Comment Thank you (Score 1) 1521
Dear Rob, thank you for making slashdot what it is today.
Google To Drop Support For H.264 In Chrome 765
Steve writes "Google just made a bold move in the HTML5 video tag battle: even though H.264 is widely used and WebM is not, the search giant has announced it will drop support for the former in Chrome. The company has not done so yet, but it has promised it will in the next couple of months. Google wants to give content publishers and developers using the HTML5 video tag an opportunity to make any necessary changes to their websites."
DOJ Gives Oracle Approval To Buy Sun 162
k33l0r writes "The BBC is reporting that the US Justice Department has approved Oracle's takeover of Sun Microsystems. The acquisition gives Oracle control over (or a leading role in), among other things, Java, MySQL, (Open)Solaris, ZFS, OpenOffice, and the NetBeans IDE. 'The European Commission has still to rule on the deal, a step that will be required before it can close. That body has indicated it will issue an initial opinion on Sept. 3, according to the Wall Street Journal. It may OK the deal at that time or launch a four-month probe of it. ... The Justice Department ruling came earlier than expected, a possible response to Sun's declining revenues and precarious business position in a steep recession, as the required reviews proceeded.' We first discussed the deal back when it was announced in April."
Submission + - New Pattern Found in Prime Numbers 1
stephen.schaubach writes: "Spanish Mathematicians have discovered a new pattern in primes that surprisingly has gone unnoticed. "Besides providing insight into the nature of primes, the finding could also have applications in areas such as fraud detection and stock market analysis." Bueno! "
Submission + - Book Review: Beginning Python Visualization (awaretek.com)
aceydacey writes: "Sometimes a picture is worth a thousand words. "Beginning Python Visualization: Creating Visual Transformation Scripts", published in February 2009 by Apress, shows how Python and its related tools can be used to easily and effectively turn raw data into visual representations that communicate effectively. The author is Shai Vaingast, a professional engineer and engineering manager who needed to train scientists and engineers to do this kind of programming work. He was looking for a tutorial and reference work, and unable to find a suitable text, wound up writing his first book. He wrote in the easy and clear style of someone comfortable and engaged with the subject matter.
The book uses several very specific examples that illustrate general principles.
The first example is using GPS data. By using Python one can extract data from GPS receivers and enter it into the computer and manipulate it to do what one wants including creating graphs and charts. In this section he shows how to use CSV, comma separated values, as a most useful file format. He shows show to extract data from real world GPS devices and import it via serial ports and the PySerial module. It would be easy for the reader to duplicate and extend this project.
The heart of the book is coverage of useful examples utilizing MatPlotLib, NumPy and SciPy. These related tools are easy to use and fully integrated with Python. MatPlotLib is for plotting data and graphs, including interactive graphs and image files. NumPy is a powerful math library comparable to commercial tools like MatLab, and SciPy extends NumPy to for the sciences. Examples are numerous and include signal analysis using Fourier transforms.
There is also a section on Image Processing using PIL, the Python Imaging Library. This is used for relatively simple image cropping and sizing and also for bit by bit image processing. Interpolation and curve fitting are also well covered. For anyone wanting an introduction to graphical analysis of statistical data, this would be an excellent resource.
The author is obviously a professional in this field. He has a knack for good organizational style and a pragmatic approach to the work. In the book he says "Most of the time, research is organized chaos. The emphasis, however, should be on organized, not chaos." A real value I got from the book is a better understanding of data files, format, and organization as well as methods and guidelines for selecting file formats and storing and organizing data to enable fast and efficient data processing. It is obvious that this book was written by a practicing engineer.
The theme of the book is that Python can be an all purpose environment for data manipulation and visualization, using nothing but free and open source tools that are easily integrated and scriptable without using multiple programming languages. The book should be an invaluable tool for scientists and engineers but it is also easily accessible to anyone interested in math and data analysis. There is no need for an advanced math background. While, as a matter of full disclosure, I have undergraduate degrees in Math and Physics, I feel the book should be easily accessible to anyone with a solid high school math background who is seriously interested in the subject. The book contains a short introductory tutorial on the basics of Python so anyone familiar with programming in any language should be fine.
The book is an easy read from front to back, and I am sure it will also be a good reference resource for the future. The writing style is very clear and unforced and I found surprisingly few errors. While the Python world has a surplus of introductory and general books, books covering this kind of specific domain are especially welcome, and we could use more on other topics by competent authors.
At 363 pages the book is a surprisingly fast read. Its methodology is to use specific, short code examples to make all the key points. Most of the code samples are well selected, short and written in clear, concise Python. This is not the kind of book that overwhelms you with massive amounts of code. Either the book was well edited or else it was written by an exceptionally lucid thinker, or both.
So, if you want to learn how to process, organize, and visualize data from various sources using the Python language, I recommend this book to you. I have also posted a podcast of an interview with the author at Python411"
The book uses several very specific examples that illustrate general principles.
The first example is using GPS data. By using Python one can extract data from GPS receivers and enter it into the computer and manipulate it to do what one wants including creating graphs and charts. In this section he shows how to use CSV, comma separated values, as a most useful file format. He shows show to extract data from real world GPS devices and import it via serial ports and the PySerial module. It would be easy for the reader to duplicate and extend this project.
The heart of the book is coverage of useful examples utilizing MatPlotLib, NumPy and SciPy. These related tools are easy to use and fully integrated with Python. MatPlotLib is for plotting data and graphs, including interactive graphs and image files. NumPy is a powerful math library comparable to commercial tools like MatLab, and SciPy extends NumPy to for the sciences. Examples are numerous and include signal analysis using Fourier transforms.
There is also a section on Image Processing using PIL, the Python Imaging Library. This is used for relatively simple image cropping and sizing and also for bit by bit image processing. Interpolation and curve fitting are also well covered. For anyone wanting an introduction to graphical analysis of statistical data, this would be an excellent resource.
The author is obviously a professional in this field. He has a knack for good organizational style and a pragmatic approach to the work. In the book he says "Most of the time, research is organized chaos. The emphasis, however, should be on organized, not chaos." A real value I got from the book is a better understanding of data files, format, and organization as well as methods and guidelines for selecting file formats and storing and organizing data to enable fast and efficient data processing. It is obvious that this book was written by a practicing engineer.
The theme of the book is that Python can be an all purpose environment for data manipulation and visualization, using nothing but free and open source tools that are easily integrated and scriptable without using multiple programming languages. The book should be an invaluable tool for scientists and engineers but it is also easily accessible to anyone interested in math and data analysis. There is no need for an advanced math background. While, as a matter of full disclosure, I have undergraduate degrees in Math and Physics, I feel the book should be easily accessible to anyone with a solid high school math background who is seriously interested in the subject. The book contains a short introductory tutorial on the basics of Python so anyone familiar with programming in any language should be fine.
The book is an easy read from front to back, and I am sure it will also be a good reference resource for the future. The writing style is very clear and unforced and I found surprisingly few errors. While the Python world has a surplus of introductory and general books, books covering this kind of specific domain are especially welcome, and we could use more on other topics by competent authors.
At 363 pages the book is a surprisingly fast read. Its methodology is to use specific, short code examples to make all the key points. Most of the code samples are well selected, short and written in clear, concise Python. This is not the kind of book that overwhelms you with massive amounts of code. Either the book was well edited or else it was written by an exceptionally lucid thinker, or both.
So, if you want to learn how to process, organize, and visualize data from various sources using the Python language, I recommend this book to you. I have also posted a podcast of an interview with the author at Python411"
Vista Not Playing Well With IPv6 232
netbuzz writes in to note that some early adopters of Microsoft Vista are reporting problems with Vista's implementation of IPv6. An example:"'We are seeing a number of applications that are IP-based that do not like the addressing scheme of IPv6,' says one user. 'We will send a print job to an IP-based printer, and the print job becomes corrupted. We're seeing this with Window's Vista machines. When IPv6 is installed, this happens without fail. As soon as we remove IPv6, all of our printer functions return to normal.'"
Comment Re:Imagine the Possibilities (Score 2, Informative) 339
some technical details on the ISRO website:
http://www.isro.org/Cartosat/Page3.htm
