Earlier start to the day! I even did some chores at home, and saw the sunrise. (It was slightly foggy and ethereally beautiful.)

The Coursera Algorithms course continues to walk that line where it’s alternately awesome and completely baffling. It’s fine. This is fine.

Started in on quicksort today — partitioning is so simple it seems like it shouldn’t be so cool. And yet.

I feel like I have a reasonably good grasp on comparing Big-O complexities of various algorithms (like O(n log n) vs. O(n)), but DETERMINING the Big-O complexity of a given algorithm is something that I understand only temporarily, and then it slips away again. (I only know that merge sort is O(n log n) because I accidentally memorized this.)

My mom (who teaches programming) says Big-O is one of those things that you get, and you are so relieved, and then you learn more and it becomes SO HARD, and then you learn more and things start to click again. I think I’m in the middle part.

Finally headed back to Concordia University’s library for afternoon studying. (Also picked up my community library card!!) Decided that panicking for several hours over not being able to build a database from super messy data is not productive, and also it makes sense that I do not yet know how to do something I haven’t done before. (Panicked about other things instead [work-related things].)

Instead of panicking about the database that isn’t (yet), I headed back to my little weather app. I added in some temperature stuff, which I’ve been way more interested in since the answer stopped being “it is hot all the time, except when it is DANGEROUSLY hot and the air is poison.”

I started a new branch to do something I’ve been excited about since well before I started this project — I’m going to gather together whatever information seems most relevant for bike commutes so I can call up that info with a single command. (Maybe I’ll fling it in a mobile app someday, so I can call it up with a single tap.)

How much will this rule? I’ll just get to be like, “yo computer, I’mma bike today,” and it will give me relevant data.

From my preliminary notes, here’s what I want to include, with aspirational stuff in italics:

• precipitation, if there’s going to be any today (later: narrow this to commute hours, so just morning/evening)
• humidity, if it’s high (also decide what constitutes “high”)
• temperature, if it’s “low” or “high” (decide what constitutes low/high) (later: only display if it’s very different from yesterday)
• visibility, if it is (or will be) pretty low at some point today
• sunset time, if it’ll be before I expect to head home
• apparent_temperature (Dark Sky defines this! how rad), if it’s very different from the actual temperature. I’m fairly certain I care more about THIS measure of temperature than the technically-true one, unless there’s a good reason to pay more attention to actual-temperature (like…danger?). Haven’t thought of one yet, though.

Maybe I’ll do something really weird like make up a “biking pleasantness score” or something so I can be all, “ugh, today’s gonna be a 12” or “oh wow, I just HAVE to bike today; it’s a 47!”