选择数组中N个均匀间隔的元素，包括第一个和最后一个 [英] Select N evenly spaced out elements in array, including first and last

问题描述

我有一个任意长度的数组，我想选择其中的N个元素，该元素均匀地隔开(大约N可能是偶数，数组长度可能是素数，等等)，其中包括第一个arr[0]元素和最后一个arr[len-1]元素.

I have an array of arbitrary length, and I want to select N elements of it, evenly spaced out (approximately, as N may be even, array length may be prime, etc) that includes the very first arr[0] element and the very last arr[len-1] element.

示例:

>>> arr = np.arange(17)
>>> arr
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16])

然后，我想制作一个类似于以下的函数，以获取数组中均匀间隔的numElems，该数组必须包含第一个和最后一个元素:

Then I want to make a function like the following to grab numElems evenly spaced out within the array, which must include the first and last element:

GetSpacedElements(numElems = 4)
>>> returns 0, 5, 11, 16

这有意义吗?

我已经尝试过arr[0:len:numElems](即使用数组start:stop:skip表示法)和一些细微的变化，但是我在这里找不到想要的东西:

I've tried arr[0:len:numElems] (i.e. using the array start:stop:skip notation) and some slight variations, but I'm not getting what I'm looking for here:

>>> arr[0:len:numElems]
array([ 0,  4,  8, 12, 16])

或

>>> arr[0:len:numElems+1]
array([ 0,  5, 10, 15])

我并不在乎中间的元素是什么，只要它们均匀地间隔开一个1的索引就可以了.但是获取正确数量的元素(包括索引零和最后一个索引)至关重要.

I don't care exactly what the middle elements are, as long as they're spaced evenly apart, off by an index of 1 let's say. But getting the right number of elements, including the index zero and last index, are critical.

希望有人能帮助我快速找到一线客，谢谢！

Hopefully someone can help me find a quick one-liner, thanks!

推荐答案

要获取等间距索引的列表，请使用np.linspace:

To get a list of evenly spaced indices, use np.linspace:

idx = np.round(np.linspace(0, len(arr) - 1, numElems)).astype(int)

接下来，索引回到arr以获取相应的值:

Next, index back into arr to get the corresponding values:

arr[idx]

在强制转换为整数之前始终使用舍入.在内部，当提供dtype参数时，linspace调用astype.因此，此方法 NOT 等效于:

Always use rounding before casting to integers. Internally, linspace calls astype when the dtype argument is provided. Therefore, this method is NOT equivalent to:

# this simply truncates the non-integer part
idx = np.linspace(0, len(array) - 1, numElems).astype(int)
idx = np.linspace(0, len(arr) - 1, numElems, dtype='int')

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