nadlan-mcp/nadlan_mcp/govmap/statistics.py

"""
Statistical calculation functions for deal data.

This module provides pure mathematical functions for analyzing real estate deal data.
"""

from collections import Counter
from typing import List
import logging
from datetime import date

from .models import Deal, DealStatistics

logger = logging.getLogger(__name__)


def calculate_deal_statistics(deals: List[Deal]) -> DealStatistics:
    """
    Calculate statistical aggregations on deal data.

    Args:
        deals: List of Deal model instances

    Returns:
        DealStatistics model with comprehensive metrics

    Raises:
        ValueError: If deals is not a valid list
    """
    if not isinstance(deals, list):
        raise ValueError("deals must be a list")

    if not deals:
        return DealStatistics(
            total_deals=0,
            price_statistics={},
            area_statistics={},
            price_per_sqm_statistics={},
            property_type_distribution={},
            date_range=None,
        )

    # Extract numeric values
    prices = []
    areas = []
    price_per_sqm_values = []
    property_types = []
    deal_dates = []

    for deal in deals:
        # Prices
        if deal.deal_amount and deal.deal_amount > 0:
            prices.append(deal.deal_amount)

        # Areas
        if deal.asset_area and deal.asset_area > 0:
            areas.append(deal.asset_area)

        # Price per sqm (use computed field)
        if deal.price_per_sqm:
            price_per_sqm_values.append(deal.price_per_sqm)

        # Property types
        if deal.property_type_description:
            property_types.append(deal.property_type_description)

        # Deal dates
        if deal.deal_date:
            deal_dates.append(deal.deal_date)

    # Calculate statistics
    price_stats = {}
    area_stats = {}
    price_per_sqm_stats = {}

    # Price statistics
    if prices:
        sorted_prices = sorted(prices)
        price_stats = {
            "mean": round(sum(prices) / len(prices), 2),
            "median": (sorted_prices[len(sorted_prices) // 2] + sorted_prices[(len(sorted_prices) - 1) // 2]) / 2,
            "min": min(prices),
            "max": max(prices),
            "p25": sorted_prices[len(sorted_prices) // 4],
            "p75": sorted_prices[(3 * len(sorted_prices)) // 4],
            "std_dev": round(calculate_std_dev(prices), 2) if len(prices) > 1 else 0,
            "total": sum(prices),
        }

    # Area statistics
    if areas:
        sorted_areas = sorted(areas)
        area_stats = {
            "mean": round(sum(areas) / len(areas), 2),
            "median": sorted_areas[len(sorted_areas) // 2],
            "min": min(areas),
            "max": max(areas),
            "p25": sorted_areas[len(sorted_areas) // 4],
            "p75": sorted_areas[(3 * len(sorted_areas)) // 4],
        }

    # Price per sqm statistics
    if price_per_sqm_values:
        sorted_pps = sorted(price_per_sqm_values)
        price_per_sqm_stats = {
            "mean": round(sum(price_per_sqm_values) / len(price_per_sqm_values), 2),
            "median": round(sorted_pps[len(sorted_pps) // 2], 2),
            "min": round(min(price_per_sqm_values), 2),
            "max": round(max(price_per_sqm_values), 2),
            "p25": round(sorted_pps[len(sorted_pps) // 4], 2),
            "p75": round(sorted_pps[(3 * len(sorted_pps)) // 4], 2),
        }

    # Property type distribution
    property_type_dist = {}
    if property_types:
        type_counts = Counter(property_types)
        property_type_dist = dict(sorted(type_counts.items()))

    # Date range
    date_range_dict = None
    if deal_dates:
        try:
            # Convert dates to ISO strings for consistent formatting
            from datetime import date as date_type
            parsed_dates = []
            for d in deal_dates:
                try:
                    # Handle date objects (from Pydantic models)
                    if isinstance(d, date_type):
                        parsed_dates.append(d.isoformat())
                    else:
                        # Handle string dates
                        date_str = str(d)
                        # Handle ISO format with timezone (e.g., "2025-01-01T00:00:00.000Z")
                        if 'T' in date_str:
                            date_str = date_str.split('T')[0]
                        parsed_dates.append(date_str)
                except (ValueError, TypeError):
                    logger.warning(f"Invalid date format: {d}")
                    continue

            if parsed_dates:
                sorted_dates = sorted(parsed_dates)
                date_range_dict = {
                    "earliest": sorted_dates[0],
                    "latest": sorted_dates[-1],
                }
        except (ValueError, TypeError):
            logger.warning("Invalid date format in date range calculation")
            pass

    return DealStatistics(
        total_deals=len(deals),
        price_statistics=price_stats,
        area_statistics=area_stats,
        price_per_sqm_statistics=price_per_sqm_stats,
        property_type_distribution=property_type_dist,
        date_range=date_range_dict,
    )


def calculate_std_dev(values: List[float]) -> float:
    """
    Calculate standard deviation of a list of values.

    Args:
        values: List of numeric values

    Returns:
        Standard deviation
    """
    if len(values) < 2:
        return 0.0
    mean = sum(values) / len(values)
    variance = sum((x - mean) ** 2 for x in values) / (len(values) - 1)
    return variance**0.5