Calibration

Calibration is performed by fitting observed visibilities to a model visibility.

The scalar equation to be minimised is:

\[S = \sum_{t,f}^{}{\sum_{i,j}^{}{w_{t,f,i,j}\left| V_{t,f,i,j}^{\text{obs}} - J_{i}{J_{j}^{*}V}_{t,f,i,j}^{\text{mod}} \right|}^{2}}\]

The least squares fit algorithm uses an iterative substitution (or relaxation) algorithm from Larry D’Addario in the late seventies.

rascil.processing_components.calibration.iterators Module

GainTable iterators for iterating through a GainTable

Functions

gaintable_timeslice_iter(gt, **kwargs)

GainTable iterator

rascil.processing_components.calibration.operations Module

Functions for calibration, including creation of gaintables, application of gaintables, and merging gaintables.

Functions

append_gaintable(gt, othergt)	Append othergt to gt
create_gaintable_from_rows(gt, rows[, makecopy])	Create a GainTable from selected rows
gaintable_plot(gt[, cc, title, ants, ...])	Standard plot of gain table