Concatenate 4-momentum objects from `vector` package

[shenvitor]

Concatenate 4-momentum objects from `vector` package

[shenvitor]

The previous way to address the problem is possible and not bad.

But now the strategy has changed.
To do the concatenation for each vector in 4-vector one by one, and not do it converted to np.ndarray then all-in-one and convert back.

"Added" since e.g. p1. px, p1.py, p1.pz, and p1.e is directly a numpy array.
Thus no conversion is needed
The latest solution for this is something like this

def concatenate_data(
    data1: tuple[MomentumNumpy4D, ...],
    data2: tuple[MomentumNumpy4D, ...],
) -> tuple[MomentumNumpy4D, ...]:
    return tuple(concatenate_vectors((pi1, pj2)) for pi1, pj2 in zip(data1, data2))


def concatenate_vectors(vectors: tuple[MomentumNumpy4D]) -> MomentumNumpy4D:
    return vector.array(
        {
            "px": np.concatenate([p.px for p in vectors]),
            "py": np.concatenate([p.py for p in vectors]),
            "pz": np.concatenate([p.pz for p in vectors]),
            "E": np.concatenate([p.e for p in vectors]),
        }
    )

[redeboer]

Thus no conversion is needed

Behind the scenes, concatenate_vectors() is still converting between numpy and the vector package, so performance-wise this is not a good solution. But for the notebook, this is fine, the implementation in that function can be replaced by a cleaner, more efficient solution later.

[shenvitor]

Done!

[shenvitor]

Thus no conversion is needed

Behind the scenes, concatenate_vectors() is still converting between numpy and the vector package, so performance-wise this is not a good solution. But for the notebook, this is fine, the implementation in that function can be replaced by a cleaner, more efficient solution later.

Agree.
Conversion is still there at the end.