"""
This plot presents a time series of areal coverage
of United States Drought Monitor for a given state of your choice. This
plot uses a JSON data service provided by the
<a href="https://droughtmonitor.unl.edu">Drought Monitor</a> website.
"""

from datetime import datetime, timedelta

import matplotlib.dates as mdates
import pandas as pd
import requests
from pyiem.exceptions import NoDataFound
from pyiem.plot import figure_axes
from pyiem.reference import state_fips, state_names
from pyiem.util import LOG

SERVICE = (
    "https://droughtmonitor.unl.edu"
    "/DmData/DataTables.aspx/ReturnTabularDMAreaPercent_"
)
COLORS = ["#ffff00", "#fcd37f", "#ffaa00", "#e60000", "#730000"]
PDICT = {"state": "Plot Individual State", "national": "Plot CONUS"}


def get_description():
    """Return a dict describing how to call this plotter"""
    desc = {"description": __doc__, "data": True}
    today = datetime.today()
    sts = today - timedelta(days=720)
    desc["arguments"] = [
        dict(
            type="select",
            default="state",
            name="s",
            options=PDICT,
            label="Plot for state or CONUS:",
        ),
        dict(type="state", name="state", default="IA", label="Select State:"),
        dict(
            type="date",
            name="sdate",
            default=sts.strftime("%Y/%m/%d"),
            label="Start Date:",
            min="2000/01/01",
        ),
        dict(
            type="date",
            name="edate",
            default=today.strftime("%Y/%m/%d"),
            label="End Date:",
            min="2000/01/01",
        ),
    ]
    return desc


def plotter(ctx: dict):
    """Go"""
    sdate = ctx["sdate"]
    edate = ctx["edate"]
    state = ctx["state"].upper()

    fips = ""
    for key, entry in state_fips.items():
        if entry == state:
            fips = f"{key:02.0f}"
    if ctx["s"] == "state":
        payload = {"area": f"'{fips}'", "statstype": "'2'", "diff": "'0'"}
        suffix = "state"
    else:
        payload = {"area": "'conus'", "statstype": "'2'", "diff": "'0'"}
        suffix = "national"
    headers = {}
    headers["Accept"] = "application/json, text/javascript, */*; q=0.01"
    headers["Content-Type"] = "application/json; charset=UTF-8"
    try:
        resp = requests.get(
            SERVICE + suffix, params=payload, headers=headers, timeout=30
        )
        resp.raise_for_status()
        jdata = resp.json()
    except Exception as exp:
        LOG.info("droughtmonitor failed: %s", exp)
        raise NoDataFound("API request to droughtmonitor failed...") from exp
    if not jdata.get("d", []):
        raise NoDataFound("No data Found.")
    df = pd.DataFrame(jdata["d"])
    for c in ["0", "1", "2", "3", "4"]:
        df[f"D{c}"] = pd.to_numeric(df[f"D{c}"])
    df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d")
    df = df[
        (df["Date"] >= pd.Timestamp(sdate))
        & (df["Date"] <= pd.Timestamp(edate))
    ]
    if df.empty:
        raise NoDataFound("No data Found.")
    df = df.sort_values("Date", ascending=True)
    df["x"] = df["Date"] + timedelta(hours=3.5 * 24)
    df = df.set_index("Date")
    df.index.name = "Date"

    df = df.reset_index()
    tt = state_names[state] if ctx["s"] == "state" else "CONUS"
    title = (
        f"Areal coverage of Drought for {tt}\n"
        f"from US Drought Monitor {df['Date'].min():%-d %B %Y} - "
        f"{df['Date'].max():%-d %B %Y}"
    )
    (fig, ax) = figure_axes(title=title, apctx=ctx)

    # HACK to get a datetime64 to datetime so matplotlib works
    xs = df["x"].to_list()
    bottom = (df["D4"] + df["D3"] + df["D2"] + df["D1"]).values
    ax.bar(
        xs,
        df["D0"].values,
        width=7,
        fc=COLORS[0],
        ec="None",
        bottom=bottom,
        label="D0 Abnormal",
    )
    bottom = (df["D4"] + df["D3"] + df["D2"]).values
    ax.bar(
        xs,
        df["D1"].values,
        bottom=bottom,
        width=7,
        fc=COLORS[1],
        ec="None",
        label="D1 Moderate",
    )
    bottom = (df["D4"] + df["D3"]).values
    ax.bar(
        xs,
        df["D2"].values,
        width=7,
        fc=COLORS[2],
        ec="None",
        bottom=bottom,
        label="D2 Severe",
    )
    bottom = df["D4"].values
    ax.bar(
        xs,
        df["D3"].values,
        width=7,
        fc=COLORS[3],
        ec="None",
        bottom=bottom,
        label="D3 Extreme",
    )
    ax.bar(
        xs,
        df["D4"].values,
        width=7,
        fc=COLORS[4],
        ec="None",
        label="D4 Exceptional",
    )

    ax.set_ylim(0, 100)
    ax.set_yticks([0, 10, 30, 50, 70, 90, 100])
    ax.set_ylabel("Percentage of Area [%]")
    ax.grid(True)
    ax.set_xlim(df["Date"].min(), df["Date"].max() + timedelta(days=7))
    ax.legend(bbox_to_anchor=(0, -0.13, 1, 0), loc="center", ncol=5)
    ax.set_position([0.1, 0.15, 0.8, 0.75])
    ax.xaxis.set_major_formatter(mdates.DateFormatter("%b\n%Y"))

    return fig, df[["Date", "None", "D0", "D1", "D2", "D3", "D4"]]
