iPython Notebook is now Jupyter… I knew it!

JupyterIt is not really news… Jupyter is the new name of the loved iPython project, and it has been for a while and as they Jupiter projects puts it themselves

The language-agnostic parts of IPython are getting a new home in Project Jupyter

As announced in the python.org page, as of version 4.0, the The Big Split from the old iPython starts. I knew this and I even tweeted about it:

All, great, right? Well I still got surprised when after updating my Python installation and tried to start my ipython notebook I got an error that ended with:

File "importstring.py", line 31, in import_item
module = __import__(package, fromlist=[obj])
ImportError: No module named notebook.notebookapp

Then I remembered and to fix my problem I simply tried installing Jupyter (*I am using Anaconda) with the following command

conda install jupyter

Et voilà!

iPython Notebook is now Jupyter… I knew it!

Markup for Fast Data Science Publication – Reblog

I am an avid user of Markdown via Mou and R Markdown (with RStudio). The facility that the iPython Notebook offers in combining code and text to be rendered in an interactive webpage is the choice for a number of things, including the 11-week Data Science course I teach at General Assembly.

As for LaTeX, well, I could not have survived my PhD without it and I still use it heavily. I have even created some videos about how to use LaTeX, you can take a loot at them

My book “Essential Matlab and Octave” was written and formatted in its entirety using LaTeX. My new book “Data Science and Analytics with Python” is having the same treatment.

markdown

I was very pleased to see the following blog post by Benjamin Bengfort. This is a reblog of that post and the original can be found here.

Markup for Fast Data Science Publication
Benjamin Bengfort

A central lesson of science is that to understand complex issues (or even simple ones), we must try to free our minds of dogma and to guarantee the freedom to publish, to contradict, and to experiment. — Carl Sagan in Billions & Billions: Thoughts on Life and Death at the Brink of the Millennium

As data scientists, it’s easy to get bogged down in the details. We’re busy implementing Python and R code to extract valuable insights from data, train effective machine learning models, or put a distributed computation system together. Many of these tasks, especially those relating to data ingestion or wrangling, are time-consuming but are the bread and butter of the data scientist’s daily grind. What we often forget, however, is that we must not only be data engineers, but also contributors to the data science corpus of knowledge.

If a data product derives its value from data and generates more data in return, then a data scientist derives their value from previously published works and should generate more publications in return. Indeed, one of the reasons that Machine Learning has grown ubiquitous (see the many Python-tagged questions related to ML on Stack Overflow) is thanks to meticulous blog posts and tools from scientific research (e.g. Scikit-Learn) that enable the rapid implementation of a variety of algorithms. Google in particular has driven the growth of data products by publishing systems papers about their methodologies, enabling the creation of open source tools like Hadoop and Word2Vec.

By building on a firm base for both software and for modeling, we are able to achieve greater results, faster. Exploration, discussion, criticism, and experimentation all enable us to have new ideas, write better code, and implement better systems by tapping into the collective genius of a data community. Publishing is vitally important to keeping this data science gravy train on the tracks for the foreseeable future.

In academia, the phrase “publish or perish” describes the pressure to establish legitimacy through publications. Clearly, we don’t want to take our rule as authors that far, but the question remains, “How can we effectively build publishing into our workflow?” The answer is through markup languages – simple, streamlined markup that we can add to plain text documents that build into a publishing layout or format. For example, the following markup languages/platforms build into the accompanying publishable formats:

  • Markdown → HTML
  • iPython Notebook (JSON + Markdown) → Interactive Code
  • reStructuredText + Sphinx → Python Documentation, ReadTheDocs.org
  • AsciiDoc → ePub, Mobi, DocBook, PDF
  • LaTeX → PDF

The great thing about markup languages is that they can be managed inline with your code workflow in the same software versioning repository. Github goes even further as to automatically render Markdown files! In this post, we’ll get you started with several markup and publication styles so that you can find what best fits into your workflow and deployment methodology.

Markdown

Markdown is the most ubiquitous of the markup languages we’ll describe in this post, and its simplicity means that it is often chosen for a variety of domains and applications, not just publishing. Markdown, originally created by John Gruber, is a text-to-HTML processor, where lightweight syntactic elements are used instead of the more heavyweight HTML tags. Markdown is intended for folks writing for the web, not designing for the web, and in some CMS systems, it is simply the way that you write, no fancy text editor required.

Markdown has seen special growth thanks to Github, which has an extended version of Markdown, usually referred to as “Github-Flavored Markdown.” This style of Markdown extends the basics of the original Markdown to include tables, syntax highlighting, and other inline formatting elements. If you create a Markdown file in Github, it is automatically rendered when viewing files on the web, and if you include a README.md in a directory, that file is rendered below the directory contents when browsing code. Github Issues are also expected to be in Markdown, further extended with tools like checkbox lists.

Markdown is used for so many applications it is difficult to name them all. Below are a select few that might prove useful to your publishing tasks.

  • Jekyll allows you to create static websites that are built from posts and pages written in Markdown.
  • Github Pages allows you to quickly publish Jekyll-generated static sites from a Github repository for free.
  • Silvrback is a lightweight blogging platform that allows you to write in Markdown (this blog is hosted on Silvrback).
  • Day One is a simple journaling app that allows you to write journal entries in Markdown.
  • iPython Notebook expects Markdown to describe blocks of code.
  • Stack Overflow expects questions, answers, and comments to be written in Markdown.
  • MkDocs is a software documentation tool written in Markdown that can be hosted on ReadTheDocs.org.
  • GitBook is a toolchain for publishing books written in Markdown to the web or as an eBook.

There are also a wide variety of editors, browser plugins, viewers, and tools available for Markdown. Both Sublime Text and Atom support Markdown and automatic preview, as well as most text editors you’ll use for coding. Mou is a desktop Markdown editor for Mac OSX and iA Writer is a distraction-free writing tool for Markdown for iOS. (Please comment your favorite tools for Windows and Android). For Chrome, extensions like Markdown Here make it easy to compose emails in Gmail via Markdown or Markdown Preview to view Markdown documents directly in the browser.

Clearly, Markdown enjoys a broad ecosystem and diverse usage. If you’re still writing HTML for anything other than templates, you’re definitely doing it wrong at this point! It’s also worth including Markdown rendering for your own projects if you have user submitted text (also great for text-processing).

Rendering Markdown can be accomplished with the Python Markdown library, usually combined with the Bleach library for sanitizing bad HTML and linkifying raw text. A simple demo of this is as follows:

First install markdown and bleach using pip:

$ pip install markdown bleach

Then create a markdown parsing function as follows:

import bleach
from markdown import markdown

def htmlize(text):
"""
This helper method renders Markdown then uses Bleach to sanitize it as
well as converting all links in text to actual anchor tags.
"""
text = bleach.clean(text, strip=True) # Clean the text by stripping bad HTML tags
text = markdown(text) # Convert the markdown to HTML
text = bleach.linkify(text) # Add links from the text and add nofollow to existing links

return text

Given a markdown file test.md whose contents are as follows:

# My Markdown Document

For more information, search on [Google](http://www.google.com).

_Grocery List:_

1. Apples
2. Bananas
3. Oranges

The following code:

>>> with open('test.md', 'r') as f:
... print htmlize(f.read())

Will produce the following HTML output:

<h1>My Markdown Document</h1>
For more information, search on <a href="http://www.google.com" rel="nofollow">Google</a>.

<em>Grocery List:</em>
<ol>
    <li>Apples</li>
    <li>Bananas</li>
    <li>Oranges</li>
</ol>

Hopefully this brief example has also served as a demonstration of how Markdown and other markup languages work to render much simpler text with lightweight markup constructs into a larger publishing framework. Markdown itself is most often used for web publishing, so if you need to write HTML, then this is the choice for you!

To learn more about Markdown syntax, please see Markdown Basics.

iPython Notebook

iPython Notebook is an web-based, interactive environment that combines Python code execution, text (marked up with Markdown), mathematics, graphs, and media into a single document. The motivation for iPython Notebook was purely scientific: How do you demonstrate or present your results in a repeatable fashion where others can understand the work you’ve done? By creating an interactive environment where code, graphics, mathematical formulas, and rich text are unified and executable, iPython Notebook gives a presentation layer to otherwise unreadable or inscrutable code. Although Markdown is a big part of iPython Notebook, it deserves a special mention because of how critical it is to the data science community.

iPython Notebook is interesting because it combines both the presentation layer as well as the markup layer. When run as a server, usually locally, the notebook is editable, explorable (a tree view will present multiple notebook files), and executable – any code written in Python in the notebook can be evaluated and run using an interactive kernel in the background. Math formula written in LaTeX are rendered using MathJax. To enhance the delivery and shareability of these notebooks, the NBViewer allows you to share static notebooks from a Github repository.

iPython Notebook comes with most scientific distributions of Python like Anaconda or Canopy, but it is also easy to install iPython with pip:

$ pip install ipython

iPython itself is an enhanced interactive Python shell or REPL that extends the basic Python REPL with many advanced features, primarily allowing for a decoupled two-process model that enables the notebook. This process model essentially runs Python as a background kernel that receives execution instructions from clients and returns responses back to them.

To start an iPython notebook execute the following command:

$ ipython notebook

This will start a local server at

http://127.0.0.1:8888

and automatically open your default browser to it. You’ll start in the “dashboard view”, which shows all of the notebooks available in the current working directory. Here you can create new notebooks and start to edit them. Notebooks are saved as .ipynb files in the local directory, a format called “Jupyter” that is simple JSON with a specific structure for representing each cell in the notebook. The Jupyter notebook files are easily reversioned via Git and Github since they are also plain text.

To learn more about iPython Notebook, please see the iPython Notebook documentation.

reStructuredText

reStructuredText is an easy-to-read plaintext markup syntax specifically designed for use in Python docstrings or to generate Python documentation. In fact, the reStructuredText parser is a component of Docutils, an open-source text processing system that is used by Sphinx to generate intelligent and beautiful software documentation, in particular the native Python documentation.

Python software has a long history of good documentation, particularly because of the idea that batteries should come included. And documentation is a very strong battery! PyPi, the Python Package Index, ensures that third party packages provide documentation, and that the documentation can be easily hosted online through Python Hosted. Because of the ease of use and ubiquity of the tools, Python programmers are known for having very consistently documented code; sometimes it’s hard to tell the standard library from third party modules!

In How to Develop Quality Python Code, I mentioned that you should use Sphinx to generate documentation for your apps and libraries in a docs directory at the top-level. Generating reStructuredText documentation in a docs directory is fairly easy:

$ mkdir docs
$ cd docs
$ sphinx-quickstart

The quickstart utility will ask you many questions to configure your documentation. Aside from the project name, author, and version (which you have to type in yourself), the defaults are fine. However, I do like to change a few things:

...
> todo: write "todo" entries that can be shown or hidden on build (y/n) [n]: y
> coverage: checks for documentation coverage (y/n) [n]: y
...
> mathjax: include math, rendered in the browser by MathJax (y/n) [n]: y

Similar to iPython Notebook, reStructured text can render LaTeX syntax mathematical formulas. This utility will create a Makefile for you; to generate HTML documentation, simply run the following command in the docs directory:

$ make html

The output will be built in the folder _build/html where you can open the index.html in your browser.

While hosting documentation on Python Hosted is a good choice, a better choice might be Read the Docs, a website that allows you to create, host, and browse documentation. One great part of Read the Docs is the stylesheet that they use; it’s more readable than older ones. Additionally, Read the Docs allows you to connect a Github repository so that whenever you push new code (and new documentation), it is automatically built and updated on the website. Read the Docs can even maintain different versions of documentation for different releases.

Note that even if you aren’t interested in the overhead of learning reStructuredText, you should use your newly found Markdown skills to ensure that you have good documentation hosted on Read the Docs. See MkDocs for document generation in Markdown that Read the Docs will render.

To learn more about reStructuredText syntax, please see the reStructuredText Primer.

AsciiDoc

When writing longer publications, you’ll need a more expressive tool that is just as lightweight as Markdown but able to handle constructs that go beyond simple HTML, for example cross-references, chapter compilation, or multi-document build chains. Longer publications should also move beyond the web and be renderable as an eBook (ePub or Mobi formats) or for print layout, e.g. PDF. These requirements add more overhead, but simplify workflows for larger media publication.

Writing for O’Reilly, I discovered that I really enjoyed working in AsciiDoc – a lightweight markup syntax, very similar to Markdown, which renders to HTML or DocBook. DocBook is very important, because it can be post-processed into other presentation formats such as HTML, PDF, EPUB, DVI, MOBI, and more, making AsciiDoc an effective tool not only for web publishing but also print and book publishing. Most text editors have an AsciiDoc grammar for syntax highlighting, in particular sublime-asciidoc and Atom AsciiDoc Preview, which make writing AsciiDoc as easy as Markdown.

AsciiDoctor is an AsciiDoc-specific toolchain for building books and websites from AsciiDoc. The project connects the various AsciiDoc tools and allows a simple command-line interface as well as preview tools. AsciiDoctor is primarily used for HTML and eBook formats, but at the time of this writing there is a PDF renderer, which is in beta. Another interesting project of O’Reilly’s is Atlas, a system for push-button publishing that manages AsciiDoc using a Git repository and wraps editorial build processes, comments, and automatic editing in a web platform. I’d be remiss not to mention GitBook which provides a similar toolchain for publishing larger books, though with Markdown.

Editor’s Note: GitBook does support AsciiDoc.

To learn more about AsciiDoc markup see AsciiDoc 101.

LaTeX

If you’ve done any graduate work in the STEM degrees then you are probably already familiar with LaTeX to write and publish articles, reports, conference and journal papers, and books. LaTeX is not a simple markup language, to say the least, but it is effective. It is able to handle almost any publishing scenario you can throw at it, including (and in particular) rendering complex mathematical formulas correctly from a text markup language. Most data scientists still use LaTeX, using MathJax or the Daum Equation Editor, if only for the math.

If you’re going to be writing PDFs or reports, I can provide two primary tips for working with LaTeX. First consider cloud-based editing with Overleaf or ShareLaTeX, which allows you to collaborate and edit LaTeX documents similarly to Google Docs. Both of these systems have many of the classes and stylesheets already so that you don’t have to worry too much about the formatting, and instead just get down to writing. Additionally, they aggregate other tools like LaTeX templates and provide templates of their own for most document types.

My personal favorite workflow, however, is to use the Atom editor with the LaTeX package and the LaTeX grammar. When using Atom, you get very nice Git and Github integration – perfect for collaboration on larger documents. If you have a TeX distribution installed (and you will need to do that on your local system, no matter what), then you can automatically build your documents within Atom and view them in PDF preview.

A complete tutorial for learning LaTeX can be found at Text Formatting with LaTeX.

Conclusion

Software developers agree that testing and documentation is vital to the successful creation and deployment of applications. However, although Agile workflows are designed to ensure that documentation and testing are included in the software development lifecycle, too often testing and documentation is left to last, or forgotten. When managing a development project, team leads need to ensure that documentation and testing are part of the “definition of done.”

In the same way, writing is vital to the successful creation and deployment of data products, and is similarly left to last or forgotten. Through publication of our work and ideas, we open ourselves up to criticism, an effective methodology for testing ideas and discovering new ones. Similarly, by explicitly sharing our methods, we make it easier for others to build systems rapidly, and in return, write tutorials that help us better build our systems. And if we translate scientific papers into practical guides, we help to push science along as well.

Don’t get bogged down in the details of writing, however. Use simple, lightweight markup languages to include documentation alongside your projects. Collaborate with other authors and your team using version control systems, and use free tools to make your work widely available. All of this is possible becasue of lightweight markup languages, and the more profecient you are at including writing in your workflow, the easier it will be to share your ideas.

Helpful Links

This post is particularly link-heavy with many references to tools and languages. For reference, here are my preferred guides for each of the Markup languages discussed:

Books to Read

Special thanks to Rebecca Bilbro for editing and contributing to this post. Without her, this would certainly have been much less readable!

As always, please follow @DistrictDataLab on Twitter and subscribe to this blog by clicking the Subscribe button on the blog home page.

Benjamin Bengfort

 

Programming Languages Ranking 2014

Well, it seems that it is that time of the month when the TIOBE index releases the rankings of programming languages. Happy to see R improving it position going from 15 to 12. Matlab is at 24 though…

The index is based on number of skilled engineers world-wide, courses and third party vendors that use each of the languages and popular search engines are used to calculate the ratings. Just remember that the TIOBE index is not about the best programming language or the language in which most lines of code have been written.

The definition of the TIOBE index can be found here. In any case here are the rankings:

Nov 2014 Nov 2013 Change Programming Language Ratings Change
1 1 C 17.469% -0.69%
2 2 Java 14.391% -2.13%
3 3 Objective-C 9.063% -0.34%
4 4 C++ 6.098% -2.27%
5 5 C# 4.985% -1.04%
6 6 PHP 3.043% -2.34%
7 8 change Python 2.589% -0.52%
8 10 change JavaScript 2.088% +0.04%
9 12 change Perl 2.073% +0.55%
10 11 change Visual Basic .NET 2.061% +0.09%
11 change Visual Basic 1.657% +1.66%
12 31 change R 1.548% +1.14%
13 9 change Transact-SQL 1.408% -1.11%
14 13 change Ruby 1.211% -0.09%
15 17 change Delphi/Object Pascal 0.957% +0.31%
16 23 change F# 0.892% +0.39%
17 18 change PL/SQL 0.870% +0.27%
18 change Swift 0.834% +0.83%
19 14 change Pascal 0.831% +0.12%
20 81 change Dart 0.816% +0.73%

Grace Hopper Doodle

 

Hopper

Once again Google puts out a doodle worth mentioning. This time they celebrate the 107th birthday anniversary of computer scientist Grace Hopper.
In case you do not know who Hopper is, well, let me smile say that she is the amazon woman behind COBOL (Common Business Oriented Language), which is still very much used today.

Grace Hopper was born in  New York in 1906  and studied Mathematics and Physics (of course) at Vassar College where she graduated in 1928. She then obtained a master’s degree at Yale in 1930 and a PhD in 1934.

Hopper joined the US Navy reserve during World War two and she was assigned to the Bureau of Ordinance Computation Project at Harvard University where she was only the third person to program the Harvard Mark I computer. She continued to work at Harvard until 1949 when she joined the Eckert-Mauchly Computer Corporation as a senior programmer.

She helped to develop the UNIVAC I, which was the second commercial computer produced in the US. In the 1950s Hopper created the first ever compiler, known as the A compiler and the first version was called the A-O.

Hopper continued to serve in the navy until 1986 when she was the oldest commissioned officer on active duty in the United States Navy.

She died in Arlington, Virginia in 1992 at the age of 85.

Grace Hopper behind my keyboard
Grace Hopper behind my keyboard (Photo credit: Alexandre Dulaunoy)

 

Relating Airy and Bessel functions

Reblogged from The Endeavour by J. D. Cook.

The Airy functions Ai(x) and Bi(x) are independent solutions to the differential equation

y'' - xy=0

For negative x they act something like sin(x) and cos(x). For positive x they act something like exp(x) and exp(-x). This isn’t surprising if you look at the differential equation. If you replace xwith a negative constant, you sines and cosines, and if you replace it with a positive constant, you get positive and negative exponentials.

The Airy functions can be related to Bessel functions as follows:

mathrm{Ai}(x)=left{ begin{array}{ll} frac{1}{3}sqrt{phantom{-}x} left(I_{-1/3}(hat{x}) - I_{1/3}(hat{x})right) & mbox{if } x > 0 \<br /><br /><br /> \<br /><br /><br /> frac{1}{3}sqrt{-x} left(J_{-1/3}(hat{x}) + J_{1/3}(hat{x})right) & mbox{if } x < 0 end{array} right.

and

mathrm{Bi}(x)=left{ begin{array}{ll} sqrt{phantom{-}x/3} left(I_{-1/3}(hat{x}) + I_{1/3}(hat{x})right) & mbox{if } x > 0 \<br /> \<br /> sqrt{-x/3} left(J_{-1/3}(hat{x}) - J_{1/3}(hat{x})right) & mbox{if } x < 0 end{array} right.

Here J is a “Bessel function of the first kind” and I is a “modified Bessel function of the first kind.” Also

hat{x}=frac{2}{3} left(sqrt{|x|}right)^3

To verify the equations above, and to show how to compute these functions in Python, here’s some code.

The SciPy function airy computes both functions, and their first derivatives, at once. I assume that’s because it doesn’t take much longer to compute all four functions than to compute one. The code for Ai2 and Bi2 below uses np.where instead of if... else so that it can operate on NumPy vectors all at once. You can plot Ai and  Ai2 and see that the two curves lie on top of each other. The same holds for Bi and  Bi2 .

from scipy.special import airy, jv, iv
from numpy import sqrt, where

def Ai(x):
    (ai, ai_prime, bi, bi_prime) = airy(x)
    return ai

def Bi(x):
    (ai, ai_prime, bi, bi_prime) = airy(x)
    return bi

def Ai2(x):
    third = 1.0/3.0
    hatx = 2*third*(abs(x))**1.5
    return where(x > 0,
        third*sqrt( x)*(iv(-third, hatx) - iv(third, hatx)),
        third*sqrt(-x)*(jv(-third, hatx) + jv(third, hatx)))

def Bi2(x):
    third = 1.0/3.0
    hatx = 2*third*(abs(x))**1.5
    return where(x > 0,
        sqrt( x/3.0)*(iv(-third, hatx) + iv(third, hatx)),
        sqrt(-x/3.0)*(jv(-third, hatx) - jv(third, hatx)))

There is a problem with Ai2 and Bi2: they return nan at 0. A more careful implementation would avoid this problem, but that’s not necessary since these functions are only for illustration. In practice, you’d simply use airy and it does the right thing at 0.

 

Programming Language Index – version 2013

A couple of years ago I had a look at the state of the TIOBE index that ranks the most popular programming languages.

So has C# finally dethrone C++ as THE language of the year? Or has LOLCODE and Brainfuck made it into the list? Well not quite, but an interesting thing is the uptake of Objective-C taking the third place! Of course an explanation can be found in the explosion of iOS apps that are developed with that language.

The usual suspects, i.e. C and Java are still at the top, followed by Objective-C and C++. It is interesting to note that they all share a very similar structure.

Position Jan 2013 Position
Jan 2012
Delta in Position Programming Language
1 2 C
2 1 Java
3 5 Objective-C
4 4 C++
5 3 C#
6 6 PHP
7 7 (Visual) Basic
8 8 Python
9 9 Perl
10 10 JavaScript
11 12 Ruby
12 24 Visual Basic .NET
13 13 Lisp
14 14 Pascal
15 11 Delphi/Object Pascal
16 17 Ada
17 23 MATLAB
18 20 Lua
19 21 Assembly
20 72 Bash

Languages in the other top ten are pretty good candidates and should not be too much of a surprise to see PHP, VB and Python there. Nice to see that languages like Pascal and Ada are still there in the top 20. But Bash? Really? How can we explain the move from 72nd to 20th?

And after that? Well, Fortran appears in place 25th… (I know!), COBOL and SQL are there and for those that have taken the R programming language to their hearts, it makes an appearance at the 26th place. An interesting addition is the appearance of the educational language Alice at the 50th place.

Position Programming Language Ratings
21 PL/SQL 0.585%
22 Transact-SQL 0.578%
23 SAS 0.571%
24 COBOL 0.496%
25 Fortran 0.462%
26 R 0.444%
27 Scheme 0.433%
28 ABAP 0.430%
29 Logo 0.389%
30 Prolog 0.359%
31 Erlang 0.334%
32 Haskell 0.331%
33 Scala 0.319%
34 Q 0.318%
35 D 0.296%
36 RPG (OS/400) 0.291%
37 Smalltalk 0.254%
38 Forth 0.239%
39 APL 0.235%
40 NXT-G 0.233%
41 ML 0.227%
42 Common Lisp 0.206%
43 ActionScript 0.195%
44 Awk 0.192%
45 F# 0.187%
46 Scratch 0.187%
47 PL/I 0.167%
48 LabVIEW 0.165%
49 Tcl 0.159%
50 Alice 0.158%

The most secret of messages… cracked!!

A model of the GCHQ headquarters in Cheltenham
A model of the GCHQ headquarters in Cheltenham (Photo credit: Wikipedia)

 

In a past post I mentioned the serendipitous discovery of an encrypted message attached to the leg of a pigeon. The message, from WWII, had eluded the experts at GCHQ and the contents of the message were therefore not known. Well, it seems that a Canadian citizen has managed to do the impossible and cracked the code. His name is Gord Young, and he has been quoted saying that it took him 17 minutes to decipher the code. How did he do it? Well, it seems that he was able to do it with the help of a code book inherited.

 

So what is the content of the most secret of messages? Mr Young says the note uses a simple World War I code to detail German troop positions in Normandy. Here are the alleged contents of the message:

 

  •  AOAKN – Artillery Observer At “K” Sector, Normandy
  •  HVPKD – Have Panzers Know Directions
  • FNFJW – Final Note [confirming] Found Jerry’s Whereabouts
  • DJHFP – Determined Jerry’s Headquarters Front Posts
  • CMPNW – Counter Measures [against] Panzers Not Working
  • PABLIZ – Panzer Attack – Blitz
  • KLDTS – Know [where] Local Dispatch Station
  • 27 / 1526 / 6 – June 27th, 1526 hours

 

Is this what the message say? Well, GCHQ is surely interested in talking to Mr Young about his work… What do you think?

 

Python for iOS

There used to be a time when computers came with tools for someone to start programming, something like a version of BASIC would get you started. That has changed to the point that some users cannot even imagine how to interact with their machines without a nice, eye-candy, even cumbersome graphical interface.
iPhones and iPads are indeed powerful devices, but in their wisdom Apple would not let you easily program them. Fortunately people are not easily convinced to drop it and recently I came across Python for iOS which is available in the AppStore. The application provides us with a simple Python interpreter that make it easy to use in these devices. The user needs to remember that the application does not create native apps, but the tool might be very handy in conjunction with a more advanced development tool. Also, it has the added bonus of allowing newcomers to start programming in devices that are largely seen to be purely as consumer ones as well as using a popular language.

Will you give it a go? Let me know what you think.

python_2 Python Python