def main():
sigma = 0.005
r_name = "R_BC"
config = ConfigLoader("config.yml")
sample_N = config.resolution_size
decays = config.get_decay(False)
decay_chain = decays.get_decay_chain(r_name)
data = config.get_data("data_origin")[0]
phsp = config.get_data("phsp_plot")[0]
dat_order = config.get_dat_order()
generate = lambda x: gauss_random(x, decay_chain, r_name, sigma, sample_N,
dat_order)
pi = generate(data)
np.savetxt("data/data.dat", pi)
np.savetxt("data/data_weight.dat",
np.ones((pi.shape[0] // len(dat_order), )))
pi = generate(phsp)
np.save("data/phsp_re.npy", pi)
np.savetxt("data/phsp_re_weight.dat",
np.ones((pi.shape[0] // len(dat_order), )))
def generate_toy_from_phspMC(Ndata, mc_file, data_file):
"""Generate toy using PhaseSpace MC from mc_file"""
config = ConfigLoader(f"{this_dir}/config_toy.yml")
config.set_params(f"{this_dir}/gen_params.json")
amp = config.get_amplitude()
data = gen_data(
amp,
Ndata=Ndata,
mcfile=mc_file,
genfile=data_file,
particles=config.get_dat_order(),
)
return data
def main():
sigma = 0.005
sigma_delta = 5
r_name = "R_BC"
config = ConfigLoader("config.yml")
sample_N = config.resolution_size
decays = config.get_decay(False)
decay_chain = decays.get_decay_chain(r_name)
data = config.get_data("data_origin")[0]
pi, total_weights = gauss_sample(data, decay_chain, "R_BC", sigma,
sample_N, config.get_dat_order())
np.savetxt("data/data.dat", pi.reshape((-1, 4)))
np.savetxt("data/data_weight.dat", np.reshape(total_weights, (-1, )))
data = config.get_data("phsp_plot")[0]
pi, total_weights = gauss_sample(data, decay_chain, "R_BC", sigma,
sample_N, config.get_dat_order())
np.save("data/phsp_re.npy", pi.reshape((-1, 4)))
np.savetxt("data/phsp_re_weight.dat", np.reshape(total_weights, (-1, )))
def generate_toy_from_phspMC(Ndata, mc_file, data_file):
"""Generate toy using PhaseSpace MC from mc_file"""
# We use ConfigLoader to read the information in the configuration file
config = ConfigLoader("config.yml")
# Set the parameters in the amplitude model
config.set_params("gen_params.json")
amp = config.get_amplitude()
# data is saved in data_file
data = gen_data(
amp,
Ndata=Ndata,
mcfile=mc_file, # input phsase space file
genfile=data_file, # saved toy data file
# use the order in config, the default is ascii order.
particles=config.get_dat_order(),
)
return data
def main():
sigma = 0.005
r_name = "DstD"
config = ConfigLoader("config_data.yml")
decays = config.get_decay(False)
decay_chain = decays.get_decay_chain(r_name)
data = config.get_data("data")[0]
angle = cal_helicity_angle(data["particle"],
decay_chain.standard_topology())
decay_chain.standard_topology()
tp_map = decay_chain.topology_map()
r_particle = tp_map[get_particle(r_name)]
mass = {}
for i in data["particle"]:
mi = data["particle"][i]["m"]
if i == r_particle:
mi = mi + tf.random.normal(mi.shape, 0, sigma, dtype=mi.dtype)
mass[i] = mi
mask = True
p4_all = {}
for i in decay_chain:
phi = angle[tp_map[i]][tp_map[i.outs[0]]]["ang"]["alpha"]
theta = angle[tp_map[i]][tp_map[i.outs[0]]]["ang"]["beta"]
m0 = mass[tp_map[i.core]]
m1 = mass[tp_map[i.outs[0]]]
m2 = mass[tp_map[i.outs[1]]]
mask = mask & (m0 >= m1 + m2)
p_square = get_relative_p2(m0, m1, m2)
p = tf.sqrt(tf.where(p_square > 0, p_square, 0))
pz = p * tf.cos(theta)
px = p * tf.sin(theta) * tf.cos(phi)
py = p * tf.sin(theta) * tf.sin(phi)
E1 = tf.sqrt(m1 * m1 + p * p)
E2 = tf.sqrt(m2 * m2 + p * p)
p1 = tf.stack([E1, px, py, pz], axis=-1)
p2 = tf.stack([E2, -px, -py, -pz], axis=-1)
p4_all[i.outs[0]] = p1
p4_all[i.outs[1]] = p2
core_boost = {}
for i in decay_chain:
if i.core != decay_chain.top:
core_boost[i.outs[0]] = i.core
core_boost[i.outs[1]] = i.core
ret = {}
for i in decay_chain.outs:
tmp = i
ret[i] = p4_all[i]
while tmp in core_boost:
tmp = core_boost[tmp]
# print(i, tmp)
ret[i] = lv.rest_vector(lv.neg(p4_all[tmp]), ret[i])
ret2 = {}
mask = tf.expand_dims(mask, -1)
for i in ret:
ret2[i] = tf.where(mask, ret[i], data["particle"][tp_map[i]]["p"])
# print(ret)
# print({i: data["particle"][tp_map[i]]["p"] for i in decay_chain.outs})
pi = np.stack([ret2[i] for i in config.get_dat_order()], axis=1)
np.savetxt("data_gauss.dat", pi.reshape((-1, 4)))