From cdb64f9d286d20527b145bce5d9a879bfe828261 Mon Sep 17 00:00:00 2001 From: ValVau <109755950+ValVau@users.noreply.github.com> Date: Tue, 22 Oct 2024 21:24:47 +0200 Subject: [PATCH] BTV developments. C_tt_workflow & QCD_soft_mu workflow. QCD studies (#101) * feat: Add new wfs & 2D correlations -scripts:Added 2D_plotter and a correlation plot maker used in the QCD studies -utils: added to AK4_parameters LSF for 22, 23; new hists for QCD smu wf and ttsemilep_valid_sf -helpers: update_branch fixed ProbC, added definitions_for_corr -workflows: added new workflows ttsemilep_valid_sf (additional c selection) and QCD soft muon wf * feat: Add new ci * Fix from comments * Polishing documentation --- .github/workflows/BTA_workflow.yml | 11 +- .github/workflows/QCD_workflow.yml | 42 +- .github/workflows/ctag_DY_workflow.yml | 11 +- .github/workflows/ctag_Wc_workflow.yml | 12 +- .github/workflows/ctag_ttbar_workflow.yml | 11 +- .github/workflows/ttbar_workflow.yml | 26 +- .github/workflows/validation.yml | 11 +- README.md | 17 + scripts/2Dhistograms.py | 766 + scripts/correlation_plots.py | 998 ++ ...BTagMu_AK4DiJet40_Mu5_run355374_362760.csv | 1169 ++ ...BTagMu_AK4DiJet40_Mu5_run366727_370790.csv | 1346 ++ ...fo_BTagMu_DiJet40_Mu5_run366727_370790.csv | 1 + .../HLTinfo_PFJet40_run355374_362760.csv | 1 + ...TagMu_AK4DiJet40_Mu5_run355374_362760.json | 1 + ...TagMu_AK4DiJet40_Mu5_run366727_370790.json | 1 + .../helpers/definitions.py | 12160 ++++++++-------- .../helpers/definitions_for_corr.py | 6124 ++++++++ .../helpers/update_branch.py | 19 +- .../utils/AK4_parameters.py | 31 +- .../utils/histogrammer.py | 113 +- .../workflows/QCD_soft_mu_validation.py | 509 + .../workflows/QCD_validation.py | 160 +- .../workflows/__init__.py | 11 +- .../workflows/ttsemilep_valid_sf.py | 375 +- 25 files changed, 17669 insertions(+), 6257 deletions(-) create mode 100644 scripts/2Dhistograms.py create mode 100644 scripts/correlation_plots.py create mode 100644 src/BTVNanoCommissioning/data/Prescales/HLTinfo_BTagMu_AK4DiJet40_Mu5_run355374_362760.csv create mode 100644 src/BTVNanoCommissioning/data/Prescales/HLTinfo_BTagMu_AK4DiJet40_Mu5_run366727_370790.csv create mode 100644 src/BTVNanoCommissioning/data/Prescales/HLTinfo_BTagMu_DiJet40_Mu5_run366727_370790.csv create mode 100644 src/BTVNanoCommissioning/data/Prescales/HLTinfo_PFJet40_run355374_362760.csv create mode 100644 src/BTVNanoCommissioning/data/Prescales/ps_weight_BTagMu_AK4DiJet40_Mu5_run355374_362760.json create mode 100644 src/BTVNanoCommissioning/data/Prescales/ps_weight_BTagMu_AK4DiJet40_Mu5_run366727_370790.json create mode 100644 src/BTVNanoCommissioning/helpers/definitions_for_corr.py create mode 100644 src/BTVNanoCommissioning/workflows/QCD_soft_mu_validation.py diff --git a/.github/workflows/BTA_workflow.yml b/.github/workflows/BTA_workflow.yml index 5fb52054..eea74212 100644 --- a/.github/workflows/BTA_workflow.yml +++ b/.github/workflows/BTA_workflow.yml @@ -36,16 +36,7 @@ jobs: steps: - uses: actions/checkout@v2 - - name: update submodules - env: - SSHKEY: ${{ secrets.GIT_CERN_SSH_PRIVATE }} - run: | - mkdir $HOME/.ssh - echo "$SSHKEY" > $HOME/.ssh/id_rsa - chmod 600 $HOME/.ssh/id_rsa - echo "HOST *" > ~/.ssh/config - echo "StrictHostKeyChecking no" >> ~/.ssh/config - git submodule update --init --recursive + - uses: cvmfs-contrib/github-action-cvmfs@v4 with: cvmfs_repositories: 'grid.cern.ch' diff --git a/.github/workflows/QCD_workflow.yml b/.github/workflows/QCD_workflow.yml index c4589eec..d9bb9adc 100644 --- a/.github/workflows/QCD_workflow.yml +++ b/.github/workflows/QCD_workflow.yml @@ -38,17 +38,7 @@ jobs: steps: - uses: actions/checkout@v2 - - name: update submodules - env: - SSHKEY: ${{ secrets.GIT_CERN_SSH_PRIVATE }} - run: | - mkdir $HOME/.ssh - echo "$SSHKEY" > $HOME/.ssh/id_rsa - ls -lrt $HOME/.ssh - chmod 600 $HOME/.ssh/id_rsa - echo "HOST *" > ~/.ssh/config - echo "StrictHostKeyChecking no" >> ~/.ssh/config - git submodule update --init --recursive + - uses: cvmfs-contrib/github-action-cvmfs@v4 with: cvmfs_repositories: 'grid.cern.ch' @@ -117,17 +107,19 @@ jobs: fi python runner.py --workflow QCD_sf --json metadata/test_bta_run3.json --executor iterative $opts - # - name: QCD mu workflows with correctionlib - # run: | - # message=$(git log -1 --pretty=format:'%s') - # if [[ $string == *"ci:skip array"* ]]; then - # opts=$(echo "$opts" | sed 's/--isArray //g') - # fi - # if [[ $string == *"ci:skip syst"* ]]; then - # opts=$(echo "$opts" | sed 's/--isSyst all//g') - # elif [[ $string == *"ci:JERC_split"* ]]; then - # opts=$(echo "$opts" | sed 's/--isSyst all/--isSyst JERC_split/g') - # elif [[ $string == *"ci:weight_only"* ]]; then - # opts=$(echo "$opts" | sed 's/--isSyst all/--isSyst weight_only/g') - # fi - # python runner.py --workflow QCD_mu --json metadata/test_bta_run3.json --executor iterative --overwrite $opts \ No newline at end of file + - name: QCD soft mu workflows with correctionlib + run: | + message=$(git log -1 --pretty=format:'%s') + if [[ $string == *"ci:skip array"* ]]; then + opts=$(echo "$opts" | sed 's/--isArray //g') + fi + if [[ $string == *"ci:skip syst"* ]]; then + opts=$(echo "$opts" | sed 's/--isSyst all//g') + elif [[ $string == *"ci:JERC_split"* ]]; then + opts=$(echo "$opts" | sed 's/--isSyst all/--isSyst JERC_split/g') + elif [[ $string == *"ci:weight_only"* ]]; then + opts=$(echo "$opts" | sed 's/--isSyst all/--isSyst weight_only/g') + fi + + python runner.py --workflow QCD_smu_sf --json metadata/test_bta_run3.json --executor iterative --overwrite $opts + diff --git a/.github/workflows/ctag_DY_workflow.yml b/.github/workflows/ctag_DY_workflow.yml index 927dc0c3..9e19a329 100644 --- a/.github/workflows/ctag_DY_workflow.yml +++ b/.github/workflows/ctag_DY_workflow.yml @@ -36,16 +36,7 @@ jobs: steps: - uses: actions/checkout@v2 - - name: update submodules - env: - SSHKEY: ${{ secrets.GIT_CERN_SSH_PRIVATE }} - run: | - mkdir $HOME/.ssh - echo "$SSHKEY" > $HOME/.ssh/id_rsa - chmod 600 $HOME/.ssh/id_rsa - echo "HOST *" > ~/.ssh/config - echo "StrictHostKeyChecking no" >> ~/.ssh/config - git submodule update --init --recursive + - uses: cvmfs-contrib/github-action-cvmfs@v4 with: cvmfs_repositories: 'grid.cern.ch' diff --git a/.github/workflows/ctag_Wc_workflow.yml b/.github/workflows/ctag_Wc_workflow.yml index c4f3fc04..bca370f6 100644 --- a/.github/workflows/ctag_Wc_workflow.yml +++ b/.github/workflows/ctag_Wc_workflow.yml @@ -38,17 +38,7 @@ jobs: steps: - uses: actions/checkout@v2 - - name: update submodules - env: - SSHKEY: ${{ secrets.GIT_CERN_SSH_PRIVATE }} - run: | - mkdir $HOME/.ssh - echo "$SSHKEY" > $HOME/.ssh/id_rsa - ls -lrt $HOME/.ssh - chmod 600 $HOME/.ssh/id_rsa - echo "HOST *" > ~/.ssh/config - echo "StrictHostKeyChecking no" >> ~/.ssh/config - git submodule update --init --recursive + - uses: cvmfs-contrib/github-action-cvmfs@v4 with: cvmfs_repositories: 'grid.cern.ch' diff --git a/.github/workflows/ctag_ttbar_workflow.yml b/.github/workflows/ctag_ttbar_workflow.yml index 10218323..d8482826 100644 --- a/.github/workflows/ctag_ttbar_workflow.yml +++ b/.github/workflows/ctag_ttbar_workflow.yml @@ -36,16 +36,7 @@ jobs: steps: - uses: actions/checkout@v2 - - name: update submodules - env: - SSHKEY: ${{ secrets.GIT_CERN_SSH_PRIVATE }} - run: | - mkdir $HOME/.ssh - echo "$SSHKEY" > $HOME/.ssh/id_rsa - chmod 600 $HOME/.ssh/id_rsa - echo "HOST *" > ~/.ssh/config - echo "StrictHostKeyChecking no" >> ~/.ssh/config - git submodule update --init --recursive + - uses: cvmfs-contrib/github-action-cvmfs@v4 with: cvmfs_repositories: 'grid.cern.ch' diff --git a/.github/workflows/ttbar_workflow.yml b/.github/workflows/ttbar_workflow.yml index 0d37e7c7..e4327638 100644 --- a/.github/workflows/ttbar_workflow.yml +++ b/.github/workflows/ttbar_workflow.yml @@ -36,16 +36,7 @@ jobs: steps: - uses: actions/checkout@v2 - - name: update submodules - env: - SSHKEY: ${{ secrets.GIT_CERN_SSH_PRIVATE }} - run: | - mkdir $HOME/.ssh - echo "$SSHKEY" > $HOME/.ssh/id_rsa - chmod 600 $HOME/.ssh/id_rsa - echo "HOST *" > ~/.ssh/config - echo "StrictHostKeyChecking no" >> ~/.ssh/config - git submodule update --init --recursive + - uses: cvmfs-contrib/github-action-cvmfs@v4 with: cvmfs_repositories: 'grid.cern.ch' @@ -112,6 +103,21 @@ jobs: opts=$(echo "$opts" | sed 's/--isSyst all/--isSyst weight_only/g') fi python runner.py --workflow ttsemilep_sf --json metadata/test_bta_run3.json --limit 1 --executor iterative $opts + + - name: semileptonic + c ttbar workflows with correctionlib + run: | + string=$(git log -1 --pretty=format:'%s') + if [[ $string == *"ci:skip array"* ]]; then + opts=$(echo "$opts" | sed 's/--isArray //g') + fi + if [[ $string == *"ci:skip syst"* ]]; then + opts=$(echo "$opts" | sed 's/--isSyst all//g') + elif [[ $string == *"ci:JERC_split"* ]]; then + opts=$(echo "$opts" | sed 's/--isSyst all/--isSyst JERC_split/g') + elif [[ $string == *"ci:weight_only"* ]]; then + opts=$(echo "$opts" | sed 's/--isSyst all/--isSyst weight_only/g') + fi + python runner.py --workflow c_ttsemilep_sf --json metadata/test_bta_run3.json --limit 1 --executor iterative $opts - name: btag dileptonic ttbar workflows with correctionlib diff --git a/.github/workflows/validation.yml b/.github/workflows/validation.yml index 521a0a23..b8628c3e 100644 --- a/.github/workflows/validation.yml +++ b/.github/workflows/validation.yml @@ -36,16 +36,7 @@ jobs: steps: - uses: actions/checkout@v2 - - name: update submodules - env: - SSHKEY: ${{ secrets.GIT_CERN_SSH_PRIVATE }} - run: | - mkdir $HOME/.ssh - echo "$SSHKEY" > $HOME/.ssh/id_rsa - chmod 600 $HOME/.ssh/id_rsa - echo "HOST *" > ~/.ssh/config - echo "StrictHostKeyChecking no" >> ~/.ssh/config - git submodule update --init --recursive + - uses: cvmfs-contrib/github-action-cvmfs@v4 with: cvmfs_repositories: 'grid.cern.ch' diff --git a/README.md b/README.md index 628c3139..33f08917 100644 --- a/README.md +++ b/README.md @@ -644,6 +644,23 @@ Extract the ROCs for different tagger and efficiencies from validation workflow python scripts/validation_plot.py -i $INPUT_COFFEA -v $VERSION ``` +### Correlation plots study + +You can perform a study of linear correlations of b-tagging input variables. Additionally, soft muon variables may be added into the study by requesting `--SMu` argument. If you wan to limit the outputs only to DeepFlavB, PNetB and RobustParTAK4B, you can use the `--limit_outputs` option. If you want to use only the set of variables used for tagger training, not just all the input variables, then use the option `--limit_inputs`. To limit number of files read, make use of option `--max_files`. In case your study requires splitting samples by flavour, use `--flavour_split`. `--split_region_b` performs a sample splitting based on the DeepFlavB >/< 0.5 + +```python +python correlation_plots.py $input_folder [--max_files $nmax_files --SMu --limit_inputs --limit_outputs --specify_MC --flavour_split --split_region_b] +``` + +### 2D plots (Correlation study-related) + +To further investigate the correlations, one can create the 2D plots of the variables used in this study. Inputs and optional arguments are the same as for the correlation plots study. + +```python +python 2Dhistogramms.py $input_folder [--max_files $nmax_files --SMu --limit_inputs --limit_outputs --specify_MC --flavour_split --split_region_b] +``` + + ## Store histograms from coffea file Use `scripts/make_template.py` to dump 1D/2D histogram from `.coffea` to `TH1D/TH2D` with hist. MC histograms can be reweighted to according to luminosity value given via `--lumi`. You can also merge several files diff --git a/scripts/2Dhistograms.py b/scripts/2Dhistograms.py new file mode 100644 index 00000000..c5cc2c31 --- /dev/null +++ b/scripts/2Dhistograms.py @@ -0,0 +1,766 @@ +import psutil +import os +from tqdm import tqdm +import uproot +import warnings +import argparse +import glob +import matplotlib.pyplot as plt +from concurrent.futures import ThreadPoolExecutor, as_completed +import pandas as pd +import numpy as np +import seaborn as sns +import awkward as ak +from BTVNanoCommissioning.helpers.definitions import definitions_dict +from BTVNanoCommissioning.helpers.definitions import disc_list + + +# Suppress the specific FutureWarning from uproot +warnings.filterwarnings("ignore", category=FutureWarning, module="uproot") +warnings.filterwarnings("ignore", category=FutureWarning, module="seaborn") + + +filtered_names = [ + name for name in disc_list if name.endswith("B") and not name.endswith("CvB") +] + + +def is_within_range(array, min_value, max_value): + """Check if all values in the array are within the specified range.""" + return ak.all((array >= min_value) & (array <= max_value)) + + +def load_single_file( + file_path, + base_dir, + chunk_size=100000, + temp_subdir="temp_data", + limit_inputs=False, + limit_outputs=False, + SMu=False, + flavour_split=False, +): + temp_dir = os.path.join(base_dir, temp_subdir) + if not os.path.exists(temp_dir): + os.makedirs(temp_dir, exist_ok=True) + + with uproot.open(file_path) as file: + if "Events" not in file: + print(f"Skipping file without 'Events' key: {file_path}") + return + + tree = file["Events"] + if tree.num_entries == 0: + print(f"Skipping empty file: {file_path}") + return + + # Apply limit_inputs filter + if limit_inputs: + filtered_definitions_dict = { + k: v + for k, v in definitions_dict.items() + if any(sub in k for sub in ["Npfcan", "Cpfcan", "sv"]) + and k.endswith("_0") + or not any(sub in k for sub in ["Npfcan", "Cpfcan", "sv"]) + } + else: + filtered_definitions_dict = definitions_dict + + # Initialize filtered_names + filtered_names = [ + name + for name in disc_list + if name.endswith("B") and not name.endswith("CvB") + ] + + # Apply limit_outputs filter + if limit_outputs: + filtered_names = [ + name + for name in filtered_names + if name.endswith("B") + and "Neg" not in name + and "btagPNetProbB" not in name + ] + + # Filter branches to include only those with 'SelJets' in their name, containing keys from filtered_definitions_dict, and matching filtered_names + branches = [ + branch + for branch in tree.keys() + if "MuonJet" in branch + and ( + any(key in branch for key in filtered_definitions_dict.keys()) + or any(name in branch for name in filtered_names) + ) + ] + + if limit_outputs: + branches = [ + branch + for branch in branches + if not any(name in branch for name in filtered_names) + or any( + name in branch for name in filtered_names if branch.endswith("B") + ) + ] + + # Include branches that have 'SMu' in their name if the SMu flag is True + if SMu: + smu_branches = [branch for branch in tree.keys() if "SoftMuon" in branch] + smu_branches.append("MuonJet_muEF") + if limit_inputs: + smu_branches = [ + "SoftMuon_tunepRelPt", + "SoftMuon_pfRelIso03_chg", + "SoftMuon_eta", + "SoftMuon_phi", + "SoftMuon_jetPtRelv2", + "SoftMuon_dxy", + "SoftMuon_dxyErr", + "SoftMuon_jetRelIso", + "SoftMuon_sip3d", + "SoftMuon_dzErr", + "SoftMuon_pfRelIso04_all", + "SoftMuon_ip3d", + "SoftMuon_pt", + "SoftMuon_ptErr", + "SoftMuon_tkRelIso", + "SoftMuon_dz", + "SoftMuon_pfRelIso03_all", + "MuonJet_muEF", + "SoftMuon_charge", + ] + branches.extend(smu_branches) + branches = list(set(branches)) # Remove duplicates + + # Include the flavour column if flavour_split is enabled + if flavour_split: + branches.append("MuonJet_hadronFlavour") + + # Extract manual ranges for x and y columns if they don't start with SelJet_ or SMu_ + x_limits_dict = {} + for x_col in branches: + if not x_col.startswith(("SelJet_", "SoftMuon_", "MuonJet_")): + x_limits = definitions_dict.get(x_col, {}).get("manual_ranges", None) + else: + x_col_str = ( + x_col.lstrip("SelJet_").lstrip("SoftMuon_").lstrip("MuonJet_") + ) + x_limits = definitions_dict.get(x_col_str, {}).get( + "manual_ranges", None + ) + + x_limits_dict[x_col] = x_limits + + for i, data_chunk in enumerate( + tree.iterate(branches, library="pd", step_size=chunk_size) + ): + if isinstance(data_chunk, tuple): + data_chunk = data_chunk[0] # Extract the DataFrame from the tuple + + # Filter the data based on MuJet_Cpfcan_ptrel_0 + ###data_chunk = data_chunk[data_chunk['MuJet_DeepJet_Cpfcan_ptrel_0'] >= -20] + + # Filter the data based on x_limits + for col, limits in x_limits_dict.items(): + if limits is not None: + min_value, max_value = limits + data_chunk = data_chunk[ + (data_chunk[col] >= min_value) & (data_chunk[col] <= max_value) + ] + temp_file_path = os.path.join( + temp_dir, f"{os.path.basename(file_path)}_chunk_{i}.parquet" + ) + data_chunk.to_parquet(temp_file_path) + + +def inspect_first_file( + file_path, limit_inputs=False, limit_outputs=False, SMu=False, flavour_split=False +): + with uproot.open(file_path) as file: + print(f"Inspecting file: {file_path}") + print("Keys:", file.keys()) + if "Events" in file: + tree = file["Events"] + print("Number of entries in 'Events':", tree.num_entries) + + # Apply limit_inputs filter + if limit_inputs: + filtered_definitions_dict = { + k: v + for k, v in definitions_dict.items() + if any(sub in k for sub in ["Npfcan", "Cpfcan", "sv"]) + and k.endswith("_0") + or not any(sub in k for sub in ["Npfcan", "Cpfcan", "sv"]) + } + else: + filtered_definitions_dict = definitions_dict + + # Initialize filtered_names + filtered_names = [ + name + for name in disc_list + if name.endswith("B") and not name.endswith("CvB") + ] + + # Apply limit_outputs filter + if limit_outputs: + filtered_names = [ + name + for name in filtered_names + if name.endswith("B") + and "Neg" not in name + and "btagPNetProbB" not in name + ] + + print(tree.keys()) + # Filter branches to include only those with 'SelJets' in their name, containing keys from definitions_dict, and matching filtered_names + + branches = [ + branch + for branch in tree.keys() + if "MuJet" in branch + and ( + any(key in branch for key in filtered_definitions_dict.keys()) + or any(name in branch for name in filtered_names) + ) + ] + if limit_outputs: + branches = [ + branch + for branch in branches + if not any(name in branch for name in filtered_names) + or any( + name in branch + for name in filtered_names + if branch.endswith(name) + ) + ] + print("Filtered Branches:", branches) + print(len(branches)) + if SMu: + smu_branches = [ + branch for branch in tree.keys() if "SoftMuon" in branch + ] + smu_branches.append("MuonJet_muEF") + print(smu_branches) + if limit_inputs: + smu_branches = [ + "SoftMuon_tunepRelPt", + "SoftMuon_pfRelIso03_chg", + "SoftMuon_eta", + "SoftMuon_phi", + "SoftMuon_jetPtRelv2", + "SoftMuon_dxy", + "SoftMuon_dxyErr", + "SoftMuon_jetRelIso", + "SoftMuon_sip3d", + "SoftMuon_dzErr", + "SoftMuon_pfRelIso04_all", + "SoftMuon_ip3d", + "SoftMuon_pt", + "SoftMuon_ptErr", + "SoftMuon_tkRelIso", + "SoftMuon_dz", + "SoftMuon_pfRelIso03_all", + "MuonJet_muEF", + "SoftMuon_charge", + ] + + branches.extend(smu_branches) + branches = list(set(branches)) # Remove duplicates + print("Branches with SMu:", smu_branches) + + # Include the flavour column if flavour_split is enabled + if flavour_split: + branches.append("MuonJet_hadronFlavour") + + print("Branches:", branches) + + # Find branches that contain any of the keys from definitions_dict + matching_keys = [ + branch + for branch in branches + if any(key in branch for key in definitions_dict.keys()) + ] + print("Branches containing keys from definitions_dict:", matching_keys) + print(len(matching_keys)) + + matching_keys = [ + branch + for branch in branches + if any(name in branch for name in filtered_names) + ] + print("Branches containing keys from definitions_dict:", matching_keys) + print(len(matching_keys)) + + # Print the filtered names + print("Filtered Names:", filtered_names) + + # Extract manual ranges for x and y columns if they don't start with SelJet_ or SMu_ + x_limits_dict = {} + for x_col in branches: + if not x_col.startswith(("SelJet_", "SoftMuon_", "MuonJet_")): + x_limits = definitions_dict.get(x_col, {}).get( + "manual_ranges", None + ) + else: + x_col_str = ( + x_col.lstrip("SelJet_").lstrip("SoftMuon_").lstrip("MuonJet_") + ) + x_limits = definitions_dict.get(x_col_str, {}).get( + "manual_ranges", None + ) + + x_limits_dict[x_col] = x_limits + # Debugging prints + # print(f"x_col: {x_col}") + # print(f"Manual ranges for {x_col}: {x_limits}") + + # Load the first chunk to inspect memory usage + data_chunk = next(tree.iterate(branches, library="pd", step_size=100000)) + if isinstance(data_chunk, tuple): + data_chunk = data_chunk[0] # Extract the DataFrame from the tuple + + # Filter the data based on x_limits + for col, limits in x_limits_dict.items(): + if limits is not None: + min_value, max_value = limits + data_chunk = data_chunk[ + (data_chunk[col] >= min_value) & (data_chunk[col] <= max_value) + ] + + memory_usage = data_chunk.memory_usage(deep=True).sum() + print( + f"Memory usage of the first DataFrame chunk: {memory_usage / (1024 ** 2):.2f} MB" + ) + else: + print("'Events' key not found in the file.") + + +def load_data( + file_paths, + base_dir, + batch_size=2, + temp_subdir="temp_data", + output_subdir="parquet_data", + max_files=None, + limit_inputs=False, + limit_outputs=False, + SMu=False, + flavour_split=False, +): + # Step 1: Inspect the nominal folder + nominal_folder = os.path.join(base_dir, "nominal") + nominal_subfolders = {} + if os.path.exists(nominal_folder): + for subfolder in os.listdir(nominal_folder): + subfolder_path = os.path.join(nominal_folder, subfolder) + if os.path.isdir(subfolder_path): + # Count the number of .root files in the subfolder + root_files = [ + f for f in os.listdir(subfolder_path) if f.endswith(".root") + ] + root_file_count = len(root_files) + # Print the count of .root files + print( + f"Subfolder: {subfolder}, Number of .root files: {root_file_count}" + ) + # Store the subfolder path for later use + nominal_subfolders[subfolder] = subfolder_path + + if max_files is not None: + file_paths = file_paths[:max_files] + + output_dir = os.path.join(base_dir, output_subdir) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + with ThreadPoolExecutor(max_workers=batch_size) as executor: + futures = [ + executor.submit( + load_single_file, + file_path, + base_dir, + temp_subdir=temp_subdir, + limit_inputs=limit_inputs, + limit_outputs=limit_outputs, + SMu=SMu, + flavour_split=flavour_split, + ) + for file_path in file_paths + ] + for future in tqdm( + as_completed(futures), total=len(futures), desc="Processing files" + ): + future.result() # Ensure any exceptions are raised + + combined_data = {} + temp_files = glob.glob(os.path.join(base_dir, temp_subdir, "*.parquet")) + print(f"Temporary files found: {len(temp_files)}") # Debugging line + event_counts = [] + for temp_file in temp_files: + df = pd.read_parquet(temp_file) + key = os.path.basename(temp_file).replace(".parquet", "") + combined_data[key] = df # Store the DataFrame directly + event_counts.append((key, len(df))) # Store the number of events and the key + + # Define ranking factors for each subfolder + ### FIXME: sumw has to change, whenever you run a different set. Pay attention!!! + ### FIXME: The former factor is the xs, the second - sumw! + ranking_factors = { + "QCD_PT-15to20_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 295600 / 142083, + "QCD_PT-20to30_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 2689000 / 5926, + "QCD_PT-30to50_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 1442000 / 339153, + "QCD_PT-50to80_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 405800 / 94942, + "QCD_PT-80to120_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 96060 / 488561, + "QCD_PT-120to170_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 23230 / 210233, + "QCD_PT-1700to300_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 7763 / 216617, + "QCD_PT-300to470_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 701.4 / 196409, + "QCD_PT-470to600_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 68.24 / 254794, + "QCD_PT-600to800_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 21.23 / 34096, + "QCD_PT-800to1000_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 3.9 / 155007, + "QCD_PT-1000_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 1.323 / 237819, + } + + # Sort event counts by the number of events in descending order + event_counts.sort(key=lambda x: x[1], reverse=True) + + # Display the ranking + print("Ranking by number of events:") + for rank, (key, count) in enumerate(event_counts, start=1): + # Strip the _chunk_0 suffix + stripped_key = key.replace("_chunk_0", "") + # Determine the parent subfolder + parent_subfolder = None + for subfolder, path in nominal_subfolders.items(): + if stripped_key in os.listdir(path): + parent_subfolder = subfolder + break + print( + f"{rank}. File: {key}, Number of events: {count}, Parent subfolder: {parent_subfolder}" + ) + + # Calculate weighted event counts + weighted_event_counts = [] + for key, count in event_counts: + stripped_key = key.replace("_chunk_0", "") + parent_subfolder = None + for subfolder, path in nominal_subfolders.items(): + if stripped_key in os.listdir(path): + parent_subfolder = subfolder + break + factor = ranking_factors.get( + parent_subfolder, 1.0 + ) # Default factor is 1.0 if not specified + weighted_count = count * factor + weighted_event_counts.append((key, weighted_count, parent_subfolder)) + + # Sort weighted event counts by the weighted number of events in descending order + weighted_event_counts.sort(key=lambda x: x[1], reverse=True) + + # Display the ranking + ### Ranking done by the yields: from the highest to the lowest + print("Ranking by weighted number of events:") + for rank, (key, weighted_count, parent_subfolder) in enumerate( + weighted_event_counts, start=1 + ): + print( + f"{rank}. File: {key}, Weighted number of events: {weighted_count}, Parent subfolder: {parent_subfolder}" + ) + + print(f"Combined data keys: {list(combined_data.keys())}") # Debugging line + return combined_data + + +variables_to_zoom = { + "MuonJet_DeepCSV_flightDistance2dSig": [2, 4], + "MuonJet_DeepCSV_flightDistance2dVal": [0, 0.4], + "MuonJet_DeepCSV_flightDistance3dSig": [1, 10], + "MuonJet_DeepCSV_flightDistance3dVal": [0, 0.5], + "MuonJet_DeepCSV_jetNSelectedTracks": [5, 12], + "MuonJet_DeepCSV_trackJetPt": [25, 60], + "MuonJet_DeepCSV_trackSip2dSigAboveCharm": [0, 2], + "MuonJet_DeepCSV_trackSip2dValAboveCharm": [0, 0.01], + "MuonJet_DeepCSV_trackSip3dSigAboveCharm": [0, 2], + "MuonJet_DeepCSV_trackSip3dValAboveCharm": [0, 0.01], + "MuonJet_DeepCSV_trackSumJetDeltaR": [0, 0.03], + "MuonJet_DeepCSV_vertexEnergyRatio": [0, 0.4], + "MuonJet_DeepCSV_vertexJetDeltaR": [0, 0.07], + "SoftMuon_ip3d": [0, 0.05], + "SoftMuon_jetPtRelv2": [0, 1], + "SoftMuon_jetRelIso": [0, 30], + "SoftMuon_pfRelIso03_all": [0, 20], + "SoftMuon_pfRelIso03_chg": [0, 20], + "SoftMuon_pfRelIso04_all": [0, 20], + "SoftMuon_sip3d": [0, 15], +} + + +# Function to print keys of definitions_dict +def print_definitions_keys(): + keys = definitions_dict.keys() + print("Keys in definitions_dict:", keys) + print(len(keys)) + + +def plot_2d_histogram( + data, x_col, y_col, output_dir, x_limits=None, y_limits=None, x_bins=50, y_bins=50 +): + plt.figure(figsize=(10, 8)) + hist = sns.histplot( + data, x=x_col, y=y_col, bins=(x_bins, y_bins), pthresh=0.1, cmap="viridis" + ) + + if x_limits is not None: + plt.xlim(x_limits) + if y_limits is not None: + plt.ylim(y_limits) + + plt.xlabel(x_col) + plt.ylabel(y_col) + plt.title(f"2D Histogram of {x_col} vs {y_col}") + plt.colorbar(hist.collections[0], label="Counts") + plt.grid(True) + plt.tight_layout() + plt.savefig(os.path.join(output_dir, f"{x_col}_vs_{y_col}.png")) + plt.close() + + +def plot_all_histograms( + data, + definitions_dict, + filtered_names, + output_dir, + include_smu=False, + flavour_split=False, + split_region_b=False, + zoom=False, +): + print("Data columns:", data.columns) + print("Definitions dict keys:", definitions_dict.keys()) + print("Filtered names:", filtered_names) + + # Extend definitions_dict with SMu columns if include_smu is True + if include_smu: + smu_columns = [col for col in data.columns if "SMu" in col] + for col in smu_columns: + if col not in definitions_dict: + definitions_dict[col] = col # Add SMu columns to definitions_dict + + print("Definitions dict keys:", definitions_dict.keys()) + + if flavour_split: + flavour_column = "MuJet_hadronFlavour" + + flavour_groups = { + "light": data[data[flavour_column] == 0], + "charm": data[data[flavour_column] == 4], + "bottom": data[data[flavour_column] == 5], + } + for flavour in flavour_groups.keys(): + flavour_data = flavour_groups[flavour] + flavour_dir = os.path.join(output_dir, f"flavour_{flavour}") + os.makedirs(flavour_dir, exist_ok=True) + plot_histograms_for_data( + flavour_data, definitions_dict, filtered_names, flavour_dir, zoom + ) + + elif split_region_b: + deepflavb_column = "MuonJet_btagDeepFlavB" + high_probB_data = data[data[deepflavb_column] > 0.5] + low_probB_data = data[data[deepflavb_column] <= 0.5] + + high_probB_dir = os.path.join(output_dir, "high_probB") + low_probB_dir = os.path.join(output_dir, "low_probB") + + os.makedirs(high_probB_dir, exist_ok=True) + os.makedirs(low_probB_dir, exist_ok=True) + + plot_histograms_for_data( + high_probB_data, definitions_dict, filtered_names, high_probB_dir, zoom + ) + plot_histograms_for_data( + low_probB_data, definitions_dict, filtered_names, low_probB_dir, zoom + ) + + else: + plot_histograms_for_data( + data, definitions_dict, filtered_names, output_dir, zoom + ) + + +def plot_histograms_for_data( + data, definitions_dict, filtered_names, output_dir, zoom=False +): + for input_col in definitions_dict.keys(): + for output_col in filtered_names: + matching_input_cols = [col for col in data.columns if input_col in col] + matching_output_cols = [col for col in data.columns if output_col in col] + + for x_col in matching_input_cols: + for y_col in matching_output_cols: + print(f"Plotting {x_col} vs {y_col}") + + # Extract manual ranges for x and y columns if they don't start with SelJet_ or SMu_ + if not x_col.startswith(("SelJet_", "SMu_", "MuJet_")): + x_limits = definitions_dict.get(x_col, {}).get( + "manual_ranges", None + ) + x_bins = definitions_dict.get(x_col, {}).get("bins", 50) + else: + x_col_str = ( + x_col.lstrip("SelJet_").lstrip("SMu_").lstrip("MuJet_") + ) + x_limits = definitions_dict.get(x_col_str, {}).get( + "manual_ranges", None + ) + x_bins = definitions_dict.get(x_col_str, {}).get("bins", 50) + + if not y_col.startswith(("SelJet_", "SMu_", "MuJet_")): + y_limits = definitions_dict.get(y_col, {}).get( + "manual_ranges", None + ) + y_bins = definitions_dict.get(x_col, {}).get("bins", 50) + else: + y_col_str = ( + y_col.lstrip("SelJet_").lstrip("SMu_").lstrip("MuJet_") + ) + y_limits = definitions_dict.get(y_col_str, {}).get( + "manual_ranges", None + ) + y_bins = definitions_dict.get(y_col_str, {}).get("bins", 50) + + # Debugging prints + print(f"x_col: {x_col}, y_col: {y_col}") + print( + f"definitions_dict[{x_col}]: {definitions_dict.get(x_col, 'Not found')}" + ) + print( + f"definitions_dict[{y_col}]: {definitions_dict.get(y_col, 'Not found')}" + ) + print(f"Manual ranges for {x_col}: {x_limits}") + print(f"Manual ranges for {y_col}: {y_limits}") + + print(f"Bin count for {x_col}: {x_bins}") + print(f"Bin count for {y_col}: {y_bins}") + # If zoom is active, create zoomed histograms + if zoom and x_col in variables_to_zoom: + zoomed_dir = os.path.join(output_dir, "zoomed_hists") + os.makedirs(zoomed_dir, exist_ok=True) + + x_zoom = variables_to_zoom[x_col] + plot_2d_histogram( + data, + x_col, + y_col, + zoomed_dir, + x_limits=x_zoom, + y_limits=y_limits, + x_bins=40, + y_bins=y_bins, + ) + + plot_2d_histogram( + data, + x_col, + y_col, + output_dir, + x_limits=x_limits, + y_limits=y_limits, + x_bins=x_bins, + y_bins=y_bins, + ) + + +def main(): + parser = argparse.ArgumentParser(description="Generate correlation plots.") + parser.add_argument( + "folder", type=str, help="The folder containing the data files." + ) + parser.add_argument( + "--max_files", + type=int, + default=None, + help="Maximum number of files to process.", + ) + parser.add_argument( + "--limit_inputs", + action="store_true", + help="Limit inputs to variables with npf, cpf, sv and _0", + ) + parser.add_argument( + "--limit_outputs", + action="store_true", + help="Limit outputs to names ending with B and not containing Neg", + ) + parser.add_argument( + "--SMu", action="store_true", help="Include branches with SMu in their name" + ) + parser.add_argument( + "--flavour_split", + action="store_true", + help="Split correlations by hadron flavour", + ) + parser.add_argument( + "--split_region_b", action="store_true", help="Split correlations by region B" + ) + parser.add_argument( + "--zoom", action="store_true", help="Zoom in on the correlation plots" + ) + args = parser.parse_args() + + data_file_paths = glob.glob( + os.path.join(args.folder, "**", "*.root"), recursive=True + ) + print(f"Data file paths: {data_file_paths}") # Debugging line + # print(data_file_paths) + # print_definitions_keys() + + if data_file_paths: + inspect_first_file( + data_file_paths[0], + limit_inputs=args.limit_inputs, + limit_outputs=args.limit_outputs, + SMu=args.SMu, + flavour_split=args.flavour_split, + ) + + parquet_dir = os.path.join(args.folder, "parquet_data") + histograms_dir = os.path.join(args.folder, "2D_histograms") + os.makedirs(histograms_dir, exist_ok=True) + + data_dict = load_data( + data_file_paths, + args.folder, + max_files=args.max_files, + limit_inputs=args.limit_inputs, + limit_outputs=args.limit_outputs, + SMu=args.SMu, + flavour_split=args.flavour_split, + ) + + # Concatenate all DataFrames if load_data returns a dictionary of DataFrames + if isinstance(data_dict, dict): + data = pd.concat(data_dict.values(), ignore_index=True) + else: + data = data_dict + + # Print data columns for debugging + print("Data columns after loading and concatenation:", data.columns) + + # Plot 2D histograms of inputs vs outputs + plot_all_histograms( + data, + definitions_dict, + filtered_names, + histograms_dir, + include_smu=args.SMu, + flavour_split=args.flavour_split, + split_region_b=args.split_region_b, + zoom=args.zoom, + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/correlation_plots.py b/scripts/correlation_plots.py new file mode 100644 index 00000000..c8cb6a1a --- /dev/null +++ b/scripts/correlation_plots.py @@ -0,0 +1,998 @@ +import psutil +import os +from tqdm import tqdm +import uproot +import warnings +import argparse +import glob +import matplotlib.pyplot as plt +from concurrent.futures import ThreadPoolExecutor, as_completed +import pandas as pd +import numpy as np +import seaborn as sns +import awkward as ak +from BTVNanoCommissioning.helpers.definitions import definitions_dict +from BTVNanoCommissioning.helpers.definitions import disc_list + + +# Suppress the specific FutureWarning from uproot +warnings.filterwarnings("ignore", category=FutureWarning, module="uproot") + +filtered_names = [ + name for name in disc_list if name.endswith("B") and not name.endswith("CvB") +] + + +def is_within_range(array, min_value, max_value): + """Check if all values in the array are within the specified range.""" + return ak.all((array >= min_value) & (array <= max_value)) + + +def load_single_file( + file_path, + base_dir, + chunk_size=100000, + temp_subdir="temp_data", + limit_inputs=False, + limit_outputs=False, + SMu=False, + flavour_split=False, +): + temp_dir = os.path.join(base_dir, temp_subdir) + if not os.path.exists(temp_dir): + os.makedirs(temp_dir, exist_ok=True) + + with uproot.open(file_path) as file: + if "Events" not in file: + print(f"Skipping file without 'Events' key: {file_path}") + return + + tree = file["Events"] + if tree.num_entries == 0: + print(f"Skipping empty file: {file_path}") + return + + # Apply limit_inputs filter + if limit_inputs: + filtered_definitions_dict = { + k: v + for k, v in definitions_dict.items() + if any(sub in k for sub in ["Npfcan", "Cpfcan", "sv"]) + and k.endswith("_0") + or not any(sub in k for sub in ["Npfcan", "Cpfcan", "sv"]) + } + else: + filtered_definitions_dict = definitions_dict + + # Initialize filtered_names + filtered_names = [ + name + for name in disc_list + if name.endswith("B") and not name.endswith("CvB") + ] + + # Apply limit_outputs filter + if limit_outputs: + filtered_names = [ + name + for name in filtered_names + if name.endswith("B") + and "Neg" not in name + and "btagPNetProbB" not in name + ] + + # Filter branches to include only those with 'SelJets' in their name, containing keys from filtered_definitions_dict, and matching filtered_names + branches = [ + branch + for branch in tree.keys() + if "MuonJet" in branch + and ( + any(key in branch for key in filtered_definitions_dict.keys()) + or any(name in branch for name in filtered_names) + ) + ] + + if limit_outputs: + branches = [ + branch + for branch in branches + if not any(name in branch for name in filtered_names) + or any( + name in branch for name in filtered_names if branch.endswith("B") + ) + ] + + # Include branches that have 'SMu' in their name if the SMu flag is True + if SMu: + smu_branches = [branch for branch in tree.keys() if "SoftMuon" in branch] + smu_branches.append("MuonJet_muEF") + if limit_inputs: + smu_branches = [ + "SoftMuon_tunepRelPt", + "SoftMuon_pfRelIso03_chg", + "SoftMuon_eta", + "SoftMuon_phi", + "SoftMuon_jetPtRelv2", + "SoftMuon_dxy", + "SoftMuon_dxyErr", + "SoftMuon_jetRelIso", + "SoftMuon_sip3d", + "SoftMuon_dzErr", + "SoftMuon_pfRelIso04_all", + "SoftMuon_ip3d", + "SoftMuon_pt", + "SoftMuon_ptErr", + "SoftMuon_tkRelIso", + "SoftMuon_dz", + "SoftMuon_pfRelIso03_all", + "MuonJet_muEF", + ] + branches.extend(smu_branches) + branches = list(set(branches)) # Remove duplicates + + # Include the flavour column if flavour_split is enabled + if flavour_split: + branches.append("MuonJet_hadronFlavour") + + # Extract manual ranges for x and y columns if they don't start with SelJet_ or SMu_ + x_limits_dict = {} + for x_col in branches: + if not x_col.startswith(("SelJet_", "SoftMuon_", "MuonJet_")): + x_limits = definitions_dict.get(x_col, {}).get("manual_ranges", None) + else: + x_col_str = ( + x_col.lstrip("SelJet_").lstrip("SoftMuon_").lstrip("MuonJet_") + ) + x_limits = definitions_dict.get(x_col_str, {}).get( + "manual_ranges", None + ) + + x_limits_dict[x_col] = x_limits + + for i, data_chunk in enumerate( + tree.iterate(branches, library="pd", step_size=chunk_size) + ): + if isinstance(data_chunk, tuple): + data_chunk = data_chunk[0] # Extract the DataFrame from the tuple + + # Filter the data based on MuJet_Cpfcan_ptrel_0 + ###data_chunk = data_chunk[data_chunk['MuJet_DeepJet_Cpfcan_ptrel_0'] >= -20] + + # Filter the data based on x_limits + for col, limits in x_limits_dict.items(): + if limits is not None: + min_value, max_value = limits + data_chunk = data_chunk[ + (data_chunk[col] >= min_value) & (data_chunk[col] <= max_value) + ] + temp_file_path = os.path.join( + temp_dir, f"{os.path.basename(file_path)}_chunk_{i}.parquet" + ) + data_chunk.to_parquet(temp_file_path) + + +def inspect_first_file( + file_path, limit_inputs=False, limit_outputs=False, SMu=False, flavour_split=False +): + with uproot.open(file_path) as file: + print(f"Inspecting file: {file_path}") + print("Keys:", file.keys()) + if "Events" in file: + tree = file["Events"] + print("Number of entries in 'Events':", tree.num_entries) + + # Apply limit_inputs filter + if limit_inputs: + filtered_definitions_dict = { + k: v + for k, v in definitions_dict.items() + if any(sub in k for sub in ["Npfcan", "Cpfcan", "sv"]) + and k.endswith("_0") + or not any(sub in k for sub in ["Npfcan", "Cpfcan", "sv"]) + } + else: + filtered_definitions_dict = definitions_dict + + # Initialize filtered_names + filtered_names = [ + name + for name in disc_list + if name.endswith("B") and not name.endswith("CvB") + ] + + # Apply limit_outputs filter + if limit_outputs: + filtered_names = [ + name + for name in filtered_names + if name.endswith("B") + and "Neg" not in name + and "btagPNetProbB" not in name + ] + + print(tree.keys()) + # Filter branches to include only those with 'SelJets' in their name, containing keys from definitions_dict, and matching filtered_names + + branches = [ + branch + for branch in tree.keys() + if "MuonJet" in branch + and ( + any(key in branch for key in filtered_definitions_dict.keys()) + or any(name in branch for name in filtered_names) + ) + ] + if limit_outputs: + branches = [ + branch + for branch in branches + if not any(name in branch for name in filtered_names) + or any( + name in branch + for name in filtered_names + if branch.endswith(name) + ) + ] + print("Filtered Branches:", branches) + print(len(branches)) + if SMu: + smu_branches = [ + branch for branch in tree.keys() if "SoftMuon" in branch + ] + smu_branches.append("MuonJet_muEF") + print(smu_branches) + if limit_inputs: + smu_branches = [ + "SoftMuon_tunepRelPt", + "SoftMuon_pfRelIso03_chg", + "SoftMuon_eta", + "SoftMuon_phi", + "SoftMuon_jetPtRelv2", + "SoftMuon_dxy", + "SoftMuon_dxyErr", + "SoftMuon_jetRelIso", + "SoftMuon_sip3d", + "SoftMuon_dzErr", + "SoftMuon_pfRelIso04_all", + "SoftMuon_ip3d", + "SoftMuon_pt", + "SoftMuon_ptErr", + "SoftMuon_tkRelIso", + "SoftMuon_dz", + "SoftMuon_pfRelIso03_all", + "MuonJet_muEF", + ] + + branches.extend(smu_branches) + branches = list(set(branches)) # Remove duplicates + print("Branches with SMu:", smu_branches) + + # Include the flavour column if flavour_split is enabled + if flavour_split: + branches.append("MuonJet_hadronFlavour") + + print("Branches:", branches) + + # Find branches that contain any of the keys from definitions_dict + matching_keys = [ + branch + for branch in branches + if any(key in branch for key in definitions_dict.keys()) + ] + print("Branches containing keys from definitions_dict:", matching_keys) + print(len(matching_keys)) + + matching_keys = [ + branch + for branch in branches + if any(name in branch for name in filtered_names) + ] + print("Branches containing keys from definitions_dict:", matching_keys) + print(len(matching_keys)) + + # Print the filtered names + print("Filtered Names:", filtered_names) + + # Extract manual ranges for x and y columns if they don't start with SelJet_ or SMu_ + x_limits_dict = {} + for x_col in branches: + if not x_col.startswith(("SelJet_", "SoftMuon_", "MuonJet_")): + x_limits = definitions_dict.get(x_col, {}).get( + "manual_ranges", None + ) + else: + x_col_str = ( + x_col.lstrip("SelJet_").lstrip("SoftMuon_").lstrip("MuonJet_") + ) + x_limits = definitions_dict.get(x_col_str, {}).get( + "manual_ranges", None + ) + + x_limits_dict[x_col] = x_limits + # Debugging prints + # print(f"x_col: {x_col}") + # print(f"Manual ranges for {x_col}: {x_limits}") + + # Load the first chunk to inspect memory usage + data_chunk = next(tree.iterate(branches, library="pd", step_size=100000)) + if isinstance(data_chunk, tuple): + data_chunk = data_chunk[0] # Extract the DataFrame from the tuple + + # Filter the data based on x_limits + for col, limits in x_limits_dict.items(): + if limits is not None: + min_value, max_value = limits + data_chunk = data_chunk[ + (data_chunk[col] >= min_value) & (data_chunk[col] <= max_value) + ] + + memory_usage = data_chunk.memory_usage(deep=True).sum() + print( + f"Memory usage of the first DataFrame chunk: {memory_usage / (1024 ** 2):.2f} MB" + ) + else: + print("'Events' key not found in the file.") + + +def load_data( + file_paths, + base_dir, + batch_size=2, + temp_subdir="temp_data", + output_subdir="parquet_data", + max_files=None, + limit_inputs=False, + limit_outputs=False, + SMu=False, + flavour_split=False, + specify_MC=False, +): + # Step 1: Inspect the nominal folder + nominal_folder = os.path.join(base_dir, "nominal") + nominal_subfolders = {} + if os.path.exists(nominal_folder): + for subfolder in os.listdir(nominal_folder): + subfolder_path = os.path.join(nominal_folder, subfolder) + if os.path.isdir(subfolder_path): + # Count the number of .root files in the subfolder + root_files = [ + f for f in os.listdir(subfolder_path) if f.endswith(".root") + ] + root_file_count = len(root_files) + # Print the count of .root files + print( + f"Subfolder: {subfolder}, Number of .root files: {root_file_count}" + ) + # Store the subfolder path for later use + nominal_subfolders[subfolder] = subfolder_path + + if max_files is not None: + file_paths = file_paths[:max_files] + + print(base_dir) + if "MC" in base_dir: + print("MC file detected") + + # Check if the base directory name contains "MC" and the specify_MC flag is active + if "MC" in base_dir and specify_MC: + # Filter file_paths to only include files from a specific subfolder + print("Filtering file paths to include files from a specific subfolder") + # specific_subfolder = "QCD_PT-50to80_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8" # Replace with the actual subfolder name + specific_subfolder = "QCD_PT-80to120_TuneCP5_13p6TeV_pythia8" + file_paths = [fp for fp in file_paths if specific_subfolder in fp] + + output_dir = os.path.join(base_dir, output_subdir) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + with ThreadPoolExecutor(max_workers=batch_size) as executor: + futures = [ + executor.submit( + load_single_file, + file_path, + base_dir, + temp_subdir=temp_subdir, + limit_inputs=limit_inputs, + limit_outputs=limit_outputs, + SMu=SMu, + flavour_split=flavour_split, + ) + for file_path in file_paths + ] + for future in tqdm( + as_completed(futures), total=len(futures), desc="Processing files" + ): + future.result() # Ensure any exceptions are raised + + combined_data = {} + temp_files = glob.glob(os.path.join(base_dir, temp_subdir, "*.parquet")) + print(f"Temporary files found: {len(temp_files)}") # Debugging line + event_counts = [] + for temp_file in temp_files: + df = pd.read_parquet(temp_file) + key = os.path.basename(temp_file).replace(".parquet", "") + combined_data[key] = df # Store the DataFrame directly + event_counts.append((key, len(df))) # Store the number of events and the key + + # Define ranking factors for each subfolder + ### FIXME: sumw has to change, whenever you run a different set. Pay attention!!! + ### FIXME: The former factor is the xs, the second - sumw! + ranking_factors = { + "QCD_PT-15to20_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 295600 / 142083, + "QCD_PT-20to30_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 2689000 / 5926, + "QCD_PT-30to50_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 1442000 / 339153, + "QCD_PT-50to80_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 405800 / 94942, + "QCD_PT-80to120_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 96060 / 488561, + "QCD_PT-120to170_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 23230 / 210233, + "QCD_PT-1700to300_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 7763 / 216617, + "QCD_PT-300to470_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 701.4 / 196409, + "QCD_PT-470to600_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 68.24 / 254794, + "QCD_PT-600to800_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 21.23 / 34096, + "QCD_PT-800to1000_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 3.9 / 155007, + "QCD_PT-1000_MuEnrichedPt5_TuneCP5_13p6TeV_pythia8": 1.323 / 237819, + "QCD_PT-50to80_TuneCP5_13p6TeV_pythia8": 16760000.0 / 306000, + "QCD_PT-80to120_TuneCP5_13p6TeV_pythia8": 2514000.0 / 362000, + "QCD_PT-120to170_TuneCP5_13p6TeV_pythia8": 442300.0 / 352000, + "QCD_PT-170to300_TuneCP5_13p6TeV_pythia8": 113400.0 / 334000, + "QCD_PT-300to470_TuneCP5_13p6TeV_pythia8": 7610.0 / 128000, + "QCD_PT-470to600_TuneCP5_13p6TeV_pythia8": 626.6 / 223000, + "QCD_PT-600to800_TuneCP5_13p6TeV_pythia8": 179.5 / 230000, + "QCD_PT-800to1000_TuneCP5_13p6TeV_pythia8": 30.69 / 38000, + "QCD_PT-1000to1400_TuneCP5_13p6TeV_pythia8": 8.956 / 143000, + "QCD_PT-1400to1800_TuneCP5_13p6TeV_pythia8": 0.8112 / 219000, + "QCD_PT-1800to2400_TuneCP5_13p6TeV_pythia8": 0.1152 / 211000, + "QCD_PT-2400to3200_TuneCP5_13p6TeV_pythia8": 0.007574 / 334000, + "QCD_PT-3200_TuneCP5_13p6TeV_pythia8": 0.0002313 / 44000, + } + + # Sort event counts by the number of events in descending order + event_counts.sort(key=lambda x: x[1], reverse=True) + + # Display the ranking + print("Ranking by number of events:") + for rank, (key, count) in enumerate(event_counts, start=1): + # Strip the _chunk_0 suffix + stripped_key = key.replace("_chunk_0", "") + # Determine the parent subfolder + parent_subfolder = None + for subfolder, path in nominal_subfolders.items(): + if stripped_key in os.listdir(path): + parent_subfolder = subfolder + break + print( + f"{rank}. File: {key}, Number of events: {count}, Parent subfolder: {parent_subfolder}" + ) + + # Calculate weighted event counts + weighted_event_counts = [] + for key, count in event_counts: + stripped_key = key.replace("_chunk_0", "") + parent_subfolder = None + for subfolder, path in nominal_subfolders.items(): + if stripped_key in os.listdir(path): + parent_subfolder = subfolder + break + factor = ranking_factors.get( + parent_subfolder, 1.0 + ) # Default factor is 1.0 if not specified + weighted_count = count * factor + weighted_event_counts.append((key, weighted_count, parent_subfolder)) + + # Sort weighted event counts by the weighted number of events in descending order + weighted_event_counts.sort(key=lambda x: x[1], reverse=True) + + # Display the ranking + ### Ranking done by the yields: from the highest to the lowest + print("Ranking by weighted number of events:") + for rank, (key, weighted_count, parent_subfolder) in enumerate( + weighted_event_counts, start=1 + ): + print( + f"{rank}. File: {key}, Weighted number of events: {weighted_count}, Parent subfolder: {parent_subfolder}" + ) + + print(f"Combined data keys: {list(combined_data.keys())}") # Debugging line + return combined_data + + +def compute_correlations(data, SMu=False, flavour_split=False, split_region_B=False): + if not data: + raise ValueError("No data available to compute correlations.") + + # Combine all data into a single DataFrame + combined_df = pd.concat(data.values(), axis=0) + print(f"Combined DataFrame shape: {combined_df.shape}") # Debugging line + print(f"Combined DataFrame columns: {combined_df.columns}") # Debugging line + + combined_df = combined_df[sorted(combined_df.columns)] + print(f"Sorted DataFrame columns: {combined_df.columns}") # Debugging line + + # Separate columns into two groups + filtered_columns = [ + col + for col in combined_df.columns + if any(name in col for name in filtered_names) + ] + definition_columns = [ + col + for col in combined_df.columns + if any(key in col for key in definitions_dict.keys()) + ] + + # Include SMu columns if the SMu flag is True + if SMu: + smu_columns = [col for col in combined_df.columns if "SoftMuon" in col] + smu_columns.append("MuonJet_muEF") # Ensure MuJet_muEF is included + definition_columns.extend(smu_columns) + definition_columns = list(set(definition_columns)) # Remove duplicates + + # Ensure SMu columns are at the end of the definition_columns list + smu_columns = [col for col in definition_columns if "SoftMuon" in col] + non_smu_columns = [col for col in definition_columns if "SoftMuon" not in col] + definition_columns = non_smu_columns + smu_columns + + # Compute the full correlation matrix + all_columns = sorted(filtered_columns + definition_columns) + if flavour_split: + flavour_column = "MuonJet_hadronFlavour" + if flavour_column not in combined_df.columns: + raise ValueError(f"Column '{flavour_column}' not found in data.") + + flavour_groups = { + "light": combined_df[combined_df[flavour_column] == 0], + "charm": combined_df[combined_df[flavour_column] == 4], + "bottom": combined_df[combined_df[flavour_column] == 5], + } + + correlation_matrices = {} + for flavour, df in flavour_groups.items(): + correlation_matrices[flavour] = df[all_columns].corr() + + return correlation_matrices, filtered_columns, definition_columns + elif split_region_B: + deepflavb_column = "MuonJet_btagDeepFlavB" + if deepflavb_column not in combined_df.columns: + raise ValueError(f"Column '{deepflavb_column}' not found in data.") + + # Split the DataFrame based on DeepFlavB + high_probB_df = combined_df[combined_df[deepflavb_column] > 0.5] + low_probB_df = combined_df[combined_df[deepflavb_column] <= 0.5] + + # Calculate correlation matrices for high and low DeepFlavB + high_probB_corr_matrix = high_probB_df[all_columns].corr() + low_probB_corr_matrix = low_probB_df[all_columns].corr() + + # Return the correlation matrices along with the filtered and definition columns + return ( + {"high_probB": high_probB_corr_matrix, "low_probB": low_probB_corr_matrix}, + filtered_columns, + definition_columns, + ) + else: + correlation_matrix = combined_df[all_columns].corr() + return correlation_matrix, filtered_columns, definition_columns + + +# Function to print keys of definitions_dict +def print_definitions_keys(): + keys = definitions_dict.keys() + print("Keys in definitions_dict:", keys) + print(len(keys)) + + +def create_label_mapping(columns): + sorted_columns = sorted(columns) + return {col: f"Label {i+1}" for i, col in enumerate(sorted_columns)} + + +def plot_correlations( + correlation_data, + filtered_columns, + definition_columns, + output_dir, + flavour_split=False, + split_region_B=False, + threshold=0.5, +): + if not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + + def plot_heatmap(matrix, title, filename, label_mapping, threshold): + num_labels = len(matrix.columns) + font_size = ( + 10 if num_labels < 30 else 6 + ) # Adjust font size based on number of labels + + plt.figure(figsize=(24, 16)) # Increase the size of the plot + sns.heatmap( + matrix, + annot=False, + cmap="coolwarm", + fmt=".2f", + cbar_kws={"label": "Correlation Coefficient"}, + ) + + # Add annotations manually + for i in range(matrix.shape[0]): + for j in range(matrix.shape[1]): + value = matrix.iloc[i, j] + if abs(value) >= threshold: + plt.text( + j + 0.5, + i + 0.5, + f"{value:.2f}", + ha="center", + va="center", + fontsize=font_size, + ) + + plt.title(title) + + # Replace x and y labels with numbers or symbols + ax = plt.gca() + ax.set_xticklabels( + [ + label_mapping.get(label.get_text(), label.get_text()) + for label in ax.get_xticklabels() + ] + ) + ax.set_yticklabels( + [ + label_mapping.get(label.get_text(), label.get_text()) + for label in ax.get_yticklabels() + ] + ) + + # Add legend to the right of the colorbar + colorbar = ax.collections[0].colorbar + colorbar.ax.set_position([0.85, 0.1, 0.03, 0.8]) # Adjust position as needed + + # Create legend handles and sort them alphabetically + sorted_label_mapping = dict(sorted(label_mapping.items())) + legend_handles = [ + plt.Line2D([0], [0], color="w", label=f"{v}: {k}") + for k, v in sorted_label_mapping.items() + ] + + # Determine the number of columns for the legend + ncol = 2 if len(legend_handles) > 20 else 1 + + # Add legend + plt.legend( + handles=legend_handles, + loc="center left", + bbox_to_anchor=(1.2, 0.5), + title="Legend", + ncol=ncol, + ) + + plt.tight_layout() + plt.savefig(filename, bbox_inches="tight") + plt.close() + + # Create label mappings + filtered_label_mapping = create_label_mapping(filtered_columns) + definition_label_mapping = create_label_mapping(definition_columns) + + # Sort columns and index alphabetically + sorted_filtered_columns = sorted(filtered_columns) + sorted_definition_columns = sorted(definition_columns) + + if flavour_split: + flavour_output_dir = os.path.join(output_dir, "flavour_splitting") + if not os.path.exists(flavour_output_dir): + os.makedirs(flavour_output_dir, exist_ok=True) + + for flavour, correlation_matrix in correlation_data.items(): + flavour_dir = os.path.join(flavour_output_dir, flavour) + if not os.path.exists(flavour_dir): + os.makedirs(flavour_dir, exist_ok=True) + + sorted_correlation_matrix = correlation_matrix.loc[ + sorted_filtered_columns + sorted_definition_columns, + sorted_filtered_columns + sorted_definition_columns, + ] + plot_heatmap( + sorted_correlation_matrix, + f"{flavour.capitalize()} Flavour Correlation Matrix", + os.path.join(flavour_dir, f"{flavour}_full_correlation_matrix.png"), + {**filtered_label_mapping, **definition_label_mapping}, + threshold, + ) + + # Plot the filtered_names x filtered_names submatrix + filtered_corr_matrix = correlation_matrix.loc[ + sorted_filtered_columns, sorted_filtered_columns + ] + plot_heatmap( + filtered_corr_matrix, + f"{flavour.capitalize()} Filtered Names Correlation Matrix", + os.path.join( + flavour_dir, f"{flavour}_filtered_names_correlation_matrix.png" + ), + filtered_label_mapping, + 0.0, + ) + + # Plot the definition_dict x definition_dict submatrix + definition_corr_matrix = correlation_matrix.loc[ + sorted_definition_columns, sorted_definition_columns + ] + plot_heatmap( + definition_corr_matrix, + f"{flavour.capitalize()} Definition Dict Correlation Matrix", + os.path.join( + flavour_dir, f"{flavour}_definition_dict_correlation_matrix.png" + ), + definition_label_mapping, + 0.75, + ) + + # Plot the definition_dict x filtered_names submatrix + def_filtered_corr_matrix = correlation_matrix.loc[ + sorted_definition_columns, sorted_filtered_columns + ] + plot_heatmap( + def_filtered_corr_matrix, + f"{flavour.capitalize()} Definition Dict x Filtered Names Correlation Matrix", + os.path.join( + flavour_dir, + f"{flavour}_definition_dict_filtered_names_correlation_matrix.png", + ), + {**definition_label_mapping, **filtered_label_mapping}, + 0.12, + ) + + # Plot the filtered_names x definition_dict submatrix + filtered_def_corr_matrix = correlation_matrix.loc[ + sorted_filtered_columns, sorted_definition_columns + ] + plot_heatmap( + filtered_def_corr_matrix, + f"{flavour.capitalize()} Filtered Names x Definition Dict Correlation Matrix", + os.path.join( + flavour_dir, + f"{flavour}_filtered_names_definition_dict_correlation_matrix.png", + ), + {**filtered_label_mapping, **definition_label_mapping}, + threshold, + ) + elif split_region_B: + for region, matrix in correlation_data.items(): + region_dir = os.path.join(output_dir, region) + if not os.path.exists(region_dir): + os.makedirs(region_dir, exist_ok=True) + + sorted_correlation_matrix = matrix.loc[ + sorted_filtered_columns + sorted_definition_columns, + sorted_filtered_columns + sorted_definition_columns, + ] + plot_heatmap( + sorted_correlation_matrix, + f"Correlation Matrix for {region} DeepFlavB", + os.path.join(region_dir, f"correlation_matrix_{region}.png"), + {**filtered_label_mapping, **definition_label_mapping}, + threshold, + ) + + # Plot the filtered_names x filtered_names submatrix + filtered_corr_matrix = matrix.loc[ + sorted_filtered_columns, sorted_filtered_columns + ] + plot_heatmap( + filtered_corr_matrix, + f"Filtered Names Correlation Matrix for {region} DeepFlavB", + os.path.join( + region_dir, f"filtered_names_correlation_matrix_{region}.png" + ), + filtered_label_mapping, + 0.0, + ) + + # Plot the definition_dict x definition_dict submatrix + definition_corr_matrix = matrix.loc[ + sorted_definition_columns, sorted_definition_columns + ] + plot_heatmap( + definition_corr_matrix, + f"Definition Dict Correlation Matrix for {region} DeepFlavB", + os.path.join( + region_dir, f"definition_dict_correlation_matrix_{region}.png" + ), + definition_label_mapping, + 0.75, + ) + + # Plot the definition_dict x filtered_names submatrix + def_filtered_corr_matrix = matrix.loc[ + sorted_definition_columns, sorted_filtered_columns + ] + plot_heatmap( + def_filtered_corr_matrix, + f"Definition Dict x Filtered Names Correlation Matrix for {region} DeepFlavB", + os.path.join( + region_dir, + f"definition_dict_filtered_names_correlation_matrix_{region}.png", + ), + {**definition_label_mapping, **filtered_label_mapping}, + 0.12, + ) + + # Plot the filtered_names x definition_dict submatrix + filtered_def_corr_matrix = matrix.loc[ + sorted_filtered_columns, sorted_definition_columns + ] + plot_heatmap( + filtered_def_corr_matrix, + f"Filtered Names x Definition Dict Correlation Matrix for {region} DeepFlavB", + os.path.join( + region_dir, + f"filtered_names_definition_dict_correlation_matrix_{region}.png", + ), + {**filtered_label_mapping, **definition_label_mapping}, + threshold, + ) + + else: + # Plot the full correlation matrix + sorted_correlation_matrix = correlation_data.loc[ + sorted_filtered_columns + sorted_definition_columns, + sorted_filtered_columns + sorted_definition_columns, + ] + plot_heatmap( + sorted_correlation_matrix, + "Full Correlation Matrix", + os.path.join(output_dir, "full_correlation_matrix.png"), + {**filtered_label_mapping, **definition_label_mapping}, + threshold, + ) + + # Plot the filtered_names x filtered_names submatrix + filtered_corr_matrix = correlation_data.loc[ + sorted_filtered_columns, sorted_filtered_columns + ] + plot_heatmap( + filtered_corr_matrix, + "Filtered Names Correlation Matrix", + os.path.join(output_dir, "filtered_names_correlation_matrix.png"), + filtered_label_mapping, + 0.0, + ) + + # Plot the definition_dict x definition_dict submatrix + definition_corr_matrix = correlation_data.loc[ + sorted_definition_columns, sorted_definition_columns + ] + plot_heatmap( + definition_corr_matrix, + "Definition Dict Correlation Matrix", + os.path.join(output_dir, "definition_dict_correlation_matrix.png"), + definition_label_mapping, + 0.75, + ) + + # Plot the definition_dict x filtered_names submatrix + def_filtered_corr_matrix = correlation_data.loc[ + sorted_definition_columns, sorted_filtered_columns + ] + plot_heatmap( + def_filtered_corr_matrix, + "Definition Dict x Filtered Names Correlation Matrix", + os.path.join( + output_dir, "definition_dict_filtered_names_correlation_matrix.png" + ), + {**definition_label_mapping, **filtered_label_mapping}, + 0.12, + ) + + # Plot the filtered_names x definition_dict submatrix + filtered_def_corr_matrix = correlation_data.loc[ + sorted_filtered_columns, sorted_definition_columns + ] + plot_heatmap( + filtered_def_corr_matrix, + "Filtered Names x Definition Dict Correlation Matrix", + os.path.join( + output_dir, "filtered_names_definition_dict_correlation_matrix.png" + ), + {**filtered_label_mapping, **definition_label_mapping}, + threshold, + ) + + +def main(): + parser = argparse.ArgumentParser(description="Generate correlation plots.") + parser.add_argument( + "folder", type=str, help="The folder containing the data files." + ) + parser.add_argument( + "--max_files", + type=int, + default=None, + help="Maximum number of files to process.", + ) + parser.add_argument( + "--limit_inputs", + action="store_true", + help="Limit inputs to variables with npf, cpf, sv and _0", + ) + parser.add_argument( + "--limit_outputs", + action="store_true", + help="Limit outputs to names ending with B and not containing Neg", + ) + parser.add_argument( + "--SMu", action="store_true", help="Include branches with SMu in their name" + ) + parser.add_argument( + "--flavour_split", + action="store_true", + help="Split correlations by hadron flavour", + ) + parser.add_argument( + "--split_region_b", action="store_true", help="Split correlations by region B" + ) + parser.add_argument( + "--specify_MC", + action="store_true", + help="Specify MC file to make better data MC comparison", + ) + args = parser.parse_args() + + data_file_paths = glob.glob( + os.path.join(args.folder, "**", "*.root"), recursive=True + ) + print(f"Data file paths: {data_file_paths}") # Debugging line + # print(data_file_paths) + # print_definitions_keys() + + if data_file_paths: + inspect_first_file( + data_file_paths[0], + limit_inputs=args.limit_inputs, + limit_outputs=args.limit_outputs, + SMu=args.SMu, + flavour_split=args.flavour_split, + ) + + output_plot_path = os.path.join(args.folder, "output_plot.png") + parquet_dir = os.path.join(args.folder, "parquet_data") + + data = load_data( + data_file_paths, + args.folder, + max_files=args.max_files, + limit_inputs=args.limit_inputs, + limit_outputs=args.limit_outputs, + SMu=args.SMu, + flavour_split=args.flavour_split, + specify_MC=args.specify_MC, + ) + correlation_matrix, filtered_cols, def_cols = compute_correlations( + data, + SMu=args.SMu, + flavour_split=args.flavour_split, + split_region_B=args.split_region_b, + ) + + # Create a separate subfolder for plots if specify_MC is active + if args.specify_MC: + output_dir = os.path.join(args.folder, "correlation_plots_MC_specified") + else: + output_dir = os.path.join(args.folder, "correlation_plots") + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plot_correlations( + correlation_matrix, + filtered_cols, + def_cols, + output_dir, + flavour_split=args.flavour_split, + split_region_B=args.split_region_b, + ) + + +if __name__ == "__main__": + main() diff --git a/src/BTVNanoCommissioning/data/Prescales/HLTinfo_BTagMu_AK4DiJet40_Mu5_run355374_362760.csv b/src/BTVNanoCommissioning/data/Prescales/HLTinfo_BTagMu_AK4DiJet40_Mu5_run355374_362760.csv new file mode 100644 index 00000000..4a2ac678 --- /dev/null +++ b/src/BTVNanoCommissioning/data/Prescales/HLTinfo_BTagMu_AK4DiJet40_Mu5_run355374_362760.csv @@ -0,0 +1,1169 @@ +# run,cmsls,prescidx,totprescval,hltpath/prescval,logic,l1bit/prescval +355374,1,7,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355374,68,6,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/0,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355374,80,5,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/0,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355381,1,7,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355381,3,9,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/0,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355418,1,11,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/0,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355419,1,7,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355419,2,15,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/0,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355419,3,11,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/0,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355429,1,7,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355429,7,11,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/0,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355429,80,7,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355435,1,7,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355435,64,15,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/0,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355442,1,7,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355443,1,13,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355444,1,13,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355445,1,13,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355454,1,7,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355455,1,7,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355456,1,7,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355558,1,9,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355559,1,9,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355679,1,9,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355679,4,12,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355680,1,12,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355768,1,3,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355768,77,2,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355769,1,2,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v13/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355862,1,3,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355862,17,0,0.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/0,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/0.00 +355862,21,3,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355862,126,11,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355863,1,11,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355870,1,11,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355870,15,3,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355871,1,3,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355871,11,11,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355872,1,11,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355872,6,10,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355872,73,11,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355872,96,3,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355892,1,3,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355892,6,11,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355912,1,11,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355913,1,11,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 +355921,1,11,256.0,HLT_BTagMu_AK4DiJet40_Mu5_v14/1,ONE,L1_Mu3_Jet35er2p5_dR_Max0p4/256.00 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]} \ No newline at end of file diff --git a/src/BTVNanoCommissioning/helpers/definitions.py b/src/BTVNanoCommissioning/helpers/definitions.py index 8c4319df..298e6bb0 100644 --- a/src/BTVNanoCommissioning/helpers/definitions.py +++ b/src/BTVNanoCommissioning/helpers/definitions.py @@ -1,6 +1,6131 @@ ## code source: Master Thesis Annika Stein ## https://git.rwth-aachen.de/annika-stein/ai_safety_2021/-/blob/master/june_21/evaluate/definitions.py +disc_list = [ + "btagDeepFlavB", + "btagDeepFlavC", + "btagTransDeepFlavB", + "btagTransDeepFlavC", + "btagDeepFlavCvL", + "btagDeepFlavCvB", + "btagDeepFlavB_b", + "btagDeepFlavB_bb", + "btagTransDeepFlavB_lepb", + "btagTransDeepFlavB_b", + "btagTransDeepFlavB_bb", + "btagTransDeepFlavB_lepb", + "btagPNetB", + "btagTransPNetB", + "btagPNetCvB", + "btagPNetCvL", + "btagPNetCvNotB", + "btagPNetProbB", + "btagPNetProbC", + "btagPNetProbG", + "btagPNetProbUDS", + "btagTransPNetProbB", + "btagTransPNetProbC", + "btagTransPNetProbG", + "btagTransPNetProbUDS", + "btagPNetQvG", + "btagPNetTauVJet", + "btagRobustParTAK4B", + "btagRobustParTAK4B_b", + "btagRobustParTAK4B_bb", + "btagRobustParTAK4B_lepb", + "btagRobustParTAK4C", + "btagRobustParTAK4G", + "btagRobustParTAK4UDS", + "btagTransRobustParTAK4B", + "btagTransRobustParTAK4B_b", + "btagTransRobustParTAK4B_bb", + "btagTransRobustParTAK4B_lepb", + "btagTransRobustParTAK4C", + "btagTransRobustParTAK4G", + "btagTransRobustParTAK4UDS", + "btagRobustParTAK4CvB", + "btagRobustParTAK4CvL", + "btagRobustParTAK4QG", + "btagUParTAK4B", + "btagUParTAK4CvL", + "btagUParTAK4CvB", + "btagUParTAK4CvNotB", + "btagUParTAK4QvG", + "btagUParTAK4TauVJet", + ## Negative tagger + "btagNegDeepFlavB", + "btagNegDeepFlavB_b", + "btagNegDeepFlavB_bb", + "btagNegDeepFlavB_lepb", + "btagNegDeepFlavC", + "btagNegDeepFlavCvB", + "btagNegDeepFlavCvL", + "btagNegDeepFlavG", + "btagNegDeepFlavQG", + "btagNegDeepFlavUDS", + "btagNegPNetB", + "btagNegPNetC", + "btagNegPNetCvB", + "btagNegPNetCvL", + "btagNegPNetProbB", + "btagNegPNetProbC", + "btagNegPNetProbG", + "btagNegPNetProbUDS", + "btagNegRobustParTAK4B", + "btagNegRobustParTAK4B_b", + "btagNegRobustParTAK4B_bb", + "btagNegRobustParTAK4B_lepb", + "btagNegRobustParTAK4C", + "btagNegRobustParTAK4CvB", + "btagNegRobustParTAK4CvL", + "btagNegRobustParTAK4G", + "btagNegRobustParTAK4QG", + "btagNegRobustParTAK4UDS", + # other prob info + "PNetRegPtRawCorr", + "PNetRegPtRawCorrNeutrino", + "PNetRegPtRawRes", + "UParTAK4RegPtRawRes", + "UParTAK4RegPtRawCorrNeutrino", + "UParTAK4RegPtRawCorr", + "Bprob", + "BprobN", + "ProbaN", +] + +definitions_dict = { + "DeepCSV_jetNSelectedTracks": { + "displayname": "Jet N Selected Tracks", + "manual_ranges": [0.0, 25], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_vertexNTracks": { + "displayname": "Vertex N Tracks", + "manual_ranges": [0.0, 25], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_vertexJetDeltaR": { + "displayname": "Vertex Jet $\\Delta R$", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackSumJetEtRatio": { + "displayname": "Track Sum Jet $E_T$ Ratio", + "manual_ranges": [0.0, 5], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 28, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_4": { + "displayname": "Track SIP 2D Sig [4]", + "manual_ranges": [-6.5, 4.5], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 22, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_4": { + "displayname": "Track $p_{T,rel}$ Ratio [4]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_5": { + "displayname": "Track $p_{T,rel}$ Ratio [5]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_0": { + "displayname": "Track $p_{T,rel}$ Ratio [0]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_1": { + "displayname": "Track $p_{T,rel}$ Ratio [1]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_2": { + "displayname": "Track $p_{T,rel}$ Ratio [2]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_3": { + "displayname": "Track $p_{T,rel}$ Ratio [3]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_vertexEnergyRatio": { + "displayname": "Vertex Energy Ratio", + "manual_ranges": [0, 2.5], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_5": { + "displayname": "Track SIP 2D Sig [5]", + "manual_ranges": [-7, 2], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 18, + "inputVar_units": None, + }, + "DeepCSV_trackSumJetDeltaR": { + "displayname": "Track Sum Jet $\\Delta R$", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackSip3dSig_0": { + "displayname": "Track SIP 3D Sig [0]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackDecayLenVal_5": { + "displayname": "Track Decay Len Val [5]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_trackDecayLenVal_4": { + "displayname": "Track Decay Len Val [4]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_vertexMass": { + "displayname": "Vertex Mass", + "manual_ranges": [0, 20], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 20, + "inputVar_units": "GeV", + }, + "DeepCSV_trackSip3dSig_3": { + "displayname": "Track SIP 3D Sig [3]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackDecayLenVal_1": { + "displayname": "Track Decay Len Val [1]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_trackDecayLenVal_0": { + "displayname": "Track Decay Len Val [0]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_trackDecayLenVal_3": { + "displayname": "Track Decay Len Val [3]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_trackDecayLenVal_2": { + "displayname": "Track Decay Len Val [2]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_trackPtRel_0": { + "displayname": "Track $p_{T,rel}$ [0]", + "manual_ranges": [-0.1, 3.1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 32, + "inputVar_units": None, + }, + "DeepCSV_jetNTracksEtaRel": { + "displayname": "Jet N Tracks $\\eta_{rel}$", + "manual_ranges": [0.0, 25], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_0": { + "displayname": "Track SIP 2D Sig [0]", + "manual_ranges": [-4.5, 16], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 21, + "inputVar_units": None, + }, + "DeepCSV_trackSip3dSigAboveCharm": { + "displayname": "Track SIP 3D Sig Above Charm", + "manual_ranges": [-6.5, 6.5], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 26, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_3": { + "displayname": "Track SIP 2D Sig [3]", + "manual_ranges": [-6, 7], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 26, + "inputVar_units": None, + }, + "DeepCSV_trackSip3dSig_4": { + "displayname": "Track SIP 3D Sig [4]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip3dSig_5": { + "displayname": "Track SIP 3D Sig [5]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_2": { + "displayname": "Track SIP 2D Sig [2]", + "manual_ranges": [-5.5, 10], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 16, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dValAboveCharm": { + "displayname": "Track SIP 2D Val Above Charm", + "manual_ranges": [-0.06, 0.06], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 24, + "inputVar_units": "cm", + }, + "DeepCSV_trackSip3dSig_1": { + "displayname": "Track SIP 3D Sig [1]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip3dSig_2": { + "displayname": "Track SIP 3D Sig [2]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_1": { + "displayname": "Track SIP 2D Sig [1]", + "manual_ranges": [-5, 13], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 18, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSigAboveCharm": { + "displayname": "Track SIP 2D Sig Above Charm", + "manual_ranges": [-5.5, 5.5], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 22, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_4": { + "displayname": "Track $\\Delta R$ [4]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_5": { + "displayname": "Track $\\Delta R$ [5]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_2": { + "displayname": "Track $\\Delta R$ [2]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_3": { + "displayname": "Track $\\Delta R$ [3]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_0": { + "displayname": "Track $\\Delta R$ [0]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_flightDistance3dVal": { + "displayname": "Flight Distance 3D Val", + "manual_ranges": [-0.1, 5], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 51, + "inputVar_units": "cm", + }, + "DeepCSV_flightDistance3dSig": { + "displayname": "Flight Distance 3D Sig", + "manual_ranges": [-0.1, 100], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 1, + "bins": 100, + "inputVar_units": None, + }, + "DeepCSV_jetNSecondaryVertices": { + "displayname": "Jet N Secondary Vertices", + "manual_ranges": [0.0, 10], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackPtRel_2": { + "displayname": "Track $p_{T,rel}$ [2]", + "manual_ranges": [-0.1, 3.1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 32, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_1": { + "displayname": "Track $\\Delta R$ [1]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRel_3": { + "displayname": "Track $p_{T,rel}$ [3]", + "manual_ranges": [-0.1, 3.1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 32, + "inputVar_units": None, + }, + "DeepCSV_vertexCategory": { + "displayname": "Vertex Category", + "manual_ranges": [-0.6, 2.6], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 26, + "inputVar_units": None, + }, + "DeepCSV_flightDistance2dVal": { + "displayname": "Flight Distance 2D Val", + "manual_ranges": [-0.1, 2.6], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 27, + "inputVar_units": "cm", + }, + "DeepCSV_trackPtRel_5": { + "displayname": "Track $p_{T,rel}$ [5]", + "manual_ranges": [-0.1, 3.1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 32, + "inputVar_units": "GeV", + }, + "DeepCSV_trackEtaRel_3": { + "displayname": "Track $\\eta_{rel}$ [3]", + "manual_ranges": [1.0, 8], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 45, + "inputVar_units": None, + }, + "DeepCSV_trackEtaRel_2": { + "displayname": "Track $\\eta_{rel}$ [2]", + "manual_ranges": [1.0, 8], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 35, + "inputVar_units": None, + }, + "DeepCSV_trackEtaRel_1": { + "displayname": "Track $\\eta_{rel}$ [1]", + "manual_ranges": [1.0, 8], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 35, + "inputVar_units": None, + }, + "DeepCSV_trackEtaRel_0": { + "displayname": "Track $\\eta_{rel}$ [0]", + "manual_ranges": [1.0, 8], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 35, + "inputVar_units": None, + }, + "DeepCSV_trackJetDistVal_4": { + "displayname": "Track Jet Dist Val [4]", + "manual_ranges": [-0.08, 0.0025], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 4, + "bins": 35, + "inputVar_units": "cm", + }, + "DeepCSV_trackJetDistVal_5": { + "displayname": "Track Jet Dist Val [5]", + "manual_ranges": [-0.08, 0.0025], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 4, + "bins": 35, + "inputVar_units": "cm", + }, + "DeepCSV_trackPtRel_1": { + "displayname": "Track $p_{T,rel}$ [1]", + "manual_ranges": [-0.1, 3.1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 32, + "inputVar_units": "GeV", + }, + "DeepCSV_trackJetDistVal_0": { + "displayname": "Track Jet Dist Val [0]", + "manual_ranges": [-0.08, 0.0025], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 4, + "bins": 35, + "inputVar_units": "cm", + }, + "DeepCSV_trackJetDistVal_1": { + "displayname": "Track Jet Dist Val [1]", + "manual_ranges": [-0.08, 0.0025], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 4, + "bins": 35, + "inputVar_units": "cm", + }, + "DeepCSV_trackJetDistVal_2": { + "displayname": "Track Jet Dist Val [2]", + "manual_ranges": [-0.08, 0.0025], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 4, + "bins": 35, + "inputVar_units": "cm", + }, + "DeepCSV_trackJetDistVal_3": { + "displayname": "Track Jet Dist Val [3]", + "manual_ranges": [-0.08, 0.0025], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 4, + "bins": 35, + "inputVar_units": "cm", + }, + "DeepCSV_flightDistance2dSig": { + "displayname": "Flight Distance 2D Sig", + "manual_ranges": [-0.1, 100], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 1, + "bins": 101, + "inputVar_units": None, + }, + "DeepCSV_trackPtRel_4": { + "displayname": "Track $p_{T,rel}$ [4]", + "manual_ranges": [-0.1, 3.1], 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"2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": None, + }, + "DeepJet_sv_d3dsig_1": { + "displayname": "3D IP Sig [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_d3dsig_2": { + "displayname": "3D IP Sig [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_d3dsig_3": { + "displayname": "3D IP Sig [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_deltaR_0": { + "displayname": "$\\Delta$R(SV,Jet) [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-0.5, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_deltaR_1": { + "displayname": "$\\Delta$R(SV,Jet) [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-0.5, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_deltaR_2": { + "displayname": "$\\Delta$R(SV,Jet) [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-0.5, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_deltaR_3": { + "displayname": "$\\Delta$R(SV,Jet) [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-0.5, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_dxy_0": { + "displayname": "2D IP Val [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_dxy_1": { + "displayname": "2D IP Val [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_dxy_2": { + "displayname": "2D IP Val [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_dxy_3": { + "displayname": "2D IP Val [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 10.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_dxysig_0": { + "displayname": "2D IP Sig[0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": None, + }, + "DeepJet_sv_dxysig_1": { + "displayname": "2D IP Sig[1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_dxysig_2": { + "displayname": "2D IP Sig[2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_dxysig_3": { + "displayname": "2D IP Sig[3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_enratio_0": { + "displayname": "$E_{SV}/E_{Jet}$ [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_enratio_1": { + "displayname": "$E_{SV}/E_{Jet}$ [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_enratio_2": { + "displayname": "$E_{SV}/E_{Jet}$ [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_enratio_3": { + "displayname": "$E_{SV}/E_{Jet}$ [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_mass_0": { + "displayname": "$m_{SV}$ [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "$GeV$", + }, + "DeepJet_sv_mass_1": { + "displayname": "$m_{SV}$ [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "$GeV$", + }, + "DeepJet_sv_mass_2": { + "displayname": "$m_{SV}$ [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "$GeV$", + }, + "DeepJet_sv_mass_3": { + "displayname": "$m_{SV}$ [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "$GeV$", + }, + "DeepJet_sv_normchi2_0": { + "displayname": "$\\chi^2$/dof [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 8.0], + "inputVar_units": None, + }, + "DeepJet_sv_normchi2_1": { + "displayname": "$\\chi^2$/dof [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 8.0], + "inputVar_units": None, + }, + "DeepJet_sv_normchi2_2": { + "displayname": "$\\chi^2$/dof [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 8.0], + "inputVar_units": None, + }, + "DeepJet_sv_normchi2_3": { + "displayname": "$\\chi^2$/dof [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 8.0], + "inputVar_units": None, + }, + "DeepJet_sv_ntracks_0": { + "displayname": "N Tracks of [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 40, + "manual_ranges": [0.0, 10.0], + "inputVar_units": None, + }, + "DeepJet_sv_ntracks_1": { + "displayname": "N Tracks of [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 10, + "manual_ranges": [0.0, 10.0], + "inputVar_units": None, + }, + "DeepJet_sv_ntracks_2": { + "displayname": "N Tracks of [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 10, + "manual_ranges": [0.0, 10.0], + "inputVar_units": None, + }, + "DeepJet_sv_ntracks_3": { + "displayname": "N Tracks of [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 10, + "manual_ranges": [0.0, 10.0], + "inputVar_units": None, + }, + "DeepJet_sv_pt_0": { + "displayname": "$p_T^{SV}$ [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": "GeV", + }, + "DeepJet_sv_pt_1": { + "displayname": "$p_T^{SV}$ [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": "GeV", + }, + "DeepJet_sv_pt_2": { + "displayname": "$p_T^{SV}$ [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": "GeV", + }, + "DeepJet_sv_pt_3": { + "displayname": "$p_T^{SV}$ [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": "GeV", + }, + "DeepJet_nCpfcand": { + "displayname": "Number of Ch. PF cand. in the jet", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 50, + "manual_ranges": [0.0, 50.0], + "inputVar_units": None, + }, + "DeepJet_nNpfcand": { + "displayname": "Number of Neu. PF cand. in the jet", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 50, + "manual_ranges": [0.0, 50.0], + "inputVar_units": None, + }, + "DeepJet_npv": { + "displayname": "Number of PV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0, 100.0], + "inputVar_units": None, + }, + "DeepJet_nsv": { + "displayname": "Number of SV in the jet", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 10, + "manual_ranges": [0, 10], + "inputVar_units": None, + }, + "PFCands_numberOFHits": { + "displayname": "Number of hits", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 50, + "manual_ranges": [0, 50], + "inputVar_units": None, + }, + "PFCands_numberOfPixelHits": { + "displayname": "Number of pixel hits", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 10, + "manual_ranges": [0, 10], + "inputVar_units": None, + }, + "PFCands_lostInnerHits": { + "displayname": "Lost inner pixel hits", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 11, + "manual_ranges": [-1, 10], + "inputVar_units": None, + }, +} + def definitions(): jetINDEX = [0, 1, 28, 41, 48, 49, 56, 57, 58, 59, 63, 64, 65] @@ -56,6040 +6181,9 @@ def definitions(): interger_variables = [59, 63, 64, 65, 66] - definitions_dict = { - "DeepCSV_jetNSelectedTracks": { - "displayname": "Jet N Selected Tracks", - "manual_ranges": [0.0, 25], - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 25, - "inputVar_units": None, - }, - "DeepCSV_vertexNTracks": { - "displayname": "Vertex N Tracks", - "manual_ranges": [0.0, 25], - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 25, - "inputVar_units": None, - }, - "DeepCSV_vertexJetDeltaR": { - "displayname": "Vertex Jet $\\Delta R$", - "manual_ranges": [-0.001, 0.301], - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 30, - "inputVar_units": None, - }, - "DeepCSV_trackSumJetEtRatio": { - "displayname": "Track Sum Jet $E_T$ Ratio", - "manual_ranges": [0.0, 5], - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 28, - "inputVar_units": None, - }, - "DeepCSV_trackSip2dSig_4": { - "displayname": "Track SIP 2D Sig [4]", - "manual_ranges": [-6.5, 4.5], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 22, - "inputVar_units": None, - }, - "DeepCSV_trackPtRatio_4": { - "displayname": "Track $p_{T,rel}$ Ratio [4]", - "manual_ranges": [-0.001, 0.301], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 30, - "inputVar_units": None, - }, - "DeepCSV_trackPtRatio_5": { - "displayname": "Track $p_{T,rel}$ Ratio [5]", - "manual_ranges": [-0.001, 0.301], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 30, - "inputVar_units": None, - }, - "DeepCSV_trackPtRatio_0": { - "displayname": "Track $p_{T,rel}$ Ratio [0]", - "manual_ranges": [-0.001, 0.301], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 30, - "inputVar_units": None, - }, - "DeepCSV_trackPtRatio_1": { - "displayname": "Track $p_{T,rel}$ Ratio [1]", - "manual_ranges": [-0.001, 0.301], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 30, - "inputVar_units": None, - }, - "DeepCSV_trackPtRatio_2": { - "displayname": "Track $p_{T,rel}$ Ratio [2]", - "manual_ranges": [-0.001, 0.301], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 30, - "inputVar_units": None, - }, - "DeepCSV_trackPtRatio_3": { - "displayname": "Track $p_{T,rel}$ Ratio [3]", - "manual_ranges": [-0.001, 0.301], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 30, - "inputVar_units": None, - }, - "DeepCSV_vertexEnergyRatio": { - "displayname": "Vertex Energy Ratio", - "manual_ranges": [0, 2.5], - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 25, - "inputVar_units": None, - }, - "DeepCSV_trackSip2dSig_5": { - "displayname": "Track SIP 2D Sig [5]", - "manual_ranges": [-7, 2], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 18, - "inputVar_units": None, - }, - "DeepCSV_trackSumJetDeltaR": { - "displayname": "Track Sum Jet $\\Delta R$", - "manual_ranges": [-0.001, 0.301], - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 30, - "inputVar_units": None, - }, - "DeepCSV_trackSip3dSig_0": { - "displayname": "Track SIP 3D Sig [0]", - "manual_ranges": [-25, 50], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 25, - "inputVar_units": None, - }, - "DeepCSV_trackDecayLenVal_5": { - "displayname": "Track Decay Len Val [5]", - "manual_ranges": [-0.1, 1], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 22, - "inputVar_units": "cm", - }, - "DeepCSV_trackDecayLenVal_4": { - "displayname": "Track Decay Len Val [4]", - "manual_ranges": [-0.1, 1], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 22, - "inputVar_units": "cm", - }, - "DeepCSV_vertexMass": { - "displayname": "Vertex Mass", - "manual_ranges": [0, 20], - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 20, - "inputVar_units": "GeV", - }, - "DeepCSV_trackSip3dSig_3": { - "displayname": "Track SIP 3D Sig [3]", - "manual_ranges": [-25, 50], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 25, - "inputVar_units": None, - }, - "DeepCSV_trackDecayLenVal_1": { - "displayname": "Track Decay Len Val [1]", - "manual_ranges": [-0.1, 1], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 22, - "inputVar_units": "cm", - }, - "DeepCSV_trackDecayLenVal_0": { - "displayname": "Track Decay Len Val [0]", - "manual_ranges": [-0.1, 1], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 22, - "inputVar_units": "cm", - }, - "DeepCSV_trackDecayLenVal_3": { - "displayname": "Track Decay Len Val [3]", - "manual_ranges": [-0.1, 1], - "ylabel_text": "Tracks", - "format_unit": "2f", - "format_unit_digits": 3, - "bins": 22, - "inputVar_units": "cm", - }, - "DeepCSV_trackDecayLenVal_2": { - "displayname": "Track Decay Len Val [2]", - 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"format_unit_digits": 2, - "bins": 100, - "manual_ranges": [0.0, 400.0], - "inputVar_units": "GeV", - }, - "DeepJet_nCpfcand": { - "displayname": "Number of Ch. PF cand. in the jet", - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 50, - "manual_ranges": [0.0, 50.0], - "inputVar_units": None, - }, - "DeepJet_nNpfcand": { - "displayname": "Number of Neu. PF cand. in the jet", - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 50, - "manual_ranges": [0.0, 50.0], - "inputVar_units": None, - }, - "DeepJet_npv": { - "displayname": "Number of PV", - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 100, - "manual_ranges": [0, 100.0], - "inputVar_units": None, - }, - "DeepJet_nsv": { - "displayname": "Number of SV in the jet", - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 10, - "manual_ranges": [0, 10], - "inputVar_units": None, - }, - "PFCands_numberOFHits": { - "displayname": "Number of hits", - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 50, - "manual_ranges": [0, 50], - "inputVar_units": None, - }, - "PFCands_numberOfPixelHits": { - "displayname": "Number of pixel hits", - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 10, - "manual_ranges": [0, 10], - "inputVar_units": None, - }, - "PFCands_lostInnerHits": { - "displayname": "Lost inner pixel hits", - "ylabel_text": "Jets", - "format_unit": "2f", - "format_unit_digits": 2, - "bins": 11, - "manual_ranges": [-1, 10], - "inputVar_units": None, - }, - } + definition = definitions_dict - return definitions_dict + return definition def SV_definitions(): diff --git a/src/BTVNanoCommissioning/helpers/definitions_for_corr.py b/src/BTVNanoCommissioning/helpers/definitions_for_corr.py new file mode 100644 index 00000000..15df39f4 --- /dev/null +++ b/src/BTVNanoCommissioning/helpers/definitions_for_corr.py @@ -0,0 +1,6124 @@ +### discriminators +disc_list = [ + "btagDeepFlavB", + "btagDeepFlavC", + "btagTransDeepFlavB", + "btagTransDeepFlavC", + "btagDeepFlavCvL", + "btagDeepFlavCvB", + "btagDeepFlavB_b", + "btagDeepFlavB_bb", + "btagTransDeepFlavB_lepb", + "btagTransDeepFlavB_b", + "btagTransDeepFlavB_bb", + "btagTransDeepFlavB_lepb", + "btagPNetB", + "btagTransPNetB", + "btagPNetCvB", + "btagPNetCvL", + "btagPNetProbB", + "btagPNetProbC", + "btagPNetProbG", + "btagPNetProbUDS", + "btagTransPNetProbB", + "btagTransPNetProbC", + "btagTransPNetProbG", + "btagTransPNetProbUDS", + "btagPNetQvG", + "btagPNetTauVJet", + "btagRobustParTAK4B", + "btagRobustParTAK4B_b", + "btagRobustParTAK4B_bb", + "btagRobustParTAK4B_lepb", + "btagRobustParTAK4C", + "btagRobustParTAK4G", + "btagRobustParTAK4UDS", + "btagTransRobustParTAK4B", + "btagTransRobustParTAK4B_b", + "btagTransRobustParTAK4B_bb", + "btagTransRobustParTAK4B_lepb", + "btagTransRobustParTAK4C", + "btagTransRobustParTAK4G", + "btagTransRobustParTAK4UDS", + "btagRobustParTAK4CvB", + "btagRobustParTAK4CvL", + "btagRobustParTAK4QG", + "btagUParTAK4B", + "btagUParTAK4CvL", + "btagUParTAK4CvB", + "btagUParTAK4CvNotB", + "btagUParTAK4QvG", + "btagUParTAK4TauVJet", + ## Negative tagger + "btagNegDeepFlavB", + "btagNegDeepFlavB_b", + "btagNegDeepFlavB_bb", + "btagNegDeepFlavB_lepb", + "btagNegDeepFlavC", + "btagNegDeepFlavCvB", + "btagNegDeepFlavCvL", + "btagNegDeepFlavG", + "btagNegDeepFlavQG", + "btagNegDeepFlavUDS", + "btagNegPNetB", + "btagNegPNetC", + "btagNegPNetCvB", + "btagNegPNetCvL", + "btagNegPNetProbB", + "btagNegPNetProbC", + "btagNegPNetProbG", + "btagNegPNetProbUDS", + "btagNegRobustParTAK4B", + "btagNegRobustParTAK4B_b", + "btagNegRobustParTAK4B_bb", + "btagNegRobustParTAK4B_lepb", + "btagNegRobustParTAK4C", + "btagNegRobustParTAK4CvB", + "btagNegRobustParTAK4CvL", + "btagNegRobustParTAK4G", + "btagNegRobustParTAK4QG", + "btagNegRobustParTAK4UDS", + # other prob info + "PNetRegPtRawCorr", + "PNetRegPtRawCorrNeutrino", + "PNetRegPtRawRes", + "UParTAK4RegPtRawRes", + "UParTAK4RegPtRawCorrNeutrino", + "UParTAK4RegPtRawCorr", + "Bprob", + "BprobN", + "ProbaN", +] + +definitions_dict = { + "DeepCSV_jetNSelectedTracks": { + "displayname": "Jet N Selected Tracks", + "manual_ranges": [0.0, 25], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_vertexNTracks": { + "displayname": "Vertex N Tracks", + "manual_ranges": [0.0, 25], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_vertexJetDeltaR": { + "displayname": "Vertex Jet $\\Delta R$", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackSumJetEtRatio": { + "displayname": "Track Sum Jet $E_T$ Ratio", + "manual_ranges": [0.0, 5], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 28, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_4": { + "displayname": "Track SIP 2D Sig [4]", + "manual_ranges": [-6.5, 4.5], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 22, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_4": { + "displayname": "Track $p_{T,rel}$ Ratio [4]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_5": { + "displayname": "Track $p_{T,rel}$ Ratio [5]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_0": { + "displayname": "Track $p_{T,rel}$ Ratio [0]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_1": { + "displayname": "Track $p_{T,rel}$ Ratio [1]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_2": { + "displayname": "Track $p_{T,rel}$ Ratio [2]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRatio_3": { + "displayname": "Track $p_{T,rel}$ Ratio [3]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_vertexEnergyRatio": { + "displayname": "Vertex Energy Ratio", + "manual_ranges": [0, 2.5], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_5": { + "displayname": "Track SIP 2D Sig [5]", + "manual_ranges": [-7, 2], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 18, + "inputVar_units": None, + }, + "DeepCSV_trackSumJetDeltaR": { + "displayname": "Track Sum Jet $\\Delta R$", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackSip3dSig_0": { + "displayname": "Track SIP 3D Sig [0]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackDecayLenVal_5": { + "displayname": "Track Decay Len Val [5]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_trackDecayLenVal_4": { + "displayname": "Track Decay Len Val [4]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_vertexMass": { + "displayname": "Vertex Mass", + "manual_ranges": [0, 20], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 20, + "inputVar_units": "GeV", + }, + "DeepCSV_trackSip3dSig_3": { + "displayname": "Track SIP 3D Sig [3]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackDecayLenVal_1": { + "displayname": "Track Decay Len Val [1]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_trackDecayLenVal_0": { + "displayname": "Track Decay Len Val [0]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_trackDecayLenVal_3": { + "displayname": "Track Decay Len Val [3]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_trackDecayLenVal_2": { + "displayname": "Track Decay Len Val [2]", + "manual_ranges": [-0.1, 1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 22, + "inputVar_units": "cm", + }, + "DeepCSV_trackPtRel_0": { + "displayname": "Track $p_{T,rel}$ [0]", + "manual_ranges": [-0.1, 3.1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 32, + "inputVar_units": None, + }, + "DeepCSV_jetNTracksEtaRel": { + "displayname": "Jet N Tracks $\\eta_{rel}$", + "manual_ranges": [0.0, 25], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_0": { + "displayname": "Track SIP 2D Sig [0]", + "manual_ranges": [-4.5, 16], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 21, + "inputVar_units": None, + }, + "DeepCSV_trackSip3dSigAboveCharm": { + "displayname": "Track SIP 3D Sig Above Charm", + "manual_ranges": [-6.5, 6.5], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 26, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_3": { + "displayname": "Track SIP 2D Sig [3]", + "manual_ranges": [-6, 7], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 26, + "inputVar_units": None, + }, + "DeepCSV_trackSip3dSig_4": { + "displayname": "Track SIP 3D Sig [4]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip3dSig_5": { + "displayname": "Track SIP 3D Sig [5]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_2": { + "displayname": "Track SIP 2D Sig [2]", + "manual_ranges": [-5.5, 10], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 16, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dValAboveCharm": { + "displayname": "Track SIP 2D Val Above Charm", + "manual_ranges": [-0.06, 0.06], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 24, + "inputVar_units": "cm", + }, + "DeepCSV_trackSip3dSig_1": { + "displayname": "Track SIP 3D Sig [1]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip3dSig_2": { + "displayname": "Track SIP 3D Sig [2]", + "manual_ranges": [-25, 50], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSig_1": { + "displayname": "Track SIP 2D Sig [1]", + "manual_ranges": [-5, 13], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 18, + "inputVar_units": None, + }, + "DeepCSV_trackSip2dSigAboveCharm": { + "displayname": "Track SIP 2D Sig Above Charm", + "manual_ranges": [-5.5, 5.5], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 22, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_4": { + "displayname": "Track $\\Delta R$ [4]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_5": { + "displayname": "Track $\\Delta R$ [5]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_2": { + "displayname": "Track $\\Delta R$ [2]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_3": { + "displayname": "Track $\\Delta R$ [3]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_0": { + "displayname": "Track $\\Delta R$ [0]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_flightDistance3dVal": { + "displayname": "Flight Distance 3D Val", + "manual_ranges": [-0.1, 5], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 51, + "inputVar_units": "cm", + }, + "DeepCSV_flightDistance3dSig": { + "displayname": "Flight Distance 3D Sig", + "manual_ranges": [-0.1, 100], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 1, + "bins": 100, + "inputVar_units": None, + }, + "DeepCSV_jetNSecondaryVertices": { + "displayname": "Jet N Secondary Vertices", + "manual_ranges": [0.0, 10], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 25, + "inputVar_units": None, + }, + "DeepCSV_trackPtRel_2": { + "displayname": "Track $p_{T,rel}$ [2]", + "manual_ranges": [-0.1, 3.1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 32, + "inputVar_units": None, + }, + "DeepCSV_trackDeltaR_1": { + "displayname": "Track $\\Delta R$ [1]", + "manual_ranges": [-0.001, 0.301], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 30, + "inputVar_units": None, + }, + "DeepCSV_trackPtRel_3": { + "displayname": "Track $p_{T,rel}$ [3]", + "manual_ranges": [-0.1, 3.1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 32, + "inputVar_units": None, + }, + "DeepCSV_vertexCategory": { + "displayname": "Vertex Category", + "manual_ranges": [-0.6, 2.6], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 26, + "inputVar_units": None, + }, + "DeepCSV_flightDistance2dVal": { + "displayname": "Flight Distance 2D Val", + "manual_ranges": [-0.1, 2.6], + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 3, + "bins": 27, + "inputVar_units": "cm", + }, + "DeepCSV_trackPtRel_5": { + "displayname": "Track $p_{T,rel}$ [5]", + "manual_ranges": [-0.1, 3.1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 32, + "inputVar_units": "GeV", + }, + "DeepCSV_trackEtaRel_3": { + "displayname": "Track $\\eta_{rel}$ [3]", + "manual_ranges": [1.0, 8], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 45, + "inputVar_units": None, + }, + "DeepCSV_trackEtaRel_2": { + "displayname": "Track $\\eta_{rel}$ [2]", + "manual_ranges": [1.0, 8], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 35, + "inputVar_units": None, + }, + "DeepCSV_trackEtaRel_1": { + "displayname": "Track $\\eta_{rel}$ [1]", + "manual_ranges": [1.0, 8], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 35, + "inputVar_units": None, + }, + "DeepCSV_trackEtaRel_0": { + "displayname": "Track $\\eta_{rel}$ [0]", + "manual_ranges": [1.0, 8], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 35, + "inputVar_units": None, + }, + "DeepCSV_trackJetDistVal_4": { + "displayname": "Track Jet Dist Val [4]", + "manual_ranges": [-0.08, 0.0025], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 4, + "bins": 35, + "inputVar_units": "cm", + }, + "DeepCSV_trackJetDistVal_5": { + "displayname": "Track Jet Dist Val [5]", + "manual_ranges": [-0.08, 0.0025], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 4, + "bins": 35, + "inputVar_units": "cm", + }, + "DeepCSV_trackPtRel_1": { + "displayname": "Track $p_{T,rel}$ [1]", + "manual_ranges": [-0.1, 3.1], + "ylabel_text": "Tracks", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 32, + "inputVar_units": "GeV", + }, + "DeepCSV_trackJetDistVal_0": { + "displayname": "Track Jet Dist Val [0]", + "manual_ranges": [-0.08, 0.0025], + 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"displayname": "$\\cos(\\overrightarrow{flight}, \\overrightarrow{p})$ [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-1.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_costhetasvpv_1": { + "displayname": "$\\cos(\\overrightarrow{flight}, \\overrightarrow{p})$ [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-1.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_costhetasvpv_2": { + "displayname": "$\\cos(\\overrightarrow{flight}, \\overrightarrow{p})$ [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-1.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_costhetasvpv_3": { + "displayname": "$\\cos(\\overrightarrow{flight}, \\overrightarrow{p})$ [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-1.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_d3d_0": { + "displayname": "3D IP Val [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 25.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_d3d_1": { + "displayname": "3D IP Val [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_d3d_2": { + "displayname": "3D IP Val [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_d3d_3": { + "displayname": "3D IP Val [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_d3dsig_0": { + "displayname": "3D IP Sig [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": None, + }, + "DeepJet_sv_d3dsig_1": { + "displayname": "3D IP Sig [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_d3dsig_2": { + "displayname": "3D IP Sig [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_d3dsig_3": { + "displayname": "3D IP Sig [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_deltaR_0": { + "displayname": "$\\Delta$R(SV,Jet) [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-0.5, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_deltaR_1": { + "displayname": "$\\Delta$R(SV,Jet) [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-0.5, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_deltaR_2": { + "displayname": "$\\Delta$R(SV,Jet) [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-0.5, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_deltaR_3": { + "displayname": "$\\Delta$R(SV,Jet) [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [-0.5, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_dxy_0": { + "displayname": "2D IP Val [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_dxy_1": { + "displayname": "2D IP Val [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_dxy_2": { + "displayname": "2D IP Val [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_dxy_3": { + "displayname": "2D IP Val [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 10.0], + "inputVar_units": "cm", + }, + "DeepJet_sv_dxysig_0": { + "displayname": "2D IP Sig[0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": None, + }, + "DeepJet_sv_dxysig_1": { + "displayname": "2D IP Sig[1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_dxysig_2": { + "displayname": "2D IP Sig[2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_dxysig_3": { + "displayname": "2D IP Sig[3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 200.0], + "inputVar_units": None, + }, + "DeepJet_sv_enratio_0": { + "displayname": "$E_{SV}/E_{Jet}$ [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_enratio_1": { + "displayname": "$E_{SV}/E_{Jet}$ [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_enratio_2": { + "displayname": "$E_{SV}/E_{Jet}$ [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_enratio_3": { + "displayname": "$E_{SV}/E_{Jet}$ [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 1.0], + "inputVar_units": None, + }, + "DeepJet_sv_mass_0": { + "displayname": "$m_{SV}$ [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "$GeV$", + }, + "DeepJet_sv_mass_1": { + "displayname": "$m_{SV}$ [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "$GeV$", + }, + "DeepJet_sv_mass_2": { + "displayname": "$m_{SV}$ [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "$GeV$", + }, + "DeepJet_sv_mass_3": { + "displayname": "$m_{SV}$ [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 20.0], + "inputVar_units": "$GeV$", + }, + "DeepJet_sv_normchi2_0": { + "displayname": "$\\chi^2$/dof [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 8.0], + "inputVar_units": None, + }, + "DeepJet_sv_normchi2_1": { + "displayname": "$\\chi^2$/dof [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 8.0], + "inputVar_units": None, + }, + "DeepJet_sv_normchi2_2": { + "displayname": "$\\chi^2$/dof [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 8.0], + "inputVar_units": None, + }, + "DeepJet_sv_normchi2_3": { + "displayname": "$\\chi^2$/dof [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 8.0], + "inputVar_units": None, + }, + "DeepJet_sv_ntracks_0": { + "displayname": "N Tracks of [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 40, + "manual_ranges": [0.0, 10.0], + "inputVar_units": None, + }, + "DeepJet_sv_ntracks_1": { + "displayname": "N Tracks of [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 10, + "manual_ranges": [0.0, 10.0], + "inputVar_units": None, + }, + "DeepJet_sv_ntracks_2": { + "displayname": "N Tracks of [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 10, + "manual_ranges": [0.0, 10.0], + "inputVar_units": None, + }, + "DeepJet_sv_ntracks_3": { + "displayname": "N Tracks of [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 10, + "manual_ranges": [0.0, 10.0], + "inputVar_units": None, + }, + "DeepJet_sv_pt_0": { + "displayname": "$p_T^{SV}$ [0] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": "GeV", + }, + "DeepJet_sv_pt_1": { + "displayname": "$p_T^{SV}$ [1] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": "GeV", + }, + "DeepJet_sv_pt_2": { + "displayname": "$p_T^{SV}$ [2] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": "GeV", + }, + "DeepJet_sv_pt_3": { + "displayname": "$p_T^{SV}$ [3] SV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0.0, 400.0], + "inputVar_units": "GeV", + }, + "DeepJet_nCpfcand": { + "displayname": "Number of Ch. PF cand. in the jet", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 50, + "manual_ranges": [0.0, 50.0], + "inputVar_units": None, + }, + "DeepJet_nNpfcand": { + "displayname": "Number of Neu. PF cand. in the jet", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 50, + "manual_ranges": [0.0, 50.0], + "inputVar_units": None, + }, + "DeepJet_npv": { + "displayname": "Number of PV", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 100, + "manual_ranges": [0, 100.0], + "inputVar_units": None, + }, + "DeepJet_nsv": { + "displayname": "Number of SV in the jet", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 10, + "manual_ranges": [0, 10], + "inputVar_units": None, + }, + "PFCands_numberOFHits": { + "displayname": "Number of hits", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 50, + "manual_ranges": [0, 50], + "inputVar_units": None, + }, + "PFCands_numberOfPixelHits": { + "displayname": "Number of pixel hits", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 10, + "manual_ranges": [0, 10], + "inputVar_units": None, + }, + "PFCands_lostInnerHits": { + "displayname": "Lost inner pixel hits", + "ylabel_text": "Jets", + "format_unit": "2f", + "format_unit_digits": 2, + "bins": 11, + "manual_ranges": [-1, 10], + "inputVar_units": None, + }, +} diff --git a/src/BTVNanoCommissioning/helpers/update_branch.py b/src/BTVNanoCommissioning/helpers/update_branch.py index d1687ad0..81115994 100644 --- a/src/BTVNanoCommissioning/helpers/update_branch.py +++ b/src/BTVNanoCommissioning/helpers/update_branch.py @@ -22,13 +22,26 @@ def missing_branch(events): events.Jet, {"btagDeepFlavB": jets.btagDeepFlavB}, ) - if hasattr(events.Jet, "btagDeepFlavCvL") and not hasattr( - events.Jet, "btagDeepFlavC" + if ( + hasattr(events.Jet, "btagDeepFlavCvL") + and hasattr(events.Jet, "btagDeepFlavUDS") + and not hasattr(events.Jet, "btagDeepFlavC") ): jets = events.Jet jets["btagDeepFlavC"] = ( events.Jet.btagDeepFlavCvL / (1.0 - events.Jet.btagDeepFlavCvL) - ) * events.Jet.btagDeepFlavB + ) * (events.Jet.btagDeepFlavG + events.Jet.btagDeepFlavUDS) + events.Jet = update( + events.Jet, + {"btagDeepFlavC": jets.btagDeepFlavC}, + ) + if hasattr(events.Jet, "btagDeepFlavCvB") and not hasattr( + events.Jet, "btagDeepFlavC" + ): + jets = events.Jet + jets["btagDeepFlavC"] = ( + events.Jet.btagDeepFlavCvB / (1.0 - events.Jet.btagDeepFlavCvB) + ) * (events.Jet.btagDeepFlavB) events.Jet = update( events.Jet, {"btagDeepFlavC": jets.btagDeepFlavC}, diff --git a/src/BTVNanoCommissioning/utils/AK4_parameters.py b/src/BTVNanoCommissioning/utils/AK4_parameters.py index 156824e4..438834c9 100644 --- a/src/BTVNanoCommissioning/utils/AK4_parameters.py +++ b/src/BTVNanoCommissioning/utils/AK4_parameters.py @@ -49,9 +49,12 @@ "Run2022D": "Summer22_22Sep2023_RunCD_V2", }, # update to latest JEC "LSF": { - "mu_json": "ScaleFactors_Muon_trackerMuons_Z_2022_Prompt_ID_ISO_schemaV2.json", "mu_ID": "NUM_TightID_DEN_TrackerMuons", "mu_Iso": "NUM_TightPFIso_DEN_TightID", + "ele_ID 2022Re-recoBCD Electron-ID-SF": "Tight", + "ele_Reco_low 2022Re-recoBCD Electron-ID-SF": "RecoBelow20", + "ele_Reco_med 2022Re-recoBCD Electron-ID-SF": "Reco20to75", + "ele_Reco_high 2022Re-recoBCD Electron-ID-SF": "RecoAbove75", }, "JPCalib": { "Run2022C": "calibeHistoWrite_Data2022C_NANO130X_v1.root", @@ -70,14 +73,12 @@ "Run2022G": "Summer22EE_22Sep2023_RunG_V2", }, "LSF": { - "ele_json": "electron.json.gz", - "mu_json": "ScaleFactors_Muon_trackerMuons_Z_2022EE_Prompt_ID_ISO_schemaV2.json", "mu_ID": "NUM_TightID_DEN_TrackerMuons", "mu_Iso": "NUM_TightPFIso_DEN_TightID", - "ele_ID 2022FG 2022FG-Electron-ID-SF": "Tight", - "ele_Reco_low 2022FG 2022FG-Electron-ID-SF": "RecoBelow20", - "ele_Reco_med 2022FG 2022FG-Electron-ID-SF": "Reco20to75", - "ele_Reco_high 2022FG 2022FG-Electron-ID-SF": "RecoAbove75", + "ele_ID 2022Re-recoE+PromptFG Electron-ID-SF": "Tight", + "ele_Reco_low 2022Re-recoE+PromptFG Electron-ID-SF": "RecoBelow20", + "ele_Reco_med 2022Re-recoE+PromptFG Electron-ID-SF": "Reco20to75", + "ele_Reco_high 2022Re-recoE+PromptFG Electron-ID-SF": "RecoAbove75", }, "jetveto": {"Run2022E jetvetomap_eep": "Winter22Run3_RunE_v1.histo.root"}, # use for BTA production, jet probablity @@ -99,6 +100,14 @@ # "Run2023C-22Sep2023_v4": "Summer23Prompt23_RunCv4_V1", # }, "JME": "jec_compiled.pkl.gz", + "LSF": { + "mu_ID": "NUM_TightID_DEN_TrackerMuons", + "mu_Iso": "NUM_TightPFIso_DEN_TightID", + "ele_ID 2023PromptC Electron-ID-SF": "Tight", + "ele_Reco_low 2023PromptC Electron-ID-SF": "RecoBelow20", + "ele_Reco_med 2023PromptC Electron-ID-SF": "Reco20to75", + "ele_Reco_high 2023PromptC Electron-ID-SF": "RecoAbove75", + }, "jetveto": {"Run2023BC jetvetomap_all": "Summer23Prompt23_RunC_v1.histo.root"}, "JPCalib": { "Run2023C-22Sep2023_v1": "calibeHistoWrite_Data2023C-22Sep2023_v1.root", @@ -116,6 +125,14 @@ "Run2023D": "Summer23BPixPrompt23_RunD_V1", }, # "JME": "jec_compiled.pkl.gz", + "LSF": { + "mu_ID": "NUM_TightID_DEN_TrackerMuons", + "mu_Iso": "NUM_TightPFIso_DEN_TightID", + "ele_ID 2023PromptD Electron-ID-SF": "Tight", + "ele_Reco_low 2023PromptD Electron-ID-SF": "RecoBelow20", + "ele_Reco_med 2023PromptD Electron-ID-SF": "Reco20to75", + "ele_Reco_high 2023PromptD Electron-ID-SF": "RecoAbove75", + }, "jetveto": { "Run2023D jetvetomap_all": "Summer23BPixPrompt23_RunD_v1.histo.root" }, # this is from Mikko https://indico.cern.ch/event/1315421/contributions/5532963/attachments/2697975/4683826/2023_08_16_jetvetomaps_v3.pdf diff --git a/src/BTVNanoCommissioning/utils/histogrammer.py b/src/BTVNanoCommissioning/utils/histogrammer.py index 3c8d81a4..963c77a6 100644 --- a/src/BTVNanoCommissioning/utils/histogrammer.py +++ b/src/BTVNanoCommissioning/utils/histogrammer.py @@ -47,6 +47,36 @@ def histogrammer(events, workflow): ) _hist_dict["nJetSVs"] = Hist.Hist(syst_axis, n_axis, Hist.storage.Weight()) + elif "QCD_smu" == workflow: + obj_list = ["jet0", "hl", "soft_l"] + _hist_dict["dr_SVjet0"] = Hist.Hist( + syst_axis, flav_axis, dr_SV_axis, Hist.storage.Weight() + ) + _hist_dict["nJetSVs"] = Hist.Hist(syst_axis, n_axis, Hist.storage.Weight()) + _hist_dict["nsmuj"] = Hist.Hist(syst_axis, n_axis, Hist.storage.Weight()) + _hist_dict["nssmu"] = Hist.Hist(syst_axis, n_axis, Hist.storage.Weight()) + + for i in ["soft_l"]: + if i == "soft_l": + _hist_dict[f"soft_l_pfRelIso04_all"] = Hist.Hist( + syst_axis, + flav_axis, + softliso_axis, + Hist.storage.Weight(), + ) + _hist_dict[f"{i}_dxy"] = Hist.Hist( + syst_axis, flav_axis, dxy_axis, Hist.storage.Weight() + ) + _hist_dict[f"{i}_dz"] = Hist.Hist( + syst_axis, flav_axis, dz_axis, Hist.storage.Weight() + ) + _hist_dict[f"{i}_ptratio"] = Hist.Hist( + syst_axis, flav_axis, ptratio_axis, Hist.storage.Weight() + ) + _hist_dict["dr_lmujetsmu"] = Hist.Hist( + syst_axis, flav_axis, dr_s_axis, Hist.storage.Weight() + ) + elif "validation" == workflow: obj_list = ["jet0", "jet1"] _hist_dict["bjet_WP_pt"] = Hist.Hist( @@ -139,9 +169,37 @@ def histogrammer(events, workflow): obj_list = ["mu", "MET"] for i in range(4): obj_list.append(f"jet{i}") + + _hist_dict[f"dr_mujet{i}"] = Hist.Hist( + syst_axis, flav_axis, dr_axis, Hist.storage.Weight() + ) + + for i in ["mu"]: + _hist_dict[f"{i}_pfRelIso04_all"] = Hist.Hist( + syst_axis, iso_axis, Hist.storage.Weight() + ) + _hist_dict[f"{i}_dxy"] = Hist.Hist( + syst_axis, dxy_axis, Hist.storage.Weight() + ) + _hist_dict[f"{i}_dz"] = Hist.Hist(syst_axis, dz_axis, Hist.storage.Weight()) + _hist_dict["w_mass"] = Hist.Hist( + syst_axis, + Hist.axis.Regular(50, 40, 120, name="mass", label="$m_{\\ell\\nu}$ [GeV]"), + Hist.storage.Weight(), + ) + elif "c_ttsemilep_sf" == workflow: + obj_list = ["mu", "MET"] + obj_list.append("cjet") + _hist_dict["dr_cjet"] = Hist.Hist( + syst_axis, flav_axis, dr_axis, Hist.storage.Weight() + ) + for i in range(4): + obj_list.append(f"jet{i}") + _hist_dict[f"dr_mujet{i}"] = Hist.Hist( syst_axis, flav_axis, dr_axis, Hist.storage.Weight() ) + for i in ["mu"]: _hist_dict[f"{i}_pfRelIso04_all"] = Hist.Hist( syst_axis, iso_axis, Hist.storage.Weight() @@ -150,6 +208,11 @@ def histogrammer(events, workflow): syst_axis, dxy_axis, Hist.storage.Weight() ) _hist_dict[f"{i}_dz"] = Hist.Hist(syst_axis, dz_axis, Hist.storage.Weight()) + _hist_dict["w_mass"] = Hist.Hist( + syst_axis, + Hist.axis.Regular(50, 40, 120, name="mass", label="$m_{\\ell\\nu}$ [GeV]"), + Hist.storage.Weight(), + ) elif "ctag_ttdilep_sf" in workflow: obj_list = ["hl", "sl", "soft_l", "MET", "z", "lmujet"] _hist_dict["z_mass"] = Hist.Hist( @@ -478,6 +541,7 @@ def histogrammer(events, workflow): "btagDeepFlavB_b", "btagDeepFlavB_bb", "btagPNetB", + "btagTransPNetB", "btagPNetCvB", "btagPNetCvL", "btagPNetCvNotB", @@ -551,17 +615,37 @@ def histogrammer(events, workflow): njet = 2 elif "ttsemilep_sf" in workflow: njet = 4 + if "btag" in disc or "ProbaN" == disc: + _hist_dict[f"c_{disc}"] = Hist.Hist( + syst_axis, + flav_axis, + Hist.axis.Regular(50, 0.0, 1, name="discr", label=disc), + Hist.storage.Weight(), + ) + elif "Bprob" in disc: + _hist_dict[f"c_{disc}"] = Hist.Hist( + syst_axis, + flav_axis, + Hist.axis.Regular(50, 0, 10, name="discr", label=disc), + Hist.storage.Weight(), + ) + elif "PNetRegPtRawRes" == disc: + _hist_dict[f"c_{disc}"] = Hist.Hist( + syst_axis, + flav_axis, + Hist.axis.Regular(40, 0, 1, name="discr", label=disc), + Hist.storage.Weight(), + ) + elif "PNetRegPtRawCorr" in disc: + _hist_dict[f"c_{disc}"] = Hist.Hist( + syst_axis, + flav_axis, + Hist.axis.Regular(40, 0, 2, name="discr", label=disc), + Hist.storage.Weight(), + ) for i in range(njet): if "Wc_sf" in workflow: - if "Trans" in disc: - _hist_dict[f"{disc}_{i}"] = Hist.Hist( - syst_axis, - flav_axis, - osss_axis, - Hist.axis.Regular(40, 0, 8, name="discr", label=disc), - Hist.storage.Weight(), - ) - elif "btag" in disc or "ProbaN" == disc: + if "btag" in disc or "ProbaN" == disc: _hist_dict[f"{disc}_{i}"] = Hist.Hist( syst_axis, flav_axis, @@ -595,13 +679,6 @@ def histogrammer(events, workflow): ) else: - if "Trans" in disc: - _hist_dict[f"{disc}_{i}"] = Hist.Hist( - syst_axis, - flav_axis, - Hist.axis.Regular(40, 0, 8, name="discr", label=disc), - Hist.storage.Weight(), - ) if "btag" in disc or "ProbaN" == disc: _hist_dict[f"{disc}_{i}"] = Hist.Hist( syst_axis, @@ -616,14 +693,14 @@ def histogrammer(events, workflow): Hist.axis.Regular(50, 0, 10, name="discr", label=disc), Hist.storage.Weight(), ) - elif "Res" == disc: + elif "PNetRegPtRawRes" == disc: _hist_dict[f"{disc}_{i}"] = Hist.Hist( syst_axis, flav_axis, Hist.axis.Regular(40, 0, 1, name="discr", label=disc), Hist.storage.Weight(), ) - elif "Corr" in disc: + elif "PNetRegPtRawCorr" in disc: _hist_dict[f"{disc}_{i}"] = Hist.Hist( syst_axis, flav_axis, diff --git a/src/BTVNanoCommissioning/workflows/QCD_soft_mu_validation.py b/src/BTVNanoCommissioning/workflows/QCD_soft_mu_validation.py new file mode 100644 index 00000000..9ab97b35 --- /dev/null +++ b/src/BTVNanoCommissioning/workflows/QCD_soft_mu_validation.py @@ -0,0 +1,509 @@ +import collections, numpy as np, awkward as ak +from coffea import processor +from coffea.analysis_tools import Weights +from BTVNanoCommissioning.utils.selection import jet_cut +from BTVNanoCommissioning.helpers.func import flatten, update, dump_lumi +from BTVNanoCommissioning.utils.histogrammer import histogrammer +from BTVNanoCommissioning.utils.array_writer import array_writer +from BTVNanoCommissioning.helpers.update_branch import missing_branch +from BTVNanoCommissioning.utils.correction import ( + load_SF, + load_lumi, + muSFs, + eleSFs, + puwei, + btagSFs, + JME_shifts, + Roccor_shifts, + jetveto, +) +from BTVNanoCommissioning.utils.selection import ( + jet_id, + mu_idiso, + ele_mvatightid, + softmu_mask, +) + +import correctionlib + + +class NanoProcessor(processor.ProcessorABC): + # Define histograms + def __init__( + self, + year="2022", + campaign="Summer22Run3", + name="", + isSyst=False, + isArray=False, + noHist=False, + chunksize=75000, + addsel=False, + ): + self._year = year + self._campaign = campaign + self.name = name + self.isSyst = isSyst + self.isArray = isArray + self.noHist = noHist + self.lumiMask = load_lumi(self._campaign) + self.chunksize = chunksize + ## Load corrections + self.SF_map = load_SF(self._campaign) + + @property + def accumulator(self): + return self._accumulator + + def process(self, events): + isRealData = not hasattr(events, "genWeight") + dataset = events.metadata["dataset"] + events = missing_branch(events) + shifts = [] + if "JME" in self.SF_map.keys() or "jetveto" in self.SF_map.keys(): + syst_JERC = self.isSyst + if self.isSyst == "JERC_split": + syst_JERC = "split" + shifts = JME_shifts( + shifts, self.SF_map, events, self._campaign, isRealData, False, True + ) + else: + if int(self._year) < 2020: + shifts = [ + ({"Jet": events.Jet, "MET": events.MET, "Muon": events.Muon}, None) + ] + else: + shifts = [ + ( + { + "Jet": events.Jet, + "MET": events.PuppiMET, + "Muon": events.Muon, + }, + None, + ) + ] + if "roccor" in self.SF_map.keys(): + shifts = Roccor_shifts(shifts, self.SF_map, events, isRealData, False) + else: + shifts[0][0]["Muon"] = events.Muon + + return processor.accumulate( + self.process_shift(update(events, collections), name) + for collections, name in shifts + ) + + def process_shift(self, events, shift_name): + isRealData = not hasattr(events, "genWeight") + dataset = events.metadata["dataset"] + _hist_event_dict = ( + {"": None} if self.noHist else histogrammer(events, "QCD_smu") + ) + if _hist_event_dict == None: + _hist_event_dict[""] + output = { + "sumw": processor.defaultdict_accumulator(float), + **_hist_event_dict, + } + + if isRealData: + output["sumw"] = len(events) + else: + output["sumw"] = ak.sum(events.genWeight) + + #################### + # Selections # + #################### + ## HLT + triggers = [ + "BTagMu_AK4DiJet40_Mu5", + ] + checkHLT = ak.Array([hasattr(events.HLT, _trig) for _trig in triggers]) + if ak.all(checkHLT == False): + raise ValueError("HLT paths:", triggers, " are all invalid in", dataset) + elif ak.any(checkHLT == False): + print(np.array(triggers)[~checkHLT], " not exist in", dataset) + trig_arrs = [ + events.HLT[_trig] for _trig in triggers if hasattr(events.HLT, _trig) + ] + req_trig = np.zeros(len(events), dtype="bool") + for t in trig_arrs: + req_trig = req_trig | t + req_lumi = np.ones(len(events), dtype="bool") + if isRealData: + req_lumi = self.lumiMask(events.run, events.luminosityBlock) + if shift_name is None: + output = dump_lumi(events[req_lumi], output) + ## Jet cuts + events.Jet = events.Jet[jet_cut(events, self._campaign)] + req_jets = ak.count(events.Jet.pt, axis=1) >= 1 + + event_level = ak.fill_none(req_lumi & req_trig & req_jets, False) + if len(events[event_level]) == 0: + return {dataset: output} + + dxySigcut = 0 + muNeEmSum = 1 + + soft_muon = events.Muon[ + softmu_mask(events, self._campaign) + & (abs(events.Muon.dxy / events.Muon.dxyErr) > dxySigcut) + & (events.Muon.pt > 5) + ] + req_softmu = ak.count(soft_muon.pt, axis=1) >= 1 + event_jet = events.Jet + mujetsel = ak.fill_none( + ( + (ak.all(event_jet.metric_table(soft_muon) <= 0.4, axis=2)) + & ((event_jet.muonIdx1 != -1) | (event_jet.muonIdx2 != -1)) + & ((event_jet.muEF + event_jet.neEmEF) < muNeEmSum) + & (event_jet.pt > 20) + & ((event_jet.pt / event_jet.E) > 0.03) + ), + False, + axis=-1, + ) + soft_muon = ak.pad_none(soft_muon, 1, axis=1) + soft_muon["dxySig"] = soft_muon.dxy / soft_muon.dxyErr + + ## Muon-jet cuts + event_jet["isMuonJet"] = mujetsel + mu_jet = event_jet[mujetsel] + otherjets = event_jet[~mujetsel] + req_mujet = ak.num(mu_jet.pt, axis=1) >= 1 + mu_jet = ak.pad_none(mu_jet, 1, axis=1) + + #################### + # Selected objects # + #################### + event_level = event_level & req_softmu & req_mujet + event_level = ak.fill_none(event_level, False) + sjets = events.Jet[event_level] + ssmu = soft_muon[event_level] + smuj = mu_jet[event_level] + nssmu = ak.count(ssmu.pt, axis=1) + nsmuj = ak.count(smuj.pt, axis=1) + njet = ak.count(sjets.pt, axis=1) + + ############### + # Selected SV # + ############### + selev = events[event_level] + if len(events[event_level]) == 0: + if self.isArray: + array_writer( + self, + events[event_level], + events, + "nominal", + dataset, + isRealData, + empty=True, + ) + return {dataset: output} + ### FIXME: Check if the JetSVs are present in the events + ###FIXME: https://gitlab.cern.ch/cms-btv-coordination/tasks/-/issues/188 + if "JetSVs" in events.fields: + valid_events = (ak.count(selev.Jet.pt, axis=1) > 0) & ( + ak.count(selev.JetSVs.pt, axis=1) > 0 + ) + print("valid_events", valid_events, len(valid_events), len(events)) + filtered_events = selev[valid_events] + + # Pad selev.JetSVs.jetIdx to match the length of selev.Jet + filtered_events.JetSVs.jetIdx = ak.pad_none( + filtered_events.JetSVs.jetIdx, len(filtered_events.Jet), clip=True + ) + + # Print the initial state of selev.Jet and selev.JetSVs.jetIdx + # print("selev.Jet", filtered_events.Jet) + # print("selev.JetSVs.jetIdx", filtered_events.JetSVs.jetIdx) + + # Filter events where the number of Jet and JetSVs are the same + # equal_length_events = ak.count(filtered_events.Jet.pt, axis=1) == ak.count(filtered_events.JetSVs.pt, axis=1) + # same_length_events = filtered_events[equal_length_events] + # print("Number of events with equal number of Jet and JetSVs:", len(same_length_events)) + # print("Same length selev.Jet", same_length_events.Jet) + # print("Same length selev.JetSVs.jetIdx", same_length_events.JetSVs.jetIdx) + + # Count and print the number of events where JetSVs is longer than Jet + # longer_jetSVs_events = ak.count(events.JetSVs.pt, axis=1) > ak.count(events.Jet.pt, axis=1) + # longer_jetSVs_events = longer_jetSVs_events & (ak.count(events.Jet.pt, axis=1) == 0) + # num_longer_jetSVs_events = ak.sum(longer_jetSVs_events) + # print("Number of events where JetSVs is longer than Jet:", num_longer_jetSVs_events) + # print("Longer SV selev.Jet", events.Jet[longer_jetSVs_events]) + # print("Longer SV selev.JetSVs.jetIdx", events.JetSVs.jetIdx[longer_jetSVs_events]) + + # Ensure that all indices are within the valid range for selev.Jet + valid_indices = (filtered_events.JetSVs.jetIdx >= 0) & ( + filtered_events.JetSVs.jetIdx < ak.num(filtered_events.Jet) + ) + if not np.all(valid_indices): + print("Warning: Some indices in selev.JetSVs.jetIdx are out of range.") + # Filter out invalid indices + filtered_events.JetSVs = filtered_events.JetSVs[valid_indices] + + # Check if selev.JetSVs.jetIdx is empty after filtering + if len(filtered_events.JetSVs.jetIdx) == 0: + print("Warning: selev.JetSVs.jetIdx is empty after filtering.") + matched_JetSVs = ak.Array([]) + lj_matched_JetSVs = ak.Array([]) + lj_SVs = ak.Array([]) + nJetSVs = ak.Array([]) + else: + # Proceed with the assignment + matched_JetSVs = filtered_events.Jet[filtered_events.JetSVs.jetIdx] + lj_matched_JetSVs = matched_JetSVs[filtered_events.JetSVs.jetIdx == 0] + lj_SVs = filtered_events.JetSVs[filtered_events.JetSVs.jetIdx == 0] + nJetSVs = ak.count(lj_SVs.pt, axis=1) + + ### FIXME: https://gitlab.cern.ch/cms-btv-coordination/tasks/-/issues/188 + # Print the final state of the variables + # print("matched_JetSVs:", matched_JetSVs) + # print("lj_matched_JetSVs:", lj_matched_JetSVs) + # print("lj_SVs:", lj_SVs) + # print("nJetSVs:", nJetSVs) + + #################### + # Weight & Geninfo # + #################### + weights = Weights(len(selev), storeIndividual=True) + if not isRealData: + weights.add("genweight", selev.genWeight) + + par_flav = (sjets.partonFlavour == 0) & (sjets.hadronFlavour == 0) + genflavor = sjets.hadronFlavour + 1 * par_flav + smflav = smuj.hadronFlavour + 1 * ( + (smuj.partonFlavour == 0) & (smuj.hadronFlavour == 0) + ) + + # genweiev = ak.flatten( + # ak.broadcast_arrays(weights.weight()[event_level], sjets["pt"])[0] + # ) + if "JetSVs" in events.fields: + if len(lj_matched_JetSVs) > 0: + lj_matched_JetSVs_par_flav = ( + lj_matched_JetSVs.partonFlavour == 0 + ) & (lj_matched_JetSVs.hadronFlavour == 0) + lj_matched_JetSVs_genflav = ( + lj_matched_JetSVs.hadronFlavour + 1 * lj_matched_JetSVs_par_flav + ) + else: + lj_matched_JetSVs_genflav = ak.Array([]) + + syst_wei = True if self.isSyst != None else False + if "PU" in self.SF_map.keys(): + puwei( + events[event_level].Pileup.nTrueInt, + self.SF_map, + weights, + syst_wei, + ) + + if "BTV" in self.SF_map.keys(): + btagSFs(sjets, self.SF_map, weights, "DeepJetC", syst_wei) + btagSFs(sjets, self.SF_map, weights, "DeepJetB", syst_wei) + btagSFs(sjets, self.SF_map, weights, "DeepCSVB", syst_wei) + btagSFs(sjets, self.SF_map, weights, "DeepCSVC", syst_wei) + else: + genflavor = ak.zeros_like(sjets.pt, dtype=int) + smflav = ak.zeros_like(smuj.pt, dtype=int) + + if isRealData and len(selev) > 0: + if self._year == "2022": + run_num = "355374_362760" + elif self._year == "2023": + run_num = "366727_370790" + pseval = correctionlib.CorrectionSet.from_file( + f"src/BTVNanoCommissioning/data/Prescales/ps_weight_{triggers[0]}_run{run_num}.json" + ) + # if 369869 in selev.run: continue + psweight = pseval["prescaleWeight"].evaluate( + selev.run, + f"HLT_{triggers[0]}", + ak.values_astype(selev.luminosityBlock, np.float32), + ) + weights.add("psweight", psweight) + genflavor = ak.zeros_like(sjets.pt, dtype=int) + if "JetSVs" in events.fields: + if len(lj_matched_JetSVs) > 0: + lj_matched_JetSVs_genflav = ak.zeros_like( + lj_matched_JetSVs.pt, dtype=int + ) + else: + lj_matched_JetSVs_genflav = ak.Array([]) + + # Systematics information + if shift_name is None: + systematics = ["nominal"] + list(weights.variations) + else: + systematics = [shift_name] + + exclude_btv = [ + "DeepCSVC", + "DeepCSVB", + "DeepJetB", + "DeepJetC", + ] # exclude b-tag SFs for btag inputs + #################### + # Fill histogram # + #################### + + for syst in systematics: + + if self.isSyst == False and syst != "nominal": + break + if self.noHist: + break + weight = ( + weights.weight() + if syst == "nominal" or syst == shift_name + else weights.weight(modifier=syst) + ) + for histname, h in output.items(): + if ( + "Deep" in histname + and "btag" not in histname + and histname in events.Jet.fields + ): + h.fill( + syst, + flatten(genflavor), + flatten(sjets[histname]), + weight=flatten(ak.broadcast_arrays(weight, sjets["pt"])[0]), + ) + elif ( + "jet" in histname and histname.replace("jet0_", "") in sjets.fields + ): + h.fill( + syst, + flatten(genflavor[:, 0]), + flatten(sjets[:, 0][histname.replace(f"jet0_", "")]), + weight=weight, + ) + elif "JetSVs_" in histname and "JetSVs" in events.fields: + if len(lj_matched_JetSVs) > 0: + h.fill( + syst, + flatten(lj_matched_JetSVs_genflav), + flatten(lj_SVs[histname.replace("JetSVs_", "")]), + weight=flatten( + ak.broadcast_arrays( + weight[valid_events], lj_matched_JetSVs["pt"] + )[0] + ), + ) + elif ( + "btag" in histname + and "0" in histname + and histname.replace("_0", "") in events.Jet.fields + ): + sel_jet = sjets[:, 0] + if syst == "nominal": + h.fill( + syst="noSF", + flav=genflavor[:, 0], + discr=np.where( + sel_jet[histname.replace("_0", "")] < 0, + -0.2, + sel_jet[histname.replace("_0", "")], + ), + weight=weight, + ) + if not isRealData and "btag" in self.SF_map.keys(): + h.fill( + syst=syst, + flav=genflavor[:, 0], + discr=np.where( + sel_jet[histname.replace("_0", "")] < 0, + -0.2, + sel_jet[histname.replace("_0", "")], + ), + weight=weight, + ) + output["soft_l_pfRelIso04_all"].fill( + syst, + flav=smflav[:, 0], + pfRelIso03_all=flatten(ssmu[:, 0].pfRelIso04_all), + weight=weight, + ) + output["soft_l_dxy"].fill( + syst, + flav=smflav[:, 0], + dxy=flatten(ssmu[:, 0].dxy), + weight=weight, + ) + output["soft_l_dz"].fill( + syst, + flav=smflav[:, 0], + dz=flatten(ssmu[:, 0].dz), + weight=weight, + ) + output["soft_l_ptratio"].fill( + syst, + flav=smflav[:, 0], + ratio=ssmu[:, 0].pt / smuj[:, 0].pt, + weight=weight, + ) + output["dr_lmujetsmu"].fill( + syst, + flav=smflav[:, 0], + dr=smuj[:, 0].delta_r(ssmu[:, 0]), + weight=weight, + ) + output["soft_l_pt"].fill( + syst, + flav=smflav[:, 0], + pt=flatten(ssmu[:, 0].pt), + weight=weight, + ) + output["soft_l_eta"].fill( + syst, + flav=smflav[:, 0], + eta=flatten(ssmu[:, 0].eta), + weight=weight, + ) + output["soft_l_phi"].fill( + syst, + flav=smflav[:, 0], + phi=flatten(ssmu[:, 0].phi), + weight=weight, + ) + output["soft_l_mass"].fill( + syst, + flav=smflav[:, 0], + mass=flatten(ssmu[:, 0].mass), + weight=weight, + ) + + output["njet"].fill(syst, njet, weight=weight) + output["nssmu"].fill(syst, nssmu, weight=weight) + output["nsmuj"].fill(syst, nsmuj, weight=weight) + if "JetSVs" in events.fields: + if len(lj_matched_JetSVs) > 0: + output["nJetSVs"].fill(syst, nJetSVs, weight=weight[valid_events]) + output["dr_SVjet0"].fill( + syst, + flatten(lj_matched_JetSVs_genflav), + flatten(abs(lj_SVs.deltaR) - 0.1), + weight=flatten( + ak.broadcast_arrays( + weight[valid_events], lj_matched_JetSVs["pt"] + )[0] + ), + ) + output["npvs"].fill(syst, flatten(selev.PV.npvsGood), weight=weight) + ####################### + # Create root files # + ####################### + if self.isArray and len(selev) > 0: + # Keep the structure of events and pruned the object size + pruned_ev = events[event_level] + pruned_ev["SelJet"] = sjets + pruned_ev["SoftMuon"] = ssmu[:, 0] + pruned_ev["MuonJet"] = smuj[:, 0] + array_writer(self, pruned_ev, events, systematics[0], dataset, isRealData) + return {dataset: output} + + def postprocess(self, accumulator): + return accumulator diff --git a/src/BTVNanoCommissioning/workflows/QCD_validation.py b/src/BTVNanoCommissioning/workflows/QCD_validation.py index 3ca714e6..36db27d3 100644 --- a/src/BTVNanoCommissioning/workflows/QCD_validation.py +++ b/src/BTVNanoCommissioning/workflows/QCD_validation.py @@ -31,6 +31,7 @@ def __init__( isArray=False, noHist=False, chunksize=75000, + addsel=False, ): self._year = year self._campaign = campaign @@ -141,11 +142,67 @@ def process_shift(self, events, shift_name): # Selected SV # ############### selev = events[event_level] - if "JetSVs" in events.Jet.fields: - matched_JetSVs = selev.Jet[selev.JetSVs.jetIdx] - lj_matched_JetSVs = matched_JetSVs[selev.JetSVs.jetIdx == 0] - lj_SVs = selev.JetSVs[selev.JetSVs.jetIdx == 0] - nJetSVs = ak.count(lj_SVs.pt, axis=1) + + ###FIXME: https://gitlab.cern.ch/cms-btv-coordination/tasks/-/issues/188 + if "JetSVs" in events.fields: + valid_events = (ak.count(selev.Jet.pt, axis=1) > 0) & ( + ak.count(selev.JetSVs.pt, axis=1) > 0 + ) + print("valid_events", valid_events, len(valid_events), len(events)) + filtered_events = selev[valid_events] + + # Pad selev.JetSVs.jetIdx to match the length of selev.Jet + filtered_events.JetSVs.jetIdx = ak.pad_none( + filtered_events.JetSVs.jetIdx, len(filtered_events.Jet), clip=True + ) + + # Print the initial state of selev.Jet and selev.JetSVs.jetIdx + # print("selev.Jet", filtered_events.Jet) + # print("selev.JetSVs.jetIdx", filtered_events.JetSVs.jetIdx) + + # Filter events where the number of Jet and JetSVs are the same + # equal_length_events = ak.count(filtered_events.Jet.pt, axis=1) == ak.count(filtered_events.JetSVs.pt, axis=1) + # same_length_events = filtered_events[equal_length_events] + # print("Number of events with equal number of Jet and JetSVs:", len(same_length_events)) + # print("Same length selev.Jet", same_length_events.Jet) + # print("Same length selev.JetSVs.jetIdx", same_length_events.JetSVs.jetIdx) + + # Count and print the number of events where JetSVs is longer than Jet + # longer_jetSVs_events = ak.count(events.JetSVs.pt, axis=1) > ak.count(events.Jet.pt, axis=1) + # longer_jetSVs_events = longer_jetSVs_events & (ak.count(events.Jet.pt, axis=1) == 0) + # num_longer_jetSVs_events = ak.sum(longer_jetSVs_events) + # print("Number of events where JetSVs is longer than Jet:", num_longer_jetSVs_events) + # print("Longer SV selev.Jet", events.Jet[longer_jetSVs_events]) + # print("Longer SV selev.JetSVs.jetIdx", events.JetSVs.jetIdx[longer_jetSVs_events]) + + # Ensure that all indices are within the valid range for selev.Jet + valid_indices = (filtered_events.JetSVs.jetIdx >= 0) & ( + filtered_events.JetSVs.jetIdx < ak.num(filtered_events.Jet) + ) + if not np.all(valid_indices): + print("Warning: Some indices in selev.JetSVs.jetIdx are out of range.") + # Filter out invalid indices + filtered_events.JetSVs = filtered_events.JetSVs[valid_indices] + + # Check if selev.JetSVs.jetIdx is empty after filtering + if len(filtered_events.JetSVs.jetIdx) == 0: + print("Warning: selev.JetSVs.jetIdx is empty after filtering.") + matched_JetSVs = ak.Array([]) + lj_matched_JetSVs = ak.Array([]) + lj_SVs = ak.Array([]) + nJetSVs = ak.Array([]) + else: + # Proceed with the assignment + matched_JetSVs = filtered_events.Jet[filtered_events.JetSVs.jetIdx] + lj_matched_JetSVs = matched_JetSVs[filtered_events.JetSVs.jetIdx == 0] + lj_SVs = filtered_events.JetSVs[filtered_events.JetSVs.jetIdx == 0] + nJetSVs = ak.count(lj_SVs.pt, axis=1) + + # Print the final state of the variables + print("matched_JetSVs:", matched_JetSVs) + print("lj_matched_JetSVs:", lj_matched_JetSVs) + print("lj_SVs:", lj_SVs) + print("nJetSVs:", nJetSVs) #################### # Weight & Geninfo # @@ -158,13 +215,16 @@ def process_shift(self, events, shift_name): # genweiev = ak.flatten( # ak.broadcast_arrays(weights.weight()[event_level], sjets["pt"])[0] # ) - if "JetSVs" in events.Jet.fields: - lj_matched_JetSVs_par_flav = (lj_matched_JetSVs.partonFlavour == 0) & ( - lj_matched_JetSVs.hadronFlavour == 0 - ) - lj_matched_JetSVs_genflav = ( - lj_matched_JetSVs.hadronFlavour + 1 * lj_matched_JetSVs_par_flav - ) + if "JetSVs" in events.fields: + if len(lj_matched_JetSVs) > 0: + lj_matched_JetSVs_par_flav = ( + lj_matched_JetSVs.partonFlavour == 0 + ) & (lj_matched_JetSVs.hadronFlavour == 0) + lj_matched_JetSVs_genflav = ( + lj_matched_JetSVs.hadronFlavour + 1 * lj_matched_JetSVs_par_flav + ) + else: + lj_matched_JetSVs_genflav = ak.Array([]) syst_wei = True if self.isSyst != None else False if "PU" in self.SF_map.keys(): puwei( @@ -196,10 +256,13 @@ def process_shift(self, events, shift_name): ) weights.add("psweight", psweight) genflavor = ak.zeros_like(sjets.pt, dtype=int) - if "JetSVs" in events.Jet.fields: - lj_matched_JetSVs_genflav = ak.zeros_like( - lj_matched_JetSVs.pt, dtype=int - ) + if "JetSVs" in events.fields: + if len(lj_matched_JetSVs) > 0: + lj_matched_JetSVs_genflav = ak.zeros_like( + lj_matched_JetSVs.pt, dtype=int + ) + else: + lj_matched_JetSVs_genflav = ak.Array([]) # Systematics information if shift_name is None: @@ -229,7 +292,7 @@ def process_shift(self, events, shift_name): ) for histname, h in output.items(): if ( - "DeepCSV" in histname + "Deep" in histname and "btag" not in histname and histname in events.Jet.fields ): @@ -248,15 +311,18 @@ def process_shift(self, events, shift_name): flatten(sjets[:, 0][histname.replace(f"jet0_", "")]), weight=weight, ) - elif "JetSVs_" in histname and "JetSVs" in events.Jet.fields: - h.fill( - syst, - flatten(lj_matched_JetSVs_genflav), - flatten(lj_SVs[histname.replace("JetSVs_", "")]), - weight=flatten( - ak.broadcast_arrays(weight, lj_matched_JetSVs["pt"])[0] - ), - ) + elif "JetSVs_" in histname and "JetSVs" in events.fields: + if len(lj_matched_JetSVs) > 0: + h.fill( + syst, + flatten(lj_matched_JetSVs_genflav), + flatten(lj_SVs[histname.replace("JetSVs_", "")]), + weight=flatten( + ak.broadcast_arrays( + weight[valid_events], lj_matched_JetSVs["pt"] + )[0] + ), + ) elif ( "btag" in histname and "0" in histname @@ -287,31 +353,31 @@ def process_shift(self, events, shift_name): ) output["njet"].fill(syst, njet, weight=weight) - if "JetSVs" in events.Jet.fields: - output["nJetSVs"].fill(syst, nJetSVs, weight=weight) - - output["dr_SVjet0"].fill( - syst, - flatten(lj_matched_JetSVs_genflav), - flatten(abs(lj_SVs.deltaR) - 0.1), - weight=flatten( - ak.broadcast_arrays(weight, lj_matched_JetSVs["pt"])[0] - ), - ) + if "JetSVs" in events.fields: + if len(lj_matched_JetSVs) > 0: + output["nJetSVs"].fill(syst, nJetSVs, weight=weight[valid_events]) + output["dr_SVjet0"].fill( + syst, + flatten(lj_matched_JetSVs_genflav), + flatten(abs(lj_SVs.deltaR) - 0.1), + weight=flatten( + ak.broadcast_arrays( + weight[valid_events], lj_matched_JetSVs["pt"] + )[0] + ), + ) output["npvs"].fill(syst, flatten(selev.PV.npvsGood), weight=weight) if not isRealData: if syst == "nominal": - output["pu"].fill( - "noPU", - flatten(selev.Pileup.nTrueInt), - weight=np.ones_like(weight), - ) - output["npvs"].fill( - "noPU", - flatten(selev.Pileup.nTrueInt), - weight=np.ones_like(weight), - ) output["pu"].fill(syst, flatten(selev.PV.npvsGood), weight=weight) + ####################### + # Create root files # + ####################### + if self.isArray: + # Keep the structure of events and pruned the object size + pruned_ev = events[event_level] + pruned_ev["SelJet"] = sjets + array_writer(self, pruned_ev, events, systematics[0], dataset, isRealData) return {dataset: output} diff --git a/src/BTVNanoCommissioning/workflows/__init__.py b/src/BTVNanoCommissioning/workflows/__init__.py index 51f8208f..a3f9fd70 100644 --- a/src/BTVNanoCommissioning/workflows/__init__.py +++ b/src/BTVNanoCommissioning/workflows/__init__.py @@ -31,6 +31,9 @@ from BTVNanoCommissioning.workflows.QCD_validation import ( NanoProcessor as QCDValidProcessor, ) +from BTVNanoCommissioning.workflows.QCD_soft_mu_validation import ( + NanoProcessor as QCDsmuValidProcessor, +) ## BTA - for SFs from BTVNanoCommissioning.workflows.BTA_producer import ( @@ -51,7 +54,12 @@ # TTBar workflows["ttdilep_sf"] = TTdilepValidSFProcessor -workflows["ttsemilep_sf"] = TTsemilepValidSFProcessor +workflows["ttsemilep_sf"] = partial( + TTsemilepValidSFProcessor, selectionModifier="tt_semilep" +) +workflows["c_ttsemilep_sf"] = partial( + TTsemilepValidSFProcessor, selectionModifier="c_tt_semilep" +) workflows["emctag_ttdilep_sf"] = CTAGEMDilepttValidSFProcessor workflows["ctag_ttdilep_sf"] = partial( @@ -69,6 +77,7 @@ ##QCD workflows["QCD_sf"] = QCDValidProcessor +workflows["QCD_smu_sf"] = QCDsmuValidProcessor # W+c workflows["ctag_Wc_sf"] = partial(CTAGWcTTValidSFProcessor, selectionModifier="WcM") diff --git a/src/BTVNanoCommissioning/workflows/ttsemilep_valid_sf.py b/src/BTVNanoCommissioning/workflows/ttsemilep_valid_sf.py index eaee6736..6663e43a 100644 --- a/src/BTVNanoCommissioning/workflows/ttsemilep_valid_sf.py +++ b/src/BTVNanoCommissioning/workflows/ttsemilep_valid_sf.py @@ -39,6 +39,7 @@ def __init__( isArray=False, noHist=False, chunksize=75000, + selectionModifier="tt_semilep", ): self._year = year self._campaign = campaign @@ -48,6 +49,8 @@ def __init__( self.noHist = noHist self.lumiMask = load_lumi(self._campaign) self.chunksize = chunksize + ### Added selection for ttbar semileptonic + self.ttaddsel = selectionModifier ## Load corrections self.SF_map = load_SF(self._campaign) @@ -58,7 +61,14 @@ def accumulator(self): def process(self, events): isRealData = not hasattr(events, "genWeight") dataset = events.metadata["dataset"] + + attributes = dir(events.Jet) + + # Filter attributes that contain 'btagDeepFlav' in their name + btag_attributes = [attr for attr in attributes if "btagDeepFlav" in attr] + events = missing_branch(events) + shifts = [] if "JME" in self.SF_map.keys(): syst_JERC = self.isSyst @@ -160,6 +170,65 @@ def process_shift(self, events, shift_name): ] req_jets = ak.num(event_jet.pt) >= 4 + if self.ttaddsel == "c_tt_semilep": + print("ttbar semileptonic selection") + # Sort jets by pt in descending order + + # Sort all jets by pt in descending order + sorted_jets = event_jet[ak.argsort(event_jet.pt, axis=-1, ascending=False)] + + # Pad the sorted jets to ensure there are at least two jets in each event + padded_jets = ak.pad_none(sorted_jets, 2, axis=1) + + # Determine the leading and subleading jets + leading_jet = padded_jets[:, 0] + subleading_jet = padded_jets[:, 1] + + c_selection = ( + event_jet.btagDeepFlavC > 0.6 + ) # & (event_jet.pt == leading_jet.pt) + ### plot # c jets , DeepFlavC of the c jets + c_selection = ak.fill_none(c_selection, False) + req_c_jets = ak.num(event_jet[c_selection].pt) >= 1 + req_c_jets = ak.fill_none(req_c_jets, False) + c_jets = event_jet[c_selection] + single_c_jet = ak.firsts(c_jets) + remaining_jets = event_jet[~c_selection] + nob_selection = remaining_jets.btagDeepFlavB < 0.5 + + nob_candidate_jets = remaining_jets[nob_selection] + + # Create combinations of single_c_jet and nob_candidate_jets + combinations = ak.cartesian( + {"c_jet": single_c_jet, "nob_jet": nob_candidate_jets}, axis=1 + ) + + # Calculate the invariant mass of each pair using the .mass attribute + masses = (combinations["c_jet"] + combinations["nob_jet"]).mass + + # Create a mask for pairs with mass between 55 and 110 + mass_mask = (masses > 50) & (masses < 110) + + # Apply the mask to get the valid combinations + valid_combinations = combinations[mass_mask] + # Create an event-level mask + event_mask = ak.any(mass_mask, axis=1) + + req_w_mass = ak.fill_none(event_mask, False) + + nob_w_selection = ( + event_jet.btagDeepFlavB < 0.5 + ) # & (event_jet.pt == subleading_jet.pt) + nob_w_selection = ak.fill_none(nob_w_selection, False) + w_cand_b_jet = ak.firsts(event_jet[nob_w_selection]) + + w_mass_lead_sublead = (single_c_jet + w_cand_b_jet).mass + + w_mask = (w_mass_lead_sublead > 50) & (w_mass_lead_sublead < 110) + w_mask = ak.fill_none(w_mask, False) + + # req_w_jets = ak.fill_none(req_w_jets, False) + ## store jet index for PFCands, create mask on the jet index jetindx = ak.mask( ak.local_index(events.Jet.pt), @@ -196,6 +265,10 @@ def process_shift(self, events, shift_name): event_level = ak.fill_none( req_trig & req_jets & req_muon & req_MET & req_lumi & req_metfilter, False ) + if self.ttaddsel == "c_tt_semilep": + + # Combine masks with the original conditions + event_level = ak.fill_none(event_level & req_w_mass & req_c_jets, False) if len(events[event_level]) == 0: if self.isArray: array_writer( @@ -214,8 +287,28 @@ def process_shift(self, events, shift_name): smu = event_muon[event_level] smu = smu[:, 0] sjets = event_jet[event_level] + + # Filter jets with btagDeepFlavC > 0.6 + ### With such a cut we reach c-purity of 70 % + jet_c_sel_hist = sjets.btagDeepFlavC > 0.6 + if self.ttaddsel == "c_tt_semilep": + scjets = sjets[jet_c_sel_hist] + + # Ensure the filtered jets are padded to maintain a consistent shape + # scjets = ak.pad_none(scjets, 4, axis=1) + + # Calculate the number of selected jets + if self.ttaddsel == "c_tt_semilep": + ncseljet = ak.count(scjets.pt, axis=1) nseljet = ak.count(sjets.pt, axis=1) + + # Limit the number of jets to 4 sjets = sjets[:, :4] + + if self.ttaddsel == "c_tt_semilep": + scjets = scjets[:, 0] + + sw = sjets[:, 0] + sjets[:, 1] smet = MET[event_level] # Find the PFCands associate with selected jets. Search from jetindex->JetPFCands->PFCand if "PFCands" in events.fields: @@ -239,7 +332,13 @@ def process_shift(self, events, shift_name): if not isRealData: weights.add("genweight", events[event_level].genWeight) par_flav = (sjets.partonFlavour == 0) & (sjets.hadronFlavour == 0) + if self.ttaddsel == "c_tt_semilep": + par_flav_c = (scjets.partonFlavour == 0) & (scjets.hadronFlavour == 0) genflavor = ak.values_astype(sjets.hadronFlavour + 1 * par_flav, int) + if self.ttaddsel == "c_tt_semilep": + genflavor_c = ak.values_astype( + scjets.hadronFlavour + 1 * par_flav_c, int + ) if len(self.SF_map.keys()) > 0: syst_wei = True if self.isSyst != False else False if "PU" in self.SF_map.keys(): @@ -256,8 +355,15 @@ def process_shift(self, events, shift_name): btagSFs(sjets, self.SF_map, weights, "DeepJetB", syst_wei) btagSFs(sjets, self.SF_map, weights, "DeepCSVB", syst_wei) btagSFs(sjets, self.SF_map, weights, "DeepCSVC", syst_wei) + if self.ttaddsel == "c_tt_semilep": + btagSFs(scjets, self.SF_map, weights, "DeepJetC", syst_wei) + btagSFs(scjets, self.SF_map, weights, "DeepJetB", syst_wei) + btagSFs(scjets, self.SF_map, weights, "DeepCSVB", syst_wei) + btagSFs(scjets, self.SF_map, weights, "DeepCSVC", syst_wei) else: genflavor = ak.zeros_like(sjets.pt, dtype=int) + if self.ttaddsel == "c_tt_semilep": + genflavor_c = ak.zeros_like(scjets.pt, dtype=int) # Systematics information if shift_name is None: @@ -285,55 +391,177 @@ def process_shift(self, events, shift_name): if syst == "nominal" or syst == shift_name else weights.weight(modifier=syst) ) - for histname, h in output.items(): - if ( - "Deep" in histname - and "btag" not in histname - and histname in events.Jet.fields - ): - h.fill( + if not self.ttaddsel == "c_tt_semilep": + for histname, h in output.items(): + if ( + "Deep" in histname + and "btag" not in histname + and histname in events.Jet.fields + ): + h.fill( + syst, + flatten(genflavor), + flatten(sjets[histname]), + weight=flatten( + ak.broadcast_arrays( + weights.partial_weight(exclude=exclude_btv), + sjets["pt"], + )[0] + ), + ) + elif ( + "PFCands" in histname + and "PFCands" in events.fields + and histname.split("_")[1] in events.PFCands.fields + ): + for i in range(4): + h.fill( + syst, + flatten( + ak.broadcast_arrays( + genflavor[:, i], + spfcands[i]["pt"], + )[0] + ), + flatten(spfcands[i][histname.replace("PFCands_", "")]), + weight=flatten( + ak.broadcast_arrays( + weights.partial_weight(exclude=exclude_btv), + spfcands[i]["pt"], + )[0] + ), + ) + elif "btag" in histname: + for i in range(4): + sel_jet = sjets[:, i] + if ( + str(i) in histname + and histname.replace(f"_{i}", "") in events.Jet.fields + ): + h.fill( + syst="noSF", + flav=genflavor[:, i], + discr=sel_jet[histname.replace(f"_{i}", "")], + weight=weight, + ) + if ( + not isRealData + and "btag" in self.SF_map.keys() + and "_b" not in histname + and "_bb" not in histname + and "_lepb" not in histname + ): + h.fill( + syst=syst, + flav=genflavor[:, i], + discr=sel_jet[histname.replace(f"_{i}", "")], + weight=weight, + ) + elif ( + "mu_" in histname and histname.replace("mu_", "") in smu.fields + ): + h.fill( + syst, + flatten(smu[histname.replace("mu_", "")]), + weight=weight, + ) + elif ( + "jet" in histname + and "dr" not in histname + and "njet" != histname + ): + for i in range(4): + sel_jet = sjets[:, i] + if str(i) in histname: + h.fill( + syst, + flatten(genflavor[:, i]), + flatten(sel_jet[histname.replace(f"jet{i}_", "")]), + weight=weight, + ) + + for i in range(4): + output[f"dr_mujet{i}"].fill( syst, - flatten(genflavor), - flatten(sjets[histname]), - weight=flatten( - ak.broadcast_arrays( - weights.partial_weight(exclude=exclude_btv), sjets["pt"] - )[0] - ), + flav=flatten(genflavor[:, i]), + dr=flatten(smu.delta_r(sjets[:, i])), + weight=weight, ) - elif ( - "PFCands" in histname - and "PFCands" in events.fields - and histname.split("_")[1] in events.PFCands.fields - ): - for i in range(4): + output["njet"].fill(syst, nseljet, weight=weight) + output["MET_pt"].fill(syst, flatten(smet.pt), weight=weight) + output["MET_phi"].fill(syst, flatten(smet.phi), weight=weight) + output["npvs"].fill( + syst, + events[event_level].PV.npvs, + weight=weight, + ) + output["w_mass"].fill( + syst, + flatten(sw.mass), + weight=weight, + ) + else: + for histname, h in output.items(): + if ( + "Deep" in histname + and "btag" not in histname + and histname in events.Jet.fields + ): + print( + "Deep in histname, btag not in histname, histname in events.Jet.fields", + histname, + scjets[histname], + ) + h.fill( + syst, + flatten(genflavor_c), + flatten(scjets[histname]), + weight=flatten( + ak.broadcast_arrays( + weights.partial_weight(exclude=exclude_btv), + scjets["pt"], + )[0] + ), + ) + elif ( + "PFCands" in histname + and "PFCands" in events.fields + and histname.split("_")[1] in events.PFCands.fields + ): + print( + "PFCands in histname, PFCands in events.fields, histname.split_[1] in events.PFCands.fields", + histname, + ) h.fill( syst, flatten( ak.broadcast_arrays( - genflavor[:, i], - spfcands[i]["pt"], + genflavor_c, + spfcands[0]["pt"], )[0] ), - flatten(spfcands[i][histname.replace("PFCands_", "")]), + flatten(spfcands[0][histname.replace("PFCands_", "")]), weight=flatten( ak.broadcast_arrays( weights.partial_weight(exclude=exclude_btv), - spfcands[i]["pt"], + spfcands[0]["pt"], )[0] ), ) - elif "btag" in histname: - for i in range(4): - sel_jet = sjets[:, i] - if ( - str(i) in histname - and histname.replace(f"_{i}", "") in events.Jet.fields - ): + elif "btag" in histname: + sel_jet = scjets + print("btag in histname", histname, scjets) + if "c_btag" in histname and "Trans" not in histname: + print( + "c_btag in histname", + histname, + scjets[histname.replace(f"c_", "")], + ) + if histname.replace(f"c_", "") in events.Jet.fields: h.fill( syst="noSF", - flav=genflavor[:, i], - discr=sel_jet[histname.replace(f"_{i}", "")], + flav=genflavor_c, + discr=sel_jet[histname.replace(f"c_", "")], weight=weight, ) if ( @@ -345,46 +573,69 @@ def process_shift(self, events, shift_name): ): h.fill( syst=syst, - flav=genflavor[:, i], - discr=sel_jet[histname.replace(f"_{i}", "")], + flav=genflavor_c, + discr=sel_jet[histname.replace(f"c_", "")], weight=weight, ) - elif "mu_" in histname and histname.replace("mu_", "") in smu.fields: - h.fill( - syst, - flatten(smu[histname.replace("mu_", "")]), - weight=weight, - ) - elif "jet" in histname and "dr" not in histname and "njet" != histname: - for i in range(4): - sel_jet = sjets[:, i] - if str(i) in histname: + elif ( + "mu_" in histname and histname.replace("mu_", "") in smu.fields + ): + print( + "mu_ in histname", + histname, + smu[histname.replace("mu_", "")], + ) + mu_array = flatten(smu[histname.replace("mu_", "")]) + print("Lengths:", len(syst), len(mu_array), len(weight)) + h.fill( + syst, + flatten(smu[histname.replace("mu_", "")]), + weight=weight, + ) + elif ( + "jet" in histname + and "dr" not in histname + and "njet" != histname + ): + if "c" in histname: + print( + "jet in histname", + histname, + scjets[histname.replace(f"cjet_", "")], + ) + jet_array = flatten(scjets[histname.replace(f"cjet_", "")]) + print( + "Lengths:", + len(syst), + len(flatten(genflavor_c)), + len(jet_array), + len(weight), + ) + h.fill( syst, - flatten(genflavor[:, i]), - flatten(sel_jet[histname.replace(f"jet{i}_", "")]), + flatten(genflavor_c), + flatten(scjets[histname.replace(f"cjet_", "")]), weight=weight, ) - for i in range(4): - output[f"dr_mujet{i}"].fill( + output[f"dr_cjet"].fill( syst, - flav=flatten(genflavor[:, i]), - dr=flatten(smu.delta_r(sjets[:, i])), + flav=flatten(genflavor_c), + dr=flatten(smu.delta_r(scjets)), weight=weight, ) - output["njet"].fill(syst, nseljet, weight=weight) - output["MET_pt"].fill(syst, flatten(smet.pt), weight=weight) - output["MET_phi"].fill(syst, flatten(smet.phi), weight=weight) - output["npvs"].fill( - syst, - events[event_level].PV.npvs, - weight=weight, - ) - if not isRealData: - output["pu"].fill( + output["njet"].fill(syst, ncseljet, weight=weight) + output["MET_pt"].fill(syst, flatten(smet.pt), weight=weight) + output["MET_phi"].fill(syst, flatten(smet.phi), weight=weight) + output["npvs"].fill( + syst, + events[event_level].PV.npvs, + weight=weight, + ) + output["w_mass"].fill( syst, - events[event_level].Pileup.nTrueInt, + flatten(sw.mass), weight=weight, ) #######################