[](https://advestis.github.io/adtypingdecorators)
[](https://www.gnu.org/licenses/gpl-3.0)
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# AdTypingDecorators
Python decorators allowing to check and/or enforce types in functions' arguments based on typing hints.
## Installation
```bash
pip install adtypingdecorators
```
## Usage
```python
import numpy as np
import pandas as pd
from adtypingdecorators import typing_raise, typing_convert, typing_warn, typing_custom
def to_array(a: int):
return np.array([a, 2 * a])
def to_array_2(a: int):
return np.array([a, 3 * a])
@typing_raise
def f_raise(a: int):
return a + 1
@typing_convert
def f_convert(a: int):
return a + 1
@typing_warn
def f_warn(a: int):
return a + 1
@typing_custom(
convertors={int: to_array, "b": to_array_2},
exclude=["c", pd.DataFrame]
)
def f_custom(a: np.ndarray, b: np.ndarray, c: np.ndarray, d: np.ndarray):
return a + 1, b + 1, c + 1, d + 1
f_raise(1) # Returns 2, as expected
# noinspection PyTypeChecker
f_raise(1.5) # Raises TypeError
# noinspection PyTypeChecker
f_convert(1.5) # Returns 2 (converted 1.5 into 1)
# noinspection PyTypeChecker
f_convert("foo") # Raises ValueError (while trying to convert 'foo' to interger)
# noinspection PyTypeChecker
f_warn(1.5) # Returns 2.5, and warns
# noinspection PyTypeChecker
a_, b_, c_, d_ = f_custom(1, 2, 3, pd.DataFrame([4]))
# a_ is np.array([2, 3])
# b_ is np.array([3, 7])
# c_ is 4
# d_ is pd.DataFrame([5])
```
