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changed: vendor pybind11 using FetchContent

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Arne Morten Kvarving 2023-09-07 15:35:09 +02:00
parent f7067897b3
commit 9c38f01997
183 changed files with 1 additions and 40073 deletions
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4
python/CMakeLists.txt
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@ -1,9 +1,7 @@
find_package(pybind11 2.2 CONFIG)
if(NOT pybind11_FOUND)
add_subdirectory( pybind11 )
# SYSTEM is supported for embedded pybind source (>= 2.3.0)
set(PYBIND11_SYSTEM "SYSTEM")
include(DownloadPyBind11)
elseif(pybind11_VERSION VERSION_GREATER_EQUAL "2.3.0")
if(CMAKE_VERSION VERSION_LESS 3.12)
# SYSTEM is only support for pybind >= 2.3.0 and cmake <= 3.12.0 (Unfortunately querying the

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python/pybind11/.appveyor.yml
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@ -1,70 +0,0 @@
version: 1.0.{build}
image:
- Visual Studio 2017
- Visual Studio 2015
test: off
skip_branch_with_pr: true
build:
parallel: true
platform:
- x64
- x86
environment:
matrix:
- PYTHON: 36
CPP: 14
CONFIG: Debug
- PYTHON: 27
CPP: 14
CONFIG: Debug
- CONDA: 36
CPP: latest
CONFIG: Release
matrix:
exclude:
- image: Visual Studio 2015
platform: x86
- image: Visual Studio 2015
CPP: latest
- image: Visual Studio 2017
CPP: latest
platform: x86
install:
- ps: |
if ($env:PLATFORM -eq "x64") { $env:CMAKE_ARCH = "x64" }
if ($env:APPVEYOR_JOB_NAME -like "*Visual Studio 2017*") {
$env:CMAKE_GENERATOR = "Visual Studio 15 2017"
$env:CMAKE_INCLUDE_PATH = "C:\Libraries\boost_1_64_0"
$env:CXXFLAGS = "-permissive-"
} else {
$env:CMAKE_GENERATOR = "Visual Studio 14 2015"
}
if ($env:PYTHON) {
if ($env:PLATFORM -eq "x64") { $env:PYTHON = "$env:PYTHON-x64" }
$env:PATH = "C:\Python$env:PYTHON\;C:\Python$env:PYTHON\Scripts\;$env:PATH"
python -W ignore -m pip install --upgrade pip wheel
python -W ignore -m pip install pytest numpy --no-warn-script-location
} elseif ($env:CONDA) {
if ($env:CONDA -eq "27") { $env:CONDA = "" }
if ($env:PLATFORM -eq "x64") { $env:CONDA = "$env:CONDA-x64" }
$env:PATH = "C:\Miniconda$env:CONDA\;C:\Miniconda$env:CONDA\Scripts\;$env:PATH"
$env:PYTHONHOME = "C:\Miniconda$env:CONDA"
conda --version
conda install -y -q pytest numpy scipy
}
- ps: |
Start-FileDownload 'http://bitbucket.org/eigen/eigen/get/3.3.3.zip'
7z x 3.3.3.zip -y > $null
$env:CMAKE_INCLUDE_PATH = "eigen-eigen-67e894c6cd8f;$env:CMAKE_INCLUDE_PATH"
build_script:
- cmake -G "%CMAKE_GENERATOR%" -A "%CMAKE_ARCH%"
-DPYBIND11_CPP_STANDARD=/std:c++%CPP%
-DPYBIND11_WERROR=ON
-DDOWNLOAD_CATCH=ON
-DCMAKE_SUPPRESS_REGENERATION=1
.
- set MSBuildLogger="C:\Program Files\AppVeyor\BuildAgent\Appveyor.MSBuildLogger.dll"
- cmake --build . --config %CONFIG% --target pytest -- /m /v:m /logger:%MSBuildLogger%
- cmake --build . --config %CONFIG% --target cpptest -- /m /v:m /logger:%MSBuildLogger%
- if "%CPP%"=="latest" (cmake --build . --config %CONFIG% --target test_cmake_build -- /m /v:m /logger:%MSBuildLogger%)
on_failure: if exist "tests\test_cmake_build" type tests\test_cmake_build\*.log*

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python/pybind11/.gitignore vendored
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@ -1,38 +0,0 @@
CMakeCache.txt
CMakeFiles
Makefile
cmake_install.cmake
.DS_Store
*.so
*.pyd
*.dll
*.sln
*.sdf
*.opensdf
*.vcxproj
*.filters
example.dir
Win32
x64
Release
Debug
.vs
CTestTestfile.cmake
Testing
autogen
MANIFEST
/.ninja_*
/*.ninja
/docs/.build
*.py[co]
*.egg-info
*~
.*.swp
.DS_Store
/dist
/build
/cmake/
.cache/
sosize-*.txt
pybind11Config*.cmake
pybind11Targets.cmake

3
python/pybind11/.gitmodules vendored
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@ -1,3 +0,0 @@
[submodule "tools/clang"]
path = tools/clang
url = ../../wjakob/clang-cindex-python3

3
python/pybind11/.readthedocs.yml
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@ -1,3 +0,0 @@
python:
version: 3
requirements_file: docs/requirements.txt

280
python/pybind11/.travis.yml
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@ -1,280 +0,0 @@
language: cpp
matrix:
include:
# This config does a few things:
# - Checks C++ and Python code styles (check-style.sh and flake8).
# - Makes sure sphinx can build the docs without any errors or warnings.
# - Tests setup.py sdist and install (all header files should be present).
# - Makes sure that everything still works without optional deps (numpy/scipy/eigen) and
# also tests the automatic discovery functions in CMake (Python version, C++ standard).
- os: linux
dist: xenial # Necessary to run doxygen 1.8.15
name: Style, docs, and pip
cache: false
before_install:
- pyenv global $(pyenv whence 2to3) # activate all python versions
- PY_CMD=python3
- $PY_CMD -m pip install --user --upgrade pip wheel setuptools
install:
- $PY_CMD -m pip install --user --upgrade sphinx sphinx_rtd_theme breathe flake8 pep8-naming pytest
- curl -fsSL https://sourceforge.net/projects/doxygen/files/rel-1.8.15/doxygen-1.8.15.linux.bin.tar.gz/download | tar xz
- export PATH="$PWD/doxygen-1.8.15/bin:$PATH"
script:
- tools/check-style.sh
- flake8
- $PY_CMD -m sphinx -W -b html docs docs/.build
- |
# Make sure setup.py distributes and installs all the headers
$PY_CMD setup.py sdist
$PY_CMD -m pip install --user -U ./dist/*
installed=$($PY_CMD -c "import pybind11; print(pybind11.get_include(True) + '/pybind11')")
diff -rq $installed ./include/pybind11
- |
# Barebones build
cmake -DCMAKE_BUILD_TYPE=Debug -DPYBIND11_WERROR=ON -DDOWNLOAD_CATCH=ON -DPYTHON_EXECUTABLE=$(which $PY_CMD) .
make pytest -j 2
make cpptest -j 2
# The following are regular test configurations, including optional dependencies.
# With regard to each other they differ in Python version, C++ standard and compiler.
- os: linux
dist: trusty
name: Python 2.7, c++11, gcc 4.8
env: PYTHON=2.7 CPP=11 GCC=4.8
addons:
apt:
packages:
- cmake=2.\*
- cmake-data=2.\*
- os: linux
dist: trusty
name: Python 3.6, c++11, gcc 4.8
env: PYTHON=3.6 CPP=11 GCC=4.8
addons:
apt:
sources:
- deadsnakes
packages:
- python3.6-dev
- python3.6-venv
- cmake=2.\*
- cmake-data=2.\*
- os: linux
dist: trusty
env: PYTHON=2.7 CPP=14 GCC=6 CMAKE=1
name: Python 2.7, c++14, gcc 4.8, CMake test
addons:
apt:
sources:
- ubuntu-toolchain-r-test
packages:
- g++-6
- os: linux
dist: trusty
name: Python 3.5, c++14, gcc 6, Debug build
# N.B. `ensurepip` could be installed transitively by `python3.5-venv`, but
# seems to have apt conflicts (at least for Trusty). Use Docker instead.
services: docker
env: DOCKER=debian:stretch PYTHON=3.5 CPP=14 GCC=6 DEBUG=1
- os: linux
dist: xenial
env: PYTHON=3.6 CPP=17 GCC=7
name: Python 3.6, c++17, gcc 7
addons:
apt:
sources:
- deadsnakes
- ubuntu-toolchain-r-test
packages:
- g++-7
- python3.6-dev
- python3.6-venv
- os: linux
dist: xenial
env: PYTHON=3.6 CPP=17 CLANG=7
name: Python 3.6, c++17, Clang 7
addons:
apt:
sources:
- deadsnakes
- llvm-toolchain-xenial-7
packages:
- python3.6-dev
- python3.6-venv
- clang-7
- libclang-7-dev
- llvm-7-dev
- lld-7
- libc++-7-dev
- libc++abi-7-dev # Why is this necessary???
- os: osx
name: Python 2.7, c++14, AppleClang 7.3, CMake test
osx_image: xcode7.3
env: PYTHON=2.7 CPP=14 CLANG CMAKE=1
- os: osx
name: Python 3.7, c++14, AppleClang 9, Debug build
osx_image: xcode9
env: PYTHON=3.7 CPP=14 CLANG DEBUG=1
# Test a PyPy 2.7 build
- os: linux
dist: trusty
env: PYPY=5.8 PYTHON=2.7 CPP=11 GCC=4.8
name: PyPy 5.8, Python 2.7, c++11, gcc 4.8
addons:
apt:
packages:
- libblas-dev
- liblapack-dev
- gfortran
# Build in 32-bit mode and tests against the CMake-installed version
- os: linux
dist: trusty
services: docker
env: DOCKER=i386/debian:stretch PYTHON=3.5 CPP=14 GCC=6 INSTALL=1
name: Python 3.4, c++14, gcc 6, 32-bit
script:
- |
# Consolidated 32-bit Docker Build + Install
set -ex
$SCRIPT_RUN_PREFIX sh -c "
set -ex
cmake ${CMAKE_EXTRA_ARGS} -DPYBIND11_INSTALL=1 -DPYBIND11_TEST=0 .
make install
cp -a tests /pybind11-tests
mkdir /build-tests && cd /build-tests
cmake ../pybind11-tests ${CMAKE_EXTRA_ARGS} -DPYBIND11_WERROR=ON
make pytest -j 2"
set +ex
cache:
directories:
- $HOME/.local/bin
- $HOME/.local/lib
- $HOME/.local/include
- $HOME/Library/Python
before_install:
- |
# Configure build variables
set -ex
if [ "$TRAVIS_OS_NAME" = "linux" ]; then
if [ -n "$CLANG" ]; then
export CXX=clang++-$CLANG CC=clang-$CLANG
EXTRA_PACKAGES+=" clang-$CLANG llvm-$CLANG-dev"
else
if [ -z "$GCC" ]; then GCC=4.8
else EXTRA_PACKAGES+=" g++-$GCC"
fi
export CXX=g++-$GCC CC=gcc-$GCC
fi
elif [ "$TRAVIS_OS_NAME" = "osx" ]; then
export CXX=clang++ CC=clang;
fi
if [ -n "$CPP" ]; then CPP=-std=c++$CPP; fi
if [ "${PYTHON:0:1}" = "3" ]; then PY=3; fi
if [ -n "$DEBUG" ]; then CMAKE_EXTRA_ARGS+=" -DCMAKE_BUILD_TYPE=Debug"; fi
set +ex
- |
# Initialize environment
set -ex
if [ -n "$DOCKER" ]; then
docker pull $DOCKER
containerid=$(docker run --detach --tty \
--volume="$PWD":/pybind11 --workdir=/pybind11 \
--env="CC=$CC" --env="CXX=$CXX" --env="DEBIAN_FRONTEND=$DEBIAN_FRONTEND" \
--env=GCC_COLORS=\ \
$DOCKER)
SCRIPT_RUN_PREFIX="docker exec --tty $containerid"
$SCRIPT_RUN_PREFIX sh -c 'for s in 0 15; do sleep $s; apt-get update && apt-get -qy dist-upgrade && break; done'
else
if [ "$PYPY" = "5.8" ]; then
curl -fSL https://bitbucket.org/pypy/pypy/downloads/pypy2-v5.8.0-linux64.tar.bz2 | tar xj
PY_CMD=$(echo `pwd`/pypy2-v5.8.0-linux64/bin/pypy)
CMAKE_EXTRA_ARGS+=" -DPYTHON_EXECUTABLE:FILEPATH=$PY_CMD"
else
PY_CMD=python$PYTHON
if [ "$TRAVIS_OS_NAME" = "osx" ]; then
if [ "$PY" = "3" ]; then
brew update && brew upgrade python
else
curl -fsSL https://bootstrap.pypa.io/get-pip.py | $PY_CMD - --user
fi
fi
fi
if [ "$PY" = 3 ] || [ -n "$PYPY" ]; then
$PY_CMD -m ensurepip --user
fi
$PY_CMD --version
$PY_CMD -m pip install --user --upgrade pip wheel
fi
set +ex
install:
- |
# Install dependencies
set -ex
cmake --version
if [ -n "$DOCKER" ]; then
if [ -n "$DEBUG" ]; then
PY_DEBUG="python$PYTHON-dbg python$PY-scipy-dbg"
CMAKE_EXTRA_ARGS+=" -DPYTHON_EXECUTABLE=/usr/bin/python${PYTHON}dm"
fi
$SCRIPT_RUN_PREFIX sh -c "for s in 0 15; do sleep \$s; \
apt-get -qy --no-install-recommends install \
$PY_DEBUG python$PYTHON-dev python$PY-pytest python$PY-scipy \
libeigen3-dev libboost-dev cmake make ${EXTRA_PACKAGES} && break; done"
else
if [ "$CLANG" = "7" ]; then
export CXXFLAGS="-stdlib=libc++"
fi
export NPY_NUM_BUILD_JOBS=2
echo "Installing pytest, numpy, scipy..."
local PIP_CMD=""
if [ -n $PYPY ]; then
# For expediency, install only versions that are available on the extra index.
travis_wait 30 \
$PY_CMD -m pip install --user --upgrade --extra-index-url https://imaginary.ca/trusty-pypi \
pytest numpy==1.15.4 scipy==1.2.0
else
$PY_CMD -m pip install --user --upgrade pytest numpy scipy
fi
echo "done."
mkdir eigen
curl -fsSL https://bitbucket.org/eigen/eigen/get/3.3.4.tar.bz2 | \
tar --extract -j --directory=eigen --strip-components=1
export CMAKE_INCLUDE_PATH="${CMAKE_INCLUDE_PATH:+$CMAKE_INCLUDE_PATH:}$PWD/eigen"
fi
set +ex
script:
- |
# CMake Configuration
set -ex
$SCRIPT_RUN_PREFIX cmake ${CMAKE_EXTRA_ARGS} \
-DPYBIND11_PYTHON_VERSION=$PYTHON \
-DPYBIND11_CPP_STANDARD=$CPP \
-DPYBIND11_WERROR=${WERROR:-ON} \
-DDOWNLOAD_CATCH=${DOWNLOAD_CATCH:-ON} \
.
set +ex
- |
# pytest
set -ex
$SCRIPT_RUN_PREFIX make pytest -j 2 VERBOSE=1
set +ex
- |
# cpptest
set -ex
$SCRIPT_RUN_PREFIX make cpptest -j 2
set +ex
- |
# CMake Build Interface
set -ex
if [ -n "$CMAKE" ]; then $SCRIPT_RUN_PREFIX make test_cmake_build; fi
set +ex
after_failure: cat tests/test_cmake_build/*.log*
after_script:
- |
# Cleanup (Docker)
set -ex
if [ -n "$DOCKER" ]; then docker stop "$containerid"; docker rm "$containerid"; fi
set +ex

157
python/pybind11/CMakeLists.txt
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@ -1,157 +0,0 @@
# CMakeLists.txt -- Build system for the pybind11 modules
#
# Copyright (c) 2015 Wenzel Jakob <wenzel@inf.ethz.ch>
#
# All rights reserved. Use of this source code is governed by a
# BSD-style license that can be found in the LICENSE file.
cmake_minimum_required(VERSION 2.8.12)
if (POLICY CMP0048)
# cmake warns if loaded from a min-3.0-required parent dir, so silence the warning:
cmake_policy(SET CMP0048 NEW)
endif()
# CMake versions < 3.4.0 do not support try_compile/pthread checks without C as active language.
if(CMAKE_VERSION VERSION_LESS 3.4.0)
project(pybind11)
else()
project(pybind11 CXX)
endif()
# Check if pybind11 is being used directly or via add_subdirectory
set(PYBIND11_MASTER_PROJECT OFF)
if (CMAKE_CURRENT_SOURCE_DIR STREQUAL CMAKE_SOURCE_DIR)
set(PYBIND11_MASTER_PROJECT ON)
endif()
option(PYBIND11_INSTALL "Install pybind11 header files?" ${PYBIND11_MASTER_PROJECT})
option(PYBIND11_TEST "Build pybind11 test suite?" ${PYBIND11_MASTER_PROJECT})
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/tools")
include(pybind11Tools)
# Cache variables so pybind11_add_module can be used in parent projects
set(PYBIND11_INCLUDE_DIR "${CMAKE_CURRENT_LIST_DIR}/include" CACHE INTERNAL "")
set(PYTHON_INCLUDE_DIRS ${PYTHON_INCLUDE_DIRS} CACHE INTERNAL "")
set(PYTHON_LIBRARIES ${PYTHON_LIBRARIES} CACHE INTERNAL "")
set(PYTHON_MODULE_PREFIX ${PYTHON_MODULE_PREFIX} CACHE INTERNAL "")
set(PYTHON_MODULE_EXTENSION ${PYTHON_MODULE_EXTENSION} CACHE INTERNAL "")
set(PYTHON_VERSION_MAJOR ${PYTHON_VERSION_MAJOR} CACHE INTERNAL "")
set(PYTHON_VERSION_MINOR ${PYTHON_VERSION_MINOR} CACHE INTERNAL "")
# NB: when adding a header don't forget to also add it to setup.py
set(PYBIND11_HEADERS
include/pybind11/detail/class.h
include/pybind11/detail/common.h
include/pybind11/detail/descr.h
include/pybind11/detail/init.h
include/pybind11/detail/internals.h
include/pybind11/detail/typeid.h
include/pybind11/attr.h
include/pybind11/buffer_info.h
include/pybind11/cast.h
include/pybind11/chrono.h
include/pybind11/common.h
include/pybind11/complex.h
include/pybind11/options.h
include/pybind11/eigen.h
include/pybind11/embed.h
include/pybind11/eval.h
include/pybind11/functional.h
include/pybind11/numpy.h
include/pybind11/operators.h
include/pybind11/pybind11.h
include/pybind11/pytypes.h
include/pybind11/stl.h
include/pybind11/stl_bind.h
)
string(REPLACE "include/" "${CMAKE_CURRENT_SOURCE_DIR}/include/"
PYBIND11_HEADERS "${PYBIND11_HEADERS}")
if (PYBIND11_TEST)
add_subdirectory(tests)
endif()
include(GNUInstallDirs)
include(CMakePackageConfigHelpers)
# extract project version from source
file(STRINGS "${PYBIND11_INCLUDE_DIR}/pybind11/detail/common.h" pybind11_version_defines
REGEX "#define PYBIND11_VERSION_(MAJOR|MINOR|PATCH) ")
foreach(ver ${pybind11_version_defines})
if (ver MATCHES "#define PYBIND11_VERSION_(MAJOR|MINOR|PATCH) +([^ ]+)$")
set(PYBIND11_VERSION_${CMAKE_MATCH_1} "${CMAKE_MATCH_2}" CACHE INTERNAL "")
endif()
endforeach()
set(${PROJECT_NAME}_VERSION ${PYBIND11_VERSION_MAJOR}.${PYBIND11_VERSION_MINOR}.${PYBIND11_VERSION_PATCH})
message(STATUS "pybind11 v${${PROJECT_NAME}_VERSION}")
option (USE_PYTHON_INCLUDE_DIR "Install pybind11 headers in Python include directory instead of default installation prefix" OFF)
if (USE_PYTHON_INCLUDE_DIR)
file(RELATIVE_PATH CMAKE_INSTALL_INCLUDEDIR ${CMAKE_INSTALL_PREFIX} ${PYTHON_INCLUDE_DIRS})
endif()
if(NOT (CMAKE_VERSION VERSION_LESS 3.0)) # CMake >= 3.0
# Build an interface library target:
add_library(pybind11 INTERFACE)
add_library(pybind11::pybind11 ALIAS pybind11) # to match exported target
target_include_directories(pybind11 INTERFACE $<BUILD_INTERFACE:${PYBIND11_INCLUDE_DIR}>
$<BUILD_INTERFACE:${PYTHON_INCLUDE_DIRS}>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>)
target_compile_options(pybind11 INTERFACE $<BUILD_INTERFACE:${PYBIND11_CPP_STANDARD}>)
add_library(module INTERFACE)
add_library(pybind11::module ALIAS module)
if(NOT MSVC)
target_compile_options(module INTERFACE -fvisibility=hidden)
endif()
target_link_libraries(module INTERFACE pybind11::pybind11)
if(WIN32 OR CYGWIN)
target_link_libraries(module INTERFACE $<BUILD_INTERFACE:${PYTHON_LIBRARIES}>)
elseif(APPLE)
target_link_libraries(module INTERFACE "-undefined dynamic_lookup")
endif()
add_library(embed INTERFACE)
add_library(pybind11::embed ALIAS embed)
target_link_libraries(embed INTERFACE pybind11::pybind11 $<BUILD_INTERFACE:${PYTHON_LIBRARIES}>)
endif()
if (PYBIND11_INSTALL)
install(DIRECTORY ${PYBIND11_INCLUDE_DIR}/pybind11 DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
# GNUInstallDirs "DATADIR" wrong here; CMake search path wants "share".
set(PYBIND11_CMAKECONFIG_INSTALL_DIR "share/cmake/${PROJECT_NAME}" CACHE STRING "install path for pybind11Config.cmake")
configure_package_config_file(tools/${PROJECT_NAME}Config.cmake.in
"${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}Config.cmake"
INSTALL_DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR})
# Remove CMAKE_SIZEOF_VOID_P from ConfigVersion.cmake since the library does
# not depend on architecture specific settings or libraries.
set(_PYBIND11_CMAKE_SIZEOF_VOID_P ${CMAKE_SIZEOF_VOID_P})
unset(CMAKE_SIZEOF_VOID_P)
write_basic_package_version_file(${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake
VERSION ${${PROJECT_NAME}_VERSION}
COMPATIBILITY AnyNewerVersion)
set(CMAKE_SIZEOF_VOID_P ${_PYBIND11_CMAKE_SIZEOF_VOID_P})
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}Config.cmake
${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake
tools/FindPythonLibsNew.cmake
tools/pybind11Tools.cmake
DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR})
if(NOT (CMAKE_VERSION VERSION_LESS 3.0))
if(NOT PYBIND11_EXPORT_NAME)
set(PYBIND11_EXPORT_NAME "${PROJECT_NAME}Targets")
endif()
install(TARGETS pybind11 module embed
EXPORT "${PYBIND11_EXPORT_NAME}")
if(PYBIND11_MASTER_PROJECT)
install(EXPORT "${PYBIND11_EXPORT_NAME}"
NAMESPACE "${PROJECT_NAME}::"
DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR})
endif()
endif()
endif()

49
python/pybind11/CONTRIBUTING.md
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@ -1,49 +0,0 @@
Thank you for your interest in this project! Please refer to the following
sections on how to contribute code and bug reports.
### Reporting bugs
At the moment, this project is run in the spare time of a single person
([Wenzel Jakob](http://rgl.epfl.ch/people/wjakob)) with very limited resources
for issue tracker tickets. Thus, before submitting a question or bug report,
please take a moment of your time and ensure that your issue isn't already
discussed in the project documentation provided at
[http://pybind11.readthedocs.org/en/latest](http://pybind11.readthedocs.org/en/latest).
Assuming that you have identified a previously unknown problem or an important
question, it's essential that you submit a self-contained and minimal piece of
code that reproduces the problem. In other words: no external dependencies,
isolate the function(s) that cause breakage, submit matched and complete C++
and Python snippets that can be easily compiled and run on my end.
## Pull requests
Contributions are submitted, reviewed, and accepted using Github pull requests.
Please refer to [this
article](https://help.github.com/articles/using-pull-requests) for details and
adhere to the following rules to make the process as smooth as possible:
* Make a new branch for every feature you're working on.
* Make small and clean pull requests that are easy to review but make sure they
do add value by themselves.
* Add tests for any new functionality and run the test suite (``make pytest``)
to ensure that no existing features break.
* Please run ``flake8`` and ``tools/check-style.sh`` to check your code matches
the project style. (Note that ``check-style.sh`` requires ``gawk``.)
* This project has a strong focus on providing general solutions using a
minimal amount of code, thus small pull requests are greatly preferred.
### Licensing of contributions
pybind11 is provided under a BSD-style license that can be found in the
``LICENSE`` file. By using, distributing, or contributing to this project, you
agree to the terms and conditions of this license.
You are under no obligation whatsoever to provide any bug fixes, patches, or
upgrades to the features, functionality or performance of the source code
("Enhancements") to anyone; however, if you choose to make your Enhancements
available either publicly, or directly to the author of this software, without
imposing a separate written license agreement for such Enhancements, then you
hereby grant the following license: a non-exclusive, royalty-free perpetual
license to install, use, modify, prepare derivative works, incorporate into
other computer software, distribute, and sublicense such enhancements or
derivative works thereof, in binary and source code form.

17
python/pybind11/ISSUE_TEMPLATE.md
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Make sure you've completed the following steps before submitting your issue -- thank you!
1. Check if your question has already been answered in the [FAQ](http://pybind11.readthedocs.io/en/latest/faq.html) section.
2. Make sure you've read the [documentation](http://pybind11.readthedocs.io/en/latest/). Your issue may be addressed there.
3. If those resources didn't help and you only have a short question (not a bug report), consider asking in the [Gitter chat room](https://gitter.im/pybind/Lobby).
4. If you have a genuine bug report or a more complex question which is not answered in the previous items (or not suitable for chat), please fill in the details below.
5. Include a self-contained and minimal piece of code that reproduces the problem. If that's not possible, try to make the description as clear as possible.
*After reading, remove this checklist and the template text in parentheses below.*
## Issue description
(Provide a short description, state the expected behavior and what actually happens.)
## Reproducible example code
(The code should be minimal, have no external dependencies, isolate the function(s) that cause breakage. Submit matched and complete C++ and Python snippets that can be easily compiled and run to diagnose the issue.)

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Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>, All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Please also refer to the file CONTRIBUTING.md, which clarifies licensing of
external contributions to this project including patches, pull requests, etc.

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recursive-include include/pybind11 *.h
include LICENSE README.md CONTRIBUTING.md

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Note:
This is a dump of the pybind11 distribution, version 2.3.0.
A more elegant solutions might be explored in the future.
![pybind11 logo](https://github.com/pybind/pybind11/raw/master/docs/pybind11-logo.png)
# pybind11 — Seamless operability between C++11 and Python
[![Documentation Status](https://readthedocs.org/projects/pybind11/badge/?version=master)](http://pybind11.readthedocs.org/en/master/?badge=master)
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**pybind11** is a lightweight header-only library that exposes C++ types in Python
and vice versa, mainly to create Python bindings of existing C++ code. Its
goals and syntax are similar to the excellent
[Boost.Python](http://www.boost.org/doc/libs/1_58_0/libs/python/doc/) library
by David Abrahams: to minimize boilerplate code in traditional extension
modules by inferring type information using compile-time introspection.
The main issue with Boost.Python—and the reason for creating such a similar
project—is Boost. Boost is an enormously large and complex suite of utility
libraries that works with almost every C++ compiler in existence. This
compatibility has its cost: arcane template tricks and workarounds are
necessary to support the oldest and buggiest of compiler specimens. Now that
C++11-compatible compilers are widely available, this heavy machinery has
become an excessively large and unnecessary dependency.
Think of this library as a tiny self-contained version of Boost.Python with
everything stripped away that isn't relevant for binding generation. Without
comments, the core header files only require ~4K lines of code and depend on
Python (2.7 or 3.x, or PyPy2.7 >= 5.7) and the C++ standard library. This
compact implementation was possible thanks to some of the new C++11 language
features (specifically: tuples, lambda functions and variadic templates). Since
its creation, this library has grown beyond Boost.Python in many ways, leading
to dramatically simpler binding code in many common situations.
Tutorial and reference documentation is provided at
[http://pybind11.readthedocs.org/en/master](http://pybind11.readthedocs.org/en/master).
A PDF version of the manual is available
[here](https://media.readthedocs.org/pdf/pybind11/master/pybind11.pdf).
## Core features
pybind11 can map the following core C++ features to Python
- Functions accepting and returning custom data structures per value, reference, or pointer
- Instance methods and static methods
- Overloaded functions
- Instance attributes and static attributes
- Arbitrary exception types
- Enumerations
- Callbacks
- Iterators and ranges
- Custom operators
- Single and multiple inheritance
- STL data structures
- Smart pointers with reference counting like ``std::shared_ptr``
- Internal references with correct reference counting
- C++ classes with virtual (and pure virtual) methods can be extended in Python
## Goodies
In addition to the core functionality, pybind11 provides some extra goodies:
- Python 2.7, 3.x, and PyPy (PyPy2.7 >= 5.7) are supported with an
implementation-agnostic interface.
- It is possible to bind C++11 lambda functions with captured variables. The
lambda capture data is stored inside the resulting Python function object.
- pybind11 uses C++11 move constructors and move assignment operators whenever
possible to efficiently transfer custom data types.
- It's easy to expose the internal storage of custom data types through
Pythons' buffer protocols. This is handy e.g. for fast conversion between
C++ matrix classes like Eigen and NumPy without expensive copy operations.
- pybind11 can automatically vectorize functions so that they are transparently
applied to all entries of one or more NumPy array arguments.
- Python's slice-based access and assignment operations can be supported with
just a few lines of code.
- Everything is contained in just a few header files; there is no need to link
against any additional libraries.
- Binaries are generally smaller by a factor of at least 2 compared to
equivalent bindings generated by Boost.Python. A recent pybind11 conversion
of PyRosetta, an enormous Boost.Python binding project,
[reported](http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf) a binary
size reduction of **5.4x** and compile time reduction by **5.8x**.
- Function signatures are precomputed at compile time (using ``constexpr``),
leading to smaller binaries.
- With little extra effort, C++ types can be pickled and unpickled similar to
regular Python objects.
## Supported compilers
1. Clang/LLVM 3.3 or newer (for Apple Xcode's clang, this is 5.0.0 or newer)
2. GCC 4.8 or newer
3. Microsoft Visual Studio 2015 Update 3 or newer
4. Intel C++ compiler 17 or newer (16 with pybind11 v2.0 and 15 with pybind11 v2.0 and a [workaround](https://github.com/pybind/pybind11/issues/276))
5. Cygwin/GCC (tested on 2.5.1)
## About
This project was created by [Wenzel Jakob](http://rgl.epfl.ch/people/wjakob).
Significant features and/or improvements to the code were contributed by
Jonas Adler,
Sylvain Corlay,
Trent Houliston,
Axel Huebl,
@hulucc,
Sergey Lyskov
Johan Mabille,
Tomasz Miąsko,
Dean Moldovan,
Ben Pritchard,
Jason Rhinelander,
Boris Schäling,
Pim Schellart,
Ivan Smirnov, and
Patrick Stewart.
### License
pybind11 is provided under a BSD-style license that can be found in the
``LICENSE`` file. By using, distributing, or contributing to this project,
you agree to the terms and conditions of this license.

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.wy-table-responsive table th {
white-space: initial !important;
}
.rst-content table.docutils td {
vertical-align: top !important;
}
div[class^='highlight'] pre {
white-space: pre;
white-space: pre-wrap;
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Chrono
======
When including the additional header file :file:`pybind11/chrono.h` conversions
from C++11 chrono datatypes to python datetime objects are automatically enabled.
This header also enables conversions of python floats (often from sources such
as ``time.monotonic()``, ``time.perf_counter()`` and ``time.process_time()``)
into durations.
An overview of clocks in C++11
------------------------------
A point of confusion when using these conversions is the differences between
clocks provided in C++11. There are three clock types defined by the C++11
standard and users can define their own if needed. Each of these clocks have
different properties and when converting to and from python will give different
results.
The first clock defined by the standard is ``std::chrono::system_clock``. This
clock measures the current date and time. However, this clock changes with to
updates to the operating system time. For example, if your time is synchronised
with a time server this clock will change. This makes this clock a poor choice
for timing purposes but good for measuring the wall time.
The second clock defined in the standard is ``std::chrono::steady_clock``.
This clock ticks at a steady rate and is never adjusted. This makes it excellent
for timing purposes, however the value in this clock does not correspond to the
current date and time. Often this clock will be the amount of time your system
has been on, although it does not have to be. This clock will never be the same
clock as the system clock as the system clock can change but steady clocks
cannot.
The third clock defined in the standard is ``std::chrono::high_resolution_clock``.
This clock is the clock that has the highest resolution out of the clocks in the
system. It is normally a typedef to either the system clock or the steady clock
but can be its own independent clock. This is important as when using these
conversions as the types you get in python for this clock might be different
depending on the system.
If it is a typedef of the system clock, python will get datetime objects, but if
it is a different clock they will be timedelta objects.
Provided conversions
--------------------
.. rubric:: C++ to Python
- ``std::chrono::system_clock::time_point`` → ``datetime.datetime``
System clock times are converted to python datetime instances. They are
in the local timezone, but do not have any timezone information attached
to them (they are naive datetime objects).
- ``std::chrono::duration`` → ``datetime.timedelta``
Durations are converted to timedeltas, any precision in the duration
greater than microseconds is lost by rounding towards zero.
- ``std::chrono::[other_clocks]::time_point`` → ``datetime.timedelta``
Any clock time that is not the system clock is converted to a time delta.
This timedelta measures the time from the clocks epoch to now.
.. rubric:: Python to C++
- ``datetime.datetime`` → ``std::chrono::system_clock::time_point``
Date/time objects are converted into system clock timepoints. Any
timezone information is ignored and the type is treated as a naive
object.
- ``datetime.timedelta`` → ``std::chrono::duration``
Time delta are converted into durations with microsecond precision.
- ``datetime.timedelta`` → ``std::chrono::[other_clocks]::time_point``
Time deltas that are converted into clock timepoints are treated as
the amount of time from the start of the clocks epoch.
- ``float`` → ``std::chrono::duration``
Floats that are passed to C++ as durations be interpreted as a number of
seconds. These will be converted to the duration using ``duration_cast``
from the float.
- ``float`` → ``std::chrono::[other_clocks]::time_point``
Floats that are passed to C++ as time points will be interpreted as the
number of seconds from the start of the clocks epoch.

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Custom type casters
===================
In very rare cases, applications may require custom type casters that cannot be
expressed using the abstractions provided by pybind11, thus requiring raw
Python C API calls. This is fairly advanced usage and should only be pursued by
experts who are familiar with the intricacies of Python reference counting.
The following snippets demonstrate how this works for a very simple ``inty``
type that that should be convertible from Python types that provide a
``__int__(self)`` method.
.. code-block:: cpp
struct inty { long long_value; };
void print(inty s) {
std::cout << s.long_value << std::endl;
}
The following Python snippet demonstrates the intended usage from the Python side:
.. code-block:: python
class A:
def __int__(self):
return 123
from example import print
print(A())
To register the necessary conversion routines, it is necessary to add
a partial overload to the ``pybind11::detail::type_caster<T>`` template.
Although this is an implementation detail, adding partial overloads to this
type is explicitly allowed.
.. code-block:: cpp
namespace pybind11 { namespace detail {
template <> struct type_caster<inty> {
public:
/**
* This macro establishes the name 'inty' in
* function signatures and declares a local variable
* 'value' of type inty
*/
PYBIND11_TYPE_CASTER(inty, _("inty"));
/**
* Conversion part 1 (Python->C++): convert a PyObject into a inty
* instance or return false upon failure. The second argument
* indicates whether implicit conversions should be applied.
*/
bool load(handle src, bool) {
/* Extract PyObject from handle */
PyObject *source = src.ptr();
/* Try converting into a Python integer value */
PyObject *tmp = PyNumber_Long(source);
if (!tmp)
return false;
/* Now try to convert into a C++ int */
value.long_value = PyLong_AsLong(tmp);
Py_DECREF(tmp);
/* Ensure return code was OK (to avoid out-of-range errors etc) */
return !(value.long_value == -1 && !PyErr_Occurred());
}
/**
* Conversion part 2 (C++ -> Python): convert an inty instance into
* a Python object. The second and third arguments are used to
* indicate the return value policy and parent object (for
* ``return_value_policy::reference_internal``) and are generally
* ignored by implicit casters.
*/
static handle cast(inty src, return_value_policy /* policy */, handle /* parent */) {
return PyLong_FromLong(src.long_value);
}
};
}} // namespace pybind11::detail
.. note::
A ``type_caster<T>`` defined with ``PYBIND11_TYPE_CASTER(T, ...)`` requires
that ``T`` is default-constructible (``value`` is first default constructed
and then ``load()`` assigns to it).
.. warning::
When using custom type casters, it's important to declare them consistently
in every compilation unit of the Python extension module. Otherwise,
undefined behavior can ensue.

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Eigen
#####
`Eigen <http://eigen.tuxfamily.org>`_ is C++ header-based library for dense and
sparse linear algebra. Due to its popularity and widespread adoption, pybind11
provides transparent conversion and limited mapping support between Eigen and
Scientific Python linear algebra data types.
To enable the built-in Eigen support you must include the optional header file
:file:`pybind11/eigen.h`.
Pass-by-value
=============
When binding a function with ordinary Eigen dense object arguments (for
example, ``Eigen::MatrixXd``), pybind11 will accept any input value that is
already (or convertible to) a ``numpy.ndarray`` with dimensions compatible with
the Eigen type, copy its values into a temporary Eigen variable of the
appropriate type, then call the function with this temporary variable.
Sparse matrices are similarly copied to or from
``scipy.sparse.csr_matrix``/``scipy.sparse.csc_matrix`` objects.
Pass-by-reference
=================
One major limitation of the above is that every data conversion implicitly
involves a copy, which can be both expensive (for large matrices) and disallows
binding functions that change their (Matrix) arguments. Pybind11 allows you to
work around this by using Eigen's ``Eigen::Ref<MatrixType>`` class much as you
would when writing a function taking a generic type in Eigen itself (subject to
some limitations discussed below).
When calling a bound function accepting a ``Eigen::Ref<const MatrixType>``
type, pybind11 will attempt to avoid copying by using an ``Eigen::Map`` object
that maps into the source ``numpy.ndarray`` data: this requires both that the
data types are the same (e.g. ``dtype='float64'`` and ``MatrixType::Scalar`` is
``double``); and that the storage is layout compatible. The latter limitation
is discussed in detail in the section below, and requires careful
consideration: by default, numpy matrices and Eigen matrices are *not* storage
compatible.
If the numpy matrix cannot be used as is (either because its types differ, e.g.
passing an array of integers to an Eigen parameter requiring doubles, or
because the storage is incompatible), pybind11 makes a temporary copy and
passes the copy instead.
When a bound function parameter is instead ``Eigen::Ref<MatrixType>`` (note the
lack of ``const``), pybind11 will only allow the function to be called if it
can be mapped *and* if the numpy array is writeable (that is
``a.flags.writeable`` is true). Any access (including modification) made to
the passed variable will be transparently carried out directly on the
``numpy.ndarray``.
This means you can can write code such as the following and have it work as
expected:
.. code-block:: cpp
void scale_by_2(Eigen::Ref<Eigen::VectorXd> v) {
v *= 2;
}
Note, however, that you will likely run into limitations due to numpy and
Eigen's difference default storage order for data; see the below section on
:ref:`storage_orders` for details on how to bind code that won't run into such
limitations.
.. note::
Passing by reference is not supported for sparse types.
Returning values to Python
==========================
When returning an ordinary dense Eigen matrix type to numpy (e.g.
``Eigen::MatrixXd`` or ``Eigen::RowVectorXf``) pybind11 keeps the matrix and
returns a numpy array that directly references the Eigen matrix: no copy of the
data is performed. The numpy array will have ``array.flags.owndata`` set to
``False`` to indicate that it does not own the data, and the lifetime of the
stored Eigen matrix will be tied to the returned ``array``.
If you bind a function with a non-reference, ``const`` return type (e.g.
``const Eigen::MatrixXd``), the same thing happens except that pybind11 also
sets the numpy array's ``writeable`` flag to false.
If you return an lvalue reference or pointer, the usual pybind11 rules apply,
as dictated by the binding function's return value policy (see the
documentation on :ref:`return_value_policies` for full details). That means,
without an explicit return value policy, lvalue references will be copied and
pointers will be managed by pybind11. In order to avoid copying, you should
explicitly specify an appropriate return value policy, as in the following
example:
.. code-block:: cpp
class MyClass {
Eigen::MatrixXd big_mat = Eigen::MatrixXd::Zero(10000, 10000);
public:
Eigen::MatrixXd &getMatrix() { return big_mat; }
const Eigen::MatrixXd &viewMatrix() { return big_mat; }
};
// Later, in binding code:
py::class_<MyClass>(m, "MyClass")
.def(py::init<>())
.def("copy_matrix", &MyClass::getMatrix) // Makes a copy!
.def("get_matrix", &MyClass::getMatrix, py::return_value_policy::reference_internal)
.def("view_matrix", &MyClass::viewMatrix, py::return_value_policy::reference_internal)
;
.. code-block:: python
a = MyClass()
m = a.get_matrix() # flags.writeable = True, flags.owndata = False
v = a.view_matrix() # flags.writeable = False, flags.owndata = False
c = a.copy_matrix() # flags.writeable = True, flags.owndata = True
# m[5,6] and v[5,6] refer to the same element, c[5,6] does not.
Note in this example that ``py::return_value_policy::reference_internal`` is
used to tie the life of the MyClass object to the life of the returned arrays.
You may also return an ``Eigen::Ref``, ``Eigen::Map`` or other map-like Eigen
object (for example, the return value of ``matrix.block()`` and related
methods) that map into a dense Eigen type. When doing so, the default
behaviour of pybind11 is to simply reference the returned data: you must take
care to ensure that this data remains valid! You may ask pybind11 to
explicitly *copy* such a return value by using the
``py::return_value_policy::copy`` policy when binding the function. You may
also use ``py::return_value_policy::reference_internal`` or a
``py::keep_alive`` to ensure the data stays valid as long as the returned numpy
array does.
When returning such a reference of map, pybind11 additionally respects the
readonly-status of the returned value, marking the numpy array as non-writeable
if the reference or map was itself read-only.
.. note::
Sparse types are always copied when returned.
.. _storage_orders:
Storage orders
==============
Passing arguments via ``Eigen::Ref`` has some limitations that you must be
aware of in order to effectively pass matrices by reference. First and
foremost is that the default ``Eigen::Ref<MatrixType>`` class requires
contiguous storage along columns (for column-major types, the default in Eigen)
or rows if ``MatrixType`` is specifically an ``Eigen::RowMajor`` storage type.
The former, Eigen's default, is incompatible with ``numpy``'s default row-major
storage, and so you will not be able to pass numpy arrays to Eigen by reference
without making one of two changes.
(Note that this does not apply to vectors (or column or row matrices): for such
types the "row-major" and "column-major" distinction is meaningless).
The first approach is to change the use of ``Eigen::Ref<MatrixType>`` to the
more general ``Eigen::Ref<MatrixType, 0, Eigen::Stride<Eigen::Dynamic,
Eigen::Dynamic>>`` (or similar type with a fully dynamic stride type in the
third template argument). Since this is a rather cumbersome type, pybind11
provides a ``py::EigenDRef<MatrixType>`` type alias for your convenience (along
with EigenDMap for the equivalent Map, and EigenDStride for just the stride
type).
This type allows Eigen to map into any arbitrary storage order. This is not
the default in Eigen for performance reasons: contiguous storage allows
vectorization that cannot be done when storage is not known to be contiguous at
compile time. The default ``Eigen::Ref`` stride type allows non-contiguous
storage along the outer dimension (that is, the rows of a column-major matrix
or columns of a row-major matrix), but not along the inner dimension.
This type, however, has the added benefit of also being able to map numpy array
slices. For example, the following (contrived) example uses Eigen with a numpy
slice to multiply by 2 all coefficients that are both on even rows (0, 2, 4,
...) and in columns 2, 5, or 8:
.. code-block:: cpp
m.def("scale", [](py::EigenDRef<Eigen::MatrixXd> m, double c) { m *= c; });
.. code-block:: python
# a = np.array(...)
scale_by_2(myarray[0::2, 2:9:3])
The second approach to avoid copying is more intrusive: rearranging the
underlying data types to not run into the non-contiguous storage problem in the
first place. In particular, that means using matrices with ``Eigen::RowMajor``
storage, where appropriate, such as:
.. code-block:: cpp
using RowMatrixXd = Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
// Use RowMatrixXd instead of MatrixXd
Now bound functions accepting ``Eigen::Ref<RowMatrixXd>`` arguments will be
callable with numpy's (default) arrays without involving a copying.
You can, alternatively, change the storage order that numpy arrays use by
adding the ``order='F'`` option when creating an array:
.. code-block:: python
myarray = np.array(source, order='F')
Such an object will be passable to a bound function accepting an
``Eigen::Ref<MatrixXd>`` (or similar column-major Eigen type).
One major caveat with this approach, however, is that it is not entirely as
easy as simply flipping all Eigen or numpy usage from one to the other: some
operations may alter the storage order of a numpy array. For example, ``a2 =
array.transpose()`` results in ``a2`` being a view of ``array`` that references
the same data, but in the opposite storage order!
While this approach allows fully optimized vectorized calculations in Eigen, it
cannot be used with array slices, unlike the first approach.
When *returning* a matrix to Python (either a regular matrix, a reference via
``Eigen::Ref<>``, or a map/block into a matrix), no special storage
consideration is required: the created numpy array will have the required
stride that allows numpy to properly interpret the array, whatever its storage
order.
Failing rather than copying
===========================
The default behaviour when binding ``Eigen::Ref<const MatrixType>`` Eigen
references is to copy matrix values when passed a numpy array that does not
conform to the element type of ``MatrixType`` or does not have a compatible
stride layout. If you want to explicitly avoid copying in such a case, you
should bind arguments using the ``py::arg().noconvert()`` annotation (as
described in the :ref:`nonconverting_arguments` documentation).
The following example shows an example of arguments that don't allow data
copying to take place:
.. code-block:: cpp
// The method and function to be bound:
class MyClass {
// ...
double some_method(const Eigen::Ref<const MatrixXd> &matrix) { /* ... */ }
};
float some_function(const Eigen::Ref<const MatrixXf> &big,
const Eigen::Ref<const MatrixXf> &small) {
// ...
}
// The associated binding code:
using namespace pybind11::literals; // for "arg"_a
py::class_<MyClass>(m, "MyClass")
// ... other class definitions
.def("some_method", &MyClass::some_method, py::arg().noconvert());
m.def("some_function", &some_function,
"big"_a.noconvert(), // <- Don't allow copying for this arg
"small"_a // <- This one can be copied if needed
);
With the above binding code, attempting to call the the ``some_method(m)``
method on a ``MyClass`` object, or attempting to call ``some_function(m, m2)``
will raise a ``RuntimeError`` rather than making a temporary copy of the array.
It will, however, allow the ``m2`` argument to be copied into a temporary if
necessary.
Note that explicitly specifying ``.noconvert()`` is not required for *mutable*
Eigen references (e.g. ``Eigen::Ref<MatrixXd>`` without ``const`` on the
``MatrixXd``): mutable references will never be called with a temporary copy.
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
contrast, has comparable 2-dimensional 1xN and Nx1 arrays, but *also* has
1-dimensional arrays of size N.
When passing a 2-dimensional 1xN or Nx1 array to Eigen, the Eigen type must
have matching dimensions: That is, you cannot pass a 2-dimensional Nx1 numpy
array to an Eigen value expecting a row vector, or a 1xN numpy array as a
column vector argument.
On the other hand, pybind11 allows you to pass 1-dimensional arrays of length N
as Eigen parameters. If the Eigen type can hold a column vector of length N it
will be passed as such a column vector. If not, but the Eigen type constraints
will accept a row vector, it will be passed as a row vector. (The column
vector takes precedence when both are supported, for example, when passing a
1D numpy array to a MatrixXd argument). Note that the type need not be
explicitly a vector: it is permitted to pass a 1D numpy array of size 5 to an
Eigen ``Matrix<double, Dynamic, 5>``: you would end up with a 1x5 Eigen matrix.
Passing the same to an ``Eigen::MatrixXd`` would result in a 5x1 Eigen matrix.
When returning an Eigen vector to numpy, the conversion is ambiguous: a row
vector of length 4 could be returned as either a 1D array of length 4, or as a
2D array of size 1x4. When encountering such a situation, pybind11 compromises
by considering the returned Eigen type: if it is a compile-time vector--that
is, the type has either the number of rows or columns set to 1 at compile
time--pybind11 converts to a 1D numpy array when returning the value. For
instances that are a vector only at run-time (e.g. ``MatrixXd``,
``Matrix<float, Dynamic, 4>``), pybind11 returns the vector as a 2D array to
numpy. If this isn't want you want, you can use ``array.reshape(...)`` to get
a view of the same data in the desired dimensions.
.. seealso::
The file :file:`tests/test_eigen.cpp` contains a complete example that
shows how to pass Eigen sparse and dense data types in more detail.

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Functional
##########
The following features must be enabled by including :file:`pybind11/functional.h`.
Callbacks and passing anonymous functions
=========================================
The C++11 standard brought lambda functions and the generic polymorphic
function wrapper ``std::function<>`` to the C++ programming language, which
enable powerful new ways of working with functions. Lambda functions come in
two flavors: stateless lambda function resemble classic function pointers that
link to an anonymous piece of code, while stateful lambda functions
additionally depend on captured variables that are stored in an anonymous
*lambda closure object*.
Here is a simple example of a C++ function that takes an arbitrary function
(stateful or stateless) with signature ``int -> int`` as an argument and runs
it with the value 10.
.. code-block:: cpp
int func_arg(const std::function<int(int)> &f) {
return f(10);
}
The example below is more involved: it takes a function of signature ``int -> int``
and returns another function of the same kind. The return value is a stateful
lambda function, which stores the value ``f`` in the capture object and adds 1 to
its return value upon execution.
.. code-block:: cpp
std::function<int(int)> func_ret(const std::function<int(int)> &f) {
return [f](int i) {
return f(i) + 1;
};
}
This example demonstrates using python named parameters in C++ callbacks which
requires using ``py::cpp_function`` as a wrapper. Usage is similar to defining
methods of classes:
.. code-block:: cpp
py::cpp_function func_cpp() {
return py::cpp_function([](int i) { return i+1; },
py::arg("number"));
}
After including the extra header file :file:`pybind11/functional.h`, it is almost
trivial to generate binding code for all of these functions.
.. code-block:: cpp
#include <pybind11/functional.h>
PYBIND11_MODULE(example, m) {
m.def("func_arg", &func_arg);
m.def("func_ret", &func_ret);
m.def("func_cpp", &func_cpp);
}
The following interactive session shows how to call them from Python.
.. code-block:: pycon
$ python
>>> import example
>>> def square(i):
... return i * i
...
>>> example.func_arg(square)
100L
>>> square_plus_1 = example.func_ret(square)
>>> square_plus_1(4)
17L
>>> plus_1 = func_cpp()
>>> plus_1(number=43)
44L
.. warning::
Keep in mind that passing a function from C++ to Python (or vice versa)
will instantiate a piece of wrapper code that translates function
invocations between the two languages. Naturally, this translation
increases the computational cost of each function call somewhat. A
problematic situation can arise when a function is copied back and forth
between Python and C++ many times in a row, in which case the underlying
wrappers will accumulate correspondingly. The resulting long sequence of
C++ -> Python -> C++ -> ... roundtrips can significantly decrease
performance.
There is one exception: pybind11 detects case where a stateless function
(i.e. a function pointer or a lambda function without captured variables)
is passed as an argument to another C++ function exposed in Python. In this
case, there is no overhead. Pybind11 will extract the underlying C++
function pointer from the wrapped function to sidestep a potential C++ ->
Python -> C++ roundtrip. This is demonstrated in :file:`tests/test_callbacks.cpp`.
.. note::
This functionality is very useful when generating bindings for callbacks in
C++ libraries (e.g. GUI libraries, asynchronous networking libraries, etc.).
The file :file:`tests/test_callbacks.cpp` contains a complete example
that demonstrates how to work with callbacks and anonymous functions in
more detail.

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Type conversions
################
Apart from enabling cross-language function calls, a fundamental problem
that a binding tool like pybind11 must address is to provide access to
native Python types in C++ and vice versa. There are three fundamentally
different ways to do this—which approach is preferable for a particular type
depends on the situation at hand.
1. Use a native C++ type everywhere. In this case, the type must be wrapped
using pybind11-generated bindings so that Python can interact with it.
2. Use a native Python type everywhere. It will need to be wrapped so that
C++ functions can interact with it.
3. Use a native C++ type on the C++ side and a native Python type on the
Python side. pybind11 refers to this as a *type conversion*.
Type conversions are the most "natural" option in the sense that native
(non-wrapped) types are used everywhere. The main downside is that a copy
of the data must be made on every Python ↔ C++ transition: this is
needed since the C++ and Python versions of the same type generally won't
have the same memory layout.
pybind11 can perform many kinds of conversions automatically. An overview
is provided in the table ":ref:`conversion_table`".
The following subsections discuss the differences between these options in more
detail. The main focus in this section is on type conversions, which represent
the last case of the above list.
.. toctree::
:maxdepth: 1
overview
strings
stl
functional
chrono
eigen
custom

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Overview
########
.. rubric:: 1. Native type in C++, wrapper in Python
Exposing a custom C++ type using :class:`py::class_` was covered in detail
in the :doc:`/classes` section. There, the underlying data structure is
always the original C++ class while the :class:`py::class_` wrapper provides
a Python interface. Internally, when an object like this is sent from C++ to
Python, pybind11 will just add the outer wrapper layer over the native C++
object. Getting it back from Python is just a matter of peeling off the
wrapper.
.. rubric:: 2. Wrapper in C++, native type in Python
This is the exact opposite situation. Now, we have a type which is native to
Python, like a ``tuple`` or a ``list``. One way to get this data into C++ is
with the :class:`py::object` family of wrappers. These are explained in more
detail in the :doc:`/advanced/pycpp/object` section. We'll just give a quick
example here:
.. code-block:: cpp
void print_list(py::list my_list) {
for (auto item : my_list)
std::cout << item << " ";
}
.. code-block:: pycon
>>> print_list([1, 2, 3])
1 2 3
The Python ``list`` is not converted in any way -- it's just wrapped in a C++
:class:`py::list` class. At its core it's still a Python object. Copying a
:class:`py::list` will do the usual reference-counting like in Python.
Returning the object to Python will just remove the thin wrapper.
.. rubric:: 3. Converting between native C++ and Python types
In the previous two cases we had a native type in one language and a wrapper in
the other. Now, we have native types on both sides and we convert between them.
.. code-block:: cpp
void print_vector(const std::vector<int> &v) {
for (auto item : v)
std::cout << item << "\n";
}
.. code-block:: pycon
>>> print_vector([1, 2, 3])
1 2 3
In this case, pybind11 will construct a new ``std::vector<int>`` and copy each
element from the Python ``list``. The newly constructed object will be passed
to ``print_vector``. The same thing happens in the other direction: a new
``list`` is made to match the value returned from C++.
Lots of these conversions are supported out of the box, as shown in the table
below. They are very convenient, but keep in mind that these conversions are
fundamentally based on copying data. This is perfectly fine for small immutable
types but it may become quite expensive for large data structures. This can be
avoided by overriding the automatic conversion with a custom wrapper (i.e. the
above-mentioned approach 1). This requires some manual effort and more details
are available in the :ref:`opaque` section.
.. _conversion_table:
List of all builtin conversions
-------------------------------
The following basic data types are supported out of the box (some may require
an additional extension header to be included). To pass other data structures
as arguments and return values, refer to the section on binding :ref:`classes`.
+------------------------------------+---------------------------+-------------------------------+
| Data type | Description | Header file |
+====================================+===========================+===============================+
| ``int8_t``, ``uint8_t`` | 8-bit integers | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``int16_t``, ``uint16_t`` | 16-bit integers | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``int32_t``, ``uint32_t`` | 32-bit integers | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``int64_t``, ``uint64_t`` | 64-bit integers | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``ssize_t``, ``size_t`` | Platform-dependent size | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``float``, ``double`` | Floating point types | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``bool`` | Two-state Boolean type | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``char`` | Character literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``char16_t`` | UTF-16 character literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``char32_t`` | UTF-32 character literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``wchar_t`` | Wide character literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``const char *`` | UTF-8 string literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``const char16_t *`` | UTF-16 string literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``const char32_t *`` | UTF-32 string literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``const wchar_t *`` | Wide string literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::string`` | STL dynamic UTF-8 string | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::u16string`` | STL dynamic UTF-16 string | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::u32string`` | STL dynamic UTF-32 string | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::wstring`` | STL dynamic wide string | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::string_view``, | STL C++17 string views | :file:`pybind11/pybind11.h` |
| ``std::u16string_view``, etc. | | |
+------------------------------------+---------------------------+-------------------------------+
| ``std::pair<T1, T2>`` | Pair of two custom types | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::tuple<...>`` | Arbitrary tuple of types | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::reference_wrapper<...>`` | Reference type wrapper | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::complex<T>`` | Complex numbers | :file:`pybind11/complex.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::array<T, Size>`` | STL static array | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::vector<T>`` | STL dynamic array | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::deque<T>`` | STL double-ended queue | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::valarray<T>`` | STL value array | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::list<T>`` | STL linked list | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::map<T1, T2>`` | STL ordered map | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::unordered_map<T1, T2>`` | STL unordered map | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::set<T>`` | STL ordered set | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::unordered_set<T>`` | STL unordered set | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::optional<T>`` | STL optional type (C++17) | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::experimental::optional<T>`` | STL optional type (exp.) | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::variant<...>`` | Type-safe union (C++17) | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::function<...>`` | STL polymorphic function | :file:`pybind11/functional.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::chrono::duration<...>`` | STL time duration | :file:`pybind11/chrono.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``std::chrono::time_point<...>`` | STL date/time | :file:`pybind11/chrono.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``Eigen::Matrix<...>`` | Eigen: dense matrix | :file:`pybind11/eigen.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``Eigen::Map<...>`` | Eigen: mapped memory | :file:`pybind11/eigen.h` |
+------------------------------------+---------------------------+-------------------------------+
| ``Eigen::SparseMatrix<...>`` | Eigen: sparse matrix | :file:`pybind11/eigen.h` |
+------------------------------------+---------------------------+-------------------------------+

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STL containers
##############
Automatic conversion
====================
When including the additional header file :file:`pybind11/stl.h`, conversions
between ``std::vector<>``/``std::deque<>``/``std::list<>``/``std::array<>``,
``std::set<>``/``std::unordered_set<>``, and
``std::map<>``/``std::unordered_map<>`` and the Python ``list``, ``set`` and
``dict`` data structures are automatically enabled. The types ``std::pair<>``
and ``std::tuple<>`` are already supported out of the box with just the core
:file:`pybind11/pybind11.h` header.
The major downside of these implicit conversions is that containers must be
converted (i.e. copied) on every Python->C++ and C++->Python transition, which
can have implications on the program semantics and performance. Please read the
next sections for more details and alternative approaches that avoid this.
.. note::
Arbitrary nesting of any of these types is possible.
.. seealso::
The file :file:`tests/test_stl.cpp` contains a complete
example that demonstrates how to pass STL data types in more detail.
.. _cpp17_container_casters:
C++17 library containers
========================
The :file:`pybind11/stl.h` header also includes support for ``std::optional<>``
and ``std::variant<>``. These require a C++17 compiler and standard library.
In C++14 mode, ``std::experimental::optional<>`` is supported if available.
Various versions of these containers also exist for C++11 (e.g. in Boost).
pybind11 provides an easy way to specialize the ``type_caster`` for such
types:
.. code-block:: cpp
// `boost::optional` as an example -- can be any `std::optional`-like container
namespace pybind11 { namespace detail {
template <typename T>
struct type_caster<boost::optional<T>> : optional_caster<boost::optional<T>> {};
}}
The above should be placed in a header file and included in all translation units
where automatic conversion is needed. Similarly, a specialization can be provided
for custom variant types:
.. code-block:: cpp
// `boost::variant` as an example -- can be any `std::variant`-like container
namespace pybind11 { namespace detail {
template <typename... Ts>
struct type_caster<boost::variant<Ts...>> : variant_caster<boost::variant<Ts...>> {};
// Specifies the function used to visit the variant -- `apply_visitor` instead of `visit`
template <>
struct visit_helper<boost::variant> {
template <typename... Args>
static auto call(Args &&...args) -> decltype(boost::apply_visitor(args...)) {
return boost::apply_visitor(args...);
}
};
}} // namespace pybind11::detail
The ``visit_helper`` specialization is not required if your ``name::variant`` provides
a ``name::visit()`` function. For any other function name, the specialization must be
included to tell pybind11 how to visit the variant.
.. note::
pybind11 only supports the modern implementation of ``boost::variant``
which makes use of variadic templates. This requires Boost 1.56 or newer.
Additionally, on Windows, MSVC 2017 is required because ``boost::variant``
falls back to the old non-variadic implementation on MSVC 2015.
.. _opaque:
Making opaque types
===================
pybind11 heavily relies on a template matching mechanism to convert parameters
and return values that are constructed from STL data types such as vectors,
linked lists, hash tables, etc. This even works in a recursive manner, for
instance to deal with lists of hash maps of pairs of elementary and custom
types, etc.
However, a fundamental limitation of this approach is that internal conversions
between Python and C++ types involve a copy operation that prevents
pass-by-reference semantics. What does this mean?
Suppose we bind the following function
.. code-block:: cpp
void append_1(std::vector<int> &v) {
v.push_back(1);
}
and call it from Python, the following happens:
.. code-block:: pycon
>>> v = [5, 6]
>>> append_1(v)
>>> print(v)
[5, 6]
As you can see, when passing STL data structures by reference, modifications
are not propagated back the Python side. A similar situation arises when
exposing STL data structures using the ``def_readwrite`` or ``def_readonly``
functions:
.. code-block:: cpp
/* ... definition ... */
class MyClass {
std::vector<int> contents;
};
/* ... binding code ... */
py::class_<MyClass>(m, "MyClass")
.def(py::init<>())
.def_readwrite("contents", &MyClass::contents);
In this case, properties can be read and written in their entirety. However, an
``append`` operation involving such a list type has no effect:
.. code-block:: pycon
>>> m = MyClass()
>>> m.contents = [5, 6]
>>> print(m.contents)
[5, 6]
>>> m.contents.append(7)
>>> print(m.contents)
[5, 6]
Finally, the involved copy operations can be costly when dealing with very
large lists. To deal with all of the above situations, pybind11 provides a
macro named ``PYBIND11_MAKE_OPAQUE(T)`` that disables the template-based
conversion machinery of types, thus rendering them *opaque*. The contents of
opaque objects are never inspected or extracted, hence they *can* be passed by
reference. For instance, to turn ``std::vector<int>`` into an opaque type, add
the declaration
.. code-block:: cpp
PYBIND11_MAKE_OPAQUE(std::vector<int>);
before any binding code (e.g. invocations to ``class_::def()``, etc.). This
macro must be specified at the top level (and outside of any namespaces), since
it instantiates a partial template overload. If your binding code consists of
multiple compilation units, it must be present in every file (typically via a
common header) preceding any usage of ``std::vector<int>``. Opaque types must
also have a corresponding ``class_`` declaration to associate them with a name
in Python, and to define a set of available operations, e.g.:
.. code-block:: cpp
py::class_<std::vector<int>>(m, "IntVector")
.def(py::init<>())
.def("clear", &std::vector<int>::clear)
.def("pop_back", &std::vector<int>::pop_back)
.def("__len__", [](const std::vector<int> &v) { return v.size(); })
.def("__iter__", [](std::vector<int> &v) {
return py::make_iterator(v.begin(), v.end());
}, py::keep_alive<0, 1>()) /* Keep vector alive while iterator is used */
// ....
.. seealso::
The file :file:`tests/test_opaque_types.cpp` contains a complete
example that demonstrates how to create and expose opaque types using
pybind11 in more detail.
.. _stl_bind:
Binding STL containers
======================
The ability to expose STL containers as native Python objects is a fairly
common request, hence pybind11 also provides an optional header file named
:file:`pybind11/stl_bind.h` that does exactly this. The mapped containers try
to match the behavior of their native Python counterparts as much as possible.
The following example showcases usage of :file:`pybind11/stl_bind.h`:
.. code-block:: cpp
// Don't forget this
#include <pybind11/stl_bind.h>
PYBIND11_MAKE_OPAQUE(std::vector<int>);
PYBIND11_MAKE_OPAQUE(std::map<std::string, double>);
// ...
// later in binding code:
py::bind_vector<std::vector<int>>(m, "VectorInt");
py::bind_map<std::map<std::string, double>>(m, "MapStringDouble");
When binding STL containers pybind11 considers the types of the container's
elements to decide whether the container should be confined to the local module
(via the :ref:`module_local` feature). If the container element types are
anything other than already-bound custom types bound without
``py::module_local()`` the container binding will have ``py::module_local()``
applied. This includes converting types such as numeric types, strings, Eigen
types; and types that have not yet been bound at the time of the stl container
binding. This module-local binding is designed to avoid potential conflicts
between module bindings (for example, from two separate modules each attempting
to bind ``std::vector<int>`` as a python type).
It is possible to override this behavior to force a definition to be either
module-local or global. To do so, you can pass the attributes
``py::module_local()`` (to make the binding module-local) or
``py::module_local(false)`` (to make the binding global) into the
``py::bind_vector`` or ``py::bind_map`` arguments:
.. code-block:: cpp
py::bind_vector<std::vector<int>>(m, "VectorInt", py::module_local(false));
Note, however, that such a global binding would make it impossible to load this
module at the same time as any other pybind module that also attempts to bind
the same container type (``std::vector<int>`` in the above example).
See :ref:`module_local` for more details on module-local bindings.
.. seealso::
The file :file:`tests/test_stl_binders.cpp` shows how to use the
convenience STL container wrappers.

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Strings, bytes and Unicode conversions
######################################
.. note::
This section discusses string handling in terms of Python 3 strings. For
Python 2.7, replace all occurrences of ``str`` with ``unicode`` and
``bytes`` with ``str``. Python 2.7 users may find it best to use ``from
__future__ import unicode_literals`` to avoid unintentionally using ``str``
instead of ``unicode``.
Passing Python strings to C++
=============================
When a Python ``str`` is passed from Python to a C++ function that accepts
``std::string`` or ``char *`` as arguments, pybind11 will encode the Python
string to UTF-8. All Python ``str`` can be encoded in UTF-8, so this operation
does not fail.
The C++ language is encoding agnostic. It is the responsibility of the
programmer to track encodings. It's often easiest to simply `use UTF-8
everywhere <http://utf8everywhere.org/>`_.
.. code-block:: c++
m.def("utf8_test",
[](const std::string &s) {
cout << "utf-8 is icing on the cake.\n";
cout << s;
}
);
m.def("utf8_charptr",
[](const char *s) {
cout << "My favorite food is\n";
cout << s;
}
);
.. code-block:: python
>>> utf8_test('🎂')
utf-8 is icing on the cake.
🎂
>>> utf8_charptr('🍕')
My favorite food is
🍕
.. note::
Some terminal emulators do not support UTF-8 or emoji fonts and may not
display the example above correctly.
The results are the same whether the C++ function accepts arguments by value or
reference, and whether or not ``const`` is used.
Passing bytes to C++
--------------------
A Python ``bytes`` object will be passed to C++ functions that accept
``std::string`` or ``char*`` *without* conversion. On Python 3, in order to
make a function *only* accept ``bytes`` (and not ``str``), declare it as taking
a ``py::bytes`` argument.
Returning C++ strings to Python
===============================
When a C++ function returns a ``std::string`` or ``char*`` to a Python caller,
**pybind11 will assume that the string is valid UTF-8** and will decode it to a
native Python ``str``, using the same API as Python uses to perform
``bytes.decode('utf-8')``. If this implicit conversion fails, pybind11 will
raise a ``UnicodeDecodeError``.
.. code-block:: c++
m.def("std_string_return",
[]() {
return std::string("This string needs to be UTF-8 encoded");
}
);
.. code-block:: python
>>> isinstance(example.std_string_return(), str)
True
Because UTF-8 is inclusive of pure ASCII, there is never any issue with
returning a pure ASCII string to Python. If there is any possibility that the
string is not pure ASCII, it is necessary to ensure the encoding is valid
UTF-8.
.. warning::
Implicit conversion assumes that a returned ``char *`` is null-terminated.
If there is no null terminator a buffer overrun will occur.
Explicit conversions
--------------------
If some C++ code constructs a ``std::string`` that is not a UTF-8 string, one
can perform a explicit conversion and return a ``py::str`` object. Explicit
conversion has the same overhead as implicit conversion.
.. code-block:: c++
// This uses the Python C API to convert Latin-1 to Unicode
m.def("str_output",
[]() {
std::string s = "Send your r\xe9sum\xe9 to Alice in HR"; // Latin-1
py::str py_s = PyUnicode_DecodeLatin1(s.data(), s.length());
return py_s;
}
);
.. code-block:: python
>>> str_output()
'Send your résumé to Alice in HR'
The `Python C API
<https://docs.python.org/3/c-api/unicode.html#built-in-codecs>`_ provides
several built-in codecs.
One could also use a third party encoding library such as libiconv to transcode
to UTF-8.
Return C++ strings without conversion
-------------------------------------
If the data in a C++ ``std::string`` does not represent text and should be
returned to Python as ``bytes``, then one can return the data as a
``py::bytes`` object.
.. code-block:: c++
m.def("return_bytes",
[]() {
std::string s("\xba\xd0\xba\xd0"); // Not valid UTF-8
return py::bytes(s); // Return the data without transcoding
}
);
.. code-block:: python
>>> example.return_bytes()
b'\xba\xd0\xba\xd0'
Note the asymmetry: pybind11 will convert ``bytes`` to ``std::string`` without
encoding, but cannot convert ``std::string`` back to ``bytes`` implicitly.
.. code-block:: c++
m.def("asymmetry",
[](std::string s) { // Accepts str or bytes from Python
return s; // Looks harmless, but implicitly converts to str
}
);
.. code-block:: python
>>> isinstance(example.asymmetry(b"have some bytes"), str)
True
>>> example.asymmetry(b"\xba\xd0\xba\xd0") # invalid utf-8 as bytes
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xba in position 0: invalid start byte
Wide character strings
======================
When a Python ``str`` is passed to a C++ function expecting ``std::wstring``,
``wchar_t*``, ``std::u16string`` or ``std::u32string``, the ``str`` will be
encoded to UTF-16 or UTF-32 depending on how the C++ compiler implements each
type, in the platform's native endianness. When strings of these types are
returned, they are assumed to contain valid UTF-16 or UTF-32, and will be
decoded to Python ``str``.
.. code-block:: c++
#define UNICODE
#include <windows.h>
m.def("set_window_text",
[](HWND hwnd, std::wstring s) {
// Call SetWindowText with null-terminated UTF-16 string
::SetWindowText(hwnd, s.c_str());
}
);
m.def("get_window_text",
[](HWND hwnd) {
const int buffer_size = ::GetWindowTextLength(hwnd) + 1;
auto buffer = std::make_unique< wchar_t[] >(buffer_size);
::GetWindowText(hwnd, buffer.data(), buffer_size);
std::wstring text(buffer.get());
// wstring will be converted to Python str
return text;
}
);
.. warning::
Wide character strings may not work as described on Python 2.7 or Python
3.3 compiled with ``--enable-unicode=ucs2``.
Strings in multibyte encodings such as Shift-JIS must transcoded to a
UTF-8/16/32 before being returned to Python.
Character literals
==================
C++ functions that accept character literals as input will receive the first
character of a Python ``str`` as their input. If the string is longer than one
Unicode character, trailing characters will be ignored.
When a character literal is returned from C++ (such as a ``char`` or a
``wchar_t``), it will be converted to a ``str`` that represents the single
character.
.. code-block:: c++
m.def("pass_char", [](char c) { return c; });
m.def("pass_wchar", [](wchar_t w) { return w; });
.. code-block:: python
>>> example.pass_char('A')
'A'
While C++ will cast integers to character types (``char c = 0x65;``), pybind11
does not convert Python integers to characters implicitly. The Python function
``chr()`` can be used to convert integers to characters.
.. code-block:: python
>>> example.pass_char(0x65)
TypeError
>>> example.pass_char(chr(0x65))
'A'
If the desire is to work with an 8-bit integer, use ``int8_t`` or ``uint8_t``
as the argument type.
Grapheme clusters
-----------------
A single grapheme may be represented by two or more Unicode characters. For
example 'é' is usually represented as U+00E9 but can also be expressed as the
combining character sequence U+0065 U+0301 (that is, the letter 'e' followed by
a combining acute accent). The combining character will be lost if the
two-character sequence is passed as an argument, even though it renders as a
single grapheme.
.. code-block:: python
>>> example.pass_wchar('é')
'é'
>>> combining_e_acute = 'e' + '\u0301'
>>> combining_e_acute
'é'
>>> combining_e_acute == 'é'
False
>>> example.pass_wchar(combining_e_acute)
'e'
Normalizing combining characters before passing the character literal to C++
may resolve *some* of these issues:
.. code-block:: python
>>> example.pass_wchar(unicodedata.normalize('NFC', combining_e_acute))
'é'
In some languages (Thai for example), there are `graphemes that cannot be
expressed as a single Unicode code point
<http://unicode.org/reports/tr29/#Grapheme_Cluster_Boundaries>`_, so there is
no way to capture them in a C++ character type.
C++17 string views
==================
C++17 string views are automatically supported when compiling in C++17 mode.
They follow the same rules for encoding and decoding as the corresponding STL
string type (for example, a ``std::u16string_view`` argument will be passed
UTF-16-encoded data, and a returned ``std::string_view`` will be decoded as
UTF-8).
References
==========
* `The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!) <https://www.joelonsoftware.com/2003/10/08/the-absolute-minimum-every-software-developer-absolutely-positively-must-know-about-unicode-and-character-sets-no-excuses/>`_
* `C++ - Using STL Strings at Win32 API Boundaries <https://msdn.microsoft.com/en-ca/magazine/mt238407.aspx>`_

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.. _embedding:
Embedding the interpreter
#########################
While pybind11 is mainly focused on extending Python using C++, it's also
possible to do the reverse: embed the Python interpreter into a C++ program.
All of the other documentation pages still apply here, so refer to them for
general pybind11 usage. This section will cover a few extra things required
for embedding.
Getting started
===============
A basic executable with an embedded interpreter can be created with just a few
lines of CMake and the ``pybind11::embed`` target, as shown below. For more
information, see :doc:`/compiling`.
.. code-block:: cmake
cmake_minimum_required(VERSION 3.0)
project(example)
find_package(pybind11 REQUIRED) # or `add_subdirectory(pybind11)`
add_executable(example main.cpp)
target_link_libraries(example PRIVATE pybind11::embed)
The essential structure of the ``main.cpp`` file looks like this:
.. code-block:: cpp
#include <pybind11/embed.h> // everything needed for embedding
namespace py = pybind11;
int main() {
py::scoped_interpreter guard{}; // start the interpreter and keep it alive
py::print("Hello, World!"); // use the Python API
}
The interpreter must be initialized before using any Python API, which includes
all the functions and classes in pybind11. The RAII guard class `scoped_interpreter`
takes care of the interpreter lifetime. After the guard is destroyed, the interpreter
shuts down and clears its memory. No Python functions can be called after this.
Executing Python code
=====================
There are a few different ways to run Python code. One option is to use `eval`,
`exec` or `eval_file`, as explained in :ref:`eval`. Here is a quick example in
the context of an executable with an embedded interpreter:
.. code-block:: cpp
#include <pybind11/embed.h>
namespace py = pybind11;
int main() {
py::scoped_interpreter guard{};
py::exec(R"(
kwargs = dict(name="World", number=42)
message = "Hello, {name}! The answer is {number}".format(**kwargs)
print(message)
)");
}
Alternatively, similar results can be achieved using pybind11's API (see
:doc:`/advanced/pycpp/index` for more details).
.. code-block:: cpp
#include <pybind11/embed.h>
namespace py = pybind11;
using namespace py::literals;
int main() {
py::scoped_interpreter guard{};
auto kwargs = py::dict("name"_a="World", "number"_a=42);
auto message = "Hello, {name}! The answer is {number}"_s.format(**kwargs);
py::print(message);
}
The two approaches can also be combined:
.. code-block:: cpp
#include <pybind11/embed.h>
#include <iostream>
namespace py = pybind11;
using namespace py::literals;
int main() {
py::scoped_interpreter guard{};
auto locals = py::dict("name"_a="World", "number"_a=42);
py::exec(R"(
message = "Hello, {name}! The answer is {number}".format(**locals())
)", py::globals(), locals);
auto message = locals["message"].cast<std::string>();
std::cout << message;
}
Importing modules
=================
Python modules can be imported using `module::import()`:
.. code-block:: cpp
py::module sys = py::module::import("sys");
py::print(sys.attr("path"));
For convenience, the current working directory is included in ``sys.path`` when
embedding the interpreter. This makes it easy to import local Python files:
.. code-block:: python
"""calc.py located in the working directory"""
def add(i, j):
return i + j
.. code-block:: cpp
py::module calc = py::module::import("calc");
py::object result = calc.attr("add")(1, 2);
int n = result.cast<int>();
assert(n == 3);
Modules can be reloaded using `module::reload()` if the source is modified e.g.
by an external process. This can be useful in scenarios where the application
imports a user defined data processing script which needs to be updated after
changes by the user. Note that this function does not reload modules recursively.
.. _embedding_modules:
Adding embedded modules
=======================
Embedded binary modules can be added using the `PYBIND11_EMBEDDED_MODULE` macro.
Note that the definition must be placed at global scope. They can be imported
like any other module.
.. code-block:: cpp
#include <pybind11/embed.h>
namespace py = pybind11;
PYBIND11_EMBEDDED_MODULE(fast_calc, m) {
// `m` is a `py::module` which is used to bind functions and classes
m.def("add", [](int i, int j) {
return i + j;
});
}
int main() {
py::scoped_interpreter guard{};
auto fast_calc = py::module::import("fast_calc");
auto result = fast_calc.attr("add")(1, 2).cast<int>();
assert(result == 3);
}
Unlike extension modules where only a single binary module can be created, on
the embedded side an unlimited number of modules can be added using multiple
`PYBIND11_EMBEDDED_MODULE` definitions (as long as they have unique names).
These modules are added to Python's list of builtins, so they can also be
imported in pure Python files loaded by the interpreter. Everything interacts
naturally:
.. code-block:: python
"""py_module.py located in the working directory"""
import cpp_module
a = cpp_module.a
b = a + 1
.. code-block:: cpp
#include <pybind11/embed.h>
namespace py = pybind11;
PYBIND11_EMBEDDED_MODULE(cpp_module, m) {
m.attr("a") = 1;
}
int main() {
py::scoped_interpreter guard{};
auto py_module = py::module::import("py_module");
auto locals = py::dict("fmt"_a="{} + {} = {}", **py_module.attr("__dict__"));
assert(locals["a"].cast<int>() == 1);
assert(locals["b"].cast<int>() == 2);
py::exec(R"(
c = a + b
message = fmt.format(a, b, c)
)", py::globals(), locals);
assert(locals["c"].cast<int>() == 3);
assert(locals["message"].cast<std::string>() == "1 + 2 = 3");
}
Interpreter lifetime
====================
The Python interpreter shuts down when `scoped_interpreter` is destroyed. After
this, creating a new instance will restart the interpreter. Alternatively, the
`initialize_interpreter` / `finalize_interpreter` pair of functions can be used
to directly set the state at any time.
Modules created with pybind11 can be safely re-initialized after the interpreter
has been restarted. However, this may not apply to third-party extension modules.
The issue is that Python itself cannot completely unload extension modules and
there are several caveats with regard to interpreter restarting. In short, not
all memory may be freed, either due to Python reference cycles or user-created
global data. All the details can be found in the CPython documentation.
.. warning::
Creating two concurrent `scoped_interpreter` guards is a fatal error. So is
calling `initialize_interpreter` for a second time after the interpreter
has already been initialized.
Do not use the raw CPython API functions ``Py_Initialize`` and
``Py_Finalize`` as these do not properly handle the lifetime of
pybind11's internal data.
Sub-interpreter support
=======================
Creating multiple copies of `scoped_interpreter` is not possible because it
represents the main Python interpreter. Sub-interpreters are something different
and they do permit the existence of multiple interpreters. This is an advanced
feature of the CPython API and should be handled with care. pybind11 does not
currently offer a C++ interface for sub-interpreters, so refer to the CPython
documentation for all the details regarding this feature.
We'll just mention a couple of caveats the sub-interpreters support in pybind11:
1. Sub-interpreters will not receive independent copies of embedded modules.
Instead, these are shared and modifications in one interpreter may be
reflected in another.
2. Managing multiple threads, multiple interpreters and the GIL can be
challenging and there are several caveats here, even within the pure
CPython API (please refer to the Python docs for details). As for
pybind11, keep in mind that `gil_scoped_release` and `gil_scoped_acquire`
do not take sub-interpreters into account.

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Exceptions
##########
Built-in exception translation
==============================
When C++ code invoked from Python throws an ``std::exception``, it is
automatically converted into a Python ``Exception``. pybind11 defines multiple
special exception classes that will map to different types of Python
exceptions:
.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
+--------------------------------------+--------------------------------------+
| C++ exception type | Python exception type |
+======================================+======================================+
| :class:`std::exception` | ``RuntimeError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::bad_alloc` | ``MemoryError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::domain_error` | ``ValueError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::invalid_argument` | ``ValueError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::length_error` | ``ValueError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::out_of_range` | ``IndexError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::range_error` | ``ValueError`` |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::stop_iteration` | ``StopIteration`` (used to implement |
| | custom iterators) |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::index_error` | ``IndexError`` (used to indicate out |
| | of bounds access in ``__getitem__``, |
| | ``__setitem__``, etc.) |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::value_error` | ``ValueError`` (used to indicate |
| | wrong value passed in |
| | ``container.remove(...)``) |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::key_error` | ``KeyError`` (used to indicate out |
| | of bounds access in ``__getitem__``, |
| | ``__setitem__`` in dict-like |
| | objects, etc.) |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::error_already_set` | Indicates that the Python exception |
| | flag has already been set via Python |
| | API calls from C++ code; this C++ |
| | exception is used to propagate such |
| | a Python exception back to Python. |
+--------------------------------------+--------------------------------------+
When a Python function invoked from C++ throws an exception, it is converted
into a C++ exception of type :class:`error_already_set` whose string payload
contains a textual summary.
There is also a special exception :class:`cast_error` that is thrown by
:func:`handle::call` when the input arguments cannot be converted to Python
objects.
Registering custom translators
==============================
If the default exception conversion policy described above is insufficient,
pybind11 also provides support for registering custom exception translators.
To register a simple exception conversion that translates a C++ exception into
a new Python exception using the C++ exception's ``what()`` method, a helper
function is available:
.. code-block:: cpp
py::register_exception<CppExp>(module, "PyExp");
This call creates a Python exception class with the name ``PyExp`` in the given
module and automatically converts any encountered exceptions of type ``CppExp``
into Python exceptions of type ``PyExp``.
When more advanced exception translation is needed, the function
``py::register_exception_translator(translator)`` can be used to register
functions that can translate arbitrary exception types (and which may include
additional logic to do so). The function takes a stateless callable (e.g. a
function pointer or a lambda function without captured variables) with the call
signature ``void(std::exception_ptr)``.
When a C++ exception is thrown, the registered exception translators are tried
in reverse order of registration (i.e. the last registered translator gets the
first shot at handling the exception).
Inside the translator, ``std::rethrow_exception`` should be used within
a try block to re-throw the exception. One or more catch clauses to catch
the appropriate exceptions should then be used with each clause using
``PyErr_SetString`` to set a Python exception or ``ex(string)`` to set
the python exception to a custom exception type (see below).
To declare a custom Python exception type, declare a ``py::exception`` variable
and use this in the associated exception translator (note: it is often useful
to make this a static declaration when using it inside a lambda expression
without requiring capturing).
The following example demonstrates this for a hypothetical exception classes
``MyCustomException`` and ``OtherException``: the first is translated to a
custom python exception ``MyCustomError``, while the second is translated to a
standard python RuntimeError:
.. code-block:: cpp
static py::exception<MyCustomException> exc(m, "MyCustomError");
py::register_exception_translator([](std::exception_ptr p) {
try {
if (p) std::rethrow_exception(p);
} catch (const MyCustomException &e) {
exc(e.what());
} catch (const OtherException &e) {
PyErr_SetString(PyExc_RuntimeError, e.what());
}
});
Multiple exceptions can be handled by a single translator, as shown in the
example above. If the exception is not caught by the current translator, the
previously registered one gets a chance.
If none of the registered exception translators is able to handle the
exception, it is handled by the default converter as described in the previous
section.
.. seealso::
The file :file:`tests/test_exceptions.cpp` contains examples
of various custom exception translators and custom exception types.
.. note::
You must call either ``PyErr_SetString`` or a custom exception's call
operator (``exc(string)``) for every exception caught in a custom exception
translator. Failure to do so will cause Python to crash with ``SystemError:
error return without exception set``.
Exceptions that you do not plan to handle should simply not be caught, or
may be explicitly (re-)thrown to delegate it to the other,
previously-declared existing exception translators.

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Functions
#########
Before proceeding with this section, make sure that you are already familiar
with the basics of binding functions and classes, as explained in :doc:`/basics`
and :doc:`/classes`. The following guide is applicable to both free and member
functions, i.e. *methods* in Python.
.. _return_value_policies:
Return value policies
=====================
Python and C++ use fundamentally different ways of managing the memory and
lifetime of objects managed by them. This can lead to issues when creating
bindings for functions that return a non-trivial type. Just by looking at the
type information, it is not clear whether Python should take charge of the
returned value and eventually free its resources, or if this is handled on the
C++ side. For this reason, pybind11 provides a several *return value policy*
annotations that can be passed to the :func:`module::def` and
:func:`class_::def` functions. The default policy is
:enum:`return_value_policy::automatic`.
Return value policies are tricky, and it's very important to get them right.
Just to illustrate what can go wrong, consider the following simple example:
.. code-block:: cpp
/* Function declaration */
Data *get_data() { return _data; /* (pointer to a static data structure) */ }
...
/* Binding code */
m.def("get_data", &get_data); // <-- KABOOM, will cause crash when called from Python
What's going on here? When ``get_data()`` is called from Python, the return
value (a native C++ type) must be wrapped to turn it into a usable Python type.
In this case, the default return value policy (:enum:`return_value_policy::automatic`)
causes pybind11 to assume ownership of the static ``_data`` instance.
When Python's garbage collector eventually deletes the Python
wrapper, pybind11 will also attempt to delete the C++ instance (via ``operator
delete()``) due to the implied ownership. At this point, the entire application
will come crashing down, though errors could also be more subtle and involve
silent data corruption.
In the above example, the policy :enum:`return_value_policy::reference` should have
been specified so that the global data instance is only *referenced* without any
implied transfer of ownership, i.e.:
.. code-block:: cpp
m.def("get_data", &get_data, return_value_policy::reference);
On the other hand, this is not the right policy for many other situations,
where ignoring ownership could lead to resource leaks.
As a developer using pybind11, it's important to be familiar with the different
return value policies, including which situation calls for which one of them.
The following table provides an overview of available policies:
.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
+--------------------------------------------------+----------------------------------------------------------------------------+
| Return value policy | Description |
+==================================================+============================================================================+
| :enum:`return_value_policy::take_ownership` | Reference an existing object (i.e. do not create a new copy) and take |
| | ownership. Python will call the destructor and delete operator when the |
| | object's reference count reaches zero. Undefined behavior ensues when the |
| | C++ side does the same, or when the data was not dynamically allocated. |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::copy` | Create a new copy of the returned object, which will be owned by Python. |
| | This policy is comparably safe because the lifetimes of the two instances |
| | are decoupled. |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::move` | Use ``std::move`` to move the return value contents into a new instance |
| | that will be owned by Python. This policy is comparably safe because the |
| | lifetimes of the two instances (move source and destination) are decoupled.|
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::reference` | Reference an existing object, but do not take ownership. The C++ side is |
| | responsible for managing the object's lifetime and deallocating it when |
| | it is no longer used. Warning: undefined behavior will ensue when the C++ |
| | side deletes an object that is still referenced and used by Python. |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::reference_internal` | Indicates that the lifetime of the return value is tied to the lifetime |
| | of a parent object, namely the implicit ``this``, or ``self`` argument of |
| | the called method or property. Internally, this policy works just like |
| | :enum:`return_value_policy::reference` but additionally applies a |
| | ``keep_alive<0, 1>`` *call policy* (described in the next section) that |
| | prevents the parent object from being garbage collected as long as the |
| | return value is referenced by Python. This is the default policy for |
| | property getters created via ``def_property``, ``def_readwrite``, etc. |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::automatic` | **Default policy.** This policy falls back to the policy |
| | :enum:`return_value_policy::take_ownership` when the return value is a |
| | pointer. Otherwise, it uses :enum:`return_value_policy::move` or |
| | :enum:`return_value_policy::copy` for rvalue and lvalue references, |
| | respectively. See above for a description of what all of these different |
| | policies do. |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::automatic_reference` | As above, but use policy :enum:`return_value_policy::reference` when the |
| | return value is a pointer. This is the default conversion policy for |
| | function arguments when calling Python functions manually from C++ code |
| | (i.e. via handle::operator()). You probably won't need to use this. |
+--------------------------------------------------+----------------------------------------------------------------------------+
Return value policies can also be applied to properties:
.. code-block:: cpp
class_<MyClass>(m, "MyClass")
.def_property("data", &MyClass::getData, &MyClass::setData,
py::return_value_policy::copy);
Technically, the code above applies the policy to both the getter and the
setter function, however, the setter doesn't really care about *return*
value policies which makes this a convenient terse syntax. Alternatively,
targeted arguments can be passed through the :class:`cpp_function` constructor:
.. code-block:: cpp
class_<MyClass>(m, "MyClass")
.def_property("data"
py::cpp_function(&MyClass::getData, py::return_value_policy::copy),
py::cpp_function(&MyClass::setData)
);
.. warning::
Code with invalid return value policies might access uninitialized memory or
free data structures multiple times, which can lead to hard-to-debug
non-determinism and segmentation faults, hence it is worth spending the
time to understand all the different options in the table above.
.. note::
One important aspect of the above policies is that they only apply to
instances which pybind11 has *not* seen before, in which case the policy
clarifies essential questions about the return value's lifetime and
ownership. When pybind11 knows the instance already (as identified by its
type and address in memory), it will return the existing Python object
wrapper rather than creating a new copy.
.. note::
The next section on :ref:`call_policies` discusses *call policies* that can be
specified *in addition* to a return value policy from the list above. Call
policies indicate reference relationships that can involve both return values
and parameters of functions.
.. note::
As an alternative to elaborate call policies and lifetime management logic,
consider using smart pointers (see the section on :ref:`smart_pointers` for
details). Smart pointers can tell whether an object is still referenced from
C++ or Python, which generally eliminates the kinds of inconsistencies that
can lead to crashes or undefined behavior. For functions returning smart
pointers, it is not necessary to specify a return value policy.
.. _call_policies:
Additional call policies
========================
In addition to the above return value policies, further *call policies* can be
specified to indicate dependencies between parameters or ensure a certain state
for the function call.
Keep alive
----------
In general, this policy is required when the C++ object is any kind of container
and another object is being added to the container. ``keep_alive<Nurse, Patient>``
indicates that the argument with index ``Patient`` should be kept alive at least
until the argument with index ``Nurse`` is freed by the garbage collector. Argument
indices start at one, while zero refers to the return value. For methods, index
``1`` refers to the implicit ``this`` pointer, while regular arguments begin at
index ``2``. Arbitrarily many call policies can be specified. When a ``Nurse``
with value ``None`` is detected at runtime, the call policy does nothing.
When the nurse is not a pybind11-registered type, the implementation internally
relies on the ability to create a *weak reference* to the nurse object. When
the nurse object is not a pybind11-registered type and does not support weak
references, an exception will be thrown.
Consider the following example: here, the binding code for a list append
operation ties the lifetime of the newly added element to the underlying
container:
.. code-block:: cpp
py::class_<List>(m, "List")
.def("append", &List::append, py::keep_alive<1, 2>());
For consistency, the argument indexing is identical for constructors. Index
``1`` still refers to the implicit ``this`` pointer, i.e. the object which is
being constructed. Index ``0`` refers to the return type which is presumed to
be ``void`` when a constructor is viewed like a function. The following example
ties the lifetime of the constructor element to the constructed object:
.. code-block:: cpp
py::class_<Nurse>(m, "Nurse")
.def(py::init<Patient &>(), py::keep_alive<1, 2>());
.. note::
``keep_alive`` is analogous to the ``with_custodian_and_ward`` (if Nurse,
Patient != 0) and ``with_custodian_and_ward_postcall`` (if Nurse/Patient ==
0) policies from Boost.Python.
Call guard
----------
The ``call_guard<T>`` policy allows any scope guard type ``T`` to be placed
around the function call. For example, this definition:
.. code-block:: cpp
m.def("foo", foo, py::call_guard<T>());
is equivalent to the following pseudocode:
.. code-block:: cpp
m.def("foo", [](args...) {
T scope_guard;
return foo(args...); // forwarded arguments
});
The only requirement is that ``T`` is default-constructible, but otherwise any
scope guard will work. This is very useful in combination with `gil_scoped_release`.
See :ref:`gil`.
Multiple guards can also be specified as ``py::call_guard<T1, T2, T3...>``. The
constructor order is left to right and destruction happens in reverse.
.. seealso::
The file :file:`tests/test_call_policies.cpp` contains a complete example
that demonstrates using `keep_alive` and `call_guard` in more detail.
.. _python_objects_as_args:
Python objects as arguments
===========================
pybind11 exposes all major Python types using thin C++ wrapper classes. These
wrapper classes can also be used as parameters of functions in bindings, which
makes it possible to directly work with native Python types on the C++ side.
For instance, the following statement iterates over a Python ``dict``:
.. code-block:: cpp
void print_dict(py::dict dict) {
/* Easily interact with Python types */
for (auto item : dict)
std::cout << "key=" << std::string(py::str(item.first)) << ", "
<< "value=" << std::string(py::str(item.second)) << std::endl;
}
It can be exported:
.. code-block:: cpp
m.def("print_dict", &print_dict);
And used in Python as usual:
.. code-block:: pycon
>>> print_dict({'foo': 123, 'bar': 'hello'})
key=foo, value=123
key=bar, value=hello
For more information on using Python objects in C++, see :doc:`/advanced/pycpp/index`.
Accepting \*args and \*\*kwargs
===============================
Python provides a useful mechanism to define functions that accept arbitrary
numbers of arguments and keyword arguments:
.. code-block:: python
def generic(*args, **kwargs):
... # do something with args and kwargs
Such functions can also be created using pybind11:
.. code-block:: cpp
void generic(py::args args, py::kwargs kwargs) {
/// .. do something with args
if (kwargs)
/// .. do something with kwargs
}
/// Binding code
m.def("generic", &generic);
The class ``py::args`` derives from ``py::tuple`` and ``py::kwargs`` derives
from ``py::dict``.
You may also use just one or the other, and may combine these with other
arguments as long as the ``py::args`` and ``py::kwargs`` arguments are the last
arguments accepted by the function.
Please refer to the other examples for details on how to iterate over these,
and on how to cast their entries into C++ objects. A demonstration is also
available in ``tests/test_kwargs_and_defaults.cpp``.
.. note::
When combining \*args or \*\*kwargs with :ref:`keyword_args` you should
*not* include ``py::arg`` tags for the ``py::args`` and ``py::kwargs``
arguments.
Default arguments revisited
===========================
The section on :ref:`default_args` previously discussed basic usage of default
arguments using pybind11. One noteworthy aspect of their implementation is that
default arguments are converted to Python objects right at declaration time.
Consider the following example:
.. code-block:: cpp
py::class_<MyClass>("MyClass")
.def("myFunction", py::arg("arg") = SomeType(123));
In this case, pybind11 must already be set up to deal with values of the type
``SomeType`` (via a prior instantiation of ``py::class_<SomeType>``), or an
exception will be thrown.
Another aspect worth highlighting is that the "preview" of the default argument
in the function signature is generated using the object's ``__repr__`` method.
If not available, the signature may not be very helpful, e.g.:
.. code-block:: pycon
FUNCTIONS
...
| myFunction(...)
| Signature : (MyClass, arg : SomeType = <SomeType object at 0x101b7b080>) -> NoneType
...
The first way of addressing this is by defining ``SomeType.__repr__``.
Alternatively, it is possible to specify the human-readable preview of the
default argument manually using the ``arg_v`` notation:
.. code-block:: cpp
py::class_<MyClass>("MyClass")
.def("myFunction", py::arg_v("arg", SomeType(123), "SomeType(123)"));
Sometimes it may be necessary to pass a null pointer value as a default
argument. In this case, remember to cast it to the underlying type in question,
like so:
.. code-block:: cpp
py::class_<MyClass>("MyClass")
.def("myFunction", py::arg("arg") = (SomeType *) nullptr);
.. _nonconverting_arguments:
Non-converting arguments
========================
Certain argument types may support conversion from one type to another. Some
examples of conversions are:
* :ref:`implicit_conversions` declared using ``py::implicitly_convertible<A,B>()``
* Calling a method accepting a double with an integer argument
* Calling a ``std::complex<float>`` argument with a non-complex python type
(for example, with a float). (Requires the optional ``pybind11/complex.h``
header).
* Calling a function taking an Eigen matrix reference with a numpy array of the
wrong type or of an incompatible data layout. (Requires the optional
``pybind11/eigen.h`` header).
This behaviour is sometimes undesirable: the binding code may prefer to raise
an error rather than convert the argument. This behaviour can be obtained
through ``py::arg`` by calling the ``.noconvert()`` method of the ``py::arg``
object, such as:
.. code-block:: cpp
m.def("floats_only", [](double f) { return 0.5 * f; }, py::arg("f").noconvert());
m.def("floats_preferred", [](double f) { return 0.5 * f; }, py::arg("f"));
Attempting the call the second function (the one without ``.noconvert()``) with
an integer will succeed, but attempting to call the ``.noconvert()`` version
will fail with a ``TypeError``:
.. code-block:: pycon
>>> floats_preferred(4)
2.0
>>> floats_only(4)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: floats_only(): incompatible function arguments. The following argument types are supported:
1. (f: float) -> float
Invoked with: 4
You may, of course, combine this with the :var:`_a` shorthand notation (see
:ref:`keyword_args`) and/or :ref:`default_args`. It is also permitted to omit
the argument name by using the ``py::arg()`` constructor without an argument
name, i.e. by specifying ``py::arg().noconvert()``.
.. note::
When specifying ``py::arg`` options it is necessary to provide the same
number of options as the bound function has arguments. Thus if you want to
enable no-convert behaviour for just one of several arguments, you will
need to specify a ``py::arg()`` annotation for each argument with the
no-convert argument modified to ``py::arg().noconvert()``.
.. _none_arguments:
Allow/Prohibiting None arguments
================================
When a C++ type registered with :class:`py::class_` is passed as an argument to
a function taking the instance as pointer or shared holder (e.g. ``shared_ptr``
or a custom, copyable holder as described in :ref:`smart_pointers`), pybind
allows ``None`` to be passed from Python which results in calling the C++
function with ``nullptr`` (or an empty holder) for the argument.
To explicitly enable or disable this behaviour, using the
``.none`` method of the :class:`py::arg` object:
.. code-block:: cpp
py::class_<Dog>(m, "Dog").def(py::init<>());
py::class_<Cat>(m, "Cat").def(py::init<>());
m.def("bark", [](Dog *dog) -> std::string {
if (dog) return "woof!"; /* Called with a Dog instance */
else return "(no dog)"; /* Called with None, dog == nullptr */
}, py::arg("dog").none(true));
m.def("meow", [](Cat *cat) -> std::string {
// Can't be called with None argument
return "meow";
}, py::arg("cat").none(false));
With the above, the Python call ``bark(None)`` will return the string ``"(no
dog)"``, while attempting to call ``meow(None)`` will raise a ``TypeError``:
.. code-block:: pycon
>>> from animals import Dog, Cat, bark, meow
>>> bark(Dog())
'woof!'
>>> meow(Cat())
'meow'
>>> bark(None)
'(no dog)'
>>> meow(None)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: meow(): incompatible function arguments. The following argument types are supported:
1. (cat: animals.Cat) -> str
Invoked with: None
The default behaviour when the tag is unspecified is to allow ``None``.
.. note::
Even when ``.none(true)`` is specified for an argument, ``None`` will be converted to a
``nullptr`` *only* for custom and :ref:`opaque <opaque>` types. Pointers to built-in types
(``double *``, ``int *``, ...) and STL types (``std::vector<T> *``, ...; if ``pybind11/stl.h``
is included) are copied when converted to C++ (see :doc:`/advanced/cast/overview`) and will
not allow ``None`` as argument. To pass optional argument of these copied types consider
using ``std::optional<T>``
Overload resolution order
=========================
When a function or method with multiple overloads is called from Python,
pybind11 determines which overload to call in two passes. The first pass
attempts to call each overload without allowing argument conversion (as if
every argument had been specified as ``py::arg().noconvert()`` as described
above).
If no overload succeeds in the no-conversion first pass, a second pass is
attempted in which argument conversion is allowed (except where prohibited via
an explicit ``py::arg().noconvert()`` attribute in the function definition).
If the second pass also fails a ``TypeError`` is raised.
Within each pass, overloads are tried in the order they were registered with
pybind11.
What this means in practice is that pybind11 will prefer any overload that does
not require conversion of arguments to an overload that does, but otherwise prefers
earlier-defined overloads to later-defined ones.
.. note::
pybind11 does *not* further prioritize based on the number/pattern of
overloaded arguments. That is, pybind11 does not prioritize a function
requiring one conversion over one requiring three, but only prioritizes
overloads requiring no conversion at all to overloads that require
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Miscellaneous
#############
.. _macro_notes:
General notes regarding convenience macros
==========================================
pybind11 provides a few convenience macros such as
:func:`PYBIND11_DECLARE_HOLDER_TYPE` and ``PYBIND11_OVERLOAD_*``. Since these
are "just" macros that are evaluated in the preprocessor (which has no concept
of types), they *will* get confused by commas in a template argument; for
example, consider:
.. code-block:: cpp
PYBIND11_OVERLOAD(MyReturnType<T1, T2>, Class<T3, T4>, func)
The limitation of the C preprocessor interprets this as five arguments (with new
arguments beginning after each comma) rather than three. To get around this,
there are two alternatives: you can use a type alias, or you can wrap the type
using the ``PYBIND11_TYPE`` macro:
.. code-block:: cpp
// Version 1: using a type alias
using ReturnType = MyReturnType<T1, T2>;
using ClassType = Class<T3, T4>;
PYBIND11_OVERLOAD(ReturnType, ClassType, func);
// Version 2: using the PYBIND11_TYPE macro:
PYBIND11_OVERLOAD(PYBIND11_TYPE(MyReturnType<T1, T2>),
PYBIND11_TYPE(Class<T3, T4>), func)
The ``PYBIND11_MAKE_OPAQUE`` macro does *not* require the above workarounds.
.. _gil:
Global Interpreter Lock (GIL)
=============================
When calling a C++ function from Python, the GIL is always held.
The classes :class:`gil_scoped_release` and :class:`gil_scoped_acquire` can be
used to acquire and release the global interpreter lock in the body of a C++
function call. In this way, long-running C++ code can be parallelized using
multiple Python threads. Taking :ref:`overriding_virtuals` as an example, this
could be realized as follows (important changes highlighted):
.. code-block:: cpp
:emphasize-lines: 8,9,31,32
class PyAnimal : public Animal {
public:
/* Inherit the constructors */
using Animal::Animal;
/* Trampoline (need one for each virtual function) */
std::string go(int n_times) {
/* Acquire GIL before calling Python code */
py::gil_scoped_acquire acquire;
PYBIND11_OVERLOAD_PURE(
std::string, /* Return type */
Animal, /* Parent class */
go, /* Name of function */
n_times /* Argument(s) */
);
}
};
PYBIND11_MODULE(example, m) {
py::class_<Animal, PyAnimal> animal(m, "Animal");
animal
.def(py::init<>())
.def("go", &Animal::go);
py::class_<Dog>(m, "Dog", animal)
.def(py::init<>());
m.def("call_go", [](Animal *animal) -> std::string {
/* Release GIL before calling into (potentially long-running) C++ code */
py::gil_scoped_release release;
return call_go(animal);
});
}
The ``call_go`` wrapper can also be simplified using the `call_guard` policy
(see :ref:`call_policies`) which yields the same result:
.. code-block:: cpp
m.def("call_go", &call_go, py::call_guard<py::gil_scoped_release>());
Binding sequence data types, iterators, the slicing protocol, etc.
==================================================================
Please refer to the supplemental example for details.
.. seealso::
The file :file:`tests/test_sequences_and_iterators.cpp` contains a
complete example that shows how to bind a sequence data type, including
length queries (``__len__``), iterators (``__iter__``), the slicing
protocol and other kinds of useful operations.
Partitioning code over multiple extension modules
=================================================
It's straightforward to split binding code over multiple extension modules,
while referencing types that are declared elsewhere. Everything "just" works
without any special precautions. One exception to this rule occurs when
extending a type declared in another extension module. Recall the basic example
from Section :ref:`inheritance`.
.. code-block:: cpp
py::class_<Pet> pet(m, "Pet");
pet.def(py::init<const std::string &>())
.def_readwrite("name", &Pet::name);
py::class_<Dog>(m, "Dog", pet /* <- specify parent */)
.def(py::init<const std::string &>())
.def("bark", &Dog::bark);
Suppose now that ``Pet`` bindings are defined in a module named ``basic``,
whereas the ``Dog`` bindings are defined somewhere else. The challenge is of
course that the variable ``pet`` is not available anymore though it is needed
to indicate the inheritance relationship to the constructor of ``class_<Dog>``.
However, it can be acquired as follows:
.. code-block:: cpp
py::object pet = (py::object) py::module::import("basic").attr("Pet");
py::class_<Dog>(m, "Dog", pet)
.def(py::init<const std::string &>())
.def("bark", &Dog::bark);
Alternatively, you can specify the base class as a template parameter option to
``class_``, which performs an automated lookup of the corresponding Python
type. Like the above code, however, this also requires invoking the ``import``
function once to ensure that the pybind11 binding code of the module ``basic``
has been executed:
.. code-block:: cpp
py::module::import("basic");
py::class_<Dog, Pet>(m, "Dog")
.def(py::init<const std::string &>())
.def("bark", &Dog::bark);
Naturally, both methods will fail when there are cyclic dependencies.
Note that pybind11 code compiled with hidden-by-default symbol visibility (e.g.
via the command line flag ``-fvisibility=hidden`` on GCC/Clang), which is
required for proper pybind11 functionality, can interfere with the ability to
access types defined in another extension module. Working around this requires
manually exporting types that are accessed by multiple extension modules;
pybind11 provides a macro to do just this:
.. code-block:: cpp
class PYBIND11_EXPORT Dog : public Animal {
...
};
Note also that it is possible (although would rarely be required) to share arbitrary
C++ objects between extension modules at runtime. Internal library data is shared
between modules using capsule machinery [#f6]_ which can be also utilized for
storing, modifying and accessing user-defined data. Note that an extension module
will "see" other extensions' data if and only if they were built with the same
pybind11 version. Consider the following example:
.. code-block:: cpp
auto data = (MyData *) py::get_shared_data("mydata");
if (!data)
data = (MyData *) py::set_shared_data("mydata", new MyData(42));
If the above snippet was used in several separately compiled extension modules,
the first one to be imported would create a ``MyData`` instance and associate
a ``"mydata"`` key with a pointer to it. Extensions that are imported later
would be then able to access the data behind the same pointer.
.. [#f6] https://docs.python.org/3/extending/extending.html#using-capsules
Module Destructors
==================
pybind11 does not provide an explicit mechanism to invoke cleanup code at
module destruction time. In rare cases where such functionality is required, it
is possible to emulate it using Python capsules or weak references with a
destruction callback.
.. code-block:: cpp
auto cleanup_callback = []() {
// perform cleanup here -- this function is called with the GIL held
};
m.add_object("_cleanup", py::capsule(cleanup_callback));
This approach has the potential downside that instances of classes exposed
within the module may still be alive when the cleanup callback is invoked
(whether this is acceptable will generally depend on the application).
Alternatively, the capsule may also be stashed within a type object, which
ensures that it not called before all instances of that type have been
collected:
.. code-block:: cpp
auto cleanup_callback = []() { /* ... */ };
m.attr("BaseClass").attr("_cleanup") = py::capsule(cleanup_callback);
Both approaches also expose a potentially dangerous ``_cleanup`` attribute in
Python, which may be undesirable from an API standpoint (a premature explicit
call from Python might lead to undefined behavior). Yet another approach that
avoids this issue involves weak reference with a cleanup callback:
.. code-block:: cpp
// Register a callback function that is invoked when the BaseClass object is colelcted
py::cpp_function cleanup_callback(
[](py::handle weakref) {
// perform cleanup here -- this function is called with the GIL held
weakref.dec_ref(); // release weak reference
}
);
// Create a weak reference with a cleanup callback and initially leak it
(void) py::weakref(m.attr("BaseClass"), cleanup_callback).release();
.. note::
PyPy (at least version 5.9) does not garbage collect objects when the
interpreter exits. An alternative approach (which also works on CPython) is to use
the :py:mod:`atexit` module [#f7]_, for example:
.. code-block:: cpp
auto atexit = py::module::import("atexit");
atexit.attr("register")(py::cpp_function([]() {
// perform cleanup here -- this function is called with the GIL held
}));
.. [#f7] https://docs.python.org/3/library/atexit.html
Generating documentation using Sphinx
=====================================
Sphinx [#f4]_ has the ability to inspect the signatures and documentation
strings in pybind11-based extension modules to automatically generate beautiful
documentation in a variety formats. The python_example repository [#f5]_ contains a
simple example repository which uses this approach.
There are two potential gotchas when using this approach: first, make sure that
the resulting strings do not contain any :kbd:`TAB` characters, which break the
docstring parsing routines. You may want to use C++11 raw string literals,
which are convenient for multi-line comments. Conveniently, any excess
indentation will be automatically be removed by Sphinx. However, for this to
work, it is important that all lines are indented consistently, i.e.:
.. code-block:: cpp
// ok
m.def("foo", &foo, R"mydelimiter(
The foo function
Parameters
----------
)mydelimiter");
// *not ok*
m.def("foo", &foo, R"mydelimiter(The foo function
Parameters
----------
)mydelimiter");
By default, pybind11 automatically generates and prepends a signature to the docstring of a function
registered with ``module::def()`` and ``class_::def()``. Sometimes this
behavior is not desirable, because you want to provide your own signature or remove
the docstring completely to exclude the function from the Sphinx documentation.
The class ``options`` allows you to selectively suppress auto-generated signatures:
.. code-block:: cpp
PYBIND11_MODULE(example, m) {
py::options options;
options.disable_function_signatures();
m.def("add", [](int a, int b) { return a + b; }, "A function which adds two numbers");
}
Note that changes to the settings affect only function bindings created during the
lifetime of the ``options`` instance. When it goes out of scope at the end of the module's init function,
the default settings are restored to prevent unwanted side effects.
.. [#f4] http://www.sphinx-doc.org
.. [#f5] http://github.com/pybind/python_example

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Python C++ interface
####################
pybind11 exposes Python types and functions using thin C++ wrappers, which
makes it possible to conveniently call Python code from C++ without resorting
to Python's C API.
.. toctree::
:maxdepth: 2
object
numpy
utilities

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.. _numpy:
NumPy
#####
Buffer protocol
===============
Python supports an extremely general and convenient approach for exchanging
data between plugin libraries. Types can expose a buffer view [#f2]_, which
provides fast direct access to the raw internal data representation. Suppose we
want to bind the following simplistic Matrix class:
.. code-block:: cpp
class Matrix {
public:
Matrix(size_t rows, size_t cols) : m_rows(rows), m_cols(cols) {
m_data = new float[rows*cols];
}
float *data() { return m_data; }
size_t rows() const { return m_rows; }
size_t cols() const { return m_cols; }
private:
size_t m_rows, m_cols;
float *m_data;
};
The following binding code exposes the ``Matrix`` contents as a buffer object,
making it possible to cast Matrices into NumPy arrays. It is even possible to
completely avoid copy operations with Python expressions like
``np.array(matrix_instance, copy = False)``.
.. code-block:: cpp
py::class_<Matrix>(m, "Matrix", py::buffer_protocol())
.def_buffer([](Matrix &m) -> py::buffer_info {
return py::buffer_info(
m.data(), /* Pointer to buffer */
sizeof(float), /* Size of one scalar */
py::format_descriptor<float>::format(), /* Python struct-style format descriptor */
2, /* Number of dimensions */
{ m.rows(), m.cols() }, /* Buffer dimensions */
{ sizeof(float) * m.cols(), /* Strides (in bytes) for each index */
sizeof(float) }
);
});
Supporting the buffer protocol in a new type involves specifying the special
``py::buffer_protocol()`` tag in the ``py::class_`` constructor and calling the
``def_buffer()`` method with a lambda function that creates a
``py::buffer_info`` description record on demand describing a given matrix
instance. The contents of ``py::buffer_info`` mirror the Python buffer protocol
specification.
.. code-block:: cpp
struct buffer_info {
void *ptr;
ssize_t itemsize;
std::string format;
ssize_t ndim;
std::vector<ssize_t> shape;
std::vector<ssize_t> strides;
};
To create a C++ function that can take a Python buffer object as an argument,
simply use the type ``py::buffer`` as one of its arguments. Buffers can exist
in a great variety of configurations, hence some safety checks are usually
necessary in the function body. Below, you can see an basic example on how to
define a custom constructor for the Eigen double precision matrix
(``Eigen::MatrixXd``) type, which supports initialization from compatible
buffer objects (e.g. a NumPy matrix).
.. code-block:: cpp
/* Bind MatrixXd (or some other Eigen type) to Python */
typedef Eigen::MatrixXd Matrix;
typedef Matrix::Scalar Scalar;
constexpr bool rowMajor = Matrix::Flags & Eigen::RowMajorBit;
py::class_<Matrix>(m, "Matrix", py::buffer_protocol())
.def("__init__", [](Matrix &m, py::buffer b) {
typedef Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic> Strides;
/* Request a buffer descriptor from Python */
py::buffer_info info = b.request();
/* Some sanity checks ... */
if (info.format != py::format_descriptor<Scalar>::format())
throw std::runtime_error("Incompatible format: expected a double array!");
if (info.ndim != 2)
throw std::runtime_error("Incompatible buffer dimension!");
auto strides = Strides(
info.strides[rowMajor ? 0 : 1] / (py::ssize_t)sizeof(Scalar),
info.strides[rowMajor ? 1 : 0] / (py::ssize_t)sizeof(Scalar));
auto map = Eigen::Map<Matrix, 0, Strides>(
static_cast<Scalar *>(info.ptr), info.shape[0], info.shape[1], strides);
new (&m) Matrix(map);
});
For reference, the ``def_buffer()`` call for this Eigen data type should look
as follows:
.. code-block:: cpp
.def_buffer([](Matrix &m) -> py::buffer_info {
return py::buffer_info(
m.data(), /* Pointer to buffer */
sizeof(Scalar), /* Size of one scalar */
py::format_descriptor<Scalar>::format(), /* Python struct-style format descriptor */
2, /* Number of dimensions */
{ m.rows(), m.cols() }, /* Buffer dimensions */
{ sizeof(Scalar) * (rowMajor ? m.cols() : 1),
sizeof(Scalar) * (rowMajor ? 1 : m.rows()) }
/* Strides (in bytes) for each index */
);
})
For a much easier approach of binding Eigen types (although with some
limitations), refer to the section on :doc:`/advanced/cast/eigen`.
.. seealso::
The file :file:`tests/test_buffers.cpp` contains a complete example
that demonstrates using the buffer protocol with pybind11 in more detail.
.. [#f2] http://docs.python.org/3/c-api/buffer.html
Arrays
======
By exchanging ``py::buffer`` with ``py::array`` in the above snippet, we can
restrict the function so that it only accepts NumPy arrays (rather than any
type of Python object satisfying the buffer protocol).
In many situations, we want to define a function which only accepts a NumPy
array of a certain data type. This is possible via the ``py::array_t<T>``
template. For instance, the following function requires the argument to be a
NumPy array containing double precision values.
.. code-block:: cpp
void f(py::array_t<double> array);
When it is invoked with a different type (e.g. an integer or a list of
integers), the binding code will attempt to cast the input into a NumPy array
of the requested type. Note that this feature requires the
:file:`pybind11/numpy.h` header to be included.
Data in NumPy arrays is not guaranteed to packed in a dense manner;
furthermore, entries can be separated by arbitrary column and row strides.
Sometimes, it can be useful to require a function to only accept dense arrays
using either the C (row-major) or Fortran (column-major) ordering. This can be
accomplished via a second template argument with values ``py::array::c_style``
or ``py::array::f_style``.
.. code-block:: cpp
void f(py::array_t<double, py::array::c_style | py::array::forcecast> array);
The ``py::array::forcecast`` argument is the default value of the second
template parameter, and it ensures that non-conforming arguments are converted
into an array satisfying the specified requirements instead of trying the next
function overload.
Structured types
================
In order for ``py::array_t`` to work with structured (record) types, we first
need to register the memory layout of the type. This can be done via
``PYBIND11_NUMPY_DTYPE`` macro, called in the plugin definition code, which
expects the type followed by field names:
.. code-block:: cpp
struct A {
int x;
double y;
};
struct B {
int z;
A a;
};
// ...
PYBIND11_MODULE(test, m) {
// ...
PYBIND11_NUMPY_DTYPE(A, x, y);
PYBIND11_NUMPY_DTYPE(B, z, a);
/* now both A and B can be used as template arguments to py::array_t */
}
The structure should consist of fundamental arithmetic types, ``std::complex``,
previously registered substructures, and arrays of any of the above. Both C++
arrays and ``std::array`` are supported. While there is a static assertion to
prevent many types of unsupported structures, it is still the user's
responsibility to use only "plain" structures that can be safely manipulated as
raw memory without violating invariants.
Vectorizing functions
=====================
Suppose we want to bind a function with the following signature to Python so
that it can process arbitrary NumPy array arguments (vectors, matrices, general
N-D arrays) in addition to its normal arguments:
.. code-block:: cpp
double my_func(int x, float y, double z);
After including the ``pybind11/numpy.h`` header, this is extremely simple:
.. code-block:: cpp
m.def("vectorized_func", py::vectorize(my_func));
Invoking the function like below causes 4 calls to be made to ``my_func`` with
each of the array elements. The significant advantage of this compared to
solutions like ``numpy.vectorize()`` is that the loop over the elements runs
entirely on the C++ side and can be crunched down into a tight, optimized loop
by the compiler. The result is returned as a NumPy array of type
``numpy.dtype.float64``.
.. code-block:: pycon
>>> x = np.array([[1, 3],[5, 7]])
>>> y = np.array([[2, 4],[6, 8]])
>>> z = 3
>>> result = vectorized_func(x, y, z)
The scalar argument ``z`` is transparently replicated 4 times. The input
arrays ``x`` and ``y`` are automatically converted into the right types (they
are of type ``numpy.dtype.int64`` but need to be ``numpy.dtype.int32`` and
``numpy.dtype.float32``, respectively).
.. note::
Only arithmetic, complex, and POD types passed by value or by ``const &``
reference are vectorized; all other arguments are passed through as-is.
Functions taking rvalue reference arguments cannot be vectorized.
In cases where the computation is too complicated to be reduced to
``vectorize``, it will be necessary to create and access the buffer contents
manually. The following snippet contains a complete example that shows how this
works (the code is somewhat contrived, since it could have been done more
simply using ``vectorize``).
.. code-block:: cpp
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
namespace py = pybind11;
py::array_t<double> add_arrays(py::array_t<double> input1, py::array_t<double> input2) {
py::buffer_info buf1 = input1.request(), buf2 = input2.request();
if (buf1.ndim != 1 || buf2.ndim != 1)
throw std::runtime_error("Number of dimensions must be one");
if (buf1.size != buf2.size)
throw std::runtime_error("Input shapes must match");
/* No pointer is passed, so NumPy will allocate the buffer */
auto result = py::array_t<double>(buf1.size);
py::buffer_info buf3 = result.request();
double *ptr1 = (double *) buf1.ptr,
*ptr2 = (double *) buf2.ptr,
*ptr3 = (double *) buf3.ptr;
for (size_t idx = 0; idx < buf1.shape[0]; idx++)
ptr3[idx] = ptr1[idx] + ptr2[idx];
return result;
}
PYBIND11_MODULE(test, m) {
m.def("add_arrays", &add_arrays, "Add two NumPy arrays");
}
.. seealso::
The file :file:`tests/test_numpy_vectorize.cpp` contains a complete
example that demonstrates using :func:`vectorize` in more detail.
Direct access
=============
For performance reasons, particularly when dealing with very large arrays, it
is often desirable to directly access array elements without internal checking
of dimensions and bounds on every access when indices are known to be already
valid. To avoid such checks, the ``array`` class and ``array_t<T>`` template
class offer an unchecked proxy object that can be used for this unchecked
access through the ``unchecked<N>`` and ``mutable_unchecked<N>`` methods,
where ``N`` gives the required dimensionality of the array:
.. code-block:: cpp
m.def("sum_3d", [](py::array_t<double> x) {
auto r = x.unchecked<3>(); // x must have ndim = 3; can be non-writeable
double sum = 0;
for (ssize_t i = 0; i < r.shape(0); i++)
for (ssize_t j = 0; j < r.shape(1); j++)
for (ssize_t k = 0; k < r.shape(2); k++)
sum += r(i, j, k);
return sum;
});
m.def("increment_3d", [](py::array_t<double> x) {
auto r = x.mutable_unchecked<3>(); // Will throw if ndim != 3 or flags.writeable is false
for (ssize_t i = 0; i < r.shape(0); i++)
for (ssize_t j = 0; j < r.shape(1); j++)
for (ssize_t k = 0; k < r.shape(2); k++)
r(i, j, k) += 1.0;
}, py::arg().noconvert());
To obtain the proxy from an ``array`` object, you must specify both the data
type and number of dimensions as template arguments, such as ``auto r =
myarray.mutable_unchecked<float, 2>()``.
If the number of dimensions is not known at compile time, you can omit the
dimensions template parameter (i.e. calling ``arr_t.unchecked()`` or
``arr.unchecked<T>()``. This will give you a proxy object that works in the
same way, but results in less optimizable code and thus a small efficiency
loss in tight loops.
Note that the returned proxy object directly references the array's data, and
only reads its shape, strides, and writeable flag when constructed. You must
take care to ensure that the referenced array is not destroyed or reshaped for
the duration of the returned object, typically by limiting the scope of the
returned instance.
The returned proxy object supports some of the same methods as ``py::array`` so
that it can be used as a drop-in replacement for some existing, index-checked
uses of ``py::array``:
- ``r.ndim()`` returns the number of dimensions
- ``r.data(1, 2, ...)`` and ``r.mutable_data(1, 2, ...)``` returns a pointer to
the ``const T`` or ``T`` data, respectively, at the given indices. The
latter is only available to proxies obtained via ``a.mutable_unchecked()``.
- ``itemsize()`` returns the size of an item in bytes, i.e. ``sizeof(T)``.
- ``ndim()`` returns the number of dimensions.
- ``shape(n)`` returns the size of dimension ``n``
- ``size()`` returns the total number of elements (i.e. the product of the shapes).
- ``nbytes()`` returns the number of bytes used by the referenced elements
(i.e. ``itemsize()`` times ``size()``).
.. seealso::
The file :file:`tests/test_numpy_array.cpp` contains additional examples
demonstrating the use of this feature.
Ellipsis
========
Python 3 provides a convenient ``...`` ellipsis notation that is often used to
slice multidimensional arrays. For instance, the following snippet extracts the
middle dimensions of a tensor with the first and last index set to zero.
.. code-block:: python
a = # a NumPy array
b = a[0, ..., 0]
The function ``py::ellipsis()`` function can be used to perform the same
operation on the C++ side:
.. code-block:: cpp
py::array a = /* A NumPy array */;
py::array b = a[py::make_tuple(0, py::ellipsis(), 0)];

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Python types
############
Available wrappers
==================
All major Python types are available as thin C++ wrapper classes. These
can also be used as function parameters -- see :ref:`python_objects_as_args`.
Available types include :class:`handle`, :class:`object`, :class:`bool_`,
:class:`int_`, :class:`float_`, :class:`str`, :class:`bytes`, :class:`tuple`,
:class:`list`, :class:`dict`, :class:`slice`, :class:`none`, :class:`capsule`,
:class:`iterable`, :class:`iterator`, :class:`function`, :class:`buffer`,
:class:`array`, and :class:`array_t`.
Casting back and forth
======================
In this kind of mixed code, it is often necessary to convert arbitrary C++
types to Python, which can be done using :func:`py::cast`:
.. code-block:: cpp
MyClass *cls = ..;
py::object obj = py::cast(cls);
The reverse direction uses the following syntax:
.. code-block:: cpp
py::object obj = ...;
MyClass *cls = obj.cast<MyClass *>();
When conversion fails, both directions throw the exception :class:`cast_error`.
.. _python_libs:
Accessing Python libraries from C++
===================================
It is also possible to import objects defined in the Python standard
library or available in the current Python environment (``sys.path``) and work
with these in C++.
This example obtains a reference to the Python ``Decimal`` class.
.. code-block:: cpp
// Equivalent to "from decimal import Decimal"
py::object Decimal = py::module::import("decimal").attr("Decimal");
.. code-block:: cpp
// Try to import scipy
py::object scipy = py::module::import("scipy");
return scipy.attr("__version__");
.. _calling_python_functions:
Calling Python functions
========================
It is also possible to call Python classes, functions and methods
via ``operator()``.
.. code-block:: cpp
// Construct a Python object of class Decimal
py::object pi = Decimal("3.14159");
.. code-block:: cpp
// Use Python to make our directories
py::object os = py::module::import("os");
py::object makedirs = os.attr("makedirs");
makedirs("/tmp/path/to/somewhere");
One can convert the result obtained from Python to a pure C++ version
if a ``py::class_`` or type conversion is defined.
.. code-block:: cpp
py::function f = <...>;
py::object result_py = f(1234, "hello", some_instance);
MyClass &result = result_py.cast<MyClass>();
.. _calling_python_methods:
Calling Python methods
========================
To call an object's method, one can again use ``.attr`` to obtain access to the
Python method.
.. code-block:: cpp
// Calculate e^π in decimal
py::object exp_pi = pi.attr("exp")();
py::print(py::str(exp_pi));
In the example above ``pi.attr("exp")`` is a *bound method*: it will always call
the method for that same instance of the class. Alternately one can create an
*unbound method* via the Python class (instead of instance) and pass the ``self``
object explicitly, followed by other arguments.
.. code-block:: cpp
py::object decimal_exp = Decimal.attr("exp");
// Compute the e^n for n=0..4
for (int n = 0; n < 5; n++) {
py::print(decimal_exp(Decimal(n));
}
Keyword arguments
=================
Keyword arguments are also supported. In Python, there is the usual call syntax:
.. code-block:: python
def f(number, say, to):
... # function code
f(1234, say="hello", to=some_instance) # keyword call in Python
In C++, the same call can be made using:
.. code-block:: cpp
using namespace pybind11::literals; // to bring in the `_a` literal
f(1234, "say"_a="hello", "to"_a=some_instance); // keyword call in C++
Unpacking arguments
===================
Unpacking of ``*args`` and ``**kwargs`` is also possible and can be mixed with
other arguments:
.. code-block:: cpp
// * unpacking
py::tuple args = py::make_tuple(1234, "hello", some_instance);
f(*args);
// ** unpacking
py::dict kwargs = py::dict("number"_a=1234, "say"_a="hello", "to"_a=some_instance);
f(**kwargs);
// mixed keywords, * and ** unpacking
py::tuple args = py::make_tuple(1234);
py::dict kwargs = py::dict("to"_a=some_instance);
f(*args, "say"_a="hello", **kwargs);
Generalized unpacking according to PEP448_ is also supported:
.. code-block:: cpp
py::dict kwargs1 = py::dict("number"_a=1234);
py::dict kwargs2 = py::dict("to"_a=some_instance);
f(**kwargs1, "say"_a="hello", **kwargs2);
.. seealso::
The file :file:`tests/test_pytypes.cpp` contains a complete
example that demonstrates passing native Python types in more detail. The
file :file:`tests/test_callbacks.cpp` presents a few examples of calling
Python functions from C++, including keywords arguments and unpacking.
.. _PEP448: https://www.python.org/dev/peps/pep-0448/

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Utilities
#########
Using Python's print function in C++
====================================
The usual way to write output in C++ is using ``std::cout`` while in Python one
would use ``print``. Since these methods use different buffers, mixing them can
lead to output order issues. To resolve this, pybind11 modules can use the
:func:`py::print` function which writes to Python's ``sys.stdout`` for consistency.
Python's ``print`` function is replicated in the C++ API including optional
keyword arguments ``sep``, ``end``, ``file``, ``flush``. Everything works as
expected in Python:
.. code-block:: cpp
py::print(1, 2.0, "three"); // 1 2.0 three
py::print(1, 2.0, "three", "sep"_a="-"); // 1-2.0-three
auto args = py::make_tuple("unpacked", true);
py::print("->", *args, "end"_a="<-"); // -> unpacked True <-
.. _ostream_redirect:
Capturing standard output from ostream
======================================
Often, a library will use the streams ``std::cout`` and ``std::cerr`` to print,
but this does not play well with Python's standard ``sys.stdout`` and ``sys.stderr``
redirection. Replacing a library's printing with `py::print <print>` may not
be feasible. This can be fixed using a guard around the library function that
redirects output to the corresponding Python streams:
.. code-block:: cpp
#include <pybind11/iostream.h>
...
// Add a scoped redirect for your noisy code
m.def("noisy_func", []() {
py::scoped_ostream_redirect stream(
std::cout, // std::ostream&
py::module::import("sys").attr("stdout") // Python output
);
call_noisy_func();
});
This method respects flushes on the output streams and will flush if needed
when the scoped guard is destroyed. This allows the output to be redirected in
real time, such as to a Jupyter notebook. The two arguments, the C++ stream and
the Python output, are optional, and default to standard output if not given. An
extra type, `py::scoped_estream_redirect <scoped_estream_redirect>`, is identical
except for defaulting to ``std::cerr`` and ``sys.stderr``; this can be useful with
`py::call_guard`, which allows multiple items, but uses the default constructor:
.. code-block:: py
// Alternative: Call single function using call guard
m.def("noisy_func", &call_noisy_function,
py::call_guard<py::scoped_ostream_redirect,
py::scoped_estream_redirect>());
The redirection can also be done in Python with the addition of a context
manager, using the `py::add_ostream_redirect() <add_ostream_redirect>` function:
.. code-block:: cpp
py::add_ostream_redirect(m, "ostream_redirect");
The name in Python defaults to ``ostream_redirect`` if no name is passed. This
creates the following context manager in Python:
.. code-block:: python
with ostream_redirect(stdout=True, stderr=True):
noisy_function()
It defaults to redirecting both streams, though you can use the keyword
arguments to disable one of the streams if needed.
.. note::
The above methods will not redirect C-level output to file descriptors, such
as ``fprintf``. For those cases, you'll need to redirect the file
descriptors either directly in C or with Python's ``os.dup2`` function
in an operating-system dependent way.
.. _eval:
Evaluating Python expressions from strings and files
====================================================
pybind11 provides the `eval`, `exec` and `eval_file` functions to evaluate
Python expressions and statements. The following example illustrates how they
can be used.
.. code-block:: cpp
// At beginning of file
#include <pybind11/eval.h>
...
// Evaluate in scope of main module
py::object scope = py::module::import("__main__").attr("__dict__");
// Evaluate an isolated expression
int result = py::eval("my_variable + 10", scope).cast<int>();
// Evaluate a sequence of statements
py::exec(
"print('Hello')\n"
"print('world!');",
scope);
// Evaluate the statements in an separate Python file on disk
py::eval_file("script.py", scope);
C++11 raw string literals are also supported and quite handy for this purpose.
The only requirement is that the first statement must be on a new line following
the raw string delimiter ``R"(``, ensuring all lines have common leading indent:
.. code-block:: cpp
py::exec(R"(
x = get_answer()
if x == 42:
print('Hello World!')
else:
print('Bye!')
)", scope
);
.. note::
`eval` and `eval_file` accept a template parameter that describes how the
string/file should be interpreted. Possible choices include ``eval_expr``
(isolated expression), ``eval_single_statement`` (a single statement, return
value is always ``none``), and ``eval_statements`` (sequence of statements,
return value is always ``none``). `eval` defaults to ``eval_expr``,
`eval_file` defaults to ``eval_statements`` and `exec` is just a shortcut
for ``eval<eval_statements>``.

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Smart pointers
##############
std::unique_ptr
===============
Given a class ``Example`` with Python bindings, it's possible to return
instances wrapped in C++11 unique pointers, like so
.. code-block:: cpp
std::unique_ptr<Example> create_example() { return std::unique_ptr<Example>(new Example()); }
.. code-block:: cpp
m.def("create_example", &create_example);
In other words, there is nothing special that needs to be done. While returning
unique pointers in this way is allowed, it is *illegal* to use them as function
arguments. For instance, the following function signature cannot be processed
by pybind11.
.. code-block:: cpp
void do_something_with_example(std::unique_ptr<Example> ex) { ... }
The above signature would imply that Python needs to give up ownership of an
object that is passed to this function, which is generally not possible (for
instance, the object might be referenced elsewhere).
std::shared_ptr
===============
The binding generator for classes, :class:`class_`, can be passed a template
type that denotes a special *holder* type that is used to manage references to
the object. If no such holder type template argument is given, the default for
a type named ``Type`` is ``std::unique_ptr<Type>``, which means that the object
is deallocated when Python's reference count goes to zero.
It is possible to switch to other types of reference counting wrappers or smart
pointers, which is useful in codebases that rely on them. For instance, the
following snippet causes ``std::shared_ptr`` to be used instead.
.. code-block:: cpp
py::class_<Example, std::shared_ptr<Example> /* <- holder type */> obj(m, "Example");
Note that any particular class can only be associated with a single holder type.
One potential stumbling block when using holder types is that they need to be
applied consistently. Can you guess what's broken about the following binding
code?
.. code-block:: cpp
class Child { };
class Parent {
public:
Parent() : child(std::make_shared<Child>()) { }
Child *get_child() { return child.get(); } /* Hint: ** DON'T DO THIS ** */
private:
std::shared_ptr<Child> child;
};
PYBIND11_MODULE(example, m) {
py::class_<Child, std::shared_ptr<Child>>(m, "Child");
py::class_<Parent, std::shared_ptr<Parent>>(m, "Parent")
.def(py::init<>())
.def("get_child", &Parent::get_child);
}
The following Python code will cause undefined behavior (and likely a
segmentation fault).
.. code-block:: python
from example import Parent
print(Parent().get_child())
The problem is that ``Parent::get_child()`` returns a pointer to an instance of
``Child``, but the fact that this instance is already managed by
``std::shared_ptr<...>`` is lost when passing raw pointers. In this case,
pybind11 will create a second independent ``std::shared_ptr<...>`` that also
claims ownership of the pointer. In the end, the object will be freed **twice**
since these shared pointers have no way of knowing about each other.
There are two ways to resolve this issue:
1. For types that are managed by a smart pointer class, never use raw pointers
in function arguments or return values. In other words: always consistently
wrap pointers into their designated holder types (such as
``std::shared_ptr<...>``). In this case, the signature of ``get_child()``
should be modified as follows:
.. code-block:: cpp
std::shared_ptr<Child> get_child() { return child; }
2. Adjust the definition of ``Child`` by specifying
``std::enable_shared_from_this<T>`` (see cppreference_ for details) as a
base class. This adds a small bit of information to ``Child`` that allows
pybind11 to realize that there is already an existing
``std::shared_ptr<...>`` and communicate with it. In this case, the
declaration of ``Child`` should look as follows:
.. _cppreference: http://en.cppreference.com/w/cpp/memory/enable_shared_from_this
.. code-block:: cpp
class Child : public std::enable_shared_from_this<Child> { };
.. _smart_pointers:
Custom smart pointers
=====================
pybind11 supports ``std::unique_ptr`` and ``std::shared_ptr`` right out of the
box. For any other custom smart pointer, transparent conversions can be enabled
using a macro invocation similar to the following. It must be declared at the
top namespace level before any binding code:
.. code-block:: cpp
PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>);
The first argument of :func:`PYBIND11_DECLARE_HOLDER_TYPE` should be a
placeholder name that is used as a template parameter of the second argument.
Thus, feel free to use any identifier, but use it consistently on both sides;
also, don't use the name of a type that already exists in your codebase.
The macro also accepts a third optional boolean parameter that is set to false
by default. Specify
.. code-block:: cpp
PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>, true);
if ``SmartPtr<T>`` can always be initialized from a ``T*`` pointer without the
risk of inconsistencies (such as multiple independent ``SmartPtr`` instances
believing that they are the sole owner of the ``T*`` pointer). A common
situation where ``true`` should be passed is when the ``T`` instances use
*intrusive* reference counting.
Please take a look at the :ref:`macro_notes` before using this feature.
By default, pybind11 assumes that your custom smart pointer has a standard
interface, i.e. provides a ``.get()`` member function to access the underlying
raw pointer. If this is not the case, pybind11's ``holder_helper`` must be
specialized:
.. code-block:: cpp
// Always needed for custom holder types
PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>);
// Only needed if the type's `.get()` goes by another name
namespace pybind11 { namespace detail {
template <typename T>
struct holder_helper<SmartPtr<T>> { // <-- specialization
static const T *get(const SmartPtr<T> &p) { return p.getPointer(); }
};
}}
The above specialization informs pybind11 that the custom ``SmartPtr`` class
provides ``.get()`` functionality via ``.getPointer()``.
.. seealso::
The file :file:`tests/test_smart_ptr.cpp` contains a complete example
that demonstrates how to work with custom reference-counting holder types
in more detail.

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.. _basics:
First steps
###########
This sections demonstrates the basic features of pybind11. Before getting
started, make sure that development environment is set up to compile the
included set of test cases.
Compiling the test cases
========================
Linux/MacOS
-----------
On Linux you'll need to install the **python-dev** or **python3-dev** packages as
well as **cmake**. On Mac OS, the included python version works out of the box,
but **cmake** must still be installed.
After installing the prerequisites, run
.. code-block:: bash
mkdir build
cd build
cmake ..
make check -j 4
The last line will both compile and run the tests.
Windows
-------
On Windows, only **Visual Studio 2015** and newer are supported since pybind11 relies
on various C++11 language features that break older versions of Visual Studio.
To compile and run the tests:
.. code-block:: batch
mkdir build
cd build
cmake ..
cmake --build . --config Release --target check
This will create a Visual Studio project, compile and run the target, all from the
command line.
.. Note::
If all tests fail, make sure that the Python binary and the testcases are compiled
for the same processor type and bitness (i.e. either **i386** or **x86_64**). You
can specify **x86_64** as the target architecture for the generated Visual Studio
project using ``cmake -A x64 ..``.
.. seealso::
Advanced users who are already familiar with Boost.Python may want to skip
the tutorial and look at the test cases in the :file:`tests` directory,
which exercise all features of pybind11.
Header and namespace conventions
================================
For brevity, all code examples assume that the following two lines are present:
.. code-block:: cpp
#include <pybind11/pybind11.h>
namespace py = pybind11;
Some features may require additional headers, but those will be specified as needed.
.. _simple_example:
Creating bindings for a simple function
=======================================
Let's start by creating Python bindings for an extremely simple function, which
adds two numbers and returns their result:
.. code-block:: cpp
int add(int i, int j) {
return i + j;
}
For simplicity [#f1]_, we'll put both this function and the binding code into
a file named :file:`example.cpp` with the following contents:
.. code-block:: cpp
#include <pybind11/pybind11.h>
int add(int i, int j) {
return i + j;
}
PYBIND11_MODULE(example, m) {
m.doc() = "pybind11 example plugin"; // optional module docstring
m.def("add", &add, "A function which adds two numbers");
}
.. [#f1] In practice, implementation and binding code will generally be located
in separate files.
The :func:`PYBIND11_MODULE` macro creates a function that will be called when an
``import`` statement is issued from within Python. The module name (``example``)
is given as the first macro argument (it should not be in quotes). The second
argument (``m``) defines a variable of type :class:`py::module <module>` which
is the main interface for creating bindings. The method :func:`module::def`
generates binding code that exposes the ``add()`` function to Python.
.. note::
Notice how little code was needed to expose our function to Python: all
details regarding the function's parameters and return value were
automatically inferred using template metaprogramming. This overall
approach and the used syntax are borrowed from Boost.Python, though the
underlying implementation is very different.
pybind11 is a header-only library, hence it is not necessary to link against
any special libraries and there are no intermediate (magic) translation steps.
On Linux, the above example can be compiled using the following command:
.. code-block:: bash
$ c++ -O3 -Wall -shared -std=c++11 -fPIC `python3 -m pybind11 --includes` example.cpp -o example`python3-config --extension-suffix`
For more details on the required compiler flags on Linux and MacOS, see
:ref:`building_manually`. For complete cross-platform compilation instructions,
refer to the :ref:`compiling` page.
The `python_example`_ and `cmake_example`_ repositories are also a good place
to start. They are both complete project examples with cross-platform build
systems. The only difference between the two is that `python_example`_ uses
Python's ``setuptools`` to build the module, while `cmake_example`_ uses CMake
(which may be preferable for existing C++ projects).
.. _python_example: https://github.com/pybind/python_example
.. _cmake_example: https://github.com/pybind/cmake_example
Building the above C++ code will produce a binary module file that can be
imported to Python. Assuming that the compiled module is located in the
current directory, the following interactive Python session shows how to
load and execute the example:
.. code-block:: pycon
$ python
Python 2.7.10 (default, Aug 22 2015, 20:33:39)
[GCC 4.2.1 Compatible Apple LLVM 7.0.0 (clang-700.0.59.1)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import example
>>> example.add(1, 2)
3L
>>>
.. _keyword_args:
Keyword arguments
=================
With a simple modification code, it is possible to inform Python about the
names of the arguments ("i" and "j" in this case).
.. code-block:: cpp
m.def("add", &add, "A function which adds two numbers",
py::arg("i"), py::arg("j"));
:class:`arg` is one of several special tag classes which can be used to pass
metadata into :func:`module::def`. With this modified binding code, we can now
call the function using keyword arguments, which is a more readable alternative
particularly for functions taking many parameters:
.. code-block:: pycon
>>> import example
>>> example.add(i=1, j=2)
3L
The keyword names also appear in the function signatures within the documentation.
.. code-block:: pycon
>>> help(example)
....
FUNCTIONS
add(...)
Signature : (i: int, j: int) -> int
A function which adds two numbers
A shorter notation for named arguments is also available:
.. code-block:: cpp
// regular notation
m.def("add1", &add, py::arg("i"), py::arg("j"));
// shorthand
using namespace pybind11::literals;
m.def("add2", &add, "i"_a, "j"_a);
The :var:`_a` suffix forms a C++11 literal which is equivalent to :class:`arg`.
Note that the literal operator must first be made visible with the directive
``using namespace pybind11::literals``. This does not bring in anything else
from the ``pybind11`` namespace except for literals.
.. _default_args:
Default arguments
=================
Suppose now that the function to be bound has default arguments, e.g.:
.. code-block:: cpp
int add(int i = 1, int j = 2) {
return i + j;
}
Unfortunately, pybind11 cannot automatically extract these parameters, since they
are not part of the function's type information. However, they are simple to specify
using an extension of :class:`arg`:
.. code-block:: cpp
m.def("add", &add, "A function which adds two numbers",
py::arg("i") = 1, py::arg("j") = 2);
The default values also appear within the documentation.
.. code-block:: pycon
>>> help(example)
....
FUNCTIONS
add(...)
Signature : (i: int = 1, j: int = 2) -> int
A function which adds two numbers
The shorthand notation is also available for default arguments:
.. code-block:: cpp
// regular notation
m.def("add1", &add, py::arg("i") = 1, py::arg("j") = 2);
// shorthand
m.def("add2", &add, "i"_a=1, "j"_a=2);
Exporting variables
===================
To expose a value from C++, use the ``attr`` function to register it in a
module as shown below. Built-in types and general objects (more on that later)
are automatically converted when assigned as attributes, and can be explicitly
converted using the function ``py::cast``.
.. code-block:: cpp
PYBIND11_MODULE(example, m) {
m.attr("the_answer") = 42;
py::object world = py::cast("World");
m.attr("what") = world;
}
These are then accessible from Python:
.. code-block:: pycon
>>> import example
>>> example.the_answer
42
>>> example.what
'World'
.. _supported_types:
Supported data types
====================
A large number of data types are supported out of the box and can be used
seamlessly as functions arguments, return values or with ``py::cast`` in general.
For a full overview, see the :doc:`advanced/cast/index` section.

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import random
import os
import time
import datetime as dt
nfns = 4 # Functions per class
nargs = 4 # Arguments per function
def generate_dummy_code_pybind11(nclasses=10):
decl = ""
bindings = ""
for cl in range(nclasses):
decl += "class cl%03i;\n" % cl
decl += '\n'
for cl in range(nclasses):
decl += "class cl%03i {\n" % cl
decl += "public:\n"
bindings += ' py::class_<cl%03i>(m, "cl%03i")\n' % (cl, cl)
for fn in range(nfns):
ret = random.randint(0, nclasses - 1)
params = [random.randint(0, nclasses - 1) for i in range(nargs)]
decl += " cl%03i *fn_%03i(" % (ret, fn)
decl += ", ".join("cl%03i *" % p for p in params)
decl += ");\n"
bindings += ' .def("fn_%03i", &cl%03i::fn_%03i)\n' % \
(fn, cl, fn)
decl += "};\n\n"
bindings += ' ;\n'
result = "#include <pybind11/pybind11.h>\n\n"
result += "namespace py = pybind11;\n\n"
result += decl + '\n'
result += "PYBIND11_MODULE(example, m) {\n"
result += bindings
result += "}"
return result
def generate_dummy_code_boost(nclasses=10):
decl = ""
bindings = ""
for cl in range(nclasses):
decl += "class cl%03i;\n" % cl
decl += '\n'
for cl in range(nclasses):
decl += "class cl%03i {\n" % cl
decl += "public:\n"
bindings += ' py::class_<cl%03i>("cl%03i")\n' % (cl, cl)
for fn in range(nfns):
ret = random.randint(0, nclasses - 1)
params = [random.randint(0, nclasses - 1) for i in range(nargs)]
decl += " cl%03i *fn_%03i(" % (ret, fn)
decl += ", ".join("cl%03i *" % p for p in params)
decl += ");\n"
bindings += ' .def("fn_%03i", &cl%03i::fn_%03i, py::return_value_policy<py::manage_new_object>())\n' % \
(fn, cl, fn)
decl += "};\n\n"
bindings += ' ;\n'
result = "#include <boost/python.hpp>\n\n"
result += "namespace py = boost::python;\n\n"
result += decl + '\n'
result += "BOOST_PYTHON_MODULE(example) {\n"
result += bindings
result += "}"
return result
for codegen in [generate_dummy_code_pybind11, generate_dummy_code_boost]:
print ("{")
for i in range(0, 10):
nclasses = 2 ** i
with open("test.cpp", "w") as f:
f.write(codegen(nclasses))
n1 = dt.datetime.now()
os.system("g++ -Os -shared -rdynamic -undefined dynamic_lookup "
"-fvisibility=hidden -std=c++14 test.cpp -I include "
"-I /System/Library/Frameworks/Python.framework/Headers -o test.so")
n2 = dt.datetime.now()
elapsed = (n2 - n1).total_seconds()
size = os.stat('test.so').st_size
print(" {%i, %f, %i}," % (nclasses * nfns, elapsed, size))
print ("}")

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Benchmark
=========
The following is the result of a synthetic benchmark comparing both compilation
time and module size of pybind11 against Boost.Python. A detailed report about a
Boost.Python to pybind11 conversion of a real project is available here: [#f1]_.
.. [#f1] http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf
Setup
-----
A python script (see the ``docs/benchmark.py`` file) was used to generate a set
of files with dummy classes whose count increases for each successive benchmark
(between 1 and 2048 classes in powers of two). Each class has four methods with
a randomly generated signature with a return value and four arguments. (There
was no particular reason for this setup other than the desire to generate many
unique function signatures whose count could be controlled in a simple way.)
Here is an example of the binding code for one class:
.. code-block:: cpp
...
class cl034 {
public:
cl279 *fn_000(cl084 *, cl057 *, cl065 *, cl042 *);
cl025 *fn_001(cl098 *, cl262 *, cl414 *, cl121 *);
cl085 *fn_002(cl445 *, cl297 *, cl145 *, cl421 *);
cl470 *fn_003(cl200 *, cl323 *, cl332 *, cl492 *);
};
...
PYBIND11_MODULE(example, m) {
...
py::class_<cl034>(m, "cl034")
.def("fn_000", &cl034::fn_000)
.def("fn_001", &cl034::fn_001)
.def("fn_002", &cl034::fn_002)
.def("fn_003", &cl034::fn_003)
...
}
The Boost.Python version looks almost identical except that a return value
policy had to be specified as an argument to ``def()``. For both libraries,
compilation was done with
.. code-block:: bash
Apple LLVM version 7.0.2 (clang-700.1.81)
and the following compilation flags
.. code-block:: bash
g++ -Os -shared -rdynamic -undefined dynamic_lookup -fvisibility=hidden -std=c++14
Compilation time
----------------
The following log-log plot shows how the compilation time grows for an
increasing number of class and function declarations. pybind11 includes many
fewer headers, which initially leads to shorter compilation times, but the
performance is ultimately fairly similar (pybind11 is 19.8 seconds faster for
the largest largest file with 2048 classes and a total of 8192 methods -- a
modest **1.2x** speedup relative to Boost.Python, which required 116.35
seconds).
.. only:: not latex
.. image:: pybind11_vs_boost_python1.svg
.. only:: latex
.. image:: pybind11_vs_boost_python1.png
Module size
-----------
Differences between the two libraries become much more pronounced when
considering the file size of the generated Python plugin: for the largest file,
the binary generated by Boost.Python required 16.8 MiB, which was **2.17
times** / **9.1 megabytes** larger than the output generated by pybind11. For
very small inputs, Boost.Python has an edge in the plot below -- however, note
that it stores many definitions in an external library, whose size was not
included here, hence the comparison is slightly shifted in Boost.Python's
favor.
.. only:: not latex
.. image:: pybind11_vs_boost_python2.svg
.. only:: latex
.. image:: pybind11_vs_boost_python2.png

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.. _classes:
Object-oriented code
####################
Creating bindings for a custom type
===================================
Let's now look at a more complex example where we'll create bindings for a
custom C++ data structure named ``Pet``. Its definition is given below:
.. code-block:: cpp
struct Pet {
Pet(const std::string &name) : name(name) { }
void setName(const std::string &name_) { name = name_; }
const std::string &getName() const { return name; }
std::string name;
};
The binding code for ``Pet`` looks as follows:
.. code-block:: cpp
#include <pybind11/pybind11.h>
namespace py = pybind11;
PYBIND11_MODULE(example, m) {
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def("setName", &Pet::setName)
.def("getName", &Pet::getName);
}
:class:`class_` creates bindings for a C++ *class* or *struct*-style data
structure. :func:`init` is a convenience function that takes the types of a
constructor's parameters as template arguments and wraps the corresponding
constructor (see the :ref:`custom_constructors` section for details). An
interactive Python session demonstrating this example is shown below:
.. code-block:: pycon
% python
>>> import example
>>> p = example.Pet('Molly')
>>> print(p)
<example.Pet object at 0x10cd98060>
>>> p.getName()
u'Molly'
>>> p.setName('Charly')
>>> p.getName()
u'Charly'
.. seealso::
Static member functions can be bound in the same way using
:func:`class_::def_static`.
Keyword and default arguments
=============================
It is possible to specify keyword and default arguments using the syntax
discussed in the previous chapter. Refer to the sections :ref:`keyword_args`
and :ref:`default_args` for details.
Binding lambda functions
========================
Note how ``print(p)`` produced a rather useless summary of our data structure in the example above:
.. code-block:: pycon
>>> print(p)
<example.Pet object at 0x10cd98060>
To address this, we could bind an utility function that returns a human-readable
summary to the special method slot named ``__repr__``. Unfortunately, there is no
suitable functionality in the ``Pet`` data structure, and it would be nice if
we did not have to change it. This can easily be accomplished by binding a
Lambda function instead:
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def("setName", &Pet::setName)
.def("getName", &Pet::getName)
.def("__repr__",
[](const Pet &a) {
return "<example.Pet named '" + a.name + "'>";
}
);
Both stateless [#f1]_ and stateful lambda closures are supported by pybind11.
With the above change, the same Python code now produces the following output:
.. code-block:: pycon
>>> print(p)
<example.Pet named 'Molly'>
.. [#f1] Stateless closures are those with an empty pair of brackets ``[]`` as the capture object.
.. _properties:
Instance and static fields
==========================
We can also directly expose the ``name`` field using the
:func:`class_::def_readwrite` method. A similar :func:`class_::def_readonly`
method also exists for ``const`` fields.
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def_readwrite("name", &Pet::name)
// ... remainder ...
This makes it possible to write
.. code-block:: pycon
>>> p = example.Pet('Molly')
>>> p.name
u'Molly'
>>> p.name = 'Charly'
>>> p.name
u'Charly'
Now suppose that ``Pet::name`` was a private internal variable
that can only be accessed via setters and getters.
.. code-block:: cpp
class Pet {
public:
Pet(const std::string &name) : name(name) { }
void setName(const std::string &name_) { name = name_; }
const std::string &getName() const { return name; }
private:
std::string name;
};
In this case, the method :func:`class_::def_property`
(:func:`class_::def_property_readonly` for read-only data) can be used to
provide a field-like interface within Python that will transparently call
the setter and getter functions:
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def_property("name", &Pet::getName, &Pet::setName)
// ... remainder ...
Write only properties can be defined by passing ``nullptr`` as the
input for the read function.
.. seealso::
Similar functions :func:`class_::def_readwrite_static`,
:func:`class_::def_readonly_static` :func:`class_::def_property_static`,
and :func:`class_::def_property_readonly_static` are provided for binding
static variables and properties. Please also see the section on
:ref:`static_properties` in the advanced part of the documentation.
Dynamic attributes
==================
Native Python classes can pick up new attributes dynamically:
.. code-block:: pycon
>>> class Pet:
... name = 'Molly'
...
>>> p = Pet()
>>> p.name = 'Charly' # overwrite existing
>>> p.age = 2 # dynamically add a new attribute
By default, classes exported from C++ do not support this and the only writable
attributes are the ones explicitly defined using :func:`class_::def_readwrite`
or :func:`class_::def_property`.
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<>())
.def_readwrite("name", &Pet::name);
Trying to set any other attribute results in an error:
.. code-block:: pycon
>>> p = example.Pet()
>>> p.name = 'Charly' # OK, attribute defined in C++
>>> p.age = 2 # fail
AttributeError: 'Pet' object has no attribute 'age'
To enable dynamic attributes for C++ classes, the :class:`py::dynamic_attr` tag
must be added to the :class:`py::class_` constructor:
.. code-block:: cpp
py::class_<Pet>(m, "Pet", py::dynamic_attr())
.def(py::init<>())
.def_readwrite("name", &Pet::name);
Now everything works as expected:
.. code-block:: pycon
>>> p = example.Pet()
>>> p.name = 'Charly' # OK, overwrite value in C++
>>> p.age = 2 # OK, dynamically add a new attribute
>>> p.__dict__ # just like a native Python class
{'age': 2}
Note that there is a small runtime cost for a class with dynamic attributes.
Not only because of the addition of a ``__dict__``, but also because of more
expensive garbage collection tracking which must be activated to resolve
possible circular references. Native Python classes incur this same cost by
default, so this is not anything to worry about. By default, pybind11 classes
are more efficient than native Python classes. Enabling dynamic attributes
just brings them on par.
.. _inheritance:
Inheritance and automatic downcasting
=====================================
Suppose now that the example consists of two data structures with an
inheritance relationship:
.. code-block:: cpp
struct Pet {
Pet(const std::string &name) : name(name) { }
std::string name;
};
struct Dog : Pet {
Dog(const std::string &name) : Pet(name) { }
std::string bark() const { return "woof!"; }
};
There are two different ways of indicating a hierarchical relationship to
pybind11: the first specifies the C++ base class as an extra template
parameter of the :class:`class_`:
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def_readwrite("name", &Pet::name);
// Method 1: template parameter:
py::class_<Dog, Pet /* <- specify C++ parent type */>(m, "Dog")
.def(py::init<const std::string &>())
.def("bark", &Dog::bark);
Alternatively, we can also assign a name to the previously bound ``Pet``
:class:`class_` object and reference it when binding the ``Dog`` class:
.. code-block:: cpp
py::class_<Pet> pet(m, "Pet");
pet.def(py::init<const std::string &>())
.def_readwrite("name", &Pet::name);
// Method 2: pass parent class_ object:
py::class_<Dog>(m, "Dog", pet /* <- specify Python parent type */)
.def(py::init<const std::string &>())
.def("bark", &Dog::bark);
Functionality-wise, both approaches are equivalent. Afterwards, instances will
expose fields and methods of both types:
.. code-block:: pycon
>>> p = example.Dog('Molly')
>>> p.name
u'Molly'
>>> p.bark()
u'woof!'
The C++ classes defined above are regular non-polymorphic types with an
inheritance relationship. This is reflected in Python:
.. code-block:: cpp
// Return a base pointer to a derived instance
m.def("pet_store", []() { return std::unique_ptr<Pet>(new Dog("Molly")); });
.. code-block:: pycon
>>> p = example.pet_store()
>>> type(p) # `Dog` instance behind `Pet` pointer
Pet # no pointer downcasting for regular non-polymorphic types
>>> p.bark()
AttributeError: 'Pet' object has no attribute 'bark'
The function returned a ``Dog`` instance, but because it's a non-polymorphic
type behind a base pointer, Python only sees a ``Pet``. In C++, a type is only
considered polymorphic if it has at least one virtual function and pybind11
will automatically recognize this:
.. code-block:: cpp
struct PolymorphicPet {
virtual ~PolymorphicPet() = default;
};
struct PolymorphicDog : PolymorphicPet {
std::string bark() const { return "woof!"; }
};
// Same binding code
py::class_<PolymorphicPet>(m, "PolymorphicPet");
py::class_<PolymorphicDog, PolymorphicPet>(m, "PolymorphicDog")
.def(py::init<>())
.def("bark", &PolymorphicDog::bark);
// Again, return a base pointer to a derived instance
m.def("pet_store2", []() { return std::unique_ptr<PolymorphicPet>(new PolymorphicDog); });
.. code-block:: pycon
>>> p = example.pet_store2()
>>> type(p)
PolymorphicDog # automatically downcast
>>> p.bark()
u'woof!'
Given a pointer to a polymorphic base, pybind11 performs automatic downcasting
to the actual derived type. Note that this goes beyond the usual situation in
C++: we don't just get access to the virtual functions of the base, we get the
concrete derived type including functions and attributes that the base type may
not even be aware of.
.. seealso::
For more information about polymorphic behavior see :ref:`overriding_virtuals`.
Overloaded methods
==================
Sometimes there are several overloaded C++ methods with the same name taking
different kinds of input arguments:
.. code-block:: cpp
struct Pet {
Pet(const std::string &name, int age) : name(name), age(age) { }
void set(int age_) { age = age_; }
void set(const std::string &name_) { name = name_; }
std::string name;
int age;
};
Attempting to bind ``Pet::set`` will cause an error since the compiler does not
know which method the user intended to select. We can disambiguate by casting
them to function pointers. Binding multiple functions to the same Python name
automatically creates a chain of function overloads that will be tried in
sequence.
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &, int>())
.def("set", (void (Pet::*)(int)) &Pet::set, "Set the pet's age")
.def("set", (void (Pet::*)(const std::string &)) &Pet::set, "Set the pet's name");
The overload signatures are also visible in the method's docstring:
.. code-block:: pycon
>>> help(example.Pet)
class Pet(__builtin__.object)
| Methods defined here:
|
| __init__(...)
| Signature : (Pet, str, int) -> NoneType
|
| set(...)
| 1. Signature : (Pet, int) -> NoneType
|
| Set the pet's age
|
| 2. Signature : (Pet, str) -> NoneType
|
| Set the pet's name
If you have a C++14 compatible compiler [#cpp14]_, you can use an alternative
syntax to cast the overloaded function:
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def("set", py::overload_cast<int>(&Pet::set), "Set the pet's age")
.def("set", py::overload_cast<const std::string &>(&Pet::set), "Set the pet's name");
Here, ``py::overload_cast`` only requires the parameter types to be specified.
The return type and class are deduced. This avoids the additional noise of
``void (Pet::*)()`` as seen in the raw cast. If a function is overloaded based
on constness, the ``py::const_`` tag should be used:
.. code-block:: cpp
struct Widget {
int foo(int x, float y);
int foo(int x, float y) const;
};
py::class_<Widget>(m, "Widget")
.def("foo_mutable", py::overload_cast<int, float>(&Widget::foo))
.def("foo_const", py::overload_cast<int, float>(&Widget::foo, py::const_));
.. [#cpp14] A compiler which supports the ``-std=c++14`` flag
or Visual Studio 2015 Update 2 and newer.
.. note::
To define multiple overloaded constructors, simply declare one after the
other using the ``.def(py::init<...>())`` syntax. The existing machinery
for specifying keyword and default arguments also works.
Enumerations and internal types
===============================
Let's now suppose that the example class contains an internal enumeration type,
e.g.:
.. code-block:: cpp
struct Pet {
enum Kind {
Dog = 0,
Cat
};
Pet(const std::string &name, Kind type) : name(name), type(type) { }
std::string name;
Kind type;
};
The binding code for this example looks as follows:
.. code-block:: cpp
py::class_<Pet> pet(m, "Pet");
pet.def(py::init<const std::string &, Pet::Kind>())
.def_readwrite("name", &Pet::name)
.def_readwrite("type", &Pet::type);
py::enum_<Pet::Kind>(pet, "Kind")
.value("Dog", Pet::Kind::Dog)
.value("Cat", Pet::Kind::Cat)
.export_values();
To ensure that the ``Kind`` type is created within the scope of ``Pet``, the
``pet`` :class:`class_` instance must be supplied to the :class:`enum_`.
constructor. The :func:`enum_::export_values` function exports the enum entries
into the parent scope, which should be skipped for newer C++11-style strongly
typed enums.
.. code-block:: pycon
>>> p = Pet('Lucy', Pet.Cat)
>>> p.type
Kind.Cat
>>> int(p.type)
1L
The entries defined by the enumeration type are exposed in the ``__members__`` property:
.. code-block:: pycon
>>> Pet.Kind.__members__
{'Dog': Kind.Dog, 'Cat': Kind.Cat}
The ``name`` property returns the name of the enum value as a unicode string.
.. note::
It is also possible to use ``str(enum)``, however these accomplish different
goals. The following shows how these two approaches differ.
.. code-block:: pycon
>>> p = Pet( "Lucy", Pet.Cat )
>>> pet_type = p.type
>>> pet_type
Pet.Cat
>>> str(pet_type)
'Pet.Cat'
>>> pet_type.name
'Cat'
.. note::
When the special tag ``py::arithmetic()`` is specified to the ``enum_``
constructor, pybind11 creates an enumeration that also supports rudimentary
arithmetic and bit-level operations like comparisons, and, or, xor, negation,
etc.
.. code-block:: cpp
py::enum_<Pet::Kind>(pet, "Kind", py::arithmetic())
...
By default, these are omitted to conserve space.

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.. _compiling:
Build systems
#############
Building with setuptools
========================
For projects on PyPI, building with setuptools is the way to go. Sylvain Corlay
has kindly provided an example project which shows how to set up everything,
including automatic generation of documentation using Sphinx. Please refer to
the [python_example]_ repository.
.. [python_example] https://github.com/pybind/python_example
Building with cppimport
========================
[cppimport]_ is a small Python import hook that determines whether there is a C++
source file whose name matches the requested module. If there is, the file is
compiled as a Python extension using pybind11 and placed in the same folder as
the C++ source file. Python is then able to find the module and load it.
.. [cppimport] https://github.com/tbenthompson/cppimport
.. _cmake:
Building with CMake
===================
For C++ codebases that have an existing CMake-based build system, a Python
extension module can be created with just a few lines of code:
.. code-block:: cmake
cmake_minimum_required(VERSION 2.8.12)
project(example)
add_subdirectory(pybind11)
pybind11_add_module(example example.cpp)
This assumes that the pybind11 repository is located in a subdirectory named
:file:`pybind11` and that the code is located in a file named :file:`example.cpp`.
The CMake command ``add_subdirectory`` will import the pybind11 project which
provides the ``pybind11_add_module`` function. It will take care of all the
details needed to build a Python extension module on any platform.
A working sample project, including a way to invoke CMake from :file:`setup.py` for
PyPI integration, can be found in the [cmake_example]_ repository.
.. [cmake_example] https://github.com/pybind/cmake_example
pybind11_add_module
-------------------
To ease the creation of Python extension modules, pybind11 provides a CMake
function with the following signature:
.. code-block:: cmake
pybind11_add_module(<name> [MODULE | SHARED] [EXCLUDE_FROM_ALL]
[NO_EXTRAS] [SYSTEM] [THIN_LTO] source1 [source2 ...])
This function behaves very much like CMake's builtin ``add_library`` (in fact,
it's a wrapper function around that command). It will add a library target
called ``<name>`` to be built from the listed source files. In addition, it
will take care of all the Python-specific compiler and linker flags as well
as the OS- and Python-version-specific file extension. The produced target
``<name>`` can be further manipulated with regular CMake commands.
``MODULE`` or ``SHARED`` may be given to specify the type of library. If no
type is given, ``MODULE`` is used by default which ensures the creation of a
Python-exclusive module. Specifying ``SHARED`` will create a more traditional
dynamic library which can also be linked from elsewhere. ``EXCLUDE_FROM_ALL``
removes this target from the default build (see CMake docs for details).
Since pybind11 is a template library, ``pybind11_add_module`` adds compiler
flags to ensure high quality code generation without bloat arising from long
symbol names and duplication of code in different translation units. It
sets default visibility to *hidden*, which is required for some pybind11
features and functionality when attempting to load multiple pybind11 modules
compiled under different pybind11 versions. It also adds additional flags
enabling LTO (Link Time Optimization) and strip unneeded symbols. See the
:ref:`FAQ entry <faq:symhidden>` for a more detailed explanation. These
latter optimizations are never applied in ``Debug`` mode. If ``NO_EXTRAS`` is
given, they will always be disabled, even in ``Release`` mode. However, this
will result in code bloat and is generally not recommended.
By default, pybind11 and Python headers will be included with ``-I``. In order
to include pybind11 as system library, e.g. to avoid warnings in downstream
code with warn-levels outside of pybind11's scope, set the option ``SYSTEM``.
As stated above, LTO is enabled by default. Some newer compilers also support
different flavors of LTO such as `ThinLTO`_. Setting ``THIN_LTO`` will cause
the function to prefer this flavor if available. The function falls back to
regular LTO if ``-flto=thin`` is not available.
.. _ThinLTO: http://clang.llvm.org/docs/ThinLTO.html
Configuration variables
-----------------------
By default, pybind11 will compile modules with the C++14 standard, if available
on the target compiler, falling back to C++11 if C++14 support is not
available. Note, however, that this default is subject to change: future
pybind11 releases are expected to migrate to newer C++ standards as they become
available. To override this, the standard flag can be given explicitly in
``PYBIND11_CPP_STANDARD``:
.. code-block:: cmake
# Use just one of these:
# GCC/clang:
set(PYBIND11_CPP_STANDARD -std=c++11)
set(PYBIND11_CPP_STANDARD -std=c++14)
set(PYBIND11_CPP_STANDARD -std=c++1z) # Experimental C++17 support
# MSVC:
set(PYBIND11_CPP_STANDARD /std:c++14)
set(PYBIND11_CPP_STANDARD /std:c++latest) # Enables some MSVC C++17 features
add_subdirectory(pybind11) # or find_package(pybind11)
Note that this and all other configuration variables must be set **before** the
call to ``add_subdirectory`` or ``find_package``. The variables can also be set
when calling CMake from the command line using the ``-D<variable>=<value>`` flag.
The target Python version can be selected by setting ``PYBIND11_PYTHON_VERSION``
or an exact Python installation can be specified with ``PYTHON_EXECUTABLE``.
For example:
.. code-block:: bash
cmake -DPYBIND11_PYTHON_VERSION=3.6 ..
# or
cmake -DPYTHON_EXECUTABLE=path/to/python ..
find_package vs. add_subdirectory
---------------------------------
For CMake-based projects that don't include the pybind11 repository internally,
an external installation can be detected through ``find_package(pybind11)``.
See the `Config file`_ docstring for details of relevant CMake variables.
.. code-block:: cmake
cmake_minimum_required(VERSION 2.8.12)
project(example)
find_package(pybind11 REQUIRED)
pybind11_add_module(example example.cpp)
Note that ``find_package(pybind11)`` will only work correctly if pybind11
has been correctly installed on the system, e. g. after downloading or cloning
the pybind11 repository :
.. code-block:: bash
cd pybind11
mkdir build
cd build
cmake ..
make install
Once detected, the aforementioned ``pybind11_add_module`` can be employed as
before. The function usage and configuration variables are identical no matter
if pybind11 is added as a subdirectory or found as an installed package. You
can refer to the same [cmake_example]_ repository for a full sample project
-- just swap out ``add_subdirectory`` for ``find_package``.
.. _Config file: https://github.com/pybind/pybind11/blob/master/tools/pybind11Config.cmake.in
Advanced: interface library target
----------------------------------
When using a version of CMake greater than 3.0, pybind11 can additionally
be used as a special *interface library* . The target ``pybind11::module``
is available with pybind11 headers, Python headers and libraries as needed,
and C++ compile definitions attached. This target is suitable for linking
to an independently constructed (through ``add_library``, not
``pybind11_add_module``) target in the consuming project.
.. code-block:: cmake
cmake_minimum_required(VERSION 3.0)
project(example)
find_package(pybind11 REQUIRED) # or add_subdirectory(pybind11)
add_library(example MODULE main.cpp)
target_link_libraries(example PRIVATE pybind11::module)
set_target_properties(example PROPERTIES PREFIX "${PYTHON_MODULE_PREFIX}"
SUFFIX "${PYTHON_MODULE_EXTENSION}")
.. warning::
Since pybind11 is a metatemplate library, it is crucial that certain
compiler flags are provided to ensure high quality code generation. In
contrast to the ``pybind11_add_module()`` command, the CMake interface
library only provides the *minimal* set of parameters to ensure that the
code using pybind11 compiles, but it does **not** pass these extra compiler
flags (i.e. this is up to you).
These include Link Time Optimization (``-flto`` on GCC/Clang/ICPC, ``/GL``
and ``/LTCG`` on Visual Studio) and .OBJ files with many sections on Visual
Studio (``/bigobj``). The :ref:`FAQ <faq:symhidden>` contains an
explanation on why these are needed.
Embedding the Python interpreter
--------------------------------
In addition to extension modules, pybind11 also supports embedding Python into
a C++ executable or library. In CMake, simply link with the ``pybind11::embed``
target. It provides everything needed to get the interpreter running. The Python
headers and libraries are attached to the target. Unlike ``pybind11::module``,
there is no need to manually set any additional properties here. For more
information about usage in C++, see :doc:`/advanced/embedding`.
.. code-block:: cmake
cmake_minimum_required(VERSION 3.0)
project(example)
find_package(pybind11 REQUIRED) # or add_subdirectory(pybind11)
add_executable(example main.cpp)
target_link_libraries(example PRIVATE pybind11::embed)
.. _building_manually:
Building manually
=================
pybind11 is a header-only library, hence it is not necessary to link against
any special libraries and there are no intermediate (magic) translation steps.
On Linux, you can compile an example such as the one given in
:ref:`simple_example` using the following command:
.. code-block:: bash
$ c++ -O3 -Wall -shared -std=c++11 -fPIC `python3 -m pybind11 --includes` example.cpp -o example`python3-config --extension-suffix`
The flags given here assume that you're using Python 3. For Python 2, just
change the executable appropriately (to ``python`` or ``python2``).
The ``python3 -m pybind11 --includes`` command fetches the include paths for
both pybind11 and Python headers. This assumes that pybind11 has been installed
using ``pip`` or ``conda``. If it hasn't, you can also manually specify
``-I <path-to-pybind11>/include`` together with the Python includes path
``python3-config --includes``.
Note that Python 2.7 modules don't use a special suffix, so you should simply
use ``example.so`` instead of ``example`python3-config --extension-suffix```.
Besides, the ``--extension-suffix`` option may or may not be available, depending
on the distribution; in the latter case, the module extension can be manually
set to ``.so``.
On Mac OS: the build command is almost the same but it also requires passing
the ``-undefined dynamic_lookup`` flag so as to ignore missing symbols when
building the module:
.. code-block:: bash
$ c++ -O3 -Wall -shared -std=c++11 -undefined dynamic_lookup `python3 -m pybind11 --includes` example.cpp -o example`python3-config --extension-suffix`
In general, it is advisable to include several additional build parameters
that can considerably reduce the size of the created binary. Refer to section
:ref:`cmake` for a detailed example of a suitable cross-platform CMake-based
build system that works on all platforms including Windows.
.. note::
On Linux and macOS, it's better to (intentionally) not link against
``libpython``. The symbols will be resolved when the extension library
is loaded into a Python binary. This is preferable because you might
have several different installations of a given Python version (e.g. the
system-provided Python, and one that ships with a piece of commercial
software). In this way, the plugin will work with both versions, instead
of possibly importing a second Python library into a process that already
contains one (which will lead to a segfault).
Generating binding code automatically
=====================================
The ``Binder`` project is a tool for automatic generation of pybind11 binding
code by introspecting existing C++ codebases using LLVM/Clang. See the
[binder]_ documentation for details.
.. [binder] http://cppbinder.readthedocs.io/en/latest/about.html

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@ -1,332 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# pybind11 documentation build configuration file, created by
# sphinx-quickstart on Sun Oct 11 19:23:48 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys
import os
import shlex
import subprocess
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, os.path.abspath('.'))
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = ['breathe']
breathe_projects = {'pybind11': '.build/doxygenxml/'}
breathe_default_project = 'pybind11'
breathe_domain_by_extension = {'h': 'cpp'}
# Add any paths that contain templates here, relative to this directory.
templates_path = ['.templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = 'pybind11'
copyright = '2017, Wenzel Jakob'
author = 'Wenzel Jakob'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '2.3'
# The full version, including alpha/beta/rc tags.
release = '2.3.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['.build', 'release.rst']
# The reST default role (used for this markup: `text`) to use for all
# documents.
default_role = 'any'
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
#pygments_style = 'monokai'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# If true, keep warnings as "system message" paragraphs in the built documents.
#keep_warnings = False
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False
# -- Options for HTML output ----------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
if not on_rtd: # only import and set the theme if we're building docs locally
import sphinx_rtd_theme
html_theme = 'sphinx_rtd_theme'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
html_context = {
'css_files': [
'_static/theme_overrides.css'
]
}
else:
html_context = {
'css_files': [
'//media.readthedocs.org/css/sphinx_rtd_theme.css',
'//media.readthedocs.org/css/readthedocs-doc-embed.css',
'_static/theme_overrides.css'
]
}
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
#html_extra_path = []
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Language to be used for generating the HTML full-text search index.
# Sphinx supports the following languages:
# 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja'
# 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr'
#html_search_language = 'en'
# A dictionary with options for the search language support, empty by default.
# Now only 'ja' uses this config value
#html_search_options = {'type': 'default'}
# The name of a javascript file (relative to the configuration directory) that
# implements a search results scorer. If empty, the default will be used.
#html_search_scorer = 'scorer.js'
# Output file base name for HTML help builder.
htmlhelp_basename = 'pybind11doc'
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
'preamble': '\DeclareUnicodeCharacter{00A0}{}',
# Latex figure (float) alignment
#'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'pybind11.tex', 'pybind11 Documentation',
'Wenzel Jakob', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
# latex_logo = 'pybind11-logo.png'
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'pybind11', 'pybind11 Documentation',
[author], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output -------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'pybind11', 'pybind11 Documentation',
author, 'pybind11', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
# If true, do not generate a @detailmenu in the "Top" node's menu.
#texinfo_no_detailmenu = False
primary_domain = 'cpp'
highlight_language = 'cpp'
def generate_doxygen_xml(app):
build_dir = os.path.join(app.confdir, '.build')
if not os.path.exists(build_dir):
os.mkdir(build_dir)
try:
subprocess.call(['doxygen', '--version'])
retcode = subprocess.call(['doxygen'], cwd=app.confdir)
if retcode < 0:
sys.stderr.write("doxygen error code: {}\n".format(-retcode))
except OSError as e:
sys.stderr.write("doxygen execution failed: {}\n".format(e))
def setup(app):
"""Add hook for building doxygen xml when needed"""
app.connect("builder-inited", generate_doxygen_xml)

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@ -1,297 +0,0 @@
Frequently asked questions
##########################
"ImportError: dynamic module does not define init function"
===========================================================
1. Make sure that the name specified in PYBIND11_MODULE is identical to the
filename of the extension library (without prefixes such as .so)
2. If the above did not fix the issue, you are likely using an incompatible
version of Python (for instance, the extension library was compiled against
Python 2, while the interpreter is running on top of some version of Python
3, or vice versa).
"Symbol not found: ``__Py_ZeroStruct`` / ``_PyInstanceMethod_Type``"
========================================================================
See the first answer.
"SystemError: dynamic module not initialized properly"
======================================================
See the first answer.
The Python interpreter immediately crashes when importing my module
===================================================================
See the first answer.
CMake doesn't detect the right Python version
=============================================
The CMake-based build system will try to automatically detect the installed
version of Python and link against that. When this fails, or when there are
multiple versions of Python and it finds the wrong one, delete
``CMakeCache.txt`` and then invoke CMake as follows:
.. code-block:: bash
cmake -DPYTHON_EXECUTABLE:FILEPATH=<path-to-python-executable> .
.. _faq_reference_arguments:
Limitations involving reference arguments
=========================================
In C++, it's fairly common to pass arguments using mutable references or
mutable pointers, which allows both read and write access to the value
supplied by the caller. This is sometimes done for efficiency reasons, or to
realize functions that have multiple return values. Here are two very basic
examples:
.. code-block:: cpp
void increment(int &i) { i++; }
void increment_ptr(int *i) { (*i)++; }
In Python, all arguments are passed by reference, so there is no general
issue in binding such code from Python.
However, certain basic Python types (like ``str``, ``int``, ``bool``,
``float``, etc.) are **immutable**. This means that the following attempt
to port the function to Python doesn't have the same effect on the value
provided by the caller -- in fact, it does nothing at all.
.. code-block:: python
def increment(i):
i += 1 # nope..
pybind11 is also affected by such language-level conventions, which means that
binding ``increment`` or ``increment_ptr`` will also create Python functions
that don't modify their arguments.
Although inconvenient, one workaround is to encapsulate the immutable types in
a custom type that does allow modifications.
An other alternative involves binding a small wrapper lambda function that
returns a tuple with all output arguments (see the remainder of the
documentation for examples on binding lambda functions). An example:
.. code-block:: cpp
int foo(int &i) { i++; return 123; }
and the binding code
.. code-block:: cpp
m.def("foo", [](int i) { int rv = foo(i); return std::make_tuple(rv, i); });
How can I reduce the build time?
================================
It's good practice to split binding code over multiple files, as in the
following example:
:file:`example.cpp`:
.. code-block:: cpp
void init_ex1(py::module &);
void init_ex2(py::module &);
/* ... */
PYBIND11_MODULE(example, m) {
init_ex1(m);
init_ex2(m);
/* ... */
}
:file:`ex1.cpp`:
.. code-block:: cpp
void init_ex1(py::module &m) {
m.def("add", [](int a, int b) { return a + b; });
}
:file:`ex2.cpp`:
.. code-block:: cpp
void init_ex2(py::module &m) {
m.def("sub", [](int a, int b) { return a - b; });
}
:command:`python`:
.. code-block:: pycon
>>> import example
>>> example.add(1, 2)
3
>>> example.sub(1, 1)
0
As shown above, the various ``init_ex`` functions should be contained in
separate files that can be compiled independently from one another, and then
linked together into the same final shared object. Following this approach
will:
1. reduce memory requirements per compilation unit.
2. enable parallel builds (if desired).
3. allow for faster incremental builds. For instance, when a single class
definition is changed, only a subset of the binding code will generally need
to be recompiled.
"recursive template instantiation exceeded maximum depth of 256"
================================================================
If you receive an error about excessive recursive template evaluation, try
specifying a larger value, e.g. ``-ftemplate-depth=1024`` on GCC/Clang. The
culprit is generally the generation of function signatures at compile time
using C++14 template metaprogramming.
.. _`faq:hidden_visibility`:
"‘SomeClass’ declared with greater visibility than the type of its field ‘SomeClass::member’ [-Wattributes]"
============================================================================================================
This error typically indicates that you are compiling without the required
``-fvisibility`` flag. pybind11 code internally forces hidden visibility on
all internal code, but if non-hidden (and thus *exported*) code attempts to
include a pybind type (for example, ``py::object`` or ``py::list``) you can run
into this warning.
To avoid it, make sure you are specifying ``-fvisibility=hidden`` when
compiling pybind code.
As to why ``-fvisibility=hidden`` is necessary, because pybind modules could
have been compiled under different versions of pybind itself, it is also
important that the symbols defined in one module do not clash with the
potentially-incompatible symbols defined in another. While Python extension
modules are usually loaded with localized symbols (under POSIX systems
typically using ``dlopen`` with the ``RTLD_LOCAL`` flag), this Python default
can be changed, but even if it isn't it is not always enough to guarantee
complete independence of the symbols involved when not using
``-fvisibility=hidden``.
Additionally, ``-fvisiblity=hidden`` can deliver considerably binary size
savings. (See the following section for more details).
.. _`faq:symhidden`:
How can I create smaller binaries?
==================================
To do its job, pybind11 extensively relies on a programming technique known as
*template metaprogramming*, which is a way of performing computation at compile
time using type information. Template metaprogamming usually instantiates code
involving significant numbers of deeply nested types that are either completely
removed or reduced to just a few instructions during the compiler's optimization
phase. However, due to the nested nature of these types, the resulting symbol
names in the compiled extension library can be extremely long. For instance,
the included test suite contains the following symbol:
.. only:: html
.. code-block:: none
_​_​Z​N​8​p​y​b​i​n​d​1​1​1​2​c​p​p​_​f​u​n​c​t​i​o​n​C​1​I​v​8​E​x​a​m​p​l​e​2​J​R​N​S​t​3​_​_​1​6​v​e​c​t​o​r​I​N​S​3​_​1​2​b​a​s​i​c​_​s​t​r​i​n​g​I​w​N​S​3​_​1​1​c​h​a​r​_​t​r​a​i​t​s​I​w​E​E​N​S​3​_​9​a​l​l​o​c​a​t​o​r​I​w​E​E​E​E​N​S​8​_​I​S​A​_​E​E​E​E​E​J​N​S​_​4​n​a​m​e​E​N​S​_​7​s​i​b​l​i​n​g​E​N​S​_​9​i​s​_​m​e​t​h​o​d​E​A​2​8​_​c​E​E​E​M​T​0​_​F​T​_​D​p​T​1​_​E​D​p​R​K​T​2​_
.. only:: not html
.. code-block:: cpp
__ZN8pybind1112cpp_functionC1Iv8Example2JRNSt3__16vectorINS3_12basic_stringIwNS3_11char_traitsIwEENS3_9allocatorIwEEEENS8_ISA_EEEEEJNS_4nameENS_7siblingENS_9is_methodEA28_cEEEMT0_FT_DpT1_EDpRKT2_
which is the mangled form of the following function type:
.. code-block:: cpp
pybind11::cpp_function::cpp_function<void, Example2, std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&, pybind11::name, pybind11::sibling, pybind11::is_method, char [28]>(void (Example2::*)(std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&), pybind11::name const&, pybind11::sibling const&, pybind11::is_method const&, char const (&) [28])
The memory needed to store just the mangled name of this function (196 bytes)
is larger than the actual piece of code (111 bytes) it represents! On the other
hand, it's silly to even give this function a name -- after all, it's just a
tiny cog in a bigger piece of machinery that is not exposed to the outside
world. So we'll generally only want to export symbols for those functions which
are actually called from the outside.
This can be achieved by specifying the parameter ``-fvisibility=hidden`` to GCC
and Clang, which sets the default symbol visibility to *hidden*, which has a
tremendous impact on the final binary size of the resulting extension library.
(On Visual Studio, symbols are already hidden by default, so nothing needs to
be done there.)
In addition to decreasing binary size, ``-fvisibility=hidden`` also avoids
potential serious issues when loading multiple modules and is required for
proper pybind operation. See the previous FAQ entry for more details.
Working with ancient Visual Studio 2008 builds on Windows
=========================================================
The official Windows distributions of Python are compiled using truly
ancient versions of Visual Studio that lack good C++11 support. Some users
implicitly assume that it would be impossible to load a plugin built with
Visual Studio 2015 into a Python distribution that was compiled using Visual
Studio 2008. However, no such issue exists: it's perfectly legitimate to
interface DLLs that are built with different compilers and/or C libraries.
Common gotchas to watch out for involve not ``free()``-ing memory region
that that were ``malloc()``-ed in another shared library, using data
structures with incompatible ABIs, and so on. pybind11 is very careful not
to make these types of mistakes.
Inconsistent detection of Python version in CMake and pybind11
==============================================================
The functions ``find_package(PythonInterp)`` and ``find_package(PythonLibs)`` provided by CMake
for Python version detection are not used by pybind11 due to unreliability and limitations that make
them unsuitable for pybind11's needs. Instead pybind provides its own, more reliable Python detection
CMake code. Conflicts can arise, however, when using pybind11 in a project that *also* uses the CMake
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:
.. code-block:: cmake
find_package(PythonInterp)
find_package(PythonLibs)
find_package(pybind11)
It will detect Python 2.7 and pybind11 will pick it as well.
In contrast this code:
.. code-block:: cmake
find_package(pybind11)
find_package(PythonInterp)
find_package(PythonLibs)
will detect Python 3.x for pybind11 and may crash on ``find_package(PythonLibs)`` afterwards.
It is advised to avoid using ``find_package(PythonInterp)`` and ``find_package(PythonLibs)`` from CMake and rely
on pybind11 in detecting Python version. If this is not possible CMake machinery should be called *before* including pybind11.
How to cite this project?
=========================
We suggest the following BibTeX template to cite pybind11 in scientific
discourse:
.. code-block:: bash
@misc{pybind11,
author = {Wenzel Jakob and Jason Rhinelander and Dean Moldovan},
year = {2017},
note = {https://github.com/pybind/pybind11},
title = {pybind11 -- Seamless operability between C++11 and Python}
}

47
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.. only: not latex
.. image:: pybind11-logo.png
pybind11 --- Seamless operability between C++11 and Python
==========================================================
.. only: not latex
Contents:
.. toctree::
:maxdepth: 1
intro
changelog
upgrade
.. toctree::
:caption: The Basics
:maxdepth: 2
basics
classes
compiling
.. toctree::
:caption: Advanced Topics
:maxdepth: 2
advanced/functions
advanced/classes
advanced/exceptions
advanced/smart_ptrs
advanced/cast/index
advanced/pycpp/index
advanced/embedding
advanced/misc
.. toctree::
:caption: Extra Information
:maxdepth: 1
faq
benchmark
limitations
reference

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.. image:: pybind11-logo.png
About this project
==================
**pybind11** is a lightweight header-only library that exposes C++ types in Python
and vice versa, mainly to create Python bindings of existing C++ code. Its
goals and syntax are similar to the excellent `Boost.Python`_ library by David
Abrahams: to minimize boilerplate code in traditional extension modules by
inferring type information using compile-time introspection.
.. _Boost.Python: http://www.boost.org/doc/libs/release/libs/python/doc/index.html
The main issue with Boost.Python—and the reason for creating such a similar
project—is Boost. Boost is an enormously large and complex suite of utility
libraries that works with almost every C++ compiler in existence. This
compatibility has its cost: arcane template tricks and workarounds are
necessary to support the oldest and buggiest of compiler specimens. Now that
C++11-compatible compilers are widely available, this heavy machinery has
become an excessively large and unnecessary dependency.
Think of this library as a tiny self-contained version of Boost.Python with
everything stripped away that isn't relevant for binding generation. Without
comments, the core header files only require ~4K lines of code and depend on
Python (2.7 or 3.x, or PyPy2.7 >= 5.7) and the C++ standard library. This
compact implementation was possible thanks to some of the new C++11 language
features (specifically: tuples, lambda functions and variadic templates). Since
its creation, this library has grown beyond Boost.Python in many ways, leading
to dramatically simpler binding code in many common situations.
Core features
*************
The following core C++ features can be mapped to Python
- Functions accepting and returning custom data structures per value, reference, or pointer
- Instance methods and static methods
- Overloaded functions
- Instance attributes and static attributes
- Arbitrary exception types
- Enumerations
- Callbacks
- Iterators and ranges
- Custom operators
- Single and multiple inheritance
- STL data structures
- Smart pointers with reference counting like ``std::shared_ptr``
- Internal references with correct reference counting
- C++ classes with virtual (and pure virtual) methods can be extended in Python
Goodies
*******
In addition to the core functionality, pybind11 provides some extra goodies:
- Python 2.7, 3.x, and PyPy (PyPy2.7 >= 5.7) are supported with an
implementation-agnostic interface.
- It is possible to bind C++11 lambda functions with captured variables. The
lambda capture data is stored inside the resulting Python function object.
- pybind11 uses C++11 move constructors and move assignment operators whenever
possible to efficiently transfer custom data types.
- It's easy to expose the internal storage of custom data types through
Pythons' buffer protocols. This is handy e.g. for fast conversion between
C++ matrix classes like Eigen and NumPy without expensive copy operations.
- pybind11 can automatically vectorize functions so that they are transparently
applied to all entries of one or more NumPy array arguments.
- Python's slice-based access and assignment operations can be supported with
just a few lines of code.
- Everything is contained in just a few header files; there is no need to link
against any additional libraries.
- Binaries are generally smaller by a factor of at least 2 compared to
equivalent bindings generated by Boost.Python. A recent pybind11 conversion
of `PyRosetta`_, an enormous Boost.Python binding project, reported a binary
size reduction of **5.4x** and compile time reduction by **5.8x**.
- Function signatures are precomputed at compile time (using ``constexpr``),
leading to smaller binaries.
- With little extra effort, C++ types can be pickled and unpickled similar to
regular Python objects.
.. _PyRosetta: http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf
Supported compilers
*******************
1. Clang/LLVM (any non-ancient version with C++11 support)
2. GCC 4.8 or newer
3. Microsoft Visual Studio 2015 or newer
4. Intel C++ compiler v17 or newer (v16 with pybind11 v2.0 and v15 with pybind11 v2.0 and a `workaround <https://github.com/pybind/pybind11/issues/276>`_ )

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Limitations
###########
pybind11 strives to be a general solution to binding generation, but it also has
certain limitations:
- pybind11 casts away ``const``-ness in function arguments and return values.
This is in line with the Python language, which has no concept of ``const``
values. This means that some additional care is needed to avoid bugs that
would be caught by the type checker in a traditional C++ program.
- The NumPy interface ``pybind11::array`` greatly simplifies accessing
numerical data from C++ (and vice versa), but it's not a full-blown array
class like ``Eigen::Array`` or ``boost.multi_array``.
These features could be implemented but would lead to a significant increase in
complexity. I've decided to draw the line here to keep this project simple and
compact. Users who absolutely require these features are encouraged to fork
pybind11.

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python/pybind11/docs/reference.rst
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.. _reference:
.. warning::
Please be advised that the reference documentation discussing pybind11
internals is currently incomplete. Please refer to the previous sections
and the pybind11 header files for the nitty gritty details.
Reference
#########
.. _macros:
Macros
======
.. doxygendefine:: PYBIND11_MODULE
.. _core_types:
Convenience classes for arbitrary Python types
==============================================
Common member functions
-----------------------
.. doxygenclass:: object_api
:members:
Without reference counting
--------------------------
.. doxygenclass:: handle
:members:
With reference counting
-----------------------
.. doxygenclass:: object
:members:
.. doxygenfunction:: reinterpret_borrow
.. doxygenfunction:: reinterpret_steal
Convenience classes for specific Python types
=============================================
.. doxygenclass:: module
:members:
.. doxygengroup:: pytypes
:members:
.. _extras:
Passing extra arguments to ``def`` or ``class_``
================================================
.. doxygengroup:: annotations
:members:
Embedding the interpreter
=========================
.. doxygendefine:: PYBIND11_EMBEDDED_MODULE
.. doxygenfunction:: initialize_interpreter
.. doxygenfunction:: finalize_interpreter
.. doxygenclass:: scoped_interpreter
Redirecting C++ streams
=======================
.. doxygenclass:: scoped_ostream_redirect
.. doxygenclass:: scoped_estream_redirect
.. doxygenfunction:: add_ostream_redirect
Python built-in functions
=========================
.. doxygengroup:: python_builtins
:members:
Inheritance
===========
See :doc:`/classes` and :doc:`/advanced/classes` for more detail.
.. doxygendefine:: PYBIND11_OVERLOAD
.. doxygendefine:: PYBIND11_OVERLOAD_PURE
.. doxygendefine:: PYBIND11_OVERLOAD_NAME
.. doxygendefine:: PYBIND11_OVERLOAD_PURE_NAME
.. doxygenfunction:: get_overload
Exceptions
==========
.. doxygenclass:: error_already_set
:members:
.. doxygenclass:: builtin_exception
:members:
Literals
========
.. doxygennamespace:: literals

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python/pybind11/docs/release.rst
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To release a new version of pybind11:
- Update the version number and push to pypi
- Update ``pybind11/_version.py`` (set release version, remove 'dev').
- Update ``PYBIND11_VERSION_MAJOR`` etc. in ``include/pybind11/detail/common.h``.
- Ensure that all the information in ``setup.py`` is up-to-date.
- Update version in ``docs/conf.py``.
- Tag release date in ``docs/changelog.rst``.
- ``git add`` and ``git commit``.
- if new minor version: ``git checkout -b vX.Y``, ``git push -u origin vX.Y``
- ``git tag -a vX.Y.Z -m 'vX.Y.Z release'``.
- ``git push``
- ``git push --tags``.
- ``python setup.py sdist upload``.
- ``python setup.py bdist_wheel upload``.
- Update conda-forge (https://github.com/conda-forge/pybind11-feedstock) via PR
- download release package from Github: ``wget https://github.com/pybind/pybind11/archive/vX.Y.Z.tar.gz``
- compute checksum: ``shasum -a 256 vX.Y.Z.tar.gz``
- change version number and checksum in ``recipe/meta.yml``
- Get back to work
- Update ``_version.py`` (add 'dev' and increment minor).
- Update version in ``docs/conf.py``
- Update version macros in ``include/pybind11/common.h``
- ``git add`` and ``git commit``.
``git push``

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python/pybind11/docs/requirements.txt
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breathe == 4.5.0

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python/pybind11/docs/upgrade.rst
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Upgrade guide
#############
This is a companion guide to the :doc:`changelog`. While the changelog briefly
lists all of the new features, improvements and bug fixes, this upgrade guide
focuses only the subset which directly impacts your experience when upgrading
to a new version. But it goes into more detail. This includes things like
deprecated APIs and their replacements, build system changes, general code
modernization and other useful information.
v2.2
====
Deprecation of the ``PYBIND11_PLUGIN`` macro
--------------------------------------------
``PYBIND11_MODULE`` is now the preferred way to create module entry points.
The old macro emits a compile-time deprecation warning.
.. code-block:: cpp
// old
PYBIND11_PLUGIN(example) {
py::module m("example", "documentation string");
m.def("add", [](int a, int b) { return a + b; });
return m.ptr();
}
// new
PYBIND11_MODULE(example, m) {
m.doc() = "documentation string"; // optional
m.def("add", [](int a, int b) { return a + b; });
}
New API for defining custom constructors and pickling functions
---------------------------------------------------------------
The old placement-new custom constructors have been deprecated. The new approach
uses ``py::init()`` and factory functions to greatly improve type safety.
Placement-new can be called accidentally with an incompatible type (without any
compiler errors or warnings), or it can initialize the same object multiple times
if not careful with the Python-side ``__init__`` calls. The new-style custom
constructors prevent such mistakes. See :ref:`custom_constructors` for details.
.. code-block:: cpp
// old -- deprecated (runtime warning shown only in debug mode)
py::class<Foo>(m, "Foo")
.def("__init__", [](Foo &self, ...) {
new (&self) Foo(...); // uses placement-new
});
// new
py::class<Foo>(m, "Foo")
.def(py::init([](...) { // Note: no `self` argument
return new Foo(...); // return by raw pointer
// or: return std::make_unique<Foo>(...); // return by holder
// or: return Foo(...); // return by value (move constructor)
}));
Mirroring the custom constructor changes, ``py::pickle()`` is now the preferred
way to get and set object state. See :ref:`pickling` for details.
.. code-block:: cpp
// old -- deprecated (runtime warning shown only in debug mode)
py::class<Foo>(m, "Foo")
...
.def("__getstate__", [](const Foo &self) {
return py::make_tuple(self.value1(), self.value2(), ...);
})
.def("__setstate__", [](Foo &self, py::tuple t) {
new (&self) Foo(t[0].cast<std::string>(), ...);
});
// new
py::class<Foo>(m, "Foo")
...
.def(py::pickle(
[](const Foo &self) { // __getstate__
return py::make_tuple(f.value1(), f.value2(), ...); // unchanged
},
[](py::tuple t) { // __setstate__, note: no `self` argument
return new Foo(t[0].cast<std::string>(), ...);
// or: return std::make_unique<Foo>(...); // return by holder
// or: return Foo(...); // return by value (move constructor)
}
));
For both the constructors and pickling, warnings are shown at module
initialization time (on import, not when the functions are called).
They're only visible when compiled in debug mode. Sample warning:
.. code-block:: none
pybind11-bound class 'mymodule.Foo' is using an old-style placement-new '__init__'
which has been deprecated. See the upgrade guide in pybind11's docs.
Stricter enforcement of hidden symbol visibility for pybind11 modules
---------------------------------------------------------------------
pybind11 now tries to actively enforce hidden symbol visibility for modules.
If you're using either one of pybind11's :doc:`CMake or Python build systems
<compiling>` (the two example repositories) and you haven't been exporting any
symbols, there's nothing to be concerned about. All the changes have been done
transparently in the background. If you were building manually or relied on
specific default visibility, read on.
Setting default symbol visibility to *hidden* has always been recommended for
pybind11 (see :ref:`faq:symhidden`). On Linux and macOS, hidden symbol
visibility (in conjunction with the ``strip`` utility) yields much smaller
module binaries. `CPython's extension docs`_ also recommend hiding symbols
by default, with the goal of avoiding symbol name clashes between modules.
Starting with v2.2, pybind11 enforces this more strictly: (1) by declaring
all symbols inside the ``pybind11`` namespace as hidden and (2) by including
the ``-fvisibility=hidden`` flag on Linux and macOS (only for extension
modules, not for embedding the interpreter).
.. _CPython's extension docs: https://docs.python.org/3/extending/extending.html#providing-a-c-api-for-an-extension-module
The namespace-scope hidden visibility is done automatically in pybind11's
headers and it's generally transparent to users. It ensures that:
* Modules compiled with different pybind11 versions don't clash with each other.
* Some new features, like ``py::module_local`` bindings, can work as intended.
The ``-fvisibility=hidden`` flag applies the same visibility to user bindings
outside of the ``pybind11`` namespace. It's now set automatic by pybind11's
CMake and Python build systems, but this needs to be done manually by users
of other build systems. Adding this flag:
* Minimizes the chances of symbol conflicts between modules. E.g. if two
unrelated modules were statically linked to different (ABI-incompatible)
versions of the same third-party library, a symbol clash would be likely
(and would end with unpredictable results).
* Produces smaller binaries on Linux and macOS, as pointed out previously.
Within pybind11's CMake build system, ``pybind11_add_module`` has always been
setting the ``-fvisibility=hidden`` flag in release mode. From now on, it's
being applied unconditionally, even in debug mode and it can no longer be opted
out of with the ``NO_EXTRAS`` option. The ``pybind11::module`` target now also
adds this flag to it's interface. The ``pybind11::embed`` target is unchanged.
The most significant change here is for the ``pybind11::module`` target. If you
were previously relying on default visibility, i.e. if your Python module was
doubling as a shared library with dependents, you'll need to either export
symbols manually (recommended for cross-platform libraries) or factor out the
shared library (and have the Python module link to it like the other
dependents). As a temporary workaround, you can also restore default visibility
using the CMake code below, but this is not recommended in the long run:
.. code-block:: cmake
target_link_libraries(mymodule PRIVATE pybind11::module)
add_library(restore_default_visibility INTERFACE)
target_compile_options(restore_default_visibility INTERFACE -fvisibility=default)
target_link_libraries(mymodule PRIVATE restore_default_visibility)
Local STL container bindings
----------------------------
Previous pybind11 versions could only bind types globally -- all pybind11
modules, even unrelated ones, would have access to the same exported types.
However, this would also result in a conflict if two modules exported the
same C++ type, which is especially problematic for very common types, e.g.
``std::vector<int>``. :ref:`module_local` were added to resolve this (see
that section for a complete usage guide).
``py::class_`` still defaults to global bindings (because these types are
usually unique across modules), however in order to avoid clashes of opaque
types, ``py::bind_vector`` and ``py::bind_map`` will now bind STL containers
as ``py::module_local`` if their elements are: builtins (``int``, ``float``,
etc.), not bound using ``py::class_``, or bound as ``py::module_local``. For
example, this change allows multiple modules to bind ``std::vector<int>``
without causing conflicts. See :ref:`stl_bind` for more details.
When upgrading to this version, if you have multiple modules which depend on
a single global binding of an STL container, note that all modules can still
accept foreign ``py::module_local`` types in the direction of Python-to-C++.
The locality only affects the C++-to-Python direction. If this is needed in
multiple modules, you'll need to either:
* Add a copy of the same STL binding to all of the modules which need it.
* Restore the global status of that single binding by marking it
``py::module_local(false)``.
The latter is an easy workaround, but in the long run it would be best to
localize all common type bindings in order to avoid conflicts with
third-party modules.
Negative strides for Python buffer objects and numpy arrays
-----------------------------------------------------------
Support for negative strides required changing the integer type from unsigned
to signed in the interfaces of ``py::buffer_info`` and ``py::array``. If you
have compiler warnings enabled, you may notice some new conversion warnings
after upgrading. These can be resolved using ``static_cast``.
Deprecation of some ``py::object`` APIs
---------------------------------------
To compare ``py::object`` instances by pointer, you should now use
``obj1.is(obj2)`` which is equivalent to ``obj1 is obj2`` in Python.
Previously, pybind11 used ``operator==`` for this (``obj1 == obj2``), but
that could be confusing and is now deprecated (so that it can eventually
be replaced with proper rich object comparison in a future release).
For classes which inherit from ``py::object``, ``borrowed`` and ``stolen``
were previously available as protected constructor tags. Now the types
should be used directly instead: ``borrowed_t{}`` and ``stolen_t{}``
(`#771 <https://github.com/pybind/pybind11/pull/771>`_).
Stricter compile-time error checking
------------------------------------
Some error checks have been moved from run time to compile time. Notably,
automatic conversion of ``std::shared_ptr<T>`` is not possible when ``T`` is
not directly registered with ``py::class_<T>`` (e.g. ``std::shared_ptr<int>``
or ``std::shared_ptr<std::vector<T>>`` are not automatically convertible).
Attempting to bind a function with such arguments now results in a compile-time
error instead of waiting to fail at run time.
``py::init<...>()`` constructor definitions are also stricter and now prevent
bindings which could cause unexpected behavior:
.. code-block:: cpp
struct Example {
Example(int &);
};
py::class_<Example>(m, "Example")
.def(py::init<int &>()); // OK, exact match
// .def(py::init<int>()); // compile-time error, mismatch
A non-``const`` lvalue reference is not allowed to bind to an rvalue. However,
note that a constructor taking ``const T &`` can still be registered using
``py::init<T>()`` because a ``const`` lvalue reference can bind to an rvalue.
v2.1
====
Minimum compiler versions are enforced at compile time
------------------------------------------------------
The minimums also apply to v2.0 but the check is now explicit and a compile-time
error is raised if the compiler does not meet the requirements:
* GCC >= 4.8
* clang >= 3.3 (appleclang >= 5.0)
* MSVC >= 2015u3
* Intel C++ >= 15.0
The ``py::metaclass`` attribute is not required for static properties
---------------------------------------------------------------------
Binding classes with static properties is now possible by default. The
zero-parameter version of ``py::metaclass()`` is deprecated. However, a new
one-parameter ``py::metaclass(python_type)`` version was added for rare
cases when a custom metaclass is needed to override pybind11's default.
.. code-block:: cpp
// old -- emits a deprecation warning
py::class_<Foo>(m, "Foo", py::metaclass())
.def_property_readonly_static("foo", ...);
// new -- static properties work without the attribute
py::class_<Foo>(m, "Foo")
.def_property_readonly_static("foo", ...);
// new -- advanced feature, override pybind11's default metaclass
py::class_<Bar>(m, "Bar", py::metaclass(custom_python_type))
...
v2.0
====
Breaking changes in ``py::class_``
----------------------------------
These changes were necessary to make type definitions in pybind11
future-proof, to support PyPy via its ``cpyext`` mechanism (`#527
<https://github.com/pybind/pybind11/pull/527>`_), and to improve efficiency
(`rev. 86d825 <https://github.com/pybind/pybind11/commit/86d825>`_).
1. Declarations of types that provide access via the buffer protocol must
now include the ``py::buffer_protocol()`` annotation as an argument to
the ``py::class_`` constructor.
.. code-block:: cpp
py::class_<Matrix>("Matrix", py::buffer_protocol())
.def(py::init<...>())
.def_buffer(...);
2. Classes which include static properties (e.g. ``def_readwrite_static()``)
must now include the ``py::metaclass()`` attribute. Note: this requirement
has since been removed in v2.1. If you're upgrading from 1.x, it's
recommended to skip directly to v2.1 or newer.
3. This version of pybind11 uses a redesigned mechanism for instantiating
trampoline classes that are used to override virtual methods from within
Python. This led to the following user-visible syntax change:
.. code-block:: cpp
// old v1.x syntax
py::class_<TrampolineClass>("MyClass")
.alias<MyClass>()
...
// new v2.x syntax
py::class_<MyClass, TrampolineClass>("MyClass")
...
Importantly, both the original and the trampoline class are now specified
as arguments to the ``py::class_`` template, and the ``alias<..>()`` call
is gone. The new scheme has zero overhead in cases when Python doesn't
override any functions of the underlying C++ class.
`rev. 86d825 <https://github.com/pybind/pybind11/commit/86d825>`_.
The class type must be the first template argument given to ``py::class_``
while the trampoline can be mixed in arbitrary order with other arguments
(see the following section).
Deprecation of the ``py::base<T>()`` attribute
----------------------------------------------
``py::base<T>()`` was deprecated in favor of specifying ``T`` as a template
argument to ``py::class_``. This new syntax also supports multiple inheritance.
Note that, while the type being exported must be the first argument in the
``py::class_<Class, ...>`` template, the order of the following types (bases,
holder and/or trampoline) is not important.
.. code-block:: cpp
// old v1.x
py::class_<Derived>("Derived", py::base<Base>());
// new v2.x
py::class_<Derived, Base>("Derived");
// new -- multiple inheritance
py::class_<Derived, Base1, Base2>("Derived");
// new -- apart from `Derived` the argument order can be arbitrary
py::class_<Derived, Base1, Holder, Base2, Trampoline>("Derived");
Out-of-the-box support for ``std::shared_ptr``
----------------------------------------------
The relevant type caster is now built in, so it's no longer necessary to
include a declaration of the form:
.. code-block:: cpp
PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>)
Continuing to do so won’t cause an error or even a deprecation warning,
but it's completely redundant.
Deprecation of a few ``py::object`` APIs
----------------------------------------
All of the old-style calls emit deprecation warnings.
+---------------------------------------+---------------------------------------------+
| Old syntax | New syntax |
+=======================================+=============================================+
| ``obj.call(args...)`` | ``obj(args...)`` |
+---------------------------------------+---------------------------------------------+
| ``obj.str()`` | ``py::str(obj)`` |
+---------------------------------------+---------------------------------------------+
| ``auto l = py::list(obj); l.check()`` | ``py::isinstance<py::list>(obj)`` |
+---------------------------------------+---------------------------------------------+
| ``py::object(ptr, true)`` | ``py::reinterpret_borrow<py::object>(ptr)`` |
+---------------------------------------+---------------------------------------------+
| ``py::object(ptr, false)`` | ``py::reinterpret_steal<py::object>(ptr)`` |
+---------------------------------------+---------------------------------------------+
| ``if (obj.attr("foo"))`` | ``if (py::hasattr(obj, "foo"))`` |
+---------------------------------------+---------------------------------------------+
| ``if (obj["bar"])`` | ``if (obj.contains("bar"))`` |
+---------------------------------------+---------------------------------------------+

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/*
pybind11/attr.h: Infrastructure for processing custom
type and function attributes
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "cast.h"
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
/// \addtogroup annotations
/// @{
/// Annotation for methods
struct is_method { handle class_; is_method(const handle &c) : class_(c) { } };
/// Annotation for operators
struct is_operator { };
/// Annotation for parent scope
struct scope { handle value; scope(const handle &s) : value(s) { } };
/// Annotation for documentation
struct doc { const char *value; doc(const char *value) : value(value) { } };
/// Annotation for function names
struct name { const char *value; name(const char *value) : value(value) { } };
/// Annotation indicating that a function is an overload associated with a given "sibling"
struct sibling { handle value; sibling(const handle &value) : value(value.ptr()) { } };
/// Annotation indicating that a class derives from another given type
template <typename T> struct base {
PYBIND11_DEPRECATED("base<T>() was deprecated in favor of specifying 'T' as a template argument to class_")
base() { }
};
/// Keep patient alive while nurse lives
template <size_t Nurse, size_t Patient> struct keep_alive { };
/// Annotation indicating that a class is involved in a multiple inheritance relationship
struct multiple_inheritance { };
/// Annotation which enables dynamic attributes, i.e. adds `__dict__` to a class
struct dynamic_attr { };
/// Annotation which enables the buffer protocol for a type
struct buffer_protocol { };
/// Annotation which requests that a special metaclass is created for a type
struct metaclass {
handle value;
PYBIND11_DEPRECATED("py::metaclass() is no longer required. It's turned on by default now.")
metaclass() {}
/// Override pybind11's default metaclass
explicit metaclass(handle value) : value(value) { }
};
/// Annotation that marks a class as local to the module:
struct module_local { const bool value; constexpr module_local(bool v = true) : value(v) { } };
/// Annotation to mark enums as an arithmetic type
struct arithmetic { };
/** \rst
A call policy which places one or more guard variables (``Ts...``) around the function call.
For example, this definition:
.. code-block:: cpp
m.def("foo", foo, py::call_guard<T>());
is equivalent to the following pseudocode:
.. code-block:: cpp
m.def("foo", [](args...) {
T scope_guard;
return foo(args...); // forwarded arguments
});
\endrst */
template <typename... Ts> struct call_guard;
template <> struct call_guard<> { using type = detail::void_type; };
template <typename T>
struct call_guard<T> {
static_assert(std::is_default_constructible<T>::value,
"The guard type must be default constructible");
using type = T;
};
template <typename T, typename... Ts>
struct call_guard<T, Ts...> {
struct type {
T guard{}; // Compose multiple guard types with left-to-right default-constructor order
typename call_guard<Ts...>::type next{};
};
};
/// @} annotations
NAMESPACE_BEGIN(detail)
/* Forward declarations */
enum op_id : int;
enum op_type : int;
struct undefined_t;
template <op_id id, op_type ot, typename L = undefined_t, typename R = undefined_t> struct op_;
inline void keep_alive_impl(size_t Nurse, size_t Patient, function_call &call, handle ret);
/// Internal data structure which holds metadata about a keyword argument
struct argument_record {
const char *name; ///< Argument name
const char *descr; ///< Human-readable version of the argument value
handle value; ///< Associated Python object
bool convert : 1; ///< True if the argument is allowed to convert when loading
bool none : 1; ///< True if None is allowed when loading
argument_record(const char *name, const char *descr, handle value, bool convert, bool none)
: name(name), descr(descr), value(value), convert(convert), none(none) { }
};
/// Internal data structure which holds metadata about a bound function (signature, overloads, etc.)
struct function_record {
function_record()
: is_constructor(false), is_new_style_constructor(false), is_stateless(false),
is_operator(false), has_args(false), has_kwargs(false), is_method(false) { }
/// Function name
char *name = nullptr; /* why no C++ strings? They generate heavier code.. */
// User-specified documentation string
char *doc = nullptr;
/// Human-readable version of the function signature
char *signature = nullptr;
/// List of registered keyword arguments
std::vector<argument_record> args;
/// Pointer to lambda function which converts arguments and performs the actual call
handle (*impl) (function_call &) = nullptr;
/// Storage for the wrapped function pointer and captured data, if any
void *data[3] = { };
/// Pointer to custom destructor for 'data' (if needed)
void (*free_data) (function_record *ptr) = nullptr;
/// Return value policy associated with this function
return_value_policy policy = return_value_policy::automatic;
/// True if name == '__init__'
bool is_constructor : 1;
/// True if this is a new-style `__init__` defined in `detail/init.h`
bool is_new_style_constructor : 1;
/// True if this is a stateless function pointer
bool is_stateless : 1;
/// True if this is an operator (__add__), etc.
bool is_operator : 1;
/// True if the function has a '*args' argument
bool has_args : 1;
/// True if the function has a '**kwargs' argument
bool has_kwargs : 1;
/// True if this is a method
bool is_method : 1;
/// Number of arguments (including py::args and/or py::kwargs, if present)
std::uint16_t nargs;
/// Python method object
PyMethodDef *def = nullptr;
/// Python handle to the parent scope (a class or a module)
handle scope;
/// Python handle to the sibling function representing an overload chain
handle sibling;
/// Pointer to next overload
function_record *next = nullptr;
};
/// Special data structure which (temporarily) holds metadata about a bound class
struct type_record {
PYBIND11_NOINLINE type_record()
: multiple_inheritance(false), dynamic_attr(false), buffer_protocol(false),
default_holder(true), module_local(false) { }
/// Handle to the parent scope
handle scope;
/// Name of the class
const char *name = nullptr;
// Pointer to RTTI type_info data structure
const std::type_info *type = nullptr;
/// How large is the underlying C++ type?
size_t type_size = 0;
/// What is the alignment of the underlying C++ type?
size_t type_align = 0;
/// How large is the type's holder?
size_t holder_size = 0;
/// The global operator new can be overridden with a class-specific variant
void *(*operator_new)(size_t) = nullptr;
/// Function pointer to class_<..>::init_instance
void (*init_instance)(instance *, const void *) = nullptr;
/// Function pointer to class_<..>::dealloc
void (*dealloc)(detail::value_and_holder &) = nullptr;
/// List of base classes of the newly created type
list bases;
/// Optional docstring
const char *doc = nullptr;
/// Custom metaclass (optional)
handle metaclass;
/// Multiple inheritance marker
bool multiple_inheritance : 1;
/// Does the class manage a __dict__?
bool dynamic_attr : 1;
/// Does the class implement the buffer protocol?
bool buffer_protocol : 1;
/// Is the default (unique_ptr) holder type used?
bool default_holder : 1;
/// Is the class definition local to the module shared object?
bool module_local : 1;
PYBIND11_NOINLINE void add_base(const std::type_info &base, void *(*caster)(void *)) {
auto base_info = detail::get_type_info(base, false);
if (!base_info) {
std::string tname(base.name());
detail::clean_type_id(tname);
pybind11_fail("generic_type: type \"" + std::string(name) +
"\" referenced unknown base type \"" + tname + "\"");
}
if (default_holder != base_info->default_holder) {
std::string tname(base.name());
detail::clean_type_id(tname);
pybind11_fail("generic_type: type \"" + std::string(name) + "\" " +
(default_holder ? "does not have" : "has") +
" a non-default holder type while its base \"" + tname + "\" " +
(base_info->default_holder ? "does not" : "does"));
}
bases.append((PyObject *) base_info->type);
if (base_info->type->tp_dictoffset != 0)
dynamic_attr = true;
if (caster)
base_info->implicit_casts.emplace_back(type, caster);
}
};
inline function_call::function_call(const function_record &f, handle p) :
func(f), parent(p) {
args.reserve(f.nargs);
args_convert.reserve(f.nargs);
}
/// Tag for a new-style `__init__` defined in `detail/init.h`
struct is_new_style_constructor { };
/**
* Partial template specializations to process custom attributes provided to
* cpp_function_ and class_. These are either used to initialize the respective
* fields in the type_record and function_record data structures or executed at
* runtime to deal with custom call policies (e.g. keep_alive).
*/
template <typename T, typename SFINAE = void> struct process_attribute;
template <typename T> struct process_attribute_default {
/// Default implementation: do nothing
static void init(const T &, function_record *) { }
static void init(const T &, type_record *) { }
static void precall(function_call &) { }
static void postcall(function_call &, handle) { }
};
/// Process an attribute specifying the function's name
template <> struct process_attribute<name> : process_attribute_default<name> {
static void init(const name &n, function_record *r) { r->name = const_cast<char *>(n.value); }
};
/// Process an attribute specifying the function's docstring
template <> struct process_attribute<doc> : process_attribute_default<doc> {
static void init(const doc &n, function_record *r) { r->doc = const_cast<char *>(n.value); }
};
/// Process an attribute specifying the function's docstring (provided as a C-style string)
template <> struct process_attribute<const char *> : process_attribute_default<const char *> {
static void init(const char *d, function_record *r) { r->doc = const_cast<char *>(d); }
static void init(const char *d, type_record *r) { r->doc = const_cast<char *>(d); }
};
template <> struct process_attribute<char *> : process_attribute<const char *> { };
/// Process an attribute indicating the function's return value policy
template <> struct process_attribute<return_value_policy> : process_attribute_default<return_value_policy> {
static void init(const return_value_policy &p, function_record *r) { r->policy = p; }
};
/// Process an attribute which indicates that this is an overloaded function associated with a given sibling
template <> struct process_attribute<sibling> : process_attribute_default<sibling> {
static void init(const sibling &s, function_record *r) { r->sibling = s.value; }
};
/// Process an attribute which indicates that this function is a method
template <> struct process_attribute<is_method> : process_attribute_default<is_method> {
static void init(const is_method &s, function_record *r) { r->is_method = true; r->scope = s.class_; }
};
/// Process an attribute which indicates the parent scope of a method
template <> struct process_attribute<scope> : process_attribute_default<scope> {
static void init(const scope &s, function_record *r) { r->scope = s.value; }
};
/// Process an attribute which indicates that this function is an operator
template <> struct process_attribute<is_operator> : process_attribute_default<is_operator> {
static void init(const is_operator &, function_record *r) { r->is_operator = true; }
};
template <> struct process_attribute<is_new_style_constructor> : process_attribute_default<is_new_style_constructor> {
static void init(const is_new_style_constructor &, function_record *r) { r->is_new_style_constructor = true; }
};
/// Process a keyword argument attribute (*without* a default value)
template <> struct process_attribute<arg> : process_attribute_default<arg> {
static void init(const arg &a, function_record *r) {
if (r->is_method && r->args.empty())
r->args.emplace_back("self", nullptr, handle(), true /*convert*/, false /*none not allowed*/);
r->args.emplace_back(a.name, nullptr, handle(), !a.flag_noconvert, a.flag_none);
}
};
/// Process a keyword argument attribute (*with* a default value)
template <> struct process_attribute<arg_v> : process_attribute_default<arg_v> {
static void init(const arg_v &a, function_record *r) {
if (r->is_method && r->args.empty())
r->args.emplace_back("self", nullptr /*descr*/, handle() /*parent*/, true /*convert*/, false /*none not allowed*/);
if (!a.value) {
#if !defined(NDEBUG)
std::string descr("'");
if (a.name) descr += std::string(a.name) + ": ";
descr += a.type + "'";
if (r->is_method) {
if (r->name)
descr += " in method '" + (std::string) str(r->scope) + "." + (std::string) r->name + "'";
else
descr += " in method of '" + (std::string) str(r->scope) + "'";
} else if (r->name) {
descr += " in function '" + (std::string) r->name + "'";
}
pybind11_fail("arg(): could not convert default argument "
+ descr + " into a Python object (type not registered yet?)");
#else
pybind11_fail("arg(): could not convert default argument "
"into a Python object (type not registered yet?). "
"Compile in debug mode for more information.");
#endif
}
r->args.emplace_back(a.name, a.descr, a.value.inc_ref(), !a.flag_noconvert, a.flag_none);
}
};
/// Process a parent class attribute. Single inheritance only (class_ itself already guarantees that)
template <typename T>
struct process_attribute<T, enable_if_t<is_pyobject<T>::value>> : process_attribute_default<handle> {
static void init(const handle &h, type_record *r) { r->bases.append(h); }
};
/// Process a parent class attribute (deprecated, does not support multiple inheritance)
template <typename T>
struct process_attribute<base<T>> : process_attribute_default<base<T>> {
static void init(const base<T> &, type_record *r) { r->add_base(typeid(T), nullptr); }
};
/// Process a multiple inheritance attribute
template <>
struct process_attribute<multiple_inheritance> : process_attribute_default<multiple_inheritance> {
static void init(const multiple_inheritance &, type_record *r) { r->multiple_inheritance = true; }
};
template <>
struct process_attribute<dynamic_attr> : process_attribute_default<dynamic_attr> {
static void init(const dynamic_attr &, type_record *r) { r->dynamic_attr = true; }
};
template <>
struct process_attribute<buffer_protocol> : process_attribute_default<buffer_protocol> {
static void init(const buffer_protocol &, type_record *r) { r->buffer_protocol = true; }
};
template <>
struct process_attribute<metaclass> : process_attribute_default<metaclass> {
static void init(const metaclass &m, type_record *r) { r->metaclass = m.value; }
};
template <>
struct process_attribute<module_local> : process_attribute_default<module_local> {
static void init(const module_local &l, type_record *r) { r->module_local = l.value; }
};
/// Process an 'arithmetic' attribute for enums (does nothing here)
template <>
struct process_attribute<arithmetic> : process_attribute_default<arithmetic> {};
template <typename... Ts>
struct process_attribute<call_guard<Ts...>> : process_attribute_default<call_guard<Ts...>> { };
/**
* Process a keep_alive call policy -- invokes keep_alive_impl during the
* pre-call handler if both Nurse, Patient != 0 and use the post-call handler
* otherwise
*/
template <size_t Nurse, size_t Patient> struct process_attribute<keep_alive<Nurse, Patient>> : public process_attribute_default<keep_alive<Nurse, Patient>> {
template <size_t N = Nurse, size_t P = Patient, enable_if_t<N != 0 && P != 0, int> = 0>
static void precall(function_call &call) { keep_alive_impl(Nurse, Patient, call, handle()); }
template <size_t N = Nurse, size_t P = Patient, enable_if_t<N != 0 && P != 0, int> = 0>
static void postcall(function_call &, handle) { }
template <size_t N = Nurse, size_t P = Patient, enable_if_t<N == 0 || P == 0, int> = 0>
static void precall(function_call &) { }
template <size_t N = Nurse, size_t P = Patient, enable_if_t<N == 0 || P == 0, int> = 0>
static void postcall(function_call &call, handle ret) { keep_alive_impl(Nurse, Patient, call, ret); }
};
/// Recursively iterate over variadic template arguments
template <typename... Args> struct process_attributes {
static void init(const Args&... args, function_record *r) {
int unused[] = { 0, (process_attribute<typename std::decay<Args>::type>::init(args, r), 0) ... };
ignore_unused(unused);
}
static void init(const Args&... args, type_record *r) {
int unused[] = { 0, (process_attribute<typename std::decay<Args>::type>::init(args, r), 0) ... };
ignore_unused(unused);
}
static void precall(function_call &call) {
int unused[] = { 0, (process_attribute<typename std::decay<Args>::type>::precall(call), 0) ... };
ignore_unused(unused);
}
static void postcall(function_call &call, handle fn_ret) {
int unused[] = { 0, (process_attribute<typename std::decay<Args>::type>::postcall(call, fn_ret), 0) ... };
ignore_unused(unused);
}
};
template <typename T>
using is_call_guard = is_instantiation<call_guard, T>;
/// Extract the ``type`` from the first `call_guard` in `Extras...` (or `void_type` if none found)
template <typename... Extra>
using extract_guard_t = typename exactly_one_t<is_call_guard, call_guard<>, Extra...>::type;
/// Check the number of named arguments at compile time
template <typename... Extra,
size_t named = constexpr_sum(std::is_base_of<arg, Extra>::value...),
size_t self = constexpr_sum(std::is_same<is_method, Extra>::value...)>
constexpr bool expected_num_args(size_t nargs, bool has_args, bool has_kwargs) {
return named == 0 || (self + named + has_args + has_kwargs) == nargs;
}
NAMESPACE_END(detail)
NAMESPACE_END(PYBIND11_NAMESPACE)

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python/pybind11/include/pybind11/buffer_info.h
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/*
pybind11/buffer_info.h: Python buffer object interface
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "detail/common.h"
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
/// Information record describing a Python buffer object
struct buffer_info {
void *ptr = nullptr; // Pointer to the underlying storage
ssize_t itemsize = 0; // Size of individual items in bytes
ssize_t size = 0; // Total number of entries
std::string format; // For homogeneous buffers, this should be set to format_descriptor<T>::format()
ssize_t ndim = 0; // Number of dimensions
std::vector<ssize_t> shape; // Shape of the tensor (1 entry per dimension)
std::vector<ssize_t> strides; // Number of entries between adjacent entries (for each per dimension)
buffer_info() { }
buffer_info(void *ptr, ssize_t itemsize, const std::string &format, ssize_t ndim,
detail::any_container<ssize_t> shape_in, detail::any_container<ssize_t> strides_in)
: ptr(ptr), itemsize(itemsize), size(1), format(format), ndim(ndim),
shape(std::move(shape_in)), strides(std::move(strides_in)) {
if (ndim != (ssize_t) shape.size() || ndim != (ssize_t) strides.size())
pybind11_fail("buffer_info: ndim doesn't match shape and/or strides length");
for (size_t i = 0; i < (size_t) ndim; ++i)
size *= shape[i];
}
template <typename T>
buffer_info(T *ptr, detail::any_container<ssize_t> shape_in, detail::any_container<ssize_t> strides_in)
: buffer_info(private_ctr_tag(), ptr, sizeof(T), format_descriptor<T>::format(), static_cast<ssize_t>(shape_in->size()), std::move(shape_in), std::move(strides_in)) { }
buffer_info(void *ptr, ssize_t itemsize, const std::string &format, ssize_t size)
: buffer_info(ptr, itemsize, format, 1, {size}, {itemsize}) { }
template <typename T>
buffer_info(T *ptr, ssize_t size)
: buffer_info(ptr, sizeof(T), format_descriptor<T>::format(), size) { }
explicit buffer_info(Py_buffer *view, bool ownview = true)
: buffer_info(view->buf, view->itemsize, view->format, view->ndim,
{view->shape, view->shape + view->ndim}, {view->strides, view->strides + view->ndim}) {
this->view = view;
this->ownview = ownview;
}
buffer_info(const buffer_info &) = delete;
buffer_info& operator=(const buffer_info &) = delete;
buffer_info(buffer_info &&other) {
(*this) = std::move(other);
}
buffer_info& operator=(buffer_info &&rhs) {
ptr = rhs.ptr;
itemsize = rhs.itemsize;
size = rhs.size;
format = std::move(rhs.format);
ndim = rhs.ndim;
shape = std::move(rhs.shape);
strides = std::move(rhs.strides);
std::swap(view, rhs.view);
std::swap(ownview, rhs.ownview);
return *this;
}
~buffer_info() {
if (view && ownview) { PyBuffer_Release(view); delete view; }
}
private:
struct private_ctr_tag { };
buffer_info(private_ctr_tag, void *ptr, ssize_t itemsize, const std::string &format, ssize_t ndim,
detail::any_container<ssize_t> &&shape_in, detail::any_container<ssize_t> &&strides_in)
: buffer_info(ptr, itemsize, format, ndim, std::move(shape_in), std::move(strides_in)) { }
Py_buffer *view = nullptr;
bool ownview = false;
};
NAMESPACE_BEGIN(detail)
template <typename T, typename SFINAE = void> struct compare_buffer_info {
static bool compare(const buffer_info& b) {
return b.format == format_descriptor<T>::format() && b.itemsize == (ssize_t) sizeof(T);
}
};
template <typename T> struct compare_buffer_info<T, detail::enable_if_t<std::is_integral<T>::value>> {
static bool compare(const buffer_info& b) {
return (size_t) b.itemsize == sizeof(T) && (b.format == format_descriptor<T>::value ||
((sizeof(T) == sizeof(long)) && b.format == (std::is_unsigned<T>::value ? "L" : "l")) ||
((sizeof(T) == sizeof(size_t)) && b.format == (std::is_unsigned<T>::value ? "N" : "n")));
}
};
NAMESPACE_END(detail)
NAMESPACE_END(PYBIND11_NAMESPACE)

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python/pybind11/include/pybind11/cast.h
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python/pybind11/include/pybind11/chrono.h
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/*
pybind11/chrono.h: Transparent conversion between std::chrono and python's datetime
Copyright (c) 2016 Trent Houliston <trent@houliston.me> and
Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "pybind11.h"
#include <cmath>
#include <ctime>
#include <chrono>
#include <datetime.h>
// Backport the PyDateTime_DELTA functions from Python3.3 if required
#ifndef PyDateTime_DELTA_GET_DAYS
#define PyDateTime_DELTA_GET_DAYS(o) (((PyDateTime_Delta*)o)->days)
#endif
#ifndef PyDateTime_DELTA_GET_SECONDS
#define PyDateTime_DELTA_GET_SECONDS(o) (((PyDateTime_Delta*)o)->seconds)
#endif
#ifndef PyDateTime_DELTA_GET_MICROSECONDS
#define PyDateTime_DELTA_GET_MICROSECONDS(o) (((PyDateTime_Delta*)o)->microseconds)
#endif
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
template <typename type> class duration_caster {
public:
typedef typename type::rep rep;
typedef typename type::period period;
typedef std::chrono::duration<uint_fast32_t, std::ratio<86400>> days;
bool load(handle src, bool) {
using namespace std::chrono;
// Lazy initialise the PyDateTime import
if (!PyDateTimeAPI) { PyDateTime_IMPORT; }
if (!src) return false;
// If invoked with datetime.delta object
if (PyDelta_Check(src.ptr())) {
value = type(duration_cast<duration<rep, period>>(
days(PyDateTime_DELTA_GET_DAYS(src.ptr()))
+ seconds(PyDateTime_DELTA_GET_SECONDS(src.ptr()))
+ microseconds(PyDateTime_DELTA_GET_MICROSECONDS(src.ptr()))));
return true;
}
// If invoked with a float we assume it is seconds and convert
else if (PyFloat_Check(src.ptr())) {
value = type(duration_cast<duration<rep, period>>(duration<double>(PyFloat_AsDouble(src.ptr()))));
return true;
}
else return false;
}
// If this is a duration just return it back
static const std::chrono::duration<rep, period>& get_duration(const std::chrono::duration<rep, period> &src) {
return src;
}
// If this is a time_point get the time_since_epoch
template <typename Clock> static std::chrono::duration<rep, period> get_duration(const std::chrono::time_point<Clock, std::chrono::duration<rep, period>> &src) {
return src.time_since_epoch();
}
static handle cast(const type &src, return_value_policy /* policy */, handle /* parent */) {
using namespace std::chrono;
// Use overloaded function to get our duration from our source
// Works out if it is a duration or time_point and get the duration
auto d = get_duration(src);
// Lazy initialise the PyDateTime import
if (!PyDateTimeAPI) { PyDateTime_IMPORT; }
// Declare these special duration types so the conversions happen with the correct primitive types (int)
using dd_t = duration<int, std::ratio<86400>>;
using ss_t = duration<int, std::ratio<1>>;
using us_t = duration<int, std::micro>;
auto dd = duration_cast<dd_t>(d);
auto subd = d - dd;
auto ss = duration_cast<ss_t>(subd);
auto us = duration_cast<us_t>(subd - ss);
return PyDelta_FromDSU(dd.count(), ss.count(), us.count());
}
PYBIND11_TYPE_CASTER(type, _("datetime.timedelta"));
};
// This is for casting times on the system clock into datetime.datetime instances
template <typename Duration> class type_caster<std::chrono::time_point<std::chrono::system_clock, Duration>> {
public:
typedef std::chrono::time_point<std::chrono::system_clock, Duration> type;
bool load(handle src, bool) {
using namespace std::chrono;
// Lazy initialise the PyDateTime import
if (!PyDateTimeAPI) { PyDateTime_IMPORT; }
if (!src) return false;
if (PyDateTime_Check(src.ptr())) {
std::tm cal;
cal.tm_sec = PyDateTime_DATE_GET_SECOND(src.ptr());
cal.tm_min = PyDateTime_DATE_GET_MINUTE(src.ptr());
cal.tm_hour = PyDateTime_DATE_GET_HOUR(src.ptr());
cal.tm_mday = PyDateTime_GET_DAY(src.ptr());
cal.tm_mon = PyDateTime_GET_MONTH(src.ptr()) - 1;
cal.tm_year = PyDateTime_GET_YEAR(src.ptr()) - 1900;
cal.tm_isdst = -1;
value = system_clock::from_time_t(std::mktime(&cal)) + microseconds(PyDateTime_DATE_GET_MICROSECOND(src.ptr()));
return true;
}
else return false;
}
static handle cast(const std::chrono::time_point<std::chrono::system_clock, Duration> &src, return_value_policy /* policy */, handle /* parent */) {
using namespace std::chrono;
// Lazy initialise the PyDateTime import
if (!PyDateTimeAPI) { PyDateTime_IMPORT; }
std::time_t tt = system_clock::to_time_t(src);
// this function uses static memory so it's best to copy it out asap just in case
// otherwise other code that is using localtime may break this (not just python code)
std::tm localtime = *std::localtime(&tt);
// Declare these special duration types so the conversions happen with the correct primitive types (int)
using us_t = duration<int, std::micro>;
return PyDateTime_FromDateAndTime(localtime.tm_year + 1900,
localtime.tm_mon + 1,
localtime.tm_mday,
localtime.tm_hour,
localtime.tm_min,
localtime.tm_sec,
(duration_cast<us_t>(src.time_since_epoch() % seconds(1))).count());
}
PYBIND11_TYPE_CASTER(type, _("datetime.datetime"));
};
// Other clocks that are not the system clock are not measured as datetime.datetime objects
// since they are not measured on calendar time. So instead we just make them timedeltas
// Or if they have passed us a time as a float we convert that
template <typename Clock, typename Duration> class type_caster<std::chrono::time_point<Clock, Duration>>
: public duration_caster<std::chrono::time_point<Clock, Duration>> {
};
template <typename Rep, typename Period> class type_caster<std::chrono::duration<Rep, Period>>
: public duration_caster<std::chrono::duration<Rep, Period>> {
};
NAMESPACE_END(detail)
NAMESPACE_END(PYBIND11_NAMESPACE)

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python/pybind11/include/pybind11/common.h
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#include "detail/common.h"
#warning "Including 'common.h' is deprecated. It will be removed in v3.0. Use 'pybind11.h'."

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python/pybind11/include/pybind11/complex.h
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/*
pybind11/complex.h: Complex number support
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "pybind11.h"
#include <complex>
/// glibc defines I as a macro which breaks things, e.g., boost template names
#ifdef I
# undef I
#endif
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
template <typename T> struct format_descriptor<std::complex<T>, detail::enable_if_t<std::is_floating_point<T>::value>> {
static constexpr const char c = format_descriptor<T>::c;
static constexpr const char value[3] = { 'Z', c, '\0' };
static std::string format() { return std::string(value); }
};
#ifndef PYBIND11_CPP17
template <typename T> constexpr const char format_descriptor<
std::complex<T>, detail::enable_if_t<std::is_floating_point<T>::value>>::value[3];
#endif
NAMESPACE_BEGIN(detail)
template <typename T> struct is_fmt_numeric<std::complex<T>, detail::enable_if_t<std::is_floating_point<T>::value>> {
static constexpr bool value = true;
static constexpr int index = is_fmt_numeric<T>::index + 3;
};
template <typename T> class type_caster<std::complex<T>> {
public:
bool load(handle src, bool convert) {
if (!src)
return false;
if (!convert && !PyComplex_Check(src.ptr()))
return false;
Py_complex result = PyComplex_AsCComplex(src.ptr());
if (result.real == -1.0 && PyErr_Occurred()) {
PyErr_Clear();
return false;
}
value = std::complex<T>((T) result.real, (T) result.imag);
return true;
}
static handle cast(const std::complex<T> &src, return_value_policy /* policy */, handle /* parent */) {
return PyComplex_FromDoubles((double) src.real(), (double) src.imag());
}
PYBIND11_TYPE_CASTER(std::complex<T>, _("complex"));
};
NAMESPACE_END(detail)
NAMESPACE_END(PYBIND11_NAMESPACE)

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python/pybind11/include/pybind11/detail/class.h
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@ -1,623 +0,0 @@
/*
pybind11/detail/class.h: Python C API implementation details for py::class_
Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "../attr.h"
#include "../options.h"
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
#if PY_VERSION_HEX >= 0x03030000
# define PYBIND11_BUILTIN_QUALNAME
# define PYBIND11_SET_OLDPY_QUALNAME(obj, nameobj)
#else
// In pre-3.3 Python, we still set __qualname__ so that we can produce reliable function type
// signatures; in 3.3+ this macro expands to nothing:
# define PYBIND11_SET_OLDPY_QUALNAME(obj, nameobj) setattr((PyObject *) obj, "__qualname__", nameobj)
#endif
inline PyTypeObject *type_incref(PyTypeObject *type) {
Py_INCREF(type);
return type;
}
#if !defined(PYPY_VERSION)
/// `pybind11_static_property.__get__()`: Always pass the class instead of the instance.
extern "C" inline PyObject *pybind11_static_get(PyObject *self, PyObject * /*ob*/, PyObject *cls) {
return PyProperty_Type.tp_descr_get(self, cls, cls);
}
/// `pybind11_static_property.__set__()`: Just like the above `__get__()`.
extern "C" inline int pybind11_static_set(PyObject *self, PyObject *obj, PyObject *value) {
PyObject *cls = PyType_Check(obj) ? obj : (PyObject *) Py_TYPE(obj);
return PyProperty_Type.tp_descr_set(self, cls, value);
}
/** A `static_property` is the same as a `property` but the `__get__()` and `__set__()`
methods are modified to always use the object type instead of a concrete instance.
Return value: New reference. */
inline PyTypeObject *make_static_property_type() {
constexpr auto *name = "pybind11_static_property";
auto name_obj = reinterpret_steal<object>(PYBIND11_FROM_STRING(name));
/* Danger zone: from now (and until PyType_Ready), make sure to
issue no Python C API calls which could potentially invoke the
garbage collector (the GC will call type_traverse(), which will in
turn find the newly constructed type in an invalid state) */
auto heap_type = (PyHeapTypeObject *) PyType_Type.tp_alloc(&PyType_Type, 0);
if (!heap_type)
pybind11_fail("make_static_property_type(): error allocating type!");
heap_type->ht_name = name_obj.inc_ref().ptr();
#ifdef PYBIND11_BUILTIN_QUALNAME
heap_type->ht_qualname = name_obj.inc_ref().ptr();
#endif
auto type = &heap_type->ht_type;
type->tp_name = name;
type->tp_base = type_incref(&PyProperty_Type);
type->tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HEAPTYPE;
type->tp_descr_get = pybind11_static_get;
type->tp_descr_set = pybind11_static_set;
if (PyType_Ready(type) < 0)
pybind11_fail("make_static_property_type(): failure in PyType_Ready()!");
setattr((PyObject *) type, "__module__", str("pybind11_builtins"));
PYBIND11_SET_OLDPY_QUALNAME(type, name_obj);
return type;
}
#else // PYPY
/** PyPy has some issues with the above C API, so we evaluate Python code instead.
This function will only be called once so performance isn't really a concern.
Return value: New reference. */
inline PyTypeObject *make_static_property_type() {
auto d = dict();
PyObject *result = PyRun_String(R"(\
class pybind11_static_property(property):
def __get__(self, obj, cls):
return property.__get__(self, cls, cls)
def __set__(self, obj, value):
cls = obj if isinstance(obj, type) else type(obj)
property.__set__(self, cls, value)
)", Py_file_input, d.ptr(), d.ptr()
);
if (result == nullptr)
throw error_already_set();
Py_DECREF(result);
return (PyTypeObject *) d["pybind11_static_property"].cast<object>().release().ptr();
}
#endif // PYPY
/** Types with static properties need to handle `Type.static_prop = x` in a specific way.
By default, Python replaces the `static_property` itself, but for wrapped C++ types
we need to call `static_property.__set__()` in order to propagate the new value to
the underlying C++ data structure. */
extern "C" inline int pybind11_meta_setattro(PyObject* obj, PyObject* name, PyObject* value) {
// Use `_PyType_Lookup()` instead of `PyObject_GetAttr()` in order to get the raw
// descriptor (`property`) instead of calling `tp_descr_get` (`property.__get__()`).
PyObject *descr = _PyType_Lookup((PyTypeObject *) obj, name);
// The following assignment combinations are possible:
// 1. `Type.static_prop = value` --> descr_set: `Type.static_prop.__set__(value)`
// 2. `Type.static_prop = other_static_prop` --> setattro: replace existing `static_prop`
// 3. `Type.regular_attribute = value` --> setattro: regular attribute assignment
const auto static_prop = (PyObject *) get_internals().static_property_type;
const auto call_descr_set = descr && PyObject_IsInstance(descr, static_prop)
&& !PyObject_IsInstance(value, static_prop);
if (call_descr_set) {
// Call `static_property.__set__()` instead of replacing the `static_property`.
#if !defined(PYPY_VERSION)
return Py_TYPE(descr)->tp_descr_set(descr, obj, value);
#else
if (PyObject *result = PyObject_CallMethod(descr, "__set__", "OO", obj, value)) {
Py_DECREF(result);
return 0;
} else {
return -1;
}
#endif
} else {
// Replace existing attribute.
return PyType_Type.tp_setattro(obj, name, value);
}
}
#if PY_MAJOR_VERSION >= 3
/**
* Python 3's PyInstanceMethod_Type hides itself via its tp_descr_get, which prevents aliasing
* methods via cls.attr("m2") = cls.attr("m1"): instead the tp_descr_get returns a plain function,
* when called on a class, or a PyMethod, when called on an instance. Override that behaviour here
* to do a special case bypass for PyInstanceMethod_Types.
*/
extern "C" inline PyObject *pybind11_meta_getattro(PyObject *obj, PyObject *name) {
PyObject *descr = _PyType_Lookup((PyTypeObject *) obj, name);
if (descr && PyInstanceMethod_Check(descr)) {
Py_INCREF(descr);
return descr;
}
else {
return PyType_Type.tp_getattro(obj, name);
}
}
#endif
/** This metaclass is assigned by default to all pybind11 types and is required in order
for static properties to function correctly. Users may override this using `py::metaclass`.
Return value: New reference. */
inline PyTypeObject* make_default_metaclass() {
constexpr auto *name = "pybind11_type";
auto name_obj = reinterpret_steal<object>(PYBIND11_FROM_STRING(name));
/* Danger zone: from now (and until PyType_Ready), make sure to
issue no Python C API calls which could potentially invoke the
garbage collector (the GC will call type_traverse(), which will in
turn find the newly constructed type in an invalid state) */
auto heap_type = (PyHeapTypeObject *) PyType_Type.tp_alloc(&PyType_Type, 0);
if (!heap_type)
pybind11_fail("make_default_metaclass(): error allocating metaclass!");
heap_type->ht_name = name_obj.inc_ref().ptr();
#ifdef PYBIND11_BUILTIN_QUALNAME
heap_type->ht_qualname = name_obj.inc_ref().ptr();
#endif
auto type = &heap_type->ht_type;
type->tp_name = name;
type->tp_base = type_incref(&PyType_Type);
type->tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HEAPTYPE;
type->tp_setattro = pybind11_meta_setattro;
#if PY_MAJOR_VERSION >= 3
type->tp_getattro = pybind11_meta_getattro;
#endif
if (PyType_Ready(type) < 0)
pybind11_fail("make_default_metaclass(): failure in PyType_Ready()!");
setattr((PyObject *) type, "__module__", str("pybind11_builtins"));
PYBIND11_SET_OLDPY_QUALNAME(type, name_obj);
return type;
}
/// For multiple inheritance types we need to recursively register/deregister base pointers for any
/// base classes with pointers that are difference from the instance value pointer so that we can
/// correctly recognize an offset base class pointer. This calls a function with any offset base ptrs.
inline void traverse_offset_bases(void *valueptr, const detail::type_info *tinfo, instance *self,
bool (*f)(void * /*parentptr*/, instance * /*self*/)) {
for (handle h : reinterpret_borrow<tuple>(tinfo->type->tp_bases)) {
if (auto parent_tinfo = get_type_info((PyTypeObject *) h.ptr())) {
for (auto &c : parent_tinfo->implicit_casts) {
if (c.first == tinfo->cpptype) {
auto *parentptr = c.second(valueptr);
if (parentptr != valueptr)
f(parentptr, self);
traverse_offset_bases(parentptr, parent_tinfo, self, f);
break;
}
}
}
}
}
inline bool register_instance_impl(void *ptr, instance *self) {
get_internals().registered_instances.emplace(ptr, self);
return true; // unused, but gives the same signature as the deregister func
}
inline bool deregister_instance_impl(void *ptr, instance *self) {
auto &registered_instances = get_internals().registered_instances;
auto range = registered_instances.equal_range(ptr);
for (auto it = range.first; it != range.second; ++it) {
if (Py_TYPE(self) == Py_TYPE(it->second)) {
registered_instances.erase(it);
return true;
}
}
return false;
}
inline void register_instance(instance *self, void *valptr, const type_info *tinfo) {
register_instance_impl(valptr, self);
if (!tinfo->simple_ancestors)
traverse_offset_bases(valptr, tinfo, self, register_instance_impl);
}
inline bool deregister_instance(instance *self, void *valptr, const type_info *tinfo) {
bool ret = deregister_instance_impl(valptr, self);
if (!tinfo->simple_ancestors)
traverse_offset_bases(valptr, tinfo, self, deregister_instance_impl);
return ret;
}
/// Instance creation function for all pybind11 types. It allocates the internal instance layout for
/// holding C++ objects and holders. Allocation is done lazily (the first time the instance is cast
/// to a reference or pointer), and initialization is done by an `__init__` function.
inline PyObject *make_new_instance(PyTypeObject *type) {
#if defined(PYPY_VERSION)
// PyPy gets tp_basicsize wrong (issue 2482) under multiple inheritance when the first inherited
// object is a a plain Python type (i.e. not derived from an extension type). Fix it.
ssize_t instance_size = static_cast<ssize_t>(sizeof(instance));
if (type->tp_basicsize < instance_size) {
type->tp_basicsize = instance_size;
}
#endif
PyObject *self = type->tp_alloc(type, 0);
auto inst = reinterpret_cast<instance *>(self);
// Allocate the value/holder internals:
inst->allocate_layout();
inst->owned = true;
return self;
}
/// Instance creation function for all pybind11 types. It only allocates space for the
/// C++ object, but doesn't call the constructor -- an `__init__` function must do that.
extern "C" inline PyObject *pybind11_object_new(PyTypeObject *type, PyObject *, PyObject *) {
return make_new_instance(type);
}
/// An `__init__` function constructs the C++ object. Users should provide at least one
/// of these using `py::init` or directly with `.def(__init__, ...)`. Otherwise, the
/// following default function will be used which simply throws an exception.
extern "C" inline int pybind11_object_init(PyObject *self, PyObject *, PyObject *) {
PyTypeObject *type = Py_TYPE(self);
std::string msg;
#if defined(PYPY_VERSION)
msg += handle((PyObject *) type).attr("__module__").cast<std::string>() + ".";
#endif
msg += type->tp_name;
msg += ": No constructor defined!";
PyErr_SetString(PyExc_TypeError, msg.c_str());
return -1;
}
inline void add_patient(PyObject *nurse, PyObject *patient) {
auto &internals = get_internals();
auto instance = reinterpret_cast<detail::instance *>(nurse);
instance->has_patients = true;
Py_INCREF(patient);
internals.patients[nurse].push_back(patient);
}
inline void clear_patients(PyObject *self) {
auto instance = reinterpret_cast<detail::instance *>(self);
auto &internals = get_internals();
auto pos = internals.patients.find(self);
assert(pos != internals.patients.end());
// Clearing the patients can cause more Python code to run, which
// can invalidate the iterator. Extract the vector of patients
// from the unordered_map first.
auto patients = std::move(pos->second);
internals.patients.erase(pos);
instance->has_patients = false;
for (PyObject *&patient : patients)
Py_CLEAR(patient);
}
/// Clears all internal data from the instance and removes it from registered instances in
/// preparation for deallocation.
inline void clear_instance(PyObject *self) {
auto instance = reinterpret_cast<detail::instance *>(self);
// Deallocate any values/holders, if present:
for (auto &v_h : values_and_holders(instance)) {
if (v_h) {
// We have to deregister before we call dealloc because, for virtual MI types, we still
// need to be able to get the parent pointers.
if (v_h.instance_registered() && !deregister_instance(instance, v_h.value_ptr(), v_h.type))
pybind11_fail("pybind11_object_dealloc(): Tried to deallocate unregistered instance!");
if (instance->owned || v_h.holder_constructed())
v_h.type->dealloc(v_h);
}
}
// Deallocate the value/holder layout internals:
instance->deallocate_layout();
if (instance->weakrefs)
PyObject_ClearWeakRefs(self);
PyObject **dict_ptr = _PyObject_GetDictPtr(self);
if (dict_ptr)
Py_CLEAR(*dict_ptr);
if (instance->has_patients)
clear_patients(self);
}
/// Instance destructor function for all pybind11 types. It calls `type_info.dealloc`
/// to destroy the C++ object itself, while the rest is Python bookkeeping.
extern "C" inline void pybind11_object_dealloc(PyObject *self) {
clear_instance(self);
auto type = Py_TYPE(self);
type->tp_free(self);
// `type->tp_dealloc != pybind11_object_dealloc` means that we're being called
// as part of a derived type's dealloc, in which case we're not allowed to decref
// the type here. For cross-module compatibility, we shouldn't compare directly
// with `pybind11_object_dealloc`, but with the common one stashed in internals.
auto pybind11_object_type = (PyTypeObject *) get_internals().instance_base;
if (type->tp_dealloc == pybind11_object_type->tp_dealloc)
Py_DECREF(type);
}
/** Create the type which can be used as a common base for all classes. This is
needed in order to satisfy Python's requirements for multiple inheritance.
Return value: New reference. */
inline PyObject *make_object_base_type(PyTypeObject *metaclass) {
constexpr auto *name = "pybind11_object";
auto name_obj = reinterpret_steal<object>(PYBIND11_FROM_STRING(name));
/* Danger zone: from now (and until PyType_Ready), make sure to
issue no Python C API calls which could potentially invoke the
garbage collector (the GC will call type_traverse(), which will in
turn find the newly constructed type in an invalid state) */
auto heap_type = (PyHeapTypeObject *) metaclass->tp_alloc(metaclass, 0);
if (!heap_type)
pybind11_fail("make_object_base_type(): error allocating type!");
heap_type->ht_name = name_obj.inc_ref().ptr();
#ifdef PYBIND11_BUILTIN_QUALNAME
heap_type->ht_qualname = name_obj.inc_ref().ptr();
#endif
auto type = &heap_type->ht_type;
type->tp_name = name;
type->tp_base = type_incref(&PyBaseObject_Type);
type->tp_basicsize = static_cast<ssize_t>(sizeof(instance));
type->tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HEAPTYPE;
type->tp_new = pybind11_object_new;
type->tp_init = pybind11_object_init;
type->tp_dealloc = pybind11_object_dealloc;
/* Support weak references (needed for the keep_alive feature) */
type->tp_weaklistoffset = offsetof(instance, weakrefs);
if (PyType_Ready(type) < 0)
pybind11_fail("PyType_Ready failed in make_object_base_type():" + error_string());
setattr((PyObject *) type, "__module__", str("pybind11_builtins"));
PYBIND11_SET_OLDPY_QUALNAME(type, name_obj);
assert(!PyType_HasFeature(type, Py_TPFLAGS_HAVE_GC));
return (PyObject *) heap_type;
}
/// dynamic_attr: Support for `d = instance.__dict__`.
extern "C" inline PyObject *pybind11_get_dict(PyObject *self, void *) {
PyObject *&dict = *_PyObject_GetDictPtr(self);
if (!dict)
dict = PyDict_New();
Py_XINCREF(dict);
return dict;
}
/// dynamic_attr: Support for `instance.__dict__ = dict()`.
extern "C" inline int pybind11_set_dict(PyObject *self, PyObject *new_dict, void *) {
if (!PyDict_Check(new_dict)) {
PyErr_Format(PyExc_TypeError, "__dict__ must be set to a dictionary, not a '%.200s'",
Py_TYPE(new_dict)->tp_name);
return -1;
}
PyObject *&dict = *_PyObject_GetDictPtr(self);
Py_INCREF(new_dict);
Py_CLEAR(dict);
dict = new_dict;
return 0;
}
/// dynamic_attr: Allow the garbage collector to traverse the internal instance `__dict__`.
extern "C" inline int pybind11_traverse(PyObject *self, visitproc visit, void *arg) {
PyObject *&dict = *_PyObject_GetDictPtr(self);
Py_VISIT(dict);
return 0;
}
/// dynamic_attr: Allow the GC to clear the dictionary.
extern "C" inline int pybind11_clear(PyObject *self) {
PyObject *&dict = *_PyObject_GetDictPtr(self);
Py_CLEAR(dict);
return 0;
}
/// Give instances of this type a `__dict__` and opt into garbage collection.
inline void enable_dynamic_attributes(PyHeapTypeObject *heap_type) {
auto type = &heap_type->ht_type;
#if defined(PYPY_VERSION)
pybind11_fail(std::string(type->tp_name) + ": dynamic attributes are "
"currently not supported in "
"conjunction with PyPy!");
#endif
type->tp_flags |= Py_TPFLAGS_HAVE_GC;
type->tp_dictoffset = type->tp_basicsize; // place dict at the end
type->tp_basicsize += (ssize_t)sizeof(PyObject *); // and allocate enough space for it
type->tp_traverse = pybind11_traverse;
type->tp_clear = pybind11_clear;
static PyGetSetDef getset[] = {
{const_cast<char*>("__dict__"), pybind11_get_dict, pybind11_set_dict, nullptr, nullptr},
{nullptr, nullptr, nullptr, nullptr, nullptr}
};
type->tp_getset = getset;
}
/// buffer_protocol: Fill in the view as specified by flags.
extern "C" inline int pybind11_getbuffer(PyObject *obj, Py_buffer *view, int flags) {
// Look for a `get_buffer` implementation in this type's info or any bases (following MRO).
type_info *tinfo = nullptr;
for (auto type : reinterpret_borrow<tuple>(Py_TYPE(obj)->tp_mro)) {
tinfo = get_type_info((PyTypeObject *) type.ptr());
if (tinfo && tinfo->get_buffer)
break;
}
if (view == nullptr || !tinfo || !tinfo->get_buffer) {
if (view)
view->obj = nullptr;
PyErr_SetString(PyExc_BufferError, "pybind11_getbuffer(): Internal error");
return -1;
}
std::memset(view, 0, sizeof(Py_buffer));
buffer_info *info = tinfo->get_buffer(obj, tinfo->get_buffer_data);
view->obj = obj;
view->ndim = 1;
view->internal = info;
view->buf = info->ptr;
view->itemsize = info->itemsize;
view->len = view->itemsize;
for (auto s : info->shape)
view->len *= s;
if ((flags & PyBUF_FORMAT) == PyBUF_FORMAT)
view->format = const_cast<char *>(info->format.c_str());
if ((flags & PyBUF_STRIDES) == PyBUF_STRIDES) {
view->ndim = (int) info->ndim;
view->strides = &info->strides[0];
view->shape = &info->shape[0];
}
Py_INCREF(view->obj);
return 0;
}
/// buffer_protocol: Release the resources of the buffer.
extern "C" inline void pybind11_releasebuffer(PyObject *, Py_buffer *view) {
delete (buffer_info *) view->internal;
}
/// Give this type a buffer interface.
inline void enable_buffer_protocol(PyHeapTypeObject *heap_type) {
heap_type->ht_type.tp_as_buffer = &heap_type->as_buffer;
#if PY_MAJOR_VERSION < 3
heap_type->ht_type.tp_flags |= Py_TPFLAGS_HAVE_NEWBUFFER;
#endif
heap_type->as_buffer.bf_getbuffer = pybind11_getbuffer;
heap_type->as_buffer.bf_releasebuffer = pybind11_releasebuffer;
}
/** Create a brand new Python type according to the `type_record` specification.
Return value: New reference. */
inline PyObject* make_new_python_type(const type_record &rec) {
auto name = reinterpret_steal<object>(PYBIND11_FROM_STRING(rec.name));
auto qualname = name;
if (rec.scope && !PyModule_Check(rec.scope.ptr()) && hasattr(rec.scope, "__qualname__")) {
#if PY_MAJOR_VERSION >= 3
qualname = reinterpret_steal<object>(
PyUnicode_FromFormat("%U.%U", rec.scope.attr("__qualname__").ptr(), name.ptr()));
#else
qualname = str(rec.scope.attr("__qualname__").cast<std::string>() + "." + rec.name);
#endif
}
object module;
if (rec.scope) {
if (hasattr(rec.scope, "__module__"))
module = rec.scope.attr("__module__");
else if (hasattr(rec.scope, "__name__"))
module = rec.scope.attr("__name__");
}
auto full_name = c_str(
#if !defined(PYPY_VERSION)
module ? str(module).cast<std::string>() + "." + rec.name :
#endif
rec.name);
char *tp_doc = nullptr;
if (rec.doc && options::show_user_defined_docstrings()) {
/* Allocate memory for docstring (using PyObject_MALLOC, since
Python will free this later on) */
size_t size = strlen(rec.doc) + 1;
tp_doc = (char *) PyObject_MALLOC(size);
memcpy((void *) tp_doc, rec.doc, size);
}
auto &internals = get_internals();
auto bases = tuple(rec.bases);
auto base = (bases.size() == 0) ? internals.instance_base
: bases[0].ptr();
/* Danger zone: from now (and until PyType_Ready), make sure to
issue no Python C API calls which could potentially invoke the
garbage collector (the GC will call type_traverse(), which will in
turn find the newly constructed type in an invalid state) */
auto metaclass = rec.metaclass.ptr() ? (PyTypeObject *) rec.metaclass.ptr()
: internals.default_metaclass;
auto heap_type = (PyHeapTypeObject *) metaclass->tp_alloc(metaclass, 0);
if (!heap_type)
pybind11_fail(std::string(rec.name) + ": Unable to create type object!");
heap_type->ht_name = name.release().ptr();
#ifdef PYBIND11_BUILTIN_QUALNAME
heap_type->ht_qualname = qualname.inc_ref().ptr();
#endif
auto type = &heap_type->ht_type;
type->tp_name = full_name;
type->tp_doc = tp_doc;
type->tp_base = type_incref((PyTypeObject *)base);
type->tp_basicsize = static_cast<ssize_t>(sizeof(instance));
if (bases.size() > 0)
type->tp_bases = bases.release().ptr();
/* Don't inherit base __init__ */
type->tp_init = pybind11_object_init;
/* Supported protocols */
type->tp_as_number = &heap_type->as_number;
type->tp_as_sequence = &heap_type->as_sequence;
type->tp_as_mapping = &heap_type->as_mapping;
/* Flags */
type->tp_flags |= Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HEAPTYPE;
#if PY_MAJOR_VERSION < 3
type->tp_flags |= Py_TPFLAGS_CHECKTYPES;
#endif
if (rec.dynamic_attr)
enable_dynamic_attributes(heap_type);
if (rec.buffer_protocol)
enable_buffer_protocol(heap_type);
if (PyType_Ready(type) < 0)
pybind11_fail(std::string(rec.name) + ": PyType_Ready failed (" + error_string() + ")!");
assert(rec.dynamic_attr ? PyType_HasFeature(type, Py_TPFLAGS_HAVE_GC)
: !PyType_HasFeature(type, Py_TPFLAGS_HAVE_GC));
/* Register type with the parent scope */
if (rec.scope)
setattr(rec.scope, rec.name, (PyObject *) type);
else
Py_INCREF(type); // Keep it alive forever (reference leak)
if (module) // Needed by pydoc
setattr((PyObject *) type, "__module__", module);
PYBIND11_SET_OLDPY_QUALNAME(type, qualname);
return (PyObject *) type;
}
NAMESPACE_END(detail)
NAMESPACE_END(PYBIND11_NAMESPACE)

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python/pybind11/include/pybind11/detail/common.h
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@ -1,807 +0,0 @@
/*
pybind11/detail/common.h -- Basic macros
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#if !defined(NAMESPACE_BEGIN)
# define NAMESPACE_BEGIN(name) namespace name {
#endif
#if !defined(NAMESPACE_END)
# define NAMESPACE_END(name) }
#endif
// Robust support for some features and loading modules compiled against different pybind versions
// requires forcing hidden visibility on pybind code, so we enforce this by setting the attribute on
// the main `pybind11` namespace.
#if !defined(PYBIND11_NAMESPACE)
# ifdef __GNUG__
# define PYBIND11_NAMESPACE pybind11 __attribute__((visibility("hidden")))
# else
# define PYBIND11_NAMESPACE pybind11
# endif
#endif
#if !(defined(_MSC_VER) && __cplusplus == 199711L) && !defined(__INTEL_COMPILER)
# if __cplusplus >= 201402L
# define PYBIND11_CPP14
# if __cplusplus >= 201703L
# define PYBIND11_CPP17
# endif
# endif
#elif defined(_MSC_VER) && __cplusplus == 199711L
// MSVC sets _MSVC_LANG rather than __cplusplus (supposedly until the standard is fully implemented)
// Unless you use the /Zc:__cplusplus flag on Visual Studio 2017 15.7 Preview 3 or newer
# if _MSVC_LANG >= 201402L
# define PYBIND11_CPP14
# if _MSVC_LANG > 201402L && _MSC_VER >= 1910
# define PYBIND11_CPP17
# endif
# endif
#endif
// Compiler version assertions
#if defined(__INTEL_COMPILER)
# if __INTEL_COMPILER < 1700
# error pybind11 requires Intel C++ compiler v17 or newer
# endif
#elif defined(__clang__) && !defined(__apple_build_version__)
# if __clang_major__ < 3 || (__clang_major__ == 3 && __clang_minor__ < 3)
# error pybind11 requires clang 3.3 or newer
# endif
#elif defined(__clang__)
// Apple changes clang version macros to its Xcode version; the first Xcode release based on
// (upstream) clang 3.3 was Xcode 5:
# if __clang_major__ < 5
# error pybind11 requires Xcode/clang 5.0 or newer
# endif
#elif defined(__GNUG__)
# if __GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 8)
# error pybind11 requires gcc 4.8 or newer
# endif
#elif defined(_MSC_VER)
// Pybind hits various compiler bugs in 2015u2 and earlier, and also makes use of some stl features
// (e.g. std::negation) added in 2015u3:
# if _MSC_FULL_VER < 190024210
# error pybind11 requires MSVC 2015 update 3 or newer
# endif
#endif
#if !defined(PYBIND11_EXPORT)
# if defined(WIN32) || defined(_WIN32)
# define PYBIND11_EXPORT __declspec(dllexport)
# else
# define PYBIND11_EXPORT __attribute__ ((visibility("default")))
# endif
#endif
#if defined(_MSC_VER)
# define PYBIND11_NOINLINE __declspec(noinline)
#else
# define PYBIND11_NOINLINE __attribute__ ((noinline))
#endif
#if defined(PYBIND11_CPP14)
# define PYBIND11_DEPRECATED(reason) [[deprecated(reason)]]
#else
# define PYBIND11_DEPRECATED(reason) __attribute__((deprecated(reason)))
#endif
#define PYBIND11_VERSION_MAJOR 2
#define PYBIND11_VERSION_MINOR 3
#define PYBIND11_VERSION_PATCH 0
/// Include Python header, disable linking to pythonX_d.lib on Windows in debug mode
#if defined(_MSC_VER)
# if (PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION < 4)
# define HAVE_ROUND 1
# endif
# pragma warning(push)
# pragma warning(disable: 4510 4610 4512 4005)
# if defined(_DEBUG)
# define PYBIND11_DEBUG_MARKER
# undef _DEBUG
# endif
#endif
#include <Python.h>
#include <frameobject.h>
#include <pythread.h>
#if defined(_WIN32) && (defined(min) || defined(max))
# error Macro clash with min and max -- define NOMINMAX when compiling your program on Windows
#endif
#if defined(isalnum)
# undef isalnum
# undef isalpha
# undef islower
# undef isspace
# undef isupper
# undef tolower
# undef toupper
#endif
#if defined(_MSC_VER)
# if defined(PYBIND11_DEBUG_MARKER)
# define _DEBUG
# undef PYBIND11_DEBUG_MARKER
# endif
# pragma warning(pop)
#endif
#include <cstddef>
#include <cstring>
#include <forward_list>
#include <vector>
#include <string>
#include <stdexcept>
#include <unordered_set>
#include <unordered_map>
#include <memory>
#include <typeindex>
#include <type_traits>
#if PY_MAJOR_VERSION >= 3 /// Compatibility macros for various Python versions
#define PYBIND11_INSTANCE_METHOD_NEW(ptr, class_) PyInstanceMethod_New(ptr)
#define PYBIND11_INSTANCE_METHOD_CHECK PyInstanceMethod_Check
#define PYBIND11_INSTANCE_METHOD_GET_FUNCTION PyInstanceMethod_GET_FUNCTION
#define PYBIND11_BYTES_CHECK PyBytes_Check
#define PYBIND11_BYTES_FROM_STRING PyBytes_FromString
#define PYBIND11_BYTES_FROM_STRING_AND_SIZE PyBytes_FromStringAndSize
#define PYBIND11_BYTES_AS_STRING_AND_SIZE PyBytes_AsStringAndSize
#define PYBIND11_BYTES_AS_STRING PyBytes_AsString
#define PYBIND11_BYTES_SIZE PyBytes_Size
#define PYBIND11_LONG_CHECK(o) PyLong_Check(o)
#define PYBIND11_LONG_AS_LONGLONG(o) PyLong_AsLongLong(o)
#define PYBIND11_LONG_FROM_SIGNED(o) PyLong_FromSsize_t((ssize_t) o)
#define PYBIND11_LONG_FROM_UNSIGNED(o) PyLong_FromSize_t((size_t) o)
#define PYBIND11_BYTES_NAME "bytes"
#define PYBIND11_STRING_NAME "str"
#define PYBIND11_SLICE_OBJECT PyObject
#define PYBIND11_FROM_STRING PyUnicode_FromString
#define PYBIND11_STR_TYPE ::pybind11::str
#define PYBIND11_BOOL_ATTR "__bool__"
#define PYBIND11_NB_BOOL(ptr) ((ptr)->nb_bool)
#define PYBIND11_PLUGIN_IMPL(name) \
extern "C" PYBIND11_EXPORT PyObject *PyInit_##name()
#else
#define PYBIND11_INSTANCE_METHOD_NEW(ptr, class_) PyMethod_New(ptr, nullptr, class_)
#define PYBIND11_INSTANCE_METHOD_CHECK PyMethod_Check
#define PYBIND11_INSTANCE_METHOD_GET_FUNCTION PyMethod_GET_FUNCTION
#define PYBIND11_BYTES_CHECK PyString_Check
#define PYBIND11_BYTES_FROM_STRING PyString_FromString
#define PYBIND11_BYTES_FROM_STRING_AND_SIZE PyString_FromStringAndSize
#define PYBIND11_BYTES_AS_STRING_AND_SIZE PyString_AsStringAndSize
#define PYBIND11_BYTES_AS_STRING PyString_AsString
#define PYBIND11_BYTES_SIZE PyString_Size
#define PYBIND11_LONG_CHECK(o) (PyInt_Check(o) || PyLong_Check(o))
#define PYBIND11_LONG_AS_LONGLONG(o) (PyInt_Check(o) ? (long long) PyLong_AsLong(o) : PyLong_AsLongLong(o))
#define PYBIND11_LONG_FROM_SIGNED(o) PyInt_FromSsize_t((ssize_t) o) // Returns long if needed.
#define PYBIND11_LONG_FROM_UNSIGNED(o) PyInt_FromSize_t((size_t) o) // Returns long if needed.
#define PYBIND11_BYTES_NAME "str"
#define PYBIND11_STRING_NAME "unicode"
#define PYBIND11_SLICE_OBJECT PySliceObject
#define PYBIND11_FROM_STRING PyString_FromString
#define PYBIND11_STR_TYPE ::pybind11::bytes
#define PYBIND11_BOOL_ATTR "__nonzero__"
#define PYBIND11_NB_BOOL(ptr) ((ptr)->nb_nonzero)
#define PYBIND11_PLUGIN_IMPL(name) \
static PyObject *pybind11_init_wrapper(); \
extern "C" PYBIND11_EXPORT void init##name() { \
(void)pybind11_init_wrapper(); \
} \
PyObject *pybind11_init_wrapper()
#endif
#if PY_VERSION_HEX >= 0x03050000 && PY_VERSION_HEX < 0x03050200
extern "C" {
struct _Py_atomic_address { void *value; };
PyAPI_DATA(_Py_atomic_address) _PyThreadState_Current;
}
#endif
#define PYBIND11_TRY_NEXT_OVERLOAD ((PyObject *) 1) // special failure return code
#define PYBIND11_STRINGIFY(x) #x
#define PYBIND11_TOSTRING(x) PYBIND11_STRINGIFY(x)
#define PYBIND11_CONCAT(first, second) first##second
#define PYBIND11_CHECK_PYTHON_VERSION \
{ \
const char *compiled_ver = PYBIND11_TOSTRING(PY_MAJOR_VERSION) \
"." PYBIND11_TOSTRING(PY_MINOR_VERSION); \
const char *runtime_ver = Py_GetVersion(); \
size_t len = std::strlen(compiled_ver); \
if (std::strncmp(runtime_ver, compiled_ver, len) != 0 \
|| (runtime_ver[len] >= '0' && runtime_ver[len] <= '9')) { \
PyErr_Format(PyExc_ImportError, \
"Python version mismatch: module was compiled for Python %s, " \
"but the interpreter version is incompatible: %s.", \
compiled_ver, runtime_ver); \
return nullptr; \
} \
}
#define PYBIND11_CATCH_INIT_EXCEPTIONS \
catch (pybind11::error_already_set &e) { \
PyErr_SetString(PyExc_ImportError, e.what()); \
return nullptr; \
} catch (const std::exception &e) { \
PyErr_SetString(PyExc_ImportError, e.what()); \
return nullptr; \
} \
/** \rst
***Deprecated in favor of PYBIND11_MODULE***
This macro creates the entry point that will be invoked when the Python interpreter
imports a plugin library. Please create a `module` in the function body and return
the pointer to its underlying Python object at the end.
.. code-block:: cpp
PYBIND11_PLUGIN(example) {
pybind11::module m("example", "pybind11 example plugin");
/// Set up bindings here
return m.ptr();
}
\endrst */
#define PYBIND11_PLUGIN(name) \
PYBIND11_DEPRECATED("PYBIND11_PLUGIN is deprecated, use PYBIND11_MODULE") \
static PyObject *pybind11_init(); \
PYBIND11_PLUGIN_IMPL(name) { \
PYBIND11_CHECK_PYTHON_VERSION \
try { \
return pybind11_init(); \
} PYBIND11_CATCH_INIT_EXCEPTIONS \
} \
PyObject *pybind11_init()
/** \rst
This macro creates the entry point that will be invoked when the Python interpreter
imports an extension module. The module name is given as the fist argument and it
should not be in quotes. The second macro argument defines a variable of type
`py::module` which can be used to initialize the module.
.. code-block:: cpp
PYBIND11_MODULE(example, m) {
m.doc() = "pybind11 example module";
// Add bindings here
m.def("foo", []() {
return "Hello, World!";
});
}
\endrst */
#define PYBIND11_MODULE(name, variable) \
static void PYBIND11_CONCAT(pybind11_init_, name)(pybind11::module &); \
PYBIND11_PLUGIN_IMPL(name) { \
PYBIND11_CHECK_PYTHON_VERSION \
auto m = pybind11::module(PYBIND11_TOSTRING(name)); \
try { \
PYBIND11_CONCAT(pybind11_init_, name)(m); \
return m.ptr(); \
} PYBIND11_CATCH_INIT_EXCEPTIONS \
} \
void PYBIND11_CONCAT(pybind11_init_, name)(pybind11::module &variable)
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
using ssize_t = Py_ssize_t;
using size_t = std::size_t;
/// Approach used to cast a previously unknown C++ instance into a Python object
enum class return_value_policy : uint8_t {
/** This is the default return value policy, which falls back to the policy
return_value_policy::take_ownership when the return value is a pointer.
Otherwise, it uses return_value::move or return_value::copy for rvalue
and lvalue references, respectively. See below for a description of what
all of these different policies do. */
automatic = 0,
/** As above, but use policy return_value_policy::reference when the return
value is a pointer. This is the default conversion policy for function
arguments when calling Python functions manually from C++ code (i.e. via
handle::operator()). You probably won't need to use this. */
automatic_reference,
/** Reference an existing object (i.e. do not create a new copy) and take
ownership. Python will call the destructor and delete operator when the
object’s reference count reaches zero. Undefined behavior ensues when
the C++ side does the same.. */
take_ownership,
/** Create a new copy of the returned object, which will be owned by
Python. This policy is comparably safe because the lifetimes of the two
instances are decoupled. */
copy,
/** Use std::move to move the return value contents into a new instance
that will be owned by Python. This policy is comparably safe because the
lifetimes of the two instances (move source and destination) are
decoupled. */
move,
/** Reference an existing object, but do not take ownership. The C++ side
is responsible for managing the object’s lifetime and deallocating it
when it is no longer used. Warning: undefined behavior will ensue when
the C++ side deletes an object that is still referenced and used by
Python. */
reference,
/** This policy only applies to methods and properties. It references the
object without taking ownership similar to the above
return_value_policy::reference policy. In contrast to that policy, the
function or property’s implicit this argument (called the parent) is
considered to be the the owner of the return value (the child).
pybind11 then couples the lifetime of the parent to the child via a
reference relationship that ensures that the parent cannot be garbage
collected while Python is still using the child. More advanced
variations of this scheme are also possible using combinations of
return_value_policy::reference and the keep_alive call policy */
reference_internal
};
NAMESPACE_BEGIN(detail)
inline static constexpr int log2(size_t n, int k = 0) { return (n <= 1) ? k : log2(n >> 1, k + 1); }
// Returns the size as a multiple of sizeof(void *), rounded up.
inline static constexpr size_t size_in_ptrs(size_t s) { return 1 + ((s - 1) >> log2(sizeof(void *))); }
/**
* The space to allocate for simple layout instance holders (see below) in multiple of the size of
* a pointer (e.g. 2 means 16 bytes on 64-bit architectures). The default is the minimum required
* to holder either a std::unique_ptr or std::shared_ptr (which is almost always
* sizeof(std::shared_ptr<T>)).
*/
constexpr size_t instance_simple_holder_in_ptrs() {
static_assert(sizeof(std::shared_ptr<int>) >= sizeof(std::unique_ptr<int>),
"pybind assumes std::shared_ptrs are at least as big as std::unique_ptrs");
return size_in_ptrs(sizeof(std::shared_ptr<int>));
}
// Forward declarations
struct type_info;
struct value_and_holder;
struct nonsimple_values_and_holders {
void **values_and_holders;
uint8_t *status;
};
/// The 'instance' type which needs to be standard layout (need to be able to use 'offsetof')
struct instance {
PyObject_HEAD
/// Storage for pointers and holder; see simple_layout, below, for a description
union {
void *simple_value_holder[1 + instance_simple_holder_in_ptrs()];
nonsimple_values_and_holders nonsimple;
};
/// Weak references
PyObject *weakrefs;
/// If true, the pointer is owned which means we're free to manage it with a holder.
bool owned : 1;
/**
* An instance has two possible value/holder layouts.
*
* Simple layout (when this flag is true), means the `simple_value_holder` is set with a pointer
* and the holder object governing that pointer, i.e. [val1*][holder]. This layout is applied
* whenever there is no python-side multiple inheritance of bound C++ types *and* the type's
* holder will fit in the default space (which is large enough to hold either a std::unique_ptr
* or std::shared_ptr).
*
* Non-simple layout applies when using custom holders that require more space than `shared_ptr`
* (which is typically the size of two pointers), or when multiple inheritance is used on the
* python side. Non-simple layout allocates the required amount of memory to have multiple
* bound C++ classes as parents. Under this layout, `nonsimple.values_and_holders` is set to a
* pointer to allocated space of the required space to hold a sequence of value pointers and
* holders followed `status`, a set of bit flags (1 byte each), i.e.
* [val1*][holder1][val2*][holder2]...[bb...] where each [block] is rounded up to a multiple of
* `sizeof(void *)`. `nonsimple.status` is, for convenience, a pointer to the
* beginning of the [bb...] block (but not independently allocated).
*
* Status bits indicate whether the associated holder is constructed (&
* status_holder_constructed) and whether the value pointer is registered (&
* status_instance_registered) in `registered_instances`.
*/
bool simple_layout : 1;
/// For simple layout, tracks whether the holder has been constructed
bool simple_holder_constructed : 1;
/// For simple layout, tracks whether the instance is registered in `registered_instances`
bool simple_instance_registered : 1;
/// If true, get_internals().patients has an entry for this object
bool has_patients : 1;
/// Initializes all of the above type/values/holders data (but not the instance values themselves)
void allocate_layout();
/// Destroys/deallocates all of the above
void deallocate_layout();
/// Returns the value_and_holder wrapper for the given type (or the first, if `find_type`
/// omitted). Returns a default-constructed (with `.inst = nullptr`) object on failure if
/// `throw_if_missing` is false.
value_and_holder get_value_and_holder(const type_info *find_type = nullptr, bool throw_if_missing = true);
/// Bit values for the non-simple status flags
static constexpr uint8_t status_holder_constructed = 1;
static constexpr uint8_t status_instance_registered = 2;
};
static_assert(std::is_standard_layout<instance>::value, "Internal error: `pybind11::detail::instance` is not standard layout!");
/// from __cpp_future__ import (convenient aliases from C++14/17)
#if defined(PYBIND11_CPP14) && (!defined(_MSC_VER) || _MSC_VER >= 1910)
using std::enable_if_t;
using std::conditional_t;
using std::remove_cv_t;
using std::remove_reference_t;
#else
template <bool B, typename T = void> using enable_if_t = typename std::enable_if<B, T>::type;
template <bool B, typename T, typename F> using conditional_t = typename std::conditional<B, T, F>::type;
template <typename T> using remove_cv_t = typename std::remove_cv<T>::type;
template <typename T> using remove_reference_t = typename std::remove_reference<T>::type;
#endif
/// Index sequences
#if defined(PYBIND11_CPP14)
using std::index_sequence;
using std::make_index_sequence;
#else
template<size_t ...> struct index_sequence { };
template<size_t N, size_t ...S> struct make_index_sequence_impl : make_index_sequence_impl <N - 1, N - 1, S...> { };
template<size_t ...S> struct make_index_sequence_impl <0, S...> { typedef index_sequence<S...> type; };
template<size_t N> using make_index_sequence = typename make_index_sequence_impl<N>::type;
#endif
/// Make an index sequence of the indices of true arguments
template <typename ISeq, size_t, bool...> struct select_indices_impl { using type = ISeq; };
template <size_t... IPrev, size_t I, bool B, bool... Bs> struct select_indices_impl<index_sequence<IPrev...>, I, B, Bs...>
: select_indices_impl<conditional_t<B, index_sequence<IPrev..., I>, index_sequence<IPrev...>>, I + 1, Bs...> {};
template <bool... Bs> using select_indices = typename select_indices_impl<index_sequence<>, 0, Bs...>::type;
/// Backports of std::bool_constant and std::negation to accommodate older compilers
template <bool B> using bool_constant = std::integral_constant<bool, B>;
template <typename T> struct negation : bool_constant<!T::value> { };
template <typename...> struct void_t_impl { using type = void; };
template <typename... Ts> using void_t = typename void_t_impl<Ts...>::type;
/// Compile-time all/any/none of that check the boolean value of all template types
#if defined(__cpp_fold_expressions) && !(defined(_MSC_VER) && (_MSC_VER < 1916))
template <class... Ts> using all_of = bool_constant<(Ts::value && ...)>;
template <class... Ts> using any_of = bool_constant<(Ts::value || ...)>;
#elif !defined(_MSC_VER)
template <bool...> struct bools {};
template <class... Ts> using all_of = std::is_same<
bools<Ts::value..., true>,
bools<true, Ts::value...>>;
template <class... Ts> using any_of = negation<all_of<negation<Ts>...>>;
#else
// MSVC has trouble with the above, but supports std::conjunction, which we can use instead (albeit
// at a slight loss of compilation efficiency).
template <class... Ts> using all_of = std::conjunction<Ts...>;
template <class... Ts> using any_of = std::disjunction<Ts...>;
#endif
template <class... Ts> using none_of = negation<any_of<Ts...>>;
template <class T, template<class> class... Predicates> using satisfies_all_of = all_of<Predicates<T>...>;
template <class T, template<class> class... Predicates> using satisfies_any_of = any_of<Predicates<T>...>;
template <class T, template<class> class... Predicates> using satisfies_none_of = none_of<Predicates<T>...>;
/// Strip the class from a method type
template <typename T> struct remove_class { };
template <typename C, typename R, typename... A> struct remove_class<R (C::*)(A...)> { typedef R type(A...); };
template <typename C, typename R, typename... A> struct remove_class<R (C::*)(A...) const> { typedef R type(A...); };
/// Helper template to strip away type modifiers
template <typename T> struct intrinsic_type { typedef T type; };
template <typename T> struct intrinsic_type<const T> { typedef typename intrinsic_type<T>::type type; };
template <typename T> struct intrinsic_type<T*> { typedef typename intrinsic_type<T>::type type; };
template <typename T> struct intrinsic_type<T&> { typedef typename intrinsic_type<T>::type type; };
template <typename T> struct intrinsic_type<T&&> { typedef typename intrinsic_type<T>::type type; };
template <typename T, size_t N> struct intrinsic_type<const T[N]> { typedef typename intrinsic_type<T>::type type; };
template <typename T, size_t N> struct intrinsic_type<T[N]> { typedef typename intrinsic_type<T>::type type; };
template <typename T> using intrinsic_t = typename intrinsic_type<T>::type;
/// Helper type to replace 'void' in some expressions
struct void_type { };
/// Helper template which holds a list of types
template <typename...> struct type_list { };
/// Compile-time integer sum
#ifdef __cpp_fold_expressions
template <typename... Ts> constexpr size_t constexpr_sum(Ts... ns) { return (0 + ... + size_t{ns}); }
#else
constexpr size_t constexpr_sum() { return 0; }
template <typename T, typename... Ts>
constexpr size_t constexpr_sum(T n, Ts... ns) { return size_t{n} + constexpr_sum(ns...); }
#endif
NAMESPACE_BEGIN(constexpr_impl)
/// Implementation details for constexpr functions
constexpr int first(int i) { return i; }
template <typename T, typename... Ts>
constexpr int first(int i, T v, Ts... vs) { return v ? i : first(i + 1, vs...); }
constexpr int last(int /*i*/, int result) { return result; }
template <typename T, typename... Ts>
constexpr int last(int i, int result, T v, Ts... vs) { return last(i + 1, v ? i : result, vs...); }
NAMESPACE_END(constexpr_impl)
/// Return the index of the first type in Ts which satisfies Predicate<T>. Returns sizeof...(Ts) if
/// none match.
template <template<typename> class Predicate, typename... Ts>
constexpr int constexpr_first() { return constexpr_impl::first(0, Predicate<Ts>::value...); }
/// Return the index of the last type in Ts which satisfies Predicate<T>, or -1 if none match.
template <template<typename> class Predicate, typename... Ts>
constexpr int constexpr_last() { return constexpr_impl::last(0, -1, Predicate<Ts>::value...); }
/// Return the Nth element from the parameter pack
template <size_t N, typename T, typename... Ts>
struct pack_element { using type = typename pack_element<N - 1, Ts...>::type; };
template <typename T, typename... Ts>
struct pack_element<0, T, Ts...> { using type = T; };
/// Return the one and only type which matches the predicate, or Default if none match.
/// If more than one type matches the predicate, fail at compile-time.
template <template<typename> class Predicate, typename Default, typename... Ts>
struct exactly_one {
static constexpr auto found = constexpr_sum(Predicate<Ts>::value...);
static_assert(found <= 1, "Found more than one type matching the predicate");
static constexpr auto index = found ? constexpr_first<Predicate, Ts...>() : 0;
using type = conditional_t<found, typename pack_element<index, Ts...>::type, Default>;
};
template <template<typename> class P, typename Default>
struct exactly_one<P, Default> { using type = Default; };
template <template<typename> class Predicate, typename Default, typename... Ts>
using exactly_one_t = typename exactly_one<Predicate, Default, Ts...>::type;
/// Defer the evaluation of type T until types Us are instantiated
template <typename T, typename... /*Us*/> struct deferred_type { using type = T; };
template <typename T, typename... Us> using deferred_t = typename deferred_type<T, Us...>::type;
/// Like is_base_of, but requires a strict base (i.e. `is_strict_base_of<T, T>::value == false`,
/// unlike `std::is_base_of`)
template <typename Base, typename Derived> using is_strict_base_of = bool_constant<
std::is_base_of<Base, Derived>::value && !std::is_same<Base, Derived>::value>;
/// Like is_base_of, but also requires that the base type is accessible (i.e. that a Derived pointer
/// can be converted to a Base pointer)
template <typename Base, typename Derived> using is_accessible_base_of = bool_constant<
std::is_base_of<Base, Derived>::value && std::is_convertible<Derived *, Base *>::value>;
template <template<typename...> class Base>
struct is_template_base_of_impl {
template <typename... Us> static std::true_type check(Base<Us...> *);
static std::false_type check(...);
};
/// Check if a template is the base of a type. For example:
/// `is_template_base_of<Base, T>` is true if `struct T : Base<U> {}` where U can be anything
template <template<typename...> class Base, typename T>
#if !defined(_MSC_VER)
using is_template_base_of = decltype(is_template_base_of_impl<Base>::check((intrinsic_t<T>*)nullptr));
#else // MSVC2015 has trouble with decltype in template aliases
struct is_template_base_of : decltype(is_template_base_of_impl<Base>::check((intrinsic_t<T>*)nullptr)) { };
#endif
/// Check if T is an instantiation of the template `Class`. For example:
/// `is_instantiation<shared_ptr, T>` is true if `T == shared_ptr<U>` where U can be anything.
template <template<typename...> class Class, typename T>
struct is_instantiation : std::false_type { };
template <template<typename...> class Class, typename... Us>
struct is_instantiation<Class, Class<Us...>> : std::true_type { };
/// Check if T is std::shared_ptr<U> where U can be anything
template <typename T> using is_shared_ptr = is_instantiation<std::shared_ptr, T>;
/// Check if T looks like an input iterator
template <typename T, typename = void> struct is_input_iterator : std::false_type {};
template <typename T>
struct is_input_iterator<T, void_t<decltype(*std::declval<T &>()), decltype(++std::declval<T &>())>>
: std::true_type {};
template <typename T> using is_function_pointer = bool_constant<
std::is_pointer<T>::value && std::is_function<typename std::remove_pointer<T>::type>::value>;
template <typename F> struct strip_function_object {
using type = typename remove_class<decltype(&F::operator())>::type;
};
// Extracts the function signature from a function, function pointer or lambda.
template <typename Function, typename F = remove_reference_t<Function>>
using function_signature_t = conditional_t<
std::is_function<F>::value,
F,
typename conditional_t<
std::is_pointer<F>::value || std::is_member_pointer<F>::value,
std::remove_pointer<F>,
strip_function_object<F>
>::type
>;
/// Returns true if the type looks like a lambda: that is, isn't a function, pointer or member
/// pointer. Note that this can catch all sorts of other things, too; this is intended to be used
/// in a place where passing a lambda makes sense.
template <typename T> using is_lambda = satisfies_none_of<remove_reference_t<T>,
std::is_function, std::is_pointer, std::is_member_pointer>;
/// Ignore that a variable is unused in compiler warnings
inline void ignore_unused(const int *) { }
/// Apply a function over each element of a parameter pack
#ifdef __cpp_fold_expressions
#define PYBIND11_EXPAND_SIDE_EFFECTS(PATTERN) (((PATTERN), void()), ...)
#else
using expand_side_effects = bool[];
#define PYBIND11_EXPAND_SIDE_EFFECTS(PATTERN) pybind11::detail::expand_side_effects{ ((PATTERN), void(), false)..., false }
#endif
NAMESPACE_END(detail)
/// C++ bindings of builtin Python exceptions
class builtin_exception : public std::runtime_error {
public:
using std::runtime_error::runtime_error;
/// Set the error using the Python C API
virtual void set_error() const = 0;
};
#define PYBIND11_RUNTIME_EXCEPTION(name, type) \
class name : public builtin_exception { public: \
using builtin_exception::builtin_exception; \
name() : name("") { } \
void set_error() const override { PyErr_SetString(type, what()); } \
};
PYBIND11_RUNTIME_EXCEPTION(stop_iteration, PyExc_StopIteration)
PYBIND11_RUNTIME_EXCEPTION(index_error, PyExc_IndexError)
PYBIND11_RUNTIME_EXCEPTION(key_error, PyExc_KeyError)
PYBIND11_RUNTIME_EXCEPTION(value_error, PyExc_ValueError)
PYBIND11_RUNTIME_EXCEPTION(type_error, PyExc_TypeError)
PYBIND11_RUNTIME_EXCEPTION(cast_error, PyExc_RuntimeError) /// Thrown when pybind11::cast or handle::call fail due to a type casting error
PYBIND11_RUNTIME_EXCEPTION(reference_cast_error, PyExc_RuntimeError) /// Used internally
[[noreturn]] PYBIND11_NOINLINE inline void pybind11_fail(const char *reason) { throw std::runtime_error(reason); }
[[noreturn]] PYBIND11_NOINLINE inline void pybind11_fail(const std::string &reason) { throw std::runtime_error(reason); }
template <typename T, typename SFINAE = void> struct format_descriptor { };
NAMESPACE_BEGIN(detail)
// Returns the index of the given type in the type char array below, and in the list in numpy.h
// The order here is: bool; 8 ints ((signed,unsigned)x(8,16,32,64)bits); float,double,long double;
// complex float,double,long double. Note that the long double types only participate when long
// double is actually longer than double (it isn't under MSVC).
// NB: not only the string below but also complex.h and numpy.h rely on this order.
template <typename T, typename SFINAE = void> struct is_fmt_numeric { static constexpr bool value = false; };
template <typename T> struct is_fmt_numeric<T, enable_if_t<std::is_arithmetic<T>::value>> {
static constexpr bool value = true;
static constexpr int index = std::is_same<T, bool>::value ? 0 : 1 + (
std::is_integral<T>::value ? detail::log2(sizeof(T))*2 + std::is_unsigned<T>::value : 8 + (
std::is_same<T, double>::value ? 1 : std::is_same<T, long double>::value ? 2 : 0));
};
NAMESPACE_END(detail)
template <typename T> struct format_descriptor<T, detail::enable_if_t<std::is_arithmetic<T>::value>> {
static constexpr const char c = "?bBhHiIqQfdg"[detail::is_fmt_numeric<T>::index];
static constexpr const char value[2] = { c, '\0' };
static std::string format() { return std::string(1, c); }
};
#if !defined(PYBIND11_CPP17)
template <typename T> constexpr const char format_descriptor<
T, detail::enable_if_t<std::is_arithmetic<T>::value>>::value[2];
#endif
/// RAII wrapper that temporarily clears any Python error state
struct error_scope {
PyObject *type, *value, *trace;
error_scope() { PyErr_Fetch(&type, &value, &trace); }
~error_scope() { PyErr_Restore(type, value, trace); }
};
/// Dummy destructor wrapper that can be used to expose classes with a private destructor
struct nodelete { template <typename T> void operator()(T*) { } };
// overload_cast requires variable templates: C++14
#if defined(PYBIND11_CPP14)
#define PYBIND11_OVERLOAD_CAST 1
NAMESPACE_BEGIN(detail)
template <typename... Args>
struct overload_cast_impl {
constexpr overload_cast_impl() {} // MSVC 2015 needs this
template <typename Return>
constexpr auto operator()(Return (*pf)(Args...)) const noexcept
-> decltype(pf) { return pf; }
template <typename Return, typename Class>
constexpr auto operator()(Return (Class::*pmf)(Args...), std::false_type = {}) const noexcept
-> decltype(pmf) { return pmf; }
template <typename Return, typename Class>
constexpr auto operator()(Return (Class::*pmf)(Args...) const, std::true_type) const noexcept
-> decltype(pmf) { return pmf; }
};
NAMESPACE_END(detail)
/// Syntax sugar for resolving overloaded function pointers:
/// - regular: static_cast<Return (Class::*)(Arg0, Arg1, Arg2)>(&Class::func)
/// - sweet: overload_cast<Arg0, Arg1, Arg2>(&Class::func)
template <typename... Args>
static constexpr detail::overload_cast_impl<Args...> overload_cast = {};
// MSVC 2015 only accepts this particular initialization syntax for this variable template.
/// Const member function selector for overload_cast
/// - regular: static_cast<Return (Class::*)(Arg) const>(&Class::func)
/// - sweet: overload_cast<Arg>(&Class::func, const_)
static constexpr auto const_ = std::true_type{};
#else // no overload_cast: providing something that static_assert-fails:
template <typename... Args> struct overload_cast {
static_assert(detail::deferred_t<std::false_type, Args...>::value,
"pybind11::overload_cast<...> requires compiling in C++14 mode");
};
#endif // overload_cast
NAMESPACE_BEGIN(detail)
// Adaptor for converting arbitrary container arguments into a vector; implicitly convertible from
// any standard container (or C-style array) supporting std::begin/std::end, any singleton
// arithmetic type (if T is arithmetic), or explicitly constructible from an iterator pair.
template <typename T>
class any_container {
std::vector<T> v;
public:
any_container() = default;
// Can construct from a pair of iterators
template <typename It, typename = enable_if_t<is_input_iterator<It>::value>>
any_container(It first, It last) : v(first, last) { }
// Implicit conversion constructor from any arbitrary container type with values convertible to T
template <typename Container, typename = enable_if_t<std::is_convertible<decltype(*std::begin(std::declval<const Container &>())), T>::value>>
any_container(const Container &c) : any_container(std::begin(c), std::end(c)) { }
// initializer_list's aren't deducible, so don't get matched by the above template; we need this
// to explicitly allow implicit conversion from one:
template <typename TIn, typename = enable_if_t<std::is_convertible<TIn, T>::value>>
any_container(const std::initializer_list<TIn> &c) : any_container(c.begin(), c.end()) { }
// Avoid copying if given an rvalue vector of the correct type.
any_container(std::vector<T> &&v) : v(std::move(v)) { }
// Moves the vector out of an rvalue any_container
operator std::vector<T> &&() && { return std::move(v); }
// Dereferencing obtains a reference to the underlying vector
std::vector<T> &operator*() { return v; }
const std::vector<T> &operator*() const { return v; }
// -> lets you call methods on the underlying vector
std::vector<T> *operator->() { return &v; }
const std::vector<T> *operator->() const { return &v; }
};
NAMESPACE_END(detail)
NAMESPACE_END(PYBIND11_NAMESPACE)

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python/pybind11/include/pybind11/detail/descr.h
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/*
pybind11/detail/descr.h: Helper type for concatenating type signatures at compile time
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "common.h"
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
#if !defined(_MSC_VER)
# define PYBIND11_DESCR_CONSTEXPR static constexpr
#else
# define PYBIND11_DESCR_CONSTEXPR const
#endif
/* Concatenate type signatures at compile time */
template <size_t N, typename... Ts>
struct descr {
char text[N + 1];
constexpr descr() : text{'\0'} { }
constexpr descr(char const (&s)[N+1]) : descr(s, make_index_sequence<N>()) { }
template <size_t... Is>
constexpr descr(char const (&s)[N+1], index_sequence<Is...>) : text{s[Is]..., '\0'} { }
template <typename... Chars>
constexpr descr(char c, Chars... cs) : text{c, static_cast<char>(cs)..., '\0'} { }
static constexpr std::array<const std::type_info *, sizeof...(Ts) + 1> types() {
return {{&typeid(Ts)..., nullptr}};
}
};
template <size_t N1, size_t N2, typename... Ts1, typename... Ts2, size_t... Is1, size_t... Is2>
constexpr descr<N1 + N2, Ts1..., Ts2...> plus_impl(const descr<N1, Ts1...> &a, const descr<N2, Ts2...> &b,
index_sequence<Is1...>, index_sequence<Is2...>) {
return {a.text[Is1]..., b.text[Is2]...};
}
template <size_t N1, size_t N2, typename... Ts1, typename... Ts2>
constexpr descr<N1 + N2, Ts1..., Ts2...> operator+(const descr<N1, Ts1...> &a, const descr<N2, Ts2...> &b) {
return plus_impl(a, b, make_index_sequence<N1>(), make_index_sequence<N2>());
}
template <size_t N>
constexpr descr<N - 1> _(char const(&text)[N]) { return descr<N - 1>(text); }
constexpr descr<0> _(char const(&)[1]) { return {}; }
template <size_t Rem, size_t... Digits> struct int_to_str : int_to_str<Rem/10, Rem%10, Digits...> { };
template <size_t...Digits> struct int_to_str<0, Digits...> {
static constexpr auto digits = descr<sizeof...(Digits)>(('0' + Digits)...);
};
// Ternary description (like std::conditional)
template <bool B, size_t N1, size_t N2>
constexpr enable_if_t<B, descr<N1 - 1>> _(char const(&text1)[N1], char const(&)[N2]) {
return _(text1);
}
template <bool B, size_t N1, size_t N2>
constexpr enable_if_t<!B, descr<N2 - 1>> _(char const(&)[N1], char const(&text2)[N2]) {
return _(text2);
}
template <bool B, typename T1, typename T2>
constexpr enable_if_t<B, T1> _(const T1 &d, const T2 &) { return d; }
template <bool B, typename T1, typename T2>
constexpr enable_if_t<!B, T2> _(const T1 &, const T2 &d) { return d; }
template <size_t Size> auto constexpr _() -> decltype(int_to_str<Size / 10, Size % 10>::digits) {
return int_to_str<Size / 10, Size % 10>::digits;
}
template <typename Type> constexpr descr<1, Type> _() { return {'%'}; }
constexpr descr<0> concat() { return {}; }
template <size_t N, typename... Ts>
constexpr descr<N, Ts...> concat(const descr<N, Ts...> &descr) { return descr; }
template <size_t N, typename... Ts, typename... Args>
constexpr auto concat(const descr<N, Ts...> &d, const Args &...args)
-> decltype(std::declval<descr<N + 2, Ts...>>() + concat(args...)) {
return d + _(", ") + concat(args...);
}
template <size_t N, typename... Ts>
constexpr descr<N + 2, Ts...> type_descr(const descr<N, Ts...> &descr) {
return _("{") + descr + _("}");
}
NAMESPACE_END(detail)
NAMESPACE_END(PYBIND11_NAMESPACE)

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python/pybind11/include/pybind11/detail/init.h
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/*
pybind11/detail/init.h: init factory function implementation and support code.
Copyright (c) 2017 Jason Rhinelander <jason@imaginary.ca>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "class.h"
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
template <>
class type_caster<value_and_holder> {
public:
bool load(handle h, bool) {
value = reinterpret_cast<value_and_holder *>(h.ptr());
return true;
}
template <typename> using cast_op_type = value_and_holder &;
operator value_and_holder &() { return *value; }
static constexpr auto name = _<value_and_holder>();
private:
value_and_holder *value = nullptr;
};
NAMESPACE_BEGIN(initimpl)
inline void no_nullptr(void *ptr) {
if (!ptr) throw type_error("pybind11::init(): factory function returned nullptr");
}
// Implementing functions for all forms of py::init<...> and py::init(...)
template <typename Class> using Cpp = typename Class::type;
template <typename Class> using Alias = typename Class::type_alias;
template <typename Class> using Holder = typename Class::holder_type;
template <typename Class> using is_alias_constructible = std::is_constructible<Alias<Class>, Cpp<Class> &&>;
// Takes a Cpp pointer and returns true if it actually is a polymorphic Alias instance.
template <typename Class, enable_if_t<Class::has_alias, int> = 0>
bool is_alias(Cpp<Class> *ptr) {
return dynamic_cast<Alias<Class> *>(ptr) != nullptr;
}
// Failing fallback version of the above for a no-alias class (always returns false)
template <typename /*Class*/>
constexpr bool is_alias(void *) { return false; }
// Constructs and returns a new object; if the given arguments don't map to a constructor, we fall
// back to brace aggregate initiailization so that for aggregate initialization can be used with
// py::init, e.g. `py::init<int, int>` to initialize a `struct T { int a; int b; }`. For
// non-aggregate types, we need to use an ordinary T(...) constructor (invoking as `T{...}` usually
// works, but will not do the expected thing when `T` has an `initializer_list<T>` constructor).
template <typename Class, typename... Args, detail::enable_if_t<std::is_constructible<Class, Args...>::value, int> = 0>
inline Class *construct_or_initialize(Args &&...args) { return new Class(std::forward<Args>(args)...); }
template <typename Class, typename... Args, detail::enable_if_t<!std::is_constructible<Class, Args...>::value, int> = 0>
inline Class *construct_or_initialize(Args &&...args) { return new Class{std::forward<Args>(args)...}; }
// Attempts to constructs an alias using a `Alias(Cpp &&)` constructor. This allows types with
// an alias to provide only a single Cpp factory function as long as the Alias can be
// constructed from an rvalue reference of the base Cpp type. This means that Alias classes
// can, when appropriate, simply define a `Alias(Cpp &&)` constructor rather than needing to
// inherit all the base class constructors.
template <typename Class>
void construct_alias_from_cpp(std::true_type /*is_alias_constructible*/,
value_and_holder &v_h, Cpp<Class> &&base) {
v_h.value_ptr() = new Alias<Class>(std::move(base));
}
template <typename Class>
[[noreturn]] void construct_alias_from_cpp(std::false_type /*!is_alias_constructible*/,
value_and_holder &, Cpp<Class> &&) {
throw type_error("pybind11::init(): unable to convert returned instance to required "
"alias class: no `Alias<Class>(Class &&)` constructor available");
}
// Error-generating fallback for factories that don't match one of the below construction
// mechanisms.
template <typename Class>
void construct(...) {
static_assert(!std::is_same<Class, Class>::value /* always false */,
"pybind11::init(): init function must return a compatible pointer, "
"holder, or value");
}
// Pointer return v1: the factory function returns a class pointer for a registered class.
// If we don't need an alias (because this class doesn't have one, or because the final type is
// inherited on the Python side) we can simply take over ownership. Otherwise we need to try to
// construct an Alias from the returned base instance.
template <typename Class>
void construct(value_and_holder &v_h, Cpp<Class> *ptr, bool need_alias) {
no_nullptr(ptr);
if (Class::has_alias && need_alias && !is_alias<Class>(ptr)) {
// We're going to try to construct an alias by moving the cpp type. Whether or not
// that succeeds, we still need to destroy the original cpp pointer (either the
// moved away leftover, if the alias construction works, or the value itself if we
// throw an error), but we can't just call `delete ptr`: it might have a special
// deleter, or might be shared_from_this. So we construct a holder around it as if
// it was a normal instance, then steal the holder away into a local variable; thus
// the holder and destruction happens when we leave the C++ scope, and the holder
// class gets to handle the destruction however it likes.
v_h.value_ptr() = ptr;
v_h.set_instance_registered(true); // To prevent init_instance from registering it
v_h.type->init_instance(v_h.inst, nullptr); // Set up the holder
Holder<Class> temp_holder(std::move(v_h.holder<Holder<Class>>())); // Steal the holder
v_h.type->dealloc(v_h); // Destroys the moved-out holder remains, resets value ptr to null
v_h.set_instance_registered(false);
construct_alias_from_cpp<Class>(is_alias_constructible<Class>{}, v_h, std::move(*ptr));
} else {
// Otherwise the type isn't inherited, so we don't need an Alias
v_h.value_ptr() = ptr;
}
}
// Pointer return v2: a factory that always returns an alias instance ptr. We simply take over
// ownership of the pointer.
template <typename Class, enable_if_t<Class::has_alias, int> = 0>
void construct(value_and_holder &v_h, Alias<Class> *alias_ptr, bool) {
no_nullptr(alias_ptr);
v_h.value_ptr() = static_cast<Cpp<Class> *>(alias_ptr);
}
// Holder return: copy its pointer, and move or copy the returned holder into the new instance's
// holder. This also handles types like std::shared_ptr<T> and std::unique_ptr<T> where T is a
// derived type (through those holder's implicit conversion from derived class holder constructors).
template <typename Class>
void construct(value_and_holder &v_h, Holder<Class> holder, bool need_alias) {
auto *ptr = holder_helper<Holder<Class>>::get(holder);
// If we need an alias, check that the held pointer is actually an alias instance
if (Class::has_alias && need_alias && !is_alias<Class>(ptr))
throw type_error("pybind11::init(): construction failed: returned holder-wrapped instance "
"is not an alias instance");
v_h.value_ptr() = ptr;
v_h.type->init_instance(v_h.inst, &holder);
}
// return-by-value version 1: returning a cpp class by value. If the class has an alias and an
// alias is required the alias must have an `Alias(Cpp &&)` constructor so that we can construct
// the alias from the base when needed (i.e. because of Python-side inheritance). When we don't
// need it, we simply move-construct the cpp value into a new instance.
template <typename Class>
void construct(value_and_holder &v_h, Cpp<Class> &&result, bool need_alias) {
static_assert(std::is_move_constructible<Cpp<Class>>::value,
"pybind11::init() return-by-value factory function requires a movable class");
if (Class::has_alias && need_alias)
construct_alias_from_cpp<Class>(is_alias_constructible<Class>{}, v_h, std::move(result));
else
v_h.value_ptr() = new Cpp<Class>(std::move(result));
}
// return-by-value version 2: returning a value of the alias type itself. We move-construct an
// Alias instance (even if no the python-side inheritance is involved). The is intended for
// cases where Alias initialization is always desired.
template <typename Class>
void construct(value_and_holder &v_h, Alias<Class> &&result, bool) {
static_assert(std::is_move_constructible<Alias<Class>>::value,
"pybind11::init() return-by-alias-value factory function requires a movable alias class");
v_h.value_ptr() = new Alias<Class>(std::move(result));
}
// Implementing class for py::init<...>()
template <typename... Args>
struct constructor {
template <typename Class, typename... Extra, enable_if_t<!Class::has_alias, int> = 0>
static void execute(Class &cl, const Extra&... extra) {
cl.def("__init__", [](value_and_holder &v_h, Args... args) {
v_h.value_ptr() = construct_or_initialize<Cpp<Class>>(std::forward<Args>(args)...);
}, is_new_style_constructor(), extra...);
}
template <typename Class, typename... Extra,
enable_if_t<Class::has_alias &&
std::is_constructible<Cpp<Class>, Args...>::value, int> = 0>
static void execute(Class &cl, const Extra&... extra) {
cl.def("__init__", [](value_and_holder &v_h, Args... args) {
if (Py_TYPE(v_h.inst) == v_h.type->type)
v_h.value_ptr() = construct_or_initialize<Cpp<Class>>(std::forward<Args>(args)...);
else
v_h.value_ptr() = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
}, is_new_style_constructor(), extra...);
}
template <typename Class, typename... Extra,
enable_if_t<Class::has_alias &&
!std::is_constructible<Cpp<Class>, Args...>::value, int> = 0>
static void execute(Class &cl, const Extra&... extra) {
cl.def("__init__", [](value_and_holder &v_h, Args... args) {
v_h.value_ptr() = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
}, is_new_style_constructor(), extra...);
}
};
// Implementing class for py::init_alias<...>()
template <typename... Args> struct alias_constructor {
template <typename Class, typename... Extra,
enable_if_t<Class::has_alias && std::is_constructible<Alias<Class>, Args...>::value, int> = 0>
static void execute(Class &cl, const Extra&... extra) {
cl.def("__init__", [](value_and_holder &v_h, Args... args) {
v_h.value_ptr() = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
}, is_new_style_constructor(), extra...);
}
};
// Implementation class for py::init(Func) and py::init(Func, AliasFunc)
template <typename CFunc, typename AFunc = void_type (*)(),
typename = function_signature_t<CFunc>, typename = function_signature_t<AFunc>>
struct factory;
// Specialization for py::init(Func)
template <typename Func, typename Return, typename... Args>
struct factory<Func, void_type (*)(), Return(Args...)> {
remove_reference_t<Func> class_factory;
factory(Func &&f) : class_factory(std::forward<Func>(f)) { }
// The given class either has no alias or has no separate alias factory;
// this always constructs the class itself. If the class is registered with an alias
// type and an alias instance is needed (i.e. because the final type is a Python class
// inheriting from the C++ type) the returned value needs to either already be an alias
// instance, or the alias needs to be constructible from a `Class &&` argument.
template <typename Class, typename... Extra>
void execute(Class &cl, const Extra &...extra) && {
#if defined(PYBIND11_CPP14)
cl.def("__init__", [func = std::move(class_factory)]
#else
auto &func = class_factory;
cl.def("__init__", [func]
#endif
(value_and_holder &v_h, Args... args) {
construct<Class>(v_h, func(std::forward<Args>(args)...),
Py_TYPE(v_h.inst) != v_h.type->type);
}, is_new_style_constructor(), extra...);
}
};
// Specialization for py::init(Func, AliasFunc)
template <typename CFunc, typename AFunc,
typename CReturn, typename... CArgs, typename AReturn, typename... AArgs>
struct factory<CFunc, AFunc, CReturn(CArgs...), AReturn(AArgs...)> {
static_assert(sizeof...(CArgs) == sizeof...(AArgs),
"pybind11::init(class_factory, alias_factory): class and alias factories "
"must have identical argument signatures");
static_assert(all_of<std::is_same<CArgs, AArgs>...>::value,
"pybind11::init(class_factory, alias_factory): class and alias factories "
"must have identical argument signatures");
remove_reference_t<CFunc> class_factory;
remove_reference_t<AFunc> alias_factory;
factory(CFunc &&c, AFunc &&a)
: class_factory(std::forward<CFunc>(c)), alias_factory(std::forward<AFunc>(a)) { }
// The class factory is called when the `self` type passed to `__init__` is the direct
// class (i.e. not inherited), the alias factory when `self` is a Python-side subtype.
template <typename Class, typename... Extra>
void execute(Class &cl, const Extra&... extra) && {
static_assert(Class::has_alias, "The two-argument version of `py::init()` can "
"only be used if the class has an alias");
#if defined(PYBIND11_CPP14)
cl.def("__init__", [class_func = std::move(class_factory), alias_func = std::move(alias_factory)]
#else
auto &class_func = class_factory;
auto &alias_func = alias_factory;
cl.def("__init__", [class_func, alias_func]
#endif
(value_and_holder &v_h, CArgs... args) {
if (Py_TYPE(v_h.inst) == v_h.type->type)
// If the instance type equals the registered type we don't have inheritance, so
// don't need the alias and can construct using the class function:
construct<Class>(v_h, class_func(std::forward<CArgs>(args)...), false);
else
construct<Class>(v_h, alias_func(std::forward<CArgs>(args)...), true);
}, is_new_style_constructor(), extra...);
}
};
/// Set just the C++ state. Same as `__init__`.
template <typename Class, typename T>
void setstate(value_and_holder &v_h, T &&result, bool need_alias) {
construct<Class>(v_h, std::forward<T>(result), need_alias);
}
/// Set both the C++ and Python states
template <typename Class, typename T, typename O,
enable_if_t<std::is_convertible<O, handle>::value, int> = 0>
void setstate(value_and_holder &v_h, std::pair<T, O> &&result, bool need_alias) {
construct<Class>(v_h, std::move(result.first), need_alias);
setattr((PyObject *) v_h.inst, "__dict__", result.second);
}
/// Implementation for py::pickle(GetState, SetState)
template <typename Get, typename Set,
typename = function_signature_t<Get>, typename = function_signature_t<Set>>
struct pickle_factory;
template <typename Get, typename Set,
typename RetState, typename Self, typename NewInstance, typename ArgState>
struct pickle_factory<Get, Set, RetState(Self), NewInstance(ArgState)> {
static_assert(std::is_same<intrinsic_t<RetState>, intrinsic_t<ArgState>>::value,
"The type returned by `__getstate__` must be the same "
"as the argument accepted by `__setstate__`");
remove_reference_t<Get> get;
remove_reference_t<Set> set;
pickle_factory(Get get, Set set)
: get(std::forward<Get>(get)), set(std::forward<Set>(set)) { }
template <typename Class, typename... Extra>
void execute(Class &cl, const Extra &...extra) && {
cl.def("__getstate__", std::move(get));
#if defined(PYBIND11_CPP14)
cl.def("__setstate__", [func = std::move(set)]
#else
auto &func = set;
cl.def("__setstate__", [func]
#endif
(value_and_holder &v_h, ArgState state) {
setstate<Class>(v_h, func(std::forward<ArgState>(state)),
Py_TYPE(v_h.inst) != v_h.type->type);
}, is_new_style_constructor(), extra...);
}
};
NAMESPACE_END(initimpl)
NAMESPACE_END(detail)
NAMESPACE_END(pybind11)

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/*
pybind11/detail/internals.h: Internal data structure and related functions
Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "../pytypes.h"
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
// Forward declarations
inline PyTypeObject *make_static_property_type();
inline PyTypeObject *make_default_metaclass();
inline PyObject *make_object_base_type(PyTypeObject *metaclass);
// The old Python Thread Local Storage (TLS) API is deprecated in Python 3.7 in favor of the new
// Thread Specific Storage (TSS) API.
#if PY_VERSION_HEX >= 0x03070000
# define PYBIND11_TLS_KEY_INIT(var) Py_tss_t *var = nullptr
# define PYBIND11_TLS_GET_VALUE(key) PyThread_tss_get((key))
# define PYBIND11_TLS_REPLACE_VALUE(key, value) PyThread_tss_set((key), (value))
# define PYBIND11_TLS_DELETE_VALUE(key) PyThread_tss_set((key), nullptr)
#else
// Usually an int but a long on Cygwin64 with Python 3.x
# define PYBIND11_TLS_KEY_INIT(var) decltype(PyThread_create_key()) var = 0
# define PYBIND11_TLS_GET_VALUE(key) PyThread_get_key_value((key))
# if PY_MAJOR_VERSION < 3
# define PYBIND11_TLS_DELETE_VALUE(key) \
PyThread_delete_key_value(key)
# define PYBIND11_TLS_REPLACE_VALUE(key, value) \
do { \
PyThread_delete_key_value((key)); \
PyThread_set_key_value((key), (value)); \
} while (false)
# else
# define PYBIND11_TLS_DELETE_VALUE(key) \
PyThread_set_key_value((key), nullptr)
# define PYBIND11_TLS_REPLACE_VALUE(key, value) \
PyThread_set_key_value((key), (value))
# endif
#endif
// Python loads modules by default with dlopen with the RTLD_LOCAL flag; under libc++ and possibly
// other STLs, this means `typeid(A)` from one module won't equal `typeid(A)` from another module
// even when `A` is the same, non-hidden-visibility type (e.g. from a common include). Under
// libstdc++, this doesn't happen: equality and the type_index hash are based on the type name,
// which works. If not under a known-good stl, provide our own name-based hash and equality
// functions that use the type name.
#if defined(__GLIBCXX__)
inline bool same_type(const std::type_info &lhs, const std::type_info &rhs) { return lhs == rhs; }
using type_hash = std::hash<std::type_index>;
using type_equal_to = std::equal_to<std::type_index>;
#else
inline bool same_type(const std::type_info &lhs, const std::type_info &rhs) {
return lhs.name() == rhs.name() || std::strcmp(lhs.name(), rhs.name()) == 0;
}
struct type_hash {
size_t operator()(const std::type_index &t) const {
size_t hash = 5381;
const char *ptr = t.name();
while (auto c = static_cast<unsigned char>(*ptr++))
hash = (hash * 33) ^ c;
return hash;
}
};
struct type_equal_to {
bool operator()(const std::type_index &lhs, const std::type_index &rhs) const {
return lhs.name() == rhs.name() || std::strcmp(lhs.name(), rhs.name()) == 0;
}
};
#endif
template <typename value_type>
using type_map = std::unordered_map<std::type_index, value_type, type_hash, type_equal_to>;
struct overload_hash {
inline size_t operator()(const std::pair<const PyObject *, const char *>& v) const {
size_t value = std::hash<const void *>()(v.first);
value ^= std::hash<const void *>()(v.second) + 0x9e3779b9 + (value<<6) + (value>>2);
return value;
}
};
/// Internal data structure used to track registered instances and types.
/// Whenever binary incompatible changes are made to this structure,
/// `PYBIND11_INTERNALS_VERSION` must be incremented.
struct internals {
type_map<type_info *> registered_types_cpp; // std::type_index -> pybind11's type information
std::unordered_map<PyTypeObject *, std::vector<type_info *>> registered_types_py; // PyTypeObject* -> base type_info(s)
std::unordered_multimap<const void *, instance*> registered_instances; // void * -> instance*
std::unordered_set<std::pair<const PyObject *, const char *>, overload_hash> inactive_overload_cache;
type_map<std::vector<bool (*)(PyObject *, void *&)>> direct_conversions;
std::unordered_map<const PyObject *, std::vector<PyObject *>> patients;
std::forward_list<void (*) (std::exception_ptr)> registered_exception_translators;
std::unordered_map<std::string, void *> shared_data; // Custom data to be shared across extensions
std::vector<PyObject *> loader_patient_stack; // Used by `loader_life_support`
std::forward_list<std::string> static_strings; // Stores the std::strings backing detail::c_str()
PyTypeObject *static_property_type;
PyTypeObject *default_metaclass;
PyObject *instance_base;
#if defined(WITH_THREAD)
PYBIND11_TLS_KEY_INIT(tstate);
PyInterpreterState *istate = nullptr;
#endif
};
/// Additional type information which does not fit into the PyTypeObject.
/// Changes to this struct also require bumping `PYBIND11_INTERNALS_VERSION`.
struct type_info {
PyTypeObject *type;
const std::type_info *cpptype;
size_t type_size, type_align, holder_size_in_ptrs;
void *(*operator_new)(size_t);
void (*init_instance)(instance *, const void *);
void (*dealloc)(value_and_holder &v_h);
std::vector<PyObject *(*)(PyObject *, PyTypeObject *)> implicit_conversions;
std::vector<std::pair<const std::type_info *, void *(*)(void *)>> implicit_casts;
std::vector<bool (*)(PyObject *, void *&)> *direct_conversions;
buffer_info *(*get_buffer)(PyObject *, void *) = nullptr;
void *get_buffer_data = nullptr;
void *(*module_local_load)(PyObject *, const type_info *) = nullptr;
/* A simple type never occurs as a (direct or indirect) parent
* of a class that makes use of multiple inheritance */
bool simple_type : 1;
/* True if there is no multiple inheritance in this type's inheritance tree */
bool simple_ancestors : 1;
/* for base vs derived holder_type checks */
bool default_holder : 1;
/* true if this is a type registered with py::module_local */
bool module_local : 1;
};
/// Tracks the `internals` and `type_info` ABI version independent of the main library version
#define PYBIND11_INTERNALS_VERSION 3
#if defined(_DEBUG)
# define PYBIND11_BUILD_TYPE "_debug"
#else
# define PYBIND11_BUILD_TYPE ""
#endif
#if defined(WITH_THREAD)
# define PYBIND11_INTERNALS_KIND ""
#else
# define PYBIND11_INTERNALS_KIND "_without_thread"
#endif
#define PYBIND11_INTERNALS_ID "__pybind11_internals_v" \
PYBIND11_TOSTRING(PYBIND11_INTERNALS_VERSION) PYBIND11_INTERNALS_KIND PYBIND11_BUILD_TYPE "__"
#define PYBIND11_MODULE_LOCAL_ID "__pybind11_module_local_v" \
PYBIND11_TOSTRING(PYBIND11_INTERNALS_VERSION) PYBIND11_INTERNALS_KIND PYBIND11_BUILD_TYPE "__"
/// Each module locally stores a pointer to the `internals` data. The data
/// itself is shared among modules with the same `PYBIND11_INTERNALS_ID`.
inline internals **&get_internals_pp() {
static internals **internals_pp = nullptr;
return internals_pp;
}
/// Return a reference to the current `internals` data
PYBIND11_NOINLINE inline internals &get_internals() {
auto **&internals_pp = get_internals_pp();
if (internals_pp && *internals_pp)
return **internals_pp;
constexpr auto *id = PYBIND11_INTERNALS_ID;
auto builtins = handle(PyEval_GetBuiltins());
if (builtins.contains(id) && isinstance<capsule>(builtins[id])) {
internals_pp = static_cast<internals **>(capsule(builtins[id]));
// We loaded builtins through python's builtins, which means that our `error_already_set`
// and `builtin_exception` may be different local classes than the ones set up in the
// initial exception translator, below, so add another for our local exception classes.
//
// libstdc++ doesn't require this (types there are identified only by name)
#if !defined(__GLIBCXX__)
(*internals_pp)->registered_exception_translators.push_front(
[](std::exception_ptr p) -> void {
try {
if (p) std::rethrow_exception(p);
} catch (error_already_set &e) { e.restore(); return;
} catch (const builtin_exception &e) { e.set_error(); return;
}
}
);
#endif
} else {
if (!internals_pp) internals_pp = new internals*();
auto *&internals_ptr = *internals_pp;
internals_ptr = new internals();
#if defined(WITH_THREAD)
PyEval_InitThreads();
PyThreadState *tstate = PyThreadState_Get();
#if PY_VERSION_HEX >= 0x03070000
internals_ptr->tstate = PyThread_tss_alloc();
if (!internals_ptr->tstate || PyThread_tss_create(internals_ptr->tstate))
pybind11_fail("get_internals: could not successfully initialize the TSS key!");
PyThread_tss_set(internals_ptr->tstate, tstate);
#else
internals_ptr->tstate = PyThread_create_key();
if (internals_ptr->tstate == -1)
pybind11_fail("get_internals: could not successfully initialize the TLS key!");
PyThread_set_key_value(internals_ptr->tstate, tstate);
#endif
internals_ptr->istate = tstate->interp;
#endif
builtins[id] = capsule(internals_pp);
internals_ptr->registered_exception_translators.push_front(
[](std::exception_ptr p) -> void {
try {
if (p) std::rethrow_exception(p);
} catch (error_already_set &e) { e.restore(); return;
} catch (const builtin_exception &e) { e.set_error(); return;
} catch (const std::bad_alloc &e) { PyErr_SetString(PyExc_MemoryError, e.what()); return;
} catch (const std::domain_error &e) { PyErr_SetString(PyExc_ValueError, e.what()); return;
} catch (const std::invalid_argument &e) { PyErr_SetString(PyExc_ValueError, e.what()); return;
} catch (const std::length_error &e) { PyErr_SetString(PyExc_ValueError, e.what()); return;
} catch (const std::out_of_range &e) { PyErr_SetString(PyExc_IndexError, e.what()); return;
} catch (const std::range_error &e) { PyErr_SetString(PyExc_ValueError, e.what()); return;
} catch (const std::exception &e) { PyErr_SetString(PyExc_RuntimeError, e.what()); return;
} catch (...) {
PyErr_SetString(PyExc_RuntimeError, "Caught an unknown exception!");
return;
}
}
);
internals_ptr->static_property_type = make_static_property_type();
internals_ptr->default_metaclass = make_default_metaclass();
internals_ptr->instance_base = make_object_base_type(internals_ptr->default_metaclass);
}
return **internals_pp;
}
/// Works like `internals.registered_types_cpp`, but for module-local registered types:
inline type_map<type_info *> &registered_local_types_cpp() {
static type_map<type_info *> locals{};
return locals;
}
/// Constructs a std::string with the given arguments, stores it in `internals`, and returns its
/// `c_str()`. Such strings objects have a long storage duration -- the internal strings are only
/// cleared when the program exits or after interpreter shutdown (when embedding), and so are
/// suitable for c-style strings needed by Python internals (such as PyTypeObject's tp_name).
template <typename... Args>
const char *c_str(Args &&...args) {
auto &strings = get_internals().static_strings;
strings.emplace_front(std::forward<Args>(args)...);
return strings.front().c_str();
}
NAMESPACE_END(detail)
/// Returns a named pointer that is shared among all extension modules (using the same
/// pybind11 version) running in the current interpreter. Names starting with underscores
/// are reserved for internal usage. Returns `nullptr` if no matching entry was found.
inline PYBIND11_NOINLINE void *get_shared_data(const std::string &name) {
auto &internals = detail::get_internals();
auto it = internals.shared_data.find(name);
return it != internals.shared_data.end() ? it->second : nullptr;
}
/// Set the shared data that can be later recovered by `get_shared_data()`.
inline PYBIND11_NOINLINE void *set_shared_data(const std::string &name, void *data) {
detail::get_internals().shared_data[name] = data;
return data;
}
/// Returns a typed reference to a shared data entry (by using `get_shared_data()`) if
/// such entry exists. Otherwise, a new object of default-constructible type `T` is
/// added to the shared data under the given name and a reference to it is returned.
template<typename T>
T &get_or_create_shared_data(const std::string &name) {
auto &internals = detail::get_internals();
auto it = internals.shared_data.find(name);
T *ptr = (T *) (it != internals.shared_data.end() ? it->second : nullptr);
if (!ptr) {
ptr = new T();
internals.shared_data[name] = ptr;
}
return *ptr;
}
NAMESPACE_END(PYBIND11_NAMESPACE)

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python/pybind11/include/pybind11/detail/typeid.h
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/*
pybind11/detail/typeid.h: Compiler-independent access to type identifiers
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include <cstdio>
#include <cstdlib>
#if defined(__GNUG__)
#include <cxxabi.h>
#endif
#include "common.h"
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
/// Erase all occurrences of a substring
inline void erase_all(std::string &string, const std::string &search) {
for (size_t pos = 0;;) {
pos = string.find(search, pos);
if (pos == std::string::npos) break;
string.erase(pos, search.length());
}
}
PYBIND11_NOINLINE inline void clean_type_id(std::string &name) {
#if defined(__GNUG__)
int status = 0;
std::unique_ptr<char, void (*)(void *)> res {
abi::__cxa_demangle(name.c_str(), nullptr, nullptr, &status), std::free };
if (status == 0)
name = res.get();
#else
detail::erase_all(name, "class ");
detail::erase_all(name, "struct ");
detail::erase_all(name, "enum ");
#endif
detail::erase_all(name, "pybind11::");
}
NAMESPACE_END(detail)
/// Return a string representation of a C++ type
template <typename T> static std::string type_id() {
std::string name(typeid(T).name());
detail::clean_type_id(name);
return name;
}
NAMESPACE_END(PYBIND11_NAMESPACE)

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python/pybind11/include/pybind11/eigen.h
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/*
pybind11/eigen.h: Transparent conversion for dense and sparse Eigen matrices
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "numpy.h"
#if defined(__INTEL_COMPILER)
# pragma warning(disable: 1682) // implicit conversion of a 64-bit integral type to a smaller integral type (potential portability problem)
#elif defined(__GNUG__) || defined(__clang__)
# pragma GCC diagnostic push
# pragma GCC diagnostic ignored "-Wconversion"
# pragma GCC diagnostic ignored "-Wdeprecated-declarations"
# ifdef __clang__
// Eigen generates a bunch of implicit-copy-constructor-is-deprecated warnings with -Wdeprecated
// under Clang, so disable that warning here:
# pragma GCC diagnostic ignored "-Wdeprecated"
# endif
# if __GNUC__ >= 7
# pragma GCC diagnostic ignored "-Wint-in-bool-context"
# endif
#endif
#if defined(_MSC_VER)
# pragma warning(push)
# pragma warning(disable: 4127) // warning C4127: Conditional expression is constant
# pragma warning(disable: 4996) // warning C4996: std::unary_negate is deprecated in C++17
#endif
#include <Eigen/Core>
#include <Eigen/SparseCore>
// Eigen prior to 3.2.7 doesn't have proper move constructors--but worse, some classes get implicit
// move constructors that break things. We could detect this an explicitly copy, but an extra copy
// of matrices seems highly undesirable.
static_assert(EIGEN_VERSION_AT_LEAST(3,2,7), "Eigen support in pybind11 requires Eigen >= 3.2.7");
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
// Provide a convenience alias for easier pass-by-ref usage with fully dynamic strides:
using EigenDStride = Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic>;
template <typename MatrixType> using EigenDRef = Eigen::Ref<MatrixType, 0, EigenDStride>;
template <typename MatrixType> using EigenDMap = Eigen::Map<MatrixType, 0, EigenDStride>;
NAMESPACE_BEGIN(detail)
#if EIGEN_VERSION_AT_LEAST(3,3,0)
using EigenIndex = Eigen::Index;
#else
using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE;
#endif
// Matches Eigen::Map, Eigen::Ref, blocks, etc:
template <typename T> using is_eigen_dense_map = all_of<is_template_base_of<Eigen::DenseBase, T>, std::is_base_of<Eigen::MapBase<T, Eigen::ReadOnlyAccessors>, T>>;
template <typename T> using is_eigen_mutable_map = std::is_base_of<Eigen::MapBase<T, Eigen::WriteAccessors>, T>;
template <typename T> using is_eigen_dense_plain = all_of<negation<is_eigen_dense_map<T>>, is_template_base_of<Eigen::PlainObjectBase, T>>;
template <typename T> using is_eigen_sparse = is_template_base_of<Eigen::SparseMatrixBase, T>;
// Test for objects inheriting from EigenBase<Derived> that aren't captured by the above. This
// basically covers anything that can be assigned to a dense matrix but that don't have a typical
// matrix data layout that can be copied from their .data(). For example, DiagonalMatrix and
// SelfAdjointView fall into this category.
template <typename T> using is_eigen_other = all_of<
is_template_base_of<Eigen::EigenBase, T>,
negation<any_of<is_eigen_dense_map<T>, is_eigen_dense_plain<T>, is_eigen_sparse<T>>>
>;
// Captures numpy/eigen conformability status (returned by EigenProps::conformable()):
template <bool EigenRowMajor> struct EigenConformable {
bool conformable = false;
EigenIndex rows = 0, cols = 0;
EigenDStride stride{0, 0}; // Only valid if negativestrides is false!
bool negativestrides = false; // If true, do not use stride!
EigenConformable(bool fits = false) : conformable{fits} {}
// Matrix type:
EigenConformable(EigenIndex r, EigenIndex c,
EigenIndex rstride, EigenIndex cstride) :
conformable{true}, rows{r}, cols{c} {
// TODO: when Eigen bug #747 is fixed, remove the tests for non-negativity. http://eigen.tuxfamily.org/bz/show_bug.cgi?id=747
if (rstride < 0 || cstride < 0) {
negativestrides = true;
} else {
stride = {EigenRowMajor ? rstride : cstride /* outer stride */,
EigenRowMajor ? cstride : rstride /* inner stride */ };
}
}
// Vector type:
EigenConformable(EigenIndex r, EigenIndex c, EigenIndex stride)
: EigenConformable(r, c, r == 1 ? c*stride : stride, c == 1 ? r : r*stride) {}
template <typename props> bool stride_compatible() const {
// To have compatible strides, we need (on both dimensions) one of fully dynamic strides,
// matching strides, or a dimension size of 1 (in which case the stride value is irrelevant)
return
!negativestrides &&
(props::inner_stride == Eigen::Dynamic || props::inner_stride == stride.inner() ||
(EigenRowMajor ? cols : rows) == 1) &&
(props::outer_stride == Eigen::Dynamic || props::outer_stride == stride.outer() ||
(EigenRowMajor ? rows : cols) == 1);
}
operator bool() const { return conformable; }
};
template <typename Type> struct eigen_extract_stride { using type = Type; };
template <typename PlainObjectType, int MapOptions, typename StrideType>
struct eigen_extract_stride<Eigen::Map<PlainObjectType, MapOptions, StrideType>> { using type = StrideType; };
template <typename PlainObjectType, int Options, typename StrideType>
struct eigen_extract_stride<Eigen::Ref<PlainObjectType, Options, StrideType>> { using type = StrideType; };
// Helper struct for extracting information from an Eigen type
template <typename Type_> struct EigenProps {
using Type = Type_;
using Scalar = typename Type::Scalar;
using StrideType = typename eigen_extract_stride<Type>::type;
static constexpr EigenIndex
rows = Type::RowsAtCompileTime,
cols = Type::ColsAtCompileTime,
size = Type::SizeAtCompileTime;
static constexpr bool
row_major = Type::IsRowMajor,
vector = Type::IsVectorAtCompileTime, // At least one dimension has fixed size 1
fixed_rows = rows != Eigen::Dynamic,
fixed_cols = cols != Eigen::Dynamic,
fixed = size != Eigen::Dynamic, // Fully-fixed size
dynamic = !fixed_rows && !fixed_cols; // Fully-dynamic size
template <EigenIndex i, EigenIndex ifzero> using if_zero = std::integral_constant<EigenIndex, i == 0 ? ifzero : i>;
static constexpr EigenIndex inner_stride = if_zero<StrideType::InnerStrideAtCompileTime, 1>::value,
outer_stride = if_zero<StrideType::OuterStrideAtCompileTime,
vector ? size : row_major ? cols : rows>::value;
static constexpr bool dynamic_stride = inner_stride == Eigen::Dynamic && outer_stride == Eigen::Dynamic;
static constexpr bool requires_row_major = !dynamic_stride && !vector && (row_major ? inner_stride : outer_stride) == 1;
static constexpr bool requires_col_major = !dynamic_stride && !vector && (row_major ? outer_stride : inner_stride) == 1;
// Takes an input array and determines whether we can make it fit into the Eigen type. If
// the array is a vector, we attempt to fit it into either an Eigen 1xN or Nx1 vector
// (preferring the latter if it will fit in either, i.e. for a fully dynamic matrix type).
static EigenConformable<row_major> conformable(const array &a) {
const auto dims = a.ndim();
if (dims < 1 || dims > 2)
return false;
if (dims == 2) { // Matrix type: require exact match (or dynamic)
EigenIndex
np_rows = a.shape(0),
np_cols = a.shape(1),
np_rstride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar)),
np_cstride = a.strides(1) / static_cast<ssize_t>(sizeof(Scalar));
if ((fixed_rows && np_rows != rows) || (fixed_cols && np_cols != cols))
return false;
return {np_rows, np_cols, np_rstride, np_cstride};
}
// Otherwise we're storing an n-vector. Only one of the strides will be used, but whichever
// is used, we want the (single) numpy stride value.
const EigenIndex n = a.shape(0),
stride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar));
if (vector) { // Eigen type is a compile-time vector
if (fixed && size != n)
return false; // Vector size mismatch
return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride};
}
else if (fixed) {
// The type has a fixed size, but is not a vector: abort
return false;
}
else if (fixed_cols) {
// Since this isn't a vector, cols must be != 1. We allow this only if it exactly
// equals the number of elements (rows is Dynamic, and so 1 row is allowed).
if (cols != n) return false;
return {1, n, stride};
}
else {
// Otherwise it's either fully dynamic, or column dynamic; both become a column vector
if (fixed_rows && rows != n) return false;
return {n, 1, stride};
}
}
static constexpr bool show_writeable = is_eigen_dense_map<Type>::value && is_eigen_mutable_map<Type>::value;
static constexpr bool show_order = is_eigen_dense_map<Type>::value;
static constexpr bool show_c_contiguous = show_order && requires_row_major;
static constexpr bool show_f_contiguous = !show_c_contiguous && show_order && requires_col_major;
static constexpr auto descriptor =
_("numpy.ndarray[") + npy_format_descriptor<Scalar>::name +
_("[") + _<fixed_rows>(_<(size_t) rows>(), _("m")) +
_(", ") + _<fixed_cols>(_<(size_t) cols>(), _("n")) +
_("]") +
// For a reference type (e.g. Ref<MatrixXd>) we have other constraints that might need to be
// satisfied: writeable=True (for a mutable reference), and, depending on the map's stride
// options, possibly f_contiguous or c_contiguous. We include them in the descriptor output
// to provide some hint as to why a TypeError is occurring (otherwise it can be confusing to
// see that a function accepts a 'numpy.ndarray[float64[3,2]]' and an error message that you
// *gave* a numpy.ndarray of the right type and dimensions.
_<show_writeable>(", flags.writeable", "") +
_<show_c_contiguous>(", flags.c_contiguous", "") +
_<show_f_contiguous>(", flags.f_contiguous", "") +
_("]");
};
// Casts an Eigen type to numpy array. If given a base, the numpy array references the src data,
// otherwise it'll make a copy. writeable lets you turn off the writeable flag for the array.
template <typename props> handle eigen_array_cast(typename props::Type const &src, handle base = handle(), bool writeable = true) {
constexpr ssize_t elem_size = sizeof(typename props::Scalar);
array a;
if (props::vector)
a = array({ src.size() }, { elem_size * src.innerStride() }, src.data(), base);
else
a = array({ src.rows(), src.cols() }, { elem_size * src.rowStride(), elem_size * src.colStride() },
src.data(), base);
if (!writeable)
array_proxy(a.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
return a.release();
}
// Takes an lvalue ref to some Eigen type and a (python) base object, creating a numpy array that
// reference the Eigen object's data with `base` as the python-registered base class (if omitted,
// the base will be set to None, and lifetime management is up to the caller). The numpy array is
// non-writeable if the given type is const.
template <typename props, typename Type>
handle eigen_ref_array(Type &src, handle parent = none()) {
// none here is to get past array's should-we-copy detection, which currently always
// copies when there is no base. Setting the base to None should be harmless.
return eigen_array_cast<props>(src, parent, !std::is_const<Type>::value);
}
// Takes a pointer to some dense, plain Eigen type, builds a capsule around it, then returns a numpy
// array that references the encapsulated data with a python-side reference to the capsule to tie
// its destruction to that of any dependent python objects. Const-ness is determined by whether or
// not the Type of the pointer given is const.
template <typename props, typename Type, typename = enable_if_t<is_eigen_dense_plain<Type>::value>>
handle eigen_encapsulate(Type *src) {
capsule base(src, [](void *o) { delete static_cast<Type *>(o); });
return eigen_ref_array<props>(*src, base);
}
// Type caster for regular, dense matrix types (e.g. MatrixXd), but not maps/refs/etc. of dense
// types.
template<typename Type>
struct type_caster<Type, enable_if_t<is_eigen_dense_plain<Type>::value>> {
using Scalar = typename Type::Scalar;
using props = EigenProps<Type>;
bool load(handle src, bool convert) {
// If we're in no-convert mode, only load if given an array of the correct type
if (!convert && !isinstance<array_t<Scalar>>(src))
return false;
// Coerce into an array, but don't do type conversion yet; the copy below handles it.
auto buf = array::ensure(src);
if (!buf)
return false;
auto dims = buf.ndim();
if (dims < 1 || dims > 2)
return false;
auto fits = props::conformable(buf);
if (!fits)
return false;
// Allocate the new type, then build a numpy reference into it
value = Type(fits.rows, fits.cols);
auto ref = reinterpret_steal<array>(eigen_ref_array<props>(value));
if (dims == 1) ref = ref.squeeze();
else if (ref.ndim() == 1) buf = buf.squeeze();
int result = detail::npy_api::get().PyArray_CopyInto_(ref.ptr(), buf.ptr());
if (result < 0) { // Copy failed!
PyErr_Clear();
return false;
}
return true;
}
private:
// Cast implementation
template <typename CType>
static handle cast_impl(CType *src, return_value_policy policy, handle parent) {
switch (policy) {
case return_value_policy::take_ownership:
case return_value_policy::automatic:
return eigen_encapsulate<props>(src);
case return_value_policy::move:
return eigen_encapsulate<props>(new CType(std::move(*src)));
case return_value_policy::copy:
return eigen_array_cast<props>(*src);
case return_value_policy::reference:
case return_value_policy::automatic_reference:
return eigen_ref_array<props>(*src);
case return_value_policy::reference_internal:
return eigen_ref_array<props>(*src, parent);
default:
throw cast_error("unhandled return_value_policy: should not happen!");
};
}
public:
// Normal returned non-reference, non-const value:
static handle cast(Type &&src, return_value_policy /* policy */, handle parent) {
return cast_impl(&src, return_value_policy::move, parent);
}
// If you return a non-reference const, we mark the numpy array readonly:
static handle cast(const Type &&src, return_value_policy /* policy */, handle parent) {
return cast_impl(&src, return_value_policy::move, parent);
}
// lvalue reference return; default (automatic) becomes copy
static handle cast(Type &src, return_value_policy policy, handle parent) {
if (policy == return_value_policy::automatic || policy == return_value_policy::automatic_reference)
policy = return_value_policy::copy;
return cast_impl(&src, policy, parent);
}
// const lvalue reference return; default (automatic) becomes copy
static handle cast(const Type &src, return_value_policy policy, handle parent) {
if (policy == return_value_policy::automatic || policy == return_value_policy::automatic_reference)
policy = return_value_policy::copy;
return cast(&src, policy, parent);
}
// non-const pointer return
static handle cast(Type *src, return_value_policy policy, handle parent) {
return cast_impl(src, policy, parent);
}
// const pointer return
static handle cast(const Type *src, return_value_policy policy, handle parent) {
return cast_impl(src, policy, parent);
}
static constexpr auto name = props::descriptor;
operator Type*() { return &value; }
operator Type&() { return value; }
operator Type&&() && { return std::move(value); }
template <typename T> using cast_op_type = movable_cast_op_type<T>;
private:
Type value;
};
// Base class for casting reference/map/block/etc. objects back to python.
template <typename MapType> struct eigen_map_caster {
private:
using props = EigenProps<MapType>;
public:
// Directly referencing a ref/map's data is a bit dangerous (whatever the map/ref points to has
// to stay around), but we'll allow it under the assumption that you know what you're doing (and
// have an appropriate keep_alive in place). We return a numpy array pointing directly at the
// ref's data (The numpy array ends up read-only if the ref was to a const matrix type.) Note
// that this means you need to ensure you don't destroy the object in some other way (e.g. with
// an appropriate keep_alive, or with a reference to a statically allocated matrix).
static handle cast(const MapType &src, return_value_policy policy, handle parent) {
switch (policy) {
case return_value_policy::copy:
return eigen_array_cast<props>(src);
case return_value_policy::reference_internal:
return eigen_array_cast<props>(src, parent, is_eigen_mutable_map<MapType>::value);
case return_value_policy::reference:
case return_value_policy::automatic:
case return_value_policy::automatic_reference:
return eigen_array_cast<props>(src, none(), is_eigen_mutable_map<MapType>::value);
default:
// move, take_ownership don't make any sense for a ref/map:
pybind11_fail("Invalid return_value_policy for Eigen Map/Ref/Block type");
}
}
static constexpr auto name = props::descriptor;
// Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
// types but not bound arguments). We still provide them (with an explicitly delete) so that
// you end up here if you try anyway.
bool load(handle, bool) = delete;
operator MapType() = delete;
template <typename> using cast_op_type = MapType;
};
// We can return any map-like object (but can only load Refs, specialized next):
template <typename Type> struct type_caster<Type, enable_if_t<is_eigen_dense_map<Type>::value>>
: eigen_map_caster<Type> {};
// Loader for Ref<...> arguments. See the documentation for info on how to make this work without
// copying (it requires some extra effort in many cases).
template <typename PlainObjectType, typename StrideType>
struct type_caster<
Eigen::Ref<PlainObjectType, 0, StrideType>,
enable_if_t<is_eigen_dense_map<Eigen::Ref<PlainObjectType, 0, StrideType>>::value>
> : public eigen_map_caster<Eigen::Ref<PlainObjectType, 0, StrideType>> {
private:
using Type = Eigen::Ref<PlainObjectType, 0, StrideType>;
using props = EigenProps<Type>;
using Scalar = typename props::Scalar;
using MapType = Eigen::Map<PlainObjectType, 0, StrideType>;
using Array = array_t<Scalar, array::forcecast |
((props::row_major ? props::inner_stride : props::outer_stride) == 1 ? array::c_style :
(props::row_major ? props::outer_stride : props::inner_stride) == 1 ? array::f_style : 0)>;
static constexpr bool need_writeable = is_eigen_mutable_map<Type>::value;
// Delay construction (these have no default constructor)
std::unique_ptr<MapType> map;
std::unique_ptr<Type> ref;
// Our array. When possible, this is just a numpy array pointing to the source data, but
// sometimes we can't avoid copying (e.g. input is not a numpy array at all, has an incompatible
// layout, or is an array of a type that needs to be converted). Using a numpy temporary
// (rather than an Eigen temporary) saves an extra copy when we need both type conversion and
// storage order conversion. (Note that we refuse to use this temporary copy when loading an
// argument for a Ref<M> with M non-const, i.e. a read-write reference).
Array copy_or_ref;
public:
bool load(handle src, bool convert) {
// First check whether what we have is already an array of the right type. If not, we can't
// avoid a copy (because the copy is also going to do type conversion).
bool need_copy = !isinstance<Array>(src);
EigenConformable<props::row_major> fits;
if (!need_copy) {
// We don't need a converting copy, but we also need to check whether the strides are
// compatible with the Ref's stride requirements
Array aref = reinterpret_borrow<Array>(src);
if (aref && (!need_writeable || aref.writeable())) {
fits = props::conformable(aref);
if (!fits) return false; // Incompatible dimensions
if (!fits.template stride_compatible<props>())
need_copy = true;
else
copy_or_ref = std::move(aref);
}
else {
need_copy = true;
}
}
if (need_copy) {
// We need to copy: If we need a mutable reference, or we're not supposed to convert
// (either because we're in the no-convert overload pass, or because we're explicitly
// instructed not to copy (via `py::arg().noconvert()`) we have to fail loading.
if (!convert || need_writeable) return false;
Array copy = Array::ensure(src);
if (!copy) return false;
fits = props::conformable(copy);
if (!fits || !fits.template stride_compatible<props>())
return false;
copy_or_ref = std::move(copy);
loader_life_support::add_patient(copy_or_ref);
}
ref.reset();
map.reset(new MapType(data(copy_or_ref), fits.rows, fits.cols, make_stride(fits.stride.outer(), fits.stride.inner())));
ref.reset(new Type(*map));
return true;
}
operator Type*() { return ref.get(); }
operator Type&() { return *ref; }
template <typename _T> using cast_op_type = pybind11::detail::cast_op_type<_T>;
private:
template <typename T = Type, enable_if_t<is_eigen_mutable_map<T>::value, int> = 0>
Scalar *data(Array &a) { return a.mutable_data(); }
template <typename T = Type, enable_if_t<!is_eigen_mutable_map<T>::value, int> = 0>
const Scalar *data(Array &a) { return a.data(); }
// Attempt to figure out a constructor of `Stride` that will work.
// If both strides are fixed, use a default constructor:
template <typename S> using stride_ctor_default = bool_constant<
S::InnerStrideAtCompileTime != Eigen::Dynamic && S::OuterStrideAtCompileTime != Eigen::Dynamic &&
std::is_default_constructible<S>::value>;
// Otherwise, if there is a two-index constructor, assume it is (outer,inner) like
// Eigen::Stride, and use it:
template <typename S> using stride_ctor_dual = bool_constant<
!stride_ctor_default<S>::value && std::is_constructible<S, EigenIndex, EigenIndex>::value>;
// Otherwise, if there is a one-index constructor, and just one of the strides is dynamic, use
// it (passing whichever stride is dynamic).
template <typename S> using stride_ctor_outer = bool_constant<
!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value &&
S::OuterStrideAtCompileTime == Eigen::Dynamic && S::InnerStrideAtCompileTime != Eigen::Dynamic &&
std::is_constructible<S, EigenIndex>::value>;
template <typename S> using stride_ctor_inner = bool_constant<
!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value &&
S::InnerStrideAtCompileTime == Eigen::Dynamic && S::OuterStrideAtCompileTime != Eigen::Dynamic &&
std::is_constructible<S, EigenIndex>::value>;
template <typename S = StrideType, enable_if_t<stride_ctor_default<S>::value, int> = 0>
static S make_stride(EigenIndex, EigenIndex) { return S(); }
template <typename S = StrideType, enable_if_t<stride_ctor_dual<S>::value, int> = 0>
static S make_stride(EigenIndex outer, EigenIndex inner) { return S(outer, inner); }
template <typename S = StrideType, enable_if_t<stride_ctor_outer<S>::value, int> = 0>
static S make_stride(EigenIndex outer, EigenIndex) { return S(outer); }
template <typename S = StrideType, enable_if_t<stride_ctor_inner<S>::value, int> = 0>
static S make_stride(EigenIndex, EigenIndex inner) { return S(inner); }
};
// type_caster for special matrix types (e.g. DiagonalMatrix), which are EigenBase, but not
// EigenDense (i.e. they don't have a data(), at least not with the usual matrix layout).
// load() is not supported, but we can cast them into the python domain by first copying to a
// regular Eigen::Matrix, then casting that.
template <typename Type>
struct type_caster<Type, enable_if_t<is_eigen_other<Type>::value>> {
protected:
using Matrix = Eigen::Matrix<typename Type::Scalar, Type::RowsAtCompileTime, Type::ColsAtCompileTime>;
using props = EigenProps<Matrix>;
public:
static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
handle h = eigen_encapsulate<props>(new Matrix(src));
return h;
}
static handle cast(const Type *src, return_value_policy policy, handle parent) { return cast(*src, policy, parent); }
static constexpr auto name = props::descriptor;
// Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
// types but not bound arguments). We still provide them (with an explicitly delete) so that
// you end up here if you try anyway.
bool load(handle, bool) = delete;
operator Type() = delete;
template <typename> using cast_op_type = Type;
};
template<typename Type>
struct type_caster<Type, enable_if_t<is_eigen_sparse<Type>::value>> {
typedef typename Type::Scalar Scalar;
typedef remove_reference_t<decltype(*std::declval<Type>().outerIndexPtr())> StorageIndex;
typedef typename Type::Index Index;
static constexpr bool rowMajor = Type::IsRowMajor;
bool load(handle src, bool) {
if (!src)
return false;
auto obj = reinterpret_borrow<object>(src);
object sparse_module = module::import("scipy.sparse");
object matrix_type = sparse_module.attr(
rowMajor ? "csr_matrix" : "csc_matrix");
if (!obj.get_type().is(matrix_type)) {
try {
obj = matrix_type(obj);
} catch (const error_already_set &) {
return false;
}
}
auto values = array_t<Scalar>((object) obj.attr("data"));
auto innerIndices = array_t<StorageIndex>((object) obj.attr("indices"));
auto outerIndices = array_t<StorageIndex>((object) obj.attr("indptr"));
auto shape = pybind11::tuple((pybind11::object) obj.attr("shape"));
auto nnz = obj.attr("nnz").cast<Index>();
if (!values || !innerIndices || !outerIndices)
return false;
value = Eigen::MappedSparseMatrix<Scalar, Type::Flags, StorageIndex>(
shape[0].cast<Index>(), shape[1].cast<Index>(), nnz,
outerIndices.mutable_data(), innerIndices.mutable_data(), values.mutable_data());
return true;
}
static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
const_cast<Type&>(src).makeCompressed();
object matrix_type = module::import("scipy.sparse").attr(
rowMajor ? "csr_matrix" : "csc_matrix");
array data(src.nonZeros(), src.valuePtr());
array outerIndices((rowMajor ? src.rows() : src.cols()) + 1, src.outerIndexPtr());
array innerIndices(src.nonZeros(), src.innerIndexPtr());
return matrix_type(
std::make_tuple(data, innerIndices, outerIndices),
std::make_pair(src.rows(), src.cols())
).release();
}
PYBIND11_TYPE_CASTER(Type, _<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[", "scipy.sparse.csc_matrix[")
+ npy_format_descriptor<Scalar>::name + _("]"));
};
NAMESPACE_END(detail)
NAMESPACE_END(PYBIND11_NAMESPACE)
#if defined(__GNUG__) || defined(__clang__)
# pragma GCC diagnostic pop
#elif defined(_MSC_VER)
# pragma warning(pop)
#endif

200
python/pybind11/include/pybind11/embed.h
View File

@ -1,200 +0,0 @@
/*
pybind11/embed.h: Support for embedding the interpreter
Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "pybind11.h"
#include "eval.h"
#if defined(PYPY_VERSION)
# error Embedding the interpreter is not supported with PyPy
#endif
#if PY_MAJOR_VERSION >= 3
# define PYBIND11_EMBEDDED_MODULE_IMPL(name) \
extern "C" PyObject *pybind11_init_impl_##name() { \
return pybind11_init_wrapper_##name(); \
}
#else
# define PYBIND11_EMBEDDED_MODULE_IMPL(name) \
extern "C" void pybind11_init_impl_##name() { \
pybind11_init_wrapper_##name(); \
}
#endif
/** \rst
Add a new module to the table of builtins for the interpreter. Must be
defined in global scope. The first macro parameter is the name of the
module (without quotes). The second parameter is the variable which will
be used as the interface to add functions and classes to the module.
.. code-block:: cpp
PYBIND11_EMBEDDED_MODULE(example, m) {
// ... initialize functions and classes here
m.def("foo", []() {
return "Hello, World!";
});
}
\endrst */
#define PYBIND11_EMBEDDED_MODULE(name, variable) \
static void PYBIND11_CONCAT(pybind11_init_, name)(pybind11::module &); \
static PyObject PYBIND11_CONCAT(*pybind11_init_wrapper_, name)() { \
auto m = pybind11::module(PYBIND11_TOSTRING(name)); \
try { \
PYBIND11_CONCAT(pybind11_init_, name)(m); \
return m.ptr(); \
} catch (pybind11::error_already_set &e) { \
PyErr_SetString(PyExc_ImportError, e.what()); \
return nullptr; \
} catch (const std::exception &e) { \
PyErr_SetString(PyExc_ImportError, e.what()); \
return nullptr; \
} \
} \
PYBIND11_EMBEDDED_MODULE_IMPL(name) \
pybind11::detail::embedded_module name(PYBIND11_TOSTRING(name), \
PYBIND11_CONCAT(pybind11_init_impl_, name)); \
void PYBIND11_CONCAT(pybind11_init_, name)(pybind11::module &variable)
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
/// Python 2.7/3.x compatible version of `PyImport_AppendInittab` and error checks.
struct embedded_module {
#if PY_MAJOR_VERSION >= 3
using init_t = PyObject *(*)();
#else
using init_t = void (*)();
#endif
embedded_module(const char *name, init_t init) {
if (Py_IsInitialized())
pybind11_fail("Can't add new modules after the interpreter has been initialized");
auto result = PyImport_AppendInittab(name, init);
if (result == -1)
pybind11_fail("Insufficient memory to add a new module");
}
};
NAMESPACE_END(detail)
/** \rst
Initialize the Python interpreter. No other pybind11 or CPython API functions can be
called before this is done; with the exception of `PYBIND11_EMBEDDED_MODULE`. The
optional parameter can be used to skip the registration of signal handlers (see the
`Python documentation`_ for details). Calling this function again after the interpreter
has already been initialized is a fatal error.
If initializing the Python interpreter fails, then the program is terminated. (This
is controlled by the CPython runtime and is an exception to pybind11's normal behavior
of throwing exceptions on errors.)
.. _Python documentation: https://docs.python.org/3/c-api/init.html#c.Py_InitializeEx
\endrst */
inline void initialize_interpreter(bool init_signal_handlers = true) {
if (Py_IsInitialized())
pybind11_fail("The interpreter is already running");
Py_InitializeEx(init_signal_handlers ? 1 : 0);
// Make .py files in the working directory available by default
module::import("sys").attr("path").cast<list>().append(".");
}
/** \rst
Shut down the Python interpreter. No pybind11 or CPython API functions can be called
after this. In addition, pybind11 objects must not outlive the interpreter:
.. code-block:: cpp
{ // BAD
py::initialize_interpreter();
auto hello = py::str("Hello, World!");
py::finalize_interpreter();
} // <-- BOOM, hello's destructor is called after interpreter shutdown
{ // GOOD
py::initialize_interpreter();
{ // scoped
auto hello = py::str("Hello, World!");
} // <-- OK, hello is cleaned up properly
py::finalize_interpreter();
}
{ // BETTER
py::scoped_interpreter guard{};
auto hello = py::str("Hello, World!");
}
.. warning::
The interpreter can be restarted by calling `initialize_interpreter` again.
Modules created using pybind11 can be safely re-initialized. However, Python
itself cannot completely unload binary extension modules and there are several
caveats with regard to interpreter restarting. All the details can be found
in the CPython documentation. In short, not all interpreter memory may be
freed, either due to reference cycles or user-created global data.
\endrst */
inline void finalize_interpreter() {
handle builtins(PyEval_GetBuiltins());
const char *id = PYBIND11_INTERNALS_ID;
// Get the internals pointer (without creating it if it doesn't exist). It's possible for the
// internals to be created during Py_Finalize() (e.g. if a py::capsule calls `get_internals()`
// during destruction), so we get the pointer-pointer here and check it after Py_Finalize().
detail::internals **internals_ptr_ptr = detail::get_internals_pp();
// It could also be stashed in builtins, so look there too:
if (builtins.contains(id) && isinstance<capsule>(builtins[id]))
internals_ptr_ptr = capsule(builtins[id]);
Py_Finalize();
if (internals_ptr_ptr) {
delete *internals_ptr_ptr;
*internals_ptr_ptr = nullptr;
}
}
/** \rst
Scope guard version of `initialize_interpreter` and `finalize_interpreter`.
This a move-only guard and only a single instance can exist.
.. code-block:: cpp
#include <pybind11/embed.h>
int main() {
py::scoped_interpreter guard{};
py::print(Hello, World!);
} // <-- interpreter shutdown
\endrst */
class scoped_interpreter {
public:
scoped_interpreter(bool init_signal_handlers = true) {
initialize_interpreter(init_signal_handlers);
}
scoped_interpreter(const scoped_interpreter &) = delete;
scoped_interpreter(scoped_interpreter &&other) noexcept { other.is_valid = false; }
scoped_interpreter &operator=(const scoped_interpreter &) = delete;
scoped_interpreter &operator=(scoped_interpreter &&) = delete;
~scoped_interpreter() {
if (is_valid)
finalize_interpreter();
}
private:
bool is_valid = true;
};
NAMESPACE_END(PYBIND11_NAMESPACE)

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/*
pybind11/exec.h: Support for evaluating Python expressions and statements
from strings and files
Copyright (c) 2016 Klemens Morgenstern <klemens.morgenstern@ed-chemnitz.de> and
Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "pybind11.h"
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
enum eval_mode {
/// Evaluate a string containing an isolated expression
eval_expr,
/// Evaluate a string containing a single statement. Returns \c none
eval_single_statement,
/// Evaluate a string containing a sequence of statement. Returns \c none
eval_statements
};
template <eval_mode mode = eval_expr>
object eval(str expr, object global = globals(), object local = object()) {
if (!local)
local = global;
/* PyRun_String does not accept a PyObject / encoding specifier,
this seems to be the only alternative */
std::string buffer = "# -*- coding: utf-8 -*-\n" + (std::string) expr;
int start;
switch (mode) {
case eval_expr: start = Py_eval_input; break;
case eval_single_statement: start = Py_single_input; break;
case eval_statements: start = Py_file_input; break;
default: pybind11_fail("invalid evaluation mode");
}
PyObject *result = PyRun_String(buffer.c_str(), start, global.ptr(), local.ptr());
if (!result)
throw error_already_set();
return reinterpret_steal<object>(result);
}
template <eval_mode mode = eval_expr, size_t N>
object eval(const char (&s)[N], object global = globals(), object local = object()) {
/* Support raw string literals by removing common leading whitespace */
auto expr = (s[0] == '\n') ? str(module::import("textwrap").attr("dedent")(s))
: str(s);
return eval<mode>(expr, global, local);
}
inline void exec(str expr, object global = globals(), object local = object()) {
eval<eval_statements>(expr, global, local);
}
template <size_t N>
void exec(const char (&s)[N], object global = globals(), object local = object()) {
eval<eval_statements>(s, global, local);
}
template <eval_mode mode = eval_statements>
object eval_file(str fname, object global = globals(), object local = object()) {
if (!local)
local = global;
int start;
switch (mode) {
case eval_expr: start = Py_eval_input; break;
case eval_single_statement: start = Py_single_input; break;
case eval_statements: start = Py_file_input; break;
default: pybind11_fail("invalid evaluation mode");
}
int closeFile = 1;
std::string fname_str = (std::string) fname;
#if PY_VERSION_HEX >= 0x03040000
FILE *f = _Py_fopen_obj(fname.ptr(), "r");
#elif PY_VERSION_HEX >= 0x03000000
FILE *f = _Py_fopen(fname.ptr(), "r");
#else
/* No unicode support in open() :( */
auto fobj = reinterpret_steal<object>(PyFile_FromString(
const_cast<char *>(fname_str.c_str()),
const_cast<char*>("r")));
FILE *f = nullptr;
if (fobj)
f = PyFile_AsFile(fobj.ptr());
closeFile = 0;
#endif
if (!f) {
PyErr_Clear();
pybind11_fail("File \"" + fname_str + "\" could not be opened!");
}
#if PY_VERSION_HEX < 0x03000000 && defined(PYPY_VERSION)
PyObject *result = PyRun_File(f, fname_str.c_str(), start, global.ptr(),
local.ptr());
(void) closeFile;
#else
PyObject *result = PyRun_FileEx(f, fname_str.c_str(), start, global.ptr(),
local.ptr(), closeFile);
#endif
if (!result)
throw error_already_set();
return reinterpret_steal<object>(result);
}
NAMESPACE_END(PYBIND11_NAMESPACE)

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/*
pybind11/functional.h: std::function<> support
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "pybind11.h"
#include <functional>
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
template <typename Return, typename... Args>
struct type_caster<std::function<Return(Args...)>> {
using type = std::function<Return(Args...)>;
using retval_type = conditional_t<std::is_same<Return, void>::value, void_type, Return>;
using function_type = Return (*) (Args...);
public:
bool load(handle src, bool convert) {
if (src.is_none()) {
// Defer accepting None to other overloads (if we aren't in convert mode):
if (!convert) return false;
return true;
}
if (!isinstance<function>(src))
return false;
auto func = reinterpret_borrow<function>(src);
/*
When passing a C++ function as an argument to another C++
function via Python, every function call would normally involve
a full C++ -> Python -> C++ roundtrip, which can be prohibitive.
Here, we try to at least detect the case where the function is
stateless (i.e. function pointer or lambda function without
captured variables), in which case the roundtrip can be avoided.
*/
if (auto cfunc = func.cpp_function()) {
auto c = reinterpret_borrow<capsule>(PyCFunction_GET_SELF(cfunc.ptr()));
auto rec = (function_record *) c;
if (rec && rec->is_stateless &&
same_type(typeid(function_type), *reinterpret_cast<const std::type_info *>(rec->data[1]))) {
struct capture { function_type f; };
value = ((capture *) &rec->data)->f;
return true;
}
}
// ensure GIL is held during functor destruction
struct func_handle {
function f;
func_handle(function&& f_) : f(std::move(f_)) {}
func_handle(const func_handle&) = default;
~func_handle() {
gil_scoped_acquire acq;
function kill_f(std::move(f));
}
};
value = [hfunc = func_handle(std::move(func))](Args... args) -> Return {
gil_scoped_acquire acq;
object retval(hfunc.f(std::forward<Args>(args)...));
/* Visual studio 2015 parser issue: need parentheses around this expression */
return (retval.template cast<Return>());
};
return true;
}
template <typename Func>
static handle cast(Func &&f_, return_value_policy policy, handle /* parent */) {
if (!f_)
return none().inc_ref();
auto result = f_.template target<function_type>();
if (result)
return cpp_function(*result, policy).release();
else
return cpp_function(std::forward<Func>(f_), policy).release();
}
PYBIND11_TYPE_CASTER(type, _("Callable[[") + concat(make_caster<Args>::name...) + _("], ")
+ make_caster<retval_type>::name + _("]"));
};
NAMESPACE_END(detail)
NAMESPACE_END(PYBIND11_NAMESPACE)

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/*
pybind11/iostream.h -- Tools to assist with redirecting cout and cerr to Python
Copyright (c) 2017 Henry F. Schreiner
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "pybind11.h"
#include <streambuf>
#include <ostream>
#include <string>
#include <memory>
#include <iostream>
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
// Buffer that writes to Python instead of C++
class pythonbuf : public std::streambuf {
private:
using traits_type = std::streambuf::traits_type;
const size_t buf_size;
std::unique_ptr<char[]> d_buffer;
object pywrite;
object pyflush;
int overflow(int c) {
if (!traits_type::eq_int_type(c, traits_type::eof())) {
*pptr() = traits_type::to_char_type(c);
pbump(1);
}
return sync() == 0 ? traits_type::not_eof(c) : traits_type::eof();
}
int sync() {
if (pbase() != pptr()) {
// This subtraction cannot be negative, so dropping the sign
str line(pbase(), static_cast<size_t>(pptr() - pbase()));
{
gil_scoped_acquire tmp;
pywrite(line);
pyflush();
}
setp(pbase(), epptr());
}
return 0;
}
public:
pythonbuf(object pyostream, size_t buffer_size = 1024)
: buf_size(buffer_size),
d_buffer(new char[buf_size]),
pywrite(pyostream.attr("write")),
pyflush(pyostream.attr("flush")) {
setp(d_buffer.get(), d_buffer.get() + buf_size - 1);
}
/// Sync before destroy
~pythonbuf() {
sync();
}
};
NAMESPACE_END(detail)
/** \rst
This a move-only guard that redirects output.
.. code-block:: cpp
#include <pybind11/iostream.h>
...
{
py::scoped_ostream_redirect output;
std::cout << "Hello, World!"; // Python stdout
} // <-- return std::cout to normal
You can explicitly pass the c++ stream and the python object,
for example to guard stderr instead.
.. code-block:: cpp
{
py::scoped_ostream_redirect output{std::cerr, py::module::import("sys").attr("stderr")};
std::cerr << "Hello, World!";
}
\endrst */
class scoped_ostream_redirect {
protected:
std::streambuf *old;
std::ostream &costream;
detail::pythonbuf buffer;
public:
scoped_ostream_redirect(
std::ostream &costream = std::cout,
object pyostream = module::import("sys").attr("stdout"))
: costream(costream), buffer(pyostream) {
old = costream.rdbuf(&buffer);
}
~scoped_ostream_redirect() {
costream.rdbuf(old);
}
scoped_ostream_redirect(const scoped_ostream_redirect &) = delete;
scoped_ostream_redirect(scoped_ostream_redirect &&other) = default;
scoped_ostream_redirect &operator=(const scoped_ostream_redirect &) = delete;
scoped_ostream_redirect &operator=(scoped_ostream_redirect &&) = delete;
};
/** \rst
Like `scoped_ostream_redirect`, but redirects cerr by default. This class
is provided primary to make ``py::call_guard`` easier to make.
.. code-block:: cpp
m.def("noisy_func", &noisy_func,
py::call_guard<scoped_ostream_redirect,
scoped_estream_redirect>());
\endrst */
class scoped_estream_redirect : public scoped_ostream_redirect {
public:
scoped_estream_redirect(
std::ostream &costream = std::cerr,
object pyostream = module::import("sys").attr("stderr"))
: scoped_ostream_redirect(costream,pyostream) {}
};
NAMESPACE_BEGIN(detail)
// Class to redirect output as a context manager. C++ backend.
class OstreamRedirect {
bool do_stdout_;
bool do_stderr_;
std::unique_ptr<scoped_ostream_redirect> redirect_stdout;
std::unique_ptr<scoped_estream_redirect> redirect_stderr;
public:
OstreamRedirect(bool do_stdout = true, bool do_stderr = true)
: do_stdout_(do_stdout), do_stderr_(do_stderr) {}
void enter() {
if (do_stdout_)
redirect_stdout.reset(new scoped_ostream_redirect());
if (do_stderr_)
redirect_stderr.reset(new scoped_estream_redirect());
}
void exit() {
redirect_stdout.reset();
redirect_stderr.reset();
}
};
NAMESPACE_END(detail)
/** \rst
This is a helper function to add a C++ redirect context manager to Python
instead of using a C++ guard. To use it, add the following to your binding code:
.. code-block:: cpp
#include <pybind11/iostream.h>
...
py::add_ostream_redirect(m, "ostream_redirect");
You now have a Python context manager that redirects your output:
.. code-block:: python
with m.ostream_redirect():
m.print_to_cout_function()
This manager can optionally be told which streams to operate on:
.. code-block:: python
with m.ostream_redirect(stdout=true, stderr=true):
m.noisy_function_with_error_printing()
\endrst */
inline class_<detail::OstreamRedirect> add_ostream_redirect(module m, std::string name = "ostream_redirect") {
return class_<detail::OstreamRedirect>(m, name.c_str(), module_local())
.def(init<bool,bool>(), arg("stdout")=true, arg("stderr")=true)
.def("__enter__", &detail::OstreamRedirect::enter)
.def("__exit__", [](detail::OstreamRedirect &self_, args) { self_.exit(); });
}
NAMESPACE_END(PYBIND11_NAMESPACE)

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/*
pybind11/operator.h: Metatemplates for operator overloading
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "pybind11.h"
#if defined(__clang__) && !defined(__INTEL_COMPILER)
# pragma clang diagnostic ignored "-Wunsequenced" // multiple unsequenced modifications to 'self' (when using def(py::self OP Type()))
#elif defined(_MSC_VER)
# pragma warning(push)
# pragma warning(disable: 4127) // warning C4127: Conditional expression is constant
#endif
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
/// Enumeration with all supported operator types
enum op_id : int {
op_add, op_sub, op_mul, op_div, op_mod, op_divmod, op_pow, op_lshift,
op_rshift, op_and, op_xor, op_or, op_neg, op_pos, op_abs, op_invert,
op_int, op_long, op_float, op_str, op_cmp, op_gt, op_ge, op_lt, op_le,
op_eq, op_ne, op_iadd, op_isub, op_imul, op_idiv, op_imod, op_ilshift,
op_irshift, op_iand, op_ixor, op_ior, op_complex, op_bool, op_nonzero,
op_repr, op_truediv, op_itruediv, op_hash
};
enum op_type : int {
op_l, /* base type on left */
op_r, /* base type on right */
op_u /* unary operator */
};
struct self_t { };
static const self_t self = self_t();
/// Type for an unused type slot
struct undefined_t { };
/// Don't warn about an unused variable
inline self_t __self() { return self; }
/// base template of operator implementations
template <op_id, op_type, typename B, typename L, typename R> struct op_impl { };
/// Operator implementation generator
template <op_id id, op_type ot, typename L, typename R> struct op_ {
template <typename Class, typename... Extra> void execute(Class &cl, const Extra&... extra) const {
using Base = typename Class::type;
using L_type = conditional_t<std::is_same<L, self_t>::value, Base, L>;
using R_type = conditional_t<std::is_same<R, self_t>::value, Base, R>;
using op = op_impl<id, ot, Base, L_type, R_type>;
cl.def(op::name(), &op::execute, is_operator(), extra...);
#if PY_MAJOR_VERSION < 3
if (id == op_truediv || id == op_itruediv)
cl.def(id == op_itruediv ? "__idiv__" : ot == op_l ? "__div__" : "__rdiv__",
&op::execute, is_operator(), extra...);
#endif
}
template <typename Class, typename... Extra> void execute_cast(Class &cl, const Extra&... extra) const {
using Base = typename Class::type;
using L_type = conditional_t<std::is_same<L, self_t>::value, Base, L>;
using R_type = conditional_t<std::is_same<R, self_t>::value, Base, R>;
using op = op_impl<id, ot, Base, L_type, R_type>;
cl.def(op::name(), &op::execute_cast, is_operator(), extra...);
#if PY_MAJOR_VERSION < 3
if (id == op_truediv || id == op_itruediv)
cl.def(id == op_itruediv ? "__idiv__" : ot == op_l ? "__div__" : "__rdiv__",
&op::execute, is_operator(), extra...);
#endif
}
};
#define PYBIND11_BINARY_OPERATOR(id, rid, op, expr) \
template <typename B, typename L, typename R> struct op_impl<op_##id, op_l, B, L, R> { \
static char const* name() { return "__" #id "__"; } \
static auto execute(const L &l, const R &r) -> decltype(expr) { return (expr); } \
static B execute_cast(const L &l, const R &r) { return B(expr); } \
}; \
template <typename B, typename L, typename R> struct op_impl<op_##id, op_r, B, L, R> { \
static char const* name() { return "__" #rid "__"; } \
static auto execute(const R &r, const L &l) -> decltype(expr) { return (expr); } \
static B execute_cast(const R &r, const L &l) { return B(expr); } \
}; \
inline op_<op_##id, op_l, self_t, self_t> op(const self_t &, const self_t &) { \
return op_<op_##id, op_l, self_t, self_t>(); \
} \
template <typename T> op_<op_##id, op_l, self_t, T> op(const self_t &, const T &) { \
return op_<op_##id, op_l, self_t, T>(); \
} \
template <typename T> op_<op_##id, op_r, T, self_t> op(const T &, const self_t &) { \
return op_<op_##id, op_r, T, self_t>(); \
}
#define PYBIND11_INPLACE_OPERATOR(id, op, expr) \
template <typename B, typename L, typename R> struct op_impl<op_##id, op_l, B, L, R> { \
static char const* name() { return "__" #id "__"; } \
static auto execute(L &l, const R &r) -> decltype(expr) { return expr; } \
static B execute_cast(L &l, const R &r) { return B(expr); } \
}; \
template <typename T> op_<op_##id, op_l, self_t, T> op(const self_t &, const T &) { \
return op_<op_##id, op_l, self_t, T>(); \
}
#define PYBIND11_UNARY_OPERATOR(id, op, expr) \
template <typename B, typename L> struct op_impl<op_##id, op_u, B, L, undefined_t> { \
static char const* name() { return "__" #id "__"; } \
static auto execute(const L &l) -> decltype(expr) { return expr; } \
static B execute_cast(const L &l) { return B(expr); } \
}; \
inline op_<op_##id, op_u, self_t, undefined_t> op(const self_t &) { \
return op_<op_##id, op_u, self_t, undefined_t>(); \
}
PYBIND11_BINARY_OPERATOR(sub, rsub, operator-, l - r)
PYBIND11_BINARY_OPERATOR(add, radd, operator+, l + r)
PYBIND11_BINARY_OPERATOR(mul, rmul, operator*, l * r)
PYBIND11_BINARY_OPERATOR(truediv, rtruediv, operator/, l / r)
PYBIND11_BINARY_OPERATOR(mod, rmod, operator%, l % r)
PYBIND11_BINARY_OPERATOR(lshift, rlshift, operator<<, l << r)
PYBIND11_BINARY_OPERATOR(rshift, rrshift, operator>>, l >> r)
PYBIND11_BINARY_OPERATOR(and, rand, operator&, l & r)
PYBIND11_BINARY_OPERATOR(xor, rxor, operator^, l ^ r)
PYBIND11_BINARY_OPERATOR(eq, eq, operator==, l == r)
PYBIND11_BINARY_OPERATOR(ne, ne, operator!=, l != r)
PYBIND11_BINARY_OPERATOR(or, ror, operator|, l | r)
PYBIND11_BINARY_OPERATOR(gt, lt, operator>, l > r)
PYBIND11_BINARY_OPERATOR(ge, le, operator>=, l >= r)
PYBIND11_BINARY_OPERATOR(lt, gt, operator<, l < r)
PYBIND11_BINARY_OPERATOR(le, ge, operator<=, l <= r)
//PYBIND11_BINARY_OPERATOR(pow, rpow, pow, std::pow(l, r))
PYBIND11_INPLACE_OPERATOR(iadd, operator+=, l += r)
PYBIND11_INPLACE_OPERATOR(isub, operator-=, l -= r)
PYBIND11_INPLACE_OPERATOR(imul, operator*=, l *= r)
PYBIND11_INPLACE_OPERATOR(itruediv, operator/=, l /= r)
PYBIND11_INPLACE_OPERATOR(imod, operator%=, l %= r)
PYBIND11_INPLACE_OPERATOR(ilshift, operator<<=, l <<= r)
PYBIND11_INPLACE_OPERATOR(irshift, operator>>=, l >>= r)
PYBIND11_INPLACE_OPERATOR(iand, operator&=, l &= r)
PYBIND11_INPLACE_OPERATOR(ixor, operator^=, l ^= r)
PYBIND11_INPLACE_OPERATOR(ior, operator|=, l |= r)
PYBIND11_UNARY_OPERATOR(neg, operator-, -l)
PYBIND11_UNARY_OPERATOR(pos, operator+, +l)
PYBIND11_UNARY_OPERATOR(abs, abs, std::abs(l))
PYBIND11_UNARY_OPERATOR(hash, hash, std::hash<L>()(l))
PYBIND11_UNARY_OPERATOR(invert, operator~, (~l))
PYBIND11_UNARY_OPERATOR(bool, operator!, !!l)
PYBIND11_UNARY_OPERATOR(int, int_, (int) l)
PYBIND11_UNARY_OPERATOR(float, float_, (double) l)
#undef PYBIND11_BINARY_OPERATOR
#undef PYBIND11_INPLACE_OPERATOR
#undef PYBIND11_UNARY_OPERATOR
NAMESPACE_END(detail)
using detail::self;
NAMESPACE_END(PYBIND11_NAMESPACE)
#if defined(_MSC_VER)
# pragma warning(pop)
#endif

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/*
pybind11/options.h: global settings that are configurable at runtime.
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "detail/common.h"
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
class options {
public:
// Default RAII constructor, which leaves settings as they currently are.
options() : previous_state(global_state()) {}
// Class is non-copyable.
options(const options&) = delete;
options& operator=(const options&) = delete;
// Destructor, which restores settings that were in effect before.
~options() {
global_state() = previous_state;
}
// Setter methods (affect the global state):
options& disable_user_defined_docstrings() & { global_state().show_user_defined_docstrings = false; return *this; }
options& enable_user_defined_docstrings() & { global_state().show_user_defined_docstrings = true; return *this; }
options& disable_function_signatures() & { global_state().show_function_signatures = false; return *this; }
options& enable_function_signatures() & { global_state().show_function_signatures = true; return *this; }
// Getter methods (return the global state):
static bool show_user_defined_docstrings() { return global_state().show_user_defined_docstrings; }
static bool show_function_signatures() { return global_state().show_function_signatures; }
// This type is not meant to be allocated on the heap.
void* operator new(size_t) = delete;
private:
struct state {
bool show_user_defined_docstrings = true; //< Include user-supplied texts in docstrings.
bool show_function_signatures = true; //< Include auto-generated function signatures in docstrings.
};
static state &global_state() {
static state instance;
return instance;
}
state previous_state;
};
NAMESPACE_END(PYBIND11_NAMESPACE)

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python/pybind11/include/pybind11/pytypes.h
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/*
pybind11/stl.h: Transparent conversion for STL data types
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "pybind11.h"
#include <set>
#include <unordered_set>
#include <map>
#include <unordered_map>
#include <iostream>
#include <list>
#include <deque>
#include <valarray>
#if defined(_MSC_VER)
#pragma warning(push)
#pragma warning(disable: 4127) // warning C4127: Conditional expression is constant
#endif
#ifdef __has_include
// std::optional (but including it in c++14 mode isn't allowed)
# if defined(PYBIND11_CPP17) && __has_include(<optional>)
# include <optional>
# define PYBIND11_HAS_OPTIONAL 1
# endif
// std::experimental::optional (but not allowed in c++11 mode)
# if defined(PYBIND11_CPP14) && (__has_include(<experimental/optional>) && \
!__has_include(<optional>))
# include <experimental/optional>
# define PYBIND11_HAS_EXP_OPTIONAL 1
# endif
// std::variant
# if defined(PYBIND11_CPP17) && __has_include(<variant>)
# include <variant>
# define PYBIND11_HAS_VARIANT 1
# endif
#elif defined(_MSC_VER) && defined(PYBIND11_CPP17)
# include <optional>
# include <variant>
# define PYBIND11_HAS_OPTIONAL 1
# define PYBIND11_HAS_VARIANT 1
#endif
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
/// Extracts an const lvalue reference or rvalue reference for U based on the type of T (e.g. for
/// forwarding a container element). Typically used indirect via forwarded_type(), below.
template <typename T, typename U>
using forwarded_type = conditional_t<
std::is_lvalue_reference<T>::value, remove_reference_t<U> &, remove_reference_t<U> &&>;
/// Forwards a value U as rvalue or lvalue according to whether T is rvalue or lvalue; typically
/// used for forwarding a container's elements.
template <typename T, typename U>
forwarded_type<T, U> forward_like(U &&u) {
return std::forward<detail::forwarded_type<T, U>>(std::forward<U>(u));
}
template <typename Type, typename Key> struct set_caster {
using type = Type;
using key_conv = make_caster<Key>;
bool load(handle src, bool convert) {
if (!isinstance<pybind11::set>(src))
return false;
auto s = reinterpret_borrow<pybind11::set>(src);
value.clear();
for (auto entry : s) {
key_conv conv;
if (!conv.load(entry, convert))
return false;
value.insert(cast_op<Key &&>(std::move(conv)));
}
return true;
}
template <typename T>
static handle cast(T &&src, return_value_policy policy, handle parent) {
if (!std::is_lvalue_reference<T>::value)
policy = return_value_policy_override<Key>::policy(policy);
pybind11::set s;
for (auto &&value : src) {
auto value_ = reinterpret_steal<object>(key_conv::cast(forward_like<T>(value), policy, parent));
if (!value_ || !s.add(value_))
return handle();
}
return s.release();
}
PYBIND11_TYPE_CASTER(type, _("Set[") + key_conv::name + _("]"));
};
template <typename Type, typename Key, typename Value> struct map_caster {
using key_conv = make_caster<Key>;
using value_conv = make_caster<Value>;
bool load(handle src, bool convert) {
if (!isinstance<dict>(src))
return false;
auto d = reinterpret_borrow<dict>(src);
value.clear();
for (auto it : d) {
key_conv kconv;
value_conv vconv;
if (!kconv.load(it.first.ptr(), convert) ||
!vconv.load(it.second.ptr(), convert))
return false;
value.emplace(cast_op<Key &&>(std::move(kconv)), cast_op<Value &&>(std::move(vconv)));
}
return true;
}
template <typename T>
static handle cast(T &&src, return_value_policy policy, handle parent) {
dict d;
return_value_policy policy_key = policy;
return_value_policy policy_value = policy;
if (!std::is_lvalue_reference<T>::value) {
policy_key = return_value_policy_override<Key>::policy(policy_key);
policy_value = return_value_policy_override<Value>::policy(policy_value);
}
for (auto &&kv : src) {
auto key = reinterpret_steal<object>(key_conv::cast(forward_like<T>(kv.first), policy_key, parent));
auto value = reinterpret_steal<object>(value_conv::cast(forward_like<T>(kv.second), policy_value, parent));
if (!key || !value)
return handle();
d[key] = value;
}
return d.release();
}
PYBIND11_TYPE_CASTER(Type, _("Dict[") + key_conv::name + _(", ") + value_conv::name + _("]"));
};
template <typename Type, typename Value> struct list_caster {
using value_conv = make_caster<Value>;
bool load(handle src, bool convert) {
if (!isinstance<sequence>(src) || isinstance<str>(src))
return false;
auto s = reinterpret_borrow<sequence>(src);
value.clear();
reserve_maybe(s, &value);
for (auto it : s) {
value_conv conv;
if (!conv.load(it, convert))
return false;
value.push_back(cast_op<Value &&>(std::move(conv)));
}
return true;
}
private:
template <typename T = Type,
enable_if_t<std::is_same<decltype(std::declval<T>().reserve(0)), void>::value, int> = 0>
void reserve_maybe(sequence s, Type *) { value.reserve(s.size()); }
void reserve_maybe(sequence, void *) { }
public:
template <typename T>
static handle cast(T &&src, return_value_policy policy, handle parent) {
if (!std::is_lvalue_reference<T>::value)
policy = return_value_policy_override<Value>::policy(policy);
list l(src.size());
size_t index = 0;
for (auto &&value : src) {
auto value_ = reinterpret_steal<object>(value_conv::cast(forward_like<T>(value), policy, parent));
if (!value_)
return handle();
PyList_SET_ITEM(l.ptr(), (ssize_t) index++, value_.release().ptr()); // steals a reference
}
return l.release();
}
PYBIND11_TYPE_CASTER(Type, _("List[") + value_conv::name + _("]"));
};
template <typename Type, typename Alloc> struct type_caster<std::vector<Type, Alloc>>
: list_caster<std::vector<Type, Alloc>, Type> { };
template <typename Type, typename Alloc> struct type_caster<std::deque<Type, Alloc>>
: list_caster<std::deque<Type, Alloc>, Type> { };
template <typename Type, typename Alloc> struct type_caster<std::list<Type, Alloc>>
: list_caster<std::list<Type, Alloc>, Type> { };
template <typename ArrayType, typename Value, bool Resizable, size_t Size = 0> struct array_caster {
using value_conv = make_caster<Value>;
private:
template <bool R = Resizable>
bool require_size(enable_if_t<R, size_t> size) {
if (value.size() != size)
value.resize(size);
return true;
}
template <bool R = Resizable>
bool require_size(enable_if_t<!R, size_t> size) {
return size == Size;
}
public:
bool load(handle src, bool convert) {
if (!isinstance<sequence>(src))
return false;
auto l = reinterpret_borrow<sequence>(src);
if (!require_size(l.size()))
return false;
size_t ctr = 0;
for (auto it : l) {
value_conv conv;
if (!conv.load(it, convert))
return false;
value[ctr++] = cast_op<Value &&>(std::move(conv));
}
return true;
}
template <typename T>
static handle cast(T &&src, return_value_policy policy, handle parent) {
list l(src.size());
size_t index = 0;
for (auto &&value : src) {
auto value_ = reinterpret_steal<object>(value_conv::cast(forward_like<T>(value), policy, parent));
if (!value_)
return handle();
PyList_SET_ITEM(l.ptr(), (ssize_t) index++, value_.release().ptr()); // steals a reference
}
return l.release();
}
PYBIND11_TYPE_CASTER(ArrayType, _("List[") + value_conv::name + _<Resizable>(_(""), _("[") + _<Size>() + _("]")) + _("]"));
};
template <typename Type, size_t Size> struct type_caster<std::array<Type, Size>>
: array_caster<std::array<Type, Size>, Type, false, Size> { };
template <typename Type> struct type_caster<std::valarray<Type>>
: array_caster<std::valarray<Type>, Type, true> { };
template <typename Key, typename Compare, typename Alloc> struct type_caster<std::set<Key, Compare, Alloc>>
: set_caster<std::set<Key, Compare, Alloc>, Key> { };
template <typename Key, typename Hash, typename Equal, typename Alloc> struct type_caster<std::unordered_set<Key, Hash, Equal, Alloc>>
: set_caster<std::unordered_set<Key, Hash, Equal, Alloc>, Key> { };
template <typename Key, typename Value, typename Compare, typename Alloc> struct type_caster<std::map<Key, Value, Compare, Alloc>>
: map_caster<std::map<Key, Value, Compare, Alloc>, Key, Value> { };
template <typename Key, typename Value, typename Hash, typename Equal, typename Alloc> struct type_caster<std::unordered_map<Key, Value, Hash, Equal, Alloc>>
: map_caster<std::unordered_map<Key, Value, Hash, Equal, Alloc>, Key, Value> { };
// This type caster is intended to be used for std::optional and std::experimental::optional
template<typename T> struct optional_caster {
using value_conv = make_caster<typename T::value_type>;
template <typename T_>
static handle cast(T_ &&src, return_value_policy policy, handle parent) {
if (!src)
return none().inc_ref();
policy = return_value_policy_override<typename T::value_type>::policy(policy);
return value_conv::cast(*std::forward<T_>(src), policy, parent);
}
bool load(handle src, bool convert) {
if (!src) {
return false;
} else if (src.is_none()) {
return true; // default-constructed value is already empty
}
value_conv inner_caster;
if (!inner_caster.load(src, convert))
return false;
value.emplace(cast_op<typename T::value_type &&>(std::move(inner_caster)));
return true;
}
PYBIND11_TYPE_CASTER(T, _("Optional[") + value_conv::name + _("]"));
};
#if PYBIND11_HAS_OPTIONAL
template<typename T> struct type_caster<std::optional<T>>
: public optional_caster<std::optional<T>> {};
template<> struct type_caster<std::nullopt_t>
: public void_caster<std::nullopt_t> {};
#endif
#if PYBIND11_HAS_EXP_OPTIONAL
template<typename T> struct type_caster<std::experimental::optional<T>>
: public optional_caster<std::experimental::optional<T>> {};
template<> struct type_caster<std::experimental::nullopt_t>
: public void_caster<std::experimental::nullopt_t> {};
#endif
/// Visit a variant and cast any found type to Python
struct variant_caster_visitor {
return_value_policy policy;
handle parent;
using result_type = handle; // required by boost::variant in C++11
template <typename T>
result_type operator()(T &&src) const {
return make_caster<T>::cast(std::forward<T>(src), policy, parent);
}
};
/// Helper class which abstracts away variant's `visit` function. `std::variant` and similar
/// `namespace::variant` types which provide a `namespace::visit()` function are handled here
/// automatically using argument-dependent lookup. Users can provide specializations for other
/// variant-like classes, e.g. `boost::variant` and `boost::apply_visitor`.
template <template<typename...> class Variant>
struct visit_helper {
template <typename... Args>
static auto call(Args &&...args) -> decltype(visit(std::forward<Args>(args)...)) {
return visit(std::forward<Args>(args)...);
}
};
/// Generic variant caster
template <typename Variant> struct variant_caster;
template <template<typename...> class V, typename... Ts>
struct variant_caster<V<Ts...>> {
static_assert(sizeof...(Ts) > 0, "Variant must consist of at least one alternative.");
template <typename U, typename... Us>
bool load_alternative(handle src, bool convert, type_list<U, Us...>) {
auto caster = make_caster<U>();
if (caster.load(src, convert)) {
value = cast_op<U>(caster);
return true;
}
return load_alternative(src, convert, type_list<Us...>{});
}
bool load_alternative(handle, bool, type_list<>) { return false; }
bool load(handle src, bool convert) {
// Do a first pass without conversions to improve constructor resolution.
// E.g. `py::int_(1).cast<variant<double, int>>()` needs to fill the `int`
// slot of the variant. Without two-pass loading `double` would be filled
// because it appears first and a conversion is possible.
if (convert && load_alternative(src, false, type_list<Ts...>{}))
return true;
return load_alternative(src, convert, type_list<Ts...>{});
}
template <typename Variant>
static handle cast(Variant &&src, return_value_policy policy, handle parent) {
return visit_helper<V>::call(variant_caster_visitor{policy, parent},
std::forward<Variant>(src));
}
using Type = V<Ts...>;
PYBIND11_TYPE_CASTER(Type, _("Union[") + detail::concat(make_caster<Ts>::name...) + _("]"));
};
#if PYBIND11_HAS_VARIANT
template <typename... Ts>
struct type_caster<std::variant<Ts...>> : variant_caster<std::variant<Ts...>> { };
#endif
NAMESPACE_END(detail)
inline std::ostream &operator<<(std::ostream &os, const handle &obj) {
os << (std::string) str(obj);
return os;
}
NAMESPACE_END(PYBIND11_NAMESPACE)
#if defined(_MSC_VER)
#pragma warning(pop)
#endif

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/*
pybind11/std_bind.h: Binding generators for STL data types
Copyright (c) 2016 Sergey Lyskov and Wenzel Jakob
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#pragma once
#include "detail/common.h"
#include "operators.h"
#include <algorithm>
#include <sstream>
NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
NAMESPACE_BEGIN(detail)
/* SFINAE helper class used by 'is_comparable */
template <typename T> struct container_traits {
template <typename T2> static std::true_type test_comparable(decltype(std::declval<const T2 &>() == std::declval<const T2 &>())*);
template <typename T2> static std::false_type test_comparable(...);
template <typename T2> static std::true_type test_value(typename T2::value_type *);
template <typename T2> static std::false_type test_value(...);
template <typename T2> static std::true_type test_pair(typename T2::first_type *, typename T2::second_type *);
template <typename T2> static std::false_type test_pair(...);
static constexpr const bool is_comparable = std::is_same<std::true_type, decltype(test_comparable<T>(nullptr))>::value;
static constexpr const bool is_pair = std::is_same<std::true_type, decltype(test_pair<T>(nullptr, nullptr))>::value;
static constexpr const bool is_vector = std::is_same<std::true_type, decltype(test_value<T>(nullptr))>::value;
static constexpr const bool is_element = !is_pair && !is_vector;
};
/* Default: is_comparable -> std::false_type */
template <typename T, typename SFINAE = void>
struct is_comparable : std::false_type { };
/* For non-map data structures, check whether operator== can be instantiated */
template <typename T>
struct is_comparable<
T, enable_if_t<container_traits<T>::is_element &&
container_traits<T>::is_comparable>>
: std::true_type { };
/* For a vector/map data structure, recursively check the value type (which is std::pair for maps) */
template <typename T>
struct is_comparable<T, enable_if_t<container_traits<T>::is_vector>> {
static constexpr const bool value =
is_comparable<typename T::value_type>::value;
};
/* For pairs, recursively check the two data types */
template <typename T>
struct is_comparable<T, enable_if_t<container_traits<T>::is_pair>> {
static constexpr const bool value =
is_comparable<typename T::first_type>::value &&
is_comparable<typename T::second_type>::value;
};
/* Fallback functions */
template <typename, typename, typename... Args> void vector_if_copy_constructible(const Args &...) { }
template <typename, typename, typename... Args> void vector_if_equal_operator(const Args &...) { }
template <typename, typename, typename... Args> void vector_if_insertion_operator(const Args &...) { }
template <typename, typename, typename... Args> void vector_modifiers(const Args &...) { }
template<typename Vector, typename Class_>
void vector_if_copy_constructible(enable_if_t<is_copy_constructible<Vector>::value, Class_> &cl) {
cl.def(init<const Vector &>(), "Copy constructor");
}
template<typename Vector, typename Class_>
void vector_if_equal_operator(enable_if_t<is_comparable<Vector>::value, Class_> &cl) {
using T = typename Vector::value_type;
cl.def(self == self);
cl.def(self != self);
cl.def("count",
[](const Vector &v, const T &x) {
return std::count(v.begin(), v.end(), x);
},
arg("x"),
"Return the number of times ``x`` appears in the list"
);
cl.def("remove", [](Vector &v, const T &x) {
auto p = std::find(v.begin(), v.end(), x);
if (p != v.end())
v.erase(p);
else
throw value_error();
},
arg("x"),
"Remove the first item from the list whose value is x. "
"It is an error if there is no such item."
);
cl.def("__contains__",
[](const Vector &v, const T &x) {
return std::find(v.begin(), v.end(), x) != v.end();
},
arg("x"),
"Return true the container contains ``x``"
);
}
// Vector modifiers -- requires a copyable vector_type:
// (Technically, some of these (pop and __delitem__) don't actually require copyability, but it seems
// silly to allow deletion but not insertion, so include them here too.)
template <typename Vector, typename Class_>
void vector_modifiers(enable_if_t<is_copy_constructible<typename Vector::value_type>::value, Class_> &cl) {
using T = typename Vector::value_type;
using SizeType = typename Vector::size_type;
using DiffType = typename Vector::difference_type;
cl.def("append",
[](Vector &v, const T &value) { v.push_back(value); },
arg("x"),
"Add an item to the end of the list");
cl.def(init([](iterable it) {
auto v = std::unique_ptr<Vector>(new Vector());
v->reserve(len_hint(it));
for (handle h : it)
v->push_back(h.cast<T>());
return v.release();
}));
cl.def("extend",
[](Vector &v, const Vector &src) {
v.insert(v.end(), src.begin(), src.end());
},
arg("L"),
"Extend the list by appending all the items in the given list"
);
cl.def("extend",
[](Vector &v, iterable it) {
const size_t old_size = v.size();
v.reserve(old_size + len_hint(it));
try {
for (handle h : it) {
v.push_back(h.cast<T>());
}
} catch (const cast_error &) {
v.erase(v.begin() + static_cast<typename Vector::difference_type>(old_size), v.end());
try {
v.shrink_to_fit();
} catch (const std::exception &) {
// Do nothing
}
throw;
}
},
arg("L"),
"Extend the list by appending all the items in the given list"
);
cl.def("insert",
[](Vector &v, SizeType i, const T &x) {
if (i > v.size())
throw index_error();
v.insert(v.begin() + (DiffType) i, x);
},
arg("i") , arg("x"),
"Insert an item at a given position."
);
cl.def("pop",
[](Vector &v) {
if (v.empty())
throw index_error();
T t = v.back();
v.pop_back();
return t;
},
"Remove and return the last item"
);
cl.def("pop",
[](Vector &v, SizeType i) {
if (i >= v.size())
throw index_error();
T t = v[i];
v.erase(v.begin() + (DiffType) i);
return t;
},
arg("i"),
"Remove and return the item at index ``i``"
);
cl.def("__setitem__",
[](Vector &v, SizeType i, const T &t) {
if (i >= v.size())
throw index_error();
v[i] = t;
}
);
/// Slicing protocol
cl.def("__getitem__",
[](const Vector &v, slice slice) -> Vector * {
size_t start, stop, step, slicelength;
if (!slice.compute(v.size(), &start, &stop, &step, &slicelength))
throw error_already_set();
Vector *seq = new Vector();
seq->reserve((size_t) slicelength);
for (size_t i=0; i<slicelength; ++i) {
seq->push_back(v[start]);
start += step;
}
return seq;
},
arg("s"),
"Retrieve list elements using a slice object"
);
cl.def("__setitem__",
[](Vector &v, slice slice, const Vector &value) {
size_t start, stop, step, slicelength;
if (!slice.compute(v.size(), &start, &stop, &step, &slicelength))
throw error_already_set();
if (slicelength != value.size())
throw std::runtime_error("Left and right hand size of slice assignment have different sizes!");
for (size_t i=0; i<slicelength; ++i) {
v[start] = value[i];
start += step;
}
},
"Assign list elements using a slice object"
);
cl.def("__delitem__",
[](Vector &v, SizeType i) {
if (i >= v.size())
throw index_error();
v.erase(v.begin() + DiffType(i));
},
"Delete the list elements at index ``i``"
);
cl.def("__delitem__",
[](Vector &v, slice slice) {
size_t start, stop, step, slicelength;
if (!slice.compute(v.size(), &start, &stop, &step, &slicelength))
throw error_already_set();
if (step == 1 && false) {
v.erase(v.begin() + (DiffType) start, v.begin() + DiffType(start + slicelength));
} else {
for (size_t i = 0; i < slicelength; ++i) {
v.erase(v.begin() + DiffType(start));
start += step - 1;
}
}
},
"Delete list elements using a slice object"
);
}
// If the type has an operator[] that doesn't return a reference (most notably std::vector<bool>),
// we have to access by copying; otherwise we return by reference.
template <typename Vector> using vector_needs_copy = negation<
std::is_same<decltype(std::declval<Vector>()[typename Vector::size_type()]), typename Vector::value_type &>>;
// The usual case: access and iterate by reference
template <typename Vector, typename Class_>
void vector_accessor(enable_if_t<!vector_needs_copy<Vector>::value, Class_> &cl) {
using T = typename Vector::value_type;
using SizeType = typename Vector::size_type;
using ItType = typename Vector::iterator;
cl.def("__getitem__",
[](Vector &v, SizeType i) -> T & {
if (i >= v.size())
throw index_error();
return v[i];
},
return_value_policy::reference_internal // ref + keepalive
);
cl.def("__iter__",
[](Vector &v) {
return make_iterator<
return_value_policy::reference_internal, ItType, ItType, T&>(
v.begin(), v.end());
},
keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
);
}
// The case for special objects, like std::vector<bool>, that have to be returned-by-copy:
template <typename Vector, typename Class_>
void vector_accessor(enable_if_t<vector_needs_copy<Vector>::value, Class_> &cl) {
using T = typename Vector::value_type;
using SizeType = typename Vector::size_type;
using ItType = typename Vector::iterator;
cl.def("__getitem__",
[](const Vector &v, SizeType i) -> T {
if (i >= v.size())
throw index_error();
return v[i];
}
);
cl.def("__iter__",
[](Vector &v) {
return make_iterator<
return_value_policy::copy, ItType, ItType, T>(
v.begin(), v.end());
},
keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
);
}
template <typename Vector, typename Class_> auto vector_if_insertion_operator(Class_ &cl, std::string const &name)
-> decltype(std::declval<std::ostream&>() << std::declval<typename Vector::value_type>(), void()) {
using size_type = typename Vector::size_type;
cl.def("__repr__",
[name](Vector &v) {
std::ostringstream s;
s << name << '[';
for (size_type i=0; i < v.size(); ++i) {
s << v[i];
if (i != v.size() - 1)
s << ", ";
}
s << ']';
return s.str();
},
"Return the canonical string representation of this list."
);
}
// Provide the buffer interface for vectors if we have data() and we have a format for it
// GCC seems to have "void std::vector<bool>::data()" - doing SFINAE on the existence of data() is insufficient, we need to check it returns an appropriate pointer
template <typename Vector, typename = void>
struct vector_has_data_and_format : std::false_type {};
template <typename Vector>
struct vector_has_data_and_format<Vector, enable_if_t<std::is_same<decltype(format_descriptor<typename Vector::value_type>::format(), std::declval<Vector>().data()), typename Vector::value_type*>::value>> : std::true_type {};
// Add the buffer interface to a vector
template <typename Vector, typename Class_, typename... Args>
enable_if_t<detail::any_of<std::is_same<Args, buffer_protocol>...>::value>
vector_buffer(Class_& cl) {
using T = typename Vector::value_type;
static_assert(vector_has_data_and_format<Vector>::value, "There is not an appropriate format descriptor for this vector");
// numpy.h declares this for arbitrary types, but it may raise an exception and crash hard at runtime if PYBIND11_NUMPY_DTYPE hasn't been called, so check here
format_descriptor<T>::format();
cl.def_buffer([](Vector& v) -> buffer_info {
return buffer_info(v.data(), static_cast<ssize_t>(sizeof(T)), format_descriptor<T>::format(), 1, {v.size()}, {sizeof(T)});
});
cl.def(init([](buffer buf) {
auto info = buf.request();
if (info.ndim != 1 || info.strides[0] % static_cast<ssize_t>(sizeof(T)))
throw type_error("Only valid 1D buffers can be copied to a vector");
if (!detail::compare_buffer_info<T>::compare(info) || (ssize_t) sizeof(T) != info.itemsize)
throw type_error("Format mismatch (Python: " + info.format + " C++: " + format_descriptor<T>::format() + ")");
auto vec = std::unique_ptr<Vector>(new Vector());
vec->reserve((size_t) info.shape[0]);
T *p = static_cast<T*>(info.ptr);
ssize_t step = info.strides[0] / static_cast<ssize_t>(sizeof(T));
T *end = p + info.shape[0] * step;
for (; p != end; p += step)
vec->push_back(*p);
return vec.release();
}));
return;
}
template <typename Vector, typename Class_, typename... Args>
enable_if_t<!detail::any_of<std::is_same<Args, buffer_protocol>...>::value> vector_buffer(Class_&) {}
NAMESPACE_END(detail)
//
// std::vector
//
template <typename Vector, typename holder_type = std::unique_ptr<Vector>, typename... Args>
class_<Vector, holder_type> bind_vector(handle scope, std::string const &name, Args&&... args) {
using Class_ = class_<Vector, holder_type>;
// If the value_type is unregistered (e.g. a converting type) or is itself registered
// module-local then make the vector binding module-local as well:
using vtype = typename Vector::value_type;
auto vtype_info = detail::get_type_info(typeid(vtype));
bool local = !vtype_info || vtype_info->module_local;
Class_ cl(scope, name.c_str(), pybind11::module_local(local), std::forward<Args>(args)...);
// Declare the buffer interface if a buffer_protocol() is passed in
detail::vector_buffer<Vector, Class_, Args...>(cl);
cl.def(init<>());
// Register copy constructor (if possible)
detail::vector_if_copy_constructible<Vector, Class_>(cl);
// Register comparison-related operators and functions (if possible)
detail::vector_if_equal_operator<Vector, Class_>(cl);
// Register stream insertion operator (if possible)
detail::vector_if_insertion_operator<Vector, Class_>(cl, name);
// Modifiers require copyable vector value type
detail::vector_modifiers<Vector, Class_>(cl);
// Accessor and iterator; return by value if copyable, otherwise we return by ref + keep-alive
detail::vector_accessor<Vector, Class_>(cl);
cl.def("__bool__",
[](const Vector &v) -> bool {
return !v.empty();
},
"Check whether the list is nonempty"
);
cl.def("__len__", &Vector::size);
#if 0
// C++ style functions deprecated, leaving it here as an example
cl.def(init<size_type>());
cl.def("resize",
(void (Vector::*) (size_type count)) & Vector::resize,
"changes the number of elements stored");
cl.def("erase",
[](Vector &v, SizeType i) {
if (i >= v.size())
throw index_error();
v.erase(v.begin() + i);
}, "erases element at index ``i``");
cl.def("empty", &Vector::empty, "checks whether the container is empty");
cl.def("size", &Vector::size, "returns the number of elements");
cl.def("push_back", (void (Vector::*)(const T&)) &Vector::push_back, "adds an element to the end");
cl.def("pop_back", &Vector::pop_back, "removes the last element");
cl.def("max_size", &Vector::max_size, "returns the maximum possible number of elements");
cl.def("reserve", &Vector::reserve, "reserves storage");
cl.def("capacity", &Vector::capacity, "returns the number of elements that can be held in currently allocated storage");
cl.def("shrink_to_fit", &Vector::shrink_to_fit, "reduces memory usage by freeing unused memory");
cl.def("clear", &Vector::clear, "clears the contents");
cl.def("swap", &Vector::swap, "swaps the contents");
cl.def("front", [](Vector &v) {
if (v.size()) return v.front();
else throw index_error();
}, "access the first element");
cl.def("back", [](Vector &v) {
if (v.size()) return v.back();
else throw index_error();
}, "access the last element ");
#endif
return cl;
}
//
// std::map, std::unordered_map
//
NAMESPACE_BEGIN(detail)
/* Fallback functions */
template <typename, typename, typename... Args> void map_if_insertion_operator(const Args &...) { }
template <typename, typename, typename... Args> void map_assignment(const Args &...) { }
// Map assignment when copy-assignable: just copy the value
template <typename Map, typename Class_>
void map_assignment(enable_if_t<std::is_copy_assignable<typename Map::mapped_type>::value, Class_> &cl) {
using KeyType = typename Map::key_type;
using MappedType = typename Map::mapped_type;
cl.def("__setitem__",
[](Map &m, const KeyType &k, const MappedType &v) {
auto it = m.find(k);
if (it != m.end()) it->second = v;
else m.emplace(k, v);
}
);
}
// Not copy-assignable, but still copy-constructible: we can update the value by erasing and reinserting
template<typename Map, typename Class_>
void map_assignment(enable_if_t<
!std::is_copy_assignable<typename Map::mapped_type>::value &&
is_copy_constructible<typename Map::mapped_type>::value,
Class_> &cl) {
using KeyType = typename Map::key_type;
using MappedType = typename Map::mapped_type;
cl.def("__setitem__",
[](Map &m, const KeyType &k, const MappedType &v) {
// We can't use m[k] = v; because value type might not be default constructable
auto r = m.emplace(k, v);
if (!r.second) {
// value type is not copy assignable so the only way to insert it is to erase it first...
m.erase(r.first);
m.emplace(k, v);
}
}
);
}
template <typename Map, typename Class_> auto map_if_insertion_operator(Class_ &cl, std::string const &name)
-> decltype(std::declval<std::ostream&>() << std::declval<typename Map::key_type>() << std::declval<typename Map::mapped_type>(), void()) {
cl.def("__repr__",
[name](Map &m) {
std::ostringstream s;
s << name << '{';
bool f = false;
for (auto const &kv : m) {
if (f)
s << ", ";
s << kv.first << ": " << kv.second;
f = true;
}
s << '}';
return s.str();
},
"Return the canonical string representation of this map."
);
}
NAMESPACE_END(detail)
template <typename Map, typename holder_type = std::unique_ptr<Map>, typename... Args>
class_<Map, holder_type> bind_map(handle scope, const std::string &name, Args&&... args) {
using KeyType = typename Map::key_type;
using MappedType = typename Map::mapped_type;
using Class_ = class_<Map, holder_type>;
// If either type is a non-module-local bound type then make the map binding non-local as well;
// otherwise (e.g. both types are either module-local or converting) the map will be
// module-local.
auto tinfo = detail::get_type_info(typeid(MappedType));
bool local = !tinfo || tinfo->module_local;
if (local) {
tinfo = detail::get_type_info(typeid(KeyType));
local = !tinfo || tinfo->module_local;
}
Class_ cl(scope, name.c_str(), pybind11::module_local(local), std::forward<Args>(args)...);
cl.def(init<>());
// Register stream insertion operator (if possible)
detail::map_if_insertion_operator<Map, Class_>(cl, name);
cl.def("__bool__",
[](const Map &m) -> bool { return !m.empty(); },
"Check whether the map is nonempty"
);
cl.def("__iter__",
[](Map &m) { return make_key_iterator(m.begin(), m.end()); },
keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
);
cl.def("items",
[](Map &m) { return make_iterator(m.begin(), m.end()); },
keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
);
cl.def("__getitem__",
[](Map &m, const KeyType &k) -> MappedType & {
auto it = m.find(k);
if (it == m.end())
throw key_error();
return it->second;
},
return_value_policy::reference_internal // ref + keepalive
);
cl.def("__contains__",
[](Map &m, const KeyType &k) -> bool {
auto it = m.find(k);
if (it == m.end())
return false;
return true;
}
);
// Assignment provided only if the type is copyable
detail::map_assignment<Map, Class_>(cl);
cl.def("__delitem__",
[](Map &m, const KeyType &k) {
auto it = m.find(k);
if (it == m.end())
throw key_error();
m.erase(it);
}
);
cl.def("__len__", &Map::size);
return cl;
}
NAMESPACE_END(PYBIND11_NAMESPACE)

28
python/pybind11/pybind11/__init__.py
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@ -1,28 +0,0 @@
from ._version import version_info, __version__ # noqa: F401 imported but unused
def get_include(user=False):
from distutils.dist import Distribution
import os
import sys
# Are we running in a virtual environment?
virtualenv = hasattr(sys, 'real_prefix') or \
sys.prefix != getattr(sys, "base_prefix", sys.prefix)
if virtualenv:
return os.path.join(sys.prefix, 'include', 'site',
'python' + sys.version[:3])
else:
dist = Distribution({'name': 'pybind11'})
dist.parse_config_files()
dist_cobj = dist.get_command_obj('install', create=True)
# Search for packages in user's home directory?
if user:
dist_cobj.user = user
dist_cobj.prefix = ""
dist_cobj.finalize_options()
return os.path.dirname(dist_cobj.install_headers)

37
python/pybind11/pybind11/__main__.py
View File

@ -1,37 +0,0 @@
from __future__ import print_function
import argparse
import sys
import sysconfig
from . import get_include
def print_includes():
dirs = [sysconfig.get_path('include'),
sysconfig.get_path('platinclude'),
get_include(),
get_include(True)]
# Make unique but preserve order
unique_dirs = []
for d in dirs:
if d not in unique_dirs:
unique_dirs.append(d)
print(' '.join('-I' + d for d in unique_dirs))
def main():
parser = argparse.ArgumentParser(prog='python -m pybind11')
parser.add_argument('--includes', action='store_true',
help='Include flags for both pybind11 and Python headers.')
args = parser.parse_args()
if not sys.argv[1:]:
parser.print_help()
if args.includes:
print_includes()
if __name__ == '__main__':
main()

2
python/pybind11/pybind11/_version.py
View File

@ -1,2 +0,0 @@
version_info = (2, 3, 0)
__version__ = '.'.join(map(str, version_info))

12
python/pybind11/setup.cfg
View File

@ -1,12 +0,0 @@
[bdist_wheel]
universal=1
[flake8]
max-line-length = 99
show_source = True
exclude = .git, __pycache__, build, dist, docs, tools, venv
ignore =
# required for pretty matrix formatting: multiple spaces after `,` and `[`
E201, E241, W504,
# camelcase 'cPickle' imported as lowercase 'pickle'
N813

108
python/pybind11/setup.py
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@ -1,108 +0,0 @@
#!/usr/bin/env python
# Setup script for PyPI; use CMakeFile.txt to build extension modules
from setuptools import setup
from distutils.command.install_headers import install_headers
from pybind11 import __version__
import os
# Prevent installation of pybind11 headers by setting
# PYBIND11_USE_CMAKE.
if os.environ.get('PYBIND11_USE_CMAKE'):
headers = []
else:
headers = [
'include/pybind11/detail/class.h',
'include/pybind11/detail/common.h',
'include/pybind11/detail/descr.h',
'include/pybind11/detail/init.h',
'include/pybind11/detail/internals.h',
'include/pybind11/detail/typeid.h',
'include/pybind11/attr.h',
'include/pybind11/buffer_info.h',
'include/pybind11/cast.h',
'include/pybind11/chrono.h',
'include/pybind11/common.h',
'include/pybind11/complex.h',
'include/pybind11/eigen.h',
'include/pybind11/embed.h',
'include/pybind11/eval.h',
'include/pybind11/functional.h',
'include/pybind11/iostream.h',
'include/pybind11/numpy.h',
'include/pybind11/operators.h',
'include/pybind11/options.h',
'include/pybind11/pybind11.h',
'include/pybind11/pytypes.h',
'include/pybind11/stl.h',
'include/pybind11/stl_bind.h',
]
class InstallHeaders(install_headers):
"""Use custom header installer because the default one flattens subdirectories"""
def run(self):
if not self.distribution.headers:
return
for header in self.distribution.headers:
subdir = os.path.dirname(os.path.relpath(header, 'include/pybind11'))
install_dir = os.path.join(self.install_dir, subdir)
self.mkpath(install_dir)
(out, _) = self.copy_file(header, install_dir)
self.outfiles.append(out)
setup(
name='pybind11',
version=__version__,
description='Seamless operability between C++11 and Python',
author='Wenzel Jakob',
author_email='wenzel.jakob@epfl.ch',
url='https://github.com/pybind/pybind11',
download_url='https://github.com/pybind/pybind11/tarball/v' + __version__,
packages=['pybind11'],
license='BSD',
headers=headers,
cmdclass=dict(install_headers=InstallHeaders),
classifiers=[
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Developers',
'Topic :: Software Development :: Libraries :: Python Modules',
'Topic :: Utilities',
'Programming Language :: C++',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.2',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'License :: OSI Approved :: BSD License'
],
keywords='C++11, Python bindings',
long_description="""pybind11 is a lightweight header-only library that
exposes C++ types in Python and vice versa, mainly to create Python bindings of
existing C++ code. Its goals and syntax are similar to the excellent
Boost.Python by David Abrahams: to minimize boilerplate code in traditional
extension modules by inferring type information using compile-time
introspection.
The main issue with Boost.Python-and the reason for creating such a similar
project-is Boost. Boost is an enormously large and complex suite of utility
libraries that works with almost every C++ compiler in existence. This
compatibility has its cost: arcane template tricks and workarounds are
necessary to support the oldest and buggiest of compiler specimens. Now that
C++11-compatible compilers are widely available, this heavy machinery has
become an excessively large and unnecessary dependency.
Think of this library as a tiny self-contained version of Boost.Python with
everything stripped away that isn't relevant for binding generation. Without
comments, the core header files only require ~4K lines of code and depend on
Python (2.7 or 3.x, or PyPy2.7 >= 5.7) and the C++ standard library. This
compact implementation was possible thanks to some of the new C++11 language
features (specifically: tuples, lambda functions and variadic templates). Since
its creation, this library has grown beyond Boost.Python in many ways, leading
to dramatically simpler binding code in many common situations.""")

239
python/pybind11/tests/CMakeLists.txt
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@ -1,239 +0,0 @@
# CMakeLists.txt -- Build system for the pybind11 test suite
#
# Copyright (c) 2015 Wenzel Jakob <wenzel@inf.ethz.ch>
#
# All rights reserved. Use of this source code is governed by a
# BSD-style license that can be found in the LICENSE file.
cmake_minimum_required(VERSION 2.8.12)
option(PYBIND11_WERROR "Report all warnings as errors" OFF)
if (CMAKE_CURRENT_SOURCE_DIR STREQUAL CMAKE_SOURCE_DIR)
# We're being loaded directly, i.e. not via add_subdirectory, so make this
# work as its own project and load the pybind11Config to get the tools we need
project(pybind11_tests CXX)
find_package(pybind11 REQUIRED CONFIG)
endif()
if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
message(STATUS "Setting tests build type to MinSizeRel as none was specified")
set(CMAKE_BUILD_TYPE MinSizeRel CACHE STRING "Choose the type of build." FORCE)
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release"
"MinSizeRel" "RelWithDebInfo")
endif()
# Full set of test files (you can override these; see below)
set(PYBIND11_TEST_FILES
test_buffers.cpp
test_builtin_casters.cpp
test_call_policies.cpp
test_callbacks.cpp
test_chrono.cpp
test_class.cpp
test_constants_and_functions.cpp
test_copy_move.cpp
test_docstring_options.cpp
test_eigen.cpp
test_enum.cpp
test_eval.cpp
test_exceptions.cpp
test_factory_constructors.cpp
test_gil_scoped.cpp
test_iostream.cpp
test_kwargs_and_defaults.cpp
test_local_bindings.cpp
test_methods_and_attributes.cpp
test_modules.cpp
test_multiple_inheritance.cpp
test_numpy_array.cpp
test_numpy_dtypes.cpp
test_numpy_vectorize.cpp
test_opaque_types.cpp
test_operator_overloading.cpp
test_pickling.cpp
test_pytypes.cpp
test_sequences_and_iterators.cpp
test_smart_ptr.cpp
test_stl.cpp
test_stl_binders.cpp
test_tagbased_polymorphic.cpp
test_union.cpp
test_virtual_functions.cpp
)
# Invoking cmake with something like:
# cmake -DPYBIND11_TEST_OVERRIDE="test_callbacks.cpp;test_picking.cpp" ..
# lets you override the tests that get compiled and run. You can restore to all tests with:
# cmake -DPYBIND11_TEST_OVERRIDE= ..
if (PYBIND11_TEST_OVERRIDE)
set(PYBIND11_TEST_FILES ${PYBIND11_TEST_OVERRIDE})
endif()
string(REPLACE ".cpp" ".py" PYBIND11_PYTEST_FILES "${PYBIND11_TEST_FILES}")
# Contains the set of test files that require pybind11_cross_module_tests to be
# built; if none of these are built (i.e. because TEST_OVERRIDE is used and
# doesn't include them) the second module doesn't get built.
set(PYBIND11_CROSS_MODULE_TESTS
test_exceptions.py
test_local_bindings.py
test_stl.py
test_stl_binders.py
)
# Check if Eigen is available; if not, remove from PYBIND11_TEST_FILES (but
# keep it in PYBIND11_PYTEST_FILES, so that we get the "eigen is not installed"
# skip message).
list(FIND PYBIND11_TEST_FILES test_eigen.cpp PYBIND11_TEST_FILES_EIGEN_I)
if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
# Try loading via newer Eigen's Eigen3Config first (bypassing tools/FindEigen3.cmake).
# Eigen 3.3.1+ exports a cmake 3.0+ target for handling dependency requirements, but also
# produces a fatal error if loaded from a pre-3.0 cmake.
if (NOT CMAKE_VERSION VERSION_LESS 3.0)
find_package(Eigen3 3.2.7 QUIET CONFIG)
if (EIGEN3_FOUND)
if (EIGEN3_VERSION_STRING AND NOT EIGEN3_VERSION_STRING VERSION_LESS 3.3.1)
set(PYBIND11_EIGEN_VIA_TARGET 1)
endif()
endif()
endif()
if (NOT EIGEN3_FOUND)
# Couldn't load via target, so fall back to allowing module mode finding, which will pick up
# tools/FindEigen3.cmake
find_package(Eigen3 3.2.7 QUIET)
endif()
if(EIGEN3_FOUND)
# Eigen 3.3.1+ cmake sets EIGEN3_VERSION_STRING (and hard codes the version when installed
# rather than looking it up in the cmake script); older versions, and the
# tools/FindEigen3.cmake, set EIGEN3_VERSION instead.
if(NOT EIGEN3_VERSION AND EIGEN3_VERSION_STRING)
set(EIGEN3_VERSION ${EIGEN3_VERSION_STRING})
endif()
message(STATUS "Building tests with Eigen v${EIGEN3_VERSION}")
else()
list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
message(STATUS "Building tests WITHOUT Eigen")
endif()
endif()
# Optional dependency for some tests (boost::variant is only supported with version >= 1.56)
find_package(Boost 1.56)
# Compile with compiler warnings turned on
function(pybind11_enable_warnings target_name)
if(MSVC)
target_compile_options(${target_name} PRIVATE /W4)
elseif(CMAKE_CXX_COMPILER_ID MATCHES "(GNU|Intel|Clang)")
target_compile_options(${target_name} PRIVATE -Wall -Wextra -Wconversion -Wcast-qual -Wdeprecated)
endif()
if(PYBIND11_WERROR)
if(MSVC)
target_compile_options(${target_name} PRIVATE /WX)
elseif(CMAKE_CXX_COMPILER_ID MATCHES "(GNU|Intel|Clang)")
target_compile_options(${target_name} PRIVATE -Werror)
endif()
endif()
endfunction()
set(test_targets pybind11_tests)
# Build pybind11_cross_module_tests if any test_whatever.py are being built that require it
foreach(t ${PYBIND11_CROSS_MODULE_TESTS})
list(FIND PYBIND11_PYTEST_FILES ${t} i)
if (i GREATER -1)
list(APPEND test_targets pybind11_cross_module_tests)
break()
endif()
endforeach()
set(testdir ${CMAKE_CURRENT_SOURCE_DIR})
foreach(target ${test_targets})
set(test_files ${PYBIND11_TEST_FILES})
if(NOT target STREQUAL "pybind11_tests")
set(test_files "")
endif()
# Create the binding library
pybind11_add_module(${target} THIN_LTO ${target}.cpp ${test_files} ${PYBIND11_HEADERS})
pybind11_enable_warnings(${target})
if(MSVC)
target_compile_options(${target} PRIVATE /utf-8)
endif()
if(EIGEN3_FOUND)
if (PYBIND11_EIGEN_VIA_TARGET)
target_link_libraries(${target} PRIVATE Eigen3::Eigen)
else()
target_include_directories(${target} PRIVATE ${EIGEN3_INCLUDE_DIR})
endif()
target_compile_definitions(${target} PRIVATE -DPYBIND11_TEST_EIGEN)
endif()
if(Boost_FOUND)
target_include_directories(${target} PRIVATE ${Boost_INCLUDE_DIRS})
target_compile_definitions(${target} PRIVATE -DPYBIND11_TEST_BOOST)
endif()
# Always write the output file directly into the 'tests' directory (even on MSVC)
if(NOT CMAKE_LIBRARY_OUTPUT_DIRECTORY)
set_target_properties(${target} PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${testdir})
foreach(config ${CMAKE_CONFIGURATION_TYPES})
string(TOUPPER ${config} config)
set_target_properties(${target} PROPERTIES LIBRARY_OUTPUT_DIRECTORY_${config} ${testdir})
endforeach()
endif()
endforeach()
# Make sure pytest is found or produce a fatal error
if(NOT PYBIND11_PYTEST_FOUND)
execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "import pytest; print(pytest.__version__)"
RESULT_VARIABLE pytest_not_found OUTPUT_VARIABLE pytest_version ERROR_QUIET)
if(pytest_not_found)
message(FATAL_ERROR "Running the tests requires pytest. Please install it manually"
" (try: ${PYTHON_EXECUTABLE} -m pip install pytest)")
elseif(pytest_version VERSION_LESS 3.0)
message(FATAL_ERROR "Running the tests requires pytest >= 3.0. Found: ${pytest_version}"
"Please update it (try: ${PYTHON_EXECUTABLE} -m pip install -U pytest)")
endif()
set(PYBIND11_PYTEST_FOUND TRUE CACHE INTERNAL "")
endif()
if(CMAKE_VERSION VERSION_LESS 3.2)
set(PYBIND11_USES_TERMINAL "")
else()
set(PYBIND11_USES_TERMINAL "USES_TERMINAL")
endif()
# A single command to compile and run the tests
add_custom_target(pytest COMMAND ${PYTHON_EXECUTABLE} -m pytest ${PYBIND11_PYTEST_FILES}
DEPENDS ${test_targets} WORKING_DIRECTORY ${testdir} ${PYBIND11_USES_TERMINAL})
if(PYBIND11_TEST_OVERRIDE)
add_custom_command(TARGET pytest POST_BUILD
COMMAND ${CMAKE_COMMAND} -E echo "Note: not all tests run: -DPYBIND11_TEST_OVERRIDE is in effect")
endif()
# Add a check target to run all the tests, starting with pytest (we add dependencies to this below)
add_custom_target(check DEPENDS pytest)
# The remaining tests only apply when being built as part of the pybind11 project, but not if the
# tests are being built independently.
if (NOT PROJECT_NAME STREQUAL "pybind11")
return()
endif()
# Add a post-build comment to show the primary test suite .so size and, if a previous size, compare it:
add_custom_command(TARGET pybind11_tests POST_BUILD
COMMAND ${PYTHON_EXECUTABLE} ${PROJECT_SOURCE_DIR}/tools/libsize.py
$<TARGET_FILE:pybind11_tests> ${CMAKE_CURRENT_BINARY_DIR}/sosize-$<TARGET_FILE_NAME:pybind11_tests>.txt)
# Test embedding the interpreter. Provides the `cpptest` target.
add_subdirectory(test_embed)
# Test CMake build using functions and targets from subdirectory or installed location
add_subdirectory(test_cmake_build)

239
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@ -1,239 +0,0 @@
"""pytest configuration
Extends output capture as needed by pybind11: ignore constructors, optional unordered lines.
Adds docstring and exceptions message sanitizers: ignore Python 2 vs 3 differences.
"""
import pytest
import textwrap
import difflib
import re
import sys
import contextlib
import platform
import gc
_unicode_marker = re.compile(r'u(\'[^\']*\')')
_long_marker = re.compile(r'([0-9])L')
_hexadecimal = re.compile(r'0x[0-9a-fA-F]+')
def _strip_and_dedent(s):
"""For triple-quote strings"""
return textwrap.dedent(s.lstrip('\n').rstrip())
def _split_and_sort(s):
"""For output which does not require specific line order"""
return sorted(_strip_and_dedent(s).splitlines())
def _make_explanation(a, b):
"""Explanation for a failed assert -- the a and b arguments are List[str]"""
return ["--- actual / +++ expected"] + [line.strip('\n') for line in difflib.ndiff(a, b)]
class Output(object):
"""Basic output post-processing and comparison"""
def __init__(self, string):
self.string = string
self.explanation = []
def __str__(self):
return self.string
def __eq__(self, other):
# Ignore constructor/destructor output which is prefixed with "###"
a = [line for line in self.string.strip().splitlines() if not line.startswith("###")]
b = _strip_and_dedent(other).splitlines()
if a == b:
return True
else:
self.explanation = _make_explanation(a, b)
return False
class Unordered(Output):
"""Custom comparison for output without strict line ordering"""
def __eq__(self, other):
a = _split_and_sort(self.string)
b = _split_and_sort(other)
if a == b:
return True
else:
self.explanation = _make_explanation(a, b)
return False
class Capture(object):
def __init__(self, capfd):
self.capfd = capfd
self.out = ""
self.err = ""
def __enter__(self):
self.capfd.readouterr()
return self
def __exit__(self, *args):
self.out, self.err = self.capfd.readouterr()
def __eq__(self, other):
a = Output(self.out)
b = other
if a == b:
return True
else:
self.explanation = a.explanation
return False
def __str__(self):
return self.out
def __contains__(self, item):
return item in self.out
@property
def unordered(self):
return Unordered(self.out)
@property
def stderr(self):
return Output(self.err)
@pytest.fixture
def capture(capsys):
"""Extended `capsys` with context manager and custom equality operators"""
return Capture(capsys)
class SanitizedString(object):
def __init__(self, sanitizer):
self.sanitizer = sanitizer
self.string = ""
self.explanation = []
def __call__(self, thing):
self.string = self.sanitizer(thing)
return self
def __eq__(self, other):
a = self.string
b = _strip_and_dedent(other)
if a == b:
return True
else:
self.explanation = _make_explanation(a.splitlines(), b.splitlines())
return False
def _sanitize_general(s):
s = s.strip()
s = s.replace("pybind11_tests.", "m.")
s = s.replace("unicode", "str")
s = _long_marker.sub(r"\1", s)
s = _unicode_marker.sub(r"\1", s)
return s
def _sanitize_docstring(thing):
s = thing.__doc__
s = _sanitize_general(s)
return s
@pytest.fixture
def doc():
"""Sanitize docstrings and add custom failure explanation"""
return SanitizedString(_sanitize_docstring)
def _sanitize_message(thing):
s = str(thing)
s = _sanitize_general(s)
s = _hexadecimal.sub("0", s)
return s
@pytest.fixture
def msg():
"""Sanitize messages and add custom failure explanation"""
return SanitizedString(_sanitize_message)
# noinspection PyUnusedLocal
def pytest_assertrepr_compare(op, left, right):
"""Hook to insert custom failure explanation"""
if hasattr(left, 'explanation'):
return left.explanation
@contextlib.contextmanager
def suppress(exception):
"""Suppress the desired exception"""
try:
yield
except exception:
pass
def gc_collect():
''' Run the garbage collector twice (needed when running
reference counting tests with PyPy) '''
gc.collect()
gc.collect()
def pytest_configure():
"""Add import suppression and test requirements to `pytest` namespace"""
try:
import numpy as np
except ImportError:
np = None
try:
import scipy
except ImportError:
scipy = None
try:
from pybind11_tests.eigen import have_eigen
except ImportError:
have_eigen = False
pypy = platform.python_implementation() == "PyPy"
skipif = pytest.mark.skipif
pytest.suppress = suppress
pytest.requires_numpy = skipif(not np, reason="numpy is not installed")
pytest.requires_scipy = skipif(not np, reason="scipy is not installed")
pytest.requires_eigen_and_numpy = skipif(not have_eigen or not np,
reason="eigen and/or numpy are not installed")
pytest.requires_eigen_and_scipy = skipif(
not have_eigen or not scipy, reason="eigen and/or scipy are not installed")
pytest.unsupported_on_pypy = skipif(pypy, reason="unsupported on PyPy")
pytest.unsupported_on_py2 = skipif(sys.version_info.major < 3,
reason="unsupported on Python 2.x")
pytest.gc_collect = gc_collect
def _test_import_pybind11():
"""Early diagnostic for test module initialization errors
When there is an error during initialization, the first import will report the
real error while all subsequent imports will report nonsense. This import test
is done early (in the pytest configuration file, before any tests) in order to
avoid the noise of having all tests fail with identical error messages.
Any possible exception is caught here and reported manually *without* the stack
trace. This further reduces noise since the trace would only show pytest internals
which are not useful for debugging pybind11 module issues.
"""
# noinspection PyBroadException
try:
import pybind11_tests # noqa: F401 imported but unused
except Exception as e:
print("Failed to import pybind11_tests from pytest:")
print(" {}: {}".format(type(e).__name__, e))
sys.exit(1)
_test_import_pybind11()

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#pragma once
/*
tests/constructor_stats.h -- framework for printing and tracking object
instance lifetimes in example/test code.
Copyright (c) 2016 Jason Rhinelander <jason@imaginary.ca>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
This header provides a few useful tools for writing examples or tests that want to check and/or
display object instance lifetimes. It requires that you include this header and add the following
function calls to constructors:
class MyClass {
MyClass() { ...; print_default_created(this); }
~MyClass() { ...; print_destroyed(this); }
MyClass(const MyClass &c) { ...; print_copy_created(this); }
MyClass(MyClass &&c) { ...; print_move_created(this); }
MyClass(int a, int b) { ...; print_created(this, a, b); }
MyClass &operator=(const MyClass &c) { ...; print_copy_assigned(this); }
MyClass &operator=(MyClass &&c) { ...; print_move_assigned(this); }
...
}
You can find various examples of these in several of the existing testing .cpp files. (Of course
you don't need to add any of the above constructors/operators that you don't actually have, except
for the destructor).
Each of these will print an appropriate message such as:
### MyClass @ 0x2801910 created via default constructor
### MyClass @ 0x27fa780 created 100 200
### MyClass @ 0x2801910 destroyed
### MyClass @ 0x27fa780 destroyed
You can also include extra arguments (such as the 100, 200 in the output above, coming from the
value constructor) for all of the above methods which will be included in the output.
For testing, each of these also keeps track the created instances and allows you to check how many
of the various constructors have been invoked from the Python side via code such as:
from pybind11_tests import ConstructorStats
cstats = ConstructorStats.get(MyClass)
print(cstats.alive())
print(cstats.default_constructions)
Note that `.alive()` should usually be the first thing you call as it invokes Python's garbage
collector to actually destroy objects that aren't yet referenced.
For everything except copy and move constructors and destructors, any extra values given to the
print_...() function is stored in a class-specific values list which you can retrieve and inspect
from the ConstructorStats instance `.values()` method.
In some cases, when you need to track instances of a C++ class not registered with pybind11, you
need to add a function returning the ConstructorStats for the C++ class; this can be done with:
m.def("get_special_cstats", &ConstructorStats::get<SpecialClass>, py::return_value_policy::reference)
Finally, you can suppress the output messages, but keep the constructor tracking (for
inspection/testing in python) by using the functions with `print_` replaced with `track_` (e.g.
`track_copy_created(this)`).
*/
#include "pybind11_tests.h"
#include <unordered_map>
#include <list>
#include <typeindex>
#include <sstream>
class ConstructorStats {
protected:
std::unordered_map<void*, int> _instances; // Need a map rather than set because members can shared address with parents
std::list<std::string> _values; // Used to track values (e.g. of value constructors)
public:
int default_constructions = 0;
int copy_constructions = 0;
int move_constructions = 0;
int copy_assignments = 0;
int move_assignments = 0;
void copy_created(void *inst) {
created(inst);
copy_constructions++;
}
void move_created(void *inst) {
created(inst);
move_constructions++;
}
void default_created(void *inst) {
created(inst);
default_constructions++;
}
void created(void *inst) {
++_instances[inst];
}
void destroyed(void *inst) {
if (--_instances[inst] < 0)
throw std::runtime_error("cstats.destroyed() called with unknown "
"instance; potential double-destruction "
"or a missing cstats.created()");
}
static void gc() {
// Force garbage collection to ensure any pending destructors are invoked:
#if defined(PYPY_VERSION)
PyObject *globals = PyEval_GetGlobals();
PyObject *result = PyRun_String(
"import gc\n"
"for i in range(2):"
" gc.collect()\n",
Py_file_input, globals, globals);
if (result == nullptr)
throw py::error_already_set();
Py_DECREF(result);
#else
py::module::import("gc").attr("collect")();
#endif
}
int alive() {
gc();
int total = 0;
for (const auto &p : _instances)
if (p.second > 0)
total += p.second;
return total;
}
void value() {} // Recursion terminator
// Takes one or more values, converts them to strings, then stores them.
template <typename T, typename... Tmore> void value(const T &v, Tmore &&...args) {
std::ostringstream oss;
oss << v;
_values.push_back(oss.str());
value(std::forward<Tmore>(args)...);
}
// Move out stored values
py::list values() {
py::list l;
for (const auto &v : _values) l.append(py::cast(v));
_values.clear();
return l;
}
// Gets constructor stats from a C++ type index
static ConstructorStats& get(std::type_index type) {
static std::unordered_map<std::type_index, ConstructorStats> all_cstats;
return all_cstats[type];
}
// Gets constructor stats from a C++ type
template <typename T> static ConstructorStats& get() {
#if defined(PYPY_VERSION)
gc();
#endif
return get(typeid(T));
}
// Gets constructor stats from a Python class
static ConstructorStats& get(py::object class_) {
auto &internals = py::detail::get_internals();
const std::type_index *t1 = nullptr, *t2 = nullptr;
try {
auto *type_info = internals.registered_types_py.at((PyTypeObject *) class_.ptr()).at(0);
for (auto &p : internals.registered_types_cpp) {
if (p.second == type_info) {
if (t1) {
t2 = &p.first;
break;
}
t1 = &p.first;
}
}
}
catch (const std::out_of_range &) {}
if (!t1) throw std::runtime_error("Unknown class passed to ConstructorStats::get()");
auto &cs1 = get(*t1);
// If we have both a t1 and t2 match, one is probably the trampoline class; return whichever
// has more constructions (typically one or the other will be 0)
if (t2) {
auto &cs2 = get(*t2);
int cs1_total = cs1.default_constructions + cs1.copy_constructions + cs1.move_constructions + (int) cs1._values.size();
int cs2_total = cs2.default_constructions + cs2.copy_constructions + cs2.move_constructions + (int) cs2._values.size();
if (cs2_total > cs1_total) return cs2;
}
return cs1;
}
};
// To track construction/destruction, you need to call these methods from the various
// constructors/operators. The ones that take extra values record the given values in the
// constructor stats values for later inspection.
template <class T> void track_copy_created(T *inst) { ConstructorStats::get<T>().copy_created(inst); }
template <class T> void track_move_created(T *inst) { ConstructorStats::get<T>().move_created(inst); }
template <class T, typename... Values> void track_copy_assigned(T *, Values &&...values) {
auto &cst = ConstructorStats::get<T>();
cst.copy_assignments++;
cst.value(std::forward<Values>(values)...);
}
template <class T, typename... Values> void track_move_assigned(T *, Values &&...values) {
auto &cst = ConstructorStats::get<T>();
cst.move_assignments++;
cst.value(std::forward<Values>(values)...);
}
template <class T, typename... Values> void track_default_created(T *inst, Values &&...values) {
auto &cst = ConstructorStats::get<T>();
cst.default_created(inst);
cst.value(std::forward<Values>(values)...);
}
template <class T, typename... Values> void track_created(T *inst, Values &&...values) {
auto &cst = ConstructorStats::get<T>();
cst.created(inst);
cst.value(std::forward<Values>(values)...);
}
template <class T, typename... Values> void track_destroyed(T *inst) {
ConstructorStats::get<T>().destroyed(inst);
}
template <class T, typename... Values> void track_values(T *, Values &&...values) {
ConstructorStats::get<T>().value(std::forward<Values>(values)...);
}
/// Don't cast pointers to Python, print them as strings
inline const char *format_ptrs(const char *p) { return p; }
template <typename T>
py::str format_ptrs(T *p) { return "{:#x}"_s.format(reinterpret_cast<std::uintptr_t>(p)); }
template <typename T>
auto format_ptrs(T &&x) -> decltype(std::forward<T>(x)) { return std::forward<T>(x); }
template <class T, typename... Output>
void print_constr_details(T *inst, const std::string &action, Output &&...output) {
py::print("###", py::type_id<T>(), "@", format_ptrs(inst), action,
format_ptrs(std::forward<Output>(output))...);
}
// Verbose versions of the above:
template <class T, typename... Values> void print_copy_created(T *inst, Values &&...values) { // NB: this prints, but doesn't store, given values
print_constr_details(inst, "created via copy constructor", values...);
track_copy_created(inst);
}
template <class T, typename... Values> void print_move_created(T *inst, Values &&...values) { // NB: this prints, but doesn't store, given values
print_constr_details(inst, "created via move constructor", values...);
track_move_created(inst);
}
template <class T, typename... Values> void print_copy_assigned(T *inst, Values &&...values) {
print_constr_details(inst, "assigned via copy assignment", values...);
track_copy_assigned(inst, values...);
}
template <class T, typename... Values> void print_move_assigned(T *inst, Values &&...values) {
print_constr_details(inst, "assigned via move assignment", values...);
track_move_assigned(inst, values...);
}
template <class T, typename... Values> void print_default_created(T *inst, Values &&...values) {
print_constr_details(inst, "created via default constructor", values...);
track_default_created(inst, values...);
}
template <class T, typename... Values> void print_created(T *inst, Values &&...values) {
print_constr_details(inst, "created", values...);
track_created(inst, values...);
}
template <class T, typename... Values> void print_destroyed(T *inst, Values &&...values) { // Prints but doesn't store given values
print_constr_details(inst, "destroyed", values...);
track_destroyed(inst);
}
template <class T, typename... Values> void print_values(T *inst, Values &&...values) {
print_constr_details(inst, ":", values...);
track_values(inst, values...);
}

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#pragma once
#include "pybind11_tests.h"
/// Simple class used to test py::local:
template <int> class LocalBase {
public:
LocalBase(int i) : i(i) { }
int i = -1;
};
/// Registered with py::module_local in both main and secondary modules:
using LocalType = LocalBase<0>;
/// Registered without py::module_local in both modules:
using NonLocalType = LocalBase<1>;
/// A second non-local type (for stl_bind tests):
using NonLocal2 = LocalBase<2>;
/// Tests within-module, different-compilation-unit local definition conflict:
using LocalExternal = LocalBase<3>;
/// Mixed: registered local first, then global
using MixedLocalGlobal = LocalBase<4>;
/// Mixed: global first, then local
using MixedGlobalLocal = LocalBase<5>;
/// Registered with py::module_local only in the secondary module:
using ExternalType1 = LocalBase<6>;
using ExternalType2 = LocalBase<7>;
using LocalVec = std::vector<LocalType>;
using LocalVec2 = std::vector<NonLocal2>;
using LocalMap = std::unordered_map<std::string, LocalType>;
using NonLocalVec = std::vector<NonLocalType>;
using NonLocalVec2 = std::vector<NonLocal2>;
using NonLocalMap = std::unordered_map<std::string, NonLocalType>;
using NonLocalMap2 = std::unordered_map<std::string, uint8_t>;
PYBIND11_MAKE_OPAQUE(LocalVec);
PYBIND11_MAKE_OPAQUE(LocalVec2);
PYBIND11_MAKE_OPAQUE(LocalMap);
PYBIND11_MAKE_OPAQUE(NonLocalVec);
//PYBIND11_MAKE_OPAQUE(NonLocalVec2); // same type as LocalVec2
PYBIND11_MAKE_OPAQUE(NonLocalMap);
PYBIND11_MAKE_OPAQUE(NonLocalMap2);
// Simple bindings (used with the above):
template <typename T, int Adjust = 0, typename... Args>
py::class_<T> bind_local(Args && ...args) {
return py::class_<T>(std::forward<Args>(args)...)
.def(py::init<int>())
.def("get", [](T &i) { return i.i + Adjust; });
};
// Simulate a foreign library base class (to match the example in the docs):
namespace pets {
class Pet {
public:
Pet(std::string name) : name_(name) {}
std::string name_;
const std::string &name() { return name_; }
};
}
struct MixGL { int i; MixGL(int i) : i{i} {} };
struct MixGL2 { int i; MixGL2(int i) : i{i} {} };

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#if !defined(__OBJECT_H)
#define __OBJECT_H
#include <atomic>
#include "constructor_stats.h"
/// Reference counted object base class
class Object {
public:
/// Default constructor
Object() { print_default_created(this); }
/// Copy constructor
Object(const Object &) : m_refCount(0) { print_copy_created(this); }
/// Return the current reference count
int getRefCount() const { return m_refCount; };
/// Increase the object's reference count by one
void incRef() const { ++m_refCount; }
/** \brief Decrease the reference count of
* the object and possibly deallocate it.
*
* The object will automatically be deallocated once
* the reference count reaches zero.
*/
void decRef(bool dealloc = true) const {
--m_refCount;
if (m_refCount == 0 && dealloc)
delete this;
else if (m_refCount < 0)
throw std::runtime_error("Internal error: reference count < 0!");
}
virtual std::string toString() const = 0;
protected:
/** \brief Virtual protected deconstructor.
* (Will only be called by \ref ref)
*/
virtual ~Object() { print_destroyed(this); }
private:
mutable std::atomic<int> m_refCount { 0 };
};
// Tag class used to track constructions of ref objects. When we track constructors, below, we
// track and print out the actual class (e.g. ref<MyObject>), and *also* add a fake tracker for
// ref_tag. This lets us check that the total number of ref<Anything> constructors/destructors is
// correct without having to check each individual ref<Whatever> type individually.
class ref_tag {};
/**
* \brief Reference counting helper
*
* The \a ref refeference template is a simple wrapper to store a
* pointer to an object. It takes care of increasing and decreasing
* the reference count of the object. When the last reference goes
* out of scope, the associated object will be deallocated.
*
* \ingroup libcore
*/
template <typename T> class ref {
public:
/// Create a nullptr reference
ref() : m_ptr(nullptr) { print_default_created(this); track_default_created((ref_tag*) this); }
/// Construct a reference from a pointer
ref(T *ptr) : m_ptr(ptr) {
if (m_ptr) ((Object *) m_ptr)->incRef();
print_created(this, "from pointer", m_ptr); track_created((ref_tag*) this, "from pointer");
}
/// Copy constructor
ref(const ref &r) : m_ptr(r.m_ptr) {
if (m_ptr)
((Object *) m_ptr)->incRef();
print_copy_created(this, "with pointer", m_ptr); track_copy_created((ref_tag*) this);
}
/// Move constructor
ref(ref &&r) : m_ptr(r.m_ptr) {
r.m_ptr = nullptr;
print_move_created(this, "with pointer", m_ptr); track_move_created((ref_tag*) this);
}
/// Destroy this reference
~ref() {
if (m_ptr)
((Object *) m_ptr)->decRef();
print_destroyed(this); track_destroyed((ref_tag*) this);
}
/// Move another reference into the current one
ref& operator=(ref&& r) {
print_move_assigned(this, "pointer", r.m_ptr); track_move_assigned((ref_tag*) this);
if (*this == r)
return *this;
if (m_ptr)
((Object *) m_ptr)->decRef();
m_ptr = r.m_ptr;
r.m_ptr = nullptr;
return *this;
}
/// Overwrite this reference with another reference
ref& operator=(const ref& r) {
print_copy_assigned(this, "pointer", r.m_ptr); track_copy_assigned((ref_tag*) this);
if (m_ptr == r.m_ptr)
return *this;
if (m_ptr)
((Object *) m_ptr)->decRef();
m_ptr = r.m_ptr;
if (m_ptr)
((Object *) m_ptr)->incRef();
return *this;
}
/// Overwrite this reference with a pointer to another object
ref& operator=(T *ptr) {
print_values(this, "assigned pointer"); track_values((ref_tag*) this, "assigned pointer");
if (m_ptr == ptr)
return *this;
if (m_ptr)
((Object *) m_ptr)->decRef();
m_ptr = ptr;
if (m_ptr)
((Object *) m_ptr)->incRef();
return *this;
}
/// Compare this reference with another reference
bool operator==(const ref &r) const { return m_ptr == r.m_ptr; }
/// Compare this reference with another reference
bool operator!=(const ref &r) const { return m_ptr != r.m_ptr; }
/// Compare this reference with a pointer
bool operator==(const T* ptr) const { return m_ptr == ptr; }
/// Compare this reference with a pointer
bool operator!=(const T* ptr) const { return m_ptr != ptr; }
/// Access the object referenced by this reference
T* operator->() { return m_ptr; }
/// Access the object referenced by this reference
const T* operator->() const { return m_ptr; }
/// Return a C++ reference to the referenced object
T& operator*() { return *m_ptr; }
/// Return a const C++ reference to the referenced object
const T& operator*() const { return *m_ptr; }
/// Return a pointer to the referenced object
operator T* () { return m_ptr; }
/// Return a const pointer to the referenced object
T* get_ptr() { return m_ptr; }
/// Return a pointer to the referenced object
const T* get_ptr() const { return m_ptr; }
private:
T *m_ptr;
};
#endif /* __OBJECT_H */

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/*
tests/pybind11_cross_module_tests.cpp -- contains tests that require multiple modules
Copyright (c) 2017 Jason Rhinelander <jason@imaginary.ca>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#include "pybind11_tests.h"
#include "local_bindings.h"
#include <pybind11/stl_bind.h>
#include <numeric>
PYBIND11_MODULE(pybind11_cross_module_tests, m) {
m.doc() = "pybind11 cross-module test module";
// test_local_bindings.py tests:
//
// Definitions here are tested by importing both this module and the
// relevant pybind11_tests submodule from a test_whatever.py
// test_load_external
bind_local<ExternalType1>(m, "ExternalType1", py::module_local());
bind_local<ExternalType2>(m, "ExternalType2", py::module_local());
// test_exceptions.py
m.def("raise_runtime_error", []() { PyErr_SetString(PyExc_RuntimeError, "My runtime error"); throw py::error_already_set(); });
m.def("raise_value_error", []() { PyErr_SetString(PyExc_ValueError, "My value error"); throw py::error_already_set(); });
m.def("throw_pybind_value_error", []() { throw py::value_error("pybind11 value error"); });
m.def("throw_pybind_type_error", []() { throw py::type_error("pybind11 type error"); });
m.def("throw_stop_iteration", []() { throw py::stop_iteration(); });
// test_local_bindings.py
// Local to both:
bind_local<LocalType, 1>(m, "LocalType", py::module_local())
.def("get2", [](LocalType &t) { return t.i + 2; })
;
// Can only be called with our python type:
m.def("local_value", [](LocalType &l) { return l.i; });
// test_nonlocal_failure
// This registration will fail (global registration when LocalFail is already registered
// globally in the main test module):
m.def("register_nonlocal", [m]() {
bind_local<NonLocalType, 0>(m, "NonLocalType");
});
// test_stl_bind_local
// stl_bind.h binders defaults to py::module_local if the types are local or converting:
py::bind_vector<LocalVec>(m, "LocalVec");
py::bind_map<LocalMap>(m, "LocalMap");
// test_stl_bind_global
// and global if the type (or one of the types, for the map) is global (so these will fail,
// assuming pybind11_tests is already loaded):
m.def("register_nonlocal_vec", [m]() {
py::bind_vector<NonLocalVec>(m, "NonLocalVec");
});
m.def("register_nonlocal_map", [m]() {
py::bind_map<NonLocalMap>(m, "NonLocalMap");
});
// The default can, however, be overridden to global using `py::module_local()` or
// `py::module_local(false)`.
// Explicitly made local:
py::bind_vector<NonLocalVec2>(m, "NonLocalVec2", py::module_local());
// Explicitly made global (and so will fail to bind):
m.def("register_nonlocal_map2", [m]() {
py::bind_map<NonLocalMap2>(m, "NonLocalMap2", py::module_local(false));
});
// test_mixed_local_global
// We try this both with the global type registered first and vice versa (the order shouldn't
// matter).
m.def("register_mixed_global_local", [m]() {
bind_local<MixedGlobalLocal, 200>(m, "MixedGlobalLocal", py::module_local());
});
m.def("register_mixed_local_global", [m]() {
bind_local<MixedLocalGlobal, 2000>(m, "MixedLocalGlobal", py::module_local(false));
});
m.def("get_mixed_gl", [](int i) { return MixedGlobalLocal(i); });
m.def("get_mixed_lg", [](int i) { return MixedLocalGlobal(i); });
// test_internal_locals_differ
m.def("local_cpp_types_addr", []() { return (uintptr_t) &py::detail::registered_local_types_cpp(); });
// test_stl_caster_vs_stl_bind
py::bind_vector<std::vector<int>>(m, "VectorInt");
m.def("load_vector_via_binding", [](std::vector<int> &v) {
return std::accumulate(v.begin(), v.end(), 0);
});
// test_cross_module_calls
m.def("return_self", [](LocalVec *v) { return v; });
m.def("return_copy", [](const LocalVec &v) { return LocalVec(v); });
class Dog : public pets::Pet { public: Dog(std::string name) : Pet(name) {}; };
py::class_<pets::Pet>(m, "Pet", py::module_local())
.def("name", &pets::Pet::name);
// Binding for local extending class:
py::class_<Dog, pets::Pet>(m, "Dog")
.def(py::init<std::string>());
m.def("pet_name", [](pets::Pet &p) { return p.name(); });
py::class_<MixGL>(m, "MixGL", py::module_local()).def(py::init<int>());
m.def("get_gl_value", [](MixGL &o) { return o.i + 100; });
py::class_<MixGL2>(m, "MixGL2", py::module_local()).def(py::init<int>());
// test_vector_bool
// We can't test both stl.h and stl_bind.h conversions of `std::vector<bool>` within
// the same module (it would be an ODR violation). Therefore `bind_vector` of `bool`
// is defined here and tested in `test_stl_binders.py`.
py::bind_vector<std::vector<bool>>(m, "VectorBool");
// test_missing_header_message
// The main module already includes stl.h, but we need to test the error message
// which appears when this header is missing.
m.def("missing_header_arg", [](std::vector<float>) { });
m.def("missing_header_return", []() { return std::vector<float>(); });
}

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/*
tests/pybind11_tests.cpp -- pybind example plugin
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#include "pybind11_tests.h"
#include "constructor_stats.h"
#include <functional>
#include <list>
/*
For testing purposes, we define a static global variable here in a function that each individual
test .cpp calls with its initialization lambda. It's convenient here because we can just not
compile some test files to disable/ignore some of the test code.
It is NOT recommended as a way to use pybind11 in practice, however: the initialization order will
be essentially random, which is okay for our test scripts (there are no dependencies between the
individual pybind11 test .cpp files), but most likely not what you want when using pybind11
productively.
Instead, see the "How can I reduce the build time?" question in the "Frequently asked questions"
section of the documentation for good practice on splitting binding code over multiple files.
*/
std::list<std::function<void(py::module &)>> &initializers() {
static std::list<std::function<void(py::module &)>> inits;
return inits;
}
test_initializer::test_initializer(Initializer init) {
initializers().push_back(init);
}
test_initializer::test_initializer(const char *submodule_name, Initializer init) {
initializers().push_back([=](py::module &parent) {
auto m = parent.def_submodule(submodule_name);
init(m);
});
}
void bind_ConstructorStats(py::module &m) {
py::class_<ConstructorStats>(m, "ConstructorStats")
.def("alive", &ConstructorStats::alive)
.def("values", &ConstructorStats::values)
.def_readwrite("default_constructions", &ConstructorStats::default_constructions)
.def_readwrite("copy_assignments", &ConstructorStats::copy_assignments)
.def_readwrite("move_assignments", &ConstructorStats::move_assignments)
.def_readwrite("copy_constructions", &ConstructorStats::copy_constructions)
.def_readwrite("move_constructions", &ConstructorStats::move_constructions)
.def_static("get", (ConstructorStats &(*)(py::object)) &ConstructorStats::get, py::return_value_policy::reference_internal)
// Not exactly ConstructorStats, but related: expose the internal pybind number of registered instances
// to allow instance cleanup checks (invokes a GC first)
.def_static("detail_reg_inst", []() {
ConstructorStats::gc();
return py::detail::get_internals().registered_instances.size();
})
;
}
PYBIND11_MODULE(pybind11_tests, m) {
m.doc() = "pybind11 test module";
bind_ConstructorStats(m);
#if !defined(NDEBUG)
m.attr("debug_enabled") = true;
#else
m.attr("debug_enabled") = false;
#endif
py::class_<UserType>(m, "UserType", "A `py::class_` type for testing")
.def(py::init<>())
.def(py::init<int>())
.def("get_value", &UserType::value, "Get value using a method")
.def("set_value", &UserType::set, "Set value using a method")
.def_property("value", &UserType::value, &UserType::set, "Get/set value using a property")
.def("__repr__", [](const UserType& u) { return "UserType({})"_s.format(u.value()); });
py::class_<IncType, UserType>(m, "IncType")
.def(py::init<>())
.def(py::init<int>())
.def("__repr__", [](const IncType& u) { return "IncType({})"_s.format(u.value()); });
for (const auto &initializer : initializers())
initializer(m);
if (!py::hasattr(m, "have_eigen")) m.attr("have_eigen") = false;
}

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python/pybind11/tests/pybind11_tests.h
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#pragma once
#include <pybind11/pybind11.h>
#if defined(_MSC_VER) && _MSC_VER < 1910
// We get some really long type names here which causes MSVC 2015 to emit warnings
# pragma warning(disable: 4503) // warning C4503: decorated name length exceeded, name was truncated
#endif
namespace py = pybind11;
using namespace pybind11::literals;
class test_initializer {
using Initializer = void (*)(py::module &);
public:
test_initializer(Initializer init);
test_initializer(const char *submodule_name, Initializer init);
};
#define TEST_SUBMODULE(name, variable) \
void test_submodule_##name(py::module &); \
test_initializer name(#name, test_submodule_##name); \
void test_submodule_##name(py::module &variable)
/// Dummy type which is not exported anywhere -- something to trigger a conversion error
struct UnregisteredType { };
/// A user-defined type which is exported and can be used by any test
class UserType {
public:
UserType() = default;
UserType(int i) : i(i) { }
int value() const { return i; }
void set(int set) { i = set; }
private:
int i = -1;
};
/// Like UserType, but increments `value` on copy for quick reference vs. copy tests
class IncType : public UserType {
public:
using UserType::UserType;
IncType() = default;
IncType(const IncType &other) : IncType(other.value() + 1) { }
IncType(IncType &&) = delete;
IncType &operator=(const IncType &) = delete;
IncType &operator=(IncType &&) = delete;
};
/// Custom cast-only type that casts to a string "rvalue" or "lvalue" depending on the cast context.
/// Used to test recursive casters (e.g. std::tuple, stl containers).
struct RValueCaster {};
NAMESPACE_BEGIN(pybind11)
NAMESPACE_BEGIN(detail)
template<> class type_caster<RValueCaster> {
public:
PYBIND11_TYPE_CASTER(RValueCaster, _("RValueCaster"));
static handle cast(RValueCaster &&, return_value_policy, handle) { return py::str("rvalue").release(); }
static handle cast(const RValueCaster &, return_value_policy, handle) { return py::str("lvalue").release(); }
};
NAMESPACE_END(detail)
NAMESPACE_END(pybind11)

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python/pybind11/tests/pytest.ini
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[pytest]
minversion = 3.0
norecursedirs = test_cmake_build test_embed
addopts =
# show summary of skipped tests
-rs
# capture only Python print and C++ py::print, but not C output (low-level Python errors)
--capture=sys
filterwarnings =
# make warnings into errors but ignore certain third-party extension issues
error
# importing scipy submodules on some version of Python
ignore::ImportWarning
# bogus numpy ABI warning (see numpy/#432)
ignore:.*numpy.dtype size changed.*:RuntimeWarning
ignore:.*numpy.ufunc size changed.*:RuntimeWarning

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python/pybind11/tests/test_buffers.cpp
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/*
tests/test_buffers.cpp -- supporting Pythons' buffer protocol
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#include "pybind11_tests.h"
#include "constructor_stats.h"
TEST_SUBMODULE(buffers, m) {
// test_from_python / test_to_python:
class Matrix {
public:
Matrix(ssize_t rows, ssize_t cols) : m_rows(rows), m_cols(cols) {
print_created(this, std::to_string(m_rows) + "x" + std::to_string(m_cols) + " matrix");
m_data = new float[(size_t) (rows*cols)];
memset(m_data, 0, sizeof(float) * (size_t) (rows * cols));
}
Matrix(const Matrix &s) : m_rows(s.m_rows), m_cols(s.m_cols) {
print_copy_created(this, std::to_string(m_rows) + "x" + std::to_string(m_cols) + " matrix");
m_data = new float[(size_t) (m_rows * m_cols)];
memcpy(m_data, s.m_data, sizeof(float) * (size_t) (m_rows * m_cols));
}
Matrix(Matrix &&s) : m_rows(s.m_rows), m_cols(s.m_cols), m_data(s.m_data) {
print_move_created(this);
s.m_rows = 0;
s.m_cols = 0;
s.m_data = nullptr;
}
~Matrix() {
print_destroyed(this, std::to_string(m_rows) + "x" + std::to_string(m_cols) + " matrix");
delete[] m_data;
}
Matrix &operator=(const Matrix &s) {
print_copy_assigned(this, std::to_string(m_rows) + "x" + std::to_string(m_cols) + " matrix");
delete[] m_data;
m_rows = s.m_rows;
m_cols = s.m_cols;
m_data = new float[(size_t) (m_rows * m_cols)];
memcpy(m_data, s.m_data, sizeof(float) * (size_t) (m_rows * m_cols));
return *this;
}
Matrix &operator=(Matrix &&s) {
print_move_assigned(this, std::to_string(m_rows) + "x" + std::to_string(m_cols) + " matrix");
if (&s != this) {
delete[] m_data;
m_rows = s.m_rows; m_cols = s.m_cols; m_data = s.m_data;
s.m_rows = 0; s.m_cols = 0; s.m_data = nullptr;
}
return *this;
}
float operator()(ssize_t i, ssize_t j) const {
return m_data[(size_t) (i*m_cols + j)];
}
float &operator()(ssize_t i, ssize_t j) {
return m_data[(size_t) (i*m_cols + j)];
}
float *data() { return m_data; }
ssize_t rows() const { return m_rows; }
ssize_t cols() const { return m_cols; }
private:
ssize_t m_rows;
ssize_t m_cols;
float *m_data;
};
py::class_<Matrix>(m, "Matrix", py::buffer_protocol())
.def(py::init<ssize_t, ssize_t>())
/// Construct from a buffer
.def(py::init([](py::buffer b) {
py::buffer_info info = b.request();
if (info.format != py::format_descriptor<float>::format() || info.ndim != 2)
throw std::runtime_error("Incompatible buffer format!");
auto v = new Matrix(info.shape[0], info.shape[1]);
memcpy(v->data(), info.ptr, sizeof(float) * (size_t) (v->rows() * v->cols()));
return v;
}))
.def("rows", &Matrix::rows)
.def("cols", &Matrix::cols)
/// Bare bones interface
.def("__getitem__", [](const Matrix &m, std::pair<ssize_t, ssize_t> i) {
if (i.first >= m.rows() || i.second >= m.cols())
throw py::index_error();
return m(i.first, i.second);
})
.def("__setitem__", [](Matrix &m, std::pair<ssize_t, ssize_t> i, float v) {
if (i.first >= m.rows() || i.second >= m.cols())
throw py::index_error();
m(i.first, i.second) = v;
})
/// Provide buffer access
.def_buffer([](Matrix &m) -> py::buffer_info {
return py::buffer_info(
m.data(), /* Pointer to buffer */
{ m.rows(), m.cols() }, /* Buffer dimensions */
{ sizeof(float) * size_t(m.cols()), /* Strides (in bytes) for each index */
sizeof(float) }
);
})
;
// test_inherited_protocol
class SquareMatrix : public Matrix {
public:
SquareMatrix(ssize_t n) : Matrix(n, n) { }
};
// Derived classes inherit the buffer protocol and the buffer access function
py::class_<SquareMatrix, Matrix>(m, "SquareMatrix")
.def(py::init<ssize_t>());
// test_pointer_to_member_fn
// Tests that passing a pointer to member to the base class works in
// the derived class.
struct Buffer {
int32_t value = 0;
py::buffer_info get_buffer_info() {
return py::buffer_info(&value, sizeof(value),
py::format_descriptor<int32_t>::format(), 1);
}
};
py::class_<Buffer>(m, "Buffer", py::buffer_protocol())
.def(py::init<>())
.def_readwrite("value", &Buffer::value)
.def_buffer(&Buffer::get_buffer_info);
class ConstBuffer {
std::unique_ptr<int32_t> value;
public:
int32_t get_value() const { return *value; }
void set_value(int32_t v) { *value = v; }
py::buffer_info get_buffer_info() const {
return py::buffer_info(value.get(), sizeof(*value),
py::format_descriptor<int32_t>::format(), 1);
}
ConstBuffer() : value(new int32_t{0}) { };
};
py::class_<ConstBuffer>(m, "ConstBuffer", py::buffer_protocol())
.def(py::init<>())
.def_property("value", &ConstBuffer::get_value, &ConstBuffer::set_value)
.def_buffer(&ConstBuffer::get_buffer_info);
struct DerivedBuffer : public Buffer { };
py::class_<DerivedBuffer>(m, "DerivedBuffer", py::buffer_protocol())
.def(py::init<>())
.def_readwrite("value", (int32_t DerivedBuffer::*) &DerivedBuffer::value)
.def_buffer(&DerivedBuffer::get_buffer_info);
}

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import struct
import pytest
from pybind11_tests import buffers as m
from pybind11_tests import ConstructorStats
pytestmark = pytest.requires_numpy
with pytest.suppress(ImportError):
import numpy as np
def test_from_python():
with pytest.raises(RuntimeError) as excinfo:
m.Matrix(np.array([1, 2, 3])) # trying to assign a 1D array
assert str(excinfo.value) == "Incompatible buffer format!"
m3 = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
m4 = m.Matrix(m3)
for i in range(m4.rows()):
for j in range(m4.cols()):
assert m3[i, j] == m4[i, j]
cstats = ConstructorStats.get(m.Matrix)
assert cstats.alive() == 1
del m3, m4
assert cstats.alive() == 0
assert cstats.values() == ["2x3 matrix"]
assert cstats.copy_constructions == 0
# assert cstats.move_constructions >= 0 # Don't invoke any
assert cstats.copy_assignments == 0
assert cstats.move_assignments == 0
# PyPy: Memory leak in the "np.array(m, copy=False)" call
# https://bitbucket.org/pypy/pypy/issues/2444
@pytest.unsupported_on_pypy
def test_to_python():
mat = m.Matrix(5, 4)
assert memoryview(mat).shape == (5, 4)
assert mat[2, 3] == 0
mat[2, 3] = 4.0
mat[3, 2] = 7.0
assert mat[2, 3] == 4
assert mat[3, 2] == 7
assert struct.unpack_from('f', mat, (3 * 4 + 2) * 4) == (7, )
assert struct.unpack_from('f', mat, (2 * 4 + 3) * 4) == (4, )
mat2 = np.array(mat, copy=False)
assert mat2.shape == (5, 4)
assert abs(mat2).sum() == 11
assert mat2[2, 3] == 4 and mat2[3, 2] == 7
mat2[2, 3] = 5
assert mat2[2, 3] == 5
cstats = ConstructorStats.get(m.Matrix)
assert cstats.alive() == 1
del mat
pytest.gc_collect()
assert cstats.alive() == 1
del mat2 # holds a mat reference
pytest.gc_collect()
assert cstats.alive() == 0
assert cstats.values() == ["5x4 matrix"]
assert cstats.copy_constructions == 0
# assert cstats.move_constructions >= 0 # Don't invoke any
assert cstats.copy_assignments == 0
assert cstats.move_assignments == 0
@pytest.unsupported_on_pypy
def test_inherited_protocol():
"""SquareMatrix is derived from Matrix and inherits the buffer protocol"""
matrix = m.SquareMatrix(5)
assert memoryview(matrix).shape == (5, 5)
assert np.asarray(matrix).shape == (5, 5)
@pytest.unsupported_on_pypy
def test_pointer_to_member_fn():
for cls in [m.Buffer, m.ConstBuffer, m.DerivedBuffer]:
buf = cls()
buf.value = 0x12345678
value = struct.unpack('i', bytearray(buf))[0]
assert value == 0x12345678

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/*
tests/test_builtin_casters.cpp -- Casters available without any additional headers
Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#include "pybind11_tests.h"
#include <pybind11/complex.h>
#if defined(_MSC_VER)
# pragma warning(push)
# pragma warning(disable: 4127) // warning C4127: Conditional expression is constant
#endif
TEST_SUBMODULE(builtin_casters, m) {
// test_simple_string
m.def("string_roundtrip", [](const char *s) { return s; });
// test_unicode_conversion
// Some test characters in utf16 and utf32 encodings. The last one (the 𝐀) contains a null byte
char32_t a32 = 0x61 /*a*/, z32 = 0x7a /*z*/, ib32 = 0x203d /*‽*/, cake32 = 0x1f382 /*🎂*/, mathbfA32 = 0x1d400 /*𝐀*/;
char16_t b16 = 0x62 /*b*/, z16 = 0x7a, ib16 = 0x203d, cake16_1 = 0xd83c, cake16_2 = 0xdf82, mathbfA16_1 = 0xd835, mathbfA16_2 = 0xdc00;
std::wstring wstr;
wstr.push_back(0x61); // a
wstr.push_back(0x2e18); // ⸘
if (sizeof(wchar_t) == 2) { wstr.push_back(mathbfA16_1); wstr.push_back(mathbfA16_2); } // 𝐀, utf16
else { wstr.push_back((wchar_t) mathbfA32); } // 𝐀, utf32
wstr.push_back(0x7a); // z
m.def("good_utf8_string", []() { return std::string(u8"Say utf8\u203d \U0001f382 \U0001d400"); }); // Say utf8‽ 🎂 𝐀
m.def("good_utf16_string", [=]() { return std::u16string({ b16, ib16, cake16_1, cake16_2, mathbfA16_1, mathbfA16_2, z16 }); }); // b‽🎂𝐀z
m.def("good_utf32_string", [=]() { return std::u32string({ a32, mathbfA32, cake32, ib32, z32 }); }); // a𝐀🎂‽z
m.def("good_wchar_string", [=]() { return wstr; }); // a‽𝐀z
m.def("bad_utf8_string", []() { return std::string("abc\xd0" "def"); });
m.def("bad_utf16_string", [=]() { return std::u16string({ b16, char16_t(0xd800), z16 }); });
// Under Python 2.7, invalid unicode UTF-32 characters don't appear to trigger UnicodeDecodeError
if (PY_MAJOR_VERSION >= 3)
m.def("bad_utf32_string", [=]() { return std::u32string({ a32, char32_t(0xd800), z32 }); });
if (PY_MAJOR_VERSION >= 3 || sizeof(wchar_t) == 2)
m.def("bad_wchar_string", [=]() { return std::wstring({ wchar_t(0x61), wchar_t(0xd800) }); });
m.def("u8_Z", []() -> char { return 'Z'; });
m.def("u8_eacute", []() -> char { return '\xe9'; });
m.def("u16_ibang", [=]() -> char16_t { return ib16; });
m.def("u32_mathbfA", [=]() -> char32_t { return mathbfA32; });
m.def("wchar_heart", []() -> wchar_t { return 0x2665; });
// test_single_char_arguments
m.attr("wchar_size") = py::cast(sizeof(wchar_t));
m.def("ord_char", [](char c) -> int { return static_cast<unsigned char>(c); });
m.def("ord_char_lv", [](char &c) -> int { return static_cast<unsigned char>(c); });
m.def("ord_char16", [](char16_t c) -> uint16_t { return c; });
m.def("ord_char16_lv", [](char16_t &c) -> uint16_t { return c; });
m.def("ord_char32", [](char32_t c) -> uint32_t { return c; });
m.def("ord_wchar", [](wchar_t c) -> int { return c; });
// test_bytes_to_string
m.def("strlen", [](char *s) { return strlen(s); });
m.def("string_length", [](std::string s) { return s.length(); });
// test_string_view
#ifdef PYBIND11_HAS_STRING_VIEW
m.attr("has_string_view") = true;
m.def("string_view_print", [](std::string_view s) { py::print(s, s.size()); });
m.def("string_view16_print", [](std::u16string_view s) { py::print(s, s.size()); });
m.def("string_view32_print", [](std::u32string_view s) { py::print(s, s.size()); });
m.def("string_view_chars", [](std::string_view s) { py::list l; for (auto c : s) l.append((std::uint8_t) c); return l; });
m.def("string_view16_chars", [](std::u16string_view s) { py::list l; for (auto c : s) l.append((int) c); return l; });
m.def("string_view32_chars", [](std::u32string_view s) { py::list l; for (auto c : s) l.append((int) c); return l; });
m.def("string_view_return", []() { return std::string_view(u8"utf8 secret \U0001f382"); });
m.def("string_view16_return", []() { return std::u16string_view(u"utf16 secret \U0001f382"); });
m.def("string_view32_return", []() { return std::u32string_view(U"utf32 secret \U0001f382"); });
#endif
// test_integer_casting
m.def("i32_str", [](std::int32_t v) { return std::to_string(v); });
m.def("u32_str", [](std::uint32_t v) { return std::to_string(v); });
m.def("i64_str", [](std::int64_t v) { return std::to_string(v); });
m.def("u64_str", [](std::uint64_t v) { return std::to_string(v); });
// test_tuple
m.def("pair_passthrough", [](std::pair<bool, std::string> input) {
return std::make_pair(input.second, input.first);
}, "Return a pair in reversed order");
m.def("tuple_passthrough", [](std::tuple<bool, std::string, int> input) {
return std::make_tuple(std::get<2>(input), std::get<1>(input), std::get<0>(input));
}, "Return a triple in reversed order");
m.def("empty_tuple", []() { return std::tuple<>(); });
static std::pair<RValueCaster, RValueCaster> lvpair;
static std::tuple<RValueCaster, RValueCaster, RValueCaster> lvtuple;
static std::pair<RValueCaster, std::tuple<RValueCaster, std::pair<RValueCaster, RValueCaster>>> lvnested;
m.def("rvalue_pair", []() { return std::make_pair(RValueCaster{}, RValueCaster{}); });
m.def("lvalue_pair", []() -> const decltype(lvpair) & { return lvpair; });
m.def("rvalue_tuple", []() { return std::make_tuple(RValueCaster{}, RValueCaster{}, RValueCaster{}); });
m.def("lvalue_tuple", []() -> const decltype(lvtuple) & { return lvtuple; });
m.def("rvalue_nested", []() {
return std::make_pair(RValueCaster{}, std::make_tuple(RValueCaster{}, std::make_pair(RValueCaster{}, RValueCaster{}))); });
m.def("lvalue_nested", []() -> const decltype(lvnested) & { return lvnested; });
// test_builtins_cast_return_none
m.def("return_none_string", []() -> std::string * { return nullptr; });
m.def("return_none_char", []() -> const char * { return nullptr; });
m.def("return_none_bool", []() -> bool * { return nullptr; });
m.def("return_none_int", []() -> int * { return nullptr; });
m.def("return_none_float", []() -> float * { return nullptr; });
// test_none_deferred
m.def("defer_none_cstring", [](char *) { return false; });
m.def("defer_none_cstring", [](py::none) { return true; });
m.def("defer_none_custom", [](UserType *) { return false; });
m.def("defer_none_custom", [](py::none) { return true; });
m.def("nodefer_none_void", [](void *) { return true; });
m.def("nodefer_none_void", [](py::none) { return false; });
// test_void_caster
m.def("load_nullptr_t", [](std::nullptr_t) {}); // not useful, but it should still compile
m.def("cast_nullptr_t", []() { return std::nullptr_t{}; });
// test_bool_caster
m.def("bool_passthrough", [](bool arg) { return arg; });
m.def("bool_passthrough_noconvert", [](bool arg) { return arg; }, py::arg().noconvert());
// test_reference_wrapper
m.def("refwrap_builtin", [](std::reference_wrapper<int> p) { return 10 * p.get(); });
m.def("refwrap_usertype", [](std::reference_wrapper<UserType> p) { return p.get().value(); });
// Not currently supported (std::pair caster has return-by-value cast operator);
// triggers static_assert failure.
//m.def("refwrap_pair", [](std::reference_wrapper<std::pair<int, int>>) { });
m.def("refwrap_list", [](bool copy) {
static IncType x1(1), x2(2);
py::list l;
for (auto &f : {std::ref(x1), std::ref(x2)}) {
l.append(py::cast(f, copy ? py::return_value_policy::copy
: py::return_value_policy::reference));
}
return l;
}, "copy"_a);
m.def("refwrap_iiw", [](const IncType &w) { return w.value(); });
m.def("refwrap_call_iiw", [](IncType &w, py::function f) {
py::list l;
l.append(f(std::ref(w)));
l.append(f(std::cref(w)));
IncType x(w.value());
l.append(f(std::ref(x)));
IncType y(w.value());
auto r3 = std::ref(y);
l.append(f(r3));
return l;
});
// test_complex
m.def("complex_cast", [](float x) { return "{}"_s.format(x); });
m.def("complex_cast", [](std::complex<float> x) { return "({}, {})"_s.format(x.real(), x.imag()); });
// test int vs. long (Python 2)
m.def("int_cast", []() {return (int) 42;});
m.def("long_cast", []() {return (long) 42;});
m.def("longlong_cast", []() {return ULLONG_MAX;});
/// test void* cast operator
m.def("test_void_caster", []() -> bool {
void *v = (void *) 0xabcd;
py::object o = py::cast(v);
return py::cast<void *>(o) == v;
});
}

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# Python < 3 needs this: coding=utf-8
import pytest
from pybind11_tests import builtin_casters as m
from pybind11_tests import UserType, IncType
def test_simple_string():
assert m.string_roundtrip("const char *") == "const char *"
def test_unicode_conversion():
"""Tests unicode conversion and error reporting."""
assert m.good_utf8_string() == u"Say utf8‽ 🎂 𝐀"
assert m.good_utf16_string() == u"b‽🎂𝐀z"
assert m.good_utf32_string() == u"a𝐀🎂‽z"
assert m.good_wchar_string() == u"a⸘𝐀z"
with pytest.raises(UnicodeDecodeError):
m.bad_utf8_string()
with pytest.raises(UnicodeDecodeError):
m.bad_utf16_string()
# These are provided only if they actually fail (they don't when 32-bit and under Python 2.7)
if hasattr(m, "bad_utf32_string"):
with pytest.raises(UnicodeDecodeError):
m.bad_utf32_string()
if hasattr(m, "bad_wchar_string"):
with pytest.raises(UnicodeDecodeError):
m.bad_wchar_string()
assert m.u8_Z() == 'Z'
assert m.u8_eacute() == u'é'
assert m.u16_ibang() == u'‽'
assert m.u32_mathbfA() == u'𝐀'
assert m.wchar_heart() == u'♥'
def test_single_char_arguments():
"""Tests failures for passing invalid inputs to char-accepting functions"""
def toobig_message(r):
return "Character code point not in range({0:#x})".format(r)
toolong_message = "Expected a character, but multi-character string found"
assert m.ord_char(u'a') == 0x61 # simple ASCII
assert m.ord_char_lv(u'b') == 0x62
assert m.ord_char(u'é') == 0xE9 # requires 2 bytes in utf-8, but can be stuffed in a char
with pytest.raises(ValueError) as excinfo:
assert m.ord_char(u'Ā') == 0x100 # requires 2 bytes, doesn't fit in a char
assert str(excinfo.value) == toobig_message(0x100)
with pytest.raises(ValueError) as excinfo:
assert m.ord_char(u'ab')
assert str(excinfo.value) == toolong_message
assert m.ord_char16(u'a') == 0x61
assert m.ord_char16(u'é') == 0xE9
assert m.ord_char16_lv(u'ê') == 0xEA
assert m.ord_char16(u'Ā') == 0x100
assert m.ord_char16(u'‽') == 0x203d
assert m.ord_char16(u'♥') == 0x2665
assert m.ord_char16_lv(u'♡') == 0x2661
with pytest.raises(ValueError) as excinfo:
assert m.ord_char16(u'🎂') == 0x1F382 # requires surrogate pair
assert str(excinfo.value) == toobig_message(0x10000)
with pytest.raises(ValueError) as excinfo:
assert m.ord_char16(u'aa')
assert str(excinfo.value) == toolong_message
assert m.ord_char32(u'a') == 0x61
assert m.ord_char32(u'é') == 0xE9
assert m.ord_char32(u'Ā') == 0x100
assert m.ord_char32(u'‽') == 0x203d
assert m.ord_char32(u'♥') == 0x2665
assert m.ord_char32(u'🎂') == 0x1F382
with pytest.raises(ValueError) as excinfo:
assert m.ord_char32(u'aa')
assert str(excinfo.value) == toolong_message
assert m.ord_wchar(u'a') == 0x61
assert m.ord_wchar(u'é') == 0xE9
assert m.ord_wchar(u'Ā') == 0x100
assert m.ord_wchar(u'‽') == 0x203d
assert m.ord_wchar(u'♥') == 0x2665
if m.wchar_size == 2:
with pytest.raises(ValueError) as excinfo:
assert m.ord_wchar(u'🎂') == 0x1F382 # requires surrogate pair
assert str(excinfo.value) == toobig_message(0x10000)
else:
assert m.ord_wchar(u'🎂') == 0x1F382
with pytest.raises(ValueError) as excinfo:
assert m.ord_wchar(u'aa')
assert str(excinfo.value) == toolong_message
def test_bytes_to_string():
"""Tests the ability to pass bytes to C++ string-accepting functions. Note that this is
one-way: the only way to return bytes to Python is via the pybind11::bytes class."""
# Issue #816
import sys
byte = bytes if sys.version_info[0] < 3 else str
assert m.strlen(byte("hi")) == 2
assert m.string_length(byte("world")) == 5
assert m.string_length(byte("a\x00b")) == 3
assert m.strlen(byte("a\x00b")) == 1 # C-string limitation
# passing in a utf8 encoded string should work
assert m.string_length(u'💩'.encode("utf8")) == 4
@pytest.mark.skipif(not hasattr(m, "has_string_view"), reason="no <string_view>")
def test_string_view(capture):
"""Tests support for C++17 string_view arguments and return values"""
assert m.string_view_chars("Hi") == [72, 105]
assert m.string_view_chars("Hi 🎂") == [72, 105, 32, 0xf0, 0x9f, 0x8e, 0x82]
assert m.string_view16_chars("Hi 🎂") == [72, 105, 32, 0xd83c, 0xdf82]
assert m.string_view32_chars("Hi 🎂") == [72, 105, 32, 127874]
assert m.string_view_return() == "utf8 secret 🎂"
assert m.string_view16_return() == "utf16 secret 🎂"
assert m.string_view32_return() == "utf32 secret 🎂"
with capture:
m.string_view_print("Hi")
m.string_view_print("utf8 🎂")
m.string_view16_print("utf16 🎂")
m.string_view32_print("utf32 🎂")
assert capture == """
Hi 2
utf8 🎂 9
utf16 🎂 8
utf32 🎂 7
"""
with capture:
m.string_view_print("Hi, ascii")
m.string_view_print("Hi, utf8 🎂")
m.string_view16_print("Hi, utf16 🎂")
m.string_view32_print("Hi, utf32 🎂")
assert capture == """
Hi, ascii 9
Hi, utf8 🎂 13
Hi, utf16 🎂 12
Hi, utf32 🎂 11
"""
def test_integer_casting():
"""Issue #929 - out-of-range integer values shouldn't be accepted"""
import sys
assert m.i32_str(-1) == "-1"
assert m.i64_str(-1) == "-1"
assert m.i32_str(2000000000) == "2000000000"
assert m.u32_str(2000000000) == "2000000000"
if sys.version_info < (3,):
assert m.i32_str(long(-1)) == "-1" # noqa: F821 undefined name 'long'
assert m.i64_str(long(-1)) == "-1" # noqa: F821 undefined name 'long'
assert m.i64_str(long(-999999999999)) == "-999999999999" # noqa: F821 undefined name
assert m.u64_str(long(999999999999)) == "999999999999" # noqa: F821 undefined name 'long'
else:
assert m.i64_str(-999999999999) == "-999999999999"
assert m.u64_str(999999999999) == "999999999999"
with pytest.raises(TypeError) as excinfo:
m.u32_str(-1)
assert "incompatible function arguments" in str(excinfo.value)
with pytest.raises(TypeError) as excinfo:
m.u64_str(-1)
assert "incompatible function arguments" in str(excinfo.value)
with pytest.raises(TypeError) as excinfo:
m.i32_str(-3000000000)
assert "incompatible function arguments" in str(excinfo.value)
with pytest.raises(TypeError) as excinfo:
m.i32_str(3000000000)
assert "incompatible function arguments" in str(excinfo.value)
if sys.version_info < (3,):
with pytest.raises(TypeError) as excinfo:
m.u32_str(long(-1)) # noqa: F821 undefined name 'long'
assert "incompatible function arguments" in str(excinfo.value)
with pytest.raises(TypeError) as excinfo:
m.u64_str(long(-1)) # noqa: F821 undefined name 'long'
assert "incompatible function arguments" in str(excinfo.value)
def test_tuple(doc):
"""std::pair <-> tuple & std::tuple <-> tuple"""
assert m.pair_passthrough((True, "test")) == ("test", True)
assert m.tuple_passthrough((True, "test", 5)) == (5, "test", True)
# Any sequence can be cast to a std::pair or std::tuple
assert m.pair_passthrough([True, "test"]) == ("test", True)
assert m.tuple_passthrough([True, "test", 5]) == (5, "test", True)
assert m.empty_tuple() == ()
assert doc(m.pair_passthrough) == """
pair_passthrough(arg0: Tuple[bool, str]) -> Tuple[str, bool]
Return a pair in reversed order
"""
assert doc(m.tuple_passthrough) == """
tuple_passthrough(arg0: Tuple[bool, str, int]) -> Tuple[int, str, bool]
Return a triple in reversed order
"""
assert m.rvalue_pair() == ("rvalue", "rvalue")
assert m.lvalue_pair() == ("lvalue", "lvalue")
assert m.rvalue_tuple() == ("rvalue", "rvalue", "rvalue")
assert m.lvalue_tuple() == ("lvalue", "lvalue", "lvalue")
assert m.rvalue_nested() == ("rvalue", ("rvalue", ("rvalue", "rvalue")))
assert m.lvalue_nested() == ("lvalue", ("lvalue", ("lvalue", "lvalue")))
def test_builtins_cast_return_none():
"""Casters produced with PYBIND11_TYPE_CASTER() should convert nullptr to None"""
assert m.return_none_string() is None
assert m.return_none_char() is None
assert m.return_none_bool() is None
assert m.return_none_int() is None
assert m.return_none_float() is None
def test_none_deferred():
"""None passed as various argument types should defer to other overloads"""
assert not m.defer_none_cstring("abc")
assert m.defer_none_cstring(None)
assert not m.defer_none_custom(UserType())
assert m.defer_none_custom(None)
assert m.nodefer_none_void(None)
def test_void_caster():
assert m.load_nullptr_t(None) is None
assert m.cast_nullptr_t() is None
def test_reference_wrapper():
"""std::reference_wrapper for builtin and user types"""
assert m.refwrap_builtin(42) == 420
assert m.refwrap_usertype(UserType(42)) == 42
with pytest.raises(TypeError) as excinfo:
m.refwrap_builtin(None)
assert "incompatible function arguments" in str(excinfo.value)
with pytest.raises(TypeError) as excinfo:
m.refwrap_usertype(None)
assert "incompatible function arguments" in str(excinfo.value)
a1 = m.refwrap_list(copy=True)
a2 = m.refwrap_list(copy=True)
assert [x.value for x in a1] == [2, 3]
assert [x.value for x in a2] == [2, 3]
assert not a1[0] is a2[0] and not a1[1] is a2[1]
b1 = m.refwrap_list(copy=False)
b2 = m.refwrap_list(copy=False)
assert [x.value for x in b1] == [1, 2]
assert [x.value for x in b2] == [1, 2]
assert b1[0] is b2[0] and b1[1] is b2[1]
assert m.refwrap_iiw(IncType(5)) == 5
assert m.refwrap_call_iiw(IncType(10), m.refwrap_iiw) == [10, 10, 10, 10]
def test_complex_cast():
"""std::complex casts"""
assert m.complex_cast(1) == "1.0"
assert m.complex_cast(2j) == "(0.0, 2.0)"
def test_bool_caster():
"""Test bool caster implicit conversions."""
convert, noconvert = m.bool_passthrough, m.bool_passthrough_noconvert
def require_implicit(v):
pytest.raises(TypeError, noconvert, v)
def cant_convert(v):
pytest.raises(TypeError, convert, v)
# straight up bool
assert convert(True) is True
assert convert(False) is False
assert noconvert(True) is True
assert noconvert(False) is False
# None requires implicit conversion
require_implicit(None)
assert convert(None) is False
class A(object):
def __init__(self, x):
self.x = x
def __nonzero__(self):
return self.x
def __bool__(self):
return self.x
class B(object):
pass
# Arbitrary objects are not accepted
cant_convert(object())
cant_convert(B())
# Objects with __nonzero__ / __bool__ defined can be converted
require_implicit(A(True))
assert convert(A(True)) is True
assert convert(A(False)) is False
@pytest.requires_numpy
def test_numpy_bool():
import numpy as np
convert, noconvert = m.bool_passthrough, m.bool_passthrough_noconvert
# np.bool_ is not considered implicit
assert convert(np.bool_(True)) is True
assert convert(np.bool_(False)) is False
assert noconvert(np.bool_(True)) is True
assert noconvert(np.bool_(False)) is False
def test_int_long():
"""In Python 2, a C++ int should return a Python int rather than long
if possible: longs are not always accepted where ints are used (such
as the argument to sys.exit()). A C++ long long is always a Python
long."""
import sys
must_be_long = type(getattr(sys, 'maxint', 1) + 1)
assert isinstance(m.int_cast(), int)
assert isinstance(m.long_cast(), int)
assert isinstance(m.longlong_cast(), must_be_long)
def test_void_caster_2():
assert m.test_void_caster()

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@ -1,100 +0,0 @@
/*
tests/test_call_policies.cpp -- keep_alive and call_guard
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#include "pybind11_tests.h"
struct CustomGuard {
static bool enabled;
CustomGuard() { enabled = true; }
~CustomGuard() { enabled = false; }
static const char *report_status() { return enabled ? "guarded" : "unguarded"; }
};
bool CustomGuard::enabled = false;
struct DependentGuard {
static bool enabled;
DependentGuard() { enabled = CustomGuard::enabled; }
~DependentGuard() { enabled = false; }
static const char *report_status() { return enabled ? "guarded" : "unguarded"; }
};
bool DependentGuard::enabled = false;
TEST_SUBMODULE(call_policies, m) {
// Parent/Child are used in:
// test_keep_alive_argument, test_keep_alive_return_value, test_alive_gc_derived,
// test_alive_gc_multi_derived, test_return_none, test_keep_alive_constructor
class Child {
public:
Child() { py::print("Allocating child."); }
Child(const Child &) = default;
Child(Child &&) = default;
~Child() { py::print("Releasing child."); }
};
py::class_<Child>(m, "Child")
.def(py::init<>());
class Parent {
public:
Parent() { py::print("Allocating parent."); }
~Parent() { py::print("Releasing parent."); }
void addChild(Child *) { }
Child *returnChild() { return new Child(); }
Child *returnNullChild() { return nullptr; }
};
py::class_<Parent>(m, "Parent")
.def(py::init<>())
.def(py::init([](Child *) { return new Parent(); }), py::keep_alive<1, 2>())
.def("addChild", &Parent::addChild)
.def("addChildKeepAlive", &Parent::addChild, py::keep_alive<1, 2>())
.def("returnChild", &Parent::returnChild)
.def("returnChildKeepAlive", &Parent::returnChild, py::keep_alive<1, 0>())
.def("returnNullChildKeepAliveChild", &Parent::returnNullChild, py::keep_alive<1, 0>())
.def("returnNullChildKeepAliveParent", &Parent::returnNullChild, py::keep_alive<0, 1>());
#if !defined(PYPY_VERSION)
// test_alive_gc
class ParentGC : public Parent {
public:
using Parent::Parent;
};
py::class_<ParentGC, Parent>(m, "ParentGC", py::dynamic_attr())
.def(py::init<>());
#endif
// test_call_guard
m.def("unguarded_call", &CustomGuard::report_status);
m.def("guarded_call", &CustomGuard::report_status, py::call_guard<CustomGuard>());
m.def("multiple_guards_correct_order", []() {
return CustomGuard::report_status() + std::string(" & ") + DependentGuard::report_status();
}, py::call_guard<CustomGuard, DependentGuard>());
m.def("multiple_guards_wrong_order", []() {
return DependentGuard::report_status() + std::string(" & ") + CustomGuard::report_status();
}, py::call_guard<DependentGuard, CustomGuard>());
#if defined(WITH_THREAD) && !defined(PYPY_VERSION)
// `py::call_guard<py::gil_scoped_release>()` should work in PyPy as well,
// but it's unclear how to test it without `PyGILState_GetThisThreadState`.
auto report_gil_status = []() {
auto is_gil_held = false;
if (auto tstate = py::detail::get_thread_state_unchecked())
is_gil_held = (tstate == PyGILState_GetThisThreadState());
return is_gil_held ? "GIL held" : "GIL released";
};
m.def("with_gil", report_gil_status);
m.def("without_gil", report_gil_status, py::call_guard<py::gil_scoped_release>());
#endif
}

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import pytest
from pybind11_tests import call_policies as m
from pybind11_tests import ConstructorStats
def test_keep_alive_argument(capture):
n_inst = ConstructorStats.detail_reg_inst()
with capture:
p = m.Parent()
assert capture == "Allocating parent."
with capture:
p.addChild(m.Child())
assert ConstructorStats.detail_reg_inst() == n_inst + 1
assert capture == """
Allocating child.
Releasing child.
"""
with capture:
del p
assert ConstructorStats.detail_reg_inst() == n_inst
assert capture == "Releasing parent."
with capture:
p = m.Parent()
assert capture == "Allocating parent."
with capture:
p.addChildKeepAlive(m.Child())
assert ConstructorStats.detail_reg_inst() == n_inst + 2
assert capture == "Allocating child."
with capture:
del p
assert ConstructorStats.detail_reg_inst() == n_inst
assert capture == """
Releasing parent.
Releasing child.
"""
def test_keep_alive_return_value(capture):
n_inst = ConstructorStats.detail_reg_inst()
with capture:
p = m.Parent()
assert capture == "Allocating parent."
with capture:
p.returnChild()
assert ConstructorStats.detail_reg_inst() == n_inst + 1
assert capture == """
Allocating child.
Releasing child.
"""
with capture:
del p
assert ConstructorStats.detail_reg_inst() == n_inst
assert capture == "Releasing parent."
with capture:
p = m.Parent()
assert capture == "Allocating parent."
with capture:
p.returnChildKeepAlive()
assert ConstructorStats.detail_reg_inst() == n_inst + 2
assert capture == "Allocating child."
with capture:
del p
assert ConstructorStats.detail_reg_inst() == n_inst
assert capture == """
Releasing parent.
Releasing child.
"""
# https://bitbucket.org/pypy/pypy/issues/2447
@pytest.unsupported_on_pypy
def test_alive_gc(capture):
n_inst = ConstructorStats.detail_reg_inst()
p = m.ParentGC()
p.addChildKeepAlive(m.Child())
assert ConstructorStats.detail_reg_inst() == n_inst + 2
lst = [p]
lst.append(lst) # creates a circular reference
with capture:
del p, lst
assert ConstructorStats.detail_reg_inst() == n_inst
assert capture == """
Releasing parent.
Releasing child.
"""
def test_alive_gc_derived(capture):
class Derived(m.Parent):
pass
n_inst = ConstructorStats.detail_reg_inst()
p = Derived()
p.addChildKeepAlive(m.Child())
assert ConstructorStats.detail_reg_inst() == n_inst + 2
lst = [p]
lst.append(lst) # creates a circular reference
with capture:
del p, lst
assert ConstructorStats.detail_reg_inst() == n_inst
assert capture == """
Releasing parent.
Releasing child.
"""
def test_alive_gc_multi_derived(capture):
class Derived(m.Parent, m.Child):
def __init__(self):
m.Parent.__init__(self)
m.Child.__init__(self)
n_inst = ConstructorStats.detail_reg_inst()
p = Derived()
p.addChildKeepAlive(m.Child())
# +3 rather than +2 because Derived corresponds to two registered instances
assert ConstructorStats.detail_reg_inst() == n_inst + 3
lst = [p]
lst.append(lst) # creates a circular reference
with capture:
del p, lst
assert ConstructorStats.detail_reg_inst() == n_inst
assert capture == """
Releasing parent.
Releasing child.
Releasing child.
"""
def test_return_none(capture):
n_inst = ConstructorStats.detail_reg_inst()
with capture:
p = m.Parent()
assert capture == "Allocating parent."
with capture:
p.returnNullChildKeepAliveChild()
assert ConstructorStats.detail_reg_inst() == n_inst + 1
assert capture == ""
with capture:
del p
assert ConstructorStats.detail_reg_inst() == n_inst
assert capture == "Releasing parent."
with capture:
p = m.Parent()
assert capture == "Allocating parent."
with capture:
p.returnNullChildKeepAliveParent()
assert ConstructorStats.detail_reg_inst() == n_inst + 1
assert capture == ""
with capture:
del p
assert ConstructorStats.detail_reg_inst() == n_inst
assert capture == "Releasing parent."
def test_keep_alive_constructor(capture):
n_inst = ConstructorStats.detail_reg_inst()
with capture:
p = m.Parent(m.Child())
assert ConstructorStats.detail_reg_inst() == n_inst + 2
assert capture == """
Allocating child.
Allocating parent.
"""
with capture:
del p
assert ConstructorStats.detail_reg_inst() == n_inst
assert capture == """
Releasing parent.
Releasing child.
"""
def test_call_guard():
assert m.unguarded_call() == "unguarded"
assert m.guarded_call() == "guarded"
assert m.multiple_guards_correct_order() == "guarded & guarded"
assert m.multiple_guards_wrong_order() == "unguarded & guarded"
if hasattr(m, "with_gil"):
assert m.with_gil() == "GIL held"
assert m.without_gil() == "GIL released"

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/*
tests/test_callbacks.cpp -- callbacks
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#include "pybind11_tests.h"
#include "constructor_stats.h"
#include <pybind11/functional.h>
#include <thread>
int dummy_function(int i) { return i + 1; }
TEST_SUBMODULE(callbacks, m) {
// test_callbacks, test_function_signatures
m.def("test_callback1", [](py::object func) { return func(); });
m.def("test_callback2", [](py::object func) { return func("Hello", 'x', true, 5); });
m.def("test_callback3", [](const std::function<int(int)> &func) {
return "func(43) = " + std::to_string(func(43)); });
m.def("test_callback4", []() -> std::function<int(int)> { return [](int i) { return i+1; }; });
m.def("test_callback5", []() {
return py::cpp_function([](int i) { return i+1; }, py::arg("number"));
});
// test_keyword_args_and_generalized_unpacking
m.def("test_tuple_unpacking", [](py::function f) {
auto t1 = py::make_tuple(2, 3);
auto t2 = py::make_tuple(5, 6);
return f("positional", 1, *t1, 4, *t2);
});
m.def("test_dict_unpacking", [](py::function f) {
auto d1 = py::dict("key"_a="value", "a"_a=1);
auto d2 = py::dict();
auto d3 = py::dict("b"_a=2);
return f("positional", 1, **d1, **d2, **d3);
});
m.def("test_keyword_args", [](py::function f) {
return f("x"_a=10, "y"_a=20);
});
m.def("test_unpacking_and_keywords1", [](py::function f) {
auto args = py::make_tuple(2);
auto kwargs = py::dict("d"_a=4);
return f(1, *args, "c"_a=3, **kwargs);
});
m.def("test_unpacking_and_keywords2", [](py::function f) {
auto kwargs1 = py::dict("a"_a=1);
auto kwargs2 = py::dict("c"_a=3, "d"_a=4);
return f("positional", *py::make_tuple(1), 2, *py::make_tuple(3, 4), 5,
"key"_a="value", **kwargs1, "b"_a=2, **kwargs2, "e"_a=5);
});
m.def("test_unpacking_error1", [](py::function f) {
auto kwargs = py::dict("x"_a=3);
return f("x"_a=1, "y"_a=2, **kwargs); // duplicate ** after keyword
});
m.def("test_unpacking_error2", [](py::function f) {
auto kwargs = py::dict("x"_a=3);
return f(**kwargs, "x"_a=1); // duplicate keyword after **
});
m.def("test_arg_conversion_error1", [](py::function f) {
f(234, UnregisteredType(), "kw"_a=567);
});
m.def("test_arg_conversion_error2", [](py::function f) {
f(234, "expected_name"_a=UnregisteredType(), "kw"_a=567);
});
// test_lambda_closure_cleanup
struct Payload {
Payload() { print_default_created(this); }
~Payload() { print_destroyed(this); }
Payload(const Payload &) { print_copy_created(this); }
Payload(Payload &&) { print_move_created(this); }
};
// Export the payload constructor statistics for testing purposes:
m.def("payload_cstats", &ConstructorStats::get<Payload>);
/* Test cleanup of lambda closure */
m.def("test_cleanup", []() -> std::function<void(void)> {
Payload p;
return [p]() {
/* p should be cleaned up when the returned function is garbage collected */
(void) p;
};
});
// test_cpp_function_roundtrip
/* Test if passing a function pointer from C++ -> Python -> C++ yields the original pointer */
m.def("dummy_function", &dummy_function);
m.def("dummy_function2", [](int i, int j) { return i + j; });
m.def("roundtrip", [](std::function<int(int)> f, bool expect_none = false) {
if (expect_none && f)
throw std::runtime_error("Expected None to be converted to empty std::function");
return f;
}, py::arg("f"), py::arg("expect_none")=false);
m.def("test_dummy_function", [](const std::function<int(int)> &f) -> std::string {
using fn_type = int (*)(int);
auto result = f.target<fn_type>();
if (!result) {
auto r = f(1);
return "can't convert to function pointer: eval(1) = " + std::to_string(r);
} else if (*result == dummy_function) {
auto r = (*result)(1);
return "matches dummy_function: eval(1) = " + std::to_string(r);
} else {
return "argument does NOT match dummy_function. This should never happen!";
}
});
class AbstractBase { public: virtual unsigned int func() = 0; };
m.def("func_accepting_func_accepting_base", [](std::function<double(AbstractBase&)>) { });
struct MovableObject {
bool valid = true;
MovableObject() = default;
MovableObject(const MovableObject &) = default;
MovableObject &operator=(const MovableObject &) = default;
MovableObject(MovableObject &&o) : valid(o.valid) { o.valid = false; }
MovableObject &operator=(MovableObject &&o) {
valid = o.valid;
o.valid = false;
return *this;
}
};
py::class_<MovableObject>(m, "MovableObject");
// test_movable_object
m.def("callback_with_movable", [](std::function<void(MovableObject &)> f) {
auto x = MovableObject();
f(x); // lvalue reference shouldn't move out object
return x.valid; // must still return `true`
});
// test_bound_method_callback
struct CppBoundMethodTest {};
py::class_<CppBoundMethodTest>(m, "CppBoundMethodTest")
.def(py::init<>())
.def("triple", [](CppBoundMethodTest &, int val) { return 3 * val; });
// test async Python callbacks
using callback_f = std::function<void(int)>;
m.def("test_async_callback", [](callback_f f, py::list work) {
// make detached thread that calls `f` with piece of work after a little delay
auto start_f = [f](int j) {
auto invoke_f = [f, j] {
std::this_thread::sleep_for(std::chrono::milliseconds(50));
f(j);
};
auto t = std::thread(std::move(invoke_f));
t.detach();
};
// spawn worker threads
for (auto i : work)
start_f(py::cast<int>(i));
});
}

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import pytest
from pybind11_tests import callbacks as m
from threading import Thread
def test_callbacks():
from functools import partial
def func1():
return "func1"
def func2(a, b, c, d):
return "func2", a, b, c, d
def func3(a):
return "func3({})".format(a)
assert m.test_callback1(func1) == "func1"
assert m.test_callback2(func2) == ("func2", "Hello", "x", True, 5)
assert m.test_callback1(partial(func2, 1, 2, 3, 4)) == ("func2", 1, 2, 3, 4)
assert m.test_callback1(partial(func3, "partial")) == "func3(partial)"
assert m.test_callback3(lambda i: i + 1) == "func(43) = 44"
f = m.test_callback4()
assert f(43) == 44
f = m.test_callback5()
assert f(number=43) == 44
def test_bound_method_callback():
# Bound Python method:
class MyClass:
def double(self, val):
return 2 * val
z = MyClass()
assert m.test_callback3(z.double) == "func(43) = 86"
z = m.CppBoundMethodTest()
assert m.test_callback3(z.triple) == "func(43) = 129"
def test_keyword_args_and_generalized_unpacking():
def f(*args, **kwargs):
return args, kwargs
assert m.test_tuple_unpacking(f) == (("positional", 1, 2, 3, 4, 5, 6), {})
assert m.test_dict_unpacking(f) == (("positional", 1), {"key": "value", "a": 1, "b": 2})
assert m.test_keyword_args(f) == ((), {"x": 10, "y": 20})
assert m.test_unpacking_and_keywords1(f) == ((1, 2), {"c": 3, "d": 4})
assert m.test_unpacking_and_keywords2(f) == (
("positional", 1, 2, 3, 4, 5),
{"key": "value", "a": 1, "b": 2, "c": 3, "d": 4, "e": 5}
)
with pytest.raises(TypeError) as excinfo:
m.test_unpacking_error1(f)
assert "Got multiple values for keyword argument" in str(excinfo.value)
with pytest.raises(TypeError) as excinfo:
m.test_unpacking_error2(f)
assert "Got multiple values for keyword argument" in str(excinfo.value)
with pytest.raises(RuntimeError) as excinfo:
m.test_arg_conversion_error1(f)
assert "Unable to convert call argument" in str(excinfo.value)
with pytest.raises(RuntimeError) as excinfo:
m.test_arg_conversion_error2(f)
assert "Unable to convert call argument" in str(excinfo.value)
def test_lambda_closure_cleanup():
m.test_cleanup()
cstats = m.payload_cstats()
assert cstats.alive() == 0
assert cstats.copy_constructions == 1
assert cstats.move_constructions >= 1
def test_cpp_function_roundtrip():
"""Test if passing a function pointer from C++ -> Python -> C++ yields the original pointer"""
assert m.test_dummy_function(m.dummy_function) == "matches dummy_function: eval(1) = 2"
assert (m.test_dummy_function(m.roundtrip(m.dummy_function)) ==
"matches dummy_function: eval(1) = 2")
assert m.roundtrip(None, expect_none=True) is None
assert (m.test_dummy_function(lambda x: x + 2) ==
"can't convert to function pointer: eval(1) = 3")
with pytest.raises(TypeError) as excinfo:
m.test_dummy_function(m.dummy_function2)
assert "incompatible function arguments" in str(excinfo.value)
with pytest.raises(TypeError) as excinfo:
m.test_dummy_function(lambda x, y: x + y)
assert any(s in str(excinfo.value) for s in ("missing 1 required positional argument",
"takes exactly 2 arguments"))
def test_function_signatures(doc):
assert doc(m.test_callback3) == "test_callback3(arg0: Callable[[int], int]) -> str"
assert doc(m.test_callback4) == "test_callback4() -> Callable[[int], int]"
def test_movable_object():
assert m.callback_with_movable(lambda _: None) is True
def test_async_callbacks():
# serves as state for async callback
class Item:
def __init__(self, value):
self.value = value
res = []
# generate stateful lambda that will store result in `res`
def gen_f():
s = Item(3)
return lambda j: res.append(s.value + j)
# do some work async
work = [1, 2, 3, 4]
m.test_async_callback(gen_f(), work)
# wait until work is done
from time import sleep
sleep(0.5)
assert sum(res) == sum([x + 3 for x in work])
def test_async_async_callbacks():
t = Thread(target=test_async_callbacks)
t.start()
t.join()

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/*
tests/test_chrono.cpp -- test conversions to/from std::chrono types
Copyright (c) 2016 Trent Houliston <trent@houliston.me> and
Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#include "pybind11_tests.h"
#include <pybind11/chrono.h>
TEST_SUBMODULE(chrono, m) {
using system_time = std::chrono::system_clock::time_point;
using steady_time = std::chrono::steady_clock::time_point;
// test_chrono_system_clock
// Return the current time off the wall clock
m.def("test_chrono1", []() { return std::chrono::system_clock::now(); });
// test_chrono_system_clock_roundtrip
// Round trip the passed in system clock time
m.def("test_chrono2", [](system_time t) { return t; });
// test_chrono_duration_roundtrip
// Round trip the passed in duration
m.def("test_chrono3", [](std::chrono::system_clock::duration d) { return d; });
// test_chrono_duration_subtraction_equivalence
// Difference between two passed in time_points
m.def("test_chrono4", [](system_time a, system_time b) { return a - b; });
// test_chrono_steady_clock
// Return the current time off the steady_clock
m.def("test_chrono5", []() { return std::chrono::steady_clock::now(); });
// test_chrono_steady_clock_roundtrip
// Round trip a steady clock timepoint
m.def("test_chrono6", [](steady_time t) { return t; });
// test_floating_point_duration
// Roundtrip a duration in microseconds from a float argument
m.def("test_chrono7", [](std::chrono::microseconds t) { return t; });
// Float durations (issue #719)
m.def("test_chrono_float_diff", [](std::chrono::duration<float> a, std::chrono::duration<float> b) {
return a - b; });
}

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from pybind11_tests import chrono as m
import datetime
def test_chrono_system_clock():
# Get the time from both c++ and datetime
date1 = m.test_chrono1()
date2 = datetime.datetime.today()
# The returned value should be a datetime
assert isinstance(date1, datetime.datetime)
# The numbers should vary by a very small amount (time it took to execute)
diff = abs(date1 - date2)
# There should never be a days/seconds difference
assert diff.days == 0
assert diff.seconds == 0
# We test that no more than about 0.5 seconds passes here
# This makes sure that the dates created are very close to the same
# but if the testing system is incredibly overloaded this should still pass
assert diff.microseconds < 500000
def test_chrono_system_clock_roundtrip():
date1 = datetime.datetime.today()
# Roundtrip the time
date2 = m.test_chrono2(date1)
# The returned value should be a datetime
assert isinstance(date2, datetime.datetime)
# They should be identical (no information lost on roundtrip)
diff = abs(date1 - date2)
assert diff.days == 0
assert diff.seconds == 0
assert diff.microseconds == 0
def test_chrono_duration_roundtrip():
# Get the difference between two times (a timedelta)
date1 = datetime.datetime.today()
date2 = datetime.datetime.today()
diff = date2 - date1
# Make sure this is a timedelta
assert isinstance(diff, datetime.timedelta)
cpp_diff = m.test_chrono3(diff)
assert cpp_diff.days == diff.days
assert cpp_diff.seconds == diff.seconds
assert cpp_diff.microseconds == diff.microseconds
def test_chrono_duration_subtraction_equivalence():
date1 = datetime.datetime.today()
date2 = datetime.datetime.today()
diff = date2 - date1
cpp_diff = m.test_chrono4(date2, date1)
assert cpp_diff.days == diff.days
assert cpp_diff.seconds == diff.seconds
assert cpp_diff.microseconds == diff.microseconds
def test_chrono_steady_clock():
time1 = m.test_chrono5()
assert isinstance(time1, datetime.timedelta)
def test_chrono_steady_clock_roundtrip():
time1 = datetime.timedelta(days=10, seconds=10, microseconds=100)
time2 = m.test_chrono6(time1)
assert isinstance(time2, datetime.timedelta)
# They should be identical (no information lost on roundtrip)
assert time1.days == time2.days
assert time1.seconds == time2.seconds
assert time1.microseconds == time2.microseconds
def test_floating_point_duration():
# Test using a floating point number in seconds
time = m.test_chrono7(35.525123)
assert isinstance(time, datetime.timedelta)
assert time.seconds == 35
assert 525122 <= time.microseconds <= 525123
diff = m.test_chrono_float_diff(43.789012, 1.123456)
assert diff.seconds == 42
assert 665556 <= diff.microseconds <= 665557

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/*
tests/test_class.cpp -- test py::class_ definitions and basic functionality
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
*/
#include "pybind11_tests.h"
#include "constructor_stats.h"
#include "local_bindings.h"
#include <pybind11/stl.h>
#if defined(_MSC_VER)
# pragma warning(disable: 4324) // warning C4324: structure was padded due to alignment specifier
#endif
// test_brace_initialization
struct NoBraceInitialization {
NoBraceInitialization(std::vector<int> v) : vec{std::move(v)} {}
template <typename T>
NoBraceInitialization(std::initializer_list<T> l) : vec(l) {}
std::vector<int> vec;
};
TEST_SUBMODULE(class_, m) {
// test_instance
struct NoConstructor {
NoConstructor() = default;
NoConstructor(const NoConstructor &) = default;
NoConstructor(NoConstructor &&) = default;
static NoConstructor *new_instance() {
auto *ptr = new NoConstructor();
print_created(ptr, "via new_instance");
return ptr;
}
~NoConstructor() { print_destroyed(this); }
};
py::class_<NoConstructor>(m, "NoConstructor")
.def_static("new_instance", &NoConstructor::new_instance, "Return an instance");
// test_inheritance
class Pet {
public:
Pet(const std::string &name, const std::string &species)
: m_name(name), m_species(species) {}
std::string name() const { return m_name; }
std::string species() const { return m_species; }
private:
std::string m_name;
std::string m_species;
};
class Dog : public Pet {
public:
Dog(const std::string &name) : Pet(name, "dog") {}
std::string bark() const { return "Woof!"; }
};
class Rabbit : public Pet {
public:
Rabbit(const std::string &name) : Pet(name, "parrot") {}
};
class Hamster : public Pet {
public:
Hamster(const std::string &name) : Pet(name, "rodent") {}
};
class Chimera : public Pet {
Chimera() : Pet("Kimmy", "chimera") {}
};
py::class_<Pet> pet_class(m, "Pet");
pet_class
.def(py::init<std::string, std::string>())
.def("name", &Pet::name)
.def("species", &Pet::species);
/* One way of declaring a subclass relationship: reference parent's class_ object */
py::class_<Dog>(m, "Dog", pet_class)
.def(py::init<std::string>());
/* Another way of declaring a subclass relationship: reference parent's C++ type */
py::class_<Rabbit, Pet>(m, "Rabbit")
.def(py::init<std::string>());
/* And another: list parent in class template arguments */
py::class_<Hamster, Pet>(m, "Hamster")
.def(py::init<std::string>());
/* Constructors are not inherited by default */
py::class_<Chimera, Pet>(m, "Chimera");
m.def("pet_name_species", [](const Pet &pet) { return pet.name() + " is a " + pet.species(); });
m.def("dog_bark", [](const Dog &dog) { return dog.bark(); });
// test_automatic_upcasting
struct BaseClass {
BaseClass() = default;
BaseClass(const BaseClass &) = default;
BaseClass(BaseClass &&) = default;
virtual ~BaseClass() {}
};
struct DerivedClass1 : BaseClass { };
struct DerivedClass2 : BaseClass { };
py::class_<BaseClass>(m, "BaseClass").def(py::init<>());
py::class_<DerivedClass1>(m, "DerivedClass1").def(py::init<>());
py::class_<DerivedClass2>(m, "DerivedClass2").def(py::init<>());
m.def("return_class_1", []() -> BaseClass* { return new DerivedClass1(); });
m.def("return_class_2", []() -> BaseClass* { return new DerivedClass2(); });
m.def("return_class_n", [](int n) -> BaseClass* {
if (n == 1) return new DerivedClass1();
if (n == 2) return new DerivedClass2();
return new BaseClass();
});
m.def("return_none", []() -> BaseClass* { return nullptr; });
// test_isinstance
m.def("check_instances", [](py::list l) {
return py::make_tuple(
py::isinstance<py::tuple>(l[0]),
py::isinstance<py::dict>(l[1]),
py::isinstance<Pet>(l[2]),
py::isinstance<Pet>(l[3]),
py::isinstance<Dog>(l[4]),
py::isinstance<Rabbit>(l[5]),
py::isinstance<UnregisteredType>(l[6])
);
});
// test_mismatched_holder
struct MismatchBase1 { };
struct MismatchDerived1 : MismatchBase1 { };
struct MismatchBase2 { };
struct MismatchDerived2 : MismatchBase2 { };
m.def("mismatched_holder_1", []() {
auto mod = py::module::import("__main__");
py::class_<MismatchBase1, std::shared_ptr<MismatchBase1>>(mod, "MismatchBase1");
py::class_<MismatchDerived1, MismatchBase1>(mod, "MismatchDerived1");
});
m.def("mismatched_holder_2", []() {
auto mod = py::module::import("__main__");
py::class_<MismatchBase2>(mod, "MismatchBase2");
py::class_<MismatchDerived2, std::shared_ptr<MismatchDerived2>,
MismatchBase2>(mod, "MismatchDerived2");
});
// test_override_static
// #511: problem with inheritance + overwritten def_static
struct MyBase {
static std::unique_ptr<MyBase> make() {
return std::unique_ptr<MyBase>(new MyBase());
}
};
struct MyDerived : MyBase {
static std::unique_ptr<MyDerived> make() {
return std::unique_ptr<MyDerived>(new MyDerived());
}
};
py::class_<MyBase>(m, "MyBase")
.def_static("make", &MyBase::make);
py::class_<MyDerived, MyBase>(m, "MyDerived")
.def_static("make", &MyDerived::make)
.def_static("make2", &MyDerived::make);
// test_implicit_conversion_life_support
struct ConvertibleFromUserType {
int i;
ConvertibleFromUserType(UserType u) : i(u.value()) { }
};
py::class_<ConvertibleFromUserType>(m, "AcceptsUserType")
.def(py::init<UserType>());
py::implicitly_convertible<UserType, ConvertibleFromUserType>();
m.def("implicitly_convert_argument", [](const ConvertibleFromUserType &r) { return r.i; });
m.def("implicitly_convert_variable", [](py::object o) {
// `o` is `UserType` and `r` is a reference to a temporary created by implicit
// conversion. This is valid when called inside a bound function because the temp
// object is attached to the same life support system as the arguments.
const auto &r = o.cast<const ConvertibleFromUserType &>();
return r.i;
});
m.add_object("implicitly_convert_variable_fail", [&] {
auto f = [](PyObject *, PyObject *args) -> PyObject * {
auto o = py::reinterpret_borrow<py::tuple>(args)[0];
try { // It should fail here because there is no life support.
o.cast<const ConvertibleFromUserType &>();
} catch (const py::cast_error &e) {
return py::str(e.what()).release().ptr();
}
return py::str().release().ptr();
};
auto def = new PyMethodDef{"f", f, METH_VARARGS, nullptr};
return py::reinterpret_steal<py::object>(PyCFunction_NewEx(def, nullptr, m.ptr()));
}());
// test_operator_new_delete
struct HasOpNewDel {
std::uint64_t i;
static void *operator new(size_t s) { py::print("A new", s); return ::operator new(s); }
static void *operator new(size_t s, void *ptr) { py::print("A placement-new", s); return ptr; }
static void operator delete(void *p) { py::print("A delete"); return ::operator delete(p); }
};
struct HasOpNewDelSize {
std::uint32_t i;
static void *operator new(size_t s) { py::print("B new", s); return ::operator new(s); }
static void *operator new(size_t s, void *ptr) { py::print("B placement-new", s); return ptr; }
static void operator delete(void *p, size_t s) { py::print("B delete", s); return ::operator delete(p); }
};
struct AliasedHasOpNewDelSize {
std::uint64_t i;
static void *operator new(size_t s) { py::print("C new", s); return ::operator new(s); }
static void *operator new(size_t s, void *ptr) { py::print("C placement-new", s); return ptr; }
static void operator delete(void *p, size_t s) { py::print("C delete", s); return ::operator delete(p); }
virtual ~AliasedHasOpNewDelSize() = default;
};
struct PyAliasedHasOpNewDelSize : AliasedHasOpNewDelSize {
PyAliasedHasOpNewDelSize() = default;
PyAliasedHasOpNewDelSize(int) { }
std::uint64_t j;
};
struct HasOpNewDelBoth {
std::uint32_t i[8];
static void *operator new(size_t s) { py::print("D new", s); return ::operator new(s); }
static void *operator new(size_t s, void *ptr) { py::print("D placement-new", s); return ptr; }
static void operator delete(void *p) { py::print("D delete"); return ::operator delete(p); }
static void operator delete(void *p, size_t s) { py::print("D wrong delete", s); return ::operator delete(p); }
};
py::class_<HasOpNewDel>(m, "HasOpNewDel").def(py::init<>());
py::class_<HasOpNewDelSize>(m, "HasOpNewDelSize").def(py::init<>());
py::class_<HasOpNewDelBoth>(m, "HasOpNewDelBoth").def(py::init<>());
py::class_<AliasedHasOpNewDelSize, PyAliasedHasOpNewDelSize> aliased(m, "AliasedHasOpNewDelSize");
aliased.def(py::init<>());
aliased.attr("size_noalias") = py::int_(sizeof(AliasedHasOpNewDelSize));
aliased.attr("size_alias") = py::int_(sizeof(PyAliasedHasOpNewDelSize));
// This test is actually part of test_local_bindings (test_duplicate_local), but we need a
// definition in a different compilation unit within the same module:
bind_local<LocalExternal, 17>(m, "LocalExternal", py::module_local());
// test_bind_protected_functions
class ProtectedA {
protected:
int foo() const { return value; }
private:
int value = 42;
};
class PublicistA : public ProtectedA {
public:
using ProtectedA::foo;
};
py::class_<ProtectedA>(m, "ProtectedA")
.def(py::init<>())
#if !defined(_MSC_VER) || _MSC_VER >= 1910
.def("foo", &PublicistA::foo);
#else
.def("foo", static_cast<int (ProtectedA::*)() const>(&PublicistA::foo));
#endif
class ProtectedB {
public:
virtual ~ProtectedB() = default;
protected:
virtual int foo() const { return value; }
private:
int value = 42;
};
class TrampolineB : public ProtectedB {
public:
int foo() const override { PYBIND11_OVERLOAD(int, ProtectedB, foo, ); }
};
class PublicistB : public ProtectedB {
public:
using ProtectedB::foo;
};
py::class_<ProtectedB, TrampolineB>(m, "ProtectedB")
.def(py::init<>())
#if !defined(_MSC_VER) || _MSC_VER >= 1910
.def("foo", &PublicistB::foo);
#else
.def("foo", static_cast<int (ProtectedB::*)() const>(&PublicistB::foo));
#endif
// test_brace_initialization
struct BraceInitialization {
int field1;
std::string field2;
};
py::class_<BraceInitialization>(m, "BraceInitialization")
.def(py::init<int, const std::string &>())
.def_readwrite("field1", &BraceInitialization::field1)
.def_readwrite("field2", &BraceInitialization::field2);
// We *don't* want to construct using braces when the given constructor argument maps to a
// constructor, because brace initialization could go to the wrong place (in particular when
// there is also an `initializer_list<T>`-accept constructor):
py::class_<NoBraceInitialization>(m, "NoBraceInitialization")
.def(py::init<std::vector<int>>())
.def_readonly("vec", &NoBraceInitialization::vec);
// test_reentrant_implicit_conversion_failure
// #1035: issue with runaway reentrant implicit conversion
struct BogusImplicitConversion {
BogusImplicitConversion(const BogusImplicitConversion &) { }
};
py::class_<BogusImplicitConversion>(m, "BogusImplicitConversion")
.def(py::init<const BogusImplicitConversion &>());
py::implicitly_convertible<int, BogusImplicitConversion>();
// test_qualname
// #1166: nested class docstring doesn't show nested name
// Also related: tests that __qualname__ is set properly
struct NestBase {};
struct Nested {};
py::class_<NestBase> base(m, "NestBase");
base.def(py::init<>());
py::class_<Nested>(base, "Nested")
.def(py::init<>())
.def("fn", [](Nested &, int, NestBase &, Nested &) {})
.def("fa", [](Nested &, int, NestBase &, Nested &) {},
"a"_a, "b"_a, "c"_a);
base.def("g", [](NestBase &, Nested &) {});
base.def("h", []() { return NestBase(); });
// test_error_after_conversion
// The second-pass path through dispatcher() previously didn't
// remember which overload was used, and would crash trying to
// generate a useful error message
struct NotRegistered {};
struct StringWrapper { std::string str; };
m.def("test_error_after_conversions", [](int) {});
m.def("test_error_after_conversions",
[](StringWrapper) -> NotRegistered { return {}; });
py::class_<StringWrapper>(m, "StringWrapper").def(py::init<std::string>());
py::implicitly_convertible<std::string, StringWrapper>();
#if defined(PYBIND11_CPP17)
struct alignas(1024) Aligned {
std::uintptr_t ptr() const { return (uintptr_t) this; }
};
py::class_<Aligned>(m, "Aligned")
.def(py::init<>())
.def("ptr", &Aligned::ptr);
#endif
}
template <int N> class BreaksBase { public: virtual ~BreaksBase() = default; };
template <int N> class BreaksTramp : public BreaksBase<N> {};
// These should all compile just fine:
typedef py::class_<BreaksBase<1>, std::unique_ptr<BreaksBase<1>>, BreaksTramp<1>> DoesntBreak1;
typedef py::class_<BreaksBase<2>, BreaksTramp<2>, std::unique_ptr<BreaksBase<2>>> DoesntBreak2;
typedef py::class_<BreaksBase<3>, std::unique_ptr<BreaksBase<3>>> DoesntBreak3;
typedef py::class_<BreaksBase<4>, BreaksTramp<4>> DoesntBreak4;
typedef py::class_<BreaksBase<5>> DoesntBreak5;
typedef py::class_<BreaksBase<6>, std::shared_ptr<BreaksBase<6>>, BreaksTramp<6>> DoesntBreak6;
typedef py::class_<BreaksBase<7>, BreaksTramp<7>, std::shared_ptr<BreaksBase<7>>> DoesntBreak7;
typedef py::class_<BreaksBase<8>, std::shared_ptr<BreaksBase<8>>> DoesntBreak8;
#define CHECK_BASE(N) static_assert(std::is_same<typename DoesntBreak##N::type, BreaksBase<N>>::value, \
"DoesntBreak" #N " has wrong type!")
CHECK_BASE(1); CHECK_BASE(2); CHECK_BASE(3); CHECK_BASE(4); CHECK_BASE(5); CHECK_BASE(6); CHECK_BASE(7); CHECK_BASE(8);
#define CHECK_ALIAS(N) static_assert(DoesntBreak##N::has_alias && std::is_same<typename DoesntBreak##N::type_alias, BreaksTramp<N>>::value, \
"DoesntBreak" #N " has wrong type_alias!")
#define CHECK_NOALIAS(N) static_assert(!DoesntBreak##N::has_alias && std::is_void<typename DoesntBreak##N::type_alias>::value, \
"DoesntBreak" #N " has type alias, but shouldn't!")
CHECK_ALIAS(1); CHECK_ALIAS(2); CHECK_NOALIAS(3); CHECK_ALIAS(4); CHECK_NOALIAS(5); CHECK_ALIAS(6); CHECK_ALIAS(7); CHECK_NOALIAS(8);
#define CHECK_HOLDER(N, TYPE) static_assert(std::is_same<typename DoesntBreak##N::holder_type, std::TYPE##_ptr<BreaksBase<N>>>::value, \
"DoesntBreak" #N " has wrong holder_type!")
CHECK_HOLDER(1, unique); CHECK_HOLDER(2, unique); CHECK_HOLDER(3, unique); CHECK_HOLDER(4, unique); CHECK_HOLDER(5, unique);
CHECK_HOLDER(6, shared); CHECK_HOLDER(7, shared); CHECK_HOLDER(8, shared);
// There's no nice way to test that these fail because they fail to compile; leave them here,
// though, so that they can be manually tested by uncommenting them (and seeing that compilation
// failures occurs).
// We have to actually look into the type: the typedef alone isn't enough to instantiate the type:
#define CHECK_BROKEN(N) static_assert(std::is_same<typename Breaks##N::type, BreaksBase<-N>>::value, \
"Breaks1 has wrong type!");
//// Two holder classes:
//typedef py::class_<BreaksBase<-1>, std::unique_ptr<BreaksBase<-1>>, std::unique_ptr<BreaksBase<-1>>> Breaks1;
//CHECK_BROKEN(1);
//// Two aliases:
//typedef py::class_<BreaksBase<-2>, BreaksTramp<-2>, BreaksTramp<-2>> Breaks2;
//CHECK_BROKEN(2);
//// Holder + 2 aliases
//typedef py::class_<BreaksBase<-3>, std::unique_ptr<BreaksBase<-3>>, BreaksTramp<-3>, BreaksTramp<-3>> Breaks3;
//CHECK_BROKEN(3);
//// Alias + 2 holders
//typedef py::class_<BreaksBase<-4>, std::unique_ptr<BreaksBase<-4>>, BreaksTramp<-4>, std::shared_ptr<BreaksBase<-4>>> Breaks4;
//CHECK_BROKEN(4);
//// Invalid option (not a subclass or holder)
//typedef py::class_<BreaksBase<-5>, BreaksTramp<-4>> Breaks5;
//CHECK_BROKEN(5);
//// Invalid option: multiple inheritance not supported:
//template <> struct BreaksBase<-8> : BreaksBase<-6>, BreaksBase<-7> {};
//typedef py::class_<BreaksBase<-8>, BreaksBase<-6>, BreaksBase<-7>> Breaks8;
//CHECK_BROKEN(8);

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