PyDDM - A generalized drift diffusion model simulator

PyDDM is a simulator and modeling framework for generalized drift-diffusion models (GDDM or DDM), with a focus on cognitive neuroscience.

Key features include:

  • Fast solutions for drift-diffusion models (DDM) and generalized drift-diffusion models (GDDM)

  • Easy and flexible syntax for building models

  • Arbitrary Python functions define parameters for drift rate, noise, bounds, non-decision time, and starting position

  • Graphical interface for exploring new models

  • Automatic parallelization for faster fitting

June 2024: PyDDM is new and improved! Check it out!

Interactive online demo on Google Colab.

Start with the tutorial. To see what PyDDM is capable of, and for example models, see the PyDDM Cookbook. Also see the FAQs for more information.

Release annoucments are posted on the pyddm-announce mailing list and on github.

Please note that PyDDM is still beta software so you may experience some glitches or uninformative error messages. Please report any problems to the bug tracker.

Examples

To simulate a simple DDM:

import pyddm
import matplotlib.pyplot as plt
model = pyddm.gddm(drift=2, noise=1.5, bound=1.3, starting_position=.1, nondecision=.1)
plt.plot(model.solve().pdf("upper_bound"))

To fit data to a simple DDM:

import pyddm, pandas
model = pyddm.gddm(drift="driftrate", bound="B", starting_position="x0", nondecision="ndtime",
                   parameters={"driftrate": (-1, 1), "B": (.5, 2), "x0": (-.5, .5), "ndtime": (0, .5)})
df = pandas.from_csv("your_data_here.csv")
sample = pyddm.Sample.from_pandas_dataframe(df, rt_column_name="rt", choice_column_name="correct")
model.fit(sample)

To use PyDDM’s GUI to visualize a complex model with leaky integration, a constant drift rate, exponentially collapsing bounds, and a variable starting position:

import pyddm
import pyddm.plot
import numpy as np
model = pyddm.gddm(drift=lambda x,leak,driftrate : driftrate - x*leak,
                   bound=lambda t,initial_B,collapse_rate : initial_B * np.exp(-collapse_rate*t),
                   starting_position="x0",
                   parameters={"leak": (0, 2),
                               "driftrate": (-3, 3),
                               "initial_B": (.5, 1.5),
                               "collapse_rate": (0, 10),
                               "x0": (-.9, .9)})

pyddm.plot.model_gui(model) # If not using a Jupyter notebook, or...
pyddm.plot.model_gui_jupyter(model) # If using a Jupyter notebook