def typeof_pyval(cls, val):
"""
This is called from numba._dispatcher as a fallback if the native code
cannot decide the type.
"""
if isinstance(val, numpy.ndarray):
# TODO complete dtype mapping
dtype = numpy_support.from_dtype(val.dtype)
ndim = val.ndim
if ndim == 0:
# is array scalar
return numpy_support.from_dtype(val.dtype)
layout = numpy_support.map_layout(val)
aryty = types.Array(dtype, ndim, layout)
return aryty
# The following are handled in the C version for exact type match
# So test these later
elif isinstance(val, INT_TYPES):
return types.int64
elif isinstance(val, float):
return types.float64
elif isinstance(val, complex):
return types.complex128
elif numpy_support.is_arrayscalar(val):
# Array scalar
return numpy_support.from_dtype(numpy.dtype(type(val)))
# Other object
else:
return types.pyobject
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_pyval(val):
"""
This is called from numba._dispatcher as a fallback if the native code
cannot decide the type.
"""
if isinstance(val, numpy.ndarray):
# TODO complete dtype mapping
dtype = numpy_support.from_dtype(val.dtype)
ndim = val.ndim
if ndim == 0:
# is array scalar
return numpy_support.from_dtype(val.dtype)
layout = numpy_support.map_layout(val)
aryty = types.Array(dtype, ndim, layout)
return aryty
# The following are handled in the C version for exact type match
# So test these later
elif isinstance(val, INT_TYPES):
return types.int32
elif isinstance(val, float):
return types.float64
elif isinstance(val, complex):
return types.complex128
elif numpy_support.is_arrayscalar(val):
# Array scalar
return numpy_support.from_dtype(numpy.dtype(type(val)))
# Other object
else:
return types.pyobject
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:
return
layout = numpy_support.map_layout(val)
readonly = not val.flags.writeable
return types.Array(dtype, val.ndim, layout, readonly=readonly)
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)
def resolve_argument_type(self, val):
"""
Return the numba type of a Python value that is being used
as a function argument. Integer types will all be considered
int64, regardless of size. Numpy arrays will be accepted in
"C" or "F" layout.
Unknown types will be mapped to pyobject.
"""
if isinstance(val, utils.INT_TYPES):
# Force all integers to be 64-bit
return types.int64
elif numpy_support.is_array(val):
dtype = numpy_support.from_dtype(val.dtype)
layout = numpy_support.map_layout(val)
return types.Array(dtype, val.ndim, layout)
tp = self.resolve_data_type(val)
if tp is None:
tp = getattr(val, "_numba_type_", types.pyobject)
return tp
def resolve_argument_type(self, val):
"""
Return the numba type of a Python value that is being used
as a function argument. Integer types will all be considered
int64, regardless of size. Numpy arrays will be accepted in
"C" or "F" layout.
Unknown types will be mapped to pyobject.
"""
if isinstance(val, utils.INT_TYPES):
# Force all integers to be 64-bit
return types.int64
elif numpy_support.is_array(val):
dtype = numpy_support.from_dtype(val.dtype)
layout = numpy_support.map_layout(val)
return types.Array(dtype, val.ndim, layout)
tp = self.resolve_data_type(val)
if tp is None:
tp = getattr(val, "_numba_type_", types.pyobject)
return tp
def resolve_data_type(self, val):
"""
Return the numba type of a Python value representing data
(e.g. a number or an array, but not more sophisticated types
such as functions, etc.)
This function can return None to if it cannot decide.
"""
if val is True or val is False:
return types.boolean
# Under 2.x, we must guard against numpy scalars (np.intXY
# subclasses Python int but get_number_type() wouldn't infer the
# right bit width -- perhaps it should?).
elif (not isinstance(val, numpy.number)
and isinstance(val, utils.INT_TYPES + (float,))):
return self.get_number_type(val)
elif val is None:
return types.none
elif isinstance(val, str):
return types.string
elif isinstance(val, complex):
return types.complex128
elif isinstance(val, tuple):
tys = [self.resolve_value_type(v) for v in val]
distinct_types = set(tys)
if len(distinct_types) == 1:
return types.UniTuple(tys[0], len(tys))
else:
return types.Tuple(tys)
elif ctypes_utils.is_ctypes_funcptr(val):
return ctypes_utils.make_function_type(val)
elif cffi_utils.SUPPORTED and cffi_utils.is_cffi_func(val):
return cffi_utils.make_function_type(val)
elif numpy_support.is_array(val):
ary = val
try:
dtype = numpy_support.from_dtype(ary.dtype)
except NotImplementedError:
return
layout = numpy_support.map_layout(ary)
readonly = not ary.flags.writeable
return types.Array(dtype, ary.ndim, layout, readonly=readonly)
elif sys.version_info >= (2, 7) and not isinstance(val, numpy.generic):
try:
m = memoryview(val)
except TypeError:
pass
else:
# Object has the buffer protocol
try:
dtype = bufproto.decode_pep3118_format(m.format, m.itemsize)
except ValueError:
pass
else:
type_class = bufproto.get_type_class(type(val))
layout = bufproto.infer_layout(m)
return type_class(dtype, m.ndim, layout=layout,
readonly=m.readonly)
if isinstance(val, numpy.dtype):
tp = numpy_support.from_dtype(val)
return types.DType(tp)
else:
# Matching here is quite broad, so we have to do it after
# the more specific matches above.
try:
return numpy_support.map_arrayscalar_type(val)
except NotImplementedError:
pass
return
def resolve_data_type(self, val):
"""
Return the numba type of a Python value representing data
(e.g. a number or an array, but not more sophisticated types
such as functions, etc.)
This function can return None to if it cannot decide.
"""
if val is True or val is False:
return types.boolean
# Under 2.x, we must guard against numpy scalars (np.intXY
# subclasses Python int but get_number_type() wouldn't infer the
# right bit width -- perhaps it should?).
elif (not isinstance(val, numpy.number)
and isinstance(val, utils.INT_TYPES + (float, ))):
return self.get_number_type(val)
elif val is None:
return types.none
elif isinstance(val, str):
return types.string
elif isinstance(val, complex):
return types.complex128
elif isinstance(val, tuple):
tys = [self.resolve_value_type(v) for v in val]
distinct_types = set(tys)
if len(distinct_types) == 1:
return types.UniTuple(tys[0], len(tys))
else:
return types.Tuple(tys)
elif ctypes_utils.is_ctypes_funcptr(val):
return ctypes_utils.make_function_type(val)
elif cffi_utils.SUPPORTED and cffi_utils.is_cffi_func(val):
return cffi_utils.make_function_type(val)
elif numpy_support.is_array(val):
ary = val
try:
dtype = numpy_support.from_dtype(ary.dtype)
except NotImplementedError:
return
layout = numpy_support.map_layout(ary)
readonly = not ary.flags.writeable
return types.Array(dtype, ary.ndim, layout, readonly=readonly)
elif sys.version_info >= (2, 7) and not isinstance(val, numpy.generic):
try:
m = memoryview(val)
except TypeError:
pass
else:
# Object has the buffer protocol
try:
dtype = bufproto.decode_pep3118_format(
m.format, m.itemsize)
except ValueError:
pass
else:
type_class = bufproto.get_type_class(type(val))
layout = bufproto.infer_layout(m)
return type_class(dtype,
m.ndim,
layout=layout,
readonly=m.readonly)
if isinstance(val, numpy.dtype):
tp = numpy_support.from_dtype(val)
return types.DType(tp)
else:
# Matching here is quite broad, so we have to do it after
# the more specific matches above.
try:
return numpy_support.map_arrayscalar_type(val)
except NotImplementedError:
pass
return
