一个数值无关的 Pyrex 类¶
| 日期 | 2006-11-04（最后修改），2006-03-29（创建） |
|---|
注意：此条目最后更新于 2006-11-04，包含的信息在今天（截至 2013 年）不再相关。
概述¶
这里介绍了 !NumInd（数值独立扩展），这是一个用 Pyrex 编写的类的示例。此类可以包装来自 Numeric、numarray 或 !NumPy 的任何对象的的信息，而无需任何依赖这些包来编译扩展。它允许您在 Python 和 C 空间中创建统一的接口。此外，您可以通过添加新方法或属性来个性化它。
为了使此扩展正常工作，您需要一个支持 数组接口 的数值包。任何这些版本都足够好
NumPy (所有版本) Numeric (>=24.2) * numarray (>=1.5.1)
NumInd：一个基于 Pyrex 的数值独立扩展¶
下面显示的 !NumInd 类接受一个 Numeric/numarray/!NumPy 对象，并创建一个可以在 Python 和 Pyrex（以及 C）空间中以统一方式访问的另一个对象。此外，它公开了一个数组接口，以便您可以使用任何 Numeric/numarray/!NumPy 重新包装此对象。所有这些功能都无需实际复制数据本身。这为开发支持 Numeric/numarray/!NumPy 三元组的应用程序打开了大门，而无需针对其中任何一个进行编译。
警告：此类主要支持同构数据集，但支持 recarrays 也不难。这无论如何都是一项正在进行的工作。
在 [ ]
# This Pyrex extension class can take a numpy/numarray/Numeric object
# as a parameter and wrap it so that its information can be accessed
# in a standard way, both in Python space and C space.
#
# Heavily based on an idea of Andrew Straw. See
# https://scipy.org.cn/Cookbook/ArrayStruct_and_Pyrex
# Very inspiring! :-)
#
# First version: 2006-03-25
# Last update: 2006-03-25
# Author: Francesc Altet
import sys
cdef extern from "Python.h":
ctypedef int Py_intptr_t
long PyInt_AsLong(object)
void Py_INCREF(object)
void Py_DECREF(object)
object PyCObject_FromVoidPtrAndDesc(void* cobj, void* desc,
void (*destr)(void *, void *))
cdef extern from "stdlib.h":
ctypedef long size_t
ctypedef long intptr_t
void *malloc(size_t size)
void free(void* ptr)
# for PyArrayInterface:
CONTIGUOUS=0x01
FORTRAN=0x02
ALIGNED=0x100
NOTSWAPPED=0x200
WRITEABLE=0x400
# byteorder dictionary
byteorder = {'<':'little', '>':'big'}
ctypedef struct PyArrayInterface:
int version # contains the integer 2 as a sanity check
int nd # number of dimensions
char typekind # kind in array --- character code of typestr
int itemsize # size of each element
int flags # flags indicating how the data should be interpreted
Py_intptr_t *shape # A length-nd array of shape information
Py_intptr_t *strides # A length-nd array of stride information
void *data # A pointer to the first element of the array
cdef void free_array_interface(void *ptr, void *arr):
arrpy = <object>arr
Py_DECREF(arrpy)
cdef class NumInd:
cdef void *data
cdef int _nd
cdef Py_intptr_t *_shape, *_strides
cdef PyArrayInterface *inter
cdef object _t_shape, _t_strides, _undarray
def __init__(self, object undarray):
cdef int i, stride
cdef object array_shape, array_strides
# Keep a reference to the underlying object
self._undarray = undarray
# Get the shape and strides C arrays
array_shape = undarray.__array_shape__
self._t_shape = array_shape
# The number of dimensions
self._nd = len(array_shape)
# The shape
self._shape = <Py_intptr_t *>malloc(self._nd*sizeof(Py_intptr_t))
for i from 0 <= i < self._nd:
self._shape[i] = self._t_shape[i]
# The strides (compute them if needed)
array_strides = undarray.__array_strides__
self._t_strides = array_strides
self._strides = <Py_intptr_t *>malloc(self._nd*sizeof(Py_intptr_t))
if array_strides:
for i from 0 <= i < self._nd:
self._strides[i] = array_strides[i]
else:
# strides is None. Compute them explicitely.
self._t_strides = [0] * self._nd
stride = int(self.typestr[2:])
for i from self._nd > i >= 0:
self._strides[i] = stride
self._t_strides[i] = stride
stride = stride * array_shape[i]
self._t_strides = tuple(self._t_strides)
# Populate the C array interface
self.inter = self._get_array_interface()
# Properties. This are visible from Python space.
# Add as many as you want.
property undarray: # Returns the underlying array
def __get__(self):
return self._undarray
property shape:
def __get__(self):
return self._t_shape
property strides:
def __get__(self):
return self._t_strides
property typestr:
def __get__(self):
return self._undarray.__array_typestr__
property readonly:
def __get__(self):
return self._undarray.__array_data__[1]
property __array_struct__:
"Allows other numerical packages to obtain a new object."
def __get__(self):
if hasattr(self._undarray, "__array_struct__"):
return self._undarray.__array_struct__
else:
# No an underlying array with __array_struct__
# Deliver an equivalent PyCObject.
Py_INCREF(self)
return PyCObject_FromVoidPtrAndDesc(<void*>self.inter,
<void*>self,
free_array_interface)
cdef PyArrayInterface *_get_array_interface(self):
"Populates the array interface"
cdef PyArrayInterface *inter
cdef object undarray, data_address, typestr
undarray = self._undarray
typestr = self.typestr
inter = <PyArrayInterface *>malloc(sizeof(PyArrayInterface))
if inter is NULL:
raise MemoryError()
inter.version = 2
inter.nd = self._nd
inter.typekind = ord(typestr[1])
inter.itemsize = int(typestr[2:])
inter.flags = 0 # initialize flags
if typestr[0] == '|':
inter.flags = inter.flags | NOTSWAPPED
elif byteorder[typestr[0]] == sys.byteorder:
inter.flags = inter.flags | NOTSWAPPED
if not self.readonly:
inter.flags = inter.flags | WRITEABLE
# XXX how to determine the ALIGNED flag?
inter.strides = self._strides
inter.shape = self._shape
# Get the data address
data_address = int(undarray.__array_data__[0], 16)
inter.data = <void*>PyInt_AsLong(data_address)
return inter
# This is just an example on how to modify the data in C space
# (and at C speed! :-)
def modify(self):
"Modify the values of the underlying array"
cdef int *data, i
data = <int *>self.inter.data
# Modify just the first row
for i from 0 <= i < self.inter.shape[self.inter.nd-1]:
data[i] = data[i] + 1
def __dealloc__(self):
free(self._shape)
free(self._strides)
free(self.inter)
使用示例¶
为了了解上述扩展的功能，请尝试对 !NumInd 扩展运行此脚本。
在 [ ]
import Numeric
import numarray
import numpy
import numind
# Create an arbitrary object for each package
nu=Numeric.arange(12)
nu.shape = (4,3)
na=numarray.arange(12)
na.shape = (4,3)
np=numpy.arange(12)
np.shape = (4,3)
# Wrap the different objects with the NumInd class
# and execute some actions on it
for obj in [nu, na, np]:
ni = numind.NumInd(obj)
print "original object type-->", type(ni.undarray)
# Print some values
print "typestr -->", ni.typestr
print "shape -->", ni.shape
print "strides -->", ni.strides
npa = numpy.asarray(ni)
print "object after a numpy re-wrapping -->", npa
ni.modify()
print "object after modification in C space -->", npa
您可以在此处查看输出 test_output.txt
另请参阅¶
- ["Cookbook/Pyrex_and_NumPy"]
- ["Cookbook/ArrayStruct_and_Pyrex"] (启发性的食谱)
章节作者: FrancescAltet
附件