numpy.i：一个NumPy的SWIG接口文件
介绍
Simple Wrapper and Interface Generator(或SWIG)是一个功能强大的工具，用于生成用于与多种脚本语言接口的包装器代码。 SWIG可以解析头文件，并且只使用代码原型创建与目标语言的接口。 但 SWIG 并不是万能的。、例如，它无法从原型获知：
double rms(double* seq, int n);
what exactly seq
is. Is it a single value to be altered inplace?
Is it an array, and if so what is its length? Is it inputonly?
Outputonly? Inputoutput? SWIG cannot determine these details,
and does not attempt to do so.
If we designed rms
, we probably made it a routine that takes an
inputonly array of length n
of double
values called seq
and returns the root mean square. The default behavior of SWIG,
however, will be to create a wrapper function that compiles, but is
nearly impossible to use from the scripting language in the way the C
routine was intended.
For Python, the preferred way of handling contiguous (or technically,
strided ) blocks of homogeneous data is with NumPy, which provides full
objectoriented access to multidimensial arrays of data. Therefore, the most
logical Python interface for the rms
function would be (including doc
string):
def rms(seq):
"""
rms: return the root mean square of a sequence
rms(numpy.ndarray) > double
rms(list) > double
rms(tuple) > double
"""
where seq
would be a NumPy array of double
values, and its
length n
would be extracted from seq
internally before being
passed to the C routine. Even better, since NumPy supports
construction of arrays from arbitrary Python sequences, seq
itself could be a nearly arbitrary sequence (so long as each element
can be converted to a double
) and the wrapper code would
internally convert it to a NumPy array before extracting its data
and length.
SWIG allows these types of conversions to be defined via a
mechanism called typemaps . This document provides information on
how to use numpy.i
, a SWIG interface file that defines a series
of typemaps intended to make the type of arrayrelated conversions
described above relatively simple to implement. For example, suppose
that the rms
function prototype defined above was in a header file
named rms.h
. To obtain the Python interface discussed above, your
SWIG interface file would need the following:
%{
#define SWIG_FILE_WITH_INIT
#include "rms.h"
%}
%include "numpy.i"
%init %{
import_array();
%}
%apply (double* IN_ARRAY1, int DIM1) {(double* seq, int n)};
%include "rms.h"
Typemaps are keyed off a list of one or more function arguments,
either by type or by type and name. We will refer to such lists as
signatures . One of the many typemaps defined by numpy.i
is used
above and has the signature (double* IN_ARRAY1, int DIM1)
. The
argument names are intended to suggest that the double*
argument
is an input array of one dimension and that the int
represents the
size of that dimension. This is precisely the pattern in the rms
prototype.
Most likely, no actual prototypes to be wrapped will have the argument
names IN_ARRAY1
and DIM1
. We use the SWIG %apply
directive to apply the typemap for onedimensional input arrays of
type double
to the actual prototype used by rms
. Using
numpy.i
effectively, therefore, requires knowing what typemaps are
available and what they do.
A SWIG interface file that includes the SWIG directives given above will produce wrapper code that looks something like:
1 PyObject *_wrap_rms(PyObject *args) {
2 PyObject *resultobj = 0;
3 double *arg1 = (double *) 0 ;
4 int arg2 ;
5 double result;
6 PyArrayObject *array1 = NULL ;
7 int is_new_object1 = 0 ;
8 PyObject * obj0 = 0 ;
9
10 if (!PyArg_ParseTuple(args,(char *)"O:rms",&obj0)) SWIG_fail;
11 {
12 array1 = obj_to_array_contiguous_allow_conversion(
13 obj0, NPY_DOUBLE, &is_new_object1);
14 npy_intp size[1] = {
15 1
16 };
17 if (!array1  !require_dimensions(array1, 1) 
18 !require_size(array1, size, 1)) SWIG_fail;
19 arg1 = (double*) array1>data;
20 arg2 = (int) array1>dimensions[0];
21 }
22 result = (double)rms(arg1,arg2);
23 resultobj = SWIG_From_double((double)(result));
24 {
25 if (is_new_object1 && array1) Py_DECREF(array1);
26 }
27 return resultobj;
28 fail:
29 {
30 if (is_new_object1 && array1) Py_DECREF(array1);
31 }
32 return NULL;
33 }
The typemaps from numpy.i
are responsible for the following lines
of code: 12–20, 25 and 30. Line 10 parses the input to the rms
function. From the format string "O:rms"
, we can see that the
argument list is expected to be a single Python object (specified
by the O
before the colon) and whose pointer is stored in
obj0
. A number of functions, supplied by numpy.i
, are called
to make and check the (possible) conversion from a generic Python
object to a NumPy array. These functions are explained in the
section Helper Functions, but hopefully their names are
selfexplanatory. At line 12 we use obj0
to construct a NumPy
array. At line 17, we check the validity of the result: that it is
nonnull and that it has a single dimension of arbitrary length. Once
these states are verified, we extract the data buffer and length in
lines 19 and 20 so that we can call the underlying C function at line
22. Line 25 performs memory management for the case where we have
created a new array that is no longer needed.
This code has a significant amount of error handling. Note the
SWIG_fail
is a macro for goto fail
, referring to the label at
line 28. If the user provides the wrong number of arguments, this
will be caught at line 10. If construction of the NumPy array
fails or produces an array with the wrong number of dimensions, these
errors are caught at line 17. And finally, if an error is detected,
memory is still managed correctly at line 30.
Note that if the C function signature was in a different order:
double rms(int n, double* seq);
that SWIG would not match the typemap signature given above with
the argument list for rms
. Fortunately, numpy.i
has a set of
typemaps with the data pointer given last:
%apply (int DIM1, double* IN_ARRAY1) {(int n, double* seq)};
This simply has the effect of switching the definitions of arg1
and arg2
in lines 3 and 4 of the generated code above, and their
assignments in lines 19 and 20.
Using numpy.i
The numpy.i
file is currently located in the tools/swig
subdirectory under the numpy
installation directory. Typically,
you will want to copy it to the directory where you are developing
your wrappers.
A simple module that only uses a single SWIG interface file should include the following:
%{
#define SWIG_FILE_WITH_INIT
%}
%include "numpy.i"
%init %{
import_array();
%}
Within a compiled Python module, import_array()
should only get
called once. This could be in a C/C++ file that you have written and
is linked to the module. If this is the case, then none of your
interface files should #define SWIG_FILE_WITH_INIT
or call
import_array()
. Or, this initialization call could be in a
wrapper file generated by SWIG from an interface file that has the
%init
block as above. If this is the case, and you have more than
one SWIG interface file, then only one interface file should
#define SWIG_FILE_WITH_INIT
and call import_array()
.
Available Typemaps
The typemap directives provided by numpy.i
for arrays of different
data types, say double
and int
, and dimensions of different
types, say int
or long
, are identical to one another except
for the C and NumPy type specifications. The typemaps are
therefore implemented (typically behind the scenes) via a macro:
%numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE)
that can be invoked for appropriate (DATA_TYPE, DATA_TYPECODE,
DIM_TYPE)
triplets. For example:
%numpy_typemaps(double, NPY_DOUBLE, int)
%numpy_typemaps(int, NPY_INT , int)
The numpy.i
interface file uses the %numpy_typemaps
macro to
implement typemaps for the following C data types and int
dimension types:
signed char
unsigned char
short
unsigned short
int
unsigned int
long
unsigned long
long long
unsigned long long
float
double
In the following descriptions, we reference a generic DATA_TYPE
, which
could be any of the C data types listed above, and DIM_TYPE
which
should be one of the many types of integers.
The typemap signatures are largely differentiated on the name given to
the buffer pointer. Names with FARRAY
are for Fortranordered
arrays, and names with ARRAY
are for Cordered (or 1D arrays).
Input Arrays
Input arrays are defined as arrays of data that are passed into a routine but are not altered inplace or returned to the user. The Python input array is therefore allowed to be almost any Python sequence (such as a list) that can be converted to the requested type of array. The input array signatures are
1D:
( DATA_TYPE IN_ARRAY1[ANY] )
( DATA_TYPE* IN_ARRAY1, int DIM1 )
( int DIM1, DATA_TYPE* IN_ARRAY1 )
2D:
( DATA_TYPE IN_ARRAY2[ANY][ANY] )
( DATA_TYPE* IN_ARRAY2, int DIM1, int DIM2 )
( int DIM1, int DIM2, DATA_TYPE* IN_ARRAY2 )
( DATA_TYPE* IN_FARRAY2, int DIM1, int DIM2 )
( int DIM1, int DIM2, DATA_TYPE* IN_FARRAY2 )
3D:
( DATA_TYPE IN_ARRAY3[ANY][ANY][ANY] )
( DATA_TYPE* IN_ARRAY3, int DIM1, int DIM2, int DIM3 )
( int DIM1, int DIM2, int DIM3, DATA_TYPE* IN_ARRAY3 )
( DATA_TYPE* IN_FARRAY3, int DIM1, int DIM2, int DIM3 )
( int DIM1, int DIM2, int DIM3, DATA_TYPE* IN_FARRAY3 )
4D:
(DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY])
(DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
(DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, , DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4)
(DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
(DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4)
The first signature listed, ( DATA_TYPE IN_ARRAY[ANY] )
is for
onedimensional arrays with hardcoded dimensions. Likewise,
( DATA_TYPE IN_ARRAY2[ANY][ANY] )
is for twodimensional arrays
with hardcoded dimensions, and similarly for threedimensional.
InPlace Arrays
Inplace arrays are defined as arrays that are modified inplace. The input values may or may not be used, but the values at the time the function returns are significant. The provided Python argument must therefore be a NumPy array of the required type. The inplace signatures are
1D:
( DATA_TYPE INPLACE_ARRAY1[ANY] )
( DATA_TYPE* INPLACE_ARRAY1, int DIM1 )
( int DIM1, DATA_TYPE* INPLACE_ARRAY1 )
2D:
( DATA_TYPE INPLACE_ARRAY2[ANY][ANY] )
( DATA_TYPE* INPLACE_ARRAY2, int DIM1, int DIM2 )
( int DIM1, int DIM2, DATA_TYPE* INPLACE_ARRAY2 )
( DATA_TYPE* INPLACE_FARRAY2, int DIM1, int DIM2 )
( int DIM1, int DIM2, DATA_TYPE* INPLACE_FARRAY2 )
3D:
( DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY] )
( DATA_TYPE* INPLACE_ARRAY3, int DIM1, int DIM2, int DIM3 )
( int DIM1, int DIM2, int DIM3, DATA_TYPE* INPLACE_ARRAY3 )
( DATA_TYPE* INPLACE_FARRAY3, int DIM1, int DIM2, int DIM3 )
( int DIM1, int DIM2, int DIM3, DATA_TYPE* INPLACE_FARRAY3 )
4D:
(DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY])
(DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
(DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, , DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4)
(DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
(DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4)
These typemaps now check to make sure that the INPLACE_ARRAY
arguments use native byte ordering. If not, an exception is raised.
There is also a “flat” inplace array for situations in which you would like to modify or process each element, regardless of the number of dimensions. One example is a “quantization” function that quantizes each element of an array inplace, be it 1D, 2D or whatever. This form checks for continuity but allows either C or Fortran ordering.
ND:
(DATA_TYPE* INPLACE_ARRAY_FLAT, DIM_TYPE DIM_FLAT)
Argout Arrays
Argout arrays are arrays that appear in the input arguments in C, but are in fact output arrays. This pattern occurs often when there is more than one output variable and the single return argument is therefore not sufficient. In Python, the conventional way to return multiple arguments is to pack them into a sequence (tuple, list, etc.) and return the sequence. This is what the argout typemaps do. If a wrapped function that uses these argout typemaps has more than one return argument, they are packed into a tuple or list, depending on the version of Python. The Python user does not pass these arrays in, they simply get returned. For the case where a dimension is specified, the python user must provide that dimension as an argument. The argout signatures are
1D:
( DATA_TYPE ARGOUT_ARRAY1[ANY] )
( DATA_TYPE* ARGOUT_ARRAY1, int DIM1 )
( int DIM1, DATA_TYPE* ARGOUT_ARRAY1 )
2D:
( DATA_TYPE ARGOUT_ARRAY2[ANY][ANY] )
3D:
( DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY] )
4D:
( DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY] )
These are typically used in situations where in C/C++, you would allocate a(n) array(s) on the heap, and call the function to fill the array(s) values. In Python, the arrays are allocated for you and returned as new array objects.
Note that we support DATA_TYPE*
argout typemaps in 1D, but not 2D
or 3D. This is because of a quirk with the SWIG typemap syntax and
cannot be avoided. Note that for these types of 1D typemaps, the
Python function will take a single argument representing DIM1
.
Argout View Arrays
Argoutview arrays are for when your C code provides you with a view of its internal data and does not require any memory to be allocated by the user. This can be dangerous. There is almost no way to guarantee that the internal data from the C code will remain in existence for the entire lifetime of the NumPy array that encapsulates it. If the user destroys the object that provides the view of the data before destroying the NumPy array, then using that array may result in bad memory references or segmentation faults. Nevertheless, there are situations, working with large data sets, where you simply have no other choice.
The C code to be wrapped for argoutview arrays are characterized by pointers: pointers to the dimensions and double pointers to the data, so that these values can be passed back to the user. The argoutview typemap signatures are therefore
1D:
( DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1 )
( DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1 )
2D:
( DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2 )
( DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2 )
( DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2 )
( DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2 )
3D:
( DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
( DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3)
( DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
( DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3)
4D:
(DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
(DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_ARRAY4)
(DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
(DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_FARRAY4)
Note that arrays with hardcoded dimensions are not supported. These cannot follow the double pointer signatures of these typemaps.
Memory Managed Argout View Arrays
A recent addition to numpy.i
are typemaps that permit argout
arrays with views into memory that is managed. See the discussion here.
1D:
(DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1)
(DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1)
2D:
(DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
(DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2)
(DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
(DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2)
3D:
(DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
(DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_ARRAY3)
(DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
(DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_FARRAY3)
4D:
(DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
(DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4)
(DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
(DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4)
Output Arrays
The numpy.i
interface file does not support typemaps for output
arrays, for several reasons. First, C/C++ return arguments are
limited to a single value. This prevents obtaining dimension
information in a general way. Second, arrays with hardcoded lengths
are not permitted as return arguments. In other words:
double[3] newVector(double x, double y, double z);
is not legal C/C++ syntax. Therefore, we cannot provide typemaps of the form:
%typemap(out) (TYPE[ANY]);
If you run into a situation where a function or method is returning a
pointer to an array, your best bet is to write your own version of the
function to be wrapped, either with %extend
for the case of class
methods or %ignore
and %rename
for the case of functions.
Other Common Types: bool
Note that C++ type bool
is not supported in the list in the
Available Typemaps section. NumPy bools are a single byte, while
the C++ bool
is four bytes (at least on my system). Therefore:
%numpy_typemaps(bool, NPY_BOOL, int)
will result in typemaps that will produce code that reference improper data lengths. You can implement the following macro expansion:
%numpy_typemaps(bool, NPY_UINT, int)
to fix the data length problem, and Input Arrays will work fine, but InPlace Arrays might fail typechecking.
Other Common Types: complex
Typemap conversions for complex floatingpoint types is also not
supported automatically. This is because Python and NumPy are
written in C, which does not have native complex types. Both
Python and NumPy implement their own (essentially equivalent)
struct
definitions for complex variables:
/* Python */
typedef struct {double real; double imag;} Py_complex;
/* NumPy */
typedef struct {float real, imag;} npy_cfloat;
typedef struct {double real, imag;} npy_cdouble;
We could have implemented:
%numpy_typemaps(Py_complex , NPY_CDOUBLE, int)
%numpy_typemaps(npy_cfloat , NPY_CFLOAT , int)
%numpy_typemaps(npy_cdouble, NPY_CDOUBLE, int)
which would have provided automatic type conversions for arrays of
type Py_complex
, npy_cfloat
and npy_cdouble
. However, it
seemed unlikely that there would be any independent (nonPython,
nonNumPy) application code that people would be using SWIG to
generate a Python interface to, that also used these definitions
for complex types. More likely, these application codes will define
their own complex types, or in the case of C++, use std::complex
.
Assuming these data structures are compatible with Python and
NumPy complex types, %numpy_typemap
expansions as above (with
the user’s complex type substituted for the first argument) should
work.
NumPy Array Scalars and SWIG
SWIG has sophisticated type checking for numerical types. For
example, if your C/C++ routine expects an integer as input, the code
generated by SWIG will check for both Python integers and
Python long integers, and raise an overflow error if the provided
Python integer is too big to cast down to a C integer. With the
introduction of NumPy scalar arrays into your Python code, you
might conceivably extract an integer from a NumPy array and attempt
to pass this to a SWIGwrapped C/C++ function that expects an
int
, but the SWIG type checking will not recognize the NumPy
array scalar as an integer. (Often, this does in fact work – it
depends on whether NumPy recognizes the integer type you are using
as inheriting from the Python integer type on the platform you are
using. Sometimes, this means that code that works on a 32bit machine
will fail on a 64bit machine.)
If you get a Python error that looks like the following:
TypeError: in method 'MyClass_MyMethod', argument 2 of type 'int'
and the argument you are passing is an integer extracted from a NumPy array, then you have stumbled upon this problem. The solution is to modify the SWIG type conversion system to accept NumPy array scalars in addition to the standard integer types. Fortunately, this capability has been provided for you. Simply copy the file:
pyfragments.swg
to the working build directory for you project, and this problem will be fixed. It is suggested that you do this anyway, as it only increases the capabilities of your Python interface.
Why is There a Second File?
The SWIG type checking and conversion system is a complicated combination of C macros, SWIG macros, SWIG typemaps and SWIG fragments. Fragments are a way to conditionally insert code into your wrapper file if it is needed, and not insert it if not needed. If multiple typemaps require the same fragment, the fragment only gets inserted into your wrapper code once.
There is a fragment for converting a Python integer to a C
long
. There is a different fragment that converts a Python
integer to a C int
, that calls the routine defined in the
long
fragment. We can make the changes we want here by changing
the definition for the long
fragment. SWIG determines the
active definition for a fragment using a “first come, first served”
system. That is, we need to define the fragment for long
conversions prior to SWIG doing it internally. SWIG allows us
to do this by putting our fragment definitions in the file
pyfragments.swg
. If we were to put the new fragment definitions
in numpy.i
, they would be ignored.
Helper Functions
The numpy.i
file contains several macros and routines that it
uses internally to build its typemaps. However, these functions may
be useful elsewhere in your interface file. These macros and routines
are implemented as fragments, which are described briefly in the
previous section. If you try to use one or more of the following
macros or functions, but your compiler complains that it does not
recognize the symbol, then you need to force these fragments to appear
in your code using:
%fragment("NumPy_Fragments");
in your SWIG interface file.
Macros

is_array(a)
Evaluates as true if a is non
NULL
and can be cast to aPyArrayObject*
. 
array_type(a)
Evaluates to the integer data type code of
a
, assuminga
can be cast to aPyArrayObject*
. 
array_numdims(a)
Evaluates to the integer number of dimensions of
a
, assuminga
can be cast to aPyArrayObject*
. 
array_dimensions(a)
Evaluates to an array of type
npy_intp
and lengtharray_numdims(a)
, giving the lengths of all of the dimensions ofa
, assuminga
can be cast to aPyArrayObject*
. 
array_size(a,i)
Evaluates to the
i
th dimension size ofa
, assuming a can be cast to aPyArrayObject*
. 
array_strides(a)
Evaluates to an array of type
npy_intp
and lengtharray_numdims(a)
, giving the stridess of all of the dimensions ofa
, assuminga
can be cast to aPyArrayObject*
. A stride is the distance in bytes between an element and its immediate neighbor along the same axis. 
array_stride(a,i)
Evaluates to the
i
th stride ofa
, assuminga
can be cast to aPyArrayObject*
. 
array_data(a)
Evaluates to a pointer of type
void*
that points to the data buffer ofa
, assuminga
can be cast to aPyArrayObject*
. 
array_descr(a)
Returns a borrowed reference to the dtype property
(PyArray_Descr*)
ofa
, assuminga
can be cast to aPyArrayObject*
. 
array_flags(a)
Returns an integer representing the flags of
a
, assuminga
can be cast to aPyArrayObject*
. 
array_enableflags(a,f)
Sets the flag represented by
f
ofa
, assuming a can be cast to aPyArrayObject*
. 
array_is_contiguous(a)
Evaluates as true if a is a contiguous array. Equivalent to
(PyArray_ISCONTIGUOUS(a))
. 
array_is_native(a)
Evaluates as true if the data buffer of a uses native byte order. Equivalent to
(PyArray_ISNOTSWAPPED(a))
. 
array_is_fortran(a)
Evaluates as true if
a
is FORTRAN ordered.
Routines

pytype_string()
Return type:
const char*
Arguments:
PyObject* py_obj
, a general Python object.
Return a string describing the type of
py_obj
. 
typecode_string()
Return type:
const char*
Arguments:
int typecode
, a NumPy integer typecode.
Return a string describing the type corresponding to the NumPy
typecode
. 
type_match()
Return type:
int
Arguments:
int actual_type
, the NumPy typecode of a NumPy array.int desired_type
, the desired NumPy typecode.
Make sure that
actual_type
is compatible withdesired_type
. For example, this allows character and byte types, or int and long types, to match. This is now equivalent toPyArray_EquivTypenums()
. 
obj_to_array_no_conversion()
Return type:
PyArrayObject*
Arguments:
PyObject* input
, a general Python object.int typecode
, the desired NumPy typecode.
Cast
input
to aPyArrayObject*
if legal, and ensure that it is of typetypecode
. Ifinput
cannot be cast, or thetypecode
is wrong, set a Python error and returnNULL
. 
obj_to_array_allow_conversion()
Return type:
PyArrayObject*
Arguments:
PyObject* input
, a general Python object.int typecode
, the desired NumPy typecode of the resulting array.int* is_new_object
, returns a value of 0 if no conversion performed, else 1.
Convert
input
to a NumPy array with the giventypecode
. On success, return a validPyArrayObject*
with the correct type. On failure, the Python error string will be set and the routine returnsNULL
. 
make_contiguous()
Return type:
PyArrayObject*
Arguments:
PyArrayObject* ary
, a NumPy array.int* is_new_object
, returns a value of 0 if no conversion performed, else 1.int min_dims
, minimum allowable dimensions.int max_dims
, maximum allowable dimensions.
Check to see if
ary
is contiguous. If so, return the input pointer and flag it as not a new object. If it is not contiguous, create a newPyArrayObject*
using the original data, flag it as a new object and return the pointer. 
make_fortran()
Return type:
PyArrayObject*
Arguments
PyArrayObject* ary
, a NumPy array.int* is_new_object
, returns a value of 0 if no conversion performed, else 1.
Check to see if
ary
is Fortran contiguous. If so, return the input pointer and flag it as not a new object. If it is not Fortran contiguous, create a newPyArrayObject*
using the original data, flag it as a new object and return the pointer. 
obj_to_array_contiguous_allow_conversion()
Return type:
PyArrayObject*
Arguments:
PyObject* input
, a general Python object.int typecode
, the desired NumPy typecode of the resulting array.int* is_new_object
, returns a value of 0 if no conversion performed, else 1.
Convert
input
to a contiguousPyArrayObject*
of the specified type. If the input object is not a contiguousPyArrayObject*
, a new one will be created and the new object flag will be set. 
obj_to_array_fortran_allow_conversion()
Return type:
PyArrayObject*
Arguments:
PyObject* input
, a general Python object.int typecode
, the desired NumPy typecode of the resulting array.int* is_new_object
, returns a value of 0 if no conversion performed, else 1.
Convert
input
to a Fortran contiguousPyArrayObject*
of the specified type. If the input object is not a Fortran contiguousPyArrayObject*
, a new one will be created and the new object flag will be set. 
require_contiguous()
Return type:
int
Arguments:
PyArrayObject* ary
, a NumPy array.
Test whether
ary
is contiguous. If so, return 1. Otherwise, set a Python error and return 0. 
require_native()
Return type:
int
Arguments:
PyArray_Object* ary
, a NumPy array.
Require that
ary
is not byteswapped. If the array is not byteswapped, return 1. Otherwise, set a Python error and return 0. 
require_dimensions()
Return type:
int
Arguments:
PyArrayObject* ary
, a NumPy array.int exact_dimensions
, the desired number of dimensions.
Require
ary
to have a specified number of dimensions. If the array has the specified number of dimensions, return 1. Otherwise, set a Python error and return 0. 
require_dimensions_n()
Return type:
int
Arguments:
PyArrayObject* ary
, a NumPy array.int* exact_dimensions
, an array of integers representing acceptable numbers of dimensions.int n
, the length ofexact_dimensions
.
Require
ary
to have one of a list of specified number of dimensions. If the array has one of the specified number of dimensions, return 1. Otherwise, set the Python error string and return 0. 
require_size()
Return type:
int
Arguments:
PyArrayObject* ary
, a NumPy array.npy_int* size
, an array representing the desired lengths of each dimension.int n
, the length ofsize
.
Require
ary
to have a specified shape. If the array has the specified shape, return 1. Otherwise, set the Python error string and return 0. 
require_fortran()
Return type:
int
Arguments:
PyArrayObject* ary
, a NumPy array.
Require the given
PyArrayObject
to to be Fortran ordered. If thePyArrayObject
is already Fortran ordered, do nothing. Else, set the Fortran ordering flag and recompute the strides.
Beyond the Provided Typemaps
There are many C or C++ array/NumPy array situations not covered by
a simple %include "numpy.i"
and subsequent %apply
directives.
A Common Example
Consider a reasonable prototype for a dot product function:
double dot(int len, double* vec1, double* vec2);
The Python interface that we want is:
def dot(vec1, vec2):
"""
dot(PyObject,PyObject) > double
"""
The problem here is that there is one dimension argument and two array
arguments, and our typemaps are set up for dimensions that apply to a
single array (in fact, SWIG does not provide a mechanism for
associating len
with vec2
that takes two Python input
arguments). The recommended solution is the following:
%apply (int DIM1, double* IN_ARRAY1) {(int len1, double* vec1),
(int len2, double* vec2)}
%rename (dot) my_dot;
%exception my_dot {
$action
if (PyErr_Occurred()) SWIG_fail;
}
%inline %{
double my_dot(int len1, double* vec1, int len2, double* vec2) {
if (len1 != len2) {
PyErr_Format(PyExc_ValueError,
"Arrays of lengths (%d,%d) given",
len1, len2);
return 0.0;
}
return dot(len1, vec1, vec2);
}
%}
If the header file that contains the prototype for double dot()
also contains other prototypes that you want to wrap, so that you need
to %include
this header file, then you will also need a %ignore
dot;
directive, placed after the %rename
and before the
%include
directives. Or, if the function in question is a class
method, you will want to use %extend
rather than %inline
in
addition to %ignore
.
A note on error handling: Note that my_dot
returns a
double
but that it can also raise a Python error. The
resulting wrapper function will return a Python float
representation of 0.0 when the vector lengths do not match. Since
this is not NULL
, the Python interpreter will not know to check
for an error. For this reason, we add the %exception
directive
above for my_dot
to get the behavior we want (note that
$action
is a macro that gets expanded to a valid call to
my_dot
). In general, you will probably want to write a SWIG
macro to perform this task.
Other Situations
There are other wrapping situations in which numpy.i
may be
helpful when you encounter them.

In some situations, it is possible that you could use the
%numpy_typemaps
macro to implement typemaps for your own types. See the Other Common Types: bool or Other Common Types: complex sections for examples. Another situation is if your dimensions are of a type other thanint
(saylong
for example):python %numpy_typemaps(double, NPY_DOUBLE, long)

You can use the code in
numpy.i
to write your own typemaps. For example, if you had a fivedimensional array as a function argument, you could cutandpaste the appropriate fourdimensional typemaps into your interface file. The modifications for the fourth dimension would be trivial.  Sometimes, the best approach is to use the
%extend
directive to define new methods for your classes (or overload existing ones) that take aPyObject*
(that either is or can be converted to aPyArrayObject*
) instead of a pointer to a buffer. In this case, the helper routines innumpy.i
can be very useful.  Writing typemaps can be a bit nonintuitive. If you have specific
questions about writing SWIG typemaps for NumPy, the
developers of
numpy.i
do monitor the Numpydiscussion and Swiguser mail lists.
A Final Note
When you use the %apply
directive, as is usually necessary to use
numpy.i
, it will remain in effect until you tell SWIG that it
shouldn’t be. If the arguments to the functions or methods that you
are wrapping have common names, such as length
or vector
,
these typemaps may get applied in situations you do not expect or
want. Therefore, it is always a good idea to add a %clear
directive after you are done with a specific typemap:
%apply (double* IN_ARRAY1, int DIM1) {(double* vector, int length)}
%include "my_header.h"
%clear (double* vector, int length);
In general, you should target these typemap signatures specifically where you want them, and then clear them after you are done.
Summary
Out of the box, numpy.i
provides typemaps that support conversion
between NumPy arrays and C arrays:
 That can be one of 12 different scalar types:
signed char
,unsigned char
,short
,unsigned short
,int
,unsigned int
,long
,unsigned long
,long long
,unsigned long long
,float
anddouble
.  That support 74 different argument signatures for each data type, including: Onedimensional, twodimensional, threedimensional and fourdimensional arrays. Inputonly, inplace, argout, argoutview, and memory managed argoutview behavior. Hardcoded dimensions, databufferthendimensions specification, and dimensionsthendatabuffer specification. Both Cordering (“last dimension fastest”) or Fortranordering (“first dimension fastest”) support for 2D, 3D and 4D arrays.
 Onedimensional, twodimensional, threedimensional and fourdimensional arrays.
 Inputonly, inplace, argout, argoutview, and memory managed argoutview behavior.
 Hardcoded dimensions, databufferthendimensions specification, and dimensionsthendatabuffer specification.
 Both Cordering (“last dimension fastest”) or Fortranordering (“first dimension fastest”) support for 2D, 3D and 4D arrays.
The numpy.i
interface file also provides additional tools for
wrapper developers, including:
 A SWIG macro (
%numpy_typemaps
) with three arguments for implementing the 74 argument signatures for the user’s choice of (1) C data type, (2) NumPy data type (assuming they match), and (3) dimension type.  Fourteen C macros and fifteen C functions that can be used to write specialized typemaps, extensions, or inlined functions that handle cases not covered by the provided typemaps. Note that the macros and functions are coded specifically to work with the NumPy C/API regardless of NumPy version number, both before and after the deprecation of some aspects of the API after version 1.6.