numpy中有arange/linspace的多维版本吗? [英] Is there a multi-dimensional version of arange/linspace in numpy?

问题描述

我想要一个2d NumPy数组(x，y)的列表，其中每个x分别位于{-5，-4.5，-4，-3.5，...，3.5、4、4.5、5}中，并且y也一样.

I would like a list of 2d NumPy arrays (x,y) , where each x is in {-5, -4.5, -4, -3.5, ..., 3.5, 4, 4.5, 5} and the same for y.

我可以做到

x = np.arange(-5, 5.1, 0.5)
y = np.arange(-5, 5.1, 0.5)

然后遍历所有可能的对，但是我敢肯定有更好的方法...

and then iterate through all possible pairs, but I'm sure there's a nicer way...

我想要一些看起来像这样的东西

I would like something back that looks like:

[[-5, -5],
 [-5, -4.5],
 [-5, -4],
 ...
 [5, 5]]

但顺序无关紧要.

推荐答案

您可以使用

You can use np.mgrid for this, it's often more convenient than np.meshgrid because it creates the arrays in one step:

import numpy as np
X,Y = np.mgrid[-5:5.1:0.5, -5:5.1:0.5]

要获得类似linspace的功能，请将步骤(即0.5)替换为一个复数，其大小指定了序列中所需的点数.使用此语法，将与上述相同的数组指定为:

For linspace-like functionality, replace the step (i.e. 0.5) with a complex number whose magnitude specifies the number of points you want in the series. Using this syntax, the same arrays as above are specified as:

X, Y = np.mgrid[-5:5:21j, -5:5:21j]

---

然后您可以创建对，如下所示:

---

You can then create your pairs as:

xy = np.vstack((X.flatten(), Y.flatten())).T

正如@ali_m所建议的，这可以全部在一行中完成:

As @ali_m suggested, this can all be done in one line:

xy = np.mgrid[-5:5.1:0.5, -5:5.1:0.5].reshape(2,-1).T

祝你好运！

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