def vectorize(ftylist_or_function=(), **kws):
"""vectorize(ftylist_or_function=(), target='cpu', identity=None, **kws)
A decorator that creates a Numpy ufunc object using Numba compiled
code. When no arguments or only keyword arguments are given,
vectorize will return a Numba dynamic ufunc (DUFunc) object, where
compilation/specialization may occur at call-time.
Args
-----
ftylist_or_function: function or iterable
When the first argument is a function, signatures are dealt
with at call-time.
When the first argument is an iterable of type signatures,
which are either function type object or a string describing
the function type, signatures are finalized at decoration
time.
Keyword Args
------------
target: str
A string for code generation target. Default to "cpu".
identity: int, str, or None
The identity (or unit) value for the element-wise function
being implemented. Allowed values are None (the default), 0, 1,
and "reorderable".
cache: bool
Turns on caching.
Returns
--------
A NumPy universal function
Examples
-------
@vectorize(['float32(float32, float32)',
'float64(float64, float64)'], identity=1)
def sum(a, b):
return a + b
@vectorize
def sum(a, b):
return a + b
@vectorize(identity=1)
def mul(a, b):
return a * b
"""
if isinstance(ftylist_or_function, str):
# Common user mistake
ftylist = [ftylist_or_function]
elif inspect.isfunction(ftylist_or_function):
return dufunc.DUFunc(ftylist_or_function, **kws)
elif ftylist_or_function is not None:
ftylist = ftylist_or_function
def wrap(func):
vec = Vectorize(func, **kws)
for sig in ftylist:
vec.add(sig)
if len(ftylist) > 0:
vec.disable_compile()
return vec.build_ufunc()
return wrap
def nopython_dufunc(self, pyfunc):
return dufunc.DUFunc(pyfunc, targetoptions=dict(nopython=True))