def any(localization, proxy_obj, arguments): if Number == type(arguments[0]): return numpy.bool_() dvar = call_utilities.parse_varargs_and_kwargs( localization, arguments, ['axis', 'out', 'keepdims'], { 'axis': [ types.NoneType, Integer, IterableDataStructureWithTypedElements(Integer) ], 'out': numpy.ndarray, 'keepdims': bool }, 'any') if isinstance(dvar, StypyTypeError): return dvar if 'out' in dvar.keys(): set_contained_elements_type(localization, dvar['out'], numpy.bool_()) return dvar['out'] if 'axis' in dvar.keys(): return call_utilities.create_numpy_array(numpy.bool_()) return numpy.bool_()
def reciprocal(localization, proxy_obj, arguments, func_name='reciprocal'): dvar = call_utilities.parse_varargs_and_kwargs( localization, arguments, ['out', 'where'], { 'out': numpy.ndarray, 'where': numpy.ndarray, }, func_name) if isinstance(dvar, StypyTypeError): return dvar if Number == type(arguments[0]): ret = call_utilities.cast_to_numpy_type(numpy.float64()) else: try: ret = call_utilities.create_numpy_array_n_dimensions( call_utilities.cast_to_numpy_type( call_utilities.get_inner_type(localization, arguments[0])), call_utilities.get_dimensions(localization, arguments[0])) except Exception as ex: return StypyTypeError(localization, str(ex)) if 'out' in dvar.keys(): set_contained_elements_type(localization, dvar['out'], ret) return dvar['out'] return ret
def clip(localization, proxy_obj, arguments): if Number == type(arguments[0]): return call_utilities.cast_to_numpy_type(arguments[0]) dvar = call_utilities.parse_varargs_and_kwargs( localization, arguments, ['a_min', 'a_max', 'out'], { 'a_min': [ Integer, IterableDataStructureWithTypedElements(Integer), types.NoneType ], 'a_max': [ Integer, IterableDataStructureWithTypedElements(Integer), types.NoneType ], 'out': numpy.ndarray, }, 'clip') if isinstance(dvar, StypyTypeError): return dvar if 'out' in dvar.keys(): set_contained_elements_type( localization, dvar['out'], get_contained_elements_type(localization, arguments[0])) return dvar['out'] return call_utilities.create_numpy_array( get_contained_elements_type(localization, arguments[0]))
def nansum(localization, proxy_obj, arguments): if Number == type(arguments[0]): return call_utilities.cast_to_numpy_type(arguments[0]) dvar = call_utilities.parse_varargs_and_kwargs( localization, arguments, ['axis', 'dtype', 'out', 'keepdims'], { 'axis': int, 'dtype': type, 'out': numpy.ndarray, 'keepdims': bool }, 'nansum') if isinstance(dvar, StypyTypeError): return dvar if 'out' in dvar.keys(): set_contained_elements_type( localization, dvar['out'], get_contained_elements_type(localization, arguments[0])) return dvar['out'] if 'axis' in dvar.keys(): return call_utilities.create_numpy_array( get_contained_elements_type(localization, arguments[0])) return call_utilities.cast_to_numpy_type( get_contained_elements_type(localization, arguments[0]))
def trace(localization, proxy_obj, arguments): dvar = call_utilities.parse_varargs_and_kwargs( localization, arguments, ['offset', 'axis1', ' axis2', 'dtype', 'out'], { 'offset': Integer, 'axis1': Integer, 'axis2': Integer, 'dtype': type, 'out': numpy.ndarray, }, 'trace') if isinstance(dvar, StypyTypeError): return dvar dim = call_utilities.get_dimensions(localization, arguments[0]) if dim == 1: return call_utilities.cast_to_numpy_type( get_contained_elements_type(localization, arguments[0])) else: ret = call_utilities.create_numpy_array( call_utilities.get_inner_type(localization, arguments[0])) if 'out' in dvar.keys(): if dim == 1 or not (call_utilities.get_dimensions( localization, dvar['out']) == 1): return StypyTypeError( localization, "Wrong dimensions of out parameter in trace call") set_contained_elements_type( localization, dvar['out'], get_contained_elements_type(localization, arguments[0])) return dvar['out'] return ret
def heappush(localization, proxy_obj, arguments): ex_type = get_contained_elements_type(localization, arguments[0]) if ex_type is UndefinedType: u = arguments[1] else: u = UnionType.add(ex_type, arguments[1]) set_contained_elements_type(localization, arguments[0], u) return types.NoneType
def __getitem__(localization, proxy_obj, arguments): if Integer == type(arguments[0]): return int() else: t = call_utilities.wrap_contained_type(tuple()) set_contained_elements_type(localization, t, int()) return t
def einsum(localization, proxy_obj, arguments): if isinstance(arguments[-1], dict): if Str == type(arguments[0]): arg_num = 2 else: arg_num = 1 dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['out', 'dtype', 'order', 'casting', 'optimize'], { 'out': IterableDataStructure, 'dtype': type, 'order': Str, 'casting': Str, 'optimize': [bool, Str] }, 'einsum', arg_num) if isinstance(dvar, StypyTypeError): return dvar val_temp = call_utilities.check_possible_values(dvar, 'order', ['C', 'F', 'A', 'K']) if isinstance(val_temp, StypyTypeError): return val_temp val_temp = call_utilities.check_possible_values(dvar, 'casting', ['no', 'equiv', 'safe', 'same_kind', 'unsafe']) if isinstance(val_temp, StypyTypeError): return val_temp val_temp = call_utilities.check_possible_values(dvar, 'optimize', ['greedy', 'optimal', False, True]) if isinstance(val_temp, StypyTypeError): return val_temp arguments = arguments[:-1] else: dvar = dict() typ = None if Str == type(arguments[0]): arg_list = arguments[1:] if Number == type(arguments[1]) and 'out' in dvar: return dvar['out'] else: arg_list = arguments for arg in arg_list: if call_utilities.is_iterable(arg): typ_temp = call_utilities.cast_to_numpy_type(call_utilities.get_inner_type(localization, arg)) typ = call_utilities.cast_to_greater_numpy_type(typ, typ_temp) union = UnionType.add(typ, call_utilities.create_numpy_array(DynamicType)) if 'out' in dvar: set_contained_elements_type(localization, dvar['out'], DynamicType) return call_utilities.create_numpy_array(DynamicType) return union
def tensordot(localization, proxy_obj, arguments): dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['axes'],{ 'axes': [Integer, IterableDataStructureWithTypedElements(Integer)], }, 'tensordot', 2) if isinstance(dvar, StypyTypeError): return dvar l = wrap_contained_type(list()) set_contained_elements_type(localization, l, DynamicType()) return call_utilities.create_numpy_array(l)
def iadd(localization, proxy_obj, arguments): if call_utilities.is_iterable( arguments[0]) and call_utilities.is_iterable(arguments[1]): if isinstance( arguments[0].get_wrapped_type(), list) and isinstance( arguments[1].get_wrapped_type(), tuple): t1 = get_contained_elements_type(localization, arguments[0]) t2 = get_contained_elements_type(localization, arguments[1]) tEnd = UnionType.add(t1, t2) set_contained_elements_type(localization, arguments[0], tEnd) return arguments[0] return None
def divide(localization, proxy_obj, arguments, func_name='divide'): dvar = call_utilities.parse_varargs_and_kwargs( localization, arguments, ['out', 'where'], { 'out': numpy.ndarray, 'where': numpy.ndarray, }, func_name, 2) if isinstance(dvar, StypyTypeError): return dvar if Number == type(arguments[0]) and Number == type(arguments[1]): return call_utilities.cast_to_greater_numpy_type( arguments[0], arguments[1]) try: dims = 0 if call_utilities.is_iterable(arguments[0]): param0 = call_utilities.get_inner_type(localization, arguments[0]) dims = call_utilities.get_dimensions(localization, arguments[0]) else: param0 = arguments[0] if call_utilities.is_iterable(arguments[1]): param1 = call_utilities.get_inner_type(localization, arguments[1]) temp = call_utilities.get_dimensions(localization, arguments[1]) if temp > dims: dims = temp else: param1 = arguments[1] if dims > 0: ret = call_utilities.create_numpy_array_n_dimensions( call_utilities.cast_to_greater_numpy_type(param0, param1), dims) else: ret = call_utilities.cast_to_greater_numpy_type(param0, param1) except Exception as ex: return StypyTypeError(localization, str(ex)) if 'out' in dvar.keys(): set_contained_elements_type(localization, dvar['out'], ret) return dvar['out'] return ret
def logical_xor(localization, proxy_obj, arguments): dvar = call_utilities.parse_varargs_and_kwargs( localization, arguments, ['out'], { 'out': numpy.ndarray, }, 'logical_xor', 2) if isinstance(dvar, StypyTypeError): return dvar if Number == type(arguments[0]) and Number == type(arguments[1]): return bool() if 'out' in dvar.keys(): set_contained_elements_type(localization, dvar['out'], bool()) return dvar['out'] return call_utilities.create_numpy_array(bool())
def outer(localization, proxy_obj, arguments): dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['out'],{ 'out': numpy.ndarray, }, 'outer', 2) if isinstance(dvar, StypyTypeError): return dvar t1 = get_contained_elements_type(localization, arguments[0]) t2 = get_contained_elements_type(localization, arguments[1]) l = wrap_contained_type(list()) set_contained_elements_type(localization, l, call_utilities.cast_to_greater_numpy_type(t1, t2)) if 'out' in dvar: set_contained_elements_type(localization, dvar['out'], l) return call_utilities.create_numpy_array(l)
def argmin(localization, proxy_obj, arguments): if len(arguments) == 0: return numpy.int32() dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['axis', 'out'], { 'axis': Integer, 'out': numpy.ndarray, }, 'argmin', 0) if isinstance(dvar, StypyTypeError): return dvar if 'out' in dvar.keys(): set_contained_elements_type(localization, dvar['out'], numpy.int32()) return dvar['out'] return call_utilities.create_numpy_array(numpy.int32())
def reduceat(localization, proxy_obj, arguments): dvar = call_utilities.parse_varargs_and_kwargs( localization, arguments, ['axis', 'dtype', 'out'], { 'axis': Integer, 'dtype': type, 'out': numpy.ndarray, }, 'reduceat', 2) if isinstance(dvar, StypyTypeError): return dvar if 'out' in dvar.keys(): set_contained_elements_type( localization, dvar['out'], get_contained_elements_type(localization, arguments[0])) return dvar['out'] return arguments[0]
def negative(localization, proxy_obj, arguments): dvar = call_utilities.parse_varargs_and_kwargs( localization, arguments, ['out'], { 'out': numpy.ndarray, }, 'logical_not') if isinstance(dvar, StypyTypeError): return dvar if Number == type(arguments[0]): return call_utilities.cast_to_numpy_type(arguments[0]) if 'out' in dvar.keys(): set_contained_elements_type( localization, dvar['out'], get_contained_elements_type(localization, arguments[0])) return dvar['out'] return call_utilities.create_numpy_array( get_contained_elements_type(localization, arguments[0]))
def sum(localization, proxy_obj, arguments): dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['axis', 'dtype', 'out'], { 'axis': [types.NoneType, Integer, IterableDataStructureWithTypedElements(Integer)], 'dtype': type, 'out': numpy.ndarray, 'keepdims': bool}, 'sum') if isinstance(dvar, StypyTypeError): return dvar r = TypeWrapper.get_wrapper_of(proxy_obj.__self__) if 'out' in dvar.keys(): set_contained_elements_type(localization, dvar['out'], get_contained_elements_type(localization, r)) return dvar['out'] if 'axis' in dvar.keys(): return call_utilities.create_numpy_array(get_contained_elements_type(localization, r)) return call_utilities.cast_to_numpy_type(get_contained_elements_type(localization, r))
def round_(localization, proxy_obj, arguments): if Number == type(arguments[0]): return call_utilities.cast_to_numpy_type(arguments[0]) dvar = call_utilities.parse_varargs_and_kwargs( localization, arguments, ['decimals', 'out'], { 'decimals': Integer, 'out': numpy.ndarray, }, 'round_') if isinstance(dvar, StypyTypeError): return dvar if 'out' in dvar.keys(): set_contained_elements_type( localization, dvar['out'], get_contained_elements_type(localization, arguments[0])) return dvar['out'] return call_utilities.create_numpy_array( get_contained_elements_type(localization, arguments[0]))
def unique(localization, proxy_obj, arguments): dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['return_index', 'return_inverse', 'return_counts'], { 'return_index': bool, 'return_inverse': bool, 'return_counts': bool, }, 'unique') if isinstance(dvar, StypyTypeError): return dvar if Number == type(arguments[0]): ret_arr = call_utilities.create_numpy_array(arguments[0]) else: ret_arr = call_utilities.create_numpy_array(get_contained_elements_type(localization, arguments[0])) if len(dvar.keys()) == 0: return ret_arr tup = wrap_type(tuple()) union = UnionType.add(ret_arr, call_utilities.create_numpy_array(numpy.int32())) if len(dvar.keys()) == 1: set_contained_elements_type(localization, tup, union) if len(dvar.keys()) == 2: union = UnionType.add(union, call_utilities.create_numpy_array(numpy.int32())) set_contained_elements_type(localization, tup, union) if len(dvar.keys()) == 3: union = UnionType.add(union, call_utilities.create_numpy_array(numpy.int32())) union = UnionType.add(union, call_utilities.create_numpy_array(numpy.int32())) set_contained_elements_type(localization, tup, union) return tup
def mean(localization, proxy_obj, arguments): dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['axis', 'dtype', 'out', 'keepdims'], { 'dtype': type, 'axis': Integer, 'out': numpy.ndarray, 'keepdims': bool, }, 'mean', 0) if isinstance(dvar, StypyTypeError): return dvar r = TypeWrapper.get_wrapper_of(proxy_obj.__self__) dims = call_utilities.get_dimensions(localization, r) if 'axis' in dvar: if dims > 1: if 'out' in dvar.keys(): set_contained_elements_type(localization, dvar['out'], numpy.int32()) return dvar['out'] return call_utilities.create_numpy_array(numpy.float64()) return numpy.float64()
def dot(localization, proxy_obj, arguments): dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['out'], { 'out': numpy.ndarray, }, 'dot') if isinstance(dvar, StypyTypeError): return dvar r = TypeWrapper.get_wrapper_of(proxy_obj.__self__) if Number == type(arguments[0]): c_t = get_contained_elements_type(localization, r) if not 'out' in dvar.keys(): return call_utilities.create_numpy_array(c_t) else: set_contained_elements_type(localization, dvar['out'], c_t) return dvar['out'] if call_utilities.is_iterable(arguments[0]): if call_utilities.get_dimensions(localization, r) == 1 and call_utilities.get_dimensions( localization, arguments[0]) == 1: return call_utilities.cast_to_greater_numpy_type( call_utilities.get_inner_type(localization, r), call_utilities.get_inner_type(localization, arguments[0])) typ = call_utilities.cast_to_greater_numpy_type(call_utilities.get_inner_type(localization, r), call_utilities.get_inner_type(localization, arguments[0])) for i in range(call_utilities.get_dimensions(localization, r)): typ = call_utilities.create_numpy_array(typ) if not 'out' in dvar.keys(): return typ else: set_contained_elements_type(localization, dvar['out'], get_contained_elements_type(localization, typ)) return dvar['out'] return r
def inner(localization, proxy_obj, arguments): dvar = call_utilities.parse_varargs_and_kwargs( localization, arguments, ['out'], { 'out': numpy.ndarray, }, 'inner') if isinstance(dvar, StypyTypeError): return dvar if Number == type(arguments[0]) and Number == type(arguments[1]): return call_utilities.cast_to_greater_numpy_type( arguments[0], arguments[1]) if Number == type(arguments[0]) and call_utilities.is_iterable( arguments[1]): c_t = get_contained_elements_type(localization, arguments[1]) if not 'out' in dvar.keys(): return call_utilities.create_numpy_array(c_t) else: set_contained_elements_type(localization, dvar['out'], c_t) return dvar['out'] if Number == type(arguments[1]) and call_utilities.is_iterable( arguments[0]): c_t = get_contained_elements_type(localization, arguments[0]) if not 'out' in dvar.keys(): return call_utilities.create_numpy_array(c_t) else: set_contained_elements_type(localization, dvar['out'], c_t) return dvar['out'] if call_utilities.is_iterable( arguments[0]) and call_utilities.is_iterable(arguments[1]): if call_utilities.get_dimensions( localization, arguments[0]) == 1 and call_utilities.get_dimensions( localization, arguments[1]) == 1: return call_utilities.cast_to_greater_numpy_type( call_utilities.get_inner_type(localization, arguments[0]), call_utilities.get_inner_type(localization, arguments[1])) # typ = call_utilities.cast_to_greater_numpy_type(call_utilities.get_inner_type(localization, arguments[0]), call_utilities.get_inner_type(localization, arguments[1])) # for i in range (call_utilities.get_dimensions(localization, arguments[0])): # typ = call_utilities.create_numpy_array(typ) typ = call_utilities.create_numpy_array(DynamicType()) if not 'out' in dvar.keys(): return typ else: set_contained_elements_type( localization, dvar['out'], get_contained_elements_type(localization, typ)) return dvar['out'] return arguments[0]
def iteritems(localization, proxy_obj, arguments): it = wrap_type(get_sample_instance_for_type("dictionary_itemiterator")) set_contained_elements_type(localization, it, arguments[0].get_wrapped_type().items()) return it