DEF CON CTF simulator

data.py

import abc
import argparse
import functools
import json
import sys
import threading
import time
from abc import abstractmethod
from typing import Optional

import numpy as np
import arcade

NUM_TEAMS = 12
VISION_RADIUS = 1
TICKS_PER_MINUTE = 60  # 1 tick per second (60 ticks per minute)
TICK_DURATION = 1 / TICKS_PER_MINUTE
BOARD_LENGTH = 240  # Length of the board
BOARD_ANGLE = 30  # Maximum angle of the board in degrees
START_SHRINK = 60
SHRINK_SPEED = 0.5  # units per minute

AD_KOTH_RATIO = 0.7

BONUS_OFFSET_TICK = 30 * TICKS_PER_MINUTE  # Bonus challenges are spawned 30 minutes after start ...
BONUS_INTERVAL_TICK = 60 * TICKS_PER_MINUTE  # ... and every 60 minutes thereafter
SERVICE_ROUND_DURATION_TICK = 1 * TICKS_PER_MINUTE  # Each ad/koth round lasts 1 minute

ATTACK_POINT_PER_ROUND = 1
DEFENSE_POINT_PER_ROUND = 1

COOLDOWN_1V1 = 5.0  # 5 minutes cooldown between 1v1 matches

IN_BOUND_DURATION = 0.5  # Every 30 seconds in bound ...
IN_BOUND_SCORE = 1  # ... 1 point for the team

SPEEDRUN_SCORE = 300  # Score for winning a speedrun
BONUS_SCORE = 600  # Bonus challenge score

KAPPA = 5.0  # Average interaction number constant
BETA = np.log(2) / 2.0  # Skill impact constant
CHI = 1  # Unexpected number of deviation per day

teams_score = [0 for _ in range(NUM_TEAMS)]


class Position:
    def __init__(self, mark: float, angle: float):
        assert 0 <= mark <= BOARD_LENGTH, "Mark must be within the board length"
        assert 0 <= angle < BOARD_ANGLE, "Angle must be within the board angle"

        self.mark = mark
        self.angle = angle

    def move(self, dist: float, direction: float) -> "Position":
        # move position towards the direction by dist
        x = self.mark * np.cos(np.deg2rad(self.angle))
        y = self.mark * np.sin(np.deg2rad(self.angle))
        x += dist * np.cos(direction)
        y += dist * np.sin(direction)
        return Position(np.clip(np.sqrt(x ** 2 + y ** 2), 0, BOARD_LENGTH), np.rad2deg(np.arctan2(y, x)) % BOARD_ANGLE)

    def randomize(self) -> "Position":
        dist = np.random.normal(scale=2)
        direction = np.random.rand() * np.pi
        return self.move(dist, direction)

    def __closest(self, position: "Position") -> tuple[float, float]:
        # return the shortest distance and corresponding angle

        # case 1: direct distance
        x1 = self.mark * np.cos(np.deg2rad(self.angle))
        y1 = self.mark * np.sin(np.deg2rad(self.angle))
        x2 = position.mark * np.cos(np.deg2rad(position.angle))
        y2 = position.mark * np.sin(np.deg2rad(position.angle))
        dist1 = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)

        # case 2: wrap around distance
        if self.angle < position.angle:
            angle1 = self.angle + BOARD_ANGLE
            angle2 = position.angle
        else:
            angle1 = self.angle
            angle2 = position.angle + BOARD_ANGLE
        a1 = self.mark * np.cos(np.deg2rad(angle1))
        b1 = self.mark * np.sin(np.deg2rad(angle1))
        a2 = position.mark * np.cos(np.deg2rad(angle2))
        b2 = position.mark * np.sin(np.deg2rad(angle2))
        dist2 = np.sqrt((a1 - a2) ** 2 + (b1 - b2) ** 2)

        if dist1 <= dist2:
            return dist1, np.arctan2(y2 - y1, x2 - x1)
        return dist2, np.arctan2(b2 - b1, a2 - a1)

    def distance(self, position: "Position") -> float:
        return self.__closest(position)[0]

    def direction_to(self, position: "Position") -> float:
        return self.__closest(position)[1]

    def __repr__(self):
        return f"Position(mark={self.mark:.2f}, angle={self.angle:.2f})"


class Point:
    COLOR: arcade.types.Color

    text: str
    pos: Position

    def __init__(self, pos: Position):
        self.pos = pos

    def draw(self, camera: arcade.camera.Camera2D, texts: list[arcade.Text]):
        x = self.pos.mark * np.cos(np.deg2rad(self.pos.angle - BOARD_ANGLE / 2))
        y = self.pos.mark * np.sin(np.deg2rad(self.pos.angle - BOARD_ANGLE / 2))

        arcade.draw_circle_filled(x, y, VISION_RADIUS, self.COLOR, num_segments=60)
        texts.append(arcade.Text(self.text, *camera.project((x + 2, y - 1)), color=self.COLOR, font_size=10))

    def retired(self, time_elapsed: float) -> bool:
        return self.time_until_retire(time_elapsed) <= 0

    def time_until_retire(self, time_elapsed: float) -> float:
        return (BOARD_LENGTH - self.pos.mark) / SHRINK_SPEED + START_SHRINK - time_elapsed

    def __repr__(self):
        return f"Point(mark={self.pos.mark:.2f}, angle={self.pos.angle:.2f})"


class Challenge(Point):
    __COUNTER: int = 0

    id: int

    def __init__(self, pos: Position):
        super().__init__(pos)

        self.id = Challenge.__COUNTER
        Challenge.__COUNTER += 1

        self.epsilon = np.random.normal(loc=1, scale=0.1)
        self.deviation = 1.0

    def retired(self, time_elapsed: float) -> bool:
        return self.time_until_retire(time_elapsed) <= 0

    def time_until_retire(self, time_elapsed: float) -> float:
        return (BOARD_LENGTH - self.pos.mark) / SHRINK_SPEED + START_SHRINK - time_elapsed

    @property
    def average_state_change_duration(self) -> float:
        raise NotImplementedError('TODO')

    def state_change_duration(self, team_z_score: float) -> float:
        return self.average_state_change_duration * np.exp(-BETA * team_z_score)


class MultipleInteractionChallenge(Challenge):
    @property
    def average_state_change_duration(self) -> float:
        return self.epsilon * self.deviation * (BOARD_LENGTH - self.pos.mark + 60) / KAPPA


class ADService(MultipleInteractionChallenge):
    COLOR = arcade.color.CAL_POLY_GREEN

    def __init__(self, pos: Position):
        super().__init__(pos)
        self.text = f"A/D ({self.id})"


class KotHChallenge(MultipleInteractionChallenge):
    COLOR = arcade.color.CANDY_APPLE_RED

    def __init__(self, pos: Position):
        super().__init__(pos)
        self.text = f"KotH ({self.id})"


class ConstantChallenge(Challenge):
    DIFFICULTY: float

    @property
    def average_state_change_duration(self) -> float:
        return self.epsilon * self.deviation * self.DIFFICULTY


class BonusChallenge(ConstantChallenge):
    COLOR = arcade.color.AZURE
    DIFFICULTY = 30.  # expected to take 30 minutes to solve (1 state change)

    def __init__(self, pos: Position):
        super().__init__(pos)
        self.text = f"Bonus ({self.id})"
        self.solved = False

    def retired(self, time_elapsed: float) -> bool:
        return self.solved


class SpeedrunChallenge(ConstantChallenge):  # 1v1
    DIFFICULTY = 15.  # expected to take 15 minutes to solve (1 state change)

    def __init__(self):
        super().__init__(None)
        Challenge._Challenge__COUNTER -= 1


class ChallengeState:
    def __init__(self, encountered_at: float):
        self.encountered_at = encountered_at
        self.bugs_found = 0
        self.bugs_committed = 0
        self.prev_state_change_duration = None


class Action(abc.ABC):
    @abstractmethod
    def perform(self, team: "Team"):
        pass


class StayStill(Action):
    def perform(self, team: "Team"):
        pass

    def __repr__(self):
        return "StayStill()"


class MoveTowards(Action):
    def __init__(self, direction: float):
        self.direction = direction

    def perform(self, team: "Team"):
        team.pos = team.pos.move(TICK_DURATION, self.direction)

    def __repr__(self):
        return f"MoveTowards(direction={self.direction:.2f})"


class Initiate1v1(Action):
    def __init__(self, opponent: int):
        self.opponent = opponent

    def perform(self, team: "Team"):
        raise Exception("never called")

    def __repr__(self):
        return f"Initiate1v1(opponent={self.opponent})"


class Perform1v1(Action):
    def __init__(self, start_at: float, team1: 'Team', team2: 'Team'):
        chall = SpeedrunChallenge()
        if team1.z_score >= team2.z_score:
            self.end_at = start_at + chall.state_change_duration(team1.z_score)
            self.winner = team1.idx
        else:
            self.end_at = start_at + chall.state_change_duration(team2.z_score)
            self.winner = team2.idx

    def perform(self, team: "Team"):
        assert team.speedrun is None
        team.speedrun = self

    def __repr__(self):
        return f"Perform1v1()"


class Team(Point):
    challenge_state: dict[int, ChallengeState]

    def __init__(self, idx: int, pos: Position, z_score: float):
        super().__init__(pos)
        self.idx = idx
        self.text = f"Team {idx + 1}"
        self.z_score = z_score
        self.challenge_states = {}
        self.score = 0.
        self.probability_1v1 = np.random.rand()
        self.speedrun = None
        self.last_score_time = 0.0

    @property
    def COLOR(self) -> arcade.types.Color:
        return arcade.color.BLUE if self.speedrun is None else arcade.color.GOLD

    def evaluate(self, elapsed: float, challenges: list[Challenge], possible_1v1_teams: list[int]) -> Action:
        if self.speedrun is not None:
            return StayStill()

        def approximate_state_change_duration(chall: Challenge) -> float:
            # Approximate using public info only (position/distance), no epsilon/deviation/z-score.
            if isinstance(chall, BonusChallenge):
                return BonusChallenge.DIFFICULTY  # public hint ~30 minutes
            if isinstance(chall, MultipleInteractionChallenge):
                # Approximate A/D & KotH using mark (farther out ≈ tougher) and interaction constant.
                return (BOARD_LENGTH - chall.pos.mark + 60) / KAPPA
            raise NotImplementedError('Unknown challenge type for approximation')

        def expected_wait_time(chall: Challenge, state: ChallengeState | None) -> float:
            if state and state.prev_state_change_duration is not None:
                return state.prev_state_change_duration  # observed rate after first bug
            return approximate_state_change_duration(chall)

        active_regular_dist = [
            self.pos.distance(c.pos) for c in challenges
            if not isinstance(c, BonusChallenge) and not c.retired(elapsed)
        ]
        nearest_regular = min(active_regular_dist) if active_regular_dist else None
        unexplored_regular_dist = [
            self.pos.distance(c.pos) for c in challenges
            if not isinstance(c, BonusChallenge) and not c.retired(elapsed) and c.id not in self.challenge_states
        ]
        nearest_unexplored_regular = min(unexplored_regular_dist) if unexplored_regular_dist else None

        def challenge_reward(chall: Challenge, distance: float, state: ChallengeState | None) -> float:
            pending = 0 if state is None else state.bugs_found - state.bugs_committed
            visited = state is not None

            if isinstance(chall, BonusChallenge):
                # Steeper distance discount so distant bonuses are rarely pursued.
                bonus_discount = float(np.exp(-distance / 12.0))
                # If there is a closer regular challenge (especially unexplored), further devalue the bonus.
                if nearest_unexplored_regular is not None and nearest_unexplored_regular + 1.5 < distance:
                    bonus_discount *= 0.35
                elif nearest_regular is not None and nearest_regular + 1.5 < distance:
                    bonus_discount *= 0.55
                reward = BONUS_SCORE * bonus_discount
            else:
                reward = 30.0  # baseline value for regular challenges
                if isinstance(chall, (ADService, KotHChallenge)):
                    # More frequent service rounds mean higher steady payoff
                    round_minutes = SERVICE_ROUND_DURATION_TICK / TICKS_PER_MINUTE
                    freq_boost = max(1.0, 5.0 / max(round_minutes, 0.1))  # calibrated to 5-min baseline
                    reward += 5.0 * freq_boost

            if pending > 0:
                reward *= 1.8 + 0.1 * (pending - 1)
            elif not visited:
                reward *= 1.5  # stronger exploration boost
            else:
                reward *= 0.1  # heavily de-prioritize waiting on explored challenges

            if visited and pending == 0:
                if isinstance(chall, BonusChallenge):
                    reward *= 0.5  # extra penalty for camping bonus
                if distance <= VISION_RADIUS:
                    return -1.0  # move away instead of idling on an empty challenge

            wait = 0.0 if pending > 0 else expected_wait_time(chall, state)
            travel_time = distance

            time_left = chall.time_until_retire(elapsed)
            if time_left < travel_time:
                return -np.inf
            safety_margin = time_left - travel_time
            if safety_margin < 3.0 and pending > 0:
                reward *= 0.25  # too risky to run back to commit with border closing
            if time_left < 10.0:
                reward *= time_left / 10.0  # deprioritize expiring challenges
            # Additional discount as safety margin shrinks (less likely to go back)
            reward *= float(np.clip(safety_margin / 8.0, 0.1, 1.0))

            return reward / (1.0 + travel_time + wait)

        # pick best challenge to move toward (or stay)
        best_target = None
        best_score = -np.inf
        best_distance = None

        for chall in challenges:
            if chall.retired(elapsed):
                continue
            state = self.challenge_states.get(chall.id)
            distance = self.pos.distance(chall.pos)
            score = challenge_reward(chall, distance, state)
            if score > best_score:
                best_score = score
                best_target = chall
                best_distance = distance

        action_from_challenges: Action = StayStill()
        if best_target is not None:
            if best_distance is not None and best_distance > VISION_RADIUS / 2:
                action_from_challenges = MoveTowards(self.pos.direction_to(best_target.pos))
            else:
                # Already close enough; staying keeps us ready to commit bugs
                action_from_challenges = StayStill()

        # consider 1v1 if nearby and conditions are decent
        has_pending_bug = any(
            state.bugs_found > state.bugs_committed for state in self.challenge_states.values()
        )
        action_1v1 = None
        if possible_1v1_teams:
            border_safety = np.clip(self.time_until_retire(elapsed) / 15.0, 0.0, 1.0)
            willingness = self.probability_1v1 * border_safety
            if has_pending_bug:
                willingness *= 0.5  # finish pending work before dueling
            willingness = float(np.clip(willingness, 0.0, 1.0))

            expected_speedrun_value = SPEEDRUN_SCORE / (SpeedrunChallenge.DIFFICULTY + 1.0)
            speedrun_utility = willingness * expected_speedrun_value

            if np.random.rand() < willingness and speedrun_utility >= best_score * 0.6:
                opponent = np.random.choice(possible_1v1_teams)
                action_1v1 = Initiate1v1(opponent)

        return action_1v1 if action_1v1 is not None else action_from_challenges

    def tick(self, action: Action, elapsed: float, challenges: list[Challenge]):
        if self.retired(elapsed):
            # out of bounds, we reset scoring timer without actually awarding points
            self.last_score_time = elapsed

        if elapsed - self.last_score_time >= IN_BOUND_DURATION:
            self.score += IN_BOUND_SCORE
            self.last_score_time = elapsed

        if self.speedrun is not None:
            if self.speedrun.end_at <= elapsed:
                if self.speedrun.winner == self.idx:
                    self.score += SPEEDRUN_SCORE
                    print(f"{elapsed:.2f}: {self.text} won the 1v1 speedrun!")
                else:
                    print(f"{elapsed:.2f}: {self.text} lost the 1v1 speedrun")
                self.speedrun = None
            else:
                assert isinstance(action, StayStill)

        if isinstance(action, Perform1v1):
            # sanity check, not in any challenges
            for chall in challenges:
                assert self.pos.distance(chall.pos) > VISION_RADIUS

        action.perform(self)

        for chall in challenges:
            if chall.retired(elapsed):
                self.challenge_states.pop(chall.id, None)
                continue

            state_change_duration = chall.state_change_duration(self.z_score)

            if chall.id in self.challenge_states:
                state = self.challenge_states[chall.id]
                bugs_found = int((elapsed - state.encountered_at) / state_change_duration)
                if bugs_found > state.bugs_found:
                    assert bugs_found - state.bugs_found == 1
                    print(f"{elapsed:.2f}: {self.text} found a new bug on {chall.text}")
                    state.bugs_found = bugs_found
                    state.prev_state_change_duration = state_change_duration

            distance = self.pos.distance(chall.pos)

            if distance <= VISION_RADIUS:
                if chall.id not in self.challenge_states:
                    print(f"{elapsed:.2f}: {self.text} encountered {chall.text}")
                    self.challenge_states[chall.id] = ChallengeState(elapsed)
                else:
                    state = self.challenge_states[chall.id]
                    state.bugs_committed = state.bugs_found
                    if state.bugs_committed > 0 and isinstance(chall, BonusChallenge):
                        self.score += BONUS_SCORE
                        chall.solved = True
                        print(f"{elapsed:.2f}: {self.text} solved the bonus challenge {chall.text}")

    def __repr__(self):
        return f"Team({self.idx}, z_score={self.z_score:.2f})"


class Board:
    challenges: list[Challenge]
    teams: list[Team]

    def __init__(self, challenges: list[Challenge], teams: list[Team]):
        self.challenges = challenges
        self.teams = teams
        self.previous_1v1 = {}
        self.__tick = 0

    def draw(self, camera: arcade.camera.Camera2D, texts: list[arcade.Text]):
        for chall in self.challenges:
            chall.draw(camera, texts)
        for team in self.teams:
            team.draw(camera, texts)

    def __calculate_bonus_spawn_location(self) -> Position:
        # Calculate current shrinking boundary position
        if self.elapsed < START_SHRINK:
            boundary_mark = BOARD_LENGTH
        else:
            boundary_mark = BOARD_LENGTH - (self.elapsed - START_SHRINK) * SHRINK_SPEED

        # Constraint 3: Must be at least 15 minutes away from boundary
        assert boundary_mark > 15
        max_mark = boundary_mark - 15

        # Sample candidate positions across the valid region
        candidates = []
        num_mark_samples = 50
        num_angle_samples = 30

        for mark in np.linspace(0, max_mark, num_mark_samples):
            for angle in np.linspace(0, BOARD_ANGLE, num_angle_samples, endpoint=False):
                pos = Position(mark, angle)

                # Calculate distances to all active teams
                distances = [pos.distance(t.pos) for t in self.teams]
                distances.sort()

                # Get the two closest teams' distances
                closest_dist = distances[0]
                second_closest_dist = distances[1]

                # Constraint 1: Must be equidistant to two closest teams (within tolerance)
                if not np.isclose(closest_dist, second_closest_dist, atol=0.5):
                    continue

                # Constraint 2: Must be at least 5 minutes from any team
                if closest_dist < 5:
                    continue

                # Calculate total distance to all teams for optimization
                total_distance = sum(distances)
                candidates.append((pos, total_distance))

        assert candidates

        # Constraint 4: Among valid candidates, find those minimizing sum of distances
        min_total = min(c[1] for c in candidates)
        best_candidates = [c[0] for c in candidates if np.isclose(c[1], min_total, rtol=0.01)]

        # Rule 5: Randomly select one if multiple solutions exist
        return best_candidates[np.random.randint(len(best_candidates))]

    def tick(self):
        # check bonus challenge spawning
        if self.__tick >= BONUS_OFFSET_TICK:
            if (self.__tick - BONUS_OFFSET_TICK) % BONUS_INTERVAL_TICK == 0:
                pos = self.__calculate_bonus_spawn_location()
                bonus_chall = BonusChallenge(pos)
                self.challenges.append(bonus_chall)
                print(f"{self.elapsed:.2f}: A new bonus challenge has spawned at {pos}")

        # A/D and KotH scoring update
        if self.__tick % SERVICE_ROUND_DURATION_TICK == 0:
            for chall in self.challenges:
                if isinstance(chall, ADService):
                    for team in self.teams:
                        if chall.id not in team.challenge_states:
                            continue
                        committed = team.challenge_states[chall.id].bugs_committed
                        for oteam in self.teams:
                            if oteam.idx == team.idx:
                                continue
                            if chall.id not in oteam.challenge_states:
                                continue
                            ocommitted = oteam.challenge_states[chall.id].bugs_committed
                            if committed > ocommitted:
                                team.score += ATTACK_POINT_PER_ROUND
                            else:
                                oteam.score += DEFENSE_POINT_PER_ROUND
                elif isinstance(chall, KotHChallenge):
                    teams_performing = []
                    for team in self.teams:
                        if chall.id in team.challenge_states:
                            committed = team.challenge_states[chall.id].bugs_committed
                            if committed > 0:
                                teams_performing.append((team, committed))

                    def compare(x, y):
                        if x[1] is None and y[1] is None:
                            return 0
                        if x[1] is None:
                            return -1
                        if y[1] is None:
                            return 1
                        if x[1] == y[1]:
                            return x[0].z_score - y[0].z_score
                        return x[1] - y[1]

                    teams_performing.sort(key=functools.cmp_to_key(compare))
                    for i, (team, _) in enumerate(teams_performing):
                        team.score += i + 1  # 1 point for last place, 2 for second last, ...

        # identify roaming teams
        roaming_teams: dict[int, Position] = {}
        for team in self.teams:
            if team.speedrun is not None:
                continue  # skip teams in 1v1 speedrun
            for chall in self.challenges:
                if team.pos.distance(chall.pos) <= VISION_RADIUS:
                    break
            else:
                roaming_teams[team.idx] = team.pos

        actions = []
        for team in self.teams:
            possible_1v1_teams = []

            if team.idx in roaming_teams:
                for (oteam_idx, oteam_pos) in roaming_teams.items():
                    if oteam_idx == team.idx:
                        continue
                    previous = self.previous_1v1.get((team.idx, oteam_idx), None)
                    if previous is not None:
                        if self.elapsed - COOLDOWN_1V1 < previous:
                            continue
                        self.previous_1v1.pop((team.idx, oteam_idx))
                    if team.pos.distance(oteam_pos) <= VISION_RADIUS:
                        possible_1v1_teams.append(oteam_idx)

            actions.append(team.evaluate(self.elapsed, self.challenges, possible_1v1_teams))

        # handle 1v1 initiations
        for i, action in enumerate(actions):
            if isinstance(action, Initiate1v1):
                opponent_action = actions[action.opponent]
                if isinstance(opponent_action, Initiate1v1) and opponent_action.opponent == i:
                    print(f"{self.elapsed:.2f}: Team {i} and Team {action.opponent} are starting a 1v1 speedrun!")
                    perform_1v1 = Perform1v1(self.elapsed, self.teams[i], self.teams[action.opponent])
                    actions[i] = perform_1v1
                    actions[action.opponent] = perform_1v1
                    self.previous_1v1[i, action.opponent] = perform_1v1.end_at
                    self.previous_1v1[action.opponent, i] = perform_1v1.end_at
                else:
                    actions[i] = StayStill()
                    self.previous_1v1[i, action.opponent] = self.elapsed

        self.__tick += 1

        for (team, action) in zip(self.teams, actions):
            team.tick(action, self.elapsed, self.challenges)

        self.challenges = [c for c in self.challenges if not c.retired(self.elapsed)]

    @property
    def elapsed(self) -> float:
        return self.__tick * TICK_DURATION


class UI(arcade.Window):
    lock: threading.Lock
    board: Optional[Board] = None

    def __init__(self):
        super().__init__()
        arcade.set_background_color(arcade.color.GRAY)

        self.camera = arcade.Camera2D(position=(BOARD_LENGTH / 2, 0), zoom=4)

        self.lock = threading.Lock()
        self.board = None

    def on_draw(self):
        self.clear()

        with self.lock:
            board = self.board

        if board is None:
            return

        texts = []

        with self.camera.activate():
            arcade.draw_arc_filled(
                0, 0,
                BOARD_LENGTH * 2, BOARD_LENGTH * 2,
                color=arcade.color.PINK,
                start_angle=-15,
                end_angle=15,
                num_segments=360,
            )

            left = BOARD_LENGTH
            if board.elapsed > START_SHRINK:
                left -= (board.elapsed - START_SHRINK) * SHRINK_SPEED

            arcade.draw_arc_filled(
                0, 0,
                left * 2, left * 2,
                color=arcade.color.WHITE,
                start_angle=-15,
                end_angle=15,
                num_segments=360,
            )

            for tick in (0, 60, 120, 180, 210, 240):
                arcade.draw_arc_outline(
                    0, 0,
                    tick * 2, tick * 2,
                    color=arcade.color.BLACK,
                    start_angle=-15,
                    end_angle=15,
                    num_segments=360,
                    border_width=0.5,
                )
                upper_pos = tick * np.cos(np.deg2rad(15)) - 2, -tick * np.sin(np.deg2rad(15)) - 4
                texts.append(
                    arcade.Text(str(tick), *self.camera.project(upper_pos), color=arcade.color.WHITE, font_size=10,
                                bold=True, align="center"))

            board.draw(self.camera, texts)

        for text in texts:
            text.draw()

        elapsed = board.elapsed * 60  # convert to seconds
        seconds = int(elapsed % 60)
        minutes = int((elapsed // 60) % 60)
        hours = int(elapsed // 3600)
        arcade.Text(f'Time: {hours:02}:{minutes:02}:{seconds:02}', 10, 10, arcade.color.WHITE, 14).draw()

        # Live scoreboard (screen space)
        scoreboard = []
        for i in range(NUM_TEAMS):
            prev_score = teams_score[i]
            cur_score = board.teams[i].score
            if BOARD_LENGTH == 120:
                cur_score *= 2  # double for short matches
            total_score = prev_score + cur_score
            scoreboard.append((i, total_score))
        scoreboard.sort(key=lambda x: x[1], reverse=True)

        y_offset = 60 + 16 * 12
        for rank, (team_idx, score) in enumerate(scoreboard, start=1):
            line = f"{rank:02d}. {board.teams[team_idx]}\tScore: {score}"
            arcade.Text(line, 10, y_offset, arcade.color.WHITE, 12).draw()
            y_offset -= 16


def generate_challenges() -> list[Challenge]:
    if BOARD_LENGTH == 240:
        locations = [
            Position(225, 0),
            Position(225, 10),
            Position(225, 20),

            Position(210, 5),
            Position(210, 15),
            Position(210, 25),

            Position(180, 0),
            Position(180, 10),
            Position(180, 20),

            Position(150, 7.5),
            Position(150, 22.5),

            Position(120, 15),

            Position(90, 7.5),
            Position(90, 22.5),

            Position(60, 15),
            Position(15, 15),
        ]
    else:
        locations = [
            Position(90, 7.5),
            Position(90, 22.5),

            Position(60, 15),
            Position(15, 15),
        ]

    challenges = []
    for loc in locations:
        pos = loc.randomize()

        if np.random.binomial(1, AD_KOTH_RATIO):
            challenges.append(ADService(pos))
        else:
            challenges.append(KotHChallenge(pos))

    if CHI > 0:
        for c in np.random.choice(challenges, size=int(CHI), replace=False):
            if np.random.rand() < 0.5:
                c.deviation = 2
            else:
                c.deviation = 0.5

            print(f"Challenge {c.text} deviation adjusted to {c.deviation:.2f}")

    return challenges


def generate_teams(z_scores: list[float]) -> list[Team]:
    team_dis = BOARD_ANGLE / NUM_TEAMS
    init_offset = np.random.rand() * team_dis

    positions = [
        Position(
            mark=BOARD_LENGTH,
            angle=init_offset + i * team_dis,
        )
        for i in range(NUM_TEAMS)
    ]
    np.random.shuffle(positions)

    return [
        Team(
            idx=i,
            pos=positions[i],
            z_score=z_scores[i],
        )
        for i in range(NUM_TEAMS)
    ]


def simulate(ui: Optional[UI], stop: threading.Event):
    teams_z_score = np.random.normal(size=NUM_TEAMS).tolist()

    for board_length in (240, 240, 120):
        global BOARD_LENGTH
        BOARD_LENGTH = board_length

        teams = generate_teams(teams_z_score)
        challenges = generate_challenges()
        board = Board(challenges, teams)

        if ui is not None:
            with ui.lock:
                ui.board = board

        match_duration = 8 * 60 if BOARD_LENGTH == 240 else 4 * 60  # minutes
        for _ in np.arange(0, match_duration, step=TICK_DURATION):
            if ui is not None:
                with ui.lock:
                    board.tick()
                time.sleep(0.001)
                if stop.is_set():
                    break
            else:
                board.tick()

        for team in teams:
            teams_score[team.idx] += team.score
            if board_length == 120:  # times 2 for short matches
                teams_score[team.idx] += team.score

    json_output = json.dumps([{'z': teams_z_score[idx], 's': teams_score[idx]} for idx in range(NUM_TEAMS)])
    sys.stderr.write(json_output + '\n')

    if ui is not None:
        with ui.lock:
            ui.board = None

    print("Final Leaderboard:")
    leaderboard = sorted(enumerate(teams_score), key=lambda x: x[1], reverse=True)
    for rank, (idx, score) in enumerate(leaderboard, start=1):
        print(f"Place {rank:02d} (z-score: {teams_z_score[idx]:.2f}): {score}")

    arcade.close_window()


def main():
    ui = UI()
    stop_event = threading.Event()
    sim = threading.Thread(target=simulate, args=(ui, stop_event))
    sim.start()
    try:
        arcade.run()
    except:
        stop_event.set()
    finally:
        stop_event.set()
        sim.join()


def main_headless():
    simulate(None, None)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="DEF CON CTF simulator.")
    parser.add_argument(
        '--headless',
        action='store_true',  # This makes it a boolean flag
        help='Run without UI'
    )
    args = parser.parse_args()
    if args.headless:
        main_headless()
    else:
        main()

data_ext.sh

#!/bin/bash

nproc_val=$(nproc)
parallel=$((nproc_val - 2))
if [ $parallel -lt 1 ]; then
    parallel=1
fi

ntrials=${1:-500}

echo "Using $parallel parallel processes. Running $ntrials trials."

seq ${ntrials} | xargs -P ${parallel} -I {} sh -c 'python3 data.py --headless 2>&1 1>/dev/null' > output.jsonl

data_vis.py

import json
from collections import Counter

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LogNorm
from scipy.stats import pearsonr


def correlate_data(data):
    sort_by_z = sorted(enumerate(data), key=lambda x: x[1]['z'])
    sort_by_s = sorted(enumerate(data), key=lambda x: x[1]['s'])
    s_rank_by_index = [0] * len(data)
    for rank, (index, _) in enumerate(sort_by_s):
        s_rank_by_index[index] = rank
    z_rank_by_index = [0] * len(data)
    for rank, (index, _) in enumerate(sort_by_z):
        z_rank_by_index[index] = rank
    correlated = [(12 - s_rank_by_index[i], 12 - z_rank_by_index[i]) for i in range(len(data))]
    return correlated


all_data = []

with open('output.jsonl', 'r') as f:
    for l in f:
        data = json.loads(l)
        all_data.extend(correlate_data(data))

counts = Counter(all_data)
xs = [p[0] for p in counts.keys()]
ys = [p[1] for p in counts.keys()]
freqs = list(counts.values())

xs_full = [d[0] for d in all_data]
ys_full = [d[1] for d in all_data]
m, b = np.polyfit(xs_full, ys_full, 1)

x_line = np.linspace(1, 12, 100)
y_line = m * x_line + b
plt.plot(x_line, y_line, color="crimson", linewidth=2, label="Correlation")
plt.legend()

r_value, p_value = pearsonr(xs_full, ys_full)
plt.gca().text(1.0, 11, f"r = {r_value:.4f}\np = {p_value:.4f}", color="crimson", fontsize=12)

plt.scatter(xs, ys, c=freqs, cmap="Blues")
plt.colorbar(label="Count")
plt.xlabel("Score Ranking")
plt.ylabel("Skill Ranking")
plt.xticks(range(1, 13, 1))
plt.yticks(range(1, 13, 1))
plt.title("Score-Skill Distribution")
plt.grid(False)
plt.show()

output.jsonl

[{"z": -0.5334121815075937, "s": 8889.0}, {"z": -0.43527614048381286, "s": 8063.0}, {"z": 1.149046930151048, "s": 11519.0}, {"z": 1.4494465863076211, "s": 12384.0}, {"z": 1.6136280244035164, "s": 12849.0}, {"z": -0.42522821202631195, "s": 9555.0}, {"z": -0.36653564920863885, "s": 11118.0}, {"z": -0.07023154113794593, "s": 10170.0}, {"z": -0.8253003624443395, "s": 7707.0}, {"z": 0.44401957762235345, "s": 9537.0}, {"z": 3.3636842079518923, "s": 22021.0}, {"z": 1.386235354807699, "s": 15901.0}]
[{"z": -0.5155287357845331, "s": 10290.0}, {"z": -0.6556191843562519, "s": 9410.0}, {"z": -1.0822702395404349, "s": 7929.0}, {"z": 0.3863569681331279, "s": 10362.0}, {"z": -0.8715403207171551, "s": 9713.0}, {"z": -0.05515948521507073, "s": 13078.0}, {"z": -0.6738176365341846, "s": 8862.0}, {"z": -1.1134521462842528, "s": 8982.0}, {"z": 1.3542727802189543, "s": 13612.0}, {"z": -0.9948636792161408, "s": 8645.0}, {"z": -0.6282332952840286, "s": 10890.0}, {"z": 3.7675320350773087, "s": 22061.0}]
[{"z": -1.0728414603315857, "s": 8397.0}, {"z": 0.07987339829404018, "s": 11132.0}, {"z": -0.5750062462956288, "s": 9674.0}, {"z": 0.537417109190004, "s": 14936.0}, {"z": -0.3563620962069197, "s": 8387.0}, {"z": -0.10640835277584981, "s": 10558.0}, {"z": 0.27914184917683876, "s": 11450.0}, {"z": 0.06733488967139922, "s": 12042.0}, {"z": 0.1438811160705471, "s": 11568.0}, {"z": 0.8280956602871609, "s": 18845.0}, {"z": 0.14340472323018405, "s": 9587.0}, {"z": -1.5465987554347065, "s": 8089.0}]
[{"z": 0.15022533571077548, "s": 12010.0}, {"z": 0.02441333808403842, "s": 10043.0}, {"z": -0.8486673111977211, "s": 8569.0}, {"z": -2.1904597866972915, "s": 6693.0}, {"z": -0.3561589859870375, "s": 10307.0}, {"z": -0.45119786002707146, "s": 9781.0}, {"z": -1.14135791530465, "s": 9075.0}, {"z": -0.5457792719479776, "s": 8441.0}, {"z": -0.3428062758317828, "s": 11772.0}, {"z": 0.11095820822997056, "s": 10732.0}, {"z": -0.39880417812586716, "s": 10778.0}, {"z": 0.8869592735720944, "s": 15910.0}]
[{"z": 0.7859615807074135, "s": 14837.0}, {"z": -0.9923990052885017, "s": 8510.0}, {"z": 0.15235454918677985, "s": 10122.0}, {"z": 0.20999414123451743, "s": 10572.0}, {"z": -1.198888724117454, "s": 7506.0}, {"z": -0.2486537746309584, "s": 9042.0}, {"z": -0.30302740527744565, "s": 8930.0}, {"z": -0.15438631188800633, "s": 9077.0}, {"z": -1.8415531060152932, "s": 7079.0}, {"z": 0.2610905067972939, "s": 10319.0}, {"z": -0.3237320689981909, "s": 8708.0}, {"z": 0.30155700021239623, "s": 10587.0}]
[{"z": -0.266688833089725, "s": 10030.0}, {"z": -0.21602569413863412, "s": 8280.0}, {"z": -1.2850670804301463, "s": 5991.0}, {"z": -0.6383084237759, "s": 9251.0}, {"z": 1.0524833860996072, "s": 11938.0}, {"z": 1.1736848617727191, "s": 16735.0}, {"z": -0.8835079564047407, "s": 8106.0}, {"z": -0.6522532218462199, "s": 7873.0}, {"z": -0.29924352194718407, "s": 8800.0}, {"z": 1.022501813542387, "s": 11154.0}, {"z": 0.16338773701803797, "s": 10543.0}, {"z": 1.323700145851782, "s": 12668.0}]
[{"z": -1.3914669888094793, "s": 8597.0}, {"z": 0.9838248682307363, "s": 15204.0}, {"z": 1.867850379160763, "s": 21003.0}, {"z": -1.6086443243835222, "s": 8687.0}, {"z": -0.3031550713300215, "s": 9320.0}, {"z": -1.7438222997478807, "s": 8130.0}, {"z": 0.7192827076087832, "s": 11059.0}, {"z": -0.6821651805995499, "s": 8409.0}, {"z": -0.0947003009399938, "s": 10875.0}, {"z": 0.4800999645219433, "s": 10462.0}, {"z": 0.5685949023949071, "s": 12245.0}, {"z": -0.9136846642113351, "s": 8319.0}]
[{"z": 0.43896830818265176, "s": 11918.0}, {"z": -0.6334087011210032, "s": 7015.0}, {"z": -1.0377615316477675, "s": 9495.0}, {"z": -0.6609940305005991, "s": 9718.0}, {"z": 0.5627227638900211, "s": 14921.0}, {"z": 1.5995377904338015, "s": 21436.0}, {"z": -1.4890846324423546, "s": 7802.0}, {"z": -0.543009209040171, "s": 11901.0}, {"z": -2.2588463640169176, "s": 7036.0}, {"z": -1.1531088569601706, "s": 9287.0}, {"z": 0.3484085009759812, "s": 13805.0}, {"z": 0.23831839681576675, "s": 9315.0}]
[{"z": -1.108623708482019, "s": 7282.0}, {"z": 1.744995415599922, "s": 15676.0}, {"z": 1.218378862056997, "s": 12780.0}, {"z": -0.0002279508993803172, "s": 12692.0}, {"z": 0.8999348668828066, "s": 15111.0}, {"z": 0.3045745856498923, "s": 10682.0}, {"z": 1.4142707559973127, "s": 12911.0}, {"z": 0.6639837970316536, "s": 10829.0}, {"z": -0.15487664020189815, "s": 9390.0}, {"z": -0.48522836190789637, "s": 6942.0}, {"z": -0.07540165316527156, "s": 8914.0}, {"z": -1.0029392312003542, "s": 7549.0}]
[{"z": -0.8274483838501769, "s": 8612.0}, {"z": -0.8936092554480293, "s": 7387.0}, {"z": 0.6711655779145059, "s": 11411.0}, {"z": 1.2028818680771933, "s": 17091.0}, {"z": 0.8709681676395261, "s": 13245.0}, {"z": 0.38103475612524323, "s": 13603.0}, {"z": 0.05570576634502052, "s": 8777.0}, {"z": -0.06557073225578233, "s": 9899.0}, {"z": -0.9388042882030223, "s": 7467.0}, {"z": 0.002852920441341293, "s": 10318.0}, {"z": 0.6631080329327578, "s": 11695.0}, {"z": -0.6024150923713898, "s": 8218.0}]
[{"z": -0.6269713775512349, "s": 9081.0}, {"z": -1.2507780199562948, "s": 7362.0}, {"z": -1.1225124270646658, "s": 9075.0}, {"z": -1.4111818339222364, "s": 7368.0}, {"z": 0.8456953837607541, "s": 12583.0}, {"z": 1.3222174112409466, "s": 12459.0}, {"z": 1.7635757395287741, "s": 17569.0}, {"z": 1.2518094106905897, "s": 14719.0}, {"z": -0.13320607629205383, "s": 10363.0}, {"z": 2.0174931064441166, "s": 17437.0}, {"z": -1.0103647082100755, "s": 9237.0}, {"z": 0.5060062966509676, "s": 10373.0}]
[{"z": 0.15224482743799223, "s": 10434.0}, {"z": 0.37784655968226555, "s": 12596.0}, {"z": -0.9486665348579261, "s": 9021.0}, {"z": -0.14448499405467785, "s": 8447.0}, {"z": 0.856385045694399, "s": 10919.0}, {"z": 0.7800046730286673, "s": 10392.0}, {"z": 1.2102761902861972, "s": 12149.0}, {"z": 1.300973129730288, "s": 15636.0}, {"z": -1.9381464715409669, "s": 8998.0}, {"z": 1.3345652474518053, "s": 15799.0}, {"z": -0.5224745359649098, "s": 9269.0}, {"z": 1.8275911065531536, "s": 17245.0}]
[{"z": 0.6157111984655109, "s": 13663.0}, {"z": -0.3742989820201893, "s": 8056.0}, {"z": 0.2878422301267494, "s": 9334.0}, {"z": -0.128956613164276, "s": 9935.0}, {"z": -0.19587333385845787, "s": 7753.0}, {"z": -1.229564119118169, "s": 7888.0}, {"z": -2.0977811299331526, "s": 7560.0}, {"z": 2.018849118978721, "s": 15088.0}, {"z": 0.7562642717630259, "s": 12738.0}, {"z": 1.8047143121480669, "s": 15427.0}, {"z": -0.7542499536472288, "s": 7982.0}, {"z": -2.5432799334660214, "s": 6851.0}]
[{"z": 0.687119213906157, "s": 11131.0}, {"z": 0.5855702747402881, "s": 10585.0}, {"z": -0.59872034055677, "s": 7747.0}, {"z": -2.3670257539921336, "s": 7703.0}, {"z": -2.508573212235696, "s": 7451.0}, {"z": -1.9119995263111635, "s": 9689.0}, {"z": 1.5096195789602704, "s": 18060.0}, {"z": 1.0663410981031898, "s": 10475.0}, {"z": 0.29601317284005607, "s": 8201.0}, {"z": -1.3989058450952292, "s": 8282.0}, {"z": 0.9563704476476466, "s": 12376.0}, {"z": 0.2993162686309126, "s": 10544.0}]
[{"z": 0.7767257562634315, "s": 7961.0}, {"z": -1.6234304304615461, "s": 7492.0}, {"z": -0.3484060010508101, "s": 8906.0}, {"z": -1.733716477667638, "s": 7444.0}, {"z": -1.219284920424117, "s": 7189.0}, {"z": -0.6986524932912769, "s": 6516.0}, {"z": 0.057646622851395914, "s": 10527.0}, {"z": -0.5113939418448941, "s": 9014.0}, {"z": -0.528538580084344, "s": 7558.0}, {"z": 1.8597382655359689, "s": 17529.0}, {"z": 1.10402413048526, "s": 14281.0}, {"z": 0.24911828188568577, "s": 8818.0}]
[{"z": 0.023133235411276327, "s": 9989.0}, {"z": 0.9817485918095448, "s": 14031.0}, {"z": 0.8447086147908511, "s": 11041.0}, {"z": 0.7165637710997936, "s": 11221.0}, {"z": -0.6756157765201594, "s": 10635.0}, {"z": 0.39346357199327453, "s": 9442.0}, {"z": -1.8847685621219938, "s": 7058.0}, {"z": 0.9012677514147108, "s": 12416.0}, {"z": -1.5329612059694744, "s": 6880.0}, {"z": -0.6898418992746626, "s": 9181.0}, {"z": -0.09497244719626599, "s": 9997.0}, {"z": -1.0170540023265437, "s": 8485.0}]
[{"z": -1.8487281658472787, "s": 8596.0}, {"z": 0.29152239778069433, "s": 14518.0}, {"z": -0.04737793080761098, "s": 11660.0}, {"z": -0.08392009209418204, "s": 10482.0}, {"z": 0.3651955136963322, "s": 14329.0}, {"z": -0.3473881490949891, "s": 10224.0}, {"z": -0.8710925307984069, "s": 9909.0}, {"z": -1.3648784826236522, "s": 9317.0}, {"z": -1.3990488951397, "s": 9321.0}, {"z": -0.011307783958013361, "s": 10767.0}, {"z": 0.6742457290640422, "s": 16987.0}, {"z": -1.0678830708702491, "s": 10608.0}]
[{"z": -0.08013412721117606, "s": 11301.0}, {"z": -0.8588515276433205, "s": 10176.0}, {"z": 0.6709925224350495, "s": 14337.0}, {"z": -0.6066547641148622, "s": 8460.0}, {"z": -0.9099700350910159, "s": 9783.0}, {"z": -1.1530856150212865, "s": 7535.0}, {"z": 0.2714118859019758, "s": 11168.0}, {"z": 0.6658999611363141, "s": 15495.0}, {"z": -0.11489416346354803, "s": 10767.0}, {"z": 0.6631947056222225, "s": 13814.0}, {"z": -0.13050964132864734, "s": 10138.0}, {"z": 0.8043259526319979, "s": 14360.0}]
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