def __init__(self,
file_out,
inputfilename="",
params_gain=PARAMS_GAIN(),
params_det=PARAMS_DET(),
params_discharge=PARAMS_PD(),
debug=False):
self.ADCPKPOS_SECTOR_AVG = 0. #Average of the fitted cluster ADC PkPos in defined (ieta,iphi) sector
self.ADCPKPOS_SECTOR_STDDEV = 0. #Std. Dev. of the fitted cluster ADC PkPos in defined (ieta,iphi) sector
self.ANA_UNI_GRANULARITY = 32
self.AVGCLUSTSIZE_SECTOR_AVG = 0. #Average of Average Cluster Size distributions in defined (ieta,iphi) sector
self.AVGCLUSTSIZE_SECTOR_STDDEV = 0. #Std. Dev. of Average Cluster Size distributions in defined (ieta,iphi) sector
self.DEBUG = debug
self.DETECTOR = params_det
self.DET_IMON_QC5_RESP_UNI = params_det.DET_IMON_QC5_RESP_UNI
self.DET_IMON_POINTS = []
self.FILE_IN = []
if len(inputfilename) > 0:
self.FILE_IN = TFile(str(inputfilename), "READ", "", 1)
self.FILE_OUT = file_out
self.GAIN_CALCULATOR = params_gain
self.GAIN_LAMBDA = 1.
self.GAIN_LAMBDA_ERR = 0.
self.GAIN_AVG_POINTS = [] #Average Gain over the entire detector
self.GAIN_STDDEV_POINTS = [
] #Std. Dev of Gain over the entire detector
self.GAIN_MAX_POINTS = [] #Max Gain over the entire detector
self.GAIN_MIN_POINTS = [] #Min Gain over the entire detector
self.G2D_MAP_ABS_RESP_UNI = TGraph2D(
) #Absolute Response Uniformity Map
self.G2D_MAP_AVG_CLUST_SIZE_ORIG = TGraph2D(
) #Absolute Avg Cluster Size Map
self.G2D_MAP_AVG_CLUST_SIZE_NORM = TGraph2D(
) #Normalized " "
self.G2D_MAP_GAIN_ORIG = TGraph2D() #Effective Gain Map
self.PD_CALCULATOR = params_discharge
self.PD_AVG_POINTS = [] #Avg P_D over entire detector
self.PD_STDDEV_POINTS = [] #Std. Dev of P_D over entire detector
self.PD_MAX_POINTS = [] #Max P_D over the entire detector
self.PD_MIN_POINTS = [] #Min P_D over the entire detector
return
def makePlot(s):
if (type(s) == TH1D):
s.SetStats(0)
x = RooRealVar("x", "x", 0, 3)
l = RooArgList(x)
dh = RooDataHist("dh", "dh", l, s)
frame = x.frame()
dh.plotOn(frame)
#frame.SetTitle(name)
#frame.GetXaxis().SetTitle(s.GetXaxis().GetTitle())
frame.GetYaxis().SetTitle(s.GetYaxis().GetTitle())
# frame.GetYaxis().SetTopMargin(0.15)
#frame.Draw(draw)
return frame
elif (type(s) == TH2D):
s.SetStats(0)
x = RooRealVar("x", "x", 0, 3)
y = RooRealVar("y", "y", 0, 3)
l = RooArgList(x, y)
dh = RooDataHist("dh", "dh", l, s)
t = TGraph2D(s)
s.SetMarkerSize(1.0)
s.SetMarkerStyle(1)
#t.GetXaxis().SetTitle(s.GetXaxis().GetTitle())
return s
elif (type(s) == TGraph2D):
s.SetMarkerSize(1.0)
s.SetMarkerStyle(1)
return s
def plot(self, gD01, gDbar01, repeat=False):
N = gD01.GetN()
xD01 = gD01.GetX()
yD01 = gD01.GetY()
zD01 = gD01.GetZ()
xDbar01 = gDbar01.GetX()
yDbar01 = gDbar01.GetY()
zDbar01 = gDbar01.GetZ()
gr1 = TGraph2D(N)
gr1.SetName("delta" + self.obj)
for i in range(N):
x1 = xD01[i]
y1 = yD01[i]
if self.obj=="gAbs":
z1 = gDbar01.Interpolate(y1,x1) / zD01[i] * 100
else:
z1 = gDbar01.Interpolate(y1, x1) - zD01[i] * 100
if (z1 < 0):
z1 += 2 * pi
#z1 *= 1/(2*pi)
#z1 = zDbar01[i] - gD01.Interpolate(y1,x1)
gr1.SetPoint(i, x1, y1, z1)
xD1 = gr1.GetX()
yD1 = gr1.GetY()
zD1 = gr1.GetZ()
#set_palette()
theta = 89.9
phi=0.1
c1 = TCanvas("c1", "c1", 4000, 4000)
gPad.SetLeftMargin(0.1)
c1.cd()
titles = {
"gArg":"#delta_{D}",
"gAbs": "|A_{#bar{D}^{0}}|/|A_{D^{0}}|"
}
title = titles[self.obj]
if (repeat):
title = "#Delta" + title
gr1.SetTitle("%s ; m^{2}_{K_{S}^{0} #pi^{+}} ; m^{2}_{K_{S}^{0} #pi^{-}}" % (title))
gr1.GetXaxis().SetLabelOffset(15)
gr1.GetYaxis().SetLabelOffset(15)
gr1.GetZaxis().SetLabelOffset(15)
gr1.SetMargin(0.02)
gr1.Draw(self.draw2D)
gPad.SetTheta(theta)
gPad.SetPhi(phi)
gPad.SetLeftMargin(0.15)
gStyle.SetPalette(55)
gPad.Update()
c1.SaveAs("%s/Delta_%s.%s" % (self.output, self.obj, self.imgtype))
return gr1
def seed3dfitSVD(name,r1,r2,r3,r4,dodraw):
g = TGraph2D()
g.SetMarkerSize(3)
g.SetMarkerStyle(20)
g.SetMarkerColor(ROOT.kRed)
g.SetLineColor(ROOT.kRed)
g.SetPoint(0,r1[2],r1[1],r1[0])
g.SetPoint(1,r2[2],r2[1],r2[0])
g.SetPoint(2,r3[2],r3[1],r3[0])
g.SetPoint(3,r4[2],r4[1],r4[0])
g.GetXaxis().SetRangeUser(290,340)
g.GetYaxis().SetRangeUser(-0.8,+0.8)
g.GetZaxis().SetRangeUser( 0 if(isel(r1[0])) else xPsideL, xEsideR if(isel(r1[0])) else 0 )
x = np.array([r1[0],r2[0],r3[0],r4[0]])
y = np.array([r1[1],r2[1],r3[1],r4[1]])
z = np.array([r1[2],r2[2],r3[2],r4[2]])
data = np.concatenate((x[:, np.newaxis],
y[:, np.newaxis],
z[:, np.newaxis]),
axis=1)
# Calculate the mean of the points, i.e. the 'center' of the cloud
datamean = data.mean(axis=0)
# Do an SVD on the mean-centered data (Singular Value Decomposition)
uu, dd, vv = np.linalg.svd(data - datamean)
# Now vv[0] contains the first principal component, i.e. the direction
# vector of the 'best fit' line in the least squares sense.
# Now generate some points along this best fit line, for plotting.
# I use -7, 7 since the spread of the data is roughly 14
# and we want it to have mean 0 (like the points we did
# the svd on). Also, it's a straight line, so we only need 2 points.
# linepts = vv[0] * np.mgrid[-7:7:2j][:, np.newaxis]
linepts = vv[0] * np.mgrid[-50:50:2j][:, np.newaxis]
# shift by the mean to get the line in the right place
linepts += datamean
if(dodraw):
lfit = TPolyLine3D()
for point in linepts:
lfit.SetNextPoint(point[2],point[1],point[0])
lfit.SetLineColor(ROOT.kBlue)
cnv = TCanvas("","",2000,2000)
view = TView.CreateView(1)
xviewmin = 0 if(isel(r1[0])) else xPsideL
xviewmax = xEsideR if(isel(r1[0])) else 0
view.SetRange(290,-0.8, xviewmin , 340,+0.8,xviewmax)
view.ShowAxis()
g.Draw("p0")
lfit.Draw("smae")
cnv.SaveAs(name)
return linepts, dd ## dd is a 1D array of the data singular values
def seed3dfit(name,r1,r2,r3,r4,dodraw):
g = TGraph2D()
g.SetMarkerSize(3)
g.SetMarkerStyle(20)
g.SetMarkerColor(ROOT.kRed)
g.SetLineColor(ROOT.kRed)
g.SetPoint(0,r1[2],r1[1],r1[0])
g.SetPoint(1,r2[2],r2[1],r2[0])
g.SetPoint(2,r3[2],r3[1],r3[0])
g.SetPoint(3,r4[2],r4[1],r4[0])
g.GetXaxis().SetRangeUser(290,340)
g.GetYaxis().SetRangeUser(-0.8,+0.8)
g.GetZaxis().SetRangeUser(0 if(isel(r1[0])) else xPsideL, xEsideR if(isel(r1[0])) else 0 )
x = np.array([r1[0],r2[0],r3[0],r4[0]])
y = np.array([r1[1],r2[1],r3[1],r4[1]])
z = np.array([r1[2],r2[2],r3[2],r4[2]])
# this will find the slope and x-intercept of a plane
# parallel to the y-axis that best fits the data
A_xz = np.vstack((x, np.ones(len(x)))).T
# m_xz, c_xz = np.linalg.lstsq(A_xz, z,rcond=None)[0]
result_xz = np.linalg.lstsq(A_xz, z,rcond=None)
m_xz, c_xz = result_xz[0]
residuals_xz = result_xz[1]
# again for a plane parallel to the x-axis
A_yz = np.vstack((y, np.ones(len(y)))).T
# m_yz, c_yz = np.linalg.lstsq(A_yz, z,rcond=None)[0]
result_yz = np.linalg.lstsq(A_yz, z,rcond=None)
m_yz, c_yz = result_yz[0]
residuals_yz = result_yz[1]
if(dodraw):
zz = np.array([300,310,320,330])
xx,yy = line3d(zz, m_xz,c_xz,m_yz,c_yz)
lfit = TPolyLine3D()
for i in range(4):
lfit.SetNextPoint(zz[i],yy[i],xx[i])
lfit.SetLineColor(ROOT.kBlue)
cnv = TCanvas("","",2000,2000)
view = TView.CreateView(1)
xviewmin = 0 if(isel(r1[0])) else xPsideL
xviewmax = xEsideR if(isel(r1[0])) else 0
view.SetRange(290,-0.8, xviewmin , 340,+0.8,xviewmax)
view.ShowAxis()
g.Draw("p0")
lfit.Draw("smae")
cnv.SaveAs(name)
return residuals_xz,residuals_yz
def MakeGraph(self, title, suffix):
self.legend_title = title.replace('.root', '').replace('-', '_')
self.suffix = suffix
if title.find('DY') != -1:
title = 'Drell-Yan events'
elif title.find('TT') != -1:
title = r't\bar{t} events'
else:
mH_value = re.findall(r'\d+', title)[2]
mA_value = re.findall(r'\d+', title)[3]
title = 'Signal events with M_{H} = %s GeV and M_{A} = %s GeV' % (
mH_value, mA_value)
# Normalize #
if self.normalize:
self.Z += self.nevents * self.norm
title += ' [Normalized]'
self.legend_title += '_norm'
# Divide by total entries #
self.Z /= self.nevents # Divide by N
self.Z *= 2 # Because -2 log L
# check for invalids (nan of inf) might be coming from log10 #
invalid_entries = np.logical_or(np.isinf(self.Z), np.isnan(self.Z))
max_Z = np.amax(self.Z[np.invert(invalid_entries)])
min_Z = np.amin(self.Z[np.invert(invalid_entries)])
self.Z[invalid_entries] = 0 # removes non physical points
# Generate graph #
graph = TGraph2D(self.N)
print('Generating TGraph2D')
manager = enlighten.get_manager()
pbar = manager.counter(total=self.N, desc='Progress', unit='Point')
for i in range(self.N):
graph.SetPoint(i, self.mA[i], self.mH[i], self.Z[i])
pbar.update()
manager.stop()
#graph = copy.deepcopy(TGraph2D(self.N,self.mA,self.mH,self.Z))
graph.SetTitle(
'Log-Likelihood : %s;M_{A} [GeV]; M_{H} [GeV]; -2log L' % (title))
graph.SetMaximum(max_Z)
graph.SetMinimum(min_Z)
graph.SetNpx(1000)
graph.SetNpy(1000)
# Save graph #
self.graph = graph
self._saveGraph()
def initialR(b, d):
tR = [
TH1D("X", ";x pos;", 100, 0, 10),
TH1D("Y", ";y pos;", 100, 0, 10),
TH1D("Z", ";z pos;", 100, 0, 10)
]
tg2d = TGraph2D()
c = 0
o = 0
for i in b.R():
if (c == d):
for j in i:
#print j
x = j[0]
y = j[1]
z = j[2]
tR[0].Fill(x)
tR[1].Fill(y)
tR[2].Fill(z)
tg2d.SetPoint(o, x, y, z)
o += 1
#print "(%f,%f,%f)" % (x,y,z)
c += 1
tR[0].GetXaxis().SetTitle("X pos @ t = %d" % d)
tR[1].GetXaxis().SetTitle("Y pos @ t = %d" % d)
tR[2].GetXaxis().SetTitle("Z pos @ t = %d" % d)
for y in xrange(3):
tR[y].GetYaxis().SetTitle("Count")
tR[y].GetYaxis().CenterTitle()
tR[y].GetXaxis().CenterTitle()
tg2d.GetXaxis().SetTitle("X")
tg2d.GetYaxis().SetTitle("Y")
tg2d.GetZaxis().SetTitle("Z")
tg2d.GetXaxis().CenterTitle()
tg2d.GetYaxis().CenterTitle()
tg2d.GetZaxis().CenterTitle()
return [tR, tg2d]
def _doProfile2d( solver, ipar1=0, type1="u", ipar2=1, type2= "m" ):
from ConstrainedFit import clsq
from ROOT import TGraph2D, TMarker, gPad
from array import array
global tg2d, hist, te1, te2, te3, tm
par1= clsq.createClsqPar( ipar1, type1, solver )
par2= clsq.createClsqPar( ipar2, type2, solver )
parval1, parerr1, name1= _getUMParErrName( par1 )
parval2, parerr2, name2= _getUMParErrName( par2 )
print( "\nChi^2 profile plot " + name1 + " - " + name2 + ":" )
corr= solver.getCorrMatrix()
icorr1= ipar1
icorr2= ipar2
if type1 == "u" or type2 == "u":
nmpar= len(solver.getMpars())
if type1 == "u":
icorr1= nmpar + ipar1
if type2 == "u":
icorr2= nmpar + ipar2
rho= corr[icorr1,icorr2]
te1= _makeEllipse( parval1, parval2, parerr1, parerr2, rho )
te2= _makeEllipse( parval1, parval2, 2.0*parerr1, 2.0*parerr2, rho )
te3= _makeEllipse( parval1, parval2, 3.0*parerr1, 3.0*parerr2, rho )
ca= clsq.clsqAnalysis( solver )
points= ca.profile2d( par1, par2 )
npoints= len(points)
tg2d= TGraph2D( npoints )
for i in range( npoints ):
point= points[i]
tg2d.SetPoint( i, point[0], point[1], point[2] )
hist= tg2d.GetHistogram()
contourlevels= array( "d", [ 1.0, 4.0, 9.0 ] )
hist.SetContour( 3, contourlevels )
hist.GetXaxis().SetTitle( name1 )
hist.GetYaxis().SetTitle( name2 )
hist.SetTitle( "Triangle fit "+name2+" vs "+name1 )
hist.Draw( "cont1" )
tm= TMarker( parval1, parval2, 20 )
tm.Draw( "s" )
te1.Draw( "s" )
te2.Draw( "s" )
te3.Draw( "s" )
gPad.Print( "triangle_errorellipse.png" )
return
def Plot(self):
self.CheckError()
if self.dataCorrect == True:
c1 = TCanvas("c1", "a template of 1d graphics output", 200, 10,
1024, 768)
gr = TGraph2D()
gr.SetTitle(self.chartName)
for index, item in enumerate(self.dataX):
gr.SetPoint(index, item, self.dataY[index], self.dataZ[index])
gr.GetXaxis().SetTitle(self.axisXName)
gr.GetYaxis().SetTitle(self.axisYName)
gr.GetZaxis().SetTitle(self.axisZName)
gr.Draw("surf1")
c1.SaveAs("2D.C")
print "2d finished"
else:
print "data Error nothing to do"
def plotDataROOT(self,
name='plotDataROOT',
ztitle='Z',
zmin=None,
zmax=None):
self.canvasData = TCanvas("canvasData", "canvasData", 0, 0, 300, 300)
gStyle.SetOptStat(0)
minX, maxX, minY, maxY, minZ, maxZ = self.autoRange(zmin, zmax)
X, Y, Z = self.getXYZpoints()
n = X.shape[0]
self.graphData = TGraph2D(name, ztitle, n, X, Y, Z)
self.graphData.SetMaximum(maxZ)
self.graphData.SetMinimum(minZ)
gStyle.SetHistTopMargin(0)
self.graphData.Draw("zpcol")
return self.graphData
def ZoomHist(graph, bins, xmin, xmax, ymin, ymax):
x = np.linspace(xmin, xmax, bins)
y = np.linspace(ymin, ymax, bins)
X, Y = np.meshgrid(x, y)
X = X.ravel()
Y = Y.ravel()
valid = np.logical_and(np.greater_equal(Y, X),
np.greater_equal(Y, 125))
X = X[valid]
Y = Y[valid]
N = X.shape[0]
new_graph = TGraph2D(N)
manager = enlighten.get_manager()
pbar = manager.counter(total=N, desc='Progress', unit='Point')
for i in range(N):
content = graph.Interpolate(X[i], Y[i])
if content != 0 or Y[i] > 125:
new_graph.SetPoint(i, X[i], Y[i], content)
pbar.update()
new_graph.SetTitle(graph.GetTitle())
return copy.deepcopy(new_graph)
def ExtrapolateGraph(graph, points):
print('Extrapolation')
new_graph = TGraph2D(N_plane)
# Extrapolation #
from scipy.optimize import curve_fit
def func(x, a, b, c, x0, y0, e1, e2):
z = (a * (x[0] - x0)**2 + b * (x[1] - y0)**2 + c * (x[0] - x0) *
(x[1] - y0)) * np.exp(-x[0] / e1 - x[1] / e2)
return z.ravel()
initial_guess = (-4e-6, -5e-7, 9e-6, 500, 800, 400, 700)
popt, pcov = curve_fit(func, (points[:, 0], points[:, 1]),
points[:, 2],
p0=initial_guess)
print(popt)
manager = enlighten.get_manager()
pbar = manager.counter(total=N_plane, desc='Progress', unit='Point')
for i in range(N_plane):
pbar.update()
m_A = mA_plane[i]
m_H = mH_plane[i]
if m_A >= m_H: #or m_H<=mh :#or m_H-m_A<mZ:
new_graph.SetPoint(i, m_A, m_H, 0)
continue
Z = func((m_A, m_H), *popt)
# Truncation #
if m_H > 1000:
Z = func((m_A, 1000), *popt)
if Z <= 0.02:
Z = 0.02
if Z <= 0.07 and m_H > 400:
Z = 0.07
new_graph.SetPoint(i, m_A, m_H, Z)
manager.stop()
return new_graph
def test_fill_graph():
n_samples = 1000
data2D = RNG.randn(n_samples, 2)
data3D = RNG.randn(n_samples, 3)
graph = TGraph()
rnp.fill_graph(graph, data2D)
graph2d = TGraph2D()
rnp.fill_graph(graph2d, data3D)
# array not 2-d
for g in (graph, graph2d):
assert_raises(ValueError, rnp.fill_graph, g, RNG.randn(10))
# length of second axis does not match dimensionality of histogram
for g in (graph, graph2d):
assert_raises(ValueError, rnp.fill_graph, g, RNG.randn(10, 4))
# wrong type
h = list()
a = RNG.randn(10)
assert_raises(TypeError, rnp.fill_graph, h, a)
def test_fill_graph():
np.random.seed(0)
data2D = np.random.randn(1E6, 2)
data3D = np.random.randn(1E4, 3)
graph = TGraph()
rnp.fill_graph(graph, data2D)
graph2d = TGraph2D()
rnp.fill_graph(graph2d, data3D)
# array not 2-d
for g in (graph, graph2d):
assert_raises(ValueError, rnp.fill_graph, g, np.random.randn(1E4))
# length of second axis does not match dimensionality of histogram
for g in (graph, graph2d):
assert_raises(ValueError, rnp.fill_graph, g, np.random.randn(1E4, 4))
# wrong type
h = list()
a = np.random.randn(100)
assert_raises(TypeError, rnp.fill_graph, h, a)
def remove_values_from_list(the_list, val):
while val in the_list:
the_list.remove(val)
for i in range(len(sys.argv)):
if string.count(sys.argv[i],'defoDump') > 0:
print 'Opening file number ' + str(i) + ' ' + sys.argv[i]
file = open(sys.argv[i])
canvas1.cd()
canvas1.Clear()
nPointsX = 0
grX = TGraph2D()
for line in file:
values = []
tokenizedLine = line.split(' ',99)
remove_values_from_list(tokenizedLine, ' ')
remove_values_from_list(tokenizedLine, '')
for token in tokenizedLine:
values.append(token)
nPointsX += 1
grX.SetPoint(nPointsX, float(values[2]), float(values[3]), float(values[6]) )
def seed2dfit(name,r1,r2,r3,r4,dodraw):
gxyz = TGraph2D()
gxyz.SetMarkerSize(1)
gxyz.SetMarkerStyle(24)
gxyz.SetMarkerColor(ROOT.kBlack)
gxyz.SetPoint(0,r1[2],r1[1],r1[0])
gxyz.SetPoint(1,r2[2],r2[1],r2[0])
gxyz.SetPoint(2,r3[2],r3[1],r3[0])
gxyz.SetPoint(3,r4[2],r4[1],r4[0])
gxyz.GetXaxis().SetRangeUser(290,340)
gxyz.GetYaxis().SetRangeUser(-0.8,+0.8)
gxyz.GetZaxis().SetRangeUser( 0 if(isel(r1[0])) else xPsideL, xEsideR if(isel(r1[0])) else 0 )
gxz = TGraph()
gxz.SetMarkerSize(1)
gxz.SetMarkerStyle(24)
gxz.SetMarkerColor(ROOT.kBlack)
gxz.SetPoint(0,r1[2],r1[0])
gxz.SetPoint(1,r2[2],r2[0])
gxz.SetPoint(2,r3[2],r3[0])
gxz.SetPoint(3,r4[2],r4[0])
gxz.GetXaxis().SetRangeUser(290,340)
if(isel(r1[0])): gxz.GetYaxis().SetRangeUser(0,xEsideR)
else: gxz.GetYaxis().SetRangeUser(xPsideL,0)
gyz = TGraph()
gyz.SetMarkerSize(1)
gyz.SetMarkerStyle(24)
gyz.SetMarkerColor(ROOT.kBlack)
gyz.SetLineColor(ROOT.kRed)
gyz.SetPoint(0,r1[2],r1[1])
gyz.SetPoint(1,r2[2],r2[1])
gyz.SetPoint(2,r3[2],r3[1])
gyz.SetPoint(3,r4[2],r4[1])
gyz.GetXaxis().SetRangeUser(290,340)
gyz.GetYaxis().SetRangeUser(-0.8,+0.8)
fxz = TF1("fxz","pol1",300,330)
fitr_xz = gxz.Fit(fxz,"Q")
fitf_xz = gxz.FindObject("fxz")
fyz = TF1("fyz","pol1",300,330)
fitr_yz = gyz.Fit(fyz,"Q")
fitf_yz = gyz.FindObject("fyz")
if(dodraw):
lfit = TPolyLine3D()
for z in [300,310,320,330]:
x = fitf_xz.Eval(z)
y = fitf_yz.Eval(z)
lfit.SetNextPoint(z,y,x)
lfit.SetLineColor(ROOT.kBlue)
cnv = TCanvas("","",1500,500)
cnv.Divide(3,1)
cnv.cd(1)
view = TView.CreateView(1)
xviewmin = 0 if(isel(r1[0])) else xPsideL
xviewmax = xEsideR if(isel(r1[0])) else 0
view.SetRange(290,-0.8, xviewmin , 340,+0.8,xviewmax)
view.ShowAxis()
gxyz.Draw("p0")
lfit.Draw("smae")
cnv.cd(2)
gxz.Draw()
cnv.cd(3)
gyz.Draw()
cnv.SaveAs(name)
chi2_xz = fitf_xz.GetChisquare()/fitf_xz.GetNDF()
chi2_yz = fitf_yz.GetChisquare()/fitf_yz.GetNDF()
prob_xz = fitf_xz.GetProb()
prob_yz = fitf_yz.GetProb()
return chi2_xz,prob_xz,chi2_yz,prob_yz
def makePlot2D(filepath,foutname,medcfg,chicfg,offshell=True):
xsecs = fcncXsecs # FIXME
limits = parseLimitFiles2D(filepath,None)
gs = {}
for g in ['exp','expup','expdown','obs','obsup','obsdown']:
gs[g] = TGraph2D()
iP=0
hgrid = TH2D('grid','grid',medcfg[0],medcfg[1],medcfg[2],chicfg[0],chicfg[1],chicfg[2])
for p in limits:
mMed = p[0]; mChi = p[1]
'''
if mMed<0.5*medcfg[1] or mMed>1.2*medcfg[2]:
continue
if mChi<0.5*chicfg[1] or mChi>1.2*chicfg[2]:
continue
'''
l = limits[p]
if l.obs==0 or l.cent==0:
print mMed,mChi
continue
hgrid.Fill(mMed,mChi)
gs['exp'].SetPoint(iP,mMed,mChi,l.cent)
gs['expup'].SetPoint(iP,mMed,mChi,l.up1)
gs['expdown'].SetPoint(iP,mMed,mChi,l.down1)
gs['obs'].SetPoint(iP,mMed,mChi,l.obs)
gs['obsup'].SetPoint(iP,mMed,mChi,l.obs/(1-XSECUNCERT))
gs['obsdown'].SetPoint(iP,mMed,mChi,l.obs/(1+XSECUNCERT))
iP += 1
hs = {}
for h in ['exp','expup','expdown','obs','obsup','obsdown']:
hs[h] = TH2D(h,h,medcfg[0],medcfg[1],medcfg[2],chicfg[0],chicfg[1],chicfg[2])
# hs[h].SetStats(0); hs[h].SetTitle('')
for iX in xrange(0,medcfg[0]):
for iY in xrange(0,chicfg[0]):
x = medcfg[1] + (medcfg[2]-medcfg[1])*iX/medcfg[0]
y = chicfg[1] + (chicfg[2]-chicfg[1])*iY/chicfg[0]
if not(offshell) and 2*y>x:
continue
val = gs[h].Interpolate(x,y)
val = max(0.01,min(100,val))
hs[h].SetBinContent(iX+1,iY+1,val)
# if h=='obs':
# print iX+1,iY+1,x,y,gs[h].Interpolate(x,y)
'''
zaxis = hs['obs'].GetZaxis()
nbins = zaxis.GetNbins()
print nbins
zaxis.SetBinLabel(1,'<10^{-2}')
zaxis.SetBinLabel(nbins,'>10')
'''
hs['obsclone'] = hs['obs'].Clone() # clone it so we can draw with different settings
for h in ['exp','expup','expdown','obsclone','obsup','obsdown']:
hs[h].SetContour(2)
hs[h].SetContourLevel(1,1)
for iX in xrange(1,medcfg[0]+1):
for iY in xrange(1,chicfg[0]+1):
if hs[h].GetBinContent(iX,iY)<=0:
hs[h].SetBinContent(iX,iY,100)
canvas = ROOT.TCanvas("canvas", "canvas", 1000, 800)
canvas.SetLogz()
frame = canvas.DrawFrame(medcfg[1],chicfg[1],medcfg[2],chicfg[2],"")
frame.GetYaxis().CenterTitle();
frame.GetYaxis().SetTitle("m_{#chi} [GeV]");
frame.GetXaxis().SetTitle("m_{V} [GeV]");
frame.GetXaxis().SetTitleOffset(1.15);
frame.GetYaxis().SetTitleOffset(1.15);
root.gStyle.SetLabelSize(0.035,"X");
root.gStyle.SetLabelSize(0.035,"Y");
root.gStyle.SetLabelSize(0.035,"Z");
frame.Draw()
##Color palette
ncontours = 999;
#root.TColor.InitializeColors();
#stops = [ 0.0000, 0.1250, 0.2500, 0.3750, 0.5000, 0.6250, 0.7500, 0.8750, 1.0000]
stops = [ 0.0000, 0.10, 0.200, 0.30, 0.4000, 0.50, 0.7500, 0.8750, 1.0000]
red = [ 243./255., 243./255., 240./255., 240./255., 241./255., 239./255., 186./255., 151./255., 129./255.]
green = [ 0./255., 46./255., 99./255., 149./255., 194./255., 220./255., 183./255., 166./255., 147./255.]
blue = [ 6./255., 8./255., 36./255., 91./255., 169./255., 235./255., 246./255., 240./255., 233./255.]
stopsArray = array('d',stops)
redArray = array('d',red)
greenArray = array('d',green)
blueArray = array('d',blue)
root.TColor.CreateGradientColorTable(9, stopsArray, redArray, greenArray, blueArray, ncontours);
root.gStyle.SetNumberContours(ncontours);
hs['obs'].SetMinimum(0.01)
hs['obs'].SetMaximum(100.)
hs['obs'].Draw("COLZ SAME")
hs['obsclone'].SetLineStyle(1)
hs['obsclone'].SetLineWidth(3)
hs['obsclone'].SetLineColor(2)
hs['obsclone'].Draw('CONT3 SAME')
ctemp = root.TCanvas()
hs['obsclone'].Draw('contlist')
ctemp.Update()
objs = root.gROOT.GetListOfSpecials().FindObject('contours')
saveobs = (objs.At(0)).First()
canvas.cd()
hs['obsup'].SetLineStyle(2)
hs['obsup'].SetLineWidth(2)
hs['obsup'].SetLineColor(2)
hs['obsup'].Draw('CONT3 SAME')
hs['obsdown'].SetLineStyle(2)
hs['obsdown'].SetLineWidth(2)
hs['obsdown'].SetLineColor(2)
hs['obsdown'].Draw('CONT3 SAME')
hs['exp'].SetLineStyle(1)
hs['exp'].SetLineWidth(3)
hs['exp'].SetLineColor(1)
hs['exp'].Draw('CONT3 SAME')
hs['expup'].SetLineStyle(2)
hs['expup'].SetLineWidth(2)
hs['expup'].SetLineColor(1)
hs['expup'].Draw('CONT3 SAME')
hs['expdown'].SetLineStyle(2)
hs['expdown'].SetLineWidth(2)
hs['expdown'].SetLineColor(1)
hs['expdown'].Draw('CONT3 SAME')
if drawLegend:
leg = root.TLegend(0.16,0.62,0.55,0.88);#,NULL,"brNDC");
leg.SetHeader('a_{FC} = b_{FC} = 0.25, g_{DM} = 1')
leg.AddEntry(hs['exp'],"Median Expected 95% CL","L");
leg.AddEntry(hs['expup'],"Exp. #pm 1 std. dev. (exp)","L");
leg.AddEntry(hs['obsclone'],"Observed 95% CL","L");
leg.AddEntry(hs['obsup'],"Obs. #pm 1 std. dev. (theory)","L");
leg.SetFillColor(0); leg.SetBorderSize(0)
leg.Draw("SAME");
# hgrid.Draw('same')
tex = root.TLatex();
tex.SetNDC();
tex.SetTextFont(42);
tex.SetLineWidth(2);
tex.SetTextSize(0.040);
tex.Draw();
tex.DrawLatex(0.62,0.94,"12.9 fb^{-1} (13 TeV)");
tex2 = root.TLatex();
tex2.SetNDC();
tex2.SetTextFont(42);
tex2.SetLineWidth(2);
tex2.SetTextSize(0.04);
tex2.SetTextAngle(270);
tex2.DrawLatex(0.965,0.93,"Observed #sigma_{95% CL}/#sigma_{theory}");
texCMS = root.TLatex(0.12,0.94,"#bf{CMS}");
texCMS.SetNDC();
texCMS.SetTextFont(42);
texCMS.SetLineWidth(2);
texCMS.SetTextSize(0.05); texCMS.Draw();
texPrelim = root.TLatex(0.2,0.94,"#it{Preliminary}");
texPrelim.SetNDC();
texPrelim.SetTextFont(42);
texPrelim.SetLineWidth(2);
texPrelim.SetTextSize(0.04); texPrelim.Draw();
root.gPad.SetRightMargin(0.15);
root.gPad.SetTopMargin(0.07);
root.gPad.SetBottomMargin(0.15);
root.gPad.RedrawAxis();
root.gPad.Modified();
root.gPad.Update();
canvas.SaveAs(foutname+'.png')
canvas.SaveAs(foutname+'.pdf')
fsave = root.TFile(foutname+'.root','RECREATE')
fsave.WriteTObject(hs['obs'],'hobserved')
fsave.WriteTObject(gs['obs'],'gobserved')
fsave.WriteTObject(hs['exp'],'hexp')
fsave.WriteTObject(gs['exp'],'gexp')
fsave.WriteTObject(saveobs,'observed')
fsave.Close()
gr68.SetLineStyle(1)
gr68.SetLineColor(1)
gr68.SetFillStyle(1001)
gr68.SetFillColor(82)
#####
gr95 = contourPlot(tree95, 0.049, 1.0, bestfit)
gr95.SetLineWidth(2)
gr95.SetLineStyle(7)
gr95.SetLineColor(1)
gr95.SetFillStyle(1001)
gr95.SetFillColor(89)
else :
#gr68 = TGraph()
#gr68 = tree68
#bestfit = BestFit(tfBF)
graph = TGraph2D(th2)
xbinsize = 0.05; ybinsize = 0.05
graph.SetNpx( int((graph.GetXmax() - graph.GetXmin())/xbinsize) )
graph.SetNpy( int((graph.GetYmax() - graph.GetYmin())/ybinsize) )
kk = graph.GetHistogram()
canv = TCanvas("canv", "canv", 600, 600)
canv.SetBottomMargin(0.10)
canv.SetLeftMargin(0.08)
canv.SetRightMargin(0.12)
if fromGrid :
graph.Draw('colz')
#c.SaveAs('plot.pdf')
gr68 = graph.GetContourList(2.3/2)
def RootifyCrossBR(self):
for processname in self.processnames:
outfilename_cross = self.crosssecfolder+'/Crosssections_%s%s.root' % (processname, self.tag)
cross_module = importlib.import_module('crosssections.ChiPsi.Crosssections_%s' % (processname))
crosssections = cross_module.crosssection
if self.submit:
outfile = TFile(outfilename_cross, 'RECREATE')
else:
print yellow('--> Would have created outfile %s' % (outfilename_cross))
print green('--> Now at sample %s' % (processname))
for lamb in self.lambdas:
if not lamb in crosssections: continue
print green(' --> Now at lambda: %s' % (get_lambdastring(lamb)))
xsecs_per_mref = crosssections[lamb]
graph2d = TGraph2D()
npoints2d=0
set_points ={}
all_combinations = get_all_combinations(preferred_configurations=preferred_configurations)
for mlq in all_combinations:
set_points[mlq] = {}
for mch in all_combinations[mlq]:
set_points[mlq][mch] = False
for mref in xsecs_per_mref:
xsecs = xsecs_per_mref[mref]
final_xsecs = []
mdeps = array('d')
sigmas = array('d')
tot_los = array('d')
tot_his = array('d')
mdeps_lo = array('d')
mdeps_hi = array('d')
for tuple in xsecs:
mdep, sigma, q_lo, q_hi, pdf = tuple
tot_lo = XsecTotErr(sigma, q_lo, pdf)
tot_hi = XsecTotErr(sigma, q_hi, pdf)
final_xsecs.append((mdep, sigma, tot_lo, tot_hi))
mdeps.append(mdep)
sigmas.append(sigma)
tot_los.append(tot_lo)
tot_his.append(tot_hi)
mdeps_lo.append(0.)
mdeps_hi.append(0.)
if 'LQLQ' in processname or 'LQTChannel' in processname:
graph2d.SetPoint(npoints2d, mdep, mref, sigma)
set_points[mdep][mref] = True
elif 'PsiPsi' in processname:
graph2d.SetPoint(npoints2d, mref, mdep, sigma)
set_points[mref][mdep] = True
else:
raise ValueError('processname does not contain \'LQLQ\' or \'PsiPsi\', what kind of process are we looking at here?')
npoints2d += 1
# make TGraph out of it
graph = TGraphAsymmErrors(len(mdeps), mdeps, sigmas, mdeps_lo, mdeps_hi, tot_los, tot_his)
xaxistitle = 'M_{LQ} [GeV]' if ('LQLQ' in processname or 'LQTChannel' in processname) else 'M_{#chi_{1}} [GeV]'
graph.GetXaxis().SetTitle('M_{LQ} [GeV]')
graph.GetYaxis().SetTitle('#sigma [pb]')
graphname = processname
if 'LQLQ' in processname or 'LQTChannel' in processname:
graphname += '_MC1%i' % (mref)
elif 'PsiPsi' in processname:
graphname += '_MLQ%i' % (mref)
else:
raise ValueError('processname does not contain \'LQLQ\' or \'PsiPsi\', what kind of process are we looking at here?')
graphname += '_L%s' % (get_lambdastring(lamb))
graph.SetName(graphname)
# print 'graphname: %s' % (graphname)
graphtitle = processname
if 'LQLQ' in processname or 'LQTChannel' in processname:
graphtitle += ', M_{#chi_{1}} = %i GeV' % (mref)
elif 'PsiPsi' in processname:
graphtitle += ', M_{LQ} = %i GeV' % (mref)
graphtitle += ', #lambda = %s' % (get_lambdastring(lamb).replace('p', '.'))
# print 'graphtitle: %s' % (graphtitle)
graph.SetTitle(graphtitle)
if self.submit:
outfile.cd()
graph.Write()
else:
print yellow(' --> Would have written graph %s to outfile' % (graphname))
# fill remaining points in 2d graph with zeros
for mlq in set_points:
for mch in set_points[mlq]:
if not set_points[mlq][mch]:
graph2d.SetPoint(npoints2d, mlq, mch, 0.)
npoints2d += 1
graph2d.SetName(processname + '_L%s' % (get_lambdastring(lamb)))
graph2d.GetXaxis().SetTitle('M_{LQ} [GeV]')
graph2d.GetYaxis().SetTitle('M_{#chi_{1}} = %i [GeV]')
graph2d.GetZaxis().SetTitle('#sigma [pb]')
graph2d.SetTitle(processname + ', #lambda = %s' % (get_lambdastring(lamb).replace('p', '.')))
if self.submit:
graph2d.Write()
else:
print yellow(' --> Would have written 2d-graph to outfile')
if self.submit:
outfile.Close()
# also rootify BRs if we are looking at the LQLQ process without decays (just for fun, could also be any other LQLQ process)
if processname == 'LQLQ':
outfilename_br = self.crosssecfolder+'/Branchingratios_%s%s.root' % (processname, self.tag)
if self.submit:
outfile = TFile(outfilename_br, 'RECREATE')
else:
print yellow('--> Would have created outfile %s' % (outfilename_br))
br_module = importlib.import_module('crosssections.ChiPsi.Branchingratios_%s' % (processname))
allbrs = br_module.branchingratio
for lamb in allbrs.keys():
brs = allbrs[lamb]
brs2d = {}
npoints2d = {}
set_points ={}
for mlq in all_combinations:
set_points[mlq] = {}
for mch in all_combinations[mlq]:
set_points[mlq][mch] = {}
for decaymode in decaymode_dict.keys():
set_points[mlq][mch][decaymode] = False
decaymodes_present = []
for mlq in sorted(brs):
mchs_per_decaymode = {}
brs_per_decaymode = {}
for mch in sorted(brs[mlq]):
for decaymode in brs[mlq][mch]:
if not decaymode in decaymodes_present:
decaymodes_present.append(decaymode)
if not decaymode in mchs_per_decaymode.keys(): mchs_per_decaymode[decaymode] = array('d')
if not decaymode in brs_per_decaymode.keys(): brs_per_decaymode[decaymode] = array('d')
# if not decaymode in set_points[mlq][mch].keys():
# # print mlq, mch, decaymode
# set_points[mlq][mch][decaymode] = False
if not decaymode in brs2d.keys():
graphname2d = processname + ('_L%s_%i_%i' % (get_lambdastring(lamb), abs(decaymode[0]), abs(decaymode[1])))
# print graphname2d
npoints2d[decaymode] = 0
brs2d[decaymode] = TGraph2D()
brs2d[decaymode].SetName(graphname2d)
brs2d[decaymode].GetXaxis().SetTitle('M_{LQ} [GeV]')
brs2d[decaymode].GetYaxis().SetTitle('M_{#chi_{1}} = %i [GeV]')
brs2d[decaymode].GetZaxis().SetTitle('BR (LQLQ#rightarrow%s)' % (decaymode_dict[decaymode]))
brs2d[decaymode].SetTitle(processname + ', %s' % (decaymode_dict[decaymode]))
mchs_per_decaymode[decaymode].append(mch)
brs_per_decaymode[decaymode].append(brs[mlq][mch][decaymode][0])
brs2d[decaymode].SetPoint(npoints2d[decaymode], mlq, mch, brs[mlq][mch][decaymode][0])
set_points[mlq][mch][decaymode] = True
npoints2d[decaymode] += 1
for decaymode in mchs_per_decaymode.keys():
graph = TGraph(len(mchs_per_decaymode[decaymode]), mchs_per_decaymode[decaymode], brs_per_decaymode[decaymode])
graphname = processname + ('_MLQ%i_L%s_%i_%i' % (mlq, get_lambdastring(lamb), abs(decaymode[0]), abs(decaymode[1])))
graph.SetName(graphname)
graph.GetXaxis().SetTitle('M_{#chi_{1}} [GeV]')
graph.GetYaxis().SetTitle('BR (LQLQ#rightarrow%s)' % (decaymode_dict[decaymode]))
graph.SetTitle(processname + ', %s' % (decaymode_dict[decaymode]))
if self.submit:
graph.Write()
else:
print yellow(' --> Would have written graph %s to outfile' % (graphname))
# fill remaining points in 2d graph with zeros
for mlq in set_points:
for mch in set_points[mlq]:
for decaymode in set_points[mlq][mch]:
if not set_points[mlq][mch][decaymode] and decaymode in decaymodes_present:
# print decaymode
# print brs2d.keys()
# print npoints2d.keys()
# print 'Setting BR for MLQ=%i, MCH=%i, decay=(%i, %i) to 0' % (mlq, mch, decaymode[0], decaymode[1])
brs2d[decaymode].SetPoint(npoints2d[decaymode], mlq, mch, 0)
npoints2d[decaymode] += 1
if self.submit:
for decaymode in brs2d:
brs2d[decaymode].Write()
else:
print yellow(' --> Would have written 2d-graphs to outfile')
outfile.Close()
ScinPlot = TH2F("Scin_" + str(phi), "Scin_" + str(phi), int(100),
float(np.mean(theta_S) - 0.05),
float(np.mean(theta_S) + 0.05), int(100),
float(np.mean(phi_S) - 0.05),
float(np.mean(phi_S) + 0.05))
CherPlot = TH2F("Cher_" + str(phi), "Cher_" + str(phi), int(100),
float(np.mean(theta_S) - 0.05),
float(np.mean(theta_S) + 0.05), int(100),
float(np.mean(phi_S) - 0.05),
float(np.mean(phi_S) + 0.05))
n = len(theta_S)
array_theta_S = np.array(theta_S, 'd')
array_phi_S = np.array(phi_S, 'd')
array_E_s = np.array(E_s, 'd')
ScinGraph = TGraph2D(n, array_theta_S, array_phi_S, array_E_s)
ScinGraph.SetName("ScinGraph_" + str(phi))
n = len(theta_C)
array_theta_C = np.array(theta_C, 'd')
array_phi_C = np.array(phi_C, 'd')
array_E_c = np.array(E_c, 'd')
CherGraph = TGraph2D(n, array_theta_C, array_phi_C, array_E_c)
CherGraph.SetName("CherGraph_" + str(phi))
MeanTheta = 0.
MeanPhi = 0.
sumtheta = 0.
sumphi = 0.
MeanThetaC = 0.
MeanPhiC = 0.
def makePlot2D(filepath, foutname, medcfg, chicfg, offshell=False):
limits = parseLimitFiles2D(filepath)
gs = {}
for g in ['exp', 'expup', 'expdown', 'obs', 'obsup', 'obsdown']:
gs[g] = TGraph2D()
iP = 0
hgrid = TH2D('grid', 'grid', medcfg[0], medcfg[1], medcfg[2], chicfg[0],
chicfg[1], chicfg[2])
for p in limits:
mMed = p[0]
mChi = p[1]
'''
if mMed<0.5*medcfg[1] or mMed>1.2*medcfg[2]:
continue
if mChi<0.5*chicfg[1] or mChi>1.2*chicfg[2]:
continue
'''
l = limits[p]
if l.obs == 0 or l.cent == 0:
print mMed, mChi
continue
hgrid.Fill(mMed, mChi)
gs['exp'].SetPoint(iP, mMed, mChi, l.cent)
gs['expup'].SetPoint(iP, mMed, mChi, l.up1)
gs['expdown'].SetPoint(iP, mMed, mChi, l.down1)
iP += 1
hs = {}
for h in ['exp', 'expup', 'expdown']:
hs[h] = TH2D(h, h, medcfg[0], medcfg[1], medcfg[2], chicfg[0],
chicfg[1], chicfg[2])
# hs[h].SetStats(0); hs[h].SetTitle('')
for iX in xrange(0, medcfg[0]):
for iY in xrange(0, chicfg[0]):
x = medcfg[1] + (medcfg[2] - medcfg[1]) * iX / medcfg[0]
y = chicfg[1] + (chicfg[2] - chicfg[1]) * iY / chicfg[0]
if not (offshell) and 2 * y > x:
val = 9999
else:
val = gs[h].Interpolate(x, y)
if val == 0:
val = 9999
val = max(0.01, min(100, val))
hs[h].SetBinContent(iX + 1, iY + 1, val)
# if h=='obs':
# print iX+1,iY+1,x,y,gs[h].Interpolate(x,y)
'''
zaxis = hs['obs'].GetZaxis()
nbins = zaxis.GetNbins()
print nbins
zaxis.SetBinLabel(1,'<10^{-2}')
zaxis.SetBinLabel(nbins,'>10')
'''
hs['expclone'] = hs['exp'].Clone()
for h in ['expup', 'expdown', 'expclone']:
hs[h].SetContour(2)
hs[h].SetContourLevel(1, 1)
for iX in xrange(1, medcfg[0] + 1):
for iY in xrange(1, chicfg[0] + 1):
if hs[h].GetBinContent(iX, iY) <= 0:
hs[h].SetBinContent(iX, iY, 100)
canvas = ROOT.TCanvas("canvas", "canvas", 1000, 800)
canvas.SetLogz()
frame = canvas.DrawFrame(medcfg[1], chicfg[1], medcfg[2], chicfg[2], "")
frame.GetYaxis().CenterTitle()
frame.GetYaxis().SetTitle("m_{#chi} [GeV]")
frame.GetXaxis().SetTitle("m_{V} [GeV]")
frame.GetXaxis().SetTitleOffset(1.15)
frame.GetYaxis().SetTitleOffset(1.15)
root.gStyle.SetLabelSize(0.035, "X")
root.gStyle.SetLabelSize(0.035, "Y")
root.gStyle.SetLabelSize(0.035, "Z")
frame.Draw()
hs['exp'].SetMinimum(0.01)
hs['exp'].SetMaximum(100.)
hs['exp'].Draw("COLZ SAME")
hs['expclone'].SetLineStyle(1)
hs['expclone'].SetLineWidth(3)
hs['expclone'].SetLineColor(1)
hs['expclone'].Draw('CONT3 SAME')
ctemp = root.TCanvas()
hs['expclone'].Draw('contlist')
ctemp.Update()
objs = root.gROOT.GetListOfSpecials().FindObject('contours')
canvas.cd()
hs['expup'].SetLineStyle(2)
hs['expup'].SetLineWidth(2)
hs['expup'].SetLineColor(1)
hs['expup'].Draw('CONT3 SAME')
hs['expdown'].SetLineStyle(2)
hs['expdown'].SetLineWidth(2)
hs['expdown'].SetLineColor(1)
hs['expdown'].Draw('CONT3 SAME')
if drawLegend:
leg = root.TLegend(0.16, 0.62, 0.57, 0.88)
#,NULL,"brNDC");
leg.SetHeader('g_{q}^{V} = 0.25, g_{DM}^{V} = 1')
leg.AddEntry(hs['expclone'], "Median Expected 95% CL", "L")
leg.AddEntry(hs['expup'], "Exp. #pm 1 std. dev. (exp)", "L")
leg.SetFillColor(0)
leg.SetBorderSize(0)
leg.Draw("SAME")
# hgrid.Draw('same')
tex = root.TLatex()
tex.SetNDC()
tex.SetTextFont(42)
tex.SetLineWidth(2)
tex.SetTextSize(0.040)
tex.Draw()
tex.DrawLatex(0.62, 0.94, "36.3 fb^{-1} (13 TeV)")
tex2 = root.TLatex()
tex2.SetNDC()
tex2.SetTextFont(42)
tex2.SetLineWidth(2)
tex2.SetTextSize(0.04)
tex2.SetTextAngle(270)
tex2.DrawLatex(0.965, 0.93, "Expected #sigma_{95% CL}/#sigma_{theory}")
texCMS = root.TLatex(0.12, 0.94, "#bf{CMS}")
texCMS.SetNDC()
texCMS.SetTextFont(42)
texCMS.SetLineWidth(2)
texCMS.SetTextSize(0.05)
texCMS.Draw()
texPrelim = root.TLatex(0.2, 0.94, "#it{Preliminary}")
texPrelim.SetNDC()
texPrelim.SetTextFont(42)
texPrelim.SetLineWidth(2)
texPrelim.SetTextSize(0.04)
texPrelim.Draw()
root.gPad.SetRightMargin(0.15)
root.gPad.SetTopMargin(0.07)
root.gPad.SetBottomMargin(0.15)
root.gPad.RedrawAxis()
root.gPad.Modified()
root.gPad.Update()
canvas.SaveAs(foutname + '.png')
canvas.SaveAs(foutname + '.pdf')
fsave = root.TFile(foutname + '.root', 'RECREATE')
fsave.WriteTObject(hs['exp'], 'hexp')
fsave.WriteTObject(gs['exp'], 'gexp')
fsave.Close()
def makePlot2D(filepath, foutname, medcfg, gqcfg, header):
limits = parseLimitFiles2D(filepath)
gs = {}
for g in ['exp', 'expup', 'expdown', 'obs', 'obsup', 'obsdown']:
gs[g] = TGraph2D()
iP = 0
hgrid = TH2D('grid', 'grid', medcfg[0], medcfg[1], medcfg[2], gqcfg[0],
gqcfg[1], gqcfg[2])
for p in limits:
l = p.limit
if l.obs == 0 or l.cent == 0:
print l.mMed, l.gQ
continue
hgrid.Fill(l.mMed, l.gQ)
gs['exp'].SetPoint(iP, l.mMed, l.gQ, l.cent)
gs['expup'].SetPoint(iP, l.mMed, l.gQ, l.up1)
gs['expdown'].SetPoint(iP, l.mMed, l.gQ, l.down1)
gs['obs'].SetPoint(iP, l.mMed, l.gQ, l.obs)
gs['obsup'].SetPoint(iP, l.mMed, l.gQ, l.obs / (1 - XSECUNCERT))
gs['obsdown'].SetPoint(iP, l.mMed, l.gQ, l.obs / (1 + XSECUNCERT))
iP += 1
hs = {}
for h in ['exp', 'expup', 'expdown', 'obs', 'obsup', 'obsdown']:
hs[h] = TH2D(h, h, medcfg[0], medcfg[1], medcfg[2], gqcfg[0], gqcfg[1],
gqcfg[2])
# hs[h].SetStats(0); hs[h].SetTitle('')
for iX in xrange(0, medcfg[0]):
for iY in xrange(0, gqcfg[0]):
x = medcfg[1] + (medcfg[2] - medcfg[1]) * iX / medcfg[0]
y = gqcfg[1] + (gqcfg[2] - gqcfg[1]) * iY / gqcfg[0]
val = gs[h].Interpolate(x, y)
if val == 0:
val = 9999
val = max(0.01, min(100, val))
hs[h].SetBinContent(iX + 1, iY + 1, val)
# if h=='obs':
# print iX+1,iY+1,x,y,gs[h].Interpolate(x,y)
'''
zaxis = hs['obs'].GetZaxis()
nbins = zaxis.GetNbins()
print nbins
zaxis.SetBinLabel(1,'<10^{-2}')
zaxis.SetBinLabel(nbins,'>10')
'''
hs['obsclone'] = hs['obs'].Clone(
) # clone it so we can draw with different settings
for h in ['exp', 'expup', 'expdown', 'obsclone', 'obsup', 'obsdown']:
hs[h].SetContour(2)
hs[h].SetContourLevel(1, 1)
for iX in xrange(1, medcfg[0] + 1):
for iY in xrange(1, gqcfg[0] + 1):
if hs[h].GetBinContent(iX, iY) <= 0:
hs[h].SetBinContent(iX, iY, 100)
global iC
canvas = ROOT.TCanvas("canvas%i" % iC, '', 1000, 800)
canvas.SetLogz()
iC += 1
frame = canvas.DrawFrame(medcfg[1], gqcfg[1], medcfg[2], gqcfg[2], "")
frame.GetYaxis().CenterTitle()
frame.GetYaxis().SetTitle(gqcfg[3])
frame.GetXaxis().SetTitle("m_{V} [TeV]")
frame.GetXaxis().SetTitleOffset(1.15)
frame.GetYaxis().SetTitleOffset(1.15)
frame.Draw()
hs['obs'].SetMinimum(0.01)
hs['obs'].SetMaximum(100.)
hs['obs'].Draw("COLZ SAME")
hs['obsclone'].SetLineStyle(1)
hs['obsclone'].SetLineWidth(3)
hs['obsclone'].SetLineColor(2)
hs['obsclone'].Draw('CONT3 SAME')
ctemp = root.TCanvas()
hs['obsclone'].Draw('contlist')
ctemp.Update()
objs = root.gROOT.GetListOfSpecials().FindObject('contours')
saveobs = (objs.At(0)).First()
canvas.cd()
hs['obsup'].SetLineStyle(2)
hs['obsup'].SetLineWidth(1)
hs['obsup'].SetLineColor(2)
hs['obsup'].Draw('CONT3 SAME')
hs['obsdown'].SetLineStyle(2)
hs['obsdown'].SetLineWidth(1)
hs['obsdown'].SetLineColor(2)
hs['obsdown'].Draw('CONT3 SAME')
hs['exp'].SetLineStyle(1)
hs['exp'].SetLineWidth(3)
hs['exp'].SetLineColor(1)
hs['exp'].Draw('CONT3 SAME')
hs['expup'].SetLineStyle(2)
hs['expup'].SetLineWidth(1)
hs['expup'].SetLineColor(1)
hs['expup'].Draw('CONT3 SAME')
hs['expdown'].SetLineStyle(2)
hs['expdown'].SetLineWidth(1)
hs['expdown'].SetLineColor(1)
hs['expdown'].Draw('CONT3 SAME')
if drawLegend:
leg = root.TLegend(0.16, 0.62, 0.57, 0.88)
#,NULL,"brNDC");
leg.SetHeader(header)
leg.AddEntry(hs['exp'], "Median Expected 95% CL", "L")
leg.AddEntry(hs['expup'], "Exp. #pm 1 std. dev. (exp)", "L")
leg.AddEntry(hs['obsclone'], "Observed 95% CL", "L")
leg.AddEntry(hs['obsup'], "Obs. #pm 1 std. dev. (theory)", "L")
leg.SetFillColor(0)
leg.SetBorderSize(0)
leg.Draw("SAME")
# hgrid.Draw('same')
tex = root.TLatex()
tex.SetNDC()
tex.SetTextFont(42)
tex.SetLineWidth(2)
tex.SetTextSize(0.040)
tex.Draw()
tex.DrawLatex(0.6, 0.94, "35.8 fb^{-1} (13 TeV)")
tex2 = root.TLatex()
tex2.SetNDC()
tex2.SetTextFont(42)
tex2.SetLineWidth(2)
tex2.SetTextSize(0.04)
tex2.SetTextAngle(270)
tex2.DrawLatex(0.965, 0.93, "Expected #sigma_{95% CL}/#sigma_{theory}")
texCMS = root.TLatex(0.12, 0.94, "#bf{CMS}")
texCMS.SetNDC()
texCMS.SetTextFont(42)
texCMS.SetLineWidth(2)
texCMS.SetTextSize(0.05)
texCMS.Draw()
# texPrelim = root.TLatex(0.2,0.94,"#it{Preliminary}");
# texPrelim.SetNDC();
# texPrelim.SetTextFont(42);
# texPrelim.SetLineWidth(2);
# texPrelim.SetTextSize(0.04); texPrelim.Draw();
root.gPad.SetRightMargin(0.15)
root.gPad.SetTopMargin(0.07)
root.gPad.SetBottomMargin(0.15)
root.gPad.RedrawAxis()
root.gPad.Modified()
root.gPad.Update()
canvas.SaveAs(foutname + '.png')
canvas.SaveAs(foutname + '.pdf')
fsave = root.TFile(foutname + '.root', 'RECREATE')
fsave.WriteTObject(hs['exp'], 'hexp')
fsave.WriteTObject(gs['exp'], 'gexp')
fsave.Close()
0.97514, 0.97068, 0.94650, 0.90751, 0.95916, 0.93991, 0.96377, 0.98120,
0.99205, 0.97595, 0.92522, 0.97245, 0.99472, 0.99782, 0.99816
])
AUC_DNN = np.array([
0.90729, 0.90475, 0.90596, 0.87725, 0.93349, 0.90674, 0.85802, 0.97674,
0.97644, 0.97210, 0.94923, 0.90743, 0.96152, 0.93995, 0.96559, 0.98142,
0.99210, 0.97635, 0.92650, 0.97343, 0.99470, 0.99726, 0.99729
])
#mH = np.array([200,200,250,250,300,300,300,500,500,500,500,500,650,800,800,800,800,800,1000,1000,1000])
#mA = np.array([50,100,50,100,50,100,200,50,100,200,300,400,50,50,100,200,400,700,50,200,500])
#AUC_MEM = np.array([0.90107,0.89736,0.90640,0.87296,0.92959,0.90424,0.85102,0.97537,0.97514,0.97068,0.94650,0.90751,0.95916,0.93991,0.96377,0.98120,0.99205,0.97595,0.92522,0.97245,0.99472])
#AUC_DNN = np.array([0.90729,0.90475,0.90596,0.87725,0.93349,0.90674,0.85802,0.97674,0.97644,0.97210,0.94923,0.90743,0.96152,0.93995,0.96559,0.98142,0.99210,0.97635,0.92650,0.97343,0.99470])
N = mA.shape[0]
graph_DNN = TGraph2D(N)
graph_MEM = TGraph2D(N)
for i in range(0, N):
graph_MEM.SetPoint(i, mA[i], mH[i], AUC_MEM[i])
graph_DNN.SetPoint(i, mA[i], mH[i], AUC_DNN[i])
graph_MEM.SetNpx(500)
graph_MEM.SetNpy(500)
graph_DNN.SetNpx(500)
graph_DNN.SetNpy(500)
canvas = TCanvas('c1', 'c1', 1800, 700)
canvas.Divide(2, 1, 0.005)
canvas.cd(1)
gPad.SetRightMargin(0.2)
def makePlot2D(filepath,foutname,medcfg,chicfg,header='',offshell=False):
limits = parseLimitFiles2D(filepath)
gs = {}
for g in ['exp','expup','expdown','obs','obsup','obsdown']:
gs[g] = TGraph2D()
iP=0
hgrid = TH2D('grid','grid',medcfg[0],medcfg[1],medcfg[2],chicfg[0],chicfg[1],chicfg[2])
for p in limits:
mMed = p[0]; mChi = p[1]
l = limits[p]
if l.obs==0 or l.cent==0:
print mMed,mChi
continue
hgrid.Fill(mMed,mChi,100)
gs['exp'].SetPoint(iP,mMed,mChi,l.cent)
gs['expup'].SetPoint(iP,mMed,mChi,l.up1)
gs['expdown'].SetPoint(iP,mMed,mChi,l.down1)
gs['obs'].SetPoint(iP,mMed,mChi,l.obs)
gs['obsup'].SetPoint(iP,mMed,mChi,l.obs/(1-XSECUNCERT))
gs['obsdown'].SetPoint(iP,mMed,mChi,l.obs/(1+XSECUNCERT))
iP += 1
hs = {}
for h in ['exp','expup','expdown','obs','obsup','obsdown']:
hs[h] = TH2D(h,h,medcfg[0],medcfg[1],medcfg[2],chicfg[0],chicfg[1],chicfg[2])
# hs[h].SetStats(0); hs[h].SetTitle('')
for iX in xrange(0,medcfg[0]):
for iY in xrange(0,chicfg[0]):
x = medcfg[1] + (medcfg[2]-medcfg[1])*iX/medcfg[0]
y = chicfg[1] + (chicfg[2]-chicfg[1])*iY/chicfg[0]
if not(offshell) and 2*y>x:
val = 9999
else:
val = gs[h].Interpolate(x,y)
if val == 0:
val = 9999
val = max(0.01,min(100,val))
hs[h].SetBinContent(iX+1,iY+1,val)
hs['obsclone'] = hs['obs'].Clone() # clone it so we can draw with different settings
for h in ['exp','expup','expdown','obsclone','obsup','obsdown']:
hs[h].SetContour(2)
hs[h].SetContourLevel(1,1)
for iX in xrange(1,medcfg[0]+1):
for iY in xrange(1,chicfg[0]+1):
if hs[h].GetBinContent(iX,iY)<=0:
hs[h].SetBinContent(iX,iY,100)
global iC
canvas = ROOT.TCanvas("canvas%i"%iC, '', 1000, 800)
canvas.SetLogz()
iC+=1
frame = canvas.DrawFrame(medcfg[1],chicfg[1],medcfg[2],chicfg[2],"")
frame.GetYaxis().CenterTitle();
#frame.GetYaxis().SetTitle("m_{A} [TeV]");
if options.thdm: frame.GetYaxis().SetTitle("m_{A} (TeV/c^{2})");
if options.zpb: frame.GetYaxis().SetTitle("m_{#chi} (TeV/c^{2})");
frame.GetXaxis().SetTitle("m_{Z'} (TeV/c^{2})");
frame.GetXaxis().SetTitleOffset(1.15);
frame.GetYaxis().SetTitleOffset(1.15);
# frame.GetXaxis().SetNdivisions(5)
frame.Draw()
htest = hs['exp']
obs_color = root.kOrange
hs['obs'].SetMinimum(0.01)
hs['obs'].SetMaximum(100.)
hs['obs'].Draw("COLZ SAME")
hs['obsclone'].SetLineStyle(1)
hs['obsclone'].SetLineWidth(3)
hs['obsclone'].SetLineColor(obs_color)
hs['obsclone'].Draw('CONT3 SAME')
ctemp = root.TCanvas()
hs['obsclone'].Draw('contlist')
ctemp.Update()
objs = root.gROOT.GetListOfSpecials().FindObject('contours')
saveobs = root.TGraph((objs.At(0)).First())
canvas.cd()
root.gStyle.SetLineStyleString(11, '40 80')
conts = {}
hs['obsup'].SetLineStyle(3)
hs['obsup'].SetLineWidth(2)
hs['obsup'].SetLineColor(obs_color)
conts['obsup'] = get_contours(hs['obsup'], canvas)[0]
conts['obsup'].Draw('L SAME')
# hs['obsup'].Draw('CONT3 SAME')
hs['obsdown'].SetLineStyle(3)
hs['obsdown'].SetLineWidth(2)
hs['obsdown'].SetLineColor(obs_color)
conts['obsdown'] = get_contours(hs['obsdown'], canvas)[0]
conts['obsdown'].Draw('L SAME')
#hs['obsdown'].Draw('CONT3 SAME')
hs['exp'].SetLineStyle(1)
hs['exp'].SetLineWidth(3)
hs['exp'].SetLineColor(1)
hs['exp'].Draw('CONT3 SAME')
conts['exp'] = get_contours(hs['exp'], canvas)[0]
conts['obsclone'] = get_contours(hs['obsclone'], canvas)[0]
hs['expup'].SetLineStyle(3)
hs['expup'].SetLineWidth(2)
hs['expup'].SetLineColor(1)
conts['expup'] = get_contours(hs['expup'], canvas)[0]
conts['expup'].Draw('L SAME')
#hs['expup'].Draw('CONT3 SAME')
hs['expdown'].SetLineStyle(3)
hs['expdown'].SetLineWidth(2)
hs['expdown'].SetLineColor(1)
conts['expdown'] = get_contours(hs['expdown'], canvas)[0]
conts['expdown'].Draw('L SAME')
#hs['expdown'].Draw('CONT3 SAME')
graphroot = TFile("limitGraphs2HDMComboTanBeta.root","RECREATE")
graphroot.cd()
h_exp = conts['exp']
h_exp.SetName("expected_curve")
h_exp.Write()
#conts['exp'].Write()
conts['expup'].Write()
conts['expdown'].Write()
#conts['obsclone'].Write()
h_obs = conts['obsclone']
h_obs.SetName("observed_curve")
h_obs.Write()
conts['obsup'].Write()
conts['obsdown'].Write()
if drawLegend:
leg = root.TLegend(0.13,0.75,0.39,0.9);#,NULL,"brNDC");
leg.SetHeader(header)
leg.AddEntry(hs['exp'],"Median expected 95% CL","L");
leg.AddEntry(hs['expup'],"Exp. #pm 1 #sigma_{experiment}","L");
leg.AddEntry(hs['obsclone'],"Observed 95% CL","L");
leg.AddEntry(hs['obsup'],"Obs. #pm 1 #sigma_{theory}","L");
leg.SetFillColor(0); leg.SetBorderSize(0)
leg.Draw("SAME");
tex = root.TLatex();
tex.SetNDC();
tex.SetTextFont(42);
tex.SetLineWidth(2);
tex.SetTextSize(0.040);
tex.Draw();
tex.DrawLatex(0.65,0.94,"35.9 fb^{-1} (13 TeV)");
coupling = root.TLatex();
coupling.SetNDC();
coupling.SetTextFont(42);
coupling.SetLineWidth(2);
coupling.SetTextSize(0.025);
coupling.SetTextColor(0);
coupling.Draw();
if options.thdm:
coupling.DrawLatex(0.15,0.70,"g_{Z'} = 0.8, g_{#chi} = 1");
coupling.DrawLatex(0.15,0.65,"m_{#chi} = 100 GeV, tan#beta = 1");
if options.zpb: coupling.DrawLatex(0.15,0.70,"g_{q} = 0.25, g_{#chi} = 1");
tex2 = root.TLatex();
tex2.SetNDC();
tex2.SetTextFont(42);
tex2.SetLineWidth(2);
tex2.SetTextSize(0.04);
tex2.SetTextAngle(90);
tex2.SetTextAlign(33)
tex2.DrawLatex(0.965,0.93,"Observed #sigma_{95% CL}/#sigma_{theory}");
texCMS = root.TLatex(0.12,0.94,"#bf{CMS}");
texCMS.SetNDC();
texCMS.SetTextFont(42);
texCMS.SetLineWidth(2);
texCMS.SetTextSize(0.05); texCMS.Draw();
root.gPad.SetRightMargin(0.15);
root.gPad.SetTopMargin(0.07);
root.gPad.SetBottomMargin(0.15);
root.gPad.RedrawAxis();
root.gPad.Modified();
root.gPad.Update();
canvas.SaveAs(foutname+'.png')
canvas.SaveAs(foutname+'.pdf')
texPrelim = root.TLatex(0.2,0.94,"");
texPrelim.SetNDC();
texPrelim.SetTextFont(42);
texPrelim.SetLineWidth(2);
texPrelim.SetTextSize(0.05); texPrelim.Draw();
canvas.SaveAs(foutname+'_prelim.png')
canvas.SaveAs(foutname+'_prelim.pdf')
canvas.SetGrid()
hgrid.Draw('BOX')
hs['obsup'].Draw('CONT3 SAME')
hs['obsdown'].Draw('CONT3 SAME')
hs['exp'].Draw('CONT3 SAME')
hs['expup'].Draw('CONT3 SAME')
hs['expdown'].Draw('CONT3 SAME')
canvas.SaveAs(foutname+'_grid.png')
canvas.SaveAs(foutname+'_grid.pdf')
fsave = root.TFile(foutname+'.root','RECREATE')
fsave.WriteTObject(hs['obs'],'hobserved')
fsave.WriteTObject(gs['obs'],'gobserved')
fsave.WriteTObject(hs['exp'],'hexp')
fsave.WriteTObject(gs['exp'],'gexp')
fsave.WriteTObject(saveobs,'observed')
fsave.Close()
def makeAndStorePlot(self, inputfilename, iSkip=2, strObsName="MPV", outputfilename="AnaSuiteClustQOutput.root", strFileOpt="RECREATE"):
#Load the data from an input text file
clustChargeData = np.loadtxt(inputfilename, skiprows=iSkip)
#Splice the clustChargeData into HVORGAIN & CLUSTSIZE
self.ARRAY_HVORGAIN = clustChargeData[:,0]
self.ARRAY_CLUSTSIZE= clustChargeData[:,1]
#Print the shape if requested
if self.DEBUG:
print "Shape of clustChargeData = {0}".format(clustChargeData.shape)
print "Shape of self.ARRAY_HVORGAIN = {0}".format(self.ARRAY_HVORGAIN.shape)
print "Shape of self.ARRAY_CLUSTSIZE = {0}".format(self.ARRAY_CLUSTSIZE.shape)
#Reparameterize in terms of Gain?
strIndepVarName = "VDrift"
if self.TRANSFORM2GAIN:
strIndepVarName = "EffGain"
#array_X = self.GAIN_CALCULATOR.calcGain(array_X)
self.ARRAY_HVORGAIN = self.GAIN_CALCULATOR.calcGain(self.ARRAY_HVORGAIN)
#Transform input observable name to uppercase
strObsName = strObsName.upper()
#Initialize the correct TGraph2D
g2D_ClustQ_Obs = TGraph2D(len(clustChargeData))
g2D_ClustQ_Obs.SetTitle("")
if strObsName == self.STROBSNAME_MPV:
#self.G2D_CLUSTQ_MPV.Set( len(clustChargeData) )
#self.G2D_CLUSTQ_MPV.SetName("g2D_ClusterChargeMPV_StripSize_vs_" + strIndepVarName)
#self.G2D_CLUSTQ_MPV.SetTitle("")
g2D_ClustQ_Obs.SetName("g2D_ClusterChargeMPV_StripSize_vs_{0}".format(strIndepVarName))
self.ARRAY_CLUSTQ_MPV = clustChargeData[:,2]
elif strObsName == self.STROBSNAME_MEAN:
#self.G2D_CLUSTQ_MEAN.Set( len(clustChargeData) )
#self.G2D_CLUSTQ_MEAN.SetName("g2D_ClusterChargeMean_StripSize_vs_" + strIndepVarName)
#self.G2D_CLUSTQ_MEAN.SetTitle("")
g2D_ClustQ_Obs.SetName("g2D_ClusterChargeMean_StripSize_vs_{0}".format(strIndepVarName))
self.ARRAY_CLUSTQ_MEAN = clustChargeData[:,2]
elif strObsName == self.STROBSNAME_SIGMA:
#self.G2D_CLUSTQ_SIGMA.Set( len(clustChargeData) )
#self.G2D_CLUSTQ_SIGMA.SetName("g2D_ClusterChargeSigma_StripSize_vs_" + strIndepVarName)
#self.G2D_CLUSTQ_SIGMA.SetTitle("")
g2D_ClustQ_Obs.SetName("g2D_ClusterChargeSigma_StripSize_vs_{0}".format(strIndepVarName))
self.ARRAY_CLUSTQ_SIGMA = clustChargeData[:,2]
else:
print "Input Observable Name: {0}".format(strObsName)
print "Was not recognized, please cross-check and re-run"
print "Exiting"
return
#Set the points
for i in range(0, len(clustChargeData)):
if strObsName == self.STROBSNAME_MPV:
#self.G2D_CLUSTQ_MPV.SetPoint(i, self.ARRAY_HVORGAIN[i], self.ARRAY_CLUSTSIZE[i], self.ARRAY_CLUSTQ_MPV[i])
g2D_ClustQ_Obs.SetPoint(i, self.ARRAY_HVORGAIN[i], self.ARRAY_CLUSTSIZE[i], self.ARRAY_CLUSTQ_MPV[i])
elif strObsName == self.STROBSNAME_MEAN:
#self.G2D_CLUSTQ_MEAN.SetPoint(i, self.ARRAY_HVORGAIN[i], self.ARRAY_CLUSTSIZE[i], self.ARRAY_CLUSTQ_MEAN[i])
g2D_ClustQ_Obs.SetPoint(i, self.ARRAY_HVORGAIN[i], self.ARRAY_CLUSTSIZE[i], self.ARRAY_CLUSTQ_MEAN[i])
elif strObsName == self.STROBSNAME_SIGMA:
#self.G2D_CLUSTQ_SIGMA.SetPoint(i, self.ARRAY_HVORGAIN[i], self.ARRAY_CLUSTSIZE[i], self.ARRAY_CLUSTQ_SIGMA[i])
g2D_ClustQ_Obs.SetPoint(i, self.ARRAY_HVORGAIN[i], self.ARRAY_CLUSTSIZE[i], self.ARRAY_CLUSTQ_SIGMA[i])
#Reshape the arrays for later analysis
self.ARRAY_HVORGAIN = np.unique(self.ARRAY_HVORGAIN)
self.ARRAY_CLUSTSIZE = np.unique(self.ARRAY_CLUSTSIZE)
if strObsName == self.STROBSNAME_MPV:
self.ARRAY_CLUSTQ_MPV = np.reshape(self.ARRAY_CLUSTQ_MPV,(len(self.ARRAY_HVORGAIN),len(self.ARRAY_CLUSTSIZE)),order='F')
elif strObsName == self.STROBSNAME_MEAN:
self.ARRAY_CLUSTQ_MEAN = np.reshape(self.ARRAY_CLUSTQ_MEAN,(len(self.ARRAY_HVORGAIN),len(self.ARRAY_CLUSTSIZE)),order='F')
elif strObsName == self.STROBSNAME_SIGMA:
self.ARRAY_CLUSTQ_SIGMA = np.reshape(self.ARRAY_CLUSTQ_SIGMA,(len(self.ARRAY_HVORGAIN),len(self.ARRAY_CLUSTSIZE)),order='F')
#Store the Plot
#outputFile = TFile(outputfilename,strFileOpt,"",1)
#dir_Out = []
#if strFileOpt == "UPDATE":
#dir_Out = outputFile.GetDirectory("ClusterChargeData")
#else:
#dir_Out = outputFile.mkdir("ClusterChargeData")
dir_Out = self.FILE_OUT.mkdir("ClusterChargeData")
dir_Out.cd()
g2D_ClustQ_Obs.Write()
outputFile.Close()
return
print "Could not find a valid value for par %s in filename: %s !" % (
par, file)
return val
ROOT.gStyle.SetPalette(1)
par1 = options.par1
par2 = options.par2
par1Latex = options.par1Latex
par2Latex = options.par2Latex
limits = {
'-2s': [TGraph2D(), 0],
'-1s': [TGraph2D(), 1],
'mean': [TGraph2D(), 2],
'+1s': [TGraph2D(), 3],
'+2s': [TGraph2D(), 4],
'obs': [TGraph2D(), 5]
}
n = 0
for file in glob(options.input):
if '.root' not in file:
print "Skipped non-root file: %s" % file
continue
par1val = extractParValue(par1, file)
par2val = extractParValue(par2, file)
def analyzeMeshes(self, dictOfMeshes, doCull=False, cullCut=0.90):
""" analyzeMeshes takes a dictionary with meshes as input, and fits it to the references
output dictionary includes a deepcopy of the input meshes
note:
this fits to zDiff = dictOfMeshes - interpolation of self.meshDict
ie. the sign is defined as (other guy) - (me)
"""
# deepcopy the meshes! no longer adjusts in place
dictOfMeshesCopy = {}
for key in list(dictOfMeshes.keys()):
theMesh = copy.deepcopy(dictOfMeshes[key])
dictOfMeshesCopy[key] = theMesh
# then we analyze the Zernike polynomials by comparing them to stored references
# analyzeDonuts returns a dictionary containing deltas and rotation angles for focus, astigmatism and coma
# as well as the hexapod adjustments
# it also makes a Canvas of plots for study
dictOfResults = {}
# loop over keys, fitting References
for key in list(dictOfMeshesCopy.keys()):
# some special cases
resultsKeyName = "%sResultDict" % (key.replace("Mesh", ""))
meshName = key
if key == "z4Mesh":
print("Python 3 debugging: %r" % self.meshDict)
dictOfResults[resultsKeyName] = self.fitToRefMesh(
self.meshDict[meshName], dictOfMeshesCopy[meshName],
self.zangleconv)
elif key == "rzeroMesh":
dictOfResults[resultsKeyName] = self.analyzeRzero(
dictOfMeshesCopy["rzeroMesh"],
dictOfMeshesCopy["chi2Mesh"], dictOfMeshesCopy["neleMesh"])
elif key == "chi2Mesh":
continue
elif key == "neleMesh":
continue
else:
# check that meshDict has the appropriate mesh
if meshName in self.meshDict:
dictOfResults[resultsKeyName] = self.fitToRefMesh(
self.meshDict[meshName], dictOfMeshesCopy[meshName])
# if we want to cull, cull and refit!
# use the fitted weight to cull - culltype="fit"
if doCull:
# this will take the wgt's from the fit and take their product
# for each point, and cull at a product of cullCut
self.cullAllMeshes(dictOfMeshesCopy, cullCut=cullCut)
for key in list(dictOfMeshesCopy.keys()):
# some special cases
resultsKeyName = "%sResultDict" % (key.replace("Mesh", ""))
meshName = key
if key == "z4Mesh":
dictOfResults[resultsKeyName] = self.fitToRefMesh(
self.meshDict[meshName], dictOfMeshesCopy[meshName],
self.zangleconv)
elif key == "rzeroMesh":
dictOfResults[resultsKeyName] = self.analyzeRzero(
dictOfMeshesCopy["rzeroMesh"],
dictOfMeshesCopy["chi2Mesh"],
dictOfMeshesCopy["neleMesh"])
elif key == "chi2Mesh":
continue
elif key == "neleMesh":
continue
else:
# check that meshDict has the appropriate mesh
if meshName in self.meshDict:
dictOfResults[resultsKeyName] = self.fitToRefMesh(
self.meshDict[meshName],
dictOfMeshesCopy[meshName])
# analyze this data and extract the hexapod coefficients
donutDict = self.calcHexapod(dictOfResults)
if len(donutDict) == 0:
goodCalc = False
else:
goodCalc = True
# add the individual fit results here too
for key in list(dictOfResults.keys()):
donutDict[key] = dictOfResults[key]
# and add the meshes too
for key in list(dictOfMeshesCopy.keys()):
donutDict[key] = dictOfMeshesCopy[key]
# cmr add X and Y coords
keylist = list(dictOfResults.keys())
key0 = keylist[0]
dict0 = dictOfResults[key0]
donutDict['deltaArrayX'] = dict0['deltaArrayX']
donutDict['deltaArrayY'] = dict0['deltaArrayY']
# make a Canvas of plots for this image
# plot Histogram of Difference before fit, after fit, and after fit vs. X,Y position
if self.paramDict["histFlag"] and "deltaArrayBefore" in dictOfResults[
"z4ResultDict"] and goodCalc:
# setup plots
gStyle.SetStatH(0.32)
gStyle.SetStatW(0.4)
gStyle.SetOptStat(1111111)
gStyle.SetMarkerStyle(20)
gStyle.SetMarkerSize(0.5)
gStyle.SetPalette(1)
gROOT.ForceStyle()
# loop over results, making plots for each
nplots = 0
plotDict = {}
for key in list(dictOfResults.keys()):
theResultDict = dictOfResults[key]
keyId = key.replace("ResultDict", "")
# special cases
if keyId == "z4":
nWavesBefore = 200.0
nWavesAfter = 200.0
else:
nWavesBefore = 2.0
nWavesAfter = 0.2
if keyId != "rzero":
nplots = nplots + 1
hBefore = hfillhist(key + "Before",
"Delta " + key + ", Before Fit",
theResultDict["deltaArrayBefore"], 200,
-nWavesBefore, nWavesBefore)
hAfter = hfillhist(key + "zAfter",
"Delta " + key + ", After Fit",
theResultDict["deltaArrayAfter"], 200,
-nWavesAfter, nWavesAfter)
hBefore2D = TGraph2D(
key + "Before2D", "Delta " + key +
", Before Fit, vs. Position;X[mm];Y[mm]",
theResultDict["deltaArrayBefore"].shape[0],
theResultDict["deltaArrayX"],
theResultDict["deltaArrayY"],
theResultDict["deltaArrayBefore"])
hAfter2D = TGraph2D(
key + "After2D", "Delta " + key +
", After Fit, vs. Position;X[mm];Y[mm]",
theResultDict["deltaArrayAfter"].shape[0],
theResultDict["deltaArrayX"],
theResultDict["deltaArrayY"],
theResultDict["deltaArrayAfter"])
plotList = [hBefore, hAfter, hBefore2D, hAfter2D]
plotDict[keyId] = plotList
# the Canvas
# unique name for our canvas
# might be that
# gROOT.SetBatch(kTRUE)
# will prevent the Canvas from appearing, but will still get made in output pickle
tstr = "canvas" + str(time.time())
canvas = TCanvas(tstr, tstr, 300 * nplots, 1000)
canvas.Divide(nplots, 4)
# plot em
jZ = 0
for iZ in range(4, 15 + 1):
key = "z%d" % (iZ)
if key in plotDict:
jZ = jZ + 1
plotList = plotDict[key]
icanvas = jZ + 0 * nplots
canvas.cd(icanvas)
plotList[0].Draw()
icanvas = jZ + 1 * nplots
canvas.cd(icanvas)
plotList[1].Draw()
icanvas = jZ + 2 * nplots
canvas.cd(icanvas)
plotList[2].Draw("zcolpcol")
icanvas = jZ + 3 * nplots
canvas.cd(icanvas)
plotList[3].Draw("zcolpcol")
# set it so that python doesn't own these ROOT object
for key in list(plotDict.keys()):
for plot in plotDict[key]:
SetOwnership(plot, False)
# save canvas in the output Dictionary
donutDict["canvas"] = canvas
# all done
return donutDict
for plane in otherplanes:
branches[plane[0]][0] = float(
randint(0, 11))
scores.append(reader.EvaluateMVA(ML))
MLscore = sum(scores) / float(len(scores))
plane_y.append(float(strip1))
plane_x.append(float(strip2))
MLscores.append(MLscore)
########## Make canvases, draw plots, and save them
c = TCanvas(
sample[0] + "_" + planey[1] + "vs" + planex[1] + "vs" +
ML, sample[0] + "_" + planey[1] + "vs" + planex[1] +
"vs" + ML)
gr = TGraph2D(len(MLscores), plane_x, plane_y, MLscores)
gr.SetName(sample[1] + " " + planey[1] + " vs " +
planex[1] + " vs " + ML + " score")
gr.Draw("COLZ")
c.Update()
gr.SetTitle(sample[1] + " " + planey[1] + " vs " +
planex[1] + " vs " + ML + " score")
gr.GetXaxis().SetTitle(planex[1])
gr.GetYaxis().SetTitle(planey[1])
gr.GetZaxis().SetTitle(ML + " score")
gr.GetZaxis().SetTitleOffset(1.2)
d_out.cd()
c.Write()
c.SaveAs(folder + "/%s.pdf" % (c.GetName()))
def calcGainMapHV(self, strDetName, hvPt):
#Create the new TGraph2D - Gain
g2D_Map_Gain_hvPt = TGraph2D( self.G2D_MAP_GAIN_ORIG.GetN() )
#g2D_Map_Gain_hvPt.SetName( "g2D_" + strDetName + "_EffGain_AllEta_" + str(int(hvPt)) )
g2D_Map_Gain_hvPt.SetName( "g2D_{0}_EffGain_AllEta_{1}".format(strDetName, int(hvPt)) )
#Create the new TGraph2D - Discharge Probability
g2D_Map_PD_hvPt = TGraph2D( self.G2D_MAP_GAIN_ORIG.GetN() )
#g2D_Map_PD_hvPt.SetName( "g2D_" + strDetName + "_PD_AllEta_" + str(int(hvPt)) )
g2D_Map_PD_hvPt.SetName( "g2D_{0}_PD_AllEta_{1}".format(strDetName, int(hvPt)) )
#Get the arrays that make the response uniformity map
array_fPx = self.G2D_MAP_GAIN_ORIG.GetX()
array_fPy = self.G2D_MAP_GAIN_ORIG.GetY()
array_fPz = self.G2D_MAP_GAIN_ORIG.GetZ()
#Calculate alpha
alpha = self.calcAlpha(hvPt)
#Loop Over all Points of self.G2D_MAP_ABS_RESP_UNI
array_Gain_Vals = np.zeros(self.G2D_MAP_ABS_RESP_UNI.GetN())
array_PD_Vals = np.zeros(self.G2D_MAP_ABS_RESP_UNI.GetN())
for i in range(0, self.G2D_MAP_ABS_RESP_UNI.GetN() ):
#Set the i^th point in self.G2D_MAP_GAIN_ORIG
array_Gain_Vals[i] = array_fPz[i] * alpha
array_PD_Vals[i] = self.PD_CALCULATOR.calcPD(array_fPz[i] * alpha)
g2D_Map_Gain_hvPt.SetPoint(i, array_fPx[i], array_fPy[i], array_fPz[i] * alpha)
g2D_Map_PD_hvPt.SetPoint(i, array_fPx[i], array_fPy[i], self.PD_CALCULATOR.calcPD(array_fPz[i] * alpha) )
#Store Average, Std. Dev., Max, & Min Gain
array_Gain_Vals = rejectOutliers(array_Gain_Vals)
self.DET_IMON_POINTS.append(hvPt)
self.GAIN_AVG_POINTS.append(np.mean(array_Gain_Vals) )
self.GAIN_STDDEV_POINTS.append(np.std(array_Gain_Vals) )
self.GAIN_MAX_POINTS.append(np.max(array_Gain_Vals) )
self.GAIN_MIN_POINTS.append(np.min(array_Gain_Vals) )
#Store Average, Std. Dev., Max & Min P_D
array_PD_Vals = rejectOutliers(array_PD_Vals)
self.PD_AVG_POINTS.append(np.mean(array_PD_Vals) )
self.PD_STDDEV_POINTS.append(np.std(array_PD_Vals) )
self.PD_MAX_POINTS.append(np.max(array_PD_Vals) )
self.PD_MIN_POINTS.append(np.min(array_PD_Vals) )
#Draw the effective gain map
#canv_Gain_Map_hvPt = TCanvas("canv_" + strDetName + "_EffGain_AllEta_" + str(int(hvPt)),"Gain Map - hvPt = " + str(hvPt),600,600)
canv_Gain_Map_hvPt = TCanvas("canv_{0}_EffGain_AllEta_{1}".format(strDetName, int(hvPt)),"Gain Map - hvPt = {0}".format(hvPt),600,600)
canv_Gain_Map_hvPt.cd()
canv_Gain_Map_hvPt.cd().SetLogz(1)
g2D_Map_Gain_hvPt.Draw("TRI2Z")
#Draw the discharge probability map
#canv_PD_Map_hvPt = TCanvas("canv_" + strDetName + "_PD_AllEta_" + str(int(hvPt)),"Discharge Probability Map - hvPt = " + str(hvPt),600,600)
canv_PD_Map_hvPt = TCanvas("canv_{0}_PD_AllEta_{1}".format(strDetName,int(hvPt)),"Discharge Probability Map - hvPt = {0}".format(hvPt),600,600)
canv_PD_Map_hvPt.cd()
canv_PD_Map_hvPt.cd().SetLogz(1)
g2D_Map_PD_hvPt.Draw("TRI2Z")
#Write the effective gain map to the output file
#dir_hvPt = self.FILE_OUT.mkdir( "GainMap_HVPt" + str(int(hvPt)) )
dir_hvPt = self.FILE_OUT.mkdir( "GainMap_HVPt{0}".format(int(hvPt)) )
dir_hvPt.cd()
canv_Gain_Map_hvPt.Write()
g2D_Map_Gain_hvPt.Write()
canv_PD_Map_hvPt.Write()
g2D_Map_PD_hvPt.Write()
return g2D_Map_Gain_hvPt
args = parser.parse_args()
N = args.nEvents
name = args.name
draw = args.draw
opt = args.opt
out = args.output
print(N)
print(name[1:])
print(opt)
title = ""
if (name == "gArg" or name == "gAbs"):
title = name[
1:] + "(A(m^{2}_{K_{S}^{0}#pi^{+}},m^{2}_{K_{S}^{0}#pi^{-}})); m^{2}_{K_{S}^{0}#pi^{+}}(GeV); m^{2}_{K_{S}^{0}#pi^{-}} (GeV)"
os.system("Generator --nEvents %i --Output %s.root %s.opt" % (N, opt, opt))
f = TFile.Open("%s.root" % (opt))
s = f.Get(name)
c = TCanvas("c_%s" % (name), name, 1000, 1000)
c.cd()
gStyle.SetPalette(55)
if (type(s) == TH2D):
s = TGraph2D(s)
s.SetMargin(0.1)
print(type(s))
s.SetTitle(title)
s.Draw(draw)
c.SaveAs("%s.png" % (out))