DesertedNonstopLoop created by nloadholtes - https://repl.it/@nloadholtes/DesertedNonstopLoop

data.txt

#1 @ 45,64: 22x22
#2 @ 291,191: 12x19
#3 @ 497,49: 24x27
#4 @ 694,889: 28x28
#5 @ 942,231: 15x24
#6 @ 463,561: 19x10
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#8 @ 612,291: 21x17
#9 @ 86,585: 12x29
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#12 @ 204,231: 28x25
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#1273 @ 587,802: 29x22
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#1275 @ 469,828: 14x3
#1276 @ 888,483: 26x10
#1277 @ 50,360: 17x27
#1278 @ 857,790: 21x29
#1279 @ 231,894: 16x12
#1280 @ 23,131: 27x29
#1281 @ 73,300: 10x12
#1282 @ 286,137: 20x25
#1283 @ 673,866: 21x27
#1284 @ 347,704: 27x23
#1285 @ 445,504: 16x25
#1286 @ 919,668: 28x20
#1287 @ 74,729: 29x11
#1288 @ 957,313: 21x13
#1289 @ 566,873: 18x16
#1290 @ 407,547: 28x17
#1291 @ 581,354: 23x22
#1292 @ 119,762: 20x28
#1293 @ 174,133: 29x26
#1294 @ 265,262: 14x19
#1295 @ 548,129: 28x11
#1296 @ 57,32: 10x29
#1297 @ 780,123: 14x28
#1298 @ 195,339: 17x20
#1299 @ 892,193: 28x11
#1300 @ 634,811: 17x27
#1301 @ 130,498: 26x10

main.py

test_data="""#1 @ 1,3: 4x4
#3 @ 5,5: 2x2
#2 @ 3,1: 4x4
#4 @ 1,4: 4x4
"""

def parser(data):
  output = []
  for line in data.split("\n"):
    if not line:
      continue
    # print(line)
    seg = line.split(" ")
    # print(seg)
    x = int(seg[2].split(",")[0])
    x1 = int(seg[3].split("x")[0])
    y = int(seg[2].split(",")[1][:-1])
    y1 = int(seg[3].split("x")[1])
    obj = {"id": seg[0], "x": [x, x+x1],
      "y": [y, y+y1]}
    obj["key"] = (x, y)
    # print(f"obj: {obj}")
    output.append(obj)
  return output

def overlap(start0, end0, start1, end1):
  over_start = max(start0, start1)
  over_end = min(end0, end1)
  overlap = max(0, over_end - over_start)
  return overlap, (over_start, over_end)

#If the x0 of an obj is less than the previous x1, check the y
# If the y0 is less than the previous y1
def process(data):
  total_overlap = 0
  overlap_areas = set()
  for prev in range(0, len(data)):
    for curr in range(prev+1, len(data)):
      if data[curr]["x"][0] > data[prev]["x"][1]:
        print("Too far in X axis")
        break
      x_overlap, x_start_end = overlap(data[prev]["x"][0], 
        data[prev]["x"][1], data[curr]["x"][0], data[curr]["x"][1])
      if x_overlap > 0:
        # print(f"[{data[curr]['id']}/{data[prev]['id']}] X Overlap: %s" % x_overlap)
        y_overlap, y_start_end = overlap(data[prev]["y"][0], 
          data[prev]["y"][1], data[curr]["y"][0], data[curr]["y"][1])
        if y_overlap:
          # TODO: Detect if this area is already claimed
          print("Y Overlap: %s" % y_overlap)
          total_overlap += (x_overlap * y_overlap)
          print("Overlap area: %s" % total_overlap)
          for x in range(*x_start_end):
            for y in range(*y_start_end):
              overlap_areas.add((x,y))
  print("Size of overlap areas: %s" % len(overlap_areas))
  print(overlap_areas)
  return total_overlap


def main():
  patches = parser(test_data)
  # patches = parser(open("data.txt").read())
  # print([x["id"] for x in patches])
  # print(patches)
  patches.sort(key=lambda x: x["key"])
  # print(patches)
  # print([x["id"] for x in patches])
  total_overlap = process(patches)
  print(f"Total overlap: {total_overlap}")
  return patches

patches = main()

# Wrong answer, too high? 115492.0
# 188499 too high
# import test
# from test import *

# test_alg()

test.py

data = [
  {"x0": 1, "x1": 5, "y0": 3,"y1": 7, "id":1},
  {"x0": 3, "x1": 5, "y0": 1,"y1": 5,"id":2},
  {"x0": 5, "x1": 7, "y0": 5,"y1": 7,"id":3},
]

def overlap(x0, y0, x1, y1):
  return max(0, min(y0, y1) - max(x0, x1))

def test_alg():
  test_data="""#1 @ 1,3: 4x4
  #3 @ 5,5: 2x2
  #2 @ 3,1: 4x4
  """
  print("===============")

  for x in range(1, len(data)):
    #x_overlap = data[x-1]["x1"] - data[x]["x0"]
    if data[x]["x0"] < data[x-1]["x1"]:
      x_overlap = overlap(data[x-1]["x0"], data[x-1]["x1"],
        data[x]["x0"], data[x]["x1"])
      if x_overlap > 0:
        print("X Overlap: %s" % x_overlap)
        y_overlap = overlap(data[x-1]["y0"], data[x-1]["y1"],
        data[x]["y0"], data[x]["y1"])
        print("Y Overlap: %s" % y_overlap)
        print("Overlap area: %s" % (x_overlap * y_overlap))