Ai/farm_data/services.py

from __future__ import annotations

from decimal import Decimal, ROUND_HALF_UP

from django.db import transaction

from location_data.models import SoilLocation
from location_data.serializers import SoilDepthDataSerializer
from plant.serializers import PlantSerializer
from weather.models import WeatherForecast

from .models import SensorData
from .serializers import WeatherForecastDetailSerializer


DEPTH_PRIORITY = ["0-5cm", "5-15cm", "15-30cm"]
DECIMAL_PRECISION = Decimal("0.000001")


def get_farm_details(farm_uuid: str):
    farm = (
        SensorData.objects.select_related("center_location", "weather_forecast")
        .prefetch_related("plants", "center_location__depths")
        .filter(farm_uuid=farm_uuid)
        .first()
    )
    if farm is None:
        return None

    center_location = farm.center_location
    weather = farm.weather_forecast
    if weather is None:
        weather = (
            center_location.weather_forecasts.order_by("-forecast_date", "-id").first()
        )

    depths = list(center_location.depths.all())
    depths.sort(key=lambda item: DEPTH_PRIORITY.index(item.depth_label) if item.depth_label in DEPTH_PRIORITY else 99)

    soil_metrics = _surface_soil_metrics(depths)
    sensor_metrics = _flatten_sensor_metrics(farm.sensor_payload)

    resolved_metrics = dict(soil_metrics)
    metric_sources = {key: "soil" for key in soil_metrics}
    for key, value in sensor_metrics.items():
        resolved_metrics[key] = value
        metric_sources[key] = "sensor"

    return {
        "farm_uuid": farm.farm_uuid,
        "center_location": {
            "id": center_location.id,
            "lat": center_location.latitude,
            "lon": center_location.longitude,
            "farm_boundary": center_location.farm_boundary,
        },
        "weather": WeatherForecastDetailSerializer(weather).data if weather else None,
        "sensor_payload": farm.sensor_payload or {},
        "soil": {
            "resolved_metrics": resolved_metrics,
            "metric_sources": metric_sources,
            "depths": SoilDepthDataSerializer(depths, many=True).data,
        },
        "plant_ids": list(farm.plants.values_list("id", flat=True)),
        "plants": PlantSerializer(farm.plants.all(), many=True).data,
        "created_at": farm.created_at,
        "updated_at": farm.updated_at,
    }


def resolve_center_location_from_boundary(farm_boundary: dict | list) -> SoilLocation:
    """
    مرز مزرعه را می‌گیرد، مرکز را محاسبه می‌کند و رکورد SoilLocation را
    ایجاد/به‌روزرسانی می‌کند.
    """
    points = _extract_boundary_points(farm_boundary)
    if not points:
        raise ValueError("farm_boundary باید حداقل 3 گوشه معتبر داشته باشد.")

    normalized_points = _normalize_points(points)
    if len(normalized_points) < 3:
        raise ValueError("farm_boundary باید حداقل 3 گوشه معتبر داشته باشد.")

    lat_sum = sum(lat for lat, _ in normalized_points)
    lon_sum = sum(lon for _, lon in normalized_points)
    count = Decimal(len(normalized_points))
    center_lat = (lat_sum / count).quantize(DECIMAL_PRECISION, rounding=ROUND_HALF_UP)
    center_lon = (lon_sum / count).quantize(DECIMAL_PRECISION, rounding=ROUND_HALF_UP)

    with transaction.atomic():
        location, _ = SoilLocation.objects.update_or_create(
            latitude=center_lat,
            longitude=center_lon,
            defaults={"farm_boundary": _serialize_boundary(farm_boundary)},
        )
    return location


def resolve_weather_for_location(location: SoilLocation) -> WeatherForecast | None:
    return (
        WeatherForecast.objects.filter(location=location)
        .order_by("-forecast_date", "-id")
        .first()
    )


def _flatten_sensor_metrics(sensor_payload: dict | None) -> dict:
    if not isinstance(sensor_payload, dict):
        return {}

    flattened = {}
    for sensor_values in sensor_payload.values():
        if not isinstance(sensor_values, dict):
            continue
        flattened.update(sensor_values)
    return flattened


def _surface_soil_metrics(depths) -> dict:
    if not depths:
        return {}

    primary_depth = depths[0]
    fields = [
        "bdod",
        "cec",
        "cfvo",
        "clay",
        "nitrogen",
        "ocd",
        "ocs",
        "phh2o",
        "sand",
        "silt",
        "soc",
        "wv0010",
        "wv0033",
        "wv1500",
    ]
    return {
        field: getattr(primary_depth, field)
        for field in fields
        if getattr(primary_depth, field) is not None
    }


def _extract_boundary_points(boundary: dict | list) -> list:
    if isinstance(boundary, dict):
        if boundary.get("type") == "Polygon":
            coordinates = boundary.get("coordinates") or []
            if coordinates and isinstance(coordinates[0], list):
                return coordinates[0]
            return []
        if "corners" in boundary:
            return boundary.get("corners") or []
    if isinstance(boundary, list):
        return boundary
    return []


def _normalize_points(points: list) -> list[tuple[Decimal, Decimal]]:
    normalized: list[tuple[Decimal, Decimal]] = []
    for point in points:
        lat = lon = None
        if isinstance(point, dict):
            lat = point.get("lat", point.get("latitude"))
            lon = point.get("lon", point.get("longitude"))
        elif isinstance(point, (list, tuple)) and len(point) >= 2:
            lon, lat = point[0], point[1]

        if lat is None or lon is None:
            continue

        lat_decimal = Decimal(str(lat))
        lon_decimal = Decimal(str(lon))
        normalized.append((lat_decimal, lon_decimal))

    if len(normalized) > 1 and normalized[0] == normalized[-1]:
        normalized = normalized[:-1]
    return normalized


def _serialize_boundary(boundary: dict | list) -> dict:
    if isinstance(boundary, dict) and boundary.get("type") == "Polygon":
        return boundary
    raw_points = boundary.get("corners") if isinstance(boundary, dict) else boundary
    normalized = _normalize_points(raw_points or [])
    coordinates = [[float(lon), float(lat)] for lat, lon in normalized]
    if coordinates and coordinates[0] != coordinates[-1]:
        coordinates.append(coordinates[0])
    return {
        "type": "Polygon",
        "coordinates": [coordinates],
    }