py-datastruct 1.1.0

Combination of struct and dataclasses for easy parsing of binary formats

py-datastruct

This is a (relatively) simple, pure-Python, no dependency library, aiming to simplify parsing and building binary data structures. It uses dataclasses as its main container type, and struct-compatible format specifiers for writing field definitions.

The way of composing structures is somewhat similar to (and inspired by) Construct. While probably not as powerful, it should give more flexibility and control over the data, as well as full IDE type hinting.

Installation

pip install py-datastruct

Usage

This simple example illustrates creating a 24-byte long structure, consisting of a 32-bit integer, an 8-byte 0xFF-filled padding, and a 12-byte bytes string.

from hexdump import hexdump
from dataclasses import dataclass
from datastruct import DataStruct
from datastruct.fields import field, padding

@dataclass
class MyStruct(DataStruct):
    my_number: int = field("I", default=123)
    _1: ... = padding(8)
    my_binary: bytes = field("12s")

my_object = MyStruct(my_binary=b"Hello Python")
print(my_object)
# MyStruct(my_number=123, my_binary=b'Hello World!')

my_object = MyStruct(my_number=5, my_binary=b"Hello World!")
print(my_object)
# MyStruct(my_number=5, my_binary=b'Hello World!')

packed = my_object.pack()
hexdump(packed)
# 00000000: 05 00 00 00 FF FF FF FF  FF FF FF FF 48 65 6C 6C  ............Hell
# 00000010: 6F 20 57 6F 72 6C 64 21                           o World!

unpacked = MyStruct.unpack(packed)
print(unpacked)
# MyStruct(my_number=5, my_binary=b'Hello World!')
print(my_object == unpacked)
# True

You might also pass a stream (file/BytesIO/etc.) to pack() and unpack(). Otherwise, pack() will create a BytesIO stream and return its contents after packing; unpack() will accept a bytes object as its parameter.

pack() and unpack() also accept custom, keyword-only arguments, that are available in the Context, throughout the entire operation.

Context

Upon starting a pack/unpack operation, a Context object is created. The context is a container scoped to the currently processed structure. It's composed of the following main elements:

• all values of the current structure - when packing; during unpacking, it contains all values of fields that were already processes (the context "grows")
• all keyword arguments passed to pack()/unpack() (for the root context only)
• all keyword arguments passed to subfield() (for child contexts only)
• _: Context - reference to the parent object's context (only when nesting DataStructs)
• self: Any - the current datastruct - note that it's a DataStruct subclass when packing, and a Container when unpacking
• G - global context - general-purpose container that is not scoped to the current structure (it's identical for nested structs)
  • io: IO[bytes] - the stream being read from/written to
  • packing: bool - whether current operation is packing
  • unpacking: bool - whether current operation is unpacking
  • root: Context - context of the topmost structure
  • tell: () -> int - function returning the current position in the stream
  • seek: (offset: int, whence: int) -> int - function allowing to seek to an absolute offset
• P - local context - general-purpose container that is different for each nested struct
  • config: Config - current DataStruct's config
  • tell: () -> int - function returning the current position in the current structure (in bytes)
  • seek: (offset: int, whence: int) -> int - function allowing to seek to an offset within the current structure
  • skip: (length: int) -> int - function allowing to skip length bytes
  • i: int - (for repeat() fields only) index of the current item of the list
  • item: Any - (for repeat() fields, in last= lambda only) item processed right before evaluation
  • self: Any - (packing only) value of the current field

The context is "general-purpose", meaning that the user can write custom values to it. All fields presented above can be accessed by lambda functions - see "Parameter evaluation".

Parameter evaluation

Most field parameters support pack/unpack-time evaluation (which means they can e.g. depend on previously read fields). Lambda expressions are then given the current context, and expected to return a simple value, that would be statically valid in this parameter.

an_unpredictable_field: int = field(lambda ctx: "I" if randint(1, 10) % 2 == 0 else "H")

Ellipsis - special value

A special value of type Ellipsis/... is used in the library, to indicate something not having a type or a value. It's not the same as None. built() fields, for example, have ... as value after creating the struct, but before packing it for the first time.

Special fields (like padding(), which don't have any value) must have ... as their type hint.

Variable-length fields

This is a simple example of using parameter evaluation to dynamically size a bytes string. Binary strings use the <len>s specifier, which can be omitted (simple int can be used instead).

@dataclass
class MyStruct(DataStruct):
    data_length: int = field("I")
    data: bytes = field(lambda ctx: ctx.data_length)

The user is still responsible for adjusting data_length after changing data. The built() field comes in handy here:

@dataclass
class MyStruct(DataStruct):
    data_length: int = built("I", lambda ctx: len(ctx.data))
    data: bytes = field(lambda ctx: ctx.data_length)

When unpacking, the data_length field will be used to dynamically size the data field. When packing, data_length will always be recalculated based on what's in data.

Wrapper fields - storing a list

Lists are also iterables, like bytes, but they store a number of items of a specific type. Thus, the repeat() field wrapper has to be used.

Wrapper fields simply require calling them first with any used parameters, then with the "base" field.

@dataclass
class MyStruct(DataStruct):
    item_count: int = built("H", lambda ctx: len(ctx.items))
    # This creates a list of 16-bit integers.
    # The list is empty by default.
    items: List[int] = repeat(lambda ctx: ctx.item_count)(field("H"))

my_object = MyStruct()
my_object.items = [0x5555, 0x4444, 0x3333, 0x2222]
my_object.item_count = 1  # this doesn't matter, as the field is rebuilt
packed = my_object.pack()
hexdump(packed)
# 00000000: 04 00 55 55 44 44 33 33  22 22

Conditional fields

They're also wrapper fields - if the condition is not met, they act like as if the field didn't exist at all.

@dataclass
class MyStruct(DataStruct):
    has_text: bool = field("?")
    text: str = cond(lambda ctx: ctx.has_text)(field("8s", default=""))

my_object = MyStruct.unpack(b"\x01HELOWRLD")
print(my_object)
# MyStruct(has_text=True, text='HELOWRLD')

my_object = MyStruct.unpack(b"\x00")
print(my_object)
# MyStruct(has_text=False, text='')

Switch fields

Switch fields are like more powerful conditional fields. The following example reads an 8/16/32-bit number, depending on the prefixing length byte. If the length is not supported, it reads the value as bytes instead.

number_length: int = field("B", default=1)
number: Union[int, bytes] = switch(lambda ctx: ctx.number_length)(
    _1=(int, field("B")),
    _2=(int, field("H")),
    _4=(int, field("I")),
    default=(bytes, field(lambda ctx: ctx.number_length)),
)

The values on the left (_1, _2, _4) are the keys. The key is picked depending on the key-lambda result (ctx.number_length). The value on the right is a tuple of the expected field type, and a field() specifier.

Since it's not possible to pass just 1 as a keyword argument, integers are looked up prefixed with an underscore as well. Enums are additionally looked up by their name and value, and booleans are looked up by lowercase true/false.

Note that you can pass (probably) any kind of field to the switch list.

To be continued

License

MIT License

Copyright (c) 2023 Kuba Szczodrzyński

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