def do_main_calculation(self, central_data, neighbour_data, sel):
self.generate_central_realisation(central_data, sel)
if self.complexity != 0:
self.generate_neighbour_realisation(neighbour_data, central_data,
sel)
# Draw this neighbour realisation repeatedly on a ring of angular position
snr, e1, e2 = self.do_position_loop()
else:
noise = self.generate_effective_noise(neighbour_data, central_data,
sel)
gal, psf = i3s.setup_simple(boxsize=32,
shear=self.g,
flux=self.central_flux,
psf_ellipticity=self.psf_e,
psf_size=self.psf_size,
neighbour=[np.inf, np.inf],
wcs=self.wcs,
opt=self.opt)
res, cat = i3s.run(gal + noise,
psf,
opt=self.opt,
return_cat=True,
show=False)
snr, e1, e2 = cat.snr, res.e1, res.e2
return snr, e1, e2
def do_position_loop(self):
"""Loop over all possible angular positions for this neighbour realisation."""
# Some empty datavectors
De1 = []
De2 = []
snr = []
theta= np.linspace(0,2,10)*np.pi
for i,t in enumerate(theta):
print " -- Level 2 iteration: %d (theta=%f)"%(i,t)
x = self.neighbour_distance*np.cos(t)
y = self.neighbour_distance*np.sin(t)
gal, psf = i3s.setup_simple(boxsize=32, shear=self.g, flux=self.central_flux, psf_ellipticity=self.psf_e, psf_size=self.psf_size, neighbour_ellipticity=self.neighbour_e, neighbour=[x,y], neighbour_flux=self.neighbour_flux, neighbour_size=self.neighbour_radius, wcs=self.wcs, opt=self.opt)
if self.neighbour_distance< 23:
wts = self.generate_mock_weightmap(gal.array, x, y)
else:
wts = np.ones_like(gal.array)
res, cat = i3s.run(gal, psf, weights=wts, opt=self.opt, return_cat=True, show=False)
De1.append(res.e1)
De2.append(res.e2)
snr.append(cat.snr)
# Compute the mean over all positions
return np.mean(snr), np.mean(De1), np.mean(De2)
def do_main_calculation(self, central_data, neighbour_data, sel):
self.generate_central_realisation(central_data, sel)
if self.complexity!=0:
self.generate_neighbour_realisation(neighbour_data, central_data, sel)
# Draw this neighbour realisation repeatedly on a ring of angular position
snr, e1, e2 = self.do_position_loop()
else:
noise = self.generate_effective_noise(neighbour_data, central_data, sel)
gal, psf = i3s.setup_simple(boxsize=32, shear=self.g, flux=self.central_flux, psf_ellipticity=self.psf_e, psf_size=self.psf_size, neighbour=[np.inf,np.inf], wcs=self.wcs, opt=self.opt)
res, cat = i3s.run(gal+noise, psf, opt=self.opt, return_cat=True, show=False)
snr, e1, e2 = cat.snr, res.e1, res.e2
return snr, e1, e2
def do_position_loop(self):
"""Loop over all possible angular positions for this neighbour realisation."""
# Some empty datavectors
De1 = []
De2 = []
snr = []
theta = np.linspace(0, 2, 10) * np.pi
for i, t in enumerate(theta):
print " -- Level 2 iteration: %d (theta=%f)" % (i, t)
x = self.neighbour_distance * np.cos(t)
y = self.neighbour_distance * np.sin(t)
gal, psf = i3s.setup_simple(boxsize=32,
shear=self.g,
flux=self.central_flux,
psf_ellipticity=self.psf_e,
psf_size=self.psf_size,
neighbour_ellipticity=self.neighbour_e,
neighbour=[x, y],
neighbour_flux=self.neighbour_flux,
neighbour_size=self.neighbour_radius,
wcs=self.wcs,
opt=self.opt)
if self.neighbour_distance < 23:
wts = self.generate_mock_weightmap(gal.array, x, y)
else:
wts = np.ones_like(gal.array)
res, cat = i3s.run(gal,
psf,
weights=wts,
opt=self.opt,
return_cat=True,
show=False)
De1.append(res.e1)
De2.append(res.e2)
snr.append(cat.snr)
# Compute the mean over all positions
return np.mean(snr), np.mean(De1), np.mean(De2)
