Serves modern approach to data structures definition - with a minimal boilerplate.
Project description
matiz
Python's 3.7 dataclasses got maried with pyyaml.
Installation:
pip install matiz
Usage example:
import dataclasses import matiz from matiz import fields @dataclasses.dataclass class Some(matiz.ObsoleteAble): id: int = fields.Integer() mac: str = fields.MacAddress() ip: str = fields.IPv4() version: int = fields.Integer(default=411)
Now the Some
object can be created as a pure python object (thanks to dataclass
)
it is equipped with __init__
, __repr__
, __eq__
and some other methods.
>>> obj = Some(123, 'aa:bb:cc:11:22:33', '192.168.11.6') >>> obj Some(id=123, mac='aa:bb:cc:11:22:33', ip='192.168.11.6', version=411) >>> print(obj) !iz/Some id: 123 ip: 192.168.11.6 mac: aa:bb:cc:11:22:33 version: 411
It's attributes are regular python attributes:
>>> obj.id 123 >>> obj.mac 'aa:bb:cc:11:22:33' >>> obj.ip '192.168.11.6' >>> obj.version = 0 # can be assigned >>> print(repr(obj)) Ints(id=123, mac='aa:bb:cc:11:22:33', ip='192.168.11.6', version=0)
It can be also deserialized from yaml directly to the custom object:
>>> yaml_string = """ !iz/Some id: 22001 ip: 12.13.14.15 mac: aa:bb:cc:11:22:33 version: 0 """ >>> import yaml >>> obj2 = yaml.load(yaml_string, Loader=yaml.Loader) >>> repr(obj2) Some(id=22001, mac='aa:bb:cc:11:22:33', ip='12.13.14.15', version=0)
To be further documented...
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