如何在numpy中更改掩蔽数组的值?

In my code, at some point I try to modify a value of a masked array, yet python seems to ignore this. I'm thinking this has to do with the way memory is stored in arrays, as if I were modifying a copy of the value and not the value itself, but I'm not well versed enough in this to have any clue how to resolve it.

在我的代码中，在某些时候，我试图修改一个蒙面数组的值，但是python似乎忽略了这一点。我认为这与内存存储在数组中的方式有关，就好像我修改了一个值的副本，而不是值本身，但是我还没有足够的知识来理解它。

Here is a simplified version of what I'm trying to do :

下面是我想做的一个简化版本:

    x = np.zeros((2,5)) # create 2D array of zeroes
    x[0][1:3] = 5       # replace some values along 1st dimension with 5

    mask = (x[0] > 0)   # create a mask to only deal with the non negative values

    x[0][mask][1] = 10  # change one of the values that is non negative 

    print x[0][mask][1] # value isn't changed in the original array

the output of this is :

它的输出是:

5.0

when it should be 10.

应该是10。

Any help would be greatly appreciated, ideally this need to be scalable (meaning I don't necessarily know the shape of x, or where the values are non-negative, or which one I will need to modify).

任何帮助都将得到极大的赞赏，理想情况下，这需要是可伸缩的(这意味着我不一定知道x的形状，或者值是非负的，或者我需要修改的值)。

I'm working with numpy 1.11.0, on python 2.7.12 on Ubuntu 16.04.2

我和numpy 1.11.0一起工作，在python 2.7.12和Ubuntu 16.04.2上

Thanks !

谢谢!

3 个解决方案

#1

Let's generalize your problem a bit:

让我们概括一下你的问题:

In [164]: x=np.zeros((2,5))
In [165]: x[0, [1, 3]] = 5      # index with a list, not a slice
In [166]: x
Out[166]: 
array([[ 0.,  5.,  0.,  5.,  0.],
       [ 0.,  0.,  0.,  0.,  0.]])

When the indexing occurs right before the =, it's part of a __setitem__ and acts on the original array. This is true whether the indexing uses slices, a list or a boolean mask.

当索引发生在=之前时，它是__setitem__的一部分，并对原始数组起作用。无论索引使用的是片、列表还是布尔蒙版，都是如此。

But a selection with the list or mask produces a copy. Further indexed assignment affects only that copy, not the original.

但是带有列表或遮罩的选项会产生一个副本。进一步的索引赋值只影响复制，而不是原始的。

In [167]: x[0, [1, 3]]
Out[167]: array([ 5.,  5.])
In [168]: x[0, [1, 3]][1] = 6
In [169]: x
Out[169]: 
array([[ 0.,  5.,  0.,  5.,  0.],
       [ 0.,  0.,  0.,  0.,  0.]])

The best way around this is to modify the mask itself:

解决这个问题的最好办法是修改面具本身:

In [170]: x[0, np.array([1,3])[1]] = 6
In [171]: x
Out[171]: 
array([[ 0.,  5.,  0.,  6.,  0.],
       [ 0.,  0.,  0.,  0.,  0.]])

If the mask is boolean, you may need to convert it to indexing array

如果掩码是布尔值，您可能需要将其转换为索引数组。

In [174]: mask = x[0]>0
In [175]: mask
Out[175]: array([False,  True, False,  True, False], dtype=bool)
In [176]: idx = np.where(mask)[0]
In [177]: idx
Out[177]: array([1, 3], dtype=int32)
In [178]: x[0, idx[1]]
Out[178]: 6.0

Or you can tweak the boolean values directly

或者可以直接调整布尔值

In [179]: mask[1]=False
In [180]: x[0,mask]
Out[180]: array([ 6.])

So in your big problem you need to be aware of when indexing produces a view and it is a copy. And you need to be comfortable with index with lists, arrays and booleans, and understand how to switch between them.

所以在你的大问题中，你需要意识到什么时候索引产生了一个视图，它是一个副本。您需要熟悉使用列表、数组和布尔值的索引，并了解如何在它们之间进行切换。

#2

It's not really a masked array what you've created:

它并不是你所创建的蒙面数组

x = np.zeros((2,5))
x[0][1:3] = 5
mask = (x[0] > 0)
mask
Out[14]: array([False,  True,  True, False, False], dtype=bool)

So, this is just a boolean array. To create a masked array you should use numpy.ma module:

这只是一个布尔数组。要创建蒙面数组，应该使用numpy。马模块:

masked_x = np.ma.array(x[0], mask=~(x[0] > 0)) # let's mask first row as you did
masked_x
Out[15]: 
masked_array(data = [-- 5.0 5.0 -- --],
             mask = [ True False False  True  True],
       fill_value = 1e+20)

Now you can change your masked array, and accordingly the main array:

现在您可以更改您的屏蔽数组，并相应地更改主数组:

masked_x[1] = 10.    
masked_x
Out[36]: 
masked_array(data = [-- 10.0 5.0 -- --],
             mask = [ True False False  True  True],
       fill_value = 1e+20)    
x
Out[37]: 
array([[  0.,  10.,   5.,   0.,   0.],
       [  0.,   0.,   0.,   0.,   0.]])

And notice that in masked arrays invalid entries marked as True.

注意，在掩蔽数组中，被标记为True的无效条目。

#3

To understand what's going on I suggest reading this http://scipy-cookbook.readthedocs.io/items/ViewsVsCopies.html

为了理解发生了什么，我建议阅读http://scipy-cookbook.readthedocs.io/items/ViewsVsCopies.html

This boils down to the misleading use of fancy indexing. The following statements are the same and as you can see it's directly setting to 10 the elements of x using mask.

这可以归结为对花哨的索引的误导使用。下面的语句是一样的，您可以看到它直接使用蒙版将x的元素设置为10。

x[0][mask] = 10
x[0,mask] = 10
x.__setitem__((0, mask), 10)

What you're doing on the other hand is the following

另一方面，你正在做的是下面的事情

x[0][mask][1] = 10
x[0,mask][1] = 10
x[0,mask].__setitem__(1, 10)
x.__getitem__((0, mask)).__setitem__(1, 10)

Which is creating a copy with __getitem__()

使用__getitem__()创建副本

In conclusion you need to rethink how to modify that single number with a different mask __setitem()__

总之，您需要重新考虑如何使用不同的掩码__setitem()__修改这个数字

#1