How to use the coffea.util.numpy.random.normal function in coffea

def test_weights():
    from coffea.processor import Weights
    
    counts, test_eta, test_pt = dummy_jagged_eta_pt()
    scale_central = np.random.normal(loc=1.0, scale=0.01, size=counts.size)
    scale_up = scale_central * 1.10
    scale_down = scale_central * 0.95
    scale_up_shift = 0.10 * scale_central
    scale_down_shift = 0.05 * scale_central

    weight = Weights(counts.size)
    weight.add('test', scale_central, weightUp=scale_up, weightDown=scale_down)
    weight.add('testShift', scale_central, weightUp=scale_up_shift,
               weightDown=scale_down_shift, shift=True)

    var_names = weight.variations
    expected_names = ['testShiftUp', 'testShiftDown', 'testUp', 'testDown']
    for name in expected_names:
        assert(name in var_names)

    test_central = weight.weight()

def dummy_four_momenta():
    np.random.seed(12345)
    nrows = 1000
    counts = np.minimum(np.random.exponential(0.5, size=nrows).astype(int), 20)
    
    px = np.random.normal(loc=20.0,scale=5.0,size=np.sum(counts))
    py = np.random.normal(loc=20.0,scale=5.0,size=np.sum(counts))
    pz = np.random.normal(loc=0, scale=55, size=np.sum(counts))
    m_pi = np.full_like(px,fill_value=0.135)
    energy = np.sqrt(px*px + py*py + pz*pz + m_pi*m_pi)
    return (counts,px,py,pz,energy)

height,
                               # weight is a reserved keyword
                               hist.Bin("mass", "weight (g=9.81m/s**2) [kg]", np.power(10., np.arange(5)-1)),],
                         
                       )
                          
    assert h_mascots_1._dense_shape == h_mascots_2._dense_shape
    assert h_mascots_2._dense_shape == h_mascots_3._dense_shape
    assert h_mascots_3._dense_shape == h_mascots_4._dense_shape
    
    assert h_mascots_1._axes == h_mascots_2._axes
    assert h_mascots_2._axes == h_mascots_3._axes
    assert h_mascots_3._axes == h_mascots_4._axes
                        
    adult_bison_h = np.random.normal(loc=2.5, scale=0.2, size=40)
    adult_bison_w = np.random.normal(loc=700, scale=100, size=40)
    h_mascots_1.fill(animal="bison", vocalization="huff", height=adult_bison_h, mass=adult_bison_w)
    goose_h = np.random.normal(loc=0.4, scale=0.05, size=1000)
    goose_w = np.random.normal(loc=7, scale=1, size=1000)
    h_mascots_1.fill(animal="goose", vocalization="honk", height=goose_h, mass=goose_w)
    crane_h = np.random.normal(loc=1, scale=0.05, size=4)
    crane_w = np.random.normal(loc=10, scale=1, size=4)
    h_mascots_1.fill(animal="crane", vocalization="none", height=crane_h, mass=crane_w)

    with pytest.raises(ValueError):
        h_mascots_1.fill(beast="crane", yelling="none", tallness=crane_h, heavitivity=crane_w)
    
    
    h_mascots_2 = h_mascots_1.copy()
    h_mascots_2.clear()
    baby_bison_h = np.random.normal(loc=.5, scale=0.1, size=20)
    baby_bison_w = np.random.normal(loc=200, scale=10, size=20)

vocalization,
                               height,
                               # weight is a reserved keyword
                               hist.Bin("mass", "weight (g=9.81m/s**2) [kg]", np.power(10., np.arange(5)-1)),],
                         
                       )
                          
    assert h_mascots_1._dense_shape == h_mascots_2._dense_shape
    assert h_mascots_2._dense_shape == h_mascots_3._dense_shape
    assert h_mascots_3._dense_shape == h_mascots_4._dense_shape
    
    assert h_mascots_1._axes == h_mascots_2._axes
    assert h_mascots_2._axes == h_mascots_3._axes
    assert h_mascots_3._axes == h_mascots_4._axes
                        
    adult_bison_h = np.random.normal(loc=2.5, scale=0.2, size=40)
    adult_bison_w = np.random.normal(loc=700, scale=100, size=40)
    h_mascots_1.fill(animal="bison", vocalization="huff", height=adult_bison_h, mass=adult_bison_w)
    goose_h = np.random.normal(loc=0.4, scale=0.05, size=1000)
    goose_w = np.random.normal(loc=7, scale=1, size=1000)
    h_mascots_1.fill(animal="goose", vocalization="honk", height=goose_h, mass=goose_w)
    crane_h = np.random.normal(loc=1, scale=0.05, size=4)
    crane_w = np.random.normal(loc=10, scale=1, size=4)
    h_mascots_1.fill(animal="crane", vocalization="none", height=crane_h, mass=crane_w)

    with pytest.raises(ValueError):
        h_mascots_1.fill(beast="crane", yelling="none", tallness=crane_h, heavitivity=crane_w)
    
    
    h_mascots_2 = h_mascots_1.copy()
    h_mascots_2.clear()
    baby_bison_h = np.random.normal(loc=.5, scale=0.1, size=20)

hist.Bin("mass", "weight (g=9.81m/s**2) [kg]", np.power(10., np.arange(5)-1)),],
                         
                       )
                          
    assert h_mascots_1._dense_shape == h_mascots_2._dense_shape
    assert h_mascots_2._dense_shape == h_mascots_3._dense_shape
    assert h_mascots_3._dense_shape == h_mascots_4._dense_shape
    
    assert h_mascots_1._axes == h_mascots_2._axes
    assert h_mascots_2._axes == h_mascots_3._axes
    assert h_mascots_3._axes == h_mascots_4._axes
                        
    adult_bison_h = np.random.normal(loc=2.5, scale=0.2, size=40)
    adult_bison_w = np.random.normal(loc=700, scale=100, size=40)
    h_mascots_1.fill(animal="bison", vocalization="huff", height=adult_bison_h, mass=adult_bison_w)
    goose_h = np.random.normal(loc=0.4, scale=0.05, size=1000)
    goose_w = np.random.normal(loc=7, scale=1, size=1000)
    h_mascots_1.fill(animal="goose", vocalization="honk", height=goose_h, mass=goose_w)
    crane_h = np.random.normal(loc=1, scale=0.05, size=4)
    crane_w = np.random.normal(loc=10, scale=1, size=4)
    h_mascots_1.fill(animal="crane", vocalization="none", height=crane_h, mass=crane_w)

    with pytest.raises(ValueError):
        h_mascots_1.fill(beast="crane", yelling="none", tallness=crane_h, heavitivity=crane_w)
    
    
    h_mascots_2 = h_mascots_1.copy()
    h_mascots_2.clear()
    baby_bison_h = np.random.normal(loc=.5, scale=0.1, size=20)
    baby_bison_w = np.random.normal(loc=200, scale=10, size=20)
    baby_bison_cutefactor = 2.5*np.ones_like(baby_bison_w)
    h_mascots_2.fill(animal="bison", vocalization="baa", height=baby_bison_h, mass=baby_bison_w, weight=baby_bison_cutefactor)

def dummy_four_momenta():
    np.random.seed(12345)
    nrows = 1000
    counts = np.minimum(np.random.exponential(0.5, size=nrows).astype(int), 20)
    
    px = np.random.normal(loc=20.0,scale=5.0,size=np.sum(counts))
    py = np.random.normal(loc=20.0,scale=5.0,size=np.sum(counts))
    pz = np.random.normal(loc=0, scale=55, size=np.sum(counts))
    m_pi = np.full_like(px,fill_value=0.135)
    energy = np.sqrt(px*px + py*py + pz*pz + m_pi*m_pi)
    return (counts,px,py,pz,energy)

assert h_mascots_1._dense_shape == h_mascots_2._dense_shape
    assert h_mascots_2._dense_shape == h_mascots_3._dense_shape
    assert h_mascots_3._dense_shape == h_mascots_4._dense_shape
    
    assert h_mascots_1._axes == h_mascots_2._axes
    assert h_mascots_2._axes == h_mascots_3._axes
    assert h_mascots_3._axes == h_mascots_4._axes
                        
    adult_bison_h = np.random.normal(loc=2.5, scale=0.2, size=40)
    adult_bison_w = np.random.normal(loc=700, scale=100, size=40)
    h_mascots_1.fill(animal="bison", vocalization="huff", height=adult_bison_h, mass=adult_bison_w)
    goose_h = np.random.normal(loc=0.4, scale=0.05, size=1000)
    goose_w = np.random.normal(loc=7, scale=1, size=1000)
    h_mascots_1.fill(animal="goose", vocalization="honk", height=goose_h, mass=goose_w)
    crane_h = np.random.normal(loc=1, scale=0.05, size=4)
    crane_w = np.random.normal(loc=10, scale=1, size=4)
    h_mascots_1.fill(animal="crane", vocalization="none", height=crane_h, mass=crane_w)

    with pytest.raises(ValueError):
        h_mascots_1.fill(beast="crane", yelling="none", tallness=crane_h, heavitivity=crane_w)
    
    
    h_mascots_2 = h_mascots_1.copy()
    h_mascots_2.clear()
    baby_bison_h = np.random.normal(loc=.5, scale=0.1, size=20)
    baby_bison_w = np.random.normal(loc=200, scale=10, size=20)
    baby_bison_cutefactor = 2.5*np.ones_like(baby_bison_w)
    h_mascots_2.fill(animal="bison", vocalization="baa", height=baby_bison_h, mass=baby_bison_w, weight=baby_bison_cutefactor)
    h_mascots_2.fill(animal="fox", vocalization="none", height=1., mass=30.)

    h_mascots = h_mascots_1 + h_mascots_2

h_mascots_1.fill(animal="bison", vocalization="huff", height=adult_bison_h, mass=adult_bison_w)
    goose_h = np.random.normal(loc=0.4, scale=0.05, size=1000)
    goose_w = np.random.normal(loc=7, scale=1, size=1000)
    h_mascots_1.fill(animal="goose", vocalization="honk", height=goose_h, mass=goose_w)
    crane_h = np.random.normal(loc=1, scale=0.05, size=4)
    crane_w = np.random.normal(loc=10, scale=1, size=4)
    h_mascots_1.fill(animal="crane", vocalization="none", height=crane_h, mass=crane_w)

    with pytest.raises(ValueError):
        h_mascots_1.fill(beast="crane", yelling="none", tallness=crane_h, heavitivity=crane_w)
    
    
    h_mascots_2 = h_mascots_1.copy()
    h_mascots_2.clear()
    baby_bison_h = np.random.normal(loc=.5, scale=0.1, size=20)
    baby_bison_w = np.random.normal(loc=200, scale=10, size=20)
    baby_bison_cutefactor = 2.5*np.ones_like(baby_bison_w)
    h_mascots_2.fill(animal="bison", vocalization="baa", height=baby_bison_h, mass=baby_bison_w, weight=baby_bison_cutefactor)
    h_mascots_2.fill(animal="fox", vocalization="none", height=1., mass=30.)

    h_mascots = h_mascots_1 + h_mascots_2
    assert h_mascots.integrate("vocalization", "h*").sum("height", "mass", "animal").values()[()] == 1040.

    species_class = hist.Cat("species_class", "where the subphylum is vertibrates")
    classes = {
        'birds': ['goose', 'crane'],
        'mammals': ['bison', 'fox'],
    }
    h_species = h_mascots.group("animal", species_class, classes)

    assert set(h_species.integrate("vocalization").values().keys()) == set([('birds',), ('mammals',)])
    nbirds_bin = np.sum((goose_h>=0.5)&(goose_h<1)&(goose_w>10)&(goose_w<100))

adult_bison_w = np.random.normal(loc=700, scale=100, size=40)
    h_mascots_1.fill(animal="bison", vocalization="huff", height=adult_bison_h, mass=adult_bison_w)
    goose_h = np.random.normal(loc=0.4, scale=0.05, size=1000)
    goose_w = np.random.normal(loc=7, scale=1, size=1000)
    h_mascots_1.fill(animal="goose", vocalization="honk", height=goose_h, mass=goose_w)
    crane_h = np.random.normal(loc=1, scale=0.05, size=4)
    crane_w = np.random.normal(loc=10, scale=1, size=4)
    h_mascots_1.fill(animal="crane", vocalization="none", height=crane_h, mass=crane_w)

    with pytest.raises(ValueError):
        h_mascots_1.fill(beast="crane", yelling="none", tallness=crane_h, heavitivity=crane_w)
    
    
    h_mascots_2 = h_mascots_1.copy()
    h_mascots_2.clear()
    baby_bison_h = np.random.normal(loc=.5, scale=0.1, size=20)
    baby_bison_w = np.random.normal(loc=200, scale=10, size=20)
    baby_bison_cutefactor = 2.5*np.ones_like(baby_bison_w)
    h_mascots_2.fill(animal="bison", vocalization="baa", height=baby_bison_h, mass=baby_bison_w, weight=baby_bison_cutefactor)
    h_mascots_2.fill(animal="fox", vocalization="none", height=1., mass=30.)

    h_mascots = h_mascots_1 + h_mascots_2
    assert h_mascots.integrate("vocalization", "h*").sum("height", "mass", "animal").values()[()] == 1040.

    species_class = hist.Cat("species_class", "where the subphylum is vertibrates")
    classes = {
        'birds': ['goose', 'crane'],
        'mammals': ['bison', 'fox'],
    }
    h_species = h_mascots.group("animal", species_class, classes)

    assert set(h_species.integrate("vocalization").values().keys()) == set([('birds',), ('mammals',)])