def test_bx2_with_parts():
np.random.seed(0)
data = np.random.randn(10000)
shifted = data + 3.
data = np.append(data, [shifted])
g1 = Extended(rename(gaussian, ['x', 'lmu', 'lsigma']), extname='N1')
g2 = Extended(rename(gaussian, ['x', 'rmu', 'rsigma']), extname='N2')
allpdf = AddPdf(g1, g2)
bx2 = BinnedChi2(allpdf, data)
bx2.draw(args=(0, 1, 10000, 3, 1, 10000), 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_bx2_with_parts():
np.random.seed(0)
data = np.random.randn(10000)
shifted = data + 3.0
data = np.append(data, [shifted])
g1 = Extended(rename(gaussian, ["x", "lmu", "lsigma"]), extname="N1")
g2 = Extended(rename(gaussian, ["x", "rmu", "rsigma"]), extname="N2")
allpdf = AddPdf(g1, g2)
bx2 = BinnedChi2(allpdf, data)
bx2.draw(args=(0, 1, 10000, 3, 1, 10000), parts=True)
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_ulh_with_parts():
np.random.seed(0)
data = np.random.randn(10000)
shifted = data + 3.0
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_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_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 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., atol=2 * minuit.errors['lmu'])
assert_allclose(minuit.values['rmu'], 3., atol=2 * minuit.errors['rmu'])
assert_allclose(minuit.values['sigma'], 1., 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_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"])