def main():
config = ConfigLoader("config.yml")
decay = config.get_amplitude().decay_group
data = config.get_data("phsp")
ret = []
for i in data:
cached_amp = build_angle_amp_matrix(decay, i)
ret.append(data_to_numpy(cached_amp))
idx = config.get_data_index("angle", "R_BC/B")
ang = data_index(data[0], idx)
np.savez("phsp.npz", ret)
for k, v in ret[0].items():
for i, amp in enumerate(v):
w = np.abs(amp)**2
w = np.sum(np.reshape(w, (amp.shape[0], -1)), axis=-1)
plt.hist(
np.cos(ang["beta"]),
weights=w,
bins=20,
histtype="step",
label="{}: {}".format(k, i),
)
plt.savefig("angle_costheta.png")
def test_load():
with write_temp_file(resonancs_str) as f:
cs = config_str.format(file_name=f)
print(cs)
with write_temp_file(cs) as g:
config = ConfigLoader(g)
config.get_amplitude()
def test_chain_phsp():
dic = yaml.full_load(config_text)
config = ConfigLoader(dic)
data = config.generate_toy(10)
data.mass_hist("(C, F, G, H)").draw()
import matplotlib.pyplot as plt
plt.savefig("chain_phsp.png")
def test_cp_decay():
with open(f"{this_dir}/config_toy.yml") as f:
config_data = yaml.full_load(f)
config_data["decay_chain"] = {"$all": {"is_cp": True}}
config = ConfigLoader(config_data)
amp = config.get_amplitude()
data = config.get_data("data")[0]
amp(data)
def test_constrains():
with write_temp_file(resonancs_str) as f:
cs = config_str.format(file_name=f)
print(cs)
with write_temp_file(cs) as g:
config = ConfigLoader(g)
amp = config.get_amplitude()
config.add_free_var_constraints(amp)
def single_fit(config_dict, data, phsp, bg):
config = ConfigLoader(config_dict)
print("\n########### initial parameters")
pprint(config.get_params())
print(config.full_decay)
fit_result = config.fit(data, phsp, bg=bg)
pprint(fit_result.params)
# fit_result.save_as("final_params.json")
return fit_result.min_nll, fit_result.ndf
def plot(params_file):
config = ConfigLoader("config.yml")
with open(params_file) as f:
params = yaml.safe_load(f)
params = params["value"]
config.plot_partial_wave(params,
plot_pull=True,
prefix="fig/",
save_pdf=True,
save_root=True)
def plot_all(
res="MI(1+)S",
config_file="config.yml",
params="final_params.json",
prefix="figure/",
):
"""plot all figure"""
config = ConfigLoader(config_file)
config.set_params(params)
particle = config.get_decay().get_particle(res)
mi, r, phi_i, r_e, phi_e = load_params(config_file, params, res)
x, y, x_e, y_e = trans_r2xy(r, phi_i, r_e, phi_e)
m = np.linspace(mi[0], mi[-1], 1000)
M_Kpm = 0.49368
M_Dpm = 1.86961
M_Dstar0 = 2.00685
M_Bpm = 5.27926
# x_new = interp1d(xi, x, "cubic")(m)
# y_new = interp1d(xi, y, "cubic")(m)
rm_new = particle.interp(m).numpy()
x_new, y_new = rm_new.real, rm_new.imag
pq = dalitz_weight(m * m, M_Bpm, M_Dstar0, M_Dpm, M_Kpm)
pq_i = dalitz_weight(mi * mi, M_Bpm, M_Dstar0, M_Dpm, M_Kpm)
phi = np.arctan2(y_new, x_new)
r2 = x_new * x_new + y_new * y_new
plot_phi(f"{prefix}phi.png", m, phi, mi, np.arctan2(y, x))
plot_x_y(
f"{prefix}r2.png",
m,
r2,
mi,
r * r,
"mass",
"$|R(m)|^2$",
ylim=(0, None),
)
plot_x_y(f"{prefix}x_y.png", x_new, y_new, x, y, "real R(m)", "imag R(m)")
plot_x_y_err(f"{prefix}x_y_err.png", x[1:-1], y[1:-1], x_e[1:-1],
y_e[1:-1])
plot_x_y(
f"{prefix}r2_pq.png",
m,
r2 * pq,
mi,
r * r * pq_i,
"mass",
"$|R(m)|^2 p \cdot q$",
ylim=(0, None),
)
plot3d_m_x_y(f"{prefix}m_r.gif", m, x_new, y_new)
def cal_fitfractions(params_file):
config = ConfigLoader("config.yml")
config.set_params(params_file)
params = config.get_params()
config.get_params_error(params)
mcdata = (
config.get_phsp_noeff()
) # use the file of PhaseSpace MC without efficiency indicated in config.yml
fit_frac, err_frac = fit_fractions(
config.get_amplitude(), mcdata, config.inv_he, params
)
print("########## fit fractions:")
fit_frac_string = ""
for i in fit_frac:
if isinstance(i, tuple):
name = "{}x{}".format(*i) # interference term
else:
name = i # fit fraction
fit_frac_string += "{} {}\n".format(
name, error_print(fit_frac[i], err_frac.get(i, None))
)
print(fit_frac_string)
print("########## fit fractions table:")
print_frac_table(
fit_frac_string
) # print the fit-fractions as a 2-D table. The codes below are just to implement the print function.
def main():
config = ConfigLoader("config.yml")
config.set_params("final_params.json")
amp = config.get_amplitude()
data = config.get_data("data_origin")[0]
phsp = config.get_data("phsp_plot")[0]
phsp_re = config.get_data("phsp_plot_re")[0]
print("data loaded")
amps = amp(phsp_re)
pw = amp.partial_weight(phsp_re)
re_weight = phsp_re["weight"]
re_size = config.resolution_size
amps = sum_resolution(amps, re_weight, re_size)
pw = [sum_resolution(i, re_weight, re_size) for i in pw]
m_idx = config.get_data_index("mass", "R_BC")
m_phsp = data_index(phsp, m_idx).numpy()
m_data = data_index(data, m_idx).numpy()
m_min, m_max = np.min(m_phsp), np.max(m_phsp)
scale = m_data.shape[0] / np.sum(amps)
get_hist = lambda m, w: Hist1D.histogram(
m, weights=w, range=(m_min, m_max), bins=100)
data_hist = get_hist(m_data, None)
phsp_hist = get_hist(m_phsp, scale * amps)
pw_hist = []
for i in pw:
pw_hist.append(get_hist(m_phsp, scale * i))
ax2 = plt.subplot2grid((4, 1), (3, 0), rowspan=1)
ax = plt.subplot2grid((4, 1), (0, 0), rowspan=3, sharex=ax2)
data_hist.draw_error(ax, label="data")
phsp_hist.draw(ax, label="fit")
for i, j in zip(pw_hist, config.get_decay()):
i.draw_kde(ax, label=str(j.inner[0]))
(data_hist - phsp_hist).draw_pull(ax2)
ax.set_ylim((1, None))
ax.legend()
ax.set_yscale("log")
ax.set_ylabel("Events/{:.1f} MeV".format((m_max - m_min) * 10))
ax2.set_xlabel("M( R_BC )")
ax2.set_ylabel("pull")
ax2.set_xlim((1.3, 1.7))
ax2.set_ylim((-5, 5))
plt.setp(ax.get_xticklabels(), visible=False)
plt.savefig("m_R_BC_fit.png")
def get_data(config_file="config.yml", init_params="init_params.json"):
config = ConfigLoader(config_file)
try:
config.set_params(init_params)
print("using {}".format(init_params))
except Exception as e:
print("using RANDOM parameters")
phsp = config.get_data("phsp")
for i in config.full_decay:
print(i)
for j in i:
print(j.get_ls_list())
print("\n########### initial parameters")
print(json.dumps(config.get_params(), indent=2))
params = config.get_params()
amp = config.get_amplitude()
pw = amp.partial_weight(phsp)
pw_if = amp.partial_weight_interference(phsp)
weight = amp(phsp)
print(weight)
return config, amp, phsp, weight, pw, pw_if
def main():
config = ConfigLoader("config.yml")
data = config.get_data("data")[0]
phsp = config.get_data("phsp")[0]
bg = config.get_data("bg")[0]
config.set_params("final_params.json")
mbc_idx = config.get_data_index("mass",
"R_BC") # ("particle", "(D, K)", "m")
mbd_idx = config.get_data_index("mass",
"R_BD") # ("particle", "(D0, K, pi)", "m")
data_idx = [mbc_idx, mbd_idx]
data_cut = np.array([data_index(data, idx) for idx in data_idx])
adapter = AdaptiveBound(data_cut**2, [[2, 2], [3, 3], [2, 2]])
bound = adapter.get_bounds()
cal_chi2_config(config,
adapter,
data,
phsp,
data_idx,
bg=bg,
data_cut=data_cut)
draw_dalitz(data_cut, bound)
def fit(final_params_file):
config = ConfigLoader(
"config.yml"
) # We use ConfigLoader to read the information in the configuration file
# config.set_params("gen_params.json") # If not set, we will use random initial parameters
fit_result = config.fit(method="BFGS")
errors = config.get_params_error(
fit_result) # calculate Hesse errors of the parameters
print("\n########## fit parameters:")
for key, value in config.get_params().items():
print(key, error_print(value, errors.get(key, None)))
fit_result.save_as(final_params_file) # save fit_result to a json file
config.plot_partial_wave(
fit_result
) # Plot distributions of variables indicated in the configuration file
fit_frac, err_frac = config.cal_fitfractions()
print("\n########## fit fractions:")
for i in fit_frac:
if not isinstance(i, tuple): # fit fraction
name = i
else:
name = "{}x{}".format(*i) # interference term
print(name + ": " + error_print(fit_frac[i], err_frac.get(i, None)))
def main():
sigma = 0.005
config = ConfigLoader("config.yml")
decay = config.get_decay()
m0 = decay.top.get_mass()
m1, m2, m3 = [i.get_mass() for i in decay.outs]
print("mass: ", m0, " -> ", m1, m2, m3)
phsp = PhaseSpaceGenerator(m0, [m1, m2, m3])
p1, p2, p3 = phsp.generate(100000)
angle = cal_angle_from_momentum({"B": p1, "C": p2, "D": p3}, decay)
amp = config.get_amplitude()
m_idx = config.get_data_index("mass", "R_BC")
m_BC = data_index(angle, m_idx)
R_BC = decay.get_particle("R_BC1")
m_R, g_R = R_BC.get_mass(), R_BC.get_width()
# import matplotlib.pyplot as plt
# x = np.linspace(1.3, 1.7, 1000)
# amp = R_BC.get_amp({"m": x}).numpy()
# plt.plot(x, np.abs(amp)**2)
# plt.show()
print("mass: ", m_R, "width: ", g_R)
amp_s2 = resolution_bw(m_BC, m_R, g_R, sigma, m1 + m2, m0 - m3)
print("|A|*R: ", amp_s2)
cut_data = simple_selection(angle, amp_s2)
ps = [data_index(cut_data, ("particle", i, "p")).numpy() for i in "BCD"]
np.savetxt("data/data_origin.dat",
np.transpose(ps, (1, 0, 2)).reshape((-1, 4)))
p1, p2, p3 = phsp.generate(100000)
np.savetxt("data/phsp.dat",
np.transpose([p1, p2, p3], (1, 0, 2)).reshape((-1, 4)))
p1, p2, p3 = phsp.generate(50000)
np.savetxt(
"data/phsp_plot.dat",
np.transpose([p1, p2, p3], (1, 0, 2)).reshape((-1, 4)),
)
def test_load():
with write_temp_file(resonancs_str) as f:
cs = config_str.format(file_name=f)
print(cs)
with write_temp_file(cs) as g:
config = ConfigLoader(g)
with open(g) as f:
data = yaml.full_load(f)
config2 = ConfigLoader(data)
config.get_amplitude()
config2.get_amplitude()
def main():
sym.init_printing()
config = ConfigLoader("config.yml")
decay_group = config.get_decay()
decay_chain = list(decay_group)[0]
print(decay_chain)
angle = get_angle_distrubution(decay_chain)
ret = []
for k, v in angle.items():
for k2, v2 in v.items():
f_theta = simplify(v2 * v2.conjugate())
print("ls: ", k)
print(" lambda:", k2)
print(" :", f_theta)
ret.append(f_theta)
# break
f_theta = ret[0]
plot_theta("costheta.png", f_theta, "theta1")
plot_theta("costheta2.png", f_theta, "theta2")
plot_theta("phi2.png", f_theta, "phi2")
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 main():
Nbins = 64
config = ConfigLoader("config.yml")
# config = MultiConfig(["config.yml"]).configs[0]
config.set_params("final_params.json")
name = "R_BC"
idx = config.get_data_index("mass", "R_BC")
# idx_costheta = (*config.get_data_index("angle", "DstD/D*"), "beta")
datas, phsps, bgs, _ = config.get_all_data()
amp = config.get_amplitude()
get_data = lambda x: data_index(x, idx).numpy()
# get_data = lambda x: np.cos(data_index(x, idx_costheta).numpy())
plot_mass(amp, datas, bgs, phsps, get_data, name, Nbins)
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 test_constrains(gen_toy):
config = ConfigLoader(f"{this_dir}/config_cfit.yml")
var_name = "A->R_CD.B_g_ls_1r"
config.config["constrains"]["init_params"] = {var_name: 1.0}
@config.register_extra_constrains("init_params")
def float_var(amp, params=None):
amp.set_params(params)
config.register_extra_constrains("init_params2", float_var)
amp = config.get_amplitude()
assert amp.get_params()[var_name] == 1.0
def cal_errors(params_file):
config = ConfigLoader("config.yml")
config.set_params(params_file)
fcn = config.get_fcn()
fcn.model.Amp.vm.rp2xy_all(
) # we can use this to transform all complex parameters to xy-coordinates, since the Hesse errors of xy are more statistically reliable
params = config.get_params()
errors, config.inv_he = cal_hesse_error(
fcn, params, check_posi_def=True, save_npy=True
) # obtain the Hesse errors and the error matrix (inverse Hessian)
print("\n########## fit parameters in XY-coordinates:")
errors = dict(zip(fcn.model.Amp.vm.trainable_vars, errors))
for key, value in config.get_params().items():
print(key, error_print(value, errors.get(key, None)))
print("\n########## correlation matrix:")
print("Matrix index:\n", fcn.model.Amp.vm.trainable_vars)
print("Correlation Coefficients:\n", corr_coef_matrix(config.inv_he)
) # obtain the correlation matrix using the inverse Hessian
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():
import argparse
parser = argparse.ArgumentParser(description="calculate fit fractions")
parser.add_argument("-c", "--config", default="config.yml")
parser.add_argument("-i", "--init_params", default="final_params.json")
parser.add_argument("-e", "--error_matrix", default="error_matrix.npy")
results = parser.parse_args()
# load model and parameters and error matrix
config = ConfigLoader(results.config)
config.set_params(results.init_params)
err_matrix = np.load(results.error_matrix)
amp = config.get_amplitude()
phsp = config.get_phsp_noeff() # get_data("phsp")[0]
cal_frac(amp, phsp, err_matrix)
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 load_config(config_file="config.yml", total_same=False):
config_files = config_file.split(",")
if len(config_files) == 1:
return ConfigLoader(config_files[0])
return MultiConfig(config_files, total_same=total_same)
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)))
def test_cfit(gen_toy):
config = ConfigLoader(f"{this_dir}/config_cfit.yml")
config.set_params(f"{this_dir}/gen_params.json")
fcn = config.get_fcn()
fcn({})
fcn.nll_grad({})
def toy_config(gen_toy):
config = ConfigLoader(f"{this_dir}/config_toy.yml")
config.set_params(f"{this_dir}/exp_params.json")
return config
R2: { mass: 0.824, width: 0.05, J: 0, P: +1}
R3: { mass: 0.824, width: 0.05, J: 0, P: +1}
"""
# %%
# The config file can be loaded by `yaml` library.
#
import matplotlib.pyplot as plt
import yaml
from tf_pwa.config_loader import ConfigLoader
from tf_pwa.histogram import Hist1D
config = ConfigLoader(yaml.full_load(config_str))
# %%
# We set parameters to a blance value. And we can generate some toy data and calclute the weights
#
input_params = {
"A->R1_a.BR1_a->C.D_total_0r": 6.0,
"A->R1_b.BR1_b->C.D_total_0r": 1.0,
"A->R2.CR2->B.D_total_0r": 2.0,
"A->R3.DR3->B.C_total_0r": 1.0,
}
config.set_params(input_params)
data = config.generate_toy(1000)
phsp = config.generate_phsp(10000)
def main():
config = ConfigLoader("config.yml")
for i, dec in enumerate(config.get_decay()):
draw_decay_struct(dec,
filename="figure/fig_{}".format(i),
format="png")