PoC,WIP: Proof of concept for extending buffer protocol string #50
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This closes numpygh-28190 and fixes another issue in the initial code that triggered the regression. Note that we may still want to avoid this, since this does lead to constructing (view compatible) structured dtypes unnecessarily here. It would also compactify the dtype. For building unnecessary dtypes, the better solution may be to just introduce a "canonical" flag to the dtypes (now that we have the space).
This is a proof of concept, that allows roundtripping code such
as:
```
a = np.arange(100000).astype("T")
print(memoryview(a).format) # just to see it
via_memview_string = np.asarray(memoryview(a))
a = np.arange(1000).astype("m8[ms]") # or even struct "m8[...],i"
print(memoryview(a).format) # just to see it
via_memview_timedelta = np.asarray(memoryview(a))
```
I opted to simply store the `descr` pointer directly. This seems
just as well because the descr owns the side-car buffer.
One could still export it to non-Python (somewhat) since accessing
that side-car buffer _doesn't_ need Python API in the end (just
struct layout information).
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This is a proof of concept, that allows roundtripping code such as:
As well as a PoC for basic Cython exposure as a typed memory-view (see below).
I opted to simply store the
descrpointer directly. This seems just as well because the descr owns the side-car buffer. One could still export it to non-Python (somewhat) since accessing that side-car buffer doesn't need Python API in the end (just struct layout information).For the times I opted to store two hex ints. From Python that is awkward, but our time enum is public anyway. (But we could also store
ns, etc. strings.)Note: This branch shows a minimal PoC extension to access NumPy string arrays via Cython (these are particularly difficulty, because one needs the allocator to access elements).
Because of this, it requires using this branch of Cython da-woods/cython#5