"""Helpers for extracting GPR objects and locator observations from results."""

from __future__ import annotations

from datetime import datetime
from typing import Any

import numpy as np

from python_app.models.dataset_model import ResultCollection, ResultPayload


def collection_payload_by_name(
    collection: ResultCollection,
    name: str,
    kind: int | None = None,
) -> ResultPayload | None:
    """Return the first collection payload matching name and optional kind."""
    for payload in collection.collection_payloads:
        if payload.processing_name != name:
            continue
        if kind is not None and int(payload.kind) != int(kind):
            continue
        return payload
    return None


def collection_payloads_by_prefix(
    collection: ResultCollection,
    prefix: str,
    kind: int | None = None,
) -> list[ResultPayload]:
    """Return collection payloads matching a processing-name prefix."""
    payloads: list[ResultPayload] = []
    for payload in collection.collection_payloads:
        if not str(payload.processing_name).startswith(prefix):
            continue
        if kind is not None and int(payload.kind) != int(kind):
            continue
        payloads.append(payload)
    return payloads


def collection_has_gpr_payloads(collection: ResultCollection) -> bool:
    """Return whether collection carries GPR-specific collection payloads."""
    return any(
        str(payload.processing_name).startswith("gpr_")
        for payload in collection.collection_payloads
    )


def gpr_object_rows(collection: ResultCollection) -> np.ndarray:
    """Return object rows as `[x_m, z_m, pair_count]` from a GPR collection."""
    points_payload = collection_payload_by_name(collection, "gpr_points", kind=4)
    if points_payload is not None:
        points = np.asarray(points_payload.table, dtype=np.float32)
        if points.ndim == 2 and points.shape[1] >= 3:
            return points[:, :3]

    centers_payload = collection_payload_by_name(collection, "gpr_region_centers", kind=4)
    if centers_payload is not None:
        centers = np.asarray(centers_payload.table, dtype=np.float32)
        if centers.ndim == 2 and centers.shape[1] >= 3:
            return centers[:, :3]

    return np.zeros((0, 3), dtype=np.float32)


def locator_observations_from_collection(
    collection: ResultCollection,
    min_pair_count: float,
    *,
    visible_bounds: tuple[float, float, float, float] | None = None,
) -> list[dict[str, float]]:
    """Build locator observations from GPR rows using pair threshold and optional X/Z bounds."""
    rows = gpr_object_rows(collection)
    if rows.size == 0:
        return []

    finite_mask = np.all(np.isfinite(rows[:, :3]), axis=1)
    visible_mask = finite_mask & (rows[:, 2] >= float(min_pair_count))
    if visible_bounds is not None:
        x_min, x_max, z_min, z_max = (float(value) for value in visible_bounds)
        visible_mask &= (
            (rows[:, 0] >= x_min)
            & (rows[:, 0] <= x_max)
            & (rows[:, 1] >= z_min)
            & (rows[:, 1] <= z_max)
        )
    filtered = rows[visible_mask]

    observations: list[dict[str, float]] = []
    for x_m, z_m, _pair_count in filtered:
        observations.append(
            {
                "dst": round(float(z_m), 2),
                "crs": round(float(x_m), 2),
            }
        )
    return observations


def build_locator_payload(
    observations: list[dict[str, float]],
    *,
    protocol_version: int,
    status: int = 1,
) -> dict[str, Any]:
    """Assemble one outbound locator payload from precomputed observations."""
    return {
        "ver": int(protocol_version),
        "tim": _format_timestamp(),
        "sts": int(status),
        "obs": observations,
    }


def _format_timestamp() -> str:
    """Return wall-clock timestamp with millisecond precision."""
    return datetime.now().strftime("%H:%M:%S.%f")[:-3]