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Getting Started

  • Overview
  • Features
  • Installation
    • Required dependencies
    • Optional dependencies
    • Instructions
  • Quickstart
    • Load required modules
    • Define plotting function
    • Load and plot sample data
    • Fit and generate 1000 random samples using Gaussian copula
    • Fit and generate 1000 random samples using fPCA
  • Examples
    • Generation
      • Univariate
        • Import libraries
        • Load sample data
        • Fit and generate using samples
        • Fit and generate using parametrized distribution
        • Stretching and unifomization
        • Exporting and saving generated data
        • Saving the generator
      • Multivariate: Independent
        • Import libraries
        • Create test data
        • Generate synthetic data
        • Generate synthetic data with modified characteristics
        • Parameterize data using quantiles
        • Parameterize data using distributions
      • Multivariate: Gaussian Copulas
        • Import libraries
        • Create a sample dataset with n samples
        • Fit a Gaussian copula with Synthia’s backend
      • Multivariate: Vine Copulas
        • Import libraries
        • Create a sample dataset with n samples
        • Fit a Vine copula with pyvinecopulib’s backend
      • Multivariate: fPCA
        • Import libraries
        • Define plotting function
        • Plot source data
        • Fit the fPCA model using 10 components
        • Generate same number of samples as in the input
        • Plot the results
      • Multivariate: Discrete and Categorical
        • Import libraries
        • Generate dummy data
        • Fit and generate new samples
    • Enhancement
      • Stretching and Uniformization
        • Import libraries
        • Define plotting function
        • Plot source data
        • Fit copula to data
        • Generate ‘streatched’ samples
        • Generate ‘more uniformly distributed’ samples

Background

  • Copulas
    • What copulas are
    • The Gaussian copula
    • Other copula families
    • Vine copulas
  • Functional Principal Component Analysis (fPCA)
    • The general idea
    • Mathematical definition
    • PCA as a basis expansion
    • PCA for synthetic data generation

Help & reference

  • API reference
    • Data generators
    • Copulas
    • Parameterizers
    • Transformers
    • Utilities
  • How to cite
  • Contributing
  • Development notes
    • Conda environment
    • Install synthia
    • Documentation
    • Docstrings
    • Testing
    • Versioning
    • Deployment
  • Copyright & License
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  • Copyright & License
  • Edit on GitHub

Copyright & License¶

MIT License

Copyright (c) 2020 D. Meyer and T. Nagler

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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© Copyright 2020 D. Meyer and T. Nagler

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