Darcy scale image analysis toolbox

Visit pmgbergen.github.io/DarSIA

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

Nordbotten, J. M., Benali, B., Both, J. W., Brattekås, B., Storvik, E., & Fernø, M. A. (2023). DarSIA: An open-source Python toolbox for two-scale image processing of dynamics in porous media. Transport in Porous Media, https://doi.org/10.1007/s11242-023-02000-9

The first release can be also found on Zenodo: 10.5281/zenodo.7515016

DarSIA is developed under Python 3.10. Clone the repository from github and enter the DarSIA folder. Then, run the following command to install:

pip install .

To install DarSIA as editable (recommended), along with the tools to develop and run tests, run the following in your virtual environment:

$ pip install -e .[dev]

petsc4py is an optional but recommended dependency for performance-critical solvers such as Wasserstein distance computation. It is not installed by default.

Linux (Ubuntu/Debian):

sudo apt-get install -y libhypre-dev libmumps-seq-dev build-essential gcc gfortran mpich cmake
pip install numpy mpi4py
PETSC_CONFIGURE_OPTIONS="--download-hypre --download-mumps --download-parmetis --download-ml --download-metis --download-scalapack" pip install petsc petsc4py
# Then install DarSIA with petsc extra
pip install .[petsc]
# Or combined with dev dependencies:
pip install .[dev,petsc]

macOS / conda:

conda install -c conda-forge petsc petsc4py

See also conda_env.yaml for a complete conda environment.

The following Python script can be applied to the test image in the examples/images folder.

import numpy as np

# Create a darsia Image: An image that also contains information of physical entities
image = darsia.imread("images/baseline.jpg", width=2.8, height=1.5)

# Use the show method to take a look at the imported image.
image.show()

# Copy the image and adds a grid on top of it.
grid_image = image.add_grid(dx=0.1, dy=0.1)
grid_image.show()

# Extract region of interest (ROI) from image (box defined by two corners):
ROI_image = image.subregion(coordinates=np.array([[1.5, 0], [2.8, 0.7]]))
ROI_image.show()

Furthermore, we encourage any user to checkout the examples in the examples folder and the jupyter notebooks in the examples/notebooks folder.

Use black (version 22.3.0), flake8 and isort formatting.