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

Bluetooth with Python 3.3

Since about version 3.3 Python supports Bluetooth sockets natively. To put this to the test I got hold of an iRacer from sparkfun . To send to New Zealand the cost was $60. The toy has an on-board Bluetooth radio that supports the RFCOMM transport protocol. The drive protocol is dead easy, you send single byte instructions when a direction or speed change is required. The bytes are broken into two nibbles: 0xXY where X is the direction and Y is the speed. For example the byte 0x16 means forwards at mid-speed. I was surprised to note the car continues carrying out the last given demand! I let pairing get dealt with by the operating system. The code to create a Car object that is drivable over Bluetooth is very straight forward in pure Python: import socket import time class BluetoothCar : def __init__ ( self , mac_address = "00:12:05:09:98:36" ): self . socket = socket . socket ( socket . AF_BLUETOOTH , socket . SOCK_STREAM , socket .

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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