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===================================
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Validation Schema with validate.py
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===================================
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--------------------------
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Using the Validator class
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--------------------------
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:Authors: `Michael Foord`_, `Nicola Larosa`_, `Mark Andrews`_
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:Version: Validate 0.2.1
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:Homepage: `Validate Homepage`_
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:License: `BSD License`_
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:Support: `Mailing List`_
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.. _Mailing List: http://lists.sourceforge.net/lists/listinfo/configobj-develop
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.. _Michael Foord: fuzzyman@voidspace.org.uk
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.. _Nicola Larosa: nico@teknico.net
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.. _Mark Andrews: mark@la-la.com
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.. _Validate Homepage: http://www.voidspace.org.uk/python/validate.html
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.. _BSD License: http://www.voidspace.org.uk/python/license.shtml
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.. contents:: Validate Manual
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Validation is used to check that supplied values conform to a specification.
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The value can be supplied as a string, e.g. from a config file. In this case
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the check will also *convert* the value to the required type. This allows you
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to add validation as a transparent layer to access data stored as strings. The
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validation checks that the data is correct *and* converts it to the expected
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Checks are also strings, and are easy to write. One generic system can be used
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to validate information from different sources, via a single consistent
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Checks look like function calls, and map to function calls. They can include
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parameters and keyword arguments. These arguments are passed to the relevant
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function by the ``Validator`` instance, along with the value being checked.
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The syntax for checks also allows for specifying a default value. This default
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value can be ``None``, no matter what the type of the check. This can be used
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to indicate that a value was missing, and so holds no useful value.
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Functions either return a new value, or raise an exception. See `Validator
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Exceptions`_ for the low down on the exception classes that ``validate.py``
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Some standard functions are provided, for basic data types; these come built
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into every validator. Additional checks are easy to write: they can be provided
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when the ``Validator`` is instantiated, or added afterwards.
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Validate was primarily written to support ConfigObj_, but is designed to be
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applicable to many other situations.
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For support and bug reports please use the ConfigObj `Mailing List`_.
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.. _ConfigObj: http://www.voidspace.org.uk/python/configobj.html
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The current version is **0.2.1**, dated 16th December 2005.
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You can get obtain validate in the following ways :
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* validate.py_ from Voidspace
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* configobj.zip from Voidspace - See the homepage of ConfigObj_ for the latest
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version and download links.
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This contains validate.py and `this document`_. (As well as ConfigObj_ and
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the ConfigObj documentation).
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* The latest development version can be obtained from the `Subversion Repository`_.
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*configobj.zip* contains `this document`_ and full `API Docs`_, generated
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automatically by EpyDoc_.
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* You can view `this document`_ online as the `Validate Homepage`_.
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* You can also browse the `API Docs`_ online.
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Validate_ is also part of the Pythonutils_ set of modules. This contains
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various other useful helper modules, and is required by many of the `Voidspace
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.. _configobj.py: http://www.voidspace.org.uk/cgi-bin/voidspace/downman.py?file=configobj.py
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.. _configobj.zip: http://www.voidspace.org.uk/cgi-bin/voidspace/downman.py?file=configobj-4.1.0.zip
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.. _validate.py: http://www.voidspace.org.uk/cgi-bin/voidspace/downman.py?file=validate.py
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.. _API Docs: http://www.voidspace.org.uk/python/configobj-api/
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.. _Subversion Repository: http://svn.rest2web.python-hosting.com/branches/configobj4/
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.. _Sourceforge: http://sourceforge.net/projects/configobj
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.. _EpyDoc: http://epydoc.sourceforge.net
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.. _pythonutils: http://www.voidspace.org.uk/python/pythonutils.html
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.. _Voidspace Python Projects: http://www.voidspace.org.uk/python
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.. _validate: http://www.voidspace.org.uk/python/validate.html
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The standard functions
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======================
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The standard functions come built-in to every ``Validator`` instance. They work
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with the following basic data types :
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plus lists of these datatypes.
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Adding additional checks is done through coding simple functions.
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The full set of standard checks are :
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:'integer': matches integer values (including negative). Takes optional 'min'
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and 'max' arguments : ::
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integer(3, 9) # any value from 3 to 9
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integer(min=0) # any positive value
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:'float': matches float values
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Has the same parameters as the integer check.
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:'bool': matches boolean values: ``True`` or ``False``.
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Acceptable string values for True are : ::
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Acceptable string values for False are : ::
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Any other value raises an error.
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:'string': matches any string. Takes optional keyword args 'min' and 'max' to
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specify min and max length of string.
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:'ip_addr': matches an Internet Protocol address, v.4, represented by a
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dotted-quad string, i.e. '1.2.3.4'.
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:'list': matches any list. Takes optional keyword args 'min', and 'max' to
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specify min and max sizes of the list.
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:'int_list': Matches a list of integers. Takes the same arguments as list.
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:'float_list': Matches a list of floats. Takes the same arguments as list.
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:'bool_list': Matches a list of boolean values. Takes the same arguments as
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:'string_list': Matches a list of strings. Takes the same arguments as list.
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:'ip_addr_list': Matches a list of IP addresses. Takes the same arguments as
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:'mixed_list': Matches a list with different types in specific positions.
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List size must match the number of arguments.
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Each position can be one of : ::
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int, str, bool, float, ip_addr
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So to specify a list with two strings followed by two integers,
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you write the check as : ::
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mixed_list(str, str, int, int)
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:'pass': matches everything: it never fails and the value is unchanged. It is
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also the default if no check is specified.
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:'option': matches any from a list of options.
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You specify this test with : ::
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option('option 1', 'option 2', 'option 3')
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The following code will work without you having to specifically add the
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from validate import Validator
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newval1 = vtor.check('integer', value1)
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newval2 = vtor.check('bool', value2)
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Of course, if these checks fail they raise exceptions. So you should wrap
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them in ``try...except`` blocks. Better still, use ConfigObj for a higher
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Using ``Validator`` is very easy. It has one public attribute and one public
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Shown below are the different steps in using ``Validator``.
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The only additional thing you need to know, is about `Writing check
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from validate import Validator
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from validate import Validator
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'check_name1': function1,
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'check_name2': function2,
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'check_name3': function3,
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vtor = Validator(fdict)
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The second method adds a set of your functions as soon as your validator is
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created. They are stored in the ``vtor.functions`` dictionary. The 'key' you
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give them in this dictionary is the name you use in your checks (not the
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original function name).
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Dictionary keys/functions you pass in can override the built-in ones if you
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The code shown above, for adding functions on instantiation, has exactly the
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same effect as the following code :
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from validate import Validator
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vtor.functions['check_name1'] = function1
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vtor.functions['check_name2'] = function2
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vtor.functions['check_name3'] = function3
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``vtor.functions``is just a dictionary that maps names to functions, so we
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could also have called ``vtor.functions.update(fdict)``.
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As we've heard, the checks map to the names in the ``functions`` dictionary.
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You've got a full list of `The standard functions`_ and the arguments they
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If you're using ``Validator`` from ConfigObj, then your checks will look like
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keyword = int_list(max=6)
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but the check part will be identical .
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If you're not using ``Validator`` from ConfigObj, then you'll need to call the
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``check`` method yourself.
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If the check fails then it will raise an exception, so you'll want to trap
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that. Here's the basic example :
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from validate import Validator, ValidateError
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check = "integer(0, 9)"
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newvalue = vtor.check(check, value)
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except ValidateError:
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print 'Check Failed.'
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print 'Check passed.'
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Although the value can be a string, if it represents a list it should
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already have been turned into a list of strings.
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Some values may not be available, and you may want to be able to specify a
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default as part of the check.
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You do this by passing the keyword ``missing=True`` to the ``check`` method, as
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well as a ``default=value`` in the check. (Constructing these checks is done
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automatically by ConfigObj: you only need to know about the ``default=value``
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check1 = 'integer(default=50)'
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check2 = 'option("val 1", "val 2", "val 3", default="val 1")'
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assert vtor.check('', check1, missing=True) == 50
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assert vtor.check('', check2, missing=True) == "val 1"
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If you pass in ``missing=True`` to the check method, then the actual value is
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ignored. If no default is specified in the check, a ``ValidateMissingValue``
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exception is raised. If a default is specified then that is passed to the
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If the check has ``default=None`` (case sensitive) then ``vtor.check`` will
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*always* return ``None`` (the object). This makes it easy to tell your program
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that this check contains no useful value when missing, i.e. the value is
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optional, and may be omitted without harm.
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It's possible that you would like your default value to be a list. It's even
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possible that you will write your own check functions - and would like to pass
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them keyword arguments as lists from within the check.
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To avoid confusing syntax with commas and quotes you use a list constructor to
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specify that keyword arguments are lists. This includes the ``default`` value.
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This makes checks look something like : ::
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checkname(default=list('val1', 'val2', 'val3'))
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If you only use Validator through ConfigObj, it traps these Exceptions for
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you. You will still need to know about them for writing your own check
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``vtor.check`` indicates that the check has failed by raising an exception.
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The appropriate error should be raised in the check function.
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The base error class is ``ValidateError``. All errors (except for ``VdtParamError``)
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raised are sub-classes of this.
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If an unrecognised check is specified then ``VdtUnknownCheckError`` is
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There are also ``VdtTypeError`` and ``VdtValueError``.
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If incorrect parameters are passed to a check function then it will (or should)
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raise ``VdtParamError``. As this indicates *programmer* error, rather than an error
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in the value, it is a subclass of ``SyntaxError`` instead of ``ValidateError``.
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This means it *won't* be caught by ConfigObj - but propagated instead.
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If the value supplied is the wrong type, then the check should raise
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``VdtTypeError``. e.g. the check requires the value to be an integer (or
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representation of an integer) and something else was supplied.
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If the value supplied is the right type, but an unacceptable value, then the
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check should raise ``VdtValueError``. e.g. the check requires the value to
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be an integer (or representation of an integer) less than ten and a higher
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Both ``VdtTypeError`` and ``VdtValueError`` are initialised with the
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incorrect value. In other words you raise them like this :
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raise VdtTypeError(value)
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raise VdtValueError(value)
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``VdtValueError`` has the following subclasses, which should be raised if
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they are more appropriate.
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* ``VdtValueTooSmallError``
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* ``VdtValueTooBigError``
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* ``VdtValueTooShortError``
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* ``VdtValueTooLongError``
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Writing check functions
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=======================
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Writing check functions is easy.
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The check function will receive the value as its first argument, followed by
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any other parameters and keyword arguments.
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If the check fails, it should raise a ``VdtTypeError`` or a
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``VdtValueError`` (or an appropriate subclass).
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All parameters and keyword arguments are *always* passed as strings. (Parsed
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from the check string).
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The value might be a string (or list of strings) and need
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converting to the right type - alternatively it might already be a list of
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integers. Our function needs to be able to handle either.
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If the check passes then it should return the value (possibly converted to the
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Here is an example function that requires a list of integers. Each integer
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must be between 0 and 99.
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It takes a single argument specifying the length of the list. (Which allows us
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to use the same check in more than one place). If the length can't be converted
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to an integer then we need to raise ``VdtParamError``.
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Next we check that the value is a list. Anything else should raise a
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``VdtTypeError``. The list should also have 'length' entries. If the list
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has more or less entries then we will need to raise a
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``VdtValueTooShortError`` or a ``VdtValueTooLongError``.
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Then we need to check every entry in the list. Each entry should be an integer
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between 0 and 99, or a string representation of an integer between 0 and 99.
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Any other type is a ``VdtTypeError``, any other value is a
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``VdtValueError`` (either too big, or too small).
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def special_list(value, length):
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Check that the supplied value is a list of integers,
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with 'length' entries, and each entry between 0 and 99.
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# length is supplied as a string
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# we need to convert it to an integer
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raise VdtParamError('length', length)
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# Check the supplied value is a list
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if not isinstance(value, list):
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raise VdtTypeError(value)
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# check the length of the list is correct
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if len(value) > length:
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raise VdtValueTooLongError(value)
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elif len(value) < length:
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raise VdtValueTooShortError(value)
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# Next, check every member in the list
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# converting strings as necessary
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if not isinstance(entry, (str, unicode, int)):
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# a value in the list
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# is neither an integer nor a string
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raise VdtTypeError(value)
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elif isinstance(entry, (str, unicode)):
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if not entry.isdigit():
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raise VdtTypeError(value)
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raise VdtValueTooSmallError(value)
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raise VdtValueTooBigError(value)
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# if we got this far, all is well
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# return the new list
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If you are only using validate from ConfigObj then the error type (*TooBig*,
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*TooSmall*, etc) is lost - so you may only want to raise ``VdtValueError``.
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If your function raises an exception that isn't a subclass of
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``ValidateError``, then ConfigObj won't trap it. This means validation will
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This is why our function starts by checking the type of the value. If we
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are passed the wrong type (e.g. an integer rather than a list) we get a
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``VdtTypeError`` rather than bombing out when we try to iterate over
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If you are using validate in another circumstance you may want to create your
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own subclasses of ``ValidateError``, that convey more specific information.
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* A regex check function ?
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* A timestamp check function ? (Using the ``parse`` function from ``DateUtil``).
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* Allow triple quotes ? (getting a bit heavy for a regex)
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Please file any bug reports to `Michael Foord`_ or the ConfigObj
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If we could pull tuples out of arguments, it would be easier
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to specify arguments for 'mixed_lists'.
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2005/12/16 Version 0.2.1
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-----------------------------
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Fixed bug so we can handle keyword argument values with commas.
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We now use a list constructor for passing list values to keyword arguments
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(including ``default``) : ::
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default=list("val", "val", "val")
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Added the ``_test`` test. {sm;:-)}
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Moved a function call outside a try...except block.
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2005/08/18 Version 0.2.0
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-----------------------------
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Updated by `Michael Foord`_ and `Nicola Larosa`_
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Does type conversion as well.
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2005/02/01 Version 0.1.0
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-----------------------------
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Initial version developed by `Michael Foord`_
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Rendering this document with docutils also needs the
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textmacros module and the PySrc CSS stuff. See
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http://www.voidspace.org.uk/python/firedrop2/textmacros.shtml
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<a href="http://www.python.org">
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<img src="images/powered_by_python.jpg" width="602" height="186" border="0" />
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<a href="http://www.opensource.org">
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<img src="images/osi-certified-120x100.gif" width="120" height="100" border="0" />
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<br /><strong>Certified Open Source</strong>
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