[Numpy-discussion] Why do mgrid and meshgrid not return broadcast arrays?

[Juan Nunez-Iglesias]

Hi Warren,

ogrid doesn’t solve my problem. Note that my code returns arrays that would evaluate as equal to the mgrid output. It’s just that they are copied in mgrid into a giant array, instead of broadcast:


In [176]: a0, b0 = np.mgrid[:5, :5]

In [177]: a1, b1 = th.broadcast_mgrid((np.arange(5), np.arange(5)))

In [178]: a0
Out[178]:
array([[0, 0, 0, 0, 0],
       [1, 1, 1, 1, 1],
       [2, 2, 2, 2, 2],
       [3, 3, 3, 3, 3],
       [4, 4, 4, 4, 4]])

In [179]: a1
Out[179]:
array([[0, 0, 0, 0, 0],
       [1, 1, 1, 1, 1],
       [2, 2, 2, 2, 2],
       [3, 3, 3, 3, 3],
       [4, 4, 4, 4, 4]])

In [180]: a0.strides
Out[180]: (40, 8)

In [181]: a1.strides
Out[181]: (8, 0)



On 9 Mar 2017, 2:05 PM +1100, Warren Weckesser <warren.weckesser at gmail.com>, wrote:
>
>
> > On Wed, Mar 8, 2017 at 9:48 PM, Juan Nunez-Iglesias <jni.soma at gmail.com> wrote:
> > > I was a bit surprised to discover that both meshgrid nor mgrid return fully instantiated arrays, when simple broadcasting (ie with stride=0 for other axes) is functionally identical and happens much, much faster.
> > >
> >
> >
> > Take a look at ogrid: https://docs.scipy.org/doc/numpy/reference/generated/numpy.ogrid.html
> >
> > Warren
> >
> >
> > > I wrote my own function to do this:
> > >
> > >
> > > def broadcast_mgrid(arrays):
> > >     shape = tuple(map(len, arrays))
> > >     ndim = len(shape)
> > >     result = []
> > >     for i, arr in enumerate(arrays, start=1):
> > >         reshaped = np.broadcast_to(arr[[...] + [np.newaxis] * (ndim - i)],
> > >                                    shape)
> > >         result.append(reshaped)
> > >     return result
> > >
> > >
> > > For even a modest-sized 512 x 512 grid, this version is close to 100x faster:
> > >
> > >
> > > In [154]: %timeit th.broadcast_mgrid((np.arange(512), np.arange(512)))
> > > 10000 loops, best of 3: 25.9 µs per loop
> > >
> > > In [156]: %timeit np.meshgrid(np.arange(512), np.arange(512))
> > > 100 loops, best of 3: 2.02 ms per loop
> > >
> > > In [157]: %timeit np.mgrid[:512, :512]
> > > 100 loops, best of 3: 4.84 ms per loop
> > >
> > >
> > > Is there a conscious design decision as to why this isn’t what meshgrid/mgrid do already? Or would a PR be welcome to do this?
> > >
> > > Thanks,
> > >
> > > Juan.
> > >
> > > _______________________________________________
> > > NumPy-Discussion mailing list
> > > NumPy-Discussion at scipy.org
> > > https://mail.scipy.org/mailman/listinfo/numpy-discussion
> > >
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20170309/ed840138/attachment.html>