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



def psl(
    close: Series, open_: Series = None,
    length: Int = None, scalar: IntFloat = None, drift: Int = None,
    offset: Int = None, **kwargs: DictLike
) -> Series:
    """Psychological Line

    This indicator compares the number of the rising bars to the total number
    of bars. In other words, it is the percentage of bars that are above the
    previous bar over a given length.

    Sources:
        * [quantshare](https://www.quantshare.com/item-851-psychological-line)

    Parameters:
        close (Series): ```close``` Series
        open_ (Series): ```open``` Series
        length (int): The period. Default: ```12```
        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
    """
    # Validate
    length = v_pos_default(length, 12)
    close = v_series(close, length)

    if close is None:
        return

    scalar = v_scalar(scalar, 100)
    drift = v_drift(drift)
    offset = v_offset(offset)

    # Calculate
    if open_ is not None:
        open_ = v_series(open_)
        diff = sign(close - open_)
    else:
        diff = sign(close.diff(drift))

    diff.fillna(0, inplace=True)
    diff[diff <= 0] = 0  # Set negative values to zero

    psl = scalar * diff.rolling(length).sum() / length

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

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

    # Name and Category
    _props = f"_{length}"
    psl.name = f"PSL{_props}"
    psl.category = "momentum"

    return psl