preplish VS root

> what's wrong with that?

Why nothing at all, of course. A REPL need not be more than a way to test and explore syntax, functions, and logical structures.

> the user experience is REPL-ish and it can help some people learn the _basics_ of the language

PREPLISH exists for Perl ^_^

https://github.com/viviparous/preplish

If this is for testing of syntax or of trivial code, it sounds like a good use-case for running a local REPL. (Example: https://github.com/viviparous/preplish)

This is a simple REPL project and the readme lists other Perl REPLs.

https://github.com/viviparous/preplish

Perl's concise syntax makes working in a REPL a pleasure. Python has a REPL but the design of the language makes it expand both in length (for loops) and in width (tabs).

I am a recent convert to working in a REPL first to test programming ideas.

I made this solution for some of my simple data wrangling: https://github.com/viviparous/preplish

I tried the module it in a Perl REPL (https://github.com/viviparous/preplish) and got the following ravings that are worthy of a US loony politician:

I agree with you that the immediate start-up and feedback is a great benefit to the coder. This is why I dislike complex, Rube-Goldbergian REPL systems.

There is a use-case for a throw-away interaction with a REPL. For example, how does $builtinFuncX work, or how would $data best be imported into a structure?

A REPL can also be a good initial approach to a more ambitious problem. In this case, a REPL can be good for focus and discipline.

If the second case is going to answer your concern and be constructive, it's necessary to be able to build the code for sharing and cleanly export the code for re-use.

I've had success tackling challenges using REPLs for Python and Perl [1] in both ways. But no tooling is going to solve the problem of a sloppy teammate who claims success just because "it compiles" and "it works on my box". A person who knows how to build good tooling goes further.

[1] https://github.com/viviparous/preplish

It is Jupyter is a Rube-Goldbergian nightmare. Python is a memory hog. There are better solutions, to be sure.

A simple REPL is all that's needed to both do A-type and B-type data exploration. (I won't use the term "data scientist", it's an exaggeration in most cases.)

Python has a REPL, R has a REPL, Perl has PDL and both a simple REPL (https://github.com/viviparous/preplish) and a more complex one (https://metacpan.org/pod/Reply).

Jupyter should not be used as an IDE because it is the wrong tool for development. A-type data explorers just want a painless UI and may not care much about the horrible agglutination of incomplete/slow/broken solutions that Jupyter represents.

I think the process of data acquisition isn't so clear-cut. Take CERN as an example: they release loads of data from various experiments under the CC0 license [1]. This isn't just a few small datasets for classroom use; we're talking big-league data, like the entire first run data from LHCb [2].

On their portal, they don't just dump the data and leave you to it. They've got guides on analysis and the necessary tools (mostly open source stuff like ROOT [3] and even VMs). This means anyone can dive in. You could potentially discover something new or build on existing experiment analyses. This setup, with open data and tools, ticks the boxes for reproducibility. But does it mean people need to recreate the data themselves?

Ideally, yeah, but realistically, while you could theoretically rebuild the LHC (since most technical details are public), it would take an army of skilled people, billions of dollars, and years to do it.

This contrasts with open source models, where you can retrain models using data to get the weights. But getting hold of the data and the cost to reproduce the weights is usually prohibitive. I get that CERN's approach might seem to counter this, but remember, they're not releasing raw data (which is mostly noise), but a more refined version. Try downloading several petabytes of raw data if not; good luck with that. But for training something like a LLM, you might need the whole dataset, which in many cases have its own problems with copyrights…etc.

[1] https://opendata.cern.ch/docs/terms-of-use

[2] https://opendata.cern.ch/docs/lhcb-releases-entire-run1-data...

[3] https://root.cern/

The odd part is that this is not just for fun. For many physicists when I was at CERN, a C++ REPL was a commonly used tool to interactively debug analyses to such a degree that many never compiled their code. Back then, I believe, it was some custom implementation included in ROOT (https://root.cern/). I even went out of my way to write C++ code compatible to it just so it could run with this implementation, otherwise some colleagues weren't interested in collaborating at all.

That Python ML code is calling C++ code running in the GPU, one more reason to use C++ across the whole stack.

CERN already used prototyping in C++, with ROOT and CINT, 20 years ago.

https://root.cern/

Nowadays it is even usable from Netbooks via Xeus.

It is more a matter of lack of exposure to C++ interpreters than anything else.

Quantum computing is a pretty interesting science too. https://home.cern/news/press-release/knowledge-sharing/cern-quantum-technology-initiative-unveils-strategic-roadmap they have to deal with lots of data streaming too https://root.cern/

I'm a user of CERN's ROOT (https://root.cern/) and while I'd usually write in C++, I've been trying to write as much Python as I can recently to get a bit better in the language.

I'm dealing with a C++ software called ROOT made by CERN, which is, if I'm not wrong, the only C++ API that we could use for data analysis such as plotting histograms, fitting multi-parameter functions and storing data in the size of TB to the disk and many more. That's the only reason why physicists still stick to this software. you can check here .

Hey, I found something which could be useful: https://root.cern