Welcome to xsdba’s documentation!

xsdba is a Python library for statistical downscaling and bias adjustment. Leveraging xarray` and dask, users can easily bias-adjust large datasets.

Table of Contents:

• xsdba: Statistical Downscaling and Bias Adjustment library
  • Features
  • Quick Install
  • Documentation
  • Credits
• Installation
  • Stable release
  • From sources
  • Extra Dependencies
    • Experimental SDBA Algorithms
• Basic Usage
• xclim Migration Guide
  • Units handling
  • xsdba global options
• Bias Adjustment and Downscaling Algorithms
  • Modular Approach
  • Grouping
  • Application in multivariate settings (climate science examples)
    • Minimum and maximum temperature
    • Relative and specific humidity
    • Radiation and precipitation
  • Usage examples
  • Experimental wrap of SBCK
  • Notes for Developers
  • User API
  • Developer API
  • SDBA Footnotes
• Contributing
  • Types of Contributions
    • Report Bugs
    • Fix Bugs
    • Implement Features
    • Write Documentation
    • Submit Feedback
  • Get Started!
  • Pull Request Guidelines
  • Tips
  • Code of Conduct
  • General notes for implementing new bias-adjustment methods
• Releasing
  • Deployment
  • Packaging
    • The simple approach
    • Releasing on conda-forge
      • Initial Release
      • Subsequent releases
    • Building sources for wide support with manylinux image
• Examples
  • Statistical Downscaling and Bias-Adjustment
    • Simple Quantile Mapping
    • Grouping
    • Modular approach
      • First example : pr and frequency adaptation
      • Second example: tas and detrending
      • Third example : Multi-method protocol - Hnilica et al. 2017
      • Fourth example : Multivariate bias-adjustment (Cannon, 2018)
        • Perform the multivariate adjustment (MBCn).
        • Let’s trigger all the computations.
      • Fifth example : Dynamical Optimal Transport Correction - Robin et al. 2019
  • Advanced tools
    • Optimization with dask
    • LOESS smoothing and detrending
      • LOESS Detrending
    • Initializing an Adjustment object from a training dataset
    • Retrieving extra output diagnostics
    • Moving window for adjustments
    • Full example: Multivariate adjustment in the additive space
      • 1. Jitter, additive space transformation and variable stacking
      • 2. Get residuals and trends
      • 3. Adjustments
      • 4. Re-trend and transform back to the physical space
    • Frequency adaption with a rolling window
    • Tests for sdba
  • Tests with %%timeit (full 30 years)
  • Test computation time as a function of number of points

• Credits
• Changelog
• Bibliography

All Modules

• xsdba

User API

• API
  • xsdba Module

Indices and tables

• Index

• Module Index

• Search Page