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 modern 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