aidas.vis.visualize_velocity_waves#

aidas.vis.visualize_velocity_waves(radar_scan: RadarImage, bg_field='velocity', axes=None, overlay=False, **kwargs)[source]#

This module will visualize the radial velocity wave mask alongside the velocity field it was derived from.

Two panels are drawn. The left one is the radial velocity of the sweep the detection used, after the quality control and the dealiasing: that is the field the mask was computed from, not the raw folded velocity in the original volume, so what is plotted and what was detected agree. The right one is the binary mask on its own, which is easier to read for band spacing and orientation than a contour drawn over a colourful velocity field.

The map is framed on the wave grid rather than on a fixed domain, because the grid reaches as far as the sweep has valid velocity.

Keyword arguments are passed into pyart.graph.RadarMapDisplay.plot_ppi_map(). See the pyart.graph.RadarMapDisplay.plot_ppi_map() documentation for more details on how to control the appearance of the velocity panel.

Parameters:

  • radar_scan (RadarImage()) – This contains the RadarImage() object with the velocity wave mask already inside, as made by aidas.model.detect_velocity_waves().

  • bg_field (str) – The variable name of the field to draw in the left panel. When it is a velocity field, the colour scale defaults to a symmetric range wide enough for the dealiased values rather than to the Nyquist velocity.

  • axes (2-sequence of cartopy.mpl.geoaxes.GeoAxes(), optional) – The axes to draw the velocity and the mask into. Both must be on a cartopy.crs.PlateCarree() projection. If None, a new figure is made.

  • overlay (bool) – Set to True to also outline the mask on the velocity panel, which is useful for checking that the detections sit on the bands they came from.

Returns:

fig, axes – The matplotlib figure and the two axis handles, velocity first.

Return type:

handles