DataFeature

class uncertainpy.DataFeature(name, evaluations=None, time=None, mean=None, variance=None, percentile_5=None, percentile_95=None, sobol_first=None, sobol_first_average=None, sobol_total=None, sobol_total_average=None, labels=[])

Store the results of each statistical metric calculated from the uncertainty quantification and sensitivity analysis for a single model/feature.

The statistical metrics can be retrieved as attributes. Additionally, DataFeature implements all standard dictionary methods, such as items, value, contains and so implemented. This means it can be indexed as a regular dictionary with the statistical metric names as keys and returns the values for that statistical metric.

Parameters:

• name (str) – Name of the model/feature.
• evaluations ({None, array_like}, optional.) – Feature or model result. Default is None.
• time ({None, array_like}, optional.) – Time evaluations for feature or model. Default is None.
• mean ({None, array_like}, optional.) – Mean of the feature or model results. Default is None.
• variance ({None, array_like}, optional.) – Variance of the feature or model results. Default is None.
• percentile_5 ({None, array_like}, optional.) – 5 percentile of the feature or model results. Default is None.
• percentile_95 ({None, array_like}, optional.) – 95 percentile of the feature or model results. Default is None.
• sobol_first ({None, array_like}, optional.) – First order sensitivity of the feature or model results. Default is None.
• sobol_first_average ({None, array_like}, optional.) – First order sensitivity of the feature or model results. Default is None.
• sobol_total ({None, array_like}, optional.) – Total effect sensitivity of the feature or model results. Default is None.
• sobol_total_average ({None, array_like}, optional.) – Average of the total effect sensitivity of the feature or model results. Default is None.
• labels (list, optional.) – A list of labels for plotting, [x-axis, y-axis, z-axis] Default is [].

Variables:

• name (str) – Name of the model/feature.
• evaluations ({None, array_like}) – Feature or model output.
• time ({None, array_like}) – Time values for feature or model.
• mean ({None, array_like}) – Mean of the feature or model results.
• variance ({None, array_like}) – Variance of the feature or model results.
• percentile_5 ({None, array_like}) – 5 percentile of the feature or model results.
• percentile_95 ({None, array_like}) – 95 percentile of the feature or model results.
• sobol_first ({None, array_like}) – First order Sobol indices (sensitivity) of the feature or model results.
• sobol_first_average ({None, array_like}) – Average of the first order Sobol indices of the feature or model results.
• sobol_total ({None, array_like}) – Total order Sobol indices (sensitivity) of the feature or model results.
• sobol_total_average ({None, array_like}) – Average of the total order Sobol indices of the feature or model results.
• labels (list) – A list of labels for plotting, [x-axis, y-axis, z-axis].

Notes

The statistical metrics calculated in Uncertainpy are:

• evaluations - the results from the model/feature evaluations.
• time - the time of the model/feature.
• mean - the mean of the model/feature.
• variance. - the variance of the model/feature.
• percentile_5 - the 5th percentile of the model/feature.
• percentile_95 - the 95th percentile of the model/feature.
• sobol_first - the first order Sobol indices (sensitivity) of the model/feature.
• sobol_first_average - the average of the first order Sobol indices (sensitivity) of the model/feature.
• sobol_total - the total order Sobol indices (sensitivity) of the model/feature.
• sobol_total_average - the average of the total order Sobol indices (sensitivity) of the model/feature.

__delitem__(statistical_metric)

Delete data for statistical_metric (set to None).

Parameters:statistical_metric (str) – Name of the statistical metric.

__getitem__(statistical_metric)

Get the data for statistical_metric.

Parameters:statistical_metric (str) – Name of the statistical metric. Returns:The data for statistical_metric. Return type:{array_like, None}

__iter__()

Iterate over each statistical metric with data.

Yields:str – Name of the statistical metric.

__len__()

Get the number of data types with data.

Returns:The number of data types with data. Return type:int

__setitem__(statistical_metric, data)

Set the data for the statistical metric.

Parameters:

• statistical_metric (str) – Name of the statistical metric.
• data ({array_like, None}) – The data for the statistical metric.

clear() → None. Remove all items from D.

get(k[, d]) → D[k] if k in D, else d. d defaults to None.

get_metrics()

Get the names of all statistical metrics that contain data (not None).

Returns:List of the names of all statistical metric that contain data. Return type:list

items() → list of D's (key, value) pairs, as 2-tuples

iteritems() → an iterator over the (key, value) items of D

iterkeys() → an iterator over the keys of D

itervalues() → an iterator over the values of D

keys() → list of D's keys

ndim()

Get the number of dimensions the data of a data type. Returns None if no evaluations or all evaluations contain numpy.nan.

Parameters:feature (str) – Name of the model or a feature. Returns:The number of dimensions of the data of the data type. Return type:int

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D

update([E, ]**F) → None. Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → list of D's values