Dispersion Analysis of Dust-Polarimetric Data and Magnetic Field (B) strength.
polBpy is an open-source library for the analysis of dust polarimetric data (Stokes I, Q, U maps; polarization angle
The development of this library was supported by the NASA/SOFIA Archival Research Program (Grant #09_0537, USRA for Villanova University).
This library is divided into two main modules:
- The dispersion module which contains routines for performing polarization-angle dispersion calculations. Capabilities include:
- Calculating the two-point dispersion (structure) function (Houde+09).
- Calculating the autocorrelation function (Houde+09).
- MCMC fitting and parameter determination (Guerra+21).
- Local dispersion (
$\mathcal{S}$ ; TBI).
- The DCF module contains functions for calculating
$B_{POS}$ using multiple David-Chandrasekhar-Fermi (DCF) approximations:
- Classical (Davis52,Chandrasekhar-Fermi53).
- Compressional (Skalidis+21)
- Large-scale flow, shear flow (Lopez-Rodriguez+21,Guerra+23)
Both the dispersion and DCF modules have the capabilities to perform corresponding analysis on a pixel-to-pixel basis resulting in maps of magnetoturbulent quantities and POS magnetic field strength.
(Always download and install the latest tagged version!)
pip install git+https://github.com/jorgueagui/polBpy.git
or
git clone --depth 1 --branch v0.1.2 https://github.com/jorgueagui/polBpy.git
cd polBpy
pip install .
(setup.py will install if not present)
- Numpy
- Scipy
- Astropy
- Matplotlib
- Emcee
- Joblib
- George
- Uncertainties
- Corner
A series of Jupyter notebooks can be found here. They show examples of basic and advanced usage of this library.
- Tutorial I: Calculation of single-value
$B_{POS}$ using all DCF approximations. - Tutorial II: Calculation of range-values and maps of
$B_{\rm POS}$ using all DCF approximations. - Tutorial III: Example of dispersion analysis for a single region.
- Tutorial IV: Example of pixel-to-pixel dispersion analysis.
- Tutorial V: Map making of
$B_{\rm POS}$ using DCF.
A list of relevant references can be found here.
The use of this library is regulated by the MIT license. Details of this license can be found here.
If you use this package for a publication, please cite it as: polBpy: a Python package for the analysis of dust polarimetric observations, Jordan A. Guerra, David T. Chuss, Dylan Paré. DOI: 10.5281/zenodo.11414008. 2024.
- SOFIA/HAWC+ Archive at IPAC: SOFIA/IRSA.
- 0.1.0, June 16, 2024. First version.
- 0.1.1, June 23, 2024. Minor patches in Tutorial V.
- 0.1.2, October 12, 2024. Add the option to output the MCMC samples to mcmc_fit. Updated Tutorial III.
For any issues found with this package, please report to jordan.guerra [at] gmail.com.