Random insight of the night: every couple years, someone stands up and bemoans the fact that programming is still primarily done through the medium of text. And surely with all the power of modern graphical systems there must be a better way. But consider:

* the most powerful tool we have as humans for handling abstract concepts is language
* our brains have several hundred millenia of optimizations for processing language
* we have about 5 millenia of experimenting with ways to represent language outside our heads, using media (paper, parchment, clay, cave walls) that don't prejudice any particular form of representation at least in two dimensions
* the most wildly successful and enduring scheme we have stuck with over all that time is linear strings of symbols. Which is text.

So it is no great surprise that text is well adapted to our latest adventure in encoding and manipulating abstract concepts.

@rafial Both accurate and also misses the fact that Excel is REGULARLY misused for scientific calculations and near-programming level things since its GUI is so intuitive for doing math on things.

Like, GUI programming is HERE, we just don't want to admit it due to how embarrassing it is.

@Canageek very good point. Excel is actually the most widely used programming environment by far.

@rafial Now what we need to do is make a cheap, easy to use version of it that is designed for what scientists are using it for it. Column labels, semantic labels, faster calculations, better dealing with mid-sized data (tens of thousands of data point range), etc

@Canageek I'm wondering, given your professional leanings if you can comment on the use of "notebook" style programming systems such as Jupyter and of course Mathematica. Do you have experience with those? And if so how do they address those needs?

Thanks @urusan, I found the article interesting, and it touched on the issue how to balance the coherence of a centrally designed tool with the need for something open, inspectable, non-gatekept, and universally accessible.

PDF started its life tied to what was once a very expensive, proprietary tool set. The outside implementations that @Canageek refers to were crucial in it becoming a universally accepted format.

I think the core idea of the computational notebook is a strong one. The question for me remains if we can arrive at a point where a notebook created 5, 10, 20 or more years ago can still be read and executed without resorting to software archeology. Even old PDFs sometimes break when viewed through new apps.

@rafial @urusan Aim for longer then that. I can compile TeX documents from the 80s, and I could run ShelX files from the 60s if I wantd to.

@Canageek @rafial You aren't processing those ShelX files on any sort of hardware (or software binaries) that existed in the late 1960's. At best, you're running the original code in an emulation of the original hardware, but you are probably running it on modern software designed to run on modern hardware

Software archeology is inevitable and even desirable

What we want is an open platform maintained by software archeology experts that lets users not sweat the details

@urusan @rafial No, they've kept updating the software since then so it can use the same input files and data files. I'm reprocessing the data using the newest version of the software using the same list of reflections that was measured using optical data from wayyyy back.

The code has been through two major rewrites in that time, so I don't know how much of the original Fortran is the same, but it doesn't matter? I'm doing the calculations on the same raw data as was measured in the 60s.

There is rarely a POINT to doing so rather then growing a new crystal but I know someone that has done it (he used Crystals rather then Shelx, but he could do that as the modern input file converter works on old data just fine)

@Canageek @rafial We're talking about 2 different things here. Of course data from over half a century ago is still useful.

The thing that's hard to keep running decades later is the code, and code is becoming more and more relevant in many areas of science.

Keeping old code alive so it can produce consistent results for future researchers is a specialized job

Ignoring the issue isn't going to stop researchers from using and publishing code, so it's best to have norms

@urusan @Canageek one other thing to keep in mind is that data formats are in some ways only relevant if there is code that consumes it. Even with a standard, at the end of the day a valid PDF document is by de-facto definition, one that can be rendered by extent software. Similar with ShelX scripts. To keep the data alive, one must also keep the code alive.

@rafial @urusan @Canageek And this is why all software should be written in FORTRAN-77 or COBOL.

@mdhughes @Canageek @urusan @rafial Any language that has a reasonably-sized human-readable bootstrap path from bare metal x86, 68000, Z80 or 6502 should be fine.

They don't exist. Yet. Except Forth and PicoLisp.

Also I'd add standard Scheme and standard CL to the list. You can still run R4RS Scheme code from 1991 in Racket and most (all? is there a pure R5RS implementation?) modern Schemes. CL hasn't been updated since 1994.

@clacke @Canageek @mdhughes @rafial Really you just need a well defined language spec (which is easier said than done).

The semantics of, say, addition isn't going to change. Once you define c = a + b means adding a and b, then assigning the value into c, then you no longer need a reference implementation and you can treat this code like a well defined data format.

Of course, I'm leaving out a lot of detail here, like what do you do on overflow?

@clacke @Canageek @mdhughes @rafial Having a reference implementation just lets you defer to the reference implementation as your spec, and if it's on a well known platform then it can be reasonably emulated on different hardware.

When you think about the reference implementation as a quasi-spec, then it becomes clear that most mainstream languages already have a reference implementation, and thus one of these quasi-specs already.

@clacke @Canageek @mdhughes @rafial In either case though, the end user doesn't care about the code archeology aspects of this.

Just because we can theoretically re-implement Python 2.5.1 as it would run on a 64-bit x86 on your future 128-bit RISC-V processor doesn't mean that you would want to

You just want to see the results, and you don't want them to differ, say because of the 64-bit vs 128-bit difference

A standard platform facilitates this

@clacke @Canageek @mdhughes @rafial Language specs and reference implementations make the code archeology work possible for the maintainers of this open platform.

It's necessary for them to be able to cope, so the end user can ultimately have a smooth experience, and get back to their scientific research.

@urusan @clacke @mdhughes @rafial See, this is a lot of focus on getting the exact same results, which for science I think is a mistake.

You don't want the same results, you want the *best* results. If newer versions of the code use 128-bit floating point numbers instead of 64-bit, GREAT. Less rounding errors.

Its like, I can create this model in Shelx or Crystals. They don't implement things EXACTLY the same, but a good, physically relevant model should be able to be created in either. If I try and do the same thing in two sets of (reliable) software and it doesn't work in one, perhaps I'm trying to do something without physical meaning?

Like, it shouldn't matter if i use the exact same Fourier transform or do analysis in R, SAS, or Python. It should give the same results. Stop focusing on code preservation and focus on making analysis platform agnostic.

@Canageek @mdhughes @urusan @rafial You want to first know that you are getting the exact same results in the part of the analysis that is supposed to be deterministic. *Then* you can upgrade things and see differences and identify whether any changes are because you broke something or because the new setup is better.

If the original setup had bugs, you want to know that, and you want to know why, and you won't be able to do that if you can't reproduce the results.

@clacke @Canageek @mdhughes @rafial Yes, this is exactly what I wanted to say.

I'd like to add that the norm of a Jupyter notebook additionally promotes the explanation of whatever you are doing in the code.

You're clearly supposed to interleave explanation (okay, now I'm doing this to the data) and code (here is exactly what I did in a format a computer can replicate).

This gives you the best of both worlds.

@clacke @Canageek @mdhughes @rafial It also helps one spot and correct errors. Maybe they meant to do one thing, but did another thing, and now all their downstream numbers are incorrect.

If all you have is their explanation (or worse, final results), with them having run hidden/unexplained code then it's not as easy to correct them, and you don't know whether their reasoning is incorrect or if it was caused by a software bug without a lot of work

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@urusan @clacke @mdhughes @rafial That is fair, I'm from an area of science where you don't go into other people's work like that very often. We are far more likely to remake a compound and do all the measurements over again then we are to try and figure out what someone else did wrong.

If we find a difference between our results and the published ones the older ones probably had an impurity or something and it isn't really worth worrying about. Heck, sometimes you even get COLOUR differences when you make literature compounds, like white crystals vs red crystals.

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