pyuncertainnumber.characterisation.un_fields

Classes

AncillaryUncertainty	Base class for all enums that describe different aspects of uncertainty.
Measurand	What is being measured or described (e.g., count, probability, distribution).
Ensemble	The collection or grouping over which variability or uncertainty is considered
Variability	The way uncertainty is expressed or summarized (e.g., point estimate, confidence).
Uncertainty_types	The type of uncertainty affecting the uncertain number.

Module Contents

class pyuncertainnumber.characterisation.un_fields.AncillaryUncertainty(*args, **kwds)

Bases: enum.Enum

Base class for all enums that describe different aspects of uncertainty.

We aim to organise the ancillary information into a list of fields to be stored with the quantitative details of the uncertain number rather than separately as comments or narrative statements in other files.

These fields are intended to represent more-or-less orthogonal descriptors that together fully characterise what we need to know about the quantity in order to use it in calculations. The collection of these fields should be rich enough to account for all relevant and useful information, but it is critical that it not seem daunting or burdensome to users. Users are never required to enter anything into any of these fields. Numbers that lack substantive justifications are indicated with visual cues but, wherever possible, they are not otherwise delayed, penalised or restricted in use. Some of the entries for these various fields can be populated automatically by computer, and several could be, especially for numbers that arise from computing with previously defined numbers.

The values for the descriptor fields can be given as arbitrary strings with arbitrary formatting or by links to other resources. The possible detail for any field is unlimited. For convenience, however, suggested entries for many fields can be selected from comprehensive (and customisable) dropdown menus.

Subclasses:

• Measurand: What is being measured or described (e.g., count, probability, distribution).

• Ensemble: The collection or grouping over which variability or uncertainty is considered (e.g., repeated measurements, flights, households).

• Variability: The way uncertainty is expressed or summarized (e.g., point estimate, confidence).

class pyuncertainnumber.characterisation.un_fields.Measurand(*args, **kwds)

Bases: AncillaryUncertainty

What is being measured or described (e.g., count, probability, distribution).

count = 'count'

tally = 'tally'

unobservable_parameter = 'unobservable parameter'

probability = 'probability'

distribution = 'distribution'

range_ = 'range'

rank = 'rank'

class pyuncertainnumber.characterisation.un_fields.Ensemble(*args, **kwds)

Bases: AncillaryUncertainty

The collection or grouping over which variability or uncertainty is considered (e.g., repeated measurements, flights, households).

repeated_measurements = 'repeated measurements'

flights = 'flights'

pressurisations = 'pressurisations'

temporal_steps = 'temporal steps'

spatial_sites = 'spatial sites'

manufactured_components = 'manufactured components'

customers = 'customers'

people = 'people'

households = 'households'

particular_population = 'particular population'

class pyuncertainnumber.characterisation.un_fields.Variability(*args, **kwds)

Bases: AncillaryUncertainty

The way uncertainty is expressed or summarized (e.g., point estimate, confidence).

point_estimate = 'point estimate'

confidence = 'confidence'

class pyuncertainnumber.characterisation.un_fields.Uncertainty_types(*args, **kwds)

Bases: AncillaryUncertainty

The type of uncertainty affecting the uncertain number.

Certain = 'certain'

Aleatory = 'aleatory'

Epistemic = 'epistemic'

Inferential = 'inferential'

Design_uncertainty = 'design uncertainty'

Vagueness = 'vagueness'

Mixture = 'mixture'