def _get_valid_output_index(source_geo_def, target_geo_def, target_lons,
target_lats, reduce_data, radius_of_influence):
"""Find indices of reduced output data"""
valid_output_index = np.ones(target_lons.size, dtype=np.bool)
if reduce_data:
if isinstance(source_geo_def, (geometry.GridDefinition,
geometry.AreaDefinition)) and \
isinstance(target_geo_def, geometry.CoordinateDefinition):
# Resampling from grid to swath
lonlat_boundary = source_geo_def.get_boundary_lonlats()
valid_output_index = \
data_reduce.get_valid_index_from_lonlat_boundaries(
lonlat_boundary[0],
lonlat_boundary[1],
target_lons,
target_lats,
radius_of_influence)
valid_output_index = valid_output_index.astype(np.bool)
# Remove illegal values
valid_out = ((target_lons >= -180) & (target_lons <= 180) &
(target_lats <= 90) & (target_lats >= -90))
# Combine reduced and legal values
valid_output_index = (valid_output_index & valid_out)
if isinstance(valid_output_index, np.ma.MaskedArray):
valid_output_index = valid_output_index.filled(False)
return valid_output_index
def _get_valid_output_index(source_geo_def, target_geo_def, target_lons,
target_lats, reduce_data, radius_of_influence):
"""Find indices of reduced output data"""
valid_output_index = np.ones(target_lons.size, dtype=np.bool)
if reduce_data:
if isinstance(source_geo_def, (geometry.GridDefinition,
geometry.AreaDefinition)) and \
isinstance(target_geo_def, geometry.CoordinateDefinition):
# Resampling from grid to swath
lonlat_boundary = source_geo_def.get_boundary_lonlats()
valid_output_index = \
data_reduce.get_valid_index_from_lonlat_boundaries(
lonlat_boundary[0],
lonlat_boundary[1],
target_lons,
target_lats,
radius_of_influence)
valid_output_index = valid_output_index.astype(np.bool)
# Remove illegal values
valid_out = ((target_lons >= -180) & (target_lons <= 180) &
(target_lats <= 90) & (target_lats >= -90))
# Combine reduced and legal values
valid_output_index = (valid_output_index & valid_out)
if isinstance(valid_output_index, np.ma.MaskedArray):
valid_output_index = valid_output_index.filled(False)
return valid_output_index
def get_valid_indices_from_lonlat_boundaries(target_geo_def, source_lons,
source_lats, radius_of_influence):
"""Get valid indices from lonlat boundaries."""
# Resampling from swath to grid or from grid to grid
lonlat_boundary = target_geo_def.get_boundary_lonlats()
# Combine reduced and legal values
return data_reduce.get_valid_index_from_lonlat_boundaries(
lonlat_boundary[0], lonlat_boundary[1], source_lons, source_lats,
radius_of_influence)
def _get_valid_input_index(source_geo_def,
target_geo_def,
reduce_data,
radius_of_influence,
nprocs=1):
"""Find indices of reduced inputput data"""
source_lons, source_lats = source_geo_def.get_lonlats(nprocs=nprocs)
source_lons = source_lons.ravel()
source_lats = source_lats.ravel()
if source_lons.size == 0 or source_lats.size == 0:
raise ValueError('Cannot resample empty data set')
elif source_lons.size != source_lats.size or \
source_lons.shape != source_lats.shape:
raise ValueError('Mismatch between lons and lats')
# Remove illegal values
valid_data = ((source_lons >= -180) & (source_lons <= 180) &
(source_lats <= 90) & (source_lats >= -90))
valid_input_index = np.ones(source_geo_def.size, dtype=np.bool)
if reduce_data:
# Reduce dataset
if (isinstance(source_geo_def, geometry.CoordinateDefinition) and
isinstance(target_geo_def, (geometry.GridDefinition,
geometry.AreaDefinition))) or \
(isinstance(source_geo_def, (geometry.GridDefinition,
geometry.AreaDefinition)) and
isinstance(target_geo_def, (geometry.GridDefinition,
geometry.AreaDefinition))):
# Resampling from swath to grid or from grid to grid
lonlat_boundary = target_geo_def.get_boundary_lonlats()
valid_input_index = \
data_reduce.get_valid_index_from_lonlat_boundaries(
lonlat_boundary[0],
lonlat_boundary[1],
source_lons, source_lats,
radius_of_influence)
# Combine reduced and legal values
valid_input_index = (valid_data & valid_input_index)
if (isinstance(valid_input_index, np.ma.core.MaskedArray)):
# Make sure valid_input_index is not a masked array
valid_input_index = valid_input_index.filled(False)
return valid_input_index, source_lons, source_lats
def _get_valid_input_index_dask(source_geo_def,
target_geo_def,
reduce_data,
radius_of_influence,
nprocs=1):
"""Find indices of reduced inputput data"""
source_lons, source_lats = source_geo_def.get_lonlats_dask()
source_lons = da.ravel(source_lons)
source_lats = da.ravel(source_lats)
if source_lons.size == 0 or source_lats.size == 0:
raise ValueError('Cannot resample empty data set')
elif source_lons.size != source_lats.size or \
source_lons.shape != source_lats.shape:
raise ValueError('Mismatch between lons and lats')
# Remove illegal values
valid_input_index = ((source_lons >= -180) & (source_lons <= 180) &
(source_lats <= 90) & (source_lats >= -90))
if reduce_data:
# Reduce dataset
if (isinstance(source_geo_def, geometry.CoordinateDefinition) and
isinstance(target_geo_def, (geometry.GridDefinition,
geometry.AreaDefinition))) or \
(isinstance(source_geo_def, (geometry.GridDefinition,
geometry.AreaDefinition)) and
isinstance(target_geo_def, (geometry.GridDefinition,
geometry.AreaDefinition))):
# Resampling from swath to grid or from grid to grid
lonlat_boundary = target_geo_def.get_boundary_lonlats()
# Combine reduced and legal values
valid_input_index &= \
data_reduce.get_valid_index_from_lonlat_boundaries(
lonlat_boundary[0],
lonlat_boundary[1],
source_lons, source_lats,
radius_of_influence)
if (isinstance(valid_input_index, np.ma.core.MaskedArray)):
# Make sure valid_input_index is not a masked array
valid_input_index = valid_input_index.filled(False)
return valid_input_index, source_lons, source_lats
