chore: [pre-commit.ci] pre-commit autoupdate (#2568)

* Update pre-commit hooks:
   - github.com/pre-commit/pre-commit-hooks: v5.0.0 → v6.0.0
   - github.com/astral-sh/ruff-pre-commit: v0.12.0 → v0.14.3
   - github.com/psf/black-pre-commit-mirror: 24.10.0 → 25.9.0
   - github.com/adamchainz/blacken-docs: 1.19.1 → 1.20.0
   - github.com/pre-commit/mirrors-mypy: v1.13.0 → v1.18.2
   - github.com/codespell-project/codespell: v2.3.0 → v2.4.1
   - github.com/python-jsonschema/check-jsonschema: 0.30.0 → 0.34.1
* Apply fixes for RUF059: unused-unpacked-variable
   - c.f. https://docs.astral.sh/ruff/rules/unused-unpacked-variable/
* Apply fixes for RUF043: pytest-raises-ambiguous-pattern
   - c.f. https://docs.astral.sh/ruff/rules/pytest-raises-ambiguous-pattern/
* Add validation and test of pyhf.readxml.process_channel's ElementTree argument being
  not None.

Co-authored-by: Giordon Stark <[email protected]>
Co-authored-by: Matthew Feickert <[email protected]>

Author: 3 people

.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]:
     """