{
    "info": {
        "author": "Radoslav Georgiev",
        "author_email": "radorado@hacksoft.io",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython"
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        "description": "\n# Simple schema validator\n\n- [Simple schema validator](#simple-schema-validator)\n    - [Basic usage](#basic-usage)\n    - [Type checking](#type-checking)\n        - [Optional types](#optional-types)\n\n\nA dead-simple utility that validates if object has a certain structure. Used in some of our projects.\n\n## Basic usage\n\n```\npip install simple_schema_validator\n```\n\nAn example:\n\nLets say we have an API that returns the following data:\n\n```json\n{\n  \"user\": 1,\n  \"profile\": {\n    \"email\": \"some@user.com\",\n    \"name\": \"Some User\",\n    \"age\": 20\n  },\n  \"tokens\": {\n    \"jwt\": \"...\",\n    \"refresh\": \"...\",\n    \"firebase\": \"...\",\n  }\n}\n```\n\nAnd we are writing a simple integration test, that wants to assure the response has a certain structure.\n\nThen we can use the schema validator like so:\n\n```python\nfrom simple_schema_validator import schema_validator\n\ndata = get_data_from_api()\n\nschema = {\n  'user': Any,\n  'profile': {\n    'email': Any,\n    'name': Any,\n    'age': Any\n  },\n  'tokens': {\n    'jwt': Any,\n    'refresh': Any,\n    'firebase': Any\n  }\n}\n\nvalidation = schema_validator(schema, data)\n\nif not result:\n    print(f'Keys in data, but not in schema: {validation.additional_keys}')\n    print(f'Keys in schema, but not in data: {validation.missing_keys}')\n    print(f'Keys with different type from schema {validation.type_errors}')\n```\n\n* `missing_keys` are those keys that are required in the `schema`, but not found in `data`.\n* `additional_keys` are those keys present in `data`, but not required by the `schema`.\n* `validation_errors` are those keys, that are having a different type in `data`, from the defined in `schema`.\n\n**Nested keys are represented with \"dot\" notation - `profile.email`, `tokens.jwt`, etc.**\n\n## Type checking\n\nThe util supports simple schema type checking.\n\nCurrently, the supported types in the schema are:\n\n* `int`\n* `float`\n* `str`\n* `bool`\n* `typing.Any` (from Python `typing` library)\n* `simple_schema_validator.types.Optional` (custom type, define in the package)\n\nIf the type is `Any`, no type checking is done.\n\nIf there's a type mismatch, the errors are placed in the `type_errors` attribute of the result, which is a list of type errors.\n\nThe general format of a single type error is:\n\n```python\n{\n  'path': 'the.path.to.the.value.in.data',\n  'expected': the_expected_type_as_defined_in_the_schema,\n  'actual': the_actual_type_of_the_value\n}\n```\n\nHere's an example:\n\n\n```python\nfrom simple_schema_validator import schema_validator, types\n\n\nschema = {\n  'user': str,\n  'profile': {\n    'email': str,\n    'name': str,\n    'age': int\n  },\n  'tokens': {\n    'jwt': str,\n    'refresh': str,\n    'firebase': str\n  }\n}\n\ndata = {\n  'user': 'Some User',\n  'profile': {\n    'email': 'someuser@hacksoft.io',\n    'name': 'Some User',\n    'age': \"29\"\n  },\n  'tokens': {\n    'jwt': 'some token value',\n    'refresh': 'some token value',\n    'firebase': 'some token value'\n  }\n\n}\n\nresult = schema_validator(schema, data)\n\n\nassert bool(result) is False\nassert result.type_errors == [{'path': 'profile.age', 'expected': int, 'actual': str}]\n```\n\n### Optional types\n\nThe schema validator support optional types.\n\nYou can do the following:\n\n```python\nfrom simple_schema_validator import schema_validator, types\n\nschema = {\n  'a': types.Optional[int]\n}\n\ndata_1 = {\n  'a': None\n}\n\ndata_2 = {\n  'a': 1\n}\n\ndata_3 = {\n  'a': 'some_string'\n}\n\nassert bool(schema_validator(schema, data_1)) is True\nassert bool(schema_validator(schema, data_2)) is True\nassert bool(schema_validator(schema, data_3)) is False\n```\n\nAdditionally, you can define optional branches in the schema:\n\n```python\nfrom simple_schema_validator import schema_validator, types\n\nschema = {\n  'a': types.Optional[{\n    'b': int\n  }]\n}\n\ndata_1 = {\n  'a': None\n}\n\ndata_2 = {\n  'a': 1\n}\n\ndata_3 = {\n  'a': {\n    'b': 1\n  }\n}\n\ndata_4 = {\n  'a': {\n    'b': 'some_string'\n  }\n}\n\nassert bool(schema_validator(schema, data_1)) is True\nassert bool(schema_validator(schema, data_2)) is False\nassert bool(schema_validator(schema, data_3)) is True\nassert bool(schema_validator(schema, data_4)) is False\n```\n\n## Examples\n\nFor examples, check the [examples](examples/) folder or the tests for the project.\n\n\n",
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