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.
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.
A review of the current ‘data_source.py’ interface with small experimental datasets in mind.
A Jupyter 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.
A Jupyter notebook, for the example documentation gallery, demonstrating how an experimentalist would use these functions to compare their data to known sources.
Intermediate level python familiarity.
Beginner or intermediate level C++ familiarity.
Beginner knowledge of version control.
An undergraduate degree in physics, nuclear engineering, computer science, or related discipline.
Familiarity with nuclear data sources and experiments.
Familiarity with HDF5 and pytables.
Professor Rachel Slaybaugh and Postdoctoral Scholar Kathryn Huff, University of California, Berkeley.