How to assign scipy.sparse matrix to NumPy array via indexing?

When I try to assign a scipy.sparse matrix s (any of the available sparse types) to a NumPy array a like this:

a[:] = s

I get a TypeError:

TypeError: float() argument must be a string or a number

Is there a way to get around this?

I know about the todense() and toarray() methods, but I'd really like to avoid the unnecessary copy and I'd prefer to use the same code for both NumPy arrays and SciPy sparse matrices. For now, I'm not concerned with getting the values from the sparse matrix being inefficient.

Is there probably some kind of wrapper around sparse matrices that works with NumPy indexing assignment?

If not, any advice how I could build such a thing by myself?

Is there a different sparse array library that cooperates with NumPy in this situation?

UPDATE:

I poked around in the NumPy sources and, searching for the error message string, I think I found the section where the indexing assignment happens in numpy/core/src/multiarray/arraytypes.c.src around line 187 in the function @TYPE@_setitem().

I still don't really get it, but at some point, the float() function seems to be called (if a is a floating-point array). So I tried to monkey-patch one of the SciPy sparse matrix classes to allow this function to be called:

import scipy
s = scipy.sparse.dok_matrix((5, 1))
def myfloat(self):
    assert self.shape == (1, 1)
    return self[0, 0]
scipy.sparse.dok.dok_matrix.__float__ = myfloat
a[:] = s

Sadly, this doesn't work because float() is called on the whole sparse matrix and not on the individual items thereof.

So I guess my new question is: how can I further change the sparse matrix class to make NumPy iterate over all the items and call float() on each of them?

ANOTHER UPDATE:

I found a sparse array module on Github (https://github.com/FRidh/sparse), which allows assignment to a NumPy array. Sadly, the features of the module are quite limited (e.g. slicing doesn't really work yet), but it might help to understand how assigning to NumPy arrays can be achieved. I'll investigate that further ...

YET ANOTHER UPDATE:

I did some more digging and found that a more interesting source file is probably numpy/core/src/multiarray/ctors.c. I suspect that the function PySequence_Check() (docs/code) is called sometime during the assignment. The simple sparse array class from https://github.com/FRidh/sparse passes the test, but it looks like the sparse matrix classes from SciPy don't (although in my opinion they are sequences).

They get checked for __array_struct__, __array_interface__ and __array__, and then it's somehow decided that they are not sequences. The attributes __getitem__ and __len__ (which all the sparse array classes have!) are not checked.

This leads me to yet another question: How can I manipulate the sparse matrix classes (or objects thereof) in a way that they pass PySequence_Check()?

I think as soon as they are recognized as sequences, assignment should work, because __getitem__() and __len__() should be sufficient for that.