Simple configuration loader for python.
Project description
zen-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, pass the config path through the env variable CONFIG
, and simply use:
from dataclasses import dataclass
from zenconfig import Config
@dataclass
class MyConfig(Config):
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, defaults toCONFIG
PATH
directly the config path
💡 When supplying both, if the env var is not set, it will use
PATH
.
User constructs will be expanded.
If the file does not exist it will be created.
You can specify the file mode via Config.FILE_MODE
.
The config can be loaded from multiple files, see fnmatch for syntax. Note that you will not be able to save if not handling exactly one file.
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. When loading from multiple files, files can be of multiple formats.
Other formats can be added by subclassing Format
: Config.register_format(MyFormat(...), ".ext1", ".ext2")
.
💡 You can re-register a format to change dumping options.
Supported schemas
Currently, those schemas are supported:
- plain dict
- dataclasses
- pydantic models - requires the
pydantic
extra - attrs - requires the
attrs
extra
The schema is automatically inferred from the config class.
Other schemas can be added by subclassing Schema
: Config.register_schema(MySchema(...))
.
You can also force the schema by directly overriding the SCHEMA
class attribute on your config.
This can be used to disable auto selection, or pass arguments to the schema instance.
⚠️ When using pydantic, you have to supply the
ClassVar
type annotations to all class variable you override otherwise pydantic will treat those as its own fields and complain.
Conversions
For all schemas and formats, common built in types are handled when dumping.
⚠️ Keep in mind that only
attrs
andpydantic
support casting when loading the config.
You can add custom encoders with Config.ENCODERS
.
For pydantic
, stick with the standard way of doing it.
Contributing
See contributing guide.
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