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I am trying to code ultrasonic sensors to count the number of cars in a parking lot. I am relatively new to Python, so I am asking here for help.
I have three parking slots, in which each of them has an ultrasonic sensor.
How do I make it so that the sensors and their counters work together? For example, when the parking slots are empty, the counter shows three parking slots available. When two parking slots are filled, the counter shows one availability, etc.
I have done the following code, and I am wondering how I could continue to achieve my objective?
# Sensor 1
def distance_1():
time.sleep(0.5)
GPIO.output(TRIG_1, True)
time.sleep(0.00001)
GPIO.output(TRIG_1, False)
print("Reading Sensor 1")
while GPIO.input(ECHO_1) == 0:
start = time.time()
while GPIO.input(ECHO_1) == 1:
end = time.time()
duration = end - start
sound = 34000 / 2
distance = duration * sound
round(distance, 0)
total = 3
count = total
if distance <= 10:
count -= 1
elif distance > 10:
count += 1
if count < 0:
count = 0
elif count > total:
count = total
print(count)
mylcd.lcd_display_string("{}".format(count), 2)
# Sensor 2
def distance_2():
time.sleep(0.5)
GPIO.output(TRIG_2, True)
time.sleep(0.00001)
GPIO.output(TRIG_2, False)
print("Reading Sensor 2")
while GPIO.input(ECHO_2) == 0:
start = time.time()
while GPIO.input(ECHO_2) == 1:
end = time.time()
duration = end - start
sound = 34000 / 2
distance = duration * sound
round(distance, 0)
total = 3
count = total
if distance <= 10:
count -= 1
elif distance > 10:
count += 1
if count < 0:
count = 0
elif count > total:
count = total
print(count)
mylcd.lcd_display_string("{}".format(count), 2)
# Sensor 3
def distance_3():
time.sleep(0.5)
GPIO.output(TRIG_3, True)
time.sleep(0.00001)
GPIO.output(TRIG_3, False)
print("Reading Sensor 3")
while GPIO.input(ECHO_3) == 0:
start = time.time()
while GPIO.input(ECHO_3) == 1:
end = time.time()
duration = end - start
sound = 34000 / 2
distance = duration * sound
round(distance, 0)
total = 3
count = total
if distance <= 10:
count -= 1
elif distance > 10:
count += 1
if count < 0:
count = 0
elif count > total:
count = total
print(count)
mylcd.lcd_display_string("{}".format(count), 2)
while True:
distance_1()
distance_2()
distance_3()
GPIO.cleanup()
The trouble with programming is there are so many ways to achieve the same result.
Looking at your code, I would suggest taking a step back and refactoring it to use Python classes instead. You have a lot of code repetition happening, and eventually, the code will break if you need to keep adding more sensors.
For example:
class Parking:
"This is a parking class"
def __init__(self, space):
self.space = space
def empty(self):
if self.space == 0:
print('Parking space is empty')
def full(self):
if self.space == 1:
print('Parking space is full')
def distance(self):
time.sleep(0.5)
GPIO.output(TRIG, True)
. . .
# Input:
sensor1 = Parking(1)
sensor2 = Parking(1)
sensor3 = Parking(0)
# Output:
sensor1.empty()
sensor2.empty()
sensor3.empty()
# Output:
sensor1.full()
sensor2.full()
sensor3.full()
You can then update a dictionary with the output to monitor the latest sensor information. Ideally, the dictionary would be written to a central file accessible by all the sensors or raspberry pis to read.
available_spaces = {"sensor1": 0, "sensor2": 1, "sensor3": 0}
I analyzed your code and I did some refactorings.
I suggest you to use constant values initialization (the config section you see in the code below). The values I used are random.
A function can be parametrized, so you can pass arguments to functions, and avoid writing the same piece of code changing only a few values in the same place.
You should set up your microcontroller when the script starts, to tell the board how you are using the pins (as input or output).
I didn't dig into on the snippet above the lcd_display_string and why are you doing those operations. I suppose that these are required to print on screen the distance.
## configuration
# trigger
TRIG_1 = 17
TRIG_2 = 27
TRIG_3 = 22
# echo
ECHO_1 = 10
ECHO_2 = 9
ECHO_3 = 11
# timings
INITIAL_DELAY = 0.5
TRIGGERING_DELAY = 0.00001
## support functions
# initializing GPIO
def set_up():
# set trigger GPIOs as output pins
GPIO.setup(TRIG_1, GPIO.OUT)
GPIO.setup(TRIG_2, GPIO.OUT)
GPIO.setup(TRIG_3, GPIO.OUT)
# set echo GPIOs as input pins
GPIO.setup(ECHO_1, GPIO.IN)
GPIO.setup(ECHO_2, GPIO.IN)
GPIO.setup(ECHO_3, GPIO.IN)
# I didn't dig into these values and why are you doing these operations. I suppose that these are required to print on screen the distance.
def print_distance_on_lcd(distance):
total = 3
count = total
if distance <= 10:
count -= 1
elif distance > 10:
count += 1
if count < 0:
count = 0
elif count > total:
count = total
print(count)
mylcd.lcd_display_string("{}".format(count), 2)
def trigger(trigger):
time.sleep(INITIAL_DELAY)
GPIO.output(trigger, True) # set output pin on HIGH state
time.sleep(TRIGGERING_DELAY)
GPIO.output(trigger, False) # set output pin on LOW state
def distance(t, echo):
trigger(t)
# initializing the variables here, allows you to use it outside the while block below
# using variable names that explains their content
start_time = time.time()
end_time = time.time()
# this block is not wrong, but unnecessary: initializing the variable like above is enough
'''
while GPIO.input(echo) == 0:
start_time = time.time()
'''
while GPIO.input(echo) == 1:
end_time = time.time()
duration = end_time - start_time
sound = 34000 / 2
distance = duration * sound
return distance
# call initialization function (this will be executed only one time)
set_up()
# loop forever
while True:
set_up()
print("Reading Sensor 1")
distance_sensor_1 = distance(TRIG_1, ECHO_1)
print_distance_on_lcd(distance_sensor_1)
print("Reading Sensor 2")
distance_sensor_2 = distance(TRIG_2, ECHO_2)
print_distance_on_lcd(distance_sensor_2)
print("Reading Sensor 3")
distance_sensor_3 = distance(TRIG_3, ECHO_3)
print_distance_on_lcd(distance_sensor_3)
GPIO.cleanup()
I originally posted this on code-review (hence the lengthy code) but failed to get an answer.
My model is based on this game https://en.wikipedia.org/wiki/Ultimatum_game . I won't go into the intuition behind it but generally speaking it functions as follows:
The game consists of a n x n lattice on which an agent is placed at each node.
During each time step, each player on each node plays against a random neighbour by playing a particular strategy.
Each of their strategies (a value between 1-9) has a propensity attached to it (which is randomly assigned and is just some number). The propensity then in turn determines the probability of playing that strategy. The probability is calculated as the propensity of that strategy over the sum of the propensities of all strategies.
If a game results in a positive payoff, then the payoffs from that game get added to the propensities for those strategies.
These propensities then determine the probabilities for their strategies in the next time step, and so on.
The simulation ends after time step N is reached.
For games with large lattices and large time steps, my code runs really really slowly. I ran cProfiler to check where the bottleneck(s) are, and as I suspected the update_probabilitiesand play_rounds functions seem to be taking up a lot time. I want to be able to run the game with gridsize of about 40x40 for about 100000+ time steps, but right now that is not happening.
So what would be a more efficient way to calculate and update the probabilities/propensities of each player in the grid? I've considered implementing NumPy arrays but I am not sure if it would be worth the hassle here?
import numpy as np
import random
from random import randint
from numpy.random import choice
from numpy.random import multinomial
import cProfile
mew = 0.001
error = 0.05
def create_grid(row, col):
return [[0 for j in range(col)] for i in range(row)]
def create_random_propensities():
propensities = {}
pre_propensities = [random.uniform(0, 1) for i in range(9)]
a = np.sum(pre_propensities)
for i in range(1, 10):
propensities[i] = (pre_propensities[i - 1]/a) * 10 # normalize sum of propensities to 10
return propensities
class Proposer:
def __init__(self):
self.propensities = create_random_propensities()
self.probabilites = []
self.demand = 0 # the amount the proposer demands for themselves
def pick_strat(self, n_trials): # gets strategy, an integer in the interval [1, 9]
results = multinomial(n_trials, self.probabilites)
i, = np.where(results == max(results))
if len(i) > 1:
return choice(i) + 1
else:
return i[0] + 1
def calculate_probability(self, dict_data, index, total_sum): # calculates probability for particular strat, taking propensity
return dict_data[index]/total_sum # of that strat as input
def calculate_sum(self, dict_data):
return sum(dict_data.values())
def initialize(self):
init_sum = self.calculate_sum(self.propensities)
for strategy in range(1, 10):
self.probabilites.append(self.calculate_probability(self.propensities, strategy, init_sum))
self.demand = self.pick_strat(1)
def update_strategy(self):
self.demand = self.pick_strat(1)
def update_probablities(self):
for i in range(9):
self.propensities[1 + i] *= 1 - mew
pensity_sum = self.calculate_sum(self.propensities)
for i in range(9):
self.probabilites[i] = self.calculate_probability(self.propensities, 1 + i, pensity_sum)
def update(self):
self.update_probablities()
self.update_strategy()
class Responder: # methods same as proposer class, can skip-over
def __init__(self):
self.propensities = create_random_propensities()
self.probabilites = []
self.max_thresh = 0 # the maximum demand they are willing to accept
def pick_strat(self, n_trials):
results = multinomial(n_trials, self.probabilites)
i, = np.where(results == max(results))
if len(i) > 1:
return choice(i) + 1
else:
return i[0] + 1
def calculate_probability(self, dict_data, index, total_sum):
return dict_data[index]/total_sum
def calculate_sum(self, dict_data):
return sum(dict_data.values())
def initialize(self):
init_sum = self.calculate_sum(self.propensities)
for strategy in range(1, 10):
self.probabilites.append(self.calculate_probability(self.propensities, strategy, init_sum))
self.max_thresh = self.pick_strat(1)
def update_strategy(self):
self.max_thresh = self.pick_strat(1)
def update_probablities(self):
for i in range(9):
self.propensities[1 + i] *= 1 - mew # stops sum of propensites from growing without bound
pensity_sum = self.calculate_sum(self.propensities)
for i in range(9):
self.probabilites[i] = self.calculate_probability(self.propensities, 1 + i, pensity_sum)
def update(self):
self.update_probablities()
self.update_strategy()
class Agent:
def __init__(self):
self.prop_side = Proposer()
self.resp_side = Responder()
self.prop_side.initialize()
self.resp_side.initialize()
def update_all(self):
self.prop_side.update()
self.resp_side.update()
class Grid:
def __init__(self, rowsize, colsize):
self.rowsize = rowsize
self.colsize = colsize
def make_lattice(self):
return [[Agent() for j in range(self.colsize)] for i in range(self.rowsize)]
#staticmethod
def von_neumann_neighbourhood(array, row, col, wrapped=True): # gets up, bottom, left, right neighbours of some node
neighbours = set([])
if row + 1 <= len(array) - 1:
neighbours.add(array[row + 1][col])
if row - 1 >= 0:
neighbours.add(array[row - 1][col])
if col + 1 <= len(array[0]) - 1:
neighbours.add(array[row][col + 1])
if col - 1 >= 0:
neighbours.add(array[row][col - 1])
#if wrapped is on, conditions for out of bound points
if row - 1 < 0 and wrapped == True:
neighbours.add(array[-1][col])
if col - 1 < 0 and wrapped == True:
neighbours.add(array[row][-1])
if row + 1 > len(array) - 1 and wrapped == True:
neighbours.add(array[0][col])
if col + 1 > len(array[0]) - 1 and wrapped == True:
neighbours.add(array[row][0])
return neighbours
def get_error_term(pay, strategy):
index_strat_2, index_strat_8 = 2, 8
if strategy == 1:
return (1 - (error/2)) * pay, error/2 * pay, index_strat_2
if strategy == 9:
return (1 - (error/2)) * pay, error/2 * pay, index_strat_8
else:
return (1 - error) * pay, error/2 * pay, 0
class Games:
def __init__(self, n_rows, n_cols, n_rounds):
self.rounds = n_rounds
self.rows = n_rows
self.cols = n_cols
self.lattice = Grid(self.rows, self.cols).make_lattice()
self.lookup_table = np.full((self.rows, self.cols), False, dtype=bool) # if player on grid has updated their strat, set to True
def reset_look_tab(self):
self.lookup_table = np.full((self.rows, self.cols), False, dtype=bool)
def run_game(self):
n = 0
while n < self.rounds:
for r in range(self.rows):
for c in range(self.cols):
if n != 0:
self.lattice[r][c].update_all()
self.lookup_table[r][c] = True
self.play_rounds(self.lattice, r, c)
self.reset_look_tab()
n += 1
def play_rounds(self, grid, row, col):
neighbours = Grid.von_neumann_neighbourhood(grid, row, col)
neighbour = random.sample(neighbours, 1).pop()
neighbour_index = [(ix, iy) for ix, row in enumerate(self.lattice) for iy, i in enumerate(row) if i == neighbour]
if self.lookup_table[neighbour_index[0][0]][neighbour_index[0][1]] == False: # see if neighbour has already updated their strat
neighbour.update_all()
player = grid[row][col]
coin_toss = randint(0, 1) # which player acts as proposer or responder in game
if coin_toss == 1:
if player.prop_side.demand <= neighbour.resp_side.max_thresh: # postive payoff
payoff, adjacent_payoff, index = get_error_term(player.prop_side.demand, player.prop_side.demand)
if player.prop_side.demand == 1 or player.prop_side.demand == 9: # extreme strategies get bonus payoffs
player.prop_side.propensities[player.prop_side.demand] += payoff
player.prop_side.propensities[index] += adjacent_payoff
else:
player.prop_side.propensities[player.prop_side.demand] += payoff
player.prop_side.propensities[player.prop_side.demand - 1] += adjacent_payoff
player.prop_side.propensities[player.prop_side.demand + 1] += adjacent_payoff
else:
return 0 # if demand > max thresh -> both get zero
if coin_toss != 1:
if neighbour.prop_side.demand <= player.resp_side.max_thresh:
payoff, adjacent_payoff, index = get_error_term(10 - neighbour.prop_side.demand, player.resp_side.max_thresh)
if player.resp_side.max_thresh == 1 or player.resp_side.max_thresh == 9:
player.resp_side.propensities[player.resp_side.max_thresh] += payoff
player.resp_side.propensities[index] += adjacent_payoff
else:
player.resp_side.propensities[player.resp_side.max_thresh] += payoff
player.resp_side.propensities[player.resp_side.max_thresh - 1] += adjacent_payoff
player.resp_side.propensities[player.resp_side.max_thresh + 1] += adjacent_payoff
else:
return 0
#pr = cProfile.Profile()
#pr.enable()
my_game = Games(10, 10, 2000) # (rowsize, colsize, n_steps)
my_game.run_game()
#pr.disable()
#pr.print_stats(sort='time')
(For those who might be wondering, the get_error_term just returns the propensities for strategies that are next to strategies that receive a positive payoff, for example if the strategy 8 works, then 7 and 9's propensities also get adjusted upwards and this is calculated by said function. And the first for loop inside update_probabilities just makes sure that the sum of propensities don't grow without bound).
I'm really new to this, and am always stuck by basic issues. Please can you show and explain my errors in this chunk of code. Right now the problem is that self is not defined, but I don't understand why.
It would be amazing if someone could clear this up.
import random
import time
class Building:
number_of_floors = 0
customer_list = []
elevator = 0
def __init__(self, floors, customers):
self.number_of_floors = floors
for customerID in range(1, customers + 1):
new = Customer(customerID,self.number_of_floors)
self.customer_list.append(new)
self.customer_list.sort(key = lambda x: x.current_floor)
self.elevator = Elevator(floors,self.customer_list)
self.run()
def run(self):
print('++++++++++++++++++++++++++ELEVATOR IS NOW STARTING+++++++++++++++')
print('Ther are %d customers in the building' % (len(self.customer_list)))
number_of_customers = len(self.customer_list)
self.output()
def output(self):
for customer in self.customer_list:
print("Customer",customer.customerID,"is on floor",customer.current_floor,"and wants to go",customer.destination_floor)
# ELEVATOR MOVING UP
while (self.elevator.current_floor <= self.number_of_floors) & (self.elevator.current_floor > 1):
self.elevator.current_floor -= 1
print(len(self.customer_list),'Customers in lift.')
print('ELEVATOR MOVING DOWN')
print('++++++++++++++++++++++++++++++++++++++++++++++++++++')
print('FLOOR',self.elevator.current_floor)
for customer in self.customer_list:
if (customer.in_elevator == True):
customer.current_floor = self.elevator.current_floor
if (slef.elevator.current_floor == customer.destination_floor) & (customer.in_elevator == True) & (customer.customer_direction == -1):
customer.in_elevator = False
self.customer_list.remove(customer)
print('Customer',customer.customerID,'has reached their destination')
print('There are',len(self.customer_list),'trapped in the elevator')
print('There are',len(Elevator.register_list),'people left on the register')
print('Elevator run is done!!!')
print('CUSTOMERS STUCK IN LIFT ARE BELOW')
for stuck in self.customer_list:
print('Cust. ID:',stuck.customerID,'Dest. Floor:',stuck.destination_floor,'Curr. Floor:',stuck.current_floor,'In Elevator',stuck.in_elevator,'Direction',stuck.customer_direction)
class Elevator:
number_of_floors = 0
register_list = []
current_floor = 0
up = 1
down = -1
def __init__(self, number_of_floors, register_list):
self.number_of_floors = number_of_floors
self.register_list = register_list
def move(self):
pass;
def register_customer(self, customers):
for reg in customers:
self.register_list.append(reg)
def cancel_customer(self, customers):
pass;
class Customer:
current_floor = 0
destination_floor = 0
customerID = 0
in_elevator = False
finished = False
customer_direction = 0
def __init__(self, customerID, floors):
self.customerID = customerID
self.current_floor = random.randint(1, floors)
self.destination_floor = random.randint(1, floors)
while self.destination_floor == self.current_floor:
self.desination_floor = random.randint(1, floors)
if self.current_floor < self.destination_floor:
self.customer_direction = 1
else:
self.customer_direction = -1
def header(): # elevator animation at beginning of program
print(" ELEVATOR OPENING ")
time.sleep(.2)
print("+++++++++++++++++++++++++++++||+++++++++++++++++++++++++++++++++++")
time.sleep(.2)
print("+++++++++++++++++++++++++| |++++++++++++++++++++++++++++++++")
time.sleep(.2)
print("++++++++++++++++| |+++++++++++++++++++")
time.sleep(.2)
print("++++++| |+++++++++")
time.sleep(.2)
print(" ")
time.sleep(.2)
print(" ELEVATOR CLOSING ")
time.sleep(.2)
print("++++++| |+++++++++")
time.sleep(.2)
print("++++++++++++++++| |+++++++++++++++++++")
time.sleep(.2)
print("+++++++++++++++++++++++++| |++++++++++++++++++++++++++++++++")
time.sleep(.2)
print("+++++++++++++++++++++++++++++||+++++++++++++++++++++++++++++++++++")
def main():
try:
floors = int(input('Enter the number of floors: '))
customers = int(input('Enter number of customers: '))
building = Building(floors, customers)
except ValueError:
print('YOU DIDNT ENTER A NUMBER. START AGAIN.')
main()
if __name__ == "__main__":
main()
You haven't indented the functions in your Building class.
I would like to start 4 independent threads which are basically methods of a class.
What am I doing wrong in the code below:
from threading import Thread
import time
import random
class Creature:
def __init__(self, name):
self.name = name
def melee(self, level):
self.melee = level
def shielding(self, level):
self.shielding = level
def health(self, total):
self.health = total
def attack(self, attacker, opponent):
while 0 != 1:
power = random.randint(1, attacker.melee)
resistance = random.randint(1, opponent.shielding)
resultant = power - resistance
if resistance > 0:
opponent.health -= resistance
if opponent.health < 0:
print(opponent.name, " is dead")
print("Opponent's health ", opponent.health)
quit()
else:
print(attacker.name, " delivered an attack of ", resistance)
print("Opponent's health ", opponent.health)
def healing(self, healed):
while 0 != 1:
if healed.health <= 0:
if healed.health < 50:
life = random.randint(1, 50)
healed.health += life
if healed.health > 100:
healed.health = 100
print(healed.name, " has healed and now has a health of ", self.health)
Monster = Creature("Wasp")
Monster.health = 100
Monster.melee = 30
Monster.shielding = 15
Player = Creature("Knight")
Player.health = 100
Player.melee = 25
Player.shielding = 20
t1 = Thread(target=Player.attack(Monster, Player))
t1.start()
t2 = Thread(target=Monster.attack(Player, Monster),)
t2.start()
t3 = Thread(target=Player.healing(Player), args=())
t3.start()
t4 = Thread(target=Monster.healing(Monster), args=())
t4.start()
Somehow I am doing something wrong since only the t1 gets started.
Why does the program start only t1? :-(
Thanks!
The problem(s) in your code are all demonstrable in this one line:
t1 = Thread(target=Player.attack(Monster, Player))
The trouble is that you're calling Player.attack, not passing it to the Thread constructor. Your attack method never returns, so you never get past the attempt to create the first Thread. You'd want to do something like this:
t1 = Thread(target = Player.attack, args = (Monster, Player))
I am currently working on my Python game, in ika, which uses python 2.5
I decided to use A* pathfinding for the AI. However, I find it too slow for my needs (3-4 enemies can lag the game, but I would like to supply up to 4-5 without problems). I know, that such complex search like A* is not mean to be scripted in python, but I am pretty sure, that my pathfinder is also implemented in the wrong way.
My question is: How can I speed up this algorithm?
I wrote my own binary heap, and there are some try: except: lines inside some functions. Those lines can create large overhead? Are there better methods maintaining the open list?
I supplied the algorithm with graphics interface, for testing purposes (when the pathfinder finishes searching, it will write the number of iterations and seconds it takes to find the path, inside the ika.txt file. Also, Pressing A will do a complete search, and S does that step by step.)
Graphical version:
http://data.hu/get/6084681/A_star.rar
Also, here is a pastebin version:
http://pastebin.com/9N8ybX5F
Here is the main code I use for pathfinding:
import ika
import time
class Node:
def __init__(self,x,y,parent=None,g=0,h=0):
self.x = x
self.y = y
self.parent = parent
self.g = g
self.h = h
def cost(self):
return self.g + self.h
def equal(self,node):
if self.x == node.x and self.y == node.y:
return True
else:
return False
class Emerald_Pathfinder:
def __init__(self):
pass
def setup(self,start,goal):
self.start = start
self.goal = goal
self.openlist = [None,start] # Implemented as binary heap
self.closedlist = {} # Implemented as hash
self.onopenlist = {} # Hash, for searching the openlist
self.found = False
self.current = None
self.iterations = 0
def lowest_cost(self):
pass
def add_nodes(self,current):
nodes = []
x = current.x
y = current.y
self.add_node(x+1,y,current,10,nodes)
self.add_node(x-1,y,current,10,nodes)
self.add_node(x,y+1,current,10,nodes)
self.add_node(x,y-1,current,10,nodes)
# Dont cut across corners
up = map.is_obstacle((x,y-1),x,y-1)
down = map.is_obstacle((x,y+1),x,y+1)
left = map.is_obstacle((x-1,y),x-1,y)
right = map.is_obstacle((x+1,y),x+1,y)
if right == False and down == False:
self.add_node(x+1,y+1,current,14,nodes)
if left == False and up == False:
self.add_node(x-1,y-1,current,14,nodes)
if right == False and up == False:
self.add_node(x+1,y-1,current,14,nodes)
if left == False and down == False:
self.add_node(x-1,y+1,current,14,nodes)
return nodes
def heuristic(self,x1,y1,x2,y2):
return (abs(x1-x2)+abs(y1-y2))*10
def add_node(self,x,y,parent,cost,list):
# If not obstructed
if map.is_obstacle((x,y),x,y) == False:
g = parent.g + cost
h = self.heuristic(x,y,self.goal.x,self.goal.y)
node = Node(x,y,parent,g,h)
list.append(node)
def ignore(self,node,current):
# If its on the closed list, or open list, ignore
try:
if self.closedlist[(node.x,node.y)] == True:
return True
except:
pass
# If the node is on the openlist, do the following
try:
# If its on the open list
if self.onopenlist[(node.x,node.y)] != None:
# Get the id number of the item on the real open list
index = self.openlist.index(self.onopenlist[(node.x,node.y)])
# If one of the coordinates equal, its not diagonal.
if node.x == current.x or node.y == current.y:
cost = 10
else:
cost = 14
# Check, is this items G cost is higher, than the current G + cost
if self.openlist[index].g > (current.g + cost):
# If so, then, make the list items parent, the current node.
self.openlist[index].g = current.g + cost
self.openlist[index].parent = current
# Now resort the binary heap, in the right order.
self.resort_binary_heap(index)
# And ignore the node
return True
except:
pass
return False
def resort_binary_heap(self,index):
m = index
while m > 1:
if self.openlist[m/2].cost() > self.openlist[m].cost():
temp = self.openlist[m/2]
self.openlist[m/2] = self.openlist[m]
self.openlist[m] = temp
m = m / 2
else:
break
def heap_add(self,node):
self.openlist.append(node)
# Add item to the onopenlist.
self.onopenlist[(node.x,node.y)] = node
m = len(self.openlist)-1
while m > 1:
if self.openlist[m/2].cost() > self.openlist[m].cost():
temp = self.openlist[m/2]
self.openlist[m/2] = self.openlist[m]
self.openlist[m] = temp
m = m / 2
else:
break
def heap_remove(self):
if len(self.openlist) == 1:
return
first = self.openlist[1]
# Remove the first item from the onopenlist
self.onopenlist[(self.openlist[1].x,self.openlist[1].y)] = None
last = self.openlist.pop(len(self.openlist)-1)
if len(self.openlist) == 1:
return last
else:
self.openlist[1] = last
v = 1
while True:
u = v
# If there is two children
if (2*u)+1 < len(self.openlist):
if self.openlist[2*u].cost() <= self.openlist[u].cost():
v = 2*u
if self.openlist[(2*u)+1].cost() <= self.openlist[v].cost():
v = (2*u)+1
# If there is only one children
elif 2*u < len(self.openlist):
if self.openlist[2*u].cost() <= self.openlist[u].cost():
v = 2*u
# If at least one child is smaller, than parent, swap them
if u != v:
temp = self.openlist[u]
self.openlist[u] = self.openlist[v]
self.openlist[v] = temp
else:
break
return first
def iterate(self):
# If the open list is empty, exit the game
if len(self.openlist) == 1:
ika.Exit("no path found")
# Expand iteration by one
self.iterations += 1
# Make the current node the lowest cost
self.current = self.heap_remove()
# Add it to the closed list
self.closedlist[(self.current.x,self.current.y)] = True
# Are we there yet?
if self.current.equal(self.goal) == True:
# Target reached
self.goal = self.current
self.found = True
print self.iterations
else:
# Add the adjacent nodes, and check them
nodes_around = self.add_nodes(self.current)
for na in nodes_around:
if self.ignore(na,self.current) == False:
self.heap_add(na)
def iterateloop(self):
time1 = time.clock()
while 1:
# If the open list is empty, exit the game
if len(self.openlist) == 1:
ika.Exit("no path found")
# Expand iteration by one
self.iterations += 1
# Make the current node the lowest cost
self.current = self.heap_remove()
# Add it to the closed list
self.closedlist[(self.current.x,self.current.y)] = True
# Are we there yet?
if self.current.equal(self.goal) == True:
# Target reached
self.goal = self.current
self.found = True
print "Number of iterations"
print self.iterations
break
else:
# Add the adjacent nodes, and check them
nodes_around = self.add_nodes(self.current)
for na in nodes_around:
if self.ignore(na,self.current) == False:
self.heap_add(na)
time2 = time.clock()
time3 = time2-time1
print "Seconds to find path:"
print time3
class Map:
def __init__(self):
self.map_size_x = 20
self.map_size_y = 15
self.obstructed = {} # Library, containing x,y couples
self.start = [2*40,3*40]
self.unit = [16*40,8*40]
def is_obstacle(self,couple,x,y):
if (x >= self.map_size_x or x < 0) or (y >= self.map_size_y or y < 0):
return True
try:
if self.obstructed[(couple)] != None:
return True
except:
return False
def render_screen():
# Draw the Character
ika.Video.DrawRect(map.start[0],map.start[1],map.start[0]+40,map.start[1]+40,ika.RGB(40,200,10),1)
# Draw walls
for x in range(0,map.map_size_x):
for y in range(0,map.map_size_y):
if map.is_obstacle((x,y),x,y) == True:
ika.Video.DrawRect(x*40,y*40,(x*40)+40,(y*40)+40,ika.RGB(168,44,0),1)
# Draw openlist items
for node in path.openlist:
if node == None:
continue
x = node.x
y = node.y
ika.Video.DrawRect(x*40,y*40,(x*40)+40,(y*40)+40,ika.RGB(100,100,100,50),1)
# Draw closedlist items
for x in range(0,map.map_size_x):
for y in range(0,map.map_size_y):
try:
if path.closedlist[(x,y)] == True:
ika.Video.DrawRect(x*40,y*40,(x*40)+20,(y*40)+20,ika.RGB(0,0,255))
except:
pass
# Draw the current square
try:
ika.Video.DrawRect(path.current.x*40,path.current.y*40,(path.current.x*40)+40,(path.current.y*40)+40,ika.RGB(128,128,128), 1)
except:
pass
ika.Video.DrawRect(mouse_x.Position(),mouse_y.Position(),mouse_x.Position()+8,mouse_y.Position()+8,ika.RGB(128,128,128), 1)
# Draw the path, if reached
if path.found == True:
node = path.goal
while node.parent:
ika.Video.DrawRect(node.x*40,node.y*40,(node.x*40)+40,(node.y*40)+40,ika.RGB(40,200,200),1)
node = node.parent
# Draw the Target
ika.Video.DrawRect(map.unit[0],map.unit[1],map.unit[0]+40,map.unit[1]+40,ika.RGB(128,40,200),1)
def mainloop():
while 1:
render_screen()
if mouse_middle.Pressed():
# Iterate pathfinder
if path.found == False:
path.iterateloop()
elif mouse_right.Pressed():
# Iterate pathfinder by one
if path.found == False:
path.iterate()
elif ika.Input.keyboard["A"].Pressed():
# Iterate pathfinder
if path.found == False:
path.iterateloop()
elif ika.Input.keyboard["S"].Pressed():
# Iterate pathfinder by one
if path.found == False:
path.iterate()
elif mouse_left.Position():
# Add a square to the map, to be obstructed
if path.iterations == 0:
x = mouse_x.Position()
y = mouse_y.Position()
map.obstructed[(int(x/40),int(y/40))] = True
# Mouse preview
x = mouse_x.Position()
y = mouse_y.Position()
mx = int(x/40)*40
my = int(y/40)*40
ika.Video.DrawRect(mx,my,mx+40,my+40,ika.RGB(150,150,150,70),1)
ika.Video.ShowPage()
ika.Input.Update()
map = Map()
path = Emerald_Pathfinder()
path.setup(Node(map.start[0]/40,map.start[1]/40),Node(map.unit[0]/40,map.unit[1]/40))
mouse_middle = ika.Input.mouse.middle
mouse_right = ika.Input.mouse.right
mouse_left = ika.Input.mouse.left
mouse_x = ika.Input.mouse.x
mouse_y = ika.Input.mouse.y
# Initialize loop
mainloop()
I appreciate any help!
(sorry for any spelling mistakes, English is not my native language)
I think a proper implementation in python will be fast enough for your purposes. But the boost library has an astar implementation and python bindings. https://github.com/erwinvaneijk/bgl-python