def test_normal_seeding():
normal1 = Normal(seed=SEED)
normal2 = Normal(seed=SEED)
assert normal1 != normal2
assert normal1.mu == normal2.mu
assert normal1.sigma == normal2.sigma
assert all(
np.array_equal(i, j) for i, j in zip(normal1.data, normal2.data))
def test_normal_variables():
normal = Normal(seed=SEED, N=7, xlim=(-3, 3))
assert normal.mean == -0.07669283956815831
assert normal.median == -0.07669283956815831
assert normal.mode == -0.07669283956815831
assert normal.variance == 0.16640308360952058
assert normal.skewness == 0
def test_normal_printing():
normal = Normal(seed=SEED, N=11)
for s in ['N=11', 'rand=0.02']:
assert s in str(normal)
for s in [
'N=11', 'xlim=(-2.1163195432171413, 1.9629338640808245)',
'seed=31415927', 'mu=-0.07669283956815831',
'sigma=0.4079253407297965', 'rand=0.02'
]:
assert s in repr(normal)
def test_normal_non_random():
normal = Normal(rand=None, mu=0.0, sigma=2, xlim=(-3, 3), N=7)
x, y = normal.data
for i, j in zip(x, [-3, -2, -1, 0, 1, 2, 3]):
assert abs(i - j) < TOLERANCE
for i, j in zip(y, [
0.0647588, 0.12098536, 0.17603266, 0.19947114, 0.17603266,
0.12098536, 0.0647588
]):
assert abs(i - j) < TOLERANCE
def test_normal_random():
x_test = [-3., -2., -1., 0., 1., 2., 3.]
y_test = [
0.01035751, -0.00656358, 0.09470932, 0.96825844, 0.03250045, 0.0073236,
0.0174672
]
normal = Normal(seed=SEED, N=7, xlim=(-3, 3))
x, y = normal.data
assert normal.mu == -0.07669283956815831
assert normal.sigma == 0.4079253407297965
for i, j in zip(x, x_test):
assert abs(i - j) < TOLERANCE
for i, j in zip(y, y_test):
assert abs(i - j) < TOLERANCE
def test_normal_exceptions():
with pytest.raises(GwydionError):
Normal(mu=2j)
with pytest.raises(GwydionError):
Normal(sigma='1234')
def test_normal_creation():
normal = Normal()
assert normal