Examples

This page provides some example configuration for common calibration tasks. To use one of the following configurations, simply copy the text into an empty file and save as a .yaml.

Note

The configuration provided in this section will work, but may not include every available option. For a full list, please see Options.

Phase-only selfcal

The following performs diagonal, phase-only selfcal using a measurement set column and produces corrected residuals and corrected weights.

Note

QuartiCal expects users to ask for all required outputs explicitly. This is to ensure that users understand precisely what is ultimately being done to their data.

input_ms:
    path: path/to.ms
    data_column: DATA
    sigma_column: SIGMA_SPECTRUM
    time_chunk: '300s'
    freq_chunk: '0'
input_model:
    recipe: MODEL_DATA
solver:
    terms:
        - P
    iter_recipe:
        - 25
output:
    products:
        - corrected_residual
        - corrected_weight
    columns:
        - CORRECTED_DATA
        - WEIGHT_SPECTRUM
dask:
    threads: 6
P:
    type: phase
    time_interval: '60s'
    freq_interval: '128'

Complex (amplitude and phase) selfcal

The following performs diagonal, phase and amplitude selfcal using a Tigger .lsm sky model and produces corrected data and corrected weights.

input_ms:
    path: path/to.ms
    data_column: DATA
    sigma_column: SIGMA_SPECTRUM
    time_chunk: '300s'
    freq_chunk: '0'
input_model:
    recipe: skymodel.lsm.html
solver:
    terms:
        - G
    iter_recipe:
        - 25
output:
    products:
        - corrected_data
        - corrected_weight
    columns:
        - CORRECTED_DATA
        - WEIGHT_SPECTRUM
dask:
    threads: 6
G:
    type: diag_complex
    time_interval: '60s'
    freq_interval: '128'

Gain and bandpass selfcal

The following performs gain and bandpass calibration simultaneously, using a measurement set column as input and produces uncorrected residuals.

input_ms:
    path: path/to.ms
    data_column: DATA
    sigma_column: SIGMA_SPECTRUM
    time_chunk: '300s'
    freq_chunk: '0'
input_model:
    recipe: MODEL_DATA
solver:
    terms:
        - G
        - B
    iter_recipe:
        - 25
        - 25
        - 10
        - 10
output:
    products:
        - residual
    columns:
        - CORRECTED_DATA
dask:
    threads: 6
G:
    type: diag_complex
    time_interval: '1'
    freq_interval: '0'
B:
    type: diag_complex
    time_interval: '0'
    freq_interval: '1'

Direction-independent and direction-dependent complex selfcal

The following performs direction-independent and direction-dependent gain calibration simultaneously, using a tagged sky model as input and produces (direction-independent) corrected residuals.

Note

Direction-dependent model specification in QuartiCal (via input_model.recipe) is flexible, allowing the use of both sky models and measurement set columns in fairly complex configurations. Here are some examples:

• COL_NAME1:COL_NAME2 This will create a model with two directions, one for each of the supplied measurement set columns.

• skymodel.lsm.html~COL_NAME:COL_NAME This will create a model with two directions, one containing the visibilities associated with the sky model minus the contribution of the MS column and the other containing just the MS column.

• skymodel.lsm.html:COL_NAME1:COL_NAME2 This will create a model with three directions, one containing the visibilities associated with the sky model, the second containing the visibilities from the first MS column and the third containing the visibilities of the second MS column.

• COL_NAME1+COL_NAME2:skymodel.lsm.html@dE This will create a model with at least two directions. This first will contain the sum of the specified MS columns and the remaining will be generated from the dE tagged sources in the sky model.

The following example makes use of a tagged Tigger .lsm file to predict visibilities in several directions.

input_ms:
    path: path/to.ms
    data_column: DATA
    sigma_column: SIGMA_SPECTRUM
    time_chunk: '300s'
    freq_chunk: '0'
input_model:
    recipe: skymodel.lsm.html@dE
solver:
    terms:
        - G
        - dE
    iter_recipe:
        - 25
        - 25
        - 10
        - 10
output:
    products:
        - corrected_residual
    columns:
        - CORRECTED_DATA
dask:
    threads: 6
G:
    type: diag_complex
    time_interval: '10'
    freq_interval: '10'
dE:
    type: complex
    time_interval: '100'
    freq_interval: '100'
    direction_dependent: true