def check_if_flux(ra, dec, radius, catalog=cat_250um):
"""
Returns flux density in the unit used for the catalog
(Jy in the case of the HELMS maps)
Returns 'Outside' if the area is outside the image.
ra: float
Right ascension of the object (degrees).
dec: float
Declination of the object (degrees).
radius: float
The radius of the galaxy (degrees).
catalog: cat_250um, cat_350um, cat_500um
The catalog used in the search. All
variables are defined above.
"""
center = SkyCoord(ra, dec, unit='deg')
radius = Quantity(radius, 'deg')
region = CircleSkyRegion(center, radius)
pixel_region = region.to_pixel(pixel_convert)
if pixel_region.radius > 500:
pixel_region.radius = 20
mask = pixel_region.to_mask(mode='exact')
data = mask.cutout(img_dat)
if data is not None:
flux_density = max_list_of_lists(data)
else:
flux_density = 'Outside'
return flux_density
def get_flux_density(ra, dec, radius, catalog=cat_250um):
"""
Returns flux density in the unit used for the catalog
(Jy in the case of the HELMS maps)
ra: float
Right ascension of the object (degrees).
dec: float
Declination of the object (degrees).
radius: float
The radius of the galaxy (degrees).
catalog: cat_250um, cat_350um, cat_500um
The catalog used in the search. All
variables are defined above.
"""
hdul = fits.open(catalog)
img_dat = hdul[1].data
pixel_convert = wcs.WCS(hdul[1])
center = SkyCoord(ra, dec, unit='deg')
radius = Angle(radius, 'deg')
region = CircleSkyRegion(center, radius)
pixel_region = region.to_pixel(pixel_convert)
if pixel_region.radius > 500:
pixel_region.radius = 50
mask = pixel_region.to_mask(mode='exact')
data = mask.cutout(img_dat)
flux_density = max_list_of_lists(data)
return flux_density
# In[ ]:
jfactory = JFactory(geom=geom,
profile=profile,
distance=profiles.DMProfile.DISTANCE_GC)
jfact = jfactory.compute_jfactor()
# In[ ]:
jfact_map = WcsNDMap(geom=geom, data=jfact.value, unit=jfact.unit)
fig, ax, im = jfact_map.plot(cmap="viridis", norm=LogNorm(), add_cbar=True)
plt.title("J-Factor [{}]".format(jfact_map.unit))
# 1 deg circle usually used in H.E.S.S. analyses
sky_reg = CircleSkyRegion(center=position, radius=1 * u.deg)
pix_reg = sky_reg.to_pixel(wcs=geom.wcs)
pix_reg.plot(ax=ax, facecolor="none", edgecolor="red", label="1 deg circle")
plt.legend()
# In[ ]:
# NOTE: https://arxiv.org/abs/1607.08142 quote 2.67e21 without the +/- 0.3 deg band around the plane
total_jfact = pix_reg.to_mask().multiply(jfact).sum()
print("J-factor in 1 deg circle around GC assuming a "
"{} is {:.3g}".format(profile.__class__.__name__, total_jfact))
# ## Gamma-ray spectra at production
#
# The gamma-ray spectrum per annihilation is a further ingredient for a dark matter analysis. The following annihilation channels are supported. For more info see https://arxiv.org/pdf/1012.4515.pdf
# In[ ]:
# get events in AND and XOR
compound_and = circle1 & circle2
compound_xor = circle1 ^ circle2
mask_and = compound_and.contains(skycoords, wcs)
skycoords_and = skycoords[mask_and]
mask_xor = compound_xor.contains(skycoords, wcs)
skycoords_xor = skycoords[mask_xor]
# plot
fig = plt.figure()
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcs, aspect='equal')
ax.scatter(skycoords.l.value, skycoords.b.value, label='all',
transform=ax.get_transform('galactic'))
ax.scatter(skycoords_xor.l.value, skycoords_xor.b.value, color='orange',
label='xor', transform=ax.get_transform('galactic'))
ax.scatter(skycoords_and.l.value, skycoords_and.b.value, color='magenta',
label='and', transform=ax.get_transform('galactic'))
circle1.to_pixel(wcs=wcs).plot(ax=ax, edgecolor='green', facecolor='none', alpha=0.8, lw=3)
circle2.to_pixel(wcs=wcs).plot(ax=ax, edgecolor='red', facecolor='none', alpha=0.8, lw=3)
ax.legend(loc='lower right')
ax.set_xlim(-0.5, dataset.config['shape'][1] - 0.5)
ax.set_ylim(-0.5, dataset.config['shape'][0] - 0.5)
plt.show()
from astropy.coordinates import SkyCoord, Angle
from regions import (
make_example_dataset,
CircleSkyRegion,
)
import matplotlib.pyplot as plt
config = dict(crpix=(18, 9), cdelt=(-10, 10), shape=(18, 36))
dataset = make_example_dataset(data='simulated', config=config)
wcs = dataset.wcs
fig = plt.figure()
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcs)
ax.set_xlim(-0.5, dataset.config['shape'][1] - 0.5)
ax.set_ylim(-0.5, dataset.config['shape'][0] - 0.5)
ax.imshow(dataset.image.data, cmap='gray', vmin=0, vmax=1,
interpolation='nearest', origin='lower')
for source in dataset.source_table:
# Plot a sky circle around each source
center = SkyCoord(source['GLON'], source['GLAT'], unit='deg', frame='galactic')
radius = Angle(20, 'deg')
region = CircleSkyRegion(center=center, radius=radius)
pix_region = region.to_pixel(wcs=wcs)
pix_region.plot(ax=ax, edgecolor='yellow', facecolor='yellow', alpha=0.5, lw=3)
plt.show()
bkg_maker = ReflectedRegionsBackgroundMaker(exclusion_mask=exclusion_mask)
safe_mask_masker = SafeMaskMaker(methods=["aeff-max"], aeff_percent=10)
# In[ ]:
get_ipython().run_cell_magic(
'time', '',
'datasets = []\n\nfor observation in observations:\n dataset = dataset_maker.run(dataset_empty, observation)\n dataset_on_off = bkg_maker.run(dataset, observation)\n dataset_on_off = safe_mask_masker.run(dataset_on_off, observation)\n datasets.append(dataset_on_off)'
)
# In[ ]:
plt.figure(figsize=(8, 8))
_, ax, _ = images["counts"].smooth("0.03 deg").plot(vmax=8)
on_region.to_pixel(ax.wcs).plot(ax=ax, edgecolor="white")
plot_spectrum_datasets_off_regions(datasets, ax=ax)
# ### Model fit
#
# The next step is to fit a spectral model, using all data (i.e. a "global" fit, using all energies).
# In[ ]:
get_ipython().run_cell_magic(
'time', '',
'spectral_model = PowerLawSpectralModel(\n index=2, amplitude=1e-11 * u.Unit("cm-2 s-1 TeV-1"), reference=1 * u.TeV\n)\nmodel = SkyModel(spectral_model=spectral_model)\nfor dataset in datasets:\n dataset.models = model\n\nfit = Fit(datasets)\nresult = fit.run()\nprint(result)'
)
# ### Spectral points
#
on_ellipse_annulus = EllipseAnnulusSkyRegion(
center=position,
inner_width=1.5 * u.deg,
outer_width=2.5 * u.deg,
inner_height=3 * u.deg,
outer_height=4 * u.deg,
angle=130 * u.deg,
)
another_position = SkyCoord(80.3, 22.0, unit="deg")
on_rectangle = RectangleSkyRegion(center=another_position,
width=2.0 * u.deg,
height=4.0 * u.deg,
angle=50 * u.deg)
# Now we plot those regions. We first create an empty map
empty_map = WcsNDMap.create(skydir=position,
width=10 * u.deg,
binsz=0.1 * u.deg,
proj="TAN")
empty_map.data += 1.0
empty_map.plot(cmap="gray", vmin=0, vmax=1)
# To plot the regions, we convert them to PixelRegion with the map wcs
on_circle.to_pixel(empty_map.geom.wcs).plot()
on_rectangle.to_pixel(empty_map.geom.wcs).plot()
on_ellipse_annulus.to_pixel(empty_map.geom.wcs).plot()
plt.show()
ax = fig.add_subplot(111, projection=wcs)
#ax.set_title("Gaussian smoothed image")
norm_xmm = ImageNormalize(smoothed_data_g2, interval=ManualInterval(vmin=0.01, vmax=100.0),
stretch=LogStretch())
# norm_xmm = ImageNormalize(smoothed_data_g2,interval=PercentileInterval(pp), stretch=AsinhStretch())
ax.imshow(smoothed_data_g2, cmap=plt.cm.hot, norm=norm_xmm, origin='lower', interpolation='nearest')
ax.xlabel = 'RA'
ax.ylabel = 'Dec'
ax.set_xlabel('RA')
ax.set_ylabel('DEC')
#
# only show the last aperture
#
circle_sky = CircleSkyRegion(center=center, radius=r_end)
pix_reg = circle_sky.to_pixel(wcs)
pix_reg.plot(ax=ax, edgecolor='yellow')
plt.savefig('/home/aaranda/tfm/results_v2/{}/{}/smoothed_g2_image_low.png'.format(target, obsid))
plt.close(fig)
(x, y, yerr) = calc_radial_profile(fits_image, center, r_start, r_end, r_step, verbose=False,
detmaskfile=detmask_file, plot=False)
energy = 1000 # at 1 keV
box = 2 * int(r_end.to(u.arcsec).value) + 1
psf_out = '{}/psf_ellbeta_{}_{}pix.fits'.format(odf_dir, energy, box)
psf_gen(center, energy, box, psf_out)
fig = plt.figure(figsize=(10, 10), dpi=100)
pp = 99.9 # colour cut percentage
fig = plt.figure()
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcs)
ax.set_xlim(-0.5, dataset.config['shape'][1] - 0.5)
ax.set_ylim(-0.5, dataset.config['shape'][0] - 0.5)
ax.imshow(dataset.image.data,
cmap='gray',
vmin=0,
vmax=1,
interpolation='nearest',
origin='lower')
for source in dataset.source_table:
# Plot a sky circle around each source
center = SkyCoord(source['GLON'],
source['GLAT'],
unit='deg',
frame='galactic')
radius = Angle(20, 'deg')
region = CircleSkyRegion(center=center, radius=radius)
pix_region = region.to_pixel(wcs=wcs)
pix_region.plot(ax,
edgecolor='yellow',
facecolor='yellow',
alpha=0.5,
lw=3)
plt.show()
# get events in AND and XOR
compound_and = circle1 & circle2
compound_xor = circle1 ^ circle2
mask_and = compound_and.contains(skycoords, wcs)
skycoords_and = skycoords[mask_and]
mask_xor = compound_xor.contains(skycoords, wcs)
skycoords_xor = skycoords[mask_xor]
# plot
fig = plt.figure()
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcs, aspect='equal')
ax.scatter(skycoords.l.value, skycoords.b.value, label='all',
transform=ax.get_transform('galactic'))
ax.scatter(skycoords_xor.l.value, skycoords_xor.b.value, color='orange',
label='xor', transform=ax.get_transform('galactic'))
ax.scatter(skycoords_and.l.value, skycoords_and.b.value, color='magenta',
label='and', transform=ax.get_transform('galactic'))
circle1.to_pixel(wcs=wcs).plot(ax, edgecolor='green', facecolor='none', alpha=0.8, lw=3)
circle2.to_pixel(wcs=wcs).plot(ax, edgecolor='red', facecolor='none', alpha=0.8, lw=3)
ax.legend(loc='lower right')
ax.set_xlim(-0.5, dataset.config['shape'][1] - 0.5)
ax.set_ylim(-0.5, dataset.config['shape'][0] - 0.5)
plt.show()
"""Example how to compute and plot reflected regions."""
from astropy.coordinates import SkyCoord, Angle
from regions import CircleSkyRegion
from gammapy.image import SkyMask
from gammapy.background import find_reflected_regions
mask = SkyMask.empty(name='Exclusion Mask', nxpix=801, nypix=701, binsz=0.01,
coordsys='CEL', xref=83.2, yref=22.7)
mask.fill_random_circles(n=8, min_rad=30, max_rad=80)
pos = SkyCoord(80.2, 23.5, unit='deg')
radius = Angle(0.4, 'deg')
test_region = CircleSkyRegion(pos, radius)
center = SkyCoord(82.8, 22.5, unit='deg')
regions = find_reflected_regions(test_region, center, mask)
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8, 5), dpi=80)
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], projection=mask.wcs)
mask.plot(ax, fig)
for reg in regions:
reg.to_pixel(mask.wcs).plot(ax, facecolor='red')
test_region.to_pixel(mask.wcs).plot(ax, facecolor='blue')
plt.show()
