import numpy as np
import matplotlib.pyplot as plt
import elecLoader as el
es = 3134.078 / 2.223
Edep = np.arange(0.1, 8, 0.1)
Evis = []
resFit, resConstFit = [], []
resCerFit, resSctFit, resCovFit = [], [], []
resCerConstFit, resCovConstFit = [], []
for i in Edep:
NPE = el.getFitNsct(i) + el.getFitNcer(i)
Evis.append(NPE / es)
resFit.append(el.getFitResol(Evis[-1]))
resConstFit.append(el.getFitConstResol(Evis[-1]))
resCerFit.append(np.sqrt(el.getFitCerSigma(Evis[-1])) / NPE)
resSctFit.append(np.sqrt(el.getFitSctSigma(Evis[-1])) / NPE)
resCovFit.append(np.sqrt(el.getFitCov(Evis[-1])) / NPE)
resCerConstFit.append(np.sqrt(el.getFitCerSigmaConst(Evis[-1])) / NPE)
resCovConstFit.append(np.sqrt(el.getFitCovConst(Evis[-1])) / NPE)
#print(el.getFitResol(Evis[-1]), el.getFitCerSigma(Evis[-1]), el.getFitSctSigma(Evis[-1]), el.getFitCov(Evis[-1]))
resFit = np.array(resFit)
resConstFit = np.array(resConstFit)
resCerFit = np.array(resCerFit)
resSctFit = np.array(resSctFit)
def fitNsct(E, As, kB):
if E > 15.9:
return el.getFitNsct(15.9, kB, As, "Sim") / 15.9 * E
else:
return el.getFitNsct(E, kB, As, "Sim")
from matplotlib import gridspec
import elecLoader as el
es = 3134.078 / 2.223
es1 = (3134.078 - 111) / 2.223
es2 = 111 / 2.223
Edep = np.arange(0.1, 8, 0.1)
Evis = np.zeros((5, len(Edep)))
resFit = np.zeros((5, len(Edep)))
num = 0
for kC in [0.0, 0.2, 0.5, 0.8, 1.0]: # cerPE be selected out
nn = 0
for i in Edep:
NPE = el.getFitNsct(i) + el.getFitNcer(i) * kC + 660.8 * 2
Evis[num, nn] = NPE / (es - kC * es2)
cerSigma2 = el.getFitCerSigma(i)
sctSigma2 = el.getFitSctSigma(i)
cov = el.getFitCov(i)
if cerSigma2 == 0 or cov == 0:
resFit[num, nn] = np.sqrt(sctSigma2 + 2 * 27.07**2) / NPE
print(num, nn, sctSigma2, NPE)
nn += 1
continue
corr = cov / np.sqrt(cerSigma2 * sctSigma2)
cerSigma2 *= (1 - kC)**2
def main():
es = 3134.078 / 2.223
fig, ax = plt.subplots()
value0 = integral(0.0065)
ax.plot(Etrue,
value0,
"-",
lw=2,
color="royalblue",
label=r"ESTAR, $kB=6.50\times10^{-3}$g/cm$^2$/MeV")
#for i, j in zip(Etrue, value0):
# print(i, j)
E2, n2 = loadGeant4("kB65")
ax.plot(E2,
n2,
"--",
lw=2,
color="royalblue",
label=r"Geant4, $kB=6.50\times10^{-3}$g/cm$^2$/MeV")
#value2 = integral(0.0063)
#ax.plot(Etrue, value2, "-", lw=2, color="red", label=r"ESTAR, $kB=6.3\times10^{-3}$g/cm$^2$/MeV")
#E4, n4 = loadGeant4("kB63")
#ax.plot(E4, n4, "-.", lw=2, color="red", label=r"Geant4, $kB=6.3\times10^{-3}$g/cm$^2$/MeV")
value1 = integral(0.0051)
ax.plot(Etrue,
value1,
"-",
lw=2,
color="slategray",
label=r"ESTAR, $kB=5.50\times10^{-3}$g/cm$^2$/MeV")
E3, n3 = loadGeant4("kB51")
ax.plot(E3,
n3,
"--",
lw=2,
color="slategray",
label=r"Geant4, $kB=5.50\times10^{-3}$g/cm$^2$/MeV")
As = 1416.1
bestFit = 5.77e-3
fitError = 0.18e-4
n4, n4min, n4max = [], [], []
for i in Etrue:
n4.append(el.getFitNsct(i, bestFit, As, "Sim"))
n4min.append(el.getFitNsct(i, bestFit - fitError, As, "Sim"))
n4max.append(el.getFitNsct(i, bestFit + fitError, As, "Sim"))
#n4 = integral(bestFit)
#n4min = integral(bestFit-fitError)
#n4max = integral(bestFit+fitError)
n4 = np.array(n4)
n4min = np.array(n4min)
n4max = np.array(n4max)
delmin = n4 - n4min
delmax = n4max - n4
n4min = n4 - delmin * 50
n4max = n4 + delmax * 50
ax.plot(Etrue,
n4 / As / Etrue,
"--",
lw=2,
color="crimson",
label=r"Geant4, $kB=5.77\times10^{-3}$g/cm$^2$/MeV")
ax.fill_between(Etrue,
n4min / As / Etrue,
n4max / As / Etrue,
color="crimson",
alpha=0.3)
#ax.plot(Etrue, n4, "--", lw=2, color="crimson", label=r"ESTAR, $kB=5.45\times10^{-3}$g/cm$^2$/MeV")
#ax.fill_between(Etrue, n4min, n4max, color="crimson", alpha=0.3)
ax.grid(True)
ax.tick_params(axis='both', which='major', labelsize=14)
ax.set_xlabel("Electron $E$ [MeV]", fontsize=15)
ax.set_ylabel("$E_{s}$/$E$", fontsize=15)
ax.legend(prop={'size': 13})
ax.semilogx()
ax.set_xlim(1e-3, 15)
ax.set_ylim(0.3, 1.1)
plt.tight_layout()
plt.savefig("Num+G4_Birk.pdf")
plt.show()
def sctpe_sim(E, kB, scale):
return eloader.getFitNsct(E, kB, scale, "Sim")
totpe = ff["c11"]["totpe"].array()
ke = ff["c11"]["ke"].array()
Y = 3134.078 / 2.223
a1, b1, c1 = 0.988, 7.89e-3, 0
a1err, b1err = 6.46e-3, 4.33e-4
p0, p1, p2, p3, p4 = 4.20, 3.64, 0.11, 405, -2.09
a2, b2, n2 = 0.939, 0.103, 1.439
a3, b3, n3 = 0.840, 0.286, 1.234
#a2, b2, n2 = 0.855, 0.262, 1.253
### calculation
Ecalc = []
for i in ke:
Nsct = el.getFitNsct(i, 6.78e-3, 1417.77, "Sim")
Ncer = wenNcer(i, p0, p1, p2, p3, p4)
Ntot = Nsct + Ncer
#k1 = ROOT.gRandom.Gaus(Ntot/Y, np.sqrt(sigma2Func(a1, b1, c1, Y, Ntot/Y)))
k1 = ROOT.gRandom.Gaus(Ntot, np.sqrt(sigma2FuncNew(a2, b2, n2, Ntot))) / Y
k2 = ROOT.gRandom.Gaus(Ntot, np.sqrt(sigma2FuncNew(a3, b3, n3, Ntot))) / Y
E1 = ROOT.gRandom.Gaus(660.8, 27.07) / Y
E2 = ROOT.gRandom.Gaus(660.8, 27.07) / Y
#Ecalc.append(k)
Ecalc.append(k2 + E1 + E2)
fig, ax = plt.subplots()
ax.hist(Ecalc,
bins=100,