Concerning memoryviews in cython, is there any advantage of typing a view with NumPy types such as np.float_t instead of simply do double if I'm working with numpy float arrays?
And should I type the cdef then the same way, doing e. g.
ctypedef np.float64_t np_float_t
...
@cython.profile(False)
@cython.wraparound(False)
@cython.boundscheck(False)
cdef np_float_t mean_1d(np_float_t [:] v) nogil:
cdef unsigned int n = v.shape[0]
cdef np_float_t n_sum = 0.
cdef Py_ssize_t i
for i in range(n):
n_sum += v[i]
return n_sum / n
If you look in the numpy header file included with cython (e.g. in the master branch, it is __init__.pxd), you'll find
ctypedef double npy_double
and
ctypedef npy_double float_t
In other words, float_t is double, so there should be no advantange to using np.float_t.