Validation and parsing library
Project description
Trafaret
========
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.
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.
========
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.
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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