Numpy: How to create a grid-like array?

Question

I want to create an array which represent X-Y panel (-50, 50). That is: [[-50, -50], [-49,-50],[-48,-50]....[50,50]], which is at length 101*101.

Clearly, I can generate through a double loop from (-50,50). I'm wondering the prefered way of doing this?

Answer 1

numpy.meshgrid is obviously the clearest way to me (as @benbo has mentioned), you need one more step to ravel or flatten the 2D grid array:

In [131]: import numpy as np
     ...: x=np.linspace(-2, 2, 5)
     ...: y=np.linspace(-2, 2, 5)
     ...: xx,yy=np.meshgrid(x,y)
     ...: coords=np.array((xx.ravel(), yy.ravel())).T

In [132]: coords
Out[132]: 
array([[-2., -2.],
       [-1., -2.],
       [ 0., -2.],
       [ 1., -2.],
       [ 2., -2.],
       [-2., -1.],
       ......
       [ 1.,  2.],
       [ 2.,  2.]])

In [133]:

Or as @John mentioned, shorten your code with np.c_ to skip the transpose:

coords=np.c_[xx.ravel(), yy.ravel()]

To benchmark:

In [156]: %timeit coords=np.array((xx.ravel(), yy.ravel())).T
100000 loops, best of 3: 14.6 µs per loop

In [157]: %timeit coords=np.c_[xx.ravel(), yy.ravel()] #not as efficient as ↑
10000 loops, best of 3: 47.6 µs per loop

Answer 2

How about this:

In [15]: import numpy as np

In [16]: a = np.arange(-3,4)

In [17]: a1 = np.tile(a, (7,1))

In [18]: np.dstack((a1, a1.T)).reshape(-1, 2)

Result:

array([[-3, -3],
       [-2, -3],
       [-1, -3],
       [ 0, -3],
       [ 1, -3],
        ....
       [-1,  3],
       [ 0,  3],
       [ 1,  3],
       [ 2,  3],
       [ 3,  3]])