Google Snappy

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a cool sounding software- yet again by the guys from California, this one enables to zip and unzip Big Data much much faster

http://news.ycombinator.com/item?id=2356735

and

https://code.google.com/p/snappy/

Snappy is a compression/decompression library. It does not aim for maximum compression, or compatibility with any other compression library; instead, it aims for very high speeds and reasonable compression. For instance, compared to the fastest mode of zlib, Snappy is an order of magnitude faster for most inputs, but the resulting compressed files are anywhere from 20% to 100% bigger. On a single core of a Core i7 processor in 64-bit mode, Snappy compresses at about 250 MB/sec or more and decompresses at about 500 MB/sec or more.

Snappy is widely used inside Google, in everything from BigTable and MapReduce to our internal RPC systems. (Snappy has previously been referred to as “Zippy” in some presentations and the likes.)

For more information, please see the README. Benchmarks against a few other compression libraries (zlib, LZO, LZF, FastLZ, and QuickLZ) are included in the source code distribution.

Introduction
============
Snappy is a compression/decompression library. It does not aim for maximum
compression, or compatibility with any other compression library; instead,
it aims for very high speeds and reasonable compression. For instance,
compared to the fastest mode of zlib, Snappy is an order of magnitude faster
for most inputs, but the resulting compressed files are anywhere from 20% to
100% bigger. (For more information, see “Performance”, below.)
Snappy has the following properties:
* Fast: Compression speeds at 250 MB/sec and beyond, with no assembler code.
See “Performance” below.
* Stable: Over the last few years, Snappy has compressed and decompressed
petabytes of data in Google’s production environment. The Snappy bitstream
format is stable and will not change between versions.
* Robust: The Snappy decompressor is designed not to crash in the face of
corrupted or malicious input.
* Free and open source software: Snappy is licensed under the Apache license,
version 2.0. For more information, see the included COPYING file.
Snappy has previously been called “Zippy” in some Google presentations
and the like.
Performance
===========
Snappy is intended to be fast. On a single core of a Core i7 processor
in 64-bit mode, it compresses at about 250 MB/sec or more and decompresses at
about 500 MB/sec or more. (These numbers are for the slowest inputs in our
benchmark suite; others are much faster.) In our tests, Snappy usually
is faster than algorithms in the same class (e.g. LZO, LZF, FastLZ, QuickLZ,
etc.) while achieving comparable compression ratios.
Typical compression ratios (based on the benchmark suite) are about 1.5-1.7x
for plain text, about 2-4x for HTML, and of course 1.0x for JPEGs, PNGs and
other already-compressed data. Similar numbers for zlib in its fastest mode
are 2.6-2.8x, 3-7x and 1.0x, respectively. More sophisticated algorithms are
capable of achieving yet higher compression rates, although usually at the
expense of speed. Of course, compression ratio will vary significantly with
the input.
Although Snappy should be fairly portable, it is primarily optimized
for 64-bit x86-compatible processors, and may run slower in other environments.
In particular:
– Snappy uses 64-bit operations in several places to process more data at
once than would otherwise be possible.
– Snappy assumes unaligned 32- and 64-bit loads and stores are cheap.
On some platforms, these must be emulated with single-byte loads
and stores, which is much slower.
– Snappy assumes little-endian throughout, and needs to byte-swap data in
several places if running on a big-endian platform.
Experience has shown that even heavily tuned code can be improved.
Performance optimizations, whether for 64-bit x86 or other platforms,
are of course most welcome; see “Contact”, below.
Usage
=====
Note that Snappy, both the implementation and the interface,
is written in C++.
To use Snappy from your own program, include the file “snappy.h” from
your calling file, and link against the compiled library.
There are many ways to call Snappy, but the simplest possible is
snappy::Compress(input, &output);
and similarly
snappy::Uncompress(input, &output);
where “input” and “output” are both instances of std::string.

LibreOffice Stable Release launched

Non Oracle Open Office completes important milestone- from the press release

The Document Foundation launches LibreOffice 3.3

The first stable release of the free office suite is available for download

The Internet, January 25, 2011 – The Document Foundation launches LibreOffice 3.3, the first stable release of the free office suite developed by the community. In less than four months, the number of developers hacking LibreOffice has grown from less than twenty in late September 2010, to well over one hundred today. This has allowed us to release ahead of the aggressive schedule set by the project.

Not only does it ship a number of new and original features, LibreOffice 3.3 is also a significant achievement for a number of reasons:

– the developer community has been able to build their own and independent process, and get up and running in a very short time (with respect to the size of the code base and the project’s strong ambitions);

– thanks to the high number of new contributors having been attracted into the project, the source code is quickly undergoing a major clean-up to provide a better foundation for future development of LibreOffice;

– the Windows installer, which is going to impact the largest and most diverse user base, has been integrated into a single build containing all language versions, thus reducing the size for download sites from 75 to 11GB, making it easier for us to deploy new versions more rapidly and lowering the carbon footprint of the entire infrastructure.

Caolán McNamara from RedHat, one of the developer community leaders, comments, “We are excited: this is our very first stable release, and therefore we are eager to get user feedback, which will be integrated as soon as possible into the code, with the first enhancements being released in February. Starting from March, we will be moving to a real time-based, predictable, transparent and public release schedule, in accordance with Engineering Steering Committee’s goals and users’ requests”. The LibreOffice development roadmap is available at http://wiki.documentfoundation.org/ReleasePlan

LibreOffice 3.3 brings several unique new features. The 10 most-popular among community members are, in no particular order:

  1. the ability to import and work with SVG files;
  2. an easy way to format title pages and their numbering in Writer;
  3. a more-helpful Navigator Tool for Writer;
  4. improved ergonomics in Calc for sheet and cell management;
  5. and Microsoft Works and Lotus Word Pro document import filters.

In addition, many great extensions are now bundled, providing

PDF import,

a slide-show presenter console,

a much improved report builder, and more besides.

A more-complete and detailed list of all the new features offered by LibreOffice 3.3 is viewable on the following web page: http://www.libreoffice.org/download/new-features-and-fixes/

LibreOffice 3.3 also provides all the new features of OpenOffice.org 3.3, such as new custom properties handling; embedding of standard PDF fonts in PDF documents; new Liberation Narrow font; increased document protection in Writer and Calc; auto decimal digits for “General” format in Calc; 1 million rows in a spreadsheet; new options for CSV import in Calc; insert drawing objects in Charts; hierarchical axis labels for Charts; improved slide layout handling in Impress; a new easier-to-use print interface; more options for changing case; and colored sheet tabs in Calc. Several of these new features were contributed by members of the LibreOffice team prior to the formation of The Document Foundation.

LibreOffice hackers will be meeting at FOSDEM in Brussels on February 5 and 6, and will be presenting their work during a one-day workshop on February 6, with speeches and hacking sessions coordinated by several members of the project.

The home of The Document Foundation is at http://www.documentfoundation.org

The home of LibreOffice is at http://www.libreoffice.org where the download page has been redesigned by the community to be more user-friendly.

*** About The Document Foundation

The Document Foundation has the mission of facilitating the evolution of the OOo Community into a new, open, independent, and meritocratic organization within the next few months. An independent Foundation is a better reflection of the values of our contributors, users and supporters, and will enable a more effective, efficient and transparent community. TDF will protect past investments by building on the achievements of the first decade, will encourage wide participation within the community, and will co-ordinate activity across the community.

*** Media Contacts for TDF

Florian Effenberger (Germany)

Mobile: +49 151 14424108 – E-mail: floeff@documentfoundation.org

Olivier Hallot (Brazil)

Mobile: +55 21 88228812 – E-mail: olivier.hallot@documentfoundation.org

Charles H. Schulz (France)

Mobile: +33 6 98655424 – E-mail: charles.schulz@documentfoundation.org

Italo Vignoli (Italy)

Mobile: +39 348 5653829 – E-mail: italo.vignoli@documentfoundation.org

Interview Ajay Ohri Decisionstats.com with DMR

From-

http://www.dataminingblog.com/data-mining-research-interview-ajay-ohri/

Here is the winner of the Data Mining Research People Award 2010: Ajay Ohri! Thanks to Ajay for giving some time to answer Data Mining Research questions. And all the best to his blog, Decision Stat!

Data Mining Research (DMR): Could you please introduce yourself to the readers of Data Mining Research?

Ajay Ohri (AO): I am a business consultant and writer based out of Delhi- India. I have been working in and around the field of business analytics since 2004, and have worked with some very good and big companies primarily in financial analytics and outsourced analytics. Since 2007, I have been writing my blog at http://decisionstats.com which now has almost 10,000 views monthly.

All in all, I wrote about data, and my hobby is also writing (poetry). Both my hobby and my profession stem from my education ( a masters in business, and a bachelors in mechanical engineering).

My research interests in data mining are interfaces (simpler interfaces to enable better data mining), education (making data mining less complex and accessible to more people and students), and time series and regression (specifically ARIMAX)
In business my research interests software marketing strategies (open source, Software as a service, advertising supported versus traditional licensing) and creation of technology and entrepreneurial hubs (like Palo Alto and Research Triangle, or Bangalore India).

DMR: I know you have worked with both SAS and R. Could you give your opinion about these two data mining tools?

AO: As per my understanding, SAS stands for SAS language, SAS Institute and SAS software platform. The terms are interchangeably used by people in industry and academia- but there have been some branding issues on this.
I have not worked much with SAS Enterprise Miner , probably because I could not afford it as business consultant, and organizations I worked with did not have a budget for Enterprise Miner.
I have worked alone and in teams with Base SAS, SAS Stat, SAS Access, and SAS ETS- and JMP. Also I worked with SAS BI but as a user to extract information.
You could say my use of SAS platform was mostly in predictive analytics and reporting, but I have a couple of projects under my belt for knowledge discovery and data mining, and pattern analysis. Again some of my SAS experience is a bit dated for almost 1 year ago.

I really like specific parts of SAS platform – as in the interface design of JMP (which is better than Enterprise Guide or Base SAS ) -and Proc Sort in Base SAS- I guess sequential processing of data makes SAS way faster- though with computing evolving from Desktops/Servers to even cheaper time shared cloud computers- I am not sure how long Base SAS and SAS Stat can hold this unique selling proposition.

I dislike the clutter in SAS Stat output, it confuses me with too much information, and I dislike shoddy graphics in the rendering output of graphical engine of SAS. Its shoddy coding work in SAS/Graph and if JMP can give better graphics why is legacy source code preventing SAS platform from doing a better job of it.

I sometimes think the best part of SAS is actually code written by Goodnight and Sall in 1970’s , the latest procs don’t impress me much.

SAS as a company is something I admire especially for its way of treating employees globally- but it is strange to see the rest of tech industry not following it. Also I don’t like over aggression and the SAS versus Rest of the Analytics /Data Mining World mentality that I sometimes pick up when I deal with industry thought leaders.

I think making SAS Enterprise Miner, JMP, and Base SAS in a completely new web interface priced at per hour rates is my wishlist but I guess I am a bit sentimental here- most data miners I know from early 2000’s did start with SAS as their first bread earning software. Also I think SAS needs to be better priced in Business Intelligence- it seems quite cheap in BI compared to Cognos/IBM but expensive in analytical licensing.

If you are a new stats or business student, chances are – you may know much more R than SAS today. The shift in education at least has been very rapid, and I guess R is also more of a platform than a analytics or data mining software.

I like a lot of things in R- from graphics, to better data mining packages, modular design of software, but above all I like the can do kick ass spirit of R community. Lots of young people collaborating with lots of young to old professors, and the energy is infectious. Everybody is a CEO in R ’s world. Latest data mining algols will probably start in R, published in journals.

Which is better for data mining SAS or R? It depends on your data and your deadline. The golden rule of management and business is -it depends.

Also I have worked with a lot of KXEN, SQL, SPSS.

DMR: Can you tell us more about Decision Stats? You have a traffic of 120′000 for 2010. How did you reach such a success?

AO: I don’t think 120,000 is a success. Its not a failure. It just happened- the more I wrote, the more people read.In 2007-2008 I used to obsess over traffic. I tried SEO, comments, back linking, and I did some black hat experimental stuff. Some of it worked- some didn’t.

In the end, I started asking questions and interviewing people. To my surprise, senior management is almost always more candid , frank and honest about their views while middle managers, public relations, marketing folks can be defensive.

Social Media helped a bit- Twitter, Linkedin, Facebook really helped my network of friends who I suppose acted as informal ambassadors to spread the word.
Again I was constrained by necessity than choices- my middle class finances ( I also had a baby son in 2007-my current laptop still has some broken keys :) – by my inability to afford traveling to conferences, and my location Delhi isn’t really a tech hub.

The more questions I asked around the internet, the more people responded, and I wrote it all down.

I guess I just was lucky to meet a lot of nice people on the internet who took time to mentor and educate me.

I tried building other websites but didn’t succeed so i guess I really don’t know. I am not a smart coder, not very clever at writing but I do try to be honest.

Basic economics says pricing is proportional to demand and inversely proportional to supply. Honest and candid opinions have infinite demand and an uncertain supply.

DMR: There is a rumor about a R book you plan to publish in 2011 :-) Can you confirm the rumor and tell us more?

AO: I just signed a contract with Springer for ” R for Business Analytics”. R is a great software, and lots of books for statistically trained people, but I felt like writing a book for the MBAs and existing analytics users- on how to easily transition to R for Analytics.

Like any language there are tricks and tweaks in R, and with a focus on code editors, IDE, GUI, web interfaces, R’s famous learning curve can be bent a bit.

Making analytics beautiful, and simpler to use is always a passion for me. With 3000 packages, R can be used for a lot more things and a lot more simply than is commonly understood.
The target audience however is business analysts- or people working in corporate environments.

Brief Bio-
Ajay Ohri has been working in the field of analytics since 2004 , when it was a still nascent emerging Industries in India. He has worked with the top two Indian outsourcers listed on NYSE,and with Citigroup on cross sell analytics where he helped sell an extra 50000 credit cards by cross sell analytics .He was one of the very first independent data mining consultants in India working on analytics products and domestic Indian market analytics .He regularly writes on analytics topics on his web site www.decisionstats.com and is currently working on open source analytical tools like R besides analytical software like SPSS and SAS.

Choosing R for business – What to consider?

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Additional features in R over other analytical packages-

1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature.  This means bugs will found, shared and corrected transparently.

2) Wide literature of training material in the form of books is available for the R analytical platform.

3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.

4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.

5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.

 

6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.

Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.

Specific to the following domains R has the following costs and benefits

  • Business Analytics
    • R is free per license and for download
    • It is one of the few analytical platforms that work on Mac OS
    • It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
    • It has open source code for customization as per GPL
    • It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
    • It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
    • Huge library of packages for regression, time series, finance and modeling
    • High quality data visualization packages
    • Data Mining
      • R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
      • Flexibility in tweaking a standard algorithm by seeing the source code
      • The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
      • Business Dashboards and Reporting
      • Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
        • For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
        • R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.

Additional factors to consider in your R installation-

There are some more choices awaiting you now-
1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R

2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.

3) Operating system sub choice- 32- bit or 64 bit.

4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.

5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?

6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.

7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.

1) Licensing Choices-
You can choose between two kinds of R installations – one is free and open source from http://r-project.org The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.

Commercial Vendors of R Language Products-
1) Revolution Analytics http://www.revolutionanalytics.com/
2) XL Solutions- http://www.experience-rplus.com/
3) Information Builder – Webfocus RStat -Rattle GUI http://www.informationbuilders.com/products/webfocus/PredictiveModeling.html
4) Blue Reference- Inference for R http://inferenceforr.com/default.aspx

  1. Choosing Operating System
      1. Windows

 

Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.

        1. Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
      1. MacOS

 

Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.

      1. Linux

 

        1. Ubuntu
        2. Red Hat Enterprise Linux
        3. Other versions of Linux

 

Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.

Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can  installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.

      1. Multiple operating systems-
        1. Virtualization vs Dual Boot-

 

You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.

  1. 64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.

 

  1. Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.

Hardware costs are a significant cost to an analytics environment and are also  remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.

    1. Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
      1. Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
      2. Workstation
    2. Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
      1. Amazon
      2. Google
      3. Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
    3. How much resources
      1. RAM-Hard Disk-Processors- for workstation computing
      2. Instances or API calls for cloud computing
  1. Interface Choices
    1. Command Line
    2. GUI
    3. Web Interfaces
  2. Software Component Choices
    1. R dependencies
    2. Packages to install
    3. Recommended Packages
  3. Additional software choices
    1. Additional legacy software
    2. Optimizing your R based computing
    3. Code Editors
      1. Code Analyzers
      2. Libraries to speed up R

citation-  R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

(Note- this is a draft in progress)

RWui :Creating R Web Interfaces on the go

Here is a great R application created by http://sysbio.mrc-bsu.cam.ac.uk

R Wui for creating R Web Interfaces

its been there for some time now- but presumably R Apache is more well known.

From-

http://sysbio.mrc-bsu.cam.ac.uk/Rwui/tutorial/Rwui_Rnews_final.pdf

The web application Rwui is used to create web interfaces  for running R scripts. All the code is generated automatically so that a fully functional web interface for an R script can be downloaded and up and running in a matter of minutes.

Rwui is aimed at R script writers who have scripts that they want people unversed in R to use. The script writer uses Rwui to create a web application that will run their R script. Rwui allows the script writer to do this without them having to do any web application programming, because Rwui generates all the code for them.

The script writer designs the web application to run their R script by entering information on a sequence of web pages. The script writer then downloads the application they have created and installs it on their own server.

http://sysbio.mrc-bsu.cam.ac.uk/Rwui/tutorial/Technical_Report.pdf

Features of web applications created by Rwui

  1. Whole range of input items available if required – text boxes, checkboxes, file upload etc.
  2. Facility for uploading of an arbitrary number of files (for example, microarray replicates).
  3. Facility for grouping uploaded files (for example, into ‘Diseased’ and ‘Control’ microarray data files).
  4. Results files displayed on results page and available for download.
  5. Results files can be e-mailed to the user.
  6. Interactive results files using image maps.
  7. Repeat analyses with different parameters and data files – new results added to results list, as a link to the corresponding results page.
  8. Real time progress information (text or graphical) displayed when running the application.

Requirements

In order to use the completed web applications created by Rwui you will need:

  1. A Java webserver such as Tomcat version 5.5 or later.
  2. Java version 1.5
  3. R – a version compatible with your R script(s).

Using Rwui

Using Rwui to create a web application for an R script simply involves:

  1. Entering details about your Rscript on a sequence of web pages.
  2. Rwui is quite flexible so you can backtrack, edit and insert, as you design your application.
  3. Rwui then generates the web application, which is Java based and platform independent.
  4. The application can be downloaded either as a .zip or .tgz file.
  5. Unpacked, the download contains all the source code and a .war file.
  6. Once the .war file is copied to the Tomcat webapps directory, the application is ready to use.
  7. Application details are saved in an ‘application definition file’ for reuse and modification.
Interested-
go click and check out a new web app from http://sysbio.mrc-bsu.cam.ac.uk/Rwui/ in a matter of minutes
Also see

SAS Lawsuit against WPS- Application Dismissed

I saw Phil Rack http://twitter.com/#!/PhilRack (whom I have interviewed before at https://decisionstats.com/2009/02/03/interview-phil-rack/ ) and whom I dont talk to since Obama won the election-

 

 

 

 

 

 

 

well Phil -creator of Bridge to R- first SAS language to R language interface- mentioned this judgment and link.

 

Probably Phil should revise the documentation of Bridge to R- lest he is sued himself!!!

Conclusion
It was for these reasons that I decided to dismiss SAS’s application.

From-

http://www.bailii.org/cgi-bin/markup.cgi?doc=/ew/cases/EWHC/Ch/2010/3012.html

 

Neutral Citation Number: [2010] EWHC 3012 (Ch)
Case No: HC09C03293

IN THE HIGH COURT OF JUSTICE
CHANCERY DIVISION
Royal Courts of Justice
Strand, London, WC2A 2LL
22 November 2010

B e f o r e :

THE HON MR JUSTICE ARNOLD
____________________
Between:
SAS INSTITUTE INC. Claimant
– and –

WORLD PROGRAMMING LIMITED Defendant

____________________

Michael Hicks (instructed by Bristows) for the Claimant
Martin Howe QC and Isabel Jamal (instructed by Speechly Bircham LLP) for the Defendant
Hearing date: 18 November 2010
____________________

HTML VERSION OF JUDGMENT
____________________

Crown Copyright ©

MR. JUSTICE ARNOLD :

Introduction
By order dated 28 July 2010 I referred certain questions concerning the interpretation of Council Directive 91/250/EEC of 14 May 1991 on the legal protection of computer programs, which was recently codified as European Parliament and Council Directive 2009/24/EC of 23 April 2009, and European Parliament and Council Directive 2001/29/EC of 22 May 2001 on the harmonisation of certain aspects of copyright and related rights in the information society to the Court of Justice of the European Union under Article 267 of the Treaty on the Functioning of the European Union. The background to the reference is set out in full in my judgment dated 23 July 2010 [2010] EWHC 1829 (Ch). The reference is presently pending before the Court of Justice as Case C-406/10. By an application notice issued on 11 October 2010 SAS applied for the wording of the questions to be amended in a number of respects. I heard that application on 18 November 2010 and refused it for reasons to be given later. This judgment contains those reasons.

The questions and the proposed amendments
I set out below the questions referred with the amendments proposed by SAS shown by strikethrough and underlining:

“A. On the interpretation of Council Directive 91/250/EEC of 14 May 1991 on the legal protection of computer programs and of Directive 2009/24/EC of the European Parliament and of the Council of 23 April 2009 (codified version):
1. Where a computer program (‘the First Program’) is protected by copyright as a literary work, is Article 1(2) to be interpreted as meaning that it is not an infringement of the copyright in the First Program for a competitor of the rightholder without access to the source code of the First Program, either directly or via a process such as decompilation of the object code, to create another program (‘the Second Program’) which replicates by copying the functions of the First Program?
2. Is the answer to question 1 affected by any of the following factors:
(a) the nature and/or extent of the functionality of the First Program;
(b) the nature and/or extent of the skill, judgment and labour which has been expended by the author of the First Program in devising and/or selecting the functionality of the First Program;
(c) the level of detail to which the functionality of the First Program has been reproduced in the Second Program;
(d) if, the Second Program includes the following matters as a result of copying directly or indirectly from the First Program:
(i) the selection of statistical operations which have been implemented in the First Program;
(ii) the selection of mathematical formulae defining the statistical operations which the First Program carries out;
(iii) the particular commands or combinations of commands by which those statistical operations may be invoked;
(iv) the options which the author of the First Program has provided in respect of various commands;
(v) the keywords and syntax recognised by the First Program;
(vi) the defaults which the author of the First Program has chosen to implement in the event that a particular command or option is not specified by the user;
(vii) the number of iterations which the First Program will perform in certain circumstances;
(e)(d) if the source code for the Second Program reproduces by copying aspects of the source code of the First Program to an extent which goes beyond that which was strictly necessary in order to produce the same functionality as the First Program?
3. Where the First Program interprets and executes application programs written by users of the First Program in a programming language devised by the author of the First Program which comprises keywords devised or selected by the author of the First Program and a syntax devised by the author of the First Program, is Article 1(2) to be interpreted as meaning that it is not an infringement of the copyright in the First Program for the Second Program to be written so as to interpret and execute such application programs using the same keywords and the same syntax?
4. Where the First Program reads from and writes to data files in a particular format devised by the author of the First Program, is Article 1(2) to be interpreted as meaning that it is not an infringement of the copyright in the First Program for the Second Program to be written so as to read from and write to data files in the same format?
5. Does it make any difference to the answer to questions 1, 2, 3 and 4 if the author of the Second Program created the Second Program without access to the source code of the First Program, either directly or via decompilation of the object code by:
(a) observing, studying and testing the functioning of the First Program; or
(b) reading a manual created and published by the author of the First Program which describes the functions of the First Program (“the Manual”) and by implementing in the Second Program the functions described in the Manual; or
(c) both (a) and (b)?
6. Where a person has the right to use a copy of the First Program under a licence, is Article 5(3) to be interpreteding as meaning that the licensee is entitled, without the authorisation of the rightholder, to perform acts of loading, running and storing the program in order to observe, test or study the functioning of the First Program so as to determine the ideas and principles which underlie any element of the program, if the licence permits the licensee to perform acts of loading, running and storing the First Program when using it for the particular purpose permitted by the licence, but the acts done in order to observe, study or test the First Program extend outside the scope of the purpose permitted by the licence and are therefore acts for which the licensee has no right to use the copy of the First Program under the licence?
7. Is Article 5(3) to be interpreted as meaning that acts of observing, testing or studying of the functioning of the First Program are to be regarded as being done in order to determine the ideas or principles which underlie any element of the First Program where they are done:
(a) to ascertain the way in which the First Program functions, in particular details which are not described in the Manual, for the purpose of writing the Second Program in the manner referred to in question 1 above;
(b) to ascertain how the First Program interprets and executes statements written in the programming language which it interprets and executes (see question 3 above);
(c) to ascertain the formats of data files which are written to or read by the First Program (see question 4 above);
(d) to compare the performance of the Second Program with the First Program for the purpose of investigating reasons why their performances differ and to improve the performance of the Second Program;
(e) to conduct parallel tests of the First Program and the Second Program in order to compare their outputs in the course of developing the Second Program, in particular by running the same test scripts through both the First Program and the Second Program;
(f) to ascertain the output of the log file generated by the First Program in order to produce a log file which is identical or similar in appearance;
(g) to cause the First Program to output data (in fact, data correlating zip codes to States of the USA) for the purpose of ascertaining whether or not it corresponds with official databases of such data, and if it does not so correspond, to program the Second Program so that it will respond in the same way as the First Program to the same input data.
B. On the interpretation of Directive 2001/29/EC of the European Parliament and of the Council of 22 May 2001 on the harmonisation of certain aspects of copyright and related rights in the information society:
8. Where the Manual is protected by copyright as a literary work, is Article 2(a) to be interpreted as meaning that it is an infringement of the copyright in the Manual for the author of the Second Program to reproduce or substantially reproduce in the Second Program any or all of the following matters described in the Manual:
(a) the selection of statistical operations which have been described in the Manual as being implemented in the First Program;
(b) the mathematical formulae used in the Manual to describe those statistical operations;
(c) the particular commands or combinations of commands by which those statistical operations may be invoked;
(d) the options which the author of the First Program has provided in respect of various commands;
(e) the keywords and syntax recognised by the First Program;
(f) the defaults which the author of the First Program has chosen to implement in the event that a particular command or option is not specified by the user;
(g) the number of iterations which the First Program will perform in certain circumstances?
9. Is Article 2(a) to be interpreted as meaning that it is an infringement of the copyright in the Manual for the author of the Second Program to reproduce or substantially reproduce in a manual describing the Second Program the keywords and syntax recognised by the First Program?”

Jurisdiction
It was common ground between counsel that, although there is no direct authority on the point, it appears that the Court of Justice would accept an amendment to questions which had previously been referred by the referring court. The Court of Justice has stated that “national courts have the widest discretion in referring matters”: see Case 166/73 Rheinmühlen Düsseldorf v Einfuhr-und Vorratstelle für Getreide under Futtermittel [1974] ECR 33 at [4]. If an appeal court substitutes questions for those referred by a lower court, the substituted questions will be answered: Case 65/77 Razanatsimba [1977] ECR 2229. Sometimes the Court of Justice itself invites the referring court to clarify its questions, as occurred in Interflora Inc v Marks & Spencer plc (No 2) [2010] EWHC 925 (Ch). In these circumstances, there does not appear to be any reason to think that, if the referring court itself had good reason to amend its questions, the Court of Justice would disregard the amendment.

Counsel for WPL submitted, however, that, as a matter of domestic procedural law, this Court had no jurisdiction to vary an order for reference once sealed unless either there had been a material change of circumstances since the order (as in Interflora) or it had subsequently emerged that the Court had made the order on a false basis. He submitted that neither of those conditions was satisfied here. In those circumstances, the only remedy of a litigant in the position of SAS was to seek to appeal to the Court of Appeal.

As counsel for WPL pointed out, CPR rule 3.1(7) confers on courts what appears to be a general power to vary or revoke their own orders. The proper exercise of that power was considered by the Court of Appeal in Collier v Williams [2006] EWCA Civ 20, [2006] 1 WLR 1945 and Roult v North West Strategic Health Authority [2009] EWCA Civ 444, [2010] 1 WLR 487.

In Collier Dyson LJ (as he then was) giving the judgment of the Court of Appeal said:

“39. We now turn to the third argument. CPR 3.1(7) gives a very general power to vary or revoke an order. Consideration was given to the circumstances in which that power might be used by Patten J in Lloyds Investment (Scandinavia) Limited v Christen Ager-Hanssen [2003] EWHC 1740 (Ch). He said at paragraph 7:
‘The Deputy Judge exercised a discretion under CPR Part 13.3. It is not open to me as a judge exercising a parallel jurisdiction in the same division of the High Court to entertain what would in effect be an appeal from that order. If the Defendant wished to challenge whether the order made by Mr Berry was disproportionate and wrong in principle, then he should have applied for permission to appeal to the Court of Appeal. I have been given no real reasons why this was not done. That course remains open to him even today, although he will have to persuade the Court of Appeal of the reasons why he should have what, on any view, is a very considerable extension of time. It seems to me that the only power available to me on this application is that contained in CPR Part 3.1(7), which enables the Court to vary or revoke an order. This is not confined to purely procedural orders and there is no real guidance in the White Book as to the possible limits of the jurisdiction. Although this is not intended to be an exhaustive definition of the circumstances in which the power under CPR Part 3.1(7) is exercisable, it seems to me that, for the High Court to revisit one of its earlier orders, the Applicant must either show some material change of circumstances or that the judge who made the earlier order was misled in some way, whether innocently or otherwise, as to the correct factual position before him. The latter type of case would include, for example, a case of material non-disclosure on an application for an injunction. If all that is sought is a reconsideration of the order on the basis of the same material, then that can only be done, in my judgment, in the context of an appeal. Similarly it is not, I think, open to a party to the earlier application to seek in effect to re-argue that application by relying on submissions and evidence which were available to him at the time of the earlier hearing, but which, for whatever reason, he or his legal representatives chose not to employ. It is therefore clear that I am not entitled to entertain this application on the basis of the Defendant’s first main submission, that Mr Berry’s order was in any event disproportionate and wrong in principle, although I am bound to say that I have some reservations as to whether he was right to impose a condition of this kind without in terms enquiring whether the Defendant had any realistic prospects of being able to comply with the condition.’
We endorse that approach. We agree that the power given by CPR 3.1(7) cannot be used simply as an equivalent to an appeal against an order with which the applicant is dissatisfied. The circumstances outlined by Patten J are the only ones in which the power to revoke or vary an order already made should be exercised under 3.1(7).”
In Roult Hughes LJ, with whom Smith and Carnwath LJJ agreed, said at [15]:

“There is scant authority upon Rule 3.1(7) but such as exists is unanimous in holding that it cannot constitute a power in a judge to hear an appeal from himself in respect of a final order. Neuberger J said as much in Customs & Excise v Anchor Foods (No 3) [1999] EWHC 834 (Ch). So did Patten J in Lloyds Investment (Scandinavia) Ltd v Ager-Hanssen [2003] EWHC 1740 (Ch). His general approach was approved by this court, in the context of case management decisions, in Collier v Williams [2006] EWCA Civ 20. I agree that in its terms the rule is not expressly confined to procedural orders. Like Patten J in Ager-Hanssen I would not attempt any exhaustive classification of the circumstances in which it may be proper to invoke it. I am however in no doubt that CPR 3.1(7) cannot bear the weight which Mr Grime’s argument seeks to place upon it. If it could, it would come close to permitting any party to ask any judge to review his own decision and, in effect, to hear an appeal from himself, on the basis of some subsequent event. It would certainly permit any party to ask the judge to review his own decision when it is not suggested that he made any error. It may well be that, in the context of essentially case management decisions, the grounds for invoking the rule will generally fall into one or other of the two categories of (i) erroneous information at the time of the original order or (ii) subsequent event destroying the basis on which it was made. The exigencies of case management may well call for a variation in planning from time to time in the light of developments. There may possibly be examples of non-procedural but continuing orders which may call for revocation or variation as they continue – an interlocutory injunction may be one. But it does not follow that wherever one or other of the two assertions mentioned (erroneous information and subsequent event) can be made, then any party can return to the trial judge and ask him to re-open any decision…..”
In the present case there has been no material change of circumstances since I made the Order dated 28 July 2010. Nor did counsel for SAS suggest that I had made the Order upon a false basis. Counsel for SAS did submit, however, that the Court of Appeal had left open the possibility that it might be proper to exercise the power conferred by rule 3.1(7) even if there had no been material change of circumstances and it was not suggested that the order in question had been made on a false basis. Furthermore, he relied upon paragraph 1.1 of the Practice Direction to CPR Part 68, which provides that “responsibility for settling the terms of the reference lies with the English court and not with the parties”. He suggested that this meant that orders for references were not subject to the usual constraints on orders made purely inter partes.

In my judgment PD68 paragraph 1.1 does not justify exercising the power conferred by rule 3.1(7) in circumstances falling outside those identified in Collier and Roult. I am therefore very doubtful that it would be a proper exercise of the power conferred on me by CPR r. 3.1(7) to vary the Order dated 28 July 2010 in the present circumstances. I prefer, however, not to rest my decision on that ground.

Discretion
Counsel for WPL also submitted that, even if this Court had jurisdiction to amend the questions, I should exercise my discretion by refusing to do so for two reasons. First, because the application was made too late. Secondly, because there was no sufficient justification for the amendments anyway. I shall consider these points separately.

Delay
The relevant dates are as follows. The judgment was handed down on 23 July 2010, a draft having been made available to the parties a few days before that. There was a hearing to consider the form of the order, and in particular the wording of the questions to be referred, on 28 July 2010. Prior to that hearing both parties submitted drafts of the questions, and the respective drafts were discussed at the hearing. Following the hearing I settled the Order, and in particular the questions. The Order was sealed on 2 August 2010. The sealed Order was received by the parties between 3 and 5 August 2010. At around the same time the Senior Master of the Queen’s Bench Division transmitted the Order to the Court of Justice. On 15 September 2010 the Registry of the Court of Justice notified the parties, Member States and EU institutions of the reference. On 1 October 2010 the United Kingdom Intellectual Property Office advertised the reference on its website and invited comments by interested parties by 7 October 2010. The latest date on which written observations on the questions referred may be filed at the Court of Justice is 8 December 2010 (two months from the date of the notification plus 10 days extension on account of distance where applicable). This period is not extendable in any circumstances.

As noted above, the application was not issued until 11 October 2010. No justification has been provided by SAS for the delay in making the application. The only explanation offered by counsel for SAS was that the idea of proposing the amendments had only occurred to those representing SAS when starting work on SAS’s written observations.

Furthermore, the application notice requested that the matter be dealt with without a hearing. In my view that was not appropriate: the application was plainly one which was likely to require at least a short hearing. Furthermore, the practical consequence of proceeding in that way was to delay the hearing of the application. The paper application was put before me on 22 October 2010. On the same day I directed that the matter be listed for hearing. In the result it was not listed for hearing until 18 November 2010. If SAS had applied for the matter to be heard urgently, I am sure that it could have been dealt with sooner.

As counsel for WPL submitted, it is likely that the parties, Member States and institutions who intend to file written observations are now at an advanced stage of preparing those observations. Indeed, it is likely that preparations would have been well advanced even on 11 October 2010. To amend the questions at this stage in the manner proposed by SAS would effectively require the Court of Justice to re-start the written procedure all over again. The amended questions would have to be translated into all the EU official languages; the parties, Member States and EU institutions would have to be notified of the amended questions; and the time for submitting written observations would have to be re-set. This would have two consequences. First, a certain amount of time, effort and money on the part of those preparing written observations would be wasted. Secondly, the progress of the case would be delayed. Those are consequences that could have been avoided if SAS had moved promptly after receiving the sealed Order.

In these circumstances, it would not in my judgment be proper to exercise any discretion I may have in favour of amending the questions.

No sufficient justification
Counsel for WPL submitted that in any event SAS’s proposed amendments were not necessary in order to enable the Court of Justice to provide guidance on the issues in this case, and therefore there was no sufficient justification for making the amendments.

Before addressing that submission directly, I think it is worth commenting more generally on the formulation of questions. As is common ground, and reflected in paragraph 1.1 of PD68, it is well established that the questions posed on a reference under Article 267 are the referring court’s questions, not the parties’. The purpose of the procedure is for the Court of Justice to provide the referring court with the guidance it needs in order to deal with the issues before it. It follows that it is for the referring court to decide how to formulate the questions.

In my view it is usually helpful for the court to have the benefit of the parties’ comments on the wording of the proposed questions, as envisaged in paragraph 1.1 of PD68. There are two main reasons for this. The first is to try to ensure that the questions are sufficiently comprehensive to enable all the issues arising to be addressed by the Court of Justice, and thus avoid the need for a further reference at a later stage of the proceedings, as occurred in the Boehringer Ingelheim v Swingward litigation. In that case Laddie J referred questions to the Court of Justice, which were answered in Case C-143/00 [2002] ECR I-3759. The Court of Appeal subsequently concluded, with regret, that the answers to those questions did not suffice to enable it to deal with the case, and referred further questions to the Court of Justice: [2004] EWCA Civ 575, [2004] ETMR 65. Those questions were answered in Case C-348/04 [2007] ECR I-3391. The second main reason is to try to ensure that the questions are clear and free from avoidable ambiguity or obscurity.

In my experience it is not uncommon for parties addressing the court on the formulation of the questions to attempt to ensure that the questions are worded in a leading manner, that is to say, in a way which suggests the desired answer. In my view that is neither proper nor profitable. It is not proper because the questions should so far as possible be impartially worded. It is not profitable because experience shows that the Court of Justice is usually not concerned with the precise wording of the questions referred, but with their legal substance. Thus the Court of Justice frequently reformulates the question in giving its answer.

As counsel for WPL pointed out, and as I have already mentioned, in the present case the parties provided me with draft questions which were discussed at a hearing. In settling the questions I took into account the parties’ drafts and their comments on each other’s drafts, but the final wording is, for better or worse, my own.

As counsel for WPL submitted, at least to some extent SAS’s proposed amendments to the questions appear designed to bring the wording closer to that originally proposed by SAS. This is particularly true of the proposed amendment to question 1. In my judgment it would not be a proper exercise of any discretion that I may have to permit such an amendment, both because it appears to be an attempt by SAS to have the question worded in a manner which it believes favours its case and because its proper remedy if it objected to my not adopting the wording it proposed was to seek to appeal to the Court of Appeal. In saying this, I do not overlook the fact that SAS proposes to move some of the words excised from question 1 to question 5.

In any event, I am not satisfied that any of the amendments are necessary either to enable the parties to present their respective arguments to the Court of Justice or to enable the Court to give guidance on any of the issues arising in this case. On the contrary, I consider that the existing questions are sufficient for these purposes. By way of illustration, I will take the biggest single amendment, which is the proposed insertion of new paragraph (d) in question 2. In my view, the matters referred to in paragraph (d) are matters that are encompassed within paragraphs (b) and/or (c); or at least can be addressed by the parties, and hence the Court of Justice, in the context provided by paragraphs (b) and/or (c). When I put this to counsel for SAS during the course of argument, he accepted it.

Other amendments counsel for SAS himself presented as merely being minor matters of clarification. In my view none of them amount to the elimination of what would otherwise be ambiguities or obscurities in the questions.

It is fair to say that SAS have identified a small typographical error in question 2 (“interpreting” should read “interpreted”), but in my view this is an obvious error which will not cause any difficulty in the proceedings before the Court of Justice.

Conclusion
It was for these reasons that I decided to dismiss SAS’s application

Here comes PySpread- 85,899,345 rows and 14,316,555 columns

A Bold GNU Head
Image via Wikipedia

Whats new/ One more open source analytics package. Built like a spreadsheet with an ability to import a million cells-

From http://pyspread.sourceforge.net/index.html

about Pyspread is a cross-platform Python spreadsheet application. It is based on and written in the programming language Python.

Instead of spreadsheet formulas, Python expressions are entered into the spreadsheet cells. Each expression returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices.

Pyspread screenshot
features In pyspread, cells expect Python expressions and return Python objects. Therefore, complex data types such as lists, trees or matrices can be handled within a single cell. Macros can be used for functions that are too complex for a single expression.

Since Python modules can be easily used without external scripts, arbitrary size rational numbers (via gmpy), fixed point decimal numbers for business calculations, (via the decimal module from the standard library) and advanced statistics including plotting functions (via RPy) can be used in the spreadsheet. Everything is directly available from each cell. Just use the grid

Data can be imported and exported using csv files or the clipboard. Other forms of data exchange is possible using external Python modules.

In  order to simplify sparse matrix editing, pyspread features a three dimensional grid that can be sized up to 85,899,345 rows and 14,316,555 columns (64 bit-systems, depends on row height and column width). Note that importing a million cells requires about 500 MB of memory.

The concept of pyspread allows doing everything from each cell that a Python script can do. This may very well include deleting your hard drive or sending your data via the Internet. Of course this is a non-issue if you sandbox properly or if you only use self developed spreadsheets. Since this is not the case for everyone (see the discussion at lwn.net), a GPG signature based trust model for spreadsheet files has been introduced. It ensures that only your own trusted files are executed on loading. Untrusted files are displayed in safe mode. You can trust a file manually. Inspect carefully.

Pyspread screenshot

requirements Pyspread runs on Linux, Windows and *nix platforms with GTK+ support. There are reports that it works with MacOS X as well. If you would like to contribute by testing on OS X please contact me.

Dependencies

Highly recommended for full functionality

  • PyMe >=0.8.1, Note for Windows™ users: If you want to use signatures without compiling PyMe try out Gpg4win.
  • gmpy >=1.1.0 and
  • rpy >=1.0.3.
maturity Pyspread is in early Beta release. This means that the core functionality is fully implemented but the program needs testing and polish.

and from the wiki

http://sourceforge.net/apps/mediawiki/pyspread/index.php?title=Main_Page

a spreadsheet with more powerful functions and data structures that are accessible inside each cell. Something like Python that empowers you to do things quickly. And yes, it should be free and it should run on Linux as well as on Windows. I looked around and found nothing that suited me. Therefore, I started pyspread.

Concept

  • Each cell accepts any input that works in a Python command line.
  • The inputs are parsed and evaluated by Python’s eval command.
  • The result objects are accessible via a 3D numpy object array.
  • String representations of the result objects are displayed in the cells.

Benefits

  • Each cell returns a Python object. This object can be anything including arrays and third party library objects.
  • Generator expressions can be used efficiently for data manipulation.
  • Efficient numpy slicing is used.
  • numpy methods are accessible for the data.

Installation

  1. Download the pyspread tarball or zip and unzip at a convenient place
  2. In case you do not have it already get and install Python, wxpython and numpy
If you want the examples to work, install gmpy, R and rpy
Really do check the version requirements that are mentioned on http://pyspread.sf.net
  1. Get install privileges (e.g. become root)
  2. Change into the directory and type
python setup.py install
Windows: Replace “python” with your Python interpreter (absolute path)
  1. Become normal user again
  2. Start pyspread by typing
pyspread
  1. Enjoy

Links

Next on Spreadsheet wishlist-

a MSI bundle /Windows Self Installer which has all dependencies bundled in it-linking to PostGresSQL 😉 etc

way to go Mr Martin Manns

mmanns < at > gmx < dot > net