Buffer Protocol

Python documentation

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 and Python 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

; for Python see

Emulating buffer types

.

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). For Python see

memoryview

.

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:

call

PyObject_GetBuffer()

with the right parameters;

call

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 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()

.

typePy_buffer

Part of the

Stable ABI

(including all members) since version 3.11.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.

PyObject

*obj

A new reference to the exporting object. The reference is owned by the consumer and automatically released (i.e. reference count 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]upto((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

.

intreadonly

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.

char*format

A NULL 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.

intndim

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 maximum number of dimensions is given by

PyBUF_MAX_NDIM

.

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.

Constants:

PyBUF_MAX_NDIM

Part of the

Stable ABI

since version 3.11.The maximum number of dimensions the memory represents. Exporters MUST respect this limit, consumers of multi-dimensional buffers SHOULD be able to handle up to PyBUF_MAX_NDIM dimensions. Currently set to 64.

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

Part of the

Stable ABI

since version 3.11.Controls the

readonly

field. If set, the exporter MUST provide a writable buffer or else report failure. Otherwise, the exporter MAY provide either a read-only or writable buffer, but the choice MUST be consistent for all consumers. For example,

PyBUF_SIMPLE

|

PyBUF_WRITABLE

can be used to request a simple writable buffer.

PyBUF_WRITEABLE

This is an alias to

PyBUF_WRITABLE

.

PyBUF_FORMAT

Part of the

Stable ABI

since version 3.11.Controls the

format

field. If set, this field MUST be filled in correctly. Otherwise, this field MUST be NULL.

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

must be |’d to any of the flags except

PyBUF_SIMPLE

, because the latter already implies format B (unsigned bytes). PyBUF_FORMAT cannot be used on its own.

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

Part of the

Stable ABI

since version 3.11.yes

yes

if needed

PyBUF_STRIDES

Part of the

Stable ABI

since version 3.11.yes

yes

NULL

PyBUF_ND

Part of the

Stable ABI

since version 3.11.yes

NULL

NULL

PyBUF_SIMPLE

Part of the

Stable ABI

since version 3.11.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

Part of the

Stable ABI

since version 3.11.yes

yes

NULL

C

PyBUF_F_CONTIGUOUS

Part of the

Stable ABI

since version 3.11.yes

yes

NULL

F

PyBUF_ANY_CONTIGUOUS

Part of the

Stable ABI

since version 3.11.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

Part of the

Stable ABI

since version 3.11.yes

yes

if needed

U

0

yes

PyBUF_FULL_RO

Part of the

Stable ABI

since version 3.11.yes

yes

if needed

U

1 or 0

yes

PyBUF_RECORDS

Part of the

Stable ABI

since version 3.11.yes

yes

NULL

U

0

yes

PyBUF_RECORDS_RO

Part of the

Stable ABI

since version 3.11.yes

yes

NULL

U

1 or 0

yes

PyBUF_STRIDED

Part of the

Stable ABI

since version 3.11.yes

yes

NULL

U

0

NULL

PyBUF_STRIDED_RO

Part of the

Stable ABI

since version 3.11.yes

yes

NULL

U

1 or 0

NULL

PyBUF_CONTIG

Part of the

Stable ABI

since version 3.11.yes

NULL

NULL

C

0

NULL

PyBUF_CONTIG_RO

Part of the

Stable ABI

since version 3.11.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:

defverify_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 """ifoffset%itemsize:returnFalseifoffset<0oroffset+itemsize>memlen:returnFalseifany(v%itemsizeforvinstrides):returnFalseifndim<=0:returnndim==0andnotshapeandnotstridesif0inshape:returnTrueimin=sum(strides[j]*(shape[j]-1)forjinrange(ndim)ifstrides[j]<=0)imax=sum(strides[j]*(shape[j]-1)forjinrange(ndim)ifstrides[j]>0)return0<=offset+iminandoffset+imax+itemsize<=memlenPIL-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 charv[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 charx[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(intndim,void*buf,Py_ssize_t*strides,Py_ssize_t*suboffsets,Py_ssize_t*indices){char*pointer=(char*)buf;inti;for(i=0;i<ndim;i++){pointer+=strides[i]*indices[i];if(suboffsets[i]>=0){pointer=*((char**)pointer)+suboffsets[i];}}return(void*)pointer;}