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Sep 8, 2020
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2 changes: 2 additions & 0 deletions .github/workflows/ci.yml
Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@ on:
jobs:
standard:
strategy:
fail-fast: false
matrix:
runs-on: [ubuntu-latest, windows-latest, macos-latest]
arch: [x64]
Expand Down Expand Up @@ -103,6 +104,7 @@ jobs:
run: python -m pip install -r tests/requirements.txt --prefer-binary

- name: Setup annotations
if: runner.os == 'Linux'
run: python -m pip install pytest-github-actions-annotate-failures

- name: Configure C++11 ${{ matrix.args }}
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8 changes: 8 additions & 0 deletions .pre-commit-config.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,14 @@ repos:
types: [file]
files: (\.cmake|CMakeLists.txt)(.in)?$

- repo: local
hooks:
- id: disallow-caps
name: Disallow improper capitalization
language: pygrep
entry: PyBind|Numpy|Cmake
exclude: .pre-commit-config.yaml

- repo: local
hooks:
- id: check-style
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2 changes: 1 addition & 1 deletion docs/advanced/cast/eigen.rst
Original file line number Diff line number Diff line change
Expand Up @@ -274,7 +274,7 @@ Vectors versus column/row matrices

Eigen and numpy have fundamentally different notions of a vector. In Eigen, a
vector is simply a matrix with the number of columns or rows set to 1 at
compile time (for a column vector or row vector, respectively). Numpy, in
compile time (for a column vector or row vector, respectively). NumPy, in
contrast, has comparable 2-dimensional 1xN and Nx1 arrays, but *also* has
1-dimensional arrays of size N.

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4 changes: 2 additions & 2 deletions docs/changelog.rst
Original file line number Diff line number Diff line change
Expand Up @@ -628,7 +628,7 @@ v2.2.0 (August 31, 2017)
in reference cycles.
`#856 <https://github.com/pybind/pybind11/pull/856>`_.

* Numpy and buffer protocol related improvements:
* NumPy and buffer protocol related improvements:

1. Support for negative strides in Python buffer objects/numpy arrays. This
required changing integers from unsigned to signed for the related C++ APIs.
Expand Down Expand Up @@ -1359,7 +1359,7 @@ Happy Christmas!
* Improved support for ``std::shared_ptr<>`` conversions
* Initial support for ``std::set<>`` conversions
* Fixed type resolution issue for types defined in a separate plugin module
* Cmake build system improvements
* CMake build system improvements
* Factored out generic functionality to non-templated code (smaller code size)
* Added a code size / compile time benchmark vs Boost.Python
* Added an appveyor CI script
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2 changes: 1 addition & 1 deletion docs/faq.rst
Original file line number Diff line number Diff line change
Expand Up @@ -285,7 +285,7 @@ CMake code. Conflicts can arise, however, when using pybind11 in a project that
Python detection in a system with several Python versions installed.

This difference may cause inconsistencies and errors if *both* mechanisms are used in the same project. Consider the following
Cmake code executed in a system with Python 2.7 and 3.x installed:
CMake code executed in a system with Python 2.7 and 3.x installed:

.. code-block:: cmake

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2 changes: 1 addition & 1 deletion docs/upgrade.rst
Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ something. The changes are:
``CMAKE_CXX_STANDARD=<number>`` instead, or any other valid CMake CXX or CUDA
standard selection method, like ``target_compile_features``.

* If you do not request a standard, PyBind11 targets will compile with the
* If you do not request a standard, pybind11 targets will compile with the
compiler default, but not less than C++11, instead of forcing C++14 always.
If you depend on the old behavior, please use ``set(CMAKE_CXX_STANDARD 14)``
instead.
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2 changes: 1 addition & 1 deletion tests/test_numpy_vectorize.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ TEST_SUBMODULE(numpy_vectorize, m) {
));

// test_type_selection
// Numpy function which only accepts specific data types
// NumPy function which only accepts specific data types
m.def("selective_func", [](py::array_t<int, py::array::c_style>) { return "Int branch taken."; });
m.def("selective_func", [](py::array_t<float, py::array::c_style>) { return "Float branch taken."; });
m.def("selective_func", [](py::array_t<std::complex<float>, py::array::c_style>) { return "Complex float branch taken."; });
Expand Down