Welcome to pystran documentation

The selection and identification of a suitable environmental model structure is more than fitting parameters of a model structure to reproduce measured data. The procedure is highly dependent on various criteria, i.e. the modelling objective, the characteristics and the scale of the system under investigation as well as the available data. Rigorous analysis of the candidate model structures is needed to support and objectify the selection of the most appropriate model structure for a specific case.

This package is currently aims to counter some current drawbacks of methods in literature described:

• It is completely written in python, open source implementation of the algorithms, no license dependence
• Documentation generated semi-automatic with textit{Sphinx, http://sphinx-doc.org/} to support users in the functionalities
• It integrates the simultaneous application and comparison of different methods more easily.

The code is part of the development for a Phd in environmental sciences, Stijn Van Hoey, stvhoey.vanhoey@ugent.be.

Contents:

• Performance metrics
• Model Parameters
• Sensitivity Analysis
  • Sensitivity Base class
  • Morris Screening method
  • Standardized Regression Coefficients (SRC) method
  • Sobol Variance based method
  • Latin-Hypercube OAT method
  • Dynamic Identifiability Analysis (DYNIA)
  • Regional Sensitivity Analysis (RSA)
  • Generalised Likelihood Uncertainty Estimation (GLUE)

Indices and tables

• Index
• Module Index
• Search Page