2013/3/3 Zaur Shibzukhov <szp...@gmail.com>:
> 2013/3/2 Stefan Behnel <stefan...@behnel.de>:
>> Hi,
>>
>> the last pull request looks good to me now.
>>
>> https://github.com/cython/cython/pull/189
>>
>> Any more comments on it?
>
> As was suggested earlier, I added `import_datetime` inline function to
> initialize PyDateTime C API instead of direct usage of "non-native" C
> macros from datetime.h.
> Now you call `import_array ()` first in the same way as is done with `numpy`.
> This approach looks natural in the light of experience with numpy.
>
I make some performance comparisons. Here example for dates.
# test_date.pyx
--------------------
Here test code:
from cpython.datetime cimport import_datetime, date_new, date
import_datetime()
from datetime import date as pydate
def test_date1():
cdef list lst = []
for year in range(1000, 2001):
for month in range(1,13):
for day in range(1, 20):
d = pydate(year, month, day)
lst.append(d)
return lst
def test_date2():
cdef list lst = []
for year in range(1000, 2001):
for month in range(1,13):
for day in range(1, 20):
d = date(year, month, day)
lst.append(d)
return lst
def test_date3():
cdef list lst = []
cdef int year, month, day
for year in range(1000, 2001):
for month in range(1,13):
for day in range(1, 20):
d = date_new(year, month, day)
lst.append(d)
return lst
def test1():
l = test_date1()
return l
def test2():
l = test_date2()
return l
def test3():
l = test_date3()
return l
Here are timings:
(py32)zbook:mytests $ python -m timeit -n 50 -r 5 -s "from
mytests.test_date import test1" "test1()"
50 loops, best of 5: 83.2 msec per loop
(py32)zbook:mytests $ python -m timeit -n 50 -r 5 -s "from
mytests.test_date import test2" "test2()"
50 loops, best of 5: 74.7 msec per loop
(py32)zbook:mytests $ python -m timeit -n 50 -r 5 -s "from
mytests.test_date import test3" "test3()"
50 loops, best of 5: 20.9 msec per loop
OSX 10.6.8 64 bit python 3.2
Shibzukhov Zaur
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