MCCS Calibration Quality Assurance Plots

This package is used to generate calibration solutions for the calibration database. Metrics and plots that probe data quality and observing conditions can help to indicate whether or not a given set of solutions is suitable. Functions to generate summary plots are discussed below. An overview of simple summary metrics is given on the QA metrics page.

The functions discussed on this page are intended to be called alongside calibrate_mccs_visibility(), using many of the same input or output data products. These functions return matplotlib figure identifiers that can be displayed from the script or notebook, or output as an image file in a standard format.

For example

import matplotlib.pyplot as plt
from ska_low_mccs_calibration.qa import generate_closure_comparison_plots
fig = generate_closure_comparison_plots(
    vis, modelvis, masked_antennas, min_uv=10, ctype="uvmax"
)
fig.savefig("closure_comparison.png")
plt.show

One or more of these functions can be called automatically in calibrate_mccs_visibility() using optional dictionary qa_plots. Any plotting functions from qa() can be listed with a True or False value, and any set to true will be called with PNG output (named “function.png” without the generate_ prefix). Use print(ska_low_mccs_calibration.qa.__all__) for a list. For example:

qa_plots = {
    "generate_gaintable_plots": True,
    "generate_visibility_comparison_plots": False,
    "generate_closure_comparison_plots": True,
}

Gaintable plots

Function generate_gaintable_plots() displays the amplitude and phase of each antenna calibration solution, with separate panels for diagonal and off-diagonal Jones matrix elements. In the example below, calibrate_mccs_visibility() was called with jones_solve=False, so the off-diagonal terms are all zero.

Gaintable scatter plots

Function generate_gaintable_scatter_plots() displays the same amplitude and phase data as generate_gaintable_plots(), as a scatter plot at the antenna locations.

Visibility plots

Function generate_visibility_plots() displays the amplitude and phase of each visibility sample as a function of baseline length – in general these will be the calibrated visibilities returned by calibrate_mccs_visibility() – along with model and residual visibilities. A separate set of panels is shown for each polarisation. In the example below, calibrate_mccs_visibility() was called with the GSM and nside=16. This is a low resolution model that allows the solver to run faster, however model errors can be seen in the plots for the longer baselines.

Visibility comparison plots

Function generate_visibility_comparison_plots() displays the same amplitude and phase data as generate_visibility_plots(), as a scatter plot of visibility values versus model visibility values. Also shown are histograms of the residual phase values.

Closure comparison plots

Function generate_closure_comparison_plots() also displays scatter plots of visibility values versus model values, however the data plotted are the closure triple products. As in generate_visibility_comparison_plots(), histograms of the residual phase values are also shown.

In the example below, the scatter plots have been coloured by the maximum baseline length in each triplet (running from blue to red), and a short baseline cutoff of 10 metres has been used. The long-baseline errors that come from using nside=16 can be seen as a wider model phase spread in red.

If calibrate_mccs_visibility() is rerun with nside=32, the phases show better alignment. There is extra structure in the YY products, which may be due to the higher power levels seen in the visibility plots above.

On the other hand, if calibrate_mccs_visibility() is rerun with skymodel="Sun", the sky is represented by a point source and the closure phases for the model visibilties are close to zero. The small offsets from zero are due to the Embedded Element Patterns in the modelled visibilities.