Examples

In this page we will collect some detailed examples of a pythonic approach to the LHC and HL-LHC masks.

Info

This is the recipe to install the needed python package. We assume that you have access to afs and to the internet.

The shortcut

Open your lxplus terminal and enter

source /afs/cern.ch/eng/tracking-tools/python_installations/activate_default_python  

and that's it.

The standard way

We suggest you setup your own installation (you will be more aware of the configuration and you will be customize it).

To setup your environment you can install a miniconda distribution. E.g.,

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
sh Miniconda3-latest-Linux-x86_64.sh

and follow the instructions.

After having activated it, install the needed packages by

pip install ipython numpy pandas scipy pyarrow matplotlib cpymad

Then install

pip install pyNAFF

In addition you need to install

• pysixtrack

  git clone https://github.com/SixTrack/pysixtrack.git
  cd pysixtrack
  pip install -e .
  cd ..
  

• sixtracktools

  git clone https://github.com/SixTrack/sixtracktools.git
  cd sixtracktools
  pip install -e .
  cd ..
  

• sixtracklib. This requires a recent version of cmake. If you are installing on your own machine it should not be a problem. On lxplus, you call another environment before the installation (thanks to K. Paraschou, G. Iadarola).

source /cvmfs/sft-nightlies.cern.ch/lcg/views/dev4cuda9/latest/x86_64-centos7-gcc62-opt/setup.sh
unset PYTHONHOME
unset PYTHONPATH

then you have to go back to your python distribution. E.g., please replace conveniently the paths below

source /afs/cern.ch/eng/tracking-tools/python_installations/miniconda3/bin/activate
export PATH=/afs/cern.ch/eng/tracking-tools/python_installations/miniconda3/bin:$PATH

Please check with

which python ipython cmake

that python and ipython are pointing to your miniconda and that cmake is pointing to

/cvmfs/sft-nightlies.cern.ch/lcg/views/dev4cuda9/Tue/x86_64-centos7-gcc62-opt/bin/cmake

Then you can proceed with

git clone https://github.com/SixTrack/sixtracklib.git
cd sixtracklib
mkdir build
cd build
cmake ..
make
cd ../python
pip install -e .
cd ..

Test it

You can download the lhcmask package.

git clone https://github.com/lhcopt/lhcmask.git

It contains the examples that you are going to test. In particular you can now test a pure pythonic mask by

cd lhcmask/python_examples/hl_lhc_collisions_python
python 000_pymask.py

or you can run a standard MAD-X file by

cd lhcmask/examples/hl_lhc_collision
python ../../unmask.py main.mask parameters_for_unmask.txt --run

Each example is the markdown version of a jupyter notebook: you can read the markdown but you are also inveted to download and execute the notebook (at the start of the markdown document you will have the link to download the notebook). The full list of notebook is available here and you can get them by cloning this repository

git clone https://gitlab.cern.ch/sterbini/lhcmaskdoc.git

Thank you for contributing.

A python mask for Run3

You can find here an example of python mask for Run3.

A python function for the luminosity leveling

You can find here a detailed description of the luminosity function we propose. An example its use can be found here.

We suggest also this interesting resource by S. Papadopoulou.

Installation of the wires

You can find here an example of installation of the wires for LHC Run 3, with several sanity checks.

MADX B2 vs B4

You can find here an example of the specifity of the MADX B2 vs B4.