"""TA Overlap View"""
__docformat__ = "numpy"

import os
from typing import List

import matplotlib.pyplot as plt
import mplfinance as mpf
import pandas as pd
from pandas.plotting import register_matplotlib_converters

import gamestonk_terminal.feature_flags as gtff
from gamestonk_terminal.common.technical_analysis import overlap_model
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal.helper_funcs import export_data, plot_autoscale

register_matplotlib_converters()


def view_ma(
    ma_type: str,
    s_ticker: str,
    s_interval: str,
    df_stock: pd.DataFrame,
    length: List[int],
    offset: int,
    export: str = "",
) -> pd.DataFrame:
    """Plots MA technical indicator

    Parameters
    ----------
    ma_type: str
        Type of moving average.  Either "EMA" "ZLMA" or "SMA"
    s_ticker : str
        Ticker
    s_interval : str
        Interval of data
    df_stock : pd.DataFrame
        Dataframe of prices
    length : List[int]
        Length of EMA window
    export : str
        Format to export data
    """
    if s_interval == "1440min":
        price_df = pd.DataFrame(
            df_stock["Adj Close"].values, columns=["Price"], index=df_stock.index
        )
    else:
        price_df = pd.DataFrame(
            df_stock["Close"].values, columns=["Price"], index=df_stock.index
        )

    l_legend = [s_ticker]
    for win in length:
        if ma_type == "EMA":
            df_ta = overlap_model.ema(s_interval, df_stock, win, offset)
            l_legend.append(f"EMA {win}")
        elif ma_type == "SMA":
            df_ta = overlap_model.sma(s_interval, df_stock, win, offset)
            l_legend.append(f"SMA {win}")
        elif ma_type == "WMA":
            df_ta = overlap_model.wma(s_interval, df_stock, win, offset)
            l_legend.append(f"WMA {win}")
        elif ma_type == "HMA":
            df_ta = overlap_model.hma(s_interval, df_stock, win, offset)
            l_legend.append(f"HMA {win}")
        elif ma_type == "ZLMA":
            df_ta = overlap_model.zlma(s_interval, df_stock, win, offset)
            l_legend.append(f"ZLMA {win}")

        price_df = price_df.join(df_ta)

    fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
    ax.set_title(f"{s_ticker} {ma_type.upper()}")

    ax.plot(price_df.index, price_df["Price"], lw=3, c="k")

    ax.set_xlabel("Time")
    ax.set_xlim([price_df.index[0], price_df.index[-1]])
    ax.set_ylabel(f"{s_ticker} Price")

    for idx in range(1, price_df.shape[1]):
        ax.plot(price_df.iloc[:, idx])

    ax.legend(l_legend)
    ax.grid(b=True, which="major", color="#666666", linestyle="-")

    if gtff.USE_ION:
        plt.ion()

    plt.gcf().autofmt_xdate()
    fig.tight_layout(pad=1)

    plt.show()
    print("")

    export_data(
        export,
        os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
        f"{ma_type.lower()}{'_'.join([str(win) for win in length])}",
        price_df,
    )


def view_vwap(
    s_ticker: str, s_interval: str, df_stock: pd.DataFrame, offset: int, export: str
):
    """Plots EMA technical indicator

    Parameters
    ----------
    s_ticker : str
        Ticker
    s_interval : str
        Interval of data
    df_stock : pd.DataFrame
        Dataframe of prices
    export : str
        Format to export data
    """

    df_stock.index = df_stock.index.tz_localize(None)
    df_stock["Day"] = [idx.date() for idx in df_stock.index]

    day_df = df_stock[df_stock.Day == df_stock.Day[-1]]

    df_vwap = overlap_model.vwap(day_df, offset)
    mc = mpf.make_marketcolors(
        up="green", down="red", edge="black", wick="black", volume="in", ohlc="i"
    )

    s = mpf.make_mpf_style(marketcolors=mc, gridstyle=":", y_on_right=True)
    addplot_result = mpf.make_addplot(df_vwap, color="k")

    mpf.plot(
        day_df,
        style=s,
        type="candle",
        addplot=addplot_result,
        volume=True,
        title=f"\n{s_ticker} {s_interval} VWAP",
    )

    if gtff.USE_ION:
        plt.ion()

    plt.show()
    print("")

    export_data(
        export,
        os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
        "VWAP",
        df_vwap,
    )