"""
This plot displays the period of consecutive days
each year with the extreme criterion meet. In the case of a tie, the
first period of the season is used for the analysis. If you select any of the
warmest options, the year must have at least 200 days of data.

<p>The dew point and feels like option only works for ASOS networks
and does a simple arthimetic mean of dew point temperatures.</p>

<p>Note that the coldest and warmest feels like temperature values are
averages of the daily minimum or maximum values, not an average of an
average feels like temperature.  This is why we can't have nice things.</p>
"""

from datetime import datetime, timedelta

import matplotlib.colors as mpcolors
import numpy as np
import pandas as pd
from matplotlib.colorbar import ColorbarBase
from matplotlib.ticker import MaxNLocator
from pyiem.database import get_sqlalchemy_conn, sql_helper
from pyiem.exceptions import NoDataFound
from pyiem.plot import figure, get_cmap, pretty_bins

PDICT = {
    "coldest_temp": "Coldest Average Temperature",
    "coldest_hitemp": "Coldest Average High Temperature",
    "coldest_lotemp": "Coldest Average Low Temperature",
    "coldest_lofeel": "Coldest Average Feels Like Temperature",
    "coldest_lodwpf": "Coldest Average Daily Low Dew Point Temperature",
    "warmest_temp": "Warmest Average Temperature",
    "warmest_hitemp": "Warmest Average High Temperature",
    "warmest_lotemp": "Warmest Average Low Temperature",
    "warmest_hifeel": "Warmest Average Daily High Feels Like Temperature",
    "warmest_hidwpf": "Warmest Average High Dew Point Temperature",
    "wettest": "Highest Precipitation",
    "driest": "Lowest Precipitation",
}
# How to get plot variable from dataframe
XREF = {
    "coldest_temp": "avg_temp",
    "coldest_hitemp": "avg_hitemp",
    "coldest_lotemp": "avg_lotemp",
    "coldest_lofeel": "avg_lofeel",
    "coldest_lodwpf": "avg_lodwpf",
    "warmest_temp": "avg_temp",
    "warmest_hitemp": "avg_hitemp",
    "warmest_lotemp": "avg_lotemp",
    "warmest_hifeel": "avg_hifeel",
    "warmest_hidwpf": "avg_hidwpf",
    "wettest": "sum_precip",
    "driest": "sum_precip",
}


def get_description():
    """Return a dict describing how to call this plotter"""
    desc = {"data": True, "cache": 86400, "description": __doc__}
    desc["arguments"] = [
        dict(
            type="sid",
            include_climodat=True,
            name="station",
            default="DSM",
            network="IA_ASOS",
            label="Select Station:",
        ),
        dict(
            type="select",
            name="var",
            default="coldest_temp",
            label="Which Metric",
            options=PDICT,
        ),
        dict(type="int", name="days", default=7, label="Over How Many Days?"),
        dict(type="cmap", name="cmap", default="jet", label="Color Ramp:"),
    ]
    return desc


def get_data(ctx: dict) -> pd.DataFrame:
    """Get the data"""
    days = ctx["days"]
    varname = ctx["var"]
    offset = 6 if varname.startswith("coldest") else 0
    if varname == "driest":
        offset = 6
    station = ctx["station"]
    if ctx["network"].endswith("CLIMATE"):
        table = "alldata"
        dbname = "coop"
        highcol = "high"
        lowcol = "low"
        highdwpf = "null::numeric"
        lowdwpf = "null::numeric"
        highfeel = "null::numeric"
        lowfeel = "null::numeric"
        precipcol = "precip"
        stationcol = "station"
    else:
        station = ctx["_nt"].sts[station]["iemid"]
        dbname = "iem"
        highcol = "max_tmpf"
        lowcol = "min_tmpf"
        highdwpf = "max_dwpf"
        lowdwpf = "min_dwpf"
        highfeel = "max_feel"
        lowfeel = "min_feel"
        precipcol = "pday"
        table = "summary"
        stationcol = "iemid"
    with get_sqlalchemy_conn(dbname) as conn:
        df = pd.read_sql(
            sql_helper(
                """
        WITH data as (
        SELECT day,
        extract(year from day + ':offset months'::interval) as season,
        avg(({highcol} + {lowcol})/2.)
            OVER (ORDER by day ASC ROWS :days preceding) as avg_temp,
        avg({highcol})
            OVER (ORDER by day ASC ROWS :days preceding) as avg_hitemp,
        avg({lowcol})
            OVER (ORDER by day ASC ROWS :days preceding) as avg_lotemp,
        avg({highfeel})
            OVER (ORDER by day ASC ROWS :days preceding) as avg_hifeel,
        avg({lowfeel})
            OVER (ORDER by day ASC ROWS :days preceding) as avg_lofeel,
        avg({highdwpf})
            OVER (ORDER by day ASC ROWS :days preceding) as avg_hidwpf,
        avg({lowdwpf})
            OVER (ORDER by day ASC ROWS :days preceding) as avg_lodwpf,
        sum({precipcol})
            OVER (ORDER by day ASC ROWS :days preceding) as sum_precip
        from {table} WHERE {stationcol} = :station and {highcol} is not null),
        agg1 as (
            SELECT season, day, avg_temp, avg_hitemp, avg_lotemp,
            sum_precip, avg_hidwpf, avg_lodwpf, avg_lofeel, avg_hifeel,
            row_number()
                OVER (PARTITION by season ORDER by avg_temp ASC nulls last,
                day ASC) as coldest_temp_rank,
            row_number()
                OVER (PARTITION by season ORDER by avg_hitemp ASC nulls last,
                day ASC) as coldest_hitemp_rank,
            row_number()
                OVER (PARTITION by season ORDER by avg_lotemp ASC nulls last,
                day ASC) as coldest_lotemp_rank,
            row_number()
                OVER (PARTITION by season ORDER by avg_lofeel ASC nulls last,
                day ASC) as coldest_lofeel_rank,
            row_number()
                OVER (PARTITION by season ORDER by avg_hidwpf DESC nulls last,
                day ASC) as warmest_hidwpf_rank,
            row_number()
                OVER (PARTITION by season ORDER by avg_temp DESC nulls last,
                day ASC) as warmest_temp_rank,
            row_number()
                OVER (PARTITION by season ORDER by avg_hitemp DESC nulls last,
                day ASC) as warmest_hitemp_rank,
            row_number()
                OVER (PARTITION by season ORDER by avg_hifeel DESC nulls last,
                day ASC) as warmest_hifeel_rank,
            row_number()
                OVER (PARTITION by season ORDER by avg_lotemp DESC nulls last,
                day ASC) as warmest_lotemp_rank,
            row_number()
                OVER (PARTITION by season ORDER by avg_lodwpf ASC nulls last,
                day ASC) as coldest_lodwpf_rank,
            row_number()
                OVER (PARTITION by season ORDER by sum_precip DESC nulls last,
                day ASC) as wettest_rank,
            row_number()
                OVER (PARTITION by season ORDER by sum_precip ASC nulls last,
                day ASC) as driest_rank,
            count(*) OVER (PARTITION by season)
            from data)
        SELECT season, day,
        extract(doy from day - ':days days'::interval)::int as doy,
        avg_temp, avg_hitemp, avg_lotemp, avg_hidwpf, avg_lodwpf,
        avg_lofeel, avg_hifeel,
        sum_precip from agg1 where {varname}_rank = 1 and count > :quorum
        """,
                highcol=highcol,
                lowcol=lowcol,
                highdwpf=highdwpf,
                lowdwpf=lowdwpf,
                highfeel=highfeel,
                lowfeel=lowfeel,
                precipcol=precipcol,
                table=table,
                stationcol=stationcol,
                varname=varname,
            ),
            conn,
            params={
                "days": days - 1,
                "offset": offset,
                "station": station,
                "quorum": 200 if varname.startswith("coldest") else days,
            },
            index_col="season",
        )
        if df.empty or df[XREF[varname]].isnull().all():
            raise NoDataFound("Error, no results returned!")

    if varname.startswith("coldest") or varname == "driest":
        df.loc[df["doy"] < 183, "doy"] += 365.0
    return df


def plotter(ctx: dict):
    """Go"""
    days = ctx["days"]
    varname = ctx["var"]
    df = get_data(ctx)

    # Don't plot zeros for precip
    if varname == "wettest":
        df = df[df["sum_precip"] > 0]
    title = ctx["_sname"]
    subtitle = PDICT.get(varname)
    if days == 1:
        subtitle = subtitle.replace("Average ", "")
    subtitle = f"{days} Day Period with {subtitle}"
    fig = figure(title=title, subtitle=subtitle, apctx=ctx)
    ax = fig.add_axes((0.05, 0.3, 0.45, 0.54))
    lax = fig.add_axes((0.05, 0.1, 0.45, 0.2))
    cax = fig.add_axes((0.05, 0.875, 0.4, 0.02))
    cmap = get_cmap(ctx["cmap"])
    minval = df[XREF[varname]].min() - 1.0
    maxval = df[XREF[varname]].max() + 1.0
    if pd.isnull(minval) or pd.isnull(maxval):
        raise NoDataFound("No data found for this station!")
    if varname in ["wettest", "driest"]:
        ramp = pretty_bins(max(0, minval), maxval)
    else:
        ramp = np.linspace(
            minval, maxval, min([int(maxval - minval), 10]), dtype="i"
        )
    norm = mpcolors.BoundaryNorm(ramp, cmap.N)
    ColorbarBase(cax, norm=norm, cmap=cmap, orientation="horizontal")
    units = "inch" if varname in ["wettest", "driest"] else "°F"
    fig.text(0.47, 0.88, units)
    bboxprops = dict(color="tan", alpha=0.5, boxstyle="round")
    ax.barh(
        df.index.values,
        [days] * len(df.index),
        left=df["doy"].values,
        color=cmap(norm(df[XREF[varname]].to_numpy())),
    )
    ax.grid(True)
    lax.grid(True)
    xticks = []
    xticklabels = []
    for i in np.arange(df["doy"].min() - 5, df["doy"].max() + 5, 1):
        ts = datetime(2000, 1, 1) + timedelta(days=int(i))
        if ts.day == 1:
            xticks.append(i)
            xticklabels.append(ts.strftime("%-d %b"))
    ax.set_xticks(xticks)
    lax.set_xticks(xticks)
    lax.set_xticklabels(xticklabels)

    counts = np.zeros(366 * 2)
    for _, row in df.iterrows():
        counts[int(row["doy"]) : int(row["doy"] + days)] += 1

    lax.bar(np.arange(366 * 2), counts, edgecolor="blue", facecolor="blue")
    lax.set_ylabel("Years")
    lax.set_xlabel("Frequency of day within period")
    ax.set_ylim(df.index.values.min() - 3, df.index.values.max() + 3)

    ax.set_xlim(df["doy"].min() - 10, df["doy"].max() + 10)
    lax.set_xlim(df["doy"].min() - 10, df["doy"].max() + 10)
    ax.yaxis.set_major_locator(MaxNLocator(prune="lower", integer=True))

    # Plot per year
    series = df[XREF[varname]]
    ax = fig.add_axes((0.59, 0.5, 0.4, 0.4))
    ax.bar(df.index.values, series.values, color="blue", width=1)
    minval = series.min() - 5
    if varname == "driest":
        minval = 0
    ax.set_ylim(bottom=minval)
    ax.text(
        0.03,
        1.01,
        "Value by Year",
        transform=ax.transAxes,
        va="bottom",
        bbox=bboxprops,
    )
    ax.grid(True)

    # CDF
    ax = fig.add_axes((0.59, 0.1, 0.4, 0.3))
    X2 = np.sort(series.to_numpy())
    ptile = np.percentile(X2, [0, 5, 50, 95, 100])
    N = len(series.values)
    F2 = np.array(range(N)) / float(N) * 100.0
    ax.plot(X2, 100.0 - F2)
    ax.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
    mysort = df.sort_values(by=XREF[varname], ascending=True)
    info = (
        f"Min: {series.min():.2f} {mysort.index[0]:.0f}\n"
        f"95th: {ptile[1]:.2f}\n"
        f"Mean: {series.mean():.2f}\n"
        f"STD: {series.std():.2f}\n"
        f"5th: {ptile[3]:.2f}\n"
        f"Max: {series.max():.2f} {mysort.index[-1]:.0f}"
    )
    ax.text(
        0.75,
        0.95,
        info,
        transform=ax.transAxes,
        va="top",
        bbox=dict(facecolor="white", edgecolor="k"),
    )
    ax.text(
        0.03,
        1.01,
        "Cumulative Distribution Function",
        transform=ax.transAxes,
        va="bottom",
        bbox=bboxprops,
    )
    ax.set_ylabel("Percentile")
    ax.grid(True)
    ax.set_xlabel(units)
    return fig, df
