import logging
import os
from datetime import datetime
from typing import List, Optional

import matplotlib
import numpy as np
import pandas as pd
from matplotlib import dates as mdates
from matplotlib import pyplot as plt

from openbb_terminal.config_terminal import theme
from openbb_terminal.decorators import check_api_key
from openbb_terminal import config_plot as cfgPlot
from openbb_terminal.cryptocurrency.overview.glassnode_model import (
    get_btc_rainbow,
)
from openbb_terminal.decorators import log_start_end
from openbb_terminal.helper_funcs import (
    export_data,
    plot_autoscale,
    is_valid_axes_count,
)

logger = logging.getLogger(__name__)


@log_start_end(log=logger)
@check_api_key(["API_GLASSNODE_KEY"])
def display_btc_rainbow(
    start_date: str = "2010-01-01",
    end_date: Optional[str] = None,
    export: str = "",
    external_axes: Optional[List[plt.Axes]] = None,
):
    """Displays bitcoin rainbow chart
    [Price data from source: https://glassnode.com]
    [Inspired by: https://blockchaincenter.net]

    Parameters
    ----------
    start_date : str
        Initial date, format YYYY-MM-DD
    end_date : Optional[str]
        Final date, format YYYY-MM-DD
    export : str
        Export dataframe data to csv,json,xlsx file
    external_axes : Optional[List[plt.Axes]], optional
        External axes (1 axis is expected in the list), by default None
    """

    if end_date is None:
        end_date = datetime.now().strftime("%Y-%m-%d")

    df_data = get_btc_rainbow(start_date, end_date)

    if df_data.empty:
        return

    # This plot has 1 axis
    if not external_axes:
        _, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfgPlot.PLOT_DPI)
    elif is_valid_axes_count(external_axes, 1):
        (ax,) = external_axes
    else:
        return

    d0 = datetime.strptime("2012-01-01", "%Y-%m-%d")
    dend = datetime.strptime(end_date, "%Y-%m-%d")

    x = range((df_data.index[0] - d0).days, (dend - d0).days + 1)

    y0 = [10 ** ((2.90 * ln_x) - 19.463) for ln_x in [np.log(val + 1400) for val in x]]
    y1 = [10 ** ((2.886 * ln_x) - 19.463) for ln_x in [np.log(val + 1375) for val in x]]
    y2 = [10 ** ((2.872 * ln_x) - 19.463) for ln_x in [np.log(val + 1350) for val in x]]
    y3 = [10 ** ((2.859 * ln_x) - 19.463) for ln_x in [np.log(val + 1320) for val in x]]
    y4 = [
        10 ** ((2.8445 * ln_x) - 19.463) for ln_x in [np.log(val + 1293) for val in x]
    ]
    y5 = [
        10 ** ((2.8295 * ln_x) - 19.463) for ln_x in [np.log(val + 1275) for val in x]
    ]
    y6 = [10 ** ((2.815 * ln_x) - 19.463) for ln_x in [np.log(val + 1250) for val in x]]
    y7 = [10 ** ((2.801 * ln_x) - 19.463) for ln_x in [np.log(val + 1225) for val in x]]
    y8 = [10 ** ((2.788 * ln_x) - 19.463) for ln_x in [np.log(val + 1200) for val in x]]

    x_dates = pd.date_range(df_data.index[0], dend, freq="d")

    ax.fill_between(x_dates, y0, y1, color="red", alpha=0.7)
    ax.fill_between(x_dates, y1, y2, color="orange", alpha=0.7)
    ax.fill_between(x_dates, y2, y3, color="yellow", alpha=0.7)
    ax.fill_between(x_dates, y3, y4, color="green", alpha=0.7)
    ax.fill_between(x_dates, y4, y5, color="blue", alpha=0.7)
    ax.fill_between(x_dates, y5, y6, color="violet", alpha=0.7)
    ax.fill_between(x_dates, y6, y7, color="indigo", alpha=0.7)
    ax.fill_between(x_dates, y7, y8, color="purple", alpha=0.7)

    ax.semilogy(df_data.index, df_data["v"].values)
    ax.set_xlim(df_data.index[0], dend)
    ax.set_title("Bitcoin Rainbow Chart")
    ax.set_ylabel("Price [USD]")

    ax.legend(
        [
            "Bubble bursting imminent!!",
            "SELL!",
            "Everyone FOMO'ing....",
            "Is this a bubble??",
            "Still cheap",
            "Accumulate",
            "BUY!",
            "Basically a Fire Sale",
            "Bitcoin Price",
        ],
        prop={"size": 8},
    )

    sample_dates = np.array(
        [
            datetime(2012, 11, 28),
            datetime(2016, 7, 9),
            datetime(2020, 5, 11),
            datetime(2024, 4, 4),
        ]
    )
    sample_dates = mdates.date2num(sample_dates)
    ax.vlines(x=sample_dates, ymin=0, ymax=max(y0), color="grey")
    for i, x in enumerate(sample_dates):
        if mdates.date2num(d0) < x < mdates.date2num(dend):
            ax.text(x, 1, f"Halving {i+1}", rotation=-90, verticalalignment="center")

    ax.minorticks_off()
    ax.yaxis.set_major_formatter(
        matplotlib.ticker.FuncFormatter(lambda x, _: int(x) if x >= 1 else x)
    )
    ax.yaxis.set_major_locator(
        matplotlib.ticker.LogLocator(base=100, subs=[1.0, 2.0, 5.0, 10.0])
    )

    theme.style_primary_axis(ax)

    if not external_axes:
        theme.visualize_output()

    export_data(
        export,
        os.path.dirname(os.path.abspath(__file__)),
        "btcrb",
        df_data,
    )