Slashdot Mirror

theodp writes "Raw intellect ain't always all it's cracked up to be, advises Ted Dziuba in his introduction to Programming Things I Wish I Knew Earlier, so don't be too stubborn to learn the things that can save you from the headaches of over-engineering. Here's some sample how-to-avoid-over-complicating-things advice: 'If Linux can do it, you shouldn't. Don't use Hadoop MapReduce until you have a solid reason why xargs won't solve your problem. Don't implement your own lockservice when Linux's advisory file locking works just fine. Don't do image processing work with PIL unless you have proven that command-line ImageMagick won't do the job. Modern Linux distributions are capable of a lot, and most hard problems are already solved for you. You just need to know where to look.' Any cautionary tips you'd like to share from your own experience?"

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 writes in the easy and clear style of someone comfortable and engaged with the subject matter." Keep reading for the rest of aceydacey's review. Beginning Python Visualization: Crafting Visual Transformation Scripts author Shai Vaingast pages 363 publisher Apress rating 9/10 reviewer aceydacey ISBN 1430218436 summary learn how to process, organize, and visualize data from various sources using the Python language 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

You can purchase Beginning Python Visualization: Crafting Visual Transformation Scripts from amazon.com. Slashdot welcomes readers' book reviews -- to see your own review here, read the book review guidelines, then visit the submission page.

baxissimo asks: "I've played around with Python a bit, and as a scripting language I quite like it. So I sat down the other day to see if I could use it to make a modest OpenGL/GUI application on Windows. The short story is I gave up. I couldn't get the Python IDE I had to run--but that didn't stop me. At first I just shrugged my shoulders and said to myself 'Ah, who needs it? I've got emacs,' and then proceeded to waste a few hours trying to cobble together an app that would run before it dawned on me that Python without a decent IDE is definitely not easier to use than C++ with an IDE. So is anyone out there actually using Python to make serious apps? What tools are you using?" "I've heard the wxPython bindings are nice for the GUI bits, so I downloaded those, and pyOpenGL, and numPy, and PIL, etc. The only recommendation I really saw anywhere for an IDE was for boaConstructor, so I got that. Unfortunately it only spit out a useless error messages on startup and died. What I'd really like to start doing is creating C++/Python hybrids, but given that I was unable to successfully debug a pure Python app, I'm wondering what it's going to be like when my bugs might be in either language. How do people deal with this? What tools help you get the job done? If there's nothing free that works, are there any commercial IDE's worth the money?"

Slashback tonight brings a few followups to recent Slashdot postings on the fate of model rocketry in the new, hypercautious America; a few Python gatherings for those who prefer that language to Perl; and a response from Los Alamos to recent claims of lax security. Enjoy!

Besides which, it's the hidden cameras that matter. An anonymous reader adds this followup to the story posted last month about Wired reporter Noah Shachtman's account of sneaking into classified areas at Los Alamos national Laboratory.

"In an email message to all Los Alamos National Laboratory employees, Pete Nanos, the current Director of LANL, responded with information suggesting that the Wired reporter who thought he had broken in to a 'top secret area' had in fact just crossed a cattle fence:

'The Wired reporter clearly did not enter a Laboratory security area. The Laboratory encompasses more than 40 square miles. The security force protects important assets within those boundaries but cannot -- and does not -- protect every square foot of property. Based on the article, it appears the reporter crossed a barbed-wire cattle fence, not a fence that protects a Los Alamos security area.
There is a small security area with several buildings (roughly 400 feet by 400 feet) near the driveway entrance to TA-33. That area is surrounded by a seven-foot-high chain-link fence topped with three strands of barbed wire. A security guard is stationed inside that area seven days a week and 24 hours a day. Clearly, the reporter did not climb that fence.
There are several other buildings outside the security area that are locked for property protection interests. They have no security interests. There are several gates and fenced areas on the TA-33 site, which are there for safety access control, not security.
It's unlikely the reporter would be prosecuted for trespassing; the Laboratory does not have law enforcement authority to prosecute, and none of the proper authorities witnessed the trespass.'"

Perhaps we can have a celebrity deathmatch. hfastedge writes "Ok, now that 2 perl conferences have been mentioned, I've been brought over the edge. Python is a language that is just as old, and arguably better from: most importantly a uniform standard of readability (enforced by using whitespace to delimit blocks (instead of {}), by avoiding overuse of cryptic symbols, and by a culture that strives to keep innovations as "pythonic"), and a rich development community. Anyway, normally, there are Python events in Europe, and a trail at O'Reilly's OSCON. But now, there is a far cheaper event taking place on March 24-28 in Washington DC: http://python.org/pycon/.

Examples of Python in action: 0, 1, 2, 3, 4, 5, 6, 7"

Fly up go phhhhhwwwtttpffffff .... MyNameIsFred writes "Slashdot recently discussed whether anti-terrorism laws would destroy model rocketry. The government has ruled, and the message is clear, "When it comes to the hobby of model rocketry, size does matter. And in this case, the magic number is 62.5 grams. That's the largest amount of propellant a single model rocket engine can have in it and still be exempt from a new set of federal rules that will go into effect May 24." What does this mean for the the big guys in model rocketry, who use engines larger than this?"

While the rest of us are still in Awe over the Compaq/Digital buyout, there is an excellant article over at sun about the situation. The future of the Alpha is at stake here, so it is probably worth reading. This one was sent to us by Fredrik Lundh