612 lines
22 KiB
Plaintext
612 lines
22 KiB
Plaintext
Metadata-Version: 1.1
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Name: voluptuous
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Version: 0.8.11
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Summary: Voluptuous is a Python data validation library
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Home-page: https://github.com/alecthomas/voluptuous
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Author: Alec Thomas
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Author-email: alec@swapoff.org
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License: BSD
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Download-URL: https://pypi.python.org/pypi/voluptuous
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Description: Voluptuous is a Python data validation library
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==============================================
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|Build Status| |Stories in Ready|
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Voluptuous, *despite* the name, is a Python data validation library. It
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is primarily intended for validating data coming into Python as JSON,
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YAML, etc.
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It has three goals:
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1. Simplicity.
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2. Support for complex data structures.
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3. Provide useful error messages.
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Contact
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-------
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Voluptuous now has a mailing list! Send a mail to
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` <mailto:voluptuous@librelist.com>`__ to subscribe. Instructions will
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follow.
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You can also contact me directly via `email <mailto:alec@swapoff.org>`__
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or `Twitter <https://twitter.com/alecthomas>`__.
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To file a bug, create a `new
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issue <https://github.com/alecthomas/voluptuous/issues/new>`__ on GitHub
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with a short example of how to replicate the issue.
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Show me an example
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------------------
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Twitter's `user search
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API <https://dev.twitter.com/docs/api/1/get/users/search>`__ accepts
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query URLs like:
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::
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$ curl 'http://api.twitter.com/1/users/search.json?q=python&per_page=20&page=1
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To validate this we might use a schema like:
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.. code:: pycon
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>>> from voluptuous import Schema
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>>> schema = Schema({
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... 'q': str,
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... 'per_page': int,
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... 'page': int,
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... })
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This schema very succinctly and roughly describes the data required by
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the API, and will work fine. But it has a few problems. Firstly, it
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doesn't fully express the constraints of the API. According to the API,
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``per_page`` should be restricted to at most 20, defaulting to 5, for
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example. To describe the semantics of the API more accurately, our
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schema will need to be more thoroughly defined:
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.. code:: pycon
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>>> from voluptuous import Required, All, Length, Range
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>>> schema = Schema({
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... Required('q'): All(str, Length(min=1)),
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... Required('per_page', default=5): All(int, Range(min=1, max=20)),
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... 'page': All(int, Range(min=0)),
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... })
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This schema fully enforces the interface defined in Twitter's
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documentation, and goes a little further for completeness.
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"q" is required:
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.. code:: pycon
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>>> from voluptuous import MultipleInvalid, Invalid
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>>> try:
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... schema({})
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "required key not provided @ data['q']"
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True
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...must be a string:
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.. code:: pycon
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>>> try:
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... schema({'q': 123})
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "expected str for dictionary value @ data['q']"
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True
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...and must be at least one character in length:
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.. code:: pycon
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>>> try:
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... schema({'q': ''})
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "length of value must be at least 1 for dictionary value @ data['q']"
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True
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>>> schema({'q': '#topic'}) == {'q': '#topic', 'per_page': 5}
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True
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"per\_page" is a positive integer no greater than 20:
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.. code:: pycon
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>>> try:
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... schema({'q': '#topic', 'per_page': 900})
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "value must be at most 20 for dictionary value @ data['per_page']"
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True
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>>> try:
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... schema({'q': '#topic', 'per_page': -10})
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "value must be at least 1 for dictionary value @ data['per_page']"
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True
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"page" is an integer >= 0:
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.. code:: pycon
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>>> try:
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... schema({'q': '#topic', 'per_page': 'one'})
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc)
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"expected int for dictionary value @ data['per_page']"
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>>> schema({'q': '#topic', 'page': 1}) == {'q': '#topic', 'page': 1, 'per_page': 5}
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True
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Defining schemas
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----------------
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Schemas are nested data structures consisting of dictionaries, lists,
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scalars and *validators*. Each node in the input schema is pattern
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matched against corresponding nodes in the input data.
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Literals
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~~~~~~~~
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Literals in the schema are matched using normal equality checks:
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.. code:: pycon
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>>> schema = Schema(1)
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>>> schema(1)
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1
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>>> schema = Schema('a string')
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>>> schema('a string')
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'a string'
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Types
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~~~~~
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Types in the schema are matched by checking if the corresponding value
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is an instance of the type:
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.. code:: pycon
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>>> schema = Schema(int)
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>>> schema(1)
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1
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>>> try:
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... schema('one')
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "expected int"
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True
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URL's
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~~~~~
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URL's in the schema are matched by using ``urlparse`` library.
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.. code:: pycon
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>>> from voluptuous import Url
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>>> schema = Schema(Url())
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>>> schema('http://w3.org')
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'http://w3.org'
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>>> try:
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... schema('one')
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "expected a URL"
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True
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Lists
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~~~~~
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Lists in the schema are treated as a set of valid values. Each element
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in the schema list is compared to each value in the input data:
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.. code:: pycon
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>>> schema = Schema([1, 'a', 'string'])
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>>> schema([1])
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[1]
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>>> schema([1, 1, 1])
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[1, 1, 1]
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>>> schema(['a', 1, 'string', 1, 'string'])
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['a', 1, 'string', 1, 'string']
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Validation functions
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~~~~~~~~~~~~~~~~~~~~
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Validators are simple callables that raise an ``Invalid`` exception when
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they encounter invalid data. The criteria for determining validity is
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entirely up to the implementation; it may check that a value is a valid
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username with ``pwd.getpwnam()``, it may check that a value is of a
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specific type, and so on.
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The simplest kind of validator is a Python function that raises
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ValueError when its argument is invalid. Conveniently, many builtin
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Python functions have this property. Here's an example of a date
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validator:
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.. code:: pycon
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>>> from datetime import datetime
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>>> def Date(fmt='%Y-%m-%d'):
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... return lambda v: datetime.strptime(v, fmt)
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.. code:: pycon
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>>> schema = Schema(Date())
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>>> schema('2013-03-03')
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datetime.datetime(2013, 3, 3, 0, 0)
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>>> try:
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... schema('2013-03')
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "not a valid value"
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True
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In addition to simply determining if a value is valid, validators may
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mutate the value into a valid form. An example of this is the
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``Coerce(type)`` function, which returns a function that coerces its
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argument to the given type:
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.. code:: python
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def Coerce(type, msg=None):
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"""Coerce a value to a type.
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If the type constructor throws a ValueError, the value will be marked as
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Invalid.
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"""
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def f(v):
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try:
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return type(v)
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except ValueError:
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raise Invalid(msg or ('expected %s' % type.__name__))
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return f
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This example also shows a common idiom where an optional human-readable
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message can be provided. This can vastly improve the usefulness of the
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resulting error messages.
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Dictionaries
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~~~~~~~~~~~~
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Each key-value pair in a schema dictionary is validated against each
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key-value pair in the corresponding data dictionary:
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.. code:: pycon
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>>> schema = Schema({1: 'one', 2: 'two'})
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>>> schema({1: 'one'})
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{1: 'one'}
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Extra dictionary keys
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^^^^^^^^^^^^^^^^^^^^^
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By default any additional keys in the data, not in the schema will
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trigger exceptions:
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.. code:: pycon
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>>> schema = Schema({2: 3})
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>>> try:
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... schema({1: 2, 2: 3})
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "extra keys not allowed @ data[1]"
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True
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This behaviour can be altered on a per-schema basis. To allow additional
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keys use ``Schema(..., extra=ALLOW_EXTRA)``:
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.. code:: pycon
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>>> from voluptuous import ALLOW_EXTRA
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>>> schema = Schema({2: 3}, extra=ALLOW_EXTRA)
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>>> schema({1: 2, 2: 3})
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{1: 2, 2: 3}
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To remove additional keys use ``Schema(..., extra=REMOVE_EXTRA)``:
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.. code:: pycon
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>>> from voluptuous import REMOVE_EXTRA
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>>> schema = Schema({2: 3}, extra=REMOVE_EXTRA)
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>>> schema({1: 2, 2: 3})
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{2: 3}
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It can also be overridden per-dictionary by using the catch-all marker
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token ``extra`` as a key:
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.. code:: pycon
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>>> from voluptuous import Extra
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>>> schema = Schema({1: {Extra: object}})
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>>> schema({1: {'foo': 'bar'}})
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{1: {'foo': 'bar'}}
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Required dictionary keys
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^^^^^^^^^^^^^^^^^^^^^^^^
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By default, keys in the schema are not required to be in the data:
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.. code:: pycon
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>>> schema = Schema({1: 2, 3: 4})
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>>> schema({3: 4})
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{3: 4}
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Similarly to how extra\_ keys work, this behaviour can be overridden
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per-schema:
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.. code:: pycon
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>>> schema = Schema({1: 2, 3: 4}, required=True)
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>>> try:
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... schema({3: 4})
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "required key not provided @ data[1]"
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True
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And per-key, with the marker token ``Required(key)``:
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.. code:: pycon
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>>> schema = Schema({Required(1): 2, 3: 4})
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>>> try:
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... schema({3: 4})
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "required key not provided @ data[1]"
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True
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>>> schema({1: 2})
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{1: 2}
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Optional dictionary keys
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^^^^^^^^^^^^^^^^^^^^^^^^
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If a schema has ``required=True``, keys may be individually marked as
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optional using the marker token ``Optional(key)``:
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.. code:: pycon
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>>> from voluptuous import Optional
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>>> schema = Schema({1: 2, Optional(3): 4}, required=True)
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>>> try:
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... schema({})
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "required key not provided @ data[1]"
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True
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>>> schema({1: 2})
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{1: 2}
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>>> try:
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... schema({1: 2, 4: 5})
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "extra keys not allowed @ data[4]"
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True
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.. code:: pycon
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>>> schema({1: 2, 3: 4})
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{1: 2, 3: 4}
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Recursive schema
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~~~~~~~~~~~~~~~~
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There is no syntax to have a recursive schema. The best way to do it is
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to have a wrapper like this:
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.. code:: pycon
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>>> from voluptuous import Schema, Any
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>>> def s2(v):
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... return s1(v)
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...
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>>> s1 = Schema({"key": Any(s2, "value")})
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>>> s1({"key": {"key": "value"}})
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{'key': {'key': 'value'}}
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Extending an existing Schema
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Often it comes handy to have a base ``Schema`` that is extended with
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more requirements. In that case you can use ``Schema.extend`` to create
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a new ``Schema``:
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.. code:: pycon
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>>> from voluptuous import Schema
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>>> person = Schema({'name': str})
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>>> person_with_age = person.extend({'age': int})
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>>> sorted(list(person_with_age.schema.keys()))
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['age', 'name']
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The original ``Schema`` remains unchanged.
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Objects
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~~~~~~~
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Each key-value pair in a schema dictionary is validated against each
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attribute-value pair in the corresponding object:
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.. code:: pycon
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>>> from voluptuous import Object
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>>> class Structure(object):
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... def __init__(self, q=None):
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... self.q = q
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... def __repr__(self):
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... return '<Structure(q={0.q!r})>'.format(self)
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...
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>>> schema = Schema(Object({'q': 'one'}, cls=Structure))
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>>> schema(Structure(q='one'))
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<Structure(q='one')>
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Allow None values
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~~~~~~~~~~~~~~~~~
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To allow value to be None as well, use Any:
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.. code:: pycon
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>>> from voluptuous import Any
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>>> schema = Schema(Any(None, int))
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>>> schema(None)
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>>> schema(5)
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5
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Error reporting
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---------------
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Validators must throw an ``Invalid`` exception if invalid data is passed
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to them. All other exceptions are treated as errors in the validator and
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will not be caught.
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Each ``Invalid`` exception has an associated ``path`` attribute
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representing the path in the data structure to our currently validating
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value, as well as an ``error_message`` attribute that contains the
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message of the original exception. This is especially useful when you
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want to catch ``Invalid`` exceptions and give some feedback to the user,
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for instance in the context of an HTTP API.
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.. code:: pycon
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>>> def validate_email(email):
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... """Validate email."""
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... if not "@" in email:
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... raise Invalid("This email is invalid.")
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... return email
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>>> schema = Schema({"email": validate_email})
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>>> exc = None
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>>> try:
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... schema({"email": "whatever"})
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc)
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"This email is invalid. for dictionary value @ data['email']"
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>>> exc.path
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['email']
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>>> exc.msg
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'This email is invalid.'
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>>> exc.error_message
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'This email is invalid.'
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The ``path`` attribute is used during error reporting, but also during
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matching to determine whether an error should be reported to the user or
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if the next match should be attempted. This is determined by comparing
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the depth of the path where the check is, to the depth of the path where
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the error occurred. If the error is more than one level deeper, it is
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reported.
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The upshot of this is that *matching is depth-first and fail-fast*.
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To illustrate this, here is an example schema:
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.. code:: pycon
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>>> schema = Schema([[2, 3], 6])
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Each value in the top-level list is matched depth-first in-order. Given
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input data of ``[[6]]``, the inner list will match the first element of
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the schema, but the literal ``6`` will not match any of the elements of
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that list. This error will be reported back to the user immediately. No
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backtracking is attempted:
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.. code:: pycon
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>>> try:
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... schema([[6]])
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... raise AssertionError('MultipleInvalid not raised')
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... except MultipleInvalid as e:
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... exc = e
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>>> str(exc) == "not a valid value @ data[0][0]"
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True
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If we pass the data ``[6]``, the ``6`` is not a list type and so will
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not recurse into the first element of the schema. Matching will continue
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on to the second element in the schema, and succeed:
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.. code:: pycon
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>>> schema([6])
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[6]
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Running tests.
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--------------
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Voluptuous is using nosetests:
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::
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$ nosetests
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Why use Voluptuous over another validation library?
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---------------------------------------------------
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**Validators are simple callables**
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No need to subclass anything, just use a function.
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**Errors are simple exceptions.**
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A validator can just ``raise Invalid(msg)`` and expect the user to
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get useful messages.
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**Schemas are basic Python data structures.**
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Should your data be a dictionary of integer keys to strings?
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``{int: str}`` does what you expect. List of integers, floats or
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strings? ``[int, float, str]``.
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**Designed from the ground up for validating more than just forms.**
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Nested data structures are treated in the same way as any other
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type. Need a list of dictionaries? ``[{}]``
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**Consistency.**
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Types in the schema are checked as types. Values are compared as
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values. Callables are called to validate. Simple.
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Other libraries and inspirations
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--------------------------------
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Voluptuous is heavily inspired by
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`Validino <http://code.google.com/p/validino/>`__, and to a lesser
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extent, `jsonvalidator <http://code.google.com/p/jsonvalidator/>`__ and
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`json\_schema <http://blog.sendapatch.se/category/json_schema.html>`__.
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I greatly prefer the light-weight style promoted by these libraries to
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the complexity of libraries like FormEncode.
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.. |Build Status| image:: https://travis-ci.org/alecthomas/voluptuous.png
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:target: https://travis-ci.org/alecthomas/voluptuous
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.. |Stories in Ready| image:: https://badge.waffle.io/alecthomas/voluptuous.png?label=ready&title=Ready
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:target: https://waffle.io/alecthomas/voluptuous
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Platform: any
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Intended Audience :: Developers
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Classifier: License :: OSI Approved :: BSD License
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Classifier: Operating System :: OS Independent
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Classifier: Programming Language :: Python :: 2
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Classifier: Programming Language :: Python :: 2.7
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.1
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Classifier: Programming Language :: Python :: 3.2
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Classifier: Programming Language :: Python :: 3.3
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Classifier: Programming Language :: Python :: 3.4
|