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:
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...
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.])