dcs-retribution/theater/frontline.py

"""Battlefield front lines."""
from __future__ import annotations
from dataclasses import dataclass

import logging
import json
from pathlib import Path
from itertools import tee
from typing import Tuple, List, Union, Dict, Optional

from dcs.mapping import Point

from .controlpoint import ControlPoint, MissionTarget

Numeric = Union[int, float]

# TODO: Dedup by moving everything to using this class.
FRONTLINE_MIN_CP_DISTANCE = 5000


def pairwise(iterable):
    "s -> (s0,s1), (s1,s2), (s2, s3), ..."
    a, b = tee(iterable)
    next(b, None)
    return zip(a, b)


@dataclass
class ComplexFrontLine:
    """
    Stores data necessary for building a multi-segment frontline.
    "points" should be ordered from closest to farthest distance originating from start_cp.position
    """

    start_cp: ControlPoint
    points: List[Point]
    # control_points: List[ControlPoint]


@dataclass
class FrontLineSegment:
    """
    Describes a line segment of a FrontLine
    """

    point_a: Point
    point_b: Point

    @property
    def attack_heading(self) -> Numeric:
        return self.point_a.heading_between_point(self.point_b)

    @property
    def attack_distance(self) -> Numeric:
        return self.point_a.distance_to_point(self.point_b)


class FrontLine(MissionTarget):
    """Defines a front line location between two control points.
    Front lines are the area where ground combat happens.
    Overwrites the entirety of MissionTarget __init__ method to allow for
    dynamic position calculation.
    """
    frontline_data: Optional[Dict[str, ComplexFrontLine]] = None

    def __init__(
        self,
        control_point_a: ControlPoint,
        control_point_b: ControlPoint,
    ) -> None:
        self.control_point_a = control_point_a
        self.control_point_b = control_point_b
        self.segments: List[FrontLineSegment] = []
        self._build_segments()
        self.name = f"Front line {control_point_a}/{control_point_b}"
        print(f"FRONTLINE SEGMENTS {len(self.segments)}")

    @property
    def position(self):
        return self._calculate_position()

    @property
    def control_points(self) -> Tuple[ControlPoint, ControlPoint]:
        """Returns a tuple of the two control points."""
        return self.control_point_a, self.control_point_b

    @property
    def middle_point(self):
        self.point_from_a(self.attack_distance / 2)

    @property
    def attack_distance(self):
        return sum(i.attack_distance for i in self.segments)

    @property
    def attack_heading(self):
        return self.active_segment.attack_heading

    @property
    def active_segment(self) -> FrontLineSegment:
        """The FrontLine segment where there can be an active conflict"""
        if self._position_distance <= self.segments[0].attack_distance:
            return self.segments[0]

        remaining_dist = self._position_distance
        for segment in self.segments:
            if remaining_dist <= segment.attack_distance:
                return segment
            else:
                remaining_dist -= segment.attack_distance
        logging.error(
            "Frontline attack distance is greater than the sum of its segments"
        )
        return self.segments[0]

    @property
    def _position_distance(self) -> float:
        """
        The distance from point a where the conflict should occur
        according to the current strength of each control point
        """
        total_strength = (
            self.control_point_a.base.strength + self.control_point_b.base.strength
        )
        if total_strength == 0:
            return self._adjust_for_min_dist(0)
        strength_pct = self.control_point_a.base.strength / total_strength
        return self._adjust_for_min_dist(strength_pct * self.attack_distance)

    @classmethod
    def load_json_frontlines(cls, terrain_name: str) -> None:
        try:
            path = Path(f"resources/frontlines/{terrain_name.lower()}.json")
            with open(path, "r") as file:
                logging.debug(f"Loading frontline from {path}...")
                data = json.load(file)
            cls.frontline_data = {
                frontline: ComplexFrontLine(
                    data[frontline]["start_cp"],
                    [Point(i[0], i[1]) for i in data[frontline]["points"]],
                )
                for frontline in data
            }
            print(cls.frontline_data)
        except OSError:
            logging.warning(f"Unable to load preset frontlines for {terrain_name}")

    def _calculate_position(self) -> Point:
        """
        The position where the conflict should occur
        according to the current strength of each control point.
        """
        return self.point_from_a(self._position_distance)

    def _build_segments(self) -> None:
        control_point_ids = "|".join(
            [str(self.control_point_a.id), str(self.control_point_b.id)]
        )
        reversed_cp_ids = "|".join(
            [str(self.control_point_b.id), str(self.control_point_a.id)]
        )
        complex_frontlines = FrontLine.frontline_data
        if (complex_frontlines) and (
            (control_point_ids in complex_frontlines)
            or (reversed_cp_ids in complex_frontlines)
        ):
            # The frontline segments must be stored in the correct order for the distance algorithms to work.
            # The points in the frontline are ordered from the id before the | to the id after.
            # First, check if control point id pair matches in order, and create segments if a match is found.
            if control_point_ids in complex_frontlines:
                point_pairs = pairwise(complex_frontlines[control_point_ids].points)
                for i in point_pairs:
                    self.segments.append(FrontLineSegment(i[0], i[1]))
            # Check the reverse order and build in reverse if found.
            elif reversed_cp_ids in complex_frontlines:
                point_pairs = pairwise(
                    reversed(complex_frontlines[reversed_cp_ids].points)
                )
                for i in point_pairs:
                    self.segments.append(FrontLineSegment(i[0], i[1]))
        # If no complex frontline has been configured, fall back to the old straight line method.
        else:
            self.segments.append(
                FrontLineSegment(
                    self.control_point_a.position, self.control_point_b.position
                )
            )

    def _adjust_for_min_dist(self, distance: Numeric) -> Numeric:
        """
        Ensures the frontline conflict is never located within the minimum distance
        constant of either end control point.
        """
        if (distance > self.attack_distance / 2) and (
            distance + FRONTLINE_MIN_CP_DISTANCE > self.attack_distance
        ):
            distance = self.attack_distance - FRONTLINE_MIN_CP_DISTANCE
        elif (distance < self.attack_distance / 2) and (
            distance < FRONTLINE_MIN_CP_DISTANCE
        ):
            distance = FRONTLINE_MIN_CP_DISTANCE
        return distance

    def point_from_a(self, distance: Numeric) -> Point:
        """
        Returns a point {distance} away from control_point_a along the frontline segments.
        """
        if distance < self.segments[0].attack_distance:
            return self.control_point_a.position.point_from_heading(
                self.segments[0].attack_heading, distance
            )
        remaining_dist = distance
        for segment in self.segments:
            if remaining_dist < segment.attack_distance:
                return self.control_point_a.position.point_from_heading(
                    segment.attack_heading, remaining_dist
                )
            else:
                remaining_dist -= segment.attack_distance