pyart.map.map_gates_to_grid¶

pyart.map.map_gates_to_grid(radars, grid_shape, grid_limits, grid_origin=None, grid_origin_alt=None, grid_projection=None, fields=None, gatefilters=False, map_roi=True, weighting_function='Barnes2', toa=17000.0, roi_func='dist_beam', constant_roi=None, z_factor=0.05, xy_factor=0.02, min_radius=500.0, h_factor=1.0, nb=1.5, bsp=1.0, **kwargs)[source]¶

Map gates from one or more radars to a Cartesian grid.

Generate a Cartesian grid of points for the requested fields from the collected points from one or more radars. For each radar gate that is not filtered a radius of influence is calculated. The weighted field values for that gate are added to all grid points within that radius. This routine scaled linearly with the number of radar gates and the effective grid size.

Parameters not defined below are identical to those in map_to_grid().

Parameters

roi_func (str or RoIFunction) – Radius of influence function. A function which takes an z, y, x grid location, in meters, and returns a radius (in meters) within which all collected points will be included in the weighting for that grid points. Examples can be found in the Typically following strings can use to specify a built in radius of influence function:

constant: constant radius of influence.

dist: radius grows with the distance from each radar.

dist_beam: radius grows with the distance from each radar and parameter are based of virtual beam sizes.

A custom RoIFunction can be defined using the RoIFunction class and defining a get_roi method which returns the radius. For efficient mapping this class should be implemented in Cython.

Returns

grids (dict) – Dictionary of mapped fields. The keys of the dictionary are given by parameter fields. Each elements is a grid_size float64 array containing the interpolated grid for that field.