def test_memoizer():
po = Powerlaw()
a = np.random.uniform(-3, -1, 2000)
b = np.random.uniform(0.1, 10, 2000)
for aa, bb in zip(a, b):
po.index = aa
po.K = bb
po(1.0)
def test_duplicate():
instance = Powerlaw()
instance.index = -2.25
instance.K = 0.5
# Duplicate it
duplicate = instance.duplicate()
# Check that we have the same results
assert duplicate(2.25) == instance(2.25)
# Check that the parameters are not linked anymore
instance.index = -1.12
assert instance.index.value != duplicate.index.value
print(instance)
print(duplicate)
def test_pickling_unpickling():
# 1d function
po = Powerlaw()
po.K = 5.35
new_po = pickle.loads(pickle.dumps(po))
assert new_po.K.value == po.K.value
# 2d function
gs = Gaussian_on_sphere()
_ = pickle.loads(pickle.dumps(gs))
# 3d function
c = Continuous_injection_diffusion()
_ = pickle.loads(pickle.dumps(c))
# composite function
po2 = Powerlaw()
li = Line()
composite = po2 * li + po2 - li + 2 * po2 / li # type: Function1D
# Change some parameter
composite.K_1 = 3.2
composite.a_2 = 1.56
dump = pickle.dumps(composite)
new_composite = pickle.loads(dump)
assert new_composite.K_1.value == composite.K_1.value
assert new_composite.a_2.value == composite.a_2.value