def test_no_nan(self):
"""
Check that no nans appear
"""
bunch = Bunch.new_from_read_builtin('maus_json_primary', NAN_SIM)
self.assertAlmostEqual(bunch.bunch_weight(), 1000.)
for hit in bunch:
self.assertFalse(math.isnan(hit['energy']))
canvas, hist = bunch.root_histogram('kinetic_energy',
'MeV',
xmin=0.,
xmax=150.)
cmp_hist = ROOT.TH1D("test", "test", hist.GetNbinsX(), 0.,
150.) # pylint: disable = E1101, C0301
for h_bin in range(1, hist.GetNbinsX() + 1):
sigma = 25.
norm = 2. * bunch.bunch_weight() / sigma / (2. * math.pi)**0.5
norm *= 150. / hist.GetNbinsX()
my_x = hist.GetBinCenter(h_bin)
my_y = math.exp(-my_x**2. / 2. / sigma**2.) * norm
cmp_hist.Fill(my_x, my_y)
cmp_hist.SetLineStyle(2)
cmp_hist.Draw("SAME")
ks_value = cmp_hist.KolmogorovTest(hist)
print "nan energy ks_value", ks_value
canvas.Update()
canvas.Print(PLOT_DIR + "/nan_energy_distribution_test.png")
self.assertGreater(ks_value, 1e-3)
def test_defaults(self):
"""
Check that the default beam parameters run and produce some number of
primary hits > 0
"""
bunch = Bunch.new_from_read_builtin('maus_json_primary', DEF_SIM)
self.assertTrue(len(bunch) > 0)
def test_defaults(self):
"""
Check that the default beam parameters run and produce some number of
primary hits > 0
"""
bunch = Bunch.new_from_read_builtin('maus_json_primary', DEF_SIM)
self.assertTrue(len(bunch) > 0)
def test_no_nan(self):
"""
Check that no nans appear
"""
bunch = Bunch.new_from_read_builtin('maus_json_primary', NAN_SIM)
self.assertAlmostEqual(bunch.bunch_weight(), 1000.)
for hit in bunch:
self.assertFalse(math.isnan(hit['energy']))
canvas, hist = bunch.root_histogram('kinetic_energy', 'MeV', xmin=0.,
xmax=150.)
cmp_hist = ROOT.TH1D("test", "test", hist.GetNbinsX(), 0., 150.) # pylint: disable = E1101, C0301
for h_bin in range(1, hist.GetNbinsX()+1):
sigma = 25.
norm = 2.*bunch.bunch_weight()/sigma/(2.*math.pi)**0.5
norm *= 150./hist.GetNbinsX()
my_x = hist.GetBinCenter(h_bin)
my_y = math.exp(-my_x**2./2./sigma**2.)*norm
cmp_hist.Fill(my_x, my_y)
cmp_hist.SetLineStyle(2)
cmp_hist.Draw("SAME")
ks_value = cmp_hist.KolmogorovTest(hist)
print "nan energy ks_value", ks_value
canvas.Update()
canvas.Print(PLOT_DIR+"/nan_energy_distribution_test.png")
self.assertGreater(ks_value, 1e-3)
def smear_test():
src_fname = '/tmp/smear_test.in'
tgt_fname = '/tmp/smear_test.out'
tgt_nevents = 10000
cov = numpy.array([
[
1000.,
0.,
0.,
0.,
0.,
],
[
0.,
600.,
0.,
0.,
0.,
],
[
0.,
0.,
1000.,
0.,
0.,
],
[
0.,
0.,
0.,
600.,
0.,
],
[
0.,
0.,
0.,
0.,
100.,
],
])
mean = numpy.array([0., 0., 0., 0., 0.])
src_dist = numpy.random.multivariate_normal(mean, cov, 1000)
fsrc = open(src_fname, 'w')
for item in src_dist:
fsrc.write(json.dumps(item.tolist()) + '\n')
fsrc.close()
SmearAndSample(src_fname, tgt_fname, "icool_for003", tgt_nevents)
fin = open(tgt_fname)
tgt_bunch = Bunch.new_from_read_builtin("icool_for003", tgt_fname)
tgt_dist = [[hit[key] for key in SmearAndSample.keys] for hit in tgt_bunch]
if len(tgt_dist) != tgt_nevents:
raise RuntimeError("Fail")
tgt_dist = numpy.array(tgt_dist).transpose()
numpy.set_printoptions(precision=8, linewidth=1000)
print numpy.cov(src_dist.transpose())
print
print numpy.cov(tgt_dist)
