Analysis in Python

The LCIO data format has Python bindings called pylcio, which allows to read and write objects using Python scripts. Such analysis is slower to run than a full-blown Marlin processor, but makes the development process much faster and more flexible.

You can find an example python script in MuC-Tutorial/analysis/pylcio/, which can be executed as:

python run.py <input_file.slcio> -o <output_file.root>

The run.py script simply provides the handling of command-line parameters and imports a so-called driver, which is executed over each event of the input file, like a C++ processor would. By default the CalHitsMCPDriver is imported, which performs matching of calorimeter hits to their corresponding MCParticles and stores data for each hit into a ROOT TTree. The TrkHitsMCPDriver drived can be used instead for a similar analysis of the tracker hits.

Each driver class follows the familiar structure with standard callbacks def __init__(), def startOfData(), def processEvent() and def endOfData(). While the presented examples store data into a TTree that needs further analysis later, it is also possible to do more elaborate analysis directly in the driver or even doing it interactively by putting using python bindings directly in a Jupyter notebook.

You can also use the set_trace() debugging function anywhere inside the processEvent() callback to pause the event loop at a certain point and examine the event content interactively.