Extensible drift diffusion modeling for Python
PyDDM - A generalized drift-diffusion model simulator
PyDDM is a simulator and modeling framework for generalized drift-diffusion models (DDM), with a focus on cognitive neuroscience.
Key features include:
- Models solved numerically using Crank-Nicolson to solve the Fokker-Planck equation (Backward Euler, analytical solutions, and particle simulations also available)
- Arbitrary functions for drift rate, noise, bounds, and initial position distribution
- Arbitrary loss function and fitting method for parameter fitting
- Optional multiprocessor support
- Optional GUI for debugging and gaining an intuition for different models
- Convenient and extensible object oriented API allows building models in a component-wise fashion
- Verified accuracy of simulations using novel program verification techniques
Normally, you can install with:
$ pip install pyddm
If you are in a shared environment (e.g. a cluster), install with:
$ pip install pyddm --user
If installing from source, download the source code, extract, and do:
$ python3 setup.py install
- Python 3.5 or above
- Numpy version 1.9.2 or higher
- Scipy version 0.16.0 or higher
- Paranoid Scientist
- Pathos (optional, for multiprocessing support)
For help on using PyDDM, see the Github Forums.
Please report bugs to https://github.com/mwshinn/pyddm/issues. This includes any problems with the documentation. Pull Requests for bugs are greatly appreciated.
Feature requests are currently not being accepted due to limited resources. If you implement a new feature in PyDDM, please do the following before submitting a Pull Request on Github:
- Make sure your code is clean and well commented
- If appropriate, update the official documentation in the docs/ directory
- Ensure there are Paranoid Scientist verification conditions to your code
- Write unit tests and optionally integration tests for your new feature (runtests.sh)
- Ensure all existing tests pass
For all other questions or comments, contact firstname.lastname@example.org.
All code is available under the MIT license. See LICENSE.txt for more information.