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.
- 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.
- 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.