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_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_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_spectrumlike_fit():
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=1, 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)
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.mean(), kT_variates.mean()], [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.
# 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.)
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.mean(), kT_variates.mean()], [sim_K, sim_kT], atol=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 = 20.0
# 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 fit_point_source(roi,
maptree,
response,
point_source_model,
bin_list,
confidence_intervals=False,
liff=False,
pixel_size=0.17,
verbose=False):
data_radius = roi.data_radius.to("deg").value
if not liff:
# This is a 3ML plugin
hawc = HAL("HAWC",
maptree,
response,
roi,
flat_sky_pixels_size=pixel_size)
hawc.set_active_measurements(bin_list=bin_list)
else:
from threeML import HAWCLike
hawc = HAWCLike("HAWC", maptree, response, fullsky=True)
hawc.set_bin_list(bin_list)
ra_roi, dec_roi = roi.ra_dec_center
hawc.set_ROI(ra_roi, dec_roi, data_radius)
if not liff:
hawc.display()
data = DataList(hawc)
jl = JointLikelihood(point_source_model, data, verbose=verbose)
point_source_model.display(complete=True)
try:
jl.set_minimizer("minuit")
except:
jl.set_minimizer("minuit")
param_df, like_df = jl.fit()
if confidence_intervals:
ci = jl.get_errors()
else:
ci = None
return param_df, like_df, ci, jl.results
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.
# 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.)
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(AssertionError):
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(AssertionError):
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_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)