Metadata-Version: 1.0
Name: pyprf-feature
Version: 1.1.2
Summary: A free & open source package for finding best-fitting                     population receptive field (PRF) models and feature                     weights for fMRI data.
Home-page: https://github.com/MSchnei/pyprf_feature
Author: Marian Schneider, Ingo Marquardt
Author-email: marian.schneider@maastrichtuniversity.nl
License: GNU General Public License Version 3
Description: |DOI|
        
        pyprf_feature
        =============
        
        A free & open source package for finding best-fitting population
        receptive field (PRF) models and feature weights for fMRI data.
        
        If you are only interested in the spatial properties of the population
        receptive fields, not preferred features, check out the `pyprf
        package <https://github.com/ingo-m/pypRF>`__.
        
        Installation
        ------------
        
        For installation, follow these steps:
        
        0. (Optional) Create conda environment
        
        .. code:: bash
        
            conda create -n env_pyprf_feature python=2.7
            source activate env_pyprf_feature
            conda install pip
        
        1. Clone repository
        
        .. code:: bash
        
            git clone https://github.com/MSchnei/pyprf_feature.git
        
        2. Install numpy, e.g. by running:
        
        .. code:: bash
        
            pip install numpy
        
        3. Install pyprf_feature with pip
        
        .. code:: bash
        
            pip install /path/to/cloned/pyprf_feature
        
        Dependencies
        ------------
        
        `**Python 2.7** <https://www.python.org/download/releases/2.7/>`__
        
        +----------------------------------------+----------------+
        | Package                                | Tested version |
        +========================================+================+
        | `NumPy <http://www.numpy.org/>`__      | 1.11.1         |
        +----------------------------------------+----------------+
        | `SciPy <http://www.scipy.org/>`__      | 0.18.0         |
        +----------------------------------------+----------------+
        | `NiBabel <http://nipy.org/nibabel/>`__ | 2.0.2          |
        +----------------------------------------+----------------+
        
        How to use
        ----------
        
        1. Present stimuli and record fMRI data
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The PsychoPy scripts in the Stimulation folder can be used for
        presenting appropriate visual stimuli.
        
        2. Prepare spatial and temporal information for experiment as arrays
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        1. Run prepro_get_spat_info.py in the prepro folder to obtain an array
           with the spatial information of the experiment. This should result in
           a 3d numpy array with shape [pixel x pixel x nr of spatial aperture
           conditions] that represents images of the spatial apertures stacked
           on top of each other.
        
        2. Run prepro_get_temp_info.py in the prepro folder to obtain an array
           with the temporal information of the experiment. This should result
           in a 2d numpy array with shape [nr of volumes across all runs x 4].
           The first column represents unique identifiers of spatial aperture
           conditions. The second column represents onset times and the third
           durations (both in s).The fourth column represents unique feature
           identifiers.
        
        3. Prepare the input data
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The input data should be motion-corrected, high-pass filtered and
        (optionally) distortion-corrected. If desired, spatial as well as
        temporal smoothing can be applied. The PrePro folder contains some
        auxiliary scripts to perform some of these functions.
        
        4. Adjust the csv file
        ~~~~~~~~~~~~~~~~~~~~~~
        
        Adjust the information in the config_default.csv file in the Analysis
        folder, such that the provided information is correct. It is recommended
        to make a specific copy of the csv file for every subject.
        
        5. Run pyprf_feature
        ~~~~~~~~~~~~~~~~~~~~
        
        Open a terminal and run
        
        ::
        
            pyprf_feature -config path/to/custom_config.csv
        
        References
        ----------
        
        This application is based on the following work:
        
        -  Dumoulin, S. O., & Wandell, B. A. (2008). Population receptive field
           estimates in human visual cortex. NeuroImage, 39(2), 647–660.
           https://doi.org/10.1016/j.neuroimage.2007.09.034
        
        -  Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., &
           Gallant, J. L. (2011). Report Reconstructing Visual Experiences from
           Brain Activity Evoked by Natural Movies, 1641–1646.
           https://doi.org/10.1016/j.cub.2011.08.031
        
        -  St-Yves, G., & Naselaris, T. (2017). The feature-weighted receptive
           field: An interpretable encoding model for complex feature spaces.
           NeuroImage, (June), 1–15.
           https://doi.org/10.1016/j.neuroimage.2017.06.035
        
        License
        -------
        
        The project is licensed under `GNU General Public License Version
        3 <http://www.gnu.org/licenses/gpl.html>`__.
        
        .. |DOI| image:: https://zenodo.org/badge/78625137.svg
           :target: https://zenodo.org/badge/latestdoi/78625137
        
Keywords: pRF,fMRI,retinotopy,feature weights
Platform: UNKNOWN
