"""BigQuery functions for computing total solvency capital requirements.

This module provides functionality to calculate the total solvency capital requirement (SCR),
which is the sum of junior and senior SCR, for active insurance policies at specific points
in time and over time series using BigQuery.
"""

from typing import TYPE_CHECKING

import pandas as pd

if TYPE_CHECKING:
    from ensuro_analytics.download.bigquery import BigQueryInterface

REQUIRED_COLUMNS = ["sr_scr", "jr_scr", "expired_on", "start", "expiration"]


def current_value(bq: "BigQueryInterface", **kwargs) -> float:
    """Compute the current value of total solvency capital.

    Args:
        bq: BigQueryInterface instance for executing queries.
        **kwargs: Additional keyword arguments (unused).

    Returns:
        The sum of junior and senior solvency capital for currently active policies.
    """
    # Build base WHERE clause
    where_conditions = [
        "start <= CURRENT_TIMESTAMP()",
        "(expired_on IS NULL OR expired_on > CURRENT_TIMESTAMP())",
    ]
    where_clause = "WHERE " + " AND ".join(where_conditions)

    # Apply filters
    where_clause = bq.generate_filter_clause(where_clause)

    query = f"""
    SELECT COALESCE(SUM(jr_scr) + SUM(sr_scr), 0) as total_scr
    FROM {bq.get_table_name()}
    {where_clause}
    """
    result = bq.execute_query(query)
    return float(result.iloc[0]["total_scr"])


def at_t(bq: "BigQueryInterface", date: pd.Timestamp, **kwargs) -> float:
    """Compute the total solvency capital at a specific point in time.

    Args:
        bq: BigQueryInterface instance for executing queries.
        date: The timestamp at which to compute the total capital.
        **kwargs: Additional keyword arguments (unused).

    Returns:
        The sum of junior and senior solvency capital for policies active at the given date.
    """
    # Build base WHERE clause
    where_conditions = [
        f"start <= '{bq._format_timestamp(date)}'",
        f"(expired_on IS NULL OR expired_on > '{bq._format_timestamp(date)}')",
    ]
    where_clause = "WHERE " + " AND ".join(where_conditions)

    # Apply filters
    where_clause = bq.generate_filter_clause(where_clause)

    query = f"""
    SELECT COALESCE(SUM(jr_scr) + SUM(sr_scr), 0) as total_scr
    FROM {bq.get_table_name()}
    {where_clause}
    """
    result = bq.execute_query(query)
    return float(result.iloc[0]["total_scr"])


def time_series(
    bq: "BigQueryInterface",
    freq: str = "1W",
    end_date: pd.Timestamp | None = None,
    start_date: pd.Timestamp | None = None,
    period_column: str = "start",
    **kwargs,
) -> pd.Series:
    """Compute a time series of total solvency capital values.

    Args:
        bq: BigQueryInterface instance for executing queries.
        freq: Frequency of the time series. Defaults to "1W" (weekly).
        end_date: Maximum date to include. Defaults to today.
        start_date: Minimum date to include. Defaults to 90 days before today.
        period_column: Column to use for period grouping.
        **kwargs: Additional keyword arguments (unused).

    Returns:
        A Series containing total solvency capital values indexed by date.
    """
    # Set default dates if not provided
    if end_date is None:
        end_date = pd.Timestamp.now()
    if start_date is None:
        start_date = end_date - pd.Timedelta(days=90)

    date_series_cte = bq._generate_date_series_cte(start_date, end_date, freq)

    # Build join conditions
    join_conditions = [
        "p.start <= DATETIME(d.period_date)",
        "(p.expired_on IS NULL OR p.expired_on > DATETIME(d.period_date))",
    ]

    # Apply filters to join
    filter_clause = bq.generate_filter_clause("")
    if filter_clause:
        filter_clause = filter_clause.replace("WHERE ", "")
        from_clause = f"""
        FROM date_series d
        LEFT JOIN {bq.get_table_name()} p
            ON {' AND '.join(join_conditions)}
            AND {filter_clause}
        """
    else:
        from_clause = f"""
        FROM date_series d
        LEFT JOIN {bq.get_table_name()} p
            ON {' AND '.join(join_conditions)}
        """

    query = f"""
    WITH {date_series_cte}
    SELECT
        d.period_date,
        COALESCE(SUM(p.jr_scr) + SUM(p.sr_scr), 0) as scr
    {from_clause}
    GROUP BY d.period_date
    ORDER BY d.period_date
    """

    result = bq.execute_query(query)
    return pd.Series(result["scr"].values, index=pd.to_datetime(result["period_date"]), name="scr")
