def _run_multi_processing(self, star_reshape, im_shape, indices):
"""
Internal function to create the residuals, derotate the images, and write the output
using multiprocessing.
"""
tmp_output = np.zeros(
(len(self.m_components), im_shape[1], im_shape[2]))
if self.m_res_mean_out_port is not None:
self.m_res_mean_out_port.set_all(tmp_output, keep_attributes=False)
if self.m_res_median_out_port is not None:
self.m_res_median_out_port.set_all(tmp_output,
keep_attributes=False)
if self.m_res_weighted_out_port is not None:
self.m_res_weighted_out_port.set_all(tmp_output,
keep_attributes=False)
if self.m_res_rot_mean_clip_out_port is not None:
self.m_res_rot_mean_clip_out_port.set_all(tmp_output,
keep_attributes=False)
angles = -1. * self.m_star_in_port.get_attribute(
'PARANG') + self.m_extra_rot
capsule = PcaMultiprocessingCapsule(
self.m_res_mean_out_port, self.m_res_median_out_port,
self.m_res_weighted_out_port, self.m_res_rot_mean_clip_out_port,
self._m_config_port.get_attribute('CPU'),
deepcopy(self.m_components), deepcopy(self.m_pca),
deepcopy(star_reshape), deepcopy(angles), im_shape, indices)
capsule.run()
def _run_multi_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 multiprocessing.
"""
cpu = self._m_config_port.get_attribute('CPU')
angles = -1. * self.m_star_in_port.get_attribute(
'PARANG') + self.m_extra_rot
tmp_output = np.zeros(
(len(self.m_components), im_shape[1], im_shape[2]))
if self.m_res_mean_out_port is not None:
self.m_res_mean_out_port.set_all(tmp_output, keep_attributes=False)
if self.m_res_median_out_port is not None:
self.m_res_median_out_port.set_all(tmp_output,
keep_attributes=False)
if self.m_res_weighted_out_port is not None:
self.m_res_weighted_out_port.set_all(tmp_output,
keep_attributes=False)
if self.m_res_rot_mean_clip_out_port is not None:
self.m_res_rot_mean_clip_out_port.set_all(tmp_output,
keep_attributes=False)
self.m_star_in_port.close_port()
self.m_reference_in_port.close_port()
if self.m_res_mean_out_port is not None:
self.m_res_mean_out_port.close_port()
if self.m_res_median_out_port is not None:
self.m_res_median_out_port.close_port()
if self.m_res_weighted_out_port is not None:
self.m_res_weighted_out_port.close_port()
if self.m_res_rot_mean_clip_out_port is not None:
self.m_res_rot_mean_clip_out_port.close_port()
if self.m_res_arr_out_ports is not None:
for pca_number in self.m_components:
self.m_res_arr_out_ports[pca_number].close_port()
if self.m_basis_out_port is not None:
self.m_basis_out_port.close_port()
capsule = PcaMultiprocessingCapsule(self.m_res_mean_out_port,
self.m_res_median_out_port,
self.m_res_weighted_out_port,
self.m_res_rot_mean_clip_out_port,
cpu, deepcopy(self.m_components),
deepcopy(self.m_pca),
deepcopy(star_reshape),
deepcopy(angles), im_shape,
indices)
capsule.run()
def _run_multi_processing(self, star_reshape: np.ndarray, im_shape: tuple,
indices: Optional[np.ndarray]) -> None:
"""
Internal function to create the residuals, derotate the images, and write the output
using multiprocessing.
"""
cpu = self._m_config_port.get_attribute('CPU')
parang = -1. * self.m_star_in_port.get_attribute(
'PARANG') + self.m_extra_rot
if self.m_ifs_data:
if 'WAVELENGTH' in self.m_star_in_port.get_all_non_static_attributes(
):
wavelength = self.m_star_in_port.get_attribute('WAVELENGTH')
else:
raise ValueError(
'The wavelengths are not found. These should be stored '
'as the \'WAVELENGTH\' attribute.')
scales = scaling_factors(wavelength)
else:
scales = None
if self.m_processing_type in ['ADI', 'SDI']:
pca_first = self.m_components
pca_secon = [-1] # Not used
elif self.m_processing_type in ['SDI+ADI', 'ADI+SDI']:
pca_first = self.m_components[0]
pca_secon = self.m_components[1]
if self.m_ifs_data:
if self.m_processing_type in ['ADI', 'SDI']:
res_shape = (len(pca_first), len(wavelength), im_shape[-2],
im_shape[-1])
elif self.m_processing_type in ['SDI+ADI', 'ADI+SDI']:
res_shape = (len(pca_first), len(pca_secon), len(wavelength),
im_shape[-2], im_shape[-1])
else:
res_shape = (len(self.m_components), im_shape[1], im_shape[2])
tmp_output = np.zeros(res_shape)
if self.m_res_mean_out_port is not None:
self.m_res_mean_out_port.set_all(tmp_output, keep_attributes=False)
if self.m_res_median_out_port is not None:
self.m_res_median_out_port.set_all(tmp_output,
keep_attributes=False)
if self.m_res_weighted_out_port is not None:
self.m_res_weighted_out_port.set_all(tmp_output,
keep_attributes=False)
if self.m_res_rot_mean_clip_out_port is not None:
self.m_res_rot_mean_clip_out_port.set_all(tmp_output,
keep_attributes=False)
self.m_star_in_port.close_port()
self.m_reference_in_port.close_port()
if self.m_res_mean_out_port is not None:
self.m_res_mean_out_port.close_port()
if self.m_res_median_out_port is not None:
self.m_res_median_out_port.close_port()
if self.m_res_weighted_out_port is not None:
self.m_res_weighted_out_port.close_port()
if self.m_res_rot_mean_clip_out_port is not None:
self.m_res_rot_mean_clip_out_port.close_port()
if self.m_basis_out_port is not None:
self.m_basis_out_port.close_port()
capsule = PcaMultiprocessingCapsule(
self.m_res_mean_out_port, self.m_res_median_out_port,
self.m_res_weighted_out_port, self.m_res_rot_mean_clip_out_port,
cpu, deepcopy(self.m_components), deepcopy(self.m_pca),
deepcopy(star_reshape), deepcopy(parang), deepcopy(scales),
im_shape, indices, self.m_processing_type)
capsule.run()