Frequently Asked Questions (FAQ)

Frequently Asked Questions (FAQ)#

Welcome! Here are answers to the most common questions about Pyralysis. If you’re new, check out the Quickstart Guide guide first!

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General Questions#

Q1: What is Pyralysis and what is it used for? A: Pyralysis is an open-source Python library for radio astronomy imaging, simulation, and optimization. It provides tools for gridding, degridding, visibility estimation, and image reconstruction using regularized maximum likelihood (RML) methods and gradient-based optimization.

Note

If you already use CASA or similar packages: imaging, image formation, image synthesis, reconstruction, and image optimization all refer to forming an image from visibilities. Pyralysis docs mix these terms; they mean the same broad task (see Glossary).

Tip

Pyralysis is designed for both beginners and advanced users. Its modular, object-oriented design makes it easy to extend and customize.

Q2: How does Pyralysis compare to other radio interferometric imaging software? A: Pyralysis differs from traditional radio interferometric imaging software in several key ways:

  • Beyond CLEAN: Implements RML-based optimization, not just heuristic greedy algorithms.

  • Modern Design: Object-oriented, modular, and easy to extend.

  • Open Source: Fully transparent and community-driven.

  • Scalable: Modular design for software extensibility, and Dask-based workflows for large visibility datasets and next-generation arrays.

Note

Pyralysis leverages Dask, Zarr, Numba, and (soon) CuPy for high-performance, scalable computing.

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Installation and Setup#

Q3: How can I install Pyralysis? A: See the Installation guide for step-by-step instructions. Stable releases are published on PyPI; use GitLab or an editable checkout for the latest unreleased changes.

Q4: What are the system requirements for Pyralysis? A: Python 3.11–3.12 and a set of scientific Python libraries (see installation guide).

Q5: How do I enable GPU acceleration in Pyralysis? A: GPU support is under development. Stay tuned!

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Using Pyralysis#

Q6: How do I load visibilities (MS or Zarr)? A: Measurement Set — use DaskMS:

from pyralysis.io import DaskMS
dataset = DaskMS(input_name="/path/to/data.ms").read()

Zarr (e.g. exported simulation or pipeline output) — use ZarrDataset:

from pyralysis.io import ZarrDataset
dataset = ZarrDataset(input_name="/path/to/dataset.zarr").read()

See also I/O Operations & Data Formats.

Q7: How do I quickly create a synthetic dataset? A: Follow the minimal example in Creating a dataset. It shows how to set up the array, configure observation settings, define a simple point source, and run the simulator.

Q7b: How do I set the output Measurement Set name when simulating? A: Simulation and writing are two steps. Simulator.simulate() only builds an in-memory Dataset — there is no simulator argument for the MS path. Set the name with DaskMS:

from pyralysis.io import DaskMS

ms_io = DaskMS(output_name="my_observation.ms")
ms_io.write(dataset=dataset, tables="ALL")

See Creating a dataset and I/O Operations & Data Formats.

Q8: How do I image (reconstruct) from visibilities with optimization? A: Define an objective function, select a line search, and run an optimizer — this is imaging / image reconstruction in Pyralysis. See Using Pyralysis for a full example. For a higher-level pattern, see Repository Examples (optimization_components / optimization_pipeline scripts).

Q9: How do I form a dirty image or dirty beam without an optimizer? A: Use DirtyMapper to grid visibilities to the dirty image and synthesized beam. See Gridding Techniques in Pyralysis and the dirtymapper_components / dirtymapper_pipeline scripts in Repository Examples.

Q10: What is the difference between ``*_components.py`` and ``*_pipeline.py`` examples? A: Components scripts build classes explicitly so you can see wiring and customize behavior. Pipeline scripts use context + steps (e.g. ImagerPipeline) for shorter, reproducible workflows. See Repository Examples.

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Data storage, weighting, and I/O#

Q12: How do I save reconstructed images to disk? A: Pyralysis supports FITS and Zarr formats. See Using Pyralysis and I/O Operations & Data Formats for examples.

Q13: What is the advantage of using Zarr over FITS for data cubes? A: Zarr is chunked and cloud-friendly: parallel reads/writes, easier integration with Dask, and scalable storage for large image cubes. FITS remains the standard for interchange of single astronomical images with rich headers.

Q14: If I change visibility weighting (uniform, robust, or radial), do I need to refresh the dataset PSF? A: Yes, if you use the analytical PSF on the Dataset (e.g. FITS BMAJ/BMIN/BPA or model restoration with dataset.psf). Those moments follow imaging weights; after you rewrite IMAGING_WEIGHT_SPECTRUM, call dataset.calculate_psf() so the beam matches the weighted data. The default ApplyVisibilityWeighting step applies uniform / robust / radial and refreshes the PSF; natural is a no-op on a freshly loaded MS (imaging weights already match WEIGHT). The optimization CLI can use a different --restore-weighting than --weighting so residual gridding and the restoring beam need not match the optimization weights. Gridding with DirtyMapper uses the same imaging weights for both the dirty image and the dirty beam.

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Didn’t find your answer?#

Note

Quickstart Guide | Using Pyralysis