-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtools.py
91 lines (78 loc) · 5.27 KB
/
tools.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
from util.routines import *
# This file is for strategic tools
def find_hits(agent, targets):
# find_hits takes a dict of (left,right) target pairs and finds routines that could hit the ball between those target pairs
# find_hits is only meant for routines that require a defined intercept time/place in the future
# find_hits should not be called more than once in a given tick, as it has the potential to use an entire tick to calculate
# Example Useage:
#targets = {"goal":(opponent_left_post,opponent_right_post), "anywhere_but_my_net":(my_right_post,my_left_post)}
#hits = find_hits(agent,targets)
# print(hits)
# >{"goal":[a ton of jump and aerial routines,in order from soonest to latest], "anywhere_but_my_net":[more routines and stuff]}
hits = {name: [] for name in targets}
struct = agent.get_ball_prediction_struct()
# Begin looking at slices 0.25s into the future
# The number of slices
i = 15
while i < struct.num_slices:
# Gather some data about the slice
intercept_time = struct.slices[i].game_seconds
time_remaining = intercept_time - agent.time
if time_remaining > 0:
ball_location = Vector3(struct.slices[i].physics.location)
ball_velocity = Vector3(
struct.slices[i].physics.velocity).magnitude()
if abs(ball_location[1]) > 5250:
break # abandon search if ball is scored at/after this point
# determine the next slice we will look at, based on ball velocity (slower ball needs fewer slices)
i += 15 - cap(int(ball_velocity//150), 0, 13)
car_to_ball = ball_location - agent.me.location
# Get the direction and magnitude of the vectors separately
direction = car_to_ball.normalize()
distance = car_to_ball.magnitude()
# How far the car must turn in order to face the ball, for forward and reverse
forward_angle = direction.angle(agent.me.forward)
backward_angle = math.pi - forward_angle
# Accounting for the average time it takes to turn and face the ball
# Backward is slightly longer as typically the car is moving forward and takes time to slow down
forward_time = time_remaining - (forward_angle * 0.318)
backward_time = time_remaining - (backward_angle * 0.418)
# If the car only had to drive in a straight line, we ensure it has enough time to reach the ball (a few assumptions are made)
forward_flag = forward_time > 0.0 and (distance*1.025 / forward_time) < (
2299 if agent.me.boost > distance/100 else max(1400, 0.8 * agent.me.velocity.flatten().magnitude()))
backward_flag = distance < 1500 and backward_time > 0.0 and (
distance*1.05 / backward_time) < 1200
# Provided everything checks out, we begin to look at the target pairs
if forward_flag or backward_flag:
for pair in targets:
# First we correct the target coordinates to account for the ball's radius
# If fits == True, the ball can be scored between the target coordinates
left, right, fits = post_correction(
ball_location, targets[pair][0], targets[pair][1])
if fits:
# Now we find the easiest direction to hit the ball in order to land it between the target points
left_vector = (left - ball_location).normalize()
right_vector = (right - ball_location).normalize()
best_shot_vector = direction.clamp(
left_vector, right_vector)
# Check to make sure our approach is inside the field
# in_field(ball_location - (200*best_shot_vector),1):
if True:
# The slope represents how close the car is to the chosen vector, higher = better
# A slope of 1.0 would mean the car is 45 degrees off
slope = find_slope(
best_shot_vector.flatten(), car_to_ball.flatten())
if forward_flag:
if (ball_location[2] <= 300 or (not in_field(ball_location, 200) and not in_field(agent.me.location, 100))) and slope > 0.0:
hits[pair].append(
jump_shot(ball_location, intercept_time, best_shot_vector, slope))
if ball_location[2] > 325 and slope > 1 and cap(ball_location[2]-400, 100, 2000) * 0.1 < agent.me.boost:
if abs((car_to_ball / forward_time) - agent.me.velocity).magnitude() - 300 < 400 * forward_time:
hits[pair].append(aerial_shot(
ball_location, intercept_time, best_shot_vector, slope))
elif backward_flag and ball_location[2] <= 280 and slope > 0.25:
hits[pair].append(
jump_shot(ball_location, intercept_time, best_shot_vector, slope, -1))
else:
i += 1
return hits