def __call__(self, *args, **kwargs):
if (self.neighborhood is not None
and len(self.neighborhood) != args[0].ndim):
raise ValueError("{} dimensional neighborhood specified for {} "
"dimensional input array".format(
len(self.neighborhood), args[0].ndim))
if 'out' in kwargs:
result = kwargs['out']
rdtype = result.dtype
rttype = numpy_support.from_dtype(rdtype)
result_type = types.npytypes.Array(
rttype, result.ndim, numpy_support.map_layout(result))
array_types = tuple([typing.typeof.typeof(x) for x in args])
array_types_full = tuple([typing.typeof.typeof(x)
for x in args] + [result_type])
else:
result = None
array_types = tuple([typing.typeof.typeof(x) for x in args])
array_types_full = array_types
if config.DEBUG_ARRAY_OPT >= 1:
print("__call__", array_types, args, kwargs)
(real_ret, typemap, calltypes) = self.get_return_type(array_types)
new_func = self._stencil_wrapper(result, None, real_ret, typemap,
calltypes, *array_types_full)
if result is None:
return new_func.entry_point(*args)
else:
return new_func.entry_point(*(args + (result, )))
def _typeof_ndarray(val, c):
try:
dtype = numpy_support.from_dtype(val.dtype)
except NotImplementedError:
raise ValueError("Unsupported array dtype: %s" % (val.dtype, ))
layout = numpy_support.map_layout(val)
readonly = not val.flags.writeable
return types.Array(dtype, val.ndim, layout, readonly=readonly)