def fit_point_source(roi, maptree, response, point_source_model, liff=False):
data_radius = roi.data_radius.to("deg").value
if not liff:
# This is a 3ML plugin
hawc = HAL("HAWC", maptree, response, roi)
else:
from threeML import HAWCLike
hawc = HAWCLike("HAWC", maptree, response, fullsky=True)
ra_roi, dec_roi = roi.ra_dec_center
hawc.set_ROI(ra_roi, dec_roi, data_radius)
hawc.set_active_measurements(1, 9)
if not liff: hawc.display()
data = DataList(hawc)
jl = JointLikelihood(point_source_model, data, verbose=True)
point_source_model.display(complete=True)
beg = time.time()
jl.set_minimizer("ROOT")
param_df, like_df = jl.fit()
# _ = jl.get_errors()
print("Fit time: %s" % (time.time() - beg))
if not liff:
hawc.display_fit(display_colorbar=True).savefig("display_fit.png")
return param_df, like_df
def test_on_point_source(ra=ra_crab,
dec=dec_crab,
liff=False,
maptree=os.path.join(test_data_path,
"maptree_1024.root"),
response=os.path.join(test_data_path,
"response.root"),
data_radius=5.0,
model_radius=10.0):
if not liff:
roi = HealpixConeROI(data_radius=data_radius,
model_radius=model_radius,
ra=ra,
dec=dec)
# This is a 3ML plugin
hawc = HAL("HAWC", maptree, response, roi)
else:
from threeML import HAWCLike
hawc = HAWCLike("HAWC",
os.path.join(test_data_path, "maptree_1024.root"),
os.path.join(test_data_path, "response.root"),
fullsky=True)
hawc.set_ROI(ra, dec, data_radius)
hawc.set_active_measurements(1, 9)
if not liff: hawc.display()
spectrum = Log_parabola()
source = PointSource("pts", ra=ra, dec=dec, spectral_shape=spectrum)
# NOTE: if you use units, you have to set up the values for the parameters
# AFTER you create the source, because during creation the function Log_parabola
# gets its units
source.position.ra.bounds = (ra - 0.5, ra + 0.5)
source.position.dec.bounds = (dec - 0.5, dec + 0.5)
if ra == ra_crab:
spectrum.piv = 10 * u.TeV # Pivot energy as in the paper of the Crab
else:
spectrum.piv = 1 * u.TeV # Pivot energy
spectrum.piv.fix = True
spectrum.K = 1e-14 / (u.TeV * u.cm**2 * u.s) # norm (in 1/(keV cm2 s))
spectrum.K.bounds = (1e-25, 1e-19) # without units energies are in keV
spectrum.beta = 0 # log parabolic beta
spectrum.beta.bounds = (-4., 2.)
spectrum.alpha = -2.5 # log parabolic alpha (index)
spectrum.alpha.bounds = (-4., 2.)
model = Model(source)
data = DataList(hawc)
jl = JointLikelihood(model, data, verbose=False)
beg = time.time()
param_df, like_df = jl.fit()
print("Fit time: %s" % (time.time() - beg))
return param_df, like_df