ENH add downloader from GIN/Wasabi, change name to shortclips/vim-2

Author: TomDLT

README.rst

@@ -22,9 +22,10 @@ To explore these tutorials, one can:
 - 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/movies/merged_for_colab.ipynb>`_.
+  `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 "Movies" tutorial.
+The tutorials are best explored in order, starting with the "Shortclips"
+tutorial.
 
 Helper Python package
 =====================
@@ -72,7 +73,8 @@ The package ``voxelwise_tutorials`` has the following dependencies:
 `nltk <https://github.com/nltk/nltk>`_,
 `pycortex <https://github.com/gallantlab/pycortex>`_,
 `himalaya <https://github.com/gallantlab/himalaya>`_,
-`pymoten <https://github.com/gallantlab/pymoten>`_.
+`pymoten <https://github.com/gallantlab/pymoten>`_,
+`datalad <https://github.com/datalad/datalad>`_.
 
 
 .. |Github| image:: https://img.shields.io/badge/github-voxelwise_tutorials-blue

doc/Makefile

@@ -33,7 +33,7 @@ notebooks:
 	python create_notebooks.py
 	$(MAKE) merge-notebooks
 
-NBDIR = ../tutorials/notebooks/movies
+NBDIR = ../tutorials/notebooks/shortclips
 
 merge-notebooks:
 	python merge_notebooks.py \

doc/index.rst

@@ -20,10 +20,10 @@ To explore these tutorials, one can:
   <https://github.com/gallantlab/voxelwise_tutorials/tree/main/tutorials/notebooks>`_
   directory)
 - run the merged notebook in
-  `Google Colab <https://colab.research.google.com/github/gallantlab/voxelwise_tutorials/blob/main/tutorials/notebooks/movies/merged_for_colab.ipynb>`_
+  `Google 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 `Movies tutorial
-<_auto_examples/index.html>`_.
+The tutorials are best explored in order, starting with the `Shortclips
+tutorial <_auto_examples/index.html>`_.
 
 The project is available on GitHub at `gallantlab/voxelwise_tutorials
 <https://github.com/gallantlab/voxelwise_tutorials>`_. On top of the tutorials
@@ -59,6 +59,6 @@ If you use one of our packages in your work (``voxelwise_tutorials``
 :ref:`[15]<gao2015>`, or ``pymoten`` :ref:`[16]<nun2021>`), please cite the
 corresponding publications.
 
-If you use one of our public datasets in your work (Movie 4T
-:ref:`[3b]<den2022>`, Movie 3T :ref:`[4b]<hut2012data>`), please cite the
+If you use one of our public datasets in your work (vim-2
+:ref:`[3b]<nis2011data>`, shortclips :ref:`[4b]<hut2012data>`), please cite the
 corresponding publications.

doc/static/crcns.png

@@ -23,8 +23,8 @@ analysis:
     different stimulus and task features simultaneously. This framework enables
     the analysis of complex naturalistic stimuli and tasks which contain a
     large number of features; for example, VM has been used with naturalistic
-    images :ref:`[1]<kay2008>` :ref:`[2]<nas2009>`, movies :ref:`[3]<nis2011>`,
-    and stories :ref:`[8]<hut2016>`.
+    images :ref:`[1]<kay2008>` :ref:`[2]<nas2009>`, shortclips
+    :ref:`[3]<nis2011>`, and stories :ref:`[8]<hut2016>`.
 
 #.
     Unlike the traditional null hypothesis testing framework, VM is not prone
@@ -141,9 +141,8 @@ Datasets
 
 .. _hut2012data:
 
-[4b] Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2020):
-    Gallant Lab Natural Movie 3T fMRI Data. CRCNS.org.
-    http://dx.doi.org/10.6080/TBD
+[4b] Huth, A. G., Nishimoto, S., Vu, A. T., Dupre la Tour, T., & Gallant, J. L. (2022).
+    Gallant Lab Natural Short Clips 3T fMRI Data. http://dx.doi.org/--TBD--
 
 Packages
 --------

doc/static/download.png

@@ -23,9 +23,10 @@
     "matplotlib",
     "networkx",
     "nltk",
-    "pycortex",
+    "pycortex>=1.2.4",
     "himalaya",
     "pymoten",
+    "datalad",
 ]
 
 extras_require = {

doc/voxelwise_modeling.rst

@@ -0,0 +1,97 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Download the data set\n\nIn this script, we download the data set from Wasabi or GIN. No account is\nrequired.\n\n## Cite this data set\n\nThis tutorial is based on publicly available data `published on GIN\n<https://gin.g-node.org/gallantlab/shortclips>`_. If you publish any work using\nthis data set, please cite the original publication [1]_, and the data set\n[2]_.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        ""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Download\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# path of the data directory\nfrom voxelwise_tutorials.io import get_data_home\ndirectory = get_data_home(dataset=\"shortclips\")\nprint(directory)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We will only use the first subject in this tutorial, but you can run the same\nanalysis on the four other subjects. Uncomment the lines in ``DATAFILES`` to\ndownload more subjects.\n\nWe also skip the stimuli files, since the dataset provides two preprocessed\nfeature spaces to perform voxelwise modeling without requiring the original\nstimuli.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from voxelwise_tutorials.io import download_datalad\n\nDATAFILES = [\n    \"features/motion_energy.hdf\",\n    \"features/wordnet.hdf\",\n    \"mappers/S01_mappers.hdf\",\n    # \"mappers/S02_mappers.hdf\",\n    # \"mappers/S03_mappers.hdf\",\n    # \"mappers/S04_mappers.hdf\",\n    # \"mappers/S05_mappers.hdf\",\n    \"responses/S01_responses.hdf\",\n    # \"responses/S02_responses.hdf\",\n    # \"responses/S03_responses.hdf\",\n    # \"responses/S04_responses.hdf\",\n    # \"responses/S05_responses.hdf\",\n    # \"stimuli/test.hdf\",\n    # \"stimuli/train_00.hdf\",\n    # \"stimuli/train_01.hdf\",\n    # \"stimuli/train_02.hdf\",\n    # \"stimuli/train_03.hdf\",\n    # \"stimuli/train_04.hdf\",\n    # \"stimuli/train_05.hdf\",\n    # \"stimuli/train_06.hdf\",\n    # \"stimuli/train_07.hdf\",\n    # \"stimuli/train_08.hdf\",\n    # \"stimuli/train_09.hdf\",\n    # \"stimuli/train_10.hdf\",\n    # \"stimuli/train_11.hdf\",\n]\n\nsource = \"https://gin.g-node.org/gallantlab/shortclips\"\n\nfor datafile in DATAFILES:\n    local_filename = download_datalad(datafile, destination=directory,\n                                      source=source)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012). A\n    continuous semantic space describes the representation of thousands of\n    object and action categories across the human brain. Neuron, 76(6),\n    1210-1224.\n\n.. [2] Huth, A. G., Nishimoto, S., Vu, A. T., Dupre la Tour, T., & Gallant, J. L. (2022).\n    Gallant Lab Natural Short Clips 3T fMRI Data. http://dx.doi.org/--TBD--\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}