Cacheable schema description and validation
Project description
Conformity is a declarative schema validation library designed for use in libraries, services, application settings, and more.
Declare a schema:
from conformity.fields import Dictionary, Float, Integer, List, UnicodeString
person = Dictionary({
"name": UnicodeString(),
"height": Float(gte=0),
"event_ids": List(Integer(gt=0)),
})Check to see if data is valid:
data = {"name": "Andrew", "height": 180.3, "event_ids": [1, "3"]}
errors = person.errors(data)
# Key event_ids: Index 1: Not an integerAnd wrap functions to validate on the way in and out:
kwargs = Dictionary({
"name": UnicodeString(),
"score": Integer(),
}, optional_keys=["score"])
@validate_call(kwargs, UnicodeString())
def greet(name, score=0):
if score > 10:
return "So nice to meet you, {}!".format(name)
else:
return "Hello, {}.".format(name)There’s support for basic string, numeric, geographic, temporal, networking, and other field types, with everything easily extensible (optionally via subclassing). Conformity also boasts support for full-blown application settings schema definition and validation complete with definable defaults, and includes Sphinx autodoc extensions to help you generate meaningful documentation for your code using Conformity.
Installation
Conformity is available in PyPi and can be installing directly via Pip or listed in setup.py, requirements.txt, or Pipfile:
pip install 'conformity~=1.26'install_requires=[
...
'conformity~=1.26',
...
]conformity~=1.26conformity = {version="~=1.26"}Documentation
The complete Conformity documentation is available on Read the Docs!
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