"""yieldfx plot"""

import calendar
import os
from datetime import date, datetime, timedelta

import pandas as pd
from metpy.units import units
from pyiem.exceptions import NoDataFound
from pyiem.meteorology import gdd
from pyiem.plot import figure_axes
from pyiem.util import c2f, mm2inch

STATIONS = {
    "ames": "Central (Ames)",
    "cobs": "Central (COBS)",
    "crawfordsville": "Southeast (Crawfordsville)",
    "kanawha": "Northern (Kanawha)",
    "lewis": "Southwest (Lewis)",
    "mcnay": "Southern (Chariton/McNay)",
    "muscatine": "Southeast (Muscatine)",
    "nashua": "Northeast (Nashua)",
    "sutherland": "Northwest (Sutherland)",
}

PLOTS = {
    "gdd": "Growing Degree Days [°F]",
    "rain": "Precipitation [in]",
    "maxt": "Daily Maximum Temperature [°F]",
    "mint": "Daily Minimum Temperature [°F]",
}


def get_description():
    """Return a dict describing how to call this plotter"""
    desc = {"description": __doc__, "data": True}
    desc["arguments"] = [
        dict(
            type="select",
            name="location",
            default="ames",
            label="Select Location:",
            options=STATIONS,
        ),
        dict(
            type="select",
            name="ptype",
            default="gdd",
            label="Select Plot Type:",
            options=PLOTS,
        ),
        dict(type="text", name="sdate", default="mar15", label="Start Date:"),
    ]
    return desc


def load(dirname, location, sdate):
    """Read a file please"""
    data = []
    idx = []
    mindoy = int(sdate.strftime("%j"))
    fn = f"{dirname}/{location}.met"
    if not os.path.isfile(fn):
        raise NoDataFound("Data file was not found.")
    with open(fn, encoding="utf8") as fh:
        for line in fh:
            line = line.strip()  # noqa
            if not line.startswith("19") and not line.startswith("20"):
                continue
            tokens = line.split()
            if int(tokens[1]) < mindoy:
                continue
            data.append(tokens)
            ts = date(int(tokens[0]), 1, 1) + timedelta(
                days=int(tokens[1]) - 1
            )
            idx.append(ts)
    if len(data[0]) < 10:
        cols = ["year", "doy", "radn", "maxt", "mint", "rain"]
    else:
        cols = [
            "year",
            "doy",
            "radn",
            "maxt",
            "mint",
            "rain",
            "gdd",
            "st4",
            "st12",
            "st24",
            "st50",
            "sm12",
            "sm24",
            "sm50",
        ]
    df = pd.DataFrame(data, index=idx, columns=cols)
    for col in cols:
        df[col] = pd.to_numeric(df[col], errors="coerce")
    if len(data[0]) < 10:
        df["gdd"] = gdd(
            units("degC") * df["maxt"].values,
            units("degC") * df["mint"].values,
        )
    df["gddcum"] = df.groupby(["year"])["gdd"].apply(lambda x: x.cumsum())
    df["raincum"] = mm2inch(
        df.groupby(["year"])["rain"].apply(lambda x: x.cumsum())
    )
    return df


def plotter(ctx: dict):
    """Go"""
    location = ctx["location"]
    ptype = ctx["ptype"]
    sdate = datetime.strptime(f"2000{ctx['sdate']}", "%Y%b%d")
    df = load("/mesonet/share/pickup/yieldfx", location, sdate)
    cdf = load("/opt/iem/scripts/yieldfx/baseline", location, sdate)

    today = date.today()
    thisyear = (
        df[df["year"] == today.year].copy().reset_index().set_index("doy")
    )

    # Drop extra day from cdf during non-leap year
    if today.year % 4 != 0:
        cdf = cdf[cdf["doy"] < 366]
        df = df[df["doy"] < 366]

    # Create a specialized result dataframe for CSV, Excel output options
    resdf = pd.DataFrame(index=thisyear.index)
    resdf.index.name = "date"
    resdf["doy"] = thisyear.index.values
    resdf = resdf.reset_index().set_index("doy")

    # write current year data back to resdf
    for _v, _u in zip(["gddcum", "raincum"], ["F", "in"], strict=False):
        resdf[f"{_v}[{_u}]"] = thisyear[_v]
    for _v in ["mint", "maxt"]:
        resdf[f"{_v}[F]"] = c2f(thisyear[_v].values)
    resdf["rain[in]"] = mm2inch(thisyear["rain"])
    for _ptype, unit in zip(["gdd", "rain"], ["F", "in"], strict=False):
        resdf[f"{_ptype}cum_climo[{unit}]"] = cdf.groupby("doy")[
            _ptype + "cum"
        ].mean()
        resdf[f"{_ptype}cum_min[{unit}]"] = df.groupby("doy")[
            _ptype + "cum"
        ].min()
        resdf[f"{_ptype}cum_max[{unit}]"] = df.groupby("doy")[
            _ptype + "cum"
        ].max()
    for _ptype in ["maxt", "mint"]:
        resdf[_ptype + "_climo[F]"] = c2f(
            cdf.groupby("doy")[_ptype].mean().values
        )
        resdf[_ptype + "_min[F]"] = c2f(df.groupby("doy")[_ptype].min().values)
        resdf[_ptype + "_max[F]"] = c2f(df.groupby("doy")[_ptype].max().values)

    (fig, ax) = figure_axes(
        apctx=ctx, title=f"{STATIONS[location]} {PLOTS[ptype]}"
    )
    if ptype in ["gdd", "rain"]:
        ax.plot(
            thisyear.index.values,
            thisyear[f"{ptype}cum"],
            zorder=4,
            color="b",
            lw=2,
            label=f"{today.year} Obs + CFS Forecast",
        )
        climo = cdf.groupby("doy")[ptype + "cum"].mean()
        ax.plot(
            climo.index.values,
            climo.values,
            lw=2,
            color="k",
            label="Climatology",
            zorder=3,
        )
        xrng = df.groupby("doy")[ptype + "cum"].max()
        nrng = df.groupby("doy")[ptype + "cum"].min()
        ax.fill_between(
            xrng.index.values,
            nrng.values,
            xrng.values,
            color="tan",
            label="Range",
            zorder=2,
        )
    else:
        ax.plot(
            thisyear.index.values,
            c2f(thisyear[ptype]),
            zorder=4,
            color="b",
            lw=2,
            label=f"{today.year} Obs + CFS Forecast",
        )
        climo = cdf.groupby("doy")[ptype].mean()
        ax.plot(
            climo.index.values,
            c2f(climo.values),
            lw=2,
            color="k",
            label="Climatology",
            zorder=3,
        )
        xrng = df.groupby("doy")[ptype].max()
        nrng = df.groupby("doy")[ptype].min()
        ax.fill_between(
            xrng.index.values,
            c2f(nrng.values),
            c2f(xrng.values),
            color="tan",
            label="Range",
            zorder=2,
        )

    ax.set_ylabel(PLOTS[ptype])
    ax.legend(loc=(0.03, -0.16), ncol=3, fontsize=12)
    ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335))
    ax.set_xticklabels(calendar.month_abbr[1:])
    ax.grid(True)
    ax.set_xlim(
        sdate.timetuple().tm_yday,
        date(today.year, 12, 31).timetuple().tm_yday,
    )
    pos = ax.get_position()
    ax.set_position([pos.x0, pos.y0 + 0.05, pos.width, pos.height * 0.95])

    return fig, resdf
