From 0a3f44fccaa449adaa072de0bef28b63cefcf154 Mon Sep 17 00:00:00 2001
From: dilpath <59329744+dilpath@users.noreply.github.com>
Date: Fri, 14 Feb 2025 00:01:18 +0100
Subject: [PATCH 01/12] update docs readme

---
 doc/examples/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/examples/README.md b/doc/examples/README.md
index 85a3b2d..e32d9cc 100644
--- a/doc/examples/README.md
+++ b/doc/examples/README.md
@@ -1 +1 @@
-These notebooks need to be run to see the output. Pre-computed output can be viewed in the documentation, at https://petab-select.readthedocs.io/en/stable/examples.html
+These notebooks need to be run to see the output. Pre-computed output and further information about the notebooks can be viewed in the documentation, at https://petab-select.readthedocs.io/en/stable/examples.html

From 94fe23b9531f8464c98e5e31e2aba22b01fac2af Mon Sep 17 00:00:00 2001
From: dilpath <59329744+dilpath@users.noreply.github.com>
Date: Fri, 14 Feb 2025 00:03:05 +0100
Subject: [PATCH 02/12] allow integer version values in problem file

---
 petab_select/plot.py    | 25 ++++++++++++-------------
 petab_select/problem.py |  2 +-
 2 files changed, 13 insertions(+), 14 deletions(-)

diff --git a/petab_select/plot.py b/petab_select/plot.py
index 04b6e69..a56bc41 100644
--- a/petab_select/plot.py
+++ b/petab_select/plot.py
@@ -100,17 +100,16 @@ def __init__(
 
     def get_default_labels(self) -> dict[ModelHash, str]:
         """Get default model labels."""
-        return (
-            {
-                model.hash: model.model_label or model.model_id or model.hash
-                for model in self.models
-            }
-            | {
-                model.predecessor_model_hash: model.predecessor_model_hash
-                for model in self.models
-            }
-            | {VIRTUAL_INITIAL_MODEL.hash: "Virtual\nInitial\nModel"}
-        )
+        labels = {}
+        for model in self.models:
+            labels[model.hash] = (
+                model.model_label or model.model_id or model.hash
+            )
+            labels[model.predecessor_model_hash] = labels.get(
+                model.predecessor_model_hash, model.predecessor_model_hash
+            )
+        labels[VIRTUAL_INITIAL_MODEL.hash] = "Virtual\nInitial\nModel"
+        return labels
 
     def augment_labels(
         self,
@@ -608,8 +607,8 @@ def graph_iteration_layers(
         for model_hash in G.nodes
     ]
 
-    # Apply custom labels
-    nx.relabel_nodes(G, mapping=plot_data.labels, copy=False)
+    # Apply custom labels. Need `copy=True` to preserve node ordering.
+    G = nx.relabel_nodes(G, mapping=plot_data.labels, copy=True)
 
     nx.draw_networkx(
         G, pos, ax=ax, node_color=node_colors, **draw_networkx_kwargs
diff --git a/petab_select/problem.py b/petab_select/problem.py
index a64e62a..4ba467c 100644
--- a/petab_select/problem.py
+++ b/petab_select/problem.py
@@ -73,7 +73,7 @@ def reset(self) -> None:
 class Problem(BaseModel):
     """The model selection problem."""
 
-    format_version: str = Field(default="1.0.0")
+    format_version: str = Field(default="1.0.0", coerce_numbers_to_str=True)
     """The file format version."""
     criterion: Annotated[
         Criterion, PlainSerializer(lambda x: x.value, return_type=str)

From 5085b7c79e9b9fc8c4dc546ca6c3a9cd26f5384f Mon Sep 17 00:00:00 2001
From: dilpath <59329744+dilpath@users.noreply.github.com>
Date: Fri, 14 Feb 2025 00:04:30 +0100
Subject: [PATCH 03/12] add notebook for non-SBML usage

---
 doc/examples/other_model_types.ipynb | 296 +++++++++++++++++++++++++++
 1 file changed, 296 insertions(+)
 create mode 100644 doc/examples/other_model_types.ipynb

diff --git a/doc/examples/other_model_types.ipynb b/doc/examples/other_model_types.ipynb
new file mode 100644
index 0000000..7450e87
--- /dev/null
+++ b/doc/examples/other_model_types.ipynb
@@ -0,0 +1,296 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2e9d5661-b193-4eb1-905c-9b54387933c1",
+   "metadata": {},
+   "source": [
+    "# Model selection with non-SBML models\n",
+    "\n",
+    "PEtab Select is currently designed for use with PEtab v1, which only supports SBML for model specification. However, the SBML model is irrelevant to PEtab Select, which only reads the parameter table of a model's PEtab problem to determine the estimated parameters (e.g. to accurately compute criteria values).\n",
+    "\n",
+    "In this notebook, we demonstrate model selection with a modeling framework that currently does not support PEtab problems (specifically, [statsmodels](https://www.statsmodels.org/stable/index.html)). Since statsmodels is a Python package, we use the Python interface of PEtab Select. Non-Python frameworks can be used with the PEtab Select command-line interface instead. Synthetic data was generated from the model\n",
+    "$$y(t) = 20t + 10t^2 + 5t^3 + \\epsilon$$\n",
+    "where $\\epsilon$ is drawn from the standard normal distribution and represents measurement noise. The hypothetical scenario is that you are provided $(t,y)$ data pairs and asked to identify the powers of $t$ that are evidenced by the data (i.e., that were used to generate the data). Here, we only consider linear regression models involving terms up to $t^5$, i.e., the superset model is\n",
+    "$$y(t) = \\sum_{i=0}^5 k_i t^i,$$\n",
+    "where $k_i$ is the $i$th coefficient in the linear regression model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2a20814-e11d-4d65-9987-b84a50abf59d",
+   "metadata": {},
+   "source": [
+    "## \"True\" model\n",
+    "\n",
+    "We start by loading the data and demonstrating model calibration of the true model using statsmodels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6889e520-e13f-4b1c-8dc4-43226e37d165",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Based on the statsmodels \"Ordinary Least Squares\" example\n",
+    "# https://www.statsmodels.org/stable/examples/notebooks/generated/ols.html\n",
+    "\n",
+    "import numpy as np\n",
+    "import petab\n",
+    "import statsmodels.api as sm\n",
+    "from petab import MEASUREMENT, TIME\n",
+    "\n",
+    "import petab_select\n",
+    "from petab_select.constants import (\n",
+    "    CANDIDATE_SPACE,\n",
+    "    ESTIMATE,\n",
+    "    TERMINATE,\n",
+    "    UNCALIBRATED_MODELS,\n",
+    "    Criterion,\n",
+    ")\n",
+    "\n",
+    "# Data was generated with the formula (see next cell to regenerate data)\n",
+    "# y(t) = 20*t + 10*t^2 + 5*t^3\n",
+    "df = petab.get_measurement_df(\n",
+    "    \"other_model_types_problem/petab/measurements.tsv\"\n",
+    ")\n",
+    "t = df[TIME].to_numpy()\n",
+    "y = df[MEASUREMENT].to_numpy()\n",
+    "\n",
+    "# Show statsmodels fit for the \"true\" model\n",
+    "true_T = np.column_stack([t**1, t**2, t**3])\n",
+    "print(sm.OLS(y, true_T).fit().summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d14b4c55-2494-4fdc-a45e-e2983e6bf838",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uncomment to regenerate data\n",
+    "\n",
+    "# import numpy as np\n",
+    "# rng = np.random.default_rng(seed=0)\n",
+    "\n",
+    "# # Measurement time points\n",
+    "# t = np.linspace(0, 10, 101)\n",
+    "# # The first three powers of x are used to generate the data\n",
+    "# T = np.column_stack([t**1, t**2, t**3])\n",
+    "# K = np.array([20, 10, 5])\n",
+    "# noise = rng.normal(size=t.size)\n",
+    "# y = np.dot(T, K) + noise\n",
+    "\n",
+    "# # Write PEtab measurement table\n",
+    "# lines = [\"observableId\\tsimulationConditionId\\ttime\\tmeasurement\"]\n",
+    "# for t_, y_ in zip(t, y, strict=True):\n",
+    "#     lines.append(f\"y\\tdefault\\t{round(t_,1)}\\t{round(y_,5)}\")\n",
+    "# content = \"\\n\".join(lines)\n",
+    "# with open(\"other_model_types_problem/petab/measurements.tsv\", \"w\") as f:\n",
+    "#     f.write(content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c566b1b7-d01b-4964-b7f6-31cb9f4e4acc",
+   "metadata": {},
+   "source": [
+    "## Model selection\n",
+    "\n",
+    "To perform model selection, we define three methods to:\n",
+    "1. calibrate a single model\n",
+    "\n",
+    "This is where we convert a PEtab Select model into a statsmodels model, fit the model with statsmodels, then save the log-likelihood value in the PEtab Select model.\n",
+    "\n",
+    "2. perform a single iteration of a model selection method involving calibration of multiple models\n",
+    "\n",
+    "This is generic code that executes the required PEtab Select commands.\n",
+    "\n",
+    "3. perform a full model selection run, involving all iterations of a model selection method\n",
+    "\n",
+    "This loads the PEtab Select problem from disk and performs all iterations of a model selection method.\n",
+    "\n",
+    "In this case, the PEtab Select problem is setup to perform backward selection. See the files in `other_model_types_problem/petab_select`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89fb34cb-3b78-4b89-b58f-78e3f90414c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calibrate_model(model: petab_select.Model, t=t, y=y):\n",
+    "    # Convert the PEtab Select model into a statsmodel model\n",
+    "    powers = [\n",
+    "        int(parameter_id[1:])\n",
+    "        for parameter_id, parameter_value in model.parameters.items()\n",
+    "        if parameter_value == ESTIMATE\n",
+    "    ]\n",
+    "    if not powers:\n",
+    "        return\n",
+    "    T = np.column_stack([t**i for i in powers])\n",
+    "    sm_model = sm.OLS(y, T)\n",
+    "\n",
+    "    # Calibrate\n",
+    "    results = sm_model.fit()\n",
+    "\n",
+    "    # Save the log-likelihood\n",
+    "    model.set_criterion(criterion=Criterion.LLH, value=results.llf)\n",
+    "\n",
+    "\n",
+    "def perform_iteration(\n",
+    "    problem: petab_select.Problem, candidate_space: petab_select.CandidateSpace\n",
+    "):\n",
+    "    # Start the iteration, request models according to the model selection method.\n",
+    "    iteration = petab_select.ui.start_iteration(\n",
+    "        problem=problem, candidate_space=candidate_space\n",
+    "    )\n",
+    "\n",
+    "    # Calibrate each model.\n",
+    "    models = iteration[UNCALIBRATED_MODELS]\n",
+    "    for model in models:\n",
+    "        if all(\n",
+    "            parameter_value != ESTIMATE\n",
+    "            for parameter_value in model.parameters.values()\n",
+    "        ):\n",
+    "            del models[model.hash]\n",
+    "            continue\n",
+    "        calibrate_model(model=model)\n",
+    "        # Compute the criterion based on the log-likelihood computed by statsmodels.\n",
+    "        model.compute_criterion(criterion=problem.criterion)\n",
+    "\n",
+    "    # End the iteration and return the results.\n",
+    "    return petab_select.ui.end_iteration(\n",
+    "        problem=problem,\n",
+    "        candidate_space=iteration[CANDIDATE_SPACE],\n",
+    "        calibrated_models=models,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def run_model_selection():\n",
+    "    # Load the PEtab Select problem.\n",
+    "    problem = petab_select.Problem.from_yaml(\n",
+    "        \"other_model_types_problem/petab_select/problem.yaml\"\n",
+    "    )\n",
+    "    candidate_space = None\n",
+    "\n",
+    "    # Perform all iterations of the model selection method until the termination signal is emitted.\n",
+    "    while True:\n",
+    "        iteration_results = perform_iteration(\n",
+    "            problem=problem, candidate_space=candidate_space\n",
+    "        )\n",
+    "        if iteration_results[TERMINATE]:\n",
+    "            break\n",
+    "        candidate_space = iteration_results[CANDIDATE_SPACE]\n",
+    "\n",
+    "    # Return the problem, which contains the calibrated models.\n",
+    "    return problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93ffcec5-3143-4906-87a8-acf60c541ce4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run the model selection method.\n",
+    "problem = run_model_selection()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a93ab68f-7831-40bd-82b0-d365cca8008c",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "As when using a PEtab-compatible calibration tool, the results from calibration with statsmodels can also be analyzed with PEtab Select.\n",
+    "\n",
+    "Firstly, we get the best model identified during model selection. It matches the \"true\" model, with the same AIC as computed by statsmodels at the top of this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba3a58af-8b13-4525-812a-9c7708292bc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_model = petab_select.analyze.get_best(\n",
+    "    models=problem.state.models, criterion=problem.criterion\n",
+    ")\n",
+    "print(f\"\"\"\n",
+    "Best model information\n",
+    "----------------------\n",
+    "PEtab Select model hash: {best_model.hash}\n",
+    "Selected powers of t: {[int(parameter_id[1:]) for parameter_id, parameter_value in best_model.parameters.items() if parameter_value == ESTIMATE]}\n",
+    "Log-Likelihood: {best_model.get_criterion(criterion=Criterion.LLH)}\n",
+    "AIC: {best_model.get_criterion(criterion=Criterion.AIC)}\n",
+    "\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92857455-d8be-4bde-adc6-e6ab8de27748",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot AIC of all models that were calibrated.\n",
+    "\n",
+    "import petab_select.plot\n",
+    "\n",
+    "plot_data = petab_select.plot.PlotData(\n",
+    "    models=problem.state.models, criterion=problem.criterion\n",
+    ")\n",
+    "\n",
+    "petab_select.plot.scatter_criterion_vs_n_estimated(plot_data=plot_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c0a4f6f-9099-4e22-a589-80cebaf466cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the history of model selection iterations.\n",
+    "\n",
+    "draw_networkx_kwargs = {\n",
+    "    \"arrowstyle\": \"-|>\",\n",
+    "    \"node_shape\": \"s\",\n",
+    "    \"node_size\": 1000,\n",
+    "    \"edgecolors\": \"k\",\n",
+    "}\n",
+    "petab_select.plot.graph_iteration_layers(\n",
+    "    plot_data=plot_data, draw_networkx_kwargs=draw_networkx_kwargs\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 583e3abb2193d0bb586477d40788d91f56c69916 Mon Sep 17 00:00:00 2001
From: dilpath <59329744+dilpath@users.noreply.github.com>
Date: Fri, 14 Feb 2025 00:21:08 +0100
Subject: [PATCH 04/12] explain user and developer notebooks

---
 doc/examples.rst | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/doc/examples.rst b/doc/examples.rst
index 2c6c6aa..32c5ca9 100644
--- a/doc/examples.rst
+++ b/doc/examples.rst
@@ -1,12 +1,19 @@
 Examples
 ========
 
-Various example notebooks.
+There are currently notebooks aimed at users and developers.
+
+Users that are working with PEtab problems should consult the model selection documentation of the calibration tool they wish to use. In this case, there is no need to use the PEtab Select package directly to perform model selection. After model selection, results can be analyzed with PEtab Select. See the API documentation for :mod:`petab_select.analyze`, or the visualization notebook.
+
+Users who wish to apply model selection methods using a calibration tool that doesn't support PEtab can refer to the "other_model_types" notebook for a demonstration using statsmodels.
+
+Developers wishing to implement model selection in their tool via PEtab Select can consult the workflow or "example_cli_famos" notebooks.
 
 .. toctree::
    :maxdepth: 1
 
-   examples/example_cli_famos.ipynb
    examples/visualization.ipynb
+   examples/other_model_types.ipynb
+   examples/example_cli_famos.ipynb
    examples/workflow_cli.ipynb
    examples/workflow_python.ipynb

From 00093469ea96af647c06ec53abd1865b6b28fd60 Mon Sep 17 00:00:00 2001
From: dilpath <59329744+dilpath@users.noreply.github.com>
Date: Fri, 14 Feb 2025 00:32:08 +0100
Subject: [PATCH 05/12] add more info for user vs dev docs; add d2d

---
 doc/index.rst | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/doc/index.rst b/doc/index.rst
index 13a2626..e02b27e 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -34,6 +34,8 @@ PEtab Select is well-integrated with:
 
 * `BasiCO <https://basico.readthedocs.io/>`_
   (`example <https://basico.readthedocs.io/en/latest/notebooks/Working_with_PEtab.html#Model-Selection>`__)
+* `Data2Dynamics <https://github.com/Data2Dynamics/d2d>`_
+  (`example <https://github.com/Data2Dynamics/d2d/wiki/Model-selection-with-PEtab-Select>`__)
 * `PEtab.jl <https://sebapersson.github.io/PEtab.jl>`_
   (`example <https://sebapersson.github.io/PEtab.jl/stable/pest_select/>`__)
 * `pyPESTO <https://pypesto.readthedocs.io/>`_
@@ -42,6 +44,15 @@ PEtab Select is well-integrated with:
 Other model calibration tools can easily be integrated using the provided
 Python library or command line interface.
 
+This documentation provides examples aimed at both users and developers.
+However, if you are a user with PEtab problem(s) and simply want to perform
+model selection, then check the file formats documented here, and the model
+selection documentation of one of the calibration tools listed above. In most
+cases, model selection is performed entirely via one of the calibration tools,
+rather than the PEtab Select package directly. After model selection, the
+analysis and visualization methods in the PEtab Select package can be used
+directly with the results from your calibration tool.
+
 Installation
 ------------
 

From 09245e528757950e3a62b876c70243fb965f20c2 Mon Sep 17 00:00:00 2001
From: dilpath <59329744+dilpath@users.noreply.github.com>
Date: Fri, 14 Feb 2025 00:58:17 +0100
Subject: [PATCH 06/12] update doc reqs

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 5462e7a..92f7d9d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -53,6 +53,7 @@ doc = [
     "readthedocs-sphinx-ext>=2.2.5",
     "sphinx-autodoc-typehints",
     "petab_select[plot]",
+    "statsmodels>=0.14.4",
 ]
 
 [project.scripts]

From d797438bdf7636ff1ba100bc60bff227b0c1e902 Mon Sep 17 00:00:00 2001
From: dilpath <59329744+dilpath@users.noreply.github.com>
Date: Fri, 14 Feb 2025 01:49:53 +0100
Subject: [PATCH 07/12] add problem files

---
 .../petab/conditions.tsv                      |   2 +
 .../other_model_types_problem/petab/dummy.xml |   4 +
 .../petab/measurements.tsv                    | 102 ++++++++++++++++++
 .../petab/observables.tsv                     |   2 +
 .../petab/parameters.tsv                      |   7 ++
 .../petab/problem.yaml                        |  11 ++
 .../petab_select/model_space.tsv              |   2 +
 .../petab_select/problem.yaml                 |   5 +
 8 files changed, 135 insertions(+)
 create mode 100644 doc/examples/other_model_types_problem/petab/conditions.tsv
 create mode 100644 doc/examples/other_model_types_problem/petab/dummy.xml
 create mode 100644 doc/examples/other_model_types_problem/petab/measurements.tsv
 create mode 100644 doc/examples/other_model_types_problem/petab/observables.tsv
 create mode 100644 doc/examples/other_model_types_problem/petab/parameters.tsv
 create mode 100644 doc/examples/other_model_types_problem/petab/problem.yaml
 create mode 100644 doc/examples/other_model_types_problem/petab_select/model_space.tsv
 create mode 100644 doc/examples/other_model_types_problem/petab_select/problem.yaml

diff --git a/doc/examples/other_model_types_problem/petab/conditions.tsv b/doc/examples/other_model_types_problem/petab/conditions.tsv
new file mode 100644
index 0000000..5d45658
--- /dev/null
+++ b/doc/examples/other_model_types_problem/petab/conditions.tsv
@@ -0,0 +1,2 @@
+conditionId
+default
diff --git a/doc/examples/other_model_types_problem/petab/dummy.xml b/doc/examples/other_model_types_problem/petab/dummy.xml
new file mode 100644
index 0000000..377893d
--- /dev/null
+++ b/doc/examples/other_model_types_problem/petab/dummy.xml
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<sbml xmlns="http://www.sbml.org/sbml/level3/version2/core" level="3" version="2">
+<model/>
+</sbml>
diff --git a/doc/examples/other_model_types_problem/petab/measurements.tsv b/doc/examples/other_model_types_problem/petab/measurements.tsv
new file mode 100644
index 0000000..638e864
--- /dev/null
+++ b/doc/examples/other_model_types_problem/petab/measurements.tsv
@@ -0,0 +1,102 @@
+observableId	simulationConditionId	time	measurement
+y	default	0.0	0.12573
+y	default	0.1	1.9729
+y	default	0.2	5.08042
+y	default	0.3	7.1399
+y	default	0.4	9.38433
+y	default	0.5	13.4866
+y	default	0.6	17.984
+y	default	0.7	21.56208
+y	default	0.8	24.25626
+y	default	0.9	28.47958
+y	default	1.0	34.37673
+y	default	1.1	40.79633
+y	default	1.2	44.71497
+y	default	1.3	53.66621
+y	default	1.4	60.07409
+y	default	1.5	68.64273
+y	default	1.6	77.53574
+y	default	1.7	87.1487
+y	default	1.8	97.97163
+y	default	1.9	109.43751
+y	default	2.0	119.87147
+y	default	2.1	133.77146
+y	default	2.2	144.97481
+y	default	2.3	160.08651
+y	default	2.4	175.62347
+y	default	2.5	190.71901
+y	default	2.6	206.7365
+y	default	2.7	224.39327
+y	default	2.8	243.70227
+y	default	2.9	264.2652
+y	default	3.0	283.99038
+y	default	3.1	306.84582
+y	default	3.2	330.08077
+y	default	3.3	355.12585
+y	default	3.4	380.33466
+y	default	3.5	407.23037
+y	default	3.6	434.22617
+y	default	3.7	464.03539
+y	default	3.8	495.54398
+y	default	3.9	528.18843
+y	default	4.0	558.74093
+y	default	4.1	596.21892
+y	default	4.2	632.18588
+y	default	4.3	669.21631
+y	default	4.4	707.78446
+y	default	4.5	747.81108
+y	default	4.6	791.73802
+y	default	4.7	835.97526
+y	default	4.8	881.16163
+y	default	4.9	927.6601
+y	default	5.0	975.35738
+y	default	5.1	1024.14668
+y	default	5.2	1077.43555
+y	default	5.3	1131.94147
+y	default	5.4	1185.63164
+y	default	5.5	1244.77012
+y	default	5.6	1304.10986
+y	default	5.7	1365.56104
+y	default	5.8	1426.77588
+y	default	5.9	1492.3333
+y	default	6.0	1559.56356
+y	default	6.1	1627.8352
+y	default	6.2	1701.77937
+y	default	6.3	1772.63909
+y	default	6.4	1848.64897
+y	default	6.5	1925.36643
+y	default	6.6	2006.66347
+y	default	6.7	2088.03536
+y	default	6.8	2171.19335
+y	default	6.9	2254.44149
+y	default	7.0	2345.05203
+y	default	7.1	2436.33869
+y	default	7.2	2529.64396
+y	default	7.3	2623.36709
+y	default	7.4	2723.54201
+y	default	7.5	2820.55457
+y	default	7.6	2923.81847
+y	default	7.7	3030.50005
+y	default	7.8	3137.20905
+y	default	7.9	3249.29739
+y	default	8.0	3360.18852
+y	default	8.1	3474.67181
+y	default	8.2	3592.86244
+y	default	8.3	3712.74385
+y	default	8.4	3835.84232
+y	default	8.5	3963.75541
+y	default	8.6	4092.46117
+y	default	8.7	4224.70956
+y	default	8.8	4357.00539
+y	default	8.9	4496.63411
+y	default	9.0	4634.71261
+y	default	9.1	4779.52941
+y	default	9.2	4923.40721
+y	default	9.3	5071.94952
+y	default	9.4	5224.76979
+y	default	9.5	5380.40645
+y	default	9.6	5537.44101
+y	default	9.7	5697.67947
+y	default	9.8	5861.01878
+y	default	9.9	6028.19348
+y	default	10.0	6200.50268
diff --git a/doc/examples/other_model_types_problem/petab/observables.tsv b/doc/examples/other_model_types_problem/petab/observables.tsv
new file mode 100644
index 0000000..627c5bc
--- /dev/null
+++ b/doc/examples/other_model_types_problem/petab/observables.tsv
@@ -0,0 +1,2 @@
+observableId	observableFormula	noiseFormula
+y	k0*time^0 + k1*time^1 + k2*time^2 + k3*time^3 + k4*time^4 + k5*time^5	1
diff --git a/doc/examples/other_model_types_problem/petab/parameters.tsv b/doc/examples/other_model_types_problem/petab/parameters.tsv
new file mode 100644
index 0000000..348fc89
--- /dev/null
+++ b/doc/examples/other_model_types_problem/petab/parameters.tsv
@@ -0,0 +1,7 @@
+parameterId	parameterScale	lowerBound	upperBound	estimate
+k0	lin	0	100	1
+k1	lin	0	100	1
+k2	lin	0	100	1
+k3	lin	0	100	1
+k4	lin	0	100	1
+k5	lin	0	100	1
diff --git a/doc/examples/other_model_types_problem/petab/problem.yaml b/doc/examples/other_model_types_problem/petab/problem.yaml
new file mode 100644
index 0000000..f8de5fc
--- /dev/null
+++ b/doc/examples/other_model_types_problem/petab/problem.yaml
@@ -0,0 +1,11 @@
+format_version: 1
+parameter_file: parameters.tsv
+problems:
+- condition_files:
+  - conditions.tsv
+  measurement_files:
+  - measurements.tsv
+  sbml_files:
+  - dummy.xml
+  observable_files:
+  - observables.tsv
diff --git a/doc/examples/other_model_types_problem/petab_select/model_space.tsv b/doc/examples/other_model_types_problem/petab_select/model_space.tsv
new file mode 100644
index 0000000..1f3e830
--- /dev/null
+++ b/doc/examples/other_model_types_problem/petab_select/model_space.tsv
@@ -0,0 +1,2 @@
+model_subspace_id	petab_yaml	k0	k1	k2	k3	k4	k5
+M	../petab/problem.yaml	0;estimate	0;estimate	0;estimate	0;estimate	0;estimate	0;estimate
diff --git a/doc/examples/other_model_types_problem/petab_select/problem.yaml b/doc/examples/other_model_types_problem/petab_select/problem.yaml
new file mode 100644
index 0000000..4bb1c85
--- /dev/null
+++ b/doc/examples/other_model_types_problem/petab_select/problem.yaml
@@ -0,0 +1,5 @@
+format_version: 1
+criterion: AIC
+method: backward
+model_space_files:
+- model_space.tsv

From 554940d5e7c22328cc119092fbc670309d3af93a Mon Sep 17 00:00:00 2001
From: Dilan Pathirana <59329744+dilpath@users.noreply.github.com>
Date: Fri, 14 Feb 2025 17:45:56 +0100
Subject: [PATCH 08/12] Update doc/examples/other_model_types.ipynb

Co-authored-by: Daniel Weindl <dweindl@users.noreply.github.com>
---
 doc/examples/other_model_types.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/examples/other_model_types.ipynb b/doc/examples/other_model_types.ipynb
index 7450e87..fbc7791 100644
--- a/doc/examples/other_model_types.ipynb
+++ b/doc/examples/other_model_types.ipynb
@@ -248,7 +248,7 @@
     "    models=problem.state.models, criterion=problem.criterion\n",
     ")\n",
     "\n",
-    "petab_select.plot.scatter_criterion_vs_n_estimated(plot_data=plot_data)"
+    "petab_select.plot.scatter_criterion_vs_n_estimated(plot_data=plot_data);"
    ]
   },
   {

From 80acdb2bceb7400f50c055b2bff60965601d7410 Mon Sep 17 00:00:00 2001
From: Dilan Pathirana <59329744+dilpath@users.noreply.github.com>
Date: Fri, 14 Feb 2025 17:46:09 +0100
Subject: [PATCH 09/12] Update doc/examples/other_model_types.ipynb

Co-authored-by: Daniel Weindl <dweindl@users.noreply.github.com>
---
 doc/examples/other_model_types.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/examples/other_model_types.ipynb b/doc/examples/other_model_types.ipynb
index fbc7791..5bba8da 100644
--- a/doc/examples/other_model_types.ipynb
+++ b/doc/examples/other_model_types.ipynb
@@ -268,7 +268,7 @@
     "}\n",
     "petab_select.plot.graph_iteration_layers(\n",
     "    plot_data=plot_data, draw_networkx_kwargs=draw_networkx_kwargs\n",
-    ")"
+    ");"
    ]
   }
  ],

From edd84be3786e4d05011bec2bad75f575006188e4 Mon Sep 17 00:00:00 2001
From: dilpath <59329744+dilpath@users.noreply.github.com>
Date: Fri, 14 Feb 2025 17:48:34 +0100
Subject: [PATCH 10/12] use rendered notebook names

---
 doc/examples.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/doc/examples.rst b/doc/examples.rst
index 32c5ca9..1627a59 100644
--- a/doc/examples.rst
+++ b/doc/examples.rst
@@ -3,11 +3,11 @@ Examples
 
 There are currently notebooks aimed at users and developers.
 
-Users that are working with PEtab problems should consult the model selection documentation of the calibration tool they wish to use. In this case, there is no need to use the PEtab Select package directly to perform model selection. After model selection, results can be analyzed with PEtab Select. See the API documentation for :mod:`petab_select.analyze`, or the visualization notebook.
+Users that are working with PEtab problems should consult the model selection documentation of the calibration tool they wish to use. In this case, there is no need to use the PEtab Select package directly to perform model selection. After model selection, results can be analyzed with PEtab Select. See the API documentation for :mod:`petab_select.analyze`, or the "Visualization gallery" notebook.
 
-Users who wish to apply model selection methods using a calibration tool that doesn't support PEtab can refer to the "other_model_types" notebook for a demonstration using statsmodels.
+Users who wish to apply model selection methods using a calibration tool that doesn't support PEtab can refer to the "Model selection with non-SBML models" notebook for a demonstration using statsmodels.
 
-Developers wishing to implement model selection in their tool via PEtab Select can consult the workflow or "example_cli_famos" notebooks.
+Developers wishing to implement model selection in their tool via PEtab Select can consult the workflow notes ("Example usage with the CLI" and "Example usage with Python 3") or "FAMoS in PEtab Select" notebooks.
 
 .. toctree::
    :maxdepth: 1

From 498eefcf9ab624b44394a0a64d1e0ea0c9c70e7c Mon Sep 17 00:00:00 2001
From: dilpath <59329744+dilpath@users.noreply.github.com>
Date: Fri, 14 Feb 2025 19:02:26 +0100
Subject: [PATCH 11/12] review

---
 doc/examples/other_model_types.ipynb | 14 +++++++++-----
 petab_select/plot.py                 | 26 +++++++++++++++++++++++++-
 2 files changed, 34 insertions(+), 6 deletions(-)

diff --git a/doc/examples/other_model_types.ipynb b/doc/examples/other_model_types.ipynb
index 5bba8da..0595fc1 100644
--- a/doc/examples/other_model_types.ipynb
+++ b/doc/examples/other_model_types.ipynb
@@ -100,17 +100,18 @@
     "## Model selection\n",
     "\n",
     "To perform model selection, we define three methods to:\n",
+    "\n",
     "1. calibrate a single model\n",
     "\n",
-    "This is where we convert a PEtab Select model into a statsmodels model, fit the model with statsmodels, then save the log-likelihood value in the PEtab Select model.\n",
+    "    This is where we convert a PEtab Select model into a statsmodels model, fit the model with statsmodels, then save the log-likelihood value in the PEtab Select model.\n",
     "\n",
     "2. perform a single iteration of a model selection method involving calibration of multiple models\n",
     "\n",
-    "This is generic code that executes the required PEtab Select commands.\n",
+    "    This is generic code that executes the required PEtab Select commands.\n",
     "\n",
     "3. perform a full model selection run, involving all iterations of a model selection method\n",
     "\n",
-    "This loads the PEtab Select problem from disk and performs all iterations of a model selection method.\n",
+    "    This loads the PEtab Select problem from disk and performs all iterations of a model selection method.\n",
     "\n",
     "In this case, the PEtab Select problem is setup to perform backward selection. See the files in `other_model_types_problem/petab_select`."
    ]
@@ -259,15 +260,18 @@
    "outputs": [],
    "source": [
     "# Plot the history of model selection iterations.\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
     "draw_networkx_kwargs = {\n",
     "    \"arrowstyle\": \"-|>\",\n",
     "    \"node_shape\": \"s\",\n",
-    "    \"node_size\": 1000,\n",
     "    \"edgecolors\": \"k\",\n",
     "}\n",
+    "fig, ax = plt.subplots(figsize=(20, 20))\n",
     "petab_select.plot.graph_iteration_layers(\n",
-    "    plot_data=plot_data, draw_networkx_kwargs=draw_networkx_kwargs\n",
+    "    plot_data=plot_data,\n",
+    "    draw_networkx_kwargs=draw_networkx_kwargs,\n",
+    "    ax=ax,\n",
     ");"
    ]
   }
diff --git a/petab_select/plot.py b/petab_select/plot.py
index a56bc41..9361acc 100644
--- a/petab_select/plot.py
+++ b/petab_select/plot.py
@@ -28,6 +28,10 @@
 """The font size of axis tick labels."""
 DEFAULT_NODE_COLOR = "darkgrey"
 """The default color of nodes in graph plots."""
+FONT_HEIGHT_WIDTH_RATIO = 2
+"""The ratio of the font height to font width. Used for graph node sizes."""
+NODE_SIZE_LABEL_SIZE_RATIO = 500
+"""The ratio of node size to label size. Used for graph node sizes."""
 
 
 __all__ = [
@@ -510,6 +514,22 @@ def scatter_criterion_vs_n_estimated(
     return ax
 
 
+def labels_to_node_sizes(G: nx.Graph) -> list[float]:
+    """Compute reasonable node sizes to scale with node label sizes."""
+    node_sizes = []
+    for label in G.nodes:
+        height = 1
+        width = len(label)
+        if "\n" in label:
+            height = len(label.split("\n"))
+            width = len(sorted(label.split("\n"), key=lambda x: len(x))[-1])
+        label_size = (
+            width if width * FONT_HEIGHT_WIDTH_RATIO > height else height
+        )
+        node_sizes.append(label_size * NODE_SIZE_LABEL_SIZE_RATIO)
+    return node_sizes
+
+
 def graph_iteration_layers(
     plot_data: PlotData,
     ax: plt.Axes = None,
@@ -541,7 +561,6 @@ def graph_iteration_layers(
     default_draw_networkx_kwargs = {
         "arrowstyle": "-|>",
         "node_shape": "s",
-        "node_size": 250,
         "edgecolors": "k",
     }
     if draw_networkx_kwargs is None:
@@ -610,6 +629,11 @@ def graph_iteration_layers(
     # Apply custom labels. Need `copy=True` to preserve node ordering.
     G = nx.relabel_nodes(G, mapping=plot_data.labels, copy=True)
 
+    draw_networkx_kwargs["node_size"] = draw_networkx_kwargs.get(
+        "node_size",
+        labels_to_node_sizes(G=G),
+    )
+
     nx.draw_networkx(
         G, pos, ax=ax, node_color=node_colors, **draw_networkx_kwargs
     )

From 0f026cc13a6ae93036b6d09d66d180686627c8f4 Mon Sep 17 00:00:00 2001
From: dilpath <59329744+dilpath@users.noreply.github.com>
Date: Sat, 15 Feb 2025 20:25:13 +0100
Subject: [PATCH 12/12] typos

---
 doc/examples.rst                     | 2 +-
 doc/examples/other_model_types.ipynb | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/examples.rst b/doc/examples.rst
index 1627a59..687924c 100644
--- a/doc/examples.rst
+++ b/doc/examples.rst
@@ -7,7 +7,7 @@ Users that are working with PEtab problems should consult the model selection do
 
 Users who wish to apply model selection methods using a calibration tool that doesn't support PEtab can refer to the "Model selection with non-SBML models" notebook for a demonstration using statsmodels.
 
-Developers wishing to implement model selection in their tool via PEtab Select can consult the workflow notes ("Example usage with the CLI" and "Example usage with Python 3") or "FAMoS in PEtab Select" notebooks.
+Developers wishing to implement model selection in their tool via PEtab Select can consult the notebooks on workflows ("Example usage with the CLI" and "Example usage with Python 3") or "FAMoS in PEtab Select".
 
 .. toctree::
    :maxdepth: 1
diff --git a/doc/examples/other_model_types.ipynb b/doc/examples/other_model_types.ipynb
index 0595fc1..57b34c8 100644
--- a/doc/examples/other_model_types.ipynb
+++ b/doc/examples/other_model_types.ipynb
@@ -113,7 +113,7 @@
     "\n",
     "    This loads the PEtab Select problem from disk and performs all iterations of a model selection method.\n",
     "\n",
-    "In this case, the PEtab Select problem is setup to perform backward selection. See the files in `other_model_types_problem/petab_select`."
+    "In this case, the PEtab Select problem is setup to perform backward selection ([see the specification files for this example](https://github.com/PEtab-dev/petab_select/tree/main/doc/examples/other_model_types_problem/petab_select))."
    ]
   },
   {