def get_ext_pulser_data():
""" Adapted from ext2.py::getEff
Just gets data and save into a npz file.
"""
from ROOT import TChain, GATDataSet
import glob
# this is the output
extData = {} # {run: [pIdx, runTime, extChan, hitE, fSlo]}
for pIdx in [19, 20, 21]:
# for pIdx in [19]:
extPulserInfo = cal.GetSpecialList()["extPulserInfo"]
attList = extPulserInfo[pIdx][0] # unused
extChan = extPulserInfo[pIdx][-1]
syncChan = wl.getChan(0, 10, 0) # 672
runList = cal.GetSpecialRuns("extPulser", pIdx)
for run in runList:
# elogs: "20 Hz, 150 second runs"
gds = GATDataSet(run)
runTime = gds.GetRunTime() # sec
# pulseRate = 20 # Hz
fList = glob.glob(dsi.specialDir +
"/lat/latSkimDS0_run%d_*.root" % run)
tt = TChain("skimTree")
for f in fList:
tt.Add(f)
tCut = "(channel==%d || channel==%d) && mH==2" % (
syncChan, extChan) # enforce correct sync
n = tt.Draw("trapENFCal:channel:fitSlo", tCut, "goff")
hitE, chan, fSlo = tt.GetV1(), tt.GetV2(), tt.GetV3()
hitE = np.asarray(
[hitE[i] for i in range(n) if chan[i] == extChan])
fSlo = np.asarray(
[fSlo[i] for i in range(n) if chan[i] == extChan])
if len(hitE) == 0:
continue
extData[run] = [pIdx, runTime, extChan, hitE, fSlo]
tt.Reset()
def getSloParams():
from ROOT import TChain, GATDataSet
extPulserInfo = calInfo.GetSpecialList()["extPulserInfo"]
syncChan = wl.getChan(0, 10, 0) # 672
# for pIdx in [19,20,21]:
pIdx = 20
runList = calInfo.GetSpecialRuns("extPulser", pIdx)
attList = extPulserInfo[pIdx][0]
extChan = extPulserInfo[pIdx][-1]
sloVals = {}
for i, run in enumerate(runList):
# elogs: "20 Hz, 150 second runs"
gds = GATDataSet(run)
runTime = gds.GetRunTime() / 1e9 # sec
pulseRate = 20 # Hz
fileList = ds.getLATRunList([run], "%s/lat" % (ds.specialDir))
latChain = TChain("skimTree")
for f in fileList:
latChain.Add("%s/lat/%s" % (ds.specialDir, f))
theCut = "(channel==%d || channel==%d) && mH==2" % (syncChan, extChan)
tNames = [
"Entry$", "mH", "channel", "trapENFCal", "fitSlo", "den90",
"den10", "kvorrT", "wfStd"
]
tvals = wl.GetVX(latChain, tNames, theCut)
n = len(tvals["Entry$"])
if n == 0: continue
hitE = [
tvals["trapENFCal"][i] for i in range(n)
if tvals["channel"][i] == extChan
]
fSlo = [
tvals["fitSlo"][i] for i in range(n)
if tvals["channel"][i] == extChan
]
kvTE = [
tvals["kvorrT"][i] / tvals["trapENFCal"][i] for i in range(n)
if tvals["channel"][i] == extChan
]
rt90 = [
tvals["den90"][i] - tvals["den10"][i] for i in range(n)
if tvals["channel"][i] == extChan
]
wStd = [
tvals["wfStd"][i] for i in range(n)
if tvals["channel"][i] == extChan
]
trigEff = 100 * len(hitE) / (runTime * pulseRate)
print("%d %.2f %.2f" % (run, trigEff, np.mean(hitE)))
sloVals[run] = [trigEff, hitE, fSlo, kvTE, rt90, wStd]
np.savez("../plots/ext2-compare.npz", sloVals)
def getEff():
""" Efficiency vs. energy, each detector in Test 3"""
from ROOT import TChain, GATDataSet
f = plt.figure()
plt.cla()
extPulserInfo = calInfo.GetSpecialList()["extPulserInfo"]
syncChan = wl.getChan(0, 10, 0) # 672
dsNum, modNum, calIdx = 0, 1, 33
calDB = db.TinyDB('../calDB.json')
pars = db.Query()
fsD = ds.getDBRecord(
"fitSlo_ds%d_idx%d_m%d_Peak" % (dsNum, calIdx, modNum), False, calDB,
pars)
bkgIdx = 75 # runs 6887-6963
thD = ds.getDBRecord("thresh_ds%d_bkgidx%d" % (dsNum, bkgIdx), False,
calDB, pars)
for pIdx in [19, 20, 21]:
# for pIdx in [19]:
runList = calInfo.GetSpecialRuns("extPulser", pIdx)
attList = extPulserInfo[pIdx][0]
extChan = extPulserInfo[pIdx][-1]
fsCut = fsD[extChan][2] # 90% value (used in LAT3)
effVals, threshVals, trigVals = [], [], []
eneVals, sloVals, rtVals = [], [], []
for i, run in enumerate(runList):
if run in [7225, 7233]:
continue
# elogs: "20 Hz, 150 second runs"
gds = GATDataSet(run)
runTime = gds.GetRunTime() # sec
pulseRate = 20 # Hz
fileList = ds.getLATRunList([run], "%s/lat" % (ds.specialDir))
latChain = TChain("skimTree")
for f in fileList:
latChain.Add("%s/lat/%s" % (ds.specialDir, f))
tNames = [
"Entry$", "mH", "channel", "trapENFCal", "fitSlo", "den90",
"den10", "threshKeV", "threshSigma"
]
theCut = "(channel==%d || channel==%d) && mH==2" % (
syncChan, extChan) # enforce correct sync
tVals = wl.GetVX(latChain, tNames, theCut)
nPass = len(tVals["Entry$"])
enfArr = [
tVals["trapENFCal"][i] for i in range(nPass)
if tVals["channel"][i] == extChan
]
sloArr = [
tVals["fitSlo"][i] for i in range(nPass)
if tVals["channel"][i] == extChan
]
rtArr = [
tVals["den90"][i] - tVals["den10"][i] for i in range(nPass)
if tVals["channel"][i] == extChan
]
if len(enfArr) == 0:
print("Run %d, No hits in channel %d found. Continuing ..." %
(run, extChan))
continue
eneVals.extend(enfArr)
sloVals.extend(sloArr)
rtVals.extend(rtArr)
thr = [
tVals["threshKeV"][i] for i in range(nPass)
if tVals["channel"][i] == extChan
][0]
sig = [
tVals["threshSigma"][i] for i in range(nPass)
if tVals["channel"][i] == extChan
][0]
if thr < 99999 and sig < 99999:
threshVals.append((thr, sig))
muE, stdE = np.mean(np.asarray(enfArr)), np.std(np.asarray(enfArr))
muF, stdF = np.mean(np.asarray(sloArr)), np.std(np.asarray(sloArr))
nTot = len(sloArr)
nAcc = len([fs for fs in sloArr if fs < fsCut])
eff = (nAcc / nTot)
effVals.append((muE, eff))
nHits = len(enfArr)
expHits = runTime * pulseRate
trigEff = nHits / expHits
trigVals.append((muE, trigEff))
print(
"pIdx %d run %d chan %d nHits %d (exp %d) muE %.2f muFS %.2f eff %.2f trigEff %.2f"
% (pIdx, run, extChan, nHits, expHits, muE, muF, eff, trigEff))
eneVals, sloVals, rtVals = np.asarray(eneVals), np.asarray(
sloVals), np.asarray(rtVals)
fig = plt.figure(figsize=(8, 8), facecolor='w')
p1 = plt.subplot(211)
p2 = plt.subplot(212)
xLo, xHi, bpX = 0, 50, 0.2
yLo, yHi, bpY = 0, 300, 1.
nbX, nbY = int((xHi - xLo) / bpX), int((yHi - yLo) / bpY)
_, _, _, im = p1.hist2d(eneVals,
sloVals,
bins=[nbX, nbY],
range=[[xLo, xHi], [yLo, yHi]],
norm=LogNorm(),
cmap='jet')
fig.colorbar(im, ax=p1)
p1.axhline(fsCut, color='black', linewidth=3)
p1.set_title("pIdx %d channel %d fsCut %.2f" %
(pIdx, extChan, fsCut))
p1.set_xlabel("trapENFCal (keV)", horizontalalignment='right', x=1.0)
p1.set_ylabel("fitSlo", horizontalalignment='right', y=1.0)
xvals = np.asarray(
[val[0] for val in sorted(effVals) if 1. < val[0] < 10.])
yvals = np.asarray(
[val[1] for val in sorted(effVals) if 1. < val[0] < 10.])
p2.plot(xvals, yvals, "o", c='b', markersize=5, label='data')
popt1kev, _ = curve_fit(threshFunc, xvals, yvals)
xnew = np.arange(0, max(xvals), 0.1)
p2.plot(xnew,
threshFunc(xnew, *popt1kev),
'r-',
label="fit mu=%.2f\nfit sig=%.2f" % tuple(popt1kev))
p2.set_title("fitSlo efficiency vs. trapENFCal")
p2.set_xlabel("trapENFCal (keV)", horizontalalignment='right', x=1.0)
p2.set_ylabel("fitSlo Efficiency (%)",
horizontalalignment='right',
y=1.0)
p2.legend(loc='best')
plt.tight_layout()
plt.savefig("../plots/efficiency_idx%d.pdf" % pIdx)
# plot 3 - show how the trigger efficiency is hurting us
# compare run by run avg, db vals, and measured trig. efficiency
fig2 = plt.figure(figsize=(9, 7), facecolor='w')
xvals = np.asarray([val[0] for val in sorted(effVals) if val[0] < 10])
yvals = np.asarray([val[1] for val in sorted(effVals) if val[0] < 10])
plt.plot(xvals, yvals, "o", c='b', markersize=10, label='data')
plt.plot(xnew,
threshFunc(xnew, *popt1kev),
'b-',
label="efficiency > 1 keV\nmu=%.2f sig=%.2f" %
tuple(popt1kev))
# threshMu = np.mean(np.asarray([val[0] for val in threshVals]))
# threshSig = np.mean(np.asarray([val[1] for val in threshVals]))
threshMu = thD[extChan][0]
threshSig = thD[extChan][1]
ytrigDB = threshFunc(xnew, threshMu, threshSig)
plt.plot(xnew,
ytrigDB,
'-',
color='gray',
label="DB trigger efficiency\nmu %.2f sig %.2f" %
(threshMu, threshSig))
trigVals = np.asarray(
[val[1] for val in sorted(trigVals) if val[0] < 10])
print("trigVals:", trigVals)
plt.plot(xvals,
trigVals,
marker='o',
linestyle='-',
color='black',
label="Meas. trigger efficiency")
ycorr = []
for idx in range(len(xvals)):
corr = threshFunc(xvals[idx], threshMu, threshSig)
print("kev %.2f eff %.2f corr %.2f corrected eff %.2f" %
(xvals[idx], yvals[idx], corr, yvals[idx] / corr))
ycorr.append(yvals[idx] / corr)
ycorr = np.asarray(ycorr)
plt.plot(xvals,
ycorr,
"o",
c='r',
markersize=7,
label='trigger eff. corrected')
poptPt7kev, _ = curve_fit(threshFunc, xvals, ycorr)
plt.plot(xnew,
threshFunc(xnew, *poptPt7kev),
'r-',
label="efficiency fit to corrected\nmu %.2f sig %.2f" %
tuple(poptPt7kev))
plt.title("fitSlo efficiency vs. trapENFCal")
plt.xlabel("trapENFCal (keV)", horizontalalignment='right', x=1.0)
plt.ylabel("fitSlo Efficiency (%)", horizontalalignment='right', y=1.0)
plt.legend(loc='best')
plt.savefig("../plots/efficiency_idx%d_corr.pdf" % pIdx)
def getEff():
""" Efficiency vs. energy, each detector in Test 3"""
from ROOT import TChain, GATDataSet
f = plt.figure()
plt.cla()
extPulserInfo = calInfo.GetSpecialList()["extPulserInfo"]
syncChan = wl.getChan(0, 10, 0) # 672
dsNum, modNum, calIdx = 0, 1, 33
calDB = db.TinyDB('../calDB.json')
pars = db.Query()
fsD = ds.getDBRecord(
"fitSlo_ds%d_idx%d_m%d_Peak" % (dsNum, calIdx, modNum), False, calDB,
pars)
bkgIdx = 75 # runs 6887-6963
thD = ds.getDBRecord("thresh_ds%d_bkgidx%d" % (dsNum, bkgIdx), False,
calDB, pars)
for pIdx in [19, 20, 21]:
# for pIdx in [19]:
runList = calInfo.GetSpecialRuns("extPulser", pIdx)
attList = extPulserInfo[pIdx][0]
extChan = extPulserInfo[pIdx][-1]
fsCut = fsD[extChan][2] # 90% value (used in LAT3)
effVals, threshVals, trigVals = [], [], []
eneVals, sloVals, rtVals = [], [], []
for i, run in enumerate(runList):
if run in [7225, 7233]:
continue
# elogs: "20 Hz, 150 second runs"
gds = GATDataSet(run)
runTime = gds.GetRunTime() # sec
pulseRate = 20 # Hz
fileList = ds.getLATRunList([run], "%s/lat" % (ds.specialDir))
latChain = TChain("skimTree")
for f in fileList:
latChain.Add("%s/lat/%s" % (ds.specialDir, f))
tNames = [
"Entry$", "mH", "channel", "trapENFCal", "fitSlo", "den90",
"den10", "threshKeV", "threshSigma"
]
theCut = "(channel==%d || channel==%d) && mH==2" % (
syncChan, extChan) # enforce correct sync
tVals = wl.GetVX(latChain, tNames, theCut)
nPass = len(tVals["Entry$"])
enfArr = [
tVals["trapENFCal"][i] for i in range(nPass)
if tVals["channel"][i] == extChan
]
sloArr = [
tVals["fitSlo"][i] for i in range(nPass)
if tVals["channel"][i] == extChan
]
rtArr = [
tVals["den90"][i] - tVals["den10"][i] for i in range(nPass)
if tVals["channel"][i] == extChan
]
if len(enfArr) == 0:
print("Run %d, No hits in channel %d found. Continuing ..." %
(run, extChan))
continue
eneVals.extend(enfArr)
sloVals.extend(sloArr)
rtVals.extend(rtArr)
def runByRun():
""" Directly confirm settings of ext pulser scripts. """
import time
from ROOT import TFile, TChain, GATDataSet, gROOT
gROOT.ProcessLine(
"gErrorIgnoreLevel = 3001;") # suppress ROOT error messages
extPDict = {
7: 674,
8: 624,
9: 688,
10: 662,
11: 608,
12: 674,
14: 608,
15: 624,
16: 688,
17: 662,
18: 674,
19: 624,
20: 688,
21: 662,
22: 690
}
syncChan = wl.getChan(0, 10, 0) # 672
syncRateNominal = 20 # Hz
calInfo = ds.CalInfo()
fig = plt.figure(figsize=(10, 6), facecolor='w')
# pIdxs = [12,14,15,16,17] # test 2 - rise time
# pIdxs = [18,19,20,21] # test 3 - attenuation
pIdxs = [22]
for pIdx in pIdxs:
runList = calInfo.GetSpecialRuns("extPulser", pIdx)
print("Range", pIdx)
extChan = extPDict[pIdx]
xArr, yArr = [], [] # we're gonna plot these
# runList = [7234]
for run in runList:
# if run in [6936,6937,6940,6942,6944, 6974, 6977]: continue # test 2
# if run in [7224] or run > 7266: continue # test 3
fileList = []
subFiles = glob.glob("%s/lat/latSkimDS%d_run%d_*.root" %
(ds.specialDir, ds.GetDSNum(run), run))
for idx in range(len(subFiles)):
thisFile = "%s/lat/latSkimDS%d_run%d_%d.root" % (
ds.specialDir, ds.GetDSNum(run), run, idx)
if not os.path.isfile(thisFile):
print("File doesn't exist: ", thisFile)
else:
fileList.append(thisFile)
latChain = TChain("skimTree")
for f in fileList:
latChain.Add(f)
tNames = [
"Entry$", "run", "channel", "mH", "trapENFCal", "den90",
"den10", "fitSlo", "localTime_s", "tOffset", "fitAmp"
]
# theCut = "(channel==%d || channel==%d) && mH==2" % (syncChan,extChan)
# theCut += " && Entry$ < 100"
theCut = "Entry$ < 200 && gain==0"
tVals = wl.GetVX(latChain, tNames, theCut)
# don't delete this
for idx in range(tVals["run"].size):
ent = tVals["Entry$"][idx]
run = tVals["run"][idx]
chan = tVals["channel"][idx]
mH = tVals["mH"][idx]
enf = tVals["trapENFCal"][idx]
d90 = tVals["den90"][idx]
d10 = tVals["den10"][idx]
fitSlo = tVals["fitSlo"][idx]
gt = tVals["localTime_s"][idx]
tOff = tVals["tOffset"][idx] * 1e-9
hitTime = gt + tOff
print("%d e%d m%d t%.8f c%-4d %-9.2f %-8.2f %.2f" %
(run, ent, mH, hitTime, chan, enf, d90 - d10, fitSlo))
continue
# make sure we only have hits from syncChan and extChan
# for entry in set(tVals["Entry$"]):
# idxs = [idx for idx in range(len(tVals["Entry$"])) if tVals["Entry$"][idx]==entry]
# chans = [tVals["channel"][idx] for idx in idxs]
# if not set([extChan,syncChan]).issubset(set(chans)):
# print("NOPE:",chans)
gds = GATDataSet(int(run))
runTime = gds.GetRunTime() / 1e9
if len(tVals["Entry$"]) == 0:
print("Run %d, %.2f sec. Found no cts." % (run, runTime))
continue
syncRate = len(set(tVals["Entry$"])) / runTime
expectedCts = runTime * syncRateNominal
extPCts = len([ch for ch in tVals["channel"] if ch == extChan])
syncCts = len([ch for ch in tVals["channel"] if ch == syncChan])
extPRate = extPCts / runTime
syncRate = syncCts / runTime
syncAmp = [
tVals["fitAmp"][i] for i in range(len(tVals["fitAmp"]))
if tVals["channel"][i] == syncChan
]
syncAmp = np.asarray(syncAmp)
muS, sigS = 0, 0
if len(syncAmp) > 0:
muS, sigS = np.mean(syncAmp), np.std(syncAmp)
extENF = [
tVals["trapENFCal"][i] for i in range(len(tVals["trapENFCal"]))
if tVals["channel"][i] == extChan
]
extENF = np.asarray(extENF)
muE, sigE = 0, 0
if len(extENF) > 0:
muE, sigE = np.mean(extENF), np.std(extENF)
print(
"Run %d, %.2f sec. #Expect %d #Sync %d (%.2f Hz) #extP %d (%.2f Hz) muE %.2f sigE %.2f muS %.2f sigS %.2f"
% (run, runTime, expectedCts, syncCts, syncRate, extPCts,
extPRate, muE, sigE, muS, sigS))
# fill the plot arrays
xArr.extend([
tVals["trapENFCal"][i] for i in range(len(tVals["trapENFCal"]))
if tVals["channel"][i] == extChan
])
yArr.extend([
tVals["fitSlo"][i] for i in range(len(tVals["fitSlo"]))
if tVals["channel"][i] == extChan
])
return
# make a plot for this range
fig.clear()
xLo, xHi, yLo, yHi = 0, 10, -20, 300 # test 3
# xLo, xHi, yLo, yHi = 50, 100, -20, 200 # test 2
bpY, bpX = 2, 0.1
nBinsY, nBinsX = int((yHi - yLo) / bpY), int((xHi - xLo) / bpX)
try:
plt.hist2d(xArr,
yArr,
bins=[nBinsX, nBinsY],
range=[[xLo, xHi], [yLo, yHi]],
norm=LogNorm())
plt.colorbar()
plt.xlabel("trapENFCal (keV)", horizontalalignment='right', x=1.0)
plt.ylabel("fitSlo", horizontalalignment='right', y=1.0)
plt.title("Range %d, Channel %d" % (pIdx, extChan))
plt.tight_layout()
plt.savefig("../plots/extPulser_idx%d.pdf" % pIdx)
except ValueError:
pass
