Merge branch 'main' into fix_cov

Author: dilpath

.github/workflows/ci.yml

@@ -44,7 +44,7 @@ jobs:
           libhdf5-serial-dev
 
     - name: Install Python dependencies
-      run: pip install -r requirements_dev.txt
+      run: pip install -r requirements_dev.txt -r requirements_upsetplot.txt
 
     - name: Run tox
       run: python -m tox

.readthedocs.yaml

@@ -17,6 +17,8 @@ python:
       path: .
       extra_requirements:
         - doc
+    # keep until upsetplot can be installed from pypi
+    - requirements: requirements_upsetplot.txt
 
 build:
   os: "ubuntu-22.04"

doc/analysis.rst

@@ -17,12 +17,12 @@ Model hashes are special objects in the library, that are generated from model-s
 
 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
+.. code-block:: python
 
    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
+.. code-block:: python
 
    model.hash.get_model(petab_select_problem)

doc/conf.py

@@ -20,7 +20,6 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = [
-    "readthedocs_ext.readthedocs",
     "sphinx.ext.napoleon",
     "sphinx.ext.autodoc",
     "sphinx.ext.intersphinx",

petab_select/analyze.py

@@ -142,14 +142,14 @@ def get_best_by_iteration(
     *args,
     **kwargs,
 ) -> dict[int, Models]:
-    """Get the best model of each iteration.
+    r"""Get the best model of each iteration.
 
     See :func:``get_best`` for additional required arguments.
 
     Args:
         models:
             The models.
-        *args, **kwargs:
+        *args, \**kwargs:
             Forwarded to :func:``get_best``.
 
     Returns:

petab_select/plot.py

@@ -15,7 +15,11 @@
 import matplotlib.ticker
 import networkx as nx
 import numpy as np
-import upsetplot
+
+try:
+    import upsetplot
+except ImportError:
+    upsetplot = None
 
 from . import analyze
 from .constants import Criterion
@@ -218,6 +222,11 @@ def upset(plot_data: PlotData) -> dict[str, matplotlib.axes.Axes | None]:
         The plot axes (see documentation from the
         `upsetplot <https://upsetplot.readthedocs.io/>`__ package).
     """
+    if upsetplot is None:
+        raise ImportError(
+            "Please install upsetplot, e.g. with the following command: "
+            "pip install git+https://github.com/jnothman/UpSetPlot.git@08f0648368122303075cbb2835ae7fea0f7f07df"
+        )
     # Get delta criterion values
     values = np.array(
         plot_data.models.get_criterion(

pyproject.toml

@@ -29,7 +29,8 @@ dependencies = [
 [project.optional-dependencies]
 plot = [
     "matplotlib>=2.2.3",
-    "upsetplot",
+    # disabled with a manual install message until https://github.com/jnothman/UpSetPlot/issues/303
+    # "upsetplot",
 ]
 test = [
     "pytest >= 5.4.3",
@@ -50,7 +51,6 @@ doc = [
     "nbconvert>=7.16.4",
     "ipykernel>= 6.23.1",
     "ipython>=7.21.0",
-    "readthedocs-sphinx-ext>=2.2.5",
     "sphinx-autodoc-typehints",
     "petab_select[plot]",
 ]

requirements_upsetplot.txt

@@ -0,0 +1 @@
+git+https://github.com/jnothman/UpSetPlot.git@08f0648368122303075cbb2835ae7fea0f7f07df