"""One timing and point-in-time eligibility contract for every execution path."""

import numpy as np
import pandas as pd

from strategies.base import BarStrategy


def session_targets(strategy, close: pd.DataFrame, open_data: pd.DataFrame | None = None,
                    bars: dict[str, pd.DataFrame] | None = None,
                    eligible: pd.DataFrame | None = None) -> pd.DataFrame:
    if close.empty:
        return pd.DataFrame(0.0, index=close.index, columns=close.columns)
    member = (eligible.reindex_like(close).fillna(False).astype(bool) if eligible is not None
              else pd.DataFrame(True, index=close.index, columns=close.columns))
    if isinstance(strategy, BarStrategy):
        inputs = dict(bars if bars is not None else {"close": close})
        inputs["eligible"] = member & inputs["close"].notna()
        weights = strategy.generate_signals(inputs)
        known = member.shift(1, fill_value=False)
    else:
        # Price history remains observable before index admission. Membership
        # restricts cross-sectional selection and the eventual execution targets.
        inputs = close if open_data is None else open_data.reindex_like(close)
        native = getattr(strategy, "accepts_eligibility", False)
        kwargs = {"eligible": member} if native else {}
        inputs = inputs if native else inputs.where(member)
        if open_data is not None:
            nxt = inputs.index[-1] + pd.Timedelta(days=1)
            padded = pd.concat([inputs, inputs.iloc[[-1]].set_axis([nxt], axis=0)])
            if native:
                kwargs["eligible"] = pd.concat([member, member.iloc[[-1]].set_axis([nxt], axis=0)])
            weights = strategy.generate_signals(padded, **kwargs).shift(-1).reindex(close.index)
            known = member
        else:
            weights = strategy.generate_signals(inputs, **kwargs)
            known = member.shift(1, fill_value=False)
    weights = weights.reindex(index=close.index, columns=close.columns).fillna(0.0)
    weights = weights.where(known & member, 0.0)
    arr = weights.to_numpy()
    if (not np.isfinite(arr).all() or (arr < -1e-10).any()
            or (weights.sum(axis=1) > 1 + 1e-8).any()):
        raise ValueError("Strategy produced non-finite, short or unfunded targets")
    return weights.clip(lower=0.0)