To help you get started, we've collected the most common ways that construct is being used within popular public projects.

Ayrx / tlsenum / tlsenum / hello_constructs.py View on Github

"ec_curves",
    UBInt16("ec_curves_length"),
    Array(lambda ctx: ctx.ec_curves_length // 2, UBInt16("named_curves"))
)

ServerName = Struct(
    "server_name",
    UBInt16("server_name_list_length"),
    UBInt8("name_type"),
    UBInt16("server_name_length"),
    Bytes("server_name", lambda ctx: ctx.server_name_length)
)

Extension = Struct(
    "extension",
    UBInt16("extension_type"),
    UBInt16("extension_length"),
    Switch("extension_struct", lambda ctx: ctx.extension_type, {
        0: ServerName,
        10: ECCurves,
        11: ECPointFormat
    })

View more ways to use construct

Construct 2.10

Construct is a powerful declarative and symmetrical parser and builder for binary data.

Instead of writing imperative code to parse a piece of data, you declaratively define a data structure that describes your data. As this data structure is not code, you can use it in one direction to parse data into Pythonic objects, and in the other direction, to build objects into binary data.

The library provides both simple, atomic constructs (such as integers of various sizes), as well as composite ones which allow you form hierarchical and sequential structures of increasing complexity. Construct features bit and byte granularity, easy debugging and testing, an easy-to-extend subclass system, and lots of primitive constructs to make your work easier:

• Fields: raw bytes or numerical types
• Structs and Sequences: combine simpler constructs into more complex ones
• Bitwise: splitting bytes into bit-grained fields
• Adapters: change how data is represented
• Arrays/Ranges: duplicate constructs
• Meta-constructs: use the context (history) to compute the size of data
• If/Switch: branch the computational path based on the context
• On-demand (lazy) parsing: read and parse only what you require
• Pointers: jump from here to there in the data stream
• Tunneling: prefix data with a byte count or compress it

Example

A Struct is a collection of ordered, named fields:

>>> format = Struct(
...     "signature" / Const(b"BMP"),
...     "width" / Int8ub,
...     "height" / Int8ub,
...     "pixels" / Array(this.width * this.height, Byte),
... )
>>> format.build(dict(width=3,height=2,pixels=[7,8,9,11,12,13]))
b'BMP\x03\x02\x07\x08\t\x0b\x0c\r'
>>> format.parse(b'BMP\x03\x02\x07\x08\t\x0b\x0c\r')
Container(signature=b'BMP')(width=3)(height=2)(pixels=[7, 8, 9, 11, 12, 13])

A Sequence is a collection of ordered fields, and differs from Array and GreedyRange in that those two are homogenous:

>>> format = Sequence(PascalString(Byte, "utf8"), GreedyRange(Byte))
>>> format.build([u"lalaland", [255,1,2]])
b'\nlalaland\xff\x01\x02'
>>> format.parse(b"\x004361789432197")
['', [52, 51, 54, 49, 55, 56, 57, 52, 51, 50, 49, 57, 55]]