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

.pre-commit-config.yaml

@@ -35,13 +35,13 @@ repos:
     - id: rst-inline-touching-normal
 
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: "v0.1.9"
+  rev: "v0.3.3"
   hooks:
     - id: ruff
       args: ["--fix", "--show-fixes"]
 
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.12.1
+    rev: 24.3.0
     hooks:
     - id: black-jupyter
       types_or: [python, pyi, jupyter]
@@ -50,10 +50,10 @@ repos:
     rev: 1.16.0
     hooks:
     - id: blacken-docs
-      additional_dependencies: [black==23.12.1]
+      additional_dependencies: [black==24.3.0]
 
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.8.0
+    rev: v1.9.0
     # check the oldest and newest supported Pythons
     hooks:
       - &mypy

docs/examples/notebooks/ImpactPlot.ipynb

@@ -172,9 +172,11 @@
     "    model, _, b, e = fitresults()\n",
     "    widths = pyhf.tensorlib.concatenate(\n",
     "        [\n",
-    "            model.config.param_set(k).width()\n",
-    "            if model.config.param_set(k).constrained\n",
-    "            else [None] * model.config.param_set(k).n_parameters\n",
+    "            (\n",
+    "                model.config.param_set(k).width()\n",
+    "                if model.config.param_set(k).constrained\n",
+    "                else [None] * model.config.param_set(k).n_parameters\n",
+    "            )\n",
     "            for k, v in model.config.par_map.items()\n",
     "        ]\n",
     "    )\n",

src/pyhf/cli/__init__.py

@@ -1,4 +1,5 @@
 """The pyhf command line interface."""
+
 from pyhf.cli.cli import pyhf as cli
 from pyhf.cli.rootio import cli as rootio
 from pyhf.cli.spec import cli as spec

src/pyhf/cli/cli.py

@@ -1,4 +1,5 @@
 """The pyhf Command Line Interface."""
+
 import logging
 
 import click

src/pyhf/cli/complete.py

@@ -1,4 +1,5 @@
 '''Shell completions for pyhf.'''
+
 import click
 
 try:

src/pyhf/cli/infer.py

@@ -1,4 +1,5 @@
 """The inference CLI group."""
+
 import logging
 
 import click

src/pyhf/cli/patchset.py

@@ -1,4 +1,5 @@
 """The pyhf spec CLI subcommand."""
+
 import logging
 
 import click

src/pyhf/cli/rootio.py

@@ -1,4 +1,5 @@
 """CLI subapps to handle conversion from ROOT."""
+
 import logging
 
 import click

src/pyhf/cli/spec.py

@@ -1,4 +1,5 @@
 """The pyhf spec CLI subcommand."""
+
 import logging
 
 import click
@@ -149,9 +150,11 @@ def inspect(workspace, output_file, measurement):
                 ('(*) ' if measurement_name == default_measurement['name'] else '')
                 + measurement_name,
                 measurement_poi,
-                ','.join(measurement_parameters)
-                if measurement_parameters
-                else '(none)',
+                (
+                    ','.join(measurement_parameters)
+                    if measurement_parameters
+                    else '(none)'
+                ),
             )
         )
 

src/pyhf/contrib/cli.py

@@ -1,4 +1,5 @@
 """CLI for functionality that will get migrated out eventually."""
+
 import logging
 import click
 from pathlib import Path

src/pyhf/contrib/viz/brazil.py

@@ -1,4 +1,5 @@
 """Brazil Band Plots."""
+
 from collections import namedtuple
 
 import matplotlib.pyplot as plt

src/pyhf/infer/calculators.py

@@ -7,6 +7,7 @@
 
 Using the calculators hypothesis tests can then be performed.
 """
+
 from pyhf.infer.mle import fixed_poi_fit
 from pyhf import get_backend
 from pyhf.infer import utils

src/pyhf/infer/intervals/__init__.py

@@ -1,4 +1,5 @@
 """Interval estimation"""
+
 import pyhf.infer.intervals.upper_limits
 
 __all__ = ["upper_limits.upper_limit"]

src/pyhf/infer/intervals/upper_limits.py

@@ -1,4 +1,5 @@
 """Interval estimation"""
+
 import numpy as np
 from scipy.optimize import toms748
 

src/pyhf/infer/mle.py

@@ -1,4 +1,5 @@
 """Module for Maximum Likelihood Estimation."""
+
 from pyhf import get_backend
 from pyhf.exceptions import UnspecifiedPOI
 

src/pyhf/interpolators/code0.py

@@ -1,4 +1,5 @@
 """Piecewise-linear Interpolation. (Code 0)."""
+
 import logging
 import pyhf
 from pyhf.tensor.manager import get_backend

src/pyhf/interpolators/code1.py

@@ -1,4 +1,5 @@
 """Piecewise-Exponential Interpolation (Code 1)."""
+
 import logging
 import math
 import pyhf

src/pyhf/interpolators/code2.py

@@ -1,4 +1,5 @@
 """Quadratic Interpolation (Code 2)."""
+
 import logging
 import pyhf
 from pyhf.tensor.manager import get_backend

src/pyhf/interpolators/code4.py

@@ -1,4 +1,5 @@
 """Polynomial Interpolation (Code 4)."""
+
 import logging
 import math
 import pyhf

src/pyhf/interpolators/code4p.py

@@ -1,4 +1,5 @@
 """Piecewise-Linear + Polynomial Interpolation (Code 4p)."""
+
 import logging
 import pyhf
 from pyhf.tensor.manager import get_backend

src/pyhf/modifiers/shapesys.py

@@ -165,9 +165,9 @@ def _reindex_access_field(self, pdfconfig):
 
                 sample_mask = self._shapesys_mask[syst_index][singular_sample_index][0]
                 access_field_for_syst_and_batch[sample_mask] = selection
-                self._access_field[
-                    syst_index, batch_index
-                ] = access_field_for_syst_and_batch
+                self._access_field[syst_index, batch_index] = (
+                    access_field_for_syst_and_batch
+                )
 
     def _precompute(self):
         tensorlib, _ = get_backend()

src/pyhf/modifiers/staterror.py

@@ -92,9 +92,12 @@
             relerrs = default_backend.sum(
                 [
                     [
-                        (modifier_data['data']['uncrt'][binnr] / nomsall[binnr]) ** 2
-                        if nomsall[binnr] > 0
-                        else 0.0
+                        (
+                            (modifier_data['data']['uncrt'][binnr] / nomsall[binnr])
+                            ** 2
+                            if nomsall[binnr] > 0
+                            else 0.0
+                        )
                         for binnr in range(len(modifier_data['data']['nom_data']))
                     ]
                     for modifier_data in self.builder_data[modname].values()
@@ -123,7 +126,7 @@
 
             # list of bools, consistent with other modifiers (no numpy.bool_)
             fixed = default_backend.tolist(sigmas == 0)
             # FIXME: sigmas that are zero will be fixed to 1.0 arbitrarily to ensure
             # non-Nan constraint term, but in a future PR need to remove constraints
             # for these
             sigmas[fixed] = 1.0
@@ -184,9 +187,9 @@
 
                 sample_mask = self._staterror_mask[syst_index][singular_sample_index][0]
                 access_field_for_syst_and_batch[sample_mask] = selection
-                self._access_field[
-                    syst_index, batch_index
-                ] = access_field_for_syst_and_batch
+                self._access_field[syst_index, batch_index] = (
+                    access_field_for_syst_and_batch
+                )
 
     def _precompute(self):
         if not self.param_viewer.index_selection:

src/pyhf/optimize/common.py

@@ -1,4 +1,5 @@
 """Common Backend Shim to prepare minimization for optimizer."""
+
 from pyhf.tensor.manager import get_backend
 from pyhf.tensor.common import _TensorViewer
 

src/pyhf/optimize/mixins.py

@@ -1,4 +1,5 @@
 """Helper Classes for use of automatic differentiation."""
+
 import logging
 
 import numpy as np
@@ -84,7 +85,7 @@
             # extract number of fixed parameters
             num_fixed_pars = len(fitted_pars) - len(fitresult.x)
 
             # FIXME: Set uncertainties for fixed parameters to 0 manually
             # https://github.com/scikit-hep/iminuit/issues/762
             # https://github.com/scikit-hep/pyhf/issues/1918
             # https://github.com/scikit-hep/cabinetry/pull/346

src/pyhf/optimize/opt_minuit.py

@@ -1,4 +1,5 @@
 """Minuit Optimizer Class."""
+
 from pyhf import exceptions
 from pyhf.optimize.mixins import OptimizerMixin
 import scipy

src/pyhf/optimize/opt_scipy.py

@@ -1,4 +1,5 @@
 """SciPy Optimizer Class."""
+
 from pyhf import exceptions
 from pyhf.optimize.mixins import OptimizerMixin
 import scipy

src/pyhf/optimize/opt_tflow.py

@@ -1,4 +1,5 @@
 """Tensorflow Backend Function Shim."""
+
 from pyhf import get_backend
 import tensorflow as tf
 

src/pyhf/parameters/paramview.py

@@ -44,9 +44,11 @@ def extract_index_access(baseviewer, subviewer, indices):
 
         # the transpose is here so that modifier code doesn't have to do it
         indices_concatenated = tensorlib.astensor(
-            tensorlib.einsum('ij->ji', stitched)
-            if len(tensorlib.shape(stitched)) > 1
-            else stitched,
+            (
+                tensorlib.einsum('ij->ji', stitched)
+                if len(tensorlib.shape(stitched)) > 1
+                else stitched
+            ),
             dtype='int',
         )
     return index_selection, stitched, indices_concatenated

src/pyhf/patchset.py

@@ -1,6 +1,7 @@
 """
 pyhf patchset provides a user-friendly interface for interacting with patchsets.
 """
+
 import logging
 import jsonpatch
 from pyhf import exceptions

src/pyhf/probability.py

@@ -1,4 +1,5 @@
 """The probability density function module."""
+
 from pyhf import get_backend
 
 __all__ = ["Independent", "Normal", "Poisson", "Simultaneous"]

src/pyhf/schema/__init__.py

@@ -1,6 +1,7 @@
 """
 See :class:`~pyhf.schema.Schema` for documentation.
 """
+
 import pathlib
 import sys
 from pyhf.schema.loader import load_schema

src/pyhf/tensor/numpy_backend.py

@@ -1,4 +1,5 @@
 """NumPy Tensor Library Module."""
+
 from __future__ import annotations
 
 import logging

src/pyhf/tensor/pytorch_backend.py

@@ -1,4 +1,5 @@
 """PyTorch Tensor Library Module."""
+
 import torch
 import torch.autograd
 from torch.distributions.utils import broadcast_all

src/pyhf/tensor/tensorflow_backend.py

@@ -1,4 +1,5 @@
 """Tensorflow Tensor Library Module."""
+
 import logging
 import tensorflow as tf
 import tensorflow_probability as tfp

src/pyhf/typing.py

@@ -35,7 +35,7 @@
 )
 
 
 # TODO: Switch to os.PathLike[str] once Python 3.8 support dropped
 PathOrStr = Union[str, "os.PathLike[str]"]
 
 Shape = Tuple[int, ...]
@@ -150,17 +150,14 @@
     precision: str
     default_do_grad: bool
 
-    def _setup(self) -> None:
-        ...
+    def _setup(self) -> None: ...
 
 
 class Optimizer(Protocol):
     name: str
 
 
 class PDF(Protocol):
-    def sample(self, sample_shape: Shape) -> Any:
-        ...
+    def sample(self, sample_shape: Shape) -> Any: ...
 
-    def log_prob(self, value: Any) -> Any:
-        ...
+    def log_prob(self, value: Any) -> Any: ...

src/pyhf/workspace.py

@@ -5,6 +5,7 @@
 * the observed data (optional)
 * fit configurations ("measurements")
 """
+
 from __future__ import annotations
 
 import collections
@@ -17,6 +18,8 @@
 from pyhf import exceptions, schema
 from pyhf.mixins import _ChannelSummaryMixin
 from pyhf.pdf import Model
+import functools
+import operator
 
 log = logging.getLogger(__name__)
 
@@ -464,8 +467,8 @@ def data(self, model, include_auxdata=True):
 
         """
         try:
-            observed_data = sum(
-                (self.observations[c] for c in model.config.channels), []
+            observed_data = functools.reduce(
+                operator.iadd, (self.observations[c] for c in model.config.channels), []
             )
         except KeyError:
             log.error(

tests/test_interpolate.py

@@ -178,9 +178,7 @@ def test_code0_validation(backend, do_tensorized_calc):
 def test_code1_validation(backend, do_tensorized_calc):
     histogramssets = [[[[0.9], [1.0], [1.1]]]]
     alphasets = pyhf.tensorlib.astensor([[-2, -1, 0, 1, 2]])
-    expected = pyhf.tensorlib.astensor(
-        [[[[0.9**2], [0.9], [1.0], [1.1], [1.1**2]]]]
-    )
+    expected = pyhf.tensorlib.astensor([[[[0.9**2], [0.9], [1.0], [1.1], [1.1**2]]]])
 
     interpolator = pyhf.interpolators.get(1, do_tensorized_calc=do_tensorized_calc)(
         histogramssets, subscribe=False