Keynote: Begin...End?

This is the talk: [VIDEO] , it is a full hour. Quite enjoyable but light on in depth technical stuff. Don't recall hem mentioning how git python running technically, it is more about how he came in the position to do so in the first place.

I started using Python full time in 1998 for cheminformatics. One of the first projects I did was to make a high-level/"Pythonic" interface to an existing C library. I described it in Dr. Dobb's Journal, at [LINK] . Going back in time to 1994, we had a molecular dynamics visualization tool called VMD. It had a home-grown scripting language. I had ideas for a new programming language designed for molecular modeling (because "tiny languages" was the Unix philosophy I had picked up). After looking around, I realized 1) I didn't know how to really make a new domain-specific programming language, and 2) there were existing programming languages that could be embedded in an application, and extended with domain-specific features. I choose Tcl, because it was a very similar, but better, than our home-grown scripting language. And it was easy for people to learn. (Yes, easier than Python.) I considered Python, but was thrown off by the whitespace. And Perl wasn't a good fit. (Perl 4 C extensions were hard to do). Instead, another visualization package, UCSF's Chimera, was the first to have Python integration. In ... the mid-1990s? In 1997 I was doing more Perl work for bioinformatics. I had to read the advanced Perl book to figure out Perl5 objects. I wrote the equivalent program in Python, and it was just so much easier, even for a neophyte Python programmer. But bioinformatics at the time was Perl-oriented (see "How Perl Saved the Human Genome Project" - the author, Lincoln Stein,

I recommend one of the recent videos by Dave Beazly [1]. He lived through and contributed to the raise of Python in scientific computing first hand in the 90s, and offers some interesting insights. Plus he's always quite an entertainer. [1] [VIDEO]

According to David Beazley, it was lucky to already being around by the time scientific computing folks were in need for dynamic languages as wrappers to help with increasingly complex hardware and projects written in lower level languages, and the only other viable alternatives back then were Perl and tcl. Watch this talk: [VIDEO] Then it just snowballed since that time.

> it is my understanding that what this paper describes was never actually shipped This reminds me of an interesting talk by David Beazley where he discuss the early days of Python, when IIRC he was a student at a national lab, and they managed to run the Python REPL in an connection machine 5 that people had a hard time programming in lower level languages. It seems the work they did back then, writing modules that allowed the Python interpreter to interoperate with scientific code was one of the foundations for it to snowball many years later. So I wonder if what this paper describes had shipped in time, if that would lead us by butterfly effect into the alternative timeline where Lisp dominates scientific computing, instead of Python. See [VIDEO]