typeguard 2.1.0

Run-time type checker for Python

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.. highlight:: python

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:

#. calling ``check_argument_types()`` from within the function body:

* debugger friendly
* cannot check the type of the return value
* does not work reliably with dynamically defined type hints (e.g. in nested functions)
#. decorating the function with ``@typechecked``:

* can check the type of the return value
* adds an extra frame to the call stack for every call to a decorated function
#. using ``with TypeChecker() as checker:``:

* 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

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

To enable type checks even in optimized mode::

@typechecked(always=True)
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']):
mypackage.run_app()

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

.. hint:: 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 <http://doc.pytest.org/en/latest/recwarn.html>`_ about that)

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
``Type``
``TypeVar`` Constraints, bound types and co/contravariance are supported
but custom generic types are not (due to type erasure)
``Union``
============== ============================================================


Project links
-------------

* `Change log <https://github.com/agronholm/typeguard/blob/master/CHANGELOG.rst>`_
* `Source repository <https://github.com/agronholm/typeguard>`_
* `Issue tracker <https://github.com/agronholm/typeguard/issues>`_