ngcasa¶

ngCASA Package

Module functions with blank descriptions have not been completed yet. As each function is completed, its description will be populated.

calibration¶

Calibration subpackage modules

`apply_calibration`
`self_cal`

deconvolution¶

Deconvolution subpackage modules

`deconvolve_adaptive_scale_pixel`
`deconvolve_fast_resolve`
`deconvolve_multiterm_clean`
`deconvolve_point_clean`
`deconvolve_rotation_measure_clean`
`feather`
`is_converged`
`linear_mosaic`
`make_mask`
`restore_model`

flagging¶

Flagging subpackage modules

These functions can be used to calculate flags and handle different flag versions. There are functions to inspect flag variables and manage the set of flag variables in a CNGI Dataset. Other functions calculate flags using different flagging methods.

`auto_clip`	Apply the clip flagging method. Data with values lower than clip_min
`auto_rflag`
`auto_tfcrop`
`auto_uvbin`
`elevation`
`extend`
`manager_add`	Add a new flag variable to the dataset. All flags in the new variable take
`manager_list`	Add a new flag variable to the dataset. All flags in the new variable are
`manager_remove`	Remove flag variable from the dataset.
`manual_flag`	Implements the ‘manual’ flagging method (equivalent to CASA6’s flagdata manual
`manual_unflag`	Unflags the selected data. Flags corresponding to the selections are unset.
`quack`
`shadow`

imaging¶

Imaging subpackage modules

`calc_image_cell_size`	Calculates the image and and cell size needed for imaging a vis_dataset.
`make_grid`	param vis_mxds Input multi-xarray Dataset with global data.
`make_gridding_convolution_function`	Currently creates a gcf to correct for the primary beams of antennas and supports heterogenous arrays (antennas with different dish sizes).
`make_image`	Creates a cube or continuum dirty image from the user specified visibility, uvw and imaging weight data. Only the prolate spheroidal convolutional gridding function is supported. See make_image_with_gcf function for creating an image with A-projection.
`make_image_with_gcf`	Creates a cube or continuum dirty image from the user specified visibility, uvw and imaging weight data. A gridding convolution function (gcf_dataset), primary beam image (img_dataset) and a primary beam weight image (img_dataset) must be supplied.
`make_imaging_weight`	Creates the imaging weight data variable that has dimensions time x baseline x chan x pol (matches the visibility data variable).
`make_mosaic_pb`	The make_pb function currently supports rotationally symmetric airy disk primary beams. Primary beams can be generated for any number of dishes.
`make_pb`	The make_pb function currently supports rotationally symmetric airy disk primary beams. Primary beams can be generated for any number of dishes.
`make_psf`	Creates a cube or continuum point spread function (psf) image from the user specified uvw and imaging weight data. Only the prolate spheroidal convolutional gridding function is supported (this will change in a future releases.)
`make_psf_with_gcf`	Creates a cube or continuum dirty image from the user specified visibility, uvw and imaging weight data. A gridding convolution function (gcf_dataset), primary beam image (img_dataset) and a primary beam weight image (img_dataset) must be supplied.
`make_sd_image`
`make_sd_psf`
`make_sd_weight_image`
`predict_modelvis_component`
`predict_modelvis_image`