{"id":5699,"date":"2020-03-20T18:06:48","date_gmt":"2020-03-20T23:06:48","guid":{"rendered":"http:\/\/steve.cooleysekula.net\/blog\/?p=5699"},"modified":"2020-03-20T18:06:56","modified_gmt":"2020-03-20T23:06:56","slug":"what-i-learned-this-week","status":"publish","type":"post","link":"https:\/\/steve.cooleysekula.net\/blog\/2020\/03\/20\/what-i-learned-this-week\/","title":{"rendered":"What I learned this week"},"content":{"rendered":"\n

I have really thrown myself into physics, since I am stuck at home (a) because there is a pandemic and (b) because SMU won’t let me on campus until tomorrow (because I was abroad when they ended work-related international travel 2 weeks ago). This has been a grand opportunity. Here are some things I learned this week.<\/p>\n\n\n\n

UPROOT and UPROOT-METHODS<\/h2>\n\n\n\n

UPROOT is awesome. It lets me utilize natively in Python files created in ROOT<\/a>. No more do I need to have ROOT compiled in the background, along with its Python interfaces. I can just import UPROOT and load ROOT files into Python Pandas dataframes, which anyway are how I prefer analyzing data these days.<\/p>\n\n\n\n

UPROOT was introduced to me by my former PhD student Matthew Feickert and my current PhD student Chris Milke. I’ve been using it for several weeks to work on a project with one of my undergraduate research students. However, for high-level physics operations, like dealing with four-vector mathematics, ROOT is hard to beat. Turns out, there is a solution.<\/p>\n\n\n\n

UPROOT-methods<\/a>! These are implementations of interfaces akin to C++ classes in ROOT that do cool physics things… like vector arithmetic! I just learned about this today and already did some Lorentz Transformations on particle vectors. I’m pretty happy about this.<\/p>\n\n\n\n

MatPlotLib Subplot Gridding!<\/h2>\n\n\n\n

Sometimes you just want to layout a bunch of graphs in a single plot in a non-uniform way. Consider the following graph:<\/p>\n\n\n\n

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An analytic model is fitted to data from a muon detector at SMU. The top plot is the number of muon candidates vs. their measured lifetime. The bottom plot is the “model pull” – the difference in each bin of the lifetime measurement between the real counts and the fitted model prediction, divided by the standard deviation of the counts in the bin.<\/figcaption><\/figure>\n\n\n\n

I need to show the fit of an analytic model (an exponential lifetime model) to the data coming from a muon detector in the basement of Fondren Science Building at SMU; below that, I need to show how well the model describes the data after optimizing the model parameterization to reproduce the data. To do this, I need a big plot at the top and a short plot at the bottom. I needed plot grid layouts!<\/p>\n\n\n\n

I found this article extremely helpful: https:\/\/towardsdatascience.com\/subplots-in-matplotlib-a-guide-and-tool-for-planning-your-plots-7d63fa632857?gi=b00175d89216<\/a> (it’s how I made that figure above!)<\/p>\n\n\n\n

Online Teaching Tips<\/h2>\n\n\n\n

How do you mute all those jerks with hot mics in Zoom? WHY WON’T MY F**KING MAC LET ME SHARE MY DESKTOP?!?!?!<\/p>\n\n\n\n

Check out these tweets.<\/p>\n\n\n\n

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Online teaching tip: when using a video system to lecture or communicate, the 1st thing you should learn (besides how to connect to the system) is how to mute all the other users. Somebody will have a hot mic. Save them the embarrassment.#TheMoreYouKnow<\/a>

Thx @FirstGenPhys<\/a>! pic.twitter.com\/B34k8EM3P1<\/a><\/p>— Stephen Sekula (@drsekula) March 20, 2020<\/a><\/blockquote>