Version [ 1.2 ] - [ AsymIntervals: A Python library for uncertainty modeling with asymmetric interval numbers ]

In this paper, we present an extension of the AsymIntervals library, designed to enhance the modelling and processing of uncertainty using Asymmetric Interval Numbers (AINs). In response to the growing demand for expressive and mathematically consistent tools for interval-based uncertainty representation, the library has been extended with a comprehensive set of interval characteristics, logical predicates, relational operators, and mathematical transformations implemented within a unified core class. The extension introduces support for advanced algebraic, trigonometric, as well as exponential and logarithmic operations, flexible construction of AIN objects from multiple input formats, sampling-based data generation, and normalization of AIN collections. Additionally, enhanced export and serialisation mechanisms enable seamless integration with numerical workflows and scientific applications. These improvements substantially broaden the applicability of AsymIntervals in decision analysis, uncertainty modelling, and computational research.