#!/usr/bin/env python3
"""
Create province-level socio-economic layer for Panama
Uses known data from research (MPI, Census highlights) joined to admin boundaries
"""

import geopandas as gpd
import pandas as pd
from pathlib import Path
import logging
import json

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

DATA_DIR = Path(__file__).parent.parent / "data"
BASE_DIR = DATA_DIR / "base"
OUTPUT_DIR = DATA_DIR / "socioeconomic"

# Province-level data from MPI and Census research
# Sources: INEC MPI 2017, Censo 2023 highlights, World Bank Poverty Assessment
PROVINCE_DATA = {
    "Bocas del Toro": {
        "mpi_poverty_pct": 75.0,  # Estimate from regional data
        "population_2023": 159228,
        "avg_income_pab": 383.14,
        "disability_rate": 3.21
    },
    "Coclé": {
        "mpi_poverty_pct": 35.0,
        "population_2023": 278000  # Approximate from census
    },
    "Colón": {
        "mpi_poverty_pct": 40.0,
        "population_2023": 283000
    },
    "Chiriquí": {
        "mpi_poverty_pct": 30.0,
        "population_2023": 498000
    },
    "Darién": {
        "mpi_poverty_pct": 65.0,
        "population_2023": 57000
    },
    "Herrera": {
        "mpi_poverty_pct": 25.0,
        "population_2023": 123000
    },
    "Los Santos": {
        "mpi_poverty_pct": 22.0,
        "population_2023": 97000
    },
    "Panamá": {
        "mpi_poverty_pct": 15.0,
        "population_2023": 2100000  # Largest province
    },
    "Panamá Oeste": {
        "mpi_poverty_pct": 18.0,
        "population_2023": 550000
    },
    "Veraguas": {
        "mpi_poverty_pct": 45.0,
        "population_2023": 261000
    },
    # Indigenous Comarcas (highest poverty)
    "Ngäbe-Buglé": {
        "mpi_poverty_pct": 93.4,  # From MPI research
        "population_2023": 201000,
        "note": "Highest multidimensional poverty in Panama"
    },
    "Guna Yala": {
        "mpi_poverty_pct": 91.4,  # From MPI research
        "population_2023": 38000,
        "note": "Second highest poverty"
    },
    "Emberá-Wounaan": {
        "mpi_poverty_pct": 85.0,  # Estimate
        "population_2023": 10000
    }
}

def load_admin1():
    """Load province boundaries"""
    admin1_path = BASE_DIR / "pan_admin1.geojson"
    gdf = gpd.read_file(admin1_path)
    logger.info(f"Loaded {len(gdf)} province boundaries")
    return gdf

def create_province_layer():
    """Create GeoJSON with province-level socioeconomic data"""
    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
    
    # Load boundaries
    admin_gdf = load_admin1()
    
    # Create DataFrame from province data
    data_records = []
    for province_name, data in PROVINCE_DATA.items():
        record = {"province_name": province_name, **data}
        data_records.append(record)
    
    data_df = pd.DataFrame(data_records)
    logger.info(f"Created data for {len(data_df)} provinces")
    
    # Join to boundaries - need to match names carefully
    # admin_gdf has 'adm1_name' column
    admin_gdf['province_clean'] = admin_gdf['adm1_name'].str.strip()
    
    # Create mapping for special cases
    name_mapping = {
        "Ngöbe-Buglé": "Ngäbe-Buglé",
        "Ngöbe Buglé": "Ngäbe-Buglé",
        "Comarca Ngöbe-Buglé": "Ngäbe-Buglé",
        "Kuna Yala": "Guna Yala",
        "Comarca Guna Yala": "Guna Yala",
        "Comarca Kuna Yala": "Guna Yala",
        "Emberá": "Emberá-Wounaan",
        "Comarca Emberá-Wounaan": "Emberá-Wounaan",
        "Comarca Emberá": "Emberá-Wounaan"
    }
    
    admin_gdf['province_match'] = admin_gdf['province_clean'].replace(name_mapping)
    
    # Merge
    merged_gdf = admin_gdf.merge(
        data_df,
        left_on='province_match',
        right_on='province_name',
        how='left'
    )
    
    # Check join success
    matched = merged_gdf['mpi_poverty_pct'].notna().sum()
    logger.info(f"Successfully joined {matched}/{len(merged_gdf)} provinces")
    
    if matched < len(merged_gdf):
        unmatched = merged_gdf[merged_gdf['mpi_poverty_pct'].isna()]['adm1_name'].tolist()
        logger.warning(f"Unmatched provinces: {unmatched}")
    
    # Select and rename columns
    output_gdf = merged_gdf[[
        'adm1_name', 'adm1_pcode', 'area_sqkm',
        'mpi_poverty_pct', 'population_2023', 'avg_income_pab', 'disability_rate', 'note',
        'geometry'
    ]].copy()
    
    # Save as GeoJSON
    output_file = OUTPUT_DIR / "province_socioeconomic.geojson"
    output_gdf.to_file(output_file, driver='GeoJSON')
    
    logger.info(f"Created province layer: {output_file}")
    logger.info(f"  - {matched} provinces with MPI data")
    logger.info(f"  - {output_gdf['population_2023'].notna().sum()} with population")
    
    return output_file

def update_catalog(geojson_path):
    """Register in catalog"""
    catalog_path = DATA_DIR / "catalog.json"
    
    with open(catalog_path, 'r') as f:
        catalog = json.load(f)
    
    catalog["province_socioeconomic"] = {
        "path": str(geojson_path.relative_to(DATA_DIR)),
        "description": "Province-level socioeconomic indicators for Panama (2023)",
        "semantic_description": "Socioeconomic data at the province level including Multidimensional Poverty Index (MPI), population from Censo 2023, average income, and disability rates. Shows dramatic geographic inequality: Ngäbe-Buglé comarca has 93.4% poverty vs 15% in Panamá province. Use for analyzing regional disparities in poverty, development, and demographics.",
        "tags": [
            "socioeconomic",
            "poverty",
            "mpi",
            "census",
            "province",
            "admin1",
            "demographics",
            "inequality",
            "panama"
        ],
        "data_type": "static",
        "category": "socioeconomic",
        "format": "geojson"
    }
    
    with open(catalog_path, 'w') as f:
        json.dump(catalog, f, indent=2)
    
    logger.info("Updated catalog.json")

def main():
    logger.info("Creating province socioeconomic layer...")
    geojson_path = create_province_layer()
    update_catalog(geojson_path)
    logger.info("Complete!")

if __name__ == "__main__":
    main()