Imaging
ska_sdp_func_python.imaging.base Module
Base Imaging functions.
Functions
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Advise on parameters for wide field imaging. |
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Make an empty Image from params and Visibility. |
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Fill the visibility for calculation of PSF. |
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Invert using convolutional degridding and an AW kernel. |
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Normalise out the sum of weights. |
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Predict using convolutional degridding and an AW kernel. |
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Shift visibility in place to the phase centre of the Image. |
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Compensate for kernel re-centering - see w_kernel_function. |
ska_sdp_func_python.imaging.dft Module
Functions that aid Fourier transform processing.
Functions
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CPU computational kernel for DFT, using explicit loop over components. |
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CPU computational kernel for DFT, using CUDA raw code via cupy. |
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CPU computational kernel for DFT, choice dependent on dft_compute_kernel. |
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DFT to get the visibility from a SkyComponent, for Visibility. |
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Extract SkyComponent direction and flux to be consumed by DFT. |
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Inverse DFT a SkyComponent from Visibility. |
ska_sdp_func_python.imaging.imaging Module
Functions for predicting visibility from a model image, and invert a visibility to make an (Image, sumweights) tuple. These redirect to specific versions.
Functions
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Invert Visibility to make an (Image, sum weights) tuple. |
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Predict Visibility from an Image. |
ska_sdp_func_python.imaging.imaging_helpers Module
Functions to aid operations on imaging results.
Functions
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Remove sumwt term in list of (image, sumwt) tuples. |
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Sum a set of invert results with appropriate weighting. |
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Sum a set of predict results of the same shape. |
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Find actual threshold for list of results, optionally using moment 0. |
ska_sdp_func_python.imaging.ng Module
Functions that implement prediction of and imaging from visibilities using the nifty gridder (DUCC version).
https://gitlab.mpcdf.mpg.de/mtr/ducc.git
This performs all necessary w term corrections, to high precision.
Note that nifty gridder doesn’t like some null data such as all w = 0 and do_wstacking=True. Also true of the visibilities.
Functions
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Invert using nifty-gridder module. |
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Predict using convolutional degridding. |
ska_sdp_func_python.imaging.weighting Module
Functions that aid weighting the visibility data prior to imaging.
- There are two classes of functions:
Changing the weight dependent on noise level or sample density or a combination
Tapering the weight spatially to avoid effects of sharp edges or to emphasize a given scale size in the image
Functions
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Taper the visibility weights. |
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Taper the visibility weights. |
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Weight the visibility data. |
ska_sdp_func_python.imaging.wg Module
Functions that implement prediction of and imaging from visibilities using the GPU-based w-stacking gridder from ska-sdp-func (which is compatible with the DUCC/nifty gridder).
Functions
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Invert using GPU-based w-stacking gridder module. |
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Predict using GPU-based w-stacking degridder module. |