Welcome to GalPaK’s documentation!

GalPaK 3D is a tool to extract Galaxy Parameters and Kinematics from 3-Dimensional data, using reverse deconvolution with the Bayesian analysis procedure Markov Chain Monte Carlo.

Authors

• Bouché N., Carfanta H., Schroetter I., Michel-Dansac L., Contini T., 2015, AJ “GalPak3D: A Bayesian parametric tool for extracting morpho-kinematics of galaxies from 3D data”
• Contacts: Nicolas.Bouche@irap.omp.eu – Antoine.Goutenoir@irap.omp.eu

Acknowledgments

If you use GalPak3D in a publication, please cite Bouche et al. 2015 and link to the ACL entry http://www.ascl.net/1501.014 Please also send me a reference to your paper.

Do’s and Don’t

The algorithm should never be used blindly and we stress that one should always

1. look at the convergence of the parameters using plot_mcmc(),
2. investigate possible covariance in the parameters using plot_correlations() and/or fix a parameter with the option known_parameters to remove the degenerancy,
3. adjust the MCMC algorithm with the option random_scale (lower than 1 [default] for higher acceptance rate and vice versa) to ensure an acceptance rate of 30 to 50%.

Parameters description

A description of the parameters meaning can be found here

Table of Contents

• Installation
  • Download Options
  • Setup
  • Dependencies
  • Development environment
• A quick overview
  • Parameter meaning
    • Galaxy Parameters
  • Input Cubes supported
  • Instruments supported
  • Incomplete Header handling overview
• GalPaK3D’s API
• Under the hood
  • Instruments
    • Overview
    • Base Instrument Class
  • Hyperspectral Cube
  • Galaxy Parameters
  • Disk Model
  • Point Spread Functions
    • Gaussian
    • Moffat
    • Interface
  • Line Spread Functions
    • Gaussian
    • MUSE
    • Interface
• Tutorials Level 0
  • Preparing the input cube
  • Test GalpaK with MUSE per default
  • Test galpak with your cube
  • Run galpak examples
  • A practical example
  • Default Model Parameters
  • Run Output Overview
  • Save the run
• Tutorials Level 1
  • MCMC chain tuning
  • Using Custom Boundaries
  • Tweaking the Random Walk
  • Plotting the Markov chain
  • Plotting the cross-correlations
  • Plotting the cubes’ images
    • Recover the parameters from an earlier save
    • Recover the chain from an earlier save
  • Cubes from another instrument
• Tutorials Level 2
  • Setting a parameter to a fixed value
  • Using line doublets
  • Customizing the Point Spread Function
    • Tweaking the PSF
    • De-activating the PSF
    • Using another PSF
    • Using custom image for the PSF
    • Making your own PSF
  • Customizing the Line Spread Function
    • Using another LSF
    • Making your own LSF
  • Creating a custom instrument
  • Adding another Galaxy Parameter
• Tutorials Level 3
  • Reading old parameter file
  • Reading old chain, recomputing parameters
  • Handling of parameter names
• Tutorials Level 4
  • Changing of chi2 statistics
• Bug reporting
• Acknowledgments

Indices and tables

• Index
• Search Page