"""
This plot looks at the bias associated with computing
24 hour precipitation totals using a given hour of the day as the
delimiter. This plot will take a number of seconds to generate, so please
be patient.  This chart attempts to address the question of if computing
24 hour precip totals at midnight or 7 AM biases the totals.  Such biases
are commmon when computing this metric for high or low temperature.
"""

from datetime import date, timezone
from zoneinfo import ZoneInfo

import numpy as np
import pandas as pd
from pyiem.database import sql_helper, with_sqlalchemy_conn
from pyiem.exceptions import NoDataFound
from pyiem.plot import figure_axes
from pyiem.util import utc
from sqlalchemy.engine import Connection


def get_description():
    """Return a dict describing how to call this plotter"""
    desc = {"description": __doc__, "data": True}
    desc["arguments"] = [
        dict(
            type="zstation",
            name="zstation",
            default="DSM",
            label="Select Station:",
            network="IA_ASOS",
        )
    ]
    return desc


@with_sqlalchemy_conn("iem")
def plotter(ctx: dict, conn: Connection | None = None):
    """Go"""
    station = ctx["zstation"]
    ts1973 = utc(1973, 1, 1)
    utcnow = utc()
    rs = conn.execute(
        sql_helper("""
    SELECT valid at time zone 'UTC', phour from hourly WHERE
    iemid = :iemid and phour > 0.009 and valid >= :sts and valid < :ets
    ORDER by valid ASC
    """),
        {
            "iemid": ctx["_nt"].sts[station]["iemid"],
            "sts": ts1973,
            "ets": utcnow,
        },
    )
    if rs.rowcount == 0:
        raise NoDataFound("No Data Found.")

    # Create storage for the data
    days = (utcnow.year - 1973 + 1) * 366
    data = np.zeros((days * 24), "f")
    minvalid = None
    # make ts1973 naive
    ts1973 = ts1973.replace(tzinfo=None)
    for row in rs:
        if minvalid is None:
            minvalid = row[0]
        data[(row[0] - ts1973).days * 24 + row[0].hour] = row[1]

    lts = utcnow.astimezone(ZoneInfo(ctx["_nt"].sts[station]["tzname"]))
    lts = lts.replace(month=7, hour=0)
    cnts = [0] * 24
    avgv = [0] * 24
    rows = []
    for hr in range(24):
        ts = lts.replace(hour=hr)
        zhour = ts.astimezone(timezone.utc).hour
        arr = np.reshape(data[zhour : (0 - 24 + zhour)], (days - 1, 24))
        tots = np.sum(arr, 1)
        cnts[hr] = np.sum(np.where(tots > 0, 1, 0))
        avgv[hr] = np.average(tots[tots > 0])
        rows.append(
            dict(
                average_precip=avgv[hr],
                events=cnts[hr],
                zhour=zhour,
                localhour=hr,
            )
        )

    df = pd.DataFrame(rows)
    title = (
        f"{ctx['_sname']} ({minvalid.year}-{date.today().year})\n"
        "Bias of 24 Hour 'Day' Split for Precipitation"
    )
    (fig, ax) = figure_axes(apctx=ctx, title=title)
    acount = np.average(cnts)
    years = utcnow.year - minvalid.year + 1
    arc = (np.array(cnts) - acount) / float(years)
    maxv = max([0 - np.min(arc), np.max(arc)])
    line = ax.plot(range(24), arc, color="b", label="Days Bias")
    ax.set_ylim(0 - maxv - 0.2, maxv + 0.2)

    ax2 = ax.twinx()
    aavg = np.average(avgv)
    aavgv = np.array(avgv) - aavg
    l2 = ax2.plot(range(24), aavgv, color="r")
    maxv = max([0 - np.min(aavgv), np.max(aavgv)])
    ax2.set_ylim(0 - maxv - 0.01, maxv + 0.01)
    ax2.set_ylabel("Bias with Average 24 Hour Precip [in/day]", color="r")
    ax.set_ylabel("Bias of Days per Year with Precip", color="b")
    ax.set_xlim(0, 24)
    ax.set_xticks((0, 4, 8, 12, 16, 20, 24))
    ax.set_xticklabels(("Mid", "4 AM", "8 AM", "Noon", "4 PM", "8 PM", "Mid"))
    ax.grid(True)
    ax.set_xlabel(
        "Hour Used for 24 Hour Summary, "
        f"Timezone: {ctx['_nt'].sts[station]['tzname']}"
    )
    box = ax.get_position()
    ax.set_position([box.x0, box.y0, box.width * 0.95, box.height])
    ax2.set_position([box.x0, box.y0, box.width * 0.95, box.height])
    ax.legend(
        [line[0], l2[0]],
        ["Days Bias", "Avg Precip Bias"],
        loc="best",
        fontsize=10,
    )
    return fig, df
