty(M // R + 1, dtype=idx_dtype)
indices = np.empty(blks, dtype=idx_dtype)
data = np.zeros((blks, R, C), dtype=self.dtype)
csr_tobsr(M, N, R, C, self.indptr.astype(idx_dtype),
self.indices.astype(idx_dtype), self.data, indptr,
indices, data.ravel())
return bsr_matrix((data, indices, indptr), shape=self.shape)
def test_estimate_blocksize(self):
mats = []
mats.append( [[0,1],[1,0]] )
mats.append( [[1,1,0],[0,0,1],[1,0,1]] )
mats.append( [[0],[0],[1]] )
mats = [array(x) for x in mats]
blks = []
blks.append( [[1]] )
blks.append( [[1,1],[1,1]] )
blks.append( [[1,1],[0,1]] )
blks.append( [[1,1,0],[1,0,1],[1,1,1]] )
blks = [array(x) for x in blks]
for A in mats:
for B in blks:
X = kron(A,B)
r,c = spfuncs.estimate_blocksize(X)
assert_(r >= B.shape[0])
assert_(c >= B.shape[1])
