By: jkrumbiegel.com

Re-posted from: https://jkrumbiegel.com/pages/2021-05-20-reading-data-from-web/index.html

Recently, I had to read in a dataset from Hillenbrand (1995), published as an annotated csv-like file on a website. The dataset describes formant frequencies of several vowel utterances from different speakers. I thought I ended up with a pretty slick implementation showing off some of the tools available in the Julia data science ecosystem.

By: jkrumbiegel.com

Re-posted from: https://jkrumbiegel.com/pages/2021-05-20-reading-data-from-web/

Recently, I had to read in a dataset from Hillenbrand (1995), published as an annotated csv-like file on a website. The dataset describes formant frequencies of several vowel utterances from different speakers. I thought I ended up with a pretty slick implementation showing off some of the tools available in the Julia data science ecosystem.

By: jkrumbiegel.com

Re-posted from: https://jkrumbiegel.com/pages/2020-10-31-tuples-and-vectors/index.html

If you’re new to Julia, here is a scenario that might have tripped you up already: Let’s define two points. Both are just a collection of two floating point numbers. But one is a Vector, written with the [] syntax, and one a Tuple, written with the () syntax. Then we make vectors of both types of points and run a short computation. Let’s see what the performance difference looks like.