def test_binomial(self):
"""
Check that we can generate a binomial distribution for event number
"""
# make a dict of bunches of xboa.Hits separated by event (spill) number
bunch_dict = Bunch.new_dict_from_read_builtin('maus_json_primary', \
BIN_SIM, 'spill')
bunch_weights = []
for bunch in bunch_dict.values():
bunch_weights.append(bunch.bunch_weight())
canvas = common.make_root_canvas("")
hist = common.make_root_histogram("generated distribution", \
bunch_weights, "bunch weights", BIN_N+1, xmin=-0.5, xmax=BIN_N+0.5)
hist.Fill(0,
1000 - hist.GetSumOfWeights()) # xboa ignores empty spills
hist.Draw()
test_hist = ROOT.TH1D("test_hist", "reference distribution", BIN_N + 1,
-0.5,
BIN_N + 0.5) # pylint: disable = E1101, C0301
for i in range(BIN_N + 1):
test_hist.SetBinContent(i, \
(ROOT.TMath.BinomialI(BIN_P, BIN_N, i-1)- ROOT.TMath.BinomialI(BIN_P, BIN_N, i))*N_SPILLS) # pylint: disable = E1101, C0301
print i, test_hist.GetBinContent(i), hist.GetBinContent(i)
test_hist.SetLineStyle(2)
test_hist.Draw("SAME")
ks_value = test_hist.KolmogorovTest(hist)
canvas.Update()
canvas.Print(PLOT_DIR + "/binomial_distribution_test.png")
print "binomial ks_value", ks_value
self.assertGreater(ks_value, 1e-3)
def test_gaussian(self):
"""
Check that the weight of each sub-beam is close to the expected weight
"""
# make a dict of bunches of xboa.Hits separated by event (spill) number
bunch = Bunch.new_dict_from_read_builtin('maus_json_primary', \
BIN_SIM, 'pid')[-13]
for key, value in {'energy':226.0, 'z':3.0, 'x':0., 'y':0., \
'px':0., 'py':0.}.iteritems():
sigma = 5.*bunch.moment([key, key])**0.5\
/float(bunch.bunch_weight())**0.5
self.assertLess(abs(bunch.mean([key])[key] - value), sigma + 1.)
covs = bunch.covariance_matrix(['x', 'px', 'y', 'py'])
test = numpy.array(\
[[1.05668599e+03, -6.33950201e+02, 0.00000000e+00, 6.34327423e+02],
[-6.33950201e+02, 1.14145263e+03, -6.34327423e+02, 0.00000000e+00],
[0.00000000e+00, -6.34327423e+02, 1.05668599e+03, -6.33950201e+02],
[6.34327423e+02, 0.00000000e+00, -6.33950201e+02, 1.14145263e+03]]
)
msg_ = "Determinants:"+str(numpy.linalg.det(test))+ \
str(numpy.linalg.det(covs))
self.assertLess(\
2.*abs(numpy.linalg.det(test) - numpy.linalg.det(covs))/ \
(numpy.linalg.det(test) + numpy.linalg.det(covs)), 0.2,
msg=msg_)
self.__cmp_matrix(test, covs)
def test_gaussian(self):
"""
Check that the weight of each sub-beam is close to the expected weight
"""
# make a dict of bunches of xboa.Hits separated by event (spill) number
bunch = Bunch.new_dict_from_read_builtin('maus_json_primary', \
BIN_SIM, 'pid')[-13]
for key, value in {'energy':226.0, 'z':3.0, 'x':0., 'y':0., \
'px':0., 'py':0.}.iteritems():
sigma = 5.*bunch.moment([key, key])**0.5\
/float(bunch.bunch_weight())**0.5
self.assertLess(abs(bunch.mean([key])[key]-value), sigma+1.)
covs = bunch.covariance_matrix(['x', 'px', 'y', 'py'])
test = numpy.array(\
[[1.05668599e+03, -6.33950201e+02, 0.00000000e+00, 6.34327423e+02],
[-6.33950201e+02, 1.14145263e+03, -6.34327423e+02, 0.00000000e+00],
[0.00000000e+00, -6.34327423e+02, 1.05668599e+03, -6.33950201e+02],
[6.34327423e+02, 0.00000000e+00, -6.33950201e+02, 1.14145263e+03]]
)
msg_ = "Determinants:"+str(numpy.linalg.det(test))+ \
str(numpy.linalg.det(covs))
self.assertLess(\
2.*abs(numpy.linalg.det(test) - numpy.linalg.det(covs))/ \
(numpy.linalg.det(test) + numpy.linalg.det(covs)), 0.2,
msg=msg_)
self.__cmp_matrix(test, covs)
def test_binomial(self):
"""
Check that we can generate a binomial distribution for event number
"""
# make a dict of bunches of xboa.Hits separated by event (spill) number
bunch_dict = Bunch.new_dict_from_read_builtin('maus_json_primary', \
BIN_SIM, 'spill')
bunch_weights = []
for bunch in bunch_dict.values():
bunch_weights.append(bunch.bunch_weight())
canvas = common.make_root_canvas("")
hist = common.make_root_histogram("generated distribution", \
bunch_weights, "bunch weights", BIN_N+1, xmin=-0.5, xmax=BIN_N+0.5)
hist.Fill(0, 1000-hist.GetSumOfWeights()) # xboa ignores empty spills
hist.Draw()
test_hist = ROOT.TH1D("test_hist", "reference distribution", BIN_N+1, -0.5, BIN_N+0.5) # pylint: disable = E1101, C0301
for i in range(BIN_N+1):
test_hist.SetBinContent(i, \
(ROOT.TMath.BinomialI(BIN_P, BIN_N, i-1)- ROOT.TMath.BinomialI(BIN_P, BIN_N, i))*N_SPILLS) # pylint: disable = E1101, C0301
print i, test_hist.GetBinContent(i), hist.GetBinContent(i)
test_hist.SetLineStyle(2)
test_hist.Draw("SAME")
ks_value = test_hist.KolmogorovTest(hist)
canvas.Update()
canvas.Print(PLOT_DIR+"/binomial_distribution_test.png")
print "binomial ks_value", ks_value
self.assertGreater(ks_value, 1e-3)
def test_weighting(self):
"""
Check that the weight of each sub-beam is close to the expected weight
"""
# make a dict of bunches of xboa.Hits separated by event (spill) number
bunch_dict = Bunch.new_dict_from_read_builtin('maus_json_primary', \
BIN_SIM, 'pid')
test_weights = {}
sum_weights = 0
for pid, bunch in bunch_dict.iteritems():
test_weights[pid] = bunch.bunch_weight()
sum_weights += test_weights[pid]
for pid, weight in test_weights.iteritems():
print pid, weight, WEIGHTS_TO_PID[pid]*sum_weights, sum_weights**0.5
self.assertLess(abs(weight - WEIGHTS_TO_PID[pid]*sum_weights), \
sum_weights**0.5 )
def test_weighting(self):
"""
Check that the weight of each sub-beam is close to the expected weight
"""
# make a dict of bunches of xboa.Hits separated by event (spill) number
bunch_dict = Bunch.new_dict_from_read_builtin('maus_json_primary', \
BIN_SIM, 'pid')
test_weights = {}
sum_weights = 0
for pid, bunch in bunch_dict.iteritems():
test_weights[pid] = bunch.bunch_weight()
sum_weights += test_weights[pid]
for pid, weight in test_weights.iteritems():
print pid, weight, WEIGHTS_TO_PID[
pid] * sum_weights, sum_weights**0.5
self.assertLess(abs(weight - WEIGHTS_TO_PID[pid]*sum_weights), \
sum_weights**0.5 )
def test_sawtooth_time(self):
"""
Check that beam maker generates sawtooth t distribution correctly
"""
bunch = Bunch.new_dict_from_read_builtin('maus_json_primary', \
BIN_SIM, 'pid')[-13]
canvas, hist = bunch.root_histogram('t', 'ns', xmin=-1e6, xmax=1e6)
cmp_hist = ROOT.TH1D("test", "test", hist.GetNbinsX(), -1e6, 1e6) # pylint: disable = E1101, C0301
for h_bin in range(1, hist.GetNbinsX()+1):
my_x = hist.GetBinCenter(h_bin)
my_y = 2.*hist.GetSumOfWeights()*h_bin/hist.GetNbinsX()**2
cmp_hist.Fill(my_x, my_y)
cmp_hist.SetLineStyle(2)
cmp_hist.Draw("SAME")
ks_value = cmp_hist.KolmogorovTest(hist)
print "sawtooth t ks_value", ks_value
canvas.Update()
canvas.Print(PLOT_DIR+"/sawtooth_time_distribution_test.png")
self.assertGreater(ks_value, 1e-3)
def test_sawtooth_time(self):
"""
Check that beam maker generates sawtooth t distribution correctly
"""
bunch = Bunch.new_dict_from_read_builtin('maus_json_primary', \
BIN_SIM, 'pid')[-13]
canvas, hist = bunch.root_histogram('t', 'ns', xmin=-1e6, xmax=1e6)
cmp_hist = ROOT.TH1D("test", "test", hist.GetNbinsX(), -1e6,
1e6) # pylint: disable = E1101, C0301
for h_bin in range(1, hist.GetNbinsX() + 1):
my_x = hist.GetBinCenter(h_bin)
my_y = 2. * hist.GetSumOfWeights() * h_bin / hist.GetNbinsX()**2
cmp_hist.Fill(my_x, my_y)
cmp_hist.SetLineStyle(2)
cmp_hist.Draw("SAME")
ks_value = cmp_hist.KolmogorovTest(hist)
print "sawtooth t ks_value", ks_value
canvas.Update()
canvas.Print(PLOT_DIR + "/sawtooth_time_distribution_test.png")
self.assertGreater(ks_value, 1e-3)
