aopp_deconv_tool.deconvolve

module documentation

Script that deconvolves the first argument with the second argument

Example invocation:
python -m aopp_deconv_tool.deconvolve './example_data/test_rebin.fits{DATA}[10:12]{CELESTIAL:(1,2)}' './example_data/fit_example_psf_000.fits[10:12]{CELESTIAL:(1,2)}'
Class IterationTracker Undocumented
Class Iterator Undocumented
Function create_plot_set Creates a set of plots that are updated every cadence steps. Useful to see exactly what a deconvolver is doing.
Function deconv_process Undocumented
Function display_process_progress Undocumented
Function exec_with_args Undocumented
Function go Thin wrapper around run() to accept string inputs. As long as the names of the arguments to this function are the same as the names expected from the command line we can do this programatically
Function init_process_context Undocumented
Function parse_args Undocumented
Function run Given a FitsSpecifier for an observation and a PSF, an output path, and a class that performs deconvolution, deconvolves the observation using the PSF.
Variable deconv_methods Undocumented
Variable _lgr Undocumented
def create_plot_set(deconvolver, cadence=1):

Creates a set of plots that are updated every cadence steps. Useful to see exactly what a deconvolver is doing.

def deconv_process(group_index, idx, deconvolver, obs, psf):

Undocumented

def display_process_progress():

Undocumented

def exec_with_args(argv):

Undocumented

def go(obs_fits_spec, psf_fits_spec, output_path=None, plot=None, deconv_method=None, deconv_method_help_FLAG=None, n_processes=None, **kwargs):

Thin wrapper around run() to accept string inputs. As long as the names of the arguments to this function are the same as the names expected from the command line we can do this programatically

def init_process_context(msg_queue):

Undocumented

def parse_args(argv):

Undocumented

def run(obs_fits_spec: aph.fits.specifier.FitsSpecifier, psf_fits_spec: aph.fits.specifier.FitsSpecifier, deconvolver: Literal[CleanModified] | Literal[LucyRichardson], output_path: str | Path = './deconv.fits', plot: bool = True, progress: int = 0, n_processes: int = mp.cpu_count() - 2):

Given a FitsSpecifier for an observation and a PSF, an output path, and a class that performs deconvolution, deconvolves the observation using the PSF.

# ARGUMENTS #
obs_fits_spec : aph.fits.specifier.FitsSpecifier

FITS file specifier for observation data, format is PATH{EXT}[SLICE](AXES). Where:

PATH : str
The path to the FITS file
EXT : str | int
The name or number of the FITS extension (defaults to PRIMARY)
SLICE : "python slice format" (i.e., [1:5, 5:10:2])
Slice of the FITS extension data to use (defaults to all data)
AXES : tuple[int,...]
Axes of the FITS extension that are "spatial" or "celestial" (i.e., RA, DEC), by default will try to infer them from the FITS extension header.
psf_fits_spec : aph.fits.specifier.FitsSpecifier
FITS file specifier for PSF data, format is same as above
output_path : str = './deconv.fits'
Path to output deconvolution to.
deconvolver : ClassInstance
Instance of Class to use for deconvolving, defaults to an instance of CleanModified
plot : bool = True
If True will plot the deconvolution progress
n_processes : int = N_cpu - 2
Number of processes to parallelise the calculations over
deconv_methods =

Undocumented

_lgr =

Undocumented