All problems have simple, easy-to-understand, logical wrong answers.
Subclassing in Python is no exception. Avoid the common pitfalls
and learn everything you need to know about how subclass in Python.
Mobile apps are the hot item of the day -- and the best mobile apps are backed by a great website. Python web developer Nate Aune and iPhone developer Anna Callahan will show you how we built a simple music web app in Django with a native iPhone app that communicates with it. Attendees of this talk will see a concrete case study of building an application that exposes an API for mobile devices.
Our web app exposes a JSON API for sending and receiving data from the mobile device. We’ll talk about why we chose Django and the TastyPie API package, and discuss other Python-based frameworks that could be used to build the API such as Pyramid, Flask and Bottle. We’ll also compare REST and custom APIs to understand best practices for building APIs designed for mobile devices.
In this talk I'll describe our successful experience in introducing Python
into a system for blood collection tube labeling in laboratory and hospital
environments, based on IHE Technical Frameworks –the industry standard
for modeling and streamlining healthcare processes– and designed to avoid
human errors and ensure process traceability.
During the talk I will explain why we chose Python in the first place,
how we've been able to leverage the language's features and
characteristics for our specific field and what problems and limitations
I will show specific instances -showing code examples too– of Python
usage in different parts of the project, including a low-level driver
for laboratory automation machinery, an asynchronous messaging module,
the implementation of IHE-compliant actors and the inevitable end-user
web application, implemented with Django.
Using Python greatly helped us in building our system, allowing
very rapid prototyping cycles for both hardware and software, but during
the talk I'll also point out what we found was missing, and what
would be nice to have to ensure Python has its proper place as a viable
platform for designing streamlined healthcare workflows
based on established international standards.
by Claude Gilbert
Python is a great language for writing programming frameworks. Python frameworks are normally addressed to software developers who are Python professionals. I developed a software package in a scientific institution, designed to be used by non-programmers, but also designed to enable customisation through programming by some users. I finally designed a three level package:
One of the challenges was to offer an application with an easy to use interface, not graphical, not web-based and not requiring Python programming. This interface was necessary for batch processing.
This talk addresses how this project was carried out, the technical solutions adopted and how Python was introduced in an operational scientific institution (http://www.ecmwf.int) where most users were Fortran programmers. Python was introduced as early as 2004 and it was a challenge to gain acceptance. I will also make a parallel with a project I am currently working on for NASA (http://gmao.gsfc.nasa.gov/).
*Desperately trying to forget technical details* summarises how I tried, using Python, to help Meteorology scientists to focus on their domain of expertise instead of constantly solving technical problems.
The disciplines of Meteorology and Climate involve numerical modelling of physical phenomena. The amount of data going in and out of the model is considerable. The organisation and the storage of data is complicated, their post-processing is a challenge. Scientists need to access and process input and output data to monitor the trends of the input data and to evaluate the performance of their models. Those statistics, diagnostics, plots and verifications are crucial to the improvement of the quality of the models. Finding the right data, decoding it, transforming it to be ready for use are necessary steps to initiate the pre-processing. All these actions are fundamentally the same between different prediction centres, but the data organisation and file formats can differ.
The London Python Code Dojo is a community organised monthly meeting for Python programmers in the UK. Variously described as social coding, developer training, "Scrapheap Challenge" for Pythonistas and "I didn't learn coding like this when I was a lad", we've forked the traditional code-dojo format and turned it into something very different.
This talk will explain and explore what happens in the dojo, how it's organised and why various changes were made to the classic dojo format. Reference will also be made to influences from music education and philosophy of education.
Hopefully, by the end of the talk you'll all want to go organise a dojo!
20th–26th June 2011