Matrix sum is 220 times slower than scipy

[Anonymous]

The following code was used to calculate the sum of sparse matrix A:

[i]pysparse B init:[/i]
B = pysparse.spmatrix.ll_mat(1,N)
val = np.ones(N)
row = np.zeros(N)
col = np.arange(0,N)
B.put(val,row,col)
[b]272 seconds[/b]
    C = pysparse.spmatrix.matrixmultiply(B,A)
    a = np.zeros(N,dtype=int)
    C.take(a,np.zeros(N,dtype=int),np.arange(0,N,dtype=int))
[b]214 seconds[/b]
    f = pysparse.spmatrix.matrixmultiply(B,A).find()
    a = np.zeros(N, dtype=np.int32)
    for i, c in enumerate(f[2]): a[c] += f[0][i]
88 seconds
    a = np.zeros(N, dtype=np.int32)
    for i in tree.dA.find()[2]: a[i] += 1
[i]Scipy.sparse:[/i]
[b]0.4 seconds[/b]
    a = np.array(scipy.sparse.csr_matrix.sum(A,0))[0]

Am I doing something wrong here or is there no simple/fast way of getting the sum of A into a numpy array?

[Anonymous]

Also, there seems to be a memory leak when using ll_matrix.nnz
(I'm not sure how to create a bug report so I write it here)