def dip_residual_method():
hh = TH2F("hh", "", 20, hodo_center - 4.0, hodo_center + 4.0, 200,
-2, 2)
cuts = 'n_tracks==1 && amp_max[{}]>1000 && amp_max[{}]>1000'.format(
self.xtal[0], self.xtal[1])
t_tree.Draw("(fit_time[{0}]-fit_time[{1}]):{2}>>hh".format(
self.xtal[0], self.xtal[1], axis[0]), cuts,
"COLZ") # Plot dt vs (X or Y)
hh.FitSlicesY() # Fit slices with gaussians
gr = TGraphErrors(t_file.Get(
"hh_1")) # hh_1 is the histo of means from FitSlicesY
## Sorry for the confusing names. We plot dt vs (X or Y), so dt is our y_var, and dx is our x_var, the distance term (ie X OR Y)
points = range(gr.GetN())
dx = array('d', gr.GetX())
dt = array('d', gr.GetY())
p1 = TF1("p1", "pol1")
TGraph(gr.GetN(), dx, dt).Fit("p1",
"WQ") # Fit dt_mean vs Y linearly
## Sum each 3 consecutive residuals, take the max from this value's abs(), and the middle index is where the "dip" is farthest from the fit, the "center"
res = [dt[i] - p1.Eval(dx[i])
for i in points] # The residual between the fit and data
sum_res = [abs(sum(res[i:i + 3])) for i in points[:-2]
] # Sum 3 residuals ([:-2] to avoid index out of range)
axis_center = dx[
sum_res.index(max(sum_res)) +
1] # +1 b/c we index the 1st out of 3 residuals, but we want the middle one
return axis_center
def write(self, setup):
#Rescaling to efficiency
normalisation = 1/self.histogram.GetBinContent(1)
#Rescaling everything to have rates
self.histogram.Scale(normalisation)
efficiencyPlot = TGraphErrors(self.histogram)
efficiencyPlot.SetName(self.cfg_ana.plot_name+"_errors")
efficiencyPlot.SetTitle(self.cfg_ana.plot_title)
for index in xrange(0, len(efficiencyPlot.GetX())):
efficiencyPlot.SetPointError(index,
efficiencyPlot.GetEX()[index],
sqrt(efficiencyPlot.GetY()[index] * normalisation)
)
c1 = TCanvas ("canvas_" + self.cfg_ana.plot_name, self.cfg_ana.plot_title, 600, 600)
c1.SetGridx()
c1.SetGridy()
efficiencyPlot.Draw("AP")
c1.Update()
c1.Write()
c1.Print(self.cfg_ana.plot_name + ".svg", "svg")
efficiencyPlot.Write()
def _convert_tgrapherrors(root_obj: TGraphErrors) -> dict:
"""
Take the values from a TGraphErrors and add them to a series
of arrays
Parameters
----------
root_obj : TGraphError
ROOT TGraphError
Returns
-------
dict
Dictionary with x, y, xerr, yerr points
"""
xm, ym, x_errm, y_errm = (root_obj.GetX(),
root_obj.GetY(),
root_obj.GetEX(),
root_obj.GetEY())
x, y, x_err, y_err = [], [], [], []
for n in range(0, root_obj.GetN()):
x.append(xm[n])
y.append(ym[n])
x_err.append(x_errm[n])
y_err.append(y_errm[n])
return {'x': np.array(x), 'y': np.array(y),
'xerr': np.array(x_err), 'yerr': np.array(y_err)}
def testIthr():
lines = get_lines('DAC_scan_ithr_0x40to0xf0.dat')
gr1 = TGraphErrors()
gr2 = TGraphErrors()
fUnit = 1000. / 0.7
yUnit = 'e^{-}'
for line in lines:
if len(line) == 0: continue
if line[0] in ['#', '\n']: continue
fs = line.rstrip().split()
ix = int(fs[0])
gr1.SetPoint(ix, float(fs[1]), float(fs[2]) * fUnit)
gr1.SetPointError(ix, 0, float(fs[3]) * fUnit)
gr2.SetPoint(ix, float(fs[1]), float(fs[4]) * fUnit)
gr2.SetPointError(ix, 0, float(fs[5]) * fUnit)
useAtlasStyle()
gStyle.SetMarkerStyle(20)
gr1.SetMarkerStyle(20)
gr1.Draw('AP')
h1 = gr1.GetHistogram()
h1.GetYaxis().SetTitle("Threshold [" + yUnit + "]")
h1.GetXaxis().SetTitle("I_{Thre} code")
# h1.GetYaxis().SetRangeUser(0,0.2)
gPad.SetTicks(1, 0)
gPad.SetRightMargin(0.16)
y1b = 0
y2b = 15
x1 = h1.GetXaxis().GetXmax()
y1 = h1.GetYaxis().GetXmin()
y2 = h1.GetYaxis().GetXmax()
raxis = TGaxis(x1, y1, x1, y2, y1b, y2b, 506, "+L")
raxis.SetLineColor(2)
raxis.SetLabelColor(2)
raxis.SetTitleColor(2)
raxis.SetTitle("ENC [" + yUnit + "]")
raxis.Draw()
nP = gr2.GetN()
Ys = gr2.GetY()
EYs = gr2.GetEY()
Y = array(
'd', [y1 + (y2 - y1) / (y2b - y1b) * (Ys[i] - y1b) for i in range(nP)])
EY = array('d', [(y2 - y1) / (y2b - y1b) * EYs[i] for i in range(nP)])
gr2x = TGraphErrors(nP, gr2.GetX(), Y, gr2.GetEX(), EY)
gr2x.SetMarkerStyle(24)
gr2x.SetLineColor(2)
gr2x.SetMarkerColor(2)
gr2x.Draw('Psame')
waitRootCmdX()
def addErrors(err1, err2):
from ROOT import TGraphErrors
from math import sqrt
newErrs = TGraphErrors(err1)
for i in range(0, newErrs.GetN()):
newErrs.SetPoint(i, newErrs.GetX()[i], err1.GetY()[i] + err2.GetY()[i])
newErrs.SetPointError(i, err1.GetErrorX(i),
sqrt(err1.GetErrorY(i)**2 + \
err2.GetErrorY(i)**2)
)
return newErrs
def GetData(file, scale=1., sed=True, title='SED', barUL=True):
GetData.Ng += 1
g = TGraphErrors(file)
gUL = TGraphErrors()
if sed:
for i in range(g.GetN()):
g.GetY()[i] = pow(g.GetX()[i], 2) * g.GetY()[i] * 1e-6 / scale
g.GetEY()[i] = pow(g.GetX()[i], 2) * g.GetEY()[i] * 1e-6 / scale
g.GetX()[i] *= 1e-3
idel = 0
nUL = 0
while idel < g.GetN():
if g.GetEY()[idel] < 0:
gUL.SetPoint(nUL, g.GetX()[idel], g.GetY()[idel])
if barUL:
gUL.SetPointError(nUL, 0, g.GetY()[idel] * 1e-5)
nUL += 1
g.RemovePoint(idel)
else:
idel += 1
if sed:
g.SetTitle(title + ";Energy [GeV];E^{2}dN/dE [TeV cm^{-2} s^{-1}]")
else:
g.SetTitle(title + ";Energy [MeV];dN/dE [cm^{-2} s^{-1} MeV^{-1}]")
g.SetLineColor(kRed)
g.SetMarkerColor(kRed)
g.SetMarkerStyle(20)
g.SetName("g%d" % GetData.Ng)
gUL.SetLineColor(g.GetLineColor())
gUL.SetName("gUL%d" % GetData.Ng)
return g, gUL
def main():
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-i", "--inputfile", dest="inputfile")
parser.add_option("",
"--spline3",
action="store_true",
dest="spline3",
default=False)
(options, args) = parser.parse_args()
from ROOT import TFile, TGraph, TGraphErrors, NULL
import csv
with open(options.inputfile) as f:
reader = csv.reader(f, delimiter=" ", skipinitialspace=True)
rows = list(reader)
outfile = TFile("%s.root" % options.inputfile, "RECREATE")
g = TGraphErrors(len(rows))
for i, row in enumerate(rows):
g.SetPoint(i, float(row[1]) / 1000., float(row[2]))
g.SetPointError(i, 0.0, float(row[3]))
if options.spline3:
g_spline3 = TGraph(10 * (g.GetN() - 1))
x = g.GetX()
y = g.GetY()
for i in range(g.GetN() - 1):
step = (x[i + 1] - x[i]) / 10
for j in range(10):
index = 10 * i + j
x_ = x[i] + j * step
y_ = g.Eval(x_, NULL, "S")
g_spline3.SetPoint(index, x_, y_)
g_spline3.SetName(options.inputfile)
g_spline3.Write()
else:
g.SetName(options.inputfile)
g.Write()
outfile.Close()
def ApplyBinShiftCorrectionGeneral(self, hist, fit):
"""
Alternative method for bin shift correction:
- Apply user-default model for bin-shift correction
- don't multiply by pt
@param hist: Input spectrum for the bin shift correction
@param fit: Model for the bin-shift correction
@return: The bin-shift corrected spectrum as graph
"""
h = deepcopy(hist)
hist.SetName("htemp")
result = TGraphErrors(h)
for i in range(0, result.GetN()):
result.GetEX()[i] = 0.
y = 0
#for now 10 iterations fixes
for k in range(0, 10):
for i in range(1, h.GetNbinsX() + 1):
y = fit.Integral(h.GetBinLowEdge(i),
h.GetBinUpEdge(i)) / h.GetBinWidth(i)
x = self.FindX(y, fit, h.GetBinLowEdge(i), h.GetBinUpEdge(i))
result.GetX()[i - 1] = x
# remove points that are 0
while result.GetY()[0] < 1.e-99:
result.RemovePoint(0)
mybin = 0
for biniter in range(0, result.GetN()):
if result.GetY()[biniter] < 1.e-99:
mybin = biniter
break
while result.RemovePoint(mybin) > 0:
continue
return result
def plotDistributionComparisonPlot(cfg):
multiGraph = TMultiGraph()
multiGraph.SetName("triggerRateMultiGraph")
tfiles = []
histograms = []
canvas = TCanvas("canvas", "canvas", 800, 800)
'''Contains the legend'''
legend = TLegend(0.3, 0.7, 0.90, 0.9)
'''Maximum value container, used to scale histograms'''
maximumY = float("-inf")
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0.05) # Upper and lower plot are joined
#pad1.SetBottomMargin(0) # Upper and lower plot are joined
pad1.SetGridx() # Vertical grid
pad1.Draw() # Draw the upper pad: pad1
pad1.cd() # pad1 becomes the current pad
for histogramFileNameAndTitle in cfg.plots:
tfile = TFile(histogramFileNameAndTitle[0])
tfiles.append(tfile)
histogram = tfile.Get(histogramFileNameAndTitle[1])
histograms.append(histogram)
if histogram.ClassName() == "TH1F":
histogram.SetStats(0) # No statistics on upper plot
maximumY = histogram.GetMaximum(
) if histogram.GetMaximum() > maximumY else maximumY
legend.AddEntry(histogram, histogramFileNameAndTitle[2], "l")
# histograms[0] settings
histograms[0].SetMarkerColor(4)
histograms[0].SetLineColor(4)
histograms[0].SetLineWidth(1)
# Y axis histograms[0] plot settings
histograms[0].GetYaxis().SetTitleSize(20)
histograms[0].GetYaxis().SetTitleFont(43)
histograms[0].GetYaxis().SetTitleOffset(1.55)
#histograms[0].Scale(1./histograms[0].GetEntries())
if histograms[0].ClassName() == "TH1F":
histograms[0].Draw(
"SAME HIST") # Draw histograms[1] on top of histograms[0]
else:
histograms[0].Draw(
"SAME APE") # Draw histograms[1] on top of histograms[0]
#multiGraph.Add(histograms[0])
if getattr(cfg, "xRange", None) is not None:
histograms[0].GetXaxis().SetRangeUser(cfg.xRange[0], cfg.xRange[1])
gPad.RedrawAxis()
if getattr(cfg, "xAxisLabel", None) is not None:
histograms[0].GetXaxis().SetTitle(cfg.xAxisLabel)
gPad.RedrawAxis()
if getattr(cfg, "yAxisLabel", None) is not None:
histograms[0].GetYaxis().SetTitle(cfg.yAxisLabel)
gPad.RedrawAxis()
if getattr(cfg, "yRange", None) is not None:
histograms[0].GetYaxis().SetRangeUser(cfg.yRange[0], cfg.yRange[1])
gPad.RedrawAxis()
else:
maximumY *= 1.1
histograms[0].GetYaxis().SetRangeUser(1e-6, maximumY)
if getattr(cfg, "logY", False):
canvas.SetLogy()
# histograms[1] settings
histograms[1].SetMarkerColor(2)
histograms[1].SetLineColor(2)
histograms[1].SetLineWidth(1)
#histograms[1].Scale(1./histograms[1].GetEntries())
if histograms[1].ClassName() == "TH1F":
histograms[1].Draw(
"SAME HIST") # Draw histograms[1] on top of histograms[0]
else:
histograms[1].Draw(
"SAME PE") # Draw histograms[1] on top of histograms[0]
#multiGraph.Add(histograms[1])
#if multiGraph.GetListOfGraphs() != None:
# multiGraph.Draw("SAME PE")
# Do not draw the Y axis label on the upper plot and redraw a small
# axis instead, in order to avoid the first label (0) to be clipped.
#histograms[0].GetYaxis().SetLabelSize(0.)
#axis = TGaxis( 0, 20, 0, maximumY, 20, maximumY, 510,"")
#axis.SetLabelFont(43) # Absolute font size in pixel (precision 3)
#axis.SetLabelSize(15)
#axis.Draw()
# Adding a small text with the chi-squared
chiSquared = 0
if (histograms[0].ClassName() == "TGraph") or (histograms[0].ClassName()
== "TGraphErrors"):
numberOfBins = histograms[0].GetN()
numberOfDegreesOfFreedom = numberOfBins
else:
numberOfBins = histograms[0].GetNbinsX()
numberOfDegreesOfFreedom = numberOfBins
for x in xrange(
1, numberOfBins + 1
): # numberOfBins contains last bin, numberOfBins+1 contains the overflow (latter excluded), underflow also excluded
if (histograms[0].ClassName()
== "TGraph") or (histograms[0].ClassName() == "TGraphErrors"):
binContent0 = histograms[0].GetY()[x - 1]
else:
binContent0 = histograms[0].GetBinContent(x)
if (histograms[1].ClassName()
== "TGraph") or (histograms[1].ClassName() == "TGraphErrors"):
binContent1 = histograms[1].GetY()[x - 1]
else:
binContent1 = histograms[1].GetBinContent(x)
bin0ErrorSquared = binContent0
bin1ErrorSquared = binContent1
#bin1ErrorSquared = 0
if (binContent0 == 0) and (binContent1 == 0):
numberOfDegreesOfFreedom -= 1 #No data means one less degree of freedom
else:
binDifferenceSquared = (binContent0 - binContent1)**2
chiSquaredTerm = binDifferenceSquared / (bin0ErrorSquared +
bin1ErrorSquared)
chiSquared += chiSquaredTerm
if chiSquaredTerm > chiSquaredWarningThreshold:
if (histograms[0].ClassName()
== "TGraph") or (histograms[0].ClassName()
== "TGraphErrors"):
print "Bin", x, "-", histograms[0].GetX()[
x - 1], "has a CS=", chiSquaredTerm
else:
print "Bin", x, "-", histograms[0].GetBinCenter(
x), "has a CS=", chiSquaredTerm
chiSquareLabel = TPaveText(0.7, 0.6, 0.9, 0.4)
chiSquareLabel.AddText("#chi^{2}/ndf = " + str(chiSquared) + "/" +
str(numberOfDegreesOfFreedom) + " = " +
str(chiSquared / numberOfDegreesOfFreedom))
chiSquareLabel.Draw()
print "FINAL CS IS", format(
chiSquared,
".2f") + "/" + str(numberOfDegreesOfFreedom) + " = " + format(
chiSquared / numberOfDegreesOfFreedom, ".2f")
legend.SetHeader(
"#chi^{2}/ndf = " + format(chiSquared, ".2f") + "/" +
str(numberOfDegreesOfFreedom) + " = " +
format(chiSquared / numberOfDegreesOfFreedom, ".2f"), "C")
legend.Draw()
# lower plot will be in pad
canvas.cd() # Go back to the main canvas before defining pad2
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetTopMargin(0)
pad2.SetBottomMargin(0.2)
pad2.SetGridx() # vertical grid
pad2.Draw()
pad2.cd() # pad2 becomes the current pad
pad2.SetGridy()
# Define the ratio plot
ratioPlot = TGraphErrors(histograms[0])
ratioPlot.SetName("ratioPlot")
graph_histo0 = TGraphErrors(histograms[0])
graph_histo1 = TGraphErrors(histograms[1])
ratioPlot.SetLineColor(1)
ratioPlot.SetMinimum(0.6) # Define Y ..
ratioPlot.SetMaximum(1.5) # .. range
#ratioPlot.Sumw2()
#ratioPlot.SetStats(0) # No statistics on lower plot
#Dividing point by point
for index in xrange(0, ratioPlot.GetN()):
if graph_histo1.GetY()[index] == 0:
ratioPlot.GetY()[index] = 0
ratioPlot.GetEY()[index] = 0
else:
ratioPlot.GetY()[index] /= graph_histo1.GetY()[index]
ratioPlot.GetEY()[index] = sqrt(
((graph_histo1.GetY()[index])**2 *
(graph_histo0.GetEY()[index])**2 +
(graph_histo0.GetY()[index])**2 *
(graph_histo1.GetEY()[index])**2) /
(graph_histo1.GetY()[index])**4)
ratioPlot.SetMarkerStyle(21)
if getattr(cfg, "xRange", None) is not None:
ratioPlot.GetXaxis().SetRangeUser(cfg.xRange[0], cfg.xRange[1])
gPad.RedrawAxis()
if getattr(cfg, "yRangeRatio", None) is not None:
ratioPlot.GetYaxis().SetRangeUser(cfg.yRangeRatio[0],
cfg.yRangeRatio[1])
gPad.RedrawAxis()
ratioPlot.Draw("APE") # Draw the ratio plot
line0 = TLine(ratioPlot.GetXaxis().GetXmin(), 1,
ratioPlot.GetXaxis().GetXmax(), 1)
line0.SetLineColor(2)
line0.SetLineWidth(2)
line0.SetLineStyle(2)
line0.Draw()
# Ratio plot (ratioPlot) settings
ratioPlot.SetTitle("") # Remove the ratio title
# Y axis ratio plot settings
ratioPlot.GetYaxis().SetTitle("Ratio #frac{blue}{red}")
ratioPlot.GetYaxis().SetNdivisions(505)
ratioPlot.GetYaxis().SetTitleSize(20)
ratioPlot.GetYaxis().SetTitleFont(43)
ratioPlot.GetYaxis().SetTitleOffset(1.55)
ratioPlot.GetYaxis().SetLabelFont(
43) # Absolute font size in pixel (precision 3)
ratioPlot.GetYaxis().SetLabelSize(15)
# X axis ratio plot settings
ratioPlot.GetXaxis().SetTitleSize(20)
ratioPlot.GetXaxis().SetTitleFont(43)
ratioPlot.GetXaxis().SetTitleOffset(4.)
ratioPlot.GetXaxis().SetLabelFont(
43) # Absolute font size in pixel (precision 3)
ratioPlot.GetXaxis().SetLabelSize(15)
xRangeBinning = getattr(cfg, "simplifiedRatioPlotXRangeBinning", None)
if xRangeBinning is not None:
simplifiedRatioPlot = TGraphErrors(len(xRangeBinning) - 1)
simplifiedRatioPlot.SetName("simplifiedRatioPlot")
ratioPlotIndex = 0
for idx in xrange(0, simplifiedRatioPlot.GetN()):
yAverage = 0.
yMax = float("-inf")
yMin = float("+inf")
nPoints = 0.
simplifiedRatioPlot.GetX()[idx] = (xRangeBinning[idx] +
xRangeBinning[idx + 1]) / 2.
simplifiedRatioPlot.GetEX()[idx] = (xRangeBinning[idx + 1] -
xRangeBinning[idx]) / 2.
while (ratioPlot.GetX()[ratioPlotIndex] < xRangeBinning[idx]):
ratioPlotIndex += 1
while ((ratioPlotIndex < ratioPlot.GetN()) and
(ratioPlot.GetX()[ratioPlotIndex] < xRangeBinning[idx + 1])
and
(ratioPlot.GetX()[ratioPlotIndex] >= xRangeBinning[idx])):
yAverage += ratioPlot.GetY()[ratioPlotIndex]
if (yMax < ratioPlot.GetY()[ratioPlotIndex] +
ratioPlot.GetEY()[ratioPlotIndex]):
yMax = ratioPlot.GetY()[ratioPlotIndex] + ratioPlot.GetEY(
)[ratioPlotIndex]
if (yMin > ratioPlot.GetY()[ratioPlotIndex] -
ratioPlot.GetEY()[ratioPlotIndex]):
yMin = ratioPlot.GetY()[ratioPlotIndex] - ratioPlot.GetEY(
)[ratioPlotIndex]
nPoints += 1.
ratioPlotIndex += 1
simplifiedRatioPlot.GetY()[idx] = yAverage / nPoints
simplifiedRatioPlot.GetEY()[idx] = (yMax - yMin) / 2.
saveFile = TFile(cfg.saveFileName, "RECREATE")
saveFile.cd()
canvas.Write()
histograms[0].Write()
histograms[1].Write()
if multiGraph.GetListOfGraphs() != None:
multiGraph.Write()
ratioPlot.Write()
if xRangeBinning is not None:
simplifiedRatioPlot.Write()
saveFile.Close()
for tfile in tfiles:
tfile.Close()
def make_1d_plots(df, c=None):
if c is None:
from ROOT import TCanvas
c = TCanvas()
c.Divide(1, 2)
import seaborn as sns
import ROOT
from ROOT import TGraphErrors, TLegend
plot = PlotData()
plot.canvas = c
graphs = plot.graphs = []
graphs_lam = plot.graphs_lam = []
leg = plot.legend = TLegend(0.68, 0.6, 0.88, 0.8)
leg.SetHeader("Centrality", 'C')
# colors = [ROOT.kRed, ROOT.kBlue, ROOT.k]
sns_colors = sns.color_palette("colorblind")
tcolors = plot.tcolors = [ROOT.TColor(ROOT.TColor.GetFreeColorIndex(), *c) for c in sns_colors]
colors = plot.colors = [c.GetNumber() for c in tcolors]
for i, (cent, cdf) in enumerate(df.groupby('cent')):
color = colors[i]
data = merge_points(cdf)
g_rinv = TGraphErrors(data.shape[0])
np.frombuffer(g_rinv.GetX())[:] = np.array(data.kT)
np.frombuffer(g_rinv.GetY())[:] = np.array(data.radius)
np.frombuffer(g_rinv.GetEY())[:] = np.array(data.radius_err) * 10
g_lam = TGraphErrors(data.shape[0])
np.frombuffer(g_lam.GetX())[:] = np.array(data.kT)
np.frombuffer(g_lam.GetY())[:] = np.array(data.lam)
np.frombuffer(g_lam.GetEY())[:] = np.array(data.lam_err) * 10
for g in (g_rinv, g_lam):
g.SetMarkerStyle(21)
g.SetMarkerSize(0.7)
g.SetMarkerColor(color)
g.SetLineColor(color)
graphs.append(g_rinv)
graphs_lam.append(g_lam)
c.cd(1)
if i == 0:
g_rinv.Draw("APE")
else:
g_rinv.Draw("P ")
c.cd(2)
if i == 0:
g_lam.Draw("APE")
else:
g_lam.Draw("P ")
cent_name = cent.replace('_', '-') + "%"
leg.AddEntry(g_rinv, cent_name, 'P')
# for g in graphs:
# g.Draw('AP')
# g.Draw('APL')
# g.SetTitle("Radius")
leg.Draw()
return plot
#gr = TGraphErrors(len(list_x_ll), x_ll, ratio_ll, x_data_unc_ll, y_data_unc_ll)
# Fitting histogram (with predefined function):
fit_x = TF1("fit_x", "pol1", 0.0, 550.0)
fit_x.SetLineColor(1)
fit_x.SetLineWidth(3)
gr.Fit(fit_x)
#Create a TGraphErrors to hold the confidence intervals
grint = TGraphErrors(len(xbins_comb))
grint.SetTitle(" ")
for i in range(0,len(xbins_comb)):
grint.SetPoint(i, gr.GetX()[i], 0)
#Compute the confidence intervals at the x points of the created graph
#(TVirtualFitter::GetFitter())->GetConfidenceIntervals(grint);
#Now the "grint" graph contains function values as its y-coordinates
#and confidence intervals as the errors on these coordinates
#Draw the graph, the function and the confidence intervals
#fit = hist->GetFunction(function_name);
chi2 = fit_x.GetChisquare()
# value of the first parameter
p1 = fit_x.GetParameter(0)
p1 = round(p1,4)
# error of the first parameter
e1 = fit_x.GetParError(0)
e1 = round(e1,4)
def ApplyBinShiftCorrection(self, hist):
"""
Apply bin-shift correction to the input spectrum using an iterative procedure
@param hist: Input spectrum
@return: Bin-shift corrected spectrum
"""
h = deepcopy(hist)
h.SetName("htemp")
# Bin shift correction performed in model specturm * pt
for i in range(1, h.GetNbinsX() + 1):
pt = h.GetBinCenter(i)
h.SetBinContent(i, h.GetBinContent(i) * pt)
h.SetBinError(i, h.GetBinError(i) * pt)
result = TGraphErrors(h)
for i in range(0, result.GetN()):
result.GetEX()[i] = 0.
fitfun = TF1("fitfun", "([0]*(1.+x/[1])^(-[2])*x)-[3]", 0.15, 100.0)
fitfun.SetParameter(0, 1000)
fitfun.SetParameter(1, 1)
fitfun.SetParameter(2, 5)
fitfun.FixParameter(3, 0)
h.Fit(fitfun, "")
self.__StableFit(h, fitfun, True)
# Iterative approach:
# - Use model to get the mean of the function inside the bin
# - Get the X where the mean is found
# - Use the new coordinate (x,y) for the next iteration of the fit
# for now 10 iterations fixed
for k in range(1, 11):
for i in range(1, h.GetNbinsX() + 1):
y = fitfun.Integral(h.GetBinLowEdge(i),
h.GetBinUpEdge(i)) / h.GetBinWidth(i)
result.GetX()[i - 1] = self.FindX(y, fitfun,
h.GetBinLowEdge(i),
h.GetBinUpEdge(i))
self.__StableFit(result, fitfun, False)
# Undo multiplication with pt
for i in range(0, result.GetN()):
pt = result.GetX()[i]
result.GetY()[i] /= pt
result.GetEY()[i] /= pt
#remove points that are 0
while result.GetY()[0] < 1.e-99:
result.RemovePoint(0)
bval = 0
for mybin in range(0, result.GetN() + 1):
if result.GetY()[bin] < 1.e-99:
bval = mybin
break
while result.RemovePoint(bval) > 0:
continue
return result
def plot2BodyDist(theFitter, pars, chi2, ndf,
Err = -1, NP = False, Prefix = "Mjj", Left = False):
from ROOT import gPad, TLatex, TCanvas, kRed, kCyan, kBlue, \
RooFit, RooPlot, RooCurve, RooAbsReal, TGraphErrors, TLine, \
RooWjjMjjFitter
if pars.includeMuons and pars.includeElectrons:
modeString = ''
elif pars.includeMuons:
modeString = 'Muon'
elif pars.includeElectrons:
modeString = 'Electron'
else:
modeString = ''
mf = theFitter.stackedPlot(False, RooWjjMjjFitter.mjj, Left)
mf.SetName("%s_Stacked" % (Prefix));
sf = theFitter.residualPlot(mf, "h_background", "dibosonPdf", False)
sf.SetName("%s_Subtracted" % (Prefix));
pf = theFitter.residualPlot(mf, "h_total", "", True)
pf.SetName("%s_Pull" % (Prefix))
pf2 = pf.emptyClone("%s_Pull_Corrected" % (Prefix))
pf2.SetMinimum(-5.)
pf2.SetMaximum(5.)
corrPull = False
lf = theFitter.stackedPlot(True, RooWjjMjjFitter.mjj, Left)
lf.SetName("%s_Stacked_Log" % (Prefix));
if Err > 0:
totalPdf = theFitter.getWorkSpace().pdf('totalPdf')
## Ntotal = totalPdf.expectedEvents(iset)
## print 'Ntotal:',Ntotal
h_dibosonPdf = sf.getCurve('h_dibosonPdf')
totalPdf.plotOn(sf,
RooFit.ProjWData(theFitter.getWorkSpace().data('data')),
RooFit.Normalization(Err, RooAbsReal.Raw),
#RooFit.AddTo('h_dibosonPdf', 1., 1.),
#RooFit.Invisible(),
RooFit.Name('h_ErrUp'),
RooFit.Range('RangeForPlot'),
RooFit.NormRange('RangeForPlot'),
RooFit.LineColor(kRed), RooFit.LineStyle(3))
h_ErrUp = sf.getCurve('h_ErrUp')
sf.remove('h_ErrUp', False)
ErrBand = TGraphErrors(h_dibosonPdf.GetN(), h_dibosonPdf.GetX(),
h_dibosonPdf.GetY())
for pt in range(1, ErrBand.GetN()):
ErrBand.SetPointError(pt, 0,
h_ErrUp.interpolate(ErrBand.GetX()[pt]))
ErrBand.SetName("ErrBand")
ErrBand.SetTitle("Uncertainty")
ErrBand.SetLineColor(kRed)
## ErrBand.SetLineWidth(0)
## ErrBand.SetLineStyle(0)
ErrBand.SetFillColor(kRed)
ErrBand.SetFillStyle(3353)
#ErrBand.Draw('ap3')
#h_ErrUp.Draw('lp')
#gPad.Update()
#gPad.WaitPrimitive()
## h_ErrUp.Draw("al")
## h_ErrUp.GetXaxis().Set(36, 40., 400.)
## gPad.Update()
## gPad.WaitPrimitive()
## h_UpBand = RooCurve("h_UpBand", "Uncertainty", h_dibosonPdf, h_ErrUp,
## 1., 1.)
## h_UpBand.SetLineStyle(3)
## h_UpBand.SetLineColor(kBlue+1)
## h_DownBand = RooCurve("h_DownBand", "Uncertainty", h_dibosonPdf, h_ErrUp,
## 1., -1.)
## h_DownBand.SetLineStyle(3)
## h_DownBand.SetLineColor(kBlue+1)
## sf.addPlotable(h_UpBand, "L")
## sf.addPlotable(h_DownBand, "L")
sf.addObject(ErrBand, "3")
#sf.Print("v")
sf.drawAfter('h_dibosonPdf', 'ErrBand')
#sf.Print("v")
sf.drawAfter('ErrBand', 'theData')
#sf.Print("v")
sf.findObject('theLegend').AddEntry(ErrBand, 'Uncertainty', 'f')
sf.findObject('theLegend').SetY1NDC(sf.findObject('theLegend').GetY1NDC() - 0.057)
sf.findObject('theLegend').SetY1(sf.findObject('theLegend').GetY1NDC())
corrPull = True
pf2.addObject(sub2pull(sf.getHist('theData'),
sf.findObject('ErrBand')),
'p0')
for item in range(0, int(pf.numItems())):
firstItem = pf.getObject(item)
if (type(firstItem) == TLine):
newLine = TLine(firstItem)
newLine.SetY1(4.)
newLine.SetY2(-4.)
pf2.addObject(newLine, 'l')
#SetOwnership(newLine, False)
if NP:
NPPdf = theFitter.makeNPPdf();
NPNorm = 4.*0.11*46.8/12.*pars.intLumi
if (modeString == 'Electron'):
if pars.njets == 2:
NPNorm *= 0.0381
elif pars.njets == 3:
NPNorm *= 0.0123
else:
if pars.njets == 2:
NPNorm *= 0.0550
elif pars.njets == 3:
NPNorm *= 0.0176
print '**** N_NP:', NPNorm,'****'
NPPdf.plotOn(sf, RooFit.ProjWData(theFitter.getWorkSpace().data('data')),
RooFit.Normalization(NPNorm, RooAbsReal.Raw),
RooFit.AddTo('h_dibosonPdf', 1., 1.),
RooFit.Name('h_NP'),
RooFit.Range('RangeForPlot'),
RooFit.NormRange('RangeForPlot'),
RooFit.LineColor(kBlue), RooFit.LineStyle(2))
h_NP = sf.getCurve('h_NP')
sf.drawBefore('h_dibosonPdf', 'h_NP')
#sf.Print("v")
sf.findObject('theLegend').AddEntry(h_NP, "CDF-like Signal", "L")
sf.findObject('theLegend').SetY1NDC(sf.findObject('theLegend').GetY1NDC() - 0.057)
sf.findObject('theLegend').SetY1(sf.findObject('theLegend').GetY1NDC())
l = TLatex()
l.SetNDC()
l.SetTextSize(0.045)
l.SetTextFont(42)
cstacked = TCanvas("cstacked", "stacked")
mf.Draw()
if (chi2 > 0):
l.DrawLatex(0.55, 0.49,
'#chi^{2}/dof = %0.3f/%d' % (chi2, ndf)
)
pyroot_logon.cmsLabel(cstacked, pars.intLumi/1000, prelim = True)
cstacked.Print('Wjj_%s_%s_%ijets_Stacked.pdf' % (Prefix, modeString,
pars.njets))
cstacked.Print('Wjj_%s_%s_%ijets_Stacked.png' % (Prefix, modeString,
pars.njets))
c2 = TCanvas("c2", "stacked_log")
c2.SetLogy()
lf.Draw()
pyroot_logon.cmsPrelim(c2, pars.intLumi/1000)
c2.Print('Wjj_%s_%s_%ijets_Stacked_log.pdf' % (Prefix, modeString,
pars.njets))
c2.Print('Wjj_%s_%s_%ijets_Stacked_log.png' % (Prefix, modeString,
pars.njets))
c3 = TCanvas("c3", "subtracted")
sf.Draw()
pyroot_logon.cmsLabel(c3, pars.intLumi/1000, prelim = True)
c3.Print('Wjj_%s_%s_%ijets_Subtracted.pdf' % (Prefix, modeString,
pars.njets))
c3.Print('Wjj_%s_%s_%ijets_Subtracted.png' % (Prefix, modeString,
pars.njets))
c4 = TCanvas("c4", "pull")
pf.Draw()
c4.SetGridy()
pyroot_logon.cmsPrelim(c4, pars.intLumi/1000)
c4.Print('Wjj_%s_%s_%ijets_Pull.pdf' % (Prefix, modeString, pars.njets))
c4.Print('Wjj_%s_%s_%ijets_Pull.png' % (Prefix, modeString, pars.njets))
c5 = None
if corrPull:
c5 = TCanvas("c5", "corrected pull")
pf2.Draw()
c5.SetGridy()
pyroot_logon.cmsPrelim(c5, pars.intLumi/1000)
c5.Print('Wjj_%s_%s_%ijets_Pull_Corrected.pdf' % (Prefix, modeString,
pars.njets))
c5.Print('Wjj_%s_%s_%ijets_Pull_Corrected.png' % (Prefix, modeString,
pars.njets))
return ([mf,sf,pf2,lf],[cstacked,c2,c3,c5])
def build_tgraphs_from_data(df,
keys=None,
xkey='kT',
skip_systematics=False,
title_dict=None):
"""
"""
from collections import defaultdict
from ROOT import TGraphErrors
if keys is None:
keys = ("Ro", "Rs", "Rl")
if title_dict is None:
title_dict = {
'Ro': "R_{out}",
'Rs': "R_{side}",
'Rl': "R_{long}",
'lam': "#lambda",
'alpha': "#alpha",
'radius': 'R_{inv}'
}
missing_titles = set(keys) - set(title_dict.keys())
if missing_titles:
raise ValueError(f"Title dict missing keys: {missing_titles}")
def _merge_points(df, key):
results = []
for kt, data in df.groupby(xkey):
errs = np.array(data[key + '_err'])
weights = errs**-2
val = (data[key] * weights).sum() / weights.sum()
err = np.sqrt(1.0 / weights.sum())
results.append([kt, val, err])
return np.array(results).T
graphs = defaultdict(dict)
for cent, cdf in df.sort_values(xkey).groupby('cent'):
for r in keys:
x = []
y = []
ye = []
x, y, ye = _merge_points(cdf, r)
xe = np.zeros_like(x)
title = [r]
gdata = TGraphErrors(x.size)
np.frombuffer(gdata.GetX(), dtype=np.float64)[:] = x
np.frombuffer(gdata.GetY(), dtype=np.float64)[:] = y
np.frombuffer(gdata.GetEY(), dtype=np.float64)[:] = ye
if skip_systematics:
gsys = None
else:
sys_key = f"{r}_sys_err"
sys_err = np.array(cdf[sys_key])
gsys = TGraphErrors(x.size)
np.frombuffer(gsys.GetX(), dtype=np.float64)[:] = x # - 0.004
np.frombuffer(gsys.GetY(), dtype=np.float64)[:] = y
np.frombuffer(gsys.GetEY(), dtype=np.float64)[:] = sys_err
np.frombuffer(gsys.GetEX(), dtype=np.float64)[:] = 0.012
graphs[r][cent] = (gdata, gsys)
return dict(graphs)
