# Data¶

Uncertainpy stores all results from the uncertainty quantification and
sensitivity analysis in `UncertaintyQuantification.data`

,
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
`Data.save`

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()
data.load("filename")
variance = data["nr_spikes"].variance
```