Source code for esmvalcore.preprocessor._volume
"""Volume and z coordinate operations on data cubes.
Allows for selecting data subsets using certain volume bounds; selecting
depth or height regions; constructing volumetric averages;
"""
import logging
from copy import deepcopy
import dask.array as da
import iris
import numpy as np
logger = logging.getLogger(__name__)
[docs]def extract_volume(cube, z_min, z_max):
"""Subset a cube based on a range of values in the z-coordinate.
Function that subsets a cube on a box (z_min, z_max)
This function is a restriction of masked_cube_lonlat();
Note that this requires the requested z-coordinate range to be the
same sign as the iris cube. ie, if the cube has z-coordinate as
negative, then z_min and z_max need to be negative numbers.
Parameters
----------
cube: iris.cube.Cube
input cube.
z_min: float
minimum depth to extract.
z_max: float
maximum depth to extract.
Returns
-------
iris.cube.Cube
z-coord extracted cube.
"""
if z_min > z_max:
# minimum is below maximum, so switch them around
zmax = float(z_min)
zmin = float(z_max)
else:
zmax = float(z_max)
zmin = float(z_min)
z_constraint = iris.Constraint(
coord_values={
cube.coord(axis='Z'): lambda cell: zmin < cell.point < zmax
})
return cube.extract(z_constraint)
def _create_cube_time(src_cube, data, times):
"""Generate a new cube with the volume averaged data.
The resultant cube is seeded with `src_cube` metadata and coordinates,
excluding any source coordinates that span the associated vertical
dimension. The `times` of interpolation are used along with the
associated source cube time coordinate metadata to add a new
time coordinate to the resultant cube.
Based on the _create_cube method from _regrid.py.
Parameters
----------
src_cube : cube
The source cube that was vertically interpolated.
data : array
The payload resulting from interpolating the source cube
over the specified times.
times : array
The array of times.
Returns
-------
cube
.. note::
If there is only one level of interpolation, the resultant cube
will be collapsed over the associated vertical dimension, and a
scalar vertical coordinate will be added.
"""
# Get the source cube vertical coordinate and associated dimension.
src_times = src_cube.coord('time')
t_dim, = src_cube.coord_dims(src_times)
if data.shape[t_dim] != len(times):
emsg = ('Mismatch between data and times for data dimension {!r}, '
'got data shape {!r} with times shape {!r}.')
raise ValueError(emsg.format(t_dim, data.shape, times.shape))
# Construct the resultant cube with the interpolated data
# and the source cube metadata.
kwargs = deepcopy(src_cube.metadata)._asdict()
result = iris.cube.Cube(data, **kwargs)
# Add the appropriate coordinates to the cube, excluding
# any coordinates that span the z-dimension of interpolation.
for coord in src_cube.dim_coords:
[dim] = src_cube.coord_dims(coord)
if dim != t_dim:
result.add_dim_coord(coord.copy(), dim)
for coord in src_cube.aux_coords:
dims = src_cube.coord_dims(coord)
if t_dim not in dims:
result.add_aux_coord(coord.copy(), dims)
for coord in src_cube.derived_coords:
dims = src_cube.coord_dims(coord)
if t_dim not in dims:
result.add_aux_coord(coord.copy(), dims)
# Construct the new vertical coordinate for the interpolated
# z-dimension, using the associated source coordinate metadata.
metadata = src_times.metadata
kwargs = {
'standard_name': metadata.standard_name,
'long_name': metadata.long_name,
'var_name': metadata.var_name,
'units': metadata.units,
'attributes': metadata.attributes,
'coord_system': metadata.coord_system,
'climatological': metadata.climatological,
}
try:
coord = iris.coords.DimCoord(times, **kwargs)
result.add_dim_coord(coord, t_dim)
except ValueError:
coord = iris.coords.AuxCoord(times, **kwargs)
result.add_aux_coord(coord, t_dim)
return result
def calculate_volume(cube):
"""Calculate volume from a cube.
This function is used when the volume netcdf fx_variables can't be found.
Parameters
----------
cube: iris.cube.Cube
input cube.
Returns
-------
float
grid volume.
"""
# ####
# Load depth field and figure out which dim is which.
depth = cube.coord(axis='z')
z_dim = cube.coord_dims(cube.coord(axis='z'))[0]
# ####
# Load z direction thickness
thickness = depth.bounds[..., 1] - depth.bounds[..., 0]
# ####
# Calculate grid volume:
area = iris.analysis.cartography.area_weights(cube)
if thickness.ndim == 1 and z_dim == 1:
grid_volume = area * thickness[None, :, None, None]
if thickness.ndim == 4 and z_dim == 1:
grid_volume = area * thickness[:, :]
return grid_volume
[docs]def volume_statistics(cube, operator):
"""Apply a statistical operation over a volume.
The volume average is weighted according to the cell volume. Cell volume
is calculated from iris's cartography tool multiplied by the cell
thickness.
Parameters
----------
cube: iris.cube.Cube
Input cube.
operator: str
The operation to apply to the cube, options are: 'mean'.
Returns
-------
iris.cube.Cube
collapsed cube.
Raises
------
ValueError
if input cube shape differs from grid volume cube shape.
"""
# TODO: Test sigma coordinates.
# TODO: Add other operations.
# ####
# Load z coordinate field and figure out which dim is which.
t_dim = cube.coord_dims('time')[0]
try:
grid_volume = cube.cell_measure('ocean_volume').core_data()
except iris.exceptions.CellMeasureNotFoundError:
logger.debug('Cell measure "ocean_volume" not found in cube. '
'Check fx_file availability.')
logger.debug('Attempting to calculate grid cell volume...')
grid_volume = calculate_volume(cube)
else:
grid_volume = da.broadcast_to(grid_volume, cube.shape)
if cube.data.shape != grid_volume.shape:
raise ValueError('Cube shape ({}) doesn`t match grid volume shape '
'({})'.format(cube.data.shape, grid_volume.shape))
# #####
# Calculate global volume weighted average
result = []
# #####
# iterate over time and z-coordinate dimensions.
for time_itr in range(cube.shape[t_dim]):
# ####
# create empty output arrays
column = []
depth_volume = []
# ####
# iterate over time and z-coordinate dimensions.
for z_itr in range(cube.shape[1]):
# ####
# Calculate weighted mean for this time and layer
if operator == 'mean':
total = cube[time_itr, z_itr].collapsed(
[cube.coord(axis='z'), 'longitude', 'latitude'],
iris.analysis.MEAN,
weights=grid_volume[time_itr, z_itr]).data
else:
raise ValueError('Volume operator ({}) not '
'recognised.'.format(operator))
column.append(total)
try:
layer_vol = np.ma.masked_where(cube[time_itr, z_itr].data.mask,
grid_volume[time_itr,
z_itr]).sum()
except AttributeError:
# ####
# No mask in the cube data.
layer_vol = grid_volume.sum()
depth_volume.append(layer_vol)
# ####
# Calculate weighted mean over the water volumn
column = np.ma.array(column)
depth_volume = np.ma.array(depth_volume)
result.append(np.ma.average(column, weights=depth_volume))
# ####
# Send time series and dummy cube to cube creating tool.
times = np.array(cube.coord('time').points.astype(float))
result = np.ma.array(result)
# #####
# Create a small dummy output array for the output cube
if operator == 'mean':
src_cube = cube[:2, :2].collapsed(
[cube.coord(axis='z'), 'longitude', 'latitude'],
iris.analysis.MEAN,
weights=grid_volume[:2, :2],
)
return _create_cube_time(src_cube, result, times)
[docs]def depth_integration(cube):
"""Determine the total sum over the vertical component.
Requires a 3D cube. The z-coordinate
integration is calculated by taking the sum in the z direction of the
cell contents multiplied by the cell thickness.
Arguments
---------
cube: iris.cube.Cube
input cube.
Returns
-------
iris.cube.Cube
collapsed cube.
"""
# ####
depth = cube.coord(axis='z')
thickness = depth.bounds[..., 1] - depth.bounds[..., 0]
if depth.ndim == 1:
slices = [None for i in cube.shape]
coord_dim = cube.coord_dims(cube.coord(axis='z'))[0]
slices[coord_dim] = slice(None)
thickness = np.abs(thickness[tuple(slices)])
ones = np.ones_like(cube.data)
weights = thickness * ones
result = cube.collapsed(cube.coord(axis='z'),
iris.analysis.SUM,
weights=weights)
result.rename('Depth_integrated_' + str(cube.name()))
# result.units = Unit('m') * result.units # This doesn't work:
# TODO: Change units on cube to reflect 2D concentration (not 3D)
# Waiting for news from iris community.
return result
[docs]def extract_transect(cube, latitude=None, longitude=None):
"""Extract data along a line of constant latitude or longitude.
Both arguments, latitude and longitude, are treated identically.
Either argument can be a single float, or a pair of floats, or can be
left empty.
The single float indicates the latitude or longitude along which the
transect should be extracted.
A pair of floats indicate the range that the transect should be
extracted along the secondairy axis.
For instance `'extract_transect(cube, longitude=-28)'` will produce a
transect along 28 West.
Also, `'extract_transect(cube, longitude=-28, latitude=[-50, 50])'` will
produce a transect along 28 West between 50 south and 50 North.
This function is not yet implemented for irregular arrays - instead
try the extract_trajectory function, but note that it is currently
very slow. Alternatively, use the regrid preprocessor to regrid along
a regular grid and then extract the transect.
Parameters
----------
cube: iris.cube.Cube
input cube.
latitude: None, float or [float, float], optional
transect latiude or range.
longitude: None, float or [float, float], optional
transect longitude or range.
Returns
-------
iris.cube.Cube
collapsed cube.
Raises
------
ValueError
slice extraction not implemented for irregular grids.
ValueError
latitude and longitude are both floats or lists; not allowed
to slice on both axes at the same time.
"""
# ###
coord_dim2 = False
second_coord_range = False
lats = cube.coord('latitude')
lons = cube.coord('longitude')
if lats.ndim == 2:
raise ValueError(
'extract_transect: Not implemented for irregular arrays!' +
'\nTry regridding the data first.')
if isinstance(latitude, float) and isinstance(longitude, float):
raise ValueError(
"extract_transect: Can't slice along lat and lon at the same time")
if isinstance(latitude, list) and isinstance(longitude, list):
raise ValueError(
"extract_transect: Can't reduce lat and lon at the same time")
for dim_name, dim_cut, coord in zip(['latitude', 'longitude'],
[latitude, longitude], [lats, lons]):
# ####
# Look for the first coordinate.
if isinstance(dim_cut, float):
coord_index = coord.nearest_neighbour_index(dim_cut)
coord_dim = cube.coord_dims(dim_name)[0]
# ####
# Look for the second coordinate.
if isinstance(dim_cut, list):
coord_dim2 = cube.coord_dims(dim_name)[0]
second_coord_range = [
coord.nearest_neighbour_index(dim_cut[0]),
coord.nearest_neighbour_index(dim_cut[1])
]
# ####
# Extracting the line of constant longitude/latitude
slices = [slice(None) for i in cube.shape]
slices[coord_dim] = coord_index
if second_coord_range:
slices[coord_dim2] = slice(second_coord_range[0],
second_coord_range[1])
return cube[tuple(slices)]
[docs]def extract_trajectory(cube, latitudes, longitudes, number_points=2):
"""Extract data along a trajectory.
latitudes and longitudes are the pairs of coordinates for two points.
number_points is the number of points between the two points.
This version uses the expensive interpolate method, but it may be
necceasiry for irregular grids.
If only two latitude and longitude coordinates are given,
extract_trajectory will produce a cube will extrapolate along a line
between those two points, and will add `number_points` points between
the two corners.
If more than two points are provided, then
extract_trajectory will produce a cube which has extrapolated the data
of the cube to those points, and `number_points` is not needed.
Parameters
----------
cube: iris.cube.Cube
input cube.
latitudes: list
list of latitude coordinates (floats).
longitudes: list
list of longitude coordinates (floats).
number_points: int
number of points to extrapolate (optional).
Returns
-------
iris.cube.Cube
collapsed cube.
Raises
------
ValueError
if latitude and longitude have different dimensions.
"""
from iris.analysis.trajectory import interpolate
if len(latitudes) != len(longitudes):
raise ValueError(
'Longitude & Latitude coordinates have different lengths')
if len(latitudes) == len(longitudes) == 2:
minlat, maxlat = np.min(latitudes), np.max(latitudes)
minlon, maxlon = np.min(longitudes), np.max(longitudes)
longitudes = np.linspace(minlon, maxlon, num=number_points)
latitudes = np.linspace(minlat, maxlat, num=number_points)
points = [('latitude', latitudes), ('longitude', longitudes)]
interpolated_cube = interpolate(cube, points) # Very slow!
return interpolated_cube