From:

array([ <16x16 sparse matrix of type '<type 'numpy.float64'>'

with 64 stored elements in Compressed Sparse Row format>], dtype=object)

I deduce that C is a 1 element dense array with dtype=object. That one element is a sparse matrix.

So I expect that

 C[0].toarray()  

will work. As the error says, a numpy array does not have a toarray method. But in this case its element does.

Since Gx and Gy are sparse, then you need to use the sparse versions of hstack and vstack, not the numpy versions. Check the type of A and B. I be those are numpy arrays, not sparse matrices.

Look what happens when I use np.hstack with a couple of sparse matrices:

In [70]: M=sparse.csr_matrix([[0,1,2],[2,3,4]])
In [71]: np.hstack([M,M])
/usr/lib/python3/dist-packages/scipy/sparse/compressed.py:298: SparseEfficiencyWarning: Comparing sparse matrices using >= and <= is inefficient, using <, >, or !=, instead.
  "using <, >, or !=, instead.", SparseEfficiencyWarning)
Out[71]: 
array([ <2x3 sparse matrix of type '<class 'numpy.int32'>'
    with 5 stored elements in Compressed Sparse Row format>,
       <2x3 sparse matrix of type '<class 'numpy.int32'>'
    with 5 stored elements in Compressed Sparse Row format>], dtype=object)

The result is not sparse, rather it dense with 2 sparse elements.

You are using the right method but in a wrong way :)

nan_to_num is a method of numpy module, not numpy.ndarray. So instead of calling nan_to_num on you data, call it on numpy module giving your data as a paramter:

import numpy as np
data = np.array([1,2,3,np.nan,np.nan,5])
data_without_nan = np.nan_to_num(data)

prints:

array([1., 2., 3., 0., 0., 5.])

In your example:

import numpy as np
val = np.nan_to_num(setTo.ravel())