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.. toctree::
   :maxdepth: 2

ARL directory

A simple directory to help those familiar with other calibration and imaging packages navigate the Algorithm Reference Library.

See also the :ref:`genindex` and :ref:`modindex`.

Data containers used by ARL

See :py:mod:`arl.data.data_models` for the following definitions:`

  • Telescope Configuration: :py:class:`arl.data.data_models.Configuration`
  • GainTable for gain solutions (as (e.g. output from solve_gaintable): :py:class:`arl.data.data_models.GainTable`
  • Image (data and WCS header): :py:class:`arl.data.data_models.Image`
  • Skycomponent ((data for a point source or a Gaussian source): :py:class:`arl.data.data_models.Skycomponent`
  • Baseline based visibility tables shape (npol,), length nvis) :py:class:`arl.data.data_models.Visibility`
  • Antenna-based visibility table, shape (nants, nants, nchan, npol), length ntimes): :py:class:`arl.data.data_models.BlockVisibility`

Create empty data set for observation (a-la makeMS)

  • For Visibility operations: :py:mod:`arl.visibility.base.create_visibility`
  • For BlockVisibility: :py:mod:`arl.visibility.base.create_blockvisibility`

Read existing Measurement Set

  • Creates a list of Visibilities, one per FIELD_ID: :py:mod:`arl.visibility.base.create_visibility_from_ms`

Gridding, degridding, and weighting

  • Convolutional gridding: :py:mod:`arl.fourier_transforms.convolutional_gridding.convolutional_grid`
  • Convolutional degridding: :py:mod:`arl.fourier_transforms.convolutional_gridding.convolutional_degrid`
  • Generate gridding kernels (standard prolate spheroidal or W-projection): :py:mod:`arl.imaging.params.get_kernel_list`

Visibility weighting and tapering

  • Weighting: :py:mod:`arl.fourier_transforms.convolutional_gridding.weight_gridding`
  • Gaussian tapering: :py:mod:`arl.imaging.weighting.taper_visibility_gaussian`
  • Tukey tapering: :py:mod:`arl.imaging.weighting.taper_visibility_tukey`

Visibility Predict

  • Predict BlockVisibility or Visibility for Skycomponent :py:mod:`arl.imaging.base.predict_skycomponent_visibility`
  • Predict by de-gridding visibilities :py:mod:`arl.imaging.imaging_context.predict_function`

Visibility Invert

  • Invert by gridding visibilities :py:mod:`arl.imaging.imaging_context.invert_function`

Deconvolution

  • Deconvolution: :py:mod:`arl.image.deconvolution.deconvolve_cube` wraps:
    • Hogbom Clean: :py:mod:`arl.image.cleaners.hogbom`
    • Multi-scale Clean: :py:mod:`arl.image.cleaners.msclean`
    • Multi-scale multi-frequency Clean: :py:mod:`arl.image.cleaners.msmfsclean`
  • Restore: :py:mod:`arl.image.deconvolution.restore_cube`

Calibration

  • Create empty gain table: :py:mod:`arl.calibration.operations.create_gaintable_from_blockvisibility`
  • Solve for complex gains: :py:mod:`arl.calibration.solvers.solve_gaintable`
  • Apply complex gains: :py:mod:`arl.calibration.operations.apply_gaintable`
  • Peel compact sources: :py:mod:`arl.calibration.peeling.peel_skycomponent_blockvisibility`

Coordinate transforms

  • Station/baseline (XYZ <-> UVW): :py:mod:`arl.util.coordinate_support`
  • Source (spherical -> tangent plane): :py:mod:`arl.util.coordinate_support`
  • Phase rotation: :py:mod:`arl.visibility.base.phaserotate_visibility`
  • Polarisation basis conversion (Linear <-> Stokes <-> Circular): :py:mod:`arl.data.polarisation`

Image

  • Import from FITS: :py:mod:`arl.image.operations.import_image_from_fits`
  • Export from FITS: :py:mod:`arl.image.operations.export_image_to_fits`
  • Reproject: :py:mod:`arl.image.operations.reproject_image`
  • Smooth image: :py:mod:`arl.image.operations.smooth_image`
  • FFT: :py:mod:`arl.image.operations.fft_image`
  • Remove continuum: :py:mod:`arl.image.operations.remove_image`
  • Convert polarisation:
    • From Stokes To Polarisation: :py:mod:`arl.image.operations.convert_stokes_to_polimage`
    • From Polarisation to Stokes: :py:mod:`arl.image.operations.convert_polimage_to_stokes`

Visibility

  • Coalesce/decoalesce (i.e. BDA) :py:mod:`arl.visibility.coalesce`
  • Append/sum/divide/QA: :py:mod:`arl.visibility.operations`
  • Remove continuum: :py:mod:`arl.visibility.operations.remove_continuum_blockvisibility`
  • Integrate across channels: :py:mod:`arl.visibility.operations.integrate_visibility_by_channel`

Graphs:

  • Perform various types of prediction and inversion of visibility data: :py:mod:`arl.graphs.delayed`
  • Perform generic image or visibility unary operations: :py:mod:`arl.graphs.generic_graphs`
  • Support testing and simulations: :py:mod:`arl.util.delayed_support`
  • The canonical pipelines: py:mod:arl.pipelines.delayed