Metadata-Version: 2.1
Name: auto-selfcal
Version: 2.0
Summary: Automated self-calibration of interferometric data
Author-email: John Tobin <jtobin@nrao.edu>, Patrick Sheehan <psheehan@nrao.edu>
Project-URL: Homepage, https://github.com/jjtobin/auto_selfcal
Project-URL: Issues, https://github.com/jjtobin/auto_selfcal/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: casatools
Requires-Dist: casatasks
Requires-Dist: casaplotms
Requires-Dist: casadata
Requires-Dist: casaviewer <=2.0.1
Requires-Dist: protobuf <=3.20.3
Provides-Extra: docs
Requires-Dist: sphinx ; extra == 'docs'
Requires-Dist: sphinx-automodapi ; extra == 'docs'
Requires-Dist: sphinx-book-theme ; extra == 'docs'
Requires-Dist: numpydoc ; extra == 'docs'
Provides-Extra: mpi
Requires-Dist: casampi ; extra == 'mpi'
Requires-Dist: mpi4py <4.0.0 ; extra == 'mpi'
Provides-Extra: test
Requires-Dist: pytest ; extra == 'test'
Requires-Dist: pytest-xdist ; extra == 'test'
Requires-Dist: submitit ; extra == 'test'

![build and test](https://github.com/psheehan/auto_selfcal/actions/workflows/run_E2E_test.yml/badge.svg)
[![codecov](https://codecov.io/github/psheehan/auto_selfcal/graph/badge.svg?token=6PLBR79LWF)](https://codecov.io/github/psheehan/auto_selfcal)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17603062.svg)](https://doi.org/10.5281/zenodo.17603062)


auto_selfcal: Self-calibration, without the hassle!
===================================================

auto_selfcal does automatic self-calibration of ALMA and VLA with (almost) no effort from you. It can handle most forms of data, including single pointing and ALMA mosaics (VLA mosaics coming soon), ephemeris data, spectral scans, and more. See below to give it a try, or check out our more extensive documentation at https://auto-selfcal.readthedocs.io.

Quickstart
----------

To use auto_selfcal with an existing monolithic CASA distribution:

```bash
git clone https://github.com/jjtobin/auto_selfcal.git

cd </path/to/pipeline/calibrated/*_targets.ms/files>

casa -c </path/to/auto_selfcal>/bin/auto_selfcal.py
```

Or to install into an existing Python environment (note that a Python version for which CASA is [available](https://casadocs.readthedocs.io/en/v6.7.0/notebooks/introduction.html#Compatibility) is required) and run from a directory where pipeline-calibrated *_targets.ms files exist:

```bash
pip install auto_selfcal

cd </path/to/pipeline/calibrated/*_targets.ms/files>

auto_selfcal
```

Acknowledging auto_selfcal
--------------------------

Love auto_selfcal and want to cite it? Please use:

```tex
@software{auto_selfcal,
  author       = {John J. Tobin and Patrick D. Sheehan and Rui Xue and Austen Fourkas},
  title        = {jjtobin/auto\_selfcal: v1.3.1},
  month        = nov,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {v1.3.1},
  doi          = {10.5281/zenodo.17603063},
  url          = {https://doi.org/10.5281/zenodo.17603063},
}
```

Acknowledgements:
-----------------

Certain functions to convert from LSRK to channel, S/N estimates, and tclean wrapper have their origins from the ALMA DSHARP large program reduction scripts.

The functions to parse the cont.dat file and convert to channel ranges (used the routine from above) was adapted from a function written by Patrick Sheehan for the ALMA eDisk large program
