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):
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, 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 type(value).__numba_ext_type
else:
return super(NumbaTypeMapper, self).from_python(value)
def numba_type_from_sig(ufunc_signature):
"""
Convert ufunc type signature string (e.g. 'dd->d') to a FunctionType
"""
args, ret = ufunc_signature.split('->')
to_numba = lambda c: minitypes.map_dtype(np.dtype(c))
signature = to_numba(ret)(*map(to_numba, args))
return signature
def numba_type_from_sig(ufunc_signature):
"""
Convert ufunc type signature string (e.g. 'dd->d') to a FunctionType
"""
args, ret = ufunc_signature.split('->')
to_numba = lambda c: minitypes.map_dtype(np.dtype(c))
signature = to_numba(ret)(*map(to_numba, args))
return signature
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 from_python(self, value):
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, 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 type(value).__numba_ext_type
elif value is numba.NULL:
return null_type
elif isinstance(value, numba.decorators.NumbaFunction):
if value.signature is None:
# autojit
return AutojitType(value)
else:
return JitType(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, value)
return super(NumbaTypeMapper, self).from_python(value)
def to_minitype(ds):
# To minitype through NumPy. Discards dimension information.
return minitypes.map_dtype(to_numpy(extract_measure(ds)))
def resolve(self):
return map_dtype(self.dtype)
def from_numpy_dtype(np_dtype):
"""
:param np_dtype: the NumPy dtype (e.g. np.dtype(np.double))
:return: a dtype type representation
"""
return dtype(minitypes.map_dtype(np_dtype))
def minitype(dtype):
return minitypes.map_dtype(dtype)
def resolve(self):
return map_dtype(self.dtype)
def to_minitype(ds):
# To minitype through NumPy. Discards dimension information.
return minitypes.map_dtype(to_numpy(extract_measure(ds)))
def from_numpy_dtype(np_dtype):
"""
:param np_dtype: the NumPy dtype (e.g. np.dtype(np.double))
:return: a dtype type representation
"""
return dtype(minitypes.map_dtype(np_dtype))
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 minitype(dtype):
return minitypes.map_dtype(dtype)
