def _snr_optimize(arg):
pos_x, pos_y = arg
_, _, snr, _ = false_alarm(image=image,
x_pos=pos_x,
y_pos=pos_y,
size=self.m_aperture,
ignore=self.m_ignore)
return -snr
def _fpf_minimize(arg):
pos_x, pos_y = arg
try:
_, _, _, fpf = false_alarm(image=image,
x_pos=pos_x,
y_pos=pos_y,
size=self.m_aperture,
ignore=self.m_ignore)
except ValueError:
fpf = float('inf')
return fpf
def gaussian_noise(self,
images,
psf,
parang,
aperture):
"""
Function to compute the (constant) variance for the likelihood function when the
variance parameter is set to gaussian (see Mawet et al. 2014). The planet is first removed
from the dataset with the values specified as *param* in the constructor of the instance.
Parameters
----------
images : numpy.ndarray
Input images.
psf : numpy.ndarray
PSF template.
parang : numpy.ndarray
Parallactic angles (deg).
aperture : dict
Properties of the circular aperture. The radius is recommended to be larger than or
equal to 0.5*lambda/D.
Returns
-------
float
Variance.
"""
pixscale = self.m_image_in_port.get_attribute("PIXSCALE")
fake = fake_planet(images=images,
psf=psf,
parang=parang,
position=(self.m_param[0]/pixscale, self.m_param[1]),
magnitude=self.m_param[2],
psf_scaling=self.m_psf_scaling)
_, res_arr = pca_psf_subtraction(images=fake,
angles=-1.*parang+self.m_extra_rot,
pca_number=self.m_pca_number)
stack = combine_residuals(method=self.m_residuals, res_rot=res_arr)
_, noise, _, _ = false_alarm(image=stack[0, ],
x_pos=aperture['pos_x'],
y_pos=aperture['pos_y'],
size=aperture['radius'],
ignore=False)
return noise**2
def contrast_limit(path_images, path_psf, noise, mask, parang, psf_scaling,
extra_rot, pca_number, threshold, aperture, residuals,
snr_inject, position):
"""
Function for calculating the contrast limit at a specified position for a given sigma level or
false positive fraction, both corrected for small sample statistics.
Parameters
----------
path_images : str
System location of the stack of images (3D).
path_psf : str
System location of the PSF template for the fake planet (3D). Either a single image or a
stack of images equal in size to science data.
noise : numpy.ndarray
Residuals of the PSF subtraction (3D) without injection of fake planets. Used to measure
the noise level with a correction for small sample statistics.
mask : numpy.ndarray
Mask (2D).
parang : numpy.ndarray
Derotation angles (deg).
psf_scaling : float
Additional scaling factor of the planet flux (e.g., to correct for a neutral density
filter). Should have a positive value.
extra_rot : float
Additional rotation angle of the images in clockwise direction (deg).
pca_number : int
Number of principal components used for the PSF subtraction.
threshold : tuple(str, float)
Detection threshold for the contrast curve, either in terms of "sigma" or the false
positive fraction (FPF). The value is a tuple, for example provided as ("sigma", 5.) or
("fpf", 1e-6). Note that when sigma is fixed, the false positive fraction will change with
separation. Also, sigma only corresponds to the standard deviation of a normal distribution
at large separations (i.e., large number of samples).
aperture : float
Aperture radius (pix) for the calculation of the false positive fraction.
residuals : str
Method used for combining the residuals ("mean", "median", "weighted", or "clipped").
position : tuple(float, float)
The separation (pix) and position angle (deg) of the fake planet.
snr_inject : float
Signal-to-noise ratio of the injected planet signal that is used to measure the amount
of self-subtraction.
Returns
-------
NoneType
None
"""
images = np.load(path_images)
psf = np.load(path_psf)
if threshold[0] == "sigma":
sigma = threshold[1]
# Calculate the FPF for a given sigma level
fpf = student_t(t_input=threshold,
radius=position[0],
size=aperture,
ignore=False)
elif threshold[0] == "fpf":
fpf = threshold[1]
# Calculate the sigma level for a given FPF
sigma = student_t(t_input=threshold,
radius=position[0],
size=aperture,
ignore=False)
else:
raise ValueError("Threshold type not recognized.")
# Cartesian coordinates of the fake planet
xy_fake = polar_to_cartesian(images, position[0], position[1] - extra_rot)
# Determine the noise level
_, t_noise, _, _ = false_alarm(image=noise[0, ],
x_pos=xy_fake[0],
y_pos=xy_fake[1],
size=aperture,
ignore=False)
# Aperture properties
im_center = center_subpixel(images)
ap_dict = {
'type': 'circular',
'pos_x': im_center[1],
'pos_y': im_center[0],
'radius': aperture
}
# Measure the flux of the star
phot_table = aperture_photometry(psf_scaling * psf[0, ],
create_aperture(ap_dict),
method='exact')
star = phot_table['aperture_sum'][0]
# Magnitude of the injected planet
flux_in = snr_inject * t_noise
mag = -2.5 * math.log10(flux_in / star)
# Inject the fake planet
fake = fake_planet(images=images,
psf=psf,
parang=parang,
position=(position[0], position[1]),
magnitude=mag,
psf_scaling=psf_scaling)
# Run the PSF subtraction
_, im_res = pca_psf_subtraction(images=fake * mask,
angles=-1. * parang + extra_rot,
pca_number=pca_number)
# Stack the residuals
im_res = combine_residuals(method=residuals, res_rot=im_res)
# Measure the flux of the fake planet
flux_out, _, _, _ = false_alarm(image=im_res[0, ],
x_pos=xy_fake[0],
y_pos=xy_fake[1],
size=aperture,
ignore=False)
# Calculate the amount of self-subtraction
attenuation = flux_out / flux_in
# Calculate the detection limit
contrast = sigma * t_noise / (attenuation * star)
# The flux_out can be negative, for example if the aperture includes self-subtraction regions
if contrast > 0.:
contrast = -2.5 * math.log10(contrast)
else:
contrast = np.nan
# Separation [pix], position antle [deg], contrast [mag], FPF
return position[0], position[1], contrast, fpf
def run(self) -> None:
"""
Run method of the module. Calculates the SNR and FPF for a specified position in a post-
processed image with the Student's t-test (Mawet et al. 2014). This approach assumes
Gaussian noise but accounts for small sample statistics.
Returns
-------
NoneType
None
"""
def _snr_optimize(arg):
pos_x, pos_y = arg
_, _, snr, _ = false_alarm(image=image,
x_pos=pos_x,
y_pos=pos_y,
size=self.m_aperture,
ignore=self.m_ignore)
return -snr
self.m_snr_out_port.del_all_data()
self.m_snr_out_port.del_all_attributes()
pixscale = self.m_image_in_port.get_attribute('PIXSCALE')
self.m_aperture /= pixscale
nimages = self.m_image_in_port.get_shape()[0]
bounds = ((self.m_position[0]+self.m_bounds[0][0], self.m_position[0]+self.m_bounds[0][1]),
(self.m_position[1]+self.m_bounds[1][0], self.m_position[1]+self.m_bounds[1][1]))
start_time = time.time()
for j in range(nimages):
progress(j, nimages, 'Calculating S/N and FPF...', start_time)
image = self.m_image_in_port[j, ]
center = center_subpixel(image)
if self.m_optimize:
result = minimize(fun=_snr_optimize,
x0=[self.m_position[0], self.m_position[1]],
method='SLSQP',
bounds=bounds,
tol=None,
options={'ftol': self.m_tolerance})
_, _, snr, fpf = false_alarm(image=image,
x_pos=result.x[0],
y_pos=result.x[1],
size=self.m_aperture,
ignore=self.m_ignore)
x_pos, y_pos = result.x[0], result.x[1]
else:
_, _, snr, fpf = false_alarm(image=image,
x_pos=self.m_position[0],
y_pos=self.m_position[1],
size=self.m_aperture,
ignore=self.m_ignore)
x_pos, y_pos = self.m_position[0], self.m_position[1]
sep_ang = cartesian_to_polar(center, y_pos, x_pos)
result = np.column_stack((x_pos, y_pos, sep_ang[0]*pixscale, sep_ang[1], snr, fpf))
self.m_snr_out_port.append(result, data_dim=2)
history = f'aperture [arcsec] = {self.m_aperture*pixscale:.2f}'
self.m_snr_out_port.copy_attributes(self.m_image_in_port)
self.m_snr_out_port.add_history('FalsePositiveModule', history)
self.m_snr_out_port.close_port()
def paco_contrast_limit(path_images, path_psf, noise, parang, psf_rad,
psf_scaling, res_scaling, pixscale, extra_rot,
threshold, aperture, snr_inject, position, algorithm):
"""
Function for calculating the contrast limit at a specified position for a given sigma level or
false positive fraction, both corrected for small sample statistics.
Parameters
----------
path_images : str
System location of the stack of images (3D).
path_psf : str
System location of the PSF template for the fake planet (3D). Either a single image or a
stack of images equal in size to science data.
noise : numpy.ndarray
Residuals of the PSF subtraction (3D) without injection of fake planets. Used to measure
the noise level with a correction for small sample statistics.
parang : numpy.ndarray
Derotation angles (deg).
psf_scaling : float
Additional scaling factor of the planet flux (e.g., to correct for a neutral density
filter). Should have a positive value.
extra_rot : float
Additional rotation angle of the images in clockwise direction (deg).
threshold : tuple(str, float)
Detection threshold for the contrast curve, either in terms of "sigma" or the false
positive fraction (FPF). The value is a tuple, for example provided as ("sigma", 5.) or
("fpf", 1e-6). Note that when sigma is fixed, the false positive fraction will change with
separation. Also, sigma only corresponds to the standard deviation of a normal distribution
at large separations (i.e., large number of samples).
aperture : float
Aperture radius (pix) for the calculation of the false positive fraction.
position : tuple(float, float)
The separation (pix) and position angle (deg) of the fake planet.
snr_inject : float
Signal-to-noise ratio of the injected planet signal that is used to measure the amount
of self-subtraction.
Returns
-------
NoneType
None
"""
images = np.load(path_images)
psf = np.load(path_psf)
if threshold[0] == "sigma":
sigma = threshold[1]
# Calculate the FPF for a given sigma level
fpf = student_t(t_input=threshold,
radius=position[0],
size=aperture,
ignore=False)
elif threshold[0] == "fpf":
fpf = threshold[1]
# Calculate the sigma level for a given FPF
sigma = student_t(t_input=threshold,
radius=position[0],
size=aperture,
ignore=False)
else:
raise ValueError("Threshold type not recognized.")
# Cartesian coordinates of the fake planet
xy_fake = polar_to_cartesian(images, position[0], position[1] - extra_rot)
# Determine the noise level
_, t_noise, _, _ = false_alarm(image=noise,
x_pos=xy_fake[0],
y_pos=xy_fake[1],
size=aperture,
ignore=False)
# Aperture properties
im_center = center_subpixel(images)
# Measure the flux of the star
ap_phot = CircularAperture((im_center[1], im_center[0]), aperture)
phot_table = aperture_photometry(psf_scaling * psf[0, ],
ap_phot,
method='exact')
star = phot_table['aperture_sum'][0]
# Magnitude of the injected planet
flux_in = snr_inject * t_noise
mag = -2.5 * math.log10(flux_in / star)
# Inject the fake planet
fake = fake_planet(images=images,
psf=psf,
parang=parang,
position=(position[0], position[1]),
magnitude=mag,
psf_scaling=psf_scaling)
path_fake_planet = os.path.split(path_images)[0] = "/"
np.save(path_fake_planet + "injected.npy", fake)
# Run the PSF subtraction
#_, im_res = pca_psf_subtraction(images=fake*mask,
# angles=-1.*parang+extra_rot,
# pca_number=pca_number)
# Stack the residuals
#im_res = combine_residuals(method=residuals, res_rot=im_res)
# Measure the flux of the fake planet
#flux_out, _, _, _ = false_alarm(image=im_res[0, ],
# x_pos=xy_fake[0],
# y_pos=xy_fake[1],
# size=aperture,
# ignore=False)
# Setup PACO
if algorithm == "fastpaco":
fp = FastPACO(image_file=path_fake_planet + "injected.npy",
angles=parang,
psf=psf,
psf_rad=psf_rad,
px_scale=pixscale,
res_scale=res_scaling,
verbose=False)
elif algorithm == "fullpaco":
fp = FullPACO(image_file=path_fake_planet + "injected.npy",
angles=parang,
psf=psf,
psf_rad=psf_rad,
px_scale=pixscale,
res_scale=res_scaling,
verbose=False)
# Run PACO
# Restrict to 1 processor since this module is called from a child process
a, b = fp.PACO(cpu=1)
# Should do something with these?
snr = b / np.sqrt(a)
flux_residual = b / a
flux_out, _, _, _ = false_alarm(image=flux_residual,
x_pos=xy_fake[0],
y_pos=xy_fake[1],
size=aperture,
ignore=False)
# Iterative, unbiased flux estimation
# This doesn't seem to give the correct results yet?
#if self.m_flux_calc:
# phi0s = fp.thresholdDetection(snr,self.m_threshold)
# init = np.array([flux[int(phi0[0]),int(phi0[1])] for phi0 in phi0s])
# ests = np.array(fp.fluxEstimate(phi0s,self.m_eps,init))
# Calculate the amount of self-subtraction
attenuation = flux_out / flux_in
# Calculate the detection limit
contrast = sigma * t_noise / (attenuation * star)
# The flux_out can be negative, for example if the aperture includes self-subtraction regions
if contrast > 0.:
contrast = -2.5 * math.log10(contrast)
else:
contrast = np.nan
# Separation [pix], position antle [deg], contrast [mag], FPF
return (position[0], position[1], contrast, fpf)
def run(self):
"""
Run method of the module. Calculates the SNR and FPF for a specified position in a post-
processed image with the Student's t-test (Mawet et al. 2014). This approach assumes
Gaussian noise but accounts for small sample statistics.
Returns
-------
NoneType
None
"""
def _fpf_minimize(arg):
pos_x, pos_y = arg
try:
_, _, _, fpf = false_alarm(image=image,
x_pos=pos_x,
y_pos=pos_y,
size=self.m_aperture,
ignore=self.m_ignore)
except ValueError:
fpf = float('inf')
return fpf
self.m_snr_out_port.del_all_data()
self.m_snr_out_port.del_all_attributes()
pixscale = self.m_image_in_port.get_attribute('PIXSCALE')
self.m_aperture /= pixscale
nimages = self.m_image_in_port.get_shape()[0]
start_time = time.time()
for j in range(nimages):
progress(j, nimages, 'Running FalsePositiveModule...', start_time)
image = self.m_image_in_port[j, ]
center = center_subpixel(image)
if self.m_optimize:
result = minimize(fun=_fpf_minimize,
x0=[self.m_position[0], self.m_position[1]],
method='Nelder-Mead',
tol=None,
options={
'xatol': self.m_tolerance,
'fatol': float('inf')
})
_, _, snr, fpf = false_alarm(image=image,
x_pos=result.x[0],
y_pos=result.x[1],
size=self.m_aperture,
ignore=self.m_ignore)
x_pos, y_pos = result.x[0], result.x[1]
else:
_, _, snr, fpf = false_alarm(image=image,
x_pos=self.m_position[0],
y_pos=self.m_position[1],
size=self.m_aperture,
ignore=self.m_ignore)
x_pos, y_pos = self.m_position[0], self.m_position[1]
sep_ang = cartesian_to_polar(center, x_pos, y_pos)
result = np.column_stack(
(x_pos, y_pos, sep_ang[0] * pixscale, sep_ang[1], snr, fpf))
self.m_snr_out_port.append(result, data_dim=2)
sys.stdout.write('Running FalsePositiveModule... [DONE]\n')
sys.stdout.flush()
history = f'aperture [arcsec] = {self.m_aperture*pixscale:.2f}'
self.m_snr_out_port.copy_attributes(self.m_image_in_port)
self.m_snr_out_port.add_history('FalsePositiveModule', history)
self.m_snr_out_port.close_port()
