def test_spectrum_like_with_background_model():
energies = np.logspace(1, 3, 51)
low_edge = energies[:-1]
high_edge = energies[1:]
sim_K = 1E-1
sim_kT = 20.
# get a blackbody source function
source_function = Blackbody(K=sim_K, kT=sim_kT)
# power law background function
background_function = Powerlaw(K=5, index=-1.5, piv=100.)
spectrum_generator = SpectrumLike.from_function(
'fake',
source_function=source_function,
background_function=background_function,
energy_min=low_edge,
energy_max=high_edge)
background_plugin = SpectrumLike.from_background('background',
spectrum_generator)
bb = Blackbody()
pl = Powerlaw()
pl.piv = 100
bkg_ps = PointSource('bkg', 0, 0, spectral_shape=pl)
bkg_model = Model(bkg_ps)
jl_bkg = JointLikelihood(bkg_model, DataList(background_plugin))
_ = jl_bkg.fit()
plugin_bkg_model = SpectrumLike('full',
spectrum_generator.observed_spectrum,
background=background_plugin)
pts = PointSource('mysource', 0, 0, spectral_shape=bb)
model = Model(pts)
# MLE fitting
jl = JointLikelihood(model, DataList(plugin_bkg_model))
result = jl.fit()
K_variates = jl.results.get_variates('mysource.spectrum.main.Blackbody.K')
kT_variates = jl.results.get_variates(
'mysource.spectrum.main.Blackbody.kT')
assert np.all(
np.isclose([K_variates.average, kT_variates.average], [sim_K, sim_kT],
rtol=0.5))
def joint_likelihood_bn090217206_nai_multicomp(data_list_bn090217206_nai6):
composite = Powerlaw() + Blackbody()
model = get_grb_model(composite)
jl = JointLikelihood(model, data_list_bn090217206_nai6, verbose=False)
return jl
def test_spectrumlike_fit():
energies = np.logspace(1, 3, 51)
low_edge = energies[:-1]
high_edge = energies[1:]
sim_K = 1e-1
sim_kT = 20.0
# get a blackbody source function
source_function = Blackbody(K=sim_K, kT=sim_kT)
# power law background function
background_function = Powerlaw(K=1, index=-1.5, piv=100.0)
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
background_function=background_function,
energy_min=low_edge,
energy_max=high_edge,
)
bb = Blackbody()
pts = PointSource("mysource", 0, 0, spectral_shape=bb)
model = Model(pts)
# MLE fitting
jl = JointLikelihood(model, DataList(spectrum_generator))
result = jl.fit()
K_variates = jl.results.get_variates("mysource.spectrum.main.Blackbody.K")
kT_variates = jl.results.get_variates(
"mysource.spectrum.main.Blackbody.kT")
assert np.all(
np.isclose([K_variates.average, kT_variates.average], [sim_K, sim_kT],
atol=1))
def test_dispersionspectrumlike_fit():
response = OGIPResponse(
get_path_of_data_file("datasets/ogip_powerlaw.rsp"))
sim_K = 1e-1
sim_kT = 20.0
# get a blackbody source function
source_function = Blackbody(K=sim_K, kT=sim_kT)
# power law background function
background_function = Powerlaw(K=1, index=-1.5, piv=100.0)
spectrum_generator = DispersionSpectrumLike.from_function(
"test",
source_function=source_function,
response=response,
background_function=background_function,
)
bb = Blackbody()
pts = PointSource("mysource", 0, 0, spectral_shape=bb)
model = Model(pts)
# MLE fitting
jl = JointLikelihood(model, DataList(spectrum_generator))
result = jl.fit()
K_variates = jl.results.get_variates("mysource.spectrum.main.Blackbody.K")
kT_variates = jl.results.get_variates(
"mysource.spectrum.main.Blackbody.kT")
assert np.all(
np.isclose([K_variates.average, kT_variates.average], [sim_K, sim_kT],
atol=1))
def test_all():
response = OGIPResponse(
get_path_of_data_file("datasets/ogip_powerlaw.rsp"))
np.random.seed(1234)
# rescale the functions for the response
source_function = Blackbody(K=1e-7, kT=500.0)
background_function = Powerlaw(K=1, index=-1.5, piv=1.0e3)
spectrum_generator = DispersionSpectrumLike.from_function(
"fake",
source_function=source_function,
background_function=background_function,
response=response)
source_function.K.prior = Log_normal(mu=np.log(1e-7), sigma=1)
source_function.kT.prior = Log_normal(mu=np.log(300), sigma=2)
ps = PointSource("demo", 0, 0, spectral_shape=source_function)
model = Model(ps)
ba = BayesianAnalysis(model, DataList(spectrum_generator))
ba.set_sampler()
ba.sample(quiet=True)
ppc = compute_ppc(ba,
ba.results,
n_sims=500,
file_name="my_ppc.h5",
overwrite=True,
return_ppc=True)
ppc.fake.plot(bkg_subtract=True)
ppc.fake.plot(bkg_subtract=False)
def test_all_statistics():
energies = np.logspace(1, 3, 51)
low_edge = energies[:-1]
high_edge = energies[1:]
# get a blackbody source function
source_function = Blackbody(K=9e-2, kT=20)
# power law background function
background_function = Powerlaw(K=1, index=-1.5, piv=100.0)
pts = PointSource("mysource", 0, 0, spectral_shape=source_function)
model = Model(pts)
# test Poisson no bkg
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
energy_min=low_edge,
energy_max=high_edge,
)
spectrum_generator.set_model(model)
spectrum_generator.get_log_like()
# test Poisson w/ Poisson bkg
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
background_function=background_function,
energy_min=low_edge,
energy_max=high_edge,
)
spectrum_generator.set_model(model)
spectrum_generator.get_log_like()
spectrum_generator._background_counts = -np.ones_like(
spectrum_generator._background_counts)
with pytest.raises(NegativeBackground):
spectrum_generator._probe_noise_models()
# test Poisson w/ ideal bkg
spectrum_generator.background_noise_model = "ideal"
spectrum_generator.get_log_like()
# test Poisson w/ gauss bkg
# test Poisson w/ Poisson bkg
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
background_function=background_function,
background_errors=0.1 * background_function(low_edge),
energy_min=low_edge,
energy_max=high_edge,
)
spectrum_generator.set_model(model)
spectrum_generator.get_log_like()
# test Gaussian w/ no bkg
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
source_errors=0.5 * source_function(low_edge),
energy_min=low_edge,
energy_max=high_edge,
)
spectrum_generator.set_model(model)
spectrum_generator.get_log_like()
def test_spectrum_like_with_background_model():
energies = np.logspace(1, 3, 51)
low_edge = energies[:-1]
high_edge = energies[1:]
sim_K = 1e-1
sim_kT = 20.0
# get a blackbody source function
source_function = Blackbody(K=sim_K, kT=sim_kT)
# power law background function
background_function = Powerlaw(K=5, index=-1.5, piv=100.0)
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
background_function=background_function,
energy_min=low_edge,
energy_max=high_edge,
)
background_plugin = SpectrumLike.from_background("background",
spectrum_generator)
bb = Blackbody()
pl = Powerlaw()
pl.piv = 100
bkg_ps = PointSource("bkg", 0, 0, spectral_shape=pl)
bkg_model = Model(bkg_ps)
jl_bkg = JointLikelihood(bkg_model, DataList(background_plugin))
_ = jl_bkg.fit()
plugin_bkg_model = SpectrumLike("full",
spectrum_generator.observed_spectrum,
background=background_plugin)
pts = PointSource("mysource", 0, 0, spectral_shape=bb)
model = Model(pts)
# MLE fitting
jl = JointLikelihood(model, DataList(plugin_bkg_model))
result = jl.fit()
K_variates = jl.results.get_variates("mysource.spectrum.main.Blackbody.K")
kT_variates = jl.results.get_variates(
"mysource.spectrum.main.Blackbody.kT")
assert np.all(
np.isclose([K_variates.average, kT_variates.average], [sim_K, sim_kT],
rtol=0.5))
## test with ogiplike
with within_directory(__example_dir):
ogip = OGIPLike("test_ogip",
observation="test.pha{1}",
background=background_plugin)
def test_assigning_source_name():
energies = np.logspace(1, 3, 51)
low_edge = energies[:-1]
high_edge = energies[1:]
sim_K = 1e-1
sim_kT = 20.0
# get a blackbody source function
source_function = Blackbody(K=sim_K, kT=sim_kT)
# power law background function
background_function = Powerlaw(K=1, index=-1.5, piv=100.0)
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
background_function=background_function,
energy_min=low_edge,
energy_max=high_edge,
)
# good name setting
bb = Blackbody()
pts = PointSource("good_name", 0, 0, spectral_shape=bb)
model = Model(pts)
# before setting model
spectrum_generator.assign_to_source("good_name")
jl = JointLikelihood(model, DataList(spectrum_generator))
_ = jl.fit()
# after setting model
pts = PointSource("good_name", 0, 0, spectral_shape=bb)
model = Model(pts)
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
background_function=background_function,
energy_min=low_edge,
energy_max=high_edge,
)
jl = JointLikelihood(model, DataList(spectrum_generator))
spectrum_generator.assign_to_source("good_name")
# after with bad name
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
background_function=background_function,
energy_min=low_edge,
energy_max=high_edge,
)
jl = JointLikelihood(model, DataList(spectrum_generator))
with pytest.raises(RuntimeError):
spectrum_generator.assign_to_source("bad_name")
# before with bad name
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
background_function=background_function,
energy_min=low_edge,
energy_max=high_edge,
)
spectrum_generator.assign_to_source("bad_name")
with pytest.raises(RuntimeError):
jl = JointLikelihood(model, DataList(spectrum_generator))
# multisource model
spectrum_generator = SpectrumLike.from_function(
"fake",
source_function=source_function,
background_function=background_function,
energy_min=low_edge,
energy_max=high_edge,
)
ps1 = PointSource("ps1", 0, 0, spectral_shape=Blackbody())
ps2 = PointSource("ps2", 0, 0, spectral_shape=Powerlaw())
model = Model(ps1, ps2)
model.ps2.spectrum.main.Powerlaw.K.fix = True
model.ps2.spectrum.main.Powerlaw.index.fix = True
spectrum_generator.assign_to_source("ps1")
dl = DataList(spectrum_generator)
jl = JointLikelihood(model, dl)
_ = jl.fit()