Metadata-Version: 2.1
Name: voxelwise-tutorials
Version: 0.1.3
Summary: Tools and tutorials for voxelwise modeling
Home-page: UNKNOWN
Maintainer: Tom Dupre la Tour
Maintainer-email: tom.dupre-la-tour@m4x.org
License: BSD (3-clause)
Description: ============================
        Voxelwise modeling tutorials
        ============================
        
        |Github| |Python| |License|
        
        Welcome to the voxelwise modeling tutorial from the
        `Gallantlab <https://gallantlab.org>`_.
        
        Tutorials
        =========
        
        This repository contains tutorials describing how to use the voxelwise modeling
        framework. Voxelwise modeling 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
        
        
        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] Deniz, F., Visconti di Oleggio Castello, M., Dupré La Tour, T., &
          Gallant, J. L. (2022). Voxelwise encoding models in functional MRI. *In
          preparation*.
        
        .. [2] Dupré La Tour, T., Eickenberg, M., & Gallant, J. L. (2022).
        	Variance decomposition with banded ridge regression. *In preparation*.
        
        .. [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.
        
        .. [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
        
Platform: UNKNOWN
Description-Content-Type: text/x-rst
Provides-Extra: docs
