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style: Update validation scripts to be more Pythonic (#1565)
* Ensure `if __name__ == "__main__"` is used * Apply isort and black to validation scripts * Use f-strings * Use list comprehensions over loops * Use snake_case
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,37 +1,37 @@ | ||
| import ROOT | ||
|
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| import json | ||
| import sys | ||
|
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| source_data = json.load(open(sys.argv[1])) | ||
| root_file = sys.argv[2] | ||
| import ROOT | ||
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| binning = source_data['binning'] | ||
| bindata = source_data['bindata'] | ||
| if __name__ == "__main__": | ||
| source_data = json.load(open(sys.argv[1])) | ||
| root_file = sys.argv[2] | ||
|
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| binning = source_data["binning"] | ||
| bin_data = source_data["bin_data"] | ||
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| f = ROOT.TFile(root_file, 'RECREATE') | ||
| data = ROOT.TH1F('data', 'data', *binning) | ||
| for i, v in enumerate(bindata['data']): | ||
| data.SetBinContent(i + 1, v) | ||
| data.Sumw2() | ||
| out_file = ROOT.TFile(root_file, "RECREATE") | ||
| data = ROOT.TH1F("data", "data", *binning) | ||
| for idx, value in enumerate(bin_data["data"]): | ||
| data.SetBinContent(idx + 1, value) | ||
| data.Sumw2() | ||
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| bkg = ROOT.TH1F('bkg', 'bkg', *binning) | ||
| for i, v in enumerate(bindata['bkg']): | ||
| bkg.SetBinContent(i + 1, v) | ||
| bkg.Sumw2() | ||
| bkg = ROOT.TH1F("bkg", "bkg", *binning) | ||
| for idx, value in enumerate(bin_data["bkg"]): | ||
| bkg.SetBinContent(idx + 1, value) | ||
| bkg.Sumw2() | ||
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| if "bkgerr" in bin_data: | ||
| bkgerr = ROOT.TH1F("bkgerr", "bkgerr", *binning) | ||
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| if 'bkgerr' in bindata: | ||
| bkgerr = ROOT.TH1F('bkgerr', 'bkgerr', *binning) | ||
| # shapesys must be as multiplicative factor | ||
| for idx, value in enumerate(bin_data["bkgerr"]): | ||
| bkgerr.SetBinContent(idx + 1, value / bkg.GetBinContent(idx + 1)) | ||
| bkgerr.Sumw2() | ||
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| # shapesys must be as multiplicative factor | ||
| for i, v in enumerate(bindata['bkgerr']): | ||
| bkgerr.SetBinContent(i + 1, v / bkg.GetBinContent(i + 1)) | ||
| bkgerr.Sumw2() | ||
| sig = ROOT.TH1F("sig", "sig", *binning) | ||
| for idx, value in enumerate(bin_data["sig"]): | ||
| sig.SetBinContent(idx + 1, value) | ||
| sig.Sumw2() | ||
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| sig = ROOT.TH1F('sig', 'sig', *binning) | ||
| for i, v in enumerate(bindata['sig']): | ||
| sig.SetBinContent(i + 1, v) | ||
| sig.Sumw2() | ||
| f.Write() | ||
| out_file.Write() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,45 +1,44 @@ | ||
| import ROOT | ||
| import sys | ||
|
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| infile = sys.argv[1] | ||
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| infile = ROOT.TFile.Open(infile) | ||
| workspace = infile.Get("combined") | ||
| data = workspace.data("obsData") | ||
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| import ROOT | ||
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| sbModel = workspace.obj("ModelConfig") | ||
| poi = sbModel.GetParametersOfInterest().first() | ||
| if __name__ == "__main__": | ||
| infile = sys.argv[1] | ||
|
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| sbModel.SetSnapshot(ROOT.RooArgSet(poi)) | ||
| infile = ROOT.TFile.Open(infile) | ||
| workspace = infile.Get("combined") | ||
| data = workspace.data("obsData") | ||
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| bModel = sbModel.Clone() | ||
| bModel.SetName("bonly") | ||
| poi.setVal(0) | ||
| bModel.SetSnapshot(ROOT.RooArgSet(poi)) | ||
| sb_model = workspace.obj("ModelConfig") | ||
| poi = sb_model.GetParametersOfInterest().first() | ||
|
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| ac = ROOT.RooStats.AsymptoticCalculator(data, bModel, sbModel) | ||
| ac.SetPrintLevel(10) | ||
| ac.SetOneSided(True) | ||
| ac.SetQTilde(True) | ||
| sb_model.SetSnapshot(ROOT.RooArgSet(poi)) | ||
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| calc = ROOT.RooStats.HypoTestInverter(ac) | ||
| calc.RunFixedScan(51, 0, 5) | ||
| calc.SetConfidenceLevel(0.95) | ||
| calc.UseCLs(True) | ||
| bkg_model = sb_model.Clone() | ||
| bkg_model.SetName("bonly") | ||
| poi.setVal(0) | ||
| bkg_model.SetSnapshot(ROOT.RooArgSet(poi)) | ||
|
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| calc = ROOT.RooStats.AsymptoticCalculator(data, bkg_model, sb_model) | ||
| calc.SetPrintLevel(10) | ||
| calc.SetOneSided(True) | ||
| calc.SetQTilde(True) | ||
|
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| result = calc.GetInterval() | ||
| test_inverter = ROOT.RooStats.HypoTestInverter(calc) | ||
| test_inverter.RunFixedScan(51, 0, 5) | ||
| test_inverter.SetConfidenceLevel(0.95) | ||
| test_inverter.UseCLs(True) | ||
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| plot = ROOT.RooStats.HypoTestInverterPlot("plot", "plot", result) | ||
| c = ROOT.TCanvas() | ||
| c.SetLogy(False) | ||
| plot.Draw("OBS EXP CLb 2CL") | ||
| c.Draw() | ||
| c.SaveAs('scan.pdf') | ||
| result = test_inverter.GetInterval() | ||
|
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| plot = ROOT.RooStats.HypoTestInverterPlot("plot", "plot", result) | ||
| canvas = ROOT.TCanvas() | ||
| canvas.SetLogy(False) | ||
| plot.Draw("OBS EXP CLb 2CL") | ||
| canvas.Draw() | ||
| canvas.SaveAs("scan.pdf") | ||
|
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| print(f'observed: {result.UpperLimit()}') | ||
| print(f"observed: {result.UpperLimit()}") | ||
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| for i in [-2, -1, 0, 1, 2]: | ||
| print(f'expected {i}: {result.GetExpectedUpperLimit(i)}') | ||
| for n_sigma in [-2, -1, 0, 1, 2]: | ||
| print(f"expected {n_sigma}: {result.GetExpectedUpperLimit(n_sigma)}") |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,43 +1,42 @@ | ||
| import ROOT | ||
| import json | ||
| import sys | ||
|
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| infile = sys.argv[1] | ||
|
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| infile = ROOT.TFile.Open(infile) | ||
| workspace = infile.Get("combined") | ||
| data = workspace.data("obsData") | ||
|
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| sbModel = workspace.obj("ModelConfig") | ||
| poi = sbModel.GetParametersOfInterest().first() | ||
|
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| sbModel.SetSnapshot(ROOT.RooArgSet(poi)) | ||
| import ROOT | ||
|
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| bModel = sbModel.Clone() | ||
| bModel.SetName("bonly") | ||
| poi.setVal(0) | ||
| bModel.SetSnapshot(ROOT.RooArgSet(poi)) | ||
| if __name__ == "__main__": | ||
| infile = sys.argv[1] | ||
|
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| ac = ROOT.RooStats.AsymptoticCalculator(data, bModel, sbModel) | ||
| ac.SetPrintLevel(10) | ||
| ac.SetOneSided(True) | ||
| ac.SetQTilde(True) | ||
| infile = ROOT.TFile.Open(infile) | ||
| workspace = infile.Get("combined") | ||
| data = workspace.data("obsData") | ||
|
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| sb_model = workspace.obj("ModelConfig") | ||
| poi = sb_model.GetParametersOfInterest().first() | ||
|
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| calc = ROOT.RooStats.HypoTestInverter(ac) | ||
| calc.SetConfidenceLevel(0.95) | ||
| calc.UseCLs(True) | ||
| calc.RunFixedScan(1, 1, 1) | ||
| sb_model.SetSnapshot(ROOT.RooArgSet(poi)) | ||
|
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| result = calc.GetInterval() | ||
| bkg_model = sb_model.Clone() | ||
| bkg_model.SetName("bonly") | ||
| poi.setVal(0) | ||
| bkg_model.SetSnapshot(ROOT.RooArgSet(poi)) | ||
|
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| index = 0 | ||
| calc = ROOT.RooStats.AsymptoticCalculator(data, bkg_model, sb_model) | ||
| calc.SetPrintLevel(10) | ||
| calc.SetOneSided(True) | ||
| calc.SetQTilde(True) | ||
|
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| w = result.GetExpectedPValueDist(index) | ||
| v = w.GetSamplingDistribution() | ||
| test_inverter = ROOT.RooStats.HypoTestInverter(calc) | ||
| test_inverter.SetConfidenceLevel(0.95) | ||
| test_inverter.UseCLs(True) | ||
| test_inverter.RunFixedScan(1, 1, 1) | ||
|
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| CLs_obs = result.CLs(index) | ||
| CLs_exp = list(v)[3:-3] | ||
| result = test_inverter.GetInterval() | ||
|
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| import json | ||
| index = 0 | ||
| dist = result.GetExpectedPValueDist(index) | ||
| CLs_obs = result.CLs(index) | ||
| CLs_exp = list(dist.GetSamplingDistribution())[3:-3] | ||
|
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| print(json.dumps({'CLs_obs': CLs_obs, 'CLs_exp': CLs_exp}, sort_keys=True, indent=4)) | ||
| print( | ||
| json.dumps({"CLs_obs": CLs_obs, "CLs_exp": CLs_exp}, sort_keys=True, indent=4) | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,36 +1,38 @@ | ||
| if __name__ == "__main__": | ||
| import ROOT | ||
| import sys | ||
| import json | ||
| import json | ||
| import sys | ||
|
|
||
| import ROOT | ||
|
|
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| if __name__ == "__main__": | ||
| infile = sys.argv[1] | ||
|
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| infile = ROOT.TFile.Open(infile) | ||
| workspace = infile.Get("combined") | ||
| data = workspace.data("obsData") | ||
|
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| sbModel = workspace.obj("ModelConfig") | ||
| poi = sbModel.GetParametersOfInterest().first() | ||
| sb_model = workspace.obj("ModelConfig") | ||
| poi = sb_model.GetParametersOfInterest().first() | ||
| poi.setVal(1) | ||
| sbModel.SetSnapshot(ROOT.RooArgSet(poi)) | ||
| sb_model.SetSnapshot(ROOT.RooArgSet(poi)) | ||
|
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| bModel = sbModel.Clone() | ||
| bModel.SetName("bonly") | ||
| bkg_model = sb_model.Clone() | ||
| bkg_model.SetName("bonly") | ||
| poi.setVal(0) | ||
| bModel.SetSnapshot(ROOT.RooArgSet(poi)) | ||
| bkg_model.SetSnapshot(ROOT.RooArgSet(poi)) | ||
|
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| ac = ROOT.RooStats.AsymptoticCalculator(data, sbModel, bModel) | ||
| ac.SetPrintLevel(10) | ||
| ac.SetOneSidedDiscovery(True) | ||
| calc = ROOT.RooStats.AsymptoticCalculator(data, sb_model, bkg_model) | ||
| calc.SetPrintLevel(10) | ||
| calc.SetOneSidedDiscovery(True) | ||
|
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| result = ac.GetHypoTest() | ||
| result = calc.GetHypoTest() | ||
| pnull_obs = result.NullPValue() | ||
| palt_obs = result.AlternatePValue() | ||
| pnull_exp = [] | ||
| for sigma in [-2, -1, 0, 1, 2]: | ||
| usecls = 0 | ||
| pnull_exp.append(ac.GetExpectedPValues(pnull_obs, palt_obs, sigma, usecls)) | ||
| usecls = 0 | ||
| pnull_exp = [ | ||
| calc.GetExpectedPValues(pnull_obs, palt_obs, sigma, usecls) | ||
| for sigma in [-2, -1, 0, 1, 2] | ||
| ] | ||
|
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| print( | ||
| json.dumps({'p0_obs': pnull_obs, 'p0_exp': pnull_exp}, sort_keys=True, indent=4) | ||
| json.dumps({"p0_obs": pnull_obs, "p0_exp": pnull_exp}, sort_keys=True, indent=4) | ||
| ) |
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