Tutorials/2_SimpleConfig/example_config.py at main · PocketCoffea/Tutorials · GitHub

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from pocket_coffea.utils.configurator import Configurator
from pocket_coffea.lib.cut_definition import Cut
from pocket_coffea.lib.cut_functions import get_nObj_min, get_HLTsel, get_nPVgood, goldenJson, eventFlags
from pocket_coffea.parameters.cuts import passthrough
from pocket_coffea.parameters.histograms import *
from pocket_coffea.lib.calibrators.common import default_calibrators_sequence
import workflow
from workflow import ZmumuBaseProcessor
from pocket_coffea.lib.weights.common import common_weights

# Register custom modules in cloudpickle to propagate them to dask workers
import cloudpickle
import custom_cut_functions
cloudpickle.register_pickle_by_value(workflow)
cloudpickle.register_pickle_by_value(custom_cut_functions)

from custom_cut_functions import *
import os
localdir = os.path.dirname(os.path.abspath(__file__))

# Loading default parameters
from pocket_coffea.parameters import defaults
default_parameters = defaults.get_default_parameters()
defaults.register_configuration_dir("config_dir", localdir+"/params")

parameters = defaults.merge_parameters_from_files(default_parameters,
                                                  f"{localdir}/params/object_preselection.yaml",
                                                  f"{localdir}/params/triggers.yaml",
                                                  f"{localdir}/params/plotting.yaml",
                                                  update=True)


cfg = Configurator(
    parameters = parameters,
    datasets = {
        "jsons": [f"{localdir}/datasets/DATA_SingleMuon_xc2.json",
                  f"{localdir}/datasets/DYJetsToLL_M-50_xc2.json"
                    ],
        "filter" : {
            "samples": ["DATA_SingleMuon",
                        "DYJetsToLL"],
            "samples_exclude" : [],
            "year": ['2018']
        }
    },

    workflow = ZmumuBaseProcessor,
    calibrators = default_calibrators_sequence,

    skim = [get_nPVgood(1), eventFlags, goldenJson, # basic skims
            get_nObj_min(1, 18., "Muon"),
            # Asking only SingleMuon triggers since we are only using SingleMuon PD data
            get_HLTsel(primaryDatasets=["SingleMuon"])],

    preselections = [dimuon_presel],
    categories = {
        "baseline": [passthrough],
    },

    weights_classes = common_weights,

    weights = {
        "common": {
            "inclusive": ["genWeight","lumi","XS",
                          "pileup",
                          "sf_mu_id","sf_mu_iso",
                          ],
            "bycategory" : {
            }
        },
        "bysample": {
        }
    },

    variations = {
        "weights": {
            "common": {
                "inclusive": [  "pileup",
                                "sf_mu_id", "sf_mu_iso"
                              ],
                "bycategory" : {
                }
            },
        "bysample": {
        }
        },
    },


   variables = {
        **muon_hists(coll="MuonGood", pos=0),
        **count_hist(name="nElectronGood", coll="ElectronGood",bins=3, start=0, stop=3),
        **count_hist(name="nMuonGood", coll="MuonGood",bins=3, start=0, stop=3),
        **count_hist(name="nJets", coll="JetGood",bins=8, start=0, stop=8),
        **count_hist(name="nBJets", coll="BJetGood",bins=8, start=0, stop=8),
        **jet_hists(coll="JetGood", pos=0),
        **jet_hists(coll="JetGood", pos=1),
        "mll" : HistConf( [Axis(coll="ll", field="mass", bins=100, start=0, stop=200, label=r"$M_{\ell\ell}$ [GeV]")] ),
    }
)