def test_draw_residual_blh_norm():
np.random.seed(0)
data = np.random.randn(1000)
blh = BinnedLH(gaussian, data)
blh.draw_residual(args=(0., 1.), norm=True)
plt.ylim(-4., 3.)
plt.xlim(-4., 3.)
def test_blh_with_parts():
np.random.seed(0)
data = np.random.randn(10000)
shifted = data + 3.
data = np.append(data, [shifted])
g1 = rename(gaussian, ['x', 'lmu', 'lsigma'])
g2 = rename(gaussian, ['x', 'rmu', 'rsigma'])
allpdf = AddPdfNorm(g1, g2)
blh = BinnedLH(allpdf, data)
blh.draw(args=(0, 1, 3, 1, 0.5), parts=True)
def test_draw_residual_blh_norm_options():
np.random.seed(0)
data = np.random.randn(1000)
blh = BinnedLH(gaussian, data)
blh.draw_residual(args=(0., 1.),
norm=True,
color='green',
capsize=2,
grid=False,
zero_line=False)
def test_BinnedLH(self):
# write a better test... this depends on subtraction
f = gaussian
assert_equal(list(describe(f)), ['x', 'mean', 'sigma'])
lh = BinnedLH(gaussian, self.data, bound=[-3, 3])
assert_equal(list(describe(lh)), ['mean', 'sigma'])
assert_almost_equal(lh(0, 1), 20.446130781601543, 1)
minuit = iminuit.Minuit(lh)
assert_equal(minuit.errordef, 0.5)
def test_BinnedLH(self):
# write a better test... this depends on subtraction
f = gaussian
assert list(describe(f)) == ["x", "mean", "sigma"]
lh = BinnedLH(gaussian, self.data, bound=[-3, 3])
assert list(describe(lh)) == ["mean", "sigma"]
assert_allclose(lh(0, 1), 20.446130781601543, atol=1)
minuit = iminuit.Minuit(lh)
assert_allclose(minuit.errordef, 0.5)
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
def test_draw_blh_extend_residual_norm():
np.random.seed(0)
data = np.random.randn(1000)
blh = BinnedLH(Extended(gaussian), data, extended=True)
blh.draw_residual(args=(0., 1., 1000), norm=True)
def test_draw_residual_blh_norm():
np.random.seed(0)
data = np.random.randn(1000)
blh = BinnedLH(gaussian, data)
blh.draw_residual(args=(0., 1.), norm=True, show_errbars=False)
def test_draw_residual_blh_norm_options():
np.random.seed(0)
data = np.random.randn(1000)
blh = BinnedLH(gaussian, data)
blh.draw_residual(args=(0., 1.), norm=True, color='green', capsize=2,
grid=False, zero_line=False)
def test_draw_residual_blh():
np.random.seed(0)
data = np.random.randn(1000)
blh = BinnedLH(gaussian, data)
blh.draw_residual(args=(0., 1.))
def test_draw_blh_extend():
np.random.seed(0)
data = np.random.randn(1000)
blh = BinnedLH(Extended(gaussian), data, extended=True)
blh.draw(args=(0., 1., 1000))
def test_draw_blh():
np.random.seed(0)
data = np.random.randn(1000)
blh = BinnedLH(gaussian, data)
blh.draw(args=(0.0, 1.0))
def test_draw_blh():
np.random.seed(0)
data = np.random.randn(1000)
blh = BinnedLH(gaussian, data)
plt.figure()
blh.draw(args=(0., 1.))
