Metadata-Version: 1.0
Name: voluptuous
Version: 0.3
Summary: Voluptuous is a Python data validation library
Home-page: http://github.com/alecthomas/voluptuous
Author: Alec Thomas
Author-email: alec@swapoff.org
License: BSD
Download-URL: http://pypi.python.org/pypi/voluptuous
Description: Voluptuous is a Python data validation library
        ==============================================
        
        Voluptuous, *despite* the name, is a Python data validation library. It is
        primarily intended for validating data coming into Python as JSON, YAML,
        etc.
        
        It has three goals:
        
        1. Simplicity.
        2. Support complex data structures.
        3. Provide useful error messages.
        
        .. contents:: Table of Contents
        
        Show me an example
        ------------------
        Twitter's `user search API
        <http://apiwiki.twitter.com/Twitter-REST-API-Method:-users-search>`_ accepts
        query URLs like::
        
        $ curl 'http://api.twitter.com/1/users/search.json?q=python&per_page=20&page=1
        
        To validate this we might use a schema like::
        
        >>> from voluptuous import Schema
        >>> schema = Schema({
        ...   'q': str,
        ...   'per_page': int,
        ...   'page': int,
        ... })
        
        This schema very succinctly and roughly describes the data required by the API,
        and will work fine. But it has a few problems. Firstly, it doesn't fully
        express the constraints of the API. According to the API, ``per_page`` should
        be restricted to at most 20, for example. To describe the semantics of the API
        more accurately, our schema will need to be more thoroughly defined::
        
        >>> from voluptuous import required, all, length, range
        >>> schema = Schema({
        ...   required('q'): all(str, length(min=1)),
        ...   'per_page': all(int, range(min=1, max=20)),
        ...   'page': all(int, range(min=0)),
        ... })
        
        This schema fully enforces the interface defined in Twitter's documentation,
        and goes a little further for completeness.
        
        "q" is required::
        
        >>> schema({})
        Traceback (most recent call last):
        ...
        Invalid: required key 'q' not provided
        
        ...must be a string::
        
        >>> schema({'q': 123})
        Traceback (most recent call last):
        ...
        Invalid: expected str for dictionary value @ data['q']
        
        ...and must be at least one character in length::
        
        >>> schema({'q': ''})
        Traceback (most recent call last):
        ...
        Invalid: length of value must be at least 1 for dictionary value @ data['q']
        >>> schema({'q': '#topic'})
        {'q': '#topic'}
        
        "per_page" is a positive integer no greater than 20::
        
        >>> schema({'q': '#topic', 'per_page': 900})
        Traceback (most recent call last):
        ...
        Invalid: value must be at most 20 for dictionary value @ data['per_page']
        >>> schema({'q': '#topic', 'per_page': -10})
        Traceback (most recent call last):
        ...
        Invalid: value must be at least 1 for dictionary value @ data['per_page']
        
        "page" is an integer >= 0::
        
        >>> schema({'q': '#topic', 'page': 'one'})
        Traceback (most recent call last):
        ...
        Invalid: expected int for dictionary value @ data['page']
        >>> schema({'q': '#topic', 'page': 1})
        {'q': '#topic', 'page': 1}
        
        Why Voluptuous over another validation library?
        -----------------------------------------------
        Most existing Python validation libraries are oriented towards validating HTML
        forms. Voluptuous can be used for this case, but is primarily intended for
        validating more complex data structures, such as those used in REST API calls.
        
        Not all libraries are tied to form validation. Some, such as `Validino
        <http://code.google.com/p/validino/>`_, support arbitrary data structures, but
        have other issues such as no longer being maintained, less than ideal error
        reporting, and so on.
        
        Defining schemas
        ----------------
        Schemas are nested data structures consisting of dictionaries, lists,
        scalars and *validators*. Each node in the input schema is pattern matched
        against corresponding nodes in the input data.
        
        Literals
        ~~~~~~~~
        Literals in the schema are matched using normal equality checks::
        
        >>> schema = Schema(1)
        >>> schema(1)
        1
        >>> schema = Schema('a string')
        >>> schema('a string')
        'a string'
        
        Lists
        ~~~~~
        Lists in the schema are treated as a set of valid values. Each element in the
        schema list is compared to each value in the input data::
        
        >>> schema = Schema([1, 'a', 'string'])
        >>> schema([1])
        [1]
        >>> schema([1, 1, 1])
        [1, 1, 1]
        >>> schema(['a', 1, 'string', 1, 'string'])
        ['a', 1, 'string', 1, 'string']
        
        Dictionaries
        ~~~~~~~~~~~~
        Each key-value pair in a schema dictionary is validated against each key-value
        pair in the corresponding data dictionary::
        
        >>> schema = Schema({1: 'one', 2: 'two'})
        >>> schema({1: 'one'})
        {1: 'one'}
        >>> schema({3: 'three'})
        Traceback (most recent call last):
        ...
        Invalid: not a valid value for dictionary key @ data[3]
        
        Validation functions
        ~~~~~~~~~~~~~~~~~~~~
        
        Validators are simple callables that raise an ``Invalid`` exception when they
        encounter invalid data. The criteria for determining validity is entirely up to
        the implementation; it may check that a value is a valid username with
        ``pwd.getpwnam()``, it may check that a value is of a specific type, and so on.
        
        In addition to simply determining if a value is valid, validators may mutate
        the value into a valid form. An example of this is the ``coerce(type)``
        function, which returns a function that coerces its argument to the given
        type::
        
        def coerce(type, msg=None):
        """Coerce a value to a type.
        
        If the type constructor throws a ValueError, the value will be marked as
        Invalid.
        """
        def f(v):
        try:
        return type(v)
        except ValueError:
        raise Invalid(msg or ('expected %s' % type.__name__))
        return f
        
        This example also shows a common idiom where an optional human-readable
        message can be provided. This can vastly improve the usefulness of the
        resulting error messages.
        
        .. _extra:
        
        Extra dictionary keys
        ~~~~~~~~~~~~~~~~~~~~~
        By default, extra keys found in the data that are not in the schema, will
        trigger exceptions::
        
        >>> schema = Schema({})
        >>> schema({1: 2})
        Traceback (most recent call last):
        ...
        Invalid: extra keys not allowed @ data[1]
        
        This behaviour can be altered on a per-schema basis with ``Schema(..., extra=True)``::
        
        >>> schema = Schema({}, extra=True)
        >>> schema({1: 2})
        {1: 2}
        
        It can also be overridden per-dictionary by using the catch-all marker token
        ``extra`` as a key::
        
        >>> from voluptuous import extra
        >>> schema = Schema({1: {extra: object}})
        >>> schema({1: {'foo': 'bar'}})
        {1: {'foo': 'bar'}}
        
        
        Required dictionary keys
        ~~~~~~~~~~~~~~~~~~~~~~~~
        By default, keys in the schema are not required to be in the data::
        
        >>> schema = Schema({1: 2, 3: 4})
        >>> schema({3: 4})
        {3: 4}
        
        Similarly to the behaviour of extra_... keys, this can be overridden
        per-schema::
        
        >>> schema = Schema({1: 2, 3: 4}, required=True)
        >>> schema({3: 4})
        Traceback (most recent call last):
        ...
        Invalid: required key 1 not provided
        
        And also per-key with the marker token ``required(key)``::
        
        >>> schema = Schema({required(1): 2, 3: 4})
        >>> schema({3: 4})
        Traceback (most recent call last):
        ...
        Invalid: required key 1 not provided
        >>> schema({1: 2})
        {1: 2}
        
        If a schema has ``required=True``, keys may be individually marked as optional
        using the marker token ``optional(key)``::
        
        >>> from voluptuous import optional
        >>> schema = Schema({1: 2, optional(3): 4}, required=True)
        >>> schema({})
        Traceback (most recent call last):
        ...
        Invalid: required key 1 not provided
        >>> schema({1: 2})
        {1: 2}
        
        Error reporting
        ---------------
        Validators must throw an ``Invalid`` exception if invalid data is passed to
        them. All other exceptions are treated as errors in the validator and will not
        be caught.
        
        Each ``Invalid`` exception has an associated ``path`` attribute representing
        the path in the data structure to our currently validating value. This is used
        during error reporting, but also during matching to determine whether an error
        should be reported to the user or if the next match should be attempted. This
        is determined by comparing the depth of the path where the check is, to the
        depth of the path where the error occurred. If the error is more than one level
        deeper, it is reported.
        
        The upshot of this is that *matching is depth-first and fail-fast*.
        
        To illustrate this, here is an example schema::
        
        >>> schema = Schema([[2, 3], 6])
        
        Each value in the top-level list is matched depth-first in-order. Given input
        data of ``[[6]]``, the inner list will match the first element of the schema,
        but the literal ``6`` will not match any of the elements of that list. This
        error will be reported back to the user immediately. No backtracking is
        attempted::
        
        >>> schema([[6]])
        Traceback (most recent call last):
        ...
        Invalid: invalid list value @ data[0][0]
        
        If we pass the data ``[6]``, the ``6`` is not a list type and so will not match
        the first element and recurse deeper. It will continue on to the second element,
        and succeed::
        
        >>> schema([6])
        [6]
        
Platform: any
