def from_python(self, value):
from numba.type_inference import module_type_inference
if isinstance(value, np.ndarray):
dtype = map_dtype(value.dtype)
return minitypes.ArrayType(dtype, value.ndim) #,
#is_c_contig=value.flags['C_CONTIGUOUS'],
#is_f_contig=value.flags['F_CONTIGUOUS'])
elif isinstance(value, np.dtype):
return numba.typesystem.from_numpy_dtype(value)
elif is_dtype_constructor(value):
return numba.typesystem.from_numpy_dtype(np.dtype(value))
elif isinstance(value, (tuple, list, dict)):
return infer_container_type(self, value)
elif isinstance(value, types.ModuleType):
return ModuleType(value)
# elif isinstance(value, (self.ctypes_func_type, self.ctypes_func_type2)):
elif is_ctypes(value):
result = from_ctypes_value(value)
if result.is_function:
pointer = ctypes.cast(value, ctypes.c_void_p).value
return PointerFunctionType(value, pointer, result)
else:
return result
elif cffi_support.is_cffi_func(value):
signature = cffi_support.get_signature(value)
pointer = cffi_support.get_pointer(value)
return PointerFunctionType(value, pointer, signature)
elif isinstance(value, minitypes.Type):
return CastType(dst_type=value)
elif hasattr(type(value), '__numba_ext_type'):
return getattr(type(value), '__numba_ext_type')
elif value is numba.NULL:
return null_type
elif numbawrapper.is_numba_wrapper(value):
return JitType(value)
elif isinstance(value, numbawrapper.NumbaSpecializingWrapper):
return AutojitType(value)
elif hasattr(value, 'from_address') and hasattr(value, 'in_dll'):
# Try to detect ctypes pointers, or default to minivect
try:
ctypes.cast(value, ctypes.c_void_p)
except ctypes.ArgumentError:
pass
else:
pass
#type = convert_from_ctypes(value)
#value = ctypes.cast(value, ctypes.c_void_p).value
#return CTypesPointerType(type, valuee
result_type = super(NumbaTypeMapper, self).from_python(value)
if result_type == object_ and module_type_inference.is_registered(value):
result = module_type_inference.module_attribute_type(value)
if result is not None:
result_type = result
else:
result_type = KnownValueType(value)
return result_type
def from_python(self, value):
from numba.type_inference import module_type_inference
if isinstance(value, np.ndarray):
dtype = map_dtype(value.dtype)
return minitypes.ArrayType(dtype, value.ndim) #,
#is_c_contig=value.flags['C_CONTIGUOUS'],
#is_f_contig=value.flags['F_CONTIGUOUS'])
elif isinstance(value, np.dtype):
return numba.typesystem.from_numpy_dtype(value)
elif is_dtype_constructor(value):
return numba.typesystem.from_numpy_dtype(np.dtype(value))
elif isinstance(value, tuple):
return tuple_
elif isinstance(value, types.ModuleType):
return ModuleType(value)
# elif isinstance(value, (self.ctypes_func_type, self.ctypes_func_type2)):
elif hasattr(value, 'errcheck'):
# ugh, ctypes
if value.argtypes is None:
return object_
restype = convert_from_ctypes(value.restype)
argtypes = map(convert_from_ctypes, value.argtypes)
return CTypesFunctionType(value, restype, argtypes)
elif isinstance(value, minitypes.Type):
return CastType(dst_type=value)
elif hasattr(type(value), '__numba_ext_type'):
return getattr(type(value), '__numba_ext_type')
elif value is numba.NULL:
return null_type
elif isinstance(value, numbawrapper.NumbaCompiledWrapper):
return JitType(value)
elif isinstance(value, numbawrapper.NumbaSpecializingWrapper):
return AutojitType(value)
elif hasattr(value, 'from_address') and hasattr(value, 'in_dll'):
# Try to detect ctypes pointers, or default to minivect
try:
ctypes.cast(value, ctypes.c_void_p)
except ctypes.ArgumentError:
pass
else:
pass
#type = convert_from_ctypes(value)
#value = ctypes.cast(value, ctypes.c_void_p).value
#return CTypesPointerType(type, valuee
result_type = super(NumbaTypeMapper, self).from_python(value)
if result_type == object_ and module_type_inference.is_registered(value):
result = module_type_inference.module_attribute_type(value)
if result is not None:
result_type = result
else:
result_type = KnownValueType(value)
return result_type
def infer_typefunc(context, call_node, func_type, default_node):
if (func_type.is_known_value and
module_type_inference.is_registered(func_type.value)):
# Try the module type inferers
result_node = module_type_inference.resolve_call_or_none(
context, call_node, func_type)
if result_node:
return result_node
return default_node
def infer_typefunc(context, call_node, func_type, default_node):
func_var = call_node.func.variable
if func_var.is_constant:
func_type = typesystem.KnownValueType(func_var.constant_value)
if (func_type.is_known_value and
module_type_inference.is_registered(func_type.value)):
# Try the module type inferers
result_node = module_type_inference.resolve_call_or_none(
context, call_node, func_type)
if result_node:
return result_node
return default_node
def is_registered(value):
from numba.type_inference import module_type_inference
return module_type_inference.is_registered(value)
def is_registered(value):
from numba.type_inference import module_type_inference
return module_type_inference.is_registered(value)
def from_python(self, value):
from numba.type_inference import module_type_inference
if isinstance(value, np.ndarray):
dtype = map_dtype(value.dtype)
return minitypes.ArrayType(dtype, value.ndim) #,
#is_c_contig=value.flags['C_CONTIGUOUS'],
#is_f_contig=value.flags['F_CONTIGUOUS'])
elif isinstance(value, np.dtype):
return numba.typesystem.from_numpy_dtype(value)
elif is_dtype_constructor(value):
return numba.typesystem.from_numpy_dtype(np.dtype(value))
elif isinstance(value, (tuple, list, dict)):
return infer_container_type(self, value)
elif isinstance(value, types.ModuleType):
return ModuleType(value)
# elif isinstance(value, (self.ctypes_func_type, self.ctypes_func_type2)):
elif is_ctypes(value):
result = from_ctypes_value(value)
if result.is_function:
pointer = ctypes.cast(value, ctypes.c_void_p).value
return PointerFunctionType(value, pointer, result)
else:
return result
elif cffi_support.is_cffi_func(value):
signature = cffi_support.get_signature(value)
pointer = cffi_support.get_pointer(value)
return PointerFunctionType(value, pointer, signature)
elif isinstance(value, minitypes.Type):
return CastType(dst_type=value)
elif hasattr(type(value), '__numba_ext_type'):
return getattr(type(value), '__numba_ext_type')
elif value is numba.NULL:
return null_type
elif numbawrapper.is_numba_wrapper(value):
return JitType(value)
elif isinstance(value, numbawrapper.NumbaSpecializingWrapper):
return AutojitType(value)
elif hasattr(value, 'from_address') and hasattr(value, 'in_dll'):
# Try to detect ctypes pointers, or default to minivect
try:
ctypes.cast(value, ctypes.c_void_p)
except ctypes.ArgumentError:
pass
else:
pass
#type = convert_from_ctypes(value)
#value = ctypes.cast(value, ctypes.c_void_p).value
#return CTypesPointerType(type, valuee
result_type = super(NumbaTypeMapper, self).from_python(value)
if result_type == object_ and module_type_inference.is_registered(
value):
result = module_type_inference.module_attribute_type(value)
if result is not None:
result_type = result
else:
result_type = KnownValueType(value)
return result_type
