def aggregate_ungridded(self, kernel):
"""
Performs aggregation for ungridded data by first generating a new grid, converting it into a cube, then
collocating using the appropriate kernel and a cube cell constraint
"""
new_cube_coords = []
new_cube_shape = []
i = 0
for coord in self.data.coords():
grid, guessed_axis = self.get_grid(coord)
if grid is None:
new_coord = self._make_fully_collapsed_coord(coord)
if grid is not None and isnan(grid.delta):
# Issue a warning and then still collapse fully
logging.warning('Coordinate ' + guessed_axis + ' was given without a grid. No need to specify '
'coordinates for complete collapse, all coordinates without a grid specified are '
'automatically collapsed for ungridded aggregation.')
new_coord = self._make_fully_collapsed_coord(coord)
if grid is not None and not isnan(grid.delta):
new_coord = self._make_partially_collapsed_coord(coord, grid, guessed_axis)
new_cube_coords.append((new_coord, i))
new_cube_shape.append(len(new_coord.points))
i += 1
if len(self._grid) != 0:
raise CoordinateNotFoundError('No coordinate found that matches {}. Please check the coordinate '
'name.'.format(self._grid.keys()))
dummy_data = numpy.reshape(numpy.arange(int(numpy.prod(new_cube_shape))) + 1.0, tuple(new_cube_shape))
aggregation_cube = iris.cube.Cube(dummy_data, dim_coords_and_dims=new_cube_coords)
collocator = GeneralGriddedCollocator()
constraint = BinnedCubeCellOnlyConstraint()
aggregated_cube = collocator.collocate(aggregation_cube, self.data, constraint, kernel)
self._add_max_min_bounds_for_collapsed_coords(aggregated_cube, self.data)
# We need to rename any variables which clash with coordinate names otherwise they will not output correctly, we
# prepend it with 'aggregated_' to make it clear which variable has been aggregated (the original coordinate
# value will not have been.)
for idx, d in enumerate(aggregated_cube):
if d.var_name in [coord.var_name for coord in aggregation_cube.coords()]:
new_name = "aggregated_" + d.var_name
aggregated_cube[idx].rename(new_name)
aggregated_cube[idx].var_name = new_name
logging.warning("Variable {} clashes with a coordinate variable name and has been renamed to: {}"
.format(d.var_name, new_name))
return aggregated_cube
def aggregate_gridded(self, kernel):
# Make sure all coordinate have bounds - important for weighting and aggregating
# Only try and guess bounds on Dim Coords
for coord in self.data.coords(dim_coords=True):
if not coord.has_bounds() and len(coord.points) > 1:
coord.guess_bounds()
logging.warning("Creating guessed bounds as none exist in file")
new_coord_number = self.data.coord_dims(coord)
self.data.remove_coord(coord.name())
self.data.add_dim_coord(coord, new_coord_number)
coords = []
for coord in self.data.coords():
grid, guessed_axis = self.get_grid(coord)
if grid is not None:
if isnan(grid.delta):
logging.info('Aggregating on ' + coord.name() + ', collapsing completely and using ' +
kernel.cell_method + ' kernel.')
coords.append(coord)
else:
raise NotImplementedError("Aggregation using partial collapse of "
"coordinates is not supported for GriddedData")
output = GriddedDataList([])
if isinstance(kernel, MultiKernel):
for sub_kernel in kernel.sub_kernels:
sub_kernel_out = self._gridded_full_collapse(coords, sub_kernel)
output.append_or_extend(sub_kernel_out)
else:
output.append_or_extend(self._gridded_full_collapse(coords, kernel))
return output
