Preprepared or easy food

• Pfeffernusse biscuits
  
• Turkish bread, most white bread
  
• regular hummous (not spicy or fancy) eg Black Swan or Chris's brand from the supermarket
  
• unflavoured rice and corn thins
  
• most seaweed flavour rice crackers
  
• Boiled eggs
  
• Chopped vegetables (not red capsicum, but I can always just not eat it if it's there)
  
• non-acidic fruit: melons, grapes, banana, pear, paw paw, lychee/rambutan, loquat, custard apple. Ok in small amounts: red apples, berries, cherries
  
• Marshmallows
  
• Mondo brand nougat
  
• nuts and dried fruit (not pine nuts, nothing too acidic)
  

http://hourann.com/blog/2009/11/08/and-now-for-a-message-about-puppy-poo

http://hourann.com/?p=654

Continuing with the dog theme: this cute little sign is in a park in Boston that was created by the Big Dig (an über-expensive project to put the city centre’s main freeway underground).

class ExampleObserver(dbus.service.Object):
    ...

    @dbus.service.method(dbus_interface=telepathy.interfaces.CLIENT_OBSERVER,
                         in_signature='ooa(oa{sv})oaoa{sv}',
                         out_signature='')
    def ObserveChannels(self, account, connection, channels, dispatch_operation,
                        requests_satisfied, observer_info):
        ...

class ExampleObserver(dbus.service.Object):
    ...

    @telepathy.decorators.Telepathy.Client.Observer.ObserveChannels
    def ObserveChannels(self, account, connection, channels, dispatch_operation,
                        requests_satisfied, observer_info):
        ...

class Decorators(object):
    methods = {
        'org.freedesktop.DBus.Properties.GetAll': [ 's', 'a{sv}' ],
        'org.freedesktop.DBus.Properties.Get': [ 'ss', 'v' ],
        'org.freedesktop.Telepathy.Client.Observer.ObserveChannels': [ 'ooa(oa{sv})oaoa{sv}', '' ],
    }

    def __init__(self, namespace):
        self._namespace = namespace

    def __getattr__(self, key):
        return Decorators('%s.%s' % (self._namespace, key))

    def __call__(self, func):
        iface = self._namespace.rsplit('.', 1)[0]
        in_sig, out_sig = self.methods[self._namespace]
        return dbus.service.method(dbus_interface=iface,
                                   in_signature=in_sig,
                                   out_signature=out_sig)(func)

    def __str__(self):
        return self._namespace

decorators = Decorators('org.freedesktop')

Announcing Armada, a system for accessing and playing about with Google Transit Feed Specification datasets.

At the moment Armada will:

• import a google transit feed to a Django (actually, geodjango) database; see “load.py”
• generate maps from that (see “map.py”)
• show you stop timetables and other very basic information, via the webserver (see “python manage.py runserver”)

I’m really keen for anyone interested to collaborate with me on this project. I’ve been very reticent in the past to work with others on spare-time projects, probably just out of pointless fear of criticism. So please, go grab the code (it’s in a Mercurial repository on google code), and if you want push access to the project let me know.

There’s pretty well no docs, so if you get stuck let me know :)

A little sketch of what I plan to add next:

• a system to create JSON ‘documents’ from the database. As an example, a JSON file with all necessary source data to generate a stop timetable off-line. This document will then be shoved into a CouchDB instance for quick lookup. Then people can write little apps which grab the JSON and generate the timetable. A reference app will be provided for each document class, written in Javascript.
• add map generation to the route view in the web interface. The map code is done, just a matter of glueing code together.
• add route timetables to the route view in the web interface
• add searching to the web interface
• query ‘stops near me’, given a point on the earth. This should be easy using Geodjango / PostGIS.
• possibly routing. Google do a good job of this already though.
• mashups! let’s overlay this data with openstreetmap. Let’s look at whether public transport is better or worse depending on socio-economic data in an area. Where are the best served areas, where are the worst? How many buses, trains, etc are on the road at a given time in the day. Plot, chart, … ! :)

There are a lot of datasets available, including data for Perth and Geraldton. I’ve only tested the PTA data with Armada, so there may be unexpected issues loading other feeds. Should be fine though. None of the fare / charging stuff is implemented, because I just didn’t care about it :)