| Name | voxelwise-tutorials JSON |
| Version |
0.1.9
JSON |
| download |
| home_page | None |
| Summary | Tools and tutorials for voxelwise modeling |
| upload_time | 2025-03-06 00:40:33 |
| maintainer | Tom Dupre la Tour |
| docs_url | None |
| author | None |
| requires_python | None |
| license | BSD (3-clause) |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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============================
Voxelwise modeling tutorials
============================
|Github| |Python| |License| |Build| |Downloads|
Welcome to the voxelwise modeling tutorial from the
`GallantLab <https://gallantlab.org>`_.
Paper
=====
If you use these tutorials for your work, consider citing the corresponding paper:
Dupré La Tour, T., Visconti di Oleggio Castello, M., & Gallant, J. L. (2024). The Voxelwise Modeling framework: a tutorial introduction to fitting encoding models to fMRI data. https://doi.org/10.31234/osf.io/t975e
You can find a copy of the paper `here <paper/voxelwise_tutorials_paper.pdf>`_.
Tutorials
=========
This repository contains tutorials describing how to use the voxelwise modeling
framework. `Voxelwise modeling
<https://gallantlab.github.io/voxelwise_tutorials/voxelwise_modeling.html>`_ is
a framework to perform functional magnetic resonance imaging (fMRI) data
analysis, fitting encoding models at the voxel level.
To explore these tutorials, one can:
- read the rendered examples in the tutorials
`website <https://gallantlab.github.io/voxelwise_tutorials/>`_ (recommended)
- run the Python scripts (`tutorials <tutorials>`_ directory)
- run the Jupyter notebooks (`tutorials/notebooks <tutorials/notebooks>`_ directory)
- run the merged notebook in
`Colab <https://colab.research.google.com/github/gallantlab/voxelwise_tutorials/blob/main/tutorials/notebooks/shortclips/merged_for_colab.ipynb>`_.
The tutorials are best explored in order, starting with the "Shortclips"
tutorial.
Helper Python package
=====================
To run the tutorials, this repository contains a small Python package
called ``voxelwise_tutorials``, with useful functions to download the
data sets, load the files, process the data, and visualize the results.
Installation
------------
To install the ``voxelwise_tutorials`` package, run:
.. code-block:: bash
pip install voxelwise_tutorials
To also download the tutorial scripts and notebooks, clone the repository via:
.. code-block:: bash
git clone https://github.com/gallantlab/voxelwise_tutorials.git
cd voxelwise_tutorials
pip install .
Developers can also install the package in editable mode via:
.. code-block:: bash
pip install --editable .
Requirements
------------
The package ``voxelwise_tutorials`` has the following dependencies:
`numpy <https://github.com/numpy/numpy>`_,
`scipy <https://github.com/scipy/scipy>`_,
`h5py <https://github.com/h5py/h5py>`_,
`scikit-learn <https://github.com/scikit-learn/scikit-learn>`_,
`matplotlib <https://github.com/matplotlib/matplotlib>`_,
`networkx <https://github.com/networkx/networkx>`_,
`nltk <https://github.com/nltk/nltk>`_,
`pycortex <https://github.com/gallantlab/pycortex>`_,
`himalaya <https://github.com/gallantlab/himalaya>`_,
`pymoten <https://github.com/gallantlab/pymoten>`_,
`datalad <https://github.com/datalad/datalad>`_.
.. |Github| image:: https://img.shields.io/badge/github-voxelwise_tutorials-blue
:target: https://github.com/gallantlab/voxelwise_tutorials
.. |Python| image:: https://img.shields.io/badge/python-3.7%2B-blue
:target: https://www.python.org/downloads/release/python-370
.. |License| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg
:target: https://opensource.org/licenses/BSD-3-Clause
.. |Build| image:: https://github.com/gallantlab/voxelwise_tutorials/actions/workflows/run_tests.yml/badge.svg
:target: https://github.com/gallantlab/voxelwise_tutorials/actions/workflows/run_tests.yml
.. |Downloads| image:: https://pepy.tech/badge/voxelwise_tutorials
:target: https://pepy.tech/project/voxelwise_tutorials
Cite as
=======
If you use one of our packages in your work (``voxelwise_tutorials`` [1]_,
``himalaya`` [2]_, ``pycortex`` [3]_, or ``pymoten`` [4]_), please cite the
corresponding publications:
.. [1] Dupré La Tour, T., Visconti di Oleggio Castello, M., & Gallant, J. L. (2024).
The Voxelwise Modeling framework: a tutorial introduction to fitting encoding models to fMRI data.
https://doi.org/10.31234/osf.io/t975e
.. [2] Dupré La Tour, T., Eickenberg, M., Nunez-Elizalde, A.O., & Gallant, J. L. (2022).
Feature-space selection with banded ridge regression. NeuroImage.
https://doi.org/10.1016/j.neuroimage.2022.119728
.. [3] Gao, J. S., Huth, A. G., Lescroart, M. D., & Gallant, J. L. (2015).
Pycortex: an interactive surface visualizer for fMRI. Frontiers in
neuroinformatics, 23. https://doi.org/10.3389/fninf.2015.00023
.. [4] Nunez-Elizalde, A.O., Deniz, F., Dupré la Tour, T., Visconti di Oleggio
Castello, M., and Gallant, J.L. (2021). pymoten: scientific python package
for computing motion energy features from video. Zenodo.
https://doi.org/10.5281/zenodo.6349625
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"description": "============================\nVoxelwise modeling tutorials\n============================\n\n|Github| |Python| |License| |Build| |Downloads|\n\nWelcome to the voxelwise modeling tutorial from the\n`GallantLab <https://gallantlab.org>`_.\n\nPaper\n=====\n\nIf you use these tutorials for your work, consider citing the corresponding paper:\n\nDupr\u00e9 La Tour, T., Visconti di Oleggio Castello, M., & Gallant, J. L. (2024). The Voxelwise Modeling framework: a tutorial introduction to fitting encoding models to fMRI data. https://doi.org/10.31234/osf.io/t975e\n\nYou can find a copy of the paper `here <paper/voxelwise_tutorials_paper.pdf>`_.\n\nTutorials\n=========\n\nThis repository contains tutorials describing how to use the voxelwise modeling\nframework. `Voxelwise modeling\n<https://gallantlab.github.io/voxelwise_tutorials/voxelwise_modeling.html>`_ is\na framework to perform functional magnetic resonance imaging (fMRI) data\nanalysis, fitting encoding models at the voxel level.\n\nTo explore these tutorials, one can:\n\n- read the rendered examples in the tutorials\n `website <https://gallantlab.github.io/voxelwise_tutorials/>`_ (recommended)\n- run the Python scripts (`tutorials <tutorials>`_ directory)\n- run the Jupyter notebooks (`tutorials/notebooks <tutorials/notebooks>`_ directory)\n- run the merged notebook in\n `Colab <https://colab.research.google.com/github/gallantlab/voxelwise_tutorials/blob/main/tutorials/notebooks/shortclips/merged_for_colab.ipynb>`_.\n\nThe tutorials are best explored in order, starting with the \"Shortclips\"\ntutorial.\n\nHelper Python package\n=====================\n\nTo run the tutorials, this repository contains a small Python package\ncalled ``voxelwise_tutorials``, with useful functions to download the\ndata sets, load the files, process the data, and visualize the results.\n\nInstallation\n------------\n\nTo install the ``voxelwise_tutorials`` package, run:\n\n.. code-block:: bash\n\n pip install voxelwise_tutorials\n\n\nTo also download the tutorial scripts and notebooks, clone the repository via:\n\n.. code-block:: bash\n\n git clone https://github.com/gallantlab/voxelwise_tutorials.git\n cd voxelwise_tutorials\n pip install .\n\n\nDevelopers can also install the package in editable mode via:\n\n.. code-block:: bash\n\n pip install --editable .\n\n\nRequirements\n------------\n\nThe package ``voxelwise_tutorials`` has the following dependencies:\n`numpy <https://github.com/numpy/numpy>`_,\n`scipy <https://github.com/scipy/scipy>`_,\n`h5py <https://github.com/h5py/h5py>`_,\n`scikit-learn <https://github.com/scikit-learn/scikit-learn>`_,\n`matplotlib <https://github.com/matplotlib/matplotlib>`_,\n`networkx <https://github.com/networkx/networkx>`_,\n`nltk <https://github.com/nltk/nltk>`_,\n`pycortex <https://github.com/gallantlab/pycortex>`_,\n`himalaya <https://github.com/gallantlab/himalaya>`_,\n`pymoten <https://github.com/gallantlab/pymoten>`_,\n`datalad <https://github.com/datalad/datalad>`_.\n\n\n.. |Github| image:: https://img.shields.io/badge/github-voxelwise_tutorials-blue\n :target: https://github.com/gallantlab/voxelwise_tutorials\n\n.. |Python| image:: https://img.shields.io/badge/python-3.7%2B-blue\n :target: https://www.python.org/downloads/release/python-370\n\n.. |License| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\n :target: https://opensource.org/licenses/BSD-3-Clause\n\n.. |Build| image:: https://github.com/gallantlab/voxelwise_tutorials/actions/workflows/run_tests.yml/badge.svg\n :target: https://github.com/gallantlab/voxelwise_tutorials/actions/workflows/run_tests.yml\n\n.. |Downloads| image:: https://pepy.tech/badge/voxelwise_tutorials\n :target: https://pepy.tech/project/voxelwise_tutorials\n\n\nCite as\n=======\n\nIf you use one of our packages in your work (``voxelwise_tutorials`` [1]_,\n``himalaya`` [2]_, ``pycortex`` [3]_, or ``pymoten`` [4]_), please cite the\ncorresponding publications:\n\n.. [1] Dupr\u00e9 La Tour, T., Visconti di Oleggio Castello, M., & Gallant, J. L. (2024).\n The Voxelwise Modeling framework: a tutorial introduction to fitting encoding models to fMRI data.\n https://doi.org/10.31234/osf.io/t975e\n\n.. [2] Dupr\u00e9 La Tour, T., Eickenberg, M., Nunez-Elizalde, A.O., & Gallant, J. L. (2022).\n Feature-space selection with banded ridge regression. NeuroImage.\n https://doi.org/10.1016/j.neuroimage.2022.119728\n\n.. [3] Gao, J. S., Huth, A. G., Lescroart, M. D., & Gallant, J. L. (2015).\n Pycortex: an interactive surface visualizer for fMRI. Frontiers in\n neuroinformatics, 23. https://doi.org/10.3389/fninf.2015.00023\n\n.. [4] Nunez-Elizalde, A.O., Deniz, F., Dupr\u00e9 la Tour, T., Visconti di Oleggio\n Castello, M., and Gallant, J.L. (2021). pymoten: scientific python package\n for computing motion energy features from video. Zenodo.\n https://doi.org/10.5281/zenodo.6349625\n",
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