def _create_resample_kdtree(source_lons,
source_lats,
valid_input_index,
nprocs=1):
"""Set up kd tree on input."""
source_lons_valid = source_lons[valid_input_index]
source_lats_valid = source_lats[valid_input_index]
if nprocs > 1:
cartesian = _spatial_mp.Cartesian_MP(nprocs)
else:
cartesian = _spatial_mp.Cartesian()
input_coords = cartesian.transform_lonlats(source_lons_valid,
source_lats_valid)
if input_coords.size == 0:
raise EmptyResult('No valid data points in input data')
# Build kd-tree on input
if nprocs > 1:
resample_kdtree = _spatial_mp.cKDTree_MP(input_coords, nprocs=nprocs)
else:
resample_kdtree = KDTree(input_coords)
return resample_kdtree
def _create_resample_kdtree(source_lons,
source_lats,
valid_input_index,
nprocs=1):
"""Set up kd tree on input"""
"""
if not isinstance(source_geo_def, geometry.BaseDefinition):
raise TypeError('source_geo_def must be of geometry type')
#Get reduced cartesian coordinates and flatten them
source_cartesian_coords = source_geo_def.get_cartesian_coords(nprocs=nprocs)
input_coords = geometry._flatten_cartesian_coords(source_cartesian_coords)
input_coords = input_coords[valid_input_index]
"""
source_lons_valid = source_lons[valid_input_index]
source_lats_valid = source_lats[valid_input_index]
if nprocs > 1:
cartesian = _spatial_mp.Cartesian_MP(nprocs)
else:
cartesian = _spatial_mp.Cartesian()
input_coords = cartesian.transform_lonlats(source_lons_valid,
source_lats_valid)
if input_coords.size == 0:
raise EmptyResult('No valid data points in input data')
# Build kd-tree on input
if kd_tree_name == 'pykdtree':
resample_kdtree = KDTree(input_coords)
elif nprocs > 1:
resample_kdtree = _spatial_mp.cKDTree_MP(input_coords, nprocs=nprocs)
else:
resample_kdtree = sp.cKDTree(input_coords)
return resample_kdtree
def _create_resample_kdtree(source_lons, source_lats, valid_input_index, nprocs=1):
"""Set up kd tree on input"""
"""
if not isinstance(source_geo_def, geometry.BaseDefinition):
raise TypeError('source_geo_def must be of geometry type')
#Get reduced cartesian coordinates and flatten them
source_cartesian_coords = source_geo_def.get_cartesian_coords(nprocs=nprocs)
input_coords = geometry._flatten_cartesian_coords(source_cartesian_coords)
input_coords = input_coords[valid_input_index]
"""
source_lons_valid = source_lons[valid_input_index]
source_lats_valid = source_lats[valid_input_index]
if nprocs > 1:
cartesian = _spatial_mp.Cartesian_MP(nprocs)
else:
cartesian = _spatial_mp.Cartesian()
input_coords = cartesian.transform_lonlats(
source_lons_valid, source_lats_valid)
if input_coords.size == 0:
raise EmptyResult('No valid data points in input data')
# Build kd-tree on input
if kd_tree_name == 'pykdtree':
resample_kdtree = KDTree(input_coords)
elif nprocs > 1:
resample_kdtree = _spatial_mp.cKDTree_MP(input_coords,
nprocs=nprocs)
else:
resample_kdtree = sp.cKDTree(input_coords)
return resample_kdtree