I'm using the numpy reduceat method for binning data. Background: I'm processing measurement data sampled at high frequencies and I need to down-sample them by extracting bin means from bins of a certain size. Since I have millions of samples, I need something fast. In principle, this works like a charm:
import numpy as np
def bin_by_npreduceat(v, nbins):
bins = np.linspace(0, len(v), nbins+1, True).astype(np.int)
return np.add.reduceat(v, bins[:-1]) / np.diff(bins)
The Problem is: NaNs can occur (rarely but it happens). Consequence: the whole bin will be NaN since I use np.add:
v = np.array([1,np.nan,3,4,5,4,3,5,6,7,3,2,5,6,9])
bin_by_npreduceat(v, 3)
Out[110]: array([nan, 5., 5.])
Anybody know how I can fix this? np.nansum unfortunately has no reduceat...
We can use a masking based method -
# Mask of NaNs
mask = np.isnan(v)
# Replace NaNs with zeros
vn = np.where(mask,0,v)
# Use add.reduceat on NaNs skipped array to get summations
# Use add.reduceat on the mask to get valid counts
# Divide them to get final output
out = np.add.reduceat(vn, bins[:-1])/np.add.reduceat(~mask, bins[:-1])