pySixDesk is a python interface to managing SixTrack jobs for large parameter scans. It allows to set up and manage job submission, gather results and analyse them.

pySixDesk comes as a python library; hence, it can be imported into a python terminal or into custom-made python scripts. The library also comes with python wrapper scripts, such that specific commands can be issued directly from the login terminal.

The interface requires python3, and it is being developed with python3.6.8.

L. Coyle (CERN), X. Lu (CSNS, CERN), A. Mereghetti (CERN).

The native environment of pySixDesk is CERN's lxplus login service; the guidelines in the following will assume that this is the case. pySixDesk embeds built-in commands for:

• AFS and openAFS for disk storage;
• kerberos, for login and user identification;
• htcondor, as batch service native at CERN;
• BOINC, as additional batch service for long-term, large simulation campaigns;
• sqlite3 and mySQL, for the database monitoring the progress of jobs and storing data;
• python3, as main language.

In case of using pySixDesk on a local machine, please make sure that the necessary packages are available;

pySixDesk is a library of utilities and it does not provide a user workspace. Hence, please keep separated the library from your own workspace.

pySixDesk is still under development. Hence, the best is to download it as a git package:

1. go to https://github.com/SixTrack/pysixdesk and fork the project. If you do not have a user account on github.com, please create one. You may also want to properly configure your ssh keys for managing possible pull requests;
2. clone your fork to your local file system (<myAccount> is the login name of your account):

git clone https://github.com/<myAccount>/pysixdesk.git

In case you need to perform some development, it is common practice with git repositories to track your changes under a branch in your git fork and, once you are happy with the changes, make a request to apply them also upstream, such that every other user can profit from your contribution:

1. add the upstream main project: git remote add upstream [email protected]:SixTrack/pysixdesk.git
2. develop on a new branch: git checkout -b <myBranch>
3. when the development is over, create a pull request via the web-interface of github.com.

It is recommended to use pySixDesk from the python shell. Please remember to use python3. On lxplus, python3 is available as python3 command, since the default python command uses version 2.7.5.

In order to use the library, it is essential to declare in your live python environment the path where the pysixdesk package can be found. This can be accomplished adding the path to the pysixdesk package to the PYTHONPATH environment variable (in the following, $pysixdesk_path is the full path to pysixdesk), eg:

export PYTHONPATH=$PYTHONPATH:$pysixdesk_path/

or to add it to the sys.path list, eg:

import sys
sys.path.append(<path_to_pysixdesk>/)

The former option can be made permanent for every python terminal in any linux terminal copying the above definition into the .bashrc file. The only drawback to this approach is that every terminal will be affected by this setting. It is probably more convenient to create an alias like the following one in you .bashrc file:

alias loadPySixDesk="export PYTHONPATH=$PYTHONPATH:$pysixdesk_path/"

This short guide will explain how to set up a quick toy study. By default the jobs will be submitted to HTCondor. If you want to use a different batch system, you need to create a new cluster class with the interface (Cluster) defined in the submission.py module and specifiy it in the config.py script.

1. prepare the workspace. To do so, you have to create an instance of the parent class StudyFactory, which handles the workspace. If no argument is given, the default location ./sandbox is used:

   from pysixdesk import Study
   from pysixdesk import WorkSpace
   myWS = WorkSpace('./myWS')

2. prepare necessary folders (e.g. ./myTest/studies/test) and copy template files (including config.py) for a study. If no argument is given, the default study name is test (if no studies are present) or study_??? (with ??? being a zero-padded index of the study, calculated from the existing ones):

   myWS.init_study('myStudy')

3. edit the config.py file to define the study (e.g. define .mask file, add scan parameters, etc...);

4. load definition of study in config.py and create/update database:

   myStudy = myWS.load_study('myStudy')
   myStudy.update_db() # only needed for a new study or when parameters are changed

5. prepare and submit pre-processing jobs (e.g. MADX job and sixtrack one turn jobs), and collect results:

   myStudy.prepare_preprocess_input()
   myStudy.submit(0, 5) # 0 stands for preprocess job, 5 is trial number 
   myStudy.collect_result(0) # collect results locally

6. prepare and submit actual sixtrack jobs, and collect results:

   myStudy.prepare_sixtrack_input()
   or
   myStudy.prepare_sixtrack_input(True) #True: submit jobs to Boinc
   myStudy.submit(1, 5) # 1 stands for sixtrack job, 5 is trial number 
   myStudy.collect_result(1) # 1 stands for sixtrack job 
   or
   myStudy.collect_result(1, True) # True: collect results from boinc spool directory

In order to use a custom cluster, make sure the file containing your custom cluster class is located in your $PYTHONPATH and simply change the cluster_class attribute in config.py to point to the desired class:

config.py:

 import cluster
 ...
 def __init__(self, name='study', location=os.getcwd()):
     super(MyStudy, self).__init__(name, location)
     self.cluster_class = cluster.custom
     ...

There are ten tables in the database:

The detailed structure of these tables, please view the doc Table.md.