import ROOT

# Silence info output for this tutorial
ROOT.RooMsgService.instance().getStream(1).removeTopic(ROOT.RooFit.Minimization)
ROOT.RooMsgService.instance().getStream(1).removeTopic(ROOT.RooFit.Fitting)

# Setting up the model and creating toy dataset
# ---------------------------------------------

# l'(x | mu, sigma) = l(x | mu, sigma) * Gauss(mu_obs | mu, 0.2)

# event observables
x = ROOT.RooRealVar("x", "x", -10, 10)

# parameters
mu = ROOT.RooRealVar("mu", "mu", 0.0, -10, 10)
sigma = ROOT.RooRealVar("sigma", "sigma", 1.0, 0.1, 2.0)

# Gaussian model for event observables
gauss = ROOT.RooGaussian("gauss", "gauss", x, mu, sigma)

# global observables (which are not parameters so they are constant)
mu_obs = ROOT.RooRealVar("mu_obs", "mu_obs", 1.0, -10, 10)
mu_obs.setConstant()
# note: alternatively, one can create a constant with default limits using `RooRealVar("mu_obs", "mu_obs", 1.0)`

# constraint pdf
constraint = ROOT.RooGaussian("constraint", "constraint", mu_obs, mu, 0.1)

# full pdf including constraint pdf
model = ROOT.RooProdPdf("model", "model", [gauss, constraint])

# Generating toy data with randomized global observables
# ------------------------------------------------------

# For most toy-based statistical procedures, it is necessary to also
# randomize the global observable when generating toy datasets.
#
# To that end, let's generate a single event from the model and take the
# global observable value (the same is done in the RooStats:ToyMCSampler
# class):

dataGlob = model.generate({mu_obs}, 1)

# Next, we temporarily set the value of `mu_obs` to the randomized value for
# generating our toy dataset:
mu_obs_orig_val = mu_obs.getVal()

ROOT.RooArgSet(mu_obs).assign(dataGlob.get(0))

# Actually generate the toy dataset. We don't generate too many events,
# otherwise, the constraint will not have much weight in the fit and the result
# looks like it's unaffected by it.
data = model.generate({x}, 50)

# When fitting the toy dataset, it is important to set the global
# observables in the fit to the values that were used to generate the toy
# dataset. To facilitate the bookkeeping of global observable values, you
# can attach a snapshot with the current global observable values to the
# dataset like this (new feature introduced in ROOT 6.26):

data.setGlobalObservables({mu_obs})

# reset original mu_obs value
mu_obs.setVal(mu_obs_orig_val)

# Fitting a model with global observables
# ---------------------------------------

# Create snapshot of original parameters to reset parameters after fitting
modelParameters = model.getParameters(data.get())
origParameters = modelParameters.snapshot()

# When you fit a model that includes global observables, you need to
# specify them in the call to RooAbsPdf::fitTo with the
# RooFit::GlobalObservables command argument. By default, the global
# observable values attached to the dataset will be prioritized over the
# values in the model, so the following fit correctly uses the randomized
# global observable values from the toy dataset:
print("1. model.fitTo(*data, GlobalObservables(mu_obs))")
print("------------------------------------------------")
model.fitTo(data, GlobalObservables=mu_obs, PrintLevel=-1, Save=True).Print()
modelParameters.assign(origParameters)

# In our example, the set of global observables is attached to the toy
# dataset. In this case, you can actually drop the GlobalObservables()
# command argument, because the global observables are automatically
# figured out from the data set (this fit result should be identical to the
# previous one).
print("2. model.fitTo(*data)")
print("---------------------")
model.fitTo(data, PrintLevel=-1, Save=True).Print()
modelParameters.assign(origParameters)

# If you want to explicitly ignore the global observables in the dataset,
# you can do that by specifying GlobalObservablesSource("model"). Keep in
# mind that now it's also again your responsibility to define the set of
# global observables.
print('3. model.fitTo(*data, GlobalObservables(mu_obs), GlobalObservablesSource("model"))')
print("------------------------------------------------")
model.fitTo(data, GlobalObservables=mu_obs, GlobalObservablesSource="model", PrintLevel=-1, Save=True).Print()
modelParameters.assign(origParameters)