如何正确掩盖一个numpy的二维数组? [英] How to properly mask a numpy 2D array?

问题描述

说我有一个二维坐标数组，看起来像

Say I have a two dimensional array of coordinates that looks something like

x = array([[1,2],[2,3],[3,4]])

到目前为止，在以前的工作中，我生成了一个面具，最终看起来像

Previously in my work so far, I generated a mask that ends up looking something like

mask = [False,False,True]

当我尝试在2D坐标矢量上使用此蒙版时，出现错误

When I try to use this mask on the 2D coordinate vector, I get an error

newX = np.ma.compressed(np.ma.masked_array(x,mask))

>>>numpy.ma.core.MaskError: Mask and data not compatible: data size 
   is 6, mask size is 3.`

我想这很有道理.因此，我尝试仅使用以下蒙版:

which makes sense, I suppose. So I tried to simply use the following mask instead:

mask2 = np.column_stack((mask,mask))
newX = np.ma.compressed(np.ma.masked_array(x,mask2))

我得到的是接近的东西:

And what I get is close:

>>>array([1,2,2,3])

达到我的期望(和想要):

to what I would expect (and want):

>>>array([[1,2],[2,3]])

必须有一种更简单的方法吗?

There must be an easier way to do this?

推荐答案

这是您要寻找的吗?

import numpy as np
x[~np.array(mask)]
# array([[1, 2],
#        [2, 3]])

或者从 numpy屏蔽数组:

newX = np.ma.array(x, mask = np.column_stack((mask, mask)))
newX

# masked_array(data =
#  [[1 2]
#  [2 3]
#  [-- --]],
#              mask =
#  [[False False]
#  [False False]
#  [ True  True]],
#        fill_value = 999999)

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