Getting started

First steps with GammaLib

GammaLib comes with a Python interface, and as first step you should verify that the Python interface works correctly. You do this by typing:

>>> import gammalib
>>> gammalib.test()

The first command loads the GammaLib Python module, the second command executes the Python unit tests. If all tests are ok the GammaLib Python module has been installed successfully. In case of problems, make sure that you’ve setup correctly the GammaLib environment. You basically need the PYTHONPATH variable set to GammaLib’s Python site-package, and you also have to make sure that GammaLib and any dependent library (cfitsio, readline, ncurses) is in the library load path (eventually you may need to adjust LD_LIBRARY_PATH, or DYLD_LIBRARY_PATH if you are on Mac OS X). This is all done automatically if you set up the environment as described here.

Now try:

>>> models = gammalib.GModels()
>>> print(models)
=== GModels ===
Number of models ..........: 0
Number of parameters ......: 0

You just alloacted your first GammaLib object, which is an empty model container.

Now let’s append a model to this container. For this, type:

>>> pos = gammalib.GSkyDir()
>>> pos.radec_deg(83.6331, 22.0145)
>>> spatial = gammalib.GModelSpatialPointSource(pos)
>>> spectral = gammalib.GModelSpectralPlaw(1.0, -2.0, gammalib.GEnergy(100, 'MeV'))
>>> model = gammalib.GModelSky(spatial, spectral)
>>> models.append(model)
>>> print(models)
=== GModels ===
 Number of models ..........: 1
 Number of parameters ......: 6
=== GModelSky ===
 Name ......................:
 Instruments ...............: all
 Instrument scale factors ..: unity
 Observation identifiers ...: all
 Model type ................: PointSource
 Model components ..........: "SkyDirFunction" * "PowerLaw" * "Constant"
 Number of parameters ......: 6
 Number of spatial par's ...: 2
  RA .......................: 83.6331 deg (fixed,scale=1)
  DEC ......................: 22.0145 deg (fixed,scale=1)
 Number of spectral par's ..: 3
  Prefactor ................: 1 +/- 0 [0,infty[ ph/cm2/s/MeV (free,scale=1,gradient)
  Index ....................: -2 +/- 0 [10,-10]  (free,scale=-2,gradient)
  PivotEnergy ..............: 100 MeV (fixed,scale=100,gradient)
 Number of temporal par's ..: 1
  Constant .................: 1 (relative value) (fixed,scale=1,gradient)

With this sequence of commands you first defined a sky direction in celestial coordinates using GSkyDir. You may recognise that this is the position of the Crab. You then used this position to define the spatial component of a sky model using GModelSpatialPointSource. As the name suggests, the spatial component is a point source. Next, you defined the spectral component using GModelSpactralPlaw: a power law with normalisation of 1 and a spectral index of -2. Then, you combined the spatial and spectral components in a sky model using GModelSky. And finally you appended the sky model to the model container allocated previously using the append method. When you then print the model container you see that is contains now one model with 6 parameters. Among them, you find the specified source position (parameters RA and DEC), the power law normalisation (parameter Prefactor) and the spectral index (parameter Index). In addition, the reference energy for the normalisation has been set to 100 MeV (parameter PivotEnergy) and the temporal component has been set by default to a constant (parameter Constant).

Suppose you want to change one of these parameters, such as the PivotEnergy that you want to set to 1 TeV. This can be done using:

>>> models[0]['PivotEnergy'].value(1e6)
>>> print(models)
...
PivotEnergy ..............: 1e+06 MeV (fixed,scale=100,gradient)
...

As the units are MeV, we had to specify a value of 1e6 to set the reference energy to 1 TeV. We did this by accessing the first model in the container using models[0] (counting in GammaLib always starts from 0). Then we addressed the PivotEnergy parameter by specifying ['PivotEnergy']. And finally we called the value method that sets the value of a particular parameter.

After all this hard work, you may save your model into a XML file by typing:

>>> models.save('test.xml')

and you can load it from an XML file in memory using:

>>> new_models = gammalib.GModels('test.xml')
>>> print(new_models)

The last print command is to convince yourself that the models have been loaded properly.

Much more is still to come. Please be a little bit patient, we’re working on it. In the meantime you may check the Doxygen documentation to see what classes and methods are available.

Getting Help

Any questions, bug reports, or suggested enhancements related to GammaLib should be submitted via the issue tracker or the mailing list.