def test_bar():
from rootpy.plotting import root2matplotlib as rplt
h = Hist(100, -5, 5)
h.FillRandom('gaus')
rplt.bar(h)
# stack
h1 = h.Clone()
stack = HistStack([h, h1])
rplt.bar(stack)
rplt.bar([h, h1], stacked=True)
rplt.bar([h, h1], stacked=False)
rplt.bar([h, h1], stacked=False, reverse=True)
def test_bar():
from rootpy.plotting import root2matplotlib as rplt
h = Hist(100, -5, 5)
h.FillRandom('gaus')
rplt.bar(h)
# stack
h1 = h.Clone()
stack = HistStack([h, h1])
rplt.bar(stack)
rplt.bar([h, h1], stacked=True)
rplt.bar([h, h1], stacked=False)
rplt.bar([h, h1], stacked=False, reverse=True)
def barhist(h, **kwargs):
"""
Draws a ROOT TH1 histogram h as a pyplot line.
kwargs:
color,
lw,
fillstyle,
label,
scaling: number of "normed",
rebin: number
"""
col = kwargs.pop("color", "blue")
#b = rplt.errorbar(h, xerr=False, color=col, mec=col, ms=0, ecolor=col, **kwargs)
lw = kwargs.pop("lw", 1)
fs = kwargs.pop("fillstyle", False)
lab = kwargs.pop("label", "")
scaling = kwargs.pop("scaling", 1.0)
rebin = kwargs.pop("rebin", 1)
kwargs_d = dict(kwargs)
if scaling == "normed":
if h.Integral() > 0:
scaling = 1.0 / h.Integral()
else:
scaling = 0.0
h = h.Clone()
h.Scale(scaling)
h.Rebin(rebin)
h.fillstyle = "hollow"
b = rplt.bar(h, lw=0, color="none", ecolor=col, label=None, **kwargs)
xs = []
ys = []
for _b in b:
xs += [_b.xy[0], _b.xy[0] + _b.get_width()]
ys += [_b.xy[1] + _b.get_height(), _b.xy[1] + _b.get_height()]
_b.set_hatch(fs)
_b.set_color(col)
plt.gca().add_patch(_b)
#kwargs_d.pop("lw")
kwargs_d.pop("edgecolor", "")
kwargs_d.pop("stacked", "")
#plt.xticks(np.arange(min(xs), max(xs), (max(xs)-min(xs)) / ))
plt.plot(xs, ys, color=col, lw=lw, label=lab, **kwargs_d)
return b
def barhist(h, **kwargs):
"""
Draws a ROOT TH1 histogram h as a pyplot line.
kwargs:
color,
lw,
fillstyle,
label,
scaling: number of "normed",
rebin: number
"""
col = kwargs.pop("color", "blue")
#b = rplt.errorbar(h, xerr=False, color=col, mec=col, ms=0, ecolor=col, **kwargs)
lw = kwargs.pop("lw", 1)
fs = kwargs.pop("fillstyle", False)
lab = kwargs.pop("label", "")
scaling = kwargs.pop("scaling", 1.0)
rebin = kwargs.pop("rebin", 1)
kwargs_d = dict(kwargs)
if scaling == "normed":
if h.Integral()>0:
scaling = 1.0 / h.Integral()
else:
scaling = 0.0
h = h.Clone()
h.Scale(scaling)
h.Rebin(rebin)
h.fillstyle = "hollow"
b = rplt.bar(h, lw=0, color="none", ecolor=col, label=None, **kwargs)
xs = []
ys = []
for _b in b:
xs += [_b.xy[0], _b.xy[0]+_b.get_width()]
ys += [_b.xy[1]+_b.get_height(), _b.xy[1]+_b.get_height()]
_b.set_hatch(fs)
_b.set_color(col)
plt.gca().add_patch(_b)
#kwargs_d.pop("lw")
kwargs_d.pop("edgecolor", "")
kwargs_d.pop("stacked", "")
#plt.xticks(np.arange(min(xs), max(xs), (max(xs)-min(xs)) / ))
plt.plot(xs, ys, color=col, lw=lw, label=lab, **kwargs_d)
return b
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)
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
plt.legend(numpoints=1)
ax1 = plt.subplot(gs[1])
yloc = plt.MaxNLocator(4)
ax1.yaxis.set_major_locator(yloc)
#ax1.set_ticks([-1, 1, 2, 3])
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
plt.xlabel('Mass [GeV]', position=(1., 0.), ha='right', fontsize=40)
plt.ylabel('data/MC', va='top', fontsize=24)
rplt.errorbar(ratio, emptybins=False)
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)
# axes = plt.subplot()
# rplt.bar(stack, stacked=True, axes=axes, yerr=False, alpha=0.5, rasterized=True, ec='grey', linewidth=1)
# rplt.errorbar(hists['Data'], xerr=False, emptybins=False, axes=axes, marker='o', ms=10)
try:
axes.set_yscale('log')
rplt.bar(stack, stacked=True, axes=axes, yerr=False, alpha=0.5, rasterized=True, ec='grey', linewidth=0)
rplt.hist(stack.sum, axes=axes, yerr=False, alpha=0.8, rasterized=True, ec='grey', linewidth=1)
tmpstack = stack.sum
for i in xrange(len(stack)):
tmpstack = tmpstack - stack[len(stack)-1-i]
rplt.hist(tmpstack, axes=axes, yerr=False, alpha=0.2, rasterized=True, ec='grey', linewidth=1)
if hists['Data'].Integral():
rplt.errorbar(hists['Data'], xerr=False, emptybins=False, axes=axes, marker='o', lw=1)
except:
# # print "no data events..."
# # continue
pass
for signalsample in signalsamples:
# print signalsample
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', position=(0., 1.), va='top', fontsize = 24)
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
plt.legend(numpoints=1)
ax1 = plt.subplot(gs[1])
yloc = plt.MaxNLocator(4)
ax1.yaxis.set_major_locator(yloc)
#ax1.set_ticks([-1, 1, 2, 3])
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
plt.xlabel('Mass', position=(1., 0.), ha='right', fontsize = 24)
plt.ylabel('data/MC', position=(0., 1.), va='top', fontsize = 24)
rplt.errorbar(ratio, emptybins=False)
# plot with ROOT
set_style('ATLAS')
canvas = Canvas(width=700, height=500)
stack.Draw()
h3.Draw('E1 same')
stack.xaxis.SetTitle('Mass')
stack.yaxis.SetTitle('Events')
legend = Legend(2)
legend.AddEntry(h1, 'F')
legend.AddEntry(h2, 'F')
legend.AddEntry(h3, 'P')
legend.Draw()
canvas.Modified()
canvas.Update()
# plot with matplotlib
fig = plt.figure(figsize=(7, 5), dpi=100, facecolor='white')
axes = plt.axes([0.15, 0.15, 0.8, 0.8])
axes.xaxis.set_minor_locator(AutoMinorLocator())
axes.yaxis.set_minor_locator(AutoMinorLocator())
axes.tick_params(which='major', labelsize=15, length=8)
axes.tick_params(which='minor', length=4)
rplt.bar(stack, stacked=True, axes=axes)
rplt.errorbar(h3, xerr=False, emptybins=False, axes=axes)
plt.xlabel('Mass', position=(1., 0.), ha='right')
plt.ylabel('Events', position=(0., 1.), va='top')
plt.legend(numpoints=1)
if not ROOT.gROOT.IsBatch():
plt.show()
label = ROOT.TText(0.3, 0.8, 'ROOT')
label.SetTextFont(43)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
# plot with matplotlib
set_style('ATLAS', mpl=True)
fig = plt.figure(figsize=(7, 5), dpi=100)
axes = plt.axes()
axes.xaxis.set_minor_locator(AutoMinorLocator())
axes.yaxis.set_minor_locator(AutoMinorLocator())
axes.yaxis.set_major_locator(MultipleLocator(20))
rplt.bar(stack, stacked=True, axes=axes)
rplt.errorbar(h3, xerr=False, emptybins=False, axes=axes)
plt.xlabel('Mass', position=(1., 0.), va='bottom', ha='right')
plt.ylabel('Events', position=(0., 1.), va='top', ha='right')
axes.xaxis.set_label_coords(1., -0.20)
axes.yaxis.set_label_coords(-0.18, 1.)
leg = plt.legend()
axes.text(0.3,
0.8,
'matplotlib',
verticalalignment='center',
horizontalalignment='center',
transform=axes.transAxes,
fontsize=20)
if not ROOT.gROOT.IsBatch():
pad1.cd()
# pad1 becomes the current pad
rootstack = ROOT.THStack(stack)
rootstack.Draw('HIST')
rootstack.GetYaxis().SetTitle("Entries")
rootstack.GetYaxis().SetTitleSize(20)
rootstack.GetYaxis().SetTitleFont(43)
rootstack.GetYaxis().SetTitleOffset(1.55)
pad1.SetLogy()
if plotWithMPL:
axes.set_yscale('log')
rplt.bar(stack,
stacked=True,
axes=axes,
yerr=False,
alpha=0.5,
rasterized=True,
ec='grey',
linewidth=0)
tmpstack = stack.sum
tmpstack.fillstyle = "none"
for i in xrange(len(stack)):
myalpha = 0.4 if i > 0 else 1.0
rplt.hist(tmpstack,
axes=axes,
yerr=False,
alpha=myalpha,
rasterized=True,
ec='grey',
linewidth=1,
fillstyle="none")
def test_bar():
from rootpy.plotting import root2matplotlib as rplt
h = Hist(100, -5, 5)
h.FillRandom('gaus')
rplt.bar(h)
# normalize the histograms
h1 /= h1.Integral()
h2 /= h2.Integral()
# set visual attributes
h1.SetFillStyle('solid')
h1.SetFillColor('green')
h1.SetLineColor('green')
h2.SetFillStyle('solid')
h2.SetFillColor('red')
h2.SetLineColor('red')
stack = HistStack()
stack.Add(h1)
stack.Add(h2)
# plot with ROOT
h1.SetTitle('Histogram of IQ: #mu=100, #sigma=15')
stack.Draw()
h1.GetXaxis().SetTitle('Smarts')
h1.GetYaxis().SetTitle('Probability')
# plot with matplotlib
rplt.bar(stack, alpha=0.75, stacked=True)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
plt.show()
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)
# axes = plt.subplot()
try:
axes.set_yscale('log')
rplt.bar(stack, stacked=True, axes=axes, yerr=False, alpha=0.5, )
if hists['Data'].Integral():
rplt.errorbar(hists['Data'], xerr=False, emptybins=False, axes=axes)
except:
# print "no data events..."
# continue
pass
for signalsample in signalsamples:
skip = 1
if any([thissig in signalsample for thissig in plottedsignals]):
skip=0
if skip:
continue
signalfile = root_open("hists/rundir_signal/"+signalsample)
try:
stack.Add(tmphist)
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)
# axes = plt.subplot()
try:
axes.set_yscale('log')
rplt.bar(
stack,
stacked=True,
axes=axes,
yerr=False,
alpha=0.5,
)
if hists['Data'].Integral():
rplt.errorbar(hists['Data'],
xerr=False,
emptybins=False,
axes=axes)
except:
# print "no data events..."
# continue
pass
for signalsample in signalsamples:
skip = 1
