chore: [pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ ci:
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
     - id: check-added-large-files
     - id: check-case-conflict
@@ -35,25 +35,25 @@ repos:
     - id: rst-inline-touching-normal
 
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: "v0.12.0"
+  rev: "v0.14.3"
   hooks:
     - id: ruff-check
       args: ["--fix", "--show-fixes"]
 
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 24.10.0
+    rev: 25.9.0
     hooks:
     - id: black-jupyter
       types_or: [python, pyi, jupyter]
 
 -   repo: https://github.com/adamchainz/blacken-docs
-    rev: 1.19.1
+    rev: 1.20.0
     hooks:
     - id: blacken-docs
       additional_dependencies: [black==24.10.0]
 
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.13.0
+    rev: v1.18.2
     # check the oldest and newest supported Pythons
     # except skip python 3.9 for numpy, due to poor typing
     hooks:
@@ -69,14 +69,14 @@ repos:
         args: ["--python-version=3.13"]
 
 -   repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.1
     hooks:
     - id: codespell
       files: ^.*\.(py|md|rst)$
       args: ["-w", "-L", "hist,gaus"]
 
 - repo: https://github.com/python-jsonschema/check-jsonschema
-  rev: 0.30.0
+  rev: 0.34.1
   hooks:
     - id: check-readthedocs
       args: ["--verbose"]

docs/development.rst

@@ -255,7 +255,7 @@ final deployment to PyPI_ can be done by creating a GitHub Release:
 #. Select the release tag that was just pushed, and set the release title to be the tag
    (e.g. ``v1.2.3``).
 #. Use the "Auto-generate release notes" button to generate a skeleton of the release
-   notes and then augment them with the preprepared release notes the release maintainer
+   notes and then augment them with the prepared release notes the release maintainer
    has written.
 #. Select "This is a pre-release" if the release is a release candidate.
 #. Select "Create a discussion for this release" if the release is a stable release.

docs/examples/notebooks/ImpactPlot.ipynb

@@ -143,24 +143,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "def calc_impact(idx, b, e, i, width, poi_index):\n",
-    "    _, _, bb, ee = fitresults([(idx, b + e)])\n",
-    "    poi_up_post = bb[poi_index]\n",
-    "\n",
-    "    _, _, bb, ee = fitresults([(idx, b - e)])\n",
-    "    poi_dn_post = bb[poi_index]\n",
-    "\n",
-    "    _, _, bb, ee = fitresults([(idx, b + width)])\n",
-    "    poi_up_pre = bb[poi_index]\n",
-    "\n",
-    "    _, _, bb, ee = fitresults([(idx, b - width)])\n",
-    "    poi_dn_pre = bb[poi_index]\n",
-    "    return np.asarray([poi_dn_post, poi_up_post, poi_dn_pre, poi_up_pre])"
-   ]
+   "source": "def calc_impact(idx, b, e, i, width, poi_index):\n    _, _, bb, _ = fitresults([(idx, b + e)])\n    poi_up_post = bb[poi_index]\n\n    _, _, bb, _ = fitresults([(idx, b - e)])\n    poi_dn_post = bb[poi_index]\n\n    _, _, bb, _ = fitresults([(idx, b + width)])\n    poi_up_pre = bb[poi_index]\n\n    _, _, bb, _ = fitresults([(idx, b - width)])\n    poi_dn_pre = bb[poi_index]\n    return np.asarray([poi_dn_post, poi_up_post, poi_dn_pre, poi_up_pre])"
   },
   {
    "cell_type": "code",

docs/examples/notebooks/binderexample/StatisticalAnalysis.ipynb

@@ -171,82 +171,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "def get_mc_counts(pars):\n",
-    "    deltas, factors = pdf.modifications(pars)\n",
-    "    allsum = pyhf.tensorlib.concatenate(\n",
-    "        deltas + [pyhf.tensorlib.astensor(pdf.nominal_rates)]\n",
-    "    )\n",
-    "    nom_plus_delta = pyhf.tensorlib.sum(allsum, axis=0)\n",
-    "    nom_plus_delta = pyhf.tensorlib.reshape(\n",
-    "        nom_plus_delta, (1,) + pyhf.tensorlib.shape(nom_plus_delta)\n",
-    "    )\n",
-    "    allfac = pyhf.tensorlib.concatenate(factors + [nom_plus_delta])\n",
-    "    return pyhf.tensorlib.product(allfac, axis=0)\n",
-    "\n",
-    "\n",
-    "animate_plot_pieces = None\n",
-    "\n",
-    "\n",
-    "def init_plot(fig, ax, par_settings):\n",
-    "    global animate_plot_pieces\n",
-    "\n",
-    "    nbins = sum(list(pdf.config.channel_nbins.values()))\n",
-    "    x = np.arange(nbins)\n",
-    "    data = np.zeros(nbins)\n",
-    "    items = []\n",
-    "    for i in [3, 2, 1, 0]:\n",
-    "        items.append(ax.bar(x, data, 1, alpha=1.0))\n",
-    "    animate_plot_pieces = (\n",
-    "        items,\n",
-    "        ax.scatter(\n",
-    "            x, workspace.data(pdf, include_auxdata=False), c=\"k\", alpha=1.0, zorder=99\n",
-    "        ),\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "def animate(ax=None, fig=None, **par_settings):\n",
-    "    global animate_plot_pieces\n",
-    "    items, obs = animate_plot_pieces\n",
-    "    pars = pyhf.tensorlib.astensor(pdf.config.suggested_init())\n",
-    "    for k, v in par_settings.items():\n",
-    "        pars[par_name_dict[k]] = v\n",
-    "\n",
-    "    mc_counts = get_mc_counts(pars)\n",
-    "    rectangle_collection = zip(*map(lambda x: x.patches, items))\n",
-    "\n",
-    "    for rectangles, binvalues in zip(rectangle_collection, mc_counts[:, 0].T):\n",
-    "        offset = 0\n",
-    "        for sample_index in [3, 2, 1, 0]:\n",
-    "            rect = rectangles[sample_index]\n",
-    "            binvalue = binvalues[sample_index]\n",
-    "            rect.set_y(offset)\n",
-    "            rect.set_height(binvalue)\n",
-    "            offset += rect.get_height()\n",
-    "\n",
-    "    fig.canvas.draw()\n",
-    "\n",
-    "\n",
-    "def plot(ax=None, order=[3, 2, 1, 0], **par_settings):\n",
-    "    pars = pyhf.tensorlib.astensor(pdf.config.suggested_init())\n",
-    "    for k, v in par_settings.items():\n",
-    "        pars[par_name_dict[k]] = v\n",
-    "\n",
-    "    mc_counts = get_mc_counts(pars)\n",
-    "    bottom = None\n",
-    "    # nb: bar_data[0] because evaluating only one parset\n",
-    "    for i, sample_index in enumerate(order):\n",
-    "        data = mc_counts[sample_index][0]\n",
-    "        x = np.arange(len(data))\n",
-    "        ax.bar(x, data, 1, bottom=bottom, alpha=1.0)\n",
-    "        bottom = data if i == 0 else bottom + data\n",
-    "    ax.scatter(\n",
-    "        x, workspace.data(pdf, include_auxdata=False), c=\"k\", alpha=1.0, zorder=99\n",
-    "    )"
-   ]
+   "source": "def get_mc_counts(pars):\n    deltas, factors = pdf.modifications(pars)\n    allsum = pyhf.tensorlib.concatenate(\n        deltas + [pyhf.tensorlib.astensor(pdf.nominal_rates)]\n    )\n    nom_plus_delta = pyhf.tensorlib.sum(allsum, axis=0)\n    nom_plus_delta = pyhf.tensorlib.reshape(\n        nom_plus_delta, (1,) + pyhf.tensorlib.shape(nom_plus_delta)\n    )\n    allfac = pyhf.tensorlib.concatenate(factors + [nom_plus_delta])\n    return pyhf.tensorlib.product(allfac, axis=0)\n\n\nanimate_plot_pieces = None\n\n\ndef init_plot(fig, ax, par_settings):\n    global animate_plot_pieces\n\n    nbins = sum(list(pdf.config.channel_nbins.values()))\n    x = np.arange(nbins)\n    data = np.zeros(nbins)\n    items = []\n    for i in [3, 2, 1, 0]:\n        items.append(ax.bar(x, data, 1, alpha=1.0))\n    animate_plot_pieces = (\n        items,\n        ax.scatter(\n            x, workspace.data(pdf, include_auxdata=False), c=\"k\", alpha=1.0, zorder=99\n        ),\n    )\n\n\ndef animate(ax=None, fig=None, **par_settings):\n    global animate_plot_pieces\n    items, _ = animate_plot_pieces\n    pars = pyhf.tensorlib.astensor(pdf.config.suggested_init())\n    for k, v in par_settings.items():\n        pars[par_name_dict[k]] = v\n\n    mc_counts = get_mc_counts(pars)\n    rectangle_collection = zip(*map(lambda x: x.patches, items))\n\n    for rectangles, binvalues in zip(rectangle_collection, mc_counts[:, 0].T):\n        offset = 0\n        for sample_index in [3, 2, 1, 0]:\n            rect = rectangles[sample_index]\n            binvalue = binvalues[sample_index]\n            rect.set_y(offset)\n            rect.set_height(binvalue)\n            offset += rect.get_height()\n\n    fig.canvas.draw()\n\n\ndef plot(ax=None, order=[3, 2, 1, 0], **par_settings):\n    pars = pyhf.tensorlib.astensor(pdf.config.suggested_init())\n    for k, v in par_settings.items():\n        pars[par_name_dict[k]] = v\n\n    mc_counts = get_mc_counts(pars)\n    bottom = None\n    # nb: bar_data[0] because evaluating only one parset\n    for i, sample_index in enumerate(order):\n        data = mc_counts[sample_index][0]\n        x = np.arange(len(data))\n        ax.bar(x, data, 1, bottom=bottom, alpha=1.0)\n        bottom = data if i == 0 else bottom + data\n    ax.scatter(\n        x, workspace.data(pdf, include_auxdata=False), c=\"k\", alpha=1.0, zorder=99\n    )"
   },
   {
    "cell_type": "markdown",

docs/likelihood.rst

@@ -115,7 +115,7 @@ constraint terms are derived implicitly as each type of modifier
 unambiguously defines the constraint terms it requires. Correlated shape
 modifiers and normalisation uncertainties have compatible constraint
 terms and thus modifiers can be declared that *share* parameters by
-re-using a name [1]_ for multiple modifiers. That is, a variation of a
+reusing a name [1]_ for multiple modifiers. That is, a variation of a
 single parameter causes a shift within sample rates due to both shape
 and normalisation variations.
 

src/pyhf/infer/__init__.py

@@ -177,7 +177,7 @@ def hypotest(
             teststat, sig_plus_bkg_distribution, bkg_only_distribution
         )
     )
-    CLsb_exp, CLb_exp, CLs_exp = calc.expected_pvalues(
+    CLsb_exp, _CLb_exp, CLs_exp = calc.expected_pvalues(
         sig_plus_bkg_distribution, bkg_only_distribution
     )
 

src/pyhf/infer/calculators.py

@@ -524,7 +524,6 @@ def expected_pvalues(self, sig_plus_bkg_distribution, bkg_only_distribution):
             :math:`\mathrm{CL}_{b}`, and :math:`\mathrm{CL}_{s}`.
         """
         # Calling pvalues is easier then repeating the CLs calculation here
-        tb, _ = get_backend()
         return list(
             map(
                 list,

src/pyhf/infer/test_statistics.py

@@ -23,7 +23,7 @@ def _qmu_like(
     If the lower bound of the POI is 0 this automatically implements
     qmu_tilde. Otherwise this is qmu (no tilde).
     """
-    tensorlib, optimizer = get_backend()
+    tensorlib, _ = get_backend()
     tmu_like_stat, (mubhathat, muhatbhat) = _tmu_like(
         mu, data, pdf, init_pars, par_bounds, fixed_params, return_fitted_pars=True
     )
@@ -44,7 +44,7 @@ def _tmu_like(
     If the lower bound of the POI is 0 this automatically implements
     tmu_tilde. Otherwise this is tmu (no tilde).
     """
-    tensorlib, optimizer = get_backend()
+    tensorlib, _ = get_backend()
     mubhathat, fixed_poi_fit_lhood_val = fixed_poi_fit(
         mu, data, pdf, init_pars, par_bounds, fixed_params, return_fitted_val=True
     )
@@ -515,7 +515,7 @@ def q0(mu, data, pdf, init_pars, par_bounds, fixed_params, return_fitted_pars=Fa
         )
         mu = 0.0
 
-    tensorlib, optimizer = get_backend()
+    tensorlib, _ = get_backend()
 
     tmu_like_stat, (mubhathat, muhatbhat) = _tmu_like(
         mu, data, pdf, init_pars, par_bounds, fixed_params, return_fitted_pars=True

src/pyhf/optimize/opt_jax.py

@@ -45,7 +45,6 @@ def wrap_objective(objective, data, pdf, stitch_pars, do_grad=False, jit_pieces=
     Returns:
         objective_and_grad (:obj:`func`): tensor backend wrapped objective,gradient pair
     """
-    tensorlib, _ = get_backend()
     # NB: tuple arguments that need to be hashable (static_argnums)
     if do_grad:
 

src/pyhf/readxml.py

@@ -283,9 +283,13 @@ def process_data(
 
 
 def process_channel(
-    channelxml: ET.ElementTree, resolver: ResolverType, track_progress: bool = False
+    channelxml: ET.ElementTree[ET.Element[str]],
+    resolver: ResolverType,
+    track_progress: bool = False,
 ) -> tuple[str, list[float], list[Sample], list[Parameter]]:
     channel = channelxml.getroot()
+    if channel is None:
+        raise RuntimeError("Root element of ElementTree is missing.")
 
     inputfile = channel.attrib.get('InputFile', '')
     histopath = channel.attrib.get('HistoPath', '')
@@ -316,7 +320,7 @@ def process_channel(
 
 
 def process_measurements(
-    toplvl: ET.ElementTree,
+    toplvl: ET.ElementTree[ET.Element[str]],
     other_parameter_configs: Sequence[Parameter] | None = None,
 ) -> list[Measurement]:
     """

src/pyhf/tensor/manager.py

@@ -142,11 +142,11 @@ def set_backend(
 
             try:
                 new_optimizer = getattr(
-                    OptimizerRetriever, f"{custom_optimizer.lower()}_optimizer"
+                    OptimizerRetriever, f"{custom_optimizer.lower()!s}_optimizer"
                 )()
             except TypeError:
                 raise exceptions.InvalidOptimizer(
-                    f"The optimizer provided is not supported: {custom_optimizer}. Select from one of the supported optimizers: scipy, minuit"
+                    f"The optimizer provided is not supported: {custom_optimizer!s}. Select from one of the supported optimizers: scipy, minuit"
                 )
         else:
             new_optimizer = custom_optimizer

tests/test_import.py

@@ -108,6 +108,17 @@ def test_process_normfactor_configs():
     assert result['ParallelMeasurement']['mu_both']['bounds'] == [[1.0, 5.0]]
 
 
+def test_process_channel_missing_root(mocker, tmp_path):
+    # mock missing ElementTree root
+    mock_channelxml = mocker.Mock(spec=ET.ElementTree)
+    mock_channelxml.getroot.return_value = None
+
+    resolver = pyhf.readxml.resolver_factory(tmp_path, [])
+
+    with pytest.raises(RuntimeError, match="Root element of ElementTree is missing"):
+        pyhf.readxml.process_channel(mock_channelxml, resolver)
+
+
 def test_import_histogram():
     data, uncert = pyhf.readxml.import_root_histogram(
         lambda x: Path("validation/xmlimport_input/data").joinpath(x),
@@ -486,7 +497,7 @@ def test_import_noChannelData(mocker, datadir):
 
     basedir = datadir.joinpath("xmlimport_noChannelData")
     with pytest.raises(
-        RuntimeError, match="Channel channel1 is missing data. See issue #1911"
+        RuntimeError, match=r"Channel channel1 is missing data. See issue #1911"
     ):
         pyhf.readxml.parse(basedir.joinpath("config/example.xml"), basedir)
 
@@ -499,7 +510,7 @@ def test_import_noChannelDataPaths(mocker, datadir):
     basedir = datadir.joinpath("xmlimport_noChannelDataPaths")
     with pytest.raises(
         NotImplementedError,
-        match="Conversion of workspaces without data is currently not supported.\nSee https://github.com/scikit-hep/pyhf/issues/566",
+        match=r"Conversion of workspaces without data is currently not supported.\nSee https://github.com/scikit-hep/pyhf/issues/566",
     ):
         pyhf.readxml.parse(basedir.joinpath("config/example.xml"), basedir)
 

tests/test_infer.py

@@ -313,7 +313,7 @@ def test_hypotest_return_calculator(
     Check that the return structure of pyhf.infer.hypotest with the
     addition of the return_calculator keyword arg is as expected
     """
-    *_, model = hypotest_args
+    *_, _model = hypotest_args
 
     # only those return flags where the toggled return value
     # is placed in front of the calculator in the returned tuple
@@ -393,7 +393,7 @@ def _make_main_pdf(self, pars):
             return pyhf.probability.Poisson(expected_main)
 
         def _make_constraint_pdf(self, pars):
-            mu, gamma = pars
+            _, gamma = pars
             return pyhf.probability.Poisson(gamma * self.factor)
 
         def expected_data(self, pars, include_auxdata=True):
@@ -499,7 +499,7 @@ def test_emperical_distribution(tmp_path, hypotest_args):
     tb = pyhf.tensorlib
     np.random.seed(0)
 
-    mu_test, data, model = hypotest_args
+    mu_test, _, model = hypotest_args
     init_pars = model.config.suggested_init()
     par_bounds = model.config.suggested_bounds()
     fixed_params = model.config.suggested_fixed()

tests/test_interpolate.py

@@ -52,7 +52,7 @@ def filled_shapes(histogramssets, alphasets):
 
 
 def test_interpolator_structure(interpcode, random_histosets_alphasets_pair):
-    histogramssets, alphasets = random_histosets_alphasets_pair
+    histogramssets, _alphasets = random_histosets_alphasets_pair
 
     interpolator = pyhf.interpolators.get(interpcode)(
         histogramssets.tolist(), subscribe=False
@@ -66,7 +66,7 @@ def test_interpolator_structure(interpcode, random_histosets_alphasets_pair):
 
 
 def test_interpolator_subscription(mocker, interpcode, random_histosets_alphasets_pair):
-    histogramssets, alphasets = random_histosets_alphasets_pair
+    histogramssets, _alphasets = random_histosets_alphasets_pair
     ename = 'tensorlib_changed'
 
     interpolator_cls = pyhf.interpolators.get(interpcode)

tests/test_optim.py

@@ -462,7 +462,7 @@ def test_minuit_set_options(mocker):
 
 def test_get_tensor_shim(monkeypatch):
     monkeypatch.setattr(pyhf.tensorlib, 'name', 'fake_backend')
-    with pytest.raises(ValueError, match="No optimizer shim for fake_backend."):
+    with pytest.raises(ValueError, match=r"No optimizer shim for fake_backend."):
         _get_tensor_shim()
 
 

tests/test_pdf.py

@@ -1356,6 +1356,6 @@ def test_multi_component_poi():
 
     with pytest.raises(
         pyhf.exceptions.InvalidModel,
-        match="The parameter 'mu' contains multiple components and is not currently supported as parameter of interest.",
+        match=r"The parameter 'mu' contains multiple components and is not currently supported as parameter of interest.",
     ):
         pyhf.Workspace(spec).model()

tests/test_public_api.py

@@ -184,7 +184,6 @@ def test_prob_models(backend):
 
 
 def test_pdf_batched(backend):
-    tb, _ = backend
     source = {
         "binning": [2, -0.5, 1.5],
         "bindata": {"data": [55.0], "bkg": [50.0], "bkgerr": [7.0], "sig": [10.0]},