ska_sdp_e2e_batch_continuum_imaging.pipelines.models.sdp.continuum_imaging_params module

Following models are copied from continuum_imaging_params.py from ska-sdp-script/ska-sdp-script-continuum-imaging package. If these model change in "continuum-imaging" processing script, then we must copy them here to ensure correct validations.

To avoid dependency on ska_sdp_scripting, ParameterBaseModel is taken from .parameter_base_model package

pydantic model ska_sdp_e2e_batch_continuum_imaging.pipelines.models.sdp.continuum_imaging_params.PreFlaggerStep[source]

Bases: ParameterBaseModel

Configuration for pre_flagger step of BPP pipeline

Config:

  • strict: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = False

  • validate_assignment: bool = True

Fields:
field freqrange: str = '[154.250..159.810]'

Frequency range to flag, expressed as [start..end]. Units: MHz

pydantic model ska_sdp_e2e_batch_continuum_imaging.pipelines.models.sdp.continuum_imaging_params.AveragerStep[source]

Bases: ParameterBaseModel

Configuration for averager step of BPP pipeline

Config:

  • strict: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = False

  • validate_assignment: bool = True

Fields:
field freqstep: int = 4

Number of frequency channels to average together.

field timestep: int = 4

Number of time samples to average together.

pydantic model ska_sdp_e2e_batch_continuum_imaging.pipelines.models.sdp.continuum_imaging_params.BandpassCalibration[source]

Bases: ParameterBaseModel

Configuration for bandpass calibration step of Instrumental calibration pipeline.

Config:

  • strict: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = False

  • validate_assignment: bool = True

Fields:
field niter: int = 50

Maximum number of iterations for the solver.

field solver: Literal['gain_substitution', 'jones_substitution', 'normal_equations', 'normal_equations_presum'] = 'jones_substitution'

Type of solver to use for the calibration.

field tol: float = 0.001

Tolerance value for solver convergence. Units: NA

pydantic model ska_sdp_e2e_batch_continuum_imaging.pipelines.models.sdp.continuum_imaging_params.ComplexGainCalibration[source]

Bases: ParameterBaseModel

Configuration for complex gain calibration of Target Instrumental calibration pipeline.

Config:

  • strict: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = False

  • validate_assignment: bool = True

Fields:
field niter: int = 500

Maximum number of iterations for the solver.

field tol: float = 1e-06

Tolerance value for solver convergence. Units: NA

pydantic model ska_sdp_e2e_batch_continuum_imaging.pipelines.models.sdp.continuum_imaging_params.IonosphericCalibration[source]

Bases: ComplexGainCalibration

Configuration for the Target Ionospheric calibration pipeline

Config:

  • strict: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = False

  • validate_assignment: bool = True

Fields:
field zernike_limit: list[int] | None = None

list of Zernike index limits: Generate all Zernikes with n + |m| <= zernike_limit[cluster_id]. If None, a default is used by the solver.

pydantic model ska_sdp_e2e_batch_continuum_imaging.pipelines.models.sdp.continuum_imaging_params.SelfCalibration[source]

Bases: ParameterBaseModel

Configuration for the self-calibration step performed using ICAL pipeline.

Config:

  • strict: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = False

  • validate_assignment: bool = True

Fields:
field max_selfcal_loops: int = 8

Maximum number of self-calibration loops to perform on the target source(s).

field min_selfcal_loops: int = 4

Minimum number of self-calibration loops to perform on the target source(s).

pydantic model ska_sdp_e2e_batch_continuum_imaging.pipelines.models.sdp.continuum_imaging_params.InstrumentalCalibration[source]

Bases: ParameterBaseModel

Configuration for Instrumental Calibration stage of the batch e2e pipeline

Config:

  • strict: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = False

  • validate_assignment: bool = True

Fields:
field averager: AveragerStep [Optional]

Averager parameters. It is used to average the visibility data.

field bandpass_calibration: BandpassCalibration [Optional]

Bandpass calibration configuration parameters

field preflagger: PreFlaggerStep [Optional]

It is used to pre-flag the visibility data.

pydantic model ska_sdp_e2e_batch_continuum_imaging.pipelines.models.sdp.continuum_imaging_params.TargetCalibration[source]

Bases: ParameterBaseModel

Configuration for Target Calibration stage of the batch e2e pipeline

Config:

  • strict: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = False

  • validate_assignment: bool = True

Fields:
field averager: AveragerStep [Optional]

Averager parameters. It is used to average the visibility data.

field complex_gain_calibration: ComplexGainCalibration [Optional]

Configuration for the Instrumental Target calibration pipeline.

field ionospheric_calibration: IonosphericCalibration [Optional]

Configuration for the Target Ionospheric calibration pipeline

field preflagger: PreFlaggerStep [Optional]

It is used to pre-flag the visibility data.

field self_calibration: SelfCalibration [Optional]

Configuration for the self-calibration pipeline.

pydantic model ska_sdp_e2e_batch_continuum_imaging.pipelines.models.sdp.continuum_imaging_params.ContinuumImaging[source]

Bases: ParameterBaseModel

Configuration for Continuum Imaging stage of the e2e pipeline.

Config:

  • strict: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = False

  • validate_assignment: bool = True

Fields:
field briggs_robust_weighting: float = -0.5

Robustness value for briggs weighting

field fit_spectral_pol: int = 3

Order of spectral polynomial to fit across channels.

field gaussian_taper: float = 0.0

The beamsize in arcseconds, for Gaussian Tapering.

field image_size: tuple[int, int] = (4000, 4000)

Image size in pixels. The sensible value for this parameter will depend on the specific data (max baseline and field of view).

Constraints:

  • strict = False

field output_channels: int = 12

Specifies the number of different frequencies for which information will be present in the output. For each output channel, a separate FITS image will be created. Note that increasing this value will increase computations needed to generate the output. The processing script might reduce the computational intensity by also setting --deconvolution-channels to an appropriate value.

field pixel_scale: float = 0.0005555555555555556

Pixel scale in degrees. Defines the image resolution.

field polarizations: str = 'stokes_i'

Output polarizations to image. This maps to the 'data_product.polarizations' parameter of the CIMG pipeline.

pydantic model ska_sdp_e2e_batch_continuum_imaging.pipelines.models.sdp.continuum_imaging_params.ContinuumImagingParams[source]

Bases: ParameterBaseModel

Batch end-to-end continuum imaging processing script parameters

Config:

  • strict: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = False

  • validate_assignment: bool = True

Fields:
field continuum_imaging: ContinuumImaging [Optional]

Configuration for Continuum Imaging stage of the batch e2e pipeline

field instrumental_calibration: InstrumentalCalibration [Optional]

Configuration for Instrumental Calibration stage of the batch e2e pipeline

field target_calibration: TargetCalibration [Optional]

Configuration for Target Calibration stage of the batch e2e pipeline