Uncertainpy stores all results from the uncertainty quantification and sensitivity analysis in, as a Data object. The Data class works similarly to a Python dictionary. The name of the model or feature is the key, while the values are DataFeature objects that stores each statistical metric in in the table below as attributes. Results can be saved and loaded through and Data.load.

Calculated statistical metric Symbol Variable
Model and feature evaluations \(U\) evaluations
Model and feature times \(t\) time
Mean \(\mathbb{E}\) mean
Variance \(\mathbb{V}\) variance
5th percentile \(P_{5}\) percentile_5
95th percentile \(P_{95}\) percentile_95
First order Sobol indices \(S\) sobol_first
Total order Sobol indices \(S_T\) sobol_total
Average of the first order Sobol indices \(\widehat{S}\) sobol_first_average
Average of the total order Sobol indices \(\widehat{S}_{T}\) sobol_total_average

An example: if we have performed uncertainty quantification of a spiking neuron model with the number of spikes as one of the features, we get load the data file and get the variance of the number of spikes by typing:

data = un.Data()
variance = data["nr_spikes"].variance

API reference