The Field class

The Field class is used to manage the overall state and parameters of observed field, including observations, images, calibration solutions, etc.

class rapthor.lib.field.Field(parset, minimal=False)

The Field object stores parameters needed for processing of the field

Parameters:

parsetdict

Parset with processing parameters

minimalbool

If True, only initialize the minimal set of required parameters

adjust_sector_boundaries()

Adjusts the imaging sector boundaries for overlaping sources

Note: this adjustment is only done when there are multiple sectors in a grid, since its purpose is to ensure that sources don’t get split between two neighboring sectors

check_selfcal_progress()

Checks whether selfcal has converged or diverged by comparing the current image noise to that of the previous cycle. A check is also done on the absolute value of the noise.

Convergence is determined by comparing the noise and dynamic range ratios to self.convergence_ratio, which is the minimum ratio of the current noise to the previous noise above which selfcal is considered to have converged (must be in the range 0.5 – 2). E.g., self.convergence_ratio = 0.95 means that the image noise must decrease by ~ 5% or more from the previous cycle for selfcal to be considered as not yet converged. The same is true for the dynamic range but reversed (the dynamic range must increase by ~ 5% or more from the previous cycle for selfcal to be considered as not yet converged).

Divergence is determined by comparing the noise ratio to self.divergence_ratio, which is the minimum ratio of the current noise to the previous noise above which selfcal is considered to have diverged (must be >= 1). E.g., divergence_ratio = 1.1 means that, if image noise worsens by ~ 10% or more from the previous cycle, selfcal is considered to have diverged.

Failure is determined by comparing the absolute value of the noise in the current cycle with the theoretical noise. If the ratio of the current median noise to the theoretical one is greater than failure_ratio, selfcal is considered to have failed.

Returns:

selfcal_statenamedtuple

The selfcal state, with the following elements:

selfcal_state.converged - True if selfcal has converged in all

sectors

selfcal_state.diverged - True if selfcal has diverged in one or

more sectors

selfcal_state.failed - True if selfcal has failed in one or

more sectors

chunk_observations(mintime)

Break observations into smaller time chunks if desired

Chunking is done if:

• obs.data_fraction < 1 (so part of an observation is to be processed)

• nobs * nsectors < nnodes (so all nodes can be used efficiently. In particular, the predict operation parallelizes over sectors and observations, so we need enough observations to allow all nodes to be occupied.)

Parameters:

mintimefloat

Minimum time in sec for a chunk

define_bright_source_sectors(index)

Defines the bright source sectors

Parameters:

indexint

Iteration index

define_imaging_sectors()

Defines the imaging sectors

define_non_calibrator_source_sectors(index)

Defines the non-calibrator source sectors

These sectors are defined if peeling of non-calibrator sources is explicitly enabled or if the antenna is LBA (where it’s always needed)

Parameters:

indexint

Iteration index

define_outlier_sectors(index)

Defines the outlier sectors

Parameters:

indexint

Iteration index

define_predict_sectors(index)

Defines the predict sectors

Parameters:

indexint

Iteration index

find_intersecting_sources()

Finds sources that intersect with the intial sector boundaries

Returns:

intersecting_source_polys: list of Polygons

List of source polygons that intersect one or more sector boundaries

get_calibration_radius()

Returns the radius in degrees that encloses all calibrators

get_obs_parameters(parameter)

Returns list of parameters for all observations

Parameters:

parameterstr

Name of parameter to return

Returns:

parameterslist

List of parameters of each observation

get_source_distances(source_dict: Dict[str, List[float]])

Returns source distances in degrees from the phase center

Parameters:

source_dictdict

Dict of source patch names and coordinates in degrees (e.g., {‘name’: [RA, Dec]})

Returns:

namesnumpy array

Array of source names

distancesnumpy array

Array of distances from the phase center in degrees

makeWCS()

Makes simple WCS object

Returns:

wastropy.wcs.WCS object

A simple TAN-projection WCS object for specified reference position

make_non_calibrator_skymodel()

Make a sky model of any non-calibrator sources

Since the peeling of non-calibrator sources uses the calibration solutions from the previous cycle (if any), the calibration patches from that cycle are applied to the current sky model to ensure agreement between the sky model patches and the calibration patches.

Note: if a previous cycle was not done (and therefore a model from it does not exist), then either peeling will be done without using calibration solutions (and therefore the patches are ignored) or a solutions file and sky model have been provided by the user, in which case the patches in the model and solutions must already agree with each other.

make_outlier_skymodel()

Make a sky model of any outlier calibration sources, not included in any imaging sector

make_skymodels(skymodel_true_sky, skymodel_apparent_sky=None, regroup=True, find_sources=False, target_flux=None, target_number=None, calibrator_max_dist_deg=None, index=0)

Groups a sky model into source and calibration patches

Grouping is done on the apparent-flux sky model if available. Note that the source names in the true- and apparent-flux models must be the same (i.e., the only differences between the two are the fluxes and spectral indices)

Parameters:

skymodel_true_skystr or LSMTool skymodel object

Filename of input makesourcedb true-flux sky model file

skymodel_apparent_skystr or LSMTool skymodel object, optional

Filename of input makesourcedb apparent-flux sky model file

regroupbool, optional

If False, the calibration sky model is not regrouped to the target flux. Instead, the existing calibration groups are used

find_sourcesbool, optional

If True, group the sky model by thresholding to find sources. This is not needed if the input sky model was filtered by PyBDSF in the imaging operation

target_fluxfloat, optional

Target flux in Jy for grouping

target_numberint, optional

Target number of patches for grouping

calibrator_max_dist_degfloat, optional

Maximum distance in degrees from phase center for grouping

indexindex

Iteration index

plot_field(skymodel_radius=0, moc=None)

Plots an overview of how the imaged field compares against the skymodel used.

Parameters:

skymodel_radiusfloat

Radius in degrees out to which the skymodel catalogue was queried.

mocstr or None

If not None, the multi-order coverage map to plot alongside the usual quantiies.

radec2xy(RA, Dec)

Returns x, y for input RA, Dec

Parameters:

RAlist

List of RA values in degrees

Declist

List of Dec values in degrees

Returns:

x, ylist, list

Lists of x and y pixel values corresponding to the input RA and Dec values

remove_skymodels()

Remove sky models to minimize memory usage

scan_h5parms()

Scans the calibration h5parms

The basic structure is checked for correctness and for the presence of amplitude solutions (which may require different processing steps).

scan_observations()

Checks input MS files and initializes the associated Observation objects

set_obs_parameters()

Sets parameters for all observations from current parset and sky model

transfer_patches(from_skymodel, to_skymodel, patch_dict=None)

Transfers the patches defined in from_skymodel to to_skymodel.

Parameters:

from_skymodelsky model

Sky model from which to transfer patches

to_skymodelsky model

Sky model to which to transfer patches

patch_dictdict, optional

Dict of patch positions

Returns:

to_skymodelsky model

Sky model with patches matching those of from_skymodel

update(step_dict, index, final=False)

Updates parameters, sky models, etc. for current step

Parameters:

step_dictdict

Dict of parameter values for given iteration

indexint

Index of iteration

finalbool, optional

If True, process as the final pass (needed for correct processing of the sky models)

update_skymodels(index, regroup, target_flux=None, target_number=None, calibrator_max_dist_deg=None, final=False)

Updates the source and calibration sky models from the output sector sky model(s)

Parameters:

indexint

Iteration index (counts starting from 1)

regroupbool

Regroup sky model. This parameter is not used for the first cycle, as its value is taken from the parset. For later cycles, it controls whether the sky models that come from imaging are to be regrouped into calibration patches. In almost all cases, regrouping should be done. The exception is when using small imaging sectors when the sources in each sector should be grouped into a single patch together.

target_fluxfloat, optional

Target flux in Jy for grouping

target_numberint, optional

Target number of patches for grouping

calibrator_max_dist_degfloat, optional

Maximum distance in degrees from phase center for grouping

finalbool, optional

If True, process as the final pass (combine initial and new sky models)

xy2radec(x, y)

Returns input RA, Dec for input x, y

Parameters:

xlist

List of x values in pixels

ylist

List of y values in pixels

Returns:

RA, Declist, list

Lists of RA and Dec values corresponding to the input x and y pixel values