{"id":204,"date":"2021-03-18T15:20:05","date_gmt":"2021-03-18T14:20:05","guid":{"rendered":"https:\/\/project.inria.fr\/cobcom\/?page_id=204"},"modified":"2021-07-30T11:41:46","modified_gmt":"2021-07-30T09:41:46","slug":"software","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/cobcom\/software\/","title":{"rendered":"Software"},"content":{"rendered":"<p><\/p>\n<h2>talon<\/h2>\n<p><a href=\"https:\/\/gitlab.inria.fr\/cobcom\/talon\">https:\/\/gitlab.inria.fr\/cobcom\/talon<\/a><\/p>\n<p><a href=\"https:\/\/gitlab.inria.fr\/cobcom\/talon\">talon<\/a> is a pure Python package that implements Tractograms As Linear Operators in Neuroimaging.<\/p>\n<p>The software provides the talon Python module, which includes all the functions and tools that are necessary for filtering a tractogram. In particular, specific functions are devoted to:<\/p>\n<ul>\n<li>Transforming a tractogram into a linear operator.<\/li>\n<li>Solving the inverse problem associated to the filtering of a tractogram.<\/li>\n<li>Use GPUs to speed up these operations.<\/li>\n<\/ul>\n<h2>Dmipy<\/h2>\n<p><a href=\"https:\/\/github.com\/AthenaEPI\/dmipy\">https:\/\/github.com\/AthenaEPI\/dmipy<\/a><\/p>\n<p>The <a href=\"https:\/\/github.com\/AthenaEPI\/dmipy\">Dmipy<\/a> (Diffusion Microstructure Imaging in Python) software package facilitates the reproducible estimation of diffusion MRI-based microstructure features. It does this by taking a completely modular approach to Microstructure Imaging. Using Dmipy you can design, fit, and recover the parameters of any multi-compartment microstructure model in usually less than 10 lines of code. Created models can be used to simulate and fit data for any PGSE-based dMRI acquisition, including single shell, multi-shell, multi-diffusion time and multi-TE acquisition schemes.<\/p>\n<h2>OpenMEEG<\/h2>\n<p><a href=\"http:\/\/openmeeg.github.io\/\">http:\/\/openmeeg.github.io\/<\/a><\/p>\n<p>The <a href=\"http:\/\/openmeeg.github.io\/\">OpenMEEG<\/a> software is a C++ opensource software for quasistatic electromagnetics, solving forward problems of EEG, MEG, ECoG, intracerebral EEG, and integrated it into several software suites for MEG\/EEG analysis and processing (Brainstorm, Fieldtrip, SPM). The last releases of OpenMEEG 2.4 with notable new features are available <a href=\"http:\/\/openmeeg.gforge.inria.fr\/download\/\">here.<\/a><\/p>\n<h2>WL-align<\/h2>\n<p><a href=\"https:\/\/osf.io\/depux\/\">https:\/\/osf.io\/depux\/<\/a><\/p>\n<p>A Python implementation of the graph alignment WL-align algorithm is made public as part of the supplementary materials of the <a href=\"https:\/\/project.inria.fr\/cobcom\/publications#nn2021\">journal article<\/a> where it is introduced. The used code and the obtained connectomes and alignments are available on the Open Science Framework at this link: <a href=\"https:\/\/osf.io\/depux\/\">https:\/\/osf.io\/depux\/<\/a>.<\/p>\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>talon https:\/\/gitlab.inria.fr\/cobcom\/talon talon is a pure Python package that implements Tractograms As Linear Operators in Neuroimaging. The software provides the talon Python module, which includes all the functions and tools that are necessary for filtering a tractogram. In particular, specific functions are devoted to: Transforming a tractogram into a linear\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/cobcom\/software\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":1185,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-204","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/cobcom\/wp-json\/wp\/v2\/pages\/204","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/cobcom\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/cobcom\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/cobcom\/wp-json\/wp\/v2\/users\/1185"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/cobcom\/wp-json\/wp\/v2\/comments?post=204"}],"version-history":[{"count":9,"href":"https:\/\/project.inria.fr\/cobcom\/wp-json\/wp\/v2\/pages\/204\/revisions"}],"predecessor-version":[{"id":308,"href":"https:\/\/project.inria.fr\/cobcom\/wp-json\/wp\/v2\/pages\/204\/revisions\/308"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/cobcom\/wp-json\/wp\/v2\/media?parent=204"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}