Schematec
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Schematec is a set of tools that makes input data validation easier.
The purpose of this code is attempt to bring simplicity to applications
logics using separation of data validation and actual data processing.
Quickstart
.. code:: python
import schematec as s
schema = s.dictionary(
id=s.integer & s.required,
name=s.string,
tags=s.array(s.string),
)
.. code:: python
data = {
... 'id': '1',
... 'name': 'Red Hot Chili Peppers',
... 'tags': ['funk', 'rock'],
... 'rank': '1',
... }
schema(data)
{'id': 1, 'name': u'Red Hot Chili Peppers', 'tags': [u'funk', u'rock']}
Concepts
Schematec module is based on three basic concepts:
Schema
^^^^^^
Term "schema" is used to describe complex data struct such as dictionary(hashmap)
or array(list). Schemas has two different types of validation (it is not related to
array schemas):
- Default - Only values with required validator are required, other values are optional
- Weak - All values are optional
schematec.exc.SchemaError
is raised in case provided data is incorrect.
Order of schema validations:
#. Unbound Validators
#. Schemas(inner)
#. Converters
#. Bound Validators
Validator
^^^^^^^^^
Term "validator" describes callable objects that perform different types of checks.
There are two types of validators in schematec:
- Bound - type related, for example "max length" validator is bound to sized type.
- Unbound - universal, for example "required" validator.
Raises schematec.exc.ValidationError
.
Schematec provides following validators:
required
check if value is provided
length
check iterable for max length
regex
check if given value is valid
Converter
^^^^^^^^^
Term "converter" is used to describe cast functions. Schematec supports subset of JSON
data types.
Basic types:
- integer(int)
- string(str)
- boolean(bool)
Containers:
- array(list)
- dictionary(dict)
Raises schematec.exc.ConvertationError
.
Convertation rules
integer
#. Any int or long value
#. Any suitable string/unicode
#. Boolean value
number
#. Any float or int or long value
#. Any suitable string/unicode
#. Boolean value
string
#. Any suitable string/unicode
#. Any int or long value
boolean
#. Boolean value
#. 0 or 1
#. '0' or '1'
#. u'0' or u'1'
dictionary
#. Any mapping value(collections.Mapping)
array
#. Any iterable value(collections.Iterable), but not a mapping
Complex Descriptors
"Schema", "validator" and "converter" are internally referenced as "descriptors". Common task is
creation of complex validation rules for a field(or "complex descriptors"). To do this use bitwise
"and" operator on descriptors:
.. code:: python
import schematec
schematec.integer & schematec.required
<schematec.abc.ComplexDescriptor object at 0x10b05a0d0>
Sugar Schema
Schematec supports additional "magic" way to define your schemas. You can use simple dicts and lists
to describe your data. For example:
.. code:: python
import schematec as s
schema = {
... 'a': [{
... 'b': s.integer,
... }]
... }
data = {
... 'a': [{'b': 1}, {'b': '1'}, {}]
... }
s.process(schema, data)
{'a': [{'b': 1}, {'b': 1}, {}]}
Examples
Recursive schema
.. code:: python
import schematec as s
schema = s.dictionary(
id=s.integer & s.required,
entity=s.dictionary(
name=s.string & s.required,
value=s.string,
)
)
.. code:: python
data = {
... 'id': 1,
... 'entity': {
... 'name': 'song',
... 'value': 'californication',
... }
... }
schema(data)
{'id': 1, 'entity': {'name': u'song', 'value': u'californication'}}
Errors handling
.. code:: python
import schematec as s
schema = s.dictionary(
id=s.integer & s.required,
entity=s.dictionary(
name=s.string & s.required,
value=s.string,
)
)
.. code:: python
data = {
... 'id': 1,
... 'entity': {
... 'value': 'californication',
... }
... }
schema(data)
Traceback (most recent call last):
File "", line 1, in
File "schematec/schema.py", line 44, in call
value = schema(value, weak=weak)
File "schematec/schema.py", line 32, in call
validator(name, data)
File "schematec/validators.py", line 12, in call
raise exc.ValidationError(name)
schematec.exc.ValidationError: name