with open('day6-input', 'r') as file:
  data = [l.strip('\n') for l in file]
import numpy as np
coords = [[int(i.rstrip(',')) for i in c.split()] for c in data]

def distance(c1, c2):
  dx = abs(c1[0]-c2[0])
  dy = abs(c1[1]-c2[1])
  return dx+dy

distances = {}
for coord in coords:
  distances[tuple(coord)] = [distance(coord, c2) for c2 in coords if c2 is not coord]

cell_coord = max(distances.items(), key=lambda x: min(x[1]))
field = np.zeros((500,500))

blacklist = set()
for c1 in [(x,y) for x in range(500) for y in range(500)]:
  distances = [distance(c1, c2) for c2 in coords]
  min_dist = min(distances)
  if distances.count(min_dist) == 1:
    cell = min(enumerate(distances), key=lambda x: x[1])[0]
    field[c1] = cell
    x, y = c1
    if x == 0 or y == 0 or x == 499 or y == 499:
      blacklist.add(cell)
a = [(field == i).sum() for i in range(len(coords)) if i not in blacklist]
print(max(a))  # Part 1

#sumarea = 0
expensive_size = 400  # Needs to fit all coords
edge_extend_size = 10000  # Increase if necessary
region = np.zeros((edge_extend_size,edge_extend_size))
for c1 in [(x,y) for x in range(-expensive_size, expensive_size) for y in range(-expensive_size, expensive_size)]:
  total_dist = sum([distance(c1, c2) for c2 in coords])
  #if total_dist < 10000:
    #sumarea += 1
  region[c1] = total_dist
#print(sumarea)

total_inc = len(coords)

r = region.copy()
for i in range(expensive_size, edge_extend_size//2+1):
  r[i,:] = r[i-1,:]+total_inc
for i in range(-expensive_size, -edge_extend_size//2, -1):
  r[i,:] = r[i+1,:]+total_inc
for i in range(expensive_size, edge_extend_size//2+1):
  r[:,i] = r[:,i-1]+total_inc
for i in range(-expensive_size, -edge_extend_size//2, -1):
  r[:,i] = r[:,i+1]+total_inc

print((r < 10000).sum())  # Part 2