No real new material today, as I'm still in the thick of it. A few updates:
The Bayesian analysis of starcraft data is a bit stuck. I've been trying to figure out adding time dependence by using gaussian processes. My first real report note from that is... They're difficult. I mean, they're awesome and pretty simple conceptually, but picking priors for them is tough, at least for me and at least for now. I do have some reading to do, there.
There are also a few more projects I want to do in that space. Adding maps to the split race model is the easiest on that part, but I feel like I don't yet know enough about model comparison to competently do that. I'm working on amending that, though.
My master's thesis is slowly seeing some progress! I've used
hypothesis for testing some of the more
dubious parts of code and it's helped me find a good number of bugs. I might
have more on that to report on in the near future.
We're going to be releasing PlasmaPy 0.4 soon ish! We're opting for more time based regular updates rather than based on cool new features, which are in the works, but not yet for this release. There's been a bunch of new stuff since January, though!
In procrastinatory news, I've been meaning to contribute back to the Starcraft 2 Protoss community a little more, and I figured I'd launch a website to aggregate current quality educational content in an open and peer reviewed manner. I'd link it here, but I'm going to be changing its URL and name - I initially wanted it to be tightly coupled to the r/AllThingsProtoss subreddit and Discord, but... let's just say not everyone is as much of a fan of open source culture as I am, so the website is going to be standalone. I'll still need to work on gathering reviewers and contributors.
Julia the programming language is really, really, really cool. I've been thinking about it more since (e)attending a seminar by Alan Edelman and, if I wasn't flooded by projects already, I'd be writing something up for this place already. For now, three recommendations:
- This anecdote from the seminar is amazing and really stuck with me:
- This example from a great talk on Julia is pretty darn cool:
- And this write-up on comparing different ODE solver packages has made me give up on using Python for that. I've been trying to use scipy's odeint to efficiently solve stuff for a while now; but one thing I hadn't considered is that defining derivatives through Python functions and then having your solver call them the myriad of times required to integrate an ODE might... not be the best idea. PV has in fact called it the worst case scenario for python, and when you think about it, it makes sense, doesn't it?
As a bonus,
diffeqpy is a workaround for
that python problem! So I'm not abandoning Python as a high level language, but
the numerics I'll probably do in Julia from now on.
And that's all this week; thanks for reading!