Regional-mom6: A Python package for automatic generation of regional configurations for the Modular Ocean Model 6

This is the bittrex R package, which is a RESTful client for the Bittrex crypto-currency exchange. This revision includes changes made for the Journal of Open Source Software.

Discontinuities, i.e. fractures, form the dominant pathways for fluid flow within crystalline bedrock. Characterizing these fracture networks involves integrating data across multiple scales. Field observations from outcrops yield macroscopic structural data, often analysed in two dimensions for geometric and topological properties using tools like fractopo (Ovaskainen 2023). Complementary three-dimensional data comes from oriented drillcores, while X-ray computed tomography (CT) imaging of laboratory rock samples provides crucial microscale details of fracture geometry and rock matrix properties. To understand fluid behaviour at larger scales relevant to practical applications, discrete fracture network (DFN) modelling is often necessary. However, constructing and simulating flow within these three-dimensional fracture models presents challenges. The required software can be complex, often proprietary, while free and open-source alternatives may lack user-friendliness or sustained development.Predicting fluid flow pathways accurately through fractured rock is essential for the safety of, e.g., geological disposal of nuclear waste, geothermal energy extraction and management of groundwater resources. A gap exists between the multi-scale field, laboratory and digitized datasets geologists can collect and the use of these observations in three-dimensional fracture and flow pathway modelling. Solutions exist, especially as proprietary software but those lack transparency due to the closed-source nature. Bridging this gap is crucial for improving the reliability of subsurface flow predictions. Furthermore, more effort is required to effectively translate the detailed data a geologist captures, encompassing field observations, digitized outcrop fracture traces, and laboratory rock sample scans, into comprehensive, upscaled, three-dimensional fracture network models, for instance, through the use of DFN-modelling.This research focuses on coupling field observations with laboratory rock sample CT-scans and flow experiments. We have characterized fracture networks at the macroscale from outcrops using established 2D analysis techniques (E.g., Ovaskainen et al. 2023). Concurrently, we have analysed microscale fracture characteristics and rock properties from laboratory rock samples using X-ray CT imaging. Our current work involves integrating these distinct datasets, which capture both field-scale distributions and lab-scale details, into preliminary three-dimensional fracture network models and flow simulations using free and open-source tools, such as MPLBM-UT (Santos et al. 2022). The emphasis has firstly been on trying to inform model construction through a geological lens, rather than focusing solely on mathematical or computational abstractions and, secondly, to use free and open-source software for this purpose to attempt to bridge the gap between the field, lab and models for a more wider field of experts.Ovaskainen, Nikolas. 2023. “Fractopo: A Python Package for Fracture Network Analysis.” Journal of Open Source Software 8 (85): 5300. https://doi.org/10.21105/joss.05300.Ovaskainen, Nikolas Aleksi, Pietari Mikael Skyttä, Nicklas Johan Nordbäck, and Jon Oskar Engström. 2023. “Detailed Investigation of Multi-Scale Fracture Networks in Glacially Abraded Crystalline Bedrock at Åland Islands, Finland.” Solid Earth 14 (6): 603–24. https://doi.org/10.5194/se-14-603-2023.Santos, Javier E., Alex Gigliotti, Abhishek Bihani, Christopher Landry, Marc A. Hesse, Michael J. Pyrcz, and Maša Prodanović. 2022. “MPLBM-UT: Multiphase LBM Library for Permeable Media Analysis.” SoftwareX 18 (June): 101097. https://doi.org/10.1016/j.softx.2022.101097.

Abedinifar et al., (2024). KielMAT: Kiel Motion Analysis Toolbox - An Open-Source Python Toolbox for Analyzing Neurological Motion Data from Various Recording Modalities. Journal of Open Source Software, 9(102), 6842, https://doi.org/10.21105/joss.06842

Denovellis et al., (2022). Spectral Connectivity: a python package for computing multitaper spectral estimates and frequency-domain brain connectivity measures on the CPU and GPU. Journal of Open Source Software, 7(80), 4840, https://doi.org/10.21105/joss.04840

Bhandari et al., (2024). servir-aces: A Python Package for Training Machine Learning Models for Remote Sensing Applications. Journal of Open Source Software, 9(99), 6729, https://doi.org/10.21105/joss.06729

Vernon et al., (2021). cerf: A Python package to evaluate the feasibility and costs of power plant siting for alternative futures. Journal of Open Source Software, 6(65), 3601, https://doi.org/10.21105/joss.03601

Mohammadzadeh et al., (2023). SarcGraph: A Python package for analyzing the contractile behavior of pluripotent stem cell-derived cardiomyocytes. Journal of Open Source Software, 8(85), 5322, https://doi.org/10.21105/joss.05322

Miranda et al., (2023). DeepOF: a Python package for supervised and unsupervised pattern recognition in mice motion tracking data. Journal of Open Source Software, 8(86), 5394, https://doi.org/10.21105/joss.05394

Kotthoff et al., (2024). open-mastr: A Python Package to Download and Process the German Energy Registry Marktstammdatenregister. Journal of Open Source Software, 9(100), 6758, https://doi.org/10.21105/joss.06758

Deformation bands, or tabular zones of localised strain, are a common manifestation of deformation in upper crustal sedimentary rocks. Any mining or energy-related engineering applications must consider the possibility of reactivating these pre-existing failure planes because doing so can cause seismicity and compartmentalise the reservoir. However, there has only been a small amount of research done on laboratory-induced deformation in rocks with natural deformation features.On a low porosity bioclastic calcarenite from the Cotiella Basin, Spanish Pyrenees, our current experimental work aims to capture, for the first time to our knowledge, the dominant failure mechanisms during the reactivation of natural deformation bands oriented at different angles to the principal stress direction. At the I12-JEEP beamline at the synchrotron facility of Diamond Light Source, UK, we carried out triaxial compression experiments using a modified version of the Mjolnir cell used by Cartwright-Taylor et al., (2022) to examine how these highly heterogeneous rocks respond to additional mechanical deformation. During the deformation experiments, 4D (time and space) x-ray tomography images (8 m voxel size resolution) were acquired. We tested confining pressures between 10 MPa and 30 MPa.The mechanical data demonstrate that the existence of natural deformation features within the tested samples weakens the material. For instance, solid samples of the host rock subjected to the same confining pressures had higher peak differential stresses. Additionally, our findings demonstrate that new deformation bands form as their angle, θ, to σ1 increases, while the reactivation of pre-exiting deformation bands in this low porosity carbonate only occurs for dipping angles close to 70o. The spatio-temporal relationships between the naturally occurring and laboratory-induced deformation bands and fractures were investigated using time-resolved x-ray tomography and Digital Volume Correlation (DVC). Volumetric and shear strain fields were calculated using the SPAM software (Stamati et al., 2020). The orientation of the recently formed failure planes is influenced by the orientation of the pre-existing bands, as well as their width and the presence (or absence) of porosity along their length. Additionally, pre-existing secondary deformation features found in the tested material trigger additional mechanical damage that either promotes the development or deflects the new failure planes.ReferencesCartwright-Taylor et al. 2022, Nature Communications 13, 6169, https://doi.org/10.1038/s41467-022-33855-zStamati et al. 2020, Journal of Open Source Software, 5(51), 2286, https://doi.org/10.21105/joss.0228

Carragher et al., (2020). c212: An R Package for the Detection of Safety Signals in Clinical Trials Using Body-Systems (System Organ Classes). Journal of Open Source Software, 5(56), 2706, https://doi.org/10.21105/joss.02706

Strittmatter et al., (2025). SweetBean: A declarative language for behavioral experiments with human and artificial participants. Journal of Open Source Software, 10(107), 7703, https://doi.org/10.21105/joss.07703

Collaboration et al., (2021). The Pencil Code, a modular MPI code for partial differential equations and particles: multipurpose and multiuser-maintained. Journal of Open Source Software, 6(58), 2807, https://doi.org/10.21105/joss.02807

Babarit et al., (2024). CN-AeroModels: A C++ implementation of aerodynamic models for wind propulsion systems of cargo ships. Journal of Open Source Software, 9(102), 6940, https://doi.org/10.21105/joss.06940

Wagg et al., (2025). cogsworth: A Gala of COSMIC proportions combining binary stellar evolution and galactic dynamics. Journal of Open Source Software, 10(105), 7400, https://doi.org/10.21105/joss.07400