from __future__ import annotations

from typing import Any

import numpy as np
import pandas as pd

from config.settings import BASELINE_HIT_PROB, BASELINE_HR_PROB


def _safe_mean(series: pd.Series) -> float | None:
    numeric = pd.to_numeric(series, errors="coerce").dropna()
    if numeric.empty:
        return None
    return float(numeric.mean())


def _safe_rate(series: pd.Series) -> float | None:
    numeric = pd.to_numeric(series, errors="coerce").dropna()
    if numeric.empty:
        return None
    return float(numeric.mean())


def _percentile(series: pd.Series, q: float) -> float | None:
    numeric = pd.to_numeric(series, errors="coerce").dropna()
    if numeric.empty:
        return None
    return float(numeric.quantile(q))

def _classify_batted_ball_direction_from_spray_angle(
    spray_angle: Any,
    stand: str,
) -> str:
    """
    First-pass direction classifier.

    Assumptions:
    - negative spray angle tends to be left-field side
    - positive spray angle tends to be right-field side
    - for RHB, pull is left-field side
    - for LHB, pull is right-field side

    Returns one of:
    - "pull"
    - "center"
    - "oppo"
    - "unknown"
    """
    try:
        angle = float(spray_angle)
    except Exception:
        return "unknown"

    stand = str(stand or "").strip().upper()
    if stand not in {"R", "L"}:
        return "unknown"

    if -15 <= angle <= 15:
        return "center"

    if stand == "R":
        if angle < -15:
            return "pull"
        if angle > 15:
            return "oppo"

    if stand == "L":
        if angle > 15:
            return "pull"
        if angle < -15:
            return "oppo"

    return "unknown"


def _build_direction_series(df: pd.DataFrame) -> pd.Series:
    """
    Priority:
    1. use existing hit_direction if present
    2. infer from spray_angle + stand if present
    3. return unknown
    """
    if "hit_direction" in df.columns:
        direction_series = df["hit_direction"].astype(str).str.strip().str.lower()
        direction_series = direction_series.replace(
            {
                "pulled": "pull",
                "pull-side": "pull",
                "opposite": "oppo",
                "opposite field": "oppo",
                "center field": "center",
                "middle": "center",
            }
        )
        return direction_series

    if "spray_angle" in df.columns and "stand" in df.columns:
        return df.apply(
            lambda row: _classify_batted_ball_direction_from_spray_angle(
                row.get("spray_angle"),
                row.get("stand"),
            ),
            axis=1,
        )

    return pd.Series(["unknown"] * len(df), index=df.index)


def _build_barrel_mask(
    launch_speed: pd.Series,
    launch_angle: pd.Series,
) -> pd.Series:
    """
    First-pass barrel approximation.
    """
    valid = pd.DataFrame(
        {
            "launch_speed": pd.to_numeric(launch_speed, errors="coerce"),
            "launch_angle": pd.to_numeric(launch_angle, errors="coerce"),
        }
    ).dropna()

    mask = pd.Series(False, index=launch_speed.index)

    if valid.empty:
        return mask

    barrel_mask_valid = (
        ((valid["launch_speed"] >= 98) & (valid["launch_angle"].between(26, 30)))
        | ((valid["launch_speed"] >= 99) & (valid["launch_angle"].between(25, 31)))
        | ((valid["launch_speed"] >= 100) & (valid["launch_angle"].between(23, 33)))
        | ((valid["launch_speed"] >= 102) & (valid["launch_angle"].between(20, 35)))
    )

    mask.loc[valid.index] = barrel_mask_valid
    return mask

def build_batter_feature_row(statcast_df: pd.DataFrame, player_name: str) -> dict[str, Any]:
    _EMPTY_SHAPE = {
        "gb_rate": 0.0,
        "ld_rate": 0.0,
        "fb_rate": 0.0,
        "popup_rate": 0.0,
        "la_sweet_spot_rate": 0.0,
        "la_optimal_hr_rate": 0.0,
    }

    if statcast_df.empty or "player_name" not in statcast_df.columns:
        return {
            "player_name": player_name,
            "ev90": None,
            "avg_launch_angle": None,
            "barrel_rate": None,
            "hard_hit_rate": None,
            "xwoba": None,
            "pull_rate": None,
            "air_ball_rate": None,
            "pull_air_rate": None,
            "pulled_hard_air_rate": None,
            "pulled_barrel_rate": None,
            "plate_appearances": 0,
            "batter_stand": "R",
            **_EMPTY_SHAPE,
        }

    df = statcast_df[statcast_df["player_name"].astype(str) == str(player_name)].copy()
    if df.empty:
        return {
            "player_name": player_name,
            "ev90": None,
            "avg_launch_angle": None,
            "barrel_rate": None,
            "hard_hit_rate": None,
            "xwoba": None,
            "pull_rate": None,
            "air_ball_rate": None,
            "pull_air_rate": None,
            "pulled_hard_air_rate": None,
            "pulled_barrel_rate": None,
            "plate_appearances": 0,
            "batter_stand": "R",
            **_EMPTY_SHAPE,
        }

    launch_speed = pd.to_numeric(df.get("launch_speed"), errors="coerce")
    launch_angle = pd.to_numeric(df.get("launch_angle"), errors="coerce")
    estimated_woba = pd.to_numeric(
        df.get("xwoba") if "xwoba" in df.columns else df.get("estimated_woba_using_speedangle"),
        errors="coerce",
    )

    ev90 = _percentile(launch_speed, 0.90)
    avg_launch_angle = _safe_mean(launch_angle)
    xwoba = _safe_mean(estimated_woba)

    hard_hit_rate = None
    if not launch_speed.dropna().empty:
        hard_hit_rate = float((launch_speed >= 95).mean())

    barrel_rate = None
    barrel_mask_series = _build_barrel_mask(launch_speed, launch_angle)
    if len(barrel_mask_series) == len(df):
        barrel_rate = float(barrel_mask_series.mean())

    # ----------------------------
    # Direction + HR-shape metrics
    # ----------------------------

    # Step 1: build spray_angle_series — prefer spray_angle column; fall back to hc_x/hc_y
    _sa_raw = pd.to_numeric(df.get("spray_angle", pd.Series(dtype=float)), errors="coerce")
    if _sa_raw.notna().any():
        spray_angle_series = _sa_raw
    elif "hc_x" in df.columns and "hc_y" in df.columns:
        _hc_x = pd.to_numeric(df["hc_x"], errors="coerce")
        _hc_y = pd.to_numeric(df["hc_y"], errors="coerce")
        spray_angle_series = pd.Series(
            np.degrees(np.arctan2(125.42 - _hc_y, _hc_x - 125.42)),
            index=df.index,
        )
    else:
        spray_angle_series = None

    # Step 2: validate direction availability
    valid_direction_mask = spray_angle_series.notna() if spray_angle_series is not None else None

    # Step 3: air-ball and hard-hit masks (independent of direction)
    air_ball_mask = pd.Series(False, index=df.index)
    valid_la = launch_angle.dropna()
    if not valid_la.empty:
        air_ball_mask = launch_angle >= 10

    hard_hit_mask = pd.Series(False, index=df.index)
    valid_ev = launch_speed.dropna()
    if not valid_ev.empty:
        hard_hit_mask = launch_speed >= 95

    # Step 4: direction-dependent masks — only created when valid direction data exists
    if valid_direction_mask is not None and valid_direction_mask.any():
        pull_mask = spray_angle_series < -15
        pulled_air_mask = pull_mask & air_ball_mask
        pulled_hard_air_mask = pull_mask & air_ball_mask & hard_hit_mask
        pulled_barrel_mask = pull_mask & barrel_mask_series
    else:
        pull_mask = None
        pulled_air_mask = None
        pulled_hard_air_mask = None
        pulled_barrel_mask = None

    # Step 5: compute rates — None when direction data is absent; never 0.0 for unknown
    pull_rate = None
    if pull_mask is not None and valid_direction_mask is not None and valid_direction_mask.any():
        pull_rate = float(pull_mask[valid_direction_mask].mean())

    air_ball_rate = None
    if len(air_ball_mask) > 0:
        air_ball_rate = float(air_ball_mask.mean())

    pull_air_rate = float(pulled_air_mask.mean()) if pulled_air_mask is not None else None
    pulled_hard_air_rate = float(pulled_hard_air_mask.mean()) if pulled_hard_air_mask is not None else None
    pulled_barrel_rate = float(pulled_barrel_mask.mean()) if pulled_barrel_mask is not None else None

    # E1a: Extract batter handedness
    batter_stand = "R"
    stand_col = "batter_stand" if "batter_stand" in df.columns else "stand"
    if stand_col in df.columns:
        mode_vals = df[stand_col].dropna().mode()
        if not mode_vals.empty:
            batter_stand = str(mode_vals.iloc[0]).strip()

    # ----------------------------
    # 12A: Batted-ball shape taxonomy
    # ----------------------------
    gb_rate = 0.0
    ld_rate = 0.0
    fb_rate = 0.0
    popup_rate = 0.0
    la_sweet_spot_rate = 0.0
    la_optimal_hr_rate = 0.0

    if "bb_type" in df.columns:
        contact_df = df[df["bb_type"].notna() & (df["bb_type"].astype(str).str.strip() != "")]
        if len(contact_df) >= 5:
            bb = contact_df["bb_type"].astype(str).str.strip().str.lower()
            n = float(len(bb))
            gb_rate = float((bb == "ground_ball").sum()) / n
            ld_rate = float((bb == "line_drive").sum()) / n
            fb_rate = float((bb == "fly_ball").sum()) / n
            popup_rate = float((bb == "popup").sum()) / n

    la_valid = launch_angle.dropna()
    if len(la_valid) >= 5:
        la_sweet_spot_rate = float(((la_valid >= 8) & (la_valid <= 32)).mean())
        la_optimal_hr_rate = float(((la_valid >= 25) & (la_valid <= 35)).mean())

    return {
        "player_name": player_name,
        "ev90": ev90,
        "avg_launch_angle": avg_launch_angle,
        "barrel_rate": barrel_rate,
        "hard_hit_rate": hard_hit_rate,
        "xwoba": xwoba,
        "pull_rate": pull_rate,
        "air_ball_rate": air_ball_rate,
        "pull_air_rate": pull_air_rate,
        "pulled_hard_air_rate": pulled_hard_air_rate,
        "pulled_barrel_rate": pulled_barrel_rate,
        "plate_appearances": int(len(df)),
        "batter_stand": batter_stand,
        "gb_rate": gb_rate,
        "ld_rate": ld_rate,
        "fb_rate": fb_rate,
        "popup_rate": popup_rate,
        "la_sweet_spot_rate": la_sweet_spot_rate,
        "la_optimal_hr_rate": la_optimal_hr_rate,
    }


def compute_batter_baseline(feature_row: dict[str, Any]) -> dict[str, float]:
    """
    EV90 is the primary quality signal.
    Outputs baseline probabilities before pitcher/context adjustments.
    """
    ev90 = feature_row.get("ev90")
    avg_launch_angle = feature_row.get("avg_launch_angle")
    barrel_rate = feature_row.get("barrel_rate")
    hard_hit_rate = feature_row.get("hard_hit_rate")
    xwoba = feature_row.get("xwoba")

    hit_prob = BASELINE_HIT_PROB   # 0.24 — empirical per-PA hit rate
    hr_prob = BASELINE_HR_PROB     # 0.036 — empirical MLB 2024 per-PA HR rate
    tb2p_prob = 0.17

    if ev90 is not None:
        hit_prob += max(-0.04, min(0.07, (float(ev90) - 100.0) * 0.004))
        hr_prob += max(-0.02, min(0.05, (float(ev90) - 101.0) * 0.0035))
        tb2p_prob += max(-0.03, min(0.06, (float(ev90) - 100.0) * 0.003))

    if hard_hit_rate is not None:
        hit_prob += max(-0.02, min(0.04, (float(hard_hit_rate) - 0.40) * 0.15))
        hr_prob += max(-0.015, min(0.035, (float(hard_hit_rate) - 0.40) * 0.12))
        tb2p_prob += max(-0.02, min(0.04, (float(hard_hit_rate) - 0.40) * 0.13))

    if barrel_rate is not None:
        hr_prob += max(-0.01, min(0.05, (float(barrel_rate) - 0.07) * 0.60))
        tb2p_prob += max(-0.015, min(0.04, (float(barrel_rate) - 0.07) * 0.45))

    if xwoba is not None:
        hit_prob += max(-0.03, min(0.05, (float(xwoba) - 0.320) * 0.45))
        tb2p_prob += max(-0.02, min(0.04, (float(xwoba) - 0.320) * 0.30))

    if avg_launch_angle is not None:
        la = float(avg_launch_angle)
        if 10 <= la <= 22:
            hit_prob += 0.01
        if 16 <= la <= 28:
            hr_prob += 0.008

    # E2: Handedness-aware baseline (LHB general advantage)
    stand = feature_row.get("batter_stand", "R")
    if stand == "L":
        hr_prob = min(hr_prob + 0.004, 0.22)
        hit_prob = min(hit_prob + 0.003, 0.50)

    hit_prob = max(0.05, min(0.50, hit_prob))
    hr_prob = max(0.005, min(0.22, hr_prob))
    tb2p_prob = max(0.03, min(0.42, tb2p_prob))

    return {
        "hit_prob_base": hit_prob,
        "hr_prob_base": hr_prob,
        "tb2p_prob_base": tb2p_prob,
    }