def main(inargs):
"""Run the program."""
# Read the data
dset_in = xarray.open_dataset(inargs.infile)
gio.check_xarrayDataset(dset_in, inargs.variable)
subset_dict = gio.get_subset_kwargs(inargs)
darray = dset_in[inargs.variable].sel(**subset_dict)
# Calculate the zonal anomaly
zonal_mean = darray.mean(dim='longitude')
zonal_anomaly = darray - zonal_mean
# Write output file
d = {}
for dim in darray.dims:
d[dim] = darray[dim]
d[inargs.variable] = (darray.dims, zonal_anomaly)
dset_out = xarray.Dataset(d)
dset_out[inargs.variable].attrs = {
'long_name': darray.attrs['long_name'],
'standard_name': darray.attrs['standard_name'],
'units': darray.attrs['units'],
'notes': 'The zonal mean has been subtracted at each time step.'
}
gio.set_global_atts(dset_out, dset_in.attrs, {
inargs.infile: dset_in.attrs['history'],
})
dset_out.to_netcdf(inargs.outfile, format='NETCDF3_CLASSIC')
def calc_zw3(ifile, var_id, ofile):
"""Calculate an index of the Southern Hemisphere ZW3 pattern.
Ref: Raphael (2004). A zonal wave 3 index for the Southern Hemisphere.
Geophysical Research Letters, 31(23), L23212.
doi:10.1029/2004GL020365.
Expected input: Raphael (2004) uses is the 500hPa geopotential height,
sea level pressure or 500hPa zonal anomalies which are constructed by
removing the zonal mean of the geopotential height from each grid point
(preferred). The running mean (and zonal mean too if using it) should
have been applied to the input data beforehand. Raphael (2004) uses a
3-month running mean.
Design notes: This function uses cdo instead of CDAT because the
cdutil library doesn't have routines for calculating the daily
climatology or stdev.
"""
# Read data
dset_in = xarray.open_dataset(ifile)
gio.check_xarrayDataset(dset_in, var_id)
# Calculate the index
groupby_op = get_groupby_op(dset_in['time'].values)
index = {}
for region in ['zw31', 'zw32', 'zw33']:
south_lat, north_lat, west_lon, east_lon = gio.regions[region]
darray = dset_in[var_id].sel(
latitude=slice(south_lat, north_lat),
longitude=slice(west_lon,
east_lon)).mean(dim=['latitude', 'longitude'])
clim = darray.groupby(groupby_op).mean(dim='time')
anom = darray.groupby(groupby_op) - clim
stdev = darray.groupby(groupby_op).std(dim='time')
norm = anom.groupby(groupby_op) / stdev
index[region] = norm.values
zw3_timeseries = (index['zw31'] + index['zw32'] + index['zw33']) / 3.0
# Write output file
d = {}
d['time'] = darray['time']
d['zw3'] = (['time'], zw3_timeseries)
dset_out = xarray.Dataset(d)
dset_out['zw3'].attrs = {
'id': 'zw3',
'long_name': 'zonal_wave_3_index',
'standard_name': 'zonal_wave_3_index',
'units': '',
'notes': 'Ref: ZW3 index of Raphael (2004)'
}
gio.set_global_atts(dset_out, dset_in.attrs,
{ifile: dset_in.attrs['history']})
dset_out.to_netcdf(ofile, format='NETCDF3_CLASSIC')
def calc_nino_new(index, ifile, var_id, base_period, ofile):
"""Calculate a new Nino index.
Ref: Ren & Jin (2011). Nino indices for two types of ENSO.
Geophysical Research Letters, 38(4), L04704.
doi:10.1029/2010GL046031.
Expected input: Sea surface temperature data.
"""
# Calculate the traditional NINO3 and NINO4 indices
regions = ['NINO3', 'NINO4']
anomaly_timeseries = {}
for reg in regions:
dset_in, anomaly_timeseries[reg] = calc_nino(reg, ifile, var_id,
base_period, None)
# Calculate the new Ren & Jin index
ntime = len(anomaly_timeseries['NINO3'])
nino_new_timeseries = numpy.ma.zeros(ntime)
for i in range(0, ntime):
nino3_val = anomaly_timeseries['NINO3'][i]
nino4_val = anomaly_timeseries['NINO4'][i]
product = nino3_val * nino4_val
alpha = 0.4 if product > 0 else 0.0
if index == 'NINOCT':
nino_new_timeseries[i] = numpy.ma.subtract(
nino3_val, (numpy.ma.multiply(nino4_val, alpha)))
elif index == 'NINOWP':
nino_new_timeseries[i] = numpy.ma.subtract(
nino4_val, (numpy.ma.multiply(nino3_val, alpha)))
# Write output
d = {}
d['time'] = dset_in['time']
d['nino' + index[4:]] = (['time'], nino_new_timeseries)
dset_out = xarray.Dataset(d)
hx = 'Ref: Ren & Jin 2011, GRL, 38, L04704. Base period: %s to %s' % (
base_period[0], base_period[1])
long_name = {}
long_name['ninoCT'] = 'nino_cold_tongue_index'
long_name['ninoWP'] = 'nino_warm_pool_index'
dset_out['nino' + index[4:]].attrs = {
'long_name': long_name['nino' + index[4:]],
'standard_name': long_name['nino' + index[4:]],
'units': 'Celsius',
'notes': hx
}
gio.set_global_atts(dset_out, dset_in.attrs,
{ifile: dset_in.attrs['history']})
dset_out.to_netcdf(ofile, format='NETCDF3_CLASSIC')
def calc_sam(ifile, var_id, ofile):
"""Calculate an index of the Southern Annular Mode.
Ref: Gong & Wang (1999). Definition of Antarctic Oscillation index.
Geophysical Research Letters, 26(4), 459-462.
doi:10.1029/1999GL900003
Expected input: Mean sea level pressure data.
Concept: Difference between the normalised zonal mean pressure
at 40S and 65S.
"""
# Read data
dset_in = xarray.open_dataset(ifile)
gio.check_xarrayDataset(dset_in, var_id)
# Calculate index
north_lat = uconv.find_nearest(dset_in['latitude'].values, -40)
south_lat = uconv.find_nearest(dset_in['latitude'].values, -65)
darray = dset_in[var_id].sel(latitude=(south_lat,
north_lat)).mean(dim='longitude')
groupby_op = get_groupby_op(darray['time'].values)
clim = darray.groupby(groupby_op).mean(dim='time')
anom = darray.groupby(groupby_op) - clim
stdev = darray.groupby(groupby_op).std(dim='time')
norm = anom.groupby(groupby_op) / stdev
sam_timeseries = norm.sel(latitude=north_lat).values - norm.sel(
latitude=south_lat).values
# Write output file
d = {}
d['time'] = darray['time']
d['sam'] = (['time'], sam_timeseries)
dset_out = xarray.Dataset(d)
hx = 'Ref: Gong & Wang (1999). GRL, 26, 459-462. doi:10.1029/1999GL900003'
dset_out['sam'].attrs = {
'long_name': 'Southern_Annular_Mode_Index',
'standard_name': 'Southern_Annular_Mode_Index',
'units': '',
'notes': hx
}
gio.set_global_atts(dset_out, dset_in.attrs,
{ifile: dset_in.attrs['history']})
dset_out.to_netcdf(ofile, format='NETCDF3_CLASSIC')
def main(inargs):
"""Run the program."""
# Read the data
dset_in = xarray.open_dataset(inargs.infile)
gio.check_xarrayDataset(dset_in, inargs.var)
darray, long_name, units = extract_data(dset_in, inargs)
# Perform task
outdata_dict = {}
if inargs.outtype == 'coefficients':
outdata_dict = _get_coefficients(darray.values,
darray['longitude'].values,
inargs.min_freq, inargs.max_freq,
long_name, units, outdata_dict)
if inargs.sign_change:
outdata_dict = _get_sign_change(darray.values, outdata_dict)
if inargs.env_max:
env_max_min_freq, env_max_max_freq = inargs.env_max
outdata_dict = _get_env_max(darray.values,
darray['longitude'].values,
env_max_min_freq, env_max_max_freq,
units, outdata_dict)
dims = darray.dims[:-1]
else:
outdata_dict = _filter_data(darray.values, darray['longitude'].values,
inargs.min_freq, inargs.max_freq,
inargs.var, long_name, units,
inargs.outtype, outdata_dict)
dims = darray.dims
# Write the output file
d = {}
for dim in dims:
d[dim] = darray[dim]
for outvar in outdata_dict.keys():
d[outvar] = (dims, outdata_dict[outvar][0])
dset_out = xarray.Dataset(d)
for outvar in outdata_dict.keys():
dset_out[outvar].attrs = outdata_dict[outvar][1]
gio.set_global_atts(dset_out, dset_in.attrs, {
inargs.infile: dset_in.attrs['history'],
})
dset_out.to_netcdf(inargs.outfile)
def calc_nino(index, ifile, var_id, base_period, ofile):
"""Calculate a Nino index.
Expected input: Sea surface temperature data.
"""
index_name = 'nino' + index[4:]
# Read the data
dset_in = xarray.open_dataset(ifile)
gio.check_xarrayDataset(dset_in, var_id)
# Calculate the index
south_lat, north_lat, west_lon, east_lon = gio.regions[index_name]
darray = dset_in[var_id].sel(
latitude=slice(south_lat, north_lat),
longitude=slice(west_lon,
east_lon)).mean(dim=['latitude', 'longitude'])
groupby_op = get_groupby_op(darray['time'].values)
clim = darray.sel(
time=slice(base_period[0], base_period[1])).groupby(groupby_op).mean()
anom = darray.groupby(groupby_op) - clim
# Write output
if ofile:
d = {}
d['time'] = darray['time']
d[index_name] = (['time'], anom.values)
dset_out = xarray.Dataset(d)
hx = 'lat: %s to %s, lon: %s to %s, base: %s to %s' % (
south_lat, north_lat, west_lon, east_lon, base_period[0],
base_period[1])
dset_out[index_name].attrs = {
'long_name': index_name + '_index',
'standard_name': index_name + '_index',
'units': 'Celsius',
'notes': hx
}
gio.set_global_atts(dset_out, dset_in.attrs,
{ifile: dset_in.attrs['history']})
dset_out.to_netcdf(ofile, format='NETCDF3_CLASSIC')
else:
return dset_in, anom.values
def main(inargs):
"""Run the program."""
# Read the data
dset_in = xray.open_dataset(inargs.infile)
#gio.check_xrayDataset(dset_in, inargs.var)
subset_dict = gio.get_subset_kwargs(inargs)
darray = dset_in[inargs.var].sel(**subset_dict)
assert darray.dims == ('time', 'latitude', 'longitude'), \
"Order of the data must be time, latitude, longitude"
# Generate datetime list
dt_list, dt_list_metadata = get_datetimes(darray, inargs.date_file)
# Calculate the composites
if not inargs.date_file:
inargs.no_sig = True
cmeans, cmean_atts, pvals, pval_atts = calc_composites(
darray, dt_list, sig_test=not inargs.no_sig)
# Write the output file
d = {}
d['latitude'] = darray['latitude']
d['longitude'] = darray['longitude']
for season in season_months.keys():
d[inargs.var + '_' + season] = (['latitude',
'longitude'], cmeans[season])
if not inargs.no_sig:
d['p_' + season] = (['latitude', 'longitude'], pvals[season])
dset_out = xray.Dataset(d)
for season in season_months.keys():
dset_out[inargs.var + '_' + season].attrs = cmean_atts[season]
if not inargs.no_sig:
dset_out['p_' + season].attrs = pval_atts[season]
output_metadata = {
inargs.infile: dset_in.attrs['history'],
}
if inargs.date_file:
output_metadata[inargs.date_file] = dt_list_metadata
gio.set_global_atts(dset_out, dset_in.attrs, output_metadata)
dset_out.to_netcdf(inargs.outfile, format='NETCDF3_CLASSIC')
def main(inargs):
"""Run the program."""
dset = xray.open_dataset(inargs.infile)
try:
dset = dset.drop('bnds')
except ValueError:
print "Did not delete time bounds variable"
try:
dset.coords['time'].attrs.pop('bounds')
except KeyError:
pass
gio.set_global_atts(dset, dset.attrs, {inargs.infile: dset.attrs['history'],})
dset.to_netcdf(inargs.outfile)
def main(inargs):
"""Run the program."""
# Read the data
dset_in_u = xray.open_dataset(inargs.infileu)
gio.check_xrayDataset(dset_in_u, inargs.varu)
dset_in_v = xray.open_dataset(inargs.infilev)
gio.check_xrayDataset(dset_in_v, inargs.varv)
subset_dict = gio.get_subset_kwargs(inargs)
darray_u = dset_in_u[inargs.varu].sel(**subset_dict)
darray_v = dset_in_v[inargs.varv].sel(**subset_dict)
lat_axis = darray_u['latitude'].values
lon_axis = darray_u['longitude'].values
axis_order = axis_letters(darray_u.dims)
# Calculate the desired quantity
data_out = calc_quantity(darray_u.values, darray_v.values, inargs.quantity,
lat_axis, lon_axis, axis_order)
# Write the output file
d = {}
for dim in darray_u.dims:
d[dim] = darray_u[dim]
for var in data_out.keys():
d[var] = (darray_u.dims, data_out[var])
dset_out = xray.Dataset(d)
for var in data_out.keys():
dset_out[var].attrs = var_atts[var]
outfile_metadata = {
inargs.infileu: dset_in_u.attrs['history'],
inargs.infilev: dset_in_v.attrs['history']
}
gio.set_global_atts(dset_out, dset_in_u.attrs, outfile_metadata)
dset_out.to_netcdf(inargs.outfile, format='NETCDF3_CLASSIC')
def calc_pwi(ifile, var_id, ofile):
"""Calculate the Planetary Wave Index.
Ref: Irving & Simmonds (2015). A novel approach to diagnosing Southern
Hemisphere planetary wave activity and its influence on regional
climate variability. Journal of Climate. 28, 9041-9057.
doi:10.1175/JCLI-D-15-0287.1.
Expected input: Wave envelope.
"""
# Read data
dset_in = xarray.open_dataset(ifile)
gio.check_xarrayDataset(dset_in, var_id)
# Calculate index
darray = dset_in[var_id].sel(latitude=slice(-70, -40))
mermax = darray.max(dim='latitude')
pwi_timeseries = mermax.median(dim='longitude')
# Write output file
d = {}
d['time'] = darray['time']
d['pwi'] = (['time'], pwi_timeseries.values)
dset_out = xarray.Dataset(d)
dset_out['pwi'].attrs = {
'long_name': 'planetary_wave_index',
'standard_name': 'planetary_wave_index',
'units': darray.attrs['units'],
'notes': 'Ref: PWI of Irving and Simmonds (2015)'
}
gio.set_global_atts(dset_out, dset_in.attrs,
{ifile: dset_in.attrs['history']})
dset_out.to_netcdf(ofile, format='NETCDF3_CLASSIC')
def calc_mi(ifile, var_id, ofile):
"""Calculate the meridional wind index.
Represents the average amplitude of the meridional wind
over the 70S to 40S latitude band.
Expected input: Meridional wind
"""
# Read data
dset_in = xarray.open_dataset(ifile)
gio.check_xarrayDataset(dset_in, var_id)
# Calculate index
darray = dset_in[var_id].sel(latitude=slice(-70, -40))
units = darray.attrs['units']
darray = (darray**2)**0.5 # absolute value
mi_timeseries = darray.mean(dim=['latitude', 'longitude'])
# Write output file
d = {}
d['time'] = darray['time']
d['pwi'] = (['time'], mi_timeseries.values)
dset_out = xarray.Dataset(d)
dset_out['pwi'].attrs = {
'long_name': 'meridional_wind_index',
'standard_name': 'meridional_wind_index',
'units': units,
'notes': 'Average amplitude of meridional wind over 70S to 40S'
}
gio.set_global_atts(dset_out, dset_in.attrs,
{ifile: dset_in.attrs['history']})
dset_out.to_netcdf(ofile, format='NETCDF3_CLASSIC')
def calc_asl(ifile, var_id, ofile):
"""Calculate the Amundsen Sea Low index.
Ref: Turner et al (2013). The Amundsen Sea Low.
International Journal of Climatology. 33(7), 1818-1829
doi:10.1002/joc.3558.
Expected input: Mean sea level pressure data.
Concept: Location and value of minimum MSLP is the region
bounded by 60-75S and 180-310E.
"""
# Read data
dset_in = xarray.open_dataset(ifile)
gio.check_xarrayDataset(dset_in, var_id)
south_lat, north_lat, west_lon, east_lon = gio.regions['asl']
darray = dset_in[var_id].sel(latitude=slice(south_lat, north_lat),
longitude=slice(west_lon, east_lon))
assert darray.dims == ('time', 'latitude', 'longitude'), \
"Order of the data must be time, latitude, longitude"
# Get axis information
lat_values = darray['latitude'].values
lon_values = darray['longitude'].values
lats, lons = uconv.coordinate_pairs(lat_values, lon_values)
# Reshape data
ntimes, nlats, nlons = darray.values.shape
darray_reshaped = numpy.reshape(darray.values, (ntimes, nlats * nlons))
# Get the ASL index info (min value for each timestep and its lat/lon)
min_values = numpy.amin(darray_reshaped, axis=1)
min_indexes = numpy.argmin(darray_reshaped, axis=1)
min_lats = numpy.take(lats, min_indexes)
min_lons = numpy.take(lons, min_indexes)
# Write the output file
d = {}
d['time'] = darray['time']
d['asl_value'] = (['time'], min_values)
d['asl_lat'] = (['time'], min_lats)
d['asl_lon'] = (['time'], min_lons)
dset_out = xarray.Dataset(d)
ref = 'Ref: Turner et al (2013). Int J Clim. 33, 1818-1829. doi:10.1002/joc.3558.'
dset_out['asl_value'].attrs = {
'long_name': 'asl_minimum_pressure',
'standard_name': 'asl_minimum_pressure',
'units': 'Pa',
'notes': ref
}
dset_out['asl_lat'].attrs = {
'long_name': 'asl_latitude',
'standard_name': 'asl_latitude',
'units': 'degrees_north',
'notes': ref
}
dset_out['asl_lon'].attrs = {
'long_name': 'asl_longitude',
'standard_name': 'asl_longitude',
'units': 'degrees_east',
'notes': ref
}
gio.set_global_atts(dset_out, dset_in.attrs,
{ifile: dset_in.attrs['history']})
dset_out.to_netcdf(ofile, format='NETCDF3_CLASSIC')
def main(inargs):
"""Run the program."""
# Read data
try:
time_constraint = gio.get_time_constraint(inargs.time)
except AttributeError:
time_constraint = iris.Constraint()
with iris.FUTURE.context(cell_datetime_objects=True):
u_cube = iris.load_cube(inargs.infileU,
inargs.longnameU & time_constraint)
v_cube = iris.load_cube(inargs.infileV,
inargs.longnameV & time_constraint)
for coords in [u_cube.coords(), v_cube.coords()]:
coord_names = [coord.name() for coord in coords]
assert coord_names == ['time', 'latitude', 'longitude']
time_coord = v_cube.coord('time')
lat_coord = v_cube.coord('latitude')
lon_coord = v_cube.coord('longitude')
# Rotate wind
np_lat, np_lon = inargs.north_pole
rotated_cs = iris.coord_systems.RotatedGeogCS(np_lat, np_lon)
urot_cube, vrot_cube = iris.analysis.cartography.rotate_winds(
u_cube, v_cube, rotated_cs)
# Regrid
target_grid_cube = make_grid(lat_coord.points, lon_coord.points, np_lat,
np_lon)
scheme = iris.analysis.Linear()
vrot_cube.coords('latitude')[0].coord_system = iris.coord_systems.GeogCS(
iris.fileformats.pp.EARTH_RADIUS)
vrot_cube.coords('longitude')[0].coord_system = iris.coord_systems.GeogCS(
iris.fileformats.pp.EARTH_RADIUS)
vrot_regridded = vrot_cube.regrid(target_grid_cube, scheme)
#could use clean_data here to remove spurious large values that regirdding produces
# Write to file
d = {}
d['time'] = ('time', time_coord.points)
d['latitude'] = ('latitude', lat_coord.points)
d['longitude'] = ('longitude', lon_coord.points)
d['vrot'] = (['time', 'latitude', 'longitude'], vrot_regridded.data)
dset_out = xray.Dataset(d)
dset_out['vrot'].attrs = {
'standard_name':
'rotated_northward_wind',
'long_name':
'rotated_northward_wind',
'units':
str(v_cube.units),
'notes':
'North Pole at lat=%s, lon=%s. Data defined on rotated grid.' %
(np_lat, np_lon)
}
gio.set_dim_atts(dset_out, str(time_coord.units))
outfile_metadata = {
inargs.infileU: u_cube.attributes['history'],
inargs.infileV: v_cube.attributes['history']
}
gio.set_global_atts(dset_out, v_cube.attributes, outfile_metadata)
dset_out.to_netcdf(inargs.outfile, ) #format='NETCDF3_CLASSIC')
def main(inargs):
"""Run the program."""
# Prepate input data
try:
time_constraint = gio.get_time_constraint(inargs.time)
except AttributeError:
time_constraint = iris.Constraint()
try:
lat_constraint = iris.Constraint(latitude=lambda y: y <= inargs.maxlat)
except AttributeError:
lat_constraint = iris.Constraint()
try:
season_constraint = season_constraints[inargs.season]
except AttributeError:
season_constraint = iris.Constraint()
with iris.FUTURE.context(cell_datetime_objects=True):
cube = iris.load_cube(
inargs.infile, inargs.longname & time_constraint
& season_constraint & lat_constraint)
coord_names = [coord.name() for coord in cube.coords()]
assert coord_names == ['time', 'latitude', 'longitude']
time_coord = cube.coord('time')
lat_coord = cube.coord('latitude')
lon_coord = cube.coord('longitude')
# Perform EOF analysis
eof_anal = EofAnalysis(
cube,
**uconv.dict_filter(vars(inargs),
uconv.list_kwargs(EofAnalysis.__init__)))
eof_cube, eof_atts = eof_anal.eof(
**uconv.dict_filter(vars(inargs), uconv.list_kwargs(eof_anal.eof)))
pc_cube, pc_atts = eof_anal.pcs(
**uconv.dict_filter(vars(inargs), uconv.list_kwargs(eof_anal.pcs)))
# Write output file
d = {}
d['time'] = ('time', time_coord.points)
d['latitude'] = ('latitude', lat_coord.points)
d['longitude'] = ('longitude', lon_coord.points)
eof_dims = ['latitude', 'longitude']
pc_dims = ['time']
for index in xrange(inargs.neofs):
d['eof' +
str(index + 1)] = (eof_dims,
eof_cube.extract(
iris.Constraint(eof_number=index)).data)
d['pc' + str(index + 1)] = (pc_dims,
pc_cube.extract(
iris.Constraint(pc_number=index)).data)
dset_out = xray.Dataset(d)
for index in xrange(inargs.neofs):
eof_var = 'eof' + str(index + 1)
pc_var = 'pc' + str(index + 1)
dset_out[eof_var].attrs = eof_atts[eof_var]
dset_out[pc_var].attrs = pc_atts[pc_var]
gio.set_dim_atts(dset_out, str(time_coord.units))
outfile_metadata = {inargs.infile: cube.attributes['history']}
gio.set_global_atts(dset_out, cube.attributes, outfile_metadata)
dset_out.to_netcdf(inargs.outfile, ) #format='NETCDF3_CLASSIC')