LED-mapper/models.py

from __future__ import annotations

import json
import math
import re
from collections import deque
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any

try:
    from unfolding import MAX_LINES, UnfoldingDescription, UnfoldingLineSummary, clockwise_sort, order_points_clockwise
except ImportError:  # pragma: no cover - supports package imports in tests/tools.
    from .unfolding import MAX_LINES, UnfoldingDescription, UnfoldingLineSummary, clockwise_sort, order_points_clockwise

MIN_NEIGHBOR_DISTANCE_MM = 34.0
MAX_NEIGHBOR_TOLERANCE_MM = 2.0
METER_LIKE_MAX_SPAN = 10.0
_DOCUMENT_KNOWN_FIELDS = {
    "background",
    "circles",
    "coordinate_unit",
    "leds",
    "lines",
    "unit",
    "unfolding",
}
_LED_POINT_KNOWN_FIELDS = {
    "block_id",
    "block_index",
    "col",
    "column",
    "id",
    "line_id",
    "line_index",
    "manual",
    "row",
    "source_id",
    "x",
    "y",
}


@dataclass
class LedPoint:
    uid: int
    source_id: int
    x: float
    y: float
    row: int
    col: int
    line_id: int | None = None
    block_id: int | None = None
    line_index: int | None = None
    block_index: int | None = None
    coordinate_unit: str = "auto"
    manual: bool = False
    extra_payload: dict[str, Any] = field(default_factory=dict)


@dataclass
class LedBlock:
    id: int
    name: str
    point_ids: list[int] = field(default_factory=list)


@dataclass
class LedLine:
    id: int
    name: str
    color: str
    blocks: list[LedBlock] = field(default_factory=list)


@dataclass
class BackgroundImage:
    path: str = ""
    offset_x: float = 0.0
    offset_y: float = 0.0
    scale_x_percent: float = 100.0
    scale_y_percent: float = 100.0
    opacity_percent: float = 100.0
    visible: bool = True

    @classmethod
    def from_payload(cls, payload) -> "BackgroundImage":  # noqa: ANN001
        if not isinstance(payload, dict):
            return cls()
        scale = _optional_float(payload.get("scale_percent"))
        scale_x = _optional_float(payload.get("scale_x_percent"))
        scale_y = _optional_float(payload.get("scale_y_percent"))
        if scale_x is None:
            scale_x = 100.0 if scale is None else scale
        if scale_y is None:
            scale_y = 100.0 if scale is None else scale
        offset_x = _optional_float(payload.get("offset_x"))
        offset_y = _optional_float(payload.get("offset_y"))
        opacity = _optional_float(payload.get("opacity_percent"))
        return cls(
            path=str(payload.get("path") or ""),
            offset_x=0.0 if offset_x is None else offset_x,
            offset_y=0.0 if offset_y is None else offset_y,
            scale_x_percent=max(1.0, float(scale_x)),
            scale_y_percent=max(1.0, float(scale_y)),
            opacity_percent=min(100.0, max(0.0, 100.0 if opacity is None else float(opacity))),
            visible=bool(payload.get("visible", True)),
        )

    def to_payload(self) -> dict[str, bool | float | str]:
        return {
            "path": self.path,
            "offset_x": self.offset_x,
            "offset_y": self.offset_y,
            "scale_x_percent": self.scale_x_percent,
            "scale_y_percent": self.scale_y_percent,
            "opacity_percent": self.opacity_percent,
            "visible": self.visible,
        }

    def is_enabled(self) -> bool:
        return bool(self.path)


class MappingDocument:
    def __init__(self) -> None:
        self.path: Path | None = None
        self.collection_name = "circles"
        self.points: list[LedPoint] = []
        self.lines: list[LedLine] = []
        self.background = BackgroundImage()
        self.unfolding = UnfoldingDescription()
        self.coordinate_unit = "auto"
        self.extra_payload: dict[str, Any] = {}
        self._next_line_id = 1
        self._next_block_id = 1

    def clear(self) -> None:
        self.path = None
        self.collection_name = "circles"
        self.points.clear()
        self.lines.clear()
        self.background = BackgroundImage()
        self.unfolding = UnfoldingDescription()
        self.coordinate_unit = "auto"
        self.extra_payload.clear()
        self._next_line_id = 1
        self._next_block_id = 1

    def load(self, path: str | Path) -> None:
        self.clear()
        self.path = Path(path)
        data = json.loads(self.path.read_text(encoding="utf-8-sig"))
        self.extra_payload = {
            key: value
            for key, value in data.items()
            if key not in _DOCUMENT_KNOWN_FIELDS
        }
        self.coordinate_unit = str(data.get("coordinate_unit", data.get("unit", "auto")))
        points_data = data.get("leds")
        self.collection_name = "leds"
        if not isinstance(points_data, list):
            points_data = data.get("circles")
            self.collection_name = "circles"
        if not isinstance(points_data, list):
            raise ValueError("JSON must contain a 'circles' or 'leds' array.")

        for index, item in enumerate(points_data):
            self.points.append(
                LedPoint(
                    uid=index,
                    source_id=int(item.get("source_id", item.get("id", index))),
                    x=float(item["x"]),
                    y=float(item["y"]),
                    row=_optional_int(item.get("row")) if item.get("row") is not None else index * 2,
                    col=(
                        _optional_int(item.get("col"))
                        if item.get("col") is not None
                        else _optional_int(item.get("column")) if item.get("column") is not None else 0
                    ),
                    line_id=_optional_int(item.get("line_id")),
                    block_id=_optional_int(item.get("block_id")),
                    line_index=_optional_int(item.get("line_index")),
                    block_index=_optional_int(item.get("block_index")),
                    coordinate_unit=self.coordinate_unit,
                    manual=bool(item.get("manual", False)),
                    extra_payload={
                        key: value
                        for key, value in item.items()
                        if key not in _LED_POINT_KNOWN_FIELDS
                    },
                )
            )

        self._load_lines(data)
        self.background = BackgroundImage.from_payload(data.get("background"))
        self.unfolding = UnfoldingDescription.from_payload(data.get("unfolding"))
        self.unfolding.max_lines = MAX_LINES
        if not self.lines and any(point.line_id is not None for point in self.points):
            self._rebuild_lines_from_points()
        self._deduplicate_block_memberships()
        self._validate_line_limit()
        self.reindex_all()

    def _load_lines(self, data: dict[str, Any]) -> None:
        lines_data = data.get("lines", [])
        if not isinstance(lines_data, list):
            return
        source_to_uid = {point.source_id: point.uid for point in self.points}
        for line_item in lines_data:
            line_id = int(line_item.get("id", self._next_line_id))
            line = LedLine(
                id=line_id,
                name=str(line_item.get("name", f"Line {line_id}")),
                color=str(line_item.get("color", default_color(line_id))),
            )
            for block_item in line_item.get("blocks", []):
                block_id = int(block_item.get("id", self._next_block_id))
                line.blocks.append(
                    LedBlock(
                        id=block_id,
                        name=str(block_item.get("name", f"Block {block_id}")),
                        point_ids=self._normalize_loaded_point_ids(
                            block_item.get("point_ids", []),
                            source_to_uid,
                            str(line_item.get("point_id_mode", block_item.get("point_id_mode", "auto"))),
                        ),
                    )
                )
                self._next_block_id = max(self._next_block_id, block_id + 1)
            self.lines.append(line)
            self._next_line_id = max(self._next_line_id, line_id + 1)

    def _rebuild_lines_from_points(self) -> None:
        for line_id in sorted({point.line_id for point in self.points if point.line_id is not None}):
            line = LedLine(int(line_id), f"Line {line_id}", default_color(int(line_id)))
            block_ids = sorted({
                point.block_id
                for point in self.points
                if point.line_id == line_id and point.block_id is not None
            })
            if not block_ids:
                ids = [
                    point.uid
                    for point in sorted(
                        self.points,
                        key=lambda point: (
                            point.line_index if point.line_index is not None else 10**9,
                            point.source_id,
                        ),
                    )
                    if point.line_id == line_id
                ]
                if ids:
                    line.blocks.append(LedBlock(self._next_block_id, "Block 1", ids))
                    self._next_block_id += 1
            for block_id in block_ids:
                ids = [
                    point.uid
                    for point in sorted(
                        self.points,
                        key=lambda point: (
                            point.block_index if point.block_index is not None else 10**9,
                            point.source_id,
                        ),
                    )
                    if point.line_id == line_id and point.block_id == block_id
                ]
                line.blocks.append(LedBlock(int(block_id), f"Block {block_id}", ids))
                self._next_block_id = max(self._next_block_id, int(block_id) + 1)
            self.lines.append(line)
            self._next_line_id = max(self._next_line_id, int(line_id) + 1)

    def save(self, path: str | Path) -> None:
        path = Path(path)
        self.reindex_all()
        payload = self.to_payload()
        path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
        self.path = path

    def to_payload(
        self,
        *,
        point_payloads: list[dict[str, Any]] | None = None,
        lines_payload: list[dict[str, Any]] | None = None,
        collection_name: str | None = None,
    ) -> dict[str, Any]:
        payload = dict(self.extra_payload)
        payload[collection_name or self.collection_name] = (
            [self.point_to_payload(point) for point in self.points]
            if point_payloads is None
            else point_payloads
        )
        payload["unfolding"] = self.describe_unfolding().to_payload()
        payload["coordinate_unit"] = self._coordinate_unit_for_save()
        payload["background"] = self.background.to_payload()
        payload["lines"] = self.lines_to_payload() if lines_payload is None else lines_payload
        return payload

    def point_to_payload(self, point: LedPoint, *, id_value: int | None = None) -> dict[str, Any]:
        payload = dict(point.extra_payload)
        payload.update(
            {
                "id": point.source_id if id_value is None else id_value,
                "x": point.x,
                "y": point.y,
                "row": point.row,
                "col": point.col,
                "line_id": point.line_id,
                "block_id": point.block_id,
                "line_index": point.line_index,
                "block_index": point.block_index,
                "manual": point.manual,
            }
        )
        return payload

    def lines_to_payload(self, *, point_id_map: dict[int, int] | None = None) -> list[dict[str, Any]]:
        return [
            {
                "id": line.id,
                "name": line.name,
                "color": line.color,
                "blocks": [
                    {
                        "id": block.id,
                        "name": block.name,
                        "point_ids": [
                            point_id_map[point_id] if point_id_map is not None else point_id
                            for point_id in block.point_ids
                            if point_id_map is None or point_id in point_id_map
                        ],
                    }
                    for block in line.blocks
                ],
                "point_id_mode": "uid",
            }
            for line in self.lines
        ]

    def add_line(self) -> LedLine:
        line_limit = self._line_limit()
        if len(self.lines) >= line_limit:
            raise ValueError(f"Mapping supports no more than {line_limit} lines.")
        line = LedLine(self._next_line_id, f"Линия {self._next_line_id}", default_color(self._next_line_id))
        self._next_line_id += 1
        self.lines.append(line)
        return line

    def add_manual_point(self, x: float, y: float) -> LedPoint:
        uid = len(self.points)
        source_id = max((point.source_id for point in self.points), default=-1) + 1
        row, col = self._grid_cell_for_coordinate(float(x), float(y))
        point = LedPoint(
            uid=uid,
            source_id=source_id,
            x=float(x),
            y=float(y),
            row=row,
            col=col,
            coordinate_unit=self._coordinate_unit_for_save(),
            manual=True,
        )
        self.points.append(point)
        return point

    def update_manual_point_position(self, point_id: int, x: float | None = None, y: float | None = None) -> None:
        point = self.get_point(point_id)
        if not point.manual:
            raise ValueError("Only manually created LED points can be moved.")
        if x is not None:
            point.x = float(x)
        if y is not None:
            point.y = float(y)
        point.row, point.col = self._grid_cell_for_coordinate(point.x, point.y, exclude_uid=point.uid)

    def add_block(self, line_id: int, point_ids: list[int]) -> LedBlock:
        line = self.get_line(line_id)
        clean_point_ids = unique_in_order([
            point_id for point_id in point_ids if 0 <= point_id < len(self.points)
        ])
        self._detach_points_from_blocks(clean_point_ids)
        block = LedBlock(self._next_block_id, self._next_block_name(line), clean_point_ids)
        self._next_block_id += 1
        line.blocks.append(block)
        for point_id in block.point_ids:
            point = self.points[point_id]
            point.line_id = line.id
            point.block_id = block.id
        self.reindex_all()
        return block

    def append_points_to_block(self, line_id: int, block_id: int, point_ids: list[int]) -> int:
        block = self._get_block(line_id, block_id)
        existing = set(block.point_ids)
        clean_point_ids = unique_in_order([
            point_id
            for point_id in point_ids
            if 0 <= point_id < len(self.points) and point_id not in existing
        ])
        if not clean_point_ids:
            return 0
        self._detach_points_from_blocks(clean_point_ids)
        block = self._get_block(line_id, block_id)
        block.point_ids.extend(clean_point_ids)
        self.reindex_all()
        return len(clean_point_ids)

    def merge_blocks(self, line_id: int, block_ids: list[int]) -> LedBlock:
        line = self.get_line(line_id)
        target_ids = set(block_ids)
        if len(target_ids) < 2:
            raise ValueError("At least two blocks are required for merging.")
        selected_blocks = [block for block in line.blocks if block.id in target_ids]
        if len(selected_blocks) != len(target_ids):
            missing = target_ids - {block.id for block in selected_blocks}
            raise KeyError(f"Unknown block ids: {sorted(missing)}")
        merged_block = selected_blocks[0]
        merged_block.point_ids = unique_in_order(
            point_id
            for block in selected_blocks
            for point_id in block.point_ids
        )
        line.blocks = [
            block
            for block in line.blocks
            if block.id not in target_ids or block.id == merged_block.id
        ]
        for point_id in merged_block.point_ids:
            point = self.points[point_id]
            point.line_id = line.id
            point.block_id = merged_block.id
        self.reindex_all()
        return merged_block

    def get_line(self, line_id: int) -> LedLine:
        for line in self.lines:
            if line.id == line_id:
                return line
        raise KeyError(f"Unknown line id: {line_id}")

    def get_point(self, point_id: int) -> LedPoint:
        return self.points[point_id]

    def _get_block(self, line_id: int, block_id: int) -> LedBlock:
        line = self.get_line(line_id)
        for block in line.blocks:
            if block.id == block_id:
                return block
        raise KeyError(block_id)

    def clear_point_assignments(self, point_ids: list[int]) -> int:
        point_id_set = set(point_ids)
        removed = 0
        for line in self.lines:
            for block in line.blocks:
                before = len(block.point_ids)
                block.point_ids = [point_id for point_id in block.point_ids if point_id not in point_id_set]
                removed += before - len(block.point_ids)

        for point_id in point_id_set:
            point = self.points[point_id]
            if point.line_id is not None or point.block_id is not None:
                point.line_id = None
                point.block_id = None
                point.line_index = None
                point.block_index = None
        self.reindex_all()
        return removed

    def remove_block(self, line_id: int, block_id: int) -> int:
        line = self.get_line(line_id)
        removed_ids: list[int] = []
        kept_blocks: list[LedBlock] = []
        for block in line.blocks:
            if block.id == block_id:
                removed_ids.extend(block.point_ids)
            else:
                kept_blocks.append(block)
        line.blocks = kept_blocks
        self._clear_points(removed_ids)
        self.reindex_all()
        return len(removed_ids)

    def remove_line(self, line_id: int) -> int:
        removed_ids: list[int] = []
        kept_lines: list[LedLine] = []
        for line in self.lines:
            if line.id == line_id:
                for block in line.blocks:
                    removed_ids.extend(block.point_ids)
            else:
                kept_lines.append(line)
        self.lines = kept_lines
        self._clear_points(removed_ids)
        self.renumber_lines()
        self.reindex_all()
        return len(removed_ids)

    def clear_all_assignments(self) -> int:
        assigned = sum(1 for point in self.points if point.line_id is not None or point.block_id is not None)
        self.lines.clear()
        self._clear_points([point.uid for point in self.points])
        self._next_line_id = 1
        self._next_block_id = 1
        return assigned

    def renumber_lines(self) -> None:
        self.order_lines_clockwise()
        id_map: dict[int, int] = {}
        for new_id, line in enumerate(self.lines, start=1):
            old_id = line.id
            id_map[old_id] = new_id
            line.id = new_id
            line.name = f"Линия {new_id}"
            line.color = default_color(new_id)

        for point in self.points:
            if point.line_id in id_map:
                point.line_id = id_map[point.line_id]

        self._next_line_id = len(self.lines) + 1

    def _validate_line_limit(self) -> None:
        line_limit = self._line_limit()
        if len(self.lines) > line_limit:
            raise ValueError(f"Mapping supports no more than {line_limit} lines.")

    def _line_limit(self) -> int:
        return MAX_LINES

    def _coordinate_unit_for_save(self) -> str:
        if self.coordinate_unit in {"m", "meter", "meters"}:
            return "m"
        if self.coordinate_unit in {"mm", "millimeter", "millimeters"}:
            return "mm"
        return "m" if coordinate_display_scale(self.points) == 1000.0 else "mm"

    def _grid_cell_for_coordinate(self, x: float, y: float, *, exclude_uid: int | None = None) -> tuple[int, int]:
        anchors = [point for point in self.points if point.uid != exclude_uid and not point.manual]
        if not anchors:
            anchors = [point for point in self.points if point.uid != exclude_uid]
        if not anchors:
            return 0, 0
        scale = coordinate_display_scale(anchors)
        step = MIN_NEIGHBOR_DISTANCE_MM / scale
        anchor = min(anchors, key=lambda point: (point.y, point.x, point.uid))
        row = anchor.row + round((y - anchor.y) / step)
        col = anchor.col + round((x - anchor.x) / step)
        return int(row), int(col)

    def order_lines_clockwise(self) -> None:
        self.lines = clockwise_sort(self.lines, self._points_for_line)

    def order_blocks_clockwise(self, line_id: int, *, reorder_leds: bool = False) -> None:
        line = self.get_line(line_id)
        if reorder_leds:
            for block in line.blocks:
                start_point_id = block.point_ids[0] if block.point_ids else None
                ordered_ids = [point.uid for point in order_points_like_grid_svg(self._points_for_block(block), start_point_id)]
                if start_point_id is not None:
                    ordered_ids = rotate_ids_to_start(ordered_ids, start_point_id)
                block.point_ids = ordered_ids
        line.blocks = clockwise_sort(line.blocks, self._points_for_block)

    def describe_unfolding(self) -> UnfoldingDescription:
        summary = UnfoldingDescription(
            max_lines=self._line_limit(),
            line_order="clockwise",
            block_order="clockwise",
            led_index_scope="line",
            block_size=self.unfolding.block_size,
        )
        for line in self.lines:
            led_count = sum(len(block.point_ids) for block in line.blocks)
            block_count = len(line.blocks) or summary.expected_block_count(led_count)
            summary.block_counts.append(UnfoldingLineSummary(line.id, led_count, block_count))
        return summary

    def _points_for_line(self, line: LedLine) -> list[LedPoint]:
        return [point for block in line.blocks for point in self._points_for_block(block)]

    def _points_for_block(self, block: LedBlock) -> list[LedPoint]:
        return [self.points[point_id] for point_id in block.point_ids if 0 <= point_id < len(self.points)]

    def _normalize_loaded_point_ids(
        self,
        values: Any,
        source_to_uid: dict[int, int],
        mode: str = "auto",
    ) -> list[int]:
        if not isinstance(values, list):
            return []
        raw_ids = [int(value) for value in values]
        if mode in {"source", "source_id"}:
            return unique_in_order([
                source_to_uid[value]
                for value in raw_ids
                if value in source_to_uid
            ])
        valid_uid_ids = [point_id for point_id in raw_ids if 0 <= point_id < len(self.points)]
        if mode == "uid":
            return unique_in_order(valid_uid_ids)
        if mode == "auto" and len(valid_uid_ids) == len(raw_ids):
            return unique_in_order(valid_uid_ids)
        return unique_in_order([
            source_to_uid[value]
            for value in raw_ids
            if value in source_to_uid
        ])

    def _deduplicate_block_memberships(self) -> None:
        seen: set[int] = set()
        for line in self.lines:
            for block in line.blocks:
                unique_ids = []
                for point_id in block.point_ids:
                    if not 0 <= point_id < len(self.points):
                        continue
                    if point_id in seen:
                        continue
                    seen.add(point_id)
                    unique_ids.append(point_id)
                block.point_ids = unique_ids

    def _detach_points_from_blocks(self, point_ids: list[int]) -> int:
        if not point_ids:
            return 0
        point_id_set = set(point_ids)
        removed = 0
        for line in self.lines:
            for block in line.blocks:
                before = len(block.point_ids)
                block.point_ids = [point_id for point_id in block.point_ids if point_id not in point_id_set]
                removed += before - len(block.point_ids)
        self._clear_points(list(point_id_set))
        return removed

    def _clear_points(self, point_ids: list[int]) -> None:
        for point_id in point_ids:
            point = self.points[point_id]
            point.line_id = None
            point.block_id = None
            point.line_index = None
            point.block_index = None

    def auto_connect_line(self, line_id: int) -> None:
        line = self.get_line(line_id)
        self.order_blocks_clockwise(line.id, reorder_leds=True)
        self.reindex_line(line.id)

    def rebuild_line_blocks_by_route(self, line_id: int) -> int:
        line = self.get_line(line_id)
        point_ids = [point_id for block in line.blocks for point_id in block.point_ids]
        if not point_ids:
            return 0
        start_ids = [block.point_ids[0] for block in line.blocks if block.point_ids]
        start_id = start_ids[0] if start_ids else point_ids[0]
        groups = self.route_groups_from_points(point_ids, start_id)
        line.blocks.clear()
        for group in groups:
            line.blocks.append(LedBlock(self._next_block_id, self._next_block_name(line), group))
            self._next_block_id += 1
        self.reindex_all()
        return len(groups)

    def auto_blocks_from_selection(self, line_id: int, point_ids: list[int], groups: list[list[int]] | None = None) -> None:
        groups = self.auto_block_preview(point_ids) if groups is None else groups
        if not groups:
            return
        self._detach_points_from_blocks([point_id for group in groups for point_id in group])
        line = self.get_line(line_id)
        for group_ids in groups:
            block = LedBlock(self._next_block_id, self._next_block_name(line), unique_in_order(group_ids))
            self._next_block_id += 1
            line.blocks.append(block)
        self.order_blocks_clockwise(line_id, reorder_leds=False)
        self.reindex_all()

    def route_groups_from_points(self, point_ids: list[int], start_point_id: int | None = None) -> list[list[int]]:
        points = [
            self.points[point_id]
            for point_id in unique_in_order(point_ids)
            if 0 <= point_id < len(self.points)
        ]
        return [
            [point.uid for point in route]
            for route in trace_route_paths_like_grid_svg(points, start_point_id)
        ]

    def add_clockwise_block_from_seed(self, line_id: int, start_point_id: int) -> LedBlock:
        if not 0 <= start_point_id < len(self.points):
            raise ValueError(f"Unknown LED point id: {start_point_id}")
        group_ids = self.clockwise_block_from_seed_preview(start_point_id)
        return self.add_clockwise_block_from_group(line_id, start_point_id, group_ids)

    def add_clockwise_block_from_group(self, line_id: int, start_point_id: int, group_ids: list[int]) -> LedBlock:
        if not 0 <= start_point_id < len(self.points):
            raise ValueError(f"Unknown LED point id: {start_point_id}")
        block = self.add_block(line_id, group_ids)
        block.point_ids = rotate_ids_to_start(block.point_ids, start_point_id)
        self.reindex_line(line_id)
        return block

    def clockwise_block_from_seed_preview(self, start_point_id: int) -> list[int]:
        if not 0 <= start_point_id < len(self.points):
            return []
        candidate_points = [
            point
            for point in self.points
            if point.uid == start_point_id or (point.line_id is None and point.block_id is None)
        ]
        threshold = min_neighbor_distance_for_points(candidate_points)
        adjacency = build_route_adjacency(candidate_points, threshold)
        route = trace_single_route_like_grid_svg(
            candidate_points,
            start_point_id,
            adjacency=adjacency,
            allowed_ids={point.uid for point in candidate_points},
            min_neighbor_distance=threshold,
        )
        if not route:
            return [start_point_id]
        return rotate_ids_to_start(
            [point.uid for point in normalize_route_clockwise(route, threshold)],
            start_point_id,
        )

    def auto_block_preview(self, point_ids: list[int]) -> list[list[int]]:
        points = [
            self.points[point_id]
            for point_id in sorted_unique(point_ids)
            if 0 <= point_id < len(self.points)
        ]
        return [
            [point.uid for point in route]
            for route in trace_route_paths_like_grid_svg(points)
        ]

    def reindex_all(self) -> None:
        for point in self.points:
            point.line_id = None
            point.block_id = None
            point.line_index = None
            point.block_index = None
        for line in self.lines:
            self.reindex_line(line.id)

    def reindex_line(self, line_id: int) -> None:
        line = self.get_line(line_id)
        line_index = 0
        for block in line.blocks:
            for block_index, point_id in enumerate(block.point_ids):
                point = self.points[point_id]
                point.line_id = line.id
                point.block_id = block.id
                point.line_index = line_index
                point.block_index = block_index
                line_index += 1

    @staticmethod
    def _next_block_name(line: LedLine) -> str:
        max_number = 0
        for block in line.blocks:
            match = re.search(r"(\d+)$", block.name)
            if match is not None:
                max_number = max(max_number, int(match.group(1)))
        return f"Блок {max_number + 1}"


def sorted_unique(values: list[int]) -> list[int]:
    return sorted(set(values))


def unique_in_order(values: list[int]) -> list[int]:
    seen: set[int] = set()
    result: list[int] = []
    for value in values:
        if value in seen:
            continue
        seen.add(value)
        result.append(value)
    return result


def rotate_ids_to_start(values: list[int], start_value: int) -> list[int]:
    if start_value not in values:
        return values[:]
    start_index = values.index(start_value)
    return [*values[start_index:], *values[:start_index]]


def normalize_route_clockwise(route: list[LedPoint], min_neighbor_distance: float) -> list[LedPoint]:
    if len(route) < 3:
        return route[:]
    if not is_valid_route_neighbor(route[-1], route[0], min_neighbor_distance):
        return route[:]
    return list(reversed(route)) if route_signed_area(route) > 0 else route[:]


def route_signed_area(route: list[LedPoint]) -> float:
    area = 0.0
    for current, next_point in zip(route, [*route[1:], route[0]]):
        area += current.x * next_point.y - next_point.x * current.y
    return area / 2.0


def _optional_int(value: Any) -> int | None:
    return None if value is None else int(value)


def _optional_float(value: Any) -> float | None:
    try:
        return None if value is None else float(value)
    except (TypeError, ValueError):
        return None


def default_color(line_id: int) -> str:
    colors = ["#e11d48", "#2563eb", "#16a34a", "#f59e0b", "#7c3aed", "#0891b2"]
    return colors[(line_id - 1) % len(colors)]


def distance(a: LedPoint, b: LedPoint) -> float:
    return math.hypot(a.x - b.x, a.y - b.y)


def are_neighbors(a: LedPoint, b: LedPoint) -> bool:
    return abs(a.row - b.row) <= 1 and abs(a.col - b.col) <= 1


def connected_groups(points: list[LedPoint]) -> list[list[LedPoint]]:
    remaining = set(point.uid for point in points)
    by_id = {point.uid: point for point in points}
    groups: list[list[LedPoint]] = []
    while remaining:
        start = remaining.pop()
        queue = [start]
        group = [by_id[start]]
        while queue:
            current_id = queue.pop()
            current = by_id[current_id]
            for candidate_id in list(remaining):
                if are_neighbors(current, by_id[candidate_id]):
                    remaining.remove(candidate_id)
                    queue.append(candidate_id)
                    group.append(by_id[candidate_id])
        groups.append(group)
    return sorted(groups, key=lambda group: min(point.uid for point in group))


def contour_groups_like_grid_svg(points: list[LedPoint]) -> list[list[LedPoint]]:
    points_by_cell: dict[tuple[int, int], list[LedPoint]] = {}
    for point in points:
        points_by_cell.setdefault((point.row, point.col), []).append(point)

    cells = set(points_by_cell)
    seen: set[tuple[int, int]] = set()
    groups: list[list[LedPoint]] = []
    neighbors = ((1, 0), (-1, 0), (0, 1), (0, -1))

    for start_cell in sorted(cells):
        if start_cell in seen:
            continue
        queue = deque([start_cell])
        seen.add(start_cell)
        group_cells: list[tuple[int, int]] = []
        while queue:
            row, col = queue.popleft()
            group_cells.append((row, col))
            for d_row, d_col in neighbors:
                next_cell = (row + d_row, col + d_col)
                if next_cell in cells and next_cell not in seen:
                    seen.add(next_cell)
                    queue.append(next_cell)
        groups.append([
            point
            for cell in group_cells
            for point in points_by_cell[cell]
        ])

    return sorted(groups, key=lambda group: min(point_sort_key_for_model(point) for point in group))


def route_groups_like_grid_svg(points: list[LedPoint]) -> list[list[LedPoint]]:
    points_by_cell: dict[tuple[int, int], list[LedPoint]] = {}
    for point in points:
        points_by_cell.setdefault((point.row, point.col), []).append(point)

    cells = set(points_by_cell)
    seen: set[tuple[int, int]] = set()
    groups: list[list[LedPoint]] = []
    neighbors = ((1, 0), (-1, 0), (0, 1), (0, -1), (1, 1), (-1, 1), (-1, -1), (1, -1))
    threshold = min_neighbor_distance_for_points(points)

    for start_cell in sorted(cells):
        if start_cell in seen:
            continue
        queue = deque([start_cell])
        seen.add(start_cell)
        group_cells: list[tuple[int, int]] = []
        while queue:
            row, col = queue.popleft()
            group_cells.append((row, col))
            for d_row, d_col in neighbors:
                next_cell = (row + d_row, col + d_col)
                if next_cell not in cells or next_cell in seen:
                    continue
                if not cells_have_valid_route_neighbor(points_by_cell[(row, col)], points_by_cell[next_cell], threshold):
                    continue
                seen.add(next_cell)
                queue.append(next_cell)
        groups.append([
            point
            for cell in group_cells
            for point in points_by_cell[cell]
        ])

    return sorted(groups, key=lambda group: min(point_sort_key_for_model(point) for point in group))


def cells_have_valid_route_neighbor(
    first_points: list[LedPoint],
    second_points: list[LedPoint],
    threshold: float,
) -> bool:
    return any(
        distance(first, second) <= max_neighbor_distance(threshold)
        for first in first_points
        for second in second_points
    )


def order_group_nearest(points: list[LedPoint]) -> list[LedPoint]:
    if len(points) <= 1:
        return points[:]
    remaining = points[:]
    ordered = [min(remaining, key=lambda point: (point.row, point.col, point.source_id))]
    remaining.remove(ordered[0])
    while remaining:
        previous = ordered[-1]
        next_point = min(remaining, key=lambda point: (distance(previous, point), point.row, point.col))
        ordered.append(next_point)
        remaining.remove(next_point)
    return ordered


def order_points_like_grid_svg(points: list[LedPoint], start_point_id: int | None = None) -> list[LedPoint]:
    if len(points) <= 1:
        return points[:]
    min_neighbor_distance = min_neighbor_distance_for_points(points)
    remaining = points[:]
    if start_point_id is None:
        start_point = min(remaining, key=point_sort_key_for_model)
    else:
        start_point = next((point for point in remaining if point.uid == start_point_id), None)
        if start_point is None:
            start_point = min(remaining, key=point_sort_key_for_model)
    ordered = [start_point]
    remaining.remove(start_point)

    while remaining:
        previous = ordered[-1]
        neighbors = [
            point
            for point in remaining
            if is_valid_route_neighbor(previous, point, min_neighbor_distance)
        ]
        if neighbors:
            next_point = min(
                neighbors,
                key=lambda point: route_neighbor_preference(previous, point, min_neighbor_distance),
            )
        else:
            next_point = min(
                remaining,
                key=lambda point: (
                    distance(previous, point),
                    point_sort_key_for_model(point),
                ),
            )
        ordered.append(next_point)
        remaining.remove(next_point)

    return ordered


def trace_route_paths_like_grid_svg(
    points: list[LedPoint],
    start_point_id: int | None = None,
) -> list[list[LedPoint]]:
    remaining = points[:]
    remaining_ids = {point.uid for point in remaining}
    routes: list[list[LedPoint]] = []
    preferred_start_id = start_point_id
    min_neighbor_distance = min_neighbor_distance_for_points(points)
    while remaining:
        if preferred_start_id is not None:
            start_point = next((point for point in remaining if point.uid == preferred_start_id), None)
        else:
            start_point = None
        if start_point is None:
            start_point = min(remaining, key=point_sort_key_for_model)
        adjacency = build_route_adjacency(remaining, min_neighbor_distance)
        route = trace_single_route_like_grid_svg(
            remaining,
            start_point.uid,
            adjacency=adjacency,
            allowed_ids=remaining_ids,
            min_neighbor_distance=min_neighbor_distance,
        )
        if not route:
            break
        route_ids = {point.uid for point in route}
        routes.append(route)
        remaining_ids -= route_ids
        remaining = [point for point in remaining if point.uid in remaining_ids]
        preferred_start_id = None
    return routes


def trace_single_route_like_grid_svg(
    points: list[LedPoint],
    start_point_id: int,
    adjacency: dict[int, list[LedPoint]] | None = None,
    allowed_ids: set[int] | None = None,
    min_neighbor_distance: float | None = None,
) -> list[LedPoint]:
    if not points:
        return []
    min_neighbor_distance = min_neighbor_distance or min_neighbor_distance_for_points(points)
    adjacency = adjacency or build_route_adjacency(points, min_neighbor_distance)
    remaining = points[:]
    remaining_ids = set(allowed_ids) if allowed_ids is not None else {point.uid for point in remaining}
    start_point = next((point for point in remaining if point.uid == start_point_id), None)
    if start_point is None:
        start_point = min(remaining, key=point_sort_key_for_model)
    route = [start_point]
    remaining.remove(start_point)
    remaining_ids.remove(start_point.uid)

    while remaining:
        previous = route[-1]
        neighbors = [
            point
            for point in adjacency.get(previous.uid, [])
            if point.uid in remaining_ids
        ]
        if not neighbors:
            neighbors = fallback_route_neighbors(
                route,
                previous,
                remaining,
                remaining_ids,
                min_neighbor_distance,
            )
        if not neighbors:
            break
        next_point = choose_next_route_point(route, neighbors, min_neighbor_distance)
        route.append(next_point)
        remaining.remove(next_point)
        remaining_ids.remove(next_point.uid)

    return route


def fallback_route_neighbors(
    route: list[LedPoint],
    current: LedPoint,
    points: list[LedPoint],
    allowed_ids: set[int],
    min_neighbor_distance: float,
) -> list[LedPoint]:
    valid_neighbors = [
        point
        for point in points
        if point.uid in allowed_ids and is_valid_route_neighbor(current, point, min_neighbor_distance)
    ]
    if valid_neighbors or len(route) < 2:
        return valid_neighbors

    trajectory_neighbors = [
        point
        for point in points
        if point.uid in allowed_ids and is_trajectory_continuation(route[-2], current, point, min_neighbor_distance)
    ]
    return trajectory_neighbors


def choose_next_route_point(
    route: list[LedPoint],
    neighbors: list[LedPoint],
    min_neighbor_distance: float,
) -> LedPoint:
    if len(route) < 2:
        return min(neighbors, key=lambda point: route_path_preference(route, point, min_neighbor_distance))

    neighbors = nearest_neighbor_band(route[-1], neighbors, min_neighbor_distance)
    previous = route[-2]
    current = route[-1]
    same_vector_direction = [
        point
        for point in neighbors
        if same_direction_vector(previous, current, point)
    ]
    if same_vector_direction:
        return min(same_vector_direction, key=lambda point: route_path_preference(route, point, min_neighbor_distance))

    forward_direction = [
        point
        for point in neighbors
        if forward_direction_vector(previous, current, point)
    ]
    if forward_direction:
        return min(forward_direction, key=lambda point: route_path_preference(route, point, min_neighbor_distance))

    return min(neighbors, key=lambda point: route_path_preference(route, point, min_neighbor_distance))


def nearest_neighbor_band(
    current: LedPoint,
    neighbors: list[LedPoint],
    min_neighbor_distance: float,
) -> list[LedPoint]:
    nearest_distance = min(distance(current, point) for point in neighbors)
    band_limit = min(nearest_distance + neighbor_tolerance(min_neighbor_distance), max_neighbor_distance(min_neighbor_distance))
    band = [point for point in neighbors if distance(current, point) <= band_limit]
    return band or [min(neighbors, key=lambda point: distance(current, point))]


def same_direction_vector(previous: LedPoint, current: LedPoint, candidate: LedPoint) -> bool:
    incoming_x = current.x - previous.x
    incoming_y = current.y - previous.y
    outgoing_x = candidate.x - current.x
    outgoing_y = candidate.y - current.y
    if incoming_x == 0 and incoming_y == 0:
        return False
    if outgoing_x == 0 and outgoing_y == 0:
        return False
    dot = incoming_x * outgoing_x + incoming_y * outgoing_y
    cross = incoming_x * outgoing_y - incoming_y * outgoing_x
    return dot > 0 and abs(cross) <= max(1e-6, 0.1 * math.hypot(incoming_x, incoming_y) * math.hypot(outgoing_x, outgoing_y))


def forward_direction_vector(previous: LedPoint, current: LedPoint, candidate: LedPoint) -> bool:
    incoming_x = current.x - previous.x
    incoming_y = current.y - previous.y
    outgoing_x = candidate.x - current.x
    outgoing_y = candidate.y - current.y
    incoming_length = math.hypot(incoming_x, incoming_y)
    outgoing_length = math.hypot(outgoing_x, outgoing_y)
    if incoming_length == 0 or outgoing_length == 0:
        return False
    dot = incoming_x * outgoing_x + incoming_y * outgoing_y
    cos_angle = dot / (incoming_length * outgoing_length)
    return cos_angle > -0.25


def is_trajectory_continuation(
    previous: LedPoint,
    current: LedPoint,
    candidate: LedPoint,
    min_neighbor_distance: float,
) -> bool:
    if not forward_direction_vector(previous, current, candidate):
        return False
    return distance(current, candidate) <= route_continuation_distance_limit(current, candidate, min_neighbor_distance)


def route_continuation_distance_limit(current: LedPoint, candidate: LedPoint, min_neighbor_distance: float) -> float:
    return min_neighbor_distance * 2.0 + neighbor_tolerance(min_neighbor_distance)


def build_route_adjacency(
    points: list[LedPoint],
    min_neighbor_distance: float,
) -> dict[int, list[LedPoint]]:
    search_radius = min_neighbor_distance * math.sqrt(2.0) + neighbor_tolerance(min_neighbor_distance)
    bucket_size = max(search_radius, 1e-9)
    points_by_bucket: dict[tuple[int, int], list[LedPoint]] = {}
    for point in points:
        bucket = (math.floor(point.x / bucket_size), math.floor(point.y / bucket_size))
        points_by_bucket.setdefault(bucket, []).append(point)

    raw_adjacency: dict[int, list[LedPoint]] = {}
    for point in points:
        candidates: list[LedPoint] = []
        bucket = (math.floor(point.x / bucket_size), math.floor(point.y / bucket_size))
        for d_x in (-1, 0, 1):
            for d_y in (-1, 0, 1):
                for candidate in points_by_bucket.get((bucket[0] + d_x, bucket[1] + d_y), []):
                    if candidate.uid == point.uid:
                        continue
                    if is_valid_route_neighbor(point, candidate, min_neighbor_distance):
                        candidates.append(candidate)
        raw_adjacency[point.uid] = candidates

    adjacency: dict[int, list[LedPoint]] = {}
    for point in points:
        candidates = [
            candidate
            for candidate in raw_adjacency[point.uid]
            if (
                not route_edge_skips_intermediate_point(
                    point,
                    candidate,
                    points_by_bucket,
                    bucket_size,
                    min_neighbor_distance,
                )
                and not is_parallel_track_jump(point, candidate, raw_adjacency)
            )
        ]
        adjacency[point.uid] = sorted(
            candidates,
            key=lambda candidate: route_neighbor_preference(point, candidate, min_neighbor_distance),
        )
    return adjacency


def route_edge_skips_intermediate_point(
    current: LedPoint,
    candidate: LedPoint,
    points_by_bucket: dict[tuple[int, int], list[LedPoint]],
    bucket_size: float,
    min_neighbor_distance: float,
) -> bool:
    segment_x = candidate.x - current.x
    segment_y = candidate.y - current.y
    segment_length_sq = segment_x * segment_x + segment_y * segment_y
    if segment_length_sq <= 0:
        return False

    segment_length = math.sqrt(segment_length_sq)
    line_tolerance = max(neighbor_tolerance(min_neighbor_distance), min_neighbor_distance * 0.25)
    end_tolerance = max(1e-6, line_tolerance * 0.25)
    min_bucket_x = math.floor((min(current.x, candidate.x) - line_tolerance) / bucket_size)
    max_bucket_x = math.floor((max(current.x, candidate.x) + line_tolerance) / bucket_size)
    min_bucket_y = math.floor((min(current.y, candidate.y) - line_tolerance) / bucket_size)
    max_bucket_y = math.floor((max(current.y, candidate.y) + line_tolerance) / bucket_size)
    nearby_points = (
        point
        for bucket_x in range(min_bucket_x, max_bucket_x + 1)
        for bucket_y in range(min_bucket_y, max_bucket_y + 1)
        for point in points_by_bucket.get((bucket_x, bucket_y), [])
    )
    for point in nearby_points:
        if point.uid in {current.uid, candidate.uid}:
            continue
        point_x = point.x - current.x
        point_y = point.y - current.y
        projection = (point_x * segment_x + point_y * segment_y) / segment_length_sq
        if projection <= 0.0 or projection >= 1.0:
            continue
        perpendicular = abs(point_x * segment_y - point_y * segment_x) / segment_length
        if perpendicular > line_tolerance:
            continue
        if distance(current, point) < segment_length - end_tolerance:
            return True
    return False


def is_parallel_track_jump(
    current: LedPoint,
    candidate: LedPoint,
    raw_adjacency: dict[int, list[LedPoint]],
) -> bool:
    d_x = candidate.x - current.x
    d_y = candidate.y - current.y
    if abs(d_x) > abs(d_y):
        return (
            route_neighbor_axis_count(current, raw_adjacency, "vertical") >= 2
            and route_neighbor_axis_count(candidate, raw_adjacency, "vertical") >= 2
        )
    if abs(d_y) > abs(d_x):
        return (
            route_neighbor_axis_count(current, raw_adjacency, "horizontal") >= 2
            and route_neighbor_axis_count(candidate, raw_adjacency, "horizontal") >= 2
        )
    return False


def route_neighbor_axis_count(
    point: LedPoint,
    raw_adjacency: dict[int, list[LedPoint]],
    axis: str,
) -> int:
    if axis == "horizontal":
        return sum(1 for candidate in raw_adjacency[point.uid] if abs(candidate.x - point.x) > abs(candidate.y - point.y))
    return sum(1 for candidate in raw_adjacency[point.uid] if abs(candidate.y - point.y) > abs(candidate.x - point.x))


def trace_closed_route(
    points: list[LedPoint],
    start_point: LedPoint,
    min_neighbor_distance: float,
    adjacency: dict[int, list[LedPoint]] | None = None,
    allowed_ids: set[int] | None = None,
) -> list[LedPoint]:
    adjacency = adjacency or build_route_adjacency(points, min_neighbor_distance)
    allowed_ids = set(allowed_ids) if allowed_ids is not None else {point.uid for point in points}
    if len(adjacency.get(start_point.uid, [])) < 2:
        return []

    route = [start_point]
    visited = {start_point.uid}
    stack: list[tuple[LedPoint, list[LedPoint], int]] = [(
        start_point,
        [point for point in adjacency[start_point.uid] if point.uid in allowed_ids],
        0,
    )]
    attempts = 0
    max_attempts = min(5000, max(1000, len(points) * 25))

    while stack and attempts < max_attempts:
        current, candidates, next_index = stack[-1]
        attempts += 1

        meaningful_continuations = [
            point
            for point in adjacency.get(current.uid, [])
            if point.uid in allowed_ids and point.uid not in visited
            and len([next_point for next_point in adjacency.get(point.uid, []) if next_point.uid in allowed_ids]) > 1
        ]
        if (
            len(route) >= 4
            and is_valid_route_neighbor(current, start_point, min_neighbor_distance)
            and not meaningful_continuations
        ):
            return route[:]

        if next_index >= len(candidates):
            stack.pop()
            removed = route.pop()
            visited.remove(removed.uid)
            continue

        candidate = candidates[next_index]
        stack[-1] = (current, candidates, next_index + 1)
        if candidate.uid in visited:
            continue

        visited.add(candidate.uid)
        route.append(candidate)
        next_candidates = [
            point
            for point in adjacency.get(candidate.uid, [])
            if point.uid in allowed_ids and point.uid not in visited
        ]
        stack.append((candidate, next_candidates, 0))

    return []


def point_sort_key_for_model(point: LedPoint) -> tuple[int, int, float, float, int]:
    return point.row, point.col, point.x, point.y, point.uid


def too_close_preference(current: LedPoint, candidate: LedPoint, min_distance: float) -> int:
    return 1 if distance(current, candidate) < min_distance else 0


def route_neighbor_preference(
    current: LedPoint,
    candidate: LedPoint,
    min_distance: float,
) -> tuple[int, int, int, float, tuple[int, int, float, float, int]]:
    return (
        too_close_preference(current, candidate, min_distance),
        diagonal_cell_preference(current, candidate),
        cardinal_direction_preference(current, candidate),
        distance(current, candidate),
        point_sort_key_for_model(candidate),
    )


def route_path_preference(
    route: list[LedPoint],
    candidate: LedPoint,
    min_distance: float,
) -> tuple[int, float, int, int, float, tuple[int, int, float, float, int]]:
    current = route[-1]
    if len(route) < 2:
        return (
            diagonal_cell_preference(current, candidate),
            0.0,
            cardinal_direction_preference(current, candidate),
            too_close_preference(current, candidate, min_distance),
            0,
            distance(current, candidate),
            point_sort_key_for_model(candidate),
        )

    previous = route[-2]
    turn_class, turn_rank = route_clockwise_turn_preference(previous, current, candidate)
    return (
        turn_class,
        turn_rank,
        diagonal_cell_preference(current, candidate),
        too_close_preference(current, candidate, min_distance),
        cardinal_direction_preference(current, candidate),
        distance(current, candidate),
        point_sort_key_for_model(candidate),
    )


def route_clockwise_turn_preference(previous: LedPoint, current: LedPoint, candidate: LedPoint) -> tuple[int, float]:
    incoming_x = current.x - previous.x
    incoming_y = current.y - previous.y
    outgoing_x = candidate.x - current.x
    outgoing_y = candidate.y - current.y
    cross = incoming_x * outgoing_y - incoming_y * outgoing_x
    angle = route_turn_angle(previous, current, candidate)
    epsilon = 1e-6
    if abs(cross) <= epsilon:
        return 0, 0.0
    if angle < math.radians(35):
        return 0, angle
    if cross < -epsilon:
        return 1, -angle
    return 2, angle


def route_turn_angle(previous: LedPoint, current: LedPoint, candidate: LedPoint) -> float:
    incoming_x = current.x - previous.x
    incoming_y = current.y - previous.y
    outgoing_x = candidate.x - current.x
    outgoing_y = candidate.y - current.y
    incoming_length = math.hypot(incoming_x, incoming_y)
    outgoing_length = math.hypot(outgoing_x, outgoing_y)
    if incoming_length == 0 or outgoing_length == 0:
        return 0.0
    dot = incoming_x * outgoing_x + incoming_y * outgoing_y
    cos_angle = max(-1.0, min(1.0, dot / (incoming_length * outgoing_length)))
    return math.acos(cos_angle)


def diagonal_cell_preference(current: LedPoint, candidate: LedPoint) -> int:
    return 1 if abs(current.row - candidate.row) == 1 and abs(current.col - candidate.col) == 1 else 0


def cardinal_direction_preference(current: LedPoint, candidate: LedPoint) -> int:
    d_x = candidate.x - current.x
    d_y = candidate.y - current.y
    if abs(d_y) >= abs(d_x) and d_y > 0:
        return 0
    if abs(d_x) > abs(d_y) and d_x < 0:
        return 1
    if abs(d_y) >= abs(d_x) and d_y < 0:
        return 2
    if abs(d_x) > abs(d_y) and d_x > 0:
        return 3
    return 4


def is_valid_route_neighbor(current: LedPoint, candidate: LedPoint, nominal_distance: float) -> bool:
    return distance(current, candidate) <= route_neighbor_distance_limit(
        current,
        candidate,
        nominal_distance,
    )


def route_neighbor_distance_limit(current: LedPoint, candidate: LedPoint, nominal_distance: float) -> float:
    if abs(current.row - candidate.row) == 1 and abs(current.col - candidate.col) == 1:
        return nominal_distance * math.sqrt(2.0) + neighbor_tolerance(nominal_distance)
    return max_neighbor_distance(nominal_distance)


def max_neighbor_distance(nominal_distance: float) -> float:
    return nominal_distance + neighbor_tolerance(nominal_distance)


def neighbor_tolerance(nominal_distance: float) -> float:
    return MAX_NEIGHBOR_TOLERANCE_MM / (MIN_NEIGHBOR_DISTANCE_MM / nominal_distance)


def min_neighbor_distance_for_points(points: list[LedPoint]) -> float:
    return MIN_NEIGHBOR_DISTANCE_MM / coordinate_display_scale(points)


def coordinate_display_scale(points: list[LedPoint]) -> float:
    if not points:
        return 1.0
    explicit_units = {point.coordinate_unit for point in points if point.coordinate_unit != "auto"}
    if explicit_units & {"m", "meter", "meters"}:
        return 1000.0
    if explicit_units & {"mm", "millimeter", "millimeters"}:
        return 1.0
    xs = [point.x for point in points]
    ys = [point.y for point in points]
    span = max(max(xs) - min(xs), max(ys) - min(ys))
    return 1000.0 if span <= METER_LIKE_MAX_SPAN else 1.0


def clockwise_preference(current: LedPoint, candidate: LedPoint) -> int:
    d_row = candidate.row - current.row
    d_col = candidate.col - current.col
    clockwise_directions = [
        (1, 0),
        (1, 1),
        (0, 1),
        (-1, 1),
        (-1, 0),
        (-1, -1),
        (0, -1),
        (1, -1),
    ]
    try:
        return clockwise_directions.index((sign(d_row), sign(d_col)))
    except ValueError:
        return len(clockwise_directions)


def same_cell_preference(current: LedPoint, candidate: LedPoint) -> int:
    return 0 if current.row == candidate.row and current.col == candidate.col else 1


def upward_left_preference(current: LedPoint, candidate: LedPoint) -> int:
    if candidate.row < current.row and candidate.col <= current.col:
        return 0
    if candidate.row < current.row:
        return 1
    return 2


def sign(value: int) -> int:
    if value > 0:
        return 1
    if value < 0:
        return -1
    return 0