In Cython when using numpy, what is the point of writing:
cimport numpy as np import numpy as np ctypedef np.int_t DTYPE_t and then using DTYPE_t everywhere instead of just using np.int_t? Does the ctypedef actually do anything differently in the resulting code here?
1 Answer
You can read the notes from the docs for cython, reading the notes they explain the reason for the use of this notation and imports.
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.) 
