teaspoon
1. Getting Started
1.1. Installation
1.2. Optional Dependencies
1.3. Issues
2. Modules
2.1. Make Data (MakeData) Module
2.1.1. Point Cloud Data Generation (PointCloud) Module
2.1.2. Dynamical Systems Library (DynSysLib) Module
2.2. Parameter Selection Module
2.2.1. Mutual Information
2.2.2. Auto-correlation
2.2.3. Fourier Spectrum Analysis
2.2.4. Permutation Auto Mutual Information
2.2.5. Multi-scale Permutation Entropy
2.2.6. False Nearest Neighbors
2.3. Signal Processing (SP) Module
2.3.1. Time Series Analysis (TSA) Tools
2.3.2. Network Representation of Time Series
2.3.3. Information Module
2.3.4. Miscellaneous
2.3.5. Texture Analysis
2.3.6. Stochastic P-Bifurcation Detection
2.4. Topological Data Analaysis (TDA) Module
2.4.1. Persistent Homology of Networks (PHN) Module
2.4.2. Distances
2.4.3. Drawing
2.4.4. Persistence
2.4.5. Zero Dimensional Sublevel Set Persistence (SLSP) Module
2.4.6. Magnitude
2.4.7. Fast Zigzag
2.5. Machine Learning (ML) Module
2.5.1. Datasets
2.5.2. Featurization
2.5.3. Classification
2.5.4. References
2.6. Data Assimilation and Forecasting (DAF)
2.6.1. Forecasting
2.6.2. Data Assimilation
3. Example Notebooks
3.1. Computing Wasserstein and Bottleneck Distances
3.2. Computing Features from Persistence Images
4. Contributing
4.1. Contributing to Documentation
5. License
6. Citing
teaspoon
5.
License
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5.
License
GNU GENERAL PUBLIC LICENSE
Version: 3, 29 June 2007