Exemple #1
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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)
Exemple #2
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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))
Exemple #3
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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)
Exemple #4
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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)
Exemple #5
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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)
Exemple #6
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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)
Exemple #7
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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)
Exemple #8
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 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'])
Exemple #9
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 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'])
Exemple #10
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 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"])