I have a 2D lattice with U × U number of grids. My goal is to create an array – of shape U^2 × U^2 – where each entry denotes the least-distance between each grid and any other grid.
Currently, for U=60, my Python script takes ~5 minutes to complete. As a novice programmer, I suspect there must be a more efficient approach – I've read about performance issues related to numpy loops. My objective is to handle U=1000.
import numpy as np np.random.seed(13) import matplotlib.pyplot as plt import time start_time = time.time() U = 60 def FromGridToList(i_, j_): return i_*U + j_ def FromListToGrid(l_): i = np.floor(l_/U) j = l_ - i*U return np.array((i,j)) Ulist = range(U**2) dist_array =  for l in Ulist: print l, 'of', U**2 di = np.array([np.abs(FromListToGrid(l)-FromListToGrid(i)) for i, x in enumerate(Ulist)]) di = np.minimum(di, U-di) dj = np.array([np.abs(FromListToGrid(l)-FromListToGrid(i)) for i, x in enumerate(Ulist)]) dj = np.minimum(dj, U-dj) d = np.sqrt(di**2+dj**2) dist_array.append(d) dist_array = np.vstack(dist_array) print time.time() - start_time, 'seconds'