from __future__ import annotations

from dataclasses import dataclass, field
from typing import Any

import numpy as np


@dataclass(slots=True)
class FrameHypothesis:
    name: str
    axes: np.ndarray
    origin: np.ndarray
    score: float
    bottom_score: float
    symmetry_score: float
    flatness_score: float
    hole_score: float
    continuity_score: float


@dataclass(slots=True)
class CandidateBand:
    candidate_id: str
    slice_index: int
    x_start: float
    x_end: float
    x_center: float
    y_min: float
    y_max: float
    y_center: float
    z_low: float
    width: float
    point_count: int
    low_point_count: int
    flatness: float
    normal_alignment: float
    score: float
    edge_ratio: float
    side: str
    y_mid: float
    bin_start: int
    bin_end: int


@dataclass(slots=True)
class SliceResult:
    slice_index: int
    x_start: float
    x_end: float
    x_center: float
    y_mid: float
    candidate_bands: list[CandidateBand] = field(default_factory=list)
    left_candidates: list[CandidateBand] = field(default_factory=list)
    right_candidates: list[CandidateBand] = field(default_factory=list)
    point_count: int = 0


@dataclass(slots=True)
class TrackSolution:
    side: str
    rank: int
    score: float
    coverage_ratio: float
    coverage_count: int
    candidate_ids: list[str | None]
    candidates: list[CandidateBand]
    score_margin: float | None
    confidence: float


@dataclass(slots=True)
class RepresentativePoint:
    side: str
    slice_index: int
    x: float
    y: float
    z: float
    width: float
    point_count: int
    residual: float
    confidence: float
    x_start: float
    x_end: float


@dataclass(slots=True)
class FittedLine:
    side: str
    x_support: np.ndarray
    y_support: np.ndarray
    z_support: np.ndarray
    y_spline: Any
    z_spline: Any
    y_residual_rms: float
    z_residual_rms: float
    low_confidence_segments: list[tuple[float, float]]
    gaps: list[tuple[float, float]]


@dataclass(slots=True)
class HoleCandidate:
    side: str
    region: str
    axis: str
    center: np.ndarray
    radius: float
    score: float
    inlier_count: int
    coverage_ratio: float
    residual: float
    confidence: float


@dataclass(slots=True)
class ReferenceLine:
    side: str
    available: bool
    point_front: np.ndarray | None
    point_back: np.ndarray | None
    direction: np.ndarray | None
    confidence: float
    residual: float | None
    candidates_front: list[HoleCandidate] = field(default_factory=list)
    candidates_back: list[HoleCandidate] = field(default_factory=list)
    alternates: list[dict[str, Any]] = field(default_factory=list)