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.
- 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.
- Generalize goodness command to use variety of statistics with option of parametric bootstrap. Also remove probability statement from the standard
- output at the end of the fit since this is not statistically valid.
- 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
Create model cross-reference table (under work at Model classification).
- Add a redshift parameter to any model by prefacing the name with z.
- Implement by passing shifted energyArray to model function.
- This will not work for the cooling flow models which require the redshift be passed into the function.
- Can we come up with something to help users convert v11 scripts with /b models into the equivalent v12 syntax?
- Look at extending the projct model so it works for the case of different numbers of data sets in each annulus (see e-mail conversation with Paul Nulsen).
- Read keywords in response file to determine hard limits when using gain fitting. Could also automatically add gain fitting to all models if the keywords are set.