shape2.lat0.fix = True
shape2.radius = 0.63 * u.degree
shape2.radius.fix = True
shape2.radius.max_value = 0.63 * u.degree
lm = Model(source, source2, source3)
bin_list = "1c 1d 1e 1f 2c 2d 2e 2f 3c 3d 3e 3f 4c 4d 4e 4f 4g 5e 5f 5g 5h 6e 6f 6g 6h 7f 7g 7h 7i 8g 8h 8i 8j 9g 9h 9i 9j 9k 9l".split()
ra, dec = 307.17, 41.17
#data_radius = 6.0
model_radius = 8.0
fits_roi = HealpixMapROI(ra = ra, dec = dec, model_radius=model_radius, roifile="roi.fits")
hawc = HAL("HAWC", maptree, response,fits_roi)
hawc.set_active_measurements(bin_list=bin_list)
#hawc.display()
# 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(hawc)
jl = JointLikelihood(lm, datalist, verbose=True)
jl.set_minimizer("ROOT")
param_df, like_df = jl.fit()
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_rois():
#test to make sure ConeROI and MapROI with a simple, circular active region return the same active pixels.
ra, dec = 100, 30
data_radius = 10
model_radius = 15
cone_roi = HealpixConeROI(data_radius=data_radius,
model_radius=model_radius,
ra=ra,
dec=dec)
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")
assert np.all(
cone_roi.active_pixels(NSIDE) == map_roi.active_pixels(NSIDE))
assert np.all(
fits_roi.active_pixels(NSIDE) == map_roi.active_pixels(NSIDE))
os.remove("roitemp.fits")
#test that all is still good after saving the ROIs to dictionaries and restoring.
cone_dict = cone_roi.to_dict()
map_dict = map_roi.to_dict()
fits_dict = fits_roi.to_dict()
cone_roi2 = get_roi_from_dict(cone_dict)
map_roi2 = get_roi_from_dict(map_dict)
fits_roi2 = get_roi_from_dict(fits_dict)
assert np.all(
cone_roi2.active_pixels(NSIDE) == map_roi.active_pixels(NSIDE))
assert np.all(
fits_roi2.active_pixels(NSIDE) == map_roi.active_pixels(NSIDE))
assert np.all(
map_roi2.active_pixels(NSIDE) == map_roi.active_pixels(NSIDE))
shape2.lat0.fix = True
shape2.radius = 0.63 * u.degree
shape2.radius.fix = True
shape2.radius.max_value = 0.63 * u.degree
lm = Model(source, source2, source3)
bin_list = "1c 1d 1e 1f 2c 2d 2e 2f 3c 3d 3e 3f 4c 4d 4e 4f 4g 5e 5f 5g 5h 6e 6f 6g 6h 7f 7g 7h 7i 8g 8h 8i 8j 9g 9h 9i 9j 9k 9l".split(
)
ra, dec = 307.17, 41.17
#data_radius = 6.0
model_radius = 8.0
fits_roi = HealpixMapROI(ra=ra,
dec=dec,
model_radius=model_radius,
roifile="roi_6aroundcocoon_nomasking.fits")
hawc = HAL("HAWC", maptree, response, fits_roi)
hawc.set_active_measurements(bin_list=bin_list)
#hawc.display()
# 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(hawc)
jl = JointLikelihood(lm, datalist, verbose=True)
jl.set_minimizer("ROOT")
param_df, like_df = jl.fit()