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adam-assist

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Table of Contents

Overview

adam-assist is a pluggable propagator class for the adam-core package that uses ASSIST for propagating orbits.

Installation

pip install adam-assist

Native wheels support CPython 3.11-3.13 on manylinux 2.17+ x86-64/AArch64 and macOS Apple silicon/Intel. Windows is currently unsupported because libassist-sys 1.2.1 wraps upstream ASSIST code that requires POSIX sys/mman.h memory mapping; no Windows port is bundled. Musllinux is also unsupported.

Pure Rust

Rust-only consumers can use the same backend from crates.io without Python:

[dependencies]
adam_core = "=0.1.0-rc.5"
adam_assist = "=0.4.0-rc.7"

use adam_assist::{AssistPropagator, AssistResult};

fn main() -> AssistResult<()> {
    let propagator = AssistPropagator::from_default_kernels()?;
    let _integrator = propagator.integrator();
    Ok(())
}

The default resolver loads DE440 and SB441-n16 through adam-core's environment, installed-package, cache, and checksummed-fetch policy. Explicit/offline users can call AssistPropagator::from_paths and can disable default resolver support with default-features = false.

Usage

Propagating Orbits

Here we initialize a set of adam_core.orbit.Orbit objects from the JPL Small Bodies Database and propagate them using the Rust-backed ASSISTPropagator class. You can manually initialize the orbits as well.

from adam_core.orbits.query.sbdb import query_sbdb
from adam_core.time import Timestamp
from adam_assist import ASSISTPropagator

# Query the JPL Small Bodies Database for a set of orbits
sbdb_orbits = query_sbdb(["2020 AV2", "A919 FB", "1993 SB"])
times = Timestamp.from_mjd([60000, 60365, 60730], scale="tdb")


propagator = ASSISTPropagator()

propagated = propagator.propagate_orbits(sbdb_orbits, times)

Of course you can define your own orbits as well.

import pyarrow as pa
from adam_core.orbits import Orbit
from adam_core.coordinates import CartesianCoordinates, Origin
from adam_core.time import Timestamp
from adam_assist import ASSISTPropagator

# Define an orbit
orbits = Orbit.from_kwargs(
  orbit_id=["1", "2", "3"],
  coordinates=CartesianCoordinates.from_kwargs(
    # use realistic cartesian coords in AU and AU/day
    x=[-1.0, 0.0, 1.0],
    y=[-1.0, 0.0, 1.0],
    z=[-1.0, 0.0, 1.0],
    vx=[-0.1, 0.0, 0.1],
    vy=[-0.1, 0.0, 0.1],
    vz=[-0.1, 0.0, 0.1],
    time=Timestamp.from_mjd([60000, 60365, 60730], scale="tdb"),
    origin=Origin.from_kwargs(code=pa.repeat("SUN", 3)),
    frame="eliptic"
  ),
)

propagator = ASSISTPropagator()

propagated = propagator.propagate_orbits(orbits)

Non-Gravitational Forces

Orbits whose non_gravitational_parameters carry Marsden-style A1/A2/A3 accelerations (au/d^2) are propagated with ASSIST's non-gravitational force. The g(r) law is selected by the ALN/NK/NM/NN/R0 columns: null constants mean the asteroid convention g(r) = (1 au / r)^2, and an explicit tuple (for example the standard Marsden comet law, or a custom shape such as 1I/'Oumuamua's) is applied simulation-wide. Batches mixing different g(r) tuples are automatically split into one simulation per force law, since ASSIST holds the constants per simulation rather than per particle.

Supported (regression-tested against JPL Horizons to tens–hundreds of metres over ±300 days): symmetric inverse-square asteroid solutions (e.g. 99942 Apophis), the standard comet Marsden law (e.g. C/2022 E3), and custom symmetric (ALN, NK, NM, NN, R0) tuples (e.g. 1I/'Oumuamua).

Not supported: the asymmetric-outgassing time offset DT (dropped with a warning by adam-core's importers — solutions that estimate DT, such as 67P or 81P, retain km-scale model error through perihelion and should not be treated as JPL-parity), thermophysical Yarkovsky (AMRAT/RHO) models, and estimated (rather than fixed) g(r) shape parameters. Non-finite A-values, partial constants tuples, and degenerate ALN <= 0 / R0 <= 0 values are rejected with a ValueError rather than silently altering the force.

Generating Ephemerides

ASSISTPropagator.generate_ephemeris performs propagation, light-time geometry, optional covariance sampling/collapse, aberration, and photometry in the Rust backend behind one public Python call. Local parallelism uses Rayon rather than adam-core's former Python/Ray composition.

from adam_core.orbits.query.sbdb import query_sbdb
from adam_core.time import Timestamp
from adam_core.observers import Observers
from adam_assist import ASSISTPropagator

# Query the JPL Small Bodies Database for a set of orbits
sbdb_orbits = query_sbdb(["2020 AV2", "A919 FB", "1993 SB"])
times = Timestamp.from_mjd([60000, 60365, 60730], scale="utc")
observers = Observers.from_code("399", times)
propagator = ASSISTPropagator()

ephemerides = propagator.generate_ephemeris(sbdb_orbits, observers)

Benchmarking

Run the complete current-only suite with:

pdm run benchmark-current

It reuses the existing propagation, nongrav, ephemeris/covariance, and collision workload builders, plus five provider-capability checks used by adam-core's public OD workflows. Orbit determination remains an adam-core product rather than a separate adam-assist surface. Results include current public Python timings, genuine Rust-owned std::time::Instant timings where available, public/native overhead, and exact workload shapes. It does not require a frozen Python environment or baseline timing cache, and all ASSIST workloads use max_processes=1. Use --quick for a smoke run or select, for example, --domains nongrav ephemeris covariance --lanes tiny small. Release CI runs 30 ASSIST product workloads plus the five Core-OD backend-capability checks, all with native timing required. After wheel acceptance, release candidates also run all 54 live JPL Horizons propagation and ephemeris accuracy cases.

The deterministic test suite uses reviewed, hash-pinned frozen outputs for all formerly two-runtime propagation, covariance, ephemeris, collision, typed mapping, and OD/Vallado parity cases. Normal pytest and the current benchmark suite do not import or launch the archived ASSIST oracle and never skip because a legacy virtual environment is absent.

Configuration

When initializing the ASSISTPropagator, you can configure several parameters that control the integration. These parameters are passed directly to REBOUND's IAS15 integrator. The IAS15 integrator is a high accuracy integrator that uses adaptive timestepping to maintain precision while optimizing performance.

  • min_dt: Minimum timestep for the integrator (default: 1e-12 days)
  • initial_dt: Initial timestep for the integrator (default: 0.001 days)
  • epsilon: Controls the adaptive timestep behavior (default: 1e-6)
  • adaptive_mode: Controls the adaptive timestep behavior (default: 1)

These parameters are passed directly to REBOUND's IAS15 integrator. The IAS15 integrator is a high accuracy integrator that uses adaptive timestepping to maintain precision while optimizing performance.

Example:

propagator = ASSISTPropagator(
  min_dt=1e-12,
  initial_dt=0.0001,
  epsilon=1e-6,
  adaptive_mode=1
)

When initializing the ASSISTPropagator, you can configure several parameters that control the integration. These parameters are passed directly to REBOUND's IAS15 integrator. The IAS15 integrator is a high accuracy integrator that uses adaptive timestepping to maintain precision while optimizing performance.

Default SPK Files

The asteroids SPK file sb441-n16.bsp contains the 16 largest asteroids in the solar system. They are listed here by number for reference:

1 Ceres 3 Juno 4 Vesta 7 Iris 10 Hygiea 15 Eunomia 16 Psyche 31 Euphrosyne 52 Europa 65 Cybele 70 Panopaea 87 Sylvia 88 Thisbe 107 Camilla 511 Davida 704 Interamnia

Project links

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  • Released:
Newer release available (0.4.0)

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Credits

Author: Kathleen Kiker

GitHub Statistics

Data verified by PyPI on Aug 28, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

GNU General Public License v3 (GPLv3)

Requires

Python <3.14, >=3.11

Provides Extra

dev

Tags

astronomy orbital mechanics propagation

Classifiers

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