在Cython中使用numpy的时候，这样写有什么意义:
cimport numpy as np
import numpy as np
ctypedef np.int_t DTYPE_t
然后到处使用 DTYPE_t 而不是只使用 np.int_t？ ctypedef 实际上在此处的结果代码中做了什么不同的事情吗？
最佳答案
您可以阅读 docs for cython 中的注释, 阅读注释，他们解释了使用这种符号和导入的原因。
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
# "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.)
关于python - Cython 中的 ctypedef 与 numpy : what is right convention?，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/22083660/