pyuncertainnumber.characterisation.uncertainNumber¶

Attributes¶

`uniform`
`lognormal`

Classes¶

UncertainNumber

Uncertain Number class

Functions¶

`I`(→ UncertainNumber)	a shortcut to construct the interval-type UN object
`D`(→ UncertainNumber)	a shortcut to construct the distribution-type UN object
`DSS`(→ UncertainNumber)	a shortcut for the Dempster-Shafer-type UN object
`norm`(*args)
`gaussian`(*args)
`normal`(*args)
`alpha`(*args)
`anglit`(*args)
`argus`(*args)
`arcsine`(*args)
`beta`(*args)
`betaprime`(*args)
`bradford`(*args)
`burr`(*args)
`burr12`(*args)
`cauchy`(*args)
`chi`(*args)
`chi2`(*args)
`cosine`(*args)
`gamma`(*args)

Module Contents¶

class pyuncertainnumber.characterisation.uncertainNumber.UncertainNumber(name=None, symbol=None, unit=None, uncertainty_type=None, essence=None, masses=None, intervals=None, distribution_parameters=None, pbox_parameters=None, hedge=None, _construct=None, nominal_value=1.0, p_flag=True, _skip_construct_init=False, measurand=None, nature=None, provenence=None, justification=None, structure=None, security=None, ensemble=None, variability=None, dependence=None, uncertainty=None, physical_quantity=None, _samples=None, **kwargs)¶

Uncertain Number class

Parameters:

intervals (-) – the interval specification for the UN object;
distribution_parameters (-) – a list of the distribution family and its parameters; e.g. [‘norm’, [0, 1]];
pbox_initialisation (-) – a list of the distribution family and its parameters; e.g. [‘norm’, ([0,1], [3,4])];

Example

Uncertain numbers can be constructed in multiple ways. For example, a canonical way allows users to fill in as many fields as possible:

>>> from pyuncertainnumber import UncertainNumber
>>> UncertainNumber(name="velocity", symbol="v", unit="m/s", intervals=[1, 2])
>>> UncertainNumber(name="velocity", symbol="v", unit="m/s", distribution_parameters=['normal', (10, 2)])
>>> UncertainNumber(name="velocity", symbol="v", unit="m/s", pbox_parameters=['normal', ([8, 12], [0.5, 1.5])])
>>> UncertainNumber(name="velocity", symbol="v", unit="m/s", essence='dempster_shafer', intervals=[[1,5], [3,6]], masses=[0.5, 0.5])

Alternatively, users can use shortcuts to quickly create UN objects and get on with calculations:

>>> import pyuncertainnumber as pun
>>> pun.I([1, 2])
>>> pun.D('gaussian', (10, 2))
>>> pun.normal([8, 12], [0.5, 1.5])
>>> pun.DSS(intervals=[[1,5], [3,6]], masses=[0.5, 0.5])

Q_¶

instances = []¶

name = None¶

symbol = None¶

uncertainty_type = None¶

essence = None¶

masses = None¶

intervals = None¶

distribution_parameters = None¶

pbox_parameters = None¶

hedge = None¶

_construct = None¶

nominal_value = 1.0¶

p_flag = True¶

_skip_construct_init = False¶

measurand = None¶

nature = None¶

provenence = None¶

justification = None¶

structure = None¶

security = None¶

ensemble = None¶

variability = None¶

dependence = None¶

uncertainty = None¶

_physical_quantity = None¶

_samples = None¶

property unit¶: get the physical quantity of the uncertain number

__init_check()¶

__init_construct()¶

the de facto parameterisation/instantiation procedure for the core constructs of the UN class

caveat:: user needs to by themselves figure out the correct shape of the ‘distribution_parameters’, such as [‘uniform’, [1,2]]

parameterised_pbox_specification()¶

_update_physical_quantity()¶

static match_pbox(keyword, parameters)¶

match the distribution keyword from the initialisation to create the underlying distribution object

Parameters:

keyword (-) – (str) the distribution keyword
parameters (-) – (list) the parameters of the distribution

init_check()¶: check if the UN initialisation specification is correct

Note

a lot of things to double check. keep an growing list: 1. unit 2. hedge: user cannot speficy both ‘hedge’ and ‘intervals’. ‘intervals’ takes precedence.

__str__()¶: string representation of the UncertainNumber

Note

the same as __reor__ for now until a better idea is proposed

__repr__() → str¶: Concise __repr__

describe(style='verbose')¶: print out a verbose description of the uncertain number

ci()¶: get 95% range confidence interval

plot(**kwargs)¶: quick plot of the uncertain number object

display(**kwargs)¶: quick plot of the uncertain number object

property construct¶

property construct_type¶

property physical_quantity¶: get the physical quantity of the uncertain number

classmethod from_hedge(hedged_language)¶: create an Uncertain Number from hedged language

classmethod fromConstruct(construct)¶: create an Uncertain Number from a construct object

classmethod fromDistribution(D, **kwargs)¶

create an Uncertain Number from a Distribution object.

Parameters:

D (-) – a Distribution object
dist_family (str) – the distribution family
dist_params (list, tuple or string) – the distribution parameters

classmethod from_Interval(u)¶

classmethod from_pbox(p)¶: genenal from pbox

classmethod from_ds(ds)¶

classmethod from_sps(sps_dist)¶

create an UN object from a parametric scipy.stats dist object #! it seems that a function will suffice :param - sps_dist: scipy.stats dist object

Note

sps_dist –> UN.Distribution object

__array_ufunc__(ufunc, method, *inputs, **kwargs)¶

_apply(method)¶

sqrt()¶

exp()¶

tanh()¶

tan()¶

log()¶

sin()¶

cos()¶

reciprocal()¶

bin_ops(other, ops)¶

__add__(other)¶: add two uncertain numbers

__radd__(other)¶

__sub__(other)¶

__mul__(other)¶: multiply two uncertain numbers

__rmul__(other)¶

__truediv__(other)¶: divide two uncertain numbers

__rtruediv__(other)¶

__pow__(other)¶: power of two uncertain numbers

__rpow__(other)¶: power of two uncertain numbers

add(other, dependency='f')¶

sub(other, dependency='f')¶

mul(other, dependency='f')¶

div(other, dependency='f')¶

pow(other, dependency='f')¶

JSON_dump(filename='UN_data.json')¶: the JSON serialisation of the UN object into the filesystem

to_pbox()¶: convert the UN object to a pbox object

pyuncertainnumber.characterisation.uncertainNumber.I(*args: str | list[numbers.Number] | pyuncertainnumber.pba.intervals.number.Interval) → UncertainNumber¶: a shortcut to construct the interval-type UN object

pyuncertainnumber.characterisation.uncertainNumber.D(*args, **kwargs) → UncertainNumber¶: a shortcut to construct the distribution-type UN object

pyuncertainnumber.characterisation.uncertainNumber.DSS(*args, **kwargs) → UncertainNumber¶: a shortcut for the Dempster-Shafer-type UN object

pyuncertainnumber.characterisation.uncertainNumber.norm(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.gaussian(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.normal(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.alpha(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.anglit(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.argus(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.arcsine(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.beta(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.betaprime(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.bradford(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.burr(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.burr12(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.cauchy(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.chi(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.chi2(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.cosine(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.gamma(*args)¶

pyuncertainnumber.characterisation.uncertainNumber.uniform¶

pyuncertainnumber.characterisation.uncertainNumber.lognormal¶