GalPaK v1.16.0



GalPaK 3D is a tool to extract the intrinsic (i.e. deconvolved) Galaxy Parameters and Kinematics from any 3-Dimensional data. The algorithm uses a disk parametric model with 10 free parameters (which can also be fixed independently) and a MCMC approach with non-traditional sampling laws in order to efficiently probe the parameter space.

More importantly, it uses the knowledge of the 3-dimensional spread-function to return the intrinsic galaxy properties and the intrinsic data-cube. The 3D spread-function class is flexible enough to handle any instrument.

One can use such an algorithm to constrain simultaneously the kinematics and morphological parameters of (non-merging, i.e. regular) galaxies observed in non-optimal seeing conditions. The algorithm can also be used on AO data or on high-quality, high-SNR data to look for non-axisymmetric structures in the residuals.


If you use galpak, please cite Bouche, N. et al. 2015 and acknowledge the ACL entry ascl:1501.014


2019-04-28 ∾ bug in PA for PSF

Since v1.12, the PA definition is anti-clockwise from y-axis (matching the disk model)

2019-04-04 ∾ New Features

Since v1.11, galpak has several new features : autorun (API) for autotune randomscale / plot_geweke for convergence / plot_vprofile with errors from chain

2019-02-10 ∾ Python3 compatibility

Since v1.10, galpak is Python >3.5 compatible.

2015-05-14 ∾ Easier setup and doit implementation

Since v1.5.0, galpak supports doit tasks and python install.

2014-12-08 ∾ Support for ALMA

Since v1.3.0, galpak can now work with ALMA data.

Set the lsf_fwhm to something less than 1 channel.

2014-10-13 ∾ Galpak gains in flexibility

Since v1.1.1, galpak.GalaxyParameters are now much more flexible.

You can add new parameters and see them evolve through the MCMC. This flexibility comes at a small performance cost, which we tried to reduce as much as we could.

Hyperspectral Cube Visualization