RosettaSilentToolbox (rstoolbox) is a Python library to visualize, analyze and select
the designs of interest from a design population. It exploits the power of pandas
to ease on the selection of decoys of interest and provide a direct interface to matplotlib
and seaborn plotting.
For a more detailed idea on the benefits of using this package, you will want to read the introductory notes.
Then you should start in the installation page.
If you are not comfortable with scripting, rstoolbox offers some direct executables to automatically generate analysis from your data.
If you want to exploit the full possibilities of the library, you should probably follow the tutorials and then explore
the API.
Have you used rstoolbox on your work? Let us know so we can highlight it!
To see the code or report a bug, please visit the github repository.
This library has been developed by members of the Laboratory of Protein Design and Immunoengineering. Members have contributed both by coding and or requesting new features.
rstoolbox would not be possible without pandas,
numpy, matplotlib
and seaborn plotting.
rstoolbox is open source.
We welcome any collaboration in order to improve it.
If you find any problem or unexpected behaviour, let us know through our issue tracker so that we can evaluate and fix it.