def test_blindfunc(): np.random.seed(0) f = BlindFunc(gaussian, "mean", "abcd", width=1.5, signflip=True) arg = f.__shift_arg__((1, 1, 1)) totest = [1.0, -2.1741271445170067, 1.0] assert_almost_equal(arg[0], totest[0]) assert_almost_equal(arg[1], totest[1]) assert_almost_equal(arg[2], totest[2]) assert_almost_equal(f.__call__(0.5, 1.0, 1.0), 0.011171196819867517) np.random.seed(575345) f = BlindFunc(gaussian, "mean", "abcd", width=1.5, signflip=True) arg = f.__shift_arg__((1, 1, 1)) assert_almost_equal(arg[0], totest[0]) assert_almost_equal(arg[1], totest[1]) assert_almost_equal(arg[2], totest[2]) assert_almost_equal(f.__call__(0.5, 1.0, 1.0), 0.011171196819867517)
def test_blindfunc(): np.random.seed(0) f = BlindFunc(gaussian, 'mean', 'abcd', width=1.5, signflip=True) arg = f.__shift_arg__((1, 1, 1)) totest = [1., -1.1665264284482637, 1.] assert_almost_equal(arg[0], totest[0]) assert_almost_equal(arg[1], totest[1]) assert_almost_equal(arg[2], totest[2]) assert_almost_equal(f.__call__(0.5, 1., 1.), 0.0995003913596) np.random.seed(575345) f = BlindFunc(gaussian, 'mean', 'abcd', width=1.5, signflip=True) arg = f.__shift_arg__((1, 1, 1)) assert_almost_equal(arg[0], totest[0]) assert_almost_equal(arg[1], totest[1]) assert_almost_equal(arg[2], totest[2]) assert_almost_equal(f.__call__(0.5, 1., 1.), 0.0995003913596)
def test_blindfunc(): np.random.seed(0) f = BlindFunc(gaussian, 'mean', 'abcd', width=1.5, signflip=True) arg = f.__shift_arg__((1, 1, 1)) totest = [1., -2.1741271445170067, 1.] assert_almost_equal(arg[0], totest[0]) assert_almost_equal(arg[1], totest[1]) assert_almost_equal(arg[2], totest[2]) assert_almost_equal(f.__call__(0.5, 1., 1.), 0.011171196819867517) np.random.seed(575345) f = BlindFunc(gaussian, 'mean', 'abcd', width=1.5, signflip=True) arg = f.__shift_arg__((1, 1, 1)) assert_almost_equal(arg[0], totest[0]) assert_almost_equal(arg[1], totest[1]) assert_almost_equal(arg[2], totest[2]) assert_almost_equal(f.__call__(0.5, 1., 1.), 0.011171196819867517)
def test_blindfunc(): np.random.seed(0) f = BlindFunc(gaussian, "mean", "abcd", width=1.5, signflip=True) arg = f.__shift_arg__((1, 1, 1)) totest = [1.0, -1.1665264284482637, 1.0] assert_almost_equal(arg[0], totest[0]) assert_almost_equal(arg[1], totest[1]) assert_almost_equal(arg[2], totest[2]) assert_almost_equal(f.__call__(0.5, 1.0, 1.0), 0.0995003913596) np.random.seed(575345) f = BlindFunc(gaussian, "mean", "abcd", width=1.5, signflip=True) arg = f.__shift_arg__((1, 1, 1)) assert_almost_equal(arg[0], totest[0]) assert_almost_equal(arg[1], totest[1]) assert_almost_equal(arg[2], totest[2]) assert_almost_equal(f.__call__(0.5, 1.0, 1.0), 0.0995003913596)
inipars= dict(m0=0, m1=0, s0=1, s1=1, f_0=0.5, error_m0=0.1, error_m1=0.1, error_s0=0.1, error_s1=0.1, error_f_0=0.1) # <codecell> # Normal fit uh1= UnbinnedLH(pdf, toydata) m1= Minuit(uh1, print_level=1, **inipars) m1.migrad(); uh1.draw(); print m1.values # <codecell> # Blind one parameter uh2= UnbinnedLH( BlindFunc(pdf, toblind='m1', seedstring='some_random_stuff', width=0.5, signflip=False), toydata) m2= Minuit(uh2, print_level=1, **inipars) m2.migrad(); uh2.draw(); print m2.values # <codecell> # Blind more than one parameter. They will be shifted by the same amount uh3= UnbinnedLH( BlindFunc(pdf, ['m0','m1'], seedstring='some_random_stuff', width=0.5, signflip=False), toydata) m3= Minuit(uh3, print_level=1, **inipars) m3.migrad(); uh3.draw(); print m3.values # <codecell>