As the title says, I'm trying to change the values of a circle in a 2D array if they fulfill a certain condition. So far I've got this from a different question on here:
import numpy as np
x = np.arange(0, 20)
y = np.arange(0, 20)
arr = np.zeros((y.size, x.size))
cx = 10.
cy = 10.
r = 4.
mask = (x[np.newaxis, :] - cx) ** 2 + (y[:, np.newaxis] - cy) ** 2 < r ** 2
arr[mask] = 1
cx and cy are the coordinates of the circle's center, r is its radius. mask contains the circle, and all of the elements in it are set to 1, which works fine. What I want to do is only change their value only if they are less than 5 or whatever some similar condition. Obviously this array only has 0s, but my real data would have other values set beforehand).
Masks are booleans, and you can combine them with other masks using bitwise operations or logical operations:
circle_mask = (x[np.newaxis, :] - cx) ** 2 + (y[:, np.newaxis] - cy) ** 2 < r ** 2
less_than_5_mask = arr < 5
arr[circle_mask & less_than_5_mask] = 1
In this case, & is the bitwise, elementwise boolean AND operator. If you wanted to replace locations that were in the circle and/or less than 5, you could use the | operator instead.
& is a faster eqivalent to np.bitwise_and in this case. Since your arrays are boolean, you can use np.logical_and to the same effect. Note that that is not the same as using the and operator, since the operator will attempt to distill the entire array into a single boolean value, causing an error.