Save `event_n_channel` which just contains s1_n_channels fields

[dachengx]

What does the code in this PR do / what does it improve?

Save an extra data_type event_n_channel to save 's1_n_channels', 's1_top_n_channels', 's1_bottom_n_channels'.

Can you briefly describe how it works?

Just copy the fields.

Can you give a minimal working example (or illustrate with a figure)?

Old vs new event_pattern_fit in run_id 049989.

----------
old nan alt_s1_area_fraction_top_continuous_probability 87.0%
new nan alt_s1_area_fraction_top_continuous_probability 87.0%
difference alt_s1_area_fraction_top_continuous_probability 0.0%
mean abs difference alt_s1_area_fraction_top_continuous_probability 0.0e+00
----------
old nan alt_s1_area_fraction_top_discrete_probability 87.0%
new nan alt_s1_area_fraction_top_discrete_probability 87.0%
difference alt_s1_area_fraction_top_discrete_probability 0.0%
mean abs difference alt_s1_area_fraction_top_discrete_probability 0.0e+00
----------
old nan alt_s1_photon_fraction_top_continuous_probability 87.0%
new nan alt_s1_photon_fraction_top_continuous_probability 87.0%
difference alt_s1_photon_fraction_top_continuous_probability 0.0%
mean abs difference alt_s1_photon_fraction_top_continuous_probability 0.0e+00
----------
old nan alt_s1_photon_fraction_top_discrete_probability 87.0%
new nan alt_s1_photon_fraction_top_discrete_probability 87.0%
difference alt_s1_photon_fraction_top_discrete_probability 0.0%
mean abs difference alt_s1_photon_fraction_top_discrete_probability 0.0e+00
----------
old nan alt_s2_2llh 1.8%
new nan alt_s2_2llh 1.8%
difference alt_s2_2llh 0.0%
mean abs difference alt_s2_2llh 0.0e+00
----------
old nan alt_s2_neural_2llh 1.6%
new nan alt_s2_neural_2llh 1.6%
difference alt_s2_neural_2llh 0.0%
mean abs difference alt_s2_neural_2llh 0.0e+00
----------
old nan s1_2llh 31.7%
new nan s1_2llh 31.7%
difference s1_2llh 0.0%
mean abs difference s1_2llh 0.0e+00
----------
old nan s1_area_fraction_top_continuous_probability 24.6%
new nan s1_area_fraction_top_continuous_probability 24.6%
difference s1_area_fraction_top_continuous_probability 0.0%
mean abs difference s1_area_fraction_top_continuous_probability 0.0e+00
----------
old nan s1_area_fraction_top_discrete_probability 24.6%
new nan s1_area_fraction_top_discrete_probability 24.6%
difference s1_area_fraction_top_discrete_probability 0.0%
mean abs difference s1_area_fraction_top_discrete_probability 0.0e+00
----------
old nan s1_bottom_2llh 31.7%
new nan s1_bottom_2llh 31.7%
difference s1_bottom_2llh 0.0%
mean abs difference s1_bottom_2llh 0.0e+00
----------
old nan s1_photon_fraction_top_continuous_probability 24.6%
new nan s1_photon_fraction_top_continuous_probability 24.6%
difference s1_photon_fraction_top_continuous_probability 0.0%
mean abs difference s1_photon_fraction_top_continuous_probability 0.0e+00
----------
old nan s1_photon_fraction_top_discrete_probability 24.6%
new nan s1_photon_fraction_top_discrete_probability 24.6%
difference s1_photon_fraction_top_discrete_probability 0.0%
mean abs difference s1_photon_fraction_top_discrete_probability 0.0e+00
----------
old nan s1_top_2llh 31.7%
new nan s1_top_2llh 31.7%
difference s1_top_2llh 0.0%
mean abs difference s1_top_2llh 0.0e+00
----------
old nan s2_2llh 0.4%
new nan s2_2llh 0.4%
difference s2_2llh 0.0%
mean abs difference s2_2llh 0.0e+00
----------
old nan s2_neural_2llh 0.1%
new nan s2_neural_2llh 0.1%
difference s2_neural_2llh 0.0%
mean abs difference s2_neural_2llh 0.0e+00
----------

[coveralls]

coverage: 93.695% (+0.03%) from 93.669% when pulling 667c5912e545e40614022e07af1fd2e023961c85 on event_n_channels into 3b8d13e59be9366b71c68503e9a95216d8846de7 on master.