def profile(dx, dy, name, bins, xlow=None, xup=None, log=False):
if xlow == None and xup == None:
prof = TProfile("prof-%s" % name, "prof-%s" % name, int(len(bins)), bins, "s")
else:
if not log:
prof = TProfile("prof-%s" % name, "prof-%s" % name, int(bins), xlow, xup, "s")
else:
if xlow <= 0:
xlow = 1
step = np.log((xup - xlow)) / bins
bins = lseq(xlow, xup, step)
prof = TProfile("prof-%s" % name, "prof-%s" % name, int(len(bins)) - 1, bins, "s")
for xi, yi in zip(dx, dy):
prof.Fill(xi, yi)
x = np.array([prof.GetBinCenter(i) for i in xrange(prof.GetNbinsX()) if prof.GetBinContent(i) > 0])
y = np.array([prof.GetBinContent(i) for i in xrange(prof.GetNbinsX()) if prof.GetBinContent(i) > 0])
yerr = np.array([prof.GetBinError(i) for i in xrange(prof.GetNbinsX()) if prof.GetBinContent(i) > 0])
n = np.array([prof.GetBinEntries(i) for i in xrange(prof.GetNbinsX()) if prof.GetBinContent(i) > 0])
return x, y, yerr, n
def generator(nuslice_tree, rootfile, pset):
n_bins = pset.n_bins
drift_distance = pset.DriftDistance
bin_width = drift_distance/n_bins
half_bin_width = bin_width/2.
xvals = np.arange(half_bin_width, drift_distance, bin_width)
xerrs = np.array([half_bin_width] * len(xvals))
dist_to_anode_bins = n_bins
dist_to_anode_low = 0.
dist_to_anode_up = drift_distance
profile_bins = n_bins
profile_option = 's' # errors are the standard deviation
dy_spreads = [None] * n_bins
dy_means = [None] * n_bins
dy_hist = TH2D("dy_hist", "#Delta y",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.dy_bins, pset.dy_low, pset.dy_up)
dy_hist.GetXaxis().SetTitle("distance from anode (cm)")
dy_hist.GetYaxis().SetTitle("y_flash - y_TPC (cm)")
dy_prof = TProfile("dy_prof", "Profile of dy_spreads in #Delta y",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.dy_low*2, pset.dy_up*2, profile_option)
dy_prof.GetXaxis().SetTitle("distance from anode (cm)")
dy_prof.GetYaxis().SetTitle("y_flash - y_TPC (cm)")
dy_h1 = TH1D("dy_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
dy_h1.GetXaxis().SetTitle("distance from anode (cm)")
dy_h1.GetYaxis().SetTitle("y_flash - y_TPC (cm)")
dz_spreads = [None] * n_bins
dz_means = [None] * n_bins
dz_hist = TH2D("dz_hist", "#Delta z",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.dz_bins, pset.dz_low, pset.dz_up)
dz_hist.GetXaxis().SetTitle("distance from anode (cm)")
dz_hist.GetYaxis().SetTitle("z_flash - z_TPC (cm)")
dz_prof = TProfile("dz_prof", "Profile of dz_spreads in #Delta z",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.dz_low*2.5, pset.dz_up*2.5, profile_option)
dz_prof.GetXaxis().SetTitle("distance from anode (cm)")
dz_prof.GetYaxis().SetTitle("z_flash - z_TPC (cm)")
dz_h1 = TH1D("dz_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
dz_h1.GetXaxis().SetTitle("distance from anode (cm)")
dz_h1.GetYaxis().SetTitle("z_flash - z_TPC (cm)")
rr_spreads = [None] * n_bins
rr_means = [None] * n_bins
rr_hist = TH2D("rr_hist", "PE Spread",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.rr_bins, pset.rr_low, pset.rr_up)
rr_hist.GetXaxis().SetTitle("distance from anode (cm)")
rr_hist.GetYaxis().SetTitle("RMS flash (cm)")
rr_prof = TProfile("rr_prof", "Profile of PE Spread",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.rr_low, pset.rr_up, profile_option)
rr_prof.GetXaxis().SetTitle("distance from anode (cm)")
rr_prof.GetYaxis().SetTitle("RMS flash (cm)")
rr_h1 = TH1D("rr_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
rr_h1.GetXaxis().SetTitle("distance from anode (cm)")
rr_h1.GetYaxis().SetTitle("RMS flash (cm)")
if detector == "sbnd":
pe_spreads = [None] * n_bins
pe_means = [None] * n_bins
pe_hist = TH2D("pe_hist", "Uncoated/Coated Ratio",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.pe_bins, pset.pe_low, pset.pe_up)
pe_hist.GetXaxis().SetTitle("distance from anode (cm)")
pe_hist.GetYaxis().SetTitle("ratio_{uncoated/coated}")
pe_prof = TProfile("pe_prof", "Profile of Uncoated/Coated Ratio",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.pe_low, pset.pe_up, profile_option)
pe_prof.GetXaxis().SetTitle("distance from anode (cm)")
pe_prof.GetYaxis().SetTitle("ratio_{uncoated/coated}")
pe_h1 = TH1D("pe_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
pe_h1.GetXaxis().SetTitle("distance from anode (cm)")
pe_h1.GetYaxis().SetTitle("ratio_{uncoated/coated}")
match_score_scatter = TH2D("match_score_scatter", "Scatter plot of match scores",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.score_hist_bins, pset.score_hist_low, pset.score_hist_up*(3./5.))
match_score_scatter.GetXaxis().SetTitle("distance from anode (cm)")
match_score_scatter.GetYaxis().SetTitle("match score (arbitrary)")
match_score_hist = TH1D("match_score", "Match Score",
pset.score_hist_bins, pset.score_hist_low, pset.score_hist_up)
match_score_hist.GetXaxis().SetTitle("match score (arbitrary)")
for e in nuslice_tree:
slice = e.charge_x
dy_hist.Fill(slice, e.flash_y - e.charge_y)
dy_prof.Fill(slice, e.flash_y - e.charge_y)
dz_hist.Fill(slice, e.flash_z - e.charge_z)
dz_prof.Fill(slice, e.flash_z - e.charge_z)
rr_hist.Fill(slice, e.flash_r)
rr_prof.Fill(slice, e.flash_r)
if detector == "sbnd":
pe_hist.Fill(slice, e.flash_ratio)
pe_prof.Fill(slice, e.flash_ratio)
# fill histograms for match score calculation from profile histograms
for ib in list(range(0, profile_bins)):
ibp = ib + 1
dy_h1.SetBinContent(ibp, dy_prof.GetBinContent(ibp))
dy_h1.SetBinError(ibp, dy_prof.GetBinError(ibp))
dy_means[int(ib)] = dy_prof.GetBinContent(ibp)
dy_spreads[int(ib)] = dy_prof.GetBinError(ibp)
dz_h1.SetBinContent(ibp, dz_prof.GetBinContent(ibp))
dz_h1.SetBinError(ibp, dz_prof.GetBinError(ibp))
dz_means[int(ib)] = dz_prof.GetBinContent(ibp)
dz_spreads[int(ib)] = dz_prof.GetBinError(ibp)
rr_h1.SetBinContent(ibp, rr_prof.GetBinContent(ibp))
rr_h1.SetBinError(ibp, rr_prof.GetBinError(ibp))
rr_means[int(ib)] = rr_prof.GetBinContent(ibp)
rr_spreads[int(ib)] = rr_prof.GetBinError(ibp)
if detector == "sbnd":
pe_h1.SetBinContent(ibp, pe_prof.GetBinContent(ibp))
pe_h1.SetBinError(ibp, pe_prof.GetBinError(ibp))
pe_means[int(ib)] = pe_prof.GetBinContent(ibp)
pe_spreads[int(ib)] = pe_prof.GetBinError(ibp)
for e in nuslice_tree:
slice = e.charge_x
# calculate match score
isl = int(slice/bin_width)
score = 0.
if dy_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. dy_spreads[isl]: {dy_spreads[isl]} ")
dy_spreads[isl] = dy_spreads[isl+1]
if dz_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. dz_spreads[isl]: {dz_spreads[isl]} ")
dz_spreads[isl] = dz_spreads[isl+1]
if rr_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. rr_spreads[isl]: {rr_spreads[isl]} ")
rr_spreads[isl] = rr_spreads[isl+1]
if detector == "sbnd" and pe_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. pe_spreads[isl]: {pe_spreads[isl]} ")
pe_spreads[isl] = pe_spreads[isl+1]
score += abs(abs(e.flash_y-e.charge_y) - dy_means[isl])/dy_spreads[isl]
score += abs(abs(e.flash_z-e.charge_z) - dz_means[isl])/dz_spreads[isl]
score += abs(e.flash_r-rr_means[isl])/rr_spreads[isl]
if detector == "sbnd" and pset.UseUncoatedPMT:
score += abs(e.flash_ratio-pe_means[isl])/pe_spreads[isl]
match_score_scatter.Fill(slice, score)
match_score_hist.Fill(score)
metrics_filename = 'fm_metrics_' + detector + '.root'
hfile = gROOT.FindObject(metrics_filename)
if hfile:
hfile.Close()
hfile = TFile(metrics_filename, 'RECREATE',
'Simple flash matching metrics for ' + detector.upper())
dy_hist.Write()
dy_prof.Write()
dy_h1.Write()
dz_hist.Write()
dz_prof.Write()
dz_h1.Write()
rr_hist.Write()
rr_prof.Write()
rr_h1.Write()
if detector == "sbnd":
pe_hist.Write()
pe_prof.Write()
pe_h1.Write()
match_score_scatter.Write()
match_score_hist.Write()
hfile.Close()
canv = TCanvas("canv")
dy_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', dy_means),
array('f', xerrs), array('f', dy_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("dy.pdf")
canv.Update()
dz_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', dz_means),
array('f', xerrs), array('f', dz_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("dz.pdf")
canv.Update()
rr_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', rr_means),
array('f', xerrs), array('f', rr_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("rr.pdf")
canv.Update()
if detector == "sbnd":
pe_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', pe_means),
array('f', xerrs), array('f', pe_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("pe.pdf")
canv.Update()
match_score_scatter.Draw()
canv.Print("match_score_scatter.pdf")
canv.Update()
match_score_hist.Draw()
canv.Print("match_score.pdf")
canv.Update()
sleep(20)
def showENC(self):
tree1 = TTree()
header = 'idX/i:vL/F:vH:A:D/i:R:W'
first = True
for f in self.dataFiles:
if first: tree1.ReadFile(f, header)
else: tree1.ReadFile(f)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', self.bins[0],
tree1.GetMinimum('vH-vL') * 0.8,
tree1.GetMaximum('vH-vL') * 1.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n,
p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
fun1a.SetLineColor(2)
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
self.lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
self.lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
if self.Info:
self.lt.DrawLatexNDC(0.185, 0.6, self.Info)
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
def showENC():
fname1 = '/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat'
tree1 = TTree()
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat',
'idX/i:vL/F:vH:A:D/i:R:W')
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan2_mod.dat'
)
tree1.Show(500)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', 50, 0.12, 0.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
# g1.SetMarkerColor(3)
# g1.SetLineColor(3)
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
# p1.Fit(fun1)
# fun1a = p1.GetFunction('fun1')
fun1a.SetLineColor(2)
# p1.Draw("Esame")
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
lt = TLatex()
lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
axes2.errorbar(x,
y,
yerr=yerr,
xerr=None,
fmt='none',
ecolor='black',
label=hist.GetTitle())
# annotations
if classifier == 'BDTGResponse_1':
axes.plot([0.3, 0.3], [0, 1], color='black', linewidth=2)
axes.text(0.35,
0.3,
'Old working pt ($\geq 0.3$)',
bbox=dict(boxstyle='larrow', fc='white', ec='black'))
oeff_s = hist_s.GetBinContent(hist_s.FindBin(0.3))
axes.annotate('(0.3,{:.3f})'.format(oeff_s),
xy=(0.3, oeff_s),
xytext=(-0.1, 0.75),
arrowprops=dict(facecolor='black',
shrink=0.1,
width=2,
headwidth=5))
elif classifier in ('BDTB', 'BDTG'):
diff, idx = 1., None
for i, eff in enumerate(eff_s):
diff_t = abs(eff - 0.842) # eff_s @ old BDTG >= 0.3
if diff > diff_t:
diff, idx = diff_t, i
axes.plot(clrange, [0.842, 0.842], color='black', linewidth=2)
axes.annotate('Working pt @ old eff {}'.format((round(cuts[idx],