# -*- coding: utf-8 -*-
""" Import numba.jit or a dummy decorator.
"""
from __future__ import division, print_function # Python 2 compatibility
__author__ = "Lilian Besson"
__version__ = "0.6"
#: Configure the use of numba
USE_NUMBA = False
USE_NUMBA = True # XXX Experimental
if not USE_NUMBA:
print("Warning: numba.jit seems to be disabled. Using a dummy decorator for numba.jit() ...") # DEBUG
# DONE I tried numba.jit() on these functions, and it DOES not give any speedup...:-( sad sad !
try:
from numba.decorators import jit
import locale # See this bug, http://numba.pydata.org/numba-doc/dev/user/faq.html#llvm-locale-bug
locale.setlocale(locale.LC_NUMERIC, 'C')
# print("Info: numba.jit seems to be available.") # DEBUG
except ImportError:
print("Warning: numba.jit seems to not be available. Using a dummy decorator for numba.jit() ...\nIf you want the speed up brought by numba.jit, try to manually install numba and check that it works (installing llvmlite can be tricky, cf. https://github.com/numba/numba#custom-python-environments") # DEBUG
USE_NUMBA = False
if not USE_NUMBA:
from functools import wraps
[docs] def jit(f):
"""Fake numba.jit decorator."""
return f # XXX isn't it enough?!
# @wraps(f)
# def wrapper(*args, **kwargs):
# """Fake docstring, shouldn't be used thanks to wraps."""
# return f(*args, **kwargs)
# return wrapper
# Only export and expose the useful functions defined here
__all__ = ["USE_NUMBA", "jit"]