def main():
name = "AmC"
secBetaArr, secAntiBetaArr = bloader.loadPrmBeta("../data/gamma/" + name +
"_J19.txt")
print(">>>>>>>>>> Loading primary beta distribution <<<<<<<<<<")
mu_arr, sigma_arr = [], []
for i in range(len(secBetaArr)):
tmppe, tmpsigma = 0, 0
for j in secBetaArr[i]:
if j == 0:
break
tmppe += eloader.getNPE(j)
tmpsigma += eloader.getSPE(j) * eloader.getSPE(j)
for j in secAntiBetaArr[i]:
if j == 0:
break
tmppe += eloader.getNPE(j) + 2 * 660.8
tmpsigma += eloader.getSPE(j) * eloader.getSPE(j) + 2 * 27.02**2
tmpsigma = np.sqrt(tmpsigma)
mu_arr.append(tmppe)
sigma_arr.append(tmpsigma)
hist = ROOT.TH2D("hist", "", 100, 8300, 9300, 100, 70, 130)
for i, j in zip(mu_arr, sigma_arr):
hist.Fill(i, j)
hist.SaveAs("AmC_model.root")
def loadPrmBeta(self):
st = time.time()
filename = ""
if self.phys == "Penelope":
filename = "../data/gamma/" + self.name + "_pene.txt"
if self.phys == "Livermore":
filename = "../data/gamma/" + self.name + "_J19.txt"
if self.name == "nC12":
filename = "../data/gamma/" + self.name + "_J19_multiple.txt"
#print("Primary beta distribution from %s" %filename)
prmBeta, prmAntiBeta = bloader.loadPrmBeta(filename)
et = time.time()
self.loadPrmBetaFlag = True
#print("Primary beta loading time : %.3f s" %(et-st))
self.prmBetaArr = prmBeta
self.prmAntiBetaArr = prmAntiBeta
def main():
es = 3134.078 / 2.223
for k in range(315, 380, 1):
# Load PrmBeta
filename = "../data/gamma/Livermore-" + str(k) + ".txt"
print(filename)
prmBeta, prmAntiBeta = bloader.loadPrmBeta(filename)
# calcSingleEvent :
nSamples = 5000
mu_arr, sigma_arr = [], []
for i in range(nSamples):
tmpnpe, tmpspe = 0, 0
for j in prmBeta[i]:
if j == 0:
break
tmpnpe += (eloader.getNPE(j))
tmpspe += (eloader.getSPE(j)**2)
for j in prmAntiBeta[i]:
if j == 0:
break
tmpnpe += (eloader.getNPE(j) + 2 * 660.8)
tmpspe += (eloader.getSPE(j)**2 + 27.07**2 * 2)
mu_arr.append(tmpnpe)
sigma_arr.append(np.sqrt(tmpspe))
mu = np.array(mu_arr)
sigma = np.array(sigma_arr)
npe, spe = 0, 0
sigma_part1, sigma_part2 = 0, 0
for i in mu:
npe += i
npe = npe / nSamples
for i, j in zip(mu, sigma):
spe += (i - npe)**2 + j**2
sigma_part1 += (i - npe)**2
sigma_part2 += j**2
spe = np.sqrt(spe / nSamples)
sigma_part1 /= nSamples
sigma_part2 /= nSamples
print((k - 300) / 10., npe / es, spe / es)
if __name__ == "__main__":
nH = singleGamma("nH")
Y = 3134.078 / 2.223
filename = "/junofs/users/miaoyu/energy_model/energyModel_Fit/new_fitter/output/NewgamNewB12_kSimQ_kNewAnaCer_kNew/NewgamNewB12_kSimQ_kNewAnaCer_kNew_rescov.txt"
par1, parerr = loadBestFit(filename, 3)
a, b, n = par1
filename = "/junofs/users/miaoyu/energy_model/energyModel_Fit/new_fitter/output/NewgamNewB12_kSimQ_kNewAnaCer_kNew/NewgamNewB12_kSimQ_kNewAnaCer_kNew_nonlcov.txt"
par1, parerr1 = loadBestFit(filename, 7)
scale, kB, p0, p1, p2, p3, p4 = par1
filename = "../data/gamma/nH_J19.txt"
prmBeta, prmAntiBeta = bloader.loadPrmBeta(filename)
# calcSingleEvent :
nSamples = 5000
mu_arr, sigma_arr = [], []
cer_arr = []
for i in range(nSamples):
tmpnpe, tmpspe = 0, 0
cernpe = 0
# Electrons :
for j in prmBeta[i]:
if j == 0:
break
onenpe = (fitNsct(j, scale, kB) + wenNcer(j, p0, p1, p2, p3, p4))
cernpe += wenNcer(j, p0, p1, p2, p3, p4)
tmpnpe += onenpe
def loadPrmBeta(self):
filename = "../data/gamma/" + self.name + "_J19.txt"
prmBeta, prmAntiBeta = bloader.loadPrmBeta(filename)
return prmBeta, prmAntiBeta
def main():
##### Livermore
filename1 = "../data/gamma/log-livermore.txt"
print(filename1)
prmBeta1, prmAntiBeta1 = bloader.loadPrmBeta(filename1)
allBeta1 = []
BetaNum1 = np.zeros(100000)
for i in range(100000):
for j in prmBeta1[i]:
allBeta1.append(j)
BetaNum1[i] += 1
for k in prmAntiBeta1[i]:
allBeta1.append(k)
BetaNum1[i] += 1
##### Penelope
filename2 = "../data/gamma/log-penelope.txt"
print(filename2)
prmBeta2, prmAntiBeta2 = bloader.loadPrmBeta(filename2)
allBeta2 = []
BetaNum2 = np.zeros(100000)
for i in range(100000):
for j in prmBeta2[i]:
allBeta2.append(j)
BetaNum2[i] += 1
for k in prmAntiBeta2[i]:
allBeta2.append(k)
BetaNum2[i] += 1
"""
### chi2 TEST
h1 = ROOT.TH1D("h1", "h1", 100, 0, 8)
h2 = ROOT.TH1D("h2", "h2", 100, 0, 8)
for i, j in zip(allBeta1, allBeta2):
h1.Fill(i)
h2.Fill(j)
chi2 = h1.KolmogorovTest(h2)
print("EnergyPdf chi2test : ", chi2)
h3 = ROOT.TH1I("h3", "h3", 50, 0, 50)
h4 = ROOT.TH1I("h4", "h4", 50, 0, 50)
for i, j in zip(BetaNum1, BetaNum2):
h3.Fill(i)
h4.Fill(j)
chi2 = h3.KolmogorovTest(h4)
print("NumberPdf chi2test : ", chi2)
"""
#fig = plt.figure(figsize=(6, 9))
#spec = gridspec.GridSpec(ncols=1, nrows=4)
#fig = plt.figure(constrained_layout=True, figsize=(6, 10))
#heights = [1, 2, 1, 2]
#spec = fig.add_gridspec(ncols=1, nrows=4, height_ratios=heights)
#ax0 = fig.add_subplot(spec[1])
#ax1 = fig.add_subplot(spec[3])
#ax2 = fig.add_subplot(spec[0])
#ax3 = fig.add_subplot(spec[2])
fig = plt.figure(constrained_layout=True, figsize=(10, 6))
heights = [1, 2]
spec = fig.add_gridspec(ncols=2, nrows=2, height_ratios=heights)
ax0 = fig.add_subplot(spec[3])
ax2 = fig.add_subplot(spec[1])
ax1 = fig.add_subplot(spec[2])
ax3 = fig.add_subplot(spec[0])
cont1, edge1 = np.histogram(allBeta1, bins=100, range=(0, 8))
cont2, edge2 = np.histogram(allBeta2, bins=100, range=(0, 8))
bin1 = np.linspace(0, 8, 100)
ax0.hist(allBeta1,
bins=100,
histtype="step",
color="blue",
label="Livermore")
ax0.hist(allBeta2,
bins=100,
histtype="step",
color="red",
label="Penelope")
#ax0.bar(bin1, cont1/np.sum(cont1), width=8/100., color="white", edgecolor="blue", label="Livermore")
#ax0.bar(bin1, cont2/np.sum(cont2), width=8/100., color="white", edgecolor="red", label="Penelope")
ax0.set_xlabel("(b) Primary beta energy [MeV]", fontsize=15)
ax0.set_ylabel("counts per bin", fontsize=15)
ax0.legend(prop={"size": 14}, ncol=2)
ax0.semilogy()
ax0.set_ylim(0, 5000000)
ax0.tick_params(axis='both', which='major', labelsize=14)
ax0.set_xlim(-0.1, 8)
#rect1 = [0.35, 0.52, 0.6, 0.4]
#ax2 = add_subplot_axes(ax0, rect1)
ax2.errorbar(bin1, (cont2 - cont1) / cont2,
yerr=np.sqrt(cont1 / cont2**2 + cont2 * cont1**2 / cont2**4),
fmt="o",
color="black",
ms=3,
mfc="w")
#ax2.set_xlabel("Primary beta energy [MeV]", fontsize=13)
ax2.set_ylabel("Bias", fontsize=13)
ax2.tick_params(axis='both', which='major', labelsize=12)
ax2.grid(True)
ax2.set_ylim(-0.3, 0.3)
ax2.set_xlim(-0.1, 8)
ax1.hist(BetaNum1,
bins=50,
range=(0, 50),
histtype="step",
color="blue",
label="Livermore")
ax1.hist(BetaNum2,
bins=50,
range=(0, 50),
histtype="step",
color="red",
label="Penelope")
ax1.set_xlabel("(a) Primary beta multiplicity", fontsize=15)
ax1.set_ylabel("counts per bin", fontsize=15)
ax1.tick_params(axis='both', which='major', labelsize=14)
ax1.legend(loc="lower center", prop={"size": 14}, ncol=1)
ax1.semilogy()
ax1.set_ylim(0, 20000)
ax1.set_xlim(0, 50)
#rect2 = [0.21, 0.24, 0.5, 0.33]
#ax3 = add_subplot_axes(ax1, rect2)
cont1, edge1 = np.histogram(BetaNum1, bins=50, range=(0, 50))
cont2, edge2 = np.histogram(BetaNum2, bins=50, range=(0, 50))
bin1 = np.linspace(0, 50, 50)
ax3.errorbar(bin1, (cont2 - cont1) / cont2,
yerr=np.sqrt(cont1 / cont2**2 + cont2 * cont1**2 / cont2**4),
fmt="o",
color="black",
ms=3,
mfc="w")
#ax3.set_xlabel("Primary beta multiplicity", fontsize=13)
ax3.set_ylabel("Bias", fontsize=13)
ax3.tick_params(axis='both', which='major', labelsize=12)
ax3.grid(True)
ax3.set_ylim(-0.3, 0.3)
ax3.set_xlim(0, 50)
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=None,
hspace=None)
plt.tight_layout()
plt.savefig("compareModel.pdf")
plt.show()