Metadata-Version: 2.1
Name: voxelwise-tutorials
Version: 0.1.1
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)
Platform: UNKNOWN
Description-Content-Type: text/x-rst
Provides-Extra: docs
License-File: LICENSE.md

============================
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 located in the `tutorials <tutorials>`_ directory
- run the Jupyter notebooks located in the
  `tutorials/notebooks <tutorials/notebooks>`_ directory
- run the merged notebook in
  `Colab <https://colab.research.google.com/github/gallantlab/voxelwise_tutorials/blob/main/tutorials/notebooks/movies_3T/merged_for_colab.ipynb>`_.

To run the tutorials yourself, first download this repository, and install the
dependencies (see below). The tutorials are best explored in order, starting
with the "Movies 3T" tutorial.

Helper Python package
=====================

To run the tutorials, this repository contains a small Python package
called ``voxelwise_tutorials``, with useful fonctions 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>`_


.. |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 this tutorial
==================

If you use this tutorial and helper package in your work, please cite our (future)
publication:

.. [1] Deniz, F., Visconti di Oleggio Castello, M., Dupré La Tour, T., & Gallant, J. L. (2021).
  Voxelwise encoding models in functional MRI. *In preparation*.

If you use ``himalaya``, please cite our (future) publication:

.. [2] Dupré La Tour, T., Eickenberg, M., & Gallant, J. L. (2021).
	Variance decomposition with banded ridge regression. *In preparation*.


