ska_sdp_wflow_pointing_offset.pipeline_base module

Base class for processing pipelines.

class ska_sdp_wflow_pointing_offset.pipeline_base.PipelineBase(*, apply_mask: bool = False, beamwidth_factor: tuple[float, float] = (0.976, 1.098), start_freq: Annotated[float | None, Gt(gt=0)] = None, end_freq: Annotated[float | None, Gt(gt=0)] = None, fit_to_sep_pol: bool = False, fit_to_vis: bool = False, num_chunks: Annotated[int, Gt(gt=0)] = 16, rfi_file: str | None = None, thresh_width: Annotated[float, Gt(gt=0)] = 1.15, use_modelvis: bool = False, use_source_offset_column: bool = False)

Bases: BaseModel, ABC

Abstract base class for processing pipelines.

Implements validated configuration parameters common to all pipelines.

apply_mask: bool

beamwidth_factor: tuple[float, float]

check_fit_to_vis_and_fit_to_sep_pol_mutual_exclusion()

Self-explanatory.

abstract property common_prefix: str

Common filepath prefix for saving data.

end_freq: float | None

abstract export_data(output_offsets: dict[str, ndarray[Any, dtype[_ScalarType_co]]], pointing_table: PointingTable, visibility: Visibility) → None

Export pipeline results, either to disk or to Kafka.

fit_to_sep_pol: bool

fit_to_vis: bool

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

abstract property ms_files: list[str]

Measurement set filepaths used as input.

num_chunks: int

classmethod num_chunks_must_not_be_two(v: int) → int

Self-explanatory.

rfi_file: str | None

start_freq: float | None

thresh_width: float

use_modelvis: bool

use_source_offset_column: bool

validate_frequency_range()

Self-explanatory.