I have data that can be represented as a 2d matrix, eg like an actual map. I would like to model random variables for x and y coordinates, whose likelihood increases the closer they are to some quantity represented on the map. I am using pm.Potential to create this likelihood, but how to I use tensors for x and y as indices to the map data. I get errors saying the slicing the array can only be done by integers, and integer tensors do not suffice.
eg. some pseudocode
import pymc as pm import numpy as np import aesara.tensor as at map = np.zeros((100, 100)) map[40:60, 40:60]=1 def mapPot(xpos, ypos, map): #here we want to slice the map and get the sum of the slice to represent the likelihood. xmin = xpos.astype(np.int32) ymin = ypos.astype(np.int32) xmax = xmin +5 ymax = ymin +5 count = np.sum(map[xmin:xmax, ymin:ymax]) return -np.log(count) with pm.Model() as model: x=pm.Uniform('xpos', lower=0, upper=99) y=pm.Uniform('ypos', lower=0, upper=99) pm.Potential('mappot', mapPot(x, y, map))