"""Fractal pattern detection and encoding for candlestick sequences."""
from __future__ import annotations

from dataclasses import dataclass
from enum import IntEnum

import numpy as np
import pandas as pd


class CandleType(IntEnum):
    DOWN = -1
    DOJI = 0
    UP = 1


@dataclass
class FractalPattern:
    sequence: tuple[CandleType, ...]
    start_idx: int
    end_idx: int
    body_ratios: tuple[float, ...]
    shadow_ratios: tuple[float, ...]

    @property
    def length(self) -> int:
        return len(self.sequence)

    @property
    def code(self) -> str:
        m = {CandleType.DOWN: "D", CandleType.DOJI: "0", CandleType.UP: "U"}
        return "".join(m[c] for c in self.sequence)

    def __hash__(self) -> int:
        return hash(self.sequence)


def classify_candle(open_: float, close: float, high: float, low: float, doji_threshold: float = 0.1) -> CandleType:
    body = abs(close - open_)
    total_range = high - low
    if total_range == 0:
        return CandleType.DOJI
    ratio = body / total_range
    if ratio < doji_threshold:
        return CandleType.DOJI
    return CandleType.UP if close > open_ else CandleType.DOWN


def encode_candles(df: pd.DataFrame, doji_threshold: float = 0.1) -> np.ndarray:
    types = np.zeros(len(df), dtype=np.int8)
    body = np.abs(df["close"].values - df["open"].values)
    total = df["high"].values - df["low"].values
    total = np.where(total == 0, 1e-10, total)
    ratio = body / total

    types[ratio < doji_threshold] = CandleType.DOJI
    bullish = (ratio >= doji_threshold) & (df["close"].values > df["open"].values)
    bearish = (ratio >= doji_threshold) & (df["close"].values <= df["open"].values)
    types[bullish] = CandleType.UP
    types[bearish] = CandleType.DOWN
    return types


def extract_body_ratios(df: pd.DataFrame) -> np.ndarray:
    body = np.abs(df["close"].values - df["open"].values)
    total = df["high"].values - df["low"].values
    total = np.where(total == 0, 1e-10, total)
    return body / total


def extract_shadow_ratios(df: pd.DataFrame) -> np.ndarray:
    o, c, h, l = df["open"].values, df["close"].values, df["high"].values, df["low"].values
    upper_shadow = h - np.maximum(o, c)
    lower_shadow = np.minimum(o, c) - l
    total = h - l
    total = np.where(total == 0, 1e-10, total)
    return (upper_shadow - lower_shadow) / total


def find_patterns(df: pd.DataFrame, min_len: int = 3, max_len: int = 6, doji_threshold: float = 0.1) -> list[FractalPattern]:
    candle_types = encode_candles(df, doji_threshold)
    body_ratios = extract_body_ratios(df)
    shadow_ratios = extract_shadow_ratios(df)
    patterns: list[FractalPattern] = []

    for length in range(min_len, max_len + 1):
        for i in range(len(df) - length):
            seq = tuple(CandleType(t) for t in candle_types[i : i + length])
            br = tuple(body_ratios[i : i + length])
            sr = tuple(shadow_ratios[i : i + length])
            patterns.append(FractalPattern(
                sequence=seq,
                start_idx=i,
                end_idx=i + length,
                body_ratios=br,
                shadow_ratios=sr,
            ))

    return patterns


def pattern_frequency(patterns: list[FractalPattern]) -> pd.DataFrame:
    from collections import Counter
    codes = [p.code for p in patterns]
    counts = Counter(codes)
    total = len(codes)
    rows = [
        {"pattern": code, "count": cnt, "freq": cnt / total, "length": len(code)}
        for code, cnt in counts.most_common()
    ]
    return pd.DataFrame(rows)


def normalize_pattern_window(df: pd.DataFrame, start: int, end: int) -> np.ndarray:
    """Normalize OHLC window to [0,1] range for comparison."""
    window = df.iloc[start:end][["open", "high", "low", "close"]].values
    mn = window.min()
    mx = window.max()
    if mx - mn == 0:
        return np.zeros_like(window)
    return (window - mn) / (mx - mn)