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https://github.com/alfred82santa/dirty-validators
Agnostic validators
https://github.com/alfred82santa/dirty-validators
Last synced: about 2 months ago
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Agnostic validators
- Host: GitHub
- URL: https://github.com/alfred82santa/dirty-validators
- Owner: alfred82santa
- License: mit
- Created: 2014-01-31T07:27:19.000Z (almost 11 years ago)
- Default Branch: develop
- Last Pushed: 2022-03-15T22:35:56.000Z (almost 3 years ago)
- Last Synced: 2024-10-13T11:26:28.748Z (2 months ago)
- Language: Python
- Size: 114 KB
- Stars: 2
- Watchers: 7
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
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README
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================
dirty-validators
================
Agnostic validators for python 3
**Freely based** on [WTF-Forms](https://github.com/wtforms/wtforms) validators.
.. warning::
Be aware about incompatible changes on `Version 0.6.0`_.
********
Features
********
- Python 3 package.
- Easy to create a validator.
- Chained validations.
- Conditional validations.
- Specific error control messages.
- Dirty model integration (https://github.com/alfred82santa/dirty-models)
- No database dependent.
- Asynchronous validators.
- Stateless validators.
- Deferred validators.
*********
Changelog
*********
-------------
Version 0.6.0
-------------
The stateless refactor. Dirty validators now are full stateless.
It means now there is no messages on validator instance. The ``is_valid`` method returns
a validation context. It is evaluated as ``True`` if there were no errors and false
otherwise. So, it is possible to still to use checks like ``if validator.is_valid(my_data):``
but we recomend to use the Walruf operator (https://peps.python.org/pep-0572/):
``if result := validator.is_valid(my_data):``.
That is important because messages are stored on context.
.. code-block:: python
# Python +3.8
if result := validator.is_valid(my_data):
# some code
else:
print(result.error_messages)
#or
if not (result := validator.is_valid(my_data)):
print(result.error_messages)
On the other hand, validation error messages now are an object. It stores error code, error message
template and context values. There is a helper function ``from_context_to_legacy_message`` in order
to convert a context to legacy message format.
- Stateless refactor
- New ``Context`` class stores state of validation process.
- New ``ValidationErrorMessage`` class to store validation error messages.
- New ``Deferred`` validator in order to build validator on runtime depending on context.
- New ``from_context_to_legacy_message`` helper.
-------------
Version 0.5.4
-------------
- Allow model validator inheritance.
-------------
Version 0.5.2
-------------
- Remove hard dependency from Dirty Models.
- Fix bug iterating ListModels.
-------------
Version 0.5.1
-------------
- Added value validators for mappings.
-------------
Version 0.5.0
-------------
- Added asynchronous validators.
-------------
Version 0.4.0
-------------
- Added ```` keyword in order to look up a field from root model of context.
- Added ``key_validator`` argument for spec validators in order to validate keys on hashmaps.
************
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('[email protected]')
# 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