"""
This chart presents violin plots of the distribution of daily
high and low temperatures on dates with and without snow cover by month.
A given month is plotted when at least 1% of the days on record for the
month had snowcover.  There
are a number of caveats due to the timing of the daily temperature and
snow cover report.  Also with the quality of the snow cover data.
"""

import calendar

import numpy as np
import pandas as pd
from matplotlib.patches import Rectangle
from pyiem.database import get_sqlalchemy_conn, sql_helper
from pyiem.exceptions import NoDataFound
from pyiem.plot import figure

from iemweb.autoplot import ARG_STATION


def get_description():
    """Return a dict describing how to call this plotter"""
    desc = {"description": __doc__, "data": True}
    desc["arguments"] = [
        ARG_STATION,
    ]
    return desc


def plotter(ctx: dict):
    """Go"""
    station = ctx["station"].upper()
    # Load all available data
    with get_sqlalchemy_conn("coop") as conn:
        df = pd.read_sql(
            sql_helper(
                "SELECT year, month, high, low, snowd from alldata "
                "WHERE station = :station and snowd is not null"
            ),
            conn,
            params={"station": station},
            index_col=None,
        )
    if df.empty:
        raise NoDataFound("No data found.")

    # Consider months that have at least 3% of days with snowcover
    total = df.groupby("month").count()
    coverdf = df[df["snowd"] > 0]
    nocoverdf = df[df["snowd"] == 0]
    snowcover = coverdf.groupby("month").count()
    freq = snowcover["snowd"] / total["snowd"]
    monthsall = freq[freq > 0.03].index.values.tolist()
    # Sort the months to place fall first
    months = [x for x in monthsall if x > 6]
    months2 = [x for x in monthsall if x < 7]
    months.extend(months2)
    title = (
        f"{ctx['_sname']}\n"
        "Daily Temp Distributions by Month by Snow Cover "
        f"({df['year'].min()}-{df['year'].max()})"
    )

    fig = figure(apctx=ctx, title=title)
    ax = [
        fig.add_axes((0.12, 0.56, 0.83, 0.32)),
        fig.add_axes((0.12, 0.1, 0.83, 0.32)),
    ]
    colors = ["r", "b"]
    res = []
    for i, lbl in enumerate(["high", "low"]):
        for j, month in enumerate(months):
            vopts = dict(
                positions=[j], showmeans=False, showextrema=False, widths=0.8
            )

            # RHS
            vals = coverdf[coverdf["month"] == month][lbl]
            v = ax[i].violinplot(vals, **vopts)
            meanval2 = vals.mean()
            ax[i].plot([j, j + 0.4], [meanval2, meanval2], c=colors[1])
            ax[i].text(
                j, meanval2, rf"{meanval2:.0f}$^\circ$", c=colors[1], ha="left"
            )
            b = v["bodies"][0]
            m = np.mean(b.get_paths()[0].vertices[:, 0])
            b.get_paths()[0].vertices[:, 0] = np.clip(
                b.get_paths()[0].vertices[:, 0], m, np.inf
            )
            b.set_color(colors[1])

            # LHS
            vals = nocoverdf[nocoverdf["month"] == month][lbl]
            meanval = vals.mean()
            if not vals.empty:
                v = ax[i].violinplot(vals, **vopts)
                ax[i].plot([j - 0.4, j], [meanval, meanval], c=colors[0])
                ax[i].text(
                    j,
                    meanval,
                    rf"{meanval:.0f}$^\circ$",
                    c=colors[0],
                    ha="right",
                )
                b = v["bodies"][0]
                paths = b.get_paths()
                if paths:
                    m = np.mean(paths[0].vertices[:, 0])
                    paths[0].vertices[:, 0] = np.clip(
                        paths[0].vertices[:, 0], -np.inf, m
                    )
                    b.set_color(colors[0])

            res.append(
                {
                    "month": month,
                    f"{lbl}_withoutsnow": meanval,
                    f"{lbl}_withsnow": meanval2,
                }
            )

        ax[i].set_xticks(range(len(months)))
        ax[i].set_xticklabels([calendar.month_abbr[m] for m in months])
        ax[i].grid(axis="y")
        ax[i].axhline(32, ls="--", color="purple", alpha=0.8, lw=0.5)
        ax[i].text(len(months) - 0.3, 32, r"32$^\circ$", color="purple")

    pr0 = Rectangle((0, 0), 1, 1, fc="r")
    pr1 = Rectangle((0, 0), 1, 1, fc="b")
    ax[0].legend(
        (pr0, pr1),
        ("Without Snow Cover", "With Snow Cover"),
        ncol=2,
        loc=(0.1, -0.35),
    )
    ax[0].set_ylabel(r"Daily High Temp $^\circ$F")
    ax[1].set_ylabel(r"Daily Low Temp $^\circ$F")

    return fig, pd.DataFrame(res)
