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 缓冲协议 . This protocol has two sides:
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:
PyObject_GetBuffer()
with the right parameters;
PyArg_ParseTuple()
(or one of its siblings) with one of the
y*
,
w*
or
s*
format codes
.
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 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
缓冲对象结构
. For obtaining a buffer, see
PyObject_GetBuffer()
.
Py_buffer
¶
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.
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.
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.
访问
((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
.
readonly
¶
An indicator of whether the buffer is read-only. This field is controlled by the
PyBUF_WRITABLE
标志。
itemsize
¶
Item size in bytes of a single element. Same as the value of
struct.calcsize()
called on non-NULL
format
值。
Important exception: If a consumer requests a buffer without the
PyBUF_FORMAT
flag,
format
会被设为
NULL
,但
itemsize
still has the value for the original format.
若
shape
is present, the equality
product(shape)
*
itemsize
==
len
still holds and the consumer can use
itemsize
to navigate the buffer.
若
shape
is
NULL
as a result of a
PyBUF_SIMPLE
或
PyBUF_WRITABLE
request, the consumer must disregard
itemsize
and assume
itemsize
==
1
.
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
标志。
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.
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.
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
。见
complex arrays
for further information.
The strides array is read-only for the consumer.
suboffsets
¶
An array of
Py_ssize_t
of length
ndim
。若
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.
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.
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.
所有
Py_buffer
fields are unambiguously defined by the request type.
The following fields are not influenced by
flags
and must always be filled in with the correct values:
obj
,
buf
,
len
,
itemsize
,
ndim
.
PyBUF_WRITABLE
can be |’d to any of the flags in the next section. Since
PyBUF_SIMPLE
is defined as 0,
PyBUF_WRITABLE
can be used as a stand-alone flag to request a simple writable buffer.
PyBUF_FORMAT
can be |’d to any of the flags except
PyBUF_SIMPLE
. The latter already implies format
B
(unsigned bytes).
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 |
|---|---|---|---|
|
yes | yes | if needed |
|
yes | yes | NULL |
|
yes | NULL | NULL |
|
NULL | NULL | NULL |
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 |
|---|---|---|---|---|
|
yes | yes | NULL | C |
|
yes | yes | NULL | F |
|
yes | yes | NULL | C or F |
|
yes | NULL | NULL | C |
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 |
|---|---|---|---|---|---|---|
|
yes | yes | if needed | U | 0 | yes |
|
yes | yes | if needed | U | 1 or 0 | yes |
|
yes | yes | NULL | U | 0 | yes |
|
yes | yes | NULL | U | 1 or 0 | yes |
|
yes | yes | NULL | U | 0 | NULL |
|
yes | yes | NULL | U | 1 or 0 | NULL |
|
yes | NULL | NULL | C | 0 | NULL |
|
yes | NULL | NULL | C | 1 or 0 | NULL |
The logical structure of NumPy-style arrays is defined by
itemsize
,
ndim
,
shape
and
strides
.
若
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
.
若
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
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;
}