solve_calibrate_chain

ska_sdp_func_python.calibration.chain_calibration.solve_calibrate_chain(vis, model_vis, gaintables=None, calibration_context='T', controls=None, iteration=0, tol=1e-06)[source]

Solve GainTables by fitting an observed visibility to a model visibility.

The context string can denote a sequence of calibrations e.g. TGB. Currently, we do not support inputting different timescales.

Parameters:

  • vis – Visibility containing the observed data_models

  • model_vis – Visibility containing the visibility predicted by a model

  • gaintables – Existing GainTables (GainTable, list or dict)

  • calibration_context – calibration contexts in order of correction e.g. ‘TGB’

  • controls – controls dictionary, modified as necessary

  • iteration – Iteration number to be compared to the ‘first_selfcal’ field.

  • tol – Iteration stops when the fractional change in the gain solution is below this tolerance

Returns:

dict(GainTables)