Esempio n. 1
0
    def tobsr(self, blocksize=None, copy=True):
        from bsr import bsr_matrix

        if blocksize is None:
            from 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)

            indptr  = np.empty(M/R + 1,    dtype=np.intc)
            indices = np.empty(blks,       dtype=np.intc)
            data    = np.zeros((blks,R,C), dtype=self.dtype)

            csr_tobsr(M, N, R, C, self.indptr, self.indices, self.data, \
                    indptr, indices, data.ravel() )

            return bsr_matrix((data,indices,indptr), shape=self.shape)
Esempio n. 2
0
    def tobsr(self, blocksize=None, copy=True):
        from bsr import bsr_matrix

        if blocksize is None:
            from 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)

            indptr = np.empty(M // R + 1, dtype=np.intc)
            indices = np.empty(blks, dtype=np.intc)
            data = np.zeros((blks, R, C), dtype=self.dtype)

            csr_tobsr(M, N, R, C, self.indptr, self.indices, self.data, \
                    indptr, indices, data.ravel() )

            return bsr_matrix((data, indices, indptr), shape=self.shape)