Skip to main content

kalast

Kalast is a thermophysical model (TPM) for binary asteroids; it applies to other airless bodies as well. Kalast is also an image simulator for spacecraft cameras, in the visible and in the infrared. Its renderer serves several other uses — viewing and interacting with meshes, generating lightcurves — see the examples/ folder.

TPM

Several solvers of the heat conduction equation are implemented, all on a variable-spacing depth grid:

  • explicit: forward Euler, plus Runge–Kutta–Chebyshev super-time-stepping for problems where the depth system is no longer tridiagonal — lateral conduction, FEM, or a GPU port
  • implicit: backward Euler, Crank–Nicolson, BDF2

Validated against analytical solutions (damped thermal wave, slab relaxation) in examples/analytical/, and pinned by tests: error budgets, amplitude decay and phase lag, and the observed order of accuracy — 1 for backward Euler, 2 for Crank–Nicolson and BDF2.

The surface boundary condition includes:

  • solar radiation
  • self- and mutual heating (thermal re-emission and reflected sunlight)

Depth BC:

  • adiabatic
  • internal flux for larger bodies

Shadows, eclipses and occultations are computed by shadow mapping on the GPU (a CPU ray-tracing path exists, and is slower).

View factors for self- and mutual heating are computed by the hemicube method on the GPU: occlusion comes free from the depth test, one render yields a whole row, and the result is stored sparse (0.3 % dense on the Didymos pair). Radiosity is first order by default, with more bounces on request. Validated to 0.07 % against the closed form for perpendicular squares.

Once surface temperatures are known, the infrared image is rendered from the observer: emitted plus reflected flux, through a camera's resolution, field of view, filters and spectral response function.

Surface roughness is treated twice, once per wavelength range. In the infrared, the Kuehrt spherical-crater model corrects the emitted flux — beaming, which makes a rough surface read hotter and flatter at low phase — with multiple scattering after Lagerros (1996) and Mueller (2007). In the visible, Hapke's macroscopic roughness (θ̄, 1984) enters the photometry and the lightcurves.

Renderer

Kalast's renderer was first written to watch TPM results — surface temperatures evolving as the bodies spin. Matplotlib and MATLAB had served before, and both slowed the CPU simulation down.

The renderer runs on the GPU through wgpu, a pure-Rust implementation of the WebGPU standard, natively on every platform.

Shadow mapping uses PCF and fits its light frustum to the bounding boxes of the bodies in the scene automatically.

Kalast is also a UI app for editing scripts and interacting with the rendered scene, in the spirit of Blender or Unity.

Shape models

Shape models represent a body's surface as triangular facets. Of the many formats, kalast reads Wavefront .obj only.

Getting started

Grab a release from https://github.com/GregoireHENRY/kalast/releases, unpack it, and run it from inside the folder.

./kalast                                       # starts the kalast UI app
./kalast examples/two_spheres/main.py          # loads a Python example
./kalast examples/crater_self_shadow/step.rs   # a Rust one
./kalast some/shape.obj                        # a mesh

There is nothing to install and nothing is written outside the folder. The archive carries its own Python, with kalast and its dependencies already in it, and the .rs examples come pre-compiled. Editing one, or opening a .rs of your own, means compiling it: if the machine has no cargo the kalast UI app fetches a minimal toolchain into toolchain/ beside the executable and reuses it afterwards. On macOS that also wants Apple's command line tools for the linker (xcode-select --install).

Packages

You can also install kalast as a package, in a Python virtual environment or a Rust project:

pip install kalast          # Python
cargo add kalast            # Rust

Repo structure

  • src/: Rust core. Written to be usable standalone by Rust users, independent of Python — the Python wrapper must not compromise its speed.
  • kalast/: Python wrapper. Provides Pythonic usage of Kalast (e.g. object references) for users less familiar with Rust. Built with maturin.
  • shaders/: wgpu shaders (.wgsl) used by the rendering pipeline.
  • examples/: Examples of usage of Kalast. Scripts under examples/old/ are earlier/superseded versions kept for reference, not maintained as user-facing examples.
  • res/: resources folder.
  • out/: default output directory for simulation results.

If you want to clone and compile it yourself

Create a virtual environment for the dependencies and the build — I recommend Astral's uv for Python. Then, from within your venv, run the following.

Build the kalast Rust extension, kalast/_rs.abi3.so (its name on macOS):

maturin develop

Build in debug (the default) while implementing features or fixing bugs. Use --release for benchmarks, and once a feature works.

maturin develop --release

Beyond the default opt-level = 3 there is nothing worth adding: lto = "fat" + codegen-units = 1 were measured on this project and gave no improvement. Recorded in Cargo.toml so it is not retried blindly.

The UI app can also be started from the Python module, here loading an example:

python -m kalast examples/two_spheres/main.py

Then import kalast from Python (or add the crate from Rust) and write your own scripts.

import kalast

Release files for kalast 0.5.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for kalast 0.5.5
File Size Uploaded
kalast-0.5.5.tar.gz 955.8 kB Details

Built distributions (wheels)

Table of built distributions (wheels) for kalast 0.5.5
File
kalast-0.5.5-cp314-abi3-win_amd64.whl CPython 3.14 abi3 Windows x86-64 Details
kalast-0.5.5-cp314-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.14 abi3 Linux glibc 2.17+ x86-64 Details
kalast-0.5.5-cp314-abi3-macosx_11_0_arm64.whl CPython 3.14 abi3 macOS 11.0+ ARM64 Details
kalast-0.5.5-cp314-abi3-macosx_10_12_x86_64.whl CPython 3.14 abi3 macOS 10.12+ x86-64 Details

Total release size: 35.7 MB

Release files / kalast-0.5.5.tar.gz

Download URL kalast-0.5.5.tar.gz
Size 955.8 kB
Tags Source
SHA-256 checksum
How to use checksums
83dcb1c8424f25230d558f0b895fb346fbbafbbca57f21c10d05b0f478a04450
BLAKE2b-256 checksum
How to use checksums
e3ce8bfdef3aebbdbb53c8a029f3af23708fdee99df2eb0385ace8b244f42e53
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 23, 2026.

Transparency log

Release files / kalast-0.5.5-cp314-abi3-win_amd64.whl

Download URL kalast-0.5.5-cp314-abi3-win_amd64.whl
Size 8.1 MB
Tags CPython 3.14 Windows x86-64 abi3
SHA-256 checksum
How to use checksums
b1db0c21231087722bdbf10695639cd7420d88bb046a8a6ee4c63f017af300c9
BLAKE2b-256 checksum
How to use checksums
d0b1fa95e654082d9f1fde799dd73baca1e59d0068a852efef32fb3c2ba44caa
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 23, 2026.

Transparency log

Release files / kalast-0.5.5-cp314-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL kalast-0.5.5-cp314-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 11.3 MB
Tags CPython 3.14 Linux glibc 2.17+ x86-64 abi3
SHA-256 checksum
How to use checksums
a160f4c6e029d1b8f3dea4f1dd9fef151bcbc6e416eb70df682a28b0bb823c53
BLAKE2b-256 checksum
How to use checksums
fedc51ab47481a98a960fa51587001cb29653f6c352eb44fc9d69091f067b12a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 23, 2026.

Transparency log

Release files / kalast-0.5.5-cp314-abi3-macosx_11_0_arm64.whl

Download URL kalast-0.5.5-cp314-abi3-macosx_11_0_arm64.whl
Size 7.5 MB
Tags CPython 3.14 abi3 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
0c505b099dd4b1fb5118e453b9cf2dcd7f34e875a2f30ddfd146ad2540fc5b89
BLAKE2b-256 checksum
How to use checksums
f2e4cce1e2655e2e52fc2074f465ab0e1a650d34bcee987893d36f5218e02fb0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 23, 2026.

Transparency log

Release files / kalast-0.5.5-cp314-abi3-macosx_10_12_x86_64.whl

Download URL kalast-0.5.5-cp314-abi3-macosx_10_12_x86_64.whl
Size 7.8 MB
Tags CPython 3.14 abi3 macOS 10.12+ x86-64
SHA-256 checksum
How to use checksums
ad7fbdb36a30989c8c4e775761b67db70b4b3dd1acaa711e20551eafeeb4a37e
BLAKE2b-256 checksum
How to use checksums
446bc3cf03a3c6a63d6f6d16e105cf64b129045a5001991771ab49df4de49eeb
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 23, 2026.

Transparency log

Release history Release notifications | RSS feed

0.5.9

5 release files

0.5.8

5 release files

0.5.7

5 release files

0.5.6

5 release files

This release

0.5.5 This release

5 release files

0.5.4

5 release files

0.5.3

5 release files

0.5.2

5 release files

0.5.1

5 release files

0.5.0

5 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page