This year I am fortunate enough to be able to attend SciPy. SciPy is a Python conference focused on scientific programming. A big shout out to the Center for Open Science for making this trip a possibility. This will be the first of a (hopefully) daily blog series in which I will briefly cover how my day went and any lasting impressions it left me with.
The conference organizers made checking in quick and painless. We were served a breakfast buffet that was surprisingly good. Of paticular interest to me were the scrambed egg mini-bagels and lemon poppyseed bread slices — yum. Breakfast as followed by a series of tutorials which registrants chose in advance.
Tutorial One: Guide to Symbolic computing with SymPy — Ondřej Certik, Mateusz Paprocki, Aaron Meurer
The SymPy tutorial was … interesting. We simulataneously listened to the lecturer discuss and demonstrate common SymPy functions while completing examples in various IPython notebooks that they provided us with. It was fast paced, too fast for me anyway, and we had to skip over a lot of material.
The tutorial docs recommended experience with IPython and ‘basic mathematics.’ However, I was quite surprised how far my definition of basic mathematics was from theirs. Unfortunately, this left me struggling to keep up with the tutorial even from early on. After a mid-session break, we briefly covered Calculus functionality before being introduced to some real world applications. This is where I became utterly lost.
While the tutorial itself felt a bit unpolished, the instructors knew there stuff. All in all SymPy seems like a really interesting tool which I plan to use. When combined with IPython notebooks I believe it could create very powerful, long lasting notes for a variety of math intensive classes. I’ll be testing this out next semester in Physics.
Tutorial Two: IPython in depth — Fernando Perez, Brian Granger
Anyone who has listened to either Fernando or Brian could have told you that his tutorial was going to be good. It was. They provided a solid tutorial environment with IPython notebooks that kept me feeling like I was actively working with them throughout the entire tutorial. Whenever anyone had a question they knew how to answer them quickly and concisely.
A few things I found of paticular interest:
- IPython Notebook: If haven’t heard of this, click the link and check out. I’m not kidding. This is a versatile web tool that is incredibly powerful. For some cool examples of what people have done with notebook (including writing a book!) click here.
- Awesome help functionality: With IPython’s built in help functionality [ ?, ??, %quickref, and %magic ] you can quickly get a syntax highlighted help description, the source for a module, or even access a nifty quick reference guide mostly eliminating the need to pop out of a notebook or console and visit online docs.
- Kickass debugger: IPython’s shell is amazing. But, I’ve found myself using PyCharm for more advanced bit of code while debugging. After learning about IPython’s magic %debug and %run -d theprogram.py that may have changed. They provide you with very powerful and easy to use debugging abilities I wasn’t even aware existed.
Day one down. Time for sleep.