Data Driven Documentation

The core idea behind the amazing javascript library D3 is about creating data driven documents. The lead developer is a technical wizard at New York Times, creating amazing interactive visualisations for breaking news stories.

Some of the most recent visualisations he has done:

I've been using the d3 library at work for a number of projects over the last year. From real time monitoring of test equipment, plotting and interacting with simulations, to creating a SVG model of a wheelchair remote, interacting with it using your finger on an iPad. I did something similar after returning from KiwiPyCon last year with a bluetooth remote controlled car - driven from a basic d3 powered website! Obviously its not just a nice javascript library that has allowed all this - Websockets and some server side Python has also been instrumental for me.

I've also been using d3 to make mock up interfaces for our tools, and to create dashboards to summarize changing data.

Although I can't use it here on blogger.com I recommend you take a look at it: d3js.org

Driveby contribution to Python Cryptography

While at PyConAU 2016 I attended the Monday sprints and spent some time looking at a proposed feature I hoped would soon be part of cryptography . As most readers of this blog will know, cryptography is a very respected project within the Python ecosystem and it was an interesting experience to see how such a prominent open source project handles contributions and reviews. The feature in question is the Diffie-Hellman Key Exchange algorithm used in many cryptography applications. Diffie-Helman Key Exchange is a way of generating a shared secret between two parties where the secret can't be determined by an eavesdropper observing the communication. DHE is extremely common - it is one of the primary methods used to provide "perfect forward secrecy" every time you initiate a TLS connection to an HTTPS website. Mathematically it is extremely elegant and the inventors were the recipients of the 2015 Turing award . I wanted to write about this particular contribution becau...

Matplotlib in Django

The official django tutorial is very good, it stops short of displaying data with matplotlib - which could be very handy for dsp or automated testing. This is an extension to the tutorial. So first you must do the official tutorial! Complete the tutorial (as of writing this up to part 4). Adding an image to a view To start with we will take a static image from the hard drive and display it on the polls index page. Usually if it really is a static image this would be managed by the webserver eg apache. For introduction purposes we will get django to serve the static image. To do this we first need to change the template. Change the template At the moment poll_list.html probably looks something like this: <h1>Django test app - Polls</h1> {% if object_list %} <ul> {% for object in object_list %} <li><a href="/polls/{{object.id}}">{{ object.question }}</a></li> {% endfor %} </ul> {% else %} <p>No polls...

Python and Gmail with IMAP

Today I had to automatically access my Gmail inbox from Python. I needed the ability to get an unread email count, the subjects of those unread emails and then download them. I found a Gmail.py library on sourceforge, but it actually opened the normal gmail webpage and site scraped the info. I wanted something much faster, luckily gmail can now be accessed with both pop and imap. After a tiny amount of research I decided imap was the better albiet slightly more difficult protocol. Enabling imap in gmail is straight forward, it was under labs. The address for gmail's imap server is: imap.gmail.com:993 Python has a library module called imaplib , we will make heavy use of that to access our emails. I'm going to assume that we have already defined two globals - username and password. To connect and login to the gmail server and select the inbox we can do: import imaplib imap_server = imaplib . IMAP4_SSL ( "imap.gmail.com" , 993 ) imap_server . login ( use...

Bits of py

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