Buffer Protocol
Certain objects available in Python wrap access to an underlying memory
array or buffer. Such objects include the built-in bytes and
bytearray, and some extension types like array.array.
Third-party libraries may define their own types for special purposes, such
as image processing or numeric analysis.
While each of these types have their own semantics, they share the common
characteristic of being backed by a possibly large memory buffer. It is
then desirable, in some situations, to access that buffer directly and
without intermediate copying.
Python provides such a facility at the C level in the form of the buffer
protocol. This protocol has two sides:
- on the producer side, a type can export a “buffer interface” which allows
objects of that type to expose information about their underlying buffer.
This interface is described in the section Buffer Object Structures;
- on the consumer side, several means are available to obtain a pointer to
the raw underlying data of an object (for example a method parameter).
Simple objects such as bytes and bytearray expose their
underlying buffer in byte-oriented form. Other forms are possible; for example,
the elements exposed by an array.array can be multi-byte values.
An example consumer of the buffer interface is the write()
method of file objects: any object that can export a series of bytes through
the buffer interface can be written to a file. While write() only
needs read-only access to the internal contents of the object passed to it,
other methods such as readinto() need write access
to the contents of their argument. The buffer interface allows objects to
selectively allow or reject exporting of read-write and read-only buffers.
There are two ways for a consumer of the buffer interface to acquire a buffer
over a target object:
In both cases, PyBuffer_Release() must be called when the buffer
isn’t needed anymore. Failure to do so could lead to various issues such as
resource leaks.
Buffer structure
Buffer structures (or simply “buffers”) are useful as a way to expose the
binary data from another object to the Python programmer. They can also be
used as a zero-copy slicing mechanism. Using their ability to reference a
block of memory, it is possible to expose any data to the Python programmer
quite easily. The memory could be a large, constant array in a C extension,
it could be a raw block of memory for manipulation before passing to an
operating system library, or it could be used to pass around structured data
in its native, in-memory format.
Contrary to most data types exposed by the Python interpreter, buffers
are not PyObject pointers but rather simple C structures. This
allows them to be created and copied very simply. When a generic wrapper
around a buffer is needed, a memoryview object
can be created.
For short instructions how to write an exporting object, see
Buffer Object Structures. For obtaining
a buffer, see PyObject_GetBuffer().
-
Py_buffer
-
void *
buf
A pointer to the start of the logical structure described by the buffer
fields. This can be any location within the underlying physical memory
block of the exporter. For example, with negative strides
the value may point to the end of the memory block.
For contiguous arrays, the value points to the beginning of
the memory block.
-
void *
obj
A new reference to the exporting object. The reference is owned by
the consumer and automatically decremented and set to NULL by
PyBuffer_Release(). The field is the equivalent of the return
value of any standard C-API function.
As a special case, for temporary buffers that are wrapped by
PyMemoryView_FromBuffer() or PyBuffer_FillInfo()
this field is NULL. In general, exporting objects MUST NOT
use this scheme.
-
Py_ssize_t
len
product(shape) * itemsize. For contiguous arrays, this is the length
of the underlying memory block. For non-contiguous arrays, it is the length
that the logical structure would have if it were copied to a contiguous
representation.
Accessing ((char *)buf)[0] up to ((char *)buf)[len-1] is only valid
if the buffer has been obtained by a request that guarantees contiguity. In
most cases such a request will be PyBUF_SIMPLE or PyBUF_WRITABLE.
-
int
readonly
An indicator of whether the buffer is read-only. This field is controlled
by the PyBUF_WRITABLE flag.
-
Py_ssize_t
itemsize
Item size in bytes of a single element. Same as the value of struct.calcsize()
called on non-NULL format values.
Important exception: If a consumer requests a buffer without the
PyBUF_FORMAT flag, format will
be set to NULL, but itemsize still has
the value for the original format.
If shape is present, the equality
product(shape) * itemsize == len still holds and the consumer
can use itemsize to navigate the buffer.
If shape is NULL as a result of a PyBUF_SIMPLE
or a PyBUF_WRITABLE request, the consumer must disregard
itemsize and assume itemsize == 1.
-
const char *
format
A NUL terminated string in struct module style syntax describing
the contents of a single item. If this is NULL, "B" (unsigned bytes)
is assumed.
This field is controlled by the PyBUF_FORMAT flag.
-
int
ndim
The number of dimensions the memory represents as an n-dimensional array.
If it is 0, buf points to a single item representing
a scalar. In this case, shape, strides
and suboffsets MUST be NULL.
The macro PyBUF_MAX_NDIM limits the maximum number of dimensions
to 64. Exporters MUST respect this limit, consumers of multi-dimensional
buffers SHOULD be able to handle up to PyBUF_MAX_NDIM dimensions.
-
Py_ssize_t *
shape
An array of Py_ssize_t of length ndim
indicating the shape of the memory as an n-dimensional array. Note that
shape[0] * ... * shape[ndim-1] * itemsize MUST be equal to
len.
Shape values are restricted to shape[n] >= 0. The case
shape[n] == 0 requires special attention. See complex arrays
for further information.
The shape array is read-only for the consumer.
-
Py_ssize_t *
strides
An array of Py_ssize_t of length ndim
giving the number of bytes to skip to get to a new element in each
dimension.
Stride values can be any integer. For regular arrays, strides are
usually positive, but a consumer MUST be able to handle the case
strides[n] <= 0. See complex arrays for further information.
The strides array is read-only for the consumer.
-
Py_ssize_t *
suboffsets
An array of Py_ssize_t of length ndim.
If suboffsets[n] >= 0, the values stored along the nth dimension are
pointers and the suboffset value dictates how many bytes to add to each
pointer after de-referencing. A suboffset value that is negative
indicates that no de-referencing should occur (striding in a contiguous
memory block).
If all suboffsets are negative (i.e. no de-referencing is needed, then
this field must be NULL (the default value).
This type of array representation is used by the Python Imaging Library
(PIL). See complex arrays for further information how to access elements
of such an array.
The suboffsets array is read-only for the consumer.
-
void *
internal
This is for use internally by the exporting object. For example, this
might be re-cast as an integer by the exporter and used to store flags
about whether or not the shape, strides, and suboffsets arrays must be
freed when the buffer is released. The consumer MUST NOT alter this
value.
Buffer request types
Buffers are usually obtained by sending a buffer request to an exporting
object via PyObject_GetBuffer(). Since the complexity of the logical
structure of the memory can vary drastically, the consumer uses the flags
argument to specify the exact buffer type it can handle.
All Py_buffer fields are unambiguously defined by the request
type.
request-independent fields
The following fields are not influenced by flags and must always be filled in
with the correct values: obj, buf,
len, itemsize, ndim.
shape, strides, suboffsets
The flags that control the logical structure of the memory are listed
in decreasing order of complexity. Note that each flag contains all bits
of the flags below it.
| Request |
shape |
strides |
suboffsets |
-
PyBUF_INDIRECT
|
yes |
yes |
if needed |
-
PyBUF_STRIDES
|
yes |
yes |
NULL |
-
PyBUF_ND
|
yes |
NULL |
NULL |
-
PyBUF_SIMPLE
|
NULL |
NULL |
NULL |
contiguity requests
C or Fortran contiguity can be explicitly requested,
with and without stride information. Without stride information, the buffer
must be C-contiguous.
| Request |
shape |
strides |
suboffsets |
contig |
-
PyBUF_C_CONTIGUOUS
|
yes |
yes |
NULL |
C |
-
PyBUF_F_CONTIGUOUS
|
yes |
yes |
NULL |
F |
-
PyBUF_ANY_CONTIGUOUS
|
yes |
yes |
NULL |
C or F |
-
PyBUF_ND
|
yes |
NULL |
NULL |
C |
compound requests
All possible requests are fully defined by some combination of the flags in
the previous section. For convenience, the buffer protocol provides frequently
used combinations as single flags.
In the following table U stands for undefined contiguity. The consumer would
have to call PyBuffer_IsContiguous() to determine contiguity.
| Request |
shape |
strides |
suboffsets |
contig |
readonly |
format |
-
PyBUF_FULL
|
yes |
yes |
if needed |
U |
0 |
yes |
-
PyBUF_FULL_RO
|
yes |
yes |
if needed |
U |
1 or 0 |
yes |
-
PyBUF_RECORDS
|
yes |
yes |
NULL |
U |
0 |
yes |
-
PyBUF_RECORDS_RO
|
yes |
yes |
NULL |
U |
1 or 0 |
yes |
-
PyBUF_STRIDED
|
yes |
yes |
NULL |
U |
0 |
NULL |
-
PyBUF_STRIDED_RO
|
yes |
yes |
NULL |
U |
1 or 0 |
NULL |
-
PyBUF_CONTIG
|
yes |
NULL |
NULL |
C |
0 |
NULL |
-
PyBUF_CONTIG_RO
|
yes |
NULL |
NULL |
C |
1 or 0 |
NULL |
Complex arrays
NumPy-style: shape and strides
The logical structure of NumPy-style arrays is defined by itemsize,
ndim, shape and strides.
If ndim == 0, the memory location pointed to by buf is
interpreted as a scalar of size itemsize. In that case,
both shape and strides are NULL.
If strides is NULL, the array is interpreted as
a standard n-dimensional C-array. Otherwise, the consumer must access an
n-dimensional array as follows:
ptr = (char *)buf + indices[0] * strides[0] + ... + indices[n-1] * strides[n-1]
item = *((typeof(item) *)ptr);
As noted above, buf can point to any location within
the actual memory block. An exporter can check the validity of a buffer with
this function:
def verify_structure(memlen, itemsize, ndim, shape, strides, offset):
"""Verify that the parameters represent a valid array within
the bounds of the allocated memory:
char *mem: start of the physical memory block
memlen: length of the physical memory block
offset: (char *)buf - mem
"""
if offset % itemsize:
return False
if offset < 0 or offset+itemsize > memlen:
return False
if any(v % itemsize for v in strides):
return False
if ndim <= 0:
return ndim == 0 and not shape and not strides
if 0 in shape:
return True
imin = sum(strides[j]*(shape[j]-1) for j in range(ndim)
if strides[j] <= 0)
imax = sum(strides[j]*(shape[j]-1) for j in range(ndim)
if strides[j] > 0)
return 0 <= offset+imin and offset+imax+itemsize <= memlen
PIL-style: shape, strides and suboffsets
In addition to the regular items, PIL-style arrays can contain pointers
that must be followed in order to get to the next element in a dimension.
For example, the regular three-dimensional C-array char v[2][2][3] can
also be viewed as an array of 2 pointers to 2 two-dimensional arrays:
char (*v[2])[2][3]. In suboffsets representation, those two pointers
can be embedded at the start of buf, pointing
to two char x[2][3] arrays that can be located anywhere in memory.
Here is a function that returns a pointer to the element in an N-D array
pointed to by an N-dimensional index when there are both non-NULL strides
and suboffsets:
void *get_item_pointer(int ndim, void *buf, Py_ssize_t *strides,
Py_ssize_t *suboffsets, Py_ssize_t *indices) {
char *pointer = (char*)buf;
int i;
for (i = 0; i < ndim; i++) {
pointer += strides[i] * indices[i];
if (suboffsets[i] >=0 ) {
pointer = *((char**)pointer) + suboffsets[i];
}
}
return (void*)pointer;
}