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 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
canvas.Print("metTriggerEfficiency_ff_H_WW_enuenu_1000events.pdf", "pdf")
label_200kHz = TLatex(220000, 0.9, "110 GeV - 200 kHz")
label_200kHz.SetTextSize(0.03)
line_200kHz = TLine(200000, 0, 200000, 1.1)
line_200kHz.SetLineColor(2)
line_200kHz.SetLineStyle(2)
canvas.SetLogx(1)
canvas.SetLogy(0)
text.SetX1NDC(0.12)
text.SetX2NDC(0.52)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events = TGraphErrors(14)
for x in xrange(1, 15):
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetPoint(x - 1, jetToMETTriggerRate.GetY()[x], metTriggerEfficiency_ff_H_WW_enuenu_1000events.GetY()[x])
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetPointError(x - 1, jetToMETTriggerRate.GetEY()[x], metTriggerEfficiency_ff_H_WW_enuenu_1000events.GetEY()[x])
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetTitle("ff_H_WW_enuenu_1000events")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetName("efficiencyPlotWithRate_ff_H_WW_enuenu_1000events")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetYaxis().SetTitle("% accepted events")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetYaxis().SetTitleOffset(1.2)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetYaxis().SetRangeUser(0, 1.1)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetMarkerColor(4)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetLineColor(1)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetMarkerStyle(21)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetXaxis().SetTitle("Rate [Hz]")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetXaxis().SetTitleOffset(1.2)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.Draw("AP")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.Write()
line_200kHz.Draw()
label_200kHz.Draw()
text.Draw()
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
class RatePlotProducerPileUp(Analyzer):
'''Analyzer creating a rate plot with pile up events and saving it to ROOT and SVG format.
Example::
rate = cfg.Analyzer(
RatePlotProducerPileUp,
file_label = 'tfile1',
plot_name = 'rate',
plot_title = 'A rate plot',
zerobias_rate = 1/25e-9,
input_objects = 'jets',
bins = [30, 40, 50, 60],
yscale = 1e6,
scale_factors = [3, 5, 4]
)
* file_label: (Facultative) Name of a TFileService. If specified, the histogram will be saved in that root file, otherwise it will be saved in a <plot_name>.png and <plot_name>.root file
* plot_name: Name of the plot (Key in the output root file).
* plot_title: Title of the plot.
* bins: Array containing the bins to be tested
* zerobias_rate: zero bias trigger rate, 40 MHz @ 25 ns bunch spacing
* input_objects: name of the particle collection
* yscale: y level of the reference line
* scale_factors: custom factors to be applied to the bin, 1 is assumed in case of missing parameter or if less factors than bins are provided
'''
'''Generates a threshold function'''
def thresholdTriggerGenerator(self, threshold):
def thresholdTrigger(ptc):
return ptc.pt() > threshold
return thresholdTrigger
def beginLoop(self, setup):
super(RatePlotProducerPileUp, self).beginLoop(setup)
self.hasTFileService = hasattr(self.cfg_ana, "file_label")
if self.hasTFileService:
servname = '_'.join([
'heppy.framework.services.tfile.TFileService',
self.cfg_ana.file_label
])
tfileservice = setup.services[servname]
self.rootfile = tfileservice.file
else:
self.rootfile = TFile(
'/'.join([self.dirName, self.cfg_ana.plot_name + '.root']),
'recreate')
bins = array("f", self.cfg_ana.bins)
self.histogram = TH1F(self.cfg_ana.plot_name, self.cfg_ana.plot_title,
len(bins) - 1, bins)
self.numberOfEvents = self.cfg_comp.nGenEvents
def process(self, event):
'''Process the event.
event must contain
* self.cfg_ana.input_objects: collection of objects to be selected
These objects must be usable by the filtering function
self.cfg_ana.trigger_func.
'''
input_collection = getattr(event, self.cfg_ana.input_objects)
#We want accept events without objects if the threshold is 0 or less
startIdx = 0
#Adding events for that thresholds
for x in range(0, len(self.cfg_ana.bins) - 1):
if self.cfg_ana.bins[x] <= 0:
self.histogram.AddBinContent(x + 1)
startIdx = x + 1
#startIdx keeps track of where the positive thresholds start
# MET is not iterable, it is a single object
# We treat here single objects
if not isinstance(input_collection, collections.Iterable):
for x in range(startIdx, len(self.cfg_ana.bins) - 1):
# Preparing the check function
trigger_func = self.thresholdTriggerGenerator(
self.cfg_ana.bins[x])
# Checking if the object passes the trigger
if trigger_func(input_collection):
self.histogram.AddBinContent(x + 1)
else:
#If no item passes the threshold I can stop
break
elif isinstance(input_collection, collections.Mapping):
# Iterating through all the objects
for x in range(startIdx, len(self.cfg_ana.bins) - 1):
# Checking what thresholds are satisfied
isPassed = False
for key, val in input_collection.iteritems():
# Preparing the check function
trigger_func = self.thresholdTriggerGenerator(
self.cfg_ana.bins[x])
# Checking if the object passes the trigger
if trigger_func(val):
self.histogram.AddBinContent(x + 1)
isPassed = True
# We don't need to check for other objects
break
if not isPassed:
#If no objects passes the threshold I can stop
break
else:
for x in range(startIdx, len(self.cfg_ana.bins) - 1):
# Checking what thresholds are satisfied
isPassed = False
for obj in input_collection:
# Preparing the check function
trigger_func = self.thresholdTriggerGenerator(
self.cfg_ana.bins[x])
# Checking if the object passes the trigger
if trigger_func(obj):
self.histogram.AddBinContent(x + 1)
isPassed = True
# We don't need to check for other objects
break
if not isPassed:
#If no objects passes the threshold I can stop
break
def write(self, setup):
self.rootfile.cd()
#Rescaling to corresponding rate
if self.cfg_ana.normalise:
normalisation = self.cfg_ana.zerobias_rate / self.numberOfEvents
self.graphErrors = TGraphErrors(self.histogram)
for x in xrange(0, self.graphErrors.GetN()):
#Rescaling everything to have rates
self.graphErrors.GetEY()[x] *= normalisation
self.graphErrors.GetY()[x] *= normalisation
self.graphErrors.SetName(self.cfg_ana.plot_name)
self.histogram = self.graphErrors
if hasattr(self.cfg_ana, "scale_factors"):
for x in xrange(0, len(self.cfg_ana.scale_factors)):
self.histogram.SetBinContent(
x + 1,
self.histogram.GetBinContent(x + 1) *
self.cfg_ana.scale_factors[x])
self.histogram.Write()
xMax = self.histogram.GetXaxis().GetXmax()
xMin = self.histogram.GetXaxis().GetXmin()
yMin = self.histogram.GetMinimum()
yMax = self.histogram.GetMaximum()
self.histogram.SetMarkerStyle(20)
self.histogram.SetMarkerColor(4)
self.histogram.SetLineColor(1)
c1 = TCanvas("canvas_" + self.cfg_ana.plot_name,
self.cfg_ana.plot_title, 600, 600)
c1.SetGridx()
c1.SetGridy()
self.histogram.Draw("PE")
c1.SetLogy(True)
c1.Update()
c1.Print(self.cfg_ana.plot_name + ".svg", "svg")
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 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)
