[英]numpy array averaging with mask
I'm looking to do some basic clustering on a boolean numpy array and I'm basically just trying to do 2d averaging with a mask, but I feel like there must be a better solution than what I've got, since it's slow and inelegant: 我正在寻找在布尔numpy数组上进行一些基本聚类的方法,而我基本上只是在尝试使用蒙版进行2d平均,但是我觉得必须有比我现有方法更好的解决方案,因为它速度慢且不雅:
def grab_window(location, array, window=(3,3)):
minimums = [min(0, i-a) for i, a in zip(location, window)]
maximums = [(i + a) for i, a in zip(location, window)]
answer = array
for i, _ in enumerate(location):
answer = answer[slice(minimums[i],maximums[i])]
return answer
And then I basically just iterate through the original array, multiplying each window by a kernel, and returning the mean of the modified window. 然后,我基本上只是遍历原始数组,将每个窗口乘以一个内核,然后返回修改后的窗口的均值。
It seems like there must be a filter or something similar that would have the same effect, but I haven't been able to find one thus far. 似乎必须有一个具有相同效果的过滤器或类似的东西,但是到目前为止我还没有找到一个。
edit: location is a tuple
of a form similar to window. 编辑:位置是类似于窗口形式的tuple
。
For instance, if we were to do the simplest version of this, with a uniform 1-ply mask I would be looking for something along these lines: 例如,如果我们要做的是最简单的版本,使用统一的1层蒙版,我会按照以下思路寻找东西:
import numpy as np
test = np.arange(0,24).reshape(6, 4)
footprint = [
[1,1,1],
[1,0,1],
[1,1,1]
]
some_function(test, footprint)
array([[ 1, 2, 3, 4],
[ 4, 5, 6, 6],
[ 8, 9, 10, 10],
[12, 13, 14, 14],
[16, 17, 18, 18],
[18, 19, 20, 21]])
Turns out scipy
totally has a function that already does this. 原来scipy
完全有一个已经做到这一点的功能。 generic_filter
actually does exactly this in a much more stable way as mentioned in How to apply ndimage.generic_filter() 正如如何应用ndimage.generic_filter()中所述, generic_filter
实际上以一种更加稳定的方式精确地做到了这一点。
Example: 例:
def some_avg(values):
return values.mean()
footprint = np.array([
[1,1,1],
[1,0,1],
[1,1,1]
])
test = test = np.arange(0,24).reshape(6, 4)
scipy.ndimage.filters.generic_filter(test, some_avg, footprint=footprint)
array([[ 1, 2, 3, 4],
[ 4, 5, 6, 6],
[ 8, 9, 10, 10],
[12, 13, 14, 14],
[16, 17, 18, 18],
[18, 19, 20, 21]])
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