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.

For questions/information, contact jasondet@gmail.com, or mmarino@gmail.com. Also see the full ORCARoot Documentation (Opens a new window).

The ORCARoot analysis application can also be run as a standalone daemon to provide data fits to ORCA. When run in this mode ORCARoot will not attempt to connect to ORCA as a data analysis package.

CENPA is currently running an ORCARoot daemon at crunch4.npl.washington.edu port 9090  and ORCA defaults to this location. However, this may change without notice. See the ORCARoot documentation for more information.

Open the fit service via Main Menu->ORCARoot or from the main configuration dialog tool bar if you have customized the tool bar to display ORCARoot icon.


Connect/Disconnect. Note that if you are connected, you can not change the port number without disconnecting first

Location that you have an ORCARoot app running as a standalone daemon.

Current connection status

If selected and the fit service is not available, ORCA will repeatedly attempt to reconnect several times/minute.

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

  3) set env vars:

     On Mac OS X:

     setenv ORDIR [path to ORCARoot directory, e.g. /home/ORCARoot]

     setenv PATH $ORDIR/bin:$PATH


     On Linux:

     setenv ORDIR [path to ORCARoot directory, e.g. /home/ORCARoot]

     setenv PATH $ORDIR/bin:$PATH


  4) 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 (jasondet@gmail.com)

  about adding them to the CVS repository. See Section II: General

  Description below for a description on how these executables generally


  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/bin 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.

II. General Description

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_t's (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_t's 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:



- 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: bit-swapping utilities for cross-platform endian issues


- 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.

- 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_t's (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


- A few data-component-specific data decoders. Note that this list is changing as objects are added to ORCA so it is not meant to be complete.

  (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


  - ORAD811ADCDecoder: another example of ORVBasicTreeDecoders and


  - 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.


- 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.


- 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


- ORBasicTreeWriter: processor that uses an ORVBasicTreeDecoder to write

  simple data (a list of UInt_t's) 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).


- 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.