"""
Sheet 8: Consolidated macro dashboard.

Aggregate time series, key ratios, stress flags from workbook and Monte Carlo.
"""

from typing import Any, Optional

import pandas as pd

from fqbm.state import FQBMState
from fqbm.sheets.monte_carlo import run_n_simulations, ShockSpec


def dashboard_aggregate(
    state: FQBMState,
    mc_runs: int = 0,
    shock_spec: Optional[ShockSpec] = None,
) -> dict[str, Any]:
    """
    Build dashboard: state snapshot, key ratios, optional MC summary.
    """
    rwa = state.Loans
    cr = (state.E_b / rwa) if rwa > 0 else 0
    ratios = {
        "capital_ratio": cr,
        "reserves_to_deposits": (state.R / state.Deposits) if state.Deposits else 0,
        "loans_to_deposits": (state.Loans / state.Deposits) if state.Deposits else 0,
    }
    snapshot = {
        "B": state.B, "R": state.R, "C": state.C,
        "Loans": state.Loans, "Deposits": state.Deposits,
        "E_cb": state.E_cb, "E_b": state.E_b,
        "S": state.S, "Spread": state.Spread, "O": state.O,
        "L_cb": getattr(state, "L_cb", 0),
    }
    out = {"state": snapshot, "ratios": ratios}
    # Part VII: shadow banking leverage ratio (bank)
    try:
        from fqbm.sheets.shadow_banking import leverage_ratio
        total_assets_bank = state.Loans
        out["ratios"]["leverage_ratio_bank"] = leverage_ratio(total_assets_bank, state.E_b)
    except Exception:
        pass

    if mc_runs > 0:
        df = run_n_simulations(mc_runs, state, shock_spec)
        out["mc_summary"] = {
            "p_insolvency": df["insolvent"].mean(),
            "p_reserve_breach": df["reserve_breach"].mean(),
            "inflation_mean": df["inflation"].mean(),
            "inflation_std": df["inflation"].std(),
            "p_debt_unsustainable": 1 - df["debt_sustainable"].mean(),
        }
    return out