Suppose I have a series of 2d coordinates (x,y) each corresponding to a weight, and after I sort them into bins (i.e. a little square area) I want to find the weight in each bin, which should be the added weights of points that fall into the bin. I used
np.digitize to find which bins my data fall into, then I added weights in each bin using a loop. My code is like this:
import numpy as np x=np.random.uniform(low=0.0, high=10.0, size=(5000,)) #x variable y=np.random.uniform(low=0.0, high=10.0, size=(5000,)) #y variable w=np.random.uniform(low=0.0,high=10.0,size=(5000,)) #weight at each (x,y) binx=np.arange(0,10,1) biny=np.arange(0,10,1) indx=np.digitize(x,binx) indy=np.digitize(y,biny) #initialise empty list weight=[*len(binx) for _ in range(len(biny))] for n in range(0,len(x)): for i in range(0,len(binx)): for j in range(0,len(biny)): if indx[n]==binx[i] and indy[n]==biny[j]: weight[i][j]=+w[n]
But the first line of the output weight is empty, which doesn’t make sense. Why does this happen? Is there a more efficient way to do what I want?
Edit: I have a good answer below (one I accepted), but I wonder how it works if I have bins as floats?
You can do this with simple indexing. First get the bin number in each direction. You don’t need
np.digitize for evenly spaced bins:
xbin = np.floor_divide(x, 1, dtype=int, casting='unsafe') ybin = np.floor_divide(y, 1, dtype=int, casting='unsafe')
This is equivalent (but faster than)
xbin = (x // 1).astype(int). Now make an output grid:
grid = np.zeros_like(w, shape=(xbin.max() + 1, ybin.max() + 1))
Now the trick to getting the addition done correctly with repeated bins is to do it in unbuffered mode. Ufuncs like
np.add have a method
at just for this purpose:
np.add.at(grid, (xbin, ybin), w)