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

.pre-commit-config.yaml

@@ -27,13 +27,13 @@ repos:
       exclude: ^validation/|\.dtd$|\.xml$
 
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: "v0.0.287"
+  rev: "v0.0.292"
   hooks:
     - id: ruff
       args: ["--fix", "--show-fixes"]
 
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.7.0
+    rev: 23.9.1
     hooks:
     - id: black-jupyter
 
@@ -62,11 +62,11 @@ repos:
     rev: 1.7.0
     hooks:
     - id: nbqa-ruff
-      additional_dependencies: [ruff==0.0.287]
+      additional_dependencies: [ruff==0.0.292]
       args: ["--extend-ignore=F821,F401,F841,F811"]
 
 -   repo: https://github.com/codespell-project/codespell
-    rev: v2.2.5
+    rev: v2.2.6
     hooks:
     - id: codespell
       files: ^.*\.(py|md|rst)$

docs/faq.rst

@@ -141,7 +141,7 @@ How did ``pyhf`` get started?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In 2017 Lukas Heinrich was discussing with colleauge Holger Schulz how it would be convenient
-to share and produce statistical results from LHC experiements if they were able to be
+to share and produce statistical results from LHC experiments if they were able to be
 created with tools that didn't require the large ``C++`` dependencies and tooling expertise as
 :math:`\HiFa{}`.
 Around the same time that Lukas began thinking on these ideas, Matthew Feickert was working on

docs/governance/ROADMAP.rst

@@ -7,7 +7,7 @@ This is the pyhf 2019 into 2020 Roadmap (Issue
 Overview and Goals
 ------------------
 
-We will follow loosely Seibert’s `Heirarchy of
+We will follow loosely Seibert’s `Hierarchy of
 Needs <https://twitter.com/FRoscheck/status/1159158552298229763>`__
 
 |Seibert Hierarchy of Needs SciPy 2019| (`Stan

src/pyhf/contrib/viz/brazil.py

@@ -112,7 +112,7 @@ def plot_brazil_band(test_pois, cls_obs, cls_exp, test_size, ax, **kwargs):
         ax (:obj:`matplotlib.axes.Axes`): The matplotlib axis object to plot on.
 
     Returns:
-        :obj:`tuple`: The :obj:`matplotlib.aritst` objects drawn.
+        :obj:`tuple`: The :obj:`matplotlib.artist` objects drawn.
     """
     line_color = kwargs.pop("color", "black")
     (cls_obs_line,) = ax.plot(

src/pyhf/tensor/jax_backend.py

@@ -19,7 +19,7 @@
         self.rate = rate
 
     def sample(self, sample_shape):
         # TODO: Support other dtypes
         return jnp.asarray(
             osp_stats.poisson(self.rate).rvs(size=sample_shape + self.rate.shape),
             dtype=jnp.float64,
@@ -89,8 +89,8 @@
 
         Args:
             tensor_in (:obj:`tensor`): The input tensor object
-            min_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The minimum value to be cliped to
-            max_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The maximum value to be cliped to
+            min_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The minimum value to be clipped to
+            max_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The maximum value to be clipped to
 
         Returns:
             JAX ndarray: A clipped `tensor`

src/pyhf/tensor/numpy_backend.py

@@ -98,8 +98,8 @@ def clip(
 
         Args:
             tensor_in (:obj:`tensor`): The input tensor object
-            min_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The minimum value to be cliped to
-            max_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The maximum value to be cliped to
+            min_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The minimum value to be clipped to
+            max_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The maximum value to be clipped to
 
         Returns:
             NumPy ndarray: A clipped `tensor`

src/pyhf/tensor/pytorch_backend.py

@@ -49,8 +49,8 @@ def clip(self, tensor_in, min_value, max_value):
 
         Args:
             tensor_in (:obj:`tensor`): The input tensor object
-            min_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The minimum value to be cliped to
-            max_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The maximum value to be cliped to
+            min_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The minimum value to be clipped to
+            max_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The maximum value to be clipped to
 
         Returns:
             PyTorch tensor: A clipped `tensor`

src/pyhf/tensor/tensorflow_backend.py

@@ -46,8 +46,8 @@ def clip(self, tensor_in, min_value, max_value):
 
         Args:
             tensor_in (:obj:`tensor`): The input tensor object
-            min_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The minimum value to be cliped to
-            max_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The maximum value to be cliped to
+            min_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The minimum value to be clipped to
+            max_value (:obj:`scalar` or :obj:`tensor` or :obj:`None`): The maximum value to be clipped to
 
         Returns:
             TensorFlow Tensor: A clipped `tensor`