structured-classes 3.1.0

Annotated classes that pack and unpack C structures.

Version 3.1.x is the last version to support Python 3.9.

structured - creating classes which pack and unpack with Python's struct module.

This is a small little library to let you leverage type hints to define classes which can also be packed and unpacked using Python's struct module. The basic usage is almost like a dataclass:

class MyClass(Structured):
  file_magic: char[4]
  version: uint8

a = MyClass(b'', 0)

Get it on PypI

with open('some_file.dat', 'rb') as ins:
  a.unpack_read(ins)

Contents

1. Hint Types: For all the types you can use as type-hints.
   • Basic Types
   • Complex Types
   • Custom Types
   • Modifiers
2. The Structured class
3. Generics
4. Serializers

Hint Types

If you just want to use the library, these are the types you use to hint your instance variables to make them detected as serialized by the packing/unpacking logic. I'll use the term serializable to mean a hinted type that results in the variable being detected by the Structured class as being handled for packing and unpacking. They're broken up into two basic catergories:

• Basic Types: Those with direct correlation to the struct format specifiers, needing no extra logic.
• Complex Types: Those still using struct for packing and unpacking, but requiring extra logic so they do not always have the same format specifier.
• Custom Types: You can use your own custom classes and specify how they should be packed and unpacked.

Almost all types use typing.Annotated under the hood to just add extra serialization information to the type they represent. For example bool8 is defined as Annotated[bool8, StructSerializer('?')], so type-checkers will properly see it as a bool.

There are four exceptions to this. For these types, almost everything should pass inspection by a type-checker, except for assignment. These are:

• char: subclassed from bytes.
• pascal: subclassed from bytes.
• unicode: subclassed from str.
• array: subclassed from list.

If you want to work around this, you can use typing.Annotated yourself to appease the type-checker:

class MyStruct1(Structured):
  name: unicode[100]

item = MyStruct('Jane Doe')
item.name = 'Jessica'   # Type-checker complains about "str incompatible with unicode".

class MyStruct2(Structured):
  name: Annotated[str, unicode[100]]

item = MyStruct('Jane Doe')
item.name = 'Jessica'   # No complaint from the type-checker.

Basic Types

Almost every format specifier in struct is supported as a type:

struct format	structured type	Python type	Notes
x	pad		(1)(3)
c	equivalent to char[1]	bytes with length 1
?	bool8	int
b	int8	int
B	uint8	int
h	int16	int
H	uint16	int
i	int32	int
I	uint32	int
q	int64	int
Q	uint64	int
n	not supported
N	not supported
e	float16	float	(2)
f	float32	float
d	float64	float
s	char	bytes	(1)
p	pascal	bytes	(1)
P	not supported

Notes:

1. These type must be indexed to specify their length. For a single byte char for example ('s'), use char[1].
2. The 16-bit float type is not supported on all platforms.
3. Pad variables are skipped and not actually assigned when unpacking, nor used when packing. There is a special metaclass hook to allow you to name all of your pad variables _, and they still all count towards the final format specifier. If you want to be able to override their type-hint in subclasses, choose a name other than _.

Consecutive variables with any of these type-hints will be combined into a single struct format specifier. Keep in mind that Python's struct module may insert extra padding bytes between (but never before or after) format specifiers, depending on the Byte Order specification used.

Example:

class MyStruct(Structured):
  a: int8
  b: int8
  c: uint32
  _: pad[4]
  d: char[10]
  _: pad[2]
  e: uint32

In this example, all of the instance variables are of the "basic" type, so the final result will be as if packing or unpacking with struct using a format of 2bI4x10s2xI. Note we took advantage of the Structured metaclass to specify the padding using the same name _.

Byte Order

All of the specifiers are supported, the default it to use no specifier:

struct specifier	ByteOrder
<	LITTLE_ENDIAN (or LE)
>	BIG_ENDIAN (or BE)
=	NATIVE_STANDARD
@	NATIVE_NATIVE
!	NETWORK

To specify a byte order, pass byte_order=ByteOrder.<option> to the Structured sub-classing machinery, like so:

class MyStruct(Structured, byte_order=ByteOrder.NETWORK):
  a: int8
  b: uint32

In this example, the NETWORK (!) specifier was used, so struct will not insert any padding bytes between variables a and b, and multi-byte values will be unpacked as Big Endian numbers.

Complex Types

All other types fall into the "complex" category. They currently consist of:

• tuple: Fixed length tuples of serializable objects.
• array: Lists of a single type of serializable object.
• char: Although char[3] (or any other integer) is considered a basic type, char also supports variable length strings.
• unicode: A wrapper around char to add automatic encoding on pack and decoding on unpack.
• unions: Unions of serializable types are supported as well.
• Structured-derived types: You can use any of your Structured-derived classes as a type-hint, and the variable will be serialized as well.
• typing.Self: This type-hint denotes that the attribute should be unpacked as an instance of the containing class itself. Note that due to the recursive posibilities this allows, care must be taken to avoid hitting the recursion limit of Python.

Tuples

Both the tuple and Tuple type-hints are supported, including TypeVars (see: Generics). To be detected as serializable, the tuple type-hint must be for a fixed sized tuple (so no elipses ...), and each type-hint in the tuple must be a serializable type.

Example:

class MyStruct(Structured):
  position: tuple[int8, int8]
  size: tuple[int8, int8]

Arrays

Arrays are lists of one kind of serializable type. You do need to specify how Structured will determine the length of the list when unpacking, and how to write it when packing. To do this, you chose a Header type. The final type-hint for your list then becomes array[<header_type>, <item_type>]. Arrays also support TypeVars.

Here are the header types:

• Header[3] (or any other positive integer): A fixed length array. No length byte is packed or unpacked, just the fixed number of items. When packing, if the list doesn't contain the fixed number of elements specified, a ValueError is raised.
• Header[uint32] (or any other uint*-type): An array with the length stored as a uint32 (or other uint*-type) just before the items.
• Header[uint32, uint16]: An array with two values stored just prior to the items. The first value (in this case a uint32) is the length of the array. The second value (in this case a uint16) denotes how many bytes of data the array items takes up. When unpacking, this size is checked against how many bytes were actually required to unpack that many items. In the case of a mismatch, a ValueError will be raises.

Example:

class MyItem(Structured):
  name: unicode[100]
  age: uint8

class MyStruct(Structured):
  students: array[Header[uint32], MyItem]

char

For unpacking bytes other than with a fixed length, you have a few more options with char:

• char[uint8] (or any other uint* type): This indicates that a value (a uint8 in this case) will be packed/unpack just prior to the bytes. The value holds the number of bytes to pack or unpack.
• char[b'\0'] (or any other single bytes): This indicates a terminated byte-string. For unpacking, bytes will be read until the terminator is encountered (the terminator will be discarded). For packing, the bytes will be written, and a terminator will be written at the end if not already written. The usual case for this is NULL-terminated byte-strings, so a quick alias for that is provided: null_char.
• char[math.inf]: This indicates that every remaining byte in the input stream should be unpacked (read to the end). Note that this means no other serialized types can occur after this item.
• unicode[math.inf]: Like char[math.inf], but the bytes are then decoded into a string.

unicode

For cases where you want to read a byte-string and treat it as text, unicode will automatically encode/decode it for you. The options are the same as for char, but with an optional second argument to specify how to encode/decode it. The second option can either be a string indicating the encoding to use (defaults to utf8), or for more complex solutions you may provide an EncoderDecoder class. Similar to char, we provide null_unicode as an alias for unicode[b'\0', 'utf8'].

class MyStruct(Structured):
  name: null_unicode
  description: unicode[255, 'ascii']
  bio: unicode[uint32, MyEncoderDecoder]

To write a custom encoder-decoder, you must subclass from EncoderDecoder and implement the two class methods encode and decode:

class MyEncoderDecoder(EncoderDecoder):
  @classmethod
  def encode(text: str) -> bytes: ...

  @classmethod
  def decode(bytestring: bytes) -> str: ...

Unions

Sometimes, the data structure you're packing/unpacking depends on certain conditions. Maybe a uint8 is used to indicate what follows next. In cases like this, Structured supports unions in its typehints. To hint for this, you need to do three things:

1. Every type in your union must be a serializable type.
2. You need create a decider which will perform the logic on deciding how to unpack the data.
3. Use typing.Annotated to indicate the decider to use for packing/unpacking.

Deciders

All deciders provide some method to take in information and produce a value to be used to make a decision. The decision is made with a "decision map", which is a mapping of value to serializable types. You can also provide a default serializable type, or None if you want an error to be raised if your decision method doesn't produce a value in the decision map.

There are currently two deciders. In addition to the decision map and default, you will need to provide a few more things for each:

• LookbackDecider: You provide a method that accepts the object to be packed/unpacked and produces a decision value. Commonly, operator.attrgetter is used here. A minor detail: for unpacking operations, the object sent to your method will not be the actual unpacked object, merely a proxy with the values unpacked so far set on it.
• LookaheadDecider: For packing, this behaves just like LookbackDecider. For unpacking, you need to specify a serializable type which is unpacked first and used as the the value to look up in the decision map. After this first value is unpacked, the data-stream is rewound back for unpacking the object.

Here are a few examples:

class MyStruct(Structured):
  a_type: uint8
  a: Annotated[uint32 | float32 | char[4], LookbackDecider(attrgetter('a_type'), {0: uint32, 1: float32}, char[4])]

This example first unpacks a uint8 and stores it in a_type. The union a polls that value with attrgetter, if the value is 0 it unpacks a uint32, if it is 1 it unpacks a float32, and if it is anything else it unpacks just 4 bytes (raw data), storing whatever was unpacked in a.

class IntRecord(Structured):
  sig: char[4]
  value: int32

class FloatRecord(Structured):
  sig: char[4]
  value: float32

class MyStruct(Structured):
  record: Annotated[IntRecord | FloatRecord, LookaheadDecider(char[4], attrgetter('record.sig'), {b'IIII': IntRecord, 'FFFF': FloatRecord}, None)]

For unpacking, this example first reads in 4 bytes (char[4]), then looks up that value in the dictionary. If it was b'IIII' then it rewinds and unpacks an IntRecord (note: IntRecord's sig attribute will be set to char[4]). If it was b'FFFF' it rewinds and unpacks a FloatRecord, and if was neither it raises an exception.

For packing, this example uses attrgetter('record.sig') on the object to decide how to pack it.

Structured

You can also type-hint with one of your Structured derived classes, and the value will be unpacked and packed just as expected. Structured doesn't fully support Generics, so make sure to read the section on that to see how to hint properly with a Generic Structured class.

Example:

class MyStruct(Structured):
  a: int8
  b: char[100]

class MyStruct2(Structured):
  magic: char[4]
  item: MyStruct

Custom Types

When the above are not enough, and your problem is fairly simple, you can use SerializeAs to tell the Structured class how to pack and unpack your custom type. To do so, you choose one of the above "basic" types to use as its serialization method, then type-hint with typing.Annotated to provide that information via a SerializeAs object.

For example, say you have a class that encapsulates an integer, providing some custom functionality. You can tell your Structured class how to pack and unpack it. Say the value will be stored as a 4-byte unsigned integer:

class MyInt:
  _wrapped: int

  def __init__(self, value: int) -> None:
    self._wrapped = value

  def __index__(self) -> int:
    return self._wrapped

class MyStruct(Structured):
  version: Annotated[MyInt, SerializeAs(uint32)]

If you use your type a lot, you can use a TypeAlias to make things easier:

MyInt32: TypeAlias = Annotated[MyInt, SerializeAs(int32)]

class MyStruct(Structured):
  version: MyInt32

Note a few things required for this to work as expected:

• Your class needs to accept a single value as its initializer, which is the value unpacked by the serializer you specified in SerializeAs.
• Your class must be compatible with your chosen type for packing as well. This means:
  • for integer-like types, it must have an __index__ method.
  • for float-like types, it must have a __float__ method.

Finally, if the __init__ requirement is too constraining, you can supply a factory method for creating your objects from the single unpacked value, and use SerializeAs.with_factory instead. The factory method must accept the single unpacked value, and return an instance of your type.

Modifiers

These are additional objects that you can include in an Annotated[...] to modify how a hinted serialized type is packed/unpacked. Currently, there is only Condition.

Condition

The Condition object signals to Structured that the hinted attribute should only be considered a serializable type if a certain condition is met. For example, a data structure that has fields added or removed as new versions are made. You could provide different Structured`-derived classes for these versions, but this opens you up to errors resulting from keeping those definition in sync with each other.

To use, create a Condition object:

# NOTE: using Python 3.11+ syntax to demonstrate the signature here
Condition[T: Structured](condition: Callable[[T], bool], *defaults)

and include it in an Annotated[...] for a serializable type:

class VersionedStruct(Structured, byte_order=ByteOrder.NETWORK):
  version: uint8
  v1_field: Annotated[uint8, Condition(lambda s: s.version >= 1, 0)]
  v3_field: Annotated[uint32, Condition(lambda s: s.version >= 3, 0)]
  v2_field: Annotated[float32, Condition(lambda s: s.version >= 3, 0.0)]

A Condition takes a callable that accepts your Structured class* and returns a bool, as well as a default value for the attribute. The callable will be called just prior to packing/unpacking the attribute to evaluate the condition. On a True condition valuation, the attribute will pack/unpack* as if hinted without the Condition. On a False condition evaluation, the attribute will be skipped for packing, or set to the default value (without touching the input data stream).

[!NOTE] For unpacking, the object sent to the condition is actually a proxy object. This object has the > same serialized attributes as the actual Structured-derived class, but only those that have > already been de-serialized.

[!CAUTION] For simple types (those that have direct struct.pack translations) some care is needed.

Recall that struct inserts padding alignment bytes where needed between format specifiers. So for example:

>>> struct.pack('BI', 1, 2)
b'\x01\x00\x00\x00\x02\x00\x00\x00`

Here, 3 padding bytes were inserted between the uint8 and the uint16. The padding inserted depends on the platform and the byte-order specifier used.

Now consider these two Structured classes:

class MyStruct1(Structured):
  version: uint8
  value: uint32

class MyStruct2(Structured):
  version: uint8
  value: Annotated[uint32, Condition(lambda s: True, 0)]

These two classes will not, in general, pack/unpack the same, even though the Condition is always True! This is because for MyStruct1, the members are serialized as BI. But for MyStruct2, they are serialized as B followed by I, so no padding bytes are inserted ever.

To deal with situations like these, you either need to manually handle the padding bytes yourself (also guarded with a Condition), or if possible use a byte order specification that does not insert padding bytes (for example, ByteOrder.NETWORK).

The Structured class

The above examples should give you the basics of defining your own Structured-derived class, but there are a few details and you probably want to know, and how to use it to pack and unpack your data.

dunders

• __init__: By default, Structured generates an __init__ for your class which requires an initializer for each of the serializable types in your definition. You can block this generated __init__ by passing init=False to the subclassing machinery. Keep in mind, whatever you decide the final class's __init__ must be compatible with being initialized in the original way (one value provided for each serializable member). Otherwise your class cannot be used as a type-hint or as the item type for array.
• __eq__: Structured instance can be compared for equality / inequality. Comparison is done by comparing each of the instance variables that are serialized. You can of course override this in your subclass to add more checks, and allow super().__eq__ to handle the serializable types.
• __str__: Structured provides a nice string representation with the values of all its serializable types.
• __repr__: The repr is almost identical to __str__, just with angled brackets (<>).

Class variables

There are three public class variables associated with your class:

• .serializer: This is the serializer (see: Serializers) used for packing and unpacking the instance variables of your class.
• .byte_order: This is a ByteOrder enum value showing the byte order and alignment option used for your class.
• .attrs: This is a tuple containing the names of the attributes which are serialized for you, in the order they were detected as serializable. This can be helpful when troubleshooting why your class isn't working the way you intended.

Packing methods

There are three ways you might pack the data contained in your class, two should be familiar from Python's struct library:

• pack() -> bytes: This just packs your data into a bytestring and returns it.
• pack_into(buffer, offset = 0) -> None: This packs your data into an object supporting the Buffer Protocol, starting at the given offset.
• pack_write(writable) -> None: This packs your data, writing to the file-like object writable (which should be open in binary mode).

Unpacking methods

Similar to packing, there are three methods for unpacking data into an already existing instance of your class. There are also three similar class methods for creating a new object from freshly unpacked data:

• unpack(buffer) -> None: Unpacks data from a bytes-like buffer, assigning values to the instance.
• unpack_from(buffer, offset=0) -> None: Like unpack, but works with an object supporting the Buffer Protocol.
• unpack_read(readable): Reads data from a file-like object (which should be open in binary mode), unpacking until all instance variables are unpacked.
• create_unpack(buffer) -> instance: Class method that unpacks from a bytes-like buffer to create a new instance of your class.
• create_unpack_from(buffer, offset=0) -> instance: Class method that unpacks from a buffer supporting the Buffer Protocol to create a new instance of your class.
• create_unpack_read(readable) -> instance: Class method that reads data from a file-like object until enough data has been processed to create a new instance of your class.

Subclassing

Subclassing from your Structured-derived class is very straight-forward. New members are inserted after previous one in the serialization order. You can redefine the type of a super-class's member and it will not change the order. For example, you could remove a super-class's serializable member entirely from serialization, by redefining its type-hint with None.

Multiple inheritance from Structured classes is not supported (so no diamonds). By default, your sub-class must also use the same ByteOrder option as its super-class. This is to prevent unintended serialization errors, so if you really want to change the ByteOrder, you can pass byte_order_mode=ByteOrderMode.OVERRIDE to the sub-classing machinery.

An example of using a different byte order than the super-class:

class MyStructLE(Structured, byte_order=ByteOrder.LE):
  a: int8
  b: int32

class MyStructBE(MyStructLE, byte_order=ByteOrder.BE, byte_order_mode=ByteOrderMode.OVERRIDE):
  pass

A simple example of extending:

class MyStructV1(Structured):
  size: uint32
  a: int8
  b: char[100]

class MyStructV2(MyStructV2):
  c: float32

Here, the sub-class will pack and unpack equivalent to the struct format 'Ib100sf'.

A an example of removing a member from serialization:

class MyStruct(Structured):
  a: int8
  b: uint32
  c: float32

class DerivedStruct(MyStruct):
  b: None

Here, the sub-class will pack and unpack equivalent to the struct format 'bf'.

Generics

Structured classes can be used with typing.Generic, and most things will work the way you want, with an extra step needed in one case. The Structured class behaves this way so as not to interfere with the typing module's usual features. A "bare" specialization of your class will act in the usual way all typing.Generic subclasses do: you can use get_origin, get_args, etc on it as usual.

In general, in order for your Structured class to "know" about the specialization arguments you pass to it and work based of that specialization, it must be subclassed. In many common cases this subclassing will be done for you though. If the TypeVar specialization happens within another Structured class, then you don't need to sub-class it yourself. Even in this case, the type-hints are not modified on the class itself, so you can do any type-hint introspection you want and they will still behave the usual way the typing module would expect.

Here's some examples to show what I mean by the specialization occuring "within" versus not:

class Inner(Generic[T, U, V], Structured):
  a: tuple[T]
  b: U
  c: array[Header[4], V]

Here, Inner is a generic Structured class, and hasn't yet been specialized at all. So all of its type-hints are not detected as serializable types.

An example of specializing this class "outside" of the class looks like this:

unhappy_object = Inner[int8, float32, bool8].create_unpack(data)

In this case, Inner gets fully specialized, but still acts exactly as typing.Generic usually does: nothing new happens. The unhappy_object gets unpacked just as if you'd never specialized Inner at all (so it has no attributes serialized). To make an instance of Inner[int8, float32, bool8], you'd have to do this:

class ConcreteInner(Inner[int8, float32, bool8]):
  pass
happy_object = ConcreteInner.create_unpack(data)

An example of specializing this class "inside" of another `Structured class looks like this:

class Outer(Structured):
  sub_item: Inner[int8, float32, bool8]
happy_object = Outer.create_unpack(data)

Here, because the specialized Inner was used as a type-hint within another Structured class, and the TypeVars are fully specialized, everything works exactly how you'd want. The sub_item instance variable correctly has all of it's attributes (a, b, and c) unpacked as a tuple[in8], a float32, and a array[Header[4], bool8] respectively.

Here's one last example of where this automatic subclassing behavior doesn't kick in:

class Outer2(Generic[T], Structured):
  sub_item: Inner[int8, float32, T]
unhappy_object = Outer2[bool8].create_unpack(data)

Here again, Outer2 is generic and not fully specialized within another Structured class so you'd have to subclass it yourself. But again, if you use Outer2 as a fully specialized type-hint within another Structured class you're good to go with no extra work.

In general:

• If the outer-most Structured is Generic, than any TypeVars it uses will not be automatically detected for serialization, even when specialized. You must sub-class it yourself to get the final implementation. Of course, if those TypeVars are never intended to be serializable types (maybe you're using the TypeVar for a completely unrelated purpose) then this doesn't really matter.
• If the outer-most Structured class doesn't use TypeVars (isn't Generic itself), then everything will automatically be handled for you.

Serializers

For those more interested in what goes on under the hood, or need more access to implement serialization of a custom type, read on to learn about what serializers are and how they work!

Serializers are use typing.Generic and typing.TypeVarTuple in their class heirarchy, so if you want to include the types the serializer unpacks this could help find errors. For example:

class MySerializer(Serializer[int, int, float]):
  ...

would indicate that this serializer packs and unpacks three items, an (int, int float).

The API

The Serializer class exposes a public API very similar to that of struct.Struct. All of these methods must be implemented (unless noted otherwise) in order to work fully.

Attributes

• .num_values: int: In most cases this can just be a class variable, this represents the number of items unpacked or packed by the serializer. For example, a StructSerializer('2I') has num_values == 2. Note that array has num_values == 1, since it unpacks a single list.
• .size: This is similar to struct.Struct.size. It holds the number of bytes required for a pack or unpack operation. However unlike struct.Struct, the serializer may not know this size until the item(s) have been fully packed or unpacked. For this reason, the .size attribute is only required to be up to date with the most recently completed pack or unpack call.

Packing methods

• .prepack(self, partial_object) -> Serializer (not required): This will be called just prior to any of the pack methods of the Serializer, with a (maybe proxy of) the Structured object to be packed. This is to allow union serializers (for example) to make decisions based on the state of the object to be packed. This method should return an appropriate serializer to be used for packing, based on the information contained in partial_object. In most cases, the default implementation will do just fine, which just returns itself unchanged.
• .pack(self, *values) -> bytes: Pack the values according to this serializer's logic. The number of items in values must be .num_values. Return the values in packed bytes form.
• .pack_into(self, buffer, offset, *values) -> None: Pack the values into an object supporting the Buffer Protocol, at the given offset.
• .pack_write(self, writable, *values) -> None: Pack the values and write them to the file-like object writable.

Unpacking methods

• .preunpack(self, partial_object) -> Serializer (not required): This will be called just prior to any of the unpack methods of the Serializer, with a (maybe proxy of) the Structured object to be unpacked. This means the only attributes guaranteed to exist on the object are those that were serialized before those handled by this serializer. Again, in most cases the default implementation should work fine, which just returns itself unchanged.
• .unpack(self, byteslike) -> Iterable: Unpack from the bytes-like object, returning the values in an iterable. In most cases, just returning the values in a tuple should be fine. Iterables are supported so that the partial-proxy objects can have their attributes set more easily during unpacking. Note: the number of values in the iterable must be .num_values. NOTE: unlike struct.unpack, the byteslike object is not required to be the exact length needed for unpacking, only at least as long as required.
• .unpack_from(self, buffer, offset=0) -> Iterable: Like .unpack, but from an object supporting the Buffer Protocol, at the given offset.
• .unpack_read(self, readable) -> Iterable: Like .unpack, but reading the data from the file-like object readable.

Other

• .with_byte_order(self, byte_order: ByteOrder) -> Serializer): Return a (possibly new) serializer configured to use the ByteOrder specified. The default implementation returns itself unchanged, but in most cases this should be overridden with a correct implementation.
• .__add__(self, other) -> Serailzer (not required): The final serializer used for a Structured class is determined by "adding" all of the individual serializers for each attribute together. In most cases the default implementation will suffice. You can provide your own implementation if optimizations can be made (for example, see StructSerializer's implementation).

The "building" Serializers

There are a few basic serializers used for building others:

• NullSerializer: This is a serializer that packs and unpacks nothing. This will be the serializer used by a Structured class if no serializable instance variables are detected. It is also used as the starting value to sum(...) when generating the final serializer for a Structured class.
• CompoundSerializer: This is a generic "chaining" serializer. Most serializers don't have an easy way to combine their logic, so CompoundSerializer handles the logic of calling the packing and unpacking methods one after another. This is a common serializer to see as the final serializer for a Structured class. This is also an interesting example to see how to handle variable .size, and handling .preunpack and .prepack.

Specific Serializers

The rest of the Serializer classes are for handling specific serialization types. They range from very simple, to quite complex.

• StructSerializer: For packing/unpacking types which can be directly related to a constant struct format string. For example, uint32 is implemented as Annotated[int, StructSerializer('I')].
• StructActionSerializer: This is the class used for StructSerializer-able custom types, but need to perform a custom callable on the result(s) to convert them to their final type. It is almost identical to StructSerializer, but calls an action on each value unpacked.
• TupleSerializer: A fairly simple serializer that handles the tuple type-hints.
• AUnion: The base for both union serializers.
• LookbackDecider: The union serializer which allows for reading attributes already unpacked on the object to make a decision.
• LookaheadDecider: The union serializer which unpacks a little data then rewinds, using the unpacked value to make a decision.
• StructuredSerializer: A fairly simple serializer to handle translating the Structured class methods into the Serializer API.
• DynamicCharSerializer: The serializer used to handle char[uint*] type-hints.
• TerminatedCharSerializer: The serializer used to handle char[b'\x00'] type-hints.
• UnicodeSerializer: A wrapper around one of the char[] serializers to handle encoding on packing and decoding on unpacking.

Type detection

This is a very internal-level detail, but may be required if you write your own Serializer class.

Almost all of the typehints use typing.Annotated to specify the Serializer instance to use for a hint. In most cases, it's as simple as creating your serializer, then defining a type using this. See all of the "basic" types for example. In some more complicated examples, which are configured via the __class_getitem__ method, these return Annotated objects with the correct serializer.

In any case, the Structured class detects the serializers by inspecting the Annotated objects for serializers. To support things like a: Annotated[int, int8], it even recursively looks inside nested Annotated objects. For most of this work, structured internally uses a singleton object structured.type_checking.annotated to help extract this information. There is a step to perform extra transformations on these Annotated extras, that a new Serializer you implement might need to work. Check out for example, TupleSerializer and StructuredSerializer on where that might be necessary.