>>> from datatype.validation import failures
>>> datatype = {'foo': [{'bar': 'int'}]}
>>> bad_value = {'foo': [{'bar': 'baz'}], 'bif': 'pow!'}
>>> failures(datatype, bad_value)
['foo[0].bar: expected int, got str', 'unexpected property "bif"']Wildcard dictionary keys:
>>> datatype = {'_any_': ['int']}
>>> good_value = {'foo': [1, 2, 3], 'bar': [3, 4, 5]}
>>> failures(datatype, good_value)
[]Datatype definitions are represented with a small set of types that should be built-in for most languages.
Required types for proper validation:
- int
- float
- string
- boolean
- dictionary (or anonymous object)
- list (or array)
DEFINITION = PRIMITIVE | LIST | DICTIONARY | TUPLE
PRIMITIVE = ["nullable "] + ("int" | "str" | "float" | "bool")
DICTIONARY = (dictionary of) key: DICTIONARY-KEY, value: DEFINITION
DICTIONARY-KEY = (["optional "] + DICTIONARY-KEY-NAME) | "_any_"
DICTIONARY-KEY-NAME = [A-Za-z0-9_]+
LIST = (list of one) DEFINITION
TUPLE = (list of more than one) DEFINITIONdefinition: "int"
example value: 5
definition: {"foo": "int"}
example value: {"foo": 5}
definition: [{"foo": ["bool"]}]
example value: [{"foo": [True, False]}, {"foo": [False, False]}]
definition: {"_any_": "int"}
example value: {"foo": 5, "bar": 7}
definition: ["int", "str"]
example value: [5, "foo"]Copyright 2011 LearningStation, Inc.
Licensed under the BSD-3 License. You may obtain a copy of the License in the LICENSE file.