我是Cython的新手，正在尝试学习如何将其与numpy一起使用以加速代码。我一直在遵循link中的教程。
我在这里复制了他们的代码：
from __future__ import division
import numpy as np
# "cimport" is used to import special compile-time information
# about the numpy module (this is stored in a file numpy.pxd which is
# currently part of the Cython distribution).
cimport numpy as np
# We now need to fix a datatype for our arrays. I've used the variable
# DTYPE for this, which is assigned to the usual NumPy runtime
# type info object.
DTYPE = np.int
# "ctypedef" assigns a corresponding compile-time type to DTYPE_t. For
# every type in the numpy module there's a corresponding compile-time
# type with a _t-suffix.
ctypedef np.int_t DTYPE_t
# The builtin min and max functions works with Python objects, and are
# so very slow. So we create our own.
# - "cdef" declares a function which has much less overhead than a normal
# def function (but it is not Python-callable)
# - "inline" is passed on to the C compiler which may inline the functions
# - The C type "int" is chosen as return type and argument types
# - Cython allows some newer Python constructs like "a if x else b", but
# the resulting C file compiles with Python 2.3 through to Python 3.0 beta.
cdef inline int int_max(int a, int b): return a if a >= b else b
cdef inline int int_min(int a, int b): return a if a <= b else b
# "def" can type its arguments but not have a return type. The type of the
# arguments for a "def" function is checked at run-time when entering the
# function.
#
# The arrays f, g and h is typed as "np.ndarray" instances. The only effect
# this has is to a) insert checks that the function arguments really are
# NumPy arrays, and b) make some attribute access like f.shape[0] much
# more efficient. (In this example this doesn't matter though.)
cimport cython
@cython.boundscheck(False)
def naive_convolve(np.ndarray[DTYPE_t, ndim=2] f, np.ndarray[DTYPE_t, ndim=2] g):
if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
raise ValueError("Only odd dimensions on filter supported")
assert f.dtype == DTYPE and g.dtype == DTYPE
# The "cdef" keyword is also used within functions to type variables. It
# can only be used at the top indendation level (there are non-trivial
# problems with allowing them in other places, though we'd love to see
# good and thought out proposals for it).
#
# For the indices, the "int" type is used. This corresponds to a C int,
# other C types (like "unsigned int") could have been used instead.
# Purists could use "Py_ssize_t" which is the proper Python type for
# array indices.
cdef int vmax = f.shape[0]
cdef int wmax = f.shape[1]
cdef int smax = g.shape[0]
cdef int tmax = g.shape[1]
cdef int smid = smax // 2
cdef int tmid = tmax // 2
cdef int xmax = vmax + 2*smid
cdef int ymax = wmax + 2*tmid
cdef np.ndarray[DTYPE_t, ndim=2] h = np.zeros([xmax, ymax], dtype=DTYPE)
cdef int s, t
cdef unsigned int x, y, v, w
# It is very important to type ALL your variables. You do not get any
# warnings if not, only much slower code (they are implicitly typed as
# Python objects).
cdef int s_from, s_to, t_from, t_to
# For the value variable, we want to use the same data type as is
# stored in the array, so we use "DTYPE_t" as defined above.
# NB! An important side-effect of this is that if "value" overflows its
# datatype size, it will simply wrap around like in C, rather than raise
# an error like in Python.
cdef DTYPE_t value
for x in range(xmax):
for y in range(ymax):
s_from = int_max(smid - x, -smid)
s_to = int_min((xmax - x) - smid, smid + 1)
t_from = int_max(tmid - y, -tmid)
t_to = int_min((ymax - y) - tmid, tmid + 1)
value = 0
for s in range(s_from, s_to):
for t in range(t_from, t_to):
v = <unsigned int>(x - smid + s)
w = <unsigned int>(y - tmid + t)
value += g[<unsigned int>(smid - s), <unsigned int>(tmid - t)] * f[v, w]
h[x, y] = value
return h
我不明白一件事。我知道
cdef从link关于Cython语言基础的定义中定义了C类型。但是，上面的示例还在例如cdef DTYPE_t value的行中定义了一个名为np.int_t的编译时类型，其中DTYPE_t实际上是np.int_t。我的问题是：
np.int和np.int_t有什么区别？它类似于python int与ctypes.c_int，但更具体于numpy吗？在那种情况下，如果我只使用cdef int而不是cdef np.int_t会一样吗？另外，我确实测试了如果将
cdef DTYPE_t value替换为cdef int value会发生什么情况。结果表明两者之间没有差异。这是原始的
cdef DTYPE_t value1个循环，最好是10个循环：每个循环93.9毫秒
这是针对修改后的
cdef int value1个循环，最好是10个循环：每个循环93.8 ms
任何帮助，将不胜感激。谢谢！
最佳答案
np.int是在Python代码中引用整数dtype的Python对象。 np.int_t是仅在Cython中存在的C typedef。 （我相信它对应于C long，而不是int。）
关于python - Cython中C类型和编译时类型的差异，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/23433318/