def align_image(image_in: np.ndarray,
im_index: int,
interpolation: str,
accuracy: float,
resize: Optional[float],
num_references: int,
subframe: Optional[float],
ref_images_reshape: np.ndarray,
ref_images_shape: Tuple[int, int, int]) -> np.ndarray:
offset = np.array([0., 0.])
# Reshape the reference images back to their original 3D shape
# The original shape can not be used directly because of util.module.update_arguments
ref_images = ref_images_reshape.reshape(ref_images_shape)
for i in range(num_references):
if subframe is None:
tmp_offset, _, _ = phase_cross_correlation(ref_images[i, :, :],
image_in,
upsample_factor=accuracy)
else:
sub_in = crop_image(image_in, None, subframe)
sub_ref = crop_image(ref_images[i, :, :], None, subframe)
tmp_offset, _, _ = phase_cross_correlation(sub_ref,
sub_in,
upsample_factor=accuracy)
offset += tmp_offset
offset /= float(num_references)
if resize is not None:
offset *= resize
sum_before = np.sum(image_in)
tmp_image = rescale(image_in,
(resize, resize),
order=5,
mode='reflect',
multichannel=False,
anti_aliasing=True)
sum_after = np.sum(tmp_image)
# Conserve flux because the rescale function normalizes all values to [0:1].
tmp_image = tmp_image*(sum_before/sum_after)
else:
tmp_image = image_in
return shift_image(tmp_image, offset, interpolation)
def locate_star(image, center, width, fwhm):
"""
Function to locate the star by finding the brightest pixel.
:param image: Input image.
:type image: ndarray
:param center: Pixel center (y, x) of the subframe. The full image is used if set to None.
:type center: (int, int)
:param width: The width (pixel) of the subframe. The full image is used if set to None.
:type width: int
:param fwhm: Full width at half maximum of the Gaussian kernel.
:type fwhm: int
:return: Position (y, x) of the brightest pixel.
:rtype: (int, int)
"""
if width is not None:
if center is None:
center = image_center_pixel(image)
image = crop_image(image, center, width)
sigma = fwhm / math.sqrt(8. * math.log(2.))
kernel = (fwhm * 2 + 1, fwhm * 2 + 1)
smooth = cv2.GaussianBlur(image, kernel, sigma)
# argmax[0] is the y position and argmax[1] is the y position
argmax = np.asarray(np.unravel_index(smooth.argmax(), smooth.shape))
if center is not None and width is not None:
argmax[0] += center[0] - (image.shape[0] - 1) // 2 # y
argmax[1] += center[1] - (image.shape[1] - 1) // 2 # x
return argmax
def _photometry(images, starpos, aperture):
check_pos_in = any(np.floor(starpos[:]-aperture[1]) < 0.)
check_pos_out = any(np.ceil(starpos[:]+aperture[1]) > images.shape[0])
if check_pos_in or check_pos_out:
phot = np.nan
else:
im_crop = crop_image(images, tuple(starpos), 2*int(math.ceil(aperture[1])))
npix = im_crop.shape[0]
x_grid = y_grid = np.linspace(-(npix-1)/2, (npix-1)/2, npix)
xx_grid, yy_grid = np.meshgrid(x_grid, y_grid)
rr_grid = np.sqrt(xx_grid*xx_grid+yy_grid*yy_grid)
if self.m_aperture[0] == 'circular':
phot = np.sum(im_crop[rr_grid < aperture[1]])
elif self.m_aperture[0] == 'annulus':
phot = np.sum(im_crop[(rr_grid > aperture[0]) & (rr_grid < aperture[1])])
elif self.m_aperture[0] == 'ratio':
phot = np.sum(im_crop[rr_grid < aperture[0]]) / \
np.sum(im_crop[(rr_grid > aperture[0]) & (rr_grid < aperture[1])])
return phot
def _crop_rotating_star(image, position, im_size, filter_size):
starpos = locate_star(image=image,
center=position,
width=self.m_search_size,
fwhm=filter_size)
return crop_image(image=image, center=starpos, size=im_size)
def _crop_around_star(image, position, im_size, fwhm):
if position is None:
center = None
width = None
else:
if position.ndim == 1:
if position[0] is None and position[1] is None:
center = None
else:
center = (int(position[1]), int(position[0]))
width = int(math.ceil(position[2] / pixscale))
elif position.ndim == 2:
center = (int(position[self.m_count,
1]), int(position[self.m_count, 0]))
width = int(math.ceil(position[self.m_count, 2] /
pixscale))
starpos = locate_star(image, center, width, fwhm)
try:
im_crop = crop_image(image, starpos, im_size)
except ValueError:
warnings.warn(
"PSF size is too large to crop the image around the brightest "
"pixel (image index = " + str(self.m_count) +
", pixel [x, y] = " + str([starpos[0]] + [starpos[1]]) +
"). Using the center of the "
"image instead.")
index.append(self.m_count)
starpos = image_center_pixel(image)
im_crop = crop_image(image, starpos, im_size)
star.append((starpos[1], starpos[0]))
self.m_count += 1
return im_crop
def _crop_around_star(image: np.ndarray,
position: Optional[Union[Tuple[int, int, float],
Tuple[None, None, float]]],
im_size: int,
fwhm: int) -> np.ndarray:
if position is None:
center = None
width = None
else:
if position[0] is None and position[1] is None:
center = None
else:
center = (position[1], position[0]) # (y, x)
width = int(math.ceil(position[2]/pixscale))
starpos = locate_star(image, center, width, fwhm)
try:
im_crop = crop_image(image, tuple(starpos), im_size)
except ValueError:
if cpu == 1:
warnings.warn(f'Chosen image size is too large to crop the image around the '
f'brightest pixel (image index = {self.m_count}, pixel [x, y] '
f'= [{starpos[0]}, {starpos[1]}]). Using the center of the '
f'image instead.')
index.append(self.m_count)
else:
warnings.warn('Chosen image size is too large to crop the image around the '
'brightest pixel. Using the center of the image instead.')
starpos = center_pixel(image)
im_crop = crop_image(image, tuple(starpos), im_size)
if cpu == 1:
star.append((starpos[1], starpos[0]))
self.m_count += 1
return im_crop
def aperture_phot(
image: np.ndarray, position: np.ndarray,
aperture: Union[Tuple[str, float, float], Tuple[str, None, float]]
) -> np.float64:
"""
Parameters
----------
image : np.ndarray
Input image (2D).
position : np.ndarray
Center position (y, x) of the aperture.
aperture : tuple(str, float, float)
Tuple with the aperture properties for measuring the
photometry around the location of the brightest pixel. The
first element contains the aperture type ('circular',
'annulus', or 'ratio'). For a circular aperture, the second
element is empty and the third element contains the aperture
radius (pix). For the other two types, the second and third
element are the inner and outer radii (pix) of the aperture.
Returns
-------
np.float64
Photometry value.
"""
check_pos_in = any(np.floor(position[:] - aperture[2]) < 0.)
check_pos_out = any(
np.ceil(position[:] + aperture[2]) > image.shape[0])
if check_pos_in or check_pos_out:
phot = np.nan
else:
im_crop = crop_image(image, tuple(position),
2 * int(math.ceil(aperture[2])))
npix = im_crop.shape[0]
x_grid = y_grid = np.linspace(-(npix - 1) / 2, (npix - 1) / 2,
npix)
xx_grid, yy_grid = np.meshgrid(x_grid, y_grid)
rr_grid = np.sqrt(xx_grid**2 + yy_grid**2)
if aperture[0] == 'circular':
phot = np.sum(im_crop[rr_grid < aperture[2]])
elif aperture[0] == 'annulus':
phot = np.sum(im_crop[(rr_grid > aperture[1])
& (rr_grid < aperture[2])])
elif aperture[0] == 'ratio':
phot = np.sum(im_crop[rr_grid < aperture[1]]) / \
np.sum(im_crop[(rr_grid > aperture[1]) & (rr_grid < aperture[2])])
return phot
def _align_image(image_in):
offset = np.array([0., 0.])
for i in range(self.m_num_references):
if self.m_subframe is None:
tmp_offset, _, _ = register_translation(
ref_images[i, :, :],
image_in,
upsample_factor=self.m_accuracy)
else:
sub_in = crop_image(image_in, None, self.m_subframe)
sub_ref = crop_image(ref_images[i, :, :], None,
self.m_subframe)
tmp_offset, _, _ = register_translation(
sub_ref, sub_in, upsample_factor=self.m_accuracy)
offset += tmp_offset
offset /= float(self.m_num_references)
if self.m_resize is not None:
offset *= self.m_resize
sum_before = np.sum(image_in)
tmp_image = rescale(image=np.asarray(image_in,
dtype=np.float64),
scale=(self.m_resize, self.m_resize),
order=5,
mode='reflect',
anti_aliasing=True,
multichannel=False)
sum_after = np.sum(tmp_image)
# Conserve flux because the rescale function normalizes all values to [0:1].
tmp_image = tmp_image * (sum_before / sum_after)
else:
tmp_image = image_in
return shift_image(tmp_image, offset, self.m_interpolation)
def crop_around_star(image: np.ndarray,
im_index: int,
position: Optional[Union[Tuple[int, int, float],
Tuple[None, None, float]]],
im_size: int,
fwhm: int,
pixscale: float,
index_out_port: Optional[OutputPort],
image_out_port: OutputPort) -> np.ndarray:
if position is None:
center = None
width = None
else:
if position[0] is None and position[1] is None:
center = None
else:
center = (position[1], position[0]) # (y, x)
width = int(math.ceil(position[2]/pixscale))
starpos = locate_star(image, center, width, fwhm)
try:
im_crop = crop_image(image, tuple(starpos), im_size)
except ValueError:
warnings.warn(f'Chosen image size is too large to crop the image around the '
f'brightest pixel (image index = {im_index}, pixel [x, y] '
f'= [{starpos[0]}, {starpos[1]}]). Using the center of the '
f'image instead.')
if index_out_port is not None:
index_out_port.append([im_index], data_dim=1)
starpos = center_pixel(image)
im_crop = crop_image(image, tuple(starpos), im_size)
return im_crop
def _crop_rotating_star(image: np.ndarray,
position: Union[Tuple[float, float], np.ndarray],
im_size: int,
filter_size: Optional[int]) -> np.ndarray:
starpos = locate_star(image=image,
center=tuple(position),
width=self.m_search_size,
fwhm=filter_size)
return crop_image(image=image,
center=tuple(starpos),
size=im_size)
def run(self) -> None:
"""
Run method of the module. Decreases the image size by cropping around an given position.
The module always returns odd-sized images.
Returns
-------
NoneType
None
"""
self.m_image_out_port.del_all_attributes()
self.m_image_out_port.del_all_data()
# Get memory and number of images to split the frames into chunks
memory = self._m_config_port.get_attribute('MEMORY')
nimages = self.m_image_in_port.get_shape()[0]
frames = memory_frames(memory, nimages)
# Convert size parameter from arcseconds to pixels
pixscale = self.m_image_in_port.get_attribute('PIXSCALE')
self.m_size = int(math.ceil(self.m_size / pixscale))
# Crop images chunk by chunk
start_time = time.time()
for i in range(len(frames[:-1])):
# Update progress bar
progress(i, len(frames[:-1]), 'Running CropImagesModule...',
start_time)
# Select and crop images in the current chunk
images = self.m_image_in_port[frames[i]:frames[i + 1], ]
images = crop_image(images, self.m_center, self.m_size, copy=False)
# Write cropped images to output port
self.m_image_out_port.append(images, data_dim=3)
# Update progress bar (cropping of images is finished)
sys.stdout.write('Running CropImagesModule... [DONE]\n')
sys.stdout.flush()
# Save history and copy attributes
history = f'image size [pix] = {self.m_size}'
self.m_image_out_port.add_history('CropImagesModule', history)
self.m_image_out_port.copy_attributes(self.m_image_in_port)
self.m_image_out_port.close_port()
def locate_star(image: np.ndarray,
center: Union[tuple, None],
width: Union[int, None],
fwhm: Union[int, None]) -> np.ndarray:
"""
Function to locate the star by finding the brightest pixel.
Parameters
----------
image : numpy.ndarray
Input image (2D).
center : tuple(int, int), None
Pixel center (y, x) of the subframe. The full image is used if set to None.
width : int, None
The width (pix) of the subframe. The full image is used if set to None.
fwhm : int, None
Full width at half maximum (pix) of the Gaussian kernel. Not used if set to None.
Returns
-------
numpy.ndarray
Position (y, x) of the brightest pixel.
"""
if width is not None:
if center is None:
center = center_pixel(image)
image = crop_image(image, center, width)
if fwhm is None:
smooth = np.copy(image)
else:
sigma = fwhm / math.sqrt(8. * math.log(2.))
kernel = (fwhm * 2 + 1, fwhm * 2 + 1)
smooth = cv2.GaussianBlur(image, kernel, sigma)
# argmax[0] is the y position and argmax[1] is the y position
argmax = np.asarray(np.unravel_index(smooth.argmax(), smooth.shape))
if center is not None and width is not None:
argmax[0] += center[0] - (image.shape[0] - 1) // 2 # y
argmax[1] += center[1] - (image.shape[1] - 1) // 2 # x
return argmax
def run(self) -> None:
"""
Run the module. The FITS files are collected from the input directory and uncompressed if
needed. The images are then sorted by the two chop positions (chop A and chop B). The
required FITS header keywords (which should be set in the configuration file) are also
imported and stored as attributes to the two output datasets in the HDF5 database.
Returns
-------
NoneType
None
"""
# clear the output ports
self.m_chopa_out_port.del_all_data()
self.m_chopa_out_port.del_all_attributes()
self.m_chopb_out_port.del_all_data()
self.m_chopb_out_port.del_all_attributes()
# uncompress the FITS files if needed
self.uncompress()
# find and sort the FITS files
files = []
for filename in os.listdir(self.m_input_location):
if filename.endswith('.fits'):
files.append(os.path.join(self.m_input_location, filename))
files.sort()
# check if there are FITS files present in the input location
assert (files), f'No FITS files found in {self.m_input_location}.'
# if cropping chop A, get pixscale and convert crop_size to pixels and swap x/y
if self.m_crop is not None:
pixscale = self._m_config_port.get_attribute('PIXSCALE')
self.m_crop = (self.m_crop[1], self.m_crop[0],
int(math.ceil(self.m_crop[2] / pixscale)))
start_time = time.time()
for i, filename in enumerate(files):
progress(i, len(files), 'Preprocessing NEAR data...', start_time)
# get the primary header data and the image shape
header, im_shape = self.read_header(filename)
# get the images of chop A and chop B
chopa, chopb = self.read_images(filename, im_shape)
if self.m_subtract:
chopa = chopa - chopb
chopb = -1. * np.copy(chopa)
if self.m_crop is not None:
chopa = crop_image(chopa,
center=self.m_crop[0:2],
size=self.m_crop[2],
copy=False)
chopb = crop_image(chopb,
center=self.m_crop[0:2],
size=self.m_crop[2],
copy=False)
if self.m_combine is not None:
if self.m_combine == 'mean':
chopa = np.mean(chopa, axis=0)
chopb = np.mean(chopb, axis=0)
elif self.m_combine == 'median':
chopa = np.median(chopa, axis=0)
chopb = np.median(chopb, axis=0)
header[self._m_config_port.get_attribute('NFRAMES')] = 1
# append the images of chop A and B
self.m_chopa_out_port.append(chopa, data_dim=3)
self.m_chopb_out_port.append(chopb, data_dim=3)
# starting value for the INDEX attribute
first_index = 0
for port in (self.m_chopa_out_port, self.m_chopb_out_port):
# set the static attributes
set_static_attr(fits_file=filename,
header=header,
config_port=self._m_config_port,
image_out_port=port,
check=True)
# set the non-static attributes
set_nonstatic_attr(header=header,
config_port=self._m_config_port,
image_out_port=port,
check=True)
# set the remaining attributes
set_extra_attr(fits_file=filename,
nimages=im_shape[0] // 2,
config_port=self._m_config_port,
image_out_port=port,
first_index=first_index)
# increase the first value of the INDEX attribute
first_index += im_shape[0] // 2
# flush the output port
port.flush()
# add history information
self.m_chopa_out_port.add_history('NearReadingModule', 'Chop A')
self.m_chopb_out_port.add_history('NearReadingModule', 'Chop B')
# close all connections to the database
self.m_chopa_out_port.close_port()
def run(self) -> None:
"""
Run method of the module. Computes the similarity between frames based on the Mean Squared
Error (MSE), the Pearson Correlation Coefficient (PCC), or the Structural Similarity (SSIM).
The correlation values are stored as non-static attribute (``MSE``, ``PCC``, or ``SSIM``)
to the input data. After running this module, the
:class:`~pynpoint.processing.frameselection.SelectByAttributeModule` can be used to select
the images with the highest correlation.
Returns
-------
NoneType
None
"""
cpu = self._m_config_port.get_attribute('CPU')
pixscale = self.m_image_in_port.get_attribute('PIXSCALE')
# get number of images
nimages = self.m_image_in_port.get_shape()[0]
# convert arcsecs to pixels
self.m_mask_radii = (math.floor(self.m_mask_radii[0] / pixscale),
math.floor(self.m_mask_radii[1] / pixscale))
self.m_window_size = int(self.m_window_size / pixscale)
# overlay the same mask over all images
images = self.m_image_in_port.get_all()
# close the port during the calculations
self.m_image_out_port.close_port()
images = crop_image(images, None, int(self.m_mask_radii[1]))
if self.m_temporal_median == 'constant':
temporal_median = np.median(images, axis=0)
else:
temporal_median = False
# compare images and store similarity
similarities = np.zeros(nimages)
pool = mp.Pool(cpu)
async_results = []
for i in range(nimages):
async_results.append(
pool.apply_async(FrameSimilarityModule._similarity,
args=(images, i, self.m_method,
self.m_window_size, temporal_median)))
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, nimages, 'Calculating image similarity...',
start_time)
# check if new processes have finished every 5 seconds
time.sleep(5)
if nfinished != nimages:
print('\r ')
print('\rCalculating image similarity... [DONE]')
# get the results for every async_result object
for async_result in async_results:
reference, similarity = async_result.get()
similarities[reference] = similarity
pool.terminate()
# reopen the port after the calculation
self.m_image_out_port.open_port()
self.m_image_out_port.add_attribute(f'{self.m_method}',
similarities,
static=False)
self.m_image_out_port.close_port()
def run(self) -> None:
"""
Run method of the module. Locates the position of the calibration spots in the center
frame. From the four spots, the position of the star behind the coronagraph is fitted,
and the images are shifted and cropped.
Returns
-------
NoneType
None
"""
@typechecked
def _get_center(
image_number: int, center: Optional[Tuple[int, int]]
) -> Tuple[np.ndarray, Tuple[int, int]]:
if center_shape[-3] > 1:
warnings.warn(
'Multiple center images found. Using the first image of the stack.'
)
if ndim == 3:
center_frame = self.m_center_in_port[0, ]
elif ndim == 4:
center_frame = self.m_center_in_port[image_number, 0, ]
if center is None:
center = center_pixel(center_frame)
else:
center = (int(np.floor(center[0])), int(np.floor(center[1])))
return center_frame, center
center_shape = self.m_center_in_port.get_shape()
im_shape = self.m_image_in_port.get_shape()
ndim = self.m_image_in_port.get_ndim()
center_frame, self.m_center = _get_center(0, self.m_center)
# Read in wavelength information or set it to default values
if ndim == 4:
wavelength = self.m_image_in_port.get_attribute('WAVELENGTH')
if wavelength is None:
raise ValueError(
'The wavelength information is required to centre IFS data. '
'Please add it via the WavelengthReadingModule before using '
'the WaffleCenteringModule.')
if im_shape[0] != center_shape[0]:
raise ValueError(
f'Number of science wavelength channels: {im_shape[0]}. '
f'Number of center wavelength channels: {center_shape[0]}. '
'Exactly one center image per wavelength is required.')
wavelength_min = np.min(wavelength)
elif ndim == 3:
# for none ifs data, use default value
wavelength = [1.]
wavelength_min = 1.
# check if science and center images have the same shape
if im_shape[-2:] != center_shape[-2:]:
raise ValueError(
'Science and center images should have the same shape.')
# Setting angle via pattern (used for backwards compability)
if self.m_pattern is not None:
if self.m_pattern == 'x':
self.m_angle = 45.
elif self.m_pattern == '+':
self.m_angle = 0.
else:
raise ValueError(
f'The pattern {self.m_pattern} is not valid. Please select '
f'either \'x\' or \'+\'.')
warnings.warn(
f'The \'pattern\' parameter will be deprecated in a future release. '
f'Please Use the \'angle\' parameter instead and set it to '
f'{self.m_angle} degrees.', DeprecationWarning)
pixscale = self.m_image_in_port.get_attribute('PIXSCALE')
self.m_sigma /= pixscale
if self.m_size is not None:
self.m_size = int(math.ceil(self.m_size / pixscale))
if self.m_dither:
dither_x = self.m_image_in_port.get_attribute('DITHER_X')
dither_y = self.m_image_in_port.get_attribute('DITHER_Y')
nframes = self.m_image_in_port.get_attribute('NFRAMES')
nframes = np.cumsum(nframes)
nframes = np.insert(nframes, 0, 0)
# size of center image, only works with odd value
ref_image_size = 21
# Arrays for the positions
x_pos = np.zeros(4)
y_pos = np.zeros(4)
# Arrays for the center position for each wavelength
x_center = np.zeros((len(wavelength)))
y_center = np.zeros((len(wavelength)))
# Loop for 4 waffle spots
for w, wave_nr in enumerate(wavelength):
# Prapre centering frame
center_frame, _ = _get_center(w, self.m_center)
center_frame_unsharp = center_frame - gaussian_filter(
input=center_frame, sigma=self.m_sigma)
for i in range(4):
# Approximate positions of waffle spots
radius = self.m_radius * wave_nr / wavelength_min
x_0 = np.floor(self.m_center[0] + radius *
np.cos(self.m_angle * np.pi / 180 + np.pi / 4. *
(2 * i)))
y_0 = np.floor(self.m_center[1] + radius *
np.sin(self.m_angle * np.pi / 180 + np.pi / 4. *
(2 * i)))
tmp_center_frame = crop_image(image=center_frame_unsharp,
center=(int(y_0), int(x_0)),
size=ref_image_size)
# find maximum in tmp image
coords = np.unravel_index(indices=np.argmax(tmp_center_frame),
shape=tmp_center_frame.shape)
y_max, x_max = coords[0], coords[1]
pixmax = tmp_center_frame[y_max, x_max]
max_pos = np.array([x_max, y_max]).reshape(1, 2)
# Check whether it is the correct maximum: second brightest pixel should be nearby
tmp_center_frame[y_max, x_max] = 0.
# introduce distance parameter
dist = np.inf
while dist > 2:
coords = np.unravel_index(
indices=np.argmax(tmp_center_frame),
shape=tmp_center_frame.shape)
y_max_new, x_max_new = coords[0], coords[1]
pixmax_new = tmp_center_frame[y_max_new, x_max_new]
# Caculate minimal distance to previous points
tmp_center_frame[y_max_new, x_max_new] = 0.
dist = np.amin(
np.linalg.norm(np.vstack((max_pos[:, 0] - x_max_new,
max_pos[:, 1] - y_max_new)),
axis=0))
if dist <= 2 and pixmax_new < pixmax:
break
max_pos = np.vstack((max_pos, [x_max_new, y_max_new]))
x_max = x_max_new
y_max = y_max_new
pixmax = pixmax_new
x_0 = x_0 - (ref_image_size - 1) / 2 + x_max
y_0 = y_0 - (ref_image_size - 1) / 2 + y_max
# create reference image around determined maximum
ref_center_frame = crop_image(image=center_frame_unsharp,
center=(int(y_0), int(x_0)),
size=ref_image_size)
# Fit the data using astropy.modeling
gauss_init = models.Gaussian2D(
amplitude=np.amax(ref_center_frame),
x_mean=x_0,
y_mean=y_0,
x_stddev=1.,
y_stddev=1.,
theta=0.)
fit_gauss = fitting.LevMarLSQFitter()
y_grid, x_grid = np.mgrid[y_0 - (ref_image_size - 1) / 2:y_0 +
(ref_image_size - 1) / 2 + 1,
x_0 - (ref_image_size - 1) / 2:x_0 +
(ref_image_size - 1) / 2 + 1]
gauss = fit_gauss(gauss_init, x_grid, y_grid, ref_center_frame)
x_pos[i] = gauss.x_mean.value
y_pos[i] = gauss.y_mean.value
# Find star position as intersection of two lines
x_center[w] = ((y_pos[0]-x_pos[0]*(y_pos[2]-y_pos[0])/(x_pos[2]-float(x_pos[0]))) -
(y_pos[1]-x_pos[1]*(y_pos[1]-y_pos[3])/(x_pos[1]-float(x_pos[3])))) / \
((y_pos[1]-y_pos[3])/(x_pos[1]-float(x_pos[3])) -
(y_pos[2]-y_pos[0])/(x_pos[2]-float(x_pos[0])))
y_center[w] = x_center[w]*(y_pos[1]-y_pos[3])/(x_pos[1]-float(x_pos[3])) + \
(y_pos[1]-x_pos[1]*(y_pos[1]-y_pos[3])/(x_pos[1]-float(x_pos[3])))
# Adjust science images
nimages = self.m_image_in_port.get_shape()[-3]
npix = self.m_image_in_port.get_shape()[-2]
nwavelengths = len(wavelength)
start_time = time.time()
for i in range(nimages):
im_storage = []
for j in range(nwavelengths):
im_index = i * nwavelengths + j
progress(im_index, nimages * nwavelengths,
'Centering the images...', start_time)
if ndim == 3:
image = self.m_image_in_port[i, ]
elif ndim == 4:
image = self.m_image_in_port[j, i, ]
shift_yx = np.array([
(float(im_shape[-2]) - 1.) / 2. - y_center[j],
(float(im_shape[-1]) - 1.) / 2. - x_center[j]
])
if self.m_dither:
index = np.digitize(i, nframes, right=False) - 1
shift_yx[0] -= dither_y[index]
shift_yx[1] -= dither_x[index]
if npix % 2 == 0 and self.m_size is not None:
im_tmp = np.zeros((image.shape[0] + 1, image.shape[1] + 1))
im_tmp[:-1, :-1] = image
image = im_tmp
shift_yx[0] += 0.5
shift_yx[1] += 0.5
im_shift = shift_image(image, shift_yx, 'spline')
if self.m_size is not None:
im_crop = crop_image(im_shift, None, self.m_size)
im_storage.append(im_crop)
else:
im_storage.append(im_shift)
if ndim == 3:
self.m_image_out_port.append(im_storage[0], data_dim=3)
elif ndim == 4:
self.m_image_out_port.append(np.asarray(im_storage),
data_dim=4)
print(f'Center [x, y] = [{x_center}, {y_center}]')
history = f'[x, y] = [{round(x_center[j], 2)}, {round(y_center[j], 2)}]'
self.m_image_out_port.copy_attributes(self.m_image_in_port)
self.m_image_out_port.add_history('WaffleCenteringModule', history)
self.m_image_out_port.close_port()
def run(self) -> None:
"""
Run method of the module. Locates the position of the calibration spots in the center
frame. From the four spots, the position of the star behind the coronagraph is fitted,
and the images are shifted and cropped.
Returns
-------
NoneType
None
"""
def _get_center(center):
center_frame = self.m_center_in_port[0, ]
if center_shape[0] > 1:
warnings.warn(
'Multiple center images found. Using the first image of the stack.'
)
if center is None:
center = center_pixel(center_frame)
else:
center = (np.floor(center[0]), np.floor(center[1]))
return center_frame, center
self.m_image_out_port.del_all_data()
self.m_image_out_port.del_all_attributes()
center_shape = self.m_center_in_port.get_shape()
im_shape = self.m_image_in_port.get_shape()
center_frame, self.m_center = _get_center(self.m_center)
if im_shape[-2:] != center_shape[-2:]:
raise ValueError(
'Science and center images should have the same shape.')
pixscale = self.m_image_in_port.get_attribute('PIXSCALE')
self.m_sigma /= pixscale
if self.m_size is not None:
self.m_size = int(math.ceil(self.m_size / pixscale))
if self.m_dither:
dither_x = self.m_image_in_port.get_attribute('DITHER_X')
dither_y = self.m_image_in_port.get_attribute('DITHER_Y')
nframes = self.m_image_in_port.get_attribute('NFRAMES')
nframes = np.cumsum(nframes)
nframes = np.insert(nframes, 0, 0)
center_frame_unsharp = center_frame - gaussian_filter(
input=center_frame, sigma=self.m_sigma)
# size of center image, only works with odd value
ref_image_size = 21
# Arrays for the positions
x_pos = np.zeros(4)
y_pos = np.zeros(4)
# Loop for 4 waffle spots
for i in range(4):
# Approximate positions of waffle spots
if self.m_pattern == 'x':
x_0 = np.floor(self.m_center[0] +
self.m_radius * np.cos(np.pi / 4. *
(2 * i + 1)))
y_0 = np.floor(self.m_center[1] +
self.m_radius * np.sin(np.pi / 4. *
(2 * i + 1)))
elif self.m_pattern == '+':
x_0 = np.floor(self.m_center[0] +
self.m_radius * np.cos(np.pi / 4. * (2 * i)))
y_0 = np.floor(self.m_center[1] +
self.m_radius * np.sin(np.pi / 4. * (2 * i)))
tmp_center_frame = crop_image(image=center_frame_unsharp,
center=(int(y_0), int(x_0)),
size=ref_image_size)
# find maximum in tmp image
coords = np.unravel_index(indices=np.argmax(tmp_center_frame),
shape=tmp_center_frame.shape)
y_max, x_max = coords[0], coords[1]
pixmax = tmp_center_frame[y_max, x_max]
max_pos = np.array([x_max, y_max]).reshape(1, 2)
# Check whether it is the correct maximum: second brightest pixel should be nearby
tmp_center_frame[y_max, x_max] = 0.
# introduce distance parameter
dist = np.inf
while dist > 2:
coords = np.unravel_index(indices=np.argmax(tmp_center_frame),
shape=tmp_center_frame.shape)
y_max_new, x_max_new = coords[0], coords[1]
pixmax_new = tmp_center_frame[y_max_new, x_max_new]
# Caculate minimal distance to previous points
tmp_center_frame[y_max_new, x_max_new] = 0.
dist = np.amin(
np.linalg.norm(np.vstack((max_pos[:, 0] - x_max_new,
max_pos[:, 1] - y_max_new)),
axis=0))
if dist <= 2 and pixmax_new < pixmax:
break
max_pos = np.vstack((max_pos, [x_max_new, y_max_new]))
x_max = x_max_new
y_max = y_max_new
pixmax = pixmax_new
x_0 = x_0 - (ref_image_size - 1) / 2 + x_max
y_0 = y_0 - (ref_image_size - 1) / 2 + y_max
# create reference image around determined maximum
ref_center_frame = crop_image(image=center_frame_unsharp,
center=(int(y_0), int(x_0)),
size=ref_image_size)
# Fit the data using astropy.modeling
gauss_init = models.Gaussian2D(amplitude=np.amax(ref_center_frame),
x_mean=x_0,
y_mean=y_0,
x_stddev=1.,
y_stddev=1.,
theta=0.)
fit_gauss = fitting.LevMarLSQFitter()
y_grid, x_grid = np.mgrid[y_0 - (ref_image_size - 1) / 2:y_0 +
(ref_image_size - 1) / 2 + 1,
x_0 - (ref_image_size - 1) / 2:x_0 +
(ref_image_size - 1) / 2 + 1]
gauss = fit_gauss(gauss_init, x_grid, y_grid, ref_center_frame)
x_pos[i] = gauss.x_mean.value
y_pos[i] = gauss.y_mean.value
# Find star position as intersection of two lines
x_center = ((y_pos[0]-x_pos[0]*(y_pos[2]-y_pos[0])/(x_pos[2]-float(x_pos[0]))) -
(y_pos[1]-x_pos[1]*(y_pos[1]-y_pos[3])/(x_pos[1]-float(x_pos[3])))) / \
((y_pos[1]-y_pos[3])/(x_pos[1]-float(x_pos[3])) -
(y_pos[2]-y_pos[0])/(x_pos[2]-float(x_pos[0])))
y_center = x_center*(y_pos[1]-y_pos[3])/(x_pos[1]-float(x_pos[3])) + \
(y_pos[1]-x_pos[1]*(y_pos[1]-y_pos[3])/(x_pos[1]-float(x_pos[3])))
nimages = self.m_image_in_port.get_shape()[0]
npix = self.m_image_in_port.get_shape()[1]
start_time = time.time()
for i in range(nimages):
progress(i, nimages, 'Centering the images...', start_time)
image = self.m_image_in_port[i, ]
shift_yx = np.array([(float(im_shape[-2]) - 1.) / 2. - y_center,
(float(im_shape[-1]) - 1.) / 2. - x_center])
if self.m_dither:
index = np.digitize(i, nframes, right=False) - 1
shift_yx[0] -= dither_y[index]
shift_yx[1] -= dither_x[index]
if npix % 2 == 0 and self.m_size is not None:
im_tmp = np.zeros((image.shape[0] + 1, image.shape[1] + 1))
im_tmp[:-1, :-1] = image
image = im_tmp
shift_yx[0] += 0.5
shift_yx[1] += 0.5
im_shift = shift_image(image, shift_yx, 'spline')
if self.m_size is not None:
im_crop = crop_image(im_shift, None, self.m_size)
self.m_image_out_port.append(im_crop, data_dim=3)
else:
self.m_image_out_port.append(im_shift, data_dim=3)
print(f'Center [x, y] = [{x_center}, {y_center}]')
history = f'[x, y] = [{round(x_center, 2)}, {round(y_center, 2)}]'
self.m_image_out_port.copy_attributes(self.m_image_in_port)
self.m_image_out_port.add_history('WaffleCenteringModule', history)
self.m_image_out_port.close_port()
def _crop(image_in, size, center):
return crop_image(image_in, center, size)
def run(self) -> None:
"""
Run method of the module. Computes the similarity between frames based on the Mean Squared
Error (MSE), the Pearson Correlation Coefficient (PCC), or the Structural Similarity (SSIM).
Returns
-------
NoneType
None
"""
# get image number and image shapes
nimages = self.m_image_in_port.get_shape()[0]
im_shape = self.m_image_in_port.get_shape()[1:]
cpu = self._m_config_port.get_attribute('CPU')
pixscale = self.m_image_in_port.get_attribute('PIXSCALE')
# convert arcsecs to pixels
self.m_mask_radii = (math.floor(self.m_mask_radii[0] / pixscale),
math.floor(self.m_mask_radii[1] / pixscale))
self.m_window_size = int(self.m_window_size / pixscale)
# overlay the same mask over all images
mask = create_mask(im_shape, self.m_mask_radii)
images = self.m_image_in_port.get_all()
# close the port during the calculations
self.m_image_out_port.close_port()
if self.m_temporal_median == 'constant':
temporal_median = np.median(images, axis=0)
else:
temporal_median = False
if self.m_method == 'SSIM':
images = crop_image(images, None, int(self.m_mask_radii[1]))
temporal_median = crop_image(temporal_median, None, int(self.m_mask_radii[1]))
else:
images *= mask
# compare images and store similarity
similarities = np.zeros(nimages)
pool = mp.Pool(cpu)
async_results = []
for i in range(nimages):
async_results.append(pool.apply_async(FrameSimilarityModule._similarity,
args=(images,
i,
self.m_method,
self.m_window_size,
temporal_median)))
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, nimages, 'Running FrameSimilarityModule', start_time)
# check if new processes have finished every 5 seconds
time.sleep(5)
# get the results for every async_result object
for async_result in async_results:
reference, similarity = async_result.get()
similarities[reference] = similarity
pool.terminate()
# reopen the port after the calculation
self.m_image_out_port.open_port()
self.m_image_out_port.add_attribute(f'{self.m_method}', similarities, static=False)
self.m_image_out_port.close_port()
def _image_cutting(image_in,
size,
center):
return crop_image(image_in, center, size)
def run(self) -> None:
"""
Run method of the module. Decreases the image size by cropping around an given position.
The module always returns odd-sized images.
Returns
-------
NoneType
None
"""
# Get memory and number of images to split the frames into chunks
memory = self._m_config_port.get_attribute('MEMORY')
nimages = self.m_image_in_port.get_shape()[0]
# Get the numnber of dimensions and shape
ndim = self.m_image_in_port.get_ndim()
im_shape = self.m_image_in_port.get_shape()
if ndim == 3:
# Number of images
nimages = im_shape[-3]
# Split into batches to comply with memory constraints
frames = memory_frames(memory, nimages)
elif ndim == 4:
# Process all wavelengths per exposure at once
frames = np.linspace(0, im_shape[-3], im_shape[-3] + 1)
# Convert size parameter from arcseconds to pixels
pixscale = self.m_image_in_port.get_attribute('PIXSCALE')
print(f'New image size (arcsec) = {self.m_size}')
self.m_size = int(math.ceil(self.m_size / pixscale))
print(f'New image size (pixels) = {self.m_size}')
if self.m_center is not None:
print(f'New image center (x, y) = {self.m_center}')
# Crop images chunk by chunk
start_time = time.time()
for i in range(len(frames[:-1])):
# Update progress bar
progress(i, len(frames[:-1]), 'Cropping images...', start_time)
# Select images in the current chunk
if ndim == 3:
images = self.m_image_in_port[frames[i]:frames[i + 1], ]
elif ndim == 4:
# Process all wavelengths per exposure at once
images = self.m_image_in_port[:, i, ]
# crop images according to input parameters
images = crop_image(images, self.m_center, self.m_size, copy=False)
# Write processed images to output port
if ndim == 3:
self.m_image_out_port.append(images, data_dim=3)
elif ndim == 4:
self.m_image_out_port.append(images, data_dim=4)
# Save history and copy attributes
history = f'image size (pix) = {self.m_size}'
self.m_image_out_port.add_history('CropImagesModule', history)
self.m_image_out_port.copy_attributes(self.m_image_in_port)
self.m_image_out_port.close_port()
