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TbUT analysis software

This page provides information about the TbUT package. The software is a part of the Kepler project. The main aim of this software is to perform whole processing of the testbeam data using Gaudi framework.

Contact persons

Christopher Betancourt Mail, Iaroslava Bezshyiko Mail, Steven Blusk Mail, Adam Dendek Mail

Installation

  • To install (you need to be in the lhcb computing group on lxplus): 
    lb-dev Kepler v3r0  
cd ./KeplerDev_v3r0
git clone https://github.com/yabezsh/Tb.git
make -j4 make install
  • Make sure, that in your config file (in Tb/.git), you have a line that has a URL with your username included: 
         url = https://sblusk@github.com/yabezsh/Tb.git

Setup

  • After each time you open a new terminal 
    SetupProject LHCb
cd ~/KeplerDev_v3r0
./run bash 
source /afs/cern.ch/project/eos/installation/lhcb/etc/setup.sh
eosmount ~/eos
cd Tb/

  • To run over the data from May testbeam: For tests, please, go to Analysys.py and Analysis_onlyPlots.py and change the line
outputPath = "$KEPLERROOT/eos_"+str(sigFile.split('-')[3])+"/lhcb/testbeam/ut/TemporaryData/May2016/DQMTest" to the folder where you want to have your outputs.

Running the TbUT software

For tests, please, go to Analysys.py and Analysis_onlyPlots.py and change the line outputPath = "$KEPLERROOT/eos_"+str(sigFile.split('-')[3])+"/lhcb/testbeam/ut/TemporaryData/May2016/DQMTest" to the folder where you want to have your outputs.

After that you can run (Oct 2016 data example): 

python -u RunAnalysis.py --board A3_Lower  --PA TTV2_01 --DUTRun 2259 --mode local --mask 1

Here, a May 2016 data example: 

python -u RunAnalysis.py --board F1 --PA FanUp --DUTRun 1073 --mode local --evts 100000

The latter example will produce all plots in "outputPath"/F1/FanUp/output_1073/Plots/ If you want to update plots without reproducing of all data analyze you can add flag "--onlyPlots 1" If you want to run over all events in run just take away the flag "--evts" (usually 1000000 events; it's better to go over all of them to see nice plots). You can run it in terminal (use "--mode local") or in batch ("--mode batch").

If you will not specify a run number ("--DUTRun 1073") it will start to process all runs from testbeam data sheet ( https://docs.google.com/spreadsheets/d/1xYgpb3AxP4JJ7Dkx1YeNV75q0nNxwPrvBhoMgyBD0N4/edit )

Two canvases will be produced, one with Alignment information and one with Performance information. They should be hecked by the user. Below is an example for Run 15046, sensor M1, 300 V bias, 0 degree rotation angle

  • Alignment Plots

Thresholds

  • Performance Plots

Thresholds

Known issues

  • The TbUT cannot read eos files. *workaround: copy analyzed file into your work directory or use eosmount as shown above.
  • TbUT (run mode) need to be executed twice to correct calculate noise and extract clusters.
  • The tools should inherit from GaudTool class.
  • The unit test have to be implemented

Developers section

General overview

Every TbUT component class is a member of TbUT namespace.

Algorithms

The TbUT is a Gaudi based application. The application is separated into two sets of Gaudi algorithms. First group is responsible for processing the testbeam data. This algorithms inherit from GaudiAlgorithm class. The names of it contains string Algorithm for instanceTbUTRawDataReaderAlgorithm .

To understand TbUT source code, the algorithm classes should be treated as a starting point

The second group was designed to prepare monitoring plots. This classes inherit from base called TbUTDataMonitorAlgorithm ( see also implementation ). According to the file name convention it's names have to contains string DataMonitorAlgorithm in their's file names. E.g. TbUTPedestalSubtractorDataMonitorAlgorithm .

Tools

That kind of classes are responsible of performing the data modification. Each of this inherit from virtual interface called IProcessingEngine, therefore they have to implement processEvent method.

Tool's Factories

This kind of classes are responsible for dynamic, depend of options, creation of proper version of tools. This classes based on Factory design pattern (see also the beautiful explanation why is is so useful). See for example Raw Data Reader Factory and it implementation

Other types

  • Data structures (inherits from GaudiKernel/DataObject ) are used to be manipulated via the tools and be stored in TES. The TbUT package contains Raw Data and Clusters .
  • Additional data structures. Can be distinguished noise and pedestals. Their are used as a auxiliary structures by the tools.
  • Additional services. Used for manipulation of the additional data structures. See for instance Noise Calculator. Each of this services has own interface ( e.g. Noise calculator are based on INoiseCalculator).

TbUT high level architecture

TbUT high Level architecture

Description of the algorithms

Raw Data Reader

This algorithm is designed to read raw data from specific input file. Currently TbUT can work with files produced by Alibava and Mamba DAQs. The heart of this algorithm is tool inherit from interface IDataReader. This tool acts fascade for specific DAQ reader.

The execution of Raw Data Reader can be modified using options:

option name brief description
InputDataType type of string, choose one of DAQs data format (Mamba or Alibava)
inputData type of string, path to the input data.
standalone type of boolean, if is true the run is executed with Kepler (haven't been tested yet)

Pedestal Subtractor

The pedestal subtraction algorithm has two phases. In first, optional one, the pedestal values are calculated. This phase is also called training. During the second one the determined pedestal values are subtracted from the raw ADC data. From technical point of view the Pedestal Subtractor Algorithm calls two types of tools. One of them calculates or retrieves pedestals values( this classes inherit from I pedestal following ) and the second one remove pedestals values from the raw data. See data flow.

Pedestal_Subtrator.png

The execution of Pedestal Subtractor Algorithm can be modified using options:

option name brief descriptionSorted ascending
standalone type of boolean, if is true the run is executed with Kepler (haven't been tested yet)
treningEntry type of integer, number of training entry
FollowingOption type of string, choose type of execution (training or run)
ChannelMaskInputLocation type of string, location of the channel mask
PedestalOutputFile type of string, path to the location where calculated pedestal have to be stored
PedestalInputFile type of string, path to the location where previously calculated pedestals are stored

Pedestal following

From the mathematical point of view the calculation of the pedestal can be described as a running average. In every training event then pedestal sum is updated. This update takes into account the previous value of the pedestal sum and the current ADC count. The pedestal sum is calculated for each channel separately. To be more precisely, the pedestal sum, $p_i(n)$, for channel $n$ and event $i$ can be expressed as follows:

$P_{i}^{sum}(n+1)=P^{sum}_{i}(n) + \frac{\Delta_{i}(n+1)}{N}$

Where the $\Delta_{i}(n+1)$ is a event to pedestal correction. This correction is expressed as:

$\Delta_{i}(n+1)=ADC_{i}(n+1)-P^{sum}_{i}(n)$

To increase the suitability of the pedestal the limit for correction is applied. If the condition: $ \left| \Delta_{i}(n+1) \right| \leq 15 $ is not fulfilled the correction value is set to 15.

To determinate the pedestal values the pedestal sum should be normalized, so: $p_{i}=\frac{P^{sum}_{i}}{N}$

Pedestal removing algorithm

The second phase of the pedestal subtraction algorithm is subtraction determined pedestal values from the raw data. This procedure can be expressed as fallows:

$ADC_{i}=ADC^{RAW}_{i}-p_{i}$

where: $ADC_{i}$ is signal value after pedestal subtraction for event $i$, $ADC^{RAW}_{i}$ is a raw data and the $p_{i}$ is the pedestal value. Each of this quantities are in the unit of ADC counts.

Additional scripts

All scripts are stored in TbUT/scripts directory. (Not yet committed)
  • Noise monitor This script takes as an input noise file. The exemplary output:

Thresholds

Operations with the git repository

  • To update your local copy with the latest version in the git repository (you will need a git account) 
git pull

  • To make modifications, you will need to be added to the list of users allowed to modify (contact Iaroslava). After you make your changes: 
git add    (w/o brackets) - designate which files will be sent to git repo (this is for new or modified files)
git commit -m"Add a comment here about your changes"
git push     (this pushes the commits to the repo... will prompt you for your password)

* To get a completely fresh working copy, see instructions under Installation, above.

-- AdamMateuszDendek - 2015-08-03

Attachments Attachments
Topic attachments
I Attachment History Action Size Date Who Comment
PNGpng AlignmentPlots_M1_s2_vb300.png r1 manage 42.4 K 2016-05-13 - 22:19 UTTestbeamService  
PNGpng Landau_example.png r1 manage 0.9 K 2016-05-10 - 07:37 ChristopherBetancourt1 Example plots from runClusterWithTrackAna.C
PNGpng Pedestal_Subtrator.png r1 manage 53.7 K 2015-08-26 - 19:21 AdamMateuszDendek Pedestal Subtractor Data Flow
PNGpng PerformancePlots_M1_s2_vb300.png r1 manage 60.7 K 2016-05-13 - 22:19 UTTestbeamService  
PNGpng TbUT_Architecture.png r1 manage 103.6 K 2015-08-26 - 18:26 AdamMateuszDendek TbUT high level architecture
PNGpng Thresholds.png r1 manage 27.8 K 2015-08-26 - 13:59 AdamMateuszDendek  
PNGpng corr_example.png r1 manage 4.3 K 2016-05-10 - 07:39 ChristopherBetancourt1 Example plots from runClusterWithTrackAna.C
PNGpng profile_example.png r1 manage 0.9 K 2016-05-10 - 07:40 ChristopherBetancourt1 Example plots from runClusterWithTrackAna.C
PNGpng res_example.png r1 manage 0.8 K 2016-05-10 - 07:40 ChristopherBetancourt1 Example plots from runClusterWithTrackAna.C
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Topic revision: r25 - 2016-10-31 - StevenBlusk
 
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