import logging
from datetime import datetime
from pathlib import Path

import polars as pl

from transform import normalize_price

log = logging.getLogger("rightmove")


def write_parquet(properties: list[dict], path: Path, channel: str) -> None:
    """Write properties list to parquet with server-ready column names.

    channel: "buy" or "rent"
    """
    if not properties:
        log.warning("No properties to write to %s", path)
        return

    # Parse first_visible_date to datetime
    listing_dates = []
    for p in properties:
        fvd = p.get("first_visible_date", "")
        if fvd:
            try:
                dt = datetime.fromisoformat(fvd.replace("Z", "+00:00"))
                listing_dates.append(dt.replace(tzinfo=None))
            except (ValueError, TypeError):
                listing_dates.append(None)
        else:
            listing_dates.append(None)

    # Derive asking price / asking rent based on channel
    if channel == "buy":
        asking_prices = [p["price"] for p in properties]
        asking_rents = [None] * len(properties)
        listing_statuses = ["For sale"] * len(properties)
    else:
        asking_prices = [None] * len(properties)
        asking_rents = [
            normalize_price(p["price"], p["price_frequency"]) for p in properties
        ]
        listing_statuses = ["For rent"] * len(properties)

    df = pl.DataFrame(
        {
            "Bedrooms": [p["Bedrooms"] for p in properties],
            "Bathrooms": [p["Bathrooms"] for p in properties],
            "Number of bedrooms & living rooms": [
                p["Number of bedrooms & living rooms"] for p in properties
            ],
            "lon": [p["lon"] for p in properties],
            "lat": [p["lat"] for p in properties],
            "Postcode": [p["Postcode"] for p in properties],
            "Address per Property Register": [
                p["Address per Property Register"] for p in properties
            ],
            "Leasehold/Freehold": [p["Leasehold/Freehold"] for p in properties],
            "Property type": [p["Property type"] for p in properties],
            "Property sub-type": [p["Property sub-type"] for p in properties],
            "Price qualifier": [p["Price qualifier"] for p in properties],
            "Total floor area (sqm)": [p["Total floor area (sqm)"] for p in properties],
            "Listing URL": [p["Listing URL"] for p in properties],
            "Listing features": [p["Listing features"] for p in properties],
            "Listing date": listing_dates,
            "Listing status": listing_statuses,
            "Asking price": asking_prices,
            "Asking rent (monthly)": asking_rents,
        },
        schema={
            "Bedrooms": pl.Int32,
            "Bathrooms": pl.Int32,
            "Number of bedrooms & living rooms": pl.Int32,
            "lon": pl.Float64,
            "lat": pl.Float64,
            "Postcode": pl.Utf8,
            "Address per Property Register": pl.Utf8,
            "Leasehold/Freehold": pl.Utf8,
            "Property type": pl.Utf8,
            "Property sub-type": pl.Utf8,
            "Price qualifier": pl.Utf8,
            "Total floor area (sqm)": pl.Float64,
            "Listing URL": pl.Utf8,
            "Listing features": pl.List(pl.Utf8),
            "Listing date": pl.Datetime("us"),
            "Listing status": pl.Utf8,
            "Asking price": pl.Int64,
            "Asking rent (monthly)": pl.Int64,
        },
    )

    # Derive asking price per sqm for buy listings
    if channel == "buy":
        df = df.with_columns(
            (pl.col("Asking price") / pl.col("Total floor area (sqm)"))
            .round(0)
            .cast(pl.Int32, strict=False)
            .alias("Asking price per sqm"),
        )

    df.write_parquet(path)
    log.info("Wrote %d properties to %s", len(df), path)