pyuncertainnumber 0.0.13

Rigorous computation with Uncertain Number in Python

PyUncertainNumber

Uncertain Number refers to a class of mathematical objects useful for risk analysis that generalize real numbers, intervals, probability distributions, interval bounds on probability distributions (i.e. probability boxes), and finite DempsterShafer structures. Refer to the informative documentation for additional details.

quick start

PyUncertainNumber can be used to easily create an UncertainNumber object, which may embody a mathematical construct such as PBox, Interval, Distribution, or DempsterShafer structure.

from pyuncertainnumber import UncertainNumber as UN

e = UN(
    name='elas_modulus', 
    symbol='E', 
    units='Pa', 
    essence='pbox', 
    distribution_parameters=['gaussian', ([0,12],[1,4])])

installation

Requirement: It requires Python >=3.11

PyUncertainNumber can be installed from PyPI. Upon activation of your virtual environment, use the code below in your terminal. For additional instructions, refer to installation guide.

pip install pyuncertainnumber

features

• PyUncertainNumber is a Python package for generic computational tasks focussing on rigourou uncertainty analysis, which provides a research-grade computing environment for uncertainty characterisation, propagation, validation and uncertainty extrapolation.
• PyUncertainNumber supports probability bounds analysis to rigorously bound the prediction for the quantity of interest with mixed uncertainty propagation.
• PyUncertainNumber also features great natural language support as such characterisatin of input uncertainty can be intuitively done by using natural language like about 7 or simple expression like [15 +- 10%], without worrying about the elicitation.
• features the save and loading of UN objects
• yields much informative results such as the combination that leads to the maximum in vertex method.

UQ multiverse

UQ is a big world (like Marvel multiverse) consisting of abundant theories and software implementations on multiple platforms. We focus mainly on the imprecise probability frameworks. Some notable examples include OpenCossan UQlab in Matlab and ProbabilityBoundsAnalysis.jl in Julia, and many others of course. PyUncertainNumber builds upon on a few pioneering projects and will continue to be dedicated to support imprecise analysis in engineering using Python.