From 8c680990c850efc1a92f17650047aac3de577c7a Mon Sep 17 00:00:00 2001
From: dilpath <59329744+dilpath@users.noreply.github.com>
Date: Tue, 7 Jan 2025 02:32:50 +0100
Subject: [PATCH] doc `ModelHash`->`Model` in analysis.rst

---
 doc/analysis.rst | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/doc/analysis.rst b/doc/analysis.rst
index bf238c2..888320c 100644
--- a/doc/analysis.rst
+++ b/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)