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Remove old wrap

release/4.3a0
Fan Jiang 2020-08-17 14:44:14 -04:00
parent b0d100b8fb
commit c7eb02969a
285 changed files with 0 additions and 54751 deletions
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316
wrap/Argument.cpp
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@ -1,316 +0,0 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Argument.ccp
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#include "Argument.h"
#include "Class.h"
#include <boost/lexical_cast.hpp>
#include <iostream>
#include <fstream>
#include <sstream>
using namespace std;
using namespace wrap;
/* ************************************************************************* */
Argument Argument::expandTemplate(const TemplateSubstitution& ts) const {
Argument instArg = *this;
instArg.type = ts.tryToSubstitite(type);
return instArg;
}
/* ************************************************************************* */
ArgumentList ArgumentList::expandTemplate(
const TemplateSubstitution& ts) const {
ArgumentList instArgList;
for(const Argument& arg: *this) {
Argument instArg = arg.expandTemplate(ts);
instArgList.push_back(instArg);
}
return instArgList;
}
/* ************************************************************************* */
string Argument::matlabClass(const string& delim) const {
string result;
for(const string& ns: type.namespaces())
result += ns + delim;
if (type.name() == "string" || type.name() == "unsigned char"
|| type.name() == "char")
return result + "char";
if (type.name() == "Vector" || type.name() == "Matrix")
return result + "double";
if (type.name() == "int" || type.name() == "size_t")
return result + "numeric";
if (type.name() == "bool")
return result + "logical";
return result + type.name();
}
/* ************************************************************************* */
void Argument::matlab_unwrap(FileWriter& file, const string& matlabName) const {
file.oss << " ";
string cppType = type.qualifiedName("::");
string matlabUniqueType = type.qualifiedName();
bool isNotScalar = !type.isScalar();
// We cannot handle scalar non const references
if (!isNotScalar && is_ref && !is_const) {
throw std::runtime_error("Cannot unwrap a scalar non-const reference");
}
if (is_ptr && type.category != Qualified::EIGEN)
// A pointer: emit an "unwrap_shared_ptr" call which returns a pointer
file.oss << "boost::shared_ptr<" << cppType << "> " << name
<< " = unwrap_shared_ptr< ";
else if (is_ref && isNotScalar && type.category != Qualified::EIGEN)
// A reference: emit an "unwrap_shared_ptr" call and de-reference the pointer
file.oss << cppType << "& " << name << " = *unwrap_shared_ptr< ";
else
// Not a pointer, or a reference to a scalar type. Therefore, emit an "unwrap" call
// unwrap is specified in matlab.h as a series of template specializations
// that know how to unpack the expected MATLAB object
// example: double tol = unwrap< double >(in[2]);
// example: Vector v = unwrap< Vector >(in[1]);
file.oss << cppType << " " << name << " = unwrap< ";
file.oss << cppType << " >(" << matlabName;
if( (is_ptr || is_ref) && isNotScalar && type.category != Qualified::EIGEN)
file.oss << ", \"ptr_" << matlabUniqueType << "\"";
file.oss << ");" << endl;
}
/* ************************************************************************* */
void Argument::proxy_check(FileWriter& proxyFile, const string& s) const {
proxyFile.oss << "isa(" << s << ",'" << matlabClass(".") << "')";
if (type.name() == "Vector")
proxyFile.oss << " && size(" << s << ",2)==1";
}
/* ************************************************************************* */
void Argument::emit_cython_pxd(
FileWriter& file, const std::string& className,
const std::vector<std::string>& templateArgs) const {
string cythonType = type.pxdClassName();
if (cythonType == "This") cythonType = className;
else if (type.isEigen())
cythonType = "const " + cythonType + "&";
else if (type.match(templateArgs))
cythonType = type.name();
// add modifier
if (!type.isEigen()) {
if (is_ptr) cythonType = "shared_ptr[" + cythonType + "]&";
if (is_ref) cythonType = cythonType + "&";
if (is_const) cythonType = "const " + cythonType;
}
file.oss << cythonType << " " << name;
}
/* ************************************************************************* */
void Argument::emit_cython_pyx(FileWriter& file) const {
file.oss << type.pyxArgumentType() << " " << name;
}
/* ************************************************************************* */
std::string Argument::pyx_convertEigenTypeAndStorageOrder() const {
if (!type.isEigen())
return "";
return name + " = " + name + ".astype(float, order=\'F\', copy=False)";
}
/* ************************************************************************* */
std::string Argument::pyx_asParam() const {
string cythonType = type.pxdClassName();
string cythonVar;
if (type.isNonBasicType()) {
cythonVar = name + "." + type.shared_pxd_obj_in_pyx();
if (!is_ptr) cythonVar = "deref(" + cythonVar + ")";
} else if (type.isEigen()) {
cythonVar = "<" + cythonType + ">" + "(Map[" + cythonType + "](" + name + "))";
} else {
cythonVar = name;
}
return cythonVar;
}
/* ************************************************************************* */
string ArgumentList::types() const {
string str;
bool first = true;
for(Argument arg: *this) {
if (!first)
str += ",";
str += arg.type.name();
first = false;
}
return str;
}
/* ************************************************************************* */
string ArgumentList::signature() const {
string sig;
bool cap = false;
for(Argument arg: *this) {
for(char ch: arg.type.name())
if (isupper(ch)) {
sig += ch;
//If there is a capital letter, we don't want to read it below
cap = true;
}
if (!cap)
sig += arg.type.name()[0];
//Reset to default
cap = false;
}
return sig;
}
/* ************************************************************************* */
string ArgumentList::names() const {
string str;
bool first = true;
for(Argument arg: *this) {
if (!first)
str += ",";
str += arg.name;
first = false;
}
return str;
}
/* ************************************************************************* */
bool ArgumentList::allScalar() const {
for(Argument arg: *this)
if (!arg.type.isScalar())
return false;
return true;
}
/* ************************************************************************* */
void ArgumentList::matlab_unwrap(FileWriter& file, int start) const {
int index = start;
for(Argument arg: *this) {
stringstream buf;
buf << "in[" << index << "]";
arg.matlab_unwrap(file, buf.str());
index++;
}
}
/* ************************************************************************* */
void ArgumentList::emit_prototype(FileWriter& file, const string& name) const {
file.oss << name << "(";
bool first = true;
for(Argument arg: *this) {
if (!first)
file.oss << ", ";
file.oss << arg.type.name() << " " << arg.name;
first = false;
}
file.oss << ")";
}
/* ************************************************************************* */
void ArgumentList::emit_cython_pxd(
FileWriter& file, const std::string& className,
const std::vector<std::string>& templateArgs) const {
for (size_t j = 0; j<size(); ++j) {
at(j).emit_cython_pxd(file, className, templateArgs);
if (j<size()-1) file.oss << ", ";
}
}
/* ************************************************************************* */
void ArgumentList::emit_cython_pyx(FileWriter& file) const {
for (size_t j = 0; j < size(); ++j) {
at(j).emit_cython_pyx(file);
if (j < size() - 1) file.oss << ", ";
}
}
/* ************************************************************************* */
std::string ArgumentList::pyx_convertEigenTypeAndStorageOrder(const std::string& indent) const {
string ret, conversion;
for (size_t j = 0; j < size(); ++j) {
conversion = at(j).pyx_convertEigenTypeAndStorageOrder();
if (!conversion.empty())
ret += indent + conversion + "\n";
}
return ret;
}
/* ************************************************************************* */
std::string ArgumentList::pyx_asParams() const {
string ret;
for (size_t j = 0; j < size(); ++j) {
ret += at(j).pyx_asParam();
if (j < size() - 1) ret += ", ";
}
return ret;
}
/* ************************************************************************* */
std::string ArgumentList::pyx_paramsList() const {
string s;
for (size_t j = 0; j < size(); ++j) {
s += "'" + at(j).name + "'";
if (j < size() - 1) s += ", ";
}
return s;
}
/* ************************************************************************* */
std::string ArgumentList::pyx_castParamsToPythonType(
const std::string& indent) const {
if (size() == 0)
return "";
// cast params to their correct python argument type to pass in the function call later
string s;
for (size_t j = 0; j < size(); ++j)
s += indent + at(j).name + " = <" + at(j).type.pyxArgumentType()
+ ">(__params[" + std::to_string(j) + "])\n";
return s;
}
/* ************************************************************************* */
void ArgumentList::proxy_check(FileWriter& proxyFile) const {
// Check nr of arguments
proxyFile.oss << "if length(varargin) == " << size();
if (size() > 0)
proxyFile.oss << " && ";
// ...and their type.names
bool first = true;
for (size_t i = 0; i < size(); i++) {
if (!first)
proxyFile.oss << " && ";
string s = "varargin{" + boost::lexical_cast<string>(i + 1) + "}";
(*this)[i].proxy_check(proxyFile, s);
first = false;
}
proxyFile.oss << "\n";
}
/* ************************************************************************* */

242
wrap/Argument.h
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Argument.h
* @brief arguments to constructors and methods
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#pragma once
#include "TemplateSubstitution.h"
#include "FileWriter.h"
#include "ReturnValue.h"
namespace wrap {
/// Argument class
struct Argument {
Qualified type;
std::string name;
bool is_const, is_ref, is_ptr;
Argument() :
is_const(false), is_ref(false), is_ptr(false) {
}
Argument(const Qualified& t, const std::string& n) :
type(t), name(n), is_const(false), is_ref(false), is_ptr(false) {
}
bool isSameSignature(const Argument& other) const {
return type == other.type
&& is_const == other.is_const && is_ref == other.is_ref
&& is_ptr == other.is_ptr;
}
bool operator==(const Argument& other) const {
return type == other.type && name == other.name
&& is_const == other.is_const && is_ref == other.is_ref
&& is_ptr == other.is_ptr;
}
Argument expandTemplate(const TemplateSubstitution& ts) const;
/// return MATLAB class for use in isa(x,class)
std::string matlabClass(const std::string& delim = "") const;
/// MATLAB code generation, MATLAB to C++
void matlab_unwrap(FileWriter& file, const std::string& matlabName) const;
/**
* emit checking argument to MATLAB proxy
* @param proxyFile output stream
*/
void proxy_check(FileWriter& proxyFile, const std::string& s) const;
/**
* emit arguments for cython pxd
* @param file output stream
*/
void emit_cython_pxd(FileWriter& file, const std::string& className,
const std::vector<std::string>& templateArgs) const;
void emit_cython_pyx(FileWriter& file) const;
std::string pyx_asParam() const;
std::string pyx_convertEigenTypeAndStorageOrder() const;
friend std::ostream& operator<<(std::ostream& os, const Argument& arg) {
os << (arg.is_const ? "const " : "") << arg.type << (arg.is_ptr ? "*" : "")
<< (arg.is_ref ? "&" : "");
return os;
}
};
/// Argument list is just a container with Arguments
struct ArgumentList: public std::vector<Argument> {
/// create a comma-separated string listing all argument types (not used)
std::string types() const;
/// create a short "signature" string
std::string signature() const;
/// create a comma-separated string listing all argument names, used in m-files
std::string names() const;
/// Check if all arguments scalar
bool allScalar() const;
ArgumentList expandTemplate(const TemplateSubstitution& ts) const;
bool isSameSignature(const ArgumentList& other) const {
for(size_t i = 0; i<size(); ++i)
if (!at(i).isSameSignature(other[i])) return false;
return true;
}
// MATLAB code generation:
/**
* emit code to unwrap arguments
* @param file output stream
* @param start initial index for input array, set to 1 for method
*/
void matlab_unwrap(FileWriter& file, int start = 0) const; // MATLAB to C++
/**
* emit MATLAB prototype
* @param file output stream
* @param name of method or function
*/
void emit_prototype(FileWriter& file, const std::string& name) const;
/**
* emit arguments for cython pxd
* @param file output stream
*/
void emit_cython_pxd(FileWriter& file, const std::string& className,
const std::vector<std::string>& templateArgs) const;
void emit_cython_pyx(FileWriter& file) const;
std::string pyx_asParams() const;
std::string pyx_paramsList() const;
std::string pyx_castParamsToPythonType(const std::string& indent) const;
std::string pyx_convertEigenTypeAndStorageOrder(const std::string& indent) const;
/**
* emit checking arguments to MATLAB proxy
* @param proxyFile output stream
*/
void proxy_check(FileWriter& proxyFile) const;
/// Output stream operator
friend std::ostream& operator<<(std::ostream& os,
const ArgumentList& argList) {
os << "(";
if (argList.size() > 0)
os << argList.front();
if (argList.size() > 1)
for (size_t i = 1; i < argList.size(); i++)
os << ", " << argList[i];
os << ")";
return os;
}
};
/* ************************************************************************* */
// http://boost-spirit.com/distrib/spirit_1_8_2/libs/spirit/doc/grammar.html
struct ArgumentGrammar: public classic::grammar<ArgumentGrammar> {
wrap::Argument& result_; ///< successful parse will be placed in here
TypeGrammar argument_type_g; ///< Type parser for Argument::type
/// Construct type grammar and specify where result is placed
ArgumentGrammar(wrap::Argument& result) :
result_(result), argument_type_g(result.type) {
}
/// Definition of type grammar
template<typename ScannerT>
struct definition: BasicRules<ScannerT> {
typedef classic::rule<ScannerT> Rule;
Rule argument_p;
definition(ArgumentGrammar const& self) {
using namespace classic;
// NOTE: allows for pointers to all types
// Slightly more permissive than before on basis/eigen type qualification
// Also, currently parses Point2*&, can't make it work otherwise :-(
argument_p = !str_p("const")[assign_a(self.result_.is_const, T)] //
>> self.argument_type_g //
>> !ch_p('*')[assign_a(self.result_.is_ptr, T)]
>> !ch_p('&')[assign_a(self.result_.is_ref, T)]
>> BasicRules<ScannerT>::name_p[assign_a(self.result_.name)];
}
Rule const& start() const {
return argument_p;
}
};
};
// ArgumentGrammar
/* ************************************************************************* */
// http://boost-spirit.com/distrib/spirit_1_8_2/libs/spirit/doc/grammar.html
struct ArgumentListGrammar: public classic::grammar<ArgumentListGrammar> {
wrap::ArgumentList& result_; ///< successful parse will be placed in here
/// Construct type grammar and specify where result is placed
ArgumentListGrammar(wrap::ArgumentList& result) :
result_(result) {
}
/// Definition of type grammar
template<typename ScannerT>
struct definition {
const Argument arg0; ///< used to reset arg
Argument arg; ///< temporary argument for use during parsing
ArgumentGrammar argument_g; ///< single Argument parser
classic::rule<ScannerT> argument_p, argumentList_p;
definition(ArgumentListGrammar const& self) :
argument_g(arg) {
using namespace classic;
argument_p = argument_g //
[classic::push_back_a(self.result_, arg)] //
[assign_a(arg, arg0)];
argumentList_p = '(' >> !argument_p >> *(',' >> argument_p) >> ')';
}
classic::rule<ScannerT> const& start() const {
return argumentList_p;
}
};
};
// ArgumentListGrammar
/* ************************************************************************* */
}// \namespace wrap

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wrap/CMakeLists.txt
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# Build/install Wrap
set(WRAP_BOOST_LIBRARIES
Boost::system
Boost::filesystem
Boost::thread
)
# Allow for disabling serialization to handle errors related to Clang's linker
option(GTSAM_WRAP_SERIALIZATION "If enabled, allows for wrapped objects to be saved via boost.serialization" ON)
# Build the executable itself
file(GLOB wrap_srcs "*.cpp")
file(GLOB wrap_headers "*.h")
list(REMOVE_ITEM wrap_srcs ${CMAKE_CURRENT_SOURCE_DIR}/wrap.cpp)
add_library(wrap_lib STATIC ${wrap_srcs} ${wrap_headers})
target_include_directories(wrap_lib PUBLIC
$<BUILD_INTERFACE:${CMAKE_SOURCE_DIR}>
)
if (NOT GTSAM_WRAP_SERIALIZATION)
target_compile_definitions(wrap_lib PUBLIC -DWRAP_DISABLE_SERIALIZE)
endif()
# Apply build flags:
gtsam_apply_build_flags(wrap_lib)
target_link_libraries(wrap_lib ${WRAP_BOOST_LIBRARIES})
gtsam_assign_source_folders(${wrap_srcs} ${wrap_headers})
add_executable(wrap wrap.cpp)
target_link_libraries(wrap PRIVATE wrap_lib)
# Set folder in Visual Studio
file(RELATIVE_PATH relative_path "${PROJECT_SOURCE_DIR}" "${CMAKE_CURRENT_SOURCE_DIR}")
set_target_properties(wrap_lib wrap PROPERTIES FOLDER "${relative_path}")
# Install wrap binary and export target
install(TARGETS wrap EXPORT GTSAM-exports DESTINATION ${CMAKE_INSTALL_BINDIR})
list(APPEND GTSAM_EXPORTED_TARGETS wrap)
set(GTSAM_EXPORTED_TARGETS "${GTSAM_EXPORTED_TARGETS}" PARENT_SCOPE)
# Install matlab header
install(FILES matlab.h DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/wrap)
# Build tests
add_subdirectory(tests)

897
wrap/Class.cpp
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Class.cpp
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#include "Class.h"
#include "utilities.h"
#include "Argument.h"
#include <unordered_set>
#include <boost/lexical_cast.hpp>
#include <boost/range/adaptor/map.hpp>
#include <boost/range/algorithm/copy.hpp>
#include <boost/tuple/tuple.hpp>
#include <boost/iterator/zip_iterator.hpp>
#include <boost/range/combine.hpp>
#include <boost/range/algorithm/remove_if.hpp>
#include <vector>
#include <iostream>
#include <fstream>
#include <sstream>
#include <iterator> // std::ostream_iterator
//#include <cstdint> // on Linux GCC: fails with error regarding needing C++0x std flags
//#include <cinttypes> // same failure as above
#include <stdint.h> // works on Linux GCC
using namespace std;
using namespace wrap;
/* ************************************************************************* */
void Class::assignParent(const Qualified& parent) {
parentClass.reset(parent);
}
/* ************************************************************************* */
boost::optional<string> Class::qualifiedParent() const {
boost::optional<string> result = boost::none;
if (parentClass)
result = parentClass->qualifiedName("::");
return result;
}
/* ************************************************************************* */
static void handleException(const out_of_range& oor,
const Class::Methods& methods) {
cerr << "Class::method: key not found: " << oor.what() << ", methods are:\n";
using boost::adaptors::map_keys;
ostream_iterator<string> out_it(cerr, "\n");
boost::copy(methods | map_keys, out_it);
}
/* ************************************************************************* */
// Method& Class::mutableMethod(Str key) {
// try {
// return methods_.at(key);
// } catch (const out_of_range& oor) {
// handleException(oor, methods_);
// throw runtime_error("Internal error in wrap");
// }
// }
/* ************************************************************************* */
const Method& Class::method(Str key) const {
try {
return methods_.at(key);
} catch (const out_of_range& oor) {
handleException(oor, methods_);
throw runtime_error("Internal error in wrap");
}
}
/* ************************************************************************* */
void Class::matlab_proxy(Str toolboxPath, Str wrapperName,
const TypeAttributesTable& typeAttributes, FileWriter& wrapperFile,
vector<string>& functionNames) const {
// Create namespace folders
createNamespaceStructure(namespaces(), toolboxPath);
// open destination classFile
string classFile = matlabName(toolboxPath);
FileWriter proxyFile(classFile, verbose_, "%");
// get the name of actual matlab object
const string matlabQualName = qualifiedName(".");
const string matlabUniqueName = qualifiedName();
const string cppName = qualifiedName("::");
// emit class proxy code
// we want our class to inherit the handle class for memory purposes
const string parent =
parentClass ? parentClass->qualifiedName(".") : "handle";
comment_fragment(proxyFile);
proxyFile.oss << "classdef " << name() << " < " << parent << endl;
proxyFile.oss << " properties\n";
proxyFile.oss << " ptr_" << matlabUniqueName << " = 0\n";
proxyFile.oss << " end\n";
proxyFile.oss << " methods\n";
// Constructor
proxyFile.oss << " function obj = " << name() << "(varargin)\n";
// Special pointer constructors - one in MATLAB to create an object and
// assign a pointer returned from a C++ function. In turn this MATLAB
// constructor calls a special C++ function that just adds the object to
// its collector. This allows wrapped functions to return objects in
// other wrap modules - to add these to their collectors the pointer is
// passed from one C++ module into matlab then back into the other C++
// module.
pointer_constructor_fragments(proxyFile, wrapperFile, wrapperName,
functionNames);
wrapperFile.oss << "\n";
// Regular constructors
boost::optional<string> cppBaseName = qualifiedParent();
for (size_t i = 0; i < constructor.nrOverloads(); i++) {
ArgumentList args = constructor.argumentList(i);
const int id = (int) functionNames.size();
constructor.proxy_fragment(proxyFile, wrapperName, (bool) parentClass, id,
args);
const string wrapFunctionName = constructor.wrapper_fragment(wrapperFile,
cppName, matlabUniqueName, cppBaseName, id, args);
wrapperFile.oss << "\n";
functionNames.push_back(wrapFunctionName);
}
proxyFile.oss << " else\n";
proxyFile.oss << " error('Arguments do not match any overload of "
<< matlabQualName << " constructor');\n";
proxyFile.oss << " end\n";
if (parentClass)
proxyFile.oss << " obj = obj@" << parentClass->qualifiedName(".")
<< "(uint64(" << ptr_constructor_key << "), base_ptr);\n";
proxyFile.oss << " obj.ptr_" << matlabUniqueName << " = my_ptr;\n";
proxyFile.oss << " end\n\n";
// Deconstructor
{
const int id = (int) functionNames.size();
deconstructor.proxy_fragment(proxyFile, wrapperName, matlabUniqueName, id);
proxyFile.oss << "\n";
const string functionName = deconstructor.wrapper_fragment(wrapperFile,
cppName, matlabUniqueName, id);
wrapperFile.oss << "\n";
functionNames.push_back(functionName);
}
proxyFile.oss
<< " function display(obj), obj.print(''); end\n %DISPLAY Calls print on the object\n";
proxyFile.oss
<< " function disp(obj), obj.display; end\n %DISP Calls print on the object\n";
// Methods
for(const Methods::value_type& name_m: methods_) {
const Method& m = name_m.second;
m.proxy_wrapper_fragments(proxyFile, wrapperFile, cppName, matlabQualName,
matlabUniqueName, wrapperName, typeAttributes, functionNames);
proxyFile.oss << "\n";
wrapperFile.oss << "\n";
}
if (hasSerialization)
serialization_fragments(proxyFile, wrapperFile, wrapperName, functionNames);
proxyFile.oss << " end\n";
proxyFile.oss << "\n";
proxyFile.oss << " methods(Static = true)\n";
// Static methods
for(const StaticMethods::value_type& name_m: static_methods) {
const StaticMethod& m = name_m.second;
m.proxy_wrapper_fragments(proxyFile, wrapperFile, cppName, matlabQualName,
matlabUniqueName, wrapperName, typeAttributes, functionNames);
proxyFile.oss << "\n";
wrapperFile.oss << "\n";
}
if (hasSerialization)
deserialization_fragments(proxyFile, wrapperFile, wrapperName,
functionNames);
proxyFile.oss << " end\n";
proxyFile.oss << "end\n";
// Close file
proxyFile.emit(true);
}
/* ************************************************************************* */
void Class::pointer_constructor_fragments(FileWriter& proxyFile,
FileWriter& wrapperFile, Str wrapperName,
vector<string>& functionNames) const {
const string matlabUniqueName = qualifiedName();
const string cppName = qualifiedName("::");
const int collectorInsertId = (int) functionNames.size();
const string collectorInsertFunctionName = matlabUniqueName
+ "_collectorInsertAndMakeBase_"
+ boost::lexical_cast<string>(collectorInsertId);
functionNames.push_back(collectorInsertFunctionName);
int upcastFromVoidId;
string upcastFromVoidFunctionName;
if (isVirtual) {
upcastFromVoidId = (int) functionNames.size();
upcastFromVoidFunctionName = matlabUniqueName + "_upcastFromVoid_"
+ boost::lexical_cast<string>(upcastFromVoidId);
functionNames.push_back(upcastFromVoidFunctionName);
}
// MATLAB constructor that assigns pointer to matlab object then calls c++
// function to add the object to the collector.
if (isVirtual) {
proxyFile.oss
<< " if (nargin == 2 || (nargin == 3 && strcmp(varargin{3}, 'void')))";
} else {
proxyFile.oss << " if nargin == 2";
}
proxyFile.oss << " && isa(varargin{1}, 'uint64') && varargin{1} == uint64("
<< ptr_constructor_key << ")\n";
if (isVirtual) {
proxyFile.oss << " if nargin == 2\n";
proxyFile.oss << " my_ptr = varargin{2};\n";
proxyFile.oss << " else\n";
proxyFile.oss << " my_ptr = " << wrapperName << "("
<< upcastFromVoidId << ", varargin{2});\n";
proxyFile.oss << " end\n";
} else {
proxyFile.oss << " my_ptr = varargin{2};\n";
}
if (!parentClass) // If this class has a base class, we'll get a base class pointer back
proxyFile.oss << " ";
else
proxyFile.oss << " base_ptr = ";
proxyFile.oss << wrapperName << "(" << collectorInsertId << ", my_ptr);\n"; // Call collector insert and get base class ptr
// C++ function to add pointer from MATLAB to collector. The pointer always
// comes from a C++ return value; this mechanism allows the object to be added
// to a collector in a different wrap module. If this class has a base class,
// a new pointer to the base class is allocated and returned.
wrapperFile.oss << "void " << collectorInsertFunctionName
<< "(int nargout, mxArray *out[], int nargin, const mxArray *in[])\n";
wrapperFile.oss << "{\n";
wrapperFile.oss << " mexAtExit(&_deleteAllObjects);\n";
// Typedef boost::shared_ptr
wrapperFile.oss << " typedef boost::shared_ptr<" << cppName << "> Shared;\n";
wrapperFile.oss << "\n";
// Get self pointer passed in
wrapperFile.oss
<< " Shared *self = *reinterpret_cast<Shared**> (mxGetData(in[0]));\n";
// Add to collector
wrapperFile.oss << " collector_" << matlabUniqueName << ".insert(self);\n";
// If we have a base class, return the base class pointer (MATLAB will call the base class collectorInsertAndMakeBase to add this to the collector and recurse the heirarchy)
boost::optional<string> cppBaseName = qualifiedParent();
if (cppBaseName) {
wrapperFile.oss << "\n";
wrapperFile.oss << " typedef boost::shared_ptr<" << *cppBaseName
<< "> SharedBase;\n";
wrapperFile.oss
<< " out[0] = mxCreateNumericMatrix(1, 1, mxUINT32OR64_CLASS, mxREAL);\n";
wrapperFile.oss
<< " *reinterpret_cast<SharedBase**>(mxGetData(out[0])) = new SharedBase(*self);\n";
}
wrapperFile.oss << "}\n";
// If this is a virtual function, C++ function to dynamic upcast it from a
// shared_ptr<void>. This mechanism allows automatic dynamic creation of the
// real underlying derived-most class when a C++ method returns a virtual
// base class.
if (isVirtual)
wrapperFile.oss << "\n"
"void " << upcastFromVoidFunctionName
<< "(int nargout, mxArray *out[], int nargin, const mxArray *in[]) {\n"
" mexAtExit(&_deleteAllObjects);\n"
" typedef boost::shared_ptr<" << cppName
<< "> Shared;\n"
" boost::shared_ptr<void> *asVoid = *reinterpret_cast<boost::shared_ptr<void>**> (mxGetData(in[0]));\n"
" out[0] = mxCreateNumericMatrix(1, 1, mxUINT32OR64_CLASS, mxREAL);\n"
" Shared *self = new Shared(boost::static_pointer_cast<" << cppName
<< ">(*asVoid));\n"
" *reinterpret_cast<Shared**>(mxGetData(out[0])) = self;\n"
"}\n";
}
/* ************************************************************************* */
Class Class::expandTemplate(const TemplateSubstitution& ts) const {
Class inst = *this;
inst.methods_ = expandMethodTemplate(methods_, ts);
inst.static_methods = expandMethodTemplate(static_methods, ts);
inst.constructor = constructor.expandTemplate(ts);
inst.deconstructor.name = inst.name();
return inst;
}
/* ************************************************************************* */
vector<Class> Class::expandTemplate(Str templateArg,
const vector<Qualified>& instantiations) const {
vector<Class> result;
for(const Qualified& instName: instantiations) {
Qualified expandedClass = (Qualified) (*this);
expandedClass.expand(instName.name());
const TemplateSubstitution ts(templateArg, instName, expandedClass);
Class inst = expandTemplate(ts);
inst.name_ = expandedClass.name();
inst.templateArgs.clear();
inst.typedefName = qualifiedName("::") + "<" + instName.qualifiedName("::")
+ ">";
inst.templateInstTypeList.push_back(instName);
inst.templateClass = *this;
result.push_back(inst);
}
return result;
}
/* ************************************************************************* */
vector<Class> Class::expandTemplate(Str templateArg,
const vector<int>& integers) const {
vector<Class> result;
for(int i: integers) {
Qualified expandedClass = (Qualified) (*this);
stringstream ss; ss << i;
string instName = ss.str();
expandedClass.expand(instName);
const TemplateSubstitution ts(templateArg, instName, expandedClass);
Class inst = expandTemplate(ts);
inst.name_ = expandedClass.name();
inst.templateArgs.clear();
inst.typedefName = qualifiedName("::") + "<" + instName + ">";
result.push_back(inst);
}
return result;
}
/* ************************************************************************* */
void Class::addMethod(bool verbose, bool is_const, Str methodName,
const ArgumentList& argumentList,
const ReturnValue& returnValue, const Template& tmplate) {
// Check if templated
if (tmplate.valid()) {
try {
templateMethods_[methodName].addOverload(methodName, argumentList,
returnValue, is_const,
tmplate.argName(), verbose);
} catch (const std::runtime_error& e) {
throw std::runtime_error("Class::addMethod: error adding " + name_ +
"::" + methodName + "\n" + e.what());
}
// Create method to expand
// For all values of the template argument, create a new method
for (const Qualified& instName : tmplate.argValues()) {
const TemplateSubstitution ts(tmplate.argName(), instName, *this);
// substitute template in arguments
ArgumentList expandedArgs = argumentList.expandTemplate(ts);
// do the same for return type
ReturnValue expandedRetVal = returnValue.expandTemplate(ts);
// Now stick in new overload stack with expandedMethodName key
// but note we use the same, unexpanded methodName in overload
string expandedMethodName = methodName + instName.name();
try {
methods_[expandedMethodName].addOverload(methodName, expandedArgs,
expandedRetVal, is_const,
instName, verbose);
} catch (const std::runtime_error& e) {
throw std::runtime_error("Class::addMethod: error adding " + name_ +
"::" + expandedMethodName + "\n" + e.what());
}
}
} else {
try {
// just add overload
methods_[methodName].addOverload(methodName, argumentList, returnValue,
is_const, boost::none, verbose);
nontemplateMethods_[methodName].addOverload(methodName, argumentList,
returnValue, is_const,
boost::none, verbose);
} catch (const std::runtime_error& e) {
throw std::runtime_error("Class::addMethod: error adding " + name_ +
"::" + methodName + "\n" + e.what());
}
}
}
/* ************************************************************************* */
void Class::erase_serialization(Methods& methods) {
Methods::iterator it = methods.find("serializable");
if (it != methods.end()) {
#ifndef WRAP_DISABLE_SERIALIZE
isSerializable = true;
#else
// cout << "Ignoring serializable() flag in class " << name << endl;
#endif
methods.erase(it);
}
it = methods.find("serialize");
if (it != methods.end()) {
#ifndef WRAP_DISABLE_SERIALIZE
isSerializable = true;
hasSerialization = true;
#else
// cout << "Ignoring serialize() flag in class " << name << endl;
#endif
methods.erase(it);
}
}
void Class::erase_serialization() {
erase_serialization(methods_);
erase_serialization(nontemplateMethods_);
}
/* ************************************************************************* */
void Class::verifyAll(vector<string>& validTypes, bool& hasSerialiable) const {
hasSerialiable |= isSerializable;
// verify all of the function arguments
//TODO:verifyArguments<ArgumentList>(validTypes, constructor.args_list);
verifyArguments<StaticMethod>(validTypes, static_methods);
verifyArguments<Method>(validTypes, methods_);
// verify function return types
verifyReturnTypes<StaticMethod>(validTypes, static_methods);
verifyReturnTypes<Method>(validTypes, methods_);
// verify parents
boost::optional<string> parent = qualifiedParent();
if (parent
&& find(validTypes.begin(), validTypes.end(), *parent)
== validTypes.end())
throw DependencyMissing(*parent, qualifiedName("::"));
}
/* ************************************************************************* */
void Class::appendInheritedMethods(const Class& cls,
const vector<Class>& classes) {
if (cls.parentClass) {
// Find parent
for(const Class& parent: classes) {
// We found a parent class for our parent, TODO improve !
if (parent.name() == cls.parentClass->name()) {
methods_.insert(parent.methods_.begin(), parent.methods_.end());
appendInheritedMethods(parent, classes);
}
}
}
}
/* ************************************************************************* */
void Class::removeInheritedNontemplateMethods(vector<Class>& classes) {
if (!parentClass) return;
// Find parent
auto parentIt = std::find_if(classes.begin(), classes.end(),
[&](const Class& cls) { return cls.name() == parentClass->name(); });
if (parentIt == classes.end()) return; // ignore if parent not found
Class& parent = *parentIt;
// Only check nontemplateMethods_
for(const string& methodName: nontemplateMethods_ | boost::adaptors::map_keys) {
// check if the method exists in its parent
// Check against parent's methods_ because all the methods of grand
// parent and grand-grand-parent, etc. are already included there
// This is to avoid looking into higher level grand parents...
auto it = parent.methods_.find(methodName);
if (it == parent.methods_.end()) continue; // if not: ignore!
Method& parentMethod = it->second;
Method& method = nontemplateMethods_[methodName];
// check if they have the same modifiers (const/static/templateArgs)
if (!method.isSameModifiers(parentMethod)) continue; // if not: ignore
// check and remove duplicate overloads
auto methodOverloads = boost::combine(method.returnVals_, method.argLists_);
auto parentMethodOverloads = boost::combine(parentMethod.returnVals_, parentMethod.argLists_);
auto result = boost::remove_if(
methodOverloads,
[&](boost::tuple<ReturnValue, ArgumentList> const& overload) {
bool found = std::find_if(
parentMethodOverloads.begin(),
parentMethodOverloads.end(),
[&](boost::tuple<ReturnValue, ArgumentList> const&
parentOverload) {
return overload.get<0>() == parentOverload.get<0>() &&
overload.get<1>().isSameSignature(parentOverload.get<1>());
}) != parentMethodOverloads.end();
return found;
});
// remove all duplicate overloads
method.returnVals_.erase(boost::get<0>(result.get_iterator_tuple()),
method.returnVals_.end());
method.argLists_.erase(boost::get<1>(result.get_iterator_tuple()),
method.argLists_.end());
}
// [Optional] remove the entire method if it has no overload
for (auto it = nontemplateMethods_.begin(), ite = nontemplateMethods_.end(); it != ite;)
if (it->second.nrOverloads() == 0) it = nontemplateMethods_.erase(it); else ++it;
}
/* ************************************************************************* */
string Class::getTypedef() const {
string result;
for(Str namesp: namespaces()) {
result += ("namespace " + namesp + " { ");
}
result += ("typedef " + typedefName + " " + name() + ";");
for (size_t i = 0; i < namespaces().size(); ++i) {
result += " }";
}
return result;
}
/* ************************************************************************* */
void Class::comment_fragment(FileWriter& proxyFile) const {
proxyFile.oss << "%class " << name() << ", see Doxygen page for details\n";
proxyFile.oss
<< "%at http://research.cc.gatech.edu/borg/sites/edu.borg/html/index.html\n";
constructor.comment_fragment(proxyFile);
if (!methods_.empty())
proxyFile.oss << "%\n%-------Methods-------\n";
for(const Methods::value_type& name_m: methods_)
name_m.second.comment_fragment(proxyFile);
if (!static_methods.empty())
proxyFile.oss << "%\n%-------Static Methods-------\n";
for(const StaticMethods::value_type& name_m: static_methods)
name_m.second.comment_fragment(proxyFile);
if (hasSerialization) {
proxyFile.oss << "%\n%-------Serialization Interface-------\n";
proxyFile.oss << "%string_serialize() : returns string\n";
proxyFile.oss << "%string_deserialize(string serialized) : returns "
<< name() << "\n";
}
proxyFile.oss << "%\n";
}
/* ************************************************************************* */
void Class::serialization_fragments(FileWriter& proxyFile,
FileWriter& wrapperFile, Str wrapperName,
vector<string>& functionNames) const {
//void Point3_string_serialize_17(int nargout, mxArray *out[], int nargin, const mxArray *in[])
//{
// typedef boost::shared_ptr<Point3> Shared;
// checkArguments("string_serialize",nargout,nargin-1,0);
// Shared obj = unwrap_shared_ptr<Point3>(in[0], "ptr_Point3");
// ostringstream out_archive_stream;
// boost::archive::text_oarchive out_archive(out_archive_stream);
// out_archive << *obj;
// out[0] = wrap< string >(out_archive_stream.str());
//}
int serialize_id = functionNames.size();
const string matlabQualName = qualifiedName(".");
const string matlabUniqueName = qualifiedName();
const string cppClassName = qualifiedName("::");
const string wrapFunctionNameSerialize = matlabUniqueName
+ "_string_serialize_" + boost::lexical_cast<string>(serialize_id);
functionNames.push_back(wrapFunctionNameSerialize);
// call
//void Point3_string_serialize_17(int nargout, mxArray *out[], int nargin, const mxArray *in[])
wrapperFile.oss << "void " << wrapFunctionNameSerialize
<< "(int nargout, mxArray *out[], int nargin, const mxArray *in[])\n";
wrapperFile.oss << "{\n";
wrapperFile.oss << " typedef boost::shared_ptr<" << cppClassName
<< "> Shared;" << endl;
// check arguments - for serialize, no arguments
// example: checkArguments("string_serialize",nargout,nargin-1,0);
wrapperFile.oss
<< " checkArguments(\"string_serialize\",nargout,nargin-1,0);\n";
// get class pointer
// example: Shared obj = unwrap_shared_ptr<Point3>(in[0], "ptr_Point3");
wrapperFile.oss << " Shared obj = unwrap_shared_ptr<" << cppClassName
<< ">(in[0], \"ptr_" << matlabUniqueName << "\");" << endl;
// Serialization boilerplate
wrapperFile.oss << " ostringstream out_archive_stream;\n";
wrapperFile.oss
<< " boost::archive::text_oarchive out_archive(out_archive_stream);\n";
wrapperFile.oss << " out_archive << *obj;\n";
wrapperFile.oss << " out[0] = wrap< string >(out_archive_stream.str());\n";
// finish
wrapperFile.oss << "}\n";
// Generate code for matlab function
// function varargout string_serialize(this, varargin)
// % STRING_SERIALIZE usage: string_serialize() : returns string
// % Doxygen can be found at http://research.cc.gatech.edu/borg/sites/edu.borg/html/index.html
// if length(varargin) == 0
// varargout{1} = geometry_wrapper(15, this, varargin{:});
// else
// error('Arguments do not match any overload of function Point3.string_serialize');
// end
// end
proxyFile.oss
<< " function varargout = string_serialize(this, varargin)\n";
proxyFile.oss
<< " % STRING_SERIALIZE usage: string_serialize() : returns string\n";
proxyFile.oss
<< " % Doxygen can be found at http://research.cc.gatech.edu/borg/sites/edu.borg/html/index.html\n";
proxyFile.oss << " if length(varargin) == 0\n";
proxyFile.oss << " varargout{1} = " << wrapperName << "("
<< boost::lexical_cast<string>(serialize_id) << ", this, varargin{:});\n";
proxyFile.oss << " else\n";
proxyFile.oss
<< " error('Arguments do not match any overload of function "
<< matlabQualName << ".string_serialize');\n";
proxyFile.oss << " end\n";
proxyFile.oss << " end\n\n";
// Generate code for matlab save function
// function sobj = saveobj(obj)
// % SAVEOBJ Saves the object to a matlab-readable format
// sobj = obj.string_serialize();
// end
proxyFile.oss << " function sobj = saveobj(obj)\n";
proxyFile.oss
<< " % SAVEOBJ Saves the object to a matlab-readable format\n";
proxyFile.oss << " sobj = obj.string_serialize();\n";
proxyFile.oss << " end\n";
}
/* ************************************************************************* */
void Class::deserialization_fragments(FileWriter& proxyFile,
FileWriter& wrapperFile, Str wrapperName,
vector<string>& functionNames) const {
//void Point3_string_deserialize_18(int nargout, mxArray *out[], int nargin, const mxArray *in[])
//{
// typedef boost::shared_ptr<Point3> Shared;
// checkArguments("Point3.string_deserialize",nargout,nargin,1);
// string serialized = unwrap< string >(in[0]);
// istringstream in_archive_stream(serialized);
// boost::archive::text_iarchive in_archive(in_archive_stream);
// Shared output(new Point3(0,0,0));
// in_archive >> *output;
// out[0] = wrap_shared_ptr(output,"Point3", false);
//}
int deserialize_id = functionNames.size();
const string matlabQualName = qualifiedName(".");
const string matlabUniqueName = qualifiedName();
const string cppClassName = qualifiedName("::");
const string wrapFunctionNameDeserialize = matlabUniqueName
+ "_string_deserialize_" + boost::lexical_cast<string>(deserialize_id);
functionNames.push_back(wrapFunctionNameDeserialize);
// call
wrapperFile.oss << "void " << wrapFunctionNameDeserialize
<< "(int nargout, mxArray *out[], int nargin, const mxArray *in[])\n";
wrapperFile.oss << "{\n";
wrapperFile.oss << " typedef boost::shared_ptr<" << cppClassName
<< "> Shared;" << endl;
// check arguments - for deserialize, 1 string argument
wrapperFile.oss << " checkArguments(\"" << matlabUniqueName
<< ".string_deserialize\",nargout,nargin,1);\n";
// string argument with deserialization boilerplate
wrapperFile.oss << " string serialized = unwrap< string >(in[0]);\n";
wrapperFile.oss << " istringstream in_archive_stream(serialized);\n";
wrapperFile.oss
<< " boost::archive::text_iarchive in_archive(in_archive_stream);\n";
wrapperFile.oss << " Shared output(new " << cppClassName << "());\n";
wrapperFile.oss << " in_archive >> *output;\n";
wrapperFile.oss << " out[0] = wrap_shared_ptr(output,\"" << matlabQualName
<< "\", false);\n";
wrapperFile.oss << "}\n";
// Generate matlab function
// function varargout = string_deserialize(varargin)
// % STRING_DESERIALIZE usage: string_deserialize() : returns Point3
// % Doxygen can be found at http://research.cc.gatech.edu/borg/sites/edu.borg/html/index.html
// if length(varargin) == 1
// varargout{1} = geometry_wrapper(18, varargin{:});
// else
// error('Arguments do not match any overload of function Point3.string_deserialize');
// end
// end
proxyFile.oss << " function varargout = string_deserialize(varargin)\n";
proxyFile.oss
<< " % STRING_DESERIALIZE usage: string_deserialize() : returns "
<< matlabQualName << "\n";
proxyFile.oss
<< " % Doxygen can be found at http://research.cc.gatech.edu/borg/sites/edu.borg/html/index.html\n";
proxyFile.oss << " if length(varargin) == 1\n";
proxyFile.oss << " varargout{1} = " << wrapperName << "("
<< boost::lexical_cast<string>(deserialize_id) << ", varargin{:});\n";
proxyFile.oss << " else\n";
proxyFile.oss
<< " error('Arguments do not match any overload of function "
<< matlabQualName << ".string_deserialize');\n";
proxyFile.oss << " end\n";
proxyFile.oss << " end\n\n";
// Generate matlab load function
// function obj = loadobj(sobj)
// % LOADOBJ Saves the object to a matlab-readable format
// obj = Point3.string_deserialize(sobj);
// end
proxyFile.oss << " function obj = loadobj(sobj)\n";
proxyFile.oss
<< " % LOADOBJ Saves the object to a matlab-readable format\n";
proxyFile.oss << " obj = " << matlabQualName
<< ".string_deserialize(sobj);\n";
proxyFile.oss << " end" << endl;
}
/* ************************************************************************* */
string Class::getSerializationExport() const {
//BOOST_CLASS_EXPORT_GUID(gtsam::SharedDiagonal, "gtsamSharedDiagonal");
return "BOOST_CLASS_EXPORT_GUID(" + qualifiedName("::") + ", \""
+ qualifiedName() + "\");";
}
/* ************************************************************************* */
void Class::python_wrapper(FileWriter& wrapperFile) const {
wrapperFile.oss << "class_<" << name() << ">(\"" << name() << "\")\n";
constructor.python_wrapper(wrapperFile, name());
for(const StaticMethod& m: static_methods | boost::adaptors::map_values)
m.python_wrapper(wrapperFile, name());
for(const Method& m: methods_ | boost::adaptors::map_values)
m.python_wrapper(wrapperFile, name());
wrapperFile.oss << ";\n\n";
}
/* ************************************************************************* */
void Class::emit_cython_pxd(FileWriter& pxdFile) const {
pxdFile.oss << "cdef extern from \"" << includeFile << "\"";
string ns = qualifiedNamespaces("::");
if (!ns.empty())
pxdFile.oss << " namespace \"" << ns << "\"";
pxdFile.oss << ":" << endl;
pxdFile.oss << " cdef cppclass " << pxdClassName() << " \"" << qualifiedName("::") << "\"";
if (templateArgs.size()>0) {
pxdFile.oss << "[";
for(size_t i = 0; i<templateArgs.size(); ++i) {
pxdFile.oss << templateArgs[i];
if (i<templateArgs.size()-1) pxdFile.oss << ",";
}
pxdFile.oss << "]";
}
if (parentClass) pxdFile.oss << "(" << parentClass->pxdClassName() << ")";
pxdFile.oss << ":\n";
constructor.emit_cython_pxd(pxdFile, *this);
if (constructor.nrOverloads()>0) pxdFile.oss << "\n";
for(const StaticMethod& m: static_methods | boost::adaptors::map_values)
m.emit_cython_pxd(pxdFile, *this);
if (static_methods.size()>0) pxdFile.oss << "\n";
for(const Method& m: nontemplateMethods_ | boost::adaptors::map_values)
m.emit_cython_pxd(pxdFile, *this);
for(const TemplateMethod& m: templateMethods_ | boost::adaptors::map_values)
m.emit_cython_pxd(pxdFile, *this);
size_t numMethods = constructor.nrOverloads() + static_methods.size() +
methods_.size() + templateMethods_.size();
if (numMethods == 0)
pxdFile.oss << " pass\n";
}
/* ************************************************************************* */
void Class::emit_cython_wrapper_pxd(FileWriter& pxdFile) const {
pxdFile.oss << "\ncdef class " << pyxClassName();
if (getParent())
pxdFile.oss << "(" << getParent()->pyxClassName() << ")";
pxdFile.oss << ":\n";
pxdFile.oss << " cdef " << shared_pxd_class_in_pyx() << " "
<< shared_pxd_obj_in_pyx() << "\n";
// cyCreateFromShared
pxdFile.oss << " @staticmethod\n";
pxdFile.oss << " cdef " << pyxClassName() << " cyCreateFromShared(const "
<< shared_pxd_class_in_pyx() << "& other)\n";
for(const StaticMethod& m: static_methods | boost::adaptors::map_values)
m.emit_cython_wrapper_pxd(pxdFile, *this);
if (static_methods.size()>0) pxdFile.oss << "\n";
}
/* ************************************************************************* */
void Class::pyxInitParentObj(FileWriter& pyxFile, const std::string& pyObj,
const std::string& cySharedObj,
const std::vector<Class>& allClasses) const {
if (parentClass) {
pyxFile.oss << pyObj << "." << parentClass->shared_pxd_obj_in_pyx() << " = "
<< "<" << parentClass->shared_pxd_class_in_pyx() << ">("
<< cySharedObj << ")\n";
// Find the parent class with name "parentClass" and point its cython obj
// to the same pointer
auto parent_it = find_if(allClasses.begin(), allClasses.end(),
[this](const Class& cls) {
return cls.pxdClassName() ==
this->parentClass->pxdClassName();
});
if (parent_it == allClasses.end()) {
cerr << "Can't find parent class: " << parentClass->pxdClassName();
throw std::runtime_error("Parent class not found!");
}
parent_it->pyxInitParentObj(pyxFile, pyObj, cySharedObj, allClasses);
}
}
/* ************************************************************************* */
void Class::pyxDynamicCast(FileWriter& pyxFile, const Class& curLevel,
const std::vector<Class>& allClasses) const {
std::string me = this->pyxClassName(), sharedMe = this->shared_pxd_class_in_pyx();
if (curLevel.parentClass) {
std::string parent = curLevel.parentClass->pyxClassName(),
parentObj = curLevel.parentClass->shared_pxd_obj_in_pyx(),
parentCythonClass = curLevel.parentClass->pxd_class_in_pyx();
pyxFile.oss << "def dynamic_cast_" << me << "_" << parent << "(" << parent
<< " parent):\n";
pyxFile.oss << " try:\n";
pyxFile.oss << " return " << me << ".cyCreateFromShared(<" << sharedMe
<< ">dynamic_pointer_cast[" << pxd_class_in_pyx() << ","
<< parentCythonClass << "](parent." << parentObj
<< "))\n";
pyxFile.oss << " except:\n";
pyxFile.oss << " raise TypeError('dynamic cast failed!')\n";
// Move up higher to one level: Find the parent class with name "parentClass"
auto parent_it = find_if(allClasses.begin(), allClasses.end(),
[&curLevel](const Class& cls) {
return cls.pxdClassName() ==
curLevel.parentClass->pxdClassName();
});
if (parent_it == allClasses.end()) {
cerr << "Can't find parent class: " << parentClass->pxdClassName();
throw std::runtime_error("Parent class not found!");
}
pyxDynamicCast(pyxFile, *parent_it, allClasses);
}
}
/* ************************************************************************* */
void Class::emit_cython_pyx(FileWriter& pyxFile, const std::vector<Class>& allClasses) const {
pyxFile.oss << "cdef class " << pyxClassName();
if (parentClass) pyxFile.oss << "(" << parentClass->pyxClassName() << ")";
pyxFile.oss << ":\n";
// __init___
pyxFile.oss << " def __init__(self, *args, **kwargs):\n";
pyxFile.oss << " cdef list __params\n";
pyxFile.oss << " self." << shared_pxd_obj_in_pyx() << " = " << shared_pxd_class_in_pyx() << "()\n";
pyxFile.oss << " if len(args)==0 and len(kwargs)==1 and kwargs.has_key('cyCreateFromShared'):\n return\n";
// Constructors
constructor.emit_cython_pyx(pyxFile, *this);
pyxFile.oss << " if (self." << shared_pxd_obj_in_pyx() << ".use_count()==0):\n";
pyxFile.oss << " raise TypeError('" << pyxClassName()
<< " construction failed!')\n";
pyxInitParentObj(pyxFile, " self", "self." + shared_pxd_obj_in_pyx(), allClasses);
pyxFile.oss << "\n";
// cyCreateFromShared
pyxFile.oss << " @staticmethod\n";
pyxFile.oss << " cdef " << pyxClassName() << " cyCreateFromShared(const "
<< shared_pxd_class_in_pyx() << "& other):\n"
<< " if other.get() == NULL:\n"
<< " raise RuntimeError('Cannot create object from a nullptr!')\n"
<< " cdef " << pyxClassName() << " return_value = " << pyxClassName() << "(cyCreateFromShared=True)\n"
<< " return_value." << shared_pxd_obj_in_pyx() << " = other\n";
pyxInitParentObj(pyxFile, " return_value", "other", allClasses);
pyxFile.oss << " return return_value" << "\n\n";
for(const StaticMethod& m: static_methods | boost::adaptors::map_values)
m.emit_cython_pyx(pyxFile, *this);
if (static_methods.size()>0) pyxFile.oss << "\n";
for(const Method& m: methods_ | boost::adaptors::map_values)
m.emit_cython_pyx(pyxFile, *this);
pyxDynamicCast(pyxFile, *this, allClasses);
pyxFile.oss << "\n\n";
}
/* ************************************************************************* */

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Class.h
* @brief describe the C++ class that is being wrapped
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#pragma once
#include "spirit.h"
#include "Template.h"
#include "Constructor.h"
#include "Deconstructor.h"
#include "Method.h"
#include "StaticMethod.h"
#include "TemplateMethod.h"
#include "TypeAttributesTable.h"
#ifdef __GNUC__
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wunused-variable"
#endif
#include <boost/lambda/bind.hpp>
#include <boost/lambda/lambda.hpp>
#ifdef __GNUC__
#pragma GCC diagnostic pop
#endif
namespace bl = boost::lambda;
#include <boost/range/adaptor/map.hpp>
#include <boost/optional.hpp>
#include <string>
#include <map>
namespace wrap {
/// Class has name, constructors, methods
class Class: public Qualified {
public:
typedef const std::string& Str;
typedef std::map<std::string, Method> Methods;
typedef std::map<std::string, StaticMethod> StaticMethods;
typedef std::map<std::string, TemplateMethod> TemplateMethods;
private:
boost::optional<Qualified> parentClass; ///< The *single* parent
Methods methods_; ///< Class methods, including all expanded/instantiated template methods -- to be serialized to matlab and Python classes in Cython pyx
Methods nontemplateMethods_; ///< only nontemplate methods -- to be serialized into Cython pxd
TemplateMethods templateMethods_; ///< only template methods -- to be serialized into Cython pxd
// Method& mutableMethod(Str key);
public:
StaticMethods static_methods; ///< Static methods
// Then the instance variables are set directly by the Module constructor
std::vector<std::string> templateArgs; ///< Template arguments
std::string typedefName; ///< The name to typedef *from*, if this class is actually a typedef, i.e. typedef [typedefName] [name]
std::vector<Qualified> templateInstTypeList; ///< the original typelist used to instantiate this class from a template.
///< Empty if it's not an instantiation. Needed for template classes in Cython pxd.
boost::optional<Qualified> templateClass = boost::none; ///< qualified name of the original template class from which this class was instantiated.
///< boost::none if not an instantiation. Needed for template classes in Cython pxd.
bool isVirtual; ///< Whether the class is part of a virtual inheritance chain
bool isSerializable; ///< Whether we can use boost.serialization to serialize the class - creates exports
bool hasSerialization; ///< Whether we should create the serialization functions
Constructor constructor; ///< Class constructors
Deconstructor deconstructor; ///< Deconstructor to deallocate C++ object
bool verbose_; ///< verbose flag
std::string includeFile;
/// Constructor creates an empty class
Class(bool verbose = true) :
parentClass(boost::none), isVirtual(false), isSerializable(false), hasSerialization(
false), deconstructor(verbose), verbose_(verbose) {
}
Class(const std::string& name, bool verbose = true)
: Qualified(name, Qualified::Category::CLASS),
parentClass(boost::none),
isVirtual(false),
isSerializable(false),
hasSerialization(false),
deconstructor(verbose),
verbose_(verbose) {}
void assignParent(const Qualified& parent);
boost::optional<std::string> qualifiedParent() const;
boost::optional<Qualified> getParent() const { return parentClass; }
size_t nrMethods() const {
return methods_.size();
}
const Method& method(Str key) const;
bool exists(Str name) const {
return methods_.find(name) != methods_.end();
}
// And finally MATLAB code is emitted, methods below called by Module::matlab_code
void matlab_proxy(Str toolboxPath, Str wrapperName,
const TypeAttributesTable& typeAttributes, FileWriter& wrapperFile,
std::vector<std::string>& functionNames) const; ///< emit proxy class
Class expandTemplate(const TemplateSubstitution& ts) const;
std::vector<Class> expandTemplate(Str templateArg,
const std::vector<Qualified>& instantiations) const;
// Create new classes with integer template arguments
std::vector<Class> expandTemplate(Str templateArg,
const std::vector<int>& integers) const;
/// Add potentially overloaded, potentially templated method
void addMethod(bool verbose, bool is_const, Str methodName,
const ArgumentList& argumentList, const ReturnValue& returnValue,
const Template& tmplate);
/// Post-process classes for serialization markers
void erase_serialization(); // non-const !
void erase_serialization(Methods& methods); // non-const !
/// verify all of the function arguments
void verifyAll(std::vector<std::string>& functionNames,
bool& hasSerialiable) const;
void appendInheritedMethods(const Class& cls,
const std::vector<Class>& classes);
void removeInheritedNontemplateMethods(std::vector<Class>& classes);
/// The typedef line for this class, if this class is a typedef, otherwise returns an empty string.
std::string getTypedef() const;
/// Returns the string for an export flag
std::string getSerializationExport() const;
/// Creates a member function that performs serialization
void serialization_fragments(FileWriter& proxyFile, FileWriter& wrapperFile,
Str wrapperName, std::vector<std::string>& functionNames) const;
/// Creates a static member function that performs deserialization
void deserialization_fragments(FileWriter& proxyFile, FileWriter& wrapperFile,
Str wrapperName, std::vector<std::string>& functionNames) const;
// emit python wrapper
void python_wrapper(FileWriter& wrapperFile) const;
// emit cython wrapper
void emit_cython_pxd(FileWriter& pxdFile) const;
void emit_cython_wrapper_pxd(FileWriter& pxdFile) const;
void emit_cython_pyx(FileWriter& pyxFile,
const std::vector<Class>& allClasses) const;
void pyxInitParentObj(FileWriter& pyxFile, const std::string& pyObj,
const std::string& cySharedObj,
const std::vector<Class>& allClasses) const;
void pyxDynamicCast(FileWriter& pyxFile, const Class& curLevel,
const std::vector<Class>& allClasses) const;
friend std::ostream& operator<<(std::ostream& os, const Class& cls) {
os << "class " << cls.name() << "{\n";
os << cls.constructor << ";\n";
for(const StaticMethod& m: cls.static_methods | boost::adaptors::map_values)
os << m << ";\n";
for(const Method& m: cls.methods_ | boost::adaptors::map_values)
os << m << ";\n";
os << "};" << std::endl;
return os;
}
private:
void pointer_constructor_fragments(FileWriter& proxyFile,
FileWriter& wrapperFile, Str wrapperName,
std::vector<std::string>& functionNames) const;
void comment_fragment(FileWriter& proxyFile) const;
};
/* ************************************************************************* */
// http://boost-spirit.com/distrib/spirit_1_8_2/libs/spirit/doc/grammar.html
struct ClassGrammar: public classic::grammar<ClassGrammar> {
Class& cls_; ///< successful parse will be placed in here
Template& template_; ///< result needs to be visible outside
/// Construct type grammar and specify where result is placed
ClassGrammar(Class& cls, Template& t) :
cls_(cls), template_(t) {
}
/// Definition of type grammar
template<typename ScannerT>
struct definition: BasicRules<ScannerT> {
using BasicRules<ScannerT>::name_p;
using BasicRules<ScannerT>::className_p;
using BasicRules<ScannerT>::comments_p;
// NOTE: allows for pointers to all types
ArgumentList args;
ArgumentListGrammar argumentList_g;
Constructor constructor0, constructor;
ReturnValue retVal0, retVal;
ReturnValueGrammar returnValue_g;
Template methodTemplate;
TemplateGrammar methodTemplate_g, classTemplate_g;
std::string methodName;
bool isConst, T, F;
// Parent class
Qualified possibleParent;
TypeGrammar classParent_g;
classic::rule<ScannerT> constructor_p, methodName_p, method_p,
staticMethodName_p, static_method_p, templateList_p, classParent_p,
functions_p, class_p;
definition(ClassGrammar const& self) :
argumentList_g(args), returnValue_g(retVal), //
methodTemplate_g(methodTemplate), classTemplate_g(self.template_), //
T(true), F(false), classParent_g(possibleParent) {
using namespace classic;
bool verbose = false; // TODO
// ConstructorGrammar
constructor_p = (className_p >> argumentList_g >> ';' >> !comments_p) //
[bl::bind(&Constructor::push_back, bl::var(constructor),
bl::var(args))] //
[clear_a(args)];
// MethodGrammar
methodName_p = lexeme_d[(upper_p | lower_p) >> *(alnum_p | '_')];
// gtsam::Values retract(const gtsam::VectorValues& delta) const;
method_p = !methodTemplate_g
>> (returnValue_g >> methodName_p[assign_a(methodName)]
>> argumentList_g >> !str_p("const")[assign_a(isConst, T)] >> ';'
>> *comments_p) //
[bl::bind(&Class::addMethod, bl::var(self.cls_), verbose,
bl::var(isConst), bl::var(methodName), bl::var(args),
bl::var(retVal), bl::var(methodTemplate))] //
[assign_a(retVal, retVal0)][clear_a(args)] //
[clear_a(methodTemplate)][assign_a(isConst, F)];
// StaticMethodGrammar
staticMethodName_p = lexeme_d[(upper_p | lower_p) >> *(alnum_p | '_')];
static_method_p = (str_p("static") >> returnValue_g
>> staticMethodName_p[assign_a(methodName)] >> argumentList_g >> ';'
>> *comments_p) //
[bl::bind(&StaticMethod::addOverload,
bl::var(self.cls_.static_methods)[bl::var(methodName)],
bl::var(methodName), bl::var(args), bl::var(retVal), boost::none,
verbose)] //
[assign_a(retVal, retVal0)][clear_a(args)];
// template<POSE, POINT>
templateList_p = (str_p("template") >> '<'
>> name_p[push_back_a(self.cls_.templateArgs)]
>> *(',' >> name_p[push_back_a(self.cls_.templateArgs)]) >> '>');
// parse a full class
classParent_p = (':' >> classParent_g >> '{') //
[bl::bind(&Class::assignParent, bl::var(self.cls_),
bl::var(possibleParent))][clear_a(possibleParent)];
functions_p = constructor_p | method_p | static_method_p;
// parse a full class
class_p = (!(classTemplate_g[push_back_a(self.cls_.templateArgs,
self.template_.argName())] | templateList_p)
>> !(str_p("virtual")[assign_a(self.cls_.isVirtual, T)])
>> str_p("class") >> className_p[assign_a(self.cls_.name_)]
>> (classParent_p | '{') >> //
*(functions_p | comments_p) >> str_p("};")) //
[bl::bind(&Constructor::initializeOrCheck, bl::var(constructor),
bl::var(self.cls_.name_), boost::none, verbose)][assign_a(
self.cls_.constructor, constructor)] //
[assign_a(self.cls_.deconstructor.name, self.cls_.name_)] //
[assign_a(constructor, constructor0)];
}
classic::rule<ScannerT> const& start() const {
return class_p;
}
};
};
// ClassGrammar
}// \namespace wrap

160
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Constructor.ccp
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#include <iostream>
#include <fstream>
#include <algorithm>
#include <boost/lexical_cast.hpp>
#include "utilities.h"
#include "Constructor.h"
#include "Class.h"
using namespace std;
using namespace wrap;
/* ************************************************************************* */
string Constructor::matlab_wrapper_name(Str className) const {
string str = "new_" + className;
return str;
}
/* ************************************************************************* */
void Constructor::proxy_fragment(FileWriter& file,
const std::string& wrapperName, bool hasParent,
const int id, const ArgumentList args) const {
size_t nrArgs = args.size();
// check for number of arguments...
file.oss << " elseif nargin == " << nrArgs;
if (nrArgs > 0) file.oss << " && ";
// ...and their types
bool first = true;
for (size_t i = 0; i < nrArgs; i++) {
if (!first) file.oss << " && ";
file.oss << "isa(varargin{" << i + 1 << "},'" << args[i].matlabClass(".")
<< "')";
first = false;
}
// emit code for calling constructor
if (hasParent)
file.oss << "\n [ my_ptr, base_ptr ] = ";
else
file.oss << "\n my_ptr = ";
file.oss << wrapperName << "(" << id;
// emit constructor arguments
for (size_t i = 0; i < nrArgs; i++) {
file.oss << ", ";
file.oss << "varargin{" << i + 1 << "}";
}
file.oss << ");\n";
}
/* ************************************************************************* */
string Constructor::wrapper_fragment(FileWriter& file, Str cppClassName,
Str matlabUniqueName,
boost::optional<string> cppBaseClassName,
int id, const ArgumentList& al) const {
const string wrapFunctionName =
matlabUniqueName + "_constructor_" + boost::lexical_cast<string>(id);
file.oss << "void " << wrapFunctionName
<< "(int nargout, mxArray *out[], int nargin, const mxArray *in[])"
<< endl;
file.oss << "{\n";
file.oss << " mexAtExit(&_deleteAllObjects);\n";
// Typedef boost::shared_ptr
file.oss << " typedef boost::shared_ptr<" << cppClassName << "> Shared;\n";
file.oss << "\n";
// Check to see if there will be any arguments and remove {} for consiseness
if (al.size() > 0) al.matlab_unwrap(file); // unwrap arguments
file.oss << " Shared *self = new Shared(new " << cppClassName << "("
<< al.names() << "));" << endl;
file.oss << " collector_" << matlabUniqueName << ".insert(self);\n";
if (verbose_)
file.oss << " std::cout << \"constructed \" << self << std::endl;" << endl;
file.oss
<< " out[0] = mxCreateNumericMatrix(1, 1, mxUINT32OR64_CLASS, mxREAL);"
<< endl;
file.oss << " *reinterpret_cast<Shared**> (mxGetData(out[0])) = self;"
<< endl;
// If we have a base class, return the base class pointer (MATLAB will call
// the base class collectorInsertAndMakeBase to add this to the collector and
// recurse the heirarchy)
if (cppBaseClassName) {
file.oss << "\n";
file.oss << " typedef boost::shared_ptr<" << *cppBaseClassName
<< "> SharedBase;\n";
file.oss << " out[1] = mxCreateNumericMatrix(1, 1, mxUINT32OR64_CLASS, "
"mxREAL);\n";
file.oss << " *reinterpret_cast<SharedBase**>(mxGetData(out[1])) = new "
"SharedBase(*self);\n";
}
file.oss << "}" << endl;
return wrapFunctionName;
}
/* ************************************************************************* */
void Constructor::python_wrapper(FileWriter& wrapperFile, Str className) const {
wrapperFile.oss << " .def(\"" << name_ << "\", &" << className
<< "::" << name_ << ");\n";
}
/* ************************************************************************* */
bool Constructor::hasDefaultConstructor() const {
for (size_t i = 0; i < nrOverloads(); i++) {
if (argumentList(i).size() == 0) return true;
}
return false;
}
/* ************************************************************************* */
void Constructor::emit_cython_pxd(FileWriter& pxdFile, const Class& cls) const {
for (size_t i = 0; i < nrOverloads(); i++) {
ArgumentList args = argumentList(i);
// generate the constructor
pxdFile.oss << " " << cls.pxdClassName() << "(";
args.emit_cython_pxd(pxdFile, cls.pxdClassName(), cls.templateArgs);
pxdFile.oss << ") " << "except +\n";
}
}
/* ************************************************************************* */
void Constructor::emit_cython_pyx(FileWriter& pyxFile, const Class& cls) const {
for (size_t i = 0; i < nrOverloads(); i++) {
ArgumentList args = argumentList(i);
pyxFile.oss << " try:\n";
pyxFile.oss << pyx_resolveOverloadParams(args, true, 3);
pyxFile.oss
<< argumentList(i).pyx_convertEigenTypeAndStorageOrder(" ");
pyxFile.oss << " self." << cls.shared_pxd_obj_in_pyx() << " = "
<< cls.shared_pxd_class_in_pyx() << "(new " << cls.pxd_class_in_pyx()
<< "(" << args.pyx_asParams() << "))\n";
pyxFile.oss << " except (AssertionError, ValueError):\n";
pyxFile.oss << " pass\n";
}
}
/* ************************************************************************* */

99
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Constructor.h
* @brief class describing a constructor + code generation
* @author Frank Dellaert
* @author Richard Roberts
**/
#pragma once
#include "OverloadedFunction.h"
#include <boost/optional.hpp>
#include <string>
#include <vector>
namespace wrap {
// Forward declaration
class Class;
// Constructor class
struct Constructor: public OverloadedFunction {
typedef const std::string& Str;
/// Constructor creates an empty class
Constructor(bool verbose = false) {
verbose_ = verbose;
}
Constructor expandTemplate(const TemplateSubstitution& ts) const {
Constructor inst = *this;
inst.argLists_ = expandArgumentListsTemplate(ts);
inst.name_ = ts.expandedClassName();
return inst;
}
/// return true if the default constructor exists
bool hasDefaultConstructor() const;
// MATLAB code generation
// toolboxPath is main toolbox directory, e.g., ../matlab
// classFile is class proxy file, e.g., ../matlab/@Point2/Point2.m
/// wrapper name
std::string matlab_wrapper_name(Str className) const;
void comment_fragment(FileWriter& proxyFile) const {
if (nrOverloads() > 0)
proxyFile.oss << "%\n%-------Constructors-------\n";
for (size_t i = 0; i < nrOverloads(); i++) {
proxyFile.oss << "%";
argumentList(i).emit_prototype(proxyFile, name_);
proxyFile.oss << "\n";
}
}
/**
* Create fragment to select constructor in proxy class, e.g.,
* if nargin == 2, obj.self = new_Pose3_RP(varargin{1},varargin{2}); end
*/
void proxy_fragment(FileWriter& file, Str wrapperName, bool hasParent,
const int id, const ArgumentList args) const;
/// cpp wrapper
std::string wrapper_fragment(FileWriter& file, Str cppClassName,
Str matlabUniqueName, boost::optional<std::string> cppBaseClassName, int id,
const ArgumentList& al) const;
/// constructor function
void generate_construct(FileWriter& file, Str cppClassName,
std::vector<ArgumentList>& args_list) const;
// emit python wrapper
void python_wrapper(FileWriter& wrapperFile, Str className) const;
// emit cython wrapper
void emit_cython_pxd(FileWriter& pxdFile, const Class& cls) const;
void emit_cython_pyx(FileWriter& pyxFile, const Class& cls) const;
friend std::ostream& operator<<(std::ostream& os, const Constructor& m) {
for (size_t i = 0; i < m.nrOverloads(); i++)
os << m.name_ << m.argLists_[i];
return os;
}
};
} // \namespace wrap

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Deconstructor.ccp
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#include <iostream>
#include <fstream>
#include <boost/lexical_cast.hpp>
#include "utilities.h"
#include "Deconstructor.h"
using namespace std;
using namespace wrap;
/* ************************************************************************* */
string Deconstructor::matlab_wrapper_name(const string& className) const {
string str = "delete_" + className;
return str;
}
/* ************************************************************************* */
void Deconstructor::proxy_fragment(FileWriter& file,
const std::string& wrapperName,
const std::string& matlabUniqueName, int id) const {
file.oss << " function delete(obj)\n";
file.oss << " " << wrapperName << "(" << id << ", obj.ptr_" << matlabUniqueName << ");\n";
file.oss << " end\n";
}
/* ************************************************************************* */
string Deconstructor::wrapper_fragment(FileWriter& file,
const string& cppClassName,
const string& matlabUniqueName,
int id) const {
const string matlabName = matlab_wrapper_name(matlabUniqueName);
const string wrapFunctionName = matlabUniqueName + "_deconstructor_" + boost::lexical_cast<string>(id);
file.oss << "void " << wrapFunctionName << "(int nargout, mxArray *out[], int nargin, const mxArray *in[])" << endl;
file.oss << "{" << endl;
file.oss << " typedef boost::shared_ptr<" << cppClassName << "> Shared;" << endl;
//Deconstructor takes 1 arg, the mxArray obj
file.oss << " checkArguments(\"" << matlabName << "\",nargout,nargin," << "1" << ");" << endl;
file.oss << " Shared *self = *reinterpret_cast<Shared**>(mxGetData(in[0]));\n";
file.oss << " Collector_" << matlabUniqueName << "::iterator item;\n";
file.oss << " item = collector_" << matlabUniqueName << ".find(self);\n";
file.oss << " if(item != collector_" << matlabUniqueName << ".end()) {\n";
file.oss << " delete self;\n";
file.oss << " collector_" << matlabUniqueName << ".erase(item);\n";
file.oss << " }\n";
file.oss << "}" << endl;
return wrapFunctionName;
}
/* ************************************************************************* */

61
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Deconstructor.h
* @brief class describing a constructor + code generation
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#pragma once
#include <string>
#include <vector>
#include "Argument.h"
namespace wrap {
// Deconstructor class
struct Deconstructor {
/// Deconstructor creates an empty class
Deconstructor(bool verbose = true) :
verbose_(verbose) {
}
// Then the instance variables are set directly by the Module deconstructor
std::string name;
bool verbose_;
// MATLAB code generation
// toolboxPath is main toolbox directory, e.g., ../matlab
// classFile is class proxy file, e.g., ../matlab/@Point2/Point2.m
/// wrapper name
std::string matlab_wrapper_name(const std::string& className) const;
/// m-file
void proxy_fragment(FileWriter& file,
const std::string& wrapperName,
const std::string& matlabUniqueName, int id) const;
/// cpp wrapper
std::string wrapper_fragment(FileWriter& file,
const std::string& cppClassName,
const std::string& matlabUniqueName,
int id) const;
};
} // \namespace wrap

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/**
* @file FileWriter.cpp
*
* @date Jan 15, 2012
* @author Alex Cunningham
*/
#include "FileWriter.h"
#include "utilities.h"
#include <fstream>
#include <iostream>
using namespace std;
using namespace wrap;
/* ************************************************************************* */
FileWriter::FileWriter(const string& filename, bool verbose,
const string& comment_str) :
verbose_(verbose), filename_(filename), comment_str_(comment_str) {
}
/* ************************************************************************* */
void FileWriter::emit(bool add_header, bool force_overwrite) const {
if (verbose_)
cerr << "generating " << filename_ << " ";
// read in file if it exists
string existing_contents;
bool file_exists = true;
try {
existing_contents = file_contents(filename_.c_str(), add_header);
} catch (const CantOpenFile& ) {
file_exists = false;
}
// Only write a file if it is new, an update, or overwrite is forced
string new_contents = oss.str();
if (force_overwrite || !file_exists || existing_contents != new_contents) {
// Binary to use LF line endings instead of CRLF
ofstream ofs(filename_.c_str(), ios::binary);
if (!ofs)
throw CantOpenFile(filename_);
// dump in stringstream
ofs << new_contents;
ofs.close();
}
if (verbose_)
cerr << " ...no update" << endl;
}
/* ************************************************************************* */

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/**
* @file FileWriter.h
*
* @brief Wrapper for writing files and avoiding overwriting existing files
* This class wraps a stream object and will check that the file is
* actually different to write the new generated file.
*
* @date Jan 15, 2012
* @author Alex Cunningham
*/
#pragma once
#include <sstream>
namespace wrap {
class FileWriter {
protected:
bool verbose_;
std::string filename_;
std::string comment_str_;
public:
std::ostringstream oss; ///< Primary stream for operating on the file
/** Create a writer with a filename and delimiter for the header comment */
FileWriter(const std::string& filename, bool verbose, const std::string& comment_str);
/** Writes the contents of the stringstream to the file, checking if actually new */
void emit(bool add_header, bool force=false) const;
};
} // \namespace wrap

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Class.h
* @brief describe the C++ class that is being wrapped
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#pragma once
#include <string>
namespace wrap {
class Class;
struct ForwardDeclaration {
Class cls;
bool isVirtual;
ForwardDeclaration() : isVirtual(false) {}
explicit ForwardDeclaration(const std::string& s) : cls(s), isVirtual(false) {}
std::string name() const { return cls.qualifiedName("::"); }
};
}

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#include "FullyOverloadedFunction.h"
using namespace std;
namespace wrap {
const std::array<std::string, 2> FullyOverloadedFunction::pythonKeywords{
{"print", "lambda"}};
/* ************************************************************************* */
std::string FullyOverloadedFunction::pyx_functionCall(
const std::string& caller,
const std::string& funcName, size_t iOverload) const {
string ret;
if (!returnVals_[iOverload].isPair && !returnVals_[iOverload].type1.isPtr &&
returnVals_[iOverload].type1.isNonBasicType()) {
ret = returnVals_[iOverload].type1.make_shared_pxd_class_in_pyx() + "(";
}
// actual function call ...
if (!caller.empty()) ret += caller + ".";
ret += funcName;
if (templateArgValue_) ret += "[" + templateArgValue_->pxd_class_in_pyx() + "]";
//... with argument list
ret += "(" + argumentList(iOverload).pyx_asParams() + ")";
if (!returnVals_[iOverload].isPair && !returnVals_[iOverload].type1.isPtr &&
returnVals_[iOverload].type1.isNonBasicType())
ret += ")";
return ret;
}
}

147
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file FullyOverloadedFunction.h
* @brief Function that can be fully overloaded: arguments and return values
* @author Frank Dellaert
* @date Nov 13, 2014
**/
#pragma once
#include "OverloadedFunction.h"
#include <array>
namespace wrap {
/**
* Signature Overload (including return value)
*/
class SignatureOverloads: public ArgumentOverloads {
public:
std::vector<ReturnValue> returnVals_;
public:
const ReturnValue& returnValue(size_t i) const {
return returnVals_.at(i);
}
void push_back(const ArgumentList& args, const ReturnValue& retVal) {
argLists_.push_back(args);
returnVals_.push_back(retVal);
}
void verifyReturnTypes(const std::vector<std::string>& validtypes,
const std::string& s) const {
for(const ReturnValue& retval: returnVals_) {
retval.type1.verify(validtypes, s);
if (retval.isPair)
retval.type2.verify(validtypes, s);
}
}
// TODO use transform ?
std::vector<ReturnValue> expandReturnValuesTemplate(
const TemplateSubstitution& ts) const {
std::vector<ReturnValue> result;
for(const ReturnValue& retVal: returnVals_) {
ReturnValue instRetVal = retVal.expandTemplate(ts);
result.push_back(instRetVal);
}
return result;
}
/// Expand templates, imperative !
void expandTemplate(const TemplateSubstitution& ts) {
// substitute template in arguments
argLists_ = expandArgumentListsTemplate(ts);
// do the same for return types
returnVals_ = expandReturnValuesTemplate(ts);
}
// emit a list of comments, one for each overload
void usage_fragment(FileWriter& proxyFile, const std::string& name) const {
unsigned int argLCount = 0;
for(ArgumentList argList: argLists_) {
argList.emit_prototype(proxyFile, name);
if (argLCount != nrOverloads() - 1)
proxyFile.oss << ", ";
else
proxyFile.oss << " : returns " << returnValue(0).returnType()
<< std::endl;
argLCount++;
}
}
// emit a list of comments, one for each overload
void comment_fragment(FileWriter& proxyFile, const std::string& name) const {
size_t i = 0;
for(ArgumentList argList: argLists_) {
proxyFile.oss << "%";
argList.emit_prototype(proxyFile, name);
proxyFile.oss << " : returns " << returnVals_[i++].returnType()
<< std::endl;
}
}
friend std::ostream& operator<<(std::ostream& os,
const SignatureOverloads& overloads) {
for (size_t i = 0; i < overloads.nrOverloads(); i++)
os << overloads.returnVals_[i] << overloads.argLists_[i] << std::endl;
return os;
}
};
class FullyOverloadedFunction: public Function, public SignatureOverloads {
public:
bool addOverload(const std::string& name, const ArgumentList& args,
const ReturnValue& retVal, boost::optional<const Qualified> instName =
boost::none, bool verbose = false) {
bool first = initializeOrCheck(name, instName, verbose);
SignatureOverloads::push_back(args, retVal);
return first;
}
// emit cython pyx function call
std::string pyx_functionCall(const std::string& caller, const std::string& funcName,
size_t iOverload) const;
/// Cython: Rename functions which names are python keywords
static const std::array<std::string, 2> pythonKeywords;
static std::string pyRename(const std::string& name) {
if (std::find(pythonKeywords.begin(), pythonKeywords.end(), name) ==
pythonKeywords.end())
return name;
else
return name + "_";
}
};
// Templated checking functions
// TODO: do this via polymorphism, use transform ?
template<class F>
inline void verifyReturnTypes(const std::vector<std::string>& validTypes,
const std::map<std::string, F>& vt) {
typedef typename std::map<std::string, F>::value_type NamedMethod;
for(const NamedMethod& namedMethod: vt)
namedMethod.second.verifyReturnTypes(validTypes);
}
} // \namespace wrap

78
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Function.ccp
* @author Frank Dellaert
* @date Nov 13, 2014
**/
#include "Function.h"
#include "utilities.h"
#include <boost/lexical_cast.hpp>
#include <boost/algorithm/string.hpp>
#include <iostream>
#include <fstream>
using namespace std;
using namespace wrap;
/* ************************************************************************* */
bool Function::initializeOrCheck(const string& name,
boost::optional<const Qualified> instName, bool verbose) {
if (name.empty())
throw runtime_error("Function::initializeOrCheck called with empty name");
// Check if this overload is give to the correct method
if (name_.empty()) {
name_ = name;
templateArgValue_ = instName;
verbose_ = verbose;
return true;
} else {
if (name_ != name || verbose_ != verbose
|| ((bool) templateArgValue_ != (bool) instName)
|| ((bool) templateArgValue_ && (bool) instName
&& !(*templateArgValue_ == *instName)))
throw runtime_error(
"Function::initializeOrCheck called with different arguments");
return false;
}
}
/* ************************************************************************* */
void Function::emit_call(FileWriter& proxyFile, const ReturnValue& returnVal,
const string& wrapperName, int id) const {
returnVal.emit_matlab(proxyFile);
proxyFile.oss << wrapperName << "(" << id;
if (!isStatic())
proxyFile.oss << ", this";
proxyFile.oss << ", varargin{:});\n";
}
/* ************************************************************************* */
void Function::emit_conditional_call(FileWriter& proxyFile,
const ReturnValue& returnVal, const ArgumentList& args,
const string& wrapperName, int id) const {
// Check all arguments
args.proxy_check(proxyFile);
// output call to C++ wrapper
proxyFile.oss << " ";
emit_call(proxyFile, returnVal, wrapperName, id);
}
/* ************************************************************************* */

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Function.h
* @brief Base class for global functions and methods
* @author Frank Dellaert
* @date Nov 13, 2014
**/
#pragma once
#include "Argument.h"
#include <boost/optional.hpp>
namespace wrap {
/// Function class
class Function {
protected:
std::string name_; ///< name of method
boost::optional<Qualified> templateArgValue_; ///< value of template argument if applicable
bool verbose_;
public:
/**
* @brief first time, fill in instance variables, otherwise check if same
* @return true if first time, false thereafter
*/
bool initializeOrCheck(const std::string& name,
boost::optional<const Qualified> instName = boost::none, bool verbose =
false);
std::string name() const {
return name_;
}
/// Only Methods are non-static
virtual bool isStatic() const {
return true;
}
std::string matlabName() const {
if (templateArgValue_)
return name_ + templateArgValue_->name();
else
return name_;
}
/// Emit function call to MATLAB (no argument checking)
void emit_call(FileWriter& proxyFile, const ReturnValue& returnVal,
const std::string& wrapperName, int id) const;
/// Emit checking arguments and function call to MATLAB
void emit_conditional_call(FileWriter& proxyFile,
const ReturnValue& returnVal, const ArgumentList& args,
const std::string& wrapperName, int id) const;
};
} // \namespace wrap

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/**
* @file GlobalFunction.cpp
*
* @date Jul 22, 2012
* @author Alex Cunningham
*/
#include "GlobalFunction.h"
#include "Class.h"
#include "utilities.h"
#include <boost/lexical_cast.hpp>
namespace wrap {
using namespace std;
/* ************************************************************************* */
void GlobalFunction::addOverload(const Qualified& overload,
const ArgumentList& args, const ReturnValue& retVal, const std::string& _includeFile,
boost::optional<const Qualified> instName, bool verbose) {
FullyOverloadedFunction::addOverload(overload.name(), args, retVal, instName,
verbose);
overloads.push_back(overload);
includeFile = _includeFile;
}
/* ************************************************************************* */
void GlobalFunction::matlab_proxy(const string& toolboxPath,
const string& wrapperName, const TypeAttributesTable& typeAttributes,
FileWriter& file, vector<string>& functionNames) const {
// cluster overloads with same namespace
// create new GlobalFunction structures around namespaces - same namespaces and names are overloads
// map of namespace to global function
typedef map<string, GlobalFunction> GlobalFunctionMap;
GlobalFunctionMap grouped_functions;
for (size_t i = 0; i < overloads.size(); ++i) {
Qualified overload = overloads.at(i);
// use concatenated namespaces as key
string str_ns = qualifiedName("", overload.namespaces());
const ReturnValue& ret = returnValue(i);
const ArgumentList& args = argumentList(i);
grouped_functions[str_ns].addOverload(overload, args, ret);
}
size_t lastcheck = grouped_functions.size();
for(const GlobalFunctionMap::value_type& p: grouped_functions) {
p.second.generateSingleFunction(toolboxPath, wrapperName, typeAttributes,
file, functionNames);
if (--lastcheck != 0)
file.oss << endl;
}
}
/* ************************************************************************* */
void GlobalFunction::generateSingleFunction(const string& toolboxPath,
const string& wrapperName, const TypeAttributesTable& typeAttributes,
FileWriter& file, vector<string>& functionNames) const {
// create the folder for the namespace
const Qualified& overload1 = overloads.front();
createNamespaceStructure(overload1.namespaces(), toolboxPath);
// open destination mfunctionFileName
string mfunctionFileName = overload1.matlabName(toolboxPath);
FileWriter mfunctionFile(mfunctionFileName, verbose_, "%");
// get the name of actual matlab object
const string matlabQualName = overload1.qualifiedName(".");
const string matlabUniqueName = overload1.qualifiedName("");
const string cppName = overload1.qualifiedName("::");
mfunctionFile.oss << "function varargout = " << name_ << "(varargin)\n";
for (size_t i = 0; i < nrOverloads(); ++i) {
const ArgumentList& args = argumentList(i);
const ReturnValue& returnVal = returnValue(i);
const int id = functionNames.size();
// Output proxy matlab code
mfunctionFile.oss << " " << (i == 0 ? "" : "else");
emit_conditional_call(mfunctionFile, returnVal, args, wrapperName, id);
// Output C++ wrapper code
const string wrapFunctionName = matlabUniqueName + "_"
+ boost::lexical_cast<string>(id);
// call
file.oss << "void " << wrapFunctionName
<< "(int nargout, mxArray *out[], int nargin, const mxArray *in[])\n";
// start
file.oss << "{\n";
// check arguments
// NOTE: for static functions, there is no object passed
file.oss << " checkArguments(\"" << matlabUniqueName
<< "\",nargout,nargin," << args.size() << ");\n";
// unwrap arguments, see Argument.cpp
args.matlab_unwrap(file, 0); // We start at 0 because there is no self object
// call method with default type and wrap result
if (returnVal.type1.name() != "void")
returnVal.wrap_result(cppName + "(" + args.names() + ")", file,
typeAttributes);
else
file.oss << cppName + "(" + args.names() + ");\n";
// finish
file.oss << "}\n";
// Add to function list
functionNames.push_back(wrapFunctionName);
}
mfunctionFile.oss << " else\n";
mfunctionFile.oss
<< " error('Arguments do not match any overload of function "
<< matlabQualName << "');" << endl;
mfunctionFile.oss << " end" << endl;
// Close file
mfunctionFile.emit(true);
}
/* ************************************************************************* */
void GlobalFunction::python_wrapper(FileWriter& wrapperFile) const {
wrapperFile.oss << "def(\"" << name_ << "\", " << name_ << ");\n";
}
/* ************************************************************************* */
void GlobalFunction::emit_cython_pxd(FileWriter& file) const {
file.oss << "cdef extern from \"" << includeFile << "\" namespace \""
<< overloads[0].qualifiedNamespaces("::")
<< "\":" << endl;
for (size_t i = 0; i < nrOverloads(); ++i) {
file.oss << " ";
returnVals_[i].emit_cython_pxd(file, "", vector<string>());
file.oss << pxdName() + " \"" + overloads[0].qualifiedName("::") +
"\"(";
argumentList(i).emit_cython_pxd(file, "", vector<string>());
file.oss << ")";
file.oss << " except +";
file.oss << "\n";
}
}
/* ************************************************************************* */
void GlobalFunction::emit_cython_pyx_no_overload(FileWriter& file) const {
string funcName = pyxName();
// Function definition
file.oss << "def " << funcName;
// modify name of function instantiation as python doesn't allow overloads
// e.g. template<T={A,B,C}> funcName(...) --> funcNameA, funcNameB, funcNameC
if (templateArgValue_) file.oss << templateArgValue_->pyxClassName();
// funtion arguments
file.oss << "(";
argumentList(0).emit_cython_pyx(file);
file.oss << "):\n";
/// Call cython corresponding function and return
file.oss << argumentList(0).pyx_convertEigenTypeAndStorageOrder(" ");
string ret = pyx_functionCall("", pxdName(), 0);
if (!returnVals_[0].isVoid()) {
file.oss << " cdef " << returnVals_[0].pyx_returnType()
<< " ret = " << ret << "\n";
file.oss << " return " << returnVals_[0].pyx_casting("ret") << "\n";
} else {
file.oss << " " << ret << "\n";
}
}
/* ************************************************************************* */
void GlobalFunction::emit_cython_pyx(FileWriter& file) const {
string funcName = pyxName();
size_t N = nrOverloads();
if (N == 1) {
emit_cython_pyx_no_overload(file);
return;
}
// Dealing with overloads..
file.oss << "def " << funcName << "(*args, **kwargs):\n";
for (size_t i = 0; i < N; ++i) {
file.oss << " success, results = " << funcName << "_" << i
<< "(args, kwargs)\n";
file.oss << " if success:\n return results\n";
}
file.oss << " raise TypeError('Could not find the correct overload')\n";
for (size_t i = 0; i < N; ++i) {
ArgumentList args = argumentList(i);
file.oss << "def " + funcName + "_" + to_string(i) + "(args, kwargs):\n";
file.oss << " cdef list __params\n";
if (!returnVals_[i].isVoid()) {
file.oss << " cdef " << returnVals_[i].pyx_returnType() << " return_value\n";
}
file.oss << " try:\n";
file.oss << pyx_resolveOverloadParams(args, false, 2); // lazy: always return None even if it's a void function
/// Call corresponding cython function
file.oss << argumentList(i).pyx_convertEigenTypeAndStorageOrder(" ");
// catch exception which indicates the parameters passed are incorrect.
file.oss << " except:\n";
file.oss << " return False, None\n\n";
string call = pyx_functionCall("", pxdName(), i);
if (!returnVals_[i].isVoid()) {
file.oss << " return_value = " << call << "\n";
file.oss << " return True, " << returnVals_[i].pyx_casting("return_value") << "\n";
} else {
file.oss << " " << call << "\n";
file.oss << " return True, None\n";
}
}
}
/* ************************************************************************* */
} // \namespace wrap

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/**
* @file GlobalFunction.h
*
* @brief Implements codegen for a global function wrapped in matlab
*
* @date Jul 22, 2012
* @author Alex Cunningham
*/
#pragma once
#include "FullyOverloadedFunction.h"
#ifdef __GNUC__
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wunused-variable"
#endif
#include <boost/lambda/bind.hpp>
#include <boost/lambda/lambda.hpp>
#ifdef __GNUC__
#pragma GCC diagnostic pop
#endif
namespace bl = boost::lambda;
namespace wrap {
struct GlobalFunction: public FullyOverloadedFunction {
std::vector<Qualified> overloads; ///< Stack of qualified names
std::string includeFile;
// adds an overloaded version of this function,
void addOverload(const Qualified& overload, const ArgumentList& args,
const ReturnValue& retVal, const std::string& _includeFile = "", boost::optional<const Qualified> instName =
boost::none, bool verbose = false);
void verifyArguments(const std::vector<std::string>& validArgs) const {
SignatureOverloads::verifyArguments(validArgs, name_);
}
void verifyReturnTypes(const std::vector<std::string>& validtypes) const {
SignatureOverloads::verifyReturnTypes(validtypes, name_);
}
// codegen function called from Module to build the cpp and matlab versions of the function
void matlab_proxy(const std::string& toolboxPath,
const std::string& wrapperName, const TypeAttributesTable& typeAttributes,
FileWriter& file, std::vector<std::string>& functionNames) const;
// emit python wrapper
void python_wrapper(FileWriter& wrapperFile) const;
// function name in Cython pxd
std::string pxdName() const { return "pxd_" + pyRename(name_); }
// function name in Python pyx
std::string pyxName() const {
std::string result = "";
for(size_t i=0; i<overloads[0].namespaces_.size(); i++){
if (i >= 1) {
result += (overloads[0].namespaces_[i] + "_");
}
}
result += pyRename(name_);
return result;
}
// emit cython wrapper
void emit_cython_pxd(FileWriter& pxdFile) const;
void emit_cython_pyx(FileWriter& pyxFile) const;
void emit_cython_pyx_no_overload(FileWriter& pyxFile) const;
private:
// Creates a single global function - all in same namespace
void generateSingleFunction(const std::string& toolboxPath,
const std::string& wrapperName, const TypeAttributesTable& typeAttributes,
FileWriter& file, std::vector<std::string>& functionNames) const;
};
typedef std::map<std::string, GlobalFunction> GlobalFunctions;
/* ************************************************************************* */
// http://boost-spirit.com/distrib/spirit_1_8_2/libs/spirit/doc/grammar.html
struct GlobalFunctionGrammar: public classic::grammar<GlobalFunctionGrammar> {
GlobalFunctions& global_functions_; ///< successful parse will be placed in here
std::vector<std::string>& namespaces_;
std::string& includeFile;
/// Construct type grammar and specify where result is placed
GlobalFunctionGrammar(GlobalFunctions& global_functions,
std::vector<std::string>& namespaces,
std::string& includeFile)
: global_functions_(global_functions),
namespaces_(namespaces),
includeFile(includeFile) {}
/// Definition of type grammar
template<typename ScannerT>
struct definition: BasicRules<ScannerT> {
// using BasicRules<ScannerT>::name_p;
// using BasicRules<ScannerT>::className_p;
using BasicRules<ScannerT>::comments_p;
ArgumentList args;
ArgumentListGrammar argumentList_g;
ReturnValue retVal0, retVal;
ReturnValueGrammar returnValue_g;
Qualified globalFunction;
classic::rule<ScannerT> globalFunctionName_p, global_function_p;
definition(GlobalFunctionGrammar const& self) :
argumentList_g(args), returnValue_g(retVal) {
using namespace classic;
bool verbose = false; // TODO
globalFunctionName_p = lexeme_d[(upper_p | lower_p) >> *(alnum_p | '_')];
// parse a global function
global_function_p = (returnValue_g >> globalFunctionName_p[assign_a(
globalFunction.name_)] >>
argumentList_g >> ';' >> *comments_p) //
[assign_a(globalFunction.namespaces_, self.namespaces_)] //
[bl::bind(
&GlobalFunction::addOverload,
bl::var(self.global_functions_)[bl::var(globalFunction.name_)],
bl::var(globalFunction), bl::var(args), bl::var(retVal), bl::var(self.includeFile),
boost::none, verbose)] //
[assign_a(retVal, retVal0)][clear_a(globalFunction)][clear_a(args)];
}
classic::rule<ScannerT> const& start() const {
return global_function_p;
}
};
};
// GlobalFunctionGrammar
}// \namespace wrap

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Method.ccp
* @author Frank Dellaert
* @author Richard Roberts
**/
#include "Method.h"
#include "Class.h"
#include "utilities.h"
#include <boost/lexical_cast.hpp>
#include <boost/algorithm/string.hpp>
#include <iostream>
#include <fstream>
using namespace std;
using namespace wrap;
/* ************************************************************************* */
bool Method::addOverload(Str name, const ArgumentList& args,
const ReturnValue& retVal, bool is_const,
boost::optional<const Qualified> instName,
bool verbose) {
bool first = MethodBase::addOverload(name, args, retVal, instName, verbose);
if (first)
is_const_ = is_const;
else if (is_const && !is_const_)
throw std::runtime_error(
"Method::addOverload: " + name +
" now designated as const whereas before it was not");
else if (!is_const && is_const_)
throw std::runtime_error(
"Method::addOverload: " + name +
" now designated as non-const whereas before it was");
return first;
}
/* ************************************************************************* */
void Method::proxy_header(FileWriter& proxyFile) const {
proxyFile.oss << " function varargout = " << matlabName()
<< "(this, varargin)\n";
}
/* ************************************************************************* */
string Method::wrapper_call(FileWriter& wrapperFile, Str cppClassName,
Str matlabUniqueName,
const ArgumentList& args) const {
// check arguments
// extra argument obj -> nargin-1 is passed !
// example: checkArguments("equals",nargout,nargin-1,2);
wrapperFile.oss << " checkArguments(\"" << matlabName()
<< "\",nargout,nargin-1," << args.size() << ");\n";
// get class pointer
// example: auto obj = unwrap_shared_ptr< Test >(in[0], "Test");
wrapperFile.oss << " auto obj = unwrap_shared_ptr<" << cppClassName
<< ">(in[0], \"ptr_" << matlabUniqueName << "\");" << endl;
// unwrap arguments, see Argument.cpp, we start at 1 as first is obj
args.matlab_unwrap(wrapperFile, 1);
// call method and wrap result
// example: out[0]=wrap<bool>(obj->return_field(t));
string expanded = "obj->" + name_;
if (templateArgValue_)
expanded += ("<" + templateArgValue_->qualifiedName("::") + ">");
return expanded;
}
/* ************************************************************************* */
void Method::emit_cython_pxd(FileWriter& file, const Class& cls) const {
for (size_t i = 0; i < nrOverloads(); ++i) {
file.oss << " ";
returnVals_[i].emit_cython_pxd(file, cls.pxdClassName(), cls.templateArgs);
const string renamed = pyRename(name_);
if (renamed != name_) {
file.oss << pyRename(name_) + " \"" + name_ + "\"" << "(";
} else {
file.oss << name_ << "(";
}
argumentList(i).emit_cython_pxd(file, cls.pxdClassName(), cls.templateArgs);
file.oss << ")";
// if (is_const_) file.oss << " const";
file.oss << " except +";
file.oss << "\n";
}
}
/* ************************************************************************* */
void Method::emit_cython_pyx_no_overload(FileWriter& file,
const Class& cls) const {
string funcName = pyRename(name_);
// leverage python's special treatment for print
if (funcName == "print_") {
file.oss << " def __repr__(self):\n";
file.oss << " strBuf = RedirectCout()\n";
file.oss << " self.print_('')\n";
file.oss << " return strBuf.str()\n";
}
// Function definition
file.oss << " def " << funcName;
// modify name of function instantiation as python doesn't allow overloads
// e.g. template<T={A,B,C}> funcName(...) --> funcNameA, funcNameB, funcNameC
if (templateArgValue_) file.oss << templateArgValue_->pyxClassName();
// function arguments
file.oss << "(self";
if (argumentList(0).size() > 0) file.oss << ", ";
argumentList(0).emit_cython_pyx(file);
file.oss << "):\n";
/// Call cython corresponding function and return
file.oss << argumentList(0).pyx_convertEigenTypeAndStorageOrder(" ");
string caller = "self." + cls.shared_pxd_obj_in_pyx() + ".get()";
string ret = pyx_functionCall(caller, funcName, 0);
if (!returnVals_[0].isVoid()) {
file.oss << " cdef " << returnVals_[0].pyx_returnType()
<< " ret = " << ret << "\n";
file.oss << " return " << returnVals_[0].pyx_casting("ret") << "\n";
} else {
file.oss << " " << ret << "\n";
}
}
/* ************************************************************************* */
void Method::emit_cython_pyx(FileWriter& file, const Class& cls) const {
string funcName = pyRename(name_);
// For template function: modify name of function instantiation as python
// doesn't allow overloads
// e.g. template<T={A,B,C}> funcName(...) --> funcNameA, funcNameB, funcNameC
string instantiatedName =
(templateArgValue_) ? funcName + templateArgValue_->pyxClassName() :
funcName;
size_t N = nrOverloads();
// It's easy if there's no overload
if (N == 1) {
emit_cython_pyx_no_overload(file, cls);
return;
}
// Dealing with overloads..
file.oss << " def " << instantiatedName << "(self, *args, **kwargs):\n";
file.oss << " cdef list __params\n";
// Define return values for all possible overloads
vector<string> return_type; // every overload has a return type, possibly void
map<string, string> return_value; // we only define one return value for every distinct type
size_t j = 1;
for (size_t i = 0; i < nrOverloads(); ++i) {
if (returnVals_[i].isVoid()) {
return_type.push_back("void");
} else {
const string type = returnVals_[i].pyx_returnType();
return_type.push_back(type);
if (return_value.count(type) == 0) {
const string value = "return_value_" + to_string(j++);
return_value[type] = value;
file.oss << " cdef " << type << " " << value << "\n";
}
}
}
for (size_t i = 0; i < nrOverloads(); ++i) {
ArgumentList args = argumentList(i);
file.oss << " try:\n";
file.oss << pyx_resolveOverloadParams(args, false, 3); // lazy: always return None even if it's a void function
/// Call corresponding cython function
file.oss << args.pyx_convertEigenTypeAndStorageOrder(" ");
string caller = "self." + cls.shared_pxd_obj_in_pyx() + ".get()";
string call = pyx_functionCall(caller, funcName, i);
if (!returnVals_[i].isVoid()) {
const string type = return_type[i];
const string value = return_value[type];
file.oss << " " << value << " = " << call << "\n";
file.oss << " return " << returnVals_[i].pyx_casting(value)
<< "\n";
} else {
file.oss << " " << call << "\n";
file.oss << " return\n";
}
file.oss << " except (AssertionError, ValueError):\n";
file.oss << " pass\n";
}
file.oss
<< " raise TypeError('Incorrect arguments or types for method call.')\n\n";
}
/* ************************************************************************* */

74
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Method.h
* @brief describes and generates code for methods
* @author Frank Dellaert
* @author Richard Roberts
**/
#pragma once
#include "MethodBase.h"
namespace wrap {
/// Method class
class Method: public MethodBase {
protected:
bool is_const_;
public:
typedef const std::string& Str;
bool addOverload(Str name, const ArgumentList& args,
const ReturnValue& retVal, bool is_const,
boost::optional<const Qualified> instName = boost::none, bool verbose =
false);
bool isStatic() const override {
return false;
}
virtual bool isConst() const {
return is_const_;
}
bool isSameModifiers(const Method& other) const {
return is_const_ == other.is_const_ &&
((templateArgValue_ && other.templateArgValue_) ||
(!templateArgValue_ && !other.templateArgValue_));
}
friend std::ostream& operator<<(std::ostream& os, const Method& m) {
for (size_t i = 0; i < m.nrOverloads(); i++)
os << m.returnVals_[i] << " " << m.name_ << m.argLists_[i];
return os;
}
void emit_cython_pxd(FileWriter& file, const Class& cls) const;
void emit_cython_pyx(FileWriter& file, const Class& cls) const;
void emit_cython_pyx_no_overload(FileWriter& file, const Class& cls) const;
private:
// Emit method header
void proxy_header(FileWriter& proxyFile) const override;
std::string wrapper_call(FileWriter& wrapperFile, Str cppClassName,
Str matlabUniqueName, const ArgumentList& args) const override;
};
} // \namespace wrap

135
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file MethodBase.ccp
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#include "Method.h"
#include "Class.h"
#include "utilities.h"
#include <boost/lexical_cast.hpp>
#include <boost/algorithm/string.hpp>
#include <iostream>
#include <fstream>
using namespace std;
using namespace wrap;
/* ************************************************************************* */
void MethodBase::proxy_wrapper_fragments(
FileWriter& proxyFile, FileWriter& wrapperFile, Str cppClassName,
Str matlabQualName, Str matlabUniqueName, Str wrapperName,
const TypeAttributesTable& typeAttributes,
vector<string>& functionNames) const {
// emit header, e.g., function varargout = templatedMethod(this, varargin)
proxy_header(proxyFile);
// Emit comments for documentation
string up_name = boost::to_upper_copy(matlabName());
proxyFile.oss << " % " << up_name << " usage: ";
usage_fragment(proxyFile, matlabName());
// Emit URL to Doxygen page
proxyFile.oss << " % "
<< "Doxygen can be found at "
"http://research.cc.gatech.edu/borg/sites/edu.borg/html/"
"index.html" << endl;
// Handle special case of single overload with all numeric arguments
if (nrOverloads() == 1 && argumentList(0).allScalar()) {
// Output proxy matlab code
// TODO: document why is it OK to not check arguments in this case
proxyFile.oss << " ";
const int id = (int)functionNames.size();
emit_call(proxyFile, returnValue(0), wrapperName, id);
// Output C++ wrapper code
const string wrapFunctionName = wrapper_fragment(
wrapperFile, cppClassName, matlabUniqueName, 0, id, typeAttributes);
// Add to function list
functionNames.push_back(wrapFunctionName);
} else {
// Check arguments for all overloads
for (size_t i = 0; i < nrOverloads(); ++i) {
// Output proxy matlab code
proxyFile.oss << " " << (i == 0 ? "" : "else");
const int id = (int)functionNames.size();
emit_conditional_call(proxyFile, returnValue(i), argumentList(i),
wrapperName, id);
// Output C++ wrapper code
const string wrapFunctionName = wrapper_fragment(
wrapperFile, cppClassName, matlabUniqueName, i, id, typeAttributes);
// Add to function list
functionNames.push_back(wrapFunctionName);
}
proxyFile.oss << " else\n";
proxyFile.oss
<< " error('Arguments do not match any overload of function "
<< matlabQualName << "." << name_ << "');" << endl;
proxyFile.oss << " end\n";
}
proxyFile.oss << " end\n";
}
/* ************************************************************************* */
string MethodBase::wrapper_fragment(
FileWriter& wrapperFile, Str cppClassName, Str matlabUniqueName,
int overload, int id, const TypeAttributesTable& typeAttributes) const {
// generate code
const string wrapFunctionName =
matlabUniqueName + "_" + name_ + "_" + boost::lexical_cast<string>(id);
const ArgumentList& args = argumentList(overload);
const ReturnValue& returnVal = returnValue(overload);
// call
wrapperFile.oss
<< "void " << wrapFunctionName
<< "(int nargout, mxArray *out[], int nargin, const mxArray *in[])\n";
// start
wrapperFile.oss << "{\n";
// get call
// for static methods: cppClassName::staticMethod<TemplateVal>
// for instance methods: obj->instanceMethod<TemplateVal>
string expanded =
wrapper_call(wrapperFile, cppClassName, matlabUniqueName, args);
expanded += ("(" + args.names() + ")");
if (returnVal.type1.name() != "void")
returnVal.wrap_result(expanded, wrapperFile, typeAttributes);
else
wrapperFile.oss << " " + expanded + ";\n";
// finish
wrapperFile.oss << "}\n";
return wrapFunctionName;
}
/* ************************************************************************* */
void MethodBase::python_wrapper(FileWriter& wrapperFile, Str className) const {
wrapperFile.oss << " .def(\"" << name_ << "\", &" << className
<< "::" << name_ << ");\n";
}
/* ************************************************************************* */

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file MethodBase.h
* @brief describes and generates code for static methods
* @author Frank Dellaert
* @author Alex Cunningham
* @author Richard Roberts
**/
#pragma once
#include "FullyOverloadedFunction.h"
namespace wrap {
// Forward declaration
class Class;
/// MethodBase class
struct MethodBase : public FullyOverloadedFunction {
typedef const std::string& Str;
// emit a list of comments, one for each overload
void comment_fragment(FileWriter& proxyFile) const {
SignatureOverloads::comment_fragment(proxyFile, matlabName());
}
void verifyArguments(const std::vector<std::string>& validArgs) const {
SignatureOverloads::verifyArguments(validArgs, name_);
}
void verifyReturnTypes(const std::vector<std::string>& validtypes) const {
SignatureOverloads::verifyReturnTypes(validtypes, name_);
}
// MATLAB code generation
// classPath is class directory, e.g., ../matlab/@Point2
void proxy_wrapper_fragments(FileWriter& proxyFile, FileWriter& wrapperFile,
Str cppClassName, Str matlabQualName,
Str matlabUniqueName, Str wrapperName,
const TypeAttributesTable& typeAttributes,
std::vector<std::string>& functionNames) const;
// emit python wrapper
void python_wrapper(FileWriter& wrapperFile, Str className) const;
protected:
virtual void proxy_header(FileWriter& proxyFile) const = 0;
std::string wrapper_fragment(
FileWriter& wrapperFile, Str cppClassName, Str matlabUniqueName,
int overload, int id,
const TypeAttributesTable& typeAttributes) const; ///< cpp wrapper
virtual std::string wrapper_call(FileWriter& wrapperFile, Str cppClassName,
Str matlabUniqueName,
const ArgumentList& args) const = 0;
};
} // \namespace wrap

649
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Module.ccp
* @author Frank Dellaert
* @author Alex Cunningham
* @author Andrew Melim
* @author Richard Roberts
**/
#include "Module.h"
#include "FileWriter.h"
#include "TypeAttributesTable.h"
#include "utilities.h"
#include <boost/filesystem.hpp>
#include <boost/lexical_cast.hpp>
#include <iostream>
#include <algorithm>
using namespace std;
using namespace wrap;
using namespace BOOST_SPIRIT_CLASSIC_NS;
namespace bl = boost::lambda;
namespace fs = boost::filesystem;
/* ************************************************************************* */
// We parse an interface file into a Module object.
// The grammar is defined using the boost/spirit combinatorial parser.
// For example, str_p("const") parses the string "const", and the >>
// operator creates a sequence parser. The grammar below, composed of rules
// and with start rule [class_p], doubles as the specs for our interface files.
/* ************************************************************************* */
/* ************************************************************************* */
// If a number of template arguments were given, generate a number of expanded
// class names, e.g., PriorFactor -> PriorFactorPose2, and add those classes
static void handle_possible_template(vector<Class>& classes,
vector<Class>& uninstantiatedClasses,
const Class& cls, const Template& t) {
uninstantiatedClasses.push_back(cls);
if (cls.templateArgs.empty() || t.empty()) {
classes.push_back(cls);
} else {
if (cls.templateArgs.size() != 1)
throw std::runtime_error(
"In-line template instantiations only handle a single template argument");
string arg = cls.templateArgs.front();
vector<Class> classInstantiations =
(t.nrValues() > 0) ? cls.expandTemplate(arg, t.argValues()) :
cls.expandTemplate(arg, t.intList());
for(const Class& c: classInstantiations)
classes.push_back(c);
}
}
static void push_typedef_pair(vector<TypedefPair>& typedefs,
const Qualified& oldType,
const Qualified& newType,
const string& includeFile) {
typedefs.push_back(TypedefPair(oldType, newType, includeFile));
}
/* ************************************************************************* */
Module::Module(const std::string& moduleName, bool enable_verbose)
: name(moduleName), verbose(enable_verbose)
{
}
/* ************************************************************************* */
Module::Module(const string& interfacePath,
const string& moduleName, bool enable_verbose)
: name(moduleName), verbose(enable_verbose)
{
// read interface file
string interfaceFile = interfacePath + "/" + moduleName + ".h";
string contents = file_contents(interfaceFile);
// execute parsing
parseMarkup(contents);
}
/* ************************************************************************* */
void Module::parseMarkup(const std::string& data) {
// The parse imperatively :-( updates variables gradually during parse
// The one with postfix 0 are used to reset the variables after parse.
//----------------------------------------------------------------------------
// Grammar with actions that build the Class object. Actions are
// defined within the square brackets [] and are executed whenever a
// rule is successfully parsed. Define BOOST_SPIRIT_DEBUG to debug.
// The grammar is allows a very restricted C++ header
// lexeme_d turns off white space skipping
// http://www.boost.org/doc/libs/1_37_0/libs/spirit/classic/doc/directives.html
// ----------------------------------------------------------------------------
// Define Rule and instantiate basic rules
typedef rule<phrase_scanner_t> Rule;
BasicRules<phrase_scanner_t> basic;
vector<string> namespaces; // current namespace tag
string currentInclude;
// parse a full class
Class cls0(verbose),cls(verbose);
Template classTemplate;
ClassGrammar class_g(cls,classTemplate);
Rule class_p = class_g //
[assign_a(cls.namespaces_, namespaces)]
[assign_a(cls.includeFile, currentInclude)][bl::bind(
&handle_possible_template, bl::var(classes),
bl::var(uninstantiatedClasses), bl::var(cls),
bl::var(classTemplate))][clear_a(classTemplate)] //
[assign_a(cls, cls0)];
// parse "gtsam::Pose2" and add to singleInstantiation.typeList
TemplateInstantiationTypedef singleInstantiation, singleInstantiation0;
TypeListGrammar<'<','>'> typelist_g(singleInstantiation.typeList);
// typedef gtsam::RangeFactor<gtsam::Pose2, gtsam::Point2> RangeFactor2D;
TypeGrammar instantiationClass_g(singleInstantiation.class_);
Rule templateSingleInstantiation_p =
(str_p("typedef") >> instantiationClass_g >>
typelist_g >>
basic.className_p[assign_a(singleInstantiation.name_)] >>
';')
[assign_a(singleInstantiation.namespaces_, namespaces)]
[push_back_a(templateInstantiationTypedefs, singleInstantiation)]
[assign_a(singleInstantiation, singleInstantiation0)];
Qualified oldType, newType;
TypeGrammar typedefOldClass_g(oldType), typedefNewClass_g(newType);
Rule typedef_p =
(str_p("typedef") >> typedefOldClass_g >> typedefNewClass_g >>
';')
[assign_a(oldType.namespaces_, namespaces)]
[assign_a(newType.namespaces_, namespaces)]
[bl::bind(&push_typedef_pair, bl::var(typedefs), bl::var(oldType),
bl::var(newType), bl::var(currentInclude))];
// Create grammar for global functions
GlobalFunctionGrammar global_function_g(global_functions, namespaces,
currentInclude);
Rule include_p = str_p("#include") >> ch_p('<') >>
(*(anychar_p - '>'))[push_back_a(includes)]
[assign_a(currentInclude)] >>
ch_p('>');
#ifdef __clang__
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wuninitialized"
#endif
Rule namespace_def_p =
(str_p("namespace")
>> basic.namespace_p[push_back_a(namespaces)]
>> ch_p('{')
>> *(include_p | class_p | templateSingleInstantiation_p | typedef_p | global_function_g | namespace_def_p | basic.comments_p)
>> ch_p('}'))
[pop_a(namespaces)];
#ifdef __clang__
#pragma clang diagnostic pop
#endif
// parse forward declaration
ForwardDeclaration fwDec0, fwDec;
Class fwParentClass;
TypeGrammar className_g(fwDec.cls);
TypeGrammar classParent_g(fwParentClass);
Rule classParent_p = (':' >> classParent_g >> ';') //
[bl::bind(&Class::assignParent, bl::var(fwDec.cls),
bl::var(fwParentClass))][clear_a(fwParentClass)];
Rule forward_declaration_p =
!(str_p("virtual")[assign_a(fwDec.isVirtual, T)])
>> str_p("class") >> className_g
>> (classParent_p | ';')
[push_back_a(forward_declarations, fwDec)]
[assign_a(cls,cls0)] // also clear class to avoid partial parse
[assign_a(fwDec, fwDec0)];
Rule module_content_p = basic.comments_p | include_p | class_p
| templateSingleInstantiation_p | forward_declaration_p
| global_function_g | namespace_def_p;
Rule module_p = *module_content_p >> !end_p;
// and parse contents
parse_info<const char*> info = parse(data.c_str(), module_p, space_p);
if(!info.full) {
printf("parsing stopped at \n%.20s\n",info.stop);
cout << "Stopped in:\n"
"class '" << cls.name_ << "'" << endl;
throw ParseFailed((int)info.length);
}
// Post-process classes for serialization markers
for(Class& cls: classes)
cls.erase_serialization();
for(Class& cls: uninstantiatedClasses)
cls.erase_serialization();
// Explicitly add methods to the classes from parents so it shows in documentation
for(Class& cls: classes)
cls.appendInheritedMethods(cls, classes);
// - Remove inherited methods for Cython classes in the pxd, otherwise Cython can't decide which one to call.
// - Only inherited nontemplateMethods_ in uninstantiatedClasses need to be removed
// because that what we serialized to the pxd.
// - However, we check against the class parent's *methods_* to avoid looking into
// its grand parent and grand-grand parent, etc., because all those are already
// added in its direct parent.
// - So this must be called *after* the above code appendInheritedMethods!!
for(Class& cls: uninstantiatedClasses)
cls.removeInheritedNontemplateMethods(uninstantiatedClasses);
// Expand templates - This is done first so that template instantiations are
// counted in the list of valid types, have their attributes and dependencies
// checked, etc.
expandedClasses = ExpandTypedefInstantiations(classes,
templateInstantiationTypedefs);
// Dependency check list
vector<string> validTypes = GenerateValidTypes(expandedClasses,
forward_declarations, typedefs);
// Check that all classes have been defined somewhere
verifyArguments<GlobalFunction>(validTypes, global_functions);
verifyReturnTypes<GlobalFunction>(validTypes, global_functions);
hasSerialiable = false;
for(const Class& cls: expandedClasses)
cls.verifyAll(validTypes,hasSerialiable);
// Create type attributes table and check validity
typeAttributes.addClasses(expandedClasses);
typeAttributes.addForwardDeclarations(forward_declarations);
for (const TypedefPair& p: typedefs)
typeAttributes.addType(p.newType);
// add Eigen types as template arguments are also checked ?
vector<ForwardDeclaration> eigen;
eigen.push_back(ForwardDeclaration("Vector"));
eigen.push_back(ForwardDeclaration("Matrix"));
typeAttributes.addForwardDeclarations(eigen);
typeAttributes.checkValidity(expandedClasses);
}
/* ************************************************************************* */
void Module::generate_matlab_wrapper(const string& toolboxPath) const {
fs::create_directories(toolboxPath);
// create the unified .cpp switch file
const string wrapperName = name + "_wrapper";
string wrapperFileName = toolboxPath + "/" + wrapperName + ".cpp";
FileWriter wrapperFile(wrapperFileName, verbose, "//");
wrapperFile.oss << "#include <wrap/matlab.h>\n";
wrapperFile.oss << "#include <map>\n";
wrapperFile.oss << "\n";
// Include boost.serialization archive headers before other class headers
if (hasSerialiable) {
wrapperFile.oss << "#include <boost/archive/text_iarchive.hpp>\n";
wrapperFile.oss << "#include <boost/archive/text_oarchive.hpp>\n";
wrapperFile.oss << "#include <boost/serialization/export.hpp>\n\n";
}
// Generate includes while avoiding redundant includes
generateIncludes(wrapperFile);
// create typedef classes - we put this at the top of the wrap file so that
// collectors and method arguments can use these typedefs
for(const Class& cls: expandedClasses)
if(!cls.typedefName.empty())
wrapperFile.oss << cls.getTypedef() << "\n";
wrapperFile.oss << "\n";
// Generate boost.serialization export flags (needs typedefs from above)
if (hasSerialiable) {
for(const Class& cls: expandedClasses)
if(cls.isSerializable)
wrapperFile.oss << cls.getSerializationExport() << "\n";
wrapperFile.oss << "\n";
}
// Generate collectors and cleanup function to be called from mexAtExit
WriteCollectorsAndCleanupFcn(wrapperFile, name, expandedClasses);
// generate RTTI registry (for returning derived-most types)
WriteRTTIRegistry(wrapperFile, name, expandedClasses);
vector<string> functionNames; // Function names stored by index for switch
// create proxy class and wrapper code
for(const Class& cls: expandedClasses)
cls.matlab_proxy(toolboxPath, wrapperName, typeAttributes, wrapperFile, functionNames);
// create matlab files and wrapper code for global functions
for(const GlobalFunctions::value_type& p: global_functions)
p.second.matlab_proxy(toolboxPath, wrapperName, typeAttributes, wrapperFile, functionNames);
// finish wrapper file
wrapperFile.oss << "\n";
finish_wrapper(wrapperFile, functionNames);
wrapperFile.emit(true);
}
/* ************************************************************************* */
void Module::generate_cython_wrapper(const string& toolboxPath, const std::string& pxdImports) const {
fs::create_directories(toolboxPath);
string pxdFileName = toolboxPath + "/" + name + ".pxd";
FileWriter pxdFile(pxdFileName, verbose, "#");
pxdFile.oss << pxdImports << "\n";
emit_cython_pxd(pxdFile);
string pyxFileName = toolboxPath + "/" + name + ".pyx";
FileWriter pyxFile(pyxFileName, verbose, "#");
emit_cython_pyx(pyxFile);
}
/* ************************************************************************* */
void Module::emit_cython_pxd(FileWriter& pxdFile) const {
// headers
pxdFile.oss << "from gtsam_eigency.core cimport *\n"
"from libcpp.string cimport string\n"
"from libcpp.vector cimport vector\n"
"from libcpp.pair cimport pair\n"
"from libcpp.set cimport set\n"
"from libcpp.map cimport map\n"
"from libcpp cimport bool\n\n";
// boost shared_ptr
pxdFile.oss << "cdef extern from \"boost/shared_ptr.hpp\" namespace \"boost\":\n"
" cppclass shared_ptr[T]:\n"
" shared_ptr()\n"
" shared_ptr(T*)\n"
" T* get()\n"
" long use_count() const\n"
" T& operator*()\n\n"
" cdef shared_ptr[T] dynamic_pointer_cast[T,U](const shared_ptr[U]& r)\n\n";
// gtsam alignment-friendly shared_ptr
pxdFile.oss << "cdef extern from \"gtsam/base/make_shared.h\" namespace \"gtsam\":\n"
" cdef shared_ptr[T] make_shared[T](const T& r)\n\n";
for(const TypedefPair& types: typedefs)
types.emit_cython_pxd(pxdFile);
//... wrap all classes
for (const Class& cls : uninstantiatedClasses) {
cls.emit_cython_pxd(pxdFile);
for (const Class& expCls : expandedClasses) {
bool matchingNonTemplated = !expCls.templateClass
&& expCls.pxdClassName() == cls.pxdClassName();
bool isTemplatedFromCls = expCls.templateClass
&& expCls.templateClass->pxdClassName() == cls.pxdClassName();
// ctypedef for template instantiations
if (isTemplatedFromCls) {
pxdFile.oss << "\n";
pxdFile.oss << "ctypedef " << expCls.templateClass->pxdClassName()
<< "[";
for (size_t i = 0; i < expCls.templateInstTypeList.size(); ++i)
pxdFile.oss << expCls.templateInstTypeList[i].pxdClassName()
<< ((i == expCls.templateInstTypeList.size() - 1) ? "" : ", ");
pxdFile.oss << "] " << expCls.pxdClassName() << "\n";
}
// Python wrapper class
if (isTemplatedFromCls || matchingNonTemplated) {
expCls.emit_cython_wrapper_pxd(pxdFile);
}
}
pxdFile.oss << "\n\n";
}
//... wrap global functions
for(const GlobalFunctions::value_type& p: global_functions)
p.second.emit_cython_pxd(pxdFile);
pxdFile.emit(true);
}
/* ************************************************************************* */
void Module::emit_cython_pyx(FileWriter& pyxFile) const {
// directives...
// allow str to automatically coerce to std::string and back (for python3)
pyxFile.oss << "# cython: c_string_type=str, c_string_encoding=ascii\n\n";
// headers...
string pxdHeader = name;
pyxFile.oss << "cimport numpy as np\n"
"import numpy as npp\n"
"cimport " << pxdHeader << "\n"
"from ."<< pxdHeader << " cimport shared_ptr\n"
"from ."<< pxdHeader << " cimport dynamic_pointer_cast\n"
"from ."<< pxdHeader << " cimport make_shared\n";
pyxFile.oss << "# C helper function that copies all arguments into a positional list.\n"
"cdef list process_args(list keywords, tuple args, dict kwargs):\n"
" cdef str keyword\n"
" cdef int n = len(args), m = len(keywords)\n"
" cdef list params = list(args)\n"
" assert len(args)+len(kwargs) == m, 'Expected {} arguments'.format(m)\n"
" try:\n"
" return params + [kwargs[keyword] for keyword in keywords[n:]]\n"
" except:\n"
" raise ValueError('Epected arguments ' + str(keywords))\n";
// import all typedefs, e.g. from gtsam_wrapper cimport Key, so we don't need to say gtsam.Key
for(const Qualified& q: Qualified::BasicTypedefs) {
pyxFile.oss << "from " << pxdHeader << " cimport " << q.pxdClassName() << "\n";
}
pyxFile.oss << "from gtsam_eigency.core cimport *\n"
"from libcpp cimport bool\n\n"
"from libcpp.pair cimport pair\n"
"from libcpp.string cimport string\n"
"from cython.operator cimport dereference as deref\n\n\n";
// all classes include all forward declarations
std::vector<Class> allClasses = expandedClasses;
for(const ForwardDeclaration& fd: forward_declarations)
allClasses.push_back(fd.cls);
for(const Class& cls: expandedClasses)
cls.emit_cython_pyx(pyxFile, allClasses);
pyxFile.oss << "\n";
//... wrap global functions
for(const GlobalFunctions::value_type& p: global_functions)
p.second.emit_cython_pyx(pyxFile);
pyxFile.emit(true);
}
/* ************************************************************************* */
void Module::generateIncludes(FileWriter& file) const {
// collect includes
vector<string> all_includes(includes);
// sort and remove duplicates
sort(all_includes.begin(), all_includes.end());
vector<string>::const_iterator last_include = unique(all_includes.begin(), all_includes.end());
vector<string>::const_iterator it = all_includes.begin();
// add includes to file
for (; it != last_include; ++it)
file.oss << "#include <" << *it << ">" << endl;
file.oss << "\n";
}
/* ************************************************************************* */
void Module::finish_wrapper(FileWriter& file, const std::vector<std::string>& functionNames) const {
file.oss << "void mexFunction(int nargout, mxArray *out[], int nargin, const mxArray *in[])\n";
file.oss << "{\n";
file.oss << " mstream mout;\n"; // Send stdout to MATLAB console
file.oss << " std::streambuf *outbuf = std::cout.rdbuf(&mout);\n\n";
file.oss << " _" << name << "_RTTIRegister();\n\n";
file.oss << " int id = unwrap<int>(in[0]);\n\n";
file.oss << " try {\n";
file.oss << " switch(id) {\n";
for(size_t id = 0; id < functionNames.size(); ++id) {
file.oss << " case " << id << ":\n";
file.oss << " " << functionNames[id] << "(nargout, out, nargin-1, in+1);\n";
file.oss << " break;\n";
}
file.oss << " }\n";
file.oss << " } catch(const std::exception& e) {\n";
file.oss << " mexErrMsgTxt((\"Exception from gtsam:\\n\" + std::string(e.what()) + \"\\n\").c_str());\n";
file.oss << " }\n";
file.oss << "\n";
file.oss << " std::cout.rdbuf(outbuf);\n"; // Restore cout
file.oss << "}\n";
}
/* ************************************************************************* */
vector<Class> Module::ExpandTypedefInstantiations(const vector<Class>& classes, const vector<TemplateInstantiationTypedef> instantiations) {
vector<Class> expandedClasses = classes;
for(const TemplateInstantiationTypedef& inst: instantiations) {
// Add the new class to the list
expandedClasses.push_back(inst.findAndExpand(classes));
}
// Remove all template classes
for(size_t i = 0; i < expandedClasses.size(); ++i)
if(!expandedClasses[i].templateArgs.empty()) {
expandedClasses.erase(expandedClasses.begin() + size_t(i));
-- i;
}
return expandedClasses;
}
/* ************************************************************************* */
vector<string> Module::GenerateValidTypes(const vector<Class>& classes, const vector<ForwardDeclaration>& forwardDeclarations, const vector<TypedefPair>& typedefs) {
vector<string> validTypes;
for(const ForwardDeclaration& fwDec: forwardDeclarations) {
validTypes.push_back(fwDec.name());
}
validTypes.push_back("void");
validTypes.push_back("string");
validTypes.push_back("int");
validTypes.push_back("bool");
validTypes.push_back("char");
validTypes.push_back("unsigned char");
validTypes.push_back("size_t");
validTypes.push_back("double");
validTypes.push_back("Vector");
validTypes.push_back("Matrix");
//Create a list of parsed classes for dependency checking
for(const Class& cls: classes) {
validTypes.push_back(cls.qualifiedName("::"));
}
for(const TypedefPair& p: typedefs) {
validTypes.push_back(p.newType.qualifiedName("::"));
}
return validTypes;
}
/* ************************************************************************* */
void Module::WriteCollectorsAndCleanupFcn(FileWriter& wrapperFile, const std::string& moduleName, const std::vector<Class>& classes) {
// Generate all collectors
for(const Class& cls: classes) {
const string matlabUniqueName = cls.qualifiedName(),
cppName = cls.qualifiedName("::");
wrapperFile.oss << "typedef std::set<boost::shared_ptr<" << cppName << ">*> "
<< "Collector_" << matlabUniqueName << ";\n";
wrapperFile.oss << "static Collector_" << matlabUniqueName <<
" collector_" << matlabUniqueName << ";\n";
}
// generate mexAtExit cleanup function
wrapperFile.oss <<
"\nvoid _deleteAllObjects()\n"
"{\n"
" mstream mout;\n" // Send stdout to MATLAB console
" std::streambuf *outbuf = std::cout.rdbuf(&mout);\n\n"
" bool anyDeleted = false;\n";
for(const Class& cls: classes) {
const string matlabUniqueName = cls.qualifiedName();
const string cppName = cls.qualifiedName("::");
const string collectorType = "Collector_" + matlabUniqueName;
const string collectorName = "collector_" + matlabUniqueName;
// The extra curly-braces around the for loops work around a limitation in MSVC (existing
// since 2005!) preventing more than 248 blocks.
wrapperFile.oss <<
" { for(" << collectorType << "::iterator iter = " << collectorName << ".begin();\n"
" iter != " << collectorName << ".end(); ) {\n"
" delete *iter;\n"
" " << collectorName << ".erase(iter++);\n"
" anyDeleted = true;\n"
" } }\n";
}
wrapperFile.oss <<
" if(anyDeleted)\n"
" cout <<\n"
" \"WARNING: Wrap modules with variables in the workspace have been reloaded due to\\n\"\n"
" \"calling destructors, call 'clear all' again if you plan to now recompile a wrap\\n\"\n"
" \"module, so that your recompiled module is used instead of the old one.\" << endl;\n"
" std::cout.rdbuf(outbuf);\n" // Restore cout
"}\n\n";
}
/* ************************************************************************* */
void Module::WriteRTTIRegistry(FileWriter& wrapperFile, const std::string& moduleName, const std::vector<Class>& classes) {
wrapperFile.oss <<
"void _" << moduleName << "_RTTIRegister() {\n"
" const mxArray *alreadyCreated = mexGetVariablePtr(\"global\", \"gtsam_" + moduleName + "_rttiRegistry_created\");\n"
" if(!alreadyCreated) {\n"
" std::map<std::string, std::string> types;\n";
for(const Class& cls: classes) {
if(cls.isVirtual)
wrapperFile.oss <<
" types.insert(std::make_pair(typeid(" << cls.qualifiedName("::") << ").name(), \"" << cls.qualifiedName(".") << "\"));\n";
}
wrapperFile.oss << "\n";
wrapperFile.oss <<
" mxArray *registry = mexGetVariable(\"global\", \"gtsamwrap_rttiRegistry\");\n"
" if(!registry)\n"
" registry = mxCreateStructMatrix(1, 1, 0, NULL);\n"
" typedef std::pair<std::string, std::string> StringPair;\n"
" for(const StringPair& rtti_matlab: types) {\n"
" int fieldId = mxAddField(registry, rtti_matlab.first.c_str());\n"
" if(fieldId < 0)\n"
" mexErrMsgTxt(\"gtsam wrap: Error indexing RTTI types, inheritance will not work correctly\");\n"
" mxArray *matlabName = mxCreateString(rtti_matlab.second.c_str());\n"
" mxSetFieldByNumber(registry, 0, fieldId, matlabName);\n"
" }\n"
" if(mexPutVariable(\"global\", \"gtsamwrap_rttiRegistry\", registry) != 0)\n"
" mexErrMsgTxt(\"gtsam wrap: Error indexing RTTI types, inheritance will not work correctly\");\n"
" mxDestroyArray(registry);\n"
" \n"
" mxArray *newAlreadyCreated = mxCreateNumericMatrix(0, 0, mxINT8_CLASS, mxREAL);\n"
" if(mexPutVariable(\"global\", \"gtsam_" + moduleName + "_rttiRegistry_created\", newAlreadyCreated) != 0)\n"
" mexErrMsgTxt(\"gtsam wrap: Error indexing RTTI types, inheritance will not work correctly\");\n"
" mxDestroyArray(newAlreadyCreated);\n"
" }\n"
"}\n"
"\n";
}
/* ************************************************************************* */
void Module::generate_python_wrapper(const string& toolboxPath) const {
fs::create_directories(toolboxPath);
// create the unified .cpp switch file
const string wrapperName = name + "_python";
string wrapperFileName = toolboxPath + "/" + wrapperName + ".cpp";
FileWriter wrapperFile(wrapperFileName, verbose, "//");
wrapperFile.oss << "#include <boost/python.hpp>\n\n";
wrapperFile.oss << "using namespace boost::python;\n";
wrapperFile.oss << "BOOST_PYTHON_MODULE(" + name + ")\n";
wrapperFile.oss << "{\n";
// write out classes
for(const Class& cls: expandedClasses) {
cls.python_wrapper(wrapperFile);
}
// write out global functions
for(const GlobalFunctions::value_type& p: global_functions)
p.second.python_wrapper(wrapperFile);
// finish wrapper file
wrapperFile.oss << "}\n";
wrapperFile.emit(true);
}
/* ************************************************************************* */

95
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@ -1,95 +0,0 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Module.h
* @brief describes module to be wrapped
* @author Frank Dellaert
* @author Richard Roberts
**/
#pragma once
#include "Class.h"
#include "GlobalFunction.h"
#include "TemplateInstantiationTypedef.h"
#include "ForwardDeclaration.h"
#include "TypedefPair.h"
#include <string>
#include <vector>
#include <map>
namespace wrap {
/**
* A module just has a name and a list of classes
*/
struct Module {
// Filled during parsing:
std::string name; ///< module name
bool verbose; ///< verbose flag
std::vector<Class> classes; ///< list of classes
std::vector<Class> uninstantiatedClasses; ///< list of template classes after instantiated
std::vector<TemplateInstantiationTypedef> templateInstantiationTypedefs; ///< list of template instantiations
std::vector<ForwardDeclaration> forward_declarations;
std::vector<std::string> includes; ///< Include statements
GlobalFunctions global_functions;
std::vector<TypedefPair> typedefs;
// After parsing:
std::vector<Class> expandedClasses;
bool hasSerialiable;
TypeAttributesTable typeAttributes;
/// constructor that parses interface file
Module(const std::string& interfacePath, const std::string& moduleName,
bool enable_verbose = true);
/// Dummy constructor that does no parsing - use only for testing
Module(const std::string& moduleName, bool enable_verbose = true);
/// non-const function that performs parsing - typically called by constructor
/// Throws exception on failure
void parseMarkup(const std::string& data);
/// MATLAB code generation:
void generate_matlab_wrapper(const std::string& path) const;
/// Cython code generation:
void generate_cython_wrapper(const std::string& path, const std::string& pxdImports = "") const;
void emit_cython_pxd(FileWriter& file) const;
void emit_cython_pyx(FileWriter& file) const;
void generateIncludes(FileWriter& file) const;
void finish_wrapper(FileWriter& file,
const std::vector<std::string>& functionNames) const;
/// Python code generation:
void generate_python_wrapper(const std::string& path) const;
private:
static std::vector<Class> ExpandTypedefInstantiations(
const std::vector<Class>& classes,
const std::vector<TemplateInstantiationTypedef> instantiations);
static std::vector<std::string> GenerateValidTypes(
const std::vector<Class>& classes,
const std::vector<ForwardDeclaration>& forwardDeclarations,
const std::vector<TypedefPair>& typedefs);
static void WriteCollectorsAndCleanupFcn(FileWriter& wrapperFile,
const std::string& moduleName, const std::vector<Class>& classes);
static void WriteRTTIRegistry(FileWriter& wrapperFile,
const std::string& moduleName, const std::vector<Class>& classes);
};
} // \namespace wrap

140
wrap/OverloadedFunction.h
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file OverloadedFunction.h
* @brief Function that can overload its arguments only
* @author Frank Dellaert
* @date Nov 13, 2014
**/
#pragma once
#include "Function.h"
#include "Argument.h"
#include <unordered_set>
namespace wrap {
/**
* ArgumentList Overloads
*/
class ArgumentOverloads {
public:
std::vector<ArgumentList> argLists_;
public:
size_t nrOverloads() const { return argLists_.size(); }
const ArgumentList& argumentList(size_t i) const { return argLists_.at(i); }
void push_back(const ArgumentList& args) { argLists_.push_back(args); }
std::vector<ArgumentList> expandArgumentListsTemplate(
const TemplateSubstitution& ts) const {
std::vector<ArgumentList> result;
for (const ArgumentList& argList : argLists_) {
ArgumentList instArgList = argList.expandTemplate(ts);
result.push_back(instArgList);
}
return result;
}
/// Expand templates, imperative !
virtual void ExpandTemplate(const TemplateSubstitution& ts) {
argLists_ = expandArgumentListsTemplate(ts);
}
void verifyArguments(const std::vector<std::string>& validArgs,
const std::string s) const {
for (const ArgumentList& argList : argLists_) {
for (Argument arg : argList) {
std::string fullType = arg.type.qualifiedName("::");
if (find(validArgs.begin(), validArgs.end(), fullType) ==
validArgs.end())
throw DependencyMissing(fullType, "checking argument of " + s);
}
}
}
friend std::ostream& operator<<(std::ostream& os,
const ArgumentOverloads& overloads) {
for (const ArgumentList& argList : overloads.argLists_)
os << argList << std::endl;
return os;
}
std::string pyx_resolveOverloadParams(const ArgumentList& args, bool isVoid,
size_t indentLevel = 2) const {
std::string indent;
for (size_t i = 0; i < indentLevel; ++i)
indent += " ";
std::string s;
s += indent + "__params = process_args([" + args.pyx_paramsList()
+ "], args, kwargs)\n";
s += args.pyx_castParamsToPythonType(indent);
if (args.size() > 0) {
for (size_t i = 0; i < args.size(); ++i) {
// For python types we can do the assert after the assignment and save list accesses
if (args[i].type.isNonBasicType() || args[i].type.isEigen()) {
std::string param = args[i].name;
s += indent + "assert isinstance(" + param + ", "
+ args[i].type.pyxArgumentType() + ")";
if (args[i].type.isEigen()) {
s += " and " + param + ".ndim == "
+ ((args[i].type.pyxClassName() == "Vector") ? "1" : "2");
}
s += "\n";
}
}
}
return s;
}
};
class OverloadedFunction : public Function, public ArgumentOverloads {
public:
bool addOverload(const std::string& name, const ArgumentList& args,
boost::optional<const Qualified> instName = boost::none,
bool verbose = false) {
bool first = initializeOrCheck(name, instName, verbose);
ArgumentOverloads::push_back(args);
return first;
}
private:
};
// Templated checking functions
// TODO: do this via polymorphism, use transform ?
template <class F>
static std::map<std::string, F> expandMethodTemplate(
const std::map<std::string, F>& methods, const TemplateSubstitution& ts) {
std::map<std::string, F> result;
typedef std::pair<const std::string, F> NamedMethod;
for (NamedMethod namedMethod : methods) {
F instMethod = namedMethod.second;
instMethod.expandTemplate(ts);
namedMethod.second = instMethod;
result.insert(namedMethod);
}
return result;
}
template <class F>
inline void verifyArguments(const std::vector<std::string>& validArgs,
const std::map<std::string, F>& vt) {
typedef typename std::map<std::string, F>::value_type NamedMethod;
for (const NamedMethod& namedMethod : vt)
namedMethod.second.verifyArguments(validArgs);
}
} // \namespace wrap

5
wrap/Qualified.cpp
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#include <wrap/Qualified.h>
namespace wrap {
std::vector<Qualified> Qualified::BasicTypedefs;
}

370
wrap/Qualified.h
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Qualified.h
* @brief Qualified name
* @author Frank Dellaert
* @date Nov 11, 2014
**/
#pragma once
#include <wrap/spirit.h>
#include <string>
#include <vector>
#include <iostream>
namespace wrap {
/**
* Class to encapuslate a qualified name, i.e., with (nested) namespaces
*/
class Qualified {
//protected:
public:
std::vector<std::string> namespaces_; ///< Stack of namespaces
std::string name_; ///< type name
static std::vector<Qualified> BasicTypedefs;
friend struct TypeGrammar;
friend class TemplateSubstitution;
public:
/// the different categories
typedef enum {
CLASS = 1, EIGEN = 2, BASIS = 3, VOID = 4
} Category;
Category category;
/// Default constructor
Qualified() :
category(VOID) {
}
/// Construct from name and optional category
Qualified(const std::string& n, Category c = CLASS) :
name_(n), category(c) {
}
/// Construct from scoped name and optional category
Qualified(const std::string& ns1, const std::string& n, Category c = CLASS) :
name_(n), category(c) {
namespaces_.push_back(ns1);
}
/// Construct from doubly scoped name and optional category
Qualified(const std::string& ns1, const std::string& ns2,
const std::string& n, Category c = CLASS) :
name_(n), category(c) {
namespaces_.push_back(ns1);
namespaces_.push_back(ns2);
}
/// Construct from arbitrarily scoped name
Qualified(std::vector<std::string> ns, const std::string& name) :
namespaces_(ns), name_(name), category(CLASS) {
}
// Destructor
virtual ~Qualified() {}
std::string name() const {
return name_;
}
std::vector<std::string> namespaces() const {
return namespaces_;
}
// Qualified is 'abused' as template argument name as well
// this function checks whether *this matches with templateArg
bool match(const std::string& templateArg) const {
return (name_ == templateArg && namespaces_.empty()); //TODO && category == CLASS);
}
bool match(const std::vector<std::string>& templateArgs) const {
for(const std::string& s: templateArgs)
if (match(s)) return true;
return false;
}
void rename(const Qualified& q) {
namespaces_ = q.namespaces_;
name_ = q.name_;
category = q.category;
}
void expand(const std::string& expansion) {
name_ += expansion;
}
bool operator==(const Qualified& other) const {
return namespaces_ == other.namespaces_ && name_ == other.name_
&& category == other.category;
}
bool empty() const {
return namespaces_.empty() && name_.empty();
}
virtual void clear() {
namespaces_.clear();
name_.clear();
category = VOID;
}
bool isScalar() const {
return (name() == "bool" || name() == "char"
|| name() == "unsigned char" || name() == "int"
|| name() == "size_t" || name() == "double");
}
bool isVoid() const {
return name() == "void";
}
bool isString() const {
return name() == "string";
}
bool isEigen() const {
return name() == "Vector" || name() == "Matrix";
}
bool isBasicTypedef() const {
return std::find(Qualified::BasicTypedefs.begin(),
Qualified::BasicTypedefs.end(),
*this) != Qualified::BasicTypedefs.end();
}
bool isNonBasicType() const {
return name() != "This" && !isString() && !isScalar() && !isEigen() &&
!isVoid() && !isBasicTypedef();
}
public:
static Qualified MakeClass(std::vector<std::string> namespaces,
const std::string& name) {
return Qualified(namespaces, name);
}
static Qualified MakeEigen(const std::string& name) {
return Qualified(name, EIGEN);
}
static Qualified MakeBasis(const std::string& name) {
return Qualified(name, BASIS);
}
static Qualified MakeVoid() {
return Qualified("void", VOID);
}
/// Return a qualified namespace using given delimiter
std::string qualifiedNamespaces(const std::string& delimiter = "") const {
std::string result;
for (std::size_t i = 0; i < namespaces_.size(); ++i)
result += (namespaces_[i] + ((i<namespaces_.size()-1)?delimiter:""));
return result;
}
/// Return a qualified string using given delimiter
std::string qualifiedName(const std::string& delimiter = "", size_t fromLevel = 0) const {
std::string result;
for (std::size_t i = fromLevel; i < namespaces_.size(); ++i)
result += (namespaces_[i] + delimiter);
result += name_;
return result;
}
/// Return a matlab file name, i.e. "toolboxPath/+ns1/+ns2/name.m"
std::string matlabName(const std::string& toolboxPath) const {
std::string result = toolboxPath;
for (std::size_t i = 0; i < namespaces_.size(); ++i)
result += ("/+" + namespaces_[i]);
result += "/" + name_ + ".m";
return result;
}
/// name of Cython classes in pxd
/// Normal classes: innerNamespace_ClassName, e.g. GaussianFactor, noiseModel_Gaussian
/// Eigen type: Vector --> VectorXd, Matrix --> MatrixXd
std::string pxdClassName() const {
if (isEigen())
return name_ + "Xd";
else if (isNonBasicType())
return "C" + qualifiedName("_", 1);
else return name_;
}
/// name of Python classes in pyx
/// They have the same name with the corresponding Cython classes in pxd
/// But note that they are different: These are Python classes in the pyx file
/// To refer to a Cython class in pyx, we need to add "pxd.", e.g. pxd.noiseModel_Gaussian
/// see the other function pxd_class_in_pyx for that purpose.
std::string pyxClassName() const {
if (isEigen())
return name_;
else
return qualifiedName("_", 1);
}
/// Python type of function arguments in pyx to interface with normal python scripts
/// Eigen types become np.ndarray (There's no Eigen types, e.g. VectorXd, in
/// Python. We have to pass in numpy array in the arguments, which will then be
/// converted to Eigen types in Cython)
std::string pyxArgumentType() const {
if (isEigen())
return "np.ndarray";
else
return qualifiedName("_", 1);
}
/// return the Cython class in pxd corresponding to a Python class in pyx
std::string pxd_class_in_pyx() const {
if (isNonBasicType()) {
return pxdClassName();
} else if (isEigen()) {
return name_ + "Xd";
} else // basic types and not Eigen
return name_;
}
/// the internal Cython shared obj in a Python class wrappper
std::string shared_pxd_obj_in_pyx() const {
return pxdClassName() + "_";
}
std::string make_shared_pxd_class_in_pyx() const {
return "make_shared[" + pxd_class_in_pyx() + "]";
}
std::string shared_pxd_class_in_pyx() const {
return "shared_ptr[" + pxd_class_in_pyx() + "]";
}
friend std::ostream& operator<<(std::ostream& os, const Qualified& q) {
os << q.qualifiedName("::");
return os;
}
};
/* ************************************************************************* */
// http://boost-spirit.com/distrib/spirit_1_8_2/libs/spirit/doc/grammar.html
struct TypeGrammar: classic::grammar<TypeGrammar> {
wrap::Qualified& result_; ///< successful parse will be placed in here
/// Construct type grammar and specify where result is placed
TypeGrammar(wrap::Qualified& result) :
result_(result) {
}
/// Definition of type grammar
template<typename ScannerT>
struct definition: BasicRules<ScannerT> {
typedef classic::rule<ScannerT> Rule;
Rule void_p, basisType_p, eigenType_p, namespace_del_p, class_p, type_p;
definition(TypeGrammar const& self) {
using namespace wrap;
using namespace classic;
typedef BasicRules<ScannerT> Basic;
// HACK: use const values instead of using enums themselves - somehow this doesn't result in values getting assigned to gibberish
static const Qualified::Category EIGEN = Qualified::EIGEN;
static const Qualified::Category BASIS = Qualified::BASIS;
static const Qualified::Category CLASS = Qualified::CLASS;
static const Qualified::Category VOID = Qualified::VOID;
void_p = str_p("void") //
[assign_a(self.result_.name_)] //
[assign_a(self.result_.category, VOID)];
basisType_p = Basic::basisType_p //
[assign_a(self.result_.name_)] //
[assign_a(self.result_.category, BASIS)];
eigenType_p = Basic::eigenType_p //
[assign_a(self.result_.name_)] //
[assign_a(self.result_.category, EIGEN)];
namespace_del_p = Basic::namespace_p //
[push_back_a(self.result_.namespaces_)] >> str_p("::");
class_p = *namespace_del_p >> Basic::className_p //
[assign_a(self.result_.name_)] //
[assign_a(self.result_.category, CLASS)];
type_p = void_p | basisType_p | class_p | eigenType_p;
}
Rule const& start() const {
return type_p;
}
};
};
// type_grammar
/* ************************************************************************* */
// http://boost-spirit.com/distrib/spirit_1_8_2/libs/spirit/doc/grammar.html
template<char OPEN, char CLOSE>
struct TypeListGrammar: public classic::grammar<TypeListGrammar<OPEN, CLOSE> > {
typedef std::vector<wrap::Qualified> TypeList;
TypeList& result_; ///< successful parse will be placed in here
/// Construct type grammar and specify where result is placed
TypeListGrammar(TypeList& result) :
result_(result) {
}
/// Definition of type grammar
template<typename ScannerT>
struct definition {
wrap::Qualified type; ///< temporary for use during parsing
TypeGrammar type_g; ///< Individual Type grammars
classic::rule<ScannerT> type_p, typeList_p;
definition(TypeListGrammar const& self) :
type_g(type) {
using namespace classic;
type_p = type_g[push_back_a(self.result_, type)][clear_a(type)];
typeList_p = OPEN >> !type_p >> *(',' >> type_p) >> CLOSE;
}
classic::rule<ScannerT> const& start() const {
return typeList_p;
}
};
};
// TypeListGrammar
/* ************************************************************************* */
// Needed for other parsers in Argument.h and ReturnType.h
static const bool T = true;
} // \namespace wrap

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# WRAP README
The wrap library wraps the GTSAM library into a MATLAB toolbox.
It was designed to be more general than just wrapping GTSAM, but a small amount of GTSAM specific code exists in matlab.h, the include file that is included by the mex files. The GTSAM-specific functionality consists primarily of handling of Eigen Matrix and Vector classes.
For notes on creating a wrap interface, see gtsam.h for what features can be wrapped into a toolbox, as well as the current state of the toolbox for gtsam. For more technical details on the interface, please read comments in matlab.h
Some good things to know:
OBJECT CREATION
- Classes are created by special constructors, e.g., new_GaussianFactorGraph_.cpp.
These constructors are called from the MATLAB class @GaussianFactorGraph.
new_GaussianFactorGraph_ calls wrap_constructed in matlab.h, see documentation there
METHOD (AND CONSTRUCTOR) ARGUMENTS
- Simple argument types of methods, such as "double", will be converted in the
mex wrappers by calling unwrap<double>, defined in matlab.h
- Vector and Matrix arguments are normally passed by reference in GTSAM, but
in gtsam.h you need to pretend they are passed by value, to trigger the
generation of the correct conversion routines unwrap<Vector> and unwrap<Matrix>
- passing classes as arguments works, provided they are passed by reference.
This triggers a call to unwrap_shared_ptr

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/**
* @file ReturnType.cpp
* @date Nov 13, 2014
* @author Frank Dellaert
*/
#include "ReturnType.h"
#include "Class.h"
#include "utilities.h"
#include <iostream>
using namespace std;
using namespace wrap;
/* ************************************************************************* */
void ReturnType::wrap_result(const string& out, const string& result,
FileWriter& wrapperFile,
const TypeAttributesTable& typeAttributes) const {
string cppType = qualifiedName("::"), matlabType = qualifiedName(".");
if (category == CLASS) {
// Handle Classes
string objCopy, ptrType;
const bool isVirtual = typeAttributes.attributes(cppType).isVirtual;
if (isPtr)
objCopy = result; // a shared pointer can always be passed as is
else {
// but if we want an actual new object, things get more complex
if (isVirtual)
// A virtual class needs to be cloned, so the whole hierarchy is
// returned
objCopy = result + ".clone()";
else {
// ...but a non-virtual class can just be copied
objCopy = "boost::make_shared<" + cppType + ">(" + result + ")";
}
}
// e.g. out[1] = wrap_shared_ptr(pairResult.second,"gtsam.Point3", false);
wrapperFile.oss << out << " = wrap_shared_ptr(" << objCopy << ",\""
<< matlabType << "\", " << (isVirtual ? "true" : "false")
<< ");\n";
} else if (isPtr) {
// Handle shared pointer case for BASIS/EIGEN/VOID
// This case does not actually occur in GTSAM wrappers, so untested!
wrapperFile.oss << " {\n boost::shared_ptr<" << qualifiedName("::")
<< "> shared(" << result << ");" << endl;
wrapperFile.oss << out << " = wrap_shared_ptr(shared,\"" << matlabType
<< "\");\n }\n";
} else if (matlabType != "void")
// Handle normal case case for BASIS/EIGEN
wrapperFile.oss << out << " = wrap< " << qualifiedName("::") << " >(" << result
<< ");\n";
}
/* ************************************************************************* */
void ReturnType::emit_cython_pxd(
FileWriter& file, const std::string& className,
const std::vector<std::string>& templateArgs) const {
string cythonType;
if (name() == "This")
cythonType = className;
else if (match(templateArgs))
cythonType = name();
else
cythonType = pxdClassName();
if (isPtr) cythonType = "shared_ptr[" + cythonType + "]";
file.oss << cythonType;
}
/* ************************************************************************* */
std::string ReturnType::pyx_returnType(bool addShared) const {
string retType = pxd_class_in_pyx();
if (isPtr || (isNonBasicType() && addShared))
retType = "shared_ptr[" + retType + "]";
return retType;
}
/* ************************************************************************* */
std::string ReturnType::pyx_casting(const std::string& var,
bool isSharedVar) const {
if (isEigen()) {
string s = "ndarray_copy(" + var + ")";
if (pyxClassName() == "Vector")
return s + ".squeeze()";
else return s;
}
else if (isNonBasicType()) {
if (isPtr || isSharedVar)
return pyxClassName() + ".cyCreateFromShared(" + var + ")";
else {
// construct a shared_ptr if var is not a shared ptr
return pyxClassName() + ".cyCreateFromShared(" + make_shared_pxd_class_in_pyx() +
+ "(" + var + "))";
}
} else
return var;
}
/* ************************************************************************* */

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/**
* @file ReturnValue.h
* @brief Encapsulates a return type of a method
* @date Nov 13, 2014
* @author Frank Dellaert
*/
#include "Qualified.h"
#include "FileWriter.h"
#include "TypeAttributesTable.h"
#include "utilities.h"
#include <iostream>
#pragma once
namespace wrap {
/**
* Encapsulates return value of a method or function
*/
struct ReturnType : public Qualified {
bool isPtr;
friend struct ReturnValueGrammar;
/// Makes a void type
ReturnType() : isPtr(false) {}
/// Constructor, no namespaces
ReturnType(const std::string& name, Category c = CLASS, bool ptr = false)
: Qualified(name, c), isPtr(ptr) {}
void clear() override {
Qualified::clear();
isPtr = false;
}
/// Check if this type is in a set of valid types
template <class TYPES>
void verify(TYPES validtypes, const std::string& s) const {
std::string key = qualifiedName("::");
if (find(validtypes.begin(), validtypes.end(), key) == validtypes.end())
throw DependencyMissing(key, "checking return type of " + s);
}
/// @param className the actual class name to use when "This" is specified
void emit_cython_pxd(FileWriter& file, const std::string& className,
const std::vector<std::string>& templateArgs) const;
std::string pyx_returnType(bool addShared = true) const;
std::string pyx_casting(const std::string& var,
bool isSharedVar = true) const;
private:
friend struct ReturnValue;
/// Example: out[1] = wrap_shared_ptr(pairResult.second,"Test", false);
void wrap_result(const std::string& out, const std::string& result,
FileWriter& wrapperFile,
const TypeAttributesTable& typeAttributes) const;
};
//******************************************************************************
// http://boost-spirit.com/distrib/spirit_1_8_2/libs/spirit/doc/grammar.html
struct ReturnTypeGrammar : public classic::grammar<ReturnTypeGrammar> {
wrap::ReturnType& result_; ///< successful parse will be placed in here
TypeGrammar type_g;
/// Construct ReturnType grammar and specify where result is placed
ReturnTypeGrammar(wrap::ReturnType& result)
: result_(result), type_g(result_) {}
/// Definition of type grammar
template <typename ScannerT>
struct definition {
classic::rule<ScannerT> type_p;
definition(ReturnTypeGrammar const& self) {
using namespace classic;
type_p = self.type_g >> !ch_p('*')[assign_a(self.result_.isPtr, T)];
}
classic::rule<ScannerT> const& start() const { return type_p; }
};
};
// ReturnTypeGrammar
} // \namespace wrap

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/**
* @file ReturnValue.cpp
* @date Dec 1, 2011
* @author Alex Cunningham
* @author Andrew Melim
* @author Richard Roberts
*/
#include "ReturnValue.h"
#include "utilities.h"
#include <iostream>
using namespace std;
using namespace wrap;
/* ************************************************************************* */
ReturnValue ReturnValue::expandTemplate(const TemplateSubstitution& ts) const {
ReturnValue instRetVal = *this;
instRetVal.type1 = ts.tryToSubstitite(type1);
if (isPair) instRetVal.type2 = ts.tryToSubstitite(type2);
return instRetVal;
}
/* ************************************************************************* */
string ReturnValue::returnType() const {
if (isPair)
return "pair< " + type1.qualifiedName("::") + ", " +
type2.qualifiedName("::") + " >";
else
return type1.qualifiedName("::");
}
/* ************************************************************************* */
string ReturnValue::matlab_returnType() const {
return isPair ? "[first,second]" : "result";
}
/* ************************************************************************* */
void ReturnValue::wrap_result(const string& result, FileWriter& wrapperFile,
const TypeAttributesTable& typeAttributes) const {
if (isPair) {
// For a pair, store the returned pair so we do not evaluate the function
// twice
wrapperFile.oss << " auto pairResult = " << result
<< ";\n";
type1.wrap_result(" out[0]", "pairResult.first", wrapperFile,
typeAttributes);
type2.wrap_result(" out[1]", "pairResult.second", wrapperFile,
typeAttributes);
} else { // Not a pair
type1.wrap_result(" out[0]", result, wrapperFile, typeAttributes);
}
}
/* ************************************************************************* */
void ReturnValue::emit_matlab(FileWriter& proxyFile) const {
string output;
if (isPair)
proxyFile.oss << "[ varargout{1} varargout{2} ] = ";
else if (type1.category != ReturnType::VOID)
proxyFile.oss << "varargout{1} = ";
}
/* ************************************************************************* */
void ReturnValue::emit_cython_pxd(
FileWriter& file, const std::string& className,
const std::vector<std::string>& templateArgs) const {
if (isPair) {
file.oss << "pair[";
type1.emit_cython_pxd(file, className, templateArgs);
file.oss << ",";
type2.emit_cython_pxd(file, className, templateArgs);
file.oss << "] ";
} else {
type1.emit_cython_pxd(file, className, templateArgs);
file.oss << " ";
}
}
/* ************************************************************************* */
std::string ReturnValue::pyx_returnType() const {
if (isVoid()) return "";
if (isPair) {
return "pair [" + type1.pyx_returnType(false) + "," +
type2.pyx_returnType(false) + "]";
} else {
return type1.pyx_returnType(true);
}
}
/* ************************************************************************* */
std::string ReturnValue::pyx_casting(const std::string& var) const {
if (isVoid()) return "";
if (isPair) {
return "(" + type1.pyx_casting(var + ".first", false) + "," +
type2.pyx_casting(var + ".second", false) + ")";
} else {
return type1.pyx_casting(var);
}
}
/* ************************************************************************* */

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/**
* @file ReturnValue.h
*
* @brief Encapsulates a return value from a method
* @date Dec 1, 2011
* @author Alex Cunningham
* @author Richard Roberts
*/
#include "ReturnType.h"
#include "TemplateSubstitution.h"
#include "FileWriter.h"
#include "TypeAttributesTable.h"
#include "utilities.h"
#pragma once
namespace wrap {
/**
* Encapsulates return type of a method or function, possibly a pair
*/
struct ReturnValue {
bool isPair;
ReturnType type1, type2;
friend struct ReturnValueGrammar;
/// Default constructor
ReturnValue() :
isPair(false) {
}
/// Construct from type
ReturnValue(const ReturnType& type) :
isPair(false), type1(type) {
}
/// Construct from pair type arguments
ReturnValue(const ReturnType& t1, const ReturnType& t2) :
isPair(true), type1(t1), type2(t2) {
}
/// Destructor
virtual ~ReturnValue() {}
virtual void clear() {
type1.clear();
type2.clear();
isPair = false;
}
bool isVoid() const {
return !isPair && !type1.isPtr && (type1.name() == "void");
}
bool operator==(const ReturnValue& other) const {
return isPair == other.isPair && type1 == other.type1
&& type2 == other.type2;
}
/// Substitute template argument
ReturnValue expandTemplate(const TemplateSubstitution& ts) const;
std::string returnType() const;
std::string matlab_returnType() const;
void wrap_result(const std::string& result, FileWriter& wrapperFile,
const TypeAttributesTable& typeAttributes) const;
void emit_matlab(FileWriter& proxyFile) const;
/// @param className the actual class name to use when "This" is specified
void emit_cython_pxd(FileWriter& file, const std::string& className,
const std::vector<std::string>& templateArgs) const;
std::string pyx_returnType() const;
std::string pyx_casting(const std::string& var) const;
friend std::ostream& operator<<(std::ostream& os, const ReturnValue& r) {
if (!r.isPair && r.type1.category == ReturnType::VOID)
os << "void";
else
os << r.returnType();
return os;
}
};
//******************************************************************************
// http://boost-spirit.com/distrib/spirit_1_8_2/libs/spirit/doc/grammar.html
struct ReturnValueGrammar: public classic::grammar<ReturnValueGrammar> {
wrap::ReturnValue& result_; ///< successful parse will be placed in here
ReturnTypeGrammar returnType1_g, returnType2_g; ///< Type parsers
/// Construct type grammar and specify where result is placed
ReturnValueGrammar(wrap::ReturnValue& result) :
result_(result), returnType1_g(result.type1), returnType2_g(result.type2) {
}
/// Definition of type grammar
template<typename ScannerT>
struct definition {
classic::rule<ScannerT> pair_p, returnValue_p;
definition(ReturnValueGrammar const& self) {
using namespace classic;
pair_p = (str_p("pair") >> '<' >> self.returnType1_g >> ','
>> self.returnType2_g >> '>')[assign_a(self.result_.isPair, T)];
returnValue_p = pair_p | self.returnType1_g;
}
classic::rule<ScannerT> const& start() const {
return returnValue_p;
}
};
};
// ReturnValueGrammar
}// \namespace wrap

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file StaticMethod.ccp
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#include "StaticMethod.h"
#include "utilities.h"
#include "Class.h"
#include <boost/lexical_cast.hpp>
#include <boost/algorithm/string.hpp>
#include <iostream>
#include <fstream>
using namespace std;
using namespace wrap;
/* ************************************************************************* */
void StaticMethod::proxy_header(FileWriter& proxyFile) const {
string upperName = matlabName();
upperName[0] = toupper(upperName[0], locale());
proxyFile.oss << " function varargout = " << upperName << "(varargin)\n";
}
/* ************************************************************************* */
string StaticMethod::wrapper_call(FileWriter& wrapperFile, Str cppClassName,
Str matlabUniqueName, const ArgumentList& args) const {
// check arguments
// NOTE: for static functions, there is no object passed
wrapperFile.oss << " checkArguments(\"" << matlabUniqueName << "." << name_
<< "\",nargout,nargin," << args.size() << ");\n";
// unwrap arguments, see Argument.cpp
args.matlab_unwrap(wrapperFile, 0); // We start at 0 because there is no self object
// call method and wrap result
// example: out[0]=wrap<bool>(staticMethod(t));
string expanded = cppClassName + "::" + name_;
if (templateArgValue_)
expanded += ("<" + templateArgValue_->qualifiedName("::") + ">");
return expanded;
}
/* ************************************************************************* */
void StaticMethod::emit_cython_pxd(FileWriter& file, const Class& cls) const {
for(size_t i = 0; i < nrOverloads(); ++i) {
file.oss << " @staticmethod\n";
file.oss << " ";
returnVals_[i].emit_cython_pxd(file, cls.pxdClassName(), cls.templateArgs);
file.oss << name_ + ((i>0)?"_" + to_string(i):"") << " \"" << name_ << "\"" << "(";
argumentList(i).emit_cython_pxd(file, cls.pxdClassName(), cls.templateArgs);
file.oss << ") except +\n";
}
}
/* ************************************************************************* */
void StaticMethod::emit_cython_wrapper_pxd(FileWriter& file,
const Class& cls) const {
if (nrOverloads() > 1) {
for (size_t i = 0; i < nrOverloads(); ++i) {
string funcName = name_ + "_" + to_string(i);
file.oss << " @staticmethod\n";
file.oss << " cdef tuple " + funcName + "(tuple args, dict kwargs)\n";
}
}
}
/* ************************************************************************* */
void StaticMethod::emit_cython_pyx_no_overload(FileWriter& file,
const Class& cls) const {
assert(nrOverloads() == 1);
file.oss << " @staticmethod\n";
file.oss << " def " << name_ << "(";
argumentList(0).emit_cython_pyx(file);
file.oss << "):\n";
/// Call cython corresponding function and return
file.oss << argumentList(0).pyx_convertEigenTypeAndStorageOrder(" ");
string call = pyx_functionCall(cls.pxd_class_in_pyx(), name_, 0);
file.oss << " ";
if (!returnVals_[0].isVoid()) {
file.oss << "return " << returnVals_[0].pyx_casting(call) << "\n";
} else
file.oss << call << "\n";
file.oss << "\n";
}
/* ************************************************************************* */
void StaticMethod::emit_cython_pyx(FileWriter& file, const Class& cls) const {
size_t N = nrOverloads();
if (N == 1) {
emit_cython_pyx_no_overload(file, cls);
return;
}
// Dealing with overloads..
file.oss << " @staticmethod # overloaded\n";
file.oss << " def " << name_ << "(*args, **kwargs):\n";
for (size_t i = 0; i < N; ++i) {
string funcName = name_ + "_" + to_string(i);
file.oss << " success, results = " << cls.pyxClassName() << "."
<< funcName << "(args, kwargs)\n";
file.oss << " if success:\n return results\n";
}
file.oss << " raise TypeError('Could not find the correct overload')\n\n";
// Create cdef methods for all overloaded methods
for(size_t i = 0; i < N; ++i) {
string funcName = name_ + "_" + to_string(i);
file.oss << " @staticmethod\n";
file.oss << " cdef tuple " + funcName + "(tuple args, dict kwargs):\n";
file.oss << " cdef list __params\n";
if (!returnVals_[i].isVoid()) {
file.oss << " cdef " << returnVals_[i].pyx_returnType() << " return_value\n";
}
file.oss << " try:\n";
ArgumentList args = argumentList(i);
file.oss << pyx_resolveOverloadParams(args, false, 3);
/// Call cython corresponding function and return
file.oss << args.pyx_convertEigenTypeAndStorageOrder(" ");
string pxdFuncName = name_ + ((i>0)?"_" + to_string(i):"");
string call = pyx_functionCall(cls.pxd_class_in_pyx(), pxdFuncName, i);
if (!returnVals_[i].isVoid()) {
file.oss << " return_value = " << call << "\n";
file.oss << " return True, " << returnVals_[i].pyx_casting("return_value") << "\n";
} else {
file.oss << " " << call << "\n";
file.oss << " return True, None\n";
}
file.oss << " except:\n";
file.oss << " return False, None\n\n";
}
}
/* ************************************************************************* */

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file StaticMethod.h
* @brief describes and generates code for static methods
* @author Frank Dellaert
* @author Alex Cunningham
* @author Richard Roberts
**/
#pragma once
#include "MethodBase.h"
namespace wrap {
/// StaticMethod class
struct StaticMethod: public MethodBase {
typedef const std::string& Str;
friend std::ostream& operator<<(std::ostream& os, const StaticMethod& m) {
for (size_t i = 0; i < m.nrOverloads(); i++)
os << "static " << m.returnVals_[i] << " " << m.name_ << m.argLists_[i];
return os;
}
void emit_cython_pxd(FileWriter& file, const Class& cls) const;
void emit_cython_wrapper_pxd(FileWriter& file, const Class& cls) const;
void emit_cython_pyx(FileWriter& file, const Class& cls) const;
void emit_cython_pyx_no_overload(FileWriter& file, const Class& cls) const;
protected:
void proxy_header(FileWriter& proxyFile) const override;
std::string wrapper_call(FileWriter& wrapperFile, Str cppClassName,
Str matlabUniqueName, const ArgumentList& args) const override;
};
} // \namespace wrap

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Template.h
* @brief Template name
* @author Frank Dellaert
* @date Nov 11, 2014
**/
#pragma once
#include <wrap/Qualified.h>
namespace wrap {
/// The template specification that goes before a method or a class
class Template {
std::string argName_;
std::vector<Qualified> argValues_;
std::vector<int> intList_;
friend struct TemplateGrammar;
public:
/// The only way to get values into a Template is via our friendly Grammar
Template() {
}
void clear() {
argName_.clear();
argValues_.clear();
intList_.clear();
}
const std::string& argName() const {
return argName_;
}
const std::vector<int>& intList() const {
return intList_;
}
const std::vector<Qualified>& argValues() const {
return argValues_;
}
bool empty() const {
return argValues_.empty() && intList_.empty();
}
size_t nrValues() const {
return argValues_.size();
}
const Qualified& operator[](size_t i) const {
return argValues_[i];
}
bool valid() const {
return !argName_.empty() && argValues_.size() > 0;
}
};
/* ************************************************************************* */
// http://boost-spirit.com/distrib/spirit_1_8_2/libs/spirit/doc/grammar.html
struct IntListGrammar: public classic::grammar<IntListGrammar > {
typedef std::vector<int> IntList;
IntList& result_; ///< successful parse will be placed in here
/// Construct type grammar and specify where result is placed
IntListGrammar(IntList& result) :
result_(result) {
}
/// Definition of type grammar
template<typename ScannerT>
struct definition {
classic::rule<ScannerT> integer_p, intList_p;
definition(IntListGrammar const& self) {
using namespace classic;
integer_p = int_p[push_back_a(self.result_)];
intList_p = '{' >> !integer_p >> *(',' >> integer_p) >> '}';
}
classic::rule<ScannerT> const& start() const {
return intList_p;
}
};
};
// IntListGrammar
/* ************************************************************************* */
// http://boost-spirit.com/distrib/spirit_1_8_2/libs/spirit/doc/grammar.html
struct TemplateGrammar: public classic::grammar<TemplateGrammar> {
Template& result_; ///< successful parse will be placed in here
TypeListGrammar<'{', '}'> argValues_g; ///< TypeList parser
IntListGrammar intList_g; ///< TypeList parser
/// Construct type grammar and specify where result is placed
TemplateGrammar(Template& result) :
result_(result), argValues_g(result.argValues_), //
intList_g(result.intList_) {
}
/// Definition of type grammar
template<typename ScannerT>
struct definition: BasicRules<ScannerT> {
classic::rule<ScannerT> templateArgValues_p;
definition(TemplateGrammar const& self) {
using classic::str_p;
using classic::assign_a;
templateArgValues_p = (str_p("template") >> '<'
>> (BasicRules<ScannerT>::name_p)[assign_a(self.result_.argName_)]
>> '=' >> (self.argValues_g | self.intList_g) >> '>');
}
classic::rule<ScannerT> const& start() const {
return templateArgValues_p;
}
};
};
// TemplateGrammar
/// Cool initializer for tests
static inline boost::optional<Template> CreateTemplate(const std::string& s) {
Template result;
TemplateGrammar g(result);
bool success = parse(s.c_str(), g, classic::space_p).full;
if (success)
return result;
else
return boost::none;
}
} // \namespace wrap

69
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Class.cpp
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#include "TemplateInstantiationTypedef.h"
#include "utilities.h"
#include <iostream>
#include <boost/optional.hpp>
using namespace std;
namespace wrap {
Class TemplateInstantiationTypedef::findAndExpand(
const vector<Class>& classes) const {
// Find matching class
boost::optional<Class const &> matchedClass;
for(const Class& cls: classes) {
if (cls.name() == class_.name() && cls.namespaces() == class_.namespaces()
&& cls.templateArgs.size() == typeList.size()) {
matchedClass.reset(cls);
break;
}
}
if (!matchedClass)
throw DependencyMissing(class_.qualifiedName("::"),
"instantiation into typedef name " + qualifiedName("::")
+ ". Ensure that the typedef provides the correct number of template arguments.");
// Instantiate it
Class classInst = *matchedClass;
for (size_t i = 0; i < typeList.size(); ++i) {
TemplateSubstitution ts(classInst.templateArgs[i], typeList[i], *this);
classInst = classInst.expandTemplate(ts);
}
// Fix class properties
classInst.name_ = name();
classInst.namespaces_ = namespaces();
classInst.templateArgs.clear();
classInst.typedefName = matchedClass->qualifiedName("::") + "<";
if (typeList.size() > 0)
classInst.typedefName += typeList[0].qualifiedName("::");
for (size_t i = 1; i < typeList.size(); ++i)
classInst.typedefName += (", " + typeList[i].qualifiedName("::"));
classInst.typedefName += ">";
classInst.templateClass = *matchedClass;
classInst.templateInstTypeList = typeList;
return classInst;
}
}

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wrap/TemplateInstantiationTypedef.h
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Class.h
* @brief describe the C++ class that is being wrapped
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#pragma once
#include <vector>
#include <string>
#include "Class.h"
namespace wrap {
struct TemplateInstantiationTypedef : public Qualified {
Qualified class_;
std::vector<Qualified> typeList;
Class findAndExpand(const std::vector<Class>& classes) const;
};
}

53
wrap/TemplateMethod.cpp
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file TemplateMethod.ccp
* @author Duy-Nguyen Ta
**/
#include "TemplateMethod.h"
#include "Class.h"
using namespace std;
using namespace wrap;
/* ************************************************************************* */
void TemplateMethod::emit_cython_pxd(FileWriter& file, const Class& cls) const {
std::vector<std::string> templateArgs = cls.templateArgs;
templateArgs.push_back(argName);
for(size_t i = 0; i < nrOverloads(); ++i) {
file.oss << " ";
returnVals_[i].emit_cython_pxd(file, cls.pxdClassName(), templateArgs);
file.oss << name_ << "[" << argName << "]" << "(";
argumentList(i).emit_cython_pxd(file, cls.pxdClassName(), templateArgs);
file.oss << ") except +\n";
}
}
/* ************************************************************************* */
bool TemplateMethod::addOverload(Str name, const ArgumentList& args,
const ReturnValue& retVal, bool is_const,
std::string _argName, bool verbose) {
argName = _argName;
bool first = MethodBase::addOverload(name, args, retVal, boost::none, verbose);
if (first)
is_const_ = is_const;
else if (is_const && !is_const_)
throw std::runtime_error(
"Method::addOverload now designated as const whereas before it was not");
else if (!is_const && is_const_)
throw std::runtime_error(
"Method::addOverload now designated as non-const whereas before it was");
return first;
}
/* ************************************************************************* */

42
wrap/TemplateMethod.h
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file TemplateMethod.h
* @brief describes and generates code for template methods
* @author Duy-Nguyen Ta
**/
#pragma once
#include "Method.h"
namespace wrap {
/// StaticMethod class
struct TemplateMethod: public Method {
std::string argName; // name of template argument
void emit_cython_pxd(FileWriter& file, const Class& cls) const;
bool addOverload(Str name, const ArgumentList& args,
const ReturnValue& retVal, bool is_const,
std::string argName, bool verbose = false);
friend std::ostream& operator<<(std::ostream& os, const TemplateMethod& m) {
for (size_t i = 0; i < m.nrOverloads(); i++)
os << "template <" << m.argName << "> " << m.returnVals_[i] << " " << m.name_ << m.argLists_[i];
return os;
}
};
} // \namespace wrap

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wrap/TemplateSubstitution.h
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file TemplateSubstitution.h
* @brief Auxiliary class for template substitutions
* @author Frank Dellaert
* @date Nov 13, 2014
**/
#pragma once
#include "ReturnType.h"
#include <string>
#include <iostream>
namespace wrap {
/**
* e.g. TemplateSubstitution("T", gtsam::Point2, gtsam::PriorFactorPoint2)
*/
class TemplateSubstitution {
std::string templateArg_;
Qualified qualifiedType_, expandedClass_;
public:
TemplateSubstitution(const std::string& a, const Qualified& t,
const Qualified& e) :
templateArg_(a), qualifiedType_(t), expandedClass_(e) {
}
std::string expandedClassName() const {
return expandedClass_.name();
}
// Substitute if needed
Qualified tryToSubstitite(const Qualified& type) const {
if (type.match(templateArg_))
return qualifiedType_;
else if (type.match("This"))
return expandedClass_;
else
return type;
}
// Substitute if needed
ReturnType tryToSubstitite(const ReturnType& type) const {
ReturnType instType = type;
if (type.match(templateArg_))
instType.rename(qualifiedType_);
else if (type.match("This"))
instType.rename(expandedClass_);
return instType;
}
friend std::ostream& operator<<(std::ostream& os,
const TemplateSubstitution& ts) {
os << ts.templateArg_ << '/' << ts.qualifiedType_.qualifiedName("::")
<< " (" << ts.expandedClass_.qualifiedName("::") << ")";
return os;
}
};
} // \namespace wrap

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wrap/TypeAttributesTable.cpp
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Class.cpp
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#include "TypeAttributesTable.h"
#include "Class.h"
#include "ForwardDeclaration.h"
#include "utilities.h"
#include <boost/range/adaptor/map.hpp>
#include <boost/range/algorithm/copy.hpp>
#include <iterator> // std::ostream_iterator
using namespace std;
namespace wrap {
/* ************************************************************************* */
const TypeAttributes& TypeAttributesTable::attributes(const string& key) const {
try {
return table_.at(key);
} catch (const out_of_range& oor) {
cerr << "TypeAttributesTable::attributes: key " << key
<< " not found. Valid keys are:\n";
using boost::adaptors::map_keys;
ostream_iterator<string> out_it(cerr, "\n");
boost::copy(table_ | map_keys, out_it);
throw runtime_error("Internal error in wrap");
}
}
/* ************************************************************************* */
void TypeAttributesTable::addType(const Qualified& cls) {
if (!table_.insert(make_pair(cls.qualifiedName("::"), TypeAttributes(false)))
.second)
throw DuplicateDefinition("types " + cls.qualifiedName("::"));
}
/* ************************************************************************* */
void TypeAttributesTable::addClasses(const vector<Class>& classes) {
for(const Class& cls: classes) {
if (!table_.insert(
make_pair(cls.qualifiedName("::"), TypeAttributes(cls.isVirtual))).second)
throw DuplicateDefinition("class " + cls.qualifiedName("::"));
}
}
/* ************************************************************************* */
void TypeAttributesTable::addForwardDeclarations(
const vector<ForwardDeclaration>& forwardDecls) {
for(const ForwardDeclaration& fwDec: forwardDecls) {
if (!table_.insert(make_pair(fwDec.name(), TypeAttributes(fwDec.isVirtual))).second)
throw DuplicateDefinition("forward defined class " + fwDec.name());
}
}
/* ************************************************************************* */
void TypeAttributesTable::checkValidity(const vector<Class>& classes) const {
for(const Class& cls: classes) {
boost::optional<string> parent = cls.qualifiedParent();
if (parent) {
// Check that class is virtual if it has a parent
if (!cls.isVirtual)
throw AttributeError(cls.qualifiedName("::"),
"Has a base class so needs to be declared virtual, change to 'virtual class "
+ cls.name() + " ...'");
// Check that parent is virtual as well
if (!table_.at(*parent).isVirtual)
throw AttributeError(*parent,
"Is the base class of " + cls.qualifiedName("::")
+ ", so needs to be declared virtual");
}
}
}
}

71
wrap/TypeAttributesTable.h
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Class.h
* @brief describe the C++ class that is being wrapped
* @author Frank Dellaert
* @author Andrew Melim
* @author Richard Roberts
**/
#include <map>
#include <string>
#include <vector>
#pragma once
namespace wrap {
// Forward declarations
class Qualified;
class Class;
struct ForwardDeclaration;
/** Attributes about valid classes, both for classes defined in this module and
* also those forward-declared from others. At the moment this only contains
* whether the class is virtual, which is used to know how to copy the class,
* and whether to try to convert it to a more derived type upon returning it.
*/
struct TypeAttributes {
bool isVirtual;
TypeAttributes() :
isVirtual(false) {
}
TypeAttributes(bool isVirtual) :
isVirtual(isVirtual) {
}
};
/** Map of type names to attributes. */
class TypeAttributesTable {
std::map<std::string, TypeAttributes> table_;
public:
/// Constructor
TypeAttributesTable() {
}
void addClasses(const std::vector<Class>& classes);
void addType(const Qualified& types);
void addForwardDeclarations(
const std::vector<ForwardDeclaration>& forwardDecls);
/// Access attributes associated with key, informative failure
const TypeAttributes& attributes(const std::string& key) const;
/// Check that all virtual classes are properly defined
void checkValidity(const std::vector<Class>& classes) const;
};
}

28
wrap/TypedefPair.h
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#pragma once
#include "Qualified.h"
namespace wrap {
struct TypedefPair {
Qualified oldType, newType;
std::string includeFile;
TypedefPair() {}
TypedefPair(const Qualified& _oldType, const Qualified& _newType,
const std::string& includeFile)
: oldType(_oldType), newType(_newType), includeFile(includeFile) {
if (!oldType.isNonBasicType() &&
std::find(Qualified::BasicTypedefs.begin(),
Qualified::BasicTypedefs.end(),
newType) == Qualified::BasicTypedefs.end())
Qualified::BasicTypedefs.push_back(newType);
}
void emit_cython_pxd(FileWriter& file) const {
file.oss << "cdef extern from \"" << includeFile << "\" namespace \""
<< oldType.qualifiedNamespaces("::") << "\":\n";
file.oss << " ctypedef " << oldType.pxdClassName() << " "
<< newType.pxdClassName() << "\n";
}
};
}

443
wrap/matlab.h
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file matlab.h
* @brief header file to be included in MATLAB wrappers
* @date 2008
* @author Frank Dellaert
* @author Alex Cunningham
* @author Andrew Melim
* @author Richard Roberts
*
* wrapping and unwrapping is done using specialized templates, see
* http://www.cplusplus.com/doc/tutorial/templates.html
*/
#include <gtsam/base/Vector.h>
#include <gtsam/base/Matrix.h>
using gtsam::Vector;
using gtsam::Matrix;
extern "C" {
#include <mex.h>
}
#include <boost/shared_ptr.hpp>
#include <boost/make_shared.hpp>
#include <list>
#include <string>
#include <sstream>
#include <typeinfo>
#include <set>
#include <streambuf>
using namespace std;
using namespace boost; // not usual, but for conciseness of generated code
// start GTSAM Specifics /////////////////////////////////////////////////
// to enable Matrix and Vector constructor for SharedGaussian:
#define GTSAM_MAGIC_GAUSSIAN
// end GTSAM Specifics /////////////////////////////////////////////////
#if defined(__LP64__) || defined(_WIN64)
// 64-bit
#define mxUINT32OR64_CLASS mxUINT64_CLASS
#else
#define mxUINT32OR64_CLASS mxUINT32_CLASS
#endif
// "Unique" key to signal calling the matlab object constructor with a raw pointer
// to a shared pointer of the same C++ object type as the MATLAB type.
// Also present in utilities.h
static const boost::uint64_t ptr_constructor_key =
(boost::uint64_t('G') << 56) |
(boost::uint64_t('T') << 48) |
(boost::uint64_t('S') << 40) |
(boost::uint64_t('A') << 32) |
(boost::uint64_t('M') << 24) |
(boost::uint64_t('p') << 16) |
(boost::uint64_t('t') << 8) |
(boost::uint64_t('r'));
//*****************************************************************************
// Utilities
//*****************************************************************************
void error(const char* str) {
mexErrMsgIdAndTxt("wrap:error", str);
}
mxArray *scalar(mxClassID classid) {
mwSize dims[1]; dims[0]=1;
return mxCreateNumericArray(1, dims, classid, mxREAL);
}
void checkScalar(const mxArray* array, const char* str) {
int m = mxGetM(array), n = mxGetN(array);
if (m!=1 || n!=1)
mexErrMsgIdAndTxt("wrap: not a scalar in ", str);
}
// Replacement streambuf for cout that writes to the MATLAB console
// Thanks to http://stackoverflow.com/a/249008
class mstream : public std::streambuf {
protected:
virtual std::streamsize xsputn(const char *s, std::streamsize n) {
mexPrintf("%.*s",n,s);
return n;
}
virtual int overflow(int c = EOF) {
if (c != EOF) {
mexPrintf("%.1s",&c);
}
return 1;
}
};
//*****************************************************************************
// Check arguments
//*****************************************************************************
void checkArguments(const string& name, int nargout, int nargin, int expected) {
stringstream err;
err << name << " expects " << expected << " arguments, not " << nargin;
if (nargin!=expected)
error(err.str().c_str());
}
//*****************************************************************************
// wrapping C++ basis types in MATLAB arrays
//*****************************************************************************
// default wrapping throws an error: only basis types are allowed in wrap
template <typename Class>
mxArray* wrap(const Class& value) {
error("wrap internal error: attempted wrap of invalid type");
return 0;
}
// specialization to string
// wraps into a character array
template<>
mxArray* wrap<string>(const string& value) {
return mxCreateString(value.c_str());
}
// specialization to char
template<>
mxArray* wrap<char>(const char& value) {
mxArray *result = scalar(mxUINT32OR64_CLASS);
*(char*)mxGetData(result) = value;
return result;
}
// specialization to unsigned char
template<>
mxArray* wrap<unsigned char>(const unsigned char& value) {
mxArray *result = scalar(mxUINT32OR64_CLASS);
*(unsigned char*)mxGetData(result) = value;
return result;
}
// specialization to bool
template<>
mxArray* wrap<bool>(const bool& value) {
mxArray *result = scalar(mxUINT32OR64_CLASS);
*(bool*)mxGetData(result) = value;
return result;
}
// specialization to size_t
template<>
mxArray* wrap<size_t>(const size_t& value) {
mxArray *result = scalar(mxUINT32OR64_CLASS);
*(size_t*)mxGetData(result) = value;
return result;
}
// specialization to int
template<>
mxArray* wrap<int>(const int& value) {
mxArray *result = scalar(mxUINT32OR64_CLASS);
*(int*)mxGetData(result) = value;
return result;
}
// specialization to double -> just double
template<>
mxArray* wrap<double>(const double& value) {
return mxCreateDoubleScalar(value);
}
// wrap a const Eigen vector into a double vector
mxArray* wrap_Vector(const gtsam::Vector& v) {
int m = v.size();
mxArray *result = mxCreateDoubleMatrix(m, 1, mxREAL);
double *data = mxGetPr(result);
for (int i=0;i<m;i++) data[i]=v(i);
return result;
}
// specialization to Eigen vector -> double vector
template<>
mxArray* wrap<gtsam::Vector >(const gtsam::Vector& v) {
return wrap_Vector(v);
}
// wrap a const Eigen MATRIX into a double matrix
mxArray* wrap_Matrix(const gtsam::Matrix& A) {
int m = A.rows(), n = A.cols();
#ifdef DEBUG_WRAP
mexPrintf("wrap_Matrix called with A = \n", m,n);
gtsam::print(A);
#endif
mxArray *result = mxCreateDoubleMatrix(m, n, mxREAL);
double *data = mxGetPr(result);
// converts from column-major to row-major
for (int j=0;j<n;j++) for (int i=0;i<m;i++,data++) *data = A(i,j);
return result;
}
// specialization to Eigen MATRIX -> double matrix
template<>
mxArray* wrap<gtsam::Matrix >(const gtsam::Matrix& A) {
return wrap_Matrix(A);
}
//*****************************************************************************
// unwrapping MATLAB arrays into C++ basis types
//*****************************************************************************
// default unwrapping throws an error
// as wrap only supports passing a reference or one of the basic types
template <typename T>
T unwrap(const mxArray* array) {
error("wrap internal error: attempted unwrap of invalid type");
return T();
}
// specialization to string
// expects a character array
// Warning: relies on mxChar==char
template<>
string unwrap<string>(const mxArray* array) {
char *data = mxArrayToString(array);
if (data==NULL) error("unwrap<string>: not a character array");
string str(data);
mxFree(data);
return str;
}
// Check for 64-bit, as Mathworks says mxGetScalar only good for 32 bit
template <typename T>
T myGetScalar(const mxArray* array) {
switch (mxGetClassID(array)) {
case mxINT64_CLASS:
return (T) *(boost::int64_t*) mxGetData(array);
case mxUINT64_CLASS:
return (T) *(boost::uint64_t*) mxGetData(array);
default:
// hope for the best!
return (T) mxGetScalar(array);
}
}
// specialization to bool
template<>
bool unwrap<bool>(const mxArray* array) {
checkScalar(array,"unwrap<bool>");
return myGetScalar<bool>(array);
}
// specialization to char
template<>
char unwrap<char>(const mxArray* array) {
checkScalar(array,"unwrap<char>");
return myGetScalar<char>(array);
}
// specialization to unsigned char
template<>
unsigned char unwrap<unsigned char>(const mxArray* array) {
checkScalar(array,"unwrap<unsigned char>");
return myGetScalar<unsigned char>(array);
}
// specialization to int
template<>
int unwrap<int>(const mxArray* array) {
checkScalar(array,"unwrap<int>");
return myGetScalar<int>(array);
}
// specialization to size_t
template<>
size_t unwrap<size_t>(const mxArray* array) {
checkScalar(array, "unwrap<size_t>");
return myGetScalar<size_t>(array);
}
// specialization to double
template<>
double unwrap<double>(const mxArray* array) {
checkScalar(array,"unwrap<double>");
return myGetScalar<double>(array);
}
// specialization to Eigen vector
template<>
gtsam::Vector unwrap< gtsam::Vector >(const mxArray* array) {
int m = mxGetM(array), n = mxGetN(array);
if (mxIsDouble(array)==false || n!=1) error("unwrap<vector>: not a vector");
#ifdef DEBUG_WRAP
mexPrintf("unwrap< gtsam::Vector > called with %dx%d argument\n", m,n);
#endif
double* data = (double*)mxGetData(array);
gtsam::Vector v(m);
for (int i=0;i<m;i++,data++) v(i) = *data;
#ifdef DEBUG_WRAP
gtsam::print(v);
#endif
return v;
}
// specialization to Eigen matrix
template<>
gtsam::Matrix unwrap< gtsam::Matrix >(const mxArray* array) {
if (mxIsDouble(array)==false) error("unwrap<matrix>: not a matrix");
int m = mxGetM(array), n = mxGetN(array);
#ifdef DEBUG_WRAP
mexPrintf("unwrap< gtsam::Matrix > called with %dx%d argument\n", m,n);
#endif
double* data = (double*)mxGetData(array);
gtsam::Matrix A(m,n);
// converts from row-major to column-major
for (int j=0;j<n;j++) for (int i=0;i<m;i++,data++) A(i,j) = *data;
#ifdef DEBUG_WRAP
gtsam::print(A);
#endif
return A;
}
/*
[create_object] creates a MATLAB proxy class object with a mexhandle
in the self property. Matlab does not allow the creation of matlab
objects from within mex files, hence we resort to an ugly trick: we
invoke the proxy class constructor by calling MATLAB with a special
uint64 value ptr_constructor_key and the pointer itself. MATLAB
allocates the object. Then, the special constructor in our wrap code
that is activated when the ptr_constructor_key is passed in passes
the pointer back into a C++ function to add the pointer to its
collector. We go through this extra "C++ to MATLAB to C++ step" in
order to be able to add to the collector could be in a different wrap
module.
*/
mxArray* create_object(const std::string& classname, void *pointer, bool isVirtual, const char *rttiName) {
mxArray *result;
mxArray *input[3];
int nargin = 2;
// First input argument is pointer constructor key
input[0] = mxCreateNumericMatrix(1, 1, mxUINT64_CLASS, mxREAL);
*reinterpret_cast<boost::uint64_t*>(mxGetData(input[0])) = ptr_constructor_key;
// Second input argument is the pointer
input[1] = mxCreateNumericMatrix(1, 1, mxUINT32OR64_CLASS, mxREAL);
*reinterpret_cast<void**>(mxGetData(input[1])) = pointer;
// If the class is virtual, use the RTTI name to look up the derived matlab type
const char *derivedClassName;
if(isVirtual) {
const mxArray *rttiRegistry = mexGetVariablePtr("global", "gtsamwrap_rttiRegistry");
if(!rttiRegistry)
mexErrMsgTxt(
"gtsam wrap: RTTI registry is missing - it could have been cleared from the workspace."
" You can issue 'clear all' to completely clear the workspace, and next time a wrapped object is"
" created the RTTI registry will be recreated.");
const mxArray *derivedNameMx = mxGetField(rttiRegistry, 0, rttiName);
if(!derivedNameMx)
mexErrMsgTxt((
"gtsam wrap: The derived class type " + string(rttiName) + " was not found in the RTTI registry. "
"Try calling 'clear all' twice consecutively - we have seen things not get unloaded properly the "
"first time. If this does not work, this may indicate an inconsistency in your wrap interface file. "
"The most likely cause for this is that a base class was marked virtual in the wrap interface "
"definition header file for gtsam or for your module, but a derived type was returned by a C++ "
"function and that derived type was not marked virtual (or was not specified in the wrap interface "
"definition header at all).").c_str());
size_t strLen = mxGetN(derivedNameMx);
char *buf = new char[strLen+1];
if(mxGetString(derivedNameMx, buf, strLen+1))
mexErrMsgTxt("gtsam wrap: Internal error reading RTTI table, try 'clear all' to clear your workspace and reinitialize the toolbox.");
derivedClassName = buf;
input[2] = mxCreateString("void");
nargin = 3;
} else {
derivedClassName = classname.c_str();
}
// Call special pointer constructor, which sets 'self'
mexCallMATLAB(1,&result, nargin, input, derivedClassName);
// Deallocate our memory
mxDestroyArray(input[0]);
mxDestroyArray(input[1]);
if(isVirtual) {
mxDestroyArray(input[2]);
delete[] derivedClassName;
}
return result;
}
/*
When the user calls a method that returns a shared pointer, we create
an ObjectHandle from the shared_pointer and return it as a proxy
class to matlab.
*/
template <typename Class>
mxArray* wrap_shared_ptr(boost::shared_ptr< Class > shared_ptr, const std::string& matlabName, bool isVirtual) {
// Create actual class object from out pointer
mxArray* result;
if(isVirtual) {
boost::shared_ptr<void> void_ptr(shared_ptr);
result = create_object(matlabName, &void_ptr, isVirtual, typeid(*shared_ptr).name());
} else {
boost::shared_ptr<Class> *heapPtr = new boost::shared_ptr<Class>(shared_ptr);
result = create_object(matlabName, heapPtr, isVirtual, "");
}
return result;
}
template <typename Class>
boost::shared_ptr<Class> unwrap_shared_ptr(const mxArray* obj, const string& propertyName) {
mxArray* mxh = mxGetProperty(obj,0, propertyName.c_str());
if (mxGetClassID(mxh) != mxUINT32OR64_CLASS || mxIsComplex(mxh)
|| mxGetM(mxh) != 1 || mxGetN(mxh) != 1) error(
"Parameter is not an Shared type.");
boost::shared_ptr<Class>* spp = *reinterpret_cast<boost::shared_ptr<Class>**> (mxGetData(mxh));
return *spp;
}
//// throw an error if unwrap_shared_ptr is attempted for an Eigen Vector
//template <>
//Vector unwrap_shared_ptr<Vector>(const mxArray* obj, const string& propertyName) {
// bool unwrap_shared_ptr_Vector_attempted = false;
// BOOST_STATIC_ASSERT(unwrap_shared_ptr_Vector_attempted, "Vector cannot be unwrapped as a shared pointer");
// return Vector();
//}
//// throw an error if unwrap_shared_ptr is attempted for an Eigen Matrix
//template <>
//Matrix unwrap_shared_ptr<Matrix>(const mxArray* obj, const string& propertyName) {
// bool unwrap_shared_ptr_Matrix_attempted = false;
// BOOST_STATIC_ASSERT(unwrap_shared_ptr_Matrix_attempted, "Matrix cannot be unwrapped as a shared pointer");
// return Matrix();
//}

70
wrap/python/pybind11/.appveyor.yml
View File

@ -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*

38
wrap/python/pybind11/.gitignore vendored
View File

@ -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
wrap/python/pybind11/.gitmodules vendored
View File

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

3
wrap/python/pybind11/.readthedocs.yml
View File

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

280
wrap/python/pybind11/.travis.yml
View File

@ -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
wrap/python/pybind11/CMakeLists.txt
View File

@ -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
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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.

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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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![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)
[![Documentation Status](https://readthedocs.org/projects/pybind11/badge/?version=stable)](http://pybind11.readthedocs.org/en/stable/?badge=stable)
[![Gitter chat](https://img.shields.io/gitter/room/gitterHQ/gitter.svg)](https://gitter.im/pybind/Lobby)
[![Build Status](https://travis-ci.org/pybind/pybind11.svg?branch=master)](https://travis-ci.org/pybind/pybind11)
[![Build status](https://ci.appveyor.com/api/projects/status/riaj54pn4h08xy40?svg=true)](https://ci.appveyor.com/project/wjakob/pybind11)
**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,
Lori A. Burns,
Sylvain Corlay,
Trent Houliston,
Axel Huebl,
@hulucc,
Sergey Lyskov
Johan Mabille,
Tomasz Miąsko,
Dean Moldovan,
Ben Pritchard,
Jason Rhinelander,
Boris Schäling,
Pim Schellart,
Henry Schreiner,
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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PROJECT_NAME = pybind11
INPUT = ../include/pybind11/
RECURSIVE = YES
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GENERATE_XML = YES
XML_OUTPUT = .build/doxygenxml
XML_PROGRAMLISTING = YES
MACRO_EXPANSION = YES
EXPAND_ONLY_PREDEF = YES
EXPAND_AS_DEFINED = PYBIND11_RUNTIME_EXCEPTION
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ALIASES += "endrst=\endverbatim"
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.wy-table-responsive table td,
.wy-table-responsive table th {
white-space: initial !important;
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vertical-align: top !important;
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div[class^='highlight'] pre {
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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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@ -1,289 +0,0 @@
.. _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.dev1'
# 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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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}
}

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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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.. _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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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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breathe == 4.5.0

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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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/*
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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/*
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(time_point_cast<system_clock::duration>(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)

2
wrap/python/pybind11/include/pybind11/common.h
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@ -1,2 +0,0 @@
#include "detail/common.h"
#warning "Including 'common.h' is deprecated. It will be removed in v3.0. Use 'pybind11.h'."

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