Commit 3d608521 authored by abdelgalil's avatar abdelgalil Committed by Ahmad Reza
Browse files

Created function to check if particle is stuck. Added more direction sequences...

Created function to check if particle is stuck. Added more direction sequences for more randomized particle navigation possibilities. General code refinement.
parent 5650df63
import random
from random import randint
import math
E = 0
SE = 1
SW = 2
W = 3
NW = 4
NE = 5
# S = 6 # S for stop and not south
black = 1
gray = 2
red = 3
green = 4
blue = 5
yellow = 6
orange = 7
cyan = 8
violet = 9
dirs = [E, SE, SW, W, NW, NE]
x_offset = [0.5, 1, 0.5, -0.5, -1, -0.5]
y_offset = [1, 0, -1, -1, 0, 1]
# TODO: (OPTIMIZATION) Add more sequences in dir_coords_array to randomize particle movement and rotation direction
dirs_array = [[E, SE, SW, W, NW, NE],
[SE, SW, W, NW, NE, E],
[SW, W, NW, NE, E, SE],
[W, NW, NE, E, SE, SW],
[NW, NE, E, SE, SW, W],
[NE, E, SE, SW, W, NW],
[E, NE, NW, W, SW, SE],
[NE, NW, W, SW, SE, E],
[NW, W, SW, SE, E, NE],
[W, SW, SE, E, NE, NW],
[SW, SE, E, NE, NW, W],
[SE, E, NE, NW, W, SW]]
# -1 = DFS, 0 = BFS
search_algorithms = [-1, 0]
# TODO (core):
# 1) Use move_to and move_in_dir instead of move_to_coords DONE
# 2) Change locations to Locations DONE
# 3) Alternative solution for get_distance and get_next_best_location X (Is get_distance allowed?)
# 4) When is the simulation successful? Evaluate metrics DONE
# 5) write down what solutions features and limitation DONE
# 6) develop an automated simulation tool
# TODO (Research):
# 1) Global vs Local (graph, visited and unvisited) DONE
# 2) 1 vs swarm
# 3) Memory limitations and computational power
# 4) Alternative solutions for stuck particles
# 5) What do particles do when they are done?
# TODO (Ideas):
# 1) P2P swarm idea
# 2) "Can" overload of zones (Kalman lectures)
class Location:
def __init__(self, coords):
self.coords = coords
self.adjacent = []
self.visited = False
def __eq__(self, other):
return self.coords == other.coords
def __str__(self):
return str(self.coords) + ' | Adjacent: ' + str([item.coords for item in self.adjacent])
# Checks if a location exists in a graph
def location_exists(graph, coords):
for location in graph:
if location.coords == coords:
return True
return False
# Returns the location from a graph given the coordinates
def get_location_with_coords(graph, coords):
for location in graph:
if location.coords == coords:
return location
return False
# Returns the direction of a location relative to the current location
def get_dir(current_location, target_location):
if target_location.coords[0] == current_location.coords[0] + x_offset[0] and target_location.coords[1] == current_location.coords[1] + y_offset[0]:
return 0
if target_location.coords[0] == current_location.coords[0] + x_offset[1] and target_location.coords[1] == current_location.coords[1] + y_offset[1]:
return 1
if target_location.coords[0] == current_location.coords[0] + x_offset[2] and target_location.coords[1] == current_location.coords[1] + y_offset[2]:
return 2
if target_location.coords[0] == current_location.coords[0] + x_offset[3] and target_location.coords[1] == current_location.coords[1] + y_offset[3]:
return 3
if target_location.coords[0] == current_location.coords[0] + x_offset[4] and target_location.coords[1] == current_location.coords[1] + y_offset[4]:
return 4
if target_location.coords[0] == current_location.coords[0] + x_offset[5] and target_location.coords[1] == current_location.coords[1] + y_offset[5]:
return 5
# Adds a new location to a graph
def add_location_to_graph(sim, graph, location):
if location in graph:
for direction in dirs:
adjacent_location_coords = sim.get_coords_in_dir(location.coords, direction)
if location_exists(graph, adjacent_location_coords):
if location in get_location_with_coords(graph, adjacent_location_coords).adjacent:
get_location_with_coords(graph, adjacent_location_coords).adjacent.append(location)
def random_walk(particle):
dir = dirs.copy()
new_dir = random.choice(dir)
# Checks if the given coordinates are valid simulator coordinates
def valid_sim_location(sim, coords):
if sim.check_coords(coords[0], coords[1]):
sim_coord = sim.coords_to_sim(coords)
if sim.get_sim_x_size() >= abs(sim_coord[0]) and sim.get_sim_y_size() >= abs(sim_coord[1]):
return True
return False
# Checks if the location at the given coordinates is a border or not
def is_border(sim, coords):
for location in sim.get_location_list():
if coords == location.coords:
if location.color == [0, 0, 0]:
return True
return False
# Initializes the new custom particle attributes
def set_particle_attributes(particle):
directions = dirs_array.copy()
search_algo = search_algorithms.copy()
direction = random.choice(directions)
search_algorithm = random.choice(search_algo)
setattr(particle, "direction", direction)
setattr(particle, "search_algorithm", search_algorithm)
setattr(particle, "unvisited_queue", [])
setattr(particle, "visited", [])
setattr(particle, "graph", [])
setattr(particle, "origin_coords", particle.coords)
setattr(particle, "start_location", Location(particle.origin_coords))
setattr(particle, "current_location", particle.start_location)
setattr(particle, "target_location", particle.start_location) # just marks origin position for now
setattr(particle, "last_visited_locations", [])
setattr(particle, "stuck_locations", [])
setattr(particle, "stuck", False)
setattr(particle, "done", False)
# Discovers the adjacent (Neighbour) locations relative to the particle's current location
def discover_adjacent_locations(sim, particle):
for direction in particle.direction:
adjacent_location_coords = sim.get_coords_in_dir(particle.current_location.coords, direction)
if not valid_sim_location(sim, adjacent_location_coords):
if is_border(sim, adjacent_location_coords):
if location_exists(particle.graph, adjacent_location_coords):
if get_location_with_coords(particle.graph, adjacent_location_coords) in particle.current_location.adjacent:
particle.current_location.adjacent.append(get_location_with_coords(particle.graph, adjacent_location_coords))
new_location = Location(adjacent_location_coords)
particle.create_location_on(adjacent_location_coords[0], adjacent_location_coords[1], color=blue)
add_location_to_graph(sim, particle.graph, new_location)
# Marks the particle's current location as visited and removes it from the particle's unvisited queue
def mark_location_as_visited(particle):
particle.current_location.visited = True
particle.unvisited_queue = [location for location in particle.unvisited_queue if location not in particle.visited]
# Returns the distance between 2 locations ########### IS THE USAGE OF THIS FUNCTION CONSIDERED GPS????????????????
def get_distance(location1, location2):
x1 = location1.coords[0]
x2 = location2.coords[0]
y1 = location1.coords[1]
y2 = location2.coords[1]
return abs(math.sqrt(((x2 - x1)**2) + ((y2 - y1)**2)))
# Returns the nearest location in the particle's unvisited queue relative to the particle's current location
def get_nearest_unvisited(particle):
possible_unvisited_locations = []
for location in particle.unvisited_queue:
possible_unvisited_locations.append((round(get_distance(particle.current_location, location)), location))
return min(possible_unvisited_locations, key=lambda t: t[0])[1]
# Returns the next best possible move if the particle's target location is not adjacent to it (path generator)
def get_next_best_location(current_location, target_location, stuck_locations):
possible_moves = []
for location in current_location.adjacent:
if location in stuck_locations:
possible_moves.append((get_distance(location, target_location), location))
if len(possible_moves) is 0:
return current_location.adjacent[randint(0, len(current_location.adjacent) - 1)]
best_location = min(possible_moves, key=lambda t: t[0])[1]
return best_location
# Returns the next closest unvisited location relative to the particle's current location
def get_next_unvisited(particle, search_algorithm):
if particle.unvisited_queue[search_algorithm] not in particle.current_location.adjacent:
return get_next_best_location(particle.current_location, get_nearest_unvisited(particle), particle.stuck_locations)
return particle.unvisited_queue[search_algorithm]
# Enables the particles to create packets with their own data and send them to one another if they are within range
def communicate(particle, communication_range):
# TODO(OPTIMIZATION) should the particles exchange unvisited locations as well? What would be the benefit?
packet = (particle.graph, particle.visited, particle.unvisited_queue)
# TODO(OPTIMIZATION) What should the communication range be?
found_particles = particle.scan_for_particle_within(hop=communication_range)
if found_particles is None:
for particle in found_particles:
particle.write_to_with(particle, particle.get_id(), packet)
# Enables the particle to extend its own data with the data recieved from other particles
def analyse_memory(sim, particle):
if particle.read_whole_memory():
for particle_id in particle.read_whole_memory():
for location in particle.read_whole_memory()[particle_id][0]:
if location not in particle.graph:
add_location_to_graph(sim, particle.graph, location)
particle.visited.extend([location for location in particle.read_whole_memory()[particle_id][1] if
location not in particle.visited])
# Checks if the particle's next location is in a stuck cycle or not
def next_location_in_stuck_nodes(particle, next_location):
if next_location in particle.last_visited_locations:
if next_location in particle.stuck_locations:
particle.stuck = True
return True
# if len(particle.stuck_locations) >= 10:
# particle.stuck_locations.pop(0)
return False
return False
def solution(sim):
done_particles = 0
for particle in sim.get_particle_list():
if sim.get_actual_round() == 1:
particle.create_location_on(particle.origin_coords[0], particle.origin_coords[1], color=blue)
add_location_to_graph(sim, particle.graph, particle.current_location)
discover_adjacent_locations(sim, particle)
# TODO(OPTIMIZATION) How often should the particles communicate if they are within range?
if sim.get_actual_round() > 30:
if sim.get_actual_round() % 15 == 0:
communicate(particle, 5)
# pass
analyse_memory(sim, particle)
# TODO(OPTIMIZATION) How many locations should form the stuck cycle? Is there a better solution?
# if len(particle.stuck_locations) >= 20:
if sim.get_actual_round() % 20 == 0:
next_location = get_next_unvisited(particle, particle.search_algorithm) # 0 for BFS, -1 for DFS
if next_location_in_stuck_nodes(particle, next_location):
next_location = particle.current_location.adjacent[
randint(0, len(particle.current_location.adjacent) - 1)]
next_direction = get_dir(particle.current_location, next_location)
particle.current_location = next_location
discover_adjacent_locations(sim, particle)
except IndexError:
particle.current_location = get_location_with_coords(particle.graph, particle.coords)
discover_adjacent_locations(sim, particle)
if particle.current_location is particle.target_location:
done_particles += 1
particle.done = True
particle.current_location = get_location_with_coords(particle.graph, particle.coords)
next_location = get_next_best_location(particle.current_location, particle.target_location, particle.stuck_locations)
if next_location_in_stuck_nodes(particle, next_location):
next_location = particle.current_location.adjacent[
randint(0, len(particle.current_location.adjacent) - 1)]
next_direction = get_dir(particle.current_location, next_location)
particle.current_location = next_location
if location_exists(particle.graph, next_location.coords):
except ValueError:
discover_adjacent_locations(sim, particle)
if done_particles == len(sim.get_particle_list()):
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