如何通过切片范围有效地索引到一维numpy数组

我有一个很大的一维数据数组.我在发生重要事件的数据中有一个索引的starts数组.我想得到一个范围数组，以便得到长度为L的窗口，每个窗口都为starts的一个起点.伪造的样本数据:

I have a big 1D array of data. I have a starts array of indexes into that data where important things happened. I want to get an array of ranges so that I get windows of length L, one for each starting point in starts. Bogus sample data:

data = np.linspace(0,10,50)
starts = np.array([0,10,21])
length = 5

我想本能地做类似的事情

I want to instinctively do something like

data[starts:starts+length]

但是实际上，我需要将starts转换为范围为"windows"的2D数组.来自功能语言，我认为它是从列表到列表列表的map，例如:

But really, I need to turn starts into 2D array of range "windows." Coming from functional languages, I would think of it as a map from a list to a list of lists, like:

np.apply_along_axis(lambda i: np.arange(i,i+length), 0, starts)

但这不会起作用，因为apply_along_axis仅允许标量返回值.

But that won't work because apply_along_axis only allows scalar return values.

您可以执行以下操作:

pairs = np.vstack([starts, starts + length]).T
ranges = np.apply_along_axis(lambda p: np.arange(*p), 1, pairs)
data[ranges]

或者您可以通过列表理解来做到这一点:

Or you can do it with a list comprehension:

data[np.array([np.arange(i,i+length) for i in starts])]

或者您可以迭代地进行. (嘘)

Or you can do it iteratively. (Bleh.)

是否有一种简洁，惯用的方法在某些特定的起点切成这样的数组? (原谅麻木的新手.)

Is there a concise, idiomatic way to slice into an array at certain start points like this? (Pardon the numpy newbie-ness.)