def test_two(self):
data01_mod = data01.copy()
data = climapy.cesm_time_from_bnds(data01_mod, min_year=2001)
assert np.array_equal(
data['time'].values[[0, -1]],
np.array(['2003-01-16T11:00:00', '2007-12-16T11:00:00'],
dtype='datetime64'))
def load_output(variable, scenario='p16a_F_Hist_2000', season='annual', apply_sf=True):
"""
Load annual/seasonal data for a specific variable and scenario.
Args:
variable: string of variable name to load (e.g. 'SWCF_d1', or 'FSNTOA+LWCF')
scenario: string scenario (default 'p16a_F_Hist_2000')
season: 'annual' (default) or name of season (e.g 'DJF')
apply_sf: apply scale factor? (default True)
Returns:
xarray DataArray
"""
# Check if data have been loaded previously
try:
data = _output_dict[(variable, scenario, season, apply_sf)]
except KeyError:
# Case 1: if '+' in variable name, call recursively
if '+' in variable:
variable1, variable2 = variable.split('+')
data1 = load_output(variable1, scenario=scenario, season=season, apply_sf=apply_sf)
data2 = load_output(variable2, scenario=scenario, season=season, apply_sf=apply_sf)
data = data1 + data2
# Case 2: if '-' in variable name, call recursively
elif '-' in variable:
variable1, variable2 = variable.split('-')
data1 = load_output(variable1, scenario=scenario, season=season, apply_sf=apply_sf)
data2 = load_output(variable2, scenario=scenario, season=season, apply_sf=apply_sf)
data = data1 - data2
# Case 3: variable name is a single variable
else:
# Read data
in_filename = '{}/{}.cam.h0.{}.nc'.format(output_dir, scenario, variable)
ds = xr.open_dataset(in_filename, decode_times=False)
# Convert time coordinates
ds = climapy.cesm_time_from_bnds(ds, min_year=1701)
# Annual/seasonal mean for each year (Jan-Dec), using arithmetic mean across months
if season == 'annual':
data = ds[variable].groupby('time.year').mean(dim='time')
else:
data = ds[variable].where(ds['time.season'] == season,
drop=True).groupby('time.year').mean(dim='time')
# Discard first two years as spin-up
data = data.where(data['year'] >= 1703, drop=True)
# Limit time period to max of 60 years
data = data.where(data['year'] <= 1762, drop=True)
# Apply scale factor?
if apply_sf:
try:
data = data * _variable_sf_dict[variable]
except KeyError:
pass
# Average across level dimension if it exists
try:
data = data.mean(dim='lev')
except ValueError:
pass
# Save data for future reference
_output_dict[(variable, scenario, season, apply_sf)] = data
return data
def load_landfrac():
"""
Load land fraction (LANDFRAC).
LANDFRAC is invariant across time and scenarios.
Returns:
xarray DataArray
"""
# Read data
in_filename = '{}/LANDFRAC/p17d_f_000.cam.h0.LANDFRAC.nc'.format(output_dir)
ds = xr.open_dataset(in_filename, decode_times=False)
# Convert time coordinates
ds = climapy.cesm_time_from_bnds(ds, min_year=1701)
# Collapse time dimension (by calculating mean across time)
data = ds['LANDFRAC'].mean(dim='time')
return data
def test_one(self):
data = climapy.cesm_time_from_bnds(data01) # by default, min_year=1701
assert np.array_equal(
data['time'].values[[0, -1]],
np.array(['1703-01-16T11:00:00', '1707-12-16T11:00:00'],
dtype='datetime64'))
def load_output_monthly(variable='TS', scenario='eas0c', f_or_b='b', apply_sf=True):
"""
Load monthly data for a specific variable and scenario.
Args:
variable: string of variable name to load (default 'TS')
scenario: string scenario name (default 'eas0c')
f_or_b: 'f' (prescribed-SST) or 'b' (coupled atmosphere-ocean; default)
apply_sf: apply scale factor? (default True)
Returns:
xarray DataArray
"""
# Check if monthly outout has been loaded previously
try:
data = _output_monthly_dict[(variable, scenario, f_or_b, apply_sf)]
except KeyError:
# If + or - in variable and the scenario is not a G16 scenario, call function recursively
if ('+' in variable or '-' in variable) and scenario in ['2000', 'eas0b', 'eas0c']:
variable1, variable2 = re.split('[+\-]', variable)
data1 = load_output_monthly(variable1, scenario=scenario, f_or_b=f_or_b,
apply_sf=apply_sf)
data2 = load_output_monthly(variable2, scenario=scenario, f_or_b=f_or_b,
apply_sf=apply_sf)
if '+' in variable:
data = data1 + data2
elif '-' in variable:
data = data1 - data2
elif scenario in ['2000', 'eas0b', 'eas0c']: # p17d simulations
# Read data
in_filename = '{}/p17d_{}_{}.cam.h0.{}.nc'.format(output_dir, f_or_b, scenario, variable)
ds = xr.open_dataset(in_filename, decode_times=False)
# Convert time coordinates
ds = climapy.cesm_time_from_bnds(ds, min_year=1701)
# Select variable
data = ds[variable]
# Split time into separate year and month dimensions
d_list = []
for m in range(1, 13): # loop over months
d = data.where(data['time.month'] == m,
drop=True).groupby('time.year').mean(dim='time')
d['month'] = m # add month coordinate
d_list.append(d)
data = xr.concat(d_list, dim='month')
# Discard spin-up
if f_or_b == 'f': # discard two years for 'f' simulations
data = data.where(data['year'] >= 1703, drop=True)
elif f_or_b == 'b': # discard 40 years for 'b' simulations
data = data.where(data['year'] >= 1741, drop=True)
elif scenario in ['pA2x', 'pR45']: # G16 prescribed-SST simulations
raise NotImplementedError('G16 prescribed-SST simulations not supported')
else: # G16 transient simulations
# Get G16 equivalent variable name
variable_g16 = load_variable_g16_dict()[variable]
# Get data for three ensemble members
da_list = []
for ic in ['f1', 'h1', 'h2']:
# Read data
in_filename = '{}/{}_{}.nc'.format(g16_dir, scenario, ic)
ds = xr.open_dataset(in_filename, decode_times=False)
# Convert time coordinates
ds = climapy.cesm_time_from_bnds(ds, min_year=1701)
# Select variable
da = ds[variable_g16]
# Split time into separate year and month dimensions
d_list = []
for m in range(1, 13): # loop over months
d = da.where(da['time.month'] == m,
drop=True).groupby('time.year').mean(dim='time')
d['month'] = m # add month coordinate
d_list.append(d)
da = xr.concat(d_list, dim='month')
# Select 2080-2099
da = da.where(da['year'] >= 2080, drop=True)
# Shift time dimension to facilitate concatenation
if ic == 'h1':
da['year'] += 20
elif ic == 'h2':
da['year'] += 40
# Append to list
da_list.append(da)
# Concatenate data from three ensemble members
data = xr.concat(da_list, dim='year')
# Apply scale factor?
if apply_sf:
try:
data = data * load_variable_sf_dict()[variable]
except KeyError:
pass
# Save result for future reference
_output_monthly_dict[(variable, scenario, f_or_b, apply_sf)] = data
return data
def load_output(variable='TS', scenario='eas0c', f_or_b='b', season='annual', apply_sf=True):
"""
Load annual/seasonal data for a specific variable and scenario.
Args:
variable: string of variable name to load (default 'TS')
scenario: string scenario name (default 'eas0c')
f_or_b: 'f' (prescribed-SST) or 'b' (coupled atmosphere-ocean; default)
season: 'annual' (default) or name of season (e.g 'DJF')
apply_sf: apply scale factor? (default True)
Returns:
xarray DataArray
"""
# If + or - in variable and the scenario is not a G16 scenario, call function recursively
if ('+' in variable or '-' in variable) and scenario in ['2000', 'eas0b', 'eas0c']:
variable1, variable2 = re.split('[+\-]', variable)
data1 = load_output(variable1, scenario=scenario, f_or_b=f_or_b, season=season,
apply_sf=apply_sf)
data2 = load_output(variable2, scenario=scenario, f_or_b=f_or_b, season=season,
apply_sf=apply_sf)
if '+' in variable:
data = data1 + data2
elif '-' in variable:
data = data1 - data2
elif scenario in ['2000', 'eas0b', 'eas0c']: # p17d simulations
# Read data
in_filename = '{}/p17d_{}_{}.cam.h0.{}.nc'.format(output_dir, f_or_b, scenario, variable)
ds = xr.open_dataset(in_filename, decode_times=False)
# Convert time coordinates
ds = climapy.cesm_time_from_bnds(ds, min_year=1701)
# Calculate annual/seasonal mean for each year (Jan-Dec), using arithmetic mean
if season == 'annual':
data = ds[variable].groupby('time.year').mean(dim='time')
else:
data = ds[variable].where(ds['time.season'] == season,
drop=True).groupby('time.year').mean(dim='time')
# Discard spin-up
if f_or_b == 'f': # discard two years for 'f' simulations
data = data.where(data['year'] >= 1703, drop=True)
elif f_or_b == 'b': # discard 40 years for 'b' simulations
data = data.where(data['year'] >= 1741, drop=True)
elif scenario in ['pA2x', 'pR45']: # G16 prescribed-SST simulations
# Get G16 equivalent variable name
variable_g16 = load_variable_g16_dict()[variable]
# Read data
in_filename = '{}/{}.nc'.format(g16_dir, scenario)
ds = xr.open_dataset(in_filename, decode_times=False)
# Convert time coordinates
ds = climapy.cesm_time_from_bnds(ds, min_year=1701)
# Calculate annual/seasonal mean for each year (Jan-Dec), using arithmetic mean
if season == 'annual':
data = ds[variable_g16].groupby('time.year').mean(dim='time')
else:
data = ds[variable_g16].where(ds['time.season'] == season,
drop=True).groupby('time.year').mean(dim='time')
# Discard two years spin-up (diff from G16, where 2 years 11 months were discarded)
data = data.where(data['year'] >= 1703, drop=True)
else: # G16 transient simulations
# Get G16 equivalent variable name
variable_g16 = load_variable_g16_dict()[variable]
# Get data for three ensemble members
da_list = []
for ic in ['f1', 'h1', 'h2']:
# Read data
in_filename = '{}/{}_{}.nc'.format(g16_dir, scenario, ic)
ds = xr.open_dataset(in_filename, decode_times=False)
# Convert time coordinates
ds = climapy.cesm_time_from_bnds(ds, min_year=1701)
# Calculate annual/seasonal mean for each year (Jan-Dec), using arithmetic mean
if season == 'annual':
da = ds[variable_g16].groupby('time.year').mean(dim='time')
else:
da = ds[variable_g16].where(ds['time.season'] == season,
drop=True).groupby('time.year').mean(dim='time')
# Select 2080-2099
da = da.where(da['year'] >= 2080, drop=True)
# Shift time dimension to facilitate concatenation
if ic == 'h1':
da['year'] += 20
elif ic == 'h2':
da['year'] += 40
# Append to list
da_list.append(da)
# Concatenate data from three ensemble members
data = xr.concat(da_list, dim='year')
# Apply scale factor?
if apply_sf:
try:
data = data * load_variable_sf_dict()[variable]
except KeyError:
pass
return data