From avlawrence at email.arizona.edu Fri Dec 4 18:55:21 2020 From: avlawrence at email.arizona.edu (Ashley Victoria Lawrence) Date: Fri, 4 Dec 2020 16:55:21 -0700 Subject: [Neuroimaging] help using SPM beta images in python Message-ID: Hello, I have beta images in nifti format which were created in SPM12 in Matlab. I would like to take these already created beta images and perform further analyses on them using python because I would like to write a searchlight program. Unfortunately I am having trouble when loading the beta images into python. I have been using mydata = nib.load(beta.nii) betadata = mydata.get_data() However, this results in me just getting NAs or zeros in the shape of my beta images which are [79,95,79] When I view my nifti images in SPM they are not empty, so I'm sure there must be something wrong with how I am loading them. Does anyone know what I might be doing wrong? I am fairly new to python so I appreciate any guidance you might be able to provide. Thank you, Ashley -- Ashley V. Lawrence, M.A. Doctoral Candidate Clinical Neuropsychology Cognition and Neuroimaging Lab University of Arizona Pronouns: She, Her, Hers -------------- next part -------------- An HTML attachment was scrubbed... URL: From arokem at gmail.com Fri Dec 4 19:21:01 2020 From: arokem at gmail.com (Ariel Rokem) Date: Fri, 4 Dec 2020 16:21:01 -0800 Subject: [Neuroimaging] help using SPM beta images in python In-Reply-To: References: Message-ID: Hi Ashley, Just a hunch. Could you try: betadata = mydata.get_fdata() Instead of: betadata = mydata.get_data() Note the single character change from "data" to "fdata". Cheers, Ariel On Fri, Dec 4, 2020 at 3:55 PM Ashley Victoria Lawrence < avlawrence at email.arizona.edu> wrote: > Hello, > > I have beta images in nifti format which were created in SPM12 in Matlab. > I would like to take these already created beta images and perform further > analyses on them using python because I would like to write a searchlight > program. Unfortunately I am having trouble when loading the beta images > into python. I have been using > > mydata = nib.load(beta.nii) > betadata = mydata.get_data() > > However, this results in me just getting NAs or zeros in the shape of my > beta images which are [79,95,79] > > When I view my nifti images in SPM they are not empty, so I'm sure there > must be something wrong with how I am loading them. > > Does anyone know what I might be doing wrong? I am fairly new to python so > I appreciate any guidance you might be able to provide. > > Thank you, > Ashley > > -- > Ashley V. Lawrence, M.A. > Doctoral Candidate > Clinical Neuropsychology > Cognition and Neuroimaging Lab > University of Arizona > Pronouns: She, Her, Hers > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > -------------- next part -------------- An HTML attachment was scrubbed... URL: From avlawrence at email.arizona.edu Fri Dec 4 18:41:07 2020 From: avlawrence at email.arizona.edu (Ashley Victoria Lawrence) Date: Fri, 4 Dec 2020 16:41:07 -0700 Subject: [Neuroimaging] help using SPM beta images in python Message-ID: Hello, I have beta images in nifti format which were created in SPM12 in Matlab. I would like to take these already created beta images and perform further analyses on them using python because I would like to write a searchlight program. Unfortunately I am having trouble when loading the beta images into python. I have been using mydata = nib.load(beta.nii) betadata = mydata.get_data() However, this results in me just getting NAs or zeros in the shape of my beta images which are [79,95,79] Does anyone know what I might be doing wrong? I am fairly new to python so I appreciate any guidance you might be able to provide. Thank you, Ashley -- Ashley V. Lawrence, M.A. Doctoral Candidate Clinical Neuropsychology Cognition and Neuroimaging Lab University of Arizona Pronouns: She, Her, Hers -------------- next part -------------- An HTML attachment was scrubbed... URL: From matthew.brett at gmail.com Thu Dec 10 06:01:33 2020 From: matthew.brett at gmail.com (Matthew Brett) Date: Thu, 10 Dec 2020 11:01:33 +0000 Subject: [Neuroimaging] Paper on Python in brain imaging education Message-ID: Hi, I just came across this paper: https://ieeexplore.ieee.org/abstract/document/9066431 X. Zhang, J. Huang, Y. Yang, X. He, R. Liu and N. Zhong, "Applying Python in Brain Science Education," 2019 International Joint Conference on Information, Media and Engineering (IJCIME), Osaka, Japan, 2019, pp. 396-400, doi: 10.1109/IJCIME49369.2019.00086. >From the abstract: """ Python and its powerful technology ecosystem provide support for the teaching and practice of brain science. In this paper, the related resources in the Python ecosystem of neuroimaging technologies were used as teaching and practice materials. This article discussed how to use Python and corresponding development tools to complete neuroimaging data preprocessing, functional connectivity analysis, multivoxel pattern analysis, and searchlight analysis in brain science teaching, and the corresponding practice processes were also demonstrated with examples. """ Nibabel does a star turn, as does NiLearn. Cheers, Matthew From elef at indiana.edu Thu Dec 10 07:41:48 2020 From: elef at indiana.edu (Eleftherios Garyfallidis) Date: Thu, 10 Dec 2020 07:41:48 -0500 Subject: [Neuroimaging] Paper on Python in brain imaging education In-Reply-To: <46f6c58a60ff4525883d53bc43ebb6c8@BL-CCI-D1S07.ads.iu.edu> References: <46f6c58a60ff4525883d53bc43ebb6c8@BL-CCI-D1S07.ads.iu.edu> Message-ID: Great to see this. Thank you for sharing. Matthew. :) On Thu, Dec 10, 2020 at 6:02 AM Matthew Brett wrote: > Hi, > > I just came across this paper: > > https://ieeexplore.ieee.org/abstract/document/9066431 > > X. Zhang, J. Huang, Y. Yang, X. He, R. Liu and N. Zhong, "Applying > Python in Brain Science Education," 2019 International Joint > Conference on Information, Media and Engineering (IJCIME), Osaka, > Japan, 2019, pp. 396-400, doi: 10.1109/IJCIME49369.2019.00086. > > From the abstract: > > """ > Python and its powerful technology ecosystem provide support for the > teaching and practice of brain science. In this paper, the related > resources in the Python ecosystem of neuroimaging technologies were > used as teaching and practice materials. This article discussed how to > use Python and corresponding development tools to complete > neuroimaging data preprocessing, functional connectivity analysis, > multivoxel pattern analysis, and searchlight analysis in brain science > teaching, and the corresponding practice processes were also > demonstrated with examples. > """ > > Nibabel does a star turn, as does NiLearn. > > Cheers, > > Matthew > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > -------------- next part -------------- An HTML attachment was scrubbed... URL: From bertrand.thirion at inria.fr Thu Dec 10 07:44:50 2020 From: bertrand.thirion at inria.fr (bthirion) Date: Thu, 10 Dec 2020 13:44:50 +0100 Subject: [Neuroimaging] Paper on Python in brain imaging education In-Reply-To: References: Message-ID: <9601659d-057e-8abf-6f21-66354098540e@inria.fr> Cool ! Bertrand On 10/12/2020 12:01, Matthew Brett wrote: > Hi, > > I just came across this paper: > > https://ieeexplore.ieee.org/abstract/document/9066431 > > X. Zhang, J. Huang, Y. Yang, X. He, R. Liu and N. Zhong, "Applying > Python in Brain Science Education," 2019 International Joint > Conference on Information, Media and Engineering (IJCIME), Osaka, > Japan, 2019, pp. 396-400, doi: 10.1109/IJCIME49369.2019.00086. > > From the abstract: > > """ > Python and its powerful technology ecosystem provide support for the > teaching and practice of brain science. In this paper, the related > resources in the Python ecosystem of neuroimaging technologies were > used as teaching and practice materials. This article discussed how to > use Python and corresponding development tools to complete > neuroimaging data preprocessing, functional connectivity analysis, > multivoxel pattern analysis, and searchlight analysis in brain science > teaching, and the corresponding practice processes were also > demonstrated with examples. > """ > > Nibabel does a star turn, as does NiLearn. > > Cheers, > > Matthew > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging From jbpoline at gmail.com Thu Dec 10 08:53:14 2020 From: jbpoline at gmail.com (JB Poline) Date: Thu, 10 Dec 2020 08:53:14 -0500 Subject: [Neuroimaging] Paper on Python in brain imaging education In-Reply-To: <9601659d-057e-8abf-6f21-66354098540e@inria.fr> References: <9601659d-057e-8abf-6f21-66354098540e@inria.fr> Message-ID: :) On Thu, Dec 10, 2020 at 7:45 AM bthirion wrote: > Cool ! > Bertrand > > On 10/12/2020 12:01, Matthew Brett wrote: > > Hi, > > > > I just came across this paper: > > > > https://ieeexplore.ieee.org/abstract/document/9066431 > > > > X. Zhang, J. Huang, Y. Yang, X. He, R. Liu and N. Zhong, "Applying > > Python in Brain Science Education," 2019 International Joint > > Conference on Information, Media and Engineering (IJCIME), Osaka, > > Japan, 2019, pp. 396-400, doi: 10.1109/IJCIME49369.2019.00086. > > > > From the abstract: > > > > """ > > Python and its powerful technology ecosystem provide support for the > > teaching and practice of brain science. In this paper, the related > > resources in the Python ecosystem of neuroimaging technologies were > > used as teaching and practice materials. This article discussed how to > > use Python and corresponding development tools to complete > > neuroimaging data preprocessing, functional connectivity analysis, > > multivoxel pattern analysis, and searchlight analysis in brain science > > teaching, and the corresponding practice processes were also > > demonstrated with examples. > > """ > > > > Nibabel does a star turn, as does NiLearn. > > > > Cheers, > > > > Matthew > > _______________________________________________ > > Neuroimaging mailing list > > Neuroimaging at python.org > > https://mail.python.org/mailman/listinfo/neuroimaging > > > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > -------------- next part -------------- An HTML attachment was scrubbed... URL: From gael.varoquaux at normalesup.org Fri Dec 11 04:47:05 2020 From: gael.varoquaux at normalesup.org (Gael Varoquaux) Date: Fri, 11 Dec 2020 10:47:05 +0100 Subject: [Neuroimaging] Paper on Python in brain imaging education In-Reply-To: References: Message-ID: <20201211094705.iayzrka3vmpqkjum@phare.normalesup.org> Cool :). Thanks for the link. I think that by now, Python is well established in brain imaging. On Thu, Dec 10, 2020 at 11:01:33AM +0000, Matthew Brett wrote: > Hi, > I just came across this paper: > https://ieeexplore.ieee.org/abstract/document/9066431 > X. Zhang, J. Huang, Y. Yang, X. He, R. Liu and N. Zhong, "Applying > Python in Brain Science Education," 2019 International Joint > Conference on Information, Media and Engineering (IJCIME), Osaka, > Japan, 2019, pp. 396-400, doi: 10.1109/IJCIME49369.2019.00086. > >From the abstract: > """ > Python and its powerful technology ecosystem provide support for the > teaching and practice of brain science. In this paper, the related > resources in the Python ecosystem of neuroimaging technologies were > used as teaching and practice materials. This article discussed how to > use Python and corresponding development tools to complete > neuroimaging data preprocessing, functional connectivity analysis, > multivoxel pattern analysis, and searchlight analysis in brain science > teaching, and the corresponding practice processes were also > demonstrated with examples. > """ > Nibabel does a star turn, as does NiLearn. > Cheers, > Matthew > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging -- Gael Varoquaux Research Director, INRIA Visiting professor, McGill http://gael-varoquaux.info http://twitter.com/GaelVaroquaux From richard.hoechenberger at gmail.com Mon Dec 14 13:32:56 2020 From: richard.hoechenberger at gmail.com (=?UTF-8?Q?Richard_H=C3=B6chenberger?=) Date: Mon, 14 Dec 2020 19:32:56 +0100 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets Message-ID: Hello all, many of you may know https://openneuro.org, the platform for sharing neuroimaging and electrophysiological data. I was experiencing a little bit of trouble downloading datasets with the official openneuro-cli application, so I started my own app, named openneuro-py. openneuro-py is a command line tool that allows one to retrieve OpenNeuro datasets. You can restrict downloading to only a subset of files or directories, or exclude specific items from the download if you wish. Interrupted downloads may be resumed at a later time. You can find installation and usage instructions, and of course the source code, at https://github.com/hoechenberger/openneuro-py This project is literally just two days old, so there are probably bugs and missing features. However, I think it could already be of use to some of you. I'd love to hear your feedback! Either open an issue on the GitHub page, or drop me an email. And if you feel like contributing ? go ahead, that would be awesome! Enjoy, and stay healthy! Richard -------------- next part -------------- An HTML attachment was scrubbed... URL: From garyfallidis at gmail.com Mon Dec 14 14:23:59 2020 From: garyfallidis at gmail.com (Eleftherios Garyfallidis) Date: Mon, 14 Dec 2020 14:23:59 -0500 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets In-Reply-To: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> References: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> Message-ID: Hi Richard, Thank you for sharing. Best, Eleftherios On Mon, Dec 14, 2020 at 1:33 PM Richard H?chenberger < richard.hoechenberger at gmail.com> wrote: > Hello all, > > many of you may know https://openneuro.org, the platform for sharing > neuroimaging and electrophysiological data. > > I was experiencing a little bit of trouble downloading datasets with the > official openneuro-cli application, so I started my own app, named > openneuro-py. > > openneuro-py is a command line tool that allows one to retrieve OpenNeuro > datasets. You can restrict downloading to only a subset of files or > directories, or exclude specific items from the download if you wish. > Interrupted downloads may be resumed at a later time. > > You can find installation and usage instructions, and of course the source > code, at https://github.com/hoechenberger/openneuro-py > > This project is literally just two days old, so there are probably bugs > and missing features. However, I think it could already be of use to some > of you. I'd love to hear your feedback! Either open an issue on the GitHub > page, or drop me an email. And if you feel like contributing ? go ahead, > that would be awesome! > > Enjoy, > and stay healthy! > > Richard > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From aldo.camargo at gmail.com Tue Dec 15 09:45:09 2020 From: aldo.camargo at gmail.com (Aldo Camargo) Date: Tue, 15 Dec 2020 09:45:09 -0500 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets In-Reply-To: References: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> Message-ID: Hi Richard, Very interesting application. One question, which dataset can be downloaded? Thanks a lot, Aldo On Mon, Dec 14, 2020 at 2:24 PM Eleftherios Garyfallidis < garyfallidis at gmail.com> wrote: > Hi Richard, > Thank you for sharing. > Best, > Eleftherios > > On Mon, Dec 14, 2020 at 1:33 PM Richard H?chenberger < > richard.hoechenberger at gmail.com> wrote: > >> Hello all, >> >> many of you may know https://openneuro.org, the platform for sharing >> neuroimaging and electrophysiological data. >> >> I was experiencing a little bit of trouble downloading datasets with the >> official openneuro-cli application, so I started my own app, named >> openneuro-py. >> >> openneuro-py is a command line tool that allows one to retrieve OpenNeuro >> datasets. You can restrict downloading to only a subset of files or >> directories, or exclude specific items from the download if you wish. >> Interrupted downloads may be resumed at a later time. >> >> You can find installation and usage instructions, and of course the >> source code, at https://github.com/hoechenberger/openneuro-py >> >> This project is literally just two days old, so there are probably bugs >> and missing features. However, I think it could already be of use to some >> of you. I'd love to hear your feedback! Either open an issue on the GitHub >> page, or drop me an email. And if you feel like contributing ? go ahead, >> that would be awesome! >> >> Enjoy, >> and stay healthy! >> >> Richard >> >> _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > -------------- next part -------------- An HTML attachment was scrubbed... URL: From markiewicz at stanford.edu Tue Dec 15 10:12:10 2020 From: markiewicz at stanford.edu (Christopher Markiewicz) Date: Tue, 15 Dec 2020 15:12:10 +0000 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets In-Reply-To: References: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> , Message-ID: Hi all, FWIW almost all public datasets have been pushed to GitHub and can be accessed via datalad (exceptions being tracked on these issues: https://github.com/OpenNeuroOrg/openneuro/issues/1741 and https://github.com/OpenNeuroOrg/openneuro/issues/1743). datalad install https://github.com/OpenNeuroDatasets/ds00WXYZ.git Datalad makes it pretty straightforward to download only the portions of the data you want. This isn't to deprecate Richard's tool. There are some things only doable through the API at present (uploading, accessing private data) and I would also find a Python interface much more comprehensible and hackable. Just want to make it clear that in many cases you can bypass the website and API altogether. Best, Chris ________________________________________ From: Neuroimaging on behalf of Aldo Camargo Sent: Tuesday, December 15, 2020 9:45 AM To: Neuroimaging analysis in Python Subject: Re: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets Hi Richard, Very interesting application. One question, which dataset can be downloaded? Thanks a lot, Aldo On Mon, Dec 14, 2020 at 2:24 PM Eleftherios Garyfallidis > wrote: Hi Richard, Thank you for sharing. Best, Eleftherios On Mon, Dec 14, 2020 at 1:33 PM Richard H?chenberger > wrote: Hello all, many of you may know https://openneuro.org, the platform for sharing neuroimaging and electrophysiological data. I was experiencing a little bit of trouble downloading datasets with the official openneuro-cli application, so I started my own app, named openneuro-py. openneuro-py is a command line tool that allows one to retrieve OpenNeuro datasets. You can restrict downloading to only a subset of files or directories, or exclude specific items from the download if you wish. Interrupted downloads may be resumed at a later time. You can find installation and usage instructions, and of course the source code, at https://github.com/hoechenberger/openneuro-py This project is literally just two days old, so there are probably bugs and missing features. However, I think it could already be of use to some of you. I'd love to hear your feedback! Either open an issue on the GitHub page, or drop me an email. And if you feel like contributing ? go ahead, that would be awesome! Enjoy, and stay healthy! Richard _______________________________________________ Neuroimaging mailing list Neuroimaging at python.org https://mail.python.org/mailman/listinfo/neuroimaging From richard.hoechenberger at gmail.com Tue Dec 15 11:46:39 2020 From: richard.hoechenberger at gmail.com (=?UTF-8?Q?Richard_H=C3=B6chenberger?=) Date: Tue, 15 Dec 2020 17:46:39 +0100 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets In-Reply-To: References: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> Message-ID: Hello Aldo, On Tue, Dec 15, 2020 at 3:46 PM Aldo Camargo wrote: > Hi Richard, > > Very interesting application. One question, which dataset can be > downloaded? > theoretically, all of those you can find on openneuro.org! Practically, I'm running into issues now and then, because the server sometimes indicates the presence of files which actually do not exist / cannot be retrieved. I'll get in touch with OpenNeuro folks to track this down. We're actually using this tool to retrieve several datasets during automated testing of one of our pipelines, and so far it has proven to work reliably. Best wishes, Richard -------------- next part -------------- An HTML attachment was scrubbed... URL: From richard.hoechenberger at gmail.com Tue Dec 15 11:50:58 2020 From: richard.hoechenberger at gmail.com (=?UTF-8?Q?Richard_H=C3=B6chenberger?=) Date: Tue, 15 Dec 2020 17:50:58 +0100 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets In-Reply-To: References: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> Message-ID: Hello, just to briefly comment on this one: On Tue, Dec 15, 2020 at 4:27 PM Christopher Markiewicz < markiewicz at stanford.edu> wrote: > Hi all, > > FWIW almost all public datasets have been pushed to GitHub and can be > accessed via datalad (exceptions being tracked on these issues: > https://github.com/OpenNeuroOrg/openneuro/issues/1741 and > https://github.com/OpenNeuroOrg/openneuro/issues/1743). > > datalad install https://github.com/OpenNeuroDatasets/ds00WXYZ.git > > I specifically wanted to avoid using datalad for several reasons: - I've seen people struggle to install (esp. on macOS) and use it (including myself) - the sync between OpenNeuro and GitHub doesn't seem to work reliably, so we were having trouble retrieving the latest revisions of datasets using datalad - as far as I understand, there's no way to download a specific revision (version number) of a dataset using datalad openneuro-py attempts to do away with these issues. Best wishes, Richard -------------- next part -------------- An HTML attachment was scrubbed... URL: From lists at onerussian.com Tue Dec 15 11:52:50 2020 From: lists at onerussian.com (Yaroslav Halchenko) Date: Tue, 15 Dec 2020 11:52:50 -0500 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets In-Reply-To: References: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> Message-ID: <20201215165250.GC1090323@lena.dartmouth.edu> On Tue, 15 Dec 2020, Christopher Markiewicz wrote: > Hi all, > FWIW almost all public datasets have been pushed to GitHub and can be accessed via datalad (exceptions being tracked on these issues: https://github.com/OpenNeuroOrg/openneuro/issues/1741 and https://github.com/OpenNeuroOrg/openneuro/issues/1743). > datalad install https://github.com/OpenNeuroDatasets/ds00WXYZ.git > Datalad makes it pretty straightforward to download only the portions of the data you want. FWIW, datalad is also accompanied with Python API for all of its functionality, so analogous command with fetching specific subjects (or any path you like) would be smth like $> python3 -c 'import datalad.api as dl; ds = dl.install("https://github.com/OpenNeuroDatasets/ds000001.git"); ds.get([f"sub-{i:02d}" for i in [1,2,3]], jobs=5)' Similarly it would work for HCP, many INDI, etc datasets. Explore some more on http://datasets.datalad.org/ and learn more about datalad at http://handbook.datalad.org/ > This isn't to deprecate Richard's tool. Agree! DataLad uses git and git-annex -- might be a bit heavy of a dependency for some use cases. We are working hard though to ensure datalad with all dependencies be easy to install (windows remains an issue somewhat, but ok for "downloader" part ;)) BUT - see https://github.com/nidata/nidata - a similar concept excercised in the past (back then it was openfmri), and covers more of other data sources. Unfortunately development stopped. If to pursue an endeavor of a pure downloader -- might be worth somehow joining forces with prior effort. - with datalad you get not just a "downloader" but overall content management not only for "source data" but for results as well. See e.g. https://github.com/ReproNim/containers/#a-typical-workflow for an example of prototypical computation workflow, where source data and results are versioned and "reproducible". Sorry for a shameless DataLad plug and some grains of salt in my follow up, I don't want to sound negative and not-supportive, but it is also hard to be unbiased with my DataLad hat on. And if project to be created/maintained beyond "an exercise", those points might better be considered. Cheers, -- Yaroslav O. Halchenko Center for Open Neuroscience http://centerforopenneuroscience.org Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 WWW: http://www.linkedin.com/in/yarik From lists at onerussian.com Tue Dec 15 12:36:54 2020 From: lists at onerussian.com (Yaroslav Halchenko) Date: Tue, 15 Dec 2020 12:36:54 -0500 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets In-Reply-To: References: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> Message-ID: <20201215173654.GH1090323@lena.dartmouth.edu> On Tue, 15 Dec 2020, Richard H?chenberger wrote: > Hello, just to briefly comment on this one: > On Tue, Dec 15, 2020 at 4:27 PM Christopher Markiewicz > wrote: > Hi all, > FWIW almost all public datasets have been pushed to GitHub and can be > accessed via datalad (exceptions being tracked on these issues: > https://github.com/OpenNeuroOrg/openneuro/issues/1741 and > https://github.com/OpenNeuroOrg/openneuro/issues/1743). > ? ? datalad install https://github.com/OpenNeuroDatasets/ds00WXYZ.git > I specifically wanted to avoid using datalad for several reasons: > * I've seen people struggle to install (esp. on macOS) and use it > (including myself) please share your difficulties! mac should be the next to the easiest to be installed/used on. http://handbook.datalad.org/en/latest/intro/installation.html?highlight=OSX#macos-osx brew install git-annex pip3 install datalad should do it, but even better, instead of pip install, you do "conda install -c conda-forge datalad" if you have conda available. If not, soon there would be installer (well, it is there but under active RF) https://github.com/datalad/datalad-installer/ so you would just do smth like pip install datalad_installer # no dependencies, light and easy python -m datalad_installer miniconda git-annex datalad and it should do the "right thing". Also the effort is (very slowly) ongoing to provide OSX build of git-annex on conda-forge. A sloppy unfinished approach: https://github.com/conda-forge/git-annex-feedstock/pull/107 If not -- please file an issue so we could fix. > * as far as I understand, there's no way to download a specific revision > (version number) of a dataset using datalad? it is just two steps procedure indeed ATM -- you would just need to "manually" checkout corresponding version you like, e.g. $> python3 -c 'import datalad.api as dl; ds = dl.install("https://github.com/OpenNeuroDatasets/ds000001.git"); ds.repo.checkout("1.0.0")' we need to make it easier, e.g. see discussion/chime-in on https://github.com/datalad/datalad/issues/1810#issuecomment-379365779 so we should add support for e.g. dl.install("https://github.com/OpenNeuroDatasets/ds000001.git at 1.0.0") or alike (already supported for RIA stores, like the one used for HCP dataset) -- Yaroslav O. Halchenko Center for Open Neuroscience http://centerforopenneuroscience.org Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 WWW: http://www.linkedin.com/in/yarik From markiewicz at stanford.edu Tue Dec 15 12:48:01 2020 From: markiewicz at stanford.edu (Christopher Markiewicz) Date: Tue, 15 Dec 2020 17:48:01 +0000 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets In-Reply-To: References: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> , Message-ID: Hi Richard, Yarik beat me to most of this, but to address this issue: > * the sync between OpenNeuro and GitHub doesn't seem to work reliably, so we were having trouble retrieving the latest revisions of datasets using datalad This is definitely a work in progress, but a lot of effort has gone into resolving the synchronization issues, and the situation is improving rapidly. If you do find cases where the website/S3/GitHub are out of sync with each other, could you check the issues linked in https://github.com/OpenNeuroOrg/openneuro/issues/1895 and make sure we know about it and can track it? Also, > * as far as I understand, there's no way to download a specific revision (version number) of a dataset using datalad Note that snapshots are all git tags. The CLI equivalent of Yarik's Python code is just `datalad install ...; git -C checkout `. --- I don't want to argue writing/using the tool. It looks useful, and there are definitely cases where it's needed at this point. I do want to warn that it may end up being a short-term solution. OpenNeuro has made no commitment to maintain a stable web API; while there's not going to be breakage for its own sake, you do have a potential moving target as the needs of the platform change. The stable interface that we're trying to work toward full support for is datalad, which has well-established storage and transport models. Anyway, apologies for redirecting this thread to being largely about datalad. Best, Chris ________________________________________ From: Neuroimaging on behalf of Richard H?chenberger Sent: Tuesday, December 15, 2020 11:50 AM To: Neuroimaging analysis in Python Subject: Re: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets Hello, just to briefly comment on this one: On Tue, Dec 15, 2020 at 4:27 PM Christopher Markiewicz > wrote: Hi all, FWIW almost all public datasets have been pushed to GitHub and can be accessed via datalad (exceptions being tracked on these issues: https://github.com/OpenNeuroOrg/openneuro/issues/1741 and https://github.com/OpenNeuroOrg/openneuro/issues/1743). datalad install https://github.com/OpenNeuroDatasets/ds00WXYZ.git I specifically wanted to avoid using datalad for several reasons: * I've seen people struggle to install (esp. on macOS) and use it (including myself) * the sync between OpenNeuro and GitHub doesn't seem to work reliably, so we were having trouble retrieving the latest revisions of datasets using datalad * as far as I understand, there's no way to download a specific revision (version number) of a dataset using datalad openneuro-py attempts to do away with these issues. Best wishes, Richard From aldo.camargo at gmail.com Tue Dec 15 13:47:30 2020 From: aldo.camargo at gmail.com (Aldo Camargo) Date: Tue, 15 Dec 2020 13:47:30 -0500 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets In-Reply-To: References: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> Message-ID: Thanks a lot Richard for the email and for your nice work Aldo On Tue, Dec 15, 2020 at 11:47 AM Richard H?chenberger < richard.hoechenberger at gmail.com> wrote: > Hello Aldo, > > On Tue, Dec 15, 2020 at 3:46 PM Aldo Camargo > wrote: > >> Hi Richard, >> >> Very interesting application. One question, which dataset can be >> downloaded? >> > > theoretically, all of those you can find on openneuro.org! > > Practically, I'm running into issues now and then, because the server > sometimes indicates the presence of files which actually do not exist / > cannot be retrieved. I'll get in touch with OpenNeuro folks to track this > down. > > We're actually using this tool to retrieve several datasets during > automated testing of one of our pipelines, and so far it has proven to work > reliably. > > Best wishes, > > Richard > > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > -------------- next part -------------- An HTML attachment was scrubbed... URL: From richard.hoechenberger at gmail.com Tue Dec 15 14:43:55 2020 From: richard.hoechenberger at gmail.com (=?UTF-8?Q?Richard_H=C3=B6chenberger?=) Date: Tue, 15 Dec 2020 20:43:55 +0100 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets In-Reply-To: <20201215165250.GC1090323@lena.dartmouth.edu> References: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> <20201215165250.GC1090323@lena.dartmouth.edu> Message-ID: Hi Yaroslav, Thanks for sharing your thoughts! On Tue, Dec 15, 2020 at 6:17 PM Yaroslav, Halchenko wrote: > > > FWIW, datalad is also accompanied with Python API for all of its > functionality, so analogous command with fetching specific subjects (or > any path you like) would be smth like > > $> python3 -c 'import datalad.api as dl; ds = dl.install(" https://github.com/OpenNeuroDatasets/ds000001.git"); ds.get([f"sub-{i:02d}" for i in [1,2,3]], jobs=5)' [...] > - with datalad you get not just a "downloader" but overall content management > not only for "source data" but for results as well. and On Tue, Dec 15, 2020 at 6:37 PM Yaroslav Halchenko wrote: > > > * as far as I understand, there's no way to download a specific revision > > (version number) of a dataset using datalad > > it is just two steps procedure indeed ATM -- you would just need to > "manually" checkout corresponding version you like, e.g. > > $> python3 -c 'import datalad.api as dl; ds = dl.install(" https://github.com/OpenNeuroDatasets/ds000001.git"); ds.repo.checkout("1.0.0")' I would say these command lines are an excellent illustration of how the two approaches and target audiences are different: datalad may be great for advanced and tech-savvy users, while openneuro-py aims to providea no-frills, easy-to-use access to OpenNeuro. You wouldn't even need to know about git in order to use it: all you need is an OpenNeuro dataset id and a Python installation. Cheers, Richard -------------- next part -------------- An HTML attachment was scrubbed... URL: From richard.hoechenberger at gmail.com Tue Dec 15 14:52:01 2020 From: richard.hoechenberger at gmail.com (=?UTF-8?Q?Richard_H=C3=B6chenberger?=) Date: Tue, 15 Dec 2020 20:52:01 +0100 Subject: [Neuroimaging] ANN: openneuro-py, a new app for downloading OpenNeuro datasets In-Reply-To: References: <2b37a83c5ef34926b54c3f1854f2df32@BL-CCI-D1S07.ads.iu.edu> Message-ID: Hello Chris, On Tue, Dec 15, 2020 at 6:48 PM Christopher Markiewicz < markiewicz at stanford.edu> wrote: > Hi Richard, > > Yarik beat me to most of this, but to address this issue: > > > * the sync between OpenNeuro and GitHub doesn't seem to work reliably, > so we were having trouble retrieving the latest revisions of datasets using > datalad > > This is definitely a work in progress, but a lot of effort has gone into > resolving the synchronization issues, and the situation is improving > rapidly. If you do find cases where the website/S3/GitHub are out of sync > with each other, could you check the issues linked in > https://github.com/OpenNeuroOrg/openneuro/issues/1895 and make sure we > know about it and can track it? > Yes, absolutely. I've been in touch using the OpenNeuro support ticket system for a number of different up- and download issues in the past (not just data sync related), and I'm currently assembling a list of issues for the next support request ;) I will definitely report problems so they can get fixed upstream. OpenNeuro has made no commitment to maintain a stable web API; while > there's not going to be breakage for its own sake, you do have a potential > moving target as the needs of the platform change While that may be true, the thing is that openneuro-py is avaiable and easy-to-use _now_, while it's not even clear whether or when the OpenNeuro API will change or go away :) (and take the openneuro-cli JS app with it.. would be a bummer!) Best wishes, Richard -------------- next part -------------- An HTML attachment was scrubbed... URL: From richard.hoechenberger at gmail.com Wed Dec 16 15:10:26 2020 From: richard.hoechenberger at gmail.com (=?UTF-8?Q?Richard_H=C3=B6chenberger?=) Date: Wed, 16 Dec 2020 21:10:26 +0100 Subject: [Neuroimaging] =?utf-8?b?QU5OOiBNTkUtQklEUyAwLjYg8J+OhA==?= Message-ID: Hello all! I am very pleased to announce that we have just released MNE-BIDS 0.6! These are challenging days for many of us, and to make your lives ever so slightly easier, we?ve been working hard to deliver this early Christmas present ? And even if you do not celebrate Christmas, we are quite certain you will like what we got for you! So ? what are you waiting for? It?s time to unwrap! What is MNE-BIDS? ================= MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python. The project website is located at https://mne.tools/mne-bids Why would you want it? ====================== MNE-BIDS links BIDS and MNE-Python with the goal to make your analyses faster to code, more robust, and facilitate data and code sharing with co-workers and collaborators. Notable changes in 0.6 ====================== - The new Inspector, which can be invoked via mne_bids.inspect_dataset(), allows you to interactively explore your raw data, change the bad channels selection, and edit mne.Annotations. It also performs automated detection of flat data segments or channels, to assist you during visual inspection. The capabilities of the inspector will be further expanded in upcoming releases of MNE-BIDS. - To further assist you during data inspection, we have added a function to summarize all events present in a dataset, mne_bids.stats.count_events(). - Sidecar JSON files can now be updated using a template via mne_bids.update_sidecar_json(). - You can now read and write FLASH MRI images using mne_bids.write_anat(). We also fixed some issues with MRI defacing along the way. - Event durations are now preserved upon reading and writing data (we used to set all event durations to zero before). Installation ============ We provide pip- and conda-installable packages. Please follow the instructions at https://mne.tools/mne-bids/stable/install.html (Note that it might take a while for the packages to become available on all mirrors, so if you're not seeing 0.6 yet, try again in an hour or two ?) Asking for feedback =================== If you're experiencing any issues with this release of MNE-BIDS, please do not hesitate to get in touch with us. You may open an issue on our GitHub issue tracker at https://github.com/mne-tools/mne-bids/issues Contributors ============ The following people have contributed to this release of MNE-BIDS: - Adam Li - Alex Rockhill - Alexandre Gramfort - Austin Hurst - Ethan Knights (? new contributor ?) - Mainak Jas - Richard H?chenberger - Stefan Appelhoff Thank you all, and take care! Richard on behalf of the MNE-BIDS team From bertrand.thirion at inria.fr Wed Dec 16 15:17:32 2020 From: bertrand.thirion at inria.fr (bthirion) Date: Wed, 16 Dec 2020 21:17:32 +0100 Subject: [Neuroimaging] =?utf-8?b?QU5OOiBNTkUtQklEUyAwLjYg8J+OhA==?= In-Reply-To: References: Message-ID: Congratulations Richard ! Bertrand On 16/12/2020 21:10, Richard H?chenberger wrote: > Hello all! > > I am very pleased to announce that we have just released MNE-BIDS 0.6! > > These are challenging days for many of us, and to make your lives ever so > slightly easier, we?ve been working hard to deliver this early Christmas > present ? And even if you do not celebrate Christmas, we are quite certain > you will like what we got for you! So ? what are you waiting for? It?s time > to unwrap! > > > What is MNE-BIDS? > ================= > > MNE-BIDS is a Python package that allows you to read and write BIDS-compatible > datasets with the help of MNE-Python. The project website is located at > https://mne.tools/mne-bids > > > Why would you want it? > ====================== > > MNE-BIDS links BIDS and MNE-Python with the goal to make your analyses faster > to code, more robust, and facilitate data and code sharing with co-workers and > collaborators. > > > Notable changes in 0.6 > ====================== > > - The new Inspector, which can be invoked via mne_bids.inspect_dataset(), > allows you to interactively explore your raw data, change the bad channels > selection, and edit mne.Annotations. It also performs automated detection of > flat data segments or channels, to assist you during visual inspection. The > capabilities of the inspector will be further expanded in upcoming releases > of MNE-BIDS. > > - To further assist you during data inspection, we have added a function to > summarize all events present in a dataset, mne_bids.stats.count_events(). > > - Sidecar JSON files can now be updated using a template via > mne_bids.update_sidecar_json(). > > - You can now read and write FLASH MRI images using mne_bids.write_anat(). > We also fixed some issues with MRI defacing along the way. > > - Event durations are now preserved upon reading and writing data (we used to > set all event durations to zero before). > > > Installation > ============ > > We provide pip- and conda-installable packages. > > Please follow the instructions at > https://mne.tools/mne-bids/stable/install.html > > (Note that it might take a while for the packages to become available on all > mirrors, so if you're not seeing 0.6 yet, try again in an hour or two ?) > > > Asking for feedback > =================== > > If you're experiencing any issues with this release of MNE-BIDS, please do not > hesitate to get in touch with us. You may open an issue on our GitHub issue > tracker at https://github.com/mne-tools/mne-bids/issues > > > Contributors > ============ > > The following people have contributed to this release of MNE-BIDS: > > - Adam Li > - Alex Rockhill > - Alexandre Gramfort > - Austin Hurst > - Ethan Knights (? new contributor ?) > - Mainak Jas > - Richard H?chenberger > - Stefan Appelhoff > > > Thank you all, and take care! > > Richard > on behalf of the MNE-BIDS team > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging From alexandre.gramfort at inria.fr Thu Dec 17 16:44:49 2020 From: alexandre.gramfort at inria.fr (Alexandre Gramfort) Date: Thu, 17 Dec 2020 22:44:49 +0100 Subject: [Neuroimaging] [ANN] MNE-Python 0.22 Message-ID: Hello everyone, we?re ahead of our typical release cycle and just published MNE-Python 0.22! ? ? ? Please find a detailed list of changes and contributors below. With this year coming to a close, we?d like to take this opportunity to thank you all for your continued support, and wish you and your loved ones Happy Holidays. Stay healthy and take care! ? All the best, Your MNE Team. A few highlights ============ - The 3D viewer of source time courses based on pyvista can now support picking labels from any freesurfer annotation. We highly recommend you now use pyvista and not pysurfer/mayavi for STC visualization. - Performing ICA is now much simpler for most users: instead of offering 3 parameters -- n_components, n_pca_components, and max_pca_components -- that would interact in often hard-to-understand ways, you can now simply pass a single parameter -- n_components -- to mne.preprocessing.ICA and get what you want. The n_pca_compoents and max_pca_components parameters have been deprecated and will be removed in MNE-Python 0.23. Please also see the ?Notable API changes? section for details. - When plotting ICA sources via .ICA.plot_sources(), right-clicking on a component name will open a properties plot (the one you previously had to create using ICA.plot_properties()). This makes exploration of ICA data more interactive. - Annotations can now be shown and hidden interactively in raw plots using a checkbox. Extremely useful for datasets with overlapping Annotations! - Source estimates can now be baseline-corrected using their new apply_baseline() method. - The new function mne.stc_near_sensors() visualizes sEEG and ECoG data. - Fiducials can now be estimated when visualizing the coregistration by passing mri_fiducials=?estimated? to mne.viz.plot_alignment(). - Numerous improvements of volumetric source space support. - When cropping the baseline period of baseline-corrected Epochs, the information about the original baseline will be preserved to retain provenance. - We now offer spatio-spectral decomposition (SSD) via mne.decoding.SSD. - New readers: mne.read_evokeds_mff() for averaged MFFs, and mne.io.read_raw_boxy() for optical imaging data recorded using ISS Imgagent I/II hardware and BOXY recording software. Notable API changes ================ We have changed a few things that will require you to adjust your code. - The n_pca_components and max_pca_components argument of mne.preprocessing.ICA has been deprecated, use n_components during initialization, and n_pca_components in ICA.apply() instead. - The trans argument of mne.extract_label_time_course() is deprecated and will be removed in 0.23 as it is no longer necessary. - The parameter event_colors in mne.viz.plot_epochs and mne.Epochs.plot() is deprecated, replaced by event_color which is consistent with mne.viz.plot_raw and provides greater flexibility. Full list of API changes: https://mne.tools/stable/whats_new.html#api-changes Full changelog =========== For a full list of improvements and API changes, see: https://mne.tools/stable/whats_new.html#version-0-22-0 Find the full documentation at https://mne.tools/ Installing the new release =================== Since quite a few things ? including dependencies ? have changed, we recommend creating a new environment with a ?fresh? installation. Please follow the installation instructions on our website: https://mne.tools/stable/install/mne_python.html Feedback ======== As usual, we welcome your bug reports, feature requests, critiques, and contributions. Development takes place on GitHub. If you would like to contribute, star ? the project, or just take a peek at the code, visit https://github.com/mne-tools/mne-python. You may follow us on Twitter: https://twitter.com/mne_news We hope you will enjoy the new features and many, many small improvements we have added, and are looking forward to receiving your feedback. Stay safe and take care! The MNE-Python developers Contributors ========== MNE-Python is a community-driven project. We are always very happy to welcome new contributors of code and documentation! 34 people contributed to this release ? and a whopping 10 were first-timers! Thank you all so very much for your time and effort, we truly appreciate it! First-time contributors: - Aniket Pradhan - Austin Hurst - Eduard Ort - Evan Hathaway - Hongjiang Ye - Jeff Stout - Jonathan Kuziek - Quianliang Li - Tod Flak - Victoria Peterson Recurring contributors: - Adam Li - Alexandre Gramfort - Christian Brodbeck - Clemens Brunner - Daniel McCloy - Denis A. Engemann - Eric Larson - Evgenii Kalenkovich - Fede Raimondo - Guillaume Favelier - Jean-Remi King - Jussi Nurminen - Keith Doelling - Kyle Mathewson - Mads Jensen - Mainak Jas - Marijn van Vliet - Mikolaj Magnuski - Olaf Hauk - Quianliang Li - Richard H?chenberger - Robert Luke - Stefan Appelhoff - Thomas Hartmann -------------- next part -------------- An HTML attachment was scrubbed... URL: From bertrand.thirion at inria.fr Thu Dec 17 16:49:28 2020 From: bertrand.thirion at inria.fr (Bertrand Thirion) Date: Thu, 17 Dec 2020 22:49:28 +0100 (CET) Subject: [Neuroimaging] [ANN] MNE-Python 0.22 In-Reply-To: References: Message-ID: <1240134974.1318849.1608241768919.JavaMail.zimbra@inria.fr> Congratulations ! Bertrand > De: "Alexandre Gramfort" > ?: "Neuroimaging analysis in Python" , > megcommunity at jiscmail.ac.uk, megcommunity at gmail.com, "Discussion and support > forum for the users of MNE Software" > Envoy?: Jeudi 17 D?cembre 2020 22:44:49 > Objet: [Neuroimaging] [ANN] MNE-Python 0.22 > Hello everyone, > we?re ahead of our typical release cycle and just published MNE-Python 0.22! ? > ? ? > Please find a detailed list of changes and contributors below. > With this year coming to a close, we?d like to take this opportunity to thank > you all for your continued support, and wish you and your loved ones Happy > Holidays. > Stay healthy and take care! ? > All the best, > Your MNE Team. > A few highlights > ============ > * > The 3D viewer of source time courses based on pyvista can now support picking > labels from any freesurfer annotation. We highly recommend you now use pyvista > and not pysurfer/mayavi for STC visualization. > * > Performing ICA is now much simpler for most users: instead of offering 3 > parameters -- n_components, n_pca_components, and max_pca_components -- that > would interact in often hard-to-understand ways, you can now simply pass a > single parameter -- n_components -- to mne.preprocessing.ICA and get what you > want. The n_pca_compoents and max_pca_components parameters have been > deprecated and will be removed in MNE-Python 0.23. Please also see the ?Notable > API changes? section for details. > * > When plotting ICA sources via .ICA.plot_sources(), right-clicking on a component > name will open a properties plot (the one you previously had to create using > ICA.plot_properties()). This makes exploration of ICA data more interactive. > * > Annotations can now be shown and hidden interactively in raw plots using a > checkbox. Extremely useful for datasets with overlapping Annotations! > * > Source estimates can now be baseline-corrected using their new apply_baseline() > method. > * > The new function mne.stc_near_sensors() visualizes sEEG and ECoG data. > * > Fiducials can now be estimated when visualizing the coregistration by passing > mri_fiducials=?estimated? to mne.viz.plot_alignment(). > * > Numerous improvements of volumetric source space support. > * > When cropping the baseline period of baseline-corrected Epochs, the information > about the original baseline will be preserved to retain provenance. > * > We now offer spatio-spectral decomposition (SSD) via mne.decoding.SSD. > * > New readers: mne.read_evokeds_mff() for averaged MFFs, and > mne.io.read_raw_boxy() for optical imaging data recorded using ISS Imgagent > I/II hardware and BOXY recording software. > Notable API changes > ================ > We have changed a few things that will require you to adjust your code. > * > The n_pca_components and max_pca_components argument of mne.preprocessing.ICA > has been deprecated, use n_components during initialization, and > n_pca_components in ICA.apply() instead. > * > The trans argument of mne.extract_label_time_course() is deprecated and will be > removed in 0.23 as it is no longer necessary. > * > The parameter event_colors in mne.viz.plot_epochs and mne.Epochs.plot() is > deprecated, replaced by event_color which is consistent with mne.viz.plot_raw > and provides greater flexibility. > Full list of API changes: > [ https://mne.tools/stable/whats_new.html#api-changes | > https://mne.tools/stable/whats_new.html#api-changes ] > Full changelog > =========== > For a full list of improvements and API changes, see: > [ https://mne.tools/stable/whats_new.html#version-0-22-0 | > https://mne.tools/stable/whats_new.html#version-0-22-0 ] > Find the full documentation at [ https://mne.tools/stable/index.html | > https://mne.tools/ ] > Installing the new release > =================== > Since quite a few things ? including dependencies ? have changed, we recommend > creating a new environment with a ?fresh? installation. Please follow the > installation instructions on our website: > [ https://mne.tools/stable/install/mne_python.html | > https://mne.tools/stable/install/mne_python.html ] > Feedback > ======== > As usual, we welcome your bug reports, feature requests, critiques, and > contributions. Development takes place on GitHub. If you would like to > contribute, star ? the project, or just take a peek at the code, visit [ > https://github.com/mne-tools/mne-python | > https://github.com/mne-tools/mne-python ] . > You may follow us on Twitter: [ https://twitter.com/mne_news | > https://twitter.com/mne_news ] > We hope you will enjoy the new features and many, many small improvements we > have added, and are looking forward to receiving your feedback. > Stay safe and take care! > The MNE-Python developers > Contributors > ========== > MNE-Python is a community-driven project. We are always very happy to welcome > new contributors of code and documentation! 34 people contributed to this > release ? and a whopping 10 were first-timers! Thank you all so very much for > your time and effort, we truly appreciate it! > First-time contributors: > * > Aniket Pradhan > * > Austin Hurst > * > Eduard Ort > * > Evan Hathaway > * > Hongjiang Ye > * > Jeff Stout > * > Jonathan Kuziek > * > Quianliang Li > * > Tod Flak > * > Victoria Peterson > Recurring contributors: > * > Adam Li > * > Alexandre Gramfort > * > Christian Brodbeck > * > Clemens Brunner > * > Daniel McCloy > * > Denis A. Engemann > * > Eric Larson > * > Evgenii Kalenkovich > * > Fede Raimondo > * > Guillaume Favelier > * > Jean-Remi King > * > Jussi Nurminen > * > Keith Doelling > * > Kyle Mathewson > * > Mads Jensen > * > Mainak Jas > * > Marijn van Vliet > * > Mikolaj Magnuski > * > Olaf Hauk > * > Quianliang Li > * > Richard H?chenberger > * > Robert Luke > * > Stefan Appelhoff > * > Thomas Hartmann > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging -------------- next part -------------- An HTML attachment was scrubbed... URL: From jbpoline at gmail.com Thu Dec 17 22:47:26 2020 From: jbpoline at gmail.com (JB Poline) Date: Thu, 17 Dec 2020 22:47:26 -0500 Subject: [Neuroimaging] [ANN] MNE-Python 0.22 In-Reply-To: <1240134974.1318849.1608241768919.JavaMail.zimbra@inria.fr> References: <1240134974.1318849.1608241768919.JavaMail.zimbra@inria.fr> Message-ID: Ditto !!! JB On Thu, Dec 17, 2020 at 4:49 PM Bertrand Thirion wrote: > Congratulations ! > Bertrand > > ------------------------------ > > *De: *"Alexandre Gramfort" > *?: *"Neuroimaging analysis in Python" , > megcommunity at jiscmail.ac.uk, megcommunity at gmail.com, "Discussion and > support forum for the users of MNE Software" < > mne_analysis at nmr.mgh.harvard.edu> > *Envoy?: *Jeudi 17 D?cembre 2020 22:44:49 > *Objet: *[Neuroimaging] [ANN] MNE-Python 0.22 > > Hello everyone, > > we?re ahead of our typical release cycle and just published MNE-Python > 0.22! ? ? ? > > Please find a detailed list of changes and contributors below. > > With this year coming to a close, we?d like to take this opportunity to > thank you all for your continued support, and wish you and your loved ones > Happy Holidays. > > Stay healthy and take care! ? > > All the best, > > Your MNE Team. > > A few highlights > > ============ > > > - > > The 3D viewer of source time courses based on pyvista can now support > picking labels from any freesurfer annotation. We highly recommend you now > use pyvista and not pysurfer/mayavi for STC visualization. > - > > Performing ICA is now much simpler for most users: instead of offering > 3 parameters -- n_components, n_pca_components, and max_pca_components -- > that would interact in often hard-to-understand ways, you can now simply > pass a single parameter -- n_components -- to mne.preprocessing.ICA and > get what you want. The n_pca_compoents and max_pca_components parameters > have been deprecated and will be removed in MNE-Python 0.23. Please also > see the ?Notable API changes? section for details. > - > > When plotting ICA sources via .ICA.plot_sources(), right-clicking on a > component name will open a properties plot (the one you previously had to > create using ICA.plot_properties()). This makes exploration of ICA data > more interactive. > - > > Annotations can now be shown and hidden interactively in raw plots > using a checkbox. Extremely useful for datasets with overlapping > Annotations! > - > > Source estimates can now be baseline-corrected using their new > apply_baseline() method. > - > > The new function mne.stc_near_sensors() visualizes sEEG and ECoG data. > - > > Fiducials can now be estimated when visualizing the coregistration by > passing mri_fiducials=?estimated? to mne.viz.plot_alignment(). > - > > Numerous improvements of volumetric source space support. > - > > When cropping the baseline period of baseline-corrected Epochs, the > information about the original baseline will be preserved to retain > provenance. > - > > We now offer spatio-spectral decomposition (SSD) via mne.decoding.SSD. > - > > New readers: mne.read_evokeds_mff() for averaged MFFs, and > mne.io.read_raw_boxy() for optical imaging data recorded using ISS > Imgagent I/II hardware and BOXY recording software. > > > > Notable API changes > > ================ > > We have changed a few things that will require you to adjust your code. > > > - > > The n_pca_components and max_pca_components argument of > mne.preprocessing.ICA has been deprecated, use n_components during > initialization, and n_pca_components in ICA.apply() instead. > - > > The trans argument of mne.extract_label_time_course() is deprecated > and will be removed in 0.23 as it is no longer necessary. > - > > The parameter event_colors in mne.viz.plot_epochs and > mne.Epochs.plot() is deprecated, replaced by event_color which is > consistent with mne.viz.plot_raw and provides greater flexibility. > > > Full list of API changes: > > https://mne.tools/stable/whats_new.html#api-changes > > > Full changelog > > =========== > > For a full list of improvements and API changes, see: > > https://mne.tools/stable/whats_new.html#version-0-22-0 > > Find the full documentation at https://mne.tools/ > > > Installing the new release > > =================== > > Since quite a few things ? including dependencies ? have changed, we > recommend creating a new environment with a ?fresh? installation. Please > follow the installation instructions on our website: > > https://mne.tools/stable/install/mne_python.html > > Feedback > > ======== > > As usual, we welcome your bug reports, feature requests, critiques, and > contributions. Development takes place on GitHub. If you would like to > contribute, star ? the project, or just take a peek at the code, visit > https://github.com/mne-tools/mne-python. > > You may follow us on Twitter: https://twitter.com/mne_news > > We hope you will enjoy the new features and many, many small improvements > we have added, and are looking forward to receiving your feedback. > > Stay safe and take care! > > The MNE-Python developers > > > Contributors > > ========== > > MNE-Python is a community-driven project. We are always very happy to > welcome new contributors of code and documentation! 34 people contributed > to this release ? and a whopping 10 were first-timers! Thank you all so > very much for your time and effort, we truly appreciate it! > > First-time contributors: > > > - > > Aniket Pradhan > - > > Austin Hurst > - > > Eduard Ort > - > > Evan Hathaway > - > > Hongjiang Ye > - > > Jeff Stout > - > > Jonathan Kuziek > - > > Quianliang Li > - > > Tod Flak > - > > Victoria Peterson > > > Recurring contributors: > > > - > > Adam Li > - > > Alexandre Gramfort > - > > Christian Brodbeck > - > > Clemens Brunner > - > > Daniel McCloy > - > > Denis A. Engemann > - > > Eric Larson > - > > Evgenii Kalenkovich > - > > Fede Raimondo > - > > Guillaume Favelier > - > > Jean-Remi King > - > > Jussi Nurminen > - > > Keith Doelling > - > > Kyle Mathewson > - > > Mads Jensen > - > > Mainak Jas > - > > Marijn van Vliet > - > > Mikolaj Magnuski > - > > Olaf Hauk > - > > Quianliang Li > - > > Richard H?chenberger > - > > Robert Luke > - > > Stefan Appelhoff > - > > Thomas Hartmann > > > > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jbpoline at gmail.com Fri Dec 18 00:20:35 2020 From: jbpoline at gmail.com (JB Poline) Date: Fri, 18 Dec 2020 00:20:35 -0500 Subject: [Neuroimaging] =?utf-8?b?QU5OOiBNTkUtQklEUyAwLjYg8J+OhA==?= In-Reply-To: References: Message-ID: Ditto !!! JB On Wed, Dec 16, 2020 at 3:19 PM bthirion wrote: > Congratulations Richard ! > > Bertrand > On 16/12/2020 21:10, Richard H?chenberger wrote: > > Hello all! > > > > I am very pleased to announce that we have just released MNE-BIDS 0.6! > > > > These are challenging days for many of us, and to make your lives ever so > > slightly easier, we?ve been working hard to deliver this early Christmas > > present ? And even if you do not celebrate Christmas, we are quite > certain > > you will like what we got for you! So ? what are you waiting for? It?s > time > > to unwrap! > > > > > > What is MNE-BIDS? > > ================= > > > > MNE-BIDS is a Python package that allows you to read and write > BIDS-compatible > > datasets with the help of MNE-Python. The project website is located at > > https://mne.tools/mne-bids > > > > > > Why would you want it? > > ====================== > > > > MNE-BIDS links BIDS and MNE-Python with the goal to make your analyses > faster > > to code, more robust, and facilitate data and code sharing with > co-workers and > > collaborators. > > > > > > Notable changes in 0.6 > > ====================== > > > > - The new Inspector, which can be invoked via mne_bids.inspect_dataset(), > > allows you to interactively explore your raw data, change the bad > channels > > selection, and edit mne.Annotations. It also performs automated > detection of > > flat data segments or channels, to assist you during visual > inspection. The > > capabilities of the inspector will be further expanded in upcoming > releases > > of MNE-BIDS. > > > > - To further assist you during data inspection, we have added a function > to > > summarize all events present in a dataset, > mne_bids.stats.count_events(). > > > > - Sidecar JSON files can now be updated using a template via > > mne_bids.update_sidecar_json(). > > > > - You can now read and write FLASH MRI images using > mne_bids.write_anat(). > > We also fixed some issues with MRI defacing along the way. > > > > - Event durations are now preserved upon reading and writing data (we > used to > > set all event durations to zero before). > > > > > > Installation > > ============ > > > > We provide pip- and conda-installable packages. > > > > Please follow the instructions at > > https://mne.tools/mne-bids/stable/install.html > > > > (Note that it might take a while for the packages to become available on > all > > mirrors, so if you're not seeing 0.6 yet, try again in an hour or two > ?) > > > > > > Asking for feedback > > =================== > > > > If you're experiencing any issues with this release of MNE-BIDS, please > do not > > hesitate to get in touch with us. You may open an issue on our GitHub > issue > > tracker at https://github.com/mne-tools/mne-bids/issues > > > > > > Contributors > > ============ > > > > The following people have contributed to this release of MNE-BIDS: > > > > - Adam Li > > - Alex Rockhill > > - Alexandre Gramfort > > - Austin Hurst > > - Ethan Knights (? new contributor ?) > > - Mainak Jas > > - Richard H?chenberger > > - Stefan Appelhoff > > > > > > Thank you all, and take care! > > > > Richard > > on behalf of the MNE-BIDS team > > _______________________________________________ > > Neuroimaging mailing list > > Neuroimaging at python.org > > https://mail.python.org/mailman/listinfo/neuroimaging > > > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > -------------- next part -------------- An HTML attachment was scrubbed... URL: From frakkopesto at gmail.com Mon Dec 21 20:31:07 2020 From: frakkopesto at gmail.com (Franco Pestilli) Date: Mon, 21 Dec 2020 19:31:07 -0600 Subject: [Neuroimaging] [JOB] Postdoctoral Fellow in Concussion Neuroscience, Machine learning and Cloud Computing References: Message-ID: We would appreciate if you could share the ad below to relevant lists. Drs. Nicholas Port and Franco Pestilli are seeking a Postdoctoral Fellow or Research Associate to join a multi-institutional team of Neuroscientists, Computer Scientists, and Engineers to work on a unique project to apply Machine learning and Cloud computing to improve our understanding of concussion and mild Traumatic Brain Injury. Applicants are invited to apply for this research Postdoctoral Fellow or Research Associate, non-tenure track position with a proposed start date of summer/fall 2021. The primary goal of the project is to use brainlife.io , and advanced machine learning methods to analyze behavioral and clinical outcomes, anatomical and diffusion weighted imaging from a large dataset (the NCAA/DOD CARE Consortium Advanced Research Core). The plan is to build predictive modeling tools available on brainlife.io that can be freely used by clinicians around the world to help in patient care management. Opportunities exist to join several additional concussion, machine learning, informatics and neuroimaging projects with the labs of Drs. Port and Pestilli. The position will be based in Dr. Port?s new, state-of-the-art laboratory within the dedicated Research Center for Elite Athletic Development in the new 66,000 square-foot South-End-Zone edition to Indiana University?s Memorial Stadium (https://iuhoosiers.com/facilities/the-excellence-academy/21 https://president.iu.edu/media/photos/2018/excellence-academy-dedication/index.html ). A major research university, Indiana University currently enrolls over 38,000 undergraduates and 10,000 graduate and professional students on the Bloomington campus, and has over 114,000 students in the University system. The School of Optometry, Program in Neuroscience and Cognitive Science Program are internationally recognized, vibrant research programs with a very collegial environment. The Department of Psychological and Brain Sciences has a research dedicated 3T Prisma, which Dr. Port uses regularly. A diverse community, Bloomington is located in a beautifully wooded and hilly area of the state, where cultural and recreational opportunities abound, housing costs are low, schools are excellent, and commute times are short. Additional information on the community of Bloomington can be found at https://www.visitbloomington.com/ . The Postdoctoral Fellow position requires a PhD in Neuroscience, Computer Science, Engineering, Informatics, Vision Science or related scientific field by the time of appointment. For the Research Associate position, a minimum of a Masters degrees and 5+ year of appropriate work experience is required. Salary and rank will be commensurate with qualifications and experience. For consideration, please submit a letter of application, curriculum vita, and the names and contact information for at least three references. Interested candidates should review the application requirements and submit their application at: http://indiana.peopleadmin.com/postings/10224 Applications received by Jan 11, 2021, will be assured priority consideration; however, the search will remain open until the position is filled. Best regards, Franco FRANCO PESTILLI, PhD | Associate Professor Department of Psychology | College of Liberal Arts | The University of Texas at Austin he/him | pestilli at utexas.edu | web | github | brainlife.io? -------------- next part -------------- An HTML attachment was scrubbed... URL: From gkassis at u.rochester.edu Tue Dec 29 20:19:12 2020 From: gkassis at u.rochester.edu (George Kassis) Date: Tue, 29 Dec 2020 20:19:12 -0500 Subject: [Neuroimaging] [PySurfer] Message-ID: Hi, I downloaded PySurfer and its extensions but every time I try to plot a Brain object, the python program just gets stuck and never runs. The program was running a while ago but now gets stuck whenever it reaches brain object. e.g. brain = Brain (subject_id, hemi, surf) Appreciate the feedback. Thanks, George kassis -------------- next part -------------- An HTML attachment was scrubbed... URL: