Fix longlong timestamps

[DonBraulio]

TL;DR: This PR fixes some issues in normalize_timestamps(), simplifying the code, avoiding issues with numpy dtypes, and improving the error handling by avoiding unnecessary checks.

For context, this fixes an issue that showed up in a notebook by @achoum, caused by numpy representing numbers using np.longlong internally instead of the usual np.int64, after loading a CSV from pandas. This can be reproduced by doing:

>>> np.array([1, 2], dtype=np.longlong).dtype
dtype('int64')
>>> np.array([1, 2], dtype=np.longlong).dtype.type
<class 'numpy.longlong'>

The bug is solved by using np.issubdtype(np.dtype, np.integer), instead of accessing my_array.dtype.type which refers to the underlying scalar type, and there are a bunch of those (see Built in scalar types).

[github-actions]

Coverage report

Main: 90.40% | PR: 90.47% | Diff: 0.08 ✅

[achoum]

Awesome.
A few nits

[github-actions]

Coverage report

Main: 90.40% | PR: 90.50% | Diff: 0.10 ✅