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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