RMNest
RMNest is an open source python package for estimating both standard and generalised rotation measures via direct fits to Stokes Q, U and V spectra.
Installation
The latest release of RMNest can be installed from PyPi by running the following
pip install rmnest
Note that while a working installation of the PSRCHIVE Python-3 bindings is not necessary for using RMNest, it is strongly recommended.
Requirements
The following packages are required to running RMNest.
-
numpy: Array manipulation
-
matplotlib: Modules for plotting
-
bilby: Inference calculations framework
-
dynesty: Modules for nested sampling
Usage
RMNest can be run directly from the command line within using rmnest.
As an example, the below command would run a standard rotation-measure fit on the provided test data after frequency-averaging to 128 channels
within a [pulse] phase window between phase = 0.45 to 0.55
rmnest archive test/2020-03-16-18\:12\:00.calib.ST -o test/output/ -l testrun --window 0.45:0.55 -f 128
Alternatively, fitting for the generalised form of Faraday rotation, sometimes referred to as Faraday conversion
(see e.g. Kennett & Melrose 1998), can be performed
by adding the --gfr and --free_alpha flags as
rmnest <archive>.ar -o <outdir> -l testrun --window 0.45:0.55 --gfr --free_alpha
Omitting the --free_alpha flag will result in the spectral exponent being fixed to 3. Details of the underlying phenomenological model can be
found in a technical document by Lower (2021).
The likelihood and Faraday rotation models, as well as the general RMFit class in fit_RM.py, can also be imported like any other API.
Issues and Contributing
If you encounter any issues with RMNest, or have in mind a feature that currently does not exist, then you can contribute by openning a Github Issue and outlining the feature.
Referencing RMNest
If you make use of RMNest in your research, we would greatly appreciate it if you cite both the ASCL entry (Lower et al. 2022) and the papers behind its development.
@software{2022ascl.soft04008L,
author = {{Lower}, Marcus E. and {Kumar}, Pravir and {Shannon}, Ryan M.},
title = "{RMNest: Bayesian approach to measuring Faraday rotation and conversion in radio signals}",
keywords = {Software},
howpublished = {Astrophysics Source Code Library, record ascl:2204.008},
year = 2022,
month = apr,
eid = {ascl:2204.008},
pages = {ascl:2204.008},
archivePrefix = {ascl},
eprint = {2204.008},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022ascl.soft04008L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
For standard rotation measure fitting, then please cite Bannister et al. (2019).
@ARTICLE{2019Sci...365..565B,
author = {{Bannister}, K.~W. and {Deller}, A.~T. and {Phillips}, C. and {Macquart}, J. -P. and {Prochaska}, J.~X. and {Tejos}, N. and {Ryder}, S.~D. and {Sadler}, E.~M. and {Shannon}, R.~M. and {Simha}, S. and {Day}, C.~K. and {McQuinn}, M. and {North-Hickey}, F.~O. and {Bhandari}, S. and {Arcus}, W.~R. and {Bennert}, V.~N. and {Burchett}, J. and {Bouwhuis}, M. and {Dodson}, R. and {Ekers}, R.~D. and {Farah}, W. and {Flynn}, C. and {James}, C.~W. and {Kerr}, M. and {Lenc}, E. and {Mahony}, E.~K. and {O'Meara}, J. and {Os{\l}owski}, S. and {Qiu}, H. and {Treu}, T. and {U}, V. and {Bateman}, T.~J. and {Bock}, D.~C. -J. and {Bolton}, R.~J. and {Brown}, A. and {Bunton}, J.~D. and {Chippendale}, A.~P. and {Cooray}, F.~R. and {Cornwell}, T. and {Gupta}, N. and {Hayman}, D.~B. and {Kesteven}, M. and {Koribalski}, B.~S. and {MacLeod}, A. and {McClure-Griffiths}, N.~M. and {Neuhold}, S. and {Norris}, R.~P. and {Pilawa}, M.~A. and {Qiao}, R. -Y. and {Reynolds}, J. and {Roxby}, D.~N. and {Shimwell}, T.~W. and {Voronkov}, M.~A. and {Wilson}, C.~D.},
title = "{A single fast radio burst localized to a massive galaxy at cosmological distance}",
journal = {Science},
keywords = {ASTRONOMY, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Cosmology and Nongalactic Astrophysics},
year = 2019,
month = aug,
volume = {365},
number = {6453},
pages = {565-570},
doi = {10.1126/science.aaw5903},
archivePrefix = {arXiv},
eprint = {1906.11476},
primaryClass = {astro-ph.HE},
adsurl = {https://ui.adsabs.harvard.edu/abs/2019Sci...365..565B},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
If you used RMNest for generalised Faraday rotation measure fitting, please include a citation to Lower (2021).
@ARTICLE{2021arXiv210809429L,
author = {{Lower}, Marcus E.},
title = "{A phenomenological model for measuring generalised Faraday rotation}",
journal = {arXiv e-prints},
keywords = {Astrophysics - High Energy Astrophysical Phenomena},
year = 2021,
month = aug,
eid = {arXiv:2108.09429},
pages = {arXiv:2108.09429},
archivePrefix = {arXiv},
eprint = {2108.09429},
primaryClass = {astro-ph.HE},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210809429L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Metadata
Release files for rmnest 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| rmnest-0.3.1.tar.gz | 13.7 kB | Without provenance | Details for rmnest-0.3.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| rmnest-0.3.1-py3-none-any.whl | Python 3 | none | any | Details for rmnest-0.3.1-py3-none-any.whl |
Total release size: 25.6 kB
Release files / rmnest-0.3.1.tar.gz
| Download URL | rmnest-0.3.1.tar.gz |
|---|---|
| Size | 13.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c30feeaf1f5b78008f98d3507990de01f2b6370ee56a8d1679993a87804a2f4b
|
|
BLAKE2b-256 checksum How to use checksums |
8a44c4137abb681cec92adcc551118fb3ba7404ad1212b33fef4d2fc2e30a931
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.18
|
Release files / rmnest-0.3.1-py3-none-any.whl
| Download URL | rmnest-0.3.1-py3-none-any.whl |
|---|---|
| Size | 11.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
3b2f1fbeee76c04fc5da3519aacfeeb8d59ed59be6d81b3c06b5881e3a0c61fd
|
|
BLAKE2b-256 checksum How to use checksums |
fcbd00e785631b6584d84bb9bb4c2fa4e3fb48eddddb3e00f670c9cfdd3acf3d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.18
|
Security for rmnest 0.3.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.