"""
This plot presents percentiles of observations from
a given sounding profile against the long term record for the site. These
percentiles are computed against all other soundings for the valid hour of
the profile of interest.  For example, a 00 UTC sounding is only compared
against other 00 UTC soundings for the given month or for the period of
record.

<br /><br />The 'Select Station' option provides some 'virtual' stations
that are spliced together archives of close by stations.  For some
locations, the place that the sounding is made has moved over the years.

<br /><br />A process runs at 3:10 and 15:10z each day to ingest the
current 0 and 12z soundings respectively.  You may not find the current
day's sounding if running this application prior to those ingest times.
"""

import calendar

import pandas as pd
from pyiem.database import get_sqlalchemy_conn, sql_helper
from pyiem.exceptions import NoDataFound
from pyiem.plot import figure
from pyiem.util import utc

PDICT = {"00": "00 UTC", "12": "12 UTC"}
PDICT2 = {
    "none": "No Comparison Limit (All Soundings)",
    "month": "Month of the Selected Profile",
}
PDICT3 = {
    "tmpc": "Air Temperature (C)",
    "dwpc": "Dew Point (C)",
    "hght": "Height (m)",
    "smps": "Wind Speed (mps)",
}
PDICT4 = {
    "same": "Compare against soundings for same hour",
    "all": "Compare against soundings at any hour",
}


def get_description():
    """Return a dict describing how to call this plotter"""
    desc = {"description": __doc__, "data": True, "cache": 86400}
    today = utc()
    desc["arguments"] = [
        dict(
            type="networkselect",
            name="station",
            network="RAOB",
            default="_OAX",
            label="Select Station:",
        ),
        dict(
            type="date",
            name="date",
            default=today.strftime("%Y/%m/%d"),
            min="1946/01/01",
            max=today.strftime("%Y/%m/%d"),
            label="Date of the Sounding:",
        ),
        dict(
            type="select",
            name="hour",
            default="00",
            options=PDICT,
            label="Which Sounding from Above Date:",
        ),
        dict(
            type="select",
            name="which",
            default="month",
            options=PDICT2,
            label="Compare this sounding against (dates):",
        ),
        dict(
            type="select",
            name="h",
            default="same",
            options=PDICT4,
            label="Compare this sounding against (hour):",
        ),
        dict(
            type="select",
            name="var",
            default="tmpc",
            options=PDICT3,
            label="Which Sounding Variable to Plot:",
        ),
    ]
    return desc


def plotter(ctx: dict):
    """Go"""
    station = ctx["station"]
    if station not in ctx["_nt"].sts:  # This is needed.
        raise NoDataFound("Unknown station metadata.")
    varname = ctx["var"]
    ts = ctx["date"]
    hour = int(ctx["hour"])
    ts = utc(ts.year, ts.month, ts.day, hour)
    which = ctx["which"]
    stations = [station]
    name = ctx["_nt"].sts[station]["name"]
    if station.startswith("_"):
        name = ctx["_nt"].sts[station]["name"].split("--")[0]
        stations = (
            ctx["_nt"].sts[station]["name"].split("--")[1].strip().split(" ")
        )
    params = {
        "stations": stations,
        "ts": ts,
    }
    vlimit = ""
    if which == "month":
        vlimit = " and extract(month from f.valid) = :month "
        params["month"] = ts.month

    hrlimit = "and extract(hour from f.valid at time zone 'UTC') = :hour "
    params["hour"] = hour
    if ctx["h"].upper() == "ALL":
        hrlimit = ""
    with get_sqlalchemy_conn("raob") as conn:
        df = pd.read_sql(
            sql_helper(
                """
        with data as (
            select f.valid,
            p.pressure, count(*) OVER (PARTITION by p.pressure),
            min(valid at time zone 'UTC') OVER () as min_valid,
            max(valid at time zone 'UTC') OVER () as max_valid,
            p.tmpc,
            rank() OVER (PARTITION by p.pressure ORDER by p.tmpc ASC)
                as tmpc_rank,
            min(p.tmpc) OVER (PARTITION by p.pressure) as tmpc_min,
            max(p.tmpc) OVER (PARTITION by p.pressure) as tmpc_max,
            p.dwpc,
            rank() OVER (PARTITION by p.pressure ORDER by p.dwpc ASC)
                as dwpc_rank,
            min(p.dwpc) OVER (PARTITION by p.pressure) as dwpc_min,
            max(p.dwpc) OVER (PARTITION by p.pressure) as dwpc_max,
            p.height as hght,
            rank() OVER (
                PARTITION by p.pressure ORDER by p.height ASC) as hght_rank,
            min(p.height) OVER (PARTITION by p.pressure) as hght_min,
            max(p.height) OVER (PARTITION by p.pressure) as hght_max,
            p.smps,
            rank() OVER (PARTITION by p.pressure ORDER by p.smps ASC)
                as smps_rank,
            min(p.smps) OVER (PARTITION by p.pressure) as smps_min,
            max(p.smps) OVER (PARTITION by p.pressure) as smps_max
            from raob_flights f JOIN raob_profile p on (f.fid = p.fid)
            WHERE f.station = ANY(:stations) {hrlimit} {vlimit}
            and p.pressure in (925, 850, 700, 500, 400, 300, 250, 200,
            150, 100, 70, 50, 10)  and
            {vname} is not null)

        select * from data where valid = :ts ORDER by pressure DESC
        """,
                hrlimit=hrlimit,
                vlimit=vlimit,
                vname=varname if varname != "hght" else "height",
            ),
            conn,
            params=params,
            index_col="pressure",
        )
    if df.empty:
        raise NoDataFound(f"Sounding for {ts:%Y-%m-%d %H:%M} was not found!")
    df = df.drop("valid", axis=1)
    for key in PDICT3:
        df[key + "_percentile"] = df[key + "_rank"] / df["count"] * 100.0
        # manual hackery to get 0 and 100th percentile
        df.loc[df[key] == df[f"{key}_max"], f"{key}_percentile"] = 100.0
        df.loc[df[key] == df[f"{key}_min"], f"{key}_percentile"] = 0.0

    title = f"{station} {name} {ts:%Y/%m/%d %H} UTC Sounding"
    tt = "All Year" if which == "none" else calendar.month_name[ts.month]
    uu = "Any Hour" if ctx["h"] == "all" else f"{hour} UTC"
    subtitle = (
        f"({pd.Timestamp(df.iloc[0]['min_valid']).year}-"
        f"{pd.Timestamp(df.iloc[0]['max_valid']).year}) Percentile Ranks "
        f"({tt}) for {PDICT3[varname]} at {uu}"
    )
    fig = figure(title=title, subtitle=subtitle, apctx=ctx)
    ax = fig.add_axes((0.1, 0.1, 0.75, 0.78))
    bars = ax.barh(
        range(len(df.index)), df[varname + "_percentile"], align="center"
    )
    y2labels = []
    fmt = "%.1f" if varname not in ["hght"] else "%.0f"
    for i, mybar in enumerate(bars):
        ptile = mybar.get_width()
        # Prevent 99.999 from showing up as 100.0
        ptile = 99.9 if 99.94 < ptile < 100 else ptile
        ax.text(
            mybar.get_width() + 1,
            i,
            f"{ptile:.1f}",
            va="center",
            bbox=dict(color="white"),
        )
        y2labels.append(
            (fmt + " (" + fmt + " " + fmt + ")")
            % (
                df.iloc[i][varname],
                df.iloc[i][varname + "_min"],
                df.iloc[i][varname + "_max"],
            )
        )
    ax.set_yticks(range(len(df.index)))
    ax.set_yticklabels([f"{a:.0f}" for a in df.index.values])
    ax.set_ylim(-0.5, len(df.index) - 0.5)
    ax.set_xlabel("Percentile [100 = highest]")
    ax.set_ylabel("Mandatory Pressure Level (hPa)")
    ax.grid(True)
    ax.set_xticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
    ax.set_xlim(0, 110)
    ax.text(1.02, 1, "Ob  (Min  Max)", transform=ax.transAxes)

    ax2 = ax.twinx()
    ax2.set_ylim(-0.5, len(df.index) - 0.5)
    ax2.set_yticks(range(len(df.index)))
    ax2.set_yticklabels(y2labels)
    return fig, df
