A non intrusive optional type checking for Python 3 using annotations
Project description
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.
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