Skip to main content

A week as an intern at Enthought, India.

A week has gone by since I started my internship at Enthought, India and what a week it has been.

As I mentioned earlier, I have been going through the documentation of a few open source projects that Enthought works on and uses for their code base; traits, traitsui, chacoenvisage and jigna for example. I am not going to describe what they are because there are other people (i.e their authors) who have done it better. The documentation for traits, traitsui, chaco and envisage are very good places to start if one is interested in using the packages and understanding their capabilities.

While going through the examples and demos of these libraries, I found a couple of bugs with simple fixes, one in the chaco library demo which needed me to explicitly declare namespace in the init files and the second in envisage that needed me to explicitly specify file path to properly load image without throwing an error. Like I said, trivial fixes that didn't need me to iteratively dig through files looking for a fix. There are a couple of more bugs that I discovered but haven't yet been able to fix. I'll try reproducing the bugs again and look for a fix.

In the process of submitting pull requests (PRs) to fix the bugs, I figured out how to change the remote in a git repository, what the difference between the HTTPS and SSH way of accessing git repositories are and how to file a PR on github. I also learnt about a cool git command called "$ git stash" which stashes away changes in the current working directory and reverts it back to the previous commit. I wish I had known this earlier, life would've been a little easier. Now I need to get used to creating and switching between branches in a repository and get comfortable with the overall git workflow.

On the python side of things, I got to learn what eggs are. Answered in brief on this stackoverflow thread and in detail here, eggs are a way to package and distribute python packages. They are similar to .jar files used by the Java language. They are apparently superseded by Wheels but that's a different matter. I also learnt that there are code checkers, pylint, pychecker and pep8 for example, that go through one's code to check if it's compliant with Python's PEP8 and more.

I also learnt an interesting thing about licensing software. I've noticed that GitHub lets you choose between a bunch of licenses, differences between which eluded me. I learnt that if a software/library is released under a GPL license, then any work built on top of this library has to be open sourced. On the other hand, if a library is released under an MIT/BSD license, then one is free to create open source and propareitary work on top of said library.

I'm sure that i'm overlooking a lot of small things here, like how I'm memorizing vim commands, other than i, ESC, :w and :q, because I don't like working on IDEs and shortcuts that make my life easier on a Mac terminal prompt. I've slowly started creating a .bashrc file for that and I now need to add color to the prompt and change the prompt to reflect what I have in mind. I doubt I'll be able to write down everything I've learnt this week.

In conclusion, I look forward to another week of learning as much, if not more, on programming, on software development, on working environments and on python.

Popular posts from this blog

Animation using GNUPlot

Animation using GNUPlotI've been trying to create an animation depicting a quasar spectrum moving across the 5 SDSS pass bands with respect to redshift. It is important to visualise what emission lines are moving in and out of bands to be able to understand the color-redshift plots and the changes in it.
I've tried doing this using the animate function in matplotlib, python but i wasn't able to make it work - meaning i worked on it for a couple of days and then i gave up, not having found solutions for my problems on the internet.
And then i came across this site, where the gunn-peterson trough and the lyman alpha forest have been depicted - in a beautiful manner. And this got me interested in using js and d3 to do the animations and make it dynamic - using sliders etc.
In the meanwhile, i thought i'd look up and see if there was a way to create animations in gnuplot and whoopdedoo, what do i find but nirvana!

In the image, you see 5 static curves and one dynam…

Pandas download statistics, PyPI and Google BigQuery - Daily downloads and downloads by latest version

Inspired by this blog post :, I wanted to play around with Google BigQuery myself. And the blog post is pretty awesome because it has sample queries. I mix and matched the examples mentioned on the blog post, intent on answering two questions - 
1. How many people download the Pandas library on a daily basis? Actually, if you think about it, it's more of a question of how many times was the pandas library downloaded in a single day, because the same person could've downloaded multiple times. Or a bot could've.
This was just a fun first query/question.
2. What is the adoption rate of different versions of the Pandas library? You might have come across similar graphs which show the adoption rate of various versions of Windows.
Answering this question is actually important because the developers should have an idea of what the most popular versions are, see whether or not users are adopting new features/changes they provide…

Adaptive step size Runge-Kutta method

I am still trying to implement an adaptive step size RK routine. So far, I've been able to implement the step-halving method but not the RK-Fehlberg. I am not able to figure out how to increase the step size after reducing it initially.

To give some background on the topic, Runge-Kutta methods are used to solve ordinary differential equations, of any order. For example, in a first order differential equation, it uses the derivative of the function to predict what the function value at the next step should be. Euler's method is a rudimentary implementation of RK. Adaptive step size RK is changing the step size depending on how fastly or slowly the function is changing. If a function is rapidly rising or falling, it is in a region that we should sample carefully and therefore, we reduce the step size and if the rate of change of the function is small, we can increase the step size. I've been able to implement a way to reduce the step size depending on the rate of change of …