{"id":25008780,"url":"https://github.com/kerkelae/disimpy","last_synced_at":"2025-04-12T17:45:04.273Z","repository":{"id":38189107,"uuid":"253285972","full_name":"kerkelae/disimpy","owner":"kerkelae","description":"Massively parallel Monte Carlo diffusion MR simulator written in Python.","archived":false,"fork":false,"pushed_at":"2024-11-02T14:20:15.000Z","size":11751,"stargazers_count":25,"open_issues_count":5,"forks_count":9,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-11T11:58:21.676Z","etag":null,"topics":["cuda","diffusion-mri","gpu-computing","monte-carlo-simulation"],"latest_commit_sha":null,"homepage":"https://disimpy.readthedocs.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kerkelae.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"license.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2020-04-05T16:57:18.000Z","updated_at":"2025-03-18T19:40:26.000Z","dependencies_parsed_at":"2024-01-15T13:36:02.538Z","dependency_job_id":"b6619cf5-7a6e-49be-a4c8-94b114e03d6c","html_url":"https://github.com/kerkelae/disimpy","commit_stats":{"total_commits":157,"total_committers":3,"mean_commits":"52.333333333333336","dds":0.05095541401273884,"last_synced_commit":"df59c33a42d1c208827c9cf59dc33306a4c92642"},"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kerkelae%2Fdisimpy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kerkelae%2Fdisimpy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kerkelae%2Fdisimpy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kerkelae%2Fdisimpy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kerkelae","download_url":"https://codeload.github.com/kerkelae/disimpy/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248519472,"owners_count":21117757,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cuda","diffusion-mri","gpu-computing","monte-carlo-simulation"],"created_at":"2025-02-05T03:28:45.690Z","updated_at":"2025-04-12T17:45:04.250Z","avatar_url":"https://github.com/kerkelae.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"*******\nDisimpy\n*******\n\nDisimpy is a Python package for generating simulated diffusion-weighted MR\nsignals that can be useful in the development and validation of data\nacquisition and analysis methods. The data is generated by Monte Carlo random\nwalk simulations that run massively parallel on Nvidia CUDA-capable GPUs. If\nyou use Disimpy in work that leads to a scientific publication, please cite\n[1]_, where the details about signal generation can also be found.\n\nRequirements and installation\n#############################\n\nFollow the `installation instructions\n\u003chttps://disimpy.readthedocs.io/en/latest/installation.html\u003e`_.\n    \nUsage example\n#############\n\nRead the `tutorial \u003chttps://disimpy.readthedocs.io/en/latest/tutorial.html\u003e`_\nto learn how to use Disimpy.\n\nValidation\n##########\n\nDisimpy's functionality has been validated by comparing its results to\nanalytical solutions and to results from other simulators (e.g., `Camino\n\u003chttp://camino.cs.ucl.ac.uk/\u003e`_ and `MISST\n\u003chttp://mig.cs.ucl.ac.uk/index.php?n=Tutorial.MISST\u003e`_), and by automated\ntesting (:code:`disimpy.tests`). Examples of simulations used for validation\nare provided `here\n\u003chttps://disimpy.readthedocs.io/en/latest/validation.html\u003e`_. However, Disimpy\nis research software and some bugs undoubtedly remain. If you find any of them\nor encounter unexpected behaviour, please open an `issue on GitHub\n\u003chttps://github.com/kerkelae/disimpy/issues\u003e`_.\n\nContribute\n##########\n\nIf you want to contribute to the development of Disimpy, start by reading the\n`contributing guidelines\n\u003chttps://disimpy.readthedocs.io/en/latest/contributing.html\u003e`_.\n\nSupport\n#######\n\nIf you have questions or need help, open an `issue on Github\n\u003chttps://github.com/kerkelae/disimpy/issues\u003e`_.\n\nReferences\n##########\n\n.. [1] Kerkelä et al., (2020). Disimpy: A massively parallel Monte Carlo\n       simulator for generating diffusion-weighted MRI data in Python. Journal\n       of Open Source Software, 5(52), 2527.\n       https://doi.org/10.21105/joss.02527\n\nSponsors\n########\n\n|\n\n.. image:: https://disimpy.readthedocs.io/en/latest/_static/nihr_gosh_brc_logo.png\n   :width: 418\n   :alt: National Institute of Health Research Great Ormond Street Biomedical Research Centre\n   :align: center\n   :target: https://www.gosh.nhs.uk/our-research/our-research-infrastructure/nihr-great-ormond-street-hospital-brc/\n\n|\n\n.. image:: https://disimpy.readthedocs.io/en/latest/_static/gsoc_logo.png\n   :width: 200\n   :alt: Google Summer of Code\n   :align: center\n   :target: https://summerofcode.withgoogle.com/\n\n|\n\n.. image:: https://disimpy.readthedocs.io/en/latest/_static/rh_logo.png\n   :width: 300\n   :alt: ResearchHub\n   :align: center\n   :target: https://www.researchhub.com/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkerkelae%2Fdisimpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkerkelae%2Fdisimpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkerkelae%2Fdisimpy/lists"}