This package performs type-check at runtime with help of type annotations.
There are two ways to perform type checks using this package.
I provide a few simple examples here. For a complete overview, have a look at the package's unit tests.
You can check an object against a type or an annotation via the check_type
function.
The function returns None
if the check was successful or raises a TypeError
in case of error.
Note that this function does not check recursively for e.g. the attributes of a class.
from typing import List, Sequence, Optional, Mapping
from dataclasses import dataclass
from runtime_type_checker import check_type
check_type("a", str) # OK
check_type(["a"], List[str]) # OK
check_type(["a", 1], Sequence[str]) # raises TypeError
@dataclass
class Foo:
a: int
b: Optional[Mapping[str, int]] = None
check_type(Foo(1), Foo) # OK
check_type(Foo(1), int) # raises TypeError
You can also type-check classes upon instance creation and functions or methods upon call through the check_types
decorator:
from typing import Optional, Mapping
from dataclasses import dataclass
from runtime_type_checker import check_types
def run_typed(f):
return check_types(dataclass(f))
@check_types
@dataclass
class Foo:
a: int
b: Optional[Mapping[str, int]] = None
Foo(1) # returns an instance of foo
Foo(0, {"a": "b"}) # raises TypeError
@check_types
def bar(a: bool, **options: str) -> str:
return options.get("b", "missing") if a else "unknown"
bar(True, b="1") # returns "1"
bar(True, c=1) # raises TypeError
- simplicity: there's only one function and one decorator to keep in mind.
- robustness: this package relies on the
typing-inspect
for the heavy lifting. This package is maintained by core contributors to the typing module, which means very little hacks on my side to work with older versions of python.
- coverage: I don't offer coverage for all features of type annotations: for example Protocol, Generators, IO are not currently supported. Generics are not really well handled.