This archive is deprecated; please use PyGDSM
Note: GSM2016 methods are in beta!!
PyGSM is a Python interface for the Global Sky Model (GSM2008) of Oliveira-Costa et. al., (2008), and recently Zheng et. al., (2016), GSM2016. The GSMs are all-sky maps in Healpix format of diffuse Galactic radio emission
from 10 MHz to 94 GHz (GSM2008) and 10 MHz to 5 THz (GSM2016).
This is not a wrapper of the original code, it is a python-based equivalent that provides a useful API which has some additional features and advantages, such as healpy integration for imaging. The GSM2008 and GSM2016 classes provided here are designed to have the same API (i.e. function names & usage). Instead of the original ASCII DAT files that contain the principal component analysis (PCA), from the GSM2008, data are stored in HDF5, which can be quickly read into memory, and takes up less space to boot. Similarly, the GSM2016 data is converted into HDF5.
The first thing to do will be to make sure you've got the dependencies:
Then clone the directory
git clone https://github.com/telegraphic/PyGSM
You need to have git-lfs installed to download the data files. If you don't, you can get these via wget:
wget -O gsm2016_components.h5 https://zenodo.org/record/3479985/files/gsm2016_components.h5?download=1
wget -O gsm_components.h5 https://zenodo.org/record/3479985/files/gsm_components.h5?download=1
Which are hosted on Zenodo.
You may then install this by running python setup.py install.
To get a quick feel of what PyGSM does, have a look at the
GSM2008 quickstart guide, and the new
GSM2016 quickstart guide.
Q. What's the difference between this and the gsm.f from the main GSM2008 website?
The gsm.f is a very basic Fortran code, which reads and writes values to and from
ASCII files, and uses a command line interface for input. If you want to run this code
on an ancient computer with nothing by Fortran installed, then gsm.f is the way to go.
In contrast, PyGSM is a Python code that leverages a lot of other Packages so that you
can do more stuff more efficiently. For example: you can view a sky model in a healpy
image; you can write a sky model to a Healpix FITS file; and believe it or not, the
Python implementation is much faster. Have a look at the
quickstart guide
to get a feel for what PyGSM does.
Q. Are the outputs of gsm.f and pygsm identical? At the moment: no. The cubic
spline interpolation implementation differs, so values will differ by as much as
a few percent. The interpolation code used in gsm.f does not have an open-source
license (it's from Numerical Recipes ), so we haven't
implemented it (one could probably come up with an equivalent that didn't infringe).
Nevertheless, the underlying PCA data are identical, and I've run tests to check that
the two outputs are indeed comparable.
Q. What's the difference between this and the Zheng et. al. github repo?
pygsm provides two classes: GlobalSkyModel2016() and GSMObserver2016(), which once instantiated
provide methods for programatically generating sky models. The Zheng et. al. github repo is a
simple, low-dependency, command line tool. Have a look at the
GSM2016 quickstart guide
to get a feel for what PyGSM does.
Q. Why is this package so large?
The package size is dominated by the PCA healpix maps, which have about 3 million points each.
They're compressed using HDF5 LZF, so are actually about 3x smaller than the *.dat
files that come in the original gsm.tar.gz file. The next biggest thing is test data,
so that the output can be compared against precomputed output from gsm.f. The package now also includes
the Zheng et. al. data, which is another ~300 MB.
Q. Why do I need h5py?
h5py is required to read the PCA data, which are stored in a HDF5 file. Reading from
HDF5 into Python is incredibly efficient, and the compression is transparent to the end user.
This means that you can't eyeball the data using vim or less or a text editor, but if
you're trying to do that on a file with millions of data points you're doing science wrong anyway.
The PCA data contained here is from http://space.mit.edu/~angelica/gsm/index.html and https://github.com/jeffzhen/gsm2016.
The original GSM2008 paper is:
A. de Oliveira-Costa, M. Tegmark, B.M. Gaensler, J. Jonas, T.L. Landecker and P. Reich
A model of diffuse Galactic radio emission from 10 MHz to 100 GHz
Mon. Not. R. Astron. Soc. 388, 247-260 (2008)
doi:10.111/j.1365-2966.2008.13376.x
Which is published in MNRAS and is also available on the arXiv.
And the GSM2016 paper is:
H. Zheng (MIT), M. Tegmark, J. Dillon, A. Liu, A. Neben, J. Jonas, P. Reich, W.Reich
An Improved Model of Diffuse Galactic Radio Emission from 10 MHz to 5 THz
which is available on the arXiv.
PyGSM has an ascl.net entry:
D. C. Price, 2016, 2.0.0, Astrophysics Source Code Library, 1603.013
All code in PyGSM is licensed under the MIT license (not the underlying data). The PCA data, by Zheng et. al. is licensed under MIT also (see https://github.com/jeffzhen/gsm2016).