allure-framework / allure-framework/allure-python
Provide a custom repr for function params in `@step` decorator
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- Python
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Descripción
I have a function like:
```
@step
def foo(x: np.ndarray, y: Dict[str, np.ndarray]): ...
```
It is marked with `@step` decorator and my problem is that the output in the allure repo is too large. I see that the special case of `represnet` is here https://github.com/allure-framework/allure-python/blob/3c74fd540021f776be7b52f8078c9d34eee5c3b6/allure-python-commons/src/utils.py#L167
So I was wondering how to handle other objects like `np.ndarray` or `Dict[str, np.ndarray]`?
One way is to subclass `dict` and give a nice `repr` to it, but what with a single numpy array?Sublassing it just so it got a better `repr` for allure looks... excessive?
Can `@step` accept an additional argument that will explicitly tell it hot to handle `obj -> str` representation?
#### I'm submitting a ...
- [x ] feature request
#### What is the current behavior?
A long text output in the allure report for types like `np.ndarray` or `Dict[str, np.ndarray]` that have long `repr`.
#### What is the expected behavior?
Make `@step` that receives explicit formatter so I can write:
```
def _my_repr(x: np.ndarray, y: Dict[str, np.ndarray]):
return format_1(x), format_2(t) # <-- custom formatting code here
@step(formatter=_my_repr)
def foo(x: np.ndarray, y: Dict[str, np.ndarray]): ...
```
#### Please tell us about your environment:
allure-pytest==2.9.45
allure-python-commons==2.9.45
pytest==6.2.5
#### Other information
I am willing on implementing that feature conditional on the fact there is a way of accepting that change to the mainstream. Will you accept a PR that parametrize `@step` in such a way?
Guía de contribución
Línea de trabajo
Comience con la lógica de representación de casos especiales en allure-python-commons/src/utils.py y siga cómo @step recopila los parámetros de las funciones. Defina cómo se debe aceptar y aplicar un formateador explícito a los parámetros y, a continuación, verifique que los arrays de numpy y los diccionarios produzcan la salida compacta solicitada sin cambiar el comportamiento predeterminado.
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Evaluación
- Stack tecnológico
- numpy, python
- Área
- testing-qa
- Tipo de issue
- Nueva funcionalidad
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Estado de actividad
- Estancado
- Claridad
- Bastante claro
- Aptitud para principiantes
- 38/100