def hawc_point_source_fitted_joint_like():
data_path = sanitize_filename(os.environ.get('HAWC_3ML_TEST_DATA_DIR'),
abspath=True)
maptree = os.path.join(data_path, _maptree_name)
response = os.path.join(data_path, _response_name)
assert os.path.exists(maptree) and os.path.exists(
response), "Data files do not exist at %s" % data_path
# The simulated source has this spectrum (credits for simulation: Colas Riviere):
# CutOffPowerLaw,3.15e-11,2.37,42.3
# at this position:
# 100,22
# Define the spectral and spatial models for the source
spectrum = Cutoff_powerlaw()
source = PointSource("TestSource",
ra=100.0,
dec=22.0,
spectral_shape=spectrum)
spectrum.K = 3.15e-11 / (u.TeV * u.cm**2 * u.s)
spectrum.K.bounds = (1e-22, 1e-18) # without units energies are in keV
spectrum.piv = 1 * u.TeV
spectrum.piv.fix = True
spectrum.index = -2.37
spectrum.index.bounds = (-4, -1)
spectrum.xc = 42.3 * u.TeV
spectrum.xc.bounds = (1 * u.TeV, 100 * u.TeV)
q = source(1 * u.keV)
assert np.isclose(q.value, 67.3458058177)
# Set up a likelihood model using the source.
# Then create a HAWCLike object using the model, the maptree, and detector
# response.
lm = Model(source)
llh = HAWCLike("HAWC", maptree, response)
llh.set_active_measurements(1, 9)
# Double check the free parameters
print("Likelihood model:\n")
print(lm)
# Set up the likelihood and run the fit
print("Performing likelihood fit...\n")
datalist = DataList(llh)
jl = JointLikelihood(lm, datalist, verbose=True)
jl.set_minimizer("ROOT")
parameter_frame, like = jl.fit(compute_covariance=False)
return jl, parameter_frame, like
def test_CommonNorm_fit():
assert is_plugin_available("HAWCLike"), "HAWCLike is not available!"
data_path = sanitize_filename(os.environ.get('HAWC_3ML_TEST_DATA_DIR'), abspath=True)
maptree = os.path.join(data_path, _maptree_name)
response = os.path.join(data_path, _response_name)
assert os.path.exists(maptree) and os.path.exists(response), "Data files do not exist at %s" % data_path
# The simulated source has this spectrum (credits for simulation: Colas Riviere):
# CutOffPowerLaw,3.15e-11,2.37,42.3
# at this position:
# 100,22
# Define the spectral and spatial models for the source
spectrum = Cutoff_powerlaw()
source = PointSource("TestSource", ra=100.0, dec=22.0, spectral_shape=spectrum)
spectrum.K = 3.15e-11 / (u.TeV * u.cm ** 2 * u.s)
spectrum.K.bounds = (1e-22, 1e-18) # without units energies are in keV
spectrum.K.fix = True
spectrum.piv = 1 * u.TeV
spectrum.piv.fix = True
spectrum.index = -2.37
spectrum.index.bounds = (-4, -1)
spectrum.index.free = False
spectrum.xc = 42.3 * u.TeV
spectrum.xc.bounds = (1 * u.TeV, 100 * u.TeV)
spectrum.xc.free = False
q = source(1 * u.keV)
assert np.isclose(q.value, 67.3458058177)
# Set up a likelihood model using the source.
# Then create a HAWCLike object using the model, the maptree, and detector
# response.
lm = Model(source)
llh = HAWCLike("HAWC", maptree, response)
llh.set_active_measurements(1, 9)
llh.activate_CommonNorm()
# Double check the free parameters
print("Likelihood model:\n")
print(lm)
# Set up the likelihood and run the fit
print("Performing likelihood fit...\n")
datalist = DataList(llh)
jl = JointLikelihood(lm, datalist, verbose=True)
jl.set_minimizer("ROOT")
parameter_frame, like = jl.fit(compute_covariance=False)
assert np.isclose(lm.HAWC_ComNorm.value, 1.0756519971562115, rtol=1e-2)
def test_set_active_measurements():
data_path = sanitize_filename(os.environ.get('HAWC_3ML_TEST_DATA_DIR'), abspath=True)
maptree = os.path.join(data_path, _maptree_name)
response = os.path.join(data_path, _response_name)
assert os.path.exists(maptree) and os.path.exists(response), "Data files do not exist at %s" % data_path
llh = HAWCLike("HAWC", maptree, response)
# Test one way
llh.set_active_measurements(1, 9)
# Test the other way
llh.set_active_measurements(bin_list=['4','5','6','7','8','9'])
def test_set_active_measurements():
data_path = sanitize_filename(os.environ.get('HAWC_3ML_TEST_DATA_DIR'), abspath=True)
maptree = os.path.join(data_path, _maptree_name)
response = os.path.join(data_path, _response_name)
assert os.path.exists(maptree) and os.path.exists(response), "Data files do not exist at %s" % data_path
llh = HAWCLike("HAWC", maptree, response)
# Test one way
llh.set_active_measurements(1, 9)
# Test the other way
llh.set_active_measurements(bin_list=['4','5','6','7','8','9'])
def test_set_active_measurements():
data_path = sanitize_filename(os.environ.get("HAWC_3ML_TEST_DATA_DIR"),
abspath=True)
maptree = os.path.join(data_path, _maptree_name)
response = os.path.join(data_path, _response_name)
assert os.path.exists(maptree) and os.path.exists(response), (
"Data files do not exist at %s" % data_path)
llh = HAWCLike("HAWC", maptree, response)
# Test one way
llh.set_active_measurements(1, 9)
# Test the other way
llh.set_active_measurements(bin_list=["4", "5", "6", "7", "8", "9"])
def test_hawc_extended_source_fit():
# Ensure test environment is valid
assert is_plugin_available("HAWCLike"), "HAWCLike is not available!"
data_path = sanitize_filename(os.environ.get('HAWC_3ML_TEST_DATA_DIR'), abspath=True)
maptree = os.path.join(data_path, _maptree_name)
response = os.path.join(data_path, _response_name)
assert os.path.exists(maptree) and os.path.exists(response), "Data files do not exist at %s" % data_path
# The simulated source has this spectrum (credits for simulation: Colas Riviere):
# CutOffPowerLaw,1.32e-07,2.37,42.3
# at this position:
# 100,22
# with a disk shape with an extension of 1.5 deg
# Define the spectral and spatial models for the source
spectrum = Cutoff_powerlaw()
shape = Disk_on_sphere()
source = ExtendedSource("ExtSource",
spatial_shape=shape,
spectral_shape=spectrum)
shape.lon0 = 100.0
shape.lon0.fix = True
shape.lat0 = 22.0
shape.lat0.fix = True
shape.radius = 1.5 * u.degree
shape.radius.bounds = (0.5 * u.degree, 1.55 * u.degree)
# shape.radius.fix = True
spectrum.K = 4.39964273e-20
spectrum.K.bounds = (1e-24, 1e-17)
spectrum.piv = 1 * u.TeV
# spectrum.piv.fix = True
spectrum.index = -2.37
spectrum.index.bounds = (-4, -1)
# spectrum.index.fix = True
spectrum.xc = 42.3 * u.TeV
spectrum.xc.bounds = (1 * u.TeV, 100 * u.TeV)
spectrum.xc.fix = True
# Set up a likelihood model using the source.
# Then create a HAWCLike object using the model, the maptree, and detector
# response.
lm = Model(source)
llh = HAWCLike("HAWC", maptree, response)
llh.set_active_measurements(1, 9)
# Double check the free parameters
print("Likelihood model:\n")
print(lm)
# Set up the likelihood and run the fit
print("Performing likelihood fit...\n")
datalist = DataList(llh)
jl = JointLikelihood(lm, datalist, verbose=True)
jl.set_minimizer("ROOT")
parameter_frame, like = jl.fit(compute_covariance=False)
# Check that we have converged to the right solution
# (the true value of course are not exactly the value simulated,
# they are just the point where the fit should converge)
assert is_within_tolerance(4.7805737823025172e-20, parameter_frame['value']['ExtSource.spectrum.main.Cutoff_powerlaw.K'])
assert is_within_tolerance(-2.44931279819,
parameter_frame['value']['ExtSource.spectrum.main.Cutoff_powerlaw.index'])
assert is_within_tolerance(1.4273457159139373, parameter_frame['value']['ExtSource.Disk_on_sphere.radius'])
assert is_within_tolerance(186389.581117, like['-log(likelihood)']['HAWC'])
# Print up the TS, significance, and fit parameters, and then plot stuff
print("\nTest statistic:")
TS = llh.calc_TS()
sigma = np.sqrt(TS)
assert is_within_tolerance(3510.26, TS)
assert is_within_tolerance(59.2475, sigma)
print("Test statistic: %g" % TS)
print("Significance: %g\n" % sigma)
# Get the differential flux at 1 TeV
diff_flux = spectrum(1 * u.TeV)
# Convert it to 1 / (TeV cm2 s)
diff_flux_TeV = diff_flux.to(1 / (u.TeV * u.cm ** 2 * u.s))
print("Norm @ 1 TeV: %s \n" % diff_flux_TeV)
assert is_within_tolerance(4.66888328668e-11, diff_flux_TeV.value)
spectrum.display()
shape.display()
def test_hawc_fullsky_options():
assert is_plugin_available("HAWCLike"), "HAWCLike is not available!"
data_path = sanitize_filename(os.environ.get('HAWC_3ML_TEST_DATA_DIR'), abspath=True)
maptree = os.path.join(data_path, _maptree_name)
response = os.path.join(data_path, _response_name)
assert os.path.exists(maptree) and os.path.exists(response), "Data files do not exist at %s" % data_path
# The simulated source has this spectrum (credits for simulation: Colas Riviere):
# CutOffPowerLaw,3.15e-11,2.37,42.3
# at this position:
# 100,22
# Define the spectral and spatial models for the source
spectrum = Cutoff_powerlaw()
source = PointSource("TestSource", ra=100.0, dec=22.0, spectral_shape=spectrum)
spectrum.K = 3.15e-11 / (u.TeV * u.cm ** 2 * u.s)
spectrum.K.bounds = (1e-22, 1e-18) # without units energies are in keV
spectrum.piv = 1 * u.TeV
spectrum.piv.fix = True
spectrum.index = -2.37
spectrum.index.bounds = (-4, -1)
spectrum.xc = 42.3 * u.TeV
spectrum.xc.bounds = (1 * u.TeV, 100 * u.TeV)
q = source(1 * u.keV)
assert np.isclose(q.value, 67.3458058177)
# Set up a likelihood model using the source.
# Then create a HAWCLike object using the model, the maptree, and detector
# response.
lm = Model(source)
# Test with fullsky=True, and try to perform a fit to verify that we throw an exception
llh = HAWCLike("HAWC", maptree, response, fullsky=True)
llh.set_active_measurements(1, 9)
# Double check the free parameters
print("Likelihood model:\n")
print(lm)
# Set up the likelihood and run the fit
print("Performing likelihood fit...\n")
datalist = DataList(llh)
with pytest.raises(RuntimeError):
jl = JointLikelihood(lm, datalist, verbose=False)
# Now we use set_ROI and this should work
llh.set_ROI(100.0, 22.0, 2.0)
jl = JointLikelihood(lm, datalist, verbose=False)
# Now test that we can use set_ROI even though fullsky=False
llh = HAWCLike("HAWC", maptree, response, fullsky=False)
llh.set_active_measurements(1, 9)
llh.set_ROI(100.0, 22.0, 1.0)
# Double check the free parameters
print("Likelihood model:\n")
print(lm)
# Set up the likelihood
print("Performing likelihood fit...\n")
datalist = DataList(llh)
jl = JointLikelihood(lm, datalist, verbose=False)
def test_hawc_extended_source_fit():
# Ensure test environment is valid
assert is_plugin_available("HAWCLike"), "HAWCLike is not available!"
data_path = sanitize_filename(os.environ.get('HAWC_3ML_TEST_DATA_DIR'),
abspath=True)
maptree = os.path.join(data_path, _maptree_name)
response = os.path.join(data_path, _response_name)
assert os.path.exists(maptree) and os.path.exists(
response), "Data files do not exist at %s" % data_path
# The simulated source has this spectrum (credits for simulation: Colas Riviere):
# CutOffPowerLaw,1.32e-07,2.37,42.3
# at this position:
# 100,22
# with a disk shape with an extension of 1.5 deg
# Define the spectral and spatial models for the source
spectrum = Cutoff_powerlaw()
shape = Disk_on_sphere()
source = ExtendedSource("ExtSource",
spatial_shape=shape,
spectral_shape=spectrum)
shape.lon0 = 100.0
shape.lon0.fix = True
shape.lat0 = 22.0
shape.lat0.fix = True
shape.radius = 1.5 * u.degree
shape.radius.bounds = (0.5 * u.degree, 1.55 * u.degree)
# shape.radius.fix = True
spectrum.K = 4.39964273e-20
spectrum.K.bounds = (1e-24, 1e-17)
spectrum.piv = 1 * u.TeV
# spectrum.piv.fix = True
spectrum.index = -2.37
spectrum.index.bounds = (-4, -1)
# spectrum.index.fix = True
spectrum.xc = 42.3 * u.TeV
spectrum.xc.bounds = (1 * u.TeV, 100 * u.TeV)
spectrum.xc.fix = True
# Set up a likelihood model using the source.
# Then create a HAWCLike object using the model, the maptree, and detector
# response.
lm = Model(source)
llh = HAWCLike("HAWC", maptree, response)
llh.set_active_measurements(1, 9)
# Double check the free parameters
print("Likelihood model:\n")
print(lm)
# Set up the likelihood and run the fit
print("Performing likelihood fit...\n")
datalist = DataList(llh)
jl = JointLikelihood(lm, datalist, verbose=True)
jl.set_minimizer("ROOT")
parameter_frame, like = jl.fit(compute_covariance=False)
# Check that we have converged to the right solution
# (the true value of course are not exactly the value simulated,
# they are just the point where the fit should converge)
assert is_within_tolerance(
4.7805737823025172e-20,
parameter_frame['value']['ExtSource.spectrum.main.Cutoff_powerlaw.K'])
assert is_within_tolerance(
-2.44931279819, parameter_frame['value']
['ExtSource.spectrum.main.Cutoff_powerlaw.index'])
assert is_within_tolerance(
1.4273457159139373,
parameter_frame['value']['ExtSource.Disk_on_sphere.radius'])
assert is_within_tolerance(186389.581117, like['-log(likelihood)']['HAWC'])
# Print up the TS, significance, and fit parameters, and then plot stuff
print("\nTest statistic:")
TS = llh.calc_TS()
sigma = np.sqrt(TS)
assert is_within_tolerance(3510.26, TS)
assert is_within_tolerance(59.2475, sigma)
print("Test statistic: %g" % TS)
print("Significance: %g\n" % sigma)
# Get the differential flux at 1 TeV
diff_flux = spectrum(1 * u.TeV)
# Convert it to 1 / (TeV cm2 s)
diff_flux_TeV = diff_flux.to(1 / (u.TeV * u.cm**2 * u.s))
print("Norm @ 1 TeV: %s \n" % diff_flux_TeV)
assert is_within_tolerance(4.66888328668e-11, diff_flux_TeV.value)
spectrum.display()
shape.display()
def test_hawc_fullsky_options():
assert is_plugin_available("HAWCLike"), "HAWCLike is not available!"
data_path = sanitize_filename(os.environ.get('HAWC_3ML_TEST_DATA_DIR'),
abspath=True)
maptree = os.path.join(data_path, _maptree_name)
response = os.path.join(data_path, _response_name)
assert os.path.exists(maptree) and os.path.exists(
response), "Data files do not exist at %s" % data_path
# The simulated source has this spectrum (credits for simulation: Colas Riviere):
# CutOffPowerLaw,3.15e-11,2.37,42.3
# at this position:
# 100,22
# Define the spectral and spatial models for the source
spectrum = Cutoff_powerlaw()
source = PointSource("TestSource",
ra=100.0,
dec=22.0,
spectral_shape=spectrum)
spectrum.K = 3.15e-11 / (u.TeV * u.cm**2 * u.s)
spectrum.K.bounds = (1e-22, 1e-18) # without units energies are in keV
spectrum.piv = 1 * u.TeV
spectrum.piv.fix = True
spectrum.index = -2.37
spectrum.index.bounds = (-4, -1)
spectrum.xc = 42.3 * u.TeV
spectrum.xc.bounds = (1 * u.TeV, 100 * u.TeV)
q = source(1 * u.keV)
assert np.isclose(q.value, 67.3458058177)
# Set up a likelihood model using the source.
# Then create a HAWCLike object using the model, the maptree, and detector
# response.
lm = Model(source)
# Test with fullsky=True, and try to perform a fit to verify that we throw an exception
llh = HAWCLike("HAWC", maptree, response, fullsky=True)
llh.set_active_measurements(1, 9)
# Double check the free parameters
print("Likelihood model:\n")
print(lm)
# Set up the likelihood and run the fit
print("Performing likelihood fit...\n")
datalist = DataList(llh)
with pytest.raises(RuntimeError):
jl = JointLikelihood(lm, datalist, verbose=False)
# Now we use set_ROI and this should work
llh.set_ROI(100.0, 22.0, 2.0)
jl = JointLikelihood(lm, datalist, verbose=False)
# Now test that we can use set_ROI even though fullsky=False
llh = HAWCLike("HAWC", maptree, response, fullsky=False)
llh.set_active_measurements(1, 9)
llh.set_ROI(100.0, 22.0, 1.0)
# Double check the free parameters
print("Likelihood model:\n")
print(lm)
# Set up the likelihood
print("Performing likelihood fit...\n")
datalist = DataList(llh)
jl = JointLikelihood(lm, datalist, verbose=False)