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
synthia
Docs
»
Examples
Edit on GitHub
Examples
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Generation
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Univariate
Multivariate: Independent
Multivariate: Gaussian Copulas
Multivariate: Vine Copulas
Multivariate: fPCA
Multivariate: Discrete and Categorical
Enhancement
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Stretching and Uniformization