optimal goaltest TODO

.idea/workspace.xml

@@ -3,7 +3,7 @@
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-      <change beforePath="$PROJECT_DIR$/plant.py" beforeDir="false" afterPath="$PROJECT_DIR$/plant.py" afterDir="false" />
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+    <SUITE FILE_PATH="coverage/SmartTractor$py.coverage" NAME="py Coverage Results" MODIFIED="1619214699579" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
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 </project>

astar.py

@@ -0,0 +1,128 @@
+from operator import itemgetter
+import copy
+import tractor
+class Istate: #stan początkowy traktora (strona, w którą patrzy, miejsce, w którym się on znajduje)
+    def __init__(self, direction, x, y):
+        self.direction = direction
+        self.x = x
+        self.y = y
+    def get_direction(self):
+        return self.direction
+    def set_direction(self, direction):
+        self.direction = direction
+    def get_x(self):
+        return self.x
+    def set_x(self, x):
+        self.x = x
+    def get_y(self):
+        return self.y
+    def set_y(self, y):
+        self.y = y
+class Node: #wierzchołek grafu
+    def __init__(self, action, direction, parent, x, y):
+        self.action = action #akcja jaką ma wykonać (obróc się w lewo, obróć się w prawo, ruch do przodu)
+        self.direction = direction
+        self.parent = parent #ojciec wierzchołka
+        self.x = x
+        self.y = y
+    def get_action(self):
+        return self.action
+    def set_action(self, action):
+        self.action = action
+    def get_direction(self):
+        return self.direction
+    def set_direction(self, direction):
+        self.direction = direction
+    def get_parent(self):
+        return self.parent
+    def set_parent(self, parent):
+        self.parent = parent
+    def get_x(self):
+        return self.x
+    def set_x(self, x):
+        self.x = x
+    def get_y(self):
+        return self.y
+    def set_y(self, y):
+        self.y = y
+def cost(map, node): #funkcja kosztu : ile kosztuje przejechanie przez dane pole
+    return map.get_field_cost(int(node.get_x()), int(node.get_y()))
+def heuristic(node, goaltest): #funkcja heurestyki : oszacowuje koszt osiągnięcia stanu końcowego (droga)
+    return abs(node.get_x() - goaltest[0]) + abs(node.get_y() - goaltest[1])
+def f(map, node, goaltest): #funkcja zwracająca sumę funkcji kosztu oraz heurestyki
+    return cost(map, node) + heuristic(node, goaltest)
+# def optimal_goal_test():
+    
+def goal_test(elem, goaltest): #funkcja sprawdzająca czy położenie traktora równa się położeniu punktu docelowego, jeśli tak zwraca prawdę, w przeciwnym wypadku fałsz
+    if elem.get_x() == goaltest[0] and elem.get_y() == goaltest[1]:
+        return True
+    else:
+        return False
+def print_moves(elem): #zwraca listę ruchów jakie należy wykonać by dotrzeć do punktu docelowego
+    moves_list = []
+    while (elem[0].get_parent() != None):
+        moves_list.append(elem[0].get_action())
+        elem = elem[0].get_parent()
+    moves_list.reverse()
+    return moves_list
+def succ(elem): #funkcja następnika, przypisuje jakie akcje są możliwe do wykonania na danym polu oraz jaki będzie stan (kierunek, położenie) po wykonaniu tej akcji
+    actions_list = []
+    temp = copy.copy(elem.get_direction())
+    if temp == 1:
+        temp = 4
+    else:
+        temp = temp - 1
+    actions_list.append(("rotate_left", (temp, elem.get_x(), elem.get_y())))
+    temp = copy.copy(elem.get_direction())
+    if temp == 4:
+        temp = 1
+    else:
+        temp = temp + 1
+    actions_list.append(("rotate_right", (temp, elem.get_x(), elem.get_y())))
+    temp_move_south = elem.get_y() + 1
+    temp_move_west = elem.get_x() - 1
+    temp_move_east = elem.get_x() + 1
+    temp_move_north = elem.get_y() - 1
+    if tractor.Tractor.is_move_allowed_succ(elem) == "x + 1":
+        actions_list.append(("move", (elem.get_direction(), temp_move_east, elem.get_y())))
+    elif tractor.Tractor.is_move_allowed_succ(elem) == "y - 1":
+        actions_list.append(("move", (elem.get_direction(), elem.get_x(), temp_move_north)))
+    elif tractor.Tractor.is_move_allowed_succ(elem) == "y + 1":
+        actions_list.append(("move", (elem.get_direction(), elem.get_x(), temp_move_south)))
+    elif tractor.Tractor.is_move_allowed_succ(elem) == "x - 1":
+        actions_list.append(("move", (elem.get_direction(), temp_move_west, elem.get_y())))
+    return actions_list
+def graphsearch(fringe, explored, istate, succ, goaltest, f, map): #przeszukiwanie grafu wszerz
+    node = Node(None, istate.get_direction(), None, istate.get_x(), istate.get_y()) #wierzchołek początkowy, stworzony ze stanu początkowego traktora
+    fringe.append((node, 0)) #wierzchołki do odwiedzenia z priorytetem
+    while True:
+        if not fringe:
+            return False
+        elem = fringe.pop(0) #zdejmujemy wierzchołek z kolejki fringe i rozpatrujemy go
+        temp = copy.copy(elem[0])
+        if goal_test(elem[0], goaltest) is True: #jeżeli osiągniemy cel w trakcie przeszukiwania grafu wszerz (wjedziemy na pole docelowe) : zwracamy listę ruchów, po których wykonaniu dotrzemy na miejsce
+            return print_moves(elem)
+        explored.append(elem) #dodajemy wierzchołek do listy wierzchołków odwiedzonych
+        for (action, state) in succ(temp): #iterujemy po wszystkich możliwych akcjach i stanach otrzymanych dla danego wierzchołka grafu
+            fringe_tuple = []
+            fringe_tuple_prio = []
+            explored_tuple = []
+            for (x, y)  in fringe:
+                fringe_tuple.append((x.get_direction(), x.get_x(), x.get_y()))
+                fringe_tuple_prio.append(((x.get_direction(), x.get_x(), x.get_y()), y))
+            for (x, y) in explored:
+                explored_tuple.append((x.get_direction(), x.get_x(), x.get_y()))
+            x = Node(action, state[0], elem, state[1], state[2])  #stworzenie nowego wierzchołka, którego rodzicem jest elem
+            p = f(map, x, goaltest) #liczy priorytet
+            if state not in fringe_tuple and state not in explored_tuple: #jeżeli stan nie znajduje się na fringe oraz nie znajduje się w liście wierzchołków odwiedzonych
+                fringe.append((x, p)) #dodanie wierzchołka na fringe
+                fringe = sorted(fringe, key=itemgetter(1)) #sortowanie fringe'a według priorytetu
+            elif state in fringe_tuple:
+                i = 0
+                for (state_prio, r) in fringe_tuple_prio:
+                    if str(state_prio) == str(state):
+                        if r > p:
+                            fringe.insert(i, (x, p)) #zamiana state, który należy do fringe z priorytetem r na state z priorytetem p (niższym)
+                            fringe = sorted(fringe, key=itemgetter(1)) #sortowanie fringe'a według priorytetu
+                            break
+                    i = i + 1

map.py

@@ -23,6 +23,32 @@ class Map:
                 temp_field = field.Field(temp_plant, temp_rect, temp_soil)
                 temp_map_field.append(temp_field)
             self.fields.append(temp_map_field)
+    def get_field_cost(self, x, y): #zwraca koszt  danego pola
+        field = self.fields[x][y]
+        if field.get_plant().get_name() == "station" and field.get_plant().get_state() == -1:
+            return 5
+        elif field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() > 0 and field.get_plant().get_state() <= 3 * definitions.BEETROOTS_GROW_TIME:
+            return 10
+        elif field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE:
+            return 4
+        elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() > 0 and field.get_plant().get_state() <= 3 * definitions.CARROTS_GROW_TIME:
+            return 10
+        elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE:
+            return 4
+        elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() > 0 and field.get_plant().get_state() <= 3 * definitions.POTATOES_GROW_TIME:
+            return 10
+        elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() == definitions.POTATOES_MAXIMUM_STATE:
+            return 4
+        elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() > 0 and field.get_plant().get_state() <= 7 * definitions.WHEAT_GROW_TIME:
+            return 10
+        elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() == definitions.WHEAT_MAXIMUM_STATE:
+            return 4
+        elif field.get_soil().get_state() is False:
+            return 2
+        elif field.get_soil().get_state() is True and field.get_soil().get_water_level() is False:
+            return 3
+        elif field.get_soil().get_state() is True and field.get_soil().get_water_level() is True:
+            return 1
     def fill_map(self): #wypełnia mapę teksturami na podstawie logicznego stanu pól
         for i in range(definitions.WIDTH_AMOUNT):
             for j in range(definitions.HEIGHT_AMOUNT):

py.py

@@ -1,3 +1,4 @@
+import astar
 import definitions
 import graph
 import map
@@ -27,7 +28,8 @@ def main():
         map1.draw_window(tractor1, tractor1_rect)
         if not move_list and plant.Plant.if_any_mature_plant(map1) is True: #jeżeli są jakieś ruchy do wykonania w move_list oraz istnieje jakaś dojrzała roślina
             istate = graph.Istate(tractor1.get_direction(), tractor1.get_x() / definitions.BLOCK_SIZE, tractor1.get_y() / definitions.BLOCK_SIZE) #stan początkowy traktora (jego orientacja oraz jego aktualne współrzędne)
-            move_list = (graph.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, tractor1))) #lista z ruchami, które należy po kolei wykonać
+            #move_list = (graph.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, tractor1))) #lista z ruchami, które należy po kolei wykonać, graph
+            move_list = (astar.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, tractor1), astar.f, map1)) #lista z ruchami, które należy po kolei wykonać, astar
         elif move_list: #jeżeli move_list nie jest pusta
             tractor1.handle_movement(move_list.pop(0), tractor1_rect) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy
         else: