Resampling an array using predefined indices in Python

Question

I have an array of numbers each corresponding to an event and the times when the events occurred. For example,

ev=[0, 14, 23, 53, 3]

time=[0, 0.4, 0.75, 0.9, 1.1]

Imagine ev vs. time to be a (right-continuous) step function which changes values at the values in the time array. Now by resampling, I mean defining a new array of time values and looking up the values of ev function at these times. I want to resample the variable ev under an evenly spaced time array. For example, if t1 is an evenly spaced array, ev1 is the corresponding event list I need.

t1=[0, 0.2, 0.4, 0.6, 0.8, 1, 1.2]

ev1=[0, 0, 14, 14, 23, 53, 3]

Is it possible to do such a resampling of an event array in Python? Is there a direct command? Thanks.

Answer 1

You can use np.searchsorted with side='right to find the index of the last item in time that is smaller than your new timings, and then use it to fetch values from the ev array:

>>> np.take(ev, np.searchsorted(time, t1, side='right')-1)
array([ 0,  0, 14, 14, 23, 53,  3])

If you first convert ev to a numpy array, fancy indecing may be more readable:

>>> ev = np.array(ev)
>>> idx = np.searchsorted(time, t1, side='right')-1
>>> ev[idx]
array([ 0,  0, 14, 14, 23, 53,  3])

Answer 2

I'm sure there's a slick sorting way to do this in pure numpy, but here's a pandas way anyhow.

>>> ev = [0, 14, 23, 53, 3]
>>> time = [0, 0.4, 0.75, 0.9, 1.1]
>>> ser = pd.Series(ev, index=time)
>>> ser
0.00     0
0.40    14
0.75    23
0.90    53
1.10     3
dtype: int64
>>> ser.reindex(np.arange(0, 1.4, 0.2), method='ffill')
0.0     0
0.2     0
0.4    14
0.6    14
0.8    23
1.0    53
1.2     3
dtype: int64