This repository is a first step in integrating the standalone C++ emulator in a python code.
It is possible to use a configuration file (config.yaml) and easily change the input configuration parameters of the Stage-2 emulator.
To create the binding between Python and C++ code, a special library has been used: cppyy. Some short example about how import C++ libraries, how to call class into Python, etc are reported in this repository on my profile.
For the moment, there is no Makefile to compile the code. The command that should be used is the following. The shared library has to be inserted in the python file run_emulator.py.
g++ -std=c++14 -shared -o lib_configuration.so L1Trigger/L1THGCal/src/backend_emulator/*.cc -g -fPIC -I.
In the main python file run_emulator.py and in data_handle/event.py data are prepared and injected.
Currently I'm studying the back-end mapping (TC to S1, S2 links and frames) to understand how the data has to be prepared starting from a simple simulated dataset. XML files here and copied in the config_files directory. The geometry is read from the XML file in data_handle/geometry.py.
To run the Stage-2 emulator using the Python wrapper, some packages need to be installed in your environment. Here, I'll list the most important ones:
I suggest creating a conda environment for easy inclusion and activation of the following packages.
conda create --name <env_name> python=3.7
conda install -c conda-forge cppyy
conda install -c conda-forge pyyaml
pip install matplotlib
pip install scipy
pip install awkward
pip install uproot
pip install mplhep
conda install gxx
# run the emulator without producing any plots
python run_emulator.py
# processing 2 events and producing plots post unpacking and post seeding
python run_emulator.py -n 2 --plot --col
# processing 100 events and plotting the seeding efficiency vs eta, pT and energy threshold
python run_emulator.py -n 100 --thr_seed
# to specified the particles (photons or pions) and pileup (0 or 200)
python run_emulator.py -n 100 --pileup PU0 --particles photons --thr_seed
# to produce simulation vs emulation comparison plots starting from cluster properties
python run_emulator.py -n 100 --plot --simulation
Other options can be displayed using python run_emulator.py --help.
Single particle plots and efficiency plots can be found here.
Some emulator parameters can be easily changes via the config yaml file. For instance, the seeding threshold (in GeV) or the seeding window width.
I'm producing new data samples using Geometry V16 following the recipe outlined here and considering Phase2Fall22campaign. Public samples (photons and pions) that I'm using are stored in this eos folder:
/eos/home-m/mchiusi/s2_emulator
The location can be change via the yaml configuration file.
These samples have been produced using threshold algorithm in the CE-E and STC for CE-H and CE-S. A simulation of best choice algorithm (BS) for the CE-E is implemented in the Python wrapper.