def test_draw_ulh_with_minuit():
np.random.seed(0)
data = np.random.randn(1000)
plt.figure()
ulh = UnbinnedLH(gaussian, data)
minuit = Minuit(ulh, mean=0, sigma=1)
ulh.draw(minuit)
def test_draw_residual_ulh_norm():
np.random.seed(0)
data = np.random.randn(1000)
ulh = UnbinnedLH(gaussian, data)
ulh.draw_residual(args=(0., 1.), norm=True)
plt.ylim(-7., 3.)
plt.xlim(-4., 3.)
def test_draw_residual_ulh_norm_options():
np.random.seed(0)
data = np.random.randn(1000)
ulh = UnbinnedLH(gaussian, data)
ulh.draw_residual(args=(0., 1.),
norm=True,
color='green',
capsize=2,
grid=False,
zero_line=False)
def test_ulh_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)
ulh = UnbinnedLH(allpdf, data)
ulh.draw(args=(0, 1, 3, 1, 0.5), parts=True)
def test_draw_simultaneous():
np.random.seed(0)
data = np.random.randn(10000)
shifted = data + 3.
g1 = rename(gaussian, ['x', 'lmu', 'sigma'])
g2 = rename(gaussian, ['x', 'rmu', 'sigma'])
ulh1 = UnbinnedLH(g1, data)
ulh2 = UnbinnedLH(g2, shifted)
sim = SimultaneousFit(ulh1, ulh2)
sim.draw(args=(0, 1, 3))
def test_draw_simultaneous_prefix():
np.random.seed(0)
data = np.random.randn(10000)
shifted = data + 3.
g1 = rename(gaussian, ['x', 'lmu', 'sigma'])
g2 = rename(gaussian, ['x', 'rmu', 'sigma'])
ulh1 = UnbinnedLH(g1, data)
ulh2 = UnbinnedLH(g2, shifted)
sim = SimultaneousFit(ulh1, ulh2, prefix=['g1_', 'g2_'])
minuit = Minuit(sim, g1_lmu=0., g1_sigma=1., g2_rmu=0., g2_sigma=1.,
print_level=0)
minuit.migrad()
sim.draw(minuit)
def test_draw_simultaneous_prefix():
np.random.seed(0)
data = np.random.randn(10000)
shifted = data + 3.0
g1 = rename(gaussian, ["x", "lmu", "sigma"])
g2 = rename(gaussian, ["x", "rmu", "sigma"])
ulh1 = UnbinnedLH(g1, data)
ulh2 = UnbinnedLH(g2, shifted)
sim = SimultaneousFit(ulh1, ulh2, prefix=["g1_", "g2_"])
minuit = Minuit(
sim, g1_lmu=0.0, g1_sigma=1.0, g2_rmu=0.0, g2_sigma=1.0, print_level=0
)
minuit.migrad()
sim.draw(minuit)
def test_UnbinnedLH(self):
f = gaussian
assert_equal(list(describe(f)), ['x', 'mean', 'sigma'])
lh = UnbinnedLH(
gaussian,
self.data,
)
assert_equal(list(describe(lh)), ['mean', 'sigma'])
assert_almost_equal(lh(0, 1), 28188.201229348757)
minuit = iminuit.Minuit(lh)
assert_equal(minuit.errordef, 0.5)
def test_UnbinnedLH(self):
f = gaussian
assert list(describe(f)) == ['x', 'mean', 'sigma']
lh = UnbinnedLH(
gaussian,
self.data,
)
assert list(describe(lh)) == ['mean', 'sigma']
assert_allclose(lh(0, 1), 28188.201229348757)
minuit = iminuit.Minuit(lh)
assert_allclose(minuit.errordef, 0.5)
def test_simultaneous(self):
np.random.seed(0)
data = np.random.randn(10000)
shifted = data + 3.0
g1 = rename(gaussian, ["x", "lmu", "sigma"])
g2 = rename(gaussian, ["x", "rmu", "sigma"])
ulh1 = UnbinnedLH(g1, data)
ulh2 = UnbinnedLH(g2, shifted)
sim = SimultaneousFit(ulh1, ulh2)
assert describe(sim) == ["lmu", "sigma", "rmu"]
minuit = iminuit.Minuit(sim, sigma=1.2, pedantic=False, print_level=0)
minuit.migrad()
assert minuit.migrad_ok()
assert_allclose(minuit.values["lmu"],
0.0,
atol=2 * minuit.errors["lmu"])
assert_allclose(minuit.values["rmu"],
3.0,
atol=2 * minuit.errors["rmu"])
assert_allclose(minuit.values["sigma"],
1.0,
atol=2 * minuit.errors["sigma"])
def test_simultaneous(self):
np.random.seed(0)
data = np.random.randn(10000)
shifted = data + 3.
g1 = rename(gaussian, ['x', 'lmu', 'sigma'])
g2 = rename(gaussian, ['x', 'rmu', 'sigma'])
ulh1 = UnbinnedLH(g1, data)
ulh2 = UnbinnedLH(g2, shifted)
sim = SimultaneousFit(ulh1, ulh2)
assert_equal(describe(sim), ['lmu', 'sigma', 'rmu'])
minuit = iminuit.Minuit(sim, sigma=1.2, pedantic=False, print_level=0)
minuit.migrad()
assert (minuit.migrad_ok())
assert_almost_equal(minuit.values['lmu'],
0.,
delta=2 * minuit.errors['lmu'])
assert_almost_equal(minuit.values['rmu'],
3.,
delta=2 * minuit.errors['rmu'])
assert_almost_equal(minuit.values['sigma'],
1.,
delta=2 * minuit.errors['sigma'])
def test_draw_ulh_extend():
np.random.seed(0)
data = np.random.randn(1000)
ulh = UnbinnedLH(Extended(gaussian), data, extended=True)
ulh.draw(args=(0., 1., 1000))
def test_draw_ulh():
np.random.seed(0)
data = np.random.randn(1000)
ulh = UnbinnedLH(gaussian, data)
ulh.draw(args=(0., 1.))
def test_draw_ulh_with_minuit():
np.random.seed(0)
data = np.random.randn(1000)
ulh = UnbinnedLH(gaussian, data)
minuit = Minuit(ulh, mean=0, sigma=1)
ulh.draw(minuit)
def test_draw_ulh_extend_residual_norm():
np.random.seed(0)
data = np.random.randn(1000)
ulh = UnbinnedLH(Extended(gaussian), data, extended=True)
ulh.draw_residual(args=(0., 1., 1000), norm=True)
plt.ylim(-7.,3.)
def test_draw_residual_ulh_norm_options():
np.random.seed(0)
data = np.random.randn(1000)
ulh = UnbinnedLH(gaussian, data)
ulh.draw_residual(args=(0., 1.), norm=True, color='green', capsize=2,
grid=False, zero_line=False)
def test_draw_residual_ulh_norm():
np.random.seed(0)
data = np.random.randn(1000)
ulh = UnbinnedLH(gaussian, data)
ulh.draw_residual(args=(0., 1.), norm=True, show_errbars=False)
def test_draw_residual_ulh():
np.random.seed(0)
data = np.random.randn(1000)
ulh = UnbinnedLH(gaussian, data)
ulh.draw_residual(args=(0.0, 1.0))
def test_draw_residual_ulh_norm():
np.random.seed(0)
data = np.random.randn(1000)
plt.figure()
ulh = UnbinnedLH(gaussian, data)
ulh.draw_residual(args=(0., 1.), norm=True)
