On Wed, 2023-02-08 at 14:31 +0100, Francesc Alted wrote:
> On Wed, Feb 8, 2023 at 1:42 PM Sebastian Berg <
> sebast...@sipsolutions.net>
> wrote:
>
> > On Wed, 2023-02-08 at 12:48 +0100, Francesc Alted wrote:
> > > Hi,
> > >
> > >
> >
> > <snip>
> >
> > > Is there a way (or an ongoing effort) to express the variety of
> > > data
> > > types
> > > in NumPy that beats the above (which seems somewhat inconsistent
> > > to
> > > me)?
> >
> > How about using the Python buffer interface format string (maybe
> > with
> > some limitations).
> >
>
> If you mean the array interface (
> https://numpy.org/doc/stable/reference/arrays.interface.html), this
> is what
> dtype.str provides (
> https://numpy.org/doc/stable/reference/generated/numpy.dtype.str.html
> ).
> But the limitation here is that structured types are represented by
> the 'V'
> char, which is not properly representing it by any means.
>
Ah, I was thinking of what the Python buffer protocol uses, which is
what struct uses:
https://docs.python.org/3/library/struct.html#module-struct
That has some annoyances for sure, and structured dtypes with field
names need rather strange syntax. Also I think padding bytes at best
are simply fields with an empty name.
But overall, it probably already does a better job than any `str()` for
basic types:
In [2]: import numpy as np
In [3]: np.array(0, dtype="i,i,2f")
Out[3]:
array((0, 0, [0., 0.]),
dtype=[('f0', '<i4'), ('f1', '<i4'), ('f2', '<f4', (2,))])
In [4]: memoryview(np.array(0, dtype="i,i,2f")).format
Out[4]: 'T{i:f0:i:f1:(2)f:f2:}'
- Sebastian
>
> > But other than that, I don't have an obvious idea right now.
> >
> > - Sebsatian
> >
> > >
> > > Thanks!
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> > > Member address: sebast...@sipsolutions.net
> >
> >
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