def infer_hfds(cube_dict, inargs):
"""Infer the downward heat flux into ocean."""
hfls_data = grids.regrid_1D(cube_dict['hfls'],
cube_dict['rns'],
'latitude',
clear_units=False)
hfss_data = grids.regrid_1D(cube_dict['hfss'],
cube_dict['rns'],
'latitude',
clear_units=False)
data_list = [hfls_data, hfss_data, cube_dict['rns']]
if inargs.hfsithermds_files:
hfsithermds_data = grids.regrid_1D(cube_dict['hfsithermds'],
cube_dict['rns'],
'latitude',
clear_units=False)
data_list.append(hfsithermds_data)
else:
hfsithermds_data = 0.0
iris.util.unify_time_units(data_list)
cube_dict['hfds-inferred'] = cube_dict[
'rns'] + hfls_data + hfss_data + hfsithermds_data
return cube_dict
def plot_ensmean(data_dict, experiment, nexperiments,
single_run=False, linestyle='-', linewidth=2.0):
"""Plot the ensemble mean.
If single_run is true, the ensemble is calculated using
only the first run from each model/physics family.
"""
target_grid = make_zonal_grid()
regridded_cube_list = iris.cube.CubeList([])
count = 0
for key, cube in data_dict.items():
model, physics, realization = key
if not single_run or ((realization == 'r1') or (model == 'FGOALS-g2')):
regridded_cube = grids.regrid_1D(cube, target_grid, 'latitude')
new_aux_coord = iris.coords.AuxCoord(count, long_name='ensemble_member', units='no_unit')
regridded_cube.add_aux_coord(new_aux_coord)
regridded_cube.cell_methods = None
regridded_cube.data = regridded_cube.data.astype(numpy.float64)
regridded_cube_list.append(regridded_cube)
count = count + 1
if len(regridded_cube_list) > 1:
equalise_attributes(regridded_cube_list)
ensemble_cube = regridded_cube_list.merge_cube()
ensemble_mean = ensemble_cube.collapsed('ensemble_member', iris.analysis.MEAN)
else:
ensemble_mean = regridded_cube_list[0]
label, color = get_ensemble_label_color(experiment, nexperiments, count, single_run)
iplt.plot(ensemble_mean, label=label, color=color, linestyle=linestyle, linewidth=linewidth)
return ensemble_mean
def plot_inferred_data(primary_data, secondary_data, y_axis_name, y_var_name,
y_standard_name, y_long_name, quantity):
"""Plot the inferred data.
inferred = primary + secondary (for quantity = OHC)
OR
inferred = primary - secondary (for HTC and SFL)
"""
primary_data.coord(y_axis_name).var_name = y_var_name
primary_data.coord(y_axis_name).standard_name = y_standard_name
primary_data.coord(y_axis_name).long_name = y_long_name
primary_data.units = ''
regridded_secondary_data = grids.regrid_1D(secondary_data,
primary_data,
y_axis_name,
clear_units=True)
if 'OHC' in quantity:
label = 'inferred ' + quantity + ' (= HTC + SFL)'
iplt.plot(primary_data + regridded_secondary_data,
label=label,
color='black',
linestyle='--')
elif 'HTC' in quantity:
label = 'inferred ' + quantity + ' (= OHC - SFL)'
iplt.plot(primary_data - regridded_secondary_data,
label=label,
color='green',
linestyle='--')
elif 'SFL' in quantity:
label = 'inferred ' + quantity + ' (= OHC - HTC)'
iplt.plot(primary_data - regridded_secondary_data,
label=label,
color='orange',
linestyle='--')