

__Searches__

- LBFGS: Limits on parameters, callback history.


__Documentation__

- Overview -- have an "advanced model composition" which using trivial examples illustrates the use of lists, dicts and
multi-level models. Make it clear they are "dumb" examples for illustration. Make it clear that the model composed
links to the results allowing API design.

- Add Model section to readthedocs, which includes muiltiple cookbooks (simple, including lists and dicts, multi-level,
search chaining).

- Turn analysis summing feature into multiple likelihood functions, write general doc, write howtolens chapter
(c1 same datasets, c2 multi dataset with different parameters, c3 writing function for parameter variation, c4 different LH functions).

- Include docs on writing and loading models / collections with jsons and adding them together.

- Show how to make model components benefits and explain the benefits of doing this.

- Explicit exampels of how to extend Analsyis (e.g. putting extra data in c0nstructor) for Python beginners.

- Prior arithmatic - based on multiwavelenght models where we can compose models with relations. But also document
and test adding parameters, divisionm, etcx.

Amy was unsure how to use an instance to plot a new component:

class LinearFit:
    def __init__(self, gradient=0.01, intercept=1.0, scatter=0.1):

        self.intercept = intercept
        self.gradient = gradient
        self.scatter = scatter

    def profile_from_xvalues(self, xvalues):

        return np.multiply(self.gradient, xvalues) + self.intercept



instance = result.samples.max_log_likelihood_instance

instance.profile_from_xvalues(xvalues=hi)


Write this clearly somewhere.

Maybe we need to split the results into two, the above stuff and Samples.


__Samples__

 - Have as_instance=True default argument in all Samples objects. Use decorator to implement the output.


__Database__

- Database: profiling tests, speed ups and tools. Use test mode to quickly generate large result libearies.
- Database Storage, not needing full Dynesty etc, only store basics, no properties so we can remake each object.


__Graphical Models__

- Graphical models: model.info file, on-the-fly summary file, output of KL divergence / evidence in factors,
  model.results of overall model. For output, include a single file which is the latest results and a folder with
  all factor results, tilted, etc. FactorGraphResult.


