snakemake VS pydantic-cli

Last year, my pull requests were on my own projects: two to for my Apple Watch telemetry recording app, one was for a workout Watch app, and the third was for the research project I was working on at the moment (this paper has since been published, "The origins and genetic interactions of KRAS mutations are allele- and tissue-specific" and the code is open source). This year, in order to enhance my learning, I challenged myself to contribute to others' projects. In my opinion, I have been quite successful with contributions to Fig, tldr, snakemake, and pymc3-examples. In addition, I have also taken up the challenge of learning about Appwrite, one of the sponsors of this year's Hacktoberfest, and producing educational content about the service. (With some encouragement by the offer of free stickers 🙃) I found this a great opportunity to learn about an essential world of programming that I had yet to deal with: backend services.

If you’re into data science, have you taken a look at Snakemake? I came across this quite recently, and now, I’m wondering why I hadn’t used it before (it really would’ve come in handy to automate a few data-wrangling processes).

A comparison with https://github.com/mpkocher/pydantic-cli is welcome.

pydantic-cli

Also, the new type annotations/hints feature added to Python is an extremely useful mechanism to clearly communicate interfaces when leveraging functions as first class citizens. This can dramatically help readability and maintainability. Here's an example library designed leveraging functional centric concepts + type annotations.

Depending on the strictness level of adoption of mypy, this can sometimes (often?) requires changing how you write code. You can see some contortions here. However, having strict type analysis enables catching a lot of issues.

https://github.com/mpkocher/pydantic-cli#other-related-tools