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Disimpy

Disimpy: A massively parallel Monte Carlo simulator for generating diffusion-weighted MRI data in Python - Published in JOSS (2020)

https://github.com/kerkelae/disimpy

Science Score: 93.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README and JOSS metadata
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

cuda diffusion-mri gpu-computing monte-carlo-simulation

Scientific Fields

Mathematics Computer Science - 84% confidence
Last synced: 4 months ago · JSON representation

Repository

Massively parallel Monte Carlo diffusion MR simulator written in Python.

Basic Info
Statistics
  • Stars: 26
  • Watchers: 2
  • Forks: 9
  • Open Issues: 5
  • Releases: 0
Topics
cuda diffusion-mri gpu-computing monte-carlo-simulation
Created almost 6 years ago · Last pushed about 1 year ago
Metadata Files
Readme License

README.rst 

*******
Disimpy
*******

Disimpy is a Python package for generating simulated diffusion-weighted MR
signals that can be useful in the development and validation of data
acquisition and analysis methods. The data is generated by Monte Carlo random
walk simulations that run massively parallel on Nvidia CUDA-capable GPUs. If
you use Disimpy in work that leads to a scientific publication, please cite
[1]_, where the details about signal generation can also be found.

Requirements and installation
#############################

Follow the `installation instructions
`_.
    
Usage example
#############

Read the `tutorial `_
to learn how to use Disimpy.

Validation
##########

Disimpy's functionality has been validated by comparing its results to
analytical solutions and to results from other simulators (e.g., `Camino
`_ and `MISST
`_), and by automated
testing (:code:`disimpy.tests`). Examples of simulations used for validation
are provided `here
`_. However, Disimpy
is research software and some bugs undoubtedly remain. If you find any of them
or encounter unexpected behaviour, please open an `issue on GitHub
`_.

Contribute
##########

If you want to contribute to the development of Disimpy, start by reading the
`contributing guidelines
`_.

Support
#######

If you have questions or need help, open an `issue on Github
`_.

References
##########

.. [1] Kerkelä et al., (2020). Disimpy: A massively parallel Monte Carlo
       simulator for generating diffusion-weighted MRI data in Python. Journal
       of Open Source Software, 5(52), 2527.
       https://doi.org/10.21105/joss.02527

Sponsors
########

|

.. image:: https://disimpy.readthedocs.io/en/latest/_static/nihr_gosh_brc_logo.png
   :width: 418
   :alt: National Institute of Health Research Great Ormond Street Biomedical Research Centre
   :align: center
   :target: https://www.gosh.nhs.uk/our-research/our-research-infrastructure/nihr-great-ormond-street-hospital-brc/

|

.. image:: https://disimpy.readthedocs.io/en/latest/_static/gsoc_logo.png
   :width: 200
   :alt: Google Summer of Code
   :align: center
   :target: https://summerofcode.withgoogle.com/

|

.. image:: https://disimpy.readthedocs.io/en/latest/_static/rh_logo.png
   :width: 300
   :alt: ResearchHub
   :align: center
   :target: https://www.researchhub.com/

Owner

  • Name: Leevi
  • Login: kerkelae
  • Kind: user

Scientist interested in artificial intelligence, neuroscience, and blockchain applications.

JOSS Publication

Disimpy: A massively parallel Monte Carlo simulator for generating diffusion-weighted MRI data in Python
Published
August 26, 2020
DOI
Volume 5, Issue 52, Page 2527
Authors
Leevi Kerkelä ORCID
UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
Fabio Nery
UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
Matt G. Hall
National Physical Laboratory, Teddington, United Kingdom, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
Chris A. Clark
UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
Editor
Kevin M. Moerman ORCID
Tags
diffusion MRI neuroscience microstructure
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GitHub Events

Total
  • Watch event: 3
  • Push event: 2
Last Year
  • Watch event: 3
  • Push event: 2

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 181
  • Total Committers: 4
  • Avg Commits per committer: 45.25
  • Development Distribution Score (DDS): 0.05
Past Year
  • Commits: 2
  • Committers: 1
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
kerkelae l****a@p****m 172
Renata Cruz r****5@g****m 5
fnery f****y@g****m 3
jhurtadomoreno 8****o 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 14
  • Total pull requests: 22
  • Average time to close issues: 5 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 4
  • Total pull request authors: 4
  • Average comments per issue: 0.86
  • Average comments per pull request: 0.09
  • Merged pull requests: 19
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • kerkelae (11)
  • alpha027 (1)
  • tsantini (1)
  • Emoryzzl (1)
Pull Request Authors
  • kerkelae (21)
  • fnery (4)
  • jhurtadomoreno (3)
  • renata-cruz (2)
Top Labels
Issue Labels
enhancement (6) documentation (3)
Pull Request Labels
enhancement (3) documentation (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 13 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 3
  • Total maintainers: 1
pypi.org: disimpy

Massively parallel diffusion MR simulator

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 13 Last month
Rankings
Dependent packages count: 7.4%
Forks count: 12.0%
Stargazers count: 13.4%
Average: 19.1%
Dependent repos count: 22.3%
Downloads: 40.5%
Maintainers (1)
kerkelae
Last synced: 4 months ago

Dependencies

docs/requirements.txt pypi
  • furo >=2023.05.20
  • ipython >=8.12.0
  • matplotlib >=3.7.1
  • nbsphinx >=0.9.2
  • numba >=0.57.0
  • numpy >=1.24.3
  • pytest >=7.3.1
  • scipy >=1.10.1
  • sphinx >=7.0.1
setup.py pypi
  • matplotlib >=3.7.1
  • numba >=0.57.0
  • numpy >=1.24.3
  • pytest >=7.3.1
  • scipy >=1.10.1