I'm totally in favor of the 'gridspace(linspaces)' version, as you probably end up wanting to create grids of other things than linspaces (e.g. a logspace grid, or a grid of random points etc.). It should be called somewhat different though. Maybe 'cartesian(arrays)'? Best, Johannes Quoting Stefan Otte (2015-05-10 16:05:02)

I just drafted different versions of the `gridspace` function: https://tmp23.tmpnb.org/user/1waoqQ8PJBJ7/notebooks/2015-05%20gridspace.ipyn...

Beste Grüße, Stefan

On Sun, May 10, 2015 at 1:40 PM, Stefan Otte wrote:

...

Hey,

quite often I want to evaluate a function on a grid in a n-D space. What I end up doing (and what I really dislike) looks something like this:

x = np.linspace(0, 5, 20) M1, M2 = np.meshgrid(x, x) X = np.column_stack([M1.flatten(), M2.flatten()]) X.shape # (400, 2)

fancy_function(X)

I don't think I ever used `meshgrid` in any other way. Is there a better way to create such a grid space?

I wrote myself a little helper function:

def gridspace(linspaces): return np.column_stack([space.flatten() for space in np.meshgrid(*linspaces)])

But maybe something like this should be part of numpy?

Best, Stefan

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On 2015-05-10 14:46:12, Jaime Fernández del Río wrote:

Isn't what you are trying to build a cartesian product function? There is a neat, efficient implementation of such a function in StackOverflow, by our own pv.:

http://stackoverflow.com/questions/1208118/using-numpy-to-build-an-array-of-...

And a slightly faster version just down that page ;) Stéfan

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. I'd really appreciate if this was be part of numpy. Should I create a pull request? Regarding combinations and permutations: I could be convenient to have as well. Cheers, Stefan

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-%... Overall, Jaime's version is the fastest. On Tue, May 12, 2015 at 2:01 PM Jaime Fernández del Río < jaime.frio@gmail.com> wrote:

On Tue, May 12, 2015 at 1:17 AM, Stefan Otte 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

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