Python BlindFunc示例

示例#1

文件： test_functor.py 项目： vishalbelsare/probfit

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)

示例#2

文件： testfunctor.py 项目： mtresch/probfit

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)

示例#3

文件： test_functor.py 项目： iminuit/probfit

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)

示例#4

文件： testfunctor.py 项目： piti118/probfit

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)

示例#5

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>