mirror of https://github.com/zulip/zulip.git
113 lines
3.6 KiB
Python
113 lines
3.6 KiB
Python
'''
|
|
This module sets up a scheme for validating that arbitrary Python
|
|
objects are correctly typed. It is totally decoupled from Django,
|
|
composable, easily wrapped, and easily extended.
|
|
|
|
A validator takes two parameters--var_name and val--and returns an
|
|
error if val is not the correct type. The var_name parameter is used
|
|
to format error messages. Validators return None when there are no errors.
|
|
|
|
Example primitive validators are check_string, check_int, and check_bool.
|
|
|
|
Compound validators are created by check_list and check_dict. Note that
|
|
those functions aren't directly called for validation; instead, those
|
|
functions are called to return other functions that adhere to the validator
|
|
contract. This is similar to how Python decorators are often parameterized.
|
|
|
|
The contract for check_list and check_dict is that they get passed in other
|
|
validators to apply to their items. This allows you to build up validators
|
|
for arbitrarily complex validators. See ValidatorTestCase for example usage.
|
|
|
|
A simple example of composition is this:
|
|
|
|
check_list(check_string)('my_list', ['a', 'b', 'c']) == None
|
|
|
|
To extend this concept, it's simply a matter of writing your own validator
|
|
for any particular type of object.
|
|
'''
|
|
from __future__ import absolute_import
|
|
import six
|
|
|
|
def check_string(var_name, val):
|
|
if not isinstance(val, six.string_types):
|
|
return '%s is not a string' % (var_name,)
|
|
return None
|
|
|
|
def check_int(var_name, val):
|
|
if not isinstance(val, int):
|
|
return '%s is not an integer' % (var_name,)
|
|
return None
|
|
|
|
def check_bool(var_name, val):
|
|
if not isinstance(val, bool):
|
|
return '%s is not a boolean' % (var_name,)
|
|
return None
|
|
|
|
def check_none_or(sub_validator):
|
|
def f(var_name, val):
|
|
if val is None:
|
|
return None
|
|
else:
|
|
return sub_validator(var_name, val)
|
|
return f
|
|
|
|
def check_list(sub_validator, length=None):
|
|
def f(var_name, val):
|
|
if not isinstance(val, list):
|
|
return '%s is not a list' % (var_name,)
|
|
|
|
if length is not None and length != len(val):
|
|
return '%s should have exactly %d items' % (var_name, length)
|
|
|
|
if sub_validator:
|
|
for i, item in enumerate(val):
|
|
vname = '%s[%d]' % (var_name, i)
|
|
error = sub_validator(vname, item)
|
|
if error:
|
|
return error
|
|
|
|
return None
|
|
return f
|
|
|
|
def check_dict(required_keys):
|
|
# required_keys is a list of tuples of
|
|
# key_name/validator
|
|
|
|
def f(var_name, val):
|
|
if not isinstance(val, dict):
|
|
return '%s is not a dict' % (var_name,)
|
|
|
|
for k, sub_validator in required_keys:
|
|
if k not in val:
|
|
return '%s key is missing from %s' % (k, var_name)
|
|
vname = '%s["%s"]' % (var_name, k)
|
|
error = sub_validator(vname, val[k])
|
|
if error:
|
|
return error
|
|
|
|
return None
|
|
|
|
return f
|
|
|
|
def check_variable_type(allowed_type_funcs):
|
|
"""
|
|
Use this validator if an argument is of a variable type (e.g. processing
|
|
properties that might be strings or booleans).
|
|
|
|
`allowed_type_funcs`: the check_* validator functions for the possible data
|
|
types for this variable.
|
|
"""
|
|
def enumerated_type_check(var_name, val):
|
|
for func in allowed_type_funcs:
|
|
if not func(var_name, val):
|
|
return None
|
|
return '%s is not an allowed_type' % (var_name,)
|
|
return enumerated_type_check
|
|
|
|
def equals(expected_val):
|
|
def f(var_name, val):
|
|
if val != expected_val:
|
|
return '%s != %r (%r is wrong)' % (var_name, expected_val, val)
|
|
return None
|
|
return f
|