[Numpy-discussion] Create a n-D grid; meshgrid alternative

[Stefan Otte]

Hey,

I just created a pull request: https://github.com/numpy/numpy/pull/5874

Best,
 Stefan

On Tue, May 12, 2015 at 3:29 PM Stefan Otte <stefan.otte at gmail.com> wrote:

> Hey,
>
> here is an ipython notebook with benchmarks of all implementations (scroll
> to the bottom for plots):
>
> https://github.com/sotte/ipynb_snippets/blob/master/2015-05%20gridspace%20-%20cartesian.ipynb
>
> Overall, Jaime's version is the fastest.
>
>
>
>
>
>
>
> On Tue, May 12, 2015 at 2:01 PM Jaime Fernández del Río <
> jaime.frio at gmail.com> wrote:
>
>> On Tue, May 12, 2015 at 1:17 AM, Stefan Otte <stefan.otte at gmail.com>
>> wrote:
>>
>>> Hello,
>>>
>>> indeed I was looking for the cartesian product.
>>>
>>> I timed the two stackoverflow answers and the winner is not quite as
>>> clear:
>>>
>>> n_elements:    10  cartesian  0.00427 cartesian2  0.00172
>>> n_elements:   100  cartesian  0.02758 cartesian2  0.01044
>>> n_elements:  1000  cartesian  0.97628 cartesian2  1.12145
>>> n_elements:  5000  cartesian 17.14133 cartesian2 31.12241
>>>
>>> (This is for two arrays as parameters: np.linspace(0, 1, n_elements))
>>> cartesian2 seems to be slower for bigger.
>>>
>>
>> On my system, the following variation on Pauli's answer is 2-4x faster
>> than his for your test cases:
>>
>> def cartesian4(arrays, out=None):
>>     arrays = [np.asarray(x).ravel() for x in arrays]
>>     dtype = np.result_type(*arrays)
>>
>>     n = np.prod([arr.size for arr in arrays])
>>     if out is None:
>>         out = np.empty((len(arrays), n), dtype=dtype)
>>     else:
>>         out = out.T
>>
>>     for j, arr in enumerate(arrays):
>>         n /= arr.size
>>         out.shape = (len(arrays), -1, arr.size, n)
>>         out[j] = arr[np.newaxis, :, np.newaxis]
>>     out.shape = (len(arrays), -1)
>>
>>     return out.T
>>
>>
>>> I'd really appreciate if this was be part of numpy. Should I create a
>>> pull request?
>>>
>>
>> There hasn't been any opposition, quite the contrary, so yes, I would go
>> ahead an create that PR. I somehow feel this belongs with the set
>> operations, rather than with the indexing ones. Other thoughts?
>>
>> Also for consideration: should it work on flattened arrays? or should we
>> give it an axis argument, and then "broadcast on the rest", a la
>> generalized ufunc?
>>
>> Jaime
>>
>> --
>> (\__/)
>> ( O.o)
>> ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes
>> de dominación mundial.
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>
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