Fixed that for you.

Yes Fortran is probably the right tool for the job in this case, but I also think that creative experimentation with programming languages, invention of new languages, new implementations of and enhancements to existing languages, are good and important, and not just a "hipster" thing. All modern programming languages and their implementations have warts and deficiences, and building a new and improved one requires a level of skill and dedication that goes far beyond hipsterism.

The work of stats guru Jeff Sagarin.

Fortran can run very fast. If all you need to do is numerical calculations, that might be enough. But that's never all you need to do: you need to preprocess your input data, or deserialize it, normalize it, join it or transform it based on some rdbms data, then you do your calculations, and then you need to graph it, serialize it, etc. Most of those tasks are somewhere between excruciating and impossible in fortran, however modern a dialect you use.

Have I disputed this fact somewhere?

>The truth is, for a lot of numeric computing, you're better off using python, which calls fortran or c code to do the heavy lifting.

Proof that Python can't get the job done in that particular area. Nevertheless there remain Python zealots who will tell you that it's the best for everything.

>Fortran can run very fast. If all you need to do is numerical calculations, that might be enough.

Actually it sounds like that is all he needs to do. Reading and spitting out a .csv file from Fortran is trivial, despite its clunky IO syntax.

Yes, when you were complaining that "These are the guys trying to cram Python into every possible goddamned use case".

Proof that Python can't get the job done in that particular area.

Python can't run at all without C code. Fortunately, Python interfaces really well with C and C++ and fortran. The fact that Python lets you be way more productive using fortran tools without having to know or suffer from fotran's glaring deficiencies seems like a huge plus to me.

Reading and spitting out a .csv file from Fortran is trivial, despite its clunky IO syntax.

Not really. People who do serious analysis need to make graphs. They need to push data back into SQL databases. They need to do all sorts of interactive analyses. They need to present their results and analytics to other people, including reviewers. They need to debug their analysis. All of that stuff is much easier with python than fortran.

Which is where the aforementioned CSV files come in handy. Heard of GNUplot?

In large scale number crunching, like climate models, numerical weather prediction, the typical case is that input data is conceptually in a regular 2d or 3d grid, and stored in binary format files (like NetCDF or HDF), as that is more efficient and saves space.

Then the heavy lifting number crunching code runs on a cluster as a batch job, reads in the data, crunches the numbers, and writes results out again in NetCDF or HDF files.

The output files are then downloaded to a desktop PC, and graphing is done with Matlab (Python is also getting more popular) or especially in meteorology with some dedicated meteorology graphing software.

The binary format input and output is probably about as efficient as it can be. Also, heavy number crunching scientist probably don't have much use for relational databases.

On the subject of being the best language for the job, Fortran does not use 10x more lines than more modern languages. In fact, in many cases it uses less. When I was working on real-time signal processing algorithms we used to prototype algorithms in Fortran[1], then implement them in C. I also used to use Python with SciPy and NumPy for analysis, as well as Matlab. Matlab's huge toolbox library notwithstanding, the Fortran implementations were usually shorter, simpler, and easier to understand than any of the other languages. It was only bias and preconceived notions that kept it from being used in the real system. I would have loved to see Fortran subroutines running the algorithms underneath the C distributed framework.

[1] Until someone in management and the customer team decided to have the scientists write their algorithms directly in the real-time C. That is a rant for another day.

As for the first, a) I touched on the fact that Fortran is a modern language and b) unless you've got examples showing two side-by-side implementations of the same algorithm with Fortran being 10x longer, then it sounds like you're just another of the aforementioned hipsters who is experiencing aversion to something just because it is not new and shiny.

Fortran is like fast, typed, compiled Matlab (with smaller standard library, but a lot of numerical libraries available all over internet).

Nut yes, exploratory data analysis, something interactive like NumPy + Matplotlib, Matlab or R might be better.

But NumPy + matplotlib is kinda painful to set up first to be interactive, R the language is wonky, and Matlab cost a lot of money (and the free alternative, Octave, has much more limited graphics, and they are kind of ugly, too).

Of course, Fortran has no graphing capabilities, so one needs some tool for that anyway.

This was written in 1983: http://www.pbm.com/~lindahl/real.programmers.html

It contains among other things the sentiment "Unix is a glorified video game."

Most of the things you are using right now used to be hipster.