Featuring the fastest CPU based open source SGP4/SDP4 propagator

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.

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 →

Force models and propagators are validated against reference implementations:

Run validation tests: zig build test

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

Only supports Linux x86_64 and macOS ARM64 (Python 3.10–3.12):

pip install astroz

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

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)

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))

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.

• 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

• 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.

• 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.

• 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.

• 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.