In order to simplify the use of the delivered data, here we provide a list of
tools that can be used to visualize and analyse the CALIFA IFS data.
Some of the tools were designed for a quick-look and basic manipulation of
generic IFS observations, while others have been developed specifically for the
There are various visualisation tools available in the community that can
facilitate the user to explore spatially the CALIFA data at any wavelength
slice. Some are generic, stand-alone tools, while others were developed for a
particular project or data format (including CALIFA).
Spatial and spectral visualisation of the CALIFA data should
not represent a limitation for potential users considering the number of
different software options available in the literature for IFS visualisation.
Examples of such tools are:
is a set of IDL routines designed to visualise, manipulate, and analyse IFS
reduced data regardless of the original instrument and spaxel size/shape, it
is able to run on practically any computer platform with minimal library
requirements (Rosales-Ortega 2011, 2012).
PINGSoft is optimised for a fast visualisation rendering, it
supports RSS and 3D cube formats, and is adapted to work natively with
the CALIFA data.
All the routines automatically identify the different FITS extensions (HDUs) of the CALIFA format (for both RSS and 3D cube versions), allowing the user to explore spatially and spectrally the IFS data, using either a graphical user interface (GUI) or command-lines, displaying simultaneously the data contained in the 1st (flux) and 3rd (bad-pixels) FITS extensions. The data can be convolved with a full set of narrow and broad-band filters for visualization and/or analysis purposes. In addition, PINGSoft includes routines to perform data manipulation and some basic analysis (see below).PINGSoft visualisation widget, which provides spatial and spectral visualisation of the data, as shown for the CALIFA object NGC4210.
- The E3D visualization tool (Sánchez et al. 2004), is a highlevel IFS package which allows interactive visualisation, spatial resampling and basic analysis, it handles RSS, 3D cube, or E3D formats, it is based on Tcl/Tk and PGPLOT.
- ds9, from version 7, this popular tool contains a new module, encompassed by the new Frame 3D option, which allows users to load and view data cubes in multiple dimensions. It supports only FITS 3D cube format.
- QFitsView, by Thomas Ott, is a generic FITS viewer program capable of handling IFS data and performing basic analysis operations on practically any OS flavour. It supports only FITS 3D cube format.
- Other general astronomical packages that include visualisation tools for IFS data are described in the IFS wiki.
- IFSview3D, is a light standalone visualization tool written in Python, using PyFITs, numPy and matplotlib, and TkInter as gui interface. It is still under development, and so far it is able to visuzalize only 3D cubes in the standard format (no RSS). You can download it from here:
Here we list more complex packages that can be used for IFS data analysis.
HIIexplorer is a package for detecting and extracting the integrated spectra of HII regions from IFS datacubes. The procedure is based on some basic assumptions: (a) H ii regions are peaky/isolated structures with a strong ionized gas emission, clearly above the continuum emission and the average ionized gas emission across the galaxy; (b) H ii regions have a typical physical size of about a hundred or a few hundreds of parsecs (e.g. Gonzalez Delgado & Perez 1997; Lopez et al. 2011; Oey et al. 2003), which corresponds to a typical projected size at the distance of the galaxies of a few arcsec for galaxies at z~0.016 (the ones used in the studies of our interest). You can download it from here:
PINGSoftPINGSoft includes several tools for spectra extraction, integration and basic analysis, including:
- Spectra extraction and integration based on geometric apertures or a user-given mask.
- Spatial binning based on fixed bins or S/N floor (including Voronoi tessellation)
- Intrinsic velocity-field correction using a wavelength cross-correlation.
- Spectra selection based on S/N on continuum and/or emission line features.
Here we list some other useful tools.
CALIFAconvert is a set of tools written in Python to split the the four extensions cubes of CALIFA data into four physical fits cubes with the corresponding content: flux intensity, error cube, mask cube and error weight, as described in the DR article. You can download it from here:
PINGSoft includes the script:split_califa, which extracts the FITS extensions of the CALIFA data, writing individual files with the appropriate headers for 3D visualisation.