pyuncertainnumber.pba.pbox_parametric

Attributes

normal

gaussian

named_pbox

Functions

makePbox(→ pyuncertainnumber.pba.pbox_abc.Pbox)

_bound_pcdf(dist_family, *args, **kwargs)

bound the parametric CDF

_parametric_bounds_array(dist_family, *args, **kwargs)

from parametric distribution specification to define the lower and upper bound of the p-box

norm(*args)

lognormal(*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)

crystalball(*args)

dgamma(*args)

dweibull(*args)

erlang(*args)

exponnorm(*args)

exponential(*args, **kwargs)

The default p-box constructor for the exponential distribution with scale parameterisation

exponweib(*args)

exponpow(*args)

f(*args)

fatiguelife(*args)

fisk(*args)

foldcauchy(*args)

foldnorm(mu, s[, steps])

genlogistic(*args)

gennorm(*args)

genpareto(*args)

genexpon(*args)

genextreme(*args)

gausshyper(*args)

gamma(*args)

gengamma(*args)

genhalflogistic(*args)

geninvgauss(*args)

gompertz(*args)

gumbel_r(*args)

gumbel_l(*args)

halfcauchy(*args)

halflogistic(*args)

halfnorm(*args)

halfgennorm(*args)

hypsecant(*args)

invgamma(*args)

invgauss(*args)

invweibull(*args)

irwinhall(*args)

jf_skew_t(*args)

johnsonsb(*args)

johnsonsu(*args)

kappa4(*args)

kappa3(*args)

ksone(*args)

kstwo(*args)

kstwobign(*args)

laplace(*args)

laplace_asymmetric(*args)

levy(*args)

levy_l(*args)

levy_stable(*args)

logistic(*args)

loggamma(*args)

loglaplace(*args)

loguniform(*args)

lomax(*args)

maxwell(*args)

mielke(*args)

moyal(*args)

nakagami(*args)

ncx2(*args)

ncf(*args)

nct(*args)

norminvgauss(*args)

pareto(*args)

pearson3(*args)

powerlaw(*args)

powerlognorm(*args)

powernorm(*args)

rdist(*args)

rayleigh(*args, **kwargs)

rel_breitwigner(*args)

rice(*args)

recipinvgauss(*args)

semicircular(*args)

skewcauchy(*args)

skewnorm(*args)

studentized_range(*args)

t(*args)

trapezoid(*args)

triang(*args)

truncweibull_min(*args)

tukeylambda(*args)

uniform_sps(*args)

vonmises(*args)

vonmises_line(*args)

wald(*args)

weibull_min(*args)

weibull_max(*args)

wrapcauchy(*args)

lognormal_weird(mean, var[, steps])

p-box for the lognormal distribution

uniform(a, b[, steps])

special case of Uniform distribution as

exponential_by_lambda(...)

Bespoke p-box constructor for the exponential distribution

trapz(a, b, c, d[, steps])

weibull(*args[, steps])

KM(k, m[, steps])

KN(k, n[, steps])

bernoulli(*args)

betabinom(*args)

betanbinom(*args)

binom(*args)

boltzmann(*args)

dlaplace(*args)

geom(*args)

hypergeom(*args)

logser(*args)

nbinom(*args)

nchypergeom_fisher(*args)

nchypergeom_wallenius(*args)

nhypergeom(*args)

planck(*args)

poisson(*args)

randint(*args)

skellam(*args)

yulesimon(*args)

zipf(*args)

zipfian(*args)

Module Contents

pyuncertainnumber.pba.pbox_parametric.makePbox(func) → pyuncertainnumber.pba.pbox_abc.Pbox
pyuncertainnumber.pba.pbox_parametric._bound_pcdf(dist_family, *args, **kwargs)

bound the parametric CDF

Note

  • top-level implemenatation

  • only support fully bounded parameters

pyuncertainnumber.pba.pbox_parametric._parametric_bounds_array(dist_family, *args, **kwargs)

from parametric distribution specification to define the lower and upper bound of the p-box

Parameters:

  • dist_family – (str) the name of the distribution

  • *args – several parameter (interval or list)

  • **kwargs – scale parameters (interval or list)

Note

  • middle level implementation

pyuncertainnumber.pba.pbox_parametric.norm(*args)
pyuncertainnumber.pba.pbox_parametric.lognormal(*args)
pyuncertainnumber.pba.pbox_parametric.alpha(*args)
pyuncertainnumber.pba.pbox_parametric.anglit(*args)
pyuncertainnumber.pba.pbox_parametric.argus(*args)
pyuncertainnumber.pba.pbox_parametric.arcsine(*args)
pyuncertainnumber.pba.pbox_parametric.beta(*args)
pyuncertainnumber.pba.pbox_parametric.betaprime(*args)
pyuncertainnumber.pba.pbox_parametric.bradford(*args)
pyuncertainnumber.pba.pbox_parametric.burr(*args)
pyuncertainnumber.pba.pbox_parametric.burr12(*args)
pyuncertainnumber.pba.pbox_parametric.cauchy(*args)
pyuncertainnumber.pba.pbox_parametric.chi(*args)
pyuncertainnumber.pba.pbox_parametric.chi2(*args)
pyuncertainnumber.pba.pbox_parametric.cosine(*args)
pyuncertainnumber.pba.pbox_parametric.crystalball(*args)
pyuncertainnumber.pba.pbox_parametric.dgamma(*args)
pyuncertainnumber.pba.pbox_parametric.dweibull(*args)
pyuncertainnumber.pba.pbox_parametric.erlang(*args)
pyuncertainnumber.pba.pbox_parametric.exponnorm(*args)
pyuncertainnumber.pba.pbox_parametric.exponential(*args, **kwargs)

The default p-box constructor for the exponential distribution with scale parameterisation

Note

scale parameterisation due to scipy.stats. Note that the “scale” argument is a must. There is an “exponential_by_lambda” constructor which uses the rate parameterisation.

Example

>>> pba.pbox_parametric.exponential(scale=[1, 2])
pyuncertainnumber.pba.pbox_parametric.exponweib(*args)
pyuncertainnumber.pba.pbox_parametric.exponpow(*args)
pyuncertainnumber.pba.pbox_parametric.f(*args)
pyuncertainnumber.pba.pbox_parametric.fatiguelife(*args)
pyuncertainnumber.pba.pbox_parametric.fisk(*args)
pyuncertainnumber.pba.pbox_parametric.foldcauchy(*args)
pyuncertainnumber.pba.pbox_parametric.foldnorm(mu, s, steps=Params.steps)
pyuncertainnumber.pba.pbox_parametric.genlogistic(*args)
pyuncertainnumber.pba.pbox_parametric.gennorm(*args)
pyuncertainnumber.pba.pbox_parametric.genpareto(*args)
pyuncertainnumber.pba.pbox_parametric.genexpon(*args)
pyuncertainnumber.pba.pbox_parametric.genextreme(*args)
pyuncertainnumber.pba.pbox_parametric.gausshyper(*args)
pyuncertainnumber.pba.pbox_parametric.gamma(*args)
pyuncertainnumber.pba.pbox_parametric.gengamma(*args)
pyuncertainnumber.pba.pbox_parametric.genhalflogistic(*args)
pyuncertainnumber.pba.pbox_parametric.geninvgauss(*args)
pyuncertainnumber.pba.pbox_parametric.gompertz(*args)
pyuncertainnumber.pba.pbox_parametric.gumbel_r(*args)
pyuncertainnumber.pba.pbox_parametric.gumbel_l(*args)
pyuncertainnumber.pba.pbox_parametric.halfcauchy(*args)
pyuncertainnumber.pba.pbox_parametric.halflogistic(*args)
pyuncertainnumber.pba.pbox_parametric.halfnorm(*args)
pyuncertainnumber.pba.pbox_parametric.halfgennorm(*args)
pyuncertainnumber.pba.pbox_parametric.hypsecant(*args)
pyuncertainnumber.pba.pbox_parametric.invgamma(*args)
pyuncertainnumber.pba.pbox_parametric.invgauss(*args)
pyuncertainnumber.pba.pbox_parametric.invweibull(*args)
pyuncertainnumber.pba.pbox_parametric.irwinhall(*args)
pyuncertainnumber.pba.pbox_parametric.jf_skew_t(*args)
pyuncertainnumber.pba.pbox_parametric.johnsonsb(*args)
pyuncertainnumber.pba.pbox_parametric.johnsonsu(*args)
pyuncertainnumber.pba.pbox_parametric.kappa4(*args)
pyuncertainnumber.pba.pbox_parametric.kappa3(*args)
pyuncertainnumber.pba.pbox_parametric.ksone(*args)
pyuncertainnumber.pba.pbox_parametric.kstwo(*args)
pyuncertainnumber.pba.pbox_parametric.kstwobign(*args)
pyuncertainnumber.pba.pbox_parametric.laplace(*args)
pyuncertainnumber.pba.pbox_parametric.laplace_asymmetric(*args)
pyuncertainnumber.pba.pbox_parametric.levy(*args)
pyuncertainnumber.pba.pbox_parametric.levy_l(*args)
pyuncertainnumber.pba.pbox_parametric.levy_stable(*args)
pyuncertainnumber.pba.pbox_parametric.logistic(*args)
pyuncertainnumber.pba.pbox_parametric.loggamma(*args)
pyuncertainnumber.pba.pbox_parametric.loglaplace(*args)
pyuncertainnumber.pba.pbox_parametric.loguniform(*args)
pyuncertainnumber.pba.pbox_parametric.lomax(*args)
pyuncertainnumber.pba.pbox_parametric.maxwell(*args)
pyuncertainnumber.pba.pbox_parametric.mielke(*args)
pyuncertainnumber.pba.pbox_parametric.moyal(*args)
pyuncertainnumber.pba.pbox_parametric.nakagami(*args)
pyuncertainnumber.pba.pbox_parametric.ncx2(*args)
pyuncertainnumber.pba.pbox_parametric.ncf(*args)
pyuncertainnumber.pba.pbox_parametric.nct(*args)
pyuncertainnumber.pba.pbox_parametric.norminvgauss(*args)
pyuncertainnumber.pba.pbox_parametric.pareto(*args)
pyuncertainnumber.pba.pbox_parametric.pearson3(*args)
pyuncertainnumber.pba.pbox_parametric.powerlaw(*args)
pyuncertainnumber.pba.pbox_parametric.powerlognorm(*args)
pyuncertainnumber.pba.pbox_parametric.powernorm(*args)
pyuncertainnumber.pba.pbox_parametric.rdist(*args)
pyuncertainnumber.pba.pbox_parametric.rayleigh(*args, **kwargs)
pyuncertainnumber.pba.pbox_parametric.rel_breitwigner(*args)
pyuncertainnumber.pba.pbox_parametric.rice(*args)
pyuncertainnumber.pba.pbox_parametric.recipinvgauss(*args)
pyuncertainnumber.pba.pbox_parametric.semicircular(*args)
pyuncertainnumber.pba.pbox_parametric.skewcauchy(*args)
pyuncertainnumber.pba.pbox_parametric.skewnorm(*args)
pyuncertainnumber.pba.pbox_parametric.studentized_range(*args)
pyuncertainnumber.pba.pbox_parametric.t(*args)
pyuncertainnumber.pba.pbox_parametric.trapezoid(*args)
pyuncertainnumber.pba.pbox_parametric.triang(*args)
pyuncertainnumber.pba.pbox_parametric.truncweibull_min(*args)
pyuncertainnumber.pba.pbox_parametric.tukeylambda(*args)
pyuncertainnumber.pba.pbox_parametric.uniform_sps(*args)
pyuncertainnumber.pba.pbox_parametric.vonmises(*args)
pyuncertainnumber.pba.pbox_parametric.vonmises_line(*args)
pyuncertainnumber.pba.pbox_parametric.wald(*args)
pyuncertainnumber.pba.pbox_parametric.weibull_min(*args)
pyuncertainnumber.pba.pbox_parametric.weibull_max(*args)
pyuncertainnumber.pba.pbox_parametric.wrapcauchy(*args)
pyuncertainnumber.pba.pbox_parametric.lognormal_weird(mean, var, steps=Params.steps)

p-box for the lognormal distribution

*Note: the parameters used are the mean and variance of the lognormal distribution

not the mean and variance of the underlying normal* See: [1]<https://en.wikipedia.org/wiki/Log-normal_distribution#Generation_and_parameters> [2]<https://stackoverflow.com/questions/51906063/distribution-mean-and-standard-deviation-using-scipy-stats>

Parameters:

  • mean – mean of the lognormal distribution

  • var – variance of the lognormal distribution

Return type:

Pbox

pyuncertainnumber.pba.pbox_parametric.uniform(a, b, steps=Params.steps)

special case of Uniform distribution as Scipy has an unbelivably strange parameterisation than common sense

Parameters:

  • a (-) – (float) lower endpoint

  • b (-) – (float) upper endpoints

pyuncertainnumber.pba.pbox_parametric.exponential_by_lambda(lamb: list | pyuncertainnumber.pba.intervals.number.Interval) → pyuncertainnumber.pba.pbox_abc.Pbox

Bespoke p-box constructor for the exponential distribution

Parameters:

lamb (-) – (list or Interval) the rate parameter of the exponential distribution

pyuncertainnumber.pba.pbox_parametric.trapz(a, b, c, d, steps=Params.steps)
pyuncertainnumber.pba.pbox_parametric.weibull(*args, steps=Params.steps)
pyuncertainnumber.pba.pbox_parametric.KM(k, m, steps=Params.steps)
pyuncertainnumber.pba.pbox_parametric.KN(k, n, steps=Params.steps)
pyuncertainnumber.pba.pbox_parametric.bernoulli(*args)
pyuncertainnumber.pba.pbox_parametric.betabinom(*args)
pyuncertainnumber.pba.pbox_parametric.betanbinom(*args)
pyuncertainnumber.pba.pbox_parametric.binom(*args)
pyuncertainnumber.pba.pbox_parametric.boltzmann(*args)
pyuncertainnumber.pba.pbox_parametric.dlaplace(*args)
pyuncertainnumber.pba.pbox_parametric.geom(*args)
pyuncertainnumber.pba.pbox_parametric.hypergeom(*args)
pyuncertainnumber.pba.pbox_parametric.logser(*args)
pyuncertainnumber.pba.pbox_parametric.nbinom(*args)
pyuncertainnumber.pba.pbox_parametric.nchypergeom_fisher(*args)
pyuncertainnumber.pba.pbox_parametric.nchypergeom_wallenius(*args)
pyuncertainnumber.pba.pbox_parametric.nhypergeom(*args)
pyuncertainnumber.pba.pbox_parametric.planck(*args)
pyuncertainnumber.pba.pbox_parametric.poisson(*args)
pyuncertainnumber.pba.pbox_parametric.randint(*args)
pyuncertainnumber.pba.pbox_parametric.skellam(*args)
pyuncertainnumber.pba.pbox_parametric.yulesimon(*args)
pyuncertainnumber.pba.pbox_parametric.zipf(*args)
pyuncertainnumber.pba.pbox_parametric.zipfian(*args)
pyuncertainnumber.pba.pbox_parametric.normal
pyuncertainnumber.pba.pbox_parametric.gaussian
pyuncertainnumber.pba.pbox_parametric.named_pbox