def test_input_output():
tm = TemplateModel('__test')
tm.alpha = -0.95
tm.beta = -2.23
fake_source = PointSource("test", ra=0.0, dec=0.0, spectral_shape=tm)
fake_model = Model(fake_source)
clone = clone_model(fake_model)
assert clone.get_number_of_point_sources() == 1
assert tm.data_file == clone.test.spectrum.main.shape.data_file
assert clone.test.spectrum.main.shape.alpha.value == tm.alpha.value
assert clone.test.spectrum.main.shape.beta.value == tm.beta.value
xx = np.linspace(1, 10, 100)
assert np.allclose(clone.test.spectrum.main.shape(xx), fake_model.test.spectrum.main.shape(xx))
# Test pickling
dump = pickle.dumps(clone)
clone2 = pickle.loads(dump)
assert clone2.get_number_of_point_sources() == 1
assert tm.data_file == clone2.test.spectrum.main.shape.data_file
assert np.allclose(clone2.test.spectrum.main.shape(xx), fake_model.test.spectrum.main.shape(xx))
# Test pickling with other functions
new_shape = tm * Powerlaw()
new_shape.index_2 = -2.256
dump2 = pickle.dumps(new_shape)
clone3 = pickle.loads(dump2)
assert clone3.index_2.value == new_shape.index_2.value
# Now save to disk and reload
fake_source2 = PointSource("test", ra=0.0, dec=0.0, spectral_shape=new_shape)
fake_model2 = Model(fake_source2)
fake_model2.save("__test.yml", overwrite=True)
# Now try to reload
reloaded_model = load_model("__test.yml")
assert reloaded_model.get_number_of_point_sources() == 1
assert np.allclose(fake_model2.test.spectrum.main.shape(xx), reloaded_model.test.spectrum.main.shape(xx))
os.remove("__test.yml")
def test_template_factory():
mo = get_comparison_function()
energies = np.logspace(1, 3, 50)
t = TemplateModelFactory('__test', 'A test template', energies, ['alpha', 'xp', 'beta'])
alpha_grid = np.linspace(-1.5, 1, 15)
beta_grid = np.linspace(-3.5, -1.6, 15)
xp_grid = np.logspace(1, 3, 20)
t.define_parameter_grid('alpha', alpha_grid)
t.define_parameter_grid('beta', beta_grid)
t.define_parameter_grid('xp', xp_grid)
for a in alpha_grid:
for b in beta_grid:
for xp in xp_grid:
mo.alpha = a
mo.beta = b
mo.xp = xp
t.add_interpolation_data(mo(energies), alpha=a, xp=xp, beta=b)
print("Data has been prepared")
t.save_data(overwrite=True)
tm = TemplateModel('__test1D')
tm(energies)
def test_template_function():
tm = TemplateModel('__test')
mo = get_comparison_function()
new_alpha_grid = np.linspace(-1.5, 1, 20)
new_beta_grid = np.linspace(-3.5, -1.6, 20)
new_xp_grid = np.logspace(1, 3, 30)
new_energies = np.logspace(1, 3, 40)
tm.K = 1
mo.K = 1
for a in new_alpha_grid:
for b in new_beta_grid:
for xp in new_xp_grid:
mo.alpha = a
mo.beta = b
mo.xp = xp
tm.alpha = a
tm.beta = b
tm.xp = xp
res1 = mo(new_energies)
res2 = tm(new_energies)
deltas = np.abs((res2 - res1) / res1)
idx = deltas > 0.1
if np.any(idx):
raise AssertionError("Interpolation precision @ %s is %s, "
"worse than 10 percent, "
"with parameters %s!" % (new_energies[idx], deltas[idx], [a,b,xp]))
def test_template_function():
tm = TemplateModel('__test')
mo = get_comparison_function()
new_alpha_grid = np.linspace(-1.5, 1, 20)
new_beta_grid = np.linspace(-3.5, -1.6, 20)
new_xp_grid = np.logspace(1, 3, 30)
new_energies = np.logspace(1, 3, 40)
tm.K = 1
mo.K = 1
for a in new_alpha_grid:
for b in new_beta_grid:
for xp in new_xp_grid:
mo.alpha = a
mo.beta = b
mo.xp = xp
tm.alpha = a
tm.beta = b
tm.xp = xp
res1 = mo(new_energies)
res2 = tm(new_energies)
deltas = np.abs((res2 - res1) / res1)
idx = deltas > 0.1
if np.any(idx):
raise AssertionError(
"Interpolation precision @ %s is %s, "
"worse than 10 percent, "
"with parameters %s!" %
(new_energies[idx], deltas[idx], [a, b, xp]))