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OrcaRoot

Created by Jason Detwiler, 2005 Updated by Michael Marino, 2007 For questions/information, contact [email protected], or [email protected].

Pre-Installation

Ensure the ROOT is installed correctly, with xml and threading enabled.
Thread should be enabled by default and xml is automatically enabled if ROOT's configure can find libxml2. Also, optionally enable fftw3 support.

I. Installation

  1. Run ./configure [--with-rootsys=/path/to/root] This should make sure your system can compile and install Orcaroot. A failure here should generate a message that can aid in rectifying the problem.

  2. make [-j#] Replace # with the number of processors or cores. This will speed up the compilation process.

  3. set env vars:

    1. On Mac OS X:

      setenv ORDIR [path to OrcaRoot directory, e.g. /home/OrcaRoot]
setenv PATH $ORDIR/Applications:$PATH
setenv DYLD_LIBRARY_PATH $ORDIR/lib:$DYLD_LIBRARY_PATH

    2. On Linux:

      setenv ORDIR [path to OrcaRoot directory, e.g. /home/OrcaRoot]
setenv PATH $ORDIR/Applications:$PATH
setenv LD_LIBRARY_PATH $ORDIR/lib:$LD_LIBRARY_PATH

  4. (SEE Bindings for info on using python bindings instead of having to build a new application.) Set up a directory for your own work. Use the files:

    • $ORDIR/UserMakefileExample (rename it Makefile) and
    • $ORDIR/Applications/orcaroot.cc (rename it something like orcaroot_[project].cc) as examples to help get you started in making your own standalone executable. If you write any generally useful Decoders or Processors, contact me ([email protected]) about adding them to the CVS repository. See Section II: General Description below for a description on how these executables generally work.

  5. Typical usage to run an orcaroot executable:

    orcaroot Run[#]
         ^^^^^^ raw data file output by ORCA 
^^^^^^^^ executable; may be named something else (like
orcaroot_[project]). Should be in the $ORDIR/Applications directory.

There are additionally several options to the provided examples executables. Entering just the executable (with no arguments or data files) will typically list the usage information and available options.

  1. Run OrcaROOT as daemon (optional) You can run OrcaROOT as daemon to do fits directly from Orca. To faciliate the configuration, a suitable init script for Debian GNU/Linux (also works on Ubuntu and probably any other LSB compliant distribution) is included.

    cp Applications/etc/default/orcaroot /etc/default/orcaroot

Open /etc/default/orcaroot with your favourite text editor and set all settings according to the comments.

```Tcsh
cp Applications/etc/init.d/orcaroot /etc/init.d/orcaroot
chmod a+x /etc/init.d/orcaroot
```

Configure your System to run /etc/init.d/orcaroot on boot. For Debian:

```Tcsh
update-rc.d orcaroot defaults
```

OrcaROOT will now be started whenever you reboot your system. To start it right now without rebooting, use

```Tcsh
/etc/init.d/orcaroot start
```

II. General Description

OrcaRoot is a ROOT-based toolkit of C++ class libraries that interface with Orca data streams. Typically, OrcaRoot is used to write the data stream directly into ROOT TTrees, histograms, and other ROOT objects, and store them in a ROOT TFile for quick and immediate processing. However, OrcaRoot is general enough to serve as the Orca data stream interface for more complex event building processes, whether or not such processes use ROOT objects or files. OrcaRoot can also act as a server daemon, handling real-time requests from ORCA to perform such tasks as curve fitting and Fourier analysis.

The reading of the Orca data stream is handled by the IO virtual class ORVReader, which has two concrete implementations: ORFileReader for reading from Orca files on disk, and ORSocketReader for reading data broadcast over a network socket. At the beginning of a run, the ORVReaders read in the Orca file header into an ORHeader object which is stored for the rest of the run. Then the ORVReaders read data records one by one into buffers of binary data that may then be passed to other objects for processing.

The format of the binary data is likely different for each DAQ component in the data stream. Therefore dedicated objects must be created to process data from each component. In general several different tasks might be desired for each DAQ component. For example, one might wish to write data from a particular component to a TTree and save it to disk AND simultaneously pass the same data to a histogram to be displayed online. To provide a clean separation between different tasks and to minimize replicated code, the processing is divided among two sets of classes: decoders and processors. Decoders handle the conversion of the binary data into more user-friendly data types, typically UInt_ts (see footnote 1), although the user is free to extract the binary data into any type of object desired. Processors are the objects that use the decoders to extract the data and perform a particular task. It is intended that a separate processor be made to perform each desired task. This improves readability, encapsulation, and configurability of the code.

Implementing OrcaRoot for a particular DAQ setup will primarily involve the simple task of writing a decoder for each data producing component, followed by the more intensive task of creating a system of processors to perform the desired tasks, and finally creating an executable in which the processors are deployed. This last step can usually be performed by simply copying and modifying the orcaroot executable source code. To ease the burden on a new user, the toolkit contains a variety of example decoders, processors, and applications. Moreover, for simple data components such as an ADC whose contents can be interpreted as a set of UInt_ts to be written to a TTree or histogramed, basic processors have been written to automate the tree writing and histogram filling tasks. See some of the example ADC decoders listed below that use this basic tree / histogram writing interface. For more complicated processing, a base class ORDataProcessor provides the following set of virtual functions to define the processing interface:

  • StartProcessing() - called before any data is read
  • StartRun() - called at the start of each run
  • ProcessMyDataRecord(long* record) - called whenever a data record associated with the processor is received; this is where typical processors perform the bulk of their work
  • EndRun() - called at the end of each run
  • EndProcessing() - called at the end of processing

By implementing the above functions appropriately, and by combining interrelated processors into ORCompoundProcessors, most processing tasks should be achievable.

The ORDataProcManager class performs the central management of the data record reading loop and the issuing of the records to the various processors active in a run. Users wishing to become familiar with the structure of the code and the flow of processing should attempt to read and understand the functions ORDataProcManager::ProcessDataStream() and it's subfunction, ORDataProcManager::ProcessRun(). It will be necessary to understand the the EReturnCode conventions used in the ORDataProcessor interface and their special meaning to an ORCompoundProcessor, of which ORDataProcManager is a derived class; see ORDataProcessor.hh and ORCompoundProcessor.hh for details.

III. Toolkit Contents

Below is a list of all of the OrcaRoot directories and a description of their contents. The directories are listed in dependency-order, and their contents are listed roughly in order of importance. See the source code for details on the particular classes.

Disclaimer: I apologize in advance that not all of the source code is properly documented. I only hope that the code itself is clear enough that the user can get a general idea of how it works without the help of extensive comments. Please contact me with any questions: [email protected]

The following provides an outline of each major base class. In all cases, header files provide more extensive

Util

  • ORLogger: utility for centralized info/error logging
  • ORVSigHandler: virtual base class for objects that need to perform special clean-up procedures on SIGINT (ctrl-c).
  • ORUtils: byte-swapping utilities for cross-platform endian issues

Decoders

  • ORVDataDecoder: virtual base classes for decoders. Derived classes must define a function that returns a string containing the path to its associated data record's description in the header.

    • Swap(): This function swaps the data when necessary (i.e. when the endianness of the DAQ computer differs from that of the OrcaROOT computer.

    • GetDataObjectPath(): This function returns the path in the xml header for a particular dataId. For example, if the dataId is located under

      <key>dataDescription</key>
<dict>
...
    <key>AnObject</key>
    <dict>
    ...
        <key>DataFromObject</key>
        <dict>
            <key>dataId</key>
            <integer>9909</integer>

...

      then GetDataObjectPath() would return AnObject:DataFromObject. It automatically searches in the dataDescription dictionary and automatically adds the dataId key.

    • GetDictionaryObjectPath(): Some records include a hardware dictionary residing in the xml header that is static information associated with the hardware such as parameters, timing, etc. If this function returns a non-zero sized string, then OrcaROOT will search for all the cards that fit this parameter.

    • For more information please see the header file.

  • ORVDigitizerDecoder: This virtual class provides an interface to which all digitizer type record decoders should adhere.

  • ORBasicDataDecoder: wrapped version of ORVDataDecoder for use primarily by ORVReader; not associated with a particular data-producing DAQ component¬

  • ORVBasicTreeDecoder: virtual base class defining interface for decoders that can be made to write their data to a simple TTree, where the branches are all UInt_ts (see ORBasicTreeWriter). Relieves the user of the need to write an entire processor for this simple task.

  • ORVHistDecoder: like ORVBasicTreeDecoder, but for TH1's (see ORHistWriter)

  • A few data-component-specific data decoders (naming convention: OR + Orca header identifier + Decoder)

    • ORRunDecoder: decodes the run data record; the name of this decoder does not follow the naming convention.
    • ORAD413ADCDecoder: example of ORVBasicTreeDecoders and ORVHistDecoders.
    • ORAD811ADCDecoder: another example of ORVBasicTreeDecoders and ORVHistDecoders.
    • ORL2551ScalersDecoder: example of reading an array of data out of a data record.
    • ORShaperShaperDecoder: another example of ORVBasicTreeDecoders and ORVHistDecoders.
    • ORTek754DScopeDataDecoder: another example of reading an array of data out of a data record.

IO

  • ORVReader: virtual base class for readers.
  • ORFileReader: reads data from an Orca file.
  • ORSocketReader: reads data from a network socket.
  • ORHeader: encapsulates Orca's xml-header.
  • ORDictionary: represents the header's xml-tree structure.

Processors

  • ORRunContext: stores global information common to all processors, for example the current run number, whether the run has started, etc.
  • ORDataProcessor: base class for data processors. Each data processor holds a pointer to an ORVDataDecoder which associates the processor with a single data record type (except for ORUtilityProcessor, whose ORVDataDecoder pointer is NULL, see below).
  • ORVTreeWriter: virtual base class for processors that write data to TTrees -- automates the TTree building, filling (if desired), and writing. In addition, it automatically adds 'default' branches, including run number, sub-run number and run-state description.
  • ORBasicTreeWriter: processor that uses an ORVBasicTreeDecoder to write simple data (a list of UInt_ts) to a TTree.
  • ORHistWriter: processor that uses an ORVHistDecoder to fill a histogram.
  • ORUtilityProcessor: base class for processors that don't process data and therefore do not need access to a decoder.
  • ORFileWriter: ORUtilityProcessor that handles the opening and closing of a ROOT TFile for each run.
  • ORCompoundDataProcessor: ORUtilityProcessor that holds a list of ORDataProcessors, executing them in-order for each of the processing interface functions. ORCompoundDataProcessors can hold other ORCompoundDataProcessors in their list.
  • A few data-component-specific processors (naming convention: associated decoder class name - Decoder + direct base class suffix, i.e. Processor or TreeWriter)
    • ORRunDataProcessor: this processor has the special task of managing the state of fgRunContext.
    • ORShaperShaperTreeWriter: simple tree writing example; ORShaperShaperDecoder is already an ORVBasicTreeDecoder so this class really isn't necessary, but is instructive.
    • ORL2551ScalersTreeWriter: tree writing example in which the tree is filled manually. This processor is obsolete as of January 2006; one obtains the same tree by give an ORBasicTreeWriter an ORL2551ScalersDecoder*. Kept for backwards compatibility (in particular, for orcaroot_minesh).
    • ORTek754DScopeDataTreeWriter: example of writing non-simple data to a tree (in this case, an array of integers representing a scope trace).

Management

  • ORDataProcManager: central class that manages processing.
  • ORProcessStopper: manages a parallel thread process that runs a UI from which orcaroot can be killed nicely after processing the current data record or halted nicely after processing for the current run completes.

Applications

  • orcaroot: the main (example) application. Users can either edit this executable to use their own processors, or use this as a starting point to create a custom application.
  • orcaroot_minesh: application used by Minesh Bacrania, a user at UW.
  • getHeaderInRootFile: shows how to extract the xml-header, which ORFileWriter stores in the output file, and re-load it into an ORHeader.
  • writeShaperTree: example of a very simple application to write ADC values to a TTree.
  • testStopper: tests/debugs the stopper thread.
  • testUtil: hello world using ORLogger.

Bindings

  • If OrcaROOT can build python bindings, it will try to build them. This allows OrcaROOT to be called through to using pyROOT. See the Bindings/README.txt file for more information.

IV. ToDo:

  • check throughput/benchmark -- is it getting all the data? Any bottlenecks?
  • graphical display capability while running / GUI
  • complex grouping schemes
  • orcaroot-quit function: takes PID (or operates on all PIDs of processes named "orcaroot"), finds CWD of process, and puts quit file in that directory (to cleanly kill orcaroot remotely)
  • socket read/readline should time-out if desired
  • factory for analyzers; can read in from file/header entry?
  • configure script
  • multiple-record-type processor, OR
  • compound processor that can manage, e.g., loading of info into event from many different data records
  • ORUtils.hh contents into a namespace.
  • Re-scope ORProcessStopper into a UI class (one of whose commands is to stop processing)
  • ORSocketReader reads into a buffer, but this isn't terrible efficient for a non-multi-core machine. Fix?

Footnotes

  1. The ROOT-defined data types, especially UShort_t, UInt_t, and ULong64_t, are preferred over C++ data types such as short, int, and long long because the ROOT versions (supposedly) have the same size in bytes on any platform. This is especially important when reading the ORCA data buffers, which are packed into 32-bit words.