{
    "info": {
        "author": "Jean-Tiare Le Bigot",
        "author_email": "jtlebigot@socialludia.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Plugins",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Software Development :: Testing"
        ],
        "description": "TL;DR Use voluptuous instead\n============================\n\n.. important::\n   We forked Voluptuous because the issues we opened were not addressed.\n   Voluputous author have fixed those issues now so, we stopped working on\n   onctuous and advise anyone to use Volutpuous instead.\n\n\nPresentation\n============\n\n**Onctuous** is a fluid and pleasing to use validation tool you will love to\nuse. Originally based on `Voluptuous <https://github.com/alecthomas/voluptuous>`_\ncode by Alec Thomas <alec@swapoff.org>, we first fixed long outstanding issues\nlike Python builtins collision and added support for default values.\n\nThe goal of **Onctuous** is to make it simple and smooth.\n - You *can* write your own validators\n - You *can* specify defaults. The best ? They are *not* required to pass validation themselves\n - You *can* write readable code. This is not based on json schema specification, on purpose\n\nYou can use Onctuous to validate ``list``, ``scalar`` (regular variables) or\n``dict``. For this purpose, you will need to define a so-called ``Schema`` and\ncall the Schema with the input to validate. In case of success, it will return\nthe validated input, possibly filtered or edited according to your rules\n\nInstallation\n============\n\n::\n\n    $ pip install onctuous\n\n\nDeveloping\n==========\n\n::\n\n    $ hg clone ssh://hg@bitbucket.org/Ludia/onctuous\n    $ pip install nose nosexcover coverage mock\n    $ python setup.py develop\n    $ nosetests\n\nWhy use Onctuous over another validation library?\n=================================================\n\nIt's:\n\n- readable\n- easy\n\n**Validators are simple callables**\n  No need to subclass anything, just use a function.\n\n**Errors are simple exceptions.**\n  A validator can just ``raise Invalid(msg)`` and expect the user to get useful\n  messages.\n\n**Schemas are basic Python data structures.**\n  Should your data be a dictionary of integer keys to strings?  ``{int: str}``\n  does what you expect. List of integers, floats or strings? ``[int, float, str]``.\n\n**Designed from the ground up for validating more than just forms.**\n  Nested data structures are treated in the same way as any other type. Need a\n  list of dictionaries? ``[{}]``\n\n**Consistency.**\n  Types in the schema are checked as types. Values are compared as values.\n  Callables are called to validate. Simple.\n\n\nExample usage\n=============\n\nValidate a scalar\n-----------------\n\n::\n\n    from onctuous import Schema\n\n    validate_is_int = Schema(int)\n\n    # Validate 42 (this will run fine)\n    validated = validate_is_int(42)\n\n    # Validate \"toto\" (this will raise ``InvalidList`` containing a list of errors)\n    validated = validate_is_int(\"toto\")\n\n\nValidate a list\n---------------\n\nUsing the same idea, you can validate a list of ``int``\n\n::\n\n    from onctuous import Schema\n\n    validate_is_int_list = Schema([int])\n\n    # This will run fine\n    validated = validate_is_int_list([42, 2, 7])\n\n    # This will raise ``InvalidList`` containing a list of errors\n    validated = validate_is_int_list([2, 7, \"toto\"])\n\n\nBut we can also use on of the bundled validators and check the URL looks to\nbe valid for example and even supply a custom error message!\n\n::\n\n    from onctuous import Schema, Url\n\n    validate_is_urls = Schema([Url(msg=\"Ooops, this is *not* a valid URL\")])\n\n    # This will run fine\n    validated = validate_is_urls([\"www.example.com\", \"ftp://user:pass@ftp.example.com:42/toto?weird/path\"])\n\n    # This will raise ``InvalidList`` containing a list of errors\n    validated = validate_is_urls([2, 7, \"toto\"])\n\n\nValidate a dictionary\n---------------------\n\nAgain, this is the same concept with some more niceties. For example, here is a\nbasic user schema:\n\n::\n\n    from onctuous import Schema, Url\n\n    validate_user = Schema({\n        'firstname': unicode,\n        'lastname': unicode,\n        'age': int,\n        'website': Url(msg=\"Ooops, this is *not* a valid URL\"),\n    })\n\n    # use it...\n\nBut wait, I don't want megative ages, do I ?\n\n::\n\n    from onctuous import Schema, Url, InRange, All\n\n    validate_user = Schema({\n        'firstname': unicode,\n        'lastname': unicode,\n        'age': All(int, InRange(min=0, msg=\"Uh, ages can not be negative...\")),\n        'website': Url(msg=\"Ooops, this is *not* a valid URL\"),\n    })\n\n    # use it...\n\nHave you noticed how this uses ``All`` to specify that both ``int`` and ``range``\nconditions must ne met ?\n\nWhat if I want to make the \"Website\" field optional ? Let me introduce ``Markers``\n\n::\n\n    from onctuous import Schema, Url, InRange, All, Optional\n\n    validate_user = Schema({\n        'firstname': unicode,\n        'lastname': unicode,\n        'age': All(int, InRange(min=0, msg=\"Uh, ages can not be negative...\")),\n        Optional('website'): Url(msg=\"Ooops, this is *not* a valid URL\"),\n    })\n\n    # use it...\n\nYou could also have used the 'Required' Marker with a default value. This is very\nusefull if you do not want to spend your whole time writing ``if key in data...``.\n\n::\n\n    from onctuous import Schema, Url, InRange, All, Required\n\n    validate_user = Schema({\n        'firstname': unicode,\n        'lastname': unicode,\n        'age': All(int, InRange(min=0, msg=\"Uh, ages can not be negative...\")),\n        Required('website', \"#\"): Url(msg=\"Ooops, this is *not* a valid URL\"),\n    })\n\n    # use it...\n\n\nIt is worth noting that that the provided default value does *not* need to pass\nvalidations. You can use it as a \"Marker\" further in you application.\n\nNested and advanced validations\n-------------------------------\n\nYou can nest shemas. You actually did it in the previous example where scalars\nare nested into a dict or a list. But you can arbitrarily nest lists into dict\nand the other way around, as you need.\n\nFor example, let's say you are writing a blog post which obviously has an author\nand maybe some tags whose len are between 3 and 20 chars included.\n\n::\n\n    from onctuous import Schema, All, Required, Length, InRange\n\n    # Same schema as user above. I just removed the Schema instanciation but\n    # could have kept it. It's just more natural\n    user = {\n        'firstname': unicode,\n        'lastname': unicode,\n        'age': All(int, InRange(min=0, msg=\"Uh, ages can not be negative...\")),\n        Required('website', \"#\"): Url(msg=\"Ooops, this is *not* a valid URL\"),\n    }\n\n    validate_post = Schema({\n        'title': unicode,\n        'body': unicode,\n        'author': user,  # look how you can split a schema into re-usable chunks!\n        Optional('tags'): [All(unicode, Length(min=3, max=20))],\n        Required('website', \"#\"): Url(msg=\"Ooops, this is *not* a valid URL\"),\n    })\n\n    # use it...\n\nThat's all for nesting.\n\nYou could also use the ``Extra`` special key to allow extra fields to be present\nwhile still being valid.\n\nWhen instanciating the schema, there are also a global ``required`` and ``extra``\nparameters that can optionally be set. They both default to ``False``\n\nGoing further\n=============\n\nThere are tons of bundled validators, `see the full API documentation\n<https://onctuous.readthedocs.org/en/latest/api/validators.html>`_ for the full\nlist\n\nRequirements\n============\n\n - Python 2.7.x\n - nose, nosexcover, coverage, mock for the tests\n\nRelated Links\n=============\n\nonctuous\n--------\n\n- **Full documentation**: https://onctuous.readthedocs.org/en/latest\n- **Report bugs**: https://bitbucket.org/Ludia/onctuous/issues\n- **Download**: http://pypi.python.org/pypi/onctuous",
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