Create time vector for different sampling frequencies

[Trybnetic]

So far, the time vector can only be created for certain sampling frequencies. This is fine for the ActiGraph data, but a more generic solution would be nice.

If someone wants to fix this problem, feel free to open a pull request. For the time being, I will add a NotImplementedError to paat.io._create_time_array() and paat.io._create_time_vector(), which will refer to this issue.

[Trybnetic]

The functions now raise NotImplementedError when the second can not be divided by the sampling rate without a remainder. However, it is questionable whether it wouldn't be easier to just round on millisecond level precision.

One way might be to follow this line of thinking:

import numpy as np

start = 1619568000000 # np.asarray(np.datetime64('today'), dtype="datetime64[ms]").astype(int)
hz = 33
n_samples = hz * 60

# round step size
time_data = np.asarray(np.arange(start, start + n_samples * hz, 1e3 // hz), dtype='datetime64[ms]')

# round when converting to datetime64
time_data = np.asarray(np.arange(start, start + n_samples * hz, 1e3 / hz), dtype='datetime64[ms]')

However, I did not test that and just leave it here for later use as this issue does not have a high priority currently