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[()] # convert 0d arrays to scalars
Eric
On Tue, 12 Dec 2017 at 11:58 Robert Kern <robert.k...@gmail.com> wrote:
On Wed, Dec 13, 2017 at 4:50 AM, Joe <solar...@posteo.org> 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
> > scalar_input = True
> >
> > # The magic happens here
> >
> > if scalar_input:
> > return np.squeeze(ret)
> > return ret
> >
> > Is this as good as it gets or do you have other suggestions?
>
> In general structure, yeah. I'd probably use `np.isscalar(x)` for the test
> and `x = np.atleast_1d(x)` for the coercion for readability, but otherwise,
> that's it.
>
> --
> Robert Kern
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