def make_tgrapherrors(name,
title,
color=1,
marker=20,
marker_size=1,
width=1,
asym_err=False,
style=1,
x=None,
y=None):
if (x and y) is None:
gr = TGraphErrors() if not asym_err else TGraphAsymmErrors()
else:
gr = TGraphErrors(len(x), array(x, 'd'), array(
y, 'd')) if not asym_err else TGraphAsymmErrors(
len(x), array(x, 'd'), array(y), 'd')
gr.SetTitle(title)
gr.SetName(name)
gr.SetMarkerStyle(marker)
gr.SetMarkerColor(color)
gr.SetLineColor(color)
gr.SetMarkerSize(marker_size)
gr.SetLineWidth(width)
gr.SetLineStyle(style)
return gr
def draw_nse_mean(self):
cent_bin1 = [0, 4, 7]
cent_bin2 = [3, 6, 8]
p_bin1 = [
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5,
3.0, 3.5, 4.0, 4.5
]
p_bin2 = [
0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5, 3.0,
3.5, 4.0, 4.5, 5.0
]
gr_mean = TGraphErrors()
gr_width = TGraphErrors()
gr_eff = TGraphErrors()
for i in range(0, len(p_bin1)):
f_gaus = self.draw_nse(cent_bin1[1], cent_bin2[1], p_bin1[i],
p_bin2[i], -1, 2)
mean = f_gaus.GetParameter(1)
mean_err = f_gaus.GetParError(1)
width = f_gaus.GetParameter(2)
width_err = f_gaus.GetParError(2)
gr_mean.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, mean)
gr_mean.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5, mean_err)
gr_width.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, width)
gr_width.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5, width_err)
p = 0.5 * (p_bin1[i] + p_bin2[i])
if p < 1:
nse_low = 0.5 * p - 1.5
else:
nse_low = -1
m = f_gaus.Integral(nse_low, 2.0)
N = f_gaus.Integral(-10.0, 10.0)
gr_eff.SetPoint(i, p, m / N)
gr_eff.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5,
eff_err(m, N))
gr_mean.SetMarkerStyle(20)
gr_mean.SetMarkerSize(1.5)
gr_mean.SetMarkerColor(632)
gr_width.SetMarkerStyle(20)
gr_width.SetMarkerSize(1.5)
gr_width.SetMarkerColor(600)
gr_eff.SetMarkerStyle(20)
gr_eff.SetMarkerSize(1.5)
gr_eff.SetMarkerColor(600)
hx = histo(0, 10, -0.2, 1.2, "p_{T}(GeV/c)", "Mean/Width")
canvas_file = self.__canvas__
canvas_file.cd()
c1 = TCanvas("c1", "c1", 1000, 800)
hx.Draw()
gr_width.Draw("psame")
gr_mean.Draw("psame")
c1.Write("nse")
c1.SaveAs("nse.png")
c2 = TCanvas("c2", "c2", 1000, 800)
hx.GetYaxis().SetTitle("n#sigma_{e} cut efficiency")
hx.Draw()
gr_eff.Draw("psame")
c2.Write("nse_eff")
c2.SaveAs("nse_eff.png")
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 plotGainSummary(self, strDetName):
#Create the Plot - Average
gDet_AvgEffGain = TGraphErrors(len(self.GAIN_AVG_POINTS))
gDet_AvgEffGain.SetName("g_{0}_EffGainAvg".format(strDetName))
#Create the Plot - Max Gain
gDet_MaxEffGain = TGraphErrors(len(self.GAIN_MAX_POINTS))
gDet_MaxEffGain.SetName("g_{0}_EffGainMax".format(strDetName))
#Create the Plot - Min Gain
gDet_MinEffGain = TGraphErrors(len(self.GAIN_MIN_POINTS))
gDet_MinEffGain.SetName("g_{0}_EffGainMin".format(strDetName))
#Set and print the points
#print "===============Printing Gain Data==============="
#print "[BEGIN_DATA]"
#print "\tVAR_INDEP,VAR_DEP,VAR_DEP_ERR"
for i in range(0, len(self.GAIN_AVG_POINTS)):
#Average
gDet_AvgEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_AVG_POINTS[i])
gDet_AvgEffGain.SetPointError(i, 0, self.GAIN_STDDEV_POINTS[i])
#print "\t%f,%f,%f"%(self.DET_IMON_POINTS[i],self.GAIN_AVG_POINTS[i],self.GAIN_STDDEV_POINTS[i])
#Max
gDet_MaxEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_MAX_POINTS[i])
#Min
gDet_MinEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_MIN_POINTS[i])
pass
#print "[END_DATA]"
#print ""
#Draw
canv_AvgEffGain = TCanvas(
"canv_{0}_EffGainAvg".format(strDetName),
"{0} Average Effective Gain".format(strDetName), 600, 600)
canv_AvgEffGain.cd()
canv_AvgEffGain.cd().SetLogy()
gDet_AvgEffGain.GetXaxis().SetTitle("HV")
gDet_AvgEffGain.GetYaxis().SetTitle("#LT Effective Gain #GT")
gDet_AvgEffGain.GetYaxis().SetRangeUser(1e2, 1e6)
gDet_AvgEffGain.SetMarkerStyle(21)
gDet_AvgEffGain.Draw("AP")
gDet_MaxEffGain.Draw("sameL")
gDet_MinEffGain.Draw("sameL")
#Write
dir_Summary = self.FILE_OUT.mkdir("Summary")
dir_Summary.cd()
canv_AvgEffGain.Write()
gDet_AvgEffGain.Write()
gDet_MaxEffGain.Write()
gDet_MinEffGain.Write()
return
def draw_tof_cut_eff(self, cent_low, cent_high):
cent_bin1 = [0, 4, 7]
cent_bin2 = [3, 6, 8]
p_bin1 = [
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5,
3.0, 3.5, 4.0, 4.5
]
p_bin2 = [
0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5, 3.0,
3.5, 4.0, 4.5, 5.0
]
gr_mean = TGraphErrors()
gr_width = TGraphErrors()
gr_eff = TGraphErrors()
for i in range(0, len(p_bin1)):
f_gaus = self.draw_tof_cut(cent_bin1[1], cent_bin2[1], p_bin1[i],
p_bin2[i])
mean = f_gaus.GetParameter(1)
mean_err = f_gaus.GetParError(1)
width = f_gaus.GetParameter(2)
width_err = f_gaus.GetParError(2)
gr_mean.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, mean)
gr_mean.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5, mean_err)
gr_width.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, width)
gr_width.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5, width_err)
m = f_gaus.Integral(0.97, 1.03)
N = f_gaus.GetParameter(0) * math.sqrt(
2 * math.pi) * f_gaus.GetParameter(2)
print m, N
gr_eff.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, m / N)
gr_eff.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5,
eff_err(m, N))
gr_mean.SetMarkerStyle(20)
gr_mean.SetMarkerSize(1.5)
gr_mean.SetMarkerColor(632)
gr_width.SetMarkerStyle(20)
gr_width.SetMarkerSize(1.5)
gr_width.SetMarkerColor(600)
gr_eff.SetMarkerStyle(20)
gr_eff.SetMarkerSize(1.5)
gr_eff.SetMarkerColor(600)
hx = histo(0, 10, -0.2, 1.2, "p_{T}(GeV/c)", "Mean/Width")
canvas_file = self.__canvas__
canvas_file.cd()
c1 = TCanvas("c1", "c1", 1000, 800)
hx.GetYaxis().SetTitle("1/#beta cut efficency")
hx.Draw()
gr_width.Draw("psame")
gr_mean.Draw("psame")
c1.Write("tof")
c1.SaveAs("tof.png")
c2 = TCanvas("c2", "c2", 1000, 800)
hx.Draw()
gr_eff.Draw("psame")
c2.Write("tof_cut_eff")
c2.SaveAs("tof_cut.png")
def plotPDSummary(self, strDetName):
#Create the Plot - Average
gDet_AvgPD = TGraphErrors(len(self.PD_AVG_POINTS))
gDet_AvgPD.SetName("g_{0}_PDAvg".format(strDetName))
#Create the Plot - Max Gain
gDet_MaxPD = TGraphErrors(len(self.PD_MAX_POINTS))
gDet_MaxPD.SetName("g_{0}_PDMax".format(strDetName))
#Create the Plot - Min Gain
gDet_MinPD = TGraphErrors(len(self.PD_MIN_POINTS))
gDet_MinPD.SetName("g_" + strDetName + "_PDMin")
gDet_MinPD.SetName("g_{0}_PDMin".format(strDetName))
#Set the points
for i in range(0, len(self.PD_AVG_POINTS)):
#Average
gDet_AvgPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_AVG_POINTS[i])
gDet_AvgPD.SetPointError(i, self.GAIN_STDDEV_POINTS[i],
self.PD_STDDEV_POINTS[i])
#Max
gDet_MaxPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_MAX_POINTS[i])
#Min
gDet_MinPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_MIN_POINTS[i])
#Draw
canv_AvgPD = TCanvas("canv_{0}_PDAvg".format(strDetName),
"{0} Discharge Probability".format(strDetName),
600, 600)
canv_AvgPD.cd()
canv_AvgPD.cd().SetLogx()
canv_AvgPD.cd().SetLogy()
gDet_AvgPD.GetXaxis().SetTitle("#LT Effective Gain #GT")
gDet_AvgPD.GetYaxis().SetTitle("Discharge Probability P_{D}")
gDet_AvgPD.GetYaxis().SetRangeUser(1e-11, 1e-6)
gDet_AvgPD.SetMarkerStyle(21)
gDet_AvgPD.Draw("AP")
gDet_MaxPD.Draw("sameL")
gDet_MinPD.Draw("sameL")
#Write
dir_Summary = self.FILE_OUT.GetDirectory("Summary")
dir_Summary.cd()
canv_AvgPD.Write()
gDet_AvgPD.Write()
gDet_MaxPD.Write()
gDet_MinPD.Write()
return
def fit(label, iter, xs_file, tfunc, xmin, xmax, is_fit=True):
''' ARGUE: 1. label of your input
2. iter name
3. data source file path and its name
4. fit function
5. minimum of tfunc
6. maximum of tfunc
7. is fit or not
'''
ipoint, gaexs, geeff, gexs = 0, TGraphAsymmErrors(0), TGraphErrors(
0), TGraphErrors(0)
ipoint_xs = 0
for line in open(xs_file):
try:
fargs = map(float, line.strip().split())
sample, ecms, lum, br, nsig, nsigerrl, nsigerrh = fargs[0], fargs[
1], fargs[2], fargs[3], fargs[4], fargs[5], fargs[6]
eff, isr, vp, N0 = fargs[7], fargs[8], fargs[9], fargs[10]
'''
USER DEFINE SECTION { formula of cross section
'''
xs = nsig / (2 * lum * eff * br * isr)
xserrl = sqrt(3) * nsigerrl / (2 * lum * eff * br * isr)
xserrh = sqrt(3) * nsigerrh / (2 * lum * eff * br * isr)
'''
} USER DEFINE SECTION
'''
gexs.Set(ipoint_xs + 1)
gexs.SetPoint(ipoint_xs, ecms, xs)
gexs.SetPointError(ipoint_xs, 0.0, xserrl)
ipoint_xs += 1
if (sample == 4700 and xmin == 4.0205): continue
gaexs.Set(ipoint + 1)
gaexs.SetPoint(ipoint, ecms, xs)
gaexs.SetPointError(ipoint, 0.0, 0.0, xserrl, xserrh)
geeff.Set(ipoint + 1)
geeff.SetPoint(ipoint, ecms, eff * isr)
geeff.SetPointError(ipoint, 0.0,
sqrt(isr * eff * (1.00 - eff) / N0))
ipoint += 1
except:
'''
'''
if is_fit:
results = gaexs.Fit(tfunc, 'S')
path_user = '******' + label + '_' + iter + '.dat'
f_user = open(path_user, 'w')
for i in xrange(int((xmax - xmin) / 0.001)):
ecm = xmin + i * 0.001
xs_ecm = tfunc.Eval(ecm)
out = str(ecm) + ' ' + str(xs_ecm) + '\n'
f_user.write(out)
f_user.close()
return gaexs, geeff, gexs, tfunc
def plotGainSummary(self, strDetName):
#Create the Plot - Average
gDet_AvgEffGain = TGraphErrors( len(self.GAIN_AVG_POINTS) )
#gDet_AvgEffGain.SetName("g_" + strDetName + "_EffGainAvg")
gDet_AvgEffGain.SetName("g_{0}_EffGainAvg".format(strDetName))
#Create the Plot - Max Gain
gDet_MaxEffGain = TGraphErrors( len(self.GAIN_MAX_POINTS) )
#gDet_MaxEffGain.SetName("g_" + strDetName + "_EffGainMax")
gDet_MaxEffGain.SetName("g_{0}_EffGainMax".format(strDetName))
#Create the Plot - Min Gain
gDet_MinEffGain = TGraphErrors( len(self.GAIN_MIN_POINTS) )
#gDet_MinEffGain.SetName("g_" + strDetName + "_EffGainMin")
gDet_MinEffGain.SetName("g_{0}_EffGainMin".format(strDetName))
#Set the points
for i in range(0, len(self.GAIN_AVG_POINTS) ):
#Average
gDet_AvgEffGain.SetPoint(i,self.DET_IMON_POINTS[i],self.GAIN_AVG_POINTS[i])
gDet_AvgEffGain.SetPointError(i,0,self.GAIN_STDDEV_POINTS[i])
#Max
gDet_MaxEffGain.SetPoint(i,self.DET_IMON_POINTS[i],self.GAIN_MAX_POINTS[i])
#Min
gDet_MinEffGain.SetPoint(i,self.DET_IMON_POINTS[i],self.GAIN_MIN_POINTS[i])
#Draw
#canv_AvgEffGain = TCanvas("canv_" + strDetName + "_EffGainAvg",strDetName + " Average Effective Gain",600,600)
canv_AvgEffGain = TCanvas("canv_{0}_EffGainAvg".format(strDetName),"{0} Average Effective Gain".format(strDetName),600,600)
canv_AvgEffGain.cd()
canv_AvgEffGain.cd().SetLogy()
gDet_AvgEffGain.GetXaxis().SetTitle("HV")
gDet_AvgEffGain.GetYaxis().SetTitle("#LT Effective Gain #GT")
gDet_AvgEffGain.GetYaxis().SetRangeUser(1e2,1e6)
gDet_AvgEffGain.SetMarkerStyle(21)
gDet_AvgEffGain.Draw("AP")
gDet_MaxEffGain.Draw("sameL")
gDet_MinEffGain.Draw("sameL")
#Write
dir_Summary = self.FILE_OUT.mkdir("Summary")
dir_Summary.cd()
canv_AvgEffGain.Write()
gDet_AvgEffGain.Write()
gDet_MaxEffGain.Write()
gDet_MinEffGain.Write()
return
def __init__(self, num_pars=0, pars=None, norm=True):
pars = pars or []
self._numPars = num_pars
self.parameter = pars
self.fCov = [[None for j in range(self._numPars)]
for i in range(self._numPars)
] # Simple matrix replacement
self._dEff_dP = list()
self.TGraph = TGraphErrors()
# Normalization factors
self._doNorm = norm
self.norm = 1.0
self.TF1.FixParameter(0, self.norm) # Normalization
self.TF1.SetRange(0, 10000) # Default range for efficiency function
self._fitInput = Pairs(lambda x: ufloat(x, 0))
# if self.parameter: # Parameters were given
# map(lambda i: self.TF1.SetParameter(i + 1, self.parameter[i]), range(1, len(pars))) # Set initial parameters
# else:
# self.parameter = [None for i in range(1, self._numPars + 1)]
#
self.TF1.SetParName(0, "N") # Normalization
for i in range(0, num_pars):
self._dEff_dP.append(None)
if num_pars <= len(string.ascii_lowercase):
self.TF1.SetParName(i + 1, string.ascii_lowercase[i])
def dip_residual_method():
hh = TH2F("hh", "", 20, hodo_center - 4.0, hodo_center + 4.0, 200,
-2, 2)
cuts = 'n_tracks==1 && amp_max[{}]>1000 && amp_max[{}]>1000'.format(
self.xtal[0], self.xtal[1])
t_tree.Draw("(fit_time[{0}]-fit_time[{1}]):{2}>>hh".format(
self.xtal[0], self.xtal[1], axis[0]), cuts,
"COLZ") # Plot dt vs (X or Y)
hh.FitSlicesY() # Fit slices with gaussians
gr = TGraphErrors(t_file.Get(
"hh_1")) # hh_1 is the histo of means from FitSlicesY
## Sorry for the confusing names. We plot dt vs (X or Y), so dt is our y_var, and dx is our x_var, the distance term (ie X OR Y)
points = range(gr.GetN())
dx = array('d', gr.GetX())
dt = array('d', gr.GetY())
p1 = TF1("p1", "pol1")
TGraph(gr.GetN(), dx, dt).Fit("p1",
"WQ") # Fit dt_mean vs Y linearly
## Sum each 3 consecutive residuals, take the max from this value's abs(), and the middle index is where the "dip" is farthest from the fit, the "center"
res = [dt[i] - p1.Eval(dx[i])
for i in points] # The residual between the fit and data
sum_res = [abs(sum(res[i:i + 3])) for i in points[:-2]
] # Sum 3 residuals ([:-2] to avoid index out of range)
axis_center = dx[
sum_res.index(max(sum_res)) +
1] # +1 b/c we index the 1st out of 3 residuals, but we want the middle one
return axis_center
def compareEEC( filename="sjm91_all.root", datafilename="../EECMC/share/OPAL/data.dat" ):
f= TFile( filename )
ao= createAnalysisObservable( f, "EEC" )
tokens= datafilename.split( "/" )
exp= tokens[3]
plotoptions= { "xmin": 0.0, "xmax": 3.14159, "ymin": 0.05, "ymax": 5.0, "markerStyle": 20,
"markerSize": 0.5, "drawas": "3", "fillcolor": 6, "title": "EEC "+exp,
"xlabel": "\chi\ [rad.]", "ylabel": "1/\sigma d\Sigma/d\chi", "logy": 1 }
tgest, tgesy= ao.plot( plotoptions )
lines= [ line.rstrip( '\n' ) for line in open( datafilename ) ]
n= len( lines )
points= TVectorD( n )
values= TVectorD( n )
errors= TVectorD( n )
perrs= TVectorD(n)
grad2rad= 3.14159/180.0
for i in range( n ):
line= (lines[i]).split()
points[i]= float(line[0])*grad2rad
values[i]= float(line[3])
errors[i]= float(line[4])
perrs[i]= 0.0
datatge= TGraphErrors( points, values, perrs, errors )
datatge.SetMarkerStyle( 20 )
datatge.SetMarkerSize( 0.5 )
datatge.Draw( "psame" )
legend= TLegend( 0.2, 0.7, 0.5, 0.85 )
datatge.SetName( "datatge" );
tgesy.SetName( "tgesy" )
legend.AddEntry( "datatge", exp+" data", "pe" )
legend.AddEntry( "tgesy", "OPAL "+filename, "f" )
legend.Draw()
return
def overlay(self, x, y, dx=None, dy=None, symbol='plus', color='black'):
npts, xx, yy, dxx, dyy = self._fillArrays(x, y, dx, dy)
if npts == 0:
return
self.graphs.append(TGraphErrors(npts, xx, yy, dxx, dyy))
self.canvas.cd()
self._drawData(self.graphs[-1], symbol, color)
def load_cck():
#model by Guillermo at. al.
f = open("data/data-dtdy-y_0-RHIC-clean_CCK.dat", "read")
sigma = []
for line in f:
if line[0] == "#": continue
p = line.split("\t")
t = float(p[3])
nucl = float(p[4])
hs = float(p[8])
sigma.append({"t": t, "nucl": nucl, "hs": hs})
#correction from gamma-Au to AuAu by Michal
k_auau = 9.0296
#scaling to XnXn
kx = 0.1702
gCCK = TGraphErrors(len(sigma))
for i in xrange(len(sigma)):
gCCK.SetPoint(i, sigma[i]["t"], sigma[i]["hs"] * k_auau * kx)
gCCK.SetLineColor(rt.kRed)
gCCK.SetLineWidth(3)
gCCK.SetLineStyle(rt.kDashed) # 9
return gCCK
def CalibrationENC(calibration_filename):
# Read data fron the calibration curve dataset
C, e_C, noise_rms, d_noise_rms, fall_time, d_fall_time, amplitude, d_amplitude = LoadCalibrationFile(calibration_filename)
noise_rms = noise_rms / 1000. # convert form \muV to mV
d_noise_rms = d_noise_rms / 1000.
ENC = (noise_rms/amplitude) * (C * 20.0) * pow(10,-15) / (1.602 * pow(10,-19) ) #pF * mV = 10^-12 * 10^-3 = 10^-15 C / 10^-19 C = 10^4 electrons
d_ENC = np.zeros([len(ENC)])
for i in range(len(ENC)):
d_ENC[i] = sqrt( d_noise_rms[i]*d_noise_rms[i]*(C[i] * 20.0/amplitude[i])*(C[i] * 20.0/amplitude[i]) + d_amplitude[i]*d_amplitude[i]*((noise_rms[i]/(amplitude[i]*amplitude[i])) * (C[i] * 20.0))*((noise_rms[i]/(amplitude[i]*amplitude[i])) * (C[i] * 20.0)) )
d_ENC = d_ENC * pow(10,-15) / (1.602 * pow(10,-19) ) # conversted to # of electrons
calib_curve = TGraphErrors(len(ENC), array('d', C[:-1].tolist()), array('d', ENC.tolist()), array('d', e_C.tolist()), array('d', d_ENC) )
# Construct the fit with a 1st and 2nd order polynomial
fit_curve = TF1('fit_curve','[1]*x + [0]',0.,110.)
fit_curve2 = TF1('fit_curve2','[0]*x*x + [1]*x + [2]',0.,110.)
# Fit the calibration curve
calib_curve.Fit(fit_curve,'R')
calib_curve.Fit(fit_curve2, 'R')
lin_par0 = fit_curve.GetParameter(0)
lin_e_par0 = fit_curve.GetParError(0)
lin_par1 = fit_curve.GetParameter(1)
lin_e_par1 = fit_curve.GetParError(1)
xx = np.linspace(0., 105, 1000)
yy = [fit_curve.Eval(x) for x in xx]
yy2 = [fit_curve2.Eval(x) for x in xx]
par = [lin_par0, lin_par1]
l = 'fit pol1 $p_0$: {:.3g} $p_1$: {:.3g}'.format(*par)
# Plot the calibration curve with matplotlib
font0 = FontProperties()
font = font0.copy()
font.set_style('italic')
font.set_weight('bold')
font.set_size('x-large')
fig, ax = plt.subplots()
ax.set_xlabel('C [pF]')
ax.set_ylabel('ENC [# of electrons]')
ax.errorbar(C, ENC, d_ENC, marker='o', color='k', linestyle='None', label='Data')
ax.plot(xx, yy, color='r', label=l)
ax.plot(xx, yy2, color='g', label='fit pol2')
ax.text(0.05,0.65, 'Group 1', verticalalignment='bottom', horizontalalignment='left',
fontproperties=font, transform=ax.transAxes)
ax.legend(loc='upper left', numpoints=1)
plt.grid()
#plt.show()
fig.savefig('../graphics/calibrationENC_diode.pdf')
plt.close(fig)
plt.clf()
return lin_par0, lin_e_par0, lin_par1, lin_e_par1
def test1a(fname='data/fpgaLin/dp02a_Mar04C1a_data_0.root'):
add_fit_menu()
t1 = TChain('reco')
t1.Add(fname)
t1.Show(0)
V = 200
gr1 = TGraphErrors()
for i in range(19):
c1.cd(i+1)
lt.DrawLatexNDC(0.2,0.4,'Ch={0:d}'.format(i))
t1.Draw('Q[{0:d}]>>hx{0:d}'.format(i),'tID!=7')
hx = gPad.GetPrimitive('hx{0:d}'.format(i))
hx.Fit('gaus')
fun1 = hx.GetFunction('gaus')
encN = 7.40*V*fun1.GetParameter(2)/fun1.GetParameter(1)
encE = 7.40*V*fun1.GetParError(2)/fun1.GetParameter(1)
gr1.SetPoint(i,i,encN)
gr1.SetPointError(i,0,encE)
c1.cd(20)
gr1.Draw('AP')
c1.cd()
waitRootCmdX()
def __init__(self, file_name):
file = open(file_name)
out = open(output, 'w')
for line in file:
#calculate errors and dump the content to output file
if '#' in line:
continue
t, vin, vout = [float(x) for x in line.split()]
te = osc_error_t(t, TIME_DIV)
vd = vin - vout
voute = osc_error_v(vout, POT_DIV)
vde = osc_error_v(vd, POT_DIV)
id = vout / R[0]
if vout:
ide = id * ((voute / vout) + (R[1] / R[0]))
else:
ide = id * (R[1] / R[0])
new_line = [vd, id, vde, ide]
new_line = ' '.join([str(x) for x in new_line]) + '\n'
out.write(new_line)
file.close()
out.close()
#create Graph. ROOT automatically reads columns
self.graph = TGraphErrors(output)
self.graph.SetMarkerStyle(7)
self.func = TF1("shockley", "[0]*(exp(x/[1])-1)", 0.1, 1)
self.func.SetParameters(5, 0.026)
def __make_summary_plot(self, name, ibin2, successful):
# Nominal histogram
successful_tmp = copy(successful)
successful_tmp.sort()
filename = f"finalcross{self.case}{self.typean}mult{ibin2}.root"
filepath = join(self.nominal_analyzer_merged.d_resultsallpdata,
filename)
nominal_histo = self.__get_histogram(filepath, name)
histos = []
ml_trials = []
for succ in successful_tmp:
filename = f"finalcross{self.case}{self.typean}mult{ibin2}.root"
filepath = join(self.analyzers_syst[succ].d_resultsallpdata,
filename)
histos.append(self.__get_histogram(filepath, name))
ml_trials.append(
list(map(itemgetter(self.mcopt), self.ml_wps[succ])))
nptbins = self.nominal_processer_mc.p_nptfinbins
gr = [TGraphErrors(0) for _ in range(nptbins)]
for ipt in range(nptbins):
gr[ipt].SetTitle("pT bin %d" % ipt)
gr[ipt].SetPoint(0, self.cent_cv_cut[ipt],
nominal_histo.GetBinContent(ipt + 1))
gr[ipt].SetPointError(0, 0.0001,
nominal_histo.GetBinError(ipt + 1))
for iml, succ in enumerate(successful_tmp):
gr[ipt].SetPoint(iml + 1, ml_trials[succ][ipt],
histos[succ].GetBinContent(ipt + 1))
gr[ipt].SetPointError(iml + 1, 0.0001,
histos[succ].GetBinError(ipt + 1))
canvas = TCanvas("cvsml%d" % ibin2, "", 1200, 800)
if len(gr) <= 6:
canvas.Divide(3, 2)
elif len(gr) <= 12:
canvas.Divide(4, 3)
else:
canvas.Divide(5, 4)
for i, graph in enumerate(gr):
canvas.cd(i + 1)
graph.Draw("a*")
save_path = join(self.nominal_analyzer_merged.d_resultsallpdata,
self.syst_out_dir,
f"ml_wp_syst_{name}_vs_MLcut_ibin2_{ibin2}.eps")
canvas.SaveAs(save_path)
save_path = join(self.nominal_analyzer_merged.d_resultsallpdata,
self.syst_out_dir,
f"ml_wp_syst_{name}_vs_MLcut_ibin2_{ibin2}.root")
file_out = TFile.Open(save_path, "RECREATE")
file_out.cd()
for i, graph in enumerate(gr):
graph.Write("%s%d" % (graph.GetName(), i))
canvas.Write()
file_out.Close()
canvas.Close()
def TGraph(x, y, yError=None, xError=None, **kwargs):
__parseDrawOptions(kwargs)
import sys
#TODO: Need to check the types
try:
assert (len(x) == len(y))
except AssertionError:
print "Oops! Data points x and y aren't of the same size!"
sys.exit()
nPoints = len(x)
from array import array
if xError is None: xError = __zeros(nPoints)
if yError is None: yError = __zeros(nPoints)
from ROOT import TGraphErrors
graph = TGraphErrors(nPoints, array('d', x), array('d', y),
array('d', xError), array('d', yError))
graph.SetTitle(__drawOptions['title'])
graph.SetMarkerStyle(__drawOptions['mstyle'])
graph.SetMarkerSize(__drawOptions['msize'])
graph.SetMarkerColor(__drawOptions['mcolour'])
graph.SetFillStyle(0)
graph.SetFillColor(0)
return graph
def __init__(self, models, bcids, values, fill=4954, workinprogress=False):
"""Initialize a summary plot of values per model and bcid."""
nbcid, nmodels = len(bcids), len(models)
order = sorted(range(nbcid), key=lambda i: bcids[i])
mini, maxi = 1.0e99, -1.0e99
multi = TMultiGraph('summary', '')
for i, mod in enumerate(models):
xval = array(
'd', [c + 0.09 * (i - 0.5 * nmodels) for c in range(nbcid)])
xerr = array('d', [0.0] * nbcid)
yval = array('d', [values[i][j][0] for j in order])
yerr = array('d', [values[i][j][1] for j in order]) #[0.0]*nbcid)
mini = min(mini, min([v - e for v, e in zip(yval, yerr)]))
maxi = max(maxi, max([v + e for v, e in zip(yval, yerr)]))
graph = TGraphErrors(nbcid, xval, yval, xerr, yerr)
graph.SetName('summary_{0}'.format(mod))
multi.Add(graph)
self._multi = multi
mini, maxi = mini - 0.08 * (maxi - mini), maxi + 0.25 * (maxi - mini)
hist = TH2F('axishist', '', nbcid, -0.5, nbcid - 0.5, 100, mini, maxi)
for i, j in enumerate(order):
hist.GetXaxis().SetBinLabel(i + 1, str(bcids[j]))
SingleGraphBase.__init__(self, hist, 'summary', fill, workinprogress)
for gr in multi.GetListOfGraphs():
self.add(gr, draw=False)
self._xtitle = 'bcid'
self._drawoption = 'AXIS'
self.markers = [markers(i) for i in range(nmodels)]
self.colors = [colors(i) for i in range(nmodels)]
self.xrange(-0.5, nbcid - 0.5)
self.yrange(mini, maxi)
def rescaleJetPlot(plot):
#loop on all data points
nPoints = plot.GetN()
# print "old:",plot.GetName(), nPoints
#create another TGraph
newPlot = TGraphErrors()
for i in xrange(0, nPoints):
dataPointX = Double(0)
dataPointY = Double(0)
error = Double(0)
plot.GetPoint(i, dataPointX, dataPointY)
#if numpy.isnan(dataPointY) : dataPointY = 0.000001
if dataPointY < 0.000001: dataPointY = 0.000001
errorX = plot.GetErrorX(i)
errorY = plot.GetErrorY(i)
newPlot.SetPoint(i, dataPointX / 1000., dataPointY)
newPlot.SetPointError(i, errorX / 1000., errorY)
# print "new:", newPlot.GetName(), newPlot.GetN()
return newPlot
def create_resolutiongraph(n, energies, sigmasmeans, energieserrors, sigmasmeanserrors, graphname):
"""Function to perform ROOT graphs of resolutions"""
#How many points
n = int(n)
TGraphresolution = TGraphErrors(n, energies, sigmasmeans, energieserrors, sigmasmeanserrors)
#Draw + DrawOptions, Fit + parameter estimation
Style = gStyle
Style.SetOptFit()
XAxis = TGraphresolution.GetXaxis() #TGraphresolution
TGraphresolution.SetMarkerColor(4)
TGraphresolution.SetMarkerStyle(20)
TGraphresolution.SetMarkerSize(2)
XAxis.SetTitle("Energy (GeV)")
YAxis = TGraphresolution.GetYaxis()
YAxis.SetTitle("Sigma/Mean")
resolutionfit = TF1("resolutionfit", '([0]/((x)**0.5))+[1]', 0, max(energies)) #somma non quadratura
TGraphresolution.Fit("resolutionfit")
a = resolutionfit.GetParameter(0)
b = resolutionfit.GetParameter(1)
TGraphresolution.Draw("AP")
gPad.SaveAs(graphname)
gPad.Close()
return a, b
def create_tgrapherrors(self, x_value, y_value, dx_value, dy_value):
"""Function to create pyroot TGraphErrors object"""
# pylint: disable=no-self-use
# Creating pyroot TGraphErrors object
x_values = array('i')
y_values = array('i')
dx_values = array('i')
dy_values = array('i')
length = len(x_value)
if (all(isinstance(x, int)
for x in x_value) and all(isinstance(x, int) for x in y_value)
and all(isinstance(x, int) for x in dx_value)
and all(isinstance(x, int) for x in dy_value)):
y_values = array('i')
x_values = array('i')
dx_values = array('i')
dy_values = array('i')
else:
y_values = array('d')
x_values = array('d')
dx_values = array('d')
dy_values = array('d')
for value in range(length):
x_values.append(x_value[value])
y_values.append(y_value[value])
dx_values.append(dx_value[value])
dy_values.append(dy_value[value])
t_object = TGraphErrors(length, x_values, y_values, dx_values,
dy_values)
graph = ru.get_graph_points(t_object)
return graph
def h1_to_graph(hx):
tx = TGraphErrors(hx.GetNbinsX())
for ibin in xrange(1, hx.GetNbinsX() + 1):
tx.SetPoint(ibin - 1, hx.GetBinCenter(ibin), hx.GetBinContent(ibin))
return tx
def write(self, setup):
#Rescaling to efficiency
normalisation = 1/self.histogram.GetBinContent(1)
#Rescaling everything to have rates
self.histogram.Scale(normalisation)
efficiencyPlot = TGraphErrors(self.histogram)
efficiencyPlot.SetName(self.cfg_ana.plot_name+"_errors")
efficiencyPlot.SetTitle(self.cfg_ana.plot_title)
for index in xrange(0, len(efficiencyPlot.GetX())):
efficiencyPlot.SetPointError(index,
efficiencyPlot.GetEX()[index],
sqrt(efficiencyPlot.GetY()[index] * normalisation)
)
c1 = TCanvas ("canvas_" + self.cfg_ana.plot_name, self.cfg_ana.plot_title, 600, 600)
c1.SetGridx()
c1.SetGridy()
efficiencyPlot.Draw("AP")
c1.Update()
c1.Write()
c1.Print(self.cfg_ana.plot_name + ".svg", "svg")
efficiencyPlot.Write()
def Sigma_Calc(g,i_,x,y,xe,ye,percentage):
#print'Creating TGraph with new uncertainty band'
# sigma factor. What to multiply uncertainties by, assuming current uncertainties are 1 sigma.
#print'percentage/100. = ',percentage/100.
sf = TMath.ErfInverse(percentage/100.)*TMath.Sqrt(2) # sigma value for desired percentage events contained in distribution
#print"sf = ",sf
#for i in range(i_ - 1):
#print'i_ = ',i_
for i in range(i_):
ye[i] = ye[i]*sf # multiply 1 sigma uncertainties by new sigma value.
#print sf,'sig errors = ',ye
#ng = TGraphErrors(i_ - 1, x, y, xe, ye)
ng = TGraphErrors(i_, x, y, xe, ye)
if percentage == 90: j = 2
if percentage == 99.5: j = 4
ng.SetMarkerStyle(g.GetMarkerStyle())
ng.SetLineStyle(g.GetLineStyle())
#ng.SetMarkerColor(g.GetMarkerColor() + j)
#ng.SetLineColor(g.GetLineColor() + j)
ng.SetFillColor(g.GetFillColor() + j)
#ng.SetName(g.GetName())
ng.SetFillStyle(g.GetFillStyle())
return ng
def Ratio(histo1,histo2, recorded, title):
#gSystem.Exec("mkdir -p ZPlots")
can1 = makeCMSCanvas(str(random.random()),"mult vs lumi ",900,700)
lumi = []
lumi_err = []
ratio = []
ratio_err = []
sumLumi = 0.
for i in range(0,len(recorded)):
sumLumi += float(recorded[i])
lumi.append(sumLumi - float(recorded[i])/2)
lumi_err.append(float(recorded[i])/2)
ratio.append(histo1[i].GetEntries()/histo2[i].GetEntries())
ratio_err.append(0)
graph1 = TGraphErrors(len(recorded),array('d',lumi),array('d',ratio),array('d',lumi_err),array('d',ratio_err))
can1.cd()
graph1.SetTitle("")
graph1.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph1.GetYaxis().SetTitle("e^{+}e^{-}/#mu^{+}#mu^{-}")
graph1.SetMarkerStyle(20)
graph1.SetMarkerSize(1)
graph1.Draw("AP")
printLumiPrelOut(can1)
can1.SaveAs("ZPlots/Z_ratio_"+title+".pdf")
can1.SaveAs("ZPlots/Z_ratio_"+title+".png")
return;
def getTGraphErrors(x, y, ex=None, ey=None):
if (ex == None):
ex = [0] * len(x)
if (ey == None):
ey = [0] * len(x)
return TGraphErrors(len(x), array('f', x), array('f', y), array('f', ex),
array('f', ey))
def makeGraph(self, name='', xtitle='', ytitle=''):
"""This function returns an instance of ROOTs TGraphErrors, made with the points in from this class.
Some of the graph's default settings are changed:
- black points
- every point has the symbol of 'x'
- x- and y-axis are centered
Arguments:
name -- ROOT internal name of graph (default = '')
xtitle -- title of x-axis (default = '')
ytitle -- title of y-axis (default = '')
"""
if self.points:
x = self.getX()
y = self.getY()
ex = self.getEX()
ey = self.getEY()
graph = TGraphErrors(self.getLength(), array.array('f', x),
array.array('f', y), array.array('f', ex),
array.array('f', ey))
graph.SetName(name)
graph.SetMarkerColor(1)
graph.SetMarkerStyle(5)
graph.SetTitle("")
graph.GetXaxis().SetTitle(xtitle)
graph.GetXaxis().CenterTitle()
graph.GetYaxis().SetTitle(ytitle)
graph.GetYaxis().CenterTitle()
return graph
else:
return None
def draw_neta_cut_eff(self, cent_low, cent_high):
cent_bin1 = [0, 4, 7]
cent_bin2 = [3, 6, 8]
p_bin1 = [
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5,
3.0, 3.5, 4.0, 4.5
]
p_bin2 = [
0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5, 3.0,
3.5, 4.0, 4.5, 5.0
]
gr_eff = TGraphErrors()
for i in range(0, len(p_bin1)):
mn = self.draw_neta_cut(cent_low, cent_high, p_bin1[i], p_bin2[i])
m = mn[0]
N = mn[1]
gr_eff.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, m / N)
gr_eff.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5,
eff_err(m, N))
gr_eff.SetMarkerStyle(20)
gr_eff.SetMarkerSize(1.5)
gr_eff.SetMarkerColor(600)
hx = histo(0, 8, -0.2, 1.2, "p_{T}(GeV/c)", "n#eta cut efficiency")
canvas_file = self.__canvas__
canvas_file.cd()
c2 = TCanvas("c2", "c2", 1000, 800)
hx.Draw()
gr_eff.Draw("psame")
c2.Write("neta_cut_eff")
c2.SaveAs("neta_cut.png")
def ZMultVsLumi(histo, recorded, outputDir, title):
#gSystem.Exec("mkdir -p ZPlots")
can1 = makeCMSCanvas(str(random.random()),"mult vs lumi ",900,700)
lumi = []
lumi_err = []
mult = []
mult_err = []
sumLumi = 0.
for i in range(0,len(recorded)):
sumLumi += float(recorded[i])
lumi.append(sumLumi - float(recorded[i])/2)
lumi_err.append(float(recorded[i])/2)
mult.append(histo[i].GetEntries()/float(recorded[i]))
mult_err.append(math.sqrt(histo[i].GetEntries()/float(recorded[i])))
graph1 = TGraphErrors(len(lumi),array('d',lumi),array('d',mult),array('d',lumi_err),array('d',mult_err))
can1.cd()
graph1.SetTitle("")
graph1.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph1.GetYaxis().SetTitle("#Z / fb^{-1}")
graph1.SetMarkerStyle(20)
graph1.SetMarkerSize(1)
graph1.Draw("AP")
printLumiPrelOut(can1)
can1.SaveAs(str(outputDir) + "/Z_multiplicity_"+title+".pdf")
can1.SaveAs(str(outputDir) + "/Z_multiplicity_"+title+".png")
return graph1;
