absmethod - only the function version
df.strwhich hold functions for datetime and string operations respectively
cls=type(args)by default, this would ensure that the method-call versions returned the type of the instance
xnd.array.ufuncis a bit of a mouthful ¯_(ツ)_/¯
[Hameer Abbasi] Then
xnd.array is a valid subclass of
xnd.xnd, but this doesn’t hold for
astropy.Quantity is valid sometimes and sometimes errors, assuming consistency of units and no conversion. If inconsistent units are supplied, it violates Liskov in this form.
If we have a much more relaxed definition, such that the
dtype and the
shape have to be consistent (
type in xnd-speak), and errors are allowed, then MaskedArray and unit become valid.
[Sameer Deshmukh] @ralf.gommers I think this message states that the latest ndtypes does not need refcounting. Let me try updating this on the ruby side and then I can update you on what is going on as of now.
An update on reference counted types and the history:
The Numba team was concerned about having reference counted types in
libndtypes. However, the more I looked at it, I can't see any harm. For example, ...
t = ndt_string(ctx); ndt_del(t)
.... is now just:
t = ndt_string(ctx); ndt_decref(t);
So refcounting is all internal to
libndtypes and has nothing to do with Python refcounting.
It is all implemented in the latest
One implementation detail: An
ndt_t * is now always
const except for the
ndt_decref(const ndt_t *) follows the convention in the Linux kernel, where
kfree() takes a
const void *.
The convention in the Linux kernel has been the source of some controversy. I understand some of the counter arguments, but on the whole I think it's a good idea. More here:
const *, which has Linus' endorsement but might lead to raised eyebrows elsewhere.
[Stefan Krah] I've implemented flexible ND arrays, which are more versatile than the offset-based
These are needed for quirky formats like
The trade-off is that they are not in a single memory block.
You can now assign subarrays of different sizes:
>>> >>> x = xnd([, [1, 2]], type="array of array of int64") >>> x xnd([, [1, 2]], type='array of array of int64') >>> x = [1000, 2000, 3000] >>> x xnd([[1000, 2000, 3000], [1, 2]], type='array of array of int64') >>>
xnd-daskshould be possible for ndarray: dask/dask#4669