TheChymera TheChymera - 11 months ago 183
Python Question

Downsample a 1D numpy array

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...

Answer Source

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.])
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