from __future__ import annotations

import io
import math
from datetime import UTC, datetime
from pathlib import Path
from uuid import UUID

from geoalchemy2.shape import to_shape
from pyproj import Transformer
from shapely.geometry import box, mapping
from shapely.ops import transform as shapely_transform

from app.core.config import Settings, get_settings
from app.core.errors import AppError
from app.services.raster_cell_selection import select_cells
from app.models import Area, Dataset
from app.schemas.thematic_raster import (
    ThematicRasterMetric,
    ThematicRasterSelectionRequest,
    ThematicRasterSelectionResponse,
    ThematicRasterSelectionSummary,
)
from app.services.thematic_raster_acquisition_service import (
    ThematicRasterAcquisitionService,
    ThematicRasterProduct,
)


class ThematicRasterAnalysisService:
    @staticmethod
    def _load_dataset(db, project_id: UUID, dataset_id: UUID) -> Dataset:
        dataset = db.get(Dataset, dataset_id)
        if not dataset or dataset.project_id != project_id:
            raise AppError(code="DATASET_NOT_FOUND", message="Dataset not found", status_code=404)
        if dataset.dataset_type != "raster" or dataset.source_name != ThematicRasterAcquisitionService.PROVIDER:
            raise AppError(
                code="INVALID_THEMATIC_RASTER_DATASET",
                message="Thematic analysis requires a governed Departement Omgeving raster dataset",
                status_code=400,
            )
        if dataset.status != "ready" or not dataset.storage_path or not Path(dataset.storage_path).is_file():
            raise AppError(code="DATASET_FILE_MISSING", message="Persisted thematic raster file is unavailable", status_code=404)
        return dataset

    @staticmethod
    def _product(dataset: Dataset) -> ThematicRasterProduct:
        source_metadata = dataset.source_metadata or {}
        product = ThematicRasterAcquisitionService._products().get(str(source_metadata.get("product_key") or ""))
        if product is None or source_metadata.get("coverage_id") != product.coverage_id:
            raise AppError(code="INVALID_THEMATIC_RASTER_METADATA", message="Thematic raster provenance is incomplete", status_code=409)
        return product

    @staticmethod
    def _selection_geometry(db, project_id: UUID, payload: ThematicRasterSelectionRequest):
        selection = box(payload.bbox.min_x, payload.bbox.min_y, payload.bbox.max_x, payload.bbox.max_y)
        if payload.area_id is None:
            return selection
        area = db.get(Area, payload.area_id)
        if not area:
            raise AppError(code="AREA_NOT_FOUND", message="Area not found", status_code=404)
        if area.project_id != project_id:
            raise AppError(code="INVALID_DATASET_SCOPE", message="Area does not belong to this project", status_code=400)
        selection = selection.intersection(to_shape(area.geometry))
        if selection.is_empty or selection.area <= 0:
            raise AppError(code="THEMATIC_RASTER_SELECTION_OUTSIDE_AREA", message="Selection does not overlap the selected work area", status_code=422)
        return selection

    @staticmethod
    def _unsupported_metrics(product: ThematicRasterProduct) -> list[str]:
        if product.metric_kind == "binary_area":
            if product.theme == "forest":
                return ["tree_count", "canopy_cover", "timber_volume", "legal_forest_boundary"]
            if product.theme == "agriculture":
                return ["declared_parcel_area", "crop_declaration", "ownership", "cadastral_area"]
            return ["object_count", "parcel_area", "current_land_use"]
        if product.metric_kind == "population_density":
            return ["current_population", "household_count", "address_level_population"]
        if product.metric_kind == "index_score":
            return ["travel_time_minutes", "current_timetable", "stop_count"]
        return ["facility_count", "opening_hours", "current_service_availability"]

    @staticmethod
    def analyze(
        db,
        project_id: UUID,
        dataset_id: UUID,
        payload: ThematicRasterSelectionRequest,
        *,
        settings: Settings | None = None,
    ) -> dict:
        resolved_settings = settings or get_settings()
        dataset = ThematicRasterAnalysisService._load_dataset(db, project_id, dataset_id)
        product = ThematicRasterAnalysisService._product(dataset)
        selection_4326 = ThematicRasterAnalysisService._selection_geometry(db, project_id, payload)
        try:
            import numpy as np
            import rasterio
            from rasterio.features import geometry_mask
            from rasterio.mask import mask
        except ImportError as exc:
            raise AppError(code="RASTER_PROCESSING_UNAVAILABLE", message="Rasterio and numpy are required for thematic raster analysis", status_code=503) from exc

        try:
            with rasterio.open(dataset.storage_path) as source:
                if source.crs is None or source.crs.to_epsg() != 31370:
                    raise AppError(code="INVALID_DATASET_CRS", message="Thematic raster CRS must be EPSG:31370", status_code=409)
                transformer = Transformer.from_crs("EPSG:4326", source.crs, always_xy=True)
                selection_metric = shapely_transform(transformer.transform, selection_4326)
                analysis_geometry = selection_metric.intersection(box(*source.bounds))
                if analysis_geometry.is_empty or analysis_geometry.area <= 0:
                    raise AppError(code="THEMATIC_RASTER_SELECTION_OUTSIDE_DATASET", message="Selection does not overlap the persisted thematic raster", status_code=422)
                min_x, min_y, max_x, max_y = analysis_geometry.bounds
                expected_cells = math.ceil((max_x - min_x) / abs(source.res[0])) * math.ceil((max_y - min_y) / abs(source.res[1]))
                if expected_cells > resolved_settings.thematic_raster_max_pixels:
                    raise AppError(
                        code="THEMATIC_RASTER_SELECTION_TOO_LARGE",
                        message="Thematic raster analysis exceeds the configured cell limit",
                        details={"pixel_count": expected_cells, "max_pixels": resolved_settings.thematic_raster_max_pixels},
                        status_code=422,
                    )
                # ``all_touched`` keeps the values of cells the selection only
                # clips, so a selection finer than one cell still has data to
                # read. Which of those cells actually count is decided by
                # ``select_cells`` below, so the normal result is unchanged.
                clipped, clipped_transform = mask(
                    source,
                    [mapping(analysis_geometry)],
                    crop=True,
                    filled=False,
                    indexes=[1],
                    all_touched=True,
                )
                band = np.ma.asarray(clipped[0], dtype="float64")
                raw = band.filled(np.nan)
                cell_selection = select_cells(
                    analysis_geometry,
                    out_shape=band.shape,
                    transform=clipped_transform,
                    cell_area_m2=abs(float(source.res[0])) * abs(float(source.res[1])),
                )
                selected = cell_selection.mask
                valid = selected & ~np.ma.getmaskarray(band) & np.isfinite(raw)
                if source.nodata is not None:
                    valid &= ~np.isclose(raw, float(source.nodata))
                values = raw[valid]
                ThematicRasterAcquisitionService._validate_values(values, product)
                selected_cell_count = int(selected.sum())
                valid_cell_count = int(values.size)
                resolution_x = abs(float(source.res[0]))
                resolution_y = abs(float(source.res[1]))
                cell_area_m2 = resolution_x * resolution_y
        except AppError:
            raise
        except Exception as exc:
            raise AppError(
                code="THEMATIC_RASTER_ANALYSIS_FAILED",
                message="The persisted thematic raster could not be analysed",
                details={"reason": str(exc)},
                status_code=500,
            ) from exc

        def metric(key: str, label: str, value: float, unit: str, method: str, *, estimate: bool = True) -> ThematicRasterMetric:
            return ThematicRasterMetric(
                metric_key=key,
                metric_label=label,
                metric_value=round(float(value), 4),
                metric_unit=unit,
                aggregation_method=method,
                is_estimate=estimate,
            )

        if product.metric_kind == "binary_area":
            positive_count = int(np.count_nonzero(values >= 0.5))
            positive_area_ha = positive_count * cell_area_m2 / 10_000.0
            positive_share = positive_count / max(1, valid_cell_count) * 100.0
            label = {
                "space_occupation": "Ruimtebeslag",
                "open_space": "Open ruimte",
                "forest": "Bos",
                "agriculture": "Akker en landbouwgrasland",
            }[product.theme]
            metrics = [
                metric(f"{product.theme}_area_ha", f"{label} in selectie", positive_area_ha, "ha", "positive_source_cells_times_cell_area"),
                metric(f"{product.theme}_share_pct", f"Aandeel {label.lower()}", positive_share, "%", "positive_source_cells_divided_by_valid_selected_cells"),
                metric("valid_raster_area_ha", "Rasteroppervlakte met bronwaarde", valid_cell_count * cell_area_m2 / 10_000.0, "ha", "valid_selected_cells_times_cell_area"),
            ]
        elif product.metric_kind == "population_density":
            estimated_population = float(values.sum() * (cell_area_m2 / 10_000.0))
            metrics = [
                metric("estimated_inhabitants", "Geraamd aantal inwoners (2019)", estimated_population, "inwoners", "sum_density_times_selected_cell_area_hectares"),
                metric("population_density_mean_per_ha", "Gemiddelde inwonersdichtheid", values.mean(), "inwoners/ha", "mean_valid_one_hectare_source_cells"),
                metric("population_density_p90_per_ha", "90e percentiel inwonersdichtheid", np.percentile(values, 90), "inwoners/ha", "percentile_90_valid_source_cells"),
            ]
        else:
            unit = "score" if product.metric_kind == "index_score" else "score (0-1)"
            label = "Knooppuntwaarde" if product.metric_kind == "index_score" else "Voorzieningenniveau"
            metrics = [
                metric(f"{product.theme}_mean", f"Gemiddelde {label.lower()}", values.mean(), unit, "mean_valid_source_cells"),
                metric(f"{product.theme}_p10", f"10e percentiel {label.lower()}", np.percentile(values, 10), unit, "percentile_10_valid_source_cells"),
                metric(f"{product.theme}_median", f"Mediaan {label.lower()}", np.percentile(values, 50), unit, "median_valid_source_cells"),
                metric(f"{product.theme}_p90", f"90e percentiel {label.lower()}", np.percentile(values, 90), unit, "percentile_90_valid_source_cells"),
            ]

        primary = metrics[0]
        response = ThematicRasterSelectionResponse(
            dataset_id=dataset.id,
            product_key=product.key,
            theme=product.theme,
            metric_kind=product.metric_kind,
            selection_bbox=payload.bbox,
            selection_area_id=payload.area_id,
            selected_cell_count=selected_cell_count,
            valid_cell_count=valid_cell_count,
            coverage_ratio=round(valid_cell_count / max(1, selected_cell_count), 6),
            cell_selection_warning=cell_selection.warning,
            resolution_m=round(max(resolution_x, resolution_y), 4),
            observation_year=product.observation_year,
            summary=ThematicRasterSelectionSummary(
                metric_label=primary.metric_label,
                metric_value=primary.metric_value,
                metric_unit=primary.metric_unit,
                aggregation_method=primary.aggregation_method,
                primary_metric_key=primary.metric_key,
                metrics=metrics,
            ),
            unsupported_metrics=ThematicRasterAnalysisService._unsupported_metrics(product),
            limitation_message=product.limitation_message,
            generated_at=datetime.now(UTC).isoformat(),
        )
        return response.model_dump(mode="json")

    @staticmethod
    def render_png(db, project_id: UUID, dataset_id: UUID, *, max_dimension: int = 1800) -> bytes:
        dataset = ThematicRasterAnalysisService._load_dataset(db, project_id, dataset_id)
        product = ThematicRasterAnalysisService._product(dataset)
        try:
            import numpy as np
            import rasterio
            from PIL import Image
            from rasterio.enums import Resampling
        except ImportError as exc:
            raise AppError(code="RASTER_PROCESSING_UNAVAILABLE", message="Rasterio, numpy and Pillow are required for thematic raster rendering", status_code=503) from exc
        palettes = {
            "space_occupation": np.asarray([[251, 231, 211], [190, 62, 51]], dtype="float64"),
            "open_space": np.asarray([[221, 238, 219], [38, 122, 70]], dtype="float64"),
            "forest": np.asarray([[223, 237, 226], [43, 117, 72]], dtype="float64"),
            "agriculture": np.asarray([[245, 237, 204], [166, 122, 35]], dtype="float64"),
            "population": np.asarray([[238, 231, 246], [103, 58, 151]], dtype="float64"),
            "accessibility": np.asarray([[233, 241, 244], [15, 118, 110]], dtype="float64"),
            "services": np.asarray([[255, 244, 191], [182, 109, 22]], dtype="float64"),
        }
        try:
            with rasterio.open(dataset.storage_path) as source:
                scale = min(1.0, max_dimension / max(source.width, source.height))
                width = max(1, round(source.width * scale))
                height = max(1, round(source.height * scale))
                resampling = Resampling.nearest if product.metric_kind == "binary_area" else Resampling.bilinear
                data = source.read(1, out_shape=(height, width), masked=True, resampling=resampling)
                values = np.asarray(data.filled(np.nan), dtype="float64")
                valid = np.isfinite(values) & ~np.ma.getmaskarray(data)
                if source.nodata is not None:
                    valid &= ~np.isclose(values, float(source.nodata))
                if product.metric_kind == "binary_area":
                    valid &= values >= 0.5
                    normalized = np.where(valid, 1.0, 0.0)
                else:
                    source_metadata = dataset.source_metadata or {}
                    lower = float(source_metadata.get("render_min_value", np.nanpercentile(values[valid], 2) if valid.any() else 0.0))
                    upper = float(source_metadata.get("render_max_value", np.nanpercentile(values[valid], 98) if valid.any() else 1.0))
                    if upper <= lower:
                        upper = lower + 1.0
                    normalized = np.clip((values - lower) / (upper - lower), 0.0, 1.0)
                colors = palettes[product.theme]
                rgba = np.zeros((height, width, 4), dtype="uint8")
                for channel in range(3):
                    rgba[:, :, channel] = (colors[0, channel] + normalized * (colors[1, channel] - colors[0, channel])).astype("uint8")
                rgba[:, :, 3] = np.where(valid, 205, 0).astype("uint8")
                output = io.BytesIO()
                Image.fromarray(rgba).save(output, format="PNG", optimize=True)
                return output.getvalue()
        except AppError:
            raise
        except Exception as exc:
            raise AppError(
                code="THEMATIC_RASTER_PREVIEW_FAILED",
                message="The persisted thematic raster could not be rendered",
                details={"reason": str(exc)},
                status_code=500,
            ) from exc