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 preSampling_inGPU(secBeta, secAntiBeta, indexarr, npearr, spearr, muarr, sigmaarr):
# shared memory usage :
sNPE = cuda.shared.array(shape=(1, 845), dtype=float32)
sSPE = cuda.shared.array(shape=(1, 845), dtype=float32)
x, y = cuda.grid(2)
# threading positioning
tx = cuda.threadIdx.x
ty = cuda.blockIdx.x
bw = cuda.blockDim.x
pos = tx + ty * bw
if pos < muarr.size:
ii = indexarr[pos]
tmpmu, tmpsigma = 0, 0
for j in secBeta[ii, :]:
if j == 0:
break
tmpmu += eloader.getNPE(j)
tmpsigma += eloader.getSPE(j)**2
for j in secAntiBeta[ii, :]:
if j == 0:
break
tmpmu += ( eloader.getNPE(j) + 660.8*2 )
tmpsigma += ( eloader.getSPE(j)**2 + 2*27.07**2 )
tmpsigma = np.sqrt(tmpsigma)
muarr[pos] = tmpmu
sigmaarr[pos] = tmpsigma
cuda.syncthreads()
def preSampling(secBeta, secAntiBeta):
st = time.time()
Nevents = 5000
mu_arr, sigma_arr = [], []
sample_id = np.random.randint(0, 5000, size=Nevents)
for ii in sample_id:
tmpmu, tmpsigma = 0, 0
for j in secBeta[ii, :]:
if j == 0:
break
tmpmu += eloader.getNPE(j)
tmpsigma += eloader.getSPE(j)**2
for j in secAntiBeta[ii, :]:
if j == 0:
break
tmpmu += ( eloader.getNPE(j) + 660.8*2 )
tmpsigma += ( eloader.getSPE(j)**2 + 2*27.07**2 )
tmpsigma = np.sqrt(tmpsigma)
mu_arr.append(tmpmu)
sigma_arr.append(tmpsigma)
et = time.time()
mu_arr = np.array(mu_arr)
sigma_arr = np.array(sigma_arr)
print("Time consumed during pre-sampling: %s s" %(et-st))
return mu_arr, sigma_arr
def calcSingleEvent(self):
st = time.time()
if self.loadPrmBetaFlag == False:
self.loadPrmBeta()
mu_arr, sigma_arr = [], []
smu_arr, ssigma_arr = [], []
cmu_arr, csigma_arr = [], []
self.nSamples = len(self.prmBetaArr)
for i in range(len(self.prmBetaArr)):
tmpnpe, tmpspe = 0, 0
tmpcnpe, tmpsnpe = 0, 0
tmpcspe, tmpsspe = 0, 0
for j in self.prmBetaArr[i]:
if j == 0:
break
if self.mode == "Truth":
tmpnpe += (eloader.getNPE(j))
tmpspe += (eloader.getSPE(j)**2)
if self.mode == "Fit":
tmpnpe += (self.getFitNPE(j))
tmpspe += (self.getFitSPE(j)**2)
tmpcnpe += self.getFitCNPE(j)
tmpsnpe += self.getFitSNPE(j)
for j in self.prmAntiBetaArr[i]:
if j == 0:
break
if self.mode == "Truth":
tmpnpe += (eloader.getNPE(j) + 2 * 660.8)
tmpspe += (eloader.getSPE(j)**2 + 27.07**2 * 2)
if self.mode == "Fit":
tmpnpe += (self.getFitNPE(j) + 2 * 660.8)
tmpspe += (self.getFitSPE(j)**2 + 27.07**2 * 2)
tmpcnpe += (self.getFitCNPE(j) + 2.49)
tmpsnpe += (self.getFitSNPE(j) + 2 * 660.8 - 2.49)
mu_arr.append(tmpnpe)
sigma_arr.append(np.sqrt(tmpspe))
cmu_arr.append(tmpcnpe)
csigma_arr.append(np.sqrt(tmpcspe))
smu_arr.append(tmpsnpe)
ssigma_arr.append(np.sqrt(tmpsspe))
mu_arr = np.array(mu_arr)
sigma_arr = np.array(sigma_arr)
cmu_arr = np.array(cmu_arr)
csigma_arr = np.array(csigma_arr)
smu_arr = np.array(smu_arr)
ssigma_arr = np.array(ssigma_arr)
et = time.time()
#print("Primary beta dist calculation time : %.3f s" %(et-st))
return mu_arr, sigma_arr, smu_arr, ssigma_arr, cmu_arr, csigma_arr
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)
def GammaPePred(secBeta, secAntiBeta):
# two-times sampling alg here
sc = 1.00
calcpe = []
ctime = []
sample_id = np.random.randint(0, 5000, size=5000)
for i in sample_id:
st = time.time()
tmppe = 0
for j in secBeta[i]:
if j == 0:
break
tmppe += random.gauss(eloader.getNPE(j), eloader.getSPE(j)*sc)
for j in secAntiBeta[i]:
if j == 0:
break
tmppe += random.gauss(eloader.getNPE(j), eloader.getSPE(j)*sc) + 2*random.gauss(660.8, 27.07*sc)
calcpe.append(tmppe)
et = time.time()
ctime.append(et-st)
return calcpe, ctime
def main():
totpe, edep = [], []
for i in range(1000, 1300, 1):
filename = "/junofs/users/miaoyu/energy_model/production/J19v1r0-Pre4/B12/user-" + str(
i) + ".root"
print(filename)
tmp_totpe, tmp_edep = readSim(filename)
totpe.extend(tmp_totpe)
for i in range(2000, 2100, 1):
filename = "/junofs/users/miaoyu/energy_model/production/J19v1r0-Pre4/B12/user-" + str(
i) + ".root"
print(filename)
tmp_totpe, tmp_edep = readSim(filename)
edep.extend(tmp_edep)
pe_pred, pe_pred1 = [], []
for i in edep:
sample = eloader.getNPE(i)
sample1 = 1.126 * eloader.getSctNPE(i) - 0.812 * eloader.getSPE(i)
#sample = random.gauss(eloader.getNPE(i), eloader.getSPE(i))
#sample1 = random.gauss((1.126* eloader.getSctNPE(i) - 0.812*eloader.getCerNPE(i)), eloader.getSPE(i))
pe_pred.append(sample)
pe_pred1.append(sample1)
plt.hist(totpe,
bins=300,
range=(0, 20000),
density=True,
histtype="step",
label="simulation")
plt.hist(pe_pred,
bins=300,
range=(0, 20000),
density=True,
histtype="step",
label="kA=1, kC=1 (nominal)")
plt.hist(pe_pred1,
bins=300,
range=(0, 20000),
density=True,
histtype="step",
label="kA=1.126, kC=-0.812")
plt.legend()
plt.xlabel("# P.E.")
plt.tight_layout()
#plt.savefig("B12_totpe.pdf")
plt.show()
#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)
resCovFit = np.array(resCovFit)
resCerConstFit = np.array(resCerConstFit)
resCovConstFit = np.array(resCovConstFit)
# Load Truth Points
NPE_truth, SPE_truth = [], []
for i in Edep:
NPE_truth.append(el.getNPE(i))
SPE_truth.append(el.getSPE(i))
NPE_truth = np.array(NPE_truth)
SPE_truth = np.array(SPE_truth)
fig, ax = plt.subplots()
#ax.plot(Evis, resFit**2, "-", lw=2, color="crimson", label=r"$\sigma_E^2$")
#ax.plot(Evis, resConstFit**2, "--", lw=2, color="crimson", label=r"$\sigma_{const}^2$")
ax.plot(Evis,
resSctFit**2,
"-",
lw=2,
color="dimgray",
label=r"$\sigma_{sct}^2$")
ax.plot(Evis,
from matplotlib import gridspec
A, kB, kC, p1, p2 = 1408.45, 6.29e-3, 0.991, 0.979, 6.13e-5
if __name__ == "__main__" :
globalES = 3134.078/2.223
Edep = np.arange(0.50, 8.10, 0.10)
Edep1 = np.arange(0.50, 8.10, 0.50)
# load simulation
npeSim = []
speSim = []
for i in Edep:
npeSim.append(el.getNPE(i))
speSim.append(el.getSPE(i))
npeSim = np.array(npeSim)
speSim = np.array(speSim)
nonlSim = npeSim / Edep / globalES
resSim = speSim/npeSim
# fitting results
npeCalc = []
npeCalc1 = []
speCalc = []
for i in Edep:
sctpe = el.getQPE(i, kB, A)
cerpe = kC * el.getCerNPE(i)
npeCalc.append((sctpe+cerpe))
speCalc.append(np.sqrt(p1*(sctpe+cerpe) + p2*(sctpe+cerpe)**2))
for i in Edep1: