"""Flares on the Sun in the 24 hours before the S0 News episode of October 26, 2026, from NOAA's records.

Usage: python3 -I sun_window.py [FOLDER]
FOLDER holds the three files published next to this script (default: the script's own folder):
  goes18-xray-1min-2026-10-25-26.csv   GOES-18 1-minute X-ray flux, 0.1-0.8 nm, October 25 10:00 to October 26 12:00 UTC
  goes18-flares-2026-10-19-26.csv      GOES-18 flare list, the 7 days to October 26, 17:36 UTC
  swpc-xra-events-2026-10-25-26.csv    the X-ray events (XRA) in the Space Weather Prediction Center's Edited Events
They are extracts of NOAA files that roll over after 3 and 7 days (services.swpc.noaa.gov/json/goes/primary/
xrays-3-day.json and xray-flares-7-day.json, and ftp.swpc.noaa.gov/pub/indices/events/2026102{5,6}events.txt),
as served on October 26, 2026, about 19:00 UTC. NOAA data are US government works, in the public domain.

Prints, for the 24 hours before the upload (11:51:41 UTC) and three other windows: the flares by class, the peak flux
and its time, the minutes at or above C1 (1e-6 W/m2) and the median flux. Standard library only. "Our analysis".
Flare classes from a peak flux F (W/m2): A < 1e-7 <= B < 1e-6 <= C < 1e-5 <= M < 1e-4 <= X.
"""
import csv
import datetime as dt
import os
import statistics
import sys

UTC = dt.timezone.utc
UPLOAD = dt.datetime(2026, 10, 26, 11, 51, 41, tzinfo=UTC)  # the video's uploadDate, 2026-10-26T04:51:41-07:00


def iso(s):
    return dt.datetime.strptime(s, "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=UTC)


def flare_class(f):
    for lim, c in ((1e-4, "X"), (1e-5, "M"), (1e-6, "C"), (1e-7, "B")):
        if f >= lim:
            return f"{c}{f / lim:.1f}"
    return f"A{f / 1e-8:.1f}"


def main():
    folder = sys.argv[1] if len(sys.argv) > 1 else os.path.dirname(os.path.abspath(__file__))
    with open(os.path.join(folder, "goes18-xray-1min-2026-10-25-26.csv"), newline="", encoding="utf-8") as f:
        flux = [(iso(r["time_utc"]), float(r["flux_w_m2"])) for r in csv.DictReader(f)]
    with open(os.path.join(folder, "goes18-flares-2026-10-19-26.csv"), newline="", encoding="utf-8") as f:
        flares = [(iso(r["max_utc"]), r["max_class"]) for r in csv.DictReader(f)]
    events = []
    with open(os.path.join(folder, "swpc-xra-events-2026-10-25-26.csv"), newline="", encoding="utf-8") as f:
        for r in csv.DictReader(f):
            day = dt.datetime.strptime(r["date"], "%Y-%m-%d").replace(tzinfo=UTC)
            t = day + dt.timedelta(hours=int(r["max_utc"][:2]), minutes=int(r["max_utc"][2:]))
            events.append((t, r["xray_class"], r["region"], r["event"]))

    windows = [
        ("24 hours before the upload", UPLOAD - dt.timedelta(hours=24), UPLOAD),
        ("24 hours before 11:00 UTC on October 26", dt.datetime(2026, 10, 25, 11, tzinfo=UTC), dt.datetime(2026, 10, 26, 11, tzinfo=UTC)),
        ("24 hours before 10:00 UTC on October 26", dt.datetime(2026, 10, 25, 10, tzinfo=UTC), dt.datetime(2026, 10, 26, 10, tzinfo=UTC)),
        ("October 25, 00:00-24:00 UTC (no 1-minute flux)", dt.datetime(2026, 10, 25, tzinfo=UTC), dt.datetime(2026, 10, 26, tzinfo=UTC)),
    ]
    for name, a, b in windows:
        print(f"== {name}: {a:%b %d %H:%M:%S} to {b:%b %d %H:%M:%S} UTC")
        fw = [x for x in flares if a <= x[0] < b]
        by = {}
        for _, c in fw:
            by[c[0]] = by.get(c[0], 0) + 1
        print(f"   GOES-18 flare list: {len(fw)} flares, by class {dict(sorted(by.items()))}")
        print("   C class or above:", ", ".join(f"{c} at {t:%d %H:%M}" for t, c in fw if c[0] in "CMX") or "none")
        ev = [x for x in events if a <= x[0] < b]
        print(f"   Edited Events, X-ray: {len(ev)} events, {sum(1 for x in ev if x[1][0] in 'CMX')} of C class or above")
        fx = [x for x in flux if a <= x[0] < b]
        if len(fx) >= 1380:  # the 1-minute extract starts at 10:00 UTC on October 25
            top = max(fx, key=lambda x: x[1])
            print(f"   1-minute flux: peak {top[1]:.3e} W/m2 ({flare_class(top[1])}) at {top[0]:%d %H:%M} UTC; "
                  f"{sum(1 for x in fx if x[1] >= 1e-6)} minutes at or above C1; median {statistics.median(v for _, v in fx):.2e} "
                  f"({flare_class(statistics.median(v for _, v in fx))}); {len(fx)} minutes")
    big = [(t, c) for t, c in flares if c[0] in "MX"]
    print("M- or X-class flares anywhere in the 7-day list:", big or "none",
          f"(the list runs {flares[0][0]:%b %d %H:%M} to {flares[-1][0]:%b %d %H:%M} UTC)")
    t19 = dt.datetime(2026, 10, 26, 8, 38, tzinfo=UTC)
    gap = UPLOAD - t19
    print(f"From the C1.9 at 08:38 UTC to the upload: {gap.seconds // 3600} h {gap.seconds % 3600 // 60} min")


if __name__ == "__main__":
    main()