How to properly pass a scipy.sparse CSR matrix to a cython function?

Question

I need to pass a scipy.sparse CSR matrix to a cython function. How do I specify the type, as one would for a numpy array?

Answer 1

Building on @SaulloCastro's answer, add this function to the .pyx file to display the attributes of a csr matrix:

def print_csr(m):
    cdef np.ndarray[cINT32, ndim=1] indices, indptr
    cdef np.ndarray[cDOUBLE, ndim=1] data
    cdef int i
    if not isinstance(m, csr_matrix):
        m = csr_matrix(m)
    indices = m.indices.astype(np.int32)
    indptr = m.indptr.astype(np.int32)
    data = m.data.astype(np.float64)
    print indptr
    for i in range(np.shape(data)[0]):
        print indices[i], data[i]

indptr does not have the same length as data, so can't be printed in the same loop.

To display the csr data like coo, you can do your own conversion with these iteration lines:

    for i in range(np.shape(indptr)[0]-1):
        for j in range(indptr[i], indptr[i+1]):
            print i, indices[j], data[j]

I assume you know how to setup and compile a pyx file.

Also, what does your cython function assume about the matrix? Does it know about the csr format? The coo format?

Or does your cython function want a regular numpy array? In that case, we are off on a rabbit trail. You just need to convert the sparse matrix to an array: x.toarray() (or x.A for short).