Metadata-Version: 2.1
Name: uncertainpy
Version: 1.2.2
Summary: A python toolbox for uncertainty quantification and sensitivity analysis tailored towards neuroscience models.
Home-page: https://github.com/simetenn/uncertainpy
Author: Simen Tennøe
License: GNU GPLv3
Description: Uncertainpy is a python toolbox for uncertainty quantification and sensitivity
        analysis tailored towards computational neuroscience.
        
        Uncertainpy is model independent and treats the model as a black box where the
        model can be left unchanged. Uncertainpy implements both quasi-Monte Carlo
        methods and polynomial chaos expansions using either point collocation or the
        pseudo-spectral method. Both of the polynomial chaos expansion methods have
        support for the rosenblatt transformation to handle dependent input parameters.
        
        Uncertainpy is feature based, i.e., if applicable, it recognizes and calculates
        the uncertainty in features of the model, as well as the model itself.
        Examples of features in neuroscience can be spike timing and the action
        potential shape.
        
        Uncertainpy is tailored towards neuroscience models, and comes with several
        common neuroscience models and features built in, but new models and features can
        easily be implemented. It should be noted that while Uncertainpy is tailored
        towards neuroscience, the implemented methods are general, and Uncertainpy can
        be used for many other types of models and features within other fields.
        
Keywords: uncertainty quantification sensitivity analysis neuroscience
Platform: UNKNOWN
Requires-Python: >=3
Provides-Extra: efel_features
Provides-Extra: network_features
Provides-Extra: all
Provides-Extra: docs
Provides-Extra: all_extras
Provides-Extra: tests
