PyAutoArray
PyAutoArray (package autoarray) is the low-level data-structure and
numerical-utility layer of the PyAuto
ecosystem. It provides masks, arrays, (y,x) coordinate grids,
imaging/interferometer datasets, inversions/pixelizations for source
reconstruction, and convolution/over-sampling operators.
PyAutoGalaxy and PyAutoLens build directly on autoarray: every grid a
profile consumes, every masked image a fit operates on, and the linear-algebra
inversions behind pixelized source reconstruction are autoarray objects. The
package supports both a NumPy and an opt-in JAX (xp=jnp) backend.
Install
pip install autoarray
Examples
A masked 2D array tied to a pixel scale:
import autoarray as aa
arr = aa.Array2D.no_mask(values=[[1.0, 2.0], [3.0, 4.0]], pixel_scales=0.1)
arr.shape_native # (2, 2)
arr.native[0, 0] # 1.0
A circular mask and the (y,x) coordinate grid of its unmasked pixels:
mask = aa.Mask2D.circular(shape_native=(50, 50), pixel_scales=0.1, radius=2.0)
mask.pixels_in_mask # 1264
grid = aa.Grid2D.from_mask(mask=mask) # shape (1264, 2)
uniform = aa.Grid2D.uniform(shape_native=(10, 10), pixel_scales=0.1)
A normalized Gaussian PSF convolver:
convolver = aa.Convolver.from_gaussian(
shape_native=(11, 11), pixel_scales=0.1, sigma=1.0, normalize=True
)
convolver.kernel.shape_native # (11, 11)
convolver.kernel.array.sum() # 1.0
Community & Contributing
PyAutoFit is built in the open by its users: everyone is welcome to ask questions, share what they have made with it, and contribute.
Questions, ideas and bug reports: the PyAutoLabs Discussions. Chat with us on Slack.
Community-built tools and tutorials, and how to contribute: the PyAutoFit community page.
Links
- Source & tests:
autoarray/,test_autoarray/ - Decorators & JAX deep dive:
docs/agents/jax_and_decorators.md - Agent/contributor instructions:
AGENTS.md - Ecosystem: PyAutoLabs on GitHub
Release files for autoarray 2026.10.12.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| autoarray-2026.10.12.1.tar.gz | 2.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| autoarray-2026.10.12.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.9 MB
Release files / autoarray-2026.10.12.1.tar.gz
| Download URL | autoarray-2026.10.12.1.tar.gz |
|---|---|
| Size | 2.2 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
bd32779c074e2a4561aea7eb668daa1b916849d49b5b4de9381de7549a865e3c
|
|
BLAKE2b-256 checksum How to use checksums |
061a01e53dd65921b74d896b9594ea895416e8ea08671fa10a9a96e6b11ea73f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.0.1 CPython/3.12.15
|
Release files / autoarray-2026.10.12.1-py3-none-any.whl
| Download URL | autoarray-2026.10.12.1-py3-none-any.whl |
|---|---|
| Size | 683.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
9163aec77747a89fbb032f4b5f2c4948f767254c933e890a22e89c2efea73f5a
|
|
BLAKE2b-256 checksum How to use checksums |
6648ba05b00a7ce05051a4f017a6d5cf2f5cfff5850239cc84a43899a676689d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.0.1 CPython/3.12.15
|
