trancit 1.0.9

TranCIT: Transient Causal Interaction Toolbox.

TranCIT: Transient Causal Interaction Toolbox

TranCIT (Transient Causal Interaction Toolbox) is a Python package for quantifying causal relationships in multivariate time series data. It provides methods for analyzing directional influences using model-based statistical tools, inspired by information-theoretic and autoregressive frameworks.

🚀 Features

• Dynamic Causal Strength (DCS): Time-varying causal relationships
• Transfer Entropy (TE): Information-theoretic causality measures
• Granger Causality (GC): Linear causality detection
• Relative Dynamic Causal Strength (rDCS): Event-based causality
• VAR-based Modeling: Vector autoregressive time series analysis
• BIC Model Selection: Automatic model order selection
• Bootstrap Support: Statistical significance testing
• DeSnap Analysis: Debiased statistical analysis
• Pipeline Architecture: Modular, stage-based analysis pipeline

📦 Installation

From PyPI (Recommended)

pip install trancit

From Source

git clone https://github.com/CMC-lab/TranCIT.git
cd TranCIT
pip install -e .

Development Installation

git clone https://github.com/CMC-lab/TranCIT.git
cd TranCIT
pip install -e ".[dev]"

🎯 Quick Start

Basic Causality Analysis

import numpy as np
from trancit import DCSCalculator, generate_signals

# Generate synthetic data
data, _, _ = generate_signals(T=1000, Ntrial=20, h=0.1, 
                             gamma1=0.5, gamma2=0.5, 
                             Omega1=1.0, Omega2=1.2)

# Create DCS calculator
calculator = DCSCalculator(model_order=4, time_mode="inhomo")

# Perform analysis
result = calculator.analyze(data)
print(f"DCS shape: {result.causal_strength.shape}")
print(f"Transfer Entropy shape: {result.transfer_entropy.shape}")

Event-Based Analysis Pipeline

import numpy as np
from trancit import PipelineOrchestrator, generate_signals
from trancit.config import PipelineConfig, PipelineOptions, DetectionParams, CausalParams

# Generate data
data, _, _ = generate_signals(T=1200, Ntrial=20, h=0.1, 
                             gamma1=0.5, gamma2=0.5, 
                             Omega1=1.0, Omega2=1.2)
original_signal = np.mean(data, axis=2)
detection_signal = original_signal * 1.5

# Configure pipeline
config = PipelineConfig(
    options=PipelineOptions(detection=True, causal_analysis=True),
    detection=DetectionParams(thres_ratio=2.0, align_type="peak", 
                            l_extract=150, l_start=75),
    causal=CausalParams(ref_time=75, estim_mode="OLS"),
)

# Run analysis
orchestrator = PipelineOrchestrator(config)
result = orchestrator.run(original_signal, detection_signal)

# Access results
if result.results.get("CausalOutput"):
    dcs_values = result.results["CausalOutput"]["OLS"]["DCS"]
    te_values = result.results["CausalOutput"]["OLS"]["TE"]
    print(f"DCS shape: {dcs_values.shape}")

Model Selection and Validation

import numpy as np
from trancit import VAREstimator, BICSelector, ModelValidator

# Generate sample data
data = np.random.randn(2, 1000, 20)  # (n_vars, n_obs, n_trials)

# BIC model selection
bic_selector = BICSelector(max_order=6, mode="biased")
bic_results = bic_selector.compute_multi_trial_BIC(data, {"Params": {"BIC": {"momax": 6, "mode": "biased"}}, "EstimMode": "OLS"})

# VAR estimation
estimator = VAREstimator(model_order=4, time_mode="inhomo")
coefficients, residuals, log_likelihood, hessian_sum = estimator.estimate_var_coefficients(
    data, model_order=4, max_model_order=6, time_mode="inhomo", lag_mode="infocrit"
)

# Model validation
validator = ModelValidator()
validation_result = validator.validate(coefficients, residuals, data)
print(f"Model stable: {validation_result.model_stability}")

📚 Documentation & Examples

For comprehensive documentation, tutorials, and API reference:

👉 ReadTheDocs Documentation

Examples

• Basic Usage: Simple causality analysis
• LFP Pipeline: Local field potential analysis
• DCS Introduction: Interactive tutorial

🔬 Scientific Background

This package implements methods from:

• Shao et al. (2023): Information theoretic measures of causal influences during transient neural events
• Granger Causality: Linear causality detection in time series
• Transfer Entropy: Information-theoretic causality measures

🧪 Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=dcs --cov-report=html

# Run linting
flake8 dcs/ tests/

# Format code
black dcs/ tests/

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

git clone https://github.com/CMC-lab/TranCIT.git
cd TranCIT
pip install -e ".[dev]"
pre-commit install

📖 Citing This Work

If you use TranCIT in your research, please cite:

@article{shao2023information,
  title={Information theoretic measures of causal influences during transient neural events},
  author={Shao, Kaidi and Logothetis, Nikos K and Besserve, Michel},
  journal={Frontiers in Network Physiology},
  volume={3},
  pages={1085347},
  year={2023},
  publisher={Frontiers Media SA}
}

And cite this software package:

@software{nouri_2025_trancit,
  author       = {Nouri, Salar and
                  Shao, Kaidi and
                  Safavi, Shervin},
  title        = {TranCIT: Transient Causal Interaction Toolbox},
  month        = aug,
  year         = 2025,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.16998396},
  url          = {https://doi.org/10.5281/zenodo.16998396},
}

📄 License

This project is licensed under the BSD 2-Clause License. See the LICENSE file for details.

🙏 Acknowledgments

• Based on research from the CMC-Lab
• Inspired by information-theoretic causality methods
• Built with support from the scientific Python community

📞 Contact

• Maintainer: Salar Nouri ([e-mail])
• Issues: GitHub Issues
• Documentation: ReadTheDocs