蒙版时出现Numpy数组尺寸损失 [英] Numpy array loss of dimension when masking

问题描述

我想选择数组的某些元素，然后根据这些值执行加权平均计算.但是，使用过滤条件会破坏数组的原始结构.形状为(2, 2, 3, 2)的arr变成一维数组.这对我来说毫无用处，因为并非所有这些元素以后都需要组合在一起(而是它们的子数组).如何避免这种变平?

I want to select certain elements of an array and perform a weighted average calculation based on the values. However, using a filter condition, destroys the original structure of the array. arr which was of shape (2, 2, 3, 2) is turned into a 1-dimensional array. This is of no use to me, as not all these elements need to be combined later on with each other (but subarrays of them). How can I avoid this flattening?

>>> arr = np.asarray([ [[[1, 11], [2, 22], [3, 33]], [[4, 44], [5, 55], [6, 66]]], [ [[7, 77], [8, 88], [9, 99]], [[0, 32], [1, 33], [2, 34] ]] ])
>>> arr
array([[[[ 1, 11],
         [ 2, 22],
         [ 3, 33]],

        [[ 4, 44],
         [ 5, 55],
         [ 6, 66]]],


       [[[ 7, 77],
         [ 8, 88],
         [ 9, 99]],

        [[ 0, 32],
         [ 1, 33],
         [ 2, 34]]]])
>>> arr.shape
(2, 2, 3, 2)
>>> arr[arr>3]
array([11, 22, 33,  4, 44,  5, 55,  6, 66,  7, 77,  8, 88,  9, 99, 32, 33,
       34])
>>> arr[arr>3].shape
(18,)

推荐答案

结帐numpy.where

http://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html

要保持相同的尺寸，您将需要一个填充值.在下面的示例中，我使用0，但您也可以使用np.nan

To keep the same dimensionality you are going to need a fill value. In the example below I use 0, but you could also use np.nan

np.where(arr>3, arr, 0)

返回

array([[[[ 0, 11],
         [ 0, 22],
         [ 0, 33]],

        [[ 4, 44],
         [ 5, 55],
         [ 6, 66]]],


       [[[ 7, 77],
         [ 8, 88],
         [ 9, 99]],

        [[ 0, 32],
         [ 0, 33],
         [ 0, 34]]]])

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