This page tracks our priorities for future enhancements to xspec. It is divided into two categories : large projects and small projects based on the estimated amount of effort required.
- Large Projects
- Provide XSPEC functions in Python.
- The best approach is to use a package such as SWIG to provide access to XSPEC objects from Python.
- This is likely to require some reworking of top-level objects.
- It is probably not a good idea to provide access to lower-level objects.
- Use bootstrap resampling to provide goodness-of-fit measures.
- Provide options for different units in plots.
- Parallelization for multi-core machines.
- OpenMP is included in gcc since 4.2 and provides simple #pragma commands.
- This is shared memory so need to avoid race conditions.
- Identify cpu-intensive models and try to add multi-threading.
- Provide XSPEC functions in Python.
- Small Projects
- Add html help tarball to release and have it install itself in spectral/help (issue #1699)
- Allow convolution components to operate directly on multiplicative models (issue #1685)
- eg (CM)A in addition to the currently allowed C(MA)
- One such component would be a partial covering modification to absorption models
- Add /* option in response to fit continue prompt to allow jumping out of nested fits (issue #1739)