lukasheinrich

{"rules": [{"type": "offset", "id": "3a4c107e-9731-4b60-9bf3-1c4a1ab519d0", "rule": {"dependencies": {"dependency_type": "jsonpath_ready", "expressions": ["makews"]}, "type": "JsonStage", "name": "plot", "scheduler": {"step": {"process": {"interpreter": "bash", "process_type": "interpolated-script-cmd", "script": "source /usr/local/bin/thisroot.sh\nhfquickplot write_vardef {combined_model} combined nominal_vals.yml\nhfquickplot plot_channel {combined_model} combined channel1 x nominal_vals.yml -c qcd,mc2,mc1,signal -o {prefit_plot}\nhfquickplot fit {combined_model} combined fitresults.yml\nhfquickplot plot_channel {combined_model} combined channel1 x fitresults.yml -c qcd,mc2,mc1,signal -o {postfit_plot}\n"}, "environment": {"workdir": null, "imagetag": "latest", "par_mounts": [], "image": "lukasheinrich/dummyanalysis", "envscript": "", "resources": [], "env": {}, "environment_type": "docker-encapsulated"}, "publisher": {"publisher_type": "frompar-pub", "outputmap": {"postfit": "postfit_plot", "prefit": "pref

lukasheinrich

source  /cvmfs/sft.cern.ch/lcg/releases/LCG_87/gcc/4.9.3/x86_64-slc6/setup.sh
source  /cvmfs/sft.cern.ch/lcg/releases/LCG_87/Python/2.7.10/x86_64-slc6-gcc49-opt/Python-env.sh
source  /cvmfs/sft.cern.ch/lcg/releases/LCG_87/numpy/1.11.0/x86_64-slc6-gcc49-opt/numpy-env.sh
source  /cvmfs/sft.cern.ch/lcg/releases/LCG_87/matplotlib/1.5.1//x86_64-slc6-gcc49-opt/matplotlib-env.sh
source /cvmfs/sft.cern.ch/lcg/releases/LCG_87/MCGenerators/rivet/2.5.4/x86_64-slc6-gcc49-opt/rivetenv.sh
export PATH=/afs/cern.ch/user/a/atlaspo/workspace/texlive/2011/bin/x86_64-linux:$PATH

rivet-mkhtml Rivet.yoda

lukasheinrich

Jets bjets, recon_jets; //note: here you create 2 empty containers "bjets" and "recon_jets"
foreach(const Jet & jet, jets) { //loop over non-empty container jets (from the projection) assign to variable "jet"
   if(jet.containsBottom()){
		 bjets.push_back(jet);} 
   else{
     recon_jets.push_back(jet);
   }
}

lukasheinrich

BEGIN YODA_HISTO1D /MonoH_Truth/jets
Path=/MonoH_Truth/jets
ScaledBy=9.76669596976556591e+02
Title=
Type=Histo1D
XLabel=
YLabel=
# Mean: 2.554401e+00
# Area: 1.000000e+00
# ID	 ID	 sumw	 sumw2	 sumwx	 sumwx2	 numEntries

lukasheinrich

import ROOT
import ROOT.RooStats
import json
import sys

def main():

    workspace_file = sys.argv[1]
    plotfile = sys.argv[2]
    resultsfile = sys.argv[3]

lukasheinrich

Sim_tf.py \
--asetup AtlasProduction,19.2.3.5 \
--AMI s2586 \
--inputEVNTFile mc.EJModelB.1400.20.EVNT.pool.root \
--outputHITSFile lukas.hits.pool.root --maxEvents 2

lukasheinrich

Reco_tf.py \                             
--asetup 20.7.5.1.1 \
--AMI r7772 \
--inputHitsFile lukas.HITS.pool.root \
--outputAODFile lukas.AOD.pool.root \
--jobNumber=1 \
--maxEvents=25 \
--inputHighPtMinbiasHitsFile \
'../mc15_13TeV.361035.Pythia8EvtGen_A2MSTW2008LO_minbias_inelastic_high.merge.HITS.e3581_s2578_s2195/*' \
--inputLowPtMinbiasHitsFile \

lukasheinrich

#!/usr/bin/env python

import ROOT
ROOT.gROOT.SetBatch(True)
import yaml
import click
import os
from xml.etree import ElementTree as etree

def get_path(basedir,relpath):

lukasheinrich

import requests
import urllib
import datetime

event = '443176'
sessionid = 175282
date = '2016-07-05'
time = '08:30:00'

sessiondata = requests.get('https://indico.cern.ch/export/event/{}/session/{}.json'.format(event,sessionid)).json()

lukasheinrich

def generate_prodconf_file(optionsAr,argdata,outputfile,eventtype = None):
    templ = '''\
from Gaudi.Configuration import importOptions
{options}
{eventtype}
from ProdConf import ProdConf
ProdConf(
{args}
)
'''