# numpy.i: a SWIG Interface File for NumPy

## Introduction

The Simple Wrapper and Interface Generator (or SWIG) is a powerful tool for generating wrapper code for interfacing to a wide variety of scripting languages. SWIG can parse header files, and using only the code prototypes, create an interface to the target language. But SWIG is not omnipotent. For example, it cannot know from the prototype:

    double rms(double* seq, int n);

what exactly `seq` is. Is it a single value to be altered in-place? Is it an array, and if so what is its length? Is it input-only? Output-only? Input-output? 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 input-only 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 object-oriented 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 array-related 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 one-dimensional 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 self-explanatory. At line 12 we use `obj0` to construct a NumPy array. At line 17, we check the validity of the result: that it is non-null 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` sub-directory 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 Fortran-ordered arrays, and names with `ARRAY` are for C-ordered (or 1D arrays).

### Input Arrays

Input arrays are defined as arrays of data that are passed into a routine but are not altered in-place 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 one-dimensional arrays with hard-coded dimensions. Likewise, `( DATA_TYPE IN_ARRAY2[ANY][ANY] )` is for two-dimensional arrays with hard-coded dimensions, and similarly for three-dimensional.

### In-Place Arrays

In-place arrays are defined as arrays that are modified in-place. 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 in-place 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” in-place 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 in-place, 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 hard-coded 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 hard-coded 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 In-Place Arrays might fail type-checking.

### Other Common Types: complex

Typemap conversions for complex floating-point 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 (non-Python, non-NumPy) 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 SWIG-wrapped 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 32-bit machine will fail on a 64-bit 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 a `PyArrayObject*`.

> array_type(a)

>   Evaluates to the integer data type code of `a`, assuming `a` can be cast to a `PyArrayObject*`.

> array_numdims(a)

>   Evaluates to the integer number of dimensions of `a`, assuming `a` can be cast to a `PyArrayObject*`.

> array_dimensions(a)

>   Evaluates to an array of type `npy_intp` and length `array_numdims(a)`, giving the lengths of all of the dimensions of `a`, assuming `a` can be cast to a `PyArrayObject*`.

> array_size(a,i)

>   Evaluates to the `i`-th dimension size of `a`, assuming `a` can be cast to a `PyArrayObject*`.

> array_strides(a)

>   Evaluates to an array of type `npy_intp` and length `array_numdims(a)`, giving the stridess of all of the dimensions of `a`, assuming `a` can be cast to a `PyArrayObject*`. 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 of `a`, assuming `a` can be cast to a `PyArrayObject*`.

> array_data(a)

>   Evaluates to a pointer of type `void*` that points to the data buffer of `a`, assuming `a` can be cast to a `PyArrayObject*`.

> array_descr(a)

>   Returns a borrowed reference to the dtype property (`PyArray_Descr*`) of `a`, assuming `a` can be cast to a `PyArrayObject*`.

> array_flags(a)

>   Returns an integer representing the flags of `a`, assuming `a` can be cast to a `PyArrayObject*`.

> array_enableflags(a,f)

>   Sets the flag represented by `f` of `a`, assuming `a` can be cast to a `PyArrayObject*`.

> 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 with `desired_type`. For example, this allows character and byte types, or int and long types, to match. This is now equivalent to `PyArray_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 a `PyArrayObject*` if legal, and ensure that it is of type `typecode`. If `input` cannot be cast, or the `typecode` is wrong, set a Python error and return `NULL`.

> 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 given `typecode`. On success, return a valid `PyArrayObject*` with the correct type. On failure, the Python error string will be set and the routine returns `NULL`.

> 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 new `PyArrayObject*` 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 new `PyArrayObject*` 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 contiguous `PyArrayObject*` of the specified type. If the input object is not a contiguous `PyArrayObject*`, 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 contiguous `PyArrayObject*` of the specified type. If the input object is not a Fortran contiguous `PyArrayObject*`, 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 byte-swapped. If the array is not byte-swapped, 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 of `exact_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 of `size`.

> > 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 the `PyArrayObject` 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 than `int` (say `long` for example):

>       %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 five-dimensional array as a function argument, you could cut-and-paste the appropriate four-dimensional 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 a `PyObject*` (that either is or can be converted to a `PyArrayObject*`) instead of a pointer to a buffer. In this case, the helper routines in `numpy.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 Numpy-discussion and Swig-user 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` and `double`.

> - That support 74 different argument signatures for each data type, including:

>   - One-dimensional, two-dimensional, three-dimensional and four-dimensional arrays.

>   - Input-only, in-place, argout, argoutview, and memory managed argoutview behavior.

>   - Hard-coded dimensions, data-buffer-then-dimensions specification, and dimensions-then-data-buffer specification.

>   - Both C-ordering (“last dimension fastest”) or Fortran-ordering (“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.

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