def DrawObjectROOT(particle, pt, ptype):
import math
phiAxis = pt * 2. * math.pi / 224. # Ellypse axis
etaAxis = pt * 9. / 224.
y = particle.phi()
try:
x = particle.eta()
except:
x = 0
ellipse = TEllipse(x, y, etaAxis, phiAxis)
if 'mltop' in ptype:
ellipse.SetLineColor(10000 + len(ptype))
ellipse.SetLineWidth(2)
# ellipse.SetFillColorAlpha(20000+len(ptype), 0.0) #Este provoca solapamiento, mejor no usar
ellipse.SetFillStyle(0) # Mejor usar este (circunferencias)
else:
# ellipse.SetLineWidth(2) #Empty
ellipse.SetLineWidth(0)
# ellipse.SetLineColor(10000+len(ptype)) #Empty
ellipse.SetFillColor(20000 + len(ptype))
# ellipse.SetFillStyle(0) #Empty
ellipse.SetFillStyle(1001)
return ellipse
def addEllipse(self, x0, y0, r1, r2, color, fillcolor=False, thickness=0.):
ellipse = TEllipse(x0, y0, r1, r2)
ellipse.SetLineColor(color)
if fillcolor:
ellipse.SetFillColor(fillcolor)
else:
ellipse.SetFillStyle(0)
if thickness:
ellipse.SetLineWidth(thickness)
self.ellipses.append(ellipse)
def covarianceMatrixEllipse(covarianceMatrix, probability=0.683):
"""Plot ellipse with a given cl from a 2x2 covariance matrix.
TODO: better to create a class to store all the information and fastly plot
within the class?
"""
k = np.sqrt(-2 * np.log(1 - probability))
secondDerivativeMatrix = np.linalg.inv(covarianceMatrix)
eigenvals, eigenvecs = np.linalg.eig(secondDerivativeMatrix)
lambd = np.sqrt(eigenvals)
angle = np.rad2deg(np.arctan(eigenvecs[1, 0] / eigenvecs[0, 0]))
ellipse = TEllipse(0, 0, k / lambd[0], k / lambd[1], 0., 360., angle)
ellipse.SetFillStyle(0)
ellipse.SetLineColor(2)
ellipse.SetLineWidth(4)
scale = max(k / lambd[0], k / lambd[1])
return ellipse, scale
def DrawObjectROOT(particle, pt, ptype) :
phiAxis = pt *2.* math.pi / 224. # Ellypse axis
etaAxis = pt *9. / 224.
y = particle.Phi()
try :
x = particle.Eta()
except:
x = 0
ellipse = TEllipse(x,y,etaAxis, phiAxis)
if 'mltop' in ptype :
ellipse.SetLineColor(10000+len(ptype))
ellipse.SetLineWidth(2)
ellipse.SetFillColorAlpha(20000+len(ptype), 0.0)
else :
ellipse.SetLineWidth(0)
ellipse.SetFillColor(20000+len(ptype))
ellipse.SetFillStyle(1001)
return ellipse
class CovarianceMatrix2DContourPlot(object):
"""2D confidence interval contour plot.
This is a base class to plot, given a 2D fit result, the contour region (that
is an ellipse in the gaussian likelihood approximation) relative to a given
confidence level, on the two dimensional space parameters.
TODO: extend the class to plot more than one covariance_matrix!
TODO: raise exceptions in the init code instead of printing some erros message!
TODO: would a name like PlotFitResult be nicer?
"""
def __init__(self, name, fit_parameters, covariance_matrix, args = dict(sigma = 1)):
"""Constructor with the fit output.
Provide the constructor with the following objects:
- name: identificative name for ROOT objects
- fit_parameters: numpy array (shape 1,2) containing the two paramters of the fit
- covariance_matrix: numpy array (shape 2,2) containing the covariance matrix of the fit
- args: dictionary to specify the type of plot you want:
It can be:
- dict(sigma = 1)
- dict(probability = 0.68)
"""
self.name = name
self.fit_parameters = fit_parameters
self.covariance_matrix = covariance_matrix
self.args = args
if len(self.args) != 1:
print "args length different than 1"
return
if 'sigma' in self.args:
self.sigma = self.args['sigma']
self.ellipse_dimension = self.sigma
elif 'probability' in self.args:
self.probability = self.args['probability']
if self.probability > 1 or self.probability < 0:
print "probability must be between 0 and 1!"
return
self.ellipse_dimension = np.sqrt(-2 * np.log(1 - self.probability))
else:
print "neither 'sigma' nor 'probability' in args"
return
def DefineEllipse(self):
self.second_derivative_matrix = np.linalg.inv(self.covariance_matrix)
self.eigenvals, self.eigenvecs = np.linalg.eig(self.second_derivative_matrix)
self.lambd = np.sqrt(self.eigenvals)
self.angle = np.rad2deg(np.arctan(self.eigenvecs[1,0]/self.eigenvecs[0,0]))
self.ellipse = TEllipse(self.fit_parameters[0, 0],
self.fit_parameters[0, 1],
self.ellipse_dimension/self.lambd[0],
self.ellipse_dimension/self.lambd[1],
0.,
360.,
self.angle)
self.ellipse.SetFillStyle(0)
self.ellipse.SetLineColor(2)
self.ellipse.SetLineWidth(4)
self.graph_axis_range = max(self.ellipse_dimension/self.lambd[0], self.ellipse_dimension/self.lambd[1])
def PrepareDraw(self, title = '', legend_title = '', xtitle = '', ytitle = ''):
self.title = title
self.legend_title = legend_title
self.xtitle = xtitle
self.ytitle = ytitle
self.x_axis = np.array([-self.graph_axis_range * 2.1,
self.graph_axis_range * 2.1,
self.fit_parameters[0, 0],
self.fit_parameters[0, 0],
self.fit_parameters[0, 0],
self.fit_parameters[0, 0]])
self.y_axis = np.array([self.fit_parameters[0, 1],
self.fit_parameters[0, 1],
self.fit_parameters[0, 1],
-self.graph_axis_range * 2.1,
self.fit_parameters[0, 1],
self.graph_axis_range * 2.1])
self.graph_axis = TGraph(6, self.x_axis, self.y_axis)
self.graph_axis.SetNameTitle("axis_" + self.name, self.title)
self.graph_axis.GetXaxis().SetTitle(xtitle)
self.graph_axis.GetYaxis().SetTitle(ytitle)
self.graph_axis.GetXaxis().SetRangeUser(self.fit_parameters[0, 0] - self.graph_axis_range * 2.,
self.fit_parameters[0, 0] + self.graph_axis_range * 2.)
self.graph_axis.GetYaxis().SetRangeUser(self.fit_parameters[0, 1] - self.graph_axis_range * 2.,
self.fit_parameters[0, 1] + self.graph_axis_range * 2.)
self.ellipse.SetFillStyle(3001)
self.ellipse.SetFillColor(2)
self.ellipse.SetLineColor(2)
self.ellipse.SetLineWidth(4)
self.legend = TLegend(0.7407705, 0.6983945, 0.9911717, 0.928899)
key, value = self.args.items()[0]
if key == 'sigma':
aux_legend_string = self.legend_title + " " + str(value) + ' #sigma'
else:
aux_legend_string = self.legend_title + " C.L. " + str(int(100 * value)) + '%'
self.legend.AddEntry(self.ellipse, aux_legend_string, "f")
def Draw(self):
TopMassStyle()
self.canvas = TCanvas("canvas_" + self.name, self.title, 800, 600)
self.canvas.cd()
self.canvas.SetGrid()
self.graph_axis.Draw("AP")
self.ellipse.Draw("same")
self.legend.Draw("same")
def MakePlots(self):
if not hasattr(self, "Numerator") or not hasattr(self, "Denominator"):
print "Either Numerator or Denominator has not been defined. Not finding hot spots..."
return
circles = []
for spots in self._hotSpotsList:
circle = TEllipse(float(spots[0]), float(spots[1]), 0.06)
circle.SetLineColor(2)
circle.SetLineWidth(1)
circle.SetFillStyle(0)
circles.append(circle)
LumiText = str.format('{0:.1f}',
self._luminosityInInvFb) + " fb^{-1} (13 TeV)"
pasLumiLatex = TLatex()
pasLumiLatex.SetNDC()
pasLumiLatex.SetTextAngle(0)
pasLumiLatex.SetTextFont(42)
pasLumiLatex.SetTextAlign(32)
pasLumiLatex.SetTextSize(0.04)
pasCMSLatex = TLatex()
pasCMSLatex.SetNDC()
pasCMSLatex.SetTextAngle(0)
pasCMSLatex.SetTextFont(62)
pasCMSLatex.SetTextAlign(12)
pasCMSLatex.SetTextSize(0.04)
pasPrelimLatex = TLatex()
pasPrelimLatex.SetNDC()
pasPrelimLatex.SetTextAngle(0)
pasPrelimLatex.SetTextFont(62)
pasPrelimLatex.SetTextAlign(12)
pasPrelimLatex.SetTextSize(0.04)
self.Denominator["histogram"].Draw('colz')
pasLumiLatex.DrawLatex(0.96, 0.93, LumiText)
pasCMSLatex.DrawLatex(0.12, 0.925, "CMS Preliminary")
self._canvas.SaveAs('fiducialMapCalc_beforeVeto_' +
self.Denominator["sample"] + '.pdf')
self.Numerator["histogram"].Draw('colz')
pasLumiLatex.DrawLatex(0.96, 0.93, LumiText)
pasCMSLatex.DrawLatex(0.12, 0.925, "CMS Preliminary")
self._canvas.SaveAs('fiducialMapCalc_afterVeto_' +
self.Numerator["sample"] + '.pdf')
self._canvas.SetLogz(False)
self.Numerator["histogram"].Divide(self.Denominator["histogram"])
self.Numerator["histogram"].Draw('colz')
if 'SingleEle' in self.Numerator["sample"]:
self.Numerator["histogram"].GetZaxis().SetRangeUser(0, 0.5)
elif 'SingleMu' in self.Numerator["sample"]:
self.Numerator["histogram"].GetZaxis().SetRangeUser(0, 0.05)
self.Numerator["histogram"].GetZaxis().SetLabelSize(0.025)
for circle in circles:
circle.Draw("same")
pasLumiLatex.DrawLatex(0.96, 0.93, LumiText)
pasCMSLatex.DrawLatex(0.12, 0.925, "CMS Preliminary")
self._canvas.SaveAs('fiducialMapCalc_efficiency_' +
self.Numerator["sample"] + '.pdf')
for xbin in range(1,
self.Numerator["histogram"].GetXaxis().GetNbins()):
for ybin in range(
1, self.Numerator["histogram"].GetYaxis().GetNbins()):
thisInefficiency = self.Numerator["histogram"].GetBinContent(
xbin, ybin)
if thisInefficiency == 0:
continue
valueInSigma = (thisInefficiency - self._meanInefficiency
) / self._stdDevInefficiency
if valueInSigma < 0:
valueInSigma = 0
self.Numerator["histogram"].SetBinContent(
xbin, ybin, valueInSigma)
self._canvas.SetLogz(False)
self.Numerator["histogram"].GetZaxis().SetLabelSize(0.04)
self.Numerator["histogram"].Draw('colz')
if 'SingleEle' in self.Numerator["sample"]:
if '2017' in self.Numerator["sample"]:
self.Numerator["histogram"].GetZaxis().SetRangeUser(0, 7)
self.Numerator["histogram"].GetZaxis().SetRangeUser(0, 12)
elif 'SingleMu' in self.Numerator["sample"]:
self.Numerator["histogram"].GetZaxis().SetRangeUser(0, 23)
self.Numerator["histogram"].GetZaxis().SetLabelSize(0.025)
for circle in circles:
circle.Draw("same")
pasLumiLatex.DrawLatex(0.96, 0.93, LumiText)
pasCMSLatex.DrawLatex(0.12, 0.925, "CMS Preliminary")
self._canvas.SaveAs('fiducialMapCalc_efficiencyInSigma_' +
self.Numerator["sample"] + '.pdf')
class CovarianceMatrix2DContourPlot(object):
"""
TODO: extend the class to plot more than one covariance_matrix!
TODO: raise exceptions in the init code instead of printing some erros message!
"""
def __init__(self, name, mean, covariance_matrix, args=dict(sigma=1)):
""" """
self.name = name
self.mean = mean
self.covariance_matrix = covariance_matrix
self.args = args
if len(self.args) != 1:
print "args length different than 1"
return
if 'sigma' in self.args:
self.sigma = self.args['sigma']
self.k = self.sigma
elif 'probability' in self.args:
self.probability = self.args['probability']
if self.probability > 1 or self.probability < 0:
print "probability must be between 0 and 1!"
return
self.k = np.sqrt(-2 * np.log(1 - self.probability))
else:
print "neither 'sigma' nor 'probability' in args"
return
self.second_derivative_matrix = np.linalg.inv(self.covariance_matrix)
self.eigenvals, self.eigenvecs = np.linalg.eig(
self.second_derivative_matrix)
self.lambd = np.sqrt(self.eigenvals)
self.angle = np.rad2deg(
np.arctan(self.eigenvecs[1, 0] / self.eigenvecs[0, 0]))
self.ellipse = TEllipse(self.mean[0], self.mean[1],
self.k / self.lambd[0], self.k / self.lambd[1],
0., 360., self.angle)
self.ellipse.SetFillStyle(0)
self.ellipse.SetLineColor(2)
self.ellipse.SetLineWidth(4)
self.scale = max(self.k / self.lambd[0], self.k / self.lambd[1])
def Draw(self):
self.canvas = TCanvas("canvas_" + self.name, self.name, 800, 600)
self.x_axis = np.array(
[-self.scale * 2.1, self.scale * 2.1, 0., 0., 0., 0.])
self.y_axis = np.array(
[0., 0., 0., -self.scale * 2.1, 0., self.scale * 2.1])
self.axis = TGraph(6, x, y)
self.axis.SetNameTitle("axis_" + self.name, self.name)
self.ellipse.SetFillStyle(3001)
self.ellipse.SetFillColor(2)
self.ellipse.SetLineColor(2)
self.ellipse.SetLineWidth(4)
self.legend = TLegend(0.7407705, 0.6983945, 0.9911717, 0.928899)
self.legend.AddEntry(self.ellipse, "FCC-ee 1 #sigma ", "f")
self.canvas.cd()
self.canvas.SetGrid()
self.axis.Draw("AP")
self.ellipse.Draw("same")
self.legend.Draw("same")
def LoadExternalPoints(self):
external_points = TGraph()
external_points.SetNameTitle()
with open(fileName) as f:
for i, line in enumerate(f):
data = line.split()
external_points.SetPoint(i, data[0], data[1])
external_points.GetXaxis().SetLimits(-6, 6)
external_points.GetYaxis().SetRangeUser(-6, 10)
external_points.GetXaxis().SetTitle("#Delta g_{L} / g_{L} (%)")
external_points.GetYaxis().SetTitle("#Delta g_{R} / g_{R} (%)")
external_points.SetTitle(
"Expected relative precision on the Zt_{L}t_{L} and Zt_{R}t_{R} couplings at FCC-ee"
)
external_points.SetMarkerSize(0.4)
external_points.SetMarkerStyle(20)
self.legend.AddEntry(external_points, "4DCHM f #leq 1.5 TeV", "p")