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Python dependency injection you can taste.
A lightweight dependency injection and result processing framework for Python using type hints. Emphasis is on simplicity, ease of use, and minimal impact on source code.
import in_n_out as ino
class Thing:
def __init__(self, name: str):
self.name = name
# use ino.inject to create a version of the function
# that will retrieve the required dependencies at call time
@ino.inject
def func(thing: Thing):
return thing.name
def give_me_a_thing() -> Thing:
return Thing("Thing")
# register a provider of Thing
ino.register_provider(give_me_a_thing)
print(func()) # prints "Thing"
def give_me_another_thing() -> Thing:
return Thing("Another Thing")
with ino.register_provider(give_me_another_thing, weight=10):
print(func()) # prints "Another Thing"
This also supports processing return values as well (injection of intentional side effects):
@ino.inject_processors
def func2(thing: Thing) -> str:
return thing.name
def greet_name(name: str):
print(f"Hello, {name}!")
ino.register_processor(greet_name)
func2(Thing('Bob')) # prints "Hello, Bob!"
Lots of other python DI frameworks exist, here are a few alternatives to consider:
FAQs
plugable dependency injection and result processing
We found that in-n-out demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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