缓冲协议¶
在 Python 中可使用一些对象来包装对底层内存数组或称 缓冲 的访问。此类对象包括内置的 bytes
和 bytearray
以及一些如 array.array
这样的扩展类型。第三方库也可能会为了特殊的目的而定义它们自己的类型,例如用于图像处理和数值分析等。
虽然这些类型中的每一种都有自己的语义,但它们具有由可能较大的内存缓冲区支持的共同特征。 在某些情况下,希望直接访问该缓冲区而无需中间复制。
Python 以 缓冲协议 的形式在 C 层级上提供这样的功能。 此协议包括两个方面:
在生产者这一方面,该类型的协议可以导出一个“缓冲区接口”,允许公开它的底层缓冲区信息。该接口的描述信息在 Buffer Object Structures 一节中;
在消费者一侧,有几种方法可用于获得指向对象的原始底层数据的指针(例如一个方法的形参)。
一些简单的对象例如 bytes
和 bytearray
会以面向字节的形式公开它们的底层缓冲区。 也可能会用其他形式;例如 array.array
所公开的元素可以是多字节值。
缓冲区接口的消费者的一个例子是文件对象的 write()
方法:任何可以输出为一系列字节流的对象可以被写入文件。然而 write()
方法只需要对于传入对象的只读权限,其他的方法,如 readinto()
需要参数内容的写入权限。缓冲区接口使得对象可以选择性地允许或拒绝读写或只读缓冲区的导出。
对于缓冲接口的消费者而言,有两种方式来获取一个目的对象的缓冲。
使用正确的参数来调用
PyObject_GetBuffer()
函数call
PyArg_ParseTuple()
(or one of its siblings) with one of they*
,w*
ors*
format codes.
在这两种情况下,当不再需要缓冲区时必须调用 PyBuffer_Release()
。如果此操作失败,可能会导致各种问题,例如资源泄漏。
缓冲区结构¶
缓冲区结构(或者简单地称为“buffers”)对于将二进制数据从另一个对象公开给Python程序员非常有用。它们还可以用作零拷贝切片机制。使用它们引用内存块的能力,可以很容易地将任何数据公开给Python程序员。内存可以是C扩展中的一个大的常量数组,也可以是在传递到操作系统库之前用于操作的原始内存块,或者可以用来传递本机内存格式的结构化数据。
与 Python 解释器公开的大多部数据类型不同,缓冲区不是 PyObject
指针而是简单的 C 结构。 这使得它们可以非常简单地创建和复制。 当需要为缓冲区加上泛型包装器时,可以创建一个 内存视图 对象。
有关如何编写并导出对象的简短说明,请参阅 缓冲区对象结构。 要获取缓冲区对象,请参阅 PyObject_GetBuffer()
。
-
Py_buffer
¶ -
void *
buf
¶ 指向由缓冲区字段描述的逻辑结构开始的指针。 这可以是导出程序底层物理内存块中的任何位置。 例如,使用负的
strides
值可能指向内存块的末尾。对于 contiguous ,‘邻接’数组,值指向内存块的开头。
-
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()
orPyBuffer_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 bePyBUF_SIMPLE
orPyBUF_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-NULLformat
values.Important exception: If a consumer requests a buffer without the
PyBUF_FORMAT
flag,format
will be set to NULL, butitemsize
still has the value for the original format.If
shape
is present, the equalityproduct(shape) * itemsize == len
still holds and the consumer can useitemsize
to navigate the buffer.If
shape
is NULL as a result of aPyBUF_SIMPLE
or aPyBUF_WRITABLE
request, the consumer must disregarditemsize
and assumeitemsize == 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
andsuboffsets
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 toPyBUF_MAX_NDIM
dimensions.
-
Py_ssize_t *
shape
¶ An array of
Py_ssize_t
of lengthndim
indicating the shape of the memory as an n-dimensional array. Note thatshape[0] * ... * shape[ndim-1] * itemsize
MUST be equal tolen
.Shape values are restricted to
shape[n] >= 0
. The caseshape[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 lengthndim
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 lengthndim
. Ifsuboffsets[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.
-
void *
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
.
readonly, format¶
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).
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.
请求 |
形状 |
步幅 |
子偏移量 |
---|---|---|---|
|
是 |
是 |
如果需要的话 |
|
是 |
是 |
NULL |
|
是 |
NULL |
NULL |
|
NULL |
NULL |
NULL |
连续性的请求¶
C or Fortran contiguity can be explicitly requested, with and without stride information. Without stride information, the buffer must be C-contiguous.
请求 |
形状 |
步幅 |
子偏移量 |
contig |
---|---|---|---|---|
|
是 |
是 |
NULL |
C |
|
是 |
是 |
NULL |
F |
|
是 |
是 |
NULL |
C 或 F |
|
是 |
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.
请求 |
形状 |
步幅 |
子偏移量 |
contig |
只读 |
格式 |
---|---|---|---|---|---|---|
|
是 |
是 |
如果需要的话 |
U |
0 |
是 |
|
是 |
是 |
如果需要的话 |
U |
1 或 0 |
是 |
|
是 |
是 |
NULL |
U |
0 |
是 |
|
是 |
是 |
NULL |
U |
1 或 0 |
是 |
|
是 |
是 |
NULL |
U |
0 |
NULL |
|
是 |
是 |
NULL |
U |
1 或 0 |
NULL |
|
是 |
NULL |
NULL |
C |
0 |
NULL |
|
是 |
NULL |
NULL |
C |
1 或 0 |
NULL |
复杂数组¶
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;
}