def run(self):
"""
Run method of the module. Uses the PARANG attributes to derotate the images (if *derotate*
is set to True) and applies an optional mean or median stacking afterwards.
Returns
-------
NoneType
None
"""
def _initialize(ndim, npix):
if ndim == 2:
nimages = 1
elif ndim == 3:
nimages = self.m_image_in_port.get_shape()[0]
if self.m_stack == 'median':
frames = [0, nimages]
else:
frames = memory_frames(memory, nimages)
if self.m_stack == 'mean':
im_tot = np.zeros((npix, npix))
else:
im_tot = None
return nimages, frames, im_tot
self.m_image_out_port.del_all_data()
self.m_image_out_port.del_all_attributes()
if self.m_image_in_port.tag == self.m_image_out_port.tag:
raise ValueError(
'Input and output port should have a different tag.')
memory = self._m_config_port.get_attribute('MEMORY')
if self.m_derotate:
parang = self.m_image_in_port.get_attribute('PARANG')
ndim = self.m_image_in_port.get_ndim()
npix = self.m_image_in_port.get_shape()[1]
nimages, frames, im_tot = _initialize(ndim, npix)
start_time = time.time()
for i, _ in enumerate(frames[:-1]):
progress(i, len(frames[:-1]), 'Running DerotateAndStackModule...',
start_time)
images = self.m_image_in_port[frames[i]:frames[i + 1], ]
if self.m_derotate:
angles = -parang[frames[i]:frames[i + 1]] + self.m_extra_rot
images = rotate_images(images, angles)
if self.m_stack is None:
if ndim == 2:
self.m_image_out_port.set_all(images[np.newaxis, ...])
elif ndim == 3:
self.m_image_out_port.append(images, data_dim=3)
elif self.m_stack == 'mean':
im_tot += np.sum(images, axis=0)
sys.stdout.write('Running DerotateAndStackModule... [DONE]\n')
sys.stdout.flush()
if self.m_stack == 'mean':
im_stack = im_tot / float(nimages)
self.m_image_out_port.set_all(im_stack[np.newaxis, ...])
elif self.m_stack == 'median':
im_stack = np.median(images, axis=0)
self.m_image_out_port.set_all(im_stack[np.newaxis, ...])
if self.m_derotate or self.m_stack is not None:
self.m_image_out_port.copy_attributes(self.m_image_in_port)
self.m_image_out_port.close_port()
def run(self) -> None:
"""
Run method of the module. Uses the ``PARANG`` attributes to derotate the images (if
``derotate`` is set to ``True``) and applies an optional mean or median stacking
along the time or wavelengths dimension afterwards.
Returns
-------
NoneType
None
"""
@typechecked
def _initialize(
ndim: int, npix: int
) -> Tuple[int, np.ndarray, Optional[np.ndarray],
Optional[np.ndarray]]:
if ndim == 2:
nimages = 1
elif ndim == 3:
nimages = self.m_image_in_port.get_shape()[-3]
if self.m_stack == 'median':
frames = np.array([0, nimages])
else:
frames = memory_frames(memory, nimages)
elif ndim == 4:
nimages = self.m_image_in_port.get_shape()[-3]
nwave = self.m_image_in_port.get_shape()[-4]
if self.m_dimension == 'time':
frames = np.linspace(0, nwave, nwave + 1)
elif self.m_dimension == 'wavelength':
frames = np.linspace(0, nimages, nimages + 1)
else:
raise ValueError(
'The dimension should be set to \'time\' or \'wavelength\'.'
)
if self.m_stack == 'mean':
if ndim == 4:
if self.m_dimension == 'time':
im_tot = np.zeros((nwave, npix, npix))
elif self.m_dimension == 'wavelength':
im_tot = np.zeros((nimages, npix, npix))
else:
im_tot = np.zeros((npix, npix))
else:
im_tot = None
if self.m_stack is None and ndim == 4:
im_none = np.zeros((nwave, nimages, npix, npix))
else:
im_none = None
return nimages, frames, im_tot, im_none
memory = self._m_config_port.get_attribute('MEMORY')
if self.m_derotate:
parang = self.m_image_in_port.get_attribute('PARANG')
ndim = self.m_image_in_port.get_ndim()
npix = self.m_image_in_port.get_shape()[-2]
nimages, frames, im_tot, im_none = _initialize(ndim, npix)
start_time = time.time()
for i, _ in enumerate(frames[:-1]):
progress(i, len(frames[:-1]),
'Derotating and/or stacking images...', start_time)
if ndim == 3:
# Get the images and ensure they have the correct 3D shape with the following
# three dimensions: (batch_size, height, width)
images = self.m_image_in_port[frames[i]:frames[i + 1], ]
elif ndim == 4:
# Process all time frames per exposure at once
if self.m_dimension == 'time':
images = self.m_image_in_port[i, :, ]
elif self.m_dimension == 'wavelength':
images = self.m_image_in_port[:, i, ]
if self.m_derotate:
if ndim == 4:
if self.m_dimension == 'time':
angles = -1. * parang + self.m_extra_rot
elif self.m_dimension == 'wavelength':
n_wavel = self.m_image_in_port.get_shape()[-4]
angles = np.full(n_wavel,
-1. * parang[i]) + self.m_extra_rot
else:
angles = -parang[frames[i]:frames[i +
1]] + self.m_extra_rot
images = rotate_images(images, angles)
if self.m_stack is None:
if ndim == 2:
self.m_image_out_port.set_all(images[np.newaxis, ...])
elif ndim == 3:
self.m_image_out_port.append(images, data_dim=3)
elif ndim == 4:
if self.m_dimension == 'time':
im_none[i] = images
elif self.m_dimension == 'wavelength':
im_none[:, i] = images
elif self.m_stack == 'mean':
if ndim == 4:
im_tot[i] = np.sum(images, axis=0)
else:
im_tot += np.sum(images, axis=0)
if self.m_stack == 'mean':
if ndim == 4:
im_stack = im_tot / float(im_tot.shape[0])
if self.m_dimension == 'time':
self.m_image_out_port.set_all(im_stack[:, np.newaxis, ...])
elif self.m_dimension == 'wavelength':
self.m_image_out_port.set_all(im_stack[np.newaxis, ...])
else:
im_stack = im_tot / float(nimages)
self.m_image_out_port.set_all(im_stack[np.newaxis, ...])
elif self.m_stack == 'median':
if ndim == 4:
images = self.m_image_in_port[:]
if self.m_dimension == 'time':
im_stack = np.median(images, axis=1)
self.m_image_out_port.set_all(im_stack[:, np.newaxis, ...])
elif self.m_dimension == 'wavelength':
im_stack = np.median(images, axis=0)
self.m_image_out_port.set_all(im_stack[np.newaxis, ...])
else:
im_stack = np.median(images, axis=0)
self.m_image_out_port.set_all(im_stack[np.newaxis, ...])
elif self.m_stack is None and ndim == 4:
if self.m_dimension == 'time':
self.m_image_out_port.set_all(im_none)
elif self.m_dimension == 'wavelength':
self.m_image_out_port.set_all(im_none)
if self.m_derotate or self.m_stack is not None:
self.m_image_out_port.copy_attributes(self.m_image_in_port)
self.m_image_out_port.close_port()