# -*- coding: utf-8 -*-
from pandas import DataFrame
from ..utils import get_drift, get_offset, verify_series

def true_range(high, low, close, drift=None, offset=None, **kwargs):
    """Indicator: True Range"""
    # Validate arguments
    high = verify_series(high)
    low = verify_series(low)
    close = verify_series(close)
    drift = get_drift(drift)
    offset = get_offset(offset)

    # Calculate Result
    prev_close = close.shift(drift)
    ranges = [high - low, high - prev_close, low - prev_close]
    true_range = DataFrame(ranges).T
    true_range = true_range.abs().max(axis=1)

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

    # Handle fills
    if 'fillna' in kwargs:
        true_range.fillna(kwargs['fillna'], inplace=True)
    if 'fill_method' in kwargs:
        true_range.fillna(method=kwargs['fill_method'], inplace=True)

    # Name and Categorize it
    true_range.name = f"TRUERANGE_{drift}"
    true_range.category = 'volatility'

    return true_range



true_range.__doc__ = \
"""True Range

An method to expand a classical range (high minus low) to include
possible gap scenarios.

Sources:
    https://www.macroption.com/true-range/

Calculation:
    Default Inputs:
        drift=1
    ABS = Absolute Value
    prev_close = close.shift(drift)
    TRUE_RANGE = ABS([high - low, high - prev_close, low - prev_close]) 

Args:
    high (pd.Series): Series of 'high's
    low (pd.Series): Series of 'low's
    close (pd.Series): Series of 'close's
    drift (int): The shift period.   Default: 1
    offset (int): How many periods to offset the result.  Default: 0

Kwargs:
    fillna (value, optional): pd.DataFrame.fillna(value)
    fill_method (value, optional): Type of fill method

Returns:
    pd.Series: New feature
"""