Source code for

Utilities for finding gates with equivalent locations between radars for easy


from copy import deepcopy
import numpy as np

from scipy.spatial import KDTree

from ..core import Radar, geographic_to_cartesian

[docs]class GateMapper(object): """ The GateMapper class will, given one radar's gate, find the gate in another radar's volume that is closest in location to the specified gate. GateMapper will use a kd-tree in order to generate a mapping function that provides the sweep and ray index in the other radar that is closest in physical location to the first radar's gate. This functionality provides easy mapping of equivalent locations between radar objects with simple indexing. In addition, returning a mapped radar object is also supported. Examples -------- >>> grid_mapper =, dest) >>> # Get the destination radar's equivalent of (2, 2) in the source radar's >>> # coordinates >>> dest_index = grid_mapper[2, 2] >>> radar_mapped = grid_mapper.mapped_radar(['reflectivity']) Parameters ---------- src_radar : pyart.core.Radar The source radar data. dest_radar : pyart.core.Radar The destination radar to map the source radar data onto. tol : float The difference in meters between the source and destination gate allowed for an adequate match. """ def __init__(self, src_radar: Radar, dest_radar: Radar, tol=500.): gate_x = dest_radar.gate_x['data'] gate_y = dest_radar.gate_y['data'] gate_z = dest_radar.gate_altitude['data'] proj_dict = {'proj': 'pyart_aeqd', 'lon_0': dest_radar.longitude["data"], 'lat_0': dest_radar.latitude["data"]} self.src_radar_x, self.src_radar_y = geographic_to_cartesian( src_radar.longitude["data"], src_radar.latitude["data"], proj_dict) data = np.stack( [gate_x.flatten(), gate_y.flatten(), gate_z.flatten()], axis=1) self._kdtree = KDTree(data) self.tolerance = tol self.dest_radar = dest_radar self.src_radar = src_radar self._index_map = np.stack( [np.nan * np.ones_like( self.src_radar.gate_x["data"]), np.nan * np.ones_like(self.src_radar.gate_x["data"])], axis=2) new_points = np.stack( [self.src_radar.gate_x["data"].flatten() + self.src_radar_x, self.src_radar.gate_y["data"].flatten() + self.src_radar_y, self.src_radar.gate_altitude["data"].flatten()], axis=1) dists, inds = self._kdtree.query(new_points, distance_upper_bound=tol) inds = np.where( np.abs(dists) < self.tolerance, inds[:], -32767).astype(int) inds = np.reshape(inds, self.src_radar.gate_x["data"].shape) self._index_map[:, :, 0] = ( inds / self.dest_radar.gate_x["data"].shape[1]).astype(int) self._index_map[:, :, 1] = ( inds - self.dest_radar.gate_x["data"].shape[1] * (inds / self.dest_radar.gate_x["data"].shape[1]).astype(int)) def __getitem__(self, key: tuple): """ Return the equivalent index in the destination radar's coordinates. """ x = (int(self._index_map[key[0], key[1], 0]), int(self._index_map[key[0], key[1], 1])) if x[0] > 0: return x else: return None, None
[docs] def mapped_radar(self, field_list): """ This returns a version of the destination radar with the fields in field_list from the source radar mapped into the destination radar's coordinate system. Parameters ---------- field_list: list of str or str The list of fields to map. Returns ------- mapped_radar: The destination radar with the fields from the source radar mapped into the destination radar's coordinate system. """ mapped_radar = deepcopy(self.dest_radar) if isinstance(field_list, str): field_list = [field_list] for field in field_list: mapped_radar.fields[field]['data'] = True, mapped_radar.fields[field]['data']) for i in range(self.src_radar.nrays): for j in range(self.src_radar.ngates): index = self[i, j] if index[0] is not None: for field in field_list: mapped_radar.fields[ field]['data'][index] = self.src_radar.fields[ field]['data'][i, j] return mapped_radar