Metadata-Version: 2.0
Name: addm-toolbox
Version: 0.1.10
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/0.1.10.tar.gz
Description-Content-Type: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Requires-Dist: deap
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy

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.