File: //proc/self/root/opt/alt/python27/lib/python2.7/site-packages/schema-0.3.1-py2.7.egg-info/PKG-INFO
Metadata-Version: 1.1
Name: schema
Version: 0.3.1
Summary: Simple data validation library
Home-page: http://github.com/halst/schema
Author: Vladimir Keleshev
Author-email: vladimir@keleshev.com
License: MIT
Description: Schema validation just got Pythonic
===============================================================================
New in version 0.2.0:
- Allow intermix literals and other schemas in dictionary keys.
I.e. ``Schema({'<id>': int, str: object})`` will check ``<id>``
for being ``int``, and will disregard other keys of type ``<str>``.
See this `StackOverflow question
<http://stackoverflow.com/questions/14588098>`_ for more.
**schema** is a library for validating Python data structures, such as those
obtained from config-files, forms, external services or command-line
parsing, converted from JSON/YAML (or something else) to Python data-types.
.. image:: https://secure.travis-ci.org/halst/schema.png?branch=master
:target: https://travis-ci.org/halst/schema
.. image:: https://coveralls.io/repos/halst/schema/badge.png?branch=master
:target: https://coveralls.io/r/halst/schema?branch=master
Example
----------------------------------------------------------------------------
Here is a quick example to get a feeling of **schema**, validating a list of
entries with personal information:
.. code:: python
>>> from schema import Schema, And, Use, Optional
>>> schema = Schema([{'name': And(str, len),
... 'age': And(Use(int), lambda n: 18 <= n <= 99),
... Optional('sex'): And(str, Use(str.lower),
... lambda s: s in ('male', 'female'))}])
>>> data = [{'name': 'Sue', 'age': '28', 'sex': 'FEMALE'},
... {'name': 'Sam', 'age': '42'},
... {'name': 'Sacha', 'age': '20', 'sex': 'Male'}]
>>> validated = schema.validate(data)
>>> assert validated == [{'name': 'Sue', 'age': 28, 'sex': 'female'},
... {'name': 'Sam', 'age': 42},
... {'name': 'Sacha', 'age' : 20, 'sex': 'male'}]
If data is valid, ``Schema.validate`` will return the validated data
(optionally converted with `Use` calls, see below).
If data is invalid, ``Schema`` will raise ``SchemaError`` exception.
Installation
-------------------------------------------------------------------------------
Use `pip <http://pip-installer.org>`_ or easy_install::
pip install schema==0.3.1
Alternatively, you can just drop ``schema.py`` file into your project—it is
self-contained.
- **schema** is tested with Python 2.6, 2.7, 3.2, 3.3 and PyPy.
- **schema** follows `semantic versioning <http://semver.org>`_.
How ``Schema`` validates data
-------------------------------------------------------------------------------
Types
~~~~~
If ``Schema(...)`` encounters a type (such as ``int``, ``str``, ``object``,
etc.), it will check if the corresponding piece of data is an instance of that type,
otherwise it will raise ``SchemaError``.
.. code:: python
>>> from schema import Schema
>>> Schema(int).validate(123)
123
>>> Schema(int).validate('123')
Traceback (most recent call last):
...
SchemaError: '123' should be instance of <type 'int'>
>>> Schema(object).validate('hai')
'hai'
Callables
~~~~~~~~~
If ``Schema(...)`` encounters a callable (function, class, or object with
``__call__`` method) it will call it, and if its return value evaluates to
``True`` it will continue validating, else—it will raise ``SchemaError``.
.. code:: python
>>> import os
>>> Schema(os.path.exists).validate('./')
'./'
>>> Schema(os.path.exists).validate('./non-existent/')
Traceback (most recent call last):
...
SchemaError: exists('./non-existent/') should evaluate to True
>>> Schema(lambda n: n > 0).validate(123)
123
>>> Schema(lambda n: n > 0).validate(-12)
Traceback (most recent call last):
...
SchemaError: <lambda>(-12) should evaluate to True
"Validatables"
~~~~~~~~~~~~~~
If ``Schema(...)`` encounters an object with method ``validate`` it will run
this method on corresponding data as ``data = obj.validate(data)``. This method
may raise ``SchemaError`` exception, which will tell ``Schema`` that that piece
of data is invalid, otherwise—it will continue validating.
As example, you can use ``Use`` for creating such objects. ``Use`` helps to use
a function or type to convert a value while validating it:
.. code:: python
>>> from schema import Use
>>> Schema(Use(int)).validate('123')
123
>>> Schema(Use(lambda f: open(f, 'a'))).validate('LICENSE-MIT')
<open file 'LICENSE-MIT', mode 'a' at 0x...>
Dropping the details, ``Use`` is basically:
.. code:: python
class Use(object):
def __init__(self, callable_):
self._callable = callable_
def validate(self, data):
try:
return self._callable(data)
except Exception as e:
raise SchemaError('%r raised %r' % (self._callable.__name__, e))
Now you can write your own validation-aware classes and data types.
Lists, similar containers
~~~~~~~~~~~~~~~~~~~~~~~~~
If ``Schema(...)`` encounters an instance of ``list``, ``tuple``, ``set`` or
``frozenset``, it will validate contents of corresponding data container
against schemas listed inside that container:
.. code:: python
>>> Schema([1, 0]).validate([1, 1, 0, 1])
[1, 1, 0, 1]
>>> Schema((int, float)).validate((5, 7, 8, 'not int or float here'))
Traceback (most recent call last):
...
SchemaError: Or(<type 'int'>, <type 'float'>) did not validate 'not int or float here'
'not int or float here' should be instance of <type 'float'>
Dictionaries
~~~~~~~~~~~~
If ``Schema(...)`` encounters an instance of ``dict``, it will validate data
key-value pairs:
.. code:: python
>>> d = Schema({'name': str,
... 'age': lambda n: 18 < 99}).validate({'name': 'Sue', 'age': 28})
>>> assert d == {'name': 'Sue', 'age': 28}
You can specify keys as schemas too:
.. code:: python
>>> schema = Schema({str: int, # string keys should have integer values
... int: None}) # int keys should be always None
>>> data = schema.validate({'key1': 1, 'key2': 2,
... 10: None, 20: None})
>>> schema.validate({'key1': 1,
... 10: 'not None here'})
Traceback (most recent call last):
...
SchemaError: None does not match 'not None here'
This is useful if you want to check certain key-values, but don't care
about other:
.. code:: python
>>> schema = Schema({'<id>': int,
... '<file>': Use(open),
... str: object}) # don't care about other str keys
>>> data = schema.validate({'<id>': 10,
... '<file>': 'README.rst',
... '--verbose': True})
You can mark a key as optional as follows:
.. code:: python
>>> from schema import Optional
>>> Schema({'name': str,
... Optional('occupation'): str}).validate({'name': 'Sam'})
{'name': 'Sam'}
**schema** has classes ``And`` and ``Or`` that help validating several schemas
for the same data:
.. code:: python
>>> from schema import And, Or
>>> Schema({'age': And(int, lambda n: 0 < n < 99)}).validate({'age': 7})
{'age': 7}
>>> Schema({'password': And(str, lambda s: len(s) > 6)}).validate({'password': 'hai'})
Traceback (most recent call last):
...
SchemaError: <lambda>('hai') should evaluate to True
>>> Schema(And(Or(int, float), lambda x: x > 0)).validate(3.1415)
3.1415
User-friendly error reporting
-------------------------------------------------------------------------------
You can pass a keyword argument ``error`` to any of validatable classes
(such as ``Schema``, ``And``, ``Or``, ``Use``) to report this error instead of
a built-in one.
.. code:: python
>>> Schema(Use(int, error='Invalid year')).validate('XVII')
Traceback (most recent call last):
...
SchemaError: Invalid year
You can see all errors that occured by accessing exception's ``exc.autos``
for auto-generated error messages, and ``exc.errors`` for errors
which had ``error`` text passed to them.
You can exit with ``sys.exit(exc.code)`` if you want to show the messages
to the user without traceback. ``error`` messages are given precedence in that
case.
A JSON API example
-------------------------------------------------------------------------------
Here is a quick example: validation of
`create a gist <http://developer.github.com/v3/gists/>`_
request from github API.
.. code:: python
>>> gist = '''{"description": "the description for this gist",
... "public": true,
... "files": {
... "file1.txt": {"content": "String file contents"},
... "other.txt": {"content": "Another file contents"}}}'''
>>> from schema import Schema, And, Use, Optional
>>> import json
>>> gist_schema = Schema(And(Use(json.loads), # first convert from JSON
... # use basestring since json returns unicode
... {Optional('description'): basestring,
... 'public': bool,
... 'files': {basestring: {'content': basestring}}}))
>>> gist = gist_schema.validate(gist)
# gist:
{u'description': u'the description for this gist',
u'files': {u'file1.txt': {u'content': u'String file contents'},
u'other.txt': {u'content': u'Another file contents'}},
u'public': True}
Using **schema** with `docopt <http://github.com/docopt/docopt>`_
-------------------------------------------------------------------------------
Assume you are using **docopt** with the following usage-pattern:
Usage: my_program.py [--count=N] <path> <files>...
and you would like to validate that ``<files>`` are readable, and that
``<path>`` exists, and that ``--count`` is either integer from 0 to 5, or
``None``.
Assuming **docopt** returns the following dict:
.. code:: python
>>> args = {'<files>': ['LICENSE-MIT', 'setup.py'],
... '<path>': '../',
... '--count': '3'}
this is how you validate it using ``schema``:
.. code:: python
>>> from schema import Schema, And, Or, Use
>>> import os
>>> s = Schema({'<files>': [Use(open)],
... '<path>': os.path.exists,
... '--count': Or(None, And(Use(int), lambda n: 0 < n < 5))})
>>> args = s.validate(args)
>>> args['<files>']
[<open file 'LICENSE-MIT', mode 'r' at 0x...>, <open file 'setup.py', mode 'r' at 0x...>]
>>> args['<path>']
'../'
>>> args['--count']
3
As you can see, **schema** validated data successfully, opened files and
converted ``'3'`` to ``int``.
Keywords: schema json validation
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Utilities
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: License :: OSI Approved :: MIT License