I have a 3D numpy array
dark = np.array(dark_ref.load())
dark.shape
(100, 384, 249)
I have a list of indices for the second and third dimension
l, r, b = np.where(dark > 9000)
len(r)
1799
len(b)
1799
and I have written this function to replace the values at indices r and b with an average of the adjacent values
def flatten_bad_band(A, x, y): # x is 3d array, y is bad coords
testarr = A.copy()
for a in range(A.shape[0]):
for i in range(A.shape[1]):
for j in range((A.shape[2])):
testarr[a, x[i], y[j]] = (testarr[a, x[i+1], y[j]] + testarr[a, x[i-1], y[j]]) /2
return testarr
test = flatten_bad_band(dark, r, b)
This seems to work, but it is slow...., and I will need to use this on much larger arrays. Is there a better way to do this? I am new to python, numpy and coding!
You can also eliminate the first for loop and let numpy do all the work along the axis. Don't know if this is faster, but since numpy has a C backend, it is more than likely that it is! It is also good practice.
Besides that, you should look into python's for loops! Your code still doesn't work as you attempt to index your list with elements of your list.
Here is an example, eliminating all unnecessary loops and also deals with cases where you attempt to average out of bounds elements. Hope this helps.
import numpy as np
Atest = np.random.randint(0,10, (5,5,5)).astype(float)
l = [1,2]
r = [2,3]
def flatten_bad_band(A, l, r):
resarr = A.copy()
testarr = np.pad(resarr, ((0,0),(1,1),(0,0)), 'constant', constant_values=0)
for idx in zip(l, r):
resarr[:,idx[0], idx[1]] = np.average(np.stack((testarr[:,idx[0], idx[1]], testarr[:,idx[0]+2, idx[1]])), axis=0)
return resarr
print(flatten_bad_band(Atest, l, r))