PorePy currently has the following distinguishing features:

• General grids in 2d and 3d, as well as mixed-dimensional grids defined by intersecting fracture networks.
• Support for analysis, visualization and gridding of fractured domains.
• Discretization of flow and transport, using finite volume methods and virtual finite elements.
• Discretization of elasticity and poro-elasticity, using finite volume methods.

PorePy is developed by the Porous Media Group at the University of Bergen, Norway. The software is developed under projects funded by the Reserach Council of Norway and Statoil.

Runscripts for most, if not all, papers that uses porepy is available at here. Note that you may have to revert to an older version of PorePy to run the examples (we try to keep the runscripts updated, but sometime fail to do so, for various reasons).

If you use PorePy in your research, we ask you to cite the following publication

E. Keilegavlen, A. Fumagalli, R. Berge, I. Stefansson, I. Berre: PorePy: An Open-Source Simulation Tool for Flow and Transport in Deformable Fractured Rocks. arXiv:1712.00460

PorePy depends on numpy, scipy and networkx, and (for the moment) also on meshio, sympy and matplotlib. The latter packages may be droped / changed later. To install (on Linux, probably also OSX), use

pip install porepy

We recommend installing from source (see below), rather than pulling from pypi. Installation by pip on Windows may cause problems with buliding the requirements (numpy etc) unless conda is used.

For more detailed install instructions, including how to access GMSH (for meshing), see Install.

PorePy is developed under Python 3. It should also be compatible with Python 2.7, however, apart from unit testing, it is not being used with Python 2, so be cautious.

To get the most current version, install from github:

git clone https://github.com/pmgbergen/porepy.git

cd porepy

pip install -r requirements.txt

Finally to install PorePy

pip install .

or for editable installs into the user directory:

pip install --user -e .

A docker image is available, courtesy of Davide Baroli. To pull the docker.io/pmgbergen/porepylib with tag py27 image from cloud infrastructure:

>  docker pull docker.io/pmgbergen/porepylib:py27

Docker will pull the py27 tag of the image pmgbergen/porepylib from docker.io based on python 2.7. The download is around 4.085 GB. The image is a great place to start experimenting with porepy and includes all dependencies already compiled for you. Once the download is complete you can start porepy for the first time. Just run:

>  docker run -ti  docker.io/pmgbergen/porepy:py27

The image is auto-deployed at each push in the branch "develop".

For more details see the docker md.

To function optimally, PorePy should have access to the pypi packages:

• pymetis (for mesh partitioning). Will be installed on Linux (not so on Windows, to avoid installation issues for the core package in the case where no C compiler is available).
• Some computationally expensive methods can be accelerated with Cython or Numba. Cython is automatically installed on many Linux systems, if not, use pip or conda. Numba can be installed using conda.
• Visualization by either matplotlib or (preferrable for larger problems) vtk/paraview. To dump data to paraview, a vtk filter must be available; the only solution we have found is from the 'conda' repositories, e.g. 'conda install -c clinicalgraphics vtk=7.1.0' (note that vtk should be version 7.0.0 or later, hence not the official channel)
• Meshing: currently by gmsh. For its configuration see Install.

To test build locally, the second command requires gmsh (see Install)

pip install -r requirements-dev.txt

python setup.py test

Confer the tutorials. Also see unit tests.

Create an issue

See license md.