{
    "info": {
        "author": "Thibaut Le Page",
        "author_email": "thilp@thilp.net",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "# Umwelt\n\n[dataclasses]: https://docs.python.org/3/library/dataclasses.html\n[pydantic]: https://pydantic-docs.helpmanual.io/\n\nDescribe a configuration schema with [dataclasses][] or [pydantic][] and\nload values from the environment, in a static-typing-friendly way.\n\n## Examples\n\n### Flat\n\n```python\n>>> os.environ[\"APP_HOSTS\"] = '[\"b.org\",\"sky.net\"]'\n>>> os.environ[\"APP_TOKEN\"] = \"very secret\"\n```\n\n```python\nfrom typing import Sequence\nfrom pydantic import SecretStr\nimport umwelt\n\nclass MyConfig:\n    hosts: Sequence[str]\n    token: SecretStr\n    replicas: int = 2\n\nconfig = umwelt.new(MyConfig, prefix=\"app\")\n```\n\n```python\n>>> dataclasses.is_dataclass(config)\nTrue\n>>> config.hosts\n[\"b.org\", \"sky.net\"]\n>>> config.token\nSecretStr('**********')\n>>> config.replicas\n2\n```\n\n### Nested\n\n```python\n>>> os.environ[\"APP_DB_PORT\"] = \"32\"\n```\n\n```python\nfrom __future__ import annotations  # for forward-references\nfrom pydantic import UrlStr\nimport umwelt\n\nclass MyConfig:\n    db: DbConfig\n    host: UrlStr = \"http://b.org\"\n\n@umwelt.subconfig\nclass DbConfig:\n    port: int\n    debug: bool = False\n\nconfig = umwelt.new(MyConfig, prefix=\"app\")\n```\n\n```python\n>>> config.host\n\"http://b.org\"\n>>> config.db.port\n32\n```\n\n## Install\n\n```shell script\n$ pip install umwelt\n```\n\n## Features\n\n### umwelt.new\n\n`umwelt.new` expects one positional argument: the config class to fill.\nUmwelt will convert it into a [dataclass][dataclasses] if it's not one already.\n\n`umwelt.new` also accepts named arguments:\n- **`source`** (by default `os.environ`) is a `Mapping[str, str]` from which\nvalues are extracted.\n- **`prefix`** can be a string or a callable. As a string, it is prepended to\nthe config field's name. As a callable, it receives the config field's name and\nits result is the source key name.\n- **`decoder`** is a callable expecting a type and a string, and returns a\nconversion of that string in that type, or in a type that pydantic can convert\nin that type.\nFor example, when umwelt's default `decoder` is called with (`List[Set[int]]`,\n`\"[[1]]\"`), it simply decodes the string from JSON and hence returns a list of\n_lists_, which pydantic properly converts into a list of _sets_. \n\n### @umwelt.subconfig\n\n`@umwelt.subconfig` tags classes so that, when they appear as field annotations\nin another config class, `umwelt.new` doesn't instantiate them from a single\n`source` value, but rather from one `source` value _per class field_.\n\nExample:\n\n```python\nclass Point:                              # no @subconfig\n    def __init__(self, s: str):           # string input\n        self.x, self.y = s.split(\",\", 1)  # arbitrary implementation\n\nclass MyConf:\n    point: Point\n\nconf = umwelt.new(MyConf, source={\"POINT\": \"1,2\"})  # one source entry\nconf.point  # <Point at 0x7f07b1d04750>\n```\n\n`conf.point` is an instance of _Point_, built by passing the input value `\"1,2\"`\ndirectly to `Point.__new__`.\nThere is only one `source` key: `POINT`.\n\nNow compare with `@umwelt.subconfig`:\n\n```python\n@umwelt.subconfig\nclass Point:\n    x: int\n    y: int\n\nclass MyConf:\n    point: Point\n\nconf = umwelt.new(MyConf, source={\"POINT_X\": \"1\", \"POINT_Y\": \"2\"})\nconf.point  # Point(x=1, y=2)\n```\n\n`conf.point` is still an instance of _Point_ (_Point_ has been made a\ndataclass by Umwelt, hence the automatic `__str__` implementation).\nThere are **two** `source` keys: `POINT_X` and `POINT_Y`, each corresponding to\na field of the _Point_ class.\n\n## Comparison with Ecological\n\nI've used [Ecological][] for a long time.\nToday, a large part of Ecological's codebase implements features already found\nin [dataclasses][] and [pydantic][], which are more mature.\nI believe Ecological's design can be dramatically simplified _and_ improved by\nenforcing a strict separation of concerns:\n\n- class scaffolding is the responsibility of [dataclasses][] (which, compared\n  to metaclasses, is simpler, more introspectable, and comes with helpers like\n  `asdict`);\n- type coercion and validation is the responsibility of [pydantic][] (which has\n  more features, e.g. nested data types, JSON Schema, serialization, etc.);\n- mapping a [pydantic][] schema (the configuration class) to a string-to-string\n  dict (like `os.environ`) is the responsibility of Umwelt.\n\nSome compatibility-breaking decisions prevent from doing this in Ecological:\n\n- Don't autoload configuration values, especially not at class definition time.\n  Instead, offer just one function (`umwelt.new`) that loads the configuration\n  when it is called.\n- Don't tie variable prefixes to configuration classes, as that doesn't play\n  well with nested configurations.\n\n[ecological]: https://github.com/jmcs/ecological\n[autoloading]: https://github.com/jmcs/ecological/issues/20\n",
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