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image:: https://github.com/netneurolab/neuromaps/raw/main/docs/_static/neuromaps_logo.png\n\n|\n\n.. image:: https://zenodo.org/badge/375755159.svg\n   :target: https://zenodo.org/badge/latestdoi/375755159\n   :alt: Zenodo record\n\n.. image:: https://img.shields.io/pypi/v/neuromaps\n   :target: https://pypi.python.org/pypi/neuromaps/\n   :alt: Latest PyPI version\n\n.. image:: https://img.shields.io/badge/docker-netneurolab/neuromaps-brightgreen.svg?logo=docker\u0026style=flat\n  :target: https://hub.docker.com/r/netneurolab/neuromaps/tags/\n  :alt: Latest Docker image\n\n.. image:: https://github.com/netneurolab/neuromaps/actions/workflows/tests.yml/badge.svg\n  :target: https://github.com/netneurolab/neuromaps/actions/workflows/tests.yml\n  :alt: run-tests status\n\n.. image:: https://github.com/netneurolab/neuromaps/actions/workflows/docs.yml/badge.svg\n  :target: https://netneurolab.github.io/neuromaps/\n  :alt: deploy-docs status\n\n|\n\nThe ``neuromaps`` toolbox is designed to help researchers make easy,\nstatistically-rigorous comparisons between brain maps (or brain annotations).\nDocumentation can be found `here \u003chttps://netneurolab.github.io/neuromaps/\u003e`_.\n\nThe accompanying paper is published in `Nature Methods \u003chttps://www.nature.com/articles/s41592-022-01625-w\u003e`_ (`postprint \u003chttps://github.com/netneurolab/neuromaps/blob/main/markello2022natmethods.pdf\u003e`_).\n\nCheck all the brain maps we have `here \u003chttps://netneurolab.github.io/neuromaps/listofmaps.html\u003e`_!\n\nFeatures\n--------\n\n- A growing library of brain maps (\"annotations\") in their original coordinate space, including microstructure, function, electrophysiology, receptors, and more\n- Robust transforms between MNI-152, fsaverage, fsLR, and CIVET spaces\n- Integrated spatial null models for statistically assessing correspondences between brain maps\n\n.. image:: https://github.com/netneurolab/neuromaps/raw/main/docs/_static/neuromaps_features.png\n\n\nInstallation requirements\n-------------------------\n\nCurrently, ``neuromaps`` works with Python 3.8+.\nYou can install stable versions of ``neuromaps`` from PyPI with ``pip install neuromaps``.\nHowever, we recommend installing from the source repository to get the latest features and bug fixes.\n\nYou can install ``neuromaps`` from the source repository with ``pip install git+https://github.com/netneurolab/neuromaps.git``\nor by cloning the repository and installing from the local directory:\n\n.. code-block:: bash\n\n    git clone https://github.com/netneurolab/neuromaps\n    cd neuromaps\n    pip install .\n\nYou will also need to have `Connectome Workbench \u003chttps://www.humanconnectome.\norg/software/connectome-workbench\u003e`_ installed and available on your path in\norder to use most of the transformation / resampling functionality of\n``neuromaps``.\n\n.. _installation:\n\nCitation\n--------\n\n**Importantly**, ``neuromaps`` implements and builds on tools that have been previously developed, and we redistribute data that was acquired elsewhere.\nIf you use the ``neuromaps`` toolbox, please ensure proper attribution of the original data sources. Here's a quick checklist:\n\n- Cite the ``neuromaps`` `paper \u003chttps://www.nature.com/articles/s41592-022-01625-w\u003e`_.\n- Cite the original papers that publish the data you are using. A complete list with references for each brain annotation can be found `in the documentation \u003chttps://netneurolab.github.io/neuromaps/listofmaps.html\u003e`_, or `in this Google Sheet \u003chttps://docs.google.com/spreadsheets/d/1oZecOsvtQEh5pQkIf8cB6CyhPKVrQuko/edit?rtpof=true\u0026sd=true#gid=1162991686\u003e`_. We also provide a standalone bibliography file and a helper function to generate the citations.\n- Cite the transformations used\n\n  - Volume-to-surface transformations (registration fusion): `Buckner et al 2011 \u003chttps://journals.physiology.org/doi/full/10.1152/jn.00339.2011\u003e`_ (original proposition) and `Wu et al 2018 \u003chttps://onlinelibrary.wiley.com/doi/10.1002/hbm.24213\u003e`_ (first implementation of MNI152 to fsaverage transformation).\n  - Surface-to-surface transformations (multimodal surface matching): `Robinson et al 2014 \u003chttps://www.sciencedirect.com/science/article/pii/S1053811914004546\u003e`_ and `Robinson et al 2018 \u003chttps://www.sciencedirect.com/science/article/pii/S1053811917308649\u003e`_.\n\n- Cite the spatial null models used (see API documentation)\n\nLicense information\n-------------------\n\nThis work is licensed under a\nCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License ``cc-by-nc-sa``.\nThe full license can be found in the\n`LICENSE \u003chttps://github.com/netneurolab/neuromaps/blob/main/neuromaps\n/LICENSE\u003e`_ file in the ``neuromaps`` distribution.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetneurolab%2Fneuromaps","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnetneurolab%2Fneuromaps","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetneurolab%2Fneuromaps/lists"}