.. title: Open source computer code
.. slug: opencode
.. date: 2023-03-24 23:52:52 UTC-07:00
.. tags: software, open source, python, fmri
.. category: neuroscience
.. link: 
.. description: 
.. type: text

.. raw:: html

   <div class="container" style="margin-bottom: 20px">
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         <img src="/images/other/github.jpg" alt="github" class="img-fluid">
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         Our 
         <a href="https://github.com/gallantlab">
         github repository</a> contains lots of open code that may be useful
         for scientific computing in general, or for fitting encoding models
         to fMRI or neurophysiology data sets.
       </div>
     </div>
   </div>

   <div class="container" style="margin-bottom: 20px">
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         <img src="/images/other/pycortex.jpg" alt="pycortex" class="img-fluid">
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         <a href="https://github.com/gallantlab/pycortex">
         Pycortex</a> is a python-based toolkit for surface visualization
         of fMRI data. (It can also be used to visualize other types
         of volumetric brain data.) The brain viewers on this site were
         all generated using Pycortex. The 2015 publication describing 
         Pycortex can be found
         <a href="https://www.frontiersin.org/articles/10.3389/fninf.2015.00023/full">
         here</a>. Documentation for Pycortex can be found
         <a href="https://gallantlab.org/pycortex/">
         here</a>.
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     </div>
   </div>

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         <img src="/images/other/cottoncandy.jpg" alt="cottoncandy" class="img-fluid">
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         <a href="https://github.com/gallantlab/cottoncandy">
         CottonCandy</a> is a scientific library for storing and accessing numpy array 
         data on an S3-compatible cloud storage instance. This is achieved 
         by reading arrays from memory and downloading arrays directly 
         into memory. This means that you don't have to download your array 
         to disk, and then load it from disk into your python session.
         A paper describing CottonCandy can be found
         <a href="https://joss.theoj.org/papers/10.21105/joss.00890.pdf">
         here</a>.
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         <img src="/images/other/himalaya.jpg" alt="himalaya" class="img-fluid">
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         <a href="https://github.com/gallantlab/himalaya">
         Himalaya</a> implements machine learning linear(ized) models in
         Python, focusing on computational efficiency for large numbers 
         of targets. Himalaya efficiently estimates linear(ized) models
         on large numbers of targets (for example, thousands of voxels in an
        fMRI experiment), it runs on both CPU and GPU hardware, and it
         provides estimators that are compatible with scikit-learn's API.
         Himalaya is routinely used in our lab to fit voxelwise encoding
         models to very large fMRI data sets. A paper describing Himalaya
         can be found
         <a href="https://www.sciencedirect.com/science/article/pii/S1053811922008497">
         here</a>.
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     </div>
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