kod alfa
This commit is contained in:
Tomasz Adamczyk
parent
8caab5a3bd
commit
007304c2ac
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@ -5,8 +5,8 @@ pygame.init()
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BLOCK_SIZE = 60
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BEETROOT = pygame.image.load(os.path.join('resources/images', 'beetroot.png'))
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BEETROOT = pygame.transform.scale(BEETROOT, (BLOCK_SIZE, BLOCK_SIZE))
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@ -19,8 +19,8 @@ BEETROOTS_STAGE_3 = pygame.image.load(os.path.join('resources/images', 'beetroot
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BEETROOTS_STAGE_3 = pygame.transform.scale(BEETROOTS_STAGE_3, (BLOCK_SIZE, BLOCK_SIZE))
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||||
CARROT = pygame.image.load(os.path.join('resources/images', 'carrot.png'))
|
||||
CARROT = pygame.transform.scale(CARROT, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
CARROTS_ADULT_COST = 4
|
||||
CARROTS_GROW_COST = 10
|
||||
CARROTS_ADULT_COST = None
|
||||
CARROTS_GROW_COST = None
|
||||
CARROTS_GROW_TIME = 5
|
||||
CARROTS_MAXIMUM_STATE = CARROTS_GROW_TIME * 3 + 1
|
||||
CARROTS_STAGE_0 = pygame.image.load(os.path.join('resources/images', 'carrots_stage_0.png'))
|
||||
@ -46,34 +46,34 @@ CART_DIRECTION_WEST = 4
|
||||
CART_FERTILIZER = 2
|
||||
CART_FUEL = 200
|
||||
CART_AMOUNT_OF_SEEDS_EACH_TYPE = 20
|
||||
CART_MAXIMUM_COLLECTED_PLANTS = 80
|
||||
CART_MAXIMUM_COLLECTED_PLANTS = 200
|
||||
CART_WATER_LEVEL = 40
|
||||
DIRT = pygame.image.load(os.path.join('resources/images', 'dirt.png'))
|
||||
DIRT = pygame.transform.scale(DIRT, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
DIRT_COST = 2
|
||||
DIRT_COST = None
|
||||
FARMLAND_DRY = pygame.image.load(os.path.join('resources/images', 'farmland_dry.png'))
|
||||
FARMLAND_DRY = pygame.transform.scale(FARMLAND_DRY, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
FARMLAND_DRY_COST = 3
|
||||
FARMLAND_DRY_COST = None
|
||||
FARMLAND_WET = pygame.image.load(os.path.join('resources/images', 'farmland_wet.png'))
|
||||
FARMLAND_WET = pygame.transform.scale(FARMLAND_WET, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
FARMLAND_WET_COST = 1
|
||||
FARMLAND_WET_COST = None
|
||||
FLOWER_DANDELION = pygame.image.load(os.path.join('resources/images', 'flower_dandelion.png'))
|
||||
FLOWER_DANDELION = pygame.transform.scale(FLOWER_DANDELION, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
FLOWER_DANDELION_COST = 5
|
||||
FLOWER_DANDELION_COST = None
|
||||
FLOWER_DANDELION_GROW_PROBABILITY = 0.01
|
||||
FLOWER_DANDELION_MAXIMUM_STATE = 1
|
||||
FONT_COLOR = (255, 255, 255)
|
||||
FONT_SIZE = 23
|
||||
FONT = pygame.font.Font(os.path.join('resources/font', 'Minecraft.ttf'), FONT_SIZE)
|
||||
FPS = 1
|
||||
FPS = 10
|
||||
HEIGHT_AMOUNT, WIDTH_AMOUNT = 10, 10
|
||||
HEIGHT_MAP, WIDTH_MAP = BLOCK_SIZE * HEIGHT_AMOUNT, BLOCK_SIZE * WIDTH_AMOUNT
|
||||
HEIGHT, WIDTH = HEIGHT_MAP + BLOCK_SIZE, WIDTH_MAP
|
||||
IMAGE_SIZE_NEURAL_NETWORK = 16
|
||||
POTATO = pygame.image.load(os.path.join('resources/images', 'potato.png'))
|
||||
POTATO = pygame.transform.scale(POTATO, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
POTATOES_ADULT_COST = 4
|
||||
POTATOES_GROW_COST = 10
|
||||
POTATOES_ADULT_COST = None
|
||||
POTATOES_GROW_COST = None
|
||||
POTATOES_GROW_TIME = 5
|
||||
POTATOES_MAXIMUM_STATE = POTATOES_GROW_TIME * 3 + 1
|
||||
POTATOES_STAGE_0 = pygame.image.load(os.path.join('resources/images', 'potatoes_stage_0.png'))
|
||||
@ -90,11 +90,11 @@ SPONGE_WET = pygame.image.load(os.path.join('resources/images', 'sponge_wet.png'
|
||||
SPONGE_WET = pygame.transform.scale(SPONGE_WET, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
STATION = pygame.image.load(os.path.join('resources/images', 'rail_normal.png'))
|
||||
STATION = pygame.transform.scale(STATION, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
STATION_COST = 6
|
||||
STATION_COST = None
|
||||
WHEAT = pygame.image.load(os.path.join('resources/images', 'wheat.png'))
|
||||
WHEAT = pygame.transform.scale(WHEAT, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
WHEAT_ADULT_COST = 4
|
||||
WHEAT_GROW_COST = 10
|
||||
WHEAT_ADULT_COST = None
|
||||
WHEAT_GROW_COST = None
|
||||
WHEAT_GROW_TIME = 5
|
||||
WHEAT_MAXIMUM_STATE = WHEAT_GROW_TIME * 7 + 1
|
||||
WHEAT_STAGE_0 = pygame.image.load(os.path.join('resources/images', 'wheat_stage_0.png'))
|
||||
|
||||
180
geneticalgorithm.py
Normal file
180
geneticalgorithm.py
Normal file
@ -0,0 +1,180 @@
|
||||
import astar
|
||||
import cart
|
||||
import definitions
|
||||
import graph
|
||||
import map
|
||||
import os
|
||||
import pickle
|
||||
import plant
|
||||
import pygame
|
||||
import random
|
||||
import station
|
||||
import treelearn
|
||||
def create_genetic_algorithm():
|
||||
if os.path.exists("resources/genetic_algorithm/optimal.pkl"): #jeżeli drzewo jest zapisane w pliku to odczytaj
|
||||
astar_costs = pickle.load(open(os.path.join('resources/genetic_algorithm', "optimal.pkl"), "rb"))
|
||||
definitions.BEETROOTS_ADULT_COST = astar_costs[0]
|
||||
definitions.BEETROOTS_GROW_COST = astar_costs[1]
|
||||
definitions.CARROTS_ADULT_COST = astar_costs[2]
|
||||
definitions.CARROTS_GROW_COST = astar_costs[3]
|
||||
definitions.DIRT_COST = astar_costs[4]
|
||||
definitions.FARMLAND_DRY_COST = astar_costs[5]
|
||||
definitions.FARMLAND_WET_COST = astar_costs[6]
|
||||
definitions.FLOWER_DANDELION_COST = astar_costs[7]
|
||||
definitions.POTATOES_ADULT_COST = astar_costs[8]
|
||||
definitions.POTATOES_GROW_COST = astar_costs[9]
|
||||
definitions.STATION_COST = astar_costs[10]
|
||||
definitions.WHEAT_ADULT_COST = astar_costs[11]
|
||||
definitions.WHEAT_GROW_COST = astar_costs[12]
|
||||
print(definitions.BEETROOTS_ADULT_COST)
|
||||
print(definitions.BEETROOTS_GROW_COST)
|
||||
else:
|
||||
astar_costs = [definitions.BEETROOTS_ADULT_COST, definitions.BEETROOTS_GROW_COST, definitions.CARROTS_ADULT_COST, definitions.CARROTS_GROW_COST, definitions.DIRT_COST, definitions.FARMLAND_DRY_COST, definitions.FARMLAND_WET_COST, definitions.FLOWER_DANDELION_COST, definitions.POTATOES_ADULT_COST, definitions.POTATOES_GROW_COST, definitions.STATION_COST, definitions.WHEAT_ADULT_COST, definitions.WHEAT_GROW_COST]
|
||||
astar_costs = make_generations(astar_costs)
|
||||
pickle.dump(astar_costs, open(os.path.join('resources/genetic_algorithm', "optimal.pkl"), "wb"))
|
||||
|
||||
def fitness(astar_costs):
|
||||
ans = solution(astar_costs)
|
||||
if ans == 0:
|
||||
return 0
|
||||
else:
|
||||
return 1 / ans
|
||||
def solution(astar_costs):
|
||||
definitions.BEETROOTS_ADULT_COST = astar_costs[0]
|
||||
definitions.BEETROOTS_GROW_COST = astar_costs[1]
|
||||
definitions.CARROTS_ADULT_COST = astar_costs[2]
|
||||
definitions.CARROTS_GROW_COST = astar_costs[3]
|
||||
definitions.DIRT_COST = astar_costs[4]
|
||||
definitions.FARMLAND_DRY_COST = astar_costs[5]
|
||||
definitions.FARMLAND_WET_COST = astar_costs[6]
|
||||
definitions.FLOWER_DANDELION_COST = astar_costs[7]
|
||||
definitions.POTATOES_ADULT_COST = astar_costs[8]
|
||||
definitions.POTATOES_GROW_COST = astar_costs[9]
|
||||
definitions.STATION_COST = astar_costs[10]
|
||||
definitions.WHEAT_ADULT_COST = astar_costs[11]
|
||||
definitions.WHEAT_GROW_COST = astar_costs[12]
|
||||
#tworzenie podstawowych obiektów
|
||||
map1 = map.Map([])
|
||||
map1.create_base_map()
|
||||
move_list = ["rotate_left", "move", "move", "move", "move", "move", "move", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "move", "move", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move"] #początkowe ruchy
|
||||
amount_of_seeds_dict = {"beetroot": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "carrot": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "potato": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "wheat": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE}
|
||||
collected_plants_dict_cart = {"beetroot": 0, "carrot": 0, "potato": 0, "wheat": 0}
|
||||
collected_plants_dict_station = {"beetroot": 0, "carrot": 0, "potato": 0, "wheat": 0}
|
||||
fertilizer_dict = {"beetroot": definitions.CART_FERTILIZER, "carrot": definitions.CART_FERTILIZER, "potato": definitions.CART_FERTILIZER, "wheat": definitions.CART_FERTILIZER}
|
||||
station1 = station.Station(collected_plants_dict_station)
|
||||
cart1 = cart.Cart(amount_of_seeds_dict, collected_plants_dict_cart, definitions.CART_DIRECTION_WEST, fertilizer_dict, definitions.CART_FUEL, definitions.CART_WATER_LEVEL, 0 * definitions.BLOCK_SIZE, 0 * definitions.BLOCK_SIZE)
|
||||
cart1_rect = pygame.Rect(cart1.get_x(), cart1.get_y(), definitions.BLOCK_SIZE, definitions.BLOCK_SIZE)
|
||||
tree = treelearn.treelearn() #tworzenie drzewa decyzyjnego
|
||||
decision = [0] #początkowa decyzja o braku powrotu do stacji (0)
|
||||
grow_flower_dandelion = False
|
||||
random_movement = False
|
||||
for run in range(1000): #liczba ruchów wózka
|
||||
if not move_list: #jeżeli są jakieś ruchy do wykonania w move_list
|
||||
grow_flower_dandelion = True
|
||||
istate = graph.Istate(cart1.get_direction(), cart1.get_x() / definitions.BLOCK_SIZE, cart1.get_y() / definitions.BLOCK_SIZE) #stan początkowy wózka (jego orientacja oraz jego aktualne miejsce)
|
||||
if plant.Plant.if_any_mature_plant(map1) is True: #jeżeli istnieje jakaś dojrzała roślina
|
||||
random_movement = False
|
||||
if decision == [0]: #jeżeli decyzja jest 0 (brak powrotu do stacji) to uprawiaj pole
|
||||
move_list = (astar.graphsearch([], astar.f, [], plant.Plant.get_closest_mature_plant(istate, map1), istate, map1, graph.succ)) #lista z ruchami, które należy po kolei wykonać, astar
|
||||
else: #jeżeli decyzja jest 1 (powrót do stacji) to wróć do stacji uzupełnić zapasy
|
||||
move_list = (graph.graphsearch([], [], (0, 0), istate, graph.succ)) #lista z ruchami, które należy po kolei wykonać, graphsearch
|
||||
else:
|
||||
random_movement = True
|
||||
elif move_list: #jeżeli move_list nie jest pusta
|
||||
cart1.handle_movement(cart1_rect, move_list.pop(0)) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy
|
||||
if random_movement is True:
|
||||
cart1.handle_movement_random(cart1_rect) #wykonuj losowe ruchy
|
||||
if grow_flower_dandelion is True:
|
||||
plant.Plant.grow_flower_dandelion(map1) #losuj urośnięcie kwiatka dandeliona
|
||||
cart1.do_work(cart1_rect, map1, station1) #wykonaj pracę na danym polu
|
||||
decision = treelearn.make_decision(cart1.get_all_amount_of_seeds(), cart1.get_all_collected_plants(), cart1.get_all_fertilizer(), cart1.get_fuel(), tree, cart1.get_water_level()) #podejmij decyzję czy wracać do stacji (0 : NIE, 1 : TAK)
|
||||
plant.Plant.grow_plants(map1) #zwiększ poziom dojrzałości roślin
|
||||
|
||||
|
||||
|
||||
print("Ile zebrał: ", station1.get_collected_plants("beetroot") + station1.get_collected_plants("carrot") + station1.get_collected_plants("potato") + station1.get_collected_plants("wheat"))
|
||||
return station1.get_collected_plants("beetroot") + station1.get_collected_plants("carrot") + station1.get_collected_plants("potato") + station1.get_collected_plants("wheat")
|
||||
|
||||
|
||||
|
||||
def make_generations(astar_costs):
|
||||
solutions = []
|
||||
ans = []
|
||||
for s in range(10): #liczba osobników zerowej generacji
|
||||
solutions.append((random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10)))
|
||||
for i in range(10): #liczba generacji
|
||||
ranked_solutions = []
|
||||
for s in solutions:
|
||||
ranked_solutions.append((fitness(s), s))
|
||||
ranked_solutions.sort()
|
||||
print(f"=== Gen {i + 1} best solution ===")
|
||||
#print(f"=== Gen {i} best solutions ===")
|
||||
print(ranked_solutions[0])
|
||||
ans.append(ranked_solutions[0])
|
||||
print(ans)
|
||||
|
||||
#można dodać warunek stopu
|
||||
|
||||
best_solutions = ranked_solutions[:4] #najlepsze 4 osobniki w danej generacji
|
||||
elements1 = []
|
||||
elements2 = []
|
||||
elements3 = []
|
||||
elements4 = []
|
||||
elements5 = []
|
||||
elements6 = []
|
||||
elements7 = []
|
||||
elements8 = []
|
||||
elements9 = []
|
||||
elements10 = []
|
||||
elements11 = []
|
||||
elements12 = []
|
||||
elements13 = []
|
||||
for s in best_solutions:
|
||||
elements1.append(s[1][0])
|
||||
elements2.append(s[1][1])
|
||||
elements3.append(s[1][2])
|
||||
elements4.append(s[1][3])
|
||||
elements5.append(s[1][4])
|
||||
elements6.append(s[1][5])
|
||||
elements7.append(s[1][6])
|
||||
elements8.append(s[1][7])
|
||||
elements9.append(s[1][8])
|
||||
elements10.append(s[1][9])
|
||||
elements11.append(s[1][10])
|
||||
elements12.append(s[1][11])
|
||||
elements13.append(s[1][12])
|
||||
|
||||
new_gen = []
|
||||
for i in range(10): # liczba osobników w kolejnej generacji
|
||||
e1 = random.choice(elements1) * random.uniform(0.99, 1.01) # mutacje
|
||||
e2 = random.choice(elements2) * random.uniform(0.99, 1.01)
|
||||
e3 = random.choice(elements3) * random.uniform(0.99, 1.01)
|
||||
e4 = random.choice(elements4) * random.uniform(0.99, 1.01)
|
||||
e5 = random.choice(elements5) * random.uniform(0.99, 1.01)
|
||||
e6 = random.choice(elements6) * random.uniform(0.99, 1.01)
|
||||
e7 = random.choice(elements7) * random.uniform(0.99, 1.01)
|
||||
e8 = random.choice(elements8) * random.uniform(0.99, 1.01)
|
||||
e9 = random.choice(elements9) * random.uniform(0.99, 1.01)
|
||||
e10 = random.choice(elements10) * random.uniform(0.99, 1.01)
|
||||
e11 = random.choice(elements11) * random.uniform(0.99, 1.01)
|
||||
e12 = random.choice(elements12) * random.uniform(0.99, 1.01)
|
||||
e13 = random.choice(elements13) * random.uniform(0.99, 1.01)
|
||||
|
||||
new_gen.append((e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12, e13))
|
||||
|
||||
solutions = new_gen
|
||||
ans.sort()
|
||||
astar_costs[0] = ans[0][1][0]
|
||||
astar_costs[1] = ans[0][1][1]
|
||||
astar_costs[2] = ans[0][1][2]
|
||||
astar_costs[3] = ans[0][1][3]
|
||||
astar_costs[4] = ans[0][1][4]
|
||||
astar_costs[5] = ans[0][1][5]
|
||||
astar_costs[6] = ans[0][1][6]
|
||||
astar_costs[7] = ans[0][1][7]
|
||||
astar_costs[8] = ans[0][1][8]
|
||||
astar_costs[9] = ans[0][1][9]
|
||||
astar_costs[10] = ans[0][1][10]
|
||||
astar_costs[11] = ans[0][1][11]
|
||||
astar_costs[12] = ans[0][1][12]
|
||||
return astar_costs
|
||||
64
plant.py
64
plant.py
@ -1,4 +1,5 @@
|
||||
import definitions
|
||||
import graph
|
||||
import random
|
||||
class Plant:
|
||||
def __init__(self, name, state):
|
||||
@ -13,6 +14,55 @@ class Plant:
|
||||
def set_state(self, state):
|
||||
self.state = state
|
||||
@staticmethod
|
||||
def get_closest_mature_plant(istate, map): # TO DO, pobiera współrzędne najbliższej dojrzałej rośliny od miejsca, w którym znajduje się traktor
|
||||
x = None #x'owa pola
|
||||
y = None #y'kowa pola
|
||||
min = None
|
||||
for i in range(definitions.WIDTH_AMOUNT):
|
||||
for j in range(definitions.HEIGHT_AMOUNT):
|
||||
field = map.fields[i][j]
|
||||
if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE:
|
||||
if min is None:
|
||||
x = i
|
||||
y = j
|
||||
min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
|
||||
else:
|
||||
if len((graph.graphsearch([], [], (x, y), istate, graph.succ))) < min:
|
||||
x = i
|
||||
y = j
|
||||
min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
|
||||
elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE:
|
||||
if min is None:
|
||||
x = i
|
||||
y = j
|
||||
min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
|
||||
else:
|
||||
if len((graph.graphsearch([], [], (x, y), istate, graph.succ))) < min:
|
||||
x = i
|
||||
y = j
|
||||
min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
|
||||
elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() == definitions.POTATOES_MAXIMUM_STATE:
|
||||
if min is None:
|
||||
x = i
|
||||
y = j
|
||||
min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
|
||||
else:
|
||||
if len((graph.graphsearch([], [], (x, y), istate, graph.succ))) < min:
|
||||
x = i
|
||||
y = j
|
||||
min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
|
||||
elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() == definitions.WHEAT_MAXIMUM_STATE:
|
||||
if min is None:
|
||||
x = i
|
||||
y = j
|
||||
min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
|
||||
else:
|
||||
if len((graph.graphsearch([], [], (x, y), istate, graph.succ))) < min:
|
||||
x = i
|
||||
y = j
|
||||
min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
|
||||
return x, y
|
||||
@staticmethod
|
||||
def grow_flower_dandelion(map): #metoda statyczna, losująca czy na danym polu ma urosnąć kwiat dandelion
|
||||
for i in range(definitions.WIDTH_AMOUNT):
|
||||
for j in range(definitions.HEIGHT_AMOUNT):
|
||||
@ -35,3 +85,17 @@ class Plant:
|
||||
field.get_plant().set_state(field.get_plant().get_state() + 1)
|
||||
elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.WHEAT_MAXIMUM_STATE:
|
||||
field.get_plant().set_state(field.get_plant().get_state() + 1)
|
||||
@staticmethod
|
||||
def if_any_mature_plant(map): #sprawdza czy na polu występuje choć jedna dojrzała roślina, jeśli tak zwraca prawdę, w przeciwnym razie zwraca fałsz
|
||||
for i in range(definitions.WIDTH_AMOUNT):
|
||||
for j in range(definitions.HEIGHT_AMOUNT):
|
||||
field = map.get_fields()[i][j]
|
||||
if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE:
|
||||
return True
|
||||
elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE:
|
||||
return True
|
||||
elif field. | ||||