Astronomical and Spacecraft Toolkit Written in Zig

Featuring the fastest CPU based open source SGP4/SDP4 propagator

Orbital Mechanics	Spacecraft Ops	Astronomy
SGP4/SDP4 propagation	CCSDS packets	FITS parsing
TLE & OMM parsing	VITA49 packets	WCS coordinates
Orbital maneuvers	Attitude determination	Star precession
Force models (J2-J4, drag, SRP, third-body)	CSPICE ephemeris	Celestial bodies
Dormand-Prince 8(7) integrator	Monte Carlo sims

Performance

Sub-meter accuracy validated against reference implementations. Automatically dispatches between SGP4 (near-earth) and SDP4 (deep-space) based on orbital period. Uses SIMD (AVX512/AVX2) to process 8 satellites simultaneously, with multithreaded constellation propagation across all available cores.

Single-Threaded (1.2M propagations, single satellite)

Implementation	Props/sec	Speedup vs python-sgp4
astroz	30.8M	11x
Rust sgp4	5.1M	1.8x
heyoka	3.8M	1.3x
satkit	3.5M	1.2x
python-sgp4	2.8M	1x

Multi-Threaded Constellation (13,478 satellites × 1,440 times)

Implementation	1 Thread	16 Threads
astroz	37.7M/s	303M/s
heyoka	15.7M/s	155.6M/s
Rust sgp4 (rayon)	4.4M/s	47.9M/s
satkit	3.5M/s	3.5M/s
python-sgp4	2.7M/s	2.7M/s

Benchmarked on AMD Ryzen 7 7840U (16 threads). All implementations using their optimal configurations (SIMD, pre-allocated outputs, batch mode).

Uses SIMD (AVX512 for 8-wide, AVX2/SSE for 4-wide) with multithreaded time-major iteration. Validated against Vallado AIAA 2006-6753 reference vectors (< 10m position error, < 1µm/s velocity error). Set ASTROZ_THREADS environment variable to control thread count (defaults to all available cores).

The Cesium visualization example propagates the entire active satellite catalog (~13,000 satellites) at interactive rates. Try the live demo →

Validated Accuracy

Force models and propagators are validated against reference implementations:

Component	Reference	Tolerance
SGP4/SDP4 propagation	python-sgp4	< 100m position
J2 RAAN drift	Vallado analytical	< 1%
Hohmann transfer ΔV	poliastro	< 0.1%
Orbital periods	Analytical	1e-10 relative
Two-body energy	Conservation law	1e-10 over 100 orbits

Run validation tests: zig build test

With CSPICE enabled (for high-precision ephemeris): zig build test -Denable-cspice=true

Python

Only supports Linux x86_64 and macOS ARM64 (Python 3.10–3.14):

pip install astroz

python-sgp4 Compatible API (Recommended)

Drop-in replacement for python-sgp4 with transparent deep-space (SDP4) support. Just change the import for instant speedup:

# Before                                    # After
from sgp4.api import Satrec, jday      →    from astroz.api import Satrec, jday

Your Code	python-sgp4	astroz	Speedup
sat.sgp4() loop	1.3M/s	2.5M/s	2x
sat.sgp4_array()	2.7M/s	15M/s	5x
SatrecArray.sgp4()	3M/s	290M/s	100x

See migration guide for optimization tips.

from astroz.api import Satrec, SatrecArray, jday, WGS72
import numpy as np

# Single satellite (same syntax as python-sgp4)
line1 = "1 25544U 98067A   24127.82853009  .00015698  00000+0  27310-3 0  9995"
line2 = "2 25544  51.6393 160.4574 0003580 140.6673 205.7250 15.50957674452123"
sat = Satrec.twoline2rv(line1, line2, WGS72)

jd, fr = jday(2024, 5, 6, 12, 0, 0.0)
error, position, velocity = sat.sgp4(jd, fr)

# Batch propagation (270-330M props/sec with SIMD)
sat_array = SatrecArray([sat1, sat2, ...])  # List of Satrec objects

# Single time point (scalars)
e, r, v = sat_array.sgp4(2460000.5, 0.5)

# Multiple time points (arrays)
jd = np.full(1440, 2460000.5)
fr = np.linspace(0, 1, 1440)
e, r, v = sat_array.sgp4(jd, fr)  # (n_sats, n_times, 3)

# Skip velocities for 30% faster propagation
e, r, _ = sat_array.sgp4(jd, fr, velocities=False)

High-Level API

Convenience functions for common workflows:

from astroz import propagate, Constellation
import numpy as np

# Load and propagate - automatically optimized for maximum performance
positions = propagate("starlink", np.arange(1440))  # 1 day at 1-min intervals
# shape: (1440, num_satellites, 3) in km, ECEF coordinates

# With options
from datetime import datetime, timezone
positions = propagate(
    "starlink",
    np.arange(1440),
    start_time=datetime(2024, 6, 15, tzinfo=timezone.utc),
    output="geodetic",  # "ecef" (default), "teme", or "geodetic"
)

# With velocities
positions, velocities = propagate("starlink", np.arange(1440), velocities=True)

# For repeated propagation, pre-parse to avoid overhead
c = Constellation("starlink")
positions = propagate(c, np.arange(1440))

Orbital Mechanics & Numerical Propagation

from astroz import hohmann_transfer, propagate_numerical, EARTH_MU, EARTH_J2, EARTH_R_EQ

# Hohmann transfer: LEO to GEO
result = hohmann_transfer(EARTH_MU, 6778, 42164)
print(f"Total ΔV: {result['total_dv']:.3f} km/s")

# Numerical propagation with J2 perturbation
state = (6778.0, 0.0, 0.0, 0.0, 7.668, 0.0)  # [x,y,z,vx,vy,vz] km, km/s
times, states = propagate_numerical(
    state, 0.0, 86400.0, 60.0, EARTH_MU,
    j2=EARTH_J2, r_eq=EARTH_R_EQ,
)

Also available: bi_elliptic_transfer, lambert, orbital_velocity, orbital_period, escape_velocity. See Python README for full details.

Usage

• Add astroz as a dependency in your build.zig.zon.

zig fetch --save https://github.com/ATTron/astroz/archive/<git_tag_or_commit_hash>.tar.gz
#or
zig fetch --save git+https://github.com/ATTron/astroz/#HEAD

• Use astroz as a module in your build.zig.

const astroz_dep = b.dependency("astroz", .{
    .target = target,
    .optimize = optimize,
});
const astroz_mod = astroz_dep.module("astroz");
exe.root_module.addImport("astroz", astroz_mod);

• Propagate any satellite — the Satellite type auto-selects SGP4 or SDP4:

const astroz = @import("astroz");

var tle = try astroz.Tle.parse(tle_string, allocator);
defer tle.deinit();

const sat = try astroz.Satellite.init(tle, astroz.constants.wgs84);
const result = try sat.propagate(60.0); // minutes from epoch
const pos = result[0]; // [x, y, z] km
const vel = result[1]; // [vx, vy, vz] km/s

Examples

Spacecraft Operations

• Force Model Propagation

  Composable force models (TwoBody, J2) with the Dormand-Prince 8(7) adaptive integrator. Shows ForceModel.wrap() and Composite for combining perturbations.

• Constellation Phasing

  Sun-synchronous orbit design and constellation plane separation using J2-induced RAAN drift.

• Orbit Orientation Determination

  Calculate spacecraft attitude and orientation.

Orbital Mechanics

• Planet Transfer & Mission Planning

  Demonstrates interplanetary transfers with mission planning (Hohmann vs Bi-Elliptic comparison) and trajectory propagation.

• Orbit Maneuvers

  Comprehensive example showing TLE-based orbit propagation with various maneuver types: impulse, plane change, and phase change.

• Monte Carlo Simulation

  Statistical analysis for mission planning with uncertainty.

• Propagation

  Analytical orbit propagation using SGP4/SDP4 with TLE input. Demonstrates direct SGP4 usage, the modular propagator interface, and the unified Satellite type that auto-dispatches between SGP4 and SDP4.

• SPICE Propagation

  High-fidelity LEO propagation with SPICE-updated Sun/Moon ephemeris. Combines TwoBody + J2 + SRP + third-body perturbations with real-time position updates.

• Cesium Satellite Visualization — Live Demo

  Interactive 3D visualization of the entire near-earth satellite catalog (~13,000 satellites) using Cesium. Features multithreaded SGP4/SDP4 propagation at ~300M props/sec, constellation filtering, search, and satellite tracking.

Telemetry & Data Handling

• Parse Vita49 / with Callback

  VITA Radio Transport (VRT) packet stream parsing.

• Parse CCSDS / with File Sync

  Parse CCSDS space packet protocol from files.

• Create CCSDS Packet / with Config

  Generate CCSDS packets for telemetry.

Astronomy & Astrometry

• Generate Image from FITS File

  Parse and render FITS astronomical image data.

• Precess Star Coordinates

  Calculate stellar precession to a target epoch.

• Calculate WCS from TLE

  Compute World Coordinate System values from orbital elements.