Some notes about this release:
- Because conda is a dreary pain in the behind, it appears to be necessary to
specify
`conda install --channel conda-forge fipy=3.4`
otherwise fipy 3.3 takes precedence for some reason.
- PETSc supports Py3k in parallel (as well as Python 2.7).
- PyTrilinos is available from conda-forge for Python 2.7.
In principle, it works with Py3k as well, but we won't be
attempting to build it to find out.
- On Python 2.7, PETSc and PyTrilinos have comparable performance.
Unfortunately, this performance lags serial PySparse by a
considerable margin. Serial SciPy lags them all.
See
https://www.ctcms.nist.gov/fipy/documentation/USAGE.html#solving-in-parallel
for discussion.
Until we can sort this out, we won't be (willingly) dropping support for
Python 2.
> On Jan 29, 2020, at 8:51 AM, Guyer, Jonathan E. Dr. (Fed) via fipy
> wrote:
>
> 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.
>
>
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