climt
paramonte
climt | paramonte | |
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1 | 4 | |
149 | 240 | |
1.3% | 5.1% | |
5.5 | 8.7 | |
about 2 months ago | 5 days ago | |
Fortran | Fortran | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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climt
paramonte
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Is fortran used at all anymore, or is it like driving around a model T car? I've got some programs written in fortran.
The ParaMonte Machine Learning library is an actively developed package in Fortran 2018 standard. The next release of the package contains about a million lines of Fortran (along with other languages). There are many more Fortran libraries, mostly in the Aerospace, Geology, Astronomy, Civil Engineering, and Petroleum industry and academia. Many electronic structure, nuclear, and plasma physics packages have been and are still developed in Fortran. Ask this question on the Fortran Community Discourse to get a more comprehensive list of current Fortran packages.
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Do any of you do modeling with pymc3 or other Bayesian-oriented packages?
Bayesian modeling is at the heart of scientific inference and uncertainty quantification. Whether the industry uses it or not, does not devalue this important approach. If they do not then it is likely that they have not yet realized its significance. But I suspect many do, in collaboration with Academia and they typically use their own specialized high-performance tools for such inferences since their models are far more complex than things that could be implemented via such high-level probabilistic programming languages as pymc3. Incidentally, our lab has developed (and is still developing) a High-Performance serial/parallel package for sampling and integration of Bayesian posteriors which is available from multiple programming languages including C/C++/Fortran/Python/MATLAB/...: https://github.com/cdslaborg/paramonte
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Can I use a function or procedure as input of a subroutine in fortran?
https://github.com/cdslaborg/paramonte/blob/e3087ef9c9b13c53c5298e4abea2bcb5043ab8af/src/kernel/Integration_mod.f90#L108
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Fit data as you like
Can you provide more information about your data? How many dimensions? 1D? Also, could you elaborate on what you mean by fitting a Gaussian to the time series data? Do you mean a Gaussian process? My lab has written a fast generic Bayesian optimizer and sampler library, in pure modern Fortran, that can not only find the best-fit parameters of your time-series model (whether polynomial, sin, ...), but can also put constraints on the uncertainties associated with the parameters. Writing a generic likelihood function for polynomial or other types of fits is quite easy. Once you write it, you simply compile and link it with this library to find the best-fit parameters of each model. The prebuilt ready-to-use versions of the library are also available on the GitHub release page.I would be happy to help you further with writing the polynomial/sin models and fitting them to your data with this library. But some further information is needed from your side to write the objective functions for different models (poly, sin, ...).
What are some alternatives?
PyGCM - A global climate model
rstan - RStan, the R interface to Stan
E3SM - Energy Exascale Earth System Model source code. NOTE: use "maint" branches for your work. Head of master is not validated.
ftl - The Fortran Template Library
LBLRTM - Line-By-Line Radiative Transfer Model by Atmospheric and Environmental Research
MultiBUGS - Multi-core BUGS for fast Bayesian inference of large hierarchical models
index - An index of our repositories
modAL - A modular active learning framework for Python
prima - PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's derivative-free optimization methods, i.e., COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. PRIMA means Reference Implementation for Powell's methods with Modernization and Amelioration, P for Powell.
Nerve - This is a basic implementation of a neural network for use in C and C++ programs. It is intended for use in applications that just happen to need a simple neural network and do not want to use needlessly complex neural network libraries.
pretty-print-confusion-matrix - Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib
SciFortran - A library of fortran modules and routines for scientific calculations (*in a way* just like scipy for python)