def _regrid_to_target(self, cube: Cube, target_grid: Cube,
regridded_title: Optional[str]) -> Cube:
"""
Regrid cube to target_grid, inherit grid attributes and update title
Args:
cube:
Cube to be regridded
target_grid:
Data on the target grid. If regridding with mask, this cube
should contain land-sea mask data to be used in adjusting land
and sea points after regridding.
regridded_title:
New value for the "title" attribute to be used after
regridding. If not set, a default value is used.
Returns:
Regridded cube with updated attributes
"""
regridder = Linear(extrapolation_mode=self.extrapolation_mode)
if "nearest" in self.regrid_mode:
regridder = Nearest(extrapolation_mode=self.extrapolation_mode)
cube = cube.regrid(target_grid, regridder)
if self.REGRID_REQUIRES_LANDMASK[self.regrid_mode]:
cube = self._adjust_landsea(cube, target_grid)
# identify grid-describing attributes on source cube that need updating
required_grid_attributes = [
attr for attr in cube.attributes if attr in MOSG_GRID_ATTRIBUTES
]
# update attributes if available on target grid, otherwise remove
for key in required_grid_attributes:
if key in target_grid.attributes:
cube.attributes[key] = target_grid.attributes[key]
else:
cube.attributes.pop(key)
cube.attributes["title"] = (MANDATORY_ATTRIBUTE_DEFAULTS["title"]
if regridded_title is None else
regridded_title)
return cube
def __init__(self, extrapolation_mode="nanmask", vicinity_radius=25000.0):
"""
Initialise class
Args:
extrapolation_mode (str):
Mode to use for extrapolating data into regions
beyond the limits of the source_data domain.
Available modes are documented in
`iris.analysis <https://scitools.org.uk/iris/docs/latest/iris/
iris/analysis.html#iris.analysis.Nearest>`_
Defaults to "nanmask".
vicinity_radius (float):
Distance in metres to search for a sea or land point.
"""
self.input_land = None
self.nearest_cube = None
self.output_land = None
self.output_cube = None
self.regridder = Nearest(extrapolation_mode=extrapolation_mode)
self.vicinity = OccurrenceWithinVicinity(vicinity_radius)
class TestCubeRegridNearest(MixinCheckingCode, tests.IrisTest):
scheme = Nearest(extrapolation_mode='extrapolate')
def regrid(self, src_cube, dst_cube, **kwargs):
return src_cube.regrid(dst_cube, scheme=self.scheme)
def _regrid_to_target(
self,
cube: Cube,
target_grid: Cube,
regridded_title: Optional[str],
regrid_mode: str,
) -> Cube:
"""
Regrid cube to target_grid, inherit grid attributes and update title
Args:
cube:
Cube to be regridded
target_grid:
Data on the target grid. If regridding with mask, this cube
should contain land-sea mask data to be used in adjusting land
and sea points after regridding.
regridded_title:
New value for the "title" attribute to be used after
regridding. If not set, a default value is used.
regrid_mode:
"bilinear","nearest","nearest-with-mask",
"nearest-2","nearest-with-mask-2","bilinear-2","bilinear-with-mask-2"
Returns:
Regridded cube with updated attributes.
"""
if regrid_mode in (
"nearest-with-mask",
"nearest-with-mask-2",
"bilinear-with-mask-2",
):
if self.landmask_name not in self.landmask_source_grid.name():
msg = "Expected {} in input_landmask cube but found {}".format(
self.landmask_name, repr(self.landmask_source_grid))
warnings.warn(msg)
if self.landmask_name not in target_grid.name():
msg = "Expected {} in target_grid cube but found {}".format(
self.landmask_name, repr(target_grid))
warnings.warn(msg)
# basic categories (1) Iris-based (2) new nearest based (3) new bilinear-based
if regrid_mode in ("bilinear", "nearest", "nearest-with-mask"):
if "nearest" in regrid_mode:
regridder = Nearest(extrapolation_mode=self.extrapolation_mode)
else:
regridder = Linear(extrapolation_mode=self.extrapolation_mode)
cube = cube.regrid(target_grid, regridder)
# Iris regridding is used, and then adjust if land_sea mask is considered
if self.REGRID_REQUIRES_LANDMASK[regrid_mode]:
cube = AdjustLandSeaPoints(
vicinity_radius=self.landmask_vicinity,
extrapolation_mode=self.extrapolation_mode,
)(cube, self.landmask_source_grid, target_grid)
# new version of nearest/bilinear option with/without land-sea mask
elif regrid_mode in (
"nearest-2",
"nearest-with-mask-2",
"bilinear-2",
"bilinear-with-mask-2",
):
cube = RegridWithLandSeaMask(
regrid_mode=regrid_mode,
vicinity_radius=self.landmask_vicinity)(
cube, self.landmask_source_grid, target_grid)
# identify grid-describing attributes on source cube that need updating
required_grid_attributes = [
attr for attr in cube.attributes if attr in MOSG_GRID_ATTRIBUTES
]
# update attributes if available on target grid, otherwise remove
for key in required_grid_attributes:
if key in target_grid.attributes:
cube.attributes[key] = target_grid.attributes[key]
else:
cube.attributes.pop(key)
cube.attributes["title"] = (MANDATORY_ATTRIBUTE_DEFAULTS["title"]
if regridded_title is None else
regridded_title)
return cube
def test_default(self):
scheme = Nearest()
self.assertEqual(scheme.extrapolation_mode, "extrapolate")
def test_invalid(self):
with self.assertRaisesRegex(ValueError, "Extrapolation mode"):
Nearest("bogus")
def create_scheme(mode=None):
kwargs = {}
if mode is not None:
kwargs["extrapolation_mode"] = mode
return Nearest(**kwargs)
# Stock cube - global grid extents (degrees).
_LAT_MIN = -90.0
_LAT_MAX = 90.0
_LAT_RANGE = _LAT_MAX - _LAT_MIN
_LON_MIN = 0.0
_LON_MAX = 360.0
_LON_RANGE = _LON_MAX - _LON_MIN
# A cached stock of standard horizontal target grids.
_CACHE = dict()
# Supported point interpolation schemes.
POINT_INTERPOLATION_SCHEMES = {
'linear': Linear(extrapolation_mode='mask'),
'nearest': Nearest(extrapolation_mode='mask'),
}
# Supported horizontal regridding schemes.
HORIZONTAL_SCHEMES = {
'linear': Linear(extrapolation_mode='mask'),
'linear_extrapolate': Linear(extrapolation_mode='extrapolate'),
'nearest': Nearest(extrapolation_mode='mask'),
'area_weighted': AreaWeighted(),
'unstructured_nearest': UnstructuredNearest(),
}
# Supported vertical interpolation schemes.
VERTICAL_SCHEMES = ('linear', 'nearest',
'linear_horizontal_extrapolate_vertical',
'nearest_horizontal_extrapolate_vertical')
def test_invalid(self):
with self.assertRaisesRegexp(ValueError, 'Extrapolation mode'):
Nearest('bogus')
two_pt_close=od(lons=[10, 11], lats=[10, 11]),
two_pt_far=od(lons=[10, -170], lats=[50, -50]),
twenty_pt_far=od(lons=lons, lats=lats))
for name, coords in d.items():
data = pyaerocom.GriddedData()
data._init_testdata_default()
num = len(coords['lats'])
sample_points = [('latitude', coords['lats']),
('longitude', coords['lons'])]
print("Current: {}, (#={})".format(name, num))
t0 = time()
data.interpolate(sample_points)
dt = time() - t0
print("Elapsed time: {:.3f} s".format(dt))
times.append(dt)
from iris.analysis import Nearest
data = pyaerocom.GriddedData()
data._init_testdata_default()
scheme = Nearest()
t0 = time()
coords, points = zip(*sample_points)
interp = scheme.interpolator(data.grid, coords)
t1 = time()
interp(points, collapse_scalar=True)
print("Create interpolator: {:.3f} s".format(t1 - t0))
print("Total: {:.3f} s".format(time() - t0))