seiza 0.10.0

Star detection, WCS fitting, and hinted/blind plate solving for astrophotography

seiza (Python)

Python bindings for seiza: star detection, WCS fitting, and hinted/blind plate solving for astrophotography, implemented in Rust. Solves typical frames in a fraction of a second.

pip install seiza

Binary wheels cover Linux (x86_64, aarch64), macOS (universal2), and Windows (x64); each is a single abi3 wheel for every CPython from 3.9 up. Type stubs are included, and solving releases the GIL.

Solve an image

import numpy as np
import seiza

# One-time: download the verified solver catalogs into the shared cache.
paths = seiza.fetch_catalogs()  # lightweight Tycho-2 set
catalog = seiza.StarCatalog.open(paths["stars-lite-tycho2.bin"])

# Detect stars in a 2D float32 (or uint8) luma array.
stars = seiza.detect(image_array)

# Hinted solve: approximate center and pixel scale. sip_order=3 also fits
# SIP distortion polynomials when enough matched stars support them.
solution = seiza.solve(
    stars, catalog, width, height,
    ra=150.1, dec=35.2, scale_arcsec_px=2.5, sip_order=3,
)
print(solution)                 # center, scale, matches, RMS
print(solution.rotation_deg, solution.flipped)
ra, dec = solution.wcs.pixel_to_world(100.0, 200.0)

open takes a file, a directory (the right catalog inside is picked — the deepest star catalog wins), or nothing at all. With no argument the standard places are searched: SEIZA_STAR_DATA / SEIZA_BLIND_INDEX, files next to the program, and the seiza setup directories (SEIZA_CATALOG_DIR). These are the same rules as the CLI's --data:

catalog = seiza.StarCatalog.open("data")   # directory
catalog = seiza.StarCatalog.open()         # after seiza setup

Stars can also be plain (x, y, flux) tuples from any other detector — the solver only needs positions and relative brightness:

solution = seiza.solve([(x1, y1, f1), (x2, y2, f2), ...], catalog, w, h,
                       ra=..., dec=..., scale_arcsec_px=...)

Blind solve

No position hint, only a plausible scale range. Uses the prebuilt whole-sky pattern index and the deep Gaia catalog:

paths = seiza.fetch_catalogs(["stars-deep-gaia17.bin", "blind-gaia16.idx"])
catalog = seiza.StarCatalog.open(paths["stars-deep-gaia17.bin"])
index = seiza.BlindIndex.open(paths["blind-gaia16.idx"])
solution = seiza.solve_blind(stars, catalog, index, width, height,
                             min_scale_arcsec_px=0.5, max_scale_arcsec_px=15.0)

For faint fields, the optional stars-deep-gaia20.bin catalog reaches Gaia G≤20 (about 9 GB). It is intentionally not included in fetch_catalogs("all"), so request it explicitly with the same G≤16 blind index:

paths = seiza.fetch_catalogs(["stars-deep-gaia20.bin", "blind-gaia16.idx"])
catalog = seiza.StarCatalog.open(paths["stars-deep-gaia20.bin"])
index = seiza.BlindIndex.open(paths["blind-gaia16.idx"])

FITS WCS output

Solutions convert directly to FITS WCS keywords (1-indexed CRPIX, TAN or TAN-SIP projection, CD matrix, and the complete A_p_q/B_p_q/AP_p_q/ BP_p_q set when distortion was fitted):

cards = solution.fits_header_cards()   # dict of keyword -> value
text = solution.fits_header_text()     # 80-column cards ending with END

The header text form is suitable for header-injection APIs — for example Siril's sirilpy scripting interface (set_image_header), which makes a seiza solve usable from a Siril Python script.

Predicted satellite tracks

After a solve, predict which satellites crossed the image while the shutter was open. Predictions come from orbital elements — they are never pixel detections. The exposure must be one continuous shutter-open interval (not a stack's total integration) and needs an observer location:

sats = seiza.SatelliteCatalog.fetch_celestrak()   # cached; ~2h refresh floor
# or offline / historical: seiza.SatelliteCatalog.open("elements.json")

result = sats.tracks_in_footprint(
    solution.wcs, width, height,
    start="2026-07-19T06:12:00Z",     # Unix seconds, RFC 3339, or tz-aware datetime
    duration_s=120.0,
    latitude=42.466, longitude=-71.1516, altitude_m=150.0,
)
for track in result.tracks:           # highest elevation first
    print(track.label, track.max_elevation_deg, track.clipped_segments)

Element records older than seven days are reported in result.stale_elements and skipped rather than silently extrapolated (max_element_age_s=None overrides). CelesTrak rate-limits repeated downloads: keep reusing one cache directory, and check sats.cache_state and sats.warning after fetch_celestrak().

Notes

• Solving and detection release the GIL; other Python threads keep running.
• Catalog files are memory-mapped and SHA-256 verified at download time; fetch_catalogs caches under the platform cache directory (override with cache_dir= or SEIZA_CACHE_DIR).
• seiza.StarCatalog.from_stars([...]) builds a small in-memory catalog for tests and synthetic fields.

License

Apache-2.0