Run-time type checker for Python
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tfiers and agronholm Fix DeprecationWarning on python 3.7 (#44)
The error in full (when typechecking for a Sequence):

``` DeprecationWarning: Using or importing the ABCs from
'collections' instead of from '' is deprecated, and in
3.8 it will stop working

  if not isinstance(value, collections.Sequence):
Latest commit f294e37 Sep 11, 2018


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This library provides run-time type checking for functions defined with argument type annotations.

The typing module introduced in Python 3.5 (and available on PyPI for older versions of Python 3) is supported. See below for details.

There are three principal ways to use type checking, each with its pros and cons:

  1. calling check_argument_types() from within the function body:
    • debugger friendly (except when running with the pydev debugger with the C extension installed)
    • cannot check the type of the return value
    • does not work reliably with dynamically defined type hints (e.g. in nested functions)
  2. decorating the function with @typechecked:
    • 100% reliable at finding the function object to be checked (does not need to check the garbage collector)
    • can check the type of the return value
    • adds an extra frame to the call stack for every call to a decorated function
  3. using with TypeChecker('packagename')::
    • emits warnings instead of raising TypeError
    • eliminates boilerplate
    • multiple TypeCheckers can be stacked/nested
    • noninvasive (only records type violations; does not raise exceptions)
    • does not work reliably with dynamically defined type hints (e.g. in nested functions)
    • may cause problems with badly behaving debuggers or profilers

If a function is called with incompatible argument types or a @typechecked decorated function returns a value incompatible with the declared type, a descriptive TypeError exception is raised.

Type checks can be fairly expensive so it is recommended to run Python in "optimized" mode (python -O or setting the PYTHONOPTIMIZE environment variable) when running code containing type checks in production. The optimized mode will disable the type checks, by virtue of removing all assert statements and setting the __debug__ constant to False.

Using check_argument_types():

from typeguard import check_argument_types

def some_function(a: int, b: float, c: str, *args: str):
    assert check_argument_types()

Using @typechecked:

from typeguard import typechecked

def some_function(a: int, b: float, c: str, *args: str) -> bool:

To enable type checks even in optimized mode:

def foo(a: str, b: int, c: Union[str, int]) -> bool:

Using TypeChecker:

from warnings import filterwarnings

from typeguard import TypeChecker, TypeWarning

# Display all TypeWarnings, not just the first one
filterwarnings('always', category=TypeWarning)

# Run your entire application inside this context block
with TypeChecker(['mypackage', 'otherpackage']):

# Alternatively, manually start (and stop) the checker:
checker = TypeChecker('mypackage')


Some other things you can do with TypeChecker:

  • display all warnings from the start with python -W always::typeguard.TypeWarning
  • redirect them to logging using logging.captureWarnings()
  • record warnings in your pytest test suite and fail test(s) if you get any (see the pytest documentation about that)

To directly check a value against the specified type:

from typeguard import check_type

check_type('variablename', [1234], List[int])

The following types from the typing package have specialized support:

Type Notes
Callable Argument count is checked but types are not (yet)
Dict Keys and values are typechecked
List Contents are typechecked
NamedTuple Field values are typechecked
Set Contents are typechecked
Tuple Contents are typechecked
TypeVar Constraints, bound types and co/contravariance are supported but custom generic types are not (due to type erasure)

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