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 (and Python 3.6 tentatively – please report any bugs). The following libraries are required:
deap
future
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_toolbox_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
If you get errors while using the toolbox under Python 3, try it with Python 2.7.
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
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 at the California Institute of Technology.
Metadata
Release files for addm-toolbox 0.1.12
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| addm_toolbox-0.1.12.tar.gz | 1.0 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| addm_toolbox-0.1.12-py2-none-any.whl | Python 2 | none | any | Details |
Total release size: 2.1 MB
Release files / addm_toolbox-0.1.12.tar.gz
| Download URL | addm_toolbox-0.1.12.tar.gz |
|---|---|
| Size | 1.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
9b3c0c0f292d037ff80116caaa739d65f68bd3135c879d5585a600fc3d93dc1a
|
|
BLAKE2b-256 checksum How to use checksums |
1b0382f9ad30f1b6b78ea2b1d8aa13ea8dc438d5ec8286366b7f96d376ff21a6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Release files / addm_toolbox-0.1.12-py2-none-any.whl
| Download URL | addm_toolbox-0.1.12-py2-none-any.whl |
|---|---|
| Size | 1.1 MB |
| Tags | Python 2 |
|
SHA-256 checksum How to use checksums |
3c35a0e0bb8f4f0c215e7a3e3c46bd299e0a6178c387f8ba7af34c59ba998d66
|
|
BLAKE2b-256 checksum How to use checksums |
0f5121bc0b3ded0212ebf4f300f95806af8822cc50dd8b49559e560e4bb2c83f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for addm-toolbox 0.1.12
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.