"""DTM generation from classified LiDAR point clouds.

Handles ground classification via PDAL/SMRF and DTM rasterisation
using scipy binned_statistic_2d. Zones without LiDAR data remain as NaN.
"""

import json
import logging
import subprocess
from pathlib import Path

import numpy as np
import rasterio
from rasterio.transform import from_bounds
from scipy.stats import binned_statistic_2d

logger = logging.getLogger("lidar")


def _create_ground_pipeline(input_laz, output_las, method):
    """Create a PDAL pipeline JSON for ground classification.

    All methods include a ReturnNumber/NumberOfReturns >= 1 filter to handle
    LiDAR HD files that may contain points with invalid return numbers.

    Pre-processing steps (PDAL recommended workflow):
    1. Reset Classification to 0
    2. ELM (Extended Local Minimum) — mark low outliers as noise (Classification=7)
    3. Statistical outlier removal
    4. Ground classification (SMRF/PMF/CSF)
    5. Extract ground points (Classification=2)

    Args:
        input_laz: Path to input LAZ/LAS file.
        output_las: Path to output classified LAS file.
        method: Ground classification method ('smrf', 'pmf', or 'csf').

    Returns:
        JSON string of the PDAL pipeline.
    """
    # Common ReturnNumber filter for LiDAR HD compatibility
    return_filter = {
        "type": "filters.range",
        "limits": "ReturnNumber[1:],NumberOfReturns[1:]"
    }

    # Reset Classification to 0 before preprocessing
    reset_classification = {
        "type": "filters.assign",
        "assignment": "Classification[:]=0"
    }

    # ELM (Extended Local Minimum) — mark low outliers as noise
    # Parameters tuned for rocky limestone terrain with low vegetation:
    # - cell=5.0m: fine resolution to capture rocky relief
    # - threshold=2.0m: high threshold to avoid marking rock outcrops as noise
    elm_filter = {
        "type": "filters.elm",
        "cell": 5.0,
        "threshold": 2.0
    }

    # Statistical outlier removal
    outlier_filter = {
        "type": "filters.outlier",
        "method": "statistical",
        "mean_k": 8,
        "multiplier": 3.0
    }

    # Classification filter (ground points only)
    ground_filter = {
        "type": "filters.range",
        "limits": "Classification[2:2]"
    }

    # Method-specific ground classification filter
    if method == 'smrf':
        ground_step = {
            "type": "filters.smrf",
            "ignore": "Classification[7:7]",
            "slope": 1.0,
            "window": 16.0,
            "threshold": 0.5,
            "scalar": 1.25
        }
    elif method == 'pmf':
        ground_step = {
            "type": "filters.pmf",
            "max_window": 33,
            "slope": 0.15,
            "max_distance": 2.5,
            "initial_distance": 0.15,
            "cell_size": 1.0
        }
    elif method == 'csf':
        ground_step = {
            "type": "filters.csf",
            "resolution": 0.5,
            "rigidness": 3,
            "smooth": True,
            "threshold": 0.5
        }
    else:
        raise ValueError(f"Méthode de classification inconnue: {method}")

    pipeline = {
        "pipeline": [
            str(input_laz),
            return_filter,
            reset_classification,
            elm_filter,
            outlier_filter,
            ground_step,
            ground_filter,
            {
                "type": "writers.las",
                "filename": str(output_las),
                "extra_dims": "all"
            }
        ]
    }
    return json.dumps(pipeline)


def create_smrf_pipeline(input_laz, output_las):
    """Create a PDAL pipeline JSON for SMRF ground classification."""
    return _create_ground_pipeline(input_laz, output_las, 'smrf')


def create_pmf_pipeline(input_laz, output_las):
    """Create a PDAL pipeline JSON for PMF ground classification."""
    return _create_ground_pipeline(input_laz, output_las, 'pmf')


def create_csf_pipeline(input_laz, output_las):
    """Create a PDAL pipeline JSON for CSF ground classification."""
    return _create_ground_pipeline(input_laz, output_las, 'csf')


def detect_ground_method(laz_file):
    """Detect the best ground classification method based on point cloud statistics.

    Auto-selects between SMRF (natural terrain) and PMF (urban) only.
    CSF is available only via --ground-classification csf (slow but robust
    on complex terrain).

    Falls back to SMRF if the file cannot be read or attributes are missing.

    Args:
        laz_file: Path to input LAZ/LAS file.

    Returns:
        String: 'smrf', 'pmf', or 'csf'
    """
    import laspy

    try:
        las = laspy.read(str(laz_file))
    except Exception as e:
        logger.warning(f"  Impossible de lire le nuage pour auto-détection: {e}")
        logger.info(f"  → Méthode: SMRF (défaut — lecture impossible)")
        return 'smrf'

    total_points = len(las.points)
    z = np.array(las.z)

    # Height variance (always available)
    z_std = float(np.std(z))
    z_range = float(np.max(z) - np.min(z))

    # Try to get NumberOfReturns (may not exist in all point formats)
    single_return_ratio = 0.0
    try:
        num_returns = np.array(las.NumberOfReturns)
        single_return_count = int(np.sum(num_returns == 1))
        single_return_ratio = single_return_count / total_points if total_points > 0 else 0
    except AttributeError:
        logger.debug("  NumberOfReturns non disponible — utilisation de la variance Z uniquement")

    logger.info(f"  Analyse du nuage: {total_points:,} points, "
                f"ratio_retours_uniques={single_return_ratio:.2f}, "
                f"écart_Z={z_std:.1f}m, amplitude_Z={z_range:.1f}m")

    # Decision logic (auto selects between SMRF and PMF only):
    # - High single-return ratio (>0.6) → urban (buildings, roads) → PMF
    # - Default → SMRF (fast, robust for most natural terrain)
    # Note: CSF is available only via --ground-classification csf (slow but robust on complex terrain)
    if single_return_ratio > 0.6:
        method = 'pmf'
        reason = f"ratio retours uniques={single_return_ratio:.2f} > 0.6 → milieu urbain"
    else:
        method = 'smrf'
        reason = f"terrain naturel standard"

    logger.info(f"  → Méthode: {method.upper()} ({reason})")
    return method


def classify_ground(laz_file, temp_dir, method='auto', force=False):
    """Classify ground points using PDAL ground classification filter.

    Args:
        laz_file: Path to input LAZ/LAS file.
        temp_dir: Directory for temporary files (pipeline.json, ground.las).
        method: Ground classification method ('auto', 'smrf', 'pmf', or 'csf').
        force: If True, reclassify even if output file already exists.

    Returns:
        Path to classified ground LAS file, or None on failure.
    """
    import laspy  # noqa: ensure available

    # Auto-detect method if requested
    if method == 'auto':
        method = detect_ground_method(laz_file)
        logger.info(f"  Classification sol: {method.upper()} (auto)")
    else:
        logger.info(f"  Classification sol: {method.upper()} (forcé)")

    # Use shared basename extraction function
    from .pipeline import _file_basename
    laz_base = _file_basename(laz_file)

    output_las = temp_dir / f"{laz_base}_ground_{method}.las"

    if output_las.exists() and not force:
        logger.info(f"  Classification {method.upper()} déjà effectuée — fichier existant réutilisé")
        return output_las

    if force and output_las.exists():
        logger.info(f"  Reclassification forcée — suppression de {output_las.name}")
        output_las.unlink()

    pipeline_json = _create_ground_pipeline(laz_file, output_las, method)
    pipeline_file = temp_dir / f"pipeline_{method}.json"

    with open(pipeline_file, 'w') as f:
        f.write(pipeline_json)

    try:
        subprocess.run(
            ["pdal", "pipeline", str(pipeline_file)],
            capture_output=True, check=True
        )
        logger.info(f"  ✓ Classification sol {method.upper()} terminée")
        return output_las
    except subprocess.CalledProcessError as e:
        logger.error(f"  ✗ Erreur classification PDAL ({method.upper()}): {e.stderr.decode()}")
        return None


def create_dtm_fast(las_file, basename, dtm_dir, resolution):
    """Create DTM using fast binning method with gap filling.

    Args:
        las_file: Path to classified ground LAS file.
        basename: Base name for output file.
        dtm_dir: Directory for output DTM GeoTIFF.
        resolution: Grid resolution in meters per pixel.

    Returns:
        Path to output DTM GeoTIFF, or None on failure.
    """
    import laspy

    logger.info("  → Génération DTM...")

    try:
        las = laspy.read(str(las_file))

        min_x, max_x = float(las.header.min[0]), float(las.header.max[0])
        min_y, max_y = float(las.header.min[1]), float(las.header.max[1])

        width = int(np.ceil((max_x - min_x) / resolution))
        height = int(np.ceil((max_y - min_y) / resolution))

        logger.debug(f"    Bounds: X[{min_x:.1f}, {max_x:.1f}] Y[{min_y:.1f}, {max_y:.1f}]")
        logger.debug(f"    Grid: {width}x{height} pixels ({len(las.points):,} points)")
        logger.info(f"    Rasterisation {width}x{height} ({len(las.points):,} points)...")

        stat = binned_statistic_2d(
            las.x, las.y, las.z,
            statistic='mean',
            bins=[width, height],
            range=[[min_x, max_x], [min_y, max_y]]
        )

        dtm = stat.statistic.T
        dtm = dtm[::-1, :]  # Flip Y so north is at top

        # Fill small gaps (< 1m from existing data) while keeping large gaps as NaN
        nan_count = np.count_nonzero(np.isnan(dtm))
        if nan_count > 0:
            total = dtm.size
            nan_pct = 100.0 * nan_count / total
            logger.info(f"    {nan_count:,} pixels sans données ({nan_pct:.1f}%)")

            max_gap_pixels = max(1, int(1.0 / resolution))
            from rasterio.fill import fillnodata
            valid_mask = ~np.isnan(dtm)
            dtm_filled = fillnodata(dtm, mask=valid_mask, max_search_distance=max_gap_pixels)
            small_gap_mask = np.isnan(dtm) & ~np.isnan(dtm_filled)
            filled_count = np.count_nonzero(small_gap_mask)
            if filled_count > 0:
                dtm = np.where(small_gap_mask, dtm_filled, dtm)
                logger.info(f"    {filled_count:,} petits trous comblés (< {max_gap_pixels}px)")
            remaining = np.count_nonzero(np.isnan(dtm))
            logger.info(f"    {remaining:,} pixels restent sans données (grands écarts)")

        # Save as GeoTIFF
        output_tif = dtm_dir / f"{basename}_dtm.tif"
        transform = from_bounds(min_x, min_y, max_x, max_y, width, height)

        with rasterio.open(
            output_tif, 'w',
            driver='GTiff', height=height, width=width,
            count=1, dtype='float32',
            crs='EPSG:2154', transform=transform,
            nodata=float('nan'),
            compress='lzw'
        ) as dst:
            dst.write(dtm.astype('float32'), 1)

        logger.info(f"    ✓ DTM créé: {output_tif.name}")
        return output_tif

    except Exception as e:
        logger.error(f"  ✗ Erreur DTM: {e}", exc_info=True)
        return None