from __future__ import annotations

from typing import Callable, Dict, List, Optional

import numpy as np
import pandas as pd

from classes.v2.data_bundle import DataBundle

# ---- Pachymetry → IOP correction (per PAPILA Table 3) ----
_PACHY_TABLE: Dict[int, int] = {
    475: +5,
    485: +4,
    495: +4,
    505: +3,
    515: +2,
    525: +1,
    535: +1,
    545: 0,
    555: -1,
    565: -1,
    575: -2,
    585: -3,
    595: -4,
    605: -4,
    615: -5,
}
_PACHY_KEYS = np.array(sorted(_PACHY_TABLE.keys()))


def _nearest_pachy_key(x: float) -> int:
    idx = int(np.argmin(np.abs(_PACHY_KEYS - float(x))))
    return int(_PACHY_KEYS[idx])


def _fit_perkins_converter(
    frames: List[pd.DataFrame], method: str
) -> Callable[[float, Optional[float]], float]:
    """
    Fit a Perkins→Pneumatic converter from pooled paired observations across all frames.
    Returns a callable: converter(perkins_value, pachymetry_value) -> float.
    Supported methods: "ratio", "ols", "lad", "multi".
    """
    combined = pd.concat(frames, ignore_index=True)
    paired = combined.dropna(subset=["Pneumatic", "Perkins"])
    pneumatic = paired["Pneumatic"].values.astype(float)
    perkins   = paired["Perkins"].values.astype(float)

    if len(paired) == 0:
        raise ValueError("No paired Pneumatic+Perkins observations found; cannot fit converter.")

    if method == "ratio":
        ratio = float((pneumatic / perkins).mean())
        def converter_ratio(p: float, pachy: Optional[float] = None) -> float:
            return p * ratio
        return converter_ratio

    elif method == "ols":
        from scipy import stats as _stats
        slope, intercept, *_ = _stats.linregress(perkins, pneumatic)
        slope, intercept = float(slope), float(intercept)
        def converter_ols(p: float, pachy: Optional[float] = None) -> float:
            return p * slope + intercept
        return converter_ols

    elif method == "lad":
        from scipy import stats as _stats
        from scipy.optimize import minimize as _minimize
        slope0, intercept0, *_ = _stats.linregress(perkins, pneumatic)
        def _lad_loss(params):
            a, b = params
            return np.abs(pneumatic - (a * perkins + b)).mean()
        res = _minimize(_lad_loss, x0=[slope0, intercept0], method="Nelder-Mead")
        slope, intercept = float(res.x[0]), float(res.x[1])
        def converter_lad(p: float, pachy: Optional[float] = None) -> float:
            return p * slope + intercept
        return converter_lad

    elif method == "multi":
        from numpy.linalg import lstsq as _lstsq
        paired_multi = combined.dropna(subset=["Pneumatic", "Perkins", "Pachymetry"])
        if len(paired_multi) == 0:
            raise ValueError("No paired Pneumatic+Perkins+Pachymetry rows; cannot fit multi method.")
        pneu  = paired_multi["Pneumatic"].values.astype(float)
        perk  = paired_multi["Perkins"].values.astype(float)
        pachy_vals = paired_multi["Pachymetry"].values.astype(float)
        X = np.column_stack([perk, pachy_vals, np.ones(len(perk))])
        coeffs, *_ = _lstsq(X, pneu, rcond=None)
        slope, pachy_coef, intercept = float(coeffs[0]), float(coeffs[1]), float(coeffs[2])
        pachy_fallback = float(pachy_vals.mean())
        def converter_multi(p: float, pachy: Optional[float] = None) -> float:
            pv = pachy if (pachy is not None and not np.isnan(pachy)) else pachy_fallback
            return p * slope + pachy_coef * pv + intercept
        return converter_multi

    else:
        raise ValueError(f"Unknown iop_corr_method: {method!r}. Choose ratio/ols/lad/multi.")


def _pick_iop(row: pd.Series, converter: Callable) -> float:
    """Prefer Pneumatic; convert Perkins to Pneumatic scale if Pneumatic is absent."""
    pneumatic = row.get("Pneumatic", np.nan)
    if not pd.isna(pneumatic):
        return float(pneumatic)
    perkins = row.get("Perkins", np.nan)
    if pd.isna(perkins):
        return np.nan
    pachy = row.get("Pachymetry", np.nan)
    return converter(float(perkins), None if pd.isna(pachy) else float(pachy))


def _correct_iop(raw_iop: float, pachy: float) -> float:
    """Return corrected IOP using nearest pachymetry bin; if pachy missing, return raw."""
    if pd.isna(raw_iop):
        return np.nan
    if pd.isna(pachy):
        return float(raw_iop)
    key = _nearest_pachy_key(float(pachy))
    return float(raw_iop) + float(_PACHY_TABLE[key])


def _apply_iop_and_drop_md(
    df: pd.DataFrame,
    converter: Callable,
    drop_raw: bool = False,
) -> pd.DataFrame:
    """Add IOP_raw/IOP_corr and drop source IOP columns + VF_MD if present (in-place safe)."""
    df["IOP_raw"] = df.apply(lambda row: _pick_iop(row, converter), axis=1)
    pachy = df.get("Pachymetry", pd.Series(np.nan, index=df.index))
    df["IOP_corr"] = [
        _correct_iop(r, p) for r, p in zip(df["IOP_raw"].values, pachy.values)
    ]
    drop_cols = [c for c in ("Pneumatic", "Perkins", "VF_MD") if c in df.columns]
    if drop_raw:
        drop_cols.append("IOP_raw")
    if drop_cols:
        df.drop(columns=drop_cols, inplace=True)
    return df


def _canonicalize_eye_column(df: pd.DataFrame) -> None:
    if "eyeID" in df.columns:
        src = "eyeID"
    else:
        src = None
        for c in df.columns:
            if "eye" in c.lower():
                src = c
                break
        if src is None:
            df["eyeID"] = "OS"
            return

    s = df[src]

    def norm(v):
        if pd.isna(v):
            return None
        x = str(v).strip().upper()
        if x in {"OS", "L", "LEFT", "0"}:
            return "OS"
        if x in {"OD", "R", "RIGHT", "1"}:
            return "OD"
        try:
            num = int(float(x))
            return "OD" if num % 2 == 1 else "OS"
        except Exception:
            return None

    mapped = s.map(norm)
    uniq = {u for u in mapped.dropna().unique().tolist()}
    if not uniq.issubset({"OS", "OD"}):
        raise ValueError(f"eyeID must be binary; found values {sorted(uniq)}")
    df["eyeID"] = mapped.fillna("OS")


def build_papila_data(
    *,
    image_dir: str,
    clinical_dir: str,
    label_col: str,
    cat_cols: List[str],
    n_splits: int = 5,
    random_seed: int = 42,
    iop_corr_method: str = "ratio",
    iop_drop_raw: bool = False,
    exclude_cols: Optional[List[str]] = None,
) -> DataBundle:
    """
    Build a DataBundle for PAPILA with dataset-specific preprocessing:
      - load OD/OS Excel sheets
      - normalize Patient ID
      - canonicalize eyeID
      - compute IOP_raw / IOP_corr, drop VF_MD
      - build feature typing & folds
    """
    _exclude = list(exclude_cols) if exclude_cols else []

    # Remove excluded cols from cat_cols too so the bundle doesn't try to encode them
    effective_cat_cols = [c for c in cat_cols if c not in _exclude]

    bundle = DataBundle(
        image_dir=image_dir,
        clinical_dir=clinical_dir,
        label_col=label_col,
        patient_col="Patient ID",
        cat_cols=effective_cat_cols,
        n_splits=n_splits,
        random_seed=random_seed,
        filename_template="RET{pid:03d}{eye}.jpg",
    )

    od = pd.read_excel(f"{clinical_dir}/patient_data_od.xlsx", header=1)
    od["eyeID"] = "OD"
    os = pd.read_excel(f"{clinical_dir}/patient_data_os.xlsx", header=1)
    os["eyeID"] = "OS"

    for frame in (od, os):
        if "Patient ID" not in frame.columns and "ID" in frame.columns:
            frame.rename(columns={"ID": "Patient ID"}, inplace=True)
        frame["Patient ID"] = frame["Patient ID"].astype(str).str.extract(r"(\d+)")[0].astype(int)
        _canonicalize_eye_column(frame)

    bundle.add_df(od, id_column="ID", exclude_cols=_exclude or None)
    bundle.add_df(os, id_column="ID", exclude_cols=_exclude or None)

    converter = _fit_perkins_converter(bundle.frames, method=iop_corr_method)
    for i in range(len(bundle.frames)):
        bundle.frames[i] = _apply_iop_and_drop_md(
            bundle.frames[i], converter=converter, drop_raw=iop_drop_raw
        )

    bundle._refresh_master_df(exclude_cols=_exclude or None)
    bundle._infer_or_validate_feature_types(exclude_cols=_exclude or None)
    bundle._compute_numeric_stats()
    bundle._build_cat_maps()
    bundle._compute_feature_dim()
    bundle._build_kfold_indices()

    return bundle