Experimental Nuclear Data Project

The experimental nuclear data project will enable experimentalists to add data such as cross sections to the PyNE framework easily and to run specific, built-in comparisons against standard data.

Brief explanation:

For an experimentalist, having a tool with which to compare their experimental data set with standard datasets is extraordinarily important. PyNE can be that tool because of the available data_source.py interface.

However, a built-in tool for automated data comparison is still needed. The PyNE toolkit should include functionality for comparing two data sources.

Expected results:

  • A review of the current ‘data_source.py’ interface with small experimental datasets in mind.
  • An IPython notebook, for the example documentation gallery, demonstrating how an experimentalist can import their experimental nuclear data into a PyNE analysis (using the data_source API).
  • Development of methods that generate relevant statistical comparisons of any data in the small experimental dataset against available data in other PyNE source.
  • An Ipython notebook, for the example documentation gallery, demonstrating how an experimentalist would use these functions to compare their data to known sources.

Knowledge Prerequisites:

Necessary :

  • Intermediate level python familiarity.
  • Beginner or intermediate level C++ familiarity.
  • Beginner knowledge of version control.

Desired :

  • An undergraduate degree in physics, nuclear engineering, computer science, or related discipline.
  • Familiarity with nuclear data sources and experiments.
  • Familiarity with HDF5 and pytables.

Mentor:

Professor Rachel Slaybaugh and Postdoctoral Scholar Kathryn Huff, University of California, Berkeley.

More Resources: