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C-API for NumPy

Author: Travis Oliphant
Discussions to:numpy-discussion@scipy.org
Created:October 2005

The C API of NumPy is (mostly) backward compatible with Numeric.

There are a few non-standard Numeric usages (that were not really part of the API) that will need to be changed:

  • If you used any of the function pointers in the PyArray_Descr structure you will have to modify your usage of those. First, the pointers are all under the member named f. So descr->cast is now descr->f->cast. In addition, the casting functions have eliminated the strides argument (use PyArray_CastTo if you need strided casting). All functions have one or two PyArrayObject * arguments at the end. This allows the flexible arrays and mis-behaved arrays to be handled.
  • The descr->zero and descr->one constants have been replaced with function calls, PyArray_Zero, and PyArray_One (be sure to read the code and free the resulting memory if you use these calls).
  • If you passed array->dimensions and array->strides around to functions, you will need to fix some code. These are now npy_intp* pointers. On 32-bit systems there won't be a problem. However, on 64-bit systems, you will need to make changes to avoid errors and segfaults.

The header files arrayobject.h and ufuncobject.h contain many defines that you may find useful. The files __ufunc_api.h and __multiarray_api.h contain the available C-API function calls with their function signatures.

All of these headers are installed to <YOUR_PYTHON_LOCATION>/site-packages/numpy/core/include

Getting arrays in C-code

All new arrays can be created using PyArray_NewFromDescr. A simple interface equivalent to PyArray_FromDims is PyArray_SimpleNew(nd, dims, typenum) and to PyArray_FromDimsAndData is PyArray_SimpleNewFromData(nd, dims, typenum, data).

This is a very flexible function.

PyObject * PyArray_NewFromDescr(PyTypeObject *subtype, PyArray_Descr *descr,
                              int nd, npy_intp *dims,
                              npy_intp *strides, char *data,
                              int flags, PyObject *obj);
subtype : PyTypeObject *
The subtype that should be created (either pass in &PyArray_Type, or obj->ob_type, where obj is a an instance of a subtype (or subclass) of PyArray_Type).
descr : PyArray_Descr *
The type descriptor for the array. This is a Python object (this function steals a reference to it). The easiest way to get one is using PyArray_DescrFromType(<typenum>). If you want to use a flexible size array, then you need to use PyArray_DescrNewFromType(<flexible typenum>) and set its elsize paramter to the desired size. The typenum in both of these cases is one of the PyArray_XXXX enumerated types.
nd : int
The number of dimensions (<MAX_DIMS)
*dims : npy_intp *
A pointer to the size in each dimension. Information will be copied from here.
*strides : npy_intp *

The strides this array should have. For new arrays created by this routine, this should be NULL. If you pass in memory for this array to use, then you can pass in the strides information as well (otherwise it will be created for you and default to C-contiguous or Fortran contiguous). Any strides will be copied into the array structure. Do not pass in bad strides information!!!!

PyArray_CheckStrides(...) can help but you must call it if you are unsure. You cannot pass in strides information when data is NULL and this routine is creating its own memory.

*data : char *

NULL for creating brand-new memory. If you want this array to wrap another memory area, then pass the pointer here. You are responsible for deleting the memory in that case, but do not do so until the new array object has been deleted. The best way to handle that is to get the memory from another Python object, INCREF that Python object after passing it's data pointer to this routine, and set the ->base member of the returned array to the Python object. You are responsible for setting PyArray_BASE(ret) to the base object. Failure to do so will create a memory leak.

If you pass in a data buffer, the flags argument will be the flags of the new array. If you create a new array, a non-zero flags argument indicates that you want the array to be in Fortran order.

flags : int
Either the flags showing how to interpret the data buffer passed in, or if a new array is created, nonzero to indicate a Fortran order array. See below for an explanation of the flags.
obj : PyObject *
If subtypes is &PyArray_Type, this argument is ignored. Otherwise, the __array_finalize__ method of the subtype is called (if present) and passed this object. This is usually an array of the type to be created (so the __array_finalize__ method must handle an array argument. But, it can be anything...)

Note: The returned array object will be unitialized unless the type is PyArray_OBJECT in which case the memory will be set to NULL.

PyArray_SimpleNew(nd, dims, typenum) is a drop-in replacement for PyArray_FromDims (except it takes npy_intp* dims instead of int* dims which matters on 64-bit systems) and it does not initialize the memory to zero.

PyArray_SimpleNew is just a macro for PyArray_New with default arguments. Use PyArray_FILLWBYTE(arr, 0) to fill with zeros.

The PyArray_FromDims and family of functions are still available and are loose wrappers around this function. These functions still take int * arguments. This should be fine on 32-bit systems, but on 64-bit systems you may run into trouble if you frequently passed PyArray_FromDims the dimensions member of the old PyArrayObject structure because sizeof(npy_intp) != sizeof(int).

Getting an arrayobject from an arbitrary Python object

PyArray_FromAny(...)

This function replaces PyArray_ContiguousFromObject and friends (those function calls still remain but they are loose wrappers around the PyArray_FromAny call).

static PyObject *
PyArray_FromAny(PyObject *op, PyArray_Descr *dtype, int min_depth,
                int max_depth, int requires, PyObject *context)
op : PyObject *
The Python object to "convert" to an array object
dtype : PyArray_Descr *
The desired data-type descriptor. This can be NULL, if the descriptor should be determined by the object. Unless FORCECAST is present in flags, this call will generate an error if the data type cannot be safely obtained from the object.
min_depth : int
The minimum depth of array needed or 0 if doesn't matter
max_depth : int
The maximum depth of array allowed or 0 if doesn't matter
requires : int

A flag indicating the "requirements" of the returned array. These are the usual ndarray flags (see NDArray flags below). In addition, there are three flags used only for the FromAny family of functions:

  • ENSURECOPY: always copy the array. Returned arrays always have CONTIGUOUS, ALIGNED, and WRITEABLE set.
  • ENSUREARRAY: ensure the returned array is an ndarray (or a bigndarray if op is one).
  • FORCECAST: cause a cast to occur regardless of whether or not it is safe.
context : PyObject *
If the Python object op is not an numpy array, but has an __array__ method, context is passed as the second argument to that method (the first is the typecode). Almost always this parameter is NULL.

PyArray_ContiguousFromAny(op, typenum, min_depth, max_depth) is equivalent to PyArray_ContiguousFromObject(...) (which is still available), except it will return the subclass if op is already a subclass of the ndarray. The ContiguousFromObject version will always return an ndarray (or a bigndarray).

Passing Data Type information to C-code

All datatypes are handled using the PyArray_Descr * structure. This structure can be obtained from a Python object using PyArray_DescrConverter and PyArray_DescrConverter2. The former returns the default PyArray_LONG descriptor when the input object is None, while the latter returns NULL when the input object is None.

See the arraymethods.c and multiarraymodule.c files for many examples of usage.

Getting at the structure of the array.

You should use the #defines provided to access array structure portions:

  • PyArray_DATA(obj) : returns a void * to the array data
  • PyArray_BYTES(obj) : return a char * to the array data
  • PyArray_ITEMSIZE(obj)
  • PyArray_NDIM(obj)
  • PyArray_DIMS(obj)
  • PyArray_DIM(obj, n)
  • PyArray_STRIDES(obj)
  • PyArray_STRIDE(obj,n)
  • PyArray_DESCR(obj)
  • PyArray_BASE(obj)

see more in arrayobject.h

NDArray Flags

The flags attribute of the PyArrayObject structure contains important information about the memory used by the array (pointed to by the data member) This flags information must be kept accurate or strange results and even segfaults may result.

There are 6 (binary) flags that describe the memory area used by the data buffer. These constants are defined in arrayobject.h and determine the bit-position of the flag. Python exposes a nice attribute- based interface as well as a dictionary-like interface for getting (and, if appropriate, setting) these flags.

Memory areas of all kinds can be pointed to by an ndarray, necessitating these flags. If you get an arbitrary PyArrayObject in C-code, you need to be aware of the flags that are set. If you need to guarantee a certain kind of array (like NPY_CONTIGUOUS and NPY_BEHAVED), then pass these requirements into the PyArray_FromAny function.

NPY_CONTIGUOUS
True if the array is (C-style) contiguous in memory.
NPY_FORTRAN
True if the array is (Fortran-style) contiguous in memory.

Notice that contiguous 1-d arrays are always both NPY_FORTRAN contiguous and C contiguous. Both of these flags can be checked and are convenience flags only as whether or not an array is NPY_CONTIGUOUS or NPY_FORTRAN can be determined by the strides, dimensions, and itemsize attributes.

NPY_OWNDATA
True if the array owns the memory (it will try and free it using PyDataMem_FREE() on deallocation --- so it better really own it).

These three flags facilitate using a data pointer that is a memory-mapped array, or part of some larger record array. But, they may have other uses...

NPY_ALIGNED
True if the data buffer is aligned for the type and the strides are multiples of the alignment factor as well. This can be checked.
NPY_WRITEABLE
True only if the data buffer can be "written" to.
NPY_UPDATEIFCOPY
This is a special flag that is set if this array represents a copy made because a user required certain flags in PyArray_FromAny and a copy had to be made of some other array (and the user asked for this flag to be set in such a situation). The base attribute then points to the "misbehaved" array (which is set read_only). When the array with this flag set is deallocated, it will copy its contents back to the "misbehaved" array (casting if necessary) and will reset the "misbehaved" array to WRITEABLE. If the "misbehaved" array was not WRITEABLE to begin with then PyArray_FromAny would have returned an error because UPDATEIFCOPY would not have been possible.

PyArray_UpdateFlags(obj, flags) will update the obj->flags for flags which can be any of NPY_CONTIGUOUS, NPY_FORTRAN, NPY_ALIGNED, or NPY_WRITEABLE.

Some useful combinations of these flags:

  • NPY_BEHAVED = NPY_ALIGNED | NPY_WRITEABLE
  • NPY_CARRAY = NPY_DEFAULT = NPY_CONTIGUOUS | NPY_BEHAVED
  • NPY_CARRAY_RO = NPY_CONTIGUOUS | NPY_ALIGNED
  • NPY_FARRAY = NPY_FORTRAN | NPY_BEHAVED
  • NPY_FARRAY_RO = NPY_FORTRAN | NPY_ALIGNED

The macro PyArray_CHECKFLAGS(obj, flags) can test any combination of flags. There are several default combinations defined as macros already (see arrayobject.h)

In particular, there are ISBEHAVED, ISBEHAVED_RO, ISCARRAY and ISFARRAY macros that also check to make sure the array is in native byte order (as determined) by the data-type descriptor.

There are more C-API enhancements which you can discover in the code, or buy the book (http://www.trelgol.com)

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