ANN: DIPY 1.1.1 - a powerful release
We are excited to announce a new release of DIPY: DIPY 1.1.1 is out! In addition: a) A new 5 day workshop available during March 16-20 to learn the theory and applications of the hundreds of methods available in DIPY 1.1.1 Intense! See the exquisite program here <https://workshop.dipy.org>. *b) Given the need for a myriad of new DIPY derivative projects, DIPY moved to its own organization in GitHub. **Long live DIPY! * *And therefore, *https://github.com/dipy/dipy* supersedes https://github.com/nipy/dipy <https://github.com/nipy/dipy> The old link will be available as a redirect link for the next 6 months.* c) Please support us by *citing** DIPY* in your papers using the following DOI: 10.3389/fninf.2014.00008 <https://www.ncbi.nlm.nih.gov/pubmed/24600385>otherwise the DIPY citation police will find you. ;) DIPY 1.1.1 (Friday, 10 January 2020) This release received contributions from 11 developers (the full release notes are at: https://dipy.org/documentation/1.1.1./release_notes/release1.1/). Thank you all for your contributions and feedback! Please click here <https://dipy.org/documentation/1.1.1./api_changes/> to check API changes. Highlights of this release include: - New module for deep learning DIPY.NN (uses TensorFlow 2.0). - Improved DKI performance and increased utilities. - Non-linear and RESTORE fits from DTI compatible now with DKI. - Numerical solutions for estimating axial, radial and mean kurtosis. - Added Kurtosis Fractional Anisotropy by Glenn et al. 2015. - Added Mean Kurtosis Tensor by Hansen et al. 2013. - Nibabel minimum version is 3.0.0. - Azure CI added and Appveyor CI removed. - New command line interfaces for LPCA, MPPCA and Gibbs Unringing. - New MTMS CSD tutorial added. - Horizon refactored and updated to support StatefulTractograms. - Speeded up all cython modules by using a smarter configuration setting. - All tutorials updated to API changes and 2 new tutorials added. - Large documentation update. - Closed 126 issues and merged 50 pull requests. Note: - Have in mind that DIPY stopped supporting Python 2 after version 0.16.0. All major Python projects have switched to Python 3. It is time that you switch too. To upgrade or install <http://dipy.org/release0.10.html> DIPY Run the following command in your terminal: <http://dipy.org/release0.10.html> pip install --upgrade dipy or conda install -c conda-forge dipy This version of DIPY depends on nibabel (3.0.0+). For visualization you need FURY (0.4.0+). Questions or suggestions? For any questions go to http://dipy.org, or send an e-mail to dipy@python.org <neuroimaging@python.org> We also have an instant messaging service and chat room available at https://gitter.im/nipy/dipy On behalf of the DIPY developers, Eleftherios Garyfallidis, Ariel Rokem, Serge Koudoro https://dipy.org/contributors
If the Scikit-learn mailing list is going to include announcements of related package releases, could we please get a line or two describing that package? I expect most readers here don't know of DIPY, or of its relevance to Scikit-learn users. (I'm still not sure why it's generally relevant to scikit-learn users.) Thanks On Fri, 17 Jan 2020 at 04:04, Eleftherios Garyfallidis < garyfallidis@gmail.com> wrote:
I was unaware of this package, and had to look it up. It's my opinion that his package is only relevant to a likely small subset of users engaged in computational neuroanatomy. I am not sure updates really belong on this list... Andrew <~~~~~~~~~~~~~~~~~~~~~~~~~~~> J. Andrew Howe, PhD LinkedIn Profile <http://www.linkedin.com/in/ahowe42> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/> Open Researcher and Contributor ID (ORCID) <http://orcid.org/0000-0002-3553-1990> Github Profile <http://github.com/ahowe42> Personal Website <http://www.andrewhowe.com> I live to learn, so I can learn to live. - me <~~~~~~~~~~~~~~~~~~~~~~~~~~~> On Sat, Jan 18, 2020 at 1:12 PM Joel Nothman <joel.nothman@gmail.com> wrote:
Hello Joel, Here is the short description about DIPY as requested. DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging. Gael and Alex from sklearn's leadership team are aware of the project and its importance to the Pythonic community. Have in mind that there is an extremely low number of community based open source projects in medical imaging in Python and DIPY is one of the very few examples that is actually stable and growing. DIPY is quite unique because it provides methods (algorithms developed from scratch in Python) for solving medical imaging problems. Some of the algorithms are very generic for example we have image registration and denoising algorithms and can be used across fields. It is true that in our website it shows that focus is on diffusion imaging but now this is changing and we will be updating all our websites and systems accordingly to explain the more generic extent of the library during the following weeks. Your call at the end. It would be nice if you can spread the word. No hard feelings otherwise. Best, Eleftherios On Sat, Jan 18, 2020 at 8:12 AM Joel Nothman <joel.nothman@gmail.com> wrote:
If the Scikit-learn mailing list is going to include announcements of related package releases, could we please get a line or two describing that package? I expect most readers here don't know of DIPY, or of its relevance to Scikit-learn users. (I'm still not sure why it's generally relevant to scikit-learn users.) Thanks On Fri, 17 Jan 2020 at 04:04, Eleftherios Garyfallidis < garyfallidis@gmail.com> wrote:
I was unaware of this package, and had to look it up. It's my opinion that his package is only relevant to a likely small subset of users engaged in computational neuroanatomy. I am not sure updates really belong on this list... Andrew <~~~~~~~~~~~~~~~~~~~~~~~~~~~> J. Andrew Howe, PhD LinkedIn Profile <http://www.linkedin.com/in/ahowe42> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/> Open Researcher and Contributor ID (ORCID) <http://orcid.org/0000-0002-3553-1990> Github Profile <http://github.com/ahowe42> Personal Website <http://www.andrewhowe.com> I live to learn, so I can learn to live. - me <~~~~~~~~~~~~~~~~~~~~~~~~~~~> On Sat, Jan 18, 2020 at 1:12 PM Joel Nothman <joel.nothman@gmail.com> wrote:
Hello Joel, Here is the short description about DIPY as requested. DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging. Gael and Alex from sklearn's leadership team are aware of the project and its importance to the Pythonic community. Have in mind that there is an extremely low number of community based open source projects in medical imaging in Python and DIPY is one of the very few examples that is actually stable and growing. DIPY is quite unique because it provides methods (algorithms developed from scratch in Python) for solving medical imaging problems. Some of the algorithms are very generic for example we have image registration and denoising algorithms and can be used across fields. It is true that in our website it shows that focus is on diffusion imaging but now this is changing and we will be updating all our websites and systems accordingly to explain the more generic extent of the library during the following weeks. Your call at the end. It would be nice if you can spread the word. No hard feelings otherwise. Best, Eleftherios On Sat, Jan 18, 2020 at 8:12 AM Joel Nothman <joel.nothman@gmail.com> wrote:
participants (3)
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Andrew Howe -
Eleftherios Garyfallidis -
Joel Nothman