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 _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 _run_single_processing(self, star_reshape, im_shape, indices):
"""
Internal function to create the residuals, derotate the images, and write the output
using a single process.
Returns
-------
NoneType
None
"""
for i, pca_number in enumerate(self.m_components):
progress(i, len(self.m_components), "Creating residuals...")
parang = -1. * self.m_star_in_port.get_attribute(
"PARANG") + self.m_extra_rot
residuals, res_rot = pca_psf_subtraction(images=star_reshape,
angles=parang,
pca_number=pca_number,
pca_sklearn=self.m_pca,
im_shape=im_shape,
indices=indices)
hist = "max PC number = " + str(np.amax(self.m_components))
# 1.) derotated residuals
if self.m_res_arr_out_ports is not None:
self.m_res_arr_out_ports[pca_number].set_all(res_rot)
self.m_res_arr_out_ports[pca_number].copy_attributes(
self.m_star_in_port)
self.m_res_arr_out_ports[pca_number].add_history(
"PcaPsfSubtractionModule", hist)
# 2.) mean residuals
if self.m_res_mean_out_port is not None:
stack = combine_residuals(method="mean", res_rot=res_rot)
self.m_res_mean_out_port.append(stack, data_dim=3)
# 3.) median residuals
if self.m_res_median_out_port is not None:
stack = combine_residuals(method="median", res_rot=res_rot)
self.m_res_median_out_port.append(stack, data_dim=3)
# 4.) noise-weighted residuals
if self.m_res_weighted_out_port is not None:
stack = combine_residuals(method="weighted",
res_rot=res_rot,
residuals=residuals,
angles=parang)
self.m_res_weighted_out_port.append(stack, data_dim=3)
# 5.) clipped mean residuals
if self.m_res_rot_mean_clip_out_port is not None:
stack = combine_residuals(method="clipped", res_rot=res_rot)
self.m_res_rot_mean_clip_out_port.append(stack, data_dim=3)
sys.stdout.write("Creating residuals... [DONE]\n")
sys.stdout.flush()
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 _run_single_processing(self, star_reshape: np.ndarray,
im_shape: Tuple[int, int, int],
indices: np.ndarray) -> None:
"""
Internal function to create the residuals, derotate the images, and write the output
using a single process.
"""
start_time = time.time()
for i, pca_number in enumerate(self.m_components):
progress(i, len(self.m_components), 'Creating residuals...',
start_time)
parang = -1. * self.m_star_in_port.get_attribute(
'PARANG') + self.m_extra_rot
residuals, res_rot = pca_psf_subtraction(
images=star_reshape,
angles=parang,
pca_number=int(pca_number),
pca_sklearn=self.m_pca,
im_shape=im_shape,
indices=indices)
hist = f'max PC number = {np.amax(self.m_components)}'
# 1.) derotated residuals
if self.m_res_arr_out_ports is not None:
self.m_res_arr_out_ports[pca_number].set_all(res_rot)
self.m_res_arr_out_ports[pca_number].copy_attributes(
self.m_star_in_port)
self.m_res_arr_out_ports[pca_number].add_history(
'PcaPsfSubtractionModule', hist)
# 2.) mean residuals
if self.m_res_mean_out_port is not None:
stack = combine_residuals(method='mean', res_rot=res_rot)
self.m_res_mean_out_port.append(stack, data_dim=3)
# 3.) median residuals
if self.m_res_median_out_port is not None:
stack = combine_residuals(method='median', res_rot=res_rot)
self.m_res_median_out_port.append(stack, data_dim=3)
# 4.) noise-weighted residuals
if self.m_res_weighted_out_port is not None:
stack = combine_residuals(method='weighted',
res_rot=res_rot,
residuals=residuals,
angles=parang)
self.m_res_weighted_out_port.append(stack, data_dim=3)
# 5.) clipped mean residuals
if self.m_res_rot_mean_clip_out_port is not None:
stack = combine_residuals(method='clipped', res_rot=res_rot)
self.m_res_rot_mean_clip_out_port.append(stack, data_dim=3)
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 _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
def run_job(self, tmp_task):
"""
Run method of PcaTaskProcessor.
Parameters
----------
tmp_task : pynpoint.util.multiproc.TaskInput
Input task.
Returns
-------
pynpoint.util.multiproc.TaskResult
Output residuals.
"""
residuals, res_rot = pca_psf_subtraction(
images=self.m_star_reshape,
angles=self.m_angles,
pca_number=tmp_task.m_input_data,
pca_sklearn=self.m_pca_model,
im_shape=self.m_im_shape,
indices=self.m_indices)
res_output = np.zeros((4, res_rot.shape[1], res_rot.shape[2]))
if self.m_requirements[0]:
res_output[0, ] = combine_residuals(method="mean", res_rot=res_rot)
if self.m_requirements[1]:
res_output[1, ] = combine_residuals(method="median",
res_rot=res_rot)
if self.m_requirements[2]:
res_output[2, ] = combine_residuals(method="weighted",
res_rot=res_rot,
residuals=residuals,
angles=self.m_angles)
if self.m_requirements[3]:
res_output[3, ] = combine_residuals(method="clipped",
res_rot=res_rot)
sys.stdout.write('.')
sys.stdout.flush()
return TaskResult(res_output, tmp_task.m_job_parameter[0])
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 PCArun(self) -> None:
"""
Run method of the module. An artificial planet is injected (based on the noise level) at a
given separation and position angle. The amount of self-subtraction is then determined and
the contrast limit is calculated for a given sigma level or false positive fraction. A
correction for small sample statistics is applied for both cases. Note that if the sigma
level is fixed, the false positive fraction changes with separation, following the
Student's t-distribution (see Mawet et al. 2014 for details).
Returns
-------
NoneType
None
"""
images = self.m_image_in_port.get_all()
psf = self.m_psf_in_port.get_all()
if psf.shape[0] != 1 and psf.shape[0] != images.shape[0]:
raise ValueError(
f'The number of frames in psf_in_tag {psf.shape} does not match with '
f'the number of frames in image_in_tag {images.shape}. The '
f'DerotateAndStackModule can be used to average the PSF frames '
f'(without derotating) before applying the ContrastCurveModule.'
)
cpu = self._m_config_port.get_attribute('CPU')
working_place = self._m_config_port.get_attribute('WORKING_PLACE')
parang = self.m_image_in_port.get_attribute('PARANG')
pixscale = self.m_image_in_port.get_attribute('PIXSCALE')
self.m_image_in_port.close_port()
self.m_psf_in_port.close_port()
if self.m_cent_size is not None:
self.m_cent_size /= pixscale
if self.m_edge_size is not None:
self.m_edge_size /= pixscale
self.m_aperture /= pixscale
pos_r = np.arange(self.m_separation[0] / pixscale,
self.m_separation[1] / pixscale,
self.m_separation[2] / pixscale)
pos_t = np.arange(self.m_angle[0] + self.m_extra_rot,
self.m_angle[1] + self.m_extra_rot, self.m_angle[2])
if self.m_cent_size is None:
index_del = np.argwhere(pos_r - self.m_aperture <= 0.)
else:
index_del = np.argwhere(
pos_r - self.m_aperture <= self.m_cent_size)
pos_r = np.delete(pos_r, index_del)
if self.m_edge_size is None or self.m_edge_size > images.shape[1] / 2.:
index_del = np.argwhere(
pos_r + self.m_aperture >= images.shape[1] / 2.)
else:
index_del = np.argwhere(
pos_r + self.m_aperture >= self.m_edge_size)
pos_r = np.delete(pos_r, index_del)
positions = []
for sep in pos_r:
for ang in pos_t:
positions.append((sep, ang))
result = []
async_results = []
# Create temporary files
tmp_im_str = os.path.join(working_place, 'tmp_images.npy')
tmp_psf_str = os.path.join(working_place, 'tmp_psf.npy')
np.save(tmp_im_str, images)
np.save(tmp_psf_str, psf)
mask = create_mask(images.shape[-2:],
(self.m_cent_size, self.m_edge_size))
_, im_res = pca_psf_subtraction(images=images * mask,
angles=-1. * parang + self.m_extra_rot,
pca_number=self.m_pca_number)
noise = combine_residuals(method=self.m_residuals, res_rot=im_res)
pool = mp.Pool(cpu)
for pos in positions:
async_results.append(
pool.apply_async(
contrast_limit,
args=(tmp_im_str, tmp_psf_str, noise, mask, parang,
self.m_psf_scaling, self.m_extra_rot,
self.m_pca_number, self.m_threshold, self.m_aperture,
self.m_residuals, self.m_snr_inject, pos)))
pool.close()
start_time = time.time()
# wait for all processes to finish
while mp.active_children():
# number of finished processes
nfinished = sum([i.ready() for i in async_results])
progress(nfinished, len(positions),
'Calculating detection limits...', start_time)
# check if new processes have finished every 5 seconds
time.sleep(5)
if nfinished != len(positions):
sys.stdout.write(
'\r ')
sys.stdout.write('\rCalculating detection limits... [DONE]\n')
sys.stdout.flush()
# get the results for every async_result object
for item in async_results:
result.append(item.get())
pool.terminate()
os.remove(tmp_im_str)
os.remove(tmp_psf_str)
result = np.asarray(result)
# Sort the results first by separation and then by angle
indices = np.lexsort((result[:, 1], result[:, 0]))
result = result[indices]
result = result.reshape((pos_r.size, pos_t.size, 4))
mag_mean = np.nanmean(result, axis=1)[:, 2]
mag_var = np.nanvar(result, axis=1)[:, 2]
res_fpf = result[:, 0, 3]
limits = np.column_stack(
(pos_r * pixscale, mag_mean, mag_var, res_fpf))
self.m_image_in_port._check_status_and_activate()
self.m_contrast_out_port._check_status_and_activate()
self.m_contrast_out_port.set_all(limits, data_dim=2)
history = f'{self.m_threshold[0]} = {self.m_threshold[1]}'
self.m_contrast_out_port.add_history('ContrastCurveModule', history)
self.m_contrast_out_port.copy_attributes(self.m_image_in_port)
self.m_contrast_out_port.close_port()
def contrast_limit(
path_images: str, path_psf: str, noise: np.ndarray, mask: np.ndarray,
parang: np.ndarray, psf_scaling: float, extra_rot: float,
pca_number: int, threshold: Tuple[str, float], aperture: float,
residuals: str, snr_inject: float,
position: Tuple[float, float]) -> Tuple[float, float, float, float]:
"""
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').
snr_inject : float
Signal-to-noise ratio of the injected planet signal that is used to measure the amount
of self-subtraction.
position : tuple(float, float)
The separation (pix) and position angle (deg) of the fake planet.
Returns
-------
float
Separation (pix).
float
Position angle (deg).
float
Contrast (mag).
float
False positive fraction.
"""
images = np.load(path_images)
psf = np.load(path_psf)
# Cartesian coordinates of the fake planet
yx_fake = polar_to_cartesian(images, position[0], position[1] - extra_rot)
# Determine the noise level
noise_apertures = compute_aperture_flux_elements(image=noise[0, ],
x_pos=yx_fake[1],
y_pos=yx_fake[0],
size=aperture,
ignore=False)
t_noise = np.std(noise_apertures, ddof=1) * \
math.sqrt(1 + 1 / (noise_apertures.shape[0]))
# get sigma from fpf or fpf from sigma
# Note that the number of degrees of freedom is given by nu = n-1 with n the number of samples.
# See Section 3 of Mawet et al. (2014) for more details on the Student's t distribution.
if threshold[0] == 'sigma':
sigma = threshold[1]
# Calculate the FPF for a given sigma level
fpf = t.sf(sigma, noise_apertures.shape[0] - 1, loc=0., scale=1.)
elif threshold[0] == 'fpf':
fpf = threshold[1]
# Calculate the sigma level for a given FPF
sigma = t.isf(fpf, noise_apertures.shape[0] - 1, loc=0., scale=1.)
else:
raise ValueError('Threshold type not recognized.')
# 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)
# 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)
flux_out_frame = im_res[0, ] - noise[0, ]
# Measure the flux of the fake planet after PCA
# the first element is the planet
flux_out = compute_aperture_flux_elements(image=flux_out_frame,
x_pos=yx_fake[1],
y_pos=yx_fake[0],
size=aperture,
ignore=False)[0]
# Calculate the amount of self-subtraction
attenuation = flux_out / flux_in
# the throughput can not be negative. However, this can happen due to numerical inaccuracies
if attenuation < 0:
attenuation = 0
# Calculate the detection limit
contrast = (sigma * t_noise + np.mean(noise_apertures)) / (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 angle [deg], contrast [mag], FPF
return position[0], position[1], contrast, fpf
def postprocessor(images: np.ndarray,
angles: np.ndarray,
scales: Optional[np.ndarray],
pca_number: Union[int, Tuple[Union[int, np.int64],
Union[int, np.int64]]],
pca_sklearn: PCA = None,
im_shape: Union[None, tuple] = None,
indices: np.ndarray = None,
mask: np.ndarray = None,
processing_type: str = 'ADI'):
"""
Function to apply different kind of post processings. It is equivalent to
:func:`~pynpoint.util.psf.pca_psf_subtraction` if ``processing_type='ADI'` and
``mask=None``.
Parameters
----------
images : np.array
Input images which should be reduced.
angles : np.ndarray
Derotation angles (deg).
scales : np.array
Scaling factors
pca_number : tuple(int, int)
Number of principal components used for the PSF subtraction.
pca_sklearn : sklearn.decomposition.pca.PCA, None
PCA object with the basis if not set to None.
im_shape : tuple(int, int, int), None
Original shape of the stack with images. Required if ``pca_sklearn`` is not set to None.
indices : np.ndarray, None
Non-masked image indices. All pixels are used if set to None.
mask : np.ndarray
Mask (2D).
processing_type : str
Post-processing type:
- ADI: Angular differential imaging.
- SDI: Spectral differential imaging.
- SDI+ADI: Spectral and angular differential imaging.
- ADI+SDI: Angular and spectral differential imaging.
Returns
-------
np.ndarray
Residuals of the PSF subtraction.
np.ndarray
Derotated residuals of the PSF subtraction.
"""
if not isinstance(pca_number, tuple):
pca_number = (pca_number, -1)
if mask is None:
mask = 1.
res_raw = np.zeros(images.shape)
res_rot = np.zeros(images.shape)
if processing_type == 'ADI':
if images.ndim == 2:
res_raw, res_rot = pca_psf_subtraction(images=images * mask,
angles=angles,
scales=None,
pca_number=pca_number[0],
pca_sklearn=pca_sklearn,
im_shape=im_shape,
indices=indices)
elif images.ndim == 4:
for i in range(images.shape[0]):
res_raw[i, ], res_rot[i, ] = pca_psf_subtraction(
images=images[i, ] * mask,
angles=angles,
scales=None,
pca_number=pca_number[0],
pca_sklearn=pca_sklearn,
im_shape=im_shape,
indices=indices)
elif processing_type == 'SDI':
for i in range(images.shape[1]):
im_scaled = sdi_scaling(images[:, i, :, :], scales)
res_raw[:, i], res_rot[:, i] = pca_psf_subtraction(
images=im_scaled * mask,
angles=np.full(scales.size, angles[i]),
scales=scales,
pca_number=pca_number[0],
pca_sklearn=pca_sklearn,
im_shape=im_shape,
indices=indices)
elif processing_type == 'SDI+ADI':
# SDI
res_raw_int = np.zeros(res_raw.shape)
for i in range(images.shape[1]):
im_scaled = sdi_scaling(images[:, i], scales)
res_raw_int[:,
i], _ = pca_psf_subtraction(images=im_scaled * mask,
angles=None,
scales=scales,
pca_number=pca_number[0],
pca_sklearn=pca_sklearn,
im_shape=im_shape,
indices=indices)
# ADI
for i in range(images.shape[0]):
res_raw[i], res_rot[i] = pca_psf_subtraction(
images=res_raw_int[i] * mask,
angles=angles,
scales=None,
pca_number=pca_number[1],
pca_sklearn=pca_sklearn,
im_shape=im_shape,
indices=indices)
elif processing_type == 'ADI+SDI':
# ADI
res_raw_int = np.zeros(res_raw.shape)
for i in range(images.shape[0]):
res_raw_int[i], _ = pca_psf_subtraction(images=images[i, ] * mask,
angles=None,
scales=None,
pca_number=pca_number[0],
pca_sklearn=pca_sklearn,
im_shape=im_shape,
indices=indices)
# SDI
for i in range(images.shape[1]):
im_scaled = sdi_scaling(res_raw_int[:, i], scales)
res_raw[:, i], res_rot[:, i] = pca_psf_subtraction(
images=im_scaled * mask,
angles=np.full(scales.size, angles[i]),
scales=scales,
pca_number=pca_number[1],
pca_sklearn=pca_sklearn,
im_shape=im_shape,
indices=indices)
elif processing_type == 'CODI':
# flatten images from 4D to 3D
ims = images.shape
im_scaled_flat = np.zeros((ims[0] * ims[1], ims[2], ims[3]))
scales_flat = np.zeros((ims[0] * ims[1]))
angles_flat = np.zeros((ims[0] * ims[1]))
for i in range(ims[1]):
im_scaled_flat[i * ims[0]:(i + 1) * ims[0]] = sdi_scaling(
images[:, i], scales)
scales_flat[i * ims[0]:(i + 1) * ims[0]] = scales
angles_flat[i * ims[0]:(i + 1) * ims[0]] = angles[i]
# codi
res_raw_flat, res_rot_flat = pca_psf_subtraction(
images=im_scaled_flat * mask,
angles=angles_flat,
scales=scales_flat,
pca_number=pca_number[0],
pca_sklearn=pca_sklearn,
im_shape=im_shape,
indices=indices)
# inflate images from 3D to 4D
for i in range(ims[1]):
res_raw[:, i] = res_raw_flat[i * ims[0]:(i + 1) * ims[0]]
res_rot[:, i] = res_rot_flat[i * ims[0]:(i + 1) * ims[0]]
return res_raw, res_rot
def pixel_variance(var_type: str, images: np.ndarray, parang: np.ndarray,
cent_size: Optional[float], edge_size: Optional[float],
pca_number: int, residuals: str,
aperture: Tuple[int, int, float], sigma: float) -> float:
"""
Function to calculate the variance of the noise. After the PSF subtraction, images are rotated
in opposite direction of the regular derotation, therefore dispersing any companion or disk
signal. The noise is measured within an annulus.
Parameters
----------
var_type : str
Variance type ('gaussian' or 'hessian').
images : numpy.ndarray
Input images (3D).
parang : numpy.ndarray
Parallactic angles.
cent_size : float, None
Radius of the central mask (pix). No mask is used when set to None.
edge_size : float, None
Outer radius (pix) beyond which pixels are masked. No outer mask is used when set to
None.
pca_number : int
Number of principal components (PCs) used for the PSF subtraction.
residuals : str
Method for combining the residuals ('mean', 'median', 'weighted', or 'clipped').
aperture : tuple(int, int, float)
Aperture position (y, x) and radius (pix).
sigma : float, None
Standard deviation (pix) of the Gaussian kernel which is used to smooth the images.
Returns
-------
float
Variance of the pixel values. Either the variance of the pixel values ('gaussian') or
the variance of the determinant of the Hessian ('hessian').
"""
mask = create_mask(images.shape[-2:], (cent_size, edge_size))
_, im_res_derot = pca_psf_subtraction(images * mask, parang, pca_number)
res_noise = combine_residuals(residuals, im_res_derot)
sep_ang = cartesian_to_polar(center_subpixel(res_noise), aperture[0],
aperture[1])
if var_type == 'gaussian':
selected = select_annulus(res_noise[0, ], sep_ang[0] - aperture[2],
sep_ang[0] + aperture[2])
elif var_type == 'hessian':
hessian_rr, hessian_rc, hessian_cc = hessian_matrix(
image=res_noise[0, ],
sigma=sigma,
mode='constant',
cval=0.,
order='rc')
hes_det = (hessian_rr * hessian_cc) - (hessian_rc * hessian_rc)
selected = select_annulus(hes_det, sep_ang[0] - aperture[2],
sep_ang[0] + aperture[2])
return float(np.var(selected))
def run(self):
"""
Run method of the module. An artificial planet is injected (based on the noise level) at a
given separation and position angle. The amount of self-subtraction is then determined and
the contrast limit is calculated for a given sigma level or false positive fraction. A
correction for small sample statistics is applied for both cases. Note that if the sigma
level is fixed, the false positive fraction changes with separation, following the
Student's t-distribution (see Mawet et al. 2014 for details).
Returns
-------
NoneType
None
"""
images = self.m_image_in_port.get_all()
psf = self.m_psf_in_port.get_all()
if psf.shape[0] != 1 and psf.shape[0] != images.shape[0]:
raise ValueError('The number of frames in psf_in_tag {0} does not match with the '
'number of frames in image_in_tag {1}. The DerotateAndStackModule can '
'be used to average the PSF frames (without derotating) before '
'applying the ContrastCurveModule.'.format(psf.shape, images.shape))
cpu = self._m_config_port.get_attribute("CPU")
parang = self.m_image_in_port.get_attribute("PARANG")
pixscale = self.m_image_in_port.get_attribute("PIXSCALE")
if self.m_cent_size is not None:
self.m_cent_size /= pixscale
if self.m_edge_size is not None:
self.m_edge_size /= pixscale
self.m_aperture /= pixscale
pos_r = np.arange(self.m_separation[0]/pixscale,
self.m_separation[1]/pixscale,
self.m_separation[2]/pixscale)
pos_t = np.arange(self.m_angle[0]+self.m_extra_rot,
self.m_angle[1]+self.m_extra_rot,
self.m_angle[2])
if self.m_cent_size is None:
index_del = np.argwhere(pos_r-self.m_aperture <= 0.)
else:
index_del = np.argwhere(pos_r-self.m_aperture <= self.m_cent_size)
pos_r = np.delete(pos_r, index_del)
if self.m_edge_size is None or self.m_edge_size > images.shape[1]/2.:
index_del = np.argwhere(pos_r+self.m_aperture >= images.shape[1]/2.)
else:
index_del = np.argwhere(pos_r+self.m_aperture >= self.m_edge_size)
pos_r = np.delete(pos_r, index_del)
sys.stdout.write("Running ContrastCurveModule...\r")
sys.stdout.flush()
positions = []
for sep in pos_r:
for ang in pos_t:
positions.append((sep, ang))
# Create a queue object which will contain the results
queue = mp.Queue()
result = []
jobs = []
working_place = self._m_config_port.get_attribute("WORKING_PLACE")
# Create temporary files
tmp_im_str = os.path.join(working_place, "tmp_images.npy")
tmp_psf_str = os.path.join(working_place, "tmp_psf.npy")
np.save(tmp_im_str, images)
np.save(tmp_psf_str, psf)
mask = create_mask(images.shape[-2:], [self.m_cent_size, self.m_edge_size])
_, im_res = pca_psf_subtraction(images=images*mask,
angles=-1.*parang+self.m_extra_rot,
pca_number=self.m_pca_number)
noise = combine_residuals(method=self.m_residuals, res_rot=im_res)
for i, pos in enumerate(positions):
process = mp.Process(target=contrast_limit,
args=(tmp_im_str,
tmp_psf_str,
noise,
mask,
parang,
self.m_psf_scaling,
self.m_extra_rot,
self.m_pca_number,
self.m_threshold,
self.m_aperture,
self.m_residuals,
self.m_snr_inject,
pos,
queue),
name=(str(os.path.basename(__file__)) + '_radius=' +
str(np.round(pos[0]*pixscale, 1)) + '_angle=' +
str(np.round(pos[1], 1))))
jobs.append(process)
for i, job in enumerate(jobs):
job.start()
if (i+1)%cpu == 0:
# Start *cpu* number of processes. Wait for them to finish and start again *cpu*
# number of processes.
for k in jobs[i+1-cpu:(i+1)]:
k.join()
elif (i+1) == len(jobs) and (i+1)%cpu != 0:
# Wait for the last processes to finish if number of processes is not a multiple
# of *cpu*
for k in jobs[(i + 1 - (i+1)%cpu):]:
k.join()
progress(i, len(jobs), "Running ContrastCurveModule...")
# Send termination sentinel to queue
queue.put(None)
while True:
item = queue.get()
if item is None:
break
else:
result.append(item)
os.remove(tmp_im_str)
os.remove(tmp_psf_str)
result = np.asarray(result)
# Sort the results first by separation and then by angle
indices = np.lexsort((result[:, 1], result[:, 0]))
result = result[indices]
result = result.reshape((pos_r.size, pos_t.size, 4))
mag_mean = np.nanmean(result, axis=1)[:, 2]
mag_var = np.nanvar(result, axis=1)[:, 2]
res_fpf = result[:, 0, 3]
limits = np.column_stack((pos_r*pixscale, mag_mean, mag_var, res_fpf))
self.m_contrast_out_port.set_all(limits, data_dim=2)
sys.stdout.write("\rRunning ContrastCurveModule... [DONE]\n")
sys.stdout.flush()
history = str(self.m_threshold[0])+" = "+str(self.m_threshold[1])
self.m_contrast_out_port.add_history("ContrastCurveModule", history)
self.m_contrast_out_port.copy_attributes(self.m_image_in_port)
self.m_contrast_out_port.close_port()
