From 2f37c64ca9a0aae75fae7bbfa581a2c4b20f39cf Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 3 Oct 2023 05:33:27 +0000 Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- docs/faq.rst | 2 +- docs/governance/ROADMAP.rst | 2 +- src/pyhf/contrib/viz/brazil.py | 2 +- src/pyhf/tensor/jax_backend.py | 4 ++-- src/pyhf/tensor/numpy_backend.py | 4 ++-- src/pyhf/tensor/pytorch_backend.py | 4 ++-- src/pyhf/tensor/tensorflow_backend.py | 4 ++-- 7 files changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/faq.rst b/docs/faq.rst index d821139463..48339ebb8e 100644 --- a/docs/faq.rst +++ b/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 diff --git a/docs/governance/ROADMAP.rst b/docs/governance/ROADMAP.rst index 65992ac5db..d3bca0d2f5 100644 --- a/docs/governance/ROADMAP.rst +++ b/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 `__ |Seibert Hierarchy of Needs SciPy 2019| (`Stan diff --git a/src/pyhf/contrib/viz/brazil.py b/src/pyhf/contrib/viz/brazil.py index 4e25a63c1b..a8955f20eb 100644 --- a/src/pyhf/contrib/viz/brazil.py +++ b/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( diff --git a/src/pyhf/tensor/jax_backend.py b/src/pyhf/tensor/jax_backend.py index dc6788a337..6ae53b7bec 100644 --- a/src/pyhf/tensor/jax_backend.py +++ b/src/pyhf/tensor/jax_backend.py @@ -89,8 +89,8 @@ 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: JAX ndarray: A clipped `tensor` diff --git a/src/pyhf/tensor/numpy_backend.py b/src/pyhf/tensor/numpy_backend.py index bb96393937..4623ea5797 100644 --- a/src/pyhf/tensor/numpy_backend.py +++ b/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` diff --git a/src/pyhf/tensor/pytorch_backend.py b/src/pyhf/tensor/pytorch_backend.py index a09d3369fe..fb0cd4ad52 100644 --- a/src/pyhf/tensor/pytorch_backend.py +++ b/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` diff --git a/src/pyhf/tensor/tensorflow_backend.py b/src/pyhf/tensor/tensorflow_backend.py index bc5a4f754f..1e51a2a885 100644 --- a/src/pyhf/tensor/tensorflow_backend.py +++ b/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`