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astroz Python Bindings

High-performance SGP4/SDP4 satellite propagation, powered by Zig with SIMD acceleration (AVX512/AVX2). Automatically handles both near-earth and deep-space satellites.

Platforms: macOS, Linux | Requires: Python 3.10+

Quick Start

python-sgp4 Compatible API (Recommended)

Drop-in replacement for python-sgp4:

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

# Single satellite (same syntax as python-sgp4)
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(satrecs)
e, r, v = sat_array.sgp4(jd, fr)  # Scalars or arrays

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

High-Level API

from astroz import propagate
import numpy as np

# Load and propagate - automatically optimized for 300M+ props/sec
positions = propagate("starlink", np.arange(1440))  # 1 day at 1-min intervals
# positions: (1440, num_satellites, 3) in km, ECEF coordinates

Loading Sources

from astroz import propagate, Constellation

# CelesTrak groups
positions = propagate("starlink", times)
positions = propagate("iss", times)
positions = propagate("gps", times)
positions = propagate("all", times)  # ~13k active satellites

# By NORAD ID
positions = propagate(None, times, norad_id=25544)  # ISS
positions = propagate(None, times, norad_id=[25544, 48274])  # Multiple

# Local file or URL (TLE or OMM JSON auto-detected)
positions = propagate("satellites.tle", times)
positions = propagate("https://example.com/tles.txt", times)

# OMM JSON (CCSDS 502.0-B-3) — supports 6+ digit NORAD IDs
positions = propagate('{"NORAD_CAT_ID":25544,"EPOCH":"2026-04-15T12:00:00.000000","MEAN_MOTION":15.489,...}', times)

# For repeated propagation, pre-parse to avoid overhead
c = Constellation("starlink")
positions1 = propagate(c, times1)
positions2 = propagate(c, times2)

Groups: all, starlink, oneweb, planet, spire, gps, glonass, galileo, beidou, stations/iss, weather, geo

Propagation

from astroz import propagate, Constellation
from datetime import datetime, timezone
import numpy as np

times = np.arange(1440)  # 1 day at 1-min intervals

# Simple (defaults: now UTC, ECEF output)
positions = propagate("starlink", times)

# With options
positions = propagate(
    "starlink",
    np.arange(14 * 1440),  # 2 weeks
    start_time=datetime(2024, 6, 1, tzinfo=timezone.utc),
    output="geodetic",  # "ecef" (default), "teme", or "geodetic"
)

# With velocities
positions, velocities = propagate("starlink", times, velocities=True)

Conjunction Screening

from astroz import screen
import numpy as np

times = np.arange(1440)

# Single target (fastest - uses fused propagate+screen, no full position array)
min_dists, min_t_indices = screen("starlink", times, threshold=50.0, target=0)
# Returns per-satellite minimum distance to target and time index

# All-vs-all screening
pairs, t_indices = screen("starlink", times, threshold=10.0)
# Returns all conjunction events within threshold

Migrating from python-sgp4

Step 1: Change the Import (2x faster)

# Before
from sgp4.api import Satrec, SatrecArray, jday

# After
from astroz.api import Satrec, SatrecArray, jday

That's it. Your existing code works unchanged and runs 2x faster.

Step 2: Use Batch Methods (5-12x faster)

If you have loops, switch to batch methods:

# Before: loop (1.3M props/sec)
results = []
for jd, fr in zip(jd_list, fr_list):
    e, r, v = sat.sgp4(jd, fr)
    results.append(r)

# After: batch (15M props/sec) - 12x faster
jd_array = np.array(jd_list)
fr_array = np.array(fr_list)
e, r, v = sat.sgp4_array(jd_array, fr_array)

Step 3: Use SatrecArray for Multiple Satellites (100x faster)

# Before: loop over satellites (1.3M props/sec)
for sat in satellites:
    e, r, v = sat.sgp4_array(jd, fr)

# After: batch all satellites (290M props/sec) - 100x faster
sat_array = SatrecArray(satellites)
e, r, v = sat_array.sgp4(jd, fr)

Step 4: Skip Velocities If Not Needed (30% faster)

# If you only need positions
e, r, _ = sat_array.sgp4(jd, fr, velocities=False)

Performance Summary

Pattern 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

Compatibility Notes

astroz supports the core python-sgp4 API for satellite propagation. Some rarely-used attributes are not implemented:

  • TLE metadata: classification, intldesg, elnum, revnum, ephtype
  • Intermediate elements: Om, am, em, im, mm, nm, om (osculating elements after propagation)
  • Element rates: argpdot, mdot, nodedot
  • Gravity constants: j2, j3, j4, mu, etc. (available via astroz.constants)

For typical satellite tracking and visualization, astroz is a full drop-in replacement.

Performance

Constellation (13,478 sats x 1,440 steps) Throughput
1 thread 37.7M props/sec
16 threads 303M props/sec

Benchmarked on AMD Ryzen 7 7840U with AVX512.

Set ASTROZ_THREADS to control thread count (defaults to all cores).

Orbital Mechanics

from astroz import (
    hohmann_transfer, bi_elliptic_transfer, lambert,
    orbital_velocity, orbital_period, escape_velocity,
    EARTH_MU,
)

# Hohmann transfer: LEO (400 km) to GEO
result = hohmann_transfer(EARTH_MU, 6778, 42164)
print(f"Total ΔV: {result['total_dv']:.3f} km/s")
print(f"Transfer time: {result['transfer_time_days']:.2f} days")

# Bi-elliptic transfer (more efficient for large radius ratios)
result = bi_elliptic_transfer(EARTH_MU, 6778, 42164, 100000)
print(f"Total ΔV: {result['total_dv']:.3f} km/s")

# Lambert solver: find transfer orbit between two positions
r1 = (7000.0, 0.0, 0.0)
r2 = (0.0, 7000.0, 0.0)
tof = 3600.0  # 1 hour
result = lambert(EARTH_MU, r1, r2, tof)
print(f"Departure velocity: {result['departure_velocity']}")

# Orbital velocity, period, escape velocity
v = orbital_velocity(EARTH_MU, 6778)          # Circular orbit at 400 km
T = orbital_period(EARTH_MU, 6778)            # Period in seconds
v_esc = escape_velocity(EARTH_MU, 6778)       # Escape velocity

Numerical Propagation

Propagate orbits with perturbation models (J2, atmospheric drag) using RK4 or Dormand-Prince 8(7) adaptive integrators. r_eq is required when using J2 or drag perturbations. Drag uses an exponential Earth atmosphere model (sea-level density 1.225 kg/m^3, scale height 7.249 km, cutoff altitude 1500 km). drag_area is in m^2 and drag_mass in kg.

from astroz import propagate_numerical, EARTH_MU, EARTH_J2, EARTH_R_EQ

# Initial state: LEO circular orbit [x, y, z, vx, vy, vz] (km, km/s)
state = (6778.0, 0.0, 0.0, 0.0, 7.668, 0.0)

# Two-body propagation (1 orbit, 60s steps)
times, states = propagate_numerical(state, 0.0, 5554.0, 60.0, EARTH_MU)

# With J2 perturbation
times, states = propagate_numerical(
    state, 0.0, 5554.0, 60.0, EARTH_MU,
    j2=EARTH_J2, r_eq=EARTH_R_EQ,
)

# With J2 + atmospheric drag
times, states = propagate_numerical(
    state, 0.0, 86400.0, 60.0, EARTH_MU,
    j2=EARTH_J2, r_eq=EARTH_R_EQ,
    drag_cd=2.2, drag_area=10.0, drag_mass=500.0,
    integrator="dp87",  # or "rk4"
)

Constants

from astroz import EARTH_MU, EARTH_R_EQ, EARTH_J2, SUN_MU, MOON_MU

print(f"Earth μ: {EARTH_MU} km³/s²")
print(f"Earth radius: {EARTH_R_EQ} km")
print(f"Earth J2: {EARTH_J2}")
print(f"Sun μ: {SUN_MU} km³/s²")
print(f"Moon μ: {MOON_MU} km³/s²")

Building

Requires Zig and Python 3.10+.

cd bindings/python
pip install -e .