Oxybrain: Author generated code for "Modeling oxygen transport in the brain: an efficient coarse-grid approach to capture perivascular gradients in the parenchyma.", Pastor-Alonso et al. PLOS COMPUTATIONAL BIOLOGY, 2024

This upload contains all author-generated python code associated to the companion article (Pastor et al., Modeling oxygen transport in the brain: an efficient coarse-grid approach to capture perivascular gradients in the parenchyma, PLOS Computational Biology, 2024, doi:10.1371/journal.pcbi.101204), as well as Comsol reference simulation data used for comparison.

The Figures_and_Tests folder contains each of the simulations appearing on the article. Subfolders Fig_4 to Fig_6 and Fig_SA correspond the 5 functional tests establishing the capabilities of the introduced multiscale method. The rest constitute the simulations that aid in the comprehension of the observed experimental differences among the cortical layers of the brain. More details are given in the Readme.txt in each subfolder.

The src folder contains the corresponding sources (written in Python taking advantage of the fast libraries Numpy, Scipy, Numba for computation, and matplotlib for plotting and figure creation.) 
The code is organized in modules, where the main files called upon execution are: 
- Module_coupling_sparse, which contains the methods relevant for the multiscale approach developed in the article
- Reconstruction_extended_space, which contains the post-processing methods for the multiscale approach
- Testing, which contains multiple methods that perform the pre-processing, solve (via the present multiscale and previous methods (classic Finite Volume and Finite Volume with Peaceman coupling approach) and their relevant post-processing methods. It essentially manipulates the previous two objects in order to ease the scripting.