zenconfig 0.1.0

Simple configuration loader for python.

simple-config

Simple configuration loader for python.

Compared to other solutions, the goal is to bring:

• simple usage for simple use cases
• multiple format support
• use objects rather than plain dict to interact with the config
• optionally use the power of pydantic for validation

Simple usage

If you don't want to configure much, you can use:

from dataclasses import dataclass
from zenconfig import Config

@dataclass
class MyConfig(Config):
    PATH = "~/myconfig.json"
    
    some_key: str
    some_optional_key: bool = False


cfg = MyConfig(some_key="hello")
cfg.save()
...
cfg = MyConfig.load()
cfg.some_optional_key = True
cfg.save()
...
cfg.clear()

Config file loading

When creating your config, you can specify at least one of those two attributes:

• ENV_PATH the environment variable name containing the path to the config file
• PATH directly the config path

💡 When supplying both, if the env var is not set, it will use PATH.

The only transformation on the path made is to expand user constructs. If the file does not exist it will be created (not parent directories though). You can specify the file mode via Config.FILE_MODE.

Read only

If you do not want to be able to modify the config from your code, you can use ReadOnlyConfig.

Supported formats

Currently, those formats are supported:

• JSON
• YAML - requires the yaml extra
• TOML - requires the toml extra

The format is automatically inferred from the config file extension. You can still specify it manually using Config.FORMAT, for custom ones or configuring dump options.

To use them:

from dataclasses import dataclass
from zenconfig import Config
from zenconfig.formats.yaml import YAMLFormat

@dataclass
class MyYAMLConfig(Config):
    PATH = "~/myconfig.yaml"

Other formats can be added by subclassing either Format or ReadOnlyFormat

Supported schemas

Currently, those schemas are supported:

• plain dict
• dataclasses
• pydantic models - requires the pydantic extra

The format is automatically inferred from the config class. You can still specify it manually using Config.SCHEMA, for custom ones or configuring dump options.

To use pydantic:

from typing import ClassVar
from pydantic import BaseModel
from zenconfig import Config

class MyPydanticConfig(Config, BaseModel):
    PATH: ClassVar[str] = "~/myconfig.yaml"

⚠️ When using pydantic, you have to supply the ClassVar type annotations otherwise pydantic will treat those as its own fields and complain.