def test_gen_toy():
np.random.seed(0)
bound = (-1, 2)
ntoy = 100000
toy = gen_toy(crystalball,
ntoy,
bound=bound,
alpha=1.,
n=2.,
mean=1.,
sigma=0.3,
quiet=False)
assert_equal(len(toy), ntoy)
htoy, bins = np.histogram(toy, bins=1000, range=bound)
ncball = Normalized(crystalball, bound)
f = lambda x: ncball(x, 1., 2., 1., 0.3)
expected = vector_apply(f, mid(bins)) * ntoy * (bins[1] - bins[0])
# print htoy[:100]
# print expected[:100]
htoy = htoy * 1.0
err = np.sqrt(expected)
chi2 = compute_chi2(htoy, expected, err)
print chi2, len(bins), chi2 / len(bins)
assert (0.9 < (chi2 / len(bins)) < 1.1)
def test_gen_toy():
np.random.seed(0)
bound = (-1, 2)
ntoy = 100000
toy = gen_toy(crystalball, ntoy, bound=bound,
alpha=1., n=2., mean=1., sigma=0.3, quiet=False)
assert_equal(len(toy), ntoy)
htoy, bins = np.histogram(toy, bins=1000, range=bound)
ncball = Normalized(crystalball, bound)
f = lambda x: ncball(x, 1., 2., 1., 0.3)
expected = vector_apply(f, mid(bins)) * ntoy * (bins[1] - bins[0])
# print htoy[:100]
# print expected[:100]
htoy = htoy * 1.0
err = np.sqrt(expected)
chi2 = compute_chi2(htoy, expected, err)
print chi2, len(bins), chi2 / len(bins)
assert(0.9 < (chi2 / len(bins)) < 1.1)
def test_gen_toy2():
pdf = gaussian
np.random.seed(0)
toy = gen_toy(pdf, 10000, (-5, 5), mean=0, sigma=1)
binlh = BinnedLH(pdf, toy, bound=(-5, 5), bins=100)
lh = binlh(0.0, 1.0)
for x in toy:
assert x < 5
assert x >= -5
assert len(toy) == 10000
assert lh / 100.0 < 1.0
def test_gen_toy2():
pdf = gaussian
np.random.seed(0)
toy = gen_toy(pdf, 10000, (-5, 5), mean=0, sigma=1)
binlh = BinnedLH(pdf, toy, bound=(-5, 5), bins=100)
lh = binlh(0., 1.)
for x in toy:
assert (x < 5)
assert (x >= -5)
assert_equal(len(toy), 10000)
assert (lh / 100. < 1.)
def test_gen_toy2():
pdf = gaussian
np.random.seed(0)
toy = gen_toy(pdf, 10000, (-5, 5), mean=0, sigma=1)
binlh = BinnedLH(pdf, toy, bound=(-5, 5), bins=100)
lh = binlh(0., 1.)
for x in toy:
assert(x < 5)
assert(x >= -5)
assert_equal(len(toy), 10000)
assert(lh / 100. < 1.)
def test_gen_toy2():
pdf = gaussian
np.random.seed(0)
toy = gen_toy(pdf, 10000, (-5, 5), mean=0, sigma=1)
binlh = BinnedLH(pdf, toy, bound=(-5, 5), bins=100)
lh = binlh(0.0, 1.0)
for x in toy:
assert x < 5
assert x >= -5
assert len(toy) == 10000
assert lh / 100.0 < 1.0
