Index: fact/tools/pyscripts/tools/fbsl.py =================================================================== --- fact/tools/pyscripts/tools/fbsl.py (revision 12813) +++ fact/tools/pyscripts/tools/fbsl.py (revision 12813) @@ -0,0 +1,110 @@ +#!/usr/bin/python +# +# Werner Lustermann +# ETH Zurich +# +import numpy as np + +import pyfact +from myhisto import * +from hist import * + +year, month, day = 2011, 11, 24 +year, month, day = str(year), str(month), str(day) +yyyymmdd = year + month + day +dfile = '023' +cfile = '012' + +path = '/data03/fact-construction/raw/' +ext = '.fits' +dfname = path + year + '/' + month + '/' + day + '/' + yyyymmdd + '_' + dfile + ext + +calfname = path + year + '/' + month + '/' + day + '/' + yyyymmdd + '_' + cfile + '.drs' + ext + +resFileName = yyyymmdd + '_' + dfile + '_' + cfile + +print 'processing files:' +print 'data: ', dfname +print 'drs-cal: ', calfname + +rd = pyfact.rawdata( dfname, calfname ) + +# rd.ReadBaseline( 'histo.root', 'bls_sum/hplt_mean' ) + +rd.NEvents = 725 +print 'processing N events ', rd.NEvents +step = 500 +nsteps = rd.NEvents / step + 1 +evranges = range(0, (nsteps+1) * step, step) + +evranges[-1] = int( rd.NEvents ) +print 'nsteps', nsteps +print evranges + +exit( -1 ) + +bslMeanValues = np.zeros( ( nsteps, rd.NPIX) ) + + +def loopEvents( first, last, hist): + """ + LOOP over all events: Do what you want, here! + """ + for ev in range( first, last ): + print 'Event', ev + rd.next() + # rd.CorrectBaseline() + hist.fillall( rd.acalData ) + + +def loopPixel( npix, phist ): + vmaxprob = np.zeros( npix ) + v_rms = np.zeros( npix ) + + for pix in range( npix ): + + hpix = phist.list[pix] + + vmaxprob[pix] = hpix.GetXaxis().GetBinCenter( hpix.GetMaximumBin() ) + bslSummaryHistos.dict['hbsl_mean'].Fill( vmaxprob[pix] ) + bslSummaryHistos.dict['hplt_mean'].SetBinContent(pix+1, vmaxprob[pix] ) + + v_rms[pix] = hpix.GetRMS() + bslSummaryHistos.dict['hbsl_rms'].Fill( v_rms[pix] ) + bslSummaryHistos.dict['hplt_rms'].SetBinContent( pix+1, v_rms[pix] ); + + return vmaxprob + +pixhist = hist_array( rd.NPIX, 400, -99.5, 100.5, + 'bsl_pix', 'Pixel Baseline', + 'sample value in mV', 'Entries / 0.5 mV' ) + +for step in range( nsteps ): + print step, 'processing events: ', evranges[step], 'to ', evranges[step+1] + + pixstephist = hist_array( rd.NPIX, 400, -99.5, 100.5, + 'bsl_pix', 'Pixel Baseline', + 'sample value in mV', 'Entries / 0.5 mV' ) + loopEvents( evranges[step], evranges[step+1], pixstephist ) + pixhist.y[:,:] += pixstephist.y[:,:] + pixstephist.SetBinContent() + + bslSummaryHistos = bslHistograms( 'bsl_sum' ) + bslMeanValues[step,:] = loopPixel( rd.NPIX, pixstephist ) + print bslMeanValues + + fname = 'h'+ str( evranges[step] ) + '_to_'+ str( evranges[step+1] ) + '.root' + SaveHistograms( [pixstephist, bslSummaryHistos], fname ) + + del pixstephist + del bslSummaryHistos + +pixhist.SetBinContent() +bslSummaryHistos = bslHistograms( 'bsl_sum' ) +loopPixel( rd.NPIX, pixhist ) + +fname = 'hbsl.root' +SaveHistograms( [pixhist, bslSummaryHistos], resFileName + '_bsl.root' ) + +np.savetxt( resFileName + '_bls.dat', bslMeanValues, fmt = '%8.3f' ) +np.save( resFileName + '_bsl.npy', bslMeanValues ) Index: fact/tools/pyscripts/tools/fbsl1.py =================================================================== --- fact/tools/pyscripts/tools/fbsl1.py (revision 12813) +++ fact/tools/pyscripts/tools/fbsl1.py (revision 12813) @@ -0,0 +1,101 @@ +#!/usr/bin/python +# +# Werner Lustermann +# ETH Zurich +# +import numpy as np + +import pyfact +from myhisto import * +from hist import * + +run_list = [ + ['012', '020', '2011', '11', '24'], + ['012', '021', '2011', '11', '24'], + ['012', '022', '2011', '11', '24'], + ['012', '023', '2011', '11', '24'] ] + + +for run in run_list: + files = pyfact.fnames( run ) + print files.specifier + files.info() + +exit( -1 ) + +rd = pyfact.rawdata( dfname, calfname ) + +# rd.ReadBaseline( 'histo.root', 'bls_sum/hplt_mean' ) + +# rd.NEvents = 100 +step = 500 +nsteps = rd.NEvents / step +evranges = range(0, (nsteps + 1) * step, step) + +evranges[-1] = int( rd.NEvents ) + +bslMeanValues = np.zeros( ( nsteps, rd.NPIX) ) + +def loopEvents( first, last, hist): + """ + LOOP over all events: Do what you want, here! + """ + for ev in range( first, last ): + print 'Event', ev + rd.next() + # rd.CorrectBaseline() + hist.fillall( rd.acalData ) + + +def loopPixel( npix, phist ): + vmaxprob = np.zeros( npix ) + v_rms = np.zeros( npix ) + + for pix in range( npix ): + + hpix = phist.list[pix] + + vmaxprob[pix] = hpix.GetXaxis().GetBinCenter( hpix.GetMaximumBin() ) + bslSummaryHistos.dict['hbsl_mean'].Fill( vmaxprob[pix] ) + bslSummaryHistos.dict['hplt_mean'].SetBinContent(pix+1, vmaxprob[pix] ) + + v_rms[pix] = hpix.GetRMS() + bslSummaryHistos.dict['hbsl_rms'].Fill( v_rms[pix] ) + bslSummaryHistos.dict['hplt_rms'].SetBinContent( pix+1, v_rms[pix] ); + + return vmaxprob + + +pixhist = hist_array( rd.NPIX, 400, -99.5, 100.5, + 'bsl_pix', 'Pixel Baseline', + 'sample value in mV', 'Entries / 0.5 mV' ) + +for step in range( nsteps ): + print step, 'processing events: ', evranges[step], 'to ', evranges[step+1] + + pixstephist = hist_array( rd.NPIX, 400, -99.5, 100.5, + 'bsl_pix', 'Pixel Baseline', + 'sample value in mV', 'Entries / 0.5 mV' ) + loopEvents( evranges[step], evranges[step+1], pixstephist ) + pixhist.y[:,:] += pixstephist.y[:,:] + pixstephist.SetBinContent() + + bslSummaryHistos = bslHistograms( 'bsl_sum' ) + bslMeanValues[step,:] = loopPixel( rd.NPIX, pixstephist ) + print bslMeanValues + + fname = 'h'+ str( evranges[step] ) + '_to_'+ str( evranges[step+1] ) + '.root' + pyfact.SaveHistograms( [pixstephist, bslSummaryHistos], fname ) + + del pixstephist + del bslSummaryHistos + +pixhist.SetBinContent() +bslSummaryHistos = bslHistograms( 'bsl_sum' ) +loopPixel( rd.NPIX, pixhist ) + +fname = 'hbsl.root' +pyfact.SaveHistograms( [pixhist, bslSummaryHistos], resFileName + '_bsl.root' ) + +np.savetxt( resFileName + '_bls.dat', bslMeanValues, fmt = '%8.3f' ) +np.save( resFileName + '_bsl.npy', bslMeanValues ) Index: fact/tools/pyscripts/tools/fbsl2.py =================================================================== --- fact/tools/pyscripts/tools/fbsl2.py (revision 12813) +++ fact/tools/pyscripts/tools/fbsl2.py (revision 12813) @@ -0,0 +1,147 @@ +#!/usr/bin/python +# +# Werner Lustermann +# ETH Zurich +# +import numpy as np + +import pyfact +from myhisto import * +from hist import * + +class baseline_ana( object): + """ + """ + def __init__( self, fnames = {}, step = 500, bsl_corr = True ): + """ + fnames : dictionary of strings : run file names + step : integer : number of events to be used for the calculation of a baseline values + bsl_corr : bool : apply baseline correction + + self: + blsMeanValues : np.array() to store computed baseline values + """ + + if fnames = {}: + print "no file names given for analysis!!!" + return (-1) + else: + print "analysing data: " + fnames.info() + + self.step = step + self.bsl_corr = True + + if bsl_corr: + print 'apply baseline correction' + else: + print 'no baseline correction' + + self.bslMeanValues = np.zeros( ( nsteps, rd.NPIX) ) + + # create the object providing the rawdata + rd = pyfact.rawdata( fnames['data'], fnames['drscal'] ) + self.nevents = rd.NEvents + + self.steps() + + # rd.ReadBaseline( 'histo.root', 'bls_sum/hplt_mean' ) + + def steps( self ): + """ compute the ranges of events + + self: + nsteps : integer : number of ranges the events are devided into + evranges : list of range boundaries of length nsteps + 1 + """ + + nsteps = self.nevents / self.step + evranges = range(0, (nsteps + 1) * self.step, self.step) + + # add the events of the last incomplete step to the previous once + evranges[-1] = int( rd.NEvents ) + + self.nsteps = nsteps + self.evranges = evranges + + print 'nevents: ', self.nevents + print 'ranges: ', self.evranges + + def loopEvents( first, last, hist): + """ + LOOP over all events: Do what you want, here! + """ + for ev in range( first, last ): + print 'Event', ev + rd.next() + # rd.CorrectBaseline() + hist.fillall( rd.acalData ) + + + def loopPixel( npix, phist ): + + vmaxprob = np.zeros( npix ) + v_rms = np.zeros( npix ) + + for pix in range( npix ): + + hpix = phist.list[pix] + + vmaxprob[pix] = hpix.GetXaxis().GetBinCenter( hpix.GetMaximumBin() ) + bslSummaryHistos.dict['hbsl_mean'].Fill( vmaxprob[pix] ) + bslSummaryHistos.dict['hplt_mean'].SetBinContent(pix+1, vmaxprob[pix] ) + + v_rms[pix] = hpix.GetRMS() + bslSummaryHistos.dict['hbsl_rms'].Fill( v_rms[pix] ) + bslSummaryHistos.dict['hplt_rms'].SetBinContent( pix+1, v_rms[pix] ); + + return vmaxprob + + + def run( self ): + """ run the analysis + """ + + pixhist = hist_array( rd.NPIX, 400, -99.5, 100.5, + 'bsl_pix', 'Pixel Baseline', + 'sample value in mV', 'Entries / 0.5 mV' ) + + for step in range( self.nsteps ): + + print self.step, 'processing events: ', self.evranges[ self.step], 'to ', + self.evranges[self.step+1] + + pixstephist = hist_array( rd.NPIX, 400, -99.5, 100.5, + 'bsl_pix', 'Pixel Baseline', + 'sample value in mV', 'Entries / 0.5 mV' ) + + self.loopEvents( evranges[step], evranges[step+1], pixstephist ) + + pixhist.y[:,:] += pixstephist.y[:,:] + pixstephist.SetBinContent() + + bslSummaryHistos = bslHistograms( 'bsl_sum' ) + self.bslMeanValues[step,:] = loopPixel( rd.NPIX, pixstephist ) + # print self.bslMeanValues + + fname = 'h'+ str( evranges[step] ) + '_to_'+ str( evranges[step+1] ) + '.root' + pyfact.SaveHistograms( [pixstephist, bslSummaryHistos], fname ) + + del pixstephist + del bslSummaryHistos + + pixhist.SetBinContent() + + bslSummaryHistos = bslHistograms( 'bsl_sum' ) + loopPixel( rd.NPIX, pixhist ) + + self.save() + # fname = 'hbsl.root' + + def save( self ): + """ save data after completion of analysis + """ + pyfact.SaveHistograms( [pixhist, bslSummaryHistos], resFileName + '_bsl.root' ) + + np.savetxt( resFileName + '_bls.dat', self.bslMeanValues, fmt = '%8.3f' ) + np.save( resFileName + '_bsl.npy', self.bslMeanValues )