JIVE Wiki

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.
====== Software Development ======


===== Software correlator =====

**This is in Arpad's stuff, but should go in this chapter**

===== Logistics software =====

//Bauke Kramer//    ''Huib says: this needs a rewrite by whom?''

Kramer took care of the maintenance of the proposaltool Northstar. The validation of the data in an uploaded sourcelist was implemented.

Creation of a datamining program for the proposal data is in progress. This program uses the xml output of the proposaltool for JIVE, which is stored in a local database. It contains the name of pi, contact auther or potential observer for each proposal. Other stored items are: the country of origin, affiliation, email, etc. The program is written in php and will have many selection possibilities.

The backup program for the EVN Data Archive was improved. The backupprocedure was changed to backup by subdirectory of experiment instead of backing up by experiment. This prevents backing up the whole experiment while small changes in the data are archived. A local webpage was created which keep track of the backups.

Other programs which needed some maintenance were log2vex (program to create vexfiles), mark5 interface program and updateftp (program to mirror station logfiles and gpsfiles from Bologna and NRAO.



//Harro Verkouter//   ''Huib is wondering if this should not go to Arpad's section''

<del>Verkouter spent time on investigating and documenting "disaster recovery" for the JIVE offline-software toolchain. This is a precautionary measure as building a working offline-software toolchain is not a turnkey-project. This is mainly due to the lack of documentation of how to deal with various external dependencies and intracacies of those dependencies when one has to build them from scratch. This is still ongoing at
the time of writing.</del>

===== ALBUS =====

ALBUS (Advanced Long Baseline User Software) is a Joint Research
Activity within the RadioNet program in the EC 6th framework. In this
project JIVE has the overall management responsibility and also
carries out a large fraction of the work-packages, including the
development and distribution of ParselTongue, which established a
reasonably large user base during this period. There were a number of
personnel changes during the period, with James Anderson leaving JIVE
and the project, while Mike Sipior and Stephen Bourke joined the team


==== Ionospheric calibration ====

The aim of the ALBUS ionospheric calibration project (carried out at
JIVE) was to develop improved methods for calibrating the ionosphere
for phase-referenced VLBI observations using current data and model
products. Two different ionospheric calibration approaches were
developed for the ALBUS project.

The first type uses a global ionosphere model to predict the
three-dimensional electron density distribution of the ionosphere
using a few simple input data.  Two such models were implemented using
external public software packages, the Parameterized Ionosphere Model
(PIM) and the International Reference Ionosphere (IRI) model.  Both of
these packages are software models which attempt to predict the
electron distribution based on past observations and ionospheric
physics.  They use only a few input data, such as the Solar flux and
the strength of the Solar wind, and the date and time of the
observation to generate their predictions.

The second approach uses measurements from Global Positioning System
(GPS) receivers.  By comparing the difference in arrival times of the
signals on the two different broadcast frequencies of the GPS
sattelites (1575.42 MHz and 1227.60 MHz), the path delay of the
ionosphere can be measured.  Typically, a given GPS receiver can see 5
to 8 GPS satellites at any given time, and various combinations of
satellite positions and ionospheric delay measurements can be made to
provide calibration information for a specific source
direction. Furthermore, by combining measurements from many GPS
receivers spread over a surrounding area, a more accurate model of the
ionosphere can be formed to make ionospheric calibration predictions.
The model used by this ALBUS software is a variation on the Minimum
Ionosphere Model.

By combining a physically-based software model (PIM or IRI in this
case) with the GPS data modeling, a more refined ionospheric
calibration model can be formed.  This essentially forms a third type
of ionospheric model developed for ALBUS. Most of the underlying
software which performs the ionospheric physical modelling or model
fitting was written by James Anderson in C/C++/FORTRAN, and a Python layer allows these
routines to be run from ParselTongue. In addition to the ionospheric
physics and GPS models, the ALBUS ionospheric software also has
options to use the standard AIPS task TECOR (which uses publicly
available Ionosphere Map Exchange, IONEX, models of the ionosphere),
or to apply no ionosphere model at all. When data files for GPS
measurements, or IONEX data, are required, the ALBUS software
automatically handles downloading the public files from the Internet,
and uncompressing and preprocessing them as necessary.

These calibration models were tested using EVN observations made
during the 2005 May EVN observing session. They targeted several
bright calibration sources, using standard phase-referencing
techniques at L-band. As all target objects were themselves bright
calibrators, the coherence loss from phase referencing and ionospheric
calibration could be measured by comparing the final measured
brightnesses of the targets when using phase referencing and various
ionospheric calibration schemes, as compared with direct fringe
fitting of the target source. The results of the tests were not very
encouraging. On average, all of the ionospheric calibration algorithms
from ALBUS and the standard AIPS TECOR task showed no net improvement
in the ionospheric calibration. The main reason for this is that the
residual ionospheric effects tend to be small-scale features
(traveling ionospheric disturbances, TIDs, and other disturbances),
which none of the models can accommodate at this point in time.

However, the ALBUS ionospheric calibration routines do seem to make
improvements for polarization measurements.  In addition to modelling
the ionospheric path delay, the ALBUS ionospheric software can also
model the ionospheric Faraday rotation.  Tests with observations using
(individually) Parkes and Westerbork show that the modelled Faraday
rotation predictions do make improvements over using no corrections

Future ionospheric calibration developments should concentrate on
measuring the small-scale structure of the ionosphere. Test show that
this requires a dense network of GPS stations surrounding individual
VLBI telescopes, and further software development to properly model
wave features in the ionosphere.  However, in the near future, the
dense GPS receiver networks required for this calibration are unlikely
to be present for most VLBI stations. Some of these findings fed into
the discussions on the calibration of LOFAR in which James Anderson
participated.


==== Wide Field Imaging ====

Wide Field VLBI imaging requires observed data to correlated with high
time and frequency resolution. While the EVN correlator has these
capabilities, handling the resulting large datasets is not well
accounted for in existing software. The work carried out focused on
providing software that facilitates the distributed processing of data
to enable wide field imaging.

Existing software such as AIPS provides excellent imaging
algorithms. The problems that a user faces when processing their data
for the purpose of wide field imaging are largely related to data
management, hardware utilisation, processing time, and data inspection
analysis. Software components were developed to address these issues.

=== Interacting with high performance computing systems ===
 

The target
environments for this wide field imaging software are commodity
clusters. To attain good utilisation of the underlying hardware, it is
essential that the user have control over the allocation of
computation and data resources. A python interface to the PBS batch
system (used on many clusters) was developed to allow sub-jobs to be
allocated to compute nodes at run time. A module (AIPSLite) was
developed to allow Python/ParselTongue code to be run in a lightweight
environment without the need for a full AIPS install, as one is often
not present on shared-use clusters. The AIPSLite module also allows
for any directory to be setup and utilised as an AIPS data area at run
time. Together, these modules provide a level of control over CPU and
disk usage on commodity clusters. Furthermore, software modules were
developed by Stephen Bourke that provide functions to determine the properties of
datasets and catalogs, perform astronomy related calculations, various
conversions and utility functions. These libraries, when loaded in
Python, complement the ParselTongue infrastructure and facilitate
application development.

=== Parallelisation ===

To accomplish parallelisation, data can be split up in many ways. For
imaging, decomposing in the frequency domain is particularly useful
for spectral line data. Decomposing by frequency is also useful for
continuum imaging as the resulting dirty images can be combined prior
to performing a so-called Clark Clean. The performance of the
Cotton-Schwab Clean is prohibitively slow on the large datasets that
wide field imaging typically necessitate, so the algorithmic
advantages of the latter are compensated by the speed of imaging in
parallel followed by a Clark Clean. This approach was implemented and
used to image regions of massive star formation observed with the
EVN. Fringe fitting is also time consuming on large datasets and is
suitable for parallelisation as separate time slices can be processed
independently. This method of parallel calibration was implemented,
however for observations currently typical the overhead associated
with the decomposition of the task are not compensated by the
resulting speed up.

=== Automated analysis and visualisation ===

A follow-on problem from VLBI wide field imaging is analysing the
resulting images, which can be extremely large. Stephen Bourke developed 
automated detection
routines, which search the field for sources and create
a text file detailing possible sources of emission. The software can
also create plots to give an overview of the field.

This approach was extensively tried on a large EVN data-set observed
at the methanol line in collaboration with Kalle Torstensson and
Huib van Langevelde. The data was sampled at 1024 (2 kHz) channels
and 0.25s intervals, allowing an area of 5 square arc-minutes to be
mapped with 4 mas resolution producing a imaged datasets of over 1012
cells (Figure 7). The processing was done utilising 128 CPUs of the
Walton (opteron) cluster at the Irish Centre for High End Computing
(ICHEC). The method proved successful, with the software performing
well and in a very stable manner. However, no other masers sources
than the known central object was found. In the future more targets
can be tried.  Needs an additional paragraph on running the stuff


{{ cepa-wide-3.png?300 |Stephen's image}}
//Facet layout for a processed data set. Each of the 4669
boxes corresponds to a data cube of dimensions 2048 _ 2048 cells with
1024 frequency channels giving a total image size of over 18
tera-pixels//


===== Infrastructure Software =====


ParselTongue was developed in the context of ALBUS to define a common
interface for developing new algorithms and disseminating the results
to the user community. By providing a Python binding for classic AIPS
it allows high level scripting for a large range of radio astronomy
applications. It provides enhanced interface functions between AIPS
and the outside world, which has been an important feature for
implementing the EVN and MERLIN pipelines.

During this period, ParselTongue has seen a number of features added,
both to the code base proper, and to the user support infrastructure
around it. Version 1.0.6 of ParselTongue was released in July 2007,
and includes support for directly modifying image pixel values, as
well as adding arbitrary data keywords to an image, for purpose of
organisation or reference. Other improvements since include the
ability to modify and extend data headers, directly access the AIPS
catalogue, and attach and manipulate tables.  In 2007 it was made
possible to a switch from the deprecated numarray Python module to the
new NumPy framework. In addition, per-task log files have been added,
allowing users more control over their output, especially when running
a large number of simultaneous processes. Also, a basic infrastructure
to simplify transferring data sets between AIPS installations has been
implemented. As with per-task log files, this infrastructure is aimed
at facilitating the use of ParselTongue in large-scale distributed
pipelines.

Recent development by Mike Sipior has focused on exploring some new
facilities for making simple parallel data reduction possible. This
would be a great help for dealing with the very large datasets coming
out of wide-field VLBI work, along with the obvious applications for
data from large radio surveys. ParselTongue development in 2008 has
concentrated on establishing a framework for easily running multiple
AIPS tasks simultaneously, from a single control script, using
user-specified queues of AIPS tasks. Task queues are simply containers
of AIPSTask objects, which, once populated, are executed in a fashion
analogous to simple AIPSTasks. Because the data objects supplied as
arguments to the individual AIPSTasks contain all of the information
required to access the data itself, no extra work is required to run
jobs on a remote machine

The ParselTongue user community has grown substantially to
approximately a hundred users, also implying an increasing number of
users filing bug reports and feature requests. A great deal of work
has been done to streamline the installation of ParselTongue and its
dependencies, lowering the bar for new users to get started. In
addition, the ParselTongue wiki at the JIVE web site
[[http://www.jive.nl/dokuwiki/doku.php]] was enhanced and includes a
facility for users to share their own scripts and ParselTongue
snippets on the aforementioned wiki. The ParselTongue mailing list
serves both to announce new releases and as a forum for users to share
experiences and ask questions about the software.




[[2007-2008:2007-2008|back to index]]