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

JDH