def output_nc_file(dataset, field_name, model_params, output_dict):
#create grid
try:
lons = dataset.lon.values
lats = dataset.lat.values
latbs = dataset.latb.values
lonbs = dataset.lonb.values
nlon = lons.shape[0]
nlat = lats.shape[0]
nlatb = latbs.shape[0]
nlonb = lonbs.shape[0]
except:
lons, lats, lonbs, latbs, nlon, nlat, nlonb, nlatb = cts.create_grid(
output_dict['manual_grid_option'])
if output_dict['is_thd']:
p_full, p_half, npfull, nphalf = cts.create_pressures()
else:
p_full = None
p_half = None
npfull = None
nphalf = None
#create times
try:
output_dict['num_years']
except KeyError:
is_climatology = True
else:
if output_dict['num_years'] == 1:
is_climatology = True
else:
is_climatology = False
num_years = output_dict['num_years']
time_arr, day_number, ntime, time_units, time_bounds = cts.create_time_arr(
num_years, is_climatology, output_dict['time_spacing_days'])
#Output it to a netcdf file.
file_name = output_dict['file_name']
variable_name = output_dict['var_name']
number_dict = {}
number_dict['nlat'] = nlat
number_dict['nlon'] = nlon
number_dict['nlatb'] = nlatb
number_dict['nlonb'] = nlonb
number_dict['npfull'] = npfull
number_dict['nphalf'] = nphalf
number_dict['ntime'] = ntime
data_out = dataset[field_name].load().data
cts.output_to_file(data_out, lats, lons, latbs, lonbs, p_full, p_half,
time_arr, time_units, file_name, variable_name,
number_dict, time_bounds)
def output_to_file(dataset, output_array, output_filename, variable_name):
"""
Simple function for outputting adjusted data as a netcdf file.
"""
nt, ny, nx = np.shape(output_array)
p_full = None
p_half = None
npfull = None
nphalf = None
#Find grid and time numbers
lats = dataset.lat.values
lons = dataset.lon.values
latbs = dataset.latb.values
lonbs = dataset.lonb.values
time_arr = dataset.time.values
ntime = nt
nlonb = dataset.lonb.values.shape[0]
nlatb = dataset.latb.values.shape[0]
#Output it to a netcdf file.
number_dict = {}
number_dict['nlat'] = ny
number_dict['nlon'] = nx
number_dict['nlatb'] = nlatb
number_dict['nlonb'] = nlonb
number_dict['npfull'] = npfull
number_dict['nphalf'] = nphalf
number_dict['ntime'] = ntime
time_units = 'days since 0000-01-01 00:00:00.0'
cts.output_to_file(output_array, lats, lons, latbs, lonbs, p_full, p_half,
time_arr, time_units, output_file_name, variable_name,
number_dict)
data_sym[i + 36, :] = data_sym[i, ::-1]
# Put longitude back in!
data = np.repeat(np.expand_dims(data_sym, axis=2), 128, axis=2)
number_dict = {}
number_dict['nlat'] = nlat
number_dict['nlon'] = nlon
number_dict['nlatb'] = nlatb
number_dict['nlonb'] = nlonb
number_dict['ntime'] = ntime
variable_name = 'sn_1.000_sst'
file_name = './sn_1.000_sst.nc'
cts.output_to_file(data, lats, lons, latbs, lonbs, None, None, time_arr,
time_units, file_name, variable_name, number_dict)
time_arr, day_number, ntime, time_units, time_bounds = cts.create_time_arr(
num_years=1, is_climatology=True, time_spacing=360)
for i in range(36, 55, 2):
data_ss = (data[i - 1, :, :] + data[i, :, :]) / 2.
data_ss = np.repeat(np.expand_dims(data_ss, axis=0), 360, axis=0)
variable_name = 'sn_1.000_sst_' + str(i)
file_name = './sn_1.000_sst_' + str(i) + '.nc'
cts.output_to_file(data_ss, lats, lons, latbs, lonbs, None, None, time_arr,
time_units, file_name, variable_name, number_dict)
nlon=len(lons)
nlat=len(lats)
nlonb=len(lonbs)
nlatb=len(latbs)
npfull=len(p_full)
nphalf=len(p_half)
ntime=len(time_arr)
#Output it to a netcdf file.
file_name='ozone_1990_cmip5_test.nc'
variable_name='ozone_1990_cmip5'
number_dict={}
number_dict['nlat']=nlat
number_dict['nlon']=nlon
number_dict['nlatb']=nlatb
number_dict['nlonb']=nlonb
number_dict['npfull']=npfull
number_dict['nphalf']=nphalf
number_dict['ntime']=ntime
time_units='days since 0000-01-01 00:00:00.0'
cts.output_to_file(ozone_new,lats,lons,latbs,lonbs,p_full,p_half,time_arr,time_units,file_name,variable_name,number_dict)
def main():
base_directory = '/scratch/sit204/sst_amip_files/'
amip_data_version = 'amip_data_version_1_1_0' #s 'amip_data_version_1_1_0' or 'amip_data_version_1_0_0'
output_name_list = {'tosbcs': 'sst', 'siconc': 'siconc'}
#Note that we are using the bcs (boundary conditions) input4MIPs files, as instructed.
# The theory is that by using the bcs files (which are mid-month values) the time-average
# of the interpolated bcs files should be equal to the time-average data provided in 'tos'
# files, not the 'tosbcs'. See http://www-pcmdi.llnl.gov/projects/amip/AMIP2EXPDSN/BCS/amip2bcs.php
# and http://www-pcmdi.llnl.gov/projects/amip/AMIP2EXPDSN/BCS/amipbc_dwnld_files/360x180/v1.0.0/nc/readme_nc
add_anomaly = False
# anomaly_type='el_nino'
months_to_include = 'all'
for variable_name in list(output_name_list.keys()):
if amip_data_version == 'amip_data_version_1_0_0':
nfiles = 50
folder_name = '/1950_1999/'
filename_prefix = 'amipbc_sst_360x180_19'
sst_all = np.zeros((nfiles, 12, 180, 360))
do_annual_mean = True
elif amip_data_version == 'amip_data_version_1_1_0':
nfiles = 1
folder_name = ''
filename_prefix = variable_name + '_input4MIPs_SSTsAndSeaIce_CMIP_PCMDI-AMIP-1-1-0_gs1x1_187001-201512'
do_annual_mean = False
elif amip_data_version == 'hadgem_t_surf':
nfiles = 1
folder_name = ''
filename_prefix = 'ts_clim'
do_annual_mean = False
for file_tick in range(nfiles):
if nfiles != 1:
filename = filename_prefix + str(file_tick + 50)
else:
filename = filename_prefix
resolution_file = Dataset(
base_directory + amip_data_version + '/' + folder_name + '/' +
filename + '.nc', 'r')
try:
lons = resolution_file.variables['longitude'][:]
lats = resolution_file.variables['latitude'][:]
except KeyError:
lons = resolution_file.variables['lon'][:]
lats = resolution_file.variables['lat'][:]
sst_in = resolution_file.variables[variable_name][:]
try:
sst_all[file_tick, :, :, :] = sst_in
except NameError:
sst_all = sst_in
except IndexError:
sst_all = sst_in
try:
no_latb_lonb = False
lonbs = resolution_file.variables['bounds_longitude'][:]
latbs = resolution_file.variables['bounds_latitude'][:]
except KeyError:
try:
lonbs = resolution_file.variables['lon_bnds'][:]
latbs = resolution_file.variables['lat_bnds'][:]
except:
no_latb_lonb = True
nlon = lons.shape[0]
nlat = lats.shape[0]
if not no_latb_lonb:
nlonb = lonbs.shape[0]
nlatb = latbs.shape[0]
lonbs_adjusted = np.zeros(nlonb + 1)
latbs_adjusted = np.zeros(nlatb + 1)
lonbs_adjusted[0:nlonb] = lonbs[:, 0]
lonbs_adjusted[nlonb] = lonbs[-1, 1]
latbs_adjusted[0:nlatb] = latbs[:, 0]
latbs_adjusted[nlatb] = latbs[-1, 1]
else:
latbs_adjusted = None
lonbs_adjusted = None
try:
day_number = resolution_file.variables['time'][:]
except:
day_number = np.ones(12)
time_arr = day_number_to_date(day_number,
calendar_type='gregorian',
units_in='days since 1870-1-1')
time_arr_adj = np.arange(15, 360, 30)
annual_mean_name = ''
if len(sst_all.shape) == 4:
sst_in = np.mean(sst_all, axis=0)
else:
sst_in = np.zeros((12, nlat, nlon))
if months_to_include == 'all':
for month_tick in np.arange(1, 13, 1):
month_idx = np.where(time_arr.month == month_tick)[0]
sst_in[month_tick - 1, :, :] = np.mean(
sst_all[month_idx, :, :], axis=0)
elif months_to_include == 'DJF':
djf_idx = np.where(
np.logical_or(
np.logical_or(time_arr.month == 1,
time_arr.month == 2),
time_arr.month == 12))
djf_mean = np.mean(sst_all[djf_idx[0], ...], axis=0)
for month_tick in np.arange(1, 13, 1):
sst_in[month_tick - 1, ...] = djf_mean
annual_mean_name = '_djf'
elif months_to_include == 'only_month_available':
for month_tick in np.arange(1, 13, 1):
month_idx = np.where(time_arr.month == month_tick)[0]
sst_in[month_tick - 1, :, :] = sst_all
if do_annual_mean:
sst_in_am = np.mean(sst_in, axis=0)
sst_in = np.zeros((12, nlat, nlon))
for month_tick in np.arange(1, 13, 1):
sst_in[month_tick - 1, :, :] = sst_in_am
annual_mean_name = '_am'
if add_anomaly and variable_name == 'tosbcs':
sst_in, shifted_lons = add_sst_anomaly(sst_in, anomaly_type)
anom_name = '_' + anomaly_type
else:
anom_name = ''
p_full = None
p_half = None
npfull = None
nphalf = None
#Find grid and time numbers
ntime = time_arr.day.shape[0]
if not no_latb_lonb:
nlonb = lonbs_adjusted.shape[0]
nlatb = latbs_adjusted.shape[0]
#Output it to a netcdf file.
variable_name = output_name_list[
variable_name] + annual_mean_name + '_clim_' + amip_data_version[
0:5] + anom_name
file_name = variable_name + '_' + amip_data_version + '.nc'
number_dict = {}
number_dict['nlat'] = nlat
number_dict['nlon'] = nlon
number_dict['npfull'] = npfull
number_dict['nphalf'] = nphalf
number_dict['ntime'] = ntime
if not no_latb_lonb:
number_dict['nlatb'] = nlatb
number_dict['nlonb'] = nlonb
time_units = 'days since 0000-01-01 00:00:00.0'
cts.output_to_file(sst_in, lats, lons, latbs_adjusted, lonbs_adjusted,
p_full, p_half, time_arr_adj, time_units, file_name,
variable_name, number_dict)
