Numpy赋值如'numpy.take' [英] Numpy assignment like 'numpy.take'
问题描述
是否可以根据拍摄功能的工作原理分配一个numpy数组？
Is it possible to assign to a numpy array along the lines of how the take functionality works?
例如。如果我有一个数组 a ，索引列表 inds ，以及所需的轴，我可以使用take作为如下：
E.g. if I have a an array a, a list of indices inds, and a desired axis, I can use take as follows:
import numpy as np
a = np.arange(12).reshape((3, -1))
inds = np.array([1, 2])
print(np.take(a, inds, axis=1))
[[ 1 2]
[ 5 6]
[ 9 10]]
这非常有用所需的指数/轴可能会在运行时发生变化。
This is extremely useful when the indices / axis needed may change at runtime.
但是，numpy不允许你这样做：
However, numpy does not let you do this:
np.take(a, inds, axis=1) = 0
print(a)
通过 numpy.put 看起来有一些有限的（1-D）支持，但我想知道是否有更清洁的方法吗？
It looks like there is some limited (1-D) support for this via numpy.put, but I was wondering if there was a cleaner way to do this?
推荐答案
In [222]: a = np.arange(12).reshape((3, -1))
...: inds = np.array([1, 2])
...:
In [223]: np.take(a, inds, axis=1)
Out[223]:
array([[ 1, 2],
[ 5, 6],
[ 9, 10]])
In [225]: a[:,inds]
Out[225]:
array([[ 1, 2],
[ 5, 6],
[ 9, 10]])
构建索引元组
In [226]: idx=[slice(None)]*a.ndim
In [227]: axis=1
In [228]: idx[axis]=inds
In [229]: a[tuple(idx)]
Out[229]:
array([[ 1, 2],
[ 5, 6],
[ 9, 10]])
In [230]: a[tuple(idx)] = 0
In [231]: a
Out[231]:
array([[ 0, 0, 0, 3],
[ 4, 0, 0, 7],
[ 8, 0, 0, 11]])
或 a [inds，：] ：
In [232]: idx=[slice(None)]*a.ndim
In [233]: idx[0]=inds
In [234]: a[tuple(idx)]
Out[234]:
array([[ 4, 0, 0, 7],
[ 8, 0, 0, 11]])
In [235]: a[tuple(idx)]=1
In [236]: a
Out[236]:
array([[0, 0, 0, 3],
[1, 1, 1, 1],
[1, 1, 1, 1]])
PP的建议：
PP's suggestion:
def put_at(inds, axis=-1, slc=(slice(None),)):
return (axis<0)*(Ellipsis,) + axis*slc + (inds,) + (-1-axis)*slc
用作 a [put_at（ind_list，axis = axis）]
我在 numpy 函数中看到了两种风格。这看起来像用于 extend_dims ，我的用于 apply_along / over_axis 。
I've seen both styles on numpy functions. This looks like one used for extend_dims, mine was used in apply_along/over_axis.
在最近的拍摄问题我/我们发现它是相当于 arr.flat [ind] 的某些raveled指数。我必须要查看它。
In a recent take question I/we figured out that it was equivalent to arr.flat[ind] for some some raveled index. I'll have to look that up.
有一个 np.put 相当于分配到 flat ：
Signature: np.put(a, ind, v, mode='raise')
Docstring:
Replaces specified elements of an array with given values.
The indexing works on the flattened target array. `put` is roughly
equivalent to:
a.flat[ind] = v
其文档还提到地方和 putmask （以及 copyto ）。
Its docs also mention place and putmask (and copyto).
numpy multidimensional indexing和'take'功能
我评论 take （没有轴）相当于：
I commented take (without axis) is equivalent to:
lut.flat[np.ravel_multi_index(arr.T, lut.shape)].T
ravel ：
In [257]: a = np.arange(12).reshape((3, -1))
In [258]: IJ=np.ix_(np.arange(a.shape[0]), inds)
In [259]: np.ravel_multi_index(IJ, a.shape)
Out[259]:
array([[ 1, 2],
[ 5, 6],
[ 9, 10]], dtype=int32)
In [260]: np.take(a,np.ravel_multi_index(IJ, a.shape))
Out[260]:
array([[ 1, 2],
[ 5, 6],
[ 9, 10]])
In [261]: a.flat[np.ravel_multi_index(IJ, a.shape)] = 100
In [262]: a
Out[262]:
array([[ 0, 100, 100, 3],
[ 4, 100, 100, 7],
[ 8, 100, 100, 11]])
并使用 put ：
In [264]: np.put(a, np.ravel_multi_index(IJ, a.shape), np.arange(1,7))
In [265]: a
Out[265]:
array([[ 0, 1, 2, 3],
[ 4, 3, 4, 7],
[ 8, 5, 6, 11]])
使用 ravel 在这种情况下是不必要的，但在其他情况下可能有用。
Use of ravel is unecessary in this case but might useful in others.
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