#!/bin/sh # ======================================================================== # # * # * This file is part of MARS, the MAGIC Analysis and Reconstruction # * Software. It is distributed to you in the hope that it can be a useful # * and timesaving tool in analysing Data of imaging Cerenkov telescopes. # * It is distributed WITHOUT ANY WARRANTY. # * # * Permission to use, copy, modify and distribute this software and its # * documentation for any purpose is hereby granted without fee, # * provided that the above copyright notice appear in all copies and # * that both that copyright notice and this permission notice appear # * in supporting documentation. It is provided "as is" without express # * or implied warranty. # * # # # Author(s): Daniela Dorner 05/2006 # # Copyright: MAGIC Software Development, 2000-2007 # # # ======================================================================== # # # ############################################################################## # # This script creates mc sequences and datasets for a psf, observation mode # and zenith range chosen by the user. # # variables, that have to be set by the user: # - path directory, where the mc sequence and dataset files are stored # be careful: don't move the sequence files afterwards, as the # paths are stored in the datasetfiles # - zdmin minimum zenith distance # - zdmax maximum zenith distance # - psf psf (at the moment mc with psf 14 and 20 is available) # - modes observation mode # the explanation for the modes can be found in # /magic/montecarlo/rawfiles/README.txt # - numruns number of runs, that are in the sequence file, which are used # for training (SequencesOn in $mcdataset) # # Remark: For the training of the RF for the energy estimation you need the # macro trainengery.C in the path $path/macros in a modified version, # i.e. the inputfile (mcdataset) and the outputfile (rf-root file) # are given as options: # $macrospath/trainenergy.C+\("\"$mcdataset\""\,"\"$rffile\""\) # # Remark: You have to run the script in your Mars directory. # ############################################################################## # to be set by the user path=/home/dorner/final_analysis # path where your mc sequence and dataset files are stored mcoutpath=$path/mc # path where you have your modified version of trainenergy.C macrospath=$path/macros # zenith range of your data zdmin=17 zdmax=36 # point spread function of your data psf=14 # observation mode of your data # for explanations see /magic/montecarlo/rawfiles/README.txt modes=( "02" ) # number of runs which are in the sequence files used for training # please adjust this number such, that you use about 30000 Events for training numruns=1 # some checks # checking if the given files and paths are existing if ! [ -e $mcoutpath ] then echo "Your outpath for the mc $mcoutpathdoes not exist." exit fi if ! [ -e $macrospath ] then echo "Your macros path $macrospath does not exist." exit fi if ! [ -e $macrospath/trainenergy.C ] then echo "Your trainenergy.C does not exist in path $macrospath." exit fi mcdatasettrain=$mcoutpath/mcdataset-for-training.txt mcdatasetsponde=$mcoutpath/mcdataset-for-sponde.txt # be careful with $date, when path changes mcpath=/magic/montecarlo zbinmin=`echo "scale=2 ; 100*(1 - c($zdmin*3.14/180))+1" | bc -l` zbinmax=`echo "scale=2 ; 100*(1 - c($zdmax*3.14/180))+1" | bc -l` zbinmin=`echo $zbinmin | cut -d. -f1` zbinmax=`echo $zbinmax | cut -d. -f1` echo "zd: min: $zdmin max: $zdmax" echo "zbin: min: $zbinmin max: $zbinmax" echo "$numruns runs are classified as test the rest as train" j=0 for mode in $modes do for (( i=$zbinmin ; i < $zbinmax ; i++ )) do zbin=`printf %02d $i` path=$mcpath/rawfiles/19$zbin/$mode/$psf runs=(`ls $path | grep Gamma | cut -d_ -f2 | sed -e 's/^0//g' -e 's/^0//g' -e 's/^0//g' -e 's/^0//g' -e 's/^0//g' -e 's/^0//g' | tr "\n" " "`) if [ "$runs" = "" ] then echo " No runs for zbin $i found. Please try to find some MC with zbin $i!" continue fi echo "found ${#runs[@]} Gamma MC files in path "$path runsforfirst="" firstrun=${runs[0]} secondrun=${runs[${numruns}]} firstrunno=`printf %08d $firstrun` secondrunno=`printf %08d $secondrun` unset runs[0] for (( k=1 ; k < $numruns ; k++ )) do runsforfirst=$runsforfirst" ${runs[$k]}" unset runs[$k] done date=`echo $path | cut -d/ -f5-7 | sed -e 's/\//-/g'` trainsequfile=$mcoutpath/sequence$firstrunno.txt trainsequences[$j]=$firstrunno # echo " writing train-sequfile "$trainsequfile echo "Sequence: $firstrun" > $trainsequfile echo "Night: $date" >> $trainsequfile echo "" >> $trainsequfile echo "CalRuns: 1" >> $trainsequfile echo "PedRuns: 2" >> $trainsequfile echo "DatRuns: $firstrun$runsforfirst" >> $trainsequfile echo "" >> $trainsequfile echo "MonteCarlo: Yes" >> $trainsequfile echo "" >> $trainsequfile testsequfile=$mcoutpath/sequence$secondrunno.txt testsequences[$j]=$secondrunno # echo "writing test-sequfile "$testsequfile echo "Sequence: $secondrun" > $testsequfile echo "Night: $date" >> $testsequfile echo "" >> $testsequfile echo "CalRuns: 1" >> $testsequfile echo "PedRuns: 2" >> $testsequfile echo "DatRuns: ${runs[@]}" >> $testsequfile echo "" >> $testsequfile echo "MonteCarlo: Yes" >> $testsequfile echo "" >> $testsequfile j=$j+1 done done echo "# test sequences: ${#testsequences[@]}" echo "# train sequences: ${#trainsequences[@]}" echo "AnalysisNumber: 1 " > $mcdatasettrain echo "" >> $mcdatasettrain echo "SequencesOn: ${trainsequences[@]}" >> $mcdatasettrain echo "" >> $mcdatasettrain echo "SequencesOff: ${testsequences[@]}" >> $mcdatasettrain echo "" >> $mcdatasettrain echo "" >> $mcdatasettrain echo "AnalysisNumber: 1 " > $mcdatasetsponde echo "" >> $mcdatasetsponde echo "SequencesOn: ${testsequences[@]}" >> $mcdatasetsponde echo "" >> $mcdatasetsponde echo "" >> $mcdatasetsponde for (( i=0 ; i < ${#testsequences[@]} ; i++ )) do numtrain=${trainsequences[$i]} notrain=`echo $numtrain | cut -c 0-4` echo "Sequence$numtrain.File: $mcoutpath/sequence$numtrain.txt" >> $mcdatasettrain echo "Sequence$numtrain.Dir: $mcpath/star/$notrain/$numtrain" >> $mcdatasettrain numtest=${testsequences[$i]} notest=`echo $numtest | cut -c 0-4` echo "Sequence$numtest.File: $mcoutpath/sequence$numtest.txt" >> $mcdatasettrain echo "Sequence$numtest.Dir: $mcpath/star/$notrain/$numtrain" >> $mcdatasettrain echo "Sequence$numtest.File: $mcoutpath/sequence$numtest.txt" >> $mcdatasetsponde echo "Sequence$numtest.Dir: $mcpath/star/$notrain/$numtrain" >> $mcdatasetsponde done # train the rf for energy estimation logfile=$mcoutpath/trainenergy.log rffile=$mcoutpath/rf-energy.root echo "Your mcdataset for training: $mcdatasettrain" echo "Your rffile: $rffile" echo "" echo "Training the RF..." root -q -b $macrospath/trainenergy.C+\("\"$mcdatasettrain\""\,"\"$rffile\""\) | tee $logfile echo "" echo "Please use rf-file $rffile in your sponde.rc, in case you want to use the RF energy estimator there. "