spy ignores negative values?

fine. But if it (mainly the function option) is something

    > that merely might be useful to someone someday, then I
    > suggest it be left out until there is a clear need. (My 2
    > cents-worth, or less.)

sounds good

    > Curiosity questions about implementation:

    > 1) What is the "tocoo" method, and what objects have it? 2)

If someone is already using a sparse matrix from scipy.sparse, it's a
lot more efficient to use the sparse matrix functionality than to do
the sparsity check myself

Help on class coo_matrix in module scipy.sparse.sparse:

class coo_matrix(spmatrix)
> A sparse matrix in coordinate list format.
> COO matrices are created either as:
> A = coo_matrix(None, dims=(m, n), [dtype])
> for a zero matrix, or as:
> A = coo_matrix((obj, ij), [dims])
> where the dimensions are optional. If supplied, we set (M, N) =
> dims.
> If not supplied, we infer these from the index arrays
> ij[0][:] and ij[1][:]
> The arguments 'obj' and 'ij' represent three arrays:
> 1. obj[:] the entries of the matrix, in any order
> 2. ij[0][:] the row indices of the matrix entries
> 3. ij[1][:] the column indices of the matrix entries
> So the following holds:
> A[ij[0][k], ij[1][k] = obj[k]

    > Is there a reason why one shouldn't simply default precision
    > to 0 and use the condition "absolute(asarray(Z)) <=
    > precision"?

Minor performance issue since thisapproach requires two passes
through the data whereas mine takes one when precision=None

    > One more miscellaneous thought: perhaps spy and spy2 should
    > be consolidated into a single function with a kwarg to
    > select the marker version or the image version? Their
    > purpose is identical (isn't it?), and it would reduce
    > namespace clutter.

Fine by me -- if you want to implement it :slight_smile: