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On the analysis itself: We're still working on some details of the data analysis. Since this concerns the initial data format, we will first fix this before we'll supply you with extensive examples. There will also be a bit of a process throughout the program, since it's hard to anticipate how fast the students will be on analysing their data. We will of course keep you updated with some code examples and details on what they should be able to get out of the analysis.

Fixed facts on the analysis:
 * We will use Jupyter notebooks. A hub has been set up by the DESY IT and now we're the beta(or alpha?)-testers (see below).
 * The programming language is python+ROOT
 * The data will be supplied in the form of ROOT TTrees
 * The two teams will perform different experiments, thus it will be two different data sets and two analysis approaches.
 * We're in the act of compiling example notebooks for you. Those will contain one possible way to analyse the data, hopefully with a lot of helpful comments. Keep in mind that many roads lead to Rome and try to advise the students accordingly.

On the Jupyter notebooks:

  1. You can access the hub here: https://naf-jhub.desy.de/
  2. Log in with your DESY credentials
  3. Spawn a server
    1. Primary group: Default (or choose your group)
    2. GPU node: No
    3. Jupyter Launch Modus: Classical Notebook
    4. Hit "Spwan"
    5. You might have to repeat that - at least that happened to me once or twice
  4. You are now in your afs home directory.
  5. To create a new notebook in the top right corner hit New->Python3 (You'll also see C++ ROOT, but it doesn't seem to be working up to now)
  6. Play around with it a bit, using python syntax

I put a few explanations and examples into the notebook attached. You can open it by following until step 4 and then clicking "Upload" at the top right.

There is also one more tutorial on ROOT in Jupyter notebooks that you can check out: https://root.cern.ch/notebooks/HowTos/HowTo_ROOT-Notebooks.html

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