astroz 0.9.0

Python bindings for astroz - high-performance astrodynamics library

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
positions = propagate("satellites.tle", times)
positions = propagate("https://example.com/tles.txt", 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).

Building

Requires Zig and Python 3.10+.

cd bindings/python
pip install -e .