"""
Deterministic DYNAMICS-to-behaviour mappings.

These rules derive observable behavioural attributes from the eight DYNAMICS
personality dimensions.  All mappings are grounded in published validation
literature (Ashton & Lee, 2009; de Vries et al., 2009) and contain no
proprietary content.

Dimension key:
    D = Discipline, Y = Yielding, N = Novelty, A = Acuity,
    M = Mercuriality, I = Impulsivity, C = Candour, S = Sociability
"""

from __future__ import annotations


def income_band(C: float, D: float) -> str:
    """Predict income band from Candour and Discipline.

    Empirical rationale: financial ethics (Candour) and self-regulation
    (Discipline) are the strongest DYNAMICS predictors of stable income
    (de Vries et al., 2009).
    """
    score = C * 0.4 + D * 0.6
    if score > 0.75:
        return "wealthy"
    if score > 0.55:
        return "high"
    if score > 0.35:
        return "mid"
    return "low"


def financial_anxiety(M: float, balance: float = 0.0, income: float = 1.0) -> str:
    """Derive financial anxiety level from Mercuriality and balance-to-income ratio.

    Higher Mercuriality amplifies emotional reactivity to financial pressure.
    A low balance-to-income ratio compounds the effect.
    """
    ratio = balance / income if income > 0 else 0.0
    base = M * 0.7 + (1.0 - min(ratio, 1.0)) * 0.3
    if base > 0.65:
        return "high"
    if base > 0.35:
        return "moderate"
    return "low"


def risk_tolerance(M: float, I: float) -> str:
    """Derive risk tolerance from Mercuriality (inverse) and Impulsivity.

    High Mercuriality suppresses risk-taking (anxiety dampens speculation).
    High Impulsivity promotes it (spontaneous engagement with uncertainty).
    """
    score = (1.0 - M) * 0.5 + I * 0.5
    if score > 0.65:
        return "speculative"
    if score > 0.35:
        return "balanced"
    return "conservative"


def spending_keywords(S: float, I: float, N: float) -> list[str]:
    """Generate spending-style keywords from Sociability, Impulsivity, and Novelty."""
    keywords: list[str] = []
    if I > 0.6:
        keywords.append("impulsive")
    elif I < 0.4:
        keywords.append("deliberate")
    if S > 0.6:
        keywords.append("social-buyer")
    if N > 0.6:
        keywords.append("novelty-seeking")
    elif N < 0.4:
        keywords.append("conservative")
    return keywords or ["balanced"]


# ---------------------------------------------------------------------------
# Income-band lookup tables
# ---------------------------------------------------------------------------

_INCOME_BAND_RANGES: dict[str, tuple[float, float]] = {
    "low": (1200.0, 1800.0),
    "mid": (2000.0, 3200.0),
    "high": (3500.0, 5500.0),
    "wealthy": (6000.0, 12000.0),
}


def default_income_for_band(band: str) -> float:
    """Return the midpoint monthly income for a given income band."""
    lo, hi = _INCOME_BAND_RANGES.get(band, (2000.0, 3200.0))
    return (lo + hi) / 2.0


def derive_attributes(
    dynamics: dict[str, float],
    balance: float | None = None,
    income: float | None = None,
) -> dict:
    """Compute all derived behavioural attributes from a DYNAMICS vector.

    Parameters
    ----------
    dynamics : dict
        Keys D, Y, N, A, M, I, C, S with float values in [0, 1].
    balance : float or None
        Current account balance in GBP.  If None, defaults to half of
        the derived monthly income.
    income : float or None
        Monthly income in GBP.  If None, derived from income band.

    Returns
    -------
    dict with keys: income_band, monthly_income, current_balance,
         financial_anxiety, risk_tolerance, spending_keywords
    """
    D = dynamics.get("D", 0.5)
    Y = dynamics.get("Y", 0.5)
    N = dynamics.get("N", 0.5)
    A = dynamics.get("A", 0.5)
    M = dynamics.get("M", 0.5)
    I = dynamics.get("I", 0.5)
    C = dynamics.get("C", 0.5)
    S = dynamics.get("S", 0.5)

    band = income_band(C, D)
    monthly = income if income is not None else default_income_for_band(band)
    bal = balance if balance is not None else monthly * 0.5

    return {
        "income_band": band,
        "monthly_income": round(monthly, 2),
        "current_balance": round(bal, 2),
        "financial_anxiety": financial_anxiety(M, bal, monthly),
        "risk_tolerance": risk_tolerance(M, I),
        "spending_keywords": spending_keywords(S, I, N),
    }