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+/store/mc/Run3Summer22NanoAODv12/GluGlutoHHto2B2Tau_kl-1p00_kt-1p00_c2-0p00_LHEweights_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v2/2550000/fa4b6af7-1c56-4b2c-9edd-834455750122.root \ No newline at end of file diff --git a/boosted_decision_tree.py b/boosted_decision_tree.py new file mode 100644 index 0000000..88cac69 --- /dev/null +++ b/boosted_decision_tree.py @@ -0,0 +1,69 @@ +import os +# Keep using Keras 2 +os.environ['TF_USE_LEGACY_KERAS'] = '1' +import locale +# locale.setlocale(locale.LC_ALL, 'en_US') + +import tensorflow_decision_forests as tfdf + +import numpy as np +import pandas as pd +import tensorflow as tf +import tf_keras +import math + +dataset_df = pd.read_csv("extracted_param_pairType_2.csv") + +# Display the first 3 examples. +print(dataset_df.head(3)) + +label = "cat" + +classes = dataset_df[label].unique().tolist() +print(f"Label classes: {classes}") + +dataset_df[label] = dataset_df[label].map(classes.index) + +def split_dataset(dataset, test_ratio=0.20): + """Splits a panda dataframe in two.""" + test_indices = np.random.rand(len(dataset)) < test_ratio + return dataset[~test_indices], dataset[test_indices] + + +train_ds_pd, test_ds_pd = split_dataset(dataset_df) +print("{} examples in training, {} examples for testing.".format( + len(train_ds_pd), len(test_ds_pd))) + +train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_ds_pd, label=label) +test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_ds_pd, label=label) + + +feature_1 = tfdf.keras.FeatureUsage(name="dau1_tauIdVSjet", semantic=tfdf.keras.FeatureSemantic.CATEGORICAL) +feature_2 = tfdf.keras.FeatureUsage(name="dau1_pt") +feature_3 = tfdf.keras.FeatureUsage(name="dau2_tauIdVSjet") +feature_4 = tfdf.keras.FeatureUsage(name="dau2_pt") +feature_5 = tfdf.keras.FeatureUsage(name="pNet_sum") +feature_6 = tfdf.keras.FeatureUsage(name="bjet1_pt") +feature_7 = tfdf.keras.FeatureUsage(name="bjet2_pt") +feature_8 = tfdf.keras.FeatureUsage(name="bjet1_eta") +feature_9 = tfdf.keras.FeatureUsage(name="bjet2_eta") +all_features = [feature_1, feature_2, feature_3, feature_4, feature_5, feature_6, feature_7, feature_8, feature_9] + +# Specify the model. +model_1 = tfdf.keras.GradientBoostedTreesModel(growing_strategy="LOCAL", num_trees=1000, max_depth=5, + split_axis="SPARSE_OBLIQUE", + categorical_algorithm="RANDOM",) + +# Train the model. +model_1.fit(train_ds) + +model_1.compile(metrics=["accuracy"]) +evaluation = model_1.evaluate(test_ds, return_dict=True) +print() + +for name, value in evaluation.items(): + print(f"{name}: {value:.4f}") + +model_1.save("my_saved_model") + +tfdf.model_plotter.plot_model_in_colab(model_1, tree_idx=0, max_depth=3) \ No newline at end of file diff --git a/cutoff_comparison.png b/cutoff_comparison.png new file mode 100644 index 0000000..a68c729 Binary files /dev/null and b/cutoff_comparison.png differ diff --git a/data_mutau_standard.json b/data_mutau_standard.json new file mode 100644 index 0000000..3e739fa --- /dev/null +++ b/data_mutau_standard.json @@ -0,0 +1,12 @@ +{ + "vals": { + "mutau": [ + 47075385.0 + ] + }, + "errs": { + "mutau": [ + 0.0 + ] + } +} \ No newline at end of file diff --git a/dnn.py b/dnn.py new file mode 100644 index 0000000..0011b0a --- /dev/null +++ b/dnn.py @@ -0,0 +1,65 @@ +import os +# Keep using Keras 2 +os.environ['TF_USE_LEGACY_KERAS'] = '1' +import locale +# locale.setlocale(locale.LC_ALL, 'en_US') + +import numpy as np +import pandas as pd +import tensorflow as tf +import tf_keras +import math + +dataset_df = pd.read_csv("extracted_param_pairType_2.csv") + +# Display the first 3 examples. +print(dataset_df.head(3)) + +label = "cat" + +classes = dataset_df[label].unique().tolist() +print(f"Label classes: {classes}") + +dataset_df[label] = dataset_df[label].map(classes.index) + +def split_dataset(dataset, test_ratio=0.20): + """Splits a panda dataframe in two.""" + test_indices = np.random.rand(len(dataset)) < test_ratio + return dataset[~test_indices], dataset[test_indices] + +normalize = tf.keras.layers.Normalization() + + + + +loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) + +train_ds_pd, test_ds_pd = split_dataset(dataset_df) +train_features = train_ds_pd.copy() +train_labels = train_features.pop('cat') +print("{} examples in training, {} examples for testing.".format( + len(train_ds_pd), len(test_ds_pd))) + +train_features = np.array(train_features) +normalize.adapt(train_features) +model = tf.keras.Sequential([ + normalize, + tf.keras.layers.Dense(256, activation='relu'), + tf.keras.layers.Dense(256, activation='relu'), + tf.keras.layers.Dense(4) +]) +# Train the model. +model.compile(loss = loss_fn, + optimizer = tf.keras.optimizers.Adam(), + metrics=['accuracy']) + +model.fit(train_features, train_labels, epochs=20) +# evaluation = model_1.evaluate(test_ds, return_dict=True) +# print() + +# for name, value in evaluation.items(): +# print(f"{name}: {value:.4f}") + +# model_1.save("my_saved_model") + +# tfdf.model_plotter.plot_model_in_colab(model_1, tree_idx=0, max_depth=3) \ No newline at end of file diff --git a/extracted_param_pairType_2.csv b/extracted_param_pairType_2.csv new file mode 100644 index 0000000..f0dd2fd --- /dev/null +++ b/extracted_param_pairType_2.csv @@ -0,0 +1,5187 @@ +dau1_pt,dau1_eta,dau1_iso,dau1_mass,dau1_tauIdVSjet,dau2_pt,dau2_eta,dau2_iso,dau2_mass,dau2_tauIdVSjet,bjet1_pt,bjet1_eta,bjet1_mass,bjet1_pNet,bjet2_pt,bjet2_eta,bjet2_mass,bjet2_pNet,pNet_sum,cat +52.75840377807617,0.9859619140625,-1.0,0.7885624766349792,8,40.46183776855469,0.47930908203125,-1.0,0.8733281493186951,8,110.75,-0.543212890625,13.4609375,0.98193359375,24.671875,-0.41192626953125,2.73828125,0.5263671875,1.50830078125,Both 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+43.41579818725586,-0.888916015625,-1.0,0.13324767351150513,7,111.53892517089844,-0.2611083984375,-1.0,1.2373046875,3,137.375,0.202392578125,12.296875,0.5537109375,29.71875,-1.01171875,4.390625,0.99853515625,1.55224609375,Both +90.57189178466797,-0.2021484375,-1.0,0.9711718559265137,8,40.3758544921875,-0.2568359375,-1.0,0.13324767351150513,8,146.5,1.234619140625,23.875,0.99951171875,105.375,0.0758514404296875,13.6328125,0.998046875,1.99755859375,Both +159.4702911376953,-0.01802825927734375,-1.0,1.38671875,3,36.69102096557617,-0.49755859375,-1.0,4.235374927520752,0,112.5,-0.0405731201171875,15.71875,0.99853515625,97.3125,-0.2882080078125,12.3359375,0.99951171875,1.998046875,QuadJet +101.73916625976562,-0.9285888671875,-1.0,1.2003076076507568,8,42.08951950073242,-1.16259765625,-1.0,0.77880859375,5,108.0,-0.581787109375,11.640625,0.9990234375,78.875,0.3106689453125,9.296875,0.7568359375,1.755859375,Both +87.94025421142578,0.46051025390625,-1.0,1.0629053115844727,8,84.58089447021484,-0.0860748291015625,-1.0,2.344374895095825,0,183.75,0.8922119140625,15.6640625,0.97900390625,75.0625,1.57177734375,12.234375,0.9462890625,1.92529296875,Both +67.65621185302734,0.52783203125,-1.0,0.9111093878746033,8,64.27448272705078,-0.3797607421875,-1.0,0.9440468549728394,8,93.125,0.234222412109375,6.15234375,0.99951171875,22.453125,1.4345703125,2.48046875,0.9384765625,1.93798828125,DiTau +127.36408996582031,-0.49945068359375,-1.0,0.3405156135559082,8,42.57854461669922,0.5933837890625,-1.0,6.0178751945495605,0,175.5,0.1324462890625,17.890625,0.6328125,91.8125,0.99267578125,13.40625,0.99951171875,1.63232421875,Both +72.8109359741211,-0.5997314453125,-1.0,0.9338750243186951,8,75.06021881103516,0.176116943359375,-1.0,0.6049844026565552,8,-1,-20,-1,-1,-1,-20,-1,-1,-1,Both +186.65646362304688,0.1768798828125,-1.0,1.1591796875,8,87.77184295654297,-0.0865325927734375,-1.0,1.0393506288528442,6,-1,-20,-1,-1,-1,-20,-1,-1,-1,DiTau +143.71376037597656,-0.8262939453125,-1.0,0.53857421875,7,67.8357925415039,0.1756591796875,-1.0,0.13324767351150513,7,323.25,-0.3416748046875,48.78125,0.96044921875,121.625,0.0005904436111450195,14.359375,0.86083984375,1.8212890625,DiTau diff --git a/inclusion/DeepJetWPs.py b/inclusion/DeepJetWPs.py new file mode 100644 index 0000000..abc10db --- /dev/null +++ b/inclusion/DeepJetWPs.py @@ -0,0 +1,37 @@ +#! /usr/bin/env python +# Example of how to read the a correctionlib JSON file + +# This is a minimal version of a tau POG example: +# For more information, see the README in +# https://gitlab.cern.ch/cms-nanoAOD/jsonpog-integration/-/tree/master/POG/TAU +#import sys; sys.path.insert(0,"correctionlib") # add correctionlib to path +from correctionlib import _core + +# This corrections file is documented in https://cms-nanoaod-integration.web.cern.ch/commonJSONSFs/ +# qhere the general correctionlib documentation is kept +fname = "/cvmfs/cms.cern.ch/rsync/cms-nanoAOD/jsonpog-integration/POG/BTV/2022_Summer22/btagging.json.gz" + +# Load CorrectionSet +if fname.endswith(".json.gz"): + import gzip + with gzip.open(fname,'rt') as file: + #data = json.load(file) + data = file.read().strip() + cset = _core.CorrectionSet.from_string(data) +else: + cset = _core.CorrectionSet.from_file(fname) + +# Load Correction objects that can be evaluated +corr = cset["deepJet_wp_values"] + +btagWPl = corr.evaluate("L") +btagWPm = corr.evaluate("M") +btagWPt = corr.evaluate("T") +btagWPxt = corr.evaluate("XT") +btagWPxxt = corr.evaluate("XXT") + +print("L WP =", btagWPl) +print("M WP =", btagWPm) +print("T WP =", btagWPt) +print("XT WP =", btagWPxt) +print("XXT WP =", btagWPxxt) diff --git a/inclusion/condor/closure.py b/inclusion/condor/closure.py index 00e0c81..138c1b8 100644 --- a/inclusion/condor/closure.py +++ b/inclusion/condor/closure.py @@ -47,7 +47,16 @@ def closure(args) : jw.add_string('echo "{} for channel ${{1}} and single trigger ${{2}} done."'.format(script)) #### Write submission file - jw.write_condor(filename=outs_submit, + if main.machine == "slurm": + jw.write_batch(filename=outs_submit, + real_exec=utils.build_script_path(script), + shell_exec=outs_job, + outfile=outs_check, + logfile=outs_log, + queue=main.queue, + machine=main.machine) + else: + jw.write_condor(filename=outs_submit, real_exec=utils.build_script_path(script), shell_exec=outs_job, outfile=outs_check, @@ -55,10 +64,10 @@ def closure(args) : queue=main.queue, machine=main.machine) - qlines = [] - for chn in args.channels: - for trig in args.closure_single_triggers: - qlines.append(' {},{}'.format(chn,trig)) + qlines = [] + for chn in args.channels: + for trig in args.closure_single_triggers: + qlines.append(' {},{}'.format(chn,trig)) - jw.write_queue( qvars=('channel', 'closure_single_trigger'), - qlines=qlines ) + jw.write_queue( qvars=('channel', 'closure_single_trigger'), + qlines=qlines ) diff --git a/inclusion/condor/discriminator.py b/inclusion/condor/discriminator.py index 5d7a54b..cdb7ae5 100644 --- a/inclusion/condor/discriminator.py +++ b/inclusion/condor/discriminator.py @@ -46,14 +46,23 @@ def discriminator(args): jw.add_string('echo "Script {} with channel {} done."'.format(script, args.channels[i])) #### Write submission file - jw.write_condor(filename=outs_submit[i], - real_exec=utils.build_script_path(script), - shell_exec=outs_job[i], - outfile=outs_check[i], - logfile=outs_log[i], - queue=main.queue, - machine=main.machine) - jw.write_queue() + if main.machine == "slurm": + jw.write_batch(filename=outs_submit[i], + real_exec=utils.build_script_path(script), + shell_exec=outs_job[i], + outfile=outs_check[i], + logfile=outs_log[i], + queue=main.queue, + machine=main.machine) + else: + jw.write_condor(filename=outs_submit[i], + real_exec=utils.build_script_path(script), + shell_exec=outs_job[i], + outfile=outs_check[i], + logfile=outs_log[i], + queue=main.queue, + machine=main.machine) + jw.write_queue() # -- Parse options if __name__ == '__main__': diff --git a/inclusion/condor/eff_and_sf.py b/inclusion/condor/eff_and_sf.py index 287e60d..c3e8309 100644 --- a/inclusion/condor/eff_and_sf.py +++ b/inclusion/condor/eff_and_sf.py @@ -56,26 +56,49 @@ def eff_and_sf(args): jw.add_string('echo "{} done."'.format(script)) #### Write submission file - jw.write_condor(filename=outs_submit, - real_exec=utils.build_script_path(script), - shell_exec=outs_job, - outfile=outs_check, - logfile=outs_log, - queue=main.queue, - machine=main.machine) + if main.machine == "slurm": + cfg = importlib.import_module(args.configuration) + input_params = [] + for chn in args.channels: + if chn == args.channels[0]: + triggercomb = utils.generate_trigger_combinations(chn, cfg.triggers, + cfg.exclusive) + else: + triggercomb += utils.generate_trigger_combinations(chn, cfg.triggers, + cfg.exclusive) + + for tcomb in set(triggercomb): + input_params.append('"{}"'.format( utils.join_name_trigger_intersection(tcomb)) ) - cfg = importlib.import_module(args.configuration) - qlines = [] - for chn in args.channels: - if chn == args.channels[0]: - triggercomb = utils.generate_trigger_combinations(chn, cfg.triggers, - cfg.exclusive) - else: - triggercomb += utils.generate_trigger_combinations(chn, cfg.triggers, - cfg.exclusive) - - for tcomb in set(triggercomb): - qlines.append(' {}'.format( utils.join_name_trigger_intersection(tcomb)) ) + jw.write_batch(filename=outs_submit, + real_exec=utils.build_script_path(script), + shell_exec=outs_job, + outfile=outs_check, + logfile=outs_log, + queue=main.queue, + machine=main.machine, + input_output_params=input_params ) + else: + jw.write_condor(filename=outs_submit, + real_exec=utils.build_script_path(script), + shell_exec=outs_job, + outfile=outs_check, + logfile=outs_log, + queue=main.queue, + machine=main.machine) - jw.write_queue( qvars=('triggercomb',), - qlines=qlines ) + cfg = importlib.import_module(args.configuration) + qlines = [] + for chn in args.channels: + if chn == args.channels[0]: + triggercomb = utils.generate_trigger_combinations(chn, cfg.triggers, + cfg.exclusive) + else: + triggercomb += utils.generate_trigger_combinations(chn, cfg.triggers, + cfg.exclusive) + + for tcomb in set(triggercomb): + qlines.append(' {}'.format( utils.join_name_trigger_intersection(tcomb)) ) + + jw.write_queue( qvars=('triggercomb',), + qlines=qlines ) diff --git a/inclusion/condor/eff_and_sf_aggr.py b/inclusion/condor/eff_and_sf_aggr.py index f822f9e..83f0a3f 100644 --- a/inclusion/condor/eff_and_sf_aggr.py +++ b/inclusion/condor/eff_and_sf_aggr.py @@ -41,17 +41,31 @@ def eff_and_sf_aggr(args): jw.add_string('echo "{} done."'.format(script)) #### Write submission file - jw.write_condor(filename=outs_submit, - real_exec=utils.build_script_path(script), - shell_exec=outs_job, - outfile=outs_check, - logfile=outs_log, - queue=main.queue, - machine=main.machine) - - qlines = [] - for chn in args.channels: - qlines.append(' {}'.format(chn)) - - jw.write_queue( qvars=('channel',), - qlines=qlines ) + if main.machine == "slurm": + input_params = [] + for chn in args.channels: + input_params.append('"{}"'.format(chn)) + + jw.write_batch(filename=outs_submit, + real_exec=utils.build_script_path(script), + shell_exec=outs_job, + outfile=outs_check, + logfile=outs_log, + queue=main.queue, + machine=main.machine, + input_output_params=input_params) + else: + jw.write_condor(filename=outs_submit, + real_exec=utils.build_script_path(script), + shell_exec=outs_job, + outfile=outs_check, + logfile=outs_log, + queue=main.queue, + machine=main.machine) + + qlines = [] + for chn in args.channels: + qlines.append(' {}'.format(chn)) + + jw.write_queue( qvars=('channel',), + qlines=qlines ) diff --git a/inclusion/condor/hadd_counts.py b/inclusion/condor/hadd_counts.py index c59d10c..52097c0 100755 --- a/inclusion/condor/hadd_counts.py +++ b/inclusion/condor/hadd_counts.py @@ -77,29 +77,38 @@ def hadd_counts(args): inputs_join = {} nchannels = len(args.channels) for out1,out2,out3,out4 in zip(outs_job,outs_submit,outs_check,outs_log): - jw.write_condor(filename=out2, + if main.machine == "slurm": + jw.write_batch(filename=out2, real_exec=utils.build_script_path(script), shell_exec=out1, outfile=out3, logfile=out4, queue=main.queue, - machine=main.machine) + machine=main.machine,) + else: + jw.write_condor(filename=out2, + real_exec=utils.build_script_path(script), + shell_exec=out1, + outfile=out3, + logfile=out4, + queue=main.queue, + machine=main.machine) - qvars = None - qlines = [] - if out1 == outs_job[0]: - qvars = ('myoutput', 'channel', 'sample') - for it,t in enumerate(targets[nchannels:]): - smpl = args.samples[ int(it/nchannels) ] - chn = args.channels[ int(it%nchannels) ] - if chn not in inputs_join: - inputs_join[chn] = [] - inputs_join[chn].append(t) - qlines.append(' {}, {}, {}'.format(t,smpl,chn)) + qvars = None + qlines = [] + if out1 == outs_job[0]: + qvars = ('myoutput', 'channel', 'sample') + for it,t in enumerate(targets[nchannels:]): + smpl = args.samples[ int(it/nchannels) ] + chn = args.channels[ int(it%nchannels) ] + if chn not in inputs_join: + inputs_join[chn] = [] + inputs_join[chn].append(t) + qlines.append(' {}, {}, {}'.format(t,smpl,chn)) - elif out1 == outs_job[1]: - qvars = ('channel', 'myinputs') - for ichn,chn in enumerate(args.channels): - qlines.append(" {}, '{}'".format(chn, ' '.join(inputs_join[chn]))) + elif out1 == outs_job[1]: + qvars = ('channel', 'myinputs') + for ichn,chn in enumerate(args.channels): + qlines.append(" {}, '{}'".format(chn, ' '.join(inputs_join[chn]))) - jw.write_queue( qvars=qvars, qlines=qlines ) + jw.write_queue( qvars=qvars, qlines=qlines ) diff --git a/inclusion/condor/hadd_eff.py b/inclusion/condor/hadd_eff.py index 0925e38..91cde55 100755 --- a/inclusion/condor/hadd_eff.py +++ b/inclusion/condor/hadd_eff.py @@ -63,7 +63,28 @@ def hadd_eff(args): #### Write submission file inputs_join = [] for out1,out2,out3,out4 in zip(outs_job,outs_submit,outs_check,outs_log): - jw.write_condor(filename=out2, + if main.machine == "slurm": + input_output_params = [] + if out1 == outs_job[0]: + for t,smpl in zip(targets[1:], args.samples): + inputs = os.path.join(args.indir, smpl, args.outprefix + '*' + args.subtag + '.root ') + inputs_join.append(t) + # join subdatasets (different MC or Data subfolders, ex: TT_fullyHad, TT_semiLep, ...) + jw.add_string('"{} {}"'.format(t, inputs)) + elif out1 == outs_job[1]: + # join MC or Data subdatasets into a single one (ex: TT) + jw.add_string('"{} {}"'.format(targets[0], ' '.join(inputs_join))) + + jw.write_batch(filename=out2, + real_exec='/dev/null', + shell_exec=out1, + outfile=out3, + logfile=out4, + queue=main.queue, + machine=main.machine, + input_output_params=input_output_params) + else: + jw.write_condor(filename=out2, real_exec='/dev/null', shell_exec=out1, outfile=out3, @@ -71,16 +92,16 @@ def hadd_eff(args): queue=main.queue, machine=main.machine) - qlines = [] - if out1 == outs_job[0]: - for t,smpl in zip(targets[1:], args.samples): - inputs = os.path.join(args.indir, smpl, args.outprefix + '*' + args.subtag + '.root ') - inputs_join.append(t) - # join subdatasets (different MC or Data subfolders, ex: TT_fullyHad, TT_semiLep, ...) - jw.add_string(' {}, {}'.format(t, inputs)) - elif out1 == outs_job[1]: - # join MC or Data subdatasets into a single one (ex: TT) - jw.add_string(' {}, {}'.format(targets[0], ' '.join(inputs_join))) + qlines = [] + if out1 == outs_job[0]: + for t,smpl in zip(targets[1:], args.samples): + inputs = os.path.join(args.indir, smpl, args.outprefix + '*' + args.subtag + '.root ') + inputs_join.append(t) + # join subdatasets (different MC or Data subfolders, ex: TT_fullyHad, TT_semiLep, ...) + jw.add_string(' {}, {}'.format(t, inputs)) + elif out1 == outs_job[1]: + # join MC or Data subdatasets into a single one (ex: TT) + jw.add_string(' {}, {}'.format(targets[0], ' '.join(inputs_join))) - jw.write_queue( qvars=('myoutput', 'myinputs'), - qlines=qlines ) + jw.write_queue( qvars=('myoutput', 'myinputs'), + qlines=qlines ) diff --git a/inclusion/condor/hadd_histo.py b/inclusion/condor/hadd_histo.py index ce8468a..79c7320 100755 --- a/inclusion/condor/hadd_histo.py +++ b/inclusion/condor/hadd_histo.py @@ -62,7 +62,28 @@ def hadd_histo(args): #### Write submission file inputs_join = [] for out1,out2,out3,out4 in zip(outs_job,outs_submit,outs_check,outs_log): - jw.write_condor(filename=out2, + if main.machine == "slurm": + input_output_params = [] + if out1 == outs_job[0]: + for t,smpl in zip(targets[1:], args.samples): + inputs = os.path.join(args.indir, smpl, args.tprefix + '*' + args.subtag + '.root') + inputs_join.append(t) + # join subdatasets (different MC or Data subfolders, ex: TT_fullyHad, TT_semiLep, ...) + input_output_params.append('"{} {}"'.format(t, inputs)) + elif out1 == outs_job[1]: + # join MC or Data subdatasets into a single one (ex: TT) + input_output_params.append('"{} {}"'.format(targets[0], ' '.join(inputs_join))) + + jw.write_batch(filename=out2, + real_exec='/dev/null', + shell_exec=out1, + outfile=out3, + logfile=out4, + queue=main.queue, + machine=main.machine, + input_output_params=input_output_params) + else: + jw.write_condor(filename=out2, real_exec='/dev/null', shell_exec=out1, outfile=out3, @@ -70,16 +91,16 @@ def hadd_histo(args): queue=main.queue, machine=main.machine) - qlines = [] - if out1 == outs_job[0]: - for t,smpl in zip(targets[1:], args.samples): - inputs = os.path.join(args.indir, smpl, args.tprefix + '*' + args.subtag + '.root') - inputs_join.append(t) - # join subdatasets (different MC or Data subfolders, ex: TT_fullyHad, TT_semiLep, ...) - qlines.append(' {}, {}'.format(t, inputs)) - elif out1 == outs_job[1]: - # join MC or Data subdatasets into a single one (ex: TT) - qlines.append(' {}, {}'.format(targets[0], ' '.join(inputs_join))) - - jw.write_queue( qvars=('myoutput', 'myinputs'), - qlines=qlines ) + qlines = [] + if out1 == outs_job[0]: + for t,smpl in zip(targets[1:], args.samples): + inputs = os.path.join(args.indir, smpl, args.tprefix + '*' + args.subtag + '.root') + inputs_join.append(t) + # join subdatasets (different MC or Data subfolders, ex: TT_fullyHad, TT_semiLep, ...) + qlines.append(' {}, {}'.format(t, inputs)) + elif out1 == outs_job[1]: + # join MC or Data subdatasets into a single one (ex: TT) + qlines.append(' {}, {}'.format(targets[0], ' '.join(inputs_join))) + + jw.write_queue( qvars=('myoutput', 'myinputs'), + qlines=qlines ) diff --git a/inclusion/condor/job_writer.py b/inclusion/condor/job_writer.py index ac18107..2b166af 100644 --- a/inclusion/condor/job_writer.py +++ b/inclusion/condor/job_writer.py @@ -4,6 +4,7 @@ import os import sys +import re parent_dir = os.path.abspath(__file__ + 3 * '/..') sys.path.insert(0, parent_dir) @@ -25,7 +26,7 @@ def add_string(self, string): self.f.write( string + self.endl ) @staticmethod - def define_output(localdir, data_folders, tag, names=''): + def define_output(localdir, data_folders, tag, names='', workflow='condor'): """ Defines where the shell and condor job files, and the HTCondor outputs will be stored. @@ -65,9 +66,12 @@ def define_output(localdir, data_folders, tag, names=''): for jd, cd, name in zip(job_d,out_d,names): mkdir(cd) job_f.append( os.path.join(jd, 'job{}.sh'.format(name)) ) - subm_f.append( os.path.join(jd, 'job{}.condor'.format(name)) ) - - base_name = 'C$(Cluster)_P$(Process)' + if main.machine == 'slurm': + subm_f.append( os.path.join(jd, 'job{}.bat'.format(name)) ) + base_name = '%x-%j' + else: + subm_f.append( os.path.join(jd, 'job{}.condor'.format(name)) ) + base_name = 'C$(Cluster)_P$(Process)' out_name = '{}.out'.format(base_name) log_name = '{}.log'.format(base_name) out_f.append( os.path.join(cd, out_name) ) @@ -116,7 +120,57 @@ def write_condor(self, filename, shell_exec, real_exec, outfile, logfile, self.f.write(m) os.system('chmod u+rwx '+ filename) - def write_queue(self, qvars=(), qlines=[]): + def write_batch(self, filename, shell_exec, real_exec, outfile, logfile, + queue, machine, input_args=[], additional_args = "", input_output_params = []): + + self.filenames.append(filename) + batch_name = os.path.dirname(shell_exec).split('/')[-1] + + if not filename: + raise ValueError("file_name must not be empty") + if not shell_exec and not real_exec: + raise ValueError("either command or executable must not be empty") + if not shell_exec: + raise ValueError("shell must not be empty") + + filenum_array = [] + arg_array = [] + if len(input_args): + filenum_array = [re.findall('(\d+)(?!.*\d)', input_arg)[0] for input_arg in input_args] + file_root_path = re.split('(\d+)(?!.*\d)', input_args[0]) + additional_args = " " + file_root_path[0] + '$SLURM_ARRAY_TASK_ID' + file_root_path[2] + + if len(input_output_params): + arg_array = ["case $SLURM_ARRAY_TASK_ID in"] + + for i in range(len(input_output_params)): + arg_array.append("\t{}) IN_N_OUT_ARGS={} ;;".format(i, input_output_params[i])) + filenum_array.append(str(i)) + arg_array.append("esac") + + additional_args = " $IN_N_OUT_ARGS" + + m = self.endl.join(('#!/usr/bin/env bash', + '#SBATCH --job-name={}'.format(batch_name), + '#SBATCH --partition=standard', + '#SBATCH --output=/dev/null', + '#SBATCH --chdir=/t3home/fbilandz/Run3', + '#SBATCH --time=00:44:59', + '#SBATCH --nodes=1', + '#SBATCH -o {}'.format(outfile), + '#SBATCH -e {}'.format(outfile.replace('.out', '.err')), + '#SBATCH --array={}'.format(','.join(filenum_array)) if len(filenum_array) else "", + '#SBATCH --cpus-per-task=1', + self.endl, + self.endl.join(arg_array) if len(arg_array) else "", + shell_exec + additional_args, + self.endl)) + + with open(filename, 'w') as self.f: + self.f.write(m) + os.system('chmod u+rwx '+ filename) + + def write_queue(self, qvars=(), qlines=[], machine="condor"): """ Works for any number variables in the queue. It is up to the user to guarantee compatibility between queue variables and lines. @@ -124,7 +178,10 @@ def write_queue(self, qvars=(), qlines=[]): extension = self.filenames[-1].split('.')[-1] if extension != self.exts[1]: self.extension_exception() - + + if machine == "slurm": + return + with open(self.filenames[-1], 'a') as self.f: if len(qvars) > 0: argstr = 'Arguments = "' @@ -164,13 +221,13 @@ def write_shell(self, filename, command, localdir, machine, eos_user='bfontana') os.system('chmod u+rwx '+ filename) def condor_specific_content(self, queue, machine): - if 'llr' in machine: + if 'llr' in machine or machine == 'slurm': assert queue in ('short', 'long') - assert machine in ('llrt3condor', 'llrt3condor7') + assert machine in ('llrt3condor', 'llrt3condor7', 'slurm') m = self.endl + self.endl.join(('T3Queue = {}'.format(queue), 'WNTag=el7', '+SingularityCmd = ""')) - if machine == 'llrt3condor': + if machine == 'llrt3condor' or machine == 'slurm': t3 = "t3" elif machine == 'llrt3condor7': t3 = "t3_tst" diff --git a/inclusion/condor/processing.py b/inclusion/condor/processing.py index d209706..edf9ee6 100755 --- a/inclusion/condor/processing.py +++ b/inclusion/condor/processing.py @@ -110,20 +110,34 @@ def processing(args): jw.add_string('echo "Process {} done in mode {}."'.format(vproc,args.mode)) #### Write submission file - jw.write_condor(filename=outs_submit[i], + if main.machine == "slurm": + input_args = [] + for listname in filelist: + input_args.append('{}'.format( listname.replace('\n','') )) + jw.write_batch(filename=outs_submit[i], real_exec=utils.build_script_path(script), shell_exec=outs_job[i], outfile=outs_check[i], logfile=outs_log[i], queue=main.queue, - machine=main.machine) + machine=main.machine, + input_args=input_args) + + else: + jw.write_condor(filename=outs_submit[i], + real_exec=utils.build_script_path(script), + shell_exec=outs_job[i], + outfile=outs_check[i], + logfile=outs_log[i], + queue=main.queue, + machine=main.machine) - qlines = [] - for listname in filelist: - qlines.append(' {}'.format( listname.replace('\n','') )) - - jw.write_queue( qvars=('filename',), - qlines=qlines ) + qlines = [] + for listname in filelist: + qlines.append(' {}'.format( listname.replace('\n','') )) + + jw.write_queue( qvars=('filename',), + qlines=qlines ) # -- Parse options if __name__ == '__main__': diff --git a/inclusion/condor/union_calculator.py b/inclusion/condor/union_calculator.py index a27bc99..5334df7 100644 --- a/inclusion/condor/union_calculator.py +++ b/inclusion/condor/union_calculator.py @@ -59,7 +59,16 @@ def union_calculator(args): jw.add_string('echo "Process {} done."'.format(proc)) #### Write submission file - jw.write_condor(filename=subs[i], + if main.machine == "slurm": + jw.write_batch(filename=subs[i], + real_exec=utils.build_script_path(script), + shell_exec=jobs[i], + outfile=checks[i], + logfile=logs[i], + queue=main.queue, + machine=main.machine) + else: + jw.write_condor(filename=subs[i], real_exec=utils.build_script_path(script), shell_exec=jobs[i], outfile=checks[i], @@ -67,10 +76,10 @@ def union_calculator(args): queue=main.queue, machine=main.machine) - qlines = [] - for listname in filelist: - for trig in args.closure_single_triggers: - qlines.append(' {},{}'.format( os.path.basename(listname).replace('\n',''), trig )) - - jw.write_queue( qvars=('filename', 'closure_single_trigger'), - qlines=qlines ) + qlines = [] + for listname in filelist: + for trig in args.closure_single_triggers: + qlines.append(' {},{}'.format( os.path.basename(listname).replace('\n',''), trig )) + + jw.write_queue( qvars=('filename', 'closure_single_trigger'), + qlines=qlines ) diff --git a/inclusion/config/main.py b/inclusion/config/main.py index f355254..2aa252c 100644 --- a/inclusion/config/main.py +++ b/inclusion/config/main.py @@ -2,9 +2,11 @@ email = 'bruno.alves@cern.ch' queue = 'short' -machine = 'llrt3condor' #'lxplus' +machine = 'slurm' #'lxplus' or 'llrt3condor' -storage = {'2018': os.path.join('/data_CMS/cms/', os.environ['USER'], 'TriggerScaleFactors'), +storage = { + '2022': os.path.join('/t3home/', os.environ['USER'], 'TriggerScaleFactors'), + '2018': os.path.join('/data_CMS/cms/', os.environ['USER'], 'TriggerScaleFactors'), '2017': os.path.join('/data_CMS/cms/', os.environ['USER'], 'TriggerScaleFactors'), '2016': os.path.join('/data_CMS/cms/', os.environ['USER'], 'TriggerScaleFactors'), '2016APV': os.path.join('/data_CMS/cms/', os.environ['USER'], 'TriggerScaleFactors')} @@ -16,7 +18,10 @@ 'subm' : 'submission', 'outs' : 'outputs'} -base_folder = {'llrt3condor': +base_folder = { + 'slurm': + os.path.join(os.environ['HOME'], 'HHbbtautau/inclusionRun3'), + 'llrt3condor': os.path.join(os.environ['HOME'], 'CMSSW_14_1_0_pre0', 'src', folders['base']), 'llrt3condor7': os.path.join(os.environ['HOME'], 'CMSSW_14_1_0_pre0', 'src', folders['base']), @@ -48,15 +53,24 @@ 'emu' : {'pairType': ('==', 5),} } # variables considered for calculating and plotting efficiencies -var_eff = ('HT20', 'met_et', 'mht_et', 'metnomu_et', 'mhtnomu_et', - 'dau1_pt', 'dau2_pt', 'dau1_eta', 'dau2_eta') +var_eff = ( + # 'met_et', 'mht_et', 'metnomu_et', 'mhtnomu_et', + 'dau1_pt', 'dau2_pt', 'dau1_eta', + 'dau1_tauIdVSjet', 'dau2_tauIdVSjet', + 'bjet1_btagDeepFlavB', 'bjet2_btagDeepFlavB' + # 'dau2_eta' + ) # variables considered for plotting MC/data comparison distributions -var_dist = ('dau1_pt', 'HH_mass') +var_dist = ('dau1_pt', +# 'HH_mass' +) # joining the two lists above var_join = set(var_eff + var_dist) -var_unionweights = ('dau1_pt', 'dau2_pt', 'dau1_eta', 'dau2_eta') +var_unionweights = ('dau1_pt', 'dau2_pt', 'dau1_eta', +# 'dau2_eta' +) trig_linear = lambda x : {'mc': x, 'data': x} @@ -64,7 +78,23 @@ # It does NOT match the 'pass_triggerbit' leaf, which is a skimmed version of the above that might change more often # One way to ensure the scheme is still correct is by running the script as shown here: # https://github.com/bfonta/useful_scripts/commit/f5e4a0096bc74c89176579a336b0f52b74cb3ed2 -trig_map = {'2018': +filterbit_map = { + '2022': { + 'IsoMu24': {'mc': {13: (1, 3)}, 'data': {13: (1, 3)}}, + 'IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1': {'mc': {13: (2, ), 15: (3, 5, 9)}, 'data': {13: (2, ), 15: (3, 5, 9)}}, + "LooseDeepTauPFTauHPS180_L2NN_eta2p1": {'mc': {}, 'data': {}}, + "PFMETNoMu120_PFMHTNoMu120_IDTight": {'mc': {}, 'data': {}}, + } +} + +trig_map = { + '2022': { + 'IsoMu24': {'mc': 0, 'data': 0}, + 'IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1': {'mc': 1, 'data': 1}, + "LooseDeepTauPFTauHPS180_L2NN_eta2p1": {'mc': 11, 'data': 11}, + "PFMETNoMu120_PFMHTNoMu120_IDTight": {'mc': 12, 'data': 12}, + }, + '2018': {'IsoMu24': {'mc': 0, 'data': 0}, 'Ele32': {'mc': 2, 'data': 2}, 'METNoMu120': {'mc': 40, 'data': 40}, @@ -98,21 +128,25 @@ } trig_map['2016APV'] = trig_map['2016'] -lep_triggers = {'2018': +lep_triggers = { + '2022': { + 'IsoMu24', 'IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1' + }, + '2018': {'Ele32', 'EleIsoTauCustom', 'IsoMu24', 'IsoMuIsoTauCustom', 'IsoDoubleTauCustom'}, '2017': {'Ele32', 'EleIsoTau', 'IsoMu27', 'IsoMuIsoTau', 'IsoDoubleTau'}, '2016': {'Ele25', 'IsoMu24', 'IsoMuIsoTau', 'IsoDoubleTau'} } -for year in ('2016', '2017', '2018'): +for year in ('2016', '2017', '2018', '2022'): assert all(x in trig_map[year].keys() for x in lep_triggers[year]) -cuts_ignored = {'HT20': (), - 'met_et': ('metnomu_et',), - 'mht_et': ('mhtnomu_et',), - 'metnomu_et': ('met_et',), - 'mhtnomu_et': ('mht_et',), +cuts_ignored = {#'HT20': (), + # 'met_et': ('metnomu_et',), + # 'mht_et': ('mhtnomu_et',), + # 'metnomu_et': ('met_et',), + # 'mhtnomu_et': ('mht_et',), 'dau1_pt': (), 'dau2_pt': ()} @@ -123,7 +157,9 @@ 'mumu': {} } ### Data and MC samples -inputs = {'2018': +inputs = { + '2022': ('/t3home/fbilandz/HHbbtautau/Run3/MC/PreprocessOutputs'), + '2018': ('/data_CMS/cms/portales/HHresonant_SKIMS/SKIMS_UL18_OpenCADI_Data/', '/data_CMS/cms/alves/HHresonant_SKIMS/SKIMS_UL18_OpenCADI_MC/'), '2017': @@ -137,7 +173,16 @@ # names of the subfolders under 'inputs' above: # dictionary that maps specific general triggers to datasets -data = {"2018": +data = { + "2022": + {'MET' : ('MET',), + 'EG' : ('EGamma',), + 'Mu' : ( + # 'SingleMuon', + 'Muon',), + 'Tau' : ('Tau',) + }, + "2018": {'MET' : ('MET',), 'EG' : ('EGamma',), 'Mu' : ('SingleMuon',), @@ -163,7 +208,13 @@ }, } -mc_processes = {"2018": +mc_processes = { + "2022": { + 'TT': ('TTToHadronic', 'TTToFullyLeptonic', + 'TTToSemiLeptonic',), + 'DY': ('DYJetsToLL_M-50') + }, + "2018": {'ggfRadions': (), 'ggfBulkGraviton': (), 'TT': ('TTToHadronic', 'TTTo2L2Nu', 'TTToSemiLeptonic',), 'DY': ('DYJetsToLL_M-50_TuneCP5_13TeV-amc', diff --git a/inclusion/config/sel_trigger_regions_22.py b/inclusion/config/sel_trigger_regions_22.py new file mode 100644 index 0000000..a1fdcdd --- /dev/null +++ b/inclusion/config/sel_trigger_regions_22.py @@ -0,0 +1,131 @@ +# coding: utf-8 + +import os +import sys +parent_dir = os.path.abspath(__file__ + 3 * '/..') +sys.path.insert(0, parent_dir) + +import inclusion +from inclusion.config import main +from inclusion.utils import utils + +bjets_cut = None +mass_cut = None #standard, inverted +custom_cut = None +category = 'baseline' + +triggers = ( + # 'METNoMu120', + 'IsoMu24', + 'IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1', + 'DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1', + # "LooseDeepTauPFTauHPS180_L2NN_eta2p1", + # "PFMETNoMu120_PFMHTNoMu120_IDTight", + 'QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65', + # "Mu50", + # "Ele28_eta2p1_WPTight_Gsf_HT150" + # 'IsoTau180' + ) +trig_custom = { + # 'EleIsoTauCustom', 'IsoMuIsoTauCustom', 'IsoDoubleTauCustom' + } +cuts = { + # 'METNoMu120': {'metnomu_et': ('>', [120,]), + # 'mhtnomu_et': ('>', [100,])}, + } + +# which triggers are exclusive to a particular channel? +exclusive = {'mutau' : ( + 'IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1',), + 'mumu' : (), + 'tautau': ( + 'DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1', + ), + 'general' : ( + # "LooseDeepTauPFTauHPS180_L2NN_eta2p1", + # "PFMETNoMu120_PFMHTNoMu120_IDTight", + 'IsoMu24', + 'QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65', + # "Mu50", + # 'METNoMu120','IsoTau180' + ),} + +inters_general = {'MET' : (), + 'EG' : (), + 'Mu' : ( + # ('METNoMu120',), + ), + 'Tau' : ()} +inters = { + 'mutau': + {'MET' : (), + 'EG' : (), + 'Mu' : (('IsoMu24',), + ('IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1',), + ('IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1', 'IsoMu24',), + ('QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65',), + ('QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65', 'IsoMu24'), + ('IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1', 'QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65',), + ('IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1', 'IsoMu24', 'QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65',), +), + 'Tau' : () + }, + 'tautau': + {'MET' : (), + 'EG' : (), + 'Mu' : (), + 'Tau' : ( + ('IsoMu24',), + ('IsoMu24', 'QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65',), + ('DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1', 'IsoMu24',), + ('DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1', 'IsoMu24', 'QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65'), + ('DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1', 'QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65',), + ('QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65',), + ('DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1',), + )} +} +for x in inters: + utils.check_inters_correctness(triggers, inters[x], inters_general, channel=x, exclusive=exclusive) + + +fit_vars = ["dau1_pt", "dau2_pt"] +pairs2D = {'IsoMu24': (('dau1_pt', 'dau2_pt'),), 'IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1': (('dau1_pt', 'dau2_pt'),), + "DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1": (('dau1_pt', 'dau2_pt',), ('dau1_tauIdVSjet', 'dau2_tauIdVSjet'),), "QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65": (('dau1_pt', 'dau2_pt'), ('bjet1_btagDeepFlavB', 'bjet2_btagDeepFlavB'),), } +assert( set(pairs2D.keys()).issubset(set(triggers)) ) +for x in pairs2D.values(): + for pair in x: + assert( pair[0] in main.var_eff and pair[1] in main.var_eff ) + +pog_pt_binedges = (0, 26, 30, 40, 50, 60, 120, 200) +binedges = { + 'dau1_pt': {'etau': pog_pt_binedges, + 'mutau': pog_pt_binedges, + 'tautau': pog_pt_binedges }, + 'dau2_pt': { + 'etau': pog_pt_binedges, + 'mutau': pog_pt_binedges, + 'tautau': pog_pt_binedges + }, + 'dau1_eta': { + 'mutau': (-2.4, -2.1, -1.2, -0.9, 0, 0.9, 1.2, 2.1, 2.4), + 'etau': (-2.4, -2.1, -1.2, -0.9, 0, 0.9, 1.2, 2.1, 2.4), + 'tautau': (-2.4, -2.1, -1.2, -0.9, 0, 0.9, 1.2, 2.1, 2.4), + }, + 'dau1_tauIdVSjet': { + 'tautau': (0, 1, 2, 3, 4, 5, 6, 7), + }, + 'dau2_tauIdVSjet': { + 'tautau': (0, 1, 2, 3, 4, 5, 6, 7), + }, + 'bjet1_btagDeepFlavB': { + 'tautau': (0, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + }, + 'bjet2_btagDeepFlavB': { + 'tautau': (0, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + } + # ("quantiles", 100, 300), + # 'metnomu_et': {'mutau' : metnomu_et_binedges['mutau'], + # 'mumu' : metnomu_et_binedges['mumu'] }, + # 'mhtnomu_et': {'mutau' : (90,360), + # 'mumu' : (90,360) }, +} \ No newline at end of file diff --git a/inclusion/quickRDF.py b/inclusion/quickRDF.py new file mode 100644 index 0000000..903822d --- /dev/null +++ b/inclusion/quickRDF.py @@ -0,0 +1,81 @@ +import os +import sys + +import ROOT + +# Enable multi-threading +# ROOT.ROOT.EnableImplicitMT() +ROOT.gROOT.SetBatch(True) + +core_dir = str(os.getcwd()).split('producer') + +sys.path.insert(1, core_dir[0]+"/python") + +import argparse +parser = argparse.ArgumentParser(description='Ntuplizer options') +parser.add_argument('-v','--tauid_version', choices=['2p1', '2p5'], dest="tauid_version", default='2p5') +parser.add_argument('-o','--outfile', dest='outfile') +parser.add_argument('-i','--inputFiles', dest='inputFiles', default = False) +parser.add_argument('-mc', '--isMC', action='store_true', default = False) +options = parser.parse_args() + +# select the version +TauID_ver = options.tauid_version +outFile = options.outfile +useFiles = options.inputFiles +isMC = options.isMC + +def create_rdataframe(folders, inputFiles=None): + if not inputFiles: + inputFiles = [] + for folder in folders: + files = os.listdir(folder) + inputFiles += [folder + f for f in files] + + return ROOT.RDataFrame("Events", tuple(inputFiles)) + +def obtain_picontuple(df): + + branches = [] + + # Tag And Probe selection {Obtaining high pure Z -> mu tau Events} + ## select muon (Tag) candidate + + df = df.Filter("HLT_IsoMu24 > 0") + + df = df.Define("pass_quadJet", "return HLT_QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65 > 0;") + df = df.Define("pass_deepTau", "return HLT_DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1 > 0;") + df = df.Define("pass_both", "return HLT_QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65 > 0 && HLT_DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1 > 0;" ) + branches = ["dau1_pt", "dau1_eta", "pass_quadJet", "pass_deepTau", "pass_both"] + branch_list = ROOT.vector('string')() + for branch_name in branches: + branch_list.push_back(branch_name) + df.Snapshot("Events", "outfile.root", branch_list) + + + +if __name__ == '__main__': + + useFiles = str(useFiles) + + if ".txt" in useFiles: + print("Using files in {}".format(useFiles)) + folders = [] + inputFiles_run3 = [] + + with open(useFiles) as f: + for line in f: + line = line.replace('\n',"") # trim newline character + if "root://" not in line and not line.startswith("/eos") and not line.startswith("/pnfs"): + inputFiles_run3.append('root://cms-xrd-global.cern.ch//'+line) # prepend with redirector + else: + inputFiles_run3.append(line) + print(inputFiles_run3) + df = ROOT.RDataFrame("Events", tuple(inputFiles_run3)) + + # else: + # print("Not a valid inputFiles argument") + # print("Use 8102, 8136, or a text file of nanoaodfile locations") + + obtain_picontuple(df) + \ No newline at end of file diff --git a/inclusion/run.py b/inclusion/run.py index 54ab7fc..aa8a990 100644 --- a/inclusion/run.py +++ b/inclusion/run.py @@ -3,6 +3,7 @@ _all_ = [ ] import os +import subprocess import sys parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.insert(0, parent_dir) @@ -104,7 +105,7 @@ '--year', required=True, type=str, - choices=('2016', '2016APV', '2017', '2018'), + choices=('2016', '2016APV', '2017', '2018', '2022'), help='Data year: impact thresholds and selections.' ) parser.add_argument( @@ -639,6 +640,22 @@ def run(self): jobs, branch=self.branch ) dag_manager.write_all() + dag_manager.cleanup() + + @lutils.WorkflowDebugger(flag=FLAGS.debug_workflow) + def requires(self): + return [Processing(mode='histos'), + Processing(mode='counts'), + HaddHisto(dataset_name=data_name, samples=data_vals), + HaddHisto(dataset_name=mc_name, samples=mc_vals), + HaddCounts(dataset_name=data_name, samples=data_vals), + HaddCounts(dataset_name=mc_name, samples=mc_vals ), + EffAndSF(), + EffAndSFAggr(), + Discriminator(), + UnionCalculator(), + Closure(), + ] class SubmitDAG(lutils.ForceRun): """Submission class.""" @@ -658,20 +675,45 @@ def edit_condor_submission_file(self, out): @lutils.WorkflowDebugger(flag=FLAGS.debug_workflow) def run(self): - outfile = self.input()[-1][0].path - com = 'condor_submit_dag -no_submit -f' - com += ' -notification Always' - com += ' -append "notify_user={}"'.format(main.email) - bname = 'Inclusion_' + FLAGS.year + '_branch' + self.branch.capitalize() - com += ' -batch_name {}'.format(bname) - com += ' -outfile_dir {} {}'.format(os.path.dirname(outfile), outfile) - - os.system(com) - time.sleep(.5) - self.edit_condor_submission_file(outfile + '.condor.sub') - time.sleep(.5) - subm_com = '{}.condor.sub'.format(outfile) - os.system(subm_com) + if main.machine == "slurm": + outfile = self.input()[-1][0].path + job_registry = {} + dependencies_dict = {} + job_ids = {} + with open(outfile, "r", encoding="utf-8") as f: + for line in f: + if "JOB" in line: + job = line.split(" ") + job_registry[job[1]] = job[2].strip() + if "CHILD" in line: + job_names = line.split() + child_name = job_names[-1] + parent_names = job_names[1:job_names.index("CHILD")] + dependencies_dict[child_name] = parent_names + for job in job_registry: + if job not in dependencies_dict or len(dependencies_dict[job]) == 0: + status = subprocess.run(["sbatch", "--parsable", "-p", "short", job_registry[job]], capture_output=True) + job_ids[job] = status.stdout.decode().strip() + else: + dependency_ids = [job_ids[dependency_name] for dependency_name in dependencies_dict[job]] + status = subprocess.run(["sbatch", "--dependency=afterok:{}".format(":".join(dependency_ids)), "--parsable", "-p", "short", job_registry[job]], capture_output=True) + job_ids[job] = status.stdout.decode().strip() + + else: + outfile = self.input()[-1][0].path + com = 'condor_submit_dag -no_submit -f' + com += ' -notification Always' + com += ' -append "notify_user={}"'.format(main.email) + bname = 'Inclusion_' + FLAGS.year + '_branch' + self.branch.capitalize() + com += ' -batch_name {}'.format(bname) + com += ' -outfile_dir {} {}'.format(os.path.dirname(outfile), outfile) + + os.system(com) + time.sleep(.5) + self.edit_condor_submission_file(outfile + '.condor.sub') + time.sleep(.5) + subm_com = '{}.condor.sub'.format(outfile) + os.system(subm_com) @lutils.WorkflowDebugger(flag=FLAGS.debug_workflow) def output(self): @@ -681,19 +723,7 @@ def output(self): @lutils.WorkflowDebugger(flag=FLAGS.debug_workflow) def requires(self): - return [ Processing(mode='histos'), - Processing(mode='counts'), - HaddHisto(dataset_name=data_name, samples=data_vals), - HaddHisto(dataset_name=mc_name, samples=mc_vals), - HaddCounts(dataset_name=data_name, samples=data_vals), - HaddCounts(dataset_name=mc_name, samples=mc_vals ), - EffAndSF(), - EffAndSFAggr(), - Discriminator(), - UnionCalculator(), - Closure(), - Dag(branch=self.branch), - ] + return [Dag(branch=self.branch)] utils.create_single_dir( data_storage ) utils.create_single_dir( targets_folder ) diff --git a/inclusion/selection.py b/inclusion/selection.py index a98737f..dcce219 100644 --- a/inclusion/selection.py +++ b/inclusion/selection.py @@ -14,12 +14,26 @@ import functools from collections import defaultdict import itertools as it +import numpy as np class EventSelection: def __init__(self, entries, isdata, year='2018', configuration=None, debug=False): self.entries = entries - self.bit = self.entries['triggerbit'] - self.run = self.entries['RunNumber'] + # self.bit = self.entries['triggerbit'] + self.filterbits = self.entries['TrigObj_filterBits'] + self.obj_id = self.entries['TrigObj_id'] + # self.HLT_IsoMu24 = self.entries['HLT_IsoMu24'] + # self.HLT_IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1 = self.entries['HLT_IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1'] + # self.HLT_LooseDeepTauPFTauHPS180_L2NN_eta2p1 = self.entries['HLT_LooseDeepTauPFTauHPS180_L2NN_eta2p1'] + # self.HLT_PFMETNoMu120_PFMHTNoMu120_IDTight = self.entries['HLT_PFMETNoMu120_PFMHTNoMu120_IDTight'] + # self.HLT_Ele30_WPTight_Gsf = self.entries['HLT_Ele30_WPTight_Gsf'] + # self.HLT_Ele24_eta2p1_WPTight_Gsf_LooseDeepTauPFTauHPS30_eta2p1_CrossL1 = self.entries['HLT_Ele24_eta2p1_WPTight_Gsf_LooseDeepTauPFTauHPS30_eta2p1_CrossL1'] + # self.HLT_DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1 = self.entries['HLT_DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1'] + # self.HLT_DoubleMediumDeepTauPFTauHPS30_L2NN_eta2p1_PFJet60 = self.entries['HLT_DoubleMediumDeepTauPFTauHPS30_L2NN_eta2p1_PFJet60'] + # self.HLT_QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65 = self.entries['HLT_QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65'] + self.entries['bjet1_btagDeepFlavB'] = self.entries['Jet_btagDeepFlavB'][self.entries['bjet1_JetIdx']] + self.entries['bjet2_btagDeepFlavB'] = self.entries['Jet_btagDeepFlavB'][self.entries['bjet2_JetIdx']] + self.run = self.entries['run'] self.isdata = isdata self.year = year self.debug = debug @@ -46,6 +60,26 @@ def check_bit(self, bitpos): bitdigit = 1 res = bool(self.bit&(bitdigit< deepJetWP[0] and - self.entries['bjet2_bID_deepFlavor'] > deepJetWP[0]) - btagM = ((self.entries['bjet1_bID_deepFlavor'] > deepJetWP[1] - and self.entries['bjet2_bID_deepFlavor'] < deepJetWP[1]) or - (self.entries['bjet1_bID_deepFlavor'] < deepJetWP[1] - and self.entries['bjet2_bID_deepFlavor'] > deepJetWP[1])) - btagMM = (self.entries['bjet1_bID_deepFlavor'] > deepJetWP[1] and - self.entries['bjet2_bID_deepFlavor'] > deepJetWP[1]) + '2018' : (0.0490, 0.2783), + '2022' : (0.0583, 0.3086)}[self.year] + btagLL = (self.entries['Jet_btagDeepFlavB'][self.entries['bjet1_JetIdx']] > deepJetWP[0] and + self.entries['Jet_btagDeepFlavB'][self.entries['bjet2_JetIdx']] > deepJetWP[0]) + btagM = ((self.entries['Jet_btagDeepFlavB'][self.entries['bjet1_JetIdx']] > deepJetWP[1] + and self.entries['Jet_btagDeepFlavB'][self.entries['bjet2_JetIdx']] < deepJetWP[1]) or + (self.entries['Jet_btagDeepFlavB'][self.entries['bjet1_JetIdx']] < deepJetWP[1] + and self.entries['Jet_btagDeepFlavB'][self.entries['bjet2_JetIdx']] > deepJetWP[1])) + btagMM = (self.entries['Jet_btagDeepFlavB'][self.entries['bjet1_JetIdx']] > deepJetWP[1] and + self.entries['Jet_btagDeepFlavB'][self.entries['bjet2_JetIdx']] > deepJetWP[1]) # common = not (self.entries['bjet1_bID_deepFlavor'] > deepJetWP[1] or # self.entries['bjet2_bID_deepFlavor'] > deepJetWP[1]) @@ -267,8 +326,8 @@ def selection_cuts(self, iso_cuts=dict(), lepton_veto=True, bjets_cut=True, dau1_eleiso = self.entries['dau1_eleMVAiso'] dau1_muiso = self.entries['dau1_iso'] dau2_muiso = self.entries['dau2_iso'] - dau1_tauiso = self.entries['dau1_deepTauVsJet'] - dau2_tauiso = self.entries['dau2_deepTauVsJet'] + dau1_tauiso = self.entries['dau1_tauIdVSjet'] + dau2_tauiso = self.entries['dau2_tauIdVSjet'] # third lepton veto nleps = self.entries['nleps'] @@ -276,9 +335,9 @@ def selection_cuts(self, iso_cuts=dict(), lepton_veto=True, bjets_cut=True, return False # require at least two b jet candidates - nbjetscand = self.entries['nbjetscand'] - if nbjetscand <= 1 and bjets_cut: - return False + # nbjetscand = self.entries['nbjetscand'] + # if nbjetscand <= 1 and bjets_cut: + # return False # Loose / Medium / Tight iso_allowed = { 'dau1_ele': 1., 'dau1_mu': 0.15, 'dau2_mu': 0.15, @@ -305,9 +364,9 @@ def selection_cuts(self, iso_cuts=dict(), lepton_veto=True, bjets_cut=True, dau2_muiso >= iso_cuts['dau2_mu']) if bool0 or bool1 or bool2 or bool3: return False - - tauH_mass = self.entries['tauH_mass'] - bH_mass = self.entries['bH_mass_raw'] + + tauH_mass = self.entries['Htt_mass' if self.isdata else 'Htt_mass_corr_Medium_corr'] + bH_mass = self.entries['Hbb_mass' if self.isdata else 'Hbb_mass_corr_Medium_corr'] mcut = bH_mass > 50 and bH_mass < 270 and tauH_mass > 20 and tauH_mass < 130 mcutinv = bH_mass < 50 or bH_mass > 270 or tauH_mass < 20 or tauH_mass > 130 opt = ('standard', 'inverted') @@ -377,11 +436,12 @@ def trigger_bits(self, trig): else: flag = False bits = self.get_trigger_bit(trig) + # filterbits = self.get_filter_bit(trig) if isinstance(bits, (tuple,list)): for bit in bits: flag = flag or self.check_bit(bit) else: - flag = self.check_bit(bits) + flag = self.check_bit(bits) # and self.check_filterbit(filterbits) return flag def var_cuts(self, trig, variables, nocut_dummy_str): diff --git a/inclusion/utils/utils.py b/inclusion/utils/utils.py index d7e4055..0c8a6f8 100644 --- a/inclusion/utils/utils.py +++ b/inclusion/utils/utils.py @@ -8,6 +8,7 @@ import itertools as it import numpy as np import h5py +import json from types import SimpleNamespace import inclusion @@ -94,9 +95,10 @@ def check_inters_correctness(triggers, dchn, dgen, channel, exclusive): d = {'dataset1': (tuple1, tuple2,), 'dataset2': (tuple3, tuple4,), ...} """ chn_inters = generate_trigger_combinations(channel, triggers, exclusive) + print(chn_inters, dchn, dgen) flatten = [ tuple(sorted(w)) for x in dchn for w in dchn[x] ] flatten += [ tuple(sorted(w)) for x in dgen for w in dgen[x] ] - + print(flatten) # type check for x in flatten: if not isinstance(x, tuple): @@ -174,18 +176,56 @@ def define_used_tree_variables(cut): in the user-provided custom cut. Repeated variables are deleted. """ - _entries = ('triggerbit', 'RunNumber', 'HHKin_mass', 'isLeptrigger', 'pairType', 'isOS', - 'MC_weight', 'IdSF_deep_2d', 'PUReweight', 'L1pref_weight', 'trigSF', 'PUjetID_SF', 'bTagweightReshape', - 'dau1_eleMVAiso', 'dau1_iso', 'dau2_iso', 'dau1_deepTauVsJet', 'dau2_deepTauVsJet', - 'nleps', 'nbjetscand', 'tauH_mass', 'bH_mass_raw', - 'bjet1_bID_deepFlavor', 'bjet2_bID_deepFlavor', - 'isVBF', 'VBFjj_mass', 'VBFjj_deltaEta', - 'isTau1real', 'isTau2real') + _entries = ('TrigObj_filterBits', + # 'triggerbit', + 'TrigObj_id', 'run', + # 'HHKinFit_mass', + # 'isTauTauJetTrigger', + 'pairType', + 'isOS', + # 'genWeight', + # 'IdSF_deep_2d', + # 'puWeight', + # 'trigSF', + # 'bTagweightReshape', + 'genHH_mass', + 'MET_pt', + 'HLT_IsoMu24', 'HLT_IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1', + 'HLT_LooseDeepTauPFTauHPS180_L2NN_eta2p1', 'HLT_PFMETNoMu120_PFMHTNoMu120_IDTight', + 'HLT_Ele30_WPTight_Gsf', 'HLT_Ele24_eta2p1_WPTight_Gsf_LooseDeepTauPFTauHPS30_eta2p1_CrossL1', + 'HLT_DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1', 'HLT_DoubleMediumDeepTauPFTauHPS30_L2NN_eta2p1_PFJet60', + # "HLT_PFHT280_QuadPFJet30_PNet2BTagMean0p55", + 'dau1_eleMVAiso', 'dau1_iso', 'dau2_iso', 'dau1_eta', 'dau2_eta', 'dau1_tauIdVSjet', 'dau2_tauIdVSjet', + # 'dau1_mass', 'dau2_mass', 'Jet_mass', + 'Jet_btagPNetB', 'Jet_btagDeepFlavB', 'Jet_pt', 'Jet_eta', 'bjet1_JetIdx', 'bjet2_JetIdx', + 'dau1_pt', 'dau2_pt', + # 'nleps', 'event', + 'isQuadJetTrigger', + # 'bjet1_filterbits', 'bjet2_filterbits', 'tau1_filterbits', 'tau2_filterbits' + # 'nbjetscand', 'SoftActivityJetHT', + # 'Htt_mass', 'Hbb_mass', + # 'bjet1_btagDeepFlavB', 'bjet2_btagDeepFlavB', + # 'isVBFtrigger', + # 'VBFjj_mass', 'VBFjj_deltaEta', + #'isTau1real', 'isTau2real', + ) + mc_corrections = { + # 'dau1_pt': '_corr_Medium', + # 'dau2_pt': '_corr_Medium', + # 'HHKinFit_mass': '_corr_Medium_corr', + # 'Htt_mass': '_corr_Medium_corr', + # 'Hbb_mass': '_corr_Medium_corr', + # 'VBFjj_mass': '_corr_Medium_corr', + # 'VBFjj_deltaEta': '_corr_Medium_corr', + # 'HH_svfit_mass': '_corr_Medium_corr' + # '_corr_Medium': ('dau1_pt', 'dau2_pt') + } + if cut is not None: _regex = tuple(set(re.findall(r'self\.entries\.(.+?)\s', cut))) else: _regex = () - return tuple(set(_entries + _regex)) + return tuple(set(_entries + _regex)), mc_corrections class dot_dict(dict): """dot.notation access to dictionary attributes""" @@ -347,7 +387,7 @@ def get_root_inputs(proc, indir, include_tree=False): mes = '[' + os.path.basename(__file__) + '] ' mes += ' The input file does not exist: {}'.format(line) raise ValueError(mes) - filelist.append(line + ':HTauTauTree') + filelist.append(line + ':Events') else: if line[:-1] not in main.corrupted_files: #filelist.append("root://eosuser.cern.ch/" + line) @@ -391,7 +431,7 @@ def is_trigger_comb_in_channel(chn, tcomb, triggers, exclusive): return split in possible_trigs def is_nan(num): - return num!= num + return num!= num or num is None def join_name_trigger_intersection(tuple_element): inters = main.inters_str @@ -409,16 +449,21 @@ def load_binning(afile, key, variables, channels): """ binedges, nbins = ({} for _ in range(2)) with h5py.File(afile, 'r') as f: + # print(f.keys(), list(f.keys()), key) + # x = {} + # print(dict(f.items())) try: - group = f[key] + group = dict(f.items())[key] except KeyError: - missing_key_print(afile, key) + missing_key_print(afile, key, f) for var in variables: + print(var) try: - subgroup = group[var] + print(dict(group.items())) + subgroup = dict(group.items())[var] except KeyError: - missing_key_print(afile, key) + missing_key_print(afile, key, f) binedges[var], nbins[var] = ({} for _ in range(2)) for chn in channels: @@ -427,7 +472,7 @@ def load_binning(afile, key, variables, channels): return binedges, nbins -def missing_key_print(afile, key): +def missing_key_print(afile, key, f): print('{} does not have key {}.'.format(afile, key)) print('Available keys: {}'.format(f.keys())) raise @@ -512,6 +557,8 @@ def get_lumi(year): return 16800 elif year == "2016APV": return 19500 + elif year == "2022": + return 38010 else: raise ValueError("Year {} not supported.".format(year)) @@ -525,6 +572,8 @@ def get_ptcuts(channel, year): ptcuts = (26,) elif year == "2017" or year == "2018": ptcuts = (33, 25, 35) + elif year == "2022": + ptcuts = (33, 25, 35) elif channel == "mutau": if "2016" in year: @@ -533,6 +582,8 @@ def get_ptcuts(channel, year): ptcuts = (28, 21, 32) elif year == "2018": ptcuts = (25, 21, 32) + elif year == "2022": + ptcuts = (25, 21, 32) elif channel == "tautau": ptcuts = (40, 40) @@ -598,8 +649,14 @@ def total_sum_weights(f, isdata): xsec_norm = 0. with open(search_str, 'r') as afile: for elem in afile: - ftmp = ROOT.TFile(elem.replace('\n', ''), "READ") - xsec_norm += ftmp.Get('h_eff').GetBinContent(1) + if '.json' in elem: + ftmp = json.load(open(elem.replace('\n', ''))) + xsec_norm += float(ftmp['nweightedevents']) + else: + ftmp = ROOT.TFile(elem.replace('\n', ''), "READ") + # Find a way to connect PreCounter with this + xsec_norm += ftmp.Get('h_eff').GetBinContent(1) + # xsec_norm += 1. return xsec_norm return None diff --git a/tests/setup_regions.py b/tests/setup_regions.py new file mode 100644 index 0000000..231b731 --- /dev/null +++ b/tests/setup_regions.py @@ -0,0 +1,220 @@ +# coding: utf-8 + +_all_ = [ 'test_trigger_regions' ] + +import os +import sys +parent_dir = os.path.abspath(__file__ + 2 * '/..') +sys.path.insert(0, parent_dir) +import argparse +import glob +import multiprocessing +import itertools as it +import csv +import numpy as np +import h5py +from collections import defaultdict as dd +import importlib +from array import array + +import inclusion +from inclusion import selection +from inclusion.config import main +from inclusion.utils import utils + +import ROOT +import hist +import pickle +import uproot + +from bokeh.plotting import figure, output_file, save +from bokeh.models import Range1d, Label + +tau = '\u03C4' +mu = '\u03BC' +pm = '\u00B1' +ditau = tau+tau + +def get_outname(channel): + utils.create_single_dir('data') + + name = "" + name += str(channel) + name += '.root' + + s = 'data/regions_{}'.format(name) + return s + +def rec_dd(): + return dd(rec_dd) + +def trigger_regions(indir, channel, year, outname): + outname = get_outname(outname) + config_module = importlib.import_module(args.configuration) + + if channel == 'etau' or channel == 'mutau': + iso1 = range(0, 8.1, 8/24) + elif channel == 'tautau': + iso1 = range(0, 401, 8) + pNet_dist = [x/12. for x in range(13)] + binning = { + 'genHH_mass': ([250, 300, 350, 400, 450, 500, 550, 600, 675, 800, 1000, 1600],), + 'dau1_iso': (iso1,), + 'dau1_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'dau2_iso': (iso1,), + 'dau2_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'dau1_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + 'dau2_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + 'dau1_tauIdVSjet': ([0, 1, 2, 3, 4, 5, 6, 7], ), + 'dau2_tauIdVSjet': ([0, 1, 2, 3, 4, 5, 6, 7], ), + 'bjet1_pNet': (pNet_dist,), + 'bjet2_pNet': (pNet_dist,), + 'bjet1_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'bjet2_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'bjet1_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + 'bjet2_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + # 'triggerbits': (range(31)) + } + + norphans, ntotal = ({k:0 for k in categories} for _ in range(2)) + + ahistos = rec_dd() + for chn in ['mutau', 'etau', 'tautau']: + for i in binning.keys(): + ahistos[chn][i] = ( + hist.Hist.new.Variable(*binning[i], name=i) + .Weight() + ) + + t_in = ROOT.TChain('Events') + glob_files = glob.glob( os.path.join(indir, 'data_*.root') ) + if len(glob_files) < 1: + raise RuntimeError("No files!") + for f in glob_files: + t_in.Add(f) + t_in.SetBranchStatus('*', 0) + _entries, mc_corrections = utils.define_used_tree_variables(cut=config_module.custom_cut) + + _entries += tuple([ x + mc_corrections[x] for x in mc_corrections]) + for ientry in _entries: + t_in.SetBranchStatus(ientry, 1) + + for entry in t_in: + # this is slow: do it once only + entries = utils.dot_dict({x: getattr(entry, x) for x in _entries}) + + if entries.pairType == -1: + continue + + if (entries.pairType == 0 or entries.pairType == 1) and (entries.isOS != 1 or entries.dau2_tauIdVSjet < 5): + continue + + if entries.pairType == 2 and (entries.isOS != 1 or entries.dau2_tauIdVSjet < 5 or entries.dau1_tauIdVSjet < 5): + continue + + if entries.pairType > 2: + continue + + sel = selection.EventSelection(entries, year=year, isdata=False, configuration=config_module) + # in_legacy, in_met, in_tau = which_region(entries, year, ptcuts, regcuts, channel, sel, + # bigtau=args.bigtau) + + w_mc = entries.genWeight + # w_pure = entries.puWweight + # # w_l1pref = entries.L1pref_weight + # w_trig = 1 # entries.trigSF + # w_idiso = entries.IdSF_deep_2d + # w_jetpu = entries.PUjetID_SF + # w_btag = entries.bTagweightReshape + + if utils.is_nan(w_mc) : w_mc=1 + # if utils.is_nan(w_pure) : w_pure=1 + # # if utils.is_nan(w_l1pref) : w_l1pref=1 + # if utils.is_nan(w_trig) : w_trig=1 + # if utils.is_nan(w_idiso) : w_idiso=1 + # if utils.is_nan(w_jetpu) : w_jetpu=1 + # if utils.is_nan(w_btag) : w_btag=1 + evt_weight = 1 # * w_btag + # if evt_weight < 0.: + # print(w_mc, w_pure, w_trig, w_idiso, w_jetpu) + tau_gen_cut = {"etau": None, "mutau": None, + "tautau": 'self.entries["isTau1real"] == 1 and self.entries["isTau2real"] == 1'} + + if entries.pairType == 0: + chn = 'mutau' + elif entries.pairType == 1: + chn = 'etau' + elif entries.pairType == 2: + chn = 'tautau' + + for i in binning.keys(): + z = 0 + if 'bjet1' in i: + if 'eta' in i: + z = entries['Jet_eta'][entries['bjet1_JetIdx']] + if 'pt' in i: + z = entries['Jet_pt'][entries['bjet1_JetIdx']] + if 'pNet' in i: + z = entries['Jet_btagPNetB'][entries['bjet1_JetIdx']] + elif 'bjet2' in i: + if 'eta' in i: + z = entries['Jet_eta'][entries['bjet2_JetIdx']] + if 'pt' in i: + z = entries['Jet_pt'][entries['bjet2_JetIdx']] + if 'pNet' in i: + z = entries['Jet_btagPNetB'][entries['bjet2_JetIdx']] + else: + z = entries[i] + + ahistos[chn][i].fill(z, weight=evt_weight) + + + # all MC and signal must be rescaled to get the correct number of events + # for key,_ in cuts.items(): + # for reg in regions: + # for cat in categories: + # for cc in ['mutau', 'etau', 'tautau']: + # print("lumi", utils.get_lumi(args.year), utils.total_sum_weights(glob_files[0].replace("PreprocessRDF", "PreCounter").replace("/cat_base_selection", "").replace(".root", ".json"), isdata=False)) + # ahistos[key][reg][cat][cc] *= (utils.get_lumi(args.year) / + # utils.total_sum_weights(glob_files[0].replace("PreprocessRDF", "PreCounter").replace("/cat_base_selection", "").replace(".root", ".json"), isdata=False)) + + file = uproot.recreate(outname) + print('Raw histograms saved in {}.'.format(outname), flush=True) + +if __name__ == '__main__': + extensions = ('png',) #('png', 'pdf') + triggers = {'etau': ("HLT_Ele30_WPTight_Gsf", + "HLT_Ele24_eta2p1_WPTight_Gsf_LooseDeepTauPFTauHPS30_eta2p1_CrossL1"), + 'mutau': ('HLT_IsoMu24', 'HLT_IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1'), + 'tautau': ('HLT_DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1',) + } + + categories = ('baseline',) #('baseline', 's1b1jresolvedMcut', 's2b0jresolvedMcut', 'sboostedLLMcut') + + # Parse input arguments + desc = 'Producer trigger histograms.\n' + desc += "Run example: python tests/test_trigger_regions.py --indir /data_CMS/cms/alves/HHresonant_SKIMS/SKIMS_UL18_EOSv4_Signal/ --masses 400 500 600 700 800 900 1000 1250 1500 --channels ETau --met_turnon 180 --region_cuts 40 40 --copy" + parser = argparse.ArgumentParser(description=desc, formatter_class=argparse.RawTextHelpFormatter) + + parser.add_argument('--indir', required=True, type=str, + help='Full path of ROOT input file') + # parser.add_argument('--masses', required=True, nargs='+', type=str, + # help='Resonance mass') + parser.add_argument('--channel', required=True, type=str, + help='Select the channel over which the workflow will be run.' ) + parser.add_argument('--year', required=True, type=str, choices=('2016', '2016APV', '2017', '2018', '2022'), + help='Select the year over which the workflow will be run.' ) + # parser.add_argument('--spin', required=True, type=int, choices=(0, 2), + # help='Select the spin hypothesis over which the workflow will be run.' ) + parser.add_argument('--configuration', dest='configuration', required=True, + help='Name of the configuration module to use.') + parser.add_argument('--outname', dest='outname', required=True, + help='Output file name.') + args = utils.parse_args(parser) + + #### run main function ### + # for sample in args.masses: + trigger_regions(args.indir, args.channel, args.year, args.outname) + + ########################### + diff --git a/tests/setup_triggerbits.py b/tests/setup_triggerbits.py new file mode 100644 index 0000000..5839001 --- /dev/null +++ b/tests/setup_triggerbits.py @@ -0,0 +1,361 @@ +# coding: utf-8 + +_all_ = [ 'test_trigger_regions' ] + +import os +import sys +parent_dir = os.path.abspath(__file__ + 2 * '/..') +sys.path.insert(0, parent_dir) +import argparse +import glob +import multiprocessing +import itertools as it +import csv +import numpy as np +import h5py +from collections import defaultdict as dd +import importlib +from array import array + +import inclusion +from inclusion import selection +from inclusion.config import main +from inclusion.utils import utils + +import ROOT +import hist +import pickle +import uproot + +from bokeh.plotting import figure, output_file, save +from bokeh.models import Range1d, Label + +tau = '\u03C4' +mu = '\u03BC' +pm = '\u00B1' +ditau = tau+tau + +def rec_dd(): + return dd(rec_dd) + +def get_outname(channel, bigtau): + utils.create_single_dir('data') + + name = "" + if bigtau: + name += '_BIGTAU' + name += '_all.root' + + s = 'data/regions_2023_postBPix_15p36-ditaujet{}'.format(name) + return s + + +def trigger_regions(indir, channel, year, deltaR): + outname = get_outname(channel, + args.bigtau) + config_module = importlib.import_module(args.configuration) + + if channel == 'etau' or channel == 'mutau': + iso1 = range(0, 8.1, 8/24) + elif channel == 'tautau': + iso1 = range(0, 401, 8) + pNet_dist = [x/12. for x in range(13)] + binning.update({ + 'genHH_mass': ([250, 300, 350, 400, 450, 500, 550, 600, 675, 800, 1000, 1600],), + 'dau1_iso': (iso1,), + # 'dau1_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'dau2_iso': (iso1,), + # 'dau2_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'dau1_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + 'dau2_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + 'dau1_tauIdVSjet': ([0, 1, 2, 3, 4, 5, 6, 7], ), + 'dau2_tauIdVSjet': ([0, 1, 2, 3, 4, 5, 6, 7], ), + 'bjet1_pNet': (pNet_dist,), + 'bjet2_pNet': (pNet_dist,), + 'bjet1_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'bjet2_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'bjet1_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + 'bjet2_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + # 'bjet1_triggerbits': ([i for i in range(32)],), + # 'bjet2_triggerbits': ([i for i in range(32)],), + # 'tau1_triggerbits': ([i for i in range(32)],), + # 'tau2_triggerbits': ([i for i in range(32)],), + # 'SoftActivityJetHT': ([0, 50, 100, 150, 200, 250, 300, 350, 400, 500, 750, 1000, 1500, 2000],) + }) + + + norphans, ntotal = ({k:0 for k in categories} for _ in range(2)) + + ahistos = rec_dd() + for chn in ['mutau', 'etau', 'tautau']: + for i in binning.keys(): + ahistos[chn][i] = ( + hist.Hist.new.Variable(*binning[i], name=i) + .Weight() + ) + + t_in = ROOT.TChain('Events') + glob_files = glob.glob( os.path.join(indir, 'data_*.root') ) + if len(glob_files) < 1: + raise RuntimeError("No files!") + for f in glob_files: + t_in.Add(f) + t_in.SetBranchStatus('*', 0) + _entries, mc_corrections = utils.define_used_tree_variables(cut=config_module.custom_cut) + + _entries += tuple([ x + mc_corrections[x] for x in mc_corrections]) + for ientry in _entries: + t_in.SetBranchStatus(ientry, 1) + + for entry in t_in: + # this is slow: do it once only + entries = utils.dot_dict({x: getattr(entry, x) for x in _entries}) + + if entries.pairType == -1: + continue + + if (entries.pairType == 0 or entries.pairType == 1) and (entries.isOS != 1 or entries.dau2_tauIdVSjet < 5): + continue + + if entries.pairType == 2 and (entries.isOS != 1 or entries.dau2_tauIdVSjet < 5 or entries.dau1_tauIdVSjet < 5): + continue + + if entries.pairType > 2: + continue + + + sel = selection.EventSelection(entries, year=year, isdata=False, configuration=config_module) + # in_legacy, in_met, in_tau = which_region(entries, year, ptcuts, regcuts, channel, sel, + # bigtau=args.bigtau) + + w_mc = entries.genWeight + # w_pure = entries.puWweight + # # w_l1pref = entries.L1pref_weight + # w_trig = 1 # entries.trigSF + # w_idiso = entries.IdSF_deep_2d + # w_jetpu = entries.PUjetID_SF + # w_btag = entries.bTagweightReshape + + if utils.is_nan(w_mc) : w_mc=1 + # if utils.is_nan(w_pure) : w_pure=1 + # # if utils.is_nan(w_l1pref) : w_l1pref=1 + # if utils.is_nan(w_trig) : w_trig=1 + # if utils.is_nan(w_idiso) : w_idiso=1 + # if utils.is_nan(w_jetpu) : w_jetpu=1 + # if utils.is_nan(w_btag) : w_btag=1 + evt_weight = 1 # * w_btag + # if evt_weight < 0.: + # print(w_mc, w_pure, w_trig, w_idiso, w_jetpu) + tau_gen_cut = {"etau": None, "mutau": None, + "tautau": 'self.entries["isTau1real"] == 1 and self.entries["isTau2real"] == 1'} + + if entries.pairType == 0: + chn = 'mutau' + elif entries.pairType == 1: + chn = 'etau' + elif entries.pairType == 2: + chn = 'tautau' + + + + for i in binning.keys(): + z = 0 + if 'bjet1' in i: + if 'eta' in i: + z = entries['Jet_eta'][entries['bjet1_JetIdx']] + if 'pt' in i: + z = entries['Jet_pt'][entries['bjet1_JetIdx']] + if 'pNet' in i: + z = entries['Jet_btagPNetB'][entries['bjet1_JetIdx']] + elif 'bjet2' in i: + if 'eta' in i: + z = entries['Jet_eta'][entries['bjet2_JetIdx']] + if 'pt' in i: + z = entries['Jet_pt'][entries['bjet2_JetIdx']] + if 'pNet' in i: + z = entries['Jet_btagPNetB'][entries['bjet2_JetIdx']] + else: + if 'triggerbits' in i: + continue + z = entries[i] + + ahistos[chn][i].fill(z, weight=evt_weight) + + # if chn == 'tautau' and entries['isQuadJetTrigger'] and entries['bjet1_filterbits'] >= 0: + # for i in range(31): + # print(i, entries["bjet1_filterbits"], bool(entries["bjet1_filterbits"] & (1 << i))) + # if bool(entries["bjet1_filterbits"] & (1 << i)): + # ahistos[chn]['bjet1_triggerbits'].fill(i) + # if bool(entries["bjet2_filterbits"] & (1 << i)): + # ahistos[chn]['bjet2_triggerbits'].fill(i) + # if bool(entries["tau1_filterbits"] & (1 << i)): + # ahistos[chn]['tau1_triggerbits'].fill(i) + # if bool(entries["tau2_filterbits"] & (1 << i)): + # ahistos[chn]['tau2_triggerbits'].fill(i) + + + file = uproot.recreate(outname) + for chn in ['mutau', 'etau', 'tautau']: + for i in binning.keys(): + file[str(chn) + "_" + str(i)] = ahistos[chn][i] + + print('Raw histograms saved in {}.'.format(outname), flush=True) + +if __name__ == '__main__': + extensions = ('png',) #('png', 'pdf') + triggers = {'etau': ("HLT_Ele30_WPTight_Gsf", + "HLT_Ele24_eta2p1_WPTight_Gsf_LooseDeepTauPFTauHPS30_eta2p1_CrossL1"), + 'mutau': ('HLT_IsoMu24', 'HLT_IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1'), + 'tautau': ('HLT_DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1',) + } + binning = { + # 'metnomu_et': (20, 0, 450), + # 'dau1_pt': (30, 0, 450), + # 'dau1_eta': (20, -2.5, 2.5), + # 'dau2_iso': (20, 0.88, 1.005), + # 'dau2_pt': (30, 0, 400), + # 'dau2_eta': (20, -2.5, 2.5), + # 'ditau_deltaR': (30, 0.3, 1.3), + # 'dib_deltaR': (25, 0, 2.5), + # 'bH_pt': (20, 70, 600), + # 'bH_mass': (30, 0, 280), + # 'tauH_mass': (30, 0, 170), + # 'tauH_pt': (30, 0, 500), + # 'tauH_SVFIT_mass': (30, 0, 250), + # 'tauH_SVFIT_pt': (20, 200, 650), + # 'bjet1_pt': (25, 10, 600), + # 'bjet2_pt': (25, 10, 550), + # 'bjet1_eta': (20, -2.5, 2.5), + # 'bjet2_eta': (20, -2.5, 2.5), + } + variables = tuple(binning.keys()) + ('HHKin_mass', 'dau1_iso') + + categories = ('baseline',) #('baseline', 's1b1jresolvedMcut', 's2b0jresolvedMcut', 'sboostedLLMcut') + + # Parse input arguments + desc = 'Producer trigger histograms.\n' + desc += "Run example: python tests/test_trigger_regions.py --indir /data_CMS/cms/alves/HHresonant_SKIMS/SKIMS_UL18_EOSv4_Signal/ --masses 400 500 600 700 800 900 1000 1250 1500 --channels ETau --met_turnon 180 --region_cuts 40 40 --copy" + parser = argparse.ArgumentParser(description=desc, formatter_class=argparse.RawTextHelpFormatter) + + parser.add_argument('--indir', required=True, type=str, + help='Full path of ROOT input file') + # parser.add_argument('--masses', required=True, nargs='+', type=str, + # help='Resonance mass') + parser.add_argument('--channel', required=True, type=str, + help='Select the channel over which the workflow will be run.' ) + parser.add_argument('--year', required=True, type=str, choices=('2016', '2016APV', '2017', '2018', '2022'), + help='Select the year over which the workflow will be run.' ) + # parser.add_argument('--spin', required=True, type=int, choices=(0, 2), + # help='Select the spin hypothesis over which the workflow will be run.' ) + parser.add_argument('--deltaR', type=float, default=0.5, help='DeltaR between the two leptons.' ) + parser.add_argument('--plot', action='store_true', + help='Reuse previously produced data for quick plot changes.') + parser.add_argument('--copy', action='store_true', + help='Copy the outputs to EOS at the end.') + parser.add_argument('--notext', action='store_true', help='Square diagram without text.') + parser.add_argument('--sequential', action='store_true', + help='Do not use the multiprocess package.') + parser.add_argument('--bigtau', action='store_true', + help='Consider a larger single tau region, reducing the ditau one.') + parser.add_argument('--met_turnon', type=float, default=180, + help='MET trigger turnon cut [GeV].' ) + parser.add_argument('--region_cuts', required=False, type=float, nargs=2, default=(190, 190), + help='High/low regions pT1 and pT2 selection cuts [GeV].' ) + parser.add_argument('--configuration', dest='configuration', required=True, + help='Name of the configuration module to use.') + args = utils.parse_args(parser) + + met_turnon = args.met_turnon + regcuts = args.region_cuts + ptcuts = utils.get_ptcuts(args.channel, args.year) + + main_dir = os.path.join(os.path.join('/t3home/', os.environ['USER'], 'TriggerScaleFactors'), + '_'.join((args.channel, *[str(x) for x in regcuts], + 'DR', str(args.deltaR), 'PT', *[str(x) for x in ptcuts], 'TURNON', + str(met_turnon)))) + if args.bigtau: + main_dir += '_BIGTAU' + + regions = ('legacy', 'met', 'tau') + + #### run main function ### + if not args.plot: + # for sample in args.masses: + trigger_regions(args.indir, args.channel, args.year, args.deltaR) + # if args.sequential: + # pass + # else: + # pool = multiprocessing.Pool(processes=6) + # pool.starmap(trigger_regions, + # zip(it.repeat(args.indir), it.repeat(args.channel), it.repeat(args.year), it.repeat(args.deltaR))) + + ########################### + + sum_stats, err_sum_stats = ([] for _ in range(2)) + contam1, contam2, contam1_errors, contam2_errors = ([] for _ in range(4)) + from_directory = os.path.join(main_dir, args.channel) + outname = get_outname(args.channel, args.bigtau) + with open(outname, "rb") as f: + ahistos = pickle.load(f) + + # write csv header, one per category + out_counts = [] + # plot histograms and fill CSV with histogram integrals + c_legacy_trg, c_met_trg, c_tau_trg = ({} for _ in range(3)) + + acounts = rec_dd() + for reg in regions: + for key,cut in ahistos.items(): + acounts[key][reg] = round(ahistos[key][reg]["baseline"].values().sum(), 2) + + # append to table, one line per region + with open(os.path.join(out_counts[categories.index(cat)], 'table.csv'), 'a') as f: + reader = csv.writer(f, delimiter=',', quotechar='|') + row = [reg] + row.extend([acounts[k][reg] for k in acounts.keys()]) + reader.writerow(row) + + if reg=='legacy': + c_legacy_trg[reg] = acounts["Base"][reg] + c_met_trg[reg] = acounts["NoBaseMET"][reg] + c_tau_trg[reg] = acounts["NoBaseNoMETTau"][reg] + elif reg=='met': + c_legacy_trg[reg] = acounts["BaseNoMET"][reg] + c_met_trg[reg] = acounts["MET"][reg] + c_tau_trg[reg] = acounts["NoBaseNoMETTau"][reg] + elif reg=='tau': + c_legacy_trg[reg] = acounts["BaseNoTau"][reg] + c_met_trg[reg] = acounts["NoBaseMETNoTau"][reg] + c_tau_trg[reg] = acounts["Tau"][reg] + + c1, c2, e1, e2 = sq_res + contam1.append(c1) + contam2.append(c2) + contam1_errors.append(e1) + contam2_errors.append(e2) + + stats_l = [c_legacy_trg['legacy'],c_met_trg['legacy'],c_tau_trg['tau'],c_legacy_trg['tau']] + stats = sum(stats_l) + estats = sum(np.sqrt(stats_l)) + sum_stats.append(stats) + err_sum_stats.append(estats) + + contam1 = [float(x) for x in contam1] + contam2 = [float(x) for x in contam2] + contam1_errors = [float(x) for x in contam1_errors] + contam2_errors = [float(x) for x in contam2_errors] + # masses = [float(x) for x in args.masses] + contamination_save('data', '_'.join([str(x) for x in regcuts]) + '_' + args.channel, + contam1, contam2, contam1_errors, contam2_errors) + stats_save('data', '_'.join([str(x) for x in regcuts]) + '_' + args.channel, + sum_stats, err_sum_stats, mode='a') + + if args.copy: + import subprocess + to_directory = os.path.join('/eos/home-b/bfontana/www/TriggerScaleFactors', main_dir) + to_directory = os.path.join(to_directory, args.channel) + + for sample in args.masses: + sample_from = os.path.join(from_directory, sample) + print('Copying: {}\t\t--->\t{}'.format(sample_from, to_directory), flush=True) + subprocess.run(['rsync', '-ah', sample_from, to_directory]) diff --git a/tests/test_compare_gains.py b/tests/test_compare_gains.py deleted file mode 100644 index 2437db0..0000000 --- a/tests/test_compare_gains.py +++ /dev/null @@ -1,71 +0,0 @@ -import os -import argparse -import json -import numpy as np - -import matplotlib -import matplotlib.pyplot as plt -import mplhep as hep -plt.style.use(hep.style.ROOT) - -def compare_gains(chn, spin): - data = {} - for mode in ('standard', 'bigtau'): - with open('data_' + chn + '_' + mode + '.json') as json_data: - data[mode] = json.load(json_data) - - chn_unicodes = {"etau": r'$bb\: e\tau$', - "mutau": r'$bb\: \mu\tau$', - "tautau": r'$bb\: \tau\tau$', - "mumu": r'$bb\: \mu\mu$'} - spin_map = {'0': "Radion", '2': "BulkGraviton"} - masses = [300, 400, 500, 600, 700, 750, 800, 850, 900, 1000, - 1250, 1500, 1750, 2000, 2500, 3000] - colors = iter(("red", "dodgerblue")) - - fig, (ax1, ax2) = plt.subplots(2, sharex=True, gridspec_kw={'height_ratios': [3., 1.]}) - plt.subplots_adjust(wspace=0, hspace=0) - - ax1.set_ylabel("Number of weighted events", fontsize=20) - for mode in ('standard', 'bigtau'): - err = [e/2. for e in data[mode]['errs'][chn]] - ax1.errorbar(masses, data[mode]['vals'][chn], - yerr=(err, err), fmt='-o', color=next(colors), label=mode) - ax1.legend(loc="upper right") - - ax2.set_ylim(-2, 15) - yticks = [0., 5., 10.] - ax2.set_yticks(yticks) - line_opt = dict(color="grey", linestyle="--") - for yval in yticks: - ax2.axhline(y=yval, **line_opt) - ax2.set_ylabel(r"Ratio - 1 [%]", fontsize=20) - ax2.set_xlabel(r"$m(X)\:\:[GeV]$", fontsize=21) - - erratio = [1/2 * np.sqrt(ea**2/a**2 + eb**2/b**2) * a/b * 100 - for a,b,ea,eb in zip(data['standard']['vals'][chn],data['bigtau']['vals'][chn], - data['standard']['errs'][chn],data['bigtau']['errs'][chn])] - ax2.errorbar(masses, [(x/y-1.)*100 for x, y in zip(data['standard']['vals'][chn],data['bigtau']['vals'][chn])], - yerr=[erratio,erratio], fmt='-o', color='black') - - hep.cms.text(' Preliminary', fontsize=22, ax=ax1) - hep.cms.lumitext(chn_unicodes[chn] + " (baseline) | " + spin_map[spin], - fontsize=21, ax=ax1) - - output = os.path.join("/eos/home-b/bfontana/www/TriggerScaleFactors/CompareRatios/", - chn + "_" + os.path.basename(__file__[:-3])) - for ext in ('.png', '.pdf'): - fig.savefig(output + ext) - print('Plot saved under {}'.format(output + ext)) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Compare two selection regions with and without bigtau flag.') - parser.add_argument('--channel', required=True, choices=('etau', 'mutau', 'tautau'), help='Which analysis channel to consider.') - parser.add_argument('--spin', required=True, choices=('0', '2'), - help='Signal spin hypothesis.') - - FLAGS = parser.parse_args() - - compare_gains(chn=FLAGS.channel, spin=FLAGS.spin) - diff --git a/tests/test_draw_kin_regions.py b/tests/test_draw_kin_regions.py deleted file mode 100644 index 307f2c8..0000000 --- a/tests/test_draw_kin_regions.py +++ /dev/null @@ -1,478 +0,0 @@ -# Coding: utf-8 - -_all_ = [ 'drawCuts' ] - -import os -import argparse -import glob -import uproot as up -import hist -import pickle - -import matplotlib; import matplotlib.pyplot as plt -import matplotlib.colors as colors -import mplhep as hep -plt.style.use(hep.style.ROOT) - -def sel_category(batch, category, year): - """Applies analysis category cuts to an awkward batch.""" - deepJetWP = {'2016' : (0.048, 0.249), - '2016APV' : (0.051, 0.260), - '2017' : (0.0532, 0.3040), - '2018' : (0.0490, 0.2783)}[year] - - if category == "sboosted": - batch = batch[(batch.isBoosted == 1) & - (batch.bjet1_bID_deepFlavor > deepJetWP[0]) & (batch.bjet2_bID_deepFlavor > deepJetWP[0])] - elif category == "s1b1jresolved": - batch = batch[(batch.isBoosted != 1) & - (((batch.bjet1_bID_deepFlavor > deepJetWP[1]) & (batch.bjet2_bID_deepFlavor < deepJetWP[1])) | - ((batch.bjet1_bID_deepFlavor < deepJetWP[1]) & (batch.bjet2_bID_deepFlavor > deepJetWP[1])))] - elif category == "s2b0jresolved": - batch = batch[(batch.isBoosted != 1) & - (batch.bjet1_bID_deepFlavor > deepJetWP[1]) & (batch.bjet2_bID_deepFlavor > deepJetWP[1])] - - return batch - -def sel_cuts(batch, channel): - """Applies analysis selection cuts to an awkward batch.""" - chn_map = {"etau": 1, "mutau": 0, "tautau": 2, "mumu": 3} - batch = batch[batch.pairType == chn_map[channel]] - - # When one only has 0 or 1 bjets the HH mass is not well defined, - # and a value of -1 is assigned. One thus has to remove the cut below - # when considering events with less than 2 b-jets. - # batch = batch[batch.HHKin_mass > 1] - - # third lepton veto - batch = batch[batch.nleps == 0] - - # require at least two b jet candidates - batch = batch[batch.nbjetscand > 1] - - # opposite sign leptons - batch = batch[batch.isOS == 1] - - # Loose / Medium / Tight - iso_allowed = { 'dau1_ele': 1., 'dau1_mu': 0.15, 'dau2_mu': 0.15, - 'dau1_tau': 5., 'dau2_tau': 5. } - - # lepton id and isolation - if channel == "etau": - batch = batch[(batch.dau1_eleMVAiso == 1.) & (batch.dau2_deepTauVsJet >= 5.)] - elif channel == "mutau": - batch = batch[(batch.dau1_iso < 0.15) & (batch.dau2_deepTauVsJet >= 5.)] - elif channel == "tautau": - batch = batch[(batch.dau1_deepTauVsJet >= 5.) & (batch.dau2_deepTauVsJet >= 5.)] - elif channel == "mumu": - batch = batch[(batch.dau1_iso < 0.15) & (batch.dau2_iso < 0.15)] - - return batch - -def getHisto(x, y, inputs, xbins, ybins, - channel, category, year, dtype, savename, save=False, other_vars=None): - avars = (x, y) if other_vars is None else (x, y, *other_vars) - for inp in inputs: - assert inp[-5:] == ".root" - - if save and os.path.isfile(savename): - with open(savename, 'rb') as f: - histogram = pickle.load(f) - return histogram - - histogram = hist.Hist( - hist.axis.Regular(*xbins, name=x), - hist.axis.Regular(*ybins, name=y), - storage=hist.storage.Double() - ) - - for ibatch, batch in enumerate(up.iterate(inputs, step_size="200MB", library='ak', - filter_name=avars)): - print("Batch {}".format(ibatch)) - batch = sel_cuts(batch, channel) - batch = sel_category(batch, category, year) - histogram.fill(getattr(batch, x), getattr(batch, y)) - - with open(savename, 'wb') as f: - pickle.dump(histogram, f) - - return histogram - -class DrawCuts(): - def __init__(self, inputs, sample, channel, category, year, dtype, save): - self.inputs = inputs - self.sample = sample - self.channel = channel - self.category = category - self.year = year - self.dtype = dtype - self.save = save - - self.other_vars = ('HHKin_mass', 'pairType', 'isOS', 'dau1_eleMVAiso', 'dau1_iso', 'dau2_iso', - 'dau1_deepTauVsJet', 'dau2_deepTauVsJet', 'nleps', 'nbjetscand', - 'bjet1_bID_deepFlavor', 'bjet2_bID_deepFlavor', 'isBoosted') - - self.outname = lambda mode: os.path.join('pickles', - '_'.join((mode, sample, channel, dtype, category, year)) + ".pkl") - - self.rect_opt = dict(facecolor='white', edgecolor="black", linewidth=2) - self.leg_opt = dict(loc="upper right" if channel!="mumu" else "upper center", - facecolor="white", edgecolor="white", framealpha=1) - - def triggers(self): - mode = "trigger" - - fig, ax = self.set_figure(16, 16) - - bins = self.get_bins(mode=mode, dtype=self.dtype) - histogram = getHisto(x="dau1_pt", y="dau2_pt", xbins=bins[0], ybins=bins[1], - channel=self.channel, category=self.category, year=self.year, - other_vars=self.other_vars, inputs=self.inputs, dtype=self.dtype, - savename=self.outname(mode), save=self.save) - - xlabel, ylabel = self.set_axis_labels(mode=mode, channel=self.channel) - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - - values = histogram.values() - cbar = hep.hist2dplot(values, histogram.axes[0].edges, histogram.axes[1].edges, - flow=None, norm=colors.LogNorm(vmin=1, vmax=values.max())) - cbar.cbar.ax.set_ylabel("No. Events", rotation=90, labelpad=0.5, loc='top') - - self.set_hep_labels(self.channel) - - self.set_lines(histogram, mode=mode) - - if self.channel == "etau": - rect = matplotlib.patches.Rectangle((76,202), 24, 35, **self.rect_opt) - elif self.channel == "mutau": - rect = matplotlib.patches.Rectangle((76,202), 24, 35, **self.rect_opt) - elif self.channel == "tautau": - rect = matplotlib.patches.Rectangle((194,203), 42, 34, **self.rect_opt) - elif self.channel == "mumu": - rect = matplotlib.patches.Rectangle((76,202), 24, 35, **self.rect_opt) - - ax.add_patch(rect) - - plt.legend(title="Triggers", **self.leg_opt) - self.savefig(mode=mode) - - def mass(self): - mode = "mass" - - fig, ax = self.set_figure(16, 16) - - bins = self.get_bins(mode=mode, dtype=self.dtype) - histogram = getHisto(x="tauH_mass", y="bH_mass", xbins=bins[0], ybins=bins[1], - channel=self.channel, category=self.category, year=self.year, - other_vars=self.other_vars, inputs=self.inputs, dtype=self.dtype, - savename=self.outname(mode), save=self.save) - - xlabel, ylabel = self.set_axis_labels(mode=mode, channel=self.channel) - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - - values = histogram.values() - cbar = hep.hist2dplot(values, histogram.axes[0].edges, histogram.axes[1].edges, - flow=None, norm=colors.LogNorm(vmin=1, vmax=values.max())) - cbar.cbar.ax.set_ylabel("No. Events", rotation=90, labelpad=0.5, loc='top') - - self.set_hep_labels(self.channel) - - self.set_lines(histogram, mode=mode) - - rect = matplotlib.patches.Rectangle((140,332), 57, 13, **self.rect_opt) - ax.add_patch(rect) - - plt.legend(loc="upper right", facecolor="white", edgecolor="white", framealpha=1) - self.savefig(mode) - - def get_bins(self, mode, dtype): - if mode == "trigger": - if self.channel == "etau": - nbinsx = 25 if dtype == "signal" else 35 - nbinsy = 25 if dtype == "signal" else 35 - xbins = (nbinsx, 5, 101) - ybins = (nbinsy, 5, 240) - elif self.channel == "mutau": - nbinsx = 25 if dtype == "signal" else 35 - nbinsy = 25 if dtype == "signal" else 35 - xbins = (nbinsx, 10, 101) - ybins = (nbinsy, 10, 240) - elif self.channel == "tautau": - nbinsx = 25 if dtype == "signal" else 40 - nbinsy = 25 if dtype == "signal" else 40 - xbins = (nbinsx, 12, 240) - ybins = (nbinsy, 12, 240) - elif self.channel == "mumu": - nbinsx = 25 if dtype == "signal" else 35 - nbinsy = 25 if dtype == "signal" else 35 - xbins = (nbinsx, 10, 170) - ybins = (nbinsy, 10, 130) - - elif mode == "mass": - nbinsx = 50 if dtype == "signal" else 100 - nbinsy = 50 if dtype == "signal" else 100 - if self.channel == "etau": - xbins = (nbinsx, 5, 200) - ybins = (nbinsy, 5, 350) - elif self.channel == "mutau": - xbins = (nbinsx, 10, 200) - ybins = (nbinsy, 10, 350) - elif self.channel == "tautau": - xbins = (nbinsx, 15, 200) - ybins = (nbinsy, 15, 350) - elif self.channel == "mumu": - xbins = (nbinsx, 10, 200) - ybins = (nbinsy, 10, 350) - - return xbins, ybins - - def savefig(self, mode): - for ext in ('.png', '.pdf',): - smpl = self.sample.replace(' ', '-').replace('+', '-') - savename = os.path.join("/eos/home-b/bfontana/www/DrawCuts/", mode) - savename = os.path.join(savename, - '_'.join(("draw_" + mode, smpl, - self.channel, self.category, self.year))) - plt.savefig(savename + ext, dpi=600) - print('Stored in {}'.format(savename + ext)) - plt.close('all') - - def set_figure(self, wsize, hsize): - fig = plt.figure(figsize=(wsize, hsize),) - ax = plt.subplot(111) - ax.title.set_size(100) - return fig, ax - - def set_axis_labels(self, mode, channel): - """Set X and Y label.""" - if mode == "trigger": - if channel == "etau": - xlabel = r"$p_T(e)$ [GeV]" - ylabel = r"$p_T(\tau)$ [GeV]" - elif channel == "mutau": - xlabel = r"$p_T(\mu)$ [GeV]" - ylabel = r"$p_T(\tau)$ [GeV]" - elif channel == "tautau": - xlabel = r"$p_T(\tau_1)$ [GeV]" - ylabel = r"$p_T(\tau_2)$ [GeV]" - elif channel == "mumu": - xlabel = r"$p_T(\mu)$ [GeV]" - ylabel = r"$p_T(\mu)$ [GeV]" - elif mode == "mass": - xlabel = r"$m_{{\tau\tau}}^{{vis}}$ [GeV]" - ylabel = r"$m_{{bb}}^{{vis}}$ [GeV]" - return xlabel, ylabel - - def set_hep_labels(self, channel): - hep.cms.text('Preliminary', fontsize=40) - chn_unicodes = {"etau": r'$bb\: e\tau$', - "mutau": r'$bb\: \mu\tau$', - "tautau": r'$bb\: \tau\tau$', - "mumu": r'$bb\: \mu\mu$'} - cat_map = {'baseline': "baseline", 'baseline_boosted': "baseline boosted", - 'boostedL_pnet': "boosted", 'res1b': "res 1b", 'res2b': "res 2b"} - sample_header = self.sample.replace('TT', r"$t\bar{t}$") - hep.cms.lumitext((chn_unicodes[self.channel] + " (" + cat_map[self.category] + ") | " + - sample_header + " (" + self.year + ")"), - fontsize=24) # r"138 $fb^{-1}$ (13 TeV)" - - def set_lines(self, h, mode): - xmin, xmax = h.axes[0].edges[0], h.axes[0].edges[-1] - ymin, ymax = h.axes[1].edges[0], h.axes[1].edges[-1] - - if mode == "trigger": - if self.channel == "etau": - if self.year == "2016": - plt.plot([26.5, 26.5], [ymin, ymax], - c='black', linewidth=10, label=r"single-e + e$\tau$") - plt.plot([xmin, 25.5, 25.5], [191., 191., ymax], - c='black', linewidth=10, label=r"single-$\tau$") - plt.plot([xmin, 25.5, 25.5], [189., 189., ymin], - c='black', linewidth=10, label=r"MET") - elif self.year == "2017" or self.year == "2018": - plt.plot([24.5, 24.5, 32.5, 32.5], [ymax, 36., 36., ymin], - c='black', linewidth=10, label=r"single-e + e$\tau$") - plt.plot([xmin, 23.5, 23.5], [191., 191., ymax], - c='red', linewidth=10, label=r"single-$\tau$") - plt.plot([xmin, 23.5, 23.5, 31.5, 31.5], [189., 189., 34., 34., ymin], - c='deepskyblue', linewidth=10, label="MET") - - elif self.channel == "mutau": - if self.year == "2016": - plt.plot([19.5, 19.5, 24.5, 24.5], [ymax, 24.5, 24.5, ymin], - c='black', linewidth=10, label=r"single-$\mu$ + $\mu\tau$") - plt.plot([xmin, 18.5, 18.5], [191., 191., ymax], - c='red', linewidth=10, label=r"single-$\tau$") - plt.plot([xmin, 18.5, 18.5, 23.5, 23.5], [189., 189., 31., 31., ymin], - c='deepskyblue', linewidth=10, label="MET") - elif self.year == "2017": - plt.plot([20.5, 20.5, 27.5, 27.5], [ymax, 33., 33., ymin], - c='black', linewidth=10, label=r"single-$\mu$ + $\mu\tau$") - plt.plot([xmin, 19.5, 19.5], [191., 191., ymax], - c='red', linewidth=10, label=r"single-$\tau$") - plt.plot([xmin, 19.5, 19.5, 26.5, 26.5], [189., 189., 31., 31., ymin], - c='deepskyblue', linewidth=10, label="MET") - elif self.year == "2018": - plt.plot([20.5, 20.5, 24.5, 24.5], [ymax, 33., 33., ymin], - c='black', linewidth=10, label=r"single-$\mu$ + $\mu\tau$") - plt.plot([xmin, 19.5, 19.5], [191., 191., ymax], - c='red', linewidth=10, label=r"single-$\tau$") - plt.plot([xmin, 19.5, 19.5, 23.5, 23.5], [189., 189., 31., 31., ymin], - c='deepskyblue', linewidth=10, label="MET") - - elif self.channel == "tautau": - plt.plot([42., 42., xmax], [ymax, 42., 42.], c='black', linewidth=10, label=r"$\tau\tau$") - plt.plot([xmin, 39., 39.], [191., 191., ymax], c='red', linewidth=10, label=r"single-$\tau$") - plt.plot([xmax, 191., 191.], [39., 39., ymin], c='red', linewidth=10) - plt.plot([189., 189., 39., 39., xmin], [ymin, 39., 39., 189., 189.], c='deepskyblue', linewidth=10, label="MET") - - elif self.channel == "mumu": - if self.year == "2016": - plt.plot([24.5, 24.5], [ymax, ymin], - c='black', linewidth=10, label=r"single-$\mu$") - elif self.year == "2017": - plt.plot([27.5, 27.5], [ymax, ymin], - c='black', linewidth=10, label=r"single-$\mu$") - elif self.year == "2018": - plt.plot([24.5, 24.5], [ymax, ymin], - c='black', linewidth=10, label=r"single-$\mu$") - - elif mode == "mass": - arrow_h = dict(color='red', width=3, head_width=10, head_length=5, shape="full") - arrow_v = dict(color='red', width=2.6, head_width=7, head_length=6, shape="full") - line_d = dict(c='red', linewidth=10) - if self.channel == "mumu": - if self.dtype == "mc": - # plt.plot([20., 130., 130., 20., 20.], [50., 50., 270., 270., 50.], - # label=r"Mass window cut", **line_d) - # plt.arrow(20., (ymax-ymin)/2, -4, 0, **arrow_h) - # plt.arrow(130., (ymax-ymin)/2, 10, 0, **arrow_h) - # plt.arrow(75., 50, 0, -12, **arrow_v) - # plt.arrow(75., 270, 0, 12, **arrow_v) - - dy_l, dy_r = 86., 95. # DY left and right cuts - tt_d, tt_u = 50., 150. # ttbar down and up cuts - - plt.plot([xmin, dy_l, dy_l], [tt_u, tt_u, ymax], **line_d, label=r"Mass window cut") - plt.plot([dy_r, dy_r, xmax], [ymax, tt_u, tt_u], **line_d) - plt.plot([xmin, dy_l, dy_l], [tt_d, tt_d, ymin], **line_d) - plt.plot([dy_r, dy_r, xmax], [ymin, tt_d, tt_d], **line_d) - - arrow_length_h, arrow_length_v = 8, 10 - # top left corner - plt.arrow(dy_l/2, tt_u, 0, arrow_length_v, **arrow_v) - plt.arrow(dy_l, (ymax-tt_u)/2. + tt_u, -arrow_length_h, 0, **arrow_h) - - # top right cornerd - plt.arrow(dy_r, (ymax-tt_u)/2. + tt_u, arrow_length_h, 0, **arrow_h) - plt.arrow((xmax-dy_r)/2.+dy_r, tt_u, 0, arrow_length_v, **arrow_v) - - # bottom left corner - plt.arrow(dy_l, tt_d/2, -arrow_length_h, 0, **arrow_h) - plt.arrow(dy_l/2., tt_d, 0, -arrow_length_v, **arrow_v) - - # bottom right corner - plt.arrow(dy_r, tt_d/2, arrow_length_h, 0, **arrow_h) - plt.arrow((xmax-dy_r)/2. + dy_r, tt_d, 0, -arrow_length_v, **arrow_v) - - elif self.dtype == "dy": - arrow_opt = dict(color='red', width=2, head_width=8, head_length=4, shape="full") - plt.plot([86., 86.], [ymin, ymax], label=r"Mass window cut", **line_d) - plt.plot([95., 95.], [ymin, ymax], **line_d) - - plt.arrow(86., (ymax-ymin)/5., 2, 0, **arrow_opt) - plt.arrow(95., 2*(ymax-ymin)/5., -2, 0, **arrow_opt) - plt.arrow(86., 3*(ymax-ymin)/5., 2, 0, **arrow_opt) - plt.arrow(95., 4*(ymax-ymin)/5., -2, 0, **arrow_opt) - - elif self.dtype == "tt": - dy_l, dy_r = 86., 95. # DY left and right cuts - tt_d, tt_u = 50., 150. # ttbar down and up cuts - - plt.plot([xmin, dy_l, dy_l], [tt_u, tt_u, ymax], **line_d, label=r"Mass window cut") - plt.plot([dy_r, dy_r, xmax], [ymax, tt_u, tt_u], **line_d) - plt.plot([xmin, dy_l, dy_l], [tt_d, tt_d, ymin], **line_d) - plt.plot([dy_r, dy_r, xmax], [ymin, tt_d, tt_d], **line_d) - - arrow_length_h, arrow_length_v = 8, 10 - # top left corner - plt.arrow(dy_l/2, tt_u, 0, arrow_length_v, **arrow_v) - plt.arrow(dy_l, (ymax-tt_u)/2. + tt_u, -arrow_length_h, 0, **arrow_h) - - # top right corner - plt.arrow(dy_r, (ymax-tt_u)/2. + tt_u, arrow_length_h, 0, **arrow_h) - plt.arrow((xmax-dy_r)/2.+dy_r, tt_u, 0, arrow_length_v, **arrow_v) - - # bottom left corner - plt.arrow(dy_l, tt_d/2, -arrow_length_h, 0, **arrow_h) - plt.arrow(dy_l/2., tt_d, 0, -arrow_length_v, **arrow_v) - - # bottom right corner - plt.arrow(dy_r, tt_d/2, arrow_length_h, 0, **arrow_h) - plt.arrow((xmax-dy_r)/2. + dy_r, tt_d, 0, -arrow_length_v, **arrow_v) - - else: - plt.plot([20., 130., 130., 20., 20.], [50., 50., 270., 270., 50.], - label=r"Mass window cut", **line_d) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description="draw cuts on top of signal or MC distributions", - formatter_class=argparse.RawTextHelpFormatter) - - parser.add_argument('--dtype', choices=('signal', 'mc', 'dy', 'tt'), default='signal', - type=str, help='Data type') - parser.add_argument('--skim_tag', required=True, type=str, help='Tag of input skims.') - parser.add_argument('--signal', choices=('Radion', 'BulkGraviton'), default='Radion', - type=str, help='Signal particle type') - parser.add_argument('--mass', default='1000', - type=str, help='Signal particle type') - parser.add_argument('--channel', required=True, choices=("etau", "mutau", "tautau", "mumu"), - type=str, help='Signal particle type') - parser.add_argument('--category', default="baseline", - choices=("baseline", "baseline_boosted", "boostedL_pnet", "res1b", "res2b"), - type=str, help='Analysis category') - parser.add_argument('--year', required=True, choices=("2016", "2016APV", "2017", "2018"), - type=str, help='Signal particle type') - parser.add_argument('--mode', default="trigger", choices=("trigger", "mass"), - type=str, help='Signal particle type') - parser.add_argument('--save', action="store_false", - help='Wether to save the histograms or to use the ones produced.') - FLAGS = parser.parse_args() - - base = "/data_CMS/cms/alves/HHresonant_SKIMS/" - if FLAGS.dtype == "signal": - base = os.path.join(base, "SKIMS_UL18_{}_Sig/".format(FLAGS.skim_tag)) - name = os.path.join(base, "GluGluTo{}ToHHTo2B2Tau_M-{}_".format(FLAGS.signal, FLAGS.mass)) - infiles = glob.glob(os.path.join(name, "output_*.root")) - elif FLAGS.dtype == "dy": - base = os.path.join(base, "SKIMS_UL18_{}_MC/".format(FLAGS.skim_tag)) - names = ["DYJetsToLL_M-50_TuneCP5_13TeV-amc"] - infiles = [os.path.join(base, name, "hadded.root") for name in names] - elif FLAGS.dtype == "tt": - base = os.path.join(base, "SKIMS_UL18_{}_MC/".format(FLAGS.skim_tag)) - names = ["TTTo2L2Nu", "TTToHadronic", "TTToSemiLeptonic"] - infiles = [os.path.join(base, name, "hadded.root") for name in names] - elif FLAGS.dtype == "mc": - base = os.path.join(base, "SKIMS_UL18_{}_MC/".format(FLAGS.skim_tag)) - names = ["DYJetsToLL_M-50_TuneCP5_13TeV-amc", - "TTTo2L2Nu", "TTToHadronic", "TTToSemiLeptonic"] - infiles = [os.path.join(base, name, "hadded.root") for name in names] - - if FLAGS.dtype == "signal": - sample = FLAGS.signal + " " + FLAGS.mass + " GeV" - elif FLAGS.dtype == "dy": - sample = "DY" - elif FLAGS.dtype == "tt": - sample = "TT" - elif FLAGS.dtype == "mc": - sample = "TT+DY" - - draw = DrawCuts(infiles, channel=FLAGS.channel, year=FLAGS.year, category=FLAGS.category, - sample=sample, dtype=FLAGS.dtype, save=FLAGS.save) - if FLAGS.mode == "trigger": - draw.triggers() - elif FLAGS.mode == "mass": - draw.mass() diff --git a/tests/test_theory.py b/tests/test_theory.py deleted file mode 100644 index 8b744cb..0000000 --- a/tests/test_theory.py +++ /dev/null @@ -1,43 +0,0 @@ -import numpy as np - -nevents = 200000 -ntriggers = 2 -eff_init_data = [0.4, 0.6, 0.2] -eff_init_mc = [0.65, 0.35, 0.15] -assert len(eff_init_data)==ntriggers+1 -assert len(eff_init_mc)==ntriggers+1 -# 0: trigger 1 -# 1: trigger 2 -# 2: trigger 1 AND trigger 2 -choices = [0,1] - -trigger_decisions_data = np.random.choice(choices, size=nevents, p=eff_init_data[:-1]) -trigger_decisions_mc = np.random.choice(choices, size=nevents, p=eff_init_mc[:-1]) -print(trigger_decisions_data) -print(trigger_decisions_mc) -occur_data = np.array([ np.count_nonzero( (trigger_decisions_data==ch)) for ch in choices ]) -occur_mc = np.array([ np.count_nonzero( (trigger_decisions_mc==ch)) for ch in choices ]) -eff_data = occur_data / nevents -eff_mc = occur_mc / nevents -print(eff_data) -print(eff_mc) - -mask_data = np.random.choice(choices, - size=trigger_decisions_data.shape, - p=[1-eff_init_data[-1],eff_init_data[-1]]).astype(bool) -mask_mc = np.random.choice(choices, - size=trigger_decisions_mc.shape, - p=[1-eff_init_mc[-1],eff_init_mc[-1]]).astype(bool) -print(mask_data) -print(mask_mc) -trigger_decisions_data[mask_data] = 2 -trigger_decisions_mc[mask_mc] = 2 -print(trigger_decisions_data) -print(trigger_decisions_mc) - -occur_data = np.array([ np.count_nonzero( np.where((trigger_decisions_data==ch))) for ch in choices ]) -occur_mc = np.array([ np.count_nonzero( np.where((trigger_decisions_mc==ch))) for ch in choices ]) -eff_data = occur_data / nevents -eff_mc = occur_mc / nevents -print(eff_data) -print(eff_mc) diff --git a/tests/test_trigger_bits_number.py b/tests/test_trigger_bits_number.py deleted file mode 100644 index 5ca6271..0000000 --- a/tests/test_trigger_bits_number.py +++ /dev/null @@ -1,76 +0,0 @@ -# coding: utf-8 - -__all__ = ['TriggerBitsNumber'] - -import unittest - -import os -import sys -parent_dir = os.path.abspath(__file__ + 2 * '/..') -sys.path.insert(0, parent_dir) - -import inclusion -from inclusion import selection -from inclusion import config - -import ROOT - -class TriggerBitsNumber(unittest.TestCase): - def setUp(self): - self.isdata = True - self.entry_names = ('triggerbit', 'RunNumber', 'PUReweight', 'lumi', - 'IdAndIsoSF_deep_pt', 'HHKin_mass', 'pairType', - 'dau1_eleMVAiso', 'dau1_iso', 'dau1_deepTauVsJet', - 'dau2_deepTauVsJet', 'nleps', 'nbjetscand', - 'tauH_SVFIT_mass', 'bH_mass_raw',) - self.entry_names += tuple(config.var_eff) - self.dummy_dataset = 'MET' # not used but required by the EventSelection class - - def my_print(self, i, n): - print('\r{} / {}'.format(i, n), flush=True, end='') - - def test_met_bits(self): - infile = '/data_CMS/cms/alves/HHresonant_SKIMS/SKIMS_UL18_Aug15Evening/MET__Run2018A/output_10.root' - f_in = ROOT.TFile(infile) - t_in = f_in.Get('HTauTauTree') - - t_in.SetBranchStatus('*', 0) - for ientry in self.entry_names: - t_in.SetBranchStatus(ientry, 1) - - cmet = 0 - nentries = t_in.GetEntriesFast() - for ientry,entry in enumerate(t_in): - if ientry%1000==0: - self.my_print(ientry, nentries) - - entries = {x: getattr(entry, x) for x in self.entry_names} - sel = selection.EventSelection(entries, self.dummy_dataset, self.isdata) - - if sel.check_bit(sel.get_trigger_bit('METNoMu120')): - cmet += 1 - - return cmet > 50 # some tunable cut - - def test_muon_bits(self): - infile = '/data_CMS/cms/alves/HHresonant_SKIMS/SKIMS_UL18_Aug15Evening/SingleMuon__Run2018A/output_10.root' - f_in = ROOT.TFile(infile) - t_in = f_in.Get('HTauTauTree') - - t_in.SetBranchStatus('*', 0) - for ientry in self.entry_names: - t_in.SetBranchStatus(ientry, 1) - - cmuon = 0 - nentries = t_in.GetEntriesFast() - for ientry,entry in enumerate(t_in): - if ientry%1000==0: - self.my_print(ientry, nentries) - - entries = {x: getattr(entry, x) for x in self.entry_names} - sel = selection.EventSelection(entries, self.dummy_dataset, self.isdata) - - if sel.check_bit(sel.get_trigger_bit('IsoMu24')): - cmuon += 1 - - return cmuon > 50 # some tunable cut diff --git a/tests/test_trigger_contaminations.py b/tests/test_trigger_contaminations.py deleted file mode 100644 index 55d8256..0000000 --- a/tests/test_trigger_contaminations.py +++ /dev/null @@ -1,161 +0,0 @@ -# coding: utf-8 - -_all_ = [ 'test_trigger_contaminations' ] - -import os -import sys -parent_dir = os.path.abspath(__file__ + 2 * '/..') -sys.path.insert(0, parent_dir) - -import inclusion -from inclusion.config import main -from inclusion.utils import utils - -import argparse -import h5py -import numpy as np -from bokeh.plotting import figure, output_file, save -from bokeh.palettes import Set1 as ColorSet -from bokeh.models import Range1d, ColumnDataSource -from bokeh.layouts import layout - -tau = '\u03C4' -mu = '\u03BC' -pm = '\u00B1' -ditau = tau+tau - -def main(args): - output_file( os.path.join(basedir, 'contaminations_' + args.region_vary + '_' + args.channel + '.html') ) - p_opt = dict(width=800, height=400, x_axis_label='x', y_axis_label='y') - title_d = {'dau1_pt': '(varying first lepton pT cut, second set to 190 GeV)', - 'dau2_pt': '(varying second lepton pT cut, first set to 190 GeV)', - 'both': '(varying both lepton pT cuts)'} - title1 = 'Contaminations in the SingleTau regions ' - p1 = figure(title=title1+title_d[args.region_vary], - tools='save,box_zoom,reset', **p_opt) - p1.xaxis.axis_label = 'm(HH) [GeV]' - p1.yaxis.axis_label = 'Contamination [%]' - p1.x_range = Range1d(0, 14.5) - p1.y_range = Range1d(-0.5, 20.) - - title2 = 'Statistics in the Single ('+tau+'+'+ditau+') and DiTau ('+ditau+'+MET) regions ' - p2 = figure(title=title2+title_d[args.region_vary], tools='save,box_zoom,reset', **p_opt) - p2.xaxis.axis_label = 'm(HH) [GeV]' - p2.yaxis.axis_label = '#Events' - - title3 = 'Normalized statistics in the Single ('+tau+'+'+ditau+') and DiTau ('+ditau+'+MET) regions ' - p3 = figure(title=title3+title_d[args.region_vary], tools='save,box_zoom,reset', **p_opt) - p3.xaxis.axis_label = 'm(HH) [GeV]' - p3.yaxis.axis_label = 'Normalized #Events' - - #linearize x axis - linear_x = [k for k in range(1,len(args.masses)+1)] - xticks = linear_x[:] - for p in (p1,p2,p3): - p.toolbar.logo = None - p.xaxis[0].ticker = xticks - p.xgrid[0].ticker = xticks - p.xgrid.grid_line_alpha = 0.2 - p.xgrid.grid_line_color = 'black' - - nshifts = len(args.region_cuts) - ns2 = int(nshifts/2) - diff = 0.10 if ns2%2==0 else 0.05 - shifts = [round((-ns2+x)*diff,2) for x in range(nshifts)] - colors = ColorSet[9] - for icut, cut in enumerate(args.region_cuts): - if args.region_vary == 'dau1_pt': - cut1, cut2 = cut, '190' - elif args.region_vary == 'dau2_pt': - cut1, cut2 = '190', cut - elif args.region_vary == 'both': - cut1, cut2 = cut, cut - label = '_'.join((str(cut1),str(cut2),args.channel)) - label_stats = label + '_stats' - - with h5py.File(os.path.join('data', label + '.hdf5'), 'r') as f: - masses = f[label][0] - x_str = [str(int(k)) for k in masses] - contam1 = f[label][1] - contam2 = f[label][2] - econtam1 = f[label][3] - econtam2 = f[label][4] - np.all(f[label_stats][1]==masses) - - stats1 = f[label_stats][1] - estats1 = f[label_stats][2] - if icut==0: - stats1_norm = stats1 - stats1_ratio = stats1 / stats1_norm - estats1_ratio = estats1 / stats1_norm - - source = ColumnDataSource({'x': [x+shifts[icut] for x in linear_x], - 'contam1': contam1, - 'contam2': contam2, - 'stats1': stats1, - 'stats1_ratio': stats1_ratio,}) - opt = dict(color=colors[icut], source=source) - - leg0 = (label[:3] if args.region_vary=='dau1_pt' else label[4:7]) + 'GeV ' - leg1 = leg0 + ditau - leg2 = leg1 + '+MET' - p1.line('x', 'contam1', line_width=1, legend_label=leg1, **opt) - p1.line('x', 'contam2', line_width=1, legend_label=leg2, line_dash='4 4', **opt) - p1.multi_line([(x+shifts[icut],x+shifts[icut]) for x in linear_x], - [(max(0,x-y/2),min(100,x+y/2)) for x,y in zip(contam1,econtam1)], - color=colors[icut], line_width=2, legend_label=leg1) - p1.multi_line([(x+shifts[icut],x+shifts[icut]) for x in linear_x], - [(max(0,x-y/2),min(100,x+y/2)) for x,y in zip(contam2,econtam2)], - color=colors[icut], line_width=2, legend_label=leg2) - ga = p1.circle('x', 'contam1', size=6, legend_label=leg1, **opt) - gb = p1.triangle('x', 'contam2', size=8, legend_label=leg2, **opt) - ga = ga.glyph - gb = gb.glyph - ga.line_color = 'black' - gb.line_color = 'black' - p1.xaxis.major_label_overrides = dict(zip(linear_x,x_str)) - - p2.multi_line([(x+shifts[icut],x+shifts[icut]) for x in linear_x], - [(max(0,x-y/2),x+y/2) for x,y in zip(stats1,estats1)], - color=colors[icut], line_width=2, legend_label=leg0) - ga = p2.circle('x', 'stats1', size=6, legend_label=leg0, **opt) - ga = ga.glyph - ga.line_color = 'black' - p2.xaxis.major_label_overrides = dict(zip(linear_x,x_str)) - - p3.multi_line([(x+shifts[icut],x+shifts[icut]) for x in linear_x], - [(max(0,x-y/2),x+y/2) for x,y in zip(stats1_ratio,estats1_ratio)], - color=colors[icut], line_width=2, legend_label=leg0) - ga = p3.circle('x', 'stats1_ratio', size=6, legend_label=leg0, **opt) - ga = ga.glyph - ga.line_color = 'black' - p3.xaxis.major_label_overrides = dict(zip(linear_x,x_str)) - p3.legend.location = 'bottom_left' - - for p in (p1,p2,p3): - p.legend.glyph_height = 15 - p.legend.glyph_width = 15 - p.legend.label_height = 9 - p.legend.label_width = 15 - p.legend.label_text_font_size = '7pt' - p.legend.click_policy = 'hide' - p.output_backend = 'svg' - - save(layout([[p1],[p2,p3]])) - - -if __name__ == '__main__': - basedir = '/eos/user/b/bfontana/www/TriggerScaleFactors/' - - parser = argparse.ArgumentParser(description='Produce plots of trigger gain VS resonance mass.') - parser.add_argument('--masses', required=True, nargs='+', type=str, - help='Resonance mass') - parser.add_argument('--region_cuts', required=True, nargs='+', type=str, - help='Region SingleTau pT cuts') - parser.add_argument('--region_vary', required=True, type=str, choices=('dau1_pt', 'dau2_pt', 'both'), - help='SingleTau region to vary') - parser.add_argument('--channel', required=True, type=str, - help='Select the channel over which the workflow will be run.' ) - args = utils.parse_args(parser) - - main(args) diff --git a/tests/test_trigger_gains.py b/tests/test_trigger_gains.py deleted file mode 100644 index 971b056..0000000 --- a/tests/test_trigger_gains.py +++ /dev/null @@ -1,287 +0,0 @@ -# coding: utf-8 - -_all_ = [ 'test_trigger_gains' ] - -import os -import sys -parent_dir = os.path.abspath(__file__ + 2 * '/..') -sys.path.insert(0, parent_dir) - -import json -import argparse -from inclusion.utils import utils -import numpy as np -from collections import defaultdict as dd -import hist -from hist.intervals import clopper_pearson_interval as clop -import pickle - -import bokeh -from bokeh.plotting import figure, output_file, save -from bokeh.models import Whisker -from bokeh.layouts import gridplot -#from bokeh.io import export_svg - -tau = '\u03C4' -mu = '\u03BC' -pm = '\u00B1' -ditau = tau+tau - -def get_outname(sample, channel, regcuts, ptcuts, met_turnon, bigtau): - utils.create_single_dir('data') - - name = sample + '_' + channel + '_' - name += '_'.join((*regcuts, 'ptcuts', *[str(x) for x in ptcuts], 'turnon', str(met_turnon))) - if bigtau: - name += '_BIGTAU' - name += '.pkl' - - s = 'data/regions_{}'.format(name) - return s - -def pp(chn): - if chn == "tautau": - return ditau - elif chn == "etau": - return "e" + tau - elif chn == "mutau": - return mu + tau - -def rec_dd(): - return dd(rec_dd) - -def set_fig(fig, legend=True): - fig.output_backend = 'svg' - fig.toolbar.logo = None - # if legend: - # fig.legend.click_policy='hide' - # fig.legend.location = 'top_left' - # fig.legend.label_text_font_size = '8pt' - fig.min_border_bottom = 5 - fig.xaxis.visible = True - fig.title.align = "left" - fig.title.text_font_size = "15px" - fig.xaxis.axis_label_text_font_style = "bold" - fig.yaxis.axis_label_text_font_style = "bold" - fig.xaxis.axis_label_text_font_size = "13px" - fig.yaxis.axis_label_text_font_size = "13px" - -def main(args): - channels = args.channels - linear_x = [k for k in range(1,len(args.masses)+1)] - edges_x = [k-0.5 for k in range(1,len(args.masses)+1)] + [len(args.masses)+0.5] - ptcuts = {chn: utils.get_ptcuts(chn, args.year) for chn in args.channels} - - nevents, errors = dd(lambda: dd(dict)), dd(lambda: dd(dict)) - ratios, eratios = dd(lambda: dd(dict)), dd(lambda: dd(dict)) - - for adir in main_dir: - dRstr = str(args.deltaR).replace('.', 'p') - if len(args.channels) == 1: - output_name = os.path.join(base_dir, 'trigger_gains_{}_{}_DR{}'.format(args.channels[0], - args.year, dRstr)) - elif len(args.channels) == 2: - output_name = os.path.join(base_dir, 'trigger_gains_{}_{}_{}_DR{}'.format(*args.channels[:2], - args.year, dRstr)) - elif len(args.channels) == 3: - output_name = os.path.join(base_dir, 'trigger_gains_all_{}_DR{}'.format(args.year, dRstr)) - if args.bigtau: - output_name += "_BIGTAU" - output_name += ".html" - output_file(output_name) - print('Saving file {}.'.format(output_name)) - - for chn in channels: - md = adir[chn] - in_base = os.path.join(base_dir, md) - - nevents[md][chn]['base'], nevents[md][chn]['met'], nevents[md][chn]['tau'] = [], [], [] - ratios[md][chn]['two'], ratios[md][chn]['met'], ratios[md][chn]['tau'] = [], [], [] - eratios[md][chn]['two'], eratios[md][chn]['met'], eratios[md][chn]['tau'] = [], [], [] - errors[md][chn] = [] - - for mass in args.masses: - outname = get_outname(mass, chn, [str(x) for x in args.region_cuts], - [str(x) for x in ptcuts[chn]], str(args.met_turnon), - args.bigtau) - - with open(outname, "rb") as f: - ahistos = pickle.load(f) - - # all regions summed - sum_base_tot = round(ahistos["Base"]["legacy"]["baseline"].values().sum() + - ahistos["Base"]["tau"]["baseline"].values().sum() + - ahistos["Base"]["met"]["baseline"].values().sum()) - - # legacy region - l1 = lambda x : round(x["legacy"]["baseline"].values().sum(), 2) - sum_base = l1(ahistos["Base"]) - sum_vbf = l1(ahistos["VBF"]) - sum_met = l1(ahistos["NoBaseMET"]) - sum_only_tau = l1(ahistos["NoBaseNoMETTau"]) - sum_tau = l1(ahistos["NoBaseTau"]) - sum_basekin = l1(ahistos["LegacyKin"]) - w2_basekin = ahistos["METKin"]["legacy"]["baseline"].variances().sum() - - # MET region - l2 = lambda x : round(x["met"]["baseline"].values().sum(), 2) - sum_metkin = l2(ahistos["METKin"]) - w2_metkin = ahistos["METKin"]["met"]["baseline"].variances().sum() - - # Single Tau region - l3 = lambda x : round(x["tau"]["baseline"].values().sum(), 2) - sum_taukin = l3(ahistos["TauKin"]) - w2_taukin = ahistos["TauKin"]["tau"]["baseline"].variances().sum() - - # hypothetical VBF region - sum_vbfkin = l2(ahistos["VBFKin"]) + l3(ahistos["VBFKin"]) - - nevents[md][chn]['base'].append(sum_basekin) - nevents[md][chn]['met'].append(sum_basekin + sum_metkin) - nevents[md][chn]['tau'].append(sum_basekin + sum_metkin + sum_taukin) - - rat_met_num = sum_basekin + sum_metkin - rat_met_all = rat_met_num / sum_base_tot - - rat_tau_num = sum_basekin + sum_taukin - rat_tau_all = rat_tau_num / sum_base_tot - - rat_all_num = sum_basekin + sum_taukin + sum_metkin - rat_all = rat_all_num / sum_base_tot - - ratios[md][chn]['met'].append(rat_met_all) - ratios[md][chn]['tau'].append(rat_tau_all) - ratios[md][chn]['two'].append(rat_all) - - e_metkin = np.sqrt(w2_metkin) - e_taukin = np.sqrt(w2_taukin) - e_basekin = np.sqrt(w2_basekin) - - e_tau_num = np.sqrt(w2_taukin + w2_basekin) - e_met_num = np.sqrt(w2_metkin + w2_basekin) - e_all_num = np.sqrt(w2_metkin + w2_taukin + w2_basekin) - - eratios[md][chn]['tau'].append(rat_tau_all * np.sqrt(e_tau_num**2/rat_tau_num**2 + 1/sum_base_tot)) - eratios[md][chn]['met'].append(rat_met_all * np.sqrt(e_met_num**2/rat_met_num**2 + 1/sum_base_tot)) - eratios[md][chn]['two'].append(rat_all * np.sqrt(e_all_num**2/rat_all_num**2 + 1/sum_base_tot)) - errors[md][chn].append(e_all_num) - - json_name = 'data_' + chn + '_' - json_name += ('bigtau' if args.bigtau else 'standard') + '.json' - with open(json_name, 'w', encoding='utf-8') as json_obj: - json_data = {"vals": {chn: nevents[adir[chn]][chn]['tau'] for chn in channels}} - json_data.update({"errs": {chn: errors[adir[chn]][chn] for chn in channels}}) - json.dump(json_data, json_obj, ensure_ascii=False, indent=4) - - opt_points = dict(size=8) - opt_line = dict(width=1.5) - colors = ('green', 'blue', 'red', 'brown') - styles = ('solid', 'dashed', 'dotdash') - legends = {'base': 'Legacy', - 'met': 'MET', 'tau': 'Single Tau', - 'two': 'MET + Single Tau', 'vbf': 'VBF'} - - x_str = [str(k) for k in args.masses] - xticks = linear_x[:] - yticks = [x for x in range(0,110,5)] - shift_one = {'met': [-0.15, 0., 0.15], 'tau': [-0.20, -0.05, 0.1], - 'vbf': [-0.10, 0.05, 0.20]} - shift_both = {'met': [-0.15, 0., 0.15], 'two': [-0.20, -0.05, 0.1]} - shift_kin = {'met': [-0.09, 0., 0.15], 'tau': [0.03, -0.05, 0.1], - 'two': [-0.03, 0.05, 0.20], 'vbf': [0.09, 0.1, 0.25]} - - for adir in main_dir: - p_opt = dict(width=800, height=400, x_axis_label='x', y_axis_label='y') - p1 = figure(title='Event number (' + pp(channels[0]) + ')', y_axis_type="linear", **p_opt) - p2 = figure(title='Acceptance Gain (' + pp(channels[0]) + ')', **p_opt) if len(channels)==1 else figure(**p_opt) - - p1.yaxis.axis_label = 'Weighted number of events' - p2.yaxis.axis_label = 'Trigger acceptance gain (w.r.t. trigger baseline) [%]' - pics = (p1, p2) - for p in pics: - set_fig(p) - - for ichn,chn in enumerate(channels): - md = adir[chn] - - p1.quad(top=nevents[md][chn]["base"], bottom=0, - left=edges_x[:-1], right=edges_x[1:], - legend_label=legends["base"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), - fill_color="dodgerblue", line_color="black") - p1.quad(top=nevents[md][chn]["met"], bottom=nevents[md][chn]["base"], - left=edges_x[:-1], right=edges_x[1:], - legend_label=legends["met"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), - fill_color="green", line_color="black") - p1.quad(top=nevents[md][chn]["tau"], bottom=nevents[md][chn]["met"], - left=edges_x[:-1], right=edges_x[1:], - legend_label=legends["tau"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), - fill_color="red", line_color="black") - - for itd,td in enumerate(('met', 'tau', 'two')): - p2.circle([x+shift_kin[td][ichn] for x in linear_x], - [(x-1)*100. for x in ratios[md][chn][td]], - color=colors[itd], fill_alpha=1., **opt_points) - p2.line([x+shift_kin[td][ichn] for x in linear_x], - [(x-1)*100. for x in ratios[md][chn][td]], - color=colors[itd], line_dash=styles[ichn], - legend_label=legends[td]+(' ('+pp(chn)+')' if len(channels)>1 else ''), **opt_line) - p2.multi_line( - [(x+shift_kin[td][ichn],x+shift_kin[td][ichn]) for x in linear_x], - [((y-1)*100-(x*50.),(y-1)*100+(x*50.)) for x,y in zip(eratios[md][chn][td],ratios[md][chn][td])], - color=colors[itd], **opt_line) - - p1.legend.location = 'top_right' - p2.legend.location = 'top_left' - for p in pics: - p.xaxis[0].ticker = xticks - p.xgrid[0].ticker = xticks - p.xgrid.grid_line_alpha = 0.2 - p.xgrid.grid_line_color = 'black' - # p.yaxis[0].ticker = yticks - # p.ygrid[0].ticker = yticks - p.ygrid.grid_line_alpha = 0.2 - p.ygrid.grid_line_color = 'black' - - p.xaxis.axis_label = "m(X) [GeV]" - - p.xaxis.major_label_overrides = dict(zip(linear_x,x_str)) - - p.legend.click_policy='hide' - - p.output_backend = 'svg' - #export_svg(p, filename='line_graph.svg') - - g = gridplot([[p] for p in pics]) - save(g, title=md) - -if __name__ == '__main__': - desc = "Produce plots of trigger gain VS resonance mass.\n" - desc += "Uses the output of test_trigger_regions.py." - desc += "When running on many channels, one should keep in mind each channel has different pT cuts." - desc += "This might imply moving sub-folders (produced by the previous script) around." - parser = argparse.ArgumentParser(description=desc, formatter_class=argparse.RawTextHelpFormatter) - - parser.add_argument('--masses', required=True, nargs='+', type=str, - help='Resonance mass') - parser.add_argument('--channels', required=True, nargs='+', type=str, - choices=('etau', 'mutau', 'tautau'), - help='Select the channel over which the workflow will be run.' ) - parser.add_argument('--year', required=True, type=str, choices=('2016', '2017', '2018'), - help='Select the year over which the workflow will be run.' ) - parser.add_argument('--deltaR', type=float, default=0.5, help='DeltaR between the two leptons.') - parser.add_argument('--bigtau', action='store_true', - help='Consider a larger single tau region, reducing the ditau one.') - parser.add_argument('--met_turnon', required=False, type=str, default=180, - help='MET trigger turnon cut [GeV].' ) - parser.add_argument('--region_cuts', required=False, type=float, nargs=2, default=(190., 190.), - help='High/low regions pT1 and pT2 selection cuts [GeV].' ) - - args = utils.parse_args(parser) - - base_dir = '/eos/home-b/bfontana/www/TriggerScaleFactors/' - main_dir = [{"etau": "Region_Spin2_190_190_PT_33_25_35_DR_{}_TURNON_200_190".format(args.deltaR), - "mutau": "Region_Spin2_190_190_PT_25_21_32_DR_{}_TURNON_200_190".format(args.deltaR), - "tautau": "Region_Spin2_190_190_PT_40_40_DR_{}_TURNON_200_190".format(args.deltaR)}, - ] - - main(args) diff --git a/tests/test_trigger_regions.py b/tests/test_trigger_regions.py deleted file mode 100644 index b1ed2c9..0000000 --- a/tests/test_trigger_regions.py +++ /dev/null @@ -1,710 +0,0 @@ -# coding: utf-8 - -_all_ = [ 'test_trigger_regions' ] - -import os -import sys -parent_dir = os.path.abspath(__file__ + 2 * '/..') -sys.path.insert(0, parent_dir) -import argparse -import glob -import multiprocessing -import itertools as it -import csv -import numpy as np -import h5py -from collections import defaultdict as dd -import importlib - -import inclusion -from inclusion import selection -from inclusion.config import main -from inclusion.utils import utils - -import ROOT -import hist -import pickle - -from bokeh.plotting import figure, output_file, save -from bokeh.models import Range1d, Label - -tau = '\u03C4' -mu = '\u03BC' -pm = '\u00B1' -ditau = tau+tau - -def contamination_save(savepath, label, c1, c2, e1, e2, m, mode='w'): - with h5py.File(os.path.join(savepath, label + '.hdf5'), mode) as f: - dset = f.create_dataset(label, (5,len(m)), dtype='f') - dset[0, :] = m - dset[1, :] = c1 - dset[2, :] = c2 - dset[3, :] = e1 - dset[4, :] = e2 - dset.cols = ['mass [GeV]', - 'contamination ' + ditau + '(%)', - 'contamination ' + ditau + ' MET (%)', - 'uncertainty' + ditau, - 'uncertainty' + ditau + ' MET'] - -def stats_save(savepath, label, c1, e1, m, mode='w'): - with h5py.File(os.path.join(savepath, label + '.hdf5'), mode) as f: - dset = f.create_dataset(label+'_stats', (3,len(m)), dtype='f') - dset[0, :] = m - dset[1, :] = c1 - dset[2, :] = e1 - dset.cols = ['mass [GeV]', - 'stats', - 'uncertainty'] - -def rec_dd(): - return dd(rec_dd) - -def square_diagram(c_legacy_trg, c_met_trg, c_tau_trg, channel, - ptcuts, text, notext=False, bigtau=False): - base = {'etau': 'e+e'+tau, 'mutau': mu+'+'+mu+tau, 'tautau': ditau} - output_file(text['out']) - print('Saving file {}'.format(text['out'])) - - topr = 9.9 - shft = 0.1 - start, b1, b2, b3 = 0.0, 2, 2.5, 7.5 - xgap = b1+shft# else b2+shft - - p = figure(title='m(X)={}GeV'.format(text['mass']), width=600, height=400, - tools='save') - p.x_range = Range1d(0, 11.5) - p.y_range = Range1d(0, 10) - p.outline_line_color = None - p.toolbar.logo = None - p.xgrid.grid_line_color = None - p.ygrid.grid_line_color = None - p.xaxis.ticker = [b1,b3]#else [b1, b2, b3] - p.xaxis.major_label_overrides = ({b1: str(ptcuts[0]), b3: str(regcuts[0])}) - - for aaxis in (p.xaxis, p.yaxis): - aaxis.axis_label_text_font_size = "12pt" - aaxis.axis_label_text_font_style = "normal" - aaxis.major_label_text_font_size = "11pt" - - p.yaxis.axis_label_standoff = 0 - p.yaxis.ticker = [b1, b3]# else [b1, b2, b3] - if len(ptcuts) > 1: - p.yaxis.major_label_overrides = ({b1: str(ptcuts[0]), b3: str(regcuts[1])}) - else: - p.yaxis.major_label_overrides = ({b1: str(ptcuts[0])}) - - p.xaxis.axis_label = r'\(p_T(\tau_1) [GeV]\)' - p.yaxis.axis_label = r'\(p_T(\tau_2) [GeV]\)' - - # add a square renderer with a size, color, and alpha - polyg_opt = dict(alpha=0.3) - p.multi_polygons(color='green', - xs=[[[[xgap,b3-shft,b3-shft,xgap]]]] if bigtau else [[[[xgap,topr,topr,xgap]]]], - ys=[[[[b3-shft,b3-shft,xgap,xgap]]]] if bigtau else [[[[topr,topr,xgap,xgap]]]], - legend_label=base[channel], **polyg_opt) - - p.legend.title = 'Regions' - p.legend.title_text_font_style = 'bold' - p.legend.border_line_color = None - p.legend.background_fill_color = 'white' - p.legend.click_policy = 'hide' - - label_opt = dict(x_units='data', y_units='data', text_font_size='10pt') - - gain = (100*float(c_met_trg['legacy']+c_tau_trg['legacy']) / - (c_legacy_trg['legacy']+c_met_trg['legacy']+c_tau_trg['legacy'])) - gain = str(round(gain,2)) - if not notext: - stats_ditau = {'legacy': Label(x=b1+0.3, y=b1+1.5, text=ditau+': '+str(c_legacy_trg['legacy']), - text_color='black', **label_opt), - 'met': Label(x=b1+0.3, y=b1+1.1, text='met && !'+ditau+': '+str(c_met_trg['legacy']), - text_color='black', **label_opt), - 'tau': Label(x=b1+0.3, y=b1+0.7, text=tau+' && !met && !'+ditau+': '+str(c_tau_trg['legacy']), - text_color='black', **label_opt), - 'gain': Label(x=b1+0.3, y=b1+0.3, text='Gain: '+gain+'%', - text_color='blue', **label_opt),} - for key,elem in stats_ditau.items(): - p.add_layout(elem) - - - try: - contam_by_tau = (100*(float(c_tau_trg['met'])+c_legacy_trg['met']) / - (c_met_trg['met']+c_tau_trg['met']+c_legacy_trg['met'])) - contam_by_tau = str(round(contam_by_tau,2)) - except ZeroDivisionError: - contam_by_tau = '0' - if not notext: - stats_met = {'met': Label(x=b1+0.3, y=1.3, text='met: '+str(c_met_trg['met']), - text_color='black', **label_opt), - 'tau': Label(x=b1+0.3, y=0.5, text=tau+' && !'+ditau+' && !met: '+str(c_tau_trg['met']), - text_color='black', **label_opt), - 'legacy': Label(x=b1+0.3, y=0.9, text=ditau+' && !met: '+str(c_legacy_trg['met']), - text_color='black', **label_opt), - 'contamination': Label(x=b1+0.3, y=0.1, text='Contam.: '+contam_by_tau+'%', - text_color='blue', **label_opt),} - for key,elem in stats_met.items(): - if key != 'contamination': - p.add_layout(elem) - - - num_ditau = float(c_legacy_trg['tau']) - num_both = num_ditau + float(c_met_trg['tau']) - den = float(c_met_trg['tau']+c_tau_trg['tau']+c_legacy_trg['tau']) - if num_ditau == 0 or num_both == 0: - contam_ditau = '0' - contam_both = '0' - err_ditau = '0' - err_both = '0' - else: - contam_ditau = 100*num_ditau/den - contam_both = 100*num_both/den - - enum_ditau = np.sqrt(c_legacy_trg['tau']) - eden_ditau = enum_ditau + np.sqrt(c_met_trg['tau']) + np.sqrt(c_tau_trg['tau']) - enum_both = np.sqrt(c_met_trg['tau']) + np.sqrt(c_legacy_trg['tau']) - eden_both = enum_both + np.sqrt(c_tau_trg['tau']) - err_ditau = contam_ditau * np.sqrt(enum_ditau**2/num_ditau**2 + eden_ditau**2/den**2) - err_both = contam_both * np.sqrt(enum_both**2/num_both**2 + eden_both**2/den**2) - - contam_ditau = str(round(contam_ditau,2)) - contam_both = str(round(contam_both,2)) - err_ditau = str(round(err_ditau,2)) - err_both = str(round(err_both,2)) - - if not notext: - stats_tau = {'tau': Label(x=b3+0.2, y=1.3, text=tau+': '+str(c_tau_trg['tau']), - text_color='black', **label_opt), - 'met': Label(x=b3+0.2, y=0.5, text='met && !'+ditau+' && !'+tau+': '+str(c_met_trg['tau']), - text_color='black', **label_opt), - 'legacy': Label(x=b3+0.2, y=0.9, text=ditau+' && !'+tau+': '+str(c_legacy_trg['tau']), - text_color='black', **label_opt), - 'contamination_both': Label(x=b3+0.2, y=0.1, - text='Contam.: ('+str(contam_both)+pm+str(err_both)+')%', - text_color='blue', **label_opt),} - for key,elem in stats_tau.items(): - if key != 'contamination_both': - p.add_layout(elem) - - line_opt = dict(color='black', line_dash='dashed', line_width=2) - p.line(x=[b1,b1], y=[start, topr+shft], **line_opt) - p.line(x=[start,topr+shft], y=[b1,b1], **line_opt) - p.line(x=[b3,b3], y=[start,topr+shft], **line_opt) - p.line(x=[start,topr+shft], y=[b3,b3], **line_opt) - - p.output_backend = 'svg' - save(p) - return contam_ditau, contam_both, err_ditau, err_both - -def get_outname(sample, channel, regcuts, ptcuts, met_turnon, bigtau): - utils.create_single_dir('data') - - name = sample + '_' + channel + '_' #*map(str, regcuts) - name += '_'.join((*[str(x) for x in regcuts], 'ptcuts', - *[str(x) for x in ptcuts], 'turnon', str(met_turnon))) - if bigtau: - name += '_BIGTAU' - name += '.pkl' - - s = 'data/regions_{}'.format(name) - return s - -def set_plot_definitions(): - ROOT.gROOT.SetBatch(ROOT.kTRUE) - ROOT.gStyle.SetOptStat(ROOT.kFALSE) - ret = {'XTitleSize' : 0.045, - 'YTitleSize' : 0.045, - 'LineWidth' : 2, - 'FrameLineWidth' : 1, - } - return ret - -def plot(hbase, hmet, htau, var, channel, sample, region, category, directory): - defs = set_plot_definitions() - - hbase2 = hbase.Clone('hbase2') - hmet2 = hmet.Clone('hmet2') - htau2 = htau.Clone('htau2') - - # Normalized shapes - c2 = ROOT.TCanvas('c2', '', 600, 400) - c2.cd() - try: - hbase2.Scale(1/hbase2.Integral()) - except ZeroDivisionError: - hbase2.Scale(0.) - try: - hmet2.Scale(1/hmet2.Integral()) - except ZeroDivisionError: - hmet2.Scale(0.) - try: - htau2.Scale(1/htau2.Integral()) - except ZeroDivisionError: - htau2.Scale(0.) - - shift_scale = 5. - amax = hbase2.GetMaximum() + (hbase2.GetMaximum()-hbase2.GetMinimum()) / shift_scale - amax = max(amax, hmet2.GetMaximum() + (hmet2.GetMaximum()-hmet2.GetMinimum()) / shift_scale) - amax = max(amax, htau2.GetMaximum() + (htau2.GetMaximum()-htau2.GetMinimum()) / shift_scale) - hbase2.SetMaximum(amax) - - hbase2.GetXaxis().SetTitleSize(defs['XTitleSize']); - hbase2.GetXaxis().SetTitle(var + ' [GeV]'); - hbase2.GetYaxis().SetTitleSize(defs['YTitleSize']); - hbase2.GetYaxis().SetTitle('a. u.'); - hbase2.SetLineWidth(defs['LineWidth']); - hbase2.SetLineColor(4); - - hmet2.SetLineWidth(defs['LineWidth']); - hmet2.SetLineColor(8); - - htau2.SetLineWidth(defs['LineWidth']); - htau2.SetLineColor(2); - - hbase2.Draw('hist') - hmet2.Draw('histsame') - htau2.Draw('histsame') - - leg2 = ROOT.TLegend(0.69, 0.77, 0.90, 0.9) - leg2.SetNColumns(1) - leg2.SetFillStyle(0) - leg2.SetBorderSize(0) - leg2.SetTextFont(43) - leg2.SetTextSize(10) - leg2.AddEntry(hbase2, 'legacy') - leg2.AddEntry(hmet2, '!legacy && MET') - leg2.AddEntry(htau2, '!legacy && !MET && tau') - - leg2.Draw('same') - - c2.Update(); - cat_dir = os.path.join(directory, sample, category) - utils.create_single_dir(cat_dir) - for ext in extensions: - c2.SaveAs( os.path.join(cat_dir, 'norm_' + region + '_' + var + '.' + ext) ) - c2.Close() - -def plot2D(histo, two_vars, channel, sample, trigger_str, category, directory, region, norm=False): - histo = histo.Clone('histo_clone') - defs = set_plot_definitions() - - c = ROOT.TCanvas('c', '', 800, 800) - c.cd() - - pad1 = ROOT.TPad('pad1', 'pad1', 0., 0., 1., 1.) - pad1.SetFrameLineWidth(defs['FrameLineWidth']) - pad1.SetLeftMargin(0.12); - pad1.SetRightMargin(0.14); - pad1.SetBottomMargin(0.12); - pad1.SetTopMargin(0.1); - pad1.Draw() - pad1.cd() - - ROOT.gStyle.SetTitleX(0.44) #title X location - ROOT.gStyle.SetTitleY(1.015) #title Y location - ROOT.gStyle.SetTitleW(0.7) #title width - ROOT.gStyle.SetTitleH(0.15) #title height - ROOT.gStyle.SetTitleColor(ROOT.kBlue, 't') - histo.SetTitle('Region: ' + region +' | Triggers: ' + trigger_str); - histo.SetTitleSize(0.02, 't') - histo.GetXaxis().SetTitle(two_vars[0]) - histo.GetXaxis().SetTitleSize(0.04) - histo.GetYaxis().SetTitle(two_vars[1]) - histo.GetYaxis().SetTitleSize(0.04) - - if norm: - try: - histo.Scale(1/histo.Integral()) - except ZeroDivisionError: - histo.Scale(0.) - histo.Draw('colz') - - c.cd() - cat_folder = os.path.join(directory, sample, category) - utils.create_single_dir(cat_folder) - c.Update(); - tstr = trigger_str.replace(' ', '_').replace('+', '_PLUS_').replace('!', 'NOT') - for ext in extensions: - c.SaveAs(os.path.join(cat_folder, - tstr + '_' + 'reg' + region + '_' + '_VS_'.join(two_vars) + '.' + ext)) - c.Close() - -def which_region(ent, year, ptcuts, regcuts, channel, met_turnon, bigtau=False): - """ - Select one of the threee non-overlapping regions: leg(acy), met and tau - - ptcuts: pT cuts, delimiting the three regions based on the HLT pT thresholds - - regcuts: region cuts, delimiting the three regions in the pT space - Example in tautau: - - ptcuts = (40, 40) - - regcuts = (190, 190) - - Eta cuts are defined with different strategies: - - tautau: eta trigger cut at 2.1, so that the MET trigger can take advantage of high-eta events for the legacy region - - e/mutau: eta cuts correspond to the analysis selection - Since different triggers in the etau and mutau channels can require different eta thresholds - (eg: single-muon has no threshold and cross-muon-tau has one at 2.1), a fully correct approach - would have to apply different cuts depending on the fired trigger bit. To avoid - this extra complication, which would very likely bring a negligible signal acceptance improvement, - the eta cuts applied correspond to the object selection in the analysis. - """ - eta1_sel = {"etau": abs(ent.dau1_eta) <= 2.5, "mutau": abs(ent.dau1_eta) <= 2.4} - eta1_trg = abs(ent.dau1_eta) <= 2.1 - eta2_trg = abs(ent.dau2_eta) <= 2.1 - - if bigtau: - if channel == "tautau": - tau = (ent.dau1_pt >= regcuts[0] and eta1_trg) or (ent.dau2_pt >= regcuts[1] and eta2_trg) - leg = ent.dau1_pt >= ptcuts[0] and ent.dau2_pt >= ptcuts[1] and eta1_trg and eta2_trg and not tau - - elif channel == "etau" and year == "2016": - tau = ent.dau2_pt >= regcuts[1] and eta2_trg - leg = ent.dau1_pt >= ptcuts[0] and eta1_trg and not tau - - else: #mutau or etau non-2016 - single_lepton_validity = ent.dau1_pt >= ptcuts[0] and eta1_sel[channel] - cross_lepton_validity = ent.dau1_pt >= ptcuts[1] and eta1_trg and ent.dau2_pt >= ptcuts[2] and eta2_trg - - tau = ent.dau2_pt >= regcuts[1] and eta2_trg - leg = (single_lepton_validity or cross_lepton_validity) and not tau - - else: - if channel == "tautau": - leg = ent.dau1_pt >= ptcuts[0] and ent.dau2_pt >= ptcuts[1] and eta1_trg and eta2_trg - tau = ((ent.dau1_pt >= regcuts[0] and eta1_trg) or (ent.dau2_pt >= regcuts[1] and eta2_trg)) and not leg - - elif channel == "etau" and year == "2016": - leg = ent.dau1_pt >= ptcuts[0] and eta1_trg - tau = ent.dau2_pt >= regcuts[1] and eta2_trg and not leg - - else: #mutau or etau non-2016 - single_lepton_validity = ent.dau1_pt >= ptcuts[0] and eta1_sel[channel] - cross_lepton_validity = ent.dau1_pt >= ptcuts[1] and eta1_trg and ent.dau2_pt >= ptcuts[2] and eta2_trg - single_tau_validity = ent.dau2_pt >= regcuts[1] and eta2_trg - - leg = single_lepton_validity or cross_lepton_validity - tau = single_tau_validity and not leg - - met = ent.metnomu_et > met_turnon and not leg and not tau - - # only one True: non-overlapping regions - assert int(leg)+int(met)+int(tau)<=1 - - return leg, met, tau - - -def test_trigger_regions(indir, sample, channel, spin, year, deltaR): - outname = get_outname(sample, channel, regcuts, ptcuts, met_turnon, - args.bigtau) - config_module = importlib.import_module(args.configuration) - if channel == 'etau' or channel == 'mutau': - iso1 = (24, 0, 8) - elif channel == 'tautau': - iso1 = (20, 0.965, 1.005) - binning.update({'HHKin_mass': (20, float(sample)-300, float(sample)+300), - 'dau1_iso': iso1}) - - full_sample = {0: 'GluGluToRadionToHHTo2B2Tau_M-' + sample + '_', - 2: 'GluGluToBulkGravitonToHHTo2B2Tau_M-' + sample + '_'}[spin] - - norphans, ntotal = ({k:0 for k in categories} for _ in range(2)) - - ahistos = rec_dd() - for reg in regions: - for cat in categories: - for htype in htypes: - ahistos[htype][reg][cat] = ( - hist.Hist.new.Regular(*binning["dau1_pt"], name="dau1pt") - .Regular(*binning["dau2_pt"], name="dau2pt") - .Weight() - ) - - t_in = ROOT.TChain('HTauTauTree') - glob_files = glob.glob( os.path.join(indir, full_sample, 'output_*.root') ) - if len(glob_files) < 1: - raise RuntimeError("No files!") - for f in glob_files: - t_in.Add(f) - t_in.SetBranchStatus('*', 0) - _entries = utils.define_used_tree_variables(cut=config_module.custom_cut) - _entries += tuple(variables) - for ientry in _entries: - t_in.SetBranchStatus(ientry, 1) - - for entry in t_in: - # this is slow: do it once only - entries = utils.dot_dict({x: getattr(entry, x) for x in _entries}) - - in_legacy, in_met, in_tau = which_region(entries, year, ptcuts, regcuts, channel, met_turnon, - bigtau=args.bigtau) - - sel = selection.EventSelection(entries, isdata=False, configuration=config_module) - - pass_trg = sel.pass_triggers(triggers[channel]) and entries.isLeptrigger - pass_met = sel.pass_triggers(('METNoMu120',)) - pass_tau = sel.pass_triggers(('IsoTau180',)) - - cuts = {'Base' : pass_trg, - 'MET' : pass_met, - 'Tau' : pass_tau, - 'VBF' : False, - 'BaseMET' : pass_trg and pass_met, - 'BaseTau' : pass_trg and pass_tau, - 'METTau' : pass_met and pass_tau, - 'NoBaseMET' : not pass_trg and pass_met, - 'BaseNoMET' : pass_trg and not pass_met, - 'NoBaseNoMETTau' : not pass_trg and not pass_met, - 'NoBaseTau' : not pass_trg and pass_tau, - 'BaseNoTau' : pass_trg and not pass_tau, - 'NoBaseMETNoTau' : not pass_trg and pass_met and not pass_tau, - 'BaseMETTau' : pass_trg and pass_met and pass_tau, - 'BaseNoMETNoTau' : pass_trg and not pass_met and not pass_tau, - 'LegacyKin' : pass_trg and in_legacy, - 'METKin' : pass_met and in_met, - 'TauKin' : pass_tau and in_tau, - 'VBFKin' : False - } - assert htypes == list(cuts.keys()) - - w_mc = entries.MC_weight - w_pure = entries.PUReweight - w_l1pref = entries.L1pref_weight - w_trig = entries.trigSF - w_idiso = entries.IdSF_deep_2d - w_jetpu = entries.PUjetID_SF - # w_btag = entries.bTagweightReshape - - if utils.is_nan(w_mc) : w_mc=1 - if utils.is_nan(w_pure) : w_pure=1 - if utils.is_nan(w_l1pref) : w_l1pref=1 - if utils.is_nan(w_trig) : w_trig=1 - if utils.is_nan(w_idiso) : w_idiso=1 - if utils.is_nan(w_jetpu) : w_jetpu=1 - # if utils.is_nan(w_btag) : w_btag=1 - - evt_weight = w_mc * w_pure * w_l1pref * w_trig * w_idiso * w_jetpu # * w_btag - - tau_gen_cut = {"etau": None, "mutau": None, - "tautau": 'self.entries["isTau1real"] == 1 and self.entries["isTau2real"] == 1'} - if utils.is_channel_consistent(channel, entries.pairType): - if not sel.selection_cuts(lepton_veto=True, bjets_cut=True, - mass_cut=config_module.mass_cut, - custom_cut=tau_gen_cut[channel]): - continue - - for cat in categories: - if sel.sel_category(cat) and entries.ditau_deltaR > deltaR: - ntotal[cat] += 1 - - if in_met: - reg = 'met' - elif in_tau: - reg = 'tau' - elif in_legacy: - reg = 'legacy' - else: - norphans[cat] += 1 - continue - assert reg in regions - - for key,cut in cuts.items(): - if cut: - ahistos[key][reg][cat].fill(dau1pt=entries["dau1_pt"], - dau2pt=entries["dau2_pt"], weight=evt_weight) - - # all MC and signal must be rescaled to get the correct number of events - for key,_ in cuts.items(): - for reg in regions: - for cat in categories: - ahistos[key][reg][cat] *= (utils.get_lumi(args.year) / - utils.total_sum_weights(glob_files[0], isdata=False)) - - with open(outname, "wb") as f: - pickle.dump(ahistos, f) - - for cat in categories: - orph_frac = float(norphans[cat])/ntotal[cat] - if orph_frac > 0.1: - print('{}% orphans ({}/{}). This is unusual.'.format(orph_frac, norphans[cat], ntotal[cat])) - print(met_region) - print(tau_region) - print(legacy_region) - print('Category {} (m(X)={}GeV) had {} orphans ({}%)'.format(cat, sample, norphans[cat], orph_frac)) - print('Raw histograms saved in {}.'.format(outname), flush=True) - -if __name__ == '__main__': - extensions = ('png',) #('png', 'pdf') - triggers = {'etau': ('Ele32', 'EleIsoTauCustom'), - 'mutau': ('IsoMu24', 'IsoMuIsoTauCustom'), - 'tautau': ('IsoDoubleTauCustom',) - } - binning = {'metnomu_et': (20, 0, 450), - 'dau1_pt': (30, 0, 450), - 'dau1_eta': (20, -2.5, 2.5), - 'dau2_iso': (20, 0.88, 1.005), - 'dau2_pt': (30, 0, 400), - 'dau2_eta': (20, -2.5, 2.5), - 'ditau_deltaR': (30, 0.3, 1.3), - 'dib_deltaR': (25, 0, 2.5), - 'bH_pt': (20, 70, 600), - 'bH_mass': (30, 0, 280), - 'tauH_mass': (30, 0, 170), - 'tauH_pt': (30, 0, 500), - 'tauH_SVFIT_mass': (30, 0, 250), - 'tauH_SVFIT_pt': (20, 200, 650), - 'bjet1_pt': (25, 10, 600), - 'bjet2_pt': (25, 10, 550), - 'bjet1_eta': (20, -2.5, 2.5), - 'bjet2_eta': (20, -2.5, 2.5), - } - variables = tuple(binning.keys()) + ('HHKin_mass', 'dau1_iso') - - categories = ('baseline',) #('baseline', 's1b1jresolvedMcut', 's2b0jresolvedMcut', 'sboostedLLMcut') - - htypes = ['Base', 'MET', 'Tau', 'VBF', 'BaseMET', 'BaseTau', 'METTau', 'NoBaseMET', 'BaseNoMET', - 'NoBaseNoMETTau', 'NoBaseTau', 'BaseNoTau', 'NoBaseMETNoTau', 'BaseMETTau', 'BaseNoMETNoTau', - 'LegacyKin', 'METKin', 'TauKin', 'VBFKin'] - - # Parse input arguments - desc = 'Producer trigger histograms.\n' - desc += "Run example: python tests/test_trigger_regions.py --indir /data_CMS/cms/alves/HHresonant_SKIMS/SKIMS_UL18_EOSv4_Signal/ --masses 400 500 600 700 800 900 1000 1250 1500 --channels ETau --met_turnon 180 --region_cuts 40 40 --copy" - parser = argparse.ArgumentParser(description=desc, formatter_class=argparse.RawTextHelpFormatter) - - parser.add_argument('--indir', required=True, type=str, - help='Full path of ROOT input file') - parser.add_argument('--masses', required=True, nargs='+', type=str, - help='Resonance mass') - parser.add_argument('--channel', required=True, type=str, - help='Select the channel over which the workflow will be run.' ) - parser.add_argument('--year', required=True, type=str, choices=('2016', '2016APV', '2017', '2018'), - help='Select the year over which the workflow will be run.' ) - parser.add_argument('--spin', required=True, type=int, choices=(0, 2), - help='Select the spin hypothesis over which the workflow will be run.' ) - parser.add_argument('--deltaR', type=float, default=0.5, help='DeltaR between the two leptons.' ) - parser.add_argument('--plot', action='store_true', - help='Reuse previously produced data for quick plot changes.') - parser.add_argument('--copy', action='store_true', - help='Copy the outputs to EOS at the end.') - parser.add_argument('--notext', action='store_true', help='Square diagram without text.') - parser.add_argument('--sequential', action='store_true', - help='Do not use the multiprocess package.') - parser.add_argument('--bigtau', action='store_true', - help='Consider a larger single tau region, reducing the ditau one.') - parser.add_argument('--met_turnon', type=float, default=180, - help='MET trigger turnon cut [GeV].' ) - parser.add_argument('--region_cuts', required=False, type=float, nargs=2, default=(190, 190), - help='High/low regions pT1 and pT2 selection cuts [GeV].' ) - parser.add_argument('--configuration', dest='configuration', required=True, - help='Name of the configuration module to use.') - args = utils.parse_args(parser) - - met_turnon = args.met_turnon - regcuts = args.region_cuts - ptcuts = utils.get_ptcuts(args.channel, args.year) - - main_dir = os.path.join('/eos/home-b/bfontana/www/TriggerScaleFactors/', - '_'.join(('Spin' + str(args.spin), args.channel, *[str(x) for x in regcuts], - 'DR', str(args.deltaR), 'PT', *[str(x) for x in ptcuts], 'TURNON', - str(met_turnon)))) - if args.bigtau: - main_dir += '_BIGTAU' - - regions = ('legacy', 'met', 'tau') - - #### run main function ### - if not args.plot: - if args.sequential: - for sample in args.masses: - test_trigger_regions(args.indir, sample, args.channel, args.spin, args.year, args.deltaR) - else: - pool = multiprocessing.Pool(processes=6) - pool.starmap(test_trigger_regions, - zip(it.repeat(args.indir), args.masses, it.repeat(args.channel), - it.repeat(args.spin), it.repeat(args.year), it.repeat(args.deltaR))) - - ########################### - - sum_stats, err_sum_stats = ([] for _ in range(2)) - contam1, contam2, contam1_errors, contam2_errors = ([] for _ in range(4)) - from_directory = os.path.join(main_dir, args.channel) - for sample in args.masses: - outname = get_outname(sample, args.channel, - (str(x) for x in regcuts), (str(x) for x in ptcuts), - str(met_turnon), args.bigtau) - with open(outname, "rb") as f: - ahistos = pickle.load(f) - - # write csv header, one per category - out_counts = [] - for cat in categories: - out_counts.append( os.path.join(from_directory, sample, cat, 'counts') ) - utils.create_single_dir(out_counts[-1]) - with open(os.path.join(out_counts[-1], 'table.csv'), 'w') as f: - reader = csv.writer(f, delimiter=',', quotechar='|') - header_row = ['Region'] - header_row.extend(htypes) - reader.writerow(header_row) - - # plot histograms and fill CSV with histogram integrals - c_legacy_trg, c_met_trg, c_tau_trg = ({} for _ in range(3)) - - acounts = rec_dd() - for reg in regions: - for key,cut in ahistos.items(): - acounts[key][reg] = round(ahistos[key][reg]["baseline"].values().sum(), 2) - - # append to table, one line per region - with open(os.path.join(out_counts[categories.index(cat)], 'table.csv'), 'a') as f: - reader = csv.writer(f, delimiter=',', quotechar='|') - row = [reg] - row.extend([acounts[k][reg] for k in acounts.keys()]) - reader.writerow(row) - - if reg=='legacy': - c_legacy_trg[reg] = acounts["Base"][reg] - c_met_trg[reg] = acounts["NoBaseMET"][reg] - c_tau_trg[reg] = acounts["NoBaseNoMETTau"][reg] - elif reg=='met': - c_legacy_trg[reg] = acounts["BaseNoMET"][reg] - c_met_trg[reg] = acounts["MET"][reg] - c_tau_trg[reg] = acounts["NoBaseNoMETTau"][reg] - elif reg=='tau': - c_legacy_trg[reg] = acounts["BaseNoTau"][reg] - c_met_trg[reg] = acounts["NoBaseMETNoTau"][reg] - c_tau_trg[reg] = acounts["Tau"][reg] - - text = {'mass': sample, - 'out': os.path.join(out_counts[categories.index(cat)], 'diagram.html')} - sq_res = square_diagram(c_legacy_trg, c_met_trg, c_tau_trg, args.channel, - [str(x) for x in ptcuts], text=text, notext=args.notext, - bigtau=args.bigtau) - c1, c2, e1, e2 = sq_res - contam1.append(c1) - contam2.append(c2) - contam1_errors.append(e1) - contam2_errors.append(e2) - - stats_l = [c_legacy_trg['legacy'],c_met_trg['legacy'],c_tau_trg['tau'],c_legacy_trg['tau']] - stats = sum(stats_l) - estats = sum(np.sqrt(stats_l)) - sum_stats.append(stats) - err_sum_stats.append(estats) - - contam1 = [float(x) for x in contam1] - contam2 = [float(x) for x in contam2] - contam1_errors = [float(x) for x in contam1_errors] - contam2_errors = [float(x) for x in contam2_errors] - masses = [float(x) for x in args.masses] - contamination_save('data', '_'.join([str(x) for x in regcuts]) + '_' + args.channel, - contam1, contam2, contam1_errors, contam2_errors, masses) - stats_save('data', '_'.join([str(x) for x in regcuts]) + '_' + args.channel, - sum_stats, err_sum_stats, masses, mode='a') - - if args.copy: - import subprocess - to_directory = os.path.join('/eos/home-b/bfontana/www/TriggerScaleFactors', main_dir) - to_directory = os.path.join(to_directory, args.channel) - - for sample in args.masses: - sample_from = os.path.join(from_directory, sample) - print('Copying: {}\t\t--->\t{}'.format(sample_from, to_directory), flush=True) - subprocess.run(['rsync', '-ah', sample_from, to_directory]) diff --git a/tests/test_trigger_stats.py b/tests/test_trigger_stats.py deleted file mode 100644 index ff69919..0000000 --- a/tests/test_trigger_stats.py +++ /dev/null @@ -1,796 +0,0 @@ -# coding: utf-8 - -_all_ = [ 'test_trigger_stats' ] - -import os -import sys -parent_dir = os.path.abspath(__file__ + 2 * '/..') -sys.path.insert(0, parent_dir) -import argparse -import glob -import multiprocessing -import itertools as it -from collections import defaultdict - -import inclusion -from inclusion import selection -from inclusion.config import main -from inclusion.utils import utils - -import ROOT - -def get_outname(suffix, mode, cut, ext): - pref = {'met': 'MET', 'tau': 'Tau', - 'tau_nomet': 'noMET_and_Tau', 'met_notau': 'MET_and_noTau'} - assert mode in list(pref.keys()) - if ext == 'csv': - s = 'counts_{}_{}_{}/table.csv'.format(suffix, pref[mode], cut) - elif ext == 'root': - utils.create_single_dir('data') - s = 'data/data_{}_{}_{}.{}'.format(suffix, pref[mode], cut, ext) - else: - raise ValueError('The {} extension is not supported.'.format(ext)) - return s - -def set_plot_definitions(): - ROOT.gROOT.SetBatch(ROOT.kTRUE) - ROOT.gStyle.SetOptStat(ROOT.kFALSE) - ret = {'BoxTextSize' : 50, - 'BoxTextFont' : 43, - 'BoxTextColor' : ROOT.kBlack, - 'XTitleSize' : 0.045, - 'YTitleSize' : 0.045, - 'LineWidth' : 2, - 'FrameLineWidth' : 1, - } - return ret - -def plot(mode, hbase, htrg, htrgcut, cut_strs, var, channel, sample, category, directory): - legends = {'met': ['MET', 'MET + cut', 'met'], - 'tau': ['Tau', 'Tau + cut', 'tau'], - 'tau_nomet': ['MET', 'MET + cuts', 'tau_nomet', 'Tau', 'Tau + cuts'], - 'met_notau': ['Tau', 'Tau + cuts', 'met_notau', 'MET', 'MET + cuts']} - - assert mode in list(legends.keys()) - - cat_dir = os.path.join(directory, sample, category) - utils.create_single_dir(cat_dir) - - if isinstance(htrg, (tuple,list)): - plot_three_histos(legends[mode], cut_strs, - hbase, htrg, htrgcut, var, channel, directory, cat_dir) - else: - plot_two_histos(legends[mode], cut_strs, - hbase, htrg, htrgcut, var, channel, directory, cat_dir) - - -def plot_three_histos(legends, cut_strs, hbase, htrg, htrgcut, var, channel, directory, cat_folder): - defs = set_plot_definitions() - - hbase1 = hbase.Clone('hbase1') - htrgcut1 = [ h.Clone('htrgcut1_' + str(ih)) for ih,h in enumerate(htrgcut) ] - - hbase2 = hbase.Clone('hbase2') - htrg2 = [ h.Clone('htrg2_' + str(ih)) for ih,h in enumerate(htrg) ] - htrgcut2 = [ h.Clone('htrg2_' + str(ih)) for ih,h in enumerate(htrgcut) ] - - c1 = ROOT.TCanvas('c1', '', 600, 400) - c1.cd() - - # Absolute shapes - max_base = hbase1.GetMaximum() + (hbase1.GetMaximum()-hbase1.GetMinimum())/5. - max_cut = max(htrgcut1[0].GetMaximum(),htrgcut1[1].GetMaximum()) + (htrgcut1[0].GetMaximum()-htrgcut1[0].GetMinimum())/5. - htrgcut1[0].SetMaximum( max(max_base, max_cut) ) - - htrgcut1[1].GetXaxis().SetTitleSize(defs['XTitleSize']); - htrgcut1[1].GetXaxis().SetTitle(var + ' [GeV]'); - htrgcut1[1].GetYaxis().SetTitleSize(defs['YTitleSize']); - htrgcut1[1].GetYaxis().SetTitle('a. u.'); - htrgcut1[1].SetLineWidth(defs['LineWidth']); - htrgcut1[1].SetLineColor(8); - - hbase1.SetLineWidth(1); - hbase1.SetLineColor(1); - htrgcut1[0].SetLineWidth(1); - htrgcut1[0].SetLineColor(1); - htrgcut1[1].SetLineWidth(1); - htrgcut1[1].SetLineColor(1); - - htrgcut1[1].Add(hbase) - htrgcut1[1].Add(htrgcut1[0]) - htrgcut1[1].SetFillColor(8) - htrgcut1[1].Draw('hist') - htrgcut1[0].Add(hbase) - htrgcut1[0].SetFillColor(2) - htrgcut1[0].Draw('histsame') - hbase1.SetFillColor(4) - hbase1.Draw('histsame') - - leg1 = ROOT.TLegend(0.65, 0.75, 0.90, 0.9) - leg1.SetNColumns(1) - leg1.SetFillStyle(0) - leg1.SetBorderSize(0) - leg1.SetTextFont(43) - leg1.SetTextSize(10) - leg1.AddEntry(htrgcut1[1], legends[3], 'F') - leg1.AddEntry(htrgcut1[0], legends[0], 'F') - leg1.AddEntry(hbase1, '+'.join(triggers[channel]), 'F') - leg1.Draw('same') - - c1.Update(); - for ext in ('png', 'pdf'): - c1.SaveAs( os.path.join(cat_folder, legends[2] + '_abs_' + var + '_' + cut_strs + '.' + ext) ) - c1.Close() - - # Normalized shapes - c2 = ROOT.TCanvas('c2', '', 600, 400) - c2.cd() - try: - for h in htrg2: - h.Scale(1/h.Integral()) - except ZeroDivisionError: - pass - try: - hbase2.Scale(1/hbase2.Integral()) - except ZeroDivisionError: - pass - try: - for h in htrgcut2: - h.Scale(1/h.Integral()) - except ZeroDivisionError: - pass - - max_base2 = hbase2.GetMaximum() + (hbase2.GetMaximum()-hbase2.GetMinimum())/5. - max_cut2 = (max(htrgcut2[0].GetMaximum(),htrgcut2[1].GetMaximum()) + - max((htrgcut2[0].GetMaximum()-htrgcut2[0].GetMinimum())/5., (htrgcut2[1].GetMaximum()-htrgcut2[1].GetMinimum())/5.)) - hbase2.SetMaximum( max(max_base2, max_cut2) ) - - hbase2.GetXaxis().SetTitleSize(defs['XTitleSize']); - hbase2.GetXaxis().SetTitle(var + ' [GeV]'); - hbase2.GetYaxis().SetTitleSize(defs['YTitleSize']); - hbase2.GetYaxis().SetTitle('Normalized to 1'); - hbase2.SetLineWidth(defs['LineWidth']); - hbase2.SetLineColor(4); - htrgcut2[0].SetLineWidth(defs['LineWidth']); - htrgcut2[0].SetLineColor(2); - htrgcut2[1].SetLineWidth(defs['LineWidth']); - htrgcut2[1].SetLineColor(8); - - #htrg2.Draw('hist') - hbase2.Draw('hist') - htrgcut2[0].Draw('histsame') - htrgcut2[1].Draw('histsame') - - leg2 = ROOT.TLegend(0.69, 0.77, 0.90, 0.9) - leg2.SetNColumns(1) - leg2.SetFillStyle(0) - leg2.SetBorderSize(0) - leg2.SetTextFont(43) - leg2.SetTextSize(10) - #leg2.AddEntry(htrg2, legends[0]) - leg2.AddEntry(htrgcut2[0], legends[1]) - leg2.AddEntry(htrgcut2[1], legends[4]) - leg2.AddEntry(hbase2, '+\n'.join(triggers[channel])) - leg2.Draw('same') - - c2.Update(); - for ext in ('png', 'pdf'): - c2.SaveAs( os.path.join(cat_folder, legends[2] + '_norm_' + var + '_' + cut_strs + '.' + ext) ) - c2.Close() - -def plot_two_histos(legends, cut_strs, hbase, htrg, htrgcut, var, channel, directory, cat_folder): - defs = set_plot_definitions() - - hbase1 = hbase.Clone('hbase1') - htrgcut1 = htrgcut.Clone('htrgcut1') - - htrg2 = htrg.Clone('htrg2') - hbase2 = hbase.Clone('hbase2') - htrgcut2 = htrgcut.Clone('htrgcut2') - - c1 = ROOT.TCanvas('c1', '', 600, 400) - c1.cd() - - # Absolute shapes - max_base = hbase1.GetMaximum() + (hbase1.GetMaximum()-hbase1.GetMinimum())/5. - max_cut = htrgcut1.GetMaximum() + (htrgcut1.GetMaximum()-htrgcut1.GetMinimum())/5. - htrgcut1.SetMaximum( max(max_base, max_cut) ) - - htrgcut1.GetXaxis().SetTitleSize(defs['XTitleSize']); - htrgcut1.GetXaxis().SetTitle(var + ' [GeV]'); - htrgcut1.GetYaxis().SetTitleSize(defs['YTitleSize']); - htrgcut1.GetYaxis().SetTitle('a. u.'); - htrgcut1.SetLineWidth(defs['LineWidth']); - htrgcut1.SetLineColor(8); - - hbase1.SetLineWidth(2); - hbase1.SetLineColor(4); - htrgcut1.SetLineWidth(2); - htrgcut1.SetLineColor(2); - - #htrg.Draw('hist') - htrgcut1.Add(hbase) - htrgcut1.SetFillColor(2) - htrgcut1.Draw('hist') - hbase1.SetFillColor(4) - hbase1.Draw('histsame') - - leg1 = ROOT.TLegend(0.69, 0.77, 0.90, 0.9) - leg1.SetNColumns(1) - leg1.SetFillStyle(0) - leg1.SetBorderSize(0) - leg1.SetTextFont(43) - leg1.SetTextSize(10) - leg1.AddEntry(htrgcut1, legends[0]) - leg1.AddEntry(hbase1, '+'.join(triggers[channel])) - leg1.Draw('same') - - c1.Update(); - for ext in ('png', 'pdf'): - c1.SaveAs( os.path.join(cat_folder, legends[2] + '_abs_' + var + '_' + cut_strs + '.' + ext) ) - c1.Close() - - # Normalized shapes - c2 = ROOT.TCanvas('c2', '', 600, 400) - c2.cd() - try: - htrg2.Scale(1/htrg2.Integral()) - except ZeroDivisionError: - pass - try: - hbase2.Scale(1/hbase2.Integral()) - except ZeroDivisionError: - pass - try: - htrgcut2.Scale(1/htrgcut2.Integral()) - except ZeroDivisionError: - pass - - max_met2 = htrg2.GetMaximum() + (htrg2.GetMaximum()-htrg2.GetMinimum())/5. - max_base2 = hbase2.GetMaximum() + (hbase2.GetMaximum()-hbase2.GetMinimum())/5. - max_cut2 = htrgcut2.GetMaximum() + (htrgcut2.GetMaximum()-htrgcut2.GetMinimum())/5. - htrg2.SetMaximum( max(max_met2, max_base2, max_cut2) ) - - htrg2.GetXaxis().SetTitleSize(defs['XTitleSize']); - htrg2.GetXaxis().SetTitle(var + ' [GeV]'); - htrg2.GetYaxis().SetTitleSize(defs['YTitleSize']); - htrg2.GetYaxis().SetTitle('Normalized to 1'); - htrg2.SetLineWidth(defs['LineWidth']); - htrg2.SetLineColor(8); - - hbase2.SetLineWidth(2); - hbase2.SetLineColor(4); - htrgcut2.SetLineWidth(2); - htrgcut2.SetLineColor(2); - - htrg2.Draw('hist') - hbase2.Draw('histsame') - htrgcut2.Draw('histsame') - - leg2 = ROOT.TLegend(0.69, 0.77, 0.90, 0.9) - leg2.SetNColumns(1) - leg2.SetFillStyle(0) - leg2.SetBorderSize(0) - leg2.SetTextFont(43) - leg2.SetTextSize(10) - leg2.AddEntry(htrg2, legends[0]) - leg2.AddEntry(htrgcut2, legends[1]) - leg2.AddEntry(hbase2, '+\n'.join(triggers[channel])) - leg2.Draw('same') - - c2.Update(); - for ext in ('png', 'pdf'): - c2.SaveAs( os.path.join(cat_folder, legends[2] + '_norm_' + var + '_' + cut_strs + '.' + ext) ) - c2.Close() - - -def plot2D(mode, hbase, htrg, htrgcut, cut_strs, two_vars, channel, sample, category, directory): - legends = {'met': ['MET', 'MET + cut', 'met'], - 'tau': ['Tau', 'Tau + cut', 'tau'], - 'met_tau': ['MET', 'MET + cuts', 'met_and_tau', 'Tau', 'Tau + cuts']} - - #htrg1 = htrg.Clone('htrg1') - hbase1 = hbase.Clone('hbase1') - htrgcut1 = htrgcut.Clone('htrgcut1') - - defs = set_plot_definitions() - c = ROOT.TCanvas('c', '', 600, 400) - - c.cd() - pad1 = ROOT.TPad('pad1', 'pad1', 0., 0., 0.5, 1.) - pad1.SetFrameLineWidth(defs['FrameLineWidth']) - pad1.SetLeftMargin(0.15); - pad1.SetRightMargin(0.0); - pad1.SetBottomMargin(0.08); - pad1.SetTopMargin(0.055); - pad1.Draw() - pad1.cd() - - hbase.GetXaxis().SetTitle(''); - hbase.GetYaxis().SetTitle(''); - try: - hbase.Scale(1/hbase.Integral()) - except ZeroDivisionError: - pass - hbase.Draw('colz'); - - # c.cd() - # pad2 = ROOT.TPad('pad2', 'pad2', 0.333, 0.0, 0.665, 1.0) - # pad2.SetFrameLineWidth(defs['FrameLineWidth']) - # pad2.SetLeftMargin(0.0); - # pad2.SetRightMargin(0.0); - # pad2.SetBottomMargin(0.08); - # pad2.SetTopMargin(0.055); - # pad2.Draw() - # pad2.cd() - - # htrg1.GetXaxis().SetTitle('') - # htrg1.GetYaxis().SetTitle(two_vars[1]) - # htrgcut1.GetYaxis().SetTitleSize(0.045) - # htrgcut1.GetYaxis().SetTitle(two_vars[1]) - # try: - # htrg1.Scale(1/htrg1.Integral()) - # except ZeroDivisionError: - # pass - # htrg1.Draw('colz'); - - c.cd() - pad3 = ROOT.TPad('pad3', 'pad3', 0.5, 0.0, 1.0, 1.0) - pad3.SetFrameLineWidth(defs['FrameLineWidth']) - pad3.SetLeftMargin(0.0); - pad3.SetRightMargin(0.15); - pad3.SetBottomMargin(0.08); - pad3.SetTopMargin(0.055); - pad3.Draw() - pad3.cd() - - htrgcut1.GetXaxis().SetTitle(two_vars[0]) - htrgcut1.GetXaxis().SetTitleSize(0.045) - try: - htrgcut1.Scale(1/htrgcut1.Integral()) - except ZeroDivisionError: - pass - htrgcut1.Draw('colz same') - - cat_folder = os.path.join(directory, sample, category) - utils.create_single_dir(cat_folder) - c.Update(); - for ext in ('png', 'pdf'): - c.SaveAs( os.path.join(cat_folder, legends[mode][2] + '_' + '_VS_'.join(two_vars) + '_' + cut_strs + '.' + ext) ) - c.Close() - -def count(mode, hbase, htrg, htrgcut, cut_strings, var, channel, sample, category, directory): - cat_folder = os.path.join(directory, sample, category) - titles = {'met': ['MET', 'MET + cut', 'Trigger baseline (no MET)', 'Fraction [%]: {[MET + Cut] / [Trigger baseline]} + 1\n'], - 'tau': ['Tau', 'Tau + cut', 'Trigger baseline (no Tau)', 'Fraction [%]: {[Tau + Cut] / [Trigger baseline]} + 1\n'], - 'tau_nomet': ['MET + Tau', 'MET + Tau + cut', 'Trigger baseline (no MET + Tau)', 'Fraction [%]: {[MET + Tau + Cut] / [Trigger baseline]} + 1\n'], - 'met_notau': ['Tau + MET', 'Tau + MET + cut', 'Trigger baseline (MET + no Tau)', 'Fraction [%]: {[MET + Tau + Cut] / [Trigger baseline]} + 1\n'],} - - def calc_frac(c1, c2): - try: - frac = c2 / (c1 + c2) - except ZeroDivisionError: - frac = 0 - return frac * 100 - - name_met = os.path.join(cat_folder, - get_outname(suffix=var, mode=mode, cut=cut_strings, ext='csv')) - utils.create_single_dir(os.path.dirname(name_met)) - with open(name_met, 'w') as f: - f.write(','.join(('Bin label', titles[mode][0], titles[mode][1], titles[mode][2], titles[mode][3]))) - for ibin in range(1, htrg.GetNbinsX()+1): - label = str(round(htrg.GetXaxis().GetBinLowEdge(ibin),2)) + ' / ' + str(round(htrg.GetXaxis().GetBinLowEdge(ibin+1),2)) - cmet = htrg.GetBinContent(ibin) - cnomet = hbase.GetBinContent(ibin) - cmetcut = htrgcut.GetBinContent(ibin) - assert cmet >= cmetcut - - frac = calc_frac(cnomet, cmetcut) - f.write(','.join((label, str(round(cmet,2)), str(round(cmetcut,2)), str(round(cnomet,2)), str(round(frac,2)))) + '\n') - - totmet = htrg.Integral(0,htrg.GetNbinsX()+1) - totnomet = hbase.Integral(0,hbase.GetNbinsX()+1) - totmetcut = htrgcut.Integral(0,htrgcut.GetNbinsX()+1) - - totfrac = calc_frac(totnomet, totmetcut) - f.write(','.join(('Total', str(round(totmet,2)), str(round(totmetcut,2)), str(round(totnomet,2)), str(round(totfrac,2)))) + '\n') - return totfrac - -def counts_total(mode, totarr, channel, category, directory): - titles = {'met': ['MET', 'MET + cut', 'Trigger baseline (no MET)', 'Fraction [%]: {[MET + Cut] / [Trigger baseline]} + 1\n'], - 'tau': ['Tau', 'Tau + cut', 'Trigger baseline (no Tau)', 'Fraction [%]: {[Tau + Cut] / [Trigger baseline]} + 1\n'], - 'tau_nomet': ['MET + Tau', 'MET + Tau + cut', 'Trigger baseline (no MET + Tau)', 'Fraction [%]: {[MET + Tau + Cut] / [Trigger baseline]} + 1\n'], - 'met_notau': ['Tau + MET', 'Tau + MET + cut', 'Trigger baseline (no Tau + MET)', 'Fraction [%]: {[MET + Tau + Cut] / [Trigger baseline]} + 1\n'],} - - name = os.path.join(directory, 'counts_total_' + category + '_' + mode, 'table.csv') - utils.create_single_dir(os.path.dirname(name)) - with open(name, 'w') as f: - f.write(','.join(('Sample', 'Fraction')) + '\n') - for sample, frac in totarr: - f.write(','.join((sample, str(round(frac,3)))) + '\n') - -def test_triger_stats(indir, sample, channel, plot_only, cut_strings): - outname = get_outname(suffix=sample+'_'+channel, mode='met', - cut=cut_strings['met_tau'], ext='root') - - if channel == 'etau' or channel == 'mutau': - iso1 = (24, 0, 8) - elif channel == 'tautau': - iso1 = binning['dau2_iso'] - binning.update({'HHKin_mass': (20, float(sample)-300, float(sample)+300), - 'dau1_iso': iso1}) - - full_sample = 'GluGluToBulkGravitonToHHTo2B2Tau_M-' + sample + '_' - - t_in = ROOT.TChain('HTauTauTree') - glob_files = glob.glob( os.path.join(indir, full_sample, 'output_*.root') ) - for f in glob_files: - t_in.Add(f) - - hBaseline = defaultdict(dict) - hMET, hMETWithCut = (defaultdict(dict) for _ in range(2)) - hTau, hTauWithCut = (defaultdict(dict) for _ in range(2)) - hTauNoMET, hTauNoMETWithCut = (defaultdict(dict) for _ in range(2)) - hMETNoTau, hMETNoTauWithCut = (defaultdict(dict) for _ in range(2)) - hOR, hORWithCut = (defaultdict(dict) for _ in range(2)) - for v in tuple(variables): - for cat in categories: - suff = lambda x : x + '_' + v + '_' + cat - hopt = ('', *binning[v]) - hBaseline[v][cat] = ROOT.TH1D(suff('hBaseline'), *hopt) - hMET[v][cat] = ROOT.TH1D(suff('hMET'), *hopt) - hMETWithCut[v][cat] = ROOT.TH1D(suff('hMETWithCut'), *hopt) - hTau[v][cat] = ROOT.TH1D(suff('hTau'), *hopt) - hTauWithCut[v][cat] = ROOT.TH1D(suff('hTauWithCut'), *hopt) - hTauNoMET[v][cat] = ROOT.TH1D(suff('hTauNoMET'), *hopt) - hTauNoMETWithCut[v][cat] = ROOT.TH1D(suff('hTauNoMETWithCut'), *hopt) - hMETNoTau[v][cat] = ROOT.TH1D(suff('hMETNoTau'), *hopt) - hMETNoTauWithCut[v][cat] = ROOT.TH1D(suff('hMETNoTauWithCut'), *hopt) - hOR[v][cat] = ROOT.TH1D(suff('hOR'), *hopt) - hORWithCut[v][cat] = ROOT.TH1D(suff('hORWithCut'), *hopt) - - hBaseline_2D = defaultdict(dict) - hMET_2D, hMETWithCut_2D = (defaultdict(dict) for _ in range(2)) - hTau_2D, hTauWithCut_2D = (defaultdict(dict) for _ in range(2)) - hOR_2D, hORWithCut_2D = (defaultdict(dict) for _ in range(2)) - for v in variables_2D: - for cat in categories: - suff = lambda x: x + '_' + '_'.join(v) + '_' + cat - hopt = ('', *binning[v[0]], *binning[v[1]]) - hBaseline_2D[v][cat] = ROOT.TH2D(suff('hBaseline_2D'), *hopt) - hMET_2D[v][cat] = ROOT.TH2D(suff('hMET_2D'), *hopt) - hMETWithCut_2D[v][cat] = ROOT.TH2D(suff('hMETWithCut_2D'), *hopt) - hTau_2D[v][cat] = ROOT.TH2D(suff('hTau_2D'), *hopt) - hTauWithCut_2D[v][cat] = ROOT.TH2D(suff('hTauWithCut_2D'), *hopt) - hOR_2D[v][cat] = ROOT.TH2D(suff('hOR_2D'), *hopt) - hORWithCut_2D[v][cat] = ROOT.TH2D(suff('hORWithCut_2D'), *hopt) - - t_in.SetBranchStatus('*', 0) - _entries = ('triggerbit', 'RunNumber', 'isLeptrigger', - 'bjet1_bID_deepFlavor', 'bjet2_bID_deepFlavor', 'isBoosted', - 'isVBF', 'VBFjj_mass', 'VBFjj_deltaEta', 'PUReweight', 'lumi', 'IdAndIsoSF_deep_pt', - 'pairType', 'dau1_eleMVAiso', 'dau1_iso', 'dau1_deepTauVsJet', 'dau2_deepTauVsJet', - 'nleps', 'nbjetscand', 'tauH_SVFIT_mass', 'bH_mass_raw',) - _entries += tuple(variables) - for ientry in _entries: - t_in.SetBranchStatus(ientry, 1) - - for entry in t_in: - # this is slow: do it once only - entries = utils.dot_dict({x: getattr(entry, x) for x in _entries}) - sel = selection.EventSelection(entries, isdata=False, configuration=None) - - # mcweight = entries.MC_weight - pureweight = entries.PUReweight - lumi = entries.lumi - idandiso = entries.IdAndIsoSF_deep_pt - - #if utils.is_nan(mcweight) : mcweight=1 - if utils.is_nan(pureweight) : pureweight=1 - if utils.is_nan(lumi) : lumi=1 - if utils.is_nan(idandiso) : idandiso=1 - - evt_weight = pureweight*lumi*idandiso - if utils.is_nan(evt_weight): - evt_weight = 1 - - if utils.is_channel_consistent(channel, entries.pairType): - if not sel.selection_cuts(lepton_veto=True, bjets_cut=True, - standard_mass_cut=True, invert_mass_cut=False): - continue - - for v in variables: - for cat in categories: - if sel.sel_category(cat): - - # passes the OR of the trigger baseline (not including METNoMu120 trigger) - pass_trg = sel.pass_triggers(triggers[channel]) and entries.isLeptrigger - if pass_trg: - hBaseline[v][cat].Fill(entries[v], evt_weight) - - met_cut_expr = entries.metnomu_et > met_cut - tau_cut_expr = ((entries.dau1_pt > tau_cut and args.channel=='tautau') or - (entries.dau2_pt > tau_cut and args.channel!='tautau')) - - # passes the METNoMu120 trigger and does *not* pass the OR of the baseline - if not pass_trg and eval(' '.join(args.custom_cut)): - if sel.pass_triggers(('METNoMu120',)): - hMET[v][cat].Fill(entries[v], evt_weight) - if met_cut_expr: - hMETWithCut[v][cat].Fill(entries[v], evt_weight) - - # passes the IsoTau180 trigger and does *not* pass the OR of the baseline - if sel.pass_triggers(('IsoTau180',)): - hTau[v][cat].Fill(entries[v], evt_weight) - if tau_cut_expr: - hTauWithCut[v][cat].Fill(entries[v], evt_weight) - - # passes the IsoTau180 trigger and does *not* pass the OR of the baseline and METNoMu120 - if sel.pass_triggers(('IsoTau180',)) and not sel.pass_triggers(('METNoMu120',)): - hTauNoMET[v][cat].Fill(entries[v], evt_weight) - if tau_cut_expr: - hTauNoMETWithCut[v][cat].Fill(entries[v], evt_weight) - - # passes the METNoMu120 trigger and does *not* pass the OR of the baseline and IsoTau180 - if sel.pass_triggers(('METNoMu120',)) and not sel.pass_triggers(('IsoTau180',)): - hMETNoTau[v][cat].Fill(entries[v], evt_weight) - if met_cut_expr: - hMETNoTauWithCut[v][cat].Fill(entries[v], evt_weight) - - # passes the METNoMu120 or the IsoTau180 triggers and does *not* pass the OR of the baseline - if sel.pass_triggers(('METNoMu120', 'IsoTau180',)): - hOR[v][cat].Fill(entries[v], evt_weight) - if ((sel.pass_triggers(('METNoMu120',)) and met_cut_expr) or - (sel.pass_triggers(('IsoTau180',)) and tau_cut_expr)): - hORWithCut[v][cat].Fill(entries[v], evt_weight) - - for v in variables_2D: - for cat in categories: - if sel.sel_category(cat): - - # passes the OR of the trigger baseline (not including METNoMu120 trigger) - pass_trg = sel.pass_triggers(triggers[channel]) and entries.isLeptrigger - if pass_trg: - hBaseline_2D[v][cat].Fill(entries[v[0]], entries[v[1]], evt_weight) - - # passes the METNoMu120 trigger and does *not* pass the OR of the baseline - if not pass_trg and eval(' '.join(args.custom_cut)): - if sel.pass_triggers(('METNoMu120',)): - hMET_2D[v][cat].Fill(entries[v[0]], entries[v[1]], evt_weight) - if met_cut_expr: - hMETWithCut_2D[v][cat].Fill(entries[v[0]], entries[v[1]], evt_weight) - - if sel.pass_triggers(('IsoTau180',)): - hTau_2D[v][cat].Fill(entries[v[0]], entries[v[1]], evt_weight) - if tau_cut_expr: - hTauWithCut_2D[v][cat].Fill(entries[v[0]], entries[v[1]], evt_weight) - - if sel.pass_triggers(('METNoMu120', 'IsoTau180',)): - hOR_2D[v][cat].Fill(entries[v[0]], entries[v[1]], evt_weight) - if ((sel.pass_triggers(('METNoMu120',)) and met_cut_expr) or - (sel.pass_triggers(('IsoTau180',)) and tau_cut_expr)): - hORWithCut_2D[v][cat].Fill(entries[v[0]], entries[v[1]], evt_weight) - - - f_out = ROOT.TFile(outname, 'RECREATE') - f_out.cd() - for cat in categories: - for v in variables: - suff = lambda x : x + '_' + v + '_' + cat - hBaseline[v][cat].Write(suff('hBaseline')) - hMET[v][cat].Write(suff('hMET')) - hMETWithCut[v][cat].Write(suff('hMETWithCut')) - hTau[v][cat].Write(suff('hTau')) - hTauWithCut[v][cat].Write(suff('hTauWithCut')) - hTauNoMET[v][cat].Write(suff('hTauNoMET')) - hTauNoMETWithCut[v][cat].Write(suff('hTauNoMETWithCut')) - hMETNoTau[v][cat].Write(suff('hMETNoTau')) - hMETNoTauWithCut[v][cat].Write(suff('hMETNoTauWithCut')) - hOR[v][cat].Write(suff('hOR')) - hORWithCut[v][cat].Write(suff('hORWithCut')) - for v in variables_2D: - suff2 = lambda x: x + '_' + '_'.join(v) + '_' + cat - hBaseline_2D[v][cat].Write(suff2('hBaseline_2D')) - hMET_2D[v][cat].Write(suff2('hMET_2D')) - hMETWithCut_2D[v][cat].Write(suff2('hMETWithCut_2D')) - hTau_2D[v][cat].Write(suff2('hTau_2D')) - hTauWithCut_2D[v][cat].Write(suff2('hTauWithCut_2D')) - hOR_2D[v][cat].Write(suff2('hOR_2D')) - hORWithCut_2D[v][cat].Write(suff2('hORWithCut_2D')) - f_out.Close() - print('Raw histograms saved in {}.'.format(outname), flush=True) - -if __name__ == '__main__': - triggers = {'etau': ('Ele32', 'EleIsoTauCustom'), - 'mutau': ('IsoMu24', 'IsoMuIsoTauCustom'), - 'tautau': ('IsoDoubleTauCustom',)} - binning = {'metnomu_et': (20, 0, 450), - 'dau1_pt': (30, 0, 450), - 'dau1_eta': (20, -2.5, 2.5), - 'dau2_iso': (20, 0.88, 1.01), - 'dau2_pt': (30, 0, 350), - 'dau2_eta': (20, -2.5, 2.5), - 'ditau_deltaR': (30, 0.3, 1.3), - 'dib_deltaR': (25, 0, 2.5), - 'bH_pt': (20, 70, 600), - 'bH_mass': (30, 0, 280), - 'tauH_mass': (30, 0, 170), - 'tauH_pt': (30, 0, 500), - 'tauH_SVFIT_mass': (30, 0, 250), - 'tauH_SVFIT_pt': (20, 200, 650), - 'bjet1_pt': (25, 10, 600), - 'bjet2_pt': (25, 10, 550), - 'bjet1_eta': (20, -2.5, 2.5), - 'bjet2_eta': (20, -2.5, 2.5), - } - variables = tuple(binning.keys()) + ('HHKin_mass', 'dau1_iso') - #variables = tuple(('dau1_pt', 'dau2_pt', 'metnomu_et', 'HHKin_mass', 'dau1_iso', 'dau2_iso')) - variables_2D = (('dau1_pt', 'dau2_pt'),)#('dau1_iso', 'dau2_iso')) - - #categories = ('baseline', 's1b1jresolvedMcut', 's2b0jresolvedMcut', 'sboostedLLMcut') - categories = ('baseline',) - met_cut = 200 - tau_cut = 190 - - # Parse input arguments - parser = argparse.ArgumentParser(description='Producer trigger histograms.') - - parser.add_argument('--indir', required=True, type=str, - help='Full path of ROOT input file') - parser.add_argument('--samples', required=True, nargs='+', type=str, - help='Full path of ROOT input file') - parser.add_argument('--channel', required=True, type=str, - help='Select the channel over which the workflow will be run.' ) - parser.add_argument('--plot_only', action='store_true', - help='Reuse previously produced data for quick plot changes.') - parser.add_argument('--plot_2D_only', action='store_true', - help='Reuse previously produced data for quick plot changes.') - parser.add_argument('--custom_cut', nargs='+', default=['True'], - help='Customisable cut provided by the user.') - parser.add_argument('--copy', action='store_true', - help='Do not copy the outputs to EOS at the end.') - parser.add_argument('--sequential', action='store_true', - help='Do not use the multiprocess package.') - args = utils.parse_args(parser) - - main_dir = 'TriggerStudy_MET'+str(met_cut)+'_SingleTau'+str(tau_cut) - cut_expr = '' - if '_'.join(args.custom_cut) != 'True': - cut_expr = '_CUT_' + '_'.join(args.custom_cut) - cut_expr = cut_expr.replace('.','_').replace(' ','_').replace('>','GT').replace('<','ST') - main_dir += cut_expr - - cut_strings = {'met': 'met' + str(met_cut) + cut_expr, - 'tau': 'tau' + str(tau_cut) + cut_expr, - 'met_tau': 'met' + str(met_cut) + '_tau' + str(tau_cut) + cut_expr} - - #### run major function ### - if args.sequential: - if not args.plot_only and not args.plot_2D_only: - for sample in args.samples: - test_triger_stats(args.indir, sample, args.channel, args.plot_only, cut_strings) - else: - if not args.plot_only and not args.plot_2D_only: - pool = multiprocessing.Pool(processes=6) - pool.starmap(test_triger_stats, zip(it.repeat(args.indir), args.samples, - it.repeat(args.channel), it.repeat(args.plot_only), - it.repeat(cut_strings))) - ########################### - - from_directory = os.path.join(main_dir, args.channel) - totcounts = {'met': {}, 'tau': {}, 'tau_nomet': {}, 'met_notau': {}} - for cat in categories: - totcounts['met'][cat] = [] - totcounts['tau'][cat] = [] - totcounts['tau_nomet'][cat] = [] - totcounts['met_notau'][cat] = [] - - for sample in args.samples: - outname = get_outname(suffix=sample+'_'+args.channel, mode='met', - cut=cut_strings['met_tau'], ext='root') - f_in = ROOT.TFile(outname, 'READ') - f_in.cd() - for cat in categories: - if not args.plot_2D_only: - for v in variables: - suff = lambda x : x + '_' + v + '_' + cat - - hBaseline = f_in.Get(suff('hBaseline')) - hMET = f_in.Get(suff('hMET')) - hMETWithCut = f_in.Get(suff('hMETWithCut')) - hTau = f_in.Get(suff('hTau')) - hTauWithCut = f_in.Get(suff('hTauWithCut')) - hTauNoMET = f_in.Get(suff('hTauNoMET')) - hTauNoMETWithCut = f_in.Get(suff('hTauNoMETWithCut')) - hMETNoTau = f_in.Get(suff('hMETNoTau')) - hMETNoTauWithCut = f_in.Get(suff('hMETNoTauWithCut')) - hOR = f_in.Get(suff('hOR')) - hORWithCut = f_in.Get(suff('hORWithCut')) - - hBaseline_c = hBaseline.Clone(suff('hBaseline') + '_c') - hMET_c = hMET.Clone(suff('hMET') + '_c') - hMETWithCut_c = hMETWithCut.Clone(suff('hMETWithCut') + '_c') - hTau_c = hTau.Clone(suff('hTau') + '_c') - hTauWithCut_c = hTauWithCut.Clone(suff('hTauWithCut') + '_c') - hTauNoMET_c = hTauNoMET.Clone(suff('hTauNoMET') + '_c') - hTauNoMETWithCut_c = hTauNoMETWithCut.Clone(suff('hTauNoMETWithCut') + '_c') - hMETNoTau_c = hMETNoTau.Clone(suff('hMETNoTau') + '_c') - hMETNoTauWithCut_c = hMETNoTauWithCut.Clone(suff('hMETNoTauWithCut') + '_c') - # hOR_c = hOR.Clone(suff('hOR') + '_c') - # hORWithCut_c = hORWithCut.Clone(suff('hORWithCut') + '_c') - - hOverlayBaseline_c = hBaseline.Clone(suff('hOverlayBaseline_') + '_c') - hOverlayMET_c = hMET.Clone(suff('hOverlayMET_') + '_c') - hOverlayBaseline_c.Add(hOverlayMET_c) - - opt = (v, args.channel, sample, cat, from_directory) - - plot('met', hBaseline, hMET, hMETWithCut, cut_strings['met'], *opt) - plot('tau', hBaseline, hTau, hTauWithCut, cut_strings['tau'], *opt) - plot('tau_nomet', hBaseline, [hMET,hTauNoMET], [hMETWithCut,hTauNoMETWithCut], - cut_strings['met_tau'], *opt) - plot('met_notau', hBaseline, [hTau,hMETNoTau], [hTauWithCut,hMETNoTauWithCut], - cut_strings['met_tau'], *opt) - - c1 = count('met', hBaseline_c, hMET_c, hMETWithCut_c, cut_strings['met'], *opt) - c2 = count('tau', hBaseline_c, hTau_c, hTauWithCut_c, cut_strings['tau'], *opt) - c3 = count('tau_nomet', hBaseline_c, hTauNoMET_c, hTauNoMETWithCut_c, - cut_strings['met_tau'],*opt) - c4 = count('tau_nomet', hOverlayBaseline_c, hTauNoMET_c, hTauNoMETWithCut_c, - cut_strings['met_tau'], *opt) - c5 = count('met_notau', hBaseline_c, hMETNoTau_c, hMETNoTauWithCut_c, - cut_strings['met_tau'],*opt) - c6 = count('met_notau', hOverlayBaseline_c, hMETNoTau_c, hMETNoTauWithCut_c, - cut_strings['met_tau'], *opt) - assert c3 <= c2 - assert c4 <= c2 - assert c4 <= c3 - assert c6 <= c5 - - totcounts['met'][cat].append((sample, c1)) - totcounts['tau'][cat].append((sample, c2)) - totcounts['tau_nomet'][cat].append((sample, c4)) - totcounts['met_notau'][cat].append((sample, c6)) - - for v in variables_2D: - opt_2D = (v, args.channel, sample, cat, from_directory) - hBaseline_2D = f_in.Get('hBaseline_2D_' + '_'.join(v)+'_'+ cat) - hMET_2D = f_in.Get('hMET_2D_' + '_'.join(v)+'_'+ cat) - hMETWithCut_2D = f_in.Get('hMETWithCut_2D_' + '_'.join(v)+'_'+ cat) - plot2D('met', hBaseline_2D, hMET_2D, hMETWithCut_2D, cut_strings['met'], *opt_2D) - hTau_2D = f_in.Get('hTau_2D_' + '_'.join(v)+'_'+ cat) - hTauWithCut_2D = f_in.Get('hTauWithCut_2D_' + '_'.join(v)+'_'+ cat) - plot2D('tau', hBaseline_2D, hTau_2D, hTauWithCut_2D, cut_strings['tau'], *opt_2D) - hOR_2D = f_in.Get('hOR_2D_' + '_'.join(v)+'_'+ cat) - hORWithCut_2D = f_in.Get('hORWithCut_2D_' + '_'.join(v)+'_'+ cat) - plot2D('met_tau', hBaseline_2D, hOR_2D, hORWithCut_2D, cut_strings['met_tau'], *opt_2D) - - f_in.Close() - - for cat in categories: - opt2 = (args.channel, cat, from_directory) - counts_total('met', totcounts['met'][cat], *opt2) - counts_total('tau', totcounts['tau'][cat], *opt2) - counts_total('tau_nomet', totcounts['tau_nomet'][cat], *opt2) - counts_total('met_notau', totcounts['met_notau'][cat], *opt2) - - if args.copy: - import subprocess - to_directory = os.path.join('/eos/user/b/bfontana/www/TriggerScaleFactors', main_dir) - to_directory = os.path.join(to_directory, args.channel) - - for m in ('met', 'tau', 'tau_nomet', 'met_notau'): - for cat in categories: - folder_name = 'counts_total_' + cat + '_' + m - folder_to = os.path.join(to_directory, folder_name) - utils.create_single_dir(folder_to) - counts_files = os.path.join(from_directory, folder_name, 'table.csv') - print('Copying: {}\t\t--->\t{}'.format(counts_files, folder_to), flush=True) - subprocess.run(['rsync', '-ah', counts_files, os.path.join(folder_to, 'table.csv')]) - - for sample in args.samples: - sample_from = os.path.join(from_directory, sample) - print('Copying: {}\t\t--->\t{}'.format(sample_from, to_directory), flush=True) - subprocess.run(['rsync', '-ah', sample_from, to_directory]) - - print('Done.') diff --git a/tests/test_util.py b/tests/test_util.py deleted file mode 100644 index 223f923..0000000 --- a/tests/test_util.py +++ /dev/null @@ -1,10 +0,0 @@ -# coding: utf-8 - -__all__ = ['TestCase'] - -import unittest - -class TestCase(unittest.TestCase): - - def test_dummy(self): - return True diff --git a/tests/trigger_efficiencies_run3.py b/tests/trigger_efficiencies_run3.py new file mode 100644 index 0000000..175c8c0 --- /dev/null +++ b/tests/trigger_efficiencies_run3.py @@ -0,0 +1,456 @@ +# coding: utf-8 + +_all_ = [ 'test_trigger_gains' ] + +import os +import sys +parent_dir = os.path.abspath(__file__ + 2 * '/..') +sys.path.insert(0, parent_dir) + +import json +import argparse +from inclusion.utils import utils +import numpy as np +from collections import defaultdict as dd +import hist +import mplhep as hep +from hist.intervals import clopper_pearson_interval as clop +import pickle +import uproot +import matplotlib.pyplot as plt + + +# import bokeh +# from bokeh.plotting import figure, output_file, save +# from bokeh.models import Whisker +# from bokeh.layouts import gridplot +# from bokeh.io import export_svg, export_png + +tau = '\u03C4' +mu = '\u03BC' +pm = '\u00B1' +ditau = tau+tau + +def get_outname(channel, regcuts, ptcuts, met_turnon, bigtau): + utils.create_single_dir('data') + + name = channel + '_' + name += '_'.join((*regcuts, 'ptcuts', *[str(x) for x in ptcuts], 'turnon', str(met_turnon))) + if bigtau: + name += '_BIGTAU' + name += '.pkl' + + s = 'data/regions_{}'.format(name) + return s + +def pp(chn): + if chn == "tautau": + return ditau + elif chn == "etau": + return "e" + tau + elif chn == "mutau": + return mu + tau + +def rec_dd(): + return dd(rec_dd) + +def set_fig(fig, legend=True): + fig.output_backend = 'svg' + fig.toolbar.logo = None + # if legend: + # fig.legend.click_policy='hide' + # fig.legend.location = 'top_left' + # fig.legend.label_text_font_size = '8pt' + fig.min_border_bottom = 5 + fig.xaxis.visible = True + fig.title.align = "left" + fig.title.text_font_size = "20px" + fig.xaxis.axis_label_text_font_style = "bold" + fig.yaxis.axis_label_text_font_style = "bold" + fig.xaxis.axis_label_text_font_size = "13px" + fig.yaxis.axis_label_text_font_size = "13px" + +def main(args): + vars = [ + # 'dau1_iso', + 'dau1_pt', + # 'dau2_iso', + 'dau2_pt', 'dau1_eta', 'dau2_eta', + 'bjet1_pNet', 'bjet2_pNet', 'bjet1_pt', + 'bjet2_pt', 'bjet1_eta', 'bjet2_eta'] + if args.channels[0] == "etau": + base_extension = "E" + channel_text = r"e$\tau_{h}$" + elif args.channels[0] == "mutau": + base_extension = "Mu" + channel_text = r"$\mu \tau_{h}$" + elif args.channels[0] == "tautau": + base_extension = "Tau" + channel_text = r"$\tau_{h} \tau_{h}$" + with uproot.open("data/regions_preEE_10p20_all.root") as file: + # from matplotlib.colors import ListedColormap + petroff6 = ["#5790fc", "#f89c20", "#e42536", "#964a8b", "#9c9ca1", "#7a21dd"] + + channel_all = file['All_baseline_{}_genHH_mass'.format(args.channels[0])] + channel_base = file['Base{}_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # channel_base_plus_met = file['Base{}ORMET_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + channel_base_plus_tau = file['Base{}ORTauTauJet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # channel_base_plus_Mu50 = file['Base{}ORMu50_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + channel_base_plus_quadJetPNet = file['Base{}OR4JetsPNet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # channel_base_plus_met = file['NoBaseMuTTJet_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_quadJetDeep = file['NoBaseMu4JetsDeepJet_baseline_{}_genHH_mass'.format(args.channels[0])] + # if args.channels[0] == "tautau": + # channel_base_all = file['Base{}ORTauTauJetOR4JetsPNet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # if args.channels[0] == "mutau": + # channel_base_all = file['Base{}ORMETORTauORMu50OR4JetsPNet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # if args.channels[0] == "etau": + # channel_base_all = file['Base{}ORMETORTauOR4JetsPNet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + + # channel_base_plus_Mu50 = file['NoBaseMuMu50_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_Ele28 = file['NoBaseEle28_baseline_{}_genHH_mass'.format(args.channels[0])] + # if args.channels[0] == "tautau": + # channel_all = file['NoBaseMuTauMETORTauORTTJet_baseline_{}_genHH_mass'.format(args.channels[0])] + # base = "DoubleMediumDeepTau" + # last_trig = "TauTauJet" + # if args.channels[0] == "mutau": + # channel_all = file['NoBaseMETORTauORMu50_baseline_{}_genHH_mass'.format(args.channels[0])] + # base = "IsoMu + CrossMuTau" + # last_trig = "Mu50" + # if args.channels[0] == "etau": + # channel_all = file['NoBaseMETORTauOREle28_baseline_{}_genHH_mass'.format(args.channels[0])] + # base = "Ele24 + CrossEleTau" + # last_trig = "Ele28HT" + var_base_tot = channel_base.variances().sum() + # var_all_tot = channel_all.variances().sum() + # err_tot_all = np.sqrt(var_all_tot / (channel_base.values().sum() ** 2) + (channel_all.values().sum() ** 2) * var_base_tot / (channel_base.values().sum() ** 4)) + err_base = np.sqrt( (1/channel_all.values() * channel_base.errors()) ** 2 + (channel_base.values()/(channel_all.values() ** 2) * channel_all.errors()) ** 2 ) + # err_base_plus_met = np.sqrt( (1/channel_all.values() * channel_base_plus_met.errors()) ** 2 + (channel_base_plus_met.values()/(channel_all.values() ** 2) * channel_all.errors()) ** 2 ) + err_base_plus_tau = np.sqrt( (1/channel_all.values() * channel_base_plus_tau.errors()) ** 2 + (channel_base_plus_tau.values()/(channel_all.values() ** 2) * channel_all.errors()) ** 2 ) + err_base_plus_quadJetPNet = np.sqrt( (1/channel_all.values() * channel_base_plus_quadJetPNet.errors()) ** 2 + (channel_base_plus_quadJetPNet.values()/(channel_all.values() ** 2) * channel_all.errors()) ** 2 ) + # err_base_plustau_quadJet = np.sqrt( (1/channel_base.values() * channel_base_plus_quadJetPNet.errors()) ** 2 + (channel_base_plus_quadJetPNet.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_met = np.sqrt( (1/channel_base.values() * channel_base_plus_met.errors()) ** 2 + (channel_base_plus_met.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_quadJetDeep = np.sqrt( (1/channel_base.values() * channel_base_plus_quadJetDeep.errors()) ** 2 + (channel_base_plus_quadJetDeep.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_all = np.sqrt( (1/channel_all.values() * channel_base_all.errors()) ** 2 + (channel_base_all.values()/(channel_all.values() ** 2) * channel_all.errors()) ** 2 ) + + # err_base_plus_Mu50 = np.sqrt( (1/channel_base.values() * channel_base_plus_Mu50.errors()) ** 2 + (channel_base_plus_Mu50.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_Ele28 = np.sqrt( (1/channel_base.values() * channel_base_plus_Ele28.errors()) ** 2 + (channel_base_plus_Ele28.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_all= np.sqrt( (1/channel_base.values() * channel_all.errors()) ** 2 + (channel_all.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + ax = channel_base.axes[0].centers() + edges = channel_base.axes[0].edges() + xerr_r = ax - edges[:-1] + xerr_l = edges[1:] - ax + + + hep.style.use("CMS") + fig, ax1 = plt.subplots() + ax2 = ax1.twinx() + ax2.set_ylabel("Weighted MC events [a.u.]") # we already handled the x-label with ax1 + print("genHH", ax, channel_all.values()) + ax2.bar(ax, height=channel_all.values(), width=xerr_r * 2, color='lightgrey', alpha=0.35, zorder=0, label="HHbbtautau") + + hep.style.use("CMS") + # ax1.errorbar(ax - 15, (channel_base.values() / channel_all.values()) * 100, yerr=err_base*100, xerr=[xerr_r - 15, xerr_l + 15], fmt='o', label="Base", color=petroff6[0], zorder=5) + # ax1.errorbar(ax - 10, (channel_base_plus_met.values() / channel_all.values()) * 100, yerr=err_base_plus_met*100, xerr=[xerr_r -10, xerr_l + 10], fmt='o', label="Base + MET", color=petroff6[1], zorder=5) + # if args.channels[0] == "tautau": + # ax1.errorbar(ax, (channel_base_plus_met.values() / channel_base.values()) * 100, yerr=err_base_plus_met*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + TauTauJet", color=petroff6[2], zorder=5) + + # ax1.errorbar(ax + 10, (channel_base_plus_quadJetDeep.values() / channel_base.values()) * 100, yerr=err_base_plus_quadJetDeep*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + QuadJetDeep", color=petroff6[3], zorder=5) + + # ax1.errorbar(ax + 15, (channel_base_plus_quadJetPNet.values() / channel_base.values()) * 100, yerr=err_base_plus_quadJetPNet*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + QuadJetPNet", color=petroff6[4], zorder=5) + # if args.channels[0] == "mutau": + # ax1.errorbar(ax + 5, (channel_base_plus_Mu50.values() / channel_base.values()) * 100, yerr=err_base_plus_Mu50*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + Mu50", color=petroff6[2], zorder=5) + # ax1.errorbar(ax + 15, (channel_base_plus_quadJetPNet.values() / channel_base.values()) * 100, yerr=err_base_plus_quadJetPNet*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + QuadJetPNet", color=petroff6[4], zorder=5) + # if args.channels[0] == "etau": + ax1.errorbar(ax - 5, channel_base.values(), yerr=channel_base.errors(), xerr=[xerr_r -5 , xerr_l +5], fmt='o', label="Base", color=petroff6[1], zorder=5) + ax1.errorbar(ax + 5, channel_base_plus_quadJetPNet.values(), yerr=channel_base_plus_quadJetPNet.errors(), xerr=[xerr_r + 5, xerr_l - 5], fmt='o', label="Base + QuadJetPNet", color=petroff6[2], zorder=5) + # ax1.errorbar(ax + 15, (channel_base_all.values() / channel_all.values()) * 100, yerr=err_base_all*100, xerr=[xerr_r + 15, xerr_l - 15], fmt='o', label="Base + TauTauJet\n+ QuadJetPNet", color=petroff6[5], zorder=5) + + # ax1.errorbar(ax + 10, (channel_base_all.values() / channel_all.values()) * 100, yerr=err_base_all*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + MET + IsoTau\n+ Mu50 + QuadJetPNet", color=petroff6[4], zorder=5) + # ax1.errorbar(ax + 10, (channel_base_all.values() / channel_all.values()) * 100, yerr=err_base_all*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + MET + IsoTau\n+ QuadJetPNet", color=petroff6[4], zorder=5) + + ax1.text(0.05, 0.94, 'Channel: '+ channel_text, horizontalalignment='left', verticalalignment='bottom', transform=ax1.transAxes, size=21) + # ax1.errorbar(ax + 20, (channel_all.values() / channel_base.values()) * 100, yerr=err_all*100, xerr=[xerr_r + 20, xerr_l - 20], fmt='o', label="Base + MET + IsoTau + {}".format( last_trig), color=petroff6[3]) + ax1.set_xlabel(r"m$_{HH}$ [GeV]") + ax1.set_ylabel("Events/bin [a. u.]") + lines, labels = ax1.get_legend_handles_labels() + lines2, labels2 = ax2.get_legend_handles_labels() + plt.legend(lines + lines2, labels + labels2, loc=4, bbox_to_anchor=(1, 0.1)) + # print("Total Gain: {} +/- {}".format(round(channel_all.values().sum()/channel_base.values().sum() * 100, 2), round(err_tot_all * 100, 2))) + hep.cms.label(lumi="9.8", com="13.6") + plt.savefig("distribution_{}_quadJet_preEE_10p11.png".format(args.channels[0])) + + for i in vars: + channel_all = file['All_baseline_{}_{}'.format(args.channels[0], i)] + channel_base_tautaujet = file['Base{}ORTauTauJetNo4JetsPNet_baseline_{}_{}'.format(base_extension, args.channels[0], i)] + # channel_base_plus_Mu50 = file['Base{}ORMu50_baseline_{}_{}'.format(base_extension, args.channels[0], i)] + channel_only_quadJetPNet = file['NoBase{}ORNoTauTauJet4JetsPNet_baseline_{}_{}'.format(base_extension, args.channels[0], i)] + + var_base_tot = channel_base.variances().sum() + # var_all_tot = channel_all.variances().sum() + # err_tot_all = np.sqrt(var_all_tot / (channel_base.values().sum() ** 2) + (channel_all.values().sum() ** 2) * var_base_tot / (channel_base.values().sum() ** 4)) + err_base_tautaujet = np.sqrt( (1/channel_all.values() * channel_base_tautaujet.errors()) ** 2 + (channel_base_tautaujet.values()/(channel_all.values() ** 2) * channel_all.errors()) ** 2 ) + # err_base_plus_met = np.sqrt( (1/channel_all.values() * channel_base_plus_met.errors()) ** 2 + (channel_base_plus_met.values()/(channel_all.values() ** 2) * channel_all.errors()) ** 2 ) + # err_base_plus_tau = np.sqrt( (1/channel_all.values() * channel_base_plus_tau.errors()) ** 2 + (channel_base_plus_tau.values()/(channel_all.values() ** 2) * channel_all.errors()) ** 2 ) + err_only_quadJetPNet = np.sqrt( (1/channel_all.values() * channel_only_quadJetPNet.errors()) ** 2 + (channel_only_quadJetPNet.values()/(channel_all.values() ** 2) * channel_all.errors()) ** 2 ) + + # err_base_plus_Mu50 = np.sqrt( (1/channel_base.values() * channel_base_plus_Mu50.errors()) ** 2 + (channel_base_plus_Mu50.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_Ele28 = np.sqrt( (1/channel_base.values() * channel_base_plus_Ele28.errors()) ** 2 + (channel_base_plus_Ele28.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_all= np.sqrt( (1/channel_base.values() * channel_all.errors()) ** 2 + (channel_all.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + ax = channel_all.axes[0].centers() + edges = channel_all.axes[0].edges() + xerr_r = ax - edges[:-1] + xerr_l = edges[1:] - ax + print(edges, xerr_r, xerr_l) + + + hep.style.use("CMS") + fig, ax1 = plt.subplots() + ax2 = ax1.twinx() + ax2.set_ylabel("Weighted MC events [a.u.]") # we already handled the x-label with ax1 + print(i, channel_all.values()) + ax2.bar(ax, height=channel_all.values(), width=xerr_r * 2, color='lightgrey', alpha=0.35, zorder=0, label="HHbbtautau") + + hep.style.use("CMS") + # ax1.errorbar(ax - 15, (channel_base.values() / channel_all.values()) * 100, yerr=err_base*100, xerr=[xerr_r - 15, xerr_l + 15], fmt='o', label="Base", color=petroff6[0], zorder=5) + ax1.errorbar(ax - xerr_r * 0.2, channel_base_tautaujet.values(), yerr=channel_base_tautaujet.errors(), xerr=[xerr_r * 0.8 , xerr_l * 1.2], fmt='o', label="Base + TauTauJet", color=petroff6[3], zorder=5) + ax1.errorbar(ax + xerr_r * 0.2, channel_only_quadJetPNet.values(), yerr=channel_only_quadJetPNet.errors(), xerr=[xerr_r * 1.2, xerr_l * 0.8], fmt='o', label="QuadJetPNet", color=petroff6[2], zorder=5) + # ax1.errorbar(ax + 15, (channel_base_all.values() / channel_all.values()) * 100, yerr=err_base_all*100, xerr=[xerr_r + 15, xerr_l - 15], fmt='o', label="Base + TauTauJet\n+ QuadJetPNet", color=petroff6[5], zorder=5) + + # ax1.errorbar(ax + 10, (channel_base_all.values() / channel_all.values()) * 100, yerr=err_base_all*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + MET + IsoTau\n+ Mu50 + QuadJetPNet", color=petroff6[4], zorder=5) + # ax1.errorbar(ax + 10, (channel_base_all.values() / channel_all.values()) * 100, yerr=err_base_all*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + MET + IsoTau\n+ QuadJetPNet", color=petroff6[4], zorder=5) + + ax1.text(0.05, 0.94, 'Channel: '+ channel_text, horizontalalignment='left', verticalalignment='bottom', transform=ax1.transAxes, size=21) + # ax1.errorbar(ax + 20, (channel_all.values() / channel_base.values()) * 100, yerr=err_all*100, xerr=[xerr_r + 20, xerr_l - 20], fmt='o', label="Base + MET + IsoTau + {}".format( last_trig), color=petroff6[3]) + ax1.set_xlabel("{}".format(i)) + ax1.set_ylabel("Events/bin [a. u.]") + lines, labels = ax1.get_legend_handles_labels() + lines2, labels2 = ax2.get_legend_handles_labels() + plt.legend(lines + lines2, labels + labels2, loc=4, bbox_to_anchor=(1, 0.1)) + # print("Total Gain: {} +/- {}".format(round(channel_all.values().sum()/channel_base.values().sum() * 100, 2), round(err_tot_all * 100, 2))) + hep.cms.label(lumi="9.8", com="13.6") + plt.savefig("distribution_{}_quadJet_preEE_10p20_{}.png".format(args.channels[0], i)) + # print(ratio.axes[0]) + return + channels = args.channels + linear_x = [k for k in range(1,2)] + edges_x = [k-0.5 for k in range(1,2)] + [1.5] + ptcuts = {chn: utils.get_ptcuts(chn, args.year) for chn in args.channels} + + nevents, errors = dd(lambda: dd(dict)), dd(lambda: dd(dict)) + ratios, eratios = dd(lambda: dd(dict)), dd(lambda: dd(dict)) + + for adir in main_dir: + dRstr = str(args.deltaR).replace('.', 'p') + if len(args.channels) == 1: + output_name = os.path.join(base_dir, 'trigger_gains_{}_{}_DR{}'.format(args.channels[0], + args.year, dRstr)) + elif len(args.channels) == 2: + output_name = os.path.join(base_dir, 'trigger_gains_{}_{}_{}_DR{}'.format(*args.channels[:2], + args.year, dRstr)) + elif len(args.channels) == 3: + output_name = os.path.join(base_dir, 'trigger_gains_all_{}_DR{}'.format(args.year, dRstr)) + if args.bigtau: + output_name += "_BIGTAU" + output_name += ".html" + output_file(output_name) + print('Saving file {}.'.format(output_name)) + + for chn in channels: + md = adir[chn] + in_base = os.path.join(base_dir, md) + + nevents[md][chn]['base'], nevents[md][chn]['met'], nevents[md][chn]['tau'] = [], [], [] + ratios[md][chn]['two'], ratios[md][chn]['met'], ratios[md][chn]['tau'] = [], [], [] + eratios[md][chn]['two'], eratios[md][chn]['met'], eratios[md][chn]['tau'] = [], [], [] + errors[md][chn] = [] + + outname = get_outname( chn, [str(x) for x in args.region_cuts], + [str(x) for x in ptcuts[chn]], str(args.met_turnon), + args.bigtau) + + print(outname) + with open(outname, "rb") as f: + ahistos = pickle.load(f) + + #all regions summed + sum_base_tot = round(ahistos["Base"]["legacy"]["baseline"].values().sum() + + ahistos["Base"]["tau"]["baseline"].values().sum() + + ahistos["Base"]["met"]["baseline"].values().sum()) + print(ahistos["Base"]["legacy"]["baseline"].values()) + #legacy region + l1 = lambda x : round(x["legacy"]["baseline"].values().sum(), 2) + sum_base = l1(ahistos["Base"]) + sum_vbf = l1(ahistos["VBF"]) + sum_met = l1(ahistos["NoBaseMET"]) + sum_only_tau = l1(ahistos["NoBaseNoMETTau"]) + sum_tau = l1(ahistos["NoBaseTau"]) + sum_basekin = l1(ahistos["LegacyKin"]) + w2_basekin = ahistos["METKin"]["legacy"]["baseline"].variances().sum() + + #MET region + l2 = lambda x : round(x["met"]["baseline"].values().sum(), 2) + sum_metkin = l2(ahistos["METKin"]) + w2_metkin = ahistos["METKin"]["met"]["baseline"].variances().sum() + + #Single Tau region + l3 = lambda x : round(x["tau"]["baseline"].values().sum(), 2) + sum_taukin = l3(ahistos["TauKin"]) + w2_taukin = ahistos["TauKin"]["tau"]["baseline"].variances().sum() + + #hypothetical VBF region + sum_vbfkin = l2(ahistos["VBFKin"]) + l3(ahistos["VBFKin"]) + + nevents[md][chn]['base'].append(sum_basekin) + nevents[md][chn]['met'].append(sum_basekin + sum_metkin) + nevents[md][chn]['tau'].append(sum_basekin + sum_metkin + sum_taukin) + + rat_met_num = sum_basekin + sum_metkin + rat_met_all = rat_met_num / sum_base_tot + + rat_tau_num = sum_basekin + sum_taukin + rat_tau_all = rat_tau_num / sum_base_tot + + rat_all_num = sum_basekin + sum_taukin + sum_metkin + rat_all = rat_all_num / sum_base_tot + + ratios[md][chn]['met'].append(rat_met_all) + ratios[md][chn]['tau'].append(rat_tau_all) + ratios[md][chn]['two'].append(rat_all) + + e_metkin = np.sqrt(w2_metkin) + e_taukin = np.sqrt(w2_taukin) + e_basekin = np.sqrt(w2_basekin) + + e_tau_num = np.sqrt(w2_taukin + w2_basekin) + e_met_num = np.sqrt(w2_metkin + w2_basekin) + e_all_num = np.sqrt(w2_metkin + w2_taukin + w2_basekin) + print(rat_tau_num, rat_met_num, rat_all_num) + + eratios[md][chn]['tau'].append(rat_tau_all * np.sqrt(e_tau_num**2/rat_tau_num**2 + 1/sum_base_tot)) + eratios[md][chn]['met'].append(rat_met_all * np.sqrt(e_met_num**2/rat_met_num**2 + 1/sum_base_tot)) + eratios[md][chn]['two'].append(rat_all * np.sqrt(e_all_num**2/rat_all_num**2 + 1/sum_base_tot)) + errors[md][chn].append(e_all_num) + + json_name = 'data_' + chn + '_' + json_name += ('bigtau' if args.bigtau else 'standard') + '.json' + with open(json_name, 'w', encoding='utf-8') as json_obj: + json_data = {"vals": {chn: nevents[adir[chn]][chn]['tau'] for chn in channels}} + json_data.update({"errs": {chn: errors[adir[chn]][chn] for chn in channels}}) + json.dump(json_data, json_obj, ensure_ascii=False, indent=4) + + opt_points = dict(size=8) + opt_line = dict(width=1.5) + colors = ('green', 'blue', 'red', 'brown') + styles = ('solid', 'dashed', 'dotdash') + legends = {'base': 'Legacy', + 'met': 'MET', 'tau': 'Single Tau', + 'two': 'MET + Single Tau', 'vbf': 'VBF'} + + x_str = [str(k) for k in [0]] + xticks = linear_x[:] + yticks = [x for x in range(0,110,5)] + shift_one = {'met': [-0.20, 0., 0.20], 'tau': [-0.20, -0.05, 0.1], + 'vbf': [-0.10, 0.05, 0.20]} + shift_both = {'met': [-0.20, 0., 0.20], 'two': [-0.20, -0.05, 0.1]} + shift_kin = {'met': [-0.09, 0., 0.20], 'tau': [0.03, -0.05, 0.1], + 'two': [-0.03, 0.05, 0.20], 'vbf': [0.09, 0.1, 0.25]} + + for adir in main_dir: + print(adir) + p_opt = dict(width=800, height=400, x_axis_label='x', y_axis_label='y') + p1 = figure(title='Event number (' + pp(channels[0]) + ')', y_axis_type="linear", **p_opt) + p2 = figure(title='Acceptance Gain (' + pp(channels[0]) + ')', **p_opt) if len(channels)==1 else figure(**p_opt) + + p1.yaxis.axis_label = 'Weighted number of events' + p2.yaxis.axis_label = 'Trigger acceptance gain (w.r.t. trigger baseline) [%]' + pics = (p1, p2) + for p in pics: + set_fig(p) + + for ichn,chn in enumerate(channels): + md = adir[chn] + print(md) + + p1.quad(top=nevents[md][chn]["base"], bottom=0, + left=edges_x[:-1], right=edges_x[1:], + legend_label=legends["base"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), + fill_color="dodgerblue", line_color="black") + p1.quad(top=nevents[md][chn]["met"], bottom=nevents[md][chn]["base"], + left=edges_x[:-1], right=edges_x[1:], + legend_label=legends["met"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), + fill_color="green", line_color="black") + p1.quad(top=nevents[md][chn]["tau"], bottom=nevents[md][chn]["met"], + left=edges_x[:-1], right=edges_x[1:], + legend_label=legends["tau"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), + fill_color="red", line_color="black") + print(md) + for itd,td in enumerate(('met', 'tau', 'two')): + p2.circle([x+shift_kin[td][ichn] for x in linear_x], + [(x-1)*100. for x in ratios[md][chn][td]], + color=colors[itd], fill_alpha=1., **opt_points) + p2.line([x+shift_kin[td][ichn] for x in linear_x], + [(x-1)*100. for x in ratios[md][chn][td]], + color=colors[itd], line_dash=styles[ichn], + legend_label=legends[td]+(' ('+pp(chn)+')' if len(channels)>1 else ''), **opt_line) + p2.multi_line( + [(x+shift_kin[td][ichn],x+shift_kin[td][ichn]) for x in linear_x], + [((y-1)*100-(x*50.),(y-1)*100+(x*50.)) for x,y in zip(eratios[md][chn][td],ratios[md][chn][td])], + color=colors[itd], **opt_line) + + p1.legend.location = 'top_right' + p2.legend.location = 'top_left' + for p in pics: + p.xaxis[0].ticker = xticks + p.xgrid[0].ticker = xticks + p.xgrid.grid_line_alpha = 0.2 + p.xgrid.grid_line_color = 'black' + # p.yaxis[0].ticker = yticks + # p.ygrid[0].ticker = yticks + p.ygrid.grid_line_alpha = 0.2 + p.ygrid.grid_line_color = 'black' + + p.xaxis.axis_label = "m(X) [GeV]" + + p.xaxis.major_label_overrides = dict(zip(linear_x,x_str)) + + p.legend.click_policy='hide' + + p.output_backend = 'svg' + #export_svg(p, filename='line_graph.svg') + + g = gridplot([[p] for p in pics]) + print(g) + save(g, title=md) + export_png(g, filename="line_graph_postEE.png") + +if __name__ == '__main__': + desc = "Produce plots of trigger gain VS resonance mass.\n" + desc += "Uses the output of test_trigger_regions.py." + desc += "When running on many channels, one should keep in mind each channel has different pT cuts." + desc += "This might imply moving sub-folders (produced by the previous script) around." + parser = argparse.ArgumentParser(description=desc, formatter_class=argparse.RawTextHelpFormatter) + + # parser.add_argument('--masses', required=True, nargs='+', type=str, + # help='Resonance mass') + parser.add_argument('--channels', required=True, nargs='+', type=str, + choices=('etau', 'mutau', 'tautau'), + help='Select the channel over which the workflow will be run.' ) + parser.add_argument('--year', required=True, type=str, choices=('2016', '2017', '2018', '2022'), + help='Select the year over which the workflow will be run.' ) + parser.add_argument('--deltaR', type=float, default=0.5, help='DeltaR between the two leptons.') + parser.add_argument('--bigtau', action='store_true', + help='Consider a larger single tau region, reducing the ditau one.') + parser.add_argument('--met_turnon', required=False, type=str, default=180, + help='MET trigger turnon cut [GeV].' ) + parser.add_argument('--region_cuts', required=False, type=float, nargs=2, default=(190, 190), + help='High/low regions pT1 and pT2 selection cuts [GeV].' ) + + args = utils.parse_args(parser) + + base_dir = '/t3home/fbilandz/TriggerScaleFactors/' + main_dir = [{"etau": "Region_Spin2_190_190_PT_33_25_35_DR_{}_TURNON_200_190".format(args.deltaR), + "mutau": "mutau_190_190_DR_0.5_PT_25_21_32_TURNON_180", + "tautau": "Region_Spin2_190_190_PT_40_40_DR_{}_TURNON_200_190".format(args.deltaR)}, + ] + + main(args) diff --git a/tests/trigger_gains_refined_run3.py b/tests/trigger_gains_refined_run3.py new file mode 100644 index 0000000..eadda11 --- /dev/null +++ b/tests/trigger_gains_refined_run3.py @@ -0,0 +1,118 @@ +# coding: utf-8 + +_all_ = [ 'test_trigger_gains' ] + +import os +import sys +parent_dir = os.path.abspath(__file__ + 2 * '/..') +sys.path.insert(0, parent_dir) + +import json +import argparse +from inclusion.utils import utils +import numpy as np +from collections import defaultdict as dd +import hist +import mplhep as hep +from hist.intervals import clopper_pearson_interval as clop +import pickle +import uproot +import matplotlib.pyplot as plt + +tau = '\u03C4' +mu = '\u03BC' +pm = '\u00B1' +ditau = tau+tau + +def main(args): + if args.channels[0] == "etau": + base_extension = "E" + channel_text = r"e$\tau_{h}$" + elif args.channels[0] == "mutau": + base_extension = "Mu" + channel_text = r"$\mu \tau_{h}$" + elif args.channels[0] == "tautau": + base_extension = "Tau" + channel_text = r"$\tau_{h} \tau_{h}$" + genHH_mass_baseline = uproot.open("data/regions_postBPix_15p1-no-trigger_all.root")['tautau_genHH_mass'] + genHH_mass_base_tau = uproot.open("data/regions_2023_postBPix_15p35-base-tau_all.root")['tautau_genHH_mass'] + genHH_mass_ditaujet = uproot.open("data/regions_2023_postBPix_15p36-ditaujet_all.root")['tautau_genHH_mass'] + genHH_mass_quadjet = uproot.open("data/regions_2023_postBPix_15p34_all.root")['tautau_genHH_mass'] + genHH_mass_both = uproot.open("data/regions_2023_postBPix_15p33_all.root")['tautau_genHH_mass'] + # ht = uproot.open("data/regions_postBPix_15p11-quadjet-confirm_all.root")['tautau_SoftActivityJetHT'] + petroff6 = ["#5790fc", "#f89c20", "#e42536", "#964a8b", "#9c9ca1", "#7a21dd"] + + err_ditaujet = genHH_mass_ditaujet.errors()/ genHH_mass_base_tau.values() + err_quadjet = genHH_mass_quadjet.errors()/ genHH_mass_base_tau.values() + err_both = genHH_mass_both.errors()/ genHH_mass_base_tau.values() + + ax = genHH_mass_baseline.axes[0].centers() + edges = genHH_mass_baseline.axes[0].edges() + xerr_r = ax - edges[:-1] + xerr_l = edges[1:] - ax + hep.style.use("CMS") + # print("CMS style") + fig, ax1 = plt.subplots() + # ax1.errorbar(ax[0:10], ht.values()[0:10], yerr=ht.errors()[0:10], xerr=[xerr_r[0:10], xerr_l[0:10]], fmt='o', label="SoftActivityJetHT", color=petroff6[2], zorder=5) + # hep.cms.label(lumi="9.96", com="13.6") + # plt.legend() + # ax1.set_xlabel(r"HT [GeV]") + # ax1.set_ylabel("N [a. u.]") + # plt.savefig("ht_postBPix_pf75.png".format(args.channels[0])) + + # return + ax1.set_ylim(-4, 30) + # print("subplots") + ax2 = ax1.twinx() + print("generated additional axis") + ax2.set_ylabel("Weighted MC events [a.u.]") # we already handled the x-label with ax1 + ax2.bar(ax, height=genHH_mass_baseline.values(), width=xerr_r * 2, color=petroff6[4], alpha=0.3, zorder=0, label="HHbbtautau") + # print(channel_base_60.values(), channel_base_70.values(), channel_base_80.values()) + # hep.style.use("CMS") + print(genHH_mass_ditaujet.values()/ genHH_mass_base_tau.values()) + ax1.errorbar(ax - 10, (genHH_mass_ditaujet.values() / genHH_mass_base_tau.values() - 1) * 100, yerr=err_ditaujet*100, xerr=[xerr_r - 10, xerr_l + 10], fmt='o', label="DiTau+Jet", color=petroff6[0], zorder=5) + ax1.errorbar(ax, (genHH_mass_quadjet.values() / genHH_mass_base_tau.values() - 1) * 100, yerr=err_quadjet*100, xerr=[xerr_r, xerr_l], fmt='o', label="QuadJet parking", color=petroff6[2], zorder=5) + ax1.errorbar(ax + 10, (genHH_mass_both.values() / genHH_mass_base_tau.values() - 1) * 100, yerr=err_both*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="QuadJet + DiTau+Jet", color=petroff6[4], zorder=5) + ax1.set_xlabel(r"m$_{HH}$ [GeV]") + ax1.set_ylabel("Gain [%]") + # print("here") + lines, labels = ax1.get_legend_handles_labels() + lines2, labels2 = ax2.get_legend_handles_labels() + plt.legend(lines + lines2, labels + labels2) + # # print("Total Gain: {} +/- {}".format(round(channel_all.values().sum()/channel_base.values().sum() * 100, 2), round(err_tot_all * 100, 2))) + hep.cms.label(lumi="9.7", com="13.6") + plt.savefig("gains_ditaujet_postBPix.png".format(args.channels[0])) + + return + +if __name__ == '__main__': + desc = "Produce plots of trigger gain VS resonance mass.\n" + desc += "Uses the output of setup_regions.py." + desc += "When running on many channels, one should keep in mind each channel has different pT cuts." + desc += "This might imply moving sub-folders (produced by the previous script) around." + parser = argparse.ArgumentParser(description=desc, formatter_class=argparse.RawTextHelpFormatter) + + # parser.add_argument('--masses', required=True, nargs='+', type=str, + # help='Resonance mass') + parser.add_argument('--channels', required=True, nargs='+', type=str, + choices=('etau', 'mutau', 'tautau'), default="tautau", + help='Select the channel over which the workflow will be run.' ) + parser.add_argument('--year', required=True, type=str, choices=('2016', '2017', '2018', '2022', '2023'), + help='Select the year over which the workflow will be run.' ) + parser.add_argument('--deltaR', type=float, default=0.5, help='DeltaR between the two leptons.') + parser.add_argument('--bigtau', action='store_true', + help='Consider a larger single tau region, reducing the ditau one.') + parser.add_argument('--met_turnon', required=False, type=str, default=180, + help='MET trigger turnon cut [GeV].' ) + parser.add_argument('--region_cuts', required=False, type=float, nargs=2, default=(190, 190), + help='High/low regions pT1 and pT2 selection cuts [GeV].' ) + + args = utils.parse_args(parser) + + base_dir = '/t3home/fbilandz/TriggerScaleFactors/' + main_dir = [{"etau": "Region_Spin2_190_190_PT_33_25_35_DR_{}_TURNON_200_190".format(args.deltaR), + "mutau": "mutau_190_190_DR_0.5_PT_25_21_32_TURNON_180", + "tautau": "Region_Spin2_190_190_PT_40_40_DR_{}_TURNON_200_190".format(args.deltaR)}, + ] + + main(args) diff --git a/tests/trigger_gains_run3.py b/tests/trigger_gains_run3.py new file mode 100644 index 0000000..d3634c5 --- /dev/null +++ b/tests/trigger_gains_run3.py @@ -0,0 +1,481 @@ +# coding: utf-8 + +_all_ = [ 'test_trigger_gains' ] + +import os +import sys +parent_dir = os.path.abspath(__file__ + 2 * '/..') +sys.path.insert(0, parent_dir) + +import json +import argparse +from inclusion.utils import utils +import numpy as np +from collections import defaultdict as dd +import hist +import mplhep as hep +from hist.intervals import clopper_pearson_interval as clop +import pickle +import uproot +import matplotlib.pyplot as plt + + +# import bokeh +# from bokeh.plotting import figure, output_file, save +# from bokeh.models import Whisker +# from bokeh.layouts import gridplot +# from bokeh.io import export_svg, export_png + +tau = '\u03C4' +mu = '\u03BC' +pm = '\u00B1' +ditau = tau+tau + +def get_outname(channel, regcuts, ptcuts, met_turnon, bigtau): + utils.create_single_dir('data') + + name = channel + '_' + name += '_'.join((*regcuts, 'ptcuts', *[str(x) for x in ptcuts], 'turnon', str(met_turnon))) + if bigtau: + name += '_BIGTAU' + name += '.pkl' + + s = 'data/regions_{}'.format(name) + return s + +def pp(chn): + if chn == "tautau": + return ditau + elif chn == "etau": + return "e" + tau + elif chn == "mutau": + return mu + tau + +def rec_dd(): + return dd(rec_dd) + +def set_fig(fig, legend=True): + fig.output_backend = 'svg' + fig.toolbar.logo = None + # if legend: + # fig.legend.click_policy='hide' + # fig.legend.location = 'top_left' + # fig.legend.label_text_font_size = '8pt' + fig.min_border_bottom = 5 + fig.xaxis.visible = True + fig.title.align = "left" + fig.title.text_font_size = "15px" + fig.xaxis.axis_label_text_font_style = "bold" + fig.yaxis.axis_label_text_font_style = "bold" + fig.xaxis.axis_label_text_font_size = "13px" + fig.yaxis.axis_label_text_font_size = "13px" + +def main(args): + vars = ['dau1_pt', + 'dau2_pt', 'dau1_eta', 'dau2_eta', + 'dau1_tauIdVSjet', 'dau2_tauIdVSjet', + 'bjet1_pNet', 'bjet2_pNet', 'bjet1_pt', + 'bjet2_pt', 'bjet1_eta', 'bjet2_eta'] + if args.channels[0] == "etau": + base_extension = "E" + channel_text = r"e$\tau_{h}$" + elif args.channels[0] == "mutau": + base_extension = "Mu" + channel_text = r"$\mu \tau_{h}$" + elif args.channels[0] == "tautau": + base_extension = "Tau" + channel_text = r"$\tau_{h} \tau_{h}$" + file70 = uproot.open("data/regions_preEE_11p2_all.root") + file60 = uproot.open("data/regions_preEE_11p3-jet-lower-pt_all.root") + file80 = uproot.open("data/regions_preEE_11p4-jet-higher-pt_all.root") + # petroff6 = ["#5790fc", "#f89c20", "#e42536", "#964a8b", "#9c9ca1", "#7a21dd"] + + # channel_base_60 = file60['Base{}_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # channel_base_70 = file70['Base{}_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # channel_base_80 = file80['Base{}_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # channel_base_plus_ttjet_60 = file60['Base{}ORTauTauJet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # channel_base_plus_ttjet_70 = file70['Base{}ORTauTauJet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # channel_base_plus_ttjet_80 = file80['Base{}ORTauTauJet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # channel_all = file60['Base{}ORTauTauJetOR4JetsPNet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + # err_base_plus_ttjet_60 = np.sqrt(channel_base_plus_ttjet_60.values())/ channel_all.values() + # err_base_plus_ttjet_70 = np.sqrt(channel_base_plus_ttjet_70.values())/ channel_all.values() + # err_base_plus_ttjet_80 = np.sqrt(channel_base_plus_ttjet_80.values())/ channel_all.values() + # ax = channel_base_60.axes[0].centers() + # edges = channel_base_60.axes[0].edges() + # xerr_r = ax - edges[:-1] + # xerr_l = edges[1:] - ax + # hep.style.use("CMS") + # print("CMS style") + # fig, ax1 = plt.subplots() + # ax1.set_ylim(-4, 30) + # print("subplots") + # ax2 = ax1.twinx() + # print("generated additional axis") + # ax2.set_ylabel("Weighted MC events [a.u.]") # we already handled the x-label with ax1 + # ax2.bar(ax, height=channel_all.values(), width=xerr_r * 2, color=petroff6[4], alpha=0.3, zorder=0, label="HHbbtautau") + # print(channel_base_60.values(), channel_base_70.values(), channel_base_80.values()) + # hep.style.use("CMS") + # ax1.errorbar(ax - 10, (channel_base_plus_ttjet_60.values() / channel_base_60.values() - 1) * 100, yerr=err_base_plus_ttjet_60*100, xerr=[xerr_r - 10, xerr_l + 10], fmt='o', label=r"p$_{t}^{jet} > 60$ GeV", color=petroff6[0], zorder=5) + # ax1.errorbar(ax, (channel_base_plus_ttjet_70.values() / channel_base_60.values() - 1) * 100, yerr=err_base_plus_ttjet_70*100, xerr=[xerr_r, xerr_l], fmt='o', label=r"p$_{t}^{jet} > 70$ GeV", color=petroff6[2], zorder=5) + # ax1.errorbar(ax + 10, (channel_base_plus_ttjet_80.values() / channel_base_60.values() - 1) * 100, yerr=err_base_plus_ttjet_80*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label=r"p$_{t}^{jet} > 80$ GeV", color=petroff6[4], zorder=5) + # ax1.set_xlabel(r"m$_{HH}$ [GeV]") + # ax1.set_ylabel("Gain [%]") + # print("here") + # lines, labels = ax1.get_legend_handles_labels() + # lines2, labels2 = ax2.get_legend_handles_labels() + # plt.legend(lines + lines2, labels + labels2) + # # print("Total Gain: {} +/- {}".format(round(channel_all.values().sum()/channel_base.values().sum() * 100, 2), round(err_tot_all * 100, 2))) + # hep.cms.label(lumi="9.8", com="13.6") + # plt.savefig("cutoff_comparison.png".format(args.channels[0])) + with uproot.open("data/regions_preEE_12p11-old-way-pf75_all.root") as file: + # from matplotlib.colors import ListedColormap + petroff6 = ["#5790fc", "#f89c20", "#e42536", "#964a8b", "#9c9ca1", "#7a21dd"] + channel_all = file['Base{}ORTauTauJetOR4JetsPNet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + channel_base = file['Base{}_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + channel_base_plus_ttjet = file['Base{}ORTauTauJet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + channel_base_plus_quadJetPNet = file['Base{}OR4JetsPNet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + all_channel = file['All_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_ttjet = file['NoBaseMuTTJet_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_quadJetDeep = file['NoBaseMu4JetsDeepJet_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_all = file['Base{}ORTauTauJetOR4JetsPNet_baseline_{}_genHH_mass'.format(base_extension, args.channels[0])] + + # channel_base_plus_Mu50 = file['NoBaseMuMu50_baseline_{}'.format(args.channels[0])] + # channel_base_plus_Ele28 = file['NoBaseEle28_baseline_{}'.format(args.channels[0])] + # if args.channels[0] == "tautau": + # channel_all = file['NoBaseMuTauMETORTauORTTJet_baseline_{}'.format(args.channels[0])] + # base = "DoubleMediumDeepTau" + # last_trig = "TauTauJet" + # if args.channels[0] == "mutau": + # channel_all = file['NoBaseMETORTauORMu50_baseline_{}'.format(args.channels[0])] + # base = "IsoMu + CrossMuTau" + # last_trig = "Mu50" + # if args.channels[0] == "etau": + # channel_all = file['NoBaseMETORTauOREle28_baseline_{}'.format(args.channels[0])] + # base = "Ele24 + CrossEleTau" + # last_trig = "Ele28HT" + var_base_tot = channel_base.variances().sum() + # var_all_tot = channel_all.variances().sum() + # err_tot_all = np.sqrt(var_all_tot / (channel_base.values().sum() ** 2) + (channel_all.values().sum() ** 2) * var_base_tot / (channel_base.values().sum() ** 4)) + err_base_plus_ttjet = np.sqrt(channel_base_plus_ttjet.values())/ channel_all.values() + err_base_plus_quadJetPNet = np.sqrt(channel_base_plus_quadJetPNet.values())/ channel_all.values() + # err_base_plus_ttjet = np.sqrt( (1/channel_base.values() * channel_base_plus_ttjet.errors()) ** 2 + (channel_base_plus_ttjet.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_quadJetDeep = np.sqrt( (1/channel_base.values() * channel_base_plus_quadJetDeep.errors()) ** 2 + (channel_base_plus_quadJetDeep.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + err_all = np.sqrt(channel_all.values())/ channel_all.values() + + # err_base_plus_Mu50 = np.sqrt( (1/channel_base.values() * channel_base_plus_Mu50.errors()) ** 2 + (channel_base_plus_Mu50.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_Ele28 = np.sqrt( (1/channel_base.values() * channel_base_plus_Ele28.errors()) ** 2 + (channel_base_plus_Ele28.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_all= np.sqrt( (1/channel_base.values() * channel_all.errors()) ** 2 + (channel_all.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + ax = channel_base.axes[0].centers() + edges = channel_base.axes[0].edges() + xerr_r = ax - edges[:-1] + xerr_l = edges[1:] - ax + print(edges, xerr_r, xerr_l) + print(channel_base.errors()/channel_base.values()) + + + hep.style.use("CMS") + print("CMS style") + fig, ax1 = plt.subplots() + ax1.set_ylim(-5, 40) + print("subplots") + ax2 = ax1.twinx() + print("generated additional axis") + ax2.set_ylabel("Weighted MC events [a.u.]") # we already handled the x-label with ax1 + ax2.bar(ax, height=all_channel.values(), width=xerr_r * 2, color=petroff6[4], alpha=0.3, zorder=0, label="HHbbtautau") + print("Initialized bars") + hep.style.use("CMS") + ax1.errorbar(ax, (channel_base_plus_ttjet.values() / channel_base.values() - 1) * 100, yerr=err_base_plus_ttjet*100, xerr=[xerr_r, xerr_l], fmt='o', label="Base + TauTauJet", color=petroff6[0], zorder=5) + ax1.errorbar(ax, (channel_base_plus_quadJetPNet.values() / channel_base.values() - 1) * 100, yerr=err_base_plus_quadJetPNet*100, xerr=[xerr_r, xerr_l], fmt='o', label="Base + QuadJetPNet", color=petroff6[1], zorder=5) + if args.channels[0] == "tautau": + # ax1.errorbar(ax, (channel_base_plus_ttjet.values() / channel_base.values()) * 100, yerr=err_base_plus_ttjet*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + TauTauJet", color=petroff6[2], zorder=5) + # ax1.errorbar(ax, (channel_base_plus_quadJetPNet.values() / channel_base.values() - 1) * 100, yerr=err_base_plus_quadJetPNet*100, xerr=[xerr_r, xerr_l], fmt='o', label="Base + QuadJetPNet", color=petroff6[1], zorder=5) + # ax1.errorbar(ax + 10, (channel_base_plus_quadJetDeep.values() / channel_base.values()) * 100, yerr=err_base_plus_quadJetDeep*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + QuadJetDeep", color=petroff6[3], zorder=5) + + ax1.errorbar(ax + 10, (channel_all.values() / channel_base.values() - 1) * 100, yerr=err_all*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + TauTauJet + QuadJetPNet", color=petroff6[5], zorder=5) + # if args.channels[0] == "mutau": + # # ax1.errorbar(ax + 5, (channel_base_plus_Mu50.values() / channel_base.values()) * 100, yerr=err_base_plus_Mu50*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + Mu50", color=petroff6[2], zorder=5) + # # ax1.errorbar(ax + 15, (channel_base_plus_quadJetPNet.values() / channel_base.values()) * 100, yerr=err_base_plus_quadJetPNet*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + QuadJetPNet", color=petroff6[4], zorder=5) + # # ax1.errorbar(ax + 10, (channel_all.values() / channel_base.values() - 1) * 100, yerr=err_all*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + TauTauJet + QuadJetPNet", color=petroff6[5], zorder=5) + # if args.channels[0] == "etau": + # ax1.errorbar(ax + 10, (channel_base_plus_quadJetPNet.values() / channel_base.values()) * 100, yerr=err_base_plus_quadJetPNet*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + QuadJetPNet", color=petroff6[4], zorder=5) + # ax1.errorbar(ax + 10, (channel_all.values() / channel_base.values() - 1) * 100, yerr=err_all*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + TauTauJet + QuadJetPNet", color=petroff6[5], zorder=5) + # ax1.errorbar(ax + 20, (channel_all.values() / channel_base.values()) * 100, yerr=err_all*100, xerr=[xerr_r + 20, xerr_l - 20], fmt='o', label="Base + MET + IsoTau + {}".format( last_trig), color=petroff6[3]) + ax1.set_xlabel(r"m$_{HH}$ [GeV]") + ax1.set_ylabel("Gain [%]") + print("here") + lines, labels = ax1.get_legend_handles_labels() + lines2, labels2 = ax2.get_legend_handles_labels() + plt.legend(lines + lines2, labels + labels2) + # print("Total Gain: {} +/- {}".format(round(channel_all.values().sum()/channel_base.values().sum() * 100, 2), round(err_tot_all * 100, 2))) + hep.cms.label(lumi="9.8", com="13.6") + plt.savefig("gains_{}_11p5_ttjet-only_pf75.png".format(args.channels[0])) + # print(ratio.axes[0]) + return + for i in vars: + channel_base_plus_ttjet = file['Base{}ORTauTauJet_baseline_{}_{}'.format(base_extension, args.channels[0], i)] + channel_only_quadJetPNet = file['NoBase{}ORNoTauTauJet4JetsPNet_baseline_{}_{}'.format(base_extension, args.channels[0], i)] + channel_all = file['Base{}ORTauTauJetOR4JetsPNet_baseline_{}_{}'.format(base_extension, args.channels[0], i)] + err_base_plus_ttjet = np.sqrt(channel_base_plus_ttjet.values()) + err_only_quadJetPNet = np.sqrt(channel_only_quadJetPNet.values()) + # err_base_plus_ttjet = np.sqrt( (1/channel_base.values() * channel_base_plus_ttjet.errors()) ** 2 + (channel_base_plus_ttjet.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_quadJetDeep = np.sqrt( (1/channel_base.values() * channel_base_plus_quadJetDeep.errors()) ** 2 + (channel_base_plus_quadJetDeep.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_Mu50 = np.sqrt( (1/channel_base.values() * channel_base_plus_Mu50.errors()) ** 2 + (channel_base_plus_Mu50.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_Ele28 = np.sqrt( (1/channel_base.values() * channel_base_plus_Ele28.errors()) ** 2 + (channel_base_plus_Ele28.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_all= np.sqrt( (1/channel_base.values() * channel_all.errors()) ** 2 + (channel_all.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + ax = channel_all.axes[0].centers() + edges = channel_all.axes[0].edges() + xerr_r = ax - edges[:-1] + xerr_l = edges[1:] - ax + print(edges, xerr_r, xerr_l) + + + hep.style.use("CMS") + fig, ax1 = plt.subplots() + ax2 = ax1.twinx() + ax2.set_ylabel("Weighted MC events [a.u.]") # we already handled the x-label with ax1 + print(i, channel_all.values()) + ax2.bar(ax, height=channel_all.values(), width=xerr_r * 2, color='lightgrey', alpha=0.35, zorder=0, label="HHbbtautau") + + hep.style.use("CMS") + # ax1.errorbar(ax - 15, (channel_base.values() / channel_all.values()) * 100, yerr=err_base*100, xerr=[xerr_r - 15, xerr_l + 15], fmt='o', label="Base", color=petroff6[0], zorder=5) + ax1.errorbar(ax - xerr_r * 0.2, channel_base_plus_ttjet.values(), yerr=err_base_plus_ttjet, xerr=[xerr_r * 0.8 , xerr_l * 1.2], fmt='o', label="Base + TauTauJet", color=petroff6[3], zorder=5) + ax1.errorbar(ax + xerr_r * 0.2, channel_only_quadJetPNet.values(), yerr=err_only_quadJetPNet, xerr=[xerr_r * 1.2, xerr_l * 0.8], fmt='o', label="QuadJetPNet", color=petroff6[2], zorder=5) + # ax1.errorbar(ax + 15, (channel_base_all.values() / channel_all.values()) * 100, yerr=err_base_all*100, xerr=[xerr_r + 15, xerr_l - 15], fmt='o', label="Base + TauTauJet\n+ QuadJetPNet", color=petroff6[5], zorder=5) + + # ax1.errorbar(ax + 10, (channel_base_all.values() / channel_all.values()) * 100, yerr=err_base_all*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + MET + IsoTau\n+ Mu50 + QuadJetPNet", color=petroff6[4], zorder=5) + # ax1.errorbar(ax + 10, (channel_base_all.values() / channel_all.values()) * 100, yerr=err_base_all*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="Base + MET + IsoTau\n+ QuadJetPNet", color=petroff6[4], zorder=5) + + ax1.text(0.05, 0.94, 'Channel: '+ channel_text, horizontalalignment='left', verticalalignment='bottom', transform=ax1.transAxes, size=21) + # ax1.errorbar(ax + 20, (channel_all.values() / channel_base.values()) * 100, yerr=err_all*100, xerr=[xerr_r + 20, xerr_l - 20], fmt='o', label="Base + MET + IsoTau + {}".format( last_trig), color=petroff6[3]) + ax1.set_xlabel("{}".format(i)) + ax1.set_ylabel("Weighted MC events [a.u.]") + lines, labels = ax1.get_legend_handles_labels() + lines2, labels2 = ax2.get_legend_handles_labels() + plt.legend(lines + lines2, labels + labels2, loc=4, bbox_to_anchor=(1, 0.1)) + # print("Total Gain: {} +/- {}".format(round(channel_all.values().sum()/channel_base.values().sum() * 100, 2), round(err_tot_all * 100, 2))) + hep.cms.label(lumi="9.8", com="13.6") + plt.savefig("gains_{}_quadJet_preEE_10p20_withOffline_{}.png".format(args.channels[0], i)) + return + channels = args.channels + linear_x = [k for k in range(1,2)] + edges_x = [k-0.5 for k in range(1,2)] + [1.5] + ptcuts = {chn: utils.get_ptcuts(chn, args.year) for chn in args.channels} + + nevents, errors = dd(lambda: dd(dict)), dd(lambda: dd(dict)) + ratios, eratios = dd(lambda: dd(dict)), dd(lambda: dd(dict)) + + for adir in main_dir: + dRstr = str(args.deltaR).replace('.', 'p') + if len(args.channels) == 1: + output_name = os.path.join(base_dir, 'trigger_gains_{}_{}_DR{}'.format(args.channels[0], + args.year, dRstr)) + elif len(args.channels) == 2: + output_name = os.path.join(base_dir, 'trigger_gains_{}_{}_{}_DR{}'.format(*args.channels[:2], + args.year, dRstr)) + elif len(args.channels) == 3: + output_name = os.path.join(base_dir, 'trigger_gains_all_{}_DR{}'.format(args.year, dRstr)) + if args.bigtau: + output_name += "_BIGTAU" + output_name += ".html" + output_file(output_name) + print('Saving file {}.'.format(output_name)) + + for chn in channels: + md = adir[chn] + in_base = os.path.join(base_dir, md) + + nevents[md][chn]['base'], nevents[md][chn]['met'], nevents[md][chn]['tau'] = [], [], [] + ratios[md][chn]['two'], ratios[md][chn]['met'], ratios[md][chn]['tau'] = [], [], [] + eratios[md][chn]['two'], eratios[md][chn]['met'], eratios[md][chn]['tau'] = [], [], [] + errors[md][chn] = [] + + outname = get_outname( chn, [str(x) for x in args.region_cuts], + [str(x) for x in ptcuts[chn]], str(args.met_turnon), + args.bigtau) + + print(outname) + with open(outname, "rb") as f: + ahistos = pickle.load(f) + + #all regions summed + sum_base_tot = round(ahistos["Base"]["legacy"]["baseline"].values().sum() + + ahistos["Base"]["tau"]["baseline"].values().sum() + + ahistos["Base"]["met"]["baseline"].values().sum()) + print(ahistos["Base"]["legacy"]["baseline"].values()) + #legacy region + l1 = lambda x : round(x["legacy"]["baseline"].values().sum(), 2) + sum_base = l1(ahistos["Base"]) + sum_vbf = l1(ahistos["VBF"]) + sum_met = l1(ahistos["NoBaseMET"]) + sum_only_tau = l1(ahistos["NoBaseNoMETTau"]) + sum_tau = l1(ahistos["NoBaseTau"]) + sum_basekin = l1(ahistos["LegacyKin"]) + w2_basekin = ahistos["METKin"]["legacy"]["baseline"].variances().sum() + + #MET region + l2 = lambda x : round(x["met"]["baseline"].values().sum(), 2) + sum_metkin = l2(ahistos["METKin"]) + w2_metkin = ahistos["METKin"]["met"]["baseline"].variances().sum() + + #Single Tau region + l3 = lambda x : round(x["tau"]["baseline"].values().sum(), 2) + sum_taukin = l3(ahistos["TauKin"]) + w2_taukin = ahistos["TauKin"]["tau"]["baseline"].variances().sum() + + #hypothetical VBF region + sum_vbfkin = l2(ahistos["VBFKin"]) + l3(ahistos["VBFKin"]) + + nevents[md][chn]['base'].append(sum_basekin) + nevents[md][chn]['met'].append(sum_basekin + sum_metkin) + nevents[md][chn]['tau'].append(sum_basekin + sum_metkin + sum_taukin) + + rat_met_num = sum_basekin + sum_metkin + rat_met_all = rat_met_num / sum_base_tot + + rat_tau_num = sum_basekin + sum_taukin + rat_tau_all = rat_tau_num / sum_base_tot + + rat_all_num = sum_basekin + sum_taukin + sum_metkin + rat_all = rat_all_num / sum_base_tot + + ratios[md][chn]['met'].append(rat_met_all) + ratios[md][chn]['tau'].append(rat_tau_all) + ratios[md][chn]['two'].append(rat_all) + + e_metkin = np.sqrt(w2_metkin) + e_taukin = np.sqrt(w2_taukin) + e_basekin = np.sqrt(w2_basekin) + + e_tau_num = np.sqrt(w2_taukin + w2_basekin) + e_met_num = np.sqrt(w2_metkin + w2_basekin) + e_all_num = np.sqrt(w2_metkin + w2_taukin + w2_basekin) + print(rat_tau_num, rat_met_num, rat_all_num) + + eratios[md][chn]['tau'].append(rat_tau_all * np.sqrt(e_tau_num**2/rat_tau_num**2 + 1/sum_base_tot)) + eratios[md][chn]['met'].append(rat_met_all * np.sqrt(e_met_num**2/rat_met_num**2 + 1/sum_base_tot)) + eratios[md][chn]['two'].append(rat_all * np.sqrt(e_all_num**2/rat_all_num**2 + 1/sum_base_tot)) + errors[md][chn].append(e_all_num) + + json_name = 'data_' + chn + '_' + json_name += ('bigtau' if args.bigtau else 'standard') + '.json' + with open(json_name, 'w', encoding='utf-8') as json_obj: + json_data = {"vals": {chn: nevents[adir[chn]][chn]['tau'] for chn in channels}} + json_data.update({"errs": {chn: errors[adir[chn]][chn] for chn in channels}}) + json.dump(json_data, json_obj, ensure_ascii=False, indent=4) + + opt_points = dict(size=8) + opt_line = dict(width=1.5) + colors = ('green', 'blue', 'red', 'brown') + styles = ('solid', 'dashed', 'dotdash') + legends = {'base': 'Legacy', + 'met': 'MET', 'tau': 'Single Tau', + 'two': 'MET + Single Tau', 'vbf': 'VBF'} + + x_str = [str(k) for k in [0]] + xticks = linear_x[:] + yticks = [x for x in range(0,110,5)] + shift_one = {'met': [-0.15, 0., 0.15], 'tau': [-0.20, -0.05, 0.1], + 'vbf': [-0.10, 0.05, 0.20]} + shift_both = {'met': [-0.15, 0., 0.15], 'two': [-0.20, -0.05, 0.1]} + shift_kin = {'met': [-0.09, 0., 0.15], 'tau': [0.03, -0.05, 0.1], + 'two': [-0.03, 0.05, 0.20], 'vbf': [0.09, 0.1, 0.25]} + + for adir in main_dir: + print(adir) + p_opt = dict(width=800, height=400, x_axis_label='x', y_axis_label='y') + p1 = figure(title='Event number (' + pp(channels[0]) + ')', y_axis_type="linear", **p_opt) + p2 = figure(title='Acceptance Gain (' + pp(channels[0]) + ')', **p_opt) if len(channels)==1 else figure(**p_opt) + + p1.yaxis.axis_label = 'Weighted number of events' + p2.yaxis.axis_label = 'Trigger acceptance gain (w.r.t. trigger baseline) [%]' + pics = (p1, p2) + for p in pics: + set_fig(p) + + for ichn,chn in enumerate(channels): + md = adir[chn] + print(md) + + p1.quad(top=nevents[md][chn]["base"], bottom=0, + left=edges_x[:-1], right=edges_x[1:], + legend_label=legends["base"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), + fill_color="dodgerblue", line_color="black") + p1.quad(top=nevents[md][chn]["met"], bottom=nevents[md][chn]["base"], + left=edges_x[:-1], right=edges_x[1:], + legend_label=legends["met"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), + fill_color="green", line_color="black") + p1.quad(top=nevents[md][chn]["tau"], bottom=nevents[md][chn]["met"], + left=edges_x[:-1], right=edges_x[1:], + legend_label=legends["tau"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), + fill_color="red", line_color="black") + print(md) + for itd,td in enumerate(('met', 'tau', 'two')): + p2.circle([x+shift_kin[td][ichn] for x in linear_x], + [(x-1)*100. for x in ratios[md][chn][td]], + color=colors[itd], fill_alpha=1., **opt_points) + p2.line([x+shift_kin[td][ichn] for x in linear_x], + [(x-1)*100. for x in ratios[md][chn][td]], + color=colors[itd], line_dash=styles[ichn], + legend_label=legends[td]+(' ('+pp(chn)+')' if len(channels)>1 else ''), **opt_line) + p2.multi_line( + [(x+shift_kin[td][ichn],x+shift_kin[td][ichn]) for x in linear_x], + [((y-1)*100-(x*50.),(y-1)*100+(x*50.)) for x,y in zip(eratios[md][chn][td],ratios[md][chn][td])], + color=colors[itd], **opt_line) + + p1.legend.location = 'top_right' + p2.legend.location = 'top_left' + for p in pics: + p.xaxis[0].ticker = xticks + p.xgrid[0].ticker = xticks + p.xgrid.grid_line_alpha = 0.2 + p.xgrid.grid_line_color = 'black' + # p.yaxis[0].ticker = yticks + # p.ygrid[0].ticker = yticks + p.ygrid.grid_line_alpha = 0.2 + p.ygrid.grid_line_color = 'black' + + p.xaxis.axis_label = "m(X) [GeV]" + + p.xaxis.major_label_overrides = dict(zip(linear_x,x_str)) + + p.legend.click_policy='hide' + + p.output_backend = 'svg' + #export_svg(p, filename='line_graph.svg') + + g = gridplot([[p] for p in pics]) + print(g) + save(g, title=md) + export_png(g, filename="line_graph_postEE.png") + +if __name__ == '__main__': + desc = "Produce plots of trigger gain VS resonance mass.\n" + desc += "Uses the output of test_trigger_regions.py." + desc += "When running on many channels, one should keep in mind each channel has different pT cuts." + desc += "This might imply moving sub-folders (produced by the previous script) around." + parser = argparse.ArgumentParser(description=desc, formatter_class=argparse.RawTextHelpFormatter) + + # parser.add_argument('--masses', required=True, nargs='+', type=str, + # help='Resonance mass') + parser.add_argument('--channels', required=True, nargs='+', type=str, + choices=('etau', 'mutau', 'tautau'), + help='Select the channel over which the workflow will be run.' ) + parser.add_argument('--year', required=True, type=str, choices=('2016', '2017', '2018', '2022'), + help='Select the year over which the workflow will be run.' ) + parser.add_argument('--deltaR', type=float, default=0.5, help='DeltaR between the two leptons.') + parser.add_argument('--bigtau', action='store_true', + help='Consider a larger single tau region, reducing the ditau one.') + parser.add_argument('--met_turnon', required=False, type=str, default=180, + help='MET trigger turnon cut [GeV].' ) + parser.add_argument('--region_cuts', required=False, type=float, nargs=2, default=(190, 190), + help='High/low regions pT1 and pT2 selection cuts [GeV].' ) + + args = utils.parse_args(parser) + + base_dir = '/t3home/fbilandz/TriggerScaleFactors/' + main_dir = [{"etau": "Region_Spin2_190_190_PT_33_25_35_DR_{}_TURNON_200_190".format(args.deltaR), + "mutau": "mutau_190_190_DR_0.5_PT_25_21_32_TURNON_180", + "tautau": "Region_Spin2_190_190_PT_40_40_DR_{}_TURNON_200_190".format(args.deltaR)}, + ] + + main(args) diff --git a/tests/trigger_orthogonal_gains_refined_run3.py b/tests/trigger_orthogonal_gains_refined_run3.py new file mode 100644 index 0000000..a46701c --- /dev/null +++ b/tests/trigger_orthogonal_gains_refined_run3.py @@ -0,0 +1,157 @@ +# coding: utf-8 + +_all_ = [ 'test_trigger_gains' ] + +import os +import sys +parent_dir = os.path.abspath(__file__ + 2 * '/..') +sys.path.insert(0, parent_dir) + +import json +import argparse +from inclusion.utils import utils +import numpy as np +from collections import defaultdict as dd +import hist +import mplhep as hep +from hist.intervals import clopper_pearson_interval as clop +import pickle +import uproot +import matplotlib.pyplot as plt + + +# import bokeh +# from bokeh.plotting import figure, output_file, save +# from bokeh.models import Whisker +# from bokeh.layouts import gridplot +# from bokeh.io import export_svg, export_png + +tau = '\u03C4' +mu = '\u03BC' +pm = '\u00B1' +ditau = tau+tau + +def get_outname(channel, regcuts, ptcuts, met_turnon, bigtau): + utils.create_single_dir('data') + + name = channel + '_' + name += '_'.join((*regcuts, 'ptcuts', *[str(x) for x in ptcuts], 'turnon', str(met_turnon))) + if bigtau: + name += '_BIGTAU' + name += '.pkl' + + s = 'data/regions_{}'.format(name) + return s + +def pp(chn): + if chn == "tautau": + return ditau + elif chn == "etau": + return "e" + tau + elif chn == "mutau": + return mu + tau + +def rec_dd(): + return dd(rec_dd) + +def set_fig(fig, legend=True): + fig.output_backend = 'svg' + fig.toolbar.logo = None + # if legend: + # fig.legend.click_policy='hide' + # fig.legend.location = 'top_left' + # fig.legend.label_text_font_size = '8pt' + fig.min_border_bottom = 5 + fig.xaxis.visible = True + fig.title.align = "left" + fig.title.text_font_size = "15px" + fig.xaxis.axis_label_text_font_style = "bold" + fig.yaxis.axis_label_text_font_style = "bold" + fig.xaxis.axis_label_text_font_size = "13px" + fig.yaxis.axis_label_text_font_size = "13px" + +def main(args): + vars = ['dau1_pt', + 'dau2_pt', 'dau1_eta', 'dau2_eta', + 'dau1_tauIdVSjet', 'dau2_tauIdVSjet', + 'bjet1_pNet', 'bjet2_pNet', 'bjet1_pt', + 'bjet2_pt', 'bjet1_eta', 'bjet2_eta'] + if args.channels[0] == "etau": + base_extension = "E" + channel_text = r"e$\tau_{h}$" + elif args.channels[0] == "mutau": + base_extension = "Mu" + channel_text = r"$\mu \tau_{h}$" + elif args.channels[0] == "tautau": + base_extension = "Tau" + channel_text = r"$\tau_{h} \tau_{h}$" + genHH_mass_baseline = uproot.open("data/regions_postBPix_15p1-no-trigger_all.root")['tautau_genHH_mass'] + genHH_mass_base_tau = uproot.open("data/regions_2023_postBPix_15p35-base-tau_all.root")['tautau_genHH_mass'] + genHH_mass_ditaujet = uproot.open("data/regions_2023_postBPix_15p36-ditaujet_all.root")['tautau_genHH_mass'] + genHH_mass_quadjet = uproot.open("data/regions_2023_postBPix_15p34_all.root")['tautau_genHH_mass'] + genHH_mass_both = uproot.open("data/regions_2023_postBPix_15p33_all.root")['tautau_genHH_mass'] + # ht = uproot.open("data/regions_postBPix_15p11-quadjet-confirm_all.root")['tautau_SoftActivityJetHT'] + petroff6 = ["#5790fc", "#f89c20", "#e42536", "#964a8b", "#9c9ca1", "#7a21dd"] + + ditaujet_gain = genHH_mass_ditaujet.values() - genHH_mass_base_tau.values() + quadjet_gain = genHH_mass_quadjet.values() - genHH_mass_base_tau.values() + overlap_gain = genHH_mass_ditaujet.values() + genHH_mass_quadjet.values() - genHH_mass_base_tau.values() - genHH_mass_both.values() + + hep.style.use("CMS") + + ditaujet_gain_err = np.sqrt(ditaujet_gain)/genHH_mass_base_tau.values() + quadjet_gain_err = np.sqrt(quadjet_gain)/genHH_mass_base_tau.values() + overlap_gain_err = np.sqrt(overlap_gain)/genHH_mass_base_tau.values() + + ax = genHH_mass_base_tau.axes[0].centers() + edges = genHH_mass_base_tau.axes[0].edges() + xerr_r = ax - edges[:-1] + xerr_l = edges[1:] - ax + + plt.figure(1) + plt.bar(x=ax, height=(ditaujet_gain/genHH_mass_base_tau.values()) * 100,width=xerr_r * 2, yerr=ditaujet_gain_err * 100, color="#fff", edgecolor=petroff6[0], ecolor=petroff6[0], label="DiTauJet & !DiTau") + + plt.bar(x=ax, height=-(quadjet_gain/genHH_mass_base_tau.values()) * 100, width=xerr_r * 2, yerr=quadjet_gain_err * 100, label="QuadJet & !DiTauJet", color="#fff", edgecolor=petroff6[1], ecolor=petroff6[1]) + plt.bar(x=ax, height=(overlap_gain/genHH_mass_base_tau.values()) * 100, width=xerr_r * 2, yerr=overlap_gain_err * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2], label="DiTauJet & QuadJet") + plt.bar(x=ax, height=-(overlap_gain/genHH_mass_base_tau.values()) * 100, width=xerr_r * 2, yerr=overlap_gain_err * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2]) + plt.axhline(y=0, color='black', linestyle='-') + ticks_locs, ticks_labels = plt.yticks() + plt.yticks(ticks_locs, [int(abs(tick)) for tick in ticks_locs]) + + plt.xlabel(r"m$_{HH}$ [GeV]") + plt.ylabel("Gain [%]") + plt.legend() + hep.cms.label(lumi="9.96", com="13.6") + plt.savefig("orthogonal_gains_postBPix.png".format(args.channels[0])) + +if __name__ == '__main__': + desc = "Produce plots of trigger gain VS resonance mass.\n" + desc += "Uses the output of test_trigger_regions.py." + desc += "When running on many channels, one should keep in mind each channel has different pT cuts." + desc += "This might imply moving sub-folders (produced by the previous script) around." + parser = argparse.ArgumentParser(description=desc, formatter_class=argparse.RawTextHelpFormatter) + + # parser.add_argument('--masses', required=True, nargs='+', type=str, + # help='Resonance mass') + parser.add_argument('--channels', required=True, nargs='+', type=str, + choices=('etau', 'mutau', 'tautau'), + help='Select the channel over which the workflow will be run.' ) + parser.add_argument('--year', required=True, type=str, choices=('2016', '2017', '2018', '2022', '2023'), + help='Select the year over which the workflow will be run.' ) + parser.add_argument('--deltaR', type=float, default=0.5, help='DeltaR between the two leptons.') + parser.add_argument('--bigtau', action='store_true', + help='Consider a larger single tau region, reducing the ditau one.') + parser.add_argument('--met_turnon', required=False, type=str, default=180, + help='MET trigger turnon cut [GeV].' ) + parser.add_argument('--region_cuts', required=False, type=float, nargs=2, default=(190, 190), + help='High/low regions pT1 and pT2 selection cuts [GeV].' ) + + args = utils.parse_args(parser) + + base_dir = '/t3home/fbilandz/TriggerScaleFactors/' + main_dir = [{"etau": "Region_Spin2_190_190_PT_33_25_35_DR_{}_TURNON_200_190".format(args.deltaR), + "mutau": "mutau_190_190_DR_0.5_PT_25_21_32_TURNON_180", + "tautau": "Region_Spin2_190_190_PT_40_40_DR_{}_TURNON_200_190".format(args.deltaR)}, + ] + + main(args) \ No newline at end of file diff --git a/tests/trigger_orthogonal_gains_run3.py b/tests/trigger_orthogonal_gains_run3.py new file mode 100644 index 0000000..442e9c7 --- /dev/null +++ b/tests/trigger_orthogonal_gains_run3.py @@ -0,0 +1,594 @@ +# coding: utf-8 + +_all_ = [ 'test_trigger_gains' ] + +import os +import sys +parent_dir = os.path.abspath(__file__ + 2 * '/..') +sys.path.insert(0, parent_dir) + +import json +import argparse +from inclusion.utils import utils +import numpy as np +from collections import defaultdict as dd +import hist +import mplhep as hep +from hist.intervals import clopper_pearson_interval as clop +import pickle +import uproot +import matplotlib.pyplot as plt + + +# import bokeh +# from bokeh.plotting import figure, output_file, save +# from bokeh.models import Whisker +# from bokeh.layouts import gridplot +# from bokeh.io import export_svg, export_png + +tau = '\u03C4' +mu = '\u03BC' +pm = '\u00B1' +ditau = tau+tau + +def get_outname(channel, regcuts, ptcuts, met_turnon, bigtau): + utils.create_single_dir('data') + + name = channel + '_' + name += '_'.join((*regcuts, 'ptcuts', *[str(x) for x in ptcuts], 'turnon', str(met_turnon))) + if bigtau: + name += '_BIGTAU' + name += '.pkl' + + s = 'data/regions_{}'.format(name) + return s + +def pp(chn): + if chn == "tautau": + return ditau + elif chn == "etau": + return "e" + tau + elif chn == "mutau": + return mu + tau + +def rec_dd(): + return dd(rec_dd) + +def set_fig(fig, legend=True): + fig.output_backend = 'svg' + fig.toolbar.logo = None + # if legend: + # fig.legend.click_policy='hide' + # fig.legend.location = 'top_left' + # fig.legend.label_text_font_size = '8pt' + fig.min_border_bottom = 5 + fig.xaxis.visible = True + fig.title.align = "left" + fig.title.text_font_size = "15px" + fig.xaxis.axis_label_text_font_style = "bold" + fig.yaxis.axis_label_text_font_style = "bold" + fig.xaxis.axis_label_text_font_size = "13px" + fig.yaxis.axis_label_text_font_size = "13px" + +def main(args): + with uproot.open("data/regions_preEE_11p4-jet-higher-pt_all.root".format(args.channels[0])) as file: + # from matplotlib.colors import ListedColormap + petroff6 = ["#5790fc", "#f89c20", "#e42536", "#964a8b", "#9c9ca1", "#7a21dd"] + channel_base = file['BaseTau_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_inclusive = file['NoBaseTauMETTau_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_met = file['NoBaseTauMETNoTau_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_tau = file['NoBaseTauNoMETTau_baseline_{}_genHH_mass'.format(args.channels[0])] + + # err_inclusive = np.sqrt( (1/channel_base.values() * channel_inclusive.errors()) ** 2 + (channel_inclusive.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_met = np.sqrt( (1/channel_base.values() * channel_base_plus_met.errors()) ** 2 + (channel_base_plus_met.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_tau = np.sqrt( (1/channel_base.values() * channel_base_plus_tau.errors()) ** 2 + (channel_base_plus_tau.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # sum_base = channel_base.values().sum() + # err_tot_base = channel_base.variances().sum() + # sum_inclusive = channel_inclusive.values().sum() + # err_tot_inclusive = channel_inclusive.variances().sum() + # sum_met = channel_base_plus_met.values().sum() + # err_tot_omet = channel_base_plus_met.variances().sum() + # sum_tau = channel_base_plus_tau.values().sum() + # err_tot_otau = channel_base_plus_tau.variances().sum() + # err_tot_met = np.sqrt((err_tot_inclusive + err_tot_omet)/(sum_base**2) + (sum_inclusive + sum_met) ** 2 * err_tot_base / (sum_base ** 4)) + # err_tot_tau = np.sqrt((err_tot_inclusive + err_tot_otau)/(sum_base**2) + (sum_inclusive + sum_tau) ** 2 * err_tot_base /(sum_base ** 4)) + # err_tot_met = round(err_tot_met * 100, 2) + # err_tot_tau = round(err_tot_tau * 100, 2) + # sum_overlap = sum_inclusive/(sum_inclusive + sum_met + sum_tau) + # sum_overlap = round(sum_overlap * 100, 2) + # err_overlap = np.sqrt((err_tot_inclusive)/((sum_inclusive + sum_met + sum_tau) ** 2) + (sum_inclusive ** 2)/((sum_inclusive + sum_met + sum_tau) ** 4)*(err_tot_inclusive + err_tot_omet + err_tot_otau)) + # err_overlap = round(err_overlap * 100, 2) + + # err_base_plus_ttjet = np.sqrt( (1/channel_base.values() * channel_base_plus_ttjet.errors()) ** 2 + (channel_base_plus_ttjet.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_Mu50 = np.sqrt( (1/channel_base.values() * channel_base_plus_Mu50.errors()) ** 2 + (channel_base_plus_Mu50.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_Ele28 = np.sqrt( (1/channel_base.values() * channel_base_plus_Ele28.errors()) ** 2 + (channel_base_plus_Ele28.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_all = np.sqrt( (1/channel_base.values() * channel_all.errors()) ** 2 + (channel_all.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + ax = channel_base.axes[0].centers() + edges = channel_base.axes[0].edges() + xerr_r = ax - edges[:-1] + xerr_l = edges[1:] - ax + print(edges, xerr_r, xerr_l) + print(channel_base.errors()/channel_base.values()) + hep.style.use("CMS") + + plt.figure(0) + # plt.bar(x=ax, height=(channel_base_plus_met.values()/channel_base.values()) * 100, bottom= (channel_inclusive.values()/channel_base.values()) * 100,width=xerr_r * 2, yerr=err_base_plus_met * 100, color="#fff", edgecolor=petroff6[0], ecolor=petroff6[0], label="MET & !Tau") + + # plt.bar(x=ax, height=-(channel_base_plus_tau.values()/channel_base.values()) * 100, bottom=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_base_plus_tau * 100, label="Tau & !MET", color="#fff", edgecolor=petroff6[1], ecolor=petroff6[1]) + # plt.bar(x=ax, height=(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2], label="MET & Tau") + # plt.bar(x=ax, height=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2]) + # plt.axhline(y=0, color='black', linestyle='-') + + # plt.text(220, 24, "Total MET Gain: {} +/- {}%".format(round((sum_inclusive + sum_met)/sum_base * 100, 2), err_tot_met)) + # plt.text(250, -30.8, "Total Tau Gain: {} +/- {}%".format(round((sum_inclusive + sum_tau)/sum_base * 100, 2), err_tot_tau)) + # print("Total MET Gain: {} +/- {}%".format(round((sum_inclusive + sum_met)/sum_base * 100, 2), err_tot_met)) + # print("Total Tau Gain: {} +/- {}%".format(round((sum_inclusive + sum_tau)/sum_base * 100, 2), err_tot_tau)) + # print(sum_inclusive, sum_met, sum_tau) + # print("Total MET/Tau overlap: {} +/- {}%".format(sum_overlap, err_overlap)) + # ticks_locs, ticks_labels = plt.yticks() + # print(ticks_locs, ticks_labels) +# set labels to absolute values and with integer representation + # plt.yticks(ticks_locs, [int(abs(tick)) for tick in ticks_locs]) + + # plt.errorbar(ax - 10, (channel_base_plus_met.values() / channel_base.values()) * 100, yerr=err_base_plus_met*100, xerr=[xerr_r - 10, xerr_l + 10], fmt='o', label="{} + MET".format(base), color=petroff6[0]) + # plt.errorbar(ax, (channel_base_plus_tau.values() / channel_base.values()) * 100, yerr=err_base_plus_tau*100, xerr=[xerr_r, xerr_l], fmt='o', label="{} + IsoTau".format(base), color=petroff6[1]) + # if args.channels[0] == "tautau": + # plt.errorbar(ax + 10, (channel_base_plus_Tau.values() / channel_base.values()) * 100, yerr=err_base_plus_ttjet*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="{} + TauTauJet".format(base), color=petroff6[2]) + # if args.channels[0] == "mutau": + # plt.errorbar(ax + 10, (channel_base_plus_Mu50.values() / channel_base.values()) * 100, yerr=err_base_plus_Mu50*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="{} + Mu50".format(base), color=petroff6[2]) + # if args.channels[0] == "etau": + # plt.errorbar(ax + 10, (channel_base_plus_Ele28.values() / channel_base.values()) * 100, yerr=err_base_plus_Ele28*100, xerr=[xerr_r + 10, xerr_l - 10], fmt='o', label="{} + Ele28".format(base), color=petroff6[2]) + + # plt.errorbar(ax + 20, (channel_all.values() / channel_base.values()) * 100, yerr=err_all*100, xerr=[xerr_r + 20, xerr_l - 20], fmt='o', label="{} + MET + IsoTau + {}".format(base, last_trig), color=petroff6[3]) + # plt.xlabel(r"m$_{HH}$ [GeV]") + # plt.ylabel("Gain [%]") + # plt.legend() + # hep.cms.label(lumi="9.8", com="13.6") + # plt.savefig("orthogonal_gains_{}.png".format(args.channels[0])) + + channel_inclusive = file['NoBaseTauTTJet4JetsPNet_baseline_{}_genHH_mass'.format(args.channels[0])] + channel_base_plus_ttjet = file['NoBaseTauTauTauJetNo4JetsPNet_baseline_{}_genHH_mass'.format(args.channels[0])] + channel_base_plus_QuadJetPNet = file['NoBaseTauNoTauTauJet4JetsPNet_baseline_{}_genHH_mass'.format(args.channels[0])] + + err_inclusive = np.sqrt( (1/channel_base.values() * channel_inclusive.errors()) ** 2 + (channel_inclusive.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + err_base_plus_ttjet = np.sqrt( (1/channel_base.values() * channel_base_plus_ttjet.errors()) ** 2 + (channel_base_plus_ttjet.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + err_base_plus_QuadJetPNet = np.sqrt( (1/channel_base.values() * channel_base_plus_QuadJetPNet.errors()) ** 2 + (channel_base_plus_QuadJetPNet.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + sum_base = channel_base.values().sum() + err_tot_base = channel_base.variances().sum() + sum_inclusive = channel_inclusive.values().sum() + err_tot_inclusive = channel_inclusive.variances().sum() + sum_ttjet = channel_base_plus_ttjet.values().sum() + err_tot_ottjet = channel_base_plus_ttjet.variances().sum() + sum_QuadJetPNet = channel_base_plus_QuadJetPNet.values().sum() + err_tot_oQuadJetPNet = channel_base_plus_QuadJetPNet.variances().sum() + err_tot_ttjet = np.sqrt((err_tot_inclusive + err_tot_ottjet)/(sum_base**2) + (sum_inclusive + sum_ttjet) ** 2 * err_tot_base / (sum_base ** 4)) + err_tot_QuadJetPNet = np.sqrt((err_tot_inclusive + err_tot_oQuadJetPNet)/(sum_base**2) + (sum_inclusive + sum_QuadJetPNet) ** 2 * err_tot_base /(sum_base ** 4)) + err_tot_ttjet = round(err_tot_ttjet * 100, 2) + err_tot_QuadJetPNet = round(err_tot_QuadJetPNet * 100, 2) + sum_overlap = sum_inclusive/(sum_inclusive + sum_ttjet + sum_QuadJetPNet) + sum_overlap = round(sum_overlap * 100, 2) + err_overlap = np.sqrt((err_tot_inclusive)/((sum_inclusive + sum_ttjet + sum_QuadJetPNet) ** 2) + (sum_inclusive ** 2)/((sum_inclusive + sum_ttjet + sum_QuadJetPNet) ** 4)*(err_tot_inclusive + err_tot_ottjet + err_tot_oQuadJetPNet)) + err_overlap = round(err_overlap * 100, 2) + + + plt.figure(1) + plt.bar(x=ax, height=(channel_base_plus_ttjet.values()/channel_base.values()) * 100, bottom= (channel_inclusive.values()/channel_base.values()) * 100,width=xerr_r * 2, yerr=err_base_plus_ttjet * 100, color="#fff", edgecolor=petroff6[0], ecolor=petroff6[0], label="TauTauJet & !Tau") + + plt.bar(x=ax, height=-(channel_base_plus_QuadJetPNet.values()/channel_base.values()) * 100, bottom=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_base_plus_QuadJetPNet * 100, label="QuadJetPNet & !TauTauJet", color="#fff", edgecolor=petroff6[1], ecolor=petroff6[1]) + plt.bar(x=ax, height=(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2], label="TauTauJet & QuadJetPNet") + plt.bar(x=ax, height=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2]) + plt.axhline(y=0, color='black', linestyle='-') + + # plt.text(220, 24, "Total ttjet Gain: {} +/- {}%".format(round((sum_inclusive + sum_ttjet)/sum_base * 100, 2), err_tot_ttjet)) + # plt.text(250, -30.8, "Total QuadJetPNet Gain: {} +/- {}%".format(round((sum_inclusive + sum_QuadJetPNet)/sum_base * 100, 2), err_tot_QuadJetPNet)) + print("Total ttjet Gain: {} +/- {}%".format(round((sum_inclusive + sum_ttjet)/sum_base * 100, 2), err_tot_ttjet)) + print("Total QuadJetPNet Gain: {} +/- {}%".format(round((sum_inclusive + sum_QuadJetPNet)/sum_base * 100, 2), err_tot_QuadJetPNet)) + print(sum_inclusive, sum_ttjet, sum_QuadJetPNet) + print("Total ttjet/QuadJetPNet overlap: {} +/- {}%".format(sum_overlap, err_overlap)) + ticks_locs, ticks_labels = plt.yticks() + print(ticks_locs, ticks_labels) +# set labels to absolute values and with integer representation + plt.yticks(ticks_locs, [int(abs(tick)) for tick in ticks_locs]) + + plt.xlabel(r"m$_{HH}$ [GeV]") + plt.ylabel("Gain [%]") + plt.legend() + hep.cms.label(lumi="27.8", com="13.6") + plt.savefig("orthogonal_gains_{}_QuadJetPNet_preEE_11p4_withOffline_pf75.png".format(args.channels[0])) + + # if args.channels[0] == "tautau": + # channel_inclusive = file['NoBaseMETTTJet_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_met = file['NoBaseMETNoTTJet_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_TTJet = file['NoBaseNoMETTTJet_baseline_{}_genHH_mass'.format(args.channels[0])] + # err_inclusive = np.sqrt( (1/channel_base.values() * channel_inclusive.errors()) ** 2 + (channel_inclusive.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_met = np.sqrt( (1/channel_base.values() * channel_base_plus_met.errors()) ** 2 + (channel_base_plus_met.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_TTJet = np.sqrt( (1/channel_base.values() * channel_base_plus_TTJet.errors()) ** 2 + (channel_base_plus_TTJet.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # sum_base = channel_base.values().sum() + # err_tot_base = channel_base.variances().sum() + # sum_inclusive = channel_inclusive.values().sum() + # err_tot_inclusive = channel_inclusive.variances().sum() + # sum_met = channel_base_plus_met.values().sum() + # err_tot_omet = channel_base_plus_met.variances().sum() + # sum_TTJet = channel_base_plus_TTJet.values().sum() + # err_tot_oTTJet = channel_base_plus_TTJet.variances().sum() + # err_tot_met = np.sqrt((err_tot_inclusive + err_tot_omet)/(sum_base**2) + (sum_inclusive + sum_met) ** 2 * err_tot_base / (sum_base ** 4)) + # err_tot_TTJet = np.sqrt((err_tot_inclusive + err_tot_oTTJet)/(sum_base**2) + (sum_inclusive + sum_TTJet) ** 2 * err_tot_base /(sum_base ** 4)) + # err_tot_met = round(err_tot_met * 100, 2) + # err_tot_TTJet = round(err_tot_TTJet * 100, 2) + # sum_overlap = sum_inclusive/(sum_inclusive + sum_met + sum_TTJet) + # sum_overlap = round(sum_overlap * 100, 2) + # err_overlap = np.sqrt((err_tot_inclusive)/((sum_inclusive + sum_met + sum_TTJet) ** 2) + (sum_inclusive ** 2)/((sum_inclusive + sum_met + sum_TTJet) ** 4)*(err_tot_inclusive + err_tot_omet + err_tot_oTTJet)) + # err_overlap = round(err_overlap * 100, 2) + + # plt.figure(1) + # plt.bar(x=ax, height=(channel_base_plus_met.values()/channel_base.values()) * 100, bottom= (channel_inclusive.values()/channel_base.values()) * 100,width=xerr_r * 2, yerr=err_base_plus_met * 100, color="#fff", edgecolor=petroff6[0], ecolor=petroff6[0], label="MET & !TTJet") + + # plt.bar(x=ax, height=-(channel_base_plus_TTJet.values()/channel_base.values()) * 100, bottom=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_base_plus_TTJet * 100, label="TTJet & !MET", color="#fff", edgecolor=petroff6[1], ecolor=petroff6[1]) + # plt.bar(x=ax, height=(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2], label="MET & TTJet") + # plt.bar(x=ax, height=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2]) + # plt.axhline(y=0, color='black', linestyle='-') + # # plt.text(250, -30.8, "Total Tau Gain: {} +/- {}%".format(round((sum_inclusive + sum_tau)/sum_base * 100, 2), err_tot_tau)) + # print("Total MET Gain: {} +/- {}%".format(round((sum_inclusive + sum_met)/sum_base * 100, 2), err_tot_met)) + # print("Total TTJet Gain: {} +/- {}%".format(round((sum_inclusive + sum_TTJet)/sum_base * 100, 2), err_tot_TTJet)) + # print("Total MET/TTJet overlap: {} +/- {}%".format(sum_overlap, err_overlap)) + # ticks_locs, ticks_labels = plt.yticks() + # print(ticks_locs, ticks_labels) + # plt.xlabel(r"m$_{HH}$ [GeV]") + # plt.ylabel("Gain [%]") + # plt.legend() + # hep.cms.label(lumi="9.8", com="13.6") + # plt.savefig("orthogonal_gains_{}_ttjet.png".format(args.channels[0])) + + # channel_inclusive = file['NoBaseTauTTJet_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_tau = file['NoBaseTauNoTTJet_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_TTJet = file['NoBaseNoTauTTJet_baseline_{}_genHH_mass'.format(args.channels[0])] + # err_inclusive = np.sqrt( (1/channel_base.values() * channel_inclusive.errors()) ** 2 + (channel_inclusive.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_tau = np.sqrt( (1/channel_base.values() * channel_base_plus_tau.errors()) ** 2 + (channel_base_plus_tau.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_TTJet = np.sqrt( (1/channel_base.values() * channel_base_plus_TTJet.errors()) ** 2 + (channel_base_plus_TTJet.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # sum_base = channel_base.values().sum() + # err_tot_base = channel_base.variances().sum() + # sum_inclusive = channel_inclusive.values().sum() + # err_tot_inclusive = channel_inclusive.variances().sum() + # sum_tau = channel_base_plus_tau.values().sum() + # err_tot_otau = channel_base_plus_tau.variances().sum() + # sum_TTJet = channel_base_plus_TTJet.values().sum() + # err_tot_oTTJet = channel_base_plus_TTJet.variances().sum() + # err_tot_tau = np.sqrt((err_tot_inclusive + err_tot_otau)/(sum_base**2) + (sum_inclusive + sum_tau) ** 2 * err_tot_base / (sum_base ** 4)) + # err_tot_TTJet = np.sqrt((err_tot_inclusive + err_tot_oTTJet)/(sum_base**2) + (sum_inclusive + sum_TTJet) ** 2 * err_tot_base /(sum_base ** 4)) + # err_tot_tau = round(err_tot_tau * 100, 2) + # err_tot_TTJet = round(err_tot_TTJet * 100, 2) + # sum_overlap = sum_inclusive/(sum_inclusive + sum_tau + sum_TTJet) + # sum_overlap = round(sum_overlap * 100, 2) + # err_overlap = np.sqrt((err_tot_inclusive)/((sum_inclusive + sum_tau + sum_TTJet) ** 2) + (sum_inclusive ** 2)/((sum_inclusive + sum_tau + sum_TTJet) ** 4)*(err_tot_inclusive + err_tot_otau + err_tot_oTTJet)) + # err_overlap = round(err_overlap * 100, 2) + + # plt.figure(2) + # plt.bar(x=ax, height=(channel_base_plus_tau.values()/channel_base.values()) * 100, bottom= (channel_inclusive.values()/channel_base.values()) * 100,width=xerr_r * 2, yerr=err_base_plus_tau * 100, color="#fff", edgecolor=petroff6[0], ecolor=petroff6[0], label="Tau & !TauTauJet") + + # plt.bar(x=ax, height=-(channel_base_plus_TTJet.values()/channel_base.values()) * 100, bottom=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_base_plus_TTJet * 100, label="TauTauJet & !Tau", color="#fff", edgecolor=petroff6[1], ecolor=petroff6[1]) + # plt.bar(x=ax, height=(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2], label="Tau & TauTauJet") + # plt.bar(x=ax, height=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2]) + # plt.axhline(y=0, color='black', linestyle='-') + # # plt.text(250, -30.8, "Total Tau Gain: {} +/- {}%".format(round((sum_inclusive + sum_tau)/sum_base * 100, 2), err_tot_tau)) + # print("Total Tau Gain: {} +/- {}%".format(round((sum_inclusive + sum_tau)/sum_base * 100, 2), err_tot_met)) + # print("Total TTJet Gain: {} +/- {}%".format(round((sum_inclusive + sum_TTJet)/sum_base * 100, 2), err_tot_TTJet)) + # print("Total Tau/TTJet overlap: {} +/- {}%".format(sum_overlap, err_overlap)) + # ticks_locs, ticks_labels = plt.yticks() + # print(ticks_locs, ticks_labels) + # plt.xlabel(r"m$_{HH}$ [GeV]") + # plt.ylabel("Gain [%]") + # plt.legend() + # hep.cms.label(lumi="9.8", com="13.6") + # plt.savefig("orthogonal_gains_{}_tau_ttjet.png".format(args.channels[0])) + # # print(ratio.axes[0]) + + # # elif args.channels[0] == "mutau": + # channel_inclusive = file['NoBaseMETMu50_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_met = file['NoBaseMETNoMu50_baseline_{}_genHH_mass'.format(args.channels[0])] + # channel_base_plus_Mu50 = file['NoBaseNoMETMu50_baseline_{}'.format(args.channels[0])] + # err_inclusive = np.sqrt( (1/channel_base.values() * channel_inclusive.errors()) ** 2 + (channel_inclusive.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_met = np.sqrt( (1/channel_base.values() * channel_base_plus_met.errors()) ** 2 + (channel_base_plus_met.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_Mu50 = np.sqrt( (1/channel_base.values() * channel_base_plus_Mu50.errors()) ** 2 + (channel_base_plus_Mu50.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # sum_base = channel_base.values().sum() + # err_tot_base = channel_base.variances().sum() + # sum_inclusive = channel_inclusive.values().sum() + # err_tot_inclusive = channel_inclusive.variances().sum() + # sum_met = channel_base_plus_met.values().sum() + # err_tot_omet = channel_base_plus_met.variances().sum() + # sum_Mu50 = channel_base_plus_Mu50.values().sum() + # err_tot_oMu50 = channel_base_plus_Mu50.variances().sum() + # err_tot_met = np.sqrt((err_tot_inclusive + err_tot_omet)/(sum_base**2) + (sum_inclusive + sum_met) ** 2 * err_tot_base / (sum_base ** 4)) + # err_tot_Mu50 = np.sqrt((err_tot_inclusive + err_tot_oMu50)/(sum_base**2) + (sum_inclusive + sum_Mu50) ** 2 * err_tot_base /(sum_base ** 4)) + # sum_overlap = sum_inclusive/(sum_inclusive + sum_met + sum_Mu50) + # sum_overlap = round(sum_overlap * 100, 2) + # err_overlap = np.sqrt((err_tot_inclusive)/((sum_inclusive + sum_met + sum_Mu50) ** 2) + (sum_inclusive ** 2)/((sum_inclusive + sum_met + sum_Mu50) ** 4)*(err_tot_inclusive + err_tot_omet + err_tot_oMu50)) + # err_tot_met = round(err_tot_met * 100, 2) + # err_tot_Mu50 = round(err_tot_Mu50 * 100, 2) + # err_overlap = round(err_overlap * 100, 2) + + # plt.figure(1) + # plt.bar(x=ax, height=(channel_base_plus_met.values()/channel_base.values()) * 100, bottom= (channel_inclusive.values()/channel_base.values()) * 100,width=xerr_r * 2, yerr=err_base_plus_met * 100, color="#fff", edgecolor=petroff6[0], ecolor=petroff6[0], label="MET & !Mu50") + + # plt.bar(x=ax, height=-(channel_base_plus_Mu50.values()/channel_base.values()) * 100, bottom=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_base_plus_Mu50 * 100, label="Mu50 & !MET", color="#fff", edgecolor=petroff6[1], ecolor=petroff6[1]) + # plt.bar(x=ax, height=(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2], label="MET & Mu50") + # plt.bar(x=ax, height=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2]) + # plt.axhline(y=0, color='black', linestyle='-') + # # plt.text(250, -30.8, "Total Tau Gain: {} +/- {}%".format(round((sum_inclusive + sum_tau)/sum_base * 100, 2), err_tot_tau)) + + + # print("Total MET Gain: {} +/- {}%".format(round((sum_inclusive + sum_met)/sum_base * 100, 2), err_tot_met)) + # print("Total Mu50 Gain: {} +/- {}%".format(round((sum_inclusive + sum_Mu50)/sum_base * 100, 2), err_tot_Mu50)) + # print("Total MET/Mu50 overlap: {} +/- {}%".format(sum_overlap, err_overlap)) + # ticks_locs, ticks_labels = plt.yticks() + # print(ticks_locs, ticks_labels) + # plt.xlabel(r"m$_{HH}$ [GeV]") + # plt.ylabel("Gain [%]") + # plt.legend() + # hep.cms.label(lumi="9.8", com="13.6") + # plt.savefig("orthogonal_gains_{}_Mu50.png".format(args.channels[0])) + + # channel_inclusive = file['NoBaseTauMu50_baseline_{}'.format(args.channels[0])] + # channel_base_plus_tau = file['NoBaseTauNoMu50_baseline_{}'.format(args.channels[0])] + # channel_base_plus_Mu50 = file['NoBaseNoTauMu50_baseline_{}'.format(args.channels[0])] + # err_inclusive = np.sqrt( (1/channel_base.values() * channel_inclusive.errors()) ** 2 + (channel_inclusive.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_tau = np.sqrt( (1/channel_base.values() * channel_base_plus_tau.errors()) ** 2 + (channel_base_plus_tau.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # err_base_plus_Mu50 = np.sqrt( (1/channel_base.values() * channel_base_plus_Mu50.errors()) ** 2 + (channel_base_plus_Mu50.values()/(channel_base.values() ** 2) * channel_base.errors()) ** 2 ) + # sum_base = channel_base.values().sum() + # err_tot_base = channel_base.variances().sum() + # sum_inclusive = channel_inclusive.values().sum() + # err_tot_inclusive = channel_inclusive.variances().sum() + # sum_tau = channel_base_plus_tau.values().sum() + # err_tot_otau = channel_base_plus_tau.variances().sum() + # sum_Mu50 = channel_base_plus_Mu50.values().sum() + # err_tot_oMu50 = channel_base_plus_Mu50.variances().sum() + # err_tot_tau = np.sqrt((err_tot_inclusive + err_tot_otau)/(sum_base**2) + (sum_inclusive + sum_tau) ** 2 * err_tot_base / (sum_base ** 4)) + # err_tot_Mu50 = np.sqrt((err_tot_inclusive + err_tot_oMu50)/(sum_base**2) + (sum_inclusive + sum_Mu50) ** 2 * err_tot_base /(sum_base ** 4)) + # err_tot_tau = round(err_tot_tau * 100, 2) + # err_tot_Mu50 = round(err_tot_Mu50 * 100, 2) + # sum_overlap = sum_inclusive/(sum_inclusive + sum_tau + sum_Mu50) + # sum_overlap = round(sum_overlap * 100, 2) + # err_overlap = np.sqrt((err_tot_inclusive)/((sum_inclusive + sum_tau + sum_Mu50) ** 2) + (sum_inclusive ** 2)/((sum_inclusive + sum_tau + sum_Mu50) ** 4)*(err_tot_inclusive + err_tot_otau + err_tot_oMu50)) + # err_overlap = round(err_overlap * 100, 2) + + # plt.figure(2) + # plt.bar(x=ax, height=(channel_base_plus_tau.values()/channel_base.values()) * 100, bottom= (channel_inclusive.values()/channel_base.values()) * 100,width=xerr_r * 2, yerr=err_base_plus_tau * 100, color="#fff", edgecolor=petroff6[0], ecolor=petroff6[0], label="Tau & !Mu50") + + # plt.bar(x=ax, height=-(channel_base_plus_Mu50.values()/channel_base.values()) * 100, bottom=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_base_plus_Mu50 * 100, label="Mu50 & !Tau", color="#fff", edgecolor=petroff6[1], ecolor=petroff6[1]) + # plt.bar(x=ax, height=(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2], label="Tau & Mu50") + # plt.bar(x=ax, height=-(channel_inclusive.values()/channel_base.values()) * 100, width=xerr_r * 2, yerr=err_inclusive * 100, color="#fff", edgecolor=petroff6[2], ecolor=petroff6[2]) + # plt.axhline(y=0, color='black', linestyle='-') + # # plt.text(250, -30.8, "Total Tau Gain: {} +/- {}%".format(round((sum_inclusive + sum_tau)/sum_base * 100, 2), err_tot_tau)) + # print("Total Tau Gain: {} +/- {}%".format(round((sum_inclusive + sum_tau)/sum_base * 100, 2), err_tot_met)) + # print("Total Mu50 Gain: {} +/- {}%".format(round((sum_inclusive + sum_Mu50)/sum_base * 100, 2), err_tot_Mu50)) + # print("Total Tau/Mu50 overlap: {} +/- {}%".format(sum_overlap, err_overlap)) + # ticks_locs, ticks_labels = plt.yticks() + # print(ticks_locs, ticks_labels) + # plt.xlabel(r"m$_{HH}$ [GeV]") + # plt.ylabel("Gain [%]") + # plt.legend() + # hep.cms.label(lumi="9.8", com="13.6") + # plt.savefig("orthogonal_gains_{}_tau_Mu50.png".format(args.channels[0])) + return + channels = args.channels + linear_x = [k for k in range(1,2)] + edges_x = [k-0.5 for k in range(1,2)] + [1.5] + ptcuts = {chn: utils.get_ptcuts(chn, args.year) for chn in args.channels} + + nevents, errors = dd(lambda: dd(dict)), dd(lambda: dd(dict)) + ratios, eratios = dd(lambda: dd(dict)), dd(lambda: dd(dict)) + + for adir in main_dir: + dRstr = str(args.deltaR).replace('.', 'p') + if len(args.channels) == 1: + output_name = os.path.join(base_dir, 'trigger_gains_{}_{}_DR{}'.format(args.channels[0], + args.year, dRstr)) + elif len(args.channels) == 2: + output_name = os.path.join(base_dir, 'trigger_gains_{}_{}_{}_DR{}'.format(*args.channels[:2], + args.year, dRstr)) + elif len(args.channels) == 3: + output_name = os.path.join(base_dir, 'trigger_gains_all_{}_DR{}'.format(args.year, dRstr)) + if args.bigtau: + output_name += "_BIGTAU" + output_name += ".html" + output_file(output_name) + print('Saving file {}.'.format(output_name)) + + for chn in channels: + md = adir[chn] + in_base = os.path.join(base_dir, md) + + nevents[md][chn]['base'], nevents[md][chn]['met'], nevents[md][chn]['tau'] = [], [], [] + ratios[md][chn]['two'], ratios[md][chn]['met'], ratios[md][chn]['tau'] = [], [], [] + eratios[md][chn]['two'], eratios[md][chn]['met'], eratios[md][chn]['tau'] = [], [], [] + errors[md][chn] = [] + + outname = get_outname( chn, [str(x) for x in args.region_cuts], + [str(x) for x in ptcuts[chn]], str(args.met_turnon), + args.bigtau) + + print(outname) + with open(outname, "rb") as f: + ahistos = pickle.load(f) + + #all regions summed + sum_base_tot = round(ahistos["Base"]["legacy"]["baseline"].values().sum() + + ahistos["Base"]["tau"]["baseline"].values().sum() + + ahistos["Base"]["met"]["baseline"].values().sum()) + print(ahistos["Base"]["legacy"]["baseline"].values()) + #legacy region + l1 = lambda x : round(x["legacy"]["baseline"].values().sum(), 2) + sum_base = l1(ahistos["Base"]) + sum_vbf = l1(ahistos["VBF"]) + sum_met = l1(ahistos["NoBaseMET"]) + sum_only_tau = l1(ahistos["NoBaseNoMETTau"]) + sum_tau = l1(ahistos["NoBaseTau"]) + sum_basekin = l1(ahistos["LegacyKin"]) + w2_basekin = ahistos["METKin"]["legacy"]["baseline"].variances().sum() + + #MET region + l2 = lambda x : round(x["met"]["baseline"].values().sum(), 2) + sum_metkin = l2(ahistos["METKin"]) + w2_metkin = ahistos["METKin"]["met"]["baseline"].variances().sum() + + #Single Tau region + l3 = lambda x : round(x["tau"]["baseline"].values().sum(), 2) + sum_taukin = l3(ahistos["TauKin"]) + w2_taukin = ahistos["TauKin"]["tau"]["baseline"].variances().sum() + + #hypothetical VBF region + sum_vbfkin = l2(ahistos["VBFKin"]) + l3(ahistos["VBFKin"]) + + nevents[md][chn]['base'].append(sum_basekin) + nevents[md][chn]['met'].append(sum_basekin + sum_metkin) + nevents[md][chn]['tau'].append(sum_basekin + sum_metkin + sum_taukin) + + rat_met_num = sum_basekin + sum_metkin + rat_met_all = rat_met_num / sum_base_tot + + rat_tau_num = sum_basekin + sum_taukin + rat_tau_all = rat_tau_num / sum_base_tot + + rat_all_num = sum_basekin + sum_taukin + sum_metkin + rat_all = rat_all_num / sum_base_tot + + ratios[md][chn]['met'].append(rat_met_all) + ratios[md][chn]['tau'].append(rat_tau_all) + ratios[md][chn]['two'].append(rat_all) + + e_metkin = np.sqrt(w2_metkin) + e_taukin = np.sqrt(w2_taukin) + e_basekin = np.sqrt(w2_basekin) + + e_tau_num = np.sqrt(w2_taukin + w2_basekin) + e_met_num = np.sqrt(w2_metkin + w2_basekin) + e_all_num = np.sqrt(w2_metkin + w2_taukin + w2_basekin) + print(rat_tau_num, rat_met_num, rat_all_num) + + eratios[md][chn]['tau'].append(rat_tau_all * np.sqrt(e_tau_num**2/rat_tau_num**2 + 1/sum_base_tot)) + eratios[md][chn]['met'].append(rat_met_all * np.sqrt(e_met_num**2/rat_met_num**2 + 1/sum_base_tot)) + eratios[md][chn]['two'].append(rat_all * np.sqrt(e_all_num**2/rat_all_num**2 + 1/sum_base_tot)) + errors[md][chn].append(e_all_num) + + json_name = 'data_' + chn + '_' + json_name += ('bigtau' if args.bigtau else 'standard') + '.json' + with open(json_name, 'w', encoding='utf-8') as json_obj: + json_data = {"vals": {chn: nevents[adir[chn]][chn]['tau'] for chn in channels}} + json_data.update({"errs": {chn: errors[adir[chn]][chn] for chn in channels}}) + json.dump(json_data, json_obj, ensure_ascii=False, indent=4) + + opt_points = dict(size=8) + opt_line = dict(width=1.5) + colors = ('green', 'blue', 'red', 'brown') + styles = ('solid', 'dashed', 'dotdash') + legends = {'base': 'Legacy', + 'met': 'MET', 'tau': 'Single Tau', + 'two': 'MET + Single Tau', 'vbf': 'VBF'} + + x_str = [str(k) for k in [0]] + xticks = linear_x[:] + yticks = [x for x in range(0,110,5)] + shift_one = {'met': [-0.15, 0., 0.15], 'tau': [-0.20, -0.05, 0.1], + 'vbf': [-0.10, 0.05, 0.20]} + shift_both = {'met': [-0.15, 0., 0.15], 'two': [-0.20, -0.05, 0.1]} + shift_kin = {'met': [-0.09, 0., 0.15], 'tau': [0.03, -0.05, 0.1], + 'two': [-0.03, 0.05, 0.20], 'vbf': [0.09, 0.1, 0.25]} + + for adir in main_dir: + print(adir) + p_opt = dict(width=800, height=400, x_axis_label='x', y_axis_label='y') + p1 = figure(title='Event number (' + pp(channels[0]) + ')', y_axis_type="linear", **p_opt) + p2 = figure(title='Acceptance Gain (' + pp(channels[0]) + ')', **p_opt) if len(channels)==1 else figure(**p_opt) + + p1.yaxis.axis_label = 'Weighted number of events' + p2.yaxis.axis_label = 'Trigger acceptance gain (w.r.t. trigger baseline) [%]' + pics = (p1, p2) + for p in pics: + set_fig(p) + + for ichn,chn in enumerate(channels): + md = adir[chn] + print(md) + + p1.quad(top=nevents[md][chn]["base"], bottom=0, + left=edges_x[:-1], right=edges_x[1:], + legend_label=legends["base"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), + fill_color="dodgerblue", line_color="black") + p1.quad(top=nevents[md][chn]["met"], bottom=nevents[md][chn]["base"], + left=edges_x[:-1], right=edges_x[1:], + legend_label=legends["met"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), + fill_color="green", line_color="black") + p1.quad(top=nevents[md][chn]["tau"], bottom=nevents[md][chn]["met"], + left=edges_x[:-1], right=edges_x[1:], + legend_label=legends["tau"]+(' ('+pp(chn)+')' if len(channels)>1 else ''), + fill_color="red", line_color="black") + print(md) + for itd,td in enumerate(('met', 'tau', 'two')): + p2.circle([x+shift_kin[td][ichn] for x in linear_x], + [(x-1)*100. for x in ratios[md][chn][td]], + color=colors[itd], fill_alpha=1., **opt_points) + p2.line([x+shift_kin[td][ichn] for x in linear_x], + [(x-1)*100. for x in ratios[md][chn][td]], + color=colors[itd], line_dash=styles[ichn], + legend_label=legends[td]+(' ('+pp(chn)+')' if len(channels)>1 else ''), **opt_line) + p2.multi_line( + [(x+shift_kin[td][ichn],x+shift_kin[td][ichn]) for x in linear_x], + [((y-1)*100-(x*50.),(y-1)*100+(x*50.)) for x,y in zip(eratios[md][chn][td],ratios[md][chn][td])], + color=colors[itd], **opt_line) + + p1.legend.location = 'top_right' + p2.legend.location = 'top_left' + for p in pics: + p.xaxis[0].ticker = xticks + p.xgrid[0].ticker = xticks + p.xgrid.grid_line_alpha = 0.2 + p.xgrid.grid_line_color = 'black' + # p.yaxis[0].ticker = yticks + # p.ygrid[0].ticker = yticks + p.ygrid.grid_line_alpha = 0.2 + p.ygrid.grid_line_color = 'black' + + p.xaxis.axis_label = "m(X) [GeV]" + + p.xaxis.major_label_overrides = dict(zip(linear_x,x_str)) + + p.legend.click_policy='hide' + + p.output_backend = 'svg' + #export_svg(p, filename='line_graph.svg') + + g = gridplot([[p] for p in pics]) + print(g) + save(g, title=md) + export_png(g, filename="line_graph.png") + +if __name__ == '__main__': + desc = "Produce plots of trigger gain VS resonance mass.\n" + desc += "Uses the output of test_trigger_regions.py." + desc += "When running on many channels, one should keep in mind each channel has different pT cuts." + desc += "This might imply moving sub-folders (produced by the previous script) around." + parser = argparse.ArgumentParser(description=desc, formatter_class=argparse.RawTextHelpFormatter) + + # parser.add_argument('--masses', required=True, nargs='+', type=str, + # help='Resonance mass') + parser.add_argument('--channels', required=True, nargs='+', type=str, + choices=('etau', 'mutau', 'tautau'), + help='Select the channel over which the workflow will be run.' ) + parser.add_argument('--year', required=True, type=str, choices=('2016', '2017', '2018', '2022'), + help='Select the year over which the workflow will be run.' ) + parser.add_argument('--deltaR', type=float, default=0.5, help='DeltaR between the two leptons.') + parser.add_argument('--bigtau', action='store_true', + help='Consider a larger single tau region, reducing the ditau one.') + parser.add_argument('--met_turnon', required=False, type=str, default=180, + help='MET trigger turnon cut [GeV].' ) + parser.add_argument('--region_cuts', required=False, type=float, nargs=2, default=(190, 190), + help='High/low regions pT1 and pT2 selection cuts [GeV].' ) + + args = utils.parse_args(parser) + + base_dir = '/t3home/fbilandz/TriggerScaleFactors/' + main_dir = [{"etau": "Region_Spin2_190_190_PT_33_25_35_DR_{}_TURNON_200_190".format(args.deltaR), + "mutau": "mutau_190_190_DR_0.5_PT_25_21_32_TURNON_180", + "tautau": "Region_Spin2_190_190_PT_40_40_DR_{}_TURNON_200_190".format(args.deltaR)}, + ] + + main(args) diff --git a/tests/trigger_regions_run3.py b/tests/trigger_regions_run3.py new file mode 100644 index 0000000..0edfbe4 --- /dev/null +++ b/tests/trigger_regions_run3.py @@ -0,0 +1,967 @@ +# coding: utf-8 + +_all_ = [ 'test_trigger_regions' ] + +import os +import sys +parent_dir = os.path.abspath(__file__ + 2 * '/..') +sys.path.insert(0, parent_dir) +import argparse +import glob +import multiprocessing +import itertools as it +import csv +import numpy as np +import h5py +from collections import defaultdict as dd +import importlib + +import inclusion +from inclusion import selection +from inclusion.config import main +from inclusion.utils import utils + +import ROOT +import hist +import pickle +import uproot + +from bokeh.plotting import figure, output_file, save +from bokeh.models import Range1d, Label + +tau = '\u03C4' +mu = '\u03BC' +pm = '\u00B1' +ditau = tau+tau + +def contamination_save(savepath, label, c1, c2, e1, e2, mode='w'): + with h5py.File(os.path.join(savepath, label + '.hdf5'), mode) as f: + dset = f.create_dataset(label, (4, len(c1)), dtype='f') + dset[0, :] = c1 + dset[1, :] = c2 + dset[2, :] = e1 + dset[3, :] = e2 + dset.cols = ['contamination ' + ditau + '(%)', + 'contamination ' + ditau + ' MET (%)', + 'uncertainty' + ditau, + 'uncertainty' + ditau + ' MET'] + +def stats_save(savepath, label, c1, e1, mode='w'): + with h5py.File(os.path.join(savepath, label + '.hdf5'), mode) as f: + dset = f.create_dataset(label+'_stats', (2, len(c1)), dtype='f') + dset[0, :] = c1 + dset[1, :] = e1 + dset.cols = ['stats', + 'uncertainty'] + +def rec_dd(): + return dd(rec_dd) + +def square_diagram(c_legacy_trg, c_met_trg, c_tau_trg, channel, + ptcuts, text, notext=False, bigtau=False): + base = {'etau': 'e+e'+tau, 'mutau': mu+'+'+mu+tau, 'tautau': ditau} + output_file(text['out']) + print('Saving file {}'.format(text['out'])) + + topr = 9.9 + shft = 0.1 + start, b1, b2, b3 = 0.0, 2, 2.5, 7.5 + xgap = b1+shft# else b2+shft + + p = figure(title='m(X)=0GeV', width=600, height=400, + tools='save') + p.x_range = Range1d(0, 11.5) + p.y_range = Range1d(0, 10) + p.outline_line_color = None + p.toolbar.logo = None + p.xgrid.grid_line_color = None + p.ygrid.grid_line_color = None + p.xaxis.ticker = [b1,b3]#else [b1, b2, b3] + p.xaxis.major_label_overrides = ({b1: str(ptcuts[0]), b3: str(regcuts[0])}) + + for aaxis in (p.xaxis, p.yaxis): + aaxis.axis_label_text_font_size = "12pt" + aaxis.axis_label_text_font_style = "normal" + aaxis.major_label_text_font_size = "11pt" + + p.yaxis.axis_label_standoff = 0 + p.yaxis.ticker = [b1, b3]# else [b1, b2, b3] + if len(ptcuts) > 1: + p.yaxis.major_label_overrides = ({b1: str(ptcuts[0]), b3: str(regcuts[1])}) + else: + p.yaxis.major_label_overrides = ({b1: str(ptcuts[0])}) + + p.xaxis.axis_label = r'\(p_T(\tau_1) [GeV]\)' + p.yaxis.axis_label = r'\(p_T(\tau_2) [GeV]\)' + + # add a square renderer with a size, color, and alpha + polyg_opt = dict(alpha=0.3) + p.multi_polygons(color='green', + xs=[[[[xgap,b3-shft,b3-shft,xgap]]]] if bigtau else [[[[xgap,topr,topr,xgap]]]], + ys=[[[[b3-shft,b3-shft,xgap,xgap]]]] if bigtau else [[[[topr,topr,xgap,xgap]]]], + legend_label=base[channel], **polyg_opt) + + p.legend.title = 'Regions' + p.legend.title_text_font_style = 'bold' + p.legend.border_line_color = None + p.legend.background_fill_color = 'white' + p.legend.click_policy = 'hide' + + label_opt = dict(x_units='data', y_units='data', text_font_size='10pt') + + gain = (100*float(c_met_trg['legacy']+c_tau_trg['legacy']) / + (c_legacy_trg['legacy']+c_met_trg['legacy']+c_tau_trg['legacy'])) + gain = str(round(gain,2)) + if not notext: + stats_ditau = {'legacy': Label(x=b1+0.3, y=b1+1.5, text=ditau+': '+str(c_legacy_trg['legacy']), + text_color='black', **label_opt), + 'met': Label(x=b1+0.3, y=b1+1.1, text='met && !'+ditau+': '+str(c_met_trg['legacy']), + text_color='black', **label_opt), + 'tau': Label(x=b1+0.3, y=b1+0.7, text=tau+' && !met && !'+ditau+': '+str(c_tau_trg['legacy']), + text_color='black', **label_opt), + 'gain': Label(x=b1+0.3, y=b1+0.3, text='Gain: '+gain+'%', + text_color='blue', **label_opt),} + for key,elem in stats_ditau.items(): + p.add_layout(elem) + + + try: + if c_met_trg['met']+c_tau_trg['met']+c_legacy_trg['met'] == 0.0: + raise ZeroDivisionError + contam_by_tau = (100*(float(c_tau_trg['met'])+c_legacy_trg['met']) / + (c_met_trg['met']+c_tau_trg['met']+c_legacy_trg['met'])) + contam_by_tau = str(round(contam_by_tau,2)) + except ZeroDivisionError: + contam_by_tau = '0' + if not notext: + stats_met = {'met': Label(x=b1+0.3, y=1.3, text='met: '+str(c_met_trg['met']), + text_color='black', **label_opt), + 'tau': Label(x=b1+0.3, y=0.5, text=tau+' && !'+ditau+' && !met: '+str(c_tau_trg['met']), + text_color='black', **label_opt), + 'legacy': Label(x=b1+0.3, y=0.9, text=ditau+' && !met: '+str(c_legacy_trg['met']), + text_color='black', **label_opt), + 'contamination': Label(x=b1+0.3, y=0.1, text='Contam.: '+contam_by_tau+'%', + text_color='blue', **label_opt),} + for key,elem in stats_met.items(): + if key != 'contamination': + p.add_layout(elem) + + + num_ditau = float(c_legacy_trg['tau']) + num_both = num_ditau + float(c_met_trg['tau']) + den = float(c_met_trg['tau']+c_tau_trg['tau']+c_legacy_trg['tau']) + if num_ditau == 0 or num_both == 0: + contam_ditau = '0' + contam_both = '0' + err_ditau = '0' + err_both = '0' + else: + contam_ditau = 100*num_ditau/den + contam_both = 100*num_both/den + + enum_ditau = np.sqrt(c_legacy_trg['tau']) + eden_ditau = enum_ditau + np.sqrt(c_met_trg['tau']) + np.sqrt(c_tau_trg['tau']) + enum_both = np.sqrt(c_met_trg['tau']) + np.sqrt(c_legacy_trg['tau']) + eden_both = enum_both + np.sqrt(c_tau_trg['tau']) + err_ditau = contam_ditau * np.sqrt(enum_ditau**2/num_ditau**2 + eden_ditau**2/den**2) + err_both = contam_both * np.sqrt(enum_both**2/num_both**2 + eden_both**2/den**2) + + contam_ditau = str(round(contam_ditau,2)) + contam_both = str(round(contam_both,2)) + err_ditau = str(round(err_ditau,2)) + err_both = str(round(err_both,2)) + + if not notext: + stats_tau = {'tau': Label(x=b3+0.2, y=1.3, text=tau+': '+str(c_tau_trg['tau']), + text_color='black', **label_opt), + 'met': Label(x=b3+0.2, y=0.5, text='met && !'+ditau+' && !'+tau+': '+str(c_met_trg['tau']), + text_color='black', **label_opt), + 'legacy': Label(x=b3+0.2, y=0.9, text=ditau+' && !'+tau+': '+str(c_legacy_trg['tau']), + text_color='black', **label_opt), + 'contamination_both': Label(x=b3+0.2, y=0.1, + text='Contam.: ('+str(contam_both)+pm+str(err_both)+')%', + text_color='blue', **label_opt),} + for key,elem in stats_tau.items(): + if key != 'contamination_both': + p.add_layout(elem) + + line_opt = dict(color='black', line_dash='dashed', line_width=2) + p.line(x=[b1,b1], y=[start, topr+shft], **line_opt) + p.line(x=[start,topr+shft], y=[b1,b1], **line_opt) + p.line(x=[b3,b3], y=[start,topr+shft], **line_opt) + p.line(x=[start,topr+shft], y=[b3,b3], **line_opt) + + p.output_backend = 'svg' + save(p) + return contam_ditau, contam_both, err_ditau, err_both + +def get_outname(channel, bigtau): + utils.create_single_dir('data') + + name = "" + if bigtau: + name += '_BIGTAU' + name += '_all.root' + + s = 'data/regions_preEE_12p11-old-way-pf75{}'.format(name) + return s + +def set_plot_definitions(): + ROOT.gROOT.SetBatch(ROOT.kTRUE) + ROOT.gStyle.SetOptStat(ROOT.kFALSE) + ret = {'XTitleSize' : 0.045, + 'YTitleSize' : 0.045, + 'LineWidth' : 2, + 'FrameLineWidth' : 1, + } + return ret + +def match_trigger_object(off_eta, off_phi, obj_id, TrigObj_id, TrigObj_filterBits, TrigObj_eta, TrigObj_phi, bits): + for iobj in range(len(TrigObj_id)): + if TrigObj_id[iobj] != obj_id: + continue + dPhi = off_phi - TrigObj_phi[iobj] + dEta = off_eta - TrigObj_eta[iobj] + delR2 = dPhi * dPhi + dEta * dEta + if delR2 > 0.25: #0.5 * 0.5 + continue + # matched_bits = True + # for bit in bits: + # if (TrigObj_filterBits[iobj] & (1<= regcuts[0] and eta1_trg) or (ent.dau2_pt >= regcuts[1] and eta2_trg) + # leg = ent.dau1_pt >= ptcuts[0] and ent.dau2_pt >= ptcuts[1] and eta1_trg and eta2_trg and not tau + + # elif channel == "etau" and year == "2016": + # tau = ent.dau2_pt >= regcuts[1] and eta2_trg + # leg = ent.dau1_pt >= ptcuts[0] and eta1_trg and not tau + + # else: #mutau or etau non-2016 + # single_lepton_validity = ent.dau1_pt >= ptcuts[0] and eta1_sel[channel] + # cross_lepton_validity = ent.dau1_pt >= ptcuts[1] and eta1_trg and ent.dau2_pt >= ptcuts[2] and eta2_trg + + # tau = ent.dau2_pt >= regcuts[1] and eta2_trg + # leg = (single_lepton_validity or cross_lepton_validity) and not tau + + if channel == "tautau": + leg = True + tau = False + # leg = ent.dau1_pt >= ptcuts[0] and ent.dau2_pt >= ptcuts[1] and eta1_trg and eta2_trg + # tau = ((ent.dau1_pt >= regcuts[0] and eta1_trg) or (ent.dau2_pt >= regcuts[1] and eta2_trg)) and not leg + + elif channel == "etau" and year == "2016": + leg = ent.dau1_pt >= ptcuts[0] and eta1_trg + tau = ent.dau2_pt >= regcuts[1] and eta2_trg and not leg + + else: #mutau or etau non-2016 + leg = True + tau = False + # leg = sel.check_bit(main.trig_map[year]['IsoMu24']['mc']) or sel.check_bit(main.trig_map[year]['IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1']['mc']) + # tau = not leg and sel.check_bit(main.trig_map[year]['LooseDeepTauPFTauHPS180_L2NN_eta2p1']['mc']) + # single_lepton_validity = ent.dau1_pt >= ptcuts[0] and eta1_sel[channel] + # cross_lepton_validity = ent.dau1_pt >= ptcuts[1] and eta1_trg and ent.dau2_pt >= ptcuts[2] and eta2_trg + # single_tau_validity = ent.dau2_pt >= regcuts[1] and eta2_trg + + # leg = single_lepton_validity or cross_lepton_validity + # tau = single_tau_validity and not leg + + met = not leg and not tau and sel.check_bit(main.trig_map[year]['PFMETNoMu120_PFMHTNoMu120_IDTight']['mc']) + + # only one True: non-overlapping regions + assert int(leg)+int(met)+int(tau)<=1 + + return leg, met, tau + +def pass_offline_selection(triggers, entry): + res = False + for trigger in triggers: + trigger_pass = entry[trigger] + if not trigger_pass: + continue + if trigger == "HLT_IsoMu24" and entry['pairType'] == 0: + res = res or (entry['dau1_pt'] > 25 and abs(entry['dau1_eta']) < 2.4) + elif trigger == "HLT_IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1" and entry['pairType'] == 0: + res = res or (entry['dau1_pt'] > 21 and entry['dau2_pt'] > 32) + elif trigger == "HLT_Ele30_WPTight_Gsf" and entry['pairType'] == 1: + res = res or (entry['dau1_pt'] > 32 and abs(entry['dau1_eta']) < 2.4) + elif trigger == "HLT_Ele24_eta2p1_WPTight_Gsf_LooseDeepTauPFTauHPS30_eta2p1_CrossL1" and entry['pairType'] == 1: + res = res or (entry['dau1_pt'] > 26 and entry['dau2_pt'] > 35) + elif trigger == "HLT_DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1" and entry['pairType'] == 2: + res = res or (entry['dau1_pt'] > 40 and entry['dau2_pt'] > 40) + elif trigger == "HLT_DoubleTightChargedIsoPFTauHPS35_Trk1_eta2p1" and entry['pairType'] == 2: + res = res or (entry['dau1_pt'] > 40 and entry['dau2_pt'] > 40) + elif trigger == "HLT_LooseDeepTauPFTauHPS180_L2NN_eta2p1": + if entry['pairType'] == 2: + res = res or (entry['dau1_pt'] > 190 or entry['dau2_pt'] > 190) + else: + res = res or entry['dau2_pt'] > 190 + elif trigger == "HLT_PFMETNoMu120_PFMHTNoMu120_IDTight": + res = res or entry["MET_pt"] > 150 + elif trigger == "HLT_Mu50": + res = res or (entry['dau1_pt'] > 51 and abs(entry['dau1_eta']) < 2.4) + elif trigger == "HLT_DoubleMediumDeepTauPFTauHPS30_L2NN_eta2p1_PFJet60" and entry['pairType'] == 2: + res = res or (entry['dau1_pt'] > 35 and entry['dau2_pt'] > 35) + elif trigger == "HLT_DoubleMediumDeepTauPFTauHPS30_L2NN_eta2p1_PFJet75" and entry['pairType'] == 2: + res = res or (entry['dau1_pt'] > 35 and entry['dau2_pt'] > 35 and (entry['bjet1_pt'] > 75 or entry['bjet2_pt'] > 75)) + elif trigger == "HLT_QuadPFJet70_50_40_30_PFBTagParticleNet_2BTagSum0p65": + if entry['pairType'] == 2: + res = res or (abs(entry['dau1_eta']) < 2.1 and abs(entry['dau2_eta']) < 2.1) + else: + res = res or (abs(entry['dau1_eta']) < 2.4 and abs(entry['dau2_eta']) < 2.1) + return res + + +def pass_triggers(triggers, entry): + res = False + for trigger in triggers: + res = res or entry[trigger] + return res + + +def trigger_regions(indir, channel, year, deltaR): + outname = get_outname(channel, + args.bigtau) + config_module = importlib.import_module(args.configuration) + + + if channel == 'etau' or channel == 'mutau': + iso1 = range(0, 8.1, 8/24) + elif channel == 'tautau': + iso1 = range(0, 401, 8) + pNet_dist = [x/12. for x in range(13)] + binning.update({ + 'genHH_mass': ([250, 300, 350, 400, 450, 500, 550, 600, 675, 800, 1000, 1600],), + 'dau1_iso': (iso1,), + 'dau1_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'dau2_iso': (iso1,), + 'dau2_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'dau1_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + 'dau2_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + 'dau1_tauIdVSjet': ([0, 1, 2, 3, 4, 5, 6, 7], ), + 'dau2_tauIdVSjet': ([0, 1, 2, 3, 4, 5, 6, 7], ), + 'bjet1_pNet': (pNet_dist,), + 'bjet2_pNet': (pNet_dist,), + 'bjet1_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'bjet2_pt': ([0, 20, 30, 40, 60, 80, 100, 125, 150, 200, 250],), + 'bjet1_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + 'bjet2_eta': ([-3, -2.5, -2.1, -1.8, -1.5, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.5, 1.8, 2.1, 2.5, 3],), + }) + + # full_sample = {0: 'GluGluToRadionToHHTo2B2Tau_M-' + sample + '_', + # 2: 'GluGluToBulkGravitonToHHTo2B2Tau_M-' + sample + '_'}[spin] + + norphans, ntotal = ({k:0 for k in categories} for _ in range(2)) + + ahistos = rec_dd() + for cat in categories: + for chn in ['mutau', 'etau', 'tautau']: + for htype in htypes: + for i in binning.keys(): + ahistos[htype][cat][chn][i] = ( + hist.Hist.new.Variable(*binning[i], name=i) + .Weight() + ) + + t_in = ROOT.TChain('Events') + glob_files = glob.glob( os.path.join(indir, 'data_*.root') ) + if len(glob_files) < 1: + raise RuntimeError("No files!") + for f in glob_files: + t_in.Add(f) + t_in.SetBranchStatus('*', 0) + _entries, mc_corrections = utils.define_used_tree_variables(cut=config_module.custom_cut) + + _entries += tuple([ x + mc_corrections[x] for x in mc_corrections]) + for ientry in _entries: + t_in.SetBranchStatus(ientry, 1) + + for entry in t_in: + # this is slow: do it once only + entries = utils.dot_dict({x: getattr(entry, x) for x in _entries}) + + if entries.pairType == -1: + continue + + if entries.pairType == 0 or entries.pairType == 1: + if entries.isOS != 1 or entries.dau2_tauIdVSjet < 5: + continue + if entries.pairType == 2: + if entries.isOS != 1 or entries.dau2_tauIdVSjet < 5 or entries.dau1_tauIdVSjet < 5: + continue + + sel = selection.EventSelection(entries, year=year, isdata=False, configuration=config_module) + # in_legacy, in_met, in_tau = which_region(entries, year, ptcuts, regcuts, channel, sel, + # bigtau=args.bigtau) + + pass_base_mu = pass_triggers(triggers['mutau'], entries) + pass_base_e = pass_triggers(triggers['etau'], entries) + pass_base_tau = pass_offline_selection(triggers['tautau'], entries) + # pass_alt_base_tau = pass_offline_selection(('HLT_DoubleTightChargedIsoPFTauHPS35_Trk1_eta2p1',), entries) + pass_met = pass_offline_selection(('HLT_PFMETNoMu120_PFMHTNoMu120_IDTight',), entries) + pass_tau = pass_offline_selection(('HLT_LooseDeepTauPFTauHPS180_L2NN_eta2p1',), entries) + # pass_mu50 = pass_offline_selection(('HLT_Mu50',), entries) + # pass_ele28 = pass_offline_selection(('HLT_Ele28_eta2p1_WPTight_Gsf_HT150', ), entries) + pass_ttjet = pass_offline_selection(("HLT_DoubleMediumDeepTauPFTauHPS30_L2NN_eta2p1_PFJet60",), entries) + # pass_4jets_deepJet = pass_triggers(("HLT_QuadPFJet103_88_75_15_DoublePFBTagDeepJet_1p3_7p7_VBF1", ), entries) + pass_4jets_pNet = pass_triggers(("HLT_QuadPFJet70_50_40_35_PFBTagParticleNet_2BTagSum0p65", ), entries) + # pass_4jets_match = match_trigger_objects_QuadJet(entries) if pass_4jets_pNet else False + cuts = { + 'All': True, + 'BaseMu' : pass_base_mu, + 'BaseE' : pass_base_e, + 'BaseTau' : pass_base_tau and entries.dau1_pt > 40 and entries.dau2_pt > 40, + # 'AltBaseTau' : pass_alt_base_tau, + # 'BaseTauNoAltBaseTau': pass_base_tau and not pass_alt_base_tau, + # 'NoBaseTauAltBaseTau': not pass_base_tau and pass_alt_base_tau, + # 'BaseTauAltBaseTau': pass_base_tau and pass_alt_base_tau, + # 'MET' : pass_met, + # 'Tau' : pass_tau, + # 'VBF' : False, + # 'BaseMuMET' : pass_base_mu and pass_met, + # 'BaseEMET' : pass_base_e and pass_met, + # 'BaseTauMET' : pass_base_tau and pass_met, + # 'BaseMuORMET' : pass_base_mu or pass_met, + # 'BaseEORMET' : pass_base_e or pass_met, + # 'BaseTauORMET' : pass_base_tau or pass_met, + # 'BaseMuTau' : pass_base_mu and pass_tau, + # 'BaseETau' : pass_base_e and pass_tau, + # 'BaseTauTau' : pass_base_tau and pass_tau, + # 'BaseMuORTau' : pass_base_mu or pass_tau, + # 'BaseEORTau' : pass_base_e or pass_tau, + # 'BaseTauORTau' : pass_base_tau or pass_tau, + # 'BaseTauORTTJet' : pass_base_tau or pass_ttjet, + # 'BaseTauORTauORTTJet': pass_base_tau or pass_tau or pass_ttjet, + # 'METTau' : pass_met and pass_tau, + # 'METORTau' : pass_met or pass_tau, + # 'BaseMuORMETORTau' : pass_base_mu or pass_met or pass_tau, + # 'BaseEORMETORTau' : pass_base_e or pass_met or pass_tau, + # 'BaseTauORMETORTau' : pass_base_tau or pass_met or pass_tau, + # 'BaseTauORMETORTauORTTJet': pass_base_tau or pass_tau or pass_met or pass_ttjet, + # 'NoBaseMuMET' : not pass_base_mu and pass_met, + # 'BaseMuNoMET' : pass_base_mu and not pass_met, + # 'NoBaseEMET' : not pass_base_e and pass_met, + # 'BaseENoMET' : pass_base_e and not pass_met, + # 'NoBaseTauMET' : not pass_base_tau and pass_met, + # 'BaseTauNoMET' : pass_base_tau and not pass_met, + # 'NoBaseMuNoMETTau' : not pass_base_mu and not pass_met and pass_tau, + # 'NoBaseENoMETTau' : not pass_base_e and not pass_met and pass_tau, + # 'NoBaseTauNoMETTau' : not pass_base_tau and not pass_met and pass_tau, + # 'NoBaseTauTau' : not pass_base_tau and pass_tau, + # 'NoBaseMuMu50' : not pass_base_mu and pass_mu50, + # 'NoBaseMuTau' : not pass_base_mu and pass_tau, + # 'NoBaseETau' : not pass_base_e and pass_tau, + # 'NoBaseMuNoMETMu50': not pass_base_mu and not pass_met and pass_mu50, + # 'NoBaseMuNoTauMu50': not pass_base_mu and not pass_tau and pass_mu50, + # 'NoBaseMuMETNoMu50': not pass_base_mu and pass_met and not pass_mu50, + # 'NoBaseMuMETMu50': not pass_base_mu and pass_met and pass_mu50, + # 'BaseTauNoTau' : pass_base_tau and not pass_tau, + # 'NoBaseTauTTJet' : pass_ttjet and not pass_base_tau, + # 'NoBaseMETNoTau' : not pass_trg and pass_met and not pass_tau, + # 'NoBaseMETORTau': not pass_trg and (pass_met or pass_tau), + # 'NoBaseMETTau': not pass_trg and pass_met and pass_tau, + # 'NoBaseMETORTauORMu50': not pass_trg and (pass_met or pass_tau or pass_mu50), + # 'NoBaseMETORTauOREle28': not pass_trg and (pass_met or pass_tau or pass_ele28), + # 'NoBaseMETNoTTJet': not pass_trg and pass_met and not pass_ttjet, + # 'NoBaseNoMETTTJet': not pass_trg and not pass_met and pass_ttjet, + # 'NoBaseMETTTJet': not pass_trg and pass_met and pass_ttjet, + # 'NoBaseTauTTJet': not pass_trg and pass_tau and pass_ttjet, + # 'NoBaseTauNoTTJet': not pass_trg and pass_tau and not pass_ttjet, + # 'NoBaseNoTauTTJet': not pass_trg and not pass_tau and pass_ttjet, + # 'NoBaseTauMu50': not pass_trg and pass_tau and pass_mu50, + # 'NoBaseTauNoMu50': not pass_trg and pass_tau and not pass_mu50, + # 'NoBaseNoTauMu50': not pass_trg and not pass_tau and pass_mu50, + # 'BaseMETTau' : pass_trg and pass_met and pass_tau, + # 'BaseNoMETNoTau' : pass_trg and not pass_met and not pass_tau, + # 'VBFKin' : False, + # 'NoBaseMETORTauORTTJet' : not pass_trg and (pass_met or pass_tau or pass_ttjet) + # 'NoBaseMu4JetsPNet': not pass_base_mu and pass_4jets_pNet, + # 'NoBaseE4JetsPNet': not pass_base_e and pass_4jets_pNet, + # 'BaseMuOR4JetsPNet': pass_base_mu or pass_4jets_pNet, + # 'BaseEOR4JetsPNet': pass_base_e or pass_4jets_pNet, + # 'BaseTauOR4JetsPNet': pass_base_tau or pass_4jets_pNet, + # 'NoBaseTau4JetsDeepJet': not pass_base_tau and pass_4jets_deepJet, + # 'NoBaseTau4JetsPNet': not pass_base_tau and pass_4jets_pNet, + # 'NoBaseTau4JetsDeepJetNo4JetsPNet': not pass_base_tau and pass_4jets_deepJet and not pass_4jets_pNet, + # 'NoBaseTauNo4JetsDeepJet4JetsPNet': not pass_base_tau and not pass_4jets_deepJet and pass_4jets_pNet, + # 'NoBaseTau4JetsDeepJet4JetsPNet': not pass_base_tau and pass_4jets_deepJet and pass_4jets_pNet, + # 'NoBaseTauNoTTJet4JetsPNet': not pass_base_tau and not pass_ttjet and pass_4jets_pNet, + # 'NoBaseTauTTJetNo4JetsPNet': not pass_base_tau and pass_ttjet and not pass_4jets_pNet, + # 'NoBaseTauTTJet4JetsPNet': not pass_base_tau and pass_ttjet and pass_4jets_pNet, + # 'NoBaseTauORMETORTauORTTJetOR4JetsPNet': not pass_base_tau and (pass_met or pass_tau or pass_ttjet or pass_4jets_pNet), + # 'NoBaseMuNoMET4JetsPNet': not pass_base_mu and not pass_met and pass_4jets_pNet, + # 'NoBaseMuMETNo4JetsPNet': not pass_base_mu and pass_met and not pass_4jets_pNet, + # 'NoBaseMuMET4JetsPNet': not pass_base_mu and pass_met and pass_4jets_pNet, + # 'NoBaseENoMET4JetsPNet': not pass_base_e and not pass_met and pass_4jets_pNet, + # 'NoBaseEMETNo4JetsPNet': not pass_base_e and pass_met and not pass_4jets_pNet, + # 'NoBaseEMET4JetsPNet': not pass_base_e and pass_met and pass_4jets_pNet, + # 'NoBaseTauNoMET4JetsPNet': not pass_base_tau and not pass_met and pass_4jets_pNet, + # 'NoBaseTauMETNo4JetsPNet': not pass_base_tau and pass_met and not pass_4jets_pNet, + # 'NoBaseTauMET4JetsPNet': not pass_base_tau and pass_met and pass_4jets_pNet, + # 'NoBaseMuTauNo4JetsPNet': not pass_base_mu and pass_tau and not pass_4jets_pNet, + # 'NoBaseMuTau4JetsPNet': not pass_base_mu and pass_tau and pass_4jets_pNet, + # 'NoBaseENoTau4JetsPNet': not pass_base_e and not pass_tau and pass_4jets_pNet, + # 'NoBaseETauNo4JetsPNet': not pass_base_e and pass_tau and not pass_4jets_pNet, + # 'NoBaseETau4JetsPNet': not pass_base_e and pass_tau and pass_4jets_pNet, + 'NoBaseTauNoTau4JetsPNet': not pass_base_tau and not pass_tau and pass_4jets_pNet, + 'NoBaseTauTauNo4JetsPNet': not pass_base_tau and pass_tau and not pass_4jets_pNet, + 'NoBaseTauTau4JetsPNet': not pass_base_tau and pass_tau and pass_4jets_pNet, + # 'BaseEORMETORTauOR4JetsPNet': pass_base_e or pass_met or pass_tau or pass_4jets_pNet, + # 'BaseMuORMETORTauORMu50OR4JetsPNet': pass_base_mu or pass_met or pass_tau or pass_mu50 or pass_4jets_pNet, + # 'BaseTauORMETORTauORTTJetOR4JetsPNet': pass_base_tau or pass_met or pass_tau or pass_ttjet or pass_4jets_pNet, + 'BaseTauORTauTauJet': pass_base_tau or pass_ttjet, + 'BaseTauOR4JetsPNet': pass_base_tau or pass_4jets_pNet, + # 'BaseTauOR4JetsPNetMatch': pass_base_tau or pass_4jets_match, + 'BaseTauORTauTauJetOR4JetsPNet': pass_base_tau or pass_ttjet or pass_4jets_pNet, + 'BaseTauORTauTauJetNo4JetsPNet': (pass_base_tau or pass_ttjet) and not pass_4jets_pNet, + 'BaseTauORTauTauJet4JetsPNet' : (pass_base_tau or pass_ttjet) and pass_4jets_pNet, + 'NoBaseTauTauTauJetNo4JetsPNet': not pass_base_tau and pass_ttjet and not pass_4jets_pNet, + 'NoBaseTauNoTauTauJet4JetsPNet': not pass_base_tau and not pass_ttjet and pass_4jets_pNet, + 'NoBaseTauORNoTauTauJet4JetsPNet': not (pass_base_tau or pass_ttjet) and pass_4jets_pNet, + # 'BaseTauORMETORTauTauJetOR4JetsPNet': pass_base_tau or pass_met or pass_ttjet or pass_4jets_pNet, + # 'BaseTauORTauORTauTauJetOR4JetsPNet': pass_base_tau or pass_tau or pass_ttjet or pass_4jets_pNet + } + # assert htypes == list(cuts.keys()) + + w_mc = entries.genWeight + # w_pure = entries.puWweight + # # w_l1pref = entries.L1pref_weight + # w_trig = 1 # entries.trigSF + # w_idiso = entries.IdSF_deep_2d + # w_jetpu = entries.PUjetID_SF + # w_btag = entries.bTagweightReshape + + if utils.is_nan(w_mc) : w_mc=1 + # if utils.is_nan(w_pure) : w_pure=1 + # # if utils.is_nan(w_l1pref) : w_l1pref=1 + # if utils.is_nan(w_trig) : w_trig=1 + # if utils.is_nan(w_idiso) : w_idiso=1 + # if utils.is_nan(w_jetpu) : w_jetpu=1 + # if utils.is_nan(w_btag) : w_btag=1 + evt_weight = 1 # * w_btag + # if evt_weight < 0.: + # print(w_mc, w_pure, w_trig, w_idiso, w_jetpu) + tau_gen_cut = {"etau": None, "mutau": None, + "tautau": 'self.entries["isTau1real"] == 1 and self.entries["isTau2real"] == 1'} + # if not pass_trg and entries.pairType == 0: + # print(channel, entries.pairType, utils.is_channel_consistent(channel, entries.pairType)) + # if utils.is_channel_consistent(channel, entries.pairType): + # if not sel.selection_cuts(lepton_veto=True, bjets_cut=True, + # mass_cut=config_module.mass_cut, + # custom_cut=tau_gen_cut[channel]): + # print("Not pass selection cut") + # continue + + for cat in categories: + # if sel.sel_category(cat): # and entries.ditau_deltaR > deltaR: + ntotal[cat] += 1 + + if entries.pairType == 0: + chn = 'mutau' + elif entries.pairType == 1: + chn = 'etau' + elif entries.pairType == 2: + chn = 'tautau' + + # if in_met: + # reg = 'met' + # elif in_tau: + # reg = 'tau' + # elif in_legacy: + # reg = 'legacy' + # else: + # norphans[cat] += 1 + # continue + # assert reg in regions + + for key,cut in cuts.items(): + if cut: + # print(key, cat, chn) + for i in binning.keys(): + z = 0 + if 'bjet1' in i: + if 'eta' in i: + z = entries['Jet_eta'][entries['bjet1_JetIdx']] + if 'pt' in i: + z = entries['Jet_pt'][entries['bjet1_JetIdx']] + if 'pNet' in i: + z = entries['Jet_btagPNetB'][entries['bjet1_JetIdx']] + elif 'bjet2' in i: + if 'eta' in i: + z = entries['Jet_eta'][entries['bjet2_JetIdx']] + if 'pt' in i: + z = entries['Jet_pt'][entries['bjet2_JetIdx']] + if 'pNet' in i: + z = entries['Jet_btagPNetB'][entries['bjet2_JetIdx']] + else: + z = entries[i] + ahistos[key][cat][chn][i].fill(z, weight=evt_weight) + + # all MC and signal must be rescaled to get the correct number of events + # for key,_ in cuts.items(): + # for reg in regions: + # for cat in categories: + # for cc in ['mutau', 'etau', 'tautau']: + # print("lumi", utils.get_lumi(args.year), utils.total_sum_weights(glob_files[0].replace("PreprocessRDF", "PreCounter").replace("/cat_base_selection", "").replace(".root", ".json"), isdata=False)) + # ahistos[key][reg][cat][cc] *= (utils.get_lumi(args.year) / + # utils.total_sum_weights(glob_files[0].replace("PreprocessRDF", "PreCounter").replace("/cat_base_selection", "").replace(".root", ".json"), isdata=False)) + + # with open(outname, "wb") as f: + # pickle.dump(ahistos, f) + file = uproot.recreate(outname) + for key,_ in cuts.items(): + for cat in categories: + for chn in ['mutau', 'etau', 'tautau']: + for i in binning.keys(): + file[str(key) + '_' + str(cat) + '_' + str(chn) + "_" + str(i)] = ahistos[key][cat][chn][i] + + for cat in categories: + orph_frac = float(norphans[cat])/ntotal[cat] + if orph_frac > 0.1: + print('{}% orphans ({}/{}). This is unusual.'.format(orph_frac, norphans[cat], ntotal[cat])) + print(met_region) + print(tau_region) + print(legacy_region) + print('Category {} (m(X)=0GeV) had {} orphans ({}%)'.format(cat, norphans[cat], orph_frac)) + print('Raw histograms saved in {}.'.format(outname), flush=True) + +if __name__ == '__main__': + extensions = ('png',) #('png', 'pdf') + triggers = {'etau': ("HLT_Ele30_WPTight_Gsf", + "HLT_Ele24_eta2p1_WPTight_Gsf_LooseDeepTauPFTauHPS30_eta2p1_CrossL1"), + 'mutau': ('HLT_IsoMu24', 'HLT_IsoMu20_eta2p1_LooseDeepTauPFTauHPS27_eta2p1_CrossL1'), + 'tautau': ('HLT_DoubleMediumDeepTauPFTauHPS35_L2NN_eta2p1',) + } + binning = { + # 'metnomu_et': (20, 0, 450), + # 'dau1_pt': (30, 0, 450), + # 'dau1_eta': (20, -2.5, 2.5), + # 'dau2_iso': (20, 0.88, 1.005), + # 'dau2_pt': (30, 0, 400), + # 'dau2_eta': (20, -2.5, 2.5), + # 'ditau_deltaR': (30, 0.3, 1.3), + # 'dib_deltaR': (25, 0, 2.5), + # 'bH_pt': (20, 70, 600), + # 'bH_mass': (30, 0, 280), + # 'tauH_mass': (30, 0, 170), + # 'tauH_pt': (30, 0, 500), + # 'tauH_SVFIT_mass': (30, 0, 250), + # 'tauH_SVFIT_pt': (20, 200, 650), + # 'bjet1_pt': (25, 10, 600), + # 'bjet2_pt': (25, 10, 550), + # 'bjet1_eta': (20, -2.5, 2.5), + # 'bjet2_eta': (20, -2.5, 2.5), + } + variables = tuple(binning.keys()) + ('HHKin_mass', 'dau1_iso') + + categories = ('baseline',) #('baseline', 's1b1jresolvedMcut', 's2b0jresolvedMcut', 'sboostedLLMcut') + + htypes = [ + 'All', 'BaseMu', 'BaseE', 'BaseTau', 'MET', 'Tau', 'VBF', 'BaseMuMET', 'BaseEMET', + 'BaseTauMET', 'BaseMuORMET', 'BaseEORMET', 'BaseTauORMET', 'BaseMuTau', 'BaseETau', 'BaseTauTau', 'BaseMuORTau', + 'BaseEORTau', 'BaseTauORTau', 'BaseTauORTTJet', 'BaseTauORTauORTTJet', + 'METTau', 'METORTau', 'BaseMuORMETORTau', 'BaseEORMETORTau' , 'BaseTauORMETORTau', 'BaseTauORMETORTauORTTJet', 'NoBaseMuMET', + 'BaseMuNoMET', 'NoBaseEMET', 'BaseENoMET', 'NoBaseTauMET', 'BaseTauNoMET', 'NoBaseMuNoMETTau', 'NoBaseENoMETTau', 'NoBaseTauNoMETTau', 'NoBaseTauTau', 'NoBaseMuTau', 'NoBaseETau', + # 'NoBaseMuNoMETMu50', 'NoBaseMuNoTauMu50', 'NoBaseMuMETNoMu50', 'NoBaseMuMETMu50', + 'BaseTauNoTau', 'NoBaseTauTTJet', + 'NoBaseMu4JetsPNet', 'NoBaseE4JetsPNet', 'BaseMuOR4JetsPNet', 'BaseEOR4JetsPNet', 'BaseTauOR4JetsPNet', 'NoBaseTau4JetsDeepJet', 'NoBaseTau4JetsPNet', 'NoBaseTau4JetsDeepJetNo4JetsPNet', + 'NoBaseTauNo4JetsDeepJet4JetsPNet', 'NoBaseTau4JetsDeepJet4JetsPNet', + 'NoBaseTauNoTTJet4JetsPNet', 'NoBaseTauTTJetNo4JetsPNet', 'NoBaseTauTTJet4JetsPNet', 'NoBaseTauORMETORTauORTTJetOR4JetsPNet', + 'NoBaseMuNoMET4JetsPNet', 'NoBaseMuMETNo4JetsPNet', 'NoBaseMuMET4JetsPNet', 'NoBaseENoMET4JetsPNet', 'NoBaseEMETNo4JetsPNet', 'NoBaseEMET4JetsPNet', 'NoBaseTauNoMET4JetsPNet' , 'NoBaseTauMETNo4JetsPNet' , + 'NoBaseTauMET4JetsPNet', 'NoBaseMuTauNo4JetsPNet', 'NoBaseMuTau4JetsPNet', 'NoBaseENoTau4JetsPNet', 'NoBaseETauNo4JetsPNet', 'NoBaseETau4JetsPNet', 'NoBaseTauNoTau4JetsPNet', 'NoBaseTauTauNo4JetsPNet', 'NoBaseTauTau4JetsPNet', + 'BaseEORMETORTauOR4JetsPNet', 'BaseTauORMETORTauORTTJetOR4JetsPNet', + 'BaseTauORTauTauJet', 'BaseTauOR4JetsPNet', 'BaseTauORTauTauJetOR4JetsPNet', 'BaseTauORTauTauJetNo4JetsPNet', 'BaseTauORTauTauJet4JetsPNet', 'NoBaseTauTauTauJetNo4JetsPNet', + 'NoBaseTauNoTauTauJet4JetsPNet', 'BaseTauORMETORTauTauJetOR4JetsPNet', 'BaseTauORTauORTauTauJetOR4JetsPNet', 'NoBaseTauORNoTauTauJet4JetsPNet'] + + # Parse input arguments + desc = 'Producer trigger histograms.\n' + desc += "Run example: python tests/test_trigger_regions.py --indir /data_CMS/cms/alves/HHresonant_SKIMS/SKIMS_UL18_EOSv4_Signal/ --masses 400 500 600 700 800 900 1000 1250 1500 --channels ETau --met_turnon 180 --region_cuts 40 40 --copy" + parser = argparse.ArgumentParser(description=desc, formatter_class=argparse.RawTextHelpFormatter) + + parser.add_argument('--indir', required=True, type=str, + help='Full path of ROOT input file') + # parser.add_argument('--masses', required=True, nargs='+', type=str, + # help='Resonance mass') + parser.add_argument('--channel', required=True, type=str, + help='Select the channel over which the workflow will be run.' ) + parser.add_argument('--year', required=True, type=str, choices=('2016', '2016APV', '2017', '2018', '2022'), + help='Select the year over which the workflow will be run.' ) + # parser.add_argument('--spin', required=True, type=int, choices=(0, 2), + # help='Select the spin hypothesis over which the workflow will be run.' ) + parser.add_argument('--deltaR', type=float, default=0.5, help='DeltaR between the two leptons.' ) + parser.add_argument('--plot', action='store_true', + help='Reuse previously produced data for quick plot changes.') + parser.add_argument('--copy', action='store_true', + help='Copy the outputs to EOS at the end.') + parser.add_argument('--notext', action='store_true', help='Square diagram without text.') + parser.add_argument('--sequential', action='store_true', + help='Do not use the multiprocess package.') + parser.add_argument('--bigtau', action='store_true', + help='Consider a larger single tau region, reducing the ditau one.') + parser.add_argument('--met_turnon', type=float, default=180, + help='MET trigger turnon cut [GeV].' ) + parser.add_argument('--region_cuts', required=False, type=float, nargs=2, default=(190, 190), + help='High/low regions pT1 and pT2 selection cuts [GeV].' ) + parser.add_argument('--configuration', dest='configuration', required=True, + help='Name of the configuration module to use.') + args = utils.parse_args(parser) + + met_turnon = args.met_turnon + regcuts = args.region_cuts + ptcuts = utils.get_ptcuts(args.channel, args.year) + + main_dir = os.path.join(os.path.join('/t3home/', os.environ['USER'], 'TriggerScaleFactors'), + '_'.join((args.channel, *[str(x) for x in regcuts], + 'DR', str(args.deltaR), 'PT', *[str(x) for x in ptcuts], 'TURNON', + str(met_turnon)))) + if args.bigtau: + main_dir += '_BIGTAU' + + regions = ('legacy', 'met', 'tau') + + #### run main function ### + if not args.plot: + # for sample in args.masses: + trigger_regions(args.indir, args.channel, args.year, args.deltaR) + # if args.sequential: + # pass + # else: + # pool = multiprocessing.Pool(processes=6) + # pool.starmap(trigger_regions, + # zip(it.repeat(args.indir), it.repeat(args.channel), it.repeat(args.year), it.repeat(args.deltaR))) + + ########################### + + sum_stats, err_sum_stats = ([] for _ in range(2)) + contam1, contam2, contam1_errors, contam2_errors = ([] for _ in range(4)) + from_directory = os.path.join(main_dir, args.channel) + outname = get_outname(args.channel, args.bigtau) + with open(outname, "rb") as f: + ahistos = pickle.load(f) + + # write csv header, one per category + out_counts = [] + for cat in categories: + out_counts.append( os.path.join(from_directory, cat, 'counts') ) + utils.create_single_dir(out_counts[-1]) + with open(os.path.join(out_counts[-1], 'table.csv'), 'w') as f: + reader = csv.writer(f, delimiter=',', quotechar='|') + header_row = ['Region'] + header_row.extend(htypes) + reader.writerow(header_row) + + # plot histograms and fill CSV with histogram integrals + c_legacy_trg, c_met_trg, c_tau_trg = ({} for _ in range(3)) + + acounts = rec_dd() + for reg in regions: + for key,cut in ahistos.items(): + acounts[key][reg] = round(ahistos[key][reg]["baseline"].values().sum(), 2) + + # append to table, one line per region + with open(os.path.join(out_counts[categories.index(cat)], 'table.csv'), 'a') as f: + reader = csv.writer(f, delimiter=',', quotechar='|') + row = [reg] + row.extend([acounts[k][reg] for k in acounts.keys()]) + reader.writerow(row) + + if reg=='legacy': + c_legacy_trg[reg] = acounts["Base"][reg] + c_met_trg[reg] = acounts["NoBaseMET"][reg] + c_tau_trg[reg] = acounts["NoBaseNoMETTau"][reg] + elif reg=='met': + c_legacy_trg[reg] = acounts["BaseNoMET"][reg] + c_met_trg[reg] = acounts["MET"][reg] + c_tau_trg[reg] = acounts["NoBaseNoMETTau"][reg] + elif reg=='tau': + c_legacy_trg[reg] = acounts["BaseNoTau"][reg] + c_met_trg[reg] = acounts["NoBaseMETNoTau"][reg] + c_tau_trg[reg] = acounts["Tau"][reg] + + text = {'out': os.path.join(out_counts[categories.index(cat)], 'diagram.html')} + sq_res = square_diagram(c_legacy_trg, c_met_trg, c_tau_trg, args.channel, + [str(x) for x in ptcuts], text=text, notext=args.notext, + bigtau=args.bigtau) + c1, c2, e1, e2 = sq_res + contam1.append(c1) + contam2.append(c2) + contam1_errors.append(e1) + contam2_errors.append(e2) + + stats_l = [c_legacy_trg['legacy'],c_met_trg['legacy'],c_tau_trg['tau'],c_legacy_trg['tau']] + stats = sum(stats_l) + estats = sum(np.sqrt(stats_l)) + sum_stats.append(stats) + err_sum_stats.append(estats) + + contam1 = [float(x) for x in contam1] + contam2 = [float(x) for x in contam2] + contam1_errors = [float(x) for x in contam1_errors] + contam2_errors = [float(x) for x in contam2_errors] + # masses = [float(x) for x in args.masses] + contamination_save('data', '_'.join([str(x) for x in regcuts]) + '_' + args.channel, + contam1, contam2, contam1_errors, contam2_errors) + stats_save('data', '_'.join([str(x) for x in regcuts]) + '_' + args.channel, + sum_stats, err_sum_stats, mode='a') + + if args.copy: + import subprocess + to_directory = os.path.join('/eos/home-b/bfontana/www/TriggerScaleFactors', main_dir) + to_directory = os.path.join(to_directory, args.channel) + + for sample in args.masses: + sample_from = os.path.join(from_directory, sample) + print('Copying: {}\t\t--->\t{}'.format(sample_from, to_directory), flush=True) + subprocess.run(['rsync', '-ah', sample_from, to_directory])