如何在numpy中对2D数组进行装箱? [英] How to bin a 2D array in numpy?

问题描述

我是numpy的新手，我有一个2D对象数组，需要将它们合并到一个较小的矩阵中，然后获取每个容器中对象的数量计数以制作热图.我在这个线程上按照答案 来创建垃圾箱并执行计数一个简单的数组，但我不确定如何将其扩展到2维.这是我到目前为止的内容:

I'm new to numpy and I have a 2D array of objects that I need to bin into a smaller matrix and then get a count of the number of objects in each bin to make a heatmap. I followed the answer on this thread to create the bins and do the counts for a simple array but I'm not sure how to extend it to 2 dimensions. Here's what I have so far:

data_matrix = numpy.ndarray((500,500),dtype=float)
# fill array with values.

bins = numpy.linspace(0,50,50)
digitized = numpy.digitize(data_matrix, bins)

binned_data = numpy.ndarray((50,50))
for i in range(0,len(bins)):
    for j in range(0,len(bins)):
        k = len(data_matrix[digitized == i:digitized == j]) # <-not does not work
        binned_data[i:j] = k

P.S.数组上的[digitized == i]表示法将返回一个二进制值数组.我在任何地方都找不到有关此符号的文档.链接将不胜感激.

P.S. the [digitized == i] notation on an array will return an array of binary values. I cannot find documentation on this notation anywhere. A link would be appreciated.

推荐答案

您可以将数组重塑为反映所需块结构的四维数组，然后沿每个块内的两个轴求和.示例:

You can reshape the array to a four dimensional array that reflects the desired block structure, and then sum along both axes within each block. Example:

>>> a = np.arange(24).reshape(4, 6)
>>> a
array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23]])
>>> a.reshape(2, 2, 2, 3).sum(3).sum(1)
array([[ 24,  42],
       [ 96, 114]])

如果a具有形状m, n，则重塑应具有以下形式

If a has the shape m, n, the reshape should have the form

a.reshape(m_bins, m // m_bins, n_bins, n // n_bins)

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