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Wed, 08 Mar 2017 19:21:07 -0800

Hi Warren,

ogrid doesnĄ¯t solve my problem. Note that my code returns arrays that would 
evaluate as equal to the mgrid output. ItĄ¯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.weckes...@gmail.com>, 
wrote:
>
>
> > On Wed, Mar 8, 2017 at 9:48 PM, Juan Nunez-Iglesias <jni.s...@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 ĻĖs 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 isnĄ¯t what 
> > > meshgrid/mgrid do already? Or would a PR be welcome to do this?
> > >
> > > Thanks,
> > >
> > > Juan.
> > >
> > > _______________________________________________
> > > NumPy-Discussion mailing list
> > > NumPy-Discussion@scipy.org
> > > https://mail.scipy.org/mailman/listinfo/numpy-discussion
> > >
>
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