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REANA example - CMS dimuon mass spectrum

image

About

This REANA reproducible analysis example demonstrates the computation of the invariant mass spectrum of two muon final states with CMS Open Data. It is based on the original code from the CERN Open Data portal.

Analysis structure

Making a research data analysis reproducible basically means to provide "runnable recipes" addressing (1) where is the input data, (2) what software was used to analyse the data, (3) which computing environments were used to run the software and (4) which computational workflow steps were taken to run the analysis. This will permit to instantiate the analysis on the computational cloud and run the analysis to obtain (5) output results.

1. Input data

The datasets directory contains files with the information about locations of the collision datasets and the list of validated runs that will be used in the analysis.

2. Analysis code

The analysis code is located in the in src directory and in demoanalyzer_cfg.py file.

3. Compute environment

In order to be able to rerun the analysis even several years in the future, we need to "encapsulate the current compute environment", for example to freeze the software package versions our analysis is using. We shall achieve this by preparing a Docker container image for our analysis steps.

This example runs within the CMSSW analysis framework that was packaged for Docker in clelange/cmssw.

4. Analysis workflow

The computational workflow is essentially sequential in nature. We can use the REANA serial workflow engine and represent the analysis workflow as follows:

                 START
                   |
                   |
                   V
+-----------------------------------------+
| (1) scram b                             |
|                                         |
+-----------------------------------------+
                   |
                   |
                   V
+-----------------------------------------+
| (2) demoanalyzer_cfg.py                 | <-- datasets
|                                         |
+-----------------------------------------+
                   |
                   | DoubleMu.root
                   |
                   V
                  STOP

5. Output results

The run will create DoubleMu.root output file in the ROOT format that contains output histograms, for example:

Running the example on REANA cloud

There are two ways to execute this analysis example on REANA.

If you would like to simply launch this analysis example on the REANA instance at CERN and inspect its results using the web interface, please click on the following badge:

image

If you would like a step-by-step guide on how to use the REANA command-line client to launch this analysis example, please read on.

We start by creating a reana.yaml file describing the above analysis structure with its inputs, code, runtime environment, computational workflow steps and expected outputs:

version: 0.6.0
inputs:
  files:
      - BuildFile.xml
      - demoanalyzer_cfg.py
  directories:
      - datasets
      - python
      - src
workflow:
  type: serial
  specification:
    steps:
      - name: demoanalyzer
        environment: 'docker.io/cmsopendata/cmssw_5_3_32'
        commands:
          - >
            source /opt/cms/cmsset_default.sh
            && scramv1 project CMSSW CMSSW_5_3_32
            && cd CMSSW_5_3_32/src
            && eval `scramv1 runtime -sh`
            && mkdir reana-demo-cms-dimuon-mass-spectrum
            && cd reana-demo-cms-dimuon-mass-spectrum
            && mkdir DimuonSpectrum2011
            && cd DimuonSpectrum2011
            && cp -r ../../../../datasets ../../../../python ../../../../src ../../../../BuildFile.xml ../../../../demoanalyzer_cfg.py .
            && scram b
            && cmsRun ./demoanalyzer_cfg.py
outputs:
  files:
    - CMSSW_5_3_32/src/reana-demo-cms-dimuon-mass-spectrum/DimuonSpectrum2011/DoubleMu.root

We can now install the REANA command-line client, run the analysis and download the resulting ROOT file containing plots:

$ # create new virtual environment
$ virtualenv ~/.virtualenvs/myreana
$ source ~/.virtualenvs/myreana/bin/activate
$ # install REANA client
$ pip install reana-client
$ # connect to some REANA cloud instance
$ export REANA_SERVER_URL=https://reana.cern.ch/
$ export REANA_ACCESS_TOKEN=XXXXXXX
$ # create new workflow
$ reana-client create -n my-analysis
$ export REANA_WORKON=my-analysis
$ # upload input code and data to the workspace
$ reana-client upload
$ # start computational workflow
$ reana-client start
$ # ... should be finished in about 1 minute
$ reana-client status
$ # download output root file with generated plots
$ reana-client download

Please see the REANA-Client documentation for more detailed explanation of typical reana-client usage scenarios.