def plot_rsp_Vs_ref(check_file, eta_min, eta_max, normX, logZ, oDir, oFormat="pdf"):
"""Plot response (l1/ref) Vs ref pt"""
app = "_normX" if normX else ""
hname = "eta_%g_%g/Histograms/h2d_rsp_gen%s" % (eta_min, eta_max, app)
fwd_bin = abs(eta_min)>2.9
if cu.exists_in_file(check_file, hname):
h2d_rsp_ref_orig = cu.get_from_file(check_file, hname)
else:
h2d_rsp_ref_orig = cu.get_from_file(check_file, "eta_%g_%g/Histograms/h2d_rsp_gen" % (eta_min, eta_max))
if normX:
h2d_rsp_ref_orig = cu.norm_vertical_bins(h2d_rsp_ref_orig, rescale_peaks=True)
h2d_rsp_ref = h2d_rsp_ref_orig.Rebin2D(2, 1, "hnew")
c = generate_canvas()
if logZ:
c.SetLogz()
app += "_log"
h2d_rsp_ref.SetTitle("%s: %g-%g;%s;%s" % (eta_l1_str, eta_min, eta_max, pt_ref_str, rsp_str))
if normX:
h2d_rsp_ref.Draw("COL")
else:
h2d_rsp_ref.Draw("COLZ")
h2d_rsp_ref.SetAxisRange(rsp_min, rsp_max, 'Y')
if fwd_bin:
h2d_rsp_ref.SetAxisRange(0, 254, 'X')
line = ROOT.TLine(0, 1, 1022, 1)
line.SetLineStyle(2)
line.SetLineWidth(2)
line.Draw()
c.SaveAs("%s/h2d_rsp_ref_%g_%g%s.%s" % (oDir, eta_min, eta_max, app, oFormat))
def get_functions_graphs_params_rootfile(root_filename):
"""Get function parameters from ROOT file
Gets object based on name in runCalibration.py
Parameters
----------
root_filename : str
Name of ROOT file to get things from
Returns
-------
all_fits : list[TF1]
Collection of TF1 objects, one per line of file ( = 1 eta bin)
all_fit_params : list[list[float]]
Collection of fit parameters, one per line of file ( = 1 eta bin)
all_graphs : list[TGraphErrors]
Colleciton of correction graphs, one per eta bin
"""
print 'Reading functions from ROOT file'
in_file = cu.open_root_file(root_filename)
all_fit_params = []
all_fits = []
all_graphs = []
# Get all the fit functions from file and their corresponding graphs
etaBins = binning.eta_bins
for i, (eta_min, eta_max) in enumerate(izip(etaBins[:-1], etaBins[1:])):
print "Eta bin:", eta_min, "-", eta_max
# get the fitted TF1
try:
fit_func = cu.get_from_file(in_file, "fitfcneta_%g_%g" % (eta_min, eta_max))
fit_params = [fit_func.GetParameter(par) for par in range(fit_func.GetNumberFreeParameters())]
print "Fit fn evaluated at 5 GeV:", fit_func.Eval(5)
except IOError:
print "No fit func"
fit_func = None
fit_params = []
all_fits.append(fit_func)
all_fit_params.append(fit_params)
# print "Fit parameters:", fit_params
# get the corresponding fit graph
fit_graph = cu.get_from_file(in_file, generate_eta_graph_name(eta_min, eta_max))
all_graphs.append(fit_graph)
in_file.Close()
return all_fits, all_fit_params, all_graphs
def plot_res_all_pt(res_file, eta_min, eta_max, oDir, oFormat="pdf"):
"""Plot a graph of resolution as a function of L1 eta."""
c = generate_canvas()
# binned by ref pt
grname = "eta_%g_%g/resRefRef_%g_%g_diff" % (eta_min, eta_max, eta_min, eta_max)
try:
graph = cu.get_from_file(res_file, grname)
except IOError:
print '!! Cannot get', grname
return None
# leg = generate_legend()
mg = ROOT.TMultiGraph()
i = 0
graph.SetLineColor(plot_colors[i])
graph.SetMarkerColor(plot_colors[i])
graph.SetMarkerStyle(plot_markers[i])
mg.Add(graph)
# leg.AddEntry(graph, plot_labels[i], "LP")
mg.Draw("ALP")
mg.GetXaxis().SetTitleSize(0.04)
mg.GetXaxis().SetTitleOffset(0.9)
# mg.GetYaxis().SetTitle(res_l1_str)
mg.GetYaxis().SetRangeUser(0, mg.GetYaxis().GetXmax() * 1.5)
mg.GetYaxis().SetRangeUser(0, 0.6)
mg.GetHistogram().SetTitle("%s;%s;%s" % (plot_title + ', %g < %s < %g' % (eta_min, eta_l1_str, eta_max), pt_ref_str, res_ref_str))
mg.Draw("ALP")
# leg.Draw()
append = ""
c.SaveAs("%s/res_ref_eta_%g_%g_diff%s.%s" % (oDir, eta_min, eta_max, append, oFormat))
def plot_eta_pt_rsp_2d(calib_file, etaBins, ptBins, oDir, oFormat='pdf'):
"""
Plot a 2D map of response for each eta, pt bin. Display the <response> from
Gaussian fit of response hist in that bin
Uses the resolution output file, since this plot of rsp & fit for pT(L1) bin,
and will be done for pre and post calib, whereas output from runCalibration
will generally only be pre calib, and is binned by ref Jet pt
"""
h_2d = ROOT.TH2D("h2d_eta_pt_rsp", ";%s;%s" % (pt_l1_str, eta_l1_str) ,
len(ptBins) - 1, array('d', ptBins),
len(etaBins) - 1, array('d', etaBins))
for eta_ind, (eta_min, eta_max) in enumerate(zip(etaBins[:-1], etaBins[1:]), 1):
for pt_ind, (pt_min, pt_max) in enumerate(zip(ptBins[:-1], ptBins[1:]), 1):
h_rsp = cu.get_from_file(calib_file, "eta_%g_%g/Histograms/res_l1_%g_%g" % (eta_min, eta_max, pt_min, pt_max))
if h_rsp.GetEntries() > 0:
# we actually use the fit to (L1-Gen)/L1, so we need to * -1 and invert
res = h_rsp.GetListOfFunctions().At(0).GetParameter(1)
rsp = 1. / (1 - res)
print rsp
h_2d.SetBinContent(pt_ind, eta_ind, rsp)
else:
h_2d.SetBinContent(pt_ind, eta_ind, 0)
c = generate_canvas()
ROOT.gStyle.SetPaintTextFormat(".2f")
ROOT.gStyle.SetPalette(56)
h_2d.Draw("COLZTEXT")
h_2d.GetZaxis().SetRangeUser(0, 2)
c.SaveAs("%s/h2d_eta_pt_rsp.%s" % (oDir, oFormat))
def make_plots(filenames, oDir, hist_names, title):
c = ROOT.TCanvas("", "", 800, 600)
c.SetTicks(1, 1)
files = [cu.open_root_file(f.filename) for f in filenames]
for hname in hist_names:
# print hname
hists = [cu.get_from_file(f, hname).Clone() for f in files]
leg = ROOT.TLegend(0.6, 0.6, 0.85, 0.85)
for i, h in enumerate(hists):
norm_hist(h)
h.Rebin(2)
h.SetTitle('%s: %s' % (title, hname))
h.SetLineColor(filenames[i].color)
if i == 0:
h.Draw("HISTE")
else:
h.Draw("HISTE SAME")
leg.AddEntry(h, filenames[i].label, "L")
leg.Draw()
outname = os.path.join(oDir, hname+'.pdf')
cu.check_dir_exists_create(os.path.dirname(outname))
c.SaveAs(os.path.join(oDir, hname+'.pdf'))
def redo_correction_fit(inputfile, outputfile, absetamin, absetamax, fitfcn):
"""Redo correction fit for a given eta bin.
Get TGraphErrors for the bin, and perform calibration curve fitting
procedure and save if successful.
inputfile: TFile. The output file from running runCalibration.py previously.
outputfile: TFile. The file you want to write the new graph & fit to.
Can be the same as inputfile.
absetamin: double
absetamax: double
fitfcn: TF1
"""
# Get relevant graph
gr = cu.get_from_file(inputfile,
generate_eta_graph_name(absetamin, absetamax))
outputfile.WriteTObject(gr, gr.GetName(),
'Overwrite') # the original graph
# Setup fitting (calculate sensible range, make sub-graph), then do fit!
sub_graph, this_fit = setup_fit(gr, fitfcn, absetamin, absetamax,
outputfile)
fit_graph, fit_params = fit_correction(sub_graph, this_fit)
outputfile.WriteTObject(this_fit, this_fit.GetName(),
'Overwrite') # function by itself
outputfile.WriteTObject(
fit_graph, fit_graph.GetName(),
'Overwrite') # has the function stored in it as well
return fit_params
def make_plot_eta_binned(input_filename, output_filename, title=''):
f = cu.open_root_file(input_filename)
tree = cu.get_from_file(f, 'valid')
hists = []
eta_bins = binning.eta_bins
# eta_bins = [0, 3, 5]
for i, (eta_min, eta_max) in enumerate(binning.pairwise(eta_bins)):
hname = "h_%g_%g" % (eta_min, eta_max)
h = ROOT.TH1D(hname, title + " PU15 - 25;response;p.d.f", 30, 0, 3)
tree.Draw("rsp>>%s" % hname, "%g < TMath::Abs(eta) && TMath::Abs(eta) < %g && numPUVertices<25 && numPUVertices >15" % (eta_min, eta_max))
h.SetLineColor(binning.eta_bin_colors[i])
h.SetLineWidth(2)
h.Scale(1. / h.Integral())
hists.append(h)
canv = ROOT.TCanvas("c", "", 600, 600)
canv.SetTicks(1, 1)
hstack = ROOT.THStack("hst", title + " PU15 - 25;response;p.d.f")
leg = ROOT.TLegend(0.6, 0.6, 0.88, 0.88)
for i, h in enumerate(hists):
hstack.Add(h)
leg.AddEntry(h, '%g < |#eta| < %g' % (eta_bins[i], eta_bins[i + 1]), 'L')
hstack.Draw("NOSTACK HIST")
leg.Draw()
canv.SaveAs(output_filename)
def plot_res_pt_bin(res_file, eta_min, eta_max, pt_min, pt_max, oDir, oFormat="pdf"):
"""Plot the L1 resolution for given L1 pT, eta bin"""
hname = "eta_%g_%g/Histograms/res_l1_%g_%g" % (eta_min, eta_max, pt_min, pt_max)
h_res = cu.get_from_file(res_file, hname)
c = generate_canvas()
h_res.Draw()
h_res.SetAxisRange(-2, 2, "X")
h_res.SetTitle(";%s;N" % res_l1_str)
c.SaveAs("%s/res_l1_eta_%g_%g_%g_%g.%s" % (oDir, eta_min, eta_max, pt_min, pt_max, oFormat))
def plot_correction_graph(calib_file, eta_min, eta_max, oDir, oFormat='pdf'):
"""Plot the graph of correction value vs pT"""
gname = generate_eta_graph_name(eta_min, eta_max)
gr = cu.get_from_file(calib_file, gname)
c = generate_canvas()
gr.Draw("ALP")
y_min = ROOT.TMath.MinElement(gr.GetN(), gr.GetY())
y_max = ROOT.TMath.MaxElement(gr.GetN(), gr.GetY())
if y_max > 5:
y_max = 5
gr.GetYaxis().SetRangeUser(y_min * 0.7, y_max * 1.1)
c.SaveAs('%s/%s.%s' % (oDir, gname, oFormat))
def get_obj(self):
"""Get object for this contribution."""
input_file = cu.open_root_file(self.file_name)
self.obj = cu.get_from_file(input_file, self.obj_name)
self.obj.SetLineWidth(self.line_width)
self.obj.SetLineColor(self.line_color)
self.obj.SetLineStyle(self.line_style)
self.obj.SetMarkerSize(self.marker_size)
self.obj.SetMarkerColor(self.marker_color)
self.obj.SetMarkerStyle(self.marker_style)
input_file.Close()
return self.obj
def plot_rsp_eta_bin(calib_file, eta_min, eta_max, oDir, oFormat="pdf"):
"""Plot the response in one eta bin"""
hname = "eta_%g_%g/Histograms/hrsp_eta_%g_%g" % (eta_min, eta_max, eta_min, eta_max)
h_rsp = cu.get_from_file(calib_file, hname)
func = h_rsp.GetListOfFunctions().At(0)
c = generate_canvas()
h_rsp.SetTitle(os.path.basename(hname) + ';response (%s)' % rsp_str)
h_rsp.Draw("HISTE")
if func:
func.Draw("SAME")
filename = "%s/h_rsp_%g_%g.%s" % (oDir, eta_min, eta_max, oFormat)
c.SaveAs(filename)
return filename
def plot_ptDiff_Vs_pt(res_file, eta_min, eta_max, oDir, oFormat='pdf'):
"""Plot (ptL1 - pTRef) Vs pTL1 for given eta bin"""
hname = "eta_%g_%g/Histograms/ptDiff_ref_2d" % (eta_min, eta_max)
h_2d = cu.get_from_file(res_file, hname)
c = generate_canvas()
ROOT.gStyle.SetPalette(55)
h_2d.RebinX(16)
# h_2d.RebinY(2)
h_2d.Draw("COLZ")
h_2d.GetYaxis().SetRangeUser(-120, 120)
c.SetLogz()
c.SetTicks(1, 1)
c.SaveAs("%s/h2d_ptDiff_ptRef_eta_%g_%g.%s" % (oDir, eta_min, eta_max, oFormat))
def get_obj(self):
"""Get object for this contribution."""
input_file = cu.open_root_file(self.file_name)
self.obj = cu.get_from_file(input_file, self.obj_name)
self.obj.SetLineWidth(self.line_width)
self.obj.SetLineColor(self.line_color)
self.obj.SetLineStyle(self.line_style)
self.obj.SetMarkerSize(self.marker_size)
self.obj.SetMarkerColor(self.marker_color)
self.obj.SetMarkerStyle(self.marker_style)
if isinstance(self.obj, ROOT.TH1):
self.obj.SetDirectory(0)
input_file.Close()
return self.obj
def plot_pt_diff(res_file, eta_min, eta_max, pt_min, pt_max, oDir, oFormat="pdf"):
"""Plot the difference between L1 and ref jet pT, for given L1 pT, eta bin"""
hname = "eta_%g_%g/Histograms/ptDiff_ref_%g_%g" % (eta_min, eta_max, pt_min, pt_max)
h_diff = cu.get_from_file(res_file, hname)
c = generate_canvas()
h_diff.Draw()
h_diff.SetMaximum(h_diff.GetMaximum() * 1.2)
# h_diff.SetLineWidth(2)
func = h_diff.GetListOfFunctions().At(0)
func.SetLineWidth(1)
h_diff.SetTitle("%g < %s < %g, %g < p_{T}^{%s} < %g;%s;N" % (eta_min, eta_l1_str, eta_max, pt_min, ref_str, pt_max, pt_diff_str))
if not os.path.exists("%s/eta_%g_%g" % (oDir, eta_min, eta_max)):
os.makedirs("%s/eta_%g_%g" % (oDir, eta_min, eta_max))
c.SaveAs("%s/eta_%g_%g/pt_diff_eta_%g_%g_%g_%g.%s" % (oDir, eta_min, eta_max, eta_min, eta_max, pt_min, pt_max, oFormat))
def plot_rsp_Vs_pt_candle_violin(check_file, eta_min, eta_max, ptVar, oDir, oFormat):
"""Plot response vs pt as a candle plot and violin plot.
Also puts fitted peak graph on there as well for comparison."""
hname = "eta_%g_%g/Histograms/h2d_rsp_%s" % (eta_min, eta_max, ptVar)
if cu.exists_in_file(check_file, hname):
h2d_rsp_pt_orig = cu.get_from_file(check_file, hname)
else:
h2d_rsp_pt_orig = cu.get_from_file(check_file, "eta_%g_%g/Histograms/h2d_rsp_%s" % (eta_min, eta_max, ptVar))
h2d_rsp_pt = h2d_rsp_pt_orig.Rebin2D(1, 1, "hnew")
c = generate_canvas()
ptStr = pt_l1_str if ptVar == 'l1' else pt_ref_str
h2d_rsp_pt.SetTitle("%s: %g-%g;%s;%s" % (eta_l1_str, eta_min, eta_max, ptStr, rsp_str))
h2d_rsp_pt.Draw("CANDLE")
# Draw fitted peaks as well
gr_name = "eta_{0:g}_{1:g}/gr_rsp_{2}_eta_{0:g}_{1:g}".format(eta_min, eta_max, 'pt' if ptVar == 'l1' else 'ptRef')
gr = cu.get_from_file(check_file, gr_name)
gr.SetLineColor(ROOT.kRed)
gr.SetMarkerColor(ROOT.kRed)
gr.SetMarkerStyle(21)
gr.Draw("LP")
max_pt = 100
gr.GetXaxis().SetLimits(0, max_pt)
h2d_rsp_pt.SetAxisRange(0, max_pt, 'X')
h2d_rsp_pt.SetAxisRange(rsp_min, rsp_max, 'Y')
line = ROOT.TLine(0, 1, max_pt, 1)
line.SetLineStyle(2)
line.SetLineWidth(2)
line.Draw()
c.SaveAs("%s/h2d_rsp_%s_%g_%g_box.%s" % (oDir, 'l1' if ptVar == 'l1' else 'ref', eta_min, eta_max, oFormat))
h2d_rsp_pt.Draw("VIOLIN")
gr.Draw("LP")
c.SaveAs("%s/h2d_rsp_%s_%g_%g_violin.%s" % (oDir, 'l1' if ptVar == 'l1' else 'ref', eta_min, eta_max, oFormat))
def plot_pt_bin(calib_file, eta_min, eta_max, pt_min, pt_max, oDir, oFormat="pdf"):
"""Plot the L1 pt in a given ref jet pt bin, for a given eta bin"""
sub_dir = "eta_%g_%g" % (eta_min, eta_max)
if not os.path.isdir("%s/%s" % (oDir, sub_dir)):
os.mkdir("%s/%s" % (oDir, sub_dir))
hname = "eta_%g_%g/Histograms/L1_pt_genpt_%g_%g" % (eta_min, eta_max, pt_min, pt_max)
# ignore histogram not found errors... naughty naughty
try:
h_pt = cu.get_from_file(calib_file, hname)
c = generate_canvas()
h_pt.Draw("HISTE")
filepath = "%s/%s/L1_pt_%g_%g_%g_%g.%s" % (oDir, sub_dir, eta_min, eta_max, pt_min, pt_max, oFormat)
c.SaveAs(filepath)
return filepath
except Exception:
print "! No histogram %s exists" % hname
def process_file(filename, eta_bins=binning.eta_bins_forward):
"""Process a ROOT file with graphs, print a mean & mean histogram for each.
Parameters
----------
filename : str
Name of ROOT file to process (from runCalibration.py)
eta_bins : list[[float, float]]
Eta bin edges.
"""
f = cu.open_root_file(filename)
for eta_min, eta_max in binning.pairwise(eta_bins):
gr = cu.get_from_file(f, generate_eta_graph_name(eta_min, eta_max))
if not gr:
raise RuntimeError("Can't get graph")
xarr, yarr = cu.get_xy(gr)
xarr, yarr = np.array(xarr), np.array(
yarr) # use numpy array for easy slicing
# Loop over all possible subgraphs, and calculate a mean for each
end = len(yarr)
means = []
while end > 0:
start = 0
while start < end:
means.append(yarr[start:end].mean())
start += 1
end -= 1
# Jackknife means
jack_means = [np.delete(yarr, i).mean() for i in range(len(yarr))]
# Do plotting & peak finding in both ROOT and MPL...not sure which is better?
# peak = plot_find_peak_mpl(means, eta_min, eta_max, os.path.dirname(os.path.realpath(filename)))
peak = plot_find_peak_root(means, eta_min, eta_max,
os.path.dirname(os.path.realpath(filename)))
jackpeak = plot_jacknife_root(
jack_means, eta_min, eta_max,
os.path.dirname(os.path.realpath(filename)))
print 'Eta bin:', eta_min, '-', eta_max
print peak
print 'jackknife mean:'
print np.array(jack_means).mean()
f.Close()
def plot_res_pt_bin(res_file, eta_min, eta_max, pt_min, pt_max, oDir, oFormat="pdf"):
"""Plot the L1 resolution for given L1 pT, eta bin"""
hname = "eta_%g_%g/Histograms/res_l1_%g_%g" % (eta_min, eta_max, pt_min, pt_max)
try:
h_res = cu.get_from_file(res_file, hname)
except IOError:
print '!! Cannot get', hname
return None
c = generate_canvas()
h_res.Draw()
h_res.SetAxisRange(-2, 2, "X")
h_res.SetTitle(";%s;N" % res_l1_str)
subdir = "%s/eta_%g_%g" % (oDir, eta_min, eta_max)
cu.check_dir_exists_create("%s/eta_%g_%g" % (oDir, eta_min, eta_max))
filename = "%s/res_l1_eta_%g_%g_%g_%g.%s" % (subdir, eta_min, eta_max, pt_min, pt_max, oFormat)
c.SaveAs(filename)
return filename
def plot_rsp_pt_graph(check_file, eta_min, eta_max, oDir, oFormat='pdf', x_range=None):
"""Plot a graph of response vs pt (L1) for a given eta bin"""
grname = "eta_%g_%g/gr_rsp_pt_eta_%g_%g" % (eta_min, eta_max, eta_min, eta_max)
graph = cu.get_from_file(check_file, grname)
c = generate_canvas(plot_title)
# leg = generate_legend()
mg = ROOT.TMultiGraph()
i = 0
graph.SetLineColor(plot_colors[i])
graph.SetMarkerColor(plot_colors[i])
graph.SetMarkerStyle(plot_markers[i])
mg.Add(graph)
# leg.AddEntry(graph, plot_labels[i], "LP")
pt_min, pt_max = (0, 1022) if not x_range else (x_range[0], x_range[1])
# lines at 1, and +/- 0.1
line_central = ROOT.TLine(pt_min, 1, pt_max, 1)
line_plus = ROOT.TLine(pt_min, 1.1, pt_max, 1.1)
line_minus = ROOT.TLine(pt_min, 0.9, pt_max, 0.9)
line_central.SetLineWidth(2)
line_central.SetLineStyle(2)
for line in [line_plus, line_minus]:
line.SetLineWidth(2)
line.SetLineStyle(3)
# bundle all graphs into a TMultiGraph - set axes limits here
mg.Draw("ALP")
mg.GetYaxis().SetRangeUser(rsp_min, rsp_max)
mg.GetXaxis().SetLimits(pt_min, pt_max)
mg.GetXaxis().SetTitleSize(0.04)
mg.GetXaxis().SetTitleOffset(0.9)
# mg.GetYaxis().SetTitleSize(0.04)
mg.Draw("ALP")
mg.GetHistogram().SetTitle("%s;%s;%s" % (plot_title + ", %g < %s < %g" % (eta_min, eta_l1_str, eta_max), pt_l1_str, rsp_str))
# leg.Draw()
[line.Draw() for line in [line_central, line_plus, line_minus]]
append = ""
c.SaveAs("%s/gr_rsp_pt_eta_%g_%g%s.%s" % (oDir, eta_min, eta_max, append, oFormat))
def plot_rsp_eta_inclusive_graph(check_file, eta_min, eta_max, pt_var, oDir, oFormat="pdf"):
"""Plot a graph of response vs L1 eta."""
grname = "eta_%g_%g/gr_rsp_%s_eta_%g_%g" % (eta_min, eta_max, pt_var, eta_min, eta_max)
graph = cu.get_from_file(check_file, grname)
c = generate_canvas(plot_title)
# leg = generate_legend() #(0.4, 0.7, 0.87, 0.87) # top right
# leg = generate_legend()
mg = ROOT.TMultiGraph()
i = 0
graph.SetLineColor(plot_colors[i])
graph.SetMarkerColor(plot_colors[i])
graph.SetMarkerStyle(plot_markers[i])
mg.Add(graph)
# leg.AddEntry(graph, plot_labels[i], "LP")
# lines at 1, and +/- 0.1
line_central = ROOT.TLine(eta_min, 1, eta_max, 1)
line_plus = ROOT.TLine(eta_min, 1.1, eta_max, 1.1)
line_minus = ROOT.TLine(eta_min, 0.9, eta_max, 0.9)
line_central.SetLineWidth(2)
line_central.SetLineStyle(2)
for line in [line_plus, line_minus]:
line.SetLineWidth(2)
line.SetLineStyle(3)
# bundle all graphs into a TMultiGraph - set axes limits here
mg.Draw("ALP")
mg.GetYaxis().SetRangeUser(rsp_min, rsp_max)
mg.GetXaxis().SetLimits(eta_min, eta_max)
mg.GetXaxis().SetTitleSize(0.04)
mg.GetXaxis().SetTitleOffset(0.9)
# mg.GetYaxis().SetTitleSize(0.04)
mg.Draw("ALP")
mg.GetHistogram().SetTitle("%s;%s;%s" % (plot_title, eta_l1_str, rsp_str))
# leg.Draw()
[line.Draw() for line in [line_central, line_plus, line_minus]]
append = ""
c.SaveAs("%s/gr_rsp_eta_%g_%g%s.%s" % (oDir, eta_min, eta_max, append, oFormat))
def plot_rsp_eta_bin_pt(calib_file, eta_min, eta_max, pt_var, pt_min, pt_max, oDir, oFormat="pdf"):
"""Plot the response in one eta bin with a pt cut"""
if eta_max <= 3:
hname = "eta_0_3/Histograms/hrsp_eta_%g_%g_%s_%g_%g" % (eta_min, eta_max, pt_var, pt_min, pt_max)
else:
hname = "eta_3_5/Histograms/hrsp_eta_%g_%g_%s_%g_%g" % (eta_min, eta_max, pt_var, pt_min, pt_max)
try:
h_rsp = cu.get_from_file(calib_file, hname)
except Exception:
return None
func = h_rsp.GetListOfFunctions().At(0)
c = generate_canvas()
h_rsp.SetTitle(os.path.basename(hname) + ';response (%s)' % rsp_str)
h_rsp.Draw("HISTE")
if func:
func.Draw("SAME")
output_filename = "%s/h_rsp_%g_%g_%s_%g_%g.%s" % (oDir, eta_min, eta_max, pt_var, pt_min, pt_max, oFormat)
c.SaveAs(output_filename)
return output_filename
def process_file(filename, eta_bins=binning.eta_bins_forward):
"""Process a ROOT file with graphs, print a mean & mean histogram for each.
Parameters
----------
filename : str
Name of ROOT file to process (from runCalibration.py)
eta_bins : list[[float, float]]
Eta bin edges.
"""
f = cu.open_root_file(filename)
for eta_min, eta_max in binning.pairwise(eta_bins):
gr = cu.get_from_file(f, generate_eta_graph_name(eta_min, eta_max))
if not gr:
raise RuntimeError("Can't get graph")
xarr, yarr = cu.get_xy(gr)
xarr, yarr = np.array(xarr), np.array(yarr) # use numpy array for easy slicing
# Loop over all possible subgraphs, and calculate a mean for each
end = len(yarr)
means = []
while end > 0:
start = 0
while start < end:
means.append(yarr[start:end].mean())
start += 1
end -= 1
# Jackknife means
jack_means = [np.delete(yarr, i).mean() for i in range(len(yarr))]
# Do plotting & peak finding in both ROOT and MPL...not sure which is better?
# peak = plot_find_peak_mpl(means, eta_min, eta_max, os.path.dirname(os.path.realpath(filename)))
peak = plot_find_peak_root(means, eta_min, eta_max, os.path.dirname(os.path.realpath(filename)))
jackpeak = plot_jacknife_root(jack_means, eta_min, eta_max, os.path.dirname(os.path.realpath(filename)))
print 'Eta bin:', eta_min, '-', eta_max
print peak
print 'jackknife mean:'
print np.array(jack_means).mean()
f.Close()
def plot_rsp_eta_exclusive_graph(check_file, eta_min, eta_max, pt_bins, pt_var, oDir, oFormat="pdf"):
"""Plot a graph of response vs L1 eta.
pt_bins is a list of pt bin edges to plot on the same graph
pt_var is the varaible (pt or ptRef)
"""
mg = ROOT.TMultiGraph()
leg = generate_legend(x1=0.65, y1=0.65)
c = generate_canvas(plot_title)
gr_name = "eta_%g_%g/gr_rsp_eta_%g_%g_{0}_{1}_{2}" % (eta_min, eta_max, eta_min, eta_max)
for i, (pt_min, pt_max) in enumerate(pt_bins):
g = cu.get_from_file(check_file, gr_name.format(pt_var, pt_min, pt_max))
g.SetLineColor(binning.eta_bin_colors[i])
g.SetMarkerColor(binning.eta_bin_colors[i])
g.SetMarkerStyle(plot_markers[0])
mg.Add(g)
# leg.AddEntry(g, plot_labels[0] + " %g < %s < %g" % (pt_min, pt_var, pt_max), "LP")
leg.AddEntry(g, " %g < %s < %g" % (pt_min, pt_var, pt_max), "LP")
# lines at 1, and +/- 0.1
line_central = ROOT.TLine(eta_min, 1, eta_max, 1)
line_plus = ROOT.TLine(eta_min, 1.1, eta_max, 1.1)
line_minus = ROOT.TLine(eta_min, 0.9, eta_max, 0.9)
line_central.SetLineWidth(2)
line_central.SetLineStyle(2)
for line in [line_plus, line_minus]:
line.SetLineWidth(2)
line.SetLineStyle(3)
# bundle all graphs into a TMultiGraph - set axes limits here
mg.Draw("ALP")
mg.GetYaxis().SetRangeUser(rsp_min, rsp_max)
mg.GetXaxis().SetLimits(eta_min, eta_max)
mg.GetXaxis().SetTitleSize(0.04)
mg.GetXaxis().SetTitleOffset(0.9)
# mg.GetYaxis().SetTitleSize(0.04)
mg.Draw("ALP")
mg.GetHistogram().SetTitle("%s;%s;%s" % (plot_title, eta_l1_str, rsp_str))
leg.Draw()
[line.Draw() for line in [line_central, line_plus, line_minus]]
c.SaveAs("%s/gr_rsp_eta_%g_%g_%s_%g_%g_binned.%s" % (oDir, eta_min, eta_max, pt_var, pt_bins[0][0], pt_bins[-1][1], oFormat))
def plot_rsp_eta_pt_bin(calib_file, eta_min, eta_max, pt_min, pt_max, oDir, oFormat="pdf"):
"""Plot the response in one pt, eta bin"""
sub_dir = "eta_%g_%g" % (eta_min, eta_max)
if not os.path.isdir("%s/%s" % (oDir, sub_dir)):
os.mkdir("%s/%s" % (oDir, sub_dir))
hname = "eta_%g_%g/Histograms/Rsp_genpt_%g_%g" % (eta_min, eta_max, pt_min, pt_max)
# ignore histogram not found errors... naughty naughty
try:
h_rsp = cu.get_from_file(calib_file, hname)
c = generate_canvas()
h_rsp.Draw("HISTE")
func = h_rsp.GetListOfFunctions().At(0)
if func:
func.Draw("SAME")
h_rsp.SetTitle("%s;%s;" % (hname, rsp_str))
filepath = "%s/%s/h_rsp_%g_%g_%g_%g.%s" % (oDir, sub_dir, eta_min, eta_max, pt_min, pt_max, oFormat)
c.SaveAs(filepath)
return filepath
except Exception:
print "! No histogram %s exists" % hname
def plot_rsp_pt_binned_graph(check_file, eta_bins, pt_var, oDir, oFormat='pdf', x_range=None):
"""Plot a graph of response vs pt_var, in bins of eta"""
mg = ROOT.TMultiGraph()
leg = generate_legend(x1=0.65, y1=0.65)
c = generate_canvas(plot_title)
gr_name = "eta_{0:g}_{1:g}/gr_rsp_%s_eta_{0:g}_{1:g}" % (pt_var)
for i, (eta_min, eta_max) in enumerate(zip(eta_bins[:-1], eta_bins[1:])):
g = cu.get_from_file(check_file, gr_name.format(eta_min, eta_max))
g.SetLineColor(binning.eta_bin_colors[i])
g.SetMarkerColor(binning.eta_bin_colors[i])
g.SetMarkerStyle(plot_markers[0])
mg.Add(g)
# leg.AddEntry(g, plot_labels[0] + " %g < |#eta^{L1}| < %g" % (eta_min, eta_var), "LP")
leg.AddEntry(g, " %g < %s < %g" % (eta_min, eta_l1_str, eta_max), "LP")
pt_min, pt_max = (0, 1022) if not x_range else (x_range[0], x_range[1])
# lines at 1, and +/- 0.1
line_central = ROOT.TLine(pt_min, 1, pt_max, 1)
line_plus = ROOT.TLine(pt_min, 1.1, pt_max, 1.1)
line_minus = ROOT.TLine(pt_min, 0.9, pt_max, 0.9)
line_central.SetLineWidth(2)
line_central.SetLineStyle(2)
for line in [line_plus, line_minus]:
line.SetLineWidth(2)
line.SetLineStyle(3)
# bundle all graphs into a TMultiGraph - set axes limits here
mg.Draw("ALP")
mg.GetYaxis().SetRangeUser(rsp_min, rsp_max)
mg.GetXaxis().SetLimits(pt_min, pt_max)
mg.GetXaxis().SetTitleSize(0.04)
mg.GetXaxis().SetTitleOffset(0.9)
# mg.GetYaxis().SetTitleSize(0.04)
mg.Draw("ALP")
xtitle = pt_l1_str if pt_var == "pt" else pt_ref_str
mg.GetHistogram().SetTitle("%s;%s;%s" % (plot_title, xtitle, rsp_str))
leg.Draw()
[line.Draw() for line in [line_central, line_plus, line_minus]]
c.SaveAs("%s/gr_rsp_%s_eta_%g_%g_binned.%s" % (oDir, pt_var, eta_bins[0], eta_bins[-1], oFormat))
def plot_pt_diff(res_file, eta_min, eta_max, pt_min, pt_max, oDir, oFormat="pdf"):
"""Plot the difference between L1 and ref jet pT, for given L1 pT, eta bin"""
hname = "eta_%g_%g/Histograms/ptDiff_ref_%g_%g" % (eta_min, eta_max, pt_min, pt_max)
try:
h_diff = cu.get_from_file(res_file, hname)
except IOError:
print '!! Cannot get', hname
return None
c = generate_canvas()
h_diff.Draw()
h_diff.SetMaximum(h_diff.GetMaximum() * 1.2)
# h_diff.SetLineWidth(2)
func = h_diff.GetListOfFunctions().At(0)
if func:
func.SetLineWidth(2)
h_diff.SetTitle("%g < %s < %g, %g < p_{T}^{%s} < %g;%s;N" % (eta_min, eta_l1_str, eta_max, pt_min, ref_str, pt_max, pt_diff_str))
subdir = "%s/eta_%g_%g" % (oDir, eta_min, eta_max)
cu.check_dir_exists_create("%s/eta_%g_%g" % (oDir, eta_min, eta_max))
filename = "%s/pt_diff_eta_%g_%g_%g_%g.%s" % (subdir, eta_min, eta_max, pt_min, pt_max, oFormat)
c.SaveAs(filename)
return filename
def make_htt_plots(input_filename, output_dir):
"""Make HTT plots for one input file.
Parameters
----------
input_filename : str
Name of pairs ROOT file.
output_dir : str
Name of output directory for plots.
"""
in_stem = os.path.splitext(os.path.basename(input_filename))[0]
output_dir = os.path.join(output_dir, in_stem)
if not os.path.isdir(output_dir):
print 'Making output dir', output_dir
os.makedirs(output_dir)
f = cu.open_root_file(input_filename)
tree = cu.get_from_file(f, "valid")
common_cut = COMMON_CUT
norm_cut = '1./nMatches' # normalisation, for event-level quantities, since we store it for each match in an event
if common_cut != '':
norm_cut += ' && %s' % common_cut
do_htt_plots(tree, output_dir, norm_cut)
do_mht_plots(tree, output_dir, norm_cut)
# Do plots where y axis is some variable of interest
do_dr_plots(tree, output_dir, common_cut)
do_rsp_plots(tree, output_dir, common_cut)
do_nvtx_plots(tree, output_dir, norm_cut)
do_njets_plots(tree, output_dir, norm_cut)
do_jet_pt_plots(tree, output_dir, common_cut)
f.Close()
def make_htt_plots(input_filename, output_dir):
"""Make HTT plots for one input file.
Parameters
----------
input_filename : str
Name of pairs ROOT file.
output_dir : str
Name of output directory for plots.
"""
in_stem = os.path.splitext(os.path.basename(input_filename))[0]
output_dir = os.path.join(output_dir, in_stem)
if not os.path.isdir(output_dir):
print 'Making output dir', output_dir
os.makedirs(output_dir)
f = cu.open_root_file(input_filename)
tree = cu.get_from_file(f, "valid")
common_cut = COMMON_CUT
norm_cut = '1./nMatches' # normalisation, for event-level quantities, since we store it for each match in an event
if common_cut != '':
norm_cut += ' && %s' % common_cut
do_htt_plots(tree, output_dir, norm_cut)
do_mht_plots(tree, output_dir, norm_cut)
# Do plots where y axis is some variable of interest
do_dr_plots(tree, output_dir, common_cut)
do_rsp_plots(tree, output_dir, common_cut)
do_nvtx_plots(tree, output_dir, norm_cut)
do_njets_plots(tree, output_dir, norm_cut)
do_jet_pt_plots(tree, output_dir, common_cut)
f.Close()
def plot_rsp_pt_hists(check_file, eta_min, eta_max, pt_bins, pt_var, oDir, oFormat='pdf'):
"""Plot component hists of response vs pt graph, for given eta bin"""
sub_dir = "eta_%g_%g" % (eta_min, eta_max)
if not os.path.isdir("%s/%s" % (oDir, sub_dir)):
os.mkdir("%s/%s" % (oDir, sub_dir))
c = generate_canvas(plot_title)
filenames = []
for i, pt_min in enumerate(pt_bins[:-1]):
pt_max = pt_bins[i + 1]
hname = "%s/Histograms/rsp_%s_%g_%g" % (sub_dir, pt_var, pt_min, pt_max)
try:
hist = cu.get_from_file(check_file, hname)
hist.SetTitle("%s;%s;N" % (plot_title, rsp_str))
hist.Draw()
filename = "%s/%s/rsp_%s_%g_%g.%s" % (oDir, sub_dir, pt_var, pt_min, pt_max, oFormat)
c.SaveAs(filename)
filenames.append(filename)
except Exception:
print '! No histogram %s exists' % hname
return filenames
def redo_correction_fit(inputfile, outputfile, absetamin, absetamax, fitfcn):
"""Redo correction fit for a given eta bin.
Get TGraphErrors for the bin, and perform calibration curve fitting
procedure and save if successful.
inputfile: TFile. The output file from running runCalibration.py previously.
outputfile: TFile. The file you want to write the new graph & fit to.
Can be the same as inputfile.
absetamin: double
absetamax: double
fitfcn: TF1
"""
# Get relevant graph
gr = cu.get_from_file(inputfile, generate_eta_graph_name(absetamin, absetamax))
outputfile.WriteTObject(gr, gr.GetName(), 'Overwrite') # the original graph
# Setup fitting (calculate sensible range, make sub-graph), then do fit!
sub_graph, this_fit = setup_fit(gr, fitfcn, absetamin, absetamax, outputfile)
fit_graph, fit_params = fit_correction(sub_graph, this_fit)
outputfile.WriteTObject(this_fit, this_fit.GetName(), 'Overwrite') # function by itself
outputfile.WriteTObject(fit_graph, fit_graph.GetName(), 'Overwrite') # has the function stored in it as well
return fit_params
def plot_l1_Vs_ref(check_file, eta_min, eta_max, logZ, oDir, oFormat="pdf"):
"""Plot l1 pt against ref jet pt, for given L1 eta bin"""
hname = "eta_%g_%g/Histograms/h2d_gen_l1" % (eta_min, eta_max)
h2d_gen_l1 = cu.get_from_file(check_file, hname)
c = generate_canvas()
app = ""
if logZ:
c.SetLogz()
app = "_log"
h2d_gen_l1.SetTitle("%s: %g-%g;%s;%s" % (eta_l1_str, eta_min, eta_max, pt_ref_str, pt_l1_str))
h2d_gen_l1.Draw("COLZ")
max_pt = 512
h2d_gen_l1.SetAxisRange(0, max_pt, 'X')
h2d_gen_l1.SetAxisRange(0, max_pt, 'Y')
# lines of constant response
line1 = ROOT.TLine(0, 0, max_pt, max_pt)
line1.SetLineStyle(1)
line1.SetLineWidth(2)
line1.Draw()
line1p25 = ROOT.TLine(0, 0, max_pt / 1.25, max_pt)
line1p25.SetLineWidth(2)
# line1p25.Draw()
line1p5 = ROOT.TLine(0, 0, max_pt / 1.5, max_pt)
line1p5.SetLineWidth(2)
# line1p5.Draw()
line0p75 = ROOT.TLine(0, 0, max_pt, max_pt * 0.75)
line0p75.SetLineWidth(2)
# line0p75.Draw()
line0p5 = ROOT.TLine(0, 0, max_pt, max_pt * 0.5)
line0p5.SetLineWidth(2)
# line0p5.Draw()
c.SaveAs("%s/h2d_gen_l1_%g_%g%s.%s" % (oDir, eta_min, eta_max, app, oFormat))
def draw_horizontal_rsp(max_pt):
# lines of constant response
line1 = ROOT.TLine(0, 1, max_pt, 1)
line1.SetLineStyle(1)
line1.SetLineWidth(2)
line1.SetLineColor(ROOT.kMagenta)
line1.Draw()
ROOT.SetOwnership(line1, False)
if __name__ == "__main__":
# L1 Ntuple file
ntuple_filename = '/hdfs/user/ra12451/L1JEC/CMSSW_7_6_0_pre7/L1JetEnergyCorrections/Stage2_Run260627/Express/run260627_expressNoJEC.root'
f_ntuple = cu.open_root_file(ntuple_filename)
reco_tree = cu.get_from_file(f_ntuple, 'l1JetRecoTree/JetRecoTree')
# Matched pairs file
pairs_filename = "/hdfs/user/ra12451/L1JEC/CMSSW_7_6_0_pre7/L1JetEnergyCorrections/Stage2_Run260627/pairs/pairs_run260627_expressNoJEC_data_ref10to5000_l10to5000_dr0p4_noCleaning_fixedEF_CSC_HLTvars.root"
f_pairs = cu.open_root_file(pairs_filename)
pairs_tree = cu.get_from_file(f_pairs, 'valid')
plot_dir = '/users/ra12451/L1JEC/CMSSW_7_6_0_pre7/src/L1Trigger/L1JetEnergyCorrections/Run260627/pfCleaning/'
if not os.path.isdir(plot_dir):
os.makedirs(plot_dir)
eta_cut = 'TMath::Abs(eta) < 0.348'
etaRef_cut = 'TMath::Abs(etaRef) < 0.348'
LS_cut = ("((LS > 91 && LS < 611) || (LS > 613 && LS < 757) || (LS > 760 && LS < 788) || (LS > 791 && LS < 1051) || (LS > 1054 && LS < 1530) || (LS > 1533 && LS < 1845))")
def make_correction_curves(inputfile, outputfile, ptBins_in, absetamin,
absetamax, fitfcn, do_genjet_plots,
do_correction_fit, pu_min, pu_max, do_burr):
"""
Do all the relevant hists and fitting, for one eta bin.
Briefly: make plots of L1 jet pT, and response (= l1/gen) for each genjet pt bin.
Then find the mean L1 jet pT for the pt bin.
Then fit a Gaussian to each response histogram, and get the mean.
Plot 1/fitted mean Vs mean L1 pT.
Then fit with given function, and return parameters.
All of these plots are stored in the TFile, outputfile.
Returns parameters of succeful fit.
inputfile: TFile. Must contain TTree named "valid", full of pair quantities.
outputfile: TFile. To store output histograms.
ptBins_in: list. Edges of pt bins used to divide up correction curve.
absetamin: float. Lower edge of eta bin, must be >= 0.
absetamax: float. Upper edge of eta bin, must be > 0.
fitfcn: TF1. Function to fit for correction curve.
do_genjet_plots: bool. Whether to make plots for reference jets. Not used
in calcualtion of correction curve, but handy for debugging.
do_correction_fit: bool. Whether to actually fit the correction curve.
pu_min: float. Cut on minimum number of PU vertices.
pu_max: float. Cut on maximum number of PU vertices.
do_burr: bool. If True, use Burr fn to fit response histograms.
The default is to use a Gaussian.
"""
print "Doing PU range: %g - %g" % (pu_min, pu_max)
print "Running over pT bins:", ptBins_in
# Input tree
tree_raw = cu.get_from_file(inputfile, "valid")
# Output folders
output_f = outputfile.mkdir('eta_%g_%g' % (absetamin, absetamax))
output_f_hists = output_f.mkdir("Histograms")
# Eta cut string
eta_cut = ROOT.TCut("TMath::Abs(eta)<%g && TMath::Abs(eta) > %g" %
(absetamax, absetamin))
# PU cut string
if hasattr(tree_raw, "numPUVertices"):
pu_cut = ROOT.TCut("numPUVertices >= %g && numPUVertices <= %g" %
(pu_min, pu_max))
else:
pu_cut = ROOT.TCut("")
# Avoid L1 saturated jets cut (from 2017 any l1 jet with a saturated tower is auto given pt=1024GeV)
avoidSaturation_cut = ROOT.TCut("pt < 1023.1")
# Total cut
total_cut = ROOT.TCut(eta_cut)
total_cut += pu_cut # need to use += and not && cos TCut all fubar
total_cut += avoidSaturation_cut
print total_cut
# Draw response (pT^L1/pT^Gen) for all pt bins
tree_raw.Draw("rsp>>hrsp_eta_%g_%g(50,0,2)" % (absetamin, absetamax),
total_cut, "goff")
hrsp_eta = ROOT.gROOT.FindObject("hrsp_eta_%g_%g" % (absetamin, absetamax))
hrsp_eta.SetTitle(";response (p_{T}^{L1}/p_{T}^{Ref});")
output_f_hists.WriteTObject(hrsp_eta)
nb, pt_min, pt_max = 2048, 0, 1024
# Draw rsp (pT^L1/pT^Gen) Vs GenJet pT
tree_raw.Draw(
"rsp:ptRef>>h2d_rsp_gen(%d,%g,%g,150,0,5)" % (nb, pt_min, pt_max),
total_cut, "goff")
h2d_rsp_gen = ROOT.gROOT.FindObject("h2d_rsp_gen")
h2d_rsp_gen.SetTitle(
";p_{T}^{Ref} [GeV];response (p_{T}^{L1}/p_{T}^{Ref})")
output_f_hists.WriteTObject(h2d_rsp_gen)
# Draw rsp (pT^L1/pT^Gen) Vs L1 pT
tree_raw.Draw(
"rsp:pt>>h2d_rsp_l1(%d,%g,%g,150,0,5)" % (nb, pt_min, pt_max),
total_cut, "goff")
h2d_rsp_l1 = ROOT.gROOT.FindObject("h2d_rsp_l1")
h2d_rsp_l1.SetTitle(";p_{T}^{L1} [GeV];response (p_{T}^{L1}/p_{T}^{Ref})")
output_f_hists.WriteTObject(h2d_rsp_l1)
# draw pT^L1 Vs pT^Gen
tree_raw.Draw(
"pt:ptRef>>h2d_gen_l1(%d,%g,%g,%d,%g,%g)" %
(nb, pt_min, pt_max, nb, pt_min, pt_max), total_cut, "goff")
h2d_gen_l1 = ROOT.gROOT.FindObject("h2d_gen_l1")
h2d_gen_l1.SetTitle(";p_{T}^{Ref} [GeV];p_{T}^{L1} [GeV]")
output_f_hists.WriteTObject(h2d_gen_l1)
# Go through and find histogram bin edges that are closest to the input pt
# bin edges, and store for future use
ptBins = []
bin_indices = []
for i, ptR in enumerate(ptBins_in[0:-1]):
bin1 = h2d_rsp_gen.GetXaxis().FindBin(ptR)
bin2 = h2d_rsp_gen.GetXaxis().FindBin(ptBins_in[i + 1]) - 1
xlow = h2d_rsp_gen.GetXaxis().GetBinLowEdge(bin1)
xup = h2d_rsp_gen.GetXaxis().GetBinLowEdge(bin2 + 1)
bin_indices.append([bin1, bin2])
ptBins.append(xlow)
ptBins.append(xup) # only need this last one
gr = ROOT.TGraphErrors() # 1/<rsp> VS ptL1
gr_gen = ROOT.TGraphErrors() # 1/<rsp> VS ptGen
grc = 0
# Iterate over pT^Gen bins, and for each:
# - Project 2D hist so we have a plot of response for given pT^Gen range
# - Fit a Gaussian (if possible) to this resp histogram to get <response>
# - Plot the L1 pT for given pT^Gen range (remember, for matched pairs)
# - Get average response, <pT^L1>, from 1D L1 pT hist
# - Add a new graph point, x=<pT^L1> y=<response> for this pT^Gen bin
for i, ptR in enumerate(ptBins[0:-1]):
bin1 = bin_indices[i][0]
bin2 = bin_indices[i][1]
# h2d_calib.GetXaxis().FindBin(ptR)
# h2d_calib.GetXaxis().FindBin(ptBins[i+1])-1
# print "Binning mis-matches", ptR, ptBins[i+1],
# h2d_calib.GetXaxis().GetBinLowEdge(bin1),h2d_calib.GetXaxis().GetBinLowEdge(bin2+1)
xlow = ptR
xhigh = ptBins[i + 1]
# Plot of response for given pT Gen bin
hrsp = h2d_rsp_gen.ProjectionY("Rsp_genpt_%g_%g" % (xlow, xhigh), bin1,
bin2)
# cut on ref jet pt
pt_cut = ROOT.TCut("ptRef < %g && ptRef > %g " % (xhigh, xlow))
total_cut = ROOT.TCut(eta_cut)
total_cut += pu_cut
total_cut += avoidSaturation_cut
total_cut += pt_cut
print total_cut
# Plots of pT L1 for given pT Gen bin
tree_raw.Draw("pt>>hpt(4000, 0, 2000)", total_cut, "goff")
hpt = ROOT.gROOT.FindObject("hpt")
hpt.SetName("L1_pt_genpt_%g_%g" % (xlow, xhigh))
# hpt = h2d_gen_l1.ProjectionX("L1_pt_genpt_%g_%g" % (xlow, xhigh))
if hrsp.GetEntries() <= 0 or hpt.GetEntries() <= 0:
print "Skipping as 0 entries"
continue
output_f_hists.WriteTObject(hpt)
# Plots of pT Gen for given pT Gen bin
if do_genjet_plots:
tree_raw.Draw("ptRef>>hpt_gen(200)", total_cut, "goff")
hpt_gen = ROOT.gROOT.FindObject("hpt_gen")
hpt_gen.SetName("gen_pt_genpt_%g_%g" % (xlow, xhigh))
output_f_hists.WriteTObject(hpt_gen)
# Fit to resposne hist to get mean response & error on mean
if do_burr:
if (absetamin > 2 and i == 0):
setup_burr3_higherEta()
setup_fn = setup_burr3 if absetamin < 2 else setup_burr3_higherEta
mean, err = do_burr_response_hist_fit(hrsp, setup_fn)
else:
mean, err = do_gauss_response_hist_fit(hrsp)
output_f_hists.WriteTObject(hrsp)
print "pT Gen: ", ptR, "-", ptBins[i + 1], "<pT L1>:", hpt.GetMean(), \
"<pT Gen>:", (hpt_gen.GetMean() if do_genjet_plots else "NA"), "<rsp>:", mean
# Since we're plotting 1/rsp on y axis, so need jacobian
err = err / (mean**2)
# add point to response graph vs pt
# store if new max/min, but only max if pt > pt of previous point
# max_pt = max(hpt.GetMean(), max_pt) if grc > 0 and hpt.GetMean() > gr.GetX()[grc-1] else max_pt
# min_pt = min(hpt.GetMean(), min_pt)
gr.SetPoint(grc, hpt.GetMean(), 1. / mean)
gr.SetPointError(grc, hpt.GetMeanError(), err)
if do_genjet_plots:
gr_gen.SetPoint(grc, hpt_gen.GetMean(), 1. / mean)
# gr_gen.SetPointError(grc, hpt.GetMeanError(), err)
grc += 1
# Label response VS pT graphs
graph_name = generate_eta_graph_name(absetamin, absetamax)
gr.SetName(graph_name)
gr.GetXaxis().SetTitle("<p_{T}^{L1}> [GeV]")
gr.GetYaxis().SetTitle("1/<p_{T}^{L1}/p_{T}^{Ref}>")
if do_genjet_plots:
gr_gen.SetName('gencorr_eta_%g_%g' % (absetamin, absetamax))
gr_gen.GetXaxis().SetTitle("<p_{T}^{Ref}> [GeV]")
gr_gen.GetYaxis().SetTitle("1/<p_{T}^{L1}/p_{T}^{Ref}>")
# Save these graphs to file
outputfile.WriteTObject(gr)
if do_genjet_plots:
outputfile.WriteTObject(gr_gen)
# Fit correction function to response vs pT graph, add params list
fit_params = []
if do_correction_fit:
sub_graph, this_fit = setup_fit(gr, fitfcn, absetamin, absetamax,
outputfile)
fit_graph, fit_params = fit_correction(sub_graph, this_fit)
outputfile.WriteTObject(this_fit) # function by itself
outputfile.WriteTObject(
fit_graph) # has the function stored in it as well
return fit_params
def main(in_args=sys.argv[1:]):
print in_args
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--pairs",
help="input ROOT file with matched pairs from RunMatcher")
parser.add_argument("--res",
help="input ROOT file with resolution plots from makeResolutionPlots.py")
parser.add_argument("--checkcal",
help="input ROOT file with calibration check plots from checkCalibration.py")
parser.add_argument("--calib",
help="input ROOT file from output of runCalibration.py")
parser.add_argument("--oDir",
help="Directory to save plots. Default is in same location as ROOT file.")
parser.add_argument("--detail",
help="Plot all the individual component hists for each eta bin. There are a lot!",
action='store_true')
parser.add_argument("--format",
help="Format for plots (PDF, png, etc). Note that 2D correlation plots will "
"always be PNGs to avoid large files.",
default="pdf")
parser.add_argument("--title",
help="Title for plots.")
parser.add_argument('--zip',
help="Zip filename for zipping up all plots. Don't include extension")
# FIXME
# parser.add_argument("--etaInd",
# help="list of eta bin index/indices to run over")
parser.add_argument("--gifs",
help="Make GIFs (only applicable if --detail is also used)",
action='store_true')
parser.add_argument("--gifexe",
help='Convert executable to use. Default is result of `which convert`')
args = parser.parse_args(args=in_args)
print args
if args.detail:
print "Warning: producing all component hists. This could take a while..."
if args.gifs:
if args.detail:
print "Making animated graphs from fit plots."
else:
print "To use the --gifs flag, you also need --detail"
if not args.gifexe:
args.gifexe = find_executable('convert')
if not args.gifexe:
print 'Cannot find convert exe, not making gifs'
args.gif = False
else:
print 'Using %s to make GIFs' % args.gifexe
# customise titles
# note the use of global keyword
if args.title:
global plot_title
plot_title = args.title
if args.oDir == os.getcwd():
print "Warning: plots will be made in $PWD!"
# auto determine output directory
if not args.oDir:
filename, stem = '', ''
if args.pairs:
filename, stem = args.pairs, 'pairs_'
elif args.checkcal:
filename, stem = args.checkcal, 'check_'
elif args.res:
filename, stem = args.res, 'res_'
elif args.calib:
filename, stem = args.calib, 'output_'
new_dir = os.path.basename(filename).replace(".root", '').replace(stem, 'showoff_')
args.oDir = os.path.join(os.path.dirname(os.path.abspath(filename)), new_dir)
cu.check_dir_exists_create(args.oDir)
print "Output directory:", args.oDir
# Choose eta
ptBins = binning.pt_bins_stage2
# Do plots with output from RunMatcher
# ------------------------------------------------------------------------
if args.pairs:
pairs_file = cu.open_root_file(args.pairs)
pairs_tree = cu.get_from_file(pairs_file, "valid")
# eta binned
for emin, emax in pairwise(binning.eta_bins):
plot_dR(pairs_tree, eta_min=emin, eta_max=emax, cut="1", oDir=args.oDir)
plot_pt_both(pairs_tree, eta_min=emin, eta_max=emax, cut="1", oDir=args.oDir)
# plot_dR(pairs_tree, eta_min=0, eta_max=5, cut="1", oDir=args.oDir) # all eta
# plot_pt_both(pairs_tree, eta_min=0, eta_max=5, cut="1", oDir=args.oDir) # all eta
plot_eta_both(pairs_tree, oDir=args.oDir) # all eta
plot_dR(pairs_tree, eta_min=0, eta_max=3, cut="1", oDir=args.oDir) # central
plot_pt_both(pairs_tree, eta_min=0, eta_max=3, cut="1", oDir=args.oDir) # central
plot_dR(pairs_tree, eta_min=3, eta_max=5, cut="1", oDir=args.oDir) # forward
plot_pt_both(pairs_tree, eta_min=3, eta_max=5, cut="1", oDir=args.oDir) # forward
pairs_file.Close()
# Do plots with output from makeResolutionPlots.py
# ------------------------------------------------------------------------
if args.res:
res_file = cu.open_root_file(args.res)
# exclusive eta graphs
for eta_min, eta_max in pairwise(binning.eta_bins):
print eta_min, eta_max
plot_res_all_pt(res_file, eta_min, eta_max, args.oDir, args.format)
if args.detail:
list_dir = os.path.join(args.oDir, 'eta_%g_%g' % (eta_min, eta_max))
cu.check_dir_exists_create(list_dir)
pt_diff_filenames = []
ptBins = binning.pt_bins_stage2_8 if eta_min < 2.9 else binning.pt_bins_stage2_8_wide
for pt_min, pt_max in pairwise(ptBins):
pt_diff_fname = plot_pt_diff(res_file, eta_min, eta_max, pt_min, pt_max, args.oDir, 'png')
pt_diff_filenames.append(pt_diff_fname)
pt_diff_filenames_file = os.path.join(list_dir, 'list_pt_diff.txt')
write_filelist(pt_diff_filenames, pt_diff_filenames_file)
if args.gifs:
make_gif(pt_diff_filenames_file, pt_diff_filenames_file.replace('.txt', '.gif'), args.gifexe)
# inclusive eta graphs
for (eta_min, eta_max) in [[0, 3], [3, 5]]:
print eta_min, eta_max
plot_res_all_pt(res_file, eta_min, eta_max, args.oDir, args.format)
plot_ptDiff_Vs_pt(res_file, eta_min, eta_max, args.oDir, args.format)
if args.detail:
list_dir = os.path.join(args.oDir, 'eta_%g_%g' % (eta_min, eta_max))
cu.check_dir_exists_create(list_dir)
pt_diff_filenames = []
ptBins = binning.pt_bins_stage2_8 if eta_min < 2.9 else binning.pt_bins_stage2_8_wide
for pt_min, pt_max in pairwise(ptBins):
pt_diff_fname = plot_pt_diff(res_file, eta_min, eta_max, pt_min, pt_max, args.oDir, 'png')
pt_diff_filenames.append(pt_diff_fname)
pt_diff_filenames_file = os.path.join(list_dir, 'list_pt_diff.txt')
write_filelist(pt_diff_filenames, pt_diff_filenames_file)
if args.gifs:
make_gif(pt_diff_filenames_file, pt_diff_filenames_file.replace('.txt', '.gif'), args.gifexe)
# plot_eta_pt_rsp_2d(res_file, binning.eta_bins, binning.pt_bins[4:], args.oDir, args.format)
# components of these:
# if args.detail:
# ptBins = binning.pt_bins_stage2_8 if not forward_bin else binning.pt_bins_stage2_8_wide
# for pt_min, pt_max in pairwise(ptBins):
# plot_pt_diff(res_file, 0, 3, pt_min, pt_max, args.oDir, args.format)
# plot_pt_diff(res_file, 0, 5, pt_min, pt_max, args.oDir, args.format)
# plot_pt_diff(res_file, 3, 5, pt_min, pt_max, args.oDir, args.format)
res_file.Close()
# Do plots with output from checkCalibration.py
# ------------------------------------------------------------------------
if args.checkcal:
etaBins = binning.eta_bins
check_file = cu.open_root_file(args.checkcal)
# ptBinsWide = list(np.arange(10, 250, 8))
# indiviudal eta bins
for eta_min, eta_max in pairwise(etaBins):
for (normX, logZ) in product([True, False], [True, False]):
plot_l1_Vs_ref(check_file, eta_min, eta_max, logZ, args.oDir, 'png')
plot_rsp_Vs_l1(check_file, eta_min, eta_max, normX, logZ, args.oDir, 'png')
plot_rsp_Vs_ref(check_file, eta_min, eta_max, normX, logZ, args.oDir, 'png')
plot_rsp_Vs_pt_candle_violin(check_file, eta_min, eta_max, "l1", args.oDir, 'png')
plot_rsp_Vs_pt_candle_violin(check_file, eta_min, eta_max, "gen", args.oDir, 'png')
if args.detail:
list_dir = os.path.join(args.oDir, 'eta_%g_%g' % (eta_min, eta_max))
cu.check_dir_exists_create(list_dir)
# print individual histograms, and make a list suitable for imagemagick to turn into a GIF
pt_plot_filenames = plot_rsp_pt_hists(check_file, eta_min, eta_max, ptBins, "pt", args.oDir, 'png')
pt_plot_filenames_file = os.path.join(list_dir, 'list_pt.txt')
write_filelist(pt_plot_filenames, pt_plot_filenames_file)
# print individual histograms, and make a list suitable for imagemagick to turn into a GIF
ptRef_plot_filenames = plot_rsp_pt_hists(check_file, eta_min, eta_max, ptBins, "ptRef", args.oDir, 'png')
ptRef_plot_filenames_file = os.path.join(list_dir, 'list_ptRef.txt')
write_filelist(ptRef_plot_filenames, ptRef_plot_filenames_file)
# make dem GIFs
if args.gifs:
for inf in [pt_plot_filenames_file, ptRef_plot_filenames_file]:
make_gif(inf, inf.replace('.txt', '.gif'), args.gifexe)
# Graph of response vs pt, but in bins of eta
x_range = [0, 150] # for zoomed-in low pt
x_range = None
plot_rsp_pt_binned_graph(check_file, etaBins, "pt", args.oDir, args.format, x_range=x_range)
plot_rsp_pt_binned_graph(check_file, etaBins, "ptRef", args.oDir, args.format, x_range=x_range)
all_rsp_pt_plot_filenames = []
all_rsp_ptRef_plot_filenames = []
# Loop over central/forward eta, do 2D plots, and graphs, and component hists
for (eta_min, eta_max) in [[0, 3], [3, 5]]:
print eta_min, eta_max
for (normX, logZ) in product([True, False], [True, False]):
plot_l1_Vs_ref(check_file, eta_min, eta_max, logZ, args.oDir, 'png')
plot_rsp_Vs_l1(check_file, eta_min, eta_max, normX, logZ, args.oDir, 'png')
plot_rsp_Vs_ref(check_file, eta_min, eta_max, normX, logZ, args.oDir, 'png')
if args.detail:
plot_rsp_pt_hists(check_file, eta_min, eta_max, ptBins, "pt", args.oDir, 'png')
plot_rsp_pt_hists(check_file, eta_min, eta_max, ptBins, "ptRef", args.oDir, 'png')
# graphs
plot_rsp_eta_exclusive_graph(check_file, eta_min, eta_max, binning.check_pt_bins, 'pt', args.oDir, args.format)
plot_rsp_eta_exclusive_graph(check_file, eta_min, eta_max, binning.check_pt_bins, 'ptRef', args.oDir, args.format)
plot_rsp_pt_graph(check_file, eta_min, eta_max, args.oDir, args.format, x_range)
plot_rsp_ptRef_graph(check_file, eta_min, eta_max, args.oDir, args.format, x_range)
for etamin, etamax in pairwise(etaBins):
if etamin < eta_min or etamax > eta_max:
continue
print etamin, etamax
this_rsp_pt_plot_filenames = []
this_rsp_ptRef_plot_filenames = []
# component hists/fits for the eta graphs, binned by pt
for pt_min, pt_max in binning.check_pt_bins:
pt_filename = plot_rsp_eta_bin_pt(check_file, etamin, etamax, 'pt', pt_min, pt_max, args.oDir, 'png')
this_rsp_pt_plot_filenames.append(pt_filename)
ptRef_filename = plot_rsp_eta_bin_pt(check_file, etamin, etamax, 'ptRef', pt_min, pt_max, args.oDir, 'png')
this_rsp_ptRef_plot_filenames.append(ptRef_filename)
pt_list_file = os.path.join(args.oDir, 'list_pt_eta_%g_%g.txt' % (etamin, etamax))
write_filelist(this_rsp_pt_plot_filenames, pt_list_file)
if args.gifs:
make_gif(pt_list_file, pt_list_file.replace('.txt', '.gif'), args.gifexe)
ptRef_list_file = os.path.join(args.oDir, 'list_ptRef_eta_%g_%g.txt' % (etamin, etamax))
write_filelist(this_rsp_ptRef_plot_filenames, ptRef_list_file)
if args.gifs:
make_gif(ptRef_list_file, ptRef_list_file.replace('.txt', '.gif'), args.gifexe)
all_rsp_pt_plot_filenames.extend(this_rsp_pt_plot_filenames)
all_rsp_ptRef_plot_filenames.extend(this_rsp_ptRef_plot_filenames)
pt_list_file = os.path.join(args.oDir, 'list_pt_eta_%g_%g.txt' % (etaBins[0], etaBins[-1]))
write_filelist(all_rsp_pt_plot_filenames, pt_list_file)
if args.gifs:
make_gif(pt_list_file, pt_list_file.replace('.txt', '.gif'), args.gifexe)
ptRef_list_file = os.path.join(args.oDir, 'list_ptRef_eta_%g_%g.txt' % (etaBins[0], etaBins[-1]))
write_filelist(all_rsp_ptRef_plot_filenames, ptRef_list_file)
if args.gifs:
make_gif(ptRef_list_file, ptRef_list_file.replace('.txt', '.gif'), args.gifexe)
check_file.Close()
# Do plots with output from runCalibration.py
# ------------------------------------------------------------------------
if args.calib:
calib_file = cu.open_root_file(args.calib)
for eta_min, eta_max in pairwise(binning.eta_bins[:-1]):
print eta_min, eta_max
# 2D correlation heat maps
for (normX, logZ) in product([True, False], [True, False]):
plot_rsp_Vs_ref(calib_file, eta_min, eta_max, normX, logZ, args.oDir, 'png')
plot_rsp_Vs_l1(calib_file, eta_min, eta_max, normX, logZ, args.oDir, 'png')
# individual fit histograms for each pt bin
if args.detail:
list_dir = os.path.join(args.oDir, 'eta_%g_%g' % (eta_min, eta_max))
cu.check_dir_exists_create(list_dir)
if eta_min > 2.9:
ptBins = binning.pt_bins_stage2_hf
rsp_plot_filenames = []
pt_plot_filenames = []
for pt_min, pt_max in pairwise(ptBins):
rsp_name = plot_rsp_eta_pt_bin(calib_file, eta_min, eta_max, pt_min, pt_max, args.oDir, 'png')
rsp_plot_filenames.append(rsp_name)
pt_name = plot_pt_bin(calib_file, eta_min, eta_max, pt_min, pt_max, args.oDir, 'png')
pt_plot_filenames.append(pt_name)
# print individual histograms, and make a list suitable for imagemagick to turn into a GIF
rsp_plot_filenames_file = os.path.join(list_dir, 'list_rsp.txt')
write_filelist(rsp_plot_filenames, rsp_plot_filenames_file)
# print individual histograms, and make a list suitable for imagemagick to turn into a GIF
pt_plot_filenames_file = os.path.join(list_dir, 'list_pt.txt')
write_filelist(pt_plot_filenames, pt_plot_filenames_file)
# make dem gifs
if args.gifs:
for inf in [pt_plot_filenames_file, rsp_plot_filenames_file]:
make_gif(inf, inf.replace('.txt', '.gif'), args.gifexe)
else:
print "To make animated gif from PNGs using a plot list:"
print "convert -dispose Background -delay 50 -loop 0 @%s "\
"pt_eta_%g_%g.gif" % (pt_plot_filenames_file, eta_min, eta_max)
# the correction curve graph
plot_correction_graph(calib_file, eta_min, eta_max, args.oDir, args.format)
calib_file.Close()
if args.zip:
print 'Zipping up files'
zip_filename = os.path.basename(args.zip.split('.')[0])
make_archive(zip_filename, 'gztar', args.oDir)
def draw_horizontal_rsp(max_pt):
# lines of constant response
line1 = ROOT.TLine(0, 1, max_pt, 1)
line1.SetLineStyle(1)
line1.SetLineWidth(2)
line1.SetLineColor(ROOT.kMagenta)
line1.Draw()
ROOT.SetOwnership(line1, False)
if __name__ == "__main__":
# L1 Ntuple file
ntuple_filename = '/hdfs/user/ra12451/L1JEC/CMSSW_7_6_0_pre7/L1JetEnergyCorrections/Stage2_Run260627/Express/run260627_expressNoJEC.root'
f_ntuple = cu.open_root_file(ntuple_filename)
reco_tree = cu.get_from_file(f_ntuple, 'l1JetRecoTree/JetRecoTree')
# Matched pairs file
pairs_filename = "/hdfs/user/ra12451/L1JEC/CMSSW_7_6_0_pre7/L1JetEnergyCorrections/Stage2_Run260627/pairs/pairs_run260627_expressNoJEC_data_ref10to5000_l10to5000_dr0p4_noCleaning_fixedEF_CSC_HLTvars.root"
f_pairs = cu.open_root_file(pairs_filename)
pairs_tree = cu.get_from_file(f_pairs, 'valid')
plot_dir = '/users/ra12451/L1JEC/CMSSW_7_6_0_pre7/src/L1Trigger/L1JetEnergyCorrections/Run260627/pfCleaning/'
if not os.path.isdir(plot_dir):
os.makedirs(plot_dir)
eta_cut = 'TMath::Abs(eta) < 0.348'
etaRef_cut = 'TMath::Abs(etaRef) < 0.348'
LS_cut = (
"((LS > 91 && LS < 611) || (LS > 613 && LS < 757) || (LS > 760 && LS < 788) || (LS > 791 && LS < 1051) || (LS > 1054 && LS < 1530) || (LS > 1533 && LS < 1845))"
def make_correction_curves(inputfile, outputfile, ptBins_in, absetamin, absetamax,
fitfcn, do_genjet_plots, do_correction_fit,
pu_min, pu_max, do_burr):
"""
Do all the relevant hists and fitting, for one eta bin.
Briefly: make plots of L1 jet pT, and response (= l1/gen) for each genjet pt bin.
Then find the mean L1 jet pT for the pt bin.
Then fit a Gaussian to each response histogram, and get the mean.
Plot 1/fitted mean Vs mean L1 pT.
Then fit with given function, and return parameters.
All of these plots are stored in the TFile, outputfile.
Returns parameters of succeful fit.
inputfile: TFile. Must contain TTree named "valid", full of pair quantities.
outputfile: TFile. To store output histograms.
ptBins_in: list. Edges of pt bins used to divide up correction curve.
absetamin: float. Lower edge of eta bin, must be >= 0.
absetamax: float. Upper edge of eta bin, must be > 0.
fitfcn: TF1. Function to fit for correction curve.
do_genjet_plots: bool. Whether to make plots for reference jets. Not used
in calcualtion of correction curve, but handy for debugging.
do_correction_fit: bool. Whether to actually fit the correction curve.
pu_min: float. Cut on minimum number of PU vertices.
pu_max: float. Cut on maximum number of PU vertices.
do_burr: bool. If True, use Burr fn to fit response histograms.
The default is to use a Gaussian.
"""
print "Doing PU range: %g - %g" % (pu_min, pu_max)
print "Running over pT bins:", ptBins_in
# Input tree
tree_raw = cu.get_from_file(inputfile, "valid")
# Output folders
output_f = outputfile.mkdir('eta_%g_%g' % (absetamin, absetamax))
output_f_hists = output_f.mkdir("Histograms")
# Eta cut string
eta_cut = ROOT.TCut("TMath::Abs(eta)<%g && TMath::Abs(eta) > %g" % (absetamax, absetamin))
# PU cut string
if hasattr(tree_raw, "numPUVertices"):
pu_cut = ROOT.TCut("numPUVertices >= %g && numPUVertices <= %g" % (pu_min, pu_max))
else:
pu_cut = ROOT.TCut("")
# Total cut
total_cut = ROOT.TCut(eta_cut)
total_cut += pu_cut # need to use += and not && cos TCut all fubar
print total_cut
# Draw response (pT^L1/pT^Gen) for all pt bins
tree_raw.Draw("rsp>>hrsp_eta_%g_%g(50,0,2)" % (absetamin, absetamax), total_cut, "goff")
hrsp_eta = ROOT.gROOT.FindObject("hrsp_eta_%g_%g" % (absetamin, absetamax))
hrsp_eta.SetTitle(";response (p_{T}^{L1}/p_{T}^{Ref});")
output_f_hists.WriteTObject(hrsp_eta)
nb, pt_min, pt_max = 2048, 0, 1024
# Draw rsp (pT^L1/pT^Gen) Vs GenJet pT
tree_raw.Draw("rsp:ptRef>>h2d_rsp_gen(%d,%g,%g,150,0,5)" % (nb, pt_min, pt_max), total_cut, "goff")
h2d_rsp_gen = ROOT.gROOT.FindObject("h2d_rsp_gen")
h2d_rsp_gen.SetTitle(";p_{T}^{Ref} [GeV];response (p_{T}^{L1}/p_{T}^{Ref})")
output_f_hists.WriteTObject(h2d_rsp_gen)
# Draw rsp (pT^L1/pT^Gen) Vs L1 pT
tree_raw.Draw("rsp:pt>>h2d_rsp_l1(%d,%g,%g,150,0,5)" % (nb, pt_min, pt_max), total_cut, "goff")
h2d_rsp_l1 = ROOT.gROOT.FindObject("h2d_rsp_l1")
h2d_rsp_l1.SetTitle(";p_{T}^{L1} [GeV];response (p_{T}^{L1}/p_{T}^{Ref})")
output_f_hists.WriteTObject(h2d_rsp_l1)
# draw pT^L1 Vs pT^Gen
tree_raw.Draw("pt:ptRef>>h2d_gen_l1(%d,%g,%g,%d,%g,%g)" % (nb, pt_min, pt_max, nb, pt_min, pt_max), total_cut, "goff")
h2d_gen_l1 = ROOT.gROOT.FindObject("h2d_gen_l1")
h2d_gen_l1.SetTitle(";p_{T}^{Ref} [GeV];p_{T}^{L1} [GeV]")
output_f_hists.WriteTObject(h2d_gen_l1)
# Go through and find histogram bin edges that are closest to the input pt
# bin edges, and store for future use
ptBins = []
bin_indices = []
for i, ptR in enumerate(ptBins_in[0:-1]):
bin1 = h2d_rsp_gen.GetXaxis().FindBin(ptR)
bin2 = h2d_rsp_gen.GetXaxis().FindBin(ptBins_in[i + 1]) - 1
xlow = h2d_rsp_gen.GetXaxis().GetBinLowEdge(bin1)
xup = h2d_rsp_gen.GetXaxis().GetBinLowEdge(bin2 + 1)
bin_indices.append([bin1, bin2])
ptBins.append(xlow)
ptBins.append(xup) # only need this last one
gr = ROOT.TGraphErrors() # 1/<rsp> VS ptL1
gr_gen = ROOT.TGraphErrors() # 1/<rsp> VS ptGen
grc = 0
# Iterate over pT^Gen bins, and for each:
# - Project 2D hist so we have a plot of response for given pT^Gen range
# - Fit a Gaussian (if possible) to this resp histogram to get <response>
# - Plot the L1 pT for given pT^Gen range (remember, for matched pairs)
# - Get average response, <pT^L1>, from 1D L1 pT hist
# - Add a new graph point, x=<pT^L1> y=<response> for this pT^Gen bin
for i, ptR in enumerate(ptBins[0:-1]):
bin1 = bin_indices[i][0]
bin2 = bin_indices[i][1]
# h2d_calib.GetXaxis().FindBin(ptR)
# h2d_calib.GetXaxis().FindBin(ptBins[i+1])-1
# print "Binning mis-matches", ptR, ptBins[i+1],
# h2d_calib.GetXaxis().GetBinLowEdge(bin1),h2d_calib.GetXaxis().GetBinLowEdge(bin2+1)
xlow = ptR
xhigh = ptBins[i + 1]
# Plot of response for given pT Gen bin
hrsp = h2d_rsp_gen.ProjectionY("Rsp_genpt_%g_%g" % (xlow, xhigh), bin1, bin2)
# cut on ref jet pt
pt_cut = ROOT.TCut("ptRef < %g && ptRef > %g " % (xhigh, xlow))
total_cut = ROOT.TCut(eta_cut)
total_cut += pu_cut
total_cut += pt_cut
print total_cut
# Plots of pT L1 for given pT Gen bin
tree_raw.Draw("pt>>hpt(4000, 0, 2000)", total_cut, "goff")
hpt = ROOT.gROOT.FindObject("hpt")
hpt.SetName("L1_pt_genpt_%g_%g" % (xlow, xhigh))
# hpt = h2d_gen_l1.ProjectionX("L1_pt_genpt_%g_%g" % (xlow, xhigh))
if hrsp.GetEntries() <= 0 or hpt.GetEntries() <= 0:
print "Skipping as 0 entries"
continue
output_f_hists.WriteTObject(hpt)
# Plots of pT Gen for given pT Gen bin
if do_genjet_plots:
tree_raw.Draw("ptRef>>hpt_gen(200)", total_cut, "goff")
hpt_gen = ROOT.gROOT.FindObject("hpt_gen")
hpt_gen.SetName("gen_pt_genpt_%g_%g" % (xlow, xhigh))
output_f_hists.WriteTObject(hpt_gen)
# Fit to resposne hist to get mean response & error on mean
if do_burr:
if (absetamin > 2 and i == 0):
setup_burr3_higherEta()
setup_fn = setup_burr3 if absetamin < 2 else setup_burr3_higherEta
mean, err = do_burr_response_hist_fit(hrsp, setup_fn)
else:
mean, err = do_gauss_response_hist_fit(hrsp)
output_f_hists.WriteTObject(hrsp)
print "pT Gen: ", ptR, "-", ptBins[i + 1], "<pT L1>:", hpt.GetMean(), \
"<pT Gen>:", (hpt_gen.GetMean() if do_genjet_plots else "NA"), "<rsp>:", mean
# Since we're plotting 1/rsp on y axis, so need jacobian
err = err / (mean**2)
# add point to response graph vs pt
# store if new max/min, but only max if pt > pt of previous point
# max_pt = max(hpt.GetMean(), max_pt) if grc > 0 and hpt.GetMean() > gr.GetX()[grc-1] else max_pt
# min_pt = min(hpt.GetMean(), min_pt)
gr.SetPoint(grc, hpt.GetMean(), 1. / mean)
gr.SetPointError(grc, hpt.GetMeanError(), err)
if do_genjet_plots:
gr_gen.SetPoint(grc, hpt_gen.GetMean(), 1. / mean)
# gr_gen.SetPointError(grc, hpt.GetMeanError(), err)
grc += 1
# Label response VS pT graphs
graph_name = generate_eta_graph_name(absetamin, absetamax)
gr.SetName(graph_name)
gr.GetXaxis().SetTitle("<p_{T}^{L1}> [GeV]")
gr.GetYaxis().SetTitle("1/<p_{T}^{L1}/p_{T}^{Ref}>")
if do_genjet_plots:
gr_gen.SetName('gencorr_eta_%g_%g' % (absetamin, absetamax))
gr_gen.GetXaxis().SetTitle("<p_{T}^{Ref}> [GeV]")
gr_gen.GetYaxis().SetTitle("1/<p_{T}^{L1}/p_{T}^{Ref}>")
# Save these graphs to file
outputfile.WriteTObject(gr)
if do_genjet_plots:
outputfile.WriteTObject(gr_gen)
# Fit correction function to response vs pT graph, add params list
fit_params = []
if do_correction_fit:
sub_graph, this_fit = setup_fit(gr, fitfcn, absetamin, absetamax, outputfile)
fit_graph, fit_params = fit_correction(sub_graph, this_fit)
outputfile.WriteTObject(this_fit) # function by itself
outputfile.WriteTObject(fit_graph) # has the function stored in it as well
return fit_params
