import numpy as np
from netCDF4 import Dataset as open_ncfile
from modelsDef import defModels, defModelsCO2piC
from maps_matplot_lib import defVarmme, averageDom
from libToE import ToEdomainhistvshistNat, ToEdomain1pctCO2vsPiC, ToEdomainrcp85vshistNat
import glob
# ----- Workspace ------
indir_hist = '/data/ericglod/Density_binning/Prod_density_april15/historical/'
indir_histNat = '/data/ericglod/Density_binning/Prod_density_april15/historicalNat/'
indir_piC = '/data/ericglod/Density_binning/Prod_density_april15/mme_piControl/'
models = defModels()
modelspiC = defModelsCO2piC()
# method_noise = 'average_std' # Compute std of the time series then average in the domains
method_noise = 'std_of_average' # Average time series in the domains then compute std
if method_noise == 'average_std':
noise_description = 'The standard deviation is computed and then averaged in the domains.'
else:
noise_description = 'The variables are averaged in the domains, then the standard deviation is computed ' \
'over those domains.'
# ----- Work ------
varname = defVarmme('salinity')
v = 'S'
# varname = defVarmme('temp'); v = 'T'
fh1d = open_ncfile(datah_1d,'r')
fhn2d = open_ncfile(datahn_2d,'r')
fhn1d = open_ncfile(datahn_1d,'r')
if name == 'ens_mean_hist' or name == '1pctCO2' or name == 'ens_mean_hist_histNat' or name == '1pctCO2vsPiC'\
or name == 'ens_mean_rcp85_histNat':
if name == 'ens_mean_hist' or name == 'ens_mean_hist_histNat' or name == 'ens_mean_rcp85_histNat':
models = defModels()
model = models[imodel]
if name == 'ens_mean_rcp85_histNat':
nb_members = len(model['hist-rcp85'])
else:
nb_members = model['props'][0]
else :
models = defModelsCO2piC()
model = models[imodel]
indir = '/data/ericglod/Density_binning/Prod_density_april15/'
if name !='ens_mean_rcp85_histNat':
if name == 'ens_mean_hist' or name == 'ens_mean_hist_histNat':
file_2d = 'mme_hist/cmip5.' + model['name'] + '.historical.ensm.an.ocn.Omon.density.ver-' + model['file_end_hist'] + '_zon2D.nc'
file_1d = 'mme_hist/cmip5.' + model['name'] + '.historical.ensm.an.ocn.Omon.density.ver-' + model['file_end_hist'] + '_zon1D.nc'
elif name == '1pctCO2' or name == '1pctCO2vsPiC':
file_2d = 'mme_1pctCO2/cmip5.' + model['name'] + '.1pctCO2.ensm.an.ocn.Omon.density.ver-' + model['file_end_CO2'] + '_zon2D.nc'
file_1d = 'mme_1pctCO2/cmip5.' + model['name'] + '.1pctCO2.ensm.an.ocn.Omon.density.ver-' + model['file_end_CO2'] + '_zon1D.nc'
data_2d = indir + file_2d
data_1d = indir + file_1d
fh2d = open_ncfile(data_2d, 'r')
fh1d = open_ncfile(data_1d, 'r')
"""
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset as open_ncfile
from maps_matplot_lib import defVarmme
from modelsDef import defModels, defModelsCO2piC
# ----- Work -----
# Directory
indir_noise = '/home/ysilvy/Density_bining/Yona_analysis/data/noise_estimate/'
models = defModels()
modelspiC = defModelsCO2piC()
domains = ['Southern ST', 'SO', 'Northern ST', 'North Atlantic', 'North Pacific']
domains1 = ['Southern ST', 'Northern ST'] # First bar chart
domains2 = ['SO', 'North Atlantic', 'North Pacific'] # Second bar chart
varname = defVarmme('salinity'); v = 'S'
# ----- Variables ------
var = varname['var_zonal_w/bowl']
legVar = varname['legVar']
unit = varname['unit']
# ----- Read noise for each model ------
