Metadata-Version: 1.1
Name: addm-toolbox
Version: 0.1.8
Summary: A toolbox for data analysis using the attentional drift-diffusion model.
Home-page: http://github.com/goptavares/aDDM-Toolbox
Author: Gabriela Tavares
Author-email: gtavares@caltech.edu
License: GPLv3
Download-URL: https://github.com/goptavares/aDDM-Toolbox/archive/0.1.8.tar.gz
Description: aDDM Toolbox
        ============
        
        This toolbox can be used to perform model fitting and to generate
        simulations for the attentional drift-diffusion model (aDDM), as well as
        for the classic version of the drift-diffusion model (DDM) without an
        attentional component.
        
        Prerequisites
        -------------
        
        aDDM-Toolbox supports Python 2.7 only and requires the following
        libraries: \* deap \* matplotlib \* numpy \* pandas \* scipy
        
        Installing
        ----------
        
        ::
        
            $ pip install addm_toolbox
        
        Running tests
        -------------
        
        To make sure everything is working correctly after installation, try
        (from a UNIX shell, not the Python interpreter):
        
        ::
        
            $ addm_run_tests
        
        This should take a while to finish, so maybe go get a cup of tea :)
        
        Getting started
        ---------------
        
        To get a feel for how the algorithm works, try:
        
        ::
        
            $ addm_demo --display-figures
        
        You can see all the arguments available for the demo using:
        
        ::
        
            $ addm_demo --help
        
        Here is a list of useful scripts which can be similarly run from a UNIX
        shell: \* addm\_demo \* ddm\_pta\_test \* addm\_pta\_test \*
        addm\_pta\_mle \* addm\_pta\_map \* addm\_simulate\_true\_distributions
        \* addm\_basinhopping \* addm\_genetic\_algorithm \* ddm\_mla \*
        addm\_mla
        
        You can also have a look directly at the code in the following modules:
        \* addm.py contains the aDDM implementation, with functions to generate
        model simulations and obtain the likelihood for a given data trial. \*
        ddm.py is equivalent to addm.py but for the DDM. \* addm\_pta\_test.py
        generates an artificial data set for a given set of aDDM parameters and
        attempts to recover these parameters through maximum a posteriori
        estimation. \* ddm\_pta\_test.py is equivalent to addm\_pta\_test.py but
        for the DDM. \* addm\_pta\_mle.py fits the aDDM to a data set by
        performing maximum likelihood estimation. \* addm\_pta\_map.py performs
        model comparison for the aDDM by obtaining a posterior distribution over
        a set of models. \* simulate\_addm\_true\_distributions.py generates
        aDDM simulations using empirical data for the fixations.
        
        Common issues
        -------------
        
        Make sure you are using the toolbox under Python 2.7, not Python 3.
        
        If you get a Python RuntimeError with the message "Python is not
        installed as a framework.", try creating the
        file ~/.matplotlib/matplotlibrc and adding the following code:
        
        ::
        
            backend: TkAgg
        
        Authors
        -------
        
        -  **Gabriela Tavares** - gtavares@caltech.edu,
           `goptavares <https://github.com/goptavares>`__
        
        License
        -------
        
        This project is licensed under the GNU GENERAL PUBLIC LICENSE - see the
        COPYING file for details.
        
        Acknowledgments
        ---------------
        
        This toolbox was developed as part of a research project in the `Rangel
        Neuroeconomics Lab <http://www.rnl.caltech.edu/>`__ at the California
        Institute of Technology.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
