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 test_goodness_of_fit():
# Let's generate some data with y = Powerlaw(x)
gen_function = Powerlaw()
# Generate a dataset using the power law, and a
# constant 30% error
x = np.logspace(0, 2, 50)
xyl_generator = XYLike.from_function(
"sim_data", function=gen_function, x=x, yerr=0.3 * gen_function(x)
)
y = xyl_generator.y
y_err = xyl_generator.yerr
fit_function = Powerlaw()
xyl = XYLike("data", x, y, y_err)
parameters, like_values = xyl.fit(fit_function)
gof, all_results, all_like_values = xyl.goodness_of_fit()
# Compute the number of degrees of freedom
n_dof = len(xyl.x) - len(fit_function.free_parameters)
# Get the observed value for chi2
obs_chi2 = 2 * like_values["-log(likelihood)"]["data"]
theoretical_gof = scipy.stats.chi2(n_dof).sf(obs_chi2)
assert np.isclose(theoretical_gof, gof["total"], rtol=0.1)
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.mean(), kT_variates.mean()], [sim_K, sim_kT], rtol=0.5))
def test_one_free_parameter_input_output():
fluxUnit = 1. / (u.TeV * u.cm**2 * u.s)
temp_file = "__test_mle.fits"
spectrum=Powerlaw()
source = PointSource( "tst", ra=100 , dec=20, spectral_shape=spectrum)
model = Model(source)
spectrum.piv = 7*u.TeV
spectrum.index = -2.3
spectrum.K = 1e-15*fluxUnit
spectrum.piv.fix = True
#two free parameters (one with units)
spectrum.index.fix = False
spectrum.K.fix = False
cov_matrix = np.diag([0.001]*2)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
#one free parameter with units
spectrum.index.fix = True
spectrum.K.fix = False
cov_matrix = np.diag([0.001]*1)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
#one free parameter without units
spectrum.index.fix = False
spectrum.K.fix = True
cov_matrix = np.diag([0.001]*1)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
def test_one_free_parameter_input_output():
fluxUnit = 1. / (u.TeV * u.cm**2 * u.s)
temp_file = "__test_mle.fits"
spectrum = Powerlaw()
source = PointSource("tst", ra=100, dec=20, spectral_shape=spectrum)
model = Model(source)
spectrum.piv = 7 * u.TeV
spectrum.index = -2.3
spectrum.K = 1e-15 * fluxUnit
spectrum.piv.fix = True
#two free parameters (one with units)
spectrum.index.fix = False
spectrum.K.fix = False
cov_matrix = np.diag([0.001] * 2)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
#one free parameter with units
spectrum.index.fix = True
spectrum.K.fix = False
cov_matrix = np.diag([0.001] * 1)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
#one free parameter without units
spectrum.index.fix = False
spectrum.K.fix = True
cov_matrix = np.diag([0.001] * 1)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
def test_dispersion_spectrum_constructor(loaded_response):
rsp = loaded_response
pl = Powerlaw()
ps = PointSource("fake", 0, 0, spectral_shape=pl)
model = Model(ps)
obs_spectrum = BinnedSpectrumWithDispersion(counts=np.ones(128),
exposure=1,
response=rsp,
is_poisson=True)
bkg_spectrum = BinnedSpectrumWithDispersion(counts=np.ones(128),
exposure=1,
response=rsp,
is_poisson=True)
specLike = DispersionSpectrumLike("fake",
observation=obs_spectrum,
background=bkg_spectrum)
specLike.set_model(model)
specLike.get_model()
specLike.write_pha("test_from_dispersion", overwrite=True)
assert os.path.exists("test_from_dispersion.pha")
assert os.path.exists("test_from_dispersion_bak.pha")
os.remove("test_from_dispersion.pha")
os.remove("test_from_dispersion_bak.pha")
def joint_likelihood_bn090217206_nai(data_list_bn090217206_nai6):
powerlaw = Powerlaw()
model = get_grb_model(powerlaw)
jl = JointLikelihood(model, data_list_bn090217206_nai6, verbose=False)
return jl
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 photometry_data_model(grond_plugin):
spec = Powerlaw() # * XS_zdust() * XS_zdust()
datalist = DataList(grond_plugin)
model = Model(PointSource("grb", 0, 0, spectral_shape=spec))
yield model, datalist
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_spectrum_constructor():
ebounds = ChannelSet.from_list_of_edges(np.array([1, 2, 3, 4, 5, 6]))
pl = Powerlaw()
ps = PointSource("fake", 0, 0, spectral_shape=pl)
model = Model(ps)
obs_spectrum = BinnedSpectrum(counts=np.ones(len(ebounds)),
exposure=1,
ebounds=ebounds,
is_poisson=True)
bkg_spectrum = BinnedSpectrum(counts=np.ones(len(ebounds)),
exposure=1,
ebounds=ebounds,
is_poisson=True)
assert np.all(obs_spectrum.counts == obs_spectrum.rates)
assert np.all(bkg_spectrum.counts == bkg_spectrum.rates)
specLike = SpectrumLike("fake",
observation=obs_spectrum,
background=bkg_spectrum)
specLike.set_model(model)
specLike.get_model()
specLike.get_simulated_dataset()
specLike.rebin_on_background(min_number_of_counts=1e-1)
specLike.remove_rebinning()
specLike.significance
specLike.significance_per_channel
obs_spectrum = BinnedSpectrum(
counts=np.ones(len(ebounds)),
count_errors=np.ones(len(ebounds)),
exposure=1,
ebounds=ebounds,
is_poisson=False,
)
bkg_spectrum = BinnedSpectrum(counts=np.ones(len(ebounds)),
exposure=1,
ebounds=ebounds,
is_poisson=True)
with pytest.raises(NotImplementedError):
specLike = SpectrumLike("fake",
observation=obs_spectrum,
background=bkg_spectrum)
# gaussian source only
obs_spectrum = BinnedSpectrum(
counts=np.ones(len(ebounds)),
count_errors=np.ones(len(ebounds)),
exposure=1,
ebounds=ebounds,
)
specLike = SpectrumLike("fake", observation=obs_spectrum, background=None)
specLike.set_model(model)
specLike.get_model()
specLike.get_simulated_dataset()
with pytest.raises(AssertionError):
specLike.rebin_on_background(min_number_of_counts=1e-1)
def test_healpixRoi(geminga_maptree, geminga_response):
#test to make sure writing a model with HealpixMapROI works fine
ra, dec = 101.7, 16.
data_radius = 9.
model_radius = 24.
m = np.zeros(hp.nside2npix(NSIDE))
vec = Sky2Vec(ra, dec)
m[hp.query_disc(NSIDE,
vec, (data_radius * u.degree).to(u.radian).value,
inclusive=False)] = 1
#hp.fitsfunc.write_map("roitemp.fits" , m, nest=False, coord="C", partial=False, overwrite=True )
map_roi = HealpixMapROI(data_radius=data_radius,
ra=ra,
dec=dec,
model_radius=model_radius,
roimap=m)
#fits_roi = HealpixMapROI(data_radius=data_radius, ra=ra, dec=dec, model_radius=model_radius, roifile="roitemp.fits")
hawc = HAL("HAWC", geminga_maptree, geminga_response, map_roi)
hawc.set_active_measurements(1, 9)
'''
Define model: Two sources, 1 point, 1 extended
Same declination, but offset in RA
Different spectral idnex, but both power laws
'''
pt_shift = 3.0
ext_shift = 2.0
# First soource
spectrum1 = Powerlaw()
source1 = PointSource("point",
ra=ra + pt_shift,
dec=dec,
spectral_shape=spectrum1)
spectrum1.K = 1e-12 / (u.TeV * u.cm**2 * u.s)
spectrum1.piv = 1 * u.TeV
spectrum1.index = -2.3
spectrum1.piv.fix = True
spectrum1.K.fix = True
spectrum1.index.fix = True
# Second source
shape = Gaussian_on_sphere(lon0=ra - ext_shift, lat0=dec, sigma=0.3)
spectrum2 = Powerlaw()
source2 = ExtendedSource("extended",
spatial_shape=shape,
spectral_shape=spectrum2)
spectrum2.K = 1e-12 / (u.TeV * u.cm**2 * u.s)
spectrum2.piv = 1 * u.TeV
spectrum2.index = -2.0
spectrum2.piv.fix = True
spectrum2.K.fix = True
spectrum2.index.fix = True
shape.lon0.fix = True
shape.lat0.fix = True
shape.sigma.fix = True
model = Model(source1, source2)
hawc.set_model(model)
# Write the model map
model_map_tree = hawc.write_model_map("test.hd5", test_return_map=True)
# Read the model back
hawc_model = map_tree_factory('test.hd5', map_roi)
# Check written model and read model are the same
check_map_trees(hawc_model, model_map_tree)
os.remove("test.hd5")
def test_model_residual_maps(geminga_maptree, geminga_response, geminga_roi):
#data_radius = 5.0
#model_radius = 7.0
output = dirname(geminga_maptree)
ra_src, dec_src = 101.7, 16.0
maptree, response, roi = geminga_maptree, geminga_response, geminga_roi
hawc = HAL("HAWC", maptree, response, roi)
# Use from bin 1 to bin 9
hawc.set_active_measurements(1, 9)
# Display information about the data loaded and the ROI
hawc.display()
'''
Define model: Two sources, 1 point, 1 extended
Same declination, but offset in RA
Different spectral index, but both power laws
'''
pt_shift = 3.0
ext_shift = 2.0
# First source
spectrum1 = Powerlaw()
source1 = PointSource("point",
ra=ra_src + pt_shift,
dec=dec_src,
spectral_shape=spectrum1)
spectrum1.K = 1e-12 / (u.TeV * u.cm**2 * u.s)
spectrum1.piv = 1 * u.TeV
spectrum1.index = -2.3
spectrum1.piv.fix = True
spectrum1.K.fix = True
spectrum1.index.fix = True
# Second source
shape = Gaussian_on_sphere(lon0=ra_src - ext_shift,
lat0=dec_src,
sigma=0.3)
spectrum2 = Powerlaw()
source2 = ExtendedSource("extended",
spatial_shape=shape,
spectral_shape=spectrum2)
spectrum2.K = 1e-12 / (u.TeV * u.cm**2 * u.s)
spectrum2.piv = 1 * u.TeV
spectrum2.index = -2.0
shape.lon0.fix = True
shape.lat0.fix = True
shape.sigma.fix = True
spectrum2.piv.fix = True
spectrum2.K.fix = True
spectrum2.index.fix = True
# Define model with both sources
model = Model(source1, source2)
# Define the data we are using
data = DataList(hawc)
# Define the JointLikelihood object (glue the data to the model)
jl = JointLikelihood(model, data, verbose=False)
# This has the effect of loading the model cache
fig = hawc.display_spectrum()
# the test file names
model_file_name = "{0}/test_model.hdf5".format(output)
residual_file_name = "{0}/test_residual.hdf5".format(output)
# Write the map trees for testing
model_map_tree = hawc.write_model_map(model_file_name,
poisson_fluctuate=True,
test_return_map=True)
residual_map_tree = hawc.write_residual_map(residual_file_name,
test_return_map=True)
# Read the maps back in
hawc_model = map_tree_factory(model_file_name, roi)
hawc_residual = map_tree_factory(residual_file_name, roi)
check_map_trees(hawc_model, model_map_tree)
check_map_trees(hawc_residual, residual_map_tree)
def test_OGIP_response_against_xspec():
# Test for various photon indexes
for index in [-0.5, 0.0, 0.5, 1.5, 2.0, 3.0, 4.0]:
print("Processing index %s" % index)
# First reset xspec
xspec.AllData.clear()
# Create a model in XSpec
mo = xspec.Model("po")
# Change the default value for the photon index
# (remember that in XSpec the definition of the powerlaw is norm * E^(-PhoIndex),
# so PhoIndex is positive normally. This is the opposite of astromodels.
mo.powerlaw.PhoIndex = index
mo.powerlaw.norm = 12.2
# Now repeat the same in 3ML
# Generate the astromodels function and set it to the same values as the XSpec power law
# (the pivot in XSpec is set to 1). Remember also that the definition in xspec has the
# sign of the photon index opposite
powerlaw = Powerlaw()
powerlaw.piv = 1.0
powerlaw.index = -mo.powerlaw.PhoIndex.values[0]
powerlaw.K = mo.powerlaw.norm.values[0]
# Exploit the fact that the power law integral is analytic
powerlaw_integral = Powerlaw()
# Remove transformation
powerlaw_integral.K._transformation = None
powerlaw_integral.K.bounds = (None, None)
powerlaw_integral.index = powerlaw.index.value + 1
powerlaw_integral.K = powerlaw.K.value / (powerlaw.index.value + 1)
powerlaw_integral.display()
integral_function = lambda e1, e2: powerlaw_integral(e2) - powerlaw_integral(e1)
# Now check that the two convoluted model give the same number of counts in each channel
# Fake a spectrum so we can actually compute the convoluted model
# Get path of response file
rsp_file = get_path_of_data_file("ogip_test_gbm_n6.rsp")
fs1 = xspec.FakeitSettings(rsp_file, exposure=1.0, fileName="_fake_spectrum.pha")
xspec.AllData.fakeit(noWrite=True, applyStats=False, settings=fs1)
# Get the expected counts
xspec_counts = mo.folded(1)
# Now get the convolution from 3ML
rsp = OGIPResponse(rsp_file)
rsp.set_function(integral_function)
threeML_counts = rsp.convolve()
# Compare them
assert np.allclose(xspec_counts, threeML_counts)
# Now do the same with a matrix with a ARF
# First reset xspec
xspec.AllData.clear()
# Then load rsp and arf in XSpec
rsp_file = get_path_of_data_file("ogip_test_xmm_pn.rmf")
arf_file = get_path_of_data_file("ogip_test_xmm_pn.arf")
fs1 = xspec.FakeitSettings(rsp_file, arf_file, exposure=1.0, fileName="_fake_spectrum.pha")
xspec.AllData.fakeit(noWrite=True, applyStats=False, settings=fs1)
# Get the expected counts
xspec_counts = mo.folded(1)
# Now get the convolution from 3ML
rsp = OGIPResponse(rsp_file, arf_file=arf_file)
rsp.set_function(integral_function)
threeML_counts = rsp.convolve()
# Compare them
assert np.allclose(xspec_counts, threeML_counts)
def test_response_against_xspec():
# Make a response and write to a FITS OGIP file
matrix, mc_energies, ebounds = get_matrix_elements()
rsp = InstrumentResponse(matrix, ebounds, mc_energies)
temp_file = "__test.rsp"
rsp.to_fits(temp_file, "TEST", "TEST", overwrite=True)
# Test for various photon indexes
for index in np.linspace(-2.0, 2.0, 10):
if index == 1.0:
# This would make the integral of the power law different, so let's just
# skip it
continue
# First reset xspec
xspec.AllData.clear()
# Create a model in XSpec
mo = xspec.Model("po")
# Change the default value for the photon index
# (remember that in XSpec the definition of the powerlaw is norm * E^(-PhoIndex),
# so PhoIndex is positive normally. This is the opposite of astromodels.
mo.powerlaw.PhoIndex = index
mo.powerlaw.norm = 12.2
# Now repeat the same in 3ML
# Generate the astromodels function and set it to the same values as the XSpec power law
# (the pivot in XSpec is set to 1). Remember also that the definition in xspec has the
# sign of the photon index opposite
powerlaw = Powerlaw()
powerlaw.piv = 1.0
powerlaw.index = -mo.powerlaw.PhoIndex.values[0]
powerlaw.K = mo.powerlaw.norm.values[0]
# Exploit the fact that the power law integral is analytic
powerlaw_integral = Powerlaw()
powerlaw_integral.K._transformation = None
powerlaw_integral.K.bounds = (None, None)
powerlaw_integral.index = powerlaw.index.value + 1
powerlaw_integral.K = powerlaw.K.value / (powerlaw.index.value + 1)
integral_function = lambda e1, e2: powerlaw_integral(e2) - powerlaw_integral(e1)
# Now check that the two convoluted model give the same number of counts in each channel
# Fake a spectrum so we can actually compute the convoluted model
# Get path of response file
fs1 = xspec.FakeitSettings(temp_file, exposure=1.0, fileName="_fake_spectrum.pha")
xspec.AllData.fakeit(noWrite=True, applyStats=False, settings=fs1)
# Get the expected counts
xspec_counts = mo.folded(1)
# Now get the convolution from 3ML
rsp.set_function(integral_function)
threeML_counts = rsp.convolve()
# Compare them
assert np.allclose(xspec_counts, threeML_counts)
os.remove(temp_file)
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()
def test_response_against_xspec():
# Make a response and write to a FITS OGIP file
matrix, mc_energies, ebounds = get_matrix_elements()
rsp = InstrumentResponse(matrix, ebounds, mc_energies)
temp_file = "__test.rsp"
rsp.to_fits(temp_file, "TEST", "TEST", overwrite=True)
# Test for various photon indexes
for index in np.linspace(-2.0, 2.0, 10):
if index == 1.0:
# This would make the integral of the power law different, so let's just
# skip it
continue
# First reset xspec
xspec.AllData.clear()
# Create a model in XSpec
mo = xspec.Model("po")
# Change the default value for the photon index
# (remember that in XSpec the definition of the powerlaw is norm * E^(-PhoIndex),
# so PhoIndex is positive normally. This is the opposite of astromodels.
mo.powerlaw.PhoIndex = index
mo.powerlaw.norm = 12.2
# Now repeat the same in 3ML
# Generate the astromodels function and set it to the same values as the XSpec power law
# (the pivot in XSpec is set to 1). Remember also that the definition in xspec has the
# sign of the photon index opposite
powerlaw = Powerlaw()
powerlaw.piv = 1.0
powerlaw.index = -mo.powerlaw.PhoIndex.values[0]
powerlaw.K = mo.powerlaw.norm.values[0]
# Exploit the fact that the power law integral is analytic
powerlaw_integral = Powerlaw()
powerlaw_integral.K._transformation = None
powerlaw_integral.K.bounds = (None, None)
powerlaw_integral.index = powerlaw.index.value + 1
powerlaw_integral.K = old_div(powerlaw.K.value, (powerlaw.index.value + 1))
integral_function = lambda e1, e2: powerlaw_integral(e2) - powerlaw_integral(e1)
# Now check that the two convoluted model give the same number of counts in each channel
# Fake a spectrum so we can actually compute the convoluted model
# Get path of response file
fs1 = xspec.FakeitSettings(
temp_file, exposure=1.0, fileName="_fake_spectrum.pha"
)
xspec.AllData.fakeit(noWrite=True, applyStats=False, settings=fs1)
# Get the expected counts
xspec_counts = mo.folded(1)
# Now get the convolution from 3ML
rsp.set_function(integral_function)
threeML_counts = rsp.convolve()
# Compare them
assert np.allclose(xspec_counts, threeML_counts)
os.remove(temp_file)
def test_OGIP_response_against_xspec():
# Test for various photon indexes
for index in [-0.5, 0.0, 0.5, 1.5, 2.0, 3.0, 4.0]:
print("Processing index %s" % index)
# First reset xspec
xspec.AllData.clear()
# Create a model in XSpec
mo = xspec.Model("po")
# Change the default value for the photon index
# (remember that in XSpec the definition of the powerlaw is norm * E^(-PhoIndex),
# so PhoIndex is positive normally. This is the opposite of astromodels.
mo.powerlaw.PhoIndex = index
mo.powerlaw.norm = 12.2
# Now repeat the same in 3ML
# Generate the astromodels function and set it to the same values as the XSpec power law
# (the pivot in XSpec is set to 1). Remember also that the definition in xspec has the
# sign of the photon index opposite
powerlaw = Powerlaw()
powerlaw.piv = 1.0
powerlaw.index = -mo.powerlaw.PhoIndex.values[0]
powerlaw.K = mo.powerlaw.norm.values[0]
# Exploit the fact that the power law integral is analytic
powerlaw_integral = Powerlaw()
# Remove transformation
powerlaw_integral.K._transformation = None
powerlaw_integral.K.bounds = (None, None)
powerlaw_integral.index = powerlaw.index.value + 1
powerlaw_integral.K = old_div(powerlaw.K.value, (powerlaw.index.value + 1))
powerlaw_integral.display()
integral_function = lambda e1, e2: powerlaw_integral(e2) - powerlaw_integral(e1)
# Now check that the two convoluted model give the same number of counts in each channel
# Fake a spectrum so we can actually compute the convoluted model
# Get path of response file
rsp_file = str(get_path_of_data_file("ogip_test_gbm_n6.rsp"))
fs1 = xspec.FakeitSettings(
rsp_file, exposure=1.0, fileName="_fake_spectrum.pha"
)
xspec.AllData.fakeit(noWrite=True, applyStats=False, settings=fs1)
# Get the expected counts
xspec_counts = mo.folded(1)
# Now get the convolution from 3ML
rsp = OGIPResponse(rsp_file)
rsp.set_function(integral_function)
threeML_counts = rsp.convolve()
# Compare them
assert np.allclose(xspec_counts, threeML_counts)
# Now do the same with a matrix with a ARF
# First reset xspec
xspec.AllData.clear()
# Then load rsp and arf in XSpec
rsp_file = str(get_path_of_data_file("ogip_test_xmm_pn.rmf"))
arf_file = str(get_path_of_data_file("ogip_test_xmm_pn.arf"))
fs1 = xspec.FakeitSettings(
rsp_file, arf_file, exposure=1.0, fileName="_fake_spectrum.pha"
)
xspec.AllData.fakeit(noWrite=True, applyStats=False, settings=fs1)
# Get the expected counts
xspec_counts = mo.folded(1)
# Now get the convolution from 3ML
rsp = OGIPResponse(rsp_file, arf_file=arf_file)
rsp.set_function(integral_function)
threeML_counts = rsp.convolve()
# Compare them
assert np.allclose(xspec_counts, threeML_counts)
