Python 如何正确屏蔽 numpy 二维数组？
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How to properly mask a numpy 2D array?
提问by Anonymous
Say I have a two dimensional array of coordinates that looks something like
假设我有一个二维坐标数组，看起来像
x = array([[1,2],[2,3],[3,4]])
x = array([[1,2],[2,3],[3,4]])
Previously in my work so far, I generated a mask that ends up looking something like
到目前为止，在我的工作中，我生成了一个最终看起来像的面具
mask = [False,False,True]
mask = [False,False,True]
When I try to use this mask on the 2D coordinate vector, I get an error
当我尝试在 2D 坐标向量上使用此蒙版时，出现错误
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.`
which makes sense, I suppose. So I tried to simply use the following mask instead:
我想这是有道理的。所以我尝试简单地使用以下掩码：
mask2 = np.column_stack((mask,mask))
newX = np.ma.compressed(np.ma.masked_array(x,mask2))
And what I get is close:
我得到的很接近：
>>>array([1,2,2,3])
>>>array([1,2,2,3])
to what I would expect (and want):
我所期望的（和想要的）：
>>>array([[1,2],[2,3]])
>>>array([[1,2],[2,3]])
There must be an easier way to do this?
必须有更简单的方法来做到这一点？
采纳答案by Psidom
Is this what you are looking for?
这是你想要的？
import numpy as np
x[~np.array(mask)]
# array([[1, 2],
# [2, 3]])
Or from numpy masked array:
或者从numpy 掩码数组：
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)
回答by hpaulj
Your xis 3x2:
你x是 3x2：
In [379]: x
Out[379]:
array([[1, 2],
[2, 3],
[3, 4]])
Make a 3 element boolean mask:
制作一个 3 元素布尔掩码：
In [380]: rowmask=np.array([False,False,True])
That can be used to select the rows where it is True, or where it is False. In both cases the result is 2d:
这可用于选择为 True 或为 False 的行。在这两种情况下，结果都是 2d：
In [381]: x[rowmask,:]
Out[381]: array([[3, 4]])
In [382]: x[~rowmask,:]
Out[382]:
array([[1, 2],
[2, 3]])
This is without using the MaskedArray subclass. To make such array, we need a mask that matches xin shape. There isn't provision for masking just one dimension.
这是不使用 MaskedArray 子类的。为了制作这样的数组，我们需要一个x形状匹配的掩码。没有规定仅屏蔽一维。
In [393]: xmask=np.stack((rowmask,rowmask),-1) # column stack
In [394]: xmask
Out[394]:
array([[False, False],
[False, False],
[ True, True]], dtype=bool)
In [395]: np.ma.MaskedArray(x,xmask)
Out[395]:
masked_array(data =
[[1 2]
[2 3]
[-- --]],
mask =
[[False False]
[False False]
[ True True]],
fill_value = 999999)
Applying compressedto that produces a raveled array: array([1, 2, 2, 3])
应用compressed到它会产生一个散乱的数组：array([1, 2, 2, 3])
Since masking is element by element, it could mask one element in row 1, 2 in row 2 etc. So in general compressing, removing the masked elements, will not yield a 2d array. The flattened form is the only general choice.
由于屏蔽是逐个元素进行的，它可以屏蔽第 1 行中的一个元素，第 2 行中的第 2 行等。因此，通常compressing，删除被屏蔽的元素不会产生二维数组。扁平形式是唯一的通用选择。
np.mamakes most sense when there's a scattering of masked values. It isn't of much value if you want want to select, or deselect, whole rows or columns.
np.ma当掩码值分散时最有意义。如果您想选择或取消选择整行或整列，它没有多大价值。
===============
================
Here are more typical masked arrays:
以下是更典型的掩码数组：
In [403]: np.ma.masked_inside(x,2,3)
Out[403]:
masked_array(data =
[[1 --]
[-- --]
[-- 4]],
mask =
[[False True]
[ True True]
[ True False]],
fill_value = 999999)
In [404]: np.ma.masked_equal(x,2)
Out[404]:
masked_array(data =
[[1 --]
[-- 3]
[3 4]],
mask =
[[False True]
[ True False]
[False False]],
fill_value = 2)
In [406]: np.ma.masked_outside(x,2,3)
Out[406]:
masked_array(data =
[[-- 2]
[2 3]
[3 --]],
mask =
[[ True False]
[False False]
[False True]],
fill_value = 999999)
回答by CrossEntropy
Since none of these solutions worked for me, I thought to write down what solution did, maybe it will useful for somebody else. I use python 3.x and I worked on two 3D arrays. One, which I call data_3Dcontains float values of recordings in a brain scan, and the other, template_3Dcontains integers which represent regions of the brain. I wanted to choose those values from data_3Dcorresponding to an integer region_codeas per template_3D:
由于这些解决方案都不适合我，我想写下解决方案的作用，也许对其他人有用。我使用 python 3.x 并且我处理了两个 3D 数组。一个，我称之为data_3D包含脑部扫描记录的浮点值，另一个template_3D包含代表大脑区域的整数。我想从选择那些值data_3D对应一个整数region_code按template_3D：
my_mask = np.in1d(template_3D, region_code).reshape(template_3D.shape)
data_3D_masked = data_3D[my_mask]
which gives me a 1D array of only relevant recordings.
这给了我一个只有相关录音的一维数组。
回答by Warren Weckesser
In your last example, the problem is not the mask. It is your use of compressed. From the docstring of compressed:
在您的最后一个示例中，问题不在于掩码。这是你的用途compressed。从文档字符串compressed：
Return all the non-masked data as a 1-D array.
So compressedflattens the nonmasked values into a 1-d array. (It has to, because there is no guarantee that the compressed data will have an n-dimensional structure.)
因此compressed将非屏蔽值展平为一维数组。（必须如此，因为不能保证压缩数据将具有 n 维结构。）
Take a look at the masked array before you compress it:
在压缩之前先看一下掩码数组：
In [8]: np.ma.masked_array(x, mask2)
Out[8]:
masked_array(data =
[[1 2]
[2 3]
[-- --]],
mask =
[[False False]
[False False]
[ True True]],
fill_value = 999999)
回答by Mendi Barel
With np.whereyou can do all sorts of things:
有了np.where你，你可以做各种各样的事情：
x_maskd = np.where(mask, x, 0)
