def test_fit(roi, maptree, response):
pts_model = point_source_model()
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()
# Get the likelihood value for the saturated model
hawc.get_saturated_model_likelihood()
data = DataList(hawc)
jl = JointLikelihood(pts_model, data, verbose=True)
param_df, like_df = jl.fit()
return jl, hawc, pts_model, param_df, like_df, data
def test_complete_analysis(roi, maptree, response, point_source_model):
# Define the ROI
# Instance the plugin
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()
# Get the likelihood value for the saturated model
hawc.get_saturated_model_likelihood()
# Look at the data
fig = hawc.display_stacked_image(smoothing_kernel_sigma=0.17)
# Save to file
fig.savefig("hal_src_stacked_image.png")
data = DataList(hawc)
jl = JointLikelihood(point_source_model, data, verbose=False)
param_df, like_df = jl.fit()
# Generate a simulated dataset and write it to disk
sim = hawc.get_simulated_dataset("HAWCsim")
sim.write("sim_resp.hd5", "sim_maptree.hd5")
# See the model in counts space and the residuals
fig = hawc.display_spectrum()
# Save it to file
fig.savefig("hal_src_residuals.png")
# Look at the different energy planes (the columns are model, data, residuals)
fig = hawc.display_fit(smoothing_kernel_sigma=0.3)
fig.savefig("hal_src_fit_planes.png")
# Compute TS
src_name = point_source_model.pts.name
jl.compute_TS(src_name, like_df)
# Compute goodness of fit with Monte Carlo
gf = GoodnessOfFit(jl)
gof, param, likes = gf.by_mc(10)
print(
"Prob. of obtaining -log(like) >= observed by chance if null hypothesis is true: %.2f"
% gof['HAWC'])
# it is a good idea to inspect the results of the simulations with some plots
# Histogram of likelihood values
fig, sub = plt.subplots()
likes.hist(ax=sub)
# Overplot a vertical dashed line on the observed value
plt.axvline(jl.results.get_statistic_frame().loc['HAWC',
'-log(likelihood)'],
color='black',
linestyle='--')
fig.savefig("hal_sim_all_likes.png")
# Plot the value of beta for all simulations (for example)
fig, sub = plt.subplots()
param.loc[(slice(None), ['pts.spectrum.main.Cutoff_powerlaw.index']),
'value'].plot()
fig.savefig("hal_sim_all_index.png")
# Free the position of the source
point_source_model.pts.position.ra.free = True
point_source_model.pts.position.dec.free = True
# Set boundaries (no need to go further than this)
ra = point_source_model.pts.position.ra.value
dec = point_source_model.pts.position.dec.value
point_source_model.pts.position.ra.bounds = (ra - 0.5, ra + 0.5)
point_source_model.pts.position.dec.bounds = (dec - 0.5, dec + 0.5)
# Fit with position free
param_df, like_df = jl.fit()
# Make localization contour
# pts.position.ra(8.362 + / - 0.00028) x
# 10
# deg
# pts.position.dec(2.214 + / - 0.00025) x
# 10
a, b, cc, fig = jl.get_contours(point_source_model.pts.position.dec, 22.13,
22.1525, 10,
point_source_model.pts.position.ra, 83.615,
83.635, 10)
plt.plot([ra], [dec], 'x')
fig.savefig("hal_src_localization.png")
# Of course we can also do a Bayesian analysis the usual way
# NOTE: here the position is still free, so we are going to obtain marginals about that
# as well
# For this quick example, let's use a uniform prior for all parameters
for parameter in point_source_model.parameters.values():
if parameter.fix:
continue
if parameter.is_normalization:
parameter.set_uninformative_prior(Log_uniform_prior)
else:
parameter.set_uninformative_prior(Uniform_prior)
# Let's execute our bayes analysis
bs = BayesianAnalysis(point_source_model, data)
samples = bs.sample(30, 20, 20)
fig = bs.corner_plot()
fig.savefig("hal_corner_plot.png")