def build_flat_plane(self):
scene = Scene(self.screen, self.camera_position, COLOUR_WHITE, self.scale)
count = 1
for x in range(count):
for y in range(count):
s = int(self.screen.width / count)
cube_material = Material(
(0 + int(random.random() * 100), 150 + int(random.random() * 100), int(1 * random.random())),
# (0,0,255),
(0.5, 0.5, 0.5),
(0.8, 0.8, 0.8),
(0.5, 0.5, 0.5),
50
)
rectangle = Rectangle(
Point(x * s - self.screen.width/2, -self.screen.height / 2, self.screen.distance + y * s),
Point(x * s - self.screen.width/2, -self.screen.height / 2, self.screen.distance + (y+1) * s),
Point((x+1) * s - self.screen.width/2, -self.screen.height / 2, self.screen.distance + y * s),
cube_material
)
scene.add_object(rectangle)
light_position = Point(-self.screen.width/4,-self.screen.height/2 + 50, self.screen.width * 4 + self.screen.distance)
light = Light(light_position)
scene.add_light(light)
return scene
def __init__(self):
super().__init__()
self.setWindowTitle("Koch Curve - Iteration 0")
self.setFixedSize(640, 480)
self.iterations = [[Point(20, self.height() - 20), Point(self.width() - 20, self.height() - 20)]]
self.current_iteration = 0
self.show()
def draw_coordinate_grid(self, num=(10, 10)):
for x in xrange(int(self.field.left), 1 + int(self.field.right), 1):
pygame.draw.line(
self.screen, GRID_COLOR if x != 0 else AXES_COLOR,
self.field.fit_on_screen(Point(x, self.field.bottom)),
self.field.fit_on_screen(Point(x, self.field.top)))
for y in xrange(int(self.field.bottom), 1 + int(self.field.top), 1):
pygame.draw.line(
self.screen, GRID_COLOR if y != 0 else AXES_COLOR,
self.field.fit_on_screen(Point(self.field.left, y)),
self.field.fit_on_screen(Point(self.field.right, y)))
def __init__(self, x, y, mass=0.3, energy=0.1, x_vel=0.0, y_vel=0.0):
"""Cells begin with a specified position, without velocity, task or destination."""
# Position, Velocity and Acceleration vectors:
self.pos = Point(float(x), float(y))
self.vel = Vector(x_vel, y_vel)
self.acl = Vector(0.0, 0.0)
# Arbitrary constants:
self.density = .005 # density is used to calculate radius
# Required for motion:
self.mass = 1
self.walk_force = 0.001
self.exerted_force = Vector(0.0, 0.0)
self.weight_management()
# Required for logic:
self.task = None
self.destination = None
self.destination_type = None
self.energy = energy
# Task jumptable:
self.TaskTable = {}
self.TaskTable[None] = self.task_none
self.TaskTable["FindingFood"] = self.task_finding_food
self.TaskTable["GettingFood"] = self.task_getting_food
# Misc:
self.color = random_color()
def _point(self, x, y, z):
rotated_x = x * self.side_length / 2
rotated_y = y * self.side_length / 2
rotated_z = z * self.side_length / 2
# apply z-axis rotation
rotated_x, rotated_y = rotated_x * math.cos(
self.z_rotation) - rotated_y * math.sin(
self.z_rotation), rotated_x * math.sin(
self.z_rotation) + rotated_y * math.cos(self.z_rotation)
# apply y-axis rotation
rotated_x, rotated_z = rotated_x * math.cos(
self.y_rotation) + rotated_z * math.sin(
self.y_rotation), -rotated_x * math.sin(
self.y_rotation) + rotated_z * math.cos(self.y_rotation)
# apply x-axis rotation
rotated_y, rotated_z = rotated_y * math.cos(
self.x_rotation) - rotated_z * math.sin(
self.x_rotation), rotated_y * math.sin(
self.x_rotation) + rotated_z * math.cos(self.x_rotation)
return Point(rotated_x + self.center.x, rotated_y + self.center.y,
rotated_z + self.center.z)
def searcher(self,
position): # - Блят! да почему она не работает как надо?!
# - Потому, что ты перепутал Х с У долбоёб!
temp_list = []
point = False
for stage in self.algorithm:
if stage == "next":
point = True
if len(temp_list) != 0:
self.priority = True
self.move_list = temp_list
return 'complite'
continue
if not (1 <= (self.position.col + stage.col) <= 8): continue
if not (1 <= (self.position.row + stage.row) <= 8): continue
if point == True:
field = self.matrix[self.position.col +
stage.col][self.position.row + stage.row]
team = field.get_team()
if field.get_team() == "free":
temp_list.append(
Point(self.position.col + stage.col,
self.position.row + stage.row))
self.move_list = temp_list
def get_intersection_point(self, line):
line_unit_vector = line.vector.unit()
n_dot_u = self.plane_normal.dot_product(line_unit_vector)
if not n_dot_u:
# The line is parallel to the plane
return None
w = Vector.from_points(self.p1, line.point)
s1 = -self.plane_normal.dot_product(w) / n_dot_u
if s1 < 0:
# The intersection point is on the wrong side of line.point
return None
intersection = Point(w.x + s1 * line_unit_vector.x + self.p1.x,
w.y + s1 * line_unit_vector.y + self.p1.y,
w.z + s1 * line_unit_vector.z + self.p1.z)
a_m = Vector.from_points(self.p1, intersection)
a_b = Vector.from_points(self.p1, self.p2)
a_d = Vector.from_points(self.p1, self.p3)
am_dot_ab = a_m.dot_product(a_b)
ab_dot_ab = a_b.dot_product(a_b)
am_dot_ad = a_m.dot_product(a_d)
ad_dot_ad = a_d.dot_product(a_d)
if (0 < am_dot_ab < ab_dot_ab) and (0 < am_dot_ad < ad_dot_ad):
return intersection
def image_coordinate(self, space_coord):
x, y = space_coord.x, space_coord.y
# y direction is opposite
return Point(
self.origin_x + x * self.cell_size,
self.origin_y - y * self.cell_size
)
def __init__(self,
x,
y,
mass=0.3,
energy=0.1,
x_vel=0.0,
y_vel=0.0,
inherited_phenotype=Phenotype()):
"""Cells begin with a specified position, without velocity, task or destination."""
# Position, Velocity and Acceleration vectors:
self.pos = Point(float(x), float(y))
self.vel = Vector(x_vel, y_vel)
self.acl = Vector(0.0, 0.0)
# Brad's Satisfaction
self.keyList = []
for i in range(0, 9):
self.keyList.append([])
for j in range(0, 9):
self.keyList[i].append(False)
# Phenotypes:
self.phenotype = inherited_phenotype # Stored for calc_variance's sake
self.emRatio = inherited_phenotype.AI.emRatio # Energy/Mass gain ratio
self.div_energy = self.phenotype.AI.div_energy # How much energy a cell needs to divide
self.div_mass = self.phenotype.AI.div_mass # How much mass a cell needs to divide
self.walk_force = self.phenotype.Static.walk_force
self.density = self.phenotype.AI.density
self.mutation_chance = self.phenotype.AI.mutation_chance # The likelihood of each phenotype mutating
if self.phenotype.AI.color == None: self.color = Cell.colors.genColor()
else: self.color = mutateColor(self.phenotype.AI.color)
# Required for motion:
self.energy = energy
self.mass = mass
self.exerted_force = Vector(0.0, 0.0)
self.weight_management()
# Required for logic:
self.task = None
self.destination = None
self.destination_type = None
# Task jumptable:
self.TaskTable = {}
self.TaskTable[None] = self.task_none
self.TaskTable["FindingFood"] = self.task_finding_food
self.TaskTable["GettingFood"] = self.task_getting_food
def reverse(self, algorithm):
new_alg = []
for i in algorithm:
if i == 'next':
new_alg.append(i)
else:
new_alg.append(Point(-i.col, -i.row))
return new_alg
def graham_angle(p1, p2):
''' Not exactly the angle of the vector P₁P₂ but something that preserves
the order (actually it flips it backwards but that's a good thing):
if ∠P₁P₂ < ∠P₁P₃ then graham_angle(p1, p2) > graham_angle(p1, p3)
'''
if p1 is p2: # this can only happen if p1 and p2 is self.min_point
return -1.01 # just to guarantee the starting point is the last one
v = Point(p2.x - p1.x, p2.y - p1.y)
# monotonic with the angle, and faster to compute
return (v.x * abs(v.x)) / squared_norm(v)
def BoundingBox(points):
xmin, ymin = points[0].x, points[0].y
xmax, ymax = xmin, ymin
for p in points:
if p.x < xmin:
xmin = p.x
elif p.x > xmax:
xmax = p.x
if p.y < ymin:
ymin = p.y
elif p.y > ymax:
ymax = p.y
return [
Point(xmin, ymin),
Point(xmin, ymax),
Point(xmax, ymax),
Point(xmax, ymin)
]
def __init__(self,
name=None,
ico="·",
team="neutral",
location=None,
matrix=None,
position=None):
Figure.__init__(self)
self.name = name
self.ico = ico
self.move_list = []
self.team = team
self.location = location #top bottom
self.position = position
self.matrix = matrix
self.priority = False
self.algorithm = [
Point(1, 1),
Point(-1, 1),
Point(-1, -1),
Point(1, -1), "next",
Point(1, 1),
Point(1, -1)
]
if location == "top":
self.algorithm = self.reverse(self.algorithm)
def BoundingBoxDC(points):
''' points must be a list of Point, or whatever has both an x and a y
member '''
if not isinstance(points, list):
raise TypeError('points must be a list of Point')
# We need to add the first point to the end
points.append(points[0])
# We use a divide and conquer algorithm to find the extrema
p_xmax = extremaDC(points, RIGHT)
p_xmin = extremaDC(points, LEFT)
p_ymax = extremaDC(points, UP)
p_ymin = extremaDC(points, DOWN)
xmin, xmax, ymin, ymax = p_xmin.x, p_xmax.x, p_ymin.y, p_ymax.y
return [
Point(xmin, ymin),
Point(xmin, ymax),
Point(xmax, ymax),
Point(xmax, ymin)
]
def task_finding_food(self):
#closest piece of food
close_food = World.food_at(self.pos, self.sight_range)
#If there is any food within distance self.sight_range, get the closest one.
if len(close_food) > 0:
closest_food = min(
close_food,
key=partial(
reduce, call,
(attrgetter("pos"), attrgetter("distance_to"),
partial(
call,
self.pos)))) # food: self.pos.distance_to(food.pos))
else:
closest_food = None
if len(close_food) == 0:
"""What the cell does should it be looking for food."""
#If you can't see food, accelerate in a random direction.
x = random.uniform(0, World.width)
y = random.uniform(0, World.height)
self.destination = Point(x, y)
self.destination_type = "Exploration"
self.calc_force()
else:
#If there is any food within distance SIGHT_RANGE, get the closest one.
#closest_food = min(close_food, key = lambda food: self.pos.distance_to(food.pos))
closest_food = min(
close_food,
key=partial(
reduce, call,
(attrgetter("pos"), attrgetter("distance_to"),
partial(
call,
self.pos)))) # food: self.pos.distance_to(food.pos))
def stop_getting_food(food):
"""After the food gets eaten, stop trying to get it."""
self.destination = self.destination_type = self.task = None
self.task = "GettingFood"
self.destination_type = "Food"
#weakref.proxy calls stop_getting_food when the food is destroyed.
self.destination = weakref.proxy(closest_food.pos,
stop_getting_food)
self.food_target = weakref.ref(closest_food)
def fillinger(even=None, ico="·", uname="neutral", location=None):
self.j += 1
self.i = 0
line = [None]
for num in range(8):
self.i += 1
if (num % 2 == 0) is even or even == None:
line.append(FreeField(name="free", ico="·", team="free"))
else:
new = Checker(name="checker",
ico=ico,
team=uname,
location=location,
matrix=self.matrix,
position=Point(self.j, self.i))
line.append(new)
self.figure_set.append(new)
return line
def _calculate_pixel_colour(self, x, y):
screen_pixel_position = Point(x - self.screen.width / 2, y - self.screen.height / 2, self.screen.distance)
camera_to_pixel_vector = Vector.from_points(self.camera_position, screen_pixel_position)
camera_to_pixel = Line(camera_to_pixel_vector, self.camera_position)
intersection_points = []
for rectangle in self.rectangles:
intersection_point_for_current = rectangle.get_intersection_point(camera_to_pixel)
if intersection_point_for_current:
intersection_points.append((rectangle, intersection_point_for_current))
if intersection_points:
closest_intersection = min(intersection_points, key=lambda p: p[1].distance_to(self.camera_position))
illumination = self._calculate_illumination(*closest_intersection)
if illumination:
return self._blinn_phong_colour(illumination)
return self.background_colour
def __init__(self, x, y, mass=0.3, energy=0.1, x_vel=0.0, y_vel=0.0, inherited_phenotype=Phenotype()):
"""Cells begin with a specified position, without velocity, task or destination."""
# Position, Velocity and Acceleration vectors:
self.pos = Point(float(x), float(y))
self.vel = Vector(x_vel, y_vel)
self.acl = Vector(0.0, 0.0)
# Brad's Satisfaction
self.keyList = []
for i in range(0,9):
self.keyList.append([])
for j in range (0,9):
self.keyList[i].append(False)
# Phenotypes:
self.phenotype = inherited_phenotype # Stored for calc_variance's sake
self.emRatio = inherited_phenotype.AI.emRatio # Energy/Mass gain ratio
self.div_energy = self.phenotype.AI.div_energy # How much energy a cell needs to divide
self.div_mass = self.phenotype.AI.div_mass # How much mass a cell needs to divide
self.walk_force = self.phenotype.Static.walk_force
self.density = self.phenotype.AI.density
self.mutation_chance = self.phenotype.AI.mutation_chance # The likelihood of each phenotype mutating
if self.phenotype.AI.color == None: self.color = Cell.colors.genColor()
else: self.color = mutateColor(self.phenotype.AI.color)
# Required for motion:
self.energy = energy
self.mass = mass
self.exerted_force = Vector(0.0, 0.0)
self.weight_management()
# Required for logic:
self.task = None
self.destination = None
self.destination_type = None
# Task jumptable:
self.TaskTable = {}
self.TaskTable[None] = self.task_none
self.TaskTable["FindingFood"] = self.task_finding_food
self.TaskTable["GettingFood"] = self.task_getting_food
def paintEvent(self, _: QPaintEvent):
qp = QPainter(self)
qp.setPen(Qt.white)
qp.setBrush(Qt.white)
qp.drawRect(self.rect())
qp.drawImage(0, 0, self.img)
center = Point(self.width() / 2, self.height() / 2)
p1 = center + Vector(120, self.angle1).to_point()
p2 = p1 + Vector(100, self.angle2).to_point()
qp.setPen(QPen(Qt.black, 2))
qp.drawLine(*center, *p1)
qp.drawLine(*p1, *p2)
self.ip.setPen(QPen(Qt.black, 2))
self.ip.drawPoint(*p2)
if self.last_point:
self.ip.drawLine(*self.last_point, *p2)
self.last_point = p2
def left_click_handler(event):
global data
if event.xdata is None or event.ydata is None:
return
point = Point(x=event.xdata, y=event.ydata)
for p in data:
# check if we clicked over a point, so we need to delete it
if plot_distance_in_points(point, p) < plot_data.marker_radius:
data.remove(p)
break
else: # if we didn't click over an existing point, we create a new one
data.append(point)
data.sort(key=lambda p: p.y, reverse=True)
plot_data.clean_plot()
if data:
ax.scatter(*zip(*data), color='C0', zorder=10, linewidth=0)
excluded = data[-1]
ax.scatter(*excluded, color='C1', zorder=10, linewidth=0)
def __init__(self, x, y, mass=0.3, energy=0.1, x_vel=0.0, y_vel=0.0, Phenotype=[2.0, 0.001, 0.5,0.6, 0.005, None, 0.0]):
"""Cells begin with a specified position, without velocity, task or destination."""
# Position, Velocity and Acceleration vectors:
self.pos = Point(float(x), float(y))
self.vel = Vector(x_vel, y_vel)
self.acl = Vector(0.0, 0.0)
# Phenotypes:
self.phenotype = Phenotype # Stored for calc_variance's sake
self.emRatio = Phenotype[0] # Energy/Mass gain ratio
self.walk_force = Phenotype[1]
self.div_energy = Phenotype[2] # How much energy a cell needs to divide
self.div_mass = Phenotype[3] # How much mass a cell needs to divide
self.density = Phenotype[4]
if Phenotype[5] == None:
self.color = random_color()
Phenotype[5] = self.color
else: self.color = Phenotype[5]
self.mutation_chance = Phenotype[6] # The likelihood of each phenotype mutating
# Required for motion:
self.energy = energy
self.mass = mass
self.exerted_force = Vector(0.0, 0.0)
self.weight_management()
# Required for logic:
self.task = None
self.destination = None
self.destination_type = None
# Task jumptable:
self.TaskTable = {}
self.TaskTable[None] = self.task_none
self.TaskTable["FindingFood"] = self.task_finding_food
self.TaskTable["GettingFood"] = self.task_getting_food
def test_Point(self):
test_p = Point(2,4)
self.assertEqual(test_p + 3, Point(5,7))
pass
def __init__(self, x, y):
self.energy = 0.5
self.pos = Point(x, y)
self.pos.fit_to_torus()
class Cell:
def __init__(self, x, y, mass=0.3, energy=0.1, x_vel=0.0, y_vel=0.0):
"""Cells begin with a specified position, without velocity, task or destination."""
# Position, Velocity and Acceleration vectors:
self.pos = Point(float(x), float(y))
self.vel = Vector(x_vel, y_vel)
self.acl = Vector(0.0, 0.0)
# Arbitrary constants:
self.density = .005 # density is used to calculate radius
# Required for motion:
self.mass = 1
self.walk_force = 0.001
self.exerted_force = Vector(0.0, 0.0)
self.weight_management()
# Required for logic:
self.task = None
self.destination = None
self.destination_type = None
self.energy = energy
# Task jumptable:
self.TaskTable = {}
self.TaskTable[None] = self.task_none
self.TaskTable["FindingFood"] = self.task_finding_food
self.TaskTable["GettingFood"] = self.task_getting_food
# Misc:
self.color = random_color()
# "Task" functions, i.e. the cell's activities during each tick, depending on its task.
def task_none(self):
"""What the cell does should it have no task."""
self.task = "FindingFood"
def task_finding_food(self):
#closest piece of food
close_food = environment.Environment().food_at(self.pos, self.sight_range)
#If there is any food within distance self.sight_range, get the closest one.
if len(close_food) > 0:
closest_food = min(close_food, key = partial(reduce, call, (attrgetter("pos"), attrgetter("distance_to"), partial(call, self.pos))))# food: self.pos.distance_to(food.pos))
else: closest_food = None
if len(close_food) == 0:
"""What the cell does should it be looking for food."""
#If you can't see food, accelerate in a random direction.
x = random.uniform(0, environment.Environment().width)
y = random.uniform(0, environment.Environment().height)
self.destination = Point(x, y)
self.destination_type = "Exploration"
self.calc_force()
else:
#If there is any food within distance SIGHT_RANGE, get the closest one.
#closest_food = min(close_food, key = lambda food: self.pos.distance_to(food.pos))
closest_food = min(close_food, key = partial(reduce, call, (attrgetter("pos"), attrgetter("distance_to"), partial(call, self.pos))))# food: self.pos.distance_to(food.pos))
def stop_getting_food(food):
"""After the food gets eaten, stop trying to get it."""
self.destination = self.destination_type = self.task = None
self.task = "GettingFood"
self.destination_type = "Food"
#weakref.proxy calls stop_getting_food when the food is destroyed.
self.destination = weakref.proxy(closest_food.pos, stop_getting_food)
self.food_target = weakref.ref(closest_food)
def task_getting_food(self):
"""What the cell does when it has found food and is attempting to get it."""
#assert(len(environment.Environment().food_at(self.destination, 0)) != 0)
distance_to_destination = self.pos.distance_to(self.destination)
print self.destination
print distance_to_destination
if distance_to_destination > self.distance_to_start_slowing_down():
self.calc_force()
for f in environment.Environment().food_at(self.pos, self.radius):
self.eat(f)
#if distance_to_destination <= self.radius:
# self.eat(self.food_target())
def update_coords(self):
"""Updates the cell's position, velocity and acceleration in that order."""
prev_vel = Vector(self.vel.x, self.vel.y)
self.pos += self.vel
self.vel += self.acl
#acl is change in velocity
#displacement = (prev_vel + self.exerted_force/self.mass/2)
#self.energy -= self.exerted_force*displacement
#self.energy -= self.exerted_force*prev_vel
self.acl = self.exerted_force - self.vel*abs(self.vel)*environment.Environment().resistance*(self.radius)/self.mass
self.exerted_force = Vector(0.0,0.0)
def calc_force(self):
"""Cells calculate how much force they are exerting (prior to resistance)."""
self.exerted_force = (self.destination - self.pos)*self.walk_force / abs(self.destination - self.pos)
#self.exerted_force = (self.destination - self.pos)*self.walk_force / (abs(self.destination - self.pos)*self.mass)
if self.energy > self.walk_force:
self.energy -= self.walk_force*1.0
else:
self.mass -= self.walk_force*3.0
"""Changes the cell's position based on its velocity, a.k.a. movement."""
def distance_to_start_slowing_down(self):
"""Calculates the distance from the destination that, once past,
the cell ought to begin slowing down to reach its destination."""
return (abs(self.vel) * self.mass) / (environment.Environment().resistance * self.radius)
def eat(self, f):
#for f in environment.Environment().food_at(self.pos, self.radius):
self.energy += f.energy/2.0
self.mass += f.energy/2.0
environment.Environment().remove_food(f)
#The above line automatically resets our task and destination by calling stop_getting_food()
def weight_management(self):
self.radius = ( 3.0*self.mass*self.density / (4.0*math.pi) )**(1/2.0)
self.sight_range = .2 + self.radius
def life_and_death(self):
if self.energy >= 0.5 and self.mass >= 0.6: #hardcoded threshold
#stats of both babbyz
newMass = self.mass/2.0
newEnergy = (self.energy - 3.0)/2.0
#make babby 1
x1 = random.uniform(self.pos.x-0.01,self.pos.x+0.01)
y1 = random.uniform(self.pos.y-0.01,self.pos.y+0.01)
environment.Environment().add_cells_at_location(x1,y1,newMass,newEnergy,self.vel.x,self.vel.y)
#make babby 2
x2 = random.uniform(self.pos.x-0.01,self.pos.x+0.01)
y2 = random.uniform(self.pos.y-0.01,self.pos.y+0.01)
environment.Environment().add_cells_at_location(x2,y2,newMass,newEnergy,self.vel.x,self.vel.y)
#make two cells at slightly different positions
environment.Environment().remove_cell(self)
elif self.mass <= 0.1:
environment.Environment().kill_cell(self)
def one_tick(self):
"""What a cell does every arbitrary unit of time."""
self.TaskTable[self.task]()
self.update_coords()
self.weight_management()
self.life_and_death()
from renderer import ImageRenderer
from scene import Screen
from scene_builder import SceneBuilder
from vector import Point
if __name__ == '__main__':
SCREEN_WIDTH = 400
SCREEN_HEIGHT = 200
SCREEN_DISTANCE = 400
screen = Screen(SCREEN_WIDTH, SCREEN_HEIGHT, SCREEN_DISTANCE)
CAMERA_X = 0
CAMERA_Y = SCREEN_HEIGHT
CAMERA_Z = 0
camera_position = Point(CAMERA_X, CAMERA_Y, CAMERA_Z)
SCALE = 1
scene = SceneBuilder(screen, camera_position, SCALE).build_flat_plane()
ImageRenderer(scene).render('trace.png')
def __init__(self, x, y): self.energy = 0.5 self.pos = Point(x,y) self.pos.fit_to_torus()
from level import Level
from obstacle import Obstacle
from vector import Point
WIDTH: int = 640
HEIGHT: int = 480
POPULATION_SIZE: int = 1000
CHECKPOINT_OPTIMIZATION_ROUNDS: int = 20
LEVEL: Level = [
Level(Point(WIDTH / 2, HEIGHT - 10), Point(WIDTH / 2, 10), [
Obstacle(Point(0, HEIGHT / 2 - 5), Point(WIDTH * 3 / 4,
HEIGHT / 2 + 5))
], []),
Level(Point(WIDTH / 8, HEIGHT * 3 / 4), Point(WIDTH / 8, HEIGHT / 4), [
Obstacle(Point(0, HEIGHT / 2 - 5), Point(WIDTH * 7 / 8,
HEIGHT / 2 + 5))
], [Point(WIDTH * 15 / 16, HEIGHT / 2)]),
Level(Point(WIDTH / 2, HEIGHT - 10), Point(WIDTH / 2, 10), [
Obstacle(Point(0, HEIGHT / 4 - 5),
Point(WIDTH * 3 / 4 + 5, HEIGHT / 4 + 5)),
Obstacle(Point(WIDTH * 3 / 4 - 5, HEIGHT / 4 - 5),
Point(WIDTH * 3 / 4 + 5, HEIGHT * 5 / 8 - 5)),
Obstacle(Point(WIDTH * 3 / 8, HEIGHT * 5 / 8 - 5),
Point(WIDTH * 3 / 4 + 5, HEIGHT * 5 / 8 + 5)),
Obstacle(Point(WIDTH / 4, HEIGHT * 3 / 8 - 5),
Point(WIDTH * 5 / 8, HEIGHT * 3 / 8 + 5)),
Obstacle(Point(WIDTH / 4 - 5, HEIGHT * 3 / 8 - 5),
Point(WIDTH / 4 + 5, HEIGHT * 3 / 4 + 5)),
Obstacle(Point(WIDTH / 4 - 5, HEIGHT * 3 / 4 - 5),
def move_like_virus(self):
if not self.bool_moving: #if the cell isn't moving
self.ticksLeft = 0 #then it's at the beginning - it's made 0 steps
# print "Not movin', trying to move... "
self.bool_moving = True #get ready to moves
# print "pos = ", self.pos
current_pos = self.pos #placeholder variable - bad habits from Java
ref_pos = Point(
0, 0) #prepare a reference frame based on current location
self.moving_path_as_string = "x^", self.curveTendency
# print "future path = ", self.moving_path_as_string
#end_of_eY = environment.Environment().height - current_pos.y
#end_of_eX = environment.Environment().width - current_pos.x
distanceTraveled = 0 #we haven't gone anywhere yet
timeLeft = int(self.lifeSpan /
3) #viruses choose three paths in their life
# print "time left = ", timeLeft
while timeLeft > 0: #we begin adding positions in the virus' trajectory
#nextPos_ref is the projected position based on a null reference frame
nextPos_ref = Point(
ref_pos.x + self.driftSpeed,
((ref_pos.x + self.driftSpeed)**self.curveTendency))
#print "for reference... ", nextPos_ref
#realPos adds the reference frame position to the actual position
realPos = Point(current_pos.x + nextPos_ref.x,
current_pos.y + nextPos_ref.y)
#print "adding... ", realPos
self.moving_path.append(realPos)
#the reference doesn't reset, but builds upon itself
ref_pos = nextPos_ref
#decrease steps left
timeLeft -= 1
#now that movement has been predicted, we prepare for next movement
#change curve which is being followed
self.curveTendency = random.randint(1, 3)
multi = random.uniform(-1, 1)
#change direction possibly
self.driftSpeed = self.maxSpeed * multi / abs(multi)
#self.driftSpeed = random.uniform(-self.maxSpeed, self.maxSpeed)
# print "curve = ", self.curveTendency
# print "drift = ", self.driftSpeed
#these should ever be used, but just in case
if self.curveTendency > 10:
self.curveTendency = 3
elif self.curveTendency <= 0:
self.curveTendency = 1
else: #we're moving!
# print "moving now"
ticksMax = len(self.moving_path) #how many steps are to be taken?
#print "path = ", self.moving_path
# print "length of path = ", ticksMax
# print "ticks left = ", self.ticksLeft
#change x and y
self.pos.x = self.moving_path[self.ticksLeft].x
self.pos.y = self.moving_path[self.ticksLeft].y
# print "moving to ", self.pos
#remeber ticksLeft? Here it's used
self.ticksLeft += 1
if self.ticksLeft == ticksMax: #when we've reached the end
self.bool_moving = False #stop
self.moving_path = [] #empty the path
ticksLeft = 0 #reset ticksLeft
class Cell:
def __init__(self, x, y, mass=0.3, energy=0.1, x_vel=0.0, y_vel=0.0, Phenotype=[2.0, 0.001, 0.5,0.6, 0.005, None, 0.0]):
"""Cells begin with a specified position, without velocity, task or destination."""
# Position, Velocity and Acceleration vectors:
self.pos = Point(float(x), float(y))
self.vel = Vector(x_vel, y_vel)
self.acl = Vector(0.0, 0.0)
# Phenotypes:
self.phenotype = Phenotype # Stored for calc_variance's sake
self.emRatio = Phenotype[0] # Energy/Mass gain ratio
self.walk_force = Phenotype[1]
self.div_energy = Phenotype[2] # How much energy a cell needs to divide
self.div_mass = Phenotype[3] # How much mass a cell needs to divide
self.density = Phenotype[4]
if Phenotype[5] == None:
self.color = random_color()
Phenotype[5] = self.color
else: self.color = Phenotype[5]
self.mutation_chance = Phenotype[6] # The likelihood of each phenotype mutating
# Required for motion:
self.energy = energy
self.mass = mass
self.exerted_force = Vector(0.0, 0.0)
self.weight_management()
# Required for logic:
self.task = None
self.destination = None
self.destination_type = None
# Task jumptable:
self.TaskTable = {}
self.TaskTable[None] = self.task_none
self.TaskTable["FindingFood"] = self.task_finding_food
self.TaskTable["GettingFood"] = self.task_getting_food
#"Task" functions, i.e. the cell's activities during each tick, depending on its task.
def task_none(self):
"""What the cell does should it have no task."""
self.task = "FindingFood"
def task_finding_food(self):
#closest piece of food
close_food = environment.Environment().food_at(self.pos, self.sight_range)
#If there is any food within distance self.sight_range, get the closest one.
if len(close_food) > 0:
closest_food = min(close_food, key = partial(reduce, call, (attrgetter("pos"), attrgetter("distance_to"), partial(call, self.pos))))# food: self.pos.distance_to(food.pos))
else: closest_food = None
if len(close_food) == 0:
"""What the cell does should it be looking for food."""
#If you can't see food, accelerate in a random direction.
x = random.uniform(0, environment.Environment().width)
y = random.uniform(0, environment.Environment().height)
self.destination = Point(x, y)
self.destination_type = "Exploration"
self.calc_force()
else:
#If there is any food within distance SIGHT_RANGE, get the closest one.
#closest_food = min(close_food, key = lambda food: self.pos.distance_to(food.pos))
closest_food = min(close_food, key = partial(reduce, call, (attrgetter("pos"), attrgetter("distance_to"), partial(call, self.pos))))# food: self.pos.distance_to(food.pos))
def stop_getting_food(food):
"""After the food gets eaten, stop trying to get it."""
self.destination = self.destination_type = self.task = None
self.task = "GettingFood"
self.destination_type = "Food"
#weakref.proxy calls stop_getting_food when the food is destroyed.
self.destination = weakref.proxy(closest_food.pos, stop_getting_food)
self.food_target = weakref.ref(closest_food)
def task_getting_food(self):
"""What the cell does when it has found food and is attempting to get it."""
#assert(len(environment.Environment().food_at(self.destination, 0)) != 0)
distance_to_destination = self.pos.distance_to(self.destination)
print self.destination
print distance_to_destination
if distance_to_destination > self.distance_to_start_slowing_down():
self.calc_force()
for f in environment.Environment().food_at(self.pos, self.radius):
self.eat(f)
#if distance_to_destination <= self.radius:
# self.eat(self.food_target())
def update_coords(self):
"""Updates the cell's position, velocity and acceleration in that order."""
prev_vel = Vector(self.vel.x, self.vel.y)
self.pos += self.vel
self.vel += self.acl
#acl is change in velocity
#displacement = (prev_vel + self.exerted_force/self.mass/2)
#self.energy -= self.exerted_force*displacement
#self.energy -= self.exerted_force*prev_vel
self.acl = self.exerted_force - self.vel*abs(self.vel)*environment.Environment().resistance*(self.radius)/self.mass
self.exerted_force = Vector(0.0,0.0)
def calc_force(self):
"""Cells calculate how much force they are exerting (prior to resistance)."""
self.exerted_force = (self.destination - self.pos)*self.walk_force / abs(self.destination - self.pos)
#self.exerted_force = (self.destination - self.pos)*self.walk_force / (abs(self.destination - self.pos)*self.mass)
if self.energy > self.walk_force:
self.energy -= self.walk_force*1.0
else:
self.mass -= self.walk_force*3.0
def distance_to_start_slowing_down(self):
"""Calculates the distance from the destination that, once past,
the cell ought to begin slowing down to reach its destination."""
return (abs(self.vel) * self.mass) / (environment.Environment().resistance * self.radius)
def eat(self, f):
"""Updates energy and mass according to set emRatio value and removes food item."""
#for f in environment.Environment().food_at(self.pos, self.radius):
self.energy += f.energy/self.emRatio
self.mass += f.energy - (f.energy/self.emRatio)
environment.Environment().remove_food(f)
#The above line automatically resets our task and destination by calling stop_getting_food()
def weight_management(self):
"""Updates radius and sight range according to mass and density"""
self.radius = ( 3.0*self.mass*self.density / (4.0*math.pi) )**(1/2.0)
self.sight_range = .2 + self.radius
def calculate_variance(self):
"""Setting variance for child cell. Called when cell duplicates""" #Currently only color varaince
newphenotype = []
##Below code needs to be rewritten##
###SOLUTION: Use fraction of acceptable margin as argument for randint modification###
newcolor = (self.phenotype[5][0] + random.randint(-15,15),self.phenotype[5][1] + random.randint(-15,15), +\
self.phenotype[5][2] + random.randint(-15,15))
while (newcolor[0]+newcolor[1]+newcolor[2])/3>150 or newcolor[0]<0 or newcolor[0]>255 or newcolor[1]<0 or newcolor[1]>255 or newcolor[2]<0 or newcolor[2]>255:
print newcolor,"sucks! Its average is either above 150 or one of its color values is impossible!"
newcolor = (self.phenotype[5][0]+random.randint(-15,15), int(self.phenotype[5][1]+random.randint(-15,15)/1.15), self.phenotype[5][2]+random.randint(-15,15))
print newcolor,"should be fine, and if it isn't, F**K."
for t in self.phenotype[:5]: newphenotype.append(t)
newphenotype.append(newcolor)
for t in self.phenotype[6:]: newphenotype.append(t)
return newphenotype
def life_and_death(self):
"""Checks if cell mass is great enough for division or low enough to cause death."""
"""Brings new cells into existance or removes cell if conditions are true."""
if self.mass >= self.div_mass and self.energy >= self.div_energy:
##Removed Jack-ese##
#Statistics for child cells
newMass = self.mass/self.emRatio
newEnergy = (self.energy - 3.0)/self.emRatio
#Create child 1
x1 = random.uniform(self.pos.x-0.01,self.pos.x+0.01)
y1 = random.uniform(self.pos.y-0.01,self.pos.y+0.01)
newPhenotype1 = self.calculate_variance()
environment.Environment().cell_list.append(Cell(x1,y1,newMass,newEnergy,self.vel.x,self.vel.y,newPhenotype1))
#Create child 2
x2 = random.uniform(self.pos.x-0.01,self.pos.x+0.01)
y2 = random.uniform(self.pos.y-0.01,self.pos.y+0.01)
newPhenotype2 = self.calculate_variance()
environment.Environment().cell_list.append(Cell(x2,y2,newMass,newEnergy,self.vel.x,self.vel.y,newPhenotype2))
#Instantiates children at slightly different positions
environment.Environment().remove_cell(self)
#Kills cell
elif self.mass <= 0.1:
environment.Environment().kill_cell(self)
def one_tick(self):
"""What a cell does every arbitrary unit of time."""
self.TaskTable[self.task]()
self.update_coords()
self.weight_management()
self.life_and_death()
def get_test_point(self) -> Point:
return Point(1, 2, 3)
class Cell(object):
colors = Colors()
def __init__(self,
x,
y,
mass=0.3,
energy=0.1,
x_vel=0.0,
y_vel=0.0,
inherited_phenotype=Phenotype()):
"""Cells begin with a specified position, without velocity, task or destination."""
# Position, Velocity and Acceleration vectors:
self.pos = Point(float(x), float(y))
self.vel = Vector(x_vel, y_vel)
self.acl = Vector(0.0, 0.0)
# Brad's Satisfaction
self.keyList = []
for i in range(0, 9):
self.keyList.append([])
for j in range(0, 9):
self.keyList[i].append(False)
# Phenotypes:
self.phenotype = inherited_phenotype # Stored for calc_variance's sake
self.emRatio = inherited_phenotype.AI.emRatio # Energy/Mass gain ratio
self.div_energy = self.phenotype.AI.div_energy # How much energy a cell needs to divide
self.div_mass = self.phenotype.AI.div_mass # How much mass a cell needs to divide
self.walk_force = self.phenotype.Static.walk_force
self.density = self.phenotype.AI.density
self.mutation_chance = self.phenotype.AI.mutation_chance # The likelihood of each phenotype mutating
if self.phenotype.AI.color == None: self.color = Cell.colors.genColor()
else: self.color = mutateColor(self.phenotype.AI.color)
# Required for motion:
self.energy = energy
self.mass = mass
self.exerted_force = Vector(0.0, 0.0)
self.weight_management()
# Required for logic:
self.task = None
self.destination = None
self.destination_type = None
# Task jumptable:
self.TaskTable = {}
self.TaskTable[None] = self.task_none
self.TaskTable["FindingFood"] = self.task_finding_food
self.TaskTable["GettingFood"] = self.task_getting_food
#"Task" functions, i.e. the cell's activities during each tick, depending on its task.
def task_none(self):
"""What the cell defaults to in the instance of having no task."""
self.task = "FindingFood"
def task_finding_food(self):
#closest piece of food
close_food = World.food_at(self.pos, self.sight_range)
#If there is any food within distance self.sight_range, get the closest one.
if len(close_food) > 0:
closest_food = min(
close_food,
key=partial(
reduce, call,
(attrgetter("pos"), attrgetter("distance_to"),
partial(
call,
self.pos)))) # food: self.pos.distance_to(food.pos))
else:
closest_food = None
if len(close_food) == 0:
"""What the cell does should it be looking for food."""
#If you can't see food, accelerate in a random direction.
x = random.uniform(0, World.width)
y = random.uniform(0, World.height)
self.destination = Point(x, y)
self.destination_type = "Exploration"
self.calc_force()
else:
#If there is any food within distance SIGHT_RANGE, get the closest one.
#closest_food = min(close_food, key = lambda food: self.pos.distance_to(food.pos))
closest_food = min(
close_food,
key=partial(
reduce, call,
(attrgetter("pos"), attrgetter("distance_to"),
partial(
call,
self.pos)))) # food: self.pos.distance_to(food.pos))
def stop_getting_food(food):
"""After the food gets eaten, stop trying to get it."""
self.destination = self.destination_type = self.task = None
self.task = "GettingFood"
self.destination_type = "Food"
#weakref.proxy calls stop_getting_food when the food is destroyed.
self.destination = weakref.proxy(closest_food.pos,
stop_getting_food)
self.food_target = weakref.ref(closest_food)
def task_getting_food(self):
"""What the cell does when it has found food and is attempting to get it."""
distance_to_destination = self.pos.distance_to(self.destination)
if distance_to_destination > self.distance_to_start_slowing_down():
self.calc_force()
for f in World.food_at(self.pos, self.radius):
self.eat(f)
def update_coords(self):
"""Updates the cell's position, velocity and acceleration in that order."""
prev_vel = Vector(self.vel.x, self.vel.y)
self.pos += self.vel
if numpy.isnan(self.pos.x):
self.pos.x = random.uniform(0, World.width)
if numpy.isnan(self.pos.y):
self.pos.y = random.uniform(0, World.width)
self.vel += self.acl
self.acl = self.exerted_force - self.vel * abs(
self.vel) * World.resistance * (self.radius) / self.mass
self.exerted_force = Vector(0.0, 0.0)
def calc_force(self):
"""Cells calculate how much force they are exerting (prior to resistance)."""
self.exerted_force = (
self.destination -
self.pos) * self.walk_force / abs(self.destination - self.pos)
if self.energy > self.walk_force:
self.energy -= self.walk_force * 1.0
else:
self.mass -= self.walk_force * 3.0
def distance_to_start_slowing_down(self):
"""Calculates the distance from the destination that, once past,
the cell ought to begin slowing down to reach its destination."""
return (abs(self.vel) * self.mass) / (World.resistance * self.radius)
def eat(self, f):
"""Updates energy and mass according to set emRatio value and removes food item."""
self.energy += f.energy / self.emRatio
self.mass += f.energy - (f.energy / self.emRatio)
World.remove_food(f)
#The above line automatically resets our task and destination by calling stop_getting_food()
def weight_management(self):
"""Updates radius and sight range according to mass and density"""
self.radius = (3.0 * self.mass * self.density / (4.0 * math.pi))**(1 /
2.0)
self.sight_range = .2 + self.radius
def determine_phenotype(self):
"""Setting variance for child cell. Called when cell duplicates"""
self.phenotype.mutate_phenotype()
def life_and_death(self):
"""Checks if cell mass is great enough for division or low enough to cause death."""
"""Brings new cells into existance or removes cell if conditions are true."""
if self.mass >= self.div_mass and self.energy >= self.div_energy:
#Statistics for child cells
newMass = self.mass / self.emRatio
newEnergy = (self.energy - 3.0) / self.emRatio
#Create child 1
x1 = random.uniform(self.pos.x - 0.01, self.pos.x + 0.01)
y1 = random.uniform(self.pos.y - 0.01, self.pos.y + 0.01)
self.determine_phenotype()
World.cell_list.append(
Cell(x1, y1, newMass, newEnergy, self.vel.x, self.vel.y,
self.phenotype))
#Create child 2
x2 = random.uniform(self.pos.x - 0.01, self.pos.x + 0.01)
y2 = random.uniform(self.pos.y - 0.01, self.pos.y + 0.01)
self.determine_phenotype()
World.cell_list.append(
Cell(x2, y2, newMass, newEnergy, self.vel.x, self.vel.y,
self.phenotype))
#Instantiates children at slightly different positions
World.remove_cell(self)
#Kills cell
elif self.mass <= 0.1:
World.kill_cell(self)
def one_tick(self):
"""What a cell does every arbitrary unit of time."""
self.TaskTable[self.task]()
self.update_coords()
self.weight_management()
self.life_and_death()
def collideWith(self, foe):
"""assumed cells are colliding"""
#get distance between cell radii as a vector
selfPos = self.pos
foePos = foe.pos
xDiff = selfPos.x - foePos.x
yDiff = selfPos.y - foePos.y
dist = math.sqrt(xDiff**2 + yDiff**2)
dist += 5
distVec = Vector(xDiff, yDiff)
distVec.x = round(distVec.x, 3)
distVec.y = round(distVec.y, 3)
unitVec = distVec / dist
unitVec.x = round(unitVec.x, 3)
unitVec.y = round(unitVec.y, 3)
#make a force vector
forcApp = 1 / dist
forcApp = round(forcApp, 3)
forcVec = unitVec * forcApp
forcVec.x = round(forcVec.x, 3)
forcVec.y = round(forcVec.y, 3)
#apply the force vector to other cell
#the target's acceration is changed
#by F/target's mass
targetPushVec = forcVec / foe.mass
targetPushVec.x = round(targetPushVec.x, 3)
targetPushVec.y = round(targetPushVec.y, 3)
foe.acl += -targetPushVec
def guessedKey(self, vKey):
vKeyO = vKey % 10 #gets the number in the ones place in vKey
vKeyT = (vKey - vKeyO) / 10 #gets the number in the tens place in vKey
if self.keyList[vKeyT][
vKeyO] == True: #if the cell already knows the key
return True
randN = round(random.random(), 2) * 100
randNO = randN % 10
randNT = (randN - randNO) / 10
#if the guess shares the same tens value and is at least 2 units away from the ones value
if vKeyT == randNT and vKeyO < randNT + 2 and vKeyO >= randNT - 2:
self.keyList[randNT][randNO] = True
return True
return False #if this is reached, all guesses have failed
def randExponent(curve, maximum):
x = random.randint(0, maximum * 100.0) / 100.0
base = (((maximum + curve) / curve))**(1.0 / maximum)
y = curve * (base**x) - curve
return y
from vector import Point
from bounding_box import BoundingBoxDC, BoundingBox
output_filename = 'rectangulo.txt'
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('vertices', type=str, help='Archivo de vértices')
parser.add_argument('metodo', type=str, help='Método a usar')
args = parser.parse_args()
# Read the vertex file
with open(args.vertices, 'r') as f:
points = []
for line in f.readlines():
points.append(Point(*map(float, line.strip().split(','))))
if args.metodo == 'inicial':
box = BoundingBox(points)
elif args.metodo == 'dyc':
box = BoundingBoxDC(points)
else:
raise Exception('Método desconocido, los métodos pueden' \
'ser "inicial" o "dyc" ')
with open(output_filename, 'w') as f:
for point in box:
f.write(str(point) + '\n')
class Cell(object):
colors = Colors()
def __init__(self, x, y, mass=0.3, energy=0.1, x_vel=0.0, y_vel=0.0, inherited_phenotype=Phenotype()):
"""Cells begin with a specified position, without velocity, task or destination."""
# Position, Velocity and Acceleration vectors:
self.pos = Point(float(x), float(y))
self.vel = Vector(x_vel, y_vel)
self.acl = Vector(0.0, 0.0)
# Brad's Satisfaction
self.keyList = []
for i in range(0,9):
self.keyList.append([])
for j in range (0,9):
self.keyList[i].append(False)
# Phenotypes:
self.phenotype = inherited_phenotype # Stored for calc_variance's sake
self.emRatio = inherited_phenotype.AI.emRatio # Energy/Mass gain ratio
self.div_energy = self.phenotype.AI.div_energy # How much energy a cell needs to divide
self.div_mass = self.phenotype.AI.div_mass # How much mass a cell needs to divide
self.walk_force = self.phenotype.Static.walk_force
self.density = self.phenotype.AI.density
self.mutation_chance = self.phenotype.AI.mutation_chance # The likelihood of each phenotype mutating
if self.phenotype.AI.color == None: self.color = Cell.colors.genColor()
else: self.color = mutateColor(self.phenotype.AI.color)
# Required for motion:
self.energy = energy
self.mass = mass
self.exerted_force = Vector(0.0, 0.0)
self.weight_management()
# Required for logic:
self.task = None
self.destination = None
self.destination_type = None
# Task jumptable:
self.TaskTable = {}
self.TaskTable[None] = self.task_none
self.TaskTable["FindingFood"] = self.task_finding_food
self.TaskTable["GettingFood"] = self.task_getting_food
#"Task" functions, i.e. the cell's activities during each tick, depending on its task.
def task_none(self):
"""What the cell defaults to in the instance of having no task."""
self.task = "FindingFood"
def task_finding_food(self):
#closest piece of food
close_food = World.food_at(self.pos, self.sight_range)
#If there is any food within distance self.sight_range, get the closest one.
if len(close_food) > 0:
closest_food = min(close_food, key = partial(reduce, call, (attrgetter("pos"), attrgetter("distance_to"), partial(call, self.pos))))# food: self.pos.distance_to(food.pos))
else: closest_food = None
if len(close_food) == 0:
"""What the cell does should it be looking for food."""
#If you can't see food, accelerate in a random direction.
x = random.uniform(0, World.width)
y = random.uniform(0, World.height)
self.destination = Point(x, y)
self.destination_type = "Exploration"
self.calc_force()
else:
#If there is any food within distance SIGHT_RANGE, get the closest one.
#closest_food = min(close_food, key = lambda food: self.pos.distance_to(food.pos))
closest_food = min(close_food, key = partial(reduce, call, (attrgetter("pos"), attrgetter("distance_to"), partial(call, self.pos))))# food: self.pos.distance_to(food.pos))
def stop_getting_food(food):
"""After the food gets eaten, stop trying to get it."""
self.destination = self.destination_type = self.task = None
self.task = "GettingFood"
self.destination_type = "Food"
#weakref.proxy calls stop_getting_food when the food is destroyed.
self.destination = weakref.proxy(closest_food.pos, stop_getting_food)
self.food_target = weakref.ref(closest_food)
def task_getting_food(self):
"""What the cell does when it has found food and is attempting to get it."""
distance_to_destination = self.pos.distance_to(self.destination)
if distance_to_destination > self.distance_to_start_slowing_down():
self.calc_force()
for f in World.food_at(self.pos, self.radius):
self.eat(f)
def update_coords(self):
"""Updates the cell's position, velocity and acceleration in that order."""
prev_vel = Vector(self.vel.x, self.vel.y)
self.pos += self.vel
if numpy.isnan(self.pos.x):
self.pos.x = random.uniform(0,World.width)
if numpy.isnan(self.pos.y):
self.pos.y = random.uniform(0,World.width)
self.vel += self.acl
self.acl = self.exerted_force - self.vel*abs(self.vel)*World.resistance*(self.radius)/self.mass
self.exerted_force = Vector(0.0,0.0)
def calc_force(self):
"""Cells calculate how much force they are exerting (prior to resistance)."""
self.exerted_force = (self.destination - self.pos)*self.walk_force / abs(self.destination - self.pos)
if self.energy > self.walk_force:
self.energy -= self.walk_force*1.0
else:
self.mass -= self.walk_force*3.0
def distance_to_start_slowing_down(self):
"""Calculates the distance from the destination that, once past,
the cell ought to begin slowing down to reach its destination."""
return (abs(self.vel) * self.mass) / (World.resistance * self.radius)
def eat(self, f):
"""Updates energy and mass according to set emRatio value and removes food item."""
self.energy += f.energy/self.emRatio
self.mass += f.energy - (f.energy/self.emRatio)
World.remove_food(f)
#The above line automatically resets our task and destination by calling stop_getting_food()
def weight_management(self):
"""Updates radius and sight range according to mass and density"""
self.radius = ( 3.0*self.mass*self.density / (4.0*math.pi) )**(1/2.0)
self.sight_range = .2 + self.radius
def determine_phenotype(self):
"""Setting variance for child cell. Called when cell duplicates"""
self.phenotype.mutate_phenotype()
def life_and_death(self):
"""Checks if cell mass is great enough for division or low enough to cause death."""
"""Brings new cells into existance or removes cell if conditions are true."""
if self.mass >= self.div_mass and self.energy >= self.div_energy:
#Statistics for child cells
newMass = self.mass/self.emRatio
newEnergy = (self.energy - 3.0)/self.emRatio
#Create child 1
x1 = random.uniform(self.pos.x-0.01,self.pos.x+0.01)
y1 = random.uniform(self.pos.y-0.01,self.pos.y+0.01)
self.determine_phenotype()
World.cell_list.append(Cell(x1,y1,newMass,newEnergy,self.vel.x,self.vel.y,self.phenotype))
#Create child 2
x2 = random.uniform(self.pos.x-0.01,self.pos.x+0.01)
y2 = random.uniform(self.pos.y-0.01,self.pos.y+0.01)
self.determine_phenotype()
World.cell_list.append(Cell(x2,y2,newMass,newEnergy,self.vel.x,self.vel.y,self.phenotype))
#Instantiates children at slightly different positions
World.remove_cell(self)
#Kills cell
elif self.mass <= 0.1:
World.kill_cell(self)
def one_tick(self):
"""What a cell does every arbitrary unit of time."""
self.TaskTable[self.task]()
self.update_coords()
self.weight_management()
self.life_and_death()
def collideWith(self, foe):
"""assumed cells are colliding"""
#get distance between cell radii as a vector
selfPos = self.pos
foePos = foe.pos
xDiff = selfPos.x - foePos.x
yDiff = selfPos.y - foePos.y
dist = math.sqrt(xDiff**2 + yDiff**2)
dist += 5
distVec = Vector(xDiff, yDiff)
distVec.x = round(distVec.x,3)
distVec.y = round(distVec.y,3)
unitVec = distVec/dist
unitVec.x = round(unitVec.x, 3)
unitVec.y = round(unitVec.y, 3)
#make a force vector
forcApp = 1/dist
forcApp = round(forcApp, 3)
forcVec = unitVec * forcApp
forcVec.x = round(forcVec.x, 3)
forcVec.y = round(forcVec.y, 3)
#apply the force vector to other cell
#the target's acceration is changed
#by F/target's mass
targetPushVec = forcVec/foe.mass
targetPushVec.x = round(targetPushVec.x, 3)
targetPushVec.y = round(targetPushVec.y, 3)
foe.acl += -targetPushVec
def guessedKey(self, vKey):
vKeyO = vKey % 10 #gets the number in the ones place in vKey
vKeyT = (vKey - vKeyO)/10 #gets the number in the tens place in vKey
if self.keyList[vKeyT][vKeyO] == True : #if the cell already knows the key
return True
randN = round(random.random(),2)*100
randNO = randN % 10
randNT = (randN - randNO)/10
#if the guess shares the same tens value and is at least 2 units away from the ones value
if vKeyT == randNT and vKeyO < randNT + 2 and vKeyO >= randNT - 2:
self.keyList[randNT][randNO] = True
return True
return False #if this is reached, all guesses have failed
def randExponent(curve,maximum):
x=random.randint(0,maximum*100.0)/100.0
base=(((maximum+curve)/curve))**(1.0/maximum)
y=curve*(base**x)-curve
return y
self.image.show()
def save(self, filepath):
if self.image is None:
raise Exception("Can't save, call render() first")
self.image.save(filepath)
def image_coordinate(self, space_coord):
x, y = space_coord.x, space_coord.y
# y direction is opposite
return Point(
self.origin_x + x * self.cell_size,
self.origin_y - y * self.cell_size
)
def clear(self):
del self._draw
if __name__ == '__main__':
g = Space2d(400, 400, 200, 200, 25)
p1 = Point.origin()
p2 = Point(1.1, 2.5)
p3 = Point(4, 4)
p4 = Point(3, 2)
g.arrow(p1, p2)
g.render()
g.arrow(p1, p3, 'red')
g.arrow(p1, p4)
g.save('vectors.png')