def display_stacked_image(self, smoothing_kernel_sigma=0.5):
"""
Display a map with all active analysis bins stacked together.
:param smoothing_kernel_sigma: sigma for the Gaussian smoothing kernel to apply
:return: a matplotlib.Figure instance
"""
# This is the resolution (i.e., the size of one pixel) of the image in arcmin
resolution = 3.0
# The image is going to cover the diameter plus 20% padding
xsize = self._get_optimal_xsize(resolution)
active_planes_bins = [self._maptree[x] for x in self._active_planes]
# Get the center of the projection for this plane
this_ra, this_dec = self._roi.ra_dec_center
# Healpix uses longitude between -180 and 180, while R.A. is between 0 and 360. We need to fix that:
longitude = ra_to_longitude(this_ra)
# Declination is already between -90 and 90
latitude = this_dec
total = None
for i, data_analysis_bin in enumerate(active_planes_bins):
# Plot data
background_map = data_analysis_bin.background_map.as_dense()
this_data = data_analysis_bin.observation_map.as_dense(
) - background_map
idx = np.isnan(this_data)
# this_data[idx] = hp.UNSEEN
if i == 0:
total = this_data
else:
# Sum only when there is no UNSEEN, so that the UNSEEN pixels will stay UNSEEN
total[~idx] += this_data[~idx]
delta_coord = (self._roi.data_radius.to("deg").value * 2.0) / 15.0
fig, sub = plt.subplots(1, 1)
proj = self._represent_healpix_map(fig, total, longitude, latitude,
xsize, resolution,
smoothing_kernel_sigma)
cax = sub.imshow(proj, origin='lower')
fig.colorbar(cax)
sub.axis('off')
hp.graticule(delta_coord, delta_coord)
return fig
def display_fit(self, smoothing_kernel_sigma=0.1, display_colorbar=False):
"""
Make a figure containing 4 maps for each active analysis bins with respectively model, data,
background and residuals. The model, data and residual maps are smoothed, the background
map is not.
:param smoothing_kernel_sigma: sigma for the Gaussian smoothing kernel, for all but
background maps
:param display_colorbar: whether or not to display the colorbar in the residuals
:return: a matplotlib.Figure
"""
n_point_sources = self._likelihood_model.get_number_of_point_sources()
n_ext_sources = self._likelihood_model.get_number_of_extended_sources()
# This is the resolution (i.e., the size of one pixel) of the image
resolution = 3.0 # arcmin
# The image is going to cover the diameter plus 20% padding
xsize = self._get_optimal_xsize(resolution)
n_active_planes = len(self._active_planes)
n_columns = 4
fig, subs = plt.subplots(n_active_planes, n_columns,
figsize=(2.7 * n_columns, n_active_planes * 2), squeeze=False)
with progress_bar(len(self._active_planes), title='Smoothing maps') as prog_bar:
images = ['None'] * n_columns
for i, plane_id in enumerate(self._active_planes):
data_analysis_bin = self._maptree[plane_id]
# Get the center of the projection for this plane
this_ra, this_dec = self._roi.ra_dec_center
# Make a full healpix map for a second
whole_map = self._get_model_map(plane_id, n_point_sources, n_ext_sources).as_dense()
# Healpix uses longitude between -180 and 180, while R.A. is between 0 and 360. We need to fix that:
longitude = ra_to_longitude(this_ra)
# Declination is already between -90 and 90
latitude = this_dec
# Background and excess maps
bkg_subtracted, _, background_map = self._get_excess(data_analysis_bin, all_maps=True)
# Make all the projections: model, excess, background, residuals
proj_model = self._represent_healpix_map(fig, whole_map,
longitude, latitude,
xsize, resolution, smoothing_kernel_sigma)
# Here we removed the background otherwise nothing is visible
# Get background (which is in a way "part of the model" since the uncertainties are neglected)
proj_data = self._represent_healpix_map(fig, bkg_subtracted,
longitude, latitude,
xsize, resolution, smoothing_kernel_sigma)
# No smoothing for this one (because a goal is to check it is smooth).
proj_bkg = self._represent_healpix_map(fig, background_map,
longitude, latitude,
xsize, resolution, None)
proj_residuals = proj_data - proj_model
# Common color scale range for model and excess maps
vmin = min(np.nanmin(proj_model), np.nanmin(proj_data))
vmax = max(np.nanmax(proj_model), np.nanmax(proj_data))
# Plot model
images[0] = subs[i][0].imshow(proj_model, origin='lower', vmin=vmin, vmax=vmax)
subs[i][0].set_title('model, bin {}'.format(data_analysis_bin.name))
# Plot data map
images[1] = subs[i][1].imshow(proj_data, origin='lower', vmin=vmin, vmax=vmax)
subs[i][1].set_title('excess, bin {}'.format(data_analysis_bin.name))
# Plot background map.
images[2] = subs[i][2].imshow(proj_bkg, origin='lower')
subs[i][2].set_title('background, bin {}'.format(data_analysis_bin.name))
# Now residuals
images[3] = subs[i][3].imshow(proj_residuals, origin='lower')
subs[i][3].set_title('residuals, bin {}'.format(data_analysis_bin.name))
# Remove numbers from axis
for j in range(n_columns):
subs[i][j].axis('off')
if display_colorbar:
for j, image in enumerate(images):
plt.colorbar(image, ax=subs[i][j])
prog_bar.increase()
fig.set_tight_layout(True)
return fig
