optypecheck 1.1

A non intrusive optional type checking for Python 3 using annotations

A non intrusive optional type checking for Python 3 using annotations

Now that Python 3 supports annotations many people are using the feature to describe the valid types for the input and output of functions and methods. This kind of usage turns the reading of code more easy besides simplifly the documentation.

Once types are listed in the annotations, why not use them to check the types? The type checking is especially valuable in the development phase.

This idea is not new and there are several implementations on the internet. Most of them using function decorators. The problem with this kind to implementation is that it pollutes the code and overloads the functions calls with type checking.

This package implements a non intrusive alternative for type checking in functions and methods. Once types are defined in annotations, no changes are required to make the verification of types. And, because it is completely optional, it can be used only in the desired environmens, like unit testings, for instance. This way, the performance of production code is not affected.

Installation

pip3 install optypecheck

Example

Create a python module, for instance utils.py

#!python
from typecheck import typecheck

def gencode(a: bytes, b: str) -> str:
    return '{}{}'.format(a[0], b)

assert typecheck(__name__)

Create a module to test, for instance test.py

#!python
from utils import gencode

def test1():
    return gencode('a', 'b') # raises TypeCheckError

def test2():
    return gencode(b'a', 'b') # no error

def test3():
    return gencode(b'a', b'b') # raises TypeCheckError

if __name__ == '__main__':
    import sys
    if len(sys.argv) == 2:
        test = getattr(sys.modules[__name__], sys.argv[1], None)
        if test:
            print(test())
            exit(0)
    print('Use: {} test1|test2|test3'.format(sys.argv[0]))

Testing with type checking:

Test1 - raises TypeCheckError for utils.test1()

#!bash
$python3 test.py test1
Traceback (most recent call last):
  File "test.py", line 21, in <module>
    test()
  File "test.py", line 8, in test1
    print(gencode('a', 'b')) # raises TypeCheckError
  File "/opt/python34/lib/python3.4/site-packages/typecheck/__init__.py", line 46, in decorated
    raise TypeCheckError(arg_error_fmt.format(name, argtype, args[i].__class__))
typecheck.TypeCheckError: Argument a expects an instance of <class 'bytes'>, <class 'str'> found

Test2 - no error for utils.test2()

#!bash
$python3 test.py test2
97b

Test3 - raises TypeCheckError for utils.test3()

#!bash
$python3 test.py test3
Traceback (most recent call last):
  File "test.py", line 21, in <module>
    test()
  File "test.py", line 14, in test3
    print(gencode(b'a', b'b')) # raises TypeCheckError
  File "/opt/python34/lib/python3.4/site-packages/typecheck/__init__.py", line 46, in decorated
    raise TypeCheckError(arg_error_fmt.format(name, argtype, args[i].__class__))
typecheck.TypeCheckError: Argument b expects an instance of <class 'str'>, <class 'bytes'> found

Testing with no type checking:

Because we use assert to call typecheck() if python is called with debug mode disabled, typecheck() is not called. This way we got rid of the overload of type checking in functions and methods.

Test1 - result of utils.test1() is wrong, but no error is reported!

#!bash
$python3 -O test.py test1
ab

Test2 - no error for utils.test2()

#!bash
$python3 -O test.py test2
97b

Test3 - result of utils.test3() is wrong, but no error is reported again!

#!bash
$python3 -O test.py test3
97b'b'

Cost of type checking

#!bash
$python3 -m timeit -s 'from test import test2' 'test2()' # with type checking
100000 loops, best of 3: 3.06 usec per loop

$python3 -O -m timeit -s 'from test import test2' 'test2()' # without type checking
1000000 loops, best of 3: 0.445 usec per loop

Type checked function is 6.87 times slower. That’s why it’s better to use it only for development and testing and, when the code is trusted, remove then with no penalties.