stdlib VS SciPy

Hopefully, the SciPy community can stay open-minded about modern Fortran libraries.

Modern Fortran is quite different from Fortran 77, while being as powerful, if not more.

In addition, there has been a significant community effort on improving and modernising the legacy packages, the ecosystem, and the language itself.

With projects like LFortran (https://lfortran.org/), fpm (https://github.com/fortran-lang/fpm), and stdlib (https://github.com/fortran-lang/stdlib), I believe that Fortran will enjoy prosperity again.

They're currently working on a Fortran standard library and it's pretty far along: https://github.com/fortran-lang/stdlib

I also like FPM and the ecosystem. In case anyone is just getting started with Fortran, definitely checkout the Fortran Standard Library project:

https://github.com/fortran-lang/stdlib

In general, it is necessary to know the length of a string in Fortran before using it. There is no general string with unspecified strength. Some libraries do provide such an object (e.g. Fortran Standard Library, but it is not available in the standard language. To obtain the length of the string in your example, you could use the length option in get_command_argument as integer :: clen character(len=:), allocatable :: string_b call get_command_argument(2, length=clen) allocate(string_b(clen)) string_b = '' call get_command_argument(2, string_b) write(*,*) string_b deallocate(string_b)

If you need to clear memory in the local scope, you need to deallocate a variable explicitly. Otherwise, all Fortran variables are cleared automatically when they go out of scope. One exception are Fortran pointers (different from C pointers) which are discouraged unless really necessary. We have a discussion for a high-level wrapper for files here: https://github.com/fortran-lang/stdlib/issues/14. So, it's in scope we just haven't gotten far with the design and implementation.

FYPP syntax is ugly, but is the best tool available for now to build the Fortran stdlib. People do not have to use the FYPP version of stdlib. There is also a clean post-processed version of the stdlib completely free of FYPP or any other FPP, which looks great: https://github.com/fortran-lang/stdlib/tree/stdlib-fpm

What if we try to evaluate this using standard-compliant Fortran? Interestingly, this is an open issue in the fortran-lang/stdlib project. f90 real(8) function f(x) real(8) :: x f = x**(1d0/3d0) endfunction I know real(8) isn't standard compliant but fixing that for this tiny example would be a headache. Then, compiling with -O3 gets us f_: movsd xmm1, QWORD PTR .LC0[rip] movsd xmm0, QWORD PTR [rdi] jmp pow .LC0: .long 1431655765 .long 1070945621

learn Fortran (supports both FP and OO, but when we say Fortran we think FP mostly). And the best way to learn is contributing. You can checkout their GitHub org (Fortran-lang) and you might be astonished to see that you too can make contributions there. But you should be ready to learn and search things on your own as well. They have a discourse group too, if you get stuck somewhere. Good luck. At the moment of writing this post they have a good first issue (Greatest Common Divisor) on their stdlib repo.

I recently started learning Fortran for a lark. It reminds me a lot of R, in some respects. It's clearly a very, very good language for doing the parts of one's job that are very math-centric. But it's equally underwhelming as a general purpose programming language.

Largely, I think, due to gaps in the library ecosystem. But there are other challenges. You can see from the install instructions on the linked page, for example, that Fortran still lacks a package manager.

What's interesting, though, is that that's changing. There are currently serious efforts to give it a "standard" library (https://github.com/fortran-lang/stdlib) and package manager (https://github.com/fortran-lang/fpm).

And I've been watching the new LFortran compiler (https://lfortran.org) with extreme interest.

I guess it is a rite of passage to rewrite it. I'm doing it for SciPy too together with Propack in [1]. Somebody already mentioned your repo. Thank you for your efforts.

[1]: https://github.com/scipy/scipy/issues/18566

Fortran codes have caused many problems for the Python package Scipy, and some of them are now being rewritten in C: e.g., https://github.com/scipy/scipy/pull/19121. Not only does R have many Fortran codes, there are also many R packages using Fortran codes: https://github.com/r-devel/r-svn, https://github.com/cran?q=&type=&language=fortran&sort=. Modern Fortran is a fine language but most legacy Fortran codes use the F77 style. When I update the R package quantreg, which uses many Fortran codes, I get a lot of warning messages. Not sure how the Fortran codes in the R ecosystem will be dealt with in the future, but they recently caused an issue in R due to the lack of compiler support for Fortran: https://blog.r-project.org/2023/08/23/will-r-work-on-64-bit-arm-windows/index.html. Some renowned packages like glmnet already have their Fortran codes rewritten in C/C++: https://cran.r-project.org/web/packages/glmnet/news/news.html

There are several lessons here: a) vanilla conda-forge numpy and scipy versions come with openblas, and it works pretty well, b) do not use netlib unless your matrices are small and you need to do a lot of SVDs, or idek why c) Apple's veclib/accelerate is super fast, but it is also numerically unstable. So much so that the scipy's devs dropped any support of it back in 2018. Like dang. That said, they are apparently are bring it back in, since the 13.3 release of macOS Ventura saw some major improvements in accelerate performance.

First, if you read through that scipy issue (https://github.com/scipy/scipy/issues/18118 ) the author was willing and able to relicense PRIMA under a 3-clause BSD license which is perfectly acceptable for scipy.

For the numerical recipes reference, there is a mention that scipy uses a slightly improved version of Powell's algorithm that is originally due to Forman Acton and presumably published in his popular book on numerical analysis, and that also happens to be described & included in numerical recipes. That is, unless the code scipy uses is copied from numerical recipes, which I presume it isn't, NR having the same algorithm doesn't mean that every other independent implementation of that algorithm falls under NR copyright.

Reading the discussions under a previous thread titled "More Descent, Less Gradient"( https://news.ycombinator.com/item?id=23004026 ), I guess people might be interested in PRIMA ( www.libprima.net ), which provides the reference implementation for Powell's renowned gradient/derivative-free (zeroth-order) optimization methods, namely COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA.

PRIMA solves general nonlinear optimizaton problems without using derivatives. It implements Powell's solvers in modern Fortran, compling with the Fortran 2008 standard. The implementation is faithful, in the sense of being mathmatically equivalent to Powell's Fortran 77 implementation, but with a better numerical performance. In contrast to the 7939 lines of Fortran 77 code with 244 GOTOs, the new implementation is structured and modularized.

There is a discussion to include the PRIMA solvers into SciPy ( https://github.com/scipy/scipy/issues/18118 ), replacing the buggy and unmaintained Fortran 77 version of COBYLA, and making the other four solvers available to all SciPy users.

if that makes your eyes bleed, what do you think about this? https://github.com/scipy/scipy/blob/main/scipy/special/specfun/specfun.f (heh)