caustics 0.11.1

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

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:

pip install caustics

Minimal Example

import matplotlib.pyplot as plt
import caustics
import torch

cosmology = caustics.FlatLambdaCDM()
sie = caustics.SIE(cosmology=cosmology, name="lens")
src = caustics.Sersic(name="source")
lnslt = caustics.Sersic(name="lenslight")

x = torch.tensor([
#   z_s  z_l   x0   y0   q    phi     b    x0   y0   q     phi    n    Re
    1.5, 0.5, -0.2, 0.0, 0.4, 1.5708, 1.7, 0.0, 0.0, 0.5, -0.985, 1.3, 1.0,
#   Ie    x0   y0   q    phi  n   Re   Ie
    5.0, -0.2, 0.0, 0.8, 0.0, 1., 1.0, 10.0
])  # fmt: skip

minisim = caustics.LensSource(
    lens=sie, source=src, lens_light=lnslt, pixelscale=0.05, pixels_x=100
)
plt.imshow(minisim(x, quad_level=3), origin="lower")
plt.axis("off")
plt.show()

Batched simulator

newx = x.repeat(20, 1)
newx += torch.normal(mean=0, std=0.1 * torch.ones_like(newx))

images = torch.vmap(minisim)(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()

Automatic Differentiation

J = torch.func.jacfwd(minisim)(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()

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!