SIMG
====

The Spectral Line Imaging Pipeline (SIMG) is a CLI-based application designed to process
visibilities from MSv4 Zarr files, and generate science-ready Spectral FITS cubes. The MSv4 schema
is defined in `xradio documentation
<https://xradio.readthedocs.io/en/latest/measurement_set/overview.html>`_. This pipeline relies on
``dask`` for distribution of work across multiple processes or multiple HPC nodes.

Inputs and Outputs
------------------

======== =========================================================================================
Inputs:  Pre-processed visibilities (MSv4), YAML configuration file, continuum model images (FITS)
Outputs: Restored spectral cubes (FITS), one per polarization.
======== =========================================================================================

High-Level Stages
-----------------

The pipeline executes the following sequence:

============================== =================================================================
Polarisation Frame Conversion: Converts input visibilities to the expected output polarisations.
Visibility Prediction:         Predict model visibilities from input model continuum images.
Continuum Subtraction:         Performs continuum subtraction in the visibility domain.
Imaging:                       Produces final spectral cubes through synthesis.
============================== =================================================================

.. mermaid::

    flowchart LR
      cimg(("Continuum Imaging <br> Pipeline <br> (CIMG)")) --> continuum-model-image[("Continuum <br> Model Image")] --> simg
      ical(("Self-Calibration <br> (ICAL)")) --> corrected-vis
      corrected-vis[("Calibrated <br> Visibilities")] --> simg
      subgraph simg ["Spectral Line Imaging Pipeline"]
        direction LR
        pol-frame-conversion["Polarisation Frame <br> Conversion"] --> predict["Visibility Prediction"]
        predict --> contsub["Continuum <br> Subtraction"]
        contsub --> imaging["Imaging"]
        imaging
      end

      simg --> spec-cube[("Restored <br> Spectral Cube")]

Key Dependencies
----------------

The pipeline integrates high-performance external libraries for core operations:

====================================================== ============================================
Library                                                Description
====================================================== ============================================
`ducc <https://mtr.pages.mpcdf.de/ducc/index.html>`_   Uses Wgridder for prediction and imaging.
`aoflagger <https://gitlab.com/aroffringa/aoflagger>`_ (Optional) Handles visibility flagging.
`radler <https://git.astron.nl/RD/radler>`_            (Optional) Manages deconvolution operations.
====================================================== ============================================

Useful Links
------------

- **Repository**:
  https://gitlab.com/ska-telescope/sdp/science-pipeline-workflows/ska-sdp-spectral-line-imaging
- **Documentation**: https://developer.skao.int/projects/ska-sdp-spectral-line-imaging/en/latest/
- **Quickstart**:
  https://developer.skao.int/projects/ska-sdp-spectral-line-imaging/en/latest/README.html
- **Status**: In development
- **Contact**: Team DHRUVA
- **Where to get help**: `#team-dhruva <https://skao.slack.com/archives/C071S0XF3RT>`_ ,
  `#help-sdp-batch-pipelines <https://skao.slack.com/archives/C0973F4MRRN>`_