def test_remove_auxiliary_variable():
p1 = Parameter('test_parameter',
1.0,
min_value=-5.0,
max_value=5.0,
delta=0.2,
desc='test',
free=False,
unit='MeV')
x = Parameter('aux_variable', 1.0)
# ax + b
law = Line()
law.a = 2.0
law.b = 1.0
p1.add_auxiliary_variable(x, law)
assert p1.value == 3.0
x.value = 4.0
assert p1.value == 6.0
p1.remove_auxiliary_variable()
assert p1.has_auxiliary_variable() == False
p1.value = -1.0
assert p1.value == -1.0
def test_remove_auxiliary_variable():
p1 = Parameter('test_parameter', 1.0, min_value=-5.0, max_value=5.0, delta=0.2, desc='test', free=False, unit='MeV')
x = Parameter('aux_variable', 1.0)
# ax + b
law = Line()
law.a = 1.0
law.b = 2.0
p1.add_auxiliary_variable(x, law)
assert p1.value == 3.0
x.value = 4.0
assert p1.value == 6.0
p1.remove_auxiliary_variable()
assert p1.has_auxiliary_variable() == False
p1.value = -1.0
assert p1.value == -1.0
def test_set_auxiliary_variable():
p1 = Parameter('test_parameter', 1.0, min_value=-5.0, max_value=5.0, delta=0.2, desc='test', free=False, unit='MeV')
x = Parameter('aux_variable', 1.0)
# ax + b
law = Line()
law.a = 1.0
law.b = 2.0
p1.add_auxiliary_variable(x, law)
assert p1.has_auxiliary_variable() == True
assert p1.value == 3.0
x.value = 4.0
assert p1.value == 6.0
# Check that assigning to the parameter doesn't produce any effect
p1.value = -1.0
assert p1.value == 6.0
def test_links_and_pickle():
import pickle
p_orig = Parameter('test_parameter',
1.0,
min_value=-5.0,
max_value=5.0,
delta=0.2,
desc='test',
free=False,
unit=u.MeV,
prior=Uniform_prior())
# Test the linkinking and pickle
# Add a link
x = Parameter('aux_variable', 1.0)
# ax + b
law = Line()
law.a = 2.0
law.b = 1.0
p_orig.add_auxiliary_variable(x, law)
# Now pickle and unpickle
d = pickle.dumps(p_orig)
p = pickle.loads(d)
assert p.has_auxiliary_variable() == True
assert p.value == 3.0
assert p.free == False
p.auxiliary_variable[0].value = 4.0
assert p.value == 6.0
# Check that assigning to the parameter doesn't produce any effect
p.value = -1.0
assert p.value == 6.0
def test_set_auxiliary_variable():
p1 = Parameter('test_parameter',
1.0,
min_value=-5.0,
max_value=5.0,
delta=0.2,
desc='test',
free=False,
unit='MeV')
x = Parameter('aux_variable', 1.0)
# ax + b
law = Line()
law.a = 2.0
law.b = 1.0
p1.add_auxiliary_variable(x, law)
assert p1.has_auxiliary_variable() == True
assert p1.value == 3.0
assert p1.free == False
x.value = 4.0
assert p1.value == 6.0
# Check that assigning to the parameter doesn't produce any effect
p1.value = -1.0
assert p1.value == 6.0
# Now check errors reporting
with pytest.raises(AttributeError):
p1.add_auxiliary_variable(1.0, law)
# Now add it twice to verify that it overwrites it
p1.add_auxiliary_variable(x, law)
p1.add_auxiliary_variable(x, law)
p1.display()
def test_time_domain_integration():
po = Powerlaw()
default_powerlaw = Powerlaw()
src = PointSource("test", ra=0.0, dec=0.0, spectral_shape=po)
m = Model(src) # type: model.Model
# Add time independent variable
time = IndependentVariable("time", 0.0, u.s)
m.add_independent_variable(time)
# Now link one of the parameters with a simple line law
line = Line()
line.a = 0.0
m.link(po.index, time, line)
# Test the display just to make sure it doesn't crash
m.display()
# Now test the average with the integral
energies = np.linspace(1, 10, 10)
results = m.get_point_source_fluxes(0, energies,
tag=(time, 0, 10)) # type: np.ndarray
assert np.all(results == 1.0)
# Now test the linking of the normalization, first with a constant then with a line with a certain
# angular coefficient
m.unlink(po.index)
po.index.value = default_powerlaw.index.value
line2 = Line()
line2.a = 0.0
line2.b = 1.0
m.link(po.K, time, line2)
time.value = 1.0
results = m.get_point_source_fluxes(0, energies, tag=(time, 0, 10))
assert np.allclose(results, default_powerlaw(energies))
# Now make something actually happen
line2.a = 1.0
line2.b = 1.0
results = m.get_point_source_fluxes(0, energies,
tag=(time, 0, 10)) # type: np.ndarray
# Compare with analytical result
def F(x):
return line2.a.value / 2.0 * x**2 + line2.b.value * x
effective_norm = (F(10) - F(0)) / 10.0
expected_results = default_powerlaw(
energies) * effective_norm # type: np.ndarray
assert np.allclose(expected_results, results)
