Python has been widely accepted by scientific community. From the invaluable scientific software packages such as numpy, scipy, mpi4py, dask, and pandas to the thousands of specialized software packages, the scientific support through Python is enormous.
StructOpt is meant to solve new problems rather than be a better tool for solving well understood problems. As a result, many of the users of StructOpt will be exploring new scientific territory and will be in the development process of creation and iteration on their tools. Python is a forerunner for development applications due to its ability to scale from off-hand scripts to large packages and applications.
Via Jupyter notebooks, Python code is on its way to becoming readable for the general community. This, combined with the drive toward more accessible and better documented scientific code, may provide a powerful combination to encourage scientific reproducability and archival. To this end, StructOpt’s data explorer is meant to ease the process of analyzing and displaying useful information.