code: def neighborhood_probability(self, threshold, radius):
          
          weights = disk(radius, dtype=np.uint8)
          thresh_data = np.zeros(self.data.shape[1:], dtype=np.uint8)
          neighbor_prob = np.zeros(self.data.shape, dtype=np.float32)
          for t in np.arange(self.data.shape[0]):
              thresh_data[self.data[t] >= threshold] = 1
              maximized = fftconvolve(thresh_data, weights, mode="same")
              maximized[maximized > 1] = 1
              maximized[maximized < 1] = 0
              neighbor_prob[t] = fftconvolve(maximized, weights, mode="same")
              thresh_data[:] = 0
          neighbor_prob[neighbor_prob < 1] = 0
          neighbor_prob /= weights.sum()
          return neighbor_prob
docs: Calculate a probability based on the number of grid points in an area that exceed a threshold.
  
          Args:
              threshold:
              radius:
  
          Returns: