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 _query_resample_kdtree(resample_kdtree,
source_geo_def,
target_geo_def,
radius_of_influence,
data_slice,
neighbours=8,
epsilon=0,
reduce_data=True,
nprocs=1):
"""Query kd-tree on slice of target coordinates."""
# Check validity of input
if not isinstance(target_geo_def, geometry.BaseDefinition):
raise TypeError('target_geo_def must be of geometry type')
elif not isinstance(radius_of_influence, (long, int, float)):
raise TypeError('radius_of_influence must be number')
elif not isinstance(neighbours, int):
raise TypeError('neighbours must be integer')
elif not isinstance(epsilon, (long, int, float)):
raise TypeError('epsilon must be number')
# Get sliced target coordinates
target_lons, target_lats = target_geo_def.get_lonlats(nprocs=nprocs,
data_slice=data_slice, dtype=source_geo_def.dtype)
# Find indiced of reduced target coordinates
valid_output_index = _get_valid_output_index(source_geo_def,
target_geo_def,
target_lons.ravel(),
target_lats.ravel(),
reduce_data,
radius_of_influence)
# Get cartesian target coordinates and select reduced set
if nprocs > 1:
cartesian = _spatial_mp.Cartesian_MP(nprocs)
else:
cartesian = _spatial_mp.Cartesian()
target_lons_valid = target_lons.ravel()[valid_output_index]
target_lats_valid = target_lats.ravel()[valid_output_index]
output_coords = cartesian.transform_lonlats(target_lons_valid,
target_lats_valid)
# pykdtree requires query points have same data type as kdtree.
try:
dt = resample_kdtree.data.dtype
except AttributeError:
# use a sensible default
dt = np.dtype('d')
output_coords = np.asarray(output_coords, dtype=dt)
# Query kd-tree
distance_array, index_array = resample_kdtree.query(output_coords,
k=neighbours,
eps=epsilon,
distance_upper_bound=radius_of_influence)
return valid_output_index, index_array, distance_array
def test_cartesian(self):
"""Test the transform_lonlats of class Cartesian."""
my_cartesian = sp.Cartesian()
coords_int = my_cartesian.transform_lonlats(self.lon, self.lat)
coords_float = my_cartesian.transform_lonlats(self.lon64, self.lat64)
coords_float32 = my_cartesian.transform_lonlats(self.lon32, self.lat32)
np.testing.assert_almost_equal(coords_float, self.exp_coords, decimal=3)
np.testing.assert_almost_equal(coords_int, coords_float, decimal=0)
self.assertIs(type(coords_float32[0, 0]), np.float32)
self.assertIs(type(coords_float[0, 0]), np.float64)
self.assertTrue(np.issubdtype(coords_int.dtype, np.floating))
def get_cartesian_coords(self, nprocs=None, data_slice=None, cache=False):
"""Retrieve cartesian coordinates of geometry definition
Parameters
----------
nprocs : int, optional
Number of processor cores to be used.
Defaults to the nprocs set when instantiating object
data_slice : slice object, optional
Calculate only cartesian coordnates for the defined slice
cache : bool, optional
Store result the result. Requires data_slice to be None
Returns
-------
cartesian_coords : numpy array
"""
if self.cartesian_coords is None:
# Coordinates are not cached
if nprocs is None:
nprocs = self.nprocs
if data_slice is None:
# Use full slice
data_slice = slice(None)
lons, lats = self.get_lonlats(nprocs=nprocs, data_slice=data_slice)
if nprocs > 1:
cartesian = _spatial_mp.Cartesian_MP(nprocs)
else:
cartesian = _spatial_mp.Cartesian()
cartesian_coords = cartesian.transform_lonlats(np.ravel(lons),
np.ravel(lats))
if isinstance(lons, np.ndarray) and lons.ndim > 1:
# Reshape to correct shape
cartesian_coords = cartesian_coords.reshape(lons.shape[0],
lons.shape[1], 3)
if cache and data_slice is None:
self.cartesian_coords = cartesian_coords
else:
# Coordinates are cached
if data_slice is None:
cartesian_coords = self.cartesian_coords
else:
cartesian_coords = self.cartesian_coords[data_slice]
return cartesian_coords
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
