CoffeaRunner

Generalized framework columnar-based analysis with coffea based on the developments from BTVNanoCommissioning and some development from PocketCoffea

Requirements

Setup

❗ Install under bash environment

# only first time, including submodules
git clone --recursive [email protected]:cms-btv-pog/BTVNanoCommissioning.git 

```bash
# only first time 
git clone --recursive [email protected]:cms-rwth/CoffeaRunner.git

For installing Miniconda, see also https://hackmd.io/GkiNxag0TUmHnnCiqdND1Q#Local-or-remote

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
# Run and follow instructions on screen
bash Miniconda3-latest-Linux-x86_64.sh

NOTE: always make sure that conda, python, and pip point to local Miniconda installation (which conda etc.).

You could simply create the environment through the existing env.yml under your conda environment

conda env create -f env.yml 

Not necessary to run framework, but helpful when identifying corrupted files and tracking the progress during said task:

conda install -c conda-forge p-tqdm

Once the environment is set up, compile the python package:

pip install -e .

Structures of code

The development of the code is driven by user-friendliness, reproducibility and efficiency.

How to run

setup enviroment first

# activate enviroement
conda activate CoffeaRunner
# setup proxy
voms-proxy-init --voms cms --vomses ~/.grid-security/vomses 

Make the list of input files (Optional)

Use the ./filefetcher/fetch.py script:

python filefetcher/fetch.py --input filefetcher/input_DAS_list.txt --output output_name.json

where the input_DAS_list.txt is a simple file with a list of dataset names extract from DAS (you need to create it yourself for the samples you want to run over), and output json file in creted in ./metadata directory.

Create compiled corretions file, like JERC (Optional)

❗ In case existing correction file doesn't work for you due to the incompatibility of cloudpickle in different python versions. Please recompile the file to get new pickle file.

Compile correction pickle files for a specific JEC campaign by changing the dict of jet_factory, and define the MC campaign and the output file name by passing it as arguments to the python script:

python -m utils.compile_jec UL17_106X data/JME/UL17_106X/jec_compiled.pkl.gz

Run the processor and create .coffea files

Get .coffea file in output directories and config.pkl for the configuration use in this events.

• Example workflow (test_wf.py): simple lepton selections, run with local iterative processor. No correction case.

python runner_wconfig.py --cfg config/example.py (-o)

optional arguments: 
    -o, --overwrite_file        Overwrite the output in the configuration
    --validate        Do not process, just check all files are accessible

• More complex usage with config/HWW2l2nu.py and the application to workflow from HplusCharm developement. See details in Advance Usage

python runner_wconfig.py --cfg config/HWW2l2nu.py

Config file

The config file in .py format is passed as the argument --cfg of the runner_wconfig.py script. The file has the following structure:

Parameter	Nested component	Type	Description	Default
dataset(required)		dict	Dataset configurations
	jsons (required)	string	Path of .json file to create with NanoAOD files (can load multiple files)
	campaign(required)	string	Campaign name
	year(required)	string	Year flag
	filter	dict	Create the list of samples, samples_exclude with the dataset name(key name stored in json file)	None
workflow(required)		python modules	Analysis workflows
output(requred)		string	Output directory name, create version tag
run_options		dict	Collections of run options
	executor	string	Executor for coffea jobs, see details in executor	iterative
	limit	int	Maximum number of files per sample to process	1
	max	int	Maximum number of chunks to process	None
	chunk	int	Chunk size, numbers of events to be processed each time. Maximum number is the default sample size	50000
	workers	int	Number of parallel threads (with futures and clusters without fixed workers)	2
	mem_per_worker	int	Set memory for condor/slurm jobs	2
	scaleout	int	Number of jobs to submit, use for cluster	20
	walltime	time	Wall time for condor/slurm jobs	03:00:00
	retries	int	Numbers of retries to submit failure jobs. Usually deal with xrootd temporary failures	20
	voms	Path	Path to your x509 proxy	None
	skipbadfiles	bool	Skip bad files where not exist or broken(BE CAREFUL WITH DATA)	False
	splitjobs	bool	Split executor and accumulator to separate jobs to avoid local memory consumption become too large	True
	compression	int	Compression level of output with lz4	3
categories		dict	Dictionary of categories with cuts to apply*	None
preselections		dict	List of preselection cuts, use for all the categories	None
weights		dict	Nested dict for correction files. Details and example in weights	None
	common	dict	Specify weights apply for all the events(inclusive) or category specific(by category)
	bysample	dict	Weights only apply for particular sample, can be applied for all the events(inclusive) or category specific(by category)
systematic		dict	dict for systematic uncertainty	None
	isJERC	bool	Run JER, JEC, MET scale uncertainty
	weights	bool	Weight files with up/down variations
userconfig		dict	Dictionary of user specific configuration, depends on workflow

*Cuts in categories or preselections don't follow, can write cuts as seperate macro

🚧 histogram and plot setup are not included in current version 🚧 implementation on export_array still under construction(would use ak.to_parquet) Use filter to exclude/include specific sample, if there's no filter then would run through all the samples in json file

Advanced usages

filter

Use filter(option) to specify samples want to processed in the json files

"dataset" : {
        "jsons": ["src/Hpluscharm/input_json/higgs_UL17.json"],
        "campaign" :"UL17",
        "year" : "2017",
        "filter": {
            "samples":["GluGluHToWWTo2L2Nu_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8"],
            "samples_exclude" : []
        }
    },

Weights

All the lumiMask, correction files (SFs, pileup weight), and JEC, JER files are under BTVNanoCommissioning/src/data/ following the substructure ${type}/${campaign}/${files}(except lumiMasks and Prescales)

Type	File type	Comments
lumiMasks	.json	Masked good lumi-section used for physics analysis
Prescales	.txt	HLT paths for prescaled triggers
PU	.pkl.gz or .histo.root	Pileup reweight files, matched MC to data
LSF	.histo.root	Lepton ID/Iso/Reco/Trigger SFs
BTV	.csv or .root	b-tagger, c-tagger SFs
JME	.txt	JER, JEC files

Example in weight_splitcat.py

• In case you have correction depends on category, i.e. different ID/objects used in the different cateogries, use "bycategory":{$category_name:$weight_dict}

• In case you have correction depends on sample ,i.e. k-factor, use "bysample":{$sample_name:$weight_nested_dict}

example with customize weight files

"weights":{
        "common":{
            "inclusive":{
                "lumiMasks":"Cert_294927-306462_13TeV_UL2017_Collisions17_GoldenJSON.txt",
                "PU": "puweight_UL17.histo.root",
                "JME": "mc_compile_jec.pkl.gz",
                "BTV": {
                    "DeepJetC": "DeepJet_ctagSF_Summer20UL17_interp.root",
                },
                "LSF": {
                    "ele_Rereco_above20 EGamma_SF2D": "egammaEffi_ptAbove20.txt_EGM2D_UL2017.histo.root",
                },
        },
            "bycategory":
            {
                "cats":
                    { 
                        "PU": "puweight_UL17.histo.root",
                    }
            }
        },
        "bysample":{
            "gchcWW2L2Nu_4f":{
                "inclusive":{
               
                "JME": "mc_compile_jec.pkl.gz",
            },
            "bycategory":
            {
                "cats2":
                    { 
                        
                        "BTV": {
                            "DeepJetC": "DeepJet_ctagSF_Summer20UL17_interp.root",
                        },
                        
                    }
            }
            }
            
        },
    }

• Use central maintained jsonpog-integration The official correction files collected in jsonpog-integration is updated by POG except lumiMask and JME still updated by maintainer. No longer to request input files in the correction_config.

See the example with `2017_UL`.

  "2017_UL": {
        # Same with custom config
        "lumiMask": "Cert_294927-306462_13TeV_UL2017_Collisions17_MuonJSON.txt",
        "JME": "jec_compiled.pkl.gz",
        # no config need to be specify for PU weights
        "PU": None,
        # Btag SFs - specify $TAGGER : $TYPE-> find [$TAGGER_$TYPE] in json file
        "BTV": {"deepCSV": "shape", "deepJet": "shape"},
        
        "LSF": {
        # Electron SF - Following the scheme: "${SF_name} ${year}": "${WP}"
        # https://github.com/cms-egamma/cms-egamma-docs/blob/master/docs/EgammaSFJSON.md
            "ele_ID 2017": "wp90iso",
            "ele_Reco 2017": "RecoAbove20",

        # Muon SF - Following the scheme: "${SF_name} ${year}": "${WP}"
        # WPs : ['NUM_GlobalMuons_DEN_genTracks', 'NUM_HighPtID_DEN_TrackerMuons', 'NUM_HighPtID_DEN_genTracks', 'NUM_IsoMu27_DEN_CutBasedIdTight_and_PFIsoTight', 'NUM_LooseID_DEN_TrackerMuons', 'NUM_LooseID_DEN_genTracks', 'NUM_LooseRelIso_DEN_LooseID', 'NUM_LooseRelIso_DEN_MediumID', 'NUM_LooseRelIso_DEN_MediumPromptID', 'NUM_LooseRelIso_DEN_TightIDandIPCut', 'NUM_LooseRelTkIso_DEN_HighPtIDandIPCut', 'NUM_LooseRelTkIso_DEN_TrkHighPtIDandIPCut', 'NUM_MediumID_DEN_TrackerMuons', 'NUM_MediumID_DEN_genTracks', 'NUM_MediumPromptID_DEN_TrackerMuons', 'NUM_MediumPromptID_DEN_genTracks', 'NUM_Mu50_or_OldMu100_or_TkMu100_DEN_CutBasedIdGlobalHighPt_and_TkIsoLoose', 'NUM_SoftID_DEN_TrackerMuons', 'NUM_SoftID_DEN_genTracks', 'NUM_TightID_DEN_TrackerMuons', 'NUM_TightID_DEN_genTracks', 'NUM_TightRelIso_DEN_MediumID', 'NUM_TightRelIso_DEN_MediumPromptID', 'NUM_TightRelIso_DEN_TightIDandIPCut', 'NUM_TightRelTkIso_DEN_HighPtIDandIPCut', 'NUM_TightRelTkIso_DEN_TrkHighPtIDandIPCut', 'NUM_TrackerMuons_DEN_genTracks', 'NUM_TrkHighPtID_DEN_TrackerMuons', 'NUM_TrkHighPtID_DEN_genTracks']

            "mu_Reco 2017_UL": "NUM_TrackerMuons_DEN_genTracks",
            "mu_HLT 2017_UL": "NUM_IsoMu27_DEN_CutBasedIdTight_and_PFIsoTight",
            "mu_ID 2017_UL": "NUM_TightID_DEN_TrackerMuons",
            "mu_Iso 2017_UL": "NUM_TightRelIso_DEN_TightIDandIPCut",
        },
    },

Systematic

Specify whether run systematics or not

"systematics": 
        {
            "JERC":False,
            "weights":False,
        }

User config (example from Hpluscharm)

Write your own configurations used in your analysis

"userconfig":{
    "export_array" : False,
    "BDT":{
        "ll":"src/Hpluscharm/MVA/xgb_output/SR_ll_scangamma_2017_gamma2.json",
        "emu":"src/Hpluscharm/MVA/xgb_output/SR_emu_scangamma_2017_gamma2.json",
    }
    }

Executors

Scale out can be notoriously tricky between different sites. Coffea's integration of slurm and dask makes this quite a bit easier and for some sites the ``native'' implementation is sufficient, e.g Condor@DESY. However, some sites have certain restrictions for various reasons, in particular Condor @CERN and @FNAL.

Local executor: "iterative", "futures"

Condor@FNAL (CMSLPC) executor: "dask/lpc"

Follow setup instructions at https://github.com/CoffeaTeam/lpcjobqueue.

Condor@CERN (lxplus) executor: "dask/lxplus"

Only one port is available per node, so its possible one has to try different nodes until hitting one with 8786 being open. Other than that, no additional configurations should be necessary.

Coffea-casa (Nebraska AF) executor: "dask/casa"

Coffea-casa is a JupyterHub based analysis-facility hosted at Nebraska. For more information and setup instuctions see https://coffea-casa.readthedocs.io/en/latest/cc_user.html

After setting up and checking out this repository (either via the online terminal or git widget utility).

Authentication is handled automatically via login auth token instead of a proxy. File paths need to replace xrootd redirector with "xcache", runner.py does this automatically.

Condor@DESY executor: "dask/condor","parsl/condor","parsl/condor/naf_lite"

Use dask executor with dask/condor, but would not as stable as parsl/condor. parsl/condor/naf_lite is utilized for lite job scheme for desy condor jobs. (1core, 1.5GB mem, < 3h run time)

Maxwell@DESY executor: "parsl/slurm

For Maxwell you need specific account if you have heavy jobs. You need to check Maxwell-DESY

Profiling

CPU profiling

For profiling the CPU time of each function please select the iterative processor and then run python as:

python -m cProfile -o profiling output.prof  runner.py --cfg profiling/mem.py

Running on a few files should be enough to get stable results.

After getting the profiler output we analyze it with the Snakeviz library

snakeviz output.prof -s 

and open on a browser the link shown by the program.

Memory profiling

For memory profiling we use the memray library:

python -m memray run -o profiling/memtest.bin runner_wconfig.py --cfg config/example.py

the output can be visualized in many ways. One of the most useful is the flamegraph:

memray flamegraph profiling/memtest.bin

then open the output .html file in you browser to explore the peak memory allocation.

Alternatively the process can be monitored live during execution by doing:

memray run --live  runner.py --cfg config/example.py

Plotting code

All the lumiMask, correction files (SFs, pileup weight), and JEC, JER files are under BTVNanoCommissioning/src/data/ following the substructure ${type}/${campaign}/${files}(except lumiMasks and Prescales)

Produce data/MC comparison, shape comparison plots from .coffea files, load configuration (yaml) files, brief intro of yaml.

Details of yaml file format would summarized in table below. Information used in data/MC script would marked with () and comparsion script with (). The required info are marked as bold style.

Take `Rereco17_94X` as an example.

python plotting/plotdataMC.py --cfg testfile/btv_datamc.yml (--debug)
python plotting/comparison.py --cfg testfile/btv_compare.yml (--debug)

Use central maintained jsonpog-integration

The official correction files collected in jsonpog-integration is updated by POG except lumiMask and JME still updated by maintainer. No longer to request input files in the correction_config.

See the example with `2017_UL`.

  "2017_UL": {
        # Same with custom config
        "lumiMask": "Cert_294927-306462_13TeV_UL2017_Collisions17_MuonJSON.txt",
        "JME": "jec_compiled.pkl.gz",
        # no config need to be specify for PU weights
        "PU": None,
        # Btag SFs - specify $TAGGER : $TYPE-> find [$TAGGER_$TYPE] in json file
        "BTV": {"deepCSV": "shape", "deepJet": "shape"},
        
        "LSF": {
        # Electron SF - Following the scheme: "${SF_name} ${year}": "${WP}"
        # https://github.com/cms-egamma/cms-egamma-docs/blob/master/docs/EgammaSFJSON.md
            "ele_ID 2017": "wp90iso",
            "ele_Reco 2017": "RecoAbove20",

        # Muon SF - Following the scheme: "${SF_name} ${year}": "${WP}"
        # WPs : ['NUM_GlobalMuons_DEN_genTracks', 'NUM_HighPtID_DEN_TrackerMuons', 'NUM_HighPtID_DEN_genTracks', 'NUM_IsoMu27_DEN_CutBasedIdTight_and_PFIsoTight', 'NUM_LooseID_DEN_TrackerMuons', 'NUM_LooseID_DEN_genTracks', 'NUM_LooseRelIso_DEN_LooseID', 'NUM_LooseRelIso_DEN_MediumID', 'NUM_LooseRelIso_DEN_MediumPromptID', 'NUM_LooseRelIso_DEN_TightIDandIPCut', 'NUM_LooseRelTkIso_DEN_HighPtIDandIPCut', 'NUM_LooseRelTkIso_DEN_TrkHighPtIDandIPCut', 'NUM_MediumID_DEN_TrackerMuons', 'NUM_MediumID_DEN_genTracks', 'NUM_MediumPromptID_DEN_TrackerMuons', 'NUM_MediumPromptID_DEN_genTracks', 'NUM_Mu50_or_OldMu100_or_TkMu100_DEN_CutBasedIdGlobalHighPt_and_TkIsoLoose', 'NUM_SoftID_DEN_TrackerMuons', 'NUM_SoftID_DEN_genTracks', 'NUM_TightID_DEN_TrackerMuons', 'NUM_TightID_DEN_genTracks', 'NUM_TightRelIso_DEN_MediumID', 'NUM_TightRelIso_DEN_MediumPromptID', 'NUM_TightRelIso_DEN_TightIDandIPCut', 'NUM_TightRelTkIso_DEN_HighPtIDandIPCut', 'NUM_TightRelTkIso_DEN_TrkHighPtIDandIPCut', 'NUM_TrackerMuons_DEN_genTracks', 'NUM_TrkHighPtID_DEN_TrackerMuons', 'NUM_TrkHighPtID_DEN_genTracks']

            "mu_Reco 2017_UL": "NUM_TrackerMuons_DEN_genTracks",
            "mu_HLT 2017_UL": "NUM_IsoMu27_DEN_CutBasedIdTight_and_PFIsoTight",
            "mu_ID 2017_UL": "NUM_TightID_DEN_TrackerMuons",
            "mu_Iso 2017_UL": "NUM_TightRelIso_DEN_TightIDandIPCut",
        },
    },

Create compiled JERC file(pkl.gz)

Parameter name	Allowed values	Description
input(Required)	list or str (wildcard options * accepted)	input .coffea files
output (Required)	str	output directory of plots with date
mergemap(Required)	dict	collect sample names, (color, label) setting for file set. details in map diction
reference & compare (Required)	dict	specify the class for comparison plots
variable(Required)	dict	variables to plot, see variables section
com	str	√s , default set to be 13TeV
inbox_text	str	text put in AnchoredText
log	str	log scale on y-axis
disable_ratio	bool	disable ratio panel for data/MC comparison plot
rescale_yields	dict	Rescale yields for particular MC collections (no overlay)
scale	dict	Scale up particular MC collections overlay on the stacked histogram
norm	bool	noramlized yield to reference sample, only for comparison plot

dict of merge maps and comparison file lists

To avoid crowded legend in the plot, we would merge a set of files with similar properties. For example, pT binned DY+jets sample would merge into DY+jets, or diboson (VV) is a collection of WW, WZ and ZZ. Or merge files with similar properties together.

Create a dict for each collection under mergemap, put the merging sets.

In plodataMC.py config files (i.e. testfile/btv_datamc.yaml), you can specify the color and label name used in the plot.

In comparison.py config file (testfile/btv_compare.yaml), color and label name and label names are created with dict under reference and compare. reference only accept one entry.

Code snipped

## plodataMC.py
mergemap:
    DY: # key name of file collections
        list: # collections of files(key of dataset name in coffea file)
            - "DYJetsToLL_M-50_TuneCP5_13p6TeV-madgraphMLM-pythia8"
            - "DYJetsToLL_M-10to50_TuneCP5_13p6TeV-madgraphMLM-pythia8"
        label : "DYjet" #Optional, if not exist would take key name of the list. i.e. DY in this case
        color : '#eb4034' #Optional, color use for this category. 
    VV: 
        list:
            - "WW_TuneCP5_13p6TeV-pythia8" 
            - "WZ_TuneCP5_13p6TeV-pythia8"
            - "ZZ_TuneCP5_13p6TeV-pythia8"
## comparison.py
mergemap :  
    runC: 
        list : 
            - "Muon_Run2022C-PromptReco-v1"
            - "SingleMuon_Run2022C-PromptReco-v1"
    Muon_Run2022C-PromptReco-v1: 
        list : 
            - "Muon_Run2022C-PromptReco-v1"  
reference: 
    Muon_Run2022C-PromptReco-v1: 
        label: RunC  #Optional, label name
        color : 'b' #Optional
        
compare: 
    # if not specify anything, leave empty value for key
    Muon_Run2022D-PromptReco-v1: 
    Muon_Run2022D-PromptReco-v2: 

Variables

Common definitions for both usage, use default settings if leave empty value for the keys. :bangbang: blind option is only used in the data/MC comparison plots to blind particular observable like BDT score.

Option	Default
xlabel	take name of key
axis	sum over all the axes
rebin	no rebinning
blind	no blind region

Code snipped

## specify variable to plot
    btagDeepFlavB_0:
        # Optional, set x label of variable
        xlabel: "deepJet Prob(b)" 
        # Optional, specify hist axis with dict
        axis : 
            syst: noSF # Optional, access bin
            flav : sum # Optional, access bin, can convert sum to sum operation later
        # Optional, rebin variable 
        rebin :  
            # Optional, you can specify the rebin axis with rebin value
            # discr: 2
            # or just put a number, would rebin distribution the last axis (usually the variable)
            2
            # One can try non-uniform  rebin now! you can specify the rebin axis with rebin value
            #discr : [-0.2,0.04,0.2,0.4,0.48,0.6,0.64,0.68,0.72,0.76,0.8,0.84,0.88,0.92,0.96,1.]
        # Optional(only for data/MC), blind variables
        blind : -10, #blind variable[-10:], if put -10,-5 would blind variable[-10:-5]
        
    ## specify variable, if not specify anything, leave empty value for key
    btagDeepFlavC_0:

    ## Accept wildcard option
    # only axis and rebin can be specify here
    btagDeepFlav* :
        axis : 
            syst: noSF
            flav : sum
        rebin :  
            discr: 2
    # Use "all" will produce plots for all the variables
    # only rebin of last axis (variable-axis) can be specify here
    all: 
        rebin: 2

Running jupyter remotely

See also https://hackmd.io/GkiNxag0TUmHnnCiqdND1Q#Remote-jupyter

1. On your local machine, edit .ssh/config:

Host lxplus*
  HostName lxplus7.cern.ch
  User <your-user-name>
  ForwardX11 yes
  ForwardAgent yes
  ForwardX11Trusted yes
Host *_f
  LocalForward localhost:8800 localhost:8800
  ExitOnForwardFailure yes

2. Connect to remote with ssh lxplus_f
3. Start a jupyter notebook:

jupyter notebook --ip=127.0.0.1 --port 8800 --no-browser

4. URL for notebook will be printed, copy and open in local browser