Feature/sklearn method pluggin

[i-a-morozov]

Added support for sklearn-like method passing to injection/orbit correction

Custom solver protocol:

class Solver:
    coef_: np.ndarray
    def fit(self, X: np.ndarray, y: np.ndarray):
         ...

Example with sklearn (should match parameter=100.0)

from sklearn.linear_model import Ridge
solver = Ridge(
    alpha=arguments.injection_regularization**2,
    fit_intercept=False,
    solver="svd"
)

...

sc.tuning.correct_injection(
    solver=solver,
    n_reps=arguments.injection_number_of_repetitions,
    n_turns=arguments.injection_number_of_turns,
    gain=arguments.injection_gain
)

[kparasch]

I like the idea! I made some comments where we can simplify a bit.

Also could you rebase to the develop branch and remove commit d47440c which is related to the other PR?

[kparasch]

Thanks!!

[kparasch]

I just merged some workflows to run both pySC and pyAML tests that are related to pySC. I will merge them also to this PR to make sure pyaml doesn't break, and then do the final merge.

[i-a-morozov]

Great, nice to have tests! I'll be doing next PRs with develop as origin.