wagascianpy 0.2.8

WAGASCI ANPAN for Python

WAGASCI ANPAN for Python v0.2.8

This collective package contains all the Python module used by the ANPAN framework of the WAGASCI/T2K experiment.

T2K experiment

T2K (Tokai-to-Kamioka) is a long-baseline off-axis neutrino oscillation experiment that focuses on measuring muon (anti)-neutrinos oscillating into electron (anti)-neutrinos. A very pure muon neutrino beam is produced at J-PARC accelerator complex and detected 295 km away at the Super-Kamiokande (SK) far detector. T2K uses a set of near detectors in order to reduce the large uncertainties on the oscillation parameters that come from the neutrino fluxes and interaction models.

WAGASCI experiment

WAGASCI (WAter-Grid-SCIintilator-Detector) is proposed to

• reduce the T2K systematic error
• measurement of the charge current cross-section ratio between water and scintillator targets with 3% accuracy
• measurement of different charged current neutrino interaction channels with high precision and large acceptance.

ANPAN

ANPAN (Acquisition Network Program for Accelerated Neutrinos) is a collective name to identify all the software used to acquire data and analyze it for the WAGASCI experiment. It is composed of many packages:

• Pyrame (C/Python2)
• Calicoes (C/Python2)
• WagasciCalibration (C++11)
• WagasciRunControl (Python2)
• WagasciDatabaseViewer (Python3)
• WagasciAnpy (this package) (Python2/3)

Dependencies

• Python2.7 or Python3.4+
• pip

This package is compatible with both Python2.7 and Python3.4+. It is tested usually with Python 3.8 and sporadically with other Python versions. Therefore, it may happen that the compatibility with Python2.7 is not perfect.

To install Python, please google "Install Python <Your favourite OS>". All the other Python dependencies can be installed using Pip.

Optional dependencies

To use the raw data decoder and spill number fixer, you have to install ROOT and the WagasciCalibration package. Please refer to the links for an in-depth explanation about the installation process.

• ROOT 6
• WagasciCalibration

To update the WAGASCI run database you need

• BorgBackup

If you do not plan to use those features you can avoid installing these dependencies.

Installation

Python 2.7

Open a terminal and issue

python2 -m pip install --upgrade --user wagascianpy

Python 3.4+

Open a terminal and issue

python3 -m pip install --upgrade --user wagascianpy

Usage

Python modules

• wagascianpy.analysis

  Contains modules for analysing the WAGASCI raw data, decoded data and slow data.

  • wagascianpy.analysis.analysis : Ctypes wrappers around WagasciCalibration libraries.
  • wagascianpy.analysis.analyzer : Takes the Ctypes wrappers and organize them in a abstract factory design patter.
  • wagascianpy.analysis.beam_summary_data : module to integrate the BSD information inside the decoded WAGASCI data. It can be used as a standalone program or as a analyzer of wagascianpy.analysis.analyzer.
  • wagascianpy.analysis.mhistory2sqlite : module to convert many mhistory files containing slow devices data into a SQLite database.
  • wagascianpy.analysis.spill : factory design patter to generate various spill objects. A spill object contains information about the spill number. It is useful when fixing the spill number bugs or when integrating the BSD information.

• wagascianpy.database

  Modules to manage the WAGASCI run database and the BSD database.

  • wagascianpy.database.bsddb : BSD database creation and access.
  • wagascianpy.database.db_record : Virtual database record that is inherited by the WAGASCI run record and BSD record.
  • wagascianpy.database.my_tinydb : Virtual database build upon the tinydb external module. It is inherited by the WAGASCI run database and BSD database.
  • wagascianpy.database.wagascidb : WAGASCI run database creation and access.

• wagascianpy.plotting

  Modules to generate history plots (plots where the X axis is time).

  • wagascianpy.plotting.colors : manage plot colors
  • wagascianpy.plotting.detector : class to store the read TTrees for each subdetector and to iterate over them.
  • wagascianpy.plotting.graph : class to build a generic graph
  • wagascianpy.plotting.harvest : class to harvest the needed data from the TTrees. It returns the X points and Y points. It is implemented using the strategy design pattern.
  • wagascianpy.plotting.marker : class to plot time markers as the maintenance days, WAGASCI runs start and stop time, electronics troubles , etc...
  • wagascianpy.plotting.plotter : class to plot the actual TMultiGraph
  • wagascianpy.plotting.topology : class to specify which subdetectors to plot and which not

• wagascianpy.program

  Modules to generate a runnable program using the analyzers of wagascianpy.analysis.analyzer. Implemented using the builder design pattern.

  • wagascianpy.program.director : director of the builder design pattern.
  • wagascianpy.program.program : final program that is builded.
  • wagascianpy.program.program_builder : builder that builds the program.

• wagascianpy.utils

  Utilities.

  • wagascianpy.utils.change_bitstream : module to change the configuration of the SPIROC chip for all the chips in one go.
  • wagascianpy.utils.environment : module to read the WAGASCI environmental variables from a file or from the shell environment.
  • wagascianpy.utils.utils : all other utilities are gathered here.