Method1: read lat/rho ToE and average in the chosen domain
Method2: read ToE computed from the already averaged signal and noise in the chosen domain
"""
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset as open_ncfile
from maps_matplot_lib import defVarmme, averageDom
from modelsDef import defModels
from libToE import findToE, ToEdomainhistvshistNat
# ----- Work ------
models = defModels()
varname = defVarmme('salinity'); v = 'S'
#varname = defVarmme('temp'); v = 'T'
#varname = defVarmme('depth'); v = 'Z'
# -- Choose method for computing ToE
#method = 'average_ToE' # Determine 2D lat/rho ToE then average in the box
method = 'average_signal' # Average signal and noise in the box, then compute ToE
# Method 2 only has ToE for salinity for now
# -- Choose which 'noise' to use for the ToE calculation (only with method 2 : average_signal)
# method_noise = 'average_std' # Average the standard deviation of PiC in the specified domains
method_noise = 'average_histNat' # Average histNat in the specified domains then determine the std of this averaged value
# -- Choose domain
domains = ['Southern ST', 'SO', 'Northern ST', 'North Atlantic', 'North Pacific']
fh2d = open_ncfile(indir_rcp85 + filercp85_2d, 'r')
fh1d = open_ncfile(indir_rcp85 + filercp85_1d, 'r')
fhn2d = open_ncfile(indirhn + filehn_2d, 'r')
fhn1d = open_ncfile(indirhn + filehn_1d, 'r')
file = 'cmip5.multimodel_Nat.rcp85.remaptoz_density.zon2D.nc'
fsigma = open_ncfile(indir_density + file, 'r')
# -------------------------------------------------------------------------------
# Build variables
# -------------------------------------------------------------------------------
# == Define variables ==
# -- Salinity or temperature
varname = defVarmme('salinity')
v = 'S'
# varname = defVarmme('temp'); v = 'T'
var = varname['var_zonal_w/bowl']
if name == 'mme_rcp85_histNat':
minmax = varname['minmax_zonal_rcp85']
else:
minmax = varname['minmax_zonal']
clevsm = varname['clevsm_zonal']
legVar = varname['legVar']
unit = varname['unit']
# == Read variables ==
lat = fh2d.variables['latitude'][:]
import glob, os
import datetime
import colormaps as cmaps
# ===== Define Work ======
work = 'histNat'
# work = 'piC'
indir_histNat = '/data/ericglod/Density_binning/Prod_density_april15/mme_histNat/'
indir_piC = '/data/ericglod/Density_binning/Prod_density_april15/mme_piControl/'
fig_dir = '/home/ysilvy/figures/models/zonal_ys/'
# varname = defVarmme('salinity'); v = 'S'
# varname = defVarmme('temp'); v = 'T'
varname = defVarmme('depth')
v = 'Z'
var = varname['var_zonal_w/bowl']
legVar = varname['legVar']
unit = varname['unit']
# Density domain
rhomin = 21
rhomid = 26
rhomax = 28
domrho = [rhomin, rhomid, rhomax]
if work == 'histNat':
indir = indir_histNat
longName = 'historicalNat'
# -- Read and build variables
if name == 'Durack&Wijffels':
varname = defVarDurack(varname)
var_diff = varname['var_change']
if lat_choice == 'one':
var_diff = f.variables[var_diff][:,:,lat1_idx,:].squeeze()
else:
var_diff = f.variables[var_diff][:,:,latmin_idx:latmax_idx+1,:].squeeze()
var_diff = np.ma.average(var_diff, axis=1, weights=weights)
density = f.variables['density'][:]
#Masked values in white
var_diff[var_diff > 4] = 0
elif name == 'mme_hist':
varname = defVarmme(varname)
var = varname['var_global']
if lat_choice == 'one':
var = f.variables[var][88:,:,lat1_idx,:].squeeze()
else:
var = f.variables[var][88:,:,latmin_idx:latmax_idx+1,:]
var = np.ma.average(var, axis=2, weights=weights)
# Difference
var_diff = np.ma.average(var[-5:,:], axis=0) - np.ma.average(var[0:5,:], axis=0)
density = f.variables['lev'][:]
else:
varname = defVar(varname)
var = varname['var']
if lat_choice == 'one':
import os
import datetime
# ----- Work -----
# Directory
indir_rcphn = '/home/ysilvy/Density_bining/Yona_analysis/data/toe_rcp85_histNat_average_signal/'
indir_rcppiC = '/home/ysilvy/Density_bining/Yona_analysis/data/toe_rcp85_PiControl_average_signal/'
models = defModels()
domains = [
'Southern ST', 'SO', 'Northern ST', 'North Atlantic', 'North Pacific'
]
varname2 = defVarmme('salinity')
v = 'S'
varname1 = defVarmme('depth')
v = 'Z'
method = 'average_signal' # Average signal and noise in the box, then compute ToE
# === INPUTS ===
# method_noise_rcphn = 'average_std' # Average the standard deviation of histNat in the specified domains
method_noise_rcp = 'average_histNat' # Average histNat in the specified domains then determine the std of this averaged value
use_piC = False # signal = hist+RCP - histNat, noise = std(histNat)
# use_piC = True # signal = hist+RCP - PiControl, noise = std(PiControl)
if use_piC == True and method_noise_rcp == 'average_histNat':
from libToE import findToE
import glob
from functions_z_analysis import maptogamma
from binDensity import rhonGrid, maskVal
# ----- Workspace ------
indir_histrcp85 = '/data/ysilvy/CMIP5_annual/'
indir_histNat = '/data/ysilvy/CMIP5_annual/'
# ----- Work ------
#models = defModels() # Error: opening the one in Density_bining/ instead of Density_bining/Yona_analysis/programs/
#varname = defVarmme('salinity'); v = 'S'
varname = defVarmme('temp')
v = 'T'
multStd = 2. # detect ToE at multStd std dev of histNat or PiControl
use_piC = False # Signal = (hist-histNat) + RCP8.5-average(histNat), noise = std(histNat)
# use_piC = True # Signal = hist + RCP8.5 - PiControl, noise = std(PiControl)
iniyear = 1860
finalyear = 2100
deltay = 10.
# Define variable properties
legVar = varname['legVar']
unit = varname['unit']
# -- Read and build variables
if name == 'Durack&Wijffels':
varname = defVarDurack(varname)
var_diff = varname['var_change']
if lat_choice == 'one':
var_diff = f.variables[var_diff][:, :, lat1_idx, :].squeeze()
else:
var_diff = f.variables[var_diff][:, :, latmin_idx:latmax_idx +
1, :].squeeze()
var_diff = np.ma.average(var_diff, axis=1, weights=weights)
density = f.variables['density'][:]
#Masked values in white
var_diff[var_diff > 4] = 0
elif name == 'mme_hist':
varname = defVarmme(varname)
var = varname['var_global']
if lat_choice == 'one':
var = f.variables[var][88:, :, lat1_idx, :].squeeze()
else:
var = f.variables[var][88:, :, latmin_idx:latmax_idx + 1, :]
var = np.ma.average(var, axis=2, weights=weights)
# Difference
var_diff = np.ma.average(var[-5:, :], axis=0) - np.ma.average(var[0:5, :],
axis=0)
density = f.variables['lev'][:]
else:
varname = defVar(varname)
var = varname['var']
if lat_choice == 'one':