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_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():
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 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 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 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 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 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 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 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"""
# 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
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 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)
# You can also fit the data fit to the data
fit_result = config.fit([data], [phsp])
err = config.get_params_error(fit_result, [data], [phsp])
# %%
# we can see that thre fit results consistant with inputs, the first one is fixed.
for var in input_params:
print(
f"in: {input_params[var]} => out: {fit_result.params[var]} +/- {err.get(var, 0.)}"
)
# %%
# We can use the amplitude to plot the fit results
amp = config.get_amplitude()
weight = amp(phsp)
partial_weight = amp.partial_weight(phsp)
# %%
# We can plot the data, Hist1D include some plot method base on matplotlib.
data_hist = Hist1D.histogram(data.get_mass("(C, D)"),
bins=60,
range=(0.25, 1.45))
mass_phsp = phsp.get_mass("(C, D)")
phsp_hist = Hist1D.histogram(mass_phsp,
weights=weight,
bins=60,
range=(0.25, 1.45))
def main():
Nbins = 72
config = ConfigLoader("config.yml")
config.set_params("final_params.json")
error_matrix = np.load("error_matrix.npy")
idx = config.get_data_index("mass", "R_BC")
datas = config.get_data("data")
phsps = config.get_data("phsp")
bgs = config.get_data("bg")
amp = config.get_amplitude()
var = amp.trainable_variables
get_data = lambda x: data_index(x, idx).numpy()
m_all = np.concatenate([get_data(i) for i in datas])
m_min = np.min(m_all) - 0.1
m_max = np.max(m_all) + 0.1
binning = np.linspace(m_min, m_max, Nbins + 1)
get_hist = lambda x, w: Hist1D.histogram(
get_data(x), bins=Nbins, range=(m_min, m_max), weights=w
)
data_hist = [get_hist(i, i.get("weight")) for i in datas]
bg_hist = [get_hist(i, np.abs(i.get("weight"))) for i in bgs]
phsp_hist = []
for dh, bh, phsp in zip(data_hist, bg_hist, phsps):
m_phsp = data_index(phsp, idx).numpy()
y_frac, grads, w_error2 = binning_gradient(
binning, amp, phsp, m_phsp, var
)
error2 = np.einsum("ij,jk,ik->i", grads, error_matrix, grads)
# error parameters and error from integration sample weights
yerr = np.sqrt(error2 + w_error2)
n_fit = dh.get_count() - bh.get_count()
phsp_hist.append(n_fit * Hist1D(binning, y_frac, yerr))
total_data = reduce(operator.add, data_hist)
ax = plt.subplot2grid((4, 1), (0, 0), rowspan=3)
total_data.draw(ax, label="data")
total_data.draw_error(ax)
total_bg = reduce(operator.add, bg_hist)
total_bg.draw_bar(ax, label="back ground", color="grey", alpha=0.5)
total_fit = reduce(operator.add, phsp_hist + bg_hist)
total_fit.draw(ax, label="fit")
total_fit.draw_error(ax)
ax.set_ylim((0, None))
ax.set_xlim((m_min, m_max))
ax.legend()
ax.set_ylabel(f"Events/ {(m_max-m_min)/Nbins:.3f} GeV")
ax2 = plt.subplot2grid((4, 1), (3, 0), rowspan=1)
(total_data - total_fit).draw_pull(ax2)
plt.setp(ax.get_xticklabels(), visible=False)
ax2.set_ylim((-5, 5))
ax2.set_xlim((m_min, m_max))
ax2.axhline(0, c="black", ls="-")
ax2.axhline(-3, c="r", ls="--")
ax2.axhline(3, c="r", ls="--")
ax2.set_xlabel("$M(BC)$/GeV", loc="right")
plt.savefig("fit_full_error.png")