Degrid UVW custom

C/C++

void sdp_degrid_uvw_custom(const sdp_Mem *grid, const sdp_Mem *uvw, const sdp_Mem *uv_kernel, const sdp_Mem *w_kernel, const double theta, const double wstep, const double channel_start_hz, const double channel_step_hz, const int32_t conjugate, sdp_Mem *vis, sdp_Error *status)

Degrid visibilities.

Degrids previously gridded visibilities using supplied convolution kernels.

Parameters:

  • grid – Input grid data with shape [chan][w][v][u][pol]

  • uvw – Visibility (u,v,w) coordinates with shape [time][baseline][3]

  • uv_kernel – (u,v)-plane kernel with shape [oversampling][stride]

  • w_kernel – w-plane kernel with shape [oversampling][stride]

  • theta – Conversion parameter from (u,v)-coordinates to (x,y)-coordinates x=u*theta

  • wstep – Conversion parameter from w-coordinates to z-coordinates z=w*wstep

  • channel_start_hz – Frequency of first channel, in Hz.

  • channel_step_hz – Frequency increment between channels, in Hz.

  • conjugate – Whether to generate conjugated visibilities

  • vis – Output Visibilities with shape [time][baseline][chan][pol]

  • status – Error status.

Python

ska_sdp_func.grid_data.degrid_uvw_custom(grid, uvw, uv_kernel, w_kernel, theta, wstep, channel_start_hz, channel_step_hz, conjugate, vis)

Degrid visibilities.

Degrids previously gridded visibilities using supplied convolution kernels.

Parameters:

  • grid (numpy.ndarray or cupy.ndarray) – Input grid data with shape [chan][w][v][u][pol]

  • uvw (numpy.ndarray or cupy.ndarray) – Visibility (u,v,w) coordinates with shape [time][baseline][3]

  • uv_kernel (numpy.ndarray or cupy.ndarray) – (u,v)-plane kernel with shape [oversampling][stride]

  • w_kernel (numpy.ndarray or cupy.ndarray) – w-plane kernel with shape [oversampling][stride]

  • theta (float) – Conversion parameter from (u,v)-coordinates to (x,y)-coordinates (i.e. x=u*theta)

  • wstep (float) – Conversion parameter from w-coordinates to z-coordinates (i.e. z=w*wstep)

  • channel_start_hz (float) – Frequency of first channel, in Hz.

  • channel_step_hz (float) – Frequency increment between channels, in Hz.

  • conjugate (bool) – Whether to generate conjugated visibilities

  • vis (numpy.ndarray or cupy.ndarray) – Output visibilities with shape [time][baseline][chan][pol]