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 2026.10.22.1
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Total release size: 3.0 MB
Release files / autoarray-2026.10.22.1.tar.gz
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