Caustics

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations - Published in JOSS (2024)

https://github.com/ciela-institute/caustics

Keywords

gravitational-lensing pytorch

Keywords from Contributors

mesh hydrology energy-system exoplanet closember community-driven icesat-2 hydraulic-modelling polygon geoscience

Scientific Fields

Mathematics Computer Science - 84% confidence

Last synced: 4 months ago · JSON representation ·

Repository

A gravitational lensing simulator for the machine learning era.

Basic Info

Host: GitHub
Owner: Ciela-Institute
License: mit
Language: Python
Default Branch: main
Homepage: https://caustics.readthedocs.io
Size: 28 MB

Statistics

Stars: 44
Watchers: 5
Forks: 14
Open Issues: 39
Releases: 27

Topics

gravitational-lensing pytorch

Created over 3 years ago · Last pushed 4 months ago

Metadata Files

Readme Contributing License Code of conduct Citation

Caustics

The lensing pipeline of the future: GPU-accelerated, automatically-differentiable, highly modular. Currently under heavy development: expect interface changes and some imprecise/untested calculations.

Installation

Simply install caustics from PyPI:

bash pip install caustics

Note: Python 3.9 through 3.12 are recommended; compatibility with versions >=3.13 is currently untested.

Minimal Example

```python

fmt: off

import matplotlib.pyplot as plt import caustics import torch

cosmology = caustics.FlatLambdaCDM() sie = caustics.SIE(cosmology=cosmology, name="lens", zl=0.5, zs=1.0, x0=-0.2, y0=0.0, q=0.4, phi=1.5708, Rein=1.7) src = caustics.Sersic(name="source", x0=0.0, y0=0.0, q=0.5, phi=-0.985, n=1.3, Re=1.0, Ie=5.0) lnslt = caustics.Sersic(name="lenslight", x0=-0.2, y0=0.0, q=0.8, phi=0.0, n=1.0, Re=1.0, Ie=10.0)

sim = caustics.LensSource(lens=sie, source=src, lenslight=lnslt, pixelscale=0.05, pixelsx=100)

plt.imshow(sim(), origin="lower") plt.axis("off") plt.show() ```

Caustics lensed image

Batched simulator

```python sim.todynamic(False) cosmology.tostatic() x = sim.buildparamstensor() newx = x.repeat(20, 1) newx += torch.normal(mean=0, std=0.1 * torch.ones_like(newx))

images = torch.vmap(sim)(newx)

fig, axarr = plt.subplots(4, 5, figsize=(20, 16)) for ax, im in zip(axarr.flatten(), images): ax.imshow(im, origin="lower") plt.show() ```

Batched Caustics lensed images

Automatic Differentiation

```python J = torch.func.jacfwd(sim)(x)

Plot the new images

fig, axarr = plt.subplots(3, 7, figsize=(20, 9)) for i, ax in enumerate(axarr.flatten()): ax.imshow(J[..., i], origin="lower") plt.show() ```

Jacobian Caustics lensed image

Documentation

Please see our documentation page for more detailed information.

Contribution

We welcome contributions from collaborators and researchers interested in our work. If you have improvements, suggestions, or new findings to share, please submit an issue or pull request. Your contributions help advance our research and analysis efforts.

To get started with your development (or fork), click the "Open with GitHub Codespaces" button below to launch a fully configured development environment with all the necessary tools and extensions.

Instruction on how to contribute to this project can be found in the CONTRIBUTION.md

Some guidelines:

Please use isort and black to format your code.
Use CamelCase for class names and snake_case for variable and method names.
Open up issues for bugs/missing features.
Use pull requests for additions to the code.
Write tests that can be run by pytest.

Thanks to our contributors so far!

Owner

Name: Ciela-Institute
Login: Ciela-Institute
Kind: organization

Repositories: 2
Profile: https://github.com/Ciela-Institute

JOSS Publication

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Published

November 22, 2024

DOI

10.21105/joss.07081

Volume 9, Issue 103, Page 7081