On Tue, Dec 12, 2017 at 6:20 PM Marten van Kerkwijk <
m.h.vankerkw...@gmail.com> wrote:
> The real magic happens when you ducktype, and ensure your function
> works both for arrays and scalars on its own. This is more often
> possible than you might think!
Sadly, this still doesn't work in a typ
On Tue, Dec 12, 2017 at 5:07 PM Mark Campanelli
wrote:
> I think I saw some other discussion recently about numpy joining forces
> with Python 3's gradual type system. Is there any draft formal proposal for
> this yet? If numpy+scipy wants to scale to "bigger" projects, I think it
> behooves the
The real magic happens when you ducktype, and ensure your function
works both for arrays and scalars on its own. This is more often
possible than you might think! If you really need, e.g., the shape,
you can do `getattr(input, 'shape', ())` and things may well work for
scalars, and also for objects
The different approaches and corresponding "code bloat" here is one of the
most annoying things I have found about using numpy+scipy. Furthermore, the
flip side to the handling of the inputs, including both type and shape, is
getting the output to match the input, including both type and shape.
Get
Hi all,
I've started working on the proposal discussed in this thread:
https://mail.python.org/pipermail/numpy-discussion/2017-December/077481.html
here:
https://github.com/numpy/numpy/pull/10213
You can see how I modified PEP 1 here:
https://github.com/numpy/numpy/pull/10213/commits/eaf78
On Wed, Dec 13, 2017 at 5:52 AM, Eric Wieser
wrote:
> Using np.iscalar is a bad idea, as it fails for 0d arrays. x.ndim is the
> better option there.
>
> I’d maybe suggest not special-casing 0d arrays though, and using:
>
> def func_for_scalars_or_vectors(x):
> x = np.asanyarray(x) # convert
Using np.iscalar is a bad idea, as it fails for 0d arrays. x.ndim is the
better option there.
I’d maybe suggest not special-casing 0d arrays though, and using:
def func_for_scalars_or_vectors(x):
x = np.asanyarray(x) # convert scalars to 0d arrays
# The magic happens here
return ret
Oh, sorry for noise...
With kind regards, -gdg
On Dec 12, 2017 23:05, "Robert Kern" wrote:
> On Wed, Dec 13, 2017 at 5:00 AM, Kirill Balunov
> wrote:
> >
> > On minor thing that instead of 'ret' there should be 'x'.
>
> No, `x` is the input. The code that actually does the computation (elided
On Wed, Dec 13, 2017 at 5:00 AM, Kirill Balunov
wrote:
>
> On minor thing that instead of 'ret' there should be 'x'.
No, `x` is the input. The code that actually does the computation (elided
here by the `# The magic happens here` comment) would have assigned to
`ret`.
--
Robert Kern
On minor thing that instead of 'ret' there should be 'x'.
With kind regards, -gdg
On Dec 12, 2017 22:51, "Joe" wrote:
Hi,
the best example I found was this one:
https://stackoverflow.com/a/29319864/7919597
def func_for_scalars_or_vectors(x):
x = np.asarray(x)
scalar_input = False
On Wed, Dec 13, 2017 at 4:50 AM, Joe wrote:
>
> Hi,
>
> the best example I found was this one:
>
> https://stackoverflow.com/a/29319864/7919597
>
> def func_for_scalars_or_vectors(x):
> x = np.asarray(x)
> scalar_input = False
> if x.ndim == 0:
> x = x[None] # Makes x 1D
>
Hi,
the best example I found was this one:
https://stackoverflow.com/a/29319864/7919597
def func_for_scalars_or_vectors(x):
x = np.asarray(x)
scalar_input = False
if x.ndim == 0:
x = x[None] # Makes x 1D
scalar_input = True
# The magic happens here
if scal
On Tue, 2017-12-12 at 14:19 +0100, Joe wrote:
> Ah, ok, now that I knew what to look for I guess I found it:
>
> "If the number of objects in the selection tuple is less than N ,
> then :
> is assumed for any subsequent dimensions."
>
> https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.in
Ah, ok, now that I knew what to look for I guess I found it:
"If the number of objects in the selection tuple is less than N , then :
is assumed for any subsequent dimensions."
https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html
This is the one, right?
Am 12.12.2017 09:09
On Tue, Dec 12, 2017 at 12:02 AM, Joe wrote:
> Hi,
>
> question says it all. I looked through the basic and advanced indexing,
> but I could not find the rule that is applied to make
> x[np.newaxis,:] and x[np.newaxis] the same.
I think it's the general rule that all indexing expressions have an
Hi,
question says it all. I looked through the basic and advanced indexing,
but I could not find the rule that is applied to make
x[np.newaxis,:] and x[np.newaxis] the same.
Kind regards,
Joe
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