login bonus betclic and expekt_login bonus m88 sports_login bonus sportingbet casino welcome bonus

Wed, 08 Mar 2017 23:29:19 -0800

Hi, Juan.

Meshgrid can actually give what you want, but you must use the options: 
copy=False  and indexing=ij.

In [7]: %timeit np.meshgrid(np.arange(512), np.arange(512))
1000 loops, best of 3: 1.24 ms per loop

In [8]: %timeit np.meshgrid(np.arange(512), np.arange(512), copy=False)
10000 loops, best of 3: 27 s per loop

In [9]: %timeit np.meshgrid(np.arange(512), np.arange(512), copy=False, 
indexing='ij')
10000 loops, best of 3: 23 s per loop

Best regards
Per A. Brodtkorb

From: NumPy-Discussion [mailto:numpy-discussion-boun...@scipy.org] On Behalf Of 
Juan Nunez-Iglesias
Sent: 9. mars 2017 04:20
To: Discussion of Numerical Python
Subject: Re: [Numpy-discussion] Why do mgrid and meshgrid not return broadcast 
arrays?

Hi Warren,

ogrid doesnt solve my problem. Note that my code returns arrays that would 
evaluate as equal to the mgrid output. Its 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<free online bettingmailto:warren.weckes...@gmail.com>>, wrote:



On Wed, Mar 8, 2017 at 9:48 PM, Juan Nunez-Iglesias 
<jni.s...@gmail.com<free online bettingmailto: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 s 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 isnt what meshgrid/mgrid 
do already? Or would a PR be welcome to do this?

Thanks,

Juan.

_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org<mailto:NumPy-Discussion@scipy.org>
https://mail.scipy.org/mailman/listinfo/numpy-discussion

_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org<mailto:NumPy-Discussion@scipy.org>
https://mail.scipy.org/mailman/listinfo/numpy-discussion
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Reply via email to