def read_data(infile_list, var, model, basin_cube):
"""Read the data files.
The CSIRO-Mk3-6-0 model seems to be formatted incorrectly
and you can't select the "global_ocean" by name
"""
cube = iris.load(infile_list,
gio.check_iris_var(var),
callback=save_history)
atts = cube[0].attributes
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube.attributes = atts
cube.attributes['history'] = history[0]
if var == 'northward_ocean_heat_transport':
if model == 'CSIRO-Mk3-6-0':
cube = cube[:, 2, :]
else:
cube = cube.extract(iris.Constraint(region='global_ocean'))
cube = timeseries.convert_to_annual(cube, full_months=True)
if basin_cube:
cube = uconv.mask_marginal_seas(cube, basin_cube)
return cube
def read_data(infiles, variable, calc_annual=False, chunk=False):
"""Load the input data."""
cube = iris.load(infiles,
gio.check_iris_var(variable),
callback=save_history)
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
if calc_annual:
cube = timeseries.convert_to_annual(cube, chunk=chunk)
coord_names = [coord.name() for coord in cube.dim_coords]
aux_coord_names = [coord.name() for coord in cube.aux_coords]
assert 'time' in coord_names
assert len(coord_names) == 3
grid_type = 'curvilinear' if aux_coord_names == ['latitude', 'longitude'
] else 'latlon'
infile_history = {}
infile_history[infiles[0]] = history[0]
cube.attributes['history'] = gio.write_metadata(file_info=infile_history)
return cube, coord_names, aux_coord_names, grid_type
def main(inargs):
"""Run the program."""
if inargs.depth:
level_constraint = iris.Constraint(depth=inargs.depth)
else:
level_constraint = iris.Constraint()
cube = iris.load(inargs.infiles,
inargs.var & level_constraint,
callback=save_history)
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
area_cube = read_area(inargs.area_file)
clim_cube = iris.load_cube(inargs.climatology,
inargs.var & level_constraint)
atts = set_attributes(inargs, cube, area_cube, clim_cube)
if inargs.smoothing:
cube = smooth_data(cube, inargs.smoothing)
area_weights = get_area_weights(clim_cube, area_cube)
metric = calc_amplification_metric(cube, clim_cube, area_weights, atts)
iris.save(metric, inargs.outfile)
def main(inargs):
"""Run the program."""
cube = iris.load(inargs.infiles, gio.check_iris_var(inargs.var), callback=save_history)
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
if inargs.annual:
cube = timeseries.convert_to_annual(cube, full_months=True)
cube, coord_names, regrid_status = grids.curvilinear_to_rectilinear(cube)
if inargs.area:
cube = multiply_by_area(cube)
if inargs.sftlf_file and inargs.realm:
sftlf_cube = iris.load_cube(inargs.sftlf_file, 'land_area_fraction')
cube = uconv.apply_land_ocean_mask(cube, sftlf_cube, inargs.realm)
zonal_aggregate = cube.collapsed('longitude', aggregation_functions[inargs.aggregation])
zonal_aggregate.remove_coord('longitude')
zonal_aggregate.attributes['history'] = gio.write_metadata(file_info={inargs.infiles[0]: history[0]})
iris.save(zonal_aggregate, inargs.outfile)
def read_data(infiles, variable, time_constraint):
"""Load the input data."""
cube = iris.load(infiles,
gio.check_iris_var(variable),
callback=save_history)
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube = cube.extract(time_constraint)
if not 'J' in str(cube.units):
cube = convert_to_joules(cube)
if variable == 'surface_downward_heat_flux_in_sea_water':
agg_method = 'sum'
elif variable == 'ocean_heat_content':
agg_method = 'mean'
cube = timeseries.convert_to_annual(cube, aggregation=agg_method)
coord_names = [coord.name() for coord in cube.dim_coords]
aux_coord_names = [coord.name() for coord in cube.aux_coords]
assert 'time' in coord_names
assert len(coord_names) == 3
grid_type = 'curvilinear' if aux_coord_names == ['latitude', 'longitude'
] else 'latlon'
return cube, coord_names, aux_coord_names, grid_type
def load_data(infile, basin):
"""Load, temporally aggregate and spatially slice input data"""
try:
with iris.FUTURE.context(cell_datetime_objects=True):
cube = iris.load(
infile,
'ocean_meridional_overturning_mass_streamfunction',
callback=save_history)
equalise_attributes(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube = cube[:, basin_index[basin], :, :]
cube = timeseries.convert_to_annual(cube)
experiment = cube.attributes['experiment_id']
if experiment == 'historicalMisc':
experiment = 'historicalAA'
depth_constraint = iris.Constraint(depth=lambda cell: cell <= 250)
sh_constraint = iris.Constraint(
latitude=lambda cell: -30.0 <= cell < 0.0)
nh_constraint = iris.Constraint(
latitude=lambda cell: 0.0 < cell <= 30.0)
sh_cube = cube.extract(depth_constraint & sh_constraint)
nh_cube = cube.extract(depth_constraint & nh_constraint)
except OSError:
sh_cube = nh_cube = experiment = None
return sh_cube, nh_cube, experiment
def get_data(filename, var, target_grid=None):
"""Read data.
Positive is defined as down.
"""
if filename:
with iris.FUTURE.context(cell_datetime_objects=True):
cube = iris.load_cube(filename, gio.check_iris_var(var))
cube = gio.check_time_units(cube)
cube = iris.util.squeeze(cube)
if target_grid:
cube, coord_names, regrid_status = grids.curvilinear_to_rectilinear(
cube, target_grid_cube=target_grid)
coord_names = [coord.name() for coord in cube.dim_coords]
if 'depth' in coord_names:
depth_constraint = iris.Constraint(depth=0)
cube = cube.extract(depth_constraint)
if 'up' in cube.standard_name:
cube.data = cube.data * -1
else:
cube = None
return cube
def get_data(filename, var, time_constraint):
"""Read data."""
cube = iris.load_cube(filename, gio.check_iris_var(var) & time_constraint)
cube = gio.check_time_units(cube)
cube = iris.util.squeeze(cube)
return cube
def concat_cubes(cube_list):
"""Concatenate an iris cube list."""
iris.util.unify_time_units(cube_list)
cube = cube_list.concatenate_cube()
cube = gio.check_time_units(cube)
return cube
def get_data(filename, var, target_grid=None):
"""Read data."""
cube = iris.load_cube(filename, gio.check_iris_var(var))
cube = gio.check_time_units(cube)
cube = iris.util.squeeze(cube)
return cube
def combine_cubes(cube_list):
"""Combine two like cubes"""
equalise_attributes(cube_list)
iris.util.unify_time_units(cube_list)
cube = cube_list.concatenate_cube()
cube = gio.check_time_units(cube)
return cube
def main(inargs):
"""Run the program."""
# Read data
cube = iris.load(inargs.infiles,
'surface_downward_eastward_stress',
callback=save_history)
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
# Prepare data
cube = timeseries.convert_to_annual(cube)
sftlf_cube = iris.load_cube(inargs.sftlf_file, 'land_area_fraction')
mask = create_land_mask(sftlf_cube, cube.shape)
cube.data = numpy.ma.asarray(cube.data)
cube.data.mask = mask
cube = cube.collapsed('longitude', iris.analysis.MEAN)
# Calculate metrics
xdata = cube.coord('latitude').points
xnew = numpy.linspace(xdata[0], xdata[-1], num=1000, endpoint=True)
hemispheres = ['sh', 'nh']
directions = ['easterly', 'westerly']
metric_dict = {}
for hemisphere, direction in itertools.product(hemispheres, directions):
metric_dict[(hemisphere, direction, 'location')] = []
metric_dict[(hemisphere, direction, 'magnitude')] = []
for ycube in cube.slices(['latitude']):
func = interp1d(xdata, ycube.data, kind='cubic')
ynew = func(xnew)
for hemisphere, direction in itertools.product(hemispheres,
directions):
loc, mag = wind_stress_metrics(xnew, ynew, hemisphere, direction)
metric_dict[(hemisphere, direction, 'location')].append(loc)
metric_dict[(hemisphere, direction, 'magnitude')].append(mag)
# Write the output file
atts = cube.attributes
infile_history = {inargs.infiles[0]: history[0]}
atts['history'] = gio.write_metadata(file_info=infile_history)
units_dict = {
'magnitude': cube.units,
'location': cube.coord('latitude').units
}
cube_list = create_outcubes(metric_dict, cube.attributes, units_dict,
cube.coord('time'))
iris.save(cube_list, inargs.outfile)
def read_infiles(infiles, var, time_constraint, ensnum):
"""Combine multiple input files into one cube"""
cube, history = gio.combine_files(infiles, var)
cube = gio.check_time_units(cube)
cube = cube.extract(time_constraint)
new_aux_coord = iris.coords.AuxCoord(ensnum, long_name='ensemble_member', units='no_unit')
cube.add_aux_coord(new_aux_coord)
return cube, history[0]
def main(inargs):
"""Run the program."""
# Read data
level_constraint, lat_constraint = get_constraints(inargs.depth,
inargs.hemisphere)
cube = iris.load(inargs.infiles,
gio.check_iris_var(inargs.var) & level_constraint,
callback=save_history)
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
# Get area file (if applicable)
if inargs.hemisphere:
cube, coord_names, regrid_status = grids.curvilinear_to_rectilinear(
cube)
cube = cube.extract(lat_constraint)
area_cube = None
else:
area_cube = read_optional(inargs.area_file)
# Mask ocean or atmosphere (if applicable)
if inargs.sftlf_file:
sftlf_file, selected_region = inargs.sftlf_file
sftlf_cube = read_optional(sftlf_file)
mask = create_mask(sftlf_cube, selected_region)
cube.data = numpy.ma.asarray(cube.data)
cube.data.mask = mask
if area_cube:
areas_dict = area_info(area_cube.copy(), mask, selected_region)
else:
areas_dict = {}
sftlf_cube = None
# Outfile attributes
atts = set_attributes(inargs, cube, area_cube, sftlf_cube, areas_dict)
# Temporal smoothing
if inargs.smoothing:
cube = smooth_data(cube, inargs.smoothing)
# Calculate metric
area_weights = get_area_weights(cube, area_cube)
if inargs.metric == 'bulk-deviation':
metric = calc_bulk_deviation(cube, area_weights, atts)
elif inargs.metric == 'mean':
metric = calc_global_mean(cube, area_weights, atts)
elif inargs.metric == 'grid-deviation':
metric = calc_grid_deviation(cube, inargs.var, area_weights, atts)
iris.save(metric, inargs.outfile)
def get_data(filenames,
var,
metadata_dict,
time_constraint,
sftlf_cube=None,
realm=None):
"""Read, merge, temporally aggregate and calculate zonal sum.
Positive is defined as down.
"""
if filenames:
with iris.FUTURE.context(cell_datetime_objects=True):
cube = iris.load(filenames, gio.check_iris_var(var))
metadata_dict[filenames[0]] = cube[0].attributes['history']
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube = iris.util.squeeze(cube)
cube = cube.extract(time_constraint)
coord_names = [coord.name() for coord in cube.dim_coords]
if 'depth' in coord_names:
depth_constraint = iris.Constraint(depth=0)
cube = cube.extract(depth_constraint)
cube = timeseries.convert_to_annual(cube, full_months=True)
cube, coord_names, regrid_status = grids.curvilinear_to_rectilinear(
cube)
cube = multiply_by_area(cube)
if 'up' in cube.standard_name:
cube.data = cube.data * -1
if sftlf_cube and realm in ['ocean', 'land']:
cube = uconv.apply_land_ocean_mask(cube, sftlf_cube, realm)
zonal_sum = cube.collapsed('longitude', iris.analysis.SUM)
zonal_sum.remove_coord('longitude')
grid_spacing = grids.get_grid_spacing(zonal_sum)
zonal_sum.data = zonal_sum.data / grid_spacing
else:
zonal_sum = None
return zonal_sum, metadata_dict
def main(inargs):
"""Run the program."""
if inargs.sftlf_file:
sftlf_cube = iris.load_cube(inargs.sftlf_file, 'land_area_fraction')
else:
sftlf_cube = None
try:
time_constraint = gio.get_time_constraint(inargs.time)
except AttributeError:
time_constraint = iris.Constraint()
with iris.FUTURE.context(cell_datetime_objects=True):
cube = iris.load(inargs.infiles, gio.check_iris_var(inargs.var))
history = cube[0].attributes['history']
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube = iris.util.squeeze(cube)
cube.attributes['history'] = gio.write_metadata(
file_info={inargs.infiles[0]: history})
cube = cube.extract(time_constraint)
cube = timeseries.convert_to_annual(cube, full_months=True)
output = {}
output['full'] = calc_fields(cube,
sftlf_cube,
inargs.aggregation,
realm=None,
area=inargs.area)
if inargs.sftlf_file:
for realm in ['ocean', 'land']:
output[realm] = calc_fields(cube,
sftlf_cube,
inargs.aggregation,
realm=realm,
area=inargs.area)
cube_list = iris.cube.CubeList()
for realm, output_cubes in output.items():
for cube in output_cubes:
cube_list.append(cube)
iris.FUTURE.netcdf_no_unlimited = True
iris.save(cube_list, inargs.outfile, netcdf_format='NETCDF3_CLASSIC')
def main(inargs):
"""Run the program."""
region_constraint = iris.Constraint(
region='atlantic_arctic_ocean'
) # "atlantic_arctic_ocean", "indian_pacific_ocean ", "global_ocean "
cube = iris.load(inargs.infiles,
'ocean_meridional_overturning_mass_streamfunction'
& region_constraint,
callback=save_history)
if not cube:
cube = iris.load(inargs.infiles,
'ocean_meridional_overturning_mass_streamfunction',
callback=save_history)
equalise_attributes(cube)
cube = cube.concatenate_cube()
cube = cube[:, 0, :, :] # index for Atlantic
else:
equalise_attributes(cube)
cube = cube.concatenate_cube()
cube.remove_coord('region')
cube = gio.check_time_units(cube)
cube = timeseries.convert_to_annual(cube)
target_lat, error = uconv.find_nearest(cube.coord('latitude').points,
30,
index=False)
cube = cube.extract(iris.Constraint(latitude=target_lat))
cube.remove_coord('latitude')
assert str(cube.units) == 'kg s-1'
cube.data = (cube.data / 1023) / 1e+6
cube.units = 'Sv'
#dim_coord_names = [coord.name() for coord in cube.dim_coords]
#vert_extents = spatial_weights.calc_vertical_weights_1D(cube.coord('depth'), dim_coord_names, cube.shape)
metric = cube.collapsed('depth', iris.analysis.MAX)
metric.remove_coord('depth')
try:
metric.attributes['history'] = gio.write_metadata(
file_info={inargs.infiles[0]: cube.attributes['history']})
except KeyError:
pass
iris.save(metric, inargs.outfile)
def main(inargs):
"""Run the program."""
cube = iris.load_cube(inargs.infile, inargs.var)
cube = gio.check_time_units(cube)
if inargs.annual:
cube = timeseries.convert_to_annual(cube, chunk=inargs.chunk)
log = cmdprov.new_log(
infile_history={inargs.infile: cube.attributes['history']},
git_repo=repo_dir)
dim_vals = {}
dim_vals['latitude'] = get_dim_vals(inargs.lats)
dim_vals['longitude'] = get_dim_vals(inargs.lons)
if inargs.levs:
dim_vals['depth'] = get_dim_vals(inargs.levs)
else:
dim_vals['depth'] = get_dim_vals(inargs.depth_bnds, bounds=True)
# Regrid from curvilinear to rectilinear if necessary
regrid_status = False
if inargs.lats:
horizontal_grid = grids.make_grid(dim_vals['latitude'],
dim_vals['longitude'])
cube, coord_names, regrid_status = grids.curvilinear_to_rectilinear(
cube, target_grid_cube=horizontal_grid)
# Regrid to new grid
if dim_vals['depth'] or not regrid_status:
sample_points = get_sample_points(cube, dim_vals)
cube = cube.interpolate(sample_points, iris.analysis.Linear())
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
if inargs.levs:
cube = spatial_weights.guess_depth_bounds(cube)
else:
cube.coord('depth').bounds = get_depth_bounds(inargs.depth_bnds)
if numpy.isnan(numpy.min(cube.data)):
cube = remove_nans(cube)
# Reinstate time dim_coord if necessary
aux_coord_names = [coord.name() for coord in cube.aux_coords]
if 'time' in aux_coord_names:
cube = iris.util.new_axis(cube, 'time')
cube.attributes['history'] = log
iris.save(cube, inargs.outfile, fill_value=1e20)
def read_data(infile, variable, calc_annual=False, chunk=False):
"""Load the input data."""
cube = iris.load_cube(infile, gio.check_iris_var(variable))
cube = gio.check_time_units(cube)
if calc_annual:
cube = timeseries.convert_to_annual(cube, chunk=chunk)
coord_names = [coord.name() for coord in cube.dim_coords]
aux_coord_names = [coord.name() for coord in cube.aux_coords]
assert 'time' in coord_names
grid_type = 'curvilinear' if aux_coord_names == ['latitude', 'longitude'
] else 'latlon'
return cube, coord_names, aux_coord_names, grid_type
def load_data(infiles, basin):
"""Load, temporally aggregate and spatially slice input data"""
with iris.FUTURE.context(cell_datetime_objects=True):
cube = iris.load(infiles, 'ocean_meridional_overturning_mass_streamfunction', callback=save_history)
equalise_attributes(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube = cube[:, basin_index[basin], : ,:]
cube = timeseries.convert_to_annual(cube)
experiment = cube.attributes['experiment_id']
depth_constraint = iris.Constraint(depth=lambda cell: cell <= 250)
lat_constraint = iris.Constraint(latitude=lambda cell: -30.0 <= cell < 30.0)
cube = cube.extract(depth_constraint & lat_constraint)
return cube, experiment
def get_data(filenames,
var,
metadata_dict,
time_constraint,
area=False,
invert_evap=False):
"""Read, merge, temporally aggregate and calculate zonal mean."""
if filenames:
with iris.FUTURE.context(cell_datetime_objects=True):
cube = iris.load(filenames, gio.check_iris_var(var))
metadata_dict[filenames[0]] = cube[0].attributes['history']
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube = iris.util.squeeze(cube)
cube = cube.extract(time_constraint)
cube = timeseries.convert_to_annual(cube, full_months=True)
cube, coord_names, regrid_status = grids.curvilinear_to_rectilinear(
cube)
assert cube.units == 'kg m-2 s-1'
cube.data = cube.data * 86400
units = 'mm/day'
if invert_evap and (var == 'water_evaporation_flux'):
cube.data = cube.data * -1
if area:
cube = spatial_weights.multiply_by_area(cube)
zonal_mean = cube.collapsed('longitude', iris.analysis.MEAN)
zonal_mean.remove_coord('longitude')
else:
zonal_mean = None
return zonal_mean, metadata_dict
def read_data(infile_list, var, basin_cube, region):
"""Read the data files.
The CSIRO-Mk3-6-0 model seems to be formatted incorrectly
and you can't select the regioins by name.
"""
cube = iris.load(infile_list,
gio.check_iris_var(var),
callback=save_history)
atts = cube[0].attributes
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube.attributes = atts
cube.attributes['history'] = history[0]
model = atts['model_id']
if var == 'northward_ocean_heat_transport':
region_index = {}
region_index['atlantic_arctic_ocean'] = 0
region_index['indian_pacific_ocean'] = 1
region_index['global_ocean'] = 2
if model == 'CSIRO-Mk3-6-0':
cube = cube[:, region_index[region], :]
else:
cube = cube.extract(iris.Constraint(region=region))
cube = timeseries.convert_to_annual(cube, full_months=True)
if basin_cube:
cube = uconv.mask_marginal_seas(cube, basin_cube)
if region != 'global_ocean':
basin_numbers = {}
basin_numbers['atlantic_arctic_ocean'] = [2, 4]
basin_numbers['indian_pacific_ocean'] = [3, 5]
cube = uconv.mask_unwanted_seas(cube, basin_cube,
basin_numbers[region])
return cube
def main(inargs):
"""Run the program."""
level_subset = gio.iris_vertical_constraint(inargs.min_depth,
inargs.max_depth)
for temperature_file in inargs.temperature_files:
temperature_cube = iris.load_cube(
temperature_file, inargs.temperature_var & level_subset)
temperature_cube = gio.check_time_units(temperature_cube)
metadata_dict = {
temperature_file: temperature_cube.attributes['history']
}
temperature_atts = temperature_cube.attributes
if inargs.annual:
temperature_cube = timeseries.convert_to_annual(temperature_cube,
chunk=inargs.chunk)
coord_names = [coord.name() for coord in temperature_cube.dim_coords]
assert coord_names[0] == 'time'
assert coord_names[1] == 'depth'
volume_data, metadata_dict = get_volume(inargs.volume_file,
inargs.area_file,
temperature_cube,
metadata_dict)
ohc_cube = calc_ohc_vertical_integral(temperature_cube,
volume_data,
inargs.density,
inargs.specific_heat,
coord_names,
chunk=inargs.chunk)
ohc_cube = add_metadata(temperature_cube, temperature_atts, ohc_cube,
metadata_dict, inargs)
ohc_file = get_outfile_name(temperature_file, annual=inargs.annual)
iris.save(ohc_cube, ohc_file)
print(ohc_file)
def load_data(filenames, standard_name, time_constraint, metadata_dict,
input_timescale):
"""Basic data loading and temporal smoothing"""
with iris.FUTURE.context(cell_datetime_objects=True):
cube = iris.load(filenames, gio.check_iris_var(standard_name))
assert cube, "files do not exist: %s" % (filenames[0])
metadata_dict[filenames[0]] = cube[0].attributes['history']
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube = cube.extract(time_constraint)
cube = iris.util.squeeze(cube)
attributes = cube.attributes
if not input_timescale == 'annual':
cube = timeseries.convert_to_annual(cube, full_months=True)
return cube, metadata_dict, attributes
def get_cube_list(infiles, agg, time_bounds=None, quick=False):
"""Read and process data."""
assert agg in ['clim', 'anom']
ensemble_cube_list = iris.cube.CubeList([])
for ensnum, ensemble_member in enumerate(infiles):
print(ensemble_member)
cube, history = gio.combine_files(
ensemble_member,
'precipitation_minus_evaporation_flux',
new_calendar='365_day')
cube = gio.check_time_units(cube)
if time_bounds:
time_constraint = gio.get_time_constraint(time_bounds)
cube = cube.extract(time_constraint)
elif quick:
cube = cube[0:120, ::]
if agg == 'clim':
cube = timeseries.convert_to_annual(cube,
aggregation='mean',
days_in_month=True)
cube = cube.collapsed('time', iris.analysis.MEAN)
elif agg == 'anom':
start_data = cube.data[0, ::]
cube = cube[-1, ::]
cube.data = cube.data - start_data
cube.remove_coord('time')
cube = regrid(cube)
new_aux_coord = iris.coords.AuxCoord(ensnum,
long_name='ensemble_member',
units='no_unit')
cube.add_aux_coord(new_aux_coord)
cube.cell_methods = ()
ensemble_cube_list.append(cube)
print("Total number of models:", len(ensemble_cube_list))
return ensemble_cube_list, history
def calc_mean(infiles, variable, lat_constraint, time_constraint, area_file):
"""Load the infiles and calculate the hemispheric mean values."""
with iris.FUTURE.context(cell_datetime_objects=True):
cube = iris.load(infiles,
variable & lat_constraint & time_constraint,
callback=save_history)
equalise_attributes(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube = timeseries.convert_to_annual(cube)
orig_units = str(cube.units)
orig_atts = cube.attributes
area_weights = get_area_weights(cube, area_file, lat_constraint)
mean = cube.collapsed(['longitude', 'latitude'],
iris.analysis.MEAN,
weights=area_weights)
return mean, orig_units, orig_atts
def main(inargs):
"""Run the program."""
# Read data
cube = iris.load(inargs.infiles,
'precipitation minus evaporation flux',
callback=save_history)
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
# Prepare data
cube = timeseries.convert_to_annual(cube)
zonal_mean_cube = cube.collapsed('longitude', iris.analysis.MEAN)
# Calculate metrics
xdata = cube.coord('latitude').points
xnew = numpy.linspace(xdata[0], xdata[-1], num=1000, endpoint=True)
metric_dict = {'nh': [], 'sh': [], 'globe': []}
for ycube in zonal_mean_cube.slices(['latitude']):
func = interp1d(xdata, ycube.data, kind='cubic')
ynew = func(xnew)
for hemisphere in ['nh', 'sh', 'globe']:
amp = pe_amplitude(xnew, ynew, hemisphere)
metric_dict[hemisphere].append(amp)
# Write the output file
atts = cube.attributes
infile_history = {inargs.infiles[0]: history[0]}
atts['history'] = gio.write_metadata(file_info=infile_history)
cube_list = create_outcubes(metric_dict, cube.attributes, cube.units,
cube.coord('time'))
iris.save(cube_list, inargs.outfile)
def main(inargs):
"""Run the program."""
cube = iris.load(inargs.infiles,
gio.check_iris_var(inargs.var),
callback=save_history)
atts = cube[0].attributes
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube.attributes = atts
orig_long_name = cube.long_name
if cube.standard_name == None:
orig_standard_name = orig_long_name.replace(' ', '_')
else:
orig_standard_name = cube.standard_name
orig_var_name = cube.var_name
# Temporal smoothing
cube = timeseries.convert_to_annual(cube, full_months=True)
# Mask marginal seas
if inargs.basin:
if '.nc' in inargs.basin:
basin_cube = iris.load_cube(inargs.basin_file)
cube = uconv.mask_marginal_seas(cube, basin_cube)
else:
basin_cube = 'create'
else:
basin_cube = None
# Regrid (if needed)
if inargs.regrid:
cube, coord_names, regrid_status = grids.curvilinear_to_rectilinear(
cube)
# Change units (remove m-2)
if inargs.area:
cube = multiply_by_area(cube, inargs.area)
cube.attributes = atts
cube.long_name = orig_long_name
cube.standard_name = orig_standard_name
cube.var_name = orig_var_name
# History
history_attribute = get_history_attribute(inargs.infiles[0], history[0])
cube.attributes['history'] = gio.write_metadata(
file_info=history_attribute)
# Calculate output for each basin
if type(basin_cube) == iris.cube.Cube:
ndim = cube.ndim
basin_array = uconv.broadcast_array(basin_cube.data,
[ndim - 2, ndim - 1], cube.shape)
basin_list = ['atlantic', 'pacific', 'indian', 'globe']
elif type(basin_cube) == str:
basin_array = uconv.create_basin_array(cube)
basin_list = ['atlantic', 'pacific', 'indian', 'globe']
else:
basin_array = None
basin_list = ['globe']
dim_coord_names = [coord.name() for coord in cube.dim_coords]
aux_coord_names = [coord.name() for coord in cube.aux_coords]
assert len(dim_coord_names) == 3
assert dim_coord_names[0] == 'time'
x_axis_name = dim_coord_names[2]
for aux_coord in aux_coord_names:
cube.remove_coord(aux_coord)
out_cubes = []
for basin_name in basin_list:
data_cube = cube.copy()
if not basin_name == 'globe':
data_cube.data.mask = numpy.where(
(data_cube.data.mask == False) &
(basin_array == basins[basin_name]), False, True)
# Zonal statistic
zonal_cube = data_cube.collapsed(
x_axis_name, aggregation_functions[inargs.zonal_stat])
zonal_cube.remove_coord(x_axis_name)
# Attributes
standard_name = 'zonal_%s_%s_%s' % (inargs.zonal_stat,
orig_standard_name, basin_name)
var_name = '%s_%s_%s' % (orig_var_name,
aggregation_abbreviations[inargs.zonal_stat],
basin_name)
iris.std_names.STD_NAMES[standard_name] = {
'canonical_units': zonal_cube.units
}
zonal_cube.standard_name = standard_name
zonal_cube.long_name = standard_name.replace('_', ' ')
zonal_cube.var_name = var_name
out_cubes.append(zonal_cube)
out_cubes = iris.cube.CubeList(out_cubes)
iris.save(out_cubes, inargs.outfile)
def main(inargs):
"""Run the program."""
# Read drift coefficients
coefficient_a_cube = iris.load_cube(inargs.coefficient_file,
'coefficient a')
coefficient_b_cube = iris.load_cube(inargs.coefficient_file,
'coefficient b')
coefficient_c_cube = iris.load_cube(inargs.coefficient_file,
'coefficient c')
coefficient_d_cube = iris.load_cube(inargs.coefficient_file,
'coefficient d')
if inargs.coefficient_check and (inargs.var in [
'sea_water_potential_temperature', 'sea_water_salinity'
]):
sanity_summary = coefficient_sanity_check(coefficient_a_cube,
coefficient_b_cube,
coefficient_c_cube,
coefficient_d_cube,
inargs.var)
else:
sanity_summary = None
# Read first data cube to get some information
first_data_cube = iris.load_cube(inargs.data_files[0],
gio.check_iris_var(inargs.var))
coord_names = [
coord.name() for coord in first_data_cube.coords(dim_coords=True)
]
assert coord_names[0] == 'time'
if inargs.annual:
assert inargs.timescale == 'annual'
first_data_cube = timeseries.convert_to_annual(first_data_cube,
chunk=12)
time_diff, branch_time, new_time_unit = time_adjustment(
first_data_cube,
coefficient_a_cube,
inargs.timescale,
branch_time=inargs.branch_time)
data_history = first_data_cube.attributes['history']
del first_data_cube
new_cubelist = []
for fnum, filename in enumerate(inargs.data_files):
# Read data
data_cube = iris.load_cube(filename, gio.check_iris_var(inargs.var))
# Reinstate time dim_coord if necessary
aux_coord_names = [coord.name() for coord in data_cube.aux_coords]
if 'time' in aux_coord_names:
data_cube = iris.util.new_axis(data_cube, 'time')
if inargs.annual:
assert inargs.timescale == 'annual'
data_cube = timeseries.convert_to_annual(data_cube, chunk=12)
data_cube = check_data_units(data_cube, coefficient_a_cube)
data_cube = gio.check_time_units(data_cube)
data_cube.cell_methods = ()
if not inargs.no_parent_check:
check_attributes(data_cube.attributes,
coefficient_a_cube.attributes)
# Sync the data time axis with the coefficient time axis
time_coord = data_cube.coord('time')
time_coord.convert_units(new_time_unit)
time_values = time_coord.points.astype(numpy.float32) - time_diff
if not inargs.no_time_check:
check_time_adjustment(time_values, coefficient_a_cube, branch_time,
fnum)
# Remove the drift
if fnum == 0:
drift_signal, start_polynomial = apply_polynomial(
time_values,
coefficient_a_cube.data,
coefficient_b_cube.data,
coefficient_c_cube.data,
coefficient_d_cube.data,
poly_start=None,
chunk=inargs.chunk)
else:
try:
start = start_polynomial[0, ::]
except IndexError:
start = start_polynomial[0]
drift_signal, scraps = apply_polynomial(time_values,
coefficient_a_cube.data,
coefficient_b_cube.data,
coefficient_c_cube.data,
coefficient_d_cube.data,
poly_start=start,
chunk=inargs.chunk)
if not inargs.dummy:
new_cube = data_cube - drift_signal
#assert new_cube.data.mask.sum() == drift_signal.mask.sum()
new_cube.data.mask = drift_signal.mask
if not inargs.no_data_check:
check_data(new_cube, data_cube, filename)
else:
print('fake run - drift signal not subtracted')
new_cube = data_cube
new_cube.metadata = data_cube.metadata
if sanity_summary:
new_cube.attributes['drift_removal'] = sanity_summary
assert (inargs.outfile[-3:] == '.nc') or (inargs.outfile[-1] == '/')
if inargs.outfile[-3:] == '.nc':
new_cubelist.append(new_cube)
elif inargs.outfile[-1] == '/':
infile = filename.split('/')[-1]
if inargs.annual:
infile = re.sub('Omon', 'Oyr', infile)
outfile = inargs.outfile + infile
metadata_dict = {
infile: data_cube.attributes['history'],
inargs.coefficient_file:
coefficient_a_cube.attributes['history']
}
new_cube.attributes['history'] = gio.write_metadata(
file_info=metadata_dict)
#assert new_cube.data.dtype == numpy.float32
iris.save(new_cube, outfile, netcdf_format='NETCDF3_CLASSIC')
print('output:', outfile)
del new_cube
del drift_signal
if inargs.outfile[-3:] == '.nc':
new_cubelist = iris.cube.CubeList(new_cubelist)
equalise_attributes(new_cubelist)
new_cubelist = new_cubelist.concatenate_cube()
try:
metadata_dict = {
inargs.data_files[0]: data_history,
inargs.coefficient_file:
coefficient_a_cube.attributes['history']
}
except KeyError:
metadata_dict = {inargs.data_files[0]: data_history}
new_cubelist.attributes['history'] = gio.write_metadata(
file_info=metadata_dict)
#assert new_cubelist[0].data.dtype == numpy.float32
iris.save(new_cubelist, inargs.outfile)
def main(inargs):
"""Run the program."""
# Read data
try:
time_constraint = gio.get_time_constraint(inargs.time_bounds)
except AttributeError:
time_constraint = iris.Constraint()
depth_constraint = gio.iris_vertical_constraint(inargs.min_depth,
inargs.max_depth)
with iris.FUTURE.context(cell_datetime_objects=True):
cube = iris.load(inargs.infiles,
gio.check_iris_var(inargs.var) & depth_constraint)
history = cube[0].attributes['history']
atts = cube[0].attributes
equalise_attributes(cube)
iris.util.unify_time_units(cube)
cube = cube.concatenate_cube()
cube = gio.check_time_units(cube)
cube = cube.extract(time_constraint)
cube = iris.util.squeeze(cube)
if 'salinity' in inargs.var:
cube = gio.salinity_unit_check(cube)
infile_metadata = {inargs.infiles[0]: history}
if inargs.annual:
cube = timeseries.convert_to_annual(cube, full_months=True)
if inargs.min_depth or inargs.max_depth:
cube = vertical_mean(cube)
agg_cube = get_agg_cube(cube, inargs.aggregation)
if inargs.regrid:
before_sum = agg_cube.data.sum()
before_mean = agg_cube.data.mean()
agg_cube, coord_names, regrid_status = grids.curvilinear_to_rectilinear(
agg_cube)
if regrid_status:
print('Warning: Data has been regridded')
print('Before sum:', '%.2E' % Decimal(before_sum))
print('After sum:', '%.2E' % Decimal(agg_cube.data.sum()))
print('Before mean:', '%.2E' % Decimal(before_mean))
print('After mean:', '%.2E' % Decimal(agg_cube.data.mean()))
if inargs.subtract_tropics:
agg_cube = subtract_tropics(agg_cube)
if inargs.land_mask:
sftlf_cube = iris.load_cube(inargs.land_mask, 'land_area_fraction')
agg_cube = uconv.apply_land_ocean_mask(agg_cube, sftlf_cube, 'ocean')
atts['history'] = gio.write_metadata(file_info=infile_metadata)
agg_cube.attributes = atts
iris.FUTURE.netcdf_no_unlimited = True
iris.save(agg_cube, inargs.outfile)
