We are pleased to announce the release of FiPy 3.4. http://www.ctcms.nist.gov/fipy
This release adds support for the PETSc solvers for solving in parallel. Pulls ----- - Add support for PETSc solvers (#701) - Assorted fixes while supporting PETSc (#700) - Fix print statements for Py3k - Resolve Gmsh issues - Dump only on processor 0 - Only write `timetests` on processor 0 - Fix conda-forge link - Upload PDF - Document `print` option of `FIPY_DISPLAY_MATRIX` - Use legacy numpy formatting when testing individual modules - Switch to matplotlib's built-in symlog scaling - Clean up tests - Assorted fixes for benchmark 8 (#699) - Stipulate `--force` option for `conda remove fipy` - Update Miniconda installation url - Replace `_CellVolumeAverageVariable` class with `Variable` expression - Fix output for bad call stack - Make CircleCI build docs on Py3k (#698) - Fix link to Nick Croft's thesis (#681) - Fix NIST header footer (#680) - Use Nixpkgs version of FiPy expression (#661) - Update the Nix recipe (#658) Fixes ----- - #692: Can't copy example scripts with the command line - #669: input() deadlock on parallel runs - #643: Automate release process ======================================================================== FiPy is an object oriented, partial differential equation (PDE) solver, written in Python, based on a standard finite volume (FV) approach. The framework has been developed in the Metallurgy Division and Center for Theoretical and Computational Materials Science (CTCMS), in the Material Measurement Laboratory (MML) at the National Institute of Standards and Technology (NIST). The solution of coupled sets of PDEs is ubiquitous to the numerical simulation of science problems. Numerous PDE solvers exist, using a variety of languages and numerical approaches. Many are proprietary, expensive and difficult to customize. As a result, scientists spend considerable resources repeatedly developing limited tools for specific problems. Our approach, combining the FV method and Python, provides a tool that is extensible, powerful and freely available. A significant advantage to Python is the existing suite of tools for array calculations, sparse matrices and data rendering. The FiPy framework includes terms for transient diffusion, convection and standard sources, enabling the solution of arbitrary combinations of coupled elliptic, hyperbolic and parabolic PDEs. Currently implemented models include phase field treatments of polycrystalline, dendritic, and electrochemical phase transformations as well as a level set treatment of the electrodeposition process. _______________________________________________ fipy mailing list fipy@nist.gov http://www.ctcms.nist.gov/fipy [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ] _______________________________________________ fipy mailing list fipy@nist.gov http://www.ctcms.nist.gov/fipy [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ]
