The cdef statement is used to declare C variables, either local or module-level:
cdef int i, j, k cdef float f, g, *h
cdef struct Grail: int age float volume cdef union Food: char *spam float *eggs cdef enum CheeseType: cheddar, edam, camembert cdef enum CheeseState: hard = 1 soft = 2 runny = 3
There is currently no special syntax for defining a constant, but you can use an anonymous enum declaration for this purpose, for example,:
cdef enum: tons_of_spam = 3
the words struct, union and enum are used only when defining a type, not when referring to it. For example, to declare a variable pointing to a Grail you would write:
cdef Grail *gp
cdef struct Grail *gp # WRONG
There is also a ctypedef statement for giving names to types, e.g.:
ctypedef unsigned long ULong ctypedef int* IntPtr
Cython uses the normal C syntax for C types, including pointers. It provides all the standard C types, namely char, short, int, long, long long as well as their unsigned versions, e.g. unsigned int. The special bint type is used for C boolean values (int with 0/non-0 values for False/True) and Py_ssize_t for (signed) sizes of Python containers.
Pointer types are constructed as in C, by appending a * to the base type they point to, e.g. int** for a pointer to a pointer to a C int. Arrays use the normal C array syntax, e.g. int. Note that Cython uses array access for pointer dereferencing, as *x is not valid Python syntax, whereas x is.
Also, the Python types list, dict, tuple, etc. may be used for static typing, as well as any user defined extension types. The Python types int and long are not available for static typing and instead interpreted as C int and long respectively, as statically typing variables with Python integer types has zero advantages.
There are two kinds of function definition in Cython:
Python functions are defined using the def statement, as in Python. They take Python objects as parameters and return Python objects.
C functions are defined using the new cdef statement. They take either Python objects or C values as parameters, and can return either Python objects or C values.
Within a Cython module, Python functions and C functions can call each other freely, but only Python functions can be called from outside the module by interpreted Python code. So, any functions that you want to “export” from your Cython module must be declared as Python functions using def. There is also a hybrid function, called cpdef. A cpdef can be called from anywhere, but uses the faster C calling conventions when being called from other Cython code. A cpdef can also be overridden by a Python method on a subclass or an instance attribute, even when called from Cython. If this happens, most performance gains are of course lost and even if it does not, there is a tiny overhead in calling a cpdef method from Cython compared to calling a cdef method.
Parameters of either type of function can be declared to have C data types, using normal C declaration syntax. For example,:
def spam(int i, char *s): ... cdef int eggs(unsigned long l, float f): ...
When a parameter of a Python function is declared to have a C data type, it is passed in as a Python object and automatically converted to a C value, if possible. In other words, the definition of spam above is equivalent to writing:
def spam(python_i, python_s): cdef int i = python_i cdef char* s = python_s ...
Automatic conversion is currently only possible for numeric types, string types and structs (composed recursively of any of these types); attempting to use any other type for the parameter of a Python function will result in a compile-time error. Care must be taken with strings to ensure a reference if the pointer is to be used after the call. Structs can be obtained from Python mappings, and again care must be taken with string attributes if they are to be used after the function returns.
C functions, on the other hand, can have parameters of any type, since they’re passed in directly using a normal C function call.
A more complete comparison of the pros and cons of these different method types can be found at Early Binding for Speed.
If no type is specified for a parameter or return value, it is assumed to be a Python object. (Note that this is different from the C convention, where it would default to int.) For example, the following defines a C function that takes two Python objects as parameters and returns a Python object:
cdef spamobjs(x, y): ...
Reference counting for these objects is performed automatically according to the standard Python/C API rules (i.e. borrowed references are taken as parameters and a new reference is returned).
The name object can also be used to explicitly declare something as a Python object. This can be useful if the name being declared would otherwise be taken as the name of a type, for example,:
cdef ftang(object int): ...
declares a parameter called int which is a Python object. You can also use object as the explicit return type of a function, e.g.:
cdef object ftang(object int): ...
In the interests of clarity, it is probably a good idea to always be explicit about object parameters in C functions.
If you don’t do anything special, a function declared with cdef that does not return a Python object has no way of reporting Python exceptions to its caller. If an exception is detected in such a function, a warning message is printed and the exception is ignored.
If you want a C function that does not return a Python object to be able to propagate exceptions to its caller, you need to declare an exception value for it. Here is an example:
cdef int spam() except -1: ...
With this declaration, whenever an exception occurs inside spam, it will immediately return with the value -1. Furthermore, whenever a call to spam returns -1, an exception will be assumed to have occurred and will be propagated.
When you declare an exception value for a function, you should never explicitly return that value. If all possible return values are legal and you can’t reserve one entirely for signalling errors, you can use an alternative form of exception value declaration:
cdef int spam() except? -1: ...
The ”?” indicates that the value -1 only indicates a possible error. In this case, Cython generates a call to PyErr_Occurred() if the exception value is returned, to make sure it really is an error.
There is also a third form of exception value declaration:
cdef int spam() except *: ...
This form causes Cython to generate a call to PyErr_Occurred() after every call to spam, regardless of what value it returns. If you have a function returning void that needs to propagate errors, you will have to use this form, since there isn’t any return value to test. Otherwise there is little use for this form.
An external C++ function that may raise an exception can be declared with:
cdef int spam() except +
See Using C++ in Cython for more details.
Some things to note:
Exception values can only declared for functions returning an integer, enum, float or pointer type, and the value must be a constant expression. Void functions can only use the except * form.
The exception value specification is part of the signature of the function. If you’re passing a pointer to a function as a parameter or assigning it to a variable, the declared type of the parameter or variable must have the same exception value specification (or lack thereof). Here is an example of a pointer-to-function declaration with an exception value:
int (*grail)(int, char*) except -1
You don’t need to (and shouldn’t) declare exception values for functions which return Python objects. Remember that a function with no declared return type implicitly returns a Python object. (Exceptions on such functions are implicitly propagated by returning NULL.)
It’s important to understand that the except clause does not cause an error to be raised when the specified value is returned. For example, you can’t write something like:
cdef extern FILE *fopen(char *filename, char *mode) except NULL # WRONG!
and expect an exception to be automatically raised if a call to fopen() returns NULL. The except clause doesn’t work that way; its only purpose is for propagating Python exceptions that have already been raised, either by a Cython function or a C function that calls Python/C API routines. To get an exception from a non-Python-aware function such as fopen(), you will have to check the return value and raise it yourself, for example,:
cdef FILE* p p = fopen("spam.txt", "r") if p == NULL: raise SpamError("Couldn't open the spam file")
In most situations, automatic conversions will be performed for the basic numeric and string types when a Python object is used in a context requiring a C value, or vice versa. The following table summarises the conversion possibilities.
|C types||From Python types||To Python types|
|[unsigned] char [unsigned] short int, long||int, long||int|
|unsigned int unsigned long [unsigned] long long||int, long||long|
|float, double, long double||int, long, float||float|
|||The conversion is to/from str for Python 2.x, and bytes for Python 3.x.|
You need to be careful when using a Python string in a context expecting a char*. In this situation, a pointer to the contents of the Python string is used, which is only valid as long as the Python string exists. So you need to make sure that a reference to the original Python string is held for as long as the C string is needed. If you can’t guarantee that the Python string will live long enough, you will need to copy the C string.
Cython detects and prevents some mistakes of this kind. For instance, if you attempt something like:
cdef char *s s = pystring1 + pystring2
then Cython will produce the error message Obtaining char* from temporary Python value. The reason is that concatenating the two Python strings produces a new Python string object that is referenced only by a temporary internal variable that Cython generates. As soon as the statement has finished, the temporary variable will be decrefed and the Python string deallocated, leaving s dangling. Since this code could not possibly work, Cython refuses to compile it.
The solution is to assign the result of the concatenation to a Python variable, and then obtain the char* from that, i.e.:
cdef char *s p = pystring1 + pystring2 s = p
It is then your responsibility to hold the reference p for as long as necessary.
Keep in mind that the rules used to detect such errors are only heuristics. Sometimes Cython will complain unnecessarily, and sometimes it will fail to detect a problem that exists. Ultimately, you need to understand the issue and be careful what you do.
Control structures and expressions follow Python syntax for the most part. When applied to Python objects, they have the same semantics as in Python (unless otherwise noted). Most of the Python operators can also be applied to C values, with the obvious semantics.
If Python objects and C values are mixed in an expression, conversions are performed automatically between Python objects and C numeric or string types.
Reference counts are maintained automatically for all Python objects, and all Python operations are automatically checked for errors, with appropriate action taken.
There are some differences in syntax and semantics between C expressions and Cython expressions, particularly in the area of C constructs which have no direct equivalent in Python.
An integer literal is treated as a C constant, and will be truncated to whatever size your C compiler thinks appropriate. To get a Python integer (of arbitrary precision) cast immediately to an object (e.g. <object>100000000000000000000). The L, LL, and U suffixes have the same meaning as in C.
There is no -> operator in Cython. Instead of p->x, use p.x
There is no unary * operator in Cython. Instead of *p, use p
There is an & operator, with the same semantics as in C.
The null C pointer is called NULL, not 0 (and NULL is a reserved word).
Type casts are written <type>value , for example,:
cdef char* p, float* q p = <char*>q
Cython determines whether a variable belongs to a local scope, the module scope, or the built-in scope completely statically. As with Python, assigning to a variable which is not otherwise declared implicitly declares it to be a variable residing in the scope where it is assigned. The type of the variable depends on type inference, except for the global module scope, where it is always a Python object.
Cython compiles calls to most built-in functions into direct calls to the corresponding Python/C API routines, making them particularly fast.
Only direct function calls using these names are optimised. If you do something else with one of these names that assumes it’s a Python object, such as assign it to a Python variable, and later call it, the call will be made as a Python function call.
|Function and arguments||Return type||Python/C API Equivalent|
|abs(obj)||object, double, ...||PyNumber_Absolute, fabs, fabsf, ...|
|exec(code, [glob, [loc]])||object|
|getattr(obj, name, [default]) (Note 1)||object||PyObject_GetAttr|
|hash(obj)||int / long||PyObject_Hash|
|pow(x, y, [z])||object||PyNumber_Power|
Note 1: Pyrex originally provided a function getattr3(obj, name, default)() corresponding to the three-argument form of the Python builtin getattr(). Cython still supports this function, but the usage is deprecated in favour of the normal builtin, which Cython can optimise in both forms.
Keep in mind that there are some differences in operator precedence between Python and C, and that Cython uses the Python precedences, not the C ones.
Cython recognises the usual Python for-in-range integer loop pattern:
for i in range(n): ...
If i is declared as a cdef integer type, it will optimise this into a pure C loop. This restriction is required as otherwise the generated code wouldn’t be correct due to potential integer overflows on the target architecture. If you are worried that the loop is not being converted correctly, use the annotate feature of the cython commandline (-a) to easily see the generated C code. See Automatic range conversion
For backwards compatibility to Pyrex, Cython also supports a more verbose form of for-loop which you might find in legacy code:
for i from 0 <= i < n: ...
for i from 0 <= i < n by s: ...
where s is some integer step size.
This syntax is deprecated and should not be used in new code. Use the normal Python for-loop instead.
Some things to note about the for-from loop:
Like other Python looping statements, break and continue may be used in the body, and the loop may have an else clause.
Historically the include statement was used for sharing declarations. Use Sharing Declarations Between Cython Modules instead.
A Cython source file can include material from other files using the include statement, for example,:
The contents of the named file are textually included at that point. The included file can contain any complete statements or declarations that are valid in the context where the include statement appears, including other include statements. The contents of the included file should begin at an indentation level of zero, and will be treated as though they were indented to the level of the include statement that is including the file.
There are other mechanisms available for splitting Cython code into separate parts that may be more appropriate in many cases. See Sharing Declarations Between Cython Modules.
Some features are available for conditional compilation and compile-time constants within a Cython source file.
A compile-time constant can be defined using the DEF statement:
DEF FavouriteFood = "spam" DEF ArraySize = 42 DEF OtherArraySize = 2 * ArraySize + 17
The right-hand side of the DEF must be a valid compile-time expression. Such expressions are made up of literal values and names defined using DEF statements, combined using any of the Python expression syntax.
The following compile-time names are predefined, corresponding to the values returned by os.uname().
UNAME_SYSNAME, UNAME_NODENAME, UNAME_RELEASE, UNAME_VERSION, UNAME_MACHINE
The following selection of builtin constants and functions are also available:
None, True, False, abs, bool, chr, cmp, complex, dict, divmod, enumerate, float, hash, hex, int, len, list, long, map, max, min, oct, ord, pow, range, reduce, repr, round, slice, str, sum, tuple, xrange, zip
A name defined using DEF can be used anywhere an identifier can appear, and it is replaced with its compile-time value as though it were written into the source at that point as a literal. For this to work, the compile-time expression must evaluate to a Python value of type int, long, float or str.:
cdef int a1[ArraySize] cdef int a2[OtherArraySize] print "I like", FavouriteFood
The IF statement can be used to conditionally include or exclude sections of code at compile time. It works in a similar way to the #if preprocessor directive in C.:
IF UNAME_SYSNAME == "Windows": include "icky_definitions.pxi" ELIF UNAME_SYSNAME == "Darwin": include "nice_definitions.pxi" ELIF UNAME_SYSNAME == "Linux": include "penguin_definitions.pxi" ELSE: include "other_definitions.pxi"
The ELIF and ELSE clauses are optional. An IF statement can appear anywhere that a normal statement or declaration can appear, and it can contain any statements or declarations that would be valid in that context, including DEF statements and other IF statements.
The expressions in the IF and ELIF clauses must be valid compile-time expressions as for the DEF statement, although they can evaluate to any Python value, and the truth of the result is determined in the usual Python way.