Quickstart Guide FITS Liberator 4
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Operating Systems
Operating Systems Interface between User & Computer Hardware Applications Programs Utilities Operating System Hardware Utilities Memory Resident File Access & Control Program Creation Memory Resident File Format Structures o Editors Access Management o Compilers Protection Schemes o Debuggers System Access & Control File Manipulation System-Wide Access o File Manipulation Resource Access o File Deletion Error Detection & Response Mechanism Program Execution Error Detection Link-Loaders Error Correction Run-Time Management Response to Unrecoverable Error I/O Device Access & Control Accounting Storage File Format Structures System Usage Collection Access Management System Performance Tuning Protection Schemes Forecasting Enhancement Requirements Billing Users for Usage Resource Manager O/S KernelKernel I/O Controller Printers, Keyboards, I/O Controller Monitors, Portions of Cameras, the O/S Etc. currently in use Computer Main System Memory Portions of Various I/O Application Devices Programs Currently in use Operating System Data Application Programs I/O Controller Storage Processor Processor Data Processor Processor Operation Allocation of Main Memory is made jointly by both the O/S and Memory Management Hardware O/S controls access to I/O devices by Application Programs O/S controls access to and use of files O/S controls access to and use of the processors, i.e., how much time can be allocated to the execution of a particular Application Program Classification of Operating Systems Interactive O/S Keyboard & Monitor Access to O/S Immediate, -
M33 Tutorial Making Color Images
M33 Tutorial Making Color Images Travis A. Rector University of Alaska Anchorage and NOAO Department of Physics and Astronomy 3211 Providence Dr., Anchorage, AK USA email: [email protected] Introduction A Note from the Author The purpose of this tutorial is to demonstrate how color images of astronomical objects can be generated from the original FITS data files. The techniques described herein are used to create many of the astronomical images you see from profes- sional observatories such as the Hubble Space Telescope, Kitt Peak, Gemini and The WIYN 0.9-meter Telescope Spitzer. In this tutorial we will make an image of M33, the Triangulum Galaxy. M33 is a spectacular face-on spiral galaxy that is relatively nearby. It is assumed that you are familiar with the prerequisites listed below. If you have problems with the tutorial itself please feel free to contact the author at the email address above. Nomenclature: FITS stands for Flexible Image Prerequisites Transport System. It is the stan- dard format for storing astronomi- To participate in this tutorial, you will need a basic understanding of the follow- cal data. ing concepts and software: • FITS datafiles • Adobe Photoshop or Photoshop Elements • The layering metaphor Description of the Data The datasets of M33 used in this tutorial were obtained with the WIYN 0.9-meter Decoding file names telescope on Kitt Peak, which is located about 40 miles west of Tucson, Arizona. The FITS files are 2048 x 2048 pixels, and about 16 Mb in size each. Datasets in The FITS filenames consist of the name of the object, an underscore, the broadband UBVRI and narrowband Ha filters are available. -
Chapter 10 Introduction to Batch Files
Instructor’s Manual Chapter 10 Lecture Notes Introduction to Batch Files Chapter 10 Introduction to Batch Files LEARNING OBJECTIVES 1. Compare and contrast batch and interactive processing. 2. Explain how batch files work. 3. Explain the purpose and function of the REM, ECHO, and PAUSE commands. 4. Explain how to stop or interrupt the batch file process. 5. Explain the function and use of replaceable parameters in batch files. 6. Explain the function of pipes, filters, and redirection in batch files. STUDENT OUTCOMES 1. Use Edit to write batch files. 2. Use COPY CON to write batch files. 3. Write and execute a simple batch file. 4. Write a batch file to load an application program. 5. Use the REM, PAUSE, and ECHO commands in batch files. 6. Terminate a batch file while it is executing. 7. Write batch files using replaceable parameters. 8. Write a batch file using pipes, filters, and redirection. CHAPTER SUMMARY 1. Batch processing means running a series of instructions without interruption. 2. Interactive processing allows the user to interface directly with the computer and update records immediately. 3. Batch files allow a user to put together a string of commands and execute them with one command. 4. Batch files must have the .BAT or .CMD file extension. 5. Windows looks first internally for a command, then for a .COM files extension, then for a .EXE file extension, and finally for a .BAT or .CMD file extension. 6. Edit is a full-screen text editor used to write batch files. 7. A word processor, if it has a means to save files in ASCII, can be used to write batch files. -
Uni Hamburg – Mainframe Summit 2010 Z/OS – the Mainframe Operating System
Uni Hamburg – Mainframe Summit 2010 z/OS – The Mainframe Operating System Appendix 2 – JES and Batchprocessing Redelf Janßen IBM Technical Sales Mainframe Systems [email protected] © Copyright IBM Corporation 2010 Course materials may not be reproduced in whole or in part without the prior written permission of IBM. 4.0.1 Introduction to the new mainframe Chapter 7: Batch processing and the Job Entry Subsystem (JES) © Copyright IBM Corp., 2010. All rights reserved. Introduction to the new mainframe Chapter 7 objectives Be able to: • Give an overview of batch processing and how work is initiated and managed in the system. • Explain how the job entry subsystem (JES) governs the flow of work through a z/OS system. © Copyright IBM Corp., 2010. All rights reserved. 3 Introduction to the new mainframe Key terms in this chapter • batch processing • procedure • execution • purge • initiator • queue • job • spool • job entry subsystem (JES) • symbolic reference • output • workload manager (WLM) © Copyright IBM Corp., 2010. All rights reserved. 4 Introduction to the new mainframe What is batch processing? Much of the work running on z/OS consists of programs called batch jobs. Batch processing is used for programs that can be executed: • With minimal human interaction • At a scheduled time or on an as-needed basis. After a batch job is submitted to the system for execution, there is normally no further human interaction with the job until it is complete. © Copyright IBM Corp., 2010. All rights reserved. 5 Introduction to the new mainframe What is JES? In the z/OS operating system, JES manages the input and output job queues and data. -
Approaches to Optimize Batch Processing on Z/OS
Front cover Approaches to Optimize Batch Processing on z/OS Apply the latest z/OS features Analyze bottlenecks Evaluate critical path Alex Louwe Kooijmans Elsie Ramos Jan van Cappelle Lydia Duijvestijn Tomohiko Kaneki Martin Packer ibm.com/redbooks Redpaper International Technical Support Organization Approaches to Optimize Batch Processing on z/OS October 2012 REDP-4816-00 Note: Before using this information and the product it supports, read the information in “Notices” on page v. First Edition (October 2012) This edition applies to all supported z/OS versions and releases. This document created or updated on October 24, 2012. © Copyright International Business Machines Corporation 2012. All rights reserved. Note to U.S. Government Users Restricted Rights -- Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents Notices . .v Trademarks . vi Preface . vii The team who wrote this paper . vii Now you can become a published author, too! . viii Comments welcome. viii Stay connected to IBM Redbooks . ix Chapter 1. Getting started . 1 1.1 The initial business problem statement. 2 1.2 How to clarify the problem statement . 3 1.3 Process to formulate a good problem statement . 3 1.4 How to create a good business case . 5 1.5 Analysis methodology . 6 1.5.1 Initialization . 6 1.5.2 Analysis. 6 1.5.3 Implementation . 10 Chapter 2. Analysis steps. 13 2.1 Setting the technical strategy . 14 2.2 Understanding the batch landscape . 15 2.2.1 Identifying where batch runs and the available resources . 15 2.2.2 Job naming conventions . 15 2.2.3 Application level performance analysis. -
Outreach Products Integrated Under Virtual Observatories and IDIS
Europlanet N4 Outreach Products integrated under Virtual Observatories and IDIS Pedro Russo (Max Planck Institute for Solar System Research) [email protected] Virtual Observatories European Virtual Observatory The EURO-VO project is open to all European astronomical data centres. Partners include ESO, the European Space Agency, and six national funding agencies, with their respective VO nodes: INAF, Italy; INSU, France; INTA, Spain; NOVA, Netherlands; PPARC, UK; RDS, Germany. Data Centre Alliance Alliance of European data centres Physical storage Publish data, metadata and services Facility Centre Centralised registry for resources, standards and certification mechanisms Support for VO technology Dissemination and scientific program Technology Centre research and development projects on the advancement of VO technology, systems and tools in response to scientific and community requirements http://www.euro-vo.org/ Virtual Observatories International Virtual Observatory Alliance Facilitate the international coordination and collaboration necessary for the development and deployment of the tools, systems and organizational structures necessary to enable the international utilization of astronomical archives as an Virtual Repository 2 integrated and interoperating virtual observatory. ! Data Format Standards ! Metadata Standards Figure 1: The International Virtual Observatory Alliance partners. http://www.ivoa.net/ Astrophysical Virtual Observatory A major European component of the Virtual Observatory is the Astrophysical Virtual Observatory (http://www.euro-vo.org) that started in November 2001 as a three- year Phase A project, funded by the European Commission (FP5) and six organizations (ESO, ESA, AstroGrid, CNRS (CDS, TERAPIX), University Louis Pasteur and the Jodrell Bank Observatory) with a total of 5 M!. A Science Working Group was established in 2002 to provide scientific advice to the AVO Project and to promote the implementation of selected science cases through demonstrations. -
Parallel Processing Here at the School of Statistics
Parallel Processing here at the School of Statistics Charles J. Geyer School of Statistics University of Minnesota http://www.stat.umn.edu/~charlie/parallel/ 1 • batch processing • R package multicore • R package rlecuyer • R package snow • grid engine (CLA) • clusters (MSI) 2 Batch Processing This is really old stuff (from 1975). But not everyone knows it. If you do the following at a unix prompt nohup nice -n 19 some job & where \some job" is replaced by an actual job, then • the job will run in background (because of &). • the job will not be killed when you log out (because of nohup). • the job will have low priority (because of nice -n 19). 3 Batch Processing (cont.) For example, if foo.R is a plain text file containing R commands, then nohup nice -n 19 R CMD BATCH --vanilla foo.R & executes the commands and puts the printout in the file foo.Rout. And nohup nice -n 19 R CMD BATCH --no-restore foo.R & executes the commands, puts the printout in the file foo.Rout, and saves all created R objects in the file .RData. 4 Batch Processing (cont.) nohup nice -n 19 R CMD BATCH foo.R & is a really bad idea! It reads in all the objects in the file .RData (if one is present) at the beginning. So you have no idea whether the results are reproducible. Always use --vanilla or --no-restore except when debugging. 5 Batch Processing (cont.) This idiom has nothing to do with R. If foo is a compiled C or C++ or Fortran main program that doesn't have command line arguments (or a shell, Perl, Python, or Ruby script), then nohup nice -n 19 foo & runs it. -
ESO's Hidden Treasures Competition
Astronomical News References Figure 2. The partici- pants at the workshop Masciadri, E. 2008, The Messenger, 134, 53 on site testing atmos- pheric data in Valparaiso, Chile arrayed by the har- Links bour. 1 Workshop web page: http://site2010.sai.msu.ru/ 2 Workshop web page: http://www.dfa.uv.cl/sitetestingdata/ 3 IAU Site Testing Instruments Working Group: http://www.ctio.noao.edu/science/iauSite/ 4 Sharing of site testing data: http://project.tmt.org/~aotarola/ST ESO’s Hidden Treasures Competition Olivier Hainaut1 Over the past two and a half years ESO The ESO Science Archive stores all the Oana Sandu1 has boosted its production of outreach data acquired on Paranal, and most of Lars Lindberg Christensen1 images, both in terms of quantity and the data obtained on La Silla since the quality, so as to become one of the best late 1990s. This archive constitutes a sources of astronomical images. In goldmine commonly used for science 1 ESO achieving this goal, the whole work flow projects (e.g., Haines et al., 2006), and for from the initial production process, technical studies (e.g., Patat et al., 2011). through to publication and promotion has But besides their scientific value, the ESO’s Hidden Treasures astropho- been optimised and strengthened. The imaging datasets in the archive also have tography competition gave amateur final outputs have been made easier to great outreach potential. astronomers the opportunity to search re-use in other products or channels by ESO’s Science Archive for a well- our partners. ESO has a small team of professional hidden cosmic gem. -
Stsci Newsletter: 2011 Volume 028 Issue 02
National Aeronautics and Space Administration Interacting Galaxies UGC 1810 and UGC 1813 Credit: NASA, ESA, and the Hubble Heritage Team (STScI/AURA) 2011 VOL 28 ISSUE 02 NEWSLETTER Space Telescope Science Institute We received a total of 1,007 proposals, after accounting for duplications Hubble Cycle 19 and withdrawals. Review process Proposal Selection Members of the international astronomical community review Hubble propos- als. Grouped in panels organized by science category, each panel has one or more “mirror” panels to enable transfer of proposals in order to avoid conflicts. In Cycle 19, the panels were divided into the categories of Planets, Stars, Stellar Rachel Somerville, [email protected], Claus Leitherer, [email protected], & Brett Populations and Interstellar Medium (ISM), Galaxies, Active Galactic Nuclei and Blacker, [email protected] the Inter-Galactic Medium (AGN/IGM), and Cosmology, for a total of 14 panels. One of these panels reviewed Regular Guest Observer, Archival, Theory, and Chronology SNAP proposals. The panel chairs also serve as members of the Time Allocation Committee hen the Cycle 19 Call for Proposals was released in December 2010, (TAC), which reviews Large and Archival Legacy proposals. In addition, there Hubble had already seen a full cycle of operation with the newly are three at-large TAC members, whose broad expertise allows them to review installed and repaired instruments calibrated and characterized. W proposals as needed, and to advise panels if the panelists feel they do not have The Advanced Camera for Surveys (ACS), Cosmic Origins Spectrograph (COS), the expertise to review a certain proposal. Fine Guidance Sensor (FGS), Space Telescope Imaging Spectrograph (STIS), and The process of selecting the panelists begins with the selection of the TAC Chair, Wide Field Camera 3 (WFC3) were all close to nominal operation and were avail- about six months prior to the proposal deadline. -
Determining the Atmospheric Wind Patterns and Cloud Development of Titan Jenny Nguyen-Ly1, Tersi Arias-Young1, Jonat
Determining the Atmospheric Wind Patterns and Cloud Development of Titan 1 1 2 Jenny Nguyen-Ly , Tersi Arias-Young , Jonathan Mitchell 1 Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Maths Science Building, 520 Portola Plaza, Los Angeles, CA 90095 2 Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles, Geology Building, 595 Charles Young Dr, East, Los Angeles, CA 90095 Abstract: The general public does not think much when they see clouds in the sky. While clouds are a constant part of the everyday life on Earth, they are also a continual phenomenon found on multiple planets and moons beyond Earth. Clouds are simply an endless source of knowledge in regards to their surroundings. Just their shapes, sizes, and movements alone hold more details than one can possibly imagine. They are a tell-tale sign of what is inside the atmosphere for a planet and its climate and weather. When clouds change in number, form, or location, this is an indication of something happening in terms of climate. This is a basis for research on the clouds of Titan, a moon orbiting around Saturn. Learning about the movement of clouds on Titan gives scientists more insight into this moon, and ultimately an improved understanding of the solar system. Moreover, knowledge about the cloud development and trends on Titan could be applied to our own understanding of our planet’s atmosphere too. To accomplish such a task, a comprehensive analysis of the online NASA Planetary Data Systems (PDS) Image Atlas Archive is needed. The Cassini-Huygens mission’s recorded images of Titan lie in this archive. -
MTS on Wikipedia Snapshot Taken 9 January 2011
MTS on Wikipedia Snapshot taken 9 January 2011 PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sun, 09 Jan 2011 13:08:01 UTC Contents Articles Michigan Terminal System 1 MTS system architecture 17 IBM System/360 Model 67 40 MAD programming language 46 UBC PLUS 55 Micro DBMS 57 Bruce Arden 58 Bernard Galler 59 TSS/360 60 References Article Sources and Contributors 64 Image Sources, Licenses and Contributors 65 Article Licenses License 66 Michigan Terminal System 1 Michigan Terminal System The MTS welcome screen as seen through a 3270 terminal emulator. Company / developer University of Michigan and 7 other universities in the U.S., Canada, and the UK Programmed in various languages, mostly 360/370 Assembler Working state Historic Initial release 1967 Latest stable release 6.0 / 1988 (final) Available language(s) English Available programming Assembler, FORTRAN, PL/I, PLUS, ALGOL W, Pascal, C, LISP, SNOBOL4, COBOL, PL360, languages(s) MAD/I, GOM (Good Old Mad), APL, and many more Supported platforms IBM S/360-67, IBM S/370 and successors History of IBM mainframe operating systems On early mainframe computers: • GM OS & GM-NAA I/O 1955 • BESYS 1957 • UMES 1958 • SOS 1959 • IBSYS 1960 • CTSS 1961 On S/360 and successors: • BOS/360 1965 • TOS/360 1965 • TSS/360 1967 • MTS 1967 • ORVYL 1967 • MUSIC 1972 • MUSIC/SP 1985 • DOS/360 and successors 1966 • DOS/VS 1972 • DOS/VSE 1980s • VSE/SP late 1980s • VSE/ESA 1991 • z/VSE 2005 Michigan Terminal System 2 • OS/360 and successors -
Implementing Batch Processing in Java EE 7 Ivar Grimstad
Implementing Batch Processing in Java EE 7 Ivar Grimstad Batch - JavaLand 2014 @ivar_grimstad About Ivar Grimstad @ivar_grimstad Batch - JavaLand 2014 @ivar_grimstad batch (plural batches) The quantity of bread or other baked goods baked at one time. We made a batch of cookies to take to the party. Source: http://en.wiktionary.org/wiki/batch Batch - JavaLand 2014 @ivar_grimstad Content • Batch Applications • Batch in Java EE 7 • Demo • Wrap-Up Batch - JavaLand 2014 @ivar_grimstad History Batch - JavaLand 2014 @ivar_grimstad Batch Applications Batch - JavaLand 2014 @ivar_grimstad Common Usages • Bulk database updates • Image processing • Conversions Batch - JavaLand 2014 @ivar_grimstad Advantages of Batch Processing • No User Interaction • Utilize Batch Windows • Repetitive Work Batch - JavaLand 2014 @ivar_grimstad Disadvantages of Batch Processing • Training • Difficult Debugging • Costly Batch - JavaLand 2014 @ivar_grimstad To The Rescue Batch Frameworks Batch - JavaLand 2014 @ivar_grimstad Batch Frameworks • Jobs, steps, decision elements, relationships • Parallel or sequential processing • State • Launch, pause and resume • Error handling Batch - JavaLand 2014 @ivar_grimstad Requirements of Batch Applications Large Data Volume Automation Robustness Reliability Performance Batch - JavaLand 2014 @ivar_grimstad Batch Processing in Java EE 7 Batch - JavaLand 2014 @ivar_grimstad The Batch Processing Framework • Batch Runtime • Job Specification • Java API – Runtime interaction – Implementation Batch - JavaLand 2014 @ivar_grimstad Batch