"""
Aggregate property prices at different geographic levels.

Responsibility: Given cleaned transaction data, compute time-weighted
summary statistics (weighted trimmed mean, median, confidence) grouped
by any geographic column.
"""

import json
import logging
import math
from pathlib import Path

import numpy as np
import polars as pl

from src.config import (
    AGGREGATED_DIR,
    AGGREGATION_LEVELS,
    SECTIONS_DIR,
    TRIM_FRACTION,
    TYPE_LOCAL_SHORT,
)

logger = logging.getLogger(__name__)

# Map each aggregation level to the column used for grouping
LEVEL_TO_COLUMN: dict[str, str] = {
    "country": "_country",
    "region": "code_region",
    "department": "code_departement",
    "commune": "code_commune",
    "postcode": "code_postal",
    "section": "code_section",
}


def weighted_trimmed_mean(
    prices: np.ndarray,
    weights: np.ndarray,
    trim: float = TRIM_FRACTION,
) -> float:
    """
    Compute a weighted trimmed mean.

    Sorts by price, trims `trim` fraction from each tail by cumulative
    weight, then computes the weighted average of the remaining middle.

    Args:
        prices:  Array of price/m2 values.
        weights: Array of temporal weights (same length as prices).
        trim:    Fraction to trim from each tail (0.20 = drop bottom/top 20%).

    Returns:
        Weighted trimmed mean, or NaN if insufficient data.
    """
    if len(prices) == 0:
        return float("nan")

    order = np.argsort(prices)
    prices = prices[order]
    weights = weights[order]

    cum_w = np.cumsum(weights)
    total_w = cum_w[-1]

    if total_w <= 0:
        return float("nan")

    lo = total_w * trim
    hi = total_w * (1.0 - trim)

    mask = (cum_w >= lo) & (cum_w <= hi)

    # Ensure at least one observation survives trimming
    if not mask.any():
        mask[len(mask) // 2] = True

    w_sel = weights[mask]
    p_sel = prices[mask]

    return float(np.average(p_sel, weights=w_sel))


def effective_sample_size(weights: np.ndarray) -> float:
    """
    Compute Kish's effective sample size for weighted observations.

    n_eff = (sum(w))^2 / sum(w^2)
    When all weights are equal, n_eff = n.
    """
    sw = weights.sum()
    if sw <= 0:
        return 0.0
    return float(sw**2 / (weights**2).sum())


def _aggregate_group(group_df: pl.DataFrame, trim: float = TRIM_FRACTION) -> dict:
    """
    Compute all stats for one geographic group.

    Returns a dict with median, wtm (weighted trimmed mean), q1, q3,
    volume, n_eff, confidence.
    """
    prices = group_df["prix_m2"].to_numpy()
    weights = group_df["temporal_weight"].to_numpy()

    volume = len(prices)
    if volume == 0:
        return {
            "median": 0.0, "wtm": 0.0, "q1": 0.0, "q3": 0.0,
            "volume": 0, "n_eff": 0.0, "confidence": 0.0,
        }

    median = float(np.median(prices))
    q1 = float(np.percentile(prices, 25))
    q3 = float(np.percentile(prices, 75))
    iqr = q3 - q1
    wtm = weighted_trimmed_mean(prices, weights, trim)
    n_eff = effective_sample_size(weights)

    # Confidence: volume component + stability component
    conf_volume = min(1.0, math.log1p(n_eff) / math.log1p(100))
    conf_stability = max(0.0, 1.0 - iqr / median) if median > 0 else 0.0
    confidence = round(0.6 * conf_volume + 0.4 * conf_stability, 3)

    return {
        "median": round(median, 1),
        "wtm": round(wtm, 1),
        "q1": round(q1, 1),
        "q3": round(q3, 1),
        "volume": volume,
        "n_eff": round(n_eff, 1),
        "confidence": confidence,
    }


def aggregate_level(
    df: pl.DataFrame,
    group_col: str,
    *,
    property_type: str | None = None,
) -> dict[str, dict]:
    """
    Compute time-weighted price statistics for one geographic level.

    Args:
        df:            Collected DataFrame with prix_m2 and temporal_weight.
        group_col:     Column to group by.
        property_type: Filter to this type_local, or None for all.

    Returns:
        Dict of {code: stats_dict}.
    """
    filtered = df
    if property_type:
        filtered = filtered.filter(pl.col("type_local") == property_type)

    if group_col == "_country":
        filtered = filtered.with_columns(pl.lit("FR").alias("_country"))

    results = {}
    for code, group_df in filtered.group_by(group_col):
        code_str = str(code[0]) if isinstance(code, tuple) else str(code)
        results[code_str] = _aggregate_group(group_df)

    return results


def aggregate_all_types(
    df: pl.DataFrame,
    group_col: str,
) -> dict[str, dict[str, dict]]:
    """
    Aggregate for all property types + combined for a given level.

    Returns:
        Nested dict: {code: {"tous": {...}, "appartement": {...}, "maison": {...}}}
    """
    combined: dict[str, dict[str, dict]] = {}

    # All types combined
    all_stats = aggregate_level(df, group_col)
    for code, stats in all_stats.items():
        combined.setdefault(code, {})["tous"] = stats

    # Per property type
    for full_name, short_name in TYPE_LOCAL_SHORT.items():
        type_stats = aggregate_level(df, group_col, property_type=full_name)
        for code, stats in type_stats.items():
            combined.setdefault(code, {})[short_name] = stats

    return combined


def aggregate_all_levels(df: pl.DataFrame) -> dict[str, dict]:
    """
    Run aggregation for all 6 geographic levels.

    Args:
        df: Collected DataFrame (not lazy).

    Returns:
        Dict mapping level name -> nested price dict.
    """
    results = {}
    for level in AGGREGATION_LEVELS:
        col = LEVEL_TO_COLUMN[level]
        logger.info("Aggregating level: %s (group by %s)", level, col)
        results[level] = aggregate_all_types(df, col)
        logger.info("  -> %d unique codes", len(results[level]))
    return results


def export_json(
    aggregated: dict[str, dict],
    output_dir: Path | None = None,
) -> None:
    """
    Export aggregated data to JSON files for the frontend.

    Section level is split into per-department files under sections/.
    All other levels produce a single JSON file each.
    """
    output_dir = output_dir or AGGREGATED_DIR
    output_dir.mkdir(parents=True, exist_ok=True)

    for level, data in aggregated.items():
        if level == "section":
            _export_sections(data, output_dir)
        else:
            path = output_dir / f"prices_{level}.json"
            with open(path, "w") as f:
                json.dump(data, f, ensure_ascii=False)
            logger.info("Exported: %s (%d entries)", path.name, len(data))


def _export_sections(
    section_data: dict[str, dict],
    output_dir: Path,
) -> None:
    """
    Split section-level data into per-department JSON files.

    Section codes start with the department code (first 2-3 chars).
    Output: sections/01.json, sections/02.json, ..., sections/2A.json, etc.
    """
    sections_dir = output_dir / "sections"
    sections_dir.mkdir(parents=True, exist_ok=True)

    by_dept: dict[str, dict] = {}
    for code, stats in section_data.items():
        # Section code format: DDDCCSSSS (dept 2-3 chars + commune + section)
        # Department is first 2 chars, except Corsica (2A, 2B) and DOM (3 chars)
        if code[:3].isdigit() and int(code[:3]) >= 970:
            dept = code[:3]  # DOM-TOM: 971, 972, etc.
        elif code[:2] in ("2A", "2B"):
            dept = code[:2]  # Corsica
        else:
            dept = code[:2]  # Mainland

        by_dept.setdefault(dept, {})[code] = stats

    for dept, dept_data in sorted(by_dept.items()):
        path = sections_dir / f"{dept}.json"
        with open(path, "w") as f:
            json.dump(dept_data, f, ensure_ascii=False)

    logger.info(
        "Exported: sections/ (%d departments, %d total sections)",
        len(by_dept),
        len(section_data),
    )