Featuring the fastest CPU based open source SGP4 propagator

Sub-meter accuracy validated against reference implementations. 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 →

pip install astroz

Drop-in replacement for python-sgp4. 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))

• 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);

• 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.

• SGP4 Propagation

  Analytical orbit propagation using SGP4 with TLE input. Demonstrates both direct SGP4 usage and the modular propagator interface.

• Cesium Satellite Visualization — Live Demo

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

• Orbit Orientation Determination

  Calculate spacecraft attitude and orientation.

• 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.