import ROOT

# Observable
x = ROOT.RooRealVar("x", "x", 0, 20)
x.setBins(40)

# Create two underlying shapes for H0 and H1
gauss_H0 = ROOT.RooGaussian("gauss_H0", "gauss_H0", x, ROOT.RooFit.RooConst(10), ROOT.RooFit.RooConst(2))
gauss_H1 = ROOT.RooGaussian("gauss_H1", "gauss_H1", x, ROOT.RooFit.RooConst(12), ROOT.RooFit.RooConst(2))

# Generate toy templates
data_H0 = gauss_H0.generate(ROOT.RooArgSet(x), 10000)
data_H1 = gauss_H1.generate(ROOT.RooArgSet(x), 10000)

# Binned templates (same binning!)
hist_H0 = data_H0.binnedClone()
hist_H1 = data_H1.binnedClone()

# Create RooHistPdfs
pdf_H0 = ROOT.RooHistPdf("pdf_H0", "pdf_H0", ROOT.RooArgSet(x), hist_H0)
pdf_H1 = ROOT.RooHistPdf("pdf_H1", "pdf_H1", ROOT.RooArgSet(x), hist_H1)

# Create pseudo-data from H1 (to simulate a test)
pseudo_data = pdf_H1.generateBinned(ROOT.RooArgSet(x), 1000)

# Dummy parameter (needed by ModelConfig)
dummy = ROOT.RooRealVar("dummy", "dummy", 0.0, -1.0, 1.0)
poi = ROOT.RooArgSet(dummy)

# ModelConfig for H0
mc_H0 = ROOT.RooStats.ModelConfig("H0")
mc_H0.SetPdf(pdf_H0)
mc_H0.SetParametersOfInterest(poi)

# ModelConfig for H1
mc_H1 = ROOT.RooStats.ModelConfig("H1")
mc_H1.SetPdf(pdf_H1)
mc_H1.SetParametersOfInterest(poi)

# Run FrequentistCalculator
fc = ROOT.RooStats.FrequentistCalculator(pseudo_data, mc_H0, mc_H1)
fc.SetToys(500, 500)

result = fc.GetHypoTest()
result.Print()

print("\n--- Results ---")
print(f"p-value for H0: {result.NullPValue():.4f}")
print(f"Significance Z: {result.Significance():.2f}σ")