def test_stack():
stack = HistStack()
assert_equal(len(stack), 0)
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='red'))
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='blue'))
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='green'))
assert_equal(len(stack), 3)
stack2 = stack.Clone()
assert_equal(stack2[2].linecolor, 'green')
# test stacked=True
a = Hist(2, 0, 1)
b = Hist(2, 0, 1)
a.Fill(0.2)
b.Fill(0.2)
b.Fill(0.8, 5)
stack = HistStack([a, b])
assert_equal(stack.min(), 2)
assert_equal(stack.max(), 5)
# test stacked=False
a = Hist(2, 0, 20)
b = Hist(2, 10, 20) # binning may be different
a.Fill(0.2)
b.Fill(15, 5)
stack = HistStack([a, b], stacked=False)
assert_equal(stack.min(), 0)
assert_equal(stack.max(), 5)
def test_stack():
stack = HistStack()
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='red'))
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='blue'))
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='green'))
assert_equal(len(stack), 3)
stack2 = stack.Clone()
assert_equal(stack2[2].linecolor, 'green')
def make_control_region_data_mc_comparision(
histograms,
histogram_name,
variable,
x_label=r'$m(\mathrm{t}\bar{\mathrm{t}})$ [GeV]',
x_min=300,
x_max=1800,
y_label='Events/50 GeV'):
qcd = histograms['QCD'][histogram_name]
ttjet = histograms['TTJet'][histogram_name]
wjets = histograms['WJets'][histogram_name]
zjets = histograms['ZJets'][histogram_name]
single_top = histograms['SingleTop'][histogram_name]
other = ttjet + wjets + zjets + single_top
data = histograms['data'][histogram_name]
data.SetMarkerSize(3)
qcd.SetTitle('QCD from data')
other.SetTitle('Combined other background')
data.SetTitle('Data')
qcd.fillcolor = 'yellow'
other.fillcolor = 'red'
qcd.fillstyle = 'solid'
other.fillstyle = 'solid'
stack = HistStack()
stack.Add(other)
stack.Add(qcd)
# plot with matplotlib
plt.figure(figsize=(16, 12), dpi=200, facecolor='white')
axes = plt.axes()
rplt.hist(stack, stacked=True, axes=axes)
rplt.errorbar(data, xerr=None, emptybins=False, axes=axes)
plt.xlabel(x_label, CMS.x_axis_title)
plt.ylabel(y_label, CMS.y_axis_title)
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
plt.title(get_title(variable), CMS.title)
plt.legend(numpoints=1, loc='upper right', prop=CMS.legend_properties)
axes.set_xlim(xmin=x_min, xmax=x_max)
axes.set_ylim(ymin=0)
plt.tight_layout()
plt.savefig(variable + '.png')
plt.savefig(variable + '.pdf')
map(h3.Fill, x1_obs)
map(h3.Fill, x2_obs)
# set visual attributes
h1.fillstyle = 'solid'
h1.fillcolor = 'green'
h1.linecolor = 'green'
h1.linewidth = 0
h2.fillstyle = 'solid'
h2.fillcolor = 'red'
h2.linecolor = 'red'
h2.linewidth = 0
stack = HistStack()
stack.Add(h1)
stack.Add(h2)
ratio = h3 / (h1 + h2)
ratio = Hist.divide(h3, h1 + h2)
# plot with matplotlib
plt.figure(figsize=(16, 12), dpi=200)
gs = gridspec.GridSpec(2, 1, height_ratios=[5, 1])
axes = plt.axes()
ax0 = plt.subplot(gs[0])
ax0.minorticks_on()
rplt.bar(stack, stacked=True)
rplt.errorbar(h3, xerr=False, emptybins=False)
# plt.xlabel('Mass', position=(1., 0.), ha='right', fontsize = 24)
plt.ylabel('Events', va='top', fontsize=40)
set_style('ATLAS')
mus = (0, -1, 2)
sigmas = (2, 1, 0.5)
events = (1000, 2000, 100)
colors = ('lawngreen', 'forestgreen', 'mistyrose')
styles = ('\\', '/', '-')
canvas = Canvas()
objects = []
# create a stack
stack = HistStack()
stack.Add(
Hist(100, -5, 5, color='salmon',
drawstyle='hist').FillRandom(F1('TMath::Gaus(x, 2, 1)'), 500))
stack.Add(
Hist(100, -5, 5, color='powderblue',
drawstyle='hist').FillRandom(F1('TMath::Gaus(x, 2, 0.6)'), 300))
objects.append(stack)
# create some random histograms
for i, (mu, sigma, n, c,
s) in enumerate(zip(mus, sigmas, events, colors, styles)):
hist = Hist(100,
-5,
5,
color=c,
fillstyle=s,
drawstyle='hist' if i % 2 == 0 else '')
compressed = background.CompressedAnalysis
print compressed
histname = "hist" + rootfile
histogram = histname + "(" + nbins + "," + xmin + "," + xmax + ")"
histname = compressed.Draw(variable + ">>" + histogram,
selection=weight * total,
drawstyle='hist',
fillstyle='solid',
linecolor='black',
fillcolor=color)
print histname
print background_name
legend.AddEntry(histname, label=background_name,
style='F') #was label=background_name
stack.Add(histname)
#=================== DATA SAMPLES ====================================#
objects = []
dataobjects = []
for (datafilename, data_weight, data_bool) in zip(data_list,
data_weight_list,
data_bool_list):
print datafilename, data_weight, data_bool
if data_bool == "Plot":
print "datafilename: ", datafilename
data = root_open(data_location + "/" + datafilename)
treesSR = data.CompressedAnalysis
datacut = total
if data_weight != "1":
print "WARNING: DATA WEIGHT IS NOT 1 PLEASE CHECK IF YOU DO NOT INTEND TO WEIGHT THE DATA"
#SelectionCriteria = Cut(Luminosity) * Cut(Weight) * total
#histogram = Hist(nbins,xmin,xmax)
temphist = treeDict[sample].Draw(variable, hist= Hist(nbins,xmin,xmax),selection = Cut(Luminosity) * Cut(Weight) * total)
temphist = temphist.merge_bins([(0,1), (nbins,nbins+1) ])
sampleStyleFunc(temphist,sample,sampleDictionary)
backgroundstacks[LegendEntry].Add(temphist.Clone())
skimmed = 1
for key in backgroundstacks:
#print "key: " + str(key )
if 'Data' not in key:
if 'Signal' in key:continue
stack.Add(backgroundstacks[key].sum.Clone())
if 'Data' in key and not BLINDED :
datastack.Add(backgroundstacks[key].sum.Clone())
# print "type of stack.mergebins" + str(type(stack.merge_bins([(nbins,nbins+1)])))
if doBreakdown:
cutflowstring, yieldstring = produceRegionBreakdown(backgroundstacks,stack,datastack,Region,CRlist,SRlist,VVCRlist,TOPCRlist,ABCDlist)
print "[BREAKDOWN][PRODUCED]"
print cutflowstring
print yieldstring
SetNegativeYieldsToZero(stack,nbins)
print "[BEFORE DRAW]" + str(round(time.time() - t0,2)) + "seconds"
drawBackgrounds(stack)
# else:
# # hists[sample].markersize = 1.2
# pass
outputROOTFile.cd()
# print histsToStack[0].Integral()
# print histsToStack
sortedHistsToStack = sorted(histsToStack,
key=lambda x: x.Integral(),
reverse=False)
# print sortedHistsToStack
for tmphist in sortedHistsToStack:
if tmphist.Integral():
stack.Add(tmphist)
try:
stack.sum.Integral()
except:
print "stack has no integral!"
continue
if plotWithMPL:
gs = mpl.gridspec.GridSpec(2, 1, height_ratios=[4, 1])
gs.update(wspace=0.00, hspace=0.00)
axes = plt.subplot(gs[0])
axes_ratio = plt.subplot(gs[1], sharex=axes)
plt.setp(axes.get_xticklabels(), visible=False)
if plotWithROOT:
def stack(x, *args, **kwargs):
## parse arguments
_data = kwargs.pop('data', None)
_bkgs = kwargs.pop('bkgs', None)
_sigs = kwargs.pop('sigs', None)
_treename = kwargs.pop('treename', None)
_datasearchpath = kwargs.pop('datasearchpath', None)
_datadrivensearchpath = kwargs.pop('datadrivensearchpath', None)
_bkgsearchpath = kwargs.pop('bkgsearchpath', None)
_sigsearchpath = kwargs.pop('sigsearchpath', None)
_lumi = kwargs.pop('lumi', None)
global data
global bkgs
global sigs
global treename
global datasearchpath
global datadrivensearchpath
global bkgsearchpath
global sigsearchpath
global lumi
data = _data or data
bkgs = _bkgs or bkgs
sigs = _sigs or sigs
treename = _treename or treename
datasearchpath = _datasearchpath or datasearchpath
datadrivensearchpath = _datadrivensearchpath or datadrivensearchpath
bkgsearchpath = _bkgsearchpath or bkgsearchpath
sigsearchpath = _sigsearchpath or sigsearchpath
if _lumi:
lumi = float(_lumi)
xtitle = kwargs.pop('xtitle', '')
ytitle = kwargs.pop('ytitle', '')
logx = bool(kwargs.pop('logx', False))
logy = bool(kwargs.pop('logy', False))
blind = kwargs.pop('blind', None)
has_blinded_data = False
## save stuff to bookkeep and return
stuff = dict()
stuff['x'] = x
## get data histogram
h_data = None
if data:
sp = datasearchpath # HACK: just data to True!
newx = '%s::%s::%s' % (sp, treename, x)
h_data = ipyhep.tree.project(newx, *args, **kwargs)
if h_data:
stuff['h_data'] = h_data
## blind the data?
if h_data and not blind is None:
if isinstance(blind, tuple):
blind1, blind2 = blind
nbins = h_data.GetNbinsX()
for i_bin in xrange(1, nbins +
2): # skip underflow (but not overflow)
xval1 = h_data.GetXaxis().GetBinLowEdge(i_bin)
xval2 = h_data.GetXaxis().GetBinUpEdge(i_bin)
if xval1 >= blind1 and xval2 <= blind2:
h_data.SetBinContent(i_bin, 0.0)
h_data.SetBinError(i_bin, 0.0)
has_blinded_data = True
else:
nbins = h_data.GetNbinsX()
for i_bin in xrange(1, nbins +
2): # skip underflow (but not overflow)
xval = h_data.GetXaxis().GetBinLowEdge(i_bin)
if xval >= blind:
h_data.SetBinContent(i_bin, 0.0)
h_data.SetBinError(i_bin, 0.0)
has_blinded_data = True
## get background histograms
h_bkgs = list()
n_bkgs = list()
if bkgs:
for bkg in bkgs:
if isinstance(bkg, list):
h_subtotal = None
for dsid in bkg:
assert isinstance(dsid, str)
h_bkg = None
if dsid.isdigit():
## mc backgrounds
sp = bkgsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
else:
## data-driven backgrounds
assert dsid == 'fakes' or dsid == 'efakes'
sp = datadrivensearchpath % dsid
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
if h_bkg:
if h_subtotal:
h_subtotal.Add(h_bkg)
else:
h_subtotal = h_bkg.Clone()
if h_subtotal:
h_bkgs.append(h_subtotal)
dsid = bkg[0]
n_bkgs.append(dsid)
else:
dsid = bkg
assert isinstance(dsid, str)
h_bkg = None
if dsid.isdigit():
## mc backgrounds
sp = bkgsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
else:
## data-driven backgrounds
assert dsid == 'fakes' or dsid == 'efakes'
sp = datadrivensearchpath % dsid
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
if h_bkg:
h_bkgs.append(h_bkg)
n_bkgs.append(dsid)
if h_bkgs:
stuff['h_bkgs'] = h_bkgs
## get signal histograms
h_sigs = list()
n_sigs = list()
if sigs:
for dsid in sigs:
sp = sigsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_sig = ipyhep.tree.project(newx, *args, **kwargs)
if h_sig:
h_sigs.append(h_sig)
n_sigs.append(dsid)
if h_sigs:
stuff['h_sigs'] = h_sigs
assert h_sigs
## style data
if h_data:
h_data.title = 'Data'
h_data.linecolor = ipyhep.style.black
h_data.linewidth = 2
h_data.markercolor = ipyhep.style.black
h_data.markerstyle = 20
h_data.markersize = 1.2
h_data.fillstyle = ipyhep.style.fill_hollow
h_data.drawstyle = 'PE'
h_data.legendstyle = 'LP'
## scale and style background histograms
if h_bkgs:
assert len(h_bkgs) == len(n_bkgs), '%s\n%s' % (h_bkgs, n_bkgs)
for h, dsid in zip(h_bkgs, n_bkgs):
sf = ipyhep.sampleops.get_sf(dsid)
if dsid.isdigit():
sf *= lumi / __ntuple_lumi
h.Scale(sf)
h.title = ipyhep.sampleops.get_label(dsid)
h.linecolor = ipyhep.style.black
h.linewidth = 1
h.markercolor = ipyhep.sampleops.get_color(dsid)
h.fillcolor = ipyhep.sampleops.get_color(dsid)
h.fillstyle = ipyhep.style.fill_solid
h.legendstyle = 'F'
## calculate stat error on total background
h_bkg_total = None
if h_bkgs:
for h_bkg in h_bkgs:
if h_bkg_total:
h_bkg_total.Add(h_bkg)
else:
h_bkg_total = h_bkg.Clone()
stuff['h_bkg_total'] = h_bkg_total
## style h_bkg_total
if h_bkg_total:
h_bkg_total.title = 'stat. uncert.'
h_bkg_total.linecolor = ipyhep.style.black
h_bkg_total.linewidth = 1
h_bkg_total.markerstyle = 0
h_bkg_total.fillcolor = ipyhep.style.dark_gray
h_bkg_total.fillstyle = ipyhep.style.fill_lines
h_bkg_total.drawstyle = 'E2'
h_bkg_total.legendstyle = 'LF'
## scale and style signal histograms
if h_sigs:
assert len(h_sigs) == len(n_sigs)
for h, dsid in zip(h_sigs, n_sigs):
sf = ipyhep.sampleops.get_sf(dsid)
sf *= lumi / __ntuple_lumi
h.Scale(sf)
h.title = ipyhep.sampleops.get_label(dsid)
h.linecolor = ipyhep.sampleops.get_color(dsid)
h.linewidth = 3
h.fillstyle = ipyhep.style.fill_hollow
h.markerstyle = 0
h.drawstyle = 'HIST'
h.legendstyle = 'L'
## build list of all_hists
all_hists = list()
main_hists = list()
if h_data:
all_hists.append(h_data)
main_hists.append(h_data)
if h_bkgs:
all_hists.extend(h_bkgs)
main_hists.extend(h_bkgs)
if h_bkg_total:
all_hists.append(h_bkg_total)
main_hists.append(h_bkg_total)
if h_sigs:
all_hists.extend(h_sigs)
## get statistics
if all_hists:
stats_list = list()
for h in all_hists:
stats_list.extend(get_stats(h))
html = convert_table_to_html(convert_stats_to_table(stats_list))
stuff['html'] = html
## renormalize for bin widths
bins = kwargs.pop('bins', None)
if bins and isinstance(bins, list):
for h in all_hists:
renormalize_for_bin_widths(h, bins)
## stack background histograms
if h_bkgs:
assert len(h_bkgs) == len(n_bkgs), '%s\n%s' % (h_bkgs, n_bkgs)
h_bkgs.reverse()
n_bkgs.reverse()
hstack = HistStack()
for h in h_bkgs:
hstack.Add(h)
hstack.title = 'stack sum'
hstack.drawstyle = 'HIST'
stuff['stack'] = hstack
h_bkgs.reverse()
n_bkgs.reverse()
# ## convert data to TGraphAsymmErrors
# g_data = None
# if h_data:
# if __use_poissonize:
# g_data = poissonize.GetPoissonizedGraph(h_data)
# else:
# g_data = ROOT.TGraphAsymmErrors()
# i_g = 0
# nbins = h_data.GetNbinsX()
# for i_bin in xrange(1, nbins+1): # skip underflow/overflow
# c = h_data.GetBinContent(i_bin)
# e = h_data.GetBinError(i_bin)
# if c != 0.0:
# g_data.SetPoint(i_g, h_data.GetBinCenter(i_bin), c)
# g_ratio.SetPointError(i_g,
# h_data.GetBinWidth(i_bin)/2.,
# h_data.GetBinWidth(i_bin)/2.,
# e,
# e)
# i_g += 1
## build list of objects to draw
objects = list()
if h_bkgs:
objects.append(stuff['stack'])
objects.append(stuff['h_bkg_total'])
if h_sigs:
objects.extend(h_sigs)
if h_data:
objects.append(h_data)
## set xlimits and ylimits
ypadding = 0.21
logy_crop_value = 7e-3
xmin, xmax, ymin, ymax = 0.0, 1.0, 0.0, 1.0
if objects:
xmin, xmax, ymin, ymax = get_limits(objects,
logx=logx,
logy=logy,
ypadding=ypadding,
logy_crop_value=logy_crop_value)
if logy:
ymin = 7e-3
else:
ymin = 0.0
xlimits = (xmin, xmax)
ylimits = (ymin, ymax)
stuff['xlimits'] = xlimits
stuff['ylimits'] = ylimits
## remove xtitle for do_ratio
_xtitle = xtitle
if h_data and h_bkg_total and kwargs.get('do_ratio'):
_xtitle = ''
## make canvas
canvas = Canvas(800, 600)
stuff['canvas'] = canvas
## draw the objects
if objects:
canvas.cd()
draw(objects,
pad=canvas,
xtitle=_xtitle,
ytitle=ytitle,
xlimits=xlimits,
ylimits=ylimits)
## set log x/y, for some reason doesn't work before draw
if logx or logy:
if logx:
canvas.SetLogx()
if logy:
canvas.SetLogy()
canvas.Update()
## draw blind_line
if has_blinded_data:
if isinstance(blind, tuple):
blind_list = list(blind)
else:
blind_list = [blind]
blind_lines = list()
for bl in blind_list:
line_y1 = ymin
line_y2 = ymax
blind_line = ROOT.TLine(bl, line_y1, bl, line_y2)
blind_line.SetLineColor(ROOT.kGray + 2)
blind_line.SetLineStyle(7)
blind_line.SetLineWidth(2)
blind_line.Draw()
blind_lines.append(blind_line)
stuff['blind_lines'] = blind_lines
canvas.Update()
## legend
lefty = True
if h_bkg_total:
lefty = is_left_sided(h_bkg_total)
elif h_data:
lefty = is_left_sided(h_data)
elif h_sigs:
lefty = is_left_sided(h_sigs[0])
if main_hists:
header = '%.1f fb^{-1}, 13 TeV' % (lumi / 1000.0)
if lefty:
legend = Legend(main_hists,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.60,
rightmargin=0.02,
entrysep=0.01,
entryheight=0.04)
else:
legend = Legend(main_hists,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.03,
rightmargin=0.59,
entrysep=0.01,
entryheight=0.04)
legend.Draw()
stuff['legend'] = legend
if h_sigs:
# header = 'ATLAS Internal'
header = ''
if lefty:
legend2 = Legend(h_sigs,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.37,
rightmargin=0.23,
entrysep=0.01,
entryheight=0.04)
else:
legend2 = Legend(h_sigs,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.20,
rightmargin=0.40,
entrysep=0.01,
entryheight=0.04)
legend2.Draw()
stuff['legend2'] = legend2
## do_ratio
if h_data and h_bkg_total and kwargs.get('do_ratio'):
## top canvas
top_canvas = stuff.pop('canvas')
stuff['top_canvas'] = top_canvas
## make SM/SM with error band: h_ratio_band
i_sfratio = int(kwargs.get('sfratio', -1))
if i_sfratio < 0: # ratio plot of Data/Model
h_ratio_band = h_bkg_total.Clone()
nbins = h_ratio_band.GetNbinsX()
for i_bin in xrange(nbins + 2):
h_ratio_band.SetBinContent(i_bin, 1.0)
c = h_bkg_total.GetBinContent(i_bin)
e = h_bkg_total.GetBinError(i_bin) / c if c > 0.0 else 0.0
h_ratio_band.SetBinError(i_bin, e)
stuff['h_ratio_band'] = h_ratio_band
else: # ratio plot of Scale Factor for ith background
hi = h_bkgs[i_sfratio]
h_ratio_band = hi.Clone()
nbins = h_ratio_band.GetNbinsX()
for i_bin in xrange(nbins + 2):
h_ratio_band.SetBinContent(i_bin, 1.0)
c = hi.GetBinContent(i_bin)
e = hi.GetBinError(i_bin) / c if c > 0.0 else 0.0
h_ratio_band.SetBinError(i_bin, e)
stuff['h_ratio_band'] = h_ratio_band
## make data/(SM) h_ratio
if i_sfratio < 0:
h_ratio = h_data.Clone()
h_ratio.Divide(h_data, h_bkg_total, 1.0, 1.0)
stuff['h_ratio'] = h_ratio
else:
## SF1 = 1.0 + (data - MCtot) / MC1
sfname = kwargs.get('sfname')
sffile = kwargs.get('sffile')
if not sfname:
sfname = 'h_sf'
hi = h_bkgs[i_sfratio]
h_numer = h_data.Clone()
h_numer.Add(h_bkg_total, -1.0)
## do the division
h_ratio = h_data.Clone(sfname)
h_ratio.Divide(h_numer, hi, 1.0, 1.0)
## add the 1.0
nbins = h_ratio.GetNbinsX()
for i_bin in xrange(nbins + 2):
c = h_ratio.GetBinContent(i_bin)
h_ratio.SetBinContent(i_bin, c + 1.0)
h_ratio_band.SetBinContent(i_bin, c + 1.0)
## ignore bins with no data for SF
for i_bin in xrange(nbins + 2):
c = h_data.GetBinContent(i_bin)
if c <= 0:
h_ratio.SetBinContent(i_bin, 0.0)
h_ratio.SetBinError(i_bin, 0.0)
h_ratio_band.SetBinError(i_bin, 0.0)
stuff['h_ratio'] = h_ratio
if sffile:
f_out = ipyhep.file.write(h_ratio, sffile)
# f_out.Close()
## convert ratio to a TGraphErrors so that Draw('E0')
## shows error bars for points off the pad
g_ratio = ROOT.TGraphErrors()
i_g = 0
for i_bin in xrange(1, nbins + 1): # skip underflow/overflow
ratio_content = h_ratio.GetBinContent(i_bin)
if ratio_content != 0.0:
g_ratio.SetPoint(i_g, h_ratio.GetBinCenter(i_bin),
ratio_content)
g_ratio.SetPointError(i_g,
h_ratio.GetBinWidth(i_bin) / 2.,
h_ratio.GetBinError(i_bin))
i_g += 1
else:
h_ratio.SetBinError(i_bin, 0.0)
stuff['g_ratio'] = g_ratio
## style ratio
h_ratio_band.title = 'bkg uncert.'
if i_sfratio < 0:
h_ratio_band.linecolor = ipyhep.style.yellow
else:
h_ratio_band.linecolor = ipyhep.style.light_gray
h_ratio_band.linewidth = 0
h_ratio_band.markerstyle = 0
if i_sfratio < 0:
h_ratio_band.fillcolor = ipyhep.style.yellow
else:
h_ratio_band.linecolor = ipyhep.style.light_gray
h_ratio_band.fillstyle = ipyhep.style.fill_solid
h_ratio_band.drawstyle = 'E2'
h_ratio_band.legendstyle = 'F'
h_ratio.title = 'ratio'
h_ratio.linecolor = ipyhep.style.black
h_ratio.linewidth = 2
h_ratio.markercolor = ipyhep.style.black
h_ratio.markerstyle = 20
h_ratio.markersize = 1.2
h_ratio.fillstyle = ipyhep.style.fill_hollow
h_ratio.drawstyle = 'PE'
h_ratio.legendstyle = 'LP'
## bottom canvas
bottom_canvas = Canvas(800, 600)
bottom_canvas.cd()
stuff['bottom_canvas'] = bottom_canvas
## set ratio ylimits
ratio_min = kwargs.get('ratio_min', -0.2)
ratio_max = kwargs.get('ratio_max', 2.2)
ratio_ylimits = (ratio_min, ratio_max)
## draw ratio band
if i_sfratio < 0:
_ytitle = 'Data / Model'
else:
hi = h_bkgs[i_sfratio]
_ytitle = 'SF(%s)' % hi.title
draw([h_ratio_band],
pad=bottom_canvas,
xtitle=xtitle,
ytitle=_ytitle,
xlimits=xlimits,
ylimits=ratio_ylimits)
## set log x/y, for some reason doesn't work before draw?
if logx:
bottom_canvas.SetLogx()
bottom_canvas.Update()
### make horiz lines in ratio plot every 0.5:
line_ys = [
y / 10.0
for y in range(10 *
int(round(ratio_min)), 10 * int(round(ratio_max)) +
5, 5)
]
line_x1 = canvas.GetUxmin()
line_x2 = canvas.GetUxmax()
line_xwidth = abs(line_x2 - line_x1)
lines = []
for line_y in line_ys:
line = ROOT.TLine(line_x1 + 0.02 * line_xwidth, line_y,
line_x2 - 0.02 * line_xwidth, line_y)
line.SetLineWidth(1)
line.SetLineStyle(7)
if line_y == 1.0:
line.SetLineColor(ROOT.kGray + 2)
else:
line.SetLineColor(ROOT.kGray + 0)
line.Draw()
lines.append(line)
stuff['lines'] = lines
## draw blind_line
if has_blinded_data:
if isinstance(blind, tuple):
blind_list = list(blind)
else:
blind_list = [blind]
blind_lines = list()
for bl in blind_list:
line_y1 = ymin
line_y2 = ymax
blind_line = ROOT.TLine(bl, line_y1, bl, line_y2)
blind_line.SetLineColor(ROOT.kGray + 2)
blind_line.SetLineStyle(7)
blind_line.SetLineWidth(2)
blind_line.Draw()
blind_lines.append(blind_line)
stuff['blind_lines2'] = blind_lines
canvas.Update()
## draw ratio
g_ratio.Draw('PE0')
# h_ratio.GetYaxis().SetRangeUser(ratio_min, ratio_max)
# h_ratio.Draw('PE,SAME')
## shared canvas
shared_canvas = Canvas(800, 800)
shared_plot = plot_shared_axis(top_canvas,
bottom_canvas,
canvas=shared_canvas,
split=0.35,
axissep=0.01)
stuff['canvas'] = shared_canvas
canvas = shared_canvas
## save figures
save = kwargs.get('save')
if save is None: # NOTE: save can be False to skip saving
save = ['pdf', 'png']
if save:
ipyhep.file.save_figures(canvas, x, save)
global results
results = stuff
return stuff
def draw(
name,
category,
data=None,
data_info=None,
model=None,
model_colors=None,
signal=None,
signal_scale=1.,
signal_on_top=False,
signal_linestyles=None,
signal_colors=None,
show_signal_error=False,
fill_signal=False,
stack_signal=True,
units=None,
plot_label=None,
ylabel='Events',
blind=False,
show_ratio=False,
ratio_range=(0, 2),
ratio_height=0.15,
ratio_margin=0.06,
output_formats=None,
systematics=None,
#systematics_components=None,
integer=False,
textsize=22,
logy=False,
logy_min=None,
separate_legends=False,
ypadding=None,
legend_position='right',
range=None,
output_name=None,
output_dir=PLOTS_DIR,
arrow_values=None,
overflow=True,
show_pvalue=False,
top_label=None,
poisson_errors=True):
if model is None and data is None and signal is None:
# insufficient input
raise ValueError("at least one of model, data, "
"or signal must be specified")
if model is not None:
if not isinstance(model, (list, tuple)):
model = [model]
if overflow:
for hist in model:
fold_overflow(hist)
if signal is not None:
if not isinstance(signal, (list, tuple)):
signal = [signal]
if overflow:
for hist in signal:
fold_overflow(hist)
if data is not None and overflow:
fold_overflow(data)
# objects will be populated with all histograms in the main pad
objects = []
legends = []
if show_ratio and (data is None or model is None):
# cannot show the ratio if data or model was not specified
show_ratio = False
if ypadding is None:
# select good defaults for log or linear scales
if logy:
ypadding = (.6, .0)
else:
ypadding = (.35, .0)
template = data or model[0]
xdivisions = min(template.nbins(), 7) if integer else 507
if show_ratio:
fig = RatioPlot(logy=logy,
ratio_title='Data / Model',
ratio_limits=(0, 2),
offset=-72,
ratio_margin=22,
prune_ratio_ticks=True)
else:
fig = SimplePlot(logy=logy)
if signal is not None:
if signal_scale != 1.:
scaled_signal = []
for sig in signal:
scaled_h = sig * signal_scale
scaled_h.SetTitle(r'%g #times %s' %
(signal_scale, sig.GetTitle()))
scaled_signal.append(scaled_h)
else:
scaled_signal = signal
if signal_colors is not None:
set_colors(scaled_signal, signal_colors)
for i, s in enumerate(scaled_signal):
s.drawstyle = 'HIST'
if fill_signal:
s.fillstyle = 'solid'
s.fillcolor = s.linecolor
s.linewidth = 0
s.linestyle = 'solid'
alpha = .75
else:
s.fillstyle = 'hollow'
s.linewidth = 3
if signal_linestyles is not None:
s.linestyle = signal_linestyles[i]
else:
s.linestyle = 'solid'
alpha = 1.
if model is not None:
if model_colors is not None:
set_colors(model, model_colors)
# create the model stack
model_stack = HistStack()
for hist in model:
hist.SetLineWidth(0)
hist.drawstyle = 'hist'
model_stack.Add(hist)
if signal is not None and signal_on_top:
for s in scaled_signal:
model_stack.Add(s)
objects.append(model_stack)
if signal is not None and not signal_on_top:
if stack_signal:
# create the signal stack
signal_stack = HistStack()
for hist in scaled_signal:
signal_stack.Add(hist)
objects.append(signal_stack)
else:
objects.extend(scaled_signal)
if model is not None:
# draw uncertainty band
total_model, high_band_model, low_band_model = uncertainty_band(
model, systematics) #, systematics_components)
high = total_model + high_band_model
low = total_model - low_band_model
error_band_model = rootpy_utils.get_band(low,
high,
middle_hist=total_model)
error_band_model.fillstyle = '/'
error_band_model.fillcolor = 13
error_band_model.linecolor = 10
error_band_model.markersize = 0
error_band_model.markercolor = 10
error_band_model.drawstyle = 'e2'
objects.append(error_band_model)
if signal is not None and show_signal_error:
total_signal, high_band_signal, low_band_signal = uncertainty_band(
signal, systematics) #, systematics_components)
high = (total_signal + high_band_signal) * signal_scale
low = (total_signal - low_band_signal) * signal_scale
if signal_on_top:
high += total_model
low += total_model
error_band_signal = rootpy_utils.get_band(low,
high,
middle_hist=total_signal *
signal_scale)
error_band_signal.fillstyle = '\\'
error_band_signal.fillcolor = 13
error_band_signal.linecolor = 10
error_band_signal.markersize = 0
error_band_signal.markercolor = 10
error_band_signal.drawstyle = 'e2'
objects.append(error_band_signal)
if data is not None and blind is not True:
# create the data histogram
if isinstance(blind, tuple):
low, high = blind
# zero out bins in blind region
for bin in data.bins():
if (low < bin.x.high <= high or low <= bin.x.low < high):
data[bin.idx] = (0., 0.)
if poisson_errors:
# convert data to TGraphAsymmErrors with Poisson errors
data_poisson = data.poisson_errors()
data_poisson.markersize = 1.2
data_poisson.drawstyle = 'PZ'
objects.append(data_poisson)
else:
# Gaussian errors
data.drawstyle = 'E0'
objects.append(data)
# draw ratio plot
if model is not None and show_ratio:
fig.cd('ratio')
total_model = sum(model)
ratio_hist = Hist.divide(data, total_model)
# remove bins where data is zero
max_dev = 0
for bin in data.bins():
if bin.value <= 0:
ratio_hist[bin.idx] = (-100, 0)
else:
ratio_value = ratio_hist[bin.idx].value
dev = abs(ratio_value - 1)
if dev > max_dev:
max_dev = dev
if max_dev < 0.2:
ratio_range = (0.8, 1.2)
elif max_dev < 0.4:
ratio_range = (0.6, 1.4)
ruler_high = (ratio_range[1] + 1.) / 2.
ruler_low = (ratio_range[0] + 1.) / 2.
ratio_hist.linecolor = 'black'
ratio_hist.linewidth = 2
ratio_hist.fillstyle = 'hollow'
ratio_hist.drawstyle = 'E0'
"""
# draw empty copy of ratio_hist first so lines will show
ratio_hist_tmp = ratio_hist.Clone()
ratio_hist_tmp.Reset()
ratio_hist_tmp.Draw()
ratio_hist_tmp.yaxis.SetLimits(*ratio_range)
ratio_hist_tmp.yaxis.SetRangeUser(*ratio_range)
ratio_hist_tmp.yaxis.SetTitle('Data / Model')
ratio_hist_tmp.yaxis.SetNdivisions(4)
# not certain why the following is needed
ratio_hist_tmp.yaxis.SetTitleOffset(style.GetTitleYOffset())
ratio_xrange = range or ratio_hist.bounds()
ratio_hist_tmp.xaxis.SetLimits(*ratio_xrange)
#ratio_hist_tmp.xaxis.SetRangeUser(*ratio_xrange)
ratio_hist_tmp.xaxis.SetTickLength(
ratio_hist_tmp.xaxis.GetTickLength() * 2)
# draw ratio=1 line
line = Line(ratio_xrange[0], 1,
ratio_xrange[1], 1)
line.linestyle = 'dashed'
line.linewidth = 2
line.Draw()
# draw high ratio line
line_up = Line(ratio_xrange[0], ruler_high,
ratio_xrange[1], ruler_high)
line_up.linestyle = 'dashed'
line_up.linewidth = 2
line_up.Draw()
# draw low ratio line
line_dn = Line(ratio_xrange[0], ruler_low,
ratio_xrange[1], ruler_low)
line_dn.linestyle = 'dashed'
line_dn.linewidth = 2
line_dn.Draw()
"""
# draw band below points on ratio plot
ratio_hist_high = Hist.divide(total_model + high_band_model,
total_model)
ratio_hist_low = Hist.divide(total_model - low_band_model,
total_model)
fig.cd('ratio')
error_band = rootpy_utils.get_band(ratio_hist_high, ratio_hist_low)
error_band.fillstyle = '/'
error_band.fillcolor = '#858585'
error_band.drawstyle = 'E2'
fig.draw('ratio', [error_band, ratio_hist], xdivisions=xdivisions)
if separate_legends:
fig.cd('main')
right_legend = Legend(len(signal) + 1 if signal is not None else 1,
pad=fig.pad('main'),
**legend_params('right', textsize))
right_legend.AddEntry(data, style='lep')
if signal is not None:
for s in reversed(scaled_signal):
right_legend.AddEntry(s, style='F' if fill_signal else 'L')
legends.append(right_legend)
if model is not None:
n_entries = len(model)
if systematics:
n_entries += 1
model_legend = Legend(n_entries,
pad=fig.pad('main'),
**legend_params('left', textsize))
for hist in reversed(model):
model_legend.AddEntry(hist, style='F')
if systematics:
model_err_band = error_band_model.Clone()
model_err_band.linewidth = 0
model_err_band.linecolor = 'white'
model_err_band.fillcolor = '#858585'
model_err_band.title = 'Uncert.'
model_legend.AddEntry(model_err_band, style='F')
legends.append(model_legend)
else:
n_entries = 1
if signal is not None:
n_entries += len(scaled_signal)
if model is not None:
n_entries += len(model)
if systematics:
n_entries += 1
fig.cd('main')
legend = Legend(n_entries,
pad=fig.pad('main'),
**legend_params(legend_position, textsize))
if data is not None:
legend.AddEntry(data, style='lep')
if signal is not None:
for s in reversed(scaled_signal):
legend.AddEntry(s, style='F' if fill_signal else 'L')
if model:
for hist in reversed(model):
legend.AddEntry(hist, style='F')
model_err_band = error_band_model.Clone()
model_err_band.linewidth = 0
model_err_band.linecolor = 'white'
model_err_band.fillcolor = '#858585'
model_err_band.title = 'Uncert.'
legend.AddEntry(model_err_band, style='F')
legends.append(legend)
# draw the objects
bounds = fig.draw('main',
objects,
ypadding=ypadding,
logy_crop_value=1E-1,
xdivisions=xdivisions)
xaxis, yaxis = fig.axes('main')
base_xaxis = xaxis
base_xaxis.range_user = template.bounds()
base_xaxis.limits = template.bounds()
xmin, xmax, ymin, ymax = bounds
if show_ratio:
base_xaxis = fig.axes('ratio')[0]
base_xaxis.range_user = template.bounds()
base_xaxis.limits = template.bounds()
# draw the legends
fig.cd('main')
for legend in legends:
legend.Draw()
label_plot(
fig.pad('main'),
template=template,
xaxis=base_xaxis,
yaxis=yaxis,
xlabel=name,
ylabel=ylabel,
units=units,
category_label=category.label,
extra_label=plot_label,
extra_label_position='right' if legend_position == 'left' else 'left',
data_info=data_info)
if logy and logy_min is not None:
yaxis.min = logy_min
ymin = logy_min
# draw arrows
if arrow_values is not None:
arrow_top = ymin + (ymax - ymin) / 2.
fig.cd('main')
for value in arrow_values:
arrow = Arrow(value, arrow_top, value, ymin, 0.05, '|>')
arrow.SetAngle(30)
arrow.SetLineWidth(2)
arrow.Draw()
if show_pvalue and data is not None and model:
fig.cd('main')
total_model = sum(model)
# show p-value and chi^2
pvalue = total_model.Chi2Test(data, 'WW')
pvalue_label = ROOT.TLatex(0.6, 0.97, "p-value={0:.2f}".format(pvalue))
pvalue_label.SetNDC(True)
pvalue_label.SetTextFont(43)
pvalue_label.SetTextSize(16)
pvalue_label.Draw()
chi2 = total_model.Chi2Test(data, 'WW CHI2/NDF')
chi2_label = ROOT.TLatex(0.78, 0.97,
"#chi^{{2}}/ndf={0:.2f}".format(chi2))
chi2_label.SetNDC(True)
chi2_label.SetTextFont(43)
chi2_label.SetTextSize(16)
chi2_label.Draw()
if top_label is not None:
fig.cd('main')
label = ROOT.TLatex(
fig.pad('main').GetLeftMargin() + 0.08, 0.97, top_label)
label.SetNDC(True)
label.SetTextFont(43)
label.SetTextSize(16)
label.Draw()
if output_name is not None:
# create the output filename
filename = 'var_{0}_{1}'.format(category.name,
output_name.lower().replace(' ', '_'))
if logy:
filename += '_logy'
filename += '_root'
if output_formats is None:
output_formats = ('png', )
# save the figure
save_canvas(fig, output_dir, filename, formats=output_formats)
return fig
def plotHistos(histos, text="", option="", statbox=True):
"""
Plots a list of histograms
"""
log = logging.getLogger('pyroplot')
import rootpy
from rootpy.plotting import Hist, HistStack
from ROOT import kRed, gPad, TPaveStats
# only for 1D and 2D type histograms:
# determine and set maximum for all histograms
stack = HistStack()
for hist in histos:
if not hist.__class__.__name__ == "Hist3D":
stack.Add(hist)
# determine maximum value and set it for all histograms
if stack.GetHists():
maxplot = stack.GetMaximum()
minplot = stack.GetMinimum()
for hist in stack.GetHists():
hist.SetMaximum(maxplot)
# special treatment for log scale Y
if gPad.GetLogy():
hist.SetMinimum(1.)
else:
# if histogram minimum is positive, set to 0.
if minplot > 0:
hist.SetMinimum(0.)
for idx, hist in enumerate(histos):
try:
thisopt = option
# if not first histo, add "same" to options so previous ones are not overwritten
if idx:
if statbox:
thisopt += "sames"
else:
thisopt += "same"
histcopy = hist.DrawCopy(thisopt)
# on first go: print identifying text on pad
if not idx and text:
markPad(text=text, x=.14, y=.85, size=0.041)
if statbox:
thisopt += "sames"
else:
thisopt += "same"
histcopy.Draw(thisopt)
gPad.Update()
if statbox:
try:
statBox = histcopy.GetListOfFunctions().FindObject("stats")
# offset from last statbox
offset = .18
# needs to be larger for Profile & 2D histos
if (hist.__class__.__name__ == "Profile"
or hist.__class__.__name__ == "Hist2D"
or hist.__class__.__name__ == "Profile2D"
or hist.__class__.__name__ == "Hist3D"):
offset = .26
statBox.SetY1NDC(statBox.GetY1NDC() - offset * (idx))
statBox.SetY2NDC(statBox.GetY2NDC() - offset * (idx))
statBox.SetTextColor(hist.GetLineColor())
statBox.SetBorderSize(2)
except AttributeError:
log.debug("Could not get statbox for histogram " +
hist.GetName())
except rootpy.ROOTError, e:
log.error(
"Drawing histogram %s caused ROOTError exception ('%s')" %
(hist.GetName(), e.msg))
gPad.Clear() # otherwise this happens again when drawing..
markPad(text="Could not draw %s ('%s')" % (hist.GetName(), e.msg),
x=0.14,
y=0.4,
size=0.023,
color=kRed)
return # give up!