sh.load_table_model("expo", "G300-0_test_exposure.fits")
sh.load_table_model("bkg", "G300-0_test_background.fits")
sh.load_psf("psf", "G300-0_test_psf.fits")
# In principle one might first want to fit the background amplitude. However the background estimation method already yields the correct normalization, so we freeze the background amplitude to unity instead of adjusting it. The (smoothed) residuals from this background model are then computed and shown.
# In[3]:
sh.set_full_model(bkg)
bkg.ampl = 1
sh.freeze(bkg)
data = sh.get_data_image().y - sh.get_model_image().y
resid = SkyImage(data=data, wcs=wcs)
resid_table = [] # Keep residual images in a list to show them later
resid_smo6 = resid.smooth(radius = 6)
resid_smo6.plot()
resid_table.append(resid_smo6)
# ### Find and fit the brightest source
# We then find the position of the maximum in the (smoothed) residuals map, and fit a (symmetrical) Gaussian source with that initial position:
# In[4]:
maxcoord = resid_smo6.lookup_max()
maxpix = resid_smo6.wcs_skycoord_to_pixel(maxcoord[0])
sh.set_method("simplex")
sh.load_image('../datasets/images/MSH15-52_counts.fits.gz')
sh.set_coord("logical")
sh.load_table_model("expo", "../datasets/images/MSH15-52_exposure.fits.gz")
sh.load_table_model("bkg", "../datasets/images/MSH15-52_background.fits.gz")
sh.load_psf("psf", "../datasets/images/MSH15-52_psf.fits.gz")
# In[54]:
sh.set_full_model(bkg)
bkg.ampl = 1
sh.freeze(bkg)
data = sh.get_data_image().y - sh.get_model_image().y
resid = SkyImage(data=data, wcs=ref_image.wcs)
resid_table = [] #Keep residual images in a list to show them later
resid_smo6 = resid.smooth(radius=6)
resid_smo6.plot(vmax=5, add_cbar=True)
resid_table.append(resid_smo6)
# In[55]:
maxcoord = resid_smo6.lookup_max()
maxpix = resid_smo6.wcs_skycoord_to_pixel(maxcoord[0])
print(maxcoord)
print(maxpix)
sh.set_full_model(
bkg + psf(sh.gauss2d.g0) * expo) # creates g0 as a gauss2d instance
def make_images(psf_sigma):
# Define width of the source and the PSF
source_sigma = 4
sigma = np.sqrt(psf_sigma**2 + source_sigma**2)
amplitude = 1E3 / (2 * np.pi * sigma**2)
source = Gaussian2D(amplitude, 99, 99, sigma, sigma)
background = Const2D(1)
model = source + background
# Define data shape
shape = (200, 200)
y, x = np.indices(shape)
# Create a new WCS object
wcs = WCS(naxis=2)
# Set up an Galactic projection
wcs.wcs.crpix = [100.5, 100.5]
wcs.wcs.cdelt = np.array([0.02, 0.02])
wcs.wcs.crval = [0, 0]
wcs.wcs.ctype = ['GLON-CAR', 'GLAT-CAR']
# Fake data
random_state = get_random_state(0)
data = random_state.poisson(model(x, y))
# Create exclusion mask
center = SkyCoord(0, 0, frame='galactic', unit='deg')
circle = CircleSkyRegion(center, 0.5 * u.deg)
exclusion = SkyImage(data=x, wcs=wcs).region_mask(circle)
# Save data
header = wcs.to_header()
mask = ~exclusion.data
hdu = fits.PrimaryHDU(data=mask.astype('int32'), header=header)
filename = 'exclusion.fits.gz'
print('Writing {}'.format(filename))
hdu.writeto(filename, clobber=True)
hdu = fits.PrimaryHDU(data=data.astype('int32'), header=header)
filename = 'counts.fits.gz'
print('Writing {}'.format(filename))
hdu.writeto(filename, clobber=True)
hdu = fits.PrimaryHDU(data=model(x, y).astype('float32'), header=header)
filename = 'model.fits.gz'
print('Writing {}'.format(filename))
hdu.writeto(filename, clobber=True)
hdu = fits.PrimaryHDU(data=background(x, y).astype('float32'),
header=header)
filename = 'background.fits.gz'
print('Writing {}'.format(filename))
hdu.writeto(filename, clobber=True)
hdu = fits.PrimaryHDU(data=source(x, y).astype('float32'), header=header)
filename = 'source.fits.gz'
print('Writing {}'.format(filename))
hdu.writeto(filename, clobber=True)
exposure = 1E12 * np.ones(shape)
hdu = fits.PrimaryHDU(data=exposure.astype('float32'), header=header)
filename = 'exposure.fits.gz'
print('Writing {}'.format(filename))
hdu.writeto(filename, clobber=True)
