HGCal link mapping studies

Installation

• If you don't have python and the relevant packages installed please run: source install_packages.sh. Note the size of the installation is around 4GB. Then each time you start a new session run: source start_mapping_env.sh

• If you have python and can install your own packages the relevant ones are:

  • numpy
  • matplotlib
  • pandas
  • pyyaml
  • scikit-learn
  • ROOT
  • root_numpy

main.py

Main file containing the option to run all functions:

• plot_lpGBTLoads, plot_ModuleLoads, : Processes MC event data and determines the average number of TCs, or words, per lpGBT
• study_mapping, : Find the optimised way of assigning lpGBTs to bundles

Run using the config file config/default.yaml, where the input options are listed for each parameter

process.py

Contains the helper functions required for each function in main.py

extract_data.cxx

Prepares the input for process.py. Takes a CMSSW output root file as input and produces a .csv file. Takes a json config file as input (default in config/extract_data.json). Run like make; ./extract_data.cxx config/extract_data.json;

rotate.py and rotate.cxx

Python and C++ implementations of the mapping between 120 degree HGCal sectors in (u,v) coordinates.

fluctuation.py

Takes as input a choice of lpgbt bundles, and bins the trigger cell data event by event There are several plotting scripts that investigate the impact of truncation on the number of trigger cells. Run using the config file config/fluctuation.yaml. Also the option to save the sum of (truncated or total) trigger cell p_T as a function of R/Z for each event.

plotbundles.py

Various plotting functions, mainly to plot the 24 R/Z histograms for each bundle, and take the ratio to the inclusive distribution over 24. Run using the config file config/plotbundles.yaml