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Validobj gives you valid objects

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Tests Coverage PyPI Conda Version RTD

Validobj

Validobj is library that takes semistructured data (for example JSON and YAML configuration files) and converts it to more structured Python objects. It places the emphasis on:

  • Good error messages (rather than avoiding extra work in the error handling code).
  • Schema defined in terms of dataclasses and other high level objects such as enums, as well as a subset of the typing module.
  • Simplicity of implementation (rather than full generality).

Validobj requires a moder Python version and has no other dependencies. It progressively supports typing features as they are implemented in the standard library and language: A limited subset of the parsing facilities work with Python 3.8, which is the minimum version. The custom validation module requires at least Python 3.9.

Documentation

https://validobj.readthedocs.io/en/latest/

Example

  1. Define a schema using dataclasses
    import dataclasses
    import enum
    from typing import Mapping, Set, Tuple, List
    
    
    class DiskPermissions(enum.Flag):
        READ = enum.auto()
        WRITE = enum.auto()
        EXECUTE = enum.auto()
    
    
    class OS(enum.Enum):
        mac = enum.auto()
        windows = enum.auto()
        linux = enum.auto()
    
    
    @dataclasses.dataclass
    class Job:
        name: str
        os: Set[OS]
        script_path: str
        framework_version: Tuple[int, int] = (1, 0)
        disk_permissions: DiskPermissions = DiskPermissions.READ
    
    
    @dataclasses.dataclass
    class CIConf:
        stages: List[Job]
        global_environment: Mapping[str, str] = dataclasses.field(default_factory=dict)
    
  2. Process a dictionary input into it using Validobj
    from validobj import parse_input
    
    inp = {
        'global_environment': {'CI_ACTIVE': '1'},
        'stages': [
            {
                'name': 'compile',
                'os': ['linux', 'mac'],
                'script_path': 'build.sh',
                'disk_permissions': ['READ', 'WRITE', 'EXECUTE'],
            },
            {
                'name': 'test',
                'os': ['linux', 'mac'],
                'script_path': 'test.sh',
                'framework_version': [4, 0],
            },
        ],
    }
    print(parse_input(inp, CIConf))
    # This results in a dataclass instance with the correct types:
    #
    #CIConf(
    #    stages=[
    #        Job(
    #            name='compile',
    #            os={<OS.linux: 3>, <OS.mac:1>},
    #            script_path='build.sh',
    #            framework_version=(1, 0),
    #            disk_permissions=<DiskPermissions.EXECUTE|WRITE|READ: 7>,
    #        ),
    #        Job(
    #            name='test',
    #            os={<OS.linux: 3>, <OS.mac: 1>},
    #            script_path='test.sh',
    #            framework_version=(4, 0),
    #            disk_permissions='<DiskPermissions.READ: 1>',
    #        ),
    #    ],
    #    global_environment={'CI_ACTIVE': '1'},
    #)
    #
    

The set of applied transformations as well as the interface to customise processing are described in the documentation

Installation

The package can be installed with pip:

python3 -m pip install validobj

As well as with conda, from the conda-forge channel:

conda install validobj -c conda-forge

The code is hosted at

https://github.com/Zaharid/validobj

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