def FillRandom(layerid, detid, nhits):
rnd = TRandom()
rnd.SetSeed()
suf = suffix(layerid, detid)
for n in range(nhits):
xtrk = rnd.Uniform(0, x_chip_sensitive)
ytrk = rnd.Uniform(-y_chip_sensitive / 2, +y_chip_sensitive / 2)
histos["h_hits" + suf].Fill(xtrk, ytrk)
def FindInitialGuess(npoints,
hexp,
hsim,
mflags=[True, True, True],
binlow=None,
binup=None,
filename='grid_initial_guess.yaml'):
import itertools
import random
from ROOT import TRandom
import numpy as np
from likelihood import chi2
import yaml
data = {}
af, bf, cf = mflags
#If for any reason the minimum is at a certain limit redefine limits around it
seed = random.randint(0, 500)
print("Finding a tentative initial guess. Using seed:", seed)
ran = TRandom(seed)
chiaux = np.inf
for step in range(npoints):
m = ran.Uniform(0, 4)
d = ran.Uniform(-100, 100)
calpars = [m, d]
fwhmpars = np.array(
[ran.Uniform(0, 2),
ran.Uniform(0, 2),
ran.Uniform(0, 2)])[mflags].tolist()
pars = fwhmpars + calpars
if (chi2(pars, hexp, hsim, mflags=mflags, binlow=binlow, binup=binup) <
chiaux):
chiaux = chi2(pars,
hexp,
hsim,
mflags=mflags,
binlow=binlow,
binup=binup)
fpars = pars
print('step:', step, 'pars:', pars, 'chisq:', chiaux)
if (af and bf and cf):
data['a'] = float(pars[0])
data['b'] = float(pars[1])
data['c'] = float(pars[2])
if (bf and cf):
data['b'] = float(pars[0])
data['c'] = float(pars[1])
data['m'] = fpars[-2]
data['d'] = fpars[-1]
data['binlow'] = binlow
data['binup'] = binup
print('Printing', filename)
with open(filename, 'w') as outfile:
yaml.dump(data, outfile, default_flow_style=False)
def run_example(save=True):
##########################################
# first define some objects to play with #
##########################################
random = TRandom(getpid())
# Create a histogram to play with
bin_edges = array("d", [2, 3, 5, 7, 11, 13, 17, 19])
hist_1 = TH1F("some_histogram_1", "", len(bin_edges) - 1, bin_edges)
hist_2 = TH1F("some_histogram_2", "", len(bin_edges) - 1, bin_edges)
# Fill histogram from a random number generation
for i in range(len(bin_edges) + 1):
hist_1.SetBinContent(i + 1, random.Poisson(42))
hist_1.SetBinError(i + 1, random.Gaus(10))
hist_2.SetBinContent(i + 1, random.Poisson(42))
hist_2.SetBinError(i + 1, random.Gaus(10))
###################################
# The actual plotting starts here #
###################################
# Generate 3 styles with constant markerstyle
styles = generate_styles(StyleObject1D, 2, markerstyle=34)
# Create a figure with one cell (1 column and one row)
figure = ROOTFigure(1, 1, size=(400, 600), row_margin=0.05, column_margin=0.1)
# plot is actually defined automatically now since it's a 1x1 grid
# hence the following should be skipped
# plot = figure.define_plot(0, 0)
# so one can add an object via the figure...
figure.add_object(hist_1, style=styles[0], label="MyHist1")
# ...or obtain the current single plot cell
plot = figure.change_plot()
# ...and add another object via
plot.add_object(hist_2, style=styles[1], label="MyHist2")
# Set some axis limits and titles explicitly
plot.axes("x", title="x_title")
plot.axes("y", limits=[0, 120], title="y_title")
figure.create()
if save:
figure.save("example_one_plot.eps")
return True
def run_example(save=True):
##########################################
# first define some objects to play with #
##########################################
random = TRandom(getpid())
# Create a histogram to play with
bin_edges = array("d", [2, 3, 5, 7, 11, 13, 17, 19])
hist_1 = TH2F("some_histogram_1", "", len(bin_edges) - 1, bin_edges, len(bin_edges) - 1, bin_edges)
#hist_2 = TH1F("some_histogram_2", "", len(bin_edges) - 1, bin_edges)
# Fill histogram from a random number generation
for i in range(len(bin_edges) + 1):
for j in range(len(bin_edges) + 1):
hist_1.SetBinContent(i + 1, j + 1, random.Poisson(42))
hist_1.SetBinError(i + 1, j + 1, random.Gaus(10))
hist_2 = hist_1.ProjectionX()
###################################
# The actual plotting starts here #
###################################
# Generate 3 styles with constant markerstyle
style = StyleObject1D()
style.draw_options = "colz"
style_2 = StyleObject1D()
# Create a figure with one cell (1 column and one row)
figure = ROOTFigure(1, 2, size=(400, 600), row_margin=((0.06, 0.005), (0.005, 0.05)), column_margin=(0.11, 0.1))
# plot is actually defined automatically now since it's a 1x1 grid
# hence the following should be skipped
plot_1 = figure.define_plot()
plot_1.axes("x", title="x_title", title_offset=2.5)
plot_1.axes("y", title="y_title_1", title_offset=1.9)
# so one can add an object via the figure...
plot_1.add_object(hist_1, style=style)
plot = figure.define_plot(share_x=plot_1)
plot.add_object(hist_2, style=style_2)
plot.axes("y", title="ProjectionX", title_offset=1.9)
figure.create()
if save:
figure.save("test_2D_plot.eps")
return True
def AddFWHM(h, name, modelpars, mflags=[True, True, True]):
import random
from ROOT import TH1I, TRandom
#seed = random.randint(0, 500)
seed = 123
ran = TRandom(seed)
nbins = h.GetNbinsX()
hout = TH1I(name, '', nbins, h.GetBinLowEdge(1),
h.GetXaxis().GetBinUpEdge(nbins))
for i in range(1, nbins + 1):
for j in range(1, int(h.GetBinContent(i)) + 1):
hout.Fill(
ran.Gaus(
h.GetBinCenter(i),
FWHM(h.GetBinCenter(i), modelpars, mflags=mflags) / 2.35))
return hout
def bkgtracks(N):
tracks = []
resolution = 0.001 ## cm (10 um)
rnd = TRandom()
rnd.SetSeed()
for i in range(N):
R = cfgmap["Rbeampipe"] - cfgmap["Wbeampipe"]
phi = rnd.Uniform(0, 2 * ROOT.TMath.Pi())
x0 = R * ROOT.TMath.Cos(phi)
y0 = R * ROOT.TMath.Sin(phi)
z0 = cfgmap["zDipoleExit"]
z4 = cfgmap["zLayer4"]
x4 = rnd.Uniform(1.15 * cfgmap["xPsideL"], 1.15 * cfgmap["xEsideR"])
y4 = rnd.Uniform(-1.15 * cfgmap["Hstave"] / 2,
1.15 * cfgmap["Hstave"] / 2)
r0 = [x0, y0, z0]
r4 = [x4, y4, z4]
z1 = cfgmap["zLayer1"]
x1 = xofz(r4, r0, z1) + rnd.Gaus(0, resolution)
y1 = yofz(r4, r0, z1) + rnd.Gaus(0, resolution)
z2 = cfgmap["zLayer2"]
x2 = xofz(r4, r0, z2) + rnd.Gaus(0, resolution)
y2 = yofz(r4, r0, z2) + rnd.Gaus(0, resolution)
z3 = cfgmap["zLayer3"]
x3 = xofz(r4, r0, z3) + rnd.Gaus(0, resolution)
y3 = yofz(r4, r0, z3) + rnd.Gaus(0, resolution)
z5 = 360
x5 = xofz(r4, r0, z5)
y5 = yofz(r4, r0, z5)
line = TPolyLine3D()
line.SetNextPoint(x0, y0, z0)
line.SetNextPoint(x1, y1, z1)
line.SetNextPoint(x2, y2, z2)
line.SetNextPoint(x3, y3, z3)
line.SetNextPoint(x4, y4, z4)
line.SetNextPoint(x5, y5, z5)
line.SetLineColor(ROOT.kRed)
tracks.append(line)
return tracks
def generateToyFunctions(normNom, normErr, p1, p1err, p2, p2err, corrMatrix, covMatrix, nToys):
from ROOT import TF1, TRandom
from math import sqrt
functions = []
formula = "[0]*TMath::Power(x, [1]+[2]*TMath::Log(x))"
gaussDist = TRandom()
x1Nom = (-0.997412*p1-0.0719019*p2)
x2Nom = (0.0719019*p1-0.997412*p2)
print "p1 is ", p1
print "p1err is ", p1err
print "x1Nom is ", x1Nom
print "p2 is ", p2
print "p2err is ", p2err
print "x2Nom is ", x2Nom
x1err = -0.997412*p1err-0.0719019*p2err
x2err = 0.0719019*p1err-0.997412*p2err
print "x1err is ", -0.997412*p1err-0.0719019*p2err
print "x2err is ", (0.0719019*p1err-0.997412*p2err)
for i in range (0, nToys):
print "--------"
print "toy %i:" % i
x1 = gaussDist.Gaus(x1Nom,x1err)
x2 = gaussDist.Gaus(x2Nom, x2err)
varp1 = (-0.997412*x1+0.0719019*x2)
varp2 = (-0.0719019*x1-0.997412*x2)
print "original p1, p2: %f, %f" % (p1, p2)
print "original p1err, p2err: %f, %f" % (p1err, p2err)
print "translates to x1, x2: %f, %f" % (x1Nom, x2Nom)
print "x1err, x2err: %f, %f" % (x1err, x2err)
print "picked x1, x2: %f, %f" % (x1, x2)
print "translates to p1, p2: %f, %f" % (varp1, varp2)
functions.append(TF1("f_%i" % i, formula, 700, 4700))
functions[-1].SetParameters(1, varp1, varp2)
if functions[-1].Integral(700, 4700) == 0:
del(functions[-1])
else:
prenorm = functions[-1].Integral(700, 4700)
norm = gaussDist.Gaus(normNom, normErr)/prenorm
print "norm is: ", norm
functions[-1].SetParameter(0, norm)
return functions
def GetContours(clusters, positions, h, hsig=None):
contourspts = []
contours = []
bkgmarkers = []
sigmarkers = []
rnd = TRandom()
rnd.SetSeed()
for i in range(len(clusters)):
cluster = clusters[i]
position = positions[i]
icol = int(rnd.Uniform(0, len(colors)))
col = colors[icol]
contourpts, contdata, contour = GetClusterContour(cluster, h, col)
# marker,issig = GetClusterMarker(position,h,col,hsig)
marker, issig = GetClusterMarker(position, h, ROOT.kGray + 1, hsig)
contourspts.append(contourpts)
contours.append(contour)
if (issig): sigmarkers.append(marker)
else: bkgmarkers.append(marker)
return contourspts, bkgmarkers, sigmarkers, contours
def Rebin(h, name, newbinwidth, xlow):
import random
from ROOT import TH1I, TRandom
binwidth = h.GetBinWidth(1)
nbins = h.GetNbinsX()
xup = h.GetXaxis().GetBinUpEdge(nbins)
L = xup - xlow
if (L % newbinwidth == 0):
newnbin = int(L / newbinwidth)
h2 = TH1I(name, "", newnbin, xlow, xup)
if (L % newbinwidth > 0):
newnbin = int(L / newbinwidth) + 1
newxup = xup + newbinwidth
h2 = TH1I(name, "", newnbin, xlow, newxup)
#seed = random.randint(0, 500)
seed = 123
ran = TRandom(seed)
for i in range(1, nbins + 1):
for j in range(1, int(h.GetBinContent(i)) + 1):
h2.Fill(
ran.Uniform(h.GetBinLowEdge(i),
h.GetXaxis().GetBinUpEdge(i)))
return h2
xval = 0.04
sigmaAtX = mres.Eval(xval)
blahh = 0
#print pdf.IntegralOneDim(xval-1.4*sigmaAtX,xval+1.4*sigmaAtX,1e-12,1e-12,ROOT.Double(blahh))
pdfIntegral = pdf.IntegralOneDim(0.0, 0.1, 1e-12, 1e-12, ROOT.Double(blahh))
if (nEvents == 0):
nEvents = pdfIntegral * 0.5 / 2.8
print nEvents
fullRange = pdf.IntegralOneDim(0.02 - 1.4 * mres.Eval(0.02),
0.06 + 1.4 * mres.Eval(0.06), 1e-12, 1e-12,
ROOT.Double(blahh)) / 2.8
print "full range has {0} independent regions".format(fullRange)
rand = TRandom()
c = TCanvas("c", "c", 800, 600)
c.Print(remainder[0] + ".pdf[")
outfile = TFile(remainder[0] + ".root", "RECREATE")
c.SetLogy(0)
n_massbins = 50
minmass = 0.02
maxmass = 0.06
data = TH1D("data", "data", 200, 0, 0.1)
pvalHist = TH1D("pval", "pval", 100000, 0, 1.0)
allpvalHist = TH1D("allpval", "allpval", 100000, 0, 1.0)
pvalCdfHist = TH1D("pvalCdf", "pvalCdf", 100000, 0, 1.0)
def seed (s):
global rootrandom
raise ValueError("s should be used! ")
rootrandom = TRandom(0xdeadbeef)
from ROOT import TRandom
#from heppy.utils.pdebug import pdebugger
raise ValueError('does not behave as python random! the seed is always the same...')
rootrandom = TRandom()
def expovariate (a):
x=rootrandom.Exp(1./a)
#pdebugger.info( x)
return x
def uniform (a, b):
x=rootrandom.Uniform(a, b)
#pdebugger.info( x)
return x
def gauss (a, b):
x= rootrandom.Gaus(a,b)
#pdebugger.info( x)
return x
def seed (s):
global rootrandom
raise ValueError("s should be used! ")
rootrandom = TRandom(0xdeadbeef)
hpx = TH1F('hpx', 'This is the px distribution', 100, -4, 4)
hpx.Print()
#hpxpy = TH2F( 'hpxpy', 'py vs px', 40, -4, 4, 40, -4, 4 )
#hprof = TProfile( 'hprof', 'Profile of pz versus px', 100, -4, 4, 0, 20 )
#ntuple = TNtuple( 'ntuple', 'Demo ntuple', 'px:py:pz:random:i' )
# Set canvas/frame attributes.
#hpx.SetFillColor( 48 )
#gBenchmark.Start( 'hsimple' )
# Initialize random number generator.
#gRandom.SetSeed()
#rannor, rndm = gRandom.Rannor, gRandom.Rndm
random = TRandom()
random.SetSeed(0)
# Fill histograms randomly.
#px, py = Double(), Double()
kUPDATE = 1000
for i in xrange(2500000):
# Generate random values.
# px, py = random.gauss(0, 1), random.gauss(0, 1)
px, py = random.Gaus(0, 1), random.Gaus(0, 1)
# pt = (px*px + py*py)**0.5
pt = math.sqrt(px * px + py * py)
# pt = (px*px + py*py)
# random = rndm(1)
# Fill histograms.
def run_example(save=True):
##########################################
# first define some objects to play with #
##########################################
random = TRandom(getpid())
# Create a histogram to play with
bin_edges = array("d", [2, 3, 5, 7, 11, 13, 17, 19])
hist_1 = TH1F("some_histogram_1", "", len(bin_edges) - 1, bin_edges)
hist_2 = TH1F("some_histogram_2", "", len(bin_edges) - 1, bin_edges)
# Fill histogram from a random number generation
for i in range(len(bin_edges) + 1):
hist_1.SetBinContent(i + 1, random.Poisson(42))
hist_1.SetBinError(i + 1, random.Gaus(10))
hist_2.SetBinContent(i + 1, random.Poisson(42))
hist_2.SetBinError(i + 1, random.Gaus(10))
###################################
# The actual plotting starts here #
###################################
# Generate 5 styles, keep markerstyle the same for all
styles = generate_styles(StyleObject1D, 5, markerstyle=21)
# the overall figure with a grid of 3 columns and 4 rows (default is 1 column and 1 row)
figure = ROOTFigure(3, 4, size=(600, 600), column_margin=(0.05, 0.025))
# define a plot from cell (1, 1) to cell (2, 2)
figure.define_plot(1, 1, 2, 2, y_log=True)
# internally, set to current plot so we can add objects
figure.add_object(hist_1, style=styles[0], label="MyLabel1")
figure.add_object(hist_2, style=styles[1], label="MyLabel2")
# define another plot and store the return value in plot2 cause we want to share its x and y-axis later
# since this should only take one cell, we only need low column and low row
plot2 = figure.define_plot(0, 0, 1, 0)
# add same objects as above
figure.add_object(hist_1, style=styles[0], label="MyLabel1")
figure.add_object(hist_2, style=styles[1], label="MyLabel2")
# now add another plot and we want to share the y-axis again
figure.define_plot(2, 0, share_y=plot2)
figure.add_object(hist_1, style=styles[2], label="MyLabel4")
# now add another plot and we want to share the x-axis
plot3 = figure.define_plot(0, 1)
figure.add_object(hist_1, style=styles[2], label="MyLabel5")
# now add another plot and we want to share the x-axis again
figure.define_plot(0, 2, 0, 3, share_x=plot3)
figure.add_object(hist_1, style=styles[2], label="MyLabel6")
plot4 = figure.define_plot(1, 3)
figure.add_object(hist_1, style=styles[2], label="MyLabel7")
figure.define_plot(2, 3, share_y=plot4)
figure.add_object(hist_1, style=styles[3], label="MyLabel8")
figure.add_object(hist_2, style=styles[4], label="MyLabel9")
# until now, nothing actually happened, the above is really only a specification
# only with this call things are actually created and drawn
figure.create()
if save:
figure.save("test_plot_arrangement.eps")
return True
def run_example(save=True):
##########################################
# first define some objects to play with #
##########################################
random = TRandom(getpid())
# Construct a TGraph to play with
n_points = 100
graph = TGraph(n_points)
for i in range(n_points):
graph.SetPoint(i, random.Gaus(16.5, 5), random.Poisson(21))
# Create a histogram to play with
bin_edges = array("d", [2, 3, 5, 7, 11, 13, 17, 19])
hist = TH1F("some_histogram", "", len(bin_edges) - 1, bin_edges)
# Fill histogram from a random number generation
for i in range(len(bin_edges) - 1):
hist.SetBinContent(i + 1, random.Poisson(42))
hist.SetBinError(i + 1, random.Gaus(10))
# And now add a function
func = TF1("func", "84*sin(x)*sin(x)/x", 1, 10)
###################################
# The actual plotting starts here #
###################################
# Generate 5 styles, keep markerstyle the same for all
styles = generate_styles(StyleObject1D, 5, markerstyle=34)
styles_graphs = generate_styles(StyleObject1D,
5,
markerstyle=21,
draw_options=["P"])
# make explicity style for the function
func_style = StyleObject1D()
func_style.linecolor = kBlack
# number of columns and rows
n_cols = 3
n_rows = 4
# Define a MxN grid with overall margins on the left, bottom, right and top
figure = make_grid(ROOTFigure, (n_cols, n_rows),
0.08,
0.05,
0.05,
0.05,
size=(1000, 1000),
x_title="x_title",
y_title="y_title")
for i in range(n_cols * n_rows):
# change current plot
figure.change_plot(i)
# Add a histogram
figure.add_object(hist,
style=styles[i % len(styles)],
label=f"MyHist{i}")
# Add another object, say a TGraph
figure.add_object(graph,
style=styles_graphs[i % len(styles_graphs)],
label=f"MyGraph_{i}")
# Add another object, now a TF1
figure.add_object(func, style=func_style, label=f"MyFunc_{i}")
# And some text
figure.add_text("Text", 0.5, 0.1)
figure.create()
if save:
figure.save("test_grid_mxn.eps")
return True
return retval
def centerOfGravity(y1, y2):
return (y1 * xmin + y2 * xmax) / (y1 + y2)
from ROOT import TH1D, TCanvas, TRandom
hin = TH1D("hin", "x_in", 100, xmin * 2., xmax * 2.)
hout = TH1D("hout", "x_out", 100, xmin * 2., xmax * 2.)
hres = TH1D("hres", "residual", 100, -30., 30.)
nev = 1000
rang = TRandom()
thickness = 150
chargePerUm = 77
totalCharge = chargePerUm * thickness
for i in range(0, nevents):
xin = rang.Uniform(xmin, xmax)
hin.Fill(xin)
leftCharge = (1 - etaFunc(xin)) * totalCharge
rightCharge = (etaFunc(xin)) * totalCharge
#print "Pos: " + str(xin)
#print "Charge: " + str(leftCharge) + " " + str(rightCharge)
sidesarr = getLogicSidesArr()
n = 0 ### init n
for n in range(Nevt):
## clear the output vectors
clusters_xyz.clear()
clusters_type.clear()
clusters_id.clear()
### embed some noise ***clusters***
ids = [
1000, 2000, 3000, 4000
] ## assuming there's no chance to have more than 1k noise clusters per layer
layer2z = {1: 300, 2: 310, 3: 320, 4: 330}
rnd = TRandom()
rnd.SetSeed()
for side in sidesarr:
for kN in range(NnoiseClusters):
for layer in layers:
x = rnd.Uniform(xPsideL, xPsideR) if (
side == "Pside") else rnd.Uniform(xEsideL, xEsideR)
y = rnd.Uniform(-0.75, +0.75)
z = layer2z[layer]
cluster = ROOT.TPolyMarker3D()
cluster.SetNextPoint(x, y, z)
clusters_xyz.push_back(cluster)
clusters_type.push_back(-1)
clusters_id.push_back(ids[layer - 1])
ids[layer - 1] += 1
def main():
##########################################################################################
##########################################################################################
##########################################################################################
### read fits to root file
# fitsEx = GetFits()
svd0Seed = ROOT.std.vector( float )()
svd1Seed = ROOT.std.vector( float )()
svd2Seed = ROOT.std.vector( float )()
chi2xzSeed = ROOT.std.vector( float )()
chi2yzSeed = ROOT.std.vector( float )()
residxzSeed = ROOT.std.vector( float )()
residyzSeed = ROOT.std.vector( float )()
issigSeed = ROOT.std.vector( int )()
iGenMatch = ROOT.std.vector( int )()
x1Seed = ROOT.std.vector( float )()
y1Seed = ROOT.std.vector( float )()
z1Seed = ROOT.std.vector( float )()
x2Seed = ROOT.std.vector( float )()
y2Seed = ROOT.std.vector( float )()
z2Seed = ROOT.std.vector( float )()
x3Seed = ROOT.std.vector( float )()
y3Seed = ROOT.std.vector( float )()
z3Seed = ROOT.std.vector( float )()
x4Seed = ROOT.std.vector( float )()
y4Seed = ROOT.std.vector( float )()
z4Seed = ROOT.std.vector( float )()
pxSeed = ROOT.std.vector( float )()
pySeed = ROOT.std.vector( float )()
pzSeed = ROOT.std.vector( float )()
eSeed = ROOT.std.vector( float )()
pxGen = ROOT.std.vector( float )()
pyGen = ROOT.std.vector( float )()
pzGen = ROOT.std.vector( float )()
eGen = ROOT.std.vector( float )()
qGen = ROOT.std.vector( float )()
iGen = ROOT.std.vector( int )()
tF = TFile("../data/root/seeds_"+proc+".root","RECREATE")
tF.cd()
tT = TTree("seeds","seeds")
tT.Branch('svd0Seed',svd0Seed)
tT.Branch('svd1Seed',svd1Seed)
tT.Branch('svd2Seed',svd2Seed)
tT.Branch('chi2xzSeed',chi2xzSeed)
tT.Branch('chi2yzSeed',chi2yzSeed)
tT.Branch('residxzSeed',residxzSeed)
tT.Branch('residyzSeed',residyzSeed)
tT.Branch('issigSeed',issigSeed)
tT.Branch('iGenMatch',iGenMatch)
tT.Branch('x1Seed',x1Seed)
tT.Branch('y1Seed',y1Seed)
tT.Branch('z1Seed',z1Seed)
tT.Branch('x2Seed',x2Seed)
tT.Branch('y2Seed',y2Seed)
tT.Branch('z2Seed',z2Seed)
tT.Branch('x3Seed',x3Seed)
tT.Branch('y3Seed',y3Seed)
tT.Branch('z3Seed',z3Seed)
tT.Branch('x4Seed',x4Seed)
tT.Branch('y4Seed',y4Seed)
tT.Branch('z4Seed',z4Seed)
tT.Branch('pxSeed',pxSeed)
tT.Branch('pySeed',pySeed)
tT.Branch('pzSeed',pzSeed)
tT.Branch('eSeed',eSeed)
tT.Branch('pxGen',pxGen)
tT.Branch('pyGen',pyGen)
tT.Branch('pzGen',pzGen)
tT.Branch('eGen',eGen)
tT.Branch('qGen',qGen)
tT.Branch('iGen',iGen)
histos = { "h_residuals_xz_sig": TH1D("residuals_xz_sig",";residuals_{xz};Tracks", 500,0,0.5),
"h_residuals_yz_sig": TH1D("residuals_yz_sig",";residuals_{yz};Tracks", 500,0,500),
"h_residuals_xz_bkg": TH1D("residuals_xz_bkg",";residuals_{xz};Tracks", 500,0,0.5),
"h_residuals_yz_bkg": TH1D("residuals_yz_bkg",";residuals_{yz};Tracks", 500,0,500),
"h_svd_dd0_sig": TH1D("svd_dd0_sig",";svd_{dd0};Tracks", 500,21,24),
"h_svd_dd0_bkg": TH1D("svd_dd0_bkg",";svd_{dd0};Tracks", 500,21,24),
"h_svd_dd1_sig": TH1D("svd_dd1_sig",";svd_{dd1};Tracks", 500,0,0.1),
"h_svd_dd1_bkg": TH1D("svd_dd1_bkg",";svd_{dd1};Tracks", 500,0,0.1),
"h_svd_dd2_sig": TH1D("svd_dd2_sig",";svd_{dd2};Tracks", 500,0,0.05),
"h_svd_dd2_bkg": TH1D("svd_dd2_bkg",";svd_{dd2};Tracks", 500,0,0.05),
"h_prob_xz_sig": TH1D("prob_xz_sig",";prob_{xz};Tracks", 500,0,1.0),
"h_prob_yz_sig": TH1D("prob_yz_sig",";prob_{yz};Tracks", 500,0,1.0),
"h_prob_xz_bkg": TH1D("prob_xz_bkg",";prob_{xz};Tracks", 500,0,1.0),
"h_prob_yz_bkg": TH1D("prob_yz_bkg",";prob_{yz};Tracks", 500,0,1.0),
"h_chi2ndf_xz_sig": TH1D("chi2ndf_xz_sig",";chi2ndf_{xz};Tracks", 500,0,0.001),
"h_chi2ndf_yz_sig": TH1D("chi2ndf_yz_sig",";chi2ndf_{yz};Tracks", 500,0,0.001),
"h_chi2ndf_xz_bkg": TH1D("chi2ndf_xz_bkg",";chi2ndf_{xz};Tracks", 500,0,0.001),
"h_chi2ndf_yz_bkg": TH1D("chi2ndf_yz_bkg",";chi2ndf_{yz};Tracks", 500,0,0.001),
"h_seed_resE" : TH1D("seed_resE", ";(E_{seed}-E_{gen})/E_{gen};Tracks", 100,-3,+3),
"h_seed_resPz": TH1D("seed_resPz",";(Pz_{seed}-Pz_{gen})/Pz_{gen};Tracks", 100,-3,+3),
"h_seed_resPy": TH1D("seed_resPy",";(Py_{seed}-Py_{gen})/Py_{gen};Tracks", 100,-10,+10),
"h_seed_resE_vs_x" : TH2D("seed_resE_vs_x", ";x;(E_{seed}-E_{gen})/E_{gen};Tracks", 100,detXmin,detXmax, 100,-5,+5),
"h_seed_resPy_vs_x" : TH2D("seed_resPy_vs_x", ";x;(Py_{seed}-Py_{gen})/Py_{gen};Tracks", 100,detXmin,detXmax, 100,-10,+10),
"h_N_sigacc": TH1D("N_sigacc", ";Track multiplicity;Events", 40,30,190),
"h_N_all_seeds": TH1D("N_all_seeds", ";Track multiplicity;Events", 40,30,190),
"h_N_matched_seeds": TH1D("N_matched_seeds", ";Track multiplicity;Events", 40,30,190),
"h_N_good_seeds": TH1D("N_good_seeds", ";Track multiplicity;Events", 40,30,190),
"h_seeding_score": TH1D("h_seeding_score", ";N_{seeds}^{matched}/N_{signa}^{in.acc} [%];Events", 20,91,101),
"h_seeding_pool": TH1D("h_seeding_pool", ";N_{seeds}^{all}/N_{signa}^{in.acc} [%];Events", 50,90,590),
}
sidesarr = getLogicSidesArr()
pdfname = "../output/pdf/seedingdemo_"+proc+".pdf"
intfile = TFile("../data/root/rec_"+proc+".root","READ")
intree = intfile.Get("res")
nevents = intree.GetEntries()
print("with %d events" % nevents)
nmax = 100000
n=0 ### init n
for event in intree:
Nsigall = 0
Nsigacc = 0
Nseeds = 0
Nmatched = 0
Ngood = 0
## clear the output vectors
svd0Seed.clear()
svd1Seed.clear()
svd2Seed.clear()
chi2xzSeed.clear()
chi2yzSeed.clear()
residxzSeed.clear()
residyzSeed.clear()
issigSeed.clear()
iGenMatch.clear()
x1Seed.clear()
y1Seed.clear()
z1Seed.clear()
x2Seed.clear()
y2Seed.clear()
z2Seed.clear()
x3Seed.clear()
y3Seed.clear()
z3Seed.clear()
x4Seed.clear()
y4Seed.clear()
z4Seed.clear()
pxSeed.clear()
pySeed.clear()
pzSeed.clear()
eSeed.clear()
pxGen.clear()
pyGen.clear()
pzGen.clear()
eGen.clear()
qGen.clear()
iGen.clear()
### start the loop
if(n>nmax): break
### draw?
dodraw = (n<=NeventsToDraw)
### container for all clusters
allpointsEside = initpoints()
allpointsPside = initpoints()
## clusters' vectors are always written out (even if empty) for all gen tracks!
## each generated track in the vector always has 4 clusters accessed via TPolyMarker3D::GetPoint()
for i in range(event.polm_clusters.size()):
###############################################################
if(proc=="bppp"):
if(sides=="e+" and event.qgen[i]<0): continue ## only positrons
if(sides=="e-" and event.qgen[i]>0): continue ## only electrons
if(proc=="trident" and event.qgen[i]<0): continue ## only positrons
###############################################################
Nsigall += 1
wgt = event.wgtgen[i]
pgen = event.pgen[i]
### cut on acceptance
if(event.acctrkgen[i]!=1): continue
Nsigacc += 1
### write the truth track momentum and its index
pxGen.push_back(pgen.Px())
pyGen.push_back(pgen.Py())
pzGen.push_back(pgen.Pz())
eGen.push_back(pgen.E())
qGen.push_back(event.qgen[i])
iGen.push_back(i)
### loop over all clusters of the track and put in the allpoints classified by the layer
for jxy in range(event.polm_clusters[i].GetN()):
rcls = [ ROOT.Double(), ROOT.Double(), ROOT.Double() ]
event.polm_clusters[i].GetPoint(jxy,rcls[0],rcls[1],rcls[2]) ### the clusters
if(rcls[0]>0): AddPoint(allpointsEside,rcls,True,i)
if(rcls[0]<0): AddPoint(allpointsPside,rcls,True,i)
Nsig4 = getNnon0(allpointsEside["Cls"][4])+getNnon0(allpointsPside["Cls"][4])
### embed some noise clusters
rnd = TRandom()
rnd.SetSeed()
for kN in range(NnoiseClusters):
for layer in layers:
for side in sidesarr:
x = 0
if(side=="Pside"): x = rnd.Uniform(xPsideL,xPsideR)
if(side=="Eside"): x = rnd.Uniform(xEsideL,xEsideR)
y = rnd.Uniform(-0.75,+0.75)
if(layer==1): z = 300
if(layer==2): z = 310
if(layer==3): z = 320
if(layer==4): z = 330
rnoise = [x,y,z]
if(side=="Pside"): AddPoint(allpointsPside,rnoise)
if(side=="Eside"): AddPoint(allpointsEside,rnoise)
Nbkgsig4 = getNnon0(allpointsEside["Cls"][4])+getNnon0(allpointsPside["Cls"][4])
### just draw the full event
drawall(pdfname+"(",allpointsEside,allpointsPside,dodraw)
### loop on the 2 sides
for side in sidesarr:
allpoints = allpointsEside if(side=="Eside") else allpointsPside
### the initial pool for pivot clusters
Nall4 = getNnon0(allpoints["Cls"][4])
### loop over the clusters and start the seeding
for j4 in range(Nall4):
r4 = getpoint(allpoints["Cls"][4],j4)
xpivot = r4[0]
### electron / positron?
particlename = getparticlename(allpoints["Cls"][4],j4)
### get the yz window
winpts_yz,winlin_yz = getyzwindow(allpoints["Cls"][4],j4)
### set the wide window starting from cluster_seed1 (window corners must be added clockwise!)
winpts_xz_wide,winlin_xz_wide = getwidewindow(allpoints["Cls"][4],j4)
### discard all clusters which are not in the wide window
widepoints = initpoints()
trimwide(allpoints,widepoints,winpts_xz_wide,winpts_yz,xpivot)
Nwide1 = getNnon0(widepoints["Cls"][1])
# if(Nwide1<1): print("Failed Nwide1")
### draw the wide window
draw(pdfname,widepoints,dodraw,particlename,winlin_yz,winlin_xz_wide)
### choose one cluster in layer 1 as the second seed
for j1 in range(Nwide1):
### get the narrow window (window corners must be added clockwise!)
winpts_xz_narr,winlin_xz_narr = getnarrwindow(allpoints["Cls"][4],widepoints["Cls"][1],j4,j1)
### discard all clusters which are not in the narrow window
narrpoints = initpoints()
trimnarr(widepoints,narrpoints,winpts_xz_narr)
Nnarr2 = getNnon0(narrpoints["Cls"][2])
Nnarr3 = getNnon0(narrpoints["Cls"][3])
### check if there are at least 1 cluster in both layer 2 and layer 3 within the narrow window
if(Nnarr2<1 or Nnarr3<1): continue
### draw the narrow window
draw(pdfname,narrpoints,dodraw,particlename,None,winlin_xz_narr)
### get the seed - note: could be that there are several combinations but the seed momentum would be identical
pseed = makeseed(allpoints["Cls"][4],widepoints["Cls"][1],j4,j1,particlename)
# if(pseed.E()>Emax or pseed.E()<Emin): print("pseed.E()>Emax or pseed.E()<Emin")
if(pseed.E()>Emax or pseed.E()<Emin): continue
### set the cluster in layer 1
r1 = getpoint(widepoints["Cls"][1],j1)
### loop on the clusters in layer 2 and 3:
for j2 in range(Nnarr2):
# for j2 in range(narrpoints["Cls"][2].GetN()):
r2 = getpoint(narrpoints["Cls"][2],j2)
for j3 in range(Nnarr3):
# for j3 in range(narrpoints["Cls"][3].GetN()):
r3 = getpoint(narrpoints["Cls"][3],j3)
Nseeds += 1
issig1 = widepoints["IsSig"][1][j1]
issig2 = narrpoints["IsSig"][2][j2]
issig3 = narrpoints["IsSig"][3][j3]
issig4 = allpoints["IsSig"][4][j4]
trkid4 = allpoints["TrkId"][4][j4]
trkid3 = narrpoints["TrkId"][3][j3]
trkid2 = narrpoints["TrkId"][2][j2]
trkid1 = widepoints["TrkId"][1][j1]
issig = (issig4 and issig1 and issig2 and issig3)
trkid = str(trkid4) if(trkid4==trkid1 and trkid1==trkid2 and trkid2==trkid3) else "mult"
issiguniq = (issig and trkid!="mult")
if(issiguniq): Nmatched += 1
### two independent 2d fits
chi2_xz,prob_xz,chi2_yz,prob_yz = seed2dfit(pdfname,r1,r2,r3,r4,dodraw)
if(issiguniq):
histos["h_chi2ndf_xz_sig"].Fill(chi2_xz)
histos["h_chi2ndf_yz_sig"].Fill(chi2_yz)
histos["h_prob_xz_sig"].Fill(prob_xz)
histos["h_prob_yz_sig"].Fill(prob_yz)
else:
histos["h_chi2ndf_xz_bkg"].Fill(chi2_xz)
histos["h_chi2ndf_yz_bkg"].Fill(chi2_yz)
histos["h_prob_xz_bkg"].Fill(prob_xz)
histos["h_prob_yz_bkg"].Fill(prob_yz)
### a single 3d fit
res_xz, res_yz = seed3dfit(pdfname,r1,r2,r3,r4,dodraw)
if(issiguniq):
histos["h_residuals_xz_sig"].Fill(res_xz)
histos["h_residuals_yz_sig"].Fill(res_yz)
else:
histos["h_residuals_xz_bkg"].Fill(res_xz)
histos["h_residuals_yz_bkg"].Fill(res_yz)
### a single 3d fit SVD
lfitpts, dd = seed3dfitSVD(pdfname,r1,r2,r3,r4,dodraw)
### set again the pseed according to lfit
cluster1 = TPolyMarker3D()
cluster2 = TPolyMarker3D()
cluster1.SetNextPoint(lfitpts[0][0],lfitpts[0][1],lfitpts[0][2])
cluster2.SetNextPoint(lfitpts[1][0],lfitpts[1][1],lfitpts[1][2])
if(isel(lfitpts[0][0])): pseed = makeseed(cluster2,cluster1,0,0,particlename)
else: pseed = makeseed(cluster1,cluster2,0,0,particlename)
if(pseed.E()>Emax or pseed.E()<Emin): continue
### the SVD alg
if(issiguniq):
histos["h_svd_dd0_sig"].Fill(dd[0])
histos["h_svd_dd1_sig"].Fill(dd[1])
histos["h_svd_dd2_sig"].Fill(dd[2])
else:
histos["h_svd_dd0_bkg"].Fill(dd[0])
histos["h_svd_dd1_bkg"].Fill(dd[1])
histos["h_svd_dd2_bkg"].Fill(dd[2])
### get the generated matched track momentum
pgen = TLorentzVector()
igen = -1
for k in range(iGen.size()):
if(iGen[k]==j4):
igen = k
break
### write out the good seeds
svd0Seed.push_back(dd[0])
svd1Seed.push_back(dd[1])
svd2Seed.push_back(dd[2])
chi2xzSeed.push_back(chi2_xz)
chi2yzSeed.push_back(chi2_yz)
residxzSeed.push_back(res_xz)
residyzSeed.push_back(res_yz)
issigSeed.push_back(issiguniq)
iGenMatch.push_back(igen)
x1Seed.push_back(r1[0])
y1Seed.push_back(r1[1])
z1Seed.push_back(r1[2])
x2Seed.push_back(r2[0])
y2Seed.push_back(r2[1])
z2Seed.push_back(r2[2])
x3Seed.push_back(r3[0])
y3Seed.push_back(r3[1])
z3Seed.push_back(r3[2])
x4Seed.push_back(r4[0])
y4Seed.push_back(r4[1])
z4Seed.push_back(r4[2])
pxSeed.push_back(pseed.Px())
pySeed.push_back(pseed.Py())
pzSeed.push_back(pseed.Pz())
eSeed.push_back(pseed.E())
### cut on some quality
isgood = (dd[1]<0.005 and dd[2]<0.0025)
if(not isgood): continue
Ngood += 1
### check perforrmance of seeding
pgen.SetPxPyPzE(pxGen[igen],pyGen[igen],pzGen[igen],eGen[igen])
resE = (pseed.E()-pgen.E())/pgen.E()
resPz = (pseed.Pz()-pgen.Pz())/pgen.Pz()
resPy = (pseed.Py()-pgen.Py())/pgen.Py()
histos["h_seed_resE"].Fill(resE)
histos["h_seed_resPz"].Fill(resPz)
histos["h_seed_resPy"].Fill(resPy)
histos["h_seed_resE_vs_x"].Fill(r4[0],resE)
histos["h_seed_resPy_vs_x"].Fill(r4[0],resPy)
histos["h_N_sigacc"].Fill(Nsigacc)
histos["h_N_all_seeds"].Fill(Nseeds)
histos["h_N_matched_seeds"].Fill(Nmatched)
histos["h_N_good_seeds"].Fill(Ngood)
histos["h_seeding_score"].Fill(Nmatched/Nsigacc*100)
histos["h_seeding_pool"].Fill(Nseeds/Nsigacc*100)
if(dodraw):
cnv = TCanvas("","",2000,2000)
cnv.SaveAs(pdfname+")")
print("Event: %g --> Nsigall=%g, Nsigacc=%g, Nseeds=%g, Nmatched=%g, Ngood=%g --> Seeds matching performance: Nmatched/Nsigacc=%5.1f%%" % (n,Nsigall,Nsigacc,Nseeds,Nmatched,Ngood,Nmatched/Nsigacc*100))
tT.Fill()
if(n%10==0 and n>0): print(" processed %d events" % n)
n+=1
print("Total events processed: ",n)
cnv = TCanvas("","",1000,1000)
cnv.Divide(2,2)
cnv.cd(1)
histos["h_chi2ndf_xz_sig"].SetLineColor(ROOT.kRed); histos["h_chi2ndf_xz_sig"].Draw()
histos["h_chi2ndf_xz_bkg"].SetLineColor(ROOT.kBlack); histos["h_chi2ndf_xz_bkg"].Draw("same")
cnv.cd(2)
histos["h_chi2ndf_yz_sig"].SetLineColor(ROOT.kRed); histos["h_chi2ndf_yz_sig"].Draw()
histos["h_chi2ndf_yz_bkg"].SetLineColor(ROOT.kBlack); histos["h_chi2ndf_yz_bkg"].Draw("same")
cnv.cd(3)
histos["h_prob_xz_sig"].SetLineColor(ROOT.kRed); histos["h_prob_xz_sig"].Draw()
histos["h_prob_xz_bkg"].SetLineColor(ROOT.kBlack); histos["h_prob_xz_bkg"].Draw("same")
cnv.cd(4)
histos["h_prob_yz_sig"].SetLineColor(ROOT.kRed); histos["h_prob_yz_sig"].Draw()
histos["h_prob_yz_bkg"].SetLineColor(ROOT.kBlack); histos["h_prob_yz_bkg"].Draw("same")
cnv.SaveAs("../output/pdf/chi2ndf_"+proc+".pdf")
cnv = TCanvas("","",1000,500)
cnv.Divide(2,1)
cnv.cd(1)
histos["h_residuals_xz_sig"].SetLineColor(ROOT.kRed); histos["h_residuals_xz_sig"].Draw()
histos["h_residuals_xz_bkg"].SetLineColor(ROOT.kBlack); histos["h_residuals_xz_bkg"].Draw("same")
cnv.cd(2)
histos["h_residuals_yz_sig"].SetLineColor(ROOT.kRed); histos["h_residuals_yz_sig"].Draw()
histos["h_residuals_yz_bkg"].SetLineColor(ROOT.kBlack); histos["h_residuals_yz_bkg"].Draw("same")
cnv.SaveAs("../output/pdf/resid3dfit_"+proc+".pdf")
cnv = TCanvas("","",1500,500)
cnv.Divide(3,1)
cnv.cd(1)
histos["h_svd_dd0_sig"].SetLineColor(ROOT.kRed); histos["h_svd_dd0_sig"].Draw()
histos["h_svd_dd0_bkg"].SetLineColor(ROOT.kBlack); histos["h_svd_dd0_bkg"].Draw("same")
cnv.cd(2)
histos["h_svd_dd1_sig"].SetLineColor(ROOT.kRed); histos["h_svd_dd1_sig"].Draw()
histos["h_svd_dd1_bkg"].SetLineColor(ROOT.kBlack); histos["h_svd_dd1_bkg"].Draw("same")
cnv.cd(3)
histos["h_svd_dd2_sig"].SetLineColor(ROOT.kRed); histos["h_svd_dd2_sig"].Draw()
histos["h_svd_dd2_bkg"].SetLineColor(ROOT.kBlack); histos["h_svd_dd2_bkg"].Draw("same")
cnv.SaveAs("../output/pdf/svd3dfit_"+proc+".pdf")
cnv = TCanvas("","",1000,1000)
cnv.Divide(2,2)
cnv.cd(1); histos["h_seed_resE"].Draw("hist")
cnv.cd(2); histos["h_seed_resPy"].Draw("hist")
cnv.cd(3); histos["h_seed_resE_vs_x"].Draw("col")
cnv.cd(4); histos["h_seed_resPy_vs_x"].Draw("col")
cnv.SaveAs("../output/pdf/seedsres_"+proc+".pdf")
cnv = TCanvas("","",1500,500)
cnv.Divide(3,1)
cnv.cd(1)
histos["h_N_sigacc"].SetLineColor(ROOT.kBlack); histos["h_N_sigacc"].Draw()
histos["h_N_all_seeds"].SetLineColor(ROOT.kBlue); histos["h_N_all_seeds"].Draw("same")
histos["h_N_matched_seeds"].SetLineColor(ROOT.kGreen); histos["h_N_matched_seeds"].Draw("same")
histos["h_N_good_seeds"].SetLineColor(ROOT.kRed); histos["h_N_good_seeds"].Draw("same")
cnv.cd(2)
histos["h_seeding_pool"].Draw("hist")
cnv.cd(3)
histos["h_seeding_score"].Draw("hist")
cnv.SaveAs("../output/pdf/seedsmult_"+proc+".pdf")
tF.cd()
tT.Write()
tF.Write()
tF.Close()
if __name__=="__main__":
main()
def run_example(save=True):
##########################################
# first define some objects to play with #
##########################################
random = TRandom(getpid())
# Create a histogram to play with
bin_edges = array("d", [2, 3, 5, 7, 11, 13, 17, 19])
hist_1 = TH1F("some_histogram_1", "", len(bin_edges) - 1, bin_edges)
hist_2 = TH1F("some_histogram_2", "", len(bin_edges) - 1, bin_edges)
# Fill histogram from a random number generation
for i in range(len(bin_edges) + 1):
hist_1.SetBinContent(i + 1, random.Poisson(42))
hist_1.SetBinError(i + 1, random.Gaus(10))
hist_2.SetBinContent(i + 1, random.Poisson(42))
hist_2.SetBinError(i + 1, random.Gaus(10))
# Ratio, make use of function to clone a ROOT object which will immediately
# acquire a new name so no problems with overlapping names and ROOT complaining
h_ratio = clone_root(hist_1)
h_ratio.Divide(hist_2)
###################################
# The actual plotting starts here #
###################################
# Generate 2 styles with constant markerstyle
styles = generate_styles(StyleObject1D, 2, markerstyles=34, markersizes=2)
# Create a figure with one cell (1 column and one row)
figure = ROOTFigure(1,
2,
size=(400, 600),
row_margin=[(0.1, 0), (0, 0.1)],
column_margin=0.1,
height_ratios=[1, 2])
# since counting starts in the bottom left corner and we use the automatic generation of plot
# the first one will be the ratio
ratio_plot = figure.define_plot()
# add the ratio
figure.add_object(h_ratio, style=styles[0])
# set axis
ratio_plot.axes("x", title="x_axis", title_offset=3)
ratio_plot.axes("y", title="Hist1 / Hist2", title_offset=1.8)
# share the x-axis with the ratio plot
main_plot = figure.define_plot(share_x=ratio_plot)
# add nominal histograms
figure.add_object(hist_1, style=styles[0], label="Hist1")
figure.add_object(hist_2, style=styles[1], label="Hist2")
# set y-axis
main_plot.axes("y", title="Entries")
# create and save
figure.create()
if save:
figure.save("example_ratio_plot.eps")
return True
def seed(s):
global rootrandom
rootrandom = TRandom(s)