Keep your files tidy: tidypath organizes your research data automatically

Researchers working with large amounts of data often waste time organizing files, creating folders, choosing names, and managing storage. The Python tool 'tidypath' solves these problems by automatically organizing files and creating clear, meaningful file names that show exactly what calculation was done. The 'savedata' decorator implements memoization: your calculation is run upon the first call, the result is saved into a highly compressed file, and it is loaded upon subsequent function calls that use the same arguments, avoiding repeated work. The 'savefig' decorator automatically saves your charts and graphs in various formats. Both features create organized folder structures ('data' and 'figs') that match your code layout (module -> submodules -> function), making it easier to find the resources later. We will show how tidypath works with your existing projects without changes, and examine its support for multiple output types (virtually any: dict, tuple, np.ndarray, pd.DataFrame, ...), file formats (lzma, csv, JSON), and figure extensions (svg, pdf, html, ...).