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 ------