genetic_alg_angela #31
@ -1,4 +1,7 @@
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from dataclasses import dataclass
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import numpy as np
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from const import *
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from typing import List, Dict, Tuple
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@ -11,6 +14,56 @@ class Position:
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col: int
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@dataclass
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class Area:
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position: Position
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width: int
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height: int
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AREAS_TO_CROSS = [
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# up above left knights spawn
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Area(position=Position(row=0, col=0),
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width=KNIGHTS_SPAWN_WIDTH,
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height=LEFT_KNIGHTS_SPAWN_FIRST_ROW),
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# down below left knights spawn
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Area(position=Position(row=LEFT_KNIGHTS_SPAWN_FIRST_ROW + KNIGHTS_SPAWN_HEIGHT, col=0),
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width=KNIGHTS_SPAWN_WIDTH,
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height=ROWS - LEFT_KNIGHTS_SPAWN_FIRST_ROW - KNIGHTS_SPAWN_HEIGHT),
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# between left knights spawn and castle
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Area(position=Position(row=0, col=KNIGHTS_SPAWN_WIDTH),
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width=CASTLE_SPAWN_FIRST_COL - KNIGHTS_SPAWN_WIDTH,
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height=ROWS),
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# up above castle
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Area(position=Position(row=0, col=CASTLE_SPAWN_FIRST_COL),
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width=2,
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height=CASTLE_SPAWN_FIRST_ROW),
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# down below castle
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Area(position=Position(row=CASTLE_SPAWN_FIRST_ROW + 2, col=CASTLE_SPAWN_FIRST_COL),
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width=2,
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height=ROWS - CASTLE_SPAWN_FIRST_ROW - 2),
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# between castle and right knights spawn
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Area(position=Position(row=0, col=CASTLE_SPAWN_FIRST_COL + 2),
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width=RIGHT_KNIGHTS_SPAWN_FIRST_COL - CASTLE_SPAWN_FIRST_COL - 2,
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height=ROWS),
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# up above right knights spawn
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Area(position=Position(row=0, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
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width=KNIGHTS_SPAWN_WIDTH,
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height=RIGHT_KNIGHTS_SPAWN_FIRST_ROW),
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# down below right knights spawn
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Area(position=Position(row=RIGHT_KNIGHTS_SPAWN_FIRST_ROW + KNIGHTS_SPAWN_HEIGHT, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
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width=KNIGHTS_SPAWN_WIDTH,
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height=ROWS - RIGHT_KNIGHTS_SPAWN_FIRST_ROW - KNIGHTS_SPAWN_HEIGHT),
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]
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def dfs(grid: npt.NDArray, visited: Dict[Tuple[int, int], bool], position: Position, rows: int, cols: int) -> None:
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visited[(position.row, position.col)] = True
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@ -51,3 +104,19 @@ def get_islands(grid: npt.NDArray, positions: List[Position], rows: int = ROWS,
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islands += 1
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return roots
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def find_neighbours(grid: npt.NDArray, col: int, row: int) -> List[Position]:
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dr = [-1, 1, 0, 0]
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dc = [0, 0, -1, 1]
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neighbours = []
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for i in range(4):
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rr = row + dr[i]
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cc = col + dc[i]
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if 0 <= rr < ROWS and 0 <= cc < COLUMNS and grid[rr][cc] == MAP_ALIASES.get('GRASS'):
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neighbours.append(Position(row=rr, col=cc))
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return neighbours
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@ -13,6 +13,8 @@ LEFT_KNIGHTS_SPAWN_FIRST_ROW = 6
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LEFT_KNIGHTS_SPAWN_FIRST_COL = 0
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RIGHT_KNIGHTS_SPAWN_FIRST_ROW = 6
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RIGHT_KNIGHTS_SPAWN_FIRST_COL = 20
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CASTLE_SPAWN_FIRST_ROW = 7
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CASTLE_SPAWN_FIRST_COL = 11
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# map aliases
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MAP_ALIASES = {
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@ -1,10 +1,13 @@
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import math
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import random
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from copy import deepcopy
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from random import randrange
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from typing import List
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import numpy as np
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import numpy.typing as npt
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from common import Position
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from common import Position, get_islands, AREAS_TO_CROSS, find_neighbours
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from const import *
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@ -16,11 +19,15 @@ class Genome:
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trees: List[Position]
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sands: List[Position]
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monsters: List[Position]
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fitness: int
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sand_islands: List[Position]
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tree_islands: List[Position]
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water_islands: List[Position]
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def __init__(self):
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self.grid = np.zeros((ROWS, COLUMNS), dtype=int)
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self.fitness = 0
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# todo: spawning castle, monsters, sand, trees in the same way as bellow
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self.knights_red = spawn_objects_in_given_area(
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grid=self.grid,
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object_alias=MAP_ALIASES.get("KNIGHT_RED"),
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@ -43,7 +50,7 @@ class Genome:
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grid=self.grid,
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object_alias=MAP_ALIASES.get("CASTLE"),
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objects_count=4,
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spawn_position_start=Position(row=7, col=9),
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spawn_position_start=Position(row=CASTLE_SPAWN_FIRST_ROW, col=CASTLE_SPAWN_FIRST_COL),
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width=2,
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height=2
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)
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@ -57,6 +64,62 @@ class Genome:
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self.monsters = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("MONSTER"),
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objects_count=MONSTERS_COUNT)
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self.sand_islands = get_islands(self.grid, self.sands)
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self.tree_islands = get_islands(self.grid, self.trees)
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self.water_islands = get_islands(self.grid, self.waters)
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def calc_fitness(self):
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score = SAND_COUNT + TREE_COUNT + WATER_COUNT
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score = score - len(self.sand_islands) - len(self.tree_islands) - len(self.water_islands)
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# todo: odległość manhattan od każdej wyspy
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self.fitness = score
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def crossover(self, partner):
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# replace a randomly selected part of the grid with partner's part
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child = Genome()
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child.grid = deepcopy(self.grid)
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area_to_cross = random.choice(AREAS_TO_CROSS)
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for row in range(area_to_cross.position.row, area_to_cross.position.row + area_to_cross.height):
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for col in range(area_to_cross.position.col, area_to_cross.position.col + area_to_cross.width):
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child.grid[row][col] = partner.grid[row][col]
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child.sand_islands = get_islands(self.grid, self.sands)
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child.tree_islands = get_islands(self.grid, self.trees)
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child.water_islands = get_islands(self.grid, self.waters)
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return child
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def mutate(self, mutation_rate: float):
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# remove 1 item from a random island and add a neighbor to another island
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if random.random() < mutation_rate:
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# select islands of the same, random type
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islands_of_same_type = random.choice([self.sand_islands, self.tree_islands, self.water_islands])
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random_index = random.randint(0, len(islands_of_same_type) - 1)
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island = islands_of_same_type[random_index]
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next_island = islands_of_same_type[(random_index + 1) % len(islands_of_same_type)]
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free_tiles_nearby = find_neighbours(self.grid, next_island.col, next_island.row)
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tile_type = self.grid[island.row][island.col]
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self.grid[island.row][island.col] = MAP_ALIASES.get('GRASS')
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islands_of_same_type.remove(island)
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# todo: if there are no free tiles around then randomize another next_island
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if len(free_tiles_nearby) > 0:
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random_free_tile = random.choice(free_tiles_nearby)
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island.row = random_free_tile.row
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island.col = random_free_tile.col
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self.grid[island.row][island.col] = tile_type
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self.sand_islands = get_islands(self.grid, self.sands)
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self.tree_islands = get_islands(self.grid, self.trees)
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self.water_islands = get_islands(self.grid, self.waters)
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def is_empty(grid: npt.NDArray, position: Position) -> bool:
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return grid[position.row, position.col] in [MAP_ALIASES.get("GRASS"), MAP_ALIASES.get("SAND")]
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@ -1,16 +1,25 @@
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from map_importer_exporter import export_map
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from genome import Genome
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from common import get_islands
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from algorithms.genetic.genome import Genome
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from algorithms.genetic.map_importer_exporter import export_map
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from population import Population
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def main() -> None:
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example_genome = Genome()
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print(example_genome.knights_red)
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print(example_genome.knights_blue)
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print(example_genome.grid)
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islands = get_islands(example_genome.grid, example_genome.knights_red)
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print(f'Roots {islands} and islands count {len(islands)}')
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# export_map(example_genome.grid) fixme: FileNotFoundError
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population_size = 500
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mutation_rate = 0.4
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population = Population(mutation_rate, population_size, 27)
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while not population.evaluate():
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# create next generation
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population.generate()
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# calc fitness
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population.calc_fitness()
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print(population.best_genome.grid)
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print("Fitness of the best: ", population.best_genome.fitness)
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export_map(population.best_genome.grid)
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if __name__ == '__main__':
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@ -2,22 +2,24 @@ import json
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import random
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import string
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from datetime import datetime
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from pathlib import Path
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import numpy
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import numpy.typing as npt
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from os import listdir
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from os.path import isfile, join
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# Save map to file
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def export_map(grid: npt.NDArray):
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json_data = {"map": grid.tolist()}
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now = datetime.now()
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file_name = "map_" + now.strftime("%Y_%m_%d_%H_%M_%S") + ".json"
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path = Path("../../resources/maps/")
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file_to_open = path / file_name
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with open("resources/maps/" + file_name, "w") as write_file:
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print(file_to_open)
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with open(file_to_open, "w+") as write_file:
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json.dump(json_data, write_file)
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print("Saved map to file " + file_name)
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@ -30,9 +32,10 @@ def import_random_map() -> object:
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# Read map from file
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def import_map(file: string) -> object:
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with open("resources/maps/" + file, "r") as read_file:
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print("Reading map from file " + file)
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def import_map(file_name: string) -> object:
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file_to_open = "resources/maps/" + file_name
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with open(file_to_open, "r") as read_file:
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print("Reading map from file " + file_name)
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decoded_json = json.load(read_file)
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decoded_grid = numpy.asarray(decoded_json["map"])
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81
algorithms/genetic/population.py
Normal file
81
algorithms/genetic/population.py
Normal file
@ -0,0 +1,81 @@
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import random
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from typing import List
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import numpy as np
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import numpy.typing as npt
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from genome import Genome
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class Population:
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population: List[Genome] = [] # array to hold the current population
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mating_pool: List[Genome] = [] # array which we will use for our "mating pool"
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generations: int = 0 # number of generations
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finished: bool = False # are we finished evolving?
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mutation_rate: float
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perfect_score: int
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best_genome: Genome
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def __init__(self, mutation_rate, population_size, perfect_score=20):
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self.mutation_rate = mutation_rate
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self.perfect_score = perfect_score
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for i in range(0, population_size):
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new_genome = Genome()
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new_genome.calc_fitness()
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self.population.append(new_genome)
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# create a new generation
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def generate(self):
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max_fitness = 0
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for genome in self.population:
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if genome.fitness > max_fitness:
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max_fitness = genome.fitness
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print("Max fitness of generation " + str(self.generations) + " = " + str(max_fitness))
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# refill the population with children from the mating pool
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new_population = []
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for genome in self.population:
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partner_a = self.accept_reject(max_fitness)
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partner_b = self.accept_reject(max_fitness)
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child = partner_a.crossover(partner_b)
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child.mutate(self.mutation_rate)
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new_population.append(child)
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self.population = new_population
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self.generations += 1
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# select random with correct probability from population
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def accept_reject(self, max_fitness: int):
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safe_flag = 0
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while safe_flag < 10000:
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partner = random.choice(self.population)
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r = random.randint(0, max_fitness)
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if r < partner.fitness:
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return partner
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safe_flag += 1
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# compute the current "most fit" member of the population
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def evaluate(self):
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record = 0
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best_index = 0
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for index in range(len(self.population)):
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genome = self.population[index]
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if genome.fitness > record:
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record = genome.fitness
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best_index = index
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self.best_genome = self.population[best_index]
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if record >= self.perfect_score:
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self.finished = True
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return self.finished
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def calc_fitness(self):
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for genome in self.population:
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genome.calc_fitness()
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0
algorithms/neural_network/__init__.py
Normal file
0
algorithms/neural_network/__init__.py
Normal file
@ -1 +0,0 @@
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{"map": [[0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 4, 2, 3, 0, 0, 3, 3, 0, 3, 4, 0, 0], [0, 3, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 1, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 1, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0, 0, 3, 5, 5, 0, 0, 0, 0, 1, 0, 1, 3, 0, 0, 7, 0, 0], [2, 0, 3, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0], [6, 6, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 2, 2, 0, 2, 0, 3, 0, 0, 7, 0], [0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [1, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
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@ -1 +0,0 @@
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{"map": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0], [1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0], [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 2, 0], [0, 1, 0, 0, 3, 0, 0, 1, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0], [6, 0, 0, 0, 1, 2, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 5, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 7, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0], [6, 2, 6, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 7, 0, 7], [0, 3, 6, 2, 0, 3, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 3, 3, 0, 0, 0, 4, 0, 0, 0, 3, 1, 0, 0, 0], [0, 3, 2, 3, 0, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
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resources/maps/map_2022_06_02_00_36_19.json
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{"map": [[3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 1, 1, 0], [2, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0], [0, 0, 6, 0, 1, 3, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 1, 5, 5, 0, 0, 1, 0, 3, 0, 0, 3, 7, 0, 0], [0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 1, 0, 0, 2, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 7, 0], [0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 4, 0, 3, 0, 3, 0, 3, 3], [3, 1, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 0, 2, 1, 1, 3, 0, 1, 0], [0, 0, 0, 0, 3, 3, 3, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3, 3, 0, 0]]}
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resources/maps/map_2022_06_02_00_38_31.json
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{"map": [[0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 3, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0], [1, 2, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 0, 3, 0, 0, 0, 2, 3, 0, 3, 0, 5, 5, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0], [0, 1, 0, 2, 3, 0, 0, 0, 2, 0, 0, 5, 5, 3, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0], [0, 6, 6, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 3], [0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 2, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3], [2, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [3, 0, 0, 0, 3, 3, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0]]}
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resources/maps/map_2022_06_02_00_42_44.json
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resources/maps/map_2022_06_02_00_42_44.json
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{"map": [[0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 6, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 1], [0, 0, 0, 6, 0, 0, 0, 6, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 3, 5, 5, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 3, 0, 0, 0, 0, 0, 0, 5, 5, 3, 2, 0, 0, 0, 0, 0, 0, 0, 7, 7], [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0], [0, 0, 0, 0, 1, 0, 3, 0, 0, 3, 3, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 2, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 6, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 3, 1, 0, 0, 0, 0, 0, 0, 2, 0, 3, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0]]}
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