def predict_ball(self, duration=5.0, dt=1 / 120):
self.about_to_score = False
self.about_to_be_scored_on = False
self.time_of_goal = -1
self.ball_predictions = []
prediction = Ball(self.ball)
# nearest_opponent = Car(min(self.get_opponents(), key=lambda opponent: distance(opponent, prediction)))
while prediction.time < self.time + duration:
# if prediction.time < self.time + 1.0:
# nearest_opponent.step(Input(), dt)
# nearest_opponent.velocity[2] = 0
# prediction.step(dt, nearest_opponent)
# else:
prediction.step(dt)
self.ball_predictions.append(Ball(prediction))
if self.time_of_goal == -1:
if self.my_goal.inside(prediction.position):
self.about_to_be_scored_on = True
self.time_of_goal = prediction.time
if self.their_goal.inside(prediction.position):
self.about_to_score = True
self.time_of_goal = prediction.time
def get_closest_point_on_trajectory(b: Ball, target: vec3):
closest = vec3(target)
while True:
b.step(1.0 / 120.0)
if abs(veclen(target - b.location)) > abs(veclen(target - closest)):
return closest
closest = vec3(b.location)
def step(self, dt):
ball = Ball(self.ball)
car = self.car
# simulate ball until it gets near the floor
while (ball.position[2] > 120 or ball.velocity[2] > 0) and ball.time < car.time + 10:
ball.step(1/60)
ball_local = local(car, ground(ball.position))
target = local(car, self.target)
shift = ground(direction(ball_local, target))
shift[1] *= 1.8
shift = normalize(shift)
max_turn = clamp(norm(car.velocity) / 800, 0, 1)
max_shift = normalize(vec3(1 - max_turn, max_turn * sign(shift[1]), 0))
if abs(shift[1]) > abs(max_shift[1]) or shift[0] < 0:
shift = max_shift
shift *= clamp(car.boost, 40, 60)
shift[1] *= clamp(norm(car.velocity)/1000, 1, 2)
self._shift_direction = normalize(world(car, shift) - car.position)
target = world(car, ball_local - shift)
speed = distance(car.position, target) / max(0.001, ball.time - car.time)
self.drive.target_speed = speed
self.drive.target_pos = target
self.drive.step(dt)
self.controls = self.drive.controls
self.finished = self.ball.position[2] < 100 or ground_distance(self.ball, self.car) > 2000
def simulate(self, bot) -> vec3:
# print('simulate intercept')
# Init vars
c = Car(bot.game.my_car)
b = Ball(bot.game.ball)
t = vec3(bot.target)
intercept = self.location
dt = 1.0 / 60.0
hit = False
min_error = None
# Drive towards intercept (moving in direction of c.forward())
c.rotation = look_at(intercept, c.up())
direction = normalize(intercept - c.location) #c.forward()
advance_distance = norm(intercept - c.location) - c.hitbox(
).half_width[0] - b.collision_radius
translation = direction * advance_distance
sim_start_state: ThrottleFrame = BoostAnalysis().travel_distance(
advance_distance, norm(c.velocity))
c.velocity = direction * sim_start_state.speed
c.location += translation
c.time += sim_start_state.time
bot.ball_predictions = [vec3(b.location)]
while b.time < c.time:
b.step(dt)
bot.ball_predictions.append(vec3(b.location))
# print(c.time, b.time)
# print(c.location, b.location)
# Simulate the collision and resulting
for i in range(60 * 3):
c.location += c.velocity * dt
b.step(dt, c)
# Check if we hit the ball yet
if norm(b.location - c.location) < (c.hitbox().half_width[0] +
b.collision_radius) * 1.05:
hit = True
# print('hit')
# Measure dist from target
error = t - b.location
if hit and (min_error == None or norm(error) < norm(min_error)):
min_error = error
# Record trajectory
bot.ball_predictions.append(vec3(b.location))
if not hit: return None
return min_error
def get_hit_frame_numbers(df: pd.DataFrame):
ball_df = df[DF_BALL_PREFIX]
hit_frame_numbers = get_hit_frame_numbers_by_ball_ang_vel(df)
# Filter by hit_team_no
hit_frame_numbers = ball_df.loc[hit_frame_numbers].index[~ball_df.loc[
hit_frame_numbers, "hit_team_no"].isna()].tolist()
logger.info(f"hit: {hit_frame_numbers}")
delta_df = df.loc[:, (DF_GAME_PREFIX, 'delta')]
RLUGame.set_mode("soccar")
filtered_hit_frame_numbers = []
for frame_number in hit_frame_numbers:
previous_frame_number = frame_number - 1
ball_previous_frame = ball_df.loc[previous_frame_number, :]
initial_ang_vel = ball_previous_frame.loc[[
'ang_vel_x', 'ang_vel_y', 'ang_vel_z'
]].values
ball = Ball()
# noinspection PyPropertyAccess
ball.location = vec3(
*ball_previous_frame.loc[['pos_x', 'pos_y', 'pos_z']])
# noinspection PyPropertyAccess
ball.velocity = vec3(
*ball_previous_frame.loc[['vel_x', 'vel_y', 'vel_z']])
# noinspection PyPropertyAccess
ball.angular_velocity = vec3(*initial_ang_vel)
frame_delta = delta_df[frame_number]
delta_simulated = 0
while delta_simulated < frame_delta:
dt = 1 / 120
time_to_simulate = min(dt, frame_delta - delta_simulated)
ball.step(time_to_simulate)
delta_simulated += time_to_simulate
simulated_ball_ang_vel = np.array([
ball.angular_velocity[0], ball.angular_velocity[1],
ball.angular_velocity[2]
])
actual_ball_ang_vel = ball_df.loc[
frame_number, ['ang_vel_x', 'ang_vel_y', 'ang_vel_z']].values
actual_ang_vel_change = ((actual_ball_ang_vel -
initial_ang_vel)**2).sum()**0.5
end_ang_vel_difference = ((simulated_ball_ang_vel -
actual_ball_ang_vel)**2).sum()**0.5
relative_ang_vel_difference = end_ang_vel_difference / actual_ang_vel_change
if relative_ang_vel_difference > 0.8:
filtered_hit_frame_numbers.append(frame_number)
return filtered_hit_frame_numbers
def predict_ball(self, time_limit=6.0, dt=1 / 120):
self.about_to_score = False
self.about_to_be_scored_on = False
self.time_of_goal = -1
self.ball_predictions = []
prediction = Ball(self.ball)
while prediction.time < self.ball.time + time_limit:
prediction.step(dt)
self.ball_predictions.append(Ball(prediction))
if self.time_of_goal == -1:
if self.my_goal.inside(prediction.position):
self.about_to_be_scored_on = True
self.time_of_goal = prediction.time
if self.their_goal.inside(prediction.position):
self.about_to_score = True
self.time_of_goal = prediction.time
def predict_ball(self, num_steps, dt):
self.about_to_score = False
self.about_to_be_scored_on = False
self.time_of_goal = -1
self.ball_predictions = []
prediction = Ball(self.ball)
for _ in range(0, num_steps):
prediction.step(dt)
self.ball_predictions.append(Ball(prediction))
if self.time_of_goal == -1:
if self.my_goal.inside(prediction.position):
self.about_to_be_scored_on = True
self.time_of_goal = prediction.time
if self.their_goal.inside(prediction.position):
self.about_to_score = True
self.time_of_goal = prediction.time
def get_output(self, packet: GameTickPacket) -> SimpleControllerState:
self.game.read_game_information(packet, self.get_rigid_body_tick(), self.get_field_info())
# make a copy of the ball's info that we can change
b = Ball(self.game.ball)
ball_predictions = []
for i in range(360):
# simulate the forces acting on the ball for 1 frame
b.step(1.0 / 120.0)
# and add a copy of new ball position to the list of predictions
ball_predictions.append(vec3(b.location))
self.renderer.begin_rendering()
red = self.renderer.create_color(255, 255, 30, 30)
self.renderer.draw_polyline_3d(ball_predictions, red)
self.renderer.end_rendering()
return controller.get_output()
def set_shots(hits_by_goal_number: Dict[int, Hit], game: Game,
df: pd.DataFrame):
ball_df = df[DF_BALL_PREFIX]
player_id_to_team = {
player.id.id: player.is_orange
for player in game.players
}
for hits_list in hits_by_goal_number.values():
for hit in hits_list:
direction_factor = -1**player_id_to_team[
hit.player_id.id] # -1 if orange, 1 if blue
ball_data = ball_df.loc[hit.frame_number]
ball = Ball()
# noinspection PyPropertyAccess
ball_location = ball_data.loc[['pos_x', 'pos_y', 'pos_z']]
ball.location = vec3(*ball_location)
# noinspection PyPropertyAccess
ball_velocity = ball_data.loc[['vel_x', 'vel_y', 'vel_z']]
ball.velocity = vec3(*ball_velocity)
# noinspection PyPropertyAccess
ball_angular_velocity = ball_data.loc[[
'ang_vel_x', 'ang_vel_y', 'ang_vel_z'
]]
ball.angular_velocity = vec3(*ball_angular_velocity)
delta_simulated = 0
while delta_simulated < SHOT_SECONDS_SIMULATED:
dt = 1 / 120
ball.step(min(dt, SHOT_SECONDS_SIMULATED - delta_simulated))
delta_simulated += dt
ball_y = ball.location[1]
if ball_y * direction_factor > FIELD_Y_LIM:
hit.shot = True
break
def get_output(self, packet: GameTickPacket) -> SimpleControllerState:
self.game.read_game_information(packet,
self.get_rigid_body_tick(),
self.get_field_info())
self.controls = SimpleControllerState()
next_state = self.state
if self.state == State.RESET:
self.timer = 0.0
b_position = Vector3(random.uniform(-1500, 1500),
random.uniform( 2500, 3500),
random.uniform( 300, 500))
b_velocity = Vector3(random.uniform(-300, 300),
random.uniform(-100, 100),
random.uniform(1000, 1500))
ball_state = BallState(physics=Physics(
location=b_position,
velocity=b_velocity,
rotation=Rotator(0, 0, 0),
angular_velocity=Vector3(0, 0, 0)
))
# this just initializes the car and ball
# to different starting points each time
c_position = Vector3(b_position.x, 0 * random.uniform(-1500, -1000), 25)
#c_position = Vector3(200, -1000, 25)
car_state = CarState(physics=Physics(
location=c_position,
velocity=Vector3(0, 800, 0),
rotation=Rotator(0, 1.6, 0),
angular_velocity=Vector3(0, 0, 0)
), boost_amount=100)
self.set_game_state(GameState(
ball=ball_state,
cars={self.game.id: car_state})
)
next_state = State.WAIT
if self.state == State.WAIT:
if self.timer > 0.2:
next_state = State.INITIALIZE
if self.state == State.INITIALIZE:
self.aerial = Aerial(self.game.my_car)
# self.aerial.up = normalize(vec3(
# random.uniform(-1, 1),
# random.uniform(-1, 1),
# random.uniform(-1, 1)))
self.turn = AerialTurn(self.game.my_car)
# predict where the ball will be
prediction = Ball(self.game.ball)
self.ball_predictions = [vec3(prediction.location)]
for i in range(100):
prediction.step(0.016666)
prediction.step(0.016666)
self.ball_predictions.append(vec3(prediction.location))
# if the ball is in the air
if prediction.location[2] > 500:
self.aerial.target = prediction.location
self.aerial.arrival_time = prediction.time
simulation = self.aerial.simulate()
# # check if we can reach it by an aerial
if norm(simulation.location - self.aerial.target) < 100:
prediction.step(0.016666)
prediction.step(0.016666)
self.aerial.target = prediction.location
self.aerial.arrival_time = prediction.time
self.target_ball = Ball(prediction)
break
next_state = State.RUNNING
if self.state == State.RUNNING:
self.log.write(f"{{\"car\":{self.game.my_car.to_json()},"
f"\"ball\":{self.game.ball.to_json()}}}\n")
self.aerial.step(self.game.time_delta)
self.controls = self.aerial.controls
if self.timer > self.timeout:
next_state = State.RESET
self.aerial = None
self.turn = None
self.timer += self.game.time_delta
self.state = next_state
self.renderer.begin_rendering()
red = self.renderer.create_color(255, 230, 30, 30)
purple = self.renderer.create_color(255, 230, 30, 230)
white = self.renderer.create_color(255, 230, 230, 230)
if self.aerial:
r = 200
x = vec3(r, 0, 0)
y = vec3(0, r, 0)
z = vec3(0, 0, r)
target = self.aerial.target
self.renderer.draw_line_3d(target - x, target + x, purple)
self.renderer.draw_line_3d(target - y, target + y, purple)
self.renderer.draw_line_3d(target - z, target + z, purple)
if self.ball_predictions:
self.renderer.draw_polyline_3d(self.ball_predictions, purple)
self.renderer.end_rendering()
return self.controls
c = Car()
c.location = vec3(-164.13, 0, 88.79)
c.velocity = vec3(1835.87, 0, 372.271)
c.angular_velocity = vec3(0, 3.78721, 0)
c.rotation = mat3(0.9983, -5.23521e-6, 0.0582877, 5.5498e-6, 1.0, -5.23521e-6,
-0.0582877, 5.5498e-6, 0.9983)
b = Ball()
b.location = vec3(0, 0, 150)
b.velocity = vec3(0, 0, 0)
b.angular_velocity = vec3(0, 0, 0)
print("before:")
print(b.location)
print(b.velocity)
print(b.angular_velocity)
print("overlapping: ", intersect(c.hitbox(), b.hitbox()))
print()
b.step(0.008333, c)
print("after:")
print(b.location)
print(b.velocity)
print(b.angular_velocity)
print("overlapping: ", intersect(c.hitbox(), b.hitbox()))
def get_output(self, packet: GameTickPacket) -> SimpleControllerState:
# Update the game values and set the state
self.game.read_game_information(packet, self.get_rigid_body_tick(),
self.get_field_info())
self.controls = SimpleControllerState()
next_state = self.state
# Reset everything
if self.state == State.RESET:
self.timer = 0.0
self.set_state_stationary()
next_state = State.WAIT
# Wait so everything can settle in, mainly for ball prediction
if self.state == State.WAIT:
if self.timer > 0.2:
next_state = State.INITIALIZE
# Initialize the drive mechanic
if self.state == State.INITIALIZE:
self.drive = Drive(self.game.my_car)
self.drive.target = self.game.ball.position + 200 * normalize(
vec3(vec2(self.game.ball.position - vec3(0, 5120, 0))))
self.drive.speed = 1400
next_state = State.DRIVING
# Start driving towards the target and check whether an aerial is possible, if so initialize the dodge
if self.state == State.DRIVING:
self.drive.target = self.game.ball.position + 200 * normalize(
vec3(vec2(self.game.ball.position - vec3(0, 5120, 0))))
self.drive.step(self.game.time_delta)
self.controls = self.drive.controls
# Initialize the aerial and aerial turn
self.aerial = Aerial(self.game.my_car)
self.turn = AerialTurn(self.game.my_car)
# predict where the ball will be
prediction = Ball(self.game.ball)
self.ball_predictions = [vec3(prediction.location)]
# Loop over 87 frames which results in a time limit of 1.45
# Seeing we jump once and hold it for 0.2s we keep our second dodge until that moment
a = time.time()
for i in range(87):
# Step the ball prediction and add it to the array for rendering
prediction.step(0.016666)
self.ball_predictions.append(vec3(prediction.location))
# TODO dont hardcode the goal vector!
# Set the target to a slight offset of the ball so we will hit it towards the target or goal
# Set the arrival time to the time the ball will be in that location
# Set the preorientation so we will look at the goal
goal = vec3(0, 5120, 0)
# self.aerial.target = prediction.location + 200 * normalize(prediction.location - goal)
self.aerial.target = vec3(
0, 0, ball_z) + 200 * normalize(prediction.location - goal)
self.aerial.arrival_time = prediction.time
print(prediction.time)
# self.aerial.target_rotation = look_at(goal - self.aerial.target, vec3(0, 0, 1))
self.aerial.target_rotation = look_at(
goal - self.game.my_car.position, vec3(0, 0, 1))
# Simulate the aerial and see whether its doable or not
simulation = self.aerial.simulate
# # check if we can reach it by an aerial
if norm(simulation.location -
self.aerial.target) < 100 and angle_between(
simulation.rotation,
self.aerial.target_rotation) < 0.01:
print(i)
print(prediction.location)
print(self.game.my_car.orientation)
print("predicted rotation")
print(simulation.rotation)
next_state = State.RUNNING
break
print("time:")
print(time.time() - a)
# Perform the aerial mechanic
if self.state == State.RUNNING:
self.aerial.step(self.game.time_delta)
self.controls = self.aerial.controls
if self.game.time == packet.game_ball.latest_touch.time_seconds:
print(self.game.my_car.double_jumped)
self.controls.jump = True
print("actual rotation")
print(self.game.my_car.orientation)
if self.timer > self.timeout:
next_state = State.RESET
self.aerial = None
self.turn = None
self.timer += self.game.time_delta
self.state = next_state
# Render the target for the aerial
self.renderer.begin_rendering()
purple = self.renderer.create_color(255, 230, 30, 230)
if self.aerial:
r = 200
x = vec3(r, 0, 0)
y = vec3(0, r, 0)
z = vec3(0, 0, r)
target = self.aerial.target
self.renderer.draw_line_3d(target - x, target + x, purple)
self.renderer.draw_line_3d(target - y, target + y, purple)
self.renderer.draw_line_3d(target - z, target + z, purple)
# self.renderer.draw_line_3d(self.game.my_car.position,
# 1000 * dot(self.aerial.target_rotation, self.game.my_car.forward()), purple)
# Render ball prediction
if self.ball_predictions:
self.renderer.draw_polyline_3d(self.ball_predictions, purple)
self.renderer.end_rendering()
return self.controls
def calculate(car: Car, ball: Ball, target: vec3, ball_predictions=None):
# Init vars
b = Ball(ball)
dt = 1.0 / 60.0
# Generate predictions of ball path
if ball_predictions is None:
ball_predictions = []
for i in range(60 * 5):
b.step(dt)
ball_predictions.append(vec3(b.location))
# Gradually converge on ball location by aiming at a location, checking time to that location,
# and then aiming at the ball's NEW position. Guaranteed to converge (typically in <10 iterations)
# unless the ball is moving away from the car faster than the car's max boost speed
intercept = Intercept(b.location)
intercept.purpose = 'ball'
intercept.boost = True
intercept_ball_position = vec3(b.location)
collision_achieved = False
last_horizontal_error = None
last_horizontal_offset = None
i = 0
max_tries = 101
analyzer = BoostAnalysis() if intercept.boost else ThrottleAnalysis()
while i < max_tries:
i += 1
fake_car = Car(car)
direction = normalize(intercept.location - car.location)
fake_car.rotation = look_at(direction, fake_car.up())
for t in range(60 * 5):
# Step car location with throttle/boost analysis data
# Not super efficient but POITROAE
frame = analyzer.travel_time(dt, norm(fake_car.velocity))
# print('in 1 frame I travel', frame.time, frame.distance, frame.speed)
fake_car.location += direction * frame.distance
fake_car.velocity = direction * frame.speed
fake_car.time += dt
ball_location = ball_predictions[t]
# Check for collision
p = closest_point_on_obb(fake_car.hitbox(), ball_location)
if norm(p - ball_location) <= ball.collision_radius:
direction_vector = p - (fake_car.location - normalize(
fake_car.forward()) * 13.88) # octane center of mass
direction_vector[2] = 0
target_direction_vector = target - ball_location
target_direction_vector[2] = 0
intercept_ball_position = ball_location
direction = atan2(direction_vector[1], direction_vector[0])
ideal_direction = atan2(target_direction_vector[1],
target_direction_vector[0])
horizontal_error = direction - ideal_direction
# intercept.location = vec3(ball_location)
# intercept.time = fake_car.time
# return intercept
# Now descend the hit direction gradient
# Kick off the gradient descent with an arbitrary seed value
if last_horizontal_error is None:
last_horizontal_error = horizontal_error
last_horizontal_offset = 0
if horizontal_error > 0:
horizontal_offset = 25
else:
horizontal_offset = 25
intercept.location = ball_location - normalize(
fake_car.left()) * horizontal_offset
break
# Recursive case of gradient descent
if horizontal_offset == last_horizontal_offset:
gradient = 0
else:
gradient = (horizontal_error - last_horizontal_error
) / (horizontal_offset -
last_horizontal_offset)
if gradient == 0:
predicted_horizontal_offset = horizontal_offset
else:
predicted_horizontal_offset = horizontal_offset - horizontal_error / gradient
# Base case (convergence)
if abs(gradient) < 0.0005:
print(f'convergence in {i} iterations')
print(f'gradient = {gradient}')
print(
f'last_horizontal_offset = {last_horizontal_offset}'
)
print(f'direction = {degrees(direction)}')
print(f'ideal direction = {degrees(ideal_direction)}')
print(f'target = {target}')
print(f'ball_location = {ball_location}')
return intercept
# Edge case exit: offset maxed out
max_horizontal_offset = car.hitbox(
).half_width[1] + ball.collision_radius
if predicted_horizontal_offset > max_horizontal_offset:
predicted_horizontal_offset = max_horizontal_offset
elif predicted_horizontal_offset < -max_horizontal_offset:
predicted_horizontal_offset = -max_horizontal_offset
last_horizontal_offset = horizontal_offset
last_horizontal_error = horizontal_error
horizontal_offset = predicted_horizontal_offset
# Return the latest intercept location and continue descending the gradient
intercept.location = ball_location - normalize(
fake_car.left()) * predicted_horizontal_offset
print(f'iteration {i}')
print(f'gradient = {gradient}')
print(f'horizontal_offset = {horizontal_offset}')
print(f'horizontal_error = {degrees(horizontal_error)}')
# print(f'ideal direction = {degrees(ideal_direction)}')
break
# Check for arrival
if norm(fake_car.location -
intercept.location) < ball.collision_radius / 2:
intercept.location = ball_location
break
if i >= max_tries:
print(
f'Warning: max tries ({max_tries}) exceeded for calculating intercept'
)
return intercept
def calculate_old(car: Car,
ball: Ball,
target: vec3,
ball_predictions=None):
# Init vars
fake_car = Car(car)
b = Ball(ball)
# Generate predictions of ball path
if ball_predictions is None:
ball_predictions = [vec3(b.location)]
for i in range(60 * 5):
b.step(1.0 / 60.0)
ball_predictions.append(vec3(b.location))
# Gradually converge on ball location by aiming at a location, checking time to that location,
# and then aiming at the ball's NEW position. Guaranteed to converge (typically in <10 iterations)
# unless the ball is moving away from the car faster than the car's max boost speed
intercept = Intercept(b.location)
intercept.purpose = 'ball'
intercept.boost = True
intercept_ball_position = vec3(b.location)
i = 0
max_tries = 100
analyzer = BoostAnalysis() if intercept.boost else ThrottleAnalysis()
while i < max_tries:
# Find optimal spot to hit the ball
optimal_hit_vector = normalize(
target - intercept_ball_position) * b.collision_radius
optimal_hit_location = intercept_ball_position - optimal_hit_vector
# Find ideal rotation, unless it intersects with ground
optimal_rotation = look_at(
optimal_hit_vector, vec3(0, 0, 1)
) #axis_to_rotation(optimal_hit_vector) # this might be wrong
fake_car.rotation = optimal_rotation
# print(f'fake_car.location {fake_car.location}')
# print(f'get_car_front_center(fake_car) {get_car_front_center(fake_car)}')
fake_car.location += optimal_hit_location - get_car_front_center(
fake_car
) # try to position the car's front center directly on top of the best hit vector
euler = rotation_to_euler(optimal_rotation)
# todo put some super precise trigonometry in here to find the max angle allowed at given height
if fake_car.location[2] <= fake_car.hitbox().half_width[0]:
euler.pitch = 0
fake_car.rotation = euler_to_rotation(
vec3(euler.pitch, euler.yaw, euler.roll))
fake_car.location += optimal_hit_location - get_car_front_center(
fake_car
) # try to position the car's front center directly on top of the best hit vector
# Adjust vertical position if it (still) intersects with ground
if fake_car.location[2] < 17.0:
fake_car.location[2] = 17.0
intercept.location = get_car_front_center(fake_car)
# Calculate jump time needed
jump_height_time = JumpAnalysis().get_frame_by_height(
intercept.location[2]).time # or solve with motion equation
# car_euler = rotation_to_euler(car.rotation)
# jump_pitch_time = (euler.pitch - car_euler.pitch) / 5.5 + 0.35 # disregarding angular acceleration
# jump_yaw_time = (euler.yaw - car_euler.yaw) / 5.5 + 0.35 # disregarding angular acceleration
# jump_roll_time = (euler.roll - car_euler.roll) / 5.5 + 0.35 # disregarding angular acceleration
# jump_time = max(jump_height_time, jump_pitch_time, jump_yaw_time, jump_roll_time)
jump_time = jump_height_time # todo revisit rotation time
# print('jump_time', jump_time)
# Calculate distance to drive before jumping (to arrive perfectly on target)
total_translation = intercept.location - get_car_front_center(car)
total_translation[2] = 0
total_distance = norm(total_translation)
start_index = analyzer.get_index_by_speed(norm(car.velocity))
start_frame = analyzer.frames[start_index]
custom_error_func = lambda frame: abs(total_distance - (
frame.distance - start_frame.distance) - frame.speed *
jump_time)
drive_analysis = analyzer.get_frame_by_error(
custom_error_func, start_index)
arrival_time = drive_analysis.time - start_frame.time + jump_time
# print('drive_analysis.time', drive_analysis.time)
# print('drive_analysis', start_index)
# arrival_time = analyzer.travel_distance(total_distance, norm(car.velocity)).time
# drive_analysis = analyzer.travel_distance(norm(intercept.location - c.location), norm(c.velocity))
ball_index = int(round(arrival_time * 60))
if ball_index >= len(ball_predictions):
intercept.location = ball_predictions[-1]
intercept.time = len(ball_predictions) / 60.0
break
ball_location = ball_predictions[ball_index]
# print(f'Iteration {i} distance {norm(ball_location + vec3(optimal_hit_vector[0], optimal_hit_vector[1], 0) - intercept.location)}')
if norm(ball_location - intercept_ball_position) <= 1:
# if norm(intercept_ball_position - get_car_front_center(fake_car)) > 100:
# intercept.location = ball_predictions[-1]
# intercept.time = len(ball_predictions) / 60.0
# return intercept
intercept.dodge = True #jump_time > 0.2
intercept.jump_time = car.time + arrival_time - jump_time
intercept.dodge_preorientation = euler_to_rotation(
vec3(euler.pitch, euler.yaw, euler.roll))
intercept.dodge_delay = jump_time
intercept.dodge_direction = normalize(vec2(optimal_hit_vector))
# print(f'intercept_ball_position', intercept_ball_position)
# print(f'intercept.location', intercept.location)
# print(f'time until jump {drive_analysis.time}')
# print(f'time now {car.time}')
# print(f'distance until jump {drive_analysis.distance}')
# print(f'total distance to target {total_distance}')
# print(f'horiz speed @ jump {drive_analysis.speed}')
# print(f'time intended to be in air {jump_time}')
# print(f'distance travelled in air {jump_time * drive_analysis.speed}')
# print(f'distance remaining to target @ jump {total_distance - drive_analysis.distance}')
# print(f'Intercept convergence in {i} iterations')
# print(f'desired roll {euler.roll}')
# print(f'actual roll {rotation_to_euler(c.rotation).roll}')
break
intercept_ball_position = vec3(ball_location)
# intercept.location = vec3(ball_location)
# intercept.location[2] = 0
intercept.time = arrival_time
i += 1
if i >= max_tries:
print(
f'Warning: max tries ({max_tries}) exceeded for calculating intercept'
)
# Intercept is only meant for ground paths (and walls/cieling are only indirectly supported)
# collision_radius = c.hitbox().half_width[2] * 2 + b.collision_radius + b.collision_radius * 8
# on_ground = intercept.location[2] <= collision_radius
# on_back_wall = abs(intercept.location[1]) >= 5120 - collision_radius
# on_side_wall = abs(intercept.location[0]) >= 4096 - collision_radius
# # on_cieling = intercept.location[2] >= 2044 - collision_radius
# reachable = on_ground # or on_back_wall or on_side_wall # or on_cieling
# if not reachable:
# return None
return intercept
def get_output(self, packet: GameTickPacket) -> SimpleControllerState:
self.game.read_game_information(packet, self.get_rigid_body_tick(),
self.get_field_info())
if self.game.ball.location[2] > 250:
if self.state == "Aerial":
self.aerial.step(self.game.time_delta)
if self.aerial.finished:
self.state = "Not Aerial"
return self.aerial.controls
else:
self.aerial = Aerial(self.game.my_car)
self.aerial.up = normalize(
vec3(random.uniform(-1, 1), random.uniform(-1, 1),
random.uniform(-1, 1)))
# predict where the ball will be
prediction = Ball(self.game.ball)
for i in range(100):
prediction.step(0.016666)
if prediction.location[2] > 500:
self.aerial.target = prediction.location
self.aerial.arrival_time = prediction.time
if self.aerial.is_viable():
self.aerial.target = prediction.location
self.aerial.arrival_time = prediction.time
self.target_ball = Ball(prediction)
self.state = "Aerial"
break
ball_location = Vector2(packet.game_ball.physics.location.x,
packet.game_ball.physics.location.y)
my_car = packet.game_cars[self.index]
car_location = Vector2(my_car.physics.location.x,
my_car.physics.location.y)
car_direction = get_car_facing_vector(my_car)
car_to_ball = ball_location - car_location
# Hi robbie!
# The,type;of,punctuation;matters!
true = shreck is love, shreck is life
main(9)
if not true:
print("https://www.twitch.tv/TehRedox is the best twitch channel")
y.yeet()
self.WHOOPITYScooPTI = 0
for i in range(packet.num_cars):
self.WHOOPITYScooPTI += packet.game_cars[i].score_info.goals
if self.WHOOPITYScooPTI > self.CountyTHIS_ALSOdonttuch:
self.CountyTHIS_ALSOdonttuch = self.WHOOPITYScooPTI
self.renderer.begin_rendering(str(y))
# commented out due to performance concerns
# self.renderer.draw_polyline_3d([[car_location.x+triforce(-20,20), car_location.y+triforce(-20,20), triforce(shreck(200),200)] for i in range(40)], self.renderer.cyan())
self.renderer.draw_rect_2d(
0, 0, 3840, 2160, True, self.renderer.create_color(
64, 246, 74, 138)) # first bot that supports 4k resolution!
self.renderer.draw_string_2d(triforce(0, 100), triforce(0, 10), 8, 8,
'BANIME', self.renderer.lime())
self.renderer.draw_string_2d(
triforce(0, 100), triforce(100, 110), 8, 8,
'SCRATCH IS \n ASSEMBLY \n (also banormies) \n https://www.twitch.tv/donutkiller_pro',
self.renderer.red())
self.renderer.end_rendering()
steer_correction_radians = car_direction.correction_to(car_to_ball)
turn = clamp11(steer_correction_radians * -3)
if self.flippityThe_CAR < 1:
self.howDoIUse_this[self.howDoIUse_this[0][5]] = True
self.flippityThe_CAR = 1
elif self.flippityThe_CAR < 2:
self.howDoIUse_this[self.howDoIUse_this[0][5]] = False
self.flippityThe_CAR = 2
elif self.flippityThe_CAR < 3:
self.howDoIUse_this[self.howDoIUse_this[0][5]] = True
self.flippityThe_CAR = 3
elif self.flippityThe_CAR < 666:
self.howDoIUse_this[self.howDoIUse_this[0][5]] = False
self.flippityThe_CAR += 6
elif self.flippityThe_CAR >= 666:
self.flippityThe_CAR = 0
self.howDoIUse_this[self.howDoIUse_this[0][0]] = 1
self.howDoIUse_this[self.howDoIUse_this[0][1]] = turn
self.howDoIUse_this[self.howDoIUse_this[0][6]] = (
abs(turn) < 0.2 and not my_car.is_super_sonic)
self.howDoIUse_this[self.howDoIUse_this[0][7]] = (
abs(turn) > 1.5 and not my_car.is_super_sonic)
return getSensible_thingToCONTROL(self.howDoIUse_this)
