GridDataSeries#

class pyvisgrid.core.gridder.GridDataSeries(grid_data: GridData, u_wave: ndarray, v_wave: ndarray, times: ndarray, num_frequencies: int, step_size: int, time_start_idx: int = 0, time_end_idx: int | None = None)[source]#

Bases: object

DataClass to save the gridded and non-gridded visibilities for a specific Stokes component for different time steps. This enables to create a time series of different steps in an observation.

Attributes:

grid_dataGridData: The GridData of the full observation.
timesnumpy.ndarray: The time steps of the observation.
u_wavenp.ndarray: The \(u\) coordinates as a multiple of the wavelength.
u_wavenp.ndarray: The \(v\) coordinates as a multiple of the wavelength.
step_sizeint: The size of each timestep (how many steps in the observation are in each iteration of the series).
time_cutoff_idxint | None, optional: The index to stop the series at. Default is None.

Methods Summary

`add_grid_data`(grid_data)	Adds the given (un)gridded visibility data to the series.
`get_uv_step`(step)	Returns the ungridded \((u,v)\) coordinates for the given timestep.
`get_vis_step`(step)	Returns the visibility data for the given timestep.

Methods Documentation

add_grid_data(grid_data: GridData) → None[source]#

Adds the given (un)gridded visibility data to the series.

Parameters:

GridData: The data to add to the series.

get_uv_step(step: int) → tuple[ndarray, ndarray][source]#

Returns the ungridded \((u,v)\) coordinates for the given timestep.

Parameters:

stepint: The step index.

Returns:

tuple[np.ndarray, np.ndarray]:: The \((u,v)\) coordinates in the order (u, v).

get_vis_step(step: int) → GridData[source]#

Returns the visibility data for the given timestep.

Parameters:

stepint: The step index.

Returns:

GridData:: The (un)gridded visibility data.