johnkeefe.net

The last two years were rough. And as 2021 ended, and a new coronavirus surge began, the outlook wasn't exactly sunny.

In an effort stay centered and battle the blues, I've turned again to my soothing practice: making things.

Back in 2015, I tried to make something every week for a year. I only averaged something every 1.7 weeks, but it was still successful fun.

So I'm doing it again for 2022.

Might be a gadget, might be a toy, might be a map, might be bread. I'll try to learn something new every time, and will share each thing here.

But make. Every week.

A 3D-printed flexi-dog

To kick things off I literally dusted off my 3D printer, which I had set aside when we got a pandemic puppy, and tried to remember how to use it.

Seemed appropriate to print a dog, so I found this Flexi Dog on Thingiverse. I downloaded the shape's .stl file, navigated PrusaSlicer to turn the object into printable slices ...

... and then used OctoPrint to actually send the dog to the printer.

It didn't work right away; the triangle at the end of the tail — in the foreground of the next picture — kept coming off the base plate during printing, leading to tangled messes. In the end, I warmed the plate an extra 5° Celsius, and that made it stick.

Several false starts and two hours of continuous printing later, I had my first "make" of 2022.

I like what's possible with triangles.

Playing with rectangular blinky grids is super fun, and I've made a weather monitor and a pulse-oximeter with those.

But there's something additionally awesome about the pattern possibilities with triangle pixels.

So when I saw a Hackaday post about building a clock display with LED triangles, I was hooked.

The short story is that I made it! It now lights up my living room with dazzling animations and a funky time display.

The longer story involves perseverance made possible by my coronavirus lockdown.

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There's something exciting about holding an object you previously only imagined — whether it's a freshly baked loaf, a tomato off a garden vine, or a printed plastic pumpkin.

I've had that feeling a lot lately, with a pandemic purchase of a 3D printer.

Rolling an object in your fingers that was previously just a digital file on the internet is ridiculously fun. It's even more rewarding if the thing conjured was something you — or your kid — dreamed up.

That's what happened with this 3D pumpkin. My daughter drew it late one night for an animation class assignment using the program she was learning, Cinema 4D.

And then we made it real.

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I've been interested in how voter turnout might affect the 2020 US election and I've wanted to play with Observable notebooks.

So I blended the two projects, and you can play with my live Observable notebook that does those calculations.

The result is an admittedly super-simplistic model of how things might turn out. But you can increase the percentage of Republican and Democratic voters nationwide and see what happens!

Notably, even if Democrats were able to boost turnout more than Republicans — say 107% vs 106% — Trump still wins.

As written, it doesn't consider nuances such as regional differences in voting turnouts, swing voters, or faithless electors. (It does, however, account for the unique ways Maine and Nebraska divide their electoral votes). But I learned a lot in the process ... and there's more to come.

All my calculations are visible in the Observable notebook itself, and the initial data prep is documented in a Github repository. For good measure, I put all the raw data in my Datasette library.

Minneapolis police report stops and other incidents by neighborhood, so I decided to calculate the racial makeup of those neighborhoods to make some comparisons — along the lines of what I've already done for New York, Chicago, and Washington, DC.

This time, though, I'm using Datasette.

I've seen creator Simon Willison tweet about Datasette, and with some extra time on my hands I took a look. It's so impressive!

With Datasette, one can publish data online easily, efficiently (even free!) and in a way that allows others to explore the data themselves using SQL and feed data visualizations and apps. At scale.

How is this not in every newsroom?

(Simon, by the way, has offered to help any newsroom interested in using Datasette — an offer I hope to take him up on someday.)

Minneapolis neighborhoods

Once again, I've married US Census blocks with other municipal zones, this time the official neighborhood map of Minneapolis.

That data is now online, served up with Datasette.

And with some nifty SQL queries, bookmarked as simple links, I can list the race and ethnic makeup of every neighborhood by raw number.

Or by percentage.

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It's become a coronavirus cliché, but for this week's #MakeEveryWeek I made sourdough bread. 

The twist: I made one loaf in the oven and one in a slow cooker.

It all started with sourdough starter, specifically this guide from Quartz colleague Tim McDonnell. This was a great project for my teens, incorporating chemistry, biology, and excellent smells.

Next was this incredibly fun and detailed sourdough recipe from Kitchn, which makes two loaves and relies on two oven-safe pots. Alas, our family has but one.

We do have a slow cooker, though. Could I make one of the loaves in that? The answer is yes!

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At-home pulse oximeters, those fingertip devices doctors use to measure the oxygen saturation in your blood, have been selling out everywhere thanks to the Covid-19 pandemic.

But as my Quartz colleague Amirta Khalid points out in this great article, most people don't need 'em. If your oxygen level is worryingly low, you'll know — you don't need a machine to tell you. Folks with some existing conditions, however, can use a pulse oximeter to help a remote doctor monitor their vitals or to adjust supplementary oxygen devices.

When Khalid mentioned she was working the story, it reminded me of the DIY "pulse ox" sensor Sparkfun sells. It, like other pulse oximeters, shines light into the skin and makes measurements based on how that light is absorbed. I've built heartbeat-driven projects before and had been exploring new ways to monitor pulse rates. So I got one.

Sparkfun warns in red letters that "this device is not intended to diagnose or treat any conditions," and I offer the same caution if you're tempted to build one. The process wasn't hard at all. I got it running quickly ... and then added an LED display for fun and flourish.

Here's how I made it, and the code, too.

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I'm incredibly lucky to be both healthy and able to work from home during this coronavirus crisis. That means I spend large chunks of my day on video calls.

As a courtesy to my family, all of whom are also working and schooling from home, I've tried to warn them when they risk being broadcast to my colleagues. 

Now I have a fun "on air" light to help! And I've put the code online so you can make one, too.

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Buy a new aquarium, and you often get hood lights that are ... meh. They're good enough, but not great.

There are plenty of high-quality replacement lights out there, but none of them had the nice, low profile of the plastic covers that came with this tank. So I decided to spruce up the existing illumination with some DYI lights — and even make them programmable with an Arduino.

That was more than a year ago. Now in coronavirus isolation, I finally made it happen.

Here's how.

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I'm working on a new neural network that tries to predict an outcome – true or false – based on 65 different variables in a table.

The tabular model I made with fast.ai is somewhat accurate at making those predictions (it's a small data set of just 5,000 rows). But to me even more interesting is determining which of the 65 features matter most. 

I knew calculating this "feature importance" was possible with random forests, but could I do it with neural nets?

It turns out I can. The trick is, essentially, to try the model without each feature. The degree to which the model gets worse with that feature missing indicates its importance – or lack of importance.

This blog post describes how to run this test, and this adaptation worked perfectly in my fast.ai notebook. Here's the code in a Gist:

Unfortunately, because my project uses internal Quartz analytics, I can't share the data or the charts I'm playing with. But with the code above, I can now "see into" the neural network and get cool insights about what's going on