jake-low

Joined 10 years ago

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531
Submissions
114
Member since
Feb 14
[ my public key: https://keybase.io/jakelow; my proof: https://keybase.io/jakelow/sigs/m1D79qyEwJ_5gwQC1bV2rHMIeMofOzCiAGSL9hSlM1c ]
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Recent submissions

Comment · 2 years ago
Comment · 2 years ago
Hi Raph! I'm the author of the blog post. I actually read your PhD thesis when I was working on this project and trying to wrap my head around splines. It was a huge help to me in understanding the landscape of the field and how to think about and compare different classes of splines. Just wanted to say thanks!
Comment · 3 years ago
Comment · 3 years ago
Comment · 5 years ago
Comment · 6 years ago
Comment · 6 years ago
This is phenomenal. I've been scraping the data from primary sources for just Washington state for the past week [0], in order to make this chart which I hacked together last weekend [1].

[0]: https://github.com/jake-low/covid-19-wa-data

[1]: https://observablehq.com/@jake-low/covid-19-in-washington-st...

Doing this for just one state was a pretty substantial effort. I imagine there are multiple people at the Times who are spending several hours a day reviewing and cleaning scraped data (seems every couple of days some formatting change breaks your scripts, or a source publishes data that later needs to be retracted).

The Times dataset appears to contain per-county case and death observations in a time series, going all the way back to the first confirmed U.S. case in January in Snohomish County, WA. This makes it by far the most comprehensive time series dataset of U.S. COVID-19 cases publicly available.

Some people in this thread linked to the Johns Hopkins CSSE dataset; I've looked at this data but it doesn't go back very far in time for the U.S., and the tables are published as daily summaries with differing table schemas which makes them hard to use out of the box. For some days earlier in March, "sublocations" aren't even structured (for example the same column contains, "Boston, MA" and "Los Angeles County", making it very hard to use). No disrespect to the team behind the JHU dataset; it attempts to cover the whole world since the outbreak began which is an incredible and difficult goal. But for mapping and studying the outbreak in the U.S., the Times dataset will likely be the best choice right now.

Huge kudos to the New York Times team for making this data freely available.

Comment · 6 years ago
This is pretty neat. A week or so ago when "MathJax turns 3.0" was on the frontpage [0], I followed a link to this very thoughtful comment [1] from 2019 by svat about the landscape of math typesetting on the web. Since then I've been thinking a little bit about MathML and how compilers from other more input-friendly formats to MathML might encourage wider adoption. So I'm excited to see this library.

I've heard of AsciiMath before but hadn't ever had a chance to play with it, so I appreciated the live demo on the project website. One thing I noticed is that AsciiMath uses whitespace around operators to determine binding order.

For example (AsciiMath on left, TeX on right)

    f(x) = x+1 / 2    ≡    f(x) = \frac{x + 1}{2}
    f(x) = x + 1/2    ≡    f(x) = x + \frac{1}{2}
When there's equal spacing around neighboring operators, normal order of operations seems to apply.

This seems fairly intuitive, but might not scale to large, complex equations as well as TeX's bracket syntax does. Nonetheless, I think I like it for simple equations.

Question for the author: you mention on the page that Mathup supports an input language inspired by AsciiMath [2]. What are the differences? Does Mathup accept a subset of AsciiMath, or are there cases where the syntax diverges? And if the latter is the case, what motivated devaiting from AsciiMath's syntax?

[0]: https://news.ycombinator.com/item?id=22582343

[1]: https://news.ycombinator.com/item?id=19347737

[2]: There's a typo in this phrase on your page, BTW: "insipired" has an extra "i"

Comment · 6 years ago
This looks like a great new introduction to D3. It uses Observable as an interactive playground for D3 example code, which I think is an ideal environment for teaching concepts like these. I hope this will help more folks get started in visualization.

If anyone hasn't given Observable a try yet, I highly encourage you to do so. It's more than just "IPython/Jupyter but for JavaScript" — it's reactive, like a spreadsheet. You define "cells" of JS code that are automatically re-evaluated whenever one of their dependencies (another cell) changes.

It's an amazingly productive programming environment. These days, most of my JavaScript projects start off as Observable notebooks because it's such a great platform for experimentation. And I find myself wishing for Observable's features whenever I'm developing in other languages.

I know reactive programming is nothing new, but Observable has been my first opportunity to use it regularly, by "backporting" reactivity into one of the languages I work in most often. Being able to use it for prototyping and visualization for the past couple years has given me a really warm, fuzzy, "what-a-time-to-be-alive" feeling.

Comment · 6 years ago
Comment · 6 years ago

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