fspec, axspec = plt.subplots() fsig, axsig = plt.subplots() if efficiencynorm == 'y' and exposurenorm == 'y': addinfo = 'expo and efficiency' if efficiencynorm == 'y' and exposurenorm == 'n': addinfo = 'efficiency only' if efficiencynorm == 'n' and exposurenorm == 'y': addinfo = 'expo only' if efficiencynorm == 'n' and exposurenorm == 'n': addinfo = 'raw' fspec.suptitle(str(names)+ ' Emin = ' + str(E1) + ' DLL cut: ' +str(dlllimit) + ' DC cut: ' +str(dclimvalue) + addinfo) first = True for runs,n in zip(allruns,names): # get the format of the dataframe df = utils.initialize_dataframe(exemplepath) # merge the runs df = utils.mergeruns(runs,folder,fname,df) expo = 0 for r in runs: expo += utils.getrunexposure(r,extnr) rmexpo = utils.removedexpofromDC(r,dfDC,dclimvalue) # print 'rmexpo = ' , rmexpo expo -= rmexpo # print 'expo = ', expo dfsel = df.query(constant.basecuts + ' & ' + dllcut + '&' + fidcut + '&' + Ecut ) for imc in imcuts: dfsel = dfsel.query(imc) rate = float(len(dfsel))/expo
import glob import argparse iteration = 4 #runs = ['run100ks1','run100ks2','run100ks3','run30ks1','run30ks2'] runs = ['run100ks1', 'run100ks2', 'run100ks3', 'run30ks1', 'run30ks2'] runsall = [ 'run100ks1', 'run100ks2', 'run100ks3', 'run30ks1', 'run30ks2', 'run30ks4' ] #runs = ['run30ks4'] dlllimit = -23 fname = 'data' folder = constant.basefolders[4] exemplepath = '/Users/gaior/DAMIC/data/official4/cryoOFF_100000s-IntW800_OS_1x100_run2/pkl/data.pkl' # get the format of the dataframe dfmoriond = utils.initialize_dataframe(exemplepath) dfall = utils.initialize_dataframe(exemplepath) dflast = utils.initialize_dataframe(exemplepath) # merge the runs dfmoriond = utils.mergeruns(runs, folder, fname, dfmoriond) dfall = utils.mergeruns(runsall, folder, fname, dfall) dflast = utils.mergeruns(['run30ks4'], folder, fname, dflast) #define the cut related to the DLL dllcut = ' ll - llc < ' + str(dlllimit) fidcut = 'sigma > 0.3 & sigma < 0.8' # perform dfsel = dfall.query(constant.basecuts + ' & ' + dllcut + '&' + fidcut) dfselM = dfmoriond.query(constant.basecuts + ' & ' + dllcut + '&' + fidcut) dfselL = dflast.query(constant.basecuts + ' & ' + dllcut + '&' + fidcut) dfselM.to_pickle(constant.outfolder + 'event/' + 'evmoriond.pkl')