The New Web-Based Hera Data Processing System at the HEASARC

The HEASARC at NASA/GSFC has provide an on-line astronomical data processing system called Hera for several years. Hera provides a complete data processing environment, including installed software packages, local data storage, and the CPU resources needed to process the user's data. The original design of Hera, however, has 2 requirements that has limited it's usefulness for some users, namely, that 1) the user must download and install a small helper program on their own computer before using Hera, and 2) Hera requires that several computer ports/sockets be allowed to communicate through any local firewalls on the users machine. Both of these restrictions can be problematic for some users, therefore we are now migrating Hera into a purely Web based environment which only requires a standard Web browser. The first release of Web Hera is now publicly available at http://heasarc.gsfc.nasa.gov/webheara/. It currently provides a standard graphical interface for running hundreds of different data processing programs that are available in the HEASARC's ftools software package. Over the next year we to add more features to Web Hera, including an interactive command line interface, and more display and line capabilities

Optimal Compression of Floating-point Astronomical Images Without Significant Loss of Information

We describe a compression method for floating-point astronomical images that gives compression ratios of 6 -- 10 while still preserving the scientifically important information in the image. The pixel values are first preprocessed by quantizing them into scaled integer intensity levels, which removes some of the uncompressible noise in the image. The integers are then losslessly compressed using the fast and efficient Rice algorithm and stored in a portable FITS format file. Quantizing an image more coarsely gives greater image compression, but it also increases the noise and degrades the precision of the photometric and astrometric measurements in the quantized image. Dithering the pixel values during the quantization process can greatly improve the precision of measurements in the images. This is especially important if the analysis algorithm relies on the mode or the median which would be similarly quantized if the pixel values are not dithered. We perform a series of experiments on both synthetic and real astronomical CCD images to quantitatively demonstrate that the magnitudes and positions of stars in the quantized images can be measured with the predicted amount of precision. In order to encourage wider use of these image compression methods, we have made available a pair of general-purpose image compression programs, called fpack and funpack, which can be used to compress any FITS format image.Comment: Accepted PAS

Lossless Astronomical Image Compression and the Effects of Noise

We compare a variety of lossless image compression methods on a large sample of astronomical images and show how the compression ratios and speeds of the algorithms are affected by the amount of noise in the images. In the ideal case where the image pixel values have a random Gaussian distribution, the equivalent number of uncompressible noise bits per pixel is given by Nbits =log2(sigma * sqrt(12)) and the lossless compression ratio is given by R = BITPIX / Nbits + K where BITPIX is the bit length of the pixel values and K is a measure of the efficiency of the compression algorithm. We perform image compression tests on a large sample of integer astronomical CCD images using the GZIP compression program and using a newer FITS tiled-image compression method that currently supports 4 compression algorithms: Rice, Hcompress, PLIO, and GZIP. Overall, the Rice compression algorithm strikes the best balance of compression and computational efficiency; it is 2--3 times faster and produces about 1.4 times greater compression than GZIP. The Rice algorithm produces 75%--90% (depending on the amount of noise in the image) as much compression as an ideal algorithm with K = 0. The image compression and uncompression utility programs used in this study (called fpack and funpack) are publicly available from the HEASARC web site. A simple command-line interface may be used to compress or uncompress any FITS image file.Comment: 20 pages, 9 figures, to be published in PAS

A History and Evaluation of Camp Shiloh

https://digitalcommons.acu.edu/crs_books/1601/thumbnail.jp

Material Handling Research Center

Issued as Reports [nos. 1-7], Annual reports [nos. 1-2], and Final project report, Project no. B-04-618 (subjects A-3501, A-8091, A-8493, B-04-F12, B-04-601, B-04-601, B-04-648, E-24-685, M-23-654

Development of the FITS tools package for multiple software environments

The HEASARC is developing a package of general purpose software for analyzing data files in FITS format. This paper describes the design philosophy which makes the software both machine-independent (it runs on VAXs, Suns, and DEC-stations) and software environment-independent. Currently the software can be compiled and linked to produce IRAF tasks, or alternatively, the same source code can be used to generate stand-alone tasks using one of two implementations of a user-parameter interface library. The machine independence of the software is achieved by writing the source code in ANSI standard Fortran or C, using the machine-independent FITSIO subroutine interface for all data file I/O, and using a standard user-parameter subroutine interface for all user I/O. The latter interface is based on the Fortran IRAF Parameter File interface developed at STScI. The IRAF tasks are built by linking to the IRAF implementation of this parameter interface library. Two other implementations of this parameter interface library, which have no IRAF dependencies, are now available which can be used to generate stand-alone executable tasks. These stand-alone tasks can simply be executed from the machine operating system prompt either by supplying all the task parameters on the command line or by entering the task name after which the user will be prompted for any required parameters. A first release of this FTOOLS package is now publicly available. The currently available tasks are described, along with instructions on how to obtain a copy of the software

ROSAT implementation of a proposed multi-mission x ray data format

Until recently little effort has been made to ensure that data from X-ray telescopes are delivered in a format that reflects the common characteristics that most X-ray datasets share. Instrument-specific data-product design hampers the comparison of X-ray measurements made by different detectors and should be avoided whenever possible. The ROSAT project and the High Energy Astrophysics Science Archive Research Center (HEASARC) have defined a set of X-ray data products ('rationalized files') for ROSAT data that can be used for distribution and archiving of data from other X-ray missions. This set of 'rationalized files' has been defined to isolate instrument-independent and instrument-specific quantities using standards FITS constructs to ensure portability. We discuss the usage of the 'rationalized files' by ROSAT for data distribution and archiving, with particular emphasis on discrimination between instrument-independent and instrument-specific quantities, and discuss application of this format to data from other X-ray missions

The Future is Hera! Analyzing Astronomical Over the Internet

Hera is the data processing facility provided by the High Energy Astrophysics Science Archive Research Center (HEASARC) at the NASA Goddard Space Flight Center for analyzing astronomical data. Hera provides all the pre-installed software packages, local disk space, and computing resources need to do general processing of FITS format data files residing on the users local computer, and to do research using the publicly available data from the High ENergy Astrophysics Division. Qualified students, educators and researchers may freely use the Hera services over the internet of research and educational purposes

Detection and prevalence of depression among adult type 2 diabetes mellitus patients attending non-communicable diseases clinics in Lilongwe, Malawi

Background: Depression is associated with chronic physical illnesses and negatively affects health outcomes. However, it often goes undiagnosed and untreated. We investigated the prevalence of depression among adult type 2 diabetes mellitus (T2DM) patients attending non-communicable diseases (NCD) clinics in Lilongwe, Malawi, and estimated the level of routine detection by NCD clinicians. This study set out to determine the prevalence of major depression and its detection among adult type 2 diabetes mellitus (T2DM) patients attending NCD clinics in Lilongwe, Malawi. Methods: In a cross-sectional study design, 323 T2DM patients aged ≥ 18 years were screened for depression with the Patient Health Questionnare-9 (PHQ-9) followed by diagnostic assessment with the Structured Clinical Interview for DSM-IV (SCID). We analysed the association between presence of major depression and sociodemographic factors using logistic regression. Results: Three quarters of the participants (76%) were females. The participants’ ages ranged from 21–79 years. Of the 323 participants, 58 (18%) met criteria for DSM-IV major depression. None of the cases of major depression had been identified by the NCD clinicians. Major depression was found not to be significantly associated with any of the sociodemographic factors. Conclusions: We found that depression is common among NCD clinic attendees with T2DM in Malawi, and poorly detected by NCD clinicians. Given the high prevalence and challenges in clinical identification, integration of depression screening with a standardized validated tool should be a high priority so as to link patients to appropriate services