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README.md

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MUFASA

MUlti-component Fitter for Astrophysical Spectral Applications.

Reference

Please cite the following paper when using the code:

  1. Chen, M. C.-Y. et al. "Velocity-Coherent Filaments in NGC 1333: Evidence for Accretion Flow?" ApJ (2020).

Installation

To install the latest version of MUFASA, clone this repository and run the following in your local directory:

pip install -e .

To pip install a 'stable' release, run:

pip install mufasa

Requirements

MUFASA runs on python > v3.6 and depends on the following packages:

  • numpy > v1.19.2

  • skimage > v0.17.2

  • spectral_cube > v0.6.0

  • pyspeckit > v1.0.1

  • reproject > v0.7.1

  • FITS_tools > v0.2

If you are running a later version of Python, for example, Python 3.11, you likely will have to install the latest versions of pyspeckit and FITS_tools directly from their respective GitHub repository.

Getting Started

Minimum Working Example

To perform an NH3 (1,1) fit automatically, up to two components, simply run the following:

from mufasa import master_fitter as mf
reg = mf.Region(cubePath, paraNameRoot, paraDir, fittype='nh3_multi_v')
reg.master_2comp_fit(snr_min=0)

In the example above, cubePath is the path to the FITS data cube, paraNameRoot is the common 'root' name to all the output files, paraDir is the directory of all the outputfiles, and fittype is the name of the line model to be fitted.

MUFASA currently offers two spectral line models, specified by the fittype argument:

  • nh3_multi_v: multi-component NH3 (1,1) model
  • n2hp_multi_v: multi-component N2H+ (1-0) model

If one wishes to fit pixels only above a specific signal-to-noise-ratio (SNR) threshold, one can specify such a threshold using the snr_min argument.