doc ModelHash->Model in analysis.rst

doc/analysis.rst

@@ -9,3 +9,20 @@ to get a quick overview over all models, as a pandas dataframe.
 
 Additionally, see the Python API docs for the :mod:`petab_select.analyze` module, which contains some methods to subset and group models,
 or compute "weights" (e.g. Akaike weights).
+
+Model hashes
+^^^^^^^^^^^^
+
+Model hashes are special objects in the library, that are generated from model-specific information that is unique within a single PEtab Select problem.
+
+This means you can reconstruct the model given some model hash. For example, with this model hash `M1-000`, you can reconstruct the :class:`petab_select.ModelHash` from a string, then reconstruct the :class:`petab_select.Model`.
+
+.. code-block:: language
+
+   ModelHash.from_hash("M1-000").get_model(petab_select_problem)
+
+You can use this to get the uncalibrated version of a calibrated model.
+
+.. code-block:: language
+
+   model.hash.get_model(petab_select_problem)