Space-filling experimental design techniques are commonly used in many computer modeling and simulation studies to explore the effects of inputs on outputs. This research presents raxpy, a Python package that leverages expressive annotation of Python functions and classes to simplify space-filling experimentation. It incorporates code introspection to derive a Python function's input space and novel algorithms to automate the design of space-filling experiments for spaces with optional and hierarchical input dimensions. In this paper, we review the criteria for design evaluation given these types of dimensions and compare the proposed algorithms with numerical experiments. The results demonstrate the ability of the proposed algorithms to create improved space-filling experiment designs. The package includes support for parallelism and distributed execution. raxpy is available as free and open-source software under a MIT license.

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