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
errrate = np.sqrt(float(len(dfsel)))/expo
a_rate = np.append(a_rate,rate)
#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')
dfsel.to_pickle(constant.outfolder + 'event/' + 'evall.pkl')
dfselL.to_pickle(constant.outfolder + 'event/' + 'ev_run30ks4.pkl')
print 'my event = ', dfsel.shape[0]
names = ['run30ks5']
allruns = [run30ks5]
extnr = len(constant.extensionlist)
a_rate = np.array([])
a_errrate = np.array([])
fspec, axspec = plt.subplots()
first = True
for runs, n in zip(allruns, names):
# get the format of the dataframe
df = utils.initialize_dataframe(exemplepath)
dfb = utils.initialize_dataframe(exemplepath)
# merge the runs
df = utils.mergeruns(runs, folder, fname, df)
dfb = utils.mergeruns(runs, folder, fnameblank, dfb)
# dfsel = df.query(constant.basecuts)
# dfselb = dfb.query(constant.basecuts)
dfsel = df.query(constant.basecuts + ' & ' + fidcut)
dfselb = dfb.query(constant.basecuts + ' & ' + fidcut)
for imc in imcuts:
dfsel = dfsel.query(imc)
dfselb = dfselb.query(imc)
plt.hist(dfsel.ll - dfsel.llc, bins=bins, histtype='step', log=True)
plt.hist(dfselb.ll - dfselb.llc, bins=bins, histtype='step', log=True)
plt.show()
# parser = argparse.ArgumentParser()
# parser.add_argument("datatype", type=str, nargs='?',choices=['sim', 'data', 'blank'] ,help="type of data tag")
# args = parser.parse_args()
# datatype = args.datatype
iteration = 4
#runs = ['run100ks1','run100ks2','run100ks3','run30ks1','run30ks2']
#runs = ['run100ks1','run100ks2','run100ks3','run30ks1','run30ks2']
runs = ['run30ks4']
dlllimit = -20
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
dfall = utils.initialize_dataframe(exemplepath)
# merge the runs
dfall = utils.mergeruns(runs,folder,fname,dfall)
#define the cut related to the DLL
dllcut = ' ll - llc < ' + str(dlllimit)
# perform cuts
#dfsel = dfall.query(constant.basecuts + ' & ' + dllcut)
dfsel = dfall.query(constant.basecuts + )
firstbin = -50
step = 0.2
lastbin = 0
bins = np.arange(firstbin, lastbin, step)
rangehigh = [-12,-8,-6]
cols = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']
for lv,c in zip(rangehigh,cols):
n,b,p = plt.hist(dfsel['ll']-dfsel['llc'],bins,lw=0,histtype='step')
cbins = b[:-1] + float(step)/2
#integral = step*sum(n)
#runs = ['run30ks4']
dlllimit = -30
fname = 'data'
folder = constant.basefolders[4]
exemplepath = '/Users/gaior/DAMIC/data/official4/cryoOFF_100000s-IntW800_OS_1x100_run2/pkl/data.pkl'
fidcut = 'sigma > 0.3 & sigma < 0.8'
dlllimits = [-23, -30, -40]
fmts = ['o', 's', 'x']
for dlllimit, fmt in zip(dlllimits, fmts):
dllcut = ' ll - llc < ' + str(dlllimit)
a_rate = np.array([])
a_errrate = np.array([])
for r in runsall:
df = utils.initialize_dataframe(exemplepath)
df = utils.mergeruns([r], folder, fname, df)
listofid = pd.unique(df.RUNID)
print ' run : ', r
print ' nr of run == ', len(listofid)
#define the cut related to the DLL
# perform
dfsel = df.query(constant.basecuts + ' & ' + dllcut + '&' + fidcut)
rinfo = constant.runinfo[r]
# exposure = rinfo[0]*rinfo[1]
exposure = rinfo[0] * len(listofid)
print 'exposure = ', exposure, ' s'
evnr = dfsel.shape[0]
print 'evnr = ', evnr
rate = evnr / exposure
errrate = np.sqrt(evnr) / exposure
a_rate = np.append(a_rate, rate)