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"""Functions to create spotdata cubes."""
import iris
import numpy as np
from iris.coords import AuxCoord, DimCoord
[docs]def build_spotdata_cube(data, name, units,
altitude, latitude, longitude, wmo_id,
scalar_coords=None,
neighbour_methods=None, neighbour_methods_dim=1,
grid_attributes=None, grid_attributes_dim=2):
"""
Function to build a spotdata cube with expected dimension and auxiliary
coordinate structure.
It can be used to create spot data cubes. In this case the data is the
spot data values at each site, and the coordinates that describe each site.
It can also be used to create cubes which describe the grid points that are
used to extract each site from a gridded field, for different selection
method. The selection methods are specified by the neighbour_methods
coordinate. The grid_attribute coordinate encapsulates information required
to extract data, for example the x/y indices that identify the grid point
neighbour.
.. See the documentation for examples of these cubes.
.. include:: extended_documentation/spotdata/build_spotdata_cube/
build_spotdata_cube_examples.rst
Args:
data (float or numpy.ndarray):
Float spot data or array of data points from several sites.
The spot index should be the first dimension if the array is
multi-dimensional (see optional additional dimensions below).
name (str):
Cube name (eg 'air_temperature')
units (str):
Cube units (eg 'K')
altitude (float or numpy.ndarray):
Float or 1d array of site altitudes in metres
latitude (float or numpy.ndarray):
Float or 1d array of site latitudes in degrees
longitude (float or numpy.ndarray):
Float or 1d array of site longitudes in degrees
wmo_id (str or list):
String or list of site 5-digit WMO identifiers
scalar_coords (list):
Optional list of iris.coords.AuxCoord instances
neighbour_methods (list):
Optional list of neighbour method names, e.g. 'nearest'
neighbour_methods_dim (int):
Data dimension to match the neighbour method list
grid_attributes (list):
Optional list of grid attribute names, e.g. x-index, y-index
grid_attributes_dim (int):
Data dimension to match the grid attributes list
"""
# construct auxiliary coordinates
alt_coord = AuxCoord(altitude, 'altitude', units='m')
lat_coord = AuxCoord(latitude, 'latitude', units='degrees')
lon_coord = AuxCoord(longitude, 'longitude', units='degrees')
id_coord = AuxCoord(wmo_id, long_name='wmo_id', units='no_unit')
aux_coords_and_dims = []
for coord in [alt_coord, lat_coord, lon_coord, id_coord]:
aux_coords_and_dims.append((coord, 0))
# append scalar coordinates
if scalar_coords is not None:
for coord in scalar_coords:
aux_coords_and_dims.append((coord, None))
# construct dimension coordinates
if np.isscalar(data):
data = np.array([data])
spot_index = DimCoord(
np.arange(len(data), dtype=np.int32), long_name='spot_index',
units='1')
dim_coords_and_dims = [(spot_index, 0)]
if neighbour_methods is not None:
neighbour_methods_coord = DimCoord(
np.arange(len(neighbour_methods), dtype=np.int32),
long_name='neighbour_selection_method', units='1')
neighbour_methods_key = AuxCoord(
neighbour_methods, long_name='neighbour_selection_method_name',
units='no_unit')
dim_coords_and_dims.append((neighbour_methods_coord,
neighbour_methods_dim))
aux_coords_and_dims.append((neighbour_methods_key,
neighbour_methods_dim))
if grid_attributes is not None:
grid_attributes_coord = DimCoord(
np.arange(len(grid_attributes), dtype=np.int32),
long_name='grid_attributes', units='1')
grid_attributes_key = AuxCoord(
grid_attributes, long_name='grid_attributes_key', units='no_unit')
dim_coords_and_dims.append((grid_attributes_coord,
grid_attributes_dim))
aux_coords_and_dims.append((grid_attributes_key,
grid_attributes_dim))
# create output cube
spot_cube = iris.cube.Cube(
data, long_name=name, units=units,
dim_coords_and_dims=dim_coords_and_dims,
aux_coords_and_dims=aux_coords_and_dims)
# rename to force a standard name to be set if name is valid
spot_cube.rename(name)
return spot_cube