def shape_variation(var, plot_range, varname, recreate, out_name):
tot_hists = OrderedDict()
tdrstyle()
for wn, w in [
("unw", Weight("1.0")),
("nominal", Weights.wjets_madgraph_shape_weight("nominal")),
("shape_up", Weights.wjets_madgraph_shape_weight("wjets_up")),
("shape_down", Weights.wjets_madgraph_shape_weight("wjets_down"))
]:
p = data_mc(
var, "2J0T_%s" % (wn),
cut, Weights.total("mu", "nominal")*w,
samples, ".", recreate, lumi_iso["mu"], plot_range=plot_range,
)
for hn, h in p.hists.items():
logger.debug("%s %.2f" % (hn, h.Integral()))
#sumw = [(sample.name, numpy.mean(root_numpy.root2array(sample.tfile.GetPath()[:-2], "trees/WJets_weights", branches=[str(w)])[str(w)])) for sample in samples]
s = sum(p.hists.values()).Clone()
tot_hists[wn] = s
hc = HistCollection(tot_hists, name=out_name)
for hn, h in hc.hists.items():
print hn, h.Integral()
canv = plot_hists_dict(hc.hists, do_chi2=False, do_ks=True, x_label=varname, legend_pos="top-left")
hc.hists.values()[0].SetTitle("shape variation")
canv.SaveAs(out_name + ".png")
hc.save(out_name)
return hc, canv
def test_plot(self):
tdrstyle()
paths = get_paths()
dataset = 'Jul15'
lepton = 'mu'
systematic = 'nominal'
iso = 'iso'
#Define the different plots to make
items = [
('tchan1', paths[dataset]['mc'][lepton][systematic][iso], 'T_t_ToLeptons.root', Cuts.n_jets(2), '1.0'),
('tbarchan1', '-.-', 'Tbar_t_ToLeptons.root', Cuts.n_jets(2), '1.0'),
('tchan2', '-.-', 'T_t_ToLeptons.root', Cuts.n_jets(2)*Cuts.n_tags(1), '1.0'),
('tbarchan2', '-.-', 'Tbar_t_ToLeptons.root', Cuts.n_jets(2)*Cuts.n_tags(1), '1.0'),
]
items = fill_list(items)
#Dewfine a callbakc for normalization
def normalize(hn, h):
norm(h)
return h
#The final plot definition to draw
plot_def = {
'var': 'mt_mu',
'range': [20, 0, 300],
'items': items,
'merge_cmds': {'signal 2J1T':['tchan2', 'tbarchan2'], 'signal 2J':['tchan1', 'tbarchan1']},
'xlab': 'M_t(W)'
#'hist_callback': normalize
}
#Now do the draw comparison
canv, hists = compare_plot(plot_def)
#Do some post-checking that everything went fine
for hn, h in hists.items():
if hn=='signal 2J':
self.assertAlmostEqual(h.Integral(), 39472.452918)
elif hn=='signal 2J1T':
self.assertAlmostEqual(h.Integral(), 16414.4804941)
print "Saving the plot to test_plot.png"
canv.SaveAs('test_plot.png')
from plots.common.sample import get_paths, Sample
from plots.common.utils import NestedDict
from plots.common.cuts import Cuts
from plots.common.cross_sections import lumi_iso
from plots.common.utils import merge_hists
from plots.common.histogram import HistCollection, norm
from plots.common.hist_plots import plot_hists_dict
import numpy, copy
from plots.common.odict import OrderedDict
from plots.common.tdrstyle import tdrstyle
import logging
if __name__=="__main__":
tdrstyle()
logging.basicConfig(level=logging.INFO)
basepath = get_paths(samples_dir="data/83a02e9_Jul22_sftotal/", dataset="latest")
sampnames = [
"TTJets_FullLept", "TTJets_SemiLept",
"T_t_ToLeptons", "Tbar_t_ToLeptons",
"W1Jets_exclusive", "W2Jets_exclusive", "W3Jets_exclusive", "W4Jets_exclusive"
]
path = basepath["mc"]["mu"]["nominal"]["iso"]
cut = Cuts.final(2,1)*Cuts.mu
var = "cos_theta"
plot_range=[20, -1, 1]
lumi = lumi_iso["mu"]
plot_args = {"x_label": "cos #theta"}
"""
A crude way to change the MC samples from nominal to a specific systematic
run.
Args:
paths: a list with the filesystem paths to change.
dest: the destination systematics.
Returns: a list with the changed paths.
"""
return map(lambda x: x.replace("nominal", dest) if "nominal" in x else x, paths)
if __name__=="__main__":
logger.setLevel(logging.DEBUG)
tdrstyle.tdrstyle()
parser = argparse.ArgumentParser(
description='Creates the final plots'
)
parser.add_argument(
"-c",
"--channel", type=str, required=False, choices=["mu", "ele"], action="append", dest="channels", default=None,
help="the lepton channel to use"
)
parser.add_argument(
"-i",
"--indir", type=str, required=False, default=(os.environ["STPOL_DIR"] + "/step3_latest"),
help="the input directory, which is expected to contain the subdirectories: mu/ele"
)
parser.add_argument(
#Anti-iso region
for s in systematics:
if s == "Nominal":
make_histogram(var, dataGroup, cuts.name, openedFiles, lumis, s,
"antiiso", cuts.antiIsoCutsData)
stack = make_stack(var, cuts.name + s, MCGroups, dataGroup,
openedFiles, s, "antiiso", lumis,
cuts.antiIsoCutsMC, cuts.antiIsoCutsData, "",
cuts.name)
stacks[var.name + s + "antiiso"] = stack
#print "cuts",cuts.isoCutsData
tdrstyle.tdrstyle()
cst = TCanvas("Histogram", "Jet Systematics in final selection", 10, 10,
1000, 1000)
lumibox = lumi_textbox(lumi_iso["mu"])
histos = []
hData = dataGroup.getHistogram(var, "Nominal", "iso" + cuts.name)
#hQCDShapeOrig = dataGroup.getHistogram(var, "Nominal", "antiiso")
hData.Draw("e1")
hData.SetMarkerStyle(20)
hData.SetAxisRange(0, 1000, "Y")
stackSys = THStack("Stacksys", "stackName")
for s in systematics:
if s == "Nominal":
stackNom = stacks[var.name + s + "iso"]
def plot_fit(fit, infile, outfile, result):
tdrstyle()
spl = fit.filename.split("__")
var = spl[1]
procstyles = OrderedDict()
procstyles["tchan"] = "T_t"
procstyles["wzjets"] = "WJets_inclusive"
procstyles["top"] = "TTJets_FullLept"
procstyles["qcd"] = "QCD"
procnames = OrderedDict()
procnames["wzjets"] = "W, Z"
procnames["top"] = "t, #bar{t}"
procnames["tchan"] = "signal (t-channel)"
procnames["qcd"] = "QCD"
hists_mc_pre = OrderedDict()
hists_mc_post = OrderedDict()
print "IN",infile
print outfile
fi1 = File(infile)
fi2 = File(outfile)
hist_data = fi1.Get(var+"__DATA")
def loadhists(f):
out = OrderedDict()
for k in procnames.keys():
out[k] = f.Get(var+"__" + k)
out[k].SetTitle(procnames[k])
Styling.mc_style(out[k], procstyles[k])
return out
hists_mc_pre = loadhists(fi1)
hists_mc_post = loadhists(fi2)
print hists_mc_pre, hists_mc_post
Styling.data_style(hist_data)
of = OutputFolder(subdir="plots/final_fit")
def plot_data_mc(hists_mc, hist_data, name):
canv = ROOT.TCanvas()
p1 = ROOT.TPad("p1", "p1", 0, 0.3, 1, 1)
p1.Draw()
p1.SetTicks(1, 1);
p1.SetGrid();
p1.SetFillStyle(0);
p1.cd()
stacks_d = OrderedDict()
print "MC",hists_mc
print "VAL",hists_mc.values()
stacks_d["mc"] = hists_mc.values()
stacks_d["data"] = [hist_data]
stacks = plot_hists_stacked(
p1,
stacks_d,
x_label=var,
y_label="",
do_log_y=True
)
leg = legend([hist_data] + list(reversed(hists_mc.values())), styles=["p", "f"])
print canv, hist_data
print get_stack_total_hist(stacks["mc"])
ratio_pad, hratio = plot_data_mc_ratio(canv, get_stack_total_hist(stacks["mc"]), hist_data)
plot_info = PlotMetaInfo(
name,
"CUT",
"WEIGHT",
[infile],
subdir=fit.name,
comments=str(result[SIGNAL])
)
of.savePlot(canv, plot_info)
canv.Close()
plot_data_mc(hists_mc_post, hist_data, "post_fit")
plot_data_mc(hists_mc_pre, hist_data, "pre_fit")
for process in histos:
dirs[process] = p.mkdir(process)
#print histos
for process in histos:
dirs[process].cd()
for subproc in histos[process]:
histos[process][subproc].Write(hist_name + subproc)
print process, subproc, hist_name
#for subproc in histos[process]:
# dirs[process] = p.mkdir(process)
p.Close()
tdrstyle()
canvases = {}
for process in histos:
#Create the canvas
canv = ROOT.TCanvas("c" + process, "c")
canv.SetWindowSize(1000, 1000)
canv.SetCanvasSize(1000, 1000)
outf.write("\\begin{frame}[fragile]\n")
outf.write("\\begin{verbatim}" + process + "\\end{verbatim}\n")
outf.write("""\\begin{center}
\\begin{tabular}{ l || c | c | r }
\hline
""")
outf.write("Systematic & yield & Chi2/NDF & p-value \\\\ \hline\n")