utils

Helper functions for constructing the pipeline

ska_sdp_distributed_self_cal_prototype.workflow.utils.calculate_observation_info(pipeline_config: PipelineConfig, processing_set_manager: ProcessingSetManager, swiftly_manager: Swiftly, gridding_manager: Gridder) → PipelineConfig[source]

Calculates observation metadata.

Parameters:

  • pipeline_config – Configuration for the pipeline.

  • processing_set_manager – Manager for visibility data.

  • swiftly_manager – Swiftly manager for the pipeline.

  • gridding_manager – Gridding manager for the pipeline.

Returns:

Updated pipeline_config.

ska_sdp_distributed_self_cal_prototype.workflow.utils.save_image(image_info: ImageInfo, image_data: numpy.ndarray, output_dir: Path, filename: str = 'dirty_image', save_fits: bool = True, save_png: bool = True) → None[source]

Save 2D array of image data as FITS and/or PNG images.

Parameters:

  • image_info – Information on phase centre, pixel size and frequency required to construct the WCS.

  • image_data – Data to save as FITS/PNG.

  • output_dir – The directory where the image will be saved.

  • filename – The filename the image will be saved into.

  • save_fits – Controls whether a FITS file is output.

  • save_png – Controls whether a PNG file is output.

Returns:

None

Notes

  • image_info is only required if saving to FITS

  • Image is rotated by 90 degrees for correct display

  • PNG image is displayed with RA/DEC axis and colour bar

ska_sdp_distributed_self_cal_prototype.workflow.utils.split_square_array(array: numpy.ndarray, n_subarray: int) → list[numpy.ndarray][source]

Split a 2D square array into n_subarray square subarrays, where n_subarray is a square number.

Parameters:

  • array – The input 2D square array to split.

  • n_subarray – The number of square subarrays (must be a square number).

Returns:

A list containing the square subarrays.

Return type:

subarrays