Houdini provides a module named inlinecpp that lets you write functions in C++ that are accessible from Python. Your C++ source code appears inline in your Python source code, and is automatically compiled into a library for you. This library is automatically loaded, and has the benefits that the code won’t be compiled when the library already exists, and the library won’t be reloaded when it’s already loaded.

The inlinecpp module provides easy access to the C++ HDK (Houdini Development Kit) from Python with only a minimal amount of code. Consider this example that allows you to call UT_String's multiMatch method from Python:

>>> import inlinecpp
>>> mymodule = inlinecpp.createLibrary(
...     name="cpp_string_library",
...     includes="#include <UT/UT_String.h>",
...     function_sources=[
... """
... bool matchesPattern(const char *str, const char *pattern)
... {
...     return UT_String(str).multiMatch(pattern);
... }
... """])
...
>>> string = "one"
>>> for pattern in "o*", "x*", "^o*":
...     print repr(string), "matches", repr(pattern), ":",
...     print mymodule.matchesPattern(string, pattern)
...
'one' matches 'o*' : True
'one' matches 'x*' : False
'one' matches '^o*' : False

This module also allows you to convert between Python objects in the hou module and the corresponding C++ HDK objects, giving you access to methods available in the HDK but not in hou. In this example, a hou.Geometry object is automatically converted to const GU_Detail *:

>>> geomodule = inlinecpp.createLibrary("cpp_geo_methods",
...     includes="#include <GU/GU_Detail.h>",
...     function_sources=["""
... int numPoints(const GU_Detail *gdp)
... { return gdp->getNumPoints(); }"""])
...
>>> geo = hou.node("/obj").createNode("geo").displayNode().geometry()
>>> geomodule.numPoints(geo)
80

This module also lets you extend hou classes using C++:

>>> inlinecpp.extendClass(hou.Geometry, "cpp_geo_methods", function_sources=["""
... int numPoints(const GU_Detail *gdp)
... { return gdp->getNumPoints(); }"""])
...
>>> geo = hou.node("/obj").createNode("geo").displayNode().geometry()
>>> geo.numPoints()
80

The following functions are available in the inlinecpp module:

createLibrary(name, includes="", function_sources=(), debug=False,
              catch_crashes=None, acquire_hom_lock=False, structs=(),
              include_dirs=(), link_dirs=(), link_libs=())

This returns a module-like object.

Create a library of C++ functions from C++ source, returning it if it’s already loaded, compiling it only if it hasn’t already been compiled.

extendClass(cls, library_name, includes="", function_sources=(), debug=False, catch_crashes=None, structs=(), include_dirs=(), link_dirs=(), link_libs=())

Extend a hou class by adding methods implemented in C++.

cls is the hou module class you're extending. The rest of the arguments are the same as for createLibrary, except acquire_hom_lock is always True. Note that this function automatically adds a #include line for the underlying C++ class.

The first parameter to your C++ functions must be a pointer to a C++ object corresponding to the hou object.

inlinecpp uses the hcustom HDK tool to compile and link your C++ code. If your C++ code fails to compile, it raises an inlinecpp.CompilerError exception containing the full output from hcustom, including the compiler errors.

Since inlinecpp uses hcustom, you must have set up your environment to use the HDK. On Windows, you need to have Microsoft Visual Studio C++ (either Express or Professional Edition) installed. You can optionally have the MSVCDir environment variable set to control which compiler inlinecpp uses, but if it is not set inlinecpp will try to automatically set it for you. On Linux and Mac, you should not need to do anything to set up the compiler environment.

If your code compiled, you can check to see if there were any compiler warnings by calling the _compiler_output method on the object returned by createLibrary. For example:

>>> mymodule = inlinecpp.createLibrary("test", function_sources=["""
... int doubleIt(int value)
... {
...     int x;
...     return value * 2;
... }
... """])
...
>>> print mymodule._compiler_output()
Making /home/user/houdiniX.Y/inlinecpp/test_Linux_x86_64_10.5.313_PM3a6t3irKe+jdb113yHpw.o and
/home/user/houdiniX.Y/inlinecpp/test_Linux_x86_64_10.5.313_PM3a6t3irKe+jdb113yHpw.so from
/home/user/houdiniX.Y/inlinecpp/test_Linux_x86_64_10.5.313_PM3a6t3irKe+jdb113yHpw.C
/home/user/houdiniX.Y/inlinecpp/test_Linux_x86_64_10.5.313_PM3a6t3irKe+jdb113yHpw.C: In function int doubleIt(int):
/home/user/houdiniX.Y/inlinecpp/test_Linux_x86_64_10.5.313_PM3a6t3irKe+jdb113yHpw.C:8: warning: unused variable x

Note that if createLibrary did not need to compile the library because it was already compiled, calling _compiler_output will force the library to compile.

Your C++ functions may only use certain parameter types. Valid parameter types are:

The following details are important to note:

• bool parameter types are only supported in Python 2.6 and up. To emulate a bool parameter type, though, use an int parameter type and pass in a Python bool object.

• If your function receives a GU_Detail *, you must pass in a hou.Geometry object that is not read-only, otherwise inlinecpp will raise a hou.GeometryPermissionError exception when you call your function. In other words, only use a GU_Detail * for functions being called from a Python SOP where you can modify the geometry. For functions that do not modify the geometry, use a const GU_Detail * parameter.

• If you use a parameter type other than the ones listed above, inlinecpp will convert the Python object passed in to a void *. For example, if you have a parameter type of int * you may pass in a Python integer containing an address of an integer array.

• Do not modify the contents of a Python string from C++ code by receiving a string as a char * and changing the data. Strings in Python are immutable, and changing a string’s contents risks invalidating Python’s internal state and crashing Houdini.

The following return types are provided by default:

The following details are important to note:

• bool return types are only supported in Python 2.6 and up. To emulate a bool return type, however, your C++ function can return an int and you can convert the result to a bool from Python.

• The best way for your functions to return a string is to return an inlinecpp::BinaryString (see below for details). However, your functions can also return strings by returning pointers to a null-terminated character array. The return type of your function determines if the array is freed after converting it to a Python string. If the return type is const char *, the array is not freed. If it is char *, the array is freed using free(). There is no way to free the character array with delete [], so if the array was allocated with new, return the strdup'd result and delete the array from your function.

You may also easily create your own return types. See below for details.

The easiest way to return a string is for your C++ function to return an inlinecpp::BinaryString and then construct that BinaryString from a std::string. The following example returns a string containing a node’s deletion script:

node_utilities = inlinecpp.createLibrary("node_utilities",
    acquire_hom_lock=True,
    includes="#include <OP/OP_Node.h>",
    function_sources=["""
inlinecpp::BinaryString deletion_script(OP_Node *node)
{
    return node->getDelScript().toStdString();
}
"""])

Sometimes your C++ code will need to return a null-terminated C-style string. If this C-style string is owned by local variables inside the function and will be freed when the function returns, you need to return a copy of the string. There are a two ways to return such a copy. The first is simply to construct a std::string from the C-style string, and return an inlinecpp::BinaryString. The second is to return an inlinecpp::BinaryString and construct it using the inlinecpp::as_binary_string function, avoiding the construction of the std::string, as illustrated below:

node_utilities = inlinecpp.createLibrary("node_utilities",
    acquire_hom_lock=True,
    includes="#include <OP/OP_Node.h>",
    function_sources=["""
inlinecpp::BinaryString deletion_script(OP_Node *node)
{
    return inlinecpp::as_binary_string(node->getDelScript().nonNullBuffer());
}
"""])

If your C++ code needs to return a C-style string that will not be freed when the function returns and does not need to be returned by the caller, you can simply use a return type of const char * (not char *). When returning large strings, this approach is more efficient than returning inlinecpp::BinaryString because it avoids an unnecesary copying of the data. Note that Python will create a copy of the string, so it’s ok if the C-style string’s contents change after your function returns. Here is an example:

example_lib = inlinecpp.createLibrary("example_lib",
    includes="#include <unistd.h>",
    function_sources=["""
const char *user_name()
{
    return getlogin();
}
"""])

Finally, if your C++ code needs to return a C-style string that does need to be freed, use a return type of char * (not const char *). Note that inlinecpp will call free() on the data, not delete []; if you need to delete the array, create a copy of it into a std::string, delete the array, and return the std::string as an inlinecpp::BinaryString. The following (artificial) example illustrates how to return a string that will be freed:

example_lib = inlinecpp.createLibrary("example_lib",
    function_sources=["""
char *simple_string()
{
    return strdup("hello world");
}
"""])

If your C++ code needs to return arbitrary binary data, you can return an inlinecpp::BinaryString that is converted into a Python str. For example, if you need to return a string that may contain null characters, you can call the set method of an inlinecpp::BinaryString object, passing in a const char * pointer to the data and the size in bytes. Here is an example:

example_lib = inlinecpp.createLibrary("example_lib",
    function_sources=["""
inlinecpp::BinaryString build_binary_data()
{
    char binary_data[] = "embedded\0null\0characters";
    inlinecpp::BinaryString result;
    result.set(binary_data, 24);
    return result;
}
"""])

You can also use inlinecpp::BinaryString to return the binary representation of an array of ints, floats, or doubles. Simply store the values in a std::vector of the appropriate type and construct the inlinecpp::BinaryString from the std::vector by calling inlinecpp::as_binary_string, as in the following example. Using Python’s array module you can easily convert the string back into an array of the appropriate type. (Note that it is possible to return an array without having to use inlinecpp::BinaryString by using array return types.)

import array

example_lib = inlinecpp.createLibrary("example_lib",
    function_sources=["""
inlinecpp::BinaryString build_int_array()
{
    std::vector<int> values(10);
    for (int i=0; i<10; ++i)
        values[i] = i;
    return inlinecpp::as_binary_string(values);
}
"""])

>>> data = example_lib.build_int_array()
>>> int_array = array.array("i", data)
>>> for value in int_array:
...     print value

Finally, if you need to return a dynamically-allocated array of ints, floats, or doubles, simply call inlinecpp::BinaryString’s set method using a pointer to the first element in the array and the size of the array, in bytes. (Note that a simpler approach is possible is possible with array return types.)

example_lib = inlinecpp.createLibrary("example_lib",
    function_sources=["""
inlinecpp::BinaryString build_int_array()
{
    int *values = new int[10];
    for (int i=0; i<10; ++i)
        values[i] = i;

    inlinecpp::BinaryString result;
    result.set((const char *)values, 10 * sizeof(int));
    delete [] values;
    return result;
}
"""])

In addition to the default return types and string return types listed above, it is possible to return structures and arrays that are nicely converted into objects and sequences in Python.

To use such return types, pass the structs parameter into createLibrary with a description of the C++ structures you would like to return from the functions in your library. Set this parameter to a sequence of descriptions, where each element in the sequence is a pair of values. The first element in the pair is the name of the structure, and the second is a sequence of member descriptions. Each member description is a pair of strings containing the member name and type. Member types may be one of the format characters used by Python’s struct module (e.g. i for integer, d for double, f for float, c for character, etc.).

The following example creates a structure named Position2D with two double members named x and y, and returns the position of a node.

>>> node_utilities = inlinecpp.createLibrary(
...     acquire_hom_lock=True,
...     name="node_utilities",
...     includes="#include <OP/OP_Node.h>",
...     structs=[("Position2D", (
...         ("x", "d"),
...         ("y", "d"),
...     ))],
...     function_sources=["""
... Position2D node_position(OP_Node *node)
... {
...     Position2D result;
...     result.x = node->getX();
...     result.y = node->getY();
...     return result;
... }
... """])
...
>>> geo_node = hou.node("/obj").createNode("geo")
>>> geo_node.setPosition((3.5, 4.5))
>>> print geo_node.position()
[3.5, 4.5]
>>> position = node_utilities.node_position(geo_node)
>>> print position
<inlinecpp.Position2D object at 0x7f10e7cf0d90>
>>> print position.x
3.5
>>> print position.y
4.5

From the previous example, note that structs are initialized in C++ by creating a struct instance and assigning to the individual members.

example_lib = inlinecpp.createLibrary(
    "example_lib",
    structs=[("ArrayPair", (
        ("ints", "*i"),
        ("doubles", "*d"),
    ))],
    function_sources=["""
ArrayPair build_array_pair()
{
    std::vector<int> ints;
    ints.push_back(2);
    ints.push_back(4);

    std::vector<double> doubles;
    doubles.push_back(1.5);
    doubles.push_back(3.5);

    ArrayPair result;
    result.ints.set(ints);
    result.doubles.set(doubles);
    return result;
}
"""])

>>> array_pair = example_lib.build_array_pair()
>>> print array_pair.ints[0]
2
>>> print array_pair.doubles[1]
3.5
>>> print zip(array_pair.ints, array_pair.doubles)
[(2, 1.5), (4, 3.5)]

node_utilities = inlinecpp.createLibrary(
    "node_utilities",
    acquire_hom_lock=True,
    includes="#include <OP/OP_Director.h>",
    structs=[("IntArray", "*i")],
    function_sources=["""
IntArray ids_of_selected_nodes()
{
    std::vector<int> ids;
    OPgetDirector()->getPickedNodeIds().toStdVector(ids);
    return ids;
}
"""])

def selected_nodes():
    return [hou.nodeBySessionId(node_id)
        for node_id in node_utilities.ids_of_selected_nodes()]

example_lib = inlinecpp.createLibrary("example_lib",
    structs=[("IntArray", "*i")],
    function_sources=["""
IntArray build_int_array()
{
    int *values = new int[10];
    for (int i=0; i<10; ++i)
        values[i] = i;

    IntArray result;
    result.set(values, 10);
    delete [] values;
    return result;
}
"""])

example_lib = inlinecpp.createLibrary("example_lib",
    structs=[("StringArray", "**c")],
    function_sources=["""
StringArray build_string_array()
{
    std::vector<std::string> result;
    result.push_back("one");
    result.push_back("two");
    return result;
}
"""])

>>> example_lib.build_string_array()
('one', 'two')

example_lib = inlinecpp.createLibrary(
    "example_lib",
    structs=(
        ("Point", (
            ("x", "i"),
            ("y", "i"),
        )),
        ("Data", (
            ("tolerance", "d"),
            ("single_point", "Point"),
            ("points", "*Point"),
        )),
    ),
    function_sources=["""
Data build_nested_struct()
{
    std::vector<Point> points;
    for (int i=0; i<5; ++i)
    {
        Point point;
        point.x = i;
        point.y = i + 1;
        points.push_back(point);
    }

    Data result;
    result.tolerance = 0.01;
    result.single_point.x = 4;
    result.single_point.y = 6;
    result.points.set(points);
    return result;
}
"""])

>>> result = example_lib.build_nested_struct()
>>> print result.tolerance
0.01
>>> print result.single_point.x
4
>>> print result.points[2].y
3

point_ref_utils = inlinecpp.createLibrary(
    "point_ref_utils",
    acquire_hom_lock=True,
    structs=(
        ("ReferencingVertex", (
            ("prim_id", "i"),
            ("vertex_id", "i"),
        )),
        ("ReferencesToPoint", "*ReferencingVertex"),
        ("ReferencesToAllPoints", "*ReferencesToPoint"),
    ),  
    includes="""
#include <GU/GU_Detail.h>
#include <GB/GB_PointRef.h>

int vertex_index(GB_Vertex &vertex, GB_Primitive &prim)
{   
    GEO_Vertex &geo_vertex = dynamic_cast<GEO_Vertex &>(vertex);
    GEO_Primitive &geo_prim = dynamic_cast<GEO_Primitive &>(prim);
    int num_vertices = geo_prim.getVertexCount();
    for (int i=0; i<num_vertices; ++i)
        if (&geo_prim.getVertex(i) == &geo_vertex)
            return i; 
    return -1;
}   
""",
    function_sources=["""
ReferencesToAllPoints vertices_referencing_points(const GU_Detail *gdp)
{   
    GB_PointRefArray point_ref_array(gdp, /*group=*/NULL);

    std::vector<ReferencesToPoint> references_to_points;
    for (int i=0; i<point_ref_array.entries(); ++i)
    {       
        std::vector<ReferencingVertex> referencing_vertices;
        for (const GB_PointRef *ref = point_ref_array(i); ref; ref=ref->next)
        {   
            ReferencingVertex referencing_vertex;
            referencing_vertex.prim_id = ref->prim->getNum();
            referencing_vertex.vertex_id = vertex_index(*ref->vtx, *ref->prim);
            referencing_vertices.push_back(referencing_vertex);
        }
        references_to_points.push_back(referencing_vertices);
    }
    return references_to_points;
}
"""])

def vertices_referencing_points(geo):
    """Return a list of values, with one entry per point in the geometry.
    Each entry in the list is a list of vertices, each of which references
    the corresponding point.
    """
    vertices_for_each_point = []
    for references_to_point in point_ref_utils.vertices_referencing_points(geo):
        vertices = []
        for reference_to_point in references_to_point:
            prim = geo.iterPrims()[reference_to_point.prim_id]
            vertices.append(prim.vertex(reference_to_point.vertex_id))
        vertices_for_each_point.append(vertices)
    return vertices_for_each_point

>>> geo = hou.node("/obj").createNode("geo").createNode("box").geometry()
>>> for point, vertices in zip(geo.points(), vertices_referencing_points(geo)):
...     print point
...     for vertex in vertices:
...         print "    ", vertex

inlinecpp is very useful when writing a Python SOP, since it gives you access to high level operations available in the C++ GU_Detail class. As well, it can be used to accelerate slow loops by re-implementing them in C++. This approach combines the benefits of using the Type Properties dialog to create your parameters (so you do not have to use C++ PRM_Templates) with the performance of a C++ implementation.

Here is an example of the code inside a Python SOP that clips geometry along a plane. It assumes a parameter for the normal (a vector of 3 floats called normal) and a parameter for the distance along that normal (a float called distance) and it creates a plane with that normal, offset from the origin by that distance, and clips the geometry with the plane.

import inlinecpp

geofuncs = inlinecpp.createLibrary(
    "example_clip_sop_library",
    acquire_hom_lock=True,
    includes="""
#include <GU/GU_Detail.h>
#include <GQ/GQ_Detail.h>
""",
    function_sources=["""
void clip(GU_Detail *gdp, float nx, float ny, float nz, float distance)
{
    GQ_Detail *gqd = new GQ_Detail(gdp);
    UT_Vector3 normal(nx, ny, nz);
    gqd->clip(normal, distance, 0);
    delete gqd;
}
"""])

nx, ny, nz = hou.parmTuple("normal").eval()
geofuncs.clip(hou.pwd().geometry(), nx, ny, nz, hou.ch("distance"))

Also see the HDK for examples comparing the same SOP written using pure Python code, Python with inlinecpp, and pure C++ using the HDK. In those examples, the inlinecpp version is just as fast as the HDK version but requires much less code and is much easier to use because of automatic compilation.

Here is another example that adds a destroyMe method to hou.Attrib (note that hou.Attrib.destroy() already exists):

import types

geomodule = inlinecpp.createLibrary("geomodule",
    acquire_hom_lock=True,
    includes="""
        #include <GU/GU_Detail.h>

        template <typename T>
        bool delete_attribute(T &attrib_dict, const char *name)
        {
            GB_Attribute *attrib = attrib_dict.find(name);
            if (!attrib)
                return false;
            attrib_dict.destroy(name, attrib->getSize(), attrib->getType());
            return true;
        }
    """, function_sources=[ """
        int delete_point_attribute(GU_Detail *gdp, const char *name)
        { return delete_attribute(gdp->pointAttribs(), name); }
    """, """
        int delete_prim_attribute(GU_Detail *gdp, const char *name)
        { return delete_attribute(gdp->primitiveAttribs(), name); }
    """, """
        int delete_vertex_attribute(GU_Detail *gdp, const char *name)
        { return delete_attribute(gdp->vertexAttribs(), name); }
    """, """
        int delete_global_attribute(GU_Detail *gdp, const char *name)
        { return delete_attribute(gdp->attribs(), name); }
    """])

attrib_type_to_delete_function = {
    hou.attribType.Point: geomodule.delete_point_attribute,
    hou.attribType.Prim: geomodule.delete_prim_attribute,
    hou.attribType.Vertex: geomodule.delete_vertex_attribute,
    hou.attribType.Global: geomodule.delete_global_attribute,
}

def destroyAttrib(attrib):
    return attrib_type_to_delete_function[attrib.type()](attrib.geometry(), attrib.name())

hou.Attrib.destroyMe = types.MethodType(destroyAttrib, None, hou.Attrib)

The following examples illustrate how to extend the hou.Node class or its subclasses:

This first example adds a method called expandGroupPattern that receives a pattern string and returns a comma-separated list of names of children nodes in groups matching that pattern.

>>> inlinecpp.extendClass(hou.Node, "cpp_node_methods", function_sources=["""
... inlinecpp::BinaryString expandGroupPattern(OP_Node *node, const char *pattern)
... {
...     UT_String result;
...     node->expandGroupPattern(pattern, result);
...     return result.toStdString();
... }"""])
...
>>> hou.node("/obj").expandGroupPattern("@group1")
'geo1,geo2,geo3'

This second example adds a setSelectable method to object node objects. (Note that this method is already available as hou.ObjNode.setSelectableInViewport()).

inlinecpp.extendClass(
    hou.ObjNode,
    "node_methods",
    includes="#include <UT/UT_UndoManager.h>",
    function_sources=["""
void setSelectable(OBJ_Node *obj_node, bool selectable)
{
    if (!obj_node->canAccess(PRM_WRITE_OK))
        return;

    UT_AutoUndoBlock undo_block("Setting selectable flag", ANYLEVEL);
    obj_node->setPickable(selectable);
}
"""])

For examples of how to use inlinecpp in Python COPs, see Writing Part of the COP in C++ in the Python COPs|/hom/pythoncops] section.

Your C++ source code cannot raise exceptions to Python. However, one technique to raise exceptions is to have your C++ functions return a code, and then create a wrapper Python function that checks the code and raises an exception if necessary. Here is the same example from above that raises a hou.PermissionError exception.

import types

cpp_node_methods = inlinecpp.createLibrary(
    "node_methods",
    acquire_hom_lock=True,
    includes="""
#include <OBJ/OBJ_Node.h>
#include <UT/UT_UndoManager.h>
""",
    function_sources=["""
bool setObjectSelectable(OBJ_Node *obj_node, bool selectable)
{
    if (!obj_node->canAccess(PRM_WRITE_OK))
        return false;

    UT_AutoUndoBlock undo_block("Setting selectable flag", ANYLEVEL);
    obj_node->setPickable(selectable);
    return true;
}
"""])

def setObjectSelectable(obj_node, selectable):
    if not cpp_node_methods.setObjectSelectable(obj_node, selectable):
        raise hou.PermissionError()

hou.ObjNode.setSelectable = types.MethodType(setObjectSelectable, None, hou.ObjNode)

You cannot return something from your C++ function that gets automatically convert to a corresponding hou object. However, you can use a Python wrapper function to do the conversion for you.

For example, OP_Node::getParmsThatReference takes a parameter name and returns a UT_PtrArray of PRM_Parm pointers. You can create a Python function that receives a hou.Parm object and returns a sequence of hou.ParmTuple objects as follows:

parm_reference_lib = inlinecpp.createLibrary(
    "parm_reference_lib",
    acquire_hom_lock=True,
    structs=[("StringArray", "**c")],
    includes="""
#include <OP/OP_Node.h>
#include <PRM/PRM_Parm.h>

// This helper function is not called from Python directly:
static std::string get_parm_tuple_path(PRM_Parm &parm_tuple)
{
    UT_String result;
    parm_tuple.getParmOwner()->getFullPath(result);
    result += "/";
    result += parm_tuple.getToken();
    return result.toStdString();
}
""",
    function_sources=["""
StringArray get_parm_tuples_referencing(OP_Node *node, const char *parm_name)
{
    std::vector<std::string> result;
    UT_PtrArray<PRM_Parm *> parm_tuples;
    UT_IntArray component_indices;
    node->getParmsThatReference(parm_name, parm_tuples, component_indices);

    // Even though we're returned an array of component indices, they'll often
    // be -1, so we can't use them.
    for (int i=0; i<parm_tuples.entries(); ++i)
        result.push_back(get_parm_tuple_path(*parm_tuples(i)));

    return result;
}
"""])

def parm_tuples_referencing(parm):
    return [hou.parmTuple(parm_tuple_path)
        for parm_tuple_path in parm_reference_lib.get_parm_tuples_referencing(
            parm.node(), parm.name())]

The C++ function in this example returns a space-separated string that is split into a list of strings, and then converted into a list of hou.ParmTuple objects.

Note that this example creates a helper function that is not exposed to Python by putting its source code in the includes parameter.

If you want to distribute your compiled library without worrying whether the user has a proper compiler environment set up, simply distribute the shared object (.so/.dll/.dylib) file from your $HOME/houdiniX.Y/inlinecpp directory that inlinecpp created. As long as the user doesn’t modify your C++ source code, the checksum will be the same and the file name of the shared object file file will be the same.

Houdini will look in all inlinecpp subdirectories of $HOUDINI_PATH when looking for the shared object, so you can put the shared library at any point in your $HOUDINI_PATH.

To determine the path to the shared file, you can call the _shared_object_path method of the module-like object. For example:

>>> mymodule = inlinecpp.createLibrary("test", function_sources=["""
... int doubleIt(int value) { return value * 2; }"""])
...
>>> mymodule._shared_object_path()
'/home/user/houdiniX.Y/inlinecpp/test_Linux_x86_64_11.0.313_3ypEJvodbs1QT4FjWEVmmg.so'

The library will have different names for different platforms and architectures, so you can distribute multiple shared objects to support multiple platforms without having to worry about conflicts between shared object names.

See the Python SOP documentation for an example of how to store your Python source code outside of a digital asset, to make it easier to edit and manage under a version control system.

Your C++ code runs inside the Houdini process, so a bug in your code can easily crash Houdini. By passing debug=True when creating your library, Houdini will compile your code without optimization and with debug symbols, making it easier for you to debug your code by attaching a standard debugger to Houdini.

By default, if you pass debug=True and do not specify a value for the catch_crashes parameter, Houdini will attempt to recover from a crash in your C++ source code and convert it into a Python RuntimeError exception containing a C++ stack trace. For example, the following code crashes because it dereferences a null pointer:

>>> mymodule = inlinecpp.createLibrary("crasher", debug=True, function_sources=["""
... void crash()
... {
...     int *p = 0;
...     *p = 3;
... }
... """])
>>> mymodule.crash()
Traceback (most recent call last):
  File "<console>", line 1, in <module>
  File "/opt/hfs/houdini/python2.6libs/inlinecpp.py", line 620, in __call__
    return self._call_c_function(*args)
  File "/opt/hfs/houdini/python2.6libs/inlinecpp.py", line 589, in call_c_function_and_catch_crashes
    return hou.runCallbackAndCatchCrashes(lambda: c_function(*args))
RuntimeError: Traceback from crash:
pyHandleCrash
UT_Signal::UT_ComboSignalHandler::operator()(int, siginfo*, void*) const
UT_Signal::processSignal(int, siginfo*, void*)
_L_unlock_15 (??:0)
crash (crasher_Linux_x86_64_10.5.313_fpk9yUhcRwBJ8R5BAVtGwQ.C:9)
ffi_call_unix64+0x4c
ffi_call+0x214
_CallProc+0x352
<unknown>

Note that the Python portion of the traceback has the most recent call last followed by the exception, and the exception contains the C++ stack trace with the most recent call first.

The relevant line of the trace is crash (crasher_Linux_x86_64_10.5.313_fpk9yUhcRwBJ8R5BAVtGwQ.C:9), telling you to look at line 9 of $HOME/houdiniX.Y/inlinecpp/crasher_Linux_x86_64_10.5.313_fpk9yUhcRwBJ8R5BAVtGwQ.C. If you look at that file you’ll see something like:

#include <UT/UT_DSOVersion.h>
#include <HOM/HOM_Module.h>

extern "C" {

void crash()
{
    int *p = 0;
    *p = 3;
}

}

The statement on line 9 is a null pointer dereference.

By by default, crash handling is enabled if and only if debug=True. You can control whether crash handling is enabled independent of the debug setting by passing a value for the catch_crashes parameter.