def test_nested_files_to_copy(self): """Test that file copying works when tables have files.""" # Create random test file testfile = "testfile.txt" with open(testfile, "w") as f: f.write("test") self.addCleanup(os.remove, testfile) # Output files testdirectory = "./testout" self.addCleanup(shutil.rmtree, testdirectory) self.addCleanup(os.remove, "submission.tar.gz") # Add resource to table, add table to Submission sub = Submission() tab = Table('test') tab.add_additional_resource("a_resource", testfile, True) sub.add_table(tab) # Write outputs sub.create_files(testdirectory) # Check that test file is actually in the tar ball with tarfile.open("submission.tar.gz", "r:gz") as tar: try: tar.getmember(testfile) except KeyError: self.fail( "Submission.create_files failed to write all files to tar ball." )
def makeCutFlow(submission, config): table = Table(config["name"]) table.description = config["description"] table.location = config["location"] #table.keywords["observables"] = ["SIG"] #table.keywords["reactions"] = ["P P --> TOP --> tt + 6j"] data1 = config["data1"] error1 = config["error1"] data2 = config["data2"] error2 = config["error2"] ##################################################################################### d = Variable("Step", is_independent=True, is_binned=False, units="") d.values = np.array(list(i for i in range(0, data1.size))) cuts = Variable("Selection requirement", is_independent=False, is_binned=False, units="") cuts.values = config["cutnames"] cuts.add_qualifier("SQRT(S)", 13, "TeV") cuts.add_qualifier("LUMINOSITY", config["lumi"], "fb$^{-1}$") obs1 = Variable("RPV $m_{\\tilde{t}}$ = 450 GeV", is_independent=False, is_binned=False, units="") obs1.values = data1 obs1.add_qualifier("SQRT(S)", 13, "TeV") obs1.add_qualifier("LUMINOSITY", config["lumi"], "fb$^{-1}$") unc_obs1 = Uncertainty("1 s.d.", is_symmetric=True) unc_obs1.values = error1 obs1.add_uncertainty(unc_obs1) obs2 = Variable("SYY $m_{\\tilde{t}}$ = 850 GeV", is_independent=False, is_binned=False, units="") obs2.values = data2 obs2.add_qualifier("SQRT(S)", 13, "TeV") obs2.add_qualifier("LUMINOSITY", config["lumi"], "fb$^{-1}$") unc_obs2 = Uncertainty("1 s.d.", is_symmetric=True) unc_obs2.values = error2 obs2.add_uncertainty(unc_obs2) table.add_variable(d) table.add_variable(cuts) table.add_variable(obs1) table.add_variable(obs2) submission.add_table(table)
def make_table(): xparam = 'kappa_V' yparam = 'kappa_F' # Load results + xsbr data inputMode = "kappas" translatePOIs = LoadTranslations("translate/pois_%s.json" % inputMode) # Extract observed results fobs = "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/flashggFinalFit/Combine/runFits_UL_redo_kVkF/output_scan2D_syst_fixedMH_v2_kappa_V_vs_kappa_F.root" f_in = ROOT.TFile(fobs) t_in = f_in.Get("limit") xvals, yvals, deltaNLL = [], [], [] ev_idx = 0 for ev in t_in: xvals.append(getattr(ev, xparam)) yvals.append(getattr(ev, yparam)) deltaNLL.append(getattr(ev, "deltaNLL")) # Convert to numpy arrays as required for interpolation x = np.asarray(xvals) y = np.asarray(yvals) dnll = np.asarray(deltaNLL) v = 2 * (deltaNLL - np.min(deltaNLL)) # Make table of results table = Table("Kappas 2D: vector boson and fermion (expected)") table.description = "Expected likelihood surface." table.location = "Results from additional material" table.keywords["reactions"] = ["P P --> H ( --> GAMMA GAMMA ) X"] pois_x = Variable(str(Translate(xparam, translatePOIs)), is_independent=True, is_binned=False) pois_y = Variable(str(Translate(yparam, translatePOIs)), is_independent=True, is_binned=False) q = Variable("Expected -2$\\Delta$NLL", is_independent=False, is_binned=False) q.add_qualifier("SQRT(S)", 13, "TeV") q.add_qualifier("MH", '125.38', "GeV") pois_x.values = x pois_y.values = y q.values = np.round(np.array(v), 2) # Add variables to table table.add_variable(pois_x) table.add_variable(pois_y) table.add_variable(q) # Add figure #table.add_image("/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/scan2D_syst_obs_kappa_g_vs_kappa_gam.pdf") return table
def test_add_image(self): """Get test PDF""" # Get test PDF some_pdf = "%s/minimal.pdf" % os.path.dirname(__file__) test_table = Table("Some Table") # This should work fine try: try: test_table.add_image(some_pdf) except RuntimeError: self.fail("Table.add_image raised an unexpected RuntimeError.") except TypeError: self.fail("Table.add_image raised an unexpected TypeError.") # Try wrong argument types wrong_type = [None, 5, {}, []] for argument in wrong_type: with self.assertRaises(TypeError): test_table.add_image(argument) # Try non-existing paths: nonexisting = ["/a/b/c/d/e", "./daskjl/aksj/asdasd.pdf"] for argument in nonexisting: with self.assertRaises(RuntimeError): test_table.add_image(argument)
def test_write_images(self): """Test the write_images function.""" test_table = Table("Some Table") # Get test PDF some_pdf = "%s/minimal.pdf" % os.path.dirname(__file__) # This should work fine test_table.add_image(some_pdf) testdir = tmp_directory_name() self.addCleanup(shutil.rmtree, testdir) try: test_table.write_images(testdir) except TypeError: self.fail("Table.write_images raised an unexpected TypeError.") # Check that output file exists expected_file = os.path.join(testdir, "minimal.png") self.assertTrue(os.path.exists(expected_file)) # Try wrong type of input argument bad_arguments = [None, 5, {}, []] for argument in bad_arguments: with self.assertRaises(TypeError): test_table.write_images(argument) self.doCleanups()
def test_create_files_with_removal(self): """Test the removal of old files in create_files()""" testdir = tmp_directory_name() # Step 1: Create test directory containing random file os.makedirs(testdir) self.addCleanup(shutil.rmtree, testdir) testfile = os.path.join(testdir, "test.txt") with open(testfile, "w") as f: f.write("test") self.assertTrue(os.path.isfile(testfile)) # Step 2: Create submission and write output to test directory # Without overwriting of files test_submission = Submission() tab = Table("test") test_submission.add_table(tab) test_submission.create_files(testdir, remove_old=False) # Test file should still exist self.assertTrue(os.path.isfile(testfile)) # Step 3: Recreate submission files with removal test_submission.create_files(testdir, remove_old=True) # Test file should no longer exist self.assertFalse(os.path.isfile(testfile))
def test_yaml_output(self): """Test yaml dump""" tmp_dir = tmp_directory_name() # Create test dictionary testlist = [("x", 1.2), ("x", 2.2), ("y", 0.12), ("y", 0.22)] testdict = defaultdict(list) for key, value in testlist: testdict[key].append(value) # Create test submission test_submission = Submission() test_table = Table("TestTable") x_variable = Variable("X", is_independent=True, is_binned=False) x_variable.values = testdict['x'] y_variable = Variable("Y", is_independent=False, is_binned=False) y_variable.values = testdict['y'] test_table.add_variable(x_variable) test_table.add_variable(y_variable) test_submission.add_table(test_table) test_submission.create_files(tmp_dir) # Test read yaml file table_file = os.path.join(tmp_dir, "testtable.yaml") try: with open(table_file, 'r') as testfile: testyaml = yaml.safe_load(testfile) except yaml.YAMLError as exc: print(exc) # Test compare yaml file to string testtxt = ( "dependent_variables:\n- header:\n name: Y\n values:\n" + " - value: 0.12\n - value: 0.22\nindependent_variables:\n" + "- header:\n name: X\n values:\n - value: 1.2\n - value: 2.2\n" ) with open(table_file, 'r') as testfile: testyaml = testfile.read() self.assertEqual(str(testyaml), testtxt) self.addCleanup(os.remove, "submission.tar.gz") self.addCleanup(shutil.rmtree, tmp_dir) self.doCleanups()
def test_create_files(self): """Test create_files() for Submission.""" testdir = tmp_directory_name() test_submission = Submission() tab = Table("test") test_submission.add_table(tab) test_submission.create_files(testdir) self.doCleanups()
def test_write_yaml(self): """Test write_yaml() for Table.""" test_table = Table("Some Table") test_variable = Variable("Some Variable") test_table.add_variable(test_variable) try: test_table.write_yaml("test_output") except TypeError: self.fail("Table.write_yaml raised an unexpected TypeError.") with self.assertRaises(TypeError): test_table.write_yaml(None)
def test_write_images_multiple_executions(self): """ write_images is supposed to only recreate output PNG files if the output file does not yet exist or is outdated relative to the input file. """ test_table = Table("Some Table") some_pdf = "%s/minimal.pdf" % os.path.dirname(__file__) test_table.add_image(some_pdf) testdir = "test_output" self.addCleanup(shutil.rmtree, testdir) expected_main_file = os.path.join(testdir, "minimal.png") expected_thumbnail_file = os.path.join(testdir, "thumb_minimal.png") # Output files should not yet exist self.assertTrue(not os.path.exists(expected_main_file)) self.assertTrue(not os.path.exists(expected_thumbnail_file)) # Run the function test_table.write_images(testdir) # Output files now exist self.assertTrue(os.path.exists(expected_main_file)) self.assertTrue(os.path.exists(expected_thumbnail_file)) # Make sure that output is not recreated if input file is unchanged modified_time_main = os.path.getmtime(expected_main_file) modified_time_thumbnail = os.path.getmtime(expected_thumbnail_file) test_table.write_images(testdir) self.assertEqual(modified_time_main, os.path.getmtime(expected_main_file)) self.assertEqual(modified_time_thumbnail, os.path.getmtime(expected_thumbnail_file)) # Make sure that a change in input file triggers recreation os.utime(some_pdf, None) test_table.write_images(testdir) self.assertTrue(modified_time_main < os.path.getmtime(expected_main_file)) self.assertTrue(modified_time_thumbnail < os.path.getmtime(expected_thumbnail_file))
def test_name_checks(self): """Test the table name checks.""" # This should work fine good_name = "64 characters or fewer" try: good_table = Table(good_name) except ValueError: self.fail("Table initializer raised unexpected ValueError.") self.assertEqual(good_name, good_table.name) # Check name that is too long too_long_name = "x"*100 # In the initializer with self.assertRaises(ValueError): _bad_table = Table(too_long_name) # Using the setter with self.assertRaises(ValueError): good_table.name = too_long_name
def test_write_yaml(self): """Test write_yaml() for Table.""" test_table = Table("Some Table") test_variable = Variable("Some Variable") test_table.add_variable(test_variable) testdir = tmp_directory_name() self.addCleanup(shutil.rmtree, testdir) try: test_table.write_yaml(testdir) except TypeError: self.fail("Table.write_yaml raised an unexpected TypeError.") with self.assertRaises(TypeError): test_table.write_yaml(None) self.doCleanups()
def test_copy_files(self): """Test the copy_files function.""" test_table = Table("Some Table") some_pdf = "%s/minimal.pdf" % os.path.dirname(__file__) testdir = tmp_directory_name() self.addCleanup(shutil.rmtree, testdir) os.makedirs(testdir) test_table.add_additional_resource("a plot",some_pdf, copy_file=True) test_table.copy_files(testdir)
def test_add_table_typechecks(self): """Test the type checks in the add_table function.""" # Verify that the type check works test_submission = Submission() test_table = Table("Some Table") test_variable = Variable("Some Variable") test_uncertainty = Uncertainty("Some Uncertainty") try: test_submission.add_table(test_table) except TypeError: self.fail("Submission.add_table raised an unexpected TypeError.") with self.assertRaises(TypeError): test_submission.add_table(5) with self.assertRaises(TypeError): test_submission.add_table([1, 3, 5]) with self.assertRaises(TypeError): test_submission.add_table("a string") with self.assertRaises(TypeError): test_submission.add_table(test_variable) with self.assertRaises(TypeError): test_submission.add_table(test_uncertainty)
def convertCorrMatrixToYaml( rootfile, label, variablex, variabley, unit, object="output_corr_matrix_syst", var="Syst. correlation" ): tab = Table(label) reader = RootFileReader(rootfile) cov = reader.read_hist_2d(object) xbins = Variable( variablex, is_independent=True, is_binned=True, units=unit) xbins.values = cov["x_edges"] ybins = Variable( variabley, is_independent=True, is_binned=True, units=unit) ybins.values = cov["y_edges"] data = Variable( var, is_independent=False, is_binned=False, units="%") data.values = cov["z"] data.add_qualifier("SQRT(S)","13","TeV") data.add_qualifier("LUMINOSITY","137","fb$^{-1}$") tab.add_variable(xbins) tab.add_variable(ybins) tab.add_variable(data) return tab
tableF4b.keywords["reactions"] = [ "P P --> TOP TOPBAR X", "P P --> TOP TOPBAR GAMMA" ] tableF4b.keywords["cmenergies"] = [13000.0] tableF4b.keywords["observables"] = ["N"] tableF4b.keywords["phrases"] = [ "Top", "Quark", "Photon", "lepton+jets", "semileptonic", "Cross Section", "Proton-Proton Scattering", "Inclusive", "Differential" ] #tableF4b.keywords() submission.add_table(tableF4b) ### ### SF table ### tabSF = Table("Table 4") tabSF.description = "Extracted scale factors for the contribution from misidentified electrons for the three data-taking periods, and the Z$\gamma$, W$\gamma$ simulations." tabSF.location = "Table 4" sfType = Variable("Scale factor", is_independent=True, is_binned=False, units="") sfType.values = [ "Misidentified electrons (2016)", "Misidentified electrons (2017)", "Misidentified electrons (2018)", "Z$\gamma$ normalization", "W$\gamma$ normalization" ] value = Variable("Value", is_independent=False, is_binned=False, units="") value.values = [2.25, 2.00, 1.52, 1.01, 1.13]
def Plot_pp_pPb_Avg_FF_and_Ratio(Comb_Dict): label_size=22 axis_size=34 plot_power = False Colors = ["red","blue"] Markers = ["s","o"] fig = plt.figure(figsize=(8,8)) pp_sys_Error = 0 p_Pb_sys_Error = 0 fig.add_axes((0.1,0.3,0.88,0.6)) for SYS,sys_col,marker in zip(reversed(Systems),reversed(Colors),reversed(Markers)): #Systematics Efficiency_Uncertainty = 0.056*Comb_Dict["%s_Combined_FF"%(SYS)] Eta_Cor = Eta_Correction #see default_value.py for value Eta_Cor_Uncertainty = Eta_Correction_Uncertainty*Comb_Dict["%s_Combined_FF"%(SYS)] if not(Apply_Eta_Correction and SYS=="p-Pb"): Eta_Cor_Uncertainty = 0 #2% otherwise FF_Central = Comb_Dict["%s_Combined_FF"%(SYS)] #Eta Correction is applied when creating Dictionary! Sys_Uncertainty = np.sqrt(Efficiency_Uncertainty**2 + Comb_Dict["%s_purity_Uncertainty"%(SYS)]**2 + Eta_Cor_Uncertainty**2) if (SYS=="pp"): pp_sys_Error = Sys_Uncertainty elif (SYS=="p-Pb"): p_Pb_sys_Error=Sys_Uncertainty #Plots if (SYS=="pp"): leg_string = SYS if (SYS=="p-Pb"): leg_string = "p$-$Pb" plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT], Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT],xerr=zT_widths[:NzT-ZT_OFF_PLOT]*0, yerr=Comb_Dict["%s_Combined_FF_Errors"%(SYS)][:NzT-ZT_OFF_PLOT],linewidth=1, fmt=marker,color=sys_col,capsize=0)#for lines plt.plot(zT_centers[:NzT-ZT_OFF_PLOT], Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT],marker,linewidth=0,color=sys_col, label=leg_string)#for legend without lines if (SYS == "pp"): Sys_Plot_pp = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Sys_Uncertainty[:NzT-ZT_OFF_PLOT]+Sys_Uncertainty[:NzT-ZT_OFF_PLOT], bottom=Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT]-Sys_Uncertainty[:NzT-ZT_OFF_PLOT],width=zT_widths[:NzT-ZT_OFF_PLOT]*2, align='center',color=sys_col,alpha=0.3,edgecolor=sys_col) else: Sys_Plot_pp = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Sys_Uncertainty[:NzT-ZT_OFF_PLOT]+Sys_Uncertainty[:NzT-ZT_OFF_PLOT], bottom=Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT]-Sys_Uncertainty[:NzT-ZT_OFF_PLOT],width=zT_widths[:NzT-ZT_OFF_PLOT]*2,align='center',color=sys_col,fill=False,edgecolor="blue") if (plot_power): model,p,chi2dof = Fit_FF_PowerLaw(Comb_Dict,SYS) plt.plot(zT_centers[:NzT-ZT_OFF_PLOT], model, sys_col,label=r"%s $\alpha = %1.2f\pm 0.1 \chi^2 = %1.2f$"%(SYS,p,chi2dof)) if (Use_MC): plt.plot(zT_centers[:NzT-ZT_OFF_PLOT],pythia_FF,'--',color="forestgreen",label="PYTHIA 8.2 Monash") plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT],pythia_FF,yerr=pythia_FF_Errors,fmt='--',color="forestgreen",capsize=0) plt.yscale('log') plt.ylabel(r"$\frac{1}{N_{\mathrm{\gamma}}}\frac{\mathrm{d}^3N}{\mathrm{d}z_{\mathrm{T}}\mathrm{d}|\Delta\varphi|\mathrm{d}\Delta\eta}$",fontsize=axis_size,y=0.76) plt.ylim(0.037,15) plt.yticks(fontsize=20) plt.xticks(fontsize=0) plt.xlim(0,0.65) plt.tick_params(which='both',direction='in',right=True,top=True,bottom=False,length=10) plt.tick_params(which='minor',length=5) #pp_sys_Error = (Comb_Dict["pp_Combined_FF"][:NzT-ZT_OFF_PLOT])*math.sqrt(Rel_pUncert["pp"]**2+0.056**2) #p_Pb_sys_Error = (Comb_Dict["p-Pb_Combined_FF"][:NzT-ZT_OFF_PLOT])*math.sqrt(Rel_pUncert["p-Pb"]**2+0.056**2+Eta_Cor**2) Chi2,NDF,Pval = Get_pp_pPb_List_Chi2(Comb_Dict["pp_Combined_FF"][:NzT-ZT_OFF_PLOT], Comb_Dict["pp_Combined_FF_Errors"][:NzT-ZT_OFF_PLOT], pp_sys_Error, Comb_Dict["p-Pb_Combined_FF"][:NzT-ZT_OFF_PLOT], Comb_Dict["p-Pb_Combined_FF_Errors"][:NzT-ZT_OFF_PLOT], p_Pb_sys_Error) leg = plt.legend(numpoints=1,frameon=True,edgecolor='white', framealpha=0.0, fontsize=label_size,handlelength=1,labelspacing=0.2,loc='lower left',bbox_to_anchor=(0.001, 0.05)) plt.annotate(r"ALICE, $\sqrt{s_{\mathrm{_{NN}}}}=5.02$ TeV",xy=(0.115,0.008),xycoords='axes fraction', ha='left',va='bottom',fontsize=label_size) plt.annotate(r"%1.0f < $p_\mathrm{T}^{\gamma}$ < %1.0f GeV/$c$"%(pTbins[0],pTbins[N_pT_Bins]),xy=(0.97, 0.81), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) plt.annotate(r"%1.1f < $p_\mathrm{T}^\mathrm{h}$ < %1.1f GeV/$c$"%(Min_Hadron_pT,Max_Hadron_pT),xy=(0.97, 0.89), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) plt.annotate("$\chi^2/\mathrm{ndf}$ = %1.1f/%i, $p$ = %1.2f"%(Chi2*NDF,NDF,Pval), xy=(0.97, 0.97), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) #HEP FF Fig5 = Table("Figure 5 Top Panel") Fig5.description = "$\gamma^\mathrm{iso}$-tagged fragmentation function for pp (red) and p$-$Pb data (blue) at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV as measured by the ALICE detector. The boxes represent the systematic uncertainties while the vertical bars indicate the statistical uncertainties. The dashed green line corresponds to PYTHIA 8.2 Monash Tune. The $\chi^2$ test for the comparison of pp and p$-$Pb data incorporates correlations among different $z_\mathrm{T}$ intervals. A constant that was fit to the ratio is shown as grey band, with the width indicating the uncertainty on the fit." Fig5.location = "Data from Figure 5 Top Panel, Page 15" Fig5.keywords["observables"] = ["$\frac{1}{N_{\mathrm{\gamma}}}\frac{\mathrm{d}^3N}{\mathrm{d}z_{\mathrm{T}}\mathrm{d}\Delta\varphi\mathrm{d}\Delta\eta}$"] Fig5.add_image("./pics/LO/zT_Rebin_8_006zT06zT13fnew/Final_FFunction_and_Ratio.pdf") # x-axis: zT zt = Variable(r"$z_\mathrm{T}$", is_independent=True, is_binned=True, units="") zt.values = zT_edges Fig5.add_variable(zt) # y-axis: p-Pb Yields pPb_data = Variable("p$-$Pb conditional yield of associated hadrons", is_independent=False, is_binned=False, units="") pPb_data.values = Comb_Dict["p-Pb_Combined_FF"] pPb_sys = Uncertainty("p-Pb Systematic", is_symmetric=True) pPb_sys.values = p_Pb_sys_Error pPb_stat = Uncertainty("p-Pb Statistical", is_symmetric=True) pPb_stat.values = Comb_Dict["p-Pb_Combined_FF_Errors"] pPb_data.add_uncertainty(pPb_sys) pPb_data.add_uncertainty(pPb_stat) # y-axis: pp Yields pp_data = Variable("pp conditional yield of associated hadrons", is_independent=False, is_binned=False, units="") pp_data.values = Comb_Dict["pp_Combined_FF"] pp_sys = Uncertainty("pp Systematic", is_symmetric=True) pp_sys.values = pp_sys_Error pp_stat = Uncertainty("pp Statistical", is_symmetric=True) pp_stat.values = Comb_Dict["pp_Combined_FF_Errors"] pp_data.add_uncertainty(pp_sys) pp_data.add_uncertainty(pp_stat) # y-axis: PYTHIA Yields pythia_data = Variable("PYTHIA conditional yield of associated hadrons", is_independent=False, is_binned=False, units="") pythia_data.values = pythia_FF pythia_stat = Uncertainty("PYTHIA Statistical", is_symmetric=True) pythia_stat.values = pythia_FF_Errors pythia_data.add_uncertainty(pythia_stat) #Add everything to the HEP Table Fig5.add_variable(pPb_data) Fig5.add_variable(pp_data) Fig5.add_variable(pythia_data) submission.add_table(Fig5) #RATIO SECOND Y_AXIS fig.add_axes((0.1,0.1,0.88,0.2)) pPb_Combined = Comb_Dict["p-Pb_Combined_FF"] pPb_Combined_Errors = Comb_Dict["p-Pb_Combined_FF_Errors"] pPb_purity_Uncertainty = Comb_Dict["p-Pb_purity_Uncertainty"] pp_Combined = Comb_Dict["pp_Combined_FF"] pp_Combined_Errors = Comb_Dict["pp_Combined_FF_Errors"] pp_purity_Uncertainty = Comb_Dict["pp_purity_Uncertainty"] Ratio = pPb_Combined/pp_Combined Ratio_Error = np.sqrt((pPb_Combined_Errors/pPb_Combined)**2 + (pp_Combined_Errors/pp_Combined)**2)*Ratio Ratio_Plot = plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio[:NzT-ZT_OFF_PLOT], yerr=Ratio_Error[:NzT-ZT_OFF_PLOT],xerr=zT_widths[:NzT-ZT_OFF_PLOT]*0, fmt='ko',capsize=0, ms=6,lw=1) #Save np.save("npy_files/%s_Averaged_FF_Ratio_%s.npy"%(Shower,description_string),Ratio) np.save("npy_files/%s_Averaged_FF_Ratio_Errors_%s.npy"%(Shower,description_string),Ratio_Error) Purity_Uncertainty = np.sqrt((pp_purity_Uncertainty/pp_Combined)**2 + (pPb_purity_Uncertainty/pPb_Combined)**2)*Ratio Efficiency_Uncertainty = np.ones(len(pPb_Combined))*0.056*math.sqrt(2)*Ratio Eta_Cor_Uncertainty = Eta_Correction_Uncertainty/Comb_Dict["p-Pb_Combined_FF"]*Ratio if (CorrectedP): Ratio_Systematic = np.sqrt(Purity_Uncertainty**2 + Efficiency_Uncertainty**2 + Eta_Cor_Uncertainty**2) Sys_Plot = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio_Systematic[:NzT-ZT_OFF_PLOT]+Ratio_Systematic[:NzT-ZT_OFF_PLOT], bottom=Ratio[:NzT-ZT_OFF_PLOT]-Ratio_Systematic[:NzT-ZT_OFF_PLOT], width=zT_widths[:NzT-ZT_OFF_PLOT]*2, align='center',color='black',alpha=0.25) ### ROOT LINEAR and CONSTANT FITS ### Ratio_TGraph = TGraphErrors() for izt in range (len(Ratio)-ZT_OFF_PLOT): Ratio_TGraph.SetPoint(izt,zT_centers[izt],Ratio[izt]) Ratio_TGraph.SetPointError(izt,0,Ratio_Error[izt]) Ratio_TGraph.Fit("pol0","S") f = Ratio_TGraph.GetFunction("pol0") chi2_red = f.GetChisquare()/f.GetNDF() pval = f.GetProb() p0 = f.GetParameter(0) p0e = f.GetParError(0) p0col = "grey" Show_Fits = True if (Show_Fits): sys_const = 0.19 #23% relative from purity + tracking #sys_const = 0.504245 #IRC plt.annotate("$c = {0:.2f} \pm {1:.2f} \pm {2:.2f}$".format(p0,p0e,sys_const), xy=(0.98, 0.9), xycoords='axes fraction', ha='right', va='top', color="black",fontsize=label_size,alpha=.9) plt.annotate(r"$p = %1.2f$"%(pval), xy=(0.98, 0.75), xycoords='axes fraction', ha='right', va='top', color="black",fontsize=label_size,alpha=.9) c_error = math.sqrt(p0e**2 + sys_const**2) plt.fill_between(np.arange(0,1.1,0.1), p0+c_error, p0-c_error,color=p0col,alpha=.3) ###LABELS/AXES### plt.axhline(y=1, color='k', linestyle='--') plt.xlabel("${z_\mathrm{T}} = p_\mathrm{T}^{\mathrm{h}}/p_\mathrm{T}^\gamma$",fontsize=axis_size-8,x=0.9) plt.ylabel(r"$\frac{\mathrm{p-Pb}}{\mathrm{pp}}$",fontsize=axis_size,y=0.5) plt.ylim((-0.0, 2.8)) plt.xticks(fontsize=20) plt.yticks([0.5,1.0,1.5,2.0,2.5],fontsize=20) plt.xlim(0,0.65) plt.tick_params(which='both',direction='in',right=True,bottom=True,top=True,length=10) plt.tick_params(which='both',direction='in',top=True,length=5) plt.savefig("pics/%s/%s/Final_FFunction_and_Ratio.pdf"%(Shower,description_string), bbox_inches = "tight") plt.show() #RATIO HEP FigRatio = Table("Figure 5 Bottom Panel") FigRatio.description = r"$\gamma^\mathrm{iso}$-tagged fragmentation function for pp (red) and p$-$Pb data (blue) at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV as measured by the ALICE detector. The boxes represent the systematic uncertainties while the vertical bars indicate the statistical uncertainties. The dashed green line corresponds to PYTHIA 8.2 Monash Tune. The $\chi^2$ test for the comparison of pp and p$-$Pb data incorporates correlations among different $z_\mathrm{T}$ intervals. A constant that was fit to the ratio is shown as grey band, with the width indicating the uncertainty on the fit." FigRatio.location = "Data from Figure 5, Bottom Panel, Page 15" FigRatio.keywords["observables"] = [r"$\frac{1}{N_{\mathrm{\gamma}}}\frac{\mathrm{d}^3N}{\mathrm{d}z_{\mathrm{T}}\mathrm{d}\Delta\varphi\mathrm{d}\Delta\eta}$"] FigRatio.add_image("./pics/LO/zT_Rebin_8_006zT06zT13fnew/Final_FFunction_and_Ratio.pdf") # x-axis: zT zt_ratio = Variable(r"$z_\mathrm{T}$", is_independent=True, is_binned=True, units="") zt_ratio.values = zT_edges FigRatio.add_variable(zt_ratio) # y-axis: p-Pb Yields Ratio_HEP = Variable("Ratio conditional yield of associated hadrons in pp and p$-$Pb", is_independent=False, is_binned=False, units="") Ratio_HEP.values = Ratio Ratio_sys = Uncertainty("Ratio Systematic", is_symmetric=True) Ratio_sys.values = Ratio_Systematic Ratio_stat = Uncertainty("Ratio Statistical", is_symmetric=True) Ratio_stat.values = Ratio_Error Ratio_HEP.add_uncertainty(Ratio_stat) Ratio_HEP.add_uncertainty(Ratio_sys) FigRatio.add_variable(Ratio_HEP) submission.add_table(FigRatio)
def convertWJetsHistToYaml( rootfile, label, channel ): tab = Table(label) reader = RootFileReader(rootfile) data = reader.read_hist_1d("dataAR") # ttg = reader.read_hist_1d("TTG_centralall") top = reader.read_hist_1d("Top_centralall") dy = reader.read_hist_1d("DY_LO_centralall") wjets = reader.read_hist_1d("WJets_centralall") # wg = reader.read_hist_1d("WG_centralall") # zg = reader.read_hist_1d("ZG_centralall") other = reader.read_hist_1d("other_centralall") qcd = reader.read_hist_1d("QCD") uncUp = reader.read_hist_1d("totalUncertainty_up") uncDown = reader.read_hist_1d("totalUncertainty_down") rootfile = ROOT.TFile(rootfile,"READ") statHist = rootfile.Get("dataAR") unc = [] statunc = [] relunc = [] tot = [] for i, i_up in enumerate(uncUp["y"]): stat = statHist.GetBinError(i+1) u = abs(i_up-uncDown["y"][i])*0.5 # all = [ ttg["y"][i], top["y"][i], dy["y"][i], wjets["y"][i], wg["y"][i], zg["y"][i], other["y"][i], qcd["y"][i] ] all = [ top["y"][i], dy["y"][i], wjets["y"][i], other["y"][i], qcd["y"][i] ] sim = sum(all) unc.append(u) statunc.append(stat) tot.append(sim) relunc.append(u*100./sim) xbins = Variable( "$m_{T}(W)$", is_independent=True, is_binned=True, units="GeV") xbins.values = data["x_edges"] ydata = Variable( "Observed", is_independent=False, is_binned=False) ydata.values = data["y"] ydata.add_qualifier("CHANNEL",channel) ydata.add_qualifier("SQRT(S)","13","TeV") ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ytot = Variable( "Total simulation", is_independent=False, is_binned=False) ytot.values = tot ytot.add_qualifier("CHANNEL",channel) ytot.add_qualifier("SQRT(S)","13","TeV") ytot.add_qualifier("LUMINOSITY","137","fb$^{-1}$") # yttg = Variable( "tt$\gamma$", is_independent=False, is_binned=False) # yttg.values = ttg["y"] # yttg.add_qualifier("CHANNEL",channel) # yttg.add_qualifier("SQRT(S)","13","TeV") # yttg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ytop = Variable( "t/tt", is_independent=False, is_binned=False) ytop.values = top["y"] ytop.add_qualifier("CHANNEL",channel) ytop.add_qualifier("SQRT(S)","13","TeV") ytop.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ydy = Variable( "Drell-Yan", is_independent=False, is_binned=False) ydy.values = dy["y"] ydy.add_qualifier("CHANNEL",channel) ydy.add_qualifier("SQRT(S)","13","TeV") ydy.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ywjets = Variable( "W+jets", is_independent=False, is_binned=False) ywjets.values = wjets["y"] ywjets.add_qualifier("CHANNEL",channel) ywjets.add_qualifier("SQRT(S)","13","TeV") ywjets.add_qualifier("LUMINOSITY","137","fb$^{-1}$") # ywg = Variable( "W$\gamma$", is_independent=False, is_binned=False) # ywg.values = wg["y"] # ywg.add_qualifier("CHANNEL",channel) # ywg.add_qualifier("SQRT(S)","13","TeV") # ywg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") # yzg = Variable( "Z$\gamma$", is_independent=False, is_binned=False) # yzg.values = zg["y"] # yzg.add_qualifier("CHANNEL",channel) # yzg.add_qualifier("SQRT(S)","13","TeV") # yzg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yother = Variable( "Other", is_independent=False, is_binned=False) yother.values = other["y"] yother.add_qualifier("CHANNEL",channel) yother.add_qualifier("SQRT(S)","13","TeV") yother.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yqcd = Variable( "Multijet", is_independent=False, is_binned=False) yqcd.values = qcd["y"] yqcd.add_qualifier("CHANNEL",channel) yqcd.add_qualifier("SQRT(S)","13","TeV") yqcd.add_qualifier("LUMINOSITY","137","fb$^{-1}$") # yunc = Variable( "Total systematic uncertainty", is_independent=False, is_binned=False) # yunc.values = unc # yunc.add_qualifier("SQRT(S)","13","TeV") # yunc.add_qualifier("LUMINOSITY","137","fb$^{-1}$") #yrelunc = Variable( "Rel. uncertainty (%)", is_independent=False, is_binned=False) #yrelunc.values = relunc yunc = Uncertainty( "syst" ) yunc.is_symmetric = True yunc.values = unc ystatunc = Uncertainty( "stat" ) ystatunc.is_symmetric = True ystatunc.values = statunc ydata.uncertainties.append(ystatunc) ytot.uncertainties.append(yunc) tab.add_variable(xbins) tab.add_variable(ydata) tab.add_variable(ytot) tab.add_variable(ywjets) tab.add_variable(yqcd) tab.add_variable(ydy) tab.add_variable(ytop) tab.add_variable(yother) # tab.add_variable(ywg) # tab.add_variable(yzg) # tab.add_variable(yttg) # tab.add_variable(yunc) return tab
def convertEFTPtHistToYaml( rootfile, label, channel ): tab = Table(label) reader = RootFileReader(rootfile) data = reader.read_hist_1d("data") tot = reader.read_hist_1d("total") totbkg = reader.read_hist_1d("total_background") ttg = reader.read_hist_1d("signal") misID = reader.read_hist_1d("misID") had = reader.read_hist_1d("fakes") other = reader.read_hist_1d("other") wg = reader.read_hist_1d("WG") qcd = reader.read_hist_1d("QCD") zg = reader.read_hist_1d("ZG") eftbf = reader.read_hist_1d("bestFit") eftctZ045 = reader.read_hist_1d("ctZ0.45") eftctZI045 = reader.read_hist_1d("ctZI0.45") eftctZm045 = reader.read_hist_1d("ctZ-0.45") rootfile = ROOT.TFile(rootfile,"READ") totHist = rootfile.Get("total") datHist = rootfile.Get("data") unc = [] statunc = [] relunc = [] for i, i_tot in enumerate(tot["y"]): u = totHist.GetBinError(i+1) ustat = datHist.GetBinError(i+1) unc.append(u) statunc.append(ustat) relunc.append(u*100./i_tot) xbins = Variable( "$p_{T}(\gamma)$", is_independent=True, is_binned=True, units="GeV") xbins.values = data["x_edges"] ydata = Variable( "Observed", is_independent=False, is_binned=False) ydata.values = data["y"] ydata.add_qualifier("CHANNEL",channel) ydata.add_qualifier("SQRT(S)","13","TeV") ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ytot = Variable( "Total simulation", is_independent=False, is_binned=False) ytot.values = tot["y"] ytot.add_qualifier("CHANNEL",channel) ytot.add_qualifier("SQRT(S)","13","TeV") ytot.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ytotbkg = Variable( "Total background", is_independent=False, is_binned=False) ytotbkg.values = totbkg["y"] ytotbkg.add_qualifier("CHANNEL",channel) ytotbkg.add_qualifier("SQRT(S)","13","TeV") ytotbkg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ywg = Variable( "$W\gamma$", is_independent=False, is_binned=False) ywg.values = wg["y"] ywg.add_qualifier("CHANNEL",channel) ywg.add_qualifier("SQRT(S)","13","TeV") ywg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yzg = Variable( "$Z\gamma$", is_independent=False, is_binned=False) yzg.values = zg["y"] yzg.add_qualifier("CHANNEL",channel) yzg.add_qualifier("SQRT(S)","13","TeV") yzg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ymisID = Variable( "Misid. e", is_independent=False, is_binned=False) ymisID.values = misID["y"] ymisID.add_qualifier("CHANNEL",channel) ymisID.add_qualifier("SQRT(S)","13","TeV") ymisID.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yhad = Variable( "Hadronic $\gamma$", is_independent=False, is_binned=False) yhad.values = had["y"] yhad.add_qualifier("CHANNEL",channel) yhad.add_qualifier("SQRT(S)","13","TeV") yhad.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yqcd = Variable( "Multijet", is_independent=False, is_binned=False) yqcd.values = qcd["y"] yqcd.add_qualifier("CHANNEL",channel) yqcd.add_qualifier("SQRT(S)","13","TeV") yqcd.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yttg = Variable( "tt$\gamma$", is_independent=False, is_binned=False) yttg.values = ttg["y"] yttg.add_qualifier("CHANNEL",channel) yttg.add_qualifier("SQRT(S)","13","TeV") yttg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yother = Variable( "Other", is_independent=False, is_binned=False) yother.values = other["y"] yother.add_qualifier("CHANNEL",channel) yother.add_qualifier("SQRT(S)","13","TeV") yother.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yeftbf = Variable( "SM-EFT best fit", is_independent=False, is_binned=False) yeftbf.values = eftbf["y"] yeftbf.add_qualifier("CHANNEL",channel) yeftbf.add_qualifier("SQRT(S)","13","TeV") yeftbf.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yeftctZ045 = Variable( "$c_{tZ} = 0.45$", is_independent=False, is_binned=False, units="($\Lambda$/TeV)$^2$") yeftctZ045.values = eftctZ045["y"] yeftctZ045.add_qualifier("CHANNEL",channel) yeftctZ045.add_qualifier("SQRT(S)","13","TeV") yeftctZ045.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yeftctZI045 = Variable( "$c^I_{tZ} = 0.45$", is_independent=False, is_binned=False, units="($\Lambda$/TeV)$^2$") yeftctZI045.values = eftctZI045["y"] yeftctZI045.add_qualifier("CHANNEL",channel) yeftctZI045.add_qualifier("SQRT(S)","13","TeV") yeftctZI045.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yeftctZm045 = Variable( "$c_{tZ} = -0.45$", is_independent=False, is_binned=False, units="($\Lambda$/TeV)$^2$") yeftctZm045.values = eftctZm045["y"] yeftctZm045.add_qualifier("CHANNEL",channel) yeftctZm045.add_qualifier("SQRT(S)","13","TeV") yeftctZm045.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yunc = Uncertainty( "syst" ) yunc.is_symmetric = True yunc.values = unc ystatunc = Uncertainty( "stat" ) ystatunc.is_symmetric = True ystatunc.values = statunc ydata.uncertainties.append(ystatunc) ytot.uncertainties.append(yunc) tab.add_variable(xbins) tab.add_variable(ydata) tab.add_variable(ytot) tab.add_variable(yeftbf) tab.add_variable(yeftctZ045) tab.add_variable(yeftctZI045) tab.add_variable(yeftctZm045) tab.add_variable(ytotbkg) tab.add_variable(yttg) tab.add_variable(yhad) tab.add_variable(ymisID) tab.add_variable(yother) tab.add_variable(ywg) tab.add_variable(yqcd) tab.add_variable(yzg) return tab
def convertUnfoldingHistToYaml( rootfile, label, variable, unit ): tab = Table(label) reader = RootFileReader(rootfile) data = reader.read_hist_1d("unfoled_spectrum") simP8 = reader.read_hist_1d("fiducial_spectrum") simHpp = reader.read_hist_1d("fiducial_spectrum_Hpp") simH7 = reader.read_hist_1d("fiducial_spectrum_H7") totalUncUp = reader.read_hist_1d("totalUncertainty_up") mcstatUncUp = reader.read_hist_1d("mcStat_up") matrixUncUp = reader.read_hist_1d("totalMatrixVariationUnc_up") statUncUp = reader.read_hist_1d("stat_up") totunc = [] reltotunc = [] statunc = [] relstatunc = [] for i, i_up in enumerate(totalUncUp["y"]): tot = data["y"][i] utot = (i_up - tot)**2 utot += (mcstatUncUp["y"][i] - tot)**2 utot += matrixUncUp["y"][i]**2 utot = sqrt(utot) ustat = statUncUp["y"][i] - tot totunc.append(utot) statunc.append(ustat) reltotunc.append(utot*100./tot) relstatunc.append(ustat*100./tot) xbins = Variable( variable, is_independent=True, is_binned=True, units=unit) xbins.values = data["x_edges"] ydata = Variable( "Observed", is_independent=False, is_binned=False) ydata.values = data["y"] ydata.add_qualifier("SQRT(S)","13","TeV") ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yunc = Uncertainty( "total" ) yunc.is_symmetric = True yunc.values = totunc ystatunc = Uncertainty( "stat" ) ystatunc.is_symmetric = True ystatunc.values = statunc # ydata.uncertainties.append(ystatunc) # ydata.uncertainties.append(yunc) ysimP8 = Variable( "Simulation MG5_aMC + Pythia8", is_independent=False, is_binned=False) ysimP8.values = simP8["y"] ysimP8.add_qualifier("SQRT(S)","13","TeV") ysimP8.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ysimH7 = Variable( "Simulation MG5_aMC + Herwig7", is_independent=False, is_binned=False) ysimH7.values = simH7["y"] ysimH7.add_qualifier("SQRT(S)","13","TeV") ysimH7.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ysimHpp = Variable( "Simulation MG5_aMC + Herwig++", is_independent=False, is_binned=False) ysimHpp.values = simHpp["y"] ysimHpp.add_qualifier("SQRT(S)","13","TeV") ysimHpp.add_qualifier("LUMINOSITY","137","fb$^{-1}$") tab.add_variable(xbins) tab.add_variable(ydata) tab.add_variable(ysimP8) tab.add_variable(ysimH7) tab.add_variable(ysimHpp) return tab
def convertSRHistToYaml( rootfile, label, variable, unit ): tab = Table(label) reader = RootFileReader(rootfile) data = reader.read_hist_1d("dataAR") gen = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat0") had = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat134") misID = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat2") qcd = reader.read_hist_1d("QCD") uncUp = reader.read_hist_1d("totalUncertainty_up") uncDown = reader.read_hist_1d("totalUncertainty_down") rootfile = ROOT.TFile(rootfile,"READ") statHist = rootfile.Get("dataAR") unc = [] statunc = [] relunc = [] tot = [] for i, i_up in enumerate(uncUp["y"]): stat = statHist.GetBinError(i+1) u = abs(i_up-uncDown["y"][i])*0.5 sim = sum([ gen["y"][i], had["y"][i], misID["y"][i], qcd["y"][i] ]) unc.append(u) statunc.append(stat) tot.append(sim) relunc.append(u*100./sim) xbins = Variable( variable, is_independent=True, is_binned=True, units=unit) xbins.values = data["x_edges"] ydata = Variable( "Observed", is_independent=False, is_binned=False) ydata.values = data["y"] ydata.add_qualifier("CHANNEL","l+jets") ydata.add_qualifier("SQRT(S)","13","TeV") ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ytot = Variable( "Total simulation", is_independent=False, is_binned=False) ytot.values = tot ytot.add_qualifier("CHANNEL","l+jets") ytot.add_qualifier("SQRT(S)","13","TeV") ytot.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ygen = Variable( "Genuine $\gamma$", is_independent=False, is_binned=False) ygen.values = gen["y"] ygen.add_qualifier("CHANNEL","l+jets") ygen.add_qualifier("SQRT(S)","13","TeV") ygen.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yhad = Variable( "Hadronic $\gamma$", is_independent=False, is_binned=False) yhad.values = had["y"] yhad.add_qualifier("CHANNEL","l+jets") yhad.add_qualifier("SQRT(S)","13","TeV") yhad.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ymisID = Variable( "Misid. e", is_independent=False, is_binned=False) ymisID.values = misID["y"] ymisID.add_qualifier("CHANNEL","l+jets") ymisID.add_qualifier("SQRT(S)","13","TeV") ymisID.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yqcd = Variable( "Multijet", is_independent=False, is_binned=False) yqcd.values = qcd["y"] yqcd.add_qualifier("CHANNEL","l+jets") yqcd.add_qualifier("SQRT(S)","13","TeV") yqcd.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yunc = Uncertainty( "syst" ) yunc.is_symmetric = True yunc.values = unc ystatunc = Uncertainty( "stat" ) ystatunc.is_symmetric = True ystatunc.values = statunc ydata.uncertainties.append(ystatunc) ytot.uncertainties.append(yunc) tab.add_variable(xbins) tab.add_variable(ydata) tab.add_variable(ytot) tab.add_variable(ygen) tab.add_variable(yhad) tab.add_variable(ymisID) tab.add_variable(yqcd) return tab
def convertMlgHistToYaml( rootfile, label, variable, channel ): tab = Table(label) reader = RootFileReader(rootfile) data = reader.read_hist_1d("dataAR") misID = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat2") wg = reader.read_hist_1d("WG_centralnoChgIsoNoSieiephotoncat0") zg = reader.read_hist_1d("ZG_centralnoChgIsoNoSieiephotoncat0") other = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat0") had = reader.read_hist_1d("TTG_centralnoChgIsoNoSieiephotoncat134") qcd = reader.read_hist_1d("QCD") uncUp = reader.read_hist_1d("totalUncertainty_up") uncDown = reader.read_hist_1d("totalUncertainty_down") rootfile = ROOT.TFile(rootfile,"READ") statHist = rootfile.Get("dataAR") unc = [] statunc = [] relunc = [] tot = [] for i, i_up in enumerate(uncUp["y"]): stat = statHist.GetBinError(i+1) u = abs(i_up-uncDown["y"][i])*0.5 all = [ misID["y"][i], wg["y"][i], zg["y"][i], other["y"][i], had["y"][i], qcd["y"][i] ] sim = sum(all) unc.append(u) statunc.append(stat) tot.append(sim) relunc.append(u*100./sim) xbins = Variable( variable, is_independent=True, is_binned=True, units="GeV") xbins.values = data["x_edges"] ydata = Variable( "Observed", is_independent=False, is_binned=False) ydata.values = data["y"] ydata.add_qualifier("CHANNEL",channel) ydata.add_qualifier("SQRT(S)","13","TeV") ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ytot = Variable( "Total simulation", is_independent=False, is_binned=False) ytot.values = tot ytot.add_qualifier("CHANNEL",channel) ytot.add_qualifier("SQRT(S)","13","TeV") ytot.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ymisID = Variable( "Misid. e", is_independent=False, is_binned=False) ymisID.values = misID["y"] ymisID.add_qualifier("CHANNEL",channel) ymisID.add_qualifier("SQRT(S)","13","TeV") ymisID.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yhad = Variable( "Hadronic $\gamma$", is_independent=False, is_binned=False) yhad.values = had["y"] yhad.add_qualifier("CHANNEL",channel) yhad.add_qualifier("SQRT(S)","13","TeV") yhad.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ywg = Variable( "W$\gamma$", is_independent=False, is_binned=False) ywg.values = wg["y"] ywg.add_qualifier("CHANNEL",channel) ywg.add_qualifier("SQRT(S)","13","TeV") ywg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yzg = Variable( "Z$\gamma$", is_independent=False, is_binned=False) yzg.values = zg["y"] yzg.add_qualifier("CHANNEL",channel) yzg.add_qualifier("SQRT(S)","13","TeV") yzg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yother = Variable( "Other", is_independent=False, is_binned=False) yother.values = other["y"] yother.add_qualifier("CHANNEL",channel) yother.add_qualifier("SQRT(S)","13","TeV") yother.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yqcd = Variable( "Multijet", is_independent=False, is_binned=False) yqcd.values = qcd["y"] yqcd.add_qualifier("CHANNEL",channel) yqcd.add_qualifier("SQRT(S)","13","TeV") yqcd.add_qualifier("LUMINOSITY","137","fb$^{-1}$") # yunc = Variable( "Total systematic uncertainty", is_independent=False, is_binned=False) # yunc.values = unc # yunc.add_qualifier("SQRT(S)","13","TeV") # yunc.add_qualifier("LUMINOSITY","137","fb$^{-1}$") #yrelunc = Variable( "Rel. uncertainty (%)", is_independent=False, is_binned=False) #yrelunc.values = relunc yunc = Uncertainty( "syst" ) yunc.is_symmetric = True yunc.values = unc ystatunc = Uncertainty( "stat" ) ystatunc.is_symmetric = True ystatunc.values = statunc ydata.uncertainties.append(ystatunc) ytot.uncertainties.append(yunc) tab.add_variable(xbins) tab.add_variable(ydata) tab.add_variable(ytot) tab.add_variable(ymisID) tab.add_variable(ywg) tab.add_variable(yzg) tab.add_variable(yother) tab.add_variable(yhad) tab.add_variable(yqcd) # tab.add_variable(yunc) return tab
def convertSRPlotToYaml(): tab = Table("Figure 6") reader = RootFileReader("../regionPlots/SR_incl.root") data = reader.read_hist_1d("0dc_2016data") tot = reader.read_hist_1d("0dc_2016total") totbkg = reader.read_hist_1d("0dc_2016total_background") ttg = reader.read_hist_1d("0dc_2016signal") misID = reader.read_hist_1d("0dc_2016misID") had = reader.read_hist_1d("0dc_2016fakes") other = reader.read_hist_1d("0dc_2016other") wg = reader.read_hist_1d("0dc_2016WG") qcd = reader.read_hist_1d("0dc_2016QCD") zg = reader.read_hist_1d("0dc_2016ZG") rootfile = ROOT.TFile("../regionPlots/SR_incl.root","READ") totHist = rootfile.Get("0dc_2016total") datHist = rootfile.Get("0dc_2016data") unc = [] statunc = [] relunc = [] for i, i_tot in enumerate(tot["y"]): u = totHist.GetBinError(i+1) ustat = datHist.GetBinError(i+1) unc.append(u) statunc.append(ustat) relunc.append(u*100./i_tot) crBinLabel = [ "SR3, e, $M_{3}$ $<$ 280 GeV", "SR3, e, 280 $\leq$ $M_{3}$ $<$ 420 GeV", "SR3, e, $M_{3}$ $\geq$ 420 GeV", "SR3, $\mu$, $M_{3}$ $<$ 280 GeV", "SR3, $\mu$, 280 $\leq$ $M_{3}$ $<$ 420 GeV", "SR3, $\mu$, $M_{3}$ $\geq$ 420 GeV", "SR4p, e, $M_{3}$ $<$ 280 GeV", "SR4p, e, 280 $\leq$ $M_{3}$ $<$ 420 GeV", "SR4p, e, $M_{3}$ $\geq$ 420 GeV", "SR4p, $\mu$, $M_{3}$ $<$ 280 GeV", "SR4p, $\mu$, 280 $\leq$ $M_{3}$ $<$ 420 GeV", "SR4p, $\mu$, $M_{3}$ $\geq$ 420 GeV", ] xbins = Variable( "Bin", is_independent=True, is_binned=False) xbins.values = crBinLabel ydata = Variable( "Observed", is_independent=False, is_binned=False) ydata.values = data["y"] ydata.add_qualifier("SQRT(S)","13","TeV") ydata.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ytot = Variable( "Total simulation", is_independent=False, is_binned=False) ytot.values = tot["y"] ytot.add_qualifier("SQRT(S)","13","TeV") ytot.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ytotbkg = Variable( "Total background", is_independent=False, is_binned=False) ytotbkg.values = totbkg["y"] ytotbkg.add_qualifier("SQRT(S)","13","TeV") ytotbkg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ywg = Variable( "$W\gamma$", is_independent=False, is_binned=False) ywg.values = wg["y"] ywg.add_qualifier("SQRT(S)","13","TeV") ywg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yzg = Variable( "$Z\gamma$", is_independent=False, is_binned=False) yzg.values = zg["y"] yzg.add_qualifier("SQRT(S)","13","TeV") yzg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") ymisID = Variable( "Misid. e", is_independent=False, is_binned=False) ymisID.values = misID["y"] ymisID.add_qualifier("SQRT(S)","13","TeV") ymisID.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yhad = Variable( "Hadronic $\gamma$", is_independent=False, is_binned=False) yhad.values = had["y"] yhad.add_qualifier("SQRT(S)","13","TeV") yhad.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yqcd = Variable( "Multijet", is_independent=False, is_binned=False) yqcd.values = qcd["y"] yqcd.add_qualifier("SQRT(S)","13","TeV") yqcd.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yttg = Variable( "tt$\gamma$", is_independent=False, is_binned=False) yttg.values = ttg["y"] yttg.add_qualifier("SQRT(S)","13","TeV") yttg.add_qualifier("LUMINOSITY","137","fb$^{-1}$") yother = Variable( "Other", is_independent=False, is_binned=False) yother.values = other["y"] yother.add_qualifier("SQRT(S)","13","TeV") yother.add_qualifier("LUMINOSITY","137","fb$^{-1}$") # yunc = Variable( "Total uncertainty", is_independent=False, is_binned=False) # yunc.values = unc # yunc.add_qualifier("SQRT(S)","13","TeV") # yunc.add_qualifier("LUMINOSITY","137","fb$^{-1}$") #yrelunc = Variable( "Rel. uncertainty (%)", is_independent=False, is_binned=False) #yrelunc.values = relunc yunc = Uncertainty( "syst" ) yunc.is_symmetric = True yunc.values = unc ystatunc = Uncertainty( "stat" ) ystatunc.is_symmetric = True ystatunc.values = statunc ydata.uncertainties.append(ystatunc) ytot.uncertainties.append(yunc) tab.add_variable(xbins) tab.add_variable(ydata) tab.add_variable(ytot) tab.add_variable(ytotbkg) tab.add_variable(yttg) tab.add_variable(ymisID) tab.add_variable(yhad) tab.add_variable(yother) tab.add_variable(ywg) tab.add_variable(yqcd) tab.add_variable(yzg) # tab.add_variable(yunc) return tab
"HEPData/inputs/smp17010/folders_dressedleptons/output_root/matrix03__XSRatioSystPt.root" ) # Read the histogram data_covariance_mm_Pt = reader_covariance_mm_Pt.read_hist_2d( "covariance_totsum_0") # Create variable objects x_covariance_mm_Pt = Variable("Bin X", is_independent=True, is_binned=True) x_covariance_mm_Pt.values = data_covariance_mm_Pt["x_edges"] y_covariance_mm_Pt = Variable("Bin Y", is_independent=True, is_binned=False) y_covariance_mm_Pt.values = data_covariance_mm_Pt["y"] z_covariance_mm_Pt = Variable("covariance Matrix", is_independent=False, is_binned=False) z_covariance_mm_Pt.values = data_covariance_mm_Pt["z"] table_covariance_XSRatio_mm_Pt = Table("cov matr norm xs aux 1a") table_covariance_XSRatio_mm_Pt.description = "Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt in the dimuon final state." table_covariance_XSRatio_mm_Pt.location = "Supplementary material" for var in [x_covariance_mm_Pt, y_covariance_mm_Pt, z_covariance_mm_Pt]: table_covariance_XSRatio_mm_Pt.add_variable(var) submission.add_table(table_covariance_XSRatio_mm_Pt) # Create a reader for the input file reader_covariance_mm_Rap = RootFileReader( "HEPData/inputs/smp17010/folders_dressedleptons/output_root/matrix03__XSRatioSystRap.root" ) # Read the histogram data_covariance_mm_Rap = reader_covariance_mm_Rap.read_hist_2d( "covariance_totsum_0") # Create variable objects x_covariance_mm_Rap = Variable("Bin X", is_independent=True, is_binned=True)
def make_table(): params = ['r_ggH', 'r_VBF', 'r_VH', 'r_top'] # Load results + xsbr data inputMode = "mu" translatePOIs = LoadTranslations("translate/pois_%s.json" % inputMode) with open( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/correlations_mu.json", "r") as jf: correlations = json.load(jf) with open( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/correlations_expected_mu.json", "r") as jf: correlations_exp = json.load(jf) # Make table of results table = Table("Correlations: production mode signal strength") table.description = "Observed and expected correlations between the parameters in the production mode signal strength fit." table.location = "Results from additional material" table.keywords["reactions"] = ["P P --> H ( --> GAMMA GAMMA ) X"] pois_x = Variable("Parameter (x)", is_independent=True, is_binned=False) pois_y = Variable("Parameter (y)", is_independent=True, is_binned=False) c = Variable("Observed correlation", is_independent=False, is_binned=False) c.add_qualifier("SQRT(S)", 13, "TeV") c.add_qualifier("MH", '125.38', "GeV") c_exp = Variable("Expected correlation", is_independent=False, is_binned=False) c_exp.add_qualifier("SQRT(S)", 13, "TeV") c_exp.add_qualifier("MH", '125.38', "GeV") poiNames_x = [] poiNames_y = [] corr = [] corr_exp = [] for ipoi in params: for jpoi in params: poiNames_x.append(str(Translate(ipoi, translatePOIs))) poiNames_y.append(str(Translate(jpoi, translatePOIs))) # Extract correlation coefficient corr.append(correlations["%s__%s" % (ipoi, jpoi)]) corr_exp.append(correlations_exp["%s__%s" % (ipoi, jpoi)]) pois_x.values = poiNames_x pois_y.values = poiNames_y c.values = np.round(np.array(corr), 3) c_exp.values = np.round(np.array(corr_exp), 3) # Add variables to table table.add_variable(pois_x) table.add_variable(pois_y) table.add_variable(c) table.add_variable(c_exp) # Add figure table.add_image( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/perproc_mu_corr.pdf" ) return table
def make_table(): params = [ 'r_ggH_0J_low', 'r_ggH_0J_high', 'r_ggH_1J_low', 'r_ggH_1J_med', 'r_ggH_1J_high', 'r_ggH_2J_low', 'r_ggH_2J_med', 'r_ggH_2J_high', 'r_ggH_BSM_low', 'r_ggH_BSM_med', 'r_ggH_BSM_high', 'r_qqH_low_mjj_low_pthjj', 'r_qqH_low_mjj_high_pthjj', 'r_qqH_high_mjj_low_pthjj', 'r_qqH_high_mjj_high_pthjj', 'r_qqH_VHhad', 'r_qqH_BSM', 'r_WH_lep_low', 'r_WH_lep_med', 'r_WH_lep_high', 'r_ZH_lep', 'r_ttH_low', 'r_ttH_medlow', 'r_ttH_medhigh', 'r_ttH_high', 'r_ttH_veryhigh', 'r_tH' ] # Load results + xsbr data inputXSBRjson = "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/flashggFinalFit/Plots/jsons/xsbr_theory_stage1p2_extended_125p38.json" inputExpResultsJson = '/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/flashggFinalFit/Plots/expected_UL_redo.json' inputObsResultsJson = '/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/flashggFinalFit/Plots/observed_UL_redo.json' inputMode = "stage1p2_extended" translatePOIs = LoadTranslations("translate/pois_%s.json" % inputMode) with open(inputXSBRjson, "r") as jsonfile: xsbr_theory = json.load(jsonfile) observed = CopyDataFromJsonFile(inputObsResultsJson, inputMode, params) expected = CopyDataFromJsonFile(inputExpResultsJson, inputMode, params) mh = float(re.sub("p", ".", inputXSBRjson.split("_")[-1].split(".json")[0])) # Make table of results table = Table("STXS stage 1.2 minimal merging scheme") table.description = "Results of the minimal merging scheme STXS fit. The best fit cross sections are shown together with the respective 68% C.L. intervals. The uncertainty is decomposed into the systematic and statistical components. The expected uncertainties on the fitted parameters are given in brackets. Also listed are the SM predictions for the cross sections and the theoretical uncertainty in those predictions." table.location = "Results from Figure 20 and Table 13" table.keywords["reactions"] = ["P P --> H ( --> GAMMA GAMMA ) X"] pois = Variable("STXS region", is_independent=True, is_binned=False) poiNames = [] for poi in params: poiNames.append(str(Translate(poi, translatePOIs))) pois.values = poiNames # Dependent variables # SM predict xsbr_sm = Variable("SM predicted cross section times branching ratio", is_independent=False, is_binned=False, units='fb') xsbr_sm.add_qualifier("SQRT(S)", 13, "TeV") xsbr_sm.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') xsbr_sm.add_qualifier("MH", '125.38', "GeV") theory = Uncertainty("Theory", is_symmetric=False) xsbr_vals = [] xsbr_hi_th, xsbr_lo_th = [], [] for poi in params: xsbr_vals.append(xsbr_theory[poi]['nominal']) xsbr_hi_th.append(xsbr_theory[poi]['High01Sigma']) xsbr_lo_th.append(-1 * abs(xsbr_theory[poi]['Low01Sigma'])) xsbr_sm.values = np.round(np.array(xsbr_vals), 3) theory.values = zip(np.round(np.array(xsbr_lo_th), 3), np.round(np.array(xsbr_hi_th), 3)) xsbr_sm.add_uncertainty(theory) # Observed cross section xsbr = Variable("Observed cross section times branching ratio", is_independent=False, is_binned=False, units='fb') xsbr.add_qualifier("SQRT(S)", 13, "TeV") xsbr.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') xsbr.add_qualifier("MH", '125.38', "GeV") # Add uncertainties tot = Uncertainty("Total", is_symmetric=False) stat = Uncertainty("Stat only", is_symmetric=False) syst = Uncertainty("Syst", is_symmetric=False) xsbr_vals = [] xsbr_hi_tot, xsbr_lo_tot = [], [] xsbr_hi_stat, xsbr_lo_stat = [], [] xsbr_hi_syst, xsbr_lo_syst = [], [] for poi in params: xsbr_vals.append(xsbr_theory[poi]['nominal'] * observed[poi]['Val']) xsbr_hi_tot.append( abs(xsbr_theory[poi]['nominal'] * observed[poi]['ErrorHi'])) xsbr_lo_tot.append( -1 * abs(xsbr_theory[poi]['nominal'] * observed[poi]['ErrorLo'])) xsbr_hi_stat.append( abs(xsbr_theory[poi]['nominal'] * observed[poi]['StatHi'])) xsbr_lo_stat.append( -1 * abs(xsbr_theory[poi]['nominal'] * observed[poi]['StatLo'])) xsbr_hi_syst.append( abs(xsbr_theory[poi]['nominal'] * observed[poi]['SystHi'])) xsbr_lo_syst.append( -1 * abs(xsbr_theory[poi]['nominal'] * observed[poi]['SystLo'])) tot.values = zip(np.round(np.array(xsbr_lo_tot), 3), np.round(np.array(xsbr_hi_tot), 3)) stat.values = zip(np.round(np.array(xsbr_lo_stat), 3), np.round(np.array(xsbr_hi_stat), 3)) syst.values = zip(np.round(np.array(xsbr_lo_syst), 3), np.round(np.array(xsbr_hi_syst), 3)) xsbr.values = np.round(np.array(xsbr_vals), 3) xsbr.add_uncertainty(tot) xsbr.add_uncertainty(stat) xsbr.add_uncertainty(syst) # Observed ratio to SM xsbrr = Variable("Observed ratio to SM", is_independent=False, is_binned=False, units='') xsbrr.add_qualifier("SQRT(S)", 13, "TeV") xsbrr.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') xsbrr.add_qualifier("MH", '125.38', "GeV") # Add uncertainties totr = Uncertainty("Total", is_symmetric=False) statr = Uncertainty("Stat only", is_symmetric=False) systr = Uncertainty("Syst", is_symmetric=False) xsbr_vals = [] xsbr_hi_tot, xsbr_lo_tot = [], [] xsbr_hi_stat, xsbr_lo_stat = [], [] xsbr_hi_syst, xsbr_lo_syst = [], [] for poi in params: xsbr_vals.append(observed[poi]['Val']) xsbr_hi_tot.append(abs(observed[poi]['ErrorHi'])) xsbr_lo_tot.append(-1 * abs(observed[poi]['ErrorLo'])) xsbr_hi_stat.append(abs(observed[poi]['StatHi'])) xsbr_lo_stat.append(-1 * abs(observed[poi]['StatLo'])) xsbr_hi_syst.append(abs(observed[poi]['SystHi'])) xsbr_lo_syst.append(-1 * abs(observed[poi]['SystLo'])) totr.values = zip(np.round(np.array(xsbr_lo_tot), 3), np.round(np.array(xsbr_hi_tot), 3)) statr.values = zip(np.round(np.array(xsbr_lo_stat), 3), np.round(np.array(xsbr_hi_stat), 3)) systr.values = zip(np.round(np.array(xsbr_lo_syst), 3), np.round(np.array(xsbr_hi_syst), 3)) xsbrr.values = np.round(np.array(xsbr_vals), 3) xsbrr.add_uncertainty(totr) xsbrr.add_uncertainty(statr) xsbrr.add_uncertainty(systr) # Expected cross section xsbr_exp = Variable("Expected cross section times branching ratio", is_independent=False, is_binned=False, units='fb') xsbr_exp.add_qualifier("SQRT(S)", 13, "TeV") xsbr_exp.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') xsbr_exp.add_qualifier("MH", '125.38', "GeV") # Add uncertainties tot_exp = Uncertainty("Total", is_symmetric=False) stat_exp = Uncertainty("Stat only", is_symmetric=False) syst_exp = Uncertainty("Syst", is_symmetric=False) xsbr_vals = [] xsbr_hi_tot, xsbr_lo_tot = [], [] xsbr_hi_stat, xsbr_lo_stat = [], [] xsbr_hi_syst, xsbr_lo_syst = [], [] for poi in params: xsbr_vals.append(xsbr_theory[poi]['nominal']) xsbr_hi_tot.append( abs(xsbr_theory[poi]['nominal'] * expected[poi]['ErrorHi'])) xsbr_lo_tot.append( -1 * abs(xsbr_theory[poi]['nominal'] * expected[poi]['ErrorLo'])) xsbr_hi_stat.append( abs(xsbr_theory[poi]['nominal'] * expected[poi]['StatHi'])) xsbr_lo_stat.append( -1 * abs(xsbr_theory[poi]['nominal'] * expected[poi]['StatLo'])) xsbr_hi_syst.append( abs(xsbr_theory[poi]['nominal'] * expected[poi]['SystHi'])) xsbr_lo_syst.append( -1 * abs(xsbr_theory[poi]['nominal'] * expected[poi]['SystLo'])) tot_exp.values = zip(np.round(np.array(xsbr_lo_tot), 3), np.round(np.array(xsbr_hi_tot), 3)) stat_exp.values = zip(np.round(np.array(xsbr_lo_stat), 3), np.round(np.array(xsbr_hi_stat), 3)) syst_exp.values = zip(np.round(np.array(xsbr_lo_syst), 3), np.round(np.array(xsbr_hi_syst), 3)) xsbr_exp.values = np.round(np.array(xsbr_vals), 3) xsbr_exp.add_uncertainty(tot_exp) xsbr_exp.add_uncertainty(stat_exp) xsbr_exp.add_uncertainty(syst_exp) # Expected ratio to SM xsbrr_exp = Variable("Expected ratio to SM", is_independent=False, is_binned=False, units='') xsbrr_exp.add_qualifier("SQRT(S)", 13, "TeV") xsbrr_exp.add_qualifier("ABS(YRAP(HIGGS))", '<2.5') xsbrr_exp.add_qualifier("MH", '125.38', "GeV") # Add uncertainties totr_exp = Uncertainty("Total", is_symmetric=False) statr_exp = Uncertainty("Stat only", is_symmetric=False) systr_exp = Uncertainty("Syst", is_symmetric=False) xsbr_vals = [] xsbr_hi_tot, xsbr_lo_tot = [], [] xsbr_hi_stat, xsbr_lo_stat = [], [] xsbr_hi_syst, xsbr_lo_syst = [], [] for poi in params: xsbr_vals.append(1.00) xsbr_hi_tot.append(abs(expected[poi]['ErrorHi'])) xsbr_lo_tot.append(-1 * abs(expected[poi]['ErrorLo'])) xsbr_hi_stat.append(abs(expected[poi]['StatHi'])) xsbr_lo_stat.append(-1 * abs(expected[poi]['StatLo'])) xsbr_hi_syst.append(abs(expected[poi]['SystHi'])) xsbr_lo_syst.append(-1 * abs(expected[poi]['SystLo'])) totr_exp.values = zip(np.round(np.array(xsbr_lo_tot), 3), np.round(np.array(xsbr_hi_tot), 3)) statr_exp.values = zip(np.round(np.array(xsbr_lo_stat), 3), np.round(np.array(xsbr_hi_stat), 3)) systr_exp.values = zip(np.round(np.array(xsbr_lo_syst), 3), np.round(np.array(xsbr_hi_syst), 3)) xsbrr_exp.values = np.round(np.array(xsbr_vals), 3) xsbrr_exp.add_uncertainty(totr_exp) xsbrr_exp.add_uncertainty(statr_exp) xsbrr_exp.add_uncertainty(systr_exp) # Add variables to table table.add_variable(pois) table.add_variable(xsbr_sm) table.add_variable(xsbr) table.add_variable(xsbrr) table.add_variable(xsbr_exp) table.add_variable(xsbrr_exp) # Add figure table.add_image( "/afs/cern.ch/work/j/jlangfor/hgg/legacy/FinalFits/UL/Dec20/CMSSW_10_2_13/src/OtherScripts/HEPdata/hepdata_lib/hig-19-015/inputs/stxs_dist_stage1p2_minimal.pdf" ) return table
from hepdata_lib import Submission, Table, Variable, Uncertainty #INITIALIZE submission = Submission() #ABSTRACT submission.read_abstract("input/abstract.txt") submission.add_link( "Webpage with all figures and tables", "https://cms-results.web.cern.ch/cms-results/public-results/publications/SMP-19-013/" ) submission.add_link("arXiv", "http://arxiv.org/abs/arXiv:2105.12780") submission.add_record_id(1865855, "inspire") #FIGURE 2 UPPER LEFT fig2_ul = Table("Figure 2 (upper left)") fig2_ul.description = "Distribution of the transverse momentum of the diphoton system for the $\mathrm{W}\gamma\gamma$ electron channel. The predicted yields are shown with their pre-fit normalisations. The observed data, the expected signal contribution and the background estimates are presented with error bars showing the corresponding statistical uncertainties." fig2_ul.location = "Data from Figure 2 on Page 6 of the preprint" fig2_ul.keywords["observables"] = ["Diphoton pT"] fig2_ul.keywords["reactions"] = [ "P P --> W GAMMA GAMMA --> ELECTRON NU GAMMA GAMMA" ] fig2_ul_in = np.loadtxt("input/fig2_ul.txt", skiprows=1) #diphoton pT fig2_ul_pt = Variable("$p_T^{\gamma\gamma}$", is_independent=True, is_binned=False, units="GeV") fig2_ul_pt.values = fig2_ul_in[:, 0]
import pandas as pd from hepdata_lib import Submission from hepdata_lib import Table from hepdata_lib import RootFileReader from hepdata_lib import Variable from hepdata_lib import Uncertainty submission = Submission() df = pd.read_csv("input.csv") df = df.fillna("") for index, fig in df.iterrows(): print(fig["figure_name"]) # create a table table = Table(fig["figure_name"]) table.description = fig["description"] table.location = fig["paper_location"] # read figures reader = RootFileReader(fig["file_location"]) if fig["type_stat"].lower() in ["tgraph", "tgrapherrors", "tgraphasymmerrors"]: stat = reader.read_graph(fig["name_stat"]) elif fig["type_stat"].lower() == "th1": stat = reader.read_hist_1d(fig["name_stat"]) elif fig["type_stat"].lower() == "th2": stat = reader.read_hist_2d(fig["name_stat"]) else: print("ERROR: {}, type not recognized!".format(fig["figure_name"])) if fig["type_syst"].lower() in ["tgraph", "tgrapherrors", "tgraphasymmerrors"]:
from hepdata_lib import Submission import numpy as np submission = Submission() submission.read_abstract("hepdata_lib/examples/example_inputs/abstract.txt") submission.add_link( "Webpage with all figures and tables", "https://cms-results.web.cern.ch/cms-results/public-results/publications/B2G-16-029/" ) submission.add_link("arXiv", "http://arxiv.org/abs/arXiv:1802.09407") submission.add_record_id(1657397, "inspire") ### Table from hepdata_lib import Table table = Table("Additional Figure 1") table.description = "Signal selection efficiency times acceptance as a function of resonance mass for a spin-2 bulk graviton decaying to WW and a spin-1 W' decaying to WZ." table.location = "Data from additional Figure 1" table.keywords["observables"] = ["ACC", "EFF"] table.keywords["reactions"] = [ "P P --> GRAVITON --> W+ W-", "P P --> WPRIME --> W+/W- Z0" ] data = np.loadtxt("hepdata_lib/examples/example_inputs/effacc_signal.txt", skiprows=2) print(data) ### Variable from hepdata_lib import Variable
import numpy as np submission = Submission() sig_digits = 3 submission.read_abstract("HEPData/inputs/hig20017/abstract.txt") submission.add_link( "Webpage with all figures and tables", "http://cms-results.web.cern.ch/cms-results/public-results/publications/HIG-20-017/index.html" ) #submission.add_link("arXiv", "http://arxiv.org/abs/arXiv:xxxx.xxxxx") #submission.add_record_id(1818160, "inspire") ### Begin Table 2 table2 = Table("Table 2") table2.description = "Summary of the impact of the systematic uncertainties on the extracted signal strength; for the case of a background-only simulated data set, i.e., assuming no contributions from the $\mathrm{H}^{\pm}$ and $\mathrm{H}^{\pm\pm}$ processes, and including a charged Higgs boson signal for values of $s_{\mathrm{H}}=1.0$ and $m_{\mathrm{H}_{5}}=500$ GeV in the GM model." table2.location = "Data from Table 2" table2.keywords["observables"] = ["Uncertainty"] table2.keywords["reactions"] = ["P P --> W W j j", "P P --> W Z j j"] table2.keywords["phrases"] = [ "Same-sign WW", "WZ", "Georgi-Machacek", "Charged Higgs", "VBF" ] data2 = np.loadtxt("HEPData/inputs/hig20017/systematics.txt", dtype='string', skiprows=2) print(data2)