fpm | SciPy | |
---|---|---|
12 | 50 | |
812 | 12,491 | |
1.4% | 1.3% | |
8.8 | 9.9 | |
5 days ago | 1 day ago | |
Fortran | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
fpm
- Fortran Package Manager (FPM): Package Manager and Build System for Fortran
- Fortran Package Manager
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How do I use fortran github package.
Make sure you have the latest fpm binary installed somewhere so that your $PATH can see it: curl -o ~/.local/bin/fpm -L https://github.com/fortran-lang/fpm/releases/download/v0.8.2/fpm-0.8.2-linux-x86_64 && chmod 0755 ~/.local/bin/fpm
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SciPy: Interested in adopting PRIMA, but little appetite for more Fortran code
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.
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The Skills Gap for Fortran Looms Large in HPC
Anyway, first release of Fortran Package Manager was in November 2020: https://github.com/fortran-lang/fpm/releases/tag/v0.1.0 - more recently than I expected.
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[RANT] I really, really wish working with compiled languages is as easy as working with Python.
There is actually a Fortran Package Manager that will hopefully make things easier in the future. It's quite new, so it might not be entirely mature yet.
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Toward Modern Fortran Tooling and a Thriving Developer Community
Author here, so I'm biased toward Fortran, though I've been enjoying learning Rust as well. I think there are a few reasons.
First, Rust's multidimensional arrays are either limited and/or difficult to use. Fast, flexible, and ergonomic multidimensional arrays and arithmetic are essential for HPC. They are possible with Rust, but my two favorite Rust books not mentioning them suggests to me that they're not the focus of the language. This may or may not change in the future.
Second, Rust may be too complex to learn for scientists who aren't paid to write software but to do research. Fortran is opposite--multidimensional whole-array arithmetic looks like you would write it as math on a whiteboard. While scientists can sure learn to program Rust effectively, I think most scientists don't think like Rust, but they do think like Fortran. For somebody not familiar with Fortran but familiar with Python, I'd say Fortran very much feels like NumPy.
Third, such ecosystem would be built in Rust from scratch. In Fortran, most of the value is already there, but needs to be made more accessible with better and more modern tooling. For example, Fortran's fpm (https://github.com/fortran-lang/fpm) is largely modeled after Rust's Cargo because we recognize the importance of good user experience when it comes to building and packaging software. With the recent Fortran-lang efforts, we study many programming language ecosystems and communities (e.g. Python, Julia, Rust, etc.) to find what could work best for modern Fortran tooling.
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Fortran Web Framework
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.
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Assembly of course!
FPM has entered the chat https://github.com/fortran-lang/fpm
SciPy
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What Is a Schur Decomposition?
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
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Fortran codes are causing problems
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
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[D] Which BLAS library to choose for apple silicon?
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.
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SciPy: Interested in adopting PRIMA, but little appetite for more Fortran code
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.
- numerically evaluating wavelets?
- Fortran in SciPy: Get rid of linalg.interpolative Fortran code
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Optimization Without Using Derivatives
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.
- What can I contribute to SciPy (or other) with my pure math skill? Iām pen and paper mathematician
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Emerging Technologies: Rust in HPC
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)
- Python
What are some alternatives?
stdlib - Fortran Standard Library
SymPy - A computer algebra system written in pure Python
json-fortran - A Modern Fortran JSON API
statsmodels - Statsmodels: statistical modeling and econometrics in Python
OpenCoarrays - A parallel application binary interface for Fortran 2018 compilers.
NumPy - The fundamental package for scientific computing with Python.
NASTRAN-95
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
neural-fortran - A parallel framework for deep learning
astropy - Astronomy and astrophysics core library
pyplot-fortran - For generating plots from Fortran using Python's matplotlib.pyplot š
or-tools - Google's Operations Research tools: