Konrad Hinsen's Blog

Posts tagged python

Stability in the SciPy ecosystem: a summary of the discussion

The plea for stability in the SciPy ecosystem that I posted last week on this blog has generated a lot of feedback, both as comments and in a lengthy Twitter thread. For the benefit of people discovering it late, here is a summary of the main arguments and my reply to them.

A plea for stability in the SciPy ecosystem

Two NumPy-related news items appeared on my Twitter feed yesterday, just a few days after I had accidentally started a somewhat heated debate myself concerning the poor reproducibility of Python-based computer-aided research. The first was the announcement of a plan for dropping support for Python 2. The second was a pointer to a recent presentation by Nathaniel Smith entitled “Inside NumPy” and dealing mainly with the NumPy team’s plans for the near future. Lots of material to think about… and comment on.

Why Python does so well in scientific computing

A few days ago, I noticed this tweet in my timeline:

That sounded like a good read for the weekend, which it was. The main argument the author makes is that C remains unsurpassed as a system integration language, because it permits interfacing with “alien” code, i.e. code written independently and perhaps even in different languages, down to assembly. In fact, C is one of the few programming languages that lets you deal with whatever data at the byte level. Most more “modern” languages prohibit such interfacing in the name of safety - the only memory you can access is memory allocated through your safe language’s runtime system. As a consequence, you are stuck in the closed universe of your language.

Reproducible research in the Python ecosystem: a reality check

A few years ago, I decided to adopt the practices of reproducible research as far as possible within the technical and social constraints I have to live with. So how reproducible is my published code over time?

A rant about software deployment in 2015

We all know that software deployment in a research environment can be a pain, but knowing this as a fact is not quite the same as experiencing it in reality. Over the last days, I spent way more time that I would have imagined on what sounds like a simple task: installing a scientific application written in Python on a Linux machine for use by a group of students in a training session. Here is an outline of the difficulties, in the hope that it will (1) help others who face similar problems and (2) contributes a little bit to improving the situation.