This contribution introduces a Python package, SymPKF, able to compute PKF dynamics for univariate statistics and when the covariance model is parameterized from the variance and the local anisotropy of the correlations. Hence, the design of the parameter dynamics is crucial, while it can be tedious to do this by hand. ![]() ![]() In the PKF, the dynamics of the covariance during the forecast step rely on the prediction of the covariance parameters. Recent research in data assimilation has led to the introduction of the parametric Kalman filter (PKF): an implementation of the Kalman filter, whereby the covariance matrices are approximated by a parameterized covariance model. Its user interface supports intuitive and exploratory modeling, its architecture makes distribution and deployment cost-efficient in heterogeneous environments. Thus, the tool UMLet provides an effective way to teach UML and to create and share UML sketches, especially in agile environments and during early life-cycle phases. It is a flyweight Java application that can easily be deployed in various development environments it features an intuitive and pop-up-free user interface, while still providing output to common high-quality publishing formats. We present the freely available modeling tool UMLet we designed to specifically address these basic issues. However, such tools aim at supporting specific life-cycle phases, but they often do not meet basic requirements arising in heterogeneous environments, UML education, early life-cycle phases, or agile processes: hassle-free tool deployment, support for fast model sketching, and flexible graphic export features. ![]() A large and growing variety of tools can support all kinds of UML modeling aspects: from model creation to advanced round-trip engineering of UML models and code.
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