How to efficiently index into a 1D numpy array via slice ranges

Question

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 w

Answer 1

If you need to do this a lot of time, you can use as_strided() to create a sliding windows array of data

data = np.linspace(0,10,50000)
length = 5
starts = np.random.randint(0, len(data)-length, 10000)

from numpy.lib.stride_tricks import as_strided
sliding_window = as_strided(data, (len(data) - length + 1, length), 
                 (data.itemsize, data.itemsize))

Then you can use:

sliding_window[starts]

to get what you want.

It's also faster than creating the index array.