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_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)