The Sector class

The Sector class is used to manage imaging parameters for an image.

class rapthor.lib.sector.Sector(name, ra, dec, width_ra, width_dec, field)

The Sector object contains various parameters for a sector of the field. Sectors are used only in image and predict operations

Parameters:

namestr

Name of sector

rafloat

RA in degrees of sector center

decfloat

Dec in degrees of sector center

width_rafloat

Width of sector in RA degrees

width_decfloat

Width of sector in Dec in degrees

fieldField object

Field object

filter_skymodel(skymodel, invert=False)

Filters input skymodel to select only sources that lie inside the sector

Parameters:

skymodelLSMTool skymodel object

Input sky model

invertbool, optional

If True, invert the selection (so select only sources that lie outside the sector)

Returns:

filtered_skymodelLSMTool skymodel object

Filtered sky model

get_distance_to_obs_center()

Return the overall minimum and maximum distance in degrees from any sector vertex (and sector center) to the phase center of the observation

Returns:

min_dist, max_distfloat, float

Minimum and maximum distance in degrees

get_matplotlib_patch(wcs=None)

Returns a matplotlib patch for the sector polygon

Parameters:

wcsWCS object, optional

WCS object defining (RA, Dec) <-> (x, y) transformation. If not given, the field’s transformation is used

Returns:

patchmatplotlib patch object

The patch for the sector polygon

get_nwavelengths(cellsize_deg, timestep_sec)

Returns nwavelengths for WSClean BL-based averaging

The value depends on the integration time given the specified maximum allowed smearing. We scale it from the imaging cell size assuming normal sampling as:

max baseline in nwavelengths = 1 / theta_rad ~= 1 / (cellsize_deg * 3 * pi / 180) nwavelengths = max baseline in nwavelengths * 2 * pi * integration time in seconds / (24 * 60 * 60) / 4

Parameters:

cellsize_degfloat

Pixel size of image in degrees

timestep_secfloat

Length of one timestep in seconds

get_obs_parameters(parameter)

Returns list of parameters for all observations

Parameters:

parameterstr

Name of parameter to return

Returns:

parameterslist

List of parameters, with one entry for each observation

get_vertices_radec()

Return the vertices as RA, Dec for the sector boundary

intialize_vertices()

Determines the vertices of the sector polygon

make_region_file(outputfile, region_format='ds9')

Make a ds9 or CASA region file for the sector boundary

Parameters:

outputfilestr

Name of output region file

region_formatstr, optional

Format of region file: ‘ds9’ or ‘casa’

make_skymodel(index)

Makes predict sky model

Parameters:

indexint

Processing cycle index

make_vertices_file()

Make a vertices file for the sector boundary

set_imaging_parameters(do_multiscale=False, recalculate_imsize=False, imaging_parameters=None, preapply_dde_solutions=False)

Sets the parameters needed for the imaging operation

Parameters:

do_multiscalebool, optional

If True, multiscale clean is done

recalculate_imsizebool, optional

If True, the image size is recalculated based on the current sector region

imaging_parametersdict, optional

Dict of imaging parameters to use instead of those defined by the field’s parset. If supplied, the following keys are expected to be present:

‘cellsize_arcsec’: cell (pixel) size in arcsec ‘robust’: Briggs robust value ‘taper_arcsec’: taper in arcsec ‘local_rms_strength’: local RMS strength factor ‘local_rms_window’: local RMS window size ‘local_rms_method’: local RMS method ‘min_uv_lambda’: minimum uv distance cut in lambda ‘max_uv_lambda’: maximum uv distance cut in lambda ‘mgain’: cleaning gain ‘idg_mode’: IDG processing mode ‘mem_gb’: maximum memory in GB ‘reweight’: reweighting flag ‘dd_psf_grid’: DD PSF grid ‘max_peak_smearing’: maximum allowed peak smearing

preapply_dde_solutionsbool, optional

If True, use setup appropriate for case in which all DDE solutions are preapplied before imaging is done

set_prediction_parameters()

Sets the predict parameters