ska_sdp_datamodels.visibility Package

Functions

import_visibility_from_hdf5(filename)

Import Visibility(s) from HDF5 format

import_flagtable_from_hdf5(filename)

Import FlagTable(s) from HDF5 format

import_staticmasktable_from_hdf5(filename)

Import StaticMaskTable(s) from HDF5 format

export_visibility_to_hdf5(vis, filename)

Export a Visibility to HDF5 format

export_flagtable_to_hdf5(ft, filename)

Export a FlagTable or list to HDF5 format

export_staticmasktable_to_hdf5(static_mask, ...)

Export a StaticMaskTable or list to HDF5 format

convert_visibility_to_hdf(vis, f)

Convert a Visibility to an HDF file

convert_hdf_to_visibility(f)

Convert HDF root to visibility

convert_flagtable_to_hdf(ft, f)

Convert a FlagTable to an HDF file

convert_hdf_to_flagtable(f)

Convert HDF root to flagtable

create_visibility(config, times, frequency, ...)

Create a Visibility object with its main data array filled with complex double-precision zeros, and its axes and other attributes adequately initialised.

create_flagtable_from_visibility(vis)

Create FlagTable matching Visibility

create_visibility_from_ms(msname[, channum, ...])

Minimal MS to Visibility converter

export_visibility_to_ms(msname, vis_list[, ...])

Minimal Visibility to MS converter

extend_visibility_to_ms(msname, bvis)

Visibility to MS converter If MS doesn't exist, use export; while if MS already exists, use extend by row.

list_ms(msname[, ack])

List sources and data descriptors in a MeasurementSet

generate_baselines(nant)

Generate mapping from antennas to baselines Note that we need to include auto-correlations since some input measurement sets may contain auto-correlations

Classes

Visibility([data_vars, coords, attrs])

Visibility xarray.Dataset class

FlagTable([data_vars, coords, attrs])

Flag table class
class ska_sdp_datamodels.visibility.vis_model.VisibilityAccessor(xarray_obj)[source]

Visibility property accessor

class ska_sdp_datamodels.visibility.vis_model.FlagTableAccessor(xarray_obj)[source]

FlagTable property accessor.

ska_sdp_datamodels.image Package

Functions

import_image_from_hdf5(filename)

Import Image(s) from HDF5 format

export_image_to_hdf5(im, filename)

Export an Image or list to HDF5 format

convert_image_to_hdf(im, f)

Convert an Image to an HDF file

convert_hdf_to_image(f)

Convert HDF root to an Image

create_image(npixel, cellsize, phasecentre)

Create an empty image consistent with the inputs.

Classes

Image([data_vars, coords, attrs])

Image class with pixels as an xarray.DataArray and the AstroPy`implementation of a World Coordinate System <http://docs.astropy.org/en/stable/wcs>`_
class ska_sdp_datamodels.image.image_model.ImageAccessor(xarray_obj)[source]

Image property accessor

ska_sdp_datamodels.calibration Package

Functions

import_pointingtable_from_hdf5(filename)

Import PointingTable(s) from HDF5 format

import_gaintable_from_hdf5(filename)

Import GainTable(s) from HDF5 format

export_pointingtable_to_hdf5(pt, filename, ...)

Export a PointingTable or list to HDF5 format

export_gaintable_to_hdf5(gt, filename)

Export a GainTable or list to HDF5 format

convert_hdf_to_pointingtable(f)

Convert HDF root to a PointingTable

convert_pointingtable_to_hdf(pt, f, ...)

Convert a PointingTable to an HDF file

convert_hdf_to_gaintable(f)

Convert HDF root to a GainTable

convert_gaintable_to_hdf(gt, f)

Convert a GainTable to an HDF file

create_gaintable_from_visibility(vis[, ...])

Create a unity- or identity-initialised GainTable consistent with the given Visibility.

create_pointingtable_from_visibility(vis[, ...])

Create pointing table from visibility.

import_gaintable_from_casa_cal_table(table_name)

Create gain table from Calibration table of CASA.

convert_pointingtable_to_json(pt)

Convert a pointingtable to a json dictionary.

convert_json_to_pointingtable(pt_json)

Convert a JSON to pointingtable.

Classes

GainTable([data_vars, coords, attrs])

Container for calibration solutions; a GainTable instance implicitly corresponds to a Visibility instance being calibrated.

PointingTable([data_vars, coords, attrs])

Pointing table with ska_sdp_datamodels:
class ska_sdp_datamodels.calibration.calibration_model.GainTableAccessor(xarray_obj)[source]

GainTable property accessor

class ska_sdp_datamodels.calibration.calibration_model.PointingTableAccessor(xarray_obj)[source]

PointingTable property accessor

ska_sdp_datamodels.sky_model Package

Functions

import_skycomponent_from_hdf5(filename)

Import SkyComponent(s) from HDF5 format

import_skymodel_from_hdf5(filename)

Import a Skymodel or list from HDF5 format

export_skycomponent_to_hdf5(sc, filename)

Export a SkyComponent or list to HDF5 format

export_skymodel_to_hdf5(sm, filename)

Export a Skymodel or list to HDF5 format

export_skymodel_to_text(skymodel, filename)

Save a SkyModel to disk in a .skymodel format, with the given filename.

convert_skycomponent_to_hdf(sc, f)

Convert SkyComponent to HDF

convert_hdf_to_skycomponent(f)

Convert HDF root to a SkyComponent

convert_skymodel_to_hdf(sm, f)

Convert a SkyModel to an HDF file

convert_hdf_to_skymodel(f)

Convert HDF to SkyModel

Classes

SkyModel([image, components, gaintable, ...])

A model for the sky, including an image, components, gaintable and a mask

SkyComponent([direction, frequency, name, ...])

SkyComponents are used to represent compact sources on the sky.

ska_sdp_datamodels.gridded_visibility Package

Functions

import_griddata_from_hdf5(filename)

Import GridData(s) from HDF5 format

import_convolutionfunction_from_hdf5(filename)

Import ConvolutionFunction(s) from HDF5 format

export_griddata_to_hdf5(gd, filename)

Export a GridData or list to HDF5 format

export_convolutionfunction_to_hdf5(cf, filename)

Export a ConvolutionFunction to HDF5 format

convert_griddata_to_hdf(gd, f)

Convert a GridData to an HDF file

convert_hdf_to_griddata(f)

Convert HDF root to a GridData

convert_convolutionfunction_to_hdf(cf, f)

Convert a ConvolutionFunction to an HDF file

convert_hdf_to_convolutionfunction(f)

Convert HDF root to a ConvolutionFunction

create_griddata_from_image(im[, ...])

Create a GridData from an image

create_convolutionfunction_from_image(im[, ...])

Create a convolution function from an image

Classes

GridData([data_vars, coords, attrs])

Class to hold Gridded data for Fourier processing

ConvolutionFunction([data_vars, coords, attrs])

Class to hold Convolution function for Fourier processing - Has four or more coordinates: [chan, pol, z, y, x] where x can be u, l; y can be v, m; z can be w, n.
class ska_sdp_datamodels.gridded_visibility.grid_vis_model.GridDataAccessor(xarray_obj)[source]

GridDataAccessor property accessor

class ska_sdp_datamodels.gridded_visibility.grid_vis_model.ConvolutionFunctionAccessor(xarray_obj)[source]

ConvolutionFunction property accessor

ska_sdp_datamodels.configuration Package

Functions

convert_configuration_to_hdf(config, f)

Convert a Configuration to an HDF file

convert_configuration_from_hdf(f)

Extract configuration from HDF

create_named_configuration([name])

Create standard configurations e.g. LOWBD2, MIDBD2.

create_configuration_from_file(antfile[, ...])

Define configuration from a text file

select_configuration(config[, names])

Select a subset of antennas based on their "names"

decimate_configuration(config[, start, ...])

Decimate a configuration

convert_configuration_to_json(config)

Convert configuration to JSON :param config: Configuration :return: JSON

convert_json_to_configuration(json_str)

Convert configuration from json string to Configuration :param json_str: json string :return: Configuration

Classes

Configuration([data_vars, coords, attrs])

A Configuration describes an array configuration
class ska_sdp_datamodels.configuration.config_model.ConfigurationAccessor(xarray_obj)[source]

Convenience methods to access the fields of the Configuration

ska_sdp_datamodels.science_data_model Package

Functions

polarisation_frame_from_names(names)

Derive polarisation_frame from names

pol_matrix_multiply(cm, vec[, polaxis])

Matrix multiply of appropriate axis of vec [...,:] by cm

congruent_polarisation(rec_frame, ...)

Are these receptor and polarisation frames congruent?

correlate_polarisation(rec_frame)

Gives the polarisation frame corresponding to a receptor frame

convert_pol_frame(polvec, ipf, opf[, polaxis])

Convert between polarisation frames.

convert_linear_to_stokes(linear[, polaxis])

Convert Linear to Stokes IQUV (complex image)

convert_stokes_to_linear(stokes[, polaxis])

Convert Stokes IQUV to Linear (complex image)

convert_circular_to_stokes(circular[, polaxis])

Convert Circular to Stokes IQUV (complex image)

convert_stokes_to_circular(stokes[, polaxis])

Convert Stokes IQUV to Circular (complex image)

convert_stokesIQUV_to_stokesI(flux_iquv)

Convert fluxes of Stokes IQUV polarisation to Stokes I

convert_stokesI_to_stokesIQUV(flux_i)

Convert fluxes of Stokes I polarisation to Stokes IQUV.

convert_linear_to_stokesI(linear)

Convert Linear to Stokes I

convert_circular_to_stokesI(circular)

Convert Circular to Stokes I

Classes

ReceptorFrame(name)

Polarisation frames for receptors

PolarisationFrame(name)

Polarisation Frame data class

QualityAssessment([origin, data, context])

Quality assessment