def _compile_for_args(self, *args, **kws):
nin = self.ufunc.nin
if kws:
if 'out' in kws:
out = kws.pop('out')
args += (out, )
if kws:
raise TypeError("unexpected keyword arguments to ufunc: %s" %
", ".join(repr(k) for k in sorted(kws)))
args_len = len(args)
assert (args_len == nin) or (args_len == nin + self.ufunc.nout)
assert not kws
argtys = []
for arg in args[:nin]:
argty = typeof(arg)
if isinstance(argty, types.Array):
argty = argty.dtype
else:
# To avoid a mismatch in how Numba types scalar values as
# opposed to Numpy, we need special logic for scalars.
# For example, on 64-bit systems, numba.typeof(3) => int32, but
# np.array(3).dtype => int64.
# Note: this will not handle numpy "duckarrays" correctly,
# including but not limited to those implementing `__array__`
# and `__array_ufunc__`.
argty = numpy_support.map_arrayscalar_type(arg)
argtys.append(argty)
return self._compile_for_argtys(tuple(argtys))
def _compile_for_args(self, *args, **kws):
nin = self.ufunc.nin
if kws:
if 'out' in kws:
out = kws.pop('out')
args += (out,)
if kws:
raise TypeError("unexpected keyword arguments to ufunc: %s"
% ", ".join(repr(k) for k in sorted(kws)))
args_len = len(args)
assert (args_len == nin) or (args_len == nin + self.ufunc.nout)
assert not kws
argtys = []
# To avoid a mismatch in how Numba types values as opposed to
# Numpy, we need to first check for scalars. For example, on
# 64-bit systems, numba.typeof(3) => int32, but
# np.array(3).dtype => int64.
for arg in args[:nin]:
if numpy_support.is_arrayscalar(arg):
argtys.append(numpy_support.map_arrayscalar_type(arg))
else:
argty = typeof(arg)
if isinstance(argty, types.Array):
argty = argty.dtype
argtys.append(argty)
return self._compile_for_argtys(tuple(argtys))
def _typeof_numpy_scalar(val, c):
try:
return numpy_support.map_arrayscalar_type(val)
except NotImplementedError:
pass