"""Build ACRIM composite TSI charts from ACRIM3composite_nnaa3.txt (NOAA NGDC). Source: NOAA NGDC / ACRIM Science Team Format: 4 columns (no header) — decimal year, TSI, uncertainty, day-of-mission Scale note: Old ACRIM radiometric scale (~1366 W/m²); ~5 W/m² higher than modern SI-traceable scale (Kopp & Lean 2011, GRL 38, L01706). Reference: Willson, R.C. (1997) Total solar irradiance trend during solar cycles 21 and 22. Science 277, 1963-1965. DOI: 10.1126/science.277.5334.1963 Attribution: ACRIM Science Team; data via NOAA NCEI/NGDC. """ from pathlib import Path import datetime import matplotlib.dates as mdates import matplotlib.pyplot as plt import pandas as pd BASE_DIR = Path(__file__).parent INPUT_FILE = BASE_DIR / "tsi_raw" / "acrim_composite" / "raw" / "ACRIM3composite_nnaa3.txt" OUT_DIR = BASE_DIR / "tsi_satellite_sources" OUT_PNG = OUT_DIR / "acrim_composite_tsi_daily_monthly_annual.png" OUT_CSV = OUT_DIR / "acrim_composite_tsi_daily.csv" OUT_MANIFEST = OUT_DIR / "acrim_composite_tsi_manifest.txt" def decimal_year_to_date(dy: float) -> datetime.date: year = int(dy) fraction = dy - year start = datetime.datetime(year, 1, 1) end = datetime.datetime(year + 1, 1, 1) secs = (end - start).total_seconds() return (start + datetime.timedelta(seconds=fraction * secs)).date() def load_daily() -> pd.DataFrame: rows = [] for line in INPUT_FILE.read_text(encoding="utf-8").splitlines(): parts = line.split() if len(parts) < 2: continue try: dy = float(parts[0]) tsi = float(parts[1]) unc = float(parts[2]) if len(parts) > 2 else float("nan") except ValueError: continue if not (1200.0 < tsi < 1500.0): continue rows.append((decimal_year_to_date(dy), tsi, unc)) df = pd.DataFrame(rows, columns=["date", "tsi", "uncertainty"]) df["date"] = pd.to_datetime(df["date"]) return df.sort_values("date").reset_index(drop=True) def build_chart(frame: pd.DataFrame) -> None: monthly = frame.set_index("date")["tsi"].resample("MS").mean().reset_index(name="tsi") annual = frame.set_index("date")["tsi"].resample("YS").mean().reset_index(name="tsi") annual["plot_date"] = annual["date"] + pd.offsets.Day(181) plt.style.use("seaborn-v0_8-whitegrid") fig, ax = plt.subplots(figsize=(12.5, 6.8), dpi=170) ax.scatter(frame["date"], frame["tsi"], s=10, color="#93c5fd", alpha=0.55, edgecolors="none", label="Daily means") ax.plot(monthly["date"], monthly["tsi"], color="#2563eb", linewidth=1.8, label="Monthly means") ax.plot(annual["plot_date"], annual["tsi"], color="#1e3a8a", linewidth=2.4, marker="o", markersize=3.4, label="Annual means") ax.set_title("ACRIM Composite TSI — ACRIM Science Team / NOAA NGDC (1978–2013)", fontsize=15, pad=14) ax.set_ylabel("Total Solar Irradiance (W m^-2)") ax.set_xlabel("Year") ax.xaxis.set_major_locator(mdates.YearLocator(2)) ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y")) plt.setp(ax.get_xticklabels(), rotation=45, ha="right", fontsize=8.5) ax.set_xlim(frame["date"].min(), frame["date"].max()) ax.legend(loc="upper right", frameon=True) note = ( "Source: ACRIM3composite_nnaa3.txt — NOAA NCEI/NGDC\n" "WARNING: Old ACRIM radiometric scale (~1366 W/m2); ~5 W/m2 above modern SI-traceable scale\n" "Ref: Willson (1997) Science 277, 1963 · DOI 10.1126/science.277.5334.1963\n" "Attribution: ACRIM Science Team | hosted by NOAA NCEI/NGDC" ) ax.text(0.015, 0.03, note, transform=ax.transAxes, fontsize=8.0, bbox=dict(boxstyle="round,pad=0.35", facecolor="white", edgecolor="#cbd5e1", alpha=0.96)) fig.tight_layout() fig.savefig(OUT_PNG, bbox_inches="tight") plt.close(fig) def write_outputs(frame: pd.DataFrame) -> None: OUT_DIR.mkdir(parents=True, exist_ok=True) out = frame.copy() out["date"] = out["date"].dt.strftime("%Y-%m-%d") out.to_csv(OUT_CSV, index=False) monthly_rows = frame.set_index("date")["tsi"].resample("MS").mean().shape[0] annual_rows = frame.set_index("date")["tsi"].resample("YS").mean().shape[0] lines = [ "ACRIM Composite TSI manifest", "", "Dataset: ACRIM composite (Willson 1997; updated through ~2013)", "File: ACRIM3composite_nnaa3.txt", "Instruments: ACRIM I (SMM), ACRIM II (UARS), ACRIM III (ACRIMSAT)", "Publisher: ACRIM Science Team; hosted by NOAA NCEI/NGDC", "Source URL: https://www.ngdc.noaa.gov/stp/space-weather/solar-data/solar-indices/" "total-solar-irradiance/acrim-3/ACRIM3composite_nnaa3.txt", "Raw file: tsi_raw/acrim_composite/raw/ACRIM3composite_nnaa3.txt", "", f"Date span: {frame['date'].min().date()} to {frame['date'].max().date()}", f"Daily rows: {len(frame)}", f"Monthly bins: {monthly_rows}", f"Annual bins: {annual_rows}", f"TSI range: {frame['tsi'].min():.4f} to {frame['tsi'].max():.4f} W m^-2", "", "Scale note: Old ACRIM radiometric scale (~1366 W/m²), ~5 W/m² above modern", " SI-traceable scale. See Kopp & Lean (2011) GRL 38, L01706.", "", "Attribution (required):", " Willson, R.C. (1997) Total solar irradiance trend during solar cycles 21 and 22.", " Science 277, 1963-1965. DOI: 10.1126/science.277.5334.1963", " Acknowledge ACRIM Science Team and NOAA NCEI/NGDC.", ] OUT_MANIFEST.write_text("\n".join(lines) + "\n") def main() -> None: if not INPUT_FILE.exists(): raise FileNotFoundError(f"Missing input: {INPUT_FILE}") OUT_DIR.mkdir(parents=True, exist_ok=True) frame = load_daily() if frame.empty: raise ValueError("No valid rows parsed from ACRIM composite file") build_chart(frame) write_outputs(frame) print("saved", OUT_PNG.name) print("saved", OUT_CSV.name) print("saved", OUT_MANIFEST.name) print("daily_rows", len(frame)) print("date_span", frame["date"].min().date(), "to", frame["date"].max().date()) print("tsi_range", round(frame["tsi"].min(), 4), "to", round(frame["tsi"].max(), 4)) if __name__ == "__main__": main()