Index: fact/tools/pyscripts/tools/fbsl2.py =================================================================== --- fact/tools/pyscripts/tools/fbsl2.py (revision 12872) +++ fact/tools/pyscripts/tools/fbsl2.py (revision 12874) @@ -13,23 +13,27 @@ """ """ - def __init__( self, fnames = {}, step = 500, bsl_corr = True ): + def __init__( self, rfiles = {}, step = 500, bsl_corr = True, + max_events = -1 ): """ - fnames : dictionary of strings : run file names - step : integer : number of events to be used for the calculation of a baseline values + rfiles : 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 - + max_events : maximum number of events to be processed + self: blsMeanValues : np.array() to store computed baseline values """ - if fnames = {}: + if rfiles == {}: print "no file names given for analysis!!!" return (-1) else: - print "analysing data: " - fnames.info() + print "baseline analysing data: " + rfiles.info() - self.step = step - self.bsl_corr = True + self.rfiles = rfiles + self.step = step + self.bsl_corr = bsl_corr if bsl_corr: @@ -38,110 +42,136 @@ 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.rd = pyfact.rawdata( rfiles.names['data'], rfiles.names['drscal'] ) + self.npix = self.rd.NPIX + + if max_events == -1: + self.nevents = self.rd.NEvents + else: + self.nevents = max_events + + self.steps() # compute the number of steps - self.steps() + self.bslMeanValues = np.zeros( ( self.nsteps, self.npix ) ) + + self.pixhist = hist_array( self.npix, 400, -99.5, 100.5, + 'bsl_pix', 'Pixel Baseline', + 'sample value in mV', 'Entries / 0.5 mV' ) + self.bslSummaryHistos = bslHistograms( 'bsl_sum' ) + + self.run() # compute baseline + self.save() # save results - # rd.ReadBaseline( 'histo.root', 'bls_sum/hplt_mean' ) + def steps( self ): + """ compute the ranges of events + + self: + nsteps : integer : number of ranges of events + evranges : list of range boundaries of length nsteps + 1 + """ + + nsteps = self.nevents / self.step + 1 + evranges = range(0, nsteps * self.step, self.step) + + # add the events of the last incomplete step to the previous once + evranges[-1] = int( self.nevents ) - def steps( self ): - """ compute the ranges of events + self.nsteps = nsteps + self.evranges = evranges - 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 ) + print 'nevents: ', self.nevents + print 'ranges: ', self.evranges - 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 loopEvents( self, first, last, hist): + """ LOOP over all events: Do what you want, here! + first : integer : first event to process + last : integer : last event to process + hist : hist_array : fill all pixel data + """ + for ev in range( first, last ): + if ev%100 == 0: print 'Event', ev + self.rd.next() + # rd.CorrectBaseline() + hist.fillall( self.rd.acalData ) - def loopPixel( npix, phist ): - - vmaxprob = np.zeros( npix ) - v_rms = np.zeros( npix ) + def loopPixel( self, npix, phist ): + """ loop over pixel and extract most probable values + """ + vmaxprob = np.zeros( npix ) + v_rms = np.zeros( npix ) - for pix in range( npix ): + for pix in range( npix ): - hpix = phist.list[pix] + 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] ) + vmaxprob[pix] = hpix.GetXaxis().GetBinCenter( hpix.GetMaximumBin() ) + self.bslSummaryHistos.dict['hbsl_mean'].Fill( vmaxprob[pix] ) + self.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 + v_rms[pix] = hpix.GetRMS() + self.bslSummaryHistos.dict['hbsl_rms'].Fill( v_rms[pix] ) + self.bslSummaryHistos.dict['hplt_rms'].SetBinContent( pix+1, + v_rms[pix] ); + + self.vmaxprob = vmaxprob - def run( self ): - """ run the analysis - """ + def run( self ): + """ run the analysis + """ + + for step in range( self.nsteps - 1 ): + + print step, 'processing events: ', self.evranges[ step ], 'to ', + print self.evranges[ step+1 ] - pixhist = hist_array( rd.NPIX, 400, -99.5, 100.5, - 'bsl_pix', 'Pixel Baseline', - 'sample value in mV', 'Entries / 0.5 mV' ) + pixstephist = hist_array( self.npix, 400, -99.5, 100.5, + 'bsl_pix_step', 'Pixel Baseline', + 'sample value in mV', 'Entries / 0.5 mV' ) + + self.loopEvents( self.evranges[step], self.evranges[step+1], + pixstephist ) - for step in range( self.nsteps ): - - print self.step, 'processing events: ', self.evranges[ self.step], 'to ', - self.evranges[self.step+1] + self.pixhist.y[:,:] += pixstephist.y[:,:] + pixstephist.SetBinContent() - 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 ) + bslSummaryHistos = bslHistograms( 'bsl_sum_step' ) + self.bslMeanValues[step,:] = self.loopPixel( self.npix, pixstephist ) + # print self.bslMeanValues - 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 ) + fname = 'h'+ str( self.evranges[step] ) + '_to_'+ str( self.evranges[step+1] ) + '.root' + SaveHistograms( [pixstephist, bslSummaryHistos], fname ) - del pixstephist - del bslSummaryHistos + del pixstephist + del bslSummaryHistos - pixhist.SetBinContent() + self.pixhist.SetBinContent() - bslSummaryHistos = bslHistograms( 'bsl_sum' ) - loopPixel( rd.NPIX, pixhist ) + self.loopPixel( self.rd.NPIX, self.pixhist ) - self.save() - # fname = 'hbsl.root' - def save( self ): - """ save data after completion of analysis - """ - pyfact.SaveHistograms( [pixhist, bslSummaryHistos], resFileName + '_bsl.root' ) + def save( self ): + """ save baseline values and histograms + """ + SaveHistograms( [ self.pixhist, self.bslSummaryHistos], + self.rfiles.names['results'] + '_bsl.root' ) + + np.savetxt( self.rfiles.names['results'] + '_bls.txt', self.bslMeanValues, + fmt = '%8.3f' ) + np.save( self.rfiles.names['results'] + '_bsl.npy', self.bslMeanValues ) + - np.savetxt( resFileName + '_bls.dat', self.bslMeanValues, fmt = '%8.3f' ) - np.save( resFileName + '_bsl.npy', self.bslMeanValues ) +if __name__ == '__main__': + """ create an instance for test purposes + + """ + runfiles = pyfact.fnames() + + runfiles.info() + + bcal = baseline_ana( rfiles = runfiles, step = 14, + bsl_corr = False, max_events = 60 )