model = get_model_from_ROOT_file('../files/test_unfolded.root', 'Higgs_125*')
# first, it is a good idea to generate a summary report to make sure everything has worked
# as expected. The summary will generate quite some information which should it make easy to spot
# errors like typos in the name of uncertainties, missing shape uncertaintie, etc.
model_summary(model)
limit_type = 'expected'
# 2. apply the methods
# 2.a. Bayesian limits
# Calculate expected and observed Bayesian limits. For faster run time of this example,
# only make a few mass points. (Omitting the 'signal_procsses' parameter completely would
# process all signals defined as signal processes before; see Section "Common Parameters"
# on the theta auto intro doxygen page for details)
plot_exp, plot_obs = bayesian_limits(model, what=limit_type, signal_processes = [['Higgs_125']])
# plot_exp and plot_obs are instances of plotutil.plotdata. they contain x/y values and
# bands. You can do many things with these objects such as inspect the x/y/ban
# data, pass then to plotutil.plot routine to make pdf plots, ...
# Here, we will just create text files of the plot data. This is useful if you want
# to apply your own plotting routines or present the result in a text Table.
plot_exp.write_txt('bayesian_limits_expected.txt')
if not limit_type == 'expected':
plot_obs.write_txt('bayesian_limits_observed.txt')
# 2.b. CLs limits
# calculate cls limit plots. The interface is very similar to bayesian_limits. However, there are a few
# more options such as the definition of the test statistic which is usually a likelihood ratio but can differ in
# which parameters are minimized and which constraints / ranges are applied during minimization.
# Here, we stay with the default which fixes beta_signal=0
from utils.theta_auto import config
model = get_model_from_ROOT_file('../files/histograms.root', 'zp*')
# first, it is a good idea to generate a summary report to make sure everything has worked
# as expected. The summary will generate quite some information which should it make easy to spot
# errors like typos in the name of uncertainties, missing shape uncertaintie, etc.
model_summary(model)
# 2. apply the methods
# 2.a. Bayesian limits
# Calculate expected and observed Bayesian limits. For faster run time of this example,
# only make a few mass points. (Omitting the 'signal_procsses' parameter completely would
# process all signals defined as signal processes before; see Section "Common Parameters"
# on the theta auto intro doxygen page for details)
plot_exp, plot_obs = bayesian_limits(model, signal_processes = [['zp1000'], ['zp2000'], ['zp3000']])
# plot_exp and plot_obs are instances of plotutil.plotdata. they contain x/y values and
# bands. You can do many things with these objects such as inspect the x/y/ban
# data, pass then to plotutil.plot routine to make pdf plots, ...
# Here, we will just create text files of the plot data. This is useful if you want
# to apply your own plotting routines or present the result in a text Table.
plot_exp.write_txt('bayesian_limits_expected.txt')
plot_obs.write_txt('bayesian_limits_observed.txt')
# 2.b. CLs limits
# calculate cls limit plots. The interface is very similar to bayesian_limits. However, there are a few
# more options such as the definition of the test statistic which is usually a likelihood ratio but can differ in
# which parameters are minimized and which constraints / ranges are applied during minimization.
# Here, we stay with the default which fixes beta_signal=0
# for the background only hypothesis and lets it float freely for the signal+background hypothesis.