Metadata-Version: 1.0
Name: dirty-validators
Version: 0.1.6
Summary: Validate library for python 3
Home-page: https://github.com/alfred82santa/dirty-validators
Author: alfred82santa
Author-email: alfred82santa@gmail.com
License: UNKNOWN
Description: ================
        dirty-validators
        ================
        
        Agnostic validators for python 3
        
        **Freely based** on [WTF-Forms](https://github.com/wtforms/wtforms) validators.
        
        ********
        Features
        ********
        - Python 3 package.
        - Easy to create a validator.
        - Chained validations.
        - Specific error control messages.
        - No database dependent.
        
        ************
        Installation
        ************
        .. code-block:: bash
        
            $ pip install dirty-validators
        
        ***********
        Basic usage
        ***********
        
        .. code-block:: python
        
            from dirty_validators.basic import EqualTo, Length, Regexp, Email
            from dirty_validators.complex import Optional, ModelValidate
        
            validator = Optional(validators=[EqualTo(comp_value="test")])
        
            assert validator.is_valid("test") is True
        
            # Chained validation
            validator_chain = Chain(validators=[Length(min=14, max=16), Regexp(regex='^abc'), Email()])
        
            assert validator_chain.is_valid('abcdefg@test.com')
        
            # Model validation
        
            class MyModelValidator(ModelValidate):
                fieldName1 = Optional(validators=[Length(min=4, max=6)])
                fieldName2 = Optional(validators=[Length(min=1, max=2)])
                fieldName3 = Required(validators=[Length(min=7, max=8)])
        
            validator_model = MyModelValidator()
        
            data = {
                "fieldName1": "1234",
                "fieldName1": "12",
                "fieldName3": "123456qw"
             }
        
            assert validator_model.is_valid(FakeModel(data)) is True
        
        .. note::
            Look at tests for more examples
        
        
        
Platform: UNKNOWN
