def get_random_hist():
h = Hist(10, -5, 5)
h.FillRandom('gaus')
return h
def test_histfactory():
# create some Samples
data = Data('data')
data.hist = get_random_hist()
a = Sample('QCD')
b = Sample('QCD')
for sample in (a, b):
sample.hist = get_random_hist()
# include some histosysts
for sysname in ('x', 'y', 'z'):
histosys = HistoSys(sysname)
histosys.high = get_random_hist()
histosys.low = get_random_hist()
sample.AddHistoSys(histosys)
# include some normfactors
for normname in ('x', 'y', 'z'):
norm = NormFactor(normname)
norm.value = 1
norm.high = 2
norm.low = 0
norm.const = False
sample.AddNormFactor(norm)
# samples must be compatible here
c = a + b
c = sum([a, b])
# create Channels
channel_a = Channel('VBF')
channel_a.data = data
channel_a.AddSample(a)
channel_b = Channel('VBF')
channel_b.data = data
channel_b.AddSample(b)
combined_channel = channel_a + channel_b
combined_channel = sum([channel_a, channel_b])
# create a Measurement
meas = Measurement('MyAnalysis')
meas.AddChannel(channel_a)
# create the workspace containing the model
workspace = make_workspace(meas, silence=True)
with TemporaryFile():
workspace.Write()
assert_true(channel_a.GetSample(a.name) is not None)
channel_a.RemoveSample(a.name)
assert_true(channel_a.GetSample(a.name) is None)
assert_true(meas.GetChannel(channel_a.name) is not None)
meas.RemoveChannel(channel_a.name)
assert_true(meas.GetChannel(channel_a.name) is None)
# test split_norm_shape
nominal = Hist(1, 0, 1)
nominal.FillRandom('gaus')
hsys = HistoSys('shape', high=nominal * 1.5, low=nominal * 0.9)
norm, shape = split_norm_shape(hsys, nominal)
assert_equal(norm.low, 0.9)
assert_equal(norm.high, 1.5)
assert_equal(shape.high[1].value, nominal[1].value)
assert_equal(shape.low[1].value, nominal[1].value)
# set the random seed
ROOT.gRandom.SetSeed(42)
np.random.seed(42)
# signal distribution
signal = 126 + 10 * np.random.randn(100)
signal_obs = 126 + 10 * np.random.randn(100)
# create histograms
h1 = Hist(30, 40, 200, title='Background', markersize=0)
h2 = h1.Clone(title='Signal')
h3 = h1.Clone(title='Data')
h3.markersize = 1.2
# fill the histograms with our distributions
h1.FillRandom('landau', 1000)
map(h2.Fill, signal)
h3.FillRandom('landau', 1000)
map(h3.Fill, signal_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
from style import set_hsg4_style
from rootpy.plotting import Hist
#from rootpy.interactive import wait
from templates import SimplePlot, RatioPlot
import ROOT
ROOT.gROOT.SetBatch()
set_hsg4_style()
hist = Hist(10, 0, 1)
hist.FillRandom('gaus')
plot = SimplePlot(xtitle='X [GeV]', ytitle='Events')
plot.draw('main', hist)
plot.SaveAs('simple.png')
plot = RatioPlot(xtitle='X [GeV]',
ytitle='Events',
ratio_title='Data / Model',
offset=-122,
ratio_margin=22,
prune_ratio_ticks=True)
plot.draw('main', hist)
plot.SaveAs('ratio.png')
#wait()
def test_init_from_hist():
h = Hist(100, -10, 10)
h.FillRandom('gaus')
g = Graph(h)
def test_poisson_errors():
h = Hist(20, -3, 3)
h.FillRandom('gaus')
g = h.poisson_errors()
def test_init_from_graph():
hist = Hist(10, 0, 1)
hist.FillRandom('gaus')
graph = Graph(hist)
hist2 = Hist(graph)
def make_plot():
canvas = Canvas(700, 500)
hist = Hist(10, -3, 3)
hist.FillRandom('gaus')
hist.Draw()
return canvas
h_t2Shape.SetBinContent(7, 20)
h_t2Shape.SetBinContent(8, 20)
h_t2Shape.SetBinContent(9, 10)
h_t2Shape.SetBinContent(10, 10)
h_t3Shape.SetBinContent(1, 20)
h_t3Shape.SetBinContent(2, 20)
h_t3Shape.SetBinContent(3, 20)
h_t3Shape.SetBinContent(4, 50)
h_t3Shape.SetBinContent(5, 50)
h_t3Shape.SetBinContent(6, 100)
h_t3Shape.SetBinContent(7, 100)
h_t3Shape.SetBinContent(8, 50)
h_t3Shape.SetBinContent(9, 50)
h_t3Shape.SetBinContent(10, 40)
if useT1: h_t1.FillRandom(h_t1Shape, nTemplate)
if useT2: h_t2.FillRandom(h_t2Shape, nTemplate)
if useT3: h_t3.FillRandom(h_t3Shape, nTemplate)
if useT4: h_t4.FillRandom(h_t4Shape, nTemplate)
pass
# h_data.Scale(nData)
# h_data.FillRandom( t1Scale * h_t1Shape + t2Scale * h_t2Shape + t3Scale * h_t3Shape, nData )
fillingHistogram = 0
if useT1: fillingHistogram += t1Scale * h_t1Shape
if useT2: fillingHistogram += t2Scale * h_t2Shape
if useT3: fillingHistogram += t3Scale * h_t3Shape
if useT4: fillingHistogram += t4Scale * h_t4Shape
if useDataFromFile:
h_data = getDataFromFile()
