Validation and parsing library
Project description
Trafaret is validation library with support to convert data structures. Sample usage:
import datetime
import trafaret as t
date = t.Dict(year=t.Int, month=t.Int, day=t.Int) >> (lambda d: datetime.datetime(**d))
assert date.check({'year': 2012, 'month': 1, 'day': 12}) == datetime.datetime(2012, 1, 12)
t.Dict creates new dict structure validator with three t.Int elements. >> operation adds lambda function to the converters of given checker. Some checkers have default converter, but when you use >> or .append, you disable default converter with your own.
This does not mean that Int will not convert numbers to int, this mean that some checkers, like String with regular expression, have special converters applied to that can be overriden by your own.
Converters can be chained. You can raise DataError in converters.
Features
Trafaret has very handy features, read below some samples.
Regex String
String can work with regular expressions, and this givs you real power:
>>> c = t.String(regex=r'^name=(\w+)$') >> (lambda m: m.groups()[0])
>>> c.check('name=Jeff')
'Jeff'
Some way you can use all re.Match power to extract from strings dicts and so on.
Dict and Key
Dict get dict with keys and checkers, like {'a': t.Int}. But instead of string key you can use Key class. And Key instance can rename given key name to something else:
>>> c = t.Dict(t.Key('uNJ') >> 'user_name': t.String})
>>> c.check({'uNJ': 'Adam'})
{'user_name': 'Adam'}
And we can do more with right converter:
>>> from trafaret.utils import fold
>>> c = t.Dict({t.Key('uNJ') >> 'user__name': t.String}) >> fold
>>> c.check({'uNJ': 'Adam'})
{'user': {'name': 'Adam'}}
We have some example of enhanced Key in extras:
>>> from trafaret.extras import KeysSubset
>>> cmp_pwds = lambda x: {'pwd': x['pwd'] if x.get('pwd') == x.get('pwd1') else DataError('Not equal')}
>>> d = Dict({KeysSubset('pwd', 'pwd1'): cmp_pwds, 'key1': String})
>>> d.check({'pwd': 'a', 'pwd1': 'a', 'key1': 'b'}).keys()
{'pwd': 'a', 'key1': 'b'}
DataError
Exception class that used in library. Exception hold errors in error attribute. For simple checkers it will be just a string. For nested structures it will be dict instance.
Trafaret
Base class for checkers. Use it to make new checkers. In derrived classes you need to implement _check or _check_val methods. _check_val must return value, _check must return None on success.
You can implement converter method if you want to convert value somehow, but want to make free for developer to apply his own converters to raw data. This used to return strings instead of Match object in String trafaret.
Subclassing
For your own trafaret creation you need to subclass Trafaret class and implement check_value or check_and_return methods. check_value can return nothing on success, check_and_return must return value. In case of failure you need to raise DataError. You can use self._failure shortcut function to do this. Check library code for samples.
Type
Checks that data is instance of given class. Just instantitate it with any class, like int, float, str. Sample:
>>> Type(int).check(4) 4
Any
Will match any element.
Or
Get other converters as args. This samples are equivalent:
>>> Or(t.Int, t.Null).check(None) None >>> (t.Int | t.Null).check(5) 5
Null
Value must be None.
Bool
Check if value is boolean:
>>> t.Bool.check(True) True
Float
Check if value is float or can be converted to. Supports lte, gte, lt, gt parameters:
>>> t.Float(gt=3.5).check(4) 4
Int
Similar to Float, but checking for int:
>>> t.Int(gt=3).check(4) 4
Atom
Value must be exactly equal to Atom first arg:
>>> t.Atom('this_key_must_be_this').check('this_key_must_be_this')
'this_key_must_be_this'
This may be useful in Dict in pair with Or statements.
String, Email, URL
Basicaly just check that arg is string. Argument allow_blank indicates if string can be blank ot not. If you will provide regex param - will return re.Match object. Default converter will return match.group() result. You will get re.Match object in converter.
Email and URL just provide regular expressions and a bit of logic for IDNA domains. Default converters return email and domain, but you will get re.Match in converter.
So, some examples to make things clear:
>>> t.String().check('werwerwer')
'werwerwer'
>>> t.String(regex='^\s+$).check(' ')
' '
>>> t.String(regex='^name=(\w+)$').check('name=Jeff')
'Jeff'
And one wild sample:
>>> todt = lambda m: datetime(*[int(i) for i in m.groups()])
>>> (t.String(regex='^year=(\d+),month=(\d+),day=(\d+)$') >> todt).check('year=2011,month=07,day=23')
datetime.datetime(2011, 7, 23, 0, 0)
List
Just List of elements of one type. In converter you will get list of converted elements. Sample:
>>> t.List(t.Int).check(range(100))
[0, 1, 2, ... 99]
>>> t.extract_error(t.List(t.Int).check(['a']))
{0: 'value cant be converted to int'}
Dict
Dict include named params. You can use for keys plain strings and Key instances. In case you provide just string keys, they will converted to Key instances. Actual checking proceeded in Key instance.
Methods:
allow_extra(*names) : where names can be key names or * to allow any additional keys.
make_optional(*names) : where names can be key names or * to make all options optional.
ignore_extra(*names): where names are the names of the keys or * to exclude listed key names or all unspecified ones from the validation process and final result
Key
Special class to create dict keys. Parameters are:
name - key name
default - default if key is not present
optional - if True allow to not provide arg
to_name - instead of key name will be returned this key
- You can provide to_name with >> operation::
Key(‘javaStyleData’) >> ‘plain_cool_data’
KeysSubset
Experimental feature, not stable API. Sometimes you need to make something with part of dict keys. So you can:
>>> join = (lambda d: {'name': ' '.join(d.values())})
>>> Dict({KeysSubset('name', 'last'): join}).check({'name': 'Adam', 'last': 'Smith'})
{'name': 'Smith Adam'}
As you can see you need to return dict from checker.
Error raise
In Dict you can just return error from checkers or converters, there is need not to raise them.
Mapping
Check both keys and values:
>>> trafaret = Mapping(String, Int)
>>> trafaret
<Mapping(<String> => <Int>)>
>>> trafaret.check({"foo": 1, "bar": 2})
{'foo': 1, 'bar': 2}
Enum
- This checker check that value one from provided. Like::
>>> Enum(1, 2, 'error').check('2') 2
Callable
Check if data is callable.
Call
Take a function that will be called in check. Function must return value or DataError.
Forward
This checker is container for any checker, that you can provide later. To provide container use provide method or << operation:
>> node = Forward() >> node << Dict(name=String, children=List[node])
guard
Decorator for function:
>>> @guard(a=String, b=Int, c=String) ... def fn(a, b, c="default"): ... '''docstring''' ... return (a, b, c)
GuardError
Derived from DataError.
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