This package aims to provide a Boost.Python-like wrapping for C++ types and functions to Julia. The idea is to write the code for the Julia wrapper in C++, and then use a one-liner on the Julia side to make the wrapped C++ library available there.
The mechanism behind this package is that functions and types are registered in C++ code that is compiled into a dynamic library. This dynamic library is then loaded into Julia, where the Julia part of this package uses the data provided through a C interface to generate functions accessible from Julia. The functions are passed to Julia either as raw function pointers (for regular C++ functions that don't need argument or return type conversion) or std::functions (for lambda expressions and automatic conversion of arguments and return types). The Julia side of this package wraps all this into Julia methods automatically.
For this to work, the user must have a C++ compiler installed which supports C++17 (e.g. GCC 7, clang 5; for macOS users that means Xcode 9.3).
With Cxx.jl it is possible to directly access C++ using the @cxx
macro from Julia.
So when facing the task of wrapping a C++ library in a Julia package, authors now have two options:
- Use Cxx.jl to write the wrapper package in Julia code (much like one uses
ccall
for wrapping a C library) - Use CxxWrap to write the wrapper completely in C++ (and one line of Julia code to load the .so)
Boost.Python also uses the latter (C++-only) approach, so translating existing Python bindings based on Boost.Python may be easier using CxxWrap.
- Support for C++ functions, member functions and lambdas
- Classes with single inheritance, using abstract base classes on the Julia side
- Trivial C++ classes can be converted to a Julia isbits immutable
- Template classes map to parametric types, for the instantiations listed in the wrapper
- Automatic wrapping of default and copy constructor (mapped to
copy
) if defined on the wrapped C++ class - Facilitate calling Julia functions from C++
Just like any registered package, in pkg mode (]
at the REPL)
add CxxWrap
CxxWrap v0.10 and later depends on the libcxxwrap_julia_jll
JLL package to manage the libcxxwrap-julia
binaries. See the libcxxwrap-julia Readme for information on how to build this library yourself and force CxxWrap to use your own version.
Let's try to reproduce the example from the Boost.Python tutorial.
Suppose we want to expose the following C++ function to Julia in a module called CppHello
:
std::string greet()
{
return "hello, world";
}
Using the C++ side of CxxWrap
, this can be exposed as follows:
#include "jlcxx/jlcxx.hpp"
JLCXX_MODULE define_julia_module(jlcxx::Module& mod)
{
mod.method("greet", &greet);
}
Once this code is compiled into a shared library (say libhello.so
) it can be used in Julia as follows:
# Load the module and generate the functions
module CppHello
using CxxWrap
@wrapmodule(() -> joinpath("path/to/built/lib","libhello"))
function __init__()
@initcxx
end
end
# Call greet and show the result
@show CppHello.greet()
The code for this example can be found in [hello.cpp
] in the examples directory of the libcxxwrap-julia
project and test/hello.jl
.
Note that the __init__
function is necessary to support precompilation, which is on by default since Julia 1.0.
The recommended way to compile the C++ code is to use CMake to discover libcxxwrap-julia
and the Julia libraries.
A full example is in the testlib
directory of libcxxwrap-julia
.
The following sequence of commands can be used to build:
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH=/path/to/libcxxwrap-julia-prefix /path/to/sourcedirectory
cmake --build . --config Release
The path for CMAKE_PREFIX_PATH
can be obtained from Julia using:
julia> using CxxWrap
julia> CxxWrap.prefix_path()
The default binaries installed with CxxWrap are cross-compiled using GCC, and thus incompatible with Visual Studio C++ (MSVC).
In MSVC 2019, it is easy to check out libcxxwrap-julia
from git, and then build it and the wrapper module from source.
Details are provided in the README.
Above, we defined the module entry point as a function JLCXX_MODULE define_julia_module(jlcxx::Module& mod)
.
In the general case, there may be multiple modules defined in a single library, and each should have its own entry point, called within the appropriate module:
JLCXX_MODULE define_module_a(jlcxx::Module& mod)
{
// add stuff for A
}
JLCXX_MODULE define_module_b(jlcxx::Module& mod)
{
// add stuff for B
}
In Julia, the name of the entry point must now be specified explicitly:
module A
using CxxWrap
@wrapmodule(() -> "mylib.so",:define_module_a)
end
module B
using CxxWrap
@wrapmodule(() -> "mylib.so",:define_module_b)
end
In specific cases, it may also be necessary to specify dlopen
flags such as RTLD_GLOBAL
.
These can be supplied in a third, optional argument to @wrapmodule
, e.g:
@wrapmodule(() -> CxxWrapCore.libcxxwrap_julia_stl, :define_cxxwrap_stl_module, Libdl.RTLD_GLOBAL)
A more extensive example, including wrapping a C++11 lambda and conversion for arrays can be found in examples/functions.cpp
and test/functions.jl
.
This test also includes some performance measurements, showing that the function call overhead is the same as using ccall
on a C function if the C++ function is a regular function and does not require argument conversion.
When std::function
is used (e.g. for C++ lambdas) extra overhead appears, as expected.
Consider the following C++ class to be wrapped:
struct World
{
World(const std::string& message = "default hello") : msg(message){}
void set(const std::string& msg) { this->msg = msg; }
std::string greet() { return msg; }
std::string msg;
~World() { std::cout << "Destroying World with message " << msg << std::endl; }
};
Wrapped in the entry point function as before and defining a module CppTypes
, the code for exposing the type and some methods to Julia is:
types.add_type<World>("World")
.constructor<const std::string&>()
.method("set", &World::set)
.method("greet", &World::greet);
Here, the first line just adds the type.
The second line adds the non-default constructor taking a string.
Finally, the two method
calls add member functions, using a pointer-to-member.
The member functions become free functions in Julia, taking their object as the first argument.
This can now be used in Julia as
w = CppTypes.World()
@test CppTypes.greet(w) == "default hello"
CppTypes.set(w, "hello")
@test CppTypes.greet(w) == "hello"
The manually added constructor using the constructor
function also creates a finalizer.
This can be disabled by adding the argument false
:
types.add_type<World>("World")
.constructor<const std::string&>(false);
The add_type
function actually builds two Julia types related to World
.
The first is an abstract type:
abstract type World end
The second is a mutable type (the "allocated" or "boxed" type) with the following structure:
mutable struct WorldAllocated <: World
cpp_object::Ptr{Cvoid}
end
This type needs to be mutable, because it must have a finalizer attached to it that deletes the held C++ object.
This means that the variable w
in the above example is of concrete type WorldAllocated
and letting it go out of scope may trigger the finalizer and delete the object.
When calling a C++ constructor, it is the responsibility of the caller to manage the lifetime of the resulting variable.
The above types are used in method generation as follows, considering for example the greet method taking a World
argument:
greet(w::World) = ccall($fpointer, Any, (Ptr{Cvoid}, WorldRef), $thunk, cconvert(WorldRef, w))
Here, the cconvert
from WorldAllocated
to WorldRef
is defined automatically when creating the type.
Warning: The ordering of the C++ code matters: types used as function arguments or return types must be added before they are used in a function.
The full code for this example and more info on immutables and bits types can be found in examples/types.cpp
and test/types.jl
.
Values returned from C++ can be checked for being null using the isnull
function.
It is possible to add methods directly to e.g. the Julia Base
module, using set_override_module
.
After calling this, all methods will be added to the specified module.
To revert to the default behavior of adding methods to the current module, call unset_override_module
.
mod.add_type<A>("A", jlcxx::julia_type("AbstractFloat", "Base"))
.constructor<double>();
mod.set_override_module(mod.julia_module());
// == will be in the wrapped module:
mod.method("==", [](A& a, A& b) { return a == b; });
mod.set_override_module(jl_base_module);
// The following methods will be in Base
mod.method("+", [](A& a, A& b) { return a + b; });
mod.method("float", [](A& a) { return a.get_val(); });
// Revert to default behavior
mod.unset_override_module();
mod.method("val", [](A& a) { return a.get_val(); });
To encapsulate inheritance, types must first inherit from each other in C++, so a static_cast
to the base type can work:
struct A
{
virtual std::string message() const = 0;
std::string data = "mydata";
};
struct B : A
{
virtual std::string message() const
{
return "B";
}
};
When adding the type, add the supertype as a second argument:
types.add_type<A>("A").method("message", &A::message);
types.add_type<B>("B", jlcxx::julia_base_type<A>());
The supertype is of type jl_datatype_t*
and using the template function jlcxx::julia_base_type
looks up the abstract type associated with A
here.
Since the concrete arguments given to ccall
are the reference types, we need a way to convert BRef
into ARef
.
To allow CxxWrap to figure out the correct static_cast to use, the hierarchy must be defined at compile time as follows:
namespace jlcxx
{
template<> struct SuperType<B> { typedef A type; };
}
There is also a variant taking a string for the type name and an optional Julia module name as second argument, which is useful for inheriting from a type defined in Julia, e.g.:
mod.add_type<Teuchos::ParameterList>("ParameterList", jlcxx::julia_type("AbstractDict", "Base"))
The value returned by add_type
also had a dt()
method, useful in the case of template types:
auto multi_vector_base = mod.add_type<Parametric<TypeVar<1>>>("MultiVectorBase");
auto vector_base = mod.add_type<Parametric<TypeVar<1>>>("VectorBase", multi_vector_base.dt());
See the test at examples/inheritance.cpp
and test/inheritance.jl
.
Enum types are converted to strongly-typed bits types on the Julia side. Consider the C++ enum:
enum MyEnum
{
EnumValA,
EnumValB
};
This is registered as follows:
JLCXX_MODULE define_types_module(jlcxx::Module& types)
{
types.add_bits<MyEnum>("MyEnum", jlcxx::julia_type("CppEnum"));
types.set_const("EnumValA", EnumValA);
types.set_const("EnumValB", EnumValB);
}
The enum constants will be available on the Julia side as CppTypes.EnumValA
and CppTypes.EnumValB
, both of type CppTypes.MyEnum
.
Wrapped C++ functions taking a MyEnum
will only accept a value of type CppTypes.MyEnum
in Julia.
The natural Julia equivalent of a C++ template class is the parametric type. The mapping is complicated by the fact that all possible parameter values must be compiled in advance, requiring a deviation from the syntax for adding a regular class. Consider the following template class:
template<typename A, typename B>
struct TemplateType
{
typedef typename A::val_type first_val_type;
typedef typename B::val_type second_val_type;
first_val_type get_first()
{
return A::value();
}
second_val_type get_second()
{
return B::value();
}
};
The code for wrapping this is:
types.add_type<Parametric<TypeVar<1>, TypeVar<2>>>("TemplateType")
.apply<TemplateType<P1,P2>, TemplateType<P2,P1>>([](auto wrapped)
{
typedef typename decltype(wrapped)::type WrappedT;
wrapped.method("get_first", &WrappedT::get_first);
wrapped.method("get_second", &WrappedT::get_second);
});
The first line adds the parametric type, using the generic placeholder Parametric
and a TypeVar
for each parameter.
On the second line, the possible instantiations are created by calling apply
on the result of add_type
.
Here, we allow for TemplateType<P1,P2>
and TemplateType<P2,P1>
to exist, where P1
and P2
are C++ classes that also must be wrapped and that fulfill the requirements for being a parameter to TemplateType
.
The argument to apply
is a functor (generic C++14 lambda here) that takes the wrapped instantiated type (called wrapped
here) as argument.
This object can then be used as before to define methods.
In the case of a generic lambda, the actual type being wrapped can be obtained using decltype
as shown on the 4th line.
Use on the Julia side:
import ParametricTypes.TemplateType, ParametricTypes.P1, ParametricTypes.P2
p1 = TemplateType{P1, P2}()
p2 = TemplateType{P2, P1}()
@test ParametricTypes.get_first(p1) == 1
@test ParametricTypes.get_second(p2) == 1
There is also an apply_combination
method to make applying all combinations of parameters shorter to write.
Full example and test including non-type parameters at: examples/parametric.cpp
and test/parametric.jl
.
The default constructor and any manually added constructor using the constructor
function will automatically create a Julia object that has a finalizer attached that calls delete to free the memory.
To write a C++ function that returns a new object that can be garbage-collected in Julia, use the jlcxx::create
function:
jlcxx::create<Class>(constructor_arg1, ...);
This will return the new C++ object wrapped in a jl_value_t*
that has a finalizer.
The copy constructor is mapped to Julia's standard copy
function. Using the .
-notation it can be used to easily create a Julia arrays from the elements of e.g. an std::vector
:
wvec = cpp_function_returning_vector()
julia_array = copy.(wvec)
Since Julia supports overloading the function call operator ()
, this can be used to wrap operator()
by just omitting the method name:
struct CallOperator
{
int operator()() const
{
return 43;
}
};
// ...
types.add_type<CallOperator>("CallOperator").method(&CallOperator::operator());
Use in Julia:
call_op = CallOperator()
@test call_op() == 43
The C++ function does not even have to be operator()
, but of course it is most logical use case.
By default, overloaded signatures for wrapper methods are generated, so a method taking a double
in C++ can be called with e.g. an Int
in Julia.
Wrapping a function like this:
mod.method("half_lambda", [](const double a) {return a*0.5;});
then yields the methods:
half_lambda(arg1::Int64)
half_lambda(arg1::Float64)
In some cases (e.g. when a template parameter depends on the number type) this is not desired, so the behavior can be disabled on a per-argument basis using the StrictlyTypedNumber
type.
Wrapping a function like this:
mod.method("strict_half", [](const jlcxx::StrictlyTypedNumber<double> a) {return a.value*0.5;});
will only yield the Julia method:
strict_half(arg1::Float64)
Note that in C++ the number value is accessed using the value
member of StrictlyTypedNumber
.
The automatic overloading can be customized.
For example, to allow passing an Int64
where a UInt64
is normally expected, the following method can be added:
CxxWrap.argument_overloads(t::Type{UInt64}) = [Int64]
Due to the fact that built-in integer types don't have an imposed size, they can't be mapped to Julia integer types in the same way on every platform. For CxxWrap, we take the following approach:
- Fixed-size types such as
int32_t
are mapped directly to their Julia equivalents - Built-in types are mapped to a named type, e.g. the C++ type
long
becomesCxxLong
in Julia. If in the given C++ implementation we havelong == int64_t
, then in JuliaCxxLong
will be an alias forInt64
, otherwise it is its own bits type.
The following table gives an overview of the mapping, where some of the Cxx*
types may actually be aliases for a Julia type:
C++ | Julia |
---|---|
int8_t |
Int8 |
uint8_t |
UInt8 |
int16_t |
Int16 |
uint16_t |
UInt16 |
int32_t |
Int32 |
uint32_t |
UInt32 |
int64_t |
Int64 |
uint64_t |
UInt64 |
bool |
CxxBool |
char |
CxxChar |
wchar_t |
CxxWchar |
signed char |
CxxSignedChar |
unsigned char |
CxxUChar |
short |
CxxShort |
unsigned short |
CxxUShort |
int |
CxxInt |
unsigned int |
CxxUInt |
long |
CxxLong |
unsigned long |
CxxULong |
long long |
CxxLongLong |
unsigned long long |
CxxULongLong |
Simple pointers and references are treated the same way, and wrapped in a struct with as a single member the pointer to the C++ object.
A reference to a pointer allows changing the referred object, e.g.:
void writepointerref(MyData*& ptrref)
{
delete ptrref;
ptrref = new MyData(30);
}
is called from Julia as:
d = PtrModif.MyData()
writepointerref(Ref(d))
Note that this modifies d
itself, so d
must be a MyDataAllocated
.
More details are in the pointer_modification
example.
In the Julia C calling convention, a boolean is a Cuchar
, so to pass a reference to a boolean to C++ you need:
bref = Ref{Cuchar}(0)
boolref(bref)
Where boolref
on the C++ side is:
mod.method("boolref", [] (bool& b)
{
b = !b;
});
Strictly speaking, the representation of bool
in C++ is implementation-defined, so this conversion relies on undefined behavior. Passing references to boolean is therefore not recommended, it is better to sidestep this by writing e.g. a wrapper function in C++ that returns a boolean by value.
Currently, std::shared_ptr
, std::unique_ptr
and std::weak_ptr
are supported transparently.
Returning one of these pointer types will return an object inheriting from SmartPointer{T}
:
types.method("shared_world_factory", []()
{
return std::shared_ptr<World>(new World("shared factory hello"));
});
The shared pointer can then be used in a function taking an object of type World
like this (the module is named CppTypes
here):
swf = CppTypes.shared_world_factory()
CppTypes.greet(swf[])
Suppose we have a "smart" pointer type defined as follows:
template<typename T>
struct MySmartPointer
{
MySmartPointer(T* ptr) : m_ptr(ptr)
{
}
MySmartPointer(std::shared_ptr<T> ptr) : m_ptr(ptr.get())
{
}
T& operator*() const
{
return *m_ptr;
}
T* m_ptr;
};
Specializing in the jlcxx
namespace:
namespace jlcxx
{
template<typename T> struct IsSmartPointerType<cpp_types::MySmartPointer<T>> : std::true_type { };
template<typename T> struct ConstructorPointerType<cpp_types::MySmartPointer<T>> { typedef std::shared_ptr<T> type; };
}
Here, the first line marks our type as a smart pointer, enabling automatic conversion from the pointer to its referenced type and adding the dereferencing pointer.
If the type uses inheritance and the hierarchy is defined using SuperType
, automatic conversion to the pointer or reference of the base type is also supported.
The second line indicates that our smart pointer can be constructed from a std::shared_ptr
, also adding auto-conversion for that case.
This is useful for a relation as in std::weak_ptr
and std::shared_ptr
, for example.
Because C++ functions often return references or pointers, writing Julia functions that operate on C++ types can be tricky. For example, writing a function like:
julia_greet(w::World) = greet_cpp(w)
If World
is a type from C++, this will only work with objects that have been constructed directly or that were returned by value from C++.
To make it work with references and pointers, we would need an additional method:
julia_greet(w::CxxWrap.CxxBaseRef{World}) = greet_cpp(w[])
Note that in the general case, both the signature and the implementation need to change, making this cumbersome when there are many functions like this.
Enter the @cxxdereference
macro.
Declaring the function like this makes sure it can accept both values and references:
@cxxdereference julia_greet(w::World) = greet_cpp(w)
The @cxxdereference
macro changes the function into:
function julia_greet(w::CxxWrap.reference_type_union(World))
w = CxxWrap.dereference_argument(w)
greet_cpp(w)
end
The type of w
is now calculated by the CxxWrap.reference_type_union
function, which resolves to Union{World, CxxWrap.CxxBaseRef{World}, CxxWrap.SmartPointer{World}}
.
The behavior of the macro can be customized by adding methods to CxxWrap.reference_type_union
and CxxWrap.dereference_argument
.
When directly adding a regular free C++ function as a method, it will be called directly using ccall
and any exception will abort the Julia program.
To avoid this, you can force wrapping it in an std::function
to intercept the exception automatically by setting the force_convert
argument to method
to true:
mod.method("test_exception", test_exception, true);
Member functions and lambdas are automatically wrapped in an std::function
and so any exceptions thrown there are always intercepted and converted to a Julia exception.
C++11 tuples can be converted to Julia tuples by including the containers/tuple.hpp
header:
#include "jlcxx/jlcxx.hpp"
#include "jlcxx/tuple.hpp"
JLCXX_MODULE define_types_module(jlcxx::Module& containers)
{
containers.method("test_tuple", []() { return std::make_tuple(1, 2., 3.f); });
}
Use in Julia:
using CxxWrap
using Base.Test
module Containers
@wrapmodule(() -> libcontainers)
export test_tuple
end
using Containers
@test test_tuple() == (1,2.0,3.0f0)
The ArrayRef
type is provided to work conveniently with array data from Julia.
Defining a function like this in C++:
void test_array_set(jlcxx::ArrayRef<double> a, const int64_t i, const double v)
{
a[i] = v;
}
This can be called from Julia as:
ta = [1.,2.]
test_array_set(ta, 0, 3.)
The ArrayRef
type provides basic functionality:
- iterators
size
[]
read-write accessorpush_back
for appending elements
Note that ArrayRef
only works with primitive types, if you need a "boxed" type it has to be made an array of Any
with type ArrayRef<jl_value_t*>
in C++.
Sometimes, a function returns a const
pointer that is an array, either of fixed size or with a size that can be determined from elsewhere in the API.
Example:
const double* const_vector()
{
static double d[] = {1., 2., 3};
return d;
}
In this simple case, the most logical way to translate this would be as a tuple:
mymodule.method("const_ptr_arg", []() { return std::make_tuple(const_vector().ptr[0], const_vector().ptr[1], const_vector().ptr[2]); });
In the case of a larger blob of heap-allocated data it makes more sense to convert this to a ConstArray
, which implements the read-only part of the Julia array interface, so it exposes the data safely to Julia in a way that can be used natively:
mymodule.method("const_vector", []() { return jlcxx::make_const_array(const_vector(), 3); });
For multi-dimensional arrays, the make_const_array
function takes multiple sizes, e.g.:
const double* const_matrix()
{
static double d[2][3] = {{1., 2., 3}, {4., 5., 6.}};
return &d[0][0];
}
// ...module definition skipped...
mymodule.method("const_matrix", []() { return jlcxx::make_const_array(const_matrix(), 3, 2); });
Note that because of the column-major convention in Julia, the sizes are in reversed order from C++, so the Julia code:
display(const_matrix())
shows:
3x2 ConstArray{Float64,2}:
1.0 4.0
2.0 5.0
3.0 6.0
An extra file has to be included to have constant array functionality: #include "jlcxx/const_array.hpp"
.
Replacing make_const_array
in the examples above by make_julia_array
creates a mutable, regular Julia array with memory owned by C++.
Directly calling Julia functions uses jl_call
from julia.h
but with a more convenient syntax and automatic argument conversion and boxing.
Use a JuliaFunction
to get a functor that can be invoked directly.
Example for calling the max
function from Base
:
mymodule.method("julia_max", [](double a, double b)
{
jlcxx::JuliaFunction max("max");
return max(a, b);
});
Internally, the arguments and return value are boxed, making this method convenient but slower than calling a regular C function.
The macro CxxWrap.@safe_cfunction
provides a wrapper around Base.@cfunction
that checks the type of the function pointer.
Example C++ function:
mymodule.method("call_safe_function", [](double(*f)(double,double))
{
if(f(1.,2.) != 3.)
{
throw std::runtime_error("Incorrect callback result, expected 3");
}
});
Use from Julia:
testf(x,y) = x+y
c_func = @safe_cfunction(testf, Float64, (Float64,Float64))
MyModule.call_safe_function(c_func)
Using types different from the expected function pointer call will result in an error. This check incurs a runtime overhead, so the idea here is that the function is converted only once and then applied many times on the C++ side.
If the result of @safe_cfunction
needs to be stored before the calling signature is known, direct conversion of the created structure (type SafeCFunction
) is also possible.
It can then be converted later using jlcxx::make_function_pointer
:
mymodule.method("call_safe_function", [](jlcxx::SafeCFunction f_data)
{
auto f = jlcxx::make_function_pointer<double(double,double)>(f_data);
if(f(1.,2.) != 3.)
{
throw std::runtime_error("Incorrect callback result, expected 3");
}
});
This method of calling a Julia function is less convenient, but the call overhead should be no larger than calling a regular C function through its pointer.
Sometimes, you may want to write additional Julia code in the module that is built from C++.
To do this, call the wrapmodule
method inside an appropriately named Julia module:
module ExtendedTypes
using CxxWrap
@wrapmodule(() -> "libextended")
export ExtendedWorld, greet
end
Here, ExtendedTypes
is a name that matches the module name passed to create_module
on the C++ side.
The @wrapmodule
call works as before, but now the functions and types are defined in the existing ExtendedTypes
module, and additional Julia code such as exports and macros can be defined.
It is also possible to replace the @wrapmodule
call with a call to @readmodule
and then separately call @wraptypes
and @wrapfunctions
.
This allows using the types before the functions get called, which is useful for overloading the argument_overloads
with types defined on the C++ side.
By default, objects that are allocated from Julia are also destroyed through a finalizer that calls delete
. If you want to override this behavior, you can specialize the jlcxx::Finalizer
class as follows, for example only doing something special in the case a tye has a getImpl
function:
namespace jlcxx
{
template<typename T>
struct Finalizer<T, SpecializedFinalizer>
{
static void finalize(T* to_delete)
{
constexpr bool has_getImpl = requires(const T& t) {
t.getImpl();
};
if constexpr(has_getImpl) {
std::cout << "calling specialized delete" << std::endl;
delete to_delete;
} else {
delete to_delete;
}
}
};
}
You can also further specialize on T
to get specific behavior depending on the concrete type.
Version 0.9 introduces basic support for the C++ standard library, with mappings for std::vector
(StdVector
) and std::string
(StdString
).
To add support for e.g. vectors of your own type World
, either just add methods that use an std::vector<World>
as an argument, or manually wrap them using jlcxx::stl::apply_stl<World>(mod);
.
For this to work, add #include "jlcxx/stl.hpp"
to your C++ file.
If the type World
contains methods that take or return std::
collections of type World
or World*
, however, you must first complete the type, so that CxxWrap can generate the type and the template specializations for the std::
collections.
In this case, you can add those methods to your type like this:
jlcxx::stl::apply_stl<World*>(mod);
mod.method("getSecondaryWorldVector", [](const World* p)->const std::vector<World*>& {
return p->getSecondaries();
});
Linking wrappers using STL support requires adding JlCxx::cxxwrap_julia_stl
to the target_link_libraries
command in CMakeLists.txt
.
The StdString
implements the Julia string interface and interprets std::string
data as UTF-8 data. Since C++ strings do not require the use of the null-character to denote the end of a string the StdString
constructors usually rely on the ncodeunits
to determin the size of the string. When constructing a StdString
from a Cstring
, Base.CodeUnits
, or Vector{UInt8}
the first null-character present will denote the end of the string.
Often, new releases of CxxWrap
also require a new release of the C++ component libcxxwrap-julia
, and a rebuild of its JLL package. To make sure everything is tested properly, the following procedure should be followed for each release that requires changing both the Julia and the C++ component:
- Merge the changes to
CxxWrap
into thetestjll
branch - Create a PR in
libcxxwrap-julia
with the required changes there and make sure it passes all tests. These tests will run using theCxxWrap#testjll
branch. - Merge the
libcxxwrap-julia
PR. This will build and publish a JLL, available through the CxxWrapTestRegistry - Make a PR in
CxxWrap
to mergetestjll
intoprerelease
. Verify that the tests pass (rerun them if needed, since the first push totestjll
will have used the old JLL version). Don't merge this PR yet. - Tag the next
libcxxwrap-julia
release and update to this new release in Yggdrasil - Wait for the new JLL to appear in the registry and then merge the PR from point 4. Verify that the tests running on the
prerelease
branch pass, by merging the PR from point 4. The difference with the tests in thetestjll
branch is that theprerelease
branch tests using the JLL in the Julia General repository. - Merge the CxxWrap
prerelease
branch intomain
and create a new release using Registrator.
-
JULIA_CPP_MODULE_BEGIN
andJULIA_CPP_MODULE_END
no longer exists, define a function with return typeJLCXX_MODULE
in the global namespace instead. By default, the Julia side expects this function to be nameddefine_julia_module
, but another name can be chosen and passed as a second argument to@wrapmodule
. -
wrap_modules
is removed, replacewrap_modules(lib_file_path)
with:module Foo using CxxWrap @wrapmodule(lib_file_path) end
-
export_symbols
is removed, since all C++ modules are now wrapped in a corresponding module declared on the Julia side, so the regular Julia export statement can be used. -
safe_cfunction
is now a macro, just likecfunction
became a macro in Julia. -
Precompilation: add this function after the
@wrapmodule
macro:function __init__() @initcxx end
- No automatic conversion between Julia
String
andstd::string
, butStdString
(which mapsstd::string
) implements the JuliaAbstractString
interface. - No automatic dereference of const ref
ArrayRef
no longer supports boxed values- Custom smart pointer: use
jlcxx::add_smart_pointer<MySmartPointer>(module, "MySmartPointer")
IsMirroredType
instead ofIsImmutable
andIsBits
, added usingmap_type
. By default,IsMirroredType
is true for trivial standard layout types, so if you want to wrap these normally (i.e. you get an unexpected errorMirrored types (marked with IsMirroredType) can't be added using add_type, map them directly to a struct instead and use map_type
) then you have to explicitly disable the mirroring for that type:template<> struct IsMirroredType<Foo> : std::false_type { };
box
C++ function takes an explicit template argument- Introduction of specific integer types, such as
CxxBool
, that map to the C++ equivalent (should be transparent except for template parameters) - Defining
SuperType
on the C++ side is now necessary for any kind of casting to base class, because the previous implementation was wrong in the case of multiple inheritance. - Use
Ref(CxxPtr(x))
for pointer or reference to pointer - Use
CxxPtr{MyData}(C_NULL)
instead ofnullptr(MyData)
- Defining a C++ supertype in C++ must now be done using the
jlcxx::julia_base_type<T>()
function instead ofjlcxx::julia_type<T>()
- Requires Julia 1.3 for the use of JLL packages
- Reorganized integer types so the fixed-size types always map to built-in Julia types
- Automatic dereferencing of smart pointers was removed, so some code may require adding the dereferencing operator
[]
explicitly. See PR #338.