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Bug 1281004: vendor voluptuous; r=gps

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@ -51,6 +51,7 @@ SEARCH_PATHS = [
'python/pyyaml/lib',
'python/requests',
'python/slugid',
'python/voluptuous',
'build',
'build/pymake',
'config',

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Copyright (c) 2010, Alec Thomas
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
- Neither the name of SwapOff.org nor the names of its contributors may
be used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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include *.md
include COPYING

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Metadata-Version: 1.1
Name: voluptuous
Version: 0.8.11
Summary: Voluptuous is a Python data validation library
Home-page: https://github.com/alecthomas/voluptuous
Author: Alec Thomas
Author-email: alec@swapoff.org
License: BSD
Download-URL: https://pypi.python.org/pypi/voluptuous
Description: Voluptuous is a Python data validation library
==============================================
|Build Status| |Stories in Ready|
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 for complex data structures.
3. Provide useful error messages.
Contact
-------
Voluptuous now has a mailing list! Send a mail to
` <mailto:voluptuous@librelist.com>`__ to subscribe. Instructions will
follow.
You can also contact me directly via `email <mailto:alec@swapoff.org>`__
or `Twitter <https://twitter.com/alecthomas>`__.
To file a bug, create a `new
issue <https://github.com/alecthomas/voluptuous/issues/new>`__ on GitHub
with a short example of how to replicate the issue.
Show me an example
------------------
Twitter's `user search
API <https://dev.twitter.com/docs/api/1/get/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:
.. code:: pycon
>>> 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, defaulting to 5, for
example. To describe the semantics of the API more accurately, our
schema will need to be more thoroughly defined:
.. code:: pycon
>>> from voluptuous import Required, All, Length, Range
>>> schema = Schema({
... Required('q'): All(str, Length(min=1)),
... Required('per_page', default=5): 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:
.. code:: pycon
>>> from voluptuous import MultipleInvalid, Invalid
>>> try:
... schema({})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data['q']"
True
...must be a string:
.. code:: pycon
>>> try:
... schema({'q': 123})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "expected str for dictionary value @ data['q']"
True
...and must be at least one character in length:
.. code:: pycon
>>> try:
... schema({'q': ''})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "length of value must be at least 1 for dictionary value @ data['q']"
True
>>> schema({'q': '#topic'}) == {'q': '#topic', 'per_page': 5}
True
"per\_page" is a positive integer no greater than 20:
.. code:: pycon
>>> try:
... schema({'q': '#topic', 'per_page': 900})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "value must be at most 20 for dictionary value @ data['per_page']"
True
>>> try:
... schema({'q': '#topic', 'per_page': -10})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "value must be at least 1 for dictionary value @ data['per_page']"
True
"page" is an integer >= 0:
.. code:: pycon
>>> try:
... schema({'q': '#topic', 'per_page': 'one'})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"expected int for dictionary value @ data['per_page']"
>>> schema({'q': '#topic', 'page': 1}) == {'q': '#topic', 'page': 1, 'per_page': 5}
True
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:
.. code:: pycon
>>> schema = Schema(1)
>>> schema(1)
1
>>> schema = Schema('a string')
>>> schema('a string')
'a string'
Types
~~~~~
Types in the schema are matched by checking if the corresponding value
is an instance of the type:
.. code:: pycon
>>> schema = Schema(int)
>>> schema(1)
1
>>> try:
... schema('one')
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "expected int"
True
URL's
~~~~~
URL's in the schema are matched by using ``urlparse`` library.
.. code:: pycon
>>> from voluptuous import Url
>>> schema = Schema(Url())
>>> schema('http://w3.org')
'http://w3.org'
>>> try:
... schema('one')
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "expected a URL"
True
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:
.. code:: pycon
>>> 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']
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.
The simplest kind of validator is a Python function that raises
ValueError when its argument is invalid. Conveniently, many builtin
Python functions have this property. Here's an example of a date
validator:
.. code:: pycon
>>> from datetime import datetime
>>> def Date(fmt='%Y-%m-%d'):
... return lambda v: datetime.strptime(v, fmt)
.. code:: pycon
>>> schema = Schema(Date())
>>> schema('2013-03-03')
datetime.datetime(2013, 3, 3, 0, 0)
>>> try:
... schema('2013-03')
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "not a valid value"
True
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:
.. code:: python
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.
Dictionaries
~~~~~~~~~~~~
Each key-value pair in a schema dictionary is validated against each
key-value pair in the corresponding data dictionary:
.. code:: pycon
>>> schema = Schema({1: 'one', 2: 'two'})
>>> schema({1: 'one'})
{1: 'one'}
Extra dictionary keys
^^^^^^^^^^^^^^^^^^^^^
By default any additional keys in the data, not in the schema will
trigger exceptions:
.. code:: pycon
>>> schema = Schema({2: 3})
>>> try:
... schema({1: 2, 2: 3})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "extra keys not allowed @ data[1]"
True
This behaviour can be altered on a per-schema basis. To allow additional
keys use ``Schema(..., extra=ALLOW_EXTRA)``:
.. code:: pycon
>>> from voluptuous import ALLOW_EXTRA
>>> schema = Schema({2: 3}, extra=ALLOW_EXTRA)
>>> schema({1: 2, 2: 3})
{1: 2, 2: 3}
To remove additional keys use ``Schema(..., extra=REMOVE_EXTRA)``:
.. code:: pycon
>>> from voluptuous import REMOVE_EXTRA
>>> schema = Schema({2: 3}, extra=REMOVE_EXTRA)
>>> schema({1: 2, 2: 3})
{2: 3}
It can also be overridden per-dictionary by using the catch-all marker
token ``extra`` as a key:
.. code:: pycon
>>> 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:
.. code:: pycon
>>> schema = Schema({1: 2, 3: 4})
>>> schema({3: 4})
{3: 4}
Similarly to how extra\_ keys work, this behaviour can be overridden
per-schema:
.. code:: pycon
>>> schema = Schema({1: 2, 3: 4}, required=True)
>>> try:
... schema({3: 4})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data[1]"
True
And per-key, with the marker token ``Required(key)``:
.. code:: pycon
>>> schema = Schema({Required(1): 2, 3: 4})
>>> try:
... schema({3: 4})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data[1]"
True
>>> schema({1: 2})
{1: 2}
Optional dictionary keys
^^^^^^^^^^^^^^^^^^^^^^^^
If a schema has ``required=True``, keys may be individually marked as
optional using the marker token ``Optional(key)``:
.. code:: pycon
>>> from voluptuous import Optional
>>> schema = Schema({1: 2, Optional(3): 4}, required=True)
>>> try:
... schema({})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data[1]"
True
>>> schema({1: 2})
{1: 2}
>>> try:
... schema({1: 2, 4: 5})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "extra keys not allowed @ data[4]"
True
.. code:: pycon
>>> schema({1: 2, 3: 4})
{1: 2, 3: 4}
Recursive schema
~~~~~~~~~~~~~~~~
There is no syntax to have a recursive schema. The best way to do it is
to have a wrapper like this:
.. code:: pycon
>>> from voluptuous import Schema, Any
>>> def s2(v):
... return s1(v)
...
>>> s1 = Schema({"key": Any(s2, "value")})
>>> s1({"key": {"key": "value"}})
{'key': {'key': 'value'}}
Extending an existing Schema
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Often it comes handy to have a base ``Schema`` that is extended with
more requirements. In that case you can use ``Schema.extend`` to create
a new ``Schema``:
.. code:: pycon
>>> from voluptuous import Schema
>>> person = Schema({'name': str})
>>> person_with_age = person.extend({'age': int})
>>> sorted(list(person_with_age.schema.keys()))
['age', 'name']
The original ``Schema`` remains unchanged.
Objects
~~~~~~~
Each key-value pair in a schema dictionary is validated against each
attribute-value pair in the corresponding object:
.. code:: pycon
>>> from voluptuous import Object
>>> class Structure(object):
... def __init__(self, q=None):
... self.q = q
... def __repr__(self):
... return '<Structure(q={0.q!r})>'.format(self)
...
>>> schema = Schema(Object({'q': 'one'}, cls=Structure))
>>> schema(Structure(q='one'))
<Structure(q='one')>
Allow None values
~~~~~~~~~~~~~~~~~
To allow value to be None as well, use Any:
.. code:: pycon
>>> from voluptuous import Any
>>> schema = Schema(Any(None, int))
>>> schema(None)
>>> schema(5)
5
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, as well as an ``error_message`` attribute that contains the
message of the original exception. This is especially useful when you
want to catch ``Invalid`` exceptions and give some feedback to the user,
for instance in the context of an HTTP API.
.. code:: pycon
>>> def validate_email(email):
... """Validate email."""
... if not "@" in email:
... raise Invalid("This email is invalid.")
... return email
>>> schema = Schema({"email": validate_email})
>>> exc = None
>>> try:
... schema({"email": "whatever"})
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"This email is invalid. for dictionary value @ data['email']"
>>> exc.path
['email']
>>> exc.msg
'This email is invalid.'
>>> exc.error_message
'This email is invalid.'
The ``path`` attribute 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:
.. code:: pycon
>>> 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:
.. code:: pycon
>>> try:
... schema([[6]])
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "not a valid value @ data[0][0]"
True
If we pass the data ``[6]``, the ``6`` is not a list type and so will
not recurse into the first element of the schema. Matching will continue
on to the second element in the schema, and succeed:
.. code:: pycon
>>> schema([6])
[6]
Running tests.
--------------
Voluptuous is using nosetests:
::
$ nosetests
Why use Voluptuous over another validation library?
---------------------------------------------------
**Validators are simple callables**
No need to subclass anything, just use a function.
**Errors are simple exceptions.**
A validator can just ``raise Invalid(msg)`` and expect the user to
get useful messages.
**Schemas are basic Python data structures.**
Should your data be a dictionary of integer keys to strings?
``{int: str}`` does what you expect. List of integers, floats or
strings? ``[int, float, str]``.
**Designed from the ground up for validating more than just forms.**
Nested data structures are treated in the same way as any other
type. Need a list of dictionaries? ``[{}]``
**Consistency.**
Types in the schema are checked as types. Values are compared as
values. Callables are called to validate. Simple.
Other libraries and inspirations
--------------------------------
Voluptuous is heavily inspired by
`Validino <http://code.google.com/p/validino/>`__, and to a lesser
extent, `jsonvalidator <http://code.google.com/p/jsonvalidator/>`__ and
`json\_schema <http://blog.sendapatch.se/category/json_schema.html>`__.
I greatly prefer the light-weight style promoted by these libraries to
the complexity of libraries like FormEncode.
.. |Build Status| image:: https://travis-ci.org/alecthomas/voluptuous.png
:target: https://travis-ci.org/alecthomas/voluptuous
.. |Stories in Ready| image:: https://badge.waffle.io/alecthomas/voluptuous.png?label=ready&title=Ready
:target: https://waffle.io/alecthomas/voluptuous
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4

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# Voluptuous is a Python data validation library
[![Build Status](https://travis-ci.org/alecthomas/voluptuous.png)](https://travis-ci.org/alecthomas/voluptuous) [![Stories in Ready](https://badge.waffle.io/alecthomas/voluptuous.png?label=ready&title=Ready)](https://waffle.io/alecthomas/voluptuous)
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 for complex data structures.
3. Provide useful error messages.
## Contact
Voluptuous now has a mailing list! Send a mail to
[<voluptuous@librelist.com>](mailto:voluptuous@librelist.com) to subscribe. Instructions
will follow.
You can also contact me directly via [email](mailto:alec@swapoff.org) or
[Twitter](https://twitter.com/alecthomas).
To file a bug, create a [new issue](https://github.com/alecthomas/voluptuous/issues/new) on GitHub with a short example of how to replicate the issue.
## Show me an example
Twitter's [user search API](https://dev.twitter.com/docs/api/1/get/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:
```pycon
>>> 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, defaulting to 5, for
example. To describe the semantics of the API more accurately, our
schema will need to be more thoroughly defined:
```pycon
>>> from voluptuous import Required, All, Length, Range
>>> schema = Schema({
... Required('q'): All(str, Length(min=1)),
... Required('per_page', default=5): 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:
```pycon
>>> from voluptuous import MultipleInvalid, Invalid
>>> try:
... schema({})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data['q']"
True
```
...must be a string:
```pycon
>>> try:
... schema({'q': 123})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "expected str for dictionary value @ data['q']"
True
```
...and must be at least one character in length:
```pycon
>>> try:
... schema({'q': ''})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "length of value must be at least 1 for dictionary value @ data['q']"
True
>>> schema({'q': '#topic'}) == {'q': '#topic', 'per_page': 5}
True
```
"per\_page" is a positive integer no greater than 20:
```pycon
>>> try:
... schema({'q': '#topic', 'per_page': 900})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "value must be at most 20 for dictionary value @ data['per_page']"
True
>>> try:
... schema({'q': '#topic', 'per_page': -10})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "value must be at least 1 for dictionary value @ data['per_page']"
True
```
"page" is an integer \>= 0:
```pycon
>>> try:
... schema({'q': '#topic', 'per_page': 'one'})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"expected int for dictionary value @ data['per_page']"
>>> schema({'q': '#topic', 'page': 1}) == {'q': '#topic', 'page': 1, 'per_page': 5}
True
```
## 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:
```pycon
>>> schema = Schema(1)
>>> schema(1)
1
>>> schema = Schema('a string')
>>> schema('a string')
'a string'
```
### Types
Types in the schema are matched by checking if the corresponding value
is an instance of the type:
```pycon
>>> schema = Schema(int)
>>> schema(1)
1
>>> try:
... schema('one')
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "expected int"
True
```
### URL's
URL's in the schema are matched by using `urlparse` library.
```pycon
>>> from voluptuous import Url
>>> schema = Schema(Url())
>>> schema('http://w3.org')
'http://w3.org'
>>> try:
... schema('one')
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "expected a URL"
True
```
### 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:
```pycon
>>> 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']
```
### 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.
The simplest kind of validator is a Python function that raises
ValueError when its argument is invalid. Conveniently, many builtin
Python functions have this property. Here's an example of a date
validator:
```pycon
>>> from datetime import datetime
>>> def Date(fmt='%Y-%m-%d'):
... return lambda v: datetime.strptime(v, fmt)
```
```pycon
>>> schema = Schema(Date())
>>> schema('2013-03-03')
datetime.datetime(2013, 3, 3, 0, 0)
>>> try:
... schema('2013-03')
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "not a valid value"
True
```
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:
```python
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.
### Dictionaries
Each key-value pair in a schema dictionary is validated against each
key-value pair in the corresponding data dictionary:
```pycon
>>> schema = Schema({1: 'one', 2: 'two'})
>>> schema({1: 'one'})
{1: 'one'}
```
#### Extra dictionary keys
By default any additional keys in the data, not in the schema will
trigger exceptions:
```pycon
>>> schema = Schema({2: 3})
>>> try:
... schema({1: 2, 2: 3})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "extra keys not allowed @ data[1]"
True
```
This behaviour can be altered on a per-schema basis. To allow
additional keys use
`Schema(..., extra=ALLOW_EXTRA)`:
```pycon
>>> from voluptuous import ALLOW_EXTRA
>>> schema = Schema({2: 3}, extra=ALLOW_EXTRA)
>>> schema({1: 2, 2: 3})
{1: 2, 2: 3}
```
To remove additional keys use
`Schema(..., extra=REMOVE_EXTRA)`:
```pycon
>>> from voluptuous import REMOVE_EXTRA
>>> schema = Schema({2: 3}, extra=REMOVE_EXTRA)
>>> schema({1: 2, 2: 3})
{2: 3}
```
It can also be overridden per-dictionary by using the catch-all marker
token `extra` as a key:
```pycon
>>> 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:
```pycon
>>> schema = Schema({1: 2, 3: 4})
>>> schema({3: 4})
{3: 4}
```
Similarly to how extra\_ keys work, this behaviour can be overridden
per-schema:
```pycon
>>> schema = Schema({1: 2, 3: 4}, required=True)
>>> try:
... schema({3: 4})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data[1]"
True
```
And per-key, with the marker token `Required(key)`:
```pycon
>>> schema = Schema({Required(1): 2, 3: 4})
>>> try:
... schema({3: 4})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data[1]"
True
>>> schema({1: 2})
{1: 2}
```
#### Optional dictionary keys
If a schema has `required=True`, keys may be individually marked as
optional using the marker token `Optional(key)`:
```pycon
>>> from voluptuous import Optional
>>> schema = Schema({1: 2, Optional(3): 4}, required=True)
>>> try:
... schema({})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data[1]"
True
>>> schema({1: 2})
{1: 2}
>>> try:
... schema({1: 2, 4: 5})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "extra keys not allowed @ data[4]"
True
```
```pycon
>>> schema({1: 2, 3: 4})
{1: 2, 3: 4}
```
### Recursive schema
There is no syntax to have a recursive schema. The best way to do it is to have a wrapper like this:
```pycon
>>> from voluptuous import Schema, Any
>>> def s2(v):
... return s1(v)
...
>>> s1 = Schema({"key": Any(s2, "value")})
>>> s1({"key": {"key": "value"}})
{'key': {'key': 'value'}}
```
### Extending an existing Schema
Often it comes handy to have a base `Schema` that is extended with more
requirements. In that case you can use `Schema.extend` to create a new
`Schema`:
```pycon
>>> from voluptuous import Schema
>>> person = Schema({'name': str})
>>> person_with_age = person.extend({'age': int})
>>> sorted(list(person_with_age.schema.keys()))
['age', 'name']
```
The original `Schema` remains unchanged.
### Objects
Each key-value pair in a schema dictionary is validated against each
attribute-value pair in the corresponding object:
```pycon
>>> from voluptuous import Object
>>> class Structure(object):
... def __init__(self, q=None):
... self.q = q
... def __repr__(self):
... return '<Structure(q={0.q!r})>'.format(self)
...
>>> schema = Schema(Object({'q': 'one'}, cls=Structure))
>>> schema(Structure(q='one'))
<Structure(q='one')>
```
### Allow None values
To allow value to be None as well, use Any:
```pycon
>>> from voluptuous import Any
>>> schema = Schema(Any(None, int))
>>> schema(None)
>>> schema(5)
5
```
## 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, as well
as an `error_message` attribute that contains the message of the original
exception. This is especially useful when you want to catch `Invalid`
exceptions and give some feedback to the user, for instance in the context of
an HTTP API.
```pycon
>>> def validate_email(email):
... """Validate email."""
... if not "@" in email:
... raise Invalid("This email is invalid.")
... return email
>>> schema = Schema({"email": validate_email})
>>> exc = None
>>> try:
... schema({"email": "whatever"})
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"This email is invalid. for dictionary value @ data['email']"
>>> exc.path
['email']
>>> exc.msg
'This email is invalid.'
>>> exc.error_message
'This email is invalid.'
```
The `path` attribute 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:
```pycon
>>> 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:
```pycon
>>> try:
... schema([[6]])
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "not a valid value @ data[0][0]"
True
```
If we pass the data `[6]`, the `6` is not a list type and so will not
recurse into the first element of the schema. Matching will continue on
to the second element in the schema, and succeed:
```pycon
>>> schema([6])
[6]
```
## Running tests.
Voluptuous is using nosetests:
$ nosetests
## Why use Voluptuous over another validation library?
**Validators are simple callables**
: No need to subclass anything, just use a function.
**Errors are simple exceptions.**
: A validator can just `raise Invalid(msg)` and expect the user to get
useful messages.
**Schemas are basic Python data structures.**
: Should your data be a dictionary of integer keys to strings?
`{int: str}` does what you expect. List of integers, floats or
strings? `[int, float, str]`.
**Designed from the ground up for validating more than just forms.**
: Nested data structures are treated in the same way as any other
type. Need a list of dictionaries? `[{}]`
**Consistency.**
: Types in the schema are checked as types. Values are compared as
values. Callables are called to validate. Simple.
## Other libraries and inspirations
Voluptuous is heavily inspired by
[Validino](http://code.google.com/p/validino/), and to a lesser extent,
[jsonvalidator](http://code.google.com/p/jsonvalidator/) and
[json\_schema](http://blog.sendapatch.se/category/json_schema.html).
I greatly prefer the light-weight style promoted by these libraries to
the complexity of libraries like FormEncode.

589
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Voluptuous is a Python data validation library
==============================================
|Build Status| |Stories in Ready|
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 for complex data structures.
3. Provide useful error messages.
Contact
-------
Voluptuous now has a mailing list! Send a mail to
` <mailto:voluptuous@librelist.com>`__ to subscribe. Instructions will
follow.
You can also contact me directly via `email <mailto:alec@swapoff.org>`__
or `Twitter <https://twitter.com/alecthomas>`__.
To file a bug, create a `new
issue <https://github.com/alecthomas/voluptuous/issues/new>`__ on GitHub
with a short example of how to replicate the issue.
Show me an example
------------------
Twitter's `user search
API <https://dev.twitter.com/docs/api/1/get/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:
.. code:: pycon
>>> 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, defaulting to 5, for
example. To describe the semantics of the API more accurately, our
schema will need to be more thoroughly defined:
.. code:: pycon
>>> from voluptuous import Required, All, Length, Range
>>> schema = Schema({
... Required('q'): All(str, Length(min=1)),
... Required('per_page', default=5): 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:
.. code:: pycon
>>> from voluptuous import MultipleInvalid, Invalid
>>> try:
... schema({})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data['q']"
True
...must be a string:
.. code:: pycon
>>> try:
... schema({'q': 123})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "expected str for dictionary value @ data['q']"
True
...and must be at least one character in length:
.. code:: pycon
>>> try:
... schema({'q': ''})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "length of value must be at least 1 for dictionary value @ data['q']"
True
>>> schema({'q': '#topic'}) == {'q': '#topic', 'per_page': 5}
True
"per\_page" is a positive integer no greater than 20:
.. code:: pycon
>>> try:
... schema({'q': '#topic', 'per_page': 900})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "value must be at most 20 for dictionary value @ data['per_page']"
True
>>> try:
... schema({'q': '#topic', 'per_page': -10})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "value must be at least 1 for dictionary value @ data['per_page']"
True
"page" is an integer >= 0:
.. code:: pycon
>>> try:
... schema({'q': '#topic', 'per_page': 'one'})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"expected int for dictionary value @ data['per_page']"
>>> schema({'q': '#topic', 'page': 1}) == {'q': '#topic', 'page': 1, 'per_page': 5}
True
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:
.. code:: pycon
>>> schema = Schema(1)
>>> schema(1)
1
>>> schema = Schema('a string')
>>> schema('a string')
'a string'
Types
~~~~~
Types in the schema are matched by checking if the corresponding value
is an instance of the type:
.. code:: pycon
>>> schema = Schema(int)
>>> schema(1)
1
>>> try:
... schema('one')
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "expected int"
True
URL's
~~~~~
URL's in the schema are matched by using ``urlparse`` library.
.. code:: pycon
>>> from voluptuous import Url
>>> schema = Schema(Url())
>>> schema('http://w3.org')
'http://w3.org'
>>> try:
... schema('one')
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "expected a URL"
True
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:
.. code:: pycon
>>> 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']
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.
The simplest kind of validator is a Python function that raises
ValueError when its argument is invalid. Conveniently, many builtin
Python functions have this property. Here's an example of a date
validator:
.. code:: pycon
>>> from datetime import datetime
>>> def Date(fmt='%Y-%m-%d'):
... return lambda v: datetime.strptime(v, fmt)
.. code:: pycon
>>> schema = Schema(Date())
>>> schema('2013-03-03')
datetime.datetime(2013, 3, 3, 0, 0)
>>> try:
... schema('2013-03')
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "not a valid value"
True
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:
.. code:: python
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.
Dictionaries
~~~~~~~~~~~~
Each key-value pair in a schema dictionary is validated against each
key-value pair in the corresponding data dictionary:
.. code:: pycon
>>> schema = Schema({1: 'one', 2: 'two'})
>>> schema({1: 'one'})
{1: 'one'}
Extra dictionary keys
^^^^^^^^^^^^^^^^^^^^^
By default any additional keys in the data, not in the schema will
trigger exceptions:
.. code:: pycon
>>> schema = Schema({2: 3})
>>> try:
... schema({1: 2, 2: 3})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "extra keys not allowed @ data[1]"
True
This behaviour can be altered on a per-schema basis. To allow additional
keys use ``Schema(..., extra=ALLOW_EXTRA)``:
.. code:: pycon
>>> from voluptuous import ALLOW_EXTRA
>>> schema = Schema({2: 3}, extra=ALLOW_EXTRA)
>>> schema({1: 2, 2: 3})
{1: 2, 2: 3}
To remove additional keys use ``Schema(..., extra=REMOVE_EXTRA)``:
.. code:: pycon
>>> from voluptuous import REMOVE_EXTRA
>>> schema = Schema({2: 3}, extra=REMOVE_EXTRA)
>>> schema({1: 2, 2: 3})
{2: 3}
It can also be overridden per-dictionary by using the catch-all marker
token ``extra`` as a key:
.. code:: pycon
>>> 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:
.. code:: pycon
>>> schema = Schema({1: 2, 3: 4})
>>> schema({3: 4})
{3: 4}
Similarly to how extra\_ keys work, this behaviour can be overridden
per-schema:
.. code:: pycon
>>> schema = Schema({1: 2, 3: 4}, required=True)
>>> try:
... schema({3: 4})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data[1]"
True
And per-key, with the marker token ``Required(key)``:
.. code:: pycon
>>> schema = Schema({Required(1): 2, 3: 4})
>>> try:
... schema({3: 4})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data[1]"
True
>>> schema({1: 2})
{1: 2}
Optional dictionary keys
^^^^^^^^^^^^^^^^^^^^^^^^
If a schema has ``required=True``, keys may be individually marked as
optional using the marker token ``Optional(key)``:
.. code:: pycon
>>> from voluptuous import Optional
>>> schema = Schema({1: 2, Optional(3): 4}, required=True)
>>> try:
... schema({})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "required key not provided @ data[1]"
True
>>> schema({1: 2})
{1: 2}
>>> try:
... schema({1: 2, 4: 5})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "extra keys not allowed @ data[4]"
True
.. code:: pycon
>>> schema({1: 2, 3: 4})
{1: 2, 3: 4}
Recursive schema
~~~~~~~~~~~~~~~~
There is no syntax to have a recursive schema. The best way to do it is
to have a wrapper like this:
.. code:: pycon
>>> from voluptuous import Schema, Any
>>> def s2(v):
... return s1(v)
...
>>> s1 = Schema({"key": Any(s2, "value")})
>>> s1({"key": {"key": "value"}})
{'key': {'key': 'value'}}
Extending an existing Schema
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Often it comes handy to have a base ``Schema`` that is extended with
more requirements. In that case you can use ``Schema.extend`` to create
a new ``Schema``:
.. code:: pycon
>>> from voluptuous import Schema
>>> person = Schema({'name': str})
>>> person_with_age = person.extend({'age': int})
>>> sorted(list(person_with_age.schema.keys()))
['age', 'name']
The original ``Schema`` remains unchanged.
Objects
~~~~~~~
Each key-value pair in a schema dictionary is validated against each
attribute-value pair in the corresponding object:
.. code:: pycon
>>> from voluptuous import Object
>>> class Structure(object):
... def __init__(self, q=None):
... self.q = q
... def __repr__(self):
... return '<Structure(q={0.q!r})>'.format(self)
...
>>> schema = Schema(Object({'q': 'one'}, cls=Structure))
>>> schema(Structure(q='one'))
<Structure(q='one')>
Allow None values
~~~~~~~~~~~~~~~~~
To allow value to be None as well, use Any:
.. code:: pycon
>>> from voluptuous import Any
>>> schema = Schema(Any(None, int))
>>> schema(None)
>>> schema(5)
5
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, as well as an ``error_message`` attribute that contains the
message of the original exception. This is especially useful when you
want to catch ``Invalid`` exceptions and give some feedback to the user,
for instance in the context of an HTTP API.
.. code:: pycon
>>> def validate_email(email):
... """Validate email."""
... if not "@" in email:
... raise Invalid("This email is invalid.")
... return email
>>> schema = Schema({"email": validate_email})
>>> exc = None
>>> try:
... schema({"email": "whatever"})
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"This email is invalid. for dictionary value @ data['email']"
>>> exc.path
['email']
>>> exc.msg
'This email is invalid.'
>>> exc.error_message
'This email is invalid.'
The ``path`` attribute 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:
.. code:: pycon
>>> 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:
.. code:: pycon
>>> try:
... schema([[6]])
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == "not a valid value @ data[0][0]"
True
If we pass the data ``[6]``, the ``6`` is not a list type and so will
not recurse into the first element of the schema. Matching will continue
on to the second element in the schema, and succeed:
.. code:: pycon
>>> schema([6])
[6]
Running tests.
--------------
Voluptuous is using nosetests:
::
$ nosetests
Why use Voluptuous over another validation library?
---------------------------------------------------
**Validators are simple callables**
No need to subclass anything, just use a function.
**Errors are simple exceptions.**
A validator can just ``raise Invalid(msg)`` and expect the user to
get useful messages.
**Schemas are basic Python data structures.**
Should your data be a dictionary of integer keys to strings?
``{int: str}`` does what you expect. List of integers, floats or
strings? ``[int, float, str]``.
**Designed from the ground up for validating more than just forms.**
Nested data structures are treated in the same way as any other
type. Need a list of dictionaries? ``[{}]``
**Consistency.**
Types in the schema are checked as types. Values are compared as
values. Callables are called to validate. Simple.
Other libraries and inspirations
--------------------------------
Voluptuous is heavily inspired by
`Validino <http://code.google.com/p/validino/>`__, and to a lesser
extent, `jsonvalidator <http://code.google.com/p/jsonvalidator/>`__ and
`json\_schema <http://blog.sendapatch.se/category/json_schema.html>`__.
I greatly prefer the light-weight style promoted by these libraries to
the complexity of libraries like FormEncode.
.. |Build Status| image:: https://travis-ci.org/alecthomas/voluptuous.png
:target: https://travis-ci.org/alecthomas/voluptuous
.. |Stories in Ready| image:: https://badge.waffle.io/alecthomas/voluptuous.png?label=ready&title=Ready
:target: https://waffle.io/alecthomas/voluptuous

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[nosetests]
doctest-extension = md
with-doctest = 1
where = .
[egg_info]
tag_build =
tag_date = 0
tag_svn_revision = 0

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try:
from setuptools import setup
except ImportError:
from distutils.core import setup
import sys
import os
import atexit
sys.path.insert(0, '.')
version = __import__('voluptuous').__version__
try:
import pypandoc
long_description = pypandoc.convert('README.md', 'rst')
with open('README.rst', 'w') as f:
f.write(long_description)
atexit.register(lambda: os.unlink('README.rst'))
except (ImportError, OSError):
print('WARNING: Could not locate pandoc, using Markdown long_description.')
with open('README.md') as f:
long_description = f.read()
description = long_description.splitlines()[0].strip()
setup(
name='voluptuous',
url='https://github.com/alecthomas/voluptuous',
download_url='https://pypi.python.org/pypi/voluptuous',
version=version,
description=description,
long_description=long_description,
license='BSD',
platforms=['any'],
py_modules=['voluptuous'],
author='Alec Thomas',
author_email='alec@swapoff.org',
classifiers=[
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Developers',
'License :: OSI Approved :: BSD License',
'Operating System :: OS Independent',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.1',
'Programming Language :: Python :: 3.2',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
],
install_requires=[
'setuptools >= 0.6b1',
],
)

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Error reporting should be accurate:
>>> from voluptuous import *
>>> schema = Schema(['one', {'two': 'three', 'four': ['five'],
... 'six': {'seven': 'eight'}}])
>>> schema(['one'])
['one']
>>> schema([{'two': 'three'}])
[{'two': 'three'}]
It should show the exact index and container type, in this case a list
value:
>>> try:
... schema(['one', 'two'])
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc) == 'expected a dictionary @ data[1]'
True
It should also be accurate for nested values:
>>> try:
... schema([{'two': 'nine'}])
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"not a valid value for dictionary value @ data[0]['two']"
>>> try:
... schema([{'four': ['nine']}])
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"not a valid value @ data[0]['four'][0]"
>>> try:
... schema([{'six': {'seven': 'nine'}}])
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"not a valid value for dictionary value @ data[0]['six']['seven']"
Errors should be reported depth-first:
>>> validate = Schema({'one': {'two': 'three', 'four': 'five'}})
>>> try:
... validate({'one': {'four': 'six'}})
... except Invalid as e:
... print(e)
... print(e.path)
not a valid value for dictionary value @ data['one']['four']
['one', 'four']
Voluptuous supports validation when extra fields are present in the
data:
>>> schema = Schema({'one': 1, Extra: object})
>>> schema({'two': 'two', 'one': 1}) == {'two': 'two', 'one': 1}
True
>>> schema = Schema({'one': 1})
>>> try:
... schema({'two': 2})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"extra keys not allowed @ data['two']"
dict, list, and tuple should be available as type validators:
>>> Schema(dict)({'a': 1, 'b': 2}) == {'a': 1, 'b': 2}
True
>>> Schema(list)([1,2,3])
[1, 2, 3]
>>> Schema(tuple)((1,2,3))
(1, 2, 3)
Validation should return instances of the right types when the types are
subclasses of dict or list:
>>> class Dict(dict):
... pass
>>>
>>> d = Schema(dict)(Dict(a=1, b=2))
>>> d == {'a': 1, 'b': 2}
True
>>> type(d) is Dict
True
>>> class List(list):
... pass
>>>
>>> l = Schema(list)(List([1,2,3]))
>>> l
[1, 2, 3]
>>> type(l) is List
True
Multiple errors are reported:
>>> schema = Schema({'one': 1, 'two': 2})
>>> try:
... schema({'one': 2, 'two': 3, 'three': 4})
... except MultipleInvalid as e:
... errors = sorted(e.errors, key=lambda k: str(k))
... print([str(i) for i in errors]) # doctest: +NORMALIZE_WHITESPACE
["extra keys not allowed @ data['three']",
"not a valid value for dictionary value @ data['one']",
"not a valid value for dictionary value @ data['two']"]
>>> schema = Schema([[1], [2], [3]])
>>> try:
... schema([1, 2, 3])
... except MultipleInvalid as e:
... print([str(i) for i in e.errors]) # doctest: +NORMALIZE_WHITESPACE
['expected a list @ data[0]',
'expected a list @ data[1]',
'expected a list @ data[2]']
Required fields in dictionary which are invalid should not have required :
>>> from voluptuous import *
>>> schema = Schema({'one': {'two': 3}}, required=True)
>>> try:
... schema({'one': {'two': 2}})
... except MultipleInvalid as e:
... errors = e.errors
>>> 'required' in ' '.join([x.msg for x in errors])
False
Multiple errors for nested fields in dicts and objects:
> \>\>\> from collections import namedtuple \>\>\> validate = Schema({
> ... 'anobject': Object({ ... 'strfield': str, ... 'intfield': int ...
> }) ... }) \>\>\> try: ... SomeObj = namedtuple('SomeObj', ('strfield',
> 'intfield')) ... validate({'anobject': SomeObj(strfield=123,
> intfield='one')}) ... except MultipleInvalid as e: ...
> print(sorted(str(i) for i in e.errors)) \# doctest:
> +NORMALIZE\_WHITESPACE ["expected int for object value @
> data['anobject']['intfield']", "expected str for object value @
> data['anobject']['strfield']"]
Custom classes validate as schemas:
>>> class Thing(object):
... pass
>>> schema = Schema(Thing)
>>> t = schema(Thing())
>>> type(t) is Thing
True
Classes with custom metaclasses should validate as schemas:
>>> class MyMeta(type):
... pass
>>> class Thing(object):
... __metaclass__ = MyMeta
>>> schema = Schema(Thing)
>>> t = schema(Thing())
>>> type(t) is Thing
True
Schemas built with All() should give the same error as the original
validator (Issue \#26):
>>> schema = Schema({
... Required('items'): All([{
... Required('foo'): str
... }])
... })
>>> try:
... schema({'items': [{}]})
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"required key not provided @ data['items'][0]['foo']"
Validator should return same instance of the same type for object:
>>> class Structure(object):
... def __init__(self, q=None):
... self.q = q
... def __repr__(self):
... return '{0.__name__}(q={1.q!r})'.format(type(self), self)
...
>>> schema = Schema(Object({'q': 'one'}, cls=Structure))
>>> type(schema(Structure(q='one'))) is Structure
True
Object validator should treat cls argument as optional. In this case it
shouldn't check object type:
>>> from collections import namedtuple
>>> NamedTuple = namedtuple('NamedTuple', ('q',))
>>> schema = Schema(Object({'q': 'one'}))
>>> named = NamedTuple(q='one')
>>> schema(named) == named
True
>>> schema(named)
NamedTuple(q='one')
If cls argument passed to object validator we should check object type:
>>> schema = Schema(Object({'q': 'one'}, cls=Structure))
>>> schema(NamedTuple(q='one')) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
MultipleInvalid: expected a <class 'Structure'>
>>> schema = Schema(Object({'q': 'one'}, cls=NamedTuple))
>>> schema(NamedTuple(q='one'))
NamedTuple(q='one')
Ensure that objects with \_\_slots\_\_ supported properly:
>>> class SlotsStructure(Structure):
... __slots__ = ['q']
...
>>> schema = Schema(Object({'q': 'one'}))
>>> schema(SlotsStructure(q='one'))
SlotsStructure(q='one')
>>> class DictStructure(object):
... __slots__ = ['q', '__dict__']
... def __init__(self, q=None, page=None):
... self.q = q
... self.page = page
... def __repr__(self):
... return '{0.__name__}(q={1.q!r}, page={1.page!r})'.format(type(self), self)
...
>>> structure = DictStructure(q='one')
>>> structure.page = 1
>>> try:
... schema(structure)
... raise AssertionError('MultipleInvalid not raised')
... except MultipleInvalid as e:
... exc = e
>>> str(exc)
"extra keys not allowed @ data['page']"
>>> schema = Schema(Object({'q': 'one', Extra: object}))
>>> schema(structure)
DictStructure(q='one', page=1)
Ensure that objects can be used with other validators:
>>> schema = Schema({'meta': Object({'q': 'one'})})
>>> schema({'meta': Structure(q='one')})
{'meta': Structure(q='one')}
Ensure that subclasses of Invalid of are raised as is.
>>> class SpecialInvalid(Invalid):
... pass
...
>>> def custom_validator(value):
... raise SpecialInvalid('boom')
...
>>> schema = Schema({'thing': custom_validator})
>>> try:
... schema({'thing': 'not an int'})
... except MultipleInvalid as e:
... exc = e
>>> exc.errors[0].__class__.__name__
'SpecialInvalid'

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