eolrb/cube.py

from dataclasses import dataclass
from typing import Dict, List, Tuple
import hashlib

M_SLICE_EDGES = ["UB", "UF", "DF", "DB"]
U_EDGES = ["UB", "UR", "UF", "UL"]
CENTERS = ["U", "F", "D", "B"]
U_CORNERS = ["UBR", "UFR", "UFL", "UBL"]


def cyclic_shift(d: Dict, keys: List, offset: int):
    if not all(key in d for key in keys):
        raise ValueError("some keys are missing from the dictionary")
    values = [d[key] for key in keys]
    effective_offset = offset % len(keys)
    shifted_values = values[effective_offset:] + values[:effective_offset]
    for key, value in zip(keys, shifted_values):
        d[key] = value


@dataclass(unsafe_hash=True)
class Edge:
    name: str
    oriented: bool


class LSECube:
    def __init__(self):
        self.reset()

    def reset(self):
        self.edges = {
            "UB": Edge("UB", True),
            "UR": Edge("UR", True),
            "UF": Edge("UF", True),
            "UL": Edge("UL", True),
            "DB": Edge("DB", True),
            "DF": Edge("DF", True),
        }
        self.centers = {
            "U": "U",
            "B": "B",
            "F": "F",
            "D": "D",
        }
        self.corners = {
            "UBR": "UBR",
            "UFR": "UFR",
            "UFL": "UFL",
            "UBL": "UBL",
        }

    def eolrb_hash(self):
        # TODO: I fucking hate this language. why can't hashing be simpelr
        hash_dict = lambda d: hashlib.md5(str(sorted(d.items())).encode()).hexdigest()
        eolrb_state = {}
        # mask edges to only care about LR edges and orientation of everything else
        for location, edge in self.edges.items():
            if edge.name in ["UR", "UL"]:
                eolrb_state[location] = edge
            else:
                eolrb_state[location] = Edge("", edge.oriented)

        eolrb_state["center_oriented"] = self.centers["U"] in ["U", "D"]
        # the value of UFR is supposed to be the key of the value "UFR" in self.corners but whatever
        eolrb_state["UFR"] = self.corners["UFR"]

        return int(
            hashlib.md5(hash_dict(eolrb_state).encode()).hexdigest(),
            16,
        )

    def __repr__(self):
        return f"{self.edges=}, {self.centers=}, {self.corners=}"

    def M(self):
        cyclic_shift(self.edges, M_SLICE_EDGES, 3)
        for edge in M_SLICE_EDGES:
            self.edges[edge].oriented = not self.edges[edge].oriented
        cyclic_shift(self.centers, CENTERS, 3)

    def M2(self):
        cyclic_shift(self.edges, M_SLICE_EDGES, 2)
        cyclic_shift(self.centers, CENTERS, 2)

    def Mp(self):
        cyclic_shift(self.edges, M_SLICE_EDGES, 1)
        for edge in M_SLICE_EDGES:
            self.edges[edge].oriented = not self.edges[edge].oriented
        cyclic_shift(self.centers, CENTERS, 1)

    def U(self):
        cyclic_shift(self.edges, U_EDGES, 3)
        cyclic_shift(self.corners, U_CORNERS, 3)

    def U2(self):
        cyclic_shift(self.edges, U_EDGES, 2)
        cyclic_shift(self.corners, U_CORNERS, 2)

    def Up(self):
        cyclic_shift(self.edges, U_EDGES, 1)
        cyclic_shift(self.corners, U_CORNERS, 1)

    def cycle_corners(self, n: int):
        cyclic_shift(self.corners, U_CORNERS, 4 - n)

    def fix_auf(self) -> str:
        match self.corners["UFR"]:
            case "UFR":
                return ""
            case "UBR":
                self.Up()
                return "U'"
            case "UBL":
                self.U2()
                return "U2"
            case "UFL":
                self.U()
                return "U"
        return ""

    def fix_m_slice(self) -> str:
        match self.centers["U"]:
            case "U":
                return ""
            case "B":
                self.Mp()
                return "M'"
            case "D":
                self.M2()
                return "M2"
            case "F":
                self.M()
                return "M"
        return ""

    def alg(self, alg: str):
        move_functions = {
            "U": self.U,
            "U2": self.U2,
            "U2'": self.U2,
            "U'": self.Up,
            "M": self.M,
            "M2": self.M2,
            "M2'": self.M2,
            "M'": self.Mp,
        }
        for move in alg.split():
            move_functions[move]()


def reverse_algorithm(moves: List[str]) -> List[str]:
    def invert_move(move):
        if move.endswith("'"):
            return move[:-1]
        elif move.endswith("2"):
            return move
        else:
            return move + "'"

    reversed_moves = [invert_move(move) for move in reversed(moves)]
    return reversed_moves


def condense_algorithm(moves: List[str]) -> List[str]:
    def apply_reduction(moves: List[str]) -> List[str]:
        result = []
        for move in moves:
            base_move = move[0]
            multiplier = 1
            if move.endswith("2"):
                multiplier = 2
            if move.endswith("'"):
                multiplier = 3
            result.extend([base_move] * multiplier)
        return result

    def reduce_moves(moves: List[str]) -> List[Tuple[str, int]]:
        if not moves:
            return []

        condensed = []
        current_element = moves[0]
        count = 1

        for element in moves[1:]:
            if element == current_element:
                count += 1
            else:
                condensed.append((current_element, count))
                current_element = element
                count = 1

        # Append the last element group
        condensed.append((current_element, count))

        return condensed

    def build_reduced_alg(moves: List[Tuple[str, int]]) -> List[str]:
        result = []
        for move in moves:
            move_type, count = move
            count = count % 4
            match count:
                case 1:
                    result.append(move_type)
                case 2:
                    result.append(move_type + "2")
                case 3:
                    result.append(move_type + "'")
        return result

    reduced_moves = apply_reduction(moves)
    counted_moves = reduce_moves(reduced_moves)
    reduced_alg = build_reduced_alg(counted_moves)
    return reduced_alg