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
Adam Dendek
Mail
Setup
Follow these steps to setup
TbUT in your working directory
- Compile the source code
make -j20
make install
- Prepare the running environment
./run bash
Running the TbUT software
The
TbUT software can be run in two modes,
Pedestal and
Run. The description below contains instructions on how to run
TbUT in
standalone mode (no other Kepler stuff).
You should also look at the
Tools for the LHCb Software Environment TWiki page for a better understanding how to run Gaudi-like application.
Pedestal mode -- computing the pedestals
- The aim of the pedestal training is to calculate the pedestals values, which will then be used in Run mode in order to subtract the pedestal from the raw data during execution of the Pedestal Subtraction algorithm.
- In this mode only 2 algorithms are executed :
Raw Data Reader
and Pedestal Subtractor
for more detailed description of this algorithms look to the developers section.
- Preparation of the options. You just need to modify the file (
Tb/TbUT/options/TbUTPedestal.py
): option name | brief description |
app.inputData | String, contains path to the input data (you have to set this, see NOTE below) |
app.isAType | Boolean, choose if sensor is type A (true) or D (false) |
app.eventMax | Integer, number of event to process |
app.app.pedestaOutputData | String, contains path to the output file (contains pedestal values in a flat list). |
- NOTE: The input files are located on EOS. Currently, you either need to copy files from EOS to a local directory (you will run out of space fast) or you can mount eos temporarily
- To mount EOS:
eosmount ~/eos
If you do this, please add eosumount ~/eos
to your ~/.logout file.
- July 2015 testbeam files for board D9, for example, are located here:
eos/lhcb/testbeam/ut/OfficialData/July2015/BoardD9/RawData/*.dat
Run mode
- In run mode the TbUT performs the whole processing of testbeam data.
- In this mode all processing algorithms are executed.
algorithm | brief description |
Raw Data Reader | Reads raw data from the DUT (Mamba board output) and saves the output into the TES |
Pedestal Subtractor | Gets raw data from the TES, removes the calculated pedestals from the Pedestal mode run, and stores the output into theTES |
Common Mode Subtractor (CMS) | Gets data after Pedestal Subtraction; removes the common mode noise, a single value per 32 Beetle channels (mean value of ADC); save the output into the TES |
Cluster Creator | Gets data after CMS, create clusters and saves it information in the TES |
Beyond the listed algorithms, a set of corresponding monitoring algorithms has been implemented. The detailed descriptions of processing and monitoring algorithms see
developers section.
- Preparation of the options. You just need to modify the file (
Tb/TbUT/options/TbUTRun.py
): option name | brief description |
app.inputData | type of string, contains path to the input data (use an absolute path, e.g. "/afs/cern.ch/user/s/sblusk/eos/lhcb/testbeam/ut/..../filename.dat" |
app.isAType | type of Boolean, choose if sensor is type A (true) or D (false) |
app..sensorType | type of String, choose if sensor is P-type ("PType") or N-type ("NType") or both - calibration run ("Both") |
app.eventMax | type of integer, number of event to process |
app.pedestalInputData | type of string, contains path to pedestal file (created during execution TbUT in Pedestal mode) |
app.eventNumberDisplay | type of integer, informs how many event snapshots will be saved (see section exemplary outputs ) |
DUT and Telescope analysis
- These steps require running with a ROOT version 5, so it is helpful to type the following before running the combination of DUT and telescope data:
LbLogin -c x86_64-slc6-gcc48-opt
SetupProject LHCb v36r2
This ensures the proper root version will be used
- After computing pedestal and CMS, the data from the DUT can be combined with the information from the telescope. Once in the Tb/TbUT directory, the script for appending the DUT data with the telescope data can be run by doing the following:
cd scripts/AddTrigTracks
make
./combDUTwithTrack -i ~/inputDUT_Tuple.root -t ~/eos/lhcb/testbeam/ut/TelescopeData/July2016/RootFiles/RunXXXX/Output/Kepler-tuple.root -n ~/inputDUT_noise.root -o outputFile.root
where inputDUT_Tuple.root is the file resulting from the the run mode, inputDUT_noise.root is the file containing the noise information (nominally the same as inputDUT_Tuple.root but without the 'Tuple' appendage), ~/Kepler-tuple.root is the data from the telescope, and outputFile.root is the desired output file name.
- Once the track information has been appended to the DUT tuple file, the final set part of the analysis can be run. This includes calculation of various quantities (efficiency, correlation between DUT X and Track X, etc.).
- Begin with supplying the output file from the previous step into ~/TbUT/scripts/ClusterWithTrackAna.h. This should be a string called "filename".
- In ~Tb/TbUT/scripts/AnalysisBase_Inputs.h, one needs to specify various parameters given below
parameter | value | description |
m_board | "M1", "M3" or "M4" | Board type |
m_bias | e.g. 200 | Bias voltage in volts |
m_sector | "PA" or "C" | Pitch adapter region or Central region |
m_scanType | "Bias" or "Angle" | Defines if this is a bias scan run or and angle scan run |
nChan | e.g. 512 | The total number of channels |
stripPitch | e.g. 0.190 | The strip pitch in mm |
isPtype | true or false | where this is n-in-p (p type) or p-in-n (n type) |
Analyze DUT data with tracking
- The final scripts can now be run using the following (if you are in ~Tb/TbUT/scripts/)
root [0] .L TBrunClusterWithTrackAna.C
root [1] TBrunClusterWithTrackAna("the/full/path/Run_Angle_Scan-M1-FanIn-88-15046_Tuple_Tracks.root","M1","2","300")
The arguments of
TBrunClusterWithTrackAna
are the
filename
, the
sensor ID
, the
sector
and the
bias voltage
.
A ROOT file will be written to the location specified in "AnalysisBase_Inputs.h" (currently $HOME/lhcb/testbeam/root_files/ )
* A pair of "summary" plots can then be produced using the *monitorPlots.C" macro:
root [0] .L monitorPlots.C
root [1] monitorPlots("M1","2","300")
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
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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
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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.
The execution of Pedestal Subtractor Algorithm can be modified using options:
option name |
brief description |
FollowingOption |
type of string, choose type of execution (training or run) |
treningEntry |
type of integer, number of training entry |
ChannelMaskInputLocation |
type of string, location of the channel mask |
PedestalInputFile |
type of string, path to the location where previously calculated pedestals are stored |
PedestalOutputFile |
type of string, path to the location where calculated pedestal have to be stored |
standalone |
type of boolean, if is true the run is executed with Kepler (haven't been tested yet) |
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, , for channel and event can be expressed as follows:
Where the is a event to pedestal correction. This correction is expressed as:
To increase the suitability of the pedestal the limit for correction is applied. If the condition:
is not fulfilled the correction value is set to 15.
To determinate the pedestal values the pedestal sum should be normalized, so:
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:
where: is signal value after pedestal subtraction for event , is a raw data and the 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:
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-- AdamMateuszDendek - 2015-08-03
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