parser = optparse.OptionParser()

parser.add_option("--lumi", help="luminosity", default=5, type=int)

#parser.add_option("--z-label", help="z axis title", default="significance in optimal cut")

parser.add_option("--text-file", help="text csv file", default=None, type=str)

parser.add_option("--outdir", help="outfile directory", default="plots")

parser.add_option("--outfilebase", help="outfile base name", default="output")

parser.add_option("--g-min", help="min gluino mass", default=800, type=float)

parser.add_option("--g-max", help="max gluino mass", default=2000, type=float)

parser.add_option("--l-min", help="min lsp mass", default=0, type=float)

parser.add_option("--l-max", help="max lsp mass", default=1300, type=float)

parser.add_option("--bin-width", help="bin width", default=100, type=float)

parser.add_option("--x-dim", help="x dimension of figure", default=800, type=float)

parser.add_option("--y-dim", help="y dimension of figure", default=600, type=float)

parser.add_option("--run1_color", help="color of run 1 line", default=46, type=int)

parser.add_option("--dorun1", help="add run 1 line to graph", default=True)

parser.add_option("--run1_csvfile", help="csv file containing run 1 exclusion points", default="run1_limit.csv", type=str)

parser.add_option("--run1_1sigma_csvfile", help="csv file containing run 1 exclusion (+1 sigma) points", default="run1_limit_1sigma.csv", type=str)

parser.add_option("--sigdir", help="directory where significances files are located", default='significances', type=str)

parser.add_option("--cutdir", help="directory where cuts files are located", default='cuts', type=str)

(options,args) = parser.parse_args()

mdict = {}

with open('mass_windows.txt', 'r') as f:

reader = csv.reader(f, delimiter='\t')

m = list(reader)

mdict = {l[0]: [l[1],l[2],l[3]] for l in m}

def masses(did):

mlist = mdict[did]

mglue = mlist[0]

mstop = mlist[1]

mlsp = mlist[2]

return mglue,mstop,mlsp

filenames = glob.glob(opts.sigdir+'/s*.b*.json')

regex = re.compile(r'{0:s}'.format(os.path.join(opts.sigdir, 's(\d+)\.b([a-fA-F\d]{32})\.json')))

dids = []

sigs = []

signals = []

bkgds = []

ratios = []

for filename in filenames:

with open(filename) as json_file:

sig_dict = json.load(json_file)

entry = sig_dict[0]

max_sig = entry['significance_scaled']

max_hash = entry['hash']

did = regex.search(filename)

signal_did = did.group(1)

with open(opts.cutdir+'/'+signal_did+'.json') as signal_json_file:

signal_dict = json.load(signal_json_file)

entry = signal_dict[max_hash]

signal = entry['scaled']*opts.lumi*1000

with open(os.path.join(opts.sigdir, '{0:s}.json'.format(did.group(2))), 'r') as f:

bkgd_dids = json.load(f)

bkgd = 0

for bkgd_did in bkgd_dids:

with open(opts.cutdir+'/'+bkgd_did+'.json') as bkgd_json_file:

bkgd_dict = json.load(bkgd_json_file)

entry = bkgd_dict[max_hash]

bkgd += entry['scaled']*opts.lumi*1000

sigs.append(max_sig)

signals.append(signal)

bkgds.append(bkgd)

ratios.append(signal/bkgd)

dids.append(signal_did)

plot_array={'sig':[],'signal':[],'bkgd':[],'mgluino':[],'mlsp':[],'ratio':[]}

for did,sig,signal,bkgd,ratio in zip(dids,sigs,signals,bkgds,ratios):

mgluino,mstop,mlsp = masses(did)

if int(mstop) == 5000:

plot_array['sig'].append(sig)

plot_array['signal'].append(signal)

plot_array['bkgd'].append(bkgd)

plot_array['ratio'].append(ratio)

plot_array['mgluino'].append(mgluino)

plot_array['mlsp'].append(mlsp)

#gStyle.SetPalette(1);

c = TCanvas("c", "", 0, 0, opts.x_dim, opts.y_dim)

c.SetRightMargin(0.16)

c.SetTopMargin(0.07)

return TH2F("grid",

axis_labels(opts,label),

nbinsx(opts),

opts.g_min,

opts.g_max,

nbinsy(opts),

opts.l_min,

for i in range(len(plot_array[label])):

g = int(plot_array['mgluino'][i])

l = int(plot_array['mlsp'][i])

z = plot_array[label][i]

sig = plot_array['sig'][i]

b = hist.FindFixBin(g,l)

if(sig>0) or not(skipNegativeSig):

xx=Long(0)

yy=Long(0)

zz=Long(0)

hist.GetBinXYZ(b,xx,yy,zz)

z_old = hist.GetBinContent(xx,yy)

newz = max(z_old,z) #for significances this makes sense. For the other quantities not so much. Oh well.

hist.SetBinContent(b,newz)

else:

hist.SetMarkerSize(800)

hist.SetMarkerColor(kWhite)

#gStyle.SetPalette(51)

gStyle.SetPaintTextFormat("1.{0:d}f".format(nSigs));

txt = TLatex()

txt.SetNDC()

txt.DrawText(0.32,0.87,"Internal")

txt.DrawText(0.2,0.82,"Simulation")

#txt.SetTextSize(0.030)

txt.SetTextSize(18)

txt.DrawLatex(0.16,0.95,"#tilde{g}-#tilde{g} production, #tilde{g} #rightarrow t #bar{t} + #tilde{#chi}^{0}_{1}")

txt.DrawLatex(0.62,0.95,"L_{int} = %d fb^{-1}, #sqrt{s} = 13 TeV"% lumi)

txt.SetTextFont(72)

txt.SetTextSize(0.05)

txt.DrawText(0.2,0.87,"ATLAS")

txt.SetTextFont(12)

txt.SetTextAngle(38)

txt.SetTextSize(0.02)

if path is None:

return

txt = TLatex()

txt.SetNDC()

txt.SetTextSize(0.030)

with open(path,'r') as f:

reader = csv.reader(f,delimiter=",")

for row in reader:

x = array('f')

y = array('f')

n = 0

with open(filename,'r') as csvfile:

reader = csv.reader(csvfile, delimiter = ' ')

for row in reader:

n += 1

x.append(float(row[0]))

y.append(float(row[1]))

gr = TGraph(n,x,y)

gr.SetLineColor(linecolor)

gr.SetLineWidth(linewidth)

gr.SetLineStyle(linestyle)

txt = TLatex()

txt.SetNDC()

txt.SetTextFont(22)

txt.SetTextSize(0.04)

txt.SetTextColor(color)

#x = array('d',[opts.g_min,opts.l_max+2*topmass,opts.g_min])

#y = array('d',[opts.g_min-2*topmass,opts.l_max,opts.l_max])

x = array('d',[1400,1600,1600,1400])

y = array('d',[600,600,800,600])

p=TPolyLine(4,x,y)

p.SetFillColor(1)

p.SetFillStyle(3001)

#p.DrawPolyLine(4,x,y)