Downsample a 1D numpy array

Question

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:

Answer 1

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 NaNs 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.])