Anonymous datatype validation
Project description
Examples
>>> 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
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)
Specification
DEFINITION = PRIMITIVE | LIST | DICTIONARY PRIMITIVE = ["nullable "] + ("int" | "str" | "float" | "bool") DICTIONARY = {DICTIONARY-KEY: DEFINITION} DICTIONARY-KEY = (["optional "] + DICTIONARY-KEY-NAME) | "_any_" DICTIONARY-KEY-NAME = [A-Za-z0-9_]+ LIST = [DEFINITION]
Definition Examples (in python)
definition: "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}
Copyright and License
Copyright 2011 LearningStation, Inc.
Licensed under the BSD-3 License. You may obtain a copy of the License in the LICENSE file.
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