def _lnlike():
"""
Internal function for the log likelihood function. Noise of each pixel is assumed to follow
either a Poisson distribution (see Wertz et al. 2017) or a Gaussian distribution with a
correction for small sample statistics (see Mawet et al. 2014).
Returns
-------
float
Log likelihood.
"""
sep, ang, mag = param
fake = fake_planet(images=images,
psf=psf,
parang=parang - extra_rot,
position=(sep / pixscale, ang),
magnitude=mag,
psf_scaling=psf_scaling)
_, im_res = pca_psf_subtraction(images=fake * mask,
angles=-1. * parang + extra_rot,
pca_number=pca_number,
indices=indices)
stack = combine_residuals(method=residuals, res_rot=im_res)
merit = merit_function(residuals=stack[0, ],
function='sum',
variance=variance,
aperture=aperture,
sigma=0.)
return -0.5 * merit
def _lnlike():
"""
Internal function for the log likelihood function.
Returns
-------
float
Log likelihood.
"""
sep, ang, mag = param
fake = fake_planet(images=images,
psf=psf,
parang=parang - extra_rot,
position=(sep / pixscale, ang),
magnitude=mag,
psf_scaling=psf_scaling)
_, im_res = pca_psf_subtraction(images=fake * mask,
angles=-1. * parang + extra_rot,
pca_number=pca_number,
indices=indices)
res_stack = combine_residuals(method=residuals, res_rot=im_res)
chi_square = merit_function(residuals=res_stack[0, ],
merit=merit,
aperture=aperture,
sigma=0.)
return -0.5 * chi_square
def _objective(arg):
sys.stdout.write('.')
sys.stdout.flush()
pos_y = arg[0]
pos_x = arg[1]
mag = arg[2]
sep_ang = cartesian_to_polar(center, pos_y, pos_x)
fake = fake_planet(images=images,
psf=psf,
parang=parang,
position=(sep_ang[0], sep_ang[1]),
magnitude=mag,
psf_scaling=self.m_psf_scaling)
mask = create_mask(fake.shape[-2:], (self.m_cent_size, self.m_edge_size))
if self.m_reference_in_port is None:
_, im_res = pca_psf_subtraction(images=fake*mask,
angles=-1.*parang+self.m_extra_rot,
pca_number=self.m_pca_number,
pca_sklearn=None,
im_shape=None,
indices=None)
else:
im_reshape = np.reshape(fake*mask, (im_shape[0], im_shape[1]*im_shape[2]))
_, im_res = pca_psf_subtraction(images=im_reshape,
angles=-1.*parang+self.m_extra_rot,
pca_number=self.m_pca_number,
pca_sklearn=sklearn_pca,
im_shape=im_shape,
indices=None)
res_stack = combine_residuals(method=self.m_residuals, res_rot=im_res)
self.m_res_out_port.append(res_stack, data_dim=3)
chi_square = merit_function(residuals=res_stack[0, ],
merit=self.m_merit,
aperture=aperture,
sigma=self.m_sigma)
position = rotate_coordinates(center, (pos_y, pos_x), -self.m_extra_rot)
res = np.asarray([position[1],
position[0],
sep_ang[0]*pixscale,
(sep_ang[1]-self.m_extra_rot) % 360.,
mag,
chi_square])
self.m_flux_position_port.append(res, data_dim=2)
return chi_square
def _objective(arg):
sys.stdout.write('.')
sys.stdout.flush()
pos_y = arg[0]
pos_x = arg[1]
mag = arg[2]
sep = math.sqrt((pos_y - center[0])**2 + (pos_x - center[1])**2)
ang = math.atan2(pos_y - center[0],
pos_x - center[1]) * 180. / math.pi - 90.
fake = fake_planet(images=images,
psf=psf,
parang=parang,
position=(sep, ang),
magnitude=mag,
psf_scaling=self.m_psf_scaling)
im_shape = (fake.shape[-2], fake.shape[-1])
mask = create_mask(im_shape, [self.m_cent_size, self.m_edge_size])
_, im_res = pca_psf_subtraction(images=fake * mask,
angles=-1. * parang +
self.m_extra_rot,
pca_number=self.m_pca_number)
stack = combine_residuals(method=self.m_residuals, res_rot=im_res)
self.m_res_out_port.append(stack, data_dim=3)
merit = merit_function(residuals=stack,
function=self.m_merit,
variance="poisson",
aperture=self.m_aperture,
sigma=self.m_sigma)
position = rotate_coordinates(center, (pos_y, pos_x),
-self.m_extra_rot)
res = np.asarray((position[1], position[0], sep * pixscale,
(ang - self.m_extra_rot) % 360., mag, merit))
self.m_flux_position_port.append(res, data_dim=2)
return merit
