For more info, please [visit the GitHub page of this project](https://github.com/snimu/parameter-checks).
### Basic example
```python
import parameter_checks as pc
@pc.hints.cleanup # be left with only type-annotations
@pc.hints.enforce # enforce the lambda but not the types
def div(a: int, b: pc.annotations.Checks[int, lambda b: b != 0]):
return a / b
div(1, 1) # returns 1.0
div(1, 0) # raises ValueError
```
As can be seen in this example, this package provides a new type-annotation:
[pc.annotations.Checks](https://github.com/snimu/parameter-checks#pcannotationschecks)
(it also provides [pc.annotations.Hooks](https://github.com/snimu/parameter-checks#pcannotationshooks), see below).
Using [@pc.hints.enforce](https://github.com/snimu/parameter-checks#pchintsenforce) on a function
will enforce the checks given to those annotations (but not the types).
[@pc.hints.cleanup](https://github.com/snimu/parameter-checks#pchintscleanup) would produce the
`div.__annotations__` of `{"a": int, "b": int}` in the example above.
### Complex example
```python
import parameter_checks as pc
def decision_boundary(fct, parameter, parameter_name, typehint):
if type(parameter) is not typehint:
err_str = f"In function {fct}, parameter {parameter_name}={parameter} " \
f"is not of type {typehint}!"
raise TypeError(err_str)
return 3 + 4 * parameter - parameter ** 2
@pc.hints.enforce
def classify(
x: pc.annotations.Hooks[float, decision_boundary],
additional_offset: pc.annotations.Checks[float, lambda b: 0 <= b <= 100] = 0.
) -> bool:
return (x + additional_offset) >= 0
if __name__ == '__main__':
assert classify(1.) is True # 6.
assert classify(2.) is True # 7.
assert classify(5.) is False # -2.
assert classify(5., 2.) is True # 0.
classify("not a float!") # raises TypeError
classify(1., -1.) # raises ValueError
```
For additional comments on this example (and much more),
[visit the GitHub page of this project](https://github.com/snimu/parameter-checks#hooks-example-2).
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