def generateM_dd(p_signal, n_files=100, n_entries=10000):
mMs = 1.0
mMs_resolution = 0.01
mMlow = 0.5
mMhigh = 1.5
masses = array.array('d', [0.1, 0.1])
features = ['p1x', 'p1y', 'p1z', 'E1', 'p2x', 'p2y', 'p2z', 'E2']
for n_file in range(n_files + 1):
if n_file % 10 == 0: print("n_file : ", n_file)
mass, p1x, p1y, p1z, E1, p2x, p2y, p2z, E2 = [], [], [], [], [], [], [], [], []
for n in range(n_entries):
choice = random.random()
if choice < p_signal:
mM = numpy.random.normal(loc=mMs, scale=mMs_resolution)
else:
mM = numpy.random.uniform(mMlow, mMhigh)
MotherMom = TLorentzVector(0.0, 0.0, 0.0, mM)
event = TGenPhaseSpace()
event.SetDecay(MotherMom, 2, masses)
while True:
weight = event.Generate()
weightmax = event.GetWtMax()
assert (0. < weight / weightmax) & (weight / weightmax < 1.)
if random.random() < weight / (weightmax): break
pd1 = event.GetDecay(0)
pd2 = event.GetDecay(1)
p1x.append(pd1.Px())
p1y.append(pd1.Py())
p1z.append(pd1.Pz())
E1.append(pd1.E())
p2x.append(pd2.Px())
p2y.append(pd2.Py())
p2z.append(pd2.Pz())
E2.append(pd2.E())
mass.append(mM)
df = pd.DataFrame(np.array([p1x, p1y, p1z, E1, p2x, p2y, p2z,
E2]).transpose(),
columns=features)
df2 = pd.DataFrame(np.array([mass]).transpose(), columns=['mM'])
#print('df : ', df)
if n_file == n_files: n_file = "optimisation_0"
#df.to_csv("../data/M-dd/M-dd_{}_res_{}_{}.txt".format(p_signal, mMs_resolution, n_file), sep="\t", header = None, index=False)
df2.to_csv("../data/M-dd/M-dd_{}_res_{}_1Dmass_{}.txt".format(
p_signal, mMs_resolution, n_file),
sep="\t",
header=None,
index=False)
Theta = ThetaMin + rndm() * (ThetaMax - ThetaMin)
vm = TLorentzVector(P * math.sin(Theta) * math.cos(Phi),
P * math.sin(Theta) * math.sin(Phi),
P * math.cos(Theta),
math.sqrt(P * P + MotherMass * MotherMass))
r = abs(gauss(0., VR))
f = rndm() * 2 * math.pi
vertex = (Vx + r * math.cos(f), Vy + r * math.sin(f),
VzMin + rndm() * (VzMax - VzMin))
parts.append(storeParticle(PDGmother, vm, vertex, 0))
# prepare decay
Decay = TGenPhaseSpace()
Decay.SetDecay(vm, len(DaughterMass), array('d', DaughterMass))
test = True
Decay.Generate()
for j, p in enumerate(PDGs):
vp = Decay.GetDecay(j)
parts.append(storeParticle(p, vp, vertex, 1))
if math.isnan(vp.Px()):
test = False
if test == False:
continue
# check photon
pi = pdic[22]
if pi.Theta() > ThetaMax:
return False
pi = pdic[1022]
if pi.Theta() > ThetaMax:
return False
return test
# generate
with open(fname, "w") as fout:
# generate event
Event = TGenPhaseSpace()
Event.SetDecay(CME, len(FSMasses), array('d', FSMasses))
# loop on events
for i in range(1, NEVENTS + 1):
# generate event
counter = 1
w = Event.Generate()
# generate vertex
vertex = (VxMin + rndm() * (VxMax - VxMin),
VyMin + rndm() * (VyMax - VyMin),
VzMin + rndm() * (VzMax - VzMin))
# write header
def decay_Ds(myd):
mygen = TGenPhaseSpace()
mygen.SetDecay(myd, 2, array.array("d", [mmuon, mmuon]))
mygen.Generate()
return [mygen.GetDecay(i) for i in range(2)]
proton = pdic[2212]
if proton.Theta() < PThetaMin:
return False
if proton.Theta() > PThetaMax:
return False
return test
h2p = TH2F("hp", "", 180, -180, 180, 180, -180, 180)
h2t = TH2F("ht", "", 180, 0, 180, 90, 0, 45)
# generate
with open(fname, "w") as fout:
# generate event
Event = TGenPhaseSpace()
Event.SetDecay(CME, len(FSMasses), array('d', FSMasses))
# loop on events
i = 0
while i < NEVENTS:
# generate event
counter = 1
w = Event.Generate()
# generate vertex
vertex = (VxMin + rndm() * (VxMax - VxMin),
VyMin + rndm() * (VyMax - VyMin),
VzMin + rndm() * (VzMax - VzMin))