datatype 0.8.0

Anonymous datatype validation

Examples

>>> from datatype.validation import failures

>>> datatype = {'foo': [{'bar': 'int'}]}
>>> bad_value = {'foo': [{'bar': 'baz'}], 'bif': 'pow!'}

>>> failures(datatype, bad_value)
['unexpected property "bif"', 'foo[0].bar: expected int, got str']

Wildcard dictionary keys:

>>> datatype = {'_any_': ['int']}
>>> good_value = {'foo': [1, 2, 3], 'bar': [3, 4, 5]}

>>> failures(datatype, good_value)
[]

Coercion:

>>> from datatype.coercion import coerce_value

>>> coerce_value(['str'], [1, 2, 3])
['1', '2', '3']

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 | 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) 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}

definition: ["int", "str"]
example value: [5, "foo"]

Copyright and License

Copyright 2011-2012 LearningStation, Inc. and Adam Wagner

Licensed under the BSD-3 License. You may obtain a copy of the License in the LICENSE file.