# # # This source code is subject to the license referenced at
# # # https://github.com/NRLMMD-GEOIPS.
"""Interpolation methods using pyresample routines."""
import logging
import numpy
from pyresample import kd_tree
LOG = logging.getLogger(__name__)
# interface = None indicates to the GeoIPS interfaces that this is not a valid
# plugin, and this module will not be added to the GeoIPS plugin registry.
# This allows including python modules within the geoips/plugins directory
# that provide helper or utility functions to the geoips plugins, but are
# not full GeoIPS plugins on their own.
interface = None
[docs]def get_data_box_definition(source_name, lons, lats):
"""Obtain pyresample geometry definitions.
For use with pyresample based reprojections
Parameters
----------
source_name : str
geoips source_name for data type
lons : ndarray
Numpy array of longitudes, 0 to 360
lats : ndarray
Numpy array of latitudes, -90 to 90
"""
from geoips.plugins.classes.interpolators.utils.boxdefinitions import (
MaskedCornersSwathDefinition,
)
from geoips.plugins.classes.interpolators.utils.boxdefinitions import (
PlanarPolygonDefinition,
)
from pyresample.geometry import GridDefinition
if source_name == "abi" and lons.ndim > 1:
data_box_definition = GridDefinition(
lons=numpy.ma.array(lons, subok=False),
lats=numpy.ma.array(lats, subok=False),
)
if source_name == "ahi":
data_box_definition = PlanarPolygonDefinition(
lons=numpy.ma.array(lons, subok=False),
lats=numpy.ma.array(lats, subok=False),
)
else:
data_box_definition = MaskedCornersSwathDefinition(
lons=numpy.ma.array(lons, subok=False),
lats=numpy.ma.array(lats, subok=False),
)
return data_box_definition
[docs]def interp_kd_tree(
list_of_arrays,
area_definition,
data_box_definition,
radius_of_influence,
interp_type="nearest",
sigmas=None,
neighbours=None,
nprocs=None,
fill_value=None,
):
"""Interpolate using pyresample's kd_tree.resample_nearest method.
Parameters
----------
list_of_array : list of numpy.ndarray
list of arrays to be interpolated
area_definition : areadef
pyresample area_definition object of current region of interest.
data_box_definition : datadef
pyresample/geoips data_box_definition specifying region covered
by source data.
radius_of_influence : float
radius of influence for interpolation
Other Parameters
----------------
interp_type : str, default='nearest'
One of 'nearest' or 'gauss' - kd_tree resampling methods.
sigmas : int, default=None
Used for interp_type 'gauss' - multiplication factor for sigmas option:
* sigmas = [sigmas]*len(list_of_arrays)
"""
dstacked_arrays = numpy.ma.dstack(list_of_arrays)
if interp_type == "nearest":
kw_args = {}
kw_args["fill_value"] = None
kw_args["radius_of_influence"] = radius_of_influence
if nprocs is not None:
kw_args["nprocs"] = nprocs
LOG.info("Using interp_type %s", interp_type)
dstacked_arrays = kd_tree.resample_nearest(
data_box_definition,
dstacked_arrays,
area_definition,
**kw_args,
)
elif interp_type == "gauss":
kw_args = {}
kw_args["sigmas"] = [4000] * len(list_of_arrays)
kw_args["fill_value"] = None
kw_args["radius_of_influence"] = radius_of_influence
if sigmas is not None:
kw_args["sigmas"] = [sigmas] * len(list_of_arrays)
if neighbours is not None:
kw_args["neighbours"] = neighbours
if nprocs is not None:
kw_args["nprocs"] = nprocs
if fill_value is not None:
kw_args["fill_value"] = fill_value
LOG.info("Using interp_type %s %s", interp_type, sigmas)
dstacked_arrays = kd_tree.resample_gauss(
data_box_definition, dstacked_arrays, area_definition, **kw_args
)
else:
raise TypeError("Unknown interp_type {0}, failing".format(interp_type))
interpolated_arrays = []
# We are explicitly expecting 2d arrays back, so if 3d, break it down.
for arrind in range(len(list_of_arrays)):
if len(dstacked_arrays.shape) == 3:
interpolated_arrays += [dstacked_arrays[:, :, arrind]]
else:
interpolated_arrays += [dstacked_arrays]
return interpolated_arrays
