def value(self): if self.type_ == list: tag = TAG_Compound(self.name) tag.tags = [x.value for x in self._value] return tag if self.type_ == NBTFile: x = NBTFile() x.name = self.name x.tags = [x.value for x in self._value] return x if self.type_ == TAG_Compound: tag = TAG_Compound(name=self.name) tag.tags = [x.value for x in self._value] tag.name = self.name return tag if self.type_ == TAG_Int_Array: tag = TAG_Int_Array(name=self.name) tag.value = self._value return tag if self.type_ == TAG_List: tag = TAG_List(type=self.extra, name=self.name) tag.tags = [x.value for x in self._value] tag.name = self.name return tag return self.type_(value=self._value, name=self.name)
def pack_nbt(s): from nbt.nbt import NBTFile, TAG_Long, TAG_Int, TAG_String, TAG_List, TAG_Compound, TAG_Byte, TAG_Double """ Pack a native Python data structure into an NBT tag. Only the following structures and types are supported: * int * float * str * unicode * dict Additionally, arbitrary iterables are supported. Packing is not lossless. In order to avoid data loss, TAG_Long and TAG_Double are preferred over the less precise numerical formats. Lists and tuples may become dicts on unpacking if they were not homogenous during packing, as a side-effect of NBT's format. Nothing can be done about this. Only strings are supported as keys for dicts and other mapping types. If your keys are not strings, they will be coerced. (Resistance is futile.) """ if isinstance(s, int): return TAG_Int(s) if isinstance(s, long): return TAG_Long(s) elif isinstance(s, float): return TAG_Double(s) elif isinstance(s, (str, unicode)): return TAG_String(s) elif isinstance(s, dict): tag = TAG_Compound() for k, v in s.items(): v = pack_nbt(v) v.name = str(k) tag.tags.append(v) return tag elif hasattr(s, "__iter__"): # We arrive at a slight quandry. NBT lists must be homogenous, unlike # Python lists. NBT compounds work, but require unique names for every # entry. On the plus side, this technique should work for arbitrary # iterables as well. tags = [pack_nbt(i) for i in s] if (len(tags) == 0): # I think this is wrong... tag = TAG_List(type=type(TAG_Byte())) return tag t = type(tags[0]) # If we're homogenous... if all(t == type(i) for i in tags): tag = TAG_List(type=t) tag.tags = tags else: tag = TAG_Compound() for i, item in enumerate(tags): item.name = str(i) tag.tags = tags return tag else: raise ValueError("Couldn't serialise type %s!" % type(s))
def pack_nbt(s): """ Pack a native Python data structure into an NBT tag. Only the following structures and types are supported: * int * float * str * unicode * dict Additionally, arbitrary iterables are supported. Packing is not lossless. In order to avoid data loss, TAG_Long and TAG_Double are preferred over the less precise numerical formats. Lists and tuples may become dicts on unpacking if they were not homogenous during packing, as a side-effect of NBT's format. Nothing can be done about this. Only strings are supported as keys for dicts and other mapping types. If your keys are not strings, they will be coerced. (Resistance is futile.) """ if isinstance(s, int): return TAG_Long(s) elif isinstance(s, float): return TAG_Double(s) elif isinstance(s, (str, unicode)): return TAG_String(s) elif isinstance(s, dict): tag = TAG_Compound() for k, v in s: v = pack_nbt(v) v.name = str(k) tag.tags.append(v) return tag elif hasattr(s, "__iter__"): # We arrive at a slight quandry. NBT lists must be homogenous, unlike # Python lists. NBT compounds work, but require unique names for every # entry. On the plus side, this technique should work for arbitrary # iterables as well. tags = [pack_nbt(i) for i in s] t = type(tags[0]) # If we're homogenous... if all(t == type(i) for i in tags): tag = TAG_List(type=t) tag.tags = tags else: tag = TAG_Compound() for i, item in enumerate(tags): item.name = str(i) tag.tags = tags return tag else: raise ValueError("Couldn't serialise type %s!" % type(s))