APL.jl VS PDM

2. ipython repl

1. pairs with jaimebuelta's artistic vs engineering dichotomy, but also plays into the scientist wearing many more hats than just programmer. Code can be two or more degrees removed from the published paper -- code isn't the passion. There isn't reason, time, or motivation to think deeply about syntax.

2. For a lot of academic work, the programming language is primarily an interface to an advanced plotting calculator. Or at least that's how I think about the popularity of SPSS and Stata. Ipython and then jupyter made this easy for python.

For what it's worth, the lab I work for is mostly using shell, R, matlab, and tiny bit of python. For numerical analysis, I like R the best. It has a leg up on the interactive interface and feels more flexible than the other two. R also has better stats libraries. But when we need to interact with external services or file formats, python is the place to look (why PyPI beat out CPAN is similar question).

Total aside: Perl's built in regexp syntax is amazing and a thing I reach for often, but regular expressions as a DSL are supported almost everywhere (like using languages other than shell to launch programs and pipes -- totally find but misses all the ergonomics of using the right tool for the job). It'd love to explore APL as an analogous numerical DSL across scripting languages. APL.jl [0] and, less practically april[1], are exciting.

[0] https://github.com/shashi/APL.jl

APL.jl might be of interest to you.

# See https://pre-commit.com for more information # See https://pre-commit.com/hooks.html for more hooks repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v3.2.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml - id: check-toml - id: check-added-large-files - repo: local hooks: - id: tox lint name: tox-validation entry: pdm run tox -e test,lint language: system files: ^src\/.+py$|pyproject.toml|^tests\/.+py$ types_or: [python, toml] pass_filenames: false - id: tox docs name: tox-docs language: system entry: pdm run tox -e docs types_or: [python, rst, toml] files: ^src\/.+py$|pyproject.toml|^docs\/ pass_filenames: false - repo: https://github.com/pdm-project/pdm rev: 2.10.4 # a PDM release exposing the hook hooks: - id: pdm-lock-check - repo: https://github.com/jumanjihouse/pre-commit-hooks rev: 3.0.0 hooks: - id: markdownlint

PDM is a solution that allows for easy creation and management of python projects. Some of the key features that will improve the management of python projects include:

You'd need a proper dependencies management tool like PDM or Poetry to exhaustively resolve and lock down all the transitive dependencies if you want to have anything closed to reproducible build.

Are you working on a Python project that uses pdm for dependency management and dotenv for local environment variable and secrets management? Do you find it frustrating when CLI tools like pgcli don't automatically pick up your .env file, forcing you to resort to npm install -g dotenv-cli? I've got a more convenient solution for you!

I first learned about PDM from a blog post written by one of the PDM contributers. The post was about OOPifying argparse to allow for easy creation/modification of subcommands that exist as their own classes/files, and to avoid maintaining a single long script with an endless number of subparser.add_argument(...) lines.

That's more or less what PEP 582 plans to do, but it's been stalled and mired in discussions for years. The PDM tool went ahead and implemented it though if you want to use it: https://github.com/pdm-project/pdm

pdm – A modern Python package and dependency manager

pdm and PEP 582 enter the chat

I'm liking PDM for a while now. Quicker than Poetry and built according to the Python package spec in mind and not as an afterthought. While it was originally meant to work with PEP 582, it works with virtual environments too (now default).

https://github.com/pdm-project/pdm