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 fit(config="config.yml", init_params="init_params.json", method="BFGS"):
"""
simple fit script
"""
# load config.yml
config = ConfigLoader(config)
# set initial parameters if have
try:
config.set_params(init_params)
print("using {}".format(init_params))
except Exception as e:
if str(e) != "[Errno 2] No such file or directory: 'init_params.json'":
print(e)
print("\nusing RANDOM parameters", flush=True)
# print("\n########### initial parameters")
# json_print(config.get_params())
# fit
data, phsp, bg, inmc = config.get_all_data()
try:
fit_result = config.fit(batch=65000, method=method)
except KeyboardInterrupt:
config.save_params("break_params.json")
raise
except Exception as e:
print(e)
config.save_params("break_params.json")
raise
json_print(fit_result.params)
fit_result.save_as("final_params.json")
# calculate parameters error
fit_error = config.get_params_error(fit_result, batch=13000)
fit_result.set_error(fit_error)
fit_result.save_as("final_params.json")
pprint(fit_error)
print("\n########## fit results:")
for k, v in config.get_params().items():
print(k, error_print(v, fit_error.get(k, None)))
# plot partial wave distribution
config.plot_partial_wave(fit_result, plot_pull=True)
# calculate fit fractions
phsp_noeff = config.get_phsp_noeff()
fit_frac, err_frac = config.cal_fitfractions({}, phsp_noeff)
print("########## fit fractions")
fit_frac_string = ""
for i in fit_frac:
if isinstance(i, tuple):
name = "{}x{}".format(*i)
else:
name = i
fit_frac_string += "{} {}\n".format(
name, error_print(fit_frac[i], err_frac.get(i, None)))
print(fit_frac_string)
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
# 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)
# 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)