def test_input_output_with_complex_functions():
my_particle_distribution = Powerlaw()
my_particle_distribution.index = -1.52
electrons = ParticleSource('electrons',
distribution_shape=my_particle_distribution)
# Now set up the synch. spectrum for our source and the source itself
synch_spectrum = _ComplexTestFunction()
# Use the particle distribution we created as source for the electrons
# producing synch. emission
synch_spectrum.particle_distribution = my_particle_distribution
synch_source = PointSource('synch_source',
ra=12.6,
dec=-13.5,
spectral_shape=synch_spectrum)
my_model = Model(electrons, synch_source)
my_model.display()
my_model.save("__test.yml")
new_model = load_model("__test.yml")
assert len(new_model.sources) == len(my_model.sources)
assert my_particle_distribution.index.value == new_model.electrons.spectrum.main.shape.index.value
def test_display():
mg = ModelGetter()
m = mg.model
m.display()
m.display(complete=True)
# Now display a model without free parameters
m = Model(PointSource("test", 0.0, 0.0, Powerlaw()))
for parameter in m.parameters.values():
parameter.fix = True
m.display()
# Now display a model without fixed parameters (very unlikely)
for parameter in m.parameters.values():
parameter.free = True
m.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)
