Skip to main content

Scattering fits of time domain radio signals (Fast Radio Bursts or pulsars).

Project description

Scatfit: Scattering fits of time domain radio signals (Fast Radio Bursts or pulsars)

PyPI latest release Documentation GitHub issues License - MIT Paper link arXiv link

This repository contains code to fit Fast Radio Burst or pulsar profiles to measure scattering and other parameters. The code is mainly developed for Python 3, but Python 2 from version 2.7 onwards should work fine.

Author

The software is primarily developed and maintained by Fabian Jankowski. For more information feel free to contact me via: fabian.jankowski at cnrs-orleans.fr.

Paper

The corresponding paper (Jankowski et al. 2023, MNRAS) is available via this NASA ADS link.

Citation

If you make use of the software, please add a link to this repository and cite our corresponding paper. See above and the CITATION and CITATION.bib files.

The code is also listed in the Astrophysics Source Code Library (ASCL).

Installation

The easiest and recommended way to install the software is through pip from the central PyPI index by running:

pip install scatfit

This will install the latest release and all its dependencies. If you need a more recent version of the software, install it directly from its GitHub software repository. For instance, to install the master branch of the code, use the following command:

pip install git+https://github.com/fjankowsk/scatfit.git@master

This will also automatically install all dependencies.

Please verify that your installation works as expected by downloading a pre-generated SIGPROC filterbank file with synthetic data that comes bundled with the GitHub repository:

wget https://github.com/fjankowsk/scatfit/raw/master/extra/fake_burst_500_DM.fil

Then run the main analysis on the filterbank data file like this:

scatfit-fitfrb fake_burst_500_DM.fil 500.0 --fitscatindex --fscrunch 128 --fast

You should see several diagnostic windows open. The terminal output should show an updated DM close to 500 pc cm$^{-3}$, a scattering index near -4.0, and a scattering time at 1 GHz of about 20 ms.

Documentation

Further documentation of the software is available on our dedicated Read the docs website.

Usage

$ scatfit-fitfrb -h
usage: scatfit-fitfrb [-h] [--compare] [--binburst bin] [--fscrunch factor] [--tscrunch factor] [--fast] [--fitrange start end]
                      [--fitscatindex]
                      [--smodel {unscattered,scattered_isotropic_analytic,scattered_isotropic_convolving,scattered_isotropic_bandintegrated,scattered_isotropic_afb_instrumental,scattered_isotropic_dfb_instrumental}]
                      [--showmodels] [--snr snr] [--publish] [-z start end]
                      filename dm

Fit a scattering model to FRB data.

positional arguments:
  filename              The name of the input filterbank file.
  dm                    The dispersion measure of the FRB.

options:
  -h, --help            show this help message and exit
  --compare             Fit an unscattered Gaussian model for comparison. (default: False)
  --binburst bin        Specify the burst location bin manually. (default: None)
  --fscrunch factor     Integrate this many frequency channels. (default: 256)
  --tscrunch factor     Integrate this many time samples. (default: 1)
  --fast                Enable fast processing. This reduces the number of MCMC steps drastically. (default: False)
  --fitrange start end  Consider only this time range of data in the fit. Increase the region for wide or highly-scattered bursts.
                        Ensure that most of the scattering tail is included in the fit. (default: [-200.0, 200.0])
  --fitscatindex        Fit the scattering times and determine the scattering index. (default: False)
  --smodel {unscattered,scattered_isotropic_analytic,scattered_isotropic_convolving,scattered_isotropic_bandintegrated,scattered_isotropic_afb_instrumental,scattered_isotropic_dfb_instrumental}
                        Use the specified scattering model. (default: scattered_isotropic_analytic)
  --showmodels          Show comparison plot of scattering models. (default: False)
  --snr snr             Only consider sub-bands above this S/N threshold. (default: 3.8)
  --publish             Output plots suitable for publication. (default: False)
  -z start end, --zoom start end
                        Zoom into this time region. (default: [-50.0, 50.0])
$ scatfit-simpulse -h
usage: scatfit-simpulse [-h]

Simulate scattered pulses.

options:
  -h, --help  show this help message and exit

Profile scattering models

Several profile scattering models, i.e. pulse broadening functions and instrumental contributions, are implemented and others can easily be added. The image below shows a selection of them.

Implemented profile scattering models

Example output

The images below show some example output from the program obtained when fitting simulated filterbank data.

Profile fit

Width scaling

Correlations

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scatfit-0.2.21.tar.gz (24.3 kB view details)

Uploaded Source

Built Distributions

scatfit-0.2.21-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (519.2 kB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

scatfit-0.2.21-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (481.9 kB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

scatfit-0.2.21-cp310-cp310-macosx_13_0_arm64.whl (101.1 kB view details)

Uploaded CPython 3.10 macOS 13.0+ ARM64

scatfit-0.2.21-cp310-cp310-macosx_12_0_x86_64.whl (105.8 kB view details)

Uploaded CPython 3.10 macOS 12.0+ x86-64

scatfit-0.2.21-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (484.4 kB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

scatfit-0.2.21-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (488.9 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

scatfit-0.2.21-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (455.8 kB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

File details

Details for the file scatfit-0.2.21.tar.gz.

File metadata

  • Download URL: scatfit-0.2.21.tar.gz
  • Upload date:
  • Size: 24.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.13

File hashes

Hashes for scatfit-0.2.21.tar.gz
Algorithm Hash digest
SHA256 eb3559be8a1ee7c765fd09f8f9bee9776b7e8fcd933b4d78c52495d3656ed3e1
MD5 4c90cf166a788375b24626cb14011bdd
BLAKE2b-256 0840ff59c7efe9c824e5102d6fdba15e957b695463780eba6425c1e44f2cc510

See more details on using hashes here.

File details

Details for the file scatfit-0.2.21-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scatfit-0.2.21-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 788f32f162472888d760510d631cb000ceb60da9cf23600f0436c0730bfd3196
MD5 927dffed3970eac96c55fad265b4ed0d
BLAKE2b-256 3d423c6e66a98ef0f220304745e77498e534f8c3e56c575cf554188244d75d72

See more details on using hashes here.

File details

Details for the file scatfit-0.2.21-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scatfit-0.2.21-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 fceadb969f01474257f5232ecde9db0285aa8f5be7da368cabc234b1c06a555c
MD5 d49c5ba0a6b99bfc3f808ec3907b8a3b
BLAKE2b-256 8a046f4b2c32d7af4103291a1ec6f200e938234d9cb71c5a42ea3da1ca5fbe8a

See more details on using hashes here.

File details

Details for the file scatfit-0.2.21-cp310-cp310-macosx_13_0_arm64.whl.

File metadata

File hashes

Hashes for scatfit-0.2.21-cp310-cp310-macosx_13_0_arm64.whl
Algorithm Hash digest
SHA256 ccad67fbdccbf46fc6e9b139e2a520bef7073ea41d2adaf8e26fd22e9ff5fcc0
MD5 b07a5debe5f885d956b5dd2cabe2edf2
BLAKE2b-256 d7f933c89119995f4a4effd22688a8c2f6d9596af0397120788bc5011026afe9

See more details on using hashes here.

File details

Details for the file scatfit-0.2.21-cp310-cp310-macosx_12_0_x86_64.whl.

File metadata

File hashes

Hashes for scatfit-0.2.21-cp310-cp310-macosx_12_0_x86_64.whl
Algorithm Hash digest
SHA256 1e00152faab2f43a58bc0ccbc26082ef03abb9cfb731cbd30f43fb269a3b666f
MD5 f08750b0735bc852f4e493e15ca4c57d
BLAKE2b-256 7c68cff7f0b790ffc75855503f3ad087eec623a1238d15687b8a53c58de81815

See more details on using hashes here.

File details

Details for the file scatfit-0.2.21-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scatfit-0.2.21-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c9c9324df55d7b4489254f7e2f33ba775b355c41f1dcc5aeb89f28ea1494ed97
MD5 e3da0557edd49b3ca4ab8833bcb61056
BLAKE2b-256 b14d5ad7d65e4259b8576758ddfcc4d59c46ceae1d890cc9d3b135c361e31f6a

See more details on using hashes here.

File details

Details for the file scatfit-0.2.21-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scatfit-0.2.21-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4ff6d2ead0e7379aafb47d069dfee9c884964b1feb8778cd6512d8bf6040af30
MD5 6302d4c4154fe7da7746feec8b8c85b1
BLAKE2b-256 d247f29c9b1e066efc75a33b9ecbbc22377219758b124403297071fae3c494a7

See more details on using hashes here.

File details

Details for the file scatfit-0.2.21-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scatfit-0.2.21-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 60fcf77b4036906b719191effd8fae5a78ab5f6a07989ac0ca512f3fa56f6d6f
MD5 4a61c698871a7276270c50309f219e44
BLAKE2b-256 e91e6703f7639393b86376bd2bb7366be9d1984becb6d1b15cc32ac33985ae78

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page