Downsample a 1D numpy array

numpy, python, resampling, scipy, signal-processing

Solution

In the simple case where your array's size is divisible by the downsampling factor (`R`), you can `reshape` your array, and take the mean along the new axis:

import numpy as np
a = np.array([1.,2,6,2,1,7])
R = 3
a.reshape(-1, R)
=> array([[ 1.,  2.,  6.],
         [ 2.,  1.,  7.]])

a.reshape(-1, R).mean(axis=1)
=> array([ 3.        ,  3.33333333])

In the general case, you can pad your array with `NaN`s to a size divisible by `R`, and take the mean using `scipy.nanmean`.

import math, scipy
b = np.append(a, [ 4 ])
b.shape
=> (7,)
pad_size = math.ceil(float(b.size)/R)*R - b.size
b_padded = np.append(b, np.zeros(pad_size)*np.NaN)
b_padded.shape
=> (9,)
scipy.nanmean(b_padded.reshape(-1,R), axis=1)
=> array([ 3.        ,  3.33333333,  4.])

Problem

I have a 1-d numpy array which I would like to downsample. Any of the following methods are acceptable if the downsampling raster doesn't perfectly fit the data: - overlap downsample intervals - convert whatever number of values remains at the end to a separate downsampled value - interpolate to fit raster basically if I have ``` 1 2 6 2 1 ``` and I am downsampling by a factor of 3, all of the following are ok: ``` 3 3 3 1.5 ``` or whatever an interpolation would give me here. I'm just looking for the fastest/easiest way to do this. I found `scipy.signal.decimate`, but that sounds like it decimates the values (takes them out as needed and only leaves one in X). `scipy.signal.resample` seems to have the right name, but I do not understand where they are going with the whole fourier thing in the description. My signal is not particularly periodic. Could you give me a hand here? This seems like a really simple task to do, but all these functions are quite intricate...

Original source