# -*- coding: utf-8 -*-
from numpy import log, log10
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import (
    v_bool,
    v_drift,
    v_offset,
    v_pos_default,
    v_scalar,
    v_series
)
from pandas_ta.volatility import atr



def chop(
    high: Series, low: Series, close: Series,
    length: Int = None, atr_length: Int = None,
    ln: bool = None, scalar: IntFloat = None, drift: Int = None,
    offset: Int = None, **kwargs: DictLike
) -> Series:
    """Choppiness Index

    This indicator, by E.W. Dreiss, attempts to determine choppiness.

    Sources:
        * E.W. Dreiss an Australian Commodity Trader
        * [motivewave](https://www.motivewave.com/studies/choppiness_index.htm)
        * [tradingview](https://www.tradingview.com/scripts/choppinessindex/)

    Parameters:
        high (Series): ```high``` Series
        low (Series): ```low``` Series
        close (Series): ```close``` Series
        length (int): The period. Default: ```14```
        atr_length (int): ATR period. Default: ```1```
        ln (bool): Use ```ln``` instead of ```log10```. Default: ```False```
        scalar (float): Scalar. Default: ```100```
        drift (int): Difference amount. Default: ```1```
        offset (int): Post shift. Default: ```0```

    Other Parameters:
        fillna (value): ```pd.DataFrame.fillna(value)```

    Returns:
        (Series): 1 column

    Note:
        * Choppy: ```~ 100```
        * Trending: ```~ 0```
    """
    # Validate
    length = v_pos_default(length, 14)
    high = v_series(high, length + 1)
    low = v_series(low, length + 1)
    close = v_series(close, length + 1)

    if high is None or low is None or close is None:
        return

    atr_length = v_pos_default(atr_length, 1)
    scalar = v_scalar(scalar, 100)
    ln = v_bool(ln, False)
    drift = v_drift(drift)
    offset = v_offset(offset)

    # Calculate
    diff = high.rolling(length).max() - low.rolling(length).min()

    atr_ = atr(high=high, low=low, close=close, length=atr_length)
    atr_sum = atr_.rolling(length).sum()

    chop = scalar
    if ln:
        chop *= (log(atr_sum) - log(diff)) / log(length)
    else:
        chop *= (log10(atr_sum) - log10(diff)) / log10(length)

    # Offset
    if offset != 0:
        chop = chop.shift(offset)

    # Fill
    if "fillna" in kwargs:
        chop.fillna(kwargs["fillna"], inplace=True)

    # Name and Category
    chop.name = f"CHOP{'ln' if ln else ''}_{length}_{atr_length}_{scalar}"
    chop.category = "trend"

    return chop