This is documentation for Orange 2.7. For the latest documentation, see Orange 3.

Writing Orange Extensions in C++

This page gives an introduction to extending Orange in C++ with emphasis on how to define interfaces to Python. Besides reading this page, we recommend studying some of existing extension modules like orangeom, and the Orange’s interface itself.

We shall first present a general picture and then focus on specific parts of the interface.

Instead of general tools for creating interfaces between C++ and Python (Swig, Sip, PyBoost...), Orange uses its own specific set of tools.

To expose a C++ object to Python, we need to mark them as exportable, select a general constructor template to use or program a specific one, we have to mark the attributes to be exported, and provide the interfaces for C++ member functions. When we give the access to mostly C++ code as it is, the interface functions have only a few lines. When we want to make the exported function more friendly, eg. allow various types of arguments or fitting the default arguments according to the given ones, these functions are longer.

To define a non-member function, we write the function itself as described in the Python’s manual (see the first chapter of “Extending and Embedding the Python Interpreter”) and then mark it with a specific keyword. Pyxtract will recognize the keyword and add it to the list of exported functions.

To define a special method, one needs to provide a function with the appropriate name constructed from the class name and the special method’s name, which is the same as in Python’s PyTypeObjects.

For instance, the elements of ExampleTable (examples) can be accessed through indexing because we defined a C function that gets an index (and the table, of course) and returns the corresponding example. Here is the function (with error detection removed for the sake of clarity).

PyObject *ExampleTable_getitem_sq(PyObject *self, int idx)
    CAST_TO(TExampleTable, table);
    return Example_FromExampleRef((*table)[idx], EXAMPLE_LOCK(PyOrange_AsExampleTable(self)));

Also, ExampleTable has a non-special method sort([list-of-attributes]). This is implemented through a C function that gets a list of attributes and calls the C++ class’ method TExampleTable::sort(const vector<int> order). To illustrate, this is a slightly simplified function (we’ve removed some flexibility regarding the parameters and the exception handling).

PyObject *ExampleTable_sort(PyObject *self, PyObject *args) PYARGS(METH_VARARGS, "() -> None")
    CAST_TO(TExampleTable, table);

    if (!args || !PyTuple_Size(args)) {

    TVarList attributes;
    varListFromDomain(PyTuple_GET_ITEM(args, 0), table->domain, attributes, true, true);
    vector<int> order;
    for(TVarList::reverse_iterator vi(attributes.rbegin()), ve(attributes.rend()); vi!=ve; vi++) {

The function casts the PyObject * into the corresponding C++ object, reads the arguments, calls the C++ functions and returns the result (None, in this case).

Interfacing with Python requires a lot of manual work, but this gives a programmer the opportunity to provide a function which accepts many different forms of arguments. The above function, for instance, accepts a list in which attributes are specified by indices, names or descriptors, all corresponding to the ExampleTable which is being sorted. Inheritance of methods, on the other hand, ensures that only the methods that are truly specific for a class need to be coded.

The part of the interface that is built automatically is taken care of by two scripts. pyprops parses all Orange’s header files and extracts all the class built-in properties. The second is pyxtract, which goes through the C++ files that contain the interface functions such as those above. It recognizes the functions that implement special or member methods and constructs the corresponding ``PyTypeObject``s.


Pyprops scans each hpp file for classes we want to export to Python). Properties can be bool, int, float, string, TValue or a wrapped Orange type.

Class definition needs to look as follows.

class [ORANGE_API] <classname>; [: public <parentclass> ]

This should be in a single line. To mark the class for export, this should be followed by __REGISTER_CLASS or __REGISTER_ABSTRACT_CLASS before any properties or components are defined. The difference between the two, as far as pyprops is concerned, is that abstract classes do not define the clone method.

To export a property, it should be defined like this.

<type> <name> //P[R|O] [>|+<alias>] <description>

Pyprops doesn’t check the type and won’t object if you use other types than those listed above. The error will be discovered later, during linking. //P signals that we want to export the property. If followed by R or O, the property is read-only or obsolete. The property can also have an alias name; > renames it and + adds an alias.

Each property needs to be declared in a separate line, e.g.

int x; //P;
int y; //P;

If we don’t want to export a certain property, we omit the //P mark. An exception to this are wrapped Orange objects: for instance, if a class has a (wrapped) pointer to the domain, PDomain and it doesn’t export it, pyxtract should still know about them because for the purpose of garbage collection. You should mark them by //C so that they are put into the list of objects that need to be counted. Failing to do so would cause a memory leak.

If a class directly or indirectly holds references to any wrapped objects that are neither properties nor components, it needs to declare traverse and clear as described in Python documentation.

Pyprops creates a ppp file for each hpp, which includes the extracted information in form of C++ structures that compile into the interface. The ppp file needs to be included in the corresponding cpp file. For instance, domain.ppp is included in domain.cpp.


Pyxtract’s job is to detect the functions that define special methods (such as printing, conversion, sequence and arithmetic related operations...) and member functions. Based on what it finds for each specific class, it constructs the corresponding ``PyTypeObject``s. For the functions to be recognized, they must follow a specific syntax.

There are two basic mechanisms for marking the functions to export. Special functions are recognized by their definition (they need to return PyObject *, void or int and their name must be of form <classname>_<functionname>). Member functions, inheritance relations, constants etc. are marked by macros such as PYARGS in the above definition of ExampleTable_sort. Most of these macros don’t do anything except for marking stuff for pyxtract.

Class declaration

Each class needs to be declared as exportable. If it’s a base class, pyxtract needs to know the data structure for the instances of this class. As for all Python objects the structure must be “derived” from PyObject (Python is written in C, so the subclasses are not derived in the C++ sense but extend the C structure instead). Most objects are derived from Orange; the only exceptions are orange.Example, orange.Value and orange.DomainDepot.

Pyxtract should also know how the class is constructed - it can have a specific constructor, one of the general constructors or no constructor at all.

The class is declared in one of the following ways (here are some examples from actual Orange code).

BASED_ON(EFMDataDescription, Orange)
This tells pyxtract that EFMDataDescription is an abstract class derived from Orange: there is no constructor for this class in Python, but the C++ class itself is not abstract and can appear and be used in Python. For example, when we construct an instance of ClassifierByLookupTable with more than three attributes, an instance of EFMDataDescription will appear in one of its fields.
ABSTRACT(ClassifierFD, Classifier)
This defines an abstract class, which will never be constructed in the C++ code. The only difference between this BASED_ON and ABSTRACT is that the former can have pickle interface, while the latter don’t need one.

Abstract C++ classes are not necessarily defined as ABSTRACT in the Python interface. For example, TClassifier is an abstract C++ class, but you can seemingly construct an instance of Classifier in Python. What happens is that there is an additional C++ class TClassifierPython, which poses as Python’s class Classifier. So the Python class Classifier is not defined as ABSTRACT or BASED_ON but using the Classifier_new function, as described below.

C_NAMED(EnumVariable, Variable, "([name=, values=, autoValues=, distributed=, getValueFrom=])")
EnumVariable is derived from Variable. Pyxtract will also create a constructor which will accept the object’s name as an optional argument. The third argument is a string that describes the constructor, eg. gives a list of arguments. IDEs for Python, such as PythonWin, will show this string in a balloon help while the programmer is typing.
C_UNNAMED(RandomGenerator, Orange, "() -> RandomGenerator")
This is similar as C_NAMED, except that the constructor accepts no name. This form is rather rare since all Orange objects can be named.
C_CALL(BayesLearner, Learner, "([examples], [weight=, estimate=] -/-> Classifier")
BayesLearner is derived from Learner. It will have a peculiar constructor. It will, as usual, first construct an instance of BayesLearner. If no arguments are given (except for, possibly, keyword arguments), it will return the constructed instance. Otherwise, it will call the Learner‘s call operator and return its result instead of BayesLearner.
C_CALL3(MakeRandomIndices2, MakeRandomIndices2, MakeRandomIndices, "[n | gen [, p0]], [p0=, stratified=, randseed=] -/-> [int]")
MakeRandomIndices2 is derived from MakeRandomIndices (the third argument). For a contrast from the C_CALL above, the corresponding constructor won’t call MakeRandomIndices call operator, but the call operator of MakeRandomIndices2 (the second argument). This constructor is often used when the parent class doesn’t provide a suitable call operator.
HIDDEN(TreeStopCriteria_Python, TreeStopCriteria)
TreeStopCriteria_Python is derived from TreeStopCriteria, but we would like to hide this class from the user. We use this definition when it is elegant for us to have some intermediate class or a class that implements some specific functionality, but don’t want to bother the user with it. The class is not completely hidden - the user can reach it through the type operator on an instance of it. This is thus very similar to a BASED_ON.
DATASTRUCTURE(Orange, TPyOrange, orange_dict)
This is for the base classes. Orange has no parent class. The C++ structure that stores it is TPyOrange; TPyOrange is essentially PyObject (again, the structure always has to be based on PyObject) but with several additional fields, among them a pointer to an instance of TOrange (the C++ base class for all Orange’s classes). orange_dict is a name of TPyOrange‘s field that points to a Python dictionary; when you have an instance bayesClassifier and you type, in Python, bayesClassifier.someMyData=15, this gets stored in orange_dict. The actual mechanism behind this is rather complicated and you most probably won’t need to use it. If you happen to need to define a class with DATASTRUCTURE, you can simply omit the last argument and give a 0 instead.

Even if the class is defined by DATASTRUCTURE, you can still specify a different constructor, most probably the last form of it (the _new function). In this case, specify a keyword ROOT as a parent and pyxtract will understand that this is the base class.

Object construction in Python is divided between two methods. The constructors we discussed above construct the essential part of the object - they allocate the necessary memory and initialize the fields far enough that the object is valid to enter the garbage collection. The second part is handled by the init method. It is, however, not forbidden to organize the things so that new does all the job. This is also the case in Orange. The only task left for init is to set any attributes that user gave as the keyword arguments to the constructor.

For instance, Python’s statement orange.EnumVariable("a", values=["a", "b", "c"]) is executed so that new constructs the variable and gives it the name, while init sets the values field.

The new operator can also accept keyword arguments. For instance, when constructing an ExampleTable by reading the data from a file, you can specify a domain (using keyword argument domain), a list of attributes to reuse if possible (use), you can tell it not to reuse the stored domain or not to store the newly constructed domain (dontCheckStored, dontStore). After the ExampleTable is constructed, init is called to set the attributes. To tell it to ignore the keyword arguments that the constructor might (or had) used, we write the following.

CONSTRUCTOR_KEYWORDS(ExampleTable, "domain use useMetas dontCheckStored dontStore filterMetas")

There’s another macro related to attributes. Let ba be an orange object, say an instance of orange.BayesLearner. If you assign new attributes as usual directly, eg. ba.myAttribute = 12, you will get a warning (you should use the object’s method setattr(name, value) to avoid it). Some objects have some attributes that cannot be implemented in C++ code, yet they are usual and useful. For instance, Graph can use attributes objects, forceMapping and returnIndices, which can only be set from Python (if you take a look at the documentation on Graph you will see why these cannot be implemented in C++). Yet, since user are allowed to set these attributes and will do so often, we don’t want to give warnings. We achieve this by

RECOGNIZED_ATTRIBUTES(Graph, "objects forceMapping returnIndices")

Special methods

Special methods act as the class built-in methods. They define what the type can do: if it, for instance, supports multiplication, it should define the operator that gets the object itself and another object and return the product (or throw an exception). If it allows for indexing, it defines an operator that gets the object itself and the index, and returns the element. These operators are low-level; most can be called from Python scripts but they are also internally by Python. For instance, if table is an ExampleTable, then for e in table: or reduce(f, table) will both work by calling the indexing operator for each table’s element. For more details, consider the Python manual, chapter “Extending and Embedding the Python Interpreter” section “Defining New Types”.

To define a method for Orange class, you need to define a function named, <classname>_<methodname>; the function should return either PyObject *, int or void. The function’s head has to be written in a single line. Regarding the arguments and the result, it should conform to Python’s specifications. Pyxtract will detect the methods and set the pointers in PyTypeObject correspondingly.

Here is a list of methods: the left column represents a method name that triggers pyxtract (these names generally correspond to special method names of Python classes as a programmer in Python sees them) and the second is the name of the field in PyTypeObject or subjugated structures. See Python documentation for description of functions’ arguments and results. Not all methods can be directly defined; for those that can’t, it is because we either use an alternative method (eg. setattro instead of setattr) or pyxtract gets or computes the data for this field in some other way.

General methods

pyxtract PyTypeObject  
dealloc tp_dealloc Frees the memory occupied by the object. You will need to define this for the classes with a new DATASTRUCTURE; if you only derive a class from some Orange class, this has been taken care of. If you have a brand new object, copy the code of one of Orange’s deallocators.
. tp_getattr Can’t be redefined since we use tp_getattro instead.
. tp_setattr Can’t be redefined since we use tp_setattro instead.
cmp tp_compare  
repr tp_repr  
. as_number (pyxtract will initialize this field if you give any of the methods from the number protocol; you needn’t care about this field)
. as_sequence (pyxtract will initialize this field if you give any of the methods from the sequence protocol)
. as_mapping (pyxtract will initialize this field if you give any of the methods from the mapping protocol)
hash tp_hash Class Orange computes a hash value from the pointer; you don’t need to overload it if your object inherits the function. If you write an independent class, just copy the code.
call tp_call  
call tp_call  
str tp_str  
getattr tp_getattro  
setattr tp_setattro  
. tp_as_buffer Pyxtract doesn’t support the buffer protocol.
. tp_flags Flags are set by pyxtract.
. tp_doc Documentation is read from the constructor definition (see above).
traverse tp_traverse Traverse is tricky (as is garbage collection in general). There’s something on it in a comment in root.hpp; besides that, study the examples. In general, if a wrapped member is exported to Python (just as, for instance, Classifier contains a Variable named classVar), you don’t need to care about it. You should manually take care of any wrapped objects not exported to Python. You probably won’t come across such cases.
clear tp_clear  
richcmp tp_richcmp  
. tp_weaklistoffset  
iter tp_iter  
iternext tp_iternext  
. tp_methods Set by pyxtract if any methods are given.
. tp_members  
. getset Pyxtract initializes this by a pointer to manually written getters/setters (see below).
. tp_base Set by pyxtract to a class specified in constructor (see above).
. tp_dict Used for class constants (eg. Classifier.GetBoth)
. tp_descrget  
. tp_descrset  
. tp_dictoffset Set by pyxtract to the field given in DATASTRUCTURE (if there is any).
init tp_init  
. tp_alloc Set to PyType_GenericAlloc
new tp_new  
. tp_free Set to _PyObject_GC_Del
. tp_is_gc  
. tp_bases  
. tp_mro  
. tp_cache  
. tp_subclasses  
. tp_weaklist  

Numeric protocol

add nb_add pow nb_power lshift nb_lshift int nb_int
sub nb_subtract neg nb_negative rshift nb_rshift long nb_long
mul nb_multiply pos nb_positive and nb_and float nb_float
div nb_divide abs nb_absolute or nb_or oct nb_oct
mod nb_remainder nonzero nb_nonzero coerce nb_coerce hex nb_hex
divmod nb_divmod inv nb_invert        

Sequence protocol

len_sq sq_length getslice sq_slice
concat sq_concat setitem_sq sq_ass_item
repeat sq_slice setslice sq_ass_slice
getitem_sq sq_item contains sq_contains

Mapping protocol

len mp_length
getitem mp_subscript
setitem mp_ass_subscript

For example, here is what gets called when you want to know the length of an example table.

int ExampleTable_len_sq(PyObject *self)
        return SELF_AS(TExampleGenerator).numberOfExamples();

PyTRY and PyCATCH take care of C++ exceptions. SELF_AS is a macro for casting, ie unwrapping the points (this is an alternative to CAST_TO).

Getting and Setting Class Attributes

Exporting of most of C++ class fields is already taken care by the lists that are compiled by pyprops. There are only a few cases in the entire Orange where we needed to manually write specific handlers for setting and getting the attributes. This needs to be done if setting needs some special processing or when simulating an attribute that does not exist in the underlying C++ class.

An example for this is class HierarchicalCluster. It contains results of a general, not necessarily binary clustering, so each node in the tree has a list branches with all the node’s children. Yet, as the usual clustering is binary, it would be nice if the node would also support attributes left and right. They are not present in C++, but we can write a function that check the number of branches; if there are none, it returns None, if there are more than two, it complains, while otherwise it returns the first branch.

PyObject *HierarchicalCluster_get_left(PyObject *self)
        CAST_TO(THierarchicalCluster, cluster);

        if (!cluster->branches)

        if (cluster->branches->size() > 2)
                    "'left' not defined (cluster has more than two subclusters)",

        return WrapOrange(cluster->branches->front());

As you can see from the example, the function needs to accept a PyObject * (the object it``self``) and return a PyObject * (the attribute value). The function name needs to be <classname>_get_<attributename>. Setting an attribute is similar; function name should be <classname>_set_<attributename>, it should accept two Python objects (the object and the attribute value) and return an int, where 0 signifies success and -1 a failure.

If you define only one of the two handlers, you’ll get a read-only or write-only attribute.

Member functions

We have already shown an example of a member function - the ExampleTable‘s method sort. The general template is PyObject *<classname>_<methodname>(<arguments>) PYARGS(<arguments-keyword>, <documentation-string>). In the case of the ExampleTable‘s sort, this looks like this.

PyObject *ExampleTable_sort(PyObject *self, PyObject *args) PYARGS(METH_VARARGS, "() -> None")

Argument type can be any of the usual Python constants stating the number and the kind of arguments, such as METH_VARARGS or METH_O - this constant gets copied to the corresponding list (browse Python documentation for PyMethodDef).

Class constants

Orange classes, as seen from Python, can also have constants, such as orange.Classifier.GetBoth. Classifier’s GetBoth is visible as a member of the class, the derived classes and all their instances (eg. BayesClassifier.GetBoth and bayes.GetBoth).

There are several ways to define such constants. If they are simple integers or floats, you can use PYCLASSCONSTANT_INT or PYCLASSCONSTANT_FLOAT, like in

PYCLASSCONSTANT_INT(Classifier, GetBoth, 2)

You can also use the enums from the class, like

PYCLASSCONSTANT_INT(C45TreeNode, Leaf, TC45TreeNode::Leaf)

Pyxtract will convert the given constant to a Python object (using PyInt_FromLong or PyFloat_FromDouble>).

When the constant is an object of some other type, use PYCLASSCONSTANT. In this form (not used in Orange so far), the third argument can be either an instance of PyObject * or a function call. In either case, the object or function must be known at the point where the pyxtract generated file is included.


Pickling is taken care of automatically if the class provides a Python constructor that can construct the object without arguments (it may accept arguments, but should be able to do without them. If there is no such constructor, the class should provide a __reduce__ method or it should explicitly declare that it cannot be pickled. If it doesn’t pyxtract will issue a warning that the class will not be picklable.

Here are the rules:

  • Classes that provide a __reduce__ method (details follow below) are pickled through that method.

  • Class Orange, the base class, already provides a __reduce__ method, which is only useful if the constructor accepts empty arguments. So, if the constructor is declared as C_NAMED, C_UNNAMED, C_CALL or C_CALL3, the class is the class will be picklable. See the warning below.

  • If the constructor is defined by _new method, and the BASED_ON definition is followed be ALLOWS_EMPTY, this signifies that it accepts empty arguments, so it will be picklable just as in the above point. For example, the constructor for the class DefaultClassifier is defined like this

    PyObject *DefaultClassifier_new(PyTypeObject *tpe, PyObject *args)
        BASED_ON(Classifier, "([defaultVal])") ALLOWS_EMPTY

and is picklable through code Orange.__reduce__. But again, see the warning below.

  • If the constructor is defined as ABSTRACT, there cannot be any instances of this class, so pyxtract will give no warning that it is not picklable.

  • The class can be explicitly defined as not picklable by NO_PICKLE macro, as in


    Such classes won’t be picklable even if they define the appropriate constructors. This effectively defined a __reduce__ method which yields an exception; if you manually provide a __reduce__ method for such a class, pyxtract will detect that the method is multiply defined.

  • If there are no suitable constructors, no __reduce__ method and no ABSTRACT or NO_PICKLE flag, pyxtract gives a warning about that.

When the constructor is used, as in points 2 and 3, pickling will only work if all fields of the C++ class can be set “manually” from Python, are set through the constructor, or are set when assigning other fields. In other words, if there are fields that are not marked as //P for pyprops, you will most probably need to manually define a __reduce__ method, as in point 1.

The details of what the __reduce__ method must do are described in the Python documentation. In our circumstances, it can be implemented in two ways which differ in what function is used for unpickling: it can either use the class’ constructor or we can define a special method for unpickling.

The former usually happens when the class has a read-only property (//PR), which is set by the constructor. For instance, AssociationRule has read-only fields left and right, which are needs to be given to the constructor. This is the __reduce__ method for the class.

PyObject *AssociationRule__reduce__(PyObject *self)
        CAST_TO(TAssociationRule, arule);
        return Py_BuildValue("O(NN)N", self->ob_type,

As described in the Python documentation, the __reduce__ should return a tuple in which the first element is the function that will do the unpickling, and the second argument are the arguments for that function. Our unpickling function is simply the classes’ type (calling a type corresponds to calling a constructor) and the arguments for the constructor are the left- and right-hand side of the rule. The third element of the tuple is classes’ dictionary.

When unpickling is more complicated - usually when the class has no constructor and contains fields of type float * or similar - we need a special unpickling function. The function needs to be directly in the modules’ namespace (it cannot be a static method of a class), so we named them __pickleLoader<classname>. Search for examples of such functions in the source code; note that the instance’s true class need to be pickled, too. Also, check how we use TCharBuffer throughout the code to store and pickle binary data as Python strings.

Be careful when manually writing the unpickler: if a C++ class derived from that class inherits its __reduce__, the corresponding unpickler will construct an instance of a wrong class (unless the unpickler functions through Python’s constructor, ob_type->tp_new). Hence, classes derived from a class which defines an unpickler have to define their own __reduce__, too.

Non-member functions and constants

Non-member functions are defined in the same way as member functions except that their names do not start with the class name. Here is how the newmetaid is implemented

PyObject *newmetaid(PyObject *, PyObject *) PYARGS(0,"() -> int")
        return PyInt_FromLong(getMetaID());

Orange also defines some non-member constants. These are defined in a similar fashion as the class constants. PYCONSTANT_INT(<constant-name>, <integer>) defines an integer constant and PYCONSTANT_FLOAT would be used for a continuous one. PYCONSTANT is used for objects of other types, as the below example that defines an (obsolete) constant MeasureAttribute_splitGain shows.

PYCONSTANT(MeasureAttribute_splitGain, (PyObject *)&PyOrMeasureAttribute_gainRatio_Type)

Class constants from the previous section are put in a pyxtract generated file that is included at the end of the file in which the constant definitions and the corresponding classes are. Global constant modules are included in another file, far away from their actual definitions. For this reason, PYCONSTANT cannot refer to any functions (the above example is an exception - all class types are declared in this same file and are thus available at the moment the above code is used). Therefore, if the constant is defined by a function call, you need to use another keyword, PYCONSTANTFUNC:

PYCONSTANTFUNC(globalRandom, stdRandomGenerator)

Pyxtract will generate a code which will, prior to calling stdRandomGenerator, declare it as a function with no arguments that returns PyObject *. Of course, you will have to define the function somewhere in your code, like this:

PyObject *stdRandomGenerator()
    return WrapOrange(globalRandom);

Another example are VarTypes. VarTypes is a tiny module inside Orange that contains nothing but five constants, representing various attribute types. From pyxtract perspective, VarTypes is a constant. This is the complete definition.

PyObject *VarTypes()
    PyObject *vartypes=PyModule_New("VarTypes");
    PyModule_AddIntConstant(vartypes, "None", (int)TValue::NONE);
    PyModule_AddIntConstant(vartypes, "Discrete", (int)TValue::INTVAR);
    PyModule_AddIntConstant(vartypes, "Continuous", (int)TValue::FLOATVAR);
    PyModule_AddIntConstant(vartypes, "Other", (int)TValue::FLOATVAR+1);
    PyModule_AddIntConstant(vartypes, "String", (int)STRINGVAR);
    return vartypes;


If you want to understand the constants completely, check the Orange’s pyxtract generated file initialization.px.

How does it all fit together

We will finish the section with a description of the files generated by the two scripts. Understanding these may be needed for debugging purposes.

File specific px files

For each compiled cpp file, pyxtract creates a px file with the same name. The file starts with externs declaring the base classes for the classes whose types are defined later on. Then follow class type definitions:

  • Method definitions (PyMethodDef). Nothing exotic here, just a table with the member functions that is pointed to by tp_methods of the PyTypeObject.

  • GetSet definitions (PyGetSetDef). Similar to methods, a list to be pointed to by tp_getset, which includes the attributes for which special handlers were written.

  • Definitions of doc strings for call operator and constructor.

  • Constants. If the class has any constants, there will be a function named void <class-name>_addConstants(). The function will create a class dictionary in the type’s tp_dict, if there is none yet. Then it will store the constants in it. The functions is called at the module initialization, file initialization.px.

  • Constructors. If the class uses generic constructors (ie, if it’s defined by C_UNNAMED, C_NAMED, C_CALL or C_CALL3), they will need to call a default object constructor, like the below one for FloatVariable. (This supposes the object is derived from TOrange! We will need to get rid of this we want pyxtract to be more general. Maybe an additional argument in DATASTRUCTURE?)

    POrange FloatVariable_default_constructor(PyTypeObject *type)
        return POrange(mlnew TFloatVariable(), type);

    If the class is abstract, pyxtract defines a constructor that will call PyOrType_GenericAbstract. PyOrType_GenericAbstract checks the type that the caller wishes to construct; if it is a type derived from this type, it permits it, otherwise it complains that the class is abstract.

  • Aliases. A list of renamed attributes.

  • PyTypeObject and the numeric, sequence and mapping protocols. PyTypeObject is named PyOr<classname>_Type_inh.

  • Definition of conversion functions. This is done by macro DEFINE_cc(<classname>) which defines int ccn_<classname>(PyObject *obj, void *ptr) - functions that can be used in PyArg_ParseTuple for converting an argument (given as PyObject * to an instance of <classname>. Nothing needs to be programmed for the conversion, it is just a cast: *(GCPtr< T##type > *)(ptr) = PyOrange_As##type(obj);). The difference between cc and ccn is that the latter accepts null pointers.

  • TOrangeType that (essentially) inherits PyTypeObject. The new definition also includes the RTTI used for wrapping (this way Orange knows which C++ class corresponds to which Python class), a pointer to the default constructor (used by generic constructors), a pointer to list of constructor keywords (CONSTRUCTOR_KEYWORDS, keyword arguments that should be ignored in a later call to init) and recognized attributes (RECOGNIZED_ATTRIBUTES, attributes that don’t yield warnings when set), a list of aliases, and pointers to cc_ and ccn_ functions. The latter are not used by Orange, since it can call the converters directly. They are here because TOrangeType is exported in a DLL while cc_ and ccn_ are not (for the sake of limiting the number of exported symbols).


Initialization.px defines the global module stuff.

First, here is a list of all TOrangeTypes. The list is used for checking whether some Python object is of Orange’s type or derived from one, for finding a Python class corresponding to a C++ class (based on C++’s RTTI). Orange also exports the list as orange._orangeClasses; this is a PyCObject so it can only be used by other Python extensions written in C.

Then come declarations of all non-member functions, followed by a PyMethodDef structure with them.

Finally, here are declarations of functions that return manually constructed constants (eg VarTypes) and declarations of functions that add class constants (eg Classifier_addConstants). The latter functions were generated by pyxtract and reside in the individual px files. Then follows a function that calls all the constant related functions declared above. This function also adds all class types to the Orange module.

The main module now only needs to call addConstants.


Externs.px declares symbols for all Orange classes, for instance

extern ORANGE_API TOrangeType PyOrDomain_Type;
#define PyOrDomain_Check(op) PyObject_TypeCheck(op, (PyTypeObject *)&PyOrDomain_Type)
int cc_Domain(PyObject *, void *);
int ccn_Domain(PyObject *, void *);
#define PyOrange_AsDomain(op) (GCPtr< TDomain >(PyOrange_AS_Orange(op)))

What and where to include?

As already mentioned, ppp files should be included (at the beginning) of the corresponding cpp files, instead of the hpp file. For instance, domain.ppp is included in domain.cpp. Each ppp should be compiled only once, all other files needing the definition of TDomain should still include domain.hpp as usual.

File-specific px files are included in the corresponding cpp files. lib_kernel.px is included at the end of lib_kernel.cpp, from which it was generated. initialization.px should preferably be included in the file that initializes the module (function initorange needs to call addConstants, which is declared in initialization.px. These px files contain definitions and must be compiled only once. externs.px contains declarations and can be included wherever needed.

For Microsoft Visual Studio, create a new, blank workspace. Specify the directory with orange sources as “Location”. Add a new project of type “Win 32 Dynamic-Link Library”; change the location back to d:aiorangesource. Make it an empty DLL project.

Whatever names you give your module, make sure that the .cpp and .hpp files you create as you go on are in orangesourcesomething (replace “something” with something), since the further instructions will suppose it.