Konrad Hinsen

I'm reminded of https://blog.khinsen.net/posts/2020/12/10/the-structure-and-interpreta… the opening paragraph is worth quoting in (almost) full: > It is often said that science rests on two pillars, experiment and theory. Which has lead some to propose one or two additional pillars for the computing age: simulation and data analysis. However, the real two pillars of science are observations and models. Observations are the input to science, in the form of numerous but incomplete and imperfect views on reality. Models are the inner state of science. They represent our current understanding of reality, which is necessarily incomplete and imperfect, but understandable and applicable. Simulation and data analysis are tools for interfacing and thus comparing observations and models. They don’t add new pillars, but transforms both of them.

> Most of the rest of us will move on since the conversions were not that hard and the improvements worth it and we were given 10+ years to do it. You are describing it as if it is for everyone easy to convert. That might be true for fast-paced start-ups and web companies which rewrite their code every few years. I work in science, in projects which have complex infrastructure and are developed in the course of many years. There is a vast amount of code which is written in the language and virtually nobody has time to rewrite and convert it. Me neither. As a consequence, Python is not really ideal for this kind of work, and I will tend to avoid it in my next job. It might not seem to matter to you, but a good part of Python's success is actually because of things like Numpy, Scientific Python, and statistics and science libaries which were written by scientists. Some, like Konrad Hinsen (one of the authors of Numpy) are now even considering to move to another language: http://blog.khinsen.net/posts/2017/11/16/a-plea-for-stability-in-the-s… The idea that converting is easy is also not generally true for any kind of infrastructure. Consider the Python Wiki: https://wiki.python.org/moin/ At the bottom of the page, you find the note "MoinMoin powered". The Wiki software is the MoinMoin wiki software. It is the only large wiki software with multi-markup support written in Python. It has so far no Python 3 port. This is because people have written it in their…

The author addressed "a container or VM image" in the followup, at http://blog.khinsen.net/posts/2017/11/22/stability-in-the-scipy-ecosys… : > There are three main reasons why this is not a sufficient solution: > 1. Freezing code is fine for archival reproducibility, as I mentioned in my original post. It is not sufficient for living code bases that people work on over decades. ... (His example was his MMTK project which started in the 1.3 days. Twenty years later, it will take a lot of work to make the 2->3 transition.) > 2. The technical solutions proposed all depend on yet more infrastructure whose longevity is uncertain. For how long will a Docker container image produced in 2017 remain usable? For how long will conda and its repositories be supported, and for how long will the binaries in these repositories function on current platforms? > 3. None of today’s code freezing approaches comes with easy-to-use tooling and clear documentation that make it accessible to the average computational scientist. The technologies are today in a “good for early adopters” state. This means we cannot rely on them to preserve today’s research even though they may well take on this role in the future. There are many issues related to reproducibility. I think it's acceptable for different people to focus on different pain points, and that one need not mention all of the possible pain points that others may have when talking about one's own concerns.

Here's the open letter criticising our entire profession for producing something as terrible as C++ and allowing scientists to get into trouble with it - http://blog.khinsen.net/posts/2020/05/18/an-open-letter-to-software-en… There are links back to other parts of the story in the text there. Effectively, they built a model that predicted some real worst-case stuff, and used that as the basis for advice they sent out to try and affect government policy in a bunch of places. When the code was finally released and examined, it was in a bad state - 15k lines in one source file, race conditions, single name globals reused all over the place. The results were trash. Then the blame started to fly.

Note that cancelling Python is my thesis, and khinsen just provides the evidence to support it. He doesn't come out and say, "Don't use Python," but I am. From my notes, here are (some? of) the posts I had in mind: - <https://blog.khinsen.net/posts/2015/11/06/a-rant-about-software-deploy… - <https://blog.khinsen.net/posts/2017/11/16/a-plea-for-stability-in-the-… - <https://blog.khinsen.net/posts/2017/01/13/sustainable-software-and-rep… - <https://blog.khinsen.net/posts/2017/11/22/stability-in-the-scipy-ecosy… - <https://blog.khinsen.net/posts/2019/02/11/the-computational-notebook-o… - <https://blog.khinsen.net/posts/2019/05/09/the-computational-notebook-o… --- > I don't like Python [...] but it's the language I'd reach for first if I was teaching someone how to code Damn, that's crazy. Considering how many of those people would be coming from Windows and Python isn't actually[1] a batteries-included situation, I'd go with JS. I'd say, "Look: here's how you can get started right away making little programs that run in your browser right now even when you don't have WiFi," then at the point where other people might start introducing Node (or Deno), I'd say, "You should really learn Go." I'd preface the JS stuff by saying, "mostly ignore EVERYTHING that you come across telling you how tech influencers on the Internet…