Ai/farm_data/services.py

from __future__ import annotations

from decimal import Decimal, ROUND_HALF_UP
from numbers import Number
import logging
import uuid
import warnings

from django.conf import settings
from django.db import transaction
from django.utils.dateparse import parse_datetime
from django.utils import timezone

import requests

from location_data.block_subdivision import create_or_get_block_subdivision
from location_data.models import BlockSubdivision, SoilLocation
from location_data.satellite_snapshot import (
    build_block_layout_metric_summary,
    build_farmer_block_aggregated_snapshot,
    build_location_block_satellite_snapshots,
)
from irrigation.serializers import IrrigationMethodSerializer
from weather.models import WeatherForecast

from .models import (
    Device,
    FarmPlantAssignment,
    ParameterUpdateLog,
    PlantCatalogSnapshot,
    SensorData,
    SensorParameter,
)
from .serializers import PlantCatalogSnapshotSerializer, WeatherForecastDetailSerializer


DECIMAL_PRECISION = Decimal("0.000001")
logger = logging.getLogger(__name__)


class ExternalDataSyncError(Exception):
    """خطا در همگام‌سازی داده از سرویس‌های بیرونی."""


class BackendSyncError(Exception):
    """خطا در همگام سازی کاتالوگ گیاه و assignmentها از Backend."""


class LegacyFarmPlantRelationWarning(DeprecationWarning):
    """هشدار برای relation قدیمی SensorData.plants."""


PARAMETER_LABEL_OVERRIDES = {
    "soil_moisture": "رطوبت خاک",
    "soil_temperature": "دمای خاک",
    "soil_ph": "pH خاک",
    "electrical_conductivity": "هدایت الکتریکی",
    "nitrogen": "نیتروژن",
    "phosphorus": "فسفر",
    "potassium": "پتاسیم",
}
PARAMETER_UNIT_OVERRIDES = {
    "soil_moisture": "%",
    "soil_temperature": "°C",
    "soil_ph": "",
    "electrical_conductivity": "dS/m",
    "nitrogen": "mg/kg",
    "phosphorus": "mg/kg",
    "potassium": "mg/kg",
}


def get_backend_plant_base_url() -> str:
    return getattr(settings, "BACKEND_PLANT_SYNC_BASE_URL", "").rstrip("/")


def get_backend_plant_timeout() -> int:
    return int(getattr(settings, "BACKEND_PLANT_SYNC_TIMEOUT", 20))


def get_backend_plant_headers() -> dict[str, str]:
    headers = {"Accept": "application/json"}
    api_key = getattr(settings, "BACKEND_PLANT_SYNC_API_KEY", "").strip()
    if api_key:
        headers["X-API-Key"] = api_key
        headers["Authorization"] = f"Api-Key {api_key}"
    return headers


def _extract_envelope_list(payload):
    if isinstance(payload, list):
        return payload
    if not isinstance(payload, dict):
        return []
    data = payload.get("data")
    if isinstance(data, list):
        return data
    result = payload.get("result")
    if isinstance(result, list):
        return result
    if isinstance(data, dict) and isinstance(data.get("result"), list):
        return data["result"]
    return []


def _normalize_growth_stages(item: dict) -> list[str]:
    stages = item.get("growth_stages")
    if isinstance(stages, list):
        return [str(stage).strip() for stage in stages if str(stage).strip()]

    growth_stage = str(item.get("growth_stage") or "").strip()
    if not growth_stage:
        return []
    return [part.strip() for part in growth_stage.replace("،", ",").split(",") if part.strip()]


def _snapshot_defaults_from_payload(item: dict) -> dict:
    source_updated_at = parse_datetime(str(item.get("updated_at") or "").strip()) if item.get("updated_at") else None
    return {
        "name": str(item.get("name") or "").strip(),
        "slug": str(item.get("slug") or "").strip(),
        "icon": str(item.get("icon") or "leaf").strip() or "leaf",
        "description": str(item.get("description") or "").strip(),
        "metadata": item.get("metadata") if isinstance(item.get("metadata"), dict) else {},
        "light": str(item.get("light") or "").strip(),
        "watering": str(item.get("watering") or "").strip(),
        "soil": str(item.get("soil") or "").strip(),
        "temperature": str(item.get("temperature") or "").strip(),
        "growth_stage": str(item.get("growth_stage") or "").strip(),
        "growth_stages": _normalize_growth_stages(item),
        "planting_season": str(item.get("planting_season") or "").strip(),
        "harvest_time": str(item.get("harvest_time") or "").strip(),
        "spacing": str(item.get("spacing") or "").strip(),
        "fertilizer": str(item.get("fertilizer") or "").strip(),
        "health_profile": item.get("health_profile") if isinstance(item.get("health_profile"), dict) else {},
        "irrigation_profile": item.get("irrigation_profile") if isinstance(item.get("irrigation_profile"), dict) else {},
        "growth_profile": item.get("growth_profile") if isinstance(item.get("growth_profile"), dict) else {},
        "is_active": bool(item.get("is_active", True)),
        "source_updated_at": source_updated_at,
    }


def sync_plant_catalog_from_backend(plant_payloads: list[dict] | None = None) -> list[PlantCatalogSnapshot]:
    if plant_payloads is None:
        base_url = get_backend_plant_base_url()
        if not base_url:
            raise BackendSyncError("BACKEND_PLANT_SYNC_BASE_URL is not configured.")
        try:
            response = requests.get(
                f"{base_url}/api/plants/",
                headers=get_backend_plant_headers(),
                timeout=get_backend_plant_timeout(),
            )
        except requests.RequestException as exc:
            raise BackendSyncError(f"Backend plant catalog request failed: {exc}") from exc
        if response.status_code >= 400:
            raise BackendSyncError(f"Backend plant catalog returned status {response.status_code}.")
        plant_payloads = _extract_envelope_list(response.json())

    snapshots: list[PlantCatalogSnapshot] = []
    with transaction.atomic():
        for item in plant_payloads or []:
            if not isinstance(item, dict):
                continue
            plant_id = item.get("id") or item.get("backend_plant_id")
            if plant_id in (None, ""):
                continue
            snapshot, _ = PlantCatalogSnapshot.objects.update_or_create(
                backend_plant_id=int(plant_id),
                defaults=_snapshot_defaults_from_payload(item),
            )
            snapshots.append(snapshot)
    return snapshots


def assign_farm_plants_from_backend_ids(farm: SensorData, backend_plant_ids: list[int] | None) -> list[PlantCatalogSnapshot]:
    if backend_plant_ids is None:
        return list(get_farm_plant_snapshots(farm))

    normalized_ids = [int(plant_id) for plant_id in backend_plant_ids]
    snapshots = list(PlantCatalogSnapshot.objects.filter(backend_plant_id__in=normalized_ids))
    snapshot_by_backend_id = {snapshot.backend_plant_id: snapshot for snapshot in snapshots}
    missing_ids = [plant_id for plant_id in normalized_ids if plant_id not in snapshot_by_backend_id]
    if missing_ids:
        raise BackendSyncError(
            "Plant catalog snapshot missing for backend ids: "
            + ", ".join(str(plant_id) for plant_id in missing_ids)
        )

    with transaction.atomic():
        FarmPlantAssignment.objects.filter(farm=farm).exclude(
            plant__backend_plant_id__in=normalized_ids
        ).delete()
        for position, backend_plant_id in enumerate(normalized_ids):
            FarmPlantAssignment.objects.update_or_create(
                farm=farm,
                plant=snapshot_by_backend_id[backend_plant_id],
                defaults={"position": position},
            )
    snapshots_in_order = [snapshot_by_backend_id[backend_plant_id] for backend_plant_id in normalized_ids]
    reconcile_legacy_farm_plants_relation(farm, snapshots_in_order)
    return snapshots_in_order


def get_farm_plant_assignments(farm: SensorData) -> list[FarmPlantAssignment]:
    return list(
        farm.plant_assignments.select_related("plant").order_by("position", "id")
    )


def get_farm_plant_snapshots(farm: SensorData) -> list[PlantCatalogSnapshot]:
    return [assignment.plant for assignment in get_farm_plant_assignments(farm)]


def reconcile_legacy_farm_plants_relation(
    farm: SensorData,
    snapshots: list[PlantCatalogSnapshot] | None = None,
) -> None:
    # AI no longer mirrors canonical plant rows locally; the legacy relation is cleared
    # so downstream services cannot accidentally read stale plant data.
    farm.plants.clear()


def get_canonical_farm_record(farm_uuid: str) -> SensorData | None:
    return (
        SensorData.objects.select_related(
            "center_location",
            "weather_forecast",
            "irrigation_method",
        )
        .prefetch_related("plant_assignments__plant")
        .filter(farm_uuid=farm_uuid)
        .first()
    )


def get_legacy_farm_plants(farm: SensorData):
    warnings.warn(
        "SensorData.plants is deprecated; use farm_data.services canonical plant snapshot helpers instead.",
        LegacyFarmPlantRelationWarning,
        stacklevel=2,
    )
    return farm.plants.all()


def get_primary_plant_snapshot(farm: SensorData) -> PlantCatalogSnapshot | None:
    assignments = get_farm_plant_assignments(farm)
    return assignments[0].plant if assignments else None


def get_farm_plant_snapshot_by_name(
    farm: SensorData,
    plant_name: str | None,
) -> PlantCatalogSnapshot | None:
    normalized_name = str(plant_name or "").strip().lower()
    if not normalized_name:
        return get_primary_plant_snapshot(farm)
    for assignment in get_farm_plant_assignments(farm):
        if assignment.plant.name.strip().lower() == normalized_name:
            return assignment.plant
    return get_primary_plant_snapshot(farm)


def clone_snapshot_as_runtime_plant(
    snapshot: PlantCatalogSnapshot | None,
    *,
    growth_stage: str | None = None,
):
    if snapshot is None:
        return None

    class RuntimePlant:
        pass

    runtime = RuntimePlant()
    for field_name in (
        "backend_plant_id",
        "name",
        "slug",
        "icon",
        "description",
        "metadata",
        "light",
        "watering",
        "soil",
        "temperature",
        "growth_stage",
        "growth_stages",
        "planting_season",
        "harvest_time",
        "spacing",
        "fertilizer",
        "health_profile",
        "irrigation_profile",
        "growth_profile",
        "is_active",
    ):
        setattr(runtime, field_name, getattr(snapshot, field_name))
    if growth_stage:
        runtime.growth_stage = growth_stage
    runtime.id = snapshot.backend_plant_id
    return runtime


def get_runtime_plant_for_farm(
    farm: SensorData,
    *,
    plant_name: str | None = None,
    growth_stage: str | None = None,
):
    snapshot = get_farm_plant_snapshot_by_name(farm, plant_name)
    return clone_snapshot_as_runtime_plant(snapshot, growth_stage=growth_stage)


def list_runtime_plants_for_farm(farm: SensorData) -> list[object]:
    return [clone_snapshot_as_runtime_plant(snapshot) for snapshot in get_farm_plant_snapshots(farm)]


def build_plant_text_from_snapshot(
    plant: PlantCatalogSnapshot | None,
    growth_stage: str,
) -> str | None:
    if plant is None:
        return None

    lines = [
        f"نام گیاه: {plant.name}",
        f"مرحله رشد: {growth_stage}",
    ]
    if plant.light:
        lines.append(f"نور مورد نیاز: {plant.light}")
    if plant.watering:
        lines.append(f"آبیاری: {plant.watering}")
    if plant.soil:
        lines.append(f"خاک مناسب: {plant.soil}")
    if plant.temperature:
        lines.append(f"دمای مناسب: {plant.temperature}")
    if plant.planting_season:
        lines.append(f"فصل کاشت: {plant.planting_season}")
    if plant.harvest_time:
        lines.append(f"زمان برداشت: {plant.harvest_time}")
    if plant.spacing:
        lines.append(f"فاصله کاشت: {plant.spacing}")
    if plant.fertilizer:
        lines.append(f"کود مناسب: {plant.fertilizer}")
    return "\n".join(lines)


def build_farm_plant_context(farm_uuid: str) -> dict | None:
    farm = get_canonical_farm_record(farm_uuid)
    if farm is None:
        return None
    assignments = get_farm_plant_assignments(farm)
    snapshots = [assignment.plant for assignment in assignments]
    return {
        "farm": farm,
        "plant_ids": [plant.backend_plant_id for plant in snapshots],
        "plants": PlantCatalogSnapshotSerializer(snapshots, many=True).data,
        "plant_snapshots": snapshots,
        "plant_assignments": assignments,
        "primary_plant": snapshots[0] if snapshots else None,
    }


def infer_sensor_parameter_data_type(value: object) -> str:
    if isinstance(value, bool):
        return "bool"
    if isinstance(value, int) and not isinstance(value, bool):
        return "int"
    if isinstance(value, float):
        return "float"
    if isinstance(value, str):
        return "string"
    if isinstance(value, list):
        return "list"
    if isinstance(value, dict):
        return "object"
    return "string"


def build_parameter_defaults(sensor_key: str, code: str, value: object) -> dict[str, object]:
    return {
        "name_fa": PARAMETER_LABEL_OVERRIDES.get(code) or code.replace("_", " ").strip(),
        "unit": PARAMETER_UNIT_OVERRIDES.get(code, ""),
        "data_type": infer_sensor_parameter_data_type(value),
        "metadata": {
            "source": "auto_discovered",
            "sensor_key": sensor_key,
            "code": code,
        },
    }


def sync_sensor_parameters_from_payload(sensor_payload: dict | None) -> list[SensorParameter]:
    if not isinstance(sensor_payload, dict):
        return []

    synced_parameters: list[SensorParameter] = []
    with transaction.atomic():
        for sensor_key, sensor_values in sensor_payload.items():
            if not isinstance(sensor_values, dict):
                continue
            for code, value in sensor_values.items():
                defaults = build_parameter_defaults(sensor_key, code, value)
                parameter, created = SensorParameter.objects.get_or_create(
                    sensor_key=sensor_key,
                    code=code,
                    defaults=defaults,
                )
                if created:
                    ParameterUpdateLog.objects.create(
                        parameter=parameter,
                        action=ParameterUpdateLog.ACTION_ADDED,
                        payload={
                            "sensor_key": parameter.sensor_key,
                            "code": parameter.code,
                            "name_fa": parameter.name_fa,
                            "unit": parameter.unit,
                            "data_type": parameter.data_type,
                            "metadata": parameter.metadata,
                            "source": "farm_data_auto_sync",
                        },
                    )
                synced_parameters.append(parameter)
    return synced_parameters


def _parse_cluster_uuid(value: object) -> uuid.UUID | None:
    if value in (None, ""):
        return None
    try:
        return uuid.UUID(str(value))
    except (TypeError, ValueError, AttributeError):
        return None


def sync_devices_from_sensor_data(farm: SensorData) -> list[Device]:
    sensor_payload = farm.sensor_payload if isinstance(farm.sensor_payload, dict) else {}
    location = farm.center_location
    synced_devices: list[Device] = []

    with transaction.atomic():
        active_sensor_names: list[str] = []
        for sensor_name, payload in sensor_payload.items():
            if not isinstance(payload, dict):
                continue
            active_sensor_names.append(sensor_name)
            device, _created = Device.objects.update_or_create(
                farm=farm,
                sensor_name=sensor_name,
                defaults={
                    "location": location,
                    "payload": payload,
                    "cluster_uuid": _parse_cluster_uuid(payload.get("cluster_uuid")),
                },
            )
            synced_devices.append(device)

        stale_devices = Device.objects.filter(farm=farm)
        if active_sensor_names:
            stale_devices = stale_devices.exclude(sensor_name__in=active_sensor_names)
        stale_devices.delete()

    return synced_devices


def get_sensor_parameter_catalog(sensor_payload: dict | None = None) -> dict[str, list[dict[str, object]]]:
    parameter_queryset = SensorParameter.objects.order_by("sensor_key", "code")
    if sensor_payload and isinstance(sensor_payload, dict):
        parameter_queryset = parameter_queryset.filter(sensor_key__in=list(sensor_payload.keys()))

    catalog: dict[str, list[dict[str, object]]] = {}
    for parameter in parameter_queryset:
        catalog.setdefault(parameter.sensor_key, []).append(
            {
                "code": parameter.code,
                "name_fa": parameter.name_fa,
                "unit": parameter.unit,
                "data_type": parameter.data_type,
                "metadata": parameter.metadata,
            }
        )
    return catalog


def get_farm_details(farm_uuid: str):
    farm = get_canonical_farm_record(farm_uuid)
    if farm is None:
        return None

    sync_sensor_parameters_from_payload(farm.sensor_payload)

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

    soil_snapshot = _build_farm_soil_snapshot(
        center_location,
        sensor_payload=farm.sensor_payload,
    )

    plant_assignments = get_farm_plant_assignments(farm)
    plant_snapshots = [assignment.plant for assignment in plant_assignments]

    return {
        "center_location": {
            "id": center_location.id,
            "lat": center_location.latitude,
            "lon": center_location.longitude,
            "farm_boundary": center_location.farm_boundary,
            "input_block_count": center_location.input_block_count,
            "block_layout": build_block_layout_metric_summary(
                center_location,
                sensor_payload=farm.sensor_payload,
            ),
        },
        "weather": WeatherForecastDetailSerializer(weather).data if weather else None,
        "sensor_payload": farm.sensor_payload or {},
        "sensor_schema": get_sensor_parameter_catalog(farm.sensor_payload),
        "soil": soil_snapshot,
        "plant_ids": [plant.backend_plant_id for plant in plant_snapshots],
        "plants": PlantCatalogSnapshotSerializer(plant_snapshots, many=True).data,
        "plant_assignments": [
            {
                "plant_id": assignment.plant.backend_plant_id,
                "position": assignment.position,
                "stage": assignment.stage,
                "metadata": assignment.metadata,
                "assigned_at": assignment.assigned_at,
                "updated_at": assignment.updated_at,
                "plant": PlantCatalogSnapshotSerializer(assignment.plant).data,
            }
            for assignment in plant_assignments
        ],
        "irrigation_method_id": farm.irrigation_method_id,
        "irrigation_method": (
            IrrigationMethodSerializer(farm.irrigation_method).data
            if farm.irrigation_method
            else None
        ),
        "created_at": farm.created_at,
        "updated_at": farm.updated_at,
        "source_metadata": {
            "soil": soil_snapshot.get("source_metadata", {}),
            "weather": {
                "source": "center_location_forecast",
                "scope": "location_center_based",
                "location_id": center_location.id,
                "note": "Weather remains tied to the farm center location.",
            },
        },
    }


def _build_farm_soil_snapshot(
    center_location: SoilLocation,
    *,
    sensor_payload: dict | None,
) -> dict[str, object]:
    # Canonical farm soil metrics now come from farmer-level block aggregation.
    aggregated_snapshot = build_farmer_block_aggregated_snapshot(
        center_location,
        sensor_payload=sensor_payload,
    )
    block_snapshots = build_location_block_satellite_snapshots(
        center_location,
        sensor_payload=sensor_payload,
    )
    if all(
        snapshot.get("status") == "missing" and not snapshot.get("resolved_metrics")
        for snapshot in block_snapshots
    ):
        block_snapshots = []

    has_explicit_blocks = bool((center_location.block_layout or {}).get("blocks"))
    resolved_metrics = dict(aggregated_snapshot.get("resolved_metrics") or {})
    metric_sources = dict(aggregated_snapshot.get("metric_sources") or {})
    compatibility_sensor_overlay_applied = False

    if not has_explicit_blocks:
        compatibility_sensor_overlay_applied = _merge_legacy_sensor_metrics_if_missing(
            resolved_metrics,
            metric_sources,
            sensor_payload,
        )

    cluster_breakdown = _build_cluster_breakdown(block_snapshots)
    return {
        "resolved_metrics": resolved_metrics,
        "metric_sources": metric_sources,
        "block_snapshots": block_snapshots,
        "satellite_snapshots": block_snapshots,
        "cluster_breakdown": cluster_breakdown,
        "source_metadata": {
            "canonical_source": "farmer_block_aggregated_snapshot",
            "aggregation_strategy": aggregated_snapshot.get("aggregation_strategy") or "missing",
            "status": aggregated_snapshot.get("status") or "missing",
            "block_count": int(aggregated_snapshot.get("block_count") or len(block_snapshots)),
            "has_explicit_blocks": has_explicit_blocks,
            "compatibility_sensor_overlay_applied": compatibility_sensor_overlay_applied,
            "policy": {
                "sensor": "cluster_mean -> block_mean -> farm_mean",
                "satellite": "cluster_mean -> block_mean -> farm_mean",
                "weather": "location_center_based",
            },
        },
    }


def _merge_legacy_sensor_metrics_if_missing(
    resolved_metrics: dict,
    metric_sources: dict,
    sensor_payload: dict | None,
) -> bool:
    sensor_metrics, sensor_metric_sources = _resolve_sensor_metrics(sensor_payload)
    applied = False
    for metric_name, metric_value in sensor_metrics.items():
        if metric_name in resolved_metrics:
            continue
        resolved_metrics[metric_name] = metric_value
        metric_sources[metric_name] = sensor_metric_sources[metric_name]
        applied = True
    return applied


def _build_cluster_breakdown(block_snapshots: list[dict[str, object]]) -> list[dict[str, object]]:
    cluster_breakdown: list[dict[str, object]] = []
    for snapshot in block_snapshots:
        block_code = str(snapshot.get("block_code") or "").strip()
        for sub_block in snapshot.get("satellite_sub_blocks") or []:
            cluster_breakdown.append(
                {
                    "block_code": block_code,
                    "source": "satellite",
                    **dict(sub_block),
                }
            )
        for sub_block in snapshot.get("sensor_sub_blocks") or []:
            cluster_breakdown.append(
                {
                    "block_code": block_code,
                    "source": "sensor",
                    **dict(sub_block),
                }
            )
    return cluster_breakdown


AI_FARM_AGGREGATION_POLICY = {
    "sensor": "cluster_mean_then_block_mean_then_farm_mean",
    "satellite": "cluster_mean_then_block_mean_then_farm_mean",
    "weather": "center_location_latest_forecast",
    "default_block_policy": "1_main_block + 1_default_sub_block_when_missing",
}


def build_ai_farm_snapshot(farm_uuid: str) -> dict[str, object] | None:
    farm = get_canonical_farm_record(farm_uuid)
    if farm is None:
        return None

    sync_sensor_parameters_from_payload(farm.sensor_payload)

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

    soil_snapshot = _build_farm_soil_snapshot(
        center_location,
        sensor_payload=farm.sensor_payload,
    )
    block_metrics = _build_ai_block_metrics(soil_snapshot.get("block_snapshots") or [])
    sub_block_metrics = _build_ai_sub_block_metrics(soil_snapshot.get("block_snapshots") or [])
    plant_assignments = get_farm_plant_assignments(farm)

    return {
        "farm_uuid": str(farm.farm_uuid),
        "aggregation_policy": dict(AI_FARM_AGGREGATION_POLICY),
        "farm_metrics": {
            "resolved_metrics": dict(soil_snapshot.get("resolved_metrics") or {}),
            "metric_sources": dict(soil_snapshot.get("metric_sources") or {}),
            "status": (soil_snapshot.get("source_metadata") or {}).get("status", "missing"),
            "aggregation_strategy": (soil_snapshot.get("source_metadata") or {}).get(
                "aggregation_strategy", "missing"
            ),
        },
        "block_metrics": block_metrics,
        "sub_block_metrics": sub_block_metrics,
        "weather": {
            "forecast": WeatherForecastDetailSerializer(weather).data if weather else None,
            "source_metadata": {
                "source": "center_location_forecast",
                "scope": "location_center_based",
                "location_id": center_location.id,
                "status": "completed" if weather else "missing",
            },
        },
        "plants": [
            {
                "plant_id": assignment.plant.backend_plant_id,
                "position": assignment.position,
                "stage": assignment.stage,
                "metadata": assignment.metadata,
                "assigned_at": assignment.assigned_at,
                "updated_at": assignment.updated_at,
                "plant": PlantCatalogSnapshotSerializer(assignment.plant).data,
            }
            for assignment in plant_assignments
        ],
        "irrigation_method": {
            "id": farm.irrigation_method_id,
            "details": (
                IrrigationMethodSerializer(farm.irrigation_method).data
                if farm.irrigation_method
                else None
            ),
            "source_metadata": {
                "source": "farm_record",
                "status": "completed" if farm.irrigation_method_id else "missing",
            },
        },
        "source_metadata": {
            "farm": {
                "farm_uuid": str(farm.farm_uuid),
                "center_location_id": center_location.id,
                "has_explicit_blocks": bool((center_location.block_layout or {}).get("blocks")),
            },
            "farm_metrics": dict(soil_snapshot.get("source_metadata") or {}),
            "block_metrics": {
                "source": "build_location_block_satellite_snapshots",
                "block_count": len(block_metrics),
                "status": "completed" if block_metrics else "missing",
            },
            "sub_block_metrics": {
                "source": "block_snapshot_sub_blocks",
                "sub_block_count": len(sub_block_metrics),
                "status": "completed" if sub_block_metrics else "missing",
            },
            "weather": {
                "source": "center_location_forecast",
                "scope": "location_center_based",
                "location_id": center_location.id,
                "note": "Weather remains tied to the farm center location.",
            },
            "plants": {
                "source": "farm_plant_assignments",
                "count": len(plant_assignments),
            },
            "irrigation_method": {
                "source": "farm_record",
                "status": "completed" if farm.irrigation_method_id else "missing",
            },
        },
    }


def get_ai_farm_snapshot_or_details(farm_uuid: str) -> dict[str, object] | None:
    """Return the canonical AI snapshot, or fall back to farm details for older consumers."""
    snapshot = build_ai_farm_snapshot(farm_uuid)
    if snapshot is None:
        return None
    return snapshot


def get_ai_snapshot_metric(snapshot: dict[str, object] | None, metric_name: str) -> object | None:
    if not isinstance(snapshot, dict):
        return None
    farm_metrics = snapshot.get("farm_metrics") or {}
    resolved_metrics = farm_metrics.get("resolved_metrics") if isinstance(farm_metrics, dict) else {}
    if isinstance(resolved_metrics, dict):
        return resolved_metrics.get(metric_name)
    return None


def get_ai_snapshot_weather(snapshot: dict[str, object] | None) -> dict[str, object]:
    if not isinstance(snapshot, dict):
        return {}
    weather_section = snapshot.get("weather") or {}
    forecast = weather_section.get("forecast") if isinstance(weather_section, dict) else None
    return forecast if isinstance(forecast, dict) else {}


def _build_ai_block_metrics(block_snapshots: list[dict[str, object]]) -> list[dict[str, object]]:
    block_metrics: list[dict[str, object]] = []
    for snapshot in block_snapshots:
        block_code = str(snapshot.get("block_code") or "").strip() or "default-block"
        block_metrics.append(
            {
                "block_code": block_code,
                "resolved_metrics": dict(snapshot.get("resolved_metrics") or {}),
                "metric_sources": dict(snapshot.get("metric_sources") or {}),
                "satellite_metrics": dict(snapshot.get("satellite_metrics") or {}),
                "sensor_metrics": dict(snapshot.get("sensor_metrics") or {}),
                "status": snapshot.get("status") or "missing",
                "aggregation_strategy": snapshot.get("aggregation_strategy") or "missing",
                "sub_block_count": int(snapshot.get("sub_block_count") or 0),
                "temporal_extent": snapshot.get("temporal_extent"),
                "source_metadata": {
                    "source": "build_location_block_satellite_snapshots",
                    "block_code": block_code,
                    "run_id": snapshot.get("run_id"),
                    "cell_count": snapshot.get("cell_count"),
                },
            }
        )
    return block_metrics


def _build_ai_sub_block_metrics(block_snapshots: list[dict[str, object]]) -> list[dict[str, object]]:
    sub_block_metrics: list[dict[str, object]] = []
    for snapshot in block_snapshots:
        block_code = str(snapshot.get("block_code") or "").strip() or "default-block"
        satellite_sub_blocks = snapshot.get("satellite_sub_blocks") or []
        sensor_sub_blocks = snapshot.get("sensor_sub_blocks") or []
        if not satellite_sub_blocks and not sensor_sub_blocks:
            sub_block_metrics.append(
                {
                    "block_code": block_code,
                    "sub_block_code": "default-sub-block",
                    "resolved_metrics": dict(snapshot.get("resolved_metrics") or {}),
                    "satellite_metrics": dict(snapshot.get("satellite_metrics") or {}),
                    "sensor_metrics": dict(snapshot.get("sensor_metrics") or {}),
                    "status": snapshot.get("status") or "missing",
                    "source_metadata": {
                        "source": "default_sub_block_compatibility",
                        "scope": "future_default_policy",
                    },
                }
            )
            continue

        sub_blocks_by_code: dict[str, dict[str, object]] = {}
        for sub_block in satellite_sub_blocks:
            sub_block_code = str(sub_block.get("sub_block_code") or sub_block.get("cluster_code") or "").strip() or "default-sub-block"
            entry = sub_blocks_by_code.setdefault(
                sub_block_code,
                {
                    "block_code": block_code,
                    "sub_block_code": sub_block_code,
                    "resolved_metrics": {},
                    "satellite_metrics": {},
                    "sensor_metrics": {},
                    "status": snapshot.get("status") or "missing",
                    "source_metadata": {
                        "source": "block_snapshot_sub_blocks",
                        "satellite_present": False,
                        "sensor_present": False,
                    },
                },
            )
            entry["satellite_metrics"] = dict(sub_block.get("resolved_metrics") or {})
            entry["resolved_metrics"].update(entry["satellite_metrics"])
            entry["source_metadata"]["satellite_present"] = True
        for sub_block in sensor_sub_blocks:
            sub_block_code = str(sub_block.get("sub_block_code") or sub_block.get("cluster_code") or "").strip() or "default-sub-block"
            entry = sub_blocks_by_code.setdefault(
                sub_block_code,
                {
                    "block_code": block_code,
                    "sub_block_code": sub_block_code,
                    "resolved_metrics": {},
                    "satellite_metrics": {},
                    "sensor_metrics": {},
                    "status": snapshot.get("status") or "missing",
                    "source_metadata": {
                        "source": "block_snapshot_sub_blocks",
                        "satellite_present": False,
                        "sensor_present": False,
                    },
                },
            )
            entry["sensor_metrics"] = dict(sub_block.get("resolved_metrics") or {})
            entry["resolved_metrics"].update(entry["sensor_metrics"])
            entry["source_metadata"]["sensor_present"] = True
        sub_block_metrics.extend(sub_blocks_by_code.values())
    return sub_block_metrics


def resolve_center_location_from_boundary(
    farm_boundary: dict | list,
    block_count: int = 1,
) -> 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.")

    center_lat, center_lon = _compute_polygon_centroid(normalized_points)
    serialized_boundary = _serialize_boundary(farm_boundary)
    normalized_block_count = max(int(block_count or 1), 1)

    with transaction.atomic():
        location, created = SoilLocation.objects.get_or_create(
            latitude=center_lat,
            longitude=center_lon,
            defaults={
                "farm_boundary": serialized_boundary,
                "input_block_count": normalized_block_count,
            },
        )
        if created:
            location.set_input_block_count(normalized_block_count)
            location.farm_boundary = serialized_boundary
            location.save(update_fields=["farm_boundary", "input_block_count", "block_layout", "updated_at"])
            if normalized_block_count == 1:
                _create_initial_block_subdivision(location, serialized_boundary)
        else:
            changed_fields = []
            if location.farm_boundary != serialized_boundary:
                location.farm_boundary = serialized_boundary
                changed_fields.append("farm_boundary")
            if location.input_block_count != normalized_block_count:
                location.set_input_block_count(normalized_block_count)
                changed_fields.extend(["input_block_count", "block_layout"])
            if changed_fields:
                changed_fields.append("updated_at")
                location.save(update_fields=changed_fields)
    return location


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


def ensure_location_and_weather_data(location: SoilLocation) -> tuple[SoilLocation, WeatherForecast | None]:
    """
    forecast آب‌وهوا را در صورت نبود/قدیمی بودن refresh می‌کند تا
    سرویس‌های downstream به‌جای دیتای seed/mock از داده provider فعال استفاده کنند.
    """
    weather_forecast = resolve_weather_for_location(location)
    needs_refresh = weather_forecast is None

    if weather_forecast is not None:
        today = timezone.localdate()
        has_upcoming_forecast = WeatherForecast.objects.filter(
            location=location,
            forecast_date__gte=today,
        ).exists()
        fetched_at = getattr(weather_forecast, "fetched_at", None)
        is_stale = fetched_at is None or (timezone.now() - fetched_at).total_seconds() >= 3 * 60 * 60
        needs_refresh = (not has_upcoming_forecast) or is_stale

    if needs_refresh:
        try:
            weather_result = apps.get_app_config("weather").update_weather_for_location(location)
        except Exception as exc:
            raise ExternalDataSyncError(f"Weather sync failed: {exc}") from exc

        if weather_result.get("status") == "error":
            raise ExternalDataSyncError(weather_result.get("error") or "Weather sync failed.")

        weather_forecast = resolve_weather_for_location(location)

    return location, weather_forecast


def _create_initial_block_subdivision(
    location: SoilLocation,
    block_boundary: dict | list,
) -> BlockSubdivision:
    subdivision, _created = create_or_get_block_subdivision(
        location=location,
        block_code="block-1",
        boundary=block_boundary,
    )
    return subdivision


def _resolve_sensor_metrics(sensor_payload: dict | None) -> tuple[dict, dict]:
    if not isinstance(sensor_payload, dict):
        return {}, {}

    readings_by_metric: dict[str, list[tuple[str, object]]] = {}
    for sensor_key, sensor_values in sorted(sensor_payload.items()):
        if not isinstance(sensor_values, dict):
            continue
        for metric_key, metric_value in sensor_values.items():
            readings_by_metric.setdefault(metric_key, []).append((sensor_key, metric_value))

    resolved_metrics = {}
    metric_sources = {}
    for metric_key, readings in readings_by_metric.items():
        resolved_value, source = _resolve_metric_readings(readings)
        resolved_metrics[metric_key] = resolved_value
        metric_sources[metric_key] = source
    return resolved_metrics, metric_sources


def _resolve_metric_readings(readings: list[tuple[str, object]]) -> tuple[object, dict[str, object]]:
    if not readings:
        return None, {"type": "sensor", "strategy": "empty", "sensor_keys": []}

    sensor_keys = [sensor_key for sensor_key, _value in readings]
    distinct_values: list[object] = []
    for _sensor_key, value in readings:
        if value not in distinct_values:
            distinct_values.append(value)

    if len(distinct_values) == 1:
        return distinct_values[0], {
            "type": "sensor",
            "strategy": "single_value",
            "sensor_keys": sensor_keys,
            "sensor_count": len(sensor_keys),
        }

    numeric_values = [_coerce_numeric(value) for value in distinct_values]
    if all(value is not None for value in numeric_values):
        average = sum(numeric_values) / len(numeric_values)
        resolved_value = _normalize_numeric_result(average, distinct_values)
        return resolved_value, {
            "type": "sensor",
            "strategy": "average",
            "sensor_keys": sensor_keys,
            "sensor_count": len(sensor_keys),
            "conflict": True,
            "distinct_values": distinct_values,
        }

    return distinct_values, {
        "type": "sensor",
        "strategy": "distinct_values",
        "sensor_keys": sensor_keys,
        "sensor_count": len(sensor_keys),
        "conflict": True,
        "distinct_values": distinct_values,
    }


def _coerce_numeric(value: object) -> float | None:
    if isinstance(value, bool):
        return None
    if isinstance(value, Number):
        return float(value)
    try:
        return float(value)
    except (TypeError, ValueError):
        return None


def _normalize_numeric_result(value: float, source_values: list[object]) -> int | float:
    if all(isinstance(item, int) and not isinstance(item, bool) for item in source_values):
        if value.is_integer():
            return int(value)
    return float(Decimal(str(value)).quantize(Decimal("0.0001"), rounding=ROUND_HALF_UP))


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],
    }


def _compute_polygon_centroid(points: list[tuple[Decimal, Decimal]]) -> tuple[Decimal, Decimal]:
    polygon = list(points)
    if polygon[0] != polygon[-1]:
        polygon.append(polygon[0])

    twice_area = Decimal("0")
    centroid_lon = Decimal("0")
    centroid_lat = Decimal("0")

    for index in range(len(polygon) - 1):
        lat1, lon1 = polygon[index]
        lat2, lon2 = polygon[index + 1]
        cross = (lon1 * lat2) - (lon2 * lat1)
        twice_area += cross
        centroid_lon += (lon1 + lon2) * cross
        centroid_lat += (lat1 + lat2) * cross

    if twice_area == 0:
        return _compute_average_center(points)

    factor = Decimal("3") * twice_area
    return (
        (centroid_lat / factor).quantize(DECIMAL_PRECISION, rounding=ROUND_HALF_UP),
        (centroid_lon / factor).quantize(DECIMAL_PRECISION, rounding=ROUND_HALF_UP),
    )


def _compute_average_center(points: list[tuple[Decimal, Decimal]]) -> tuple[Decimal, Decimal]:
    lat_sum = sum(lat for lat, _ in points)
    lon_sum = sum(lon for _, lon in points)
    count = Decimal(len(points))
    return (
        (lat_sum / count).quantize(DECIMAL_PRECISION, rounding=ROUND_HALF_UP),
        (lon_sum / count).quantize(DECIMAL_PRECISION, rounding=ROUND_HALF_UP),
    )