Event preselection

This framework is designed for n-tuples (and friend trees) produced with CROWN as input. All information for the preselection step is defined in configuration files in the configs/ANALYSIS/ERA/ folder using the common_settings.yaml file and a more specific config file.

The preselection config has the following parameters:

parameter	type	description
channel	string	tau pair decay channels ("et", "mt", "tt")
processes	dict	process parameters are explained below
event_selection	dict	with this parameter all selections that should be applied are defined. This is basically a dictionary of cuts where the key is the name of a cut and the value is the cut itself as a string e.g. had_tau_pt: "pt_2 > 30". The name of a cut is not really important, it is only used as an output information in the terminal. A cut can only use variables which are in the ntuples.
mc_weights	dict	weight parameter are defined below
emb_weights	dict	all weights that should be applied for embedded samples are defined. Like for event_selection a weight can directly be specified and is then applied to all samples the same way e.g. single_trigger: "trg_wgt_single_mu24ormu27"
output_features	list	the to be saved/needed features for the later calculations are listed, this also includes features from friend trees if friends were specified in common_settings.yaml

In processes all the processes are defined that should be preprocessed.
The names are also used for the output file naming after the processing.
Each process needs two specifications:

subparameter	type	description
tau_gen_modes	list	split of the events corresponding to the origin of the hadronic tau
samples	list	list of all sample tags corresponding to the specific process

The tau_gen_modes have following modes:

subparameter	type	description
T	string	genuine tau
J	string	jet misidentified as a tau
L	string	lepton misidentified as a tau
all	string	if no split should be performed

In mc_weights all weights that should be applied for simulated samples are defined.
There are two types of weights.

1. Similar to event_selection, a weight can directly be specified and is then applied to all samples in the same way e.g. lep_id: "id_wgt_mu_1"

2. But some weights are either sample specific or need additional information. Currently implemented options are:

   subparameter	type	description
   generator	string	The normal generator weight is applied to all samples, if they aren't specified in the "stitching" sub-group. Stitching weights might be needed for DY+jets or W+jets, depending on which samples are used for them.
   lumi	string	luminosity scaling, this depends on the era and uses the era parameter of the config to get the correct weight, so basically it's not relevant what is in the string
   Z_pt_reweight	string	reweighting of the Z boson pt, the weight in the ntuple is used and only applied to DY+jets
   Top_pt_reweight	string	reweighting of the top quark pt, the weight in the ntuple is used and only applied to ttbar

Scale factors for b-tagging and tau ID vs jet are applied on the fly during the FF calculation step.

To run the preselection step, execute the python script and specify the config file (relative path possible):

python preselection.py --config-file configs/PATH/CONFIG.yaml

Further there are additional optional parameters:

1. --nthreads=SOME_INTEGER to define the number of threads for the multiprocessing pool to run the sample processing in parallel. Default value is 8 (this should normally cover running all of the samples in parallel).
2. --ncores=SOME_INTEGER to define the number of cores that should be used for each pool thread to speed up the ROOT dataframe calculation. Default value is 2.