2. Validation Schema with validate.py ¶

2.1. Using the Validator class ¶

Authors:: Michael Foord, Nicola Larosa, Rob Dennis, Eli Courtwright, Mark Andrews
Version:: Validate 2.0.0
Date:: 2014/02/08
Homepage:: Github Page
License:: BSD License
Support:: Mailing List

2.1.1. Introduction ¶

Validation is used to check that supplied values conform to a specification.

The value can be supplied as a string, e.g. from a config file. In this case the check will also convert the value to the required type. This allows you to add validation as a transparent layer to access data stored as strings. The validation checks that the data is correct and converts it to the expected type.

Checks are also strings, and are easy to write. One generic system can be used to validate information from different sources via a single consistent mechanism.

Checks look like function calls, and map to function calls. They can include parameters and keyword arguments. These arguments are passed to the relevant function by the Validator instance, along with the value being checked.

The syntax for checks also allows for specifying a default value. This default value can be None, no matter what the type of the check. This can be used to indicate that a value was missing, and so holds no useful value.

Functions either return a new value, or raise an exception. See Validator Exceptions for the low down on the exception classes that validate.py defines.

Some standard functions are provided, for basic data types; these come built into every validator. Additional checks are easy to write: they can be provided when the Validator is instantiated, or added afterwards.

Validate was primarily written to support ConfigObj, but is designed to be applicable to many other situations.

For support and bug reports please use the ConfigObj Github Page

2.1.2. Downloading ¶

The current version is 2.0.0, dated 8th February 2014.

You can obtain validate in the following ways :

2.1.2.1. Files ¶

validate.py from Github Page
The latest development version can be obtained from the Github Page.

2.1.3. The standard functions ¶

The standard functions come built-in to every Validator instance. They work with the following basic data types :

integer
float
boolean
string
ip_addr

plus lists of these datatypes.

Adding additional checks is done through coding simple functions.

The full set of standard checks are :

‘integer’:

matches integer values (including negative). Takes optional ‘min’ and ‘max’ arguments:

integer()
integer(3, 9)    # any value from 3 to 9
integer(min=0) # any positive value
integer(max=9)

‘float’:

matches float values Has the same parameters as the integer check.

‘boolean’:

matches boolean values: True or False.

Acceptable string values for True are:

true, on, yes, 1

Acceptable string values for False are:

false, off, no, 0

Any other value raises an error.

‘string’:

matches any string. Takes optional keyword args ‘min’ and ‘max’ to specify min and max length of string.

‘ip_addr’:

matches an Internet Protocol address, v.4, represented by a dotted-quad string, i.e. ‘1.2.3.4’.

‘list’:

matches any list. Takes optional keyword args ‘min’, and ‘max’ to specify min and max sizes of the list. The list checks always return a list.

force_list:

matches any list, but if a single value is passed in will coerce it into a list containing that value. Useful for configobj if the user forgot the trailing comma to turn a single value into a list.

‘tuple’:

matches any list. This check returns a tuple rather than a list.

‘int_list’:

Matches a list of integers. Takes the same arguments as list.

‘float_list’:

Matches a list of floats. Takes the same arguments as list.

‘bool_list’:

Matches a list of boolean values. Takes the same arguments as list.

‘string_list’:

Matches a list of strings. Takes the same arguments as list.

‘ip_addr_list’:

Matches a list of IP addresses. Takes the same arguments as list.

‘mixed_list’:

Matches a list with different types in specific positions. List size must match the number of arguments.

Each position can be one of:

int, str, boolean, float, ip_addr

So to specify a list with two strings followed by two integers, you write the check as:

mixed_list(str, str, int, int)

‘pass’:

matches everything: it never fails and the value is unchanged. It is also the default if no check is specified.

‘option’:

matches any from a list of options. You specify this test with:

option('option 1', 'option 2', 'option 3')

The following code will work without you having to specifically add the functions yourself.

from validate import Validator
#
vtor = Validator()
newval1 = vtor.check('integer', value1)
newval2 = vtor.check('boolean', value2)
# etc ...

Note

Of course, if these checks fail they raise exceptions. So you should wrap them in try...except blocks. Better still, use ConfigObj for a higher level interface.

2.1.4. Using Validator ¶

Using Validator is very easy. It has one public attribute and one public method.

Shown below are the different steps in using Validator.

The only additional thing you need to know, is about Writing check functions.

2.1.4.1. Instantiate ¶

from validate import Validator
vtor = Validator()

or even :

from validate import Validator
#
fdict = {
    'check_name1': function1,
    'check_name2': function2,
    'check_name3': function3,
}
#
vtor = Validator(fdict)

The second method adds a set of your functions as soon as your validator is created. They are stored in the vtor.functions dictionary. The ‘key’ you give them in this dictionary is the name you use in your checks (not the original function name).

Dictionary keys/functions you pass in can override the built-in ones if you want.

2.1.4.2. Adding functions ¶

The code shown above, for adding functions on instantiation, has exactly the same effect as the following code :

from validate import Validator
#
vtor = Validator()
vtor.functions['check_name1'] = function1
vtor.functions['check_name2'] = function2
vtor.functions['check_name3'] = function3

vtor.functions is just a dictionary that maps names to functions, so we could also have called vtor.functions.update(fdict).

2.1.4.3. Writing the check ¶

As we’ve heard, the checks map to the names in the functions dictionary. You’ve got a full list of The standard functions and the arguments they take.

If you’re using Validator from ConfigObj, then your checks will look like:

keyword = int_list(max=6)

but the check part will be identical .

2.1.4.4. The check method ¶

If you’re not using Validator from ConfigObj, then you’ll need to call the check method yourself.

If the check fails then it will raise an exception, so you’ll want to trap that. Here’s the basic example :

from validate import Validator, ValidateError
#
vtor = Validator()
check = "integer(0, 9)"
value = 3
try:
    newvalue = vtor.check(check, value)
except ValidateError:
    print 'Check Failed.'
else:
    print 'Check passed.'

Caution

Although the value can be a string, if it represents a list it should already have been turned into a list of strings.

2.1.4.4.1. Default Values ¶

Some values may not be available, and you may want to be able to specify a default as part of the check.

You do this by passing the keyword missing=True to the check method, as well as a default=value in the check. (Constructing these checks is done automatically by ConfigObj: you only need to know about the default=value part) :

check1 = 'integer(default=50)'
check2 = 'option("val 1", "val 2", "val 3", default="val 1")'

assert vtor.check(check1, '', missing=True) == 50
assert vtor.check(check2, '', missing=True) == "val 1"

If you pass in missing=True to the check method, then the actual value is ignored. If no default is specified in the check, a ValidateMissingValue exception is raised. If a default is specified then that is passed to the check instead.

If the check has default=None (case sensitive) then vtor.check will always return None (the object). This makes it easy to tell your program that this check contains no useful value when missing, i.e. the value is optional, and may be omitted without harm.

Note

As of version 0.3.0, if you specify default='None' (note the quote marks around None) then it will be interpreted as the string 'None'.

2.1.4.4.2. List Values ¶

It’s possible that you would like your default value to be a list. It’s even possible that you will write your own check functions - and would like to pass them keyword arguments as lists from within the check.

To avoid confusing syntax with commas and quotes you use a list constructor to specify that keyword arguments are lists. This includes the default value. This makes checks look something like:

checkname(default=list('val1', 'val2', 'val3'))

2.1.4.5. get_default_value ¶

Validator instances have a get_default_value method. It takes a check string (the same string you would pass to the check method) and returns the default value, converted to the right type. If the check doesn’t define a default value then this method raises a KeyError.

If the check has been seen before then it will have been parsed and cached already, so this method is not expensive to call (however the conversion is done each time).

2.1.5. Validator Exceptions ¶

Note

If you only use Validator through ConfigObj, it traps these Exceptions for you. You will still need to know about them for writing your own check functions.

vtor.check indicates that the check has failed by raising an exception. The appropriate error should be raised in the check function.

The base error class is ValidateError. All errors (except for VdtParamError) raised are sub-classes of this.

If an unrecognised check is specified then VdtUnknownCheckError is raised.

There are also VdtTypeError and VdtValueError.

If incorrect parameters are passed to a check function then it will (or should) raise VdtParamError. As this indicates programmer error, rather than an error in the value, it is a subclass of SyntaxError instead of ValidateError.

Note

This means it won’t be caught by ConfigObj - but propagated instead.

If the value supplied is the wrong type, then the check should raise VdtTypeError. e.g. the check requires the value to be an integer (or representation of an integer) and something else was supplied.

If the value supplied is the right type, but an unacceptable value, then the check should raise VdtValueError. e.g. the check requires the value to be an integer (or representation of an integer) less than ten and a higher value was supplied.

Both VdtTypeError and VdtValueError are initialised with the incorrect value. In other words you raise them like this :

raise VdtTypeError(value)
#
raise VdtValueError(value)

VdtValueError has the following subclasses, which should be raised if they are more appropriate.

VdtValueTooSmallError
VdtValueTooBigError
VdtValueTooShortError
VdtValueTooLongError

2.1.6. Writing check functions ¶

Writing check functions is easy.

The check function will receive the value as its first argument, followed by any other parameters and keyword arguments.

If the check fails, it should raise a VdtTypeError or a VdtValueError (or an appropriate subclass).

All parameters and keyword arguments are always passed as strings. (Parsed from the check string).

The value might be a string (or list of strings) and need converting to the right type - alternatively it might already be a list of integers. Our function needs to be able to handle either.

If the check passes then it should return the value (possibly converted to the right type).

And that’s it !

2.1.6.1. Example ¶

Here is an example function that requires a list of integers. Each integer must be between 0 and 99.

It takes a single argument specifying the length of the list. (Which allows us to use the same check in more than one place). If the length can’t be converted to an integer then we need to raise VdtParamError.

Next we check that the value is a list. Anything else should raise a VdtTypeError. The list should also have ‘length’ entries. If the list has more or less entries then we will need to raise a VdtValueTooShortError or a VdtValueTooLongError.

Then we need to check every entry in the list. Each entry should be an integer between 0 and 99, or a string representation of an integer between 0 and 99. Any other type is a VdtTypeError, any other value is a VdtValueError (either too big, or too small).

def special_list(value, length):
    """
    Check that the supplied value is a list of integers,
    with 'length' entries, and each entry between 0 and 99.
    """
    # length is supplied as a string
    # we need to convert it to an integer
    try:
        length = int(length)
    except ValueError:
        raise VdtParamError('length', length)
    #
    # Check the supplied value is a list
    if not isinstance(value, list):
        raise VdtTypeError(value)
    #
    # check the length of the list is correct
    if len(value) > length:
        raise VdtValueTooLongError(value)
    elif len(value) < length:
        raise VdtValueTooShortError(value)
    #
    # Next, check every member in the list
    # converting strings as necessary
    out = []
    for entry in value:
        if not isinstance(entry, (str, unicode, int)):
            # a value in the list
            # is neither an integer nor a string
            raise VdtTypeError(value)
        elif isinstance(entry, (str, unicode)):
            if not entry.isdigit():
                raise VdtTypeError(value)
            else:
                entry = int(entry)
        if entry < 0:
            raise VdtValueTooSmallError(value)
        elif entry > 99:
            raise VdtValueTooBigError(value)
        out.append(entry)
    #
    # if we got this far, all is well
    # return the new list
    return out

If you are only using validate from ConfigObj then the error type (TooBig, TooSmall, etc) is lost - so you may only want to raise VdtValueError.

Caution

If your function raises an exception that isn’t a subclass of ValidateError, then ConfigObj won’t trap it. This means validation will fail.

This is why our function starts by checking the type of the value. If we are passed the wrong type (e.g. an integer rather than a list) we get a VdtTypeError rather than bombing out when we try to iterate over the value.

If you are using validate in another circumstance you may want to create your own subclasses of ValidateError which convey more specific information.

2.1.7. Known Issues ¶

The following parses and then blows up. The resulting error message is confusing:

checkname(default=list(1, 2, 3, 4)

This is because it parses as: checkname(default="list(1", 2, 3, 4). That isn’t actually unreasonable, but the error message won’t help you work out what has happened.

2.1.8. TODO ¶

A regex check function ?
A timestamp check function ? (Using the parse function from DateUtil perhaps).

2.1.9. ISSUES ¶

Note

Please file any bug reports to the Github Page

If we could pull tuples out of arguments, it would be easier to specify arguments for ‘mixed_lists’.

2.1.10. CHANGELOG ¶

2.1.10.1. 2014/02/08 - Version 2.0.0 ¶

Python 3 single-source compatibility at the cost of a more restrictive set of versions: 2.6, 2.7, 3.2, 3.3 (otherwise unchanged)
New maintainers: Rob Dennis and Eli Courtwright
New home on github

2.1.10.2. 2009/10/25 - Version 1.0.1 ¶

BUGFIX: Fixed compatibility with Python 2.3.

2.1.10.3. 2009/04/13 - Version 1.0.0 ¶

BUGFIX: can now handle multiline strings.
Addition of ‘force_list’ validation option.

As the API is stable and there are no known bugs or outstanding feature requests I am marking this 1.0.

2.1.10.4. 2008/02/24 - Version 0.3.2 ¶

BUGFIX: Handling of None as default value fixed.

2.1.10.5. 2008/02/05 - Version 0.3.1 ¶

BUGFIX: Unicode checks no longer broken.

2.1.10.6. 2008/02/05 - Version 0.3.0 ¶

Improved performance with a parse cache.

New get_default_value method. Given a check it returns the default value (converted to the correct type) or raises a KeyError if the check doesn’t specify a default.

Added ‘tuple’ check and corresponding ‘is_tuple’ function (which always returns a tuple).

BUGFIX: A quoted ‘None’ as a default value is no longer treated as None, but as the string ‘None’.

BUGFIX: We weren’t unquoting keyword arguments of length two, so an empty string didn’t work as a default.

BUGFIX: Strings no longer pass the ‘is_list’ check. Additionally, the list checks always return lists.

A couple of documentation bug fixes.

Removed CHANGELOG from module.

default=list("val", "val", "val")

Added the _test test.

Moved a function call outside a try…except block.

2.1.10.11. 2005/08/18 Version 0.2.0 ¶

Updated by Michael Foord and Nicola Larosa

Does type conversion as well.

2.1.10.12. 2005/02/01 Version 0.1.0 ¶

Initial version developed by Michael Foord and Mark Andrews.

2. Validation Schema with validate.py ¶

2.1. Using the Validator class ¶

2.1.1. Introduction ¶

2.1.2. Downloading ¶

2.1.2.1. Files ¶

2.1.3. The standard functions ¶

2.1.4. Using Validator ¶

2.1.4.1. Instantiate ¶

2.1.4.2. Adding functions ¶

2.1.4.3. Writing the check ¶

2.1.4.4. The check method ¶

2.1.4.4.1. Default Values ¶

2.1.4.4.2. List Values ¶

2.1.4.5. get_default_value ¶

2.1.5. Validator Exceptions ¶

2.1.6. Writing check functions ¶

2.1.6.1. Example ¶

2.1.7. Known Issues ¶

2.1.8. TODO ¶

2.1.9. ISSUES ¶

2.1.10. CHANGELOG ¶

2.1.10.1. 2014/02/08 - Version 2.0.0 ¶

2.1.10.2. 2009/10/25 - Version 1.0.1 ¶

2.1.10.3. 2009/04/13 - Version 1.0.0 ¶

2.1.10.4. 2008/02/24 - Version 0.3.2 ¶

2.1.10.5. 2008/02/05 - Version 0.3.1 ¶

2.1.10.6. 2008/02/05 - Version 0.3.0 ¶

2.1.10.7. 2007/02/04 Version 0.2.3 ¶

2.1.10.8. 2006/12/17 Version 0.2.3-alpha1 ¶

2.1.10.9. 2006/04/29 Version 0.2.2 ¶

2.1.10.10. 2005/12/16 Version 0.2.1 ¶

2.1.10.11. 2005/08/18 Version 0.2.0 ¶

2.1.10.12. 2005/02/01 Version 0.1.0 ¶

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