Projects tagged ‘fortran’ and ‘science’

Linalg is a fast, LAPACK-based library for real and complex matrices in Ruby. Current functionality includes: singular value decomposition, eigenvectors and eigenvalues of a general matrix, least ... [More] squares, LU, QR, Schur, Cholesky, stand-alone LAPACK bindings. [Less]

Urin & Rykovanov

PyMCT Start PageIntroductionPyMCT consists of a suite of software packages necessary to build and run a Python Coupler like PyCCSM. MCT is a high performance regridding and parallel communication ... [More] package designed to address issues of coupling multiple scientific models on different scales and grids to one another. ArchitectureSystem OverviewPyMCT wraps the Fortran 90 MCT library and makes it available to Python Programmers. This wrapping is not normally possible due to complex issues with the many different interpretations of the Fortran 90 standard. However, a powrful tool, the CCA Babel project allowed us to address these issues and completely wrap the MCT functionality into Python. ComponentsThe following components are necessary to build and use MCT and PyMCT MCTFortran 90 Compiler MPI (we use MPICH2) BabelC, Java, and Fortran compilers ( Portland Group Fortran is NOT supported! ) Python Interpreter Chasm Numpy or Numeric Python array packages Python MPIThere are a number of Python bindings to MPI out there, but the only one that completely meets the requirements of our package is our own solution, MaroonMPI. Depending on your application you may have reasonable success with other bindings. NetCDFConfiguration and InstallationPlease see our InstallPyMCT page. [Less]

This is the Clear Climate Code project, reimplementing GISTEMP in Python for clarity. See http://clearclimatecode.org/

Computational Steering Library and Toolkit"Moving the bottleneck out of the hardware and back into the human mind" What is Computational Steering?Computational steering refers to the process by ... [More] which a scientist interacts with a running application. Typically, this application will be some form of simulation by which the scientist seeks to learn about some physical system. It can be useful for the scientist to monitor the progress of the simulation by monitoring the values of various parameters. Better still, the scientist may want to try "what if" scenarios - what happens if I alter the value of this system parameter? This is computational steering at its simplest. For large, complex systems the scientist will often need to see some form of visual feedback from the steered system as it evolves in order to inform any further steering decisions; we call this "on-line visualization." Library and ToolkitThe software offered here was developed as part of the EPSRC funded RealityGrid project by the Research Computing Services group at The University of Manchester. PackagesThe main packages are as follows: Steering Library - the core library and examples (C/C++ and Fortran). Steering Library Wrappers - Python, Perl and Java wrappers for the above. Generic Qt Steering Client - An example GUI for steering an application. WSRF Tools - WSRF-based Steering Web Services and Registry Middleware. VTK Module - VTK classes for on-line visualization of a steered code. Code RepositoryWe use Git for version control so the source code isn't hosted here on Google Code. See the CodeRepository page for more information. BugsIf you find any bugs in any of the code, please use the issue tracker via the tab above. If you can attach a patch with your report, even better! Mailing ListIf you download this software we would really appreciate it if you subscribed to our (very low volume) mailing list. This allows us to let you know when a new version is released and it allows you to ask us (and other users) questions. Join here: http://listserv.manchester.ac.uk/cgi-bin/wa?A0=COMP-STEERING Related ProjectsWSRF::Lite [Less]

OOrb contains, e.g., the statistical orbital ranging method (hereafter referred to as Ranging). Ranging is used to solve the orbital inverse problem of computing non-Gaussian orbital-element ... [More] probability density functions (p.d.f.s) based on input astrometry. Ranging is optimized for cases where the amount of astrometry is scarce or spans a relatively short time span. Ranging-based methods have successfully been applied to a variety of different topics such as rigorous ephemeris prediction, orbital-element-distribution studies for trans-neptunian objects, the computation of invariant collision probabilities between NEOs and the Earth, detecting linkages between astrometric asteroid observations within an apparition as well as between apparitions, and in the rigorous analysis of the impact of orbital arc-length and/or astrometric uncertainty on the uncertainty of the resulting orbits. In OOrb, tools for making ephemeris predictions and classification of objects (i.e., NEO-MBO-TNO) are also available. [Less]