#acl All:read

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.

 * 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.