astro-resolver 0.4.0

Astrophotography toolkit: object name resolution, FITS subframe quality analysis.

astro-resolver

Astrophotography toolkit for FITS workflows and imaging pipelines. Two independent packages, one library:

Package	Purpose
astro_resolver	Turns messy OBJECT headers into clean folder names
astro_quality	Analyses FITS subframes for stacking quality

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Installation

pip install astro-resolver

# With star detection / FWHM support (recommended):
pip install "astro-resolver[quality]"

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astro_resolver — Object name resolution

Resolves FITS OBJECT header values like "M 81", "sh2-101" or "barnard 33" into filesystem-safe folder names such as "M81_BodesGalaxy" or "B33_HorseheadNebula".

Features

• Local-first — SIMBAD only as a last resort
• Solar System, Caldwell, custom aliases, OpenNGC (~14k objects), Sharpless and Barnard catalogs bundled offline
• Stellar catalog IDs (HD, HIP, SAO, TYC, …) skip the SIMBAD name round-trip and go straight to coordinate lookup
• Coordinate fallback via SIMBAD when name lookup fails
• Output is always filesystem-safe (no spaces, special chars)

Resolution priority

1. Solar System (local)
2. Caldwell (local)
3. Custom aliases (local)
4. OpenNGC offline (~14k NGC/IC objects)
5. Sharpless catalog (Sh 2-xxx HII regions)
6. Barnard catalog (dark nebulae)
7. SIMBAD by name (online)
8. SIMBAD by coordinates (online)
9. Sanitized header string as fallback

Usage

from astro_resolver import resolve_object_name

resolve_object_name("M 81")           # → "M81_BodesGalaxy"
resolve_object_name("sh2-101")        # → "Sh2-101_TulipNebula"
resolve_object_name("barnard 33")     # → "B33_HorseheadNebula"

# Stellar catalog ID with coordinates → SIMBAD coord lookup
resolve_object_name("HD 1", ra="00 05 09", dec="+67 50 24")

The resolver accepts an optional log callable so it can be plugged into Siril scripts (log=siril.log) or any other host environment.

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astro_quality — Subframe quality analysis

Analyses FITS light frames for stacking quality. Supports any camera (DSLR, ZWO ASI, QHY, DWARF, …) — Bayer RAW and mono alike. Calibration frames (Flats, Darks, Bias) are detected and skipped automatically via filename patterns and the IMAGETYP FITS header.

Metrics per subframe

Metric	Description	Direction
background_noise	Sky background σ (ADU, sigma-clipped)	lower = better
snr_proxy	Signal/noise ratio proxy	higher = better
gradient	Spatial background variation (light pollution gradient)	lower = better
saturation_pct	Fraction of saturated pixels (%)	lower = better
star_count	Number of detected stars (transparency proxy)	higher = better
fwhm_median	Median star width in luminance pixels (focus/seeing)	lower = better
eccentricity	Median star roundness 0=round 1=line (tracking quality)	lower = better

Star count, FWHM and eccentricity require photutils (pip install "astrolib[quality]").

Scoring

Each subframe receives a relative quality score 0–100 within its session. Scores are relative: a score of 90 in a poor night may be worse in absolute terms than 70 in an excellent night. Use raw metric values for cross-session comparison.

Grade	Score	Meaning
A	85–100	Excellent
B	70–84	Good
C	50–69	Average
D	30–49	Poor
F	0–29	Reject

Usage

Analyse a single frame:

from pathlib import Path
from astro_quality import analyze_fits, score_sub, grade, compute_session_stats

result = analyze_fits(Path("IC443_0042.fits"))
if not result["error"]:
    m = result["metrics"]
    print(f"Noise: {m['background_noise']:.1f} ADU")
    print(f"SNR:   {m['snr_proxy']:.1f}")
    print(f"Stars: {m['star_count']}")   # -1 if photutils not installed

Score a session (relative quality):

from astro_quality import analyze_fits, compute_session_stats, score_sub, grade

results = [analyze_fits(f) for f in session_fits_files]
stats   = compute_session_stats(results)

for r in results:
    s = score_sub(r["metrics"], stats)
    print(f"{grade(s)} ({s:.1f})  {r['filename']}")

Scan an archive and build a quality report:

from pathlib import Path
from astro_quality import build_report_data
from astro_quality.archive import iter_sessions, iter_fits
from astro_quality.cache import load_cache, save_cache

ARCHIVE = Path("z:/AstroArchiv/Objects")
CACHE   = Path("quality_cache.json")

cache = load_cache(CACHE)

for obj, sess, sess_path in iter_sessions(ARCHIVE):
    for f in iter_fits(sess_path):          # skips Flats/Darks automatically
        if str(f) not in cache:
            result = analyze_fits(f)
            result["object"]  = obj
            result["session"] = sess
            cache[str(f)] = result

save_cache(cache, CACHE)

report = build_report_data(cache, obj_filter="IC443")
for sess, data in report["IC443_GemA"].items():
    print(f"{sess}: {data['score_mean']:.1f} ({data['grade']})  "
          f"noise={data['stats']['noise_med']:.1f}")

Calibration frame detection:

from astro_quality.archive import is_calib_frame
from pathlib import Path

is_calib_frame(Path("Flat_30s_ISO800_0001.fit"))   # True
is_calib_frame(Path("Light_M51_120s_0001.fit"))    # False

Archive layout expected by iter_sessions / iter_fits

<root>/
├── IC443_GemA/
│   ├── 2026-01-19_DWARF_3/
│   │   ├── IC 443_60s60_..._0001.fits   ← light frames
│   │   └── Flat_200ms_..._0001.fits     ← skipped automatically
│   └── 2026-04-01_DWARF_3/
│       └── ...
└── M51_WhirlpoolGalaxy/
    ├── 2026-04-25_ZWO_ASI585MC_Pro/
    │   └── Light_M51_120s_..._0001.fit
    └── presets/                          ← skipped (non-session dir)

Non-session subdirectories (presets, flats, calibration, masters, …) are skipped automatically.

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Data sources

• OpenNGC — CC BY-SA 4.0
• SIMBAD via astroquery

License

MIT