ssm-simulators 0.12.2

SSMS is a package collecting simulators and training data generators for cognitive science, neuroscience, and approximate bayesian computation

SSMS (Sequential Sampling Model Simulators)

Python Package to collect simulators for Sequential Sampling Models.

Find the package documentation here.

Quick Start

The ssms package serves two purposes.

1. Easy access to fast simulators of sequential sampling models
2. Support infrastructure to construct training data for various approaches to likelihood / posterior amortization

A number of tutorial notebooks are available under the /notebooks directory.

Installation

pip install ssm-simulators

Recommended: Install via conda-forge for full parallel support:

conda install -c conda-forge ssm-simulators

[!NOTE] Parallel Execution Requirements:

For multi-threaded simulation (n_threads > 1), the package requires:

• OpenMP: For parallel loop execution
• GSL (GNU Scientific Library): For validated random number generation

conda-forge users: Both dependencies are automatically included.

pip users: Install system dependencies first:

# macOS
brew install libomp gsl

# Ubuntu/Debian
sudo apt-get install libgomp-dev libgsl-dev

Then reinstall: pip install --force-reinstall ssm-simulators

Without these dependencies, the package works in single-threaded mode using NumPy.

[!NOTE] Building from source or developing this package requires a C compiler (such as GCC). On Linux, you can install GCC with:

sudo apt-get install build-essential

Most users installing from PyPI wheels do not need to install GCC.

Parallel Execution Details

When using n_threads > 1, the package uses GSL's validated Ziggurat algorithm for Gaussian random number generation, ensuring statistically correct simulations.

Thread Limit: The maximum supported number of threads is 256 (compile-time limit). Requesting more threads will raise a ValueError. This limit exists because per-thread random number generator states are allocated as static arrays for performance.

from ssms.basic_simulators import simulator

# Single-threaded (uses NumPy RNG)
result = simulator.simulator(model='ddm', theta=theta, n_samples=10000, n_threads=1)

# Multi-threaded (uses GSL Ziggurat RNG, requires OpenMP + GSL)
result = simulator.simulator(model='ddm', theta=theta, n_samples=10000, n_threads=8)

Check your installation's parallel capabilities:

from cssm._openmp_status import print_status
print_status()

Command Line Interface

The package exposes a command-line tool, generate, for creating training data from a YAML configuration file.

generate --config-path <path/to/config.yaml> --output <output/directory> [--log-level INFO]

• --config-path: Path to your YAML configuration file (optional, uses default if not provided).
• --output: Directory where generated data will be saved (required).
• --n-files: (Optional) Number of data files to generate. Default is 1 file.
• --estimator-type: (Optional) Likelihood estimator type (kde or pyddm). Overrides YAML config if specified.
• --log-level: (Optional) Set the logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL). Default is WARNING.

Below is a sample YAML configuration you can use with the generate command:

MODEL: 'ddm'
GENERATOR_APPROACH: 'lan'

PIPELINE:
  N_PARAMETER_SETS: 100
  N_SUBRUNS: 20

SIMULATOR:
  N_SAMPLES: 2000
  DELTA_T: 0.001

TRAINING:
  N_SAMPLES_PER_PARAM: 200

ESTIMATOR:
  TYPE: 'kde'  # Options: 'kde' (default) or 'pyddm'

Configuration file parameter details follow.

Top-Level Parameters:

Option	Definition
MODEL	The type of model you want to simulate (e.g., ddm, angle, levy)
GENERATOR_APPROACH	Type of generator used to generate data (lan or cpn)

PIPELINE Section:

Option	Definition
N_PARAMETER_SETS	Number of parameter vectors that are used for training
N_SUBRUNS	Number of repetitions of each call to generate data

SIMULATOR Section:

Option	Definition
N_SAMPLES	Number of samples a simulation run should entail for a given parameter set
DELTA_T	Time discretization step used in numerical simulation of the model. Interval between updates of evidence-accumulation.

TRAINING Section:

Option	Definition
N_SAMPLES_PER_PARAM	Number of times the kernel density estimate (KDE) is evaluated after creating the KDE from simulations of each set of model parameters

ESTIMATOR Section:

Option	Definition
TYPE	Likelihood estimator type: kde (default) or pyddm

To make your own configuration file, you can copy the example above into a new .yaml file and modify it with your preferences.

If you are using uv (see below), you can use the uv run command to run generate from the command line

This will generate training data according to your configuration and save it in the specified output directory.

Key Features

Custom Parameter Transforms

Register custom transformations to apply model-specific modifications to sampled parameters:

from ssms import register_transform_function
import numpy as np

# Register a custom transform
def exponential_drift(theta: dict) -> dict:
    if 'v' in theta:
        theta['v'] = np.exp(theta['v'])
    return theta

register_transform_function("exp_v", exponential_drift)

# Use in model configuration
model_config = {
    "name": "my_model",
    "params": ["v", "a", "z", "t"],
    "param_bounds": [...],
    "parameter_transforms": [
        {"type": "exp_v"}  # Your custom transform
    ]
}

Tutorial

Check the basic tutorial in our documentation.

Advanced: Dependency Management with uv

We use uv for fast and efficient dependency management. To get started:

1. Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Install dependencies (including development):

uv sync --all-groups  # Installs all dependency groups

Building Cython Extensions from Source

For development or to rebuild with different settings:

# Clean environment and sync
rm -rf .venv && uv sync

# Rebuild Cython extensions (editable install)
uv pip install --python .venv/bin/python -e . --reinstall

Important: Always use uv pip install --python .venv/bin/python to ensure extensions are built for the correct Python version in your virtual environment.

Contributing

We welcome contributions from the community! Whether you want to add a new model, improve documentation, or fix bugs, your help is appreciated.

Contributing New Models

Want to add your own sequential sampling model to the package? Check out our comprehensive guide:

📖 Contributing New Models Tutorial

This guide walks you through three levels of contribution:

• Level 1: Add boundary/drift variants (~15 min)
• Level 2: Implement Python simulators (~20 min)
• Level 3: Create high-performance Cython implementations (~30 min)

Other Contributions

For bug reports, feature requests, or general questions:

• Open an issue on GitHub Issues
• Check existing issues to avoid duplicates
• Provide clear descriptions and reproducible examples

Cite ssm-simulators

Please use the this DOI to cite ssm-simulators: https://doi.org/10.5281/zenodo.17156205